R², also known as the coefficient of determination, is a statistical measure used to ascertain the strength of the relationship between dependent and independent variables. It is particularly useful for linear regression models, as it helps in estimating how well the model can predict future outcomes. In this article, we will explore how to calculate R² using Microsoft Excel.

Step 1: Organize Your Data

Before you begin, ensure that your dataset consists of both dependent (Y) and independent (X) variables. Ideally, this should be arranged into two columns, with the dependent variable in one column and the independent variable in the other.

Step 2: Calculate Slope and Intercept

To determine the R² value, you first need to find the slope and intercept of the linear regression equation. To do this in Excel, do the following:

1. Click on an empty cell where you want to display the slope value.

2. Type “=SLOPE(” in this cell without quotes.

3. Highlight or select the range of cells containing your dependent variable (Y) data followed by a comma.

4. Now, highlight or select the range of cells containing your independent variable (X) data.

5. Close the parenthesis and press Enter.

Now repeat this process for intercept using “=INTERCEPT(” instead of “=SLOPE(“.

Step 3: Calculate Predicted Y Values

Next, you need to calculate predicted Y values using the slope and intercept values obtained in Step 2:

1. In a new column, adjacent to your dataset, type “=(Intercept)+((Slope)*(X Value))” without quotes.

2. Replace “Intercept” with the cell reference that contains the calculated intercept value.

3. Replace “Slope” with the cell reference that contains the calculated slope value.

4. Replace “X Value” with a reference to the Independent Variable cell.

5. Drag this formula down for all rows of data in your dataset.

Step 4: Calculate Sum of Squares

Now, calculate the sum of squares for both regression (explained variation) and total (unexplained variation):

1. In a new column, calculate the difference between each predicted Y value and the mean Y value. Use “=Predicted Value – AVERAGE(Y Values Range)”.

2. Square each result, and then sum these squared differences.

3. Next, calculate the difference between each original Y value and mean Y value. Use “=(Y Value) – AVERAGE(Y Values Range)”.

4. Square each result, and then sum these squared differences.

Step 5: Calculate R²

Finally, calculate the R² value by dividing the sum of squares regression by the total sum of squares:

1. In a new cell, type “=Sum of Squares Regression / Total Sum of Squares” without quotes.

2. Replace “Sum of Squares Regression” with your calculated value from Step 4.

3. Replace “Total Sum of Squares” with your calculated value from Step 4.

Now you have successfully computed the R² value in Excel! This helps you better assess the accuracy of your linear regression model and predict data trends accordingly.