Random maths:
∑ i = 1 n a i μ i {\displaystyle \sum _{i=1}^{n}a_{i}\mu _{i}}
∑ i = 1 n ∑ j = 1 m a i b j Cov ( Y i , X j ) {\displaystyle \sum _{i=1}^{n}\sum _{j=1}^{m}a_{i}b_{j}\operatorname {Cov} (Y_{i},X_{j})}
n ( r N ) ( N − r N ) ( N − n N − 1 ) {\displaystyle n\left({\frac {r}{N}}\right)\left({\frac {N-r}{N}}\right)\left({\frac {N-n}{N-1}}\right)}
E ( g ( Y 1 ) | Y 2 = y 2 ) = ∫ − ∞ ∞ g ( y 1 ) f ( y 1 | y 2 ) d y 1 {\displaystyle \operatorname {E} (g(Y_{1})|Y_{2}=y_{2})=\int _{-\infty }^{\infty }g(y_{1})f(y_{1}|y_{2})dy_{1}}
∫ − ∞ ∞ x n ! ( n − 1 ) ! ( 1 − F ( x ) ) n − 1 f ( x ) d x = ∫ − ∞ ∞ x f ( x ) d x {\displaystyle \int _{-\infty }^{\infty }x{\frac {n!}{(n-1)!}}(1-F(x))^{n-1}f(x)dx=\int _{-\infty }^{\infty }xf(x)dx}
( n − 1 ) S 2 σ 2 {\displaystyle {\frac {(n-1)S^{2}}{\sigma ^{2}}}} S 2 {\displaystyle S^{2}} X 2 {\displaystyle \mathrm {X} ^{2}} μ {\displaystyle \mu } σ 2 {\displaystyle \sigma ^{2}}
n ( Y ¯ − μ S ) {\displaystyle {\sqrt {n}}\left({\frac {{\bar {Y}}-\mu }{S}}\right)}
( X ¯ − Y ¯ ) − ( μ 1 − μ 2 ) σ 1 2 n 1 + σ 2 2 n 2 {\displaystyle {\frac {\left({\bar {X}}-{\bar {Y}}\right)-\left(\mu _{1}-\mu _{2}\right)}{\sqrt {{\frac {\sigma _{1}^{2}}{n_{1}}}+{\frac {\sigma _{2}^{2}}{n_{2}}}}}}}
( X ¯ − Y ¯ ) − ( μ 1 − μ 2 ) S p 2 n 1 + S p 2 n 2 {\displaystyle {\frac {\left({\bar {X}}-{\bar {Y}}\right)-\left(\mu _{1}-\mu _{2}\right)}{\sqrt {{\frac {S_{p}^{2}}{n_{1}}}+{\frac {S_{p}^{2}}{n_{2}}}}}}}
Y ¯ {\displaystyle {\bar {Y}}}
N ( μ , σ 2 ) {\displaystyle N(\mu ,\sigma ^{2})}
N ( μ 1 , σ 1 2 ) {\displaystyle N(\mu _{1},\sigma _{1}^{2})} N ( μ 2 , σ 2 2 ) {\displaystyle N(\mu _{2},\sigma _{2}^{2})}