A brief introduction to probability and statistics

This brief version of the authors' classic text retains the traditional outline for the coverage of descriptive and inferential statistics. The user-friendly presentation includes features such as Key Concepts and Formulas, and helps students grasp the material while not sacrificing the statistical integrity of the subject. MINITABO (Versions 12 and 13) is used exclusively as the computer package for statistical analysis in this text."

Introduction: An Invitation to Statistics	p. 1
The Population and the Sample	p. 3
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. 10
1.3 Graphs for Categorical Data	p. 11
1.4 Graphs for Quantitative Data	p. 18
1.5 Relative Frequency Histograms	p. 25
About Minitab--Introduction to Minitab	p. 34
Case Study How Is Your Blood Pressure?	p. 45
2 Describing Data with Numerical Measures	p. 47
2.1 Describing a Set of Data with Numerical Measures	p. 48
2.2 Measures of Center	p. 48
2.3 Measures of Variability	p. 55
2.4 On the Practical Significance of the Standard Deviation	p. 62
2.5 A Check on the Calculation of s	p. 66
2.6 Measures of Relative Standing	p. 72
2.7 The Box Plot	p. 77
About Minitab--Numerical Descriptive Measures	p. 84
Case Study The Boys of Summer	p. 91
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
About Minitab--Describing Bivariate Data	p. 108
Case Study Do You Think Your Dishes Are Really Clean?	p. 116
4 Probability and Probability Distributions	p. 118
4.1 The Role of Probability in Statistics	p. 119
4.2 Events and the Sample Space	p. 119
4.3 Calculating Probabilities Using Simple Events	p. 123
4.4 Useful Counting Rules (Optional)	p. 130
4.5 Event Composition and Event Relations	p. 138
4.6 Conditional Probability and Independence	p. 141
4.7 Bayes' Rule (Optional)	p. 152
4.8 Discrete Random Variables and Their Probability Distributions	p. 158
About Minitab--Discrete Probability Distributions	p. 169
Case Study Probability and Decision Making in the Congo	p. 177
5 Several Useful Discrete Distributions	p. 179
5.1 Introduction	p. 180
5.2 The Binomial Probability Distribution	p. 180
5.3 The Poisson Probability Distribution	p. 193
5.4 The Hypergeometric Probability Distribution	p. 199
About Minitab--Binomial and Poisson Probabilities	p. 203
Case Study A Mystery: Cancers Near a Reactor	p. 212
6 The Normal Probability Distribution	p. 214
6.1 Probability Distributions for Continuous Random Variables	p. 215
6.2 The Normal Probability Distribution	p. 217
6.3 Tabulated Areas of the Normal Probability Distribution	p. 218
6.4 The Normal Approximation to the Binomial Probability Distribution (Optional)	p. 228
About Minitab--Normal Probabilities	p. 236
Case Study The Long and the Short of It	p. 242
7 Sampling Distributions	p. 244
7.1 Introduction	p. 245
7.2 Sampling Plans and Experimental Designs	p. 245
7.3 Statistics and Sampling Distributions	p. 250
7.4 The Central Limit Theorem	p. 253
7.5 The Sampling Distribution of the Sample Mean	p. 256
7.6 The Sampling Distribution of the Sample Proportion	p. 263
7.7 A Sampling Application: Statistical Process Control (Optional)	p. 268
About Minitab--The Central Limit Theorem at Work	p. 276
Case Study Sampling the Roulette at Monte Carlo	p. 284
8 Large-Sample Estimation	p. 286
8.1 Where We've Been	p. 287
8.2 Where We're Going--Statistical Inference	p. 287
8.3 Types of Estimators	p. 289
8.4 Point Estimation	p. 289
8.5 Interval Estimation	p. 298
8.6 Estimating the Difference Between Two Population Means	p. 308
8.7 Estimating the Difference Between Two Binomial Proportions	p. 314
8.8 One-Sided Confidence Bounds	p. 319
8.9 Choosing the Sample Size	p. 320
Case Study How Reliable Is That Poll?	p. 334
9 Large-Sample Tests of Hypotheses	p. 336
9.1 Testing Hypotheses about Population Parameters	p. 337
9.2 A Statistical Test of Hypothesis	p. 337
9.3 A Large-Sample Test about a Population Mean	p. 341
9.4 A Large-Sample Test of Hypothesis for the Difference between Two Population Means	p. 354
9.5 A Large-Sample Test of Hypothesis for a Binomial Proportion	p. 361
9.6 A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions	p. 366
9.7 Some Comments on Testing Hypotheses	p. 372
Case Study An Aspirin a Day ...?	p. 379
10 Inference from Small Samples	p. 382
10.1 Introduction	p. 383
10.2 Student's t Distribution	p. 383
10.3 Small-Sample Inferences Concerning a Population Mean	p. 387
10.4 Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples	p. 395
10.5 Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test	p. 406
10.6 Inferences Concerning a Population Variance	p. 416
10.7 Comparing Two Population Variances	p. 424
10.8 Revisiting the Small-Sample Assumptions	p. 432
About Minitab--Small-Sample Testing and Estimation	p. 434
Case Study How Would You Like a Four-Day Work Week?	p. 449
11 The Analysis of Variance	p. 451
11.1 The Design of an Experiment	p. 452
11.2 What Is an Analysis of Variance?	p. 453
11.3 The Assumptions for an Analysis of Variance	p. 454
11.4 The Completely Randomized Design: A One-Way Classification	p. 454
11.5 The Analysis of Variance for a Completely Randomized Design	p. 455
11.6 Ranking Population Means	p. 468
11.7 Revisiting the Analysis of Variance Assumptions	p. 472
11.8 A Brief Summary	p. 477
About Minitab--Analysis of Variance Procedures	p. 478
Case Study "Are You at Risk?"	p. 482
12 Linear Regression and Correlation	p. 484
12.1 Introduction	p. 485
12.2 A Simple Linear Probabilistic Model	p. 485
12.3 The Method of Least Squares	p. 488
12.4 An Analysis of Variance for Linear Regression	p. 491
12.5 Testing the Usefulness of the Linear Regression Model	p. 495
12.6 Estimation and Prediction Using the Fitted Line	p. 503
12.7 Revisiting the Regression Assumptions	p. 510
12.8 Correlation Analysis	p. 515
About Minitab--Linear Regression Procedures	p. 522
Case Study Is Your Car "Made in the U.S.A."?	p. 530
13 Analysis of Categorical Data	p. 533
13.1 A Description of the Experiment	p. 534
13.2 Pearson's Chi-Square Statistic	p. 535
13.3 Testing Specified Cell Probabilities: The Goodness-of-Fit Test	p. 536
13.4 Contingency Tables: A Two-Way Classification	p. 541
13.5 Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals	p. 548
13.6 The Equivalence of Statistical Tests	p. 554
13.7 Other Applications of the Chi-Square Test	p. 555
About Minitab--The Chi-Square Test	p. 557
Case Study Can a Marketing Approach Improve Library Services?	p. 567
Appendix I Tables	p. 569
Table 1 Cumulative Binomial Probabilities	p. 569
Table 2 Cumulative Poisson Probabilities	p. 576
Table 3 Normal Curve Areas	p. 578
Table 4 Critical Values of t	p. 579
Table 5 Critical Values of Chi-Square	p. 580
Table 6 Percentage Points of the F Distribution	p. 582
Table 7 Random Numbers	p. 590
Table 8 Percentage Points of the Studentized Range, q(k, df)	p. 592
Answers to Selected Exercises	p. 596
Index	p. 615

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