INDEX
Foundations
- Bayes' Theorem
- Bayesian Statistics
- Bernoulli Distribution
- Beta Distribution
- Bias-Variance Trade-off
- Binomial Distribution
- Central Limit Theorem (CLT)
- Chi-Square Distribution
- Correlation vs Causation
- Derivatives & Gradients
- Descriptive Statistics
- Eigenvalues & Eigenvectors
- Exponential Distribution
- F-Distribution
- Factor Analysis (EFA & CFA)
- Gamma Distribution
- Law of Large Numbers
- Log Transformations
- Matrix Multiplication
- Maximum Likelihood Estimation (MLE)
- Normal Distribution
- Poisson Distribution
- Standard Deviation
- Standard Error
- T-Distribution
- Uniform Distribution
Hypothesis Testing
- A-B Testing
- Bonferroni Correction
- Chi-Square Test of Independence
- Confidence Intervals
- Degrees of Freedom
- Effect Size Measures
- Factorial Design (2k)
- Fisher's Exact Test
- Hypothesis Testing (P-Value & CI)
- Kendall's Tau
- Kruskal-Wallis Test
- Levene's Test
- Mann-Whitney U Test
- MANOVA
- McNemar's Test
- Mixed ANOVA (Between-Within)
- One-Sample t-test
- One-Way ANOVA
- Pearson Correlation
- Power Analysis
- Repeated Measures ANOVA
- Shapiro-Wilk Test
- Spearman's Rank Correlation
- Student's T-Test
- Tukey's HSD
- Two-Way ANOVA
- Type I & Type II Errors
- Welch's ANOVA
- Welch's T-Test
- Wilcoxon Signed-Rank Test
- Z-Test
Regression Analysis
- Binary Logistic Regression
- Breusch-Pagan Test
- Cook's Distance
- Hosmer-Lemeshow Test
- Lasso Regression
- Leverage (Hat Matrix)
- Logistic Regression
- Multinomial Logistic Regression (MNLogit)
- Multiple Linear Regression
- Negative Binomial Regression
- Outlier Analysis (Standardized Residuals)
- Poisson Regression
- Probit Regression
- Quantile Regression
- Ramsey RESET Test
- Regression Discontinuity Design (RDD)
- Regularization
- Ridge Regression
- Simple Linear Regression
- VIF (Variance Inflation Factor)
- Weighted Least Squares (WLS)
- White Test
- Zero-Inflated Models
Machine Learning
- Bayesian Statistics via Probabilistic Programming
- Bootstrap Methods
- Confusion Matrix
- Cross-Validation
- Decision Tree
- Gradient Boosting (XGBoost)
- Gradient Descent
- K-Means Clustering
- K-Nearest Neighbors (KNN)
- Model Evaluation Metrics
- Naive Bayes
- Overfitting & Underfitting
- Principal Component Analysis (PCA)
- Probabilistic Programming
- Random Forest
- ROC & AUC
- Support Vector Machines (SVM)
- Train-Test Split
Time Series
- ARIMA Models
- Auto-Correlation (ACF & PACF)
- Breusch-Godfrey Test
- Durbin-Watson Test
- GARCH Models
- Granger Causality
- Stationarity (ADF & KPSS)
- Vector Autoregression (VAR)