A Fresh Approach: Computational Technique
Preface: Updating Computational Technique
The story in more detail
The story continues …
1
Introduction
1.1
The Choice of Software
1.2
The R Command Console
1.3
Invoking an Operation
1.3.1
Naming and Storing Values
1.3.2
Assignment vs Algebra
1.3.3
Connecting Computations
1.3.4
Numbers and Arithmetic
1.3.5
Aside: Complex numbers
1.3.6
Types of Objects
2
Data in R
2.1
Preliminaries
2.2
Reading Tabular Data into R
2.3
Data Frames
2.4
Variables in Data Frames
2.5
Adding a New Variable
2.6
Sampling from a Sample Frame
3
Describing Variation
3.1
Simple Statistical Calculations
3.1.1
Aside: The “base” versions
3.2
Simple Statistical Graphics
3.2.1
Histograms and Distributions
3.2.2
Density Plots
3.2.3
Box-and-Whisker Plots
3.3
Displays of Categorical Variables
4
Groupwise models
4.1
Model Values and Residuals
5
Confidence intervals
5.1
Finding a Sampling Distribution through Bootstrapping
5.2
Computing Grade-Point Averages
6
Language of models
6.1
Bi-variate Plots
6.1.1
Quantitative Explanatory Variable
6.1.2
Categorical Explanatory Variable
6.1.3
Multiple Explanatory Variables
6.2
Fitting Models and Finding Model Values
6.2.1
Interactions and Main Effects
6.2.2
Transformation Terms
7
Model formulas and coefficients
7.1
Examining model coefficients
7.2
Other Useful Operators
8
Fitting models to data
8.1
Sums of Squares
8.2
Redundancy
9
Correlation and partitioning of variation
10
Total and partial relationships
10.1
Adjustment
11
Modeling randomness
11.1
Random Draws from Probability Models
11.2
Standard Probability Models
11.3
Quantiles and Coverage Intervals
11.4
Percentiles
12
Confidence in models
12.1
Confidence Intervals from Standard Errors
12.2
Bootstrapping Confidence Intervals
12.3
Prediction Confidence Intervals
13
The logic of hypothesis testing
14
Testing whole models
14.1
The Permutation Test
14.2
First-Principle Tests
15
Testing parts of models
15.1
ANOVA reports
15.2
Non-Parametric Statistics
16
Models of Yes/No Variables
16.1
Fitting Logistic Models
16.2
Fitted Model Values
16.3
Which Level is “Yes”?
16.4
Analysis of Deviance
17
Causation
18
Experiment
Statistical Modeling: A Fresh Approach
Statistical Modeling: Computational Technique
Chapter 17
Causation