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Statistical Comparisons • Paired or unpaired t tests. Reports P values and confidence intervals. • Automatically generate volcano plot (difference vs. P value) from multiple t test analysis. • Nonparametric Mann-Whitney test, including confidence interval of difference of medians. • Kolmogorov-Smirnov test to compare two groups. • Wilcoxon test with confidence interval of median.
• Perform many t tests at once, using False Discovery Rate (or Bonferroni multiple comparisons) to choose which comparisons are discoveries to study further. • Ordinary or repeated measures ANOVA followed by the Tukey, Newman-Keuls, Dunnett, Bonferroni or Holm-Sidak multiple comparison tests, the post-test for trend, or Fisher’s Least Significant tests. • One-way ANOVA without assuming populations with equal standard deviations using Brown-Forsythe and Welch ANOVA, followed by appropriate comparisons tests (Games-Howell, Tamhane T2, Dunnett T3) • Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values. • Greenhouse-Geisser correction so repeated measures one-, two-, and three-way ANOVA do not have to assume sphericity.
When this is chosen, multiple comparison tests also do not assume sphericity. • Kruskal-Wallis or Friedman nonparametric one-way ANOVA with Dunn's post test. • Fisher's exact test or the chi-square test. Calculate the relative risk and odds ratio with confidence intervals. • Two-way ANOVA, even with missing values with some post tests. • Two-way ANOVA, with repeated measures in one or both factors. Tukey, Newman-Keuls, Dunnett, Bonferroni, Holm-Sidak, or Fisher’s LSD multiple comparisons testing main and simple effects.
• Three-way ANOVA (limited to two levels in two of the factors, and any number of levels in the third). • Analysis of repeated measures data (one-, two-, and three-way) using a mixed effects model (similar to repeated measures ANOVA, but capable of handling missing data). • Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).
• Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model). Nonlinear Regression • Fit one of our 105 built-in equations, or enter your own. Now including family of growth equations: exponential growth, exponential plateau, Gompertz, logistic, and beta (growth and then decay). • Enter differential or implicit equations. • Enter different equations for different data sets. • Global nonlinear regression – share parameters between data sets. • Robust nonlinear regression.
• Automatic outlier identification or elimination. • Compare models using extra sum-of-squares F test or AICc. • Compare parameters between data sets. • Apply constraints.
• Differentially weight points by several methods and assess how well your weighting method worked. • Accept automatic initial estimated values or enter your own.
• Automatically graph curve over specified range of X values. • Quantify precision of fits with SE or CI of parameters.
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Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate). • Quantify symmetry of imprecision with Hougaard’s skewness.
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• Plot confidence or prediction bands. • Test normality of residuals. • Runs or replicates test of adequacy of model. • Report the covariance matrix or set of dependencies.