Statistics Explained

Perry R. Hinton

Routledge | 2014 - 3ª edição | 377 páginas | rar - pdf | Mb

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Statistics Explained is an accessible introduction to statistical concepts and ideas. It makes few assumptions about the reader’s statistical knowledge, carefully explaining each step of the analysis and the logic behind it. The book:

• provides a clear explanation of statistical analysis and the key statistical tests employed in analysing research data
• gives accessible explanations of how and why statistical tests are used
• includes a wide range of practical, easy-to-understand worked examples

Building on the international success of earlier editions, this fully updated revision includes developments in statistical analysis, with new sections explaining concepts such as bootstrapping and structural equation modelling. A new chapter - ‘Samples and Statistical Inference’ - explains how data can be analysed in detail to examine its suitability for certain statistical tests.

The friendly and straightforward style of the text makes it accessible to all those new to statistics, as well as more experienced students requiring a concise guide. It is suitable for students and new researchers in disciplines including Psychology, Education, Sociology, Sports Science, Nursing, Communication, and Media and Business Studies.

Presented in full colour and with an updated, reader-friendly layout, this new edition also comes with a companion website featuring supplementary resources for students. Unobtrusive cross-referencing makes it the ideal companion to Perry R. Hinton’s SPSS Explained, also published by Routledge.

Perry R. Hinton has many years of experience in teaching statistics to students from a wide range of disciplines and his understanding of the problems students face forms the basis of this book.

The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day

David J. Hand

Scientific American | 2014 | 288 páginas | rar - epub | 376 kb

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In The Improbability Principle, the renowned statistician David J. Hand argues that extraordinarily rare events are anything but. In fact, they’re commonplace. Not only that, we should all expect to experience a miracle roughly once every month.
But Hand is no believer in superstitions, prophecies, or the paranormal. His definition of “miracle” is thoroughly rational. No mystical or supernatural explanation is necessary to understand why someone is lucky enough to win the lottery twice, or is destined to be hit by lightning three times and still survive. All we need, Hand argues, is a firm grounding in a powerful set of laws: the laws of inevitability, of truly large numbers, of selection, of the probability lever, and of near enough.
Together, these constitute Hand’s groundbreaking Improbability Principle. And together, they explain why we should not be so surprised to bump into a friend in a foreign country, or to come across the same unfamiliar word four times in one day. Hand wrestles with seemingly less explicable questions as well: what the Bible and Shakespeare have in common, why financial crashes are par for the course, and why lightning does strike the same place (and the same person) twice. Along the way, he teaches us how to use the Improbability Principle in our own lives—including how to cash in at a casino and how to recognize when a medicine is truly effective.
An irresistible adventure into the laws behind “chance” moments and a trusty guide for understanding the world and universe we live in, The Improbability Principle will transform how you think about serendipity and luck, whether it’s in the world of business and finance or you’re merely sitting in your backyard, tossing a ball into the air and wondering where it will land.

Contents
Title Page
Dedication
Epigraph
Preface
1. The Mystery
2. A Capricious Universe
3. What Is Chance?
4. The Law of Inevitability
5. The Law of Truly Large Numbers
6. The Law of Selection
7. The Law of the Probability Lever
8. The Law of Near Enough
9. The Human Mind
10. Life, the Universe, and Everything
11. How to Use the Improbability Principle
Epilogue
Appendix A: Mind-Numbingly Large and Mind-Bogglingly Small
Appendix B: Rules of Chance
Notes
Index
Also by David J. Hand
A Note About the Author

Outro livro do mesmo autor:

Statistics : a very short introduction por D J Hand Idioma: Inglês   Editora: Oxford ; New York : Oxford University Press, 2008.

Social Statistics: Managing Data, Conducting Analyses, Presenting Results

Thomas J. Linneman

Routledge | 2014 - 2ª edição | 657 páginas | rar - pdf | 9,3 Mb

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Many fundamentally important decisions about our social life are a function of how well we understand and analyze DATA. This sounds so obvious but it is so misunderstood. Social statisticians struggle with this problem in their teaching constantly. This book and its approach is the ally and support of all instructors who want to accomplish this hugely important teaching goal.

This innovative text for undergraduate social statistics courses is, (as one satisfied instructor put it), a "breath of fresh air." It departs from convention by not covering some techniques and topics that have been in social stat textbooks for 30 years, but that are no longer used by social scientists today. It also includes techniques that conventional wisdom has previously thought to be the province of graduate level courses.

Linneman’s text is for those instructors looking for a thoroughly "modern" way to teach quantitative thinking, problem-solving, and statistical analysis to their students…an undergraduate social statistics course that recognizes the increasing ubiquity of analytical tools in our data-driven age and therefore the practical benefit of learning how to "do statistics," to "present results" effectively (to employers as well as instructors), and to "interpret" intelligently the quantitative arguments made by others.

BRIEF CONTENTS

Preface xxix

Acknowledgments xxxvii

Chapter 1: Life in a Data-Laden Age: Finding and Managing Datasets 1

Chapter 2: The Art of Visual Storytelling: Creating Accurate Tables and Graphs 46

Chapter 3: Summarizing Center and Diversity: Basic Descriptive Statistics 92

Chapter 4: Using Sample Crosstabs to Talk about Populations: The Chi-Square Test 141

Chapter 5: Using a Sample Mean or Proportion to Talk about a Population: Confidence Intervals 189

Chapter 6: Using Multiple Sample Means to Talk about Populations: t-Tests and ANOVA 231

Chapter 7: Give Me One Good Reason Why: Bivariate Correlation and Regression 264

Chapter 8: Using Sample Slopes to Talk about Populations: Inference and Regression 303

Chapter 9: It’s All Relative: Dichotomies as Independent Variables in Regression 326

Chapter 10: Above and Beyond: The Logic of Controlling and the Power of Nested Regression Models 348

Chapter 11: Some Slopes Are Bigger than Others: Calculating and Interpreting Beta Coefficients 384

Chapter 12: Different Slopes for Different Folks: Interaction Effects 402

Chapter 13: Explaining Dichotomous Outcomes: Logistic Regression 435

Chapter 14: Visualizing Causal Stories: Path Analysis 467

Chapter 15: Questioning the Greatness of Straightness: Nonlinear Relationships 493

Chapter 16: Problems and Prospects: Regression Diagnostics, Advanced Techniques, and Where to Go Now 532

Appendix A: Variables and Indexes from the Datasets Used in the End-of-Chapter Exercises A-1

Appendix B: 86 Articles That Use Statistics in Less Than Scary Ways B-1

Appendix C: Statistical Tables C-1

Appendix D: Answers to Selected End-of-Chapter Exercises D-1

Bibliography R-1

Glossary/Index I-1

Probability and Statistics: A Didactic Introduction

José I. Barragués, Adolfo Morais e Jenaro Guisasola

CRC Press | 2014 | páginas | rar - pdf | 6,35 Mb

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With contributions by leaders in the field, this book provides a comprehensive introduction to the foundations of probability and statistics. Each of the chapters covers a major topic and offers an intuitive view of the subject matter, methodologies, concepts, terms, and related applications. The book is suitable for use for entry level courses in first year university studies of Science and Engineering, higher level courses, postgraduate university studies and for the research community.

Contents
Preface vii
1. Descriptive Statistics 1
Nicholas Watier, Claude Lamontagne and Sylvain Chartier
2. Probability 38
José I. Barragués, Adolfo Morais and Jenaro Guisasola
3. Random Variables 124
Verônica Y. Kataoka, Irene M. Cazorla, Hugo Hernandez and
Claudia Borim da Silva
4. Sampling 176
Giovanni Boscaino and Ornella Giambalvo
5. Point Estimation and Statistical Intervals 210
Martin Griffiths
6. Tests of Hypotheses 252
Martin Griffiths
7. Analysis of Variance 293
David L. Trumpower and Sait Atas
8. Factor Analysis 330
Marta B. Quaglino and José A. Pagura
9. Discriminant Analysis 384
T. Ramayah, Joshua Ignatius, Jasmine Yeap Ai Leen and Lo May Chiun
10. Multiple Regression Analysis 416
María V. López, María C. Fabrizio and María C. Plencovich
Index 469
Color Plate Section 475

Karl Pearson: The Scientific Life in a Statistical Age

Theodore M. Porter

Princeton University Press | 2006 | 353 páginas | rar - pdf |2,8 Mb

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Karl Pearson, founder of modern statistics, came to this field by way of passionate early studies of philosophy and cultural history as well as ether physics and graphical geometry. His faith in science grew out of a deeply moral quest, reflected also in his socialism and his efforts to find a new basis for relations between men and women. This biography recounts Pearson's extraordinary intellectual adventure and sheds new light on the inner life of science.
Theodore Porter's intensely personal portrait of Pearson extends from religious crisis and sexual tensions to metaphysical and even mathematical anxieties. Pearson sought to reconcile reason with enthusiasm and to achieve the impersonal perspective of science without sacrificing complex individuality. Even as he longed to experience nature directly and intimately, he identified science with renunciation and positivistic detachment. Porter finds a turning point in Pearson's career, where his humanistic interests gave way to statistical ones, in his Grammar of Science(1892), in which he attempted to establish scientific method as the moral educational basis for a refashioned culture.
In this original and engaging book, a leading historian of modern science investigates the interior experience of one man's scientific life while placing it in a rich tapestry of social, political, and intellectual movements.

Contents
Preface and Acknowledgments vii
CHAPTER ONE Introduction: An Improbable Personage 1
CHAPTER TWO Lehrjahre of a Poetic Wrangler 13
CHAPTER THREE Apostle of Renunciation: A New Werther 43
CHAPTER FOUR Pearson’s Progress: A Nineteenth-Century Passion Play 69
CHAPTER FIVE Cultural Historian in a Political Age 91
CHAPTER SIX Intellectual Love and the Woman Question 125
CHAPTER SEVEN Ether Squirts and the Inaccessibility of Nature 178
CHAPTER EIGHT Scientific Education and Graphical Statistics 215
CHAPTER NINE The Statistical Reformation 249
CHAPTER TEN Epilogue: Composing a Life 297
Bibliography 315
Index 329

Outro livro do mesmo autor:

Trust in numbers : the pursuit of objectivity in science and public life por Theodore M Porter Idioma: Inglês   Editora: Princeton, N.J. : Princeton University Press, ©1995.

Naked Statistics: Stripping the Dread from the Data

Charles Wheelan


W. W. Norton & Company | 2014 | 304 páginas | epub | 1 Mb

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Once considered tedious, the field of statistics is rapidly evolving into a discipline Hal Varian, chief economist at Google, has actually called “sexy.” From batting averages and political polls to game shows and medical research, the real-world application of statistics continues to grow by leaps and bounds. How can we catch schools that cheat on standardized tests? How does Netflix know which movies you’ll like? What is causing the rising incidence of autism? As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.

For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis. He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.

And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game showLet’s Make a Deal—and you’ll come away with insights each time. With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.

Contents

Introduction: Why I hated calculus but love statistics

1 What’s the Point?

2 Descriptive Statistics: Who was the best baseball player of all time?

3 Deceptive Description: “He’s got a great personality!” and other true but grossly misleading statements

4 Correlation: How does Netflix know what movies I like?

5 Basic Probability: Don’t buy the extended warranty on your $99 printer

5½ The Monty Hall Problem

6 Problems with Probability: How overconfident math geeks nearly destroyed the global financial system

7 The Importance of Data: “Garbage in, garbage out”

8 The Central Limit Theorem: The Lebron James of statistics

9 Inference: Why my statistics professor thought I might have cheated

10 Polling: How we know that 64 percent of Americans support the death penalty (with a sampling error ± 3 percent)

11 Regression Analysis: The miracle elixir

12 Common Regression Mistakes: The mandatory warning label

13 Program Evaluation: Will going to Harvard change your life?

Conclusion: Five questions that statistics can help answer

Appendix: Statistical software

Notes

Acknowledgments

Statistics in Plain English

Timothy C Urdan

Routledge | 2010 - 3.ª edição  | 223 páginas | rar - epub | 1,24 Mb

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PDF | 2,6 Mb
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This inexpensive paperback provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used. This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication.

Lawrence Erlbaum | 2005 - 2ª edição | PDF | 199 páginas | 12,56 Mb

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4shared.com

Statistics in Plain English, 2/e provides a brief, simple overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly. It presents brief explanations of statistical concepts and techniques in simple, everyday language. Each self-contained chapter consists of three sections. The first describes the statistic, including how it is used and what information it provides. The second section reviews how it works, how to calculate the formula, the strengths and weaknesses of the technique, and the conditions needed for its use. The final section provides examples that use and interpret the statistic. A glossary of terms and symbols is also included.

Study Guide for Practical Statistics for Educators

 Ruth Ravid e Elizabeth Oyer

Rowman & Littlefield Publishers | 2011 - 4ª edição | páginas | rar -pdf | 680 kb

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The fourth edition of the Study Guide for Practical Statistics for Educators serves as a user-friendly and accessible way for students to better understand, review, and apply the concepts introduced in its companion textbook, Practical Statistics for Educators (Ravid, 2011). Since the first edition of this study guide came out in 1994, thousands of students in educational statistics courses and their professors have found it to be an excellent guide with clear and easy-to-follow instructions and examples. The study guide allows students to reinforce and test their knowledge of the concepts addressed in each chapter of the textbook. At the end of each chapter, the best answer for each exercise is given, along with an explanation for why the correct answer is better than the other choices. New in this edition are accompanying Excel exercises, so students may perform data analysis with this commonly-used software, using data available on the web-based portal that accompanies the guide.

Contents
Preface vii
1 An Overview of Educational Research 1
2 Basic Concepts in Statistics 9
3 Organizing and Graphing Data 15
4 Measure of Central Tendency 27
5 Measures of Variability 33
6 The Normal Curve and Standard Scores 39
7 Interpreting Test Scores 45
8 Correlations 49
9 Prediction and Regression 57
10 t Test 65
11 Analysis of Variance 75
12 Chi Square 81
13 Reliability 87
14 Validity 91
15 Planning and Conducting Research Studies 95
About the Authors 101

Practical Statistics for Educators

Ruth Ravid

Rowman & Littlefield Publishers | 2010 - 4ª edição | 273 páginas | pdf | 1 Mb

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Practical Statistics for Educators, 4th edition focuses on the application of research and statistics as applied specifically to education. Since the first edition came out in 1994, thousands of students in educational statistics courses and their professors have found it to be an excellent textbook. Educational practitioners have also appreciated keeping this book on their reference shelf. Now in its fourth edition, this well-regarded text is a clear and easy-to-follow manual for use in introductory statistics or action research courses. Ruth Ravid concentrates on the essential concepts in educational statistics including when to use various statistical tests and how to interpret the results. Testing and test score interpretation, reliability, and validity are included to help students understand these topics which are essential for practitioners in education. 
Real-life examples, used generously throughout, are taken from the field of education and presented to illustrate the various concepts and terms. Chapter previews and summaries, as well as a glossary of the main terms and concepts, help readers navigate the book, focus on the most important points, and build upon the knowledge gained from each chapter.
New in this edition are updated and improved graphics, revised and enhanced text, and examples. Lengthy appendixes-tables are deleted and their relevant sections are integrated into the chapters. Detailed and complicated computational steps have also been eliminated.

Contents
Part I. Introduction 
Chapter 1: An Overview of Educational Research Basic (Pure), Applied, and Action Research Quantitative vs. Qualitative Research Experimental vs. Nonexperimental Research Summary 
Chapter 2: Basic Concepts in Statistics  Variables and Measurement Scales Populations and Samples Parameters and Statistics Methods of Sampling Sample Bias Size of Sample Parametric and Nonparametric Statistics Descriptive and Inferential Statistics Using Hypotheses in Research Probability and Level of Significance Errors in Decision Making Degrees of Freedom Effect Size Using Samples to Estimate Population Values Steps in the Process of Hypothesis Testing And Finally... Summary 
Part II. Descriptive Statistics 
Chapter 3: Organizing and Graphing Data Organizing Data Graphing Data Drawing Accurate Graphs Summary 
Chapter 4: Measures of Central Tendency Mode Median Mean Comparing the Mode, Median, and Mean Summary 
Chapter 5: Measures of Variability The Range Standard Deviation and Variance Summary 
Part III. The Normal Curve and Standard Scores 
Chapter 6: The Normal Curve and Standard Scores The Normal Curve Standard Scores Summary 
Chapter 7: Interpreting Test Scores Norm-Referenced Tests Criterion-Referenced Tests Summary 
Part IV. Measuring Relationships 
Chapter 8: Correlation Pearson Product Moment Factors Affecting the Correlation The Coefficient of Determination and Effect Size Intercorrelation Tables Correlation Tables Summary 
Chapter 9: Prediction and Regression Simple Regression Multiple Regression Summary 
Part V. Inferential Statistics 
Chapter 10: t test Hypotheses for t Tests Independent-Samples t Test An Example of a t Test for Independent Samples t Test for Paired Samples An Example of a t Test for Paired Samples t Test for a Single Sample An Example of a t Test for a Single Sample Summary 
Chapter 11: Analysis of Variance One-Way ANOVA Conceptualizing the One-Way ANOVA Hypotheses for a One-Way ANOVA The ANOVA Summary Table Further Interpretation of the F Ratio An Example of a One-Way ANOVA Post Hoc Comparisons Two-Way ANOVA Conceptualizing the Two-Way ANOVA Hypotheses for the Two-Way ANOVA Graphing the Interaction The Two-Way ANOVA Summary Table An Example of a Two-Way ANOVA Summary 
Chapter 12: Chi Square Test Assumptions for the Chi Square Test The Chi Square Test of Independence Summary 
Part VI. Reliability and Validity 
Chapter 13: Reliability Understanding the Theory of Reliability Methods of Assessing Reliability The Standard Error of Measurement Factors Affecting Reliability How High Should the Reliability Be? Summary 
Chapter 14: Validity Content Validity Criterion-Related Validity Concurrent Validity Predictive Validity Construct Validity Face Validity Assessing Validity Test Bias Summary Part Seven: Conducting Your Own Research 
Chapter 15: Planning and Conducting Research Studies Research Ethics The Research Proposal Introduction Literature Review Methodology References The Research Report Results Discussion Summary 
Chapter 16: Choosing the Right Statistical Test Choosing a Statistical Test: A Decision Flowchart Examples Scenarios 

Statistics on the Table: The History of Statistical Concepts and Methods

Stephen M. Stigler

Harvard University Press | 2002 | 499 páginas | djvu | 5 Mb

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This lively collection of essays examines in witty detail the history of some of the concepts involved in bringing statistical argument "to the table," and some of the pitfalls that have been encountered. The topics range from seventeenth-century medicine and the circulation of blood, to the cause of the Great Depression and the effect of the California gold discoveries of 1848 upon price levels, to the determinations of the shape of the Earth and the speed of light, to the meter of Virgil's poetry and the prediction of the Second Coming of Christ. The title essay tells how the statistician Karl Pearson came to issue the challenge to put "statistics on the table" to the economists Marshall, Keynes, and Pigou in 1911. The 1911 dispute involved the effect of parental alcoholism upon children, but the challenge is general and timeless: important arguments require evidence, and quantitative evidence requires statistical evaluation. Some essays examine deep and subtle statistical ideas such as the aggregation and regression paradoxes; others tell of the origin of the Average Man and the evaluation of fingerprints as a forerunner of the use of DNA in forensic science. Several of the essays are entirely nontechnical; all examine statistical ideas with an ironic eye for their essence and what their history can tell us about current disputes.

Contents
Acknowledgments IX
Introduction 1
I. Statistics and Social Science
1 Karl Pearson and the Cambridge Economists 13
2 The Average Man Is 168 Years Old 51
3 Jevons as Statistician 66
4 Jevons on the King-Davenant Law of Demand 80
5 Francis Ysidro Edgeworth, Statistician 87
II. Galtonian Ideas
6 Galton and Identification by Fingerprints 131
7 Stochastic Simulation in the Nineteenth Century 141
8 The History of Statistics in 1933 157
9 Regression toward the Mean 173
10 Statistical Concepts in Psychology 189
III. Some Seventeenth-Century Explorers
11 Apollo Mathematicus 203
12 The Dark Ages of Probability 239
13 John Craig and the Probability of History 252
IV Questions of Discovery
14 Stigler's Law of Eponymy 277
15 Who Discovered Bayes's Theorem? 291
16 Daniel Bernoulli, Leonhard Euler, and Maximum Likelihood 302
17 Gauss and the Invention of Least Squares 320
18 Cauchy and the Witch of Agnesi 332
19 Karl Pearson and Degrees of Freedom 338
V Questions of Standards
20 Statistics and Standards
21 The Trial of the Pyx
22 Normative Terminology
with H. Kruskal

Examples and Problems in Mathematical Statistics

(Wiley Series in Probability and Statistics) 

Shelemyahu Zacks

 Wiley | 2014 | 654 páginas | rar - pdf |  2,8 Mb

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Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises

With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory andapplication and presents numerous problem-solving examples that illustrate the relatednotations and proven results.

Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features:

• Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving

• More than 430 unique exercises with select solutions

• Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis

Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.

Table of Contents

Preface xv

List of Random Variables xvii

List of Abbreviations xix

1 Basic Probability Theory 1

PART I: THEORY, 1

1.1 Operations on Sets, 1

1.2 Algebra and σ-Fields, 2

1.3 Probability Spaces, 4

1.4 Conditional Probabilities and Independence, 6

1.5 Random Variables and Their Distributions, 8

1.6 The Lebesgue and Stieltjes Integrals, 12

1.7 Joint Distributions, Conditional Distributions and Independence, 21

1.8 Moments and Related Functionals, 26

1.9 Modes of Convergence, 35

1.10 Weak Convergence, 39

1.11 Laws of Large Numbers, 41

1.12 Central Limit Theorem, 44

1.13 Miscellaneous Results, 47

PART II: EXAMPLES, 56

PART III: PROBLEMS, 73

PART IV: SOLUTIONS TO SELECTED PROBLEMS, 93

2 Statistical Distributions 106

PART I: THEORY, 106

2.1 Introductory Remarks, 106

2.2 Families of Discrete Distributions, 106

2.3 Some Families of Continuous Distributions, 109

2.4 Transformations, 118

2.5 Variances and Covariances of Sample Moments, 120

2.6 Discrete Multivariate Distributions, 122

2.7 Multinormal Distributions, 125

2.8 Distributions of Symmetric Quadratic Forms of Normal Variables, 130

2.9 Independence of Linear and Quadratic Forms of Normal Variables, 132

2.10 The Order Statistics, 133

2.11 t-Distributions, 135

2.12 F-Distributions, 138

2.13 The Distribution of the Sample Correlation, 142

2.14 Exponential Type Families, 144

2.15 Approximating the Distribution of the Sample Mean: Edgeworth and Saddlepoint Approximations, 146

PART II: EXAMPLES, 150

PART III: PROBLEMS, 167

PART IV: SOLUTIONS TO SELECTED PROBLEMS, 181

3 Sufficient Statistics and the Information in Samples 191

PART I: THEORY, 191

3.1 Introduction, 191

3.2 Definition and Characterization of Sufficient Statistics, 192

3.3 Likelihood Functions and Minimal Sufficient Statistics, 200

3.4 Sufficient Statistics and Exponential Type Families, 202

3.5 Sufficiency and Completeness, 203

3.6 Sufficiency and Ancillarity, 205

3.7 Information Functions and Sufficiency, 206

3.8 The Fisher Information Matrix, 212

3.9 Sensitivity to Changes in Parameters, 214

PART II: EXAMPLES, 216

PART III: PROBLEMS, 230

PART IV: SOLUTIONS TO SELECTED PROBLEMS, 236

4 Testing Statistical Hypotheses 246

PART I: THEORY, 246

4.1 The General Framework, 246

4.2 The Neyman–Pearson Fundamental Lemma, 248

4.3 Testing One-Sided Composite Hypotheses in MLR Models, 251

4.4 Testing Two-Sided Hypotheses in One-Parameter Exponential Families, 254

4.5 Testing Composite Hypotheses with Nuisance Parameters—Unbiased Tests, 256

4.6 Likelihood Ratio Tests, 260

4.7 The Analysis of Contingency Tables, 271

4.8 Sequential Testing of Hypotheses, 275

PART II: EXAMPLES, 283

PART III: PROBLEMS, 298

PART IV: SOLUTIONS TO SELECTED PROBLEMS, 307

5 Statistical Estimation 321

PART I: THEORY, 321

5.1 General Discussion, 321

5.2 Unbiased Estimators, 322

5.3 The Efficiency of Unbiased Estimators in Regular Cases, 328

5.4 Best Linear Unbiased and Least-Squares Estimators, 331

5.5 Stabilizing the LSE: Ridge Regressions, 335

5.6 Maximum Likelihood Estimators, 337

5.7 Equivariant Estimators, 341

5.8 Estimating Equations, 346

5.9 Pretest Estimators, 349

5.10 Robust Estimation of the Location and Scale Parameters of Symmetric Distributions, 349

PART II: EXAMPLES, 353

PART III: PROBLEMS, 381

PART IV: SOLUTIONS OF SELECTED PROBLEMS, 393

6 Confidence and Tolerance Intervals 406

PART I: THEORY, 406

6.1 General Introduction, 406

6.2 The Construction of Confidence Intervals, 407

6.3 Optimal Confidence Intervals, 408

6.4 Tolerance Intervals, 410

6.5 Distribution Free Confidence and Tolerance Intervals, 412

6.6 Simultaneous Confidence Intervals, 414

6.7 Two-Stage and Sequential Sampling for Fixed Width Confidence Intervals, 417

PART II: EXAMPLES, 421

PART III: PROBLEMS, 429

PART IV: SOLUTION TO SELECTED PROBLEMS, 433

7 Large Sample Theory for Estimation and Testing 439

PART I: THEORY, 439

7.1 Consistency of Estimators and Tests, 439

7.2 Consistency of the MLE, 440

7.3 Asymptotic Normality and Efficiency of Consistent Estimators, 442

7.4 Second-Order Efficiency of BAN Estimators, 444

7.5 Large Sample Confidence Intervals, 445

7.6 Edgeworth and Saddlepoint Approximations to the Distribution of the MLE: One-Parameter Canonical Exponential Families, 446

7.7 Large Sample Tests, 448

7.8 Pitman’s Asymptotic Efficiency of Tests, 449

7.9 Asymptotic Properties of Sample Quantiles, 451

PART II: EXAMPLES, 454

PART III: PROBLEMS, 475

PART IV: SOLUTION OF SELECTED PROBLEMS, 479

8 Bayesian Analysis in Testing and Estimation 485

PART I: THEORY, 485

8.1 The Bayesian Framework, 486

8.2 Bayesian Testing of Hypothesis, 491

8.3 Bayesian Credibility and Prediction Intervals, 501

8.4 Bayesian Estimation, 502

8.5 Approximation Methods, 506

8.6 Empirical Bayes Estimators, 513

PART II: EXAMPLES, 514

PART III: PROBLEMS, 549

PART IV: SOLUTIONS OF SELECTED PROBLEMS, 557

9 Advanced Topics in Estimation Theory 563

PART I: THEORY, 563

9.1 Minimax Estimators, 563

9.2 Minimum Risk Equivariant, Bayes Equivariant, and Structural Estimators, 565

9.3 The Admissibility of Estimators, 570

PART II: EXAMPLES, 585

PART III: PROBLEMS, 592

PART IV: SOLUTIONS OF SELECTED PROBLEMS, 596

References 601

Author Index 613

Subject Index 617

The Manga Guide to Statistics

Shin Takahashi

No Starch Press | 2008 | 236 páginas | rar - pdf | 9,4 Mb

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Think you can't have fun learning statistics? Think again.

The Manga Guide to Statistics will teach you everything you need to know about this essential discipline, while entertaining you at the same time. With its unique combination of Japanese-style comics called manga and serious educational content, the EduManga format is already a hit in Japan.

In The Manga Guide to Statistics, our heroine Rui is determined to learn about statistics to impress the dreamy Mr. Igarashi and begs her father for a tutor. Soon she's spending her Saturdays with geeky, bespectacled Mr. Yamamoto, who patiently teaches her all about the fundamentals of statistics: topics like data categorization, averages, graphing, and standard deviation.

After all her studying, Rui is confident in her knowledge of statistics, including complex concepts like probability, coefficients of correlation, hypothesis tests, and tests of independence. But is it enough to impress her dream guy? Or maybe there's someone better, right in front of her?

Reluctant statistics students of all ages will enjoy learning along with Rui in this charming, easy-to-read guide, which uses real-world examples like teen magazine quizzes, bowling games, test scores, and ramen noodle prices. Examples, exercises, and answer keys help you follow along and check your work. An appendix showing how to perform statistics calculations in Microsoft Excel makes it easy to put Rui's lessons into practice.

This EduManga book is a translation from a bestselling series in Japan, co-published with Ohmsha, Ltd. of Tokyo, Japan.

Damned Lies and Statistics: Untangling Numbers from the Media, Politicians, and Activists

Joel Best

University of California Press | 2001 | 203 páginas | rar - pdf | 1 Mb

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Does the number of children gunned down double each year? Does anorexia kill 150,000 young women annually? Do white males account for only a sixth of new workers? Startling statistics shape our thinking about social issues. But all too often, these numbers are wrong. This book is a lively guide to spotting bad statistics and learning to think critically about these influential numbers. Damned Lies and Statistics is essential reading for everyone who reads or listens to the news, for students, and for anyone who relies on statistical information to understand social problems.
Joel Best bases his discussion on a wide assortment of intriguing contemporary issues that have garnered much recent media attention, including abortion, cyberporn, homelessness, the Million Man March, teen suicide, the U.S. census, and much more. Using examples from the New York Times, the Washington Post, and other major newspapers and television programs, he unravels many fascinating examples of the use, misuse, and abuse of statistical information.
In this book Best shows us exactly how and why bad statistics emerge, spread, and come to shape policy debates. He recommends specific ways to detect bad statistics, and shows how to think more critically about "stat wars," or disputes over social statistics among various experts. Understanding this book does not require sophisticated mathematical knowledge; Best discusses the most basic and most easily understood forms of statistics, such as percentages, averages, and rates.
This accessible book provides an alternative to either naively accepting the statistics we hear or cynically assuming that all numbers are meaningless. It shows how anyone can become a more intelligent, critical, and empowered consumer of the statistics that inundate both the social sciences and our media-saturated lives.

Contents
Introduction: the worst social statistic ever
The importance of social statistics
Soft facts: sources of bad statistics
Mutant statistics: methods for mangling numbers
Apples and oranges: inappropriate comparisons
Stat wars: conflicts over social statistics
Thinking about social statistics: the critical approach.

Outros livros do mesmo autor, disponíveis no blog:

More Damned Lies and Statistics: How Numbers Confuse Public Issues, 2004

Chances Are: The Only Statistic Book You'll Ever Need

Steve Slavin

Madison Books | 1998 | 220 páginas | rar - pdf | 5,3 Mb

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Chances Are is the first book to make statistics accessible to everyone, regardless of how much math you remember from school.

Contents
Acknowledgments
How to Use this Book
Chapter 1. Presentation of Statistical Data
Simple Tables, Line Graphs, Bar Graphs, Histograms, Pie Charts, Complex Graphs
Chapter 2. Basic Arithmetic Operations
Converting Proper Fractions into Decimals, Rounding, Multiplying Decimals, Interpolation, Extrapolation, Fast Multiplication and Division, Fast Multiplication, Fast Division, Manipulating Negative Numbers
Chapter 3. The Mean, Median, Mode, and Range
The Mean, Simple Mean, Weighted Average, The Median, The Mode, The Range, Finding the Mean, Median, Mode, and Range
Chapter 4. Frequency Distribution 49
Frequency Distributions, Graphing Frequency Distributions, Line Graphs, Bar Graphs, Histograms, Cumulative Frequency
Chapter 5. Percents 65
Changing Decimals into Percents , Changing Fractions into Percents, Changing Percents into Decimals , Finding Percentage Changes , Percentage Distribution , Finding Percentage Distributions , Pie Charts
Chapter 6. Percentiles 85
Using a Cumulative Percentage Graph , Deciles and Quartiles , Finding Scores, Percentiles, Deciles, and Quartiles on the Cumulative Percentage Graph , Obtaining Percentile Ranks from Frequency Distribution Tables , Finding the Cumulative Frequency and Setting True Limits
Chapter 7. Standard Deviation 103
Calculating Standard Deviation with Ungrovped Scores , Calculating Standard Deviation with Grouped Scores
Chapter 8. The Normal Curve 121
The Standard Nonnal Distribution , Finding the Percentile Rank , Z-Scores , Finding Percent of Cases Falling between Two Scores 135
Chapter 9. Probability
Sampling with Replacement, The Addition Rule, The Multiplication Rule, Sampling without Replacement, Double Counting, Probability and the Normal Curve, One- and Two-Tailed Probability Values
Chapter 10. Correlation
Positive, Negative, and Perfect Correlations, Calculating the Coefficient of Correlation
Chapter 11. Prediction and Regression
Predicting Individual Scores, Freehand Regression Line Fitting, Constructing Regression Lines by Predicting Y-Scores, Using Regression Lines

Analyzing Wimbledon: The Power of Statistics

Franc Klaassen e Jan R. Magnus

 Oxford University Press | 2014 | 269 páginas | rar - pdf | 1,5 Mb

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The game of tennis raises many questions that are of interest to a statistician. Is it true that beginning to serve in a set gives an advantage? Are new balls an advantage? Is the seventh game in a set particularly important? Are top players more stable than other players? Do real champions win the big points? These and many other questions are formulated as "hypotheses" and tested statistically. 

Analyzing Wimbledon also discusses how the outcome of a match can be predicted (even while the match is in progress), which points are important and which are not, how to choose an optimal service strategy, and whether "winning mood" actually exists in tennis. Aimed at readers with some knowledge of mathematics and statistics, the book uses tennis (Wimbledon in particular) as a vehicle to illustrate the power and beauty of statistical reasoning.

Introduction to Probability and Statistics

William Mendenhall, Robert J. Beaver e Barbara M. Beaver

Cengage Learning | 2012 - 14 ª edição | 753 páginas | rar - pdf |12,2 Mb

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Used by hundreds of thousands of students, INTRODUCTION TO PROBABILITY AND STATISTICS, Fourteenth Edition, blends proven coverage with new innovations to ensure you gain a solid understanding of statistical concepts--and see their relevance to your everyday life. The new edition retains the text's straightforward presentation and traditional outline for descriptive and inferential statistics while incorporating modern technology--including computational software and interactive visual tools--to help you master statistical reasoning and skillfully interpret statistical results. Drawing from decades of classroom teaching experience, the authors clearly illustrate how to apply statistical procedures as they explain how to describe real sets of data, what statistical tests mean in terms of practical application, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. Statistics can be an intimidating course, but with this text you will be well prepared. With its thorough explanations, insightful examples, practical exercises, and innovative technology features, this text equips you with a firm foundation in statistical concepts, as well as the tools to apply them to the world around you.

Contents
INTRODUCTION 1
DESCRIBING DATA WITH GRAPHS 7
DESCRIBING DATA WITH NUMERICAL MEASURES 50
DESCRIBING BIVARIATE DATA 94
PROBABILITY AND PROBABILITY DISTRIBUTIONS 123
SEVERAL USEFUL DISCRETE DISTRIBUTIONS 175
THE NORMAL PROBABILITY DISTRIBUTION 209
SAMPLING DISTRIBUTIONS 242
LARGE-SAMPLE ESTIMATION 281
LARGE-SAMPLE TESTS OF HYPOTHESES 324
INFERENCE FROM SMALL SAMPLES 364
THE ANALYSIS OF VARIANCE 425
LINEAR REGRESSION AND CORRELATION 482
MULTIPLE REGRESSION ANALYSIS 530
ANALYSIS OF CATEGORICAL DATA 574
NONPARAMETRIC STATISTICS 606
APPENDIX I 655
DATA SOURCES 688
ANSWERS TO SELECTED EXERCISES 700
INDEX 714

Mathematical Theory of Democracy

Andranik Tangian

Springer | 2014 | 629 páginas | rar - pdf | 3,7 Mb

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The mathematical theory of democracy deals with selection of representatives who make decisions on behalf of the whole society. In this book, the notion of representativeness is operationalized with the index of popularity (the average percentage of the population whose opinion is represented on a number of issues) and the index of universality (the frequency of cases when the opinion of a majority is represented). These indices are applied to evaluate and study the properties of single representatives (e.g. president) and representative bodies (e.g. parliament, magistrate, cabinet, jury, coalition). To bridge representative and direct democracy, an election method is proposed that is based not on voting but on indexing candidates with respect to the electorate’s political profile. In addition, societal and non-societal applications are considered.

Contents
History: Athenian Democracy
Echoes of Democracy in Ancient Rome
Revival of Democracy in Italian Mediaval City-Republics
Enlightenment and the End of Traditional Democracy
Modernity and Schism in Understanding Democracy
Theory: Direct Democracy
Dictatorship and Democracy
Representative Democracy
Statistically Testing the Representative Capacity
Concluding Discussion: Bridging Representative and Direct Democracies
Applications: Simple Applications
Application to Collective Multicriteria Decisions
Application to Stock Exchange Predictions
Application to Traffic Control
Appendix: Computational Formulas
Probabilities of Unequal Choices by Vote and by Candidate Scores
Statistical Significance of Representative Capacity.

The Empire of Chance: How Probability Changed Science and Everyday Life

(Ideas in Context)

Gerd Gigerenzer, Zeno Swijtink, Theodore Porter, Lorraine Daston, John Beatty, Lorenz Kruger

Cambridge University Press | 1990 | 360 páginas | rar - pdf | 35,4 Mb

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This book tells how quantitative ideas of chance have transformed the natural and social sciences as well as everyday life over the past three centuries. A continuous narrative connects the earliest application of probability and statistics in gambling and insurance to the most recent forays into law, medicine, polling, and baseball. Separate chapters explore the theoretical and methodological impact on biology, physics, and psychology. In contrast to the literature on the mathematical development of probability and statistics, this book centers on how these technical innovations recreated our conceptions of nature, mind, and society.

CONTENTS
Acknowledgments page xi
Introduction xiii
1 Classical probabilities, 1660-1840 1
1.1 Introduction 1
1.2 The beginnings 2
1.3 The classical interpretation 6
1.4 Determinism 11
1.5 Reasonableness 14
1.6 Risk in gambling and insurance 19
1.7 Evidence and causes 26
1.8 The moral sciences 32
1.9 Conclusion 34
2 Statistical probabilities, 1820-1900 37
2.1 Introduction 37
2.2 Statistical regularity and l'homme moyen 38
2.3 Opposition to statistics 45
2.4 Statistics and variation 48
2.5 The error law and correlation 53
2.6 The statistical critique of determinism 59
2.7 Conclusion 68
3 The inference experts 70
3.1 In want of a "system of mean results" 70
3.2 Analysis of variance 73
3.3 Fisher's antecedents: early significance tests and comparative experimentation
3.4 The controversy: Fisher vs. Neyman and Pearson 90
3.5 Hybridization: the silent solution 106
3.6 The statistical profession: intellectual autonomy 109
3.7 The statistical profession: institutions and influence 115
3.8 Conclusion 120
4 Chance and life: controversies in modem biology 123
4.1 Introduction 123
4.2 Spontaneity and control: chance in physiology 124
4.3 Coincidence and design: chance in natural history 132
4.4 Correlations and causes: chance in genetics 141
4.5 Sampling and selection: chance in evolutionary biology 152
5 The probabilistic revolution in physics 163
5.1 The background: classical physics 163
5.2 Probability in classical physics: the epistemic interpretation
5.3 Three limitations of classical physics: sources of probabilism
5.4 Comments on the three limitations 175
5.5 Mass phenomena and propensities 179
5.6 Explanations from probabilistic assumptions 182
5.7 The puzzle of irreversibility in time 187
5.8 The discontinuity underlying all change 190
6 Statistics of the mind 203
6.1 Introduction 203
6.2 The pre-statistical period 204
6.3 The new tools 205
6.4 From tools to theories of mind 211
6.5 A case study: from thinking to judgments under uncertainty
6.6 The return of the reasonable man 226

6.7 Conclusion 233