Accounting Decision Support Tools

The assignment consists of five questions that cover various topics in accounting decision support tools. Question 1 discusses the decision making process and its steps, while Questions 2-5 focus on financial analysis, regression models, and break-even analysis. The questions require students to apply theoretical concepts to real-world scenarios, such as optimizing a fish vendor's sales, analyzing hotel overbooking policies, predicting car prices using regression models, and calculating break-even points for products A and B.

Accounting Decision Support
Tools
Assignment - III
Student Name
[Pick the date]

Question 1
(a) The decision making process comprises of a host of defined steps as illustrated as follows.
 Objective definition of the expectations from the decision carried out by decision
maker. Futher, the possible outcomes that are likely from the the decision also got to
be listed.
 Adeqaute brainstoriming must be perfomed so as to identify the various alternatives
that the decision makers can potentially choose.
 Taking into consideration the states of nature along with the chances of occurrence of
these states, the alternative analysis needs to be carried out.
 The payoff expeted from each alternative under the different states of nature must be
clearly defined in monetary terms.
 Finally the decision maker should take the decision based on the payoff matrix
derived along with decision making model.
(b) In decision analysis framework, alternatives refer to the different strategies or course of
action that the decision maker can possibly pursue. The challenge for the decision maker is
to determine the optimum alternative considering the states of nature which are future
situations that can arise and are out of control of decision maker. For example states of nature
may be in the form of economic outlook being optimistic, neutral and pessimistic. Based on
the underlying likelihood of the states of nature, suitable needs to be decided.
(c) (1) The conditional profits matrix for the possible opetions for purchase is shown below:
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(2) Case: Let the fish vendor is an optimist
The best suitable option for an optimist would be 30kg sea food.
(3) Case: Let the fish vendor is an pessimistic
The best suitable option for a pessimistic would be 10kg sea food.
(4) Case: Let the fish vendor is applying Laplace rule
The best suitable option through Laplace rule would be 25kg sea food.
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(5) Case: Let the fish vendor is applying regret matrix
The best suitable option through regret matrix would be 25kg sea food.
(6) Case: Let the fish vendor is agreed to use the method to maximize the expected monetary
value
The best suitable option through regret matrix would be 25kg sea food.
(7) Weekly demand foe fillets = Normal distribution
Mean = 20 kg
Standard deviation = 5 kg
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Total kg buy each week in regards to maximize the expected profit =?
Question 2
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Question 3
(a) Excel setup to simulate the 30 days prediction of various costs.
(b) The model representing the value and formula is shown below:
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(c) Total average daily cost for the overbooking for the hotel is computed through the
simulation model and is represented below:
(d) The advice based on the above computations has been offered below.
Date : 11th May, 2018
From: STUDENT NAME
To: Manager
Dear Sir
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The aim of the simulation model that has been formulated based on available inputs is to
critically analyse the current policy adhered by the hotel dealing with the issue of overbooking.
The main takeaway of this model is in the form of average cost computations from a host of
random repetitions. This clearly highlights flaw with the present hotel policy on overbooking as
it is not cost efficient. In order to achieve the same, it is advisable that four overbookings must be
accepted and the current practice of three overbooking must be discontinued.
Yours faithfully
STUDENT NAME
Question 4
a) Model A(Simple Regression)
From the excel generated regression output, the following observations can be made.
 The significance of βmileage is confirmed since p value (0.002) < α (0.05).
 The R2 value highlights the independent variable’s ability to offer explanation to 72.21%
of change seen in the car price.
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Model B(Simple Regression)
From the excel generated regression output, the following observations can be made.
 The significance of βage is confirmed since p value (0.002) < α (0.05).
 The R2 value highlights the independent variable’s ability to offer explanation to 73.11%
of change seen in the car price.
Model C
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From the excel generated regression output, the following observations can be made.
 The insignificance of βage & βmileage is confirmed since respective p value < α (0.10).
 The R2 value highlights the independent variables joint ability to offer explanation to
73.88% of change seen in the car price.
Optimum Model
Model A – Significant independent variable. R2 = 0.7221
Model B - Significant independent variable. R2 = 0.7311
Model C – Insignificant independent variables. Hence rejected.
From the above summary, it is evident that the preferred model would be Model B which has age
as the predictor variable for car price.
b) In relation to the simple regression models i.e. Model A and Model B, considering the both
have just predictor variable, the superior model would be one which has a higher coefficient
of determination which for the given case is Model B. Besides, the inverse relation between
price and age and also mileage does not cause any surprise. This is because as the car
becomes aged, the price comes out down on account of depreciation and newer model coming
in the market which are more advanced. Also, higher mileage implies higher internal damage
to the components of the car and hence the car price would dip.
c) The multiple regression model is based on a host of assumptions. One of these is that
multicollinearity must be absent. This implies that the independent variables must be exhibit
any significant correlation. However, this assumption is being violated for the given multiple
regression model which is why this model is not advisable to be used for prediction.
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Question 5
(a) Number of units of product a required to manufacture to be at break-even is calculated
through model.
Profit = 0, (break-even)
Required units of A to manufacture = 300 units of A
(b) Number of units of product a required to manufacture for the profit of $1600 is calculated
through model.
Profit = $1600
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Required units of A to manufacture = 500 units of A
(c) Number of units of product A and B required manufacturing for the profit of $20,000 is
calculated through model.
Product A: Product B = 2:1
Profit = $20,000
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Required units of A to manufacture = 2000 units of A
Required units of B to manufacture = 1000 units of B
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