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Application Of Multivariate Analysis On The Incidence Of Four Selected Common Child Killer Diseases Among Children Aged 0 -11 Years PDF Download

Download complete project materials   on Application Of Multivariate Analysis On The Incidence Of Four Selected Common Child Killer Diseases Among Children Aged 0 -11 Years PDF Download  from  chapter one to five with references


This project is titled “Application of Multivariate Analysis (MANOVA)” of four selected common child killer diseases among children aged 0-11 years for the reported cases of Measles D1, Malaria D2, Pneumonia D3 and Diarrheal D4.  The data used for the analysis is secondary in nature which was obtained from Yusuf Dantsoho Memorial Hospital Kaduna from 2010-2014.

This analysis on is aimed to find out if there is significant different between gender of children suffered from the four common child killer diseases and also to find out if there is significant difference between the diseases suffered by (is affected) the children and to test the equality of covariance across the gender male and female using Box’s M, in order to achieve the stated aimed of this project, the statistical tools used were Pillais Frace, Hotteling’s Frace, Wilks Lambda and Roy’s Largest Root, which implies that the overall there is no significant difference between gender of children (male and female), with the respect to the effect of the four common child killer disease, that means both male and female child are equally suffering from the effects of those diseases.

The analysis also showed that there is no significant differences between the diseases (D1, D2, D3 and D4) with respect to the suffering of the diseases by the child. This indicated that all the four diseases are equally affected the children and the box’s test is significant and the assumptions are violated. Based on the findings, we recommend that the parents should ensure that their children receive vaccines, drugs and antibiotics that prevents or reduces the occurrence of these diseases.


Title page

Declaration         Approval page


Acknowledgement       Abstract

Symbols and abbreviation

Table of content


1.0     Introduction

1.1     Background of study

1.2     Background information about yusuf dantsoho memorial hospital

1.3     Statement of the problem

1.4     Aims and objectives

1.5     Significance of the study

1.6     Scope and limitation

1.7     Defination of terms         


2.0     Introduction        


3.0     Introduction

3.1     Methods of data collection

3.2     Source of data collection

3.3     Statistical tools used

3.4     Method of data analysis

3.5     The canonical correlation analysis has the following test hypothesis


4.0    Introduction

4.1     Data presentation

4.2     Data analysis

4.3     Results


5.0     Introduction

5.1     Summary

  1. 2    Conclusion

5.3     Recommendation

5.4     Study implication

5.5     Suggestion for further study






Statistics has been defined as the scientific method of collecting, organizing, presenting, analyzing, and interpreting numerical data with the aim of drawing a valid conclusion based on the outcome of the analysis. The importance of applying statistical tools (method) to any data is to analyze, predict and draw valid conclusions as well as given suggestions based on the result on the analysis.

            1.1     BACKGROUND OF STUDY

Johnson, Richard A. Wcherm, Dean W. (2007) applied Multivariate  statistical Analysis (six ed). Prentice. Hall has defined multivariate analysis as a subdivision of statistics encompassing the simultaneous observation and analysis of more than one outcome variable. The application of multivariate statistics is multivariate analysis.

Multivariate statistics concern understanding the different aims and background of each of the different forms of multivariate analysis, and how they relate to each other. The practical implementation of multivariate statistics to a particular problem may involve several types of univariate and multivariate analysis in order to understand the relationships between variable and their relevance to the actual problem being studied.

Certain types of problem involving multivariate data for example linear regression and regression are not usually considered as special cases of multivariate statistics because the analysis is deal with by considering the (univariate) conditional distribution of a single outcome variable give the other variables.

Multiple statistical analyses refer to multiple advanced techniques for examining relationship among multiple variable at the same time. Researchers use the multiple procedures in studies that involve more than one dependent variable (also known as the outcome or phenomenon of interest), more than one independent variable (also known as a predictor) or both, upper-level undergraduate courses and graduate courses in statistics teach multivariate statistical analysis. This type of analysis is desirable because researchers often hypothesize that a given outcome of interest is affected or influenced by more than one thing.

Multivariate analyses is also important in social sciences research because researchers in these fields are often unable to use randomized laboratory    experiments that their counterparts in medicine and natural sciences often use instead, many social scientists must rely on quasi-experimental design in which the experimental and control groups may have initial differences that could affect or bias the outcome of the study.

Multivariate analyses try to statistically account for these differences and adjust outcome measures to control for the potion that can be attributed to the differences.

Statistical software programs such as SAS strata and SPSS can perform multivariate statistical analysis. These programs are frequently used by university researchers and other research professionals, spreadsheet programs can perform some multivariate analyses, but are intended for more general use and may have limited abilities than a specialized statistical software package.

There are many different procedures or models with its own types of analysis, which includes:


The use of multivariate analysis includes:

  1. Design for capability (also known as capability based design)
  2. Inverse design, where any variable can be treated as an independent variable.
  3. Analysis Of Alternative (AOA), the selection of concept to fulfill a customer need.
  4. Analysis of concept with respect to changing scenarios.
  5. Identification of critical design-drivers and correlations acroshierarchical levels.

In general canonical correlation analysis is a method for exploring the relationship between two multivariate set of variables (vectors), all measured on the same individual.

According to Johnson, Richard A. Wichem, W. (2007) applied multivariate statistical analysis is the aim of canonical correlation analysis is to find the best linear combination between two multivariate data set that maximizes the correlation coefficient between them.

This is particularly useful to determine the relationship between criterion measures and the set of their explanatory factors. This technique involves, first, the reduction of the dimensions of the two multivariate datasets by rejection, and second, the calculation of the relationship (measured by the correlation coefficient) between the two projections of the datasets.

The further statistical tools are Pilliar’s Trace, Wilk’s Lambda, Hotelling Trace and Roy’s Largest Root.


Yusuf Dantsoho Memorial Hospital (YDMH) is a 240-bed secondary care hospital located in Tudun Wada, Kaduna metropolis. It is made of two major sections; the Adult Section and the Pediatric Section. YDMH is one of the largest hospitals in Kaduna metropolis, it has a staff strength of 583 made of 29 doctors (including 13 consultants) 244 nursing staff while pharmacy staff are 57 laboratory staff and other 237 staff (professional administrative and clerical, covering the various departments).

Globally each year, more than eleven million children die from the effects of disease. In some countries, such as Nigeria more than one in five children die before they get to their fifth birthday, and many of those who do survive are unable to grow well and develop to their potentials World Health Organization (WHO).

Seven out of Ten of childhood deaths in developing countries can be attributed to just four main causes, or often to a combination of them: pneumonia, diarrhea, malaria and measles (WHO). Around the world, three out of every four children seen by health services are suffering from at least one of these conditions.

For one thing, two or more potentially fatal infections diseases of childhood can occur simultaneously, particularly if they are associated with shared risk factors. Unsanitary environmental conditions in the home, for example can contribute to the incidence of both diarrhea and pneumonia (United Nation Millennium project: material and child Health).


For a quite long period of decades, it was observed and noticed that the most four common child killer diseases are measles (D1), malaria (D2), pneumonia (D3) and diarrhea (D4). Therefore, it would very interesting to study these four common child killer diseases in terms of differences that might exist between the gender of children (male and female) and the disease(s) that mostly affecting the children in order to get clear direction on affect of the diseases on children.

            1.4 AIMS AND OBJECTIVES

The main objective of this research project is to sight the significance difference between the gender (male and female) of the children suffered from the four common child killer diseases and also to sight the significance difference between the diseases D1, D2, D3, D4 suffered by the children.

  1. To find out if there is significant different between gender of children suffered from the four common child killer diseases.
  2. To find out if there if significant difference between the diseases suffered by (is affecting) the children.

            1.5     SIGNIFICANCE OF THE STUDY

The following could be considered as the significance of this study

  1. It could be used by the government to improve in addressing those four common child killer diseases in various communities.
  2. It could be source of information to parents to report any case they notice on their child or children.
  3. It could be used by health/ medical practitioners, doctors and other users in handling those four common child killer diseases.
  4. Also it may serve as information to the general public with respect to those four common child killer diseases.

           1.6     SCOPE AND LIMITATION

This research work covers only the reported cases of measles D1, malaria D2, pneumonia D3 and diarrhea D4 among children aged 0-11 years from 2010 to 2014 observation. The data of this research was collected from the record office of Yusuf Dantsoho Memorial Hospital (YDMH) Kaduna, due to time fact and accuracy; because conditions in the hospital can hardly be generalized to cover those in the children aged 0-11 years from thousand of similar hospitals within the country.

It is restricted to the record office of the hospital, data collected are based on four selected common child killer disease




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