1. The relationship between life expectancy and income per person in 2007 (Europe Central /East Asia and Pacific). The Data for Europe and Central Asia The Data for East Asia and Pacific The box and whisker graph Chi-squared test Null hypothesis Validity Limitations Conclusion

2. The data chosen in order to have a strong correlation and to demonstrate that as the GDP increases, life expectancy also increases. Europe And Central Asia /2007 Income per person (GDP)Life expectancy (y value) 1.Turkmenistan511560.634 2.Ukraine654267.733 3.Azerbaijan764670.041 4.Turkey858671.738 5.Bulgaria1092372.95 6.Montenegro1142874.03 7.Poland1541575.517 8.Portugal2052379.12 9.Spain2842780.745 10.France3066781.14 Next Slide

3. Is there a correlation between the income per person and the life expectancy in Europe? The ‘r’ value (0.943) calculated by taking a square root of r 2 Both have a strong positive correlation First Slide

4. Another data with different countries East Asia and Pacific /2007Income per person (GDP)Life expectancy (y value) Indonesia353570.474 Samoa505371.38 China612872.919 Malaysia1246074.117 South Korea2343879.445 New Zealand2492680.145 Japan3163782.87 Australia3408481.67 Hong Kong, China3995082.18 Singapore4535280.117 Next Slide

5. Is there a correlation between the income per person and the life expectancy in East Asia and Pacific? The ‘r’ value is 0.988The correlation remains strong positive First Slide

6. The box and whisker graph Europe Next Slide

7. The box and whisker graph East Asia and Pacific First Slide

8. Chi-squared test A chi-squared test-statistical hypothesis test in which the sampling distribution of the test statistic is a chi-squared distribution when the null hypothesis is true, or any in which this is asymptotically true. Both Europe and East Asia and Pacific: Null Hypothesis: The income per person is dependent from the life expectancy. Alternative Hypothesis: The income per person is independent from the life expectancy. First Slide

9. Null hypothesis rejected: calculated value is greater then the critical one. Europe East Asia and Pacific  2 Critical 3.841  2 Calculated 6.666 Accept H 0 ?Reject the Null Hypothesis  2 Critical 3.841  2 Calculated 10 Accept H 0 ?Reject the Null Hypothesis First Slide

10. Validity Calculations are correct due to the use of calculator and manually. Chi Square test was done by calculator Correlation analysis was done manually Both double-checked by GDC to make sure that the calculations are 100% correct. First Slide

11. Limitations The insufficient data collected The validity of data The error that was made while collecting the data Not everyone was included in the calculation of income Not everyone was included in the calculation of life expectancy First Slide

12. Conclusion The data was not chosen randomly, but in order to get positive correlation. The graphs, Chi Squared -used to show positive correlation and relationship between life expectancy and income per person. There is a correlation in between two compared variables in terms of correlation coefficient (‘r’). The null hypothesis has to be rejected because the critical value was always less then the calculated one. First Slide

13. Columns from the excel First Slide

14. Scatter diagrams from the excel First Slide