Question

In: Statistics and Probability

Given student data: USING R please y=c(81.72, 91.5, 90, 75.21, 68.11, 95.27, 95, 89.71, 92.5) x1=c(60,...

Given student data: USING R please

y=c(81.72, 91.5, 90, 75.21, 68.11, 95.27, 95, 89.71, 92.5)

x1=c(60, 62, 68, 59, 61, 70, 70, 65, 66)

x=c('B', 'A', 'B', 'A', 'A', 'B', 'B', 'B', 'A')

Y represents the grade scores per semester , x1 represents the student’s height in inches, and x2 represents the major( A = architecture, B = business)

Question 1

1. Build a linear regression model relating grade scores y to student height x1 and the type of major x2.

2. Test whether the type of major relates to the student performance using alpha = 0.05

3. Comment on model adequacy by performing a residual analysis.

Question 2

Alter the model developed in question 1 to add an interaction term between student height and the type of major.

1. Does the interaction term have a significant effect on the student performance using alpha=0.05?

2. Is there an effect of the type of major on student performance?

3. Comment on model adequacy by performing a residual analysis.

Which model should we choose?

Solutions

Expert Solution

Question 1 with 3 sub parts is completely solved.

Request: Please post the next question as a separate question to be answered. It helps to provide a detailed solution.


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