Product Sales Analysis

8/10/2019 Product Sales Analysis

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CHAPTER ONE

INTRODUCTION

1.0 Background of the Study

Statisticians have played an increasing and important role in nearly all

phases of human endeavor. The influence of statisticians has now

spread to manufacturing, agriculture, business, communication,

economics, education, psychology, political science, sociology and

numerous other fields of life. Statistics is a body of scientific methods

and theory of collecting, organizing, summarizing, presenting and

analyzing data and as well drawing valid conclusions and making

reasonable decision. (Williams 1941)

Since statistics is a body of scientific methods as regards decision

making, its application is found in all discipline of human knowledge,

especially the sciences, where the available information can be put in

some kind of numerical forms. In fact, this project tends to study the

effect of crude oil revenue and the strength it has on the economy.

Production of crude oil can be said to be the burning of organic raw

materials, refined materials by chemical means into new products.

In many economies or business situations, two or more variables are

closely related and their relationship may help to predict the futurestate of the economy or the degree of the association may help to

analyze the performance of the economy or business. For example,

there must be a close relationship between the wages paid to workers

and their productivity. The degree of the association between these


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variables will enable the employer to decide on whether to increase

wages from workers. It is this relationship between variables that

seems to depend on regression analysis.

Crude oil is a naturally occurring flammable liquid consisting of a

complex mixture of hydrocarbons of various molecular weights and

other liquid organic compounds, that are found in geologic

formations beneath the Earth's surface. A fossil fuel, it is formed when

large quantities of dead organisms, usually zooplankton and algae, are

buried underneath sedimentary rock and undergo intense heat and

pressure. (George 1956)

Petroleum is recovered mostly through oil drilling. This comes after the

studies of structural geology (at the reservoir scale), sedimentary basin

analysis, and reservoir characterization (mainly in terms of porosity and

permeable structures). It is refined and separated, most easily

by boiling point, into a large number of consumer products, from

petrol (or gasoline ) and kerosene to asphalt and chemical reagents used

to make plastics and pharmaceuticals. Petroleum is used in

manufacturing a wide variety of materials, and it is estimated that the

world consumes about 88 million barrels each day.

The use of fossil fuels such as petroleum can have a negative impact on

Earth's biosphere, releasing pollutants and greenhouse gases into the

air and damaging ecosystems through events such as oil spills. Concern

over the depletion of the earth's finite reserves of oil, and the effect this

would have on a society dependent on it, is a field known as peak oil.
http://en.wikipedia.org/wiki/Flammable_liquidhttp://en.wikipedia.org/wiki/Hydrocarbonhttp://en.wikipedia.org/wiki/Organic_compoundhttp://en.wikipedia.org/wiki/Formation_(stratigraphy)http://en.wikipedia.org/wiki/Formation_(stratigraphy)http://en.wikipedia.org/wiki/Earthhttp://en.wikipedia.org/wiki/Fossil_fuelhttp://en.wikipedia.org/wiki/Zooplanktonhttp://en.wikipedia.org/wiki/Algaehttp://en.wikipedia.org/wiki/Sedimentary_rockhttp://en.wikipedia.org/wiki/Oil_drillinghttp://en.wikipedia.org/wiki/Boiling_pointhttp://en.wikipedia.org/wiki/Petrolhttp://en.wikipedia.org/wiki/Gasolinehttp://en.wikipedia.org/wiki/Kerosenehttp://en.wikipedia.org/wiki/Asphalthttp://en.wikipedia.org/wiki/Reagenthttp://en.wikipedia.org/wiki/Plastichttp://en.wikipedia.org/wiki/Pharmaceuticalshttp://en.wikipedia.org/wiki/Barrel_(unit)http://en.wikipedia.org/wiki/Oil_spillhttp://en.wikipedia.org/wiki/Oil_depletionhttp://en.wikipedia.org/wiki/Non-renewable_resourcehttp://en.wikipedia.org/wiki/Peak_oilhttp://en.wikipedia.org/wiki/Peak_oilhttp://en.wikipedia.org/wiki/Non-renewable_resourcehttp://en.wikipedia.org/wiki/Oil_depletionhttp://en.wikipedia.org/wiki/Oil_spillhttp://en.wikipedia.org/wiki/Barrel_(unit)http://en.wikipedia.org/wiki/Pharmaceuticalshttp://en.wikipedia.org/wiki/Plastichttp://en.wikipedia.org/wiki/Reagenthttp://en.wikipedia.org/wiki/Asphalthttp://en.wikipedia.org/wiki/Kerosenehttp://en.wikipedia.org/wiki/Gasolinehttp://en.wikipedia.org/wiki/Petrolhttp://en.wikipedia.org/wiki/Boiling_pointhttp://en.wikipedia.org/wiki/Oil_drillinghttp://en.wikipedia.org/wiki/Sedimentary_rockhttp://en.wikipedia.org/wiki/Algaehttp://en.wikipedia.org/wiki/Zooplanktonhttp://en.wikipedia.org/wiki/Fossil_fuelhttp://en.wikipedia.org/wiki/Earthhttp://en.wikipedia.org/wiki/Formation_(stratigraphy)http://en.wikipedia.org/wiki/Formation_(stratigraphy)http://en.wikipedia.org/wiki/Organic_compoundhttp://en.wikipedia.org/wiki/Hydrocarbonhttp://en.wikipedia.org/wiki/Flammable_liquid


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Product sales analysis is the prediction of future sales of a product, either

judgmental or based on previous sales patterns. A further definition shows it

is a careful examination of a company's sales records, that is done to measure

the company's performance and to try and improve it or the break-down of

sales figures by region, product, customer, market, etc for a given period as a

control measure sales aptitude tests

A sales analysis report includes sales-related metrics, also called

key performance indicators, for a specified time-period. Sales analysis

reports provide a record of past performance and can be used as a tool

to predict future business performance.

Sales analysis reports are used to measure and monitor sales

department performance. Sales managers use these reports to develop

sales strategies, better understand past results and to help forecast

future results. Sales representatives use these reports to closely track

their performance against sales goals and to plan and prioritize sales

activities. Finance and human resources staff members use these

reports to calculate sales compensation and bonus payouts for sales

department employees.

1.1 Aims and Objectives of Study

1. To fit a regression model to the crude oil variables.

2. To study and describe the nature of correlation of the crude oil

variables.
http://en.mimi.hu/marketingweb/sales.htmlhttp://en.mimi.hu/marketingweb/product.htmlhttp://en.mimi.hu/marketingweb/customer.htmlhttp://en.mimi.hu/marketingweb/market.htmlhttp://en.mimi.hu/marketingweb/control.htmlhttp://en.mimi.hu/marketingweb/sales.htmlhttp://en.mimi.hu/marketingweb/sales.htmlhttp://en.mimi.hu/marketingweb/control.htmlhttp://en.mimi.hu/marketingweb/market.htmlhttp://en.mimi.hu/marketingweb/customer.htmlhttp://en.mimi.hu/marketingweb/product.htmlhttp://en.mimi.hu/marketingweb/sales.html


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1.2 Significance of Study

The study will be of importance to NNPC as it will help them to predict

what the projected revenue will be in the near future.

1.3 Scope and Limitations

1.3.1 Scope

The formation of the Nigeria National Petroleum Corporation in 1977

was an off- shoot of different development in the countrys oil industry.

Commercial oil findings were made in 1956 by shell in Olobiri present

day of Bayelsa State. This was after about half a country of exploration

activities in the southwestern Nigeria where the explorers made

bitumen.

The success of shell in Niger Delta attracted other companies including

Mobil, Texaco, Gulf (now Chevron), Agip, Esso and Safrap (now Elf) into

the country to take up concessions that had been relinquished by shell.

In 1963, the first offshore, finds were made by Gulf (Okan-1), Mobil

(Ata-1) and Texaco (Kulama-1). Since then the Nigeria oil industry has

gone through rapid expansion, notwithstanding the discourteous effects

of the civil war between 1967 and 1970, and of course, the recent

community problems have invariably grounded several operators. It

was believed that if government had more say in the running of the

country, the situation about the production of crude oil would take a

new look regardless of the effect. Unfortunately the Ministry of

Petroleum resources played mainly regulatory roles in the industry


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then. Today, government participation stands at 55 percent in shell and

60 percent in Chevron, Mobil, Agip, Elf and Texaco.

In 1971, shortly before the country joined the Organization of

Petroleum Exporting Countries (OPEC) as its tenth member. The

Nigerian National Oil Corporation (NNOC) was established as an organ

for exercising control over the industry which was dominated by

multinationals. The corporation also provided a platform for

government to take up participating interest in the operations of the

companies starting with 331.3% in Agip has on the 1965 agreement,

which allowed the government to acquire interest in the company in the

event of a successful exploration effort. Other acquisitions were to

follow and by 1974, the acquisition had covered all the operating

companies with the percentage of government interest increased to

60%, thus given birth to the joint ventures relationship between

government and the major oil producing companies, a relationship

which subsists till today.

NNPC was formed through the merge of the NNOC and the Ministry of

Petroleum Resources in April 1977. The Corporation was given powers

and operational interest in refining, petrochemicals and products

transportation as well as marketing. This was in addition to its

exploration and production activities that were carried out mainly

offshore Niger Delta by its forerunner of the NNOC. Between 1978 and

1989, NNPC constructed refineries in Warri, Kaduna and Port Harcourt

and took over the 35,000 barred shell refinery established in Port

Harcourt in 1965. In addition, the corporation constructed several


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kilometers of pipelines, pumps stations and depots for distribution of

petroleum of petroleum products throughout the country and

pioneered the exploration activities in the Chard Basin around

Maiduguri in 1982. Products retail, which hitherto was firmly in the

hands of major multinational oil companies, was deregulated to

accommodate indigenous marketers.

In 1990, with a view of improving the countrys oil and gas revenue

base, oil exploration, which has progressively moved offshore Niger

Delta, was further extended into frontier areas including the deep

offshore and the inland basins of Anambra, Benin(Dahomey) and Benue

where acreages were allocated to several multinationals after signing a

producing sharing contract with NNPC.

Nnpc.com

1.3.2 Limitations

Nigeria as a country is lagging behind in area of research. Most times the

reason is not that Nigerian student are not hard working rather the fact

is that effort made in getting relevant information in most cases proves

abortive. This is one of the challenges and limitations we had in carrying

out this research. As statisticians, without a well presented data, the

overall result will be highly misleading. Also sourcing for this

information was really tasking and cumbersome.


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1.4 Literature Review

A lot of literates have contributed to the theme of the research. Some ofwhich includes:

Considine and Larson (1995): in a research on Uncertainty and the

convenience yield in crude oil price backwardations examined why

firms hold stocks of crude oil, particularly during price backwardations

when spot prices exceed prices for forward delivery. Using a stochastic

control model, the paper showed that the equilibrium value of

inventories contains: the conventional Hotelling principle; the

convenience yield from the classical theory of storage; and an option

value related to price uncertainty. Her empirical results suggest that a

convenience yield and risk premium are important elements of crude

oil price backwardations.

Herbert (1996) in his write-up on Data analysis of sales of natural gas to

households in the United States was of the view that Regression diagnosticsand a time series analysis of residuals are used to help define regression

equations and to identify, the weaknesses of these equations for explaining

monthly deliveries of natural gas to residential customers in the United

States for the time period April 1979 through March 1983. According to

him, more than 99% of the monthly variation in deliveries is explained by a

linear regression equation which includes heating degree days, coolingdegree days, and the price of natural gas as independent variables. Final

estimated relationships yield useful monthly and annual estimates of natural

gas deliveries to residential customers in the United States for the time

period April 1983 through March 1984. Most importantly, the estimated
http://www.tandfonline.com/action/doSearch?action=runSearch&type=advanced&result=true&prevSearch=%2Bauthorsfield%3A(Herbert%2C+John+H.)http://www.tandfonline.com/action/doSearch?action=runSearch&type=advanced&result=true&prevSearch=%2Bauthorsfield%3A(Herbert%2C+John+H.)


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results when used in conjunction with the diagnostics and the time series

analysis of the residuals indicate the possible strengths, weaknesses, and

applicability of the estimated relationships.

Borenstein (1997) in a paper on comparing crude oil and gasoline

prices he tested and confirmed that retail gasoline prices respond more

quickly to increases than to decreases in crude oil prices. Among the

possible sources of this asymmetry are production/inventory

adjustment lags and market power of some sellers. By analyzing price

transmission at different points in the distribution chain, we attempt to

shed light on these theories. Spot prices for generic gasoline show

asymmetry in responding to crude oil price changes, which may reflect

inventory adjustment effects. Asymmetry also appears in the response

of retail prices to wholesale price changes, possibly indicating short-run

market power among retailers.

Wang and Mulllins (1999) in an article on Fluorescence lifetime

studies of crude oil measured fluorescence lifetimes of a series of crude

oil at various concentrations for UV-visible excitation and emission

wavelengths. The lifetime results are compared with fluorescence

spectra and quantum yields for these solutions. The concentration

effects of energy transfer and quenching are large and result in a

significant decrease in fluorescence lifetimes for high concentrations

and for heavy crude oils. Thus, radiationless processes dominate in

energy transfer. At high concentrations, energy transfer produces large

red shifts in fluorescence emission spectra, while quenching produces a


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large reduction in quantum yields. Stern-Volmer analyses of lifetime

and quenching data show a linear dependence of energy transfer and

quenching rates on concentration. The rate constants are consistent

with collisions which are very efficient at energy transfer and

quenching, and the rates of these two processes are comparable.

Fleming and Ostdiek (1999) in an article on the impact of energy

derivates on the crude oil market examined the effects of energy

derivatives trading on the crude oil market. There is a common public

and regulatory perception that derivative securities increase volatilityand can have a destabilizing effect on the underlying market. Consistent

with this view, we find an abnormal increase in volatility for three

consecutive weeks following the introduction of NYMEX crude

oil futures. While there is also evidence of a longer-term volatility

increase, this is likely due to exogenous factors, such as the continuing

deregulation of the energy markets. Subsequent introductions of crude

oil options and derivatives on other energy commodities have no effect

on crude oil volatility. We also examine the effects of derivatives trading

on the depth and liquidity of the crude oil market. This analysis reveals

a strong inverse relation between the open interest in crude oil futures

and spot market volatility. Specifically, when open interest is greater,

the volatility shock associated with a given unexpected increase in

volume is much smaller.

Timothy and Larson (2001) in an article on Risk premiums on inventory

tested for the presence of risk premiums on crude oil and natural gas.


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The econometric analysis followed from a stochastic model in which the

equilibrium value of inventories depends on a convenience yield and an

option value related to price uncertainty. The empirical findings provide

rather strong support for the presence of risk premiums and also

evidence for the existence of convenience yields. The risk premiums

rose sharply with greater price volatility and help to explain why prices

for immediate sales often exceed prices for future delivery.

Krichene (2002) in a paper on World crude oil and natural gas

examined world markets for crude oil and natural gas over the period1918 1999; it analyzes the time-series properties of output and prices

and estimates demand and supply elasticities during 1918 1973 and

1973 1999. Oil and gas prices were stable during the first period; they

became volatile afterwards, reflecting deep changes in the market

structure following the oil shock in 1973. Demand price elasticities were

too low; however, demand income elasticities were high. Supply price

elasticities were also too low. The elasticity estimates help to explain

the market power of the oil producers and price volatility in response to

shocks, and corroborate elasticity estimates in energy studies.

Fong and See (2002) in a paper on Markov switching model on future

crude oil prices examined the temporal behaviour of volatility of daily

returns on crude oil futures using a generalized regime switching model

that allows for abrupt changes in mean and variance, GARCH dynamics,

basis-driven time-varying transition probabilities and conditional

leptokurtosis. This flexible model enables us to capture many complex


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features of conditional volatility within a relatively parsimonious set-up.

We show that regime shifts are clearly present in the data and dominate

GARCH effects. Within the high volatility state, a negative basis is more

likely to increase regime persistence than a positive basis, a finding

which is consistent with previous empirical research on the theory of

storage, e.g. Ng and Pirrong (1994). The volatility regimes identified by

our model correlate well with major events affecting supply and

demand for oil. Out-of-sample tests indicate that the regime switching

model performs noticeably better than non-switching models

regardless of evaluation criteria. We conclude that regime switchingmodels provide a useful framework for the financial historian interested

in studying factors behind the evolution of volatility and to oil futures

traders interested short-term volatility forecasts.

Santis (2003) in a book on crude oil price fluctuation explained

why crude oil prices fluctuate, the main cause being the quota regime,

which characterizes the OPEC agreements. Given that the Saudi

oil supply is inelastic in the short term, a shock in the oil market is

accommodated by an immediate price change. By contrast, a dominant

firm behaviour in the long term causes an output change, which is

accompanied by a smaller price change. This explains why oil prices

overshoot. The results of a general equilibrium model applied to Saudi

Arabia support this analysis. They also indicate that Saudi Arabia does

not have any incentive for altering the crude oil market equilibrium

with either positive or negative supply shocks, as its welfare declines;

and that it has an incentive (disincentive) for intervening if a negative
http://www.sciencedirect.com/science/article/pii/S0140988301000871http://www.sciencedirect.com/science/article/pii/S0140988301000871


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(positive) demand shock hits the crude oil market. A second set of

simulations is designed to understand what kind of OECD policy might

help to bring down prices. A tax cut would worsen the situation,

whereas policies that can increase the price elasticity of demand seem

to be very effective.

Horn (2003) in a write- up OPECs optimal crude oil price was of

the view that OPEC decided to stabilize oil prices within a range of 22

28 US-Dollar/barrel of crude oil. Such an oil-price-level is far beyond the

short and long run marginal costs of oil production, beyond even that inregions with particularly high costs. Nevertheless, OPEC may achieve its

goal if world demand for oil increases substantially in the future and

oil resources outside the OPEC are not big enough to accordingly

increase production. In this case OPEC, which controls about 78% of

world oil reserves, has to supply a large share of that demand increase.

If we assume OPEC will behave as a partial monopolist on the oil

market, which takes into consideration the reaction of the other

producers to its own sales strategy, it can reach its price target. Lower

prices before 2020 are probable only if the OPEC cartel breaks up.

Higher prices are possible if production outside OPEC is inelastic as

assumed by some geologists, but they would probably stimulate the

production of unconventional oil based on oil sand or coal. Crude

oil prices above 30 US-Dollar/barrel are therefore probably not

sustainable for a long period.


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Kaufmann and Laskowski (2005): In a write-up on the causes of

the relationship between the price of crude oil and refined petroleum

products revisited the issue of asymmetries in the relation between the

price of crude oil and refined petroleum products in the United States.

An econometric analysis of monthly data indicates that the asymmetric

relation between the price of crude oil and motor gasoline is generated

by refinery utilization rates and inventory behavior. The asymmetric

relation between the price of crude oil and home heating oil probably is

generated by contractual arrangements between retailers and

consumers. Together, these results imply that price asymmetries maybe generated by efficient markets. Under these conditions, there is little

justification for policy interventions to reduce or eliminate price

asymmetries in motor gasoline and home heating oil markets

Krichene (2005) A simultaneous equation model for world crude oil

markets A model for world crude oil and natural gas markets is

estimated. It confirms low price and high income elasticities of demand

for both crude oil and natural gas, which explains the market power of

oil producers and price volatility following shocks. The paper

establishes a relationship between oil prices, changes in the nominal

effective exchange rate (NEER) of the U.S. dollar, and the U.S. interest

rates, thereby identifying demand shocks arising from monetary policy.

Both interest rates and the NEER are shown to influence crude prices

inversely. The results imply that crude oil prices should be included in

the policy rule equation of an inflation targeting model.


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Baumeister and Peersman (2007) in a book on Sources of the volatility

puzzle in the crude oil market noted that a remarkable feature of the

crude oil market is a dramatic rise in oil price volatility over time which

has been accompanied by a substantial fall in oil production volatility.

We investigate the sources of this opposite evolution of both oil market

variables. Our main finding is that the observed volatility puzzle can be

rationalized by the fact that the price elasticities of both oil supply and

oil demand have decreased considerably over time. This implies that

small disturbances on either side of the oil market generate large price

reactions but only modest quantity adjustments. We also document thatstructural shocks which shift the oil demand and supply curves have

become smaller in the more recent past thereby even mitigating

oil price fluctuations.

Duan et al (2008) in a journal on The dynamics of online word-of-mouth

and product sales: An empirical investigation of the movie industrynoted that there are growing interests in understanding how word-of-

mouth (WOM) on the Internet is generated and how it influences

consumers purchase decisions at retail outlets. A unique aspect of the

WOM effect is the presence of a positive feedback mechanism between

WOM and retail sales. They characterize the process through a dynamic

simultaneous equation system, in which we separate the effect of online

WOM as both a pre-cursor to and an outcome of retail sales. They apply

our approach to the movie industry, showing that both a movie's box

office revenue and WOM valence significantly influence WOM volume.

WOM volume in turn leads to higher box office performance. This


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positive feedback mechanism highlights the importance of WOM in

generating and sustaining retail revenue.

Cooper and Kleinschmidt (2009) in their article on Success Factors in

Product Innovation focused on the factors responsible for the success of

product innovation. Management of the 1980s and 1990s faces a

dilemma in product innovation. On the one hand, there is increasing

pressure to develop and launch more new products. On the other hand,

product innovation remains a very high-risk endeavor, fraught with

difficulties and littered with failures. According to the author, if

businesses are to survive and prosper, managers must become more

judicious at selecting new product winners, and at effectively managing

the new product process from product idea through to launch. In recent

years, there have been a number of investigations into the success

factors in product innovation and have yielded many insights into why

new products succeed or fail. Traditionally, new product success has

been measured in one way, namely in financial terms. But as one

research team points out that financial return is one of the most easily

quantifiable industrial parameters, it is far from the only important one.

Simester (2011) in his paper on the Effect of Search Costs on the

Concentration of Product Sales investigated the Internets Long Tail

phenomenon. By analyzing data collected from a multi-channel retailer,

it provides empirical evidence that the Internet channel exhibits a

significantly less concentrated sales distribution when compared with

traditional channels. Previous explanations for this result have focused

on differences in product availability between channels. However, he


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demonstrated that the result survives even when the Internet and

traditional channels share exactly the same product availability and

prices. Instead, he found consumers usage of Internet search and

discovery tools, such as recommendation engines, are associated with

an increase the share of niche products. They concluded that the

Internets Long Tail is not solely due to the increase in product selection

but may also partly reflect lower search costs on the Internet. If the

relationships we uncover persist, the underlying trends in technology

portend an ongoing shift in the distribution of product sales.


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CHAPTER TWO

DATA COLLECTION

2.1 Method of Data Collection

report from In this project, the method of data collection used is

documentation and Nigeria National Petroleum Corporation (NNPC),

Headquarter Lagos (Annex), Ikoyi and the annual report of statistics

department Central Bank of Nigeria (CBN).

2.2 Limitation of Data Collection

The limitation of data collection is that the data are only in Crude oil

production on NNPC and the data collected are on the actual revenue

from crude oil production, number of manual labour used in the

production of crude oil, cost of equipment used for the production of

crude oil and the number of hours worked in the production of crude oil

from 2000 2011 and all the variables are in aggregate..

2.3 Data Collected

The data collected includes:

Revenue: This is the amount the organization makes as profit at

the end of production from both the locally-used and the exported

crude oil.


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Labour: This is the number of persons used in the production of

crude oil.

Cost of Equipment: This is the amount spent of the purchase of

equipment used in the production of crude oil.

Manhour: This the total number of hours worked in the

production factory.

YEAR Revenue

(N MILLION)

Labour Cost of Equipment

(N MILLION)

2000 47.054 578.00 5.80

2001 116.427 585.00 5.80

2002 112.090 580.00 5.00

2003 110.500 589.00 13.60

2004 173.200 597.00 15.90

2005 101.177 613.00 14.50

2006 156.989 645.00 18.90

2007 150.020 693.00 20.20

2008 218.000 658.00 25.00

2009

224.581 663.00 37.90

2010 350.659 370.00 49.60


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2.4 Problems Encountered

The process of getting relevant information in most cases is always

cumbersome. This is one of the challenges we had in carrying out the

research. As a statistician, without a well-presented data, the overall result

will be highly misleading how much more when there is no data. Also,

sourcing for necessary information was really tasking. Too many factors

militated against the quick completion of this research but to mention a few.


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CHAPTER THREE

ANALYSIS OF DATA

3.0 Data Analysis

The data for this research to be analyzed are presented in section 2.3

(Data Presentation) and SPSS software will also be used to do analysis.

In the data analysis the variables for the analysis in millions of naira are

actual revenue from crude oil production and Projected revenue from

crude oil production denoted as Y and X 1 respectively.

With the aid of SPSS the following are the means, standard deviation

and variables of the crude oil variables

= i = 1,2,3, , n

S2 = ( )

Descriptive Statistics

Mean Std. Deviation N

Revenue 160.0634 81.87160 11

Labour 597.3636 84.99326 11

Cost_of_Equipment 19.2909 13.90536 11


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3.1 Regression Model and Parameters Estimation

The regression model is multiple linear as given below

Y i = 0 + 1 x 1i + ei

Interpretation of Parameters

Y i = Predicted value of the dependent variable ( y) in the ith trial

x 1i = A value of the independent variable ( x 1) in the ith trial

The last condition on the error ( ) term indicates that the error terms

are uncorrelated.

Regression Coefficients

Coefficients

Model Unstandardized

Coefficients Standardized

Coefficients t Sig. 95.0% Confidence Interval for

Std. Error Lower

Bound Upper Bound

1

(Constant) 141.832 81.635 1.737 .121 -46.418 330.082

Labour -.136 .124 -.142 -1.097 .305 -.423 .150 Cost_of_Equipment 5.167 .760 .878 6.800 .000 3.415 6.919

a. Dependent Variable: Revenue


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Yi = 141.832 - 0.136X 1i + 5.167X 2i

Goodness of FitGoodness of fit test of the regression model

Yi = 141.832 - 0.136X 1i + 5.167X 2i

H0: 0 = 1 = 2 = 3 =0 (The regression coefficient are thesame)

H1: i i1 for at least one i i 1

The Analysis of Variance (ANOVA) table is a result from SPSS and

will be used to test the hypothesis.

= .05

ANOVA Table

ANOVA a

Model Sum of Squares df Mean Square F Sig.

1

Regression 62339.688 3 20779.896 31.016 .000

Residual 4689.802 7 669.972

Total 67029.490 10


b. Predictors: (Constant), Manhour, Cost_of_Equipment, Labour

Decision Rule

Reject H 0 if F-Ratio > F ,(3,7)df and accept if otherwise at 5% level of


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Significance.

F ,(3,7)df = 4.35

Decision

Since F-Ratio > F ,(3,7)df i.e 31.018 > 4.35

We reject H 0 and conclude that the regression coefficients are not

the same.

b. Coefficient of Multiple Determination

Coefficient of multiple determination ( RY(X1X2X3) 2) of the model is0.887

Model Summary

Model R R

Square Adjusted

RSquare

Std. Error of

the Estimate Change Statistics

R SquareChange

FChange

df1 df2 Sig. F Change

1 .942 a .887 .859 30.77218 .887 31.393 2 8 .000

a. Predictors: (Constant), Cost_of_Equipment, Labour

b. Dependent Variable: Revenue

An R2-value of .887 1 shows the efficiency of the model .


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C. Residual Statistics

The mean of the residual is 0

Residuals Statistics a

Minimum Maximum Mean Std. Deviation N

Predicted Value 88.5895 347.6696 160.0634 77.10653 11

Std. Predicted Value -.927 2.433 .000 1.000 11

Standard Error of Predicted

Value 9.634 30.135 15.018 5.999 11

Adjusted Predicted Value 81.1825 277.7157 154.9018 64.50852 11

Residual -45.94175 36.70490 .00000 27.52347 11

Std. Residual -1.493 1.193 .000 .894 11

Stud. Residual -1.700 1.332 .019 1.018 11

Deleted Residual -59.53435 72.94328 5.16160 41.77710 11 Stud. Deleted Residual -1.989 1.412 -.004 1.077 11

Mahal. Distance .071 8.681 1.818 2.503 11

Cook's Distance .000 1.796 .254 .522 11

Centered Leverage Value .007 .868 .182 .250 11



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3.2 Table of Correlation

A correlation matrix is simply a rectangular array of numbers which

gives the correlation coefficients between a single variable and every

other variables in the investigation. The correlation coefficient between

a variable and itself is always 1, hence the principal diagonal of the

correlation matrix contains 1s. The correlation coefficients above and

below the principal diagonal are the same.

The sign (positive or negative) of the correlation = the sign of the slope

of straight line. It is calculated with the model below

=

Correlations

Revenue Labour Cost_of_Equipment

Pearson Correlation

Revenue 1.000 -.483 .933

Labour -.483 1.000 -.390

Cost_of_Equipment .933 -.390 1.000

Sig. (1-tailed) Revenue . .066 .000

Labour .066 . .118

Cost_of_Equipment .000 .118 .

N

Revenue 11 11 11

Labour 11 11 11

Cost_of_Equipment 11 11 11

The correlation between revenue and cost of equipment is the greatest.

( y,x 2 = .933)


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CHAPTER FOUR

SUMMARY, CONCLUSION AND RECOMMENDATION

4.1 Summary

The exploration and production of crude oil has been the bedrock of

Nations economy (OPEC Nations) There has been a lot of views to the

crude oil activities in Nigeria. According to Nashawi (2003) in his write-

up on forecasting world crude oil production using multicyclic hubbert

models concluded that the rapid growth in fuel demand has forced the

policy makers worldwide to include uninterrupted crude oil supply as a

vital priority in their economic and strategic planning. The view shown

by Ayadi (2005) in an article on OPEC review on the oil price fluctuation

and the Nigerian economy summarized that an increase in oil prices

does not necessarily lead to an increase in industrial production in

Nigeria.

With all these postulations, a raw data was collected showing therevenue from crude oil production, the amount of labour used in the

course of the production and the cost of the equipment used over the

period 2000-2011. The analysis was run using SPSSV21.

4.2 Conclusion

Results led to the development of the model Yi = 141.832 - 0.136X 1i +

5.167X 2i showing that the amount of labour used in the production of

crude oil in the considered years negatively affected the revenue

generated as it could be seen in the correlation coefficient of -0.483.

Both the cost of equipment happens to be the bedrock for the revenue


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as it had a positive impact on the revenue generated. This could be seen

in their positive correlation with the revenue.

An R2 = .887 in the regression models and a residual mean of 0 confirms

the genuinety of the models.

4.3 Recommendation

In the light of the above data interpretations and conclusions, the

following recommendations should be considered:

The Nation should see crude oil production as her major source ofincome and as such deploy more man-power to facilitate the productionof much more quantity of this product.

The oil sector should maintain the cost and quality of the machines usedin their productions.

Finally, the oil sector point should be highly observant and know whento take advantage of other Nations oil prices that are members of OPEC.


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REFERENCESAyadi Zeller (2005): An Introduction to Bayeisan, Inference in Econometrics

John Willey, sons Inc, London. (44-82)

Williams S. Anderson (1941): Statistics for business and Economics South Western College publishing Cincinnati Ohio. 7 th edition (144-186)

Baumeister N. and Peersman H. (2007): Sources of the volatility puzzle in thecrude oil market. Social Science Electronic Publishing, Inc (112-128)

Borenstein G. (1997): Comparing crude oil and gasoline prices MassachusettsInstitute of Technology, Petroleum Encyclopedia Britannica (11thed.). Cambridge University Press. (250-275)

Considine N. and Larson K. (1995): Uncertainty and the convenience yield incrude oil price backwardness (880-890)

Cooper J. and Kleinschmidt S. (2009): Success Factors in Product InnovationElsevier Science Inc; DOI: 10.1111/1540-5885.1250374 (112-130)

Duan et al (2008): The Dynamics of online word-of-mouth and product (670-700)

Fleming G. and Ostdiek M. (1999): Impact of energy derivates on the crude

oil market. Elsevier Science B.V (512-530)Fong H and See D. (2002): A Markov switching model on future crude oil

prices. Elsevier Science B.V, (667-700)

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Herbert J.L (1996): Data Analysis of Sales of Natural Gas to Household in theUnited States309 -.Elsevie Science B.V, (442-520)

Horn K. (2003): OPECs optimal crude oil price Elsevier Science B.V (708-715)Kaufmann T. & Laskowski Y. (2005): Causes of the relationship between the

price of crude oil and refined petroleum products Elsevier ScienceB.V, (98-108)

Krichene K. (2002): World crude oil and natural gas. Elsevier Ltd (109-225)
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Krichene H. (2005): A Simultaneous equation model for world crude oilmarkets Social Science Electronic Publishing, Inc (1120-1130)

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Timothy F. and Larson M. (2001): Risk premium on inventory, John Wiley &sons, Inc (98-111)

Wang D. & Mullins F. (1999): Fluorescence lifetime studies of crude oilElsevier Science B.V (44-52)
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