Probability and statistics Books

Book SynopsisEveryone wants to succeed in life. But what causes some of us to be more successful than others? Is it really down to skill and strategy - or something altogether more unpredictable? This book intends to change the way you think about business and the world.

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Book SynopsisThe phenomenal international bestseller that shows us how to stop trying to predict everything - and take advantage of uncertaintyWhat have the invention of the wheel, Pompeii, the Wall Street Crash, Harry Potter and the internet got in common? Why are all forecasters con-artists? Why should you never run for a train or read a newspaper? This book is all about Black Swans: the random events that underlie our lives, from bestsellers to world disasters. Their impact is huge; they''re impossible to predict; yet after they happen we always try to rationalize them. ''Taleb is a bouncy and even exhilarating guide ... I came to relish what he said, and even develop a sneaking affection for him as a person'' Will Self, Independent on Sunday''He leaps like some superhero of the mind'' Boyd Tonkin, IndependentTrade ReviewA fascinating study of how we are regularly taken for suckers by the unexpected * Guardian *Like the conversation of a raconteur ... hugely enjoyable - compelling * Financial Times *It has altered modern thinking * The Times *Confirms his status as a guru for every would-be Damien Hirst, George Soros and aspirant despot * Sunday Times *The Black Swan changed my view of how the world works -- Daniel Kahneman, author of Thinking, Fast and SlowGreat fun... brash, stubborn, entertaining, opinionated, curious, cajoling -- Stephen J. Dubner, co-author of FreakonomicsThe most prophetic voice of all * GQ *

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Book SynopsisA guide to critical thinking in the ''post-truth'' era, from the author of Sunday Times best-seller The Organized Mind We live in a world of information overload. Facts and figures on absolutely everything are at our fingertips, but are too often biased, distorted, or outright lies. From unemployment figures to voting polls, IQ tests to divorce rates, we''re bombarded by seemingly plausible statistics on how people live and what they think. Daniel Levitin teaches us how to effectively ask ourselves: can we really know that? And how do they know that? In this eye-opening, accessible guide filled with fascinating examples and practical takeaways, acclaimed neuroscientist Daniel Levitin shows us how learning to understand statistics will enable you to make better, smarter judgements on the world around you.Trade ReviewIn a post-truth world, Levitin's book is an invaluable primer on how to sort the fact from the fiction * Sunday Times *The world is awash with data, but not always with accurate information. [Levitin] does a terrific job of illustrating the difference between the two with precision and delightful good humour -- Charles Wheelan, author of Naked EconomicsDeservedly a bestseller -- Independent on 'The Organized Mind'Smart, important, exquisitely written -- Daniel Gilbert on 'The Organized Mind'The Organized Mind is the perfect antidote to the effects of information overload. Loved it. -- Scott Turow, New York Times bestselling author of 'Identical' and 'Innocent' on 'The Organized Mind'Daniel Levitin's field guide is a critical thinking primer for our shrill, data-drenched age. From the way averages befuddle to the logical fallacies that sneak by us, every page is enlightening -- Charles Duhigg, author of The Power of Habit and Smarter, Faster, BetterValuable tools for anyone willing to evaluate claims and get to the truth of the matter * Kirkus Reviews *As a lucid guide to critical thinking about statistics, information and assertion it is profoundly welcome * Observer *

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

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

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Book Synopsis''A great introduction to a crucial topic'' Bill Gates''Perhaps the most popular book on statistics ever published ... It''s a marvel ... gave me a peek behind the curtain of statistical manipulation, showing me how the swindling was done so that I would not be fooled again'' Tim HarfordIn 1954, Darrell Huff decided enough was enough. Fed up with politicians, advertisers and journalists using statistics to sensationalise, inflate, confuse, oversimplify and - on occasion - downright lie, he decided to shed light on their ill-informed and sneaky ways. How to Lie with Statistics is the result - the definitive and hilarious primer in the ways statistics are used to deceive.With over one and half million copies sold around the world, it has delighted generations of readers with its cheeky takes on the ins and outs of samples, averages, errors, graphs and indexes. And in the modern world of big data and misinformation, Huff remains the perfect guide tTrade ReviewMore relevant than ever . . . a great introduction to the use of statistics -- Bill GatesA hilarious exploration of mathematical mendacity.... Every time you pick it up, what happens? Bang goes another illusion! * New York Times *A pleasantly subversive little book guaranteed to undermine your faith in the almighty statistic * Atlantic *

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Book SynopsisTrade ReviewThe human mind soaks up the images and narratives conveyed by the press, but they are a highly nonrandom sample of reality: the lurid, the sudden, the photogenic. Smil's title says it all: to understand the world, you need to follow the trendlines, not the headlines. This is a compelling, fascinating, and most important, realistic portrait of the world and where it's going -- Steven PinkerThe best book to read to better understand our world. Once in a while a book comes along that helps us see our planet more clearly. By showing us numbers about science, health, green technology and more, Smil's book does just that. It should be on every bookshelf! -- Linda Yueh, author of The Great EconomistsImportant -- Mark Zuckerberg, on EnergyOne of the world's foremost thinkers on development history and a master of statistical analysis . . . The nerd's nerd * Guardian *A book for anyone confused by statistics or dubious of data in a world where numbers seem to mean everything and nothing. Vaclav Smil's new book reveals why diesel isn't as bad as you think, how much food is really being wasted, what actually makes people happy, and much more. * BBC Science Focus magazine *Outstanding perspectives on the world we live in providing context on mankind's challenges ranging from demographics through globalization, innovation and the environment -- Sarah Riopelle, Senior portfolio manager at RBC Global Asset Management * Bloomberg, 'Best Books of 2022' *

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Book SynopsisHandbook and reference guide for students and practitioners of statistical regression-based analyses in R Handbook of Regression Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical programming language. The authors' thorough treatment of classical regression analysis in the first edition is complemented here by their discussion of more advanced topics including time-to-event survival data and longitudinal and clustered data. The book further pays particular attention to methods that have become prominent in the last few decades as increasingly large data sets have made new techniques and applications possible. These include: Regularization methodsSmoothing methodsTree-based methods In the new edition of the Handbook, the data analyst's toolkit is explored and expanded. Examples are drawn from a wide variety of real-life applications and data sets. All the utilized R code and data are available via an author-maintained website. Of interest to undergraduate and graduate students taking courses in statistics and regression, the Handbook of Regression Analysis will also be invaluable to practicing data scientists and statisticians.Table of ContentsPreface to the Second Edition xv Preface to the First Edition xix Part I The Multiple Linear Regression Model 1 Multiple Linear Regression 3 1.1 Introduction 3 1.2 Concepts and Background Material 4 1.2.1 The Linear Regression Model 4 1.2.2 Estimation Using Least Squares 5 1.2.3 Assumptions 8 1.3 Methodology 9 1.3.1 Interpreting Regression Coefficients 9 1.3.2 Measuring the Strength of the Regression Relationship 10 1.3.3 Hypothesis Tests and Confidence Intervals for β 12 1.3.4 Fitted Values and Predictions 13 1.3.5 Checking Assumptions Using Residual Plots 14 1.4 Example —Estimating Home Prices 15 1.5 Summary 19 2 Model Building 23 2.1 Introduction 23 2.2 Concepts and Background Material 24 2.2.1 Using Hypothesis Tests to Compare Models 24 2.2.2 Collinearity 26 2.3 Methodology 29 2.3.1 Model Selection 29 2.3.2 Example—Estimating Home Prices (continued) 31 2.4 Indicator Variables and Modeling Interactions 38 2.4.1 Example—Electronic Voting and the 2004 Presidential Election 40 2.5 Summary 46 Part II Addressing Violations of Assumptions 3 Diagnostics for Unusual Observations 53 3.1 Introduction 53 3.2 Concepts and Background Material 54 3.3 Methodology 56 3.3.1 Residuals and Outliers 56 3.3.2 Leverage Points 57 3.3.3 Influential Points and Cook’s Distance 58 3.4 Example— Estimating Home Prices (continued) 60 3.5 Summary 63 4 Transformations and Linearizable Models 67 4.1 Introduction 67 4.2 Concepts and Background Material: The Log-Log Model 69 4.3 Concepts and Background Material: Semilog Models 69 4.3.1 Logged Response Variable 70 4.3.2 Logged Predictor Variable 70 4.4 Example— Predicting Movie Grosses After One Week 71 4.5 Summary 77 5 Time Series Data and Autocorrelation 79 5.1 Introduction 79 5.2 Concepts and Background Material 81 5.3 Methodology: Identifying Autocorrelation 83 5.3.1 The Durbin-Watson Statistic 83 5.3.2 The Autocorrelation Function (ACF) 84 5.3.3 Residual Plots and the Runs Test 85 5.4 Methodology: Addressing Autocorrelation 86 5.4.1 Detrending and Deseasonalizing 86 5.4.2 Example— e-Commerce Retail Sales 87 5.4.3 Lagging and Differencing 93 5.4.4 Example— Stock Indexes 94 5.4.5 Generalized Least Squares (GLS): The Cochrane-Orcutt Procedure 99 5.4.6 Example— Time Intervals Between Old Faithful Geyser Eruptions 100 5.5 Summary 104 Part III Categorical Predictors 6 Analysis of Variance 109 6.1 Introduction 109 6.2 Concepts and Background Material 110 6.2.1 One-Way ANOVA 110 6.2.2 Two-Way ANOVA 111 6.3 Methodology 113 6.3.1 Codings for Categorical Predictors 113 6.3.2 Multiple Comparisons 118 6.3.3 Levene’s Test and Weighted Least Squares 120 6.3.4 Membership in Multiple Groups 123 6.4 Example—DVD Sales of Movies 125 6.5 Higher-Way ANOVA 130 6.6 Summary 132 7 Analysis of Covariance 135 7.1 Introduction 135 7.2 Methodology 136 7.2.1 Constant Shift Models 136 7.2.2 Varying Slope Models 137 7.3 Example —International Grosses of Movies 137 7.4 Summary 142 Part IV Non-Gaussian Regression Models 8 Logistic Regression 145 8.1 Introduction 145 8.2 Concepts and Background Material 147 8.2.1 The Logit Response Function 148 8.2.2 Bernoulli and Binomial Random Variables 149 8.2.3 Prospective and Retrospective Designs 149 8.3 Methodology 152 8.3.1 Maximum Likelihood Estimation 152 8.3.2 Inference, Model Comparison, and Model Selection 153 8.3.3 Goodness-of-Fit 155 8.3.4 Measures of Association and Classification Accuracy 157 8.3.5 Diagnostics 159 8.4 Example— Smoking and Mortality 159 8.5 Example— Modeling Bankruptcy 163 8.6 Summary 168 9 Multinomial Regression 173 9.1 Introduction 173 9.2 Concepts and Background Material 174 9.2.1 Nominal Response Variable 174 9.2.2 Ordinal Response Variable 176 9.3 Methodology 178 9.3.1 Estimation 178 9.3.2 Inference, Model Comparisons, and Strength of Fit 178 9.3.3 Lack of Fit and Violations of Assumptions 180 9.4 Example— City Bond Ratings 180 9.5 Summary 184 10 Count Regression 187 10.1 Introduction 187 10.2 Concepts and Background Material 188 10.2.1 The Poisson Random Variable 188 10.2.2 Generalized Linear Models 189 10.3 Methodology 190 10.3.1 Estimation and Inference 190 10.3.2 Offsets 191 10.4 Overdispersion and Negative Binomial Regression 192 10.4.1 Quasi-likelihood 192 10.4.2 Negative Binomial Regression 193 10.5 Example— Unprovoked Shark Attacks in Florida 194 10.6 Other Count Regression Models 201 10.7 Poisson Regression and Weighted Least Squares 203 10.7.1 Example— International Grosses of Movies (continued) 204 10.8 Summary 206 11 Models for Time-to-Event (Survival) Data 209 11.1 Introduction 210 11.2 Concepts and Background Material 211 11.2.1 The Nature of Survival Data 211 11.2.2 Accelerated Failure Time Models 212 11.2.3 The Proportional Hazards Model 214 11.3 Methodology 214 11.3.1 The Kaplan-Meier Estimator and the Log-Rank Test 214 11.3.2 Parametric (Likelihood) Estimation 219 11.3.3 Semiparametric (Partial Likelihood) Estimation 221 11.3.4 The Buckley-James Estimator 223 11.4 Example—The Survival of Broadway Shows (continued) 223 11.5 Left-Truncated/Right-Censored Data and Time-Varying Covariates 230 11.5.1 Left-Truncated/Right-Censored Data 230 11.5.2 Example—The Survival of Broadway Shows (continued) 233 11.5.3 Time-Varying Covariates 233 11.5.4 Example—Female Heads of Government 235 11.6 Summary 238 Part V Other Regression Models 12 Nonlinear Regression 243 12.1 Introduction 243 12.2 Concepts and Background Material 244 12.3 Methodology 246 12.3.1 Nonlinear Least Squares Estimation 246 12.3.2 Inference for Nonlinear Regression Models 247 12.4 Example —Michaelis-Menten Enzyme Kinetics 248 12.5 Summary 252 13 Models for Longitudinal and Nested Data 255 13.1 Introduction 255 13.2 Concepts and Background Material 257 13.2.1 Nested Data and ANOVA 257 13.2.2 Longitudinal Data and Time Series 258 13.2.3 Fixed Effects Versus Random Effects 259 13.3 Methodology 260 13.3.1 The Linear Mixed Effects Model 260 13.3.2 The Generalized Linear Mixed Effects Model 262 13.3.3 Generalized Estimating Equations 262 13.3.4 Nonlinear Mixed Effects Models 263 13.4 Example —Tumor Growth in a Cancer Study 264 13.5 Example —Unprovoked Shark Attacks in the United States 269 13.6 Summary 275 14 Regularization Methods and Sparse Models 277 14.1 Introduction 277 14.2 Concepts and Background Material 278 14.2.1 The Bias–Variance Tradeoff 278 14.2.2 Large Numbers of Predictors and Sparsity 279 14.3 Methodology 280 14.3.1 Forward Stepwise Regression 280 14.3.2 Ridge Regression 281 14.3.3 The Lasso 281 14.3.4 Other Regularization Methods 283 14.3.5 Choosing the Regularization Parameter(s) 284 14.3.6 More Structured Regression Problems 285 14.3.7 Cautions About Regularization Methods 286 14.4 Example— Human Development Index 287 14.5 Summary 289 Part VI Nonparametric and Semiparametric Models 15 Smoothing and Additive Models 295 15.1 Introduction 296 15.2 Concepts and Background Material 296 15.2.1 The Bias–Variance Tradeoff 296 15.2.2 Smoothing and Local Regression 297 15.3 Methodology 298 15.3.1 Local Polynomial Regression 298 15.3.2 Choosing the Bandwidth 298 15.3.3 Smoothing Splines 299 15.3.4 Multiple Predictors, the Curse of Dimensionality, and Additive Models 300 15.4 Example— Prices of German Used Automobiles 301 15.5 Local and Penalized Likelihood Regression 304 15.5.1 Example— The Bechdel Rule and Hollywood Movies 305 15.6 Using Smoothing to Identify Interactions 307 15.6.1 Example— Estimating Home Prices (continued) 308 15.7 Summary 310 16 Tree-Based Models 313 16.1 Introduction 314 16.2 Concepts and Background Material 314 16.2.1 Recursive Partitioning 314 16.2.2 Types of Trees 317 16.3 Methodology 318 16.3.1 CART 318 16.3.2 Conditional Inference Trees 319 16.3.3 Ensemble Methods 320 16.4 Examples 321 16.4.1 Estimating Home Prices (continued) 321 16.4.2 Example—Courtesy in Airplane Travel 322 16.5 Trees for Other Types of Data 327 16.5.1 Trees for Nested and Longitudinal Data 327 16.5.2 Survival Trees 328 16.6 Summary 332 Bibliography 337 Index 343

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Book SynopsisThis essential textbook presents the basics of dental statistics in an accessible way, combining explanation in non-technical language with key messages, practical examples, suggestions for further reading and exercises complete with detailed solutions. There is an emphasis on the principles and application of statistics without the use of algebra.The statistical material is strongly rooted in practical examples drawn from a wide range of journal articles representing both dental health care delivery and clinical dentistry. The perspective is international, with papers drawn from a variety of settings around the world. Many articles are recent and report contemporary developments in dental care.The intended audience includes dental students and practitioners, those engaged in dental research and other health care professionals. For students and tutors, it covers the undergraduate curriculum, and the exercises and solutions make it ideal for course use. For practitionerTrade Review"The author, who has a long track record of communicating statistics to non-experts, has for over 20 years steeped himself in the world of dental health care. The fruits of his experience are demonstrated by the rich set of examples which he brings to every chapter of the book. The subject matter of each chapter are applicable to any field involving statistical reasoning, but the authoritative way in which he relates every piece of teaching to realistic questions for dental care practitioners is most convincing. Previous generations of the dental community who have attempted to learn statistics have often had to turn to medical statistics textbooks. However, Smeeton has managed to break this mould and demonstrate properly the application of statistics in dentistry. His treatment of statistical concepts is orthodox and sound, but this third edition of the book has evolved to meet changing needs of the intended audience, including a welcome chapter on evidence-based dentistry. I anticipate many undergraduate and postgraduate dental students will find this book to be their primary source for their use of statistics."—Richard Morris, Professor in Medical Statistics, University of Bristol"The first edition of Nigel Smeeton’s highly accessible guide to research design and statistics was a breakthrough in providing a simple and easy to read guide to conducting research studies in dentistry. And now this third edition, which comprises 18 chapters, spans the principles of research design, broad aspects of the ethics of research, principles of statistical analysis and the appraisal of research (including peer review of manuscripts). Each chapter provides an introduction to the topic it will address which outlines the importance of the topic and the areas that will be covered, this is followed by the main body of the chapter, and is concluded with a short test for the reader to appraise their own learning. The author draws extensively on published dental research to provide examples. The most remarkable aspect of this book is that there is so little mathematics in it. The focus of the learning is understanding how statistics work without delving into the formulae. The third edition brings a new chapter on evidence based dental practice which is becoming increasingly important for policy and practice. There are also some new sections on advanced techniques – which with the increased availability of statistical software are being used more widely.There is little doubt that this is a very useful book for undergraduate dental students learning the skills of research design and critical appraisal of scientific research which will form part of the foundation for their future learning and continuing professional development. Throughout the text the key learning points are emphasised, and there are exercises to test the new learning. I would strongly recommend it for undergraduate courses. However I also believe it will be very useful for qualified practitioners who are keen to develop an awareness of statistical issues, in order to support an evidence-based approach to dental practice."–J T Newton, King’s College London Dental InstituteTable of ContentsPreface Preface to the second edition Preface to the first edition Introduction Planning a study Types of study in dental research Sampling Randomised controlled trials Ethical considerations The Normal distribution Diagnostic tests Sampling variation Introduction to hypothesis tests Comparing two means Dealing with proportions and categorical data Comparing several means Regression, correlation and agreement Non-Normally distributed data The choice of sample size Evidence based dentistry Statistical refereeing Appendix Solutions to exercises Index

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Book SynopsisLeonard Mlodinow''s The Drunkard''s Walk: How Randomness Rules Our Lives is an exhilarating, eye-opening guide to understanding our random world.Randomness and uncertainty surround everything we do. So why are we so bad at understanding them? The same tools that help us understand the random paths of molecules can be applied to the randomness that governs so many aspects of our everyday lives, from winning the lottery to road safety, and reveals the truth about the success of sporting heroes and film stars, and even how to make sense of a blood test.The Drunkard''s Walk reveals the psychological illusions that prevent us understanding everything from stock-picking to wine-tasting - read it, or risk becoming another victim of chance.''A wonderfully readable guide to how the mathematical laws of randomness affect our lives'' Stephen Hawking, author of A Brief History of TimeTrade Review'Mlodinow writes in a breezy style, interspersing probabilistic mind-benders with portraits of theorists ! The result is a readable crash course in randomness.' New York Times 'If you're strong enough to have some of your favorite assumptions challenged, please read the Drunkard's Walk, a history, explanation, and exaltation of probability theory.' Fortune magazine

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Book SynopsisStatistics For Dummies, 2nd Edition (9781119293521) was previously published as Statistics For Dummies, 2nd Edition (9780470911082). 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 About This Book 1 Conventions Used in This Book 2 What You’re Not to Read 3 Foolish Assumptions 3 How This Book Is Organized 3 Part 1: Vital Statistics about Statistics 3 Part 2: Number-Crunching Basics 4 Part 3: Distributions and the Central Limit Theorem 4 Part 4: Guesstimating and Hypothesizing with Confidence 4 Part 5: Statistical Studies and the Hunt for a Meaningful Relationship 5 Part 6: The Part of Tens 5 Icons Used in This Book 6 Where to Go from Here 6 Part 1: Vital Statistics About Statistics 7 Chapter 1: Statistics in a Nutshell 9 Thriving in a Statistical World 10 Designing Appropriate Studies 11 Surveys 11 Experiments 12 Collecting Quality Data 13 Selecting a good sample 13 Avoiding bias in your data 14 Creating Effective Summaries 14 Descriptive statistics 15 Charts and graphs 15 Determining Distributions 16 Performing Proper Analyses 17 Margin of error and confidence intervals 18 Hypothesis tests 19 Correlation, regression, and two-way tables 20 Drawing Credible Conclusions 21 Reeling in overstated results 21 Questioning claims of cause and effect 21 Becoming a Sleuth, Not a Skeptic 22 Chapter 2: The Statistics of Everyday Life 23 Statistics and the Media: More Questions than Answers? 24 Probing popcorn problems 24 Venturing into viruses 24 Comprehending crashes 25 Mulling malpractice 26 Belaboring the loss of land 26 Scrutinizing schools 27 Studying sports 28 Banking on business news 28 Touring the travel news 29 Surveying sexual stats 29 Breaking down weather reports 30 Musing about movies 30 Highlighting horoscopes 31 Using Statistics at Work 31 Delivering babies — and information 31 Posing for pictures 32 Poking through pizza data 32 Statistics in the office 33 Chapter 3: Taking Control: So Many Numbers, So Little Time 35 Detecting Errors, Exaggerations, and Just Plain Lies 36 Checking the math 36 Uncovering misleading statistics 37 Looking for lies in all the right places 44 Feeling the Impact of Misleading Statistics 44 Chapter 4: Tools of the Trade 47 Statistics: More than Just Numbers 47 Grabbing Some Basic Statistical Jargon 49 Data 50 Data set 51 Variable 51 Population 51 Sample, random, or otherwise 52 Statistic 54 Parameter 54 Bias 55 Mean (Average) 55 Median 56 Standard deviation 56 Percentile 57 Standard score 57 Distribution and normal distribution 58 Central Limit Theorem 59 z-values 60 Experiments 60 Surveys (Polls) 62 Margin of error 62 Confidence interval 63 Hypothesis testing 64 p-values 65 Statistical significance 66 Correlation versus causation 67 Part 2: Number-Crunching Basics 69 Chapter 5: Means, Medians, and More 71 Summing Up Data with Descriptive Statistics 71 Crunching Categorical Data: Tables and Percents 72 Measuring the Center with Mean and Median 75 Averaging out to the mean 75 Splitting your data down the median 77 Comparing means and medians: Histograms 78 Accounting for Variation 80 Reporting the standard deviation 81 Being out of range 84 Examining the Empirical Rule (68-95-99.7) 85 Measuring Relative Standing with Percentiles 87 Calculating percentiles 88 Interpreting percentiles 89 Gathering a five-number summary 93 Exploring interquartile range 94 Chapter 6: Getting the Picture: Graphing Categorical Data 95 Take Another Little Piece of My Pie Chart 96 Tallying personal expenses 96 Bringing in a lotto revenue 97 Ordering takeout 98 Projecting age trends 99 Raising the Bar on Bar Graphs 101 Tracking transportation expenses 101 Making a lotto profit 103 Tipping the scales on a bar graph 104 Pondering pet peeves 105 Chapter 7: Going by the Numbers: Graphing Numerical Data 107 Handling Histograms 108 Making a histogram 108 Interpreting a histogram 111 Putting numbers with pictures 115 Detecting misleading histograms 117 Examining Boxplots 120 Making a boxplot 120 Interpreting a boxplot 121 Tackling Time Charts 127 Interpreting time charts 127 Understanding variability: Time charts versus histograms 128 Spotting misleading time charts 128 Part 3: Distributions And The Central Limit Theorem 133 Chapter 8: Random Variables and the Binomial Distribution 135 Defining a Random Variable 136 Discrete versus continuous 136 Probability distributions 137 The mean and variance of a discrete random variable 138 Identifying a Binomial 139 Checking binomial conditions step by step 140 No fixed number of trials 140 More than success or failure 141 Trials are not independent 141 Probability of success (p) changes 141 Finding Binomial Probabilities Using a Formula 142 Finding Probabilities Using the Binomial Table 144 Finding probabilities for specific values of X 145 Finding probabilities for X greater-than, less-than, or between two values 146 Checking Out the Mean and Standard Deviation of the Binomial 146 CHAPTER 9: The Normal Distribution 149 Exploring the Basics of the Normal Distribution 150 Meeting the Standard Normal (Z-) Distribution 152 Checking out Z 153 Standardizing from X to Z 153 Finding probabilities for Z with the Z-table 155 Finding Probabilities for a Normal Distribution 156 Finding X When You Know the Percent 158 Figuring out a percentile for a normal distribution 159 Translating tricky wording in percentile problems 161 Normal Approximation to the Binomial 162 CHAPTER 10: The t-Distribution 165 Basics of the t-Distribution 165 Comparing the t- and Z-distributions 165 Discovering the effect of variability on t-distributions 167 Using the t-Table 167 Finding probabilities with the t-table 168 Figuring percentiles for the t-distribution 168 Picking out t*-values for confidence intervals 169 Studying Behavior Using the t-Table 170 Chapter 11: Sampling Distributions and the Central Limit Theorem 171 Defining a Sampling Distribution 172 The Mean of a Sampling Distribution 174 Measuring Standard Error 174 Sample size and standard error 175 Population standard deviation and standard error 176 Looking at the Shape of a Sampling Distribution 178 Case 1: The distribution of X is normal 178 Case 2: The distribution of X is not normal—enter the Central Limit Theorem 178 Finding Probabilities for the Sample Mean 181 The Sampling Distribution of the Sample Proportion 183 Finding Probabilities for the Sample Proportion 185 Part 4: Guesstimating And Hypothesizing With Confidence 187 Chapter 12: Leaving Room for a Margin of Error 189 Seeing the Importance of That Plus or Minus 190 Finding the Margin of Error: A General Formula 191 Measuring sample variability 191 Calculating margin of error for a sample proportion 193 Reporting results 194 Calculating margin of error for a sample mean 195 Being confident you’re right 197 Determining the Impact of Sample Size 197 Sample size and margin of error 198 Bigger isn’t always (that much) better! 198 Keeping margin of error in perspective 199 Chapter 13: Confidence Intervals: Making Your Best Guesstimate 201 Not All Estimates Are Created Equal 202 Linking a Statistic to a Parameter 203 Getting with the Jargon 203 Interpreting Results with Confidence 204 Zooming In on Width 205 Choosing a Confidence Level 206 Factoring In the Sample Size 208 Counting On Population Variability 209 Calculating a Confidence Interval for a Population Mean 210 Case 1: Population standard deviation is known 210 Case 2: Population standard deviation is unknown and/or n is small 212 Figuring Out What Sample Size You Need 213 Determining the Confidence Interval for One Population Proportion 214 Creating a Confidence Interval for the Difference of Two Means 216 Case 1: Population standard deviations are known 216 Case 2: Population standard deviations are unknown and/or sample sizes are small 218 Estimating the Difference of Two Proportions 219 Spotting Misleading Confidence Intervals 221 Chapter 14: Claims, Tests, and Conclusions 223 Setting Up the Hypotheses 224 Defining the null 224 What’s the alternative? 225 Gathering Good Evidence (Data) 226 Compiling the Evidence: The Test Statistic 226 Gathering sample statistics 227 Measuring variability using standard errors 227 Understanding standard scores 228 Calculating and interpreting the test statistic 228 Weighing the Evidence and Making Decisions: p-Values 229 Connecting test statistics and p-values 229 Defining a p-value 230 Calculating a p-value 230 Making Conclusions 231 Setting boundaries for rejecting Ho 232 Testing varicose veins 233 Assessing the Chance of a Wrong Decision 233 Making a false alarm: Type-1 errors 234 Missing out on a detection: Type-2 errors 234 Chapter 15: Commonly Used Hypothesis Tests: Formulas and Examples 237 Testing One Population Mean 238 Handling Small Samples and Unknown Standard Deviations: The t-Test 240 Putting the t-test to work 241 Relating t to Z 241 Handling negative t-values 242 Examining the not-equal-to alternative 242 Testing One Population Proportion 243 Comparing Two (Independent) Population Averages 245 Testing for an Average Difference (The Paired t-Test) 247 Comparing Two Population Proportions 251 Part 5: Statistical Studies And The Hunt For A Meaningful Relationship 255 Chapter 16: Polls, Polls, and More Polls 257 Recognizing the Impact of Polls 258 Getting to the source 258 Surveying what’s hot 260 Impacting lives 260 Behind the Scenes: The Ins and Outs of Surveys 262 Planning and designing a survey 263 Selecting the sample 266 Carrying out a survey 268 Interpreting results and finding problems 271 Chapter 17: Experiments: Medical Breakthroughs or Misleading Results? 275 Boiling Down the Basics of Studies 276 Looking at the lingo of studies 276 Observing observational studies 277 Examining experiments 278 Designing a Good Experiment 278 Designing the experiment to make comparisons 279 Selecting the sample size 281 Choosing the subjects 283 Making random assignments 283 Controlling for confounding variables 284 Respecting ethical issues 286 Collecting good data 287 Analyzing the data properly 289 Making appropriate conclusions 290 Making Informed Decisions 292 Chapter 18: Looking for Links: Correlation and Regression 293 Picturing a Relationship with a Scatterplot 294 Making a scatterplot 295 Interpreting a scatterplot 296 Quantifying Linear Relationships Using the Correlation 297 Calculating the correlation 297 Interpreting the correlation 298 Examining properties of the correlation 300 Working with Linear Regression 301 Figuring out which variable is X and which is Y 301 Checking the conditions 302 Calculating the regression line 302 Interpreting the regression line 304 Putting it all together with an example: The regression line for the crickets 306 Making Proper Predictions 306 Explaining the Relationship: Correlation versus Cause and Effect 308 Chapter 19: Two-Way Tables and Independence 311 Organizing a Two-Way Table 312 Setting up the cells 313 Figuring the totals 314 Interpreting Two-Way Tables 315 Singling out variables with marginal ­distributions 315 Examining all groups — a joint distribution 317 Comparing groups with conditional distributions 321 Checking Independence and Describing Dependence 324 Checking for independence 324 Describing a dependent relationship 327 Cautiously Interpreting Results 329 Checking for legitimate cause and effect 329 Projecting from sample to population 330 Making prudent predictions 331 Resisting the urge to jump to conclusions 332 Part 6: The Part Of Tens 333 Chapter 20: Ten Tips for the Statistically Savvy Sleuth 335 Pinpoint Misleading Graphs 335 Pie charts 336 Bar graphs 336 Time charts 337 Histograms 339 Uncover Biased Data 339 Search for a Margin of Error 340 Identify Non-Random Samples 341 Sniff Out Missing Sample Sizes 342 Detect Misinterpreted Correlations 343 Reveal Confounding Variables 344 Inspect the Numbers 344 Report Selective Reporting 345 Expose the Anecdote 346 Chapter 21: Ten Surefire Exam Score Boosters 349 Know What You Don’t Know, and then Do Something about It 350 Avoid “Yeah-Yeah” Traps 351 Yeah-yeah trap #1 352 Yeah-yeah trap #2 352 Make Friends with Formulas 354 Make an “If-Then-How” Chart 355 Figure Out What the Question Is Asking 357 Label What You’re Given 358 Draw a Picture 360 Make the Connection and Solve the Problem 361 Do the Math — Twice 362 Analyze Your Answers 363 Appendix: Tables For Reference 365 Index 375

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Book SynopsisDiscover football''s astonishing hidden rules in The Numbers Game by Chris Anderson and David Sally*Fully updated with a new World Cup chapter* Football has always been a numbers game: 4-4-2, the big number 9 and 3 points for a win. But what if up until now we''ve been focusing on the wrong numbers? What if the numbers that really matter, the ones that hold the key to winning matches, are actually 2.66, 53.4, 50/50, and 0 > 1? What if managers only make a 15% difference? What if Chelsea should have bought Darren Bent?In this incisive, myth-busting book, Chris Anderson, former goalkeeper turned football statistics guru, and David Sally, former baseball pitcher turned behavioural economist, show that every shred of knowledge we can gather can help us to love football and understand it even more. You''ll discover why stopping a goal is more valuable than scoring one, why corners should be taken short, and why it is better to improve yTrade ReviewDoes the impossible of making the beautiful game even more beautiful -- Malcolm GladwellA must-read . . . Chris Anderson and David Sally have the ability to see football in a way few have before them. Be warned: The Numbers Game will change the way you think about your favourite team or player, and change the way you watch the beautiful game. -- Billy Beane, General Manager of the Oakland A's, the subject of MoneyballA fascinating and stylish investigation into a rapidly developing way of understanding football -- Jonathan Wilson, author of Inverting the Pyramid: The History of Football TacticsWhether you are a traditionalist or a numbers nut you can enjoy this book. It's thorough, accessible, and devoid of the absolute truths so many on both sides of the debate peddle. -- Gabriele Marcotti, football broadcaster and authorIt is the book that could change the game forever * Times *You need to like football. Millions of people do. And they should rush to read this book immediately. The game they love will take on new depth, colour and subtlety -- Ed Smith * The Times *

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Book SynopsisThis series has been developed specifically for the Cambridge International AS & A Level Mathematics (9709) syllabus to be examined from 2020. Cambridge International AS & A Level Mathematics: Probability & Statistics 1 matches the corresponding unit of the syllabus, with a clear and logical progression through. It contains materials on topics such as data, variation, probability, permutations and combinations, binomial and geometric distributions, and normal distribution. This coursebook contains a variety of features including recap sections for students to check their prior knowledge, detailed explanations and worked examples, end-of-chapter and cross-topic review exercises and ''Explore'' tasks to encourage deeper thinking around mathematical concepts. Answers to coursebook questions are at the back of the book.Table of Contents1. Representation of data; 2. Measures of central tendency; 3. Measures of variation; Cross-topic revision exercise 1; 4. Probability; 5. Permutations and combinations; Cross-topic revision exercise 2; 6. Probability distributions; 7. The binomial and geometric distributions; 8. The normal distribution; Cross-topic revision exercise 3; Practice paper; Answers; Glossary; Index.

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Book SynopsisWe are the safest humans who ever lived - the statistics prove it. And yet the media tells a different story with its warnings and scare stories. How is it possible that anxiety has become the stuff of daily life?In this ground-breaking, compulsively readable book, Dan Gardner shows how our flawed strategies for perceiving risk influence our lives, often with unforeseen and sometimes-tragic consequences. He throws light on our paranoia about everything from paedophiles to terrorism and reveals how the most significant threats are actually the mundane risks to which we pay little attention.Speaking to psychologists and scientists, as well as looking at the influence of the media and politicians, Gardner uncovers one of the central puzzles of our time: why are the safest people in history living in a culture of fear?Trade ReviewExcellent ... Gardner analyses everything from the media's predilection for irrational scare stories to the cynical use of fear by politicians pushing a particular agenda ... A cheery corrective to modern paranoia * Economist *Terrific ... exceptionally good - has the clarity of Malcolm Gladwell * Evening Standard *Enlivening ... a fascinating insight into the peculiar and devastating nature of human fear * Sunday Telegraph *Stimulating ... where writers such as Richard Dawkins, Christopher Hitchens and Francis Wheen have been content largely to enumerate the errors of less rational men and women, Dan Gardner has collated part of what we need to diagnose the problem * Independent on Sunday *Beautifully observed * Observer *

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Book SynopsisAbout our authors Richard D. De?Veaux? is an internationally known educator and consultant. He has taught at the Wharton School and the Princeton University School of Engineering, where he won a Lifetime Award for Dedication and Excellence in Teaching. He is the C. Carlisle and M. Tippit Professor of Statistics at Williams College, where he has taught since 1994. Dick has won both the Wilcoxon and Shewell awards from the American Society for Quality. He is a fellow of the American Statistical Association (ASA) and an elected member of the International Statistical Institute (ISI). In 2008, he was named Statistician of the Year by the Boston Chapter of the ASA, and was the 2018-2021 Vice-President of the ASA. Dick is also well known in industry, where for more than 30 years he has consulted for such Fortune 500 companies as American Express, Hewlett-Packard, Alcoa, DuPont, Pillsbury, General Electric, and Chemical Bank. Because he consulted with Mickey Hart

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Book SynopsisThe International Bestseller by ''The Galileo of number crunchers'' (Independent)Every time we choose a route to work, decide whether to go on a second date, or set aside money for a rainy day, we are making a prediction about the future. Yet from the financial crisis to ecological disasters, we routinely fail to foresee hugely significant events, often at great cost to society. The rise of ''big data'' has the potential to help us predict the future, yet much of it is misleading, useless or distracting.In The Signal and the Noise, the New York Times political forecaster Nate Silver, who accurately predicted the results of every state in the 2012 US election, reveals how we can all develop better foresight in an uncertain world. From the stock market to the poker table, from earthquakes to the economy, he takes us on an enthralling insider''s tour of the high-stakes world of forecasting, showing how we can all learn to detect the true signals amid a noise of data. ''Remarkable and rewarding'' Matthew D''Ancona, Sunday Telegraph''A lucid explanation of how to think probabilistically'' GuardianTrade ReviewOutstanding... I was hooked -- Tim Harford * Financial Times *One of the more momentous books of the decade * The New York Times Book Review *A lucid explanation of how to think probabilistically * Guardian *The inhabitants of Westminster are speed-reading The Signal and the Noise... They will find the book remarkable and rewarding * Sunday Telegraph *Is there anything now that Nate Silver could tell us that we wouldn't believe? * Jonathan Freedland *Fascinating... our age's Brunel -- Bryan Appleyard * Sunday Times *A surprisingly accessible peek into the world of mathematical probability -- Daily TelegraphThe Galileo of number crunchers * Independent *A 34-year old Delphic Oracle * Daily Beast *

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Book SynopsisAn essential guide to the ways data can improve decision making. Statistics are everywhere: in news reports, at the doctor's office, and in every sort of forecast, from the stock market to the weather. Blogger, teacher, and computer scientist Allen B. Downey knows well that people have an innate ability both to understand statistics and to be fooled by them. As he makes clear in this accessible introduction to statistical thinking, the stakes are big. Simple misunderstandings have led to incorrect medical prognoses, underestimated the likelihood of large earthquakes, hindered social justice efforts, and resulted in dubious policy decisions. There are right and wrong ways to look at numbers, and Downey will help you see which are which. Probably Overthinking It uses real data to delve into real examples with real consequences, drawing on cases from health campaigns, political movements, chess rankings, and more. He lays out common pitfallslike the base rate fallacy, length-biased sampling, and Simpson's paradoxand shines a light on what we learn when we interpret data correctly, and what goes wrong when we don't. Using data visualizations instead of equations, he builds understanding from the basics to help you recognize errors, whether in your own thinking or in media reports. Even if you have never studied statisticsor if you have and forgot everything you learnedthis book will offer new insight into the methods and measurements that help us understand the world.Trade Review“Downey’s pure love for the subject shines through abundantly, as does his social conscience and belief in the importance of statistical methods to illuminate the greatest, most challenging issues of our time.” -- Aubrey Clayton, author of Bernoulli’s Fallacy: Statistical Illogic and the Crisis of Modern Science“Probably Overthinking It shows how fascinating and interesting statistics can be. Readers don’t need to be expert mathematicians. They just need to bring their curiosity about the world.” -- Ravin Kumar, data scientist at Google“Probably Overthinking It is a delightful exposition of commonly-encountered statistical fallacies and paradoxes and why they matter. The illustrations are powerful and the prose is exceptionally clear. There are few domains of human activity to which the lessons of this volume are not applicable.” -- Samuel H. Preston, coauthor of Demography: Measuring and Modeling Population Processes“Mark Twain once observed that ‘facts are stubborn things, but statistics are more pliable.’ Downey understands just how that happens, even to people who are not trying to obfuscate. It was an honest researcher who in 1971 found data that seemed to indicate smoking by pregnant women might be good for their babies—a misinterpretation that may have delayed anti-smoking measures by a decade. In this clear and cogent analysis, Downey explains why the data was misunderstood, as well as much else. It is a valuable book.” -- Floyd Norris, Johns Hopkins University, former chief financial correspondent for the New York TimesTable of ContentsIntroduction 1. Are You Normal? Hint: No 2. Relay Races and Revolving Doors 3. Defy Tradition, Save the World 4. Extremes, Outliers, and GOATs 5. Better Than New 6. Jumping to Conclusions 7. Causation, Collision, and Confusion 8. The Long Tail of Disaster 9. Fairness and Fallacy 10. Penguins, Pessimists, and Paradoxes 11. Changing Hearts and Minds 12. Chasing the Overton Window Epilogue Acknowledgments Bibliography Index

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Book SynopsisTable of Contents1. Introduction to Statistics and Data Analysis 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability 1.2 Sampling Procedures; Collection of Data 1.3 Measures of Location: The Sample Mean and Median Exercises 1.4 Measures of Variability Exercises 1.5 Discrete and Continuous Data 1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods 1.7 General Types of Statistical Studies: Designed Experiment, Observational Study, and Retrospective Study Exercises 2. Probability 2.1 Sample Space 2.2 Events Exercises 2.3 Counting Sample Points Exercises 2.4 Probability of an Event 2.5 Additive Rules Exercises 2.6 Conditional Probability, Independence and Product Rules Exercises 2.7 Bayes' Rule Exercises Review Exercises 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 3. Random Variables and Probability Distributions 3.1 Concept of a Random Variable 3.2 Discrete Probability Distributions 3.3 Continuous Probability Distributions Exercises 3.4 Joint Probability Distributions Exercises Review Exercises 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 4. Mathematical Expectation 4.1 Mean of a Random Variable Exercises 4.2 Variance and Covariance of Random Variables Exercises 4.3 Means and Variances of Linear Combinations of Random Variables 4.4 Chebyshev's Theorem Exercises Review Exercises 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 5. Some Discrete Probability Distributions 5.1 Introduction and Motivation 5.2 Binomial and Multinomial Distributions Exercises 5.3 Hypergeometric Distribution Exercises 5.4 Negative Binomial and Geometric Distributions 5.5 Poisson Distribution and the Poisson Process Exercises Review Exercises 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 6. Some Continuous Probability Distributions 6.1 Continuous Uniform Distribution 6.2 Normal Distribution 6.3 Areas under the Normal Curve 6.4 Applications of the Normal Distribution Exercises 6.5 Normal Approximation to the Binomial Exercises 6.6 Gamma and Exponential Distributions 6.7 Chi-Squared Distribution 6.8 Beta Distribution 6.9 Lognormal Distribution (Optional) 6.10 Weibull Distribution (Optional) Exercises Review Exercises 6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters 7. Functions of Random Variables (Optional) 7.1 Introduction 7.2 Transformations of Variables 7.3 Moments and Moment-Generating Functions Exercises 8. Sampling Distributions and More Graphical Tools 8.1 Random Sampling and Sampling Distributions 8.2 Some Important Statistics Exercises 8.3 Sampling Distributions 8.4 Sampling Distribution of Means and the Central Limit Theorem Exercises 8.5 Sampling Distribution of S2 8.6 t-Distribution 8.8 Quantile and Probability Plots Exercises Review Exercises 8.9 Potential Misconceptions and Hazards; Relationship to Mate

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Book SynopsisThis book demonstrates the importance of computer-generated statistical analyses in behavioral science research, particularly those using the R software environment. Statistical methods are being increasingly developed and refined by computer scientists, with expertise in writing efficient and elegant computer code. Unfortunately, many researchers lack this programming background, leaving them to accept on faith the black-box output that emerges from the sophisticated statistical models they frequently use. Building on the author’s previous volume, Linear Models in Matrix Form, this text bridges the gap between computer science and research application, providing easy-to-follow computer code for many statistical analyses using the R software environment. The text opens with a foundational section on linear algebra, then covers a variety of advanced topics, including robust regression, model selection based on bias and efficiency, nonlinear models and optimization routines, generalized linear models, and survival and time-series analysis. Each section concludes with a presentation of the computer code used to illuminate the analysis, as well as pointers to packages in R that can be used for similar analyses and nonstandard cases. The accessible code and breadth of topics make this book an ideal tool for graduate students or researchers in the behavioral sciences who are interested in performing advanced statistical analyses without having a sophisticated background in computer science and mathematics.Table of ContentsLinear Equations.- Least Squares Estimation.- Linear Regression.- Eigen Decomposition.- Singular Value Decomposition.- Generalized Least Squares Estimation.- Robust Regression.- Model Selection and Biased Estimation.- Cubic Splines and Additive Models.- Nonlinear Regression and Optimization.- Generalized Linear Models.- Survival Analysis.- Time Series Analysis.- Mixed Effects Models.

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Book SynopsisMaking good decisions under conditions of uncertainty - which is the norm - requires a sound appreciation of the way random chance works. As analysis and modelling of most aspects of the world, and all measurement, are necessarily imprecise and involve uncertainties of varying degrees, the understanding and management of probabilities is central to much work in the sciences and economics. In this Very Short Introduction, John Haigh introduces the ideas of probability and different philosophical approaches to probability, and gives a brief account of the history of development of probability theory, from Galileo and Pascal to Bayes, Laplace, Poisson, and Markov. He describes the basic probability distributions, and goes on to discuss a wide range of applications in science, economics, and a variety of other contexts such as games and betting. He concludes with an intriguing discussion of coincidences and some curious paradoxes. ABOUT THE SERIES: The Very Short Introductions series from Oxford University Press contains hundreds of titles in almost every subject area. These pocket-sized books are the perfect way to get ahead in a new subject quickly. Our expert authors combine facts, analysis, perspective, new ideas, and enthusiasm to make interesting and challenging topics highly readable.Trade ReviewAn excellent and provocative introduction to a fascinating and underappreciated subject. * Mathematical Gazette *Table of Contents1. Fundamentals ; 2. The workings of probability ; 3. Historical sketch ; 4. Chance experiments ; 5. Making sense of probabilities ; 6. Games people play ; 7. Applications in science and operations research ; 8. Other applications ; 9. Curiosities and dilemmas ; Appendix - Answers to questions posed

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Book SynopsisThis self-contained textbook introduces all the relevant mathematical concepts needed to understand and use machine learning methods, with a minimum of prerequisites. Topics include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics.Trade Review'This book provides great coverage of all the basic mathematical concepts for machine learning. I'm looking forward to sharing it with students, colleagues, and anyone interested in building a solid understanding of the fundamentals.' Joelle Pineau, McGill University, Montreal'The field of machine learning has grown dramatically in recent years, with an increasingly impressive spectrum of successful applications. This comprehensive text covers the key mathematical concepts that underpin modern machine learning, with a focus on linear algebra, calculus, and probability theory. It will prove valuable both as a tutorial for newcomers to the field, and as a reference text for machine learning researchers and engineers.' Christopher Bishop, Microsoft Research Cambridge'This book provides a beautiful exposition of the mathematics underpinning modern machine learning. Highly recommended for anyone wanting a one-stop-shop to acquire a deep understanding of machine learning foundations.' Pieter Abbeel, University of California, Berkeley'Really successful are the numerous explanatory illustrations, which help to explain even difficult concepts in a catchy way. Each chapter concludes with many instructive exercises. An outstanding feature of this book is the additional material presented on the website …' Volker H. Schulz, SIAM ReviewTable of Contents1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.

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Book SynopsisEvery day, most of us will read or watch something in the news that is based on statistics in some way. Sometimes it''ll be obvious - ''X people develop cancer every year'' - and sometimes less obvious - ''How smartphones destroyed a generation''. Statistics are an immensely powerful tool for understanding the world, but in the wrong hands they can be dangerous.Introducing you to the common mistakes that journalists make and the tricks they sometimes deploy, HOW TO READ NUMBERS is a vital guide that will help you understand when and how to trust the numbers in the news - and, just as importantly, when not to.Trade ReviewA charming, practical and insightful guide. You might not even notice how much you're learning - you'll be too busy having fun -- TIM HARFORD, author of HOW TO MAKE THE WORLD ADD UPA vital plea to take statistics more seriously - the prose being as clear and elegant as the numbers -- SATHNAM SANGHERA, author of EMPIRELANDReading this book is strongly correlated with not looking stupid. Highly recommended -- HELEN LEWIS, author of Difficult WomenAn excellent guide to everyday statistics . . . the authors do a splendid job of stringing words together so smartly that even difficult concepts are explained and so understood with ease. [A] timely and lively book -- Manjit Kumar * THE TIMES *Wonderfully written - incredibly readable. It should be made compulsory reading for everyone before they leave school -- EVAN DAVISAn erudite, enlightening guide to the numbers we read in the news - and why they are so often wrong. The authors make sense of dense material and offer engrossing insights into sampling bias, statistical significance and the dangers of believing the casual language used in newspapers * INDEPENDENT *[A] fascinating, easy-to-read explanation of how to interpret numbers in the news . . . their enlightening book provides us with the tools to spot when we're being led astray -- Nick Rennison * DAILY MAIL *An absolute lifesaver . . . Breezy, easy to read, funny and loaded with useful information -- IAN DUNT, author of HOW TO BE A LIBERALA great combination of important and accessible -- MISHAL HUSAINBrilliant . . . part of the joy of How to Read Numbers is how light and fun it is. At the end of the process, you'll be better equipped to understand what it means when a glass of red wine can both increase and decrease your chances of getting cancer, how many portions of fruit and veg you need to eat each day, and any number of stories about numbers you might read or hear * THE BIG ISSUE *

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Book SynopsisWhether you’re a student or an adult looking to refresh your knowledge, Barron’s Painless Statistics provides review and practice in an easy, step-by-step format.An essential resource for: Virtual learning Homeschool Learning pods Supplementing classes/in-person learning Inside you’ll find: Clear examples for all topics, including data and distributions, basic probability, confidence intervals, bivariate statistics, and much more Diagrams, charts, and instructive math illustrations Painless tips, common pitfalls, and informative sidebars Math talk boxes that translate complex “math speak” into easy-to-understand language Brain Tickler quizzes throughout each chapter to test your progress

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Book SynopsisA refreshing, student-focused introduction to the use of statistics in the study of the biosciences. Emphasising why statistical techniques are essential tools for bioscientists, Biomeasurement removes the stigma attached to statistics by giving students the confidence to use key techniques for themselves.Trade ReviewReview from previous edition Biomeasurement does a wonderful job of keeping the biology the focus of analysis, and highlighting the fact that statistics is simply another useful tool to help biological understanding. * Dr Shane Richards, Biological and Biomedical Sciences, University of Durham, UK *This book represents the best I have seen for teaching undergraduate biologists statistics. * Dr Chris Venditti, Department of Biological Sciences, University of Hull, UK *It demystifies and clarifies topics that students can normally find confusing and challenging. It is a must for biologists! * Dr Maria G. Tuohy, School of Natural Sciences, National University of Ireland, Galway *This book was a blessing when I got it for my first year, and I'm still finding it helpful in my second year! It is so easy to navigate; you can read it cover to cover if you are very confused, or just dip into the topics you need. I would definitely recommend it to any students doing a bioscience degree which involves statistical elements. * Bethany Richmond, student at the University of Warwick *As a student with limited mathematical ability, new to statistics who believed I would not be able to pass this module, I read this book chapter by chapter prior to my weekly lectures and everything fell into place without a struggle. A 100% necessary purchase. A 100% necessary read. The book is appealing and very easy to navigate. I have tried to read other statistics books aimed at beginners but this was the only book which I clearly understood. * Julie Carter, student at Anglia Ruskin University *Table of Contents1: Why am I reading this book?2: Getting to grips with the basics3: Describing a single sample4: Inferring and estimating5: Choosing the right test and graph6: Overview of null hypothesis significance testing7: Tests on frequencies8: Tests of difference: two unrelated samples9: Tests of difference: two related samples10: Tests of difference: more than two samples11: Tests of relationship: regression12: Tests of relationship: correlation13: Introducing the generalized linear model: general linear model14: More on the generalized linear model: logistic and loglinear modelsAppendix I How to enter data into SPSSAppendix II Statistical tables of critical valuesAppendix III Summary guidance on reporting statistical resultsAppendix IV Statistics and experimental designRelated Titles

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Book SynopsisStochastic Modelling of Big Data in Finance provides a rigorous overview and exploration of stochastic modelling of big data in finance (BDF). The book describes various stochastic models, including multivariate models, to deal with big data in finance. This includes data in high-frequency and algorithmic trading, specifically in limit order books (LOB), and shows how those models can be applied to different datasets to describe the dynamics of LOB, and to figure out which model is the best with respect to a specific data set. The results of the book may be used to also solve acquisition, liquidation and market making problems, and other optimization problems in finance.Features Self-contained book suitable for graduate students and post-doctoral fellows in financial mathematics and data science, as well as for practitioners working in the financial industry who deal with big data All results are presented visually to aid in understanding oTable of Contents1. A Brief Introduction: Stochastic Modelling of Big Data in Finance. 1.1. Introduction. 1.2. Big Data in Finance: Limit Order Books. 1.3. Stochastic Modelling of Big Data in Finance: Limit Order Books (LOB). 1.4 Illustration and Justification of Our Method to Study Big Data in Finance. 1.5. Methodological Aspects of Using the Models. 1.6. Conclusion. I. Semi-Markovian Modelling of Big Data in Finance. 2. A Semi-Markovian Modelling of Big Data in Finance. 2.1. Introduction. 2.2. A Semi-Markovian Modeling of Limit Order Markets. 2.3. Main Probabilistic Results. 2.4. Diffusion Limit of the Price Process. 2.5. Numerical Results. 2.6. More Big Data. 2.7. Conclusion. 3. General Semi-Markovian Modelling of Big Data in Finance. 3.1. Introduction. 3.2. Reviewing the Assumptions with Our New Data Sets. 3.3. General Semi-Markov Model for the Limit Order Book with Two States. 3.4. General Semi-Markov Model for the Limit Order Book with arbitrary number of states. 3.5. Discussion on Price Spreads. 3.6. Conclusion. II. Modelling of Big Data in Finance with Hawkes Processes. 4. A Brief Introduction to Hawkes Processes. 4.1. Introduction. 4.2. Definition of Hawkes Processes (HPs). 4.3. Compound Hawkes Processes. 4.4. Limit Theorems for Hawkes Processes: LLN and FCLT. 4.5. Limit Theorems for Poisson Processes: LLN and FCLT. 4.6. Stylized Properties of Hawkes Process. 4.7. Conclusion. 5. Stochastic Modelling of Big Data in Finance with CHP. 5.1. Introduction. 5.2. Definitions of HP, CHP and RSCHP. 5.3. Diffusion Limits and LLNs for CHP and RSCHP in Limit Order Books. 5.4. Numerical Examples and Parameters Estimations. 5.5. Conclusion. 6. Stochastic Modelling of Big Data in Finance with GCHP. 6.1. A Brief Introduction and Literature Review. 6.2. Diffusion Limits and LLNs. 6.3. Empirical Results. 6.4. Conclusion. 7. Quantitative and Comparative Analyses of Big Data with GCHP. 7.1. Introduction. 7.2. Theoretical Analysis. 7.3. Application. 7.4. Hawkes Process and Models Calibrations. 7.5. Error Measurement. 7.6. Conclusion. III. Multivariate Modelling of Big Data in Finance. 8. Multivariate General Compound Hawkes Processes in BDF. 8.1. Introduction. 8.2. Hawkes Processes and Limit Theorems. 8.3. Multivariate General Compound Hawkes Processes (MGCHP) and Limit Theorems. 8.4. FCLT II for MGCHP: Deterministic Centralization. 8.5. Numerical Example. 8.6. Conclusion. 9. Multivariate General Compound Point Processes in BDF. 9.1. Introduction. 9.2. Definition of Multivariate General Compound Point Process (MGCPP). 9.3. LLNs and Diffusion Limits for MGCPP. 9.4. Diffusion Limit for the MGCPP: Deterministic Centralization. 9.5. Conclusion. IV. Appendix: Basics in Stochastic Processes

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Book SynopsisDespite its seemingly deterministic nature, the study of whole numbers, especially prime numbers, has many interactions with probability theory, the theory of random processes and events. This surprising connection was first discovered around 1920, but in recent years the links have become much deeper and better understood. Aimed at beginning graduate students, this textbook is the first to explain some of the most modern parts of the story. Such topics include the Chebychev bias, universality of the Riemann zeta function, exponential sums and the bewitching shapes known as Kloosterman paths. Emphasis is given throughout to probabilistic ideas in the arguments, not just the final statements, and the focus is on key examples over technicalities. The book develops probabilistic number theory from scratch, with short appendices summarizing the most important background results from number theory, analysis and probability, making it a readable and incisive introduction to this beautiful arTrade Review'an excellent resource for someone trying to enter the field of probabilistic number theory' Bookshelf by Notices of the American Mathematical Society'The book contains many exercises and three appendices presenting the material from analysis, probability and number theory that is used. Certainly the book is a good read for a mathematicians interested in the interaction between probability theory and number theory. The techniques used in the book appear quite advanced to us, so we would recommend the book for students at a graduate but not at an undergraduate level.' Jörg Neunhäuserer, Mathematical ReviewsTable of Contents1. Introduction; 2. Classical probabilistic number theory; 3. The distribution of values of the Riemann zeta function, I; 4. The distribution of values of the Riemann zeta function, II; 5. The Chebychev bias; 6. The shape of exponential sums; 7. Further topics; Appendix A. Analysis; Appendix B. Probability; Appendix C. Number theory; References; Index.

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Book SynopsisThis class-tested textbook will provide in-depth coverage of the fundamentals of machine learning, with an exploration of applications in information security. The book will cover malware detection, cryptography, and intrusion detection. The book will be relevant for students in machine learning and computer security courses.

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This book sheds new light on stochastic calculus, the branch of mathematics that is most widely applied in financial engineering and mathematical finance. The first book to introduce pathwise formulae for the stochastic integral, it provides a simple but rigorous treatment of the subject, including a range of advanced topics. The book discusses in-depth topics such as quadratic variation, Ito formula, and Emery topology. The authors briefly addresses continuous semi-martingales to obtain growth estimates and study solution of a stochastic differential equation (SDE) by using the technique of random time change. Later, by using Metivier–Pellaumail inequality, the solutions to SDEs driven by general semi-martingales are discussed. The connection of the theory with mathematical finance is briefly discussed and the book has extensive treatment on the representation of martingales as stochastic integrals and a second fundamental theorem of asset pricing. Intended for undergraduate- and beginning graduate-level students in the engineering and mathematics disciplines, the book is also an excellent reference resource for applied mathematicians and statisticians looking for a review of the topic.

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Book SynopsisStatistics for Engineers and Scientists stands out for its clear presentation of applied statistics. The book takes a practical approach to methods of statistical modeling and data analysis that are most often used in scientific work. This edition features a unique approach highlighted by an engaging writing style that explains difficult concepts clearly, along with the use of contemporary real world data sets, to help motivate students and show direct connections to industry and research. While focusing on practical applications of statistics, the text makes extensive use of examples to motivate fundamental concepts and to develop intuition.The new edition of Statistics for Engineers and Scientists is also available in McGraw Hill Connect, featuring SmartBook 2.0, Adaptive Learning Assignments, and more!Table of ContentsChapter 1: Sampling and Descriptive StatisticsChapter 2: ProbabilityChapter 3: Propagation of ErrorChapter 4: Commonly Used DistributionsChapter 5: Confidence IntervalsChapter 6: Hypothesis TestingChapter 7: Correlation and Simple Linear RegressionChapter 8: Multiple RegressionChapter 9: Factorial ExperimentsChapter 10: Statistical Quality Control

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Book SynopsisAlan C. Acock's A Gentle Introduction to Stata, Revised Sixth Edition is aimed at new Stata users who want to become proficient in Stata. After reading this introductory text, new users will be able to not only use Stata well but also learn new aspects of Stata.Acock assumes that the user is not familiar with any statistical software. This assumption of a blank slate is central to the structure and contents of the book. Acock starts with the basics; for example, the part of the book that deals with data management begins with a careful and detailed example of turning survey data on paper into a Stata-ready dataset. When explaining how to go about basic exploratory statistical procedures, Acock includes notes that will help the reader develop good work habits. This mixture of explaining good Stata habits and explaining good statistical habits continues throughout the book.Acock is quite careful to teach the reader all aspects of using Stata. He covers data management, good work habits (including the use of basic do-files), basic exploratory statistics (including graphical displays), and analyses using the standard array of basic statistical tools (correlation, linear and logistic regression, and parametric and nonparametric tests of location and dispersion). He also successfully introduces some more advanced topics such as multiple imputation and multilevel modeling in a very approachable manner. Acock teaches Stata commands by using the menus and dialog boxes while still stressing the value of Stata commands and do-files. In this way, he ensures that all types of users can build good work habits. Each chapter has exercises that the motivated reader can use to reinforce the material.The tone of the book is friendly and conversational without ever being glib or condescending. Important asides and notes about terminology are set off in boxes, which makes the text easy to read without any convoluted twists or forward referencing. Rather than splitting topics by their Stata implementation, Acock arranges the topics as they would appear in a basic statistics textbook; graphics and postestimation are woven into the material naturally. Real datasets, such as the General Social Surveys from 2002, 2006, and 2016, are used throughout the book.The focus of the book is especially helpful for those in the behavioral and social sciences because the presentation of basic statistical modeling is supplemented with discussions of effect sizes and standardized coefficients. Various selection criteria, such as semipartial correlations, are discussed for model selection. Acock also covers a variety of commands available for evaluating reliability and validity of measurements.The revised sixth edition is fully up to date for Stata 17, including updated discussion and images of Stata's interface and modern command syntax. In addition, examples include new features such as the table command and collect suite for creating and exporting customized tables as well as the option for creating graphs with transparency.Table of ContentsGetting started Entering data Preparing data for analysis Working with commands, do-files, and results Descriptive statistics and graphs for one variable Statistics and graphs for two categorical variables Tests for one or two means Bivariate correlation and regression Analysis of variance Multiple regression Logistic regression Measurement, reliability, and validity Structural equation and generalized structural equation modeling Working with missing values—multiple imputation An introduction to multilevel analysis Item response theory (IRT) What’s next? Glossary of acronyms Glossary of mathematical and statistical symbols References

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Book SynopsisTable of ContentsIntroduction 1 About This Book 1 About the Fourth Edition 2 Foolish Assumptions 2 Icons Used in This Book 3 Beyond the Book 3 Where to Go from Here 3 Part 1: Getting Started with SPSS 5 Chapter 1: Introducing SPSS 7 SPSS’s Job, Our Job, and Your Job 7 SPSS’s job 8 Our job 8 Your job 9 Garbage In, Garbage Out: Recognizing the Importance of Good Data 9 Talking to SPSS: Can You Hear Me Now? 12 The graphical user interface 12 Syntax 12 Programmability 13 How SPSS works 13 Getting Help When You Need It 15 Chapter 2: Finding the Best SPSS for You 17 Campus Editions 19 Subscription Plans 20 Commercial Editions 22 What’s New in Version 27 24 Chapter 3: Getting to Know SPSS by Running a Simple Session 25 Opening a Dataset 25 Running an Analysis 27 Interpreting Results 30 Creating Graphs 33 Investigating Data 37 Part 2: Getting Data into and out of SPSS 43 Chapter 4: Understanding SPSS Data: Defining Metadata 45 Entering Variable Definitions on the Variable View Tab 46 Name 47 Type 47 Width 51 Decimals 52 Label 52 Values 53 Missing 54 Columns 55 Align 55 Measure 55 Role 56 Entering and Viewing Data Items on the Data View Tab 56 Chapter 5: Opening Data Files 59 Getting Acquainted with the SPSS File Format 59 Reading Simple Data from a Text File 60 Transferring Data from Another Program 65 Reading an Excel file 67 Reading from an unknown program type 68 Saving Data 69 Chapter 6: Getting Data and Results from SPSS 71 Exporting Data to Another Program 71 Navigating SPSS Statistics Viewer 72 Moving SPSS Output to Other Applications 74 Copying and pasting output 74 Exporting output 75 Printing Data 80 Chapter 7: More about Defining Your Data 81 Working with Dates and Times 82 Using the Date and Time Wizard 84 Creating and Using a Multiple-Response Set 86 Copying Data Properties 90 Part 3: Messing with Data in SPSS 95 Chapter 8: The Transform and Data Menus 97 Sorting Cases 97 Selecting the Data You Want to Look At 100 Splitting Data for Easier Analysis 103 Counting Case Occurrences 104 Recoding Variables 107 Recoding into different variables 107 Automatic recoding 110 Binning 113 Optimal Binning 117 Chapter 9: Computing New Variables 119 Calculating a New Variable with a Formula 120 Calculating a New Variable with a Condition 122 Using System Variables 124 Contrasting $Sysmis with SYSMIS 125 Understanding Missing Data in Formulas 127 Efficiently Calculating with Multiple Formulas 129 Chapter 10: Some Useful Functions 133 The LENGTH Function 134 The ANY Function 137 The MEAN Function and Missing Data 139 RND, TRUNC, and MOD 141 Logicals, the MISSING Function, and the NOT Function 143 String Parsing and Nesting Functions 144 Calculating Lags 146 Chapter 11: Combining Files 147 Merging Files by Adding Cases 147 Merging Files by Adding Variables 152 Part 4: Graphing Data 161 Chapter 12: On the Menu: Graphing Choices in SPSS 163 Building Graphs the Chart Builder Way 164 The Gallery tab 164 The Basic Elements tab 168 The Groups/Point ID tab 169 The Titles/Footnotes tab 170 The Element Properties tab 170 The Chart Appearance tab 176 The Options tab 177 Building Graphs with Graphboard Template Chooser 178 Chapter 13: Building Graphs Using Chart Builder 183 Simple Graphs 184 Simple scatterplots 184 Simple dot plots 185 Simple bar graphs 186 Simple error bars 187 Simple histograms 189 Population pyramids 191 Stacked area charts 192 Fancy Graphs 194 Charts with multiple lines 194 Colored scatterplots 196 Scatterplot matrices 198 Stacked bar charts 199 Pie charts 200 Clustered range bar graphs 202 Differenced area graphs 202 Dual-axis graph 204 Fancy Maps Using Graphboard Template Chooser 205 Heat map 206 Choropleth of values 206 Coordinates on a reference map 209 Part 5: Analyzing Data 211 Chapter 14: Using Descriptive Statistics 213 Looking at Levels of Measurement 213 Defining the four levels of measurement 214 Defining summary statistics 215 Focusing on Frequencies for Categorical Variables 217 Understanding Frequencies for Continuous Variables 221 Summarizing Continuous Variables with the Descriptives Procedure 224 Chapter 15: Knowing When Not to Trust Your Data 227 Sampling 227 Understanding Sample Size 228 Testing Hypotheses 229 Calculating Confidence Intervals 231 Conducting In-Depth Hypothesis Testing 232 Using the Normal Distribution 235 Working with Z-Scores 236 Chapter 16: Testing One Group 239 Conducting Inferential Tests 239 Running the Chi-Square Goodness of Fit Test 240 Running the One-Sample T-Test Procedure 246 Chapter 17: Showing Relationships between Categorical Variables 251 Running the Crosstabs Procedure 252 Running the Chi-Square Test of Independence 256 Comparing Column Proportions 260 Adding Control Variables 261 Creating a Clustered Bar Chart 264 Chapter 18: Showing Relationships between Continuous Dependent and Categorical Independent Variables 267 Conducting Inferential Tests 268 Using the Compare Means Dialog 268 Running the Independent-Samples T-Test Procedure 269 Comparing the Means Graphically 275 Running the Summary Independent-Samples T-Test Procedure 277 Running the Paired-Samples T-Test Procedure 280 Chapter 19: Showing Relationships between Continuous Variables 285 Viewing Relationships 286 Running the Bivariate Procedure 288 Running the Simple Linear Regression Procedure 292 Part 6: Getting More Advanced with Analyzing Data 301 Chapter 20: Doing More Advanced Analyses 303 Running the One-Way ANOVA Procedure 303 Conducting Post Hoc Tests 311 Comparing Means Graphically 314 Running the Multiple Linear Regression Procedure 315 Viewing Relationships 325 Chapter 21: What Is Normal Anyway? 327 Understanding Nonparametric Tests 328 Understanding Distributions 328 Running a Nonparametric Independent Samples Test 331 Running a Nonparametric Related Samples Test 338 Chapter 22: When to Do What 345 Determining Which Statistical Test to Perform 346 Using Advanced Techniques 350 Part 7: Making SPSS Your Own 351 Chapter 23: Changing Settings 353 General Options 354 Language Options 356 Viewer Options 357 Data Options 358 Currency Options 360 Output Options 361 Chart Options 362 Pivot Tables Options 364 File Locations Options 365 Scripts Options 366 Multiple Imputations Options 368 Syntax Editor Options 369 Privacy Options 370 Chapter 24: Editing Charts and Chart Templates 371 Changing and Editing Axes 372 Changing the axis range 372 Scaling the axis range 373 Changing Style: Lines and Symbols 376 Editing chart lines 376 Editing data points 378 Applying Templates 380 Chapter 25: Editing Tables 383 Working with TableLooks 384 Style Output 387 Pivoting Trays 390 Part 8: Programming SPSS with Command Syntax 393 Chapter 26: Getting Acquainted with Syntax 395 Pasting 396 Performing a Series of Related Compute Statements 399 Labeling 400 Repeatedly Generating the Same Report 400 Chapter 27: Adding Syntax to Your Toolkit 403 Your Wish Is My Command 404 Understanding Keywords 405 Declaring Data 406 Commenting Your Way to Clarity 407 Running Your Code 408 Controlling Flow and Executing Conditionals 410 IF 410 DO IF 411 SELECT IF 412 Part 9: The Part of Tens 413 Chapter 28: Ten (or So) Modules You Can Add to SPSS 415 The Advanced Statistics Module 416 The Custom Tables Module 416 The Regression Module 418 The Categories Module 418 The Data Preparation Module 419 The Decision Trees Module 419 The Forecasting Module 420 The Missing Values Module 421 The Bootstrapping Module 421 The Complex Samples Module 422 The Conjoint Module 422 The Direct Marketing Module 422 The Exact Tests Module 423 The Neural Networks Module 424 Chapter 29: Ten Useful SPSS Resources 425 Supporting Websites for This Book 425 LinkedIn and LinkedIn Groups 426 IBM SPSS Statistics Certification 427 IBM Data Science Community 427 SPSSX-L 427 Online Videos 428 Twitter 429 Live Instruction 430 Asynchronous Instruction and Tutorials 431 SPSS Statistics for Data Analysis and Visualization 432 Chapter 30: Ten SPSS Statistics Gotchas 433 Failing to Declare Level of Measurement 433 Conflating String Values with Labels 434 Failing to Declare Missing Data 435 Failing to Find Add-on Modules and Plug-Ins 435 Failing to Meet Statistical and Software Assumptions 437 Confusing Pasting Syntax with Copy and Paste 438 Thinking You Create Variables in SPSS as You Do in Excel 438 Getting Confused by Listwise Deletion 439 Losing Track of Your Active Dataset 440 Forgetting to Turn Off Select and Split and Weight 441 Index 443

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Book SynopsisTHE CLASSIC GUIDE, NOW FULLY REVISED AND UPDATEDWhy do we need Statistics?What do terms like ''dispersion'', ''correlation'', ''normal distribution'' and ''significance'' actually mean?How can I learn how to think statistically?This bestselling introduction is for anyone who wants to know how statistics works and the powerful ideas behind it. Teaching through words and diagrams instead of requiring you to do complex calculations, it assumes no expert knowledge and makes the subject accessible even to readers who consider themselves non-mathematical. This clear and informative ''tutorial in print'' includes questions for you to respond to in the light of what you have read so far, ensuring your developing ability to think statistically.

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Book SynopsisA guide to using statistics properly in social science.

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Book SynopsisPreface Statistics is seldom the most eagerly anticipated course of a business student. Our goal in writing this casebook and the companion volume (Business Analysis Using Regression) was to change that impression by showing how statistics yields insights and answers interesting business questions.Trade Review"The manuscripts are divided into "classes" which tackle various statistical concepts. Material becomes increasingly complex as the data in later sections exhibit multiple deviations from ideal conditions. The casebooks use a combination of explanatory text and software output to guide the student. Almost no mathematics appears in these volumes; the authors make traditional texts available to their students who are curious about the technical details...I anticipate students will wholeheartedly endorse the FSW casebooks. The material is easy to digest as, for instance, the authors cleverly interweave probability, the standard error of the mean, and control charts. The casebooks effectively relay the message that statistics is relevant and doable. Ideally, that is the message that should be sent in all introductory business statistics courses." Marlene Smith, University of Colorado-DenverTable of ContentsOverview and Foundations * Statistical Summaries of Data * Sources of Variation * Standard Error * Confidence Intervals * Sampling * Making Decisions * Designing Tests for Better Comparisons * Confounding Effects in Tests: A Case Study * Covariance, Correlation, and Portfolios * A Preview of Regression

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Book SynopsisWhy is it that incredibly unlikely phenomena actually happen quite regularly and why should we, in fact, expect such things to happen? Here, in this highly original book - aimed squarely at anyone with an interest in coincidences, probability or gambling - eminent statistician David Hand answers this question by weaving together various strands of probability into a unified explanation, which he calls the improbability principle.This is a book that will appeal not only to those who love stories about startling coincidences and extraordinarily rare events, but also to those who are interested in how a single bold idea links areas as diverse as gambling, the weather, airline disasters and creative writing as well as the origin of life and even the universe. The Improbability Principle will change your perspective on how the world works and tell you what the Bible code and Shakespeare have in common, how to win the lottery, why Apple''s song shuffling was made less random to seem more random. Oh and why lightning does in fact strike twice...Trade ReviewA hugely entertaining eye-opener about how misuse of statistics can skew our view of the world * Daily Mail *Lively and lucid . . . an intensely useful (as well as a remarkably entertaining) book . . . * Salon *In my experience, it is very rare to find a book that is both erudite and entertaining. Yet The Improbability Principle is such a book. Surely this cannot be due to chance alone! -- Hal Varian, Google’s Chief EconomistAn elegant, astoundingly clear and enjoyable combination of subtle statistical thinking and real-world events. -- Andrew Dilnot, co-author of 'The Numbers Game'As someone who happened to meet his future wife on a plane, on an airline he rarely used, I wholeheartedly endorse David Hand’s fascinating guide to improbability, a subject which affects the lives of all, yet until now has lacked a coherent exposition of its underlying principles. -- Gordon Woo, catastrophist at Risk Management Solutions

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Book SynopsisA First Course in Ergodic Theory provides readers with an introductory course in Ergodic Theory. This textbook has been developed from the authorsâ own notes on the subject, which they have been teaching since the 1990s. Over the years they have added topics, theorems, examples and explanations from various sources. The result is a book that is easy to teach from and easy to learn from â designed to require only minimal prerequisites.Features Suitable for readers with only a basic knowledge of measure theory, some topology and a very basic knowledge of functional analysis Perfect as the primary textbook for a course in Ergodic Theory Examples are described and are studied in detail when new properties are presented. Trade Review"A First Course in Ergodic Theory by Dajani and Kalle provides not only a crystal clear introduction to the core of ergodic theory, but also goes down paths previously accessible only through the research literature. The book covers ergodic theorems, invariant measures, entropy and the variational principle. But it also covers piecewise monotone interval maps, Perron-Frobenius operators, natural extensions, and the useful lemma of Knopp. Another theme is the theory of conservative nonsingular and infinite measure preserving transformations. All of this is illustrated via numerous examples from (not necessarily regular) continued fractions and other number expansions, the authors’ specialty. Throughout the book, the proofs patiently explain details often ignored. An excellent appendix provides a reference to needed results from topology, measure theory, probability and functional analysis."– E. Arthur (Robbie) Robinson, Jr., Professor of Mathematics at George Washington University and co-author of The Mathematics of Politics"This textbook is a delightful introduction to Ergodic Theory. It starts at a basic level, giving intuitive explanations and motivations, and concludes with more advanced topics such as variational principle and infinite ergodic theory. The style is very crisp, and many of the results are proved. Examples which are primarily taken from number theory run as a red thread through the manuscript. This makes this textbook quite different from other classic textbooks on the subject. It’s very easy to build an advanced UG or a postgraduate lecture course around this material."– Sebastian van Strien, Imperial College LondonTable of ContentsPreface. Author Bios. 1. Measure preservingness and basic examples. 1.1. What is Ergodic Theory. 1.2. Measure Preserving Transformations. 1.3. Basic Examples. 2. Recurrence and Ergodicity. 2.1. Recurrence. 2.2. Ergodicity. 2.3. Examples of Ergodic Transformations. 3. The Pointwise Ergodic Theorem and its consequences. 3.2. Normal Numbers. 3.3. Characterization of Irreducible Markov Chains. 3.4. Mixing. 4. More Ergodic Theorem. The mean Ergodic Theorem. 4.2. The Hurewicz Erogdic Theorem. 5. Measure Preserving Isomorphisms. 5.2. Factor Maps. 5.3. Natural Extensions. 6. The Perron–Frobenius Operator. 6.1. Absolutely Continuous Invariants Measures. 6.2. Exactness. Densities for Piecewise Monotnoe Interval Maps. 7. Invariant Measures for Continuous Transformations. 7.1. Existence. 7.2. Unique Ergodicity and Inform Distributions. 7.3. Some Topological Dynamics. 8. Continued Fractions. 8.1. Basic Properties of Regular Continue Fractions. 8.2. Ergodic Properties of Gauss Map. 8.3. Natural Extension and the Doeblin–Lenstra Conjecture. 8.4. Other Continue Fraction Transformation. 9. Entropy. 9.1. Randomness and Information. 9.2. Definitions and Properties. Calculation of Entropy and Examples. 9.4. The Shannon–McMillan–Breiman Theorem. 9.5. Lochs’ Theorem. 10. The Variational Principle. 10.1 Topological Entropy. 10.2. Main Theorem. 10.3. Measures of Maximal Entropy. 11. Infinite Ergodic Theory. 11.1 Examples of Infinite Measure Dynamical Systems. 11.2. Conservative and Dissipative Part. 11.3. Induced Systems. 11.4. Jump Transformations. 11.5. Ergodic Theorem for Infinite Measure Systems. 12. Appendix. 12.1. Topology. 12.2. Measure Theory. 12.3 Lebesgue Spaces. 12.4. Lebesgue Integration and Convergence Results. 12.5. Hilbert’s Spaces. 12.6. Borel Measures on Compact Metric Spaces. 12.7. Functions of Bounded Variation. Bibliography. Index.

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Book SynopsisThis textbook shows how to bring theoretical concepts from finance and econometrics to the data. Focusing on coding and data analysis with Python, we show how to conduct research in empirical finance from scratch. We start by introducing the concepts of tidy data and coding principles using pandas, numpy, and plotnine. Code is provided to prepare common open-source and proprietary financial data sources (CRSP, Compustat, Mergent FISD, TRACE) and organize them in a database. We reuse these data in all the subsequent chapters, which we keep as self-contained as possible. The empirical applications range from key concepts of empirical asset pricing (beta estimation, portfolio sorts, performance analysis, Fama-French factors) to modeling and machine learning applications (fixed effects estimation, clustering standard errors, difference-in-difference estimators, ridge regression, Lasso, Elastic net, random forests, neural networks) and portfolio optimization techniques.Key Feature

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

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Book SynopsisThis series has been developed specifically for the Cambridge International AS & A Level Mathematics (9709) syllabus to be examined from 2020. Cambridge International AS & A Level Mathematics: Probability & Statistics 2 matches the corresponding unit of the syllabus, with a clear and logical progression through. It contains materials on topics such as hypothesis testing, Poisson distribution, linear combinations and continuous random variables, and sampling. This coursebook contains a variety of features including recap sections for students to check their prior knowledge, detailed explanations and worked examples, end-of-chapter and cross-topic review exercises and ''Explore'' tasks to encourage deeper thinking around mathematical concepts. Answers to coursebook questions are at the back of the book.Table of Contents1. Hypothesis testing; 2. Poisson distribution; 3. Linear combinations of random variables; Cross-topic revision exercise 1; 4. Continuous random variables; 5. Sampling; 6. Interpretation of sample data; Cross-topic revision exercise 2; Practice paper; Answers; Glossary; Index.

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Book SynopsisDo you want to use R to tell stories? This book was written for youwhether you already know some R or have never coded before.Most R texts focus only on programming or statistical theory. Practical R for Mass Communication and Journalism gives you ideas, tools, and techniques for incorporating data and visualizations into your narratives.You'll see step by step how to: Analyze airport flight delays, restaurant inspections, and election results Map bank locations, median incomes, and new voting districts Compare campaign contributions to final election results Extract data from PDFs Whip messy data into shape for analysis Scrape data from a website Create graphics ranging from simple, static charts to interactive visualizations for the Web If you work or plan to work in a neTrade Review"Practical R for Mass Communication and Journalism looks to me like a fabulous resource for those folks who always wanted to learn some more R but were afraid to ask. Definitely recommended." ~Carl Howe, Director of Education, RStudio"The book can provide a good starting point into working with R. It covers a lot of perspectives that are expected in newsrooms all over the world, especially working with geospatial data. It also provides a lot of good examples and interesting additional resources. The packages used are also mainly part of the standard corpus of R-packages." ~Benedict Witzenberger, Süddeutsche Zeitung"I am the data editor of a mid-sized newsroom. I have long wished for an Intro to R book that was geared toward journalists, not data scientists. I’ve found that fellow journalists are much more likely to pick up on the intricacies of a computing language like R when they encounter it through a relatable example, like visualizing Election Night votes or analyzing a city council budget. Additionally, there are some R functions that simply aren’t useful for the quantitative needs of most journalists. This is what I appreciated the most about the book – its practical nature (the title doesn’t lie!) Machlis focuses on the concepts that data journalists most frequently encounter and spends little to no time on those they don’t…I also appreciated Chapter 17, "An R Project from Start to Finish." This chapter is exactly why I’ve wanted a journalism-specific Intro to R project that I can recommend to my colleagues" ~Ryann Jones, Deputy Editor, Data at ProPublica"I like the book. It’s conversationally written, it walks you through common problems in data journalism and for the most part uses the most common libraries to analyze and visualize data…The book’s instructional approach is the real value – it seems aimed at an audience that needs a narrative in order to understand code and analysis. Conveniently, that pretty well describes journalism students and working professionals…I would recommend publication. It advances the field of data journalism and presents a solid text for instructors or practitioners who are interested in R for analysis." ~Matthew Waite"I NEED THIS BOOK. I may adopt it as a textbook." ~Alberto Cairo, University of Miami"I’m reading this book now and it is terrific. Highly recommended for anyone interested in learning R. I will be using this book in my Data Analysis for Journalists class in the spring." ~Rob Wells, University of Arkansas"Sharon Machlis' 'Practical R for Mass Communication and Journalism' is based on the author's workshops for journalists. This book dives straight into doing the kinds of things a busy reporter or news analyst needs to do to meet a 5:00 pm deadline: data cleaning, presentation-quality graphics, and maps take precedence over control flow or the niceties of variable scope. I particularly enjoyed the way each chapter starts with a realistic project and works through what's needed to build it. People who've never programmed before will be a little intimidated by how many packages they need to download if they try to work through the material on their own, but the instructions are clear, and the author's enthusiasm for her material shines through in every example." ~Greg Wilson, RStudio"Practical R for Mass Communication and Journalism looks to me like a fabulous resource for those folks who always wanted to learn some more R but were afraid to ask. Definitely recommended." ~Carl Howe, Director of Education, RStudio"The book can provide a good starting point into working with R. It covers a lot of perspectives that are expected in newsrooms all over the world, especially working with geospatial data. It also provides a lot of good examples and interesting additional resources. The packages used are also mainly part of the standard corpus of R-packages." ~Benedict Witzenberger, Süddeutsche Zeitung"I am the data editor of a mid-sized newsroom. I have long wished for an Intro to R book that was geared toward journalists, not data scientists. I’ve found that fellow journalists are much more likely to pick up on the intricacies of a computing language like R when they encounter it through a relatable example, like visualizing Election Night votes or analyzing a city council budget. Additionally, there are some R functions that simply aren’t useful for the quantitative needs of most journalists. This is what I appreciated the most about the book – its practical nature (the title doesn’t lie!) Machlis focuses on the concepts that data journalists most frequently encounter and spends little to no time on those they don’t…I also appreciated Chapter 17, "An R Project from Start to Finish." This chapter is exactly why I’ve wanted a journalism-specific Intro to R project that I can recommend to my colleagues" ~Ryann Jones, Deputy Editor, Data at ProPublica"I like the book. It’s conversationally written, it walks you through common problems in data journalism and for the most part uses the most common libraries to analyze and visualize data…The book’s instructional approach is the real value – it seems aimed at an audience that needs a narrative in order to understand code and analysis. Conveniently, that pretty well describes journalism students and working professionals…I would recommend publication. It advances the field of data journalism and presents a solid text for instructors or practitioners who are interested in R for analysis." ~Matthew Waite"I NEED THIS BOOK. I may adopt it as a textbook." ~Alberto Cairo, University of Miami"I’m reading this book now and it is terrific. Highly recommended for anyone interested in learning R. I will be using this book in my Data Analysis for Journalists class in the spring." ~Rob Wells, University of Arkansas"Sharon Machlis' 'Practical R for Mass Communication and Journalism' is based on the author's workshops for journalists. This book dives straight into doing the kinds of things a busy reporter or news analyst needs to do to meet a 5:00 pm deadline: data cleaning, presentation-quality graphics, and maps take precedence over control flow or the niceties of variable scope. I particularly enjoyed the way each chapter starts with a realistic project and works through what's needed to build it. People who've never programmed before will be a little intimidated by how many packages they need to download if they try to work through the material on their own, but the instructions are clear, and the author's enthusiasm for her material shines through in every example." ~Greg Wilson, RStudioTable of ContentsIntroduction. Get Started With R in a Few Easy Steps. See How Much You Can Do in a Few Lines of Code. Import data into R. Project: Snowfall data; Skill: Basic data exploration. Project: Raw Snowfall data; Skill: Reshape data. Project: Airport Delays by Airline; Skill: Analyze data by groups. Map Median: Household Income; Skills: Simple mapping, saving graphics. Population Density and Election Results: Is There a Relationship?; Skills: Joining tables, correlations, basic linear regression. Easily Reproducible Reports With R Markdown; Skills: Generate a Word doc or HTML file from an R script.

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