Business Economics Assignment Analysing Aspects of Managerial Economics
Question
Task:
Prepare a detailed business economics assignment addressing the following questions:
1) From the given table, calculate elasticity of price, total revenue and marginal revenue. Also, explain the relationship between AR and MR?
Price 
Quantity 
Total Revenue 
Marginal Revenue 
6 
0 


5 
100 


4 
200 


3 
300 


2 
400 


1 
500 


0 
600 


2) Demand forecasting is not a speculative exercise into the unknown. It is essentially a reasonable judgement of future probabilities of the market events based on scientific background. Explain the statement by elaborating different qualitative and quantitative methods of demand forecasting.
3) a) Define elasticity of supply and find the price from the given statement:
If Es of a good is 2 and a firm supplies 200 units at price of Rs 8 per unit, then at what price will the firm supply 250 units?
b) Calculate the elasticity of supply if a 15% increase in the price of soya bean oil increases its supply from 300 to 345 units.
Answer
Answer to question 1 of business economics assignment:
Elasticity of price:
Price elasticity as per the given table can be determined through dividing the percentage change in quantity demand with the percentage change in the price (MéndezCarbajo&Asarta. 2017). In order to determine the percentage change in demand following formula has been utilised:
% change in quantity = (Q_2 Q_1)/((Q_2+ Q_1)/2) x 100
To determine the percentage change in price, following formula has been utilised:
% change in price = (P_2 P_1)/((P_2+ P_1)/2) x 100
Considering the above formula, following table has been produced:
Point 
Price 
Quantity 
% change in price 
% change in quantity 
Elasticity 
A 
0 
600 

B 
1 
500 
2.00 
0.18 
0.09 
C 
2 
400 
0.67 
0.22 
0.33 
D 
3 
300 
0.40 
0.29 
0.71 
E 
4 
200 
0.29 
0.40 
1.40 
F 
5 
100 
0.22 
0.67 
3.00 
G 
6 
0 
0.18 
2.00 
11.00 
Table 1: Elasticity of price calculation
From the table 1, it can be seen that from point B to D, demand curve is inelastic where change in price leads to small change in output. From point E to G, demand curve is elastic in this interval and a change in price can lead to higher change in output.
Calculation of Total revenue and Marginal revenue:
Price 
Quantity 
Total revenue 
Marginal revenue 
6 
0 
0 

5 
100 
500 
5 
4 
200 
800 
3 
3 
300 
900 
1 
2 
400 
800 
1 
1 
500 
500 
3 
0 
600 
0 
5 
Table 2: Calculation of Total revenue and Marginal revenue
From the table 2, calculation of Total Revenue (TR) and Marginal Revenue (MR) can be seen. Here to determine Total Revenue, following formula has been utilised:
TR= Price x Quantity
In order to calculate the Marginal Revenue, following formula has been used:
MR = (Change in TR)/(Change in quantity)
Relation between TR and MR:
There is a positive relationship between the TR and MR until there is rise in TR. This means, when there is rise in TR, MR will be positive. Once, TR reaches to maximum point, MR will be zero or in other words, when MR is zero, TR reaches to its maximum point (Bergman, 2020). When TR starts to fall, MR becomes negative or in other word, when MR becomes negative, TR starts to fall. This can be seen from table 2, where till fourth price point, TR increased and MR remain positive. Next to this, as the TR started to fall, MR becomes negative.
Answer to question 2:
Forecasting is a statistical method which is utilised to determine future possibilities occurrence of events based upon the past trends. As per Hofmann andRutschmann (2018), forecasting is utilised when it is believed that there are past trends and there is possibility that it can be continued in future too and there is available information to determine significant trend statistically. There are two types of forecasting method which are quantitative forecasting and another one is qualitative forecasting (Scheidt et al., 2020).
These two types of forecasting method can be subdivided into several other type of forecasting which can be seen figure 1:
Figure 1: Forecasting approaches
As per figure 1, it can be seen that there are two different approached of quantitative forecasting, which is cause and effect and Time Series Forecasting. In time series forecasting, approximation is considered underpinning the past scenario in order to predict the future trend. On the other hand, Cause and Effect model predicts potential outcome under similar condition underpinning statistical relation among the factors under question and the independent variables (Merkuryeva et al., 2019). This is basically business simulation, which is utilised in business world widely to predict outcome of the dependent variable under certain situation of the independent variables.
On the other hand, qualitative forecasting is of four types, which is opinion of the jury where forecasting is done underpinning the opinion of the experts in the field. Next there is aggregate opinion, where salesperson predict future demand based upon the expertise. Thirdly there is consumer expectation model which produce forecasting based upon the survey outcome that companies perform frequently to understand market situation(Merkuryeva et al., 2019). Here statistical model is also fitted to determine the factors of influence and their power to influence. Lastly, there is expert assessment where bunch of experts provide their outcome and repeated game is played until consensus is produced.
Thus, forecasting both in qualitative as well as quantitative form is not based on speculation rather it has strong evidence and statistical support to defence the probable market outcomes based on scientific background and previous trends.
Answer to question 3:
3a.
Elasticity of Supply:
Price Elasticity of Supply or the PES is a measurement of sensitivity of quantity supplied of a good or service due to a change in price of the same (Mason& Roberts, 2018). In simple words, PES determinethe amount of change in goods and service supply due to change in the price.
Calculation of Price:
Given that,
Elasticity of supply (Es) = 2
Firm supply (Q1) = 200 units
Price (P1) = Rs 8 per unit
New supply needed (Q2) = 250
Price (P2) =?
% change in quantity supplied = [(Q2 – Q1)/Q2] *100
Hence, % change in quantity supplied = [(250 – 200)/250] * 100 = .2 * 100 = 20%
% change in price = [(P2 – P1)/P2] * 100
Hence, % change in price = [(P2 – 8)/P2] * 100
As per formula of price elasticity of supply = % change in supply / % change in price = 2
Putting values in price elasticity of supply equation:
2 = (.2 * 100) /[(P2 – 8)/P2] * 100
2 = .2/ (P2 – 8)/P2
2 = .2P2 / P2 – 8
2P2 – 16 = .2P2
2P2  .2P2 = 16
1.8P2 = 16
P2 = 8.88
Hence, it can be stated that, at Rs 8.88 firm will supply 250 units of the good. 3b.
Calculation of Elasticity of Supply:
As per the given information,
Percentage change in price of soya bean oil = 15%
Initial supply of soya bean (Q1) = 300 unit
New supply of soya bean (Q2) = 345 unit
Elasticity of supply =?
% change in supply = [(Q2 – Q1)/Q2] *100 = [(345 – 300) / 345] * 100
Hence, % change in supply = (45/345) * 100 = 13.04%
As per formula of price elasticity of supply = % change in supply / % change in price
Hence, Elasticity of Supply = 13.04%/15% = .86
Hence, the elasticity of supply of the Soya bean is inelastic (0.86), thus, change in price leads to small change in quantity supply.
Reference:
Bergman, M. (2020).Price Elasticity of Supply. Microeconomics for Managers.
Hofmann, E., &Rutschmann, E. (2018). Big data analytics and demand forecasting in supply chains: a conceptual analysis. The International Journal of Logistics Management.
Mason, C. F., & Roberts, G. (2018). Price elasticity of supply and productivity: an analysis of natural gas wells in Wyoming. The Energy Journal, 39(Special Issue 1).
MéndezCarbajo, D., &Asarta, C. J. (2017).Using FRED data to teach price elasticity of demand.Business economics assignment The Journal of Economic Education, 48(3), 176185.
Merkuryeva, G., Valberga, A., & Smirnov, A. (2019). Demand forecasting in pharmaceutical supply chains: A case study. Procedia Computer Science, 149, 310.
vomScheidt, F., Medinová, H., Ludwig, N., Richter, B., Staudt, P., &Weinhardt, C. (2020). Data analytics in the electricity sector–A quantitative and qualitative literature review. Energy and AI, 1, 100009.