| Preface |
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xxi | |
| Acknowledgments |
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xxiii | |
| About the Author |
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xxiv | |
| Chapter 1 A Gentle Introduction |
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1 | (42) |
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How Much Math Do I Need to Do Statistics? |
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2 | (1) |
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The General Purpose of Statistics: Understanding the World |
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2 | (1) |
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Another Purpose of Statistics: Making an Argument or a Decision |
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2 | (1) |
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3 | (3) |
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One Role: The Curious Detective |
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3 | (1) |
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Another Role: The Honest Attorney |
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4 | (1) |
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A Final Role: A Good Storyteller |
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5 | (1) |
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Liberal and Conservative Statisticians |
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6 | (1) |
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Descriptive and Inferential Statistics |
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7 | (1) |
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Experiments Are Designed to Test Theories and Hypotheses |
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8 | (1) |
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9 | (1) |
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9 | (2) |
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Eight Essential Questions of Any Survey or Study |
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11 | (6) |
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1 Who Was Surveyed or Studied? |
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11 | (1) |
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2 Why Did the People Participate in the Study? |
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11 | (1) |
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3 Was There a Control Group, and Did the Control Group Receive a Placebo? |
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12 | (1) |
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4 How Many People Participated in the Study? |
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13 | (1) |
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5 How Were the Questions Worded to the Participants in the Study? |
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13 | (2) |
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6 Was Causation Assumed From a Correlational Study? |
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15 | (1) |
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7 Who Paid for the Study? |
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15 | (1) |
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8 Was the Study Published in a Peer-Reviewed Journal? |
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16 | (1) |
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On Making Samples Representative of the Population |
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17 | (1) |
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Experimental Design and Statistical Analysis as Controls |
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18 | (1) |
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The Language of Statistics |
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19 | (1) |
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On Conducting Scientific Experiments |
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20 | (1) |
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The Dependent Variable and Measurement |
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20 | (1) |
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21 | (1) |
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21 | (1) |
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Measurement Scales: The Difference Between Continuous and Discrete Variables |
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22 | (1) |
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Types of Measurement Scales |
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22 | (2) |
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22 | (1) |
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23 | (1) |
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23 | (1) |
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24 | (1) |
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Rounding Numbers and Rounding Error |
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24 | (2) |
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26 | (1) |
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26 | (2) |
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28 | (1) |
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History Trivia: Achenwall to Nightingale |
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29 | (1) |
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30 | (1) |
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31 | (1) |
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32 | (4) |
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36 | (7) |
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37 | (1) |
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37 | (1) |
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Changing the Display Format for New Numeric Variables |
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38 | (1) |
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39 | (1) |
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39 | (1) |
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Numeric Variables Versus String Variables |
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40 | (1) |
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41 | (1) |
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Changing the Folder Where Your Data Are Saved |
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41 | (1) |
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Opening and Saving Your Data Files |
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42 | (1) |
| Chapter 2 Descriptive Statistics: Understanding Distributions of Numbers |
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43 | (40) |
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The Purpose of Graphs and Tables: Making Arguments and Decisions |
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44 | (4) |
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How a Good Graph Stopped a Cholera Epidemic |
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45 | (2) |
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How Bad Graphs and Tables Contributed to the Space Shuttle Challenger Explosion |
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47 | (1) |
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How a Poor PowerPoint® Presentation Contributed to the Space Shuttle Columbia Disaster |
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48 | (1) |
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A Summary of the Purpose of Graphs and Tables |
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48 | (3) |
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1 Document the Sources of Statistical Data and Their Characteristics |
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48 | (1) |
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2 Make Appropriate Comparisons |
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49 | (1) |
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3 Demonstrate the Mechanisms of Cause and Effect and Express the Mechanisms Quantitatively |
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49 | (1) |
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4 Recognize the Inherent Multivariate Nature of Analytic Problems |
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50 | (1) |
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5 Inspect and Evaluate Alternative Hypotheses |
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50 | (1) |
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51 | (1) |
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52 | (3) |
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Shapes of Frequency Distributions |
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55 | (1) |
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Grouping Data Into Intervals |
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56 | (2) |
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Advice on Grouping Data Into Intervals |
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58 | (1) |
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1 Choose Interval Widths That Reduce Your Data to 5 to 10 Intervals |
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58 | (1) |
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2 Choose the Size of Your Interval Widths Based on Understandable Units, for Example, Multiples of 5 or 10 |
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59 | (1) |
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3 Make Sure That Your Chosen Intervals Do Not Overlap |
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59 | (1) |
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The Cumulative Frequency Distribution |
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59 | (1) |
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Cumulative Percentages, Percentiles, and Quartiles |
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60 | (2) |
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62 | (1) |
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Non-normal Frequency Distributions |
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63 | (1) |
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On the Importance of the Shapes of Distributions |
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64 | (1) |
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Additional Thoughts About Good Graphs Versus Bad Graphs |
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64 | (2) |
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64 | (1) |
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64 | (1) |
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Changing Scales Midstream (or Mid-Axis) |
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65 | (1) |
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65 | (1) |
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65 | (1) |
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PowerPoint® Graphs and Presentations |
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66 | (1) |
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History Trivia: De Moivre to Tukey |
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66 | (2) |
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68 | (1) |
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68 | (1) |
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69 | (4) |
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73 | (10) |
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Creating a Frequency Distribution |
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73 | (3) |
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76 | (1) |
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77 | (1) |
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Understanding Skewness and Kurtosis |
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78 | (1) |
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Describing the Total Autistic Symptoms Data |
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79 | (2) |
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Describing the Schizoid Personality Disorder Data |
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81 | (2) |
| Chapter 3 Statistical Parameters: Measures of Central Tendency and Variation |
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83 | (25) |
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Measures of Central Tendency |
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83 | (5) |
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84 | (1) |
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85 | (2) |
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85 | (1) |
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86 | (1) |
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87 | (1) |
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Choosing Among Measures of Central Tendency |
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88 | (1) |
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89 | (1) |
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Uncertain or Equivocal Results |
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90 | (1) |
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90 | (3) |
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91 | (1) |
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91 | (2) |
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Correcting for Bias in the Sample Standard Deviation |
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93 | (1) |
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How the Square Root of x2 Is Almost Equivalent to Taking the Absolute Value of x |
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93 | (1) |
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The Computational Formula for Standard Deviation |
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94 | (1) |
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94 | (1) |
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The Sampling Distribution of Means, the Central Limit Theorem, and the Standard Error of the Mean |
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95 | (1) |
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The Use of the Standard Deviation for Prediction |
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96 | (1) |
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Practical Uses of the Empirical Rule: As a Definition of an Outlier |
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97 | (1) |
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Practical Uses of the Empirical Rule: Prediction and IQ Tests |
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97 | (1) |
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98 | (1) |
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History Trivia: Fisher to Eels |
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98 | (1) |
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99 | (1) |
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99 | (1) |
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100 | (3) |
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103 | (5) |
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Generating Central Tendency and Variation Statistics |
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103 | (5) |
| Chapter 4 Standard Scores, the z Distribution, and Hypothesis Testing |
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108 | (35) |
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109 | (1) |
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The Classic Standard Score: The z Score and the z Distribution |
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110 | (1) |
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111 | (1) |
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More Practice on Converting Raw Data Into z Scores |
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111 | (2) |
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Converting z Scores to Other Types of Standard Scores |
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113 | (2) |
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115 | (1) |
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Interpreting Negative z Scores |
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116 | (1) |
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Testing the Predictions of the Empirical Rule With the z Distribution |
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116 | (1) |
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Why Is the z Distribution so Important? |
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117 | (1) |
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How We Use the z Distribution to Test Experimental Hypotheses |
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117 | (1) |
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More Practice With the z Distribution and TScores |
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118 | (13) |
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Example 1: Finding the Area in a z Distribution That Falls Above a Known Score Where the Known Score Is Above the Mean |
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118 | (1) |
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Example 2: Finding the Area in a z Distribution That Falls Below a Known Score Where the Known Score Is Above the Mean |
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119 | (3) |
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Example 3: Finding the Area in a z Distribution That Falls Below a Known Score Where the Known Score Is Below the Mean |
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122 | (1) |
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Example 4: Finding The Area in a z Distribution That Falls Above a Known Score Where the Known Score Is Below the Mean |
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123 | (2) |
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Example 5: Finding The Area in a z Distribution That Falls Between Two Known Scores Where Both Known Scores Are Above the Mean |
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125 | (2) |
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Example 6: Finding The Area in a z Distribution That Falls Between Two Known Scores Where One Known Score Is Above the Mean and One Is Below the Mean |
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127 | (1) |
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Example 7: Finding the Area in a z Distribution That Falls Between Two Known Scores |
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128 | (3) |
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Summarizing Scores Through Percentiles |
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131 | (1) |
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History Trivia: Karl Pearson to Egon Pearson |
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132 | (2) |
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134 | (1) |
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134 | (1) |
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135 | (2) |
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137 | (6) |
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Transforming Raw Scores Into z Scores |
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138 | (2) |
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Transforming z Scores Into T Scores |
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140 | (3) |
| Chapter 5 Inferential Statistics: The Controlled Experiment, Hypothesis Testing, and the z Distribution |
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143 | (38) |
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Hypothesis Testing in the Controlled Experiment |
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145 | (1) |
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Hypothesis Testing: The Big Decision |
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146 | (1) |
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How the Big Decision Is Made: Back to the z Distribution |
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146 | (2) |
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The Parameter of Major Interest in Hypothesis Testing: The Mean |
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148 | (1) |
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Nondirectional and Directional Alternative Hypotheses |
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149 | (1) |
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A Debate: Retain the Null Hypothesis or Fait to Reject the Null Hypothesis |
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149 | (1) |
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The Null Hypothesis as a Nonconservative Beginning |
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150 | (1) |
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The Four Possible Outcomes in Hypothesis Testing |
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151 | (1) |
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1 Correct Decision: Retain Ho When Ho Is Actually True |
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151 | (1) |
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2 Type I Error: Reject Ho When Ho Is Actually True |
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151 | (1) |
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3 Correct Decision: Reject Ho When Ho Is Actually False |
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151 | (1) |
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4 Type II Error: Retain Ho When Ho Is Actually False |
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152 | (1) |
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152 | (1) |
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Significant and Nonsignificant Findings |
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152 | (1) |
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Trends, and Does God Really Love the 05 Level of Significance More Than the .06 Level? |
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153 | (1) |
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Directional or Nondirectional Alternative Hypotheses: Advantages and Disadvantages |
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154 | (5) |
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Did Nuclear Fusion Occur? |
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154 | (1) |
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155 | (1) |
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How Reliable Is the Source of the Claim? |
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155 | (1) |
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Does This Source Often Make Similar Claims? |
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156 | (1) |
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Have the Claims Been Verified by Another Source? |
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156 | (1) |
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How Does the Claim Fit With Known Natural Scientific Laws? |
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157 | (1) |
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Can the Claim Be Disproven, or Has Only Supportive Evidence Been Sought? |
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158 | (1) |
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Do the Claimants' Personal Beliefs and Biases Drive Their Conclusions or Vice Versa? |
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159 | (1) |
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Conclusions About Science and Pseudoscience |
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159 | (1) |
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The Most Critical Elements in the Detection of Baloney in Suspicious Studies and Fraudulent Claims |
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160 | (1) |
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Can Statistics Solve Every Problem? |
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161 | (1) |
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161 | (7) |
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161 | (1) |
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The Definition of the Probability of an Event |
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162 | (1) |
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The Multiplication Theorem of Probability |
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162 | (1) |
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Combinations Theorem of Probability |
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163 | (1) |
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Permutations Theorem of Probability |
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164 | (3) |
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167 | (1) |
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167 | (1) |
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168 | (1) |
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168 | (1) |
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History Trivia: Egon Pearson to Karl Pearson |
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168 | (2) |
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170 | (1) |
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170 | (1) |
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170 | (4) |
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174 | (7) |
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Removing a Case From a Data Set |
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174 | (1) |
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174 | (1) |
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175 | (1) |
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176 | (1) |
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176 | (1) |
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Selecting a Particular Condition for Analysis Within a Data Set |
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177 | (2) |
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Copying Selected Cases or Conditions to a New Data Set |
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179 | (2) |
| Chapter 6 An Introduction to Correlation and Regression |
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181 | (49) |
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Correlation: Use and Abuse |
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183 | (2) |
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A Warning: Correlation Does Not Imply Causation |
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185 | (3) |
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1 Marijuana Use and Heroin Use Are Positively Correlated |
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186 | (1) |
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2 Milk Use Is Positively Correlated to Cancer Rates |
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186 | (1) |
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3 Weekly Church Attendance Is Negatively Correlated With Drug Abuse |
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186 | (1) |
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4 Lead Levels Are Positively Correlated With Antisocial Behavior |
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187 | (1) |
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5 The Risk of Getting Alzheimer's Disease Is Negatively Correlated With Smoking Cigarettes |
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187 | (1) |
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6 Sexual Activity Is Negatively Correlated With Increases in Education |
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187 | (1) |
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7 An Active Sex Life Is Positively Correlated With Longevity |
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187 | (1) |
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8 Coffee Drinking Is Negatively Correlated With Suicidal Risk |
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188 | (1) |
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9 Excessive Drinking and Smoking Causes Women to Be Abused |
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188 | (1) |
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Another Warning: Chance Is Lumpy |
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188 | (1) |
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Correlation and Prediction |
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189 | (1) |
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The Four Common Types of Correlation |
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189 | (1) |
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The Pearson Product-Moment Correlation Coefficient |
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189 | (3) |
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Testing for the Significance of a Correlation Coefficient |
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192 | (1) |
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Obtaining the Critical Values of the t Distribution |
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193 | (2) |
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Step 1: Choose a One-Tailed or Two-Tailed Test of Significance |
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194 | (1) |
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Step 2: Choose the Level of Significance |
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194 | (1) |
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Step 3: Determine the Degrees of Freedom (df) |
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194 | (1) |
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Step 4: Determine Whether the t from the Formula (Called the Derived t) Exceeds the Tabled Critical Values From the t Distribution |
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194 | (1) |
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If the Null Hypothesis Is Rejected |
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195 | (1) |
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Representing the Pearson Correlation Graphically: The Scatterplot |
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195 | (1) |
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Fitting the Points With a Straight Line: The Assumption of a Linear Relationship |
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196 | (1) |
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Interpretation of the Slope of the Best-Fitting Line |
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197 | (1) |
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The Assumption of Homoscedasticity |
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198 | (1) |
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The Coefficient of Determination: How Much One Variable Accounts for Variation in Another Variable-The Interpretation of r2 |
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199 | (1) |
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Quirks in the Interpretation of Significant and Nonsignificant Correlation Coefficients |
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200 | (1) |
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201 | (1) |
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Reading the Regression Line |
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202 | (5) |
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The World Is a Complex Place: Any Single Behavior Is Most Often Caused by Multiple Variables |
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203 | (1) |
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204 | (1) |
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205 | (1) |
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205 | (2) |
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Final Thoughts About Multiple Regression Analyses: A Warning About the Interpretation of the Significant Beta Coefficients |
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207 | (1) |
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208 | (1) |
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Significance Test for Spearman's r |
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209 | (1) |
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210 | (1) |
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Point-Biserial Correlation |
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211 | (3) |
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Testing for the Significance of the Point-Biserial Correlation Coefficient |
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214 | (1) |
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215 | (1) |
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Testing for the Significance of Phi |
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216 | (1) |
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History Trivia: Galton to Fisher |
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217 | (1) |
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218 | (1) |
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219 | (1) |
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219 | (5) |
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224 | (6) |
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Analyzing the Pearson Product-Moment Correlation |
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224 | (1) |
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225 | (3) |
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Using the Paste Function in the Syntax Editor |
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228 | (2) |
| Chapter 7 The t Test for Independent Groups |
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230 | (27) |
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The Statistical Analysis of the Controlled Experiment |
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230 | (1) |
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One t Test but Two Designs |
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231 | (2) |
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Assumptions of the Independent tTest |
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232 | (1) |
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232 | (1) |
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Normality of the Dependent Variable |
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233 | (1) |
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233 | (1) |
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The Formula for the Independent t Test |
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233 | (1) |
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You Must Remember This! An Overview of Hypothesis Testing With the tTest |
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234 | (1) |
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What Does the t Test Do? Components of the t Test Formula |
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234 | (1) |
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What If the Two Variances Are Radically Different From One Another? |
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235 | (1) |
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235 | (5) |
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Steps in the tTest Formula |
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236 | (2) |
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Testing the Null Hypothesis |
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238 | (1) |
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Steps in Determining Significance |
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238 | (1) |
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When Ho Has Been Rejected |
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239 | (1) |
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240 | (1) |
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The Power of a Statistical Test |
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240 | (1) |
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241 | (1) |
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The Correlation Coefficient of Effect Size |
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241 | (1) |
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Another Measure of Effect Size: Cohen's d |
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242 | (1) |
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243 | (3) |
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Estimating the Standard Error |
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246 | (2) |
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History Trivia: Gosset and Guinness Brewery |
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248 | (1) |
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248 | (1) |
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249 | (1) |
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249 | (4) |
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253 | (4) |
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Conducting a t Test for Independent Groups |
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253 | (1) |
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Interpreting a t Test for Independent Groups |
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254 | (1) |
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Conducting a tTest for Independent Groups for a Different Variable |
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255 | (1) |
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Interpreting a t Test for Independent Groups for a Different Variable |
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256 | (1) |
| Chapter 8 The t Test for Dependent Groups |
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257 | (24) |
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Variations on the Controlled Experiment |
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257 | (2) |
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258 | (1) |
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258 | (1) |
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258 | (1) |
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258 | (1) |
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259 | (3) |
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259 | (1) |
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Assumptions of the Dependent t Test |
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259 | (1) |
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Why the Dependent t Test May Be More Powerful Than the Independent t Test |
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259 | (1) |
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How to Increase the Power of a t Test |
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260 | (1) |
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Drawbacks of the Dependent t Test Designs |
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260 | (1) |
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One-Tailed or Two-Tailed Tests of Significance |
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261 | (1) |
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Hypothesis Testing and the Dependent t Test: Design 1 |
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261 | (1) |
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Design 1 (Same Participants or Repeated Measures): A Computational Example |
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262 | (4) |
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Determination of Effect Size |
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265 | (1) |
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Design 2 (Matched Pairs): A Computational Example |
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266 | (3) |
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Determination of Effect Size |
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269 | (1) |
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Design 3 (Same Participants and Balanced Presentation): A Computational Example |
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269 | (4) |
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Determination of Effect Size |
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272 | (1) |
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History Trivia: Fisher to Pearson |
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273 | (1) |
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273 | (1) |
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274 | (1) |
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274 | (4) |
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278 | (3) |
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Conducting a t Test for Dependent Groups |
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278 | (1) |
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Interpreting a t Test for Dependent Groups |
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279 | (2) |
| Chapter 9 Analysis of Variance (ANOVA): One-Factor Completely Randomized Design |
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281 | (23) |
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A Limitation of Multiple t Tests and a Solution |
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282 | (1) |
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The Equally Unacceptable Bonferroni Solution |
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282 | (1) |
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The Acceptable Solution: An Analysis of Variance |
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282 | (1) |
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The Null and Alternative Hypotheses in ANOVA |
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283 | (1) |
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The Beauty and Elegance of the FTest Statistic |
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283 | (1) |
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284 | (1) |
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How Can There Be Two Different Estimates of Within-Groups Variance? |
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285 | (1) |
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286 | (1) |
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287 | (1) |
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287 | (1) |
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What a Significant ANOVA Indicates |
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288 | (1) |
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288 | (3) |
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Degrees of Freedom for the Numerator |
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291 | (1) |
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Degrees of Freedom for the Denominator |
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291 | (1) |
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Determining Effect Size in ANOVA: Omega Squared |
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292 | (1) |
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Another Measure of Effect Size: Eta |
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293 | (1) |
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History Trivia: Gosset to Fisher |
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294 | (2) |
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296 | (1) |
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296 | (1) |
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297 | (4) |
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301 | (3) |
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301 | (1) |
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Downloading the Data Set to Your Desktop |
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301 | (1) |
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Conducting a One-Factor Completely Randomized ANOVA |
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301 | (2) |
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Interpreting a One-Factor Completely Randomized ANOVA |
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303 | (1) |
| Chapter 10 After a Significant ANOVA: Multiple Comparison Tests |
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304 | (19) |
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Conceptual Overview of Tukey's Test |
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305 | (1) |
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Computation of Tukey's HSD Test |
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305 | (3) |
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What to Do If the Number of Error Degrees of Freedom Is Not Listed in the Table of Tukey's q Values |
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308 | (1) |
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Determining What It All Means |
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308 | (1) |
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309 | (1) |
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On the Importance of Nonsignificant Mean Differences |
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310 | (1) |
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310 | (1) |
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311 | (1) |
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311 | (1) |
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311 | (1) |
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312 | (1) |
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312 | (3) |
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315 | (8) |
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315 | (1) |
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Downloading the Data Set to Your Desktop |
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315 | (1) |
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Conducting a Multiple Comparison Test |
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316 | (2) |
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Interpreting a Multiple Comparison Test |
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318 | (1) |
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Conducting a Multiple Comparison Test for Another Variable |
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319 | (2) |
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Interpreting a Multiple Comparison Test for Another Variable |
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321 | (2) |
| Chapter 11 Analysis of Variance (ANOVA): One-Factor Repeated-Measures Design |
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323 | (16) |
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The Repeated-Measures ANOVA |
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323 | (1) |
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Assumptions of the One-Factor Repeated-Measures ANOVA |
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324 | (1) |
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324 | (4) |
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Determining Effect Size in ANOVA |
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328 | (1) |
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329 | (1) |
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329 | (1) |
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330 | (2) |
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332 | (7) |
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332 | (1) |
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Downloading the Data Set to Your Desktop |
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333 | (1) |
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Conducting a One-Factor Repeated-Measures Design ANOVA |
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333 | (3) |
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Interpreting a One-Factor Repeated-Measures Design ANOVA |
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336 | (3) |
| Chapter 12 Factorial ANOVA: Two-Factor Completely Randomized Design |
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339 | (16) |
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339 | (1) |
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The Most Important Feature of a Factorial Design: The Interaction |
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340 | (1) |
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Fixed and Random Effects and In Situ Designs |
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340 | (1) |
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The Null Hypotheses in a Two-Factor ANOVA |
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341 | (1) |
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Assumptions and Unequal Numbers of Participants |
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341 | (1) |
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341 | (6) |
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Computation of the First Main Effect |
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343 | (1) |
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Computation of the Second Main Effect |
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343 | (1) |
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Computation of the Interaction Between the Two Main Effects |
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344 | (2) |
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Interpretation of the Results |
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346 | (1) |
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347 | (1) |
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347 | (1) |
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348 | (3) |
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351 | (4) |
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351 | (1) |
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Downloading the Data Set to Your Desktop |
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352 | (1) |
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Conducting an ANOVA Two-Factor Completely Randomized Design |
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352 | (1) |
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Interpreting an ANOVA Two-Factor Completely Randomized Design |
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353 | (2) |
| Chapter 13 Post Hoc Analysis of Factorial ANOVA |
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355 | (26) |
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Main Effect Interpretation: Gender |
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355 | (1) |
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Why a Multiple Comparison Test Is Unnecessary for a Two-Level Main Effect, and When Is a Multiple Comparison Test Necessary? |
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356 | (1) |
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357 | (1) |
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Multiple Comparison Test for the Main Effect for Age |
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358 | (2) |
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Warning: Limit Your Main Effect Conclusions When the Interaction Is Significant |
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360 | (1) |
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Multiple Comparison Tests |
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360 | (1) |
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Interpretation of the Interaction Effect |
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361 | (4) |
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364 | (1) |
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365 | (1) |
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ADHD Men Versus ADHD Women |
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365 | (1) |
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365 | (1) |
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Writing Up the Results Journal Style |
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365 | (1) |
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366 | (1) |
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Exploring the Possible Outcomes in a Two-Factor ANOVA |
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366 | (2) |
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Determining Effect Size in a Two-Factor ANOVA |
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368 | (1) |
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History Trivia: Fisher and Smoking |
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369 | (1) |
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370 | (1) |
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371 | (1) |
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371 | (3) |
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374 | (7) |
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374 | (1) |
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Downloading the Data Set to Your Desktop |
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374 | (1) |
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Conducting a Post Hoc Analysis of Factorial ANOVA |
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375 | (2) |
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Interpreting a Post Hoc Analysis of Factorial ANOVA for the Main Effect |
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377 | (1) |
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Conducting a Post Hoc Analysis of a Significant Interaction in Factorial ANOVA With a Group Variable |
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378 | (1) |
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Interpreting a Post Hoc Analysis of a Significant Interaction in Factorial ANOVA With a Group Variable |
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379 | (2) |
| Chapter 14 Factorial ANOVA: Additional Designs |
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381 | (31) |
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381 | (1) |
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Overview of the Split-Plot ANOVA |
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382 | (1) |
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382 | (7) |
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Main Effect: Social Facilitation |
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388 | (1) |
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388 | (1) |
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Interaction: Social Facilitation x Trials |
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389 | (1) |
|
Two-Factor ANOVA: Repeated Measures on Both Factors Design |
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389 | (1) |
|
Overview of the Repeated-Measures ANOVA |
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389 | (1) |
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390 | (8) |
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398 | (1) |
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398 | (1) |
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399 | (3) |
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402 | (10) |
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|
402 | (1) |
|
Downloading the Data Set to Your Desktop |
|
|
403 | (1) |
|
Conducting a Split-Plot ANOVA |
|
|
403 | (3) |
|
A Second Two-Factor ANOVA Design in SPSS |
|
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406 | (1) |
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|
407 | (1) |
|
Conducting a Repeated-Measures ANOVA |
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|
408 | (4) |
| Chapter 15 Nonparametric Statistics: The Chi-Square Test and Other Nonparametric Tests |
|
412 | (30) |
|
Overview of the Purpose of Chi-Square |
|
|
413 | (1) |
|
Overview of Chi-Square Designs |
|
|
414 | (1) |
|
Chi-Square Test: Two-Cell Design (Equal Probabilities Type) |
|
|
414 | (3) |
|
Computation of the Two-Cell Design |
|
|
415 | (2) |
|
The Chi-Square Distribution |
|
|
417 | (1) |
|
Assumptions of the Chi-Square Test |
|
|
417 | (1) |
|
Chi-Square Test: Two-Cell Design (Different Probabilities Type) |
|
|
418 | (2) |
|
Computation of the Two-Cell Design |
|
|
418 | (2) |
|
Interpreting a Significant Chi-Square Test for a Newspaper |
|
|
420 | (1) |
|
Chi-Square Test: Three-Cell Experiment (Equal Probabilities Type) |
|
|
420 | (2) |
|
Computation of the Three-Cell Design |
|
|
420 | (2) |
|
Chi-Square Test: Two-by-Two Design |
|
|
422 | (3) |
|
Computation of the Two-by-Two Design |
|
|
423 | (2) |
|
What to Do After a Chi-Square Test Is Significant |
|
|
425 | (1) |
|
When Cell Frequencies Are Less Than 5 Revisited |
|
|
426 | (6) |
|
Other Nonparametric Tests |
|
|
427 | (1) |
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|
427 | (3) |
|
Wilcoxon Test for Two Dependent Groups |
|
|
430 | (2) |
|
History Trivia: Pearson and Biometrika |
|
|
432 | (1) |
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|
432 | (1) |
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|
433 | (1) |
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|
433 | (3) |
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|
436 | (6) |
|
Building a Data Set for a Chi-Square Test |
|
|
436 | (2) |
|
Conducting a Chi-Square Test |
|
|
438 | (2) |
|
Interpreting a Chi-Square Test |
|
|
440 | (2) |
| Chapter 16 Other Statistical Topics, Parameters, and Tests |
|
442 | (18) |
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|
|
443 | (1) |
|
Health Science Statistics |
|
|
443 | (7) |
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|
443 | (4) |
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|
447 | (1) |
|
Parameters of Mortality and Morbidity |
|
|
448 | (2) |
|
Additional Statistical Analyses and Multivariate Statistics |
|
|
450 | (5) |
|
|
|
450 | (1) |
|
Multivariate Analysis of Variance |
|
|
450 | (1) |
|
Multivariate Analysis of Covariance |
|
|
451 | (1) |
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|
|
451 | (1) |
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|
|
452 | (1) |
|
Structural Equation Modeling |
|
|
453 | (1) |
|
|
|
453 | (1) |
|
|
|
454 | (1) |
|
Linear Discriminant Function Analysis |
|
|
454 | (1) |
|
A Summary of Multivariate Statistics |
|
|
455 | (1) |
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|
|
456 | (1) |
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|
456 | (1) |
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|
457 | (1) |
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|
|
457 | (3) |
| Appendix A: z Distribution |
|
460 | (14) |
| Appendix B: t Distribution |
|
474 | (2) |
| Appendix C: Spearman's Correlation |
|
476 | (1) |
| Appendix D: Chi-Square Distribution |
|
477 | (2) |
| Appendix E: F Distribution |
|
479 | (6) |
| Appendix F: Tukey's Table |
|
485 | (2) |
| Appendix G: Mann-Whitney U Critical Values |
|
487 | (3) |
| Appendix H: Wilcoxon Signed-Rank Test Critical Values |
|
490 | (1) |
| Appendix I: Answers to Odd-Numbered Test Yourself Questions |
|
491 | (3) |
| Glossary |
|
494 | (8) |
| References |
|
502 | |
| Index |
|
50 | |
