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# Essential Statistics w/Student CD

## 9781429234467

**by**Moore, David S.

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### Summary

W.H. Freeman is excited to be publishing a new text by David Moore: ** Essential Statistics**.

David Moore’s considerable experience as a statistician and instructor, and his commitment to producing high-quality, innovative introductory statistics textbooks motivated him to create *Essential Statistics. *The text offers the same highly successful approach and pedagogy of David Moore’s bestselling *The Basic Practice of Statistics *(BPS), Fifth Edition, but in a briefer, more concise format. Through careful rewriting, he has shortened and simplified explanations, to better highlight the key, *essential*, statistical ideas and methods students need to know.

The text is based on three principles: balanced content, the importance of ideas, and experience with data. Using a “just the basics” approach, the text clarifies and simplifies important concepts and methods, while engaging students with contemporary, realistic examples. Throughout the book, exercises help students check and apply their skills. A four-step problem-solving process in examples and exercises encourage good habits that go beyond graphs and calculations to ask, “What do the data tell me?”

*Essential Statistics* is what its name suggests: a basic introduction to statistical ideas and methods that aims to equip students to carry out common statistical procedures and to follow statistical reasoning in their fields of study and in their future employment.

### Author Biography

David S. Moore is Shanti S. Gupta Distinguished Professor of Statistics, Emeritus, at Purdue University and was 1998 president of the American Statistical Association. He received his A.B. from Princeton and his Ph.D. from Cornell, both in mathematics. He has written many research papers in statistical theory and served on the editorial boards of several major journals. Professor Moore is an elected fellow of the American Statistical Association and of the Institute of Mathematical Statistics and an elected member of the International Statistical Institute. He has served as program director for statistics and probability at the National Science Foundation.

In recent years, Professor Moore has devoted his attention to the teaching of statistics. He was the content developer for the Annenberg/Corporation for Public Broadcasting college-level telecourse *Against All Odds: Inside Statistics* and for the series of video modules *Statistics: Decisions through Data*, intended to aid the teaching of statistics in schools. He is the author of influential articles on statistics education and of several leading texts. Professor Moore has served as president of the International Association for Statistical Education and has received the Mathematical Association of America’s national award for distinguished college or university teaching of mathematics.

### Table of Contents

**PART I: **Exploring Data 1

**CHAPTER 1 **Picturing Distributions with Graphs 3

Individuals and variables / 3

Categorical variables: pie charts and bar graphs / 5

Quantitative variables: histograms / 10

Interpreting histograms / 12

Quantitative variables: stemplots / 16

Time plots / 19

**CHAPTER 2 D**escribing Distributions with Numbers 29

Measuring center: the mean / 29

Measuring center: the median / 31

Comparing the mean and the median / 32

Measuring spread: the quartiles / 33

The five-number summary and boxplots / 34

Measuring spread: the standard deviation / 37

Choosing measures of center and spread / 39

Using technology / 40

Organizing a statistical problem / 40

**CHAPTER 3 **The Normal Distributions 51

Density curves / 51

Describing density curves / 54

Normal distributions / 55

The 68-95-99.7 rule / 57

The standard Normal distribution / 59

Finding Normal proportions / 61

Using the standard Normal table / 62

Finding a value given a proportion / 65

**CHAPTER 4 **Scatterplots and Correlation 73

Explanatory and response variables / 73

Displaying relationships: scatterplots / 74

Interpreting scatterplots / 76

Measuring linear association: correlation / 79

Facts about correlation / 80

**CHAPTER 5 **Regression 91

Regression lines / 91

The least-squares regression line / 94

Using technology / 95

Facts about least-squares regression / 97

Residuals / 98

Influential observations / 101

Cautions about correlation and regression / 103

Association does not imply causation / 105

**CHAPTER 6 **Exploring Data: Part I Review 115

Part I Summary / 115

Review Exercises / 116

Supplementary Exercises / 121

**PART II: **From Exploration to Inference 127

**CHAPTER 7 **Producing Data: Sampling 129

Population versus sample / 129

How to sample badly / 131

Simple random samples / 132

Inference about the population / 136

Cautions about sample surveys / 137

**CHAPTER 8 **Producing Data: Experiments 145

Observation versus experiment / 145

Subjects, factors, treatments / 147

How to experiment badly / 149

Randomized comparative experiments / 150

The logic of randomized comparative experiments / 153

Cautions about experimentation / 154

Matched pairs designs / 156

**CHAPTER 9 **Introducing Probability 163

The idea of probability / 164

Probability models / 166

Probability rules / 168

Discrete probability models / 171

Continuous probability models / 172

Random variables / 176

iv ** Starred material is not required for later parts of the text.*

**CHAPTER 10 **Sampling Distributions 183

Parameters and statistics / 183

Statistical estimation and the law of large numbers / 184

Sampling distributions / 187

The mean and standard deviation of ¯*x */ 189

The central limit theorem / 190

**CHAPTER 11** General Rules of Probability* 199

Independence and the multiplication rule / 199

The general addition rule / 203

Conditional probability / 205

The general multiplication rule / 20

Tree diagrams / 208

**CHAPTER 12 **Binomial Distributions* 217

The binomial setting and binomial distributions / 217

Binomial distributions in statistical sampling / 218

Binomial probabilities / 219

Binomial mean and standard deviation / 221

The Normal approximation to binomial distributions / 223

**CHAPTER 13 **Introduction to Inference 231

The reasoning of statistical estimation / 232

Confidence intervals for a population mean / 235

The reasoning of statistical tests / 238

Stating hypotheses / 241*P*-values / 242

Tests for a population mean / 245

Statistical significance / 248

**CHAPTER 14 **Thinking about Inference 257

Conditions for inference in practice / 257

How confidence intervals behave / 261

Sample size for confidence intervals / 263

How significance tests behave / 264

**CHAPTER 15 **From Exploration to Inference: Part II Review 273

Part II Summary / 273

Review Exercises / 275

Supplementary Exercises / 279

Optional Exercises / 281

**PART III: **Inference about Variables 283

**CHAPTER 16 **Inference about a Population Mean 285

Conditions for inference about a mean / 285

The *t *distributions / 286

The one-sample *t *confidence interval / 288

The one-sample *t *test / 291

Using technology / 293

Matched pairs *t *procedures / 295

Robustness of *t *procedures / 297

**CHAPTER 17 **Two-Sample Problems 307

Comparing two population means / 308

Two-sample *t *procedures / 310

Using technology / 315

Robustness again / 317

**CHAPTER 18 **Inference about a Population Proportion 327

The sample proportion ˆ*p */ 328

Large-sample confidence intervals for a proportion / 330

Choosing the sample size / 332

Significance tests for a proportion / 334

**CHAPTER 19 **Comparing Two Proportions 341

Two-sample problems: proportions / 341

The sampling distribution of a difference between proportions / 342

Large-sample confidence intervals form comparing proportions / 343

Using technology / 344

Significance tests for comparing proportions / 346

**CHAPTER 20 **Inference about Variables: Part III Review 353

Statistics in Outline / 353

Part III Summary / 354

Review Exercises / 356

Supplementary Exercises / 359

**PART IV: **Inference about Relationships 363

**CHAPTER 21 **Two Categorical Variables: The Chi-Square Test 365

Two-way tables / 365

Is there a relationship? Expected cell counts / 370

The chi-square test / 372

Data analysis for chi-square / 374

Another use of the chi-square test / 378

The chi-square distributions / 380

The chi-square test for goodness of fit / 382

**CHAPTER 22 **Inference for Regression 393

Conditions for regression inference / 395

Estimating the parameters / 396

Using technology / 399

Testing the hypothesis of no linear relationship / 401

Testing lack of correlation / 403

Confidence intervals for the regression slope / 404

Inference about prediction / 405

Checking the conditions for inference / 408

**CHAPTER 23 **One-Way Analysis of Variance:

Comparing Several Means 421

The analysis of variance *F *test / 423

Using technology / 425

The idea of analysis of variance / 429

Conditions for ANOVA / 431*F *distributions and degrees of freedom / 435

**NOTES AND DATA SOURCES** / 445

**TABLES** / 463

TABLE A Standard Normal Probabilities / 464

TABLE B Random Digits / 466

TABLE C *t *Distribution Critical Values / 467

TABLE D Chi-Square Distribution Critical Values / 468

**ANSWERS TO SELECTED EXERCISES** / 469

*INDEX / 495*

Additional Material (available on the *Essential Statistics *CD and Web site www.whfreeman.com/essentialstats)

**CHAPTER 24 **Nonparametric Tests

Comparing two samples: the Wilcoxon rank sum test

The Normal approximation for *W*Using technology

What hypotheses does Wilcoxon test?

Dealing with ties in rank tests

Matched pairs: the Wilcoxon signed rank test

The Normal approximation for

*W*+

Dealing with ties in the signed rank test

Commentary: Data Ethics

Applets for Interactive Learning