The final answer has to be ordered exactly like the output and the residual row must be present.

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5.7 LAB: Regression with nonlinear relationships Load the mtcars.csv data set into a data frame. Create a new column in the data frame, exp_wt, that is the negative exponentiation of of the column wt. Use the ols function to perform a multiple regression with mpg as the response variable and wt and exp_wt, in that order, as the predictor variables. Create an analysis of variance table using the results of the multiple regression. Ex: If the column qsec is used as the response variable instead of mpg, the output is: wt sum_sq df 0.397038 1.0 0.120078 0.077859 1.0 0.023547 Residual 95.888614 29.0 exp wt 518506.3413430.qx3zqy7 F NaN PR (>F) 0.731452 0.879105 NaN 5.7 LAB: Regression with nonlinear relationships Load the mtcars.csv data set into a data frame. Create a new column in the data frame, exp_wt, that is the negative exponentiation of of the column wt. Use the ols function to perform a multiple regression with mpg as the response variable and wt and exp_wt, in that order, as the predictor variables. Create an analysis of variance table using the results of the multiple regression. Ex: If the column qsec is used as the response variable instead of mpg, the output is: wt sum_sq df 0.397038 1.0 0.120078 0.077859 1.0 0.023547 Residual 95.888614 29.0 exp wt 518506.3413430.qx3zqy7 F NaN PR (>F) 0.731452 0.879105 NaN