Summary
1. Click Stat::Fit from the Tools menu.
2. Copy the column of data that you want to use within Stat::Fit.
3. Click in the open space directly across from the number 1 and press CTRL + V to paste data.
4. Click the Auto::Fit button.
5. In the Auto::Fit dialog, select unbounded, lower bound or assigned lower bound and click OK.
- Users with statistical background may enjoy experimenting with the power of Stat::Fit here.
6. Click on the distribution you wish to use in the dialog displayed. This will generate the Comparison graph.
- The Comparison graph allows you to compare your actual data against the selected distribution. The bars represent your data, while the line represents the distribution that will fit that data.
- Other comparative graphs are accessible through the Results option of the Fit menu.
7. With the best distribution selected, click the Export button. Click OK to export the distribution to the clipboard.
8. Now return to ProcessModel and paste the distribution in the desired field or Action logic using the CTRL-V shortcut.
Detailed Information
Stat::Fit® is a comprehensive yet user-friendly curve-fitting package. Stat::Fit will take raw data from spreadsheets, text files, or manual input and convert that data into the appropriate distribution for instant input into ProcessModel software.
It automatically fits continuous distributions, compares distribution types, and provides an absolute measure of each distribution’s acceptability. It also translates the fitted distribution into specific forms for use in ProcessModel products. It is developed by our technology partners at Geer Mountain Software.
Stat::Fit statistically fits your data to the most useful analytical distribution. Its operation is intuitive, yet its help file is extensive. The Auto::Fit function automatically fits continuous distributions, provides relative comparisons between distribution types, and an absolute measure of each distribution’s acceptability. The Export function translates the fitted distribution into ProcessModel. Some of the features are included below
Stat::Fit takes raw data (e.g. collected service times) and turns them into a single distribution that represents the collected data. For example, data collected on the length of breakdowns can be turned into a single distribution and be placed in a ProcessModel field.
Stat::Fit is accessed from the Tools menu. It allows you to improve the accuracy of your models by using collected data to determine the best distribution to use in order to reflect that data (See “Distributions”).
Distribution Fitting:
For Many distributions:
Beta, Binomial, Chi-Squared, Erlang, Exponential, Extreme ValueIA, Extreme Value IB, Gamma, Geometric, Inverse Gaussian, Inverse Weibull, Johnson SB, Johnson SU, Logarithmic, Logistic, Loglogistic, Lognormal, Normal, Pareto, Pearson V, Pearson VI, Poisson, Power Function, Rayleigh, Triangular, Uniform, Weibull.
Descriptive Statistics:
Mean, Median, Mode, Standard Deviation, Variance, Coefficient of Variation, Skewness, Kurtosis.
Parameter Estimates:
Maximum Likelihood, Moments.
Goodness of Fit Tests:
Chi-squared, Kolmogorov-Smirnov, Anderson-Darling.
Graphical Analysis:
Density graphs, Distribution Graphs, Difference graphs, Box Plots, Q-Q plot, P-P plot, Scatter plot, Autocorrelation graphs.
Additional Features:
Built-in random variate generator, Data manipulation options, Distribution Viewer, Distribution Percentiles
Continuous Distributions vs. Discrete Distributions
Distribution fittings are built-in functions that generate random numbers using predetermined patterns. Distributions may be discrete, randomly returning one value among a specified list of values, or they can be continuous and interpolate randomly according to the pattern provided by the input table or parameters. There are several steps in determining the best distribution to use given raw data from observations of the process being modeled. First, you must determine whether the data is discrete or continuous, then follow the appropriate instructions. For instructions on finding the best discrete or continuous distribution, see Discrete distribution below.
Stat::Fit is capable of much more than fitting data to distributions, but you need only take advantage of a few of its easy-to-use features when fitting your data to a ProcessModel distribution.
Continuous Distribution
The following example shows you how Stat::Fit can help you create more accurate models. A bank wants to model its teller operations, including the amount of time that it takes to serve each customer. Therefore, for a week, the time each customer spent with a teller is recorded. The data is entered in a text file which can be read by Stat::Fit. Using Stat::Fit, the data is analyzed and an activity time distribution is found that accurately reflects the amount of time required to serve a customer.
Discrete Distribution
The following example shows how a restaurant could use ProcessModel to model its seating operation. The number of customers is a quantity of discrete entities. Therefore, the Stat::Fit component of ProcessModel would take data about the number of customers who enter in each group, create a discrete distribution to represent that data, and place the distribution in the Quantity field for Arrivals in the ProcessModel for the restaurant.
Determine the best discrete distribution from raw data
1. Click Stat::Fit from the Tools menu.
2. Copy the column of data that you want to use within Stat::Fit.
3. Click in the open space directly across from the number 1 and press CTRL + V to paste data.
4. Click the Auto::Fit button 
.
5. In the Auto::Fit dialog, select discrete distributions as shown below and click OK.
Stat::Fit then calculates the best distribution choices and displays them along with their rank (the higher the rank, the better the fit)
6. Click on the name of the distribution that best fits the data.
7. With the best distribution found, follow the instructions for exporting data to ProcessModel found in fitting continuous data.