통계-공식
다음은 Tutorialspoint 통계 자습서에서 사용되는 통계 공식 목록입니다. 각 공식은 공식 사용 방법을 설명하는 웹 페이지에 연결됩니다.
ㅏ
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 -중앙값 = $ \ frac {N + 1} {2}) ^ {th} \ 항목 $의 값
Arithmetic Range -$ {계수 \ of \ 범위 = \ frac {LS} {L + S}} $
비
Best Point Estimation -$ {MLE = \ frac {S} {T}} $
Binomial Distribution -$ {P (Xx)} = ^ {n} {C_x} {Q ^ {nx}}. {p ^ x} $
씨
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 {when $ a \ le x \ le b $} \\ 0, & \ text {when $ x \ lt a $ 또는 $ x \ gt b $} \ end {cases} $
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!}} $
디
Deciles Statistics-$ {D_i = l + \ frac {h} {f} (\ frac {iN} {10}-c); 나는 = 1,2,3 ..., 9} $
Deciles Statistics-$ {D_i = l + \ frac {h} {f} (\ frac {iN} {10}-c); 나는 = 1,2,3 ..., 9} $
에프
Factorial-$ {n! = 1 \ times 2 \ times 3 ... \ times n} $
지
Geometric Mean -$ GM = \ sqrt [n] {x_1x_2x_3 ... x_n} $
Geometric Probability Distribution -$ {P (X = x) = p \ times 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)}} $
나는
Interval Estimation -$ {\ mu = \ bar x \ pm Z _ {\ frac {\ alpha} {2}} \ frac {\ sigma} {\ sqrt n}} $
엘
Logistic Regression -$ {\ pi (x) = \ frac {e ^ {\ alpha + \ beta x}} {1 + e ^ {\ alpha + \ beta x}}} $
미디엄
Mean Deviation -$ {MD} = \ frac {1} {N} \ sum {| XA |} = \ frac {\ sum {| D |}} {N} $
Mean Difference -$ {평균 \ 차이 = \ 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}} $
엔
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}}} $
영형
One Proportion Z Test -$ {z = \ frac {\ hat p -p_o} {\ sqrt {\ frac {p_o (1-p_o)} {n}}}} $
피
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 {Number \ of \ favourable \ cases} {Total \ number \ of \ same \ possible \ cases} = \ frac {m} {n}} $
Probability Additive Theorem -$ {P (A \ 또는 \ B) = P (A) + P (B) \\ [7pt] P (A \ 컵 B) = P (A) + P (B)} $
Probability Multiplicative Theorem -$ {P (A \ and \ B) = P (A) \ times P (B) \\ [7pt] P (AB) = P (A) \ times 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)}} $
Probability Density Function -$ {P (a \ le X \ le b) = \ int_a ^ bf (x) d_x} $
아르 자형
Reliability Coefficient -$ {신뢰성 \ 계수, \ RC = (\ frac {N} {(N-1)}) \ times (\ frac {(Total \ Variance \-Sum \ of \ Variance)} {Total Variance})} $
Residual Sum of Squares -$ {RSS = \ sum_ {i = 0} ^ n (\ epsilon_i) ^ 2 = \ sum_ {i = 0} ^ n (y_i-(\ alpha + \ beta x_i)) ^ 2} $
에스
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 -$ {Sum \ of \ Squares \ = \ sum (x_i-\ bar x) ^ 2} $
티
Trimmed Mean -$ \ mu = \ frac {\ sum {X_i}} {n} $