STAT697-01_s19-team-3_data_analysis_by_MG.sas

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**************** 80-character banner for column width reference ***************;
* (set window width to banner width to calibrate line length to 80 characters *;
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* set relative file import path to current directory (using standard SAS trick);
X "cd ""%substr(%sysget(SAS_EXECFILEPATH),1,%eval(%length(%sysget(SAS_EXECFILEPATH))-%length(%sysget(SAS_EXECFILENAME))))""";

* load external file that will generate final analytic file;
%include '.\STAT697-01_s19-team-3_data_preparation.sas';
    
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* Research Question Analysis Starting Point;
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title1 justify=left
'Research Question: Which country has the highest inequaality-adjusted life expectancy index?'
;

title2 justify=left
'Rationale: This should help identify the which country has the lowest education index and make appropriate action'
;

footnote1 justify=left
'The country has the most life expectancy index does not always has the highest human development index.'
;

*
Note: This compares the column "Country" to 
the column of "Inequality-adjusted life expectancy Index from 2018 
Statistical_Annex_Table_3.

Limitations: The value in this dataset need to be numeric to perform mean and 
median calculation. 

Methodology: To proc sort the data in descending order and find the country 
has the highest lift expectancy index which is Albania

Followup Steps: Clear out any missing value.
;

* output first five row of resulting sorted data to better visualize the results;

proc sort 
        data=country_analytic_file_raw (obs=5)
        out = country_analystic_file_raw_Q1
    ;
    by 
        descending 
        adjusted_life_index
        HDI
    ;
run;
    
proc report data=country_analystic_file_raw_Q1;

    columns
        Country
        adjusted_life_index
        HDI
    ;
run;

* clear titles/footnotes;
title;
footnote;

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* Research Question Analysis Starting Point;
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title1 justify=left
'Research Question: What is the correleation between year of school and income per capita?'
;

title2 justify=left
'Rationale: This should help demonstrate whehter school can be a important factor in national income value.'
;

footnote1 justify=left
'Based on the correlation table, there is a positive correlation between these two variable'
;

*
Note: This compares the column "Expected years of 
schooling" to the column "Estimated gross national income per capita" from 
2018 Statistical_Annex_Table_4

Limitations: The value in this dataset need to be numeric to perform mean and 
median calculation

Methodology: To plot a scatterplot to visualize the trend of the reseach question.
According to the graph, the longer of the female education, the higher of the female 
gross national income

Followup Steps: Run a linear regresssio analysis to determine whether there is a 
strong correleation between the two variables.;

* Construct a scatter plot to show the relationship between the two interested 
variable;

proc sgplot 
        data=country_analytic_file_raw;
        scatter x=Year_School_Female y=Estimated_gross_national_income_
        ;
run;


data work; 
    set country_analytic_file_raw;
    if 
        Year_School_Female  ne '..'
    then
        do;
            Year_School_Female2 = input(Year_School_Female, best7.);
        end;

    if 
        Estimated_gross_national_income_ ne '..'
    then
        do;
            Estimated_income_fe = input(Estimated_gross_national_income_, best7.);
        end;
run;

proc corr
        data=work
        nosimple
    ;
    var
        Year_School_Female2
        Estimated_income_fe
    ;
run;

* clear titles/footnotes;
title;
footnote;

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* Research Question Analysis Starting Point;
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title1 justify=left
'Research Question: Which country is experiencing the most severe multidimensional poverty?'
;

title2 justify=left
'Rationale: This should help Non-profit organizaion to appropriate allocate resources to the needest country.'
;

footnote1 justify=left
'Looks like majority of afrian country are in severe multidimenstional poverty'
;

*
Note: This compares the column "Country" from 
sat15 to the column "Population in severe multidimensional poverty from 2018 
Statistical_Annex_Table_6.

Limitations: missing value may need to removed to perform general linear model

Methodology: To sort the selected variable and find the highest severe 
multidimensional poverty.

Followup Steps: To make a boxplot of the data to see the distribution of data
;

* To sort the data in descending order to find the max value;

proc sort 
        data=country_analytic_file_raw
    ;
    by 
        descending 
        Population_in_severe_multidimens
    ;

run;

proc print;
run;

* clear titles/footnotes;
title;
footnote;