Mean : μ = ∑ x i n μ = n ∑ x i
Median : Middle value in a sorted list.
Mode : Most frequent value in a dataset.
Variance : σ 2 = ∑ ( x i − μ ) 2 n σ 2 = n ∑ ( x i − μ ) 2
Standard Deviation : σ = σ 2 σ = σ 2
Covariance : Cov ( X , Y ) = ∑ ( x i − μ x ) ( y i − μ y ) n Cov ( X , Y ) = n ∑ ( x i − μ x ) ( y i − μ y )
Correlation Coefficient : r = Cov ( X , Y ) σ x σ y r = σ x σ y Cov ( X , Y )
Probability : P ( A ) = Number of favorable outcomes Total number of outcomes P ( A ) = Total number of outcomes Number of favorable outcomes
Conditional Probability : P ( A ∣ B ) = P ( A ∩ B ) P ( B ) P ( A ∣ B ) = P ( B ) P ( A ∩ B )
Bayes' Theorem : P ( A ∣ B ) = P ( B ∣ A ) P ( A ) P ( B ) P ( A ∣ B ) = P ( B ) P ( B ∣ A ) P ( A )
Binomial Distribution : P ( X = k ) = ( n k ) p k ( 1 − p ) n − k P ( X = k ) = ( k n ) p k ( 1 − p ) n − k
Poisson Distribution : P ( X = k ) = λ k e − λ k ! P ( X = k ) = k ! λ k e − λ
Normal Distribution : f ( x ) = 1 σ 2 Ï€ e − ( x − μ ) 2 2 σ 2 f ( x ) = σ 2 Ï€ 1 e − 2 σ 2 ( x − μ ) 2
Z-Score : z = x − μ σ z = σ x − μ
Chi-Square Test : χ 2 = ∑ ( O i − E i ) 2 E i χ 2 = ∑ E i ( O i − E i ) 2
T-Test : t = x ˉ − μ s / n t = s / n x ˉ − μ
F-Test : F = σ 1 2 σ 2 2 F = σ 2 2 σ 1 2
Regression Line : y = m x + b y = m x + b
Coefficient of Determination : R 2 = Explained Variation Total Variation R 2 = Total Variation Explained Variation
Standard Error : S E = σ n SE = n σ
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