Statistical Consulting Center
Need statistical help on your project?
The Mathematics Department provides free statistical support to UNI faculty, staff, and students through the Statistical Consulting Center (SCC). Statistical help on your research project may include assistance with study design, data collection, sample size requirements, use of statistical software, data analysis, and data interpretation. The SCC also offers statistical software short courses.
Contact Justine Radunzel, the SCC Coordinator, at justine.radunzel@uni.edu to set up your appointment or to inquire about and register for one or more of the upcoming short courses being offered.
SPSS Short Courses
- SPSS Introductory Short Course Session 1 - Data Manipulation
When: Tuesday, November 11, 3:30 to 4:45 pm
Where: Wright Hall 110
Learn about entering and reading data into SPSS, validating data read in, changing data types, and recoding variables. This session is suitable for beginners and assumes no previous knowledge.
- SPSS Introductory Short Course Session 2 - More on Data Manipulation
When: Tuesday, November 18, 3:30 to 4:45 pm
Where: Wright Hall 110
Learn about combining data sources in SPSS, creating new variables, dealing with missing data, and subsetting and exporting data. This session is suitable for beginners.
- SPSS Introductory Short Course Session 3 - Graphics
When: Tuesday, December 2, 3:30 to 4:45 pm
Where: Wright Hall 110
Learn about SPSS graphics capabilities. This session is suitable for beginners.
- SPSS Introductory Short Course Session 4 - Basic Inferential Statistics
When: Tuesday, December 9, 3:30 to 4:45 pm
Where: Wright Hall 110
Learn about conducting chi-square tests, t-tests, and simple linear regression in SPSS. This session is suitable for beginners with some basic statistical knowledge.
- SPSS Introductory Short Course Session 5 - Multiple Linear Regression
When: TBD, Spring 2026
Where: TBD
Learn about conducting multiple linear regression in SPSS. This session is suitable for beginners with some basic knowledge of linear regression.
- SPSS Introductory Short Course Session 6 - Logistic Regression
When: TBD, Spring 2026
Where: TBD
Learn about conducting logistic regression in SPSS. This session is suitable for beginners with some basic knowledge of logistic regression.
R/RSTUDIO Short Courses
- R/RStudio Introductory Short Course Session 1 - Data Manipulation
When: Tuesday, November 11, 9:30 to 10:45 am
Where: Wright Hall 110
Learn about reading data into R, validating data read in, changing data types, and combining data sources. This session is suitable for beginners with some basic programming experience.
- R/RStudio Introductory Short Course Session 2 - More on Data Manipulation
When: Tuesday, November 18, 9:30 to 10:45 am
Where: Wright Hall 110
Learn about creating new variables, dealing with missing data, creating built-in functions, and subsetting and exporting data in R. This session is suitable for beginners with some basic programming experience who have taken Part 1.
- R/RStudio Introductory Short Course Session 3 - Graphics with ggplot2
When: Tuesday, December 2, 9:30 to 10:45 am
Where: Wright Hall 110
Learn about creating graphics using ggplot2 package in R. This session is suitable for beginners who have taken the first two sessions.
- R/RStudio Introductory Short Course Session 4 - Basic Inferential Statistics
When: Tuesday, December 9, 9:30 to 10:45 am
Where: Wright Hall 110
Learn about conducting chi-square tests, t-tests, and simple linear regression in R. This session is suitable for beginners with some basic knowledge of inferential statistics.
- R/RStudio Introductory Short Course Session 5 - Multiple Linear Regression
When: TBD, Spring 2026
Where: TBD
Learn about conducting multiple linear regression in R. This session is suitable for beginners with some basic knowledge of linear regression.
- R/RStudio Introductory Short Course Session 6 - Logistic Regression
When: TBD, Spring 2026
Where: TBD
Learn about conducting logistic regression in R. This session is suitable for beginners with some basic knowledge of logistic regression.