Statistics: The Art and Science of Learning from Data, Fifth Edition
By Alan Agresti, Christine Franklin and Bernhard Klingenberg
Contents:
Preface 10
Part One Gathering and Exploring Data
Chapter 1 Statistics: The Art and Science
of Learning From Data 24
1.1 Using Data to Answer Statistical Questions 25
1.2 Sample Versus Population 30
1.3 Organizing Data, Statistical Software, and the New Field of Data Science 42
Chapter Summary 52
Chapter Exercises 53
Chapter 2 Exploring Data With Graphs
and Numerical Summaries 57
2.1 Different Types of Data 58
2.2 Graphical Summaries of Data 64
2.3 Measuring the Center of Quantitative Data 82
2.4 Measuring the Variability of Quantitative Data 90
2.5 Using Measures of Position to Describe Variability 98
2.6 Linear Transformations and Standardizing 110
2.7 Recognizing and Avoiding Misuses of Graphical
Summaries 117
Chapter Summary 122
Chapter Exercises 125
Chapter 3 Exploring Relationships
Between Two Variables 134
3.1 The Association Between Two Categorical Variables 136
3.2 The Relationship Between Two Quantitative Variables 146
3.3 Linear Regression: Predicting the Outcome of a Variable 160
3.4 Cautions in Analyzing Associations 175
Chapter Summary 194
Chapter Exercises 196
Chapter 4 Gathering Data 204
4.1 Experimental and Observational Studies 205
4.2 Good and Poor Ways to Sample 213
4.3 Good and Poor Ways to Experiment 223
4.4 Other Ways to Conduct Experimental and
Nonexperimental Studies 228
Chapter Summary 240
Chapter Exercises 241
Part Two
Probability, Probability Distributions,
and Sampling Distributions
Chapter 5 Probability in Our Daily Lives 252
5.1 How Probability Quantifies Randomness 253
5.2 Finding Probabilities 261
5.3 Conditional Probability 275
5.4 Applying the Probability Rules 284
Chapter Summary 298
Chapter Exercises 300
Chapter 6 Random Variables and
Probability Distributions 307
6.1 Summarizing Possible Outcomes and Their
Probabilities 308
6.2 Probabilities for Bell-Shaped Distributions: The Normal
Distribution 321
6.3 Probabilities When Each Observation Has Two Possible
Outcomes: The Binomial Distribution 334
Chapter Summary 345
Chapter Exercises 347
Chapter 7 Sampling Distributions 354
7.1 How Sample Proportions Vary Around the Population Proportion 355
7.2 How Sample Means Vary Around the Population Mean 367
7.3 Using the Bootstrap to Find Sampling
Distributions 380
Chapter Summary 390
Chapter Exercises 392
Part Three Inferential Statistics
Chapter 8 Statistical Inference:
Confidence Intervals 400
8.1 Point and Interval Estimates of Population
Parameters 401
8.2 Confidence Interval for a Population Proportion 409
8.3 Confidence Interval for a Population Mean 426
8.4 Bootstrap Confidence Intervals 439
Chapter Summary 447
Chapter Exercises 449
Chapter 9 Statistical Inference:
Significance Tests About Hypotheses 457
9.1 Steps for Performing a Significance Test 458
9.2 Significance Test About a Proportion 464
9.3 Significance Test About a Mean 481
9.4 Decisions and Types of Errors in Significance Tests 493
9.5 Limitations of Significance Tests 498
9.6 The Likelihood of a Type II Error and the Power of a Test 506
Chapter Summary 513
Chapter Exercises 515
Chapter 10 Comparing Two Groups 522
10.1 Categorical Response: Comparing Two Proportions 524
10.2 Quantitative Response: Comparing Two Means 539
10.3 Comparing Two Groups With Bootstrap or Permutation Resampling 554
10.4 Analyzing Dependent Samples 568
10.5 Adjusting for the Effects of Other Variables 581
Chapter Summary 587
Chapter Exercises 590
Part Four
Extended Statistical Methods and Models for
Analyzing Categorical and Quantitative Variables
Chapter 11 Categorical Data Analysis 602
11.1 Independence and Dependence (Association) 603
11.2 Testing Categorical Variables for Independence 608
11.3 Determining the Strength of the Association 622
11.4 Using Residuals to Reveal the Pattern of Association 631
11.5 Fisher’s Exact and Permutation Tests 635
Chapter Summary 643
Chapter Exercises 645
Chapter 12 Regression Analysis 652
12.1 The Linear Regression Model 653
12.2 Inference About Model Parameters and the Relationship 663
12.3 Describing the Strength of the Relationship 670
12.4 How the Data Vary Around the Regression Line 681
12.5 Exponential Regression: A Model for Nonlinearity 693
Chapter Summary 698
Chapter Exercises 701
Chapter 13 Multiple Regression 707
13.1 Using Several Variables to Predict a Response 708
13.2 Extending the Correlation Coefficient and R2 for Multiple Regression 714
13.3 Inferences Using Multiple Regression 720
13.4 Checking a Regression Model Using Residual Plots 731
13.5 Regression and Categorical Predictors 737
13.6 Modeling a Categorical Response: Logistic Regression 743
Chapter Summary 752
Chapter Exercises 754
Chapter 14 Comparing Groups: Analysis of Variance Methods 760
14.1 One-Way ANOVA: Comparing Several Means 761
14.2 Estimating Differences in Groups for a Single Factor 772
14.3 Two-Way ANOVA: Exploring Two Factors and Their Interaction 781
Chapter Summary 795
Chapter Exercises 797
Chapter 15 Nonparametric Statistics 804
15.1 Compare Two Groups by Ranking 805
15.2 Nonparametric Methods for Several Groups and for
Dependent Samples 816
Chapter Summary 827
Chapter Exercises 829
Appendix A-833
Answers A-839
Index I-865
Index of Applications I-872
Credits C-877