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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
  127. CASE STUDY: Is Your Car “Made in the U.S.A.”?
  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
  165. Answers to Selected Exercises
  166. Index
  167. Credits
  168. Answers to MyPersonal Trainer Exercises
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