ANALYSIS ON SALES OF EGG FROM 2000-2009 USING TIME SERIES (A CASE STUDY OF NICE-FARM OLUANA AREA AKINGBILE MONIYA IBADAN OYO STATE NIGERIA)

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ABSTRACT
            This project is written to know the rate in which people are consuming an egg, through scientific approach in detecting fluctuation at Nice-Farm Oluana  area Moniya Ibadan, Oyo State Nigeria.
In this research work, I have been able to use a secondary data method of collection to analyze data  both theoretical and practical.
The statistical tools used to analyze the data collection is Time Series analysis and which i tested for Trend, Seasonal variation, Cyclical variation and Irregular variation. And I used the least square method to analyze and to calculate in order to have to good result.
In conclusion, the result shows that there will be decrease in the egg in nearest future if the birds did not lay egg very well because of the negative signs arrived when calculating for the forecasted years.




TABLE OF CONTENT
Title page                                                                                                       
Abstract
Certification
Dedication
Acknowledgement
Table of content
CHAPTER ONE
1.0.      Introduction
1.1.      Time series
1.2.      Uses of Time series
1.3.      Kind of Time series
1.4.      Aims and Objectives
1.5.      Scope and coverage
1.6.      Advantages of Time series
1.7.      Types of Time series
1.8.      The Problem encountered during the collection of Data
1.9.      Utility of Time Series
1.10.    Component of Time series
CHAPTER TWO
2.0.      Literature review
2.1.      Definition of Some terms
2.2.      The history of poultry development in Nigeria
2.3.      The Importance of poultry in our lives
CHAPTER THREE
3.0.      Methodology
3.1.      Method of estimating the trend
3.2.      Method of estimating seasonal variation
3.3.      Estimating the seasonal index
3.4.      Estimation of Cyclical Variation using multiplicative model
3.5.      Estimation of irregular variation
3.6.      Deaseasonalization of the data
3.7.      Graphical Presentation

CHAPTER FOUR
4.0.      Data Analysis
4.1.      Estimation of trend using least square method
4.2.      Estimation of trend using moving average method
4.3.      Estimation of seasonal variation using additive model
4.4.      Estimation of Deasonalized data using additive model
4.5.      Estimation of Deasonalization data using multiplicative model
4.6.      Estimation of cyclical variation using additive model
4.7.      Estimation of cyclical variation using multiplicative model
4.8.      Estimation of irregular variation using additive model
4.9.      Prediction
4.10.    Forecasting the additive model
4.11.    Forecasting the multiplicative model with least square trend
4.12     The Tabulation from below shows the predicted value for the next
            Four year (2000-2009) additive model
4.13.    Using multiplicative model to obtain the predicted value
CHAPTER FIVE
5.0.      Findings, Recommendation and Conclusion
5.1.      Result and Interpretation
5.2.      Recommendation
5.3.      Conclusion


REFERENCES
APPENDIX





CHAPTER ONE
1.0       INTRODUCTION
1.1       TIME SERIES:
            A time series is a collection of observations of well-defined data items obtained through repeated measurements over time. For example measuring the value of retail sales each month of the year would comprise a time series. This is because sales revenue is well defined, and consistently measured at equally spaced intervals. Data collected irregularly or only once are not time series.
            An observed time series can be decomposed into three components. The trend (long term direction). The seasonal (Systematic, Calendar related movements) and the irregular (unsystematic, short term fluctuations).      
It involves classifying and studying pattern of movement of the variables over regular interval of time.
The role of time series cannot be over emphasized especially for research workers who work on available information i.e data collected over a period of time to forcast or predict of prefigure what is likely to happen in the nearest future.
It helps in understanding past behavior of particular variable, it also helps in planning future occurrence and guide against making a wrong decision mathematically time series can be represented by t he letters Y1 Y2 Y3 ……Yn of the variable Y and t1 t2 t3….tn of the variable t.
Y and T is a function which can be symbolically written as Y = P(t).
1.2.      USES OF TIME SERIES
(1)   It helps to understand the past behavior of a variable and determine the part direction of movement of a series as well as the rate of growth.
(2)   It enables us to estimate the seasonal variation with a view to reduce it effect on the highly seasonal data.
(3)   It enables us to make forecast about the future.
(4)   The decomposition and study of the various components of the series is of paramount importance to a business manger in the planning of future operation and in the formulation of executive policy decision.
(5)   It reveals the future occurrence of a particular variable by studying it past behaviours.
1.3.      KIND OF TIME SERIES
            There are four kind of time series and it shall be explantiate as follows.
(1)   PHYSICAL TIME SERIES:- The time series which occur in physical sciences like Meteorology, Geography,  Geophysical science are called physical time series. A good example is timely record of atmospheric temperature.
(2)   MARKETING TIME SERIES:-This is the time series that study the behavior of finished products in the market in terms of sales and quality produced. An appropriate example is monthly sales of fruit juice, eggs, chicken e.t.c.
(3)   DEMOGRAPHIC TIME SERIES:-This is the time series that deals with the study of population in a given geographical area at a particular time. It involves measures of virtual statistic such as mortality rate, fertility rate e.t.c.
(4)   ECONOMIC TIME SERIES:-This time series study the changes in the economic of a given case study Example are: A nation, A company. A community. E.t.c
1.4.      AIMS AND OBJECTIVES
            The aims of collecting data on sales of egg
(1)   To help in planning future operation since the future can not be properly planned for without forecasting event.
(2)   To know whether the establishment are making profit or not.
(3)   To know the rate in which people are consuming an egg, through scientific approach in detecting fluctuation
(4)   To encourage those who have intension to embark on similar research work.
(5)   To know what exactly the establishment is likely to come across in the nearest future.
1.5.      SCOPE AND COVERAGE
            This project covers a period of ten years on quarterly sales of egg at Nice farm, Oluana Area Akingible Moniya Ibadan, Oyo state. Nigeria. From the year 2000 to 2009. Due to the time constraint the data used in this research is a secondary data i.e is a kind of data that was transcribed from the record.....


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