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

## 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).

## 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

1 Probability
2 Random variables
3
4 Modeling
5 Inference
6
7 Regression
8
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.