Statistical Analysis та статистичний аналіз

🎓 Hypothesis Testing

t-test

Types: One-sample, two-sample, paired.

Application: For means comparison.

Assumptions: Normal distribution, equal variances.

Chi-square Test

Types: Goodness of fit, independence.

Application: For categorical data.

Goal: To verify associations.

ANOVA

Concept: Analysis of Variance.

Application: For comparing means of multiple groups.

Types: One-way, two-way ANOVA.

🔧 Regression

Linear Regression

Concept: Linear dependency between features and target.

Estimation: OLS (Ordinary Least Squares).

Application: For continuous targets.

Logistic Regression

Concept: For binary classification.

Estimation: Maximum Likelihood.

Application: For probabilities.

Multiple Regression

Concept: Multiple features for prediction.

Advantages: Considers multiple factors.

Application: For complex relationships.

📚 Practical Examples

Example 1: t-test for A/B testing

Hypotheses: Define null and alternative.

t-test: Perform a two-sample t-test.

Solution: Interpret the p-value, make a decision.

Example 2: Linear Regression

Model: Identify features and target.

Fitting: Train a linear regression.

Evaluation: Evaluate R², p-values of coefficients.

Try it live

Everything above runs in your browser — open Dimensionality Reduction: PCA, t-SNE & UMAP and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.