Posc/Uapp 816

Class Notes

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.

Class 1 Notes

Introduction to applied statistics, desktop computing, the internet

Class 2 Notes

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

Class 3 Notes

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

Class 4 Notes

Confidence intervals for means and differences of means, MINITAB procedures

Class 5 Notes

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

Class 6 Notes

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

Class 7 Notes

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

Class 8 Notes

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

Class 9 Notes

Causal inference with regression analysis, examples, OLS

Class 10 Notes

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

Class 11 Notes

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

Class 11 - A Notes

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

Class 12 Notes

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

Class 13 Notes

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

Class 14 Notes

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

Class 15 Notes

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

Class 16 Notes

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

Class 17 Notes - First part

Residuals, transformations, ladder powers

Class 17 Notes - Second part

Examples

Class 18 Notes

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

Class 19 Notes

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

Class 20 Notes

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

Class 21 Notes

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

Class 22 Notes

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

Class 23 Notes

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

Class 24 Notes

Strategies for building multiple regression models.

Applied Statistics page

H. T. Reynolds page