Statistical Workshops

When is open-registration for Fall 2017 SAS classes?

  • Friday, September 8, 9:00am:  Open registration for “Intro-to-SAS” and “Linear Multiple Regression.” 
    • WORKSHOPS FULL, REGISTRATION CLOSED
  • Friday, September 29, 9:00am:  Open registration for “Cleaning Dirty Data” and “One-Way-ANOVA.”
  • Friday, October 13, 9:00am:  Open registration for “Logistic Binary Regression” and “Repeated-Measures ANOVA.”

On the day registration opens for one or more SAS classes you wish to attend, use your UIC email account (i.e., joesmith@uic.edu) to send your registration-request to ACCC’s SAS-instructor at: stats@uic.edu.

In your request(s), please state the particular SAS class(es) for which you wish to register (i.e., “I am requesting that you register me for the Intro-to-SAS class held on Tuesday, September 19,” – or - “I am requesting that you register me for the Cleaning Dirty Data class held on Tuesday, October 17, and for the One-Way ANOVA class held on Tuesday, October 24.”) Remember, you cannot register for an SAS class prior to the date and time when registration opens for that particular class. So, for example, if someone submits a request to attend Cleaning Dirty Data class before the start of open-registration for that class (i.e., 9:00am on September 29), then their request to attend Cleaning Dirty Data class will NOT be processed. This rule is inflexible.

Locations

This Fall, all SAS classes will be in the College of Pharmacy building (833 S. Wood Street). The Linear-Regression class on September 26th will be in Conference Room 231, but all other SAS classes will be in the brand-new Conference Room # B-8.


Introductory-level Workshops

Introduction to SAS

  • 9/18, 9:00am to 1:00pm, COP-B8​ --- WORKSHOP FULL, REGISTRATION CLOSED
  • 9/19, 9:00am to 1:00pm, COP-B8​ --- WORKSHOP FULL, REGISTRATION CLOSED

Topics covered in this workshop include an overview of the SAS layout, defining and creating SAS libraries, importing Excel spreadsheets into SAS, SAS statements “DATA”, “INFILE”, “INPUT”; creating SAS variables, row & column include/exclude commands, variable labels, date and currency definition options, PROC statements for “PRINT”, “MEANS”, “FREQ”, and tables-options.  This workshop is offered twice this semester and the same material will be covered in each session.

Recommended prerequisite: Completion of a 200-level course in statistics.

Intermediate-level Workshops

Linear Multiple Regression using SAS

  • 9/26, 9:00am to 12:30pm, COP-231​ --- WORKSHOP FULL, REGISTRATION CLOSED

Linear Multiple Regression: A start-to-finish example of some of the various procedures and options for univariate screening of data prior to producing a multiple-regression model (e.g., addressing common confounds to producing models, decision-rules for addressing missing-data issues, formulas to partially-ameliorate issues arising from data Missing Completely At Random, regression-selection methods, diagnostic plots, and detection of multicolinearity).

Recommended prerequisites: SAS basic skills, comfort with using a syntax programming language, and completion of a 400-level course in statistics.

Data-Management/Cleaning Dirty Data with SAS

  • ​10/10, 9:00am to 1:30pm, COP-B8

Taught as a hands-on lesson with a realistic sample dataset and narrative context, this workshop teaches programming protocols for the screening and cleaning of various types of data errors (e.g., missing or invalid primary-keys, alphabetical entries in numeric fields, invalid values, data mis-keyed into incorrect fields). Lessons are useful for all professionals that manage and/or analyze data.

Prerequisites: Completion of ACCC's “Introduction to SAS” and “Linear Multiple Regression” workshops, OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

One-Way ANOVA Using SAS

  • ​10/17, 9:00am to 1:00pm, COP-B8

Analysis of Variance (ANOVA) is one of the most commonly-used statistical tests.  This class will cover “One-Way-Between-Subjects”, one of the most basic and frequently used models, employed to assess whether group-averages differ meaningfully due to some attribute(s) specific to one or more groups being studied.

Topics covered in this workshop include importing various genres of research data into SAS, mathematical assumptions that must be met in order for ANOVA test results to be valid and reliable, relative effects of mathematical assumption violations on ANOVA testing, relative effectiveness of widely-accepted countermeasures to ANOVA assumption violations, using tabular or graphical means to identify assumption violations, and producing ANOVA graphs.

Recommended prerequisites: Completion of a 300- or 400-level course in statistics, completion of ACCC's “Introduction to SAS” and “Linear Multiple Regression"; OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

Advanced-level Workshop

Logistic Binary Regression Using SAS

  • ​10/24, 9:00am to 1:30pm, COP-B8

Logistic Regression is the third-leg of the mathematically logical triangle, which includes Linear Regression and ANOVA.  This class will cover Logistic Binary Regression, which is employed to build predictive models when the outcome of research interest is dichotomous (e.g., “patient died/survived” or “customer purchased/didn’t purchase”). Like the Linear Regression and ANOVA workshops, this class is comprised of hands-on, applied SAS programming exercises.  To instill the course with “real world” context and also provide a sense of topical continuity, all instruction will revolve around the same dataset: an authentic medical-trauma research study.

Topics covered in this workshop include: 1) a brief refresher on the mathematics that drive Logistic Binary Regression and of the circumstances under which this is the best procedure to use for predictive-model building; 2) extensive diagnostic data-screening to assess whether mathematical and scientific assumptions have been met sufficiently for a constructed model’s predictions to be valid and reliable; 3) individual case-deletion diagnostics to identify outliers that disproportionately influence the estimation of correlation coefficients during the building of the predictive model; 4) case-by-case qualitative analysis of the model’s predictive failures; 5) Plots, ROC Curve and S-graphs, and, 6) a brief introduction to the topic of “Propensity Score Matching” as a countermeasure for predictive-modeling confounds occasioned by substantial aggregate dissimilarities between Control and Treatment groups.

Recommended prerequisites: completion of 400-level statistics classes, an aptitude for and an interest in multivariate statistical testing; and, a level of competency writing SAS programming language which is equivalent to having completed the other four ACCC-sponsored SAS workshops.

Repeated Measures Analysis of Variance

  • ​11/7, 9:00am to 1:30pm, COP-B8

Due to its particular usefulness as a hypothesis-test for evaluating experimental treatments in human-subjects studies over time, r-ANOVA is widely used for clinical research as well as many other topically diverse areas like agriculture, economics, engineering, marketing, and psychology.

This workshop will cover 1) a brief refresher of the r-ANOVA formula and of the "mathematical mechanics" that it performs,  2) some conditions under which r-ANOVA may - or may not - be the most appropriate hypothesis-test for a dataset, 3) a few basic diagnostic protocols to help determine if, and how much, the mathematical properties of a dataset could weaken or even confound r-ANOVA test-results, 4) practical examples of  some types of research confounds that cannot be directly assessed through statistical diagnostics-tests, 5) methods commonly used to partially-compensate for some violations of assumptions,  6) the SAS programming syntax required to perform metric-diagnostic tests, run the F-Test and alternatives to the F-Test, run post-hoc tests and alternative post-hoc tests, generate graphs for diagnostic-assessments and presentation of r-ANOVA test-results; 7) reporting of test-results and interpretations. 

Required homework: 8-12 hours of pre-class homework will be assigned 1 week before class starts. Completion of this homework is a required prerequisite.

Recommended prerequisites: completion of graduate-level statistical coursework which covered the mathematics and assumptions of r-ANOVA, a level of competency writing SAS programming language.


Equipment

These conference rooms are more comfortable than computer-labs, but they are not equipped with desktop computers. Attendees are required to attend class with SAS installed on their laptops. So if you do not have SAS installed on your laptop computer, and if ACCC’s SAS instructor approves your request to register for any of the SAS classes, you must obtain and install SAS on your computer as soon as possible. Your best option is to visit the Webstore (http://webstore.illinois.edu) and order DVDs of the *full* version of SAS 9.4 for Teaching and Research. DVD delivery can take several days, and after you receive the DVDs, you will need some additional time to install SAS - it is a big piece of software - and then test that SAS is working correctly on your computer. If SAS does not install correctly, Webstore staff can provide helpful advice (webstore@illinois.edu).

Knowledge-Prerequisites

First, each SAS class is a compressed module of real-world training and hands-on exercises, designed to teach attendees how to write the SAS code needed to perform procedures related to that particular SAS class. Additionally, each SAS class provides a foundation of knowledge designed to help attendees comprehend the lessons taught in subsequent SAS classes. Without knowing how to write basic SAS syntax such as the lessons taught in Intro-to-SAS, it would not be possible for attendees to comprehend the lessons taught in any of the post-Intro SAS classes. Without knowing how to write code to perform the procedures taught in the Linear Multiple Regression and One-Way ANOVA classes, it would be very difficult for attendees to comprehend the coding lessons taught in the Logistic Binary Regression or Repeated-Measures ANOVA classes. In other words, unless you have obtained equivalent SAS programming skills though other educational or practical experience, you might find it difficult to comprehend advanced SAS programming without first learning intermediate-level SAS programming (i.e., it would be like taking Chemistry 201 without first taking Chemistry 101).

Second, at the start of each post-Intro class, the SAS instructor will briefly describe the statistical test(s) to be performed in that class, in order to help refresh the memories of attendees who have not utilized their statistical training in several years. However, refreshers cannot help people who have *never* received formal education in the mathematics and underlying mathematical assumptions that govern these statistical tests. For example, it is only appropriate to attend a class in how to write SAS code to run Repeated-Measures ANOVA if attendees already understand the mathematics and mathematical assumptions that underlie Repeated-Measures ANOVA testing. In past years, within 15-40 minutes after the start of a post-Intro SAS class, there have been a few attendees who have stood up and walked out of the classroom. But before those attendees departed, they admitted that they had made the mistake of believing that they could benefit by learning the SAS programming to perform statistical tests that they did not understand. To summarize, if you have not completed any statistical coursework for Linear Regression, then you should not register for the SAS/Linear Multiple Regression class. If you are uncertain as to whether you know enough about statistics to attend a post-Intro SAS class, you may submit such questions/concerns to ACCC’s SAS instructor at: stats@uic.edu.

Registration Procedures

Please review our Statistical Software Workshop Guidelines and Registration Procedures.

Please email stats@uic.edu with any questions.  

 

Last updated: 

September 11, 2017