Applied Biostats
Preface
1 Introduction to Stats
I Introduction to R
2 Day one of R and RStudio
3 Handling data in R
4 Collecting and storing data
II Describing and visualizing data
5 Visualizing data in R – An intro to ggplot
6 Data presentation and visualization. Goals, considerations, and best practices
7 Making betteR figures
8 Summarizing data
9 Review of R and New tricks
Potential Datasets For Projects
10 Sampling
11 Uncertainty
12 First Major Assignment
13 Review and reflect on the most important stats concepts we have learned so far
- 13.1 Questions to consider
III Probability and hypothesis testing
14 Probabilistic thinking
15 Simulate for Probabilistic thinking
16 Hypothesis Testing
17 Shuffling labels to generate a null
18 Associations between continuous variables
19 Shuffling two categorical variables
20 Reflect Review and Relax
21 Normal distribution
22 Samples from a normal distribution
23 Linear Models
24 Two samples from normal distributions
25 More than two samples from normal distributions
26 Predicting one continuous variable from another
27 Predicting one continuous variable from two (or more) things
28 More considerations when predicting one continuous variable from two (or more) things
29 Review + Fun data sets if you need them
- 29.1 Review
- 29.2 Quiz
Potential Datasets For Projects
IV Big picture – what is are we trying to do?
30 Designing scientific studies
31 Break
32 Causal Inference
33 Bias and bad science
V Beyond the standard linear model
34 Likelihood based inference
35 Analyzing proportions
36 Generalized linear models I: Yes/No
37 Generalized linear models II: Count Data
38 Random effects
39 Mixed models
References
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Applied Biostats – BIOL3272 UMN – Fall 2022

Chapter 12 First Major Assignment

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