2022 - 2023 Undergraduate Bulletin


Search for courses listed in this bulletin. To find a semester course schedule (including instructors, meeting times and locations), go to mynmu.nmu.edu.

DATA 109 Introduction to Statistics 4 cr.  (4-0-0)
  • Offered: Fall Winter Summer
  • Prerequisites: MA 100 (“C- or better) or appropriate math placement.

The study of descriptive and inferential statistics, with an emphasis on hypothesis testing and an introduction to linear regression and ANOVA in a statistical package such as R.


Formerly MA 109 and MA 171.

DATA 309 Data Visualization and Programming in R 4 cr.  (4-0-0)
  • Offered: Fall
  • Prerequisites: DATA 109 (“C-” or better) or instructor permission.

The course covers practical issues in statistical computing which includes programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting R code. We will use these skills to develop data visualizations in R. Topics in statistical data analysis will provide working examples.

DATA 409 Experimental Design 4 cr.  (4-0-0)
  • Offered: Winter
  • Prerequisites: DATA 109 (“C-” or better) or instructor permission.

Define quantitatively the most efficient ways to obtain knowledge from experiments with differing constraints for number of treatments, replicates, classes of experimental objectives, and blocking procedures in terms of the general linear model.

DATA 472 Multiple Regression and ANOVA 4 cr.  (4-0-0)
  • Offered: Winter
  • Prerequisites: DATA 309 and either MA 371, or DATA 109, or graduate level standing.

Generalized linear models are introduced after a brief review of hypothesis testing and simple linear regression. Multiple regression and one-way and two-way ANOVA  are discussed in detail.

DATA 475 Time Series and Logistic Regression 4 cr.  (4-0-0)
  • Offered: On demand
  • Prerequisites: MA 472 or instructor permission.

Introduction to techniques used in time-series analyses and logistic regression. Topics include seasonality, lag operators, construction of stationary time series models (moving average and autoregression), the construction of  nonlinear stochastic models (ARIMA), and binary response variables.

DATA 478 Bayesian Analysis 4 cr.  (4-0-0)
  • Offered: Fall
  • Prerequisites: DATA 472 (“C-” or better) or instructor permission.

The aim of this course is to equip students with the skills to perform and interpret Bayesian statistical analyses.  Modern advances in computing have allowed many complicated models, which are difficult to analyze using ‘classical’ (frequentist) methods, to be readily analyzed using Bayesian methodology.