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Statistical Thinking: A Simulation Approach to Modeling Uncertainty (STAT 216 edition)
Introduction
Course Material
Participation in the Learning Process
1
Introduction to modeling and simulation
1.1
Modeling and simulation
1.2
Generating Data from Models
1.3
Activity: Pet factories
1.3.1
Explore a pre-built sampler
1.3.2
Creating data factories
1.4
Monte Carlo Simulation
1.4.1
Example of a Monte Carlo Simulation Study
1.4.2
Monte Carlo Simulation Assumptions
1.4.3
Monte Carlo Simulation in Practice
1.5
Activity: Building Monte Carlo simulations
Model-simulate-evaluate
1.5.1
Coin Flips
1.5.2
Monte Carlo Simulation 2: Generating a Sample of Students
1.5.3
Automating the simulation process
1.6
Regularity in randomness
1.6.1
How many trials do you need to run?
1.7
Describing Distributions
1.7.1
Shape
1.7.2
Center / “typical value”
1.7.3
Spread / variabilty
1.7.4
Potential outliers
1.7.5
Putting It All Together
1.8
Activity: Football kicking contest
1.8.1
Where should the judge stand?
1.8.2
How far does she run?
1.9
The mean and standard deviation
1.9.1
Mean
1.9.2
Standard deviation (SD)
1.9.3
Range of likely results
1.10
Review activity: Montana political parties
1.11
Unit 1 summary
1.11.1
You should be able to
1.11.2
You should understand
1.11.3
TinkerPlots™ skills
1.11.4
Vocab
2
Modeling Sampling Variation
2.1
Statistical hypothesis testing
2.1.1
Simulation Process for Evaluating Hypotheses
2.2
Activity: Monday breakups
2.3
Null hypotheses
2.4
A closer look at statistical hypothesis testing
2.4.1
Example: Monday breakups
2.4.2
Summary
2.5
Activity: Facial prototying
2.6
Activity: A preference for 7?
2.7
Activity: Gender and the U.S. Congress
2.8
Review activity: Racial disparities in police stops
2.9
Unit 2 summary
2.9.1
You should be able to
2.9.2
You should understand
2.9.3
TinkerPlots™ skills
2.9.4
Vocab
3
Experimental Variation and the Randomization Test
3.1
Activity: Comparing study designs
3.2
Random assignment and experimental variation
3.2.1
Comparing study designs
3.2.2
Experimental Variation
3.3
Activity: Memorization
3.3.1
Preliminary analysis and experimental variation
3.3.2
Statistical analysis
3.4
Activity: Sleep deprivation
3.5
Quantifying Results: p-Values
3.5.1
Adjustment for Simulation Results
3.5.2
p-Values as Evidence
3.5.3
Six Principles about p-Values
3.6
Activity: Contagious yawns
3.7
Internal Validity Evidence and Random Assignment
3.8
Activity: Strength shoe
3.8.1
Confounding variables
3.8.2
Random assignment
3.9
Review activity: Gender coding and expectations
3.10
Unit 3 summary
3.10.1
You should be able to
3.10.2
You should understand
3.10.3
TinkerPlots™ skills
3.10.4
Vocab
4
Sampling Variation and the Bootstrap Test
4.1
Sampling Variation
Bootstrapping
4.2
Activity: Speed skating
4.3
Activity: Crazy in love - Representative samples
4.4
External Validity Evidence and Random Sampling
Statistical Bias
4.5
Activity: Sample size exploration
4.6
Validity Evidence and Inferences
4.7
Activity: Validity evidence: Studies of Peanut Allergies
Study Design #1
Study Design #2
Study Design #3
4.8
Observational Studies and the Bootstrap Test
4.8.1
Drawing inferences from observational studies
4.8.2
Analyzing Data from Observational Studies
4.9
Activity: Murderous nurse
4.10
Summative activity: Movie sequels
5
Statistical estimation
5.1
Activity: Kissing the ‘right’ way
5.2
Compatibility intervals and margin of error
5.2.1
Quantification of Uncertainty: Margin of Error
5.3
Activity: Cuddling with dogs
5.4
Uncertainty and Bias
5.5
Activity: CEO compensation
5.6
Activity: Comparing cuddling preferences
5.7
Activity: Statistical estimates in the news
5.8
Activity: Comparing estimates from random samples
5.9
Extension activity: Estimating effect size
5.10
Summative activity: Montana YRBS
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Statistical Thinking: A Simulation Approach to Modeling Uncertainty (UM STAT 216 edition)
5.8
Activity: Comparing estimates from random samples
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