| A Note to the Student: Why I Wrote This Book |
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xviii | |
| And a (Little) Note to the Instructor |
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xx | |
| Acknowledgments |
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xxi | |
| And Now, About the Third Edition... |
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xxii | |
| About the Author |
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xxiv | |
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Part I Yippee! I'm in Statistics |
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1 | (40) |
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1 Statistics or Sadistics? It's Up to You |
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5 | (36) |
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5 | (1) |
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6 | (1) |
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A Five-Minute History of Statistics |
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6 | (2) |
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Statistics: What It Is (and Isn't) |
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8 | (1) |
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What Are Descriptive Statistics? |
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9 | (1) |
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What Are Inferential Statistics? |
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9 | (1) |
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10 | (1) |
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Tooling Around With the Analysis ToolPak |
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11 | (1) |
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What Am I Doing in a Statistics Class? |
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12 | (1) |
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Ten Ways to Use This Book (and Learn Statistics at the Same Time!) |
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13 | (3) |
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16 | (1) |
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17 | (1) |
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Key to "How Much Excel" Icons |
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17 | (1) |
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18 | (2) |
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18 | (1) |
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18 | (2) |
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Little
Chapter 1a. All You Need to Know About Formulas and Functions |
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20 | (1) |
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20 | (1) |
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21 | (2) |
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Operator, Operator---Get Me a Formula! |
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23 | (1) |
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23 | (1) |
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24 | (1) |
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25 | (6) |
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Using Functions in Formulas |
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31 | (1) |
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We're Taking Names: Naming Ranges |
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32 | (1) |
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32 | (5) |
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35 | (1) |
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35 | (2) |
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Little
Chapter 1b. All You Need to Know About Using the Amazing Analysis ToolPak |
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37 | (1) |
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A Look at the Analysis ToolPak |
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38 | (1) |
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39 | (2) |
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Part II Σigma Freud and Descriptive Statistics |
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41 | (120) |
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2 Computing and Understanding Averages: Means to an End |
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43 | (26) |
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44 | (1) |
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And Now... Using Excel's AVERAGE Function |
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45 | (2) |
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47 | (1) |
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Computing a Weighted Mean |
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48 | (3) |
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51 | (1) |
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And Now... Using Excel's MEDIAN Function |
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52 | (3) |
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55 | (1) |
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55 | (1) |
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And Now... Using Excel's MODE.SNGL Function |
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56 | (2) |
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58 | (1) |
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And Now... Using Excel's MODE.MULT Function |
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59 | (2) |
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Using the Amazing Analysis ToolPak to Compute Descriptive Statistics |
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61 | (3) |
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Make the Analysis ToolPak Output Pretty |
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64 | (1) |
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65 | (4) |
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66 | (1) |
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66 | (3) |
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3 Vive la Difference: Understanding Variability |
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69 | (15) |
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Why Understanding Variability Is Important |
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69 | (1) |
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70 | (1) |
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Computing the Standard Deviation |
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71 | (2) |
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And Now... Using Excel's STDEV.S Function |
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73 | (3) |
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Why n - 1? What's Wrong With Just n? |
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76 | (1) |
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77 | (1) |
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78 | (1) |
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78 | (1) |
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And Now... Using Excel's VAR.S Function |
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79 | (1) |
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The Standard Deviation Versus the Variance |
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80 | (1) |
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Using the Amazing Analysis ToolPak (Again!) |
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81 | (3) |
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81 | (1) |
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81 | (3) |
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4 A Picture Really Is Worth a Thousand Words |
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84 | (32) |
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84 | (1) |
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Ten Ways to a Great Figure (Eat Less and Exercise More?) |
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85 | (1) |
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First Things First: Creating a Frequency Distribution |
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86 | (1) |
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The Classiest of Intervals |
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87 | (1) |
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The Plot Thickens: Creating a Histogram |
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88 | (2) |
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90 | (1) |
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Using the Amazing Analysis ToolPak to Create a Histogram |
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90 | (4) |
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The Next Step: A Frequency Polygon |
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94 | (1) |
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95 | (1) |
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Fat and Skinny Frequency Distributions |
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96 | (1) |
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97 | (1) |
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97 | (1) |
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98 | (1) |
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99 | (2) |
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101 | (1) |
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Your First Excel Chart: A Moment to Remember (Sigh) |
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102 | (2) |
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Excellent Charts Part Deux: Making Charts Pretty |
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104 | (4) |
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108 | (1) |
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108 | (1) |
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108 | (1) |
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109 | (1) |
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110 | (6) |
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114 | (1) |
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114 | (2) |
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5 Ice Cream and Crime: Computing Correlation Coefficients |
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116 | (24) |
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What Are Correlations All About? |
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116 | (1) |
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Types of Correlation Coefficients: Flavor 1 and Flavor 2 |
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117 | (1) |
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118 | (1) |
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Computing a Simple Correlation Coefficient |
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119 | (2) |
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And Now... Using Excel's CORREL Function |
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121 | (2) |
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A Visual Picture of a Correlation: The Scatterplot |
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123 | (3) |
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Using Excel to Create a Scatterplot |
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126 | (1) |
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Bunches of Correlations: The Correlation Matrix |
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127 | (1) |
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More Excel---Bunches of Correlations a la Excel |
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128 | (1) |
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Using the Amazing Analysis ToolPak to Compute Correlations |
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129 | (2) |
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Understanding What the Correlation Coefficient Means |
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131 | (1) |
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132 | (1) |
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A Determined Effort: Squaring the Correlation Coefficient |
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133 | (1) |
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As More Ice Cream Is Eaten... the Crime Rate Goes Up (or Association Versus Causality) |
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134 | (2) |
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136 | (4) |
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136 | (1) |
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137 | (3) |
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6 Just the Truth: An Introduction to Understanding Reliability and Validity |
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140 | (21) |
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An Introduction to Reliability and Validity |
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140 | (1) |
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What's Up With This Measurement Stuff? |
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141 | (1) |
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All About Measurement Scales |
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142 | (1) |
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A Rose by Any Other Name: The Nominal Level of Measurement |
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142 | (1) |
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Any Order Is Fine With Me: The Ordinal Level of Measurement |
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143 | (1) |
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1 + 1 = 2: The Interval Level of Measurement |
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143 | (1) |
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Can Anyone Have Nothing of Anything? The Ratio Level of Measurement |
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143 | (1) |
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144 | (1) |
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Reliability---Doing It Again Until You Get It Right |
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145 | (1) |
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Test Scores---Truth or Dare |
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145 | (1) |
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Observed Score = True Score + Error Score |
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146 | (1) |
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Different Types of Reliability |
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146 | (6) |
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How Big Is Big? Interpreting Reliability Coefficients |
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152 | (1) |
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And If You Can't Establish Reliability... Then What? |
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152 | (1) |
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153 | (1) |
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Validity---Whoa! What Is the Truth? |
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153 | (1) |
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Different Types of Validity |
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154 | (3) |
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And If You Can't Establish Validity... Then What? |
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157 | (1) |
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157 | (1) |
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Validity and Reliability: Really Close Cousins |
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158 | (3) |
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159 | (1) |
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159 | (2) |
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Part III Taking Chances for Fun and Profit |
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161 | (40) |
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7 Hypotheticals and You: Testing Your Questions |
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163 | (14) |
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So You Want to Be a Scientist... |
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163 | (1) |
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164 | (1) |
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165 | (1) |
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The Purposes of the Null Hypothesis |
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166 | (1) |
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167 | (1) |
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The Nondirectional Research Hypothesis |
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168 | (1) |
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The Directional Research Hypothesis |
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169 | (2) |
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Some Differences Between the Null Hypothesis and the Research Hypothesis |
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171 | (1) |
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What Makes a Good Hypothesis? |
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172 | (5) |
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175 | (1) |
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175 | (2) |
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8 Are Your Curves Normal? Probability and Why It Counts |
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177 | (24) |
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177 | (1) |
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The Normal Curve (aka the Bell-Shaped Curve) |
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178 | (1) |
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179 | (3) |
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182 | (4) |
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Our Favorite Standard Score: The z Score |
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186 | (3) |
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Using Excel to Compute z Scores |
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189 | (3) |
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192 | (3) |
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What z Scores Really Represent |
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195 | (2) |
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Hypothesis Testing and z Scores: The First Step |
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197 | (4) |
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198 | (1) |
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198 | (3) |
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Part IV Significantly Different: Using Inferential Statistics |
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201 | (154) |
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9 Significantly Significant: What It Means for You and Me |
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203 | (18) |
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The Concept of Significance |
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203 | (1) |
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204 | (2) |
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The World's Most Important Table (for This Semester Only) |
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206 | (1) |
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207 | (1) |
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208 | (2) |
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Significance Versus Meaningfulness |
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210 | (1) |
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An Introduction to Inferential Statistics |
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211 | (1) |
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212 | (1) |
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How to Select What Test to Use |
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212 | (1) |
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Here's How to Use the Chart |
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213 | (2) |
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An Introduction to Tests of Significance |
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215 | (1) |
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How a Test of Significance Works: The Plan |
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215 | (2) |
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Here's the Picture That's Worth a Thousand Words |
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217 | (1) |
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Confidence Intervals---Be Even More Confident |
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218 | (3) |
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219 | (1) |
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220 | (1) |
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10 Only the Lonely: The One-Sample Z-Test |
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221 | (10) |
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Introduction to the One-Sample Z-Test |
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221 | (1) |
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The Path to Wisdom and Knowledge |
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222 | (2) |
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Computing the Test Statistic |
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224 | (1) |
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225 | (2) |
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So How Do I Interpret z = 2.38, p < .05? |
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227 | (1) |
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Using the Excel Z.TEST Function to Compute the z Value |
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228 | (3) |
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229 | (1) |
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230 | (1) |
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11 t(ea) for Two: Tests Between the Means of Different Groups |
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231 | (17) |
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Introduction to the t-Test for Independent Samples |
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231 | (1) |
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The Path to Wisdom and Knowledge |
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232 | (2) |
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Computing the Test Statistic |
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234 | (1) |
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234 | (4) |
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So How Do I Interpret t(58) = -0.14, p > .05? |
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238 | (1) |
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And Now... Using Excel's T.TEST Function |
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238 | (3) |
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Using the Amazing Analysis ToolPak to Compute the t Value |
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241 | (2) |
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243 | (1) |
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Special Effects: Are Those Differences for Real? |
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243 | (1) |
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Computing and Understanding the Effect Size |
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244 | (2) |
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A Very Cool Effect Size Calculator |
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246 | (2) |
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247 | (1) |
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247 | (1) |
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12 t(ea) for Two (Again): Tests Between the Means of Related Groups |
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248 | (14) |
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Introduction to the t-Test for Dependent Samples |
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248 | (1) |
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The Path to Wisdom and Knowledge |
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249 | (2) |
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Computing the Test Statistic |
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251 | (3) |
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So How Do I Interpret t(24) = 2.45, p < .05? |
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254 | (1) |
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And Now... Using Excel's T.TEST Function |
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255 | (2) |
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Using the Amazing Analysis ToolPak to Compute the t Value |
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257 | (5) |
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260 | (1) |
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260 | (2) |
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13 Two Groups Too Many? Try Analysis of Variance |
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262 | (17) |
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Introduction to Analysis of Variance |
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262 | (1) |
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The Path to Wisdom and Knowledge |
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263 | (1) |
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Different Flavors of ANOVA |
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263 | (3) |
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Computing the F-Test Statistic |
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266 | (6) |
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So How Do I Interpret F(2.27) = 8.80, p < .05? |
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272 | (1) |
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And Now... Using Excel's EDIST and ETEST Functions |
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273 | (1) |
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Using the Amazing Analysis ToolPak to Compute the F Value |
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273 | (6) |
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277 | (1) |
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277 | (2) |
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14 Two Too Many Factors: Factorial Analysis of Variance---A Brief Introduction |
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279 | (14) |
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Introduction to Factorial Analysis of Variance |
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279 | (1) |
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Two Flavors of Factorial ANOVA |
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280 | (1) |
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The Path to Wisdom and Knowledge |
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281 | (2) |
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283 | (1) |
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The Main Event: Main Effects in Factorial ANOVA |
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284 | (1) |
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Even More Interesting: Interaction Effects |
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285 | (2) |
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Computing the ANOVA F Statistic Using the Amazing Analysis ToolPak |
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287 | (6) |
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291 | (1) |
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292 | (1) |
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15 Cousins or Just Good Friends? Testing Relationships Using the Correlation Coefficient |
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293 | (10) |
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Introduction to Testing the Correlation Coefficient |
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293 | (1) |
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The Path to Wisdom and Knowledge |
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294 | (2) |
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Computing the Test Statistic |
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296 | (3) |
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So How Do I Interpret r(28) = .393, p < .05? |
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299 | (1) |
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Causes and Associations (Again!) |
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300 | (1) |
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Significance Versus Meaningfulness (Again, Again!) |
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300 | (3) |
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301 | (1) |
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301 | (2) |
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16 Predicting Who'll Win the Super Bowl: Using Linear Regression |
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303 | (21) |
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What Is Prediction All About? |
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303 | (2) |
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305 | (3) |
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Drawing the World's Best Line (for Your Data) |
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308 | (3) |
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And Now... Using Excel's SLOPE Function |
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311 | (3) |
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And Now... Using Excel's INTERCEPT Function |
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314 | (2) |
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Computing the Regression Equation Using the Amazing Analysis ToolPak |
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316 | (2) |
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How Good Is Our Prediction? |
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318 | (1) |
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The More Predictors, the Better? Maybe |
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319 | (2) |
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The Big Rule When It Comes to Using Multiple Predictor Variables |
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321 | (3) |
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321 | (1) |
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322 | (2) |
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17 What to Do When You're Not Normal: Chi-Square and Some Other Nonparametric Tests |
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324 | (12) |
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Introduction to Nonparametric Statistics |
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324 | (1) |
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Introduction to One-Sample Chi-Square |
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325 | (1) |
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Computing the Chi-Square Test Statistic |
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326 | (3) |
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So How Do I Interpret Χ2 = 20.6, p < .05? |
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329 | (1) |
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And Now... Using Excel's CHISQ.TEST Function |
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330 | (2) |
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Other Nonparametric Tests You Should Know About |
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332 | (4) |
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334 | (1) |
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334 | (2) |
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18 Some Other (Important) Statistical Procedures You Should Know About |
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336 | (8) |
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337 | (1) |
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Multivariate Analysis of Variance |
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337 | (1) |
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Repeated Measures Analysis of Variance |
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338 | (1) |
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339 | (1) |
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339 | (1) |
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340 | (1) |
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340 | (1) |
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340 | (2) |
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342 | (1) |
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Structural Equation Modeling |
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342 | (2) |
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343 | (1) |
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19 A Statistical Software Sampler |
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344 | (11) |
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Selecting the Perfect Statistics Software |
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345 | (1) |
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346 | (1) |
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The Free Stuff and Open Source Stuff |
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347 | (2) |
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349 | (6) |
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353 | (2) |
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Part V Ten Things You'll Want to Know and Remember |
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355 | (11) |
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20 The Ten (or More) Best (and Most Fun) Internet Sites for Statistics Stuff |
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357 | (6) |
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How About Studying Statistics in Stockholm? |
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357 | (1) |
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358 | (1) |
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Who's Who and What's Happened |
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359 | (1) |
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359 | (1) |
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359 | (1) |
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360 | (1) |
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361 | (1) |
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361 | (1) |
|
Online Statistical Teaching Materials |
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361 | (1) |
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And, of Course, YouTube... |
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362 | (1) |
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21 The Ten Commandments of Data Collection |
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363 | (3) |
| Appendix A Excel-erate Your Learning: All You Need to Know About Excel |
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366 | (6) |
| Appendix B Tables |
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372 | (14) |
| Appendix C Data Sets |
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386 | (24) |
| Appendix D Answers to Practice Questions |
|
410 | (29) |
| Appendix E The Reward: The Brownie Recipe |
|
439 | (2) |
| Glossary |
|
441 | (8) |
| Index |
|
449 | |
