Lognormality test and likelihood of sampling from normal (Gaussian) vs.Normality testing by four methods (new: Anderson-Darling).Frequency distributions (bin to histogram), including cumulative histograms.Mean or geometric mean with confidence intervals.Calculate descriptive statistics: min, max, quartiles, mean, SD, SEM, CI, CV, skewness, kurtosis.Specify variables defining axis coordinates, color, and size.Use results in downstream applications like Principal Component Regression.Automatically generated Scree Plots, Loading Plots, Biplots, and more.Component selection via Parallel Analysis (Monte Carlo simulation), Kaiser criterion (Eigenvalue threshold), Proportion of Variance threshold, and more.Automatically generate graphs of estimated survival curves for any set of predictor variable values. Perform semi-parametric survival analysis that allows for the inclusion of additional continuous or categorical predictor variables (covariates). Perform nonparametric survival analysis for different groups, and compare the estimated survival curves for each group with the log-rank test (including test for trend). Fit straight lines to two data sets and determine the intersection point and both slopes.Easily interpolate points from the best fit curve.Report the covariance matrix or set of dependencies.Runs or replicates test of adequacy of model.Quantify symmetry of imprecision with Hougaard’s skewness.Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate). Quantify precision of fits with SE or CI of parameters.Automatically graph curve over specified range of X values.Accept automatic initial estimated values or enter your own.Differentially weight points by several methods and assess how well your weighting method worked.Compare models using extra sum-of-squares F test or AICc.Automatic outlier identification or elimination.
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