ABSTRACT
This project work reveled the rate at
which people are infected with malaria the least square method used for
analysis showed that people are infected with malaria irrespective of the time
and seasons of a successive year,
There is no noticeable direction as
regarding the number of patient treated for malaria over time.
Also, the analysis from
autoregressive moving average report shows that both autoregressive and moving
average of order four were both appropriate while the report from
autocorrelation and autocovanance does not indicate any noticeable trend in the
number of patients treated for malaria.
TABLE
CONTENT
Title page
Certification
Dedication
Acknowledgement
Abstract
Table of contents
CHAPTER ONE
1.0
INTRODUCTION
1.1
BACKGROUND OF STUDY
1.2
SCOPE AND COVERAGE OF THE STUDY
1.3
SOURCES OF DATA COLLECTION
1.4
LIMITATION OF THE STUDY
CHAPTER TWO
2.0
LIMITATION REVIEW
2.1
TYPES OF TIME SERIES
2.2
PROCESS USED IN TIME SERIES
2.3
IMPORTANCE OF TIME SERIES
2.4
ANALYSIS OF TIME SERIES
2.5
ESTIMATION OF TREND
2.6
DESEASONALIZATION OF DATA
2.7
FORECASTING METHOD
2.8
STATIONARITY
CHAPTER
THREE
3.0
DESIGN METHODOLOGY
3.1 TYPE OF
DATA COLLECTED
3.2 METHOD
OF ANALYSIS
CHAPTER
FOUR
4.0
DATA PRESENTATION AND ANALYSIS
4.1
ESTIMATION OF TREND BY THE METHOD OF
LEAST SQUARE
4.2
TIME SERIES DECOMPOSITION REPORT
4.3
AUTOCORRELATION REPORT
4.4
AUTOMATIC ARMA REPORT
4.5
PORTMANTEAU TEST SECTIO
CHAPTER
FIVE
5.0
SUMMARY OF FINDING CONCLUSION AND
RECOMMENDATION
5.1
SUMMARY OF FINDING
5.2
CONCLUSION
5.3
RECOMMENDATION
REFERENCE
CHAPTER
ONE
1.0
INTRODUCTION
The term time series refers to one the quantitative method
used in determination pattern in data collected over time e.g weekly monthly,
quarterly or yearly.
Time
service is the statistic tool or methodology that can be used to transform past
experience to predict future event which would enable the researcher or
organization to plan.
It
gives information about how the particular case of study has been behaving in
the past and present and such information can be used in prediction The number
of people treated for malaria fever at the otan Ayegbaju management hospital.
Comprehensive health centre otan. We are going to seen how change occur over
mouths in each year in the occurrence of the disease in the hospital. As a result of this, we will be able to know
certain factor responsible for increase or decrease in the rate of infection of
the disease over the period of time.
Record
of time series data can be made in the following ways:-
A.
THROUGH CUMULATIVE FIGURES:- these
represent value of input through the quarter. We must always bear in mind the
different when handling time series data and as certain which particular type
we are dealing with in every case.
B.
CUMULATIVE TYPE ADDED COMPILATION:- some
cases when an added compilation introduced for the cumulative type of data the
figure which are related to month of the year and not the total for month. further more the
characteristic movement, seasonal variation Irregular variation in the analysis
of time series, we have two types of model are generally accepted as good
approximation of the true data association among the component of observed
data, they are the most commonly assumed relationship between time series and
its components. These are additive model and Multiplicative mode. All time
series contain at least on of four of its components. These components are:-
1.
Long term trend
2.
Seasonal variation
3.
Cyclical variation
4.
Irregular or random variation value
LONG
TERM TREND COMPONENT
This
can be referred to the general path in which time series graph appear to follow
over a long period of time, in other word, it is the long-term increase or
decrease in a variable being measured over time for example a company planning
her expense on goods to produce in the next three or four years has consider
demand at a particular time.
GRAPHICAL
REPRESENTATION OF TREND
SEASONAL
COMPONENT
These are sort term variation from the trend
that occur regular with the passage of time series of many products like ice
cream, soft drink, ran during ileya turkey during chrismas and new and year period are subjects of such variation.
There changes are visually identical or almost identical in natures that follow
duringGRAPHICAL
REPRESENTATION OF SEASONAL CHANGE
CYCLICAL
COMPONENT
Data
collected how every, they can contain cyclical effect in a time series are
represented by wave-like fluctuation around a long term trends. The change
occurs in economics activities due to some facture like booms. Recess. Cyclical
fluctuation repest them selve in a general pattern in the long-term. But occur
with different frequencies and intensities.
Thus, they can be isolated but not totally
predicted
GRAPHICAL REPRESENTATION OF CYCLICAL CHANGE
IRREGULAR OR RANDOM COMPONENT
This
venation cannot BE attributed to any of three previously discussed component in
the sense that is unpredictable.
Irregulars flotation can be cause by many factor such as
war, flood drought and other human as action. Two type of irregular variation may
exit in a time series viz. minor and major irregularities minor irregularities
show up as serivtooth like pattern are under the long term trend. These
irregularited are in organization long term operation:
Major irregularities are significant one-time unpredictable
change in the time series due to such external and uncontrollable factors asan
oil embryo, war drought e.t.c
GRAPHICAL
PRESENTATIOPN OF IRREGULAR VARIATION
MODELS
OF TIME SERIES
We
usually denote the component of time series as T,TS,C and I: There Are Two
Types Of Modern That Are Appropriate For Joining Component Of Time Series these
moderns are additive and multiplicative modern.
The
additive moder assumes that the valve of the original data is the sum total of
other four elements it is:
T=T+S+C+I
where
T -
is value of the originally conserved data (dependent)
T
- is
the value of secular or trend
S
- is
the value ofr cyclical venation and
I
- is the value of irregular venation
Mufti
active modern on the other hand assumes that the value of the observed data is
the Y = TSCI
1.1 BACKGROUND OF STUDY
one
of the factor that determine the population of a country, state local
government e.t.c is death rate, that is to say, the more the disease infected
the population of such an area and vice-versa. The fact prompted the writer
into the study of quarterly number of people given treatment for malaria at the
comprehensive health centre otan ayegbaju, in addition to that it is done to
know weather infected people come to the hospital fur test or they stay back
due to the old custom self medication.
In
order to carry our the analysis data will be collected from daily record of the
hospital at record department over some years to get all necessity information
so as to carry out computation and predict about the nearest future by using
secondary method of data collection.
1.2.1 SCOPE
AND COVERAGE OF THE STUDT
This
project work was carried out on the number of people treated for malaria fever
between year 2001 to 2010. The data was collected from the comprehensive health
centre Otan Ayegbaju osun state.
AIM
AND OBJECTIVE OF THE STUDY
i.
to know whether the yearly spread of malaria is
increasing pr decreasing.
ii.
To formulate a model that can best explain the
relationship between malaria increases over the years
iii.
To use the modern to forecast the occurrence of
malaria
iv.
To plot the graph of the original data i.e
occurrence of malaria against year correlogram and moving average.
1.3
SOURCES OF DATA COLLECTION
Data
used for research are of two main sources: these source are primary and
secondary data.
Primary
data are fresh data which are collected for the task at hand. An example of
such is census registration for cards.
Secondary
data on the other hand are data dreaded in existence. They are originally
collected for some purpose other than research current problem. They can be
collected from school, hospital organization, government agencies, newspaper,
monthly or annual report e.t.c thus a secondary data is used in this project.
1.4 LIMITATION OF THE STUDY
As
we know that a researcher is bound to face certain problem. various problem
were encountered before during and after data collection
The
problem are:-
Poor
storage, which make the transfer of data difficult Data were not properly
recorded and in some cases, we have missing value.
Also
data for some period are missing from the record office: those were available
were poorly recorded this establishing one of the major.
Disadvantage
of a secondary data usage data
collection
:-
before the data was released to me, I had to present a cover letter from the
HOD and my student identify card and also promised that the data would be used
for statistical purpose only.
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