MKAN1-UC 5103代写、代做Analytics、Java，Python，c/c++程序语言代做

日期：2020-05-21 11:08

Marketing Analytics Spring 2020 Final Exam

MKAN1-UC 5103 Marketing Analytics Spring 2020 Final Exam

Instructions (total 3 points):

? Develop the SAS syntax for each question in both Part I and Part II below and

save all syntax in ONE SAS file called "YourLastName_MA_Final_Exam"

You will need to save your Final Exam as a PDF File and submit that file. To save

as PDF, you can use the menu for Print and instead of a printer you will select

“Save as PDF” (cmd and P, on a Mac then select from bottom left, or Ctrl and P

on Windows then Destination “Save as PDF”).

Please inspect your PDF file before you submit it to ensure that no detail is cut

off (this will reduce your points). If any detail does not display on the PDF, you

may need to resize and add extra lines accordingly.

? Submit that file under this “Final Exam” Assignment as your answer to Final Exam

? Start your SAS file with this header by using SAS comments syntax:

o Course Name - Semester Year

o Your name

o Final Exam - date you complete the assignment

? Start your answer to each question below with the Question # by using SAS

comments syntax

? Keep your syntax in a clear format

? Please copy and paste the syntax generated into your SAS file even if you use

point-and-click methods/tasks

Questions (total 107 points):

Part I – sales csv dataset

1. (3 points) (1) Import “sales.csv” file into a SAS data set named “sales” in one of

your libraries. (2) Use proper syntax statements to rename the name of the

“QUANTITYORDERED” column in the sales data set into “quantity” and the

“PRICEEACH” column into “price”. (3) Use proper syntax statements to create a

new column in the data set called “revenue” where revenue = quantity * price.

2. (6 points) Based on Question 1, (1) create a Bar Chart showing the total number

of revenue the store earned by year id. (2) Use SAS comments syntax to describe

the information that you see from RESULTS (explain the chart).

3. (6 points) Based on Question 1, (1) create a Line Chart showing the average

quantity the store sold by month of the year. (2) Use SAS comments syntax to

describe the information that you see from RESULTS (explain the chart).

4. (6 points) Based on Question 1, (1) create a Pie Chart showing the total quantity

by deal size. (2) Use SAS comments syntax to describe the information that you

see from RESULTS (explain the chart).

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Marketing Analytics Spring 2020 Final Exam

5. (6 points) Based on Question 1, (1) create a Mosaic Plot where y-axis is year id

and x-axis is product line. (2) Use SAS comments syntax to describe the

information that you see from RESULTS (explain the chart).

6. (6 points) Based on Question 1, (1) Create a Box Plot showing the revenue the

store earned by product line. Also, in Settings APPEARANCE, add a proper title

for the chart. (2) Use SAS comments syntax to describe the information that you

see from RESULTS (explain the plot).

7. (9 points) Based on Question 1, (1) use point and click Distribution Analysis Task

under Statistics to analyze the distribution of the “revenue” variable (make sure

in OPTIONS, check Histogram and goodness-of-fit tests, and Normal quantilequantile

plot). (2) Use SAS comments syntax to answer the questions below:

a. From the histogram, what you can conclude?

b. From goodness-of-fit tests, what is the null hypothesis? Based on the pvalue,

do we reject the null hypothesis? Then, what can we conclude

from the test?

c. What does Q-Q plot tell us?

8. (9 points) Based on Question 1, (1) use point and click One-sample T Test Task

under Statistics to test if the mean of the revenue variable is equal to 3,000. (2)

Use SAS comments syntax to answer the questions below:

a. What is the assumption of one-sample t-test? Does the target variable

“revenue” meet the assumption? How do you know?

b. What is the null hypothesis for this t-test? Do we reject the null

hypothesis? Why or why not? What will you conclude?

9. (10 points) Based on Question 1, (1) use point and click One-Way ANOVA Task

under Linear Models to test if the means of the revenue in different product line

groups are the same or not. (2) Use SAS comments syntax to answer the

questions below:

a. What are the two assumptions of One-Way ANOVA? Choose one

assumption to answer: Does the target variable “revenue” meet the

assumption you choose? How do you know?

b. What is the null hypothesis for this One-Way ANOVA test? Do we reject

the null hypothesis? Why or why not? What will you conclude?

c. List three specific pairs of product line groups where the mean revenues

are significantly different (significance level = 0.05)? How do you know

they have significant different means in revenue?

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Marketing Analytics Spring 2020 Final Exam

Part II – skincancer csv dataset

Dataset Description

State State Name in US

Lat Latitude

Mort Skincancer mortality cases

Ocean If it is near Ocean or not -- Yes 1; No 0

Long Longitude

1. (2 points) Import “skincancer.csv” file into a SAS data set named “skincancer” in

one of your libraries.

2. (6 points) Based on Question 1, (1) create a Scatter Plot showing the relationship

between latitude and skincancer mortality cases. (2) Use SAS comments syntax

to describe the information that you see from RESULTS (explain the plot).

3. (9 points) Based on Question 1, (1) use point and click Correlation Analysis Task

under Statistics to explore the linear relationship between mortality, latitude and

longitude (Make sure to display p-values under OPTIONS-Statistics). (2) Use SAS

comments syntax to answer the questions below:

a. What are the relationships between each pair of the three variables?

How do you know?

b. Which correlation amongst the three variables is/are significant? How do

you know?

4. (10 points) Based on Question 1, (1) use point and click Two-sample T Test Task

under Statistics to test if the means of the mortality cases near ocean and far

away from ocean are the same or not. (2) Use SAS comments syntax to answer

the questions below:

a. What are the two assumptions of Two-sample t-test? Does the target

variable meet both assumptions or not? How do you know?

b. What is the null hypothesis for this Two-sample t-test? Do we reject the

null hypothesis? Why or why not? What will you conclude?

c. Is there any limitation in this two-sample t-test? If yes, please explain. If

no, why not?

5. (10 points) Based on Question 1, (1) create a Linear Regression Model to predict

the mortality cases by using latitude and longitude variables. (2) Use SAS

comments syntax to answer the questions below:

a. Explain the meanings of different p-values in the results. What shall we

conclude about the different variables and the model?

b. What is the value of R Square? What does it mean?

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Marketing Analytics Spring 2020 Final Exam

6. (9 points) Based on Question 1, (1) create a Linear Regression Model to predict

the mortality cases by using latitude and the interaction effect between ocean

and latitude. (2) Use SAS comments syntax to answer the questions below:

a. Explain the meanings of different p-values in the results. What shall we

conclude about the different variables and the model?

b. Is this model better than the model in Question 5 above? How do you

know?

Note:

? This is a 110-point Final Exam. You need to submit your answers by the due time,

answer satisfactorily all points in each question and follow these instructions

fully. Late submissions will receive automatic 0 points.

? This is an individual take-home Final Exam. Any teamwork found will receive

automatic 0 points.