**You can find copies of the class notes
here. To view and print them you must have Adobe Reader,
a free program loaded in your machine. Normaly, you can just
click on the notes and Reader will start.**

For Windows 95: Adobe Reader Version 3.01.

**You can obtain the program from the University's Technology
page by looking for software. Or you can downloaded it
directly from Adobe. Most University machines have this
program installed.**

- Introduction to applied statistics, desktop computing, the internet

- MINITAB and SPSS (to a lesser extent), saving files, worksheets.

- Small sample test of means, t distribution, critical value and region.

- Confidence intervals for means and differences of means, MINITAB procedures

- One-way and two-way frequency distributions, cross-classifications, chi square test of independence.

- Cross-classifications, chi square test, remarks about significance testing, strength of association, odds ratio, log odds ratio.

- Odds ratios for I X J tables; comments about the chi square test, linear regression, scatterplot, slope, intercept.

- Basic regression model, least squares idea, regression coefficients, interpretation of coefficients

- Causal inference with regression analysis, examples, OLS

- Inference for regression, t tests, analysis of variance, F tests, confidence intervals, multiple R, coefficient of determination.

- Short set of notes that discuss the properties of the multiple regression coefficient or coefficient of determination; that is, R-squared.

- Example of hypothesis test on regression coefficients, confidence intervals, correlation coefficient, interpretation of coefficients.

- Standardized regression (beta weights, standardized regression coefficients), multiple regression model, partial regression coefficients

- Extended example of multiple regression, t and F tests, confidence intervals, standardized regression coefficients, model building.

- Regression with categorical independent variables, dummy coding, "effects," interaction.

- Multiple regression with categorical variables, interaction, examples of F tests.

- Regression assumptions, residuals, standardized residuals, partial regression plots, plots of residuals versus fitted values

- Residuals, transformations, ladder powers

- Examples

- Simultaneous confidence intervals for partial regression coefficients, multicolinearity, colinearity, variance inflation factor (VIF), causation, randomization, experimental design, internal validity, external validity.

- Time series, intervention analysis, dummy variables, time counters, simple intervention models

- Time series, autocorrelation, lagged variables, Durbin-Watson statistic, intervention.

- Time series processes, autocorrelation, moving average, ARIMA, logistic regression.

- Models for log odds (logits), log odds, probabilities, linear probability model, complete separation of data, parameter interpretation.

- Logistic regression, inference, correctly classified rate,
.**The Bell Curve**

- Strategies for building multiple regression models.

Copyright © 1997 H. T. Reynolds

This Home Page was created by WebEdit,Saturday, February 14, 1998

Most recent revision Wednesday, May 28, 1998