If the process sigma is entered manually, the program will use 6*(process sigma) as the value of comparison.
Program written, modified, or edited at StatSoft, Inc.}

RandomAccess;
If DisplayMessageBox (MB_YESNO, 'Proceed with Analysis?', 'To estimate the process sigma from the data, the topmost
Scrollsheet must be from Variance Components as described in the comments.  If this is the case or if you want to enter
the process sigma manually, then press Yes.  Otherwise press No.') = IDNO then stop;
VarCompScroll := GetScrollsheet (0);

{Set up needed arrays and initialize variables}
ReDim Measurement(Ncases), Master(Ncases), d(Ncases), Corr(Ncases,2), R(2,2), ExtractDetails(Ncases);
Sxy := 0;
Sxx := 0;

{Select the two variables for analysis and place the data into the appropriate arrays}
if (SelectVariables2 ('Select One Variable from Each List', 1, 1, MeasVar, MeasCount, 'Variable with Measurements', 1, 1,
MasterVar, MasterCount, 'Variable with Master Values')=0) then stop;
for i := 1 to Ncases do
	begin
	Measurement(i) := Data(i, MeasVar);
	Master(i) := Data(i, MasterVar);
	d(i) := Measurement(i) - Master(i);
	Corr(i,1) := Master(i);
	Corr(i,2) := d(i);
	end;

{Calculate regression line and R-squared}
ValMean(d, 1, Ncases, Meand);
ValMean(Master, 1, Ncases, MeanMaster);
for i := 1 to Ncases do
	begin
	tempxy := d(i)*Master(i);
	tempxx := Master(i)^2;
	Sxy := Sxy + tempxy;
	Sxx := Sxx + tempxx;
	end;
Sxy := Sxy - Ncases*Meand*MeanMaster;
Sxx := Sxx - Ncases*MeanMaster^2;
slope := Sxy/Sxx;
intercept := Meand - slope*MeanMaster;
MatrixCorrelations(Corr, 1, R);
RSquared := R(1,2)^2;

{Report regression results}
ReDim Result1(3);
Result1(1) := intercept; Result1(2) := slope; Result1(3) := RSquared;
output1 := NewScrollsheet(1, 3, Result1, 'Fitted Line and R-Squared', ?RowNames$, 'Intercept|Slope|R-Squared');

VectorDualSort (Master, d, SORT_ASCENDING);

ValidCases := Ncases;

{Determine the number of different master values and get information about sample size}
for i := 1 to Ncases do
	begin
	if i = 1 then
		begin
		ExtractDetails(1) := 1;
		DistinctGroups := 1;
		end
	else if (Valid(Master(i))) and (Master(i) > Master(i-1)) then
		begin
		DistinctGroups := DistinctGroups + 1;
		ExtractDetails(DistinctGroups) := i;
		end;
	if Valid(Master(i)) = 0 then ValidCases := ValidCases - 1;
	end;

{Calculate the mean bias for each master value}
ReDim BiasNMaster(DistinctGroups, 2);
for i := 1 to DistinctGroups do
	begin
	if i < DistinctGroups then NoToObtain := ExtractDetails(i+1) - ExtractDetails(i)
		else NoToObtain := ValidCases - ExtractDetails(i) + 1;
	ReDim TempData(NoToObtain);
	for j := 1 to NoToObtain do TempData(j) := d(ExtractDetails(i) + j - 1);
	ValMean(TempData, 1, NoToObtain, BiasNMaster(i, 1));
	BiasNMaster(i, 2) := Master(ExtractDetails(i));
	end;

{Report the mean bias for each master value}
output2 := NewScrollsheet(DistinctGroups, 2, BiasNMaster, 'Average Bias by Master Value', ?RowNames$, 'Mean
Bias|Master Value');

{Prepare titles and data for graph1}
Title1$ := 'Gage Linearity Study for ' + VarName(MeasVar);
Title2$ := 'Bias = ' + Str(intercept, 8, 3) + Str(slope, 8, 3) + ' * Master';
Title3$ := 'R-Squared = ' + Str(RSquared, 5, 3);
ReDim BiasMeans(DistinctGroups), DistinctMaster(DistinctGroups);

{Produce graph with bias & bias means plotted against the master values.  Also plot the regression line and report its
equation with the value of R-squared}
graph1 := NewGraph (Scatterplot, Title1$, 'Accuracy Average', 'Master Part Measurement', NCases, Master, d);
MatrixGetColumn (BiasNMaster, 1, BiasMeans);
MatrixGetColumn (BiasNMaster, 2, DistinctMaster);
GraphAddPlot (Graph1, Scatterplot, 'Bias Means', DistinctGroups, DistinctMaster, BiasMeans);
GraphSetPlotFitting (Graph1, 1, FIT_LINEAR);
GraphSetTitle (Graph1, Title_Top2, Title2$);
GraphSetTitle (Graph1, Title_Top3, Title3$);
DisplayGraph(Graph1);

{Choose how to estimate process sigma and calculate it}
EstMeth := DisplayListBox ('Choose Method to Estimate Process Sigma', 'Specify Value|Estimate from Data', 1);
if EstMeth=0 then stop
	else if EstMeth=2 then
		begin
		{If this method is chosen, the topmost scrollsheet must be the output from Variance Components
		 described at the beginning of this program}
		ReDim VarComp (2,1);
		ScrollsheetGetMatrix (VarCompScroll, 1, 1, VarComp);
		ProcSigma := 5.15 * sqrt(VarComp(1,1) + VarComp(2,1));
		end
	else
		begin
		if DisplayNumericInputBox ('Enter Process Sigma', 'Process Sigma', ProcSigma)=0 then stop
		else ProcSigma := 6 * ProcSigma;
		end;

{Calculate gage linearity and accuracy statistics}
Linearity := abs(slope)*ProcSigma;
PercLinearity := 100*abs(slope);
ValMean(d, 1, Ncases, Bias);
PercBias := 100*abs(Bias)/ProcSigma;

{Report gage linearity and accuracy statistics}
ReDim Result3(4);
Result3(1) := Linearity; Result3(2) := PercLinearity; Result3(3) := Bias; Result3(4) := PercBias;
output3 := NewScrollsheet (4, 1, Result3, 'Gage Linearity and Accuracy', 'Linearity|% Linearity|Bias|% Bias', 'Value');

{Produce graph of percent linearity and accuracy}
Redim Ybar(2), Xbar(2);
Ybar(1) := PercLinearity; Ybar(2) := PercBias;
Xbar(1) := 0; Xbar(2) := 1;
graph2 := NewGraph (Barplot, ?Top_Title$, 'Percent', ?Bottom_Title$, 2, Xbar, Ybar);
GraphSetScaleTextLabels (Graph2, AX_X, 2, Xbar, 'Linearity|Accuracy');
GraphSetPlot2DLayout (Graph2, 1, BarPlot, ?DataLabels, ?BarStyle, 0.8, ?DevLevel, ?IsRightAxis);

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