Subject: 205
Title: Statistical Methods for Research with SAS
Units: 4.0
School: College of Letters and Science LS
Department: Statistics STA
Effective Term: 2008 Fall Quarter
Learning Activities
- Lecture - 3.0 hours
- Laboratory - 1.0 hours
Description
Focus on linear statistical models widely used in scientific research. Emphasis on concepts, methods and data analysis using SAS. Topics include simple and multiple linear regression, polynomial regression, diagnostics, model selection, variable transformation, factorial designs and ANCOVA.
Prerequisites
An introductory upper division statistics course and some knowledge of vectors and matrices; STA 100, or STA 102, or STA 103 suggested or the equivalent.
Expanded Course Description
Summary of Course Content:
(approximate # of lectures) (4) Simple linear regression: Data sets, model, estimation of parameters, inference for regression coefficients, confidence interval, prediction interval, concept of R2, general linear test, ANOVA decomposition of total sum of squares. (1) Diagnostics: residual plot, normal probability plots etc, Box-Cox transformation. (1) Vector-Matrix notations: Re-expression of simple linear model and associated methodologies in vector-matrix notations. (4) Multiple regression: Data sets, model, parameter estimation, R2, problems of multicollinearity, partial F-tests, partial R2, polynomial regression, interaction models, coding of qualitative variables. (4) Diagnostics and Model building: All subsets regression, stepwise methods, models selection criteria such as Mallows Cp, AIC, BIC. (4) One-factor fixed effects ANOVA: Data sets, model, estimation, hypothesis testing and confidence intervals (including simultaneous confidence intervals), rewriting of one factor ANOVA model as a regression model. (1) Diagnostics: residual plot, normal probability plot, unequal variance, Box-Cox transformation. (3) Two factor fixed effects ANOVA: Data sets, additive and interaction models, estimation and testing, coding of variables and modeling two-factor ANOVA by the regression method. (2) Three or higher factor ANOVA models: Data sets, models, inference. (2) Analysis of covariance: Data sets, models, regression approach, inference. (1) Introduction to other procedures such as mixed effects models, logistic regression etc.
Illustrative Reading:
1. A Second Course in Statistics: Regression Analysis: by Mendenhall, W. and Sincich, T. 2003, Prentice Hall. 2. Regression and ANOVA: An Integrated Approach Using SAS Software, by Muller, K. E. and Fetterman, B. A.
Potential Course Overlap:
There is some overlap with materials taught in Statistics 106 and Statistics 108. However, Statistics 205 covers much more material than each of these courses. It will be taught at a level that is more advanced mathematically and computationally. This course also has some overlap with Statistics 232AB. Since Statistics 232 series is a requirement for the Ph.D. students in Statistics, its coverage is far wider and is taught at a substantially higher level.