Call the mtcars dataset into R. ( mathrm{R}>operatorname{attach} ) (mtcars) mpg Miles/(US) gallon cyl Number of cylinders disp Displacement (cu.in.) hp Gross horsepower drat Rear axle ratio wt Weight ( (mathrm{lb} / 1000) ) qsec ( 1 / 4 ) mile time vs Engine shape ( (0= ) Standard, ( 1=mathrm{V} )-shaped ( ) ) am Transmission ( 0 = automatic, ( 1= ) manual ( ) ) gear Number of forward gears carb Number of carburetors A) Build a linear regression model predicting qsec from cyl, disp, hp, am, and gear. Use a ( p )-value threshold of ( 15 % ) for model building and comparison. B) Build a logistic regression model predicting am from mpg, cyl, hp, vs, and carb. Use a ( p )-value threshold of ( 15 % ) for model building and comparison.

Call the mtcars dataset into R. R>attach (mtcars) mpg Miles/(US) gallon cyl Number of cylinders disp Displacement (cu.in.) hp Gross horsepower drat Rear axle ratio wt Weight (lb/1000) qsec 1/4 mile time vs Engine shape (0= Standard, 1=V-shaped ) am Transmission (0= automatic, 1= manual ) gear Number of forward gears carb Number of carburetors A) Build a linear regression model predicting qsec from cyl, disp, hp, am, and gear. Use a p-value threshold of 15% for model building and comparison. B) Build a logistic regression model predicting am from mpg, cyl, hp, vs, and carb. Use a p-value threshold of 15% for model building and comparison