Sunday, June 1, 2014

Excel Classification Table: Logistic Regression’s Percentage Correct of Predicted Results in Excel 2010 and Excel 2013

This is one of the following seven articles on Logistic Regression in Excel

Logistic Regression Overview

Logistic Regression in 7 Steps in Excel 2010 and Excel 2013

R Square For Logistic Regression Overview

Excel R Square Tests: Nagelkerke, Cox and Snell, and Log-Linear Ratio in Excel 2010 and Excel 2013

Likelihood Ratio Is Better Than Wald Statistic To Determine if the Variable Coefficients Are Significant For Excel 2010 and Excel 2013

Excel Classification Table: Logistic Regression’s Percentage Correct of Predicted Results in Excel 2010 and Excel 2013

Hosmer- Lemeshow Test in Excel – Logistic Regression Goodness-of-Fit Test in Excel 2010 and Excel 2013

 

Excel Classification Table:

Logistic Regression’s

Percentage Correct of

Predicted Results

Perhaps the most easily understood and intuitive way to present the results of binary logistic regression is to create a classification table of the output as follows:

logistic regression,regression, classification table, excel, excel 2010, excel 2013, solver, excel solver, statistics (Click On Image To See a Larger Version)

A probability of greater than 0.500 is recorded as a “1” in the Predicted column. The classification table can be set up to automatically create the columns of 1’s by using Excel If-Then-Else statements. An example of an If-Then-Else Excel formula that might be placed in the upper left cell of the table under the heading “1 Predicted Correctly” might the following:

=IF(AND(A5=1,E5>0.5),1,"")

logistic regression,regression, classification table, excel, excel 2010, excel 2013, solver, excel solver, statistics (Click On Image To See a Larger Version)

One overall metric that is commonly calculated is the PAC, the Percentage Accuracy in Classification.

PAC = [ (number of correct positives) + (number of correct negatives) ] / [total observations]

PAC = [ 8 + 9 ] / 20 = 85 percent

 

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