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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