| Preface |
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xxi | |
| Chapter 1 The Where, Why, and How of Data Collection |
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1 | (30) |
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What Is Business Statistics? |
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2 | (5) |
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2 | (3) |
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3 | (2) |
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5 | (2) |
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5 | (1) |
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5 | (2) |
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Procedures for Collecting Data |
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7 | (7) |
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7 | (4) |
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Written Questionnaires and Surveys |
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9 | (2) |
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Direct Observation and Personal Interviews |
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11 | (1) |
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Other Data Collection Methods |
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11 | (1) |
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12 | (2) |
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12 | (1) |
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12 | (1) |
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12 | (1) |
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12 | (1) |
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12 | (1) |
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13 | (1) |
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13 | (1) |
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13 | (1) |
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Populations, Samples, and Sampling Techniques |
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14 | (6) |
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14 | (1) |
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Parameters and Statistics |
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15 | (1) |
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15 | (5) |
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16 | (4) |
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Data Types and Data Measurement Levels |
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20 | (5) |
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Quantitative and Qualitative Data |
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20 | (1) |
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Time-Series Data and Cross-Sectional Data |
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21 | (1) |
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21 | (4) |
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21 | (1) |
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22 | (1) |
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22 | (1) |
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22 | (3) |
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A Brief Introduction to Data Mining |
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25 | (2) |
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Data Mining-Finding the Important, Hidden Relationships in Data |
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25 | (2) |
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27 | (2) |
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29 | (1) |
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29 | (1) |
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Video Case 1: Statistical Data Collection @ McDonald's |
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30 | (1) |
| Chapter 2 Graphs, Charts, and Tables-Describing Your Data |
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31 | (50) |
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Frequency Distributions and Histograms |
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32 | (21) |
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33 | (4) |
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Grouped Data Frequency Distributions |
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37 | (4) |
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Steps for Grouping Data into Classes |
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38 | (3) |
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41 | (4) |
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43 | (2) |
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Relative Frequency Histograms and Ogives |
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45 | (1) |
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Joint Frequency Distributions |
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46 | (7) |
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Bar Charts, Pie Charts, and Stem and Leaf Diagrams |
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53 | (10) |
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53 | (4) |
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57 | (2) |
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59 | (4) |
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Line Charts and Scatter Diagrams |
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63 | (11) |
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63 | (3) |
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66 | (3) |
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Descriptive Statistics and Data Mining |
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69 | (12) |
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69 | (1) |
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70 | (4) |
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74 | (1) |
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75 | (1) |
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75 | (1) |
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75 | (3) |
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Video Case 2: Drive-Thru Service Times @ McDonald's |
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78 | (1) |
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Case 2.1: Server Downtime |
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79 | (1) |
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Case 2.2: Hudson Valley Apples, Inc. |
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79 | (1) |
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Case 2.3: Welco Lumber Company-Part A |
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80 | (1) |
| Chapter 3 Describing Data Using Numerical Measures |
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81 | (59) |
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Measures of Center and Location |
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81 | (21) |
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Parameters and Statistics |
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82 | (1) |
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82 | (3) |
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85 | (1) |
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The Impact of Extreme Values on the Mean |
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86 | (1) |
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87 | (1) |
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Skewed and Symmetric Distributions |
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88 | (1) |
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89 | (1) |
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Applying the Measures of Central Tendency |
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90 | (2) |
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91 | (1) |
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Other Measures of Location |
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92 | (3) |
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92 | (1) |
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93 | (2) |
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95 | (1) |
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95 | (1) |
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95 | (2) |
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97 | (5) |
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102 | (11) |
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103 | (1) |
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103 | (1) |
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Population Variance and Standard Deviation |
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104 | (3) |
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Sample Variance and Standard Deviation |
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107 | (6) |
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Using the Mean and Standard Deviation Together |
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113 | (10) |
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113 | (3) |
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115 | (1) |
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116 | (1) |
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117 | (6) |
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123 | (1) |
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124 | (1) |
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125 | (1) |
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125 | (4) |
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Video Case 3: Drive-Thru Service Times @ McDonald's |
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129 | (1) |
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Case 3.1: WGI-Human Resources |
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130 | (1) |
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Case 3.2: National Call Center |
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131 | (1) |
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Case 3.3: Welco Lumber Company-Part B |
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131 | (1) |
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Case 3.4: AJ's Fitness Center |
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132 | (1) |
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Chapters 1-3 Special Review Section |
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133 | (7) |
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133 | (3) |
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136 | (2) |
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Review Case 1: State Department of Insurance |
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138 | (1) |
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138 | (2) |
| Chapter 4 Introduction to Probability |
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140 | (42) |
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The Basics of Probability |
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141 | (12) |
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Important Probability Terms |
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141 | (4) |
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141 | (1) |
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142 | (2) |
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Mutually Exclusive Events |
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144 | (1) |
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Independent and Dependent Events |
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145 | (1) |
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Methods of Assigning Probability |
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145 | (8) |
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Classical Probability Assessment |
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146 | (1) |
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Relative Frequency Assessment |
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147 | (2) |
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Subjective Probability Assessment |
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149 | (4) |
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153 | (23) |
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153 | (7) |
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Possible Values and the Summation of Possible Values |
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153 | (1) |
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Addition Rule for Individual Outcomes |
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153 | (3) |
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156 | (1) |
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Addition Rule for Any Two Events |
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157 | (3) |
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Addition Rule for Mutually Exclusive Events |
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160 | (1) |
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160 | (5) |
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163 | (1) |
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Conditional Probability for Independent Events |
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163 | (2) |
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165 | (3) |
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Multiplication Rule for Two Events |
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165 | (1) |
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166 | (1) |
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Multiplication Rule for Independent Events |
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166 | (2) |
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168 | (8) |
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176 | (1) |
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177 | (1) |
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177 | (1) |
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177 | (3) |
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Case 4.1: Great Air Commuter Service |
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180 | (1) |
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Case 4.2: Let's Make a Deal |
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181 | (1) |
| Chapter 5 Discrete Probability Distributions |
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182 | (42) |
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Introduction to Discrete Probability Distributions |
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183 | (7) |
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183 | (1) |
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Displaying Discrete Probability Distributions Graphically |
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183 | (1) |
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Mean and Standard Deviation of Discrete Distributions |
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184 | (6) |
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184 | (1) |
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Calculating the Standard Deviation |
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185 | (5) |
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The Binomial Probability Distribution |
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190 | (14) |
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The Binomial Distribution |
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190 | (1) |
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Characteristics of the Binomial Distribution |
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191 | (13) |
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192 | (2) |
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194 | (1) |
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Using the Binomial Distribution Table |
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195 | (1) |
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Mean and Standard Deviation of the Binomial Distribution |
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196 | (3) |
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Additional Information about the Binomial Distribution |
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199 | (5) |
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Other Discrete Probability Distributions |
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204 | (13) |
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204 | (5) |
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Characteristics of the Poisson Distribution |
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204 | (2) |
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Poisson Probability Distribution Table |
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206 | (2) |
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The Mean and Standard Deviation of the Poisson Distribution |
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208 | (1) |
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The Hypergeometric Distribution |
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209 | (16) |
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The Hypergeometric Distribution with More Than Two Possible Outcomes per Trial |
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213 | (4) |
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217 | (1) |
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218 | (1) |
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218 | (1) |
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218 | (3) |
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Case 5.1: SaveMor Pharmacies |
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221 | (1) |
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Case 5.2: Arrowmark Vending |
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222 | (1) |
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Case 5.3: Boise Cascade Corporation |
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222 | (2) |
| Chapter 6 Introduction to Continuous Probability Distributions |
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224 | (31) |
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The Normal Probability Distribution |
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225 | (15) |
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225 | (1) |
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The Standard Normal Distribution |
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226 | (14) |
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Using the Standard Normal Table |
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228 | (8) |
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Approximate Areas under the Normal Curve |
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236 | (4) |
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Other Continuous Probability Distributions |
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240 | (8) |
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Uniform Probability Distribution |
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240 | (2) |
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The Exponential Probability Distribution |
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242 | (6) |
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248 | (1) |
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249 | (1) |
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249 | (1) |
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249 | (4) |
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Case 6.1: State Entitlement Programs |
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253 | (1) |
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Case 6.2: Credit Data, Inc. |
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253 | (1) |
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Case 6.3: American Oil Company |
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254 | (1) |
| Chapter 7 Introduction to Sampling Distributions |
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255 | (40) |
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Sampling Error: What It Is and Why It Happens |
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256 | (8) |
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Calculating Sampling Error |
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256 | (8) |
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The Role of Sample Size in Sampling Error |
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259 | (5) |
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Sampling Distribution of the Mean |
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264 | (15) |
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Simulating the Sampling Distribution for X |
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265 | (7) |
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Sampling from Normal Populations |
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267 | (5) |
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The Central Limit Theorem |
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272 | (7) |
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Sampling Distribution of a Proportion |
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279 | (10) |
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279 | (3) |
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Sampling Distribution of p |
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282 | (7) |
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289 | (1) |
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290 | (1) |
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290 | (1) |
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290 | (4) |
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Case 7.1: Carpita Bottling Company |
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294 | (1) |
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Case 7.2: Truck Safety Inspection |
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294 | (1) |
| Chapter 8 Estimating Single Population Parameters |
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295 | (41) |
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Point and Confidence Interval Estimates for a Population Mean |
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296 | (18) |
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Point Estimates and Confidence Intervals |
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296 | (1) |
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Confidence Interval Estimate for the Population Mean, CT Known |
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297 | (7) |
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Confidence Interval Calculation |
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299 | (2) |
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Impact of the Confidence Level on the Interval Estimate |
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301 | (3) |
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Impact of the Sample Size on the Interval Estimate |
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304 | (1) |
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Confidence Interval Estimates for the Population Mean, sigma Unknown |
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304 | (1) |
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304 | (10) |
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Estimation with Larger Sample Sizes |
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310 | (4) |
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Determining the Required Sample Size for Estimating a Population Mean |
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314 | (7) |
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Determining the Required Sample Size for Estimating mu, sigma Known |
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315 | (1) |
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Determining the Required Sample Size for Estimating mu, sigma Unknown |
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316 | (5) |
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Estimating a Population Proportion |
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321 | (8) |
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Confidence Interval Estimate for a Population Proportion |
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321 | (2) |
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Determining the Required Sample Size for Estimating a Population Proportion |
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323 | (6) |
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329 | (1) |
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330 | (1) |
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330 | (1) |
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331 | (2) |
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Video Case 4: New Product Introductions @ McDonald's |
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333 | (1) |
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Case 8.1: Management Solutions, Inc. |
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334 | (1) |
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Case 8.2: Federal Aviation Administration |
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334 | (1) |
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334 | (2) |
| Chapter 9 Introduction to Hypothesis Testing |
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336 | (50) |
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Hypothesis Tests for Means |
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337 | (21) |
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Formulating the Hypotheses |
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337 | (4) |
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Null and Alternative Hypotheses |
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337 | (1) |
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337 | (1) |
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Testing a Research Hypothesis |
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338 | (1) |
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Testing a Claim about the Population |
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338 | (2) |
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Types of Statistical Errors |
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340 | (1) |
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Significance Level and Critical Value |
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341 | (1) |
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Hypothesis Test for p, v Known |
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342 | (6) |
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Calculating Critical Values |
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342 | (2) |
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Decision Rules and Test Statistics |
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344 | (3) |
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347 | (1) |
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Types of Hypothesis Tests |
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348 | (1) |
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p-Value for Two-Tailed Tests |
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349 | (2) |
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Hypothesis Test for mu, or sigma Unknown |
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351 | (7) |
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Hypothesis Tests for a Proportion |
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358 | (7) |
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Testing a Hypothesis about a Single Population Proportion |
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358 | (7) |
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365 | (11) |
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365 | (2) |
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Controlling Alpha and Beta |
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367 | (4) |
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371 | (5) |
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376 | (1) |
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377 | (1) |
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378 | (1) |
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378 | (5) |
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Video Case 4: New Product Introductions McDonald's |
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383 | (1) |
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Case 9.1: Campbell Brewery, Inc.-Part 1 |
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383 | (1) |
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384 | (2) |
| Chapter 10 Estimation and Hypothesis Testing for Two Population Parameters |
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386 | (49) |
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Estimation for Two Population Means Using Independent Samples |
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387 | (11) |
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Estimating the Difference between Two Population Means When sigma1 and sigma2 Are Known, Using Independent Samples |
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387 | (2) |
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Estimating the Difference between Two Means When sigma1 and sigma2 Are Unknown, Using Independent Samples |
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389 | (9) |
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What If the Population Variances Are Not Equal? |
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393 | (5) |
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Hypothesis Tests for Two Population Means Using Independent Samples |
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398 | (13) |
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Testing for mu1 - mu2 When sigma1 and sigma2 Are Known, Using Independent Samples |
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398 | (2) |
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400 | (1) |
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Testing mu1-mu2 when sigma1 and sigma2 Are Unknown, Using Independent Samples |
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400 | (11) |
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What If the Population Variances Are Not Equal? |
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407 | (4) |
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Interval Estimation and Hypothesis Tests for Paired Samples |
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411 | (8) |
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411 | (3) |
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Hypothesis Testing for Paired Samples |
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414 | (5) |
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Estimation and Hypothesis Tests for Two Population Proportions |
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419 | (8) |
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Estimating the Difference between Two Population Proportions |
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419 | (1) |
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Hypothesis Tests for the Difference between Two Population Proportions |
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420 | (7) |
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427 | (1) |
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428 | (1) |
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429 | (1) |
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429 | (3) |
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Case 10.1: Motive Power Company-Part 1 |
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432 | (1) |
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Case 10.2: Hamilton Marketing Services |
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433 | (1) |
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Case 10.3: Green Valley Assembly Company |
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433 | (1) |
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Case 10.4: U-Need-It Rental Agency |
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434 | (1) |
| Chapter 11 Hypothesis Tests and Estimation for Population Variances |
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435 | (27) |
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Hypothesis Tests and Estimation for a Single Population Variance |
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435 | (10) |
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Chi-Square Test for One Population Variance |
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436 | (5) |
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Interval Estimation for a Population Variance |
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441 | (4) |
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Hypothesis Tests for Two Population Variances |
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445 | (12) |
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F-Test for Two Population Variances |
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445 | (18) |
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Additional F-Test Considerations |
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453 | (4) |
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457 | (1) |
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458 | (1) |
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458 | (1) |
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458 | (2) |
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Case 11.1: Motive Power Company-Part 2 |
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460 | (2) |
| Chapter 12 Analysis of Variance |
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462 | (68) |
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One-Way Analysis of Variance |
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463 | (20) |
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Introduction to One-Way ANOVA |
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463 | (1) |
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Partitioning the Sum of Squares |
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464 | (1) |
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465 | (2) |
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467 | (11) |
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The Tukey-Kramer Procedure for Multiple Comparisons |
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473 | (5) |
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Fixed Effects Versus Random Effects in Analysis of Variance |
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478 | (5) |
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Randomized Complete Block Analysis of Variance |
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483 | (11) |
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Randomized Complete Block ANOVA |
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483 | (7) |
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487 | (3) |
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Fisher's Least Significant Difference Test |
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490 | (4) |
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Two-Factor Analysis of Variance with Replication |
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494 | (11) |
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Two-Factor ANOVA with Replications |
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495 | (6) |
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498 | (3) |
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A Caution about Interaction |
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501 | (4) |
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505 | (1) |
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506 | (1) |
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506 | (1) |
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506 | (3) |
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Video Case 3: Drive-Thru Service Times @ McDonald's |
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509 | (1) |
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Case 12.1: Agency for New Americans |
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510 | (1) |
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Case 12.2: McLaughlin Salmon Works |
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511 | (1) |
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Case 12.3: NW Pulp and Paper |
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511 | (1) |
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Case 12.4: Quinn Restoration |
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512 | (2) |
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Business Statistics Capstone Project |
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512 | (2) |
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Chapters 8-12 Special Review Section |
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514 | (16) |
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514 | (12) |
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526 | (1) |
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527 | (2) |
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529 | (1) |
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Business Statistics Capstone Project |
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529 | (1) |
| Chapter 13 Goodness-of-Fit Tests and Contingency Analysis |
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530 | (29) |
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Introduction to Goodness-of-Fit Tests |
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530 | (14) |
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Chi-Square Goodness-of-Fit Test |
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531 | (13) |
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Introduction to Contingency Analysis |
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544 | (10) |
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544 | (4) |
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548 | (2) |
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Chi-Square Test Limitations |
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550 | (4) |
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554 | (1) |
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555 | (1) |
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555 | (1) |
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555 | (2) |
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Case 13.1: American Oil Company |
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557 | (1) |
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Case 13.2: Bentford Electronics-Part 1 |
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558 | (1) |
| Chapter 14 introduction to Linear Regression and Correlation Analysis |
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559 | (53) |
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Scatter Plots and Correlation |
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560 | (10) |
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The Correlation Coefficient |
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560 | (10) |
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Significance Test for the Correlation |
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562 | (4) |
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Cause-and-Effect Interpretations |
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566 | (4) |
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Simple Linear Regression Analysis |
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570 | (22) |
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The Regression Model and Assumptions |
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570 | (1) |
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Meaning of the Regression Coefficients |
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571 | (5) |
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Least Squares Regression Properties |
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576 | (4) |
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Significance Tests in Regression Analysis |
|
|
580 | (12) |
|
The Coefficient of Determination, R2 |
|
|
580 | (3) |
|
Significance of the Slope Coefficient |
|
|
583 | (9) |
|
Uses for Regression Analysis |
|
|
592 | (12) |
|
Regression Analysis for Description |
|
|
592 | (3) |
|
Regression Analysis for Prediction |
|
|
595 | (3) |
|
Confidence Interval for the Average y, Given x |
|
|
595 | (1) |
|
Prediction Interval for a Particular y, Given x |
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|
596 | (2) |
|
Common Problems Using Regression Analysis |
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|
598 | (6) |
|
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|
604 | (1) |
|
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|
605 | (1) |
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606 | (1) |
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606 | (3) |
|
Case 14.1: A & A Industrial Products |
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|
609 | (1) |
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Case 14.2: Sapphire Coffee-Part 1 |
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|
610 | (1) |
|
Case 14.3: Alamar Industries |
|
|
610 | (1) |
|
Case 14.4: Continental Trucking |
|
|
611 | (1) |
| Chapter 15 Multiple Regression Analysis and Model Building |
|
612 | (71) |
|
Introduction to Multiple Regression Analysis |
|
|
613 | (18) |
|
Basic Model-Building Concepts |
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|
615 | (24) |
|
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|
615 | (1) |
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616 | (1) |
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|
616 | (3) |
|
Computing the Regression Equation |
|
|
619 | (1) |
|
The Coefficient of Determination |
|
|
620 | (1) |
|
|
|
620 | (1) |
|
Is the Model Significant? |
|
|
621 | (1) |
|
Are the Individual Variables Significant? |
|
|
622 | (1) |
|
Is the Standard Deviation of the Regression Model Too Large? |
|
|
623 | (2) |
|
Is Multicollinearity a Problem? |
|
|
625 | (1) |
|
Confidence Interval Estimation for Regression Coefficients |
|
|
626 | (5) |
|
Using Qualitative Independent Variables |
|
|
631 | (8) |
|
Possible Improvements to the First City Appraisal Model |
|
|
635 | (4) |
|
Working with Nonlinear Relationships |
|
|
639 | (15) |
|
Analyzing Interaction Effects |
|
|
643 | (4) |
|
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|
647 | (7) |
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|
654 | (10) |
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|
654 | (1) |
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|
655 | (3) |
|
Standard Stepwise Regression |
|
|
658 | (1) |
|
|
|
659 | (5) |
|
Determining the Aptness of the Model |
|
|
664 | (11) |
|
|
|
664 | (8) |
|
|
|
664 | (3) |
|
Do the Residuals Have Equal Variances at all Levels of Each x Variable? |
|
|
667 | (1) |
|
Are the Residuals Independent? |
|
|
667 | (1) |
|
Checking for Normally Distributed Error Terms |
|
|
668 | (4) |
|
|
|
672 | (3) |
|
|
|
675 | (1) |
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|
676 | (1) |
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|
676 | (1) |
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|
676 | (4) |
|
Case 15.1: Dynamic Scales, Inc. |
|
|
680 | (1) |
|
Case 15.2: Glaser Machine Works |
|
|
681 | (1) |
|
Case 15.3: Hawlins Manufacturing |
|
|
681 | (1) |
|
Case 15.4: Sapphire Coffee-Part 2 |
|
|
682 | (1) |
|
Case 15.5: Wendell Motors |
|
|
682 | (1) |
| Chapter 16 Analyzing and Forecasting Time-Series Data |
|
683 | (60) |
|
Introduction to Forecasting, Time-Series Data, and Index Numbers |
|
|
683 | (14) |
|
General Forecasting Issues |
|
|
684 | (1) |
|
Components of a lime Series |
|
|
684 | (3) |
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|
685 | (1) |
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|
685 | (2) |
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|
687 | (1) |
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|
687 | (1) |
|
Introduction to Index Numbers |
|
|
687 | (2) |
|
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|
689 | (1) |
|
Weighted Aggregate Price Indexes |
|
|
690 | (3) |
|
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|
691 | (1) |
|
|
|
692 | (1) |
|
Commonly Used Index Numbers |
|
|
693 | (1) |
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|
693 | (1) |
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|
694 | (1) |
|
|
|
694 | (1) |
|
Using Index Numbers to Deflate a Time Series |
|
|
694 | (3) |
|
Trend-Based Forecasting Techniques |
|
|
697 | (26) |
|
Developing a Trend-Based Forecasting Model |
|
|
697 | (3) |
|
Comparing the Forecast Values to the Actual Data |
|
|
700 | (8) |
|
|
|
702 | (4) |
|
|
|
706 | (2) |
|
Nonlinear Trend Forecasting |
|
|
708 | (4) |
|
|
|
712 | (1) |
|
Adjusting for Seasonality |
|
|
712 | (11) |
|
Computing Seasonal Indexes |
|
|
713 | (3) |
|
The Need to Normalize the Indexes |
|
|
716 | (1) |
|
|
|
716 | (2) |
|
Using Dummy Variables to Represent Seasonality |
|
|
718 | (5) |
|
Forecasting Using Smoothing Methods |
|
|
723 | (11) |
|
|
|
724 | (19) |
|
Single Exponential Smoothing |
|
|
724 | (4) |
|
Double Exponential Smoothing |
|
|
728 | (6) |
|
|
|
734 | (1) |
|
|
|
735 | (1) |
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|
735 | (1) |
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|
|
736 | (3) |
|
Video Case 2: Restaurant Location and Re-imaging Decisions @ McDonald's |
|
|
739 | (1) |
|
Case 16.1: Park Falls Chamber of Commerce |
|
|
740 | (1) |
|
Case 16.2: The St. Louis Companies |
|
|
741 | (1) |
|
Case 16.3: Wagner Machine Works |
|
|
741 | (2) |
| Chapter 17 Introduction to Nonparametric Statistics |
|
743 | (31) |
|
The Wilcoxon Signed Rank Test for One Population Median |
|
|
743 | (6) |
|
The Wilcoxon Signed Rank Test-Single Population |
|
|
744 | (5) |
|
Nonparametric Tests for Two Population Medians |
|
|
749 | (12) |
|
|
|
749 | (3) |
|
Mann-Whitney U-Test-Large Samples |
|
|
752 | (9) |
|
The Wilcoxon Matched-Pairs Signed Rank Test |
|
|
754 | (2) |
|
|
|
756 | (1) |
|
Large-Sample Wilcoxon Test |
|
|
756 | (5) |
|
Kruskal-Wallis One-Way Analysis of Variance |
|
|
761 | (7) |
|
Limitations and Other Considerations |
|
|
765 | (3) |
|
|
|
768 | (1) |
|
|
|
769 | (1) |
|
|
|
770 | (3) |
|
Case 17.1: Bentford Electronics-Part 2 |
|
|
773 | (1) |
| Chapter 18 Introduction to Quality and Statistical Process Control |
|
774 | (27) |
|
Introduction to Statistical Process Control Charts |
|
|
774 | (22) |
|
The Existence of Variation |
|
|
775 | (2) |
|
|
|
775 | (1) |
|
|
|
776 | (1) |
|
The Predictability of Variation: Understanding the Normal Distribution |
|
|
776 | (1) |
|
|
|
776 | (1) |
|
Introducing Statistical Process Control Charts |
|
|
777 | (1) |
|
|
|
778 | (18) |
|
|
|
782 | (4) |
|
|
|
786 | (3) |
|
|
|
789 | (1) |
|
|
|
789 | (3) |
|
|
|
792 | (4) |
|
|
|
796 | (1) |
|
|
|
797 | (1) |
|
|
|
798 | (1) |
|
Case 18.1: Izbar Precision Casters, Inc. |
|
|
799 | (2) |
| Appendices |
|
801 | (42) |
|
Appendix A Random Numbers Table |
|
|
802 | (1) |
|
Appendix B Cumulative Binomial Distribution Table |
|
|
803 | (13) |
|
Appendix C Cumulative Poisson Probability Distribution Table |
|
|
816 | (5) |
|
Appendix D Standard Normal Distribution Table |
|
|
821 | (1) |
|
Appendix E Exponential Distribution Table |
|
|
822 | (1) |
|
Appendix F Values of t for Selected Probabilities |
|
|
823 | (1) |
|
Appendix G Values of x2 for Selected Probabilities |
|
|
824 | (1) |
|
Appendix H FDistribution Table |
|
|
825 | (6) |
|
Appendix I Distribution of the Studentized Range (q-values) |
|
|
831 | (2) |
|
Appendix J Critical Values of r in the Runs Test |
|
|
833 | (1) |
|
Appendix K Mann-Whitney UTest Probabilities (n < 9) |
|
|
834 | (2) |
|
Appendix L Mann-Whitney U Test Critical Values (9 < or = to n < or = to 20) |
|
|
836 | (2) |
|
Appendix M Critical Values of Tin the Wilcoxon Matched-Pairs Signed-Ranks Test (n < or = to 25) |
|
|
838 | (1) |
|
Appendix N Critical Values dL and dU of the Durbin-Watson Statistic D |
|
|
839 | (2) |
|
Appendix O Lower and Upper Critical Values W of Wilcoxon Signed-Ranks Test |
|
|
841 | (1) |
|
Appendix P Control Chart Factors |
|
|
842 | (1) |
| Answers to Selected Odd-Numbered Problems |
|
843 | (20) |
| References |
|
863 | (4) |
| Glossary |
|
867 | (6) |
| Index |
|
873 | |
