Continuous probability distributions

A density function is defined by: \begin{displaymath}
f(x,\mu,\sigma)=\frac{1}{\sqrt{2\pi}\sigma}\exp\bigg(-\frac{(x-\mu)^2}{2\sigma^2}\bigg), \label{r_normalny_fun}
\end{displaymath}

where:

$-\infty<x<+\infty$,

$\mu$ – an expected value of population (its measure is mean),

$\sigma$ – standard deviation.

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Normal distribution is a symmetrical distribution for a perpendicular line to axis of abscissae going through the points designating the mean, mode and median.

Normal distribution with a mean of $\mu=0$ and $\sigma=1$ ($N(0,1)$), is so called a standardised normal distribution.

A density function is defined by: \begin{displaymath}
f(x,df)=\frac{\Gamma(\frac{df+1}{2})}{\Gamma(\frac{df}{2})\sqrt{df\pi}}\left(1+\frac{x^2}{df}\right)^{-\frac{df+1}{2}},
\end{displaymath}

where:

$-\infty<x<+\infty$,

$df$ – degrees of freedom (sample size is decreased by the number of limitations in given calculations),

$\Gamma$ is a Gamma function.

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Density function is defined by: \begin{displaymath}
f(x,df)=\frac{1}{2^{\frac{df}{2}}\Gamma{\frac{df}{2}}}x^{\frac{df}{2}-1}e^{-\frac{x}{2}},
\end{displaymath}

where:

$x>0$,

$df$ – degrees of freedom (sample size is decreased by the number of limitations in given calculations),

$\Gamma$ is a Gamma function.

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A density function is defined by: \begin{displaymath}
F(x,df_1,df_2)=\frac{\sqrt{\frac{(df_1x)^{df_1}d_2^{df_2}}{(df_1x+df_2)^{df_1+df_2}}}}{xB\left(\frac{df_1}{2},\frac{df_2}{2}\right)},
\end{displaymath}

where:

$x>0$,

$df_1$, $df_1$ – degrees of freedom (it is assumed that if $X$ i $Y$ are independent with a $\chi^2$ distribution with adequately $df_1$ and $df_2$ degrees of freedom, than $F=\frac{X/df_1}{Y/df_2}$ has a F Snedecor distribution $F(df_1,df_2)$),

$B$ is a Beta function.

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