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).
• Comparison of data from nested data tables using nested t test or nested one-way ANOVA (using mixed effects model).
• Multifactor ANOVA for any number of factors with comprehensive effect size reporting.
• Comprehensive reporting for ANOVA, contingency table analyses, and t tests – including effect sizes as well as confidence intervals.

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. Confidence intervals can be symmetrical (as is traditional) or asymmetrical (which is more accurate).
• Quantify symmetry of imprecision with Hougaard’s skewness.
• 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.
• Easily interpolate points from the best fit curve.
• Fit straight lines to two data sets and determine the intersection point and both slopes.
• Perform nonlinear regression from Multiple Variables tables with drag-and-drop assignment of independent (X), dependent (Y), and grouping variables.

Survival Analysis

• Kaplan-Meier survival analysis. 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).
• Cox proportional hazards regression. Perform semi-parametric survival analysis that allows for the inclusion of additional continuous or categorical predictor variables (covariates). Automatically generate graphs of estimated survival curves for any set of predictor variable values.
• Perform survival analysis from Multiple Variables tables with flexible variable assignment.