CIS615 Unit One Assignment
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PARK UNIVERSITY
CIS621 Data Analysis for Business
Analytics
Title: Case Study Analysis Godrej Properties: Divergent dividend
decision
Spring 2 Grad 2024
Professor Jamila Kridan
03-17-2024
Stephen M. Hill
Decision made by Godrej Properties In the highly competitive and ever-changing real estate industry, companies often face complex and multifaceted decision-making processes. In this context, Godrej Properties, a well-known real estate developer in India,
made a strategic decision regarding its dividend policy that has significant implications for the company and its shareholders. This decision involved a departure from the company's previous dividend distribution practices, with Godrej Properties refraining from paying dividends for four consecutive years. The company justified this decision by emphasizing its focus on reinvesting profits into high-return investment opportunities rather than distributing dividends to shareholders. Despite its historical consistency in paying dividends, the board believed that retaining capital for growth would ultimately maximize shareholder value in the long term. By analyzing the factors that led to this decision and its potential impacts, we can gain insights into the complex decision-making processes that companies in dynamic industries must navigate.
Data-Driven Decision Analysis
The decision made by Godrej Properties appears to be informed by a combination of strategic considerations and industry trends rather than solely driven by data. While the company cited the pursuit of high-return
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Problem:
Sales of industrial vacuum cleaners at Yarena Supply CO. over the past 13 months are as follows:
Sales in P1,000
Month
Sales in P1,000
Month
11
January
14
August
14
February
17
September
15
March
12
October
10
April
14
November
15
May
16
December
17
June
11
January
11
July
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2. THE PROJECT
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Refer to the following sensitivity report:
Variable Cells
Cell
Name
Final Value
Reduced Cost
Objective Coefficient
AllowableIncrease
AllowableDecrease
$B$2
Product_1
0
−2
5
2
1E+30
$B$3
Product_2
175
0
7
1E+30
1
$B$4
Product_3
0
−1.5
9
1.5
1E+30
Constraints
Cell
Name
Final Value
Shadow Price
Constraint R.H.Side
AllowableIncrease
AllowableDecrease
$H$9
Resource_A
0
0
100
1E+30
100
$H$10
Resource_B
525
0
800
1E+30
275
$H$11
Resource_C
700
1.75
700
366.6666667
700
If the objective function coefficient for Product_1 were increased to a value of 8, which of the following would be TRUE?
The optimal solution would change.
The optimal objective function value would stay the same.
Product_1 would enter the solution (the final value of the variable Product_1 would be greater than zero).
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Refer to the following sensitivity report:
Variable Cells
Cell
Name
Final Value
Reduced Cost
Objective Coefficient
AllowableIncrease
AllowableDecrease
$B$2
Product_1
0
−2
5
2
1E+30
$B$3
Product_2
175
0
7
1E+30
1
$B$4
Product_3
0
−1.5
9
1.5
1E+30
Constraints
Cell
Name
Final Value
Shadow Price
Constraint R.H.Side
AllowableIncrease
AllowableDecrease
$H$9
Resource_A
0
0
100
1E+30
100
$H$10
Resource_B
525
0
800
1E+30
275
$H$11
Resource_C
700
1.75
700
366.6666667
700
If the right-hand side of Resource_C increases to 750, which of the following would be true
The objective function value will increase by $1.75
The objective function value will decrease by $1.75
The objective function value will increase by $87.50
The objective function value will decrease by $87.50
None of the answer choices is correct
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Refer to the following sensitivity report:
Variable Cells
Cell
Name
Final Value
Reduced Cost
Objective Coefficient
AllowableIncrease
AllowableDecrease
$B$2
Product_1
0
−2
5
2
1E+30
$B$3
Product_2
175
0
7
1E+30
1
$B$4
Product_3
0
−1.5
9
1.5
1E+30
Constraints
Cell
Name
Final Value
Shadow Price
Constraint R.H.Side
AllowableIncrease
AllowableDecrease
$H$9
Resource_A
0
0
100
1E+30
100
$H$10
Resource_B
525
0
800
1E+30
275
$H$11
Resource_C
700
1.75
700
366.6666667
700
If the objective function coefficient for Product_1 were decreased to a value of 3, which of the following would be TRUE?
The optimal solution would change.
The optimal objective function value would stay the same.
Product_1 would enter the solution (the final value of the variable Product_1 would be greater than zero).
A. 1 only
B. 2 only
C. 3 only
D. 1, 2 and 3
E. Only 1 and 2
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1.Alabama llc collected sales dara for four months, janurary through april. the data is janurary-17, feburary -24, march -25 and april -26. usung a three month moving average. the forcaste calue of may is
a. 21
b.22
c.23
d.24
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Period (t)
Enrollment (1000s)
1
6.5
2
8.1
3
8.4
4
10.2
5
12.5
6
13.3
7
13.7
8
17.2
9
18.1
Develop a linear regression with a dependent variable of “Enrollment (1000s).
A.What is the value of R square? (Make sure two decimal places.)
B.What is the value of intercept? (Make sure two decimal places.)
C.What is the coefficient value of Period (t)? (Make sure two decimal places.)
D.Is the coefficient of Period (t) significant? (Yes:1, No: 0)
E.What is the forecast for year 10?
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4.
In the past, Peter Kelle's tire dealership in Baton Rouge sold an average of
1,100
radials each year. In the past 2 years,
220
and
250,
respectively were sold in fall,
360
and
300
in winter,
150
and
160
in spring, and
320
and
440
in summer. With a major expansion planned, Kelle projects sales next year to increase to
1,300
radials.
Part 2
Based on next year's projected sales, the demand for each season is going to be (enter your responses as whole numbers):
Season
Demand
Fall
__________
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Microsoft Excel 16.0 Sensitivity Report
Worksheet: [Book1]Sheet7
Report Created: 9/26/2020 10:43:05 AM
Engine: Standard LP/Quadratic
Objective Cell (Min)
Cell
Name
Final Value
$B$13
Obj Func AN
4135000
Decision Variable Cells
Final
Reduced
Objective
Allowable
Allowable
Cell
Name
Value
Cost
Coefficient
Increase
Decrease
$B$11
variables AN
55000
0
33
7.0000001
41.0000001
$C$11
variables AO
0
7
48
1E+30
7
$D$11
variables BN
25000
0
27
8.0000001
7.0000001
$E$11
variables BO
47000
0
35
7.0000001
8.0000001
Constraints
Final
Shadow
Constraint
Allowable
Allowable
Cell
Name
Value
Price
R.H. Side
Increase
Decrease
$B$16
LHS
55000
41
55000
10500
47000
$B$17
LHS
72000
35
72000
10500
47000
$B$18
LHS
80000
-8
80000
47000
10500
$B$19
LHS
47000
0
57500
1E+30
10500
The answers to the questions…
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A method of estimating future demand usedfor decision making in order to set reasonable targets and control orlimit uncertainties or risks.
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