Statistics for Behavioral Science Majors
- Page ID
- 39710
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\(\newcommand{\avec}{\mathbf a}\) \(\newcommand{\bvec}{\mathbf b}\) \(\newcommand{\cvec}{\mathbf c}\) \(\newcommand{\dvec}{\mathbf d}\) \(\newcommand{\dtil}{\widetilde{\mathbf d}}\) \(\newcommand{\evec}{\mathbf e}\) \(\newcommand{\fvec}{\mathbf f}\) \(\newcommand{\nvec}{\mathbf n}\) \(\newcommand{\pvec}{\mathbf p}\) \(\newcommand{\qvec}{\mathbf q}\) \(\newcommand{\svec}{\mathbf s}\) \(\newcommand{\tvec}{\mathbf t}\) \(\newcommand{\uvec}{\mathbf u}\) \(\newcommand{\vvec}{\mathbf v}\) \(\newcommand{\wvec}{\mathbf w}\) \(\newcommand{\xvec}{\mathbf x}\) \(\newcommand{\yvec}{\mathbf y}\) \(\newcommand{\zvec}{\mathbf z}\) \(\newcommand{\rvec}{\mathbf r}\) \(\newcommand{\mvec}{\mathbf m}\) \(\newcommand{\zerovec}{\mathbf 0}\) \(\newcommand{\onevec}{\mathbf 1}\) \(\newcommand{\real}{\mathbb R}\) \(\newcommand{\twovec}[2]{\left[\begin{array}{r}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\ctwovec}[2]{\left[\begin{array}{c}#1 \\ #2 \end{array}\right]}\) \(\newcommand{\threevec}[3]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\cthreevec}[3]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \end{array}\right]}\) \(\newcommand{\fourvec}[4]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\cfourvec}[4]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \end{array}\right]}\) \(\newcommand{\fivevec}[5]{\left[\begin{array}{r}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\cfivevec}[5]{\left[\begin{array}{c}#1 \\ #2 \\ #3 \\ #4 \\ #5 \\ \end{array}\right]}\) \(\newcommand{\mattwo}[4]{\left[\begin{array}{rr}#1 \amp #2 \\ #3 \amp #4 \\ \end{array}\right]}\) \(\newcommand{\laspan}[1]{\text{Span}\{#1\}}\) \(\newcommand{\bcal}{\cal B}\) \(\newcommand{\ccal}{\cal C}\) \(\newcommand{\scal}{\cal S}\) \(\newcommand{\wcal}{\cal W}\) \(\newcommand{\ecal}{\cal E}\) \(\newcommand{\coords}[2]{\left\{#1\right\}_{#2}}\) \(\newcommand{\gray}[1]{\color{gray}{#1}}\) \(\newcommand{\lgray}[1]{\color{lightgray}{#1}}\) \(\newcommand{\rank}{\operatorname{rank}}\) \(\newcommand{\row}{\text{Row}}\) \(\newcommand{\col}{\text{Col}}\) \(\renewcommand{\row}{\text{Row}}\) \(\newcommand{\nul}{\text{Nul}}\) \(\newcommand{\var}{\text{Var}}\) \(\newcommand{\corr}{\text{corr}}\) \(\newcommand{\len}[1]{\left|#1\right|}\) \(\newcommand{\bbar}{\overline{\bvec}}\) \(\newcommand{\bhat}{\widehat{\bvec}}\) \(\newcommand{\bperp}{\bvec^\perp}\) \(\newcommand{\xhat}{\widehat{\xvec}}\) \(\newcommand{\vhat}{\widehat{\vvec}}\) \(\newcommand{\uhat}{\widehat{\uvec}}\) \(\newcommand{\what}{\widehat{\wvec}}\) \(\newcommand{\Sighat}{\widehat{\Sigma}}\) \(\newcommand{\lt}{<}\) \(\newcommand{\gt}{>}\) \(\newcommand{\amp}{&}\) \(\definecolor{fillinmathshade}{gray}{0.9}\)- 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.
- 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.