STAT 720
1
Welcome to STAT 720!
1.1
About this course:
1.1.1
Logistics
1.2
Learning goals
1.3
On notation
1.4
Why do designed experiments exist?
1.4.1
Example
1.5
For tomorrow
2
Basic types of designed experiments
2.1
Announcements
2.2
The golden rules of designed experiments
2.3
Other concepts in designed experiments
2.4
Types of designs - the basics
2.4.1
Completely randomized design (CRD)
2.4.2
Randomized complete block design (RCBD)
2.4.3
Incomplete block design (IBD)
2.5
Skeleton ANOVA tables
2.5.1
Practice examples
2.6
Homeworks
3
Basic types of designed experiments
3.1
Review
3.2
Types of designs
3.2.1
Completely randomized design (CRD)
3.2.2
Randomized complete block design (RCBD)
3.3
Building an ANOVA skeleton using design (aka topographical) and treatment elements
3.4
Linear model good old friend
3.4.1
The most common assumptions behind most software
3.5
Some Practice
3.6
To do
4
Linear models, ANOVA shells applied to the more basic experiment designs
4.1
Announcements
4.2
Review
4.3
Linear models
4.3.1
The most common model - Assumptions
4.3.2
Connection between this and your classical ANOVA table
4.3.3
Sorghum example
4.4
Takehomes
4.4.1
If you don’t include the design elements (blocks), their portion of the variance goes to the error
4.5
Tomorrow
5
Review, organizing data and other helpful tips
5.1
Kahoot! as review
5.2
Organizing data
5.3
Homework
6
The treatment structure
6.1
Announcements
6.2
Review
6.3
Treatment structure
6.3.1
Types of treatment structures
6.4
Where is the treatment structure connected to the statistical model?
6.5
Tomorrow:
7
What you ask of a designed experiment
7.1
Announcements
7.2
Review
7.3
ANOVA
7.3.1
In case you were wondering: ANOVA and types of sums of squares
7.4
Setting the stage: Estimated marginal means aka least squares means
7.4.1
Example:
7.4.2
Discussion
7.5
Tomorrow
8
Applied examples
8.1
Announcements
8.2
Applied example for today
8.2.1
Discussion
8.3
Some topics that will arise today
8.4
Tomorrow & Friday
9
Applied Linear Mixed Models I
9.1
Announcements
9.2
Applied Linear Mixed Models
9.2.1
Review: random variables
9.3
Review: Designs
9.4
Split-plot designs
10
Hierarchical (Multilevel) Designs
10.1
Announcements
10.2
Review: Hierarchical Designs
10.2.1
An applied example:
10.3
Building the ANOVA skeleton using design (aka topographical) and treatment elements
10.4
Blocks: fixed of random?
10.5
Reading
11
Hierarchical (Multilevel) Designs and their implementation in R
11.1
Review
11.2
Split-plot designs
11.3
General advice to model data with some type of structure
12
Review: hierarchical multilevel models
12.1
Exercises
12.1.1
Data structure
12.1.2
Designed experiment
12.1.3
Stat model
12.1.4
Bonus
12.2
Acknowledgements
13
Statistical inference and experiment designs
13.1
Announcements
13.2
Why do we care about standard errors and degrees of freedom?
13.2.1
The hypothesis test explanation
13.2.2
The signal & noise explanation
13.2.3
Applied case I – CRD
13.2.4
Biological significance (and others) matter as much as statistical significance
13.2.5
Applied case II – RCBD
13.3
Applied case III – split-plot design
13.4
Tomorrow
14
Review: hierarchical multilevel models – cont.
14.1
Announcements
14.2
Hierarchical designs
14.3
Review
14.3.1
Treatment structure
14.3.2
Design structure
14.4
Exercises
15
Statistical inference and experiment designs
15.1
Announcements
15.2
Why do we care about standard errors and degrees of freedom?
15.2.1
The hypothesis test explanation
15.2.2
The signal & noise explanation
15.2.3
Applied case I – CRD
15.2.4
Biological significance (and others) matter as much as statistical significance
15.2.5
Applied case II – RCBD
15.3
Applied case III – split-plot design
15.4
Tomorrow
16
Semester Project
16.1
Learning objectives
16.2
Partial deadlines
16.2.1
Project proposal - Due Friday June 12 at 2pm CT
16.2.2
Written report - Due Wednesday July 1 at 2pm CT for peer review
16.2.3
Oral presentation - Somewhere between July 8 - July 14
16.2.4
FINAL written report - Due Wednesday July 16 at 11:59pm CT
16.2.5
Practical reads
Published with bookdown
STAT 720 - Design of Experiments
Day 5
Review, organizing data and other helpful tips
June 5th, 2026
5.1
Kahoot! as review
Code on the whiteboard
5.2
Organizing data
Data Organization in Spreadsheets
Excel sheet
Analyze as randomized—Why dropping block effects in designed experiments is a bad idea (Fret et al., 2024)
5.3
Homework
Homework is posted and due in a week