Statistical Software

Last changed: 22 December 2023

Many different statistical software are used at SLU. The most common are R, SAS and JMP, together with Minitab that is often used as teaching software in statistics.

Statistics in statistical software

Overview over common statistical methods with links to SAS and R

Teaching of statistical programming

In our Ph.D. courses data exercises are done in R, several of our undergraduate courses use R, while others use Mintab, which is a program with simpler user interface, but less possibilities to do statistical analysis. We do not use JMP in our courses, but this a good option for you who want to do more complex analysis and do not want to write code. SAS is another advanced statistical program that we can help you with.

At the moment we do not offer courses with statistical programming as main contents, but you have always the possibility ask questions on statistical software and programming in our consultancy services (länk) or drop-in hours.

Occassionally workshops in R tidyverse and R Markdown are given. Check the calendar!

Statistical programming – teaching material

The following materials were prepared by Statistics@SLU. See further down for more additional links.


Introduction to SAS

This link contains the contents of a former course called Basic SAS programming.


Introduction to Minitab 

Descriptive statistics and basic ANOVA and regression models in Minitab.


Introduction to Rstudio

Dataset: cats.txt

R tidyverse

R programming can be done in base R or using the R tidyverse. The following material is produced for our R tidyverse workshops and new material will appear here as it becomes available. R tidyverse is a collection of packages that makes it easier to handle data. For example, it provides easy to read code to filter out observations using certain conditions, to select specific variables, create new variables or to compute group means or other data summaries.

Using the functions filter,  group_by and summerize on Phytoplankton data

Using the functions group_by, filter and map on Chlorophyll data

Using the functions gather, mutate and ifelse on Glucose data

Using the functions lubridate and dates on Phosphate data

Parallel computations in R.

Plots and descriptive statistics in R

Making boxplots and confidence interval plots with Milkfat data

Plots with secondary axis with Nutrient loads data

Interrater agreement for continuous variables, data

Statistical programming – Useful links

Statistics in statistical software
IDRE (Institute for Digital Research and Education) at UCLA has many good descriptions of doing statistical analysis in various statistical software.

SAS - SAS user guide (documentation on syntax for all procedures):

Minitab - Minitab support

JMP -JMP documentation

R -R manuals

Several free textbooks on R are available, among them Modern Statistics with R ( and R for Data Science (