Introduction to probability and statistics /

Introduction to probability and statistics /

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Bibliographic Details
Author / Creator:Mendenhall, William
Edition:6th ed.
Imprint:Boston : Duxbury Press, c1983.
Description:xii, 646, 64 p. : ill. ; 24 cm.
Language:English
Subject:
Mathematical statistics
Probabilities
Mathematical statistics.
Probabilities.
Format: Print Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/768347
Hidden Bibliographic Details
ISBN:0871503654
Notes:Includes bibliographies and index.
Table of Contents:
  • Introduction: An Invitation to Statistics
  • The Population and the Sample
  • Descriptive and Inferential Statistics
  • Achieving the Objective of Inferential Statistics: The Necessary Steps
  • 1. Describing Data with Graphs
  • 1.1. Variables and Data
  • 1.2. Types of Variables
  • 1.3. Graphs for Categorical Data
  • 1.4. Graphs for Quantitative Data
  • 1.5. Relative Frequency Histograms
  • 2. Describing Data with Numerical Measures
  • 2.1. Describing a Set of Data with Numerical Measures
  • 2.2. Measures of Center
  • 2.3. Measures of Variability
  • 2.4. On the Practical Significance of the Standard Deviation
  • 2.5. A Check on the Calculation of s
  • 2.6. Measures of Relative Standing
  • 2.7. The Five-Number Summary and the Box Plot
  • 3. Describing Bivariate Data
  • 3.1. Bivariate Data
  • 3.2. Graphs for Qualitative Variables
  • 3.3. Scatterplots for Two Quantitative Variables
  • 3.4. Numerical Measures for Quantitative Bivariate Data
  • 4. Probability and Probability Distributions
  • 4.1. The Role of Probability in Statistics
  • 4.2. Events and the Sample Space
  • 4.3. Calculating Probabilities Using Simple Events
  • 4.4. Useful Counting Rules (Optional)
  • 4.5. Event Relations and Probability Rules
  • 4.6. Conditional Probability, Independence, and the Multiplicative Rule
  • 4.7. Bayes' Rule (Optional)
  • 4.8. Discrete Random Variables and Their Probability Distributions
  • 5. Several Useful Discrete Distributions
  • 5.1. Introduction
  • 5.2. The Binomial Probability Distribution
  • 5.3. The Poisson Probability Distribution
  • 5.4. The Hypergeometric Probability Distribution
  • 6. The Normal Probability Distribution
  • 6.1. Probability Distributions for Continuous Random Variables
  • 6.2. The Normal Probability Distribution
  • 6.3. Tabulated Areas of the Normal Probability Distribution
  • 6.4. The Normal Approximation to the Binomial Probability Distribution (Optional)
  • 7. Sampling Distributions
  • 7.1. Introduction
  • 7.2. Sampling Plans and Experimental Designs
  • 7.3. Statistics and Sampling Distributions
  • 7.4. The Central Limit Theorem
  • 7.5. The Sampling Distribution of the Sample Mean
  • 7.6. The Sampling Distribution of the Sample Proportion
  • 7.7. A Sampling Application: Statistical Process Control (Optional)
  • 8. Large-Sample Estimation
  • 8.1. Where We've Been
  • 8.2. Where We're Going--Statistical Inference
  • 8.3. Types of Estimators
  • 8.4. Point Estimation
  • 8.5. Interval Estimation
  • 8.6. Estimating the Difference between Two Population Means
  • 8.7. Estimating the Difference between Two Binomial Proportions
  • 8.8. One-Sided Confidence Bounds
  • 8.9. Choosing the Sample Size
  • 9. Large-Sample Tests of Hypotheses
  • 9.1. Testing Hypotheses about Population Parameters
  • 9.2. A Statistical Test of Hypothesis
  • 9.3. A Large-Sample Test about a Population Mean
  • 9.4. A Large-Sample Test of Hypothesis for the Difference between Two Population Means
  • 9.5. A Large-Sample Test of Hypothesis for a Binomial Proportion
  • 9.6. A Large-Sample Test of Hypothesis for the Difference between Two Binomial Proportions
  • 9.7. Some Comments on Testing Hypotheses
  • 10. Inference from Small Samples
  • 10.1. Introduction
  • 10.2. Student's t Distribution
  • 10.3. Small-Sample Inferences Concerning a Population Mean
  • 10.4. Small-Sample Inferences for the Difference between Two Population Means: Independent Random Samples
  • 10.5. Small-Sample Inferences for the Difference between Two Means: A Paired-Difference Test
  • 10.6. Inferences Concerning a Population Variance
  • 10.7. Comparing Two Population Variances
  • 10.8. Revisiting the Small-Sample Assumptions
  • 11. The Analysis of Variance
  • 11.1. The Design of an Experiment
  • 11.2. What Is an Analysis of Variance?
  • 11.3. The Assumptions for an Analysis of Variance
  • 11.4. The Completely Randomized Design: A One-Way Classification
  • 11.5. The Analysis of Variance for a Completely Randomized Design
  • 11.6. Ranking Population Means
  • 11.7. The Randomized Block Design: A Two-Way Classification
  • 11.8. The Analysis of Variance for a Randomized Block Design
  • 11.9. The a x b Factorial Experiment: A Two-Way Classification
  • 11.10. The Analysis of Variance for an a x b Factorial Experiment
  • 11.11. Revisiting the Analysis of Variance Assumptions
  • 11.12. A Brief Summary
  • 12. Linear Regression and Correlation
  • 12.1. Introduction
  • 12.2. A Simple Linear Probabilistic Model
  • 12.3. The Method of Least Squares
  • 12.4. An Analysis of Variance for Linear Regression
  • 12.5. Testing the Usefulness of the Linear Regression Model
  • 12.6. Diagnostic Tools for Checking the Regression Assumptions
  • 12.7. Estimation and Prediction Using the Fitted Line
  • 12.8. Correlation Analysis
  • 13. Multiple Regression Analysis
  • 13.1. Introduction
  • 13.2. The Multiple Regression Model
  • 13.3. A Multiple Regression Analysis
  • 13.4. A Polynomial Regression Model
  • 13.5. Using Quantitative and Qualitative Predictor Variables in a Regression Model
  • 13.6. Testing Sets of Regression Coefficients
  • 13.7. Interpreting Residual Plots
  • 13.8. Stepwise Regression Analysis
  • 13.9. Misinterpreting a Regression Analysis
  • 13.10. Steps to Follow When Building a Multiple Regression Model
  • 14. Analysis of Categorical Data
  • 14.1. A Description of the Experiment
  • 14.2. Pearson's Chi-Square Statistic
  • 14.3. Testing Specified Cell Probabilities: The Goodness-of-Fit Test
  • 14.4. Contingency Tables: A Two-Way Classification
  • 14.5. Comparing Several Multinomial Populations: A Two-Way Classification with Fixed Row or Column Totals
  • 14.6. The Equivalence of Statistical Tests
  • 14.7. Other Applications of the Chi-Square Test
  • 15. Nonparametric Statistics
  • 15.1. Introduction
  • 15.2. The Wilcoxon Rank Sum Test: Independent Random Samples
  • 15.3. The Sign Test for a Paired Experiment
  • 15.4. A Comparison of Statistical Tests
  • 15.5. The Wilcoxon Signed-Rank Test for a Paired Experiment
  • 15.6. The Kruskal-Wallis H Test for Completely Randomized Designs
  • 15.7. The Friedman F[subscript r] Test for Randomized Block Designs
  • 15.8. Rank Correlation Coefficient
  • 15.9. Summary
  • Appendix I
  • Table 1. Cumulative Binomial Probabilities
  • Table 2. Cumulative Poisson Probabilities
  • Table 3. Areas under the Normal Curve
  • Table 4. Critical Values of t
  • Table 5. Critical Values of Chi-Square
  • Table 6. Percentage Points of the F Distribution
  • Table 7. Critical Values of T for the Wilcoxon Rank Sum Test, n[subscript 1] [less than or equal] n[subscript 2]
  • Table 8. Critical Values of T for the Wilcoxon Signed-Rank Test, n = 5(1)50
  • Table 9. Critical Values of Spearman's Rank Correlation Coefficient for a One-Tailed Test
  • Table 10. Random Numbers
  • Table 11. Percentage Points of the Studentized Range, q[subscript [alpha](k, df)
  • Answers to Selected Exercises
  • Index
  • Credits
The John Crerar Library bookplate

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