Syllabus

Instructor

Office Hours -- Spring 2013

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 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

Date	Assigned Homework Problems	Date of Quiz that Includes This Material
Monday, January 14	Read Section 7.1 carefully!	Wednesday, January 16
Wednesday, January 16	Chapter 7 Exercises (pg. 365+): Concept Questions 2, 5, 18 Exercise 3(c) [just plot the data points, and comment]	Friday, January 18
Friday, January 18	Chapter 7 Exercises (pg. 365+): Exercises 1(a), 2(a) [also interpret coefficients] Exercise 5(a) [also interpret coefficients, data given at bottom of web page]	Wednesday, January 23
Wednesday, January 23	Chapter 7 Exercises (pg. 365+): Concept Questions 4, 12 Exercises 1(b), 1(d) [just do ANOVA F-test, not t-test]	Friday, January 25
Friday, January 25	Chapter 7 Exercises (pg. 365+): Concept Question 15 Exercises 1(d) [just do t-test, not F-test], 3(a,c,d), 7(a,b) [Data for #3 and #7 given at bottom of web page]	Monday, January 28
Monday, January 28	Chapter 7 Exercises (pg. 365+): Concept Questions 10, 11, 14 Exercise 3(b) [compute 95% PI for Y when X = 76] Exercise 5(b) [compute 95% CI for E(Y|X) and 95% PI for Y when X = 60, and interpret both] (You can use R for both of these problems) [Data for #3 and #5 given at bottom of web page]	Wednesday, January 30
Wednesday, January 30	Chapter 7 Exercises (pg. 365+): Concept Questions 2, 13, 19, 20 Exercise 3(e) [also interpret r^2] [Data for #3 given at bottom of web page]	Monday, February 4
Friday, February 1	Chapter 7 Exercises (pg. 365+): Concept Questions 1, 3 Exercise 7(c) [do Q-Q and residual plots] Exercise 9(a,b) [do Q-Q and residual plots] [Data for #7 and #9 given at bottom of web page]	Monday, February 4
Monday, February 4	Chapter 8 Exercises (pg. 450+): Exercise 8(a) [use R, and interpret estimated coefficients] [Data for #8 given at bottom of web page]	Wednesday, February 6
Wednesday, February 6	Chapter 8 Exercises (pg. 450+): Concept Question 13 Exercise 8(a) [use R, and perform t-test about DASH100] Exercise 13(a) [use R, and perform t-test about SQFT] Exercise 16(a) [hint: do 'full vs. reduced' F-test] [Data for #8 and #13 given at bottom of web page]	Monday, February 11
Friday, February 8	Chapter 8 Exercises (pg. 450+): Concept Question 1, 3 [hint for #3: use formula on p. 408 bottom] Exercise 5(a) [use R, and also get 90% PI for weight of tree with DBH=8, height=50, age=14, grav=0.4] [Data for #5 given at bottom of web page]	Monday, February 11
Monday, February 11	Chapter 8 Exercises (pg. 450+): Concept Questions 9, 11	Wednesday, February 13
Wednesday, February 13	Chapter 8 Exercises (pg. 450+): Concept Questions 12, 14 Exercises 6(a,b,c), 7(a,b,c), 8(b) [use R] [Data for #6, #7, and #8 given at bottom of web page]	Monday, February 18
Friday, February 15	Chapter 8 Exercises (pg. 450+): Concept Question 5 Exercise 8(c) [use R] [Data for #8 given at bottom of web page]	Monday, February 18
Monday, February 18	Chapter 6 Exercises (pg. 310+): Concept Question 2	Monday, February 25
Wednesday, February 20	Chapter 6 Exercises (pg. 310+): Concept Questions 9, 10, 11, 15	Monday, February 25
Monday, February 25	Chapter 6 Exercises (pg. 310+): Concept Question 12 Exercises 4(a), 12(a), 15(a) [use R to do ANOVA F-test] [Data for #4, #12, and #15 given at bottom of web page]	Wednesday, February 27
Wednesday, February 27	Chapter 6 Exercises (pg. 310+): Concept Question 7 In class on Friday we will use the data in Exercise 2(a) and do the full ANOVA table and F-test using the ANOVA formulas.	Monday, March 4
Monday, March 4	Chapter 6 Exercises (pg. 310+): Concept Question 6 Exercise 12(b) [use R to test whether mean diameter for media 'WA' and 'TWA' is significantly different than mean diameter for 'RDA' and 'PDA', using a contrast] [Data for #12 given at bottom of web page]	Wednesday, March 6
Wednesday, March 6	Chapter 6 Exercises (pg. 310+): Concept Question 3	Monday, March 18
Friday, March 8	Chapter 6 Exercises (pg. 310+): Exercise 12(b) [use R to do Tukey multiple comparisons procedure], Exercise 15(b) [use R to do Tukey procedure, NOT the Duncan procedure] [Data for # 12 and #15 given at bottom of web page]	Monday, March 18
Monday, March 18	Chapter 9 Exercises (pg. 509+): Concept Question 1(a,c) Exercises 15(a), 16(a) [Hint: A "profile plot" is the type of plot we have been drawing in class, similar to Figure 9.1 on p. 488]	Wednesday, March 20
Wednesday, March 20	Chapter 9 Exercises (pg. 509+): Exercises 2, 3, 9 [For each of these problems, use R to do the overall F-test, the F-test for interaction, and (if necessary), the F-tests for main effects] [Data for #2, #3 and #9 given at bottom of web page]	Monday, March 25
Friday, March 22	Chapter 9 Exercises (pg. 509+): Exercise 9 [Use R to compare the mean volume for the coldest storage temperature versus the mean volume for the other temperatures, when the eggs are stored for 15 days] [Data for #9 given at bottom of web page]	Monday, March 25
Monday, March 25	Chapter 9 Exercises (pg. 509+): Exercise 9 [Use R to perform multiple comparisons among the means for all pairs of factor level combinations, using Tukey's procedure] [Data for #9 given at bottom of web page]	Monday, April 1
Friday, March 29	Chapter 9 Exercises (pg. 509+): Concept Question 3(c)	Monday, April 1
Monday, April 1	Chapter 10 Exercises (pg. 565+): Concept Question 1 [Hint: read page 541]	Wednesday, April 3
Wednesday, April 3	Chapter 10 Exercises (pg. 565+): Exercise 10 [Use R to test for a year effect and for significant variation among routes.] [Data for #10 given at bottom of web page]	Monday, April 8
Friday, April 5	Chapter 10 Exercises (pg. 565+): Exercise 1 [Use R to test for a treatment effect and for significant variation among blocks.] [Data for #1 given at bottom of web page]	Monday, April 8
Monday, April 8	Read notes on Latin Squares carefully!	Wednesday, April 10
Wednesday, April 10	Chapter 11 Exercises (pg. 619+): Concept Question 1(a,b)	Monday, April 15
Friday, April 12	Chapter 11 Exercises (pg. 619+): Exercise 9(a) [Use R to conduct F-tests based on Type III SS] [Data (and R code with hints) for #9 given at bottom of web page]	Monday, April 15
Monday, April 15	Chapter 11 Exercises (pg. 619+): Exercise 1 [Use R to fit ANCOVA model, and test for an effect of WWT on FWT] [Data for #1 given at bottom of web page]	Wednesday, April 17
Wednesday, April 17	Chapter 11 Exercises (pg. 619+): Exercise 1, 4(a) [Use R to fit ANCOVA model, and test for an effect of Weaning Time on FWT] [For 4(a), test for the effects of both the covariate and the factor] [Data for #1 and #4 given at bottom of web page]	Monday, April 22
Friday, April 17	Chapter 11 Exercises (pg. 619+): Exercise 1, 4(b) [For #1, use R to fit ANCOVA model and test whether equal-slopes model is sufficient] [For 4(b), test whether equal-slopes model is sufficient] [Data for #1 and #4 given at bottom of web page]	Monday, April 22
Monday, April 22	Chapter 13 Exercises (pg. 684+): Exercise 3(b) [Use R to fit logistic regression model; find estimates for beta_0 and beta_1, and plot fitted curve] [Data for #3 given at bottom of web page]	Wednesday, April 24
Wednesday, April 24	Chapter 13 Exercises (pg. 684+): Exercise 3(b) [Use R to fit logistic regression model; find estimates (and 95% CI) for odds ratio, and interpret] [Data for #3 given at bottom of web page]	Monday, April 29
Friday, April 26	Chapter 13 Exercises (pg. 684+): Exercise 3(b) [Use R to conduct Wald test and LR test for the significance of venticle size] [Data for #3 given at bottom of web page]	Monday, April 29

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

Available Computing Resources: R is available as a free download (from the CRAN home page) and students who want SAS can buy a copy from USC Computer Services.
These packages are also available on the computers in the labs in LeConte College (and a few other buildings).
SAS is available in LC 124 and possibly PSC 109. R is available in LC 124 and LC 303A. Help in using R can be found on the CRAN home page.

Downloading Instructions for R

• Instructions for Downloading R

Computing Tips: Some Review

• Introduction to SAS which should be a good instruction for the software

• 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 11)	SAS example (TIF data) Output for SAS example	R example (TIF data) Output for R example

Some additional code

• Example R code for polynomial regression models and multiplicative models

• Example R code for stepwise regression and more variable selection

• Example R code for multiple logistic 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 2013, we will primarily learn to use the software package R to do the needed computations.
You will need access to a computer with R (available as a free download from the CRAN home page). This can easily be downloaded onto your home computer.
Alternatively, you could use the software package SAS (available in the LeConte College computer labs).
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.
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:

HELPFUL SAS RESOURCE:

• SAS information from the STAT 516 course page

Data Sets

• Fire damage data set (plain text)

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

• U.S. Cities data set (plain text) for Chapter 7, problem 6

• Heating Cost data set (plain text) for Chapter 7, problem 7

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

• Birth Rate data set (plain text) for Chapter 7, problem 9 -- 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

• 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

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

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

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

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

• 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

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

• 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

• Post-Test 2 Review Sheet

Formula Sheets

Exam Solutions

• Exam 1 Solutions (Version A -- white cover)

• Exam 1 Solutions (Version B -- yellow cover)

• Exam 2 Solutions (Version A -- yellow cover)

• Exam 2 Solutions (Version B -- white cover)

Exams

• Midterm 1: February 22
• Midterm 2: March 27
• Final Exam: Wednesday, May 1, 9:00 a.m.