Mathematics 4803 Analysis of Variance

Professor Karen H. Smith

Office Boyd 313

Office Hours M, W ,F 11 :00-12:00 a.m.

M, W 1:00-3:30 p.m.

Tu. 10:30 -12:30


Text        Applied Linear Statistical Models 4th ed.

By John Neter, Michael Kutner, Chris Nachtsheim, William Wasserman




The purpose of this course is to learn when, why, and how analysis of variance statistical testing is used. Random and fixed effects ANOV A models will be studied extensively using the statistical software Minitab. Multiple comparison methods such as Bonferroni, Scheffe, and Tukey will be used.


Learning Outcomes


Students will be able to recognize the various ANOV A models and when they are appropriate (L7, L11).


Students will analyze ANOV A models to determine if they satisfy the assumptions of valid models (L7, L11).


Students will identify the appropriate multiple comparison method needed according to the information desired (L7, L11).


Students will utilize residual analysis to detect departures from ANOVA assumptions (L7, L11).


Material Covered Chapters 16-21


Topics Covered


Single factor ANOV A model

Estimation and testing of factor level effects

Tukey, Scheffe, and Bonferroni multiple comparison procedures

Residual Analysis

Tests for constant variance

Overview of remedial measures

Two-Factor analysis of variance

Analysis of factor effects in two-factor studies

Two-factor studies-one case per treatment




Three Exams 75%

Final 25%


Students may elect to complete a project consisting of lor more problems at the end of each chapter that relate to a specific data set in the appendix. The project grade will be used to replace any exam grade that is lower than the project grade. The project is due the last class meeting: