![]() ![]() Texts: Applying Multiple Linear Regression to the 'mtcars' Datasetįirst, we need to load the "mtcars" dataset in R. You can explore this technique as an additional aspect of your analysis. Backward elimination involves iteratively removing non-significant variables from the model to improve its accuracy. Optional: You may consider using backward elimination as a variable selection method to refine your multiple linear regression model. You are making your assessment based on your results) (Note: I realize we haven't talked about specific methods for determining performance. Did the model do a good job of predicting? Why or why not? Provide supporting analysis and interpretation of the results. Evaluation of the model's predictive performance. ![]() ![]() Identification of the independent variables you chose and an explanation of why you selected them. Your R code for performing multiple linear regression. Make sure to visualize the results and compare them to your test set.ĭeliverables: Please submit an R code or R markdown that includes: This assignment provides an opportunity to practice the concepts of multiple linear regression, including model fitting, interpreting coefficients, and evaluating the model's performance. Goal: Your goal is to perform multiple linear regression by fitting a linear model to predict "mpg" based on a combination of independent variables. You need to select the independent variables that you believe will have the most significant impact on predicting mpg. Unlike simple linear regression, multiple linear regression allows us to consider multiple predictors simultaneously. Your task is to apply multiple linear regression to predict the fuel efficiency (mpg) based on a combination of independent variables. Summary of Assignment: The "mtcars" dataset in R contains information about various car models and their fuel efficiency (miles per gallon). SOLVED: Texts: Applying Multiple Linear Regression to the 'mtcars' Dataset ![]()
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