Analysis of Variance

Analysis of Variance
Scientific method, statistical thinking, sources of variation, completely randomized design, ANOVA, power and sample size considerations, multiple testing, randomized complete blocks, factorial designs, interactions. Introduction to statistical software.
 Hours3.0 Credit, 3.0 Lecture, 0.0 Lab
 PrerequisitesSTAT 121; or STAT 201
 RecommendedMath 112.
 TaughtFall, Winter, Spring
 ProgramsContaining STAT 230
Course Outcomes: 


Create datasets in R and SAS from space-, comma-, tab-delimited text files.

Summary Statistics

Compute summary statistics from R and SAS datasets

Create Graphics

Create graphics in R and SAS for exploratory data analysis and communicating results


Understand the concept of variability in data and the attempt to identify sources of that variability

Writing Statistical Models

Practice writing statistical models

Experimental Design

Understand the basics of experimental design, including the definition of the experimental unit, response, variable, factor(s), and level(s) of a basic experiment, and the role of randomization and replication to permit causal inference

Analyze Data

Analyze data from 'Treatment-Control' or 'A/B' experiments using professional statistical software

Randomized Design

Analyze data from completely randomized designs using professional statistical software

Block Design

Analyze data from randomized complete block designs using professional statistical software

Two-Factor Factorial Design

Analyze data from two-factor factorial designs using professional statistical software

Confidence Intervals

Demonstrate the impact of increasing the number of replicates on confidence intervals and hypothesis tests

Technical Reports

Work in teams to write a technical report and make a technical presentation of a designed experiment