---
title: Regression Analysis Software | Multiple Regression Software
description: Statgraphics STATBEANS® can perform as multiple regression software processes! Check out these tools from Statgraphics for regression analysis software!
---

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<https://www.statgraphics.com/statbeans/calculations/multipleregression#anchor3>

# Calculation STATBEANS®

 

**STATBEAN Name:** MultipleRegression

 

**Purpose:** Fits a multiple regression model to describe the relationship between Y and one or more numeric predictor variables by letting Statgraphics products to function as regression analysis software.

**DataSource for the multiple regression software: **any. 

**Regression Analysis Software Read/Write Properties**

| **Name** | **Type** | **Description** | **Possible Values** | **Default Value** |
| --- | --- | --- | --- | --- |
| includeConstant | boolean | Whether to include a constant term in the model. | true,false | true |
| maximumSteps | int | Maximum number of steps if stepwise selection. | 1+ | 1000 |
| pToEnter | double | P value to enter coefficient into model. | 0.000001-0.999999 | 0.05 |
| pToRemove | double | P value to remove coefficient into model. | 0.000001-0.999999, >= pToEnter | 0.05 |
| selectionMethod | String | Selection method for independent variables. | "All", "Forward," "Backward" | "All" |
| tablewiseExclusion | boolean | Whether all rows of the data table containing a missing value in any column should be excluded from the calculations. | true,false | false |
| tolerance | double | Conditioning tolerance for aborting matrix inversion. | >0 and <=0.000001 | 0.0000000001 |
| xVariableNames | String\[\] | The names of the column with data values to be used for the independent (X) variables. | Any string. | "" |
| yVariableName | String | The name of the column with data values to be used for the dependent (Y) variable. | Any string. | "Y" |

**Other Public Methods**

| **Name** | **Description** | **Arguments** | **Return Value** |
| --- | --- | --- | --- |
| double getAdjustedRSquared() | Returns the adjusted coefficient of determination. | None. | Adjusted R-squared, or missingValueCode if model cannot be fit. |
| void getCoefficientPValues(double\[numberOfCoefficients\]) | Returns the P-values for the estimated coefficients. | Double output array. | None. |
| void getCoefficients(double\[numberOfCoefficients\]) | Returns the estimated coefficients. | Double output array. | None. |
| void getCooksDistance(double c\[n\]) | Returns Cook's distance corresponding to each row in the datasource. | Double output array. | None. |
| void getDegreesOfFreedom(int df\[3\]) | Returns the degrees of freedom corresponding to the sums of squares. | Double output array. | None. |
| void getDFFITS(double d\[n\]) | Returns the DFFITS statistic corresponding to each row in the datasource. | Double output array. | None. |
| double getDurbinWatson() | Returns the Durbin-Watson statistic. | None. | DW, or missingValueCode if model cannot be fit. |
| void getInModel(boolean\[\]) | Returns true if an X variable is in the final model. | Boolean output array. | None. |
| int getNumberOfCoefficients() | Returns the number of coefficients in the fitted model. | None. | Number of coefficients. |
| void getLeverages(double h\[n\]) | Returns the leverage corresponding to each row in the datasource. | Double output array. | None. |
| double getLowerConfidenceLimit(double x\[\],double conflevel) | Returns the lower confidence limit for the mean value of Y. | Values of X at which to make prediction, and the percentage confidence. | Lower limit. |
| double getLowerPredictionLimit(double x\[\],double meansize,double conflevel) | Returns the lower prediction limit for a new value of Y. | Values of X at which to make prediction,number of observations at X, and the percentage confidence. | Lower limit. |
| void getMahalanobisDistance(double c\[n\]) | Returns the Mahalanobis distance corresponding to each row in the datasource. | Double output array. | None. |
| double getMeanAbsoluteError() | Returns the residual mean absolute error. | None. | MAE, or missingValueCode if model cannot be fit. |
| double getMeanSquaredError() | Returns the residual mean squared error. | None. | MSE, or missingValueCode if model cannot be fit. |
| double getModelPValue() | Returns the P-value for the fitted model. | None. | P-value. |
| void getPredictedValues(double p\[n\]) | Returns the predicted value of Y corresponding to each row in the datasource. | Double output array. | None. |
| double getPrediction(double x\[\]) | Returns the predicted value of Y. | Value of X's at which to make prediction. | Predicted value. |
| double getResidualDegreesOfFreedom() | Returns the d.f. for the error term used to estimate the standard errors. | None. | Residual df, or 0 if model cannot be fit. |
| void getResiduals(double r\[n\]) | Returns the residual corresponding to each row in the datasource. | Double output array. | Residual or missingValueCode. |
| double getResidualStandardError() | Returns the estimated standard deviation of the residuals. | None. | Standard error of the estimate, or missingValueCode if model cannot be fit. |
| double getRSquared() | Returns the coefficient of determination. | None. | R-squared, or missingValueCode if model cannot be fit. |
| double getSampleSize() | Returns the number of non-missing data values. | None. | Sample size. |
| void getStandardErrors(double\[numberOfCoefficients\]) | Returns the coefficient standard errors. | Double output array. | None. |
| void getStudentizedResiduals(double s\[n\]) | Returns the studentized deleted residual corresponding to each row in the datasource. | Double output array. | None. |
| void getSumsOfSquares(double ss\[3\]) | Returns the following sums of squares: total, model, residual. | Double output array. | None. |
| void getTypeISumsOfSquares(double\[\]) | Returns the reduction in the residual sum of squares as each variable is entered into the model. | Double output array. | None. |
| double getUpperConfidenceLimit(double x\[\],double conflevel) | Returns the upper confidence limit for the mean value of Y. | Values of X at which to make prediction, and the percentage confidence. | Upper limit. |
| double getUpperPredictionLimit(double x\[\],double meansize,double conflevel) | Returns the upper prediction limit for a new value of Y. | Values of X at which to make prediction, number of observations at X, and the percentage confidence. | Upper limit. |
| void getVIF(double\[\]) | Returns the variance inflation factors for each X variable in the final model. | Double output array. | None. |
| void getXTXInverse(double xtxinv\[\]) | Returns the inverted X-transpose-X matrix. | Output array of dimension equal to number of coefficients in model squared. | Inverted matrix. |

**Output Variables**

| **Name** | **Description** |
| --- | --- |
| CooksD | Cook's distance corresponding to each row in the datasource. |
| DFFITS | The DFFITS statistic corresponding to each row in the datasource. |
| Leverage | The leverage corresponding to each row in the datasource. |
| MahalanobisD | The Mahalanobis distance corresponding to each row in the datasource. |
| Predicted | The predicted value of Y corresponding to each row in the datasource. |
| Residual | The residual corresponding to each row in the datasource. |
| SResidual | The studentized deleted residual corresponding to each row in the datasource. |

Other properties are inherited from the general [CalculationStatbean](https://www.statgraphics.com/statbeans/calculations) class. 

**Code Sample** 

//create a datasource bean   
FileDataSource fileDataSource1 = new STATBEANS.FileDataSource(); 

//set the file name to be read   
fileDataSource1.setFileName("c:\\\\statbeans\\\\samples\\\\cardata.txt"); 

//create a calculation bean   
MultipleRegression multipleRegression1 = new STATBEANS.MultipleRegression(); 

//set the column names   
multipleRegression1.setYVariableName("mpg");   
java.lang.String\[\] tempString = new String\[3\];   
tempString\[0\] = "weight";   
tempString\[1\] = "horsepower";   
tempString\[2\] = "displace";   
multipleRegression1.setXVariableNames(tempString); 

//create a table bean   
MultipleRegressionTable multipleRegressionTable1 = new STATBEANS.MultipleRegressionTable(); 

//create plot beans   
MultipleRegressionComponentPlot multipleRegressionPlot1 = new STATBEANS.MultipleRegressionComponentPlot();   
MultipleRegression2DResponsePlot multipleRegressionPlot2 = new STATBEANS.MultipleRegression2DResponsePlot();   
MultipleRegressionContourPlot multipleRegressionPlot3 = new STATBEANS.MultipleRegressionContourPlot();   
MultipleRegressionSurfacePlot multipleRegressionPlot4 = new STATBEANS.MultipleRegressionSurfacePlot(); 

//set the column for the x axis   
multipleRegression1.setXVariableName("weight");   
//make the calculation bean a listener for changes in the FileDataSource bean   
fileDataSource1.addDataChangeListener(multipleRegression1.listenerForDataChange); 

//make the table and plot beans listeners for changes in the calculation bean   
multipleRegression1.addDataChangeListener(multipleRegressionTable1.listenerForDataChange);   
multipleRegression1.addDataChangeListener(multipleRegressionPlot1.listenerForDataChange);   
multipleRegression1.addDataChangeListener(multipleRegressionPlot2.listenerForDataChange);   
multipleRegression1.addDataChangeListener(multipleRegressionPlot3.listenerForDataChange);   
multipleRegression1.addDataChangeListener(multipleRegressionPlot4.listenerForDataChange); 

//set holdat values   
double hold\[\]=new double\[3\];   
hold\[0\]=2500;   
hold\[1\]=90;   
hold\[2\]=150;   
multipleRegressionPlot2.setHoldAt(hold);   
multipleRegressionPlot3.setHoldAt(hold);   
multipleRegressionPlot4.setHoldAt(hold); 

//instruct the fileDataSource bean to read the file   
fileDataSource1.readData();

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