Title:
Introduction to probability and statistics
Personal Author:
Edition:
9th ed.
Publication Information:
Belmont,Calif : Duxbury Press, 1994
Physical Description:
1 computer disk ; 3 1/2 in.
ISBN:
9780534208868
General Note:
Accompanies text with the same title : QA276.M47 1994
System raquirements : IBM PC or compatible
Added Author:
Available:*
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Summary
Summary
This classic text, focuses on statistical inference as the objective of statistics, emphasizes inference making, and features a highly polished and meticulous execution, with outstanding exercises. This revision introduces a range of modern ideas, while preserving the overall classical framework..
Table of Contents
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 |