Statbeans

Calculation STATBEANS®

 

STATBEAN Name: SimpleRegression

 

Purpose: Regression software that fits any of 12 linear and non-linear models to describe the relationship between two numeric variables for simple linear regression analysis.

Regression Software DataSource: any. 

Read/Write Properties For Simple Linear Regression Analysis
Name Type Description Possible Values Default Value
includeConstant boolean Whether to include a constant term in the model. true,false true
includeLackOfFit boolean Whether to divide the residual sum of squares into lack-of-fit and pure error components. true,false false
modelType String Type of model to be fit. "Linear",
"Exponential",
"Reciprocal-Y",
"Reciprocal-X",
"Double reciprocal",
"Logarithmic-X",
"Multiplicative",
"Square root-X",
"Square root-Y",
"S-curve",
"Logistic",
"Log probit"		 "Linear"
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
xVariableName String The name of the column with data values to be used for the independent (X) variable. Any string. "X"
yVariableName String The name of the column with data values to be used for the dependent (Y) variable. Any string. "Y"

Other Public Methods For Simple Linear Regression Analysis
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 getCooksDistance(double c[n]) Returns Cook's distance corresponding to each row in the datasource. Double output array. None.
double getCorrelationCoefficient() Returns the sample correlation coefficient. None. r, or missingValueCode if model cannot be fit.
void getDegreesOfFreedom(int df[5]) 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.
double getIntercept() Returns the estimated intercept. None. Estimated intercept, or missingValueCode if model cannot be fit.
double getLackOfFitPValue() Returns the P-value for lack-of-fit test. None. P-value.
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. Value 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. Value 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 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 getReverseLowerConfidenceLimit(double y,double conflevel) Returns the value of X for the lower confidence limit at a predicted value of Y. Predicted value of Y, and the percentage confidence. Value of X.
double getReverseLowerPredictionLimit(double y,double meansize,double conflevel) Returns the value of X for the lower prediction limit at a predicted value of Y. Predicted value of Y, number of observations at X, and the percentage confidence. Value of X.
double getReversePrediction(double y) Returns the value of X associated with a predicted value of Y. Predicted value of Y. Value of X.
double getReverseUpperConfidenceLimit(double y,double conflevel) Returns the value of X for the upper confidence limit at a predicted value of Y. Predicted value of Y, and the percentage confidence. Value of X.
double getReverseUpperPredictionLimit(double y,double meansize,double conflevel) Returns the value of X for the upper prediction limit at a predicted value of Y. Predicted value of Y, number of observations at X, and the percentage confidence. Value of X.
double getRSquared() Returns the coefficient of determination. None. R-squared, or missingValueCode if model cannot be fit.
double getSlope() Returns the estimated slope. None. Estimated slope, or missingValueCode if model cannot be fit.
double getSampleSize() Returns the number of non-missing data values. None. Sample size.
double getStandardErrorIntercept() Returns the standard error for the estimated intercept. None. Standard error, or missingValueCode if model cannot be fit.
double getStandardErrorSlope() Returns the standard error for the estimated slope. None. Standard error, or missingValueCode if model cannot be fit.
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[5]) Returns the following sums of squares: total, model, residual, lack-of-fit, pure error. Double output array. None.
double getUpperConfidenceLimit(double x,double conflevel) Returns the upper confidence limit for the mean value of Y. Value 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. Value of X at which to make prediction, number of observations at X, and the percentage confidence. Upper limit.

Regression Software 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.
Regression Software 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 
SimpleRegression simpleRegression1 = new STATBEANS.SimpleRegression(); 

//create a table bean 
SimpleRegressionTable simpleRegressionTable1 = new STATBEANS.SimpleRegressionTable(); 

//create a plot bean 
SimpleRegressionPlot simpleRegressionPlot1 = new STATBEANS.SimpleRegressionPlot(); 

//set the columns for the x and y axes 
simpleRegression1.setYVariableName("mpg"); 
simpleRegression1.setXVariableName("weight"); 

//make the calculation bean a listener for changes in the FileDataSource bean 
fileDataSource1.addDataChangeListener(simpleRegression1.listenerForDataChange); 

//make the table and plot beans listeners for changes in the calculation bean 
simpleRegression1.addDataChangeListener(simpleRegressionTable1.listenerForDataChange); 
simpleRegression1.addDataChangeListener(simpleRegressionPlot1.listenerForDataChange); 

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