# Create a distribution plot with varying parameters

In the Distribution Plot dialog box, specify the distribution and parameters for your graph. You can enter up to 3 different values for each parameter.

## Binomial

Complete the following steps to enter the parameters for the binomial distribution. You can enter up to 3 values for each parameter.
1. In Number of trials, enter sample sizes.
2. In Event probabilities, enter numbers between 0 and 1 for each probability.

## Chi-square

In Degrees of freedom values, enter the degrees of freedom to specify each chi-square distribution. For more information, go to Chi-square distribution.

## Exponential

Complete the following steps to enter the parameters for the exponential distribution.
1. In Scales, enter the scale parameter for each distribution. The scale parameter equals the mean when the threshold parameter equals 0.
2. In Thresholds, enter the lower bounds of each distribution.

## F

In Numerator df values and Denominator df values, enter the degrees of freedom to specify each F-distribution. You can vary up to 3 degrees of freedom values for the numerator and denominator.

## Geometric

Complete the following steps to enter the parameters for the geometric distribution.
1. In Event probabilities, enter numbers between 0 and 1 for the probability that the outcome of interest occurs. An occurrence is called an "event".
2. From Model, select one of the following to specify the number to model.
• Total number of trials: The number of trials includes both events and nonevents.
• Only the number of non-events: Do not include the number of events.

## Integer

Complete the following steps to enter the parameters of the integer distribution.
1. In Minimum values, enter the lower end points of the distributions.
2. In Maximum values, enter the upper end points of the distributions.

## Lognormal

Complete the following steps to enter the parameters for the lognormal distribution.
1. In Location, enter values that represent the location of the peak of the related normal distributions.
2. In Scale, enter values that represent the spread of the related normal distributions.
3. In Threshold, enter the lower bounds of each distribution.

## Normal

The normal distribution is a continuous distribution that is common in hypothesis tests and modeling.

The normal distribution is the most common statistical distribution because approximate normality occurs naturally in many situations. Many statistical analyses require that data come from normally distributed populations and, thus, may not work well with nonnormal data.

Complete the following steps to enter the parameters for the normal distribution.

1. In Means, enter the values for the center of the distributions.
2. In Standard deviations, enter the values for the spread of the distributions.

## Poisson

In Means, enter the values for the average rate of occurrence. For more information, go to Poisson distribution.

## t

In Degrees of freedom values, enter the degrees of freedom to specify each t-distribution. For more information, go to t-distribution.

## Uniform

Complete the following steps to enter the parameters for the uniform distribution.
1. In Lower endpoints, enter the minimum values for the distributions.
2. In Upper endpoints, enter the maximum values for the distributions.

## Weibull

Complete the following steps to enter the parameters for the Weibull distribution.

1. In Shape parameter, enter the shape parameters to define each Weibull distribution.
2. In Scale parameter, enter scale parameters to define each Weibull distribution.
3. In Threshold parameter, enter the lower bounds of each Weibull distribution.

### Example

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