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