Preface | p. ix |
Introduction | p. 1 |
What is the value of statistics? | p. 3 |
Brief introduction to the history of statistics | p. 5 |
General statistical definitions | p. 5 |
Types of variables | p. 7 |
Scales of measurement | p. 8 |
Summary | p. 12 |
Data Representation | p. 16 |
Tabular display of distributions | p. 18 |
Graphical display of distributions | p. 23 |
Percentiles | p. 29 |
SPSS | p. 33 |
Summary | p. 34 |
Univariate Population Parameters and Sample Statistics | p. 39 |
Summation notation | p. 40 |
Measures of central tendency | p. 41 |
Measures of dispersion | p. 45 |
SPSS | p. 53 |
Summary | p. 55 |
The Normal Distribution and Standard Scores | p. 59 |
The normal distribution | p. 60 |
Standard scores | p. 65 |
Skewness and kurtosis statistics | p. 68 |
SPSS | p. 72 |
Summary | p. 73 |
Introduction to Probability and Sample Statistics | p. 77 |
Brief introduction to probability | p. 78 |
Sampling and estimation | p. 81 |
Summary | p. 87 |
Introduction to Hypothesis Testing: Inferences About a Single Mean | p. 92 |
Types of hypotheses | p. 93 |
Types of decision errors | p. 95 |
Level of significance [Alpha] | p. 98 |
Overview of steps in the decision-making process | p. 100 |
Inferences about [Mu] when [sigma] is known | p. 101 |
Type II error [Beta] and power [1 - Beta] | p. 105 |
Statistical versus practical significance | p. 108 |
Inferences about [Mu] when [sigma] is unknown | p. 109 |
SPSS | p. 113 |
Summary | p. 114 |
Inferences About the Difference Between Two Means | p. 119 |
New concepts | p. 120 |
Inferences about two independent means | p. 122 |
Inferences about two dependent means | p. 129 |
SPSS | p. 133 |
Summary | p. 134 |
Inferences About Proportions | p. 140 |
Inferences about proportions involving the normal distribution | p. 141 |
Inferences about proportions involving the chi-square distribution | p. 151 |
SPSS | p. 156 |
Summary | p. 158 |
Inferences About Variances | p. 162 |
New concepts | p. 163 |
Inferences about a single variance | p. 164 |
Inferences about two dependent variances | p. 166 |
Inferences about two or more independent variances (homogeneity of variance tests) | p. 168 |
SPSS | p. 172 |
Summary | p. 173 |
Bivariate Measures of Association | p. 176 |
Scatterplot | p. 177 |
Covariance | p. 179 |
Pearson product-moment correlation coefficient | p. 182 |
Inferences about the Pearson product-moment correlation coefficient | p. 183 |
Some issues regarding correlations | p. 186 |
Other measures of association | p. 188 |
SPSS | p. 191 |
Summary | p. 192 |
One-Factor Analysis of Variance-Fixed-Effects Model | p. 196 |
Characteristics of the one-factor ANOVA model | p. 198 |
The layout of the data | p. 200 |
ANOVA theory | p. 201 |
The ANOVA model | p. 206 |
Assumptions and violation of assumptions | p. 210 |
The unequal n's or unbalanced design | p. 213 |
Alternative ANOVA procedures | p. 213 |
SPSS | p. 215 |
Summary | p. 217 |
Multiple Comparison Procedures | p. 222 |
Concepts of multiple comparison procedures | p. 224 |
Selected multiple comparison procedures | p. 228 |
SPSS | p. 241 |
Summary | p. 242 |
Factorial Analysis of Variance-Fixed-Effects Model | p. 247 |
The two-factor ANOVA model | p. 249 |
Three-factor and higher-order ANOVA | p. 265 |
Factorial ANOVA with unequal n's | p. 267 |
SPSS | p. 268 |
Summary | p. 269 |
Introduction to Analysis of Covariance: The One-Factor Fixed-Effects Model With a Single Covariate | p. 277 |
Characteristics of the model | p. 278 |
The layout of the data | p. 281 |
The ANCOVA model | p. 281 |
The ANCOVA summary table | p. 282 |
Partitioning the sums of squares | p. 283 |
Adjusted means and related procedures | p. 283 |
Assumptions and violation of assumptions | p. 286 |
An example | p. 289 |
ANCOVA without randomization | p. 292 |
More complex ANCOVA models | p. 293 |
Nonparametric ANCOVA procedures | p. 293 |
SPSS | p. 293 |
Summary | p. 294 |
Random-and Mixed-Effects Analysis of Variance Models | p. 301 |
The one-factor random-effects model | p. 303 |
The two-factor random-effects model | p. 306 |
The two-factor mixed-effects model | p. 309 |
The one-factor repeated measures design | p. 313 |
The two-factor split-plot or mixed design | p. 319 |
SPSS | p. 325 |
Summary | p. 331 |
Hierarchical and Randomized Block Analysis of Variance Models | p. 335 |
The two-factor hierarchical model | p. 336 |
The two-factor randomized block design for n = 1 | p. 343 |
The two-factor randomized block design for n > 1 | p. 350 |
The Friedman test | p. 350 |
Comparison of various ANOVA models | p. 351 |
SPSS | p. 353 |
Summary | p. 357 |
Simple Linear Regression | p. 361 |
The concepts of simple linear regression | p. 362 |
The population simple linear regression model | p. 364 |
The sample simple linear regression model | p. 365 |
SPSS | p. 381 |
Summary | p. 383 |
Multiple Regression | p. 387 |
Partial and semipartial correlations | p. 388 |
Multiple linear regression | p. 390 |
Other regression models | p. 403 |
SPSS | p. 408 |
What's next? | p. 408 |
Summary | p. 410 |
References | p. 415 |
Appendix Tables | p. 425 |
Answers | p. 449 |
Index | p. 463 |
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