Introduction to Probability and Statistics / Edition 10

Introduction to Probability and Statistics / Edition 10

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Introduction to Probability and Statistics / Edition 10

In Tenth Edition, the authors retain a traditional outline for the coverage of descriptive and inferential statistics. Much of the book was rewritten to make it more user-friendly, without sacrificing the statistical integrity of the presentation. This edition features MINITABÖ 12 for Microsoft« Windows exclusively as the computer package for statistical analysis in this edition.

Product Details

ISBN-13: 9780534361723
Publisher: Brooks/Cole
Publication date: 11/28/1998
Edition description: Older Edition

About the Author

Robert J. Beaver graduated from Bloomsburg State Teachers College (now Bloomsburg University) in 1959. In 1964, he completed an MS in Mathematics from Bucknell University and enrolled in the graduate program in Statistics chaired by William Mendenhall at the University of Florida. After completing a PhD in Statistics with a minor in Mathematics, Beaver took the position of Assistant Professor at the newly formed Department of Statistics headed by F. N. David at the University of California, Riverside. Dr. Beaver spent his entire career at Riverside, moving through the ranks to Full Professor. His research areas included paired comparisons and skewed distributions including the skewed normal, skewed Cauchy, and skewed logistic. During his time at Riverside, Beaver served as chairman of the Department of Statistics as well as chairman of the faculty of the College of Natural and Agricultural Sciences. After 35 years at Riverside, he retired in 2005 as Professor Emeritus and Statistician. Beaver has been an author for Cengage since 1966, authoring and co-authoring various Study Guides and textbooks with William Mendenhall and other authors. He and his wife Barbara have two children and four grandchildren.

Barbara M. Beaver received a B.A. in Mathematics (Magna Cum Laude) from Bucknell University in 1968 and a Master of Statistics degree from the University of Florida in 1970. She was employed as an Associate in Statistics at the Department of Statistics, University of California, Riverside from 1971-1977 on a part time basis. In 1992, she began a full-time appointment as a Lecturer in the Department of Statistics, teaching a variety of introductory statistics classes at the University of California, Riverside. She retired from teaching in 2008. Barbara has been an author and technical editor for Cengage since 1968, developing Solutions Manuals and Study Guides for a number of introductory statistics texts, and participating as a co-author with William Mendenhall and Robert J. Beaver on several statistics textbooks. Until her retirement, Barbara also worked as a professional musician, playing viola in the Riverside County Philharmonic, the Redlands Symphony, and various freelance groups in the Inland Empire area. She and her husband Bob have two children and four grandchildren.

Table of Contents

Introduction: An Invitation to Statistics1
The Population and the Sample2
Descriptive and Inferential Statistics3
Achieving the Objective of Inferential Statistics: The Necessary Steps4
1Describing Data with Graphs7
1.1Variables and Data8
1.2Types of Variables9
1.3Graphs for Categorical Data11
1.4Graphs for Quantitative Data17
1.5Relative Frequency Histograms23
2Describing Data with Numerical Measures50
2.1Describing a Set of Data with Numerical Measures51
2.2Measures of Center51
2.3Measures of Variability57
2.4On the Practical Significance of the Standard Deviation63
2.5A Check on the Calculation of s67
2.6Measures of Relative Standing73
2.7The Five-Number Summary and the Box Plot76
3Describing Bivariate Data93
3.1Bivariate Data94
3.2Graphs for Qualitative Variables94
3.3Scatterplots for Two Quantitative Variables98
3.4Numerical Measures for Quantitative Bivariate Data100
4Probability and Probability Distributions119
4.1The Role of Probability in Statistics120
4.2Events and the Sample Space120
4.3Calculating Probabilities Using Simple Events123
4.4Useful Counting Rules (Optional)129
4.5Event Relations and Probability Rules136
4.6Conditional Probability, Independence, and the Multiplicative Rule140
4.7Bayes' Rule (Optional)149
4.8Discrete Random Variables and Their Probability Distributions154
5Several Useful Discrete Distributions174
5.2The Binomial Probability Distribution175
5.3The Poisson Probability Distribution187
5.4The Hypergeometric Probability Distribution191
6The Normal Probability Distribution205
6.1Probability Distributions for Continuous Random Variables206
6.2The Normal Probability Distribution208
6.3Tabulated Areas of the Normal Probability Distribution210
6.4The Normal Approximation to the Binomial Probability Distribution (Optional)220
7Sampling Distributions236
7.2Sampling Plans and Experimental Designs237
7.3Statistics and Sampling Distributions241
7.4The Central Limit Theorem243
7.5The Sampling Distribution of the Sample Mean247
7.6The Sampling Distribution of the Sample Proportion253
7.7A Sampling Application: Statistical Process Control (Optional)258
8Large-Sample Estimation274
8.1Where We've Been275
8.2Where We're Going--Statistical Inference275
8.3Types of Estimators276
8.4Point Estimation277
8.5Interval Estimation284
8.6Estimating the Difference between Two Population Means294
8.7Estimating the Difference between Two Binomial Proportions299
8.8One-Sided Confidence Bounds303
8.9Choosing the Sample Size305
9Large-Sample Tests of Hypotheses320
9.1Testing Hypotheses about Population Parameters321
9.2A Statistical Test of Hypothesis321
9.3A Large-Sample Test about a Population Mean324
9.4A Large-Sample Test of Hypothesis for the Difference between Two Population Means337
9.5A Large-Sample Test of Hypothesis for a Binomial Proportion343
9.6A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions348
9.7Some Comments on Testing Hypotheses353
10Inference from Small Samples362
10.2Student's t Distribution363
10.3Small-Sample Inferences Concerning a Population Mean367
10.4Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples375
10.5Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test386
10.6Inferences Concerning a Population Variance394
10.7Comparing Two Population Variances401
10.8Revisiting the Small-Sample Assumptions409
11The Analysis of Variance426
11.1The Design of an Experiment427
11.2What Is an Analysis of Variance?428
11.3The Assumptions for an Analysis of Variance428
11.4The Completely Randomized Design: A One-Way Classification429
11.5The Analysis of Variance for a Completely Randomized Design430
11.6Ranking Population Means442
11.7The Randomized Block Design: A Two-Way Classification445
11.8The Analysis of Variance for a Randomized Block Design446
11.9The a x b Factorial Experiment: A Two-Way Classification458
11.10The Analysis of Variance for an a x b Factorial Experiment459
11.11Revisiting the Analysis of Variance Assumptions467
11.12A Brief Summary470
12Linear Regression and Correlation483
12.2A Simple Linear Probabilistic Model484
12.3The Method of Least Squares486
12.4An Analysis of Variance for Linear Regression489
12.5Testing the Usefulness of the Linear Regression Model494
12.6Diagnostic Tools for Checking the Regression Assumptions502
12.7Estimation and Prediction Using the Fitted Line506
12.8Correlation Analysis513
13Multiple Regression Analysis532
13.2The Multiple Regression Model533
13.3A Multiple Regression Analysis534
13.4A Polynomial Regression Model540
13.5Using Quantitative and Qualitative Predictor Variables in a Regression Model548
13.6Testing Sets of Regression Coefficients556
13.7Interpreting Residual Plots559
13.8Stepwise Regression Analysis560
13.9Misinterpreting a Regression Analysis561
13.10Steps to Follow When Building a Multiple Regression Model563
14Analysis of Categorical Data575
14.1A Description of the Experiment576
14.2Pearson's Chi-Square Statistic577
14.3Testing Specified Cell Probabilities: The Goodness-of-Fit Test578
14.4Contingency Tables: A Two-Way Classification582
14.5Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals590
14.6The Equivalence of Statistical Tests594
14.7Other Applications of the Chi-Square Test595
15Nonparametric Statistics610
15.2The Wilcoxon Rank Sum Test: Independent Random Samples611
15.3The Sign Test for a Paired Experiment620
15.4A Comparison of Statistical Tests625
15.5The Wilcoxon Signed-Rank Test for a Paired Experiment626
15.6The Kruskal-Wallis H Test for Completely Randomized Designs632
15.7The Friedman F[subscript r] Test for Randomized Block Designs638
15.8Rank Correlation Coefficient643
Appendix I663
Table 1Cumulative Binomial Probabilities664
Table 2Cumulative Poisson Probabilities670
Table 3Areas under the Normal Curve672
Table 4Critical Values of t675
Table 5Critical Values of Chi-Square676
Table 6Percentage Points of the F Distribution678
Table 7Critical Values of T for the Wilcoxon Rank Sum Test, n[subscript 1] [less than or equal] n[subscript 2]686
Table 8Critical Values of T for the Wilcoxon Signed-Rank Test, n = 5(1)50688
Table 9Critical Values of Spearman's Rank Correlation Coefficient for a One-Tailed Test689
Table 10Random Numbers690
Table 11Percentage Points of the Studentized Range, q[subscript [alpha](k, df)692
Answers to Selected Exercises696

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