About The Authors |
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xix | |
Preface |
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xxiii | |
Chapter 1 Data and Statistics |
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1 | (32) |
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Statistics in Practice: Bloomberg Businessweek |
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2 | (1) |
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1.1 Applications in Business and Economics |
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3 | (2) |
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3 | (1) |
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3 | (1) |
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4 | (1) |
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4 | (1) |
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4 | (1) |
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4 | (1) |
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5 | (5) |
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Elements, Variables, and Observations |
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5 | (1) |
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5 | (2) |
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Categorical and Quantitative Data |
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7 | (1) |
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Cross-Sectional and Time Series Data |
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8 | (2) |
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10 | (3) |
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10 | (1) |
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11 | (1) |
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12 | (1) |
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13 | (1) |
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13 | (1) |
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1.4 Descriptive Statistics |
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13 | (2) |
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1.5 Statistical Inference |
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15 | (1) |
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16 | (1) |
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1.7 Big Data and Data Mining |
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17 | (2) |
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1.8 Computers and Statistical Analysis |
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19 | (1) |
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1.9 Ethical Guidelines for Statistical Practice |
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19 | (2) |
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21 | (1) |
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21 | (1) |
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22 | (8) |
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Appendix 1.1 Opening and Saving DATA Files and Converting to Stacked form with JMP |
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30 | (3) |
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Appendix 1.2 Getting Started with R and RStudio (MindTap Reader) |
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Appendix 1.3 Basic Data Manipulation in R (MindTap Reader) |
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Chapter 2 Descriptive Statistics: Tabular and Graphical Displays |
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33 | (74) |
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Statistics in Practice: Colgate-Palmolive Company |
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34 | (1) |
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2.1 Summarizing Data for a Categorical Variable |
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35 | (7) |
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35 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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36 | (1) |
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Bar Charts and Pie Charts |
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37 | (5) |
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2.2 Summarizing Data for a Quantitative Variable |
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42 | (15) |
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42 | (2) |
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Relative Frequency and Percent Frequency Distributions |
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44 | (1) |
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45 | (1) |
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45 | (2) |
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47 | (1) |
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47 | (10) |
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2.3 Summarizing Data for Two Variables Using Tables |
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57 | (8) |
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57 | (2) |
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59 | (6) |
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2.4 Summarizing Data for Two Variables Using Graphical Displays |
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65 | (6) |
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Scatter Diagram and Trendline |
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65 | (1) |
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Side-by-Side and Stacked Bar Charts |
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66 | (5) |
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2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays |
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71 | (6) |
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Creating Effective Graphical Displays |
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71 | (1) |
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Choosing the Type of Graphical Display |
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72 | (1) |
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73 | (2) |
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Data Visualization in Practice: Cincinnati Zoo and Botanical Garden |
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75 | (2) |
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77 | (1) |
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78 | (1) |
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79 | (1) |
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80 | (5) |
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Case Problem 1: Pelican Stores |
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85 | (1) |
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Case Problem 2: Movie Theater Releases |
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86 | (1) |
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Case Problem 3: Queen City |
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87 | (1) |
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Case Problem 4: Cut-Rate Machining, Inc. |
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88 | (2) |
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Appendix 2.1 Creating Tabular and Graphical Presentations with JMP |
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90 | (3) |
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Appendix 2.2 Creating Tabular and Graphical Presentations with Excel |
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93 | (14) |
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Appendix 2.3 Creating Tabular and Graphical Presentations with R (MindTap Reader) |
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Chapter 3 Descriptive Statistics: Numerical Measures |
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107 | (70) |
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Statistics in Practice: Small Fry Design |
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108 | (1) |
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109 | (13) |
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109 | (2) |
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111 | (1) |
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112 | (1) |
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113 | (2) |
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115 | (1) |
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115 | (1) |
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116 | (6) |
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3.2 Measures of Variability |
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122 | (7) |
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123 | (1) |
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123 | (1) |
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123 | (2) |
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125 | (1) |
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126 | (3) |
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3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers |
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129 | (8) |
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129 | (1) |
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130 | (1) |
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131 | (1) |
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132 | (2) |
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134 | (3) |
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3.4 Five-Number Summaries and Boxplots |
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137 | (5) |
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138 | (1) |
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138 | (1) |
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Comparative Analysis Using Boxplots |
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139 | (3) |
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3.5 Measures of Association Between Two Variables |
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142 | (8) |
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142 | (2) |
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Interpretation of the Covariance |
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144 | (2) |
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146 | (1) |
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Interpretation of the Correlation Coefficient |
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147 | (3) |
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3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness |
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150 | (3) |
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153 | (1) |
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154 | (1) |
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155 | (1) |
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156 | (6) |
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Case Problem 1: Pelican Stores |
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162 | (1) |
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Case Problem 2: Movie Theater Releases |
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163 | (1) |
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Case Problem 3: Business Schools of Asia-Pacific |
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164 | (1) |
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Case Problem 4: Heavenly Chocolates Website Transactions |
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164 | (2) |
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Case Problem 5: African Elephant Populations |
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166 | (2) |
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Appendix 3.1 Descriptive Statistics with JMP |
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168 | (3) |
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Appendix 3.2 Descriptive Statistics with Excel |
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171 | (6) |
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Appendix 3.3 Descriptive Statistics with R (MindTap Reader) |
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Chapter 4 Introduction to Probability |
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177 | (46) |
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Statistics in Practice: National Aeronautics and Space Administration |
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178 | (1) |
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4.1 Random Experiments, Counting Rules, and Assigning Probabilities |
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179 | (10) |
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Counting Rules, Combinations, and Permutations |
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180 | (4) |
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184 | (1) |
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Probabilities for the KP&L Project |
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185 | (4) |
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4.2 Events and Their Probabilities |
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189 | (4) |
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4.3 Some Basic Relationships of Probability |
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193 | (6) |
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193 | (1) |
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194 | (5) |
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4.4 Conditional Probability |
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199 | (8) |
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202 | (1) |
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202 | (5) |
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207 | (5) |
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210 | (2) |
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212 | (1) |
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213 | (1) |
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214 | (1) |
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214 | (5) |
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Case Problem 1: Hamilton County Judges |
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219 | (2) |
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Case Problem 2: Rob's Market |
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221 | (2) |
Chapter 5 Discrete Probability Distributions |
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223 | (59) |
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Statistics in Practice: Voter Waiting Times in Elections |
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224 | (1) |
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225 | (3) |
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Discrete Random Variables |
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225 | (1) |
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Continuous Random Variables |
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225 | (3) |
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5.2 Developing Discrete Probability Distributions |
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228 | (5) |
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5.3 Expected Value and Variance |
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233 | (5) |
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233 | (1) |
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233 | (5) |
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5.4 Bivariate Distributions, Covariance, and Financial Portfolios |
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238 | (9) |
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A Bivariate Empirical Discrete Probability Distribution |
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238 | (3) |
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241 | (3) |
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244 | (3) |
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5.5 Binomial Probability Distribution |
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247 | (11) |
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248 | (1) |
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Martin Clothing Store Problem |
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249 | (4) |
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Using Tables of Binomial Probabilities |
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253 | (1) |
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Expected Value and Variance for the Binomial Distribution |
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254 | (4) |
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5.6 Poisson Probability Distribution |
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258 | (4) |
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An Example Involving Time Intervals |
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259 | (1) |
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An Example Involving Length or Distance Intervals |
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260 | (2) |
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5.7 Hypergeometric Probability Distribution |
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262 | (3) |
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265 | (1) |
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266 | (1) |
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266 | (2) |
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268 | (4) |
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Case Problem 1: Go Bananas! Breakfast Cereal |
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272 | (1) |
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Case Problem 2: McNeil's Auto Mall |
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272 | (1) |
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Case Problem 3: Grievance Committee at Tuglar Corporation |
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273 | (2) |
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Appendix 5.1 Discrete Probability Distributions with JMP |
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275 | (3) |
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Appendix 5.2 Discrete Probability Distributions with Excel |
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278 | (4) |
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Appendix 5.3 Discrete Probability Distributions with R (MindTap Reader) |
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Chapter 6 Continuous Probability Distributions 281 Statistics in Practice: Procter & Gamble |
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282 | (37) |
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6.1 Uniform Probability Distribution |
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283 | (4) |
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Area as a Measure of Probability |
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284 | (3) |
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6.2 Normal Probability Distribution |
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287 | (12) |
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287 | (2) |
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Standard Normal Probability Distribution |
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289 | (5) |
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Computing Probabilities for Any Normal Probability Distribution |
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294 | (1) |
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Grear Tire Company Problem |
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294 | (5) |
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6.3 Normal Approximation of Binomial Probabilities |
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299 | (3) |
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6.4 Exponential Probability Distribution |
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302 | (3) |
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Computing Probabilities for the Exponential Distribution |
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302 | (1) |
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Relationship Between the Poisson and Exponential Distributions |
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303 | (2) |
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305 | (1) |
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305 | (1) |
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306 | (1) |
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306 | (3) |
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Case Problem 1: Specialty Toys |
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309 | (2) |
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Case Problem 2: Gebhardt Electronics |
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311 | (1) |
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Appendix 6.1 Continuous Probability Distributions with JMP |
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312 | (5) |
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Appendix 6.2 Continuous Probability Distributions with Excel |
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317 | (2) |
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Appendix 6.3 Continuous Probability Distribution with R (MindTap Reader) |
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Chapter 7 Sampling and Sampling Distributions |
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319 | (54) |
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Statistics in Practice: Meadwestvaco Corporation |
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320 | (1) |
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7.1 The Electronics Associates Sampling Problem |
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321 | (1) |
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322 | (5) |
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Sampling from a Finite Population |
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322 | (2) |
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Sampling from an Infinite Population |
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324 | (3) |
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327 | (4) |
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329 | (2) |
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7.4 Introduction to Sampling Distributions |
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331 | (2) |
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7.5 Sampling Distribution of x |
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333 | (10) |
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334 | (1) |
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334 | (1) |
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Form of the Sampling Distribution of x |
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335 | (2) |
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Sampling Distribution of x for the EAI Problem |
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337 | (1) |
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Practical Value of the Sampling Distribution of x |
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338 | (1) |
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Relationship Between the Sample Size and the Sampling Distribution of x |
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339 | (4) |
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7.6 Sampling Distribution of p |
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343 | (6) |
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344 | (1) |
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344 | (1) |
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Form of the Sampling Distribution of p |
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345 | (1) |
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Practical Value of the Sampling Distribution of p |
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345 | (4) |
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7.7 Properties of Point Estimators |
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349 | (2) |
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349 | (1) |
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350 | (1) |
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351 | (1) |
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7.8 Other Sampling Methods |
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351 | (3) |
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Stratified Random Sampling |
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352 | (1) |
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352 | (1) |
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353 | (1) |
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353 | (1) |
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354 | (1) |
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7.9 Big Data and Standard Errors of Sampling Distributions |
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354 | (6) |
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354 | (1) |
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355 | (1) |
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356 | (1) |
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Understanding What Big Data Is |
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356 | (1) |
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Implications of Big Data for Sampling Error |
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357 | (3) |
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360 | (1) |
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361 | (1) |
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362 | (1) |
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363 | (3) |
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Case Problem: Marion Dairies |
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366 | (1) |
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Appendix 7.1 The Expected Value and Standard Deviation of x |
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367 | (1) |
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Appendix 7.2 Random Sampling with JMP |
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368 | (3) |
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Appendix 7.3 Random Sampling with Excel |
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371 | (2) |
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Appendix 7.4 Random Sampling with R (MindTap Reader) |
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Chapter 8 Interval Estimation |
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373 | (44) |
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Statistics in Practice: Food Lion |
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374 | (1) |
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8.1 Population Mean: σ Known |
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375 | (6) |
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Margin of Error and the Interval Estimate |
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375 | (4) |
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379 | (2) |
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8.2 Population Mean: σ Unknown |
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381 | (9) |
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Margin of Error and the Interval Estimate |
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382 | (3) |
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385 | (1) |
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385 | (1) |
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Summary of Interval Estimation Procedures |
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386 | (4) |
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8.3 Determining the Sample Size |
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390 | (3) |
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8.4 Population Proportion |
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393 | (5) |
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Determining the Sample Size |
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394 | (4) |
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8.5 Big Data and Confidence Intervals |
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398 | (3) |
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Big Data and the Precision of Confidence Intervals |
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398 | (1) |
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Implications of Big Data for Confidence Intervals |
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399 | (2) |
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401 | (1) |
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402 | (1) |
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402 | (1) |
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403 | (3) |
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Case Problem 1: Young Professional Magazine |
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406 | (1) |
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Case Problem 2: Gulf Real Estate Properties |
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407 | (2) |
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Case Problem 3: Metropolitan Research, Inc. |
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409 | (1) |
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Appendix 8.1 Interval Estimation with JMP |
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410 | (3) |
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Appendix 8.2 Interval Estimation Using Excel |
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413 | (4) |
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Appendix 8.3 Interval Estimation with R (MindTap Reader) |
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Chapter 9 Hypothesis Tests |
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417 | (64) |
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Statistics in Practice: John Morrell & Company |
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418 | (1) |
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9.1 Developing Null and Alternative Hypotheses |
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419 | (3) |
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The Alternative Hypothesis as a Research Hypothesis |
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419 | (1) |
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The Null Hypothesis as an Assumption to Be Challenged |
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420 | (1) |
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Summary of Forms for Null and Alternative Hypotheses |
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421 | (1) |
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9.2 Type I and Type II Errors |
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422 | (3) |
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9.3 Population Mean: σ Known |
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425 | (14) |
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425 | (5) |
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430 | (3) |
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Summary and Practical Advice |
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433 | (1) |
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Relationship Between Interval Estimation and Hypothesis Testing |
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434 | (5) |
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9.4 Population Mean: σ Unknown |
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439 | (6) |
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439 | (1) |
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440 | (1) |
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Summary and Practical Advice |
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441 | (4) |
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9.5 Population Proportion |
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445 | (5) |
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447 | (3) |
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9.6 Hypothesis Testing and Decision Making |
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450 | (1) |
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9.7 Calculating the Probability of Type II Errors |
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450 | (5) |
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9.8 Determining the Sample Size for a Hypothesis Test About a Population Mean |
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455 | (4) |
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9.9 Big Data and Hypothesis Testing |
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459 | (3) |
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Big Data, Hypothesis Testing, and p Values |
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459 | (1) |
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Implications of Big Data in Hypothesis Testing |
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460 | (2) |
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462 | (1) |
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462 | (1) |
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463 | (1) |
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463 | (4) |
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Case Problem 1: Quality Associates, Inc. |
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467 | (2) |
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Case Problem 2: Ethical Behavior of Business Students at Bayview University |
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469 | (2) |
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Appendix 9.1 Hypothesis Testing with JMP |
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471 | (4) |
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Appendix 9.2 Hypothesis Testing with Excel |
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475 | (6) |
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Appendix 9.3 Hypothesis Testing with R (MindTap Reader) |
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Chapter 10 Inference About Means and Proportions with Two Populations |
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481 | (44) |
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Statistics in Practice: U.S. Food and Drug Administration |
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482 | (1) |
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10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known |
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483 | (6) |
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Interval Estimation of μ1 - μ2 |
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483 | (2) |
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Hypothesis Tests About μ1 - μ2 |
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485 | (2) |
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487 | (2) |
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10.2 Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown |
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489 | (8) |
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Interval Estimation of - μ1 - μ2 |
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489 | (2) |
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Hypothesis Tests About μ1 - μ2 |
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491 | (2) |
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493 | (4) |
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10.3 Inferences About the Difference Between Two Population Means: Matched Samples |
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497 | (6) |
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10.4 Inferences About the Difference Between Two Population Proportions |
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503 | (6) |
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Interval Estimation of p1 - p2 |
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503 | (2) |
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Hypothesis Tests About p1 - p2 |
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505 | (4) |
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509 | (1) |
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509 | (1) |
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509 | (2) |
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511 | (3) |
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514 | (1) |
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Appendix 10.1 Inferences About Two Populations with JMP |
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515 | (4) |
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Appendix 10.2 Inferences About Two Populations with Excel |
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519 | (6) |
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Appendix 10.3 Inferences about Two Populations with R (MindTap Reader) |
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Chapter 11 Inferences About Population Variances |
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525 | (28) |
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Statistics in Practice: U.S. Government Accountability Office |
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526 | (1) |
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11.1 Inferences About a Population Variance |
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527 | (10) |
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527 | (4) |
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531 | (6) |
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11.2 Inferences About Two Population Variances |
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537 | (7) |
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544 | (1) |
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544 | (1) |
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544 | (2) |
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Case Problem 1: Air Force Training Program |
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546 | (1) |
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Case Problem 2: Meticulous Drill & Reamer |
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547 | (2) |
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Appendix 11.1 Population Variances with JMP |
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549 | (2) |
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Appendix 11.2 Population Variances with Excel |
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551 | (2) |
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Appendix 11.3 Population Variances with R (MindTap Reader) |
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Chapter 12 Comparing Multiple Proportions, Test of Independence and Goodness of Fit |
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553 | (44) |
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Statistics in Practice: United Way |
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554 | (1) |
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12.1 Testing the Equality of Population Proportions for Three or More Populations |
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555 | (10) |
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A Multiple Comparison Procedure |
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560 | (5) |
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12.2 Test of Independence |
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565 | (8) |
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12.3 Goodness of Fit Test |
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573 | (9) |
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Multinomial Probability Distribution |
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573 | (3) |
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Normal Probability Distribution |
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576 | (6) |
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582 | (1) |
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582 | (1) |
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583 | (1) |
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583 | (4) |
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Case Problem 1: A Bipartisan Agenda for Change |
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587 | (1) |
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Case Problem 2: Fuentes Salty Snacks, Inc. |
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588 | (1) |
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Case Problem 3: Fresno Board Games |
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588 | (2) |
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Appendix 12.1 Chi-Square Tests with JMP |
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590 | (3) |
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Appendix 12.2 Chi-Square Tests with Excel |
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593 | (4) |
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Appendix 12.3 Chi-Squared Tests with R (MindTap Reader) |
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Chapter 13 Experimental Design and Analysis of Variance |
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597 | (56) |
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Statistics in Practice: Burke Marketing Services, Inc. |
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598 | (1) |
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13.1 An Introduction to Experimental Design and Analysis of Variance |
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599 | (5) |
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600 | (1) |
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Assumptions for Analysis of Variance |
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601 | (1) |
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Analysis of Variance: A Conceptual Overview |
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601 | (3) |
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13.2 Analysis of Variance and the Completely Randomized Design |
|
|
604 | (11) |
|
Between-Treatments Estimate of Population Variance |
|
|
605 | (1) |
|
Within-Treatments Estimate of Population Variance |
|
|
606 | (1) |
|
Comparing the Variance Estimates: The F Test |
|
|
606 | (2) |
|
|
608 | (1) |
|
Computer Results for Analysis of Variance |
|
|
609 | (1) |
|
Testing for the Equality of k Population Means: An Observational Study |
|
|
610 | (5) |
|
13.3 Multiple Comparison Procedures |
|
|
615 | (6) |
|
|
615 | (2) |
|
|
617 | (4) |
|
13.4 Randomized Block Design |
|
|
621 | (6) |
|
Air Traffic Controller Stress Test |
|
|
621 | (2) |
|
|
623 | (1) |
|
Computations and Conclusions |
|
|
623 | (4) |
|
13.5 Factorial Experiment |
|
|
627 | (8) |
|
|
629 | (1) |
|
Computations and Conclusions |
|
|
629 | (6) |
|
|
635 | (1) |
|
|
635 | (1) |
|
|
636 | (2) |
|
|
638 | (5) |
|
Case Problem 1: Wentworth Medical Center |
|
|
643 | (1) |
|
Case Problem 2: Compensation for Sales Professionals |
|
|
644 | (1) |
|
Case Problem 3: Touristopia Travel |
|
|
644 | (2) |
|
Appendix 13.1 Analysis of Variance with JMP |
|
|
646 | (3) |
|
Appendix 13.2 Analysis of Variance with Excel |
|
|
649 | (4) |
|
Appendix 13.3 Analysis Variance with R (MindTap Reader) |
|
|
Chapter 14 Simple Linear Regression |
|
653 | (78) |
|
Statistics in Practice: Alliance Data Systems |
|
|
654 | (1) |
|
14.1 Simple Linear Regression Model |
|
|
655 | (3) |
|
Regression Model and Regression Equation |
|
|
655 | (1) |
|
Estimated Regression Equation |
|
|
656 | (2) |
|
14.2 Least Squares Method |
|
|
658 | (10) |
|
14.3 Coefficient of Determination |
|
|
668 | (7) |
|
|
671 | (4) |
|
|
675 | (1) |
|
14.5 Testing for Significance |
|
|
676 | (8) |
|
|
676 | (1) |
|
|
677 | (2) |
|
Confidence Interval for β1 |
|
|
679 | (1) |
|
|
679 | (2) |
|
Some Cautions About the Interpretation of Significance Tests |
|
|
681 | (3) |
|
14.6 Using the Estimated Regression Equation |
|
|
|
for Estimation and Prediction |
|
|
684 | (1) |
|
|
685 | (1) |
|
Confidence Interval for the Mean Value of y |
|
|
685 | (1) |
|
Prediction Interval for an Individual Value of y |
|
|
686 | (5) |
|
|
691 | (3) |
|
14.8 Residual Analysis: Validating Model Assumptions |
|
|
694 | (9) |
|
|
695 | (2) |
|
|
697 | (1) |
|
|
698 | (1) |
|
|
699 | (4) |
|
14.9 Residual Analysis: Outliers and Influential Observations |
|
|
703 | (7) |
|
|
703 | (1) |
|
Detecting Influential Observations |
|
|
704 | (6) |
|
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression |
|
|
710 | (1) |
|
|
711 | (1) |
|
|
711 | (1) |
|
|
712 | (2) |
|
|
714 | (7) |
|
Case Problem 1: Measuring Stock Market Risk |
|
|
721 | (1) |
|
Case Problem 2: U.S. Department of Transportation |
|
|
721 | (1) |
|
Case Problem 3: Selecting a Point-and-Shoot Digital Camera |
|
|
722 | (1) |
|
Case Problem 4: Finding the Best Car Value |
|
|
723 | (1) |
|
Case Problem 5: Buckeye Creek Amusement Park |
|
|
724 | (2) |
|
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas |
|
|
726 | (1) |
|
Appendix 14.2 A Test for Significance Using Correlation |
|
|
727 | (1) |
|
Appendix 14.3 Simple Linear Regression with JMP |
|
|
727 | (1) |
|
Appendix 14.4 Regression Analysis with Excel |
|
|
728 | (3) |
|
Appendix 14.5 Simple Linear Regression with R (MindTap Reader) |
|
|
Chapter 15 Multiple Regression |
|
731 | (69) |
|
Statistics in Practice: 84.51° |
|
|
732 | (1) |
|
15.1 Multiple Regression Model |
|
|
733 | (1) |
|
Regression Model and Regression Equation |
|
|
733 | (1) |
|
Estimated Multiple Regression Equation |
|
|
733 | (1) |
|
15.2 Least Squares Method |
|
|
734 | (9) |
|
An Example: Butler Trucking Company |
|
|
735 | (2) |
|
Note on Interpretation of Coefficients |
|
|
737 | (6) |
|
15.3 Multiple Coefficient of Determination |
|
|
743 | (3) |
|
|
746 | (1) |
|
15.5 Testing for Significance |
|
|
747 | (6) |
|
|
747 | (3) |
|
|
750 | (1) |
|
|
750 | (3) |
|
15.6 Using the Estimated Regression Equation for Estimation and Prediction |
|
|
753 | (2) |
|
15.7 Categorical Independent Variables |
|
|
755 | (9) |
|
An Example: Johnson Filtration, Inc. |
|
|
756 | (2) |
|
Interpreting the Parameters |
|
|
758 | (2) |
|
More Complex Categorical Variables |
|
|
760 | (4) |
|
|
764 | (7) |
|
|
766 | (1) |
|
Studentized Deleted Residuals and Outliers |
|
|
766 | (1) |
|
|
767 | (1) |
|
Using Cook's Distance Measure to Identify Influential Observations |
|
|
767 | (4) |
|
|
771 | (11) |
|
Logistic Regression Equation |
|
|
772 | (1) |
|
Estimating the Logistic Regression Equation |
|
|
773 | (1) |
|
|
774 | (1) |
|
|
775 | (1) |
|
Interpreting the Logistic Regression Equation |
|
|
776 | (2) |
|
|
778 | (4) |
|
15.10 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression |
|
|
782 | (1) |
|
|
783 | (1) |
|
|
783 | (1) |
|
|
784 | (2) |
|
|
786 | (4) |
|
Case Problem 1: Consumer Research, Inc. |
|
|
790 | (1) |
|
Case Problem 2: Predicting Winnings for NASCAR Drivers |
|
|
791 | (1) |
|
Case Problem 3: Finding the Best Car Value |
|
|
792 | (2) |
|
Appendix 15.1 Multiple Linear Regression with JMP |
|
|
794 | (2) |
|
Appendix 15.2 Logistic Regression with JMP |
|
|
796 | (1) |
|
Appendix 15.3 Multiple Regression with Excel |
|
|
797 | (3) |
|
Appendix 15.4 Multiple Linear Regression with R (MindTap Reader) |
|
|
|
Appendix 15.5 Logistics Regression with R (MindTap Reader) |
|
|
Appendix A References And Bibliography |
|
800 | (2) |
Appendix B Tables |
|
802 | (27) |
Appendix C Summation Notation |
|
829 | (2) |
Appendix D Answers To Even-Numbered Exercises (Mindtap Reader) |
|
Appendix E Microsoft Excel 2016 And Tools For Statistical Analysis |
|
831 | (8) |
Appendix F Computing P-Values With JMP And Excel |
|
839 | (4) |
Index |
|
843 | |