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The parameters of a distribution are variables that are included in an example’s density function so that the distribution can be adapted to a variety of situations. There are many differen

 

The parameters of a distribution are variables that are included in an example’s density function so that the distribution can be adapted to a variety of situations. There are many different parameters of a distribution, but of greatest importance are the parameters we outline below:

2 Parameters: The two parameters determine the average and standard deviation of the distribution. Such distributions are represented as a point on a skewness-kurtosis plot as they have fixed values of the skewness and kurtosis. Examples are the exponential, normal and uniform distributions.

3 Parameters: The three parameters determine the average, standard deviation and skewness of the distribution. Such distributions are represented as a curve on a skewness-kurtosis plot as the kurtosis depends of the skewness. Examples are the gamma and log-normal distributions.

4 Parameters: The four parameters determine the average, standard deviation, skewness and kurtosis of the distribution. Such distributions are represented as a region on a skewness-kurtosis plot as they can take on a variety of skewness and kurtosis values. Examples are the beta, Johnson and Pearson distributions

See? Not so bad, right? We’re gonna magically dance our way through this material, no sweat

What are some practical examples where each of the four parameters might be used?

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    The post The parameters of a distribution are variables that are included in an example’s density function so that the distribution can be adapted to a variety of situations. There are many differen first appeared on Writeden.

    The parameters of a distribution are variables that are included in an example’s density function so that the distribution can be adapted to a variety of situations. There are many differen
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