Estadísticas - Fórmulas

A continuación se muestra la lista de fórmulas de estadísticas utilizadas en los tutoriales de estadísticas de Tutorialspoint. Cada fórmula está vinculada a una página web que describe cómo usar la fórmula.

UN

• Adjusted R-Squared - $ {R_ {adj} ^ 2 = 1 - [\ frac {(1-R ^ 2) (n-1)} {nk-1}]} $

• Arithmetic Mean - $ \ bar {x} = \ frac {_ {\ sum {x}}} {N} $

• Arithmetic Median - Mediana = Valor de $ \ frac {N + 1} {2}) ^ {th} \ item $

• Arithmetic Range - $ {Coeficiente \ de \ Rango = \ frac {LS} {L + S}} $

segundo

• Best Point Estimation - $ {MLE = \ frac {S} {T}} $

• Binomial Distribution - $ {P (Xx)} = ^ {n} {C_x} {Q ^ {nx}}. {P ^ x} $

C

• Chebyshev's Theorem - $ {1- \ frac {1} {k ^ 2}} $

• Circular Permutation - $ {P_n = (n-1)!} $

• Cohen's kappa coefficient - $ {k = \ frac {p_0 - p_e} {1-p_e} = 1 - \ frac {1-p_o} {1-p_e}} $

• Combination - $ {C (n, r) = \ frac {n!} {R! (Nr)!}} $

• Combination with replacement - $ {^ nC_r = \ frac {(n + r-1)!} {r! (n-1)!}} $

• Continuous Uniform Distribution - f (x) = $ \ begin {cases} 1 / (ba), & \ text {cuando $ a \ le x \ le b $} \\ 0, & \ text {cuando $ x \ lt a $ o $ x \ gt b $} \ end {casos} $

• Coefficient of Variation - $ {CV = \ frac {\ sigma} {X} \ times 100} $

• Correlation Co-efficient - $ {r = \ frac {N \ sum xy - (\ sum x) (\ sum y)} {\ sqrt {[N \ sum x ^ 2 - (\ sum x) ^ 2] [N \ sum y ^ 2 - (\ sum y) ^ 2]}}} $

• Cumulative Poisson Distribution - $ {F (x, \ lambda) = \ sum_ {k = 0} ^ x \ frac {e ^ {- \ lambda} \ lambda ^ x} {k!}} $

re

• Deciles Statistics- $ {D_i = l + \ frac {h} {f} (\ frac {iN} {10} - c); i = 1,2,3 ..., 9} $

• Deciles Statistics- $ {D_i = l + \ frac {h} {f} (\ frac {iN} {10} - c); i = 1,2,3 ..., 9} $

F

• Factorial- $ {n! = 1 \ times 2 \ times 3 ... \ times n} $

GRAMO

• Geometric Mean - $ GM = \ sqrt [n] {x_1x_2x_3 ... x_n} $

• Geometric Probability Distribution - $ {P (X = x) = p \ veces q ^ {x-1}} $

• Grand Mean - $ {X_ {GM} = \ frac {\ sum x} {N}} $

H

• Harmonic Mean - $ HM = \ frac {W} {\ sum (\ frac {W} {X})} $

• Harmonic Mean - $ HM = \ frac {W} {\ sum (\ frac {W} {X})} $

• Hypergeometric Distribution - $ {h (x; N, n, K) = \ frac {[C (k, x)] [C (Nk, nx)]} {C (N, n)}} $

yo

• Interval Estimation - $ {\ mu = \ bar x \ pm Z _ {\ frac {\ alpha} {2}} \ frac {\ sigma} {\ sqrt n}} $

L

• Logistic Regression - $ {\ pi (x) = \ frac {e ^ {\ alpha + \ beta x}} {1 + e ^ {\ alpha + \ beta x}}} $

METRO

• Mean Deviation - $ {MD} = \ frac {1} {N} \ sum {| XA |} = \ frac {\ sum {| D |}} {N} $

• Mean Difference - $ {Media \ Diferencia = \ frac {\ sum x_1} {n} - \ frac {\ sum x_2} {n}} $

• Multinomial Distribution - $ {P_r = \ frac {n!} {(N_1!) (N_2!) ... (n_x!)} {P_1} ^ {n_1} {P_2} ^ {n_2} ... {P_x} ^ { n_x}} $

norte

• Negative Binomial Distribution - $ {f (x) = P (X = x) = (x-1r-1) (1-p) x-rpr} $

• Normal Distribution - $ {y = \ frac {1} {\ sqrt {2 \ pi}} e ^ {\ frac {- (x - \ mu) ^ 2} {2 \ sigma}}} $

O

• One Proportion Z Test - $ {z = \ frac {\ hat p -p_o} {\ sqrt {\ frac {p_o (1-p_o)} {n}}}} $

PAGS

• Permutation - $ {{^ nP_r = \ frac {n!} {(nr)!}} $

• Permutation with Replacement - $ {^ nP_r = n ^ r} $

• Poisson Distribution - $ {P (Xx)} = {e ^ {- m}}. \ Frac {m ^ x} {x!} $

• probability - $ {P (A) = \ frac {Número \ de \ casos \ favorables} {Número \ total \ de \ casos \ igualmente \ probables} = \ frac {m} {n}} $

• Probability Additive Theorem - $ {P (A \ o \ B) = P (A) + P (B) \\ [7pt] P (A \ cup B) = P (A) + P (B)} $

• Probability Multiplicative Theorem - $ {P (A \ y \ B) = P (A) \ veces P (B) \\ [7pt] P (AB) = P (A) \ veces P (B)} $

• Probability Bayes Theorem - $ {P (A_i / B) = \ frac {P (A_i) \ times P (B / A_i)} {\ sum_ {i = 1} ^ k P (A_i) \ times P (B / A_i)}} PS

• Probability Density Function - $ {P (a \ le X \ le b) = \ int_a ^ bf (x) d_x} $

R

• Reliability Coefficient - $ {Fiabilidad \ Coeficiente, \ RC = (\ frac {N} {(N-1)}) \ times (\ frac {(Total \ Varianza \ - Suma \ de \ Varianza)} {Varianza total})} $

• Residual Sum of Squares - $ {RSS = \ sum_ {i = 0} ^ n (\ epsilon_i) ^ 2 = \ sum_ {i = 0} ^ n (y_i - (\ alpha + \ beta x_i)) ^ 2} $

S

• Shannon Wiener Diversity Index - $ {H = \ sum [(p_i) \ times ln (p_i)]} $

• Standard Deviation - $ \ sigma = \ sqrt {\ frac {\ sum_ {i = 1} ^ n {(x- \ bar x) ^ 2}} {N-1}} $

• Standard Error ( SE ) - $ SE_ \ bar {x} = \ frac {s} {\ sqrt {n}} $

• Sum of Square - $ {Suma \ de \ cuadrados \ = \ sum (x_i - \ bar x) ^ 2} $

T

• Trimmed Mean - $ \ mu = \ frac {\ sum {X_i}} {n} $