## Prism

Analyze, graph and present scientific data faster than ever with Prism!

## Statistical Comparisons

• 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).

## Column Statistics

• 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.

## Nonlinear Regression

• 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).

## Simulations

• 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.

## Other Calculations

• Area under the curve, with confidence interval.
• Transform data.
• Normalize.
• Identify outliers.
• Normality tests.
• Transpose tables.
• Subtract baseline (and combine columns).
• Compute each value as a fraction of its row, column or grand total.