| Preface |
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xvi | |
| About the Authors |
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xx | |
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Part I Introduction and Descriptive Statistics |
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1 Introduction: Defining the Role of Statistics in Business |
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4 | (1) |
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Why Should You Learn Statistics? |
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4 | (1) |
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4 | (1) |
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How Does Learning Statistics Increase Your Decision-Making Flexibility? |
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4 | (1) |
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5 | (1) |
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Statistics Looks at the Big Picture |
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5 | (1) |
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Statistics Does Not Ignore the Individual |
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Looking at Data With Pictures and Summaries |
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5 | (1) |
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5 | (1) |
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1.3 The Five Basic Activities of Statistics |
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6 | (3) |
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Designing a Plan for Data Collection |
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6 | (1) |
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6 | (1) |
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6 | (1) |
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Estimating an Unknown Quantity |
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7 | (1) |
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8 | (1) |
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1.4 Data Mining and Big Data |
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9 | (5) |
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14 | (1) |
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15 | (1) |
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1.7 End-of-Chapter Materials |
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16 | (3) |
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16 | (1) |
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16 | (1) |
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16 | (1) |
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17 | (1) |
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18 | (1) |
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2 Data Structures: Classifying the Various Types of Data Sets |
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19 | (2) |
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20 | (1) |
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20 | (1) |
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21 | (1) |
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2.2 Quantitative Data: Numbers |
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21 | (1) |
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Discrete Quantitative Data |
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22 | (1) |
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Continuous Quantitative Data |
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22 | (1) |
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Watch Out for Meaningless Numbers |
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22 | (1) |
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2.3 Qualitative Data: Categories |
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22 | (1) |
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23 | (1) |
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23 | (1) |
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2.4 Time-Series and Cross-Sectional Data |
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23 | (1) |
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2.5 Sources of Data, including the Internet |
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24 | (15) |
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Primary and Secondary Data |
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24 | (1) |
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Observational Study and Experiment |
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25 | (1) |
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Finding and Using Data From the Internet |
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26 | (13) |
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2.6 End-of-Chapter Materials |
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39 | (7) |
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39 | (1) |
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39 | (1) |
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39 | (1) |
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40 | (4) |
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44 | (1) |
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44 | (2) |
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3 Histograms: Looking at the Distribution of Data |
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46 | (1) |
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46 | (1) |
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3.2 Using a Histogram to Display the Frequencies |
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47 | (3) |
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Histograms and Bar Charts |
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49 | (1) |
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50 | (1) |
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3.4 Skewed Distributions and Data Transformation |
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51 | (6) |
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The Trouble With Skewness |
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54 | (1) |
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Transformation to the Rescue |
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55 | (1) |
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Interpreting and Computing the Logarithm |
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56 | (1) |
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3.5 Bimodal Distributions With Two Groups |
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57 | (2) |
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58 | (1) |
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59 | (4) |
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60 | (3) |
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3.7 Data Mining With Histograms |
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63 | (2) |
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3.8 End-of-Chapter Materials |
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65 | (11) |
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65 | (1) |
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65 | (1) |
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65 | (1) |
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66 | (8) |
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74 | (1) |
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74 | (1) |
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74 | (2) |
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4 Landmark Summaries: Interpreting Typical Values and Percentiles |
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4.1 What Is The Most Typical Value? |
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76 | (2) |
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The Average: A Typical Value for Quantitative Data |
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76 | (2) |
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4.2 The Weighted Average: Adjusting for Importance |
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78 | (2) |
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4.3 The Median: A Typical Value for Quantitative and Ordinal Data |
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80 | (4) |
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4.4 The Mode: A Typical Value Even for Nominal Data |
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84 | (2) |
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Which Summary Should You Use? |
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85 | (1) |
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4.2 What Percentile Is It? |
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86 | (8) |
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Extremes, Quartiles, and Box Plots |
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86 | (4) |
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The Cumulative Distribution Function Displays the Percentiles |
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90 | (4) |
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4.3 End-of-Chapter Materials |
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94 | (12) |
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94 | (1) |
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95 | (1) |
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95 | (1) |
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95 | (7) |
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102 | (1) |
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102 | (1) |
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103 | (3) |
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5 Variability: Dealing with Diversity |
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5.1 The Standard Deviation: The Traditional Choice |
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106 | (12) |
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Definition and Formula for the Standard Deviation and the Variance |
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106 | (1) |
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Using a Calculator or a Computer |
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107 | (1) |
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Interpreting the Standard Deviation |
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108 | (2) |
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Interpreting the Standard Deviation for a Normal Distribution |
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110 | (7) |
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The Sample and the Population Standard Deviations |
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117 | (1) |
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5.2 The Range: Quick and Superficial |
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118 | (1) |
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5.3 The Coefficient of Variation: A Relative Variability Measure |
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119 | (1) |
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5.4 Effects of Adding to or Rescaling the Data |
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120 | (3) |
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5.5 End-of-Chapter Materials |
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123 | (15) |
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123 | (1) |
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124 | (1) |
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124 | (1) |
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124 | (9) |
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133 | (1) |
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133 | (1) |
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133 | (5) |
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6 Probability: Understanding Random Situations |
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6.1 An Example: Is it Behind Door Number 1, Door Number 2, or Door Number 3? |
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138 | (1) |
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6.2 How Can You Analyze Uncertainty? |
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139 | (2) |
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The Random Experiment: A Precise Definition of a Random Situation |
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139 | (1) |
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The Sample Space: A List of What Might Happen |
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140 | (1) |
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The Outcome: What Actually Happens |
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140 | (1) |
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Events: Either They Happen or They Do Not |
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141 | (1) |
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6.3 How Likely Is An Event? |
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141 | (5) |
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Every Event Has a Probability |
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141 | (1) |
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Where Do Probabilities Come From? |
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142 | (1) |
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Relative Frequency and the Law of Large Numbers |
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142 | (2) |
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144 | (1) |
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144 | (1) |
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144 | (1) |
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Bayesian and Non-Bayesian Analysis |
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145 | (1) |
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6.4 How Can You Combine Information About More Than One Event? |
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146 | (6) |
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Venn Diagrams Help You See All the Possibilities |
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146 | (1) |
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146 | (1) |
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The Complement (Not) Rule |
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147 | (1) |
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147 | (1) |
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What If Both Events Cannot Happen at Once? |
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147 | (1) |
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The Intersection (and) Rule for Mutually Exclusive Events |
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147 | (1) |
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147 | (1) |
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The Union (or) Rule for Mutually Exclusive Events |
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148 | (1) |
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Finding or From and and Vice Versa |
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148 | (1) |
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One Event Given Another: Reflecting Current Information |
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149 | (1) |
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The Rule for Finding a Conditional Probability Given Certain Information |
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150 | (1) |
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Conditional Probabilities for Mutually Exclusive Events |
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151 | (1) |
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151 | (1) |
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The Intersection (and) Rule for Independent Events |
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152 | (1) |
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The Relationship Between Independent and Mutually Exclusive Events |
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152 | (1) |
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6.5 What Is the Best Way to Solve Probability Problems? |
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152 | (9) |
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152 | (2) |
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Rules for Probability Trees |
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154 | (6) |
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160 | (1) |
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6.6 End-of-Chapter Materials |
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161 | (11) |
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161 | (1) |
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162 | (1) |
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163 | (1) |
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163 | (6) |
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169 | (1) |
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169 | (1) |
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169 | (3) |
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7 Random Variables: Working with Uncertain Numbers |
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7.1 Discrete Random Variables |
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172 | (3) |
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Finding the Mean and Standard Deviation |
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172 | (3) |
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7.2 The Binomial Distribution |
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175 | (6) |
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Definition of Binomial Distribution and Proportion |
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175 | (1) |
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Finding the Mean and Standard Deviation the Easy Way |
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176 | (2) |
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Finding the Probabilities |
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178 | (3) |
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7.3 The Normal Distribution |
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181 | (7) |
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Visualize Probabilities as the Area Under the Curve |
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182 | (1) |
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Finding Probabilities for a Normal Distribution |
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182 | (2) |
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Solving Word Problems for Normal Probabilities |
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184 | (3) |
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The Four Different Probability Calculations |
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187 | (1) |
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Be Careful: Things Need Not Be Normal! |
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187 | (1) |
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7.4 The Normal Approximation to the Binomial |
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188 | (2) |
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7.5 Two Other Distributions: The Poisson and the Exponential |
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190 | (4) |
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190 | (3) |
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The Exponential Distribution |
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193 | (1) |
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7.6 End-of-Chapter Materials |
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194 | (12) |
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194 | (1) |
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195 | (1) |
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195 | (1) |
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196 | (5) |
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201 | (1) |
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201 | (1) |
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201 | (5) |
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Part III Statistical Inference |
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8 Random Sampling: Planning Ahead for Data Gathering |
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8.1 Populations and Samples |
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206 | (2) |
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What Is a Representative Sample? |
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207 | (1) |
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A Sample Statistic and a Population Parameter |
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208 | (1) |
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208 | (5) |
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Selecting a Random Sample |
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209 | (1) |
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Sampling by Shuffling the Population |
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209 | (4) |
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8.3 The Sampling Distribution and the Central Limit Theorem |
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213 | (3) |
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8.4 A Standard Error Is an Estimated Standard Deviation |
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216 | (5) |
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How Close Is the Sample Average to the Population Mean? About One Standard Error |
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217 | (2) |
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Correcting for Small Populations |
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219 | (1) |
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The Standard Error of the Binomial Proportion |
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220 | (1) |
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8.5 Other Sampling Methods |
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221 | (5) |
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The Stratified Random Sample |
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222 | (2) |
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The Systematic Sample Is Not Recommended |
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224 | (2) |
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8.6 End-of-Chapter Materials |
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226 | (12) |
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226 | (1) |
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227 | (1) |
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227 | (1) |
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228 | (5) |
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233 | (1) |
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234 | (1) |
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234 | (4) |
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9 Confidence Intervals: Admitting That Estimates Are Not Exact |
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9.1 The Confidence Interval for a Population Mean or a Population Percentage |
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238 | (10) |
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Critical t Values and the f Distribution |
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240 | (1) |
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The Widely Used 95% Confidence Interval |
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241 | (5) |
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246 | (2) |
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9.2 Assumptions Needed for Validity |
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248 | (3) |
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249 | (1) |
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250 | (1) |
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9.3 Interpreting a Confidence Interval |
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251 | (2) |
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Which Event Has a 95% Probability? |
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252 | (1) |
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Your Lifetime Track Record |
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253 | (1) |
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9.4 One-Sided Confidence Intervals |
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253 | (2) |
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Be Careful! You Cannot Always Use a One-Sided Interval |
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253 | (1) |
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Computing the One-Sided Interval |
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254 | (1) |
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255 | (3) |
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9.6 End-of-Chapter Materials |
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258 | (10) |
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258 | (1) |
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259 | (1) |
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259 | (1) |
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260 | (5) |
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265 | (1) |
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266 | (1) |
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266 | (2) |
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10 Hypothesis Testing: Deciding Between Reality and Coincidence |
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10.1 Hypotheses Are Not Created Equal! |
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268 | (3) |
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268 | (1) |
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269 | (1) |
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Results, Decisions, and p-Values |
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269 | (1) |
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270 | (1) |
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10.2 Testing the Population Mean Against a Known Reference Value: The t-Test |
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271 | (7) |
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Using the p-Value: The Easy Way |
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271 | (1) |
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Using the Confidence Interval: The Intuitive Way, Same Answer |
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272 | (4) |
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Using the t-Statistic: A Traditional Way, Same Answer |
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276 | (2) |
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10.3 Interpreting a Hypothesis Test |
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278 | (4) |
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Errors: Type I and Type II |
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278 | (1) |
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Assumptions Needed for Validity |
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279 | (1) |
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Hypotheses Have No Probabilities of Being True or False |
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279 | (1) |
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Statistical Significance and Test Levels |
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280 | (1) |
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281 | (1) |
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282 | (5) |
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283 | (4) |
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10.5 Testing Whether or Not a New Observation Comes From the Same Population |
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287 | (1) |
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288 | (7) |
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289 | (2) |
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291 | (4) |
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10.7 End-of-Chapter Materials |
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295 | (19) |
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295 | (2) |
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297 | (1) |
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297 | (1) |
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298 | (9) |
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307 | (1) |
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308 | (1) |
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308 | (6) |
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Part IV Regression and Time Series |
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11 Correlation and Regression: Measuring and Predicting Relationships |
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11.1 Exploring Relationships Using Scatterplots and Correlations |
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314 | (19) |
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The Scatterplot Shows You the Relationship |
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314 | (4) |
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Correlation Measures the Strength of the Relationship |
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318 | (1) |
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The Formula for the Correlation |
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319 | (1) |
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The Various Types of Relationships |
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319 | (1) |
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319 | (4) |
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323 | (2) |
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325 | (2) |
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327 | (2) |
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329 | (2) |
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331 | (1) |
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Correlation Is Not Causation |
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332 | (1) |
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11.2 Regression: Prediction of One Variable From Another |
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333 | (20) |
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A Straight Line Summarizes a Linear Relationship |
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333 | (2) |
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335 | (1) |
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Finding a Line Based on Data |
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335 | (4) |
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339 | (1) |
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The Standard Error of Estimate: How Large Are the Prediction Errors? |
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339 | (1) |
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R2: How Much Is Explained? |
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340 | (1) |
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Confidence Intervals and Hypothesis Tests for Regression |
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340 | (1) |
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The Linear Model Assumption Defines the Population |
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340 | (1) |
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Standard Errors for the Slope and Intercept |
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341 | (1) |
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Confidence Intervals for Regression Coefficients |
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342 | (1) |
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Testing Whether the Relationship Is Real or Coincidence |
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342 | (1) |
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Other Methods of Testing the Significance of a Relationship |
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343 | (1) |
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Computer Results for the Production Cost Data |
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343 | (3) |
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Other Tests of a Regression Coefficient |
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346 | (1) |
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A New Observation: Uncertainty and the Confidence Interval |
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347 | (1) |
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The Mean of Y: Uncertainty and the Confidence Interval |
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348 | (1) |
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Regression Can Be Misleading |
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349 | (1) |
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The Linear Model May Be Wrong |
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350 | (1) |
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Predicting Intervention From Observed Experience Is Difficult |
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351 | (1) |
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The Intercept May Not Be Meaningful |
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351 | (1) |
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Explaining V From X Versus Explaining X From Y |
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351 | (1) |
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A Hidden "Third Factor" May Be Helpful |
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352 | (1) |
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11.3 End-of-Chapter Materials |
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353 | (20) |
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353 | (2) |
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355 | (1) |
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355 | (1) |
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356 | (12) |
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368 | (1) |
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369 | (1) |
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369 | (4) |
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12 Multiple Regression: Predicting One Variable From Several Others |
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12.1 Interpreting the Results of a Multiple Regression |
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373 | (15) |
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Regression Coefficients and the Regression Equation |
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374 | (2) |
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Interpreting the Regression Coefficients |
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376 | (2) |
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Predictions and Prediction Errors |
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378 | (1) |
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How Good Are the Predictions? |
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379 | (1) |
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Typical Prediction Error: Standard Error of Estimate |
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379 | (1) |
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Percent Variation Explained: R2 |
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379 | (1) |
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Inference in Multiple Regression |
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379 | (2) |
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381 | (1) |
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Is the Model Significant? The F Test or R2 Test |
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382 | (2) |
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Which Variables Are Significant? A t Test for Each Coefficient |
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384 | (2) |
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Other Tests for a Regression Coefficient |
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386 | (1) |
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Which Variables Explain the Most? |
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386 | (1) |
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Comparing the Standardized Regression Coefficients |
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386 | (1) |
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Comparing the Correlation Coefficients |
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387 | (1) |
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12.2 Pitfalls and Problems in Multiple Regression |
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388 | (13) |
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Multicollinearity: Are the Explanatory Variables Too Similar? |
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388 | (5) |
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Variable Selection: Are You Using the Wrong Variables? |
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393 | (1) |
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Prioritizing the List of X Variables |
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393 | (1) |
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Automating the Variable Selection Process |
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394 | (1) |
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Model Misspecification: Does the Regression Equation Have the Wrong Form? |
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394 | (1) |
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Exploring the Data to See Nonlinearity or Unequal Variability |
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395 | (1) |
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Using the Diagnostic Plot to Decide If You Have a Problem |
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396 | (3) |
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Using Percent Changes to Model an Economic Time Series |
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399 | (2) |
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12.3 Dealing With Nonlinear Relationships and Unequal Variability |
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401 | (8) |
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Transforming to a Linear Relationship: Interpreting the Results |
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401 | (3) |
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Fitting a Curve With Polynomial Regression |
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404 | (2) |
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Modeling Interaction Between Two X Variables |
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406 | (3) |
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12.4 Indicator Variables: Predicting From Categories |
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409 | (5) |
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Interpreting and Testing Regression Coefficients for Indicator Variables |
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410 | (3) |
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413 | (1) |
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12.5 End-of-Chapter Materials |
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414 | (20) |
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414 | (1) |
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415 | (1) |
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415 | (1) |
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416 | (14) |
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430 | (1) |
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430 | (1) |
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431 | (3) |
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13 Report Writing: Communicating the Results of a Multiple Regression |
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13.1 How to Organize Your Report |
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434 | (3) |
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The Executive Summary Paragraph |
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435 | (1) |
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435 | (1) |
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The Analysis and Methods Section |
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435 | (1) |
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The Conclusion and Summary Section |
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436 | (1) |
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436 | (1) |
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437 | (1) |
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437 | (1) |
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Think About Your Audience |
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437 | (1) |
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What to Write First? Next? Last? |
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437 | (1) |
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437 | (1) |
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13.3 Example: A Quick Pricing Formula for Customer Inquiries |
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438 | (4) |
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13.4 End-of-Chapter Materials |
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442 | (4) |
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442 | (1) |
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442 | (1) |
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442 | (1) |
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443 | (1) |
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444 | (1) |
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444 | (2) |
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14 Time Series: Understanding Changes Over Time |
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14.1 An Overview of Time-Series Analysis |
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446 | (6) |
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14.2 Trend-Seasonal Analysis |
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452 | (10) |
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Trend and Cyclic: The Moving Average |
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454 | (1) |
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Seasonal Index: The Average Ratio-to-Moving-Average Indicates Seasonal Behavior |
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455 | (1) |
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Seasonal Adjustment: The Series Divided by the Seasonal Index |
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456 | (2) |
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Long-Term Trend and Seasonally Adjusted Forecast: The Regression Line |
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458 | (1) |
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Forecast: The Seasonalized Trend |
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458 | (4) |
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14.3 Modeling Cyclic Behavior Using Box-Jenkins ARIMA Processes |
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462 | (10) |
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A Random Noise Process Has No Memory: The Starting Point |
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465 | (1) |
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An Autoregressive (AR) Process Remembers Where It Was |
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465 | (3) |
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A Moving-Average (MA) Process Has a Limited Memory |
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468 | (1) |
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The Autoregressive Moving-Average (ARMA) Process Combines AR and MA |
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469 | (1) |
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A Pure Integrated (I) Process Remembers Where It Was and Then Moves at Random |
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470 | (1) |
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The Autoregressive Integrated Moving-Average (ARIMA) Process Remembers Its Changes |
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471 | (1) |
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14.4 End-of-Chapter Materials |
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472 | (14) |
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472 | (1) |
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473 | (1) |
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474 | (1) |
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475 | (6) |
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481 | (5) |
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Part V Methods and Applications |
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15 ANOVA: Testing for Differences Among Many Samples and Much More |
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15.1 Using Box Plots to Look at Many Samples at Once |
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486 | (2) |
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15.2 The F Test Tells You If the Averages Are Significantly Different |
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488 | (10) |
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The Data Set and Sources of Variation |
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488 | (1) |
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488 | (1) |
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489 | (1) |
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489 | (2) |
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491 | (1) |
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The Result of the F Test Using the F Table |
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491 | (1) |
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Computer Output: The One-Way ANOVA Table With p-Value for the F Test |
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491 | (7) |
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15.3 The Least Significant Difference Test: Which Pairs Are Different? |
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498 | (2) |
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15.4 More Advanced ANOVA Designs |
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500 | (4) |
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Variety Is the Spice of Life |
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500 | (1) |
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500 | (1) |
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501 | (1) |
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Analysis of Covariance (ANCOVA) |
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501 | (1) |
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Multivariate Analysis of Variance (MANOVA) |
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501 | (1) |
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How to Read an ANOVA Table |
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501 | (3) |
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15.5 End-of-Chapter Materials |
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504 | (8) |
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504 | (1) |
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505 | (1) |
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505 | (1) |
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505 | (4) |
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509 | (1) |
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510 | (2) |
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16 Nonparametrics: Testing With Ordinal Data or Nonnormal Distributions |
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16.1 Testing the Median Against a Known Reference Value |
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512 | (5) |
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512 | (1) |
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513 | (1) |
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513 | (4) |
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16.2 Testing for Differences in Paired Data |
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517 | (1) |
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Using the Sign Test on the Differences |
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517 | (1) |
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517 | (1) |
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517 | (1) |
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16.3 Testing to See If Two Unpaired Samples Are Significantly Different |
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518 | (5) |
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The Procedure Is Based on the Ranks of All of the Data |
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518 | (1) |
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519 | (1) |
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519 | (4) |
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16.4 End-of-Chapter Materials |
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523 | (9) |
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523 | (1) |
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524 | (1) |
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524 | (1) |
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525 | (4) |
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529 | (1) |
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529 | (3) |
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17 Chi-Squared Analysis: Testing for Patterns in Qualitative Data |
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17.1 Summarizing Qualitative Data by Using Counts and Percentages |
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532 | (1) |
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17.2 Testing If Population Percentages Are Equal to Known Reference Values |
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533 | (3) |
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The Chi-Squared Test for Equality of Percentages |
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533 | (3) |
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17.3 Testing for Association Between Two Qualitative Variables |
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536 | (6) |
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The Meaning of Independence |
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536 | (1) |
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The Chi-Squared Test for Independence |
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536 | (6) |
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17.4 End-of-Chapter Materials |
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542 | (9) |
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542 | (1) |
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543 | (1) |
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543 | (1) |
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543 | (4) |
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547 | (1) |
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547 | (4) |
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18 Quality Control: Recognizing and Managing Variation |
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18.1 Processes and Causes of Variation |
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551 | (2) |
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The Pareto Diagram Shows Where to Focus Attention |
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552 | (1) |
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18.2 Control Charts and How to Read Them |
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553 | (1) |
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The Control Limits Show If a Single Observation Is Out of Control |
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553 | (1) |
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How to Spot Trouble Even Within the Control Limits |
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554 | (1) |
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18.3 Charting a Quantitative Measurement With X and R Charts |
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554 | (6) |
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18.4 Charting the Percent Defective |
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560 | (3) |
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18.5 End-of-Chapter Materials |
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563 | (9) |
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563 | (1) |
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563 | (1) |
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563 | (1) |
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564 | (6) |
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570 | (2) |
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19 Statistical (Machine) Learning: Using Complex Models With Large Data Sets |
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19.1 Training and Testing Data Sets |
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572 | (2) |
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19.2 The R Programming Language |
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574 | (1) |
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575 | (11) |
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575 | (8) |
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583 | (3) |
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19.4 Unsupervised Learning |
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586 | (7) |
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587 | (3) |
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590 | (3) |
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593 | (4) |
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593 | (2) |
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595 | (1) |
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596 | (1) |
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19.6 End-of-Chapter Materials |
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597 | (4) |
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597 | (1) |
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597 | (1) |
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597 | (1) |
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598 | (3) |
| Appendix A Employee Database |
|
601 | (2) |
| Appendix B Donations Database |
|
603 | (4) |
| Appendix C Self-Test: Solutions to Selected Problems and Database Exercises |
|
607 | (14) |
| Appendix D Statistical Tables |
|
621 | (34) |
| Index |
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655 | |
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