Executive summary
Life expectancy can be best defined as the average number of years a person will live. The exact number of years a person can be alive is determined by the exposure to several factors, which differ from an individual to another and the surroundings the person operates in (Morita, 2005).
For this experiment that was conducted with sole aim of determining how the four factors affect the number of years an average person can live, statistical methods of analysis have been employed in determining the severity of each factor the existence of mankind (Hogue ,2000).
This has mainly been achieved by analyzing the effects of the aforementioned in a group of people who were exposed to the factors for a specific period. Correlation an regression analysis have been used as very useful instruments to bring out the reality beneath the related observations (Morita, 2005)
.It has been noted from the experiment that prolonged exposure to cellular phones poses a great danger to the long term survival of mankind. This is because of the radioactive emissions these devices have. On the same note, exposure to carbon dioxide will bring out the same devastating effects as the cellular phones do (Morita 2005).
However, the analysis has several weaknesses that can make the findings not eligible to be used independently without augmenting the information with different sources:
Only a small group of persons were considered for testis. As such, the group tested is taken as a representative of the entire group of living mankind, making it to be based more on assumptions than reality.
Different people would show different results if exposed to the above factors, rising questions on why the findings from the research should be standardized to reflect the condition of the entire human race.
Most of the data was collected in conditions of unavoidable haste and as such errors may have made in the efforts to beat deadlines.
7) From the analysis brought forth in the calculation, it is evident that the statistical significance of the two selected variables does agree with correlation significance.
This is due to the fact that from the correlation analysis, factors X4 and X6 (Health expenditure per capita and mobile cellular subscriptions) have the highest correlation to life expectancy, and as a result the exposure to the said factors have a very high chance of determining the number of years the person under question will live (Morita 2005).
Below is a summary of the above.
In the above table, test statistic shows the calculated value. This highlights the alternate hypothesis (H1). H0 is the same for all the four variables, and as a result we can conclude that Factors X3,X4 and X6 have one thing in common, positive correlation with life expectancy. But of the three factors, it is X4 and X6 that will have a significant value in determining life expectancy (Morita, 2005).
8) The best regression model that will sufficiently explain the life expectancy of any species of mankind exposed to the above conditions will be given as follows
X2=aX3+bX4+cX6+C
Whereby:
X3, X4 and X6 are all the dependent variables, with X2 the independent variable. The above is informed by the fact that X3, X3 and X4 have a positive correlation with life expectancy; hence giving the equation has full inclusivity since all relevant factors have been considered irrespective of how material they are (Hitoshi, 2001).
C represents a constant that is not related to the variables explained above, but is important as far as the accuracy and effectiveness of the equation is brought to test (Hermes, 2010).
References
Hermes, O. (2010). Business System Planning. s.l.:VDM Verlag Dr. Mueller e.K..
Hitoshi, M., (2001). Inflation rate factor breakdown. population study, pp. Vol 20, iss 4, 45-49.
Hoque, Z. & James, W.( 2000). Linking balanced scorecard measures to size and market factors:
Impact on organizational performance. Journal Management Accounting Research, p. 12.
Morita, Y. (2005). Reviewing the fundermental prices of WTI oil. Energy economics, pp. 31(2),
34-9.
