Appendices
Distributions
t
distribution
Statistical Analysis Using R: Online Notes
Introduction
Preface: Inference
1. Univariate Techniques
Univariate likelihood
Hypothesis tests
Estimator bias and variance
Exact confidence intervals
Identifying distributions
Coding it up in R
2. Useful Approximations
The Newton-Raphson method
Gradient descent
The Central Limit Theorem
The Student’s
t
distribution
Handy rules of thumb
Coding it up in R
3. Linear Regression
Simple regression
Multiple regression
Variance decomposition
Coding it up in R
4. Regression Robustness
Regression robustness
Heteroskedasticity
Autocorrelation
Non-normal errors
Non-linearity
Coding it up in R
5. Feature Engineering
Categorical predictors
Revisiting ANOVA
Interaction effects
Feature engineering
Coding it up in R
6. Model Selection
Overfitting
Predictor importance
Multiple comparisons
Model comparisons
Selection algorithms
7. Building GLMs
Generalizing regression
Linear predictors and link functions
Logistic regression
Building new GLMs
8. Assessing GLMs
Probit regression
GLM goodness-of-fit
Classification metrics
9. Count Regressions
Poisson models
Overdispersed models
Adjusting the zero counts
10. Polytomous Models
Multinomial regression
Ordinal regression
Coding it up in R
Appendices
Distributions
Bernoulli distribution
Binomial distribution
Chi-squared distribution
Exponential distribution
F
distribution
Geometric distribution
Negative binomial distribution
Normal distribution
Poisson distribution
t
distribution
Uniform distribution
Proofs
List of Symbols
Appendices
Distributions
t
distribution
t
distribution
Poisson distribution
Uniform distribution