Mathematics Books

Book SynopsisTable of ContentsForeword xvii Part One: Introduction 1 Matt Blumberg Introduction 2 Chapter 1: The Nature of a CXO's Role 9 Chapter 2: Scaling a CXO 12 Part Two: Finance and Administration 15 Jack Sinclair Chief Financial Officer 16 Chapter 3: In the Beginning: Laying the CFO Foundation 19 Chapter 4: Fundraising 22 Chapter 5: Size of Opportunity 25 Chapter 6: Financial Plan 27 Chapter 7: Unit Economics and KPIs 30 Chapter 8: Investor Ecosystem Research 32 Chapter 9: Pricing and Valuation 34 Chapter 10: Due Diligence and Corporate Documentation 37 Chapter 11: Using External Counsel 40 Chapter 12: Operational Accounting 42 Chapter 13: Treasury and Cash Management 49 Chapter 14: Building an In-House Accounting Team 52 Chapter 15: International Operations 55 Chapter 16: Strategic Finance 58 Chapter 17: Other Areas to Partner With 67 Chapter 18: High Impact Areas for the Startup CFO as Partner 71 Chapter 19: Board and Shareholder Management 77 Chapter 20: Equity 80 Chapter 21: Mergers and Acquisitions (M&A) 85 Chapter 22: Bonus Section: WhatWe Used for Our Internal Systems WhenWe Started Bolster 91 Chapter 23: CEO-to-CEO Advice About the Finance Role 97 Matt Blumberg Part Three: People and Human Resources 101 Cathy Hawley Chief People Officer 102 Chapter 24: Values and Culture 105 Chapter 25: Diversity, Equity, and Inclusion (DE&I) 111 Chapter 26: Building Your Team 113 Chapter 27: Organizational Design and Operating Systems 118 Chapter 28: Team Development 124 Chapter 29: Leadership Development 127 Chapter 30: Talent and Performance Management 130 Chapter 31: Career Pathing 132 Chapter 32: Role-Specific Learning and Development 134 Chapter 33: Employee Engagement 136 Chapter 34: Rewards and Recognition 138 Chapter 35: Reductions in Force 140 Chapter 36: Recruiting 142 Chapter 37: Onboarding 149 Chapter 38: Compensation 152 Chapter 39: People Operations 154 Chapter 40: Systems 164 Chapter 41: CEO-to-CEO Advice About the People/HR Role 167 Matt Blumberg Part Four: Marketing 173 Nick Badgett and Holly Enneking Chief Marketing Officer 174 Chapter 42: Where to Start 177 Chapter 43: Generating Demand for Sales 181 Chapter 44: Supporting the Company Culture 186 Chapter 45: Breaking Down Marketing’s Functions 191 Chapter 46: Events 204 Chapter 47: Content and Communications 212 Chapter 48: Product Marketing 218 Chapter 49: Marketing Operations 223 Chapter 50: Sales Development 226 Chapter 51: Marketing as a Partner/Collaborating with the Rest of the C-suite 233 Chapter 52: Building a Marketing Machine (Scaleup) 236 Chapter 53: CEO-to-CEO Advice About the Marketing Role 243 Matt Blumberg Part Five: Sales 249 Anita Absey Chief Revenue Officer 250 Chapter 54: In the Beginning: From Prospect to Customer 251 Chapter 55: Hiring the Right People 254 Chapter 56: Profile of Successful Salespeople 257 Chapter 57: Some Myth Busting 260 Chapter 58: Compensating Sales Team Members 262 Chapter 59: Pipeline 266 Chapter 60: Scaling the Sales Organization 268 Chapter 61: Scaling Your Team Through Culture 271 Chapter 62: Scaling Sales Process and Methodology 276 Chapter 63: Scaling the Operating System 279 Chapter 64: Marketing Alignment 282 Chapter 65: Market Assessment and Alignment 286 Chapter 66: Expanding Distribution Channels 288 Chapter 67: Geographic Expansion 291 Chapter 68: Pricing and Packaging 294 Chapter 69: CEO-to-CEO Advice About the Sales Role 300 Matt Blumberg Part Six: Business/Corporate Development 305 Ken Takahashi Chief Business Development Officer 306 Chapter 70: How to Make the Biggest Impact as a CBDO 311 Chapter 71: Building Your Influence Internally 314 Chapter 72: Building Your Influence Externally 318 Chapter 73: Where Internal and External Meet: Your Relationship with Your CEO 323 Chapter 74: Influence Meets Operating System 325 Chapter 75: Develop External Trust for the Company 327 Chapter 76: Build Your Influence in Strategy 329 Chapter 77: Building Your Influence in Business Development 330 Chapter 78: When Things GoWrong in a Partnership…and They Will 335 Chapter 79: Geographic Expansion 338 Chapter 80: M&A: Buy Side 341 Chapter 81: M&A: Sell Side 344 Chapter 82: CEO-to-CEO Advice About the Business/Corporate Development Role 348 Matt Blumberg Part Seven: Customer Success/Account Management 353 George Bilbrey Chief Customer Officer 354 Chapter 83: Five Misperceptions 357 Chapter 84: Startup Customer Success Organization 360 Chapter 85: Scaling the Service Organization 362 Chapter 86: Timing: When to Hire Your Team 366 Chapter 87: Customer Segmentation and Journey 368 Chapter 88: Understanding Customers 372 Chapter 89: Understanding Customers Through Metrics 374 Chapter 90: Foundations of a Great Customer Service Organization 379 Chapter 91: Building an Effective Team 385 Chapter 92: Partnering with the Organization 387 Chapter 93: Five Eternal Questions 391 Chapter 94: CEO-to-CEO Advice About the Customer Success Role 396 Matt Blumberg Part Eight: Product and Engineering 401 Shawn Nussbaum Chief Product Officer and Chief Technology Officer 402 Chapter 95: The Product Development Leaders 405 Chapter 96: Product Development Culture 412 Chapter 97: Technical Strategy: Proportional Engineering Investment and Managing Technical Debt 416 Chapter 98: Shifting to a New Development Culture 424 Chapter 99: Starting Things 427 Chapter 100: Hiring Product Development Team Members 434 Chapter 101: Increasing the Funnel and Building Diverse Teams 442 Chapter 102: Retaining and Career Pathing People 446 Chapter 103: Hiring and Growing Leaders 449 Chapter 104: Organizing, Collaborating with, and Motivating Effective Teams 455 Chapter 105: Due Diligence and Lessons Learned from a Sale Process 468 Chapter 106: Selling Your Company: Preparation 475 Chapter 107: Selling Your Company: Telling the Story 479 Chapter 108: CEO-to-CEO Advice About the Product/Engineering Role 482 Matt Blumberg Part Nine: Privacy 487 Dennis Dayman Chief Privacy Officer 488 Chapter 109: The Role of Privacy Officer 491 Chapter 110: Privacy Advice for Startups 494 Chapter 111: Legal Documents 500 Chapter 112: The European Union 505 Chapter 113: Data Mapping 507 Chapter 114: Data Breach 510 Chapter 115: Least Privileged Access 515 Chapter 116: Employee Training Engagement 519 Chapter 117: Building Your Privacy Team in a Startup 522 Chapter 118: Building Your Privacy Team as You Scaleup 525 Chapter 119: Certifications 527 Chapter 120: Assessments 529 Chapter 121: CEO-to-CEO Advice About the Privacy Role 536 Matt Blumberg Part Ten: Operations 541 Jack Sinclair Chief Operating Officer 542 Chapter 122: CEO-to-CEO Advice About the Operating Role 549 Matt Blumberg Part Eleven: The Future of ExecutiveWork 551 Chapter 123: The Future of ExecutiveWork 553 Matt Blumberg Chapter 124: Fractional Chief Financial Officer 556 John McCarthy Chapter 125: Fractional Chief People Officer 562 Courtney Graeber Chapter 126: Fractional Chief Marketing Officer 567 Scott Kabat Chapter 127: Fractional Chief Revenue Officer 571 B.J. Bushur Chapter 128: Fractional Chief Revenue Officer 576 Sherri Sklar Chapter 129: Fractional Chief Business Development Officer 580 Jon Guttenberg Chapter 130: Fractional Chief Customer Officer 586 Amy Mustoe Chapter 131: Fractional Chief Product/Technology Officer 590 Drew Dillon Chapter 132: Fractional Chief Privacy Officer 594 Teresa Troester-Falk Conclusion 599 Epilogue 601 Pete Birkeland References 603 Acknowledgments 604 About the Authors 606 Index 613

Read more less

Book SynopsisTable of ContentsIntroduction 1 About This Book 1 Foolish Assumptions 3 Icons Used in This Book 3 Beyond the Book 4 Where to Go from Here 4 Part 1: Tackling Data Analysis and Model-Building Basics 7 Chapter 1: Beyond Number Crunching: The Art and Science of Data Analysis 9 Data Analysis: Looking before You Crunch 9 Nothing (not even a straight line) lasts forever 10 Data snooping isn’t cool 11 No (data) fishing allowed 12 Getting the Big Picture: An Overview of Stats II 13 Population parameter 13 Sample statistic 13 Confidence interval 14 Hypothesis test 14 Analysis of variance (ANOVA) 15 Multiple comparisons 15 Interaction effects 16 Correlation 16 Linear regression 17 Chi-square tests 18 Chapter 2: Finding the Right Analysis for the Job 21 Categorical versus Quantitative Variables 22 Statistics for Categorical Variables 23 Estimating a proportion 23 Comparing proportions 24 Looking for relationships between categorical variables 25 Building models to make predictions 26 Statistics for Quantitative Variables 27 Making estimates 27 Making comparisons 28 Exploring relationships 28 Predicting y using x 30 Avoiding Bias 31 Measuring Precision with Margin of Error 33 Knowing Your Limitations 35 Chapter 3: Having the Normal and Sampling Distributions in Your Back Pocket 37 Recognizing the VIP Distribution — the Normal 38 Characterizing the normal 38 Standardizing to the standard normal (Z-) distribution 38 Using the normal table 40 Finding probabilities for the normal distribution 41 Finally Getting Comfortable with Sampling Distributions 42 The mean and standard error of a sampling distribution 42 Sampling distribution of X 43 Sampling distribution of ˆp 44 Heads Up! Building Confidence Intervals and Hypothesis Tests 45 Confidence interval for the population mean 45 Confidence interval for the population proportion 46 Hypothesis test for population mean 46 Hypothesis test for the population proportion 47 Chapter 4: Reviewing Confidence Intervals and Hypothesis Tests 49 Estimating Parameters by Using Confidence Intervals 50 Getting the basics: The general form of a confidence interval 50 Finding the confidence interval for a population mean 51 What changes the margin of error? 52 Interpreting a confidence interval 55 What’s the Hype about Hypothesis Tests? 56 What Ho and Ha really represent 56 Gathering your evidence into a test statistic 57 Determining strength of evidence with a p-value 57 False alarms and missed opportunities: Type I and II errors 58 The power of a hypothesis test 60 Part 2: Using Different Types of Regression to Make Predictions 65 Chapter 5: Getting in Line with Simple Linear Regression 67 Exploring Relationships with Scatterplots and Correlations 68 Using scatterplots to explore relationships 69 Collating the information by using the correlation coefficient 70 Building a Simple Linear Regression Model 71 Finding the best-fitting line to model your data 72 The y-intercept of the regression line 73 The slope of the regression line 74 Making point estimates by using the regression line 75 No Conclusion Left Behind: Tests and Confidence Intervals for Regression 75 Scrutinizing the slope 76 Inspecting the y-intercept 78 Building confidence intervals for the average response 80 Making the band with prediction intervals 81 Checking the Model’s Fit (The Data, Not the Clothes!) 83 Defining the conditions 84 Finding and exploring the residuals 85 Using r2 to measure model fit 89 Scoping for outliers 90 Knowing the Limitations of Your Regression Analysis 92 Avoiding slipping into cause-and-effect mode 92 Extrapolation: The ultimate no-no 93 Sometimes you need more than one variable 94 Chapter 6: Multiple Regression with Two X Variables 95 Getting to Know the Multiple Regression Model 96 Discovering the uses of multiple regression 96 Looking at the general form of the multiple regression model 96 Stepping through the analysis 97 Looking at x’s and y’s 97 Collecting the Data 98 Pinpointing Possible Relationships 100 Making scatterplots 100 Correlations: Examining the bond 101 Checking for Multicolinearity 104 Finding the Best-Fitting Model for Two x Variables 105 Getting the multiple regression coefficients 106 Interpreting the coefficients 107 Testing the coefficients 108 Predicting y by Using the x Variables 110 Checking the Fit of the Multiple Regression Model 111 Noting the conditions 112 Plotting a plan to check the conditions 112 Checking the three conditions 114 Chapter 7: How Can I Miss You If You Won’t Leave? Regression Model Selection 117 Getting a Kick out of Estimating Punt Distance 118 Brainstorming variables and collecting data 118 Examining scatterplots and correlations 120 Just Like Buying Shoes: The Model Looks Nice, But Does It Fit? 123 Assessing the fit of multiple regression models 124 Model selection procedures 125 Chapter 8: Getting Ahead of the Learning Curve with Nonlinear Regression 129 Anticipating Nonlinear Regression 130 Starting Out with Scatterplots 131 Handling Curves in the Road with Polynomials 133 Bringing back polynomials 134 Searching for the best polynomial model 136 Using a second-degree polynomial to pass the quiz 138 Assessing the fit of a polynomial model 141 Making predictions 143 Going Up? Going Down? Go Exponential! 145 Recollecting exponential models 145 Searching for the best exponential model 146 Spreading secrets at an exponential rate 148 Chapter 9: Yes, No, Maybe So: Making Predictions by Using Logistic Regression 153 Understanding a Logistic Regression Model 154 How is logistic regression different from other regressions? 154 Using an S-curve to estimate probabilities 155 Interpreting the coefficients of the logistic regression model 156 The logistic regression model in action 157 Carrying Out a Logistic Regression Analysis 158 Running the analysis in Minitab 158 Finding the coefficients and making the model 160 Estimating p 161 Checking the fit of the model 162 Fitting the movie model 162 Part 3: Analyzing Variance with Anova 167 Chapter 10: Testing Lots of Means? Come On Over to ANOVA! 169 Comparing Two Means with a t-Test 170 Evaluating More Means with ANOVA 171 Spitting seeds: A situation just waiting for ANOVA 172 Walking through the steps of ANOVA 173 Checking the Conditions 174 Verifying independence 174 Looking for what’s normal 174 Taking note of spread 176 Setting Up the Hypotheses 178 Doing the F-Test 179 Running ANOVA in Minitab 180 Breaking down the variance into sums of squares 180 Locating those mean sums of squares 182 Figuring the F-statistic 183 Making conclusions from ANOVA 184 What’s next? 186 Checking the Fit of the ANOVA Model 186 Chapter 11: Sorting Out the Means with Multiple Comparisons 189 Following Up after ANOVA 190 Comparing cellphone minutes: An example 190 Setting the stage for multiple comparison procedures 192 Pinpointing Differing Means with Fisher and Tukey .193 Fishing for differences with Fisher’s LSD 194 Separating the turkeys with Tukey’s test 197 Examining the Output to Determine the Analysis 198 So Many Other Procedures, So Little Time! 199 Controlling for baloney with the Bonferroni adjustment 200 Comparing combinations by using Scheffé’s method 201 Finding out whodunit with Dunnett’s test 202 Staying cool with Student Newman-Keuls 202 Duncan’s multiple range test 202 Chapter 12: Finding Your Way through Two-Way ANOVA 205 Setting Up the Two-Way ANOVA Model 206 Determining the treatments 206 Stepping through the sums of squares 207 Understanding Interaction Effects 209 What is interaction, anyway? 209 Interacting with interaction plots 210 Testing the Terms in Two-Way ANOVA .213 Running the Two-Way ANOVA Table 214 Interpreting the results: Numbers and graphs 214 Are Whites Whiter in Hot Water? Two-Way ANOVA Investigates 217 Chapter 13: Regression and ANOVA: Surprise Relatives! 221 Seeing Regression through the Eyes of Variation 222 Spotting variability and finding an “x-planation” 222 Getting results with regression 223 Assessing the fit of the regression model 225 Regression and ANOVA: A Meeting of the Models 226 Comparing sums of squares 226 Dividing up the degrees of freedom 228 Bringing regression to the ANOVA table 229 Relating the F- and t-statistics: The final frontier 230 Part 4: Building Strong Connections with Chi-Square Tests and Nonparametrics 233 Chapter 14: Forming Associations with Two-Way Tables 235 Breaking Down a Two-Way Table 236 Organizing data into a two-way table 236 Filling in the cell counts 237 Making marginal totals 238 Breaking Down the Probabilities 239 Marginal probabilities 239 Joint probabilities 241 Conditional probabilities 242 Trying To Be Independent 247 Checking for independence between two categories 247 Checking for independence between two variables 249 Demystifying Simpson’s Paradox 250 Experiencing Simpson’s Paradox 250 Figuring out why Simpson’s Paradox occurs 253 Keeping one eye open for Simpson’s Paradox 254 Chapter 15: Being Independent Enough for the Chi-Square Test 257 The Chi-Square Test for Independence 258 Collecting and organizing the data 259 Determining the hypotheses 261 Figuring expected cell counts 261 Checking the conditions for the test 262 Calculating the Chi-square test statistic 263 Finding your results on the Chi-square table 266 Drawing your conclusions 269 Putting the Chi-square to the test 271 Comparing Two Tests for Comparing Two Proportions 272 Getting reacquainted with the Z-test for two population proportions 273 Equating Chi-square tests and Z-tests for a two-by-two table 274 Chapter 16: Using Chi-Square Tests for Goodness-of-Fit (Your Data, Not Your Jeans) 279 Finding the Goodness-of-Fit Statistic 280 What’s observed versus what’s expected 280 Calculating the goodness-of-fit statistic 282 Interpreting the Goodness-of-Fit Statistic Using a Chi-Square 284 Checking the conditions before you start 285 The steps of the Chi-square goodness-of-fit test 286 Chapter 17: Rebels Without a Distribution — Nonparametric Procedures 291 Arguing for Nonparametric Statistics 292 No need to fret if conditions aren’t met 292 The median’s in the spotlight for a change 293 So, what’s the catch? 295 Mastering the Basics of Nonparametric Statistics 296 Sign 296 Chapter 18: All Signs Point to the Sign Test 299 Reading the Signs: The Sign Test 300 Testing the median in real estate 302 Estimating the median 304 Testing matched pairs 306 Part 5: Putting it all Together: Multi-Stage Analysis of A Large Data Set 309 Chapter 19: Conducting a Multi-Stage Analysis of a Large Data Set 311 Steps Involved in Working with a Large Data Set 311 Wrangling Data 313 Discovery 313 Structuring 314 Cleaning 315 Enriching 315 Validating 316 Publishing 317 Visualizing Data 317 Exploring the Data 319 Looking for Relationships 319 Building Models and Making Inferences 320 Sharing the Story 321 Who is the audience? 322 Make an outline 322 Include an executive summary 323 Check your writing 323 Chapter 20: A Statistician Watches the Movies 325 Examining the Movie Variables and Asking Questions 326 Visualizing the Movie Data 327 Categorical movie variables 328 Quantitative movie variables 329 Doing Descriptive Dirty Work 332 Looking for Relationships 333 Relationships between quantitative movie variables 333 Relationships between two categorical variables 337 Relationships between quantitative and categorical variables 338 Building a Model for Predicting U.S Revenue 340 Writing It Up 343 Chapter 21: Looking Inside the Refrigerator 347 Refrigerator Data — The Variables 348 Exploring the Data 348 Analyzing the Data 350 Writing It Up 358 Part 6: The Part of Tens 361 Chapter 22: Ten Common Errors in Statistical Conclusions 363 Claiming These Statistics Prove 363 It’s Not Technically Statistically Significant, But 364 Concluding That x Causes y 365 Assuming the Data Was Normal 366 Only Reporting “Important” Results 366 Assuming a Bigger Sample Is Always Better 367 It’s Not Technically Random, But 369 Assuming That 1,000 Responses Is 1,000 Responses 369 Of Course the Results Apply to the General Population 371 Deciding Just to Leave It Out 372 Chapter 23: Ten Ways to Get Ahead by Knowing Statistics 375 Asking the Right Questions 375 Being Skeptical 376 Collecting and Analyzing Data Correctly 377 Calling for Help 378 Retracing Someone Else’s Steps 379 Putting the Pieces Together 379 Checking Your Answers 380 Explaining the Output 381 Making Convincing Recommendations 382 Establishing Yourself as the Statistics Go-To Person 383 Chapter 24: Ten Cool Jobs That Use Statistics 385 Pollster 386 Data Scientist 387 Ornithologist (Bird Watcher) 387 Sportscaster or Sportswriter 388 Journalist 390 Crime Fighter 390 Medical Professional 391 Marketing Executive 392 Lawyer 393 Appendix A: Reference Tables 395 Index 409

Read more less

Book SynopsisPractice your way to a great grade in Algebra I Algebra I: 1001 Practice Problems For Dummies gives you 1,001 opportunities to practice solving problems on all the major topics in Algebra Iin the book and online! Get extra help with tricky subjects, solidify what you've already learned, and get in-depth walk-throughs for every problem with this useful book. These practice problems and detailed answer explanations will get you solving for x in no-time, no matter what your skill level. Thanks to Dummies, you have a resource to you put key concepts into practice. Work through practice problems on all Algebra I topics covered in classStep through detailed solutions for every problem to build your understandingAccess practice questions online to study anywhere, any timeImprove your grade and up your study game with practice, practice, practiceThe material presented in Algebra I: 1001 Practice Problems For Dummies is an excellent resource for students, as well as parents and tutors looking to help supplement classroom instruction. Algebra I: 1001 Practice Problems For Dummies (9781119883470) was previously published as 1,001 Algebra I Practice Problems For Dummies (9781118446713). While this version features a new Dummies cover and design, the content is the same as the prior release and should not be considered a new or updated product.Table of ContentsIntroduction 1 Part 1: The Questions 5 Chapter 1: Signing on with Signed Numbers 7 Chapter 2: Recognizing Algebraic Properties and Notation 13 Chapter 3: Working with Fractions and Decimals 17 Chapter 4: Making Exponential Expressions and Operations More Compatible 23 Chapter 5: Raking in Radicals 29 Chapter 6: Creating More User-Friendly Algebraic Expressions 35 Chapter 7: Multiplying by One or More Terms 41 Chapter 8: Dividing Algebraic Expressions 47 Chapter 9: Factoring Basics 53 Chapter 10: Factoring Binomials 57 Chapter 11: Factoring Quadratic Trinomials 61 Chapter 12: Other Factoring Techniques 65 Chapter 13: Solving Linear Equations 69 Chapter 14: Taking on Quadratic Equations 73 Chapter 15: Solving Polynomials with Powers Three and Higher 79 Chapter 16: Reining in Radical and Absolute Value Equations 83 Chapter 17: Making Inequalities More Fair 87 Chapter 18: Using Established Formulas 93 Chapter 19: Using Formulas in Geometric Story Problems 101 Chapter 20: Tackling Traditional Story Problems 107 Chapter 21: Graphing Basics 113 Chapter 22: Using the Algebra of Lines 119 Chapter 23: Other Graphing Topics 123 Part 2: The Answers 127 Chapter 24: The Answers 129 Index 443

Read more less

Book SynopsisTable of ContentsIntroduction 1 Part 1: The Questions 5 Chapter 1: The Big Four Operations 7 Chapter 2: Less than Zero: Working with Negative Numbers 11 Chapter 3: You’ve Got the Power: Powers and Roots 17 Chapter 4: Following Orders: Order of Operations 23 Chapter 5: Big Four Word Problems 29 Chapter 6: Divided We Stand 35 Chapter 7: Factors and Multiples 43 Chapter 8: Word Problems about Factors and Multiples 49 Chapter 9: Fractions 53 Chapter 10: Decimals 63 Chapter 11: Percents 69 Chapter 12: Ratios and Proportions 75 Chapter 13: Word Problems for Fractions, Decimals, and Percents 79 Chapter 14: Scientific Notation 87 Chapter 15: Weights and Measures 91 Chapter 16: Geometry 97 Chapter 17: Graphing 109 Chapter 18: Statistics and Probability 115 Chapter 19: Set Theory 123 Chapter 20: Algebraic Expressions 127 Chapter 21: Solving Algebraic Equations 133 Chapter 22: Solving Algebra Word Problems 139 Part 2: The Answers 143 Chapter 23: Answers 145 Index 407

Read more less

Book SynopsisTable of ContentsIntroduction 1 Part 1: The Questions 5 Chapter 1: Reviewing Algebra Basics 7 Chapter 2: Solving Quadratic Equations and Nonlinear Inequalities 13 Chapter 3: Solving Radical and Rational Equations 21 Chapter 4: Graphs and Equations of Lines 27 Chapter 5: Functions 33 Chapter 6: Quadratic Functions and Relations 39 Chapter 7: Polynomial Functions and Equations 45 Chapter 8: Rational Functions 51 Chapter 9: Exponential and Logarithmic Functions 57 Chapter 10: Conic Sections 65 Chapter 11: Systems of Linear Equations 73 Chapter 12: Systems of Nonlinear Equations and Inequalities 79 Chapter 13: Working with Complex Numbers 85 Chapter 14: Matrices 91 Chapter 15: Sequences and Series 97 Chapter 16: Sets 103 Chapter 17: Counting Techniques and Probability 109 Part 2: The Answers 117 Chapter 18: The Answers 119 Index 499

Read more less

Book SynopsisTable of ContentsIntroduction 1 Part 1: Making Friends with the Calculator 5 Chapter 1: Starting with the Basics 7 Chapter 2: Doing Basic Arithmetic 25 Chapter 3: Dealing with Fractions 35 Chapter 4: Solving Equations 41 Part 2: Taking Your Calculator Relationship to the Next Level 53 Chapter 5: Working with Complex Numbers 55 Chapter 6: Understanding the Math Menu and Submenus 61 Chapter 7: The Angle and Test Menus 69 Chapter 8: Creating and Editing Matrices 79 Part 3: Graphing and Analyzing Functions 89 Chapter 9: Graphing Functions 91 Chapter 10: Exploring Functions 111 Chapter 11: Evaluating Functions 127 Chapter 12: Graphing Inequalities 143 Chapter 13: Graphing Parametric Equations 155 Chapter 14: Graphing Polar Equations 163 Part 4: Working with Probability and Statistics 173 Chapter 15: Probability 175 Chapter 16: Dealing with Statistical Data 183 Chapter 17: Analyzing Statistical Data 193 Part 5: Doing More with Your Calculator 209 Chapter 18: Communicating with a PC Using TI Connect CE Software 211 Chapter 19: Communicating Between Calculators 221 Chapter 20: Fun with Images 227 Chapter 21: Managing Memory 231 Part 6: The Part of Tens 237 Chapter 22: Ten Essential Skills 239 Chapter 23: Ten Common Errors 243 Chapter 24: Ten Common Error Messages 249 Part 7: Appendices 253 Appendix A: Creating Calculator Programs 255 Appendix B: Controlling Program Input and Output 259 Appendix C: Controlling Program Flow 269 Appendix D: Introducing Python Programming 281 Appendix E: Mastering the Basics of Python Programming 287 Index 293

Read more less

Book SynopsisCatrin Radcliffe is a tutor of mathematics and statistics at Oxford Brookes University, UK.Table of ContentsIntroduction PART 1: GETTING STARTED 1. What does your assignment ask you to do? 2. How will you do it? 3. Defining your research question 4. Tips for designing your questionnaire 5. How to enter your data into a spreadsheet PART 2: UNDERSTANDING AND DESCRIBING YOUR DATA 6. What type of data do you have? 7. Descriptive statistics 8. What plot should you use? PART 3: HOW DO STATISTICAL TESTS WORK? 9. What is a statistical hypothesis? 10. Using probability distributions in statistical tests 11. Statistics, "errors" and interpretation PART 4: WHAT STATISTICAL TEST DO YOU NEED? 12. The statistics signpost 13. Statistical flowcharts 14. Case studies PART 5: THE STATISTICAL PROCESS 15. You the researcher 16. You the interpreter Symbols explained Useful resources References Index.

Read more less

Book SynopsisSince the first edition of this book published, Bayesian networks have become even more important for applications in a vast array of fields. This second edition includes new material on influence diagrams, learning from data, value of information, cybersecurity, debunking bad statistics, and much more. Focusing on practical real-world problem-solving and model building, as opposed to algorithms and theory, it explains how to incorporate knowledge with data to develop and use (Bayesian) causal models of risk that provide more powerful insights and better decision making than is possible from purely data-driven solutions.Features Provides all tools necessary to build and run realistic Bayesian network models Supplies extensive example models based on real risk assessment problems in a wide range of application domains provided; for example, finance, safety, systems reliability, law, forensics, cybersecurity and more IntrodTrade ReviewPraise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Praise for the first edition: "By offering many attractive examples of Bayesian networks and by making use of software that allows one to play with the networks, readers will definitely get a feel for what can be done with Bayesian networks. … the power and also uniqueness of the book stem from the fact that it is essentially practice oriented, but with a clear aim of equipping the developer of Bayesian networks with a clear understanding of the underlying theory. Anyone involved in everyday decision making looking for a better foundation of what is now mainly based on intuition will learn something from the book."—Peter J.F. Lucas, Journal of Statistical Theory and Practice, Vol. 8, March 2014 "… very useful to practitioners, professors, students, and anyone interested in understanding the application of Bayesian networks to risk assessment and decision analysis. Having many years of experience in the area, I highly recommend the book."—William E. Vesely, International Journal of Performability Engineering, July 2013 "Risk Assessment and Decision Analysis with Bayesian Networks is a brilliant book. Being a non-mathematician, I’ve found all of the other books on BNs to be an impenetrable mass of mathematical gobble-de-gook. This, in my view, has slowed the uptake of BNs in many disciplines because people simply cannot understand why you would use them and how you can use them. This book finally makes BNs comprehensible, and I plan to develop a risk assessment course at the University of Queensland using this book as the recommended textbook."—Carl Smith, School of Agriculture and Food Sciences, The University of Queensland "… although there have been several excellent books dedicated to Bayesian networks and related methods, these books tend to be aimed at readers who already have a high level of mathematical sophistication … . As such they are not accessible to readers who are not already proficient in those subjects. This book is an exciting development because it addresses this problem. … it should be understandable by any numerate reader interested in risk assessment and decision making. The book provides sufficient motivation and examples (as well as the mathematics and probability where needed from scratch) to enable readers to understand the core principles and power of Bayesian networks. However, the focus is on ensuring that readers can build practical Bayesian network models … readers are provided with a tool that performs the propagation, so they will be able to build their own models to solve real-world risk assessment problems."—From the Foreword by Judea Pearl, UCLA Computer Science Department and 2011 Turing Award winner "Let's be honest, most risk assessment methodologies are guesses, and not very good ones at that. People collect statistics about what they can see and then assume it tells them something about what they can't. The problem is that people assume the world follows nice distributions embedded in the world's fabric and that we simply need a little data to get the parameters right. Fenton and Neil take readers on an excellent journey through a more modern and appropriate way to make sense of uncertainty by leveraging prior beliefs and emerging evidence. Along the way they provide a wakeup call for the classic statistical views of risk and eloquently show the biases, fallacies and misconceptions that exist in such a view, and how dangerous they are for those making decisions.The book is not condescending to those without a mathematical background and is not too simple for those who do. It sets a nice tone which focuses more on how readers should think about risk and uncertainty and then uses a wealth of practical examples to show them how Bayesian methods can deliver powerful insights.After reading this book, you should be in no doubt that not only is it possible to model risk from the perspective of understanding how it behaves, but also that is necessarily the only sensible way to do so if you want to do something useful with your model and make correct decisions from it.Anyone aspiring to work, or already working, in the field of risk is well advised to read this book and put it into practice."—Neil Cantle, Milliman "The lovely thing about Risk Assessment and Decision Analysis with Bayesian Networks is that it holds your hand while it guides you through this maze of statistical fallacies, p-values, randomness and subjectivity, eventually explaining how Bayesian networks work and how they can help to avoid mistakes. There are loads of vivid examples (for instance, one explaining the Monty Hall problem), and it doesn’t skim over any of the technical details …"—Angela Saini (MIT Knight Science Journalism Fellow 2012-2013) on her blog, December 2012 "As computational chip size and product development cycle time approach zero, survival in the software industry becomes predicated on three related capabilities: prediction, diagnosis, and causality. These are the competitive advantages in 21st century software design testing. Fenton and Neil not only make a compelling case for Bayesian inference, but they also meticulously and patiently guide software engineers previously untrained in probability theory toward competence in mathematics. We have been waiting for decades for the last critical component that will make Bayesian a household word in industry: the incredible combination of an accessible software tool and an accompanying and brilliantly written textbook. Now software testers have the math, the algorithms, the tool, and the book. We no longer have any excuses for not dramatically raising our technology game to meet that challenge of continuous testing. Fenton and Neil came to our rescue, and just in the nick of time. Thanks, guys."—Michael Corning, Microsoft Corporation "This is an awesome book on using Bayesian networks for risk assessment and decision analysis. What makes this book so great is both its content and style. Fenton and Neil explain how the Bayesian networks work and how they can be built and applied to solve various decision-making problems in different areas. Even more importantly, the authors very clearly demonstrate motivations and advantages for using Bayesian networks over other modelling techniques. The core ideas are illustrated by lots of examples—from toy models to real-world applications. In contrast with many other books, this one is very easy to follow and does not require a strong mathematical or statistical background. I highly recommend this book to all researchers, students and practitioners who would like to go beyond traditional statistics or automated data mining techniques and incorporate expert knowledge in their models."—Dr. Lukasz Radlinski, Szczecin University "It is the first book that takes the art and science of developing Bayesian network models for actual problems as seriously as the underlying mathematics. The reader will obtain a good understanding of the methods as they are introduced through well-motivated and intuitive examples and attractive case studies. The authors do this in such a way that readers with little previous exposure to probability theory and statistics will be able to grasp and appreciate the power of Bayesian networks. While this in itself is already a major achievement, the authors go far beyond this by providing very close and pragmatic links between model building and the required techniques. It, thus, shares insights that are mostly missing from other textbooks, making this book also of interest to advanced readers, lecturers and researchers in the area."—Prof.dr. Peter Lucas, Institute for Computing and Information Sciences, Radboud University Nijmegen, and Leiden Institute of Advanced Computer Science, Leiden University "This book gives a thorough account of Bayesian networks, one of the most widely used frameworks for reasoning with uncertainty, and their application in domains as diverse as system reliability modelling and legal reasoning. The book's central premise is that ‘essentially, all models are wrong, but some are useful’ (G.E.P. Box), and the book distinguishes itself by focusing on the art of building useful models for risk assessment and decision analysis rather than on delving into mathematical detail of the models that are built. The authors are renowned for their ability to put Bayesian network technology into practical use, and it is therefore no surprise that the book is filled to the brim with motivating and relevant examples. With the accompanying evaluation copy of the excellent AgenaRisk software, readers can easily play around with the examples and gain valuable insights of how the models behave ‘at work.’ I believe this book should be of interest to practitioners working with risk assessment and decision making and also as a valuable textbook in undergraduate courses on probability and risk."—Helge Langseth, Norwegian University of Science and Technology "Bayesian networks are revolutionizing the way experts assess and manage uncertainty. This is the first book to explain this powerful new tool to a non-specialist audience. It takes us on a compelling journey from the basics of probability to sophisticated networks of system design, finance and crime. This trip is greatly supported by free software, allowing readers to explore and develop Bayesian networks for themselves. The style is accessible and entertaining, without sacrificing conceptual or mathematical rigor. This book is a must-read for anyone wanting to learn about Bayesian networks; it provides the know-how and software so that we can all share this adventure into risk and uncertainty."—David Lagnado, Senior Lecturer in Cognitive and Decision Sciences, University College London "This is the book I have wanted to see for many years. Whilst we are entitled to see appropriate duty of care in any risk management scenario, ill-informed practice is in fact prevalent in industry and society. There is little real excuse for this as classical decision theory has a long established history, and it can now be operationalized in complex scenarios using the Bayesian network technology that is a core theme of this book. The problem has been that most books on Bayesian networks and decision theory focus in depth on the technical foundations, and provide little in the way of practical guidance on how to use the technology to support real-world risk assessment and decision making.In contrast, Norman Fenton and Martin Neil have provided a clearly written and highly readable book that is packed with informative and insightful examples. I had fun reading it, but there is also sufficient technical detail so that one can obtain a deep understanding of the subject from studying the book. It is a joy, and one that I keep dipping back into."—Paul Krause, Professor of Software Engineering, University of Surrey "Given the massive uncertainties managers now need to operate within, this book is both vital and timely. Fenton and Neil’s explanation of how to create practical models that simulate real-life strategic scenarios gives hard-pressed managers a new tool that they can use to understand potential impacts and opportunities. This book should be required reading for anyone involved in strategy, business planning, or significant decision-making."—Rob Wirszycz, Celaton Limited Table of ContentsThere Is More to Assessing Risk Than Statistics. The Need for Causal, Explanatory Models in Risk Assessment. Measuring Uncertainty: The Inevitability of Subjectivity. The Basics of Probability. Bayes’ Theorem and Conditional Probability. From Bayes’ Theorem to Bayesian Networks. Defining the Structure of Bayesian Networks. Building and Eliciting Node Probability Tables. Numeric Variables and Continuous Distribution Functions. Hypothesis Testing and Confidence Intervals. Modeling Operational Risk. Systems Reliability Modeling. Bayes and the Law. Learning Bayesian Networks. Decision making, Influence Diagrams and Value of information. Bayesian networks in forensics. Using Bayesian networks to debunk bad statistics. Bayesian networks for football prediction. Appendix A: The Basics of Counting. Appendix B: The Algebra of Node Probability Tables. Appendix C: Junction Tree Algorithm. Appendix D: Dynamic Discretization. Appendix E: Statistical Distributions.

Read more less

Book SynopsisPrevious editions of this popular textbook offered an accessible and practical introduction to numerical analysis. An Introduction to Numerical Methods: A MATLAB Approach, Fourth Edition continues to present a wide range of useful and important algorithms for scientific and engineering applications. The authors use MATLAB to illustrate each numerical method, providing full details of the computed results so that the main steps are easily visualized and interpreted. This edition also includes a new chapter on Dynamical Systems and Chaos.Features Covers the most common numerical methods encountered in science and engineering Illustrates the methods using MATLAB Presents numerous examples and exercises, with selected answers at the back of the book

Read more less

Book SynopsisDesigned for a one-semester course at the junior undergraduate level, Transformational Plane Geometry takes a hands-on, interactive approach to teaching plane geometry. The book is self-contained, defining basic concepts from linear and abstract algebra gradually as needed.The text adheres to the National Council of Teachers of Mathematics Principles and Standards for School Mathematics and the Common Core State Standards Initiative Standards for Mathematical Practice. Future teachers will acquire the skills needed to effectively apply these standards in their classrooms. Following Felix Klein's Erlangen Program, the book provides students in pure mathematics and students in teacher training programs with a concrete visual alternative to Euclid's purely axiomatic approach to plane geometry. It enables geometrical visualization in three ways: Key concepts are motivated with explorTrade Review"This book is designed for a one-semester course at the junior undergraduate level and turns especially to future educators in the USA. … The arrangement and clarity of the text meet the most demanding pedagogical and mathematical requirements. Highlights of the book are the classification of isometries and similarities of the Euclidean plane. … a wonderful first step into transformational plane geometry …"—Zentralblatt MATH 1311 Table of ContentsAxioms of Euclidean Plane Geometry. Theorems of Euclidean Plane Geometry. Introduction to Transformations, Isometries, and Similarities. Translations, Rotations, and Reflections. Compositions of Translations, Rotations, and Reflections. Classification of Isometries. Symmetry of Plane Figures. Similarity. Appendix. Bibliography. Index.

Read more less

Book SynopsisUsing data from one season of NBA games, Basketball Data Science: With Applications in R is the perfect book for anyone interested in learning and applying data analytics in basketball. Whether assessing the spatial performance of an NBA player''s shots or doing an analysis of the impact of high pressure game situations on the probability of scoring, this book discusses a variety of case studies and hands-on examples using a custom R package. The codes are supplied so readers can reproduce the analyses themselves or create their own. Assuming a basic statistical knowledge, Basketball Data Science with R is suitable for students, technicians, coaches, data analysts and applied researchers.Features: One of the first books to provide statistical and data mining methods for the growing field of analytics in basketball Presents tools for modelling graphs and figures to visualize the data Includes real woTrade Review"This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the forward: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combined with software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "This book provides a unique insight into the use of Statistics in Basketball. I am not aware of any similar text and this is a much welcomed book. It covers applications to Basketball of a good number of statistical methods. The book starts by describing the different types of data in Basketball and how to create summary statistics and different plots. Several advanced methods are described later to exploit the available information and discover patterns in the data. Furthermore, FOCUS sections throughout the book provide interesting case studies on important aspects of the game. The associated R package BasketballAnalyzeR, developed by the authors, is extensively used in the book to develop the examples. This book will be of interest to those working in sport data science as well as those with a passion for Basketball." –Virgilio Gomez Rubio From the foreword: "I am grateful to [the authors] for sharing this ‘philosophical’ approach in their valuable work. I think that it is the correct route for bringing [coaches and analysts] closer together and achieving the maximum pooling of knowledge."–Ettore Messina, Head Coach, Olimpia Militano, former Assistant Coach, San Antonio Spurs "Overall, I think this is an excellent book and it was super fun to read. It will certainly have an impact on the sports data science community." –Patrick Mair, Harvard University "The analysis is sophisticated but well-grounded. The depth of the authors' training in statistical methodology and experience analyzing data comes through clearly, filling the readers with confidence. In writing this practical but fascinating book, they have brought this expertise to bear on quantifying basketball in a way that could be indispensable for coaches, players and analysts, and tremendously interesting for fans." –Jason Osborne, North Carolina State University "My overall impression of Basketball Data Science with Applications in R is that it's exactly the sort of book I would recommend to an instructor or able student of statistics in sport" –Jack Davis, Simon Fraser University “The real strength of this book is that it is meant to be hands-on. As part of the text, the authors provide access to a custom-built package in R, along with an excellent pre-prepared data set (one full season’s worth of NBA box score and play-by-play data). The authors then guide the reader through many examples of building graphs and tables using their R package and data. The graphs are often intricate and visually detailed, but the text shows how to make them quickly, giving detailed instructions. I imagine that a reader looking to get into basketball analysis could find this book very exciting, because it provides a quick and easy entry point into conducting sophisticated analyses and making visually arresting graphs and figures. A reader can easily follow along and replicate everything that is done in the book. Or, what is more likely, the reader can skim through the text until they come to a plot that looks particularly cool, and then by reading the surrounding section they can quickly learn how to do such an analysis for themselves.” –Brian Skinner, MIT "This book I know by heart and like it very much. It is a nice collection of data science methods for basketball analysis combinedwith software code examples (in the statistical programming language R)."–Prof. Dr. Andreas Groll, Technische Universität Dortmund "For those interested in any level of statistical data analysis in basketball, specifically in R, Basketball Data Science: With Applications in R would be a valuable addition to their library. Further, this text would be quite useful for a course in sports data focusing on basketball or for a student’s research project." Russ Goodman, Central College, Iowa, USA, Mathematical Association of America, April 2023. Table of Contents1. Introduction. 2. Finding Groups in Data. 3. Finding Structures in Data with Machine Learning. 4. Modelling Relationships in Basketball. 5. Concluding Remarks and Future Perspectives.

Read more less

Book SynopsisBasic Transport Phenomena in Biomedical Engineering, Fourth Edition, brings together fundamental engineering and life science principles, with specific attention paid to the momentum and mass transport concepts applicable to the design of medical devices. Such an analysis highlights the chemical and physical transport processes used in the development of artificial organs, bioartificial organs, controlled drug delivery systems, and tissue engineering. Basic Transport Phenomena in Biomedical Engineering, Fourth Edition, furthermore provides a basic review of units and dimensions with some tips for solving engineering problems; an investigation of thermodynamic concepts with an emphasis on the properties of solutions; and an in-depth exploration of body fluids, osmosis and membrane filtration, the physical and flow properties of blood, solute transport, oxygen transport, and pharmacokinetic analysis. This text is written with curious and inquisitive students in mind who wish toTrade Review"This is an excellent undergraduate biotransport text that presents material in a logical, easily understandable fashion. The book does a great job of incorporating problem solving and dimensional analysis using topics that are relevant and timely. It is a pleasure to teach from this textbook."— Christopher Brigham, University of Massachusetts Dartmouth, USA"The text provides a comprehensive introduction to a complex topic which brings together a number of different scientific disciplines. In addition, the text provides the student with some worked examples to enhance understanding."— Nicholas Hoenich, Newcastle University, UK"My course at Rutgers consistently has garnered very positive feedback from the students, and I am delighted that the book is being continually updated and aligned to suit the newer demands of our academic discipline."— Prabhas Moghe, Rutgers University, New Jersey, USATable of Contents1 Introduction2 A review of thermodynamic concepts3 Physical properties of the body fluids and the cell membrane4 The physical and flow properties of blood and other fluids5 Mass transfer fundamentals6 Mass transfer in heterogeneous materials7 Oxygen transport in biological systems8 Pharmacokinetic analysis9 Extracorporeal devices10 Tissue engineering and regenerative medicine11 Bioartificial organs

Read more less

Book SynopsisThe ideal study guide for success in Basic Mathâupdated with the latest strategies and hundreds of practice questionsPractice makes perfectâand this study guide gives you all the practice you need to gain mastery in Basic Math. Whether youâre a high school or college student, or a self-studying adult, the hundreds of exercises in Practice Makes Perfect: Basic Math Review and Workbook, Third Edition will help you become comfortable, and ultimately gain confidence with the material.This updated edition features the latest strategies and lesson instruction in an accessible format, with thorough review followed immediately by a variety of practice questions. Covering all the essential basic math topics, this book will give you everything you need to help with your schoolwork, exams, and everyday life!Features: Hundreds of updated practice questions, including the latest question types Updated lesson instruction and the latest math st

Read more less

Book SynopsisTable of ContentsP. What is Number Theory? 1. The Integers. Numbers and Sequences. Sums and Products. Mathematical Induction. The Fibonacci Numbers. 2. Integer Representations and Operations. Representations of Integers. Computer Operations with Integers. Complexity of Integer Operations. 3. Primes and Greatest Common Divisors. Prime Numbers. The Distribution of Primes. Greatest Common Divisors. The Euclidean Algorithm. The Fundemental Theorem of Arithmetic. Factorization Methods and Fermat Numbers. Linear Diophantine Equations. 4. Congruences. Introduction to Congruences. Linear Congrences. The Chinese Remainder Theorem. Solving Polynomial Congruences. Systems of Linear Congruences. Factoring Using the Pollard Rho Method. 5. Applications of Congruences. Divisibility Tests. The perpetual Calendar. Round Robin Tournaments. Hashing Functions. Check Digits. 6. Some Special Congruences. Wilson's Theorem and Fermat's Little Theorem. Pseudoprimes. Euler's Theorem. 7. Multiplicative Functions. The Euler Phi-Function. The Sum and Number of Divisors. Perfect Numbers and Mersenne Primes. Mobius Inversion. Partitions. 8. Cryptology. Character Ciphers. Block and Stream Ciphers. Exponentiation Ciphers. Knapsack Ciphers. Cryptographic Protocols and Applications. 9. Primitive Roots. The Order of an Integer and Primitive Roots. Primitive Roots for Primes. The Existence of Primitive Roots. Index Arithmetic. Primality Tests Using Orders of Integers and Primitive Roots. Universal Exponents. 10. Applications of Primitive Roots and the Order of an Integer. Pseudorandom Numbers. The EIGamal Cryptosystem. An Application to the Splicing of Telephone Cables. 11. Quadratic Residues. Quadratic Residues and nonresidues. The Law of Quadratic Reciprocity. The Jacobi Symbol. Euler Pseudoprimes. Zero-Knowledge Proofs. 12. Decimal Fractions and Continued. Decimal Fractions. Finite Continued Fractions. Infinite Continued Fractions. Periodic Continued Fractions. Factoring Using Continued Fractions. 13. Some Nonlinear Diophantine Equations. Pythagorean Triples. Fermat's Last Theorem. Sums of Squares. Pell's Equation. Congruent Numbers. 14. The Gaussian Integers. Gaussian Primes. Unique Factorization of Gaussian Integers. Gaussian Integers and Sums of Squares.

Read more less

Book SynopsisMarvin Bittinger has been teaching math at the university level for more than thirty-eight years. Since 1968, he has been employed at Indiana University Purdue University Indianapolis, and is now professor emeritus of mathematics education. Professor Bittinger has authored over 190 publications on topics ranging from basic mathematics to algebra and trigonometry to applied calculus. He received his BA in mathematics from Manchester College and his PhD in mathematics education from Purdue University. Special honors include Distinguished Visiting Professor at the United States Air Force Academy and his election to the Manchester College Board of Trustees from 1992 to 1999.Table of ContentsR. Functions, Graphs, and Models R.1 Graphs and Equations R.2 Functions and Models R.3 Finding Domain and Range R.4 Slope and Linear Functions R.5 Nonlinear Functions and Models R.6 Mathematical Modeling and Curve Fitting Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application Average Price of a Movie Ticket 1. Differentiation 1.1 Limits: A Numerical and Graphical Approach 1.2 Algebraic Limits and Continuity 1.3 Average Rates of Change 1.4 Differentiation Using Limits of Difference Quotients 1.5 The Power and Sum—Difference Rules 1.6 The Product and Quotient Rules 1.7 The Chain Rule 1.8 Higher-Order Derivatives Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–Path of a Baseball: The Tale of the Tape 2. Applications of Differentiation 2.1 Using First Derivatives to Classify Maximum and Minimum Values and Sketch Graphs 2.2 Using Second Derivatives to Classify Maximum and Minimum Values and Sketch Graphs 2.3 Graph Sketching: Asymptotes and Rational Functions 2.4 Using Derivatives to Find Absolute Maximum and Minimum Values 2.5 Maximum—Minimum Problems; Business, Economics, and General Applications 2.6 Marginals and Differentials 2.7 Elasticity of Demand 2.8 Implicit Differentiation and Related Rates Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–Maximum Sustainable Harvest 3. Exponential and Logarithmic Functions 3.1 Exponential Functions 3.2 Logarithmic Functions 3.3 Applications: Uninhibited and Limited Growth Models 3.4 Applications: Decay 3.5 The Derivatives of ax and loga x 3.6 A Business Application: Amortization Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–The Business of Motion Picture Revenue and DVD Release 4. Integration 4.1 Antidifferentiation 4.2 Antiderivatives as Areas 4.3 Area and Definite Integrals 4.4 Properties of Definite Integrals 4.5 Integration Techniques: Substitution 4.6 Integration Techniques: Integration by Parts 4.7 Integration Techniques: Tables Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–Business: Distribution of Wealth 5. Applications of Integration 5.1 Consumer Surplus and Producer Surplus 5.2 Integrating Growth and Decay Models 5.3 Improper Integrals 5.4 Probability 5.5 Probability: Expected Value; The Normal Distribution 5.6 Volume 5.7 Differential Equations Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–Curve Fitting and Volumes of Containers 6. Functions of Several Variables 6.1 Functions of Several Variables 6.2 Partial Derivatives 6.3 Maximum—Minimum Problems 6.4 An Application: The Least-Squares Technique 6.5 Constrained Optimization 6.6 Double Integrals Chapter Summary Chapter Review Exercises Chapter Test Extended Technology Application–Minimizing Employees’ Travel Time in a Building Cumulative Revi

Read more less

Book SynopsisTable of Contents1. Functions 1.1 Functions and Their Graphs 1.2 Combining Functions; Shifting and Scaling Graphs 1.3 Trigonometric Functions 1.4 Graphing with Software 1.5 Exponential Functions 1.6 Inverse Functions and Logarithms 2. Limits and Continuity 2.1 Rates of Change and Tangent Lines to Curves 2.2 Limit of a Function and Limit Laws 2.3 The Precise Definition of a Limit 2.4 One-Sided Limits 2.5 Continuity 2.6 Limits Involving Infinity; Asymptotes of Graphs Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 3. Derivatives 3.1 Tangent Lines and the Derivative at a Point 3.2 The Derivative as a Function 3.3 Differentiation Rules 3.4 The Derivative as a Rate of Change 3.5 Derivatives of Trigonometric Functions 3.6 The Chain Rule 3.7 Implicit Differentiation 3.8 Derivatives of Inverse Functions and Logarithms 3.9 Inverse Trigonometric Functions 3.10 Related Rates 3.11 Linearization and Differentials Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 4. Applications of Derivatives 4.1 Extreme Values of Functions on Closed Intervals 4.2 The Mean Value Theorem 4.3 Monotonic Functions and the First Derivative Test 4.4 Concavity and Curve Sketching 4.5 Indeterminate Forms and L’Hôpital’s Rule 4.6 Applied Optimization 4.7 Newton’s Method 4.8 Antiderivatives Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 5. Integrals 5.1 Area and Estimating with Finite Sums 5.2 Sigma Notation and Limits of Finite Sums 5.3 The Definite Integral 5.4 The Fundamental Theorem of Calculus 5.5 Indefinite Integrals and the Substitution Method 5.6 Definite Integral Substitutions and the Area Between Curves Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 6. Applications of Definite Integrals 6.1 Volumes Using Cross-Sections 6.2 Volumes Using Cylindrical Shells 6.3 Arc Length 6.4 Areas of Surfaces of Revolution 6.5 Work 6.6 Moments and Centers of Mass Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 7. Integrals and Transcendental Functions 7.1 The Logarithm Defined as an Integral 7.2 Exponential Change and Separable Differential Equations 7.3 Hyperbolic Functions Questions to Guide Your Review Practice Exercises Additional and Advanced Exercises 8. Techniques of Integration 8.1 Integration by Parts 8.2 Trigonometric Integrals 8.3 Trigonometric Substitutions 8.4 Integration of Rational Functions by Partial Fractions

Read more less

Book SynopsisDavid C. Lay, University of MarylandCollege Park Steven R. Lay, Lee University Judi J. McDonald, Washington State University

Read more less

Book SynopsisTable of Contents Chapter 1: First-Order Differential Equations Chapter 2: Mathematical Models and Numerical Methods Chapter 3: Linear Systems and Matrices Chapter 4: Vector Spaces Chapter 5: Higher-Order Linear Differential Equations Chapter 6: Eigenvalues and Eigenvectors Chapter 7: Linear Systems of Differential Equations Chapter 8: Matrix Exponential Methods Chapter 9: Nonlinear Systems and Phenomena Chapter 10: Laplace Transform Methods Chapter 11: Power Series Methods Appendix A: Existence and Uniqueness of Solutions Appendix B: Theory of Determinants Answers to Selected Problems Index Download the detailed table of contents

Read more less

Book SynopsisFranklin D. Demana Frank Demana received his master's and Ph.D. degrees in mathematics from Michigan State University. Currently, he is Professor Emeritus of Mathematics at The Ohio State University. As an active supporter of the use of technology to teach and learn mathematics, he is cofounder of the international Teachers Teaching with Technology (T3) professional development program. He has been the director or codirector of more than $10 million of National Science Foundation (NSF) and foundational grant activities, including a $3 million grant from the U.S. Department of Education Mathematics and Science Educational Research program awarded to The Ohio State University. Along with frequent presentations at professional meetings, he has published a variety of articles in the areas of computer-and calculator-enhanced mathematics instruction. Dr. Demana is also cofounder (with Bert Waits) of the annual International Conference on Technology in Collegiate MaTable of Contents Functions and Graphs Polynomial, Power, and Rational Functions Exponential, Logistic, and Logarithmic Functions Trigonometric Functions Analytic Trigonometry Applications of Trigonometry Systems and Matrices Analytic Geometry in Two and Three Dimensions Discrete Mathematics Statistics and Probability An Introduction to Calculus: Limits, Derivatives, and Integrals Algebra Review Logic Key Formulas

Read more less

Book SynopsisAbout our authors David F. Groebner is Professor Emeritus of Production Management in the College of Business and Economics at Boise State University. He has bachelor's and master's degrees in engineering and a Ph.D. in business administration. After working as an engineer, he has taught statistics and related subjects for 27 years. In addition to writing textbooks and academic papers, Groebner has worked extensively with both small and large organizations, including Hewlett-Packard, Boise Cascade, Albertson's, and Ore-Ida. He has worked with numerous government agencies, including Boise City and the U.S. Air Force. Patrick W. Shannon, Ph.D. is Dean and Professor of Supply Chain Operations Management in the College of Business and Economics at Boise State University. In addition to his administrative responsibilities, he has taught graduate and undergraduate courses in business statistics, quality management, and production and operaTable of Contents The Where, Why, and How of Data Graphs, Charts, and Tables: Describing Your Data Describing Data Using Numerical Measures 1 - 3 SPECIAL REVIEW SECTION Introduction to Probability Discrete Probability Distributions Introduction to Continuous Probability Distributions Introduction to Sampling Distributions Estimating Single Population Parameters Introduction to Hypothesis Testing Estimation and Hypothesis Testing for Two Population Parameters Hypothesis Tests and Estimation for Population Variances Analysis of Variance 8 - 12 SPECIAL REVIEW SECTION Goodness-of-Fit Tests and Contingency Analysis Introduction to Linear Regression and Correlation Analysis Multiple Regression Analysis and Model Building Analyzing and Forecasting Time-Series Data Introduction to Nonparametric Statistics Introducing Business Analytics Introduction to Decision Analysis (Online) Introduction to Quality and Statistical Process Control (Online) APPENDICES A to P

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisMark Triola, MD, FACP is the Associate Dean for Educational Informatics at NYU School of Medicine, the founding director of the NYU Langone Medical Center Institute for Innovations in Medical Education (IIME), and an Associate Professor of Medicine. Dr. Triola's research focuses on precision education and the use of AI tools to efficiently personalize medical education for individual learners and give new insights to their programs and coaches. His lab develops new learning technologies and AI-driven educational interventions and also works to define educationally sensitive patient and system outcomes that can be used to assess the impact of training. Dr. Triola and IIME have been funded by the National Institutes of Health, the Josiah Macy Jr. Foundation, the Department of Education, the Department of Defense, and the American Medical Association's Accelerating Change in Medical Education program. Mario F. Triola is a ProfeTable of Contents INTRODUCTION TO STATISTICS 1-1 Statistical and Critical Thinking 1-2 Types of Data 1-3 Collecting Sample Data 1-4 Ethics in Statistics (download only) EXPLORING DATA WITH TABLES AND GRAPHS 2-1 Frequency Distributions for Organizing and Summarizing Data 2-2 Histograms 2-3 Graphs That Enlighten and Graphs That Deceive 2-4 Scatterplots, Correlation, and Regression DESCRIBING, EXPLORING, AND COMPARING DATA 3-1 Measures of Center 3-2 Measures of Variation 3-3 Measures of Relative Standing and Boxplots PROBABILITY 4-1 Basic Concepts of Probability 4-2 Addition Rule and Multiplication Rule 4-3 Complements, Conditional Probability, and Bayes' Theorem 4-4 Risks and Odds 4-5 Rates of Mortality, Fertility, and Morbidity 4-6 Counting DISCRETE PROBABILITY DISTRIBUTIONS 5-1 Probability Distributions 5-2 Binomial Probability Distributions 5-3 Poisson Probability Distributions NORMAL PROBABILITY DISTRIBUTIONS 6-1 The Standard Normal Distribution 6-2 Real Applications of Normal Distributions 6-3 Sampling Distributions and Estimators 6-4 The Central Limit Theorem 6-5 Assessing Normality 6-6 Normal as Approximation to Binomial (download only) ESTIMATING PARAMETERS AND DETERMINING SAMPLE SIZES 7-1 Estimating a Population Proportion 7-2 Estimating a Population Mean 7-3 Estimating a Population Standard Deviation or Variance 7-4 Bootstrapping: Using Technology for Estimates HYPOTHESIS TESTING 8-1 Basics of Hypothesis Testing 8-2 Testing a Claim About a Proportion 8-3 Testing a Claim About a Mean 8-4 Testing a Claim About a Standard Deviation or Variance 8-5 Resampling: Using Technology for Hypothesis Testing INFERENCES FROM TWO SAMPLES 9-1 Two Proportions 9-2 Two Means: Independent Samples 9-3 Matched Pairs 9-4 Two Variances or Standard Deviations 9-5 Resampling: Using Technology for Inferences CORRELATION AND REGRESSION 10-1 Correlation 10-2 Regression 10-3 Prediction Intervals and Variation 10-4 Multiple Regression 10-5 Dummy Variables and Logistic Regression GOODNESS-OF-FIT AND CONTINGENCY TABLES 11-1 Goodness-of-Fit 11-2 Contingency Tables ANALYSIS OF VARIANCE 12-1 One-Way ANOVA 12-2 Two-Way ANOVA NONPARAMETRIC TESTS 13-1 Basics of Nonparametric Tests 13-2 Sign Test 13-3 Wilcoxon Signed-Ranks Test for Matched Pairs 13-4 Wilcoxon Rank-Sum Test for Two Independent Samples 13-5 Kruskal-Wallis Test for Three or More Samples 13-6 Rank Correlation SURVIVAL ANALYSIS 14-1 Life Tables 14-2 Kaplan-Meier Survival Analysis APPENDICES A: Tables and Formulas B: Data Sets C: Websites and Bibliography of Books D: Answers to Odd-Numbered Section Exercises (and all Quick Quizzes, all Review Exercises, and all Cumulative Review Exercises) Subject Index

Read more less

Book SynopsisAbout our author Robert F. Blitzer is a native of Manhattan.  He received a Bachelor of Arts degree with dual majors in mathematics and psychology (and a minor in English literature) from the City College of New York. His unique combination of academic interests led him toward a Master of Arts in mathematics from the University of Miami and a doctorate in behavioral sciences from Nova University. Bob's love for teaching mathematics was nourished for nearly 30 years at Miami Dade College, where he received numerous teaching awards, including Innovator of the Year from the League for Innovations in the Community College and an endowed chair based on excellence in the classroom. In addition to Precalculus, Bob has written textbooks covering developmental mathematics, introductory algebra, intermediate algebra, trigonometry, college algebra, algebra & trigonometry, and liberal arts mathematics, all published by Pearson. When not secluded iTable of ContentsP. Prerequisites: Fundamental Concepts of Algebra P.1 Algebraic Expressions, Mathematical Models, and Real Numbers P.2 Exponents and Scientific Notation P.3 Radicals and Rational Exponents P.4 Polynomials P.5 Factoring Polynomials P.6 Rational Expressions P.7 Equations P.8 Modeling with Equations P.9 Linear Inequalities and Absolute Value Inequalities Summary, Review, and Test Review Exercises Chapter P Test 1. Functions and Graphs 1.1 Graphs and Graphing Utilities 1.2 Basics of Functions and Their Graphs 1.3 More on Functions and Their Graphs 1.4 Linear Functions and Slope 1.5 More on Slope 1.6 Transformations of Functions 1.7 Combinations of Functions; Composite Functions 1.8 Inverse Functions 1.9 Distance and Midpoint Formulas; Circles 1.10 Modeling with Functions Summary, Review, and Test Review Exercises Chapter 1 Test 2. Polynomial and Rational Functions 2.1 Complex Numbers 2.2 Quadratic Functions 2.3 Polynomial Functions and Their Graphs 2.4 Dividing Polynomials; Remainder and Factor Theorems 2.5 Zeros of Polynomial Functions 2.6 Rational Functions and Their Graphs 2.7 Polynomial and Rational Inequalities 2.8 Modeling Using Variation Summary, Review, and Test Review Exercises Chapter 2 Test Cumulative Review Exercises (Chapters P–2) 3. Exponential and Logarithmic Functions 3.1 Exponential Functions 3.2 Logarithmic Functions 3.3 Properties of Logarithms 3.4 Exponential and Logarithmic Equations 3.5 Exponential Growth and Decay; Modeling Data Summary, Review, and Test Review Exercises Chapter 3 Test Cumulative Review Exercises (Chapters P–3) 4. Trigonometric Functions 4.1 Angles and Radian Measure 4.2 Trigonometric Functions: The Unit Circle 4.3 Right Triangle Trigonometry 4.4 Trigonometric Functions of Any Angle 4.5 Graphs of Sine and Cosine Functions 4.6 Graphs of Other Trigonometric Functions 4.7 Inverse Trigonometric Functions 4.8 Applications of Trigonometric Functions Summary, Review, and Test Review Exercises Chapter 4 Test Cumulative Review Exercises (Chapters P–4) 5. Analytic Trigonometry 5.1 Verifying Trigonometric Identities 5.2 Sum and Difference Formulas 5.3 Double-Angle, Power-Reducing, and Half-Angle Formulas 5.4 Product-to-Sum and Sum-to-Product Formulas 5.5 Trigonometric Equations Summary, Review, and Test Review Exercises Chapter 5 Test Cumulative Review Exercises (Chapters P–5) 6. Additional Topics in Trigonometry 6.1 The Law of Sines 6.2 The Law of Cosines 6.3 Polar Coordinates 6.4 Graphs of Polar Equations 6.5 Complex Numbers in Polar Form; DeMoivre's Theorem 6.6 Vectors 6.7 The Dot Product Summary, Review, and Test Review Exercises Chapter 6 Test Cumulative Review Exercises (Chapters P–6) 7. Systems of Equations and Inequalities 7.1 Systems of Linear Equations in Two Variables 7.2 Systems of Linear Equations in Three Variables 7.3 Partial Fractions 7.4 Systems of Nonlinear Equations in Two Variables 7.5 Systems of Inequalities 7.6 Linear Programming Summary, Review, and Test Review Exercises Chapter 7 Test Cumulative Review Exercises (Chapters P–7) 8. Matrices and Determinants 8.1 Matrix Solutions to Linear Systems 8.2 Inconsistent and Dependent Systems and Their Applications 8.3 Matrix Operations and Their Applications 8.4 Multiplicative Inverses of Matrices and Matrix Equations 8.5 Determinants and Cramer's Rule Summary, Review, and Test Review Exercises Chapter 8 Test Cumulative Review Exercises (Chapters P–8) 9. Conic Sections and Analytic Geometry 9.1 The Ellipse 9.2 The Hyperbola 9.3 The Parabola 9.4 Rotation of Axes 9.5 Parametric Equations 9.6 Conic Sections in Polar Coordinates Summary, Review, and Test Review Exercises Chapter 9 Test Cumulative Review Exercises (Chapters P–9) 10. Sequences, Induction, and Probability 10.1 Sequences and Summation Notation 10.2 Arithmetic Sequences 10.3 Geometric Sequences and Series 10.4 Mathematical Induction 10.5 The Binomial Theorem 10.6 Counting Principles, Permutations, and Combinations 10.7 Probability Summary, Review, and Test Review Exercises Chapter 10 Test Cumulative Review Exercises (Chapters P–10) 11. Introduction to Calculus 11.1 Finding Limits Using Tables and Graphs 11.2 Finding Limits Using Properties of Limits 11.3 Limits and Continuity 11.4 Introduction to Derivatives Summary, Review, and Test Review Exercises Chapter 11 Test Cumulative Review Exercises (Chapters P–11) Appendix A: Where Did That Come From? Selected Proofs Appendix B: The Transition from Precalculus to Calculus Answers to Selected Exercises Subject Index Credits

Read more less

Book SynopsisGain a solid understanding of the principles of trigonometry and how these concepts apply to real life with McKeague/Turner's TRIGONOMETRY. The book presents contemporary concepts in brief, manageable sections using current, detailed examples and interesting applications. Captivating illustrations such as cycling, the Ferris wheel, and the human cannonball show trigonometry in action. Unique Historical Vignettes offer a fascinating glimpse at how many of the central ideas in trigonometry began. The text is easy to read, and important theorems and definitions are boxed so they can be quickly identified for study purposes.Table of Contents1. THE SIX TRIGONOMETRIC FUNCTIONS. Angles, Degrees, and Special Triangles.The Rectangular Coordinate System. Definition I: Trigonometric Functions. Introduction to Identities. More on Identities. 2. RIGHT TRIANGLE TRIGONOMETRY. Definition II: Right Triangle Trigonometry. Calculators and Trigonometric Functions of an Acute Angle. Solving Right Triangles. Applications. Vectors: A Geometric Approach. 3. RADIAN MEASURE. Reference Angle. Radians and Degrees. Definition III: Circular Functions. Arc Length and Area of a Sector. Velocities. 4. GRAPHING AND INVERSE FUNCTIONS. Basic Graphs. Amplitude, Reflection, and Period. Vertical and Horizontal Translations. The Other Trigonometric Functions. Finding an Equation From its Graph. Graphing Combinations of Functions. Inverse Trigonometric Functions. 5. IDENTITIES AND FORMULAS. Proving Identities. Sum and Difference Formulas. Double-Angle Formulas. Half-Angle Formulas. Additional Identities. 6. EQUATIONS. Solving Trigonometric Equations. More on Trigonometric Equations. Trigonometric Equations Involving Multiple Angles. Parametric Equations and Further Graphing. 7. TRIANGLES. The Law of Sines. The Law of Cosines. The Ambiguous Case. The Area of a Triangle. Vectors: An Algebraic Approach. Vectors: The Dot Product. 8. COMPLEX NUMBERS AND POLAR COORDINATES. Complex Numbers. Trigonometric Form for Complex Numbers. Products and Quotients in Trigonometric Form. Roots of a Complex Number. Polar Coordinates. Equations in Polar Coordinates and Their Graphs. APPENDIX A REVIEW OF TOPICS. A.1 Review of Algebra. A.2 Review of Geometry. A.3 Introduction to Functions. A.4 The Inverse of a Function. Answers to Selected Exercises. Index.

Read more less

Book SynopsisA high school student can create deep Q-learning code to control her robot, without any understanding of the meaning of ''deep'' or ''Q'', or why the code sometimes fails. This book is designed to explain the science behind reinforcement learning and optimal control in a way that is accessible to students with a background in calculus and matrix algebra. A unique focus is algorithm design to obtain the fastest possible speed of convergence for learning algorithms, along with insight into why reinforcement learning sometimes fails. Advanced stochastic process theory is avoided at the start by substituting random exploration with more intuitive deterministic probing for learning. Once these ideas are understood, it is not difficult to master techniques rooted in stochastic control. These topics are covered in the second part of the book, starting with Markov chain theory and ending with a fresh look at actor-critic methods for reinforcement learning.Trade Review'Control Systems and Reinforcement Learning is a densely packed book with a vivid, conversational style. It speaks both to computer scientists interested in learning about the tools and techniques of control engineers and to control engineers who want to learn about the unique challenges posed by reinforcement learning and how to address these challenges. The author, a world-class researcher in control and probability theory, is not afraid of strong and perhaps controversial opinions, making the book entertaining and attractive for open-minded readers. Everyone interested in the "why" and "how" of RL will use this gem of a book for many years to come.' Csaba Szepesvári, Canada CIFAR AI Chair, University of Alberta, and Head of the Foundations Team at DeepMind'This book is a wild ride, from the elements of control through to bleeding-edge topics in reinforcement learning. Aimed at graduate students and very good undergraduates who are willing to invest some effort, the book is a lively read and an important contribution.' Shane G. Henderson, Charles W. Lake, Jr. Chair in Productivity, Cornell University'Reinforcement learning, now the de facto workhorse powering most AI-based algorithms, has deep connections with optimal control and dynamic programing. Meyn explores these connections in a marvelous manner and uses them to develop fast, reliable iterative algorithms for solving RL problems. This excellent, timely book from a leading expert on stochastic optimal control and approximation theory is a must-read for all practitioners in this active research area.' Panagiotis Tsiotras, David and Andrew Lewis Chair and Professor, Guggenheim School of Aerospace Engineering, Georgia Institute of TechnologyTable of Contents1. Introduction; Part I. Fundamentals Without Noise: 2. Control crash course; 3. Optimal control; 4. ODE methods for algorithm design; 5. Value function approximations; Part II. Reinforcement Learning and Stochastic Control: 6. Markov chains; 7. Stochastic control; 8. Stochastic approximation; 9. Temporal difference methods; 10. Setting the stage, return of the actors; A. Mathematical background; B. Markov decision processes; C. Partial observations and belief states; References; Glossary of Symbols and Acronyms; Index.

Read more less

Book SynopsisOptimization techniques are at the core of data science, including data analysis and machine learning. An understanding of basic optimization techniques and their fundamental properties provides important grounding for students, researchers, and practitioners in these areas. This text covers the fundamentals of optimization algorithms in a compact, self-contained way, focusing on the techniques most relevant to data science. An introductory chapter demonstrates that many standard problems in data science can be formulated as optimization problems. Next, many fundamental methods in optimization are described and analyzed, including: gradient and accelerated gradient methods for unconstrained optimization of smooth (especially convex) functions; the stochastic gradient method, a workhorse algorithm in machine learning; the coordinate descent approach; several key algorithms for constrained optimization problems; algorithms for minimizing nonsmooth functions arising in data science; foundTrade Review'This delightful compact tome gives the reader all the results they should have in their pocket to contribute to optimization and statistical learning. With the clean, elegant derivations of many of the foundational optimization methods underlying modern large-scale data analysis, everyone from students just getting started to researchers knowing this book inside and out will be well-positioned for both using the algorithms and developing new ones for machine learning, optimization, and statistics.' John C. Duchi, Stanford University'Optimization algorithms play a vital role in the rapidly evolving field of machine learning, as well as in signal processing, statistics and control. Numerical optimization is a vast field, however, and a student wishing to learn the methods required in the world of data science could easily get lost in the literature. This book does a superb job of presenting the most important algorithms, providing both their mathematical foundations and lucid motivations for their development. Written by two of the foremost experts in the field, this book gently guides a reader without prior knowledge of optimization towards the methods and concepts that are central in modern data science applications.' Jorge Nocedal, Northwestern University'This timely introductory book gives a rigorous view of continuous optimization techniques which are being used in machine learning. It is an excellent resource for those who are interested in understanding the mathematical concepts behind commonly used machine learning techniques.' Shai Shalev-Shwartz, Hebrew University of Jerusalem'This textbook is a much-needed exposition of optimization techniques, presented with conciseness and precision, with emphasis on topics most relevant for data science and machine learning applications. I imagine that this book will be immensely popular in university courses across the globe, and become a standard reference used by researchers in the area.' Amitabh Basu, Johns Hopkins UniversityTable of Contents1. Introduction; 2. Foundations of smooth optimization; 3. Descent methods; 4. Gradient methods using momentum; 5. Stochastic gradient; 6. Coordinate descent; 7. First-order methods for constrained optimization; 8. Nonsmooth functions and subgradients; 9. Nonsmooth optimization methods; 10. Duality and algorithms; 11. Differentiation and adjoints.

Read more less

Book SynopsisDavid Sandwell developed this advanced textbook over a period of nearly 30 years for his graduate course at Scripps Institution of Oceanography. The book augments the classic textbook Geodynamics by Don Turcotte and Jerry Schubert, presenting more complex and foundational mathematical methods and approaches to geodynamics. The main new tool developed in the book is the multi-dimensional Fourier transform for solving linear partial differential equations. The book comprises nineteen chapters, including: the latest global data sets; quantitative plate tectonics; plate driving forces associated with lithospheric heat transfer and subduction; the physics of the earthquake cycle; postglacial rebound; and six chapters on gravity field development and interpretation. Each chapter has a set of student exercises that make use of the higher-level mathematical and numerical methods developed in the book. Solutions to the exercises are available online for course instructors, on request.Trade Review'Advanced Geodynamics brings the unique perspective of a leading geophysicist to the solution of a wide array of problems in geodynamics. The approach emphasizes the use of advanced mathematics, in particular the Fourier transform, to obtain a quantitative understanding of the processes involved in shaping the Earth's surface. The advanced mathematical approach not only enhances the elegance of the solutions, but it enables the consideration of many problems not accessible with less sophisticated mathematical methods. The choice of problems benefits from the deep physical insights of the author to their solutions. The book discusses the physical processes involved in plate tectonics and the earthquake cycle and provides the latest relevant observational data sets. An emphasis is also placed on the use of gravity data to learn about these processes. The book is the product of decades of teaching by the author and is a must read for students of the physics of the Earth with the appropriate mathematical background.' Gerald Schubert, University of California, Los Angeles; co-author of Geodynamics'Most authors would find writing a sequel to Turcotte and Schubert's classic book on Geodynamics a daunting task. Not so for David Sandwell, whose first book is a wonderful mix of observations and theory, elegant mathematics and a focus on the oceans and the Fourier method which together help illuminate some of the fundamental physical processes that underlie plate tectonics.' Tony Watts, University of Oxford; author of Isostasy and Flexure of the Lithosphere'Advanced Geodynamics: The Fourier Transform Method by David Sandwell is a godsend for advanced undergraduate students, graduate students, and researchers actively engaged in the broad area of geodynamics. It complements the classic Geodynamics book by Turcotte & Schubert in a way nothing else could: by elevating the treatment to real, cutting-edge research problems via Fourier transforms that deliver simple and elegant solutions to complicated science problems.' Paul Wessel, University of HawaiiTable of Contents1. Observations Related to Plate Tectonics; 2. Fourier Transform Methods in Geophysics; 3. Plate Kinematics; 4. Marine Magnetic Anomalies; 5. Cooling of the Oceanic Lithosphere; 6. A Brief Review of Elasticity; 7. Crustal Structure, Isostasy, Swell Push Force, and Rheology; 8. Flexure of the Lithosphere; 9. Flexure Examples; 10. Elastic Solutions for Strike-Slip Faulting; 11. Heat Flow Paradox; 12. The Gravity Field of the Earth, Part I; 13. Reference Earth Model: WGS84; 14. Laplace's Equation in Spherical Coordinates; 15. Laplace's Equation in Cartesian Coordinates and Satellite Altimetry; 16. Poisson's Equation in Cartesian Coordinates; 17. Gravity/Topography Transfer Function and Isostatic Geoid Anomalies; 18. Postglacial Rebound; 19. Driving Forces of Plate Tectonics; References; Index.

Read more less

Book SynopsisContaining a large and varied set of problems, this rich resource will allow students to stretch their mathematical abilities beyond the school syllabus, and bridge the gap to university-level mathematics. Many proofs are provided to better equip students for the transition to university. The author covers substantial extension material using the language of sixth form mathematics, thus enabling students to understand the more complex material. Exercises are carefully chosen to introduce students to some central ideas, without building up large amounts of abstract technology. There are over 1500 carefully graded exercises, with hints included in the text, and solutions available online. Historical and contextual asides highlight each area of mathematics and show how it has developed over time.Trade Review'The text is quite reader friendly, with over 1,500 graded exercises (most with hints) presented throughout the text, not just at the ends of sections or chapters. As a result, readers are encouraged and motivated to take the time and make the effort to understand each concept as they proceed. Earl exerts a positive influence on beginning students as they decide whether to pursue a degree in mathematics. Perhaps best of all, students get a glimpse of the breadth of areas they can pursue within the mathematics realm - even at the early stages of their study. Summing Up: Recommended.' J. T. Zerger, Choice'The biggest stumbling-block for many students about to go to university to study mathematics is not the lack of syllabus covered but the lethal attitude 'you don't need this for the exam'. This book will provide much of what they need … It is also a good resource for school and college departments looking for questions on FM material that will stretch their best learners.' Owen Toller, Mathematical GazetteTable of ContentsIntroduction; 1. Complex numbers; 2. Induction; 3. Vectors and matrices; 4. More on matrices; 5. Techniques of integration; 6. Differential equations; 7. Hints to selected exercises; Bibliography; Index.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the AQA AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the compulsory content for AS and first year of A Level. It balances accessible exposition with many worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the AQA A Level Further Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the compulsory content for second year of A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the AQA AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the Statistics content for AS and A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the AQA AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the Mechanics content for AS and A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the OCR A Level Further Mathematics specification for first teaching from 2017, this print Student Book covers the Pure Core content for the second year of A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Includes answers to aid independent study.

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources to help students with learning and revision. Written for the AQA AS/A Level Further Mathematics specification for first teaching from 2017, this print Student Book covers the Mechanics content for AS and A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Includes answers to aid independent study. This book has entered an AQA approval process.Table of Contents1. Work, energy and power 1; 2. Dimensional analysis; 3. Momentum and collisions 1; 4. Circular motion 1; 5. Work, energy and power 2; Focus on proof 1; Focus on problem solving 1; Focus on modelling 1; Cross-topic review exercise 1; 6. Momentum and collisions 2; 7. Circular motion 2; 8. Centres of mass; 9. Moments and couples; Focus on proof 2; Focus on problem solving 2; Focus on modelling 2; Cross-topic review exercise 2

Read more less

Book SynopsisNew 2017 Cambridge A Level Maths and Further Maths resources help students with learning and revision. Written for the OCR A Level Mathematics specification for first teaching from 2017, this print Student Book and Cambridge Elevate edition covers the content for the second year of A Level. It balances accessible exposition with a wealth of worked examples, exercises and opportunities to test and consolidate learning, providing a clear and structured pathway for progressing through the course. It is underpinned by a strong pedagogical approach, with an emphasis on skills development and the synoptic nature of the course. Available online and on tablet devices through the Cambridge Elevate app. Includes answers to aid independent study.

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book Synopsis

Read more less

Book SynopsisTable of ContentsPART I: MECHANICS. 1. Physics and Measurement. 2. Motion in One Dimension. 3. Vectors. 4. Motion in Two Dimensions. 5. The Laws of Motion. 6. Circular Motion and Other Applications of Newton's Laws. 7. Energy of a System. 8. Conservation of Energy. 9. Linear Momentum and Collisions. 10. Rotation of a Rigid Object About a Fixed Axis. 11. Angular Momentum. 12. Static Equilibrium and Elasticity. 13. Universal Gravitation. 14. Fluid Mechanics. PART II: OSCILLATIONS AND MECHANICAL WAVES. 15. Oscillatory Motion. 16. Wave Motion. 17. Superposition and Standing Waves. PART III: THERMODYNAMICS. 18. Temperature. 19. The First Law of Thermodynamics. 20. The Kinetic Theory of Gases. 21. Heat Engines, Entropy, and the Second Law of Thermodynamics. Part IV: ELECTRICITY AND MAGNETISM. 22. Electric Fields. 23. Continuous Charge Distributions and Gauss's Law. 24. Electric Potential. 25. Capacitance and Dielectrics. 26. Current and Resistance. 27. Direct-Current Circuits. 28. Magnetic Fields. 29. Sources of the Magnetic Field. 30. Faraday's Law. 31. Inductance. 32. Alternating-Current Circuits. 33. Electromagnetic Waves. PART V: LIGHT AND OPTICS. 34. The Nature of Light and the Principles of Ray Optics 35. Image Formation. 36. Wave Optics. 37. Diffraction Patterns and Polarization. PART VI: MODERN PHYSICS. 38. Relativity. APPENDICES. A. Tables. B. Mathematics Review. C. Periodic Table of the Elements. D. SI Units. Answers to Quick Quizzes and Odd-Numbered Problems. Index.

Read more less

Book SynopsisA concise introduction to database design concepts, methods, and techniques in and out of the cloud In the newly revised second edition of Beginning Database Design Solutions: Understanding and Implementing Database Design Concepts for the Cloud and Beyond, Second Edition, award-winning programming instructor and mathematician Rod Stephens delivers an easy-to-understand guide to designing and implementing databases both in and out of the cloud. Without assuming any prior database design knowledge, the author walks you through the steps you'll need to take to understand, analyze, design, and build databases. In the book, you'll find clear coverage of foundational database concepts along with hands-on examples that help you practice important techniques so you can apply them to your own database designs, as well as: Downloadable source code that illustrates the concepts discussed in the book Best practices for reliable, platform-agnostTable of ContentsIntroduction xxv Part 1: Introduction to Databases and Database Design Chapter 1: Database Design Goals 3 The Importance of Design 4 Information Containers 6 Strengths and Weaknesses of Information Containers 8 Desirable Database Features 9 Crud 10 Retrieval 10 Consistency 11 Validity 11 Easy Error Correction 12 Speed 13 Atomic Transactions 13 Acid 14 Base 16 NewSQL 17 Persistence and Backups 17 Low Cost and Extensibility 18 Ease of Use 19 Portability 19 Security 20 Sharing 21 Ability to Perform Complex Calculations 21 CAP Theorem 22 Cloud Considerations 22 Legal and Security Considerations 23 Consequences of Good and Bad Design 24 Summary 26 Chapter 2: Relational Overview 29 Picking a Database 30 Relational Points of View 31 Table, Rows, and Columns 32 Relations, Attributes, and Tuples 34 Keys 34 Indexes 36 Constraints 37 Domain Constraints 37 Check Constraints 37 Primary Key Constraints 38 Unique Constraints 38 Foreign Key Constraints 38 Database Operations 40 Popular RDBs 41 Spreadsheets 43 Summary 44 Chapter 3: NoSQL OVERVIEW 47 The Cloud 47 Picking a Database 50 NoSQL Philosophy 50 NoSQL Databases 50 Document Databases 51 Key- Value Database 52 Column- Oriented Databases 53 Graph Databases 53 Street Networks 54 Communication Networks 55 Social Media Apps 55 E- Commerce Programs 55 Algorithms 56 Hierarchical Databases 56 Less Exotic Options 59 Flat Files 59 XML Files 60 XML Basics 61 XML Practices 64 XML Summary 66 JSON Files 67 Spreadsheets 69 More Exotic Options 70 Object 70 Deductive 70 Dimensional 70 Temporal 71 Database Pros and Cons 72 Relational 72 General NoSQL 73 Quick Guidelines 74 Summary 76 Part 2: Database Design Process and Techniques Chapter 4: Understanding User Needs 83 Make a Plan 84 Bring a List of Questions 85 Functionality 85 Data Needs 86 Data Integrity 86 Security 87 Environment 88 Meet the Customers 88 Learn Who’s Who 89 Pick the Customers’ Brains 93 Walk a Mile in the User’s Shoes 93 Study Current Operations 94 Brainstorm 94 Look to the Future 95 Understand the Customers’ Reasoning 96 Learn What the Customers Really Need 97 Prioritize 98 Verify Your Understanding 99 Create the Requirements Document 101 Make Use Cases 102 Decide Feasibility 106 Summary 106 Chapter 5: Translating User Needs Into Data Models 111 What Are Data Models? 112 User Interface Models 114 Semantic Object Models 118 Classes and Objects 119 Cardinality 120 Identifiers 120 Putting It Together 121 Semantic Views 122 Class Types 124 Simple Objects 124 Composite Objects 124 Compound Objects 125 Hybrid Objects 125 Association Objects 126 Inherited Objects 128 Comments and Notes 129 Entity- Relationship Models 130 Entities, Attributes, and Identifiers 131 Relationships 132 Cardinality 133 Inheritance 134 Additional Conventions 136 Comments and Notes 137 Relational Models 137 Converting Semantic Object Models 138 Converting ER Diagrams 140 Summary 142 Chapter 6: Extracting Business Rules 145 What Are Business Rules? 145 Identifying Key Business Rules 147 Extracting Key Business Rules 152 Multi- Tier Applications 154 Summary 158 Chapter 7: Normalizing Data 163 What Is Normalization? 163 First Normal Form (1NF) 164 Second Normal Form (2NF) 173 Third Normal Form (3NF) 177 Stopping at Third Normal Form 181 Boyce- Codd Normal Form (BCNF) 181 Fourth Normal Form (4NF) 185 Fifth Normal Form (5NF) 190 Domain/Key Normal Form (DKNF) 193 Essential Redundancy 195 The Best Level of Normalization 197 NoSQL Normalization 197 Summary 199 Chapter 8: Designing Databases to Support Software 203 Plan Ahead 204 Document Everything 204 Consider Multi- Tier Architecture 205 Convert Domains into Tables 205 Keep Tables Focused 206 Use Three Kinds of Tables 207 Use Naming Conventions 209 Allow Some Redundant Data 210 Don’t Squeeze in Everything 211 Summary 212 Chapter 9: Using Common Design Patterns 215 Associations 216 Many- to- Many Associations 216 Multiple Many- to- Many Associations 216 Multiple- Object Associations 218 Repeated Attribute Associations 221 Reflexive Associations 222 One- to- One Reflexive Associations 223 One- to- Many Reflexive Associations 224 Hierarchical Data 225 Hierarchical Data with NoSQL 228 Network Data 229 Network Data with NoSQL 231 Temporal Data 232 Effective Dates 232 Deleted Objects 233 Deciding What to Temporalize 234 Logging and Locking 236 Audit Trails 236 Turnkey Records 237 Summary 238 Chapter 10: Avoiding Common Design Pitfalls 241 Lack of Preparation 241 Poor Documentation 242 Poor Naming Standards 242 Thinking Too Small 244 Not Planning for Change 245 Too Much Normalization 248 Insufficient Normalization 248 Insufficient Testing 249 Performance Anxiety 249 Mishmash Tables 250 Not Enforcing Constraints 253 Obsession with IDs 253 Not Defining Natural Keys 256 Summary 257 Part 3: a Detailed Case Study Chapter 11: Defining User Needs and Requirements 263 Meet the Customers 263 Pick the Customers’ Brains 265 Determining What the System Should Do 265 Determining How the Project Should Look 267 Determining What Data Is Needed for the User Interface 268 Determining Where the Data Should Come From 269 Determining How the Pieces of Data Are Related 269 Determining Performance Needs 271 Determining Security Needs 272 Determining Data Integrity Needs 273 Write Use Cases 275 Write the Requirements Document 279 Demand Feedback 280 Summary 281 Chapter 12: Building a Data Model 283 Semantic Object Modeling 283 Building an Initial Semantic Object Model 283 Improving the Semantic Object Model 286 Entity- Relationship Modeling 289 Building an ER Diagram 289 Building a Combined ER Diagram 291 Improving the Entity- Relationship Diagram 293 Relational Modeling 294 Putting It All Together 298 Summary 299 Chapter 13: Extracting Business Rules 303 Identifying Business Rules 303 Courses 304 CustomerCourses 306 Customers 307 Pets 307 Employees 307 Orders 307 OrderItems 308 InventoryItems 308 TimeEntries 308 Shifts 309 Persons 309 Phones 309 Vendors 309 Drawing a New Relational Model 310 Summary 310 Chapter 14: Normalizing and Refining 313 Improving Flexibility 313 Verifying First Normal Form 315 Verifying Second Normal Form 318 Pets 319 TimeEntries 320 Verifying Third Normal Form 321 Summary 323 Part 4: Example Programs Chapter 15: Example Overview 327 Tool Choices 327 Jupyter Notebook 329 Visual Studio 331 Database Adapters 332 Packages in Jupyter Notebook 333 Packages in Visual Studio 334 Program Passwords 336 Summary 336 Chapter 16: MariaDB IN PYTHON 339 Install MariaDB 340 Run HeidiSQL 340 Create the Program 343 Install pymysql 344 Create the Database 344 Define Tables 346 Create Data 348 Fetch Data 350 Summary 352 Chapter 17: MariaDB IN C# 355 Create the Program 355 Install MySqlConnector 356 Create the Database 356 Define Tables 358 Create Data 360 Fetch Data 364 Summary 366 Chapter 18: PostgreSQL IN PYTHON 369 Install PostgreSQL 370 Run pgAdmin 371 Design the Database 371 Create a User 371 Create the Database 373 Define the Tables 374 Define the customers Table 374 Define the orders Table 376 Define the order_items Table 377 Create the Program 378 Install Psycopg 379 Connect to the Database 379 Delete Old Data 380 Create Customer Data 380 Create Order Data 382 Create Order Item Data 383 Close the Connection 384 Perform Queries 384 Summary 386 Chapter 19: PostgreSQL IN C# 389 Create the Program 389 Install Npgsql 389 Connect to the Database 390 Delete Old Data 391 Create Customer Data 392 Create Order Data 393 Create Order Item Data 395 Display Orders 396 Summary 399 Chapter 20: Neo4j AuraDB IN PYTHON 401 Install Neo4j AuraDB 402 Nodes and Relationships 404 Cypher 404 Create the Program 405 Install the Neo4j Database Adapter 405 Action Methods 405 delete_all_nodes 406 make_node 407 make_link 407 execute_node_query 408 find_path 409 Org Chart Methods 410 build_org_chart 410 query_org_chart 411 Main Program 412 Summary 414 Chapter 21: Neo4j AuraDB IN C# 417 Create the Program 418 Install the Neo4j Driver 418 Action Methods 419 DeleteAllNodes 419 MakeNode 420 MakeLink 421 ExecuteNodeQuery 422 FindPath 422 Org Chart Methods 423 BuildOrgChart 424 QueryOrgChart 424 Main 426 Summary 428 Chapter 22: MongoDB ATLAS IN PYTHON 431 Not Normal but Not Abnormal 432 XML, JSON, and BSON 432 Install MongoDB Atlas 434 Find the Connection Code 436 Create the Program 439 Install the PyMongo Database Adapter 439 Helper Methods 440 person_string 440 connect_to_db 441 delete_old_data 442 create_data 442 query_data 444 Main Program 449 Summary 450 Chapter 23: MongoDB ATLAS IN C# 453 Create the Program 454 Install the MongoDB Database Adapter 454 Helper Methods 454 PersonString 455 DeleteOldData 456 CreateData 457 QueryData 458 Main Program 462 Summary 465 Chapter 24: Apache Ignite in Python 467 Install Apache Ignite 468 Start a Node 468 Without Persistence 469 With Persistence 470 Create the Program 470 Install the pyignite Database Adapter 471 Define the Building Class 471 Save Data 471 Read Data 473 Demonstrate Volatile Data 473 Demonstrate Persistent Data 474 Summary 474 Chapter 25: Apache Ignite in C# 477 Create the Program 477 Install the Ignite Database Adapter 478 The Main Program 479 The Building Class 480 The WriteData Method 480 The ReadData Method 482 Demonstrate Volatile Data 483 Demonstrate Persistent Data 483 Summary 483 Part 5: Advanced Topics Chapter 26: Introduction to Sql 489 Background 491 Finding More Information 491 Standards 492 Multistatement Commands 493 Basic Syntax 495 Command Overview 495 Create Table 498 Create Index 503 Drop 504 Insert 504 Select 506 SELECT Clause 506 FROM Clause 507 WHERE Clause 511 GROUP BY Clause 511 ORDER BY Clause 512 Update 513 Delete 514 Summary 515 Chapter 27: Building Databases with Sql Scripts 519 Why Bother with Scripts? 519 Script Categories 520 Database Creation Scripts 520 Basic Initialization Scripts 520 Data Initialization Scripts 520 Cleanup Scripts 521 Saving Scripts 521 Ordering SQL Commands 522 Summary 531 Chapter 28: Database Maintenance 533 Backups 533 Data Warehousing 537 Repairing the Database 538 Compacting the Database 538 Performance Tuning 538 Summary 542 Chapter 29: Database Security 545 The Right Level of Security 545 Passwords 546 Single- Password Databases 546 Individual Passwords 546 Operating System Passwords 547 Good Passwords 547 Privileges 548 Initial Configuration and Privileges 553 Too Much Security 553 Physical Security 554 Summary 555 Appendix A: Exercise Solutions 557 Appendix B: Sample Relational Designs 649 Glossary 671 Index 683

Read more less

Book SynopsisThe easy way to understand and retain all the concepts taught in pre-calculus classes Pre-Calculus All-in-One For Dummies is a great resource if you want to do you best in Pre-Calculus. Packed with lessons, examples, and practice problems in the book, plus extra chapter quizzes online, it gives you absolutely everything you need to succeed in pre-calc. Unlike your textbook, this book presents the essential topics clearly and concisely, so you can really understand the stuff you learn in class, score high on your tests (including the AP Pre-Calculus exam!), and get ready to confidently move ahead to upper-level math courses. And if you need a refresher before launching into calculus, look no furtherthis book has your back. Review what you learned in algebra and geometry, then dig into pre-calculus Master logarithms, exponentials, conic sections, linear equations, and beyond Get easy-to-understand explanations that match the methods your teaTable of ContentsIntroduction 1 Unit 1: Getting Started with Pre-Calculus 5 Chapter 1: Preparing for Pre-Calculus 7 Chapter 2: Operating with Real Numbers 25 Chapter 3: Cementing the Building Blocks of Pre-Calculus Functions 43 Chapter 4: Operating on Functions 65 Unit 2: Getting the Grip on Graphing 93 Chapter 5: Graphing Polynomial Functions 95 Chapter 6: Exponential and Logarithmic Functions 131 Chapter 7: Piece-Wise and Greatest-Integer Functions 159 Unit 3: The Essentials of Trigonometry 171 Chapter 8: Circling In on Angles 173 Chapter 9: Homing In on the Friendliest Angles 201 Chapter 10: Picturing Basic Trig Functions and Reciprocal Functions 217 Chapter 11: Graphing and Transforming Trig Functions 237 Unit 4: Identities and Special Triangles 267 Chapter 12: Identifying with Trig Identities: The Basics 269 Chapter 13: Advancing with Advanced Identities 297 Chapter 14: Getting the Slant on Oblique Triangles 327 Unit 5: Analytic Geometry 361 Chapter 15: Coordinating with Complex Numbers 363 Chapter 16: Warming Up to Polar Coordinates 379 Chapter 17: Relating Conics to Sliced Cones 399 Unit 6: Systems, Sequences, and Series 443 Chapter 18: Streamlining Systems of Equations 445 Chapter 19: Making Matrices Work 473 Chapter 20: Sequences and Series 499 Chapter 21: Expanding Binomials for the Real World 519 Unit 7: Onward to Calculus 533 Chapter 22: Lining Up the Tools 535 Chapter 23: Proceeding with Successful Procedures 557 Index 573

Read more less

Book SynopsisMake some headway in the notoriously tough subject of business statistics Business Statistics For Dummies helps you understand the core concepts and principles of business statistics, and how they relate to the business world. This book tracks to a typical introductory course offered at the undergraduate, so you know you'll find all the content you need to pass your class and get your degree. You'll get an introduction to statistical problems and processes common to the world of global business and economics. Written in clear and simple language, Business Statistics For Dummies gives you an introduction to probability, sampling techniques and distributions, and drawing conclusions from data. You'll also discover how to use charts and graphs to visualize the most important properties of a data set. Grasp the core concepts, principles, and methods of business statistics Learn tricky concepts with simplified explanations and illustrative graphsTable of ContentsIntroduction 1 Part 1: Getting Started with Business Statistics 5 Chapter 1: The Art and Science of Business Statistics 7 Chapter 2: Pictures Tell the Story: Graphical Representations of Data 21 Chapter 3: Identifying the Center of a Data Set 35 Chapter 4: Measuring Variation in a Data Set 53 Chapter 5: Measuring How Data Sets Are Related to Each Other 71 Part 2: Probability Theory and Probability Distributions 95 Chapter 6: Probability Theory: Measuring the Likelihood of Events 97 Chapter 7: Probability Distributions and Random Variables 115 Chapter 8: The Binomial and Poisson Distributions 127 Chapter 9: The Normal Distribution: So Many Possibilities! 145 Chapter 10: Sampling Techniques and Distributions 165 Part 3: Drawing Conclusions from Samples 185 Chapter 11: Confidence Intervals and the Student’s t-Distribution 187 Chapter 12: Testing Hypotheses about the Population Mean 205 Chapter 13: Applications of the Chi-Square Distribution 245 Chapter 14: Applications of the F-Distribution 273 Part 4: More Advanced Techniques: Regression Analysis and Spreadsheet Modeling 287 Chapter 15: Simple Regression Analysis 289 Chapter 16: Key Statistical Techniques in Excel 317 Part 5: The Part of Tens 343 Chapter 17: Ten Common Errors That Arise in Statistical Analysis 345 Chapter 18: (Almost) Ten Key Categories of Formulas for Business Statistics 353 Index 363

Read more less