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**Power regression** is a type of non-linear regression that takes on the following form:

**y = ax ^{b}**

where:

**y:**The response variable**x:**The predictor variable**a, b:**The regression coefficients that describe the relationship betweenÂ*x*and*y*

This type of regression is used to model situations where the response variable is equal to the predictor variable raised to a power.

The following step-by-step example shows how to perform power regression for a given dataset in R.

**Step 1: Create the Data**

First, letâ€™s create some fake data for two variables: x and y.

#create data x=1:20 y=c(1, 8, 5, 7, 6, 20, 15, 19, 23, 37, 33, 38, 49, 50, 56, 52, 70, 89, 97, 115)

**Step 2: Visualize the Data**

Next, letâ€™s create a scatterplot to visualize the relationship between x and y:

#create scatterplot plot(x, y)

From the plot we can see that there exists a clear power relationship between the two variables. Thus, it seems like a good idea to fit a power regression equation to the data instead of a linear regression model.

**Step 3: Fit the Power Regression Model**

Next, weâ€™ll use theÂ **lm()** function to fit a regression model to the data, specifying that R should use the log of the response variable and the log of the predictor variable when fitting the model:

#fit the model model #view the output of the model summary(model) Call: lm(formula = log(y) ~ log(x)) Residuals: Min 1Q Median 3Q Max -0.67014 -0.17190 -0.05341 0.16343 0.93186 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 0.15333 0.20332 0.754 0.461 log(x) 1.43439 0.08996 15.945 4.62e-12 *** --- Signif. codes: 0 â€˜***â€™ 0.001 â€˜**â€™ 0.01 â€˜*â€™ 0.05 â€˜.â€™ 0.1 â€˜ â€™ 1 Residual standard error: 0.3187 on 18 degrees of freedom Multiple R-squared: 0.9339, Adjusted R-squared: 0.9302 F-statistic: 254.2 on 1 and 18 DF, p-value: 4.619e-12

The overall F-value of the model is 252.1 and the corresponding p-value is extremely small (4.619e-12), which indicates that the model as a whole is useful.

Using the coefficients from the output table, we can see that the fitted power regression equation is:

**ln(y) = 0.15333 + 1.43439ln(x)**

ApplyingÂ *e* to both sides, we can rewrite the equation as:

**y = e**^{ 0.15333 + 1.43439ln(x)}**y = 1.1657x**^{1.43439}

We can use this equation to predict the response variable,Â *y*, based on the value of the predictor variable,Â *x*.

For example, if *x* = 12, then we would predict that *y* would beÂ **41.167**:

y = 1.1657(12)^{1.43439} = 41.167

**Bonus:** Feel free to use this online Power Regression Calculator to automatically compute the power regression equation for a given predictor and response variable.

**Additional Resources**

How to Perform Multiple Linear Regression in R

How to Perform Exponential Regression in R

How to Perform Logarithmic Regression in R