QMB3200- Statistix10 project

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until Nov 18 friday at 5pmstatistix10 software knowledge required.QMB 3200 FALL 2022 Project Submission 3
Complete your work in this document in black type. Leave the existing document in the blue
type it is currently in and do not edit the document except to add your responses below each
prompt. Submit to Canvas as a single doc or docx file; submissions consisting of multiple files or
files submitted in inaccessible formats will not be graded. Check to make sure you submit the
correct file; if you need to re-submit after the due date there will be a penalty of 25%. The
assignment is due by November 18th at 5:00pm; late work will be penalized 10% per day or
portion of a day that it is late. This assignment is worth 45 points. You will use your student
data set (cars) to run all analyses. NOTE: YOU MUST INCLUDE PRINTUTS IN ORDER FOR YOUR
WORK TO BE GRADED. WITHOUT PRINTOUTS, YOUR GRADE WILL BE ZERO.
Refer to the video in Course Kaltura if you have any questions regarding how to correctly
complete this assignment.
Delete one of your vehicle models from your data set, preferably the vehicle that was in
the middle when you ranked your vehicle models by price in Project Submission 2.
Recode the levels of Model Vehicle as 0 for one model and 1 for the other. We will no
longer be using the variable “Year Manufactured” so you can delete that if you want.
Add the quadratic term and the interaction terms. You will then start with the complete
2nd-Order Model and “build down” through the entire model-testing process, until you
have obtained the best-fitting model. Use this to respond to the following items. Refer
to the video in Course Kaltura if you have any questions about what to do to your data
set before you can begin your work.
Complete 2nd-Order Model: E(Y) = β0 +β1×1 + β2×12 +β3×2 +β4x1x2 + β5x12x2
Part 1: Model Building
A.1. Global F Test: run a test of the complete 2nd order model. Insert the STATISTIX
Printout of the results of this test. (3 points)
A.2 Fill in the following information for your Global F test (1 point per response/ total 5
points)
Test: H0:
Ha:
Test Statistic:
P-value:
Conclusion:
B. 1. Quadratics test: run a quadratics test. Insert the STATISTIX Printout of the results
of this test. (3 points).
B.2. Fill in the following information for your quadratics test (1 point per response/ total
7 points)
Full Model:
Reduced Model:
Test: H0:
H a:
Test Statistic:
P-value:
Conclusion:
C.1. Interactions Test: run an interactions test. Insert the STATISTIX Printout of the
results of this test. (3 points)
C.2.Fill in the following information for your interactions test (1 point per
response/total 7 points)
Full Model:
Reduced Model:
Test: H0:
Ha:
Test Statistic: ________________
P-value: ______________
Conclusion:
D. Would your model building be finished after the interaction test you ran in Part C.1.?
Why or why not? (2 points)
Part 2: Final Model Interpretations
A. Identify the least squares prediction equation for your final model after all the
testing was completed. You do not need to include the Printouts of any additional
tests conducted, just the results of the best test. Use the values from the Printout
and write the prediction equation below (use coefficient values, not betas). (3
points)
ŷ=
B. Report and interpret the R2 and the 2s associated with your best model. (4 points)
C. Would you use your model in practice? Why or why not? (3 points)
Part 3: Using the model
A. Using your best model, predict the price and estimate the mean price for Case # 17
in your data set. Insert the STATISTIX Predicted and Fitted Intervals Printout of the
results. (3 points)
B. Interpret the interval for the predicted value. (1 point)
C. Interpret the interval for the fitted value. (1 point)
price
miles
43998
34998
40998
28998
34998
39998
31998
36998
29998
36998
41998
34998
30998
35998
42998
23998
31998
40998
32998
50998
37998
30998
29998
45998
28998
year
14000
64000
54000
46000
26000
19000
36000
38000
20000
63000
42000
32000
78000
17000
14000
75000
57000
11000
35000
5000
51000
22000
35000
12000
78000
model
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1

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