Perform semi-parametric survival analysis that allows for the inclusion of additional continuous or categorical predictor variables (covariates). Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify "significant" findings or discoveries.Identify outliers using Grubbs or ROUT method.One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.Create QQ Plot as part of normality testing.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.Ĭalculate 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.
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