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Input X Range - Enter the reference for the range of independent data. Microsoft Excel orders independent variables from this range in ascending order from left to right. The maximum number of independent variables is 16.

Labels - Select if the first row or column of your input range or ranges contains labels. Clear if your input has no labels; Excel generates appropriate data labels for the output table.

Confidence Level - Select to include an additional level in the summary output table. In the box, enter the confidence level you want applied in addition to the default 95 percent level.

Constant is Zero - Select to force the regression line to pass through the origin.

Output Range - Enter the reference for the upper-left cell of the output table. Allow at least seven columns for the summary output table, which includes an anova table, coefficients, standard error of y estimate, r2 values, number of observations, and standard error of coefficients.

New Worksheet Ply - Click to insert a new worksheet in the current workbook and paste the results starting at cell A1 of the new worksheet. To name the new worksheet, type a name in the box.

New Workbook - Click to create a new workbook and paste the results on a new worksheet in the new workbook.

Residuals - Select to include residuals in the residuals output table.

Standardized Residuals - Select to include standardized residuals in the residuals output table.

Residual Plots - Select to generate a chart for each independent variable versus the residual.

Line Fit Plots - Select to generate a chart for predicted values versus the observed values.

Normal Probability Plots - Select to generate a chart that plots normal probability.

Enter the reference for the range of dependent data. The range must consist of a single column of data.

The Regression analysis tool performs linear regression analysis by using the "least squares" method to fit a line through a set of observations. You can analyze how a single dependent variable is affected by the values of one or more independent variables.

For example, you can analyze how an athlete's performance is affected by such factors as age, height, and weight. You can apportion shares in the performance measure to each of these three factors, based on a set of performance data, and then use the results to predict the performance of a new, untested athlete.