6
Bogus
Lessons in Statistical Thinking
Preface
1-18 from ModernDive
1
Data frames
2
Introduction to data graphics
3
Reading R
4
Databases
5
Operations on variables
6
Bogus
6
Bogus
8
Tidy data
6
Bogus
10
Exploratory Data Analysis
11
Residuals
12
Regression modeling
6
Bogus
14
Graphics for regression models
15
Categorical response variables
16
Specialized regression
6
Bogus
18
Machine learning
Statistical Thinking
19
Variation
20
DAGs and simulation
21
Signal and noise
22
Sampling and sampling variation
23
Confidence intervals
24
Effect size
25
Mechanics of prediction
26
Constructing a prediction interval
27
Review of Lessons 19-26
28
Covariates
29
Covariates eat variance
30
Confounding
31
Spurious correlation
32
Experiment and random assignment
33
Measuring and accumulating risk
34
Constructing a classifier
35
Accounting for prevalence
36
Hypothesis testing
37
Calculating a p-value
38
Putting p-values in context
39
Review of Lessons 28-38
Appendices
Software used in the Lessons
Important word pairs
40
Project: Transcribing Data
6
Bogus
Foobar
5
Operations on variables
8
Tidy data