This article examines the data to determine the elements influencing demand for Royal Dutch Shell's product. Petroleum consumption from Canada (EIA1961), the United Kingdom (EIA1959), Germany (EIA1957), and the United States (EIA2005) were utilized as samples. Japan (EIA1962), South Korea (EIA1963), Italy (EIA1958), France (EIA1956), petroleum prices, coal and gas prices, income, and time period from 2000 to 2017. Two regression analysis models will be used to compare which one is best. The bivariate regression model, which compares only petroleum use and prices, will be utilized. The multivariate model of regression analysis which entails prices of other related goods such as coal and gas prices and income from various countries will be used.
Firstly, it is known that the function of demand is represent in the form of in which e< 0 and value of A is a constant. In this case the natural logarithm of both consumption quantity and the petroleum price will be calculated then a regression analysis will be run and relevant graphs drawn to find the relationships. Secondly the multivariate model for demand estimation will employ the use of the equation of where the natural logarithm of the variables are used. M represent the income, Py and Px are prices of the product Y and X respectively as well as Qy and Qx to present the quantity consumption of product Y and X respectively. The excel data analysis pack will be utilized to make a conclusion on the demand function for the Royal Dutch Shell products
Estimating constant-price elasticity of demand function
The demand function of the petroleum for various countries are determined in the attached excel file. The functions are arrived at after performing a bi-variate linear regression analysis of the petroleum consumption with the corresponding price for every country. The tables below show the regression output for the various countries and the corresponding graphs of graphs of ln (Q) versus ln (P)
United States
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.409
R Square
0.167
Adjusted R Square
0.115
Standard Error
0.033
Observations
18.000
ANOVA
df
SS
MS
F
Significance F
Regression
1.000
0.004
0.004
3.214
0.092
Residual
16.000
0.018
0.001
Total
17.000
0.021
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
11.093
0.062
180.314
0.000
10.963
11.224
10.963
11.224
ln Petro price
-0.027
0.015
-1.793
0.092
-0.059
0.005
-0.059
0.005
The demand equation for US becomes ln (Q) = 11.093 – 0.027 ln (P)
From the above data output, the demand function shows that the demand for petroleum in US is elastic and it obeys the demand cure , whereby an increase in price leads to a decrease in in consumption and vice versa. The graph gives an illustration for it all.
United Kingdom
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.569
R Square
0.323
Adjusted R Square
0.281
Standard Error
0.056
Observations
18.000
ANOVA
df
SS
MS
F
Significance F
Regression
1.000
0.024
0.024
7.642
0.014
Residual
16.000
0.051
0.003
Total
17.000
0.075
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
8.801
0.104
85.005
0.000
8.581
9.020
8.581
9.020
ln Petro price
-0.070
0.025
-2.764
0.014
-0.124
-0.016
-0.124
-0.016
Demand equation for UK: ln (Q) = 8.801 – 0.070 ln (P)
The same with demand for US, in UK the demand is elastic and law of demand is followed
The data analysis, output and graphs the other countries are illustrated in the attached excel file. The following table gives a summary of the demand equation of petroleum from various countries.
Country
Demand function
United State
ln (Q) = 11.093 – 0.027 ln (P)
United Kingdom
ln(Q) = 8.801 – 0.070 ln(P)
Canada
ln(Q) = 8.555+ 0.066 ln(P)
Germany
ln(Q) = 9.269 – 0.0816 ln(P)
Italy
ln(Q) = 9.108 – 0.163 ln(P)
Japan
ln(Q) = 10.003 – 0.106 ln(P)
South Korea
ln(Q) = 8.690 + 0.031 ln(P)
From the above table and the output from data analysis, it can be seen that only in Canada and South Korea where the demand for petroleum consumption is inelastic and that the law of demand does not apply for their cases.
Ordinary least square (OLS) is an important tool in modeling the demand function of a product. The slope obtained from the OLS out is very important as it clearly shows the elasticity of demand to the own-price of the product. At 1% increase in own-price of the product, there will be a less significant decrease in in consumer consumption hence the responsiveness will be generally low. The rate of response is estimated or determined by the slope of the OLS curves whereby for this case Japan will be more likely affected by an increase of 1% in prices, this is because Japan has the highest slope. As it can be seen from the output of OLS, the law of demand is only applying to US, UK, Japan, Italy, and Germany where demand is elastic while Canada and South Korea do not obey the law of demand
The values of‘t’ statistic, p-value, R2, adjusted-R2, and the ‘F’ test across all the countries is very low. These values are always the determinant for the relation or the correlation that exist between the parameters being measured. With low value of R-Squared, we can conclude that the quantity demanded for petroleum across the eight countries is weakly correlated with the prevailing market prices.
The scenario for the small chai in Newcastle cannot be recommended since with inelastic nature of demand in UK, the customer will respond slowly and later a stabilized market is realized. The increase in price also will shy away the customers and instead stick to the prevailing market prices.
An expectation in future shortages as announced by OPEC will automatically lead to an increase in demand so that the consumers can store for future use. When the Wall Street predict future reduction in demand due to recession , the consumers will tend to buy more at the current time and this will result into increase in price at the current time.
Estimating multivariate demand function
The output of the multivariate demand function is illustrate in the excel file attached below. With this, there is a strong correlation in the use of income as a factor affecting the demand of the petroleum across the countries
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.965086173
R Square
0.931391322
Adjusted R Square
0.868891322
Standard Error
2.965610161
Observations
17
ANOVA
df
SS
MS
F
Significance F
Regression
1
1910.298517
1910.298517
217.2066495
2.49E-10
Residual
16
140.717498
8.794843627
Total
17
2051.016015
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
0
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
99.99165
0.076989528
0.005223903
14.73793233
9.94955E-11
0.065915
0.088064
0.065915
0.088064
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.999943
R Square
0.999887
Adjusted R Square
0.941063
Standard Error
0.120292
Observations
18
ANOVA
df
SS
MS
F
Significance F
Regression
1
2171.430795
2171.431
150063.8
3.28E-33
Residual
17
0.245990862
0.01447
Total
18
2171.676786
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
0
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
ln US
1.144823
0.002955291
387.3807
5.5E-35
1.138588
1.151058
1.138588
1.151058
United Kingdom
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.998621
R Square
0.997245
Adjusted R Square
0.938421
Standard Error
0.460018
Observations
18
ANOVA
df
SS
MS
F
Significance F
Regression
1
1302.086863
1302.087
6153.051
4.03E-22
Residual
17
3.597479686
0.211616
Total
18
1305.684343
Coefficients
Standard Error
t Stat
P-value
Lower 95%
Upper 95%
Lower 95.0%
Upper 95.0%
Intercept
0
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
#N/A
1.754852
0.022371509
78.44139
3.33E-23
1.707652
1.802052
1.707652
1.802052
The independent variable to be concerned with is the income as it bring a significance change in the analysis. The substitute too is important they all make sense in analysis of the product
Discussion
The model of data analysis by OLS is a critical one. The challenges with it is that the output for Bi-variate or multivariate analysis does not truly relates to the actual data in the field. The analysis work in a form of estimation manner where by a large data is confined into an equation which results into an outliers
The samples of data that would have been used or included while estimating the factors affecting demand of petroleum include: government policies on various nations, demand for complementary products of petroleum, technology, supplementary product or the substitute products.
The use of multivariate model of ordinary least square method of linear regression is more plausible than bi-variate model. This is because in multivariate, many factors and data affecting the demand are put in place and considered as compared to bi-variate where the own-price of the product is used.
Conclusion
The demand or the consumption rate of petroleum across many country is a point of concern.It can be evident from the above analysis that indeed most countries , developed countries has a significant rate of demand fo the petroleum product. It is advisable that the government of these country to step in and look into other source of energy rather than depending ion petroleum. We can also conclude that income, substitutes are significantly affecting the demand for the petroleum. Lastly, the use of OLS in data analysis is recommended in analysis of large data such as this one where multivariate model is most preferred
Part B
Answers for the case study for the book store in Newcastle
50 percent discount is a good deal for consumers because one may only be in need of one 1 time so buying at discount will be an automatic saving to them.
This is because the offer was still good but it covered the initial aim of the stock turn over. The customer are always enticed with low prices. As one come into the market, the amount of money she/he has will detect the number of items she will buy so the better the discount.
The clerk was willing to such an offer of buying one and getting the other one free because at 50% discount her profit margin that she would have made initially is getting squeezed and diminishing, the fact being that with such offer they will sale most likely 3 to 4 times of the price they bought with or even end up selling more.
The worker hired ought not to be fired and I totally disagree. From the management view, one should come to know that none of two workers can produce and perform at the same level in the working place. Firing a worker or calling his/her services to a termination is always an act that is considered as a negative remark as you hire the workers. Workers are the human resources in any organization set up, and they stand to be motivated so that they can produce quality services. Just with reason that a worker cannot perform in same level as hi/her co-principal in a work place does not qualify him/her to be incompetent or inefficient. Each and every worker learns differently and have varying rates in adapting to a system, what the management need is only time. Comparing the employers is a totally wrong and unacceptable. Organizations has a system of setting their own parameters that they can use to evaluate their workers on different levels of performance. In most instances a time span of 6 months to one year is given to employees after which their performance is evaluated after which the efficiency level of employees is arrived at. There will be no cause of alarm if a worker meets the given parameters that have set and he/she is over or under performing as compared to his/her core-worker.
Works Cited
Sadorsky, Perry. "Risk factors in stock returns of Canadian oil and gas companies." Energy economics 23.1 (2001): 17-28.
Levenberg, Kenneth. "A method for the solution of certain non-linear problems in least squares." Quarterly of applied mathematics 2.2 (1944): 164-168.
Frynas, J. George. "Royal Dutch/Shell." New Political Economy 8.2 (2003): 275-285.
Livesey, Sharon M. "Eco-identity as discursive struggle: royal Dutch/Shell, Brent Spar, and Nigeria." The Journal of Business Communication (1973) 38.1 (2001): 58-91.
El-Sharif, Idris, et al. "Evidence on the nature and extent of the relationship between oil prices and equity values in the UK." Energy Economics 27.6 (2005): 819-830.
