Procedures and Packages

Some commonly occuring statistical analysis tasks are implemented in Igor 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