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# STATS ASSIGNMENT

- Category:Statistics
- Document type:Assignment
- Level:Undergraduate
- Page:2
- Words:1010

**Introduction**

The objective of the assignment is for the exploration would an exploration of variables within a dataset and for the investigation of relations between the different variables. The World Health Organization utilizes information when comparing the systems of health care for different countries. As such, the variable that is being investigated within this dataset would be the life expectancy of countries and how it relates to the other variables which in this case would be the per capita gross domestic product, an indicator whether the nation is in the OECD and the years of education that most people within the nations go through as of below or above 8 years which is the nominal standard. These four variables are economically and socially related in terms of status and living standards which prevail on these nations. The life expectancy adjusted to disability is a combination of both elements.

Basic descriptive statistics and graphs

From the information above we can see the majority of life expectancy figures lie below the age of 50 tasking into account disability. That means most people in the sample countries taken do not live beyond the age of 55. According to the graph, there are specified populations with high chances of living past 60 even when disabilities are taken into the picture. These nations have the highest standard of living among the other populations provided.

The box plot above also reiterates the same information with a majority of the sample provided having lower population life expectancies than 60 percent. The remainder of the population which is the minority has a life expectancy above 60 and a higher standard of living.

The normality test indicates whether to reject or accept the null hypothesis that the data comes from a normally distributed population. The normality test and the probability plots are the best ways of judging normality for the smaller samples within a population. According to the graph the P-value is less than 0.005 which means that we can accept the null hypothesis which means it came from a normally distributed plot (Ghasemi & Zahediasl, 2012).

**Comparative Statistics and Side-by-Side Boxplots**

According to the data above, there is a high percentage of the sample population which does not belong to the OECD nations. The range or life expectancy for the ‘no’ nations goes from early 40s to early 60s. There is a higher population which is below the average of 53 years. That means that the graph is skewed to the left. On the other hand, the graph on the right is contrary in that the average is much higher for the life expectancy at 70 years for the ‘yes’ nations. It is skewed to the right in this regard. This is an indication the countries which belong to the OECD have a better standard of living as compared to the nations which do not considering there is a higher life expectancy average for the ‘yes’ nations as compared to the ‘no’, nations.

**Cross-tabulation**

**Tabulated Statistics: OECD, EDUC **

Rows: OECD Columns: EDUC

High low All

no 16 108 124

12.90 87.10 100.00

45.71 93.91 82.67

10.67 72.00 82.67

Yes 19 7 26

73.08 26.92 100.00

54.29 6.09 17.33

12.67 4.67 17.33

All 35 115 150

23.33 76.67 100.00

100.00 100.00 100.00

23.33 76.67 100.00

Cell Contents: Count

According to the data in the table above, the first structure shows samples of the countries which are not in the OECD. The low column of highs shows majority of the nations have education statistics below 8 years, while the high column show the minority of nations which do not belong to the OECD that have education statistics above 8 years. The statistics are different for the ‘yes’ category which belongs to the OECD. Majority of these nations which are in the high category have education statistics above 8 years. A minority of nations have education statistics below 8 years in the ‘yes’ category for the OECD states. This means the populations within the OECD nations have a higher chance of pursuing education beyond 8 years than the populations of the countries that do not belong to the OECD.

**Scatterplot **

From the graph it is evident that majority of the activity is occurring within the first periods of the GDPC which is in the x axis and represents the gross domestic product per capita between 1000 and 10,000. These are the poor to the middle income countries which are the majority of the sample provided and that of the globe generally. The life expectancy from the graph seems to range from 28 to 67 years of age with the majority sitting in the 50s. As the x-axis progresses, the y axis maintains a particular level of life expectancy which means the higher earning countries with big GDPC values tend to have the highest life expectancy accounting for disability.

The graph above shows the inclusion of the EDUC variable which indicates clearly what educational standards have to do with the GDPC and life expectancy variables. The higher the life expectancy variables, the more likely it is going to be from nations from a high GDPC and high standards of education. The lower the life expectancy means a lower GDPC in most cases and the lower the education standards of the nation.

**Conclusion**

This paper was to research the connections between statistical variables of the population in terms of economy and education and how they relate to length and quality of life. The four variables which have been provided including the GDPC, life expectancy accounting for disability, EDUC and the membership of the countries in the OECD are all related as concerns social and economic status. OECD nations tend to have higher GDPC and EDUC which also correlates to their higher life expectancies. These nations with higher standards on education and have stronger economies tend to have populations that live longer quite possibly because they have better qualities of life.

**Reference**

Ghasemi, A., Zahediasl, S. (2012). Normality Tests for Statistical Analysis: A Guide for Non-

Statisticians. Int J Endocrinol Metab. Spring; 10(2): 486–489.