Statistics for Behavioral Science Majors
- Page ID
- 39710
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- 1: Naming, Collecting Data and Research Design
- In today’s society, we are bombarded with data on all forms of media. The question is who, how and where does the data originate. That’s why I am excited about this chapter because we will explore types of data and its’ influences of collecting the data.
- 2: Descriptive Statistics
- Both graphical and numerical methods of summarizing data make up the branch of statistics known as descriptive statistics. This section introduces numerical measurements to describe sample data.
- 2.1: Frequency and Group Frequency Distribution
- 2.2: Histograms, Frequency Polygons, and Time Series Graphs
- 2.3: Stem-and-Leaf Graphs (Stemplots), Line Graphs, and Bar Graphs
- 2.4: Central Tendency
- 2.5: Basic Concepts of Variation
- 2.6: Skewness and the Mean, Median, and Mode
- 2.7: Measures of Position
- 2.8: Descriptive Statistics Formulas
- 2.9: Descriptive Statistics (Exercises)
- 3: Probability
- The basic principles of probability, including complements, union/intersection, independence, conditional probability, and counting rules.
- 4: Discrete Probability Distributions
- Probability distributions of discrete random variables, which can only take on particular values in a range.
- 5: Continuous Probability Distributions
- Probability distributions of continuous random variables, which can take on an infinite number of random values in an interval.
- 6: Confidence Intervals for One Population
- Developing confidence intervals based on a sample for a single population.
- 6.1: Introduction
- 6.2: Confidence Interval for a Proportion
- 6.3: Sample Size Calculation for a Proportion
- 6.4: Z-Interval for a Mean
- 6.5: t-Interval for a Mean
- 6.6: Interpreting a Confidence Interval
- 6.7: Sample Size for a Mean
- 6.8: One Sample Confidence Interval Formulas
- 6.9: One Sample Confidence Interval Exercises
- 7: Hypothesis Tests for One Population
- Using hypothesis testing to evaluate claims about single-population parameters.
- 8: Hypothesis Tests and Confidence Intervals for Two Populations
- Hypothesis testing and developing confidence intervals for two groups. Determining if the two groups are dependent (related) or independent (not related) from one another.
- 9: Correlation and Regression
- This chapter covers how to determine whether a linear relationship exists between sets of quantitative data, and, if it does, how to make predictions for a population based on the data.
- 10: The Chi-Square Distribution
- 10.1: Prelude to The Chi-Square Distribution
- 10.2: Facts About the Chi-Square Distribution
- 10.3: Goodness-of-Fit Test
- 10.4: Test of Independence
- 10.5: Test for Homogeneity
- 10.6: Comparison of the Chi-Square Tests
- 10.7: Test of a Single Variance
- 10.8: Lab 1- Chi-Square Goodness-of-Fit (Worksheet)
- 10.9: Lab 2- Chi-Square Test of Independence (Worksheet)
- 10.E: The Chi-Square Distribution (Exercises)
- 11: Analysis of Variance
- The F-test (for ANOVA) is a statistical test for testing the equality of k population means. Includes discussion of one-way and two-way ANOVAs, and identifying means that differ significantly from each other using the Bonferroni test.
- 12: Nonparametric Tests
- This chapter provides alternative methods to some of the previously covered, parametric tests (z-, t-, and F-tests) when the assumptions necessary for these parametric tests are not met. Includes the sign test, Wilcoxon signed-rank test, and the Mann-Whitney U test.

