| Forward | |
| Introduction to Statistics |
| | 1 | (11) |
| Why Should I Care About Statistics? |
| | 1 | (1) |
| | 2 | (1) |
| So, What Exactly Are Statistics? |
| | 3 | (1) |
| How Difficult Will This Be If I'm Not a Mathematical Genius? |
| | 4 | (1) |
| | 4 | (1) |
| | 4 | (1) |
| | 4 | (1) |
| | 5 | (5) |
| | 10 | (2) |
| Collecting and Measuring Data |
| | 12 | (31) |
| | 12 | (1) |
| | 12 | (4) |
| | 16 | (4) |
| | 20 | (1) |
| | 21 | (1) |
| | 21 | (1) |
| | 22 | (1) |
| | 23 | (20) |
| | 43 | (22) |
| | 43 | (1) |
| | 43 | (1) |
| | 44 | (3) |
| | 47 | (1) |
| Measures of Central Tendency |
| | 48 | (6) |
| | 54 | (4) |
| | 58 | (1) |
| | 58 | (1) |
| | 59 | (2) |
| | 61 | (1) |
| | 62 | (3) |
| Working with Distributions |
| | 65 | (19) |
| Types of Normal Distributions |
| | 65 | (1) |
| | 65 | (5) |
| | 70 | (1) |
| | 71 | (2) |
| Distribution of Differences |
| | 73 | (4) |
| | 77 | (1) |
| | 77 | (1) |
| | 77 | (3) |
| | 80 | (1) |
| | 81 | (3) |
| Hypothesis Testing and the z-test |
| | 84 | (20) |
| Introduction to Inferential Statistics |
| | 84 | (1) |
| | 84 | (2) |
| Null and Alternative Hypotheses |
| | 86 | (3) |
| Probability and Hypothesis Testing |
| | 89 | (2) |
| | 91 | (6) |
| | 97 | (1) |
| | 98 | (1) |
| | 99 | (1) |
| | 99 | (3) |
| | 102 | (1) |
| | 103 | (1) |
| | 104 | (21) |
| | 104 | (1) |
| | 105 | (2) |
| Critical Values and the t-distribution |
| | 107 | (2) |
| Degrees of Freedom, Critical Values, and the t-Table |
| | 109 | (1) |
| | 110 | (3) |
| | 113 | (1) |
| | 113 | (1) |
| | 113 | (1) |
| | 113 | (5) |
| | 118 | (4) |
| | 122 | (3) |
| Single-Factor Analysis of Variance (ANOVA) |
| | 125 | (24) |
| | 125 | (2) |
| Overview of Single-Factor Analysis of Variance |
| | 127 | (4) |
| | 131 | (2) |
| Calculating the F-Statistic |
| | 133 | (1) |
| Critical Values and the F-table |
| | 134 | (1) |
| An Anova (F-Test) Example |
| | 135 | (3) |
| | 138 | (1) |
| | 139 | (1) |
| | 139 | (1) |
| | 139 | (3) |
| | 142 | (4) |
| | 146 | (3) |
| Multiple-Factor Analysis of Variance |
| | 149 | (28) |
| Introduction to Multiple-Factor ANOVA |
| | 149 | (1) |
| | 149 | (3) |
| Main and Interaction Effects |
| | 152 | (4) |
| Conducting a Multiple Factor Analysis of Variance |
| | 156 | (5) |
| An Example of Multiple-Factor ANOVA |
| | 161 | (3) |
| | 164 | (1) |
| | 165 | (1) |
| | 165 | (8) |
| | 173 | (4) |
| | 177 | (18) |
| What is Simple Correlation? |
| | 177 | (2) |
| Computing a Simple Correlation: the Pearson r |
| | 179 | (3) |
| Testing a Correlation for Statistical Significance |
| | 182 | (3) |
| Correlation Does Not Equal Causation |
| | 185 | (1) |
| Amount of Variance Explained |
| | 185 | (1) |
| An Example of Correlation |
| | 186 | (1) |
| | 187 | (1) |
| | 188 | (1) |
| | 188 | (2) |
| | 190 | (3) |
| | 193 | (2) |
| | 195 | (15) |
| From Correlation to Regression |
| | 195 | (1) |
| Deriving the Regression Equation |
| | 196 | (1) |
| | 197 | (2) |
| Significance of the Regression Equation |
| | 199 | (5) |
| An Example of Bivariate Linear Regression |
| | 204 | (1) |
| | 205 | (1) |
| | 205 | (1) |
| | 205 | (1) |
| | 206 | (1) |
| | 207 | (3) |
| | 210 | (26) |
| Beyond Simple Correlation and Bivariate Linear Regression |
| | 210 | (3) |
| | 213 | (1) |
| Testing the Significance of a Multiple Regression Equation |
| | 214 | (2) |
| Types of Multiple Regression |
| | 216 | (4) |
| An Example of Multiple Regression |
| | 220 | (2) |
| | 222 | (1) |
| | 223 | (1) |
| | 223 | (3) |
| | 226 | (5) |
| | 231 | (5) |
| | 236 | (21) |
| Parametric vs. Nonparametric Tests |
| | 236 | (1) |
| | 236 | (2) |
| Chi-Square Goodness-of-Fit Test (One Sample) |
| | 238 | (1) |
| Chi-Square Test of Association (Multiple Samples) |
| | 239 | (2) |
| Beyond the Chi-Square Test |
| | 241 | (2) |
| An Example of Chi-Square Analysis |
| | 243 | (2) |
| | 245 | (1) |
| | 245 | (1) |
| | 246 | (3) |
| | 249 | (3) |
| | 252 | (5) |
| Appendices | | 257 | (1) |
| | 257 | (7) |
| Appendix B: Chapter Problem Answers |
| | 264 | (10) |
| Appendix C: Computer Problem Answers |
| | 274 | |
