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
Class 1 Notes
- Introduction to applied statistics, desktop computing,
Class 2 Notes
- MINITAB and SPSS (to a lesser extent), saving files,
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,
Class 8 Notes
- Basic regression model, least squares idea, regression
coefficients, interpretation of coefficients
Class 9 Notes
- Causal inference with regression analysis, examples,
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
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,
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
Class 17 Notes - First part
- Residuals, transformations, ladder powers
Class 17 Notes - Second part
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,
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
Copyright © 1997 H. T. Reynolds
This Home Page was created by WebEdit,Saturday, February 14, 1998
Most recent revision Wednesday, May 28, 1998