Mathematical and statistical software Books

Book SynopsisThis Brief provides a roadmap for the R language and programming environment with signposts to further resources and documentation.Table of ContentsIntroduction to R.- Data Structures.- Tables and Graphs.- Hypothesis Tests.- Linear Models.

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Book SynopsisTable of ContentsPreface.- 1 Introduction.- 2 Statistical Learning.- 3 Linear Regression.- 4 Classification.- 5 Resampling Methods.- 6 Linear Model Selection and Regularization.- 7 Moving Beyond Linearity.- 8 Tree-Based Methods.- 9 Support Vector Machines.- 10 Deep Learning.- 11 Survival Analysis and Censored Data.- 12 Unsupervised Learning.- 13 Multiple Testing.- Index.

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Book SynopsisStraightforward, clear, and applied, this book will give you the theoretical and practical basis you need to apply data analysis techniques to real data. Combining key statistical concepts with detailed technical advice, it addresses common themes and problems presented by real research, and shows you how to adjust your techniques and apply your statistical knowledge to a range of datasets. It also embeds code and software output throughout and is supported by online resources to enable practice and safe experimentation. The book includes: · Original case studies and data sets · Practical exercises and lists of commands for each chapter · Downloadable Stata programmes created to work alongside chapters · A wide range of detailed applications using Stata · Step-by-step guidance on writing the relevant code. This is the perfect text for anyone doing statistical research in the social sciences getting started using Stata for data analysis. Trade ReviewNewly updated, now with more advanced content, this book remains a must have for those studying applied statistics. The book is practically orientated with intuitive theoretical explanations, a wide array "how-to-do-it" examples and an engaging narrative. You won’t’ be sorry! -- Franz BuschaThis is a most impressive teaching and learning resource. Mehmetoglu and Jakobsen expertly introduce introductory to advanced social science data analysis skills in a clear and engaging manner. This text teaches students how to do data analysis in a transparent and principled manner. -- Roxanne ConnellyMehmetoglu and Jakobsen′s book offers a concise, yet comprehensive, introduction to the statistical methods that are widely used in data analysis. In addition to presenting a thorough overview of the basics of conducting empirical research, the book also emphasizes how to use Stata to analyze data in practice. This book is an excellent starting point for those who are interested in empirical work. -- Hector H. SandovalTable of ContentsChapter 1. Research and Statistics Chapter 2. Introduction to Stata Chapter 3. Simple (Bivariate) Regression Chapter 4. Multiple Regression Chapter 5. Dummy-Variable Regression Chapter 6. Interaction/Moderation Effects Using Regression Chapter 7. Linear Regression Assumptions and Diagnostics Chapter 8. Logistic Regression Chapter 9. Survival Analysis Chapter 10. Multilevel Analysis Chapter 11. Panel Data Analysis Chapter 12. Time Series Analysis Chapter 13. Exploratory Factor Analysis Chapter 14. Structural Equation Modelling and Confirmatory Factor Analysis Chapter 15. Advanced Statistical Techniques Chapter 16. Programming and Dynamic Reporting Using Stata

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Book SynopsisThis accessible guide walks you through the math necessary to thrive in the AI field such as focusing on real-world applications rather than dense academic theory. Engineers, data scientists, and students alike will examine mathematical topics critical for AI-including regression, neural networks, optimization, backpropagation, and Markov chains.

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Book SynopsisThe bestselling beginner Arduino guide, updated with new projects! Exploring Arduino makes electrical engineering and embedded software accessible. Learn step by step everything you need to know about electrical engineering, programming, and human-computer interaction through a series of increasingly complex projects.Table of ContentsIntroduction xxv Part I Arduino Engineering Basics 1 1 Getting Started and Understanding the Arduino Landscape 3 Exploring the Arduino Ecosystem 4 Arduino Functionality 5 The Microcontroller 7 Programming Interfaces 8 Input/Output: GPIO, ADCs, and Communication Busses 9 Power 9 Arduino Boards 11 Creating Your First Program 15 Downloading and Installing the Arduino IDE 16 Running the IDE and Connecting to the Arduino 17 Breaking Down Your First Program 18 Summary 21 2 Digital Inputs, Outputs, and Pulse-Width Modulation 23 Digital Outputs 24 Wiring Up an LED and Using Breadboards 24 Working with Breadboards 24 Wiring LEDs 25 Programming Digital Outputs 29 Using For Loops 30 Pulse-Width Modulation with analogWrite() 31 Reading Digital Inputs 35 Reading Digital Inputs with Pull-Down Resistors 35 Working with “Bouncy” Buttons 38 Building a Controllable RGB LED Nightlight 42 Summary 46 3 Interfacing with Analog Sensors 47 Understanding Analog and Digital Signals 48 Comparing Analog and Digital Signals 48 Converting an Analog Signal to Digital 49 Reading Analog Sensors with the Arduino: analogRead() 51 Reading a Potentiometer 51 Using Analog Sensors 56 Using Variable Resistors to Make Your Own Analog Sensors 60 Using Resistive Voltage Dividers 61 Using Analog Inputs to Control Analog Outputs 64 Summary 66 Part II Interfacing with Your Environment 67 4 Using Transistors and Driving DC Motors 69 Driving DC Motors 70 Handling High-Current Inductive Loads 71 Using Transistors as Switches 72 Using Protection Diodes73 Using a Secondary Power Source 74 Wiring the Motor 74 Controlling Motor Speed with PWM 76 Using an H-Bridge to Control DC Motor Direction 78 Building an H-Bridge Circuit 80 Operating an H-Bridge Circuit 82 Building a Roving Robot 86 Choosing the Robot Parts 87 Selecting a Motor and Gearbox 87 Powering Your Robot 87 Constructing the Robot 89 Writing the Robot Software 92 Bringing It Together 96 Summary 97 5 Driving Stepper and Servo Motors 99 Driving Servo Motors 100 Understanding the Difference between Continuous Rotation and Standard Servos 100 Understanding Servo Control 101 Controlling a Servo 104 Building a Sweeping Distance Sensor 105 Understanding and Driving Stepper Motors 109 How Bipolar Stepper Motors Work 111 Making Your Stepper Move 113 Building a “One-Minute Chronograph” 117 Wiring and Building the Chronograph 117 Programming the Chronograph 119 Summary 124 6 Making Sounds and Music 125 Understanding How Speakers Work 126 The Properties of Sound 126 How a Speaker Produces Sound 128 Using tone() to Make Sounds 129 Including a Definition File 129 Wiring the Speaker 130 Making Sound Sequences 133 Using Arrays 133 Making Note and Duration Arrays 134 Completing the Program 134 Understanding the Limitations of the tone() Function 136 Building a Micro Piano 136 Summary 139 7 USB Serial Communication 141 Understanding the Arduino’s Serial Communication Capabilities 142 Arduino Boards with an Internal or External FTDI or Silicon Labs USB-to-Serial Converter 143 Arduino Boards with a Secondary USB-Capable ATmega MCU Emulating a Serial Converter 146 Arduino Boards with a Single USB-Capable MCU 147 Arduino Boards with USB-Host Capabilities 147 Listening to the Arduino 148 Using print Statements 148 Using Special Characters 150 Changing Data Type Representations 152 Talking to the Arduino 152 Configuring the Arduino IDE’s Serial Monitor to Send Command Strings 152 Reading Incoming Data from a Computer or Other Serial Device 153 Telling the Arduino to Echo Incoming Data 153 Understanding the Differences between Chars and Ints 154 Sending Single Characters to Control an LED 156 Sending Lists of Values to Control an RGB LED 158 Talking to a Desktop App 161 Installing Processing 162 Controlling a Processing Sketch from Your Arduino 163 Sending Data from Processing to Your Arduino 166 Summary 169 8 Emulating USB Devices 171 Emulating a Keyboard 173 Typing Data into the Computer 173 Commanding Your Computer to Do Your Bidding 177 Emulating a Mouse 178 Summary 182 9 Shift Registers 183 Understanding Shift Registers 184 Sending Parallel and Serial Data 185 Working with the 74HC595 Shift Register 186 Understanding the Shift Register pin Functions 186 Understanding How the Shift Register Works 187 Shifting Serial Data from the Arduino 189 Converting Between Binary and Decimal Formats 192 Controlling Light Animations with a Shift Register 192 Building a “Light Rider” 192 Responding to Inputs with an LED Bar Graph 194 Summary 197 Part III Communication Interfaces 199 10 The I2C Bus 201 History of the I2C Bus 202 I2C Hardware Design 203 Communication Scheme and ID Numbers 203 Hardware Requirements and Pull-Up Resistors 206 Communicating with an I2C Temperature Probe 208 Setting Up the Hardware208 Referencing the Datasheet 210 Writing the Software 212 Combining Shift Registers, Serial Communication, and I2C Communications 214 Building the Hardware for a Temperature Monitoring System 214 Modifying the Embedded Program 215 Writing the Processing Sketch 218 Summary 221 11 The SPI Bus and Third-Party Libraries 223 Overview of the SPI Bus 224 SPI Hardware and Communication Design 225 Hardware Configuration 225 Communication Scheme 227 Comparing SPI to I2C and UART 227 Communicating with an SPI Accelerometer 228 What is an Accelerometer? 229 Gathering Information from the Datasheet 231 Setting Up the Hardware233 Writing the Software 235 Installing the Adafruit Sensor Libraries 236 Leveraging the Library 237 Creating an Audiovisual Instrument Using a 3-Axis Accelerometer 241 Setting Up the Hardware242 Modifying the Software 242 Summary 246 12 Interfacing with Liquid Crystal Displays 247 Setting Up the LCD 248 Using the LiquidCrystal Library to Write to the LCD 251 Adding Text to the Display 252 Creating Special Characters and Animations 254 Building a Personal Thermostat 258 Setting Up the Hardware 258 Displaying Data on the LCD 261 Adjusting the Set Point with a Button 264 Adding an Audible Warning and a Fan 265 Bringing It All Together: The Complete Program 266 Taking This Project to the Next Level 270 Summary 271 Part IV Digging Deeper and Combining Functions 273 13 Interrupts and Other Special Functions 275 Using Hardware Interrupts 276 Knowing the Tradeoffs Between Polling and Interrupting 277 Ease of Implementation (Software) 277 Ease of Implementation (Hardware) 277 Multitasking 278 Acquisition Accuracy 278 Understanding the Arduino Hardware Interrupt Capabilities 278 Building and Testing a Hardware-Debounced Button Interrupt Circuit 279 Creating a Hardware-Debouncing Circuit 280 Assembling the Complete Test Circuit 284 Writing the Software 285 Using Timer Interrupts 288 Understanding Timer Interrupts 288 Getting the Library 289 Executing Two Tasks Simultaneously(ish) 289 Building an Interrupt-Driven Sound Machine 290 Sound Machine Hardware 291 Sound Machine Software 291 Summary 294 14 Data Logging with SD Cards 295 Getting Ready for Data Logging 296 Formatting Data with CSV Files 297 Preparing an SD Card for Data Logging 297 Formatting Your SD Card Using a Windows PC 298 Formatting Your SD Card Using Mac OS 300 Formatting Your SD Card Using Linux 302 Interfacing the Arduino with an SD Card 304 SD Card Shields 304 SD Card SPI Interface 307 Writing to an SD Card 307 Reading from an SD Card 312 Real-Time Clocks 317 Understanding Real-Time Clocks 317 Communicating with a Real-Time Clock 317 Using the RTC Arduino Third-Party Library 318 Using a Real-Time Clock 319 Installing the RTC and SD Card Modules 319 Updating the Software 320 Building an Entrance Logger 327 Logger Hardware 328 Logger Software 329 Data Analysis 334 Summary 335 Part V Going Wireless 337 15 Wireless RF Communications 339 The Electromagnetic Spectrum 340 The Spectrum 342 How Your RF Link Will Send and Receive Data 343 Receiving Key Presses with the RF Link 346 Connecting Your Receiver 346 Programming Your Receiver 347 Making a Wireless Doorbell 351 Wiring the Receiver 351 Programming the Receiver 351 The Start of Your Smart Home—Controlling a Lamp 354 Your Home’s AC Power 356 How a Relay Works 356 Programming the Relay Control 358 Hooking up Your Lamp and Relay to the Arduino 360 Summary 361 16 Bluetooth Connectivity 363 Demystifying Bluetooth 364 Bluetooth Standards and Versions 364 Bluetooth Profiles and BTLE GATT Services 365 Communication between Your Arduino and Your Phone 366 Reading a Sensor over BTLE 366 Adding Support for Third-Party Boards to the Arduino IDE 367 Installing the BTLE Module Library 369 Programming the Feather Board 369 Connecting Your Smartphone to Your BTLE Transmitter 377 Sending Commands from Your Phone over BTLE 379 Parsing Command Strings 380 Commanding Your BTLE Device with Natural Language 384 Controlling an AC Lamp with Bluetooth 389 How Your Phone “Pairs” to BTLE Devices 389 Writing the Proximity Control Software 390 Pairing Your Phone 394 Pairing an Android Phone 394 Pairing an iPhone 395 Make Your Lamp React to Your Presence 396 Summary 397 17 Wi-Fi and the Cloud 399 The Web, the Arduino, and You 400 Networking Lingo 401 The Internet vs. the World Wide Web vs. the Cloud 401 IP Address 401 Network Address Translation 402 MAC Address 402 HTML 402 HTTP and HTTPS 402 GET/POST 403 DHCP 403 DNS 403 Clients and Servers 403 Your Wi-Fi–Enabled Arduino 404 Controlling Your Arduino from the Web 404 Setting Up the I/O Control Hardware 404 Preparing the Arduino IDE for Use with the Feather Board.406 Ensuring the Wi-Fi Library is Matched to the Wi-Fi Module’s Firmware 407 Checking the WINC1500’s Firmware Version 408 Updating the WINC1500’s Firmware 408 Writing an Arduino Server Sketch 408 Connecting to the Network and Retrieving an IP Address via DHCP 409 Writing the Code for a Bare-Minimum Web Server 412 Controlling Your Arduino from Inside and Outside Your Local Network 423 Controlling Your Arduino over the Local Network 423 Using Port Forwarding to Control Your Arduino from Anywhere 425 Interfacing with Web APIs 427 Using a Weather API428 Creating an Account with the API Service Provider 429 Understanding How APIs are Structured 430 JSON-Formatted Data and Your Arduino 430 Fetching and Parsing Weather Data 431 Getting the Local Temperature from the Web on Your Arduino 433 Completing the Live Temperature Display 440 Wiring up the LED Readout Display 440 Driving the Display with Temperature Data 443 Summary 449 Appendix A: Deciphering Datasheets and Schematics 451 Index 461

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Book SynopsisWritten in a clear and lively tone, Statistics Using IBM SPSS provides a data-centric approach to statistics with integrated SPSS (version 22) commands, ensuring that students gain both a deep conceptual understanding of statistics and practical facility with the leading statistical software package. With one hundred worked examples, the textbook guides students through statistical practice using real data and avoids complicated mathematics. Numerous end-of-chapter exercises allow students to apply and test their understanding of chapter topics, with detailed answers available online. The third edition has been updated throughout and includes a new chapter on research design, new topics (including weighted mean, resampling with the bootstrap, the role of the syntax file in workflow management, and regression to the mean) and new examples and exercises. Student learning is supported by a rich suite of online resources, including answers to end-of-chapter exercises, real data sets, PowerTrade Review'This is the third edition of a very popular and useful text. The focus is on using SPSS in the research process. The chapters have illustrative exercises and meaningful real data problem sets that not only make it convenient for teaching but also provide realistic experiences for students that will stay with them for many years. The book does a very good job presenting the challenge of data analysis and the experience of being a serious researcher looking at important problems; it illustrates how a variety of quantitative methods can be applied to real data to tease out and evaluate the inferences suggested by that data. I strongly recommend this book to instructors of a one- or two-semester introductory statistics course.' Robert W. Lissitz, University of Maryland'This text by Weinberg and Abramowitz is an excellent choice for an undergraduate or introductory graduate course for non-majors. Stressing concepts over computation, it focuses on essential material for students in education and the social sciences. The book reads easily, like a set of well-constructed lectures that begin with simple fundamental concepts. Yet modern and relatively advanced topics, such as uses of the bootstrap, are also treated. Rather than focusing on hand calculations, the book integrates instruction on using SPSS directly into the text. This enables student exploration of actual research data sets, beginning in the first chapters.' James E. Corter, Columbia University'This book covers a broad range of topics in introductory statistics, employing a hands-on, problem-based approach. The latest edition expands an already long list of topics to include bootstrap techniques and experimental design considerations. By providing detailed, worked-through examples based on real data and substantive research questions, the authors guide the student through the data analysis process from beginning to end. However, this is no 'cookbook' - each section builds on the concepts and techniques established previously, and the reader is encouraged to explore the nuances involved in effective statistical analysis. What is particularly unique about the authors' exposition is that it can be read on many levels; this book will serve well as a course textbook or as a handy reference for the applied researcher.' Marc A. Scott, New York UniversityTable of Contents1. Introduction; 2. Examining univariate distributions; 3. Measures of location, spread, and skewness; 4. Re-expressing variables; 5. Exploring relationships between two variables; 6. Simple linear regression; 7. Probability fundamentals; 8. Theoretical probability models; 9. The role of sampling in inferential statistics; 10. Inferences involving the mean of a single population when σ is known; 11. Inferences involving the mean when σ is not known: one- and two-sample designs; 12. Research design: introduction and overview; 13. One-way analysis of variance; 14. Two-way analysis of variance; 15. Correlation and simple regression as inferential techniques; 16. An introduction to multiple regression; 17. Nonparametric methods.

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Book SynopsisThe updated guide to the newest graphing calculator from Texas Instruments The TI-Nspire graphing calculator is popular among high school and college students as a valuable tool for calculus, AP calculus, and college-level algebra courses. Its use is allowed on the major college entrance exams.Table of ContentsIntroduction 1 Part I: Getting to Know Your TI-Nspire Handheld 9 Chapter 1: Using TI-Nspire for the First Time 11 Chapter 2: Understanding the Document Structure 25 Chapter 3: Creating and Editing Documents 37 Chapter 4: Linking Handhelds 47 Part II: The Calculator Application 51 Chapter 5: Entering and Evaluating Expressions 53 Chapter 6: Working with Variables 69 Chapter 7: Using the Calculator Application with Other Applications 77 Chapter 8: Using the Calculator Application with TI-Nspire CAS 85 Part III: The Graphs Application 99 Chapter 9: Working with Graphs 101 Chapter 10: Using the Graphs Application to Do Calculus 131 Part IV: The Geometry Application 135 Chapter 11: Working with Geometric Objects 137 Chapter 12: Using an Analytic Window in the Geometry Application 159 Part V: The Lists & Spreadsheet Application 165 Chapter 13: Applying What You Already Know about Spreadsheets 167 Chapter 14: Working with Data 177 Chapter 15: Constructing Scatter Plots and Performing Regressions 189 Chapter 16: Manual and Automatic Data Capture 201 Part VI: The Data & Statistics and Vernier DataQuest Applications 209 Chapter 17: Constructing Statistical Graphs 211 Chapter 18: Working with Single-Variable Data 215 Chapter 19: Working with Two-Variable Data 227 Chapter 20: Data Collection 237 Part VII: The Notes Application 249 Chapter 21: The Why and How of Using Notes 251 Chapter 22: Taking Notes to a Whole New Level 255 Part VIII: TI-Nspire Computer Software 261 Chapter 23: Getting Started with TI-Nspire Computer Software 263 Chapter 24: File Creation and Display in Documents Workspace 271 Chapter 25: File Management with Content Workspace 287 Part IX: The Part of Tens 295 Chapter 26: Ten Great Tips and Shortcuts 297 Chapter 27: Ten Common Problems Resolved 305 Appendix A: Safeguarding in Press-to-Test Mode 311 Appendix B: Basic Programming 315 Appendix C: Working with Libraries 331 Index 337

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Book SynopsisArguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware.AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance.Danske Bank''s wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank''s systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives Table of ContentsModern Computational Finance xi Preface by Leif Andersen xv Acknowledgments xix Introduction xxi About the Companion C++ Code xxv PART I Modern Parallel Programming 1 Introduction 3 CHAPTER 1 Effective C++ 17 CHAPTER 2 Modern C++ 25 2.1 Lambda expressions 25 2.2 Functional programming in C++ 28 2.3 Move semantics 34 2.4 Smart pointers 41 CHAPTER 3 Parallel C++ 47 3.1 Multi-threaded Hello World 49 3.2 Thread management 50 3.3 Data sharing 55 3.4 Thread local storage 56 3.5 False sharing 57 3.6 Race conditions and data races 62 3.7 Locks 64 3.8 Spinlocks 66 3.9 Deadlocks 67 3.10 RAII locks 68 3.11 Lock-free concurrent design 70 3.12 Introduction to concurrent data structures 72 3.13 Condition variables 74 3.14 Advanced synchronization 80 3.15 Lazy initialization 83 3.16 Atomic types 86 3.17 Task management 89 3.18 Thread pools 96 3.19 Using the thread pool 108 3.20 Debugging and optimizing parallel programs 113 PART II Parallel Simulation 123 Introduction 125 CHAPTER 4 Asset Pricing 127 4.1 Financial products 127 4.2 The Arbitrage Pricing Theory 140 4.3 Financial models 151 CHAPTER 5 Monte-Carlo 185 5.1 The Monte-Carlo algorithm 185 5.2 Simulation of dynamic models 192 5.3 Random numbers 200 5.4 Better random numbers 202 CHAPTER 6Serial Implementation 213 6.1 The template simulation algorithm 213 6.2 Random number generators 223 6.3 Concrete products 230 6.4 Concrete models 245 6.5 User interface 263 6.6 Results 268 CHAPTER 7 Parallel Implementation 271 7.1 Parallel code and skip ahead 271 7.2 Skip ahead with mrg32k3a 276 7.3 Skip ahead with Sobol 282 7.4 Results 283 PART III Constant Time Differentiation 285 Introduction 287 CHAPTER 8 Manual Adjoint Differentiation 295 8.1 Introduction to Adjoint Differentiation 295 8.2 Adjoint Differentiation by hand 308 8.3 Applications in machine learning and finance 315 CHAPTER 9 Algorithmic Adjoint Differentiation 321 9.1 Calculation graphs 322 9.2 Building and applying DAGs 328 9.3 Adjoint mathematics 340 9.4 Adjoint accumulation and DAG traversal 344 9.5 Working with tapes 349 CHAPTER 10 Effective AAD and Memory Management 357 10.1 The Node class 359 10.2 Memory management and the Tape class 362 10.3 The Number class 379 10.4 Basic instrumentation 398 CHAPTER 11 Discussion and Limitations 401 11.1 Inputs and outputs 401 11.2 Higher-order derivatives 402 11.3 Control flow 402 11.4 Memory 403 CHAPTER 12 Differentiation of the Simulation Library 407 12.1 Active code 407 12.2 Serial code 409 12.3 User interface 417 12.4 Serial results 424 12.5 Parallel code 426 12.6 Parallel results 433 CHAPTER 13 Check-Pointing and Calibration 439 13.1 Check-pointing 439 13.2 Explicit calibration 448 13.3 Implicit calibration 475 CHAPTER 14 Multiple Differentiation in Almost Constant Time 483 14.1 Multidimensional differentiation 483 14.2 Traditional Multidimensional AAD 484 14.3 Multidimensional adjoints 485 14.4 AAD library support 487 14.5 Instrumentation of simulation algorithms 494 14.6 Results 499 CHAPTER 15 Acceleration with Expression Templates 503 15.1 Expression nodes 504 15.2 Expression templates 507 15.3 Expression templated AAD code 524 Debugging AAD Instrumentation 541 Conclusion 547 References 549 Index 555

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Book SynopsisWith the book in one hand and a 3D printed model in the other, readers can find deeper meaning while holding a hyperbolic honeycomb, touching the twists of a torus knot, or caressing the curves of a Klein quartic.Trade ReviewMy best advice is to go out and buy yourself a copy of the book. Chalkdust Magazine The breadth of Segerman's 3D printing explorations is impressive. Coupled with the clarity of his explanations of the mathematics behind those explorations, this book becomes an easy recommendation for any reader interested in learning some beautiful mathematical ideas. Journal of Mathematics and the Arts No previous mathematical maturity is required. The work is a good addition to any academic library. Highly recommended Choice I have great difficulty thinking about Visualizing Mathematics with 3D Printing as "just a book." The careful choice, quality and effectiveness of the 140+ images in the book is outstanding. What Segerman has developed is much bigger than a book he has developed a whole platform to complement the book and explore mathematical concepts. Visualizing Mathematics with 3D printing allows the reader to manipulate with a computer or 3D print the objects discussed, making it possible to physically interact with the concepts. Mathematical Association of AmericaTable of ContentsPrefaceAcknowledgments1. Symmetry2. Polyhedra3. Four-Dimensional Space4. Tilings and Curvature5. Knots6. Surfaces7. MenagerieAppendix AAppendix BIndex

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Book SynopsisLearn how to program by diving into the R language, and then use your newfound skills to solve practical data science problems. With this book, you'll learn how to load data, assemble and disassemble data objects, navigate R's environment system, write your own functions, and use all of R's programming tools

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Book SynopsisComputational complexity is one of the most beautiful fields of modern mathematics, and it is increasingly relevant to other sciences ranging from physics to biology. But this beauty is often buried underneath layers of unnecessary formalism, and exciting recent results like interactive proofs, phase transitions, and quantum computing are usually considered too advanced for the typical student. This book bridges these gaps by explaining the deep ideas of theoretical computer science in a clear and enjoyable fashion, making them accessible to non-computer scientists and to computer scientists who finally want to appreciate their field from a new point of view. The authors start with a lucid and playful explanation of the P vs. NP problem, explaining why it is so fundamental, and so hard to resolve. They then lead the reader through the complexity of mazes and games; optimization in theory and practice; randomized algorithms, interactive proofs, and pseudorandomness; Markov chains and phase transitions; and the outer reaches of quantum computing. At every turn, they use a minimum of formalism, providing explanations that are both deep and accessible. The book is intended for graduate and undergraduate students, scientists from other areas who have long wanted to understand this subject, and experts who want to fall in love with this field all over again.Trade ReviewA creative, insightful, and accessible introduction to the theory of computing, written with a keen eye toward the frontiers of the field and a vivid enthusiasm for the subject matter. * Jon Kleinberg, Cornell University *To put it bluntly: this book rocks! It's 900+ pages of awesome. It somehow manages to combine the fun of a popular book with the intellectual heft of a textbook, so much so that I don't know what to call it (but whatever the genre is, there needs to be more of it!). * Scott Aaronson, Massachusetts Institute of Technology *Moore and Mertens guide the reader through the interesting field of computational complexity in a clear, broadly accessible and informal manner, while systematically explaining the main concepts and approaches in this area and the existing links to other disciplines. The book is comprehensive and can be easily used as a textbook, at both advanced undergraduate and postgraduate levels, but is equally useful for researchers in neighbouring disciplines, such as statistical physics [...]. Some of the material covered, such as approximability issues and Probabilistically Checkable Proofs is typically not presented in books of this type, and the authors do an excellent job in presenting them very clearly and convincingly. * David Saad, Aston University, Birmingham *A treasure trove of ideas, concepts and information on algorithms and complexity theory. Serious material presented in the most delightful manner! * Vijay Vazirani, Georgia Instituute of Technology *In a class by itself - in The Nature of Computation, Cristopher Moore and Stephan Mertens have produced one of the most successful attempts to capture the broad scope and intellectual depth of theoretical computer science as it is practiced today. The Nature of Computation is one of those books you can open to a random page and find something amazing, surprising and, often, very funny. * American Scientist *a comprehensive, accessible, and highly enjoyable book that conveys the key intellectual contributions of the theory of computing ... a valuable resource for any educator * Haris Aziz, SIGACT *The book is highly recommended for all interested readers: in or out of courses, students undergraduate or graduate, researchers in other fields eager to learn the subject, or scholars already in the field who wish to enrich their current understanding. It makes for a great textbook in a conventional theory of computing course, as I can testify from recent personal experience (I used it once; Ill use it again!). With its broad and deep wealth of information, it would be a top contender for one of my desert island books.TNoC speaks directly, clearly, convincingly, and entetainingly, but also goes much further: it inspires. * Frederic Green, SIGACT *Table of Contents1. Prologue ; 2. The Basics ; 3. Insights and Algorithms ; 4. Needles in a Haystack: The class NP ; 5. Who is the Hardest One of All: NP-Completeness ; 6. The Deep Question: P vs. NP ; 7. Memory, Paths and games ; 8. Grand Unified Theory of Computation ; 9. Simply the Best: Optimization ; 10. The Power of Randomness ; 11. Random Walks and Rapid Mixing ; 12. Counting, Sampling, and Statistical Physics ; 13. When Formulas Freeze: Phase Transitions in Computation ; 14. Quantum Computing ; 15. Epilogue ; 16. Appendix: Mathematical Tools

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Book SynopsisFrom a review of the first edition: Modern Data Science with R is rich with examples and is guided by a strong narrative voice. What's more, it presents an organizing framework that makes a convincing argument that data science is a course distinct from applied statistics (The American Statistician).Modern Data Science with R is a comprehensive data science textbook for undergraduates that incorporates statistical and computational thinking to solve real-world data problems. Rather than focus exclusively on case studies or programming syntax, this book illustrates how statistical programming in the state-of-the-art R/RStudio computing environment can be leveraged to extract meaningful information from a variety of data in the service of addressing compelling questions.The second edition is updated to reflect the growing influence of the tidyverse set of packages. All code in the book has been revised and styled to be more readable and easier to undTrade Review"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." (Hunter Glanz Cal Poly San Luis Obispo)"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." (Lynn Collen, St. Cloud Stat Univ.)"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." (Matthew Beckman, Penn State University)"[...] To answer a wide range of modern research questions, this book by Baumer, Kaplan, and Horton features an excellent introduction to data wrangling, visualization, statistical modeling, machine learning, and other advanced statistical applications through the RStudio environment following the tidyverse syntax. [...] Overall, Modern Data Science with R, 2nd edition serves as an excellent introductory resource to help develop techniques to extract, transform, visualize, and learn from datasets through the R environment. It focuses on implementing those techniques in R and does not provide a theoretical background for the discussed methods. The book will be a perfect reference for a broad audience ranging from undergraduates in data science courses to advanced graduate students and professionals from a variety of research fields."-Kohma Arai and Vyacheslav Lyubchich, in Technometrics, July 2022"Overall, I enjoyed reading this book. The authors were very good at creating a complete tool for studying data science. Therefore, I recommend this book, for its content, writing, and organization, to graduate students in data science and statistics. I also recommend the book to professionals who should prepare themselves for the challenges they are going to face in the future with the voluminous and heterogenous amount of data that should be timely analyzed to extract meaningful information to guide action."-Georgios Nikolopoulos, in ISCB News, June 2022"The authors have successfully completed the job of choosing the content with relevant topics and, deciding the extent of knowledge to be delivered, and finally, putting them in an understandable sequence. This is a well-written book and does not cover much theory. .. The book’s second edition contents are updated, expanded, revised, split, rewritten and rearranged compared to the first edition. The key changes are the use of recently developed R packages, .... (and) updated exercises in the chapters ..."-Shalabh,in Journal of the Royal Statistical Society Series A, August 2021"[This book] provides an excellent basis for statisticians who want to dig deeper into, for example, data handling, for computer scientists who aim to strengthen their knowledge of statistical methods as well as for all other researchers who are interested in data science in general. ... Each section is structured as an interplay between R-code and explanatory text for understanding. The division into several stand-alone segments is an advantage, because the reader may easily choose the section she or he is interested in without missing relevant information. A key feature of the book is its focus on different example data sets that are available via R-packages or from URLs that are embedded in the text. These data sets are used to illustrate the methodology presented using R-code. Their availability allows the reader to reproduce the code while working with the book. ... It can be warmly recommended to practical researchers who seek a comprehensive overview of different topics in data science with focus on implementations in R."-Annika Hoyer, in Biometrical Journal, August 2021"This text continues to be fantastic! There are a number of courses for which I would require this book and others that I would recommend it as a supplement. I would likely require it for courses focused on computing in R or courses in data science. I would include it as a recommended text in introductory and other statistics courses that used R as the software of choice, where this text could be used as a supplemental resource in how to use R to work with data." -Hunter Glanz, Cal Poly San Luis Obispo"Easy for students to read and relate to the exercises and examples. Many questions and hands-on activities with data sets to practice skills." -Lynn Collen, St. Cloud Stat University"I used the first edition of this book as the primary text for an intermediate data science course a few years ago and I liked it very much…I think that the technical breadth, writing style, and level of difficulty are very clear strengths. Also, my students and I found the `tidyverse` approach to be particularly well-suited for teaching and learning R…and I love that the MDSR book includes such complete code. Students can program everything they see in the book, and often times there are tips & tricks for them to discover along the way just by studying expert code provided by the authors. This really sets MDSR apart from other books I considered for the course." -Matthew Beckman, Penn State University"The authors have covered almost all aspects of data science, a revolutionary field that marries elements of computational thinking and traditional statistical theory. The book can thus equip the readers with the necessary knowledge and skills to extract data from a variety of sources, restructure observations in a form that allows analysis, store data in efficient databases, and work effectively on massive and complex data sets in order to produce actionable information."- Georgios Nikolopoulos, University of Cyprus, ISCB Book Reviews, June 2022.Table of ContentsI Part I: Introduction to Data Science. 1. Prologue: Why data science? 2. Data visualization. 3. A grammar for graphics. 4. Data wrangling on one table. 5. Data wrangling on multiple tables. 6. Tidy data. 7. Iteration. 8. Data science ethics. II. Part II: Statistics and Modeling. 9. Statistical foundations. 10. Predictive modeling. 11. Supervised learning. 12. Unsupervised learning. 13. Simulation. III Part III: Topics in Data Science. 14. Dynamic and customized data graphics. 15. Database querying using SQL. 16. Database administration. 17. Working with spatial data. 18.Geospatial computations. 19. Text as data. 20. Network science. IV Part IV: Appendices.

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Book SynopsisThis textbook offers a refreshingly clear and digestible introduction to statistical analysis for psychology using the user-friendly jamovi software. The authors provide a concise, practical guide that takes students from the early stages of research design, with a jargon-free explanation of terminology, and walks them through key analyses such as the t-test, ANOVA, correlation, chi-square, and linear regression. The book features written interpretations to help learners identify relevant statistics along the way. With fascinating examples from psychological research, as well as screenshots and activities from jamovi, this text is sure to encourage even the most reluctant statistics student. The comprehensive companion website provides an extra helping hand, with practice datasets and a full suite of tutorial videos to help consolidate understanding. This is essential reading for psychology students using jamovi for their courses in Research Methods and Statistics or Data Analysis.Trade ReviewJamovi for Psychologists offers a complete overview of topics in introductory statistics in an easy, conversational tone. But what makes it especially valuable is its practical emphasis—how to use very accessible software, fully understand its output, and appropriately report the results. It’s the kind of book students will actually find useful! * Andy Luttrell, Ball State University, USA *Jamovi for Psychologists is an excellent resource for those learning to use jamovi as part of a statistics course or for those seeking to better understand the wide range of statistical tests available in the software. The straightforward step-by-step instructions and conceptual framing of statistical analyses will help faculty make statistics and jamovi more accessible to students. * Andrew Mienaltowski, Western Kentucky University, USA *Jamovi for Psychologists, is a friendly introduction to the accessible statistics package jamovi. It is well-pitched for psychologists beginning to learn about statistics and includes concise but thorough guides throughout. * Piers Fleming, University of East Anglia, UK *Table of Contents1. Research Design 2. Data Preparation, Common Assumptions, and Descriptive Statistics 3. P-Values, Effect Sizes and 95% confidence intervals 4. Statistical Power 5. Reliability and Validity 6. Correlations 7. Chi Square 8. Independent T-Tests 9. Paired T-Tests 10. Comparing multiple means for Between-subjects designs (One-way ANOVA & Kruskal-Wallis) 11. Comparing multiple means for Repeated measures designs (one-way ANOVA and Friedman’s ANOVA) 12. Factorial ANOVA (assessing effects of multiple independent variables) 13. Simple, Multiple, and Hierarchical Linear Regression.

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Book SynopsisPsychoPy is an open-source software package for creating rich, dynamic experiments in psychology, neuroscience and linguistics. Written by its creator, this book walks you through the steps of building experiments in PsychoPy, from using images to discovering lesser-known features, and from analysing data to debugging your experiment. Divided into three parts and with unique extension exercises to guide you at whatever level you are at, this textbook is the perfect tool for teaching practical undergraduate classes on research methods, as well as acting as a comprehensive reference text for the professional scientist. Essential reading for anyone using PsychoPy software, the second edition has been fully updated and includes multiple new chapters about features included in recent versions of PsychoPy, including running studies online and collecting survey data. Part I teaches you all the basic skills you need (and some more advanced tips along the way) to design experiments in behavioral sciences. Each chapter introduces anew concept but will offer a series of working experiments that you can build on. Part II presents more details important for professional scientists intending to use PsychoPy for published research. This part is recommended reading for science professionals in any discipline. Part III covers a range of specialist topics, such as those doing fMRI research, or those studying visual perception. "This book fills an incredibly important gap in the field. Many users of PsychoPy will be excited to learn that there is now a highly accessible and well-designed written guide to refine their skills." – Susanne Quadflieg, University of BristolTrade ReviewThe 2020 pandemic has forced a lot of researchers to move their physical lab experiments online. If you are already doing this, or thinking about it, the second edition of "Building Experiments in PsychoPy" is a must have. The new edition offers sage advice on online data collection and provides a walkthrough on how to use some of its new components (e.g., creating surveys via Forms. ). -- Jason GellerIn ye olden days when I was a student, we had to script our experiments, which was tedious and error-prone, or use proprietary software, which was expensive and inflexible. This is why I love PsychoPy Builder, and Building Experiments in PsychoPy is a great resource for today‘s budding experimenters. -- Heiða María SigurðardóttirTable of ContentsChapter 1: Introduction PART I: FOR THE BEGINNER Chapter 2: Building your first experiment Chapter 3: Using images: A study into face perception Chapter 4: Timing and brief stimuli: Posner cueing Chapter 5: Running studies online Chapter 6: Creating dynamic stimuli (revealing text and moving stimuli) Chapter 7: Providing feedback: Simple code components Chapter 8: Collecting survey data using forms Chapter 9: Using sliders Chapter 10: Randomizing and counterbalancing blocks of trials: A bilingual Stroop task Chapter 11: Using the mouse for input: Creating a visual search task PART II: FOR THE PROFESSIONAL Chapter 12: Implementing research designs with randomization Chapter 13: Coordinates and color spaces Chapter 14: Understanding your computer timing issues Chapter 15: Monitors and monitor center Chapter 16: Debugging your experiment Chapter 17: Pro tips, tricks, and lesser-known features PART III: FOR THE SPECIALIST Chapter 18: Psychophysics, stimuli and staircases Chapter 19: Building an FMRI study Chapter 20: Building an EEG study Chapter 21: Add eye tracking to your experiment Appendices

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Book SynopsisWhether you are new to Stata graphics or a seasoned veteran, this book will teach you how to use Stata to make publication-quality graphs that will stand out and enhance your statistical results. With over 1,200 illustrated examples and quick-reference tabs, this book quickly guides you to the information you need for creating and customizing high-quality graphs for any type of statistical data. Each graph is displayed in full color with simple and clear instructions that illustrate how to create and customize graphs using Stata commands. Whether you use this book as a learning tool or a quick reference, you will have the power of Stata graphics at your fingertips.Table of Contents1. Introduction 2. Twoway graphs 3. Scatterplot matrix graphs 4. Bar graphs 5. Box plots 6. Dot plots 7. Pie charts 8. Options available for most graphs 9. Standard options available for all graphs 10. Styles for changing the look of graphs 11. Appendix

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Book SynopsisThis book provides hands-on guidance for researchers and practitioners in criminal justice and criminology to perform statistical analyses and data visualization in the free and open-source software R. It offers a step-by-step guide for beginners to become familiar with the RStudio platform and tidyverse set of packages. This volume will help users master the fundamentals of the R programming language, providing tutorials in each chapter that lay out research questions and hypotheses centering around a real criminal justice dataset, such as data from the National Survey on Drug Use and Health, National Crime Victimization Survey, Youth Risk Behavior Surveillance System, The Monitoring the Future Study, and The National Youth Survey. Users will also learn how to manipulate common sources of agency data, such as calls-for-service (CFS) data. The end of each chapter includes exercises that reinforce the R tutorial examples, designed to help master the software as well as to provide practice on statistical concepts, data analysis, and interpretation of results. The text can be used as a stand-alone guide to learning R or it can be used as a companion guide to an introductory statistics textbook, such as Basic Statistics in Criminal Justice (2020).Table of Contents1. Getting started.2. Managing your data.3. Data visualization.4. Spatiotemporal data visualization and basic crime analysis.5. Descriptive statistics: measures of central tendency.6. Descriptive statistics: measures of dispersion.7. Statistical inference in criminal justice research.8. Defining the observed significance level of a test.9. Hypothesis testing using the binomial distribution.10. Chi-square: a test commonly used for nominal-level measures.11. The normal distribution and its application to tests of statistical significance.12. Comparing means in two samples.13. Analysis of variance.14. Measures of association for nominal and ordinal variables.15. Measuring association for interval data.16. Introduction to regression analysis.

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Book SynopsisTrade Review"A thorough, very clearly written overview on the subject of machine learning for those with the prerequisite mathematical tools of calculus, linear algebra and probability."---Jonathan Shock, Mathemafrica"Valuable."---J. Brzezinski, Choice

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Book SynopsisTable of ContentsIntroduction 1 About This Book 2 What’s New in This Edition 2 What’s New in Excel (Microsoft 365) 3 Foolish Assumptions 3 Icons Used in This Book 4 Where to Go from Here 5 Beyond This Book 5 Part 1: Getting Started With Statistical Analysis With Excel: A Marriage Made In Heaven 7 Chapter 1: Evaluating Data in the Real World 9 The Statistical (and Related) Notions You Just Have to Know 9 Samples and populations 10 Variables: Dependent and independent 11 Types of data 12 A little probability 13 Inferential Statistics: Testing Hypotheses 14 Null and alternative hypotheses 15 Two types of error 16 Some Excel Fundamentals 18 Autofilling cells 22 Referencing cells 25 Chapter 2: Understanding Excel’s Statistical Capabilities 29 Getting Started 30 Setting Up for Statistics 32 Worksheet functions 32 Quickly accessing statistical functions 36 Array functions 38 What’s in a name? An array of possibilities 41 Creating Your Own Array Formulas 50 Using data analysis tools 51 Additional data analysis tool packages 56 Accessing Commonly Used Functions 58 The New Analyze Data Tool 59 Data from Pictures! 60 Part 2: Describing Data 63 Chapter 3: Show-and-Tell: Graphing Data 65 Why Use Graphs? 65 Examining Some Fundamentals 67 Gauging Excel’s Graphics (Chartics?) Capabilities 68 Becoming a Columnist 69 Stacking the Columns 73 Slicing the Pie 74 A word from the wise 76 Drawing the Line 77 Adding a Spark 80 Passing the Bar 82 The Plot Thickens 84 Finding Another Use for the Scatter Chart 88 Chapter 4: Finding Your Center 91 Means: The Lore of Averages 91 Calculating the mean 92 AVERAGE and AVERAGEA 93 AVERAGEIF and AVERAGEIFS 95 TRIMMEAN 99 Other means to an end 100 Medians: Caught in the Middle 102 Finding the median 102 MEDIAN 103 Statistics à la Mode 104 Finding the mode 104 MODE.SNGL and MODE.MULT 104 Chapter 5: Deviating from the Average 107 Measuring Variation 108 Averaging squared deviations: Variance and how to calculate it 108 VAR.P and VARPA 111 Sample variance 113 VAR.S and VARA 114 Back to the Roots: Standard Deviation 114 Population standard deviation 115 STDEV.P and STDEVPA 115 Sample standard deviation 116 STDEV.S and STDEVA 116 The missing functions: STDEVIF and STDEVIFS 117 Related Functions 121 DEVSQ 121 Average deviation 122 AVEDEV 123 Chapter 6: Meeting Standards and Standings 125 Catching Some Z’s 126 Characteristics of z-scores 126 Bonds versus the Bambino 127 Exam scores 128 STANDARDIZE 128 Where Do You Stand? 131 RANK.EQ and RANK.AVG 131 LARGE and SMALL 133 PERCENTILE.INC and PERCENTILE.EXC 134 PERCENTRANK.INC and PERCENTRANK.EXC 137 Data analysis tool: Rank and Percentile 138 Chapter 7: Summarizing It All 141 Counting Out 141 COUNT, COUNTA, COUNTBLANK, COUNTIF, COUNTIFS 141 The Long and Short of It 144 MAX, MAXA, MIN, and MINA 144 Getting Esoteric 145 SKEW and SKEW.P 146 KURT 148 Tuning In the Frequency 150 FREQUENCY 150 Data analysis tool: Histogram 152 Can You Give Me a Description? 154 Data analysis tool: Descriptive Statistics 154 Be Quick About It! 156 Instant Statistics 159 Chapter 8: What’s Normal? 161 Hitting the Curve 161 Digging deeper 162 Parameters of a normal distribution 163 NORM.DIST 165 NORM.INV 167 A Distinguished Member of the Family 168 NORM.S.DIST 169 NORM.S.INV 170 PHI and GAUSS 170 Graphing a Standard Normal Distribution 171 Part 3: Drawing Conclusions From Data 173 Chapter 9: The Confidence Game: Estimation 175 Understanding Sampling Distributions 176 An EXTREMELY Important Idea: The Central Limit Theorem 177 (Approximately) simulating the Central Limit Theorem 178 The Limits of Confidence 183 Finding confidence limits for a mean 183 CONFIDENCE.NORM 186 Fit to a t 187 CONFIDENCE.T 188 Chapter 10: One-Sample Hypothesis Testing 189 Hypotheses, Tests, and Errors 190 Hypothesis Tests and Sampling Distributions 191 Catching Some Z’s Again 193 Z.TEST 196 t for One 197 T.DIST, T.DIST.RT, and T.DIST.2T 198 T.INV and T.INV.2T 200 Visualizing a t-Distribution 201 Testing a Variance 203 CHISQ.DIST and CHISQ.DIST.RT 205 CHISQ.INV and CHISQ.INV.RT 206 Visualizing a Chi-Square Distribution 208 Chapter 11: Two-Sample Hypothesis Testing 211 Hypotheses Built for Two 211 Sampling Distributions Revisited 212 Applying the Central Limit Theorem 213 Z’s once more 215 Data analysis tool: z-Test: Two Sample for Means 216 t for Two 219 Like peas in a pod: Equal variances 220 Like p’s and q’s: Unequal variances 221 T.TEST 222 Data analysis tool: t-Test: Two Sample 223 A Matched Set: Hypothesis Testing for Paired Samples 227 T.TEST for matched samples 228 Data analysis tool: t-Test: Paired Two Sample for Means 230 t-tests on the iPad with StatPlus 232 Testing Two Variances 235 Using F in conjunction with t 237 F.TEST 238 F.DIST and F.DIST.RT 240 F.INV and F.INV.RT 241 Data analysis tool: F-test: Two Sample for Variances 242 Visualizing the F-Distribution 244 Chapter 12: Testing More Than Two Samples 247 Testing More than Two 247 A thorny problem 248 A solution 249 Meaningful relationships 253 After the F-test 254 Data analysis tool: Anova: Single Factor 258 Comparing the means 260 Another Kind of Hypothesis, Another Kind of Test 262 Working with repeated measures ANOVA 262 Getting trendy 264 Data analysis tool: Anova: Two-Factor Without Replication 268 Analyzing trend 271 ANOVA on the iPad 272 ANOVA on the iPad: Another Way 274 Repeated Measures ANOVA on the iPad 277 Chapter 13: Slightly More Complicated Testing 281 Cracking the Combinations 281 Breaking down the variances 282 Data analysis tool: Anova: Two-Factor Without Replication 284 Cracking the Combinations Again 286 Rows and columns 286 Interactions 287 The analysis 288 Data analysis tool: Anova: Two-Factor With Replication 289 Two Kinds of Variables — at Once 292 Using Excel with a Mixed Design 293 Graphing the Results 298 After the ANOVA 300 Two-Factor ANOVA on the iPad 300 Chapter 14: Regression: Linear and Multiple 303 The Plot of Scatter 303 Graphing a line 305 Regression: What a Line! 307 Using regression for forecasting 309 Variation around the regression line 309 Testing hypotheses about regression 311 Worksheet Functions for Regression 317 SLOPE, INTERCEPT, STEYX 318 FORECAST.LINEAR 319 Array function: TREND 319 Array function: LINEST 323 Data Analysis Tool: Regression 325 Working with tabled output 327 Opting for graphical output 329 Juggling Many Relationships at Once: Multiple Regression 330 Excel Tools for Multiple Regression 331 TREND revisited 331 LINEST revisited 333 Regression data analysis tool revisited 336 Regression Analysis on the iPad 338 Chapter 15: Correlation: The Rise and Fall of Relationships 341 Scatterplots Again 341 Understanding Correlation 342 Correlation and Regression 345 Testing Hypotheses about Correlation 347 Is a correlation coefficient greater than zero? 348 Do two correlation coefficients differ? 349 Worksheet Functions for Correlation 350 CORREL and PEARSON 350 RSQ 351 COVARIANCE.P and COVARIANCE.S 352 Data Analysis Tool: Correlation 353 Tabled output 354 Multiple correlation 355 Partial correlation 356 Semipartial correlation 357 Data Analysis Tool: Covariance 358 Using Excel to Test Hypotheses about Correlation 358 Worksheet functions: FISHER, FISHERINV 359 Correlation Analysis on the iPad 360 Chapter 16: It’s About Time 363 A Series and Its Components 363 A Moving Experience 364 Lining up the trend 365 Data analysis tool: Moving Average 365 How to Be a Smoothie, Exponentially 368 One-Click Forecasting 369 Working with Time Series on the iPad 374 Chapter 17: Nonparametric Statistics 379 Independent Samples 380 Two samples: Mann-Whitney U test 380 More than two samples: Kruskal-Wallis one-way ANOVA 382 Matched Samples 383 Two samples: Wilcoxon matched-pairs signed ranks 384 More than two samples: Friedman two-way ANOVA 386 More than two samples: Cochran’s Q 387 Correlation: Spearman’s rS 389 A Heads-Up 391 Part 4: Probability 393 Chapter 18: Introducing Probability 395 What Is Probability? 395 Experiments, trials, events, and sample spaces 396 Sample spaces and probability 396 Compound Events 397 Union and intersection 397 Intersection, again 398 Conditional Probability 399 Working with the probabilities 400 The foundation of hypothesis testing 400 Large Sample Spaces 400 Permutations 401 Combinations 402 Worksheet Functions 403 FACT 403 PERMUT and PERMUTIONA 403 COMBIN and COMBINA 404 Random Variables: Discrete and Continuous 405 Probability Distributions and Density Functions 405 The Binomial Distribution 407 Worksheet Functions 409 BINOM.DIST and BINOM.DIST.RANGE 409 NEGBINOM.DIST 411 Hypothesis Testing with the Binomial Distribution 412 BINOM.INV 413 More on hypothesis testing 414 The Hypergeometric Distribution 415 HYPGEOM.DIST 416 Chapter 19: More on Probability 419 Discovering Beta 419 BETA.DIST 421 BETA.INV 423 Poisson 424 POISSON.DIST 425 Working with Gamma 427 The gamma function and GAMMA 427 The gamma distribution and GAMMA.DIST 428 GAMMA.INV 430 Exponential 431 EXPON.DIST 431 Chapter 20: Using Probability: Modeling and Simulation 433 Modeling a Distribution 434 Plunging into the Poisson distribution 434 Visualizing the Poisson distribution 435 Working with the Poisson distribution 436 Using POISSON.DIST again 437 Testing the model’s fit 437 A word about CHISQ.TEST 440 Playing ball with a model 441 A Simulating Discussion 444 Taking a chance: The Monte Carlo method 444 Loading the dice 444 Data analysis tool: Random Number Generation 445 Simulating the Central limit Theorem 448 Simulating a business 452 Chapter 21: Estimating Probability: Logistic Regression 457 Working Your Way Through Logistic Regression 458 Mining with XLMiner 460 Part 5: The Part of Tens 465 Chapter 22: Ten (12, Actually) Statistical and Graphical Tips and Traps 467 Significant Doesn’t Always Mean Important 467 Trying to Not Reject a Null Hypothesis Has a Number of Implications 468 Regression Isn’t Always Linear 468 Extrapolating Beyond a Sample Scatterplot Is a Bad Idea 469 Examine the Variability Around a Regression Line 469 A Sample Can Be Too Large 470 Consumers: Know Your Axes 470 Graphing a Categorical Variable as a Quantitative Variable Is Just Plain Wrong 471 Whenever Appropriate, Include Variability in Your Graph 472 Be Careful When Relating Statistics Textbook Concepts to Excel 472 It’s Always a Good Idea to Use Named Ranges in Excel 472 Statistical Analysis with Excel on the iPad Is Pretty Good! 473 Chapter 23: Ten Topics (Thirteen, Actually) That Just Don’t Fit Elsewhere 475 Graphing the Standard Error of the Mean 475 Probabilities and Distributions 479 PROB 479 WEIBULL.DIST 479 Drawing Samples 480 Testing Independence: The True Use of CHISQ.TEST 481 Logarithmica Esoterica 484 What is a logarithm? 484 What is e? 486 LOGNORM.DIST 489 LOGNORM.INV 490 Array Function: LOGEST 491 Array Function: GROWTH 494 The logs of Gamma 497 Sorting Data 498 Part 6: Appendices 501 Appendix A: When Your Data Live Elsewhere 503 Appendix B: Tips for Teachers (and Learners) 507 Augmenting Analyses Is a Good Thing 507 Understanding ANOVA 508 Revisiting regression 510 Simulating Data Is Also a Good Thing 512 When All You Have Is a Graph 514 Appendix C: More on Excel Graphics 515 Tasting the Bubbly 515 Taking Stock 516 Scratching the Surface 518 On the Radar 519 Growing a Treemap and Bursting Some Sun 520 Building a Histogram 521 Ordering Columns: Pareto 522 Of Boxes and Whiskers 523 3D Maps 524 Filled Maps 527 Appendix D: The Analysis of Covariance 529 Covariance: A Closer Look 529 Why You Analyze Covariance 530 How You Analyze Covariance 531 ANCOVA in Excel 532 Method 1: ANOVA 533 Method 2: Regression 537 After the ANCOVA 540 And One More Thing 542 Index 545

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Book SynopsisThe goals of this text are to develop the skills and an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing dependent data. A useful feature of the presentation is the inclusion of nontrivial data sets illustrating the richness of potential applications to problems in the biological, physical, and social sciences as well as medicine. The text presents a balanced and comprehensive treatment of both time and frequency domain methods with an emphasis on data analysis.Numerous examples using data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and the analysis of economic and financial problems. The text can be used for a one semester/quarter introductory time series course where the prerequisites are an understanding of linear regression, basic calculus-based probability skills, and math skills at the Trade Review"The intended audience of the book are mathematics undergraduates taking a one semester course on time series. . . The authors frame learning time series primarily by extending concepts from linear models. Personally, I favour this approach, since it allows the book to clearly signpost similarities and differences between concepts in both topics and provides a natural learning progression from what most undergraduate students will already be familiar with . . .This book successfully delivers a practical tool-based approach to time series analysis at an introductory level, complementing the existing texts from the authors, which are aimed at a more advanced audience."~Matthew Nunes, Journal Times Series AnalysisTable of Contents1. Time Series Characteristics. 2. Time Series Regression and EDA. 3. ARIMA Models. 4. Spectral Analysis and Filtering. 5. Some Additional Topics.

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Book SynopsisModelling Survival Data in Medical Research, Fourth Edition, describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring.This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data.Features:Presents an accessible account oTable of Contents1. Survival analysis 2. Some non-parametric procedures 3. The Cox regression model 4. Model checking in the Cox regression model 5. Parametric regression models 6. Flexible parametric models 7. Model checking in parametric models 8. Time-dependent variables 9. Interval-censored survival data 10. Frailty models 11. Non-proportional hazards and institutional comparisons 12 Competing risks 13. Multiple events and event history modelling 14. Dependent censoring 15. Sample size requirements for a survival study 16. Bayesian survival analysis 17. Survival Analysis with R

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Book SynopsisThe Second Edition of Advanced Issues in Partial Least Squares Structural Equation Modeling offers a straightforward and practical guide to PLS-SEM for users ready to go further than the basics of A Primer on Partial Least Squares Structural Equation Modeling,Third Edition. Even in this advanced guide, the authors have limited the emphasis on equations, formulas, and Greek symbols, and instead rely on detailed explanations of the fundamentals of PLS-SEM and provide general guidelines for understanding and evaluating the results of applying the method. A single study on corporate reputation features as an example throughout the book, along with a single software package (SmartPLS 4.0) to provide a seamless learning experience. The approach of this book is based on the authors' many years of conducting research and teaching methodology courses, including developing the SmartPLS software. The prTrade Review"Excellent guide on how to use smart pls. Good starter product for understanding the underlying concepts." -- Saurabh Gupta"Must have if you want to do PLS" -- Jason XiongTable of ContentsChapter 1: An Overview of Recent and Emerging Developments in PLS-SEM Chapter 2: Higher-order Constructs Chapter 3: Advanced Modeling and Model Assessment Chapter 4: Advanced Results Illustration Chapter 5: Modeling Observed Heterogeneity Chapter 6: Modeling Unobserved Heterogeneity

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Book SynopsisA hands-on guide to using Excel in the business context First published in 2012, Using Excel for Business and Financial Modelling contains step-by-step instructions of how to solve common business problems using financial models, including downloadable Excel templates, a list of shortcuts and tons of practical tips and techniques you can apply straight away. Whilst there are many hundreds of tools, features and functions in Excel, this book focuses on the topics most relevant to finance professionals. It covers these features in detail from a practical perspective, but also puts them in context by applying them to practical examples in the real world. Learn to create financial models to help make business decisions whilst applying modelling best practice methodology, tools and techniques. Provides the perfect mix of practice and theory Helps you become a DIY Excel modelling specialist Includes updates for Excel 2019/365 and Excel for Table of ContentsPreface xi Chapter 1 What is Financial Modelling? 1 What’s the Difference Between a Spreadsheet and a Financial Model? 3 Types and Purposes of Financial Models 5 Tool Selection 6 What Skills Do You Need to Be a Good Financial Modeller? 17 The “Ideal” Financial Modeller 23 Summary 27 Chapter 2 Building a Model 29 Model Design 29 The Golden Rules for Model Design 31 Design Issues 32 The Workbook Anatomy of a Model 33 Project Planning Your Model 36 Model Layout Flowcharting 37 Steps to Building a Model 39 Information Requests 47 Version-Control Documentation 49 Summary 50 Chapter 3 Best-Practice Principles of Modelling 51 Document Your Assumptions 51 Linking, Not Hardcoding 52 Enter Data Only Once 53 Avoid Bad Habits 53 Use Consistent Formulas 53 Format and Label Clearly 54 Methods and Tools of Assumptions Documentation 55 Linked Dynamic Text Assumptions Documentation 62 What Makes a Good Model? 65 Summary 67 Chapter 4 Financial Modelling Techniques 69 The Problem with Excel 69 Error Avoidance Strategies 71 How Long Should a Formula Be? 76 Linking to External Files 78 Building Error Checks 81 Circular References 85 Summary 90 Chapter 5 Using Excel in Financial Modelling 91 Formulas and Functions in Excel 91 Excel Versions 94 Handy Excel Shortcuts 100 Cell Referencing Best Practices 104 Named Ranges 107 Basic Excel Functions 110 Logical Functions 114 Nesting Logical Functions 117 Summary 125 Chapter 6 Functions for Financial Modelling 127 Aggregation Functions 127 LOOKUP Functions 139 Nesting Index and Match 150 OFFSET Function 153 Regression Analysis 158 Choose Function 164 Working with Dates 165 Financial Project Evaluation Functions 171 Loan Calculations 177 Summary 183 Chapter 7 Tools for Model Display 185 Basic Formatting 185 Custom Formatting 186 Conditional Formatting 191 Sparklines 195 Bulletproofing Your Model 199 Customising the Display Settings 203 Form Controls 210 Summary 226 Chapter 8 Tools for Financial Modelling 227 Hiding Sections of a Model 227 Grouping 233 Array Formulas 234 Goal Seeking 240 Structured Reference Tables 242 PivotTables 245 Macros 254 Summary 263 Chapter 9 Common Uses of Tools in Financial Modelling 265 Escalation Methods for Modelling 265 Understanding Nominal and Effective (Real) Rates 270 Calculating a Cumulative Sum (Running Totals) 274 How to Calculate a Payback Period 275 Weighted Average Cost of Capital (WACC) 278 Building a Tiering Table 282 Modelling Depreciation Methods 286 Break-Even Analysis 295 Summary 300 Chapter 10 Model Review 301 Rebuilding an Inherited Model 301 Improving Model Performance 312 Auditing a Financial Model 317 Summary 323 Appendix: QA Log 323 Chapter 11 Stress Testing, Scenarios, and Sensitivity Analysis in Financial Modelling 325 What are the Differences Between Scenario, Sensitivity, and What-If Analyses? 326 Overview of Scenario Analysis Tools and Methods 328 Advanced Conditional Formatting 337 Comparing Scenario Methods 340 Adding Probability to a Data Table 350 Summary 351 Chapter 12 Presenting Model Output 353 Preparing an Oral Presentation for Model Results 353 Preparing a Graphic or Written Presentation for Model Results 355 Chart Types 358 Working with Charts 367 Handy Charting Hints 374 Dynamic Named Ranges 376 Charting with Two Different Axes and Chart Types 382 Bubble Charts 384 Creating a Dynamic Chart 387 Waterfall Charts 391 Summary 395 About the Author 397 About the Website 399 Index 403

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Book SynopsisA start-to-finish guide to one of the most useful programming languages for researchers in a variety of fields In the newly revised Third Edition of The R Book, a team of distinguished teachers and researchers delivers a user-friendly and comprehensive discussion of foundational and advanced topics in the R software language, which is used widely in science, engineering, medicine, economics, and other fields. The book is designed to be used as both a complete textreadable from cover to coverand as a reference manual for practitioners seeking authoritative guidance on particular topics. This latest edition offers instruction on the use of the RStudio GUI, an easy-to-use environment for those new to R. It provides readers with a complete walkthrough of the R language, beginning at a point that assumes no prior knowledge of R and very little previous knowledge of statistics. Readers will also find: A thorough introduction to fundamental conceptTable of ContentsPreface 1 Getting started 1 2 Technical background 17 3 Essentials of the R language 55 4 Data input and dataframes 195 5 Graphics 235 6 Graphics in more detail 289 7 Tables 357 8 Probability distributions in R 369 9 Testing 401 10 Regression 433 11 Generalised Linear Models 495 12 Generalised Additive Models 575 13 Mixed-effect models 599 14 Non-linear regression 627 15 Survival analysis 651 16 Designed experiments 669 17 Meta-analysis 701 18 Time Series 717 19 Multivariate Statistics 743 20 Classification and regression trees 765 21 Spatial Statistics 785 22 Bayesian Statistics 807 23 Simulation models 833

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Book SynopsisR Markdown: The Definitive Guide is the first official book authored by the core R Markdown developers that provides a comprehensive and accurate reference to the R Markdown ecosystem. With R Markdown, you can easily create reproducible data analysis reports, presentations, dashboards, interactive applications, books, dissertations, websites, and journal articles, while enjoying the simplicity of Markdown and the great power of R and other languages. In this book, you will learn Basics: Syntax of Markdown and R code chunks, how to generate figures and tables, and how to use other computing languages Built-in output formats of R Markdown: PDF/HTML/Word/RTF/Markdown documents and ioslides/Slidy/Beamer/PowerPoint presentations Extensions and applications: Dashboards, Tufte handouts, xaringan/reveal.js presentations, websites, books, journal articles, and interactive tutorials Advanced topics: Parameterized reports, HTML widgets, document templates, custom output formats, and Shiny documents. Yihui Xie is a software engineer at RStudio. He has authored and co-authored several R packages, including knitr, rmarkdown, bookdown, blogdown, shiny, xaringan, and animation. He has published three other books, Dynamic Documents with R and knitr, bookdown: Authoring Books and Technical Documents with R Markdown, and blogdown: Creating Websites with R Markdown.J.J. Allaire is the founder of RStudio and the creator of the RStudio IDE. He is an author of several packages in the R Markdown ecosystem including rmarkdown, flexdashboard, learnr, and radix.Garrett Grolemund is the co-author of R for Data Science and author of Hands-On Programming with R. He wrote the lubridate R package and works for RStudio as an advocate who trains engineers to do data science with R and the Tidyverse.Trade Review"The manuscript offers a detailed documentation of the R Markdown document format and its related packages for R (e.g. knitr, rmarkdown, flexdashboard, shiny). These packages form an important ecosystem for reproducible research using R and are widely used across academia and the private sector. All the authors have been key contributors to developing the core R Markdown packages and are knowledgable about the inner workings of these functions and all the available options to customize published documents…The target audience for this manuscript would be experienced R users who frequently use R Markdown to generate publications for a variety of mediums (articles, books, information dashboards, interactive web applications, etc.)…While this book is strongly related to the author’s previous book (Dynamic Documents with R and knitr), a wider range of readers should find this new manuscript useful for its focus on the broad range of output formats generated by R Markdown and how to customize those outputs." ~Benjamin Soltoff, Department of Computational Social Science, University of Chicago"A main strength of the software described herein is that it facilitates reproducible documents incorporating analyses and figures. The first topics covered in chapters 6-13 include handout and presentation formats that could be used effectively for teaching or presenting statistical results. The other topics focus on larger scale documents such as complex websites, books, and academic journal articles. From academic teaching and research to industry and other settings, the material covered by this book allows statisticians and data scientists to disseminate results in a highly effective manner." ~David Whitney, Department of Biostatistics, University of Washington"This book will be a valuable reference for students, academics, and professionals using R – that is to say, any one in a wide (and growing) variety of fields focused on practical data analysis including statistics, machine learning, the social sciences, etc. There is increasing awareness that nearly any occasion calling for analysis code also calls for some amount of corresponding documentation, explanation, and/or interpretation. Rapid improvement in tools for R markdown has made integrating code and text less and less of a chore, and therefore more and more common – even among users new to R. R markdown is a popular choice now for a range of formats including blog posts, user manuals, books, dissertations, and undergraduate homework assignments. I personally use R markdown for nearly all of my website content, presentations, research papers, and to generate reports for the clients of my statistical consulting business. Because of its many applications, however, the ecosystem of R markdown tools has become unwieldy, and many tutorials reference outdated techniques or unnecessary workarounds. A definitive guide has been long needed." ~Rose Hartman, UnderstandingData"This book is so far the most comprehensive reference for the R Markdown format and its associated extensions and tools. On a high level, Part I and II (Chapter 1-4) of this book cover the basic use of the R Markdown document and the knitr and rmarkdown packages, which are helpful for new users to quickly get started. Part III (Chapter 5-13) introduces a lot of new developments and powerful tools for R Markdown, including creating presentations, authoring books, building websites, writing journal articles, etc. In my personal point of view this is the most attractive part of this book, as it opens a new world for users who have only used R Markdown to create ordinary documents." ~Yixuan Qui, Department of Statistics, Purdue University"This book represents a valuable contribution to the target field due to its exploration of a wide range of features in the markdown language. If other books on this topic exist, this one has the advantage that the authors have already made significant contributions to the markdown language in the R platform and certainly have a comprehensive understanding of the topic…I recommend this book for publication because the topic is sophisticated and complex, and the interested audience will certainly be satisfied with the clarity of presentation and the depth that the authors reach in their exploratory examples" ~Jon Katz, data analyst"The manuscript offers a detailed documentation of the R Markdown document format and its related packages for R (e.g. knitr, rmarkdown, flexdashboard, shiny). These packages form an important ecosystem for reproducible research using R and are widely used across academia and the private sector. All the authors have been key contributors to developing the core R Markdown packages and are knowledgable about the inner workings of these functions and all the available options to customize published documents…The target audience for this manuscript would be experienced R users who frequently use R Markdown to generate publications for a variety of mediums (articles, books, information dashboards, interactive web applications, etc.)…While this book is strongly related to the author’s previous book (Dynamic Documents with R and knitr), a wider range of readers should find this new manuscript useful for its focus on the broad range of output formats generated by R Markdown and how to customize those outputs." ~Benjamin Soltoff, Department of Computational Social Science, University of Chicago"A main strength of the software described herein is that it facilitates reproducible documents incorporating analyses and figures. The first topics covered in chapters 6-13 include handout and presentation formats that could be used effectively for teaching or presenting statistical results. The other topics focus on larger scale documents such as complex websites, books, and academic journal articles. From academic teaching and research to industry and other settings, the material covered by this book allows statisticians and data scientists to disseminate results in a highly effective manner." ~David Whitney, Department of Biostatistics, University of Washington"This book will be a valuable reference for students, academics, and professionals using R – that is to say, any one in a wide (and growing) variety of fields focused on practical data analysis including statistics, machine learning, the social sciences, etc. There is increasing awareness that nearly any occasion calling for analysis code also calls for some amount of corresponding documentation, explanation, and/or interpretation. Rapid improvement in tools for R markdown has made integrating code and text less and less of a chore, and therefore more and more common – even among users new to R. R markdown is a popular choice now for a range of formats including blog posts, user manuals, books, dissertations, and undergraduate homework assignments. I personally use R markdown for nearly all of my website content, presentations, research papers, and to generate reports for the clients of my statistical consulting business. Because of its many applications, however, the ecosystem of R markdown tools has become unwieldy, and many tutorials reference outdated techniques or unnecessary workarounds. A definitive guide has been long needed." ~Rose Hartman, UnderstandingData"This book is so far the most comprehensive reference for the R Markdown format and its associated extensions and tools. On a high level, Part I and II (Chapter 1-4) of this book cover the basic use of the R Markdown document and the knitr and rmarkdown packages, which are helpful for new users to quickly get started. Part III (Chapter 5-13) introduces a lot of new developments and powerful tools for R Markdown, including creating presentations, authoring books, building websites, writing journal articles, etc. In my personal point of view this is the most attractive part of this book, as it opens a new world for users who have only used R Markdown to create ordinary documents." ~Yixuan Qui, Department of Statistics, Purdue University"This book represents a valuable contribution to the target field due to its exploration of a wide range of features in the markdown language. If other books on this topic exist, this one has the advantage that the authors have already made significant contributions to the markdown language in the R platform and certainly have a comprehensive understanding of the topic…I recommend this book for publication because the topic is sophisticated and complex, and the interested audience will certainly be satisfied with the clarity of presentation and the depth that the authors reach in their exploratory examples" ~Jon Katz, data analystTable of ContentsI Get Started 1.Installation 2. Basics Example applications Airbnb’s knowledge repository Homework assignments on RPubs Personalized mails Employer Health Benefits Survey Journal articles Dashboards at eelloo Books Websites Compile an R Markdown document Cheat sheets Output formats Markdown syntax Inline formatting Block-level elements Math expressions R code chunks and inline R code Figures Tables Other language engines Python Shell scripts SQL Rcpp Stan JavaScript and CSS Julia C and Fortran Interactive documents HTML widgets Shiny documents II Output Formats 3. Documents HTML document Table of contents Section numbering Tabbed sections Appearance and style Figure options Data frame printing Code folding MathJax equations Document dependencies Advanced customization Shared options HTML fragments Notebook Using Notebooks Saving and sharing Notebook format PDF document Table of contents Figure options Data frame printing Syntax highlighting LaTeX options LaTeX packages for citations Advanced customization Other features Word document Other features OpenDocument Text document Other features Rich Text Format document Other features Markdown document Markdown variants Other features R package vignette 4. Presentations ioslides presentation Display modes Incremental bullets Visual appearance Code highlighting Adding a logo Tables Advanced layout Text color Presenter mode Printing and PDF output Custom templates Other features Slidy presentation Display modes Text size Footer elements Other features Beamer presentation Themes Slide level Other features PowerPoint presentation Custom templates Other features III Extensions 5. Dashboards Layout Row-based layouts Attributes on sections Multiple pages Story boards Components Value boxes Gauges Text annotations Navigation bar Shiny Getting started A Shiny dashboard example Input sidebar Learning more 6. Tufte Handouts Headings Figures Margin figures Arbitrary margin content Full-width figures Main column figures Sidenotes References Tables Block quotes Responsiveness Sans-serif fonts and epigraphs Customize CSS styles 7. xaringan Presentations Get started Keyboard shortcuts Slide formatting Slides and properties The title slide Content classes Incremental slides Presenter notes yolo: true Build and preview slides CSS and themes Some tips Autoplay slides Countdown timer Highlight code lines Working offline Macros Disadvantages 8. revealjs Presentations Display modes Appearance and style Smaller text Slide transitions Slide backgrounds -D presentations Custom CSS Slide IDs and classes Styling text spans revealjs options revealjs plugins Other features 9. Community Formats Lightweight Pretty HTML Documents Usage Package vignettes The rmdformats package Shower presentations 10. Websites Get started The directory structure Deployment Other site generators rmarkdown’s site generator A simple example Site authoring Common elements Site navigation HTML generation Site configuration Publishing websites Additional examples Custom site generators 11. HTML Documentation for R Packages Get started Components Home page Function reference Articles News Navigation bar 12. Books Get started Project structure Index file Rmd files _bookdownyml _outputyml Markdown extensions Number and reference equations Theorems and proofs Special headers Text references Cross referencing Output Formats HTML LaTeX/PDF E-books A single document Editing Build the book Preview a chapter Serve the book RStudio addins Publishing RStudio Connect Other services Publishers 13. Journals Get started Articles templates Using a template LaTeX content Linking with bookdown Contributing templates 14. Interactive Tutorials Get started Tutorial types Exercises Solutions Hints Quiz questions Videos Shiny components Navigation and progress tracking IV Advanced Topics 15. Parameterized reports Declaring parameters Using parameters Knitting with parameters The Knit button Knit with custom parameters The interactive user interface Publishing 16. HTML Widgets Overview A widget example (sigmajs) File layout Dependencies R binding JavaScript binding Demo Creating your own widgets Requirements Scaffolding Other packages Widget sizing Specifying a sizing policy JavaScript resize method Advanced topics Data transformation Passing JavaScript functions Custom widget HTML Create a widget without an R package 17. Document Templates Template structure Supporting files Custom Pandoc templates Sharing your templates 18. Creating New Formats Deriving from built-in formats Fully custom formats Using a new format 19. Shiny Documents Getting started Deployment ShinyAppsio Shiny Server / RStudio Connect Embedded Shiny apps Inline applications External applications Shiny widgets The shinyApp() function Example: k-Means clustering Widget size and layout Multiple pages Delayed rendering Output arguments for render functions A caveat

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Book SynopsisPraise for the First Edition:. . . the book serves as an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation. Tzvetan Semerdjiev, Zentralblatt MathComputational statistics and statistical computing are two areas within statistics that may be broadly described as computational, graphical, and numerical approaches to solving statistical problems. Like its bestselling predecessor, Statistical Computing with R, Second Edition covers the traditional core material of these areas with an emphasis on using the R language via an examples-based approach. The new edition is up-to-date with the many advances that have been made in recent years. Features Provides an overview of cTrade ReviewPraise for the First Edition:"… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." —Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137 "Statistical computing and computational statistics are two areas of statistics described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R comprises, thorough and examples-based approach, the conventional core material of computational statistics with an emphasis on R... This book includes standard statistical computing topics using the R language... All examples in the text are realised in R. Software is actively maintained, it has good connectivity to various types of data and other systems, and it is versatile. In addition, R is very stable and reliable... The book also includes exercises and applications in all chapters, as well as coverage of recent advances including R Studio. Many examples are included, fully implemented in the R statisticalcomputing environment, and the R code for the examples can be downloaded from the author’s website. Most examples and exercises apply datasets accessible in the R distribution or simulated data. The author, Maria L. Rizzo, is a Full Professor at the Department of Mathematics and Statistics of Bowling Green State University (US) and is an expert on Applied Statistics, Statistical Computing, and Energy Statistics... After finishing the book, I feel that it is a well-written text useful for biostatisticians and graduate teachers, principally because it is written by a leading expert who is engaged in statistical modelling and methodological developments and applications in the real world. In my opinion, the book is a must-have for the interested biostatistician audience."- Luca Bertolaccini, ISCB December 2019 Praise for the First Edition:"… an excellent tutorial on the R language, providing examples that illustrate programming concepts in the context of practical computational problems. The book will be of great interest for all specialists working on computational statistics and Monte Carlo methods for modeling and simulation." —Tzvetan Semerdjiev, Zentralblatt Math, 2008, Vol. 1137 "Statistical computing and computational statistics are two areas of statistics described as computational, graphical, and numerical approaches to solving statistical problems. Statistical Computing with R comprises, thorough and examples-based approach, the conventional core material of computational statistics with an emphasis on R... This book includes standard statistical computing topics using the R language... All examples in the text are realised in R. Software is actively maintained, it has good connectivity to various types of data and other systems, and it is versatile. In addition, R is very stable and reliable... The book also includes exercises and applications in all chapters, as well as coverage of recent advances including R Studio. Many examples are included, fully implemented in the R statistical computing environment, and the R code for the examples can be downloaded from the author’s website. Most examples and exercises apply datasets accessible in the R distribution or simulated data. The author, Maria L. Rizzo, is a Full Professor at the Department of Mathematics and Statistics of Bowling Green State University (US) and is an expert on Applied Statistics, Statistical Computing, and Energy Statistics... After finishing the book, I feel that it is a well-written text useful for biostatisticians and graduate teachers, principally because it is written by a leading expert who is engaged in statistical modelling and methodological developments and applications in the real world. In my opinion, the book is a must-have for the interested biostatistician audience."- Luca Bertolaccini, ISCB December 2019 "...This book tries to keep a balance between theory and practice, with more focus on the latter...also provides plenty of R codes to help the readers practice what they learned from the book. As stated in the preface, the targeted readers of this book are graduate students and advanced undergraduates with preparation in the relevant mathematics foundations. From this point of view, the content of the book fits well to the anticipated audience...I really appreciate the section on “finding source code” in Chapter 15. A lot of the libraries in R are written in C or Fortran. Occasionally, we need to dig into those codes and make changes to suit our needs. It is very helpful in our daily research to be able to find the source code and compile the changes...Finally, I would like to give credit to the author on making their code available on github. This makes it convenient for readers to try the code themselves without lots of typing. It also allows the authors to easily make updated code available to readers."- Ling Leng, JASA, September 2020 Table of ContentsIntroduction. Probability and Statistics Review. Methods for Generating Random Variables. Visualization of Multivariate Data. Monte Carlo Integration and Variance Reduction. Monte Carlo Methods in Inference. Bootstrap and Jackknife. Permutation Tests. Markov Chain Monte Carlo Methods. Probability Density Estimation. Smoothing and Nonparametric Regression. High Dimensional Data. Numerical Methods in R. Optimization.

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Book SynopsisAn Introduction to Stata for Health Researchers, Fifth Edition updates this classic book that has become a standard reference for health researchers. As with previous editions, readers will learn to work effectively in Stata to perform data management, compute descriptive statistics, create meaningful graphs, fit regression models, and perform survival analysis. The fifth edition adds examples of performing power, precision, and sample-size analysis; working with Unicode characters; managing data with ICD-9 and ICD-10 codes; and creating customized tables.With many worked examples and downloadable datasets, this text is the ideal resource for hands-on learning, whether for students in a statistics course or for researchers in fields such as epidemiology, biostatistics, and public health who are learning to use Stata's tools for health research.Table of ContentsI The basics 1. Getting started 2. Getting help—and more 3. Command syntax II Data management 4. Variables 5. Getting data in and out of Stata 6. Adding explanatory text to data 7. Calculations 8. Commands affecting data structure 9. Taking good care of your data III Analysis 10. Description and simple analysis 11. Regression analysis 12. Time-to-event data 13. Power, precision, and sample-size analysis 14. Measurement and diagnosis 15. Miscellaneous IV Graphs 16. Graphs V Advanced topics 17. Advanced topics

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Book SynopsisThis second edition of Data Management Using Stata focuses on tasks that bridge the gap between raw data and statistical analysis. It has been updated throughout to reflect new data management features that have been added over the last 10 years. Such features include the ability to read and write a wide variety of file formats, the ability to write highly customized Excel files, the ability to have multiple Stata datasets open at once, and the ability to store and manipulate string variables stored as Unicode. Further, this new edition includes a new chapter illustrating how to write Stata programs for solving data management tasks. As in the original edition, the chapters are organized by data management areas: reading and writing datasets, cleaning data, labeling datasets, creating variables, combining datasets, processing observations across subgroups, changing the shape of datasets, and programming for data management. Within each chapter, each section is a self-contained lesson illustrating a particular data management task (for instance, creating date variables or automating error checking) via examples. This modular design allows you to quickly identify and implement the most common data management tasks without having to read background information first. In addition to the “nuts and bolts” examples, author Michael Mitchell alerts users to common pitfalls (and how to avoid them) and provides strategic data management advice. This book can be used as a quick reference for solving problems as they arise or can be read as a means for learning comprehensive data management skills. New users will appreciate this book as a valuable way to learn data management, while experienced users will find this information to be handy and time saving—there is a good chance that even the experienced user will learn some new tricks.Table of ContentsIntroduction. Reading and importing data files. Saving and exporting data files. Data cleaning. Labeling datasets. Creating variables. Combining datasets. Processing observations across subgroups. Changing the shape of your data. Programming for data management: Part I. Programming for data management: Part II.

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Book SynopsisThe modern world is awash with data. The R Project is a statistical environment and programming language that can help to make sense of it all. A huge open-source project, R has become enormously popular because of its power and flexibility. With R you can organise, analyse and visualise data. This clear and methodical book will help you learn how to use R from the ground up, giving you a start in the world of data science. Learning about data is important in many academic and business settings, and R offers a potent and adaptable programming toolbox. The book covers a range of topics, including: importing/exporting data, summarising data, visualising data, managing and manipulating data objects, data analysis (regression, ANOVA and association among others) and programming functions. Regardless of your background or specialty, you'll find this book the perfect primer on data analysis, data visualisation and data management, and a springboard for further exploration.Table of Contents1. A brief introduction to R 2. Basic math 3. Introduction to R objects 4. Making and importing data objects 5. Managing and exporting data objects 6. R object types and their properties 7. Working with data objects 8. Manipulating data objects 9. Summarizing data 10. Tabulation 11. Graphics: basic charts 12. Graphics: adding to plots 13. Graphics: advanced methods 14. Analyze data: statistical analyses 15. Programming tools Appendix Index

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Book SynopsisThis book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles. Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints. The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for data science.Trade Review“Having taught data analytics at the introductory graduate level, I welcome the authors’ textbook as an essential resource for training well-grounded entry-level data scientists. … A data scientist shall provide competent data science professional services to a client. … Training in both the theory and practice of data analytics is a requirement for such competence. The authors’ textbook definitely provides a valuable resource for such training.” (Harry J. Foxwell, Computing Reviews, July 7, 2022)Table of Contents1 A First Look at Data.- 2 Sampling Plans and Estimates.- 3 Probability Theory.- 4 Random Variables and Distributions.- 5 Estimation.- 6 Multiple Random Variables.- 7 Making Decisions in Uncertainty.- 8 Bayesian Statistics.

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Book SynopsisThis open access book prepares current and aspiring analytics professionals to effectively address this need by curating key concepts spanning the entire analytics lifecycle, along with step-by-step instructions for their applications to real-world problems, using ubiquitous and freely available open-source software. This book does not assume prior knowledge of statistics, how to query databases, or how to write performant code; early chapters include an introduction to R and SQL as well as an overview of statistical foundations.Human capital is an organization’s most important asset. Without the knowledge and skills of people, an organization can accomplish nothing. The acquisition, development, and retention of critical talent has become increasingly more complex and challenging, and organizations are making significant investments to gain a deeper, data-informed understanding of organizational phenomena impacting the bottom line. By the end of this book, readers will be able to: • Design and conduct empirical research • Query and wrangle data using SQL • Profile, clean, and analyze data using R • Apply appropriate statistical and ML models to a range of people analytics use cases • Package and present analyses to communicate impactful insights to stakeholdersTable of Contents1. Getting Started.- 2. Introduction to R.- 3. Introduction to SQL.- 4. Research Design.- 5. Measurement & Sampling.- 6. Data Preparation.- 7. Descriptive Statistics.- 8. Statistical Inference.- 9. Analysis of Differences.- 10. Linear Regression.- 11. Linear Model Extensions.- 12. Logistic Regression.- 13. Predictive Modeling.- 14. Unsupervised Learning.- 15. Data Visualization.- 16. Data Storytelling.

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Book SynopsisThis book introduces descriptive statistics and covers a broad range of topics of interest to students and researchers in various applied science disciplines. This includes measures of location, spread, skewness, and kurtosis; absolute and relative measures; and classification of spread, skewness, and kurtosis measures, L-moment based measures, van Zwet ordering of kurtosis, and multivariate kurtosis. Several novel topics are discussed including the recursive algorithm for sample variance; simplification of complicated summation expressions; updating formulas for sample geometric, harmonic and weighted means; divide-and-conquer algorithms for sample variance and covariance; L-skewness; spectral kurtosis, etc. A large number of exercises are included in each chapter that are drawn from various engineering fields along with examples that are illustrated using the R programming language. Basic concepts are introduced before moving on to computational aspects. Some applications in bioinformatics, finance, metallurgy, pharmacokinetics (PK), solid mechanics, and signal processing are briefly discussed. Every analyst who works with numeric data will find the discussion very illuminating and easy to follow.Table of ContentsDescriptive Statistics.- Measures of Location.- Measures of Spread.- Measures of Skewness and Kurtosis.

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Book SynopsisThis book provides a unique tour of university mathematics with the help of Python. Written in the spirit of mathematical exploration and investigation, the book enables students to utilise Python to enrich their understanding of mathematics through: Calculation: performing complex calculations and numerical simulations instantly Visualisation: demonstrating key theorems with graphs, interactive plots and animations Extension: using numerical findings as inspiration for making deeper, more general conjectures. This book is for all learners of mathematics, with the primary audience being mathematics undergraduates who are curious to see how Python can enhance their understanding of core university material. The topics chosen represent a mathematical overview of what students typically study in the first and second years at university, namely analysis, calculus, vector calculus and geometry, differential equations and dynamical systems, linear algebra, abstract algebra and number theory, probability and statistics. As such, it can also serve as a preview of university mathematics for high-school students. The prerequisites for reading the book are a familiarity with standard A-Level mathematics (or equivalent senior high-school curricula) and a willingness to learn programming. For mathematics lecturers and teachers, this book is a useful resource on how Python can be seamlessly incorporated into the mathematics syllabus, assuming only basic knowledge of programming.Table of Contents1 Analysis.- 2 Calculus.- 3 Vector Calculus and Geometry.- 4 Differential Equations and Dynamical Systems.- 5 Linear Algebra.- 6 Abstract Algebra and Number Theory.- 7 Probability.- 8 Statistics.- Appendix A: Python 101.

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Book SynopsisBootstrapping is a conceptually simple statistical technique to increase the quality of estimates, conduct robustness checks and compute standard errors for virtually any statistic. This book provides an intelligible and compact introduction for students, scientists and practitioners. It not only gives a clear explanation of the underlying concepts but also demonstrates the application of bootstrapping using Python and Stata.

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Book SynopsisThis book shows you how to analyze data sets systematically and to use Excel 2019 to extract information from data almost effortlessly. Both are (not) an art!The statistical methods are presented and discussed using a single data set. This makes it clear how the methods build on each other and gradually more and more information can be extracted from the data. The Excel functions used are explained in detail - the procedure can be easily transferred to other data sets. Various didactic elements facilitate orientation and working with the book: At the checkpoints, the most important aspects from each chapter are briefly summarized. In the freak knowledge section, more advanced aspects are addressed to whet the appetite for more. All examples are calculated with hand and Excel. Numerous applications and solutions as well as further data sets are available on the author's internet platform. This book is a translation of the original German 2nd edition Statistik angewandt mit Excel by Franz Kronthaler, published by Springer-Verlag GmbH Germany, part of Springer Nature in 2021. The translation was done with the help of artificial intelligence (machine translation by the service DeepL.com). A subsequent human revision was done primarily in terms of content, so that the book will read stylistically differently from a conventional translation. Springer Nature works continuously to further the development of tools for the production of books and on the related technologies to support the authors.Table of ContentsPart 1 - Basic knowledge and tools to apply statistics.- Statistics is fun.- Excel: A brief introduction and the statistical possibilities.- Part 2 - Describe, nothing but describe.- Mean values: How people and objects behave on average.- Scatter: The deviation from average behavior.- Graphs: The possibility to represent data visually.- Correlation: Of the correlation.- Ratio and index numbers: The chance to generate new things from old knowledge.- Part 3 - From Few to All.- Of Data and the Truth.- Hypotheses: Just a specification of the question.- Normal distribution and other test distributions.- Hypothesis testing: What is Valid?.- Part 4 - Procedures for Testing Hypotheses.- The Mean Test.- The Test for Difference of Means in Independent Samples.- The Test for Difference of Means in Dependent Samples.- The Analysis of Variance for Testing for Group Differences in More than Two Groups.- The Test for Correlation in Metric, Ordinal, and Nominal Data.- Further Test Procedures for Nominal Variables.- Summary Part IV - Overview of testing procedures.- Part 5 - Regression analysis.- The linear single regression.- The multiple regression analysis.- Part 6 - What's next.- Brief report on a research question.- Further statistical procedures.- Interesting and further statistics books.- Another data set to practice on - Intern of a company.- Appendix.

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Book SynopsisThis book helps the reader make use of the mathematical models of biological phenomena starting from the basics of programming and computer simulation. Computer simulations based on a mathematical model enable us to find a novel biological mechanism and predict an unknown biological phenomenon. Mathematical biology could further expand the progress of modern life sciences. Although many biologists are interested in mathematical biology, they do not have experience in mathematics and computer science. An educational course that combines biology, mathematics, and computer science is very rare to date. Published books for mathematical biology usually explain the theories of established mathematical models, but they do not provide a practical explanation for how to solve the differential equations included in the models, or to establish such a model that fits with a phenomenon of interest. MATLAB is an ideal programming platform for the beginners of computer science. This book starts from the very basics about how to write a programming code for MATLAB (or Octave), explains how to solve ordinary and partial differential equations, and how to apply mathematical models to various biological phenomena such as diabetes, infectious diseases, and heartbeats. Some of them are original models, newly developed for this book. Because MATLAB codes are embedded and explained throughout the book, it will be easy to catch up with the text. In the final chapter, the book focuses on the mathematical model of the proneural wave, a phenomenon that guarantees the sequential differentiation of neurons in the brain. This model was published as a paper from the author’s lab (Sato et al., PNAS 113, E5153, 2016), and was intensively explained in the book chapter “Notch Signaling in Embryology and Cancer”, published by Springer in 2020. This book provides the reader who has a biological background with invaluable opportunities to learn and practice mathematical biology.Table of Contents1. Preparation.- 2. Introduction to MATLAB programming .- 3. Simulating time variations in life phenomena.- 4. Simulating temporal and spatial changes in biological phenomena.

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Book SynopsisOffers an introduction to the modern statistical methods for ecology. This book shows how to construct statistical models for data, estimate their parameters and confidence limits, and interpret the results. It covers statistical frameworks, the philosophy of statistical modeling, and critical mathematical functions and probability distributions.Trade Review"Bolker's book is a must-buy for anyone wanting to fit data to models and go beyond hypothesis testing, but it is certainly not an 'introductory' text in the sense of 'simple'. This book is a tour de force for anyone who studied ecology for his or her interest of nature's working. But it is the one single book that can propel the statistical novice to the cutting edge of statistical ecology--albeit with blood, sweat and tears."--Carsten F. Dormann, Basic and Applied Ecology "[A] must for natural scientists and for statisticians who are interested in ecological applications... Numerous enlightening footnotes, meaningful graphics and direct speech are evidence of substantial classroom experience of the author... The book addresses students and researchers who have or have had some basic knowledge in ecology, mathematics and statistics. Delivering many examples and profound details on numerical aspects of maximum likelihood estimation, the book may also give a red line for a course in computational statistics."--Martin Schlather, Biometrical Journal "[T]his book succeeds both in explaining how to analyze many types of ecological data, and in clearly describing the theoretical background behind some common analyses and approaches. I expect to refer to it often."--Lynda D. Prior, Austral EcologyTable of ContentsAcknowledgments ix Chapter 1: Introduction and Background 1 1.1 Introduction 1 1.2 What This Book Is Not About 3 1.3 Frameworks for Modeling 5 1.4 Frameworks for Statistical Inference 10 1.5 Frameworks for Computing 17 1.6 Outline of the Modeling Process 20 1.7 R Supplement 22 Chapter 2: Exploratory Data Analysis and Graphics 29 2.1 Introduction 29 2.2 Getting Data into R 30 2.3 Data Types 34 2.4 Exploratory Data Analysis and Graphics 40 2.5 Conclusion 59 2.6 R Supplement 59 Chapter 3: Deterministic Functions for Ecological Modeling 72 3.1 Introduction 72 3.2 Finding Out about Functions Numerically 73 3.3 Finding Out about Functions Analytically 76 3.4 Bestiary of Functions 87 3.5 Conclusion 100 3.6 R Supplement 100 Chapter 4: Probability and Stochastic Distributions for Ecological Modeling 103 4.1 Introduction: Why Does Variability Matter? 103 4.2 Basic Probability Theory 104 4.3 Bayes' Rule 107 4.4 Analyzing Probability Distributions 115 4.5 Bestiary of Distributions 120 4.6 Extending Simple Distributions: Compounding and Generalizing 137 4.7 R Supplement 141 Chapter 5: Stochastic Simulation and Power Analysis 147 5.1 Introduction 147 5.2 Stochastic Simulation 148 5.3 Power Analysis 156 Chapter 6: Likelihood and All That 169 6.1 Introduction 169 6.2 Parameter Estimation: Single Distributions 169 6.3 Estimation for More Complex Functions 182 6.4 Likelihood Surfaces, Profiles, and Confidence Intervals 187 6.5 Confidence Intervals for Complex Models: Quadratic Approximation 196 6.6 Comparing Models 201 6.7 Conclusion 220 Chapter 7: Optimization and All That 222 7.1 Introduction 222 7.2 Fitting Methods 223 7.3 Markov Chain Monte Carlo 233 7.4 Fitting Challenges 241 7.5 Estimating Confidence Limits of Functions of Parameters 250 7.6 R Supplement 258 Chapter 8: Likelihood Examples 263 8.1 Tadpole Predation 263 8.2 Goby Survival 276 8.3 Seed Removal 283 Chapter 9: Standard Statistics Revisited 298 9.1 Introduction 298 9.2 General Linear Models 300 9.3 Nonlinearity: Nonlinear Least Squares 306 9.4 Nonnormal Errors: Generalized Linear Models 308 9.5 R Supplement 312 Chapter 10: Modeling Variance 316 10.1 Introduction 316 10.2 Changing Variance within Blocks 318 10.3 Correlations: Time-Series and Spatial Data 320 10.4 Multilevel Models: Special Cases 324 10.5 General Multilevel Models 327 10.6 Challenges 333 10.7 Conclusion 334 10.8 R Supplement 335 Chapter 11: Dynamic Models 337 11.1 Introduction 337 11.2 Simulating Dynamic Models 338 11.3 Observation and Process Error 342 11.4 Process and Observation Error 344 11.5 SIMEX 346 11.6 State-Space Models 348 11.7 Conclusions 357 11.8 R Supplement 360 Chapter 12: Afterword 362 Appendix Algebra and Calculus Basics 363 A.1 Exponentials and Logarithms 363 A.2 Differential Calculus 364 A.3 Partial Differentiation 364 A.4 Integral Calculus 365 A.5 Factorials and the Gamma Function 365 A.6 Probability 365 A.7 The Delta Method 366 A.8 Linear Algebra Basics 366 Bibliography 369 Index of R Arguments, Functions, and Packages 383 General Index 389

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Book SynopsisAn introduction to a popular programming language for neuroscience research, taking the reader from beginning to intermediate and advanced levels of MATLAB programming.MATLAB is one of the most popular programming languages for neuroscience and psychology research. Its balance of usability, visualization, and widespread use makes it one of the most powerful tools in a scientist's toolbox. In this book, Mike Cohen teaches brain scientists how to program in MATLAB, with a focus on applications most commonly used in neuroscience and psychology. Although most MATLAB tutorials will abandon users at the beginner's level, leaving them to sink or swim, MATLAB for Brain and Cognitive Scientists takes readers from beginning to intermediate and advanced levels of MATLAB programming, helping them gain real expertise in applications that they will use in their work.The book offers a mix of instructive text and rigorous explanations of MATLAB code along with programming tips

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Book SynopsisDiscover everything you want to know about Microsoft's newest version of Windows in this easy-to-use guide; from the most essential tasks that you'll want to perform, to solving the most common problems you'll encounter.Table of ContentsTop 10 Windows 8 Tips for the Over 50s 1. Shut Down Windows 2. Turn Live Tiles Off or On 3. Move Among Apps Quickly 4. Make icons easier to see in File Explorer 5. Back up data quickly and easily 6. Connect to a Free Wireless Hot Spot 7. Pin a Website to the Start Screen 8. Email a Photo 9. Install a digital camera, web cam, or smart phone 10. Install Anti-Virus Software 1 Learn Windows 8 Basics Know What Kind of Device you have Set up Windows 8 Consider a Microsoft Account Log In to Windows 8 Explore the Start Screen Open and Close an App Access Charms Understand Charms Access the Traditional Desktop Explore File Explorer Switch to a Microsoft Account Shut Down Windows 2 Make Windows 8 Easier to Use, See, and Navigate Change the Volume Change the Screen Resolution Personalize the Color of the Start Screen Background Turn Live Tiles Off or On Make App Tiles Larger or Smaller Reposition Apps on the Start Screen Add a Tile to the Start Screen Remove a Tile from the Start Screen Log In with Fewer Keystrokes Create Shortcuts on the Desktop Pin Items to the Taskbar Explore Accessibility Options Explore Touch Techniques 3 Use Apps to Be More Efficient Check Your Local Weather Throw Away your Physical Maps Travel without Leaving your Home Get the Latest Sports News and Follow a Team Switch to a Digital Personal Calendar Create a New Event in Calendar Explore your Piece of the Cloud Upload a File to Sky Drive Access Your Files on Sky Drive from Anywhere Shop the Windows Store Use your Free App Move among Open Apps Quickly 4 Use desktop Applications Find the desktop Applications Write a letter with Notepad Save a letter with Notepad Print a letter with Notepad Use the calculator Take a picture of what’s on the screen Share a screen shot Record and save a sound clip Play a sound clip Explore other desktop apps 5 Locate and Manage the Data you keep and Acquire Explore your Libraries Save data to a library Create a Folder or Subfolder Copy or Move a File or Folder Delete a File or Folder Explore your personal folders Search for a File Browse for a file from a desktop app Change the Size of an Open Window Use Snap, Peek, and Shake Make icons easier to see Move data to Public Folders Back up data quickl

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Book SynopsisUnderstanding Regression Analysis unifies diverse regression applications including the classical model, ANOVA models, generalized models including Poisson, Negative binomial, logistic, and survival, neural networks, and decision trees under a common umbrella -- namely, the conditional distribution model. It explains why the conditional distribution model is the correct model, and it also explains (proves) why the assumptions of the classical regression model are wrong. Unlike other regression books, this one from the outset takes a realistic approach that all models are just approximations. Hence, the emphasis is to model Nature's processes realistically, rather than to assume (incorrectly) that Nature works in particular, constrained ways.Key features of the book include: Numerous worked examples using the R software Key points and self-study questions displayed just-in-time within chapters <Trade Review"...The authors suggest their book is suitable for those who are “research-oriented”, regardless of any prior advanced training in statistics...I particularly like the emphasis on assumptions. Rather than discuss regression in idealized terms, Westfall and Arias are upfront about why assumptions are often wrong in practice, and what an analyst can do about violations. These discussions are woven into many of the chapters, and in some cases, they are featured in stand-alone chapters...I am a fan of learning statistics by doing, so the large amount of R code woven into the book’s chapters and the hands-on exercises at the end of each chapter are valuable and a welcomed feature of the book...To me, this textbook would be most suitable for a one-semester survey course in statistical methods for students outside of biostatistics or statistics. A motivated student could even use this book for self-study...Overall, I believe this is a worthwhile addition to the literature."- Ryan Andrews, ISCB News, June 2021 Table of Contents1. Introduction to Regression Models 2. Estimating Regression Model Parameters3. The Classical Model and Its Consequences4. Evaluating Assumptions5. Transformations6. The Multiple Regression Model7. Multiple Regression from the Matrix Point of View8. R-squared, Adjusted R-Squared, the F Test, and Multicollinearity9. Polynomial Models and Interaction (Moderator) Analysis10. ANOVA, ANCOVA, and Other Applications of Indicator Variables11. Variable Selection12. Heteroscedasticity and Non-independence13. Models for Binary, Nominal, and Ordinal Response Variables14. Models for Poisson and Negative Binomial Response15. Censored Data Models16. Outliers, Identification, Problems, and Remedies (Good and Bad)17. Neural Network Regression 18. Regression Trees19. Bookend

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Book SynopsisPractical introduction to crime mapping and spatial data analysis using R and R Studio. Crime mapping and analysis of crime problems using spatially explicit data has become a central feature of law enforcement agencies across the world. Criminology degrees have begun to adapt their curriculums to foster the skills required for these jobs.Trade Review"I think overall the book is pitched perfectly and the step by step approach with code will act as an excellent training resources as well as reference guide.”-Ruth Weir, City, University of London"Overall, this is a great book! It is written in an accessible style, is up to date and covers the foundational material one would want a student to understand. As an experienced R user, I was delighted to learn something. Staying abreast of the fast-developing packages is nearly a full-time job, so I see this book as highly useful to many readers. The authors do a great job illustrating the main concepts of import but also pointing readers to places to follow up for more detailed treatments.”-Michael Townsley, Professor of Criminology and Criminal Justice, Griffith UniversityTable of Contents1. Producing your First Crime Map 2. Basic Geospatial Operations in R 3. Mapping Rates and Counts 4. Variations of Thematic Mapping 5. Basics of Cartographic Design: Elements of a Map 6. Time Matters 7. Spatial Point Patterns of Crime Events 8. Crime Along Spatial Networks 9. Spatial Dependence and Autocorrelation 10. Detecting Hot Spots and Repeats 11. Spatial Regression Models 12. Spatial Heterogeneity and Regression 13. Appendix: A Quick Intro to R and RStudio 14. Appendix B: Regression Analysis (A Refresher) 15. Appendix C: Sourcing Geographical Data for Crime Analysis

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Book SynopsisLibrarians understand the need to store, use and analyze data related to their collection, patrons and institution, and there has been consistent interest over the last 10 years to improve data management, analysis, and visualization skills within the profession. However, librarians find it difficult to move from out-of-the-box proprietary software applications to the skills necessary to perform the range of data science actions in code. This book will focus on teaching R through relevant examples and skills that librarians need in their day-to-day lives that includes visualizations but goes much further to include web scraping, working with maps, creating interactive reports, machine learning, and others. While there's a place for theory, ethics, and statistical methods, librarians need a tool to help them acquire enough facility with R to utilize data science skills in their daily work, no matter what type of library they work at (academic, public or special). By walking through eTable of Contents1. Introduction 2. Using RStudio’s IDE 3. Tidying data with dplyr 4. Visualizing your project with ggplot2 5. Webscraping with rvest 6. Mapping with tmap 7. Textual Analysis with tidytext 8. Creating Dynamic Documents with rmarkdown 9. Creating a flexdashboard 10. Creating an interactive dashboard with shiny 11. Using tidymodels to Understand Machine Learning 12. Conclusion Appendix A. Dependencies Appendix B. Additional Skills

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Book SynopsisThis is the first book to provide life scientists with a practical guide to using experimental design and statistics when running animal experiments. The chapters cover a range of design types and analysis techniques employed by practitioners, using non-mathematical terms and drawing on real-life examples.Trade Review'At last, a readable statistics book focusing solely on preclinical experimental designs, data and its analysis that should form part of an in-vivo scientist's personal library. The author's unique insight into the statistical needs of preclinical scientists has allowed them to compile a non-technical guide that can facilitate sound experimental design, meaningful data analysis and appropriate scientific conclusions. I would also encourage all readers to download and explore 'InVivoStat', a powerful software package that both my group and I use on a daily basis.' Darrel J. Pemberton, Janssen Research and Development'This book provides an indispensable reference for any in-vivo scientist. It addresses common pitfalls in animal experiments and provides tangible advice to address sources of bias, thus increasing the robustness of the data. … The text links experimental design and statistical analysis in a practical way, easily accessible without any prior statistical knowledge. The statistical concepts are described in plain English, avoiding overuse of mathematical formulas and illustrated with numerous examples relevant to biomedical scientists. … This book will help scientists improve the design of animal experiments and give them the confidence to use more complex designs, enabling more efficient use of animals and reducing the number of experimental animals needed overall.' Nathalie Percie du Sert, National Centre for the Replacement, Refinement and Reduction of Animals in Research'This book will transform the way biomedical scientists plan their work and interpret their results. Although the subject matter covers complex points, it is easy to read and packed with relevant examples. There are two particularly striking features. First, at no point do the authors resort to mathematical equations as a substitute for explaining the concepts. Secondly, they explain why the choice of experimental design is so important, why the design affects the statistical analysis and how to ensure the choice of the most appropriate statistical test. The final section describes how to use InvivoStat (a software package, assembled by the authors), which enables researchers to put into practice all the points covered in this book. This is an invaluable combination of resources that should be within easy reach of anyone carrying out experiments in the biomedical sciences, especially if their work involves using live animals.' Clare Stanford, University College LondonTable of ContentsPreface; Acknowledgements; 1. Introduction; 2. Statistical concepts; 3. Experimental design; 4. Randomisation; 5. Statistical analysis; 6. Analysis using InVivoStat; 7. Conclusion; Glossary; References; Index.

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Book Synopsis...I know of no better book of its kind... (Journal of the Royal Statistical Society, Vol 169 (1), January 2006) A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to awide range of disciplines. Step-by-step instructionshelp the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling. Includes numerous worked examples and exercises within each chapter.Table of ContentsPreface xi Chapter 1 Fundamentals 1 Everything Varies 2 Significance 3 Good and Bad Hypotheses 3 Null Hypotheses 3 p Values 3 Interpretation 4 Model Choice 4 Statistical Modelling 5 Maximum Likelihood 6 Experimental Design 7 The Principle of Parsimony (Occam’s Razor) 8 Observation, Theory and Experiment 8 Controls 8 Replication: It’s the ns that Justify the Means 8 How Many Replicates? 9 Power 9 Randomization 10 Strong Inference 14 Weak Inference 14 How Long to Go On? 14 Pseudoreplication 15 Initial Conditions 16 Orthogonal Designs and Non-Orthogonal Observational Data 16 Aliasing 16 Multiple Comparisons 17 Summary of Statistical Models in R 18 Organizing Your Work 19 Housekeeping within R 20 References 22 Further Reading 22 Chapter 2 Dataframes 23 Selecting Parts of a Dataframe: Subscripts 26 Sorting 27 Summarizing the Content of Dataframes 29 Summarizing by Explanatory Variables 30 First Things First: Get to Know Your Data 31 Relationships 34 Looking for Interactions between Continuous Variables 36 Graphics to Help with Multiple Regression 39 Interactions Involving Categorical Variables 39 Further Reading 41 Chapter 3 Central Tendency 42 Further Reading 49 Chapter 4 Variance 50 Degrees of Freedom 53 Variance 53 Variance: A Worked Example 55 Variance and Sample Size 58 Using Variance 59 A Measure of Unreliability 60 Confidence Intervals 61 Bootstrap 62 Non-constant Variance: Heteroscedasticity 65 Further Reading 65 Chapter 5 Single Samples 66 Data Summary in the One-Sample Case 66 The Normal Distribution 70 Calculations Using z of the Normal Distribution 76 Plots for Testing Normality of Single Samples 79 Inference in the One-Sample Case 81 Bootstrap in Hypothesis Testing with Single Samples 81 Student’s t Distribution 82 Higher-Order Moments of a Distribution 83 Skew 84 Kurtosis 86 Reference 87 Further Reading 87 Chapter 6 Two Samples 88 Comparing Two Variances 88 Comparing Two Means 90 Student’s t Test 91 Wilcoxon Rank-Sum Test 95 Tests on Paired Samples 97 The Binomial Test 98 Binomial Tests to Compare Two Proportions 100 Chi-Squared Contingency Tables 100 Fisher’s Exact Test 105 Correlation and Covariance 108 Correlation and the Variance of Differences between Variables 110 Scale-Dependent Correlations 112 Reference 113 Further Reading 113 Chapter 7 Regression 114 Linear Regression 116 Linear Regression in R 117 Calculations Involved in Linear Regression 122 Partitioning Sums of Squares in Regression: SSY = SSR + SSE 125 Measuring the Degree of Fit, r 2 133 Model Checking 134 Transformation 135 Polynomial Regression 140 Non-Linear Regression 142 Generalized Additive Models 146 Influence 148 Further Reading 149 Chapter 8 Analysis of Variance 150 One-Way ANOVA 150 Shortcut Formulas 157 Effect Sizes 159 Plots for Interpreting One-Way ANOVA 162 Factorial Experiments 168 Pseudoreplication: Nested Designs and Split Plots 173 Split-Plot Experiments 174 Random Effects and Nested Designs 176 Fixed or Random Effects? 177 Removing the Pseudoreplication 178 Analysis of Longitudinal Data 178 Derived Variable Analysis 179 Dealing with Pseudoreplication 179 Variance Components Analysis (VCA) 183 References 184 Further Reading 184 Chapter 9 Analysis of Covariance 185 Further Reading 192 Chapter 10 Multiple Regression 193 The Steps Involved in Model Simplification 195 Caveats 196 Order of Deletion 196 Carrying Out a Multiple Regression 197 A Trickier Example 203 Further Reading 211 Chapter 11 Contrasts 212 Contrast Coefficients 213 An Example of Contrasts in R 214 A Priori Contrasts 215 Treatment Contrasts 216 Model Simplification by Stepwise Deletion 218 Contrast Sums of Squares by Hand 222 The Three Kinds of Contrasts Compared 224 Reference 225 Further Reading 225 Chapter 12 Other Response Variables 226 Introduction to Generalized Linear Models 228 The Error Structure 229 The Linear Predictor 229 Fitted Values 230 A General Measure of Variability 230 The Link Function 231 Canonical Link Functions 232 Akaike’s Information Criterion (AIC) as a Measure of the Fit of a Model 233 Further Reading 233 Chapter 13 Count Data 234 A Regression with Poisson Errors 234 Analysis of Deviance with Count Data 237 The Danger of Contingency Tables 244 Analysis of Covariance with Count Data 247 Frequency Distributions 250 Further Reading 255 Chapter 14 Proportion Data 256 Analyses of Data on One and Two Proportions 257 Averages of Proportions 257 Count Data on Proportions 257 Odds 259 Overdispersion and Hypothesis Testing 260 Applications 261 Logistic Regression with Binomial Errors 261 Proportion Data with Categorical Explanatory Variables 264 Analysis of Covariance with Binomial Data 269 Further Reading 272 Chapter 15 Binary Response Variable 273 Incidence Functions 275 ANCOVA with a Binary Response Variable 279 Further Reading 284 Chapter 16 Death and Failure Data 285 Survival Analysis with Censoring 287 Further Reading 290 Appendix Essentials of the R Language 291 R as a Calculator 291 Built-in Functions 292 Numbers with Exponents 294 Modulo and Integer Quotients 294 Assignment 295 Rounding 295 Infinity and Things that Are Not a Number (NaN) 296 Missing Values (NA) 297 Operators 298 Creating a Vector 298 Named Elements within Vectors 299 Vector Functions 299 Summary Information from Vectors by Groups 300 Subscripts and Indices 301 Working with Vectors and Logical Subscripts 301 Addresses within Vectors 304 Trimming Vectors Using Negative Subscripts 304 Logical Arithmetic 305 Repeats 305 Generate Factor Levels 306 Generating Regular Sequences of Numbers 306 Matrices 307 Character Strings 309 Writing Functions in R 310 Arithmetic Mean of a Single Sample 310 Median of a Single Sample 310 Loops and Repeats 311 The ifelse Function 312 Evaluating Functions with apply 312 Testing for Equality 313 Testing and Coercing in R 314 Dates and Times in R 315 Calculations with Dates and Times 319 Understanding the Structure of an R Object Using str 320 Reference 322 Further Reading 322 Index 323

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