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Dev.H@P@@ Axis@ Residuals, Gaussian - HistogramTPgauss_demo.vi This VI is designed to acqaint you with gaussian distributed random numbers. The gaussian white noise VI is used to generate a user selected number of random numbers that follow the gaussian distribution with mean of zero and standard deviation of one. This generator generates a long (fixed) sequence of random numbers. We select a subset of these random numbers by using the uniform random number generator to choose a (random) starting value. The random numbers are displayed sequentially in the order in which they are generated. There should be no correlation in the sequence of values, e.g. they should be uniformly distributed about positive and negative values. Next a histogram of the values is generated. The values are classified into one of 21 bins varying between -4 and +4. The classified data is known as a histogram. It is ploted as a bar chart. Superimposed is a gaussian random distribution of mean zero and standard deviation one. The x values are obtained from the bins generated in the histogram. Each x vaule is the mean of the extreme values (min and max) of each bin. The gaussian function must be calculated so that it corresponds to the histogram. Thus one calculates N*G(x)*Dx as a function of x where, N is the total number of random numbers generated, and Dx is the width of the bins. Revised 4-3-03 by jbc%.2f%.2f%.0f%.0f%.1f%.0f%.0f%.4f%.4f%.1f%.1f%.0f%.0f%.0f%.0f%.0f%.0f%.0f%.0f%.0fDǓE)DDCM)4ǀ<fv9IEIEIEIEI9 DTHPDM88  ~ &@ Number of Samples(@lowerupper inclusion @# bins @error @ number: 0 to 1 $@@ Histogram@@ XL@  @@ X@@ HistogramT@@DP @ lower @ upper*@lowerupperbothneithers inclusionBins@@ Axis@ max.@P @total @below @above # outside@ min @error @# bins@lowerupper inclusion @@ AxisdX@@4P@@ Axis@@@ y HistogramHistogram of Noise Values!0*P0!!!:*@@ Gaussian Distributed NoiseB2P@@ Axis@@ Histogram@ 4(P@@ Axis@ @@ yTH@P@@ Axis@ Residuals, Gaussian - Histogram0$@P0!!!8,@Sample PopulationWeighting (Sample)@ min>.@P @total @below @above # outside@ max`T@@DP @ lower @ upper*@lowerupperbothneithers inclusionBins@ variance$@ standard deviation @ mean~: @@ X,@Sample PopulationWeighting (Sample) @ mean@ standard deviation@ variance @samples @seed6&@@ Gaussian noise pattern|p8  @error&@@ Gaussian noise pattern @seed@ standard deviation @samples@ y:*P@@ Axis@@ yN>@4P@@ Axis@@@ y Histogram @ Mean@ Std. 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