Stat

LabTalk Object Type:

External Object

The stat object provides for the following operations:

Linear regression.
Polynomial regression.
Multiple regression.
Descriptive statistics.

Please see the fitlr, fitpoly, stats X-Function.

1 Properties:
2 Methods:
3 Examples:

Properties:

Common Stat Object Properties:

Property	Access	Description
stat.adrsq	Read only numeric	Adjust Coefficient of Determination (R squared).
stat.chiSqrErr	Read/write numeric	Multiply the error on the parameters with the reduced chi-square value. 1 = enable, 0 = disable.
stat.cod	Read only numeric	Coefficient of Determination (R squared).
stat.confLevel	Read/write numeric	Confidence level for calculating confidence and prediction limits.
stat.errBarData$	Read/write string	Name of the error bar column to be used for calculating weights.
stat.fitxData$	Read/write string	Name of the X column to be fitted.
stat.fityData$	Read/write string	Name of the Y column to be fitted.
stat.fValue	Read only numeric	F test value (MSR/MSE).
stat.lclData$	Read/write string	Name of the lower control line.
stat.lplData$	Read/write string	Name of the lower prediction line.
stat.makeX.fitnpts	Read/write numeric	Number of points for the fitted curve.
stat.makeX.fitX1	Read/write numeric	First X value to be used when making the FitX$ dataset with the MakeX() method.
stat.makeX.fitX2	Read/write numeric	Last X value to be used when making the FitX$ dataset with the MakeX() method.
stat.makeX.margin	Read/write numeric	Percent of the raw data's range, of which to extend the fit X dataset beyond.
stat.MSE	Read only numeric	Mean sum of squares of residuals.
stat.MSR	Read only numeric	Mean sum of squares due to regression.
stat.npts	Read only numeric	Number of valid data points
stat.pvalue	Read only numeric	P-value for F test.
stat.r	Read only numeric	Correlation coefficient.
stat.resData$	Read only string	Name of the residual column.
stat.sd	Read only numeric	Standard deviation of the fit.
stat.SSE	Read only numeric	Residual sum of squares.
stat.SSR	Read only numeric	Sum of squares due to regression.
stat.SSTO	Read only numeric	Total sum of squares.
stat.uclData$	Read/write string	Name of the upper control line.
stat.uplData$	Read/write string	Name of the upper prediction line.
stat.wTotal	Read only numeric	Total weight.
stat.xMean	Read only numeric	Mean value of the X column.
stat.yMean	Read only numeric	Mean value of the Y column

Stat Object Properties Specific to Linear Regression:

Property	Access	Description
stat.lr.a	Read only numeric	Intercept.
stat.lr.ap	Read only numeric	P-value for the t test of a.
stat.lr.ase	Read only numeric	Standard error of a.
stat.lr.b	Read only numeric	Slope.
stat.lr.bp	Read only numeric	P-value for the t-test of b.
stat.lr.bse	Read only numeric	Standard error of b.
stat.lr.t	Read only numeric	t value corresponding to the confidence level.

Stat Object Properties Specific to Polynomial Regression:

Property	Access	Description
stat.pr.a, stat.pr.b1, etc.	Read only numeric	Fitted parameters (a is the intercept).
stat.pr.ap	Read only numeric	P-value of the t-test for parameter a.
stat.pr.ase, stat.pr.bse1, etc.	Read only numeric	Standard error of a and b[i].
stat.pr.bp1, stat.pr.bp2, etc.	Read only numeric	P-values of the t-tests for parameters bi.
stat.pr.order	Read/write numeric	Order of the polynomial.
stat.pr.t	Read only numeric	t value according to the confidence level.

Stat Object Properties Specific to Multiple Regression:

Property	Access	Description
stat.mr.a	Read only numeric	Intercept.
stat.mr.ap	Read only numeric	P-value of t-test for parameter a.
stat.mr.ase	Read only numeric	Intercept's error
stat.mr.bestr2wks$	Internal use only	Not implemented in Origin.
stat.mr.bi	Read only numeric	Fitting parameters (i = 1 .. 9).
stat.mr.bpi	Read only numeric	P-values of t-test for parameters bi (i = 1 ..9).
stat.mr.bsei	Read only numeric	b[i]'s errors (i = 1 .. 9).
stat.mr.NumX	Read only numeric	The total number of X columns.
stat.mr.pIn	Read only numeric	Critical P-value to enter a variable in stepwise regression.
stat.mr.pOut	Read only numeric	Critical P-value to remove a variable in stepwise regression.
stat.mr.t	Read only numeric	t value according to confidence level.
stat.mr.XDatai$	Read/write string	X column names (i = 1 ..9).
stat.mr.YData$	Read/write string	Y column name

Stat Object Properties Specific to Descriptive Statistics:

Property	Access	Description
stat.DS.ad	--	Not currently implemented.
stat.DS.CIL	Read only numeric	Lower confidence limit about the mean (the percentage is set with stat.ds.confLev).
stat.DS.CIU	Read only numeric	Upper confidence limit about the mean (the percentage is set with stat.ds.confLev).
stat.DS.cName1$	--	Not currently implemented.
stat.DS.confLev	Read/write numeric	Confidence level (set to 0.95 by default). Set this property to load stat.ds.ciu and stat.ds.cil.
stat.DS.data$	Read/write string	Name of dataset used to calculate descriptive statistics.
stat.DS.geoMean		Not currently implemented.
stat.DS.interpolate	Read/write numeric	Use interpolation when finding the quartiles/percentiles. 1 = enable, 0 = disable.
stat.DS.kurt	Read only numeric	Kurtosis of the data. Kurtosis measures the long-tailedness or peakedness of the distribution of a random variable relative to the normal or Gaussian distribution with the same mean and variance.
stat.DS.max	Read only numeric	Maximum of the data.
stat.DS.mean	Read only numeric	Mean value of the data.
stat.DS.medCIL	--	Not currently implemented. Lower confidence limit about the median.
stat.DS.medCIU	--	Not currently implemented. Lower confidence limit about the median.
stat.DS.median	Read only numeric.	Median of the data.
stat.DS.min	Read only numeric	Minimum of the data.
stat.DS.missing	Read only numeric	Number of missing values in the dataset.
stat.DS.more	Read/write numeric	Perform advanced statistics. 1 = enable, 0 = disable.
stat.DS.percent	Read/write numeric	The percentile to calculate when stat.ds() is performed. The percentile is loaded into stat.ds.percentile.
stat.DS.percentile	Read only numeric	Percentiles of the data.
stat.DS.quart75	Read only numeric	The upper quartile (75th percentile).
stat.DS.quartl25	Read only numeric	The lower quartile (25th percentile).
stat.DS.quartl50	Read only numeric	The second quartile (50th percentile).
stat.DS.range	Read only numeric	Range of the data.
stat.DS.sd	Read only numeric	Standard deviation.
stat.DS.se	Read only numeric	Standard error of the mean.
stat.DS.size	Read only numeric	Number of data points in the dataset.
stat.DS.skew	Read only numeric	Skewness of the data.
stat.DS.ssq	Read only numeric	Sum of squares
stat.DS.sum	Read only numeric	Sum of the data.
stat.DS.testNorm	--	Not currently implemented.
stat.DS.var	Read only numeric	Sample variance.

Methods:

Common Stat Object Methods:

Method	Description
stat.makeX()	Make an X dataset in fitX$ that spans from FitX1 to FitX2.
*stat.name(CtrlBit, WksName_ColName, NewColNam1[, NewColNam2, ..., NewColNamn])*	CtrlBit is either 1 or 0. When CtrlBit = 1, concatenate WksName_ColName to WksNameColName. This becomes the name of the new worksheet. Scan for already existing datasets: WksNameColName_NewColNam1, WksNameColName_NewColNam2, ..WksNameColName_NewColNamn. If a dataset already exists, then generate a new worksheet name (WksNameColNamen, where n = 1, 2, etc.) and scan again. When no duplicate names are found, set stat.name.worksheet$ to WksNameColName or WksNameColNamen. When CtrlBit = 0, concatenate WksName_ColName to WksNameColName. Do not search for previously existing datasets with identical names. Set stat.name.worksheet$ to WksNameColName.
stat.reset()	Reset all parameter values to their initial, unassigned values.

Stat Object Methods Specific to Linear Regression:

Method	Description
stat.lr([z])	Linear regression for given datasets. When "z" is specified, the fitting line goes through the origin. For example : stat.data$ = %H_B; stat.errbardata$ = %H_C; stat.lr(); stat.lr.a = ; //Display intercept in Script window stat.lr.b = ; //Display slope in Script window

Method

Description

stat.lr([z])

Linear regression for given datasets. When "z" is specified, the fitting line goes through the origin.

For example :

stat.data$ = %H_B;
stat.errbardata$ = %H_C;
stat.lr();
stat.lr.a = ;
//Display intercept in Script window
stat.lr.b = ;
//Display slope in Script
window

Stat Object Methods Specific to Polynomial Regression:

Method	Description
stat.pr()	Polynomial regression for given datasets. For example: stat.data$ = %H_B; stat.fitxdata$ = %H_D; //(optional) stat.fitydata$ = %H_E; //(optional) stat.pr();

Method

Description

stat.pr()

Polynomial regression for given datasets. For example:

stat.data$ = %H_B;
stat.fitxdata$ = %H_D; //(optional)
stat.fitydata$ = %H_E; //(optional)
stat.pr();

Stat Object Methods Specific to Multiple Regression:

Method	Description
stat.mr()	Multiple regression for the given datasets. stat.mr();

Stat Object Methods Specific to Descriptive Statistics:

Method	Description
stat.DS()	Descriptive and basic statistics for given dataset.

Examples:

This script performs a second order polynomial regression. It assumes that a Data1 worksheet is active and contains four columns: an X column with data, a Y column named B with data, an empty column named fitx, and an empty column named fity.

First create a script file containing the following script and save this file as PolyNom.OGS to your Origin software folder:

//Performs polynomial regression 
//%1 = order 
//%2 = name of dataset to fit (y variable) 
//%3 = name of dataset to store fitted x data 
//%4 = name of dataset to store fitted y data 
//%5 = number of points to create fitted curve 
[main] 
stat.reset(); //Reset the DLL 
stat.pr.order = %1; //order 
stat.data$ = %2; //dataset to fit 
stat.fitxdata$ = %3; //dataset to store fitted x values 
stat.fitYdata$ = %4; //dataset to store fitted y values 
stat.makeX.fitnpts = %5; //number of points of regression curve 
limit %2; //finds limiting values for the dataset to fit 
stat.makex.fitx1 = limit.xmin; 
stat.makex.fitx2 = limit.xmax; 
stat.makex(); 
stat.PR();

Then execute the following script to run the script file and perform the polynomial regression:

run.section(polynom.ogs, main, 2 
data1_b data1_fitx data1_fity 
100);