Modelling Factors Affecting Lung Capacity

Main Article Content

Wanyonyi Maurice

Abstract

This study aims to evaluate modelling factors affecting lung capacity using linear Regression model. The study employed multiple regression models which were used to fit the factors affecting lung capacity. The factors affect lung capacity includes the following; age, gender, smoking and height. The objectives of the study were; fitting regression model on factors affecting lung capacity, determining the relationship between age and height with lung capacity. The study aim also includes predicting the value of lung capacity using the fitted model.

The data used in this study was a secondary source which was obtained from Marin [1]. The dataset is publicly available on their website. The data had 725 observations. Since multiple linear regression model was employed in this study, the model was of the form;

1.JPG

Where;

Lung capacity is the dependent variable,   2.JPG   are the coefficients (parameters) to be estimated,

Age and Height are the independent variables while 31.JPG is the random error component. The methods of parameter estimation discussed under this study include; maximum likelihood estimator and the least square estimator.

The data for this study were analyzed using SPSS and R software which are statistical software used for data analysis. From the analysis of variance table, a p-value of 0.00 was recorded which is less than alpha (alpha= 0.05). This implies that the overall model is significant.

From the model formulated, it was concluded that height and age greatly affect lung capacity. The model formulated can be used to predict the value of lung capacity provided the values of Age and Height are known. Also from the descriptive statistics, it is deduced that gender and smoking greatly affect lung capacity.

Keywords:
Model, lung capacity, multiple regression, maximum likelihood estimator, least square estimator.

Article Details

How to Cite
Maurice, W. (2020). Modelling Factors Affecting Lung Capacity. Journal of Advances in Mathematics and Computer Science, 34(6), 1-18. https://doi.org/10.9734/jamcs/2019/v34i630229
Section
Original Research Article

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