| Preface |
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xxi | |
| About the Authors |
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xxvii | |
| Chapter 1 Data and Statistics |
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1 | (22) |
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Statistics in Practice: Business Week |
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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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5 | (3) |
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Elements, Variables, and Observations |
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5 | (1) |
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6 | (1) |
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Qualitative and Quantitative Data |
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7 | (1) |
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Cross-Sectional and Time Series Data |
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7 | (1) |
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1.3 Data Sources 8 Existing Sources |
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8 | (4) |
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9 | (3) |
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12 | (1) |
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1.4 Descriptive Statistics |
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12 | (2) |
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1.5 Statistical Inference |
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14 | (2) |
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1.6 Computers and Statistical Analysis |
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16 | (1) |
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16 | (1) |
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16 | (1) |
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17 | (6) |
| Chapter 2 Descriptive Statistics: Tabular and Graphical Presentations |
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23 | (53) |
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Statistics in Practice: Colgate-Palmolive Company |
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24 | (1) |
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2.1 Summarizing Qualitative Data |
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25 | (6) |
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25 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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26 | (1) |
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Bar Graphs and Pie Charts |
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26 | (5) |
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2.2 Summarizing Quantitative Data |
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31 | (9) |
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31 | (1) |
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Relative Frequency and Percent Frequency Distributions |
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32 | (1) |
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32 | (1) |
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33 | (1) |
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34 | (2) |
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36 | (4) |
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2.3 Exploratory Data Analysis: The Stein-and-Leaf Display |
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40 | (5) |
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2.4 Crosstabulations and Scatter Diagrams |
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45 | (9) |
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45 | (2) |
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47 | (2) |
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Scatter Diagram and Trendline |
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49 | (5) |
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54 | (2) |
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56 | (1) |
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57 | (1) |
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57 | (6) |
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Case Problem Pelican Stores |
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63 | (1) |
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Appendix 2.1 Using Minitab for Tabular and Graphical Presentations |
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64 | (2) |
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Appendix 2.2 Using Excel for Tabular and Graphical Presentations |
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66 | (10) |
| Chapter 3 Descriptive Statistics: Numerical Measures |
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76 | (63) |
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Statistics in Practice: Small Fry Design |
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77 | (1) |
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78 | (9) |
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78 | (1) |
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79 | (1) |
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80 | (1) |
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81 | (1) |
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82 | (5) |
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3.2 Measures of Variability |
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87 | (7) |
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87 | (1) |
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88 | (1) |
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88 | (2) |
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90 | (1) |
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91 | (3) |
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3.3 Measures of Distribution Shape, Relative Location, and Detecting |
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94 | (1) |
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94 | (1) |
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94 | (2) |
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96 | (1) |
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97 | (1) |
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98 | (3) |
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3.4 Exploratory Data Analysis |
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101 | (4) |
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101 | (1) |
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102 | (3) |
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3.5 Measures of Association Between Two Variables |
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105 | (9) |
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106 | (1) |
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Interpretation of the Covariance |
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107 | (3) |
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110 | (1) |
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Interpretation of the Correlation Coefficient |
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111 | (3) |
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3.6 The Weighted Mean and Working with Grouped Data |
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114 | (6) |
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115 | (1) |
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116 | (4) |
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120 | (1) |
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121 | (1) |
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122 | (2) |
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124 | (5) |
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Case Problem 1 Pelican Stores |
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129 | (1) |
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Case Problem 2 National Health Care Association |
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130 | (1) |
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Case Problem 3 Business Schools of Asia-Pacific |
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131 | (2) |
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Appendix 3.1 Descriptive Statistics Using Minitab |
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133 | (2) |
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Appendix 3.2 Descriptive Statistics Using Excel |
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135 | (4) |
| Chapter 4 Introduction to Probability |
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139 | (45) |
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Statistics in Practice: Morton International |
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140 | (1) |
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4.1 Experiments, Counting Rules, and Assigning Probabilities |
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141 | (10) |
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Counting Rules, Combinations, and Permutations |
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142 | (4) |
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146 | (2) |
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Probabilities for the KP&L Project |
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148 | (3) |
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4.2 Events and Their Probabilities |
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151 | (4) |
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4.3 Some Basic Relationships of Probability |
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155 | (6) |
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155 | (1) |
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156 | (5) |
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4.4 Conditional Probability |
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161 | (8) |
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165 | (1) |
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165 | (4) |
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169 | (6) |
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173 | (2) |
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175 | (1) |
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175 | (1) |
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176 | (1) |
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177 | (4) |
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Case Problem Hamilton County Judges |
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181 | (3) |
| Chapter 5 Discrete Probability Distributions |
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184 | (39) |
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Statistics in Practice: Citibank |
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185 | (1) |
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185 | (3) |
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Discrete Random Variables |
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186 | (1) |
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Continuous Random Variables |
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187 | (1) |
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5.2 Discrete Probability Distributions |
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188 | (6) |
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5.3 Expected Value and Variance |
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194 | (4) |
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194 | (1) |
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194 | (4) |
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5.4 Binomial Probability Distribution |
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198 | (10) |
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199 | (1) |
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Martin Clothing Store Problem |
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200 | (4) |
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Using Tables of Binomial Probabilities |
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204 | (1) |
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Expected Value and Variance for the Binomial Distribution |
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205 | (3) |
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5.5 Poisson Probability Distribution |
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208 | (4) |
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An Example Involving Time Intervals |
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209 | (2) |
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An Example Involving Length or Distance Intervals |
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211 | (1) |
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5.6 Hypergeometric Probability Distribution |
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212 | (3) |
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215 | (1) |
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216 | (1) |
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217 | (1) |
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218 | (2) |
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Appendix 5.1 Discrete Probability Distributions with Minitab |
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220 | (1) |
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Appendix 5.2 Discrete Probability Distributions with Excel |
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221 | (2) |
| Chapter 6 Continuous Probability Distributions |
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223 | (34) |
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Statistics in Practice: Procter & Gamble |
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224 | (1) |
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6.1 Uniform Probability Distribution |
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225 | (4) |
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Area as a Measure of Probability |
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226 | (3) |
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6.2 Normal Probability Distribution |
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229 | (14) |
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229 | (2) |
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Standard Normal Probability Distribution |
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231 | (6) |
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Computing Probabilities for Any Normal Distribution |
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237 | (1) |
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Grear Tire Company Problem |
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238 | (5) |
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6.3 Normal Approximation of Binomial Probabilities |
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243 | (3) |
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6.4 Exponential Probability Distribution |
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246 | (4) |
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Computing Probabilities for the Exponential Distribution |
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246 | (2) |
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Relationship Between the Poisson and Exponential Distributions |
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248 | (2) |
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250 | (1) |
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250 | (1) |
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250 | (1) |
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251 | (3) |
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Case Problem Specialty Toys |
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254 | (1) |
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Appendix 6.1 Continuous Probability Distributions with Minitab |
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255 | (1) |
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Appendix 6.2 Continuous Probability Distributions with Excel |
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256 | (1) |
| Chapter 7 Sampling and Sampling Distributions |
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257 | (40) |
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Statistics in Practice: MeadWestvaco Corporation |
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258 | (1) |
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7.1 The Electronics Associates Sampling Problem |
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259 | (1) |
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7.2 Simple Random Sampling |
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260 | (4) |
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Sampling from a Finite Population |
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260 | (1) |
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Sampling from an Infinite Population |
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261 | (3) |
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264 | (3) |
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7.4 Introduction to Sampling Distributions |
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267 | (3) |
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7.5 Sampling Distribution of x |
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270 | (9) |
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270 | (1) |
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271 | (1) |
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Form of the Sampling Distribution of x |
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272 | (1) |
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Sampling Distribution of x for the EAI Problem |
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273 | (1) |
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Practical Value of the Sampling Distribution of x |
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274 | (1) |
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Relationship Between the Sample Size and the Sampling Distribution of x |
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275 | (4) |
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7.6 Sampling Distribution of p |
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279 | (5) |
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280 | (1) |
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280 | (1) |
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Form of the Sampling Distribution of p |
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281 | (1) |
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Practical Value of the Sampling Distribution of p |
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281 | (3) |
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7.7 Properties of Point Estimators |
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284 | (3) |
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285 | (1) |
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286 | (1) |
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287 | (1) |
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7.8 Other Sampling Methods |
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287 | (3) |
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Stratified Random Sampling |
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287 | (1) |
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288 | (1) |
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288 | (1) |
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289 | (1) |
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289 | (1) |
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290 | (1) |
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290 | (1) |
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291 | (1) |
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292 | (2) |
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Appendix 7.1 The Expected Value and Standard Deviation of x |
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294 | (1) |
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Appendix 7.2 Random Sampling with Minitab |
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295 | (1) |
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Appendix 7.3 Random Sampling with Excel |
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296 | (1) |
| Chapter 8 Interval Estimation |
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297 | (39) |
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Statistics in Practice: Food Lion |
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298 | (1) |
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8.1 Population Mean: σ Known |
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299 | (6) |
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Margin of Error and the Interval Estimate |
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300 | (3) |
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303 | (2) |
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8.2 Population Mean: σ Unknown |
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305 | (9) |
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Margin of Error and the Interval Estimate |
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306 | (3) |
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309 | (1) |
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309 | (2) |
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Summary of Interval Estimation Procedures |
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311 | (3) |
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8.3 Determining the Sample Size |
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314 | (3) |
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8.4 Population Proportion |
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317 | (5) |
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Determining the Sample Size |
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319 | (3) |
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322 | (1) |
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323 | (1) |
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324 | (1) |
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324 | (3) |
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Case Problem 1 Bock Investment Services |
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327 | (1) |
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Case Problem 2 Gulf Real Estate Properties |
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327 | (3) |
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Case Problem 3 Metropolitan Research, Inc. |
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330 | (1) |
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Appendix 8.1 Interval Estimation with Minitab |
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331 | (1) |
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Appendix 8.2 Interval Estimation Using Excel |
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332 | (4) |
| Chapter 9 Hypothesis Testing |
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336 | (57) |
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Statistics in Practice: John Morrell & Company |
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337 | (1) |
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9.1 Developing Null and Alternative Hypotheses |
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338 | (2) |
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Testing Research Hypotheses |
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338 | (1) |
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Testing the Validity of a Claim |
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338 | (1) |
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Testing in Decision-Making Situations |
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339 | (1) |
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Summary of Forms for Null and Alternative Hypotheses |
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339 | (1) |
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9.2 Type I and Type II Errors |
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340 | (3) |
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9.3 Population Mean: σ Known |
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343 | (15) |
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343 | (6) |
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349 | (3) |
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Summary and Practical Advice |
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352 | (1) |
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Relationship Between Interval Estimation and Hypothesis Testing |
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353 | (5) |
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9.4 Population Mean: σ Unknown |
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358 | (7) |
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358 | (2) |
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360 | (1) |
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Summary and Practical Advice |
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361 | (4) |
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9.5 Population Proportion |
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365 | (5) |
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367 | (3) |
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9.6 Hypothesis Testing and Decision Making |
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370 | (1) |
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9.7 Calculating the Probability of Type II Errors |
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371 | (5) |
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9.8 Determining the Sample Size for Hypothesis Test About a Population Mean |
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376 | (4) |
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380 | (1) |
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380 | (1) |
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381 | (1) |
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381 | (3) |
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Case Problem 1 Quality Associates, Inc. |
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384 | (1) |
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Case Problem 2 Unemployment Study |
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385 | (1) |
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Appendix 9.1 Hypothesis Testing with Minitab |
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386 | (2) |
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Appendix 9.2 Hypothesis Testing with Excel |
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388 | (5) |
| Chapter 10 Statistical Inference About Means and Proportions with Two Populations |
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393 | (39) |
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Statistics in Practice: Fisons Corporation |
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394 | (1) |
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10.1 Inferences About the Difference Between Two Population Means: σl and σ2, Known |
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395 | (7) |
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Interval Estimate of μ1-μ2 |
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395 | (2) |
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Hypothesis Tests About μ1-μ2 |
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397 | (2) |
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399 | (3) |
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10.2 Inferences About the Difference Between Two Population Means: σl and σ2 Unknown |
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402 | (9) |
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Interval Estimation of μ1-μ2 |
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402 | (1) |
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Hypothesis Tests About μ1-μ2 |
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403 | (2) |
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405 | (6) |
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10.3 Inferences About the Difference Between Two Population Means: Matched Samples |
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411 | (6) |
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10.4 Inferences About the Difference Between Two Population Proportions |
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417 | (5) |
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Interval Estimation of ρ1-ρ2 |
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417 | (2) |
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Hypothesis Tests About μ1-μ2 |
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419 | (3) |
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422 | (1) |
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423 | (1) |
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423 | (2) |
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425 | (2) |
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427 | (1) |
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Appendix 10.1 Inferences About Two Populations Using Minitab |
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428 | (2) |
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Appendix 10.2 Inferences About Two Populations Using Excel |
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430 | (2) |
| Chapter 11 Inferences About Population Variances |
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432 | (25) |
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Statistics in Practice: U.S. General Accounting Office |
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433 | (1) |
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11.1 Inferences About a Population Variance |
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434 | (9) |
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434 | (4) |
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438 | (5) |
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11.2 Inferences About Two Population Variances |
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443 | (7) |
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450 | (1) |
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450 | (1) |
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451 | (1) |
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Case Problem Air Force Training Program |
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452 | (2) |
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Appendix 11.1 Population Variances with Minitab |
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454 | (1) |
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Appendix 11.2 Population Variances with Excel |
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455 | (2) |
| Chapter 12 Tests of Goodness of Fit and Independence |
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457 | (33) |
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Statistics in Practice: United Way |
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458 | (1) |
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12.1 Goodness of Fit Test: A Multinomial Population |
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459 | (5) |
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12.2 Test of Independence |
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464 | (7) |
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12.3 Goodness of Fit Test: Poisson and Normal Distributions |
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471 | (10) |
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472 | (3) |
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475 | (6) |
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481 | (1) |
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481 | (1) |
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481 | (1) |
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482 | (3) |
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Case Problem A Bipartisan Agenda for Change |
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485 | (1) |
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Appendix 12.1 Tests of Goodness of Fit and Independence Using Minitab |
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486 | (1) |
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Appendix 12.2 Tests of Goodness of Fit and Independence Using Excel |
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487 | (3) |
| Chapter 13 Analysis of Variance and Experimental Design |
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490 | (63) |
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Statistics in Practice: Burke Marketing Services, Inc. |
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491 | (1) |
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13.1 An Introduction to Analysis of Variance |
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491 | (4) |
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Assumptions for Analysis of Variance |
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493 | (1) |
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493 | (2) |
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13.2 Analysis of Variance: Testing for the Equality of k Population Means |
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495 | (10) |
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Between-Treatments Estimate of Population Variance |
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496 | (1) |
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Within-Treatments Estimate of Population Variance |
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497 | (1) |
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Comparing the Variance Estimates: The F Test |
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498 | (2) |
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500 | (1) |
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Computer Results for Analysis of Variance |
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500 | (5) |
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13.3 Multiple Comparison Procedures |
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505 | (6) |
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506 | (2) |
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508 | (3) |
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13.4 An Introduction to Experimental Design |
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511 | (2) |
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512 | (1) |
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13.5 Completely Randomized Designs |
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513 | (6) |
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Between-Treatments Estimate of Population Variance |
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514 | (1) |
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Within-Treatments Estimate of Population Variance |
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514 | (1) |
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Comparing the Variance Estimates: The F Test 514 |
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515 | (1) |
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515 | (4) |
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13.6 Randomized Block Design |
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519 | (7) |
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Air Traffic Controller Stress Test |
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520 | (1) |
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521 | (1) |
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Computations and Conclusions |
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522 | (4) |
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13.7 Factorial Experiments |
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526 | (8) |
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527 | (1) |
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Computations and Conclusions |
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528 | (6) |
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534 | (1) |
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534 | (1) |
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535 | (2) |
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537 | (8) |
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Case Problem 1 Wentworth Medical Center |
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545 | (1) |
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Case Problem 2 Compensation for ID Professionals |
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546 | (1) |
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Appendix 13.1 Analysis of Variance and Experimental Design with Minitab |
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547 | (1) |
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Appendix 13.2 Analysis of Variance and Experimental Design with Excel |
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548 | (5) |
| Chapter 14 Simple Linear Regression |
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553 | (82) |
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Statistics in Practice: Alliance Data Systems |
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554 | (1) |
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14.1 Simple Linear Regression Model |
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555 | (3) |
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Regression Model and Regression Equation |
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555 | (1) |
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Estimated Regression Equation |
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556 | (2) |
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14.2 Least Squares Method |
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558 | (11) |
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14.3 Coefficient of Determination |
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569 | (7) |
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572 | (4) |
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576 | (2) |
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14.5 Testing for Significance |
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578 | (9) |
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578 | (1) |
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579 | (1) |
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Confidence Interval for β1 |
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580 | (1) |
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581 | (2) |
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Some Cautions About the Interpretation of Significance Tests |
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583 | (4) |
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14.6 Using the Estimated Regression Equation for Estimation and Prediction |
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587 | (6) |
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587 | (1) |
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587 | (1) |
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Confidence Interval for the Mean Value of y |
|
|
588 | (1) |
|
Prediction Interval for an Individual Value of y |
|
|
589 | (4) |
|
|
|
593 | (5) |
|
14.8 Residual Analysis: Validating Model Assumptions |
|
|
598 | (9) |
|
|
|
599 | (1) |
|
|
|
600 | (1) |
|
|
|
600 | (3) |
|
|
|
603 | (4) |
|
14.9 Residual Analysis: Outliers and Influential Observations |
|
|
607 | (7) |
|
|
|
607 | (2) |
|
Detecting Influential Observations |
|
|
609 | (5) |
|
|
|
614 | (1) |
|
|
|
615 | (1) |
|
|
|
616 | (2) |
|
|
|
618 | (5) |
|
Case Problem 1 Spending and Student Achievement |
|
|
623 | (2) |
|
Case Problem 2 U.S. Department of Transportation |
|
|
625 | (1) |
|
Case Problem 3 Alumni Giving |
|
|
626 | (2) |
|
Case Problem 4 Major League Baseball Team Values |
|
|
628 | (1) |
|
Appendix 14.1 Calculus-Based Derivation of Least Squares Formulas |
|
|
629 | (1) |
|
Appendix 14.2 A Test for Significance Using Correlation |
|
|
630 | (1) |
|
Appendix 14.3 Regression Analysis with Minitab |
|
|
631 | (1) |
|
Appendix 14.4 Regression Analysis with Excel |
|
|
632 | (3) |
| Chapter 15 Multiple Regression |
|
635 | (68) |
|
Statistics in Practice: International Paper |
|
|
636 | (1) |
|
15.1 Multiple Regression Model |
|
|
637 | (1) |
|
Regression Model and Regression Equation |
|
|
637 | (1) |
|
Estimated Multiple Regression Equation |
|
|
637 | (1) |
|
15.2 Least Squares Method |
|
|
638 | (8) |
|
An Example: Butler Trucking Company |
|
|
639 | (2) |
|
Note on Interpretation of Coefficients |
|
|
641 | (5) |
|
15.3 Multiple Coefficient of Determination |
|
|
646 | (3) |
|
|
|
649 | (1) |
|
15.5 Testing for Significance |
|
|
650 | (8) |
|
|
|
651 | (3) |
|
|
|
654 | (1) |
|
|
|
654 | (4) |
|
15.6 Using the Estimated Regression Equation for Estimation and Prediction |
|
|
658 | (2) |
|
15.7 Qualitative Independent Variables |
|
|
660 | (8) |
|
An Example: Johnson Filtration, Inc. |
|
|
660 | (2) |
|
Interpreting the Parameters |
|
|
662 | (2) |
|
More Complex Qualitative Variables |
|
|
664 | (4) |
|
|
|
668 | (7) |
|
|
|
669 | (1) |
|
Studentized Deleted Residuals and Outliers |
|
|
670 | (1) |
|
|
|
671 | (1) |
|
Using Cook's Distance Measure to Identify Influential Observations |
|
|
671 | (4) |
|
|
|
675 | (12) |
|
Logistic Regression Equation |
|
|
676 | (1) |
|
Estimating the Logistic Regression Equation |
|
|
677 | (2) |
|
|
|
679 | (1) |
|
|
|
680 | (1) |
|
Interpreting the Logistic Regression Equation |
|
|
680 | (3) |
|
|
|
683 | (4) |
|
|
|
687 | (1) |
|
|
|
687 | (1) |
|
|
|
688 | (2) |
|
|
|
690 | (5) |
|
Case Problem 1 Consumer Research, Inc. |
|
|
695 | (1) |
|
Case Problem 2 NFL Quarterback Rating |
|
|
696 | (2) |
|
Case Problem 3 Predicting Student Proficiency Test Scores |
|
|
698 | (1) |
|
Case Problem 4 Alumni Giving |
|
|
699 | (1) |
|
Appendix 15.1 Multiple Regression with Minitab |
|
|
699 | (1) |
|
Appendix 15.2 Multiple Regression with Excel |
|
|
699 | (3) |
|
Appendix 15.3 Logistic Regression with Minitab |
|
|
702 | (1) |
| Chapter 16 Regression Analysis: Model Building |
|
703 | (54) |
|
Statistics in Practice: Monsanto Company |
|
|
704 | (1) |
|
16.1 General Linear Model |
|
|
705 | (15) |
|
Modeling Curvilinear Relationships |
|
|
705 | (4) |
|
|
|
709 | (2) |
|
Transformations Involving the Dependent Variable |
|
|
711 | (4) |
|
Nonlinear Models That Are Intrinsically Linear |
|
|
715 | (5) |
|
16.2 Determining When to Add or Delete Variables |
|
|
720 | (7) |
|
|
|
722 | (1) |
|
|
|
723 | (4) |
|
16.3 Analysis of a Larger Problem |
|
|
727 | (3) |
|
16.4 Variable Selection Procedures |
|
|
730 | (7) |
|
|
|
731 | (1) |
|
|
|
732 | (1) |
|
|
|
733 | (1) |
|
|
|
733 | (1) |
|
|
|
734 | (3) |
|
|
|
737 | (7) |
|
Autocorrelation and the Durbin-Watson Test |
|
|
738 | (6) |
|
16.6 Multiple Regression Approach to Analysis of Variance and Experimental Design |
|
|
744 | (4) |
|
|
|
748 | (1) |
|
|
|
748 | (1) |
|
|
|
749 | (1) |
|
|
|
749 | (4) |
|
Case Problem 1 Unemployment Study |
|
|
753 | (2) |
|
Case Problem 2 Fuel Economy for Cars |
|
|
755 | (1) |
|
Case Problem 3 Predicting Graduation Rates for Colleges and Universities |
|
|
755 | (2) |
| Chapter 17 Index Numbers |
|
757 | (21) |
|
Statistics in Practice: U.S. Department of Labor, Bureau of Labor Statistics |
|
|
758 | (1) |
|
|
|
759 | (1) |
|
17.2 Aggregate Price Indexes |
|
|
759 | (4) |
|
17.3 Computing an Aggregate Price Index from Price Relatives |
|
|
763 | (2) |
|
17.4 Some Important Price Indexes |
|
|
765 | (2) |
|
|
|
765 | (1) |
|
|
|
765 | (1) |
|
|
|
766 | (1) |
|
17.5 Deflating a Series By Price Indexes |
|
|
767 | (4) |
|
17.6 Price Indexes: Other Considerations |
|
|
771 | (1) |
|
|
|
771 | (1) |
|
Selection of a Base Period |
|
|
771 | (1) |
|
|
|
771 | (1) |
|
|
|
772 | (2) |
|
|
|
774 | (1) |
|
|
|
774 | (1) |
|
|
|
774 | (1) |
|
|
|
775 | (3) |
| Chapter 18 Forecasting |
|
778 | (47) |
|
Statistics in Practice: Nevada Occupational Health Clinic |
|
|
779 | (1) |
|
18.1 Components of a Time Series |
|
|
780 | (3) |
|
|
|
780 | (2) |
|
|
|
782 | (1) |
|
|
|
783 | (1) |
|
|
|
783 | (1) |
|
|
|
783 | (10) |
|
|
|
783 | (2) |
|
|
|
785 | (2) |
|
|
|
787 | (6) |
|
|
|
793 | (6) |
|
18.4 Trend and Seasonal Components |
|
|
799 | (10) |
|
|
|
799 | (1) |
|
Calculating the Seasonal Indexes |
|
|
800 | (4) |
|
Deseasonalizing the Time Series |
|
|
804 | (1) |
|
Using the Deseasonalized Time Series to Identify Trend |
|
|
804 | (3) |
|
|
|
807 | (1) |
|
Models Based on Monthly Data |
|
|
807 | (1) |
|
|
|
807 | (2) |
|
|
|
809 | (2) |
|
18.6 Qualitative Approaches |
|
|
811 | (1) |
|
|
|
811 | (1) |
|
|
|
812 | (1) |
|
|
|
812 | (1) |
|
|
|
812 | (1) |
|
|
|
812 | (1) |
|
|
|
813 | (1) |
|
|
|
814 | (1) |
|
|
|
814 | (5) |
|
Case Problem 1 Forecasting Food and Beverage Sales |
|
|
819 | (1) |
|
Case Problem 2 Forecasting Lost Sales |
|
|
820 | (1) |
|
Appendix 18.1 Forecasting with Minitab |
|
|
821 | (2) |
|
Appendix 18.2 Forecasting with Excel |
|
|
823 | (2) |
| Chapter 19 Nonparametric Methods |
|
825 | (34) |
|
Statistics in Practice: West Shell Realtors |
|
|
826 | (2) |
|
|
|
828 | (5) |
|
|
|
828 | (2) |
|
|
|
830 | (1) |
|
Hypothesis Test About a Median |
|
|
831 | (2) |
|
19.2 Wilcoxon Signed-Rank Test |
|
|
833 | (5) |
|
19.3 Mann-Whitney-Wilcoxon Test |
|
|
838 | (8) |
|
|
|
838 | (2) |
|
|
|
840 | (6) |
|
|
|
846 | (4) |
|
|
|
850 | (4) |
|
Test for Significant Rank Correlation |
|
|
852 | (2) |
|
|
|
854 | (1) |
|
|
|
855 | (1) |
|
|
|
855 | (1) |
|
|
|
856 | (3) |
| Chapter 20 Statistical Methods for Quality Control |
|
859 | (30) |
|
Statistics in Practice: Dow Chemical U.S.A. |
|
|
860 | (1) |
|
20.1 Statistical Process Control |
|
|
861 | (14) |
|
|
|
862 | (1) |
|
x Chart: Process Mean and Standard Deviation Known |
|
|
863 | (2) |
|
x Chart: Process Mean and Standard Deviation Unknown |
|
|
865 | (2) |
|
|
|
867 | (2) |
|
|
|
869 | (3) |
|
|
|
872 | (1) |
|
Interpretation of Control Charts |
|
|
872 | (3) |
|
|
|
875 | (9) |
|
KALI, Inc.: An Example of Acceptance Sampling |
|
|
876 | (1) |
|
Computing the Probability of Accepting a Lot |
|
|
877 | (3) |
|
Selecting an Acceptance Sampling Plan |
|
|
880 | (1) |
|
|
|
881 | (3) |
|
|
|
884 | (1) |
|
|
|
884 | (1) |
|
|
|
885 | (1) |
|
|
|
886 | (2) |
|
Appendix 20.1 Control Charts with Minitab |
|
|
888 | (1) |
| Chapter 21 Decision Analysis |
|
889 | (37) |
|
Statistics in Practice: Ohio Edison Company |
|
|
890 | (1) |
|
|
|
891 | (2) |
|
|
|
892 | (1) |
|
|
|
892 | (1) |
|
21.2 Decision Making with Probabilities |
|
|
893 | (8) |
|
|
|
893 | (2) |
|
Expected Value of Perfect Information |
|
|
895 | (6) |
|
21.3 Decision Analysis with Sample Information |
|
|
901 | (11) |
|
|
|
902 | (1) |
|
|
|
903 | (3) |
|
Expected Value of Sample Information |
|
|
906 | (6) |
|
21.4 Computing Branch Probabilities Using Bayes' Theorem |
|
|
912 | (4) |
|
|
|
916 | (1) |
|
|
|
917 | (1) |
|
|
|
918 | (1) |
|
Case Problem Lawsuit Defense Strategy |
|
|
918 | (1) |
|
Appendix 21.1 Solving the PDC Problem with TreePlan |
|
|
919 | |
| Chapter 22 Sample Survey On CD |
|
| Appendix A References and Bibliography |
|
926 | (2) |
| Appendix B Tables |
|
928 | (29) |
| Appendix C Summation Notation |
|
957 | (2) |
| Appendix D Self-Test Solutions and Answers to Even-Numbered Exercises |
|
959 | (51) |
| Index |
|
1010 | |
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