| Preface |
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xv | |
| Acknowledgments |
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xix | |
| Glossary of Symbols |
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xxi | |
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Part I Descriptive Statistics |
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1 | (122) |
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3 | (20) |
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4 | (1) |
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Descriptive and Inferential Statistics |
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5 | (1) |
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Populations, Samples, Parameters, and Statistics |
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6 | (1) |
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6 | (2) |
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Independent and Dependent Variables |
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8 | (1) |
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9 | (1) |
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10 | (6) |
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16 | (1) |
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17 | (3) |
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20 | (1) |
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21 | (1) |
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21 | (2) |
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Frequency Distributions and Graphs |
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23 | (19) |
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The Purpose of Descriptive Statistics |
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24 | (1) |
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Regular Frequency Distributions |
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25 | (1) |
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Cumulative Frequency Distributions |
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26 | (1) |
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Grouped Frequency Distributions |
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27 | (3) |
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30 | (5) |
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Shapes of Frequency Distributions |
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35 | (2) |
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37 | (1) |
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38 | (1) |
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39 | (1) |
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40 | (1) |
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40 | (2) |
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Transformed Scores I: Percentiles |
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42 | (14) |
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43 | (1) |
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Definition of Percentile and Percentile Rank |
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43 | (1) |
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44 | (8) |
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Deciles, Quartiles, and the Median |
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52 | (1) |
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52 | (1) |
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53 | (1) |
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54 | (1) |
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54 | (1) |
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54 | (2) |
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Measures of Central Tendency |
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56 | (13) |
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57 | (1) |
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58 | (6) |
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64 | (2) |
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66 | (1) |
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66 | (1) |
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67 | (1) |
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67 | (1) |
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68 | (1) |
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68 | (1) |
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69 | (16) |
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The Concept of Variability |
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70 | (2) |
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72 | (1) |
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The Semi-Interquartile Range |
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73 | (1) |
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The Standard Deviation and Variance |
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74 | (6) |
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80 | (2) |
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82 | (1) |
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83 | (1) |
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83 | (1) |
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84 | (1) |
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Additional Techniques for Describing Batches of Data |
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85 | (9) |
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86 | (2) |
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88 | (3) |
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91 | (1) |
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91 | (1) |
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92 | (1) |
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92 | (1) |
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92 | (2) |
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Transformed Scores II: z and T Scores |
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94 | (14) |
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95 | (1) |
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Rules for Changing X and σ |
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96 | (2) |
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Standard Scores (z Scores) |
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98 | (2) |
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100 | (2) |
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102 | (1) |
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103 | (1) |
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104 | (2) |
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106 | (1) |
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106 | (1) |
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106 | (2) |
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108 | (15) |
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109 | (1) |
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110 | (1) |
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Parameters of the Normal Distribution |
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111 | (1) |
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Tables of the Standard Normal Distribution |
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111 | (1) |
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Characteristics of the Normal Curve |
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112 | (1) |
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113 | (6) |
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119 | (1) |
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120 | (1) |
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121 | (1) |
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121 | (1) |
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121 | (2) |
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Part II Basic Inferential Statistics |
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123 | (164) |
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Introduction to Statistical Inference |
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125 | (25) |
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126 | (1) |
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The Goals of Inferential Statistics |
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127 | (1) |
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128 | (4) |
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The Standard Error of the Mean |
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132 | (3) |
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The z Score for Sample Means |
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135 | (2) |
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137 | (7) |
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Assumptions Required by the Statistical Test for the Mean of a Single Population |
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144 | (1) |
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144 | (2) |
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146 | (2) |
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148 | (1) |
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149 | (1) |
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149 | (1) |
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The One-Sample t Test and Interval Estimation |
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150 | (17) |
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The Statistical Test for the Mean of a Single Population When σ Is Not Known: The t Distributions |
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151 | (4) |
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155 | (4) |
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The Standard Error of a Proportion |
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159 | (3) |
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162 | (2) |
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164 | (1) |
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165 | (1) |
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166 | (1) |
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166 | (1) |
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Testing Hypotheses about the Difference between the Means of Two Populations |
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167 | (30) |
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The Standard Error of the Difference |
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169 | (4) |
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Estimating the Standard Error of the Difference |
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173 | (1) |
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The t Test for Two Sample Means |
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174 | (3) |
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Confidence Intervals for the Difference of Two Population Means |
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177 | (2) |
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Using the t Test for Two Sample Means: Some General Considerations |
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179 | (2) |
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Measuring Size of an Effect |
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181 | (1) |
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The t Test for Matched Samples |
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182 | (6) |
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188 | (3) |
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191 | (2) |
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193 | (2) |
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195 | (1) |
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195 | (2) |
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Linear Correlation and Prediction |
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197 | (44) |
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198 | (3) |
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Describing the Linear Relationship between Two Variables |
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201 | (9) |
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Interpreting the Magnitude of a Pearson r |
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210 | (2) |
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When Is It Important That Pearson's r be Large? |
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212 | (2) |
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Testing the Significance of the Correlation Coefficient |
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214 | (3) |
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Prediction and Linear Regression |
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217 | (8) |
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Measuring Prediction Error: The Standard Error of Estimate |
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225 | (3) |
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228 | (2) |
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230 | (3) |
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233 | (1) |
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234 | (1) |
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235 | (1) |
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Appendix: Equivalence of the Various Formulas for r |
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236 | (5) |
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The Connection between Correlation and the t Test |
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241 | (14) |
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242 | (1) |
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The Point-Biserial Correlation Coefficient |
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243 | (3) |
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The Proportion of Variance Accounted For in Your Samples |
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246 | (1) |
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Estimating the Proportion of Variance Accounted For in the Population |
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247 | (2) |
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249 | (1) |
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250 | (1) |
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251 | (1) |
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252 | (1) |
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252 | (3) |
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Introduction to Power Analysis |
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255 | (32) |
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256 | (1) |
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Concepts of Power Analysis |
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257 | (2) |
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The Test of the Mean of a Single Population |
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259 | (5) |
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The Significance Test of the Proportion of a Single Population |
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264 | (2) |
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The Significance Test of a Pearson r |
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266 | (1) |
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Testing the Difference between Independent Means |
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267 | (5) |
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Testing the Difference between the Means of Two Matched Populations |
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272 | (1) |
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Choosing a Value for d for a Power Analysis Involving Independent Means |
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273 | (2) |
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Using Power Analysis to Interpret the Results of Null Hypothesis Tests |
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275 | (2) |
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277 | (4) |
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281 | (2) |
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283 | (1) |
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284 | (1) |
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284 | (3) |
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Part III Analysis of Variance Methods |
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287 | (100) |
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One-Way Analysis of Variance |
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289 | (25) |
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290 | (1) |
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The General Logic of ANOVA |
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291 | (4) |
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295 | (6) |
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Comparing the One-Way ANOVA with the t Test |
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301 | (1) |
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A Simplified ANOVA Formula for Equal Sample Sizes |
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302 | (3) |
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Effect Size for the One-Way ANOVA |
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305 | (1) |
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306 | (3) |
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309 | (1) |
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310 | (1) |
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311 | (1) |
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312 | (1) |
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Appendix: Proof That the Total Sum of Squares Is Equal to the Sum of the Between-Group and the Within-Group Sum of Squares |
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312 | (2) |
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314 | (18) |
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315 | (1) |
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Fisher's Protected t Tests |
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316 | (3) |
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Tukey's Honestly Significant Difference (HSD) |
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319 | (3) |
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Other Multiple Comparison Procedures |
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322 | (2) |
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Planned and Complex Comparisons |
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324 | (3) |
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327 | (1) |
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328 | (1) |
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329 | (1) |
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330 | (1) |
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330 | (2) |
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Introduction to Factorial Design: Two-Way Analysis of Variance |
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332 | (27) |
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333 | (1) |
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334 | (8) |
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The Meaning of Interaction |
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342 | (4) |
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Following Up a Significant Interaction |
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346 | (3) |
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349 | (3) |
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352 | (3) |
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355 | (1) |
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356 | (2) |
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358 | (1) |
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359 | (28) |
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360 | (1) |
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Calculating the One-Way RM ANOVA |
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360 | (3) |
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Rationale for the RM ANOVA Error Term |
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363 | (2) |
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Assumptions of the RM ANOVA |
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365 | (2) |
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The RM versus RB Design: An Introduction to Issues of Experimental Design |
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367 | (4) |
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371 | (6) |
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377 | (5) |
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382 | (2) |
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384 | (1) |
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384 | (1) |
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384 | (3) |
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Part IV Nonparametric Statistics |
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387 | (78) |
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Introduction to Probability and Nonparametric Methods |
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389 | (20) |
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390 | (1) |
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391 | (3) |
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The Binomial Distribution |
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394 | (6) |
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The Sign Test for Matched Samples |
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400 | (2) |
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402 | (1) |
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403 | (2) |
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405 | (1) |
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406 | (1) |
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406 | (3) |
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409 | (23) |
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Chi Square and Goodness of Fit: One-Variable Problems |
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410 | (4) |
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Chi Square as a Test of Independence: Two-Variable Problems |
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414 | (6) |
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Measures of Strength of Association in Two-Variable Tables |
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420 | (3) |
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423 | (2) |
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425 | (2) |
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427 | (1) |
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428 | (1) |
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429 | (3) |
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432 | (33) |
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433 | (3) |
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The Difference between the Locations of Two Independent Samples: The Rank-Sum Test |
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436 | (4) |
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Differences among the Locations of Two or More Independent Samples: The Kruskal-Wallis H Test |
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440 | (4) |
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The Difference between the Locations of Two Matched Samples: The Wilcoxon Test |
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444 | (5) |
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The Relationship between Two Ranked Variables: The Spearman Rank-Order Correlation |
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449 | (3) |
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452 | (3) |
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455 | (6) |
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461 | (1) |
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461 | (1) |
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462 | (3) |
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465 | (34) |
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467 | (16) |
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483 | (13) |
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Data from Sara's Experiment |
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496 | (3) |
| Glossary of Terms |
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499 | (7) |
| References |
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506 | (1) |
| Index |
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507 | |