Note: Supplemental materials are not guaranteed with Rental or Used book purchases.
Purchase Benefits
What is included with this book?
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.
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.
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 Describing 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 ˆp / 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