Syllabus

Instructor

Office Hours -- Fall 2026

Course Meeting Times

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 R.J. Freund, W. J. Wilson and D. L. Mohr, 2010.

Course Notes

• Chapter 7 notes, Part 1 (Word document) or Chapter 7 notes, Part 1 (pdf format)

• Chapter 7 notes, Part 2 (Word document) or Chapter 7 notes, Part 2 (pdf format)

• Chapter 8 notes, Part 1 (Word document) or Chapter 8 notes, Part 1 (pdf format)

• Chapter 8 notes, Part 2 (Word document) or Chapter 8 notes, Part 2 (pdf format)

• Chapter 6 notes, Part 1 (Word document) or Chapter 6 notes, Part 1 (pdf format)

• Chapter 6 notes, Part 2 (Word document) or Chapter 6 notes, Part 2 (pdf format)

• Chapter 9 notes, Part 1 (Word document) or Chapter 9 notes, Part 1 (pdf format)

• Chapter 9 notes, Part 2 (Word document) or Chapter 9 notes, Part 2 (pdf format)

• Chapter 10 notes, Part 1 (Word document) or Chapter 10 notes, Part 1 (pdf format)

• Chapter 10 notes, Part 2 (Word document) or Chapter 10 notes, Part 2 (pdf format)

• Chapter 11 notes, Part 1 (Word document) or Chapter 11 notes, Part 1 (pdf format)

• Chapter 11/13 notes, Part 2 (Word document) or Chapter 11/13 notes, Part 2 (pdf format)

Homework Assignments

• Homework 1, Fall 2026 (due Friday, September 4 by 11:59 pm)

Useful Tables from the Textbook

• t-table from textbook

• F-table from textbook

• Tukey's studentized range table from textbook

Homework Solutions

STAT 516 Supplementary Material

• Section 7.7 Supplement from the STAT 516 course page

• Section 7.8 Supplement from the STAT 516 course page

• Section 8.3 Supplement from the STAT 516 course page

• Section 8.8 Supplement from the STAT 516 course page

• Section 8.9 Supplement from the STAT 516 course page

• Section 6.4 Supplement from the STAT 516 course page

• Section 6.5 Supplement from the STAT 516 course page

• Section 9.3 Supplement from the STAT 516 course page

Access Instructions for SAS

• SAS OnDemand Entry Page

• SAS OnDemand Control Center

Downloading Instructions for R

• Instructions for Downloading R

Computing Tips: Some Review

• Introduction to SAS Studio which should help you get started with SAS Studio

• Instructions for SAS: summary statistics and graphs

• Example of PROC ANOVA in SAS

Computer Code for Class Examples

Statistical Topic	Example in SAS Code	Example in R Code
Simple Linear Regression and Correlation (Chapter 7)	SAS example: (House data) Output for SAS example	R example: (House data) Output for R example
Multiple Linear Regression (Chapter 8)	SAS example (California rain data) Output for SAS example	R example (California rain data) Output for R example
Transformations (Chapter 7-8)	SAS example (surgical data) Output for SAS example	R example (surgical data) Output for R example
One-Way Analysis of Variance (Chapter 6)	SAS example: (Rice data) Output for SAS example	R example: (Rice data) Output for R example
Two-Way ANOVA & Factorial Experiments (Chapter 9)	SAS example (Gas mileage data) Output for SAS example	R example: (Gas mileage data) Output for R example
Randomized Block Design (Chapter 10)	SAS example (Wheat data) Output for SAS example	R example (Wheat data) Output for R example
Randomized Block Design with Sampling (Chapter 10)	SAS example: (Rubber data) Output for SAS example	R example: (Rubber data) Output for R example
Latin Square Design (Chapter 10)	SAS example (Productivity data) Output for SAS example	R example: (Productivity data) Output for R example
Analysis of Unbalanced Data (Chapter 11)	SAS example (Table 11.3 data) Output for SAS example	R example (Table 11.3 data) Output for R example
Analysis of Covariance (ANCOVA) (Chapter 11)	SAS example (Trigonometry scores data) Output for SAS example	R example (Trigonometry scores data) Output for R example
Logistic Regression (Chapter 13)	SAS example (TIF data) Output for SAS example	R example (TIF data) Output for R example

Some additional code

• Example SAS code for polynomial regression models and multiplicative models

• Example R code for polynomial regression models and multiplicative models

• Example SAS code for stepwise regression and more variable selection

• Example R code for stepwise regression and more variable selection

• Example SAS code for one-way ANOVA using dummy variables

• Example SAS code for multiple logistic regression

• Example R code for multiple logistic regression

• Example SAS code for Poisson (count) regression

• Example R code for Poisson (count) regression

Some additional data examples

• Table of Music/Productivity Data for the Latin Square Example from class

Required Computing Resources: Some problems in this course involve significant computations, and for fall 2026, we will primarily learn to use the R software to do the needed computations.
You can use the software package R (available as a free download from the CRAN home page). This can easily be downloaded onto your home computer.
Example code in both R and SAS will be provided on the course web page, but we will use R for the in-class examples.
Help in using R can be found on the CRAN home page.

HELPFUL R RESOURCE:

Data Sets

• Size-Price data set from Chapter 7 (plain text)

• Simple Basket Goals data set (plain text) for Chapter 7, problem 5 -- with R commands for creating a data frame

• Birth Rate data set (plain text) for Chapter 7, problem 9 -- with R commands for creating a data frame

• Heating Cost data set (plain text) for Chapter 7, problem 7 -- with R commands for creating a data frame

• Basket Goals data set (plain text) for Chapter 8, problem 8 -- with R commands for creating a data frame

• Liver data set (plain text) for Chapter 8, problem 10 -- with R commands for creating a data frame

• Apartment Rent data set (plain text) for Chapter 8, problem 13 -- with R commands for creating a data frame

• Tree Weight data set (plain text) for Chapter 8, problem 5 -- with R commands for creating a data frame

• Health data set (plain text) for Chapter 8, problem 6 -- with R commands for creating a data frame

• Irrigation data set (plain text) for Chapter 8, problem 7 -- with R commands for creating a data frame

• Bank data set (plain text) for Chapter 6, problem 15 -- with R commands for creating a data frame

• Table 9.22 set (plain text) for Chapter 9, problem 3 -- with R commands for creating a data frame

• Cake data set (plain text) for Chapter 9, problem 9 -- with R commands for creating a data frame

• Oyster data set (plain text) for Chapter 10, problem 1 -- with R commands for creating a data frame

• Bird Count data set (plain text) for Chapter 10, problem 10 -- with R commands for creating a data frame

• Recognition data set (plain text) for Chapter 11, problem 9 -- with R commands for creating a data frame

• Tree data set (plain text) for Chapter 11, problem 4 -- with R commands for creating a data frame

• Brain data set (plain text) for Chapter 13, problem 3 -- with R commands for creating a data frame

Review Sheets

Formula Sheets

Exams

• Midterm 1: Wednesday, September 23
• Midterm 2: Wednesday, October 28
• Final Exam: Wednesday, December 9, 12:30 p.m.