Please need help with solutions. It involves R programming

Questions Open the mtcars dataset in R. A description of the variables is below: R>attach(mtcars) mpg Miles/(US) gallon cyl Number of cylinders disp Displacement (cu.in.) hp Gross horsepower drat Rear axle ratio wt Weight (1b/1000) qsec 1/4 mile time VS am Engine shape (0 = Standard, 1 = V-shaped) Transmission (0 = automatic, | = manual) gear Number of forward gears carb Number of carburetors 9) 2) Answer the following using the mtcars dataset. A) Give the correlation matrix from the variables mpg, disp, hp, and carb. B) Produce the principal components from the variables X|=mpg, X>=disp, X3=hp and X4=carb. Calculate the first and second principal component score from an observation of X, =20, X>;=300, X3= 150, and X,=4. C) Give the proportion of variance explained by each principal component. D) How many principal component variables should be chosen from Part B? Explain using both a scree plot and Kaisers' Method. E) Produce a histogram of the first principal component scores. Answer the following using the mtcars dataset. A) Give the linear discriminant functions from the variables Y=cyl, X|= drat, X;=wt, and X;=qsec. Calculate the first and second linear discriminant score from an observation of X,=3,.X2=4, and_X3= 18. B) Give a histogram of the first linear discriminant scores for each Y value. C) Conduct the Box's M-test to determine whether to use linear discriminant classification or quadratic discriminant classification. D) Classify Y from X, , X>, and X3 using linear discriminant classification. Provide the confusion matrix and the misclassification rate. E) Classify Y from 1), X2, and_X3 using quadratic discriminant classification. Provide the confusion matrix and the misclassification rate