DATA ANALYSIS

Data Analysis: Statistics Business Research for Decision Making

[Name of Student]

Executive Summary

There are different factors that influence the life expectancy of an individual. The healthcare organizations operating in countries around the globe are striving to cope with the concerns that influence the life expectancy of individuals. Furthermore, these organizations aim to explore the factors through which the life expectancy of individuals can be enhanced. This paper aims to highlight different factors that can influence the life expectancy of an individual. The paper incorporates diverse study variables in order to gauge their influence on the life expectancy of an individual. There are different statistical techniques selected to analyse the data representing the study variables in order to validate the relationship and influence depicted by the study variables on life expectancy of an individual.

Data Analysis: Statistics Business Research for Decision Making

Graphical Presentation of Data via Scatter Plots

The graphical presentation of study variables was facilitated by scatter plots. The independent variables incorporated in this study are CO2 emission, health expenditure, GDP growth and mobile subscription. Furthermore, the dependent variable incorporated for analysis is life expectancy of individuals.

The values representing study variables are closely attached with the relationship line drawn from the point of origin. The value of R2 revealed by scatter plot is 0.781. This value highlights the significance of the model derived from the scatter plot which states that there is 78.1% assurance level representing thus relationship.

The scatter plot representing the relationship between GDP growth and life expectancy reveals negative correlation between variables included in research framework. Hence, it can be stated that a unit increase in GDP growth will cause unit decrease in life expectancy of individuals.

The scatter plot mentioned below depicts the relationship between mobile subscriptions. The analysis of the scatter diagram assures that there is positive relationship between mobile subscriptions and life expectancy of individuals. Furthermore, the values representing study variables are closely associated; hence, it can be interpreted that the relationship between the variables is extremely significant. The value of R2 extracted from analysis is 0.992 which depicts 99.2% assurance level representing the model of relationship derived from the analysis of data.

Analysis of Data via Implication of Descriptive Statistics

In order to present the data statistically, the data was analysed via the implication of descriptive statistics. The descriptive analysis of the data states that mean value of life expectancy is 79.7183 which represents average life span of individuals included in the sample size. Furthermore, the values 1.21051 and 1.465 represent standard deviation and variance of the study variable. The mean score for CO2emissions (metric tons per capita) is 17.6943 which represent the average number of metric tons per capita. In addition, the standard deviation and variance representing the study variable are 0.57988 and 0.336 respectively.

The mean value representing health expenditure per capita (current US$) is 2399.5769 which depicts average health expenditure per capita. Moreover, the standard deviation and variance representing the variable are 932.65932 and 869853.39 respectively. The mean score representing GDP growth (annual %) is ...