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  • https://stats.libretexts.org/Workbench/Introduction_to_Statistical_Methods_(Yuba_College)/06%3A_Discrete_Probability_Distributions/6.05%3A_Chapter_6_Formulas
    Discrete Distribution Variance: σ 2 = ∑(x i 2 ∙P(x i )) – μ 2 Geometric Distribution: P(X = x) = p ∙ q (x – 1) , x = 1, 2, 3, … Binomial Distribution: P(X = x) = n C x ·p x ·q (n-x ) , x = 0, 1, 2, … ...Discrete Distribution Variance: σ 2 = ∑(x i 2 ∙P(x i )) – μ 2 Geometric Distribution: P(X = x) = p ∙ q (x – 1) , x = 1, 2, 3, … Binomial Distribution: P(X = x) = n C x ·p x ·q (n-x ) , x = 0, 1, 2, … , n Hypergeometric Distribution: P(X = x) = \(\frac{a C_{x} \cdot {}_b C_{n-x}}{ _{N} C_{n}}\) Unit Change for Poisson Distribution: New μ = old μ(\(\frac{\text { new units }}{\text { old units }}\)) Poisson Distribution: P(X = x) = \(\frac{e^{-\mu} \mu^{x}}{x !}\)
  • https://stats.libretexts.org/Workbench/Statistics_for_Behavioral_Science_Majors/04%3A_Discrete_Probability_Distributions/4.06%3A_Discrete_Probability_Formulas
    Discrete Distribution Mean: μ = Σ(x i ∙ P(x i )) Discrete Distribution Variance: σ 2 = ∑(x i 2 ∙P(x i )) – μ 2 Geometric Distribution: P(X = x) = p ∙ q (x – 1) , x = 1, 2, 3, … Binomial Distribution: ...Discrete Distribution Mean: μ = Σ(x i ∙ P(x i )) Discrete Distribution Variance: σ 2 = ∑(x i 2 ∙P(x i )) – μ 2 Geometric Distribution: P(X = x) = p ∙ q (x – 1) , x = 1, 2, 3, … Binomial Distribution: P(X = x) = n C x ·p x ·q (n-x ) , x = 0, 1, 2, … , n Hypergeometric Distribution: P(X = x) = \(\frac{a C_{x} \cdot {}_b C_{n-x}}{ _{N} C_{n}}\) Poisson Distribution: P(X = x) = \(\frac{e^{-\mu} \mu^{x}}{x !}\)

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