# Solution Manual for Introduction to Probability and Statistics 13th Edition by William Mendenhall

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### Solution Manual for Introduction to Probability and Statistics 13th Edition by William Mendenhall

#### Solution Manual for Introduction to Probability and Statistics 13th Edition by William Mendenhall

Used by hundreds of thousands of students since its first edition, INTRODUCTION TO PROBABILITY AND STATISTICS, Thirteenth Edition, continues to blend the best of its proven coverage with new innovations. While retaining the straightforward presentation and traditional outline for descriptive and inferential statistics, this new edition incorporates helpful learning aids like MyPersonal Trainer, MyApplet, and MyTip to ensure that students learn and understand the relevance of the material. Written for the higher end of the traditional introductory statistics market, the book takes advantage of modern technology–including computational software and interactive visual tools–to facilitate statistical reasoning as well as the interpretation of statistical results. In addition to showing how to apply statistical procedures, the authors explain how to describe real sets of data meaningfully, what the statistical tests mean in terms of their practical applications, how to evaluate the validity of the assumptions behind statistical tests, and what to do when statistical assumptions have been violated. Users will also appreciate the book’s error-free material and exercises. The new edition retains the statistical integrity, examples, exercises, and exposition that have made this text a market leader–and builds upon this tradition of excellence with new technology integration.

1. List of Applications
2. Preface
3. Contents
4. Introduction: Train Your Brain for Statistics
5. THE POPULATION AND THE SAMPLE
6. DESCRIPTIVE AND INFERENTIAL STATISTICS
7. ACHIEVING THE OBJECTIVE OF INFERENTIAL STATISTICS: THE NECESSARY STEPS
8. TRAINING YOUR BRAIN FOR STATISTICS
9. Chapter 1: Describing Data with Graphs
10. 1.1: VARIABLES AND DATA
11. 1.2: TYPES OF VARIABLES
12. 1.3: GRAPHS FOR CATEGORICAL DATA
13. 1.4: GRAPHS FOR QUANTITATIVE DATA
14. 1.5: RELATIVE FREQUENCY HISTOGRAMS
15. CHAPTER REVIEW
16. CASE STUDY: How Is Your Blood Pressure?
17. Chapter 2: Describing Data with Numerical Measures
18. 2.1: DESCRIBING A SET OF DATA WITH NUMERICAL MEASURES
19. 2.2: MEASURES OF CENTER
20. 2.3: MEASURES OF VARIABILITY
21. 2.4: ON THE PRACTICAL SIGNIFICANCE OF THE STANDARD DEVIATION
22. 2.5: A CHECK ON THE CALCULATION OF s
23. 2.6: MEASURES OF RELATIVE STANDING
24. 2.7: THE FIVE-NUMBER SUMMARY AND THE BOX PLOT
25. CHAPTER REVIEW
26. CASE STUDY: The Boys of Summer
27. Chapter 3: Describing Bivariate Data
28. 3.1: BIVARIATE DATA
29. 3.2: GRAPHS FOR QUALITATIVE VARIABLES
30. 3.3: SCATTERPLOTS FOR TWO QUANTITATIVE VARIABLES
31. 3.4: NUMERICAL MEASURES FOR QUANTITATIVE BIVARIATE DATA
32. CHAPTER REVIEW
33. CASE STUDY: Are Your Dishes Really Clean?
34. Chapter 4: Probability and Probability Distributions
35. 4.1: THE ROLE OF PROBABILITY IN STATISTICS
36. 4.2: EVENTS AND THE SAMPLE SPACE
37. 4.3: CALCULATING PROBABILITIES USING SIMPLE EVENTS
38. 4.4: USEFUL COUNTING RULES (OPTIONAL)
39. 4.5: EVENT RELATIONS AND PROBABILITY RULES
40. 4.6: INDEPENDENCE, CONDITIONAL PROBABILITY, AND THE MULTIPLICATION RULE
41. 4.7: BAYES’ RULE (OPTIONAL)
42. 4.8: DISCRETE RANDOM VARIABLES AND THEIR PROBABILITY DISTRIBUTIONS
43. CHAPTER REVIEW
44. CASE STUDY: Probability and Decision Making in the Congo
45. Chapter 5: Several Useful Discrete Distributions
46. 5.1: INTRODUCTION
47. 5.2: THE BINOMIAL PROBABILITY DISTRIBUTION
48. 5.3: THE POISSON PROBABILITY DISTRIBUTION
49. 5.4: THE HYPERGEOMETRIC PROBABILITY DISTRIBUTION
50. CHAPTER REVIEW
51. CASE STUDY: A Mystery: Cancers Near a Reactor
52. Chapter 6: The Normal Probability Distribution
53. 6.1: PROBABILITY DISTRIBUTIONS FOR CONTINUOUS RANDOM VARIABLES
54. 6.2: THE NORMAL PROBABILITY DISTRIBUTION
55. 6.3: TABULATED AREAS OF THE NORMAL PROBABILITY DISTRIBUTION
56. 6.4: THE NORMAL APPROXIMATION TO THE BINOMIAL PROBABILITY DISTRIBUTION (OPTIONAL)
57. CHAPTER REVIEW
58. CASE STUDY: The Long and Short of It
59. Chapter 7: Sampling Distributions
60. 7.1: INTRODUCTION
61. 7.2: SAMPLING PLANS AND EXPERIMENTAL DESIGNS
62. 7.3: STATISTICS AND SAMPLING DISTRIBUTIONS
63. 7.4: THE CENTRAL LIMIT THEOREM
64. 7.5: THE SAMPLING DISTRIBUTION OF THE SAMPLE MEAN
65. 7.6: THE SAMPLING DISTRIBUTION OF THE SAMPLE PROPORTION
66. 7.7: A SAMPLING APPLICATION: STATISTICAL PROCESS CONTROL (OPTIONAL)
67. CHAPTER REVIEW
68. CASE STUDY: Sampling the Roulette at Monte Carlo
69. Chapter 8: Large-Sample Estimation
70. 8.1: WHERE WE’VE BEEN
71. 8.2: WHERE WE’RE GOING—STATISTICAL INFERENCE
72. 8.3: TYPES OF ESTIMATORS
73. 8.4: POINT ESTIMATION
74. 8.5: INTERVAL ESTIMATION
75. 8.6: ESTIMATING THE DIFFERENCE BETWEEN TWO POPULATION MEANS
76. 8.7: ESTIMATING THE DIFFERENCE BETWEEN TWO BINOMIAL PROPORTIONS
77. 8.8: ONE-SIDED CONFIDENCE BOUNDS
78. 8.9: CHOOSING THE SAMPLE SIZE
79. CHAPTER REVIEW
80. CASE STUDY: How Reliable Is That Poll? CBS News: How and Where America Eats
81. Chapter 9: Large-Sample Tests of Hypotheses
82. 9.1: TESTING HYPOTHESES ABOUT POPULATION PARAMETERS
83. 9.2: A STATISTICAL TEST OF HYPOTHESIS
84. 9.3: A LARGE-SAMPLE TEST ABOUT A POPULATION MEAN
85. 9.4: A LARGE-SAMPLE TEST OF HYPOTHESIS FOR THE DIFFERENCE BETWEEN TWO POPULATION MEANS
86. 9.5: A LARGE-SAMPLE TEST OF HYPOTHESIS FOR A BINOMIAL PROPORTION
87. 9.6: A LARGE-SAMPLE TEST OF HYPOTHESIS FOR THE DIFFERENCE BETWEEN TWO BINOMIAL PROPORTIONS
88. 9.7: SOME COMMENTS ON TESTING HYPOTHESES
89. CHAPTER REVIEW
90. CASE STUDY: An Aspirin a Day . . . ?
91. Chapter 10: Inference from Small Samples
92. 10.1: INTRODUCTION
93. 10.2: STUDENT’S t DISTRIBUTION
94. 10.3: SMALL-SAMPLE INFERENCES CONCERNING A POPULATION MEAN
95. 10.4: SMALL-SAMPLE INFERENCES FOR THE DIFFERENCE BETWEEN TWO POPULATION MEANS: INDEPENDENT RANDOM SA
96. 10.5: SMALL-SAMPLE INFERENCES FOR THE DIFFERENCE BETWEEN TWO MEANS: A PAIRED-DIFFERENCE TEST
97. 10.6: INFERENCES CONCERNING A POPULATION VARIANCE
98. 10.7: COMPARING TWO POPULATION VARIANCES
99. 10.8: REVISITING THE SMALL-SAMPLE ASSUMPTIONS
100. CHAPTER REVIEW
101. CASE STUDY: How Would You Like a Four-Day Workweek?
102. Chapter 11: The Analysis of Variance
103. 11.1: THE DESIGN OF AN EXPERIMENT
104. 11.2: WHAT IS AN ANALYSIS OF VARIANCE?
105. 11.3: THE ASSUMPTIONS FOR AN ANALYSIS OF VARIANCE
106. 11.4: THE COMPLETELY RANDOMIZED DESIGN: A ONE-WAY CLASSIFICATION
107. 11.5: THE ANALYSIS OF VARIANCE FOR A COMPLETELY RANDOMIZED DESIGN
108. 11.6: RANKING POPULATION MEANS
109. 11.7: THE RANDOMIZED BLOCK DESIGN: A TWO-WAY CLASSIFICATION
110. 11.8: THE ANALYSIS OF VARIANCE FOR A RANDOMIZED BLOCK DESIGN
111. 11.9: THE a x b FACTORIAL EXPERIMENT: A TWO-WAY CLASSIFICATION
112. 11.10: THE ANALYSIS OF VARIANCE FOR AN a x b FACTORIAL EXPERIMENT
113. 11.11: REVISITING THE ANALYSIS OF VARIANCE ASSUMPTIONS
114. 11.12: A BRIEF SUMMARY
115. CHAPTER REVIEW
116. CASE STUDY: “A Fine Mess”
117. Chapter 12: Linear Regression and Correlation
118. 12.1: INTRODUCTION
119. 12.2: A SIMPLE LINEAR PROBABILISTIC MODEL
120. 12.3: THE METHOD OF LEAST SQUARES
121. 12.4: AN ANALYSIS OF VARIANCE FOR LINEAR REGRESSION
122. 12.5: TESTING THE USEFULNESS OF THE LINEAR REGRESSION MODEL
123. 12.6: DIAGNOSTIC TOOLS FOR CHECKING THE REGRESSION ASSUMPTIONS
124. 12.7: ESTIMATION AND PREDICTION USING THE FITTED LINE
125. 12.8: CORRELATION ANALYSIS
126. CHAPTER REVIEW
128. Chapter 13: Multiple Regression Analysis
129. 13.1: INTRODUCTION
130. 13.2: THE MULTIPLE REGRESSION MODEL
131. 13.3: A MULTIPLE REGRESSION ANALYSIS
132. 13.4: A POLYNOMIAL REGRESSION MODEL
133. 13.5: USING QUANTITATIVE AND QUALITATIVE PREDICTOR VARIABLES IN A REGRESSION MODEL
134. 13.6: TESTING SETS OF REGRESSION COEFFICIENTS
135. 13.7: INTERPRETING RESIDUAL PLOTS
136. 13.8: STEPWISE REGRESSION ANALYSIS
137. 13.9: MISINTERPRETING A REGRESSION ANALYSIS
138. 13.10: STEPS TO FOLLOW WHEN BUILDING A MULTIPLE REGRESSION MODEL
139. CHAPTER REVIEW
140. CASE STUDY: “Made in the U.S.A.”—Another Look
141. Chapter 14: Analysis of Categorical Data
142. 14.1: A DESCRIPTION OF THE EXPERIMENT
143. 14.2: PEARSON’S CHI-SQUARE STATISTIC
144. 14.3: TESTING SPECIFIED CELL PROBABILITIES: THE GOODNESS-OF-FIT TEST
145. 14.4: CONTINGENCY TABLES: A TWO-WAY CLASSIFICATION
146. 14.5: COMPARING SEVERAL MULTINOMIAL POPULATIONS: A TWO-WAY CLASSIFICATION WITH FIXED ROW OR COLUMN T
147. 14.6: THE EQUIVALENCE OF STATISTICAL TESTS
148. 14.7: OTHER APPLICATIONS OF THE CHI-SQUARE TEST
149. CHAPTER REVIEW
150. CASE STUDY: Can a Marketing Approach Improve Library Services?
151. Chapter 15: Nonparametric Statistics
152. 15.1: INTRODUCTION
153. 15.2: THE WILCOXON RANK SUM TEST: INDEPENDENT RANDOM SAMPLES
154. 15.3: THE SIGN TEST FOR A PAIRED EXPERIMENT
155. 15.4: A COMPARISON OF STATISTICAL TESTS
156. 15.5: THE WILCOXON SIGNED-RANK TEST FOR A PAIRED EXPERIMENT
157. 15.6: THE KRUSKAL–WALLIS H-TEST FOR COMPLETELY RANDOMIZED DESIGNS
158. 15.7: THE FRIEDMAN Fr-TEST FOR RANDOMIZED BLOCK DESIGNS
159. 15.8: RANK CORRELATION COEFFICIENT
160. 15.9: SUMMARY
161. CHAPTER REVIEW
162. CASE STUDY: How’s Your Cholesterol Level?
163. Appendix I: Tables
164. Data Sources