# SAS Assignment Help

The illustration is provided to exhibit the standard of our SAS Assignment Help. The analysis using SAS has been done to examine differences in the levels of poverty amid 7 regions of the 84 countries. GDP comparison has been done in the SAS Homework Help using histogram since we can analyse the normality easily with it. Box plot is also used as it shows the comparison in a more clear way.

The boxplot reveals quite high differences across regions, with the Western world being better the Middle East, which, in turn, has higher GDP per capita as compared to all other regions. There is no significant difference between all other regions.

Several outliers are visible: Hing Kong and Singapore sas assignment help, have much higher GDP/capita as compared to the other countries in Asa. Libya is better off all countries in Africa.

We can compare with the histograms by regions, displayed in the next pages. The information that we receive is basically the same, but the box-plot provides it in a more clear way. The advantage of the histograms is that the show us the sas homework help actual distribution and we can more easier assess its normality.

The boxplots for the two genders look pretty similar pay for sas assignment with respect to differences across regions. People in Western World and in Eastern Europe, in this order, are expected to live more than in other regions, irrespectively of which gender one considers.We can also notice that Middle East and Latin American have higher life expectancies than Africa, for both genders.

The boxplots for the two genders look pretty similar pay for sas assignment with respect to differences across regions. People in Western World and in Eastern Europe, in this order, are expected to live more than in other regions, irrespectively of which gender one considers.We can also notice that Middle East and Latin American have higher life expectancies than Africa, for both genders.

Comparing the values in the two graphs, it looks like female are likely to live more than male in all regions.

Bolivia, Portugal and Greece are outliers in their regions when it comes to male life expectancy. Each of the three countries has lower life expectancy for men as compared to central tendency in its group (the estimated life expectancies for do my sas assignment each of these societies fall below the third quartile.Similarly, Bolivia is the outlier in case of female life expectancy.

The two variables look pretty well correlated. When one increases its levels, the other sas project help increases its levels as well. The graph shows no important outlier and suggests the presence of linear association. However, the graph is not the best solution by itself to study the correlation between the two continuous variables. It has the advantage to offer a visual hint about the relation, but we need to test the form of the relation and to assess its size. Firstly, we repeat the graph, adding regression and loess lines, to visually inspect the linearity.

The relation looks quite linear. We can compute now the correlation coefficient.

The value of the Pearson correlation is 0.98256, being significant at p<.0001. We conclude that he correlation coefficient is significantly online sas tutor different from zero, and the relation is very strong, the two variable varying in an almost identical way (r=.098).

We inspect now the differences between the two groups for each country. The average difference between the life expectancy of male and female is -4.67 years, with a standard deviation of 2.37. the minimum is -9.7 years, while the maximum is 2.8 years.

A boxplot shows the extreme values. Iran is the country where the maximum difference is recorded. Greece is the place for the minimal one.

A t-test shows if the average difference differs from 0. It turns out that statistics using sas assignment help the mean difference of -4.67 is significantly different from 0 at p<.0001 (t(96)=-19.38).

One can also test if the average life expectancy for man make my sas assignment and women is the same by using a paired-samples t-test. The results are identical:

We inspect the scatterplots. For help, the loess curves are requested as well. The relationship between birth rates and death rates looks rather quadratic. Lower death rates seem sas assignment solution associated with both lower and higher birth rates.

The relationship of GNP/capita and death rate does not look linear either. Low GNP means higher death rates. The death rate decreases with GNP, but it soon reaches a plateau, therefore a hyperbolic relation is expected (death rates are proportional with 1/GNP).

Finally, the relation between infant mortality and death rates sas expert help looks linear, although several outliers are visible.

We repeat the analysis, stressing this time the regions.

Apparently, the relationship between birth rates and death rates is different depending on region: there is a relation of inverse proportionality in Eastern Europe, a direct proportionality help with sas assignment in the Middle East, an exponential connection in Asia, etc.

In fact, since the ranges of the birth rates are quite different across regions, the partial curves describe parts of the whole variation, and one may conclude that we still have an overall quadratic relationship (death rates initially decrease with the increasing birth rates, but after a certain point they star increasing). The exception seems to be the sas assignment help Western World where the relation seems to be described by a negative quadratic relation.

When it comes to the association between death rates and GNP, one may see really contradictory patterns: in the Middle East, Asia, and Africa, death rates decrease with GNP/capita; in Eastern Europe, on contrary, they increase!; in Latin America, for the same levels data analysis using sas assignment help of GNP/capita, a quadratic relation is noticeable.

Finally, in the case of association between infant mortality and death rates, all regions but astern Europe display a positive linear dependency, even if slopes may be help with sas homework

different (in particular for the Western World). However, Eastern Europe depicts the opposite situation with a negative linear dependency.

-Migration rates may affect both birth sas homework help rates and death rates, since international migration tends to depend on positive self-selection: the youngest are to migrate.

- Along with GNP, growth rates may impact on birth rates.
- Inequality may be important: average GNP/capita may hide segmentation into poor and rich, with high infant mortality rates sas assignment help being associated to large numbers of poor.