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

This course meets

Office hours are

  • Instructor: TBD on Zoom
  • TA: TBD

Relevant course pages

Textbook

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

Other resources:

Software

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.

Prerequisite

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

Q&A

Please use the Canvas discussion forum.

Schedule

Week Topic OpenIntro
1 Probability Ch 3
2 Random variables Ch 4
3    
4 Modeling  
5 Inference  
6   Ch 5
7   Ch 6
8   Ch 7
9 midterm  
10 Simple linear regression Ch 8
11 Multiple regression Ch 9
12 ANOVA  
13 Contrasts  
14 Model selection  
  final exam  

Center for Excellence in Learning and Teaching Recommendations

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