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Time Series Analysis On Consumption Of Electricity In Kwara State
[A CASE STUDY OFFA NEPA DISTRICT OFFICE FROM 2001-2015] -
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CHAPTER THREE
3.0 METHODOLOGY
This chapter contains the process by which data is collected and the time series techniques used in the analysis of the data.
3.1 SOURCES OF DATA
The data used in this project is majorly secondary data, obtained from record of monthly sales consumption of electricity in Kwara State Ilorin District from 1989 to 2003, the record of the account and sales department of NEPA Office Ilorin.
Secondary data: This is the process by which data (information) collected for one purpose if now used for another purpose apart from the one that is collected for, it can be administrative record, product of published research work of both governmental and non-governmental organization, involve in this administration.
3.2 METHOD OF DATA ANALYSIS
The model of data used in this project is time series analysis which contain the time series method to be used in the analysis.
3.3 METHOD OF ESTIMATING TREND
According to Schamn’s outline series, the method of estimating trend include:
(i) Least square estimating methods
(ii) Moving average methods
(iii) Semi-moving average methods
(iv) Free hand methods
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ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---
TABLE OF CONTENTS - [ Total Page(s): 1 ]TABLE OF CONTENTCOVER PAGE APPROVAL PAGE DEDICATION ACKNOWLEDGEMENTTABLE OF CONTENT1.1 INTRODUCTION1.2 BACKGROUND AND ITS OPERATION 1.3 SIGNIFICANCE1.4 SCOPE OF STUDY1.5 AIMS AND OBJECTIVE 1.6 PROBLEMS AND LIMITATION OF STUDY 1.7 DEFINITION OF TERMS 1.8 ABBREVIATION USEDCHAPTER TWO 2.0 LITERATURE REVIEW2.1 METHOD OF DATA ANALYSIS 2.2 TIME SERIES ANALYSIS 2.3 IMPORTANCE OF TIME SERIES 2.4 NATURE OF TIME SERIES 2.5 ... Continue reading---
CHAPTER ONE - [ Total Page(s): 2 ]CHAPTER ONE1.0 INTRODUCTION The primary aim of National Electrical Power Authority (NEPA) is in cardinal point which is to generate, transmission, distribution and sales electricity within and outside country. National Electric Power Authority NEPA can be regarded as heart beat of the nation economy as the operation of machines use in industries and most household equipment depend on electricity. Today National Electric Power Authority (NEPA) meets total maximum energy demand from t ... Continue reading---
BIBLIOGRAPHY - [ Total Page(s): 1 ]BIBLIOGRAPHYFreud J.E. William F.J (1970) “Modern Business Statistics†Pitman Publishing bid great Britain Murray .R, Spiegal (1961) “Theory and problems of Statistics†in S.I (Schaum’s outline series)Notational Electric Power Authority 2003 Press Clip (NEPA transformation newsletter Vol.0033) J.B BABALOLAJ.B BABALOLA “Statistics with applications (in behavioural Science, Business and Engineering) Revised Editions MR. .I.O Azeez “ ... Continue reading---
CHAPTER TWO - [ Total Page(s): 14 ]CHAPTER TWO2.0 LITERATURE REVIEW SOURCES OF DATA COLLECTION Data are piece of information collected for a certain purpose, in statistic, we can categorizes data into two types; the primary data and secondary data. 2.1 PRIMARY DATA These are data collected at sources. This is the collection of such data in direct from the object of the interest i.e. data collected as a result of research methodology e.g. result of the question and sample survey. 2.2 SECONDARY DATA ... Continue reading---
CHAPTER FOUR - [ Total Page(s): 10 ]Since we must eliminate seasonal variation, the graph label Fig. 11. The seasonal variation in our time series and it is used to remove the effect of seasonal from time series which is called de-seasonalizing a time series. This graph made us proceed be deseasonilize our data as table 4.4 Moving average graph and original data of the montly consumption from 1998-2003 The graph of the original data shows in the figure by the solid line graph of the moving average is shown in broken ... Continue reading---
CHAPTER FIVE - [ Total Page(s): 1 ]CHAPTER FIVE5.1 SUMMARY OF FINDINGS The success of any analysis can only but judged by extent to which to which the objectives of the research have been achieved. While the series contain the upward trend figure 1 and 2 shows it to be very week one, the same conclusion can be drawn about the seasonal factor, these shows in table 4:3. However a different conclusion can be drawn about = periodic cyclical reregulation factor. By residual reasoning, it can be inferred that ... Continue reading---
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ABSRACT - [ Total Page(s): 1 ]ABSTRACT HERE ... Continue reading---