Read My Data

Here, I will read the data, and read, and read...

[readmydata-blog]

Here are all eight frequency tables of my data

table1-code1; breast cancer percentage

codes from 1 to 6 are assigned to show the percentage areas.

1: 1% - 10%; 2: 10%-20%; 3: 20%-30%; 4: 30%-50%; 5: 50%-75%; 6: 75-100%

table2-code2; GDP per Capita in USD

codes from 1 to 7 are assigned to show the GDP per capita areas.

1: GDP<2000; 2: 2000<GDP<5000; 3: 5000<GDP<10000; 4: 10000<GDP<20000 ; 5: 20000<GDP<35000 ; 6: 35000<GDP<50000; 7: GDP>50000

table3-code3; CO2 Emission per Capita

codes from 1 to 5 are assigned.

1: CO2<1; 2: 1<CO2<5; 3: 5< CO2 <10; 4: 10< CO2 <20; 5: CO2>20

table4-code4; Female Employment Percentage

codes from 1 to 5 are assigned.

1: 1% - 20%; 2: 20%-40%; 3: 40%-60%; 4: 60%-80%; 5: >80%

table5-code5; Internet Use Rate

codes from 1 to 5 are assigned.

1: <1%; 2: 1%-10%; 3: 10%-30%; 4: 30%-50%; 5: >50%

table6-code6; Life Expectancy in Years

codes from 1 to 5 are assigned.

1: 40<Life Exp<50; 2: 50< Life Exp <60; 3: 60< Life Exp <70; 4: 70< Life Exp <80; 5: Life Exp >80

table7-code7; Urban Rate

codes from 1 to 6 are assigned.

1: Urban<30%; 2: 30%-50%; 3: 50%-60%; 4: 60%-70%; 5: 70%-80%; 6: Urban 80%-100%

table8-code8; Urban Rate

codes from 1 to 6 are assigned.

1: use<1%; 2: 1%-5%; 3: 5%-15%; 4: 615%-30%; 5: 30%-50%; 6: use>50%

table9-code9; Cholesterol

codes from 1 to 4 are assigned.

1: 4-4.5; 2: 4.5-5; 3: 5-5.5; 4: >5.5

[readmydata-blog]

My First Program Ever

Note: In order to see the tables, please refer to the next post on this blog

[readmydata-blog]

Research Question

I am interested in doing research about topics related to the social and economic development and wellbeing of societies.  Having gone through the Gapminder codebook, I found my interest in health particularly in breast cancer rate in different countries. Since, initially I do not have concrete idea that which variables and factors are the most relevant ones to this topic, I like to include almost all variables in codebook.

In order to set my own codebook, I will exclude four variables from the Gapminder codebook including HIV Rate, Armed Forces Rate, Oil per Person, and Polity Score. However, I will add some new variables such as cell phones (per 100 person) ownership, and level of cholesterol.

Based on my interest, I will be analyzing to see if there is any relationship between the breast cancer and factors mentioned above which I will include in my codebook.

Hypothesis

The breast cancer rate increases if

GDP per capita decreases

CO2 emissions increases

Female employment rate decreases

Internet use rate decreases

Life expectancy increases

Urban rate increases

Cell phone use/number increases

Obesity increases

Literature Review

Risk factors, anything that can increase the risk of developing breast cancer, are mainly divided into two parts; uncontrollable and controllable. The first one consists of all those factors above which we do not have control over such as age, family history and medical history. However, the second type of factors are those which are within our control. Examples could be diet, weight, exercise, alcohol consumption, smoking and etc.1

Breastcancer.org

http://www.breastcancer.org/symptoms/understand_bc/risk/factors

The number of new cases of female breast cancer has slightly decreased through time in the U.S. between mid-1970s and 2013. Also, the number of death caused by breast cancer has experienced a slight decline through the same period.2

National Cancer Institute, Surveillance, Epidemiology, and End Results Program

http://seer.cancer.gov/statfacts/html/breast.html

[readmydata-blog]

I find this incredibly interesting.

[readmydata-blog]

Ready to READ

I am pretty excited to follow up the assignment I need to do in this interesting Data Analysis and Interpretation MOOC.

I wanna rock on reading and interpreting data.