BUS660 Grand Canyon University Advertising Sales Forecast Paper I need two documents: one is microsoft document and the other one is excel calculation. I s

BUS660 Grand Canyon University Advertising Sales Forecast Paper I need two documents: one is microsoft document and the other one is
excel calculation. I sent excel instruction sheet from the professor. Course Code
BUS-660
Class Code
BUS-660-O500
Criteria
Content
Percentage
70.0%
Develop Regression Model A
10.0%
Predict Advertising Sales Using Regression Model
A
10.0%
Develop Regression Model B
10.0%
Predict Advertising Sales Using Regression Model
B
10.0%
Explanation of Approach and Evaluation of
Outcomes of Regression Models
20.0%
Excel Spreadsheet
10.0%
Organization and Effectiveness
20.0%
Thesis Development and Purpose
7.0%
Argument Logic and Construction
8.0%
Mechanics of Writing (includes spelling,
punctuation, grammar, language use)
5.0%
Format
10.0%
Paper Format (use of appropriate style for the
major and assignment)
5.0%
Documentation of Sources (citations, footnotes,
references, bibliography, etc., as appropriate to
assignment and style)
5.0%
Total Weightage
100%
Assignment Title
Multiple Regression Models Case Study: Web Video on Demand
Unsatisfactory (0.00%)
A regression model that predicts the amount of advertising
sales based on the number of viewers and the length of the
program is not included.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model A is
not included.
A regression model that predicts the amount of advertising
sales based on the number of viewers, the length of the
program, and the average viewer age is not included.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model B is
not included.
An explanation of the approach used, along with an
evaluation of the outcomes of each regression model, is not
included.
A copy of the Excel spreadsheet file used to design the
regression model and to determine statistical significance is
not included.
Paper lacks any discernible overall purpose or organizing
claim.
Statement of purpose is not justified by the conclusion. The
conclusion does not support the claim made. Argument is
incoherent and uses noncredible sources.
Surface errors are pervasive enough that they impede
communication of meaning. Inappropriate word choice or
sentence construction is used.
Template is not used appropriately or documentation format
is rarely followed correctly.
Sources are not documented.
Total Points
100.0
Less than Satisfactory (74.00%)
A regression model that predicts the amount of advertising
sales based on the number of viewers and the length of the
program is present, but it lacks detail or is incomplete.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model A is
included, but it lacks accuracy or is incomplete.
A regression model that predicts the amount of advertising
sales based on the number of viewers, the length of the
program, and the average viewer age is included, but it lacks
detail or is incomplete.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model B is
included, but it lacks accuracy or is incomplete.
An explanation of the approach used, along with an
evaluation of the outcomes of each regression model, is
included, but lacks detail or is incomplete.
A copy of the Excel spreadsheet file used to design the
regression model and to determine statistical significance is
included, but is inaccurate or incomplete.
Thesis is insufficiently developed or vague. Purpose is not
clear.
Sufficient justification of claims is lacking. Argument lacks
consistent unity. There are obvious flaws in the logic. Some
sources have questionable credibility.
Frequent and repetitive mechanical errors distract the
reader. Inconsistencies in language choice (register) or word
choice are present. Sentence structure is correct but not
varied.
Appropriate template is used, but some elements are missing
or mistaken. A lack of control with formatting is apparent.
Documentation of sources is inconsistent or incorrect, as
appropriate to assignment and style, with numerous
formatting errors.
Satisfactory (79.00%)
A regression model that predicts the amount of advertising
sales based on the number of viewers and the length of the
program is present.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model A is
included.
A regression model that predicts the amount of advertising
sales based on the number of viewers, the length of the
program, and the average viewer age is present.
A prediction of the potential advertising sales generated by
the documentary special presented in Regression Model B is
included.
An explanation of the approach used, along with an
evaluation of the outcomes of each regression model, is
included.
A copy of the Excel spreadsheet file used to design the
regression model and to determine statistical significance is
included.
Thesis is apparent and appropriate to purpose.
Argument is orderly, but may have a few inconsistencies. The
argument presents minimal justification of claims. Argument
logically, but not thoroughly, supports the purpose. Sources
used are credible. Introduction and conclusion bracket the
thesis.
Some mechanical errors or typos are present, but they are
not overly distracting to the reader. Correct and varied
sentence structure and audience-appropriate language are
employed.
Appropriate template is used. Formatting is correct, although
some minor errors may be present.
Sources are documented, as appropriate to assignment and
style, although some formatting errors may be present.
Good (87.00%)
A regression model that predicts the amount of advertising
sales based on the number of viewers and the length of the
program is clearly developed.
A clearly developed prediction of the potential advertising
sales generated by the documentary special presented in
Regression Model A is provided.
A regression model that predicts the amount of advertising
sales based on the number of viewers, the length of the
program, and the average viewer age is clearly developed.
A clearly developed prediction of the potential advertising
sales generated by the documentary special presented in
Regression Model B is provided.
An explanation of the approach used, along with an
evaluation of the outcomes of each regression model, is clear
and well-developed.
A complete copy of the Excel spreadsheet file used to design
the regression model and to determine statistical significance
is included.
Thesis is clear and forecasts the development of the paper.
Thesis is descriptive and reflective of the arguments and
appropriate to the purpose.
Argument shows logical progressions. Techniques of
argumentation are evident. There is a smooth progression of
claims from introduction to conclusion. Most sources are
authoritative.
Prose is largely free of mechanical errors, although a few may
be present. The writer uses a variety of effective sentence
structures and figures of speech.
Appropriate template is fully used. There are virtually no
errors in formatting style.
Sources are documented, as appropriate to assignment and
style, and format is mostly correct.
Excellent (100.00%)
A complete and accurate regression model that predicts the
amount of advertising sales based on the number of viewers
and the length of the program is developed.
A comprehensive prediction of the potential advertising sales
generated by the documentary special presented in
Regression Model A is accurately developed with supporting
details.
A complete and accurate regression model that predicts the
amount of advertising sales based on the number of viewers,
the length of the program, and the average viewer is
developed.
A comprehensive prediction of the potential advertising sales
generated by the documentary special presented in
Regression Model B is accurately developed with supporting
details.
A comprehensive explanation of the approach used, along
with an evaluation of the outcomes of each regression model,
is thoroughly developed and well-supported.
A detailed and accurate copy of the Excel spreadsheet file
used to design the regression model and to determine
statistical significance is provided.
Comments
Thesis is comprehensive and contains the essence of the
paper. Thesis statement makes the purpose of the paper
clear.
Clear and convincing argument that presents a persuasive
claim in a distinctive and compelling manner. All sources are
authoritative.
Writer is clearly in command of standard, written, academic
English.
All format elements are correct.
Sources are completely and correctly documented, as
appropriate to assignment and style, and format is free of
error.
Points Earned
Multiple Regression Models Case Study: Web
Video on Demand
Web Video on Demand (WVOD) is an Internet video-on-demand streaming service. The
company offers a subscription service for $5.99/month, which includes access to all
programming and 30-second commercial intervals.
In the last year, the company has recently begun producing its own programming, including 30-,
60-, and 120-minute television shows, specials, and films. Programming has been developed for
teen audiences as well as adults.
The following data represent the amount of money brought in through advertising sales, the
average number of viewers, length of the program, and the average viewer age per program.
Advertising Sales
($)
28,000
25,500
31,000
29,000
20,500
14,500
27,000
23,500
19,500
23,000
18,000
29,500
30,000
25,000
22,500
Average # of
Viewers
(Millions)
10.1
11.4
19.9
13.6
12.5
3.5
15.1
3.7
4.3
12.2
5.1
15.9
16.8
8.5
9.1
Length of Program
(Minutes)
30
30
60
60
60
30
60
30
30
120
120
60
120
120
30
Average Viewer Age
(Years)
30
25
30
38
20
15
24
17
19
45
19
28
31
58
43
The WVOD executives are in the process of evaluating a partnership with several independent
filmmakers to fund and distribute socially conscious and diverse programming. The executives
have asked for regression models to be developed based on specific needs. The three regression
model requests and programming details are included below.
The WVOD executives would like to see a regression model that predicts the amount of
advertising sales based on the number of viewers and the length of the program. Develop this
© 2019. Grand Canyon University. All Rights Reserved.
regression model (“Regression Model A”). Web Video on Demand would like to acquire a 60minute documentary special about social media and bullying. The special is aimed at teen
viewers and is estimated to bring in 3.2 million viewers. Based on the regression model, predict
the advertising sales that could be generated by the special.
The WVOD executives would also like to see a regression model that predicts the amount of
advertising sales based on the number of viewers, the length of the program, and the average
viewer age. Develop this regression model (“Regression Model B”). Web Video on Demand
may acquire a 2-hour film that was a hit with critics and audiences at several international film
festivals. Initial customer surveys indicate that the film could bring in 14.1 viewers and the
average viewer age would be 32. Use this information to predict the advertising sales.
2
Advertising
Sales ($)
28.000
25.500
31.000
29.000
20.500
14.500
27.000
23.500
19.500
23.000
18.000
29.500
30.000
25.000
22.500
Average # of
Viewers
(Millions)
10,1
11,4
19,9
13,6
12,5
3,5
15,1
3,7
4,3
12,2
5,1
15,9
16,8
8,5
9,1
Length of
Program
(Minutes)
Average Viewer
Age (years)
30
30
60
60
60
30
60
30
30
120
120
60
120
120
30
30
25
30
38
20
15
24
17
19
45
19
28
31
58
43
Multiple Regression Models Case
Study: Web Video on Demand
Review “Multiple Regression Models Case Study: Web Video on Demand for this topic’s case study, predicting advertisin
sales for an Internet video-on-demand streaming service.
After developing Regression Model A and Regression Model B, prepare a 250-500-word executive summary of your findi
Explain your approach and evaluate the outcomes of your regression models.
Submit a copy of the Excel spreadsheet file you used to design your regression model and to determine statistical
significance.
Note: Students should use Excel’s regression option to perform the regression.
Use an Excel spreadsheet file for the calculations and explanations. Cells should contain the formulas (i.e., if a formul
was used to calculate the entry in that cell). Students are highly encouraged to use the “Multiple Regression Dataset”
resource to complete this assignment.
Mac users can use StatPlus:mac LE, free of charge, from Analystsoft.
Prepare the written portion of this assignment according to the guidelines found in the APA Style Guide, located in the
Student Success Center. An abstract is not required.
This assignment uses a rubric. Please review the rubric prior to beginning the assignment to become familiar with the
expectations for successful completion.
You are required to submit this assignment to Lopes Write. Please refer to the directions in the Student Success Center
Attachments
BUS-660-RS-TAMultipleRegressionModelsCase StudyWebVideoonDemand.docx
@ BUS-660-RS-TMultipleRegressionDataset.xlsx

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