Statistiken

Coursera: Wesleyan_U

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Final comments

and it's done.

Thank you Lisa Dierker and the team of Wesleyan University. I did enjoy this course, my first from coursera.

I was able to at least experience (somewhat program in) SAS! 

And of course to very helpful tumblrs (tumblr users?) and other students.  n

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PASSION DRIVEN STATISTICS: FINAL PROJECT

Oil reserves of the world becomes more and more scare. The hungriest countries that need it most will have to make a move to protect and strengthen their hold of the world's remaining oil reserves. One way to do this is to become a consistent world power, esp. in military.  Furthermore, the least democratic (autocratic in that sense) countries' leaders may have the greatest hold and control of its spending without having to face the people's usual non-conformity with military.

The study looks at the countries of different amounts of oil consumption and if that may relate to perceive amounts of possible military action to protect that level of consumption. It also take into consideration different democracy level and how that could affect the association.

Sample

Total number of matching records (n=56) of the 3 variables used: amrmedforcesrate, oilperperson, polityscore were from the Gapminder dataset.

Gapminder [http://www.gapminder.org] is a non-profit organization promoting sustainable global development and achievement of the United Nations Millennium Development Goals.

Measures

polityscore: 2009 Democracy score (polity); A measure calculated by subtracting an autocracy score from a democracy score. The summary measure of a country's democratic and free nature. -10 is the lowest value, 10 is the highest sourced by Polity IV Project.

armedforcesrate: Armed forces personnel (% of total labor force) sourced by Work Development Indicators

oilperperson: 2010 oil Consumption per capita in tonnes per year and person sourced by BP.

Univariate

The average armed forces personnel is only 1.264% of the total labor force.

The global oil consumption per capita is 1.7 tonnes/year.

An average of 3.716 for the polity score and a standard deviation of 6.304 shows a negative skewness of -0.733 for this variable. 

Figure 0: Armed forces rate grouped in Quartiles in comparison with the oil consumption variable.

Bivariate

A Pearson correlation of coefficient analysis showed that the oil consumption per capita (quantitative explanatory variable) is positively and significantly associated with the armed forces personnel's percent in the total labor force (quantitative response variable); r = 0.52156, p< 0.0001. That is, the more a country consumes oil,  the more likely the country is to increase its armed forces personnel. 

The polity score of a country did moderate the correlation of coefficient's significance. It shows that that the countries with very low polity score (Figure 1) maintains the significant correlation while the countries with a low polity score (Figure 2) and the countries with a high polity score (Figure 3) shows no statistically significant correlation between the armed forces rate and oil consumption per capita variables.

Figure 1: A scatter plot of the association of oil consumption per capita to the armed forces personnel percentage, grouped by poligroup1 [countries with polity score between -10 to -1] that has Pearson's coefficient correlation(r) of 0.6612 with p>.0101.

 Figure 2: A scatter plot of the association of oil consumption per capita to the armed forces personnel percentage, grouped by poligroup2 [countries with polity score between 1 to 5] that has Pearson's coefficient correlation(r) of .34072 with a non-significant p value.

Figure 3: A scatter plot of the association of oil consumption per capita to the armed forces personnel percentage, grouped by poligroup3 [countries with polity score between 6 to 10] that has Pearson's coefficient correlation(r) of -.10423 with a non-significant p value.

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Week 4: C. Assignment 8 (Correlation)

The correlation between Armed forces rate ( and Oil Consumption per capita was 0..52156 (p=.0001), suggesting that only 27% of the variance in number of current armed forces percent on the labor force  can be explained by the oil consumption per capita. Does that make any sense?

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Week 4: B. Assignment 7 (Chi Square)

When examining the association between armed forces rate (categorical response variable) and oil consumption per capita (categorical explanatory variable), a chi-square test of independence revealed nothing or something I am not able to comprehend.

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Week 4: A. Assignment 6 (ANOVA)

This is only one of the multiple categorical variable to quantitative variable I could test with ANOVA. The one represented here is what I submitted.

The Variables:

armedforcesrate var [Armed Forces Personnel (% of total labor force)] v. oilperperson [Oil Consumption per capita]

Since both are quantitative variable, I have to turn one into a categorical variable. I also did this for the bar chart to represent better visually. The oilperperson variable is divided into four Q1, Q2, Q3, and Q4 as represented in the program below and named in OILGROUP.

The Hypothesis:

H0: There is no relationship between Armed Forces Personnel and Oil Consumption per Capita.

Ha: There is a relationship between Armed Forces Personnel and Oil Consumption per Capita.

Conclusion (ANOVA):

When examining the association between Armed forces rate (quantitative response variable) and Oil Group [consumption of oil per person in group] (categorical explanatory variable), an Analysis of Variance (ANOVA) revealed the association between the variables, F(3,52)=0.33, p=0.8041. Because my p value is greater than 0.05, I'm going to accept the null hypothesis, and say that these means are equal and there is no association between the Armed Forces Personnel and Oil Group.

Post Hoc Test:

Using Duncan's Multiple Range Test it is clear that there are NO statistically significant difference among the 4 oil groups. 

[statistiken]

Week 3: B. Assignment 5 (Graphing II) [Part 2]

Now for my Query 2 of the correlation between the var of Armed Forces Rate to Morbidity (HIV rate and Breast Cancer per 100th). Here is my initial program for that.

I tried to combine both breastcancer and hivrate in comparison with armedforces rate in a single scatter plot and here is the result:

Hivrate is clearly unreadable while breastcancer seems to be something, haha I can't analyze it. I just love that it's not concentrated on that corner. So, I have to redo my SAS program for the unreadable hivrate. Shown here:

And produced a bar chart seen here:

Not promising at all. so tried producing the scatter plot for breastcancer alone in comparison with the armedforces. 

Is this positive? Is this negative? Is it anything? Null result then eh??

Anyway I tried making a bar chart for it so as I can visualize it better:

Here is the program needed.

And now the bar chart!! i can sense something here. Sense is not needed in analysis of the graphs though. I still think it might lead onto something. 

Next week, here I come.

[statistiken]

Week 3: B. Assignment 5 (Graphing II) [Part 1]

Now everything starts to make sense. I'm loving it. Especially when it turns out there is a somewhat relation between my chosen variables.

For my Query1 of correlation* between 2010 oil Consumption per capita (tonnes per year and person) to Armed forces personnel (% of total labor force) this turned out. It seems to be a positive relation but it looks too clustered to that corner too so I did the C>Q.

This time around, the example for the video lecture used Gapminder! Yey. After doing this grouping. This the bar chart out of this prog.

NOOOOOOO!!!!! Almost a negative slope then boom. Fuck. Nice try for me though.

NOTE: * I've been misspelling correlation all this time. Thank you tumblr spell check.

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Week 3: A. Assignment 4 (Graphing I) for Passion Driven Statistics. I used the 'oilperperson' variable for this assignment; did the step-by-step procedure through the Bar Chart Wizard; prints this. 

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Week 3: -Z. Hypothesis Formulation Ver 3.0

My Query 1 is: Is there a significant correlation between the rate of the armed forces of a country to its consumption of oil? 

For the few tumblr people who left replies, they find this OK so this query remains.

My Query 2 is: Are the countries who spend more on military spends less on health? (In this case, I will look on the hiv rate and breast cancer rate)

But for Q2 a two who left a reply/answer noticed my dumbness in formulation. Thank you wmtumbls and hsquare12. 

Oh yeah hivrate and breast cancer rate does not tell how much is spent. So I'll rephrase it to the simplest possible query (as suggested in the video lectures):

Query 2 [2.0]: Is there a significant correlation between armed forces rate and morbidity? (hiv rate, breast cancer)

Both are still weak. I'll improve it on Ver 4.0

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Week 2: B. Assignment #3 (beta)

DATA MANAGEMENT

I understand all what Lisa instructed in the video lectures. The logic behind the changes and how to accomplish it in the program. I just don't need those same changes in my gapminder tables. I guess I need some data management in my tables, they are not looking pretty fly. I just don't know SAS to do it. So here is my assignment #2:

[desitur]

A lot of our classmates are going ahead with week 2 videos and Assignment 2. So do I. My reason is that a big change will affect my life in mid-April, so I’ll try to take advantage of the quietness of these days.

Then, let’s go: in the first lecture of Week 2, instructor Linda Dierker shows...

[statistiken]

Week 2: A. Assignment #2

I started working on my program in SAS by following the video lecture. Here it is:

Checked the log and viewed the result. These pictures are the part of the result tab:

eh?

oh?

ah?

So what now? These figures are not promising. I don't see the countries and why are the frequencies being counted. I don't understand anything here.

I guess that is why I'm taking up this course. I need to know how to analyze such and make use of these data.

Now you've seen what I am able to accomplish so far. Please help me. Start criticizing and scolding me esp. in the SAS part. Thank you.

[amitk-iitm]

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Week 2: -Z. Hypothesis Formulation Ver. 2.0

I finally made up my mind on my research questions!

From my chosen variables: armedforcesrate, oilperperson, hivrate, and breastcancerper100th.

Hypothesis I

Is there a relation between the rate of the armed forces of a country to its consumption of oil?

My query is, are the countries who are oil hungry boost more on defense and military stuff because war?

Hypothesis II

Are the countries who spend more on military spends less on health? (In this case, I will look on the hiv rate and breast cancer rate)

            The more a country love oil and force, the least they mind health?

There it is! Does that make any sense? Is this at least feasible? I mean, please help me, should I abandoned them and formulate better ones? Am I wrong to choose only from the data within the codebook?

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Week 1:C. Assignment #1

It took me two days to finish this assignment in the Quiz tab. Ha! It's because I don't know what to write in the variable names. From how I understand, it is to be chosen from the codebook  of my chosen dataset while having my hypothesis/research questions in mind. I really hope that this is the way it should be done.

I ended up with choosing these variables:

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Week 1: B. Hypothesis Formulation

This part is fucking hard. I stared at my notepad for 10 minutes, then skimmed the gapminder.org for 1 hour and 15 tabs of different tables are open. From those I finally formulated about 4 hypothesis I find interesting. 

I went back to the classroom and it came to me: CODEBOOK. F. I'm back at one.

[statistiken]

Hi I'm Jin

I forgot introduce myself. I am Jin, 24, an Aeronautical Engineering student. It's our summer break so I'm trying to maximize my time by educating myself.

Coursera is fucking amazing. I fucking love these MOOCs! Coursera is really up there.