We can use this equation to predict the response variable, y, based on the value of the predictor variable, x. Using the coefficients from the output table, we can see that the fitted exponential regression equation is:Īpplying e to both sides, we can rewrite the equation as: The overall F-value of the model is 204.006 and the corresponding p-value is extremely small, which indicates that the model as a whole is useful. Once you click OK, the output of the exponential regression model will be shown: In the new window that pops up, fill in the following information: In the window that pops up, click Regression. If you don’t see Data Analysis as an option, you need to first load the Analysis ToolPak. To do so, click the Data tab along the top ribbon, then click Data Analysis within the Analysis group. Next, we’ll fit the exponential regression model. Step 3: Fit the Exponential Regression Model Next, we need to create a new column that represents the natural log of the response variable y: Step 2: Take the Natural Log of the Response Variable Step 1: Create the Dataįirst, let’s create a fake dataset that contains 20 observations: The following step-by-step example shows how to perform exponential regression in Excel. a, b: The regression coefficients that describe the relationship between x and y.The equation of an exponential regression model takes the following form: Exponential decay: Decay begins rapidly and then slows down to get closer and closer to zero. Exponential growth: Growth begins slowly and then accelerates rapidly without bound.Ģ. Exponential regression is a type of regression model that can be used to model the following situations:ġ.
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