TIME SERIES ANALYSIS ON THE TOTAL NUMBER OF PATIENTS TREATED FOR MALARIA FEVER

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Product Category: Projects

Product Code: 00001469

No of Pages: 38

No of Chapters: 5

File Format: Microsoft Word

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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 during

GRAPHICAL 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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