November 19, 2010
NAME: The Statistics of Poverty and Inequality
SIZE: 97 observations, 8 variables
For 97 countries in the world, data are given for birth rates, death rates, infant death rates, life expectancies for males and females, and Gross National Product.
Day, A. (ed.) (1992), _The Annual Register 1992_, 234, London: Longmans.
_U.N.E.S.C.O. 1990 Demographic Year Book_ (1990), New York: United Nations.
1 - 6 Live birth rate per 1,000 of population
7 - 14 Death rate per 1,000 of population
15 - 22 Infant deaths per 1,000 of population under 1 year old 23 - 30 Life expectancy at birth for males
31 - 38 Life expectancy at birth for females
39 - 46 Gross National Product per capita in U.S. dollars
47 - 52 Country Group
1 = Eastern Europe
2 = South America and Mexico
3 = Western Europe, North America, Japan, Australia, New Zealand 4 = Middle East
5 = Asia
6 = Africa
53 - 74 Country
Values are aligned and delimited by blanks.
Missing values are denoted with *.
The Statistics of Poverty and Inequality
This paper describes a case study based on data taken from the U.N.E.S.C.O. 1990 Demographic Year Book and The Annual Register 1992 giving birth rates, death rates, life expectancies, and Gross National Products for 97 countries.
When reviewing the statistics about poverty and inequality in countries around the world, It was found that: From running a birth rate histogram on all the countries, it is hard to determine what the birth rate means are per country or country group. A statistics summary was run and it was found that the mean birth rate for all the countries data was 29.22989. We were then interested in the correlation between birth rates and GNP. Country groups and Group GNP’s were then compared to answer the four following questions: (1) Does Low GNP = Low Birth rate? (2) Does the higher the GNP mean a higher birth rate? (3) Which countries have the highest/Lowest birth rate? (4) Which countries have the highest/lowest GNP? The data for life expectancy for females at birth shows that it has a right positive skew. The life expectancy for males at birth have a left negative skew. This data makes sense because women have a higher life expectancy than men. The death rate data shows that it is normal. The mean, median and mode are close in proximity. The difference between the highest male/female life expectancy is by 7.9 years. The difference between the lowest male/female life expectancy is only by .2! There is a huge gap between the two. To answer the question, does the life expectancy at birth for males on average exceed the life expectancy at birth for females, the Student’s t-distribution, one-tailed test with an unknown standard deviation. The t-stat test with one tail in order to test the Ho, which states that the life expectancy at birth for males is greater than the life expectancy at birth for females. Null: Ho:u is greater than/equal to the #
Alternative: Ha:u is less than the #
The data for the life expectancy and live birth rate were entered into the simple linear regression calculator. The life expectancy at birth for males data served as the dependent variable, and the live birth rate served as the independent variable. After analyzing the data of performing an ANOVA of live birth rates vs. country groups, it was found that a one way anova produced a p-value of
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