Some commonly occuring statistical analysis tasks are implemented in Igor Pro® procedures. They include the 1D Statistics Report package, the ANOVA Power Panel, plotting and convenience functions.
The 1D Statistics Report package is designed to simplify the analysis of a single 1D wave. The package produces a formatted notebook containing the results of the operations WaveStats and StatsQuantiles followed by several graphs. The graphs include a lag plot, an autocorrelation plot, a histogram, a spectral plot, box plot and a normal-probability plot.
The ANOVA Power Panel lets you compute various quantities relating to a one-way ANOVA for fixed-effect model. You can use the panel for experiment design.
Plotting Functions
| Function Name | What it does |
| statsAutoCorrPlot() | plots the autocorrelation of a wave |
| statsBoxPlot() | creates a single box plot |
| statsPlotHistogram() | creates a simple histogram plot |
| statsPlotLag() | creates a lag plot |
| statsProbPlot() | creates a probability plot ala NIST |
| WM_PlotBiHistogram() | creates a bi-histogram plot |
A collection of convenience functions is listed below. These functions are available by including AllStatsProcedures.ipf.
Convenience Functions
| Function Name | What it does |
| WM_2MeanConfidenceIntervals() | computes the confidence limits for two populations means |
| WM_BernoulliCdf() | returns the Bernulli CDF |
| WM_CIforPooledMean() | computes the confidence intervals for pooled means |
| WM_CompareCorrelations() | compares two correlation coefficients |
| WM_EstimateMinDetectableDiff() | computes minimum detectable difference for single sample |
| WM_EstimateReqSampleSize() | estimate the required sample size given sample variance |
| WM_EstimateReqSampleSize2() | estimate the required sample size given sample variance and power |
| WM_EstimateSampleSizeForDif() | computes sample size required to detect a specified difference in means |
| WM_GetANOVA1Power() | computes power in fixed effects one way ANOVA |
| WM_GetGeometricAverage() | computes a geometric average |
| WM_GetHarmonicMean() | computes the harmonic average |
| WM_GetPooledMean() | computes the pooled mean for two distributions from populations with same means |
| WM_GetPooledVariance() | computes the pooled variance for two populations |
| WM_MCPointOnRegressionLines() | tests the difference between two points which lie on two lines |
| WM_MeanConfidenceInterval() | computes the confidence interval about the mean |
| WM_OneTailStudentA() | returns the one-tail result for StudentA |
| WM_OneTailStudentT() | returns the one-tail result for StudentT |
| WM_RankForTies() | ranks data and accounts for possible ties |
| WM_RankLetterGradesWithTies() | ranks letter grades |
| WM_RegressionInversePrediction() | computes an inverse prediction for linear regression |
| WM_VarianceConfidenceInterval() | computes confidence interval for population variance |
| WM_WilcoxonPairedRanks() | computes positive and negative ranks for Wilcoxon Paired Ranks test |
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