STATISTICS 101 Spring
2005
Instructor: Zhengyuan Zhu
Summary: This course presents regression analysis and related
techniques, and is recommended for students throughout the natural and social
sciences who are interested in applying regression analysis in their research
and/or understanding the statistical concepts underlying the methodology. The
topics include simple and multiple linear regression, matrix representation of
the regression model, statistical inferences for regression model, diagnostics
and remedies for multicollinearity, outlier and influential cases, polynomial
regression and interaction regression models, model selection, weighted least
square procedure for unequal error variances, and ANOVA model and test.
Statistical software SAS will be used throughout the course to demonstrate how
to apply the techniques on real data. The main purposes of this course is to
let students know how to use regression methods properly in data analysis and
lay the foundation for more advanced studies in statistics.
Prerequisites: Stat 31 or equivalent. Some familiarity with matrix
algebra recommended, but not required.
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