Syllabus

Instructor

Office Hours -- Spring 2020

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)

Some Filled-in Course Notes

• Chapter 6 course notes, part 1

• Chapter 6 course notes, part 2

• Chapter 9 course notes, part 1

• Chapter 9 course notes, part 2

Homework Assignments

• Homework 1, Spring 2020 (due Friday, January 31)

• Photocopies of pages for Homework 1, Spring 2020

• Homework 2, Spring 2020 (due Wednesday, February 12)

• Homework 3, Spring 2020 (due Friday, February 28)

• Homework 4, Spring 2020 (due Wednesday, March 18 March 25)
(Instructions for uploading via Blackboard) (NOTE: Please use Chrome or Firefox when uploading assignments in Blackboard, not Safari!)

Useful Tables from the Textbook

• t-table from textbook

• F-table from textbook

• Tukey's studentized range table from textbook

Homework Solutions

• Homework 1 Example Solutions, Spring 2020

• Homework 2 Example Solutions, Spring 2020

• Homework 3 Example Solutions, Spring 2020

• Homework 4 Example Solutions, Spring 2016

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

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 spring 2020, we will primarily learn to use the SAS software to do the needed computations.
You will need access to a computer with SAS Studio (available for free to students in this class). This can easily be accessed on your own computer through the internet. You will be emailed instructions about how to create a SAS OnDemand for Academics account and use SAS Studio.
Alternatively, you could 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 SAS for the in-class examples.
Both R and SAS are available on the computers in the labs in LeConte College. Help in using R can be found on the CRAN home page.

HELPFUL R RESOURCE:

Data Sets

• Fire damage data set (plain text)

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

• U.S. Cities data set (plain text) for Chapter 7, problem 6 -- with SAS commands for creating a data set

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

• English Grades data set (plain text) for Chapter 7, problem 3

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

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

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

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

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

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

• Concrete data set (plain text) for Chapter 6, problem 4

• Insecticide data set (plain text) for Chapter 6, problem 9 -- with SAS commands for creating a data set

• Fungus data set (plain text) for Chapter 6, problem 12

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

• Table 9.21 data set (plain text) for Chapter 9, problem 2

• Roast data set (plain text) for Chapter 9, problem 4

• Paper data set -- with SAS commands for creating a data set

• buildingsdata.txt data set (plain text) for Chapter 10 -- with SAS commands for creating a data set

• wheatdataLS.txt data set (plain text) for Chapter 10 -- with SAS commands for creating a data set

• Pig data set (plain text) for Chapter 11, problem 1

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

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

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

• 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

• 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

• Test 1 Review Sheet

• Test 2 Review Sheet

Formula Sheets

Information about Take-home Midterm Exam

• Test 2 information (Word document), Test 2 information (pdf) (your submission is due to be uploaded into Blackboard by Wednesday, April 1, by 11:59 p.m.)

• SAS code to create SAS data sets for problems in take-home Test 2

Online Lectures

• 75-minute online lecture covering March 23 class and Part 1 of March 25 class (.mp4 file)

• filled-in notes for online lecture covering March 23 class and Part 1 of March 25 class (pdf file)

• 75-minute online lecture covering Part 2 of March 25 class and March 27 class (.mp4 file)

• filled-in notes for online lecture covering Part 2 of March 25 class and March 27 class (pdf file)

• 75-minute online lecture covering March 30 class and Part 1 of April 1 class (.mp4 file)

• filled-in notes for online lecture covering March 30 class and Part 1 of April 1 class (pdf file)

• 75-minute online lecture covering Part 2 of April 1 class and April 3 class (.mp4 file)

• filled-in notes for online lecture covering Part 2 of April 1 class and April 3 class (pdf file)

• 75-minute online lecture covering April 6 class and Part 1 of April 8 class (.mp4 file)

• filled-in notes for online lecture covering April 6 class and Part 1 of April 8 class (pdf file)

Exams

• Midterm 1: Wednesday, February 19
• Midterm 2: Friday, March 27 Exam 2 will be a take-home exam, will be posted on March 26, and will be due on April 1
• Final Exam: Wednesday, April 29 (12:30 p.m.) WILL BE TAKE-HOME