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>Mod 2 Case – LR –

Year

1 Year 1

00)

1 January

2 February

3 March

4 April

421 5 May

June

7 July

298 8 August

9 September

10 October

November

300 298 12 December

0 0

– 0 – 0

ERROR:#DIV/0!

														New Star Grocery Company	Insert chart here
Customers		Sales			0	Number	Month	Customers (x)	Sales (y)	XY	X2	Y2
		January	1	8	5	2	3
	February	2	4	301
		March	3	7	3	10
	April		421	38	9
		May	425
		June		259		300	6
	July			298	318
		August	321
		September		215	202
		October	282	265
		November	235	3	12	11
		December
	Totals			– 0
Mean	0.00
X-bar	Y-bar
	b1												ERROR:#DIV/0!
	b0
	Y= b0+ b1x

Year 2
Forecast

New Star Grocery Company
Sales
b1 ERROR:#DIV/0! Year 2

b0 ERROR:#DIV/0! January 215 ERROR:#DIV/0! ERROR:#DIV/0!

259 ERROR:#DIV/0! ERROR:#DIV/0!

Y= b0+ b1x March

ERROR:#DIV/0! ERROR:#DIV/0!

May

ERROR:#DIV/0! ERROR:#DIV/0!

June

ERROR:#DIV/0! ERROR:#DIV/0!

August

ERROR:#DIV/0! ERROR:#DIV/0!

September

ERROR:#DIV/0! ERROR:#DIV/0!

October

ERROR:#DIV/0! ERROR:#DIV/0!

November

ERROR:#DIV/0! ERROR:#DIV/0!

December

ERROR:#DIV/0! ERROR:#DIV/0!

Totals

ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0!

Customers (x)	Actual Y(t)	Forecast F(t)	Variance
February
325
April	354
258
199
July	254
299
264
198
223
261
259.08

Scenario:

You

are a consultant who works for the Diligent Consulting Group

Your client, the New Star

Grocery Co

pany, believes that there may be a relationship between the number of customers who visit

the store during any given month (“customer traffic”) and the

total sales for that same month. In other

words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client

has collected customer traffic data over the past 1

–

month period, and monthly sales for that same 12

-m

onth period (Year 1).

Case Assignment

Using the customer traffic data and matching sales for each month of Year 1, create a Linear Regression

(LR) equation in Excel, assuming all assumptions for linear regression have been met. Use the Excel

template provided (see “Module

Case

–

Year 1”

spreadsheet tab), and be sure to include your LR

chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart.

After you have developed the LR equation above, you will use the LR equation to forecast sales for Year

(see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has

collected customer traffic data for Year 2. Your role is to complete the sales forecast using the LR equation

from Step 1 above.

After you have forecas

t Year 2 sales, your Professor will provide you with 12 months of actual sales data

for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and

average (total) variances from forecast to actual sales.

To complet

e the Module 2 Case, write a report for the client that describes the process you used above,

and that analyzes the results for Year 2. (What is the difference between forecast vs. actual sales for Year

—

by month and for the year as a whole?) Make a recom

mendation concerning how the LR equation

might be used by New Star Grocery Company to forecast future sales.

Data:

Download the Module 2 Case template here:

Data chart for BUS520 Case 2

. Use this template to

complete your Excel analysis.

Assignment Expectations

Excel Analysis

Conduct accurate and

complete Linear Regression analysis in Excel. Use Excel support to find information

on linear regression in Excel:

https://support.office.com/en

–

us/Search/

results?query=linear+regression

Written Report

Length requirements:

–

5 pages minimum

(not including Cover and Reference

pages).

NOTE:

You must submit 4

–

5 pages of

written discussion and analysis

This means that

you should avoid use of tables and charts a

s “space fillers.”

Provide a brief introduction to/background of the problem.

Your written (in Word) analysis should discuss the logic and rationale used to develop the LR

equation and chart.

Provide complete, meaningful, and accurate recommendation(s) con

cerning how the New Star

Grocery Company might use the LR equation to forecast future sales. (For example, how

reliable is the LR equation in predicting future sales?) What other recommendations do you

have for the client?

Write clearly, simply, and logica

lly. Use double

–

spaced, black Verdana or Times Roman font in 12

pt. type size.

Have an introduction at the beginning to introduce the topics and use keywords as headings to

organize the report.

Avoid redundancy and general statements such as “All organizat

ions exist to make a profit.” Make

every sentence count.

Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if

absolutely necessary, should rarely exceed five words.

Upload both your written report and Excel file to th

e case 2 Dropbox.

Here are some guidelines on how to build critical thinking skills.

· Emerald Group Publishing. (n.d.). Developing Critical Thinking. Retrieved from

http://www.emeraldinsight.com/learning/study_skills/skills/critical_thinking.htm

Scenario:

You are a consultant who works for the Diligent Consulting Group. Your client, the New Star

Grocery Company, believes that there may be a relationship between the number of customers who visit

the store during any given month (“customer traffic”) and the
total sales for that same month. In other
words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client
has collected customer traffic data over the past 12
–
month period, and monthly sales for that same 12
–
m
onth period (Year 1).

Case Assignment

Using the customer traffic data and matching sales for each month of Year 1, create a Linear Regression
(LR) equation in Excel, assuming all assumptions for linear regression have been met. Use the Excel
template provided (see “Module 2 Case
–

LR
–
Year 1”
spreadsheet tab), and be sure to include your LR
chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart.

After you have developed the LR equation above, you will use the LR equation to forecast sales for Year

2
(see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has
collected customer traffic data for Year 2. Your role is to complete the sales forecast using the LR equation
from Step 1 above.

After you have forecas
t Year 2 sales, your Professor will provide you with 12 months of actual sales data
for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and
average (total) variances from forecast to actual sales.

To complet
e the Module 2 Case, write a report for the client that describes the process you used above,
and that analyzes the results for Year 2. (What is the difference between forecast vs. actual sales for Year
2
—
by month and for the year as a whole?) Make a recom
mendation concerning how the LR equation
might be used by New Star Grocery Company to forecast future sales.

Data:

Download the Module 2 Case template here:

Data chart for BUS520 Case 2

. Use this template to
complete your Excel analysis.

Assignment Expectations

Excel Analysis

Conduct accurate and

complete Linear Regression analysis in Excel. Use Excel support to find information
on linear regression in Excel:

https://support.office.com/en
–
us/Search/
results?query=linear+regression

Written Report

Length requirements:

4
–
5 pages minimum

(not including Cover and Reference
pages).

NOTE:

You must submit 4
–
5 pages of

written discussion and analysis
.

This means that
you should avoid use of tables and charts a
s “space fillers.”

Provide a brief introduction to/background of the problem.

Your written (in Word) analysis should discuss the logic and rationale used to develop the LR
equation and chart.

Provide complete, meaningful, and accurate recommendation(s) con
cerning how the New Star
Grocery Company might use the LR equation to forecast future sales. (For example, how
reliable is the LR equation in predicting future sales?) What other recommendations do you
have for the client?

Write clearly, simply, and logica
lly. Use double
–
spaced, black Verdana or Times Roman font in 12
pt. type size.

Have an introduction at the beginning to introduce the topics and use keywords as headings to
organize the report.

Avoid redundancy and general statements such as “All organizat
ions exist to make a profit.” Make
every sentence count.

Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if
absolutely necessary, should rarely exceed five words.

Upload both your written report and Excel file to th
e case 2 Dropbox.

Scenario: You are a consultant who works for the Diligent Consulting Group. Your client, the New Star

Grocery Company, believes that there may be a relationship between the number of customers who visit

the store during any given month (“customer traffic”) and the total sales for that same month. In other

words, the greater the customer traffic, the greater the sales for that month. To test this theory, the client

has collected customer traffic data over the past 12-month period, and monthly sales for that same 12-

month period (Year 1).

Case Assignment

template provided (see “Module 2 Case – LR –Year 1” spreadsheet tab), and be sure to include your LR

chart (with a trend line) where noted. Also, be sure that you include the LR formula within your chart.

After you have developed the LR equation above, you will use the LR equation to forecast sales for Year 2

(see the second Excel spreadsheet tab labeled “Year 2 Forecast”). You will note that the customer has
collected customer traffic data for Year 2. Your role is to complete the sales forecast using the LR equation

from Step 1 above.

After you have forecast Year 2 sales, your Professor will provide you with 12 months of actual sales data

for Year 2. You will compare the sales forecast with the actual sales for Year 2, noting the monthly and

average (total) variances from forecast to actual sales.

To complete the Module 2 Case, write a report for the client that describes the process you used above,

and that analyzes the results for Year 2. (What is the difference between forecast vs. actual sales for Year

2—by month and for the year as a whole?) Make a recommendation concerning how the LR equation

might be used by New Star Grocery Company to forecast future sales.

Data: Download the Module 2 Case template here: Data chart for BUS520 Case 2. Use this template to

complete your Excel analysis.

Assignment Expectations

Excel Analysis

Conduct accurate and complete Linear Regression analysis in Excel. Use Excel support to find information

on linear regression in Excel: https://support.office.com/en-us/Search/results?query=linear+regression

Written Report

 Length requirements: 4–5 pages minimum (not including Cover and Reference

pages). NOTE: You must submit 4–5 pages of written discussion and analysis. This means that

you should avoid use of tables and charts as “space fillers.”

 Provide a brief introduction to/background of the problem.

 Your written (in Word) analysis should discuss the logic and rationale used to develop the LR

equation and chart.

 Provide complete, meaningful, and accurate recommendation(s) concerning how the New Star

Grocery Company might use the LR equation to forecast future sales. (For example, how
reliable is the LR equation in predicting future sales?) What other recommendations do you

have for the client?

 Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12

pt. type size.

 Have an introduction at the beginning to introduce the topics and use keywords as headings to

organize the report.

 Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make

every sentence count.

 Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if

absolutely necessary, should rarely exceed five words.

 Upload both your written report and Excel file to the case 2 Dropbox.

Scenario: You are a consultant for the Diligent Consulting Group (DCG). You have completed the first assignment, developing and testing a forecasting method that uses Linear Regression (LR) techniques (Module 2 Case). However, the consulting manager at DCG wants to try a different forecasting method as well. Now you decide to try Single Exponential Smoothing (SES) to forecast sales.

Using this Excel template:

Data chart for BUS520 SLP 2

, do the following:

Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled “Year 2 Forecast – MAPE”).

Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled “SES – MAPE”). Use 0.15 and 0.90 alphas.

Compare the MAPE calculated for the LR forecast (#1 above) with the MAPEs calculated using SES.

Then write a report to your boss in which you discuss the results obtained above. Using calculated MAPE values, make a recommendation concerning which method appears to be more accurate for the Year 2 data: SES or Linear Regression.

SLP Assignment Expectations

Analysis

Conduct accurate and complete SES analysis in Excel. You may also check the following link for your reference:

https://support.office.com/en-US/article/data-analysis-7e71735c-c471-47e1-84ef-a8c23dc3098b

Written Report

Length requirements: 2 – 3 pages minimum (not including Cover and Reference pages). NOTE: You must submit 2 – 3 pages of written discussion and analysis. This means that you should avoid use of tables and charts as “space fillers.”

· Provide a brief introduction to/background of the problem.

· Complete a written analysis that supports your Excel analysis, discussing the assumptions, rationale, and logic used to complete your SES forecast.

· Give complete, meaningful, and accurate recommendation(s) relating to whether LR or SES is more accurate in predicting sales.

· Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12 pt. type size.

· Have an introduction at the beginning to introduce the topics and use keywords as headings to organize the report.

· Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make every sentence count.

· Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if absolutely necessary, should rarely exceed five words.

· Upload both your written report and Excel file to the SLP 2 Dropbox .

Scenario:

You are a consultant for the Diligent Consulting Group (DCG). You have completed the first
assignment, developing and testing a forecasting method that uses Linear Regression (LR) techniques
(Module 2 Case). However, the consulting manager at DCG wants to

try a different forecasting method as
well. Now you decide to try Single Exponential Smoothing (SES) to forecast sales.

Using this Excel template:

Data chart for BUS520 SLP 2
, do the following:

Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled “Year 2
Forecast
–

MAPE”).

Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled “SES
–

MAPE”).
Use 0.15 and 0.90 alphas.

Comp
are the MAPE calculated for the LR forecast (#1 above) with the MAPEs calculated using SES.

Then write a report to your boss in which you discuss the results obtained above. Using calculated MAPE
values, make a recommendation concerning which method appear
s to be more accurate for the Year 2
data: SES or Linear Regression.

SLP Assignment Expectations

Analysis

Conduct accurate and complete SES analysis in Excel. You may also check the following link for your
reference:

https://support.office.com/en
–
US/article/data
–
analysis
–
7e71735c
–
c471
–
47e1
–
84ef
–
a8c23dc3098b

Written Report

L
ength requirements:

2
–

3 pages minimum

(not including Cover and Re
ference

pages).

NOTE:

You must submit 2
–

3 pages of

written discussion and analysis.

This means that
you should avoid use of tables and charts as “space fillers.”

Provide a brief introduction to/background of the problem.

Complete a written analysis that
supports your Excel analysis, discussing the assumptions,
rationale, and logic used to complete your SES forecast.

Give complete, meaningful, and accurate recommendation(s) relating to whether LR or SES is
more accurate in predicting sales.

Write clearly,
simply, and logically. Use double
–
spaced, black Verdana or Times Roman font in 12
pt. type size.

Have an introduction at the beginning to introduce the topics and use keywords as headings to
organize the report.

Avoid redundancy and general statements such

as “All organizations exist to make a profit.” Make
every sentence count.

Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if
absolutely necessary, should rarely exceed five words.

Upload both your written report an
d Excel file to the SLP 2 Dropbox .

Scenario: You are a consultant for the Diligent Consulting Group (DCG). You have completed the first
assignment, developing and testing a forecasting method that uses Linear Regression (LR) techniques
(Module 2 Case). However, the consulting manager at DCG wants to try a different forecasting method as
well. Now you decide to try Single Exponential Smoothing (SES) to forecast sales.
Using this Excel template: Data chart for BUS520 SLP 2, do the following:
Calculate the MAPE for Year 2 Linear Regression forecast (use the first spreadsheet tab labeled “Year 2
Forecast – MAPE”).
Calculate forecasted sales for Year 2 using SES (use the second spreadsheet tab labeled “SES – MAPE”).
Use 0.15 and 0.90 alphas.
Compare the MAPE calculated for the LR forecast (#1 above) with the MAPEs calculated using SES.
Then write a report to your boss in which you discuss the results obtained above. Using calculated MAPE
values, make a recommendation concerning which method appears to be more accurate for the Year 2
data: SES or Linear Regression.
SLP Assignment Expectations
Analysis
Conduct accurate and complete SES analysis in Excel. You may also check the following link for your
reference: https://support.office.com/en-US/article/data-analysis-7e71735c-c471-47e1-84ef-a8c23dc3098b
Written Report
 Length requirements: 2 – 3 pages minimum (not including Cover and Reference
pages). NOTE: You must submit 2 – 3 pages of written discussion and analysis. This means that
you should avoid use of tables and charts as “space fillers.”
 Provide a brief introduction to/background of the problem.
 Complete a written analysis that supports your Excel analysis, discussing the assumptions,
rationale, and logic used to complete your SES forecast.
 Give complete, meaningful, and accurate recommendation(s) relating to whether LR or SES is
more accurate in predicting sales.
 Write clearly, simply, and logically. Use double-spaced, black Verdana or Times Roman font in 12
pt. type size.
 Have an introduction at the beginning to introduce the topics and use keywords as headings to
organize the report.
 Avoid redundancy and general statements such as “All organizations exist to make a profit.” Make
every sentence count.
 Paraphrase the facts using your own words and ideas, employing quotes sparingly. Quotes, if
absolutely necessary, should rarely exceed five words.
 Upload both your written report and Excel file to the SLP 2 Dropbox .

Year 2
Forecast MA
PE

Year 2

= Mean error

= Mean percentage error

ME MPE

New Star Grocery Company

Sales

Customers (x)

Actual Y(t)

Forecast

F(t)

Y(t) – F(t)

APE

January

215

February

259

Y= b0+ b1x

March

325

April

354

May

258

June

199

July

254

August

299

September

264

October

198

November

223

December

261

MPE

MAPE

MAPE = Mean absolute percentage error

SES – MAPE

New Star Grocery Company

Alpha

Year 2

F(t) Y(t) – F(t) PE APE F(t) Y(t) – F(t) PE APE

January
February
March
April
May
June
July
August

September

October MAPE = Mean absolute percentage error
November
December

ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0!

ME MPE MAPE ME MPE MAPE

	Alpha
0.15	0.9
Sales, Y(t)
ME = Mean error
MPE = Mean percentage error
		ERROR:#DIV/0!

Year 2
Forecast MA
PE

Year 2

= Mean error

= Mean percentage error

ME MPE

New Star Grocery Company

Sales

Customers (x)

Actual Y(t)

Forecast

F(t)

Y(t) – F(t)

APE

January

215

February

259

Y= b0+ b1x

March

325

April

354

May

258

June

199

July

254

August

299

September

264

October

198

November

223

December

261

MPE

MAPE

MAPE = Mean absolute percentage error

SES – MAPE

New Star Grocery Company

Alpha

Year 2

F(t) Y(t) – F(t) PE APE F(t) Y(t) – F(t) PE APE

January
February
March
April
May
June
July
August

September

October MAPE = Mean absolute percentage error
November
December

ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0! ERROR:#DIV/0!

ME MPE MAPE ME MPE MAPE

	Alpha
0.15	0.9
Sales, Y(t)
ME = Mean error
MPE = Mean percentage error
		ERROR:#DIV/0!

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Year 2 Forecast

Year 2 Forecast MA PE

SES – MAPE

Year 2 Forecast MA PE

SES – MAPE

Year 2
Forecast

Year 2
Forecast MA
PE

Year 2
Forecast MA
PE