Data Analysis Plot Types

TeraPlot provides the following Data Analysis/Statistics plot types. A good source of reference information on these plot types is: NIST/SEMATECH Engineering Statistics Handbook.

  • Histogram. Draws a Histogram of the supplied sample data and reports associated statistical parameters such as Mean, Median, Variance, Standard Deviation, Skewness and Kurtosis

  • Linear Regression. Performs linear regression on the supplied sample data for certain predefined fitting functions. The most common example is a least-squares fit to a straight line y = ax +b, but other plot forms are available, i.e. exponential, power, reciprocal, and polynomial. The resulting fit is displayed as a plot of the original data points plus the fitted line. The line coefficients a and b are reported along with a "goodness of fit" measure. A table of the original x and y points, along with the associated fitted points an residuals is also given.

  • Box Plot. The Box Plot consists of a set box diagrams, each based based on its own dataset, provided as s column of input samples within a multi-column grid. Each box gives a pictorial summary of various statistical features of the data set such as median, quartiles and outliers.

  • Probability Plot. The probablity plot can be used to assess how close a set of sample data corresponds to a given distribution type.

  • PPCC Plot. A probability plot calculates, for a given distribution type, the location and scale parameters which best best fit the sample data. If the distribution also has a shape parameter, this must be supplied. The PPCC plot calculates multiple probability plots for a range of shape parameters and reports the shape parameter providing the best fit.

  • Statistical Distributions. Plots statistical distributions (either PDF or CDF) based on supplied distribution parameters. Available distribution types are: Normal, Exponential, Lognormal, Weibull, Gamma, Binomial.

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