This website is designed to host course material for STAT 587 Section 2 (Engineering) - Statistical Methods for Research Workers at Iowa State University.

This course meets

Office hours are

  • Instructor: W 3-4, R 4-5 on Zoom
  • TA: F 11-12 on TBD

Relevant course pages


There is no required textbook for this course. Here are some free resources that can be used:

Other resources:


We will be using the Statistical Software R. I will be using RStudio as the interface to R. Although both will be available on lab computers, I suggest you install R and RStudio on your own laptop or desktop (or both).

Install links:

Course Description

Methods of analyzing and interpreting experimental and survey data. Statistical concepts and models; estimation; hypothesis tests with continuous and discrete data; simple and multiple linear regression and correlation; introduction to analysis of variance and blocking.

Course Objectives

  • Understand the difference between a population and a sample.
  • Learn to use statistical methods, e.g. t-tests, rank sum tests, ANOVA, regression, etc., to analyze experimental and observational data.
  • Perform, check assumptions, and interpret multiple linear regression.
  • Write, interpret, and critically evaluate statements such as
    • No significant differences were observed between A/H5N1_HA N182K- and A/H5N1_HA Q222L,G2245-inoculated animals, as calculated by comparing the viral titer (Mann-Whitney test, P=0.589 and 0.818 for nose and throat titers, respectively).
    • … estimate gross carbon emissions across tropical regions between 2000 and 2005 as 0.81 petagram of carbon per year, with a 90% prediction interval of 0.57 to 1.22 petagrams of carbon per year.


The prerequisite for the course is previous enrollment in one of the following statistics (STAT) courses: 101, 104, 105, 201, or 226.


Please use the Canvas discussion forum.


Week Topic Reading
1 Probability  
2 Random variables  
4 Modeling  
5 Inference  
7 Regression  
9 midterm  
  spring break  
10 Simple linear regression  
11 Multiple regression  
12 ANOVA  
13 Contrasts  
14 Model selection  
15 Poisson and logistic regression  
  final exam  

Center for Excellence in Learning and Teaching Recommendations

This course abides by the Center for Excellence in Learning and Teaching Recommendations.