Learn to calculate the coefficient of determination, R², in Excel to assess the predictive power of your data models. This metric reflects the proportion of variability in a dependent variable that is predictable from independent variables.
While Excel provides the tools for this statistical function, it involves multiple steps and can be error-prone. We'll detail these steps and subsequently demonstrate why Sourcetable offers a more streamlined and efficient approach for calculating R².
To calculate R-squared using Excel's built-in formula, select the cell where you want the result to appear, enter =RSQ([Data set 1], [Data set 2]), and press Enter. Excel requires two data sets, representing the independent and dependent variables, to compute the R-squared value, reflecting how well data fits the regression model.
Alternatively, calculate correlation with =CORREL([Data set 1], [Data set 2]). To find R-squared, square the correlation result in Excel, offering insight into the variance explained by the independent variable.
The coefficient of determination can also be determined using the formula R2 = 1 - (RSS/TSS), where RSS stands for residual sum of squares, and TSS denotes total sum of squares. This method reveals the proportion of variance in the dependent variable that the independent variable accounts for.
For multiple regression models, use the adjusted R-squared formula 1 - (1 - R2)*(n - 1)/(n - k - 1), where n is the number of data points, and k is the number of variables. Adjusted R-squared compensates for the number of variables and provides a more accurate measure of the goodness of fit.
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