project submission 3

QMB 3200 FALL 2022 Project Submission 3Complete 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)
QMB 3200 FALL 2022Project Submission 2
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 .docxfile; 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 Sunday, October
23 at 11:59pm; late work will be penalized 10% per day or portion of a day that it is late. You
will use your student data set (cars) to run all analyses.
Part 1.
a. Run an ANOVA to compare the mean price of your three vehicle models. Insert a
screenshot of the Printout. (6 points)
b. Is there a significant difference between the means (just state yes or no)?(2 points)
Yes
c. Run a Tukey post hoc test to rank the means of the prices of the models (even if there
was not a significant difference). Insert a screenshot of your Printout. (6 points)
d. List your vehicle models (use vehicle names, not numbers!) in order from that with the
highest mean price to that with the lowest mean price (indicate any “ties”).(3 points)
Civic
Altima2.5
FusionS
PART 2.
a. Imagine that you have reason to believe that the majority ofused vehicles for sale were
newer cars (produced in the year 2016 or after). Test this assumption with a binomial
test of proportions. Insert your Printout. (6 points)
b. Interpret the results of the above analysis. (3 points)
The result of the binomial test of proportion indicates that believe is not appropriate and
most of the used vehicles for sale are not newer cars.
PART 3.
a. Run a simple linear regression of your student data set using the price of the vehicle as
the dependent variable (y) and the mileage as the independent variable (x). Insert your
Printout. (6 points)
b. Is this a useful model? Interpret the p-value based on a one-tailed test. (2 points)
The model not useful as the p-value is more than 0.05 which indicate that the model has higher
variance and is inappropriate to use
c. What is the y-intercept of the model? (2 points)
The y-intercept of the model is 16536.7
d. Can the y-intercept be practically interpreted in this case (yes or no)? (2 points)
Yes, the intercept can be practically interpreted as the price of vehicle does not depends
on milege only, it has always a basic price which has been given here in the form of
intercept.
e. What is the slope of the model? (2 points)
The slope of the model is −0.05854
f. What is the R² of the model? (2 points)
The R2 of the model is 0.0433 or 43.3%.
g. Interpret the R² of this model. (3 points)
The R2 value of 0.0433 or 43.3% indicate that the model is able to explain the 43.3% in
the price value.
QMB 3200 Fall 2022 Project Submission 1
You will need to have collected your Student Data Set (cars) before you can begin this
assignment. See the video in Course Kaltura for more information.
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. Do not put your name on this document. Submit to Canvas as a single .doc or .docx file
(assignments submitted in any other format will not be graded). If you submit an incorrect
document, an incomplete document, or a file in an improper format, you MAY be allowed to resubmit (although this is not guaranteed) at a penalty of 25% (absolutely no exceptions). The
assignment is due by Friday, September 23 at 5:00pm. Late assignments are assessed a penalty
of 10% of the total grade per day or portion of the day.
The point value for each response follows each prompt; however, you may also have point(s)
deducted for failing to follow the instructions.
PART 1: Use the student data set that you collected(cars data) to answer the following
questions. 9 points total.
a. Identify your experimental unit. (1 pt.)
Automobiles
b. Identify the four variables of your data set and give the type of variable (quantitative or
qualitative) for each.(1 pt. each)
Variable 1: Price Type: Quantitative
Variable 2: Mileage
Variable 3: Year
Variable 4: Model
Type: Quantitative
Type: Quantitative
Type: Qualitative
PART 2: Use the Statistix 10 software to create a histogram of the price of your vehicles.8
points total.
a. Insert a clear screenshot of the histogram of the price of your vehicles (5 pts.).
b. By looking at the histogram above, do you expect there to be any outliers in your
sample? Why or why not? (2 pts.)
Yes, By looking at the histogram, it can be observed that some of the values lies outside
the overall pattern of the distribution which indicate presence of outliers.
c. How would we determine of there were in fact any outliers? Describe a method that
was discussed in class. (1 pt.)
To determine if there were in fact any outliers, we may use box plots
PART 3: Use the Statistix 10 software to complete the following section. 13 points total.
a. Run the descriptive statistics for the price of your 75 vehicles, finding the mean, the
standard deviation, and the confidence interval. Insert a screenshot of the printout
below. Make sure the screenshot is legible. (5 points)
b. Based on the Printout above, what is the average price of the used vehicle? Put the
value in the appropriate unit of measure. (2 pts.)
The average price of the used vehicle is found to be $13775.
c. What is the standard deviation?Put the value in the appropriate unit of measure. (2 pts.)
The standard deviation is found to be $1959.2
d. What is the 95% confidence interval?(1 pt.)
The 95% confidence interval for mean price is found to be [13324, 14226].
e. What is the practical interpretation of the confidence interval?(Use the appropriate
units of measure. (3 pts.)
A 95% confidence interval of the mean represents the possible range in which the mean
will lie. The 95% confidence interval for the price here is found to be [13324, 14226]
which suggests that the mean value of the price of cars lie in the range $13324 to
$14226.
PART 4. Submit this document to Canvas, then CHECK TO MAKE SURE YOU SUBMITTED THE
CORRECT DOCUMENT AND THAT IT IS IN THE CORRECT FORMAT! This part is not worth any
points, but if you neglect to do so and submit an incorrect document in error, you will be
penalized a 25% point deduction IF you are allowed to re-submit. DO YOURSELF A FAVOR
AND TAKE THE TIME TO CHECK!
Price
11995
12999
12750
9995
13202
13986
12922
14315
12322
11432
11766
10388
11987
12380
13995
13990
12532
12990
11988
14000
11520
10550
10495
11917
13227
15715
9950
14250
14580
13897
19981
12901
15955
16987
14000
13948
13815
15777
11999
11998
15975
15689
15991
14991
13820
13799
11770
15950
17044
Year
0
1
0
0
1
1
1
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
1
Mileage
50185
46525
42051
52037
49602
40206
43368
37614
47725
56700
57975
44456
54242
45598
42480
47926
39536
42362
51120
41383
62953
67228
40829
50087
65032
63591
53027
41819
49539
57969
51498
60550
48680
39481
37578
45294
44105
46481
38456
41715
52596
42565
41760
50856
43799
38231
57019
47452
40001
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
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
14900
15900
12956
15038
13642
14996
16995
14991
12000
12020
12495
16995
13995
13680
12040
12900
16997
14800
16896
13652
12500
11999
14829
14500
16988
13988
0
0
0
1
0
1
1
1
1
0
1
0
1
0
1
1
1
0
1
1
0
0
0
0
1
0
46998
57579
44870
44609
51900
45280
48815
42547
43602
44891
44957
43621
39149
57337
50503
40495
40517
47804
47598
40404
48517
45498
39016
46803
41942
40055
2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3