(Solved): WPC 300 : Assignment 6: Linear Regression-CarsData.jmp...

wpc300

Assignment 6: Linear Regression

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Quiz Instructions

• Read the Assignment Instructions.

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• Start with datafile: CarsData.jmp
• Change the file name to: YourFirstName_YourLastName_Assignment6.jmp
• You must use JMP to answer the following multiple-choice questions. 
• Note: When you are asked to submit a screen capture, you need to make sure that your name is part of the capture. 

Question 1        2.5 pts

You have been asked to test if 'city mileage' of a car can be predicted based on the 'Fuel Tank capacity'. Which of the following statements is correct?

Group of answer choices

• The test shows that the variables are significantly correlated. Dependent variable ‘City mileage car’ is predicted as City Mileage (MPG) = 45.587166 - 1.3934743*Fuel Tank Capacity
• The test shows that both variables are correlated. The dependent variable ‘Fuel Tank Capacity’ is predicted as Fuel Tank Capacity = 45.587166 + 1.3934743*City Mileage of Car.
• Dependent variable ‘Fuel Tank Capacity’ is negatively correlated with independent variable ‘City Mileage of Car’
• The test shows that both variables are not correlated. Dependent variable City mileage car is predicted as City Mileage (MPG) = 45.587166 + 1.3934743*Fuel Tank Capacity

Question 2        2.5 pts

What is the coefficient of determination between 'City Mileage' and 'Weight'? [Save the script to the data file]

Group of answer choices

• 47.05
• -0.008
• 0.71
• 0.66

Question 3        2.5 pts

The team wants to find out if there are any other variables that are significantly correlated, with a correlation coefficient greater than +0.8 or less than -0.8. Execute an appropriate analysis to answer this question. Which of the following combinations of variables satisfy this condition? [Save the script to the data file]

Group of answer choices

• Luggage Capacity and weight
• Maximum Horsepower & Engine Size
• Fuel Tank Capacity & Weight
• Weight & Maximum Horsepower

Question 4        2.5 pts

Use the least square method to develop a linear model to predict the city mileage of a car using all other variables in the data file as independent variables, except ‘Model’ & ‘Manufacturer’. Remove the insignificant parameters from the model one by one by checking the log(worth) of each parameter and removing the least important parameter first from the model. Which of the following variables remain significant in the final model? [Save Script to the datafile]

Group of answer choices

• Luggage capacity & Weight
• Rear seat room & Weight
• Maximum Horsepower & Fuel Tank Capacity
• Vehicle category & Weight

Question 5        2.5 pts

What is the coefficient of determination of model equation obtained from Question 4?

Group of answer choices

• 0.80
• 0.43
• 0.78
• 0.64

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Question 6        2.5 pts

The team also asked you to check for any multi-collinearity effects in your model obtained from Question 4. After testing for any multi-collinearity effects (using VIF), what did you find out?

Group of answer choices

• Fuel Tank Capacity’, ‘Width’ and ‘Weight’ variables show multi-collinearity effects in the model
• ‘Vehicle Category’ and ‘Engine Size’ variables show multi-collinearity effects in the model
• ‘Passenger capacity’ and ‘Weight’ variables show multi-collinearity effects in the model
• ‘Passenger capacity’ and ‘Length’ variables show multi-collinearity effects in the model

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Question 7        2.5 pts

If you discovered multi-collinearity effects in the model, remove the variables in question one at a time (starting from the highest VIF) from the model and then stop when you don’t need to remove any further variable(s) from the model based on accepted VIF and p-values. After this process, submit the screenshot of the ‘Effect Summary’ of the final model. 

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Question 8        2.5 pts

Based on the model in question 7,  what are the strongest and weakest variables in predicting the city mileage of a car?

Group of answer choices

• Vehicle Category (strongest) and Passenger Capacity (weakest)
• Fuel Tank Capacity (strongest) and Passenger Capacity (weakest)
• Weight (strongest) and ‘Length’ (weakest)
• Passenger Capacity (strongest) and Weight (weakest)

Question 9        2.5 pts

What is the model equation for the final model in Question 7 to predict City mileage of a car? [Save script] Submit a screenshot.

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Question 10      2.5 pts

Submit the JMP data file with saved script for all the analysis to answer questions from this assignment.

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