گستردگی انواع تحلیلهای آماری موجود در گراف پد را ببینید.
Statistical Comparisons
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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
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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
Simulations
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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
Principal Component Analysis (PCA)
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Component selection via Parallel Analysis (Monte Carlo simulation), Kaiser criterion (Eigenvalue threshold), Proportion of Variance threshold, and more
Automatically generated Scree Plots, Loading Plots, Biplots, and more
Use results in downstream applications like Principal Component Regression
Multiple Variable Graphing
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Specify variables defining axis coordinates, color, and size