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 |

Table of Contents provided by Ingram. All Rights Reserved. |