📌 Descriptive Statistics:
⦁ Mean = (Σx) / n
⦁ Median = Middle value
⦁ Mode = Most frequent value
⦁ Variance (σ²) = Σ(x - μ)² / n
⦁ Std Dev (σ) = √Variance
⦁ Range = Max - Min
⦁ IQR = Q3 - Q1
📌 Probability Basics:
⦁ P(A) = Outcomes A / Total Outcomes
⦁ P(A ∩ B) = P(A) × P(B) (if independent)
⦁ P(A ∪ B) = P(A) + P(B) - P(A ∩ B)
⦁ Conditional: P(A|B) = P(A ∩ B) / P(B)
⦁ Bayes’ Theorem: P(A|B) = [P(B|A) × P(A)] / P(B)
📌 Common Distributions:
⦁ Binomial (fixed trials)
⦁ Normal (bell curve)
⦁ Poisson (rare events over time)
⦁ Uniform (equal probability)
📌 Inferential Stats:
⦁ Z-score = (x - μ) / σ
⦁ Central Limit Theorem: sampling dist ≈ Normal
⦁ Confidence Interval: CI = x ± z*(σ/√n)
📌 Hypothesis Testing:
⦁ H₀ = No effect; H₁ = Effect present
⦁ p-value < α → Reject H₀
⦁ Tests: t-test (small samples), z-test (known σ), chi-square (categorical data)
📌 Correlation:
⦁ Pearson: linear relation (–1 to 1)
⦁ Spearman: rank-based correlation
🧪 Tools to Practice:
Python packages:
scipy.stats, statsmodels, pandas Visualization:
seaborn, matplotlib💡 Quick tip: Use these formulas to crush interviews and build solid ML foundations!
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