Preface |
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xix | |
About the Authors |
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xxv | |
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Chapter 1 Data and Statistics |
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1 | (34) |
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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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1.6 Statistical Analysis Using Microsoft Excel |
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16 | (4) |
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Data Sets and Excel Worksheets |
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17 | (1) |
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Using Excel for Statistical Analysis |
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18 | (2) |
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20 | (1) |
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1.8 Big Data and Data Mining |
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21 | (1) |
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1.9 Ethical Guidelines for Statistical Practice |
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22 | (13) |
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24 | (1) |
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24 | (1) |
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25 | (10) |
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Appendix 1.1 Getting Started with R and RStudio (MindTap Reader) |
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Appendix 1.2 Basic Data Manipulation in R (MindTap Reader) |
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Chapter 2 Descriptive Statistics: Tabular and Graphical Displays |
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35 | (68) |
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Statistics in Practice: Colgate-Palmolive Company |
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36 | (1) |
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2.1 Summarizing Data for a Categorical Variable |
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37 | (10) |
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37 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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38 | (1) |
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Using Excel to Construct a Frequency Distribution, a Relative Frequency Distribution, and a Percent Frequency Distribution |
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39 | (1) |
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Bar Charts and Pie Charts |
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40 | (2) |
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Using Excel to Construct a Bar Chart |
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42 | (5) |
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2.2 Summarizing Data for a Quantitative Variable |
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47 | (18) |
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47 | (2) |
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Relative Frequency and Percent Frequency Distributions |
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49 | (1) |
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Using Excel to Construct a Frequency Distribution |
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50 | (1) |
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51 | (1) |
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52 | (2) |
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Using Excel's Recommended Charts Tool to Construct a Histogram |
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54 | (1) |
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55 | (1) |
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56 | (9) |
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2.3 Summarizing Data for Two Variables Using Tables |
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65 | (10) |
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65 | (3) |
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Using Excel's PivotTable Tool to Construct a Crosstabulation |
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68 | (1) |
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69 | (6) |
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2.4 Summarizing Data for Two Variables Using Graphical Displays |
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75 | (10) |
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Scatter Diagram and Trendline |
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76 | (1) |
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Using Excel to Construct a Scatter Diagram and a Trendline |
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77 | (2) |
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Side-by-Side and Stacked Bar Charts |
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79 | (2) |
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Using Excel's Recommended Charts Tool to Construct Side-by-Side and Stacked Bar Charts |
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81 | (4) |
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2.5 Data Visualization: Best Practices in Creating Effective Graphical Displays |
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85 | (13) |
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Creating Effective Graphical Displays |
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85 | (1) |
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Choosing the Type of Graphical Display |
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86 | (1) |
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86 | (2) |
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Data Visualization in Practice: Cincinnati Zoo and Botanical Garden |
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88 | (2) |
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90 | (1) |
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91 | (1) |
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92 | (1) |
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93 | (5) |
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Case Problem 1 Pelican Stores |
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98 | (1) |
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Case Problem 2 Movie Theater Releases |
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99 | (1) |
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Case Problem 3 Queen City |
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100 | (1) |
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Case Problem 4 Cut-Rate Machining, Inc. |
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100 | (3) |
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Appendix 2.1 Creating Tabular and Graphical Presentations with R (MindTap Reader) |
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Chapter 3 Descriptive Statistics: Numerical Measures |
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103 | (68) |
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Statistics in Practice: Small Fry Design |
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104 | (1) |
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105 | (16) |
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105 | (2) |
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107 | (1) |
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108 | (1) |
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Using Excel to Compute the Mean, Median, and Mode |
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109 | (1) |
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109 | (2) |
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111 | (1) |
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Using Excel to Compute the Geometric Mean |
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112 | (1) |
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113 | (1) |
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114 | (1) |
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Using Excel to Compute Percentiles and Quartiles |
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115 | (6) |
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3.2 Measures of Variability |
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121 | (9) |
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122 | (1) |
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122 | (1) |
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122 | (2) |
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124 | (1) |
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Using Excel to Compute the Sample Variance and Sample Standard Deviation |
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125 | (1) |
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126 | (1) |
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Using Excel's Descriptive Statistics Tool |
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126 | (4) |
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3.3 Measures of Distribution Shape, Relative Location, and Detecting Outliers |
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130 | (8) |
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130 | (1) |
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131 | (1) |
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132 | (1) |
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133 | (1) |
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134 | (4) |
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3.4 Five-Number Summaries and Boxplots |
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138 | (6) |
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138 | (1) |
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138 | (1) |
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Using Excel to Construct a Boxplot |
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139 | (1) |
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Comparative Analysis Using Boxplots |
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139 | (1) |
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Using Excel to Construct a Comparative Analysis Using Boxplots |
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140 | (4) |
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3.5 Measures of Association Between Two Variables |
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144 | (9) |
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144 | (2) |
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Interpretation of the Covariance |
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146 | (2) |
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148 | (1) |
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Interpretation of the Correlation Coefficient |
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149 | (2) |
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Using Excel to Compute the Sample Covariance and Sample Correlation Coefficient |
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151 | (2) |
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3.6 Data Dashboards: Adding Numerical Measures to Improve Effectiveness |
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153 | (12) |
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156 | (1) |
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157 | (1) |
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158 | (1) |
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159 | (6) |
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Case Problem 1 Pelican Stores |
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165 | (1) |
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Case Problem 2 Movie Theater Releases |
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166 | (1) |
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Case Problem 3 Business Schools of Asia-Pacific |
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167 | (1) |
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Case Problem 4 Heavenly Chocolates Website Transactions |
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167 | (2) |
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Case Problem 5 African Elephant Populations |
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169 | (2) |
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Appendix 3.1 Descriptive Statistics with R (MindTap Reader) |
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Chapter 4 Introduction to Probability |
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171 | (46) |
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Statistics in Practice: National Aeronautics and Space Administration |
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172 | (1) |
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4.1 Experiments, Counting Rules, and Assigning Probabilities |
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173 | (10) |
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Counting Rules, Combinations, and Permutations |
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174 | (4) |
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178 | (1) |
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Probabilities for the KP&L Project |
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179 | (4) |
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4.2 Events and Their Probabilities |
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183 | (4) |
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4.3 Some Basic Relationships of Probability |
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187 | (6) |
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187 | (1) |
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188 | (5) |
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4.4 Conditional Probability |
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193 | (8) |
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196 | (1) |
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196 | (5) |
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201 | (12) |
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204 | (2) |
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206 | (1) |
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207 | (1) |
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208 | (1) |
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208 | (5) |
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Case Problem 1 Hamilton County Judges |
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213 | (2) |
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Case Problem 2 Rob's Market |
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215 | (2) |
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Chapter 5 Discrete Probability Distributions |
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217 | (56) |
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Statistics in Practice: Voter Waiting Times in Elections |
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218 | (1) |
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218 | (3) |
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Discrete Random Variables |
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219 | (1) |
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Continuous Random Variables |
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220 | (1) |
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5.2 Developing Discrete Probability Distributions |
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221 | (5) |
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5.3 Expected Value and Variance |
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226 | (7) |
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226 | (1) |
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227 | (1) |
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Using Excel to Compute the Expected Value, Variance, and Standard Deviation |
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228 | (5) |
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5.4 Bivariate Distributions, Covariance, and Financial Portfolios |
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233 | (9) |
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A Bivariate Empirical Discrete Probability Distribution |
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233 | (3) |
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236 | (3) |
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239 | (3) |
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5.5 Binomial Probability Distribution |
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242 | (10) |
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242 | (2) |
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Martin Clothing Store Problem |
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244 | (4) |
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Using Excel to Compute Binomial Probabilities |
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248 | (1) |
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Expected Value and Variance for the Binomial Distribution |
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249 | (3) |
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5.6 Poisson Probability Distribution |
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252 | (5) |
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An Example Involving Time Intervals |
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253 | (1) |
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An Example Involving Length or Distance Intervals |
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254 | (1) |
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Using Excel to Compute Poisson Probabilities |
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254 | (3) |
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5.7 Hypergeometric Probability Distribution |
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257 | (11) |
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Using Excel to Compute Hypergeometric Probabilities |
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259 | (2) |
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261 | (1) |
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262 | (1) |
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263 | (1) |
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264 | (4) |
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Case Problem 1 Go Bananas! Breakfast Cereal |
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268 | (1) |
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Case Problem 2 McNeil's Auto Mall |
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269 | (1) |
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Case Problem 3 Grievance Committee at Tuglar Corporation |
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270 | (1) |
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Case Problem 4 Sagittarius Casino |
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270 | (3) |
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Appendix 5.1 Discrete Probability Distributions with R (MindTap Reader) |
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Chapter 6 Continuous Probability Distributions |
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273 | (32) |
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Statistics in Practice: Procter & Gamble |
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274 | (1) |
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6.1 Uniform Probability Distribution |
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275 | (4) |
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Area as a Measure of Probability |
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276 | (3) |
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6.2 Normal Probability Distribution |
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279 | (14) |
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279 | (2) |
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Standard Normal Probability Distribution |
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281 | (4) |
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Computing Probabilities for Any Normal Probability Distribution |
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285 | (1) |
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Grear Tire Company Problem |
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286 | (2) |
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Using Excel to Compute Normal Probabilities |
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288 | (5) |
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6.3 Exponential Probability Distribution |
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293 | (8) |
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Computing Probabilities for the Exponential Distribution |
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294 | (1) |
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Relationship Between the Poisson and Exponential Distributions |
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295 | (1) |
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Using Excel to Compute Exponential Probabilities |
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295 | (3) |
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298 | (1) |
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298 | (1) |
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298 | (1) |
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299 | (2) |
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Case Problem 1 Specialty Toys |
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301 | (1) |
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Case Problem 2 Gebhardt Electronics |
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302 | (3) |
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Appendix 6.1 Continuous Probability Distributions with R (MindTap Reader) |
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Chapter 7 Sampling and Sampling Distributions |
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305 | (50) |
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Statistics in Practice: The Food and Agriculture Organization |
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306 | (1) |
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7.1 The Electronics Associates Sampling Problem |
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307 | (1) |
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308 | (8) |
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Sampling from a Finite Population |
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308 | (4) |
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Sampling from an Infinite Population |
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312 | (4) |
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316 | (3) |
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317 | (2) |
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7.4 Introduction to Sampling Distributions |
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319 | (3) |
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7.5 Sampling Distribution of x |
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322 | (9) |
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322 | (1) |
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322 | (2) |
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Form of the Sampling Distribution of x |
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324 | (1) |
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Sampling Distribution of x for the EAI Problem |
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324 | (1) |
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Practical Value of the Sampling Distribution of x |
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325 | (2) |
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Relationship Between the Sample Size and the Sampling Distribution of x |
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327 | (4) |
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7.6 Sampling Distribution of p |
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331 | (6) |
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332 | (1) |
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332 | (1) |
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Form of the Sampling Distribution of p |
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333 | (1) |
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Practical Value of the Sampling Distribution of p |
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333 | (4) |
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7.7 Other Sampling Methods |
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337 | (2) |
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Stratified Random Sampling |
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337 | (1) |
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337 | (1) |
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338 | (1) |
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338 | (1) |
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339 | (1) |
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7.8 Practical Advice: Big Data and Errors in Sampling |
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339 | (16) |
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339 | (1) |
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340 | (1) |
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341 | (1) |
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Understanding What Big Data Is |
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342 | (1) |
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Implications of Big Data for Sampling Error |
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343 | (5) |
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348 | (1) |
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348 | (1) |
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349 | (1) |
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350 | (3) |
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Case Problem: Marion Dairies |
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353 | (2) |
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Appendix 7.1 Random Sampling with R (MindTap Reader) |
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Chapter 8 Interval Estimation |
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355 | (42) |
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Statistics in Practice: Food Lion |
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356 | (1) |
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8.1 Population Mean: σ Known |
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357 | (7) |
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Margin of Error and the Interval Estimate |
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357 | (4) |
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361 | (1) |
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362 | (2) |
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8.2 Population Mean: σ Unknown |
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364 | (10) |
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Margin of Error and the Interval Estimate |
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365 | (3) |
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368 | (1) |
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369 | (1) |
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369 | (2) |
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Summary of Interval Estimation Procedures |
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371 | (3) |
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8.3 Determining the Sample Size |
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374 | (3) |
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8.4 Population Proportion |
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377 | (7) |
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378 | (2) |
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Determining the Sample Size |
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380 | (4) |
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8.5 Practical Advice: Big Data and Interval Estimation |
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384 | (8) |
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Big Data and the Precision of Confidence Intervals |
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384 | (1) |
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Implications of Big Data for Confidence Intervals |
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385 | (2) |
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387 | (1) |
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388 | (1) |
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388 | (1) |
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389 | (3) |
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Case Problem 1 Young Professional Magazine |
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392 | (1) |
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Case Problem 2 GULF Real Estate Properties |
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393 | (2) |
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Case Problem 3 Metropolitan Research, Inc. |
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395 | (2) |
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Appendix 8.1 Interval Estimation with R (MindTap Reader) |
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Chapter 9 Hypothesis Tests |
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397 | (48) |
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Statistics in Practice: John Morrell & Company |
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398 | (1) |
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9.1 Developing Null and Alternative Hypotheses |
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399 | (3) |
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The Alternative Hypothesis as a Research Hypothesis |
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399 | (1) |
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The Null Hypothesis as an Assumption to Be Challenged |
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400 | (1) |
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Summary of Forms for Null and Alternative Hypotheses |
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401 | (1) |
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9.2 Type I and Type II Errors |
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402 | (3) |
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9.3 Population Mean: σ Known |
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405 | (15) |
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405 | (5) |
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410 | (3) |
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413 | (1) |
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Summary and Practical Advice |
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414 | (1) |
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Relationship Between Interval Estimation and Hypothesis Testing |
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415 | (5) |
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9.4 Population Mean: σ Unknown |
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420 | (8) |
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421 | (1) |
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422 | (1) |
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423 | (2) |
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Summary and Practical Advice |
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425 | (3) |
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9.5 Population Proportion |
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428 | (6) |
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430 | (1) |
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431 | (3) |
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9.6 Practical Advice: Big Data and Hypothesis Testing |
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434 | (8) |
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Big Data, Hypothesis Testing, and p-Values |
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434 | (2) |
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Implications of Big Data in Hypothesis Testing |
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436 | (1) |
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437 | (1) |
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438 | (1) |
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438 | (1) |
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439 | (3) |
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Case Problem 1 Quality Associates, Inc. |
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442 | (1) |
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Case Problem 2 Ethical Behavior of Business Students at Bayview University |
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443 | (2) |
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Appendix 9.1 Hypothesis Testing with R (MindTap Reader) |
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Chapter 10 Inference About Means and Proportions with Two Populations |
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445 | (44) |
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Statistics in Practice: U.S. Food and Drug Administration |
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446 | (1) |
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10.1 Inferences About the Difference Between Two Population Means: σ1 and σ2 Known |
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447 | (9) |
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Interval Estimation of μ1 -- μ2 |
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447 | (2) |
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Using Excel to Construct a Confidence Interval |
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449 | (2) |
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Hypothesis Tests About μ1 -- μ2 |
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451 | (1) |
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Using Excel to Conduct a Hypothesis Test |
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452 | (2) |
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454 | (2) |
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10.2 Inferences About the Difference Between Two Population Means: σ1 and σ2 Unknown |
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456 | (11) |
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Interval Estimation of μ1 -- μ2 |
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457 | (1) |
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Using Excel to Construct a Confidence Interval |
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458 | (2) |
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Hypothesis Tests About μ1 -- μ2 |
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460 | (2) |
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Using Excel to Conduct a Hypothesis Test |
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462 | (1) |
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463 | (4) |
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10.3 Inferences About the Difference Between Two Population Means: Matched Samples |
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467 | (7) |
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Using Excel to Conduct a Hypothesis Test |
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469 | (5) |
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10.4 Inferences About the Difference Between Two Population Proportions |
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474 | (15) |
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Interval Estimation of p1 -- p2 |
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474 | (2) |
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Using Excel to Construct a Confidence Interval |
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476 | (1) |
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Hypothesis Tests About p1 -- p2 |
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477 | (2) |
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Using Excel to Conduct a Hypothesis Test |
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479 | (4) |
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483 | (1) |
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483 | (1) |
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483 | (2) |
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485 | (3) |
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488 | (1) |
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Appendix 10.1 Inferences About Two Populations with R (MindTap Reader) |
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Chapter 11 Inferences About Population Variances |
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489 | (28) |
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Statistics in Practice: U.S. Government Accountability Office |
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490 | (1) |
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11.1 Inferences About a Population Variance |
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491 | (12) |
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491 | (4) |
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Using Excel to Construct a Confidence Interval |
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495 | (1) |
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496 | (2) |
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Using Excel to Conduct a Hypothesis Test |
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498 | (5) |
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11.2 Inferences About Two Population Variances |
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503 | (10) |
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Using Excel to Conduct a Hypothesis Test |
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507 | (4) |
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511 | (1) |
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511 | (1) |
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511 | (2) |
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Case Problem 1 Air Force Training Program |
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513 | (1) |
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Case Problem 2 Meticulous Drill & Reamer |
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514 | (3) |
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Appendix 11.1 Population Variances with R (MindTap Reader) |
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Chapter 12 Tests of Goodness of Fit, Independence, and Multiple Proportions |
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517 | (34) |
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Statistics in Practice: United Way |
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518 | (1) |
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12.1 Goodness of Fit Test |
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519 | (6) |
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Multinomial Probability Distribution |
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519 | (4) |
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Using Excel to Conduct a Goodness of Fit Test |
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523 | (2) |
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12.2 Test of Independence |
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525 | (9) |
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Using Excel to Conduct a Test of Independence |
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529 | (5) |
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12.3 Testing for Equality of Three or More Population Proportions |
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534 | (13) |
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A Multiple Comparison Procedure |
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537 | (2) |
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Using Excel to Conduct a Test of Multiple Proportions |
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539 | (4) |
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543 | (1) |
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544 | (1) |
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544 | (1) |
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|
544 | (3) |
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Case Problem 1 A Bipartisan Agenda for Change |
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|
547 | (1) |
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Case Problem 2 Fuentes Salty Snacks, Inc. |
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|
548 | (1) |
|
Case Problem 3 Fresno Board Games |
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|
549 | (2) |
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Appendix 12.1 Chi-Square Tests with R (MindTap Reader) |
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|
|
Chapter 13 Experimental Design and Analysis of Variance |
|
|
551 | (54) |
|
Statistics in Practice: Burke, Inc. |
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|
552 | (1) |
|
13.1 An Introduction to Experimental Design and Analysis of Variance |
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|
553 | (5) |
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|
554 | (2) |
|
Assumptions for Analysis of Variance |
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|
556 | (1) |
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Analysis of Variance: A Conceptual Overview |
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|
556 | (2) |
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13.2 Analysis of Variance and the Completely Randomized Design |
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|
558 | (12) |
|
Between-Treatments Estimate of Population Variance |
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|
559 | (1) |
|
Within-Treatments Estimate of Population Variance |
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|
560 | (1) |
|
Comparing the Variance Estimates: The F Test |
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|
561 | (1) |
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|
562 | (1) |
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|
563 | (1) |
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Testing for the Equality of k Population Means: An Observational Study |
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|
564 | (6) |
|
13.3 Multiple Comparison Procedures |
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|
570 | (5) |
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|
570 | (2) |
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|
572 | (3) |
|
13.4 Randomized Block Design |
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|
575 | (9) |
|
Air Traffic Controller Stress Test |
|
|
576 | (1) |
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|
577 | (1) |
|
Computations and Conclusions |
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|
578 | (1) |
|
|
579 | (5) |
|
13.5 Factorial Experiment |
|
|
584 | (17) |
|
|
585 | (1) |
|
Computations and Conclusions |
|
|
586 | (3) |
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|
589 | (4) |
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|
593 | (1) |
|
|
594 | (1) |
|
|
595 | (1) |
|
Completely Randomized Design |
|
|
595 | (1) |
|
Multiple Comparison Procedures |
|
|
596 | (1) |
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|
596 | (1) |
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|
596 | (1) |
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|
596 | (5) |
|
Case Problem 1 Wentworth Medical Center |
|
|
601 | (1) |
|
Case Problem 2 Compensation for Sales Professionals |
|
|
602 | (1) |
|
Case Problem 3 TourisTopia Travel |
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|
603 | (2) |
|
Appendix 13.1 Analysis of Variance with R (MindTap Reader) |
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|
|
Chapter 14 Simple Linear Regression |
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|
605 | (80) |
|
Statistics in Practice: walmart.com |
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|
606 | (1) |
|
14.1 Simple Linear Regression Model |
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|
607 | (3) |
|
Regression Model and Regression Equation |
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|
607 | (2) |
|
Estimated Regression Equation |
|
|
609 | (1) |
|
14.2 Least Squares Method |
|
|
610 | (11) |
|
Using Excel to Construct a Scatter Diagram, Display the Estimated Regression Line, and Display the Estimated Regression Equation |
|
|
614 | (7) |
|
14.3 Coefficient of Determination |
|
|
621 | (8) |
|
Using Excel to Compute the Coefficient of Determination |
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|
625 | (1) |
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626 | (3) |
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|
629 | (2) |
|
14.5 Testing for Significance |
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|
631 | (8) |
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|
631 | (1) |
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|
632 | (1) |
|
Confidence Interval for β1 |
|
|
633 | (1) |
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|
634 | (2) |
|
Some Cautions About the Interpretation of Significance Tests |
|
|
636 | (3) |
|
14.6 Using the Estimated Regression Equation for Estimation and Prediction |
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|
639 | (7) |
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|
640 | (1) |
|
Confidence Interval for the Mean Value of y |
|
|
640 | (1) |
|
Prediction Interval for an Individual Value of y |
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|
641 | (5) |
|
14.7 Excel's Regression Tool |
|
|
646 | (5) |
|
Using Excel's Regression Tool for the Armand's Pizza Parlors Example |
|
|
646 | (1) |
|
Interpretation of Estimated Regression Equation Output |
|
|
647 | (1) |
|
Interpretation of ANOVA Output |
|
|
648 | (1) |
|
Interpretation of Regression Statistics Output |
|
|
649 | (2) |
|
14.8 Residual Analysis: Validating Model Assumptions |
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|
651 | (12) |
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|
652 | (1) |
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|
653 | (2) |
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|
655 | (2) |
|
Using Excel to Construct a Residual Plot |
|
|
657 | (3) |
|
|
660 | (3) |
|
14.9 Outliers and Influential Observations |
|
|
663 | (7) |
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|
663 | (2) |
|
Detecting Influential Observations |
|
|
665 | (5) |
|
14.10 Practical Advice: Big Data and Hypothesis Testing in Simple Linear Regression |
|
|
670 | (8) |
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|
671 | (1) |
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|
671 | (1) |
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|
672 | (2) |
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|
674 | (4) |
|
Case Problem 1 Measuring Stock Market Risk |
|
|
678 | (1) |
|
Case Problem 2 U.S. Department of Transportation |
|
|
679 | (1) |
|
Case Problem 3 Selecting a Point-and-Shoot Digital Camera |
|
|
680 | (1) |
|
Case Problem 4 Finding the Best Car Value |
|
|
681 | (1) |
|
Case Problem 5 Buckeye Creek Amusement Park |
|
|
682 | (1) |
|
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas |
|
|
683 | (1) |
|
Appendix 14.2 A Test for Significance Using Correlation |
|
|
684 | (1) |
|
Appendix 14.3 Simple Linear Regression with R (MindTap Reader) |
|
|
|
Chapter 15 Multiple Regression |
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|
685 | (49) |
|
Statistics in Practice: International Paper |
|
|
686 | (1) |
|
15.1 Multiple Regression Model |
|
|
687 | (1) |
|
Regression Model and Regression Equation |
|
|
687 | (1) |
|
Estimated Multiple Regression Equation |
|
|
687 | (1) |
|
15.2 Least Squares Method |
|
|
688 | (10) |
|
An Example: Butler Trucking Company |
|
|
689 | (2) |
|
Using Excel's Regression Tool to Develop the Estimated Multiple Regression Equation |
|
|
691 | (2) |
|
Note on Interpretation of Coefficients |
|
|
693 | (5) |
|
15.3 Multiple Coefficient of Determination |
|
|
698 | (2) |
|
|
700 | (2) |
|
15.5 Testing for Significance |
|
|
702 | (6) |
|
|
702 | (2) |
|
|
704 | (1) |
|
|
705 | (3) |
|
15.6 Using the Estimated Regression Equation for Estimation and Prediction |
|
|
708 | (2) |
|
15.7 Categorical Independent Variables |
|
|
710 | (8) |
|
An Example: Johnson Filtration, Inc. |
|
|
710 | (2) |
|
Interpreting the Parameters |
|
|
712 | (1) |
|
More Complex Categorical Variables |
|
|
713 | (5) |
|
|
718 | (4) |
|
|
718 | (1) |
|
Standardized Residual Plot Against y |
|
|
719 | (3) |
|
15.9 Practical Advice: Big Data and Hypothesis Testing in Multiple Regression |
|
|
722 | (7) |
|
|
723 | (1) |
|
|
723 | (1) |
|
|
724 | (1) |
|
|
725 | (4) |
|
Case Problem 1 Consumer Research, Inc. |
|
|
729 | (1) |
|
Case Problem 2 Predicting Winnings for NASCAR Drivers |
|
|
730 | (2) |
|
Case Problem 3 Finding the Best Car Value |
|
|
732 | (2) |
|
Appendix 15.1 Multiple Linear Regression with R (MindTap Reader) |
|
|
Appendix A References and Bibliography |
|
734 | (2) |
Appendix B Tables |
|
736 | (11) |
Appendix C Summation Notation |
|
747 | (2) |
Appendix D Answers to Even-Numbered Exercises (MindTap Reader) |
|
Appendix E Microsoft Excel and Tools for Statistical Analysis |
|
749 | (8) |
Appendix F Microsoft Excel Online and Tools for Statistical Analysis |
|
757 | (8) |
Index |
|
765 | |