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Applied Biostatistics
Preface
Motivating biology and datasets
Types of Variables
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SECTION I: Intro to R
1. Getting started with R
• 1. R tips
• 1. Functions
• 1. Vectors and other objects
• 1. Variable assignment
• 1. R Packages
• 1. R Scripts
• 1. Getting started summary
2. Introduction to ggplot
• 2. A continuous variable
• 2. Saving a ggplot
• 2. Continuous by/categorical x
• 2. Two categorical variables
• 2. Two continuous variables
• 2. Many explanatory vars
• 2. ggplot summary
3. Reproducible Science
• 3. Collecting data
• 3. Storing data
• 3. Loading data
• 3. Check & prep data
• 3. Reproducible analyses
• 3. Reproducibility summary
4. Data in R
• 4. Checking Data Review
• 4. Modifying columns
• 4. Choose rows
• 4. Data in R: Code
• 4. Data in R summary
5. Simple Summaries
• 5. Summarizing shape
• 5. Changing shape
• 5. Summarizing the center
• 5. Summarizing variability
• 5. Summarizing Data: Code
• 5. Summarizing summary
6. Associations: Part I
• 6. Two categorical vars
• 6. Categorical + numeric
• 6. Associations I: Script
• 6. Association Summary: I
7. Associations: Part II
• 7. Revisiting two cats
• 7. Two numeric vars
• 7. Associations II: Script
• 7. Association II Summary
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Section II: Stats Foundations
8. Intro to Sampling
• 8. Sampling
• 8. Sampling Error
• 8. Sampling Bias
• 8. Non-independence
• 8. Sampling Better
• 8. Sampling summary
9. Uncertainty
• 9. Bootstrap
• 9. Confidence Intervals
• 9. Bootstrapping w/
infer
• 9. Gotchas
• 9. Bootstrap script
• 9. Uncertainty summary
10. Null Hypothesis Significance Testing
• 10. Statistical Hypotheses
• 10. P-Values
• 10. Statistical Significance
• 10. Considerations for NHST
• 10. NHST summary
11. Shuffling
• 11. The frogs
• 11. Permute
• 11. Structured Permutation
• 11. Shuffling summary
12. Study Design
• 12. What do you want to know?
• 12. Threats to Validity
• 12. Experimental Design
• 12. Power and Precision
• 12. Observational studies
• 12. Study design summary
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Section III: Linear Models
13. Linear Models
• 13. The Mean as
lm(y ~ 1)
• 13. Residuals
• 13. Categorical predictor
• 13. Linear regression
• 13. Two predictors
• 13. Linear model summary
14. Normal distribution
• 14. Normal Introduction
• 14. Normal Properties
• 14. Normal Math
• 14. Is It Normal?
• 14. The Normal is Common
• 14. Make It Normal
• 14. Normal Summary
Optional: Simulating from the Normal Distribution
15. The t distribution
• 15. t: Example Data
• 15. Data summaries for t
• 15. Assumptions of t
• 15. Uncertain-t
• 15. One sample t-test
• 15. One sample t-test in R
• 15. The paired t-test
• 15. t Summary
16. Comparing two means
• 16. Visualizing two groups
• 16. Two-t Assumptions
• 16. Two-t Calculations
• 16. Uncertain-2t
• 16. Two-sample t-test
• 16. Two t Summary
17. F this!
• 17. F the ratio of variance
• 17. F Calculations
• 17. F and ANOVA in R
• 17.
F
-inding connec
t
ions
• 17. F (ANOVA) summary
18. >2 Groups
• 18. Multiple testing problem
• 18. ANOVA is a linear model
• 18. ANOVA assumptions
• 18. ANOVA Example
• 18. Post hoc tests
• 18. Significance groups
• 18. R ANOVA pipeline
• 18. ANOVA summary
19. Regression
• 19. Regression summaries
• 19. Regression: a linear model
• 19. Regression assumptions
• 19. Regression: Uncertainty
• 19. Regression: NHST
• 19. Regression caveats
• 19. Regression: R workflow
• 19. Regression Summary
20. Multiple Predictors
• 20. Extending linear models
• 20. Visualizing >1 predictor
• 20. Multiple regression assumptions
• 20. Parsing effect sizes
• 20. P-values & NHST
• 20. Uncertainty
• 20. Multiple Regression in R
• 20. Multiple regression summary
21. Interactions
• 21. Interactions in linear models
• 21. Significant Interactions
• 21. Quantifying Interactions
• 21. Interactions “gotchas”
• 21. BONUS: Polynomial regression
• 21. Interactions summary
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Additional Resources
Supplements
• Supplement: Effect Sizes
References
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Types of Variables
SECTION I: Intro to R