Solved Assignments and Essays for Business Studies | Desklib

This article provides solved assignments and essays for Business Studies. It covers topics such as mortgage payments, linear programming, regression analysis, and economic order quantity. The content includes tables, formulas, and graphs to aid in understanding. The subject, course code, and college/university are not mentioned.

Running head: BUSINESS STUDIES
Business Studies
Name of Student:
Name of University:
Author’s Note:

1BUSINESS STUDIES
Table of Contents
Question 1........................................................................................................................................2
Question 2........................................................................................................................................3
Question 3........................................................................................................................................4
Question 4........................................................................................................................................7
Question 5......................................................................................................................................10
Reference.......................................................................................................................................14

2BUSINESS STUDIES
Question 1
1.a At first, we need to identify the monthly payment to be made as per the current conditions.
The loan repayment is being done for 25 years. The monthly is pay computed by using the PMT
function. This is computed as PMT (3.90%/12, 300, -487500).
1.b John’s monthly payments for each quarter needs to be considered as per the increase in the
interest rate of BoE by 4%. At a rate of 40.076%, Jeff will no longer be able to make the monthly
payment. The computation for the monthly payment has been considered with a cumulative
increase 0.25% on the initial repayment amount. The last monthly payment is assed with
multiplying 1.0025 with the 188th repayment amount of £ 4071.78.

3BUSINESS STUDIES
1.c It is seen that Jeff cannot exceed the monthly payment of £ 3200 for the mortgage. This needs
to be repaid with a total of 211 payments. In a similar way the monthly payment amount is
calculated by using the PMT function.

4BUSINESS STUDIES

5BUSINESS STUDIES
Question 2
2.a The formulation of the linear program using the excel solver is listed as follows. We have set
the decision variables to maximize the profit. The main constraints of the solver are seen with the
demand of the units ordered.
The revenue for the individual categories of the microwave is seen as £1,00,000 for cheap
variant, £20,000 for the average variant and £1,75,000 for the Premium variant. The Profit is
calculated by subtracting the cost of inputs such as Forming hours, Machining hours, Assembly
hours and Testing hours from the total revenue.
2b. (a)
In case the maximum demand for the premium model is limited to 1000, the profit of the
company will increase to £1,22,078.75.

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2b. (b)
In case the maximum available machine hours is 20000, the production hours will reduce.
However, this will have a positive impact on the profit. The profit will increase from
£1,07,262.50 to £1,14,487.50 in this case.
2c.
The given scenario presented with the formulation of the problem with the solver has
been able to depict that there is an overproduction of the materials at present. The demand for the
products has been 2000 for the cheap, 1200 for the average and 700 for the premium. This has
been able to signify that there is a need for optimizing the monthly production units to maximize
the profit (Safa et al. 2014).
In case the demand for the premium model was 1000, it would be sufficient for the
company to produce 2000 units for the cheap product, 1200 for the average product and 1000 for
the premium product. In addition to this, in case the company decides to change the maximum
available machine hours to 20000 units then it needs to produce 2000 of the cheap ovens, 1200
of the average ovens and 700 of the premium ovens (Fredendall and Hill 2016).
In order to market the products as per the motto to earn maximum profit, the strategy
needs to be considered with the formulation of a plan which will aim at promoting the products
which are in the premium and cheap category. The rationale for such a decision is seen to be
based on the demand constraint. It has been seen that the demand for the cheap and the average
product are highest. In this strategy the company will be able to keep the costs down and
maximum profit (Silva et al. 2017).
Question 3
3a. The summary of the distribution of the expected grades for both the exams is shown below as
follows:
Parameter
QM
Exam
Grade
Acting
Exam
Grade
Mean 44.13 55.38
Median 32.00 57.75
Mode 8.00 61.00
Standard Deviation 30.22 26.24
Sample Variance 913.02 688.75
Confidence Level
(95.0%) 11.28 9.80

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3b.
As per the given scenario it needs to be discerned that it is not possible to reject the null
value. Moreover, as per the lower and upper limit of the confidence interval it can be stated that
with an upper limit of 46.53 and lower limit of -22.11, there is no significant difference between
at 95% confidence interval (Bain 2017).
Student QM Study Hours QM
Exam
Grade
Acting
Study
hours
Acting
Exam
Grade
Difference
1 25 80.0 45 96.0 -71.00
2 5 8.0 3 29.0 -24.00
3 18 16.0 14 46.0 -28.00
4 29 87.0 49 32.5 -3.50
5 17 95.0 11 61.0 -44.00
6 39 14.0 19 61.0 -22.00
7 49 9.5 43 12.0 37.00
8 25 10.0 44 46.5 -21.50
9 6 35.0 0 95.0 -89.00
10 22 58.0 38 66.0 -44.00
11 37 8.0 38 24.0 13.00
12 31 89.0 50 62.0 -31.00
13 18 57.0 17 60.5 -42.50
14 45 22.0 33 10.0 35.00
15 5 39.5 33 61.0 -56.00
16 4 90.0 42 36.0 -32.00
17 17 23.0 45 39.0 -22.00
18 29 55.0 34 86.0 -57.00
19 16 74.0 15 87.0 -71.00
20 22 29.0 29 100.0 -78.00
21 28 69.5 49 76.0 -48.00
22 6 27.0 39 50.5 -44.50
23 21 12.0 31 55.0 -34.00
24 4 82.0 14 67.5 -63.50
25 24 26.0 10 94.0 -70.00
26 12 13.0 17 33.0 -21.00
27 4 23.0 34 85.0 -81.00
28 38 24.5 45 33.0 5.00
29 31 77.0 3 19.0 12.00
30 5 71.0 44 38.0 -33.00
Margin of error 12.20936308
Mean -34.32

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Lower Bound -22.11
Upper Bound 46.53
3c. As per the summary output of QM study hours and QM exam grade regression analysis, the
significance level is determined with 0.35. On comparing this value with summary output for
Acting Study hours and Acting Exam Grade, the significance of the regression is depicted with
0.4. Therefore, we can say that Acting exam study hours has more impact on acting exam grades.
The computed result is significant in nature (Fox 2015).
Output for QM study hours and QM exam grade
df SS
MS
F
Significance F
Regression 1 152.48 152.48 0.90 0.35
Residual 28 4737.392 169.19256
Total 29 4889.867
Output for Acting Study hours and Acting Exam Grade
df SS
MS
F
Significance F
Regression 1 182.4 182.4 0.7 0.4
Residual 28 6890.8 246.1
Total 29 7073.2
3d. By constructing a model for QM study hours and QM exam grade for students studying more
than 20 Hours the significance level is 0.218. However, when compared this value with
Summary output for Acting Study hours and Acting Exam Grade for students studying more than
20 Hours, the significance level is 0.33. Therefore, it can be inferred that the Acting Study hours
and Acting Exam Grade for students studying more than 20 Hours has higher impact than QM
study hours and QM exam grade for students studying more than 20 Hours (Darlington and
Hayes 2016). This result is similar to the previous findings. The second model constructed for
Summary output for QM study hours and QM exam grade for students studying less than 20
Hours show the significance level of 0.989. On comparing this value with Acting Study hours
and Acting Exam Grade for students studying less than 20 Hours the significance level is 0.867.
As per this interpretation it can be stated that QM study hours and QM exam grade for students
studying less than 20 Hours has higher impact than Acting Study hours and Acting Exam Grade
for students studying less than 20 Hours. It can be noted that for both the subjects there is in
significant relation between study hours and exam grade. Therefore, this model is better than the
former one (Chatterjee and Hadi 2015).

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Output for QM study hours and QM exam grade for students studying more than 20 Hours
df
SS
MS
F
Significance F
Regression 1 113.222 113.222 1.658557 0.21867933
Residual 14 955.7155 68.26539
Total 15 1068.938
Output for Acting Study hours and Acting Exam Grade for students studying more than 20
Hours
df
SS
MS
F
Significance F
Regression 1 41.25181 41.25181 0.973416 0.337664757
Residual 17 720.4324 42.37838
Total 18 761.6842
3e.
In my opinion, the given data consists of several inconsistencies. The most noteworthy
aspect is that several occasions despite of very few study hours the students have been able to
achieve a very high grade. This form of results is unrealistic in a real exam. For instance, there is
a student specialised in acting with zero hours of acting study and still achieved a grade of 95 in
the examination. Similarly for QM study there are students with four hours, five hours of study is
still achieved a grade of 82 and 71 in the examination respectively. On comparing these results
students studying for 49 hours and 45 hours, the achieved grade is only 9.5 and 22 in the
examination respectively. This shows that such a replication will not be possible for students
which actually sat the exam (Bolin, Hayes and Andrew 2014).
Question 4
4a. The EOQ for the given data is computed as 4264 units
No Change in any factors
Units £
Annual Demand (D) 250000
Annual cost for carrying one unit (C') 0.55
Annual cost per order (S') 20.00
Economic Order Quantity (EOQ) SQRT(2xDxS')/C

10BUSINESS STUDIES
Economic Order Quantity (EOQ) 4264
4b.
Simultaneous Increase of 7.5% on all factors
Units £
Annual Demand (D) 268750
Annual cost for carrying one unit (C') 0.59
Annual cost per order (S') 21.50
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4421
Simultaneous Decrease of 7.5% on all factors
Units £
Annual Demand (D) 231250
Annual cost for carrying one unit (C') 0.51
Annual cost per order (S') 18.50
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4101
Increase of 7.5% in Annual Demand (D)
Units £
Annual Demand (D) 268750
Annual cost for carrying one unit (C') 0.55
Annual cost per order (S') 20.00
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4421
Increase of 7.5% in Annual cost for carrying one unit (C')
Units £
Annual Demand (D) 250000
Annual cost for carrying one unit (C') 0.59
Annual cost per order (S') 20.00
Economic Order Quantity (EOQ) SQRT(2xDxS')/C

11BUSINESS STUDIES
Economic Order Quantity (EOQ) 4113
Increase of 7.5% in Annual cost per order (S')
Units £
Annual Demand (D) 250000
Annual cost for carrying one unit (C') 0.55
Annual cost per order (S') 21.50
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4421
Decrease of 7.5% in Annual Demand (D)
Units £
Annual Demand (D) 231250
Annual cost for carrying one unit (C') 0.55
Annual cost per order (S') 20.00
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4101
Decrease of 7.5% in Annual cost for carrying one unit (C')
Units £
Annual Demand (D) 250000
Annual cost for carrying one unit (C') 0.51
Annual cost per order (S') 20.00
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4434
Decrease of 7.5% in Annual cost per order (S')
Units £
Annual Demand (D) 250000
Annual cost for carrying one unit (C') 0.55
Annual cost per order (S') 18.50
Economic Order Quantity (EOQ) SQRT(2xDxS')/C
Economic Order Quantity (EOQ) 4101
4c.

12BUSINESS STUDIES
The recommendation plan has been followed with all the factors taken into consideration
sequentially. Firstly, in case there is no change in any factors, then the EOQ will be 4264 units
for the product. In case of simultaneous increase of 7.5% on all factors the EOQ would be 4421
units. On taking the third scenario, of simultaneous decrease of 7.5% on all factors, the EOQ will
be 4101 units. In case there is an increase of 7.5% in Annual Demand (D), the company would
need to order 4421 units which is similar to the case of simultaneous increase of 7.5% on all
factors. On taking the scenario of Increase of 7.5% in Annual cost for carrying one unit (C') the
EOQ is determined with 4113 units. Increase of 7.5% in Annual cost per order (S') needs to be
sufficed with an EOQ of 4421 units (Nia, Far and Niaki 2014). Decrease of 7.5% in Annual
Demand (D) needs to be accommodated with an EOQ of 4101 units which is similar to
simultaneous decrease of 7.5% on all factors. Decrease of 7.5% in Annual cost for carrying one
unit (C') will be having an EOQ of 4434 units. Lastly, decrease of 7.5% in Annual cost per order
(S') will be having an EOQ of 4101. Throughout the observations it can be depicted that Annual
Demand (D) is having the highest impact on the changes in EOQ followed by Annual cost per
order (S') (Taleizadeh and Pentico 2014).
Question 5
As per the given scenario the recommendation for building and equipping the factory in
one of the two countries is made with capital budgeting tools. Some of the main capital
budgeting tools used for the case includes the competition of “NPV, IRR, payback period and
profitability index” (Almazan, Chen and Titman 2017). In case the cost of capital is 11.25%
Allen, plc. should choose country A four setting up the factory. This decision was based on a
higher NPV of 1023401 for country A in compared to 956313.1 for country B and profitability
index of 1.49 for country A in compared to 1.46 for country B. Despite of a lower payback
period and higher IRR for setting up the factory in country B it is recommended for Allen, plc.
for choosing country A. This rational is based on the rule that in case of conflict it is best to
choose the NPV as it reflects a better primary goal, that is the growth of financial wealth (Andor,
Mohanty and Toth 2015). In case the cost of capital increases to 12.5% or decreases to 10% the
decision will be consistent in nature.
Country A
Cost of Capital 11.25%
Year
Now 1 2 3 4
Cost of Factory -2100000
Annual Profit/Loss 3,00,000 5,50,000 1250000 2300000
Total Cash Flows -2100000 3,00,000 5,50,000 12,50,000 23,00,000
Discounting Factor 0 0.90 0.81 0.73 0.65
Present Value of Cashflows -2100000 269662.92 444388.33 907841.338 1501508
Cumulative Cash Flow 269662.92 714051.26 1621892.59 3123401
Net Present Value 1023401.0
Payback Period -2100000 -1800000 -1250000 0.0 2300000
IRR 26%
Payback Period Cost of investment/annual net cash flow

13BUSINESS STUDIES
Payback Period 3 Years
Profitability Index PV of future cash flows/Initial Investment
Profitability Index 1.49
Country B
Cost of Capital 11.25%
Year
Now 1 2 3 4
Cost of Factory -2100000
Annual Profit/Loss 8,00,000 8,00,000 800000
170000
0
Total Cash Flows -2100000 8,00,000 8,00,000 800000
170000
0
Discounting Factor 0 0.90 0.81 0.73 0.65
Present Value of Cashflows -2100000 719101.12 646383.03 581018.456
110981
1
Cumulative Cash Flow 719101.12 1365484.2 1946502.61
305631
3
Net Present Value 956313.1
Payback Period -2100000.0 -1300000 -500000 300000
200000
0
IRR 29%
Payback Period Cost of investment/annual net cash flow
Payback Period 2.625 Years
Profitability Index PV of future cash flows/Initial Investment
Profitability Index 1.46
In case Cost of Capital increases to 12.5%
Country A
Cost of Capital 12.50%
Year
Now 1 2 3 4
Cost of Factory -2100000
Annual Profit/Loss 3,00,000 5,50,000 1250000 2300000
Total Cash Flows -2100000 3,00,000 5,50,000 12,50,000 23,00,000
Discounting Factor 0 0.89 0.79 0.70 0.62
Present Value of Cashflows -2100000 266666.667 434567.901 877914.952 1435879
Cumulative Cash Flow 266666.667 701234.568 1579149.52 3015028
Net Present Value 915028.2
Profitability Index PV of future cash flows/Initial Investment

14BUSINESS STUDIES
Profitability Index 1.44
Country B
Cost of Capital 12.50%
Year
Now 1 2 3 4
Cost of Factory -2100000
Annual Profit/Loss 8,00,000 8,00,000 800000 1700000
Total Cash Flows -2100000 8,00,000 8,00,000 800000 1700000
Discounting Factor 0 0.89 0.79 0.70 0.62
Present Value of Cashflows -2100000 711111.111 632098.765 561865.5693 1061302
Cumulative Cash Flow 711111.111 1343209.88 1905075.446 2966377
Net Present Value 866377.1
Profitability Index PV of future cash flows/Initial Investment
Profitability Index 1.41
In case Cost of Capital decreases to 10%
Country A
Cost of Capital 10.00%
Year
Now 1 2 3 4
Cost of Factory -2100000
Annual Profit/Loss
3,00,00
0
5,50,00
0 1250000 2300000
Total Cash Flows -2100000
3,00,00
0
5,50,00
0 12,50,000 23,00,000
Discounting Factor 0 0.91 0.83 0.75 0.68
Present Value of Cashflows -2100000 272727 454545 939143.501 1570931
Cumulative Cash Flow 272727 727273 1666416.23 3237347
Net Present Value 1137347.2
Profitability Index PV of future cash flows/Initial Investment
Profitability Index 1.54
Country B
Cost of Capital 10.00%
Year
Now 1 2 3 4

15BUSINESS STUDIES
Cost of Factory -2100000
Annual Profit/Loss
8,00,00
0
8,00,00
0 800000 1700000
Total Cash Flows -2100000
8,00,00
0
8,00,00
0 800000 1700000
Discounting Factor 0 0.91 0.83 0.75 0.68
Present Value of Cashflows -2100000 727273 661157 601051.841 1161123
Cumulative Cash Flow 727273
138843
0 1989481.59 3150604
Net Present Value 1050604.5
Profitability Index PV of future cash flows/Initial Investment
Profitability Index 1.50

16BUSINESS STUDIES
Reference
Almazan, A., Chen, Z. and Titman, S., 2017. Firm Investment and Stakeholder Choices: A Top‐
Down Theory of Capital Budgeting. The Journal of Finance, 72(5), pp.2179-2228.
Andor, G., Mohanty, S.K. and Toth, T., 2015. Capital budgeting practices: A survey of Central
and Eastern European firms. Emerging Markets Review, 23, pp.148-172.
Bain, L., 2017. Statistical analysis of reliability and life-testing models: theory and methods.
Routledge.
Bolin, J.H.; Hayes and Andrew F.(2014). Introduction to Mediation, Moderation, and
Conditional Process Analysis: A Regression‐Based Approach. New York, NY: The Guilford
Press. Journal of Educational Measurement, 51(3), pp.335-337.
Chatterjee, S. and Hadi, A.S., 2015. Regression analysis by example. John Wiley & Sons.
Darlington, R.B. and Hayes, A.F., 2016. Regression analysis and linear models: Concepts,
applications, and implementation. Guilford Publications.
Fox, J., 2015. Applied regression analysis and generalized linear models. Sage Publications.
Fredendall, L.D. and Hill, E., 2016. Basics of supply chain management. CRC Press.
Nia, A.R., Far, M.H. and Niaki, S.T.A., 2014. A fuzzy vendor managed inventory of multi-item
economic order quantity model under shortage: An ant colony optimization
algorithm. International Journal of Production Economics, 155, pp.259-271.
Safa, M., Shahi, A., Haas, C.T. and Hipel, K.W., 2014. Supplier selection process in an
integrated construction materials management model. Automation in Construction, 48, pp.64-73.
Silva, A., Rosano, M., Stocker, L. and Gorissen, L., 2017. From waste to sustainable materials
management: Three case studies of the transition journey. Waste management, 61, pp.547-557.
Taleizadeh, A.A. and Pentico, D.W., 2014. An economic order quantity model with partial
backordering and all-units discount. International Journal of Production Economics, 155,
pp.172-184.