Statistical Workshops

All statistical workshops are free and registration is first-come/first-seated.  Attendees must contact the instructor 24 hours before the class starts to request data and syntax files be emailed to them (stats@uic.edu).

Introductory-level Workshops

Introduction to SAS (Jan 27 | Jan 28)

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.

Introduction to SPSS (Feb 3 | Feb 4)

Topics covered in this workshop include the different kinds of SPSS files, navigating the SPSS Graphical User Interface, using SPSS syntax, the variable-attributes screen, FREQUENCIES dialogue, importing Excel and text files into SPSS, the partial-correlation formula, cross-tabulations, and some basic graph functions. 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.

Hands On: Big Data, SAS Visual Analytics 6.2, and High-Performance Data Mining (Jan 21)

In celebration of SAS Day at UIC, SAS consultants are presenting a free, hands-on workshop on Big Data, SAS Visual Analytics, and High-Performance Data Mining on Jan 21st, 2014.

This workshop will include an introduction to Big Data Analytics in which we will analyze several real-world examples of companies with big data problems using SAS Enterprise Miner 9.4. Big Data will then be used to explore the capabilities of SAS Visual Analytics 6.2 for SAS Cloud, including: automatic charting, bar charts, line charts, scatter plots, bubble plots, histograms and box plots, heat maps, geo maps, tree maps, correlation matrices and regression, decision trees, and forecast analysis. We will conclude with an overview of SAS High-Performance Data Mining, which allows you to develop predictive models using complete data, not just a subset, and thousands of variables to produce more accurate and timely insights.

There are no prerequisites for this workshop.

Intermediate-level Workshops

Linear Multiple Regression and Intro to Data Management (SAS, Feb 11 | SPSS, Feb 12)

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).

Data Management: Merging, aggregation of rows by stipulated classifying variables, index and substring functions for working with alphabetical variables.

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

NOTE: The Linear Multiple Regression segment comprises the majority of this workshop; coverage of the Data Management portion may be cancelled if the Multiple Regression training module runs overtime.

Cleaning Dirty Data (SAS, Feb 18 | SPSS, Feb 19)

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/SPSS” and “Linear Multiple Regression/Data-Management” workshops, OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

One-Way Analysis of Variance (SAS, Feb 25 | SPSS, Feb 26)

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 or SPSS, 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/SPSS” and “Linear Multiple Regression/Data-Management” workshops; OR equivalent knowledge acquired through academics and/or workplace experience. Comfort with using a syntax programming language.

Last updated: 

January 06, 2014

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