![]() ![]() The x-axis displays the fitted values and the y-axis displays the residuals. ![]() Click on the first option for Scatter within the Charts area. Then, navigate to the INSERT tab along the top ribbon. ![]() Make sure there are no blank rows or columns between the title and category headings and the body of the data or Excel will plot the blank spaces. Select both the numeric data and adjacent row and column headings. Hold the “Ctrl” key and highlight cells D2:D13. Select the cells that contain the data and text you want to include in the chart. Step 6: Create the residual plot. Highlight cells A2:A13. ![]() This will copy the formula in cell D2 to the rest of the cells in the column: Enter B2-C2 in cell D2. Then, click cell D2 and double-click the small “Fill Handle” at the bottom right of the cell. This will copy the formula in cell C2 to the rest of the cells in the column: Then, click cell C2 and double-click the small “Fill Handle” at the bottom right of the cell. Step 4: Calculate the predicted values. Enter the trendline equation in cell C2, replacing “x” with “A1” like so: The trend line equation will now be displayed on the scatterplot: Leave “Linear” selected and check “Display Equation on Chart.” Close the “Format Trendline” panel. Step 3: Display trend line equation on the scatterplot. Click “Add Chart Elements” from the DESIGN tab, then “Trendline”, and then “More Trendline Option. Step 2: Create a scatterplot. Highlight the values in cells A2:B13. Step 1: Enter the data values in the first two columns. For example, enter the values for the predictor variable in A2:A13 and the values for the response variable in B2:B13. Use the following steps to create a residual plot in Excel: This tutorial explains how to create a residual plot for a simple linear regression model in Excel. This type of plot is often used to assess whether or not a linear regression model is appropriate for a given dataset and to check for heteroscedasticity of residuals. A residual plot is a type of plot that displays the fitted values against the residual values for a regression model. ![]()
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