516—Statistical Methods II (3) (Prereq: a grade of C or higher in STAT 515 or STAT 509 or equivalent) Applications and principles of linear models. Simple and multiple linear regression, analysis of variance for basic designs, multiple comparisons, random effects, and analysis of covariance. Statistical packages such as SAS.
Usually Offered: Spring and Summer Semesters
Purpose: To complete a basic two course sequence (in conjunction with STAT 515 or 509) in statistical techniques available to the general practitioner for analyzing experimental data. To introduce students in many different disciplines to multiple regression and analysis of variance for basic experimental designs. To provide students with the knowledge to implement and interpret these standard linear models.
Current Textbook: Statistical Methods, Third Edition, by Freund, Mohr, and Wilson, Academic Press, 2010.
|Simple Linear Regression: least squares estimation, inferences for regression, the ANOVA table, the correlation coefficient, use of SAS||
|Multiple Linear Regression: inference for multiple regression, residual diagnostics, leverage and influence, transformations, multicollinearity, model selection||
|One-way Analysis of Variance (ANOVA): traditional analysis of variance notation, using linear regression, multiple comparisons, contrasts, assumption checking||
|ANOVA for Standard Experimental Designs: two-way ANOVA, higher order factorial designs, unbalanced data, incomplete data, randomized block designs, random effects||
|Other Models: Analysis of Covariance, and selections from: nested designs, repeated measures*, nonlinear regression, logistic regression, proportional hazards*||
* indicates topic not covered in the text
The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.