# (Solved): wpc 300 Quiz: HOA 5: Linear Regression ...

**WPC 300**

**Quiz: HOA 5: Linear Regression **

**Question 1 3 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?

- 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.
- 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 not correlated. Dependent variable City mileage car is predicted as City Mileage (MPG) = 45.587166 + 1.3934743*Fuel Tank Capacity
- Dependent variable 'Fuel Tank Capacity' is negatively correlated with independent variable 'City Mileage of Car'

**Question 2 3 pts **

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

- -0.008
- 0.66
- 47.05
- 0.71

**Question 3 3 pts **

The team wants to find out if there are any other variables that are significantly correlated with City Mileage, with a correlation coefficient of greater than +1- 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]

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

**Question 4 3 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]

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

**Question 5 **

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

- 0.80
- 0.64
- 0.78
- 0.43

**Question 6 3 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?

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

**Question 7 3 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 3pts**

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

- Weight (strongest) and 'Length' (weakest)
- Passenger Capacity (strongest) and Weight (weakest)
- Vehicle Category (strongest) and Passenger Capacity (weakest)
- Fuel Tank Capacity (strongest) and Passenger Capacity (weakest)

**Question 9 3 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 3 pts **

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

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