In this HW, you will develop a linear regression model that describe mileage (mpg) in the Auto dataset (ignore the origin and name columns) using forward selection,Divide the data into a training dataset (80%) and a testing dataset (20%)Use forward selection on the training dataset to find the three most important parameters that affect mpg: use minimum residual sum of squares (RSS) value as the rule for variable selection. Include tables of computed model parameters and their p-values.Apply the obtained model to the testing dataset. Compare results with the performance on the training dataI am using R to code this so far this is what I have1-data <- read.csv("Auto.csv")split_data <- sample(c(rep(0, 0.8 * nrow(data)), rep(1, 0.2 * nrow(data))))2-model_train <- lm(formula = mpg ~ cyl+ disp + hp + wght+ acc + year, data = data[split_data == 0, ])not sure what to do next, any help?

# Finding Sxy Sxy = 0 for xi, yi in zip(x_train, y_train): Sxy += (xi - mean_x) * (yi -…

Answered: n this HW, you will develop a linear…

Computer Networking: A Top-Down Approach (7th Edition)

7th Edition

ISBN:9780133594140

Author:James Kurose, Keith Ross

Publisher:James Kurose, Keith Ross

Chapter1: Computer Networks And The Internet

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Problem R1RQ: What is the difference between a host and an end system? List several different types of end...

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In this HW, you will develop a linear regression model that describe mileage (mpg) in the Auto dataset (ignore the origin and name columns) using forward selection,

Divide the data into a training dataset (80%) and a testing dataset (20%)
Use forward selection on the training dataset to find the three most important parameters that affect mpg: use minimum residual sum of squares (RSS) value as the rule for variable selection. Include tables of computed model parameters and their p-values.
Apply the obtained model to the testing dataset. Compare results with the performance on the training data

I am using R to code this so far this is what I have

data <- read.csv("Auto.csv")

split_data <- sample(c(rep(0, 0.8 * nrow(data)), rep(1, 0.2 * nrow(data))))

model_train <- lm(formula = mpg ~ cyl+ disp + hp + wght+ acc + year, data = data[split_data == 0, ])

not sure what to do next, any help?

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