BUEC 333讲解、Statistical Analysis讲解、辅导Java,Python程序、辅导c/c++,Python
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Summer 2019
Assignment 04
due by Thursday, July 18, 11:59 PM
You have created the model to find out what determines the retention rates, in assignment 3. Return to
your model and answer the following 6 questions.
1. Is there any good reason to expect imperfect multicollinearity in your equation? Explain. (2)
2. Test for multicollinearity. Explain your choice of the detection method, show the output of R that
you have used, and present the results (i.e., whether you have detected severe multicollinearity or
not). (4)
3. Is there any good reason to expect serial correlation in your equation? Explain. (2)
4. Test for serial correlation. Explain your choice of the detection method, show the output of R that
you have used, and present the results (i.e., whether you have detected serial correlation or not).
(4)
5. Given the results of all your tests above, discuss in short whether you could make changes to your
original model to improve it. (3)
6. Discuss in short what variables (if they were made available) could be added to your original
model to improve it. (3)
Summer 2019
Assignment 04
due by Thursday, July 18, 11:59 PM
You have created the model to find out what determines the retention rates, in assignment 3. Return to
your model and answer the following 6 questions.
1. Is there any good reason to expect imperfect multicollinearity in your equation? Explain. (2)
2. Test for multicollinearity. Explain your choice of the detection method, show the output of R that
you have used, and present the results (i.e., whether you have detected severe multicollinearity or
not). (4)
3. Is there any good reason to expect serial correlation in your equation? Explain. (2)
4. Test for serial correlation. Explain your choice of the detection method, show the output of R that
you have used, and present the results (i.e., whether you have detected serial correlation or not).
(4)
5. Given the results of all your tests above, discuss in short whether you could make changes to your
original model to improve it. (3)
6. Discuss in short what variables (if they were made available) could be added to your original
model to improve it. (3)