Pak Patch
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Pak Patch
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Code
Draw from a distribution
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Concepts
Advice
Affine function
Autocorrelation
Bayes formula
Bias - Types
Central limit theorem
Column space
Data leakage
Distribution - Beta
Distribution - Binomial
Distribution - Gamma
Distribution - Joint
Distribution - Normal
Distribution - Uniform
Distribution- Exponential
Dual space
Error
Feature engineering
Interactions
Least squares estimates
Linear projections
Linear regression
Loss functions
Marginalization
Matrix types
Mixed effect model
Model selection
Multiplication rule (probability)
Negative control outcomes
Nullspace
Overfitting
Polynomial (linear regression)
Principal component analysis
Residuals
ROC (Receiver operating characteristic)
Singular value decomposition
SMOTE (Synthetic minority oversampling technique)
Stepwise variable selection
Supervised learning
Test - Durbin-Watson
Test - Chow
Test - Cohen's D
Theorem - Frisch-Waugh-Lovell
Theorem - Gauss-Markov
Transformation - Arcsine
Transformation - Logit
Transformation- Box-Cox
Transformation
Industry-specific
ADaM
EHR (electronic health records)
Projects
Hot-dog Regression
Mambo_5_Bayesian
Tech
Classes
Linked lists
Object-tracking
R packages
Web-scraping
Workflow
Wrangling (Code)
{manim}
Books I'm currently reading through
Home Page
Likelihood
Theorem - Gauss-Markov
An important theorem for
Linear regression
.
Main takeaway