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Concepts
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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
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ADaM
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Hot-dog Regression
Mambo_5_Bayesian
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{manim}
Books I'm currently reading through
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Python
import numpy np.random.normal(0, 1, 1000)