STAT 587-2 (Engineering) Statistical Methods for Research Workers
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
- MWF 1:10-2 @ Ross Hall 0124
- R 2:10-4 @ Zoom
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:
- OpenIntro Statistics
- Online Statistics Education
- R for Data Science
- Data Camp
- Introduction to R for Data Science
- Introduction to Linear Regression Analysis (5) by Montgomery, Peck, and Vining
- Steve Vardeman’s Notes
- Coursera Data Science Specialization
- The Statistical Sleuth
- Data Collection and Analysis
- Data Analysis for Scientists and Engineers
- An Introduction to Statistical Learning
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).
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.
- 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.
|10||Simple linear regression|
|15||Poisson and logistic regression|
Center for Excellence in Learning and Teaching Recommendations
This course abides by the Center for Excellence in Learning and Teaching Recommendations.