## Table of contents for Introduction to statistics and data analysis for the behavioral sciences / Robert S. Lockhart.

Bibliographic record and links to related information available from the Library of Congress catalog

Information from electronic data provided by the publisher. May be incomplete or contain other coding.

**1. The Purpose of Statistical Data Analysis**

The Overall Goal of Statistical Data Analysis

Variables

**2. Graphical and Numerical Descriptions of Data**

Displaying Data as Distributions of Frequencies

Numerical Descriptions of Frequency Distributions

Linear Transformations of Scale and z-Scores

**3. Modeling Data and the Estimation of Parameters**

Imperfect Predictions, Models, and Residuals

Estimating the Parameters of a Model

Fitting Models with Categorical Predictor Variables

**4. Probabilities Distributions**

The Law of Large Numbers and the Meaning of Probability

Discrete Probability Distributions

The Central Limit Theorem and the Normal

Distribution

Obtaining Probabilities for Normally Distributed Variables

The Normal Distribution as a Model for Residuals

**5. Sampling Distributions and Interval Estimation**

The Sampling Distribution of the Mean

Calculating Confidence Intervals

Interpreting Confidence Intervals

**6. Experiments with Two Independent Groups**

Independent Groups Design

Analyzing Data from Independent Group Designs with Two Treatments

Deciding Between Models Using the t-distribution Directly

Decision Error Rates: The Neyman-Pearson Tradition

Overview and Evaluation

**7. Larger Experiments with Independent Groups--Analysis of Variance**

Models for Experiments With More Than Two Conditions

Evaluating the Null Model: Analysis of Variance

The Analysis of Variance of Factorial Designs

**8. Increasing the Precision of an Experiment**

Choosing an Appropriate Value of n in Two-Condition Experiments

Reducing Residuals by Using Matched Pairs

Matching and Within-Subjects Designs with More than Two Conditions

**9. Experiments with Quantitative Predictor and Response Variables--Simple Linear Regression Examples**

The Linear Model (Review)

Making Predictions

Correlation

Assumptions and Factors Influencing Correlation and Regression

**10. Analyzing Data From Studies With Categorical Predictor and Response **

Variables (Count Data)

Models for Proportions

Testing Goodness of Fit

Testing Independence in Two-Way Tables

**11. Review**

An Overview of Statistical Data Analysis

Extensions

**Appendixes**

Library of Congress subject headings for this publication:

Social sciences -- Statistical methods.