Introduction to probability and statistics

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INTRODUCTION TO PROBABILITY AND STATISTICS is one of the first texts published by Duxbury and has been blending innovation with tradition for over thirty years. It was the first statistics text to include case studies in it, and now in the eleventh edition, this text is the first to include java applets in the body of the text. It has been used by hundreds of thousands of students since its first edition. This new edition retains the excellent examples, exercises and exposition that have made it a market leader, and builds upon this tradition of excellence with new technology integration.

Introduction: An Invitation to Statistics	p. 1
The Population and the Sample	p. 2
Descriptive and Inferential Statistics	p. 3
Achieving the Objective of Inferential Statistics: The Necessary Steps	p. 4
1 Describing Data with Graphs	p. 7
1.1 Variables and Data	p. 8
1.2 Types of Variables	p. 9
1.3 Graphs for Categorical Data	p. 11
1.4 Graphs for Quantitative Data	p. 17
1.5 Relative Frequency Histograms	p. 23
2 Describing Data with Numerical Measures	p. 50
2.1 Describing a Set of Data with Numerical Measures	p. 51
2.2 Measures of Center	p. 51
2.3 Measures of Variability	p. 57
2.4 On the Practical Significance of the Standard Deviation	p. 63
2.5 A Check on the Calculation of s	p. 67
2.6 Measures of Relative Standing	p. 73
2.7 The Five-Number Summary and the Box Plot	p. 76
3 Describing Bivariate Data	p. 93
3.1 Bivariate Data	p. 94
3.2 Graphs for Qualitative Variables	p. 94
3.3 Scatterplots for Two Quantitative Variables	p. 98
3.4 Numerical Measures for Quantitative Bivariate Data	p. 100
4 Probability and Probability Distributions	p. 119
4.1 The Role of Probability in Statistics	p. 120
4.2 Events and the Sample Space	p. 120
4.3 Calculating Probabilities Using Simple Events	p. 123
4.4 Useful Counting Rules (Optional)	p. 129
4.5 Event Relations and Probability Rules	p. 136
4.6 Conditional Probability, Independence, and the Multiplicative Rule	p. 140
4.7 Bayes' Rule (Optional)	p. 149
4.8 Discrete Random Variables and Their Probability Distributions	p. 154
5 Several Useful Discrete Distributions	p. 174
5.1 Introduction	p. 175
5.2 The Binomial Probability Distribution	p. 175
5.3 The Poisson Probability Distribution	p. 187
5.4 The Hypergeometric Probability Distribution	p. 191
6 The Normal Probability Distribution	p. 205
6.1 Probability Distributions for Continuous Random Variables	p. 206
6.2 The Normal Probability Distribution	p. 208
6.3 Tabulated Areas of the Normal Probability Distribution	p. 210
6.4 The Normal Approximation to the Binomial Probability Distribution (Optional)	p. 220
7 Sampling Distributions	p. 236
7.1 Introduction	p. 237
7.2 Sampling Plans and Experimental Designs	p. 237
7.3 Statistics and Sampling Distributions	p. 241
7.4 The Central Limit Theorem	p. 243
7.5 The Sampling Distribution of the Sample Mean	p. 247
7.6 The Sampling Distribution of the Sample Proportion	p. 253
7.7 A Sampling Application: Statistical Process Control (Optional)	p. 258
8 Large-Sample Estimation	p. 274
8.1 Where We've Been	p. 275
8.2 Where We're Going--Statistical Inference	p. 275
8.3 Types of Estimators	p. 276
8.4 Point Estimation	p. 277
8.5 Interval Estimation	p. 284
8.6 Estimating the Difference between Two Population Means	p. 294
8.7 Estimating the Difference between Two Binomial Proportions	p. 299
8.8 One-Sided Confidence Bounds	p. 303
8.9 Choosing the Sample Size	p. 305
9 Large-Sample Tests of Hypotheses	p. 320
9.1 Testing Hypotheses about Population Parameters	p. 321
9.2 A Statistical Test of Hypothesis	p. 321
9.3 A Large-Sample Test about a Population Mean	p. 324
9.4 A Large-Sample Test of Hypothesis for the Difference between Two Population Means	p. 337
9.5 A Large-Sample Test of Hypothesis for a Binomial Proportion	p. 343
9.6 A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions	p. 348
9.7 Some Comments on Testing Hypotheses	p. 353
10 Inference from Small Samples	p. 362
10.1 Introduction	p. 363
10.2 Student's t Distribution	p. 363
10.3 Small-Sample Inferences Concerning a Population Mean	p. 367
10.4 Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples	p. 375
10.5 Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test	p. 386
10.6 Inferences Concerning a Population Variance	p. 394
10.7 Comparing Two Population Variances	p. 401
10.8 Revisiting the Small-Sample Assumptions	p. 409
11 The Analysis of Variance	p. 426
11.1 The Design of an Experiment	p. 427
11.2 What Is an Analysis of Variance?	p. 428
11.3 The Assumptions for an Analysis of Variance	p. 428
11.4 The Completely Randomized Design: A One-Way Classification	p. 429
11.5 The Analysis of Variance for a Completely Randomized Design	p. 430
11.6 Ranking Population Means	p. 442
11.7 The Randomized Block Design: A Two-Way Classification	p. 445
11.8 The Analysis of Variance for a Randomized Block Design	p. 446
11.9 The a x b Factorial Experiment: A Two-Way Classification	p. 458
11.10 The Analysis of Variance for an a x b Factorial Experiment	p. 459
11.11 Revisiting the Analysis of Variance Assumptions	p. 467
11.12 A Brief Summary	p. 470
12 Linear Regression and Correlation	p. 483
12.1 Introduction	p. 484
12.2 A Simple Linear Probabilistic Model	p. 484
12.3 The Method of Least Squares	p. 486
12.4 An Analysis of Variance for Linear Regression	p. 489
12.5 Testing the Usefulness of the Linear Regression Model	p. 494
12.6 Diagnostic Tools for Checking the Regression Assumptions	p. 502
12.7 Estimation and Prediction Using the Fitted Line	p. 506
12.8 Correlation Analysis	p. 513
13 Multiple Regression Analysis	p. 532
13.1 Introduction	p. 533
13.2 The Multiple Regression Model	p. 533
13.3 A Multiple Regression Analysis	p. 534
13.4 A Polynomial Regression Model	p. 540
13.5 Using Quantitative and Qualitative Predictor Variables in a Regression Model	p. 548
13.6 Testing Sets of Regression Coefficients	p. 556
13.7 Interpreting Residual Plots	p. 559
13.8 Stepwise Regression Analysis	p. 560
13.9 Misinterpreting a Regression Analysis	p. 561
13.10 Steps to Follow When Building a Multiple Regression Model	p. 563
14 Analysis of Categorical Data	p. 575
14.1 A Description of the Experiment	p. 576
14.2 Pearson's Chi-Square Statistic	p. 577
14.3 Testing Specified Cell Probabilities: The Goodness-of-Fit Test	p. 578
14.4 Contingency Tables: A Two-Way Classification	p. 582
14.5 Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals	p. 590
14.6 The Equivalence of Statistical Tests	p. 594
14.7 Other Applications of the Chi-Square Test	p. 595
15 Nonparametric Statistics	p. 610
15.1 Introduction	p. 611
15.2 The Wilcoxon Rank Sum Test: Independent Random Samples	p. 611
15.3 The Sign Test for a Paired Experiment	p. 620
15.4 A Comparison of Statistical Tests	p. 625
15.5 The Wilcoxon Signed-Rank Test for a Paired Experiment	p. 626
15.6 The Kruskal-Wallis H Test for Completely Randomized Designs	p. 632
15.7 The Friedman F[subscript r] Test for Randomized Block Designs	p. 638
15.8 Rank Correlation Coefficient	p. 643
15.9 Summary	p. 650
Appendix I	p. 663
Table 1 Cumulative Binomial Probabilities	p. 664
Table 2 Cumulative Poisson Probabilities	p. 670
Table 3 Areas under the Normal Curve	p. 672
Table 4 Critical Values of t	p. 675
Table 5 Critical Values of Chi-Square	p. 676
Table 6 Percentage Points of the F Distribution	p. 678
Table 7 Critical Values of T for the Wilcoxon Rank Sum Test, n[subscript 1] [less than or equal] n[subscript 2]	p. 686
Table 8 Critical Values of T for the Wilcoxon Signed-Rank Test, n = 5(1)50	p. 688
Table 9 Critical Values of Spearman's Rank Correlation Coefficient for a One-Tailed Test	p. 689
Table 10 Random Numbers	p. 690
Table 11 Percentage Points of the Studentized Range, q[subscript [alpha](k, df)	p. 692
Answers to Selected Exercises	p. 696
Index	p. 715
Credits	p. 719

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