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

Applied Biostats – BIOL3272 UMN – Fall 2022

Chapter 16 Project 2

Please work on your null hypothesis significance testing project – rubric here.