Jason Newsom's
Stats Notes
"Web Lectures," notes, and handouts on introductory graduate-level statistics
 © 1999-2007 Jason T. Newsom

 

 

About Stats Notes

 

§          Stats Notes contains over 25 "Web Lectures",  notes, and handouts on introductory graduate-level statistics.  Topics range from sampling distributions to logistic regression (scroll down to get started)

§          This material was designed for a long distance course I taught a couple of years ago.  I no longer teach the course, but I teach a two-course sequence which covers much of the same material in more depth
(Data Analysis I and Data Analysis II)

§          The course was designed for masters students with health backgrounds, and so the examples are often medical or biological in nature. 

§         The statistical notation follows the author of the text used for the class (Wayne W. Daniel, Biostatistics, 7th Ed.) and is different from what I currently use in my courses.  This is a problem with nearly all statistics textbooks—there is no standardized notation across different authors.  In the long run, it is probably healthy to be exposed to another notation system, even though it may be confusing if you have to use two systems at the same time.

 

 

 

Disclaimers

§          I am not always right.

§         There may be a few typos or other minor errors (what do you expect for free?).
I would appreciate a brief note if you find any (newsomj@pdx.edu).

Web Lectures

 

1/ Basic Concepts

Types of scales & levels of measurement

Sampling distributions

Significance testing

Sample Size

Normal and binomial probability distributions

Examples of Distributions and Descriptive Graphs

2/ Differences Between Groups with Continuous Outcomes: t-tests

Between groups t-test

Example of between groups t-test

Within Subjects/Repeated Measures/Paired t

3/ Differences Between Groups with Continuous Outcomes: ANOVA

ANOVA (comparing two or more group means)

Within-subjects ANOVA

4/ Differences Between Groups with Categorical Outcomes: Chi-square

Chi-square: Goodness of fit and group differences when the dependent variable is dichotomous

5/ Complex Differences Between Groups: Factorial ANOVA and Multi-way Frequency Tables

Interactions and Factorial ANOVA

Graphs of main effects without interactions

Graphs of interactions with and without main effects

More on chi-square

6/ Association Among Variables

Correlation

Point-biserial correlation, Phi, & Cramer's V

Correlation and Causation

7/ Association and Prediction: Regression

Simple Regression

R2 and tests of significance

Multiple Regression

More on Multiple Regression

8/ Association and Prediction: Logistic

Logistic Regression

Multiple Logistic Regression

9/ Overview

Overview: A Hitchhiker's Guide to Analyses

 

Handouts
Handouts are available for the following topics at the Data Analysis I and II sites.
(Sorry, but handouts will be unavailable for topics yet to be covered during the current session)

Data Analysis I:  T-tests, Chi-square, ANOVA

Threats to internal validity

t-tests

Choosing the Correct Statistical Test

Contingency Chi-square

Post Hoc Tests

One-way ANOVA Definitional Formulas and Example

Factorial ANOVA Definitional Formulas and Example

Graphs of Possible Factorial Results in the Eyewitness Example

Within-subjects ANOVA Definitional Formulas and Example

Assumptions for Within-subjects ANOVA

Mixed Factorial ANOVA

Data Analysis II:  Regression and Logistic

Simple Regression Hand Computation Example

Correlations and Scatterplots (Lab 1)

Model Building Procedures

Multiple Regression Example – Salary Data

Hierarchical Multiple Regression Example—Salary Data

Suppression Example—Exercise Data

Coding of Categorical Predictors and ANCOVA

Coding Example for 4 Categories

Partial and Semipartial Correlation SPSS Output

Remedies for Assumption Violations in Regression

SPSS Macro for Interactions and Simple Slope Tests

Simple Slope Tests Example Output

Mediation Analysis with Regression

Chi-square

Logistic Regression

More on Model Fit and Significance of Predictors with Logistic Regression

Logit, Probit, and Other Link Functions

Further Readings