Probabilistic Data Science for Psychology
Deutsch
Psychometric procedures
Welcome
Mathematical foundations
1
Language and logic
2
Sets
3
Sums, products, powers
4
Functions
5
Sequences, limits, continuity
6
Differential calculus
7
Integral calculus
8
Vectors
9
Matrices
10
Descriptive statistics
Imperative programming
11
Basic concepts of computer science
12
Arithmetic and variables
13
Data structures
14
Vectors
15
Matrices and arrays
16
Lists, dataframes, and tibbles
17
Data management
18
Control structures
Probability theory
19
Probability spaces
20
Elementary probabilities
21
Random variables
22
Random vectors
23
Expectations
24
Variances
25
Covariances
26
Inequalities
27
Limits
28
Transformations
29
Normal distributions
Inference
30
Frequentist inference
31
Point estimation
32
Confidence intervals
33
Hypothesis tests
General linear model
34
Regression
35
Correlation
36
Model formulation
37
Parameter estimation
38
T-statistics
39
F-statistics
40
T-tests
41
One-way analysis of variance
42
Two-way analysis of variance
43
Partial correlation
44
Multiple regression
Multivariate inference
45
Multivariate descriptive statistics
46
One-sample T
\(^2\)
tests
47
One-way analysis of variance
48
Canonical correlation analysis
Psychometric procedures
49
Psychological tests
50
Classical test theory
51
Factor Analysis
References
Psychometric procedures
48
Canonical correlation analysis
49
Psychological tests