STA 441: Methods of Applied Statistics
University of Toronto Mississauga,
Winter/Spring 2020
Instructor:
Jerry Brunner
What to say about SAS
(in a job interview).
I had a course where we used SAS OnDemand, so that's SAS Statistics running in the SAS Studio environment. We read data from plain test data files using a simple form of the input statement and we used proc import to read from Excel spreadsheets. We used assignment statements and if statements to create new variables, and proc format to label the values. We used arrays and do loops in the data step. We used proc reg and proc glm for univariate and multivariate regression and analysis of variance, and we used proc logistic for regular logistic regression and multinomial logit models. We used proc mixed as well as proc glm to analyze repeated measures data when the dependent variable was assumed normal. We used proc autoreg for time series analysis. We used proc nlmixed to fit mixed logistic models when the outcome was binary and repeated measures. We used ODS to send results to proc iml for further calculations, and we also used ODS select sometimes to limit the output.
If they ask about the
put
statement, say "Oh, that's like a print statement for writing on the log file."
If they ask about
SASgraph
, say "We didn't use it. My professor said it was scary."
The final exam will be online, on Saturday April 18th from 14pm. Click HERE for more information, including office hours.
On Thursday April 16th around 3 pm, I posted some
practice questions on the multinomial logit model (with answers) on the
final exam information page.
There will be one more office hour the day of the exam: Saturday 10:3011:30.
Link to Quiz 9 solution is working now.
Answer to 2018 Final Question 7 is posted on the
final exam information page.
 Syllabus
 SAS OnDemand: Run SAS for free online.
 Brief instructions for using SAS OnDemand.
 More computing Resources: Information and Links
 Formula Sheet
PDF
View LaTeX
Download LaTeX
 Assignments: Solutions are not available. Think about the questions.
 Assignment
1: Quiz on Monday January 13th.
 Assignment
2: Quiz on Monday January 20th.
 Assignment
3: Quiz on Monday January 27th.
 Assignment
4: Quiz on Monday February 3d.
 Assignment
5: Quiz on Monday February 10th.
 Assignment
6: Quiz on Monday February 24th.
 Assignment
7: Quiz on Monday March 2nd.
 Assignment
8: Quiz on Monday March 9th.
 Assignment
9: Quiz on Monday March 16th.
 Assignment
10: Quiz on Monday March 23d.
 Assignment
11: Quiz on Monday March 30.
 Lecture Overheads Some of these present concepts, and some show SAS input and output. You might want to print the SAS programs and bring them to class so you can write notes on them during lecture.
 Introduction to data analysis: See Chapter 1.
 Introduction to SAS: See Chapter 2.
 SAS Example One: Basic descriptive statistics
on the cars data. See Chapter 2.
 Oneway ANOVA with multiple comparisons: Concepts. See Chapter 3.
 SAS Example Two: Analysis if the scab disease data.
 SAS Example Three: The Berkeley graduate admissions data. See Chapter 4.
 Multiple regression: Concepts. See Chapter 5.
 SAS Example Four: Regression on the cars data.
See Chapter 5.
 SAS Example Five: Read and describe the math data.
 SAS Example Six: Regression on the math data.
 SAS Example Seven : Residual analysis of the math data.
 SAS Example Eight : Replication and prediction for the math data.
 Logistic regression: Concepts. See Chapter 6.
 SAS Example Nine: Logistic regression on the math data, Part One.
 SAS Example Ten: Logistic regression on the math data, Part Two.
 Factorial analysis of variance: Concepts. See Chapter 7.
 SAS Example Eleven: Factorial analysis of variance on the rotten potato data.
 Multivariate regression and analysis of variance: Concepts. See Chapter 9.
 SAS Example Twelve: Multivariate analysis of the little tubes data data.
 Within Cases Part One: Multivariate and mixed model approaches to within cases. See Chapter 9.
 SAS Example Thirteen: Multivariate approach to within cases.
 Within Cases Part 2 Concepts: The covariance structure approach. See Chapter 9.
 SAS Example Fourteen: Covariance structure approach to within cases.
 Binary within cases
 SAS Example Fifteen: Binary within cases + with proc nlmixed.
 Introduction to Time Series
 SAS Example Sixteen: Time series with proc autoreg.
 SAS Example Seventeen: Time series structure for repeated measures with proc mixed.
 Selection of sample size by statistical power
 SAS Example Eighteen: Power and sample size with proc iml.
 Logistic regression with more than two outcomes: Concepts
 SAS Example Nineteen: Multinomial logit models with proc logistic.
 Permutation and randomization tests
 SAS Example Twenty: Permutation and randomization tests with proc npar1way and proc multtest.
 Sample size Part 2: The sample and population variation methods
 SAS Example Twentyone: Sample size with the sample variation and population variation methods  writing on the log file.
 Lecture Videos
 Monday March 16th
 Wednesday March 18th
 Monday March 23d
 Wednesday March 25th
 Monday March 30th
 Wednesday April 1st
 Textbook
 Chapter 1: Vocabulary and basic concepts used throughout the course, and also a brief discussion of elementary significance tests.
 Chapter 2: Introduction to SAS. The unix/linux details do not apply to this class.
 Chapter 3: Comparing several means.
 Chapter 4: Simple categorical data analysis of the Berkeley graduate admissions data
 Chapter 5: Multiple regression
 Chapter 6: Logistic regression
 Chapter 7: Factorial analysis of variance
 Chapter 8: Selecting sample size
 Chapter 9: Multivariate and withincases

 Home page for the textbook. This has the current version of the full book. You are responsible for the individual chapters posted above.
All course materials prepared by Jerry are licensed under a Creative Commons AttributionShareAlike 3.0 (or later) Unported License.