Analyze, graph and present scientific data faster than ever with Prism!
- Paired or unpaired t tests. Reports P values and confidence intervals.
- 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 Discover Rate (or Bonferroni multiple comparisons)to choose which comparisons are discoveries to study further.
- Ordinary or repeated measures one-way 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.
- Many multiple comparisons test are accompanied by confidence intervals and multiplicity adjusted P values.
- Greenhouse-Geisser correction so repeated measures one-way ANOVA does 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, Bonferron, Holm-Sidak, or Fishers 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).
- Kaplan-Meier survival analysis. Compare curves with the log-rank test (including test for trend).
- Calculate min, max, quartiles, mean, SD, SEM, CI, CV,
- Mean or geometric mean with confidence intervals.
- Frequency distributions (bin to histogram), including cumulative histograms.
- Normality testing by three methods.
- One sample t test or Wilcoxon test to compare the column mean (or median) with a theoretical value.
- Skewness and Kurtosis.
- Identify outliers using Grubbs or ROUT method.
Linear Regression And Correlation
- Calculate slope and intercept with confidence intervals.
- Force the regression line through a specified point.
- Fit to replicate Y values or mean Y.
- Test for departure from linearity with a runs test.
- Calculate and graph residuals.
- Compare slopes and intercepts of two or more regression lines.
- Interpolate new points along the standard curve.
- Pearson or Spearman (nonparametric) correlation.
- Analyze a stack of P values, using Bonferroni multiple comparisons or the FDR approach to identify “significant” findings or discoveries.
- Fit one of our 105 built-in equations, or enter your own.
- 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.
Clinical (Diagnostic) Lab Statistics
- Bland-Altman plots.
- Receiver operator characteristic (ROC) curves.
- Deming regression (type ll linear regression).
- Simulate XY, Column or Contingency tables.
- Repeat analyses of simulated data as a Monte-Carlo analysis.
- Plot functions from equations you select or enter and parameter values you choose.
- Area under the curve, with confidence interval.
- Transform data.
- Identify outliers.
- Normality tests.
- Transpose tables.
- Subtract baseline (and combine columns).
- Compute each value as a fraction of its row, column or grand total.