Month: November 2015

Basic Regression on Urban Population Growth and GDP per Capita

Continuing with Regression Modelling in Practice… If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? Through the past two courses, Data Analysis Tools and Data Management and Visualization, I established that there was a correlation between urban population and GDP per capita. For this assignment, my primary explanatory variable is Urban Population Growth rate and response variable is GDP per capita, both figures are from 2010. This is my code in Python: import pandas import numpy import seaborn import matplotlib.pyplot as plt import statsmodels.formula.api as smf import statsmodels.stats.multicomp as multi gapminder = pandas.read_csv(‘Data1.csv’, low_memory=False) gapminder[‘GDP2010’] = gapminder[‘GDP2010’].replace(0,numpy.nan) gapminder[‘GDPGrowth2010’] = gapminder[‘GDPGrowth2010’].replace(0,numpy.nan) gapminder[‘UrbanPop2010’] = gapminder[‘UrbanPop2010’].replace(0,numpy.nan) gapminder[‘UrbanPopGrowth2010’] = gapminder[‘UrbanPopGrowth2010’].replace(0,numpy.nan) gapminder = gapminder[[‘Country’, ‘UrbanPop2010’, ‘UrbanPopGrowth2010’, ‘GDP2010’, ‘GDPGrowth2010’]] gapminder = gapminder.dropna() PopDes = gapminder[‘UrbanPopGrowth2010’].describe() print (PopDes) RegData = gapminder[[‘Country’, ‘UrbanPopGrowth2010’, ‘GDP2010’]] RegData[‘UrbanPopGrowth2010’] = RegData[‘UrbanPopGrowth2010’] – RegData[‘UrbanPopGrowth2010′].mean() print (RegData.describe()) UrbanReg = smf.ols(formula=’GDP2010 ~ UrbanPopGrowth2010′, data=RegData).fit() print (UrbanReg.summary()) seaborn.regplot(x=’UrbanPopGrowth2010′, y=’GDP2010’, fit_reg=True, data=RegData) plt.xlabel(‘Urban Population Growth …

Bombing Oil Fields and Fighting Islamic State

Has anyone given thought to the political, economic, and environmental consequences of such destruction? Has anyone thought about the devastating environmental impact of bombing oil fields? For the most part, the majority of the people under IS control are innocent. Yet, this sort of destruction and retaliation by the West, is exactly what drives the youth to join in the extremist movement. How can anyone be alright with the destruction of their livelihood and of their environment?

In Speaking of Data: Gapminder

This is the start of the third course, Regression Modeling in Practice, in the Data Analysis and Interpretations Specialization by Wesleyan University through Coursera. The first assignment is to provide a description of the data I have been working with – what is the sample, how the data is collected and how I managed the data. If you have been following along with my work, you will know that I am interested in the relationship between urbanization and economic development and am posing the general question of whether urbanization drives economic growth? My sample consists of countries, territories, and other political entities such as disputed territories, dependent territories, or semi-autonomous city-states like Hong Kong. According to Gapminder, where my data was downloaded, this list consists of 193 UN Nations, 51 other entities, 4 French overseas territories, 10 former states, and 2 ad-hoc areas totaling 260 (or N=260). However, because not every entity has data in the indicators I am using, the number of entities in my work is reduced to 164 (or N=164). In the case …

The Moderating Variable

Last Lesson in Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. This is the last lesson in the Data Analysis Tools course. After analyzing for correlations between variables, this assignment focuses on moderating variables. A moderating variable is one that influences the strength and direction of the association between the explanatory and response variables. Last time, I established that there were correlations between the amount of urbanization, as measured by percentage of total population in cities with over 1 million people, urban population growth, and GDP per capita. Additionally, I found that there was a correlation between total populations in cities and urban population growth. I suspect that one of these two variables might be a moderating variable. I first looked at total …

Correlations! Urbanization and Economic Development in Rich and Poor Countries

Continuing with Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. Finally! Quantitative to quantitative variable analysis! This is the lesson I have been waiting for. With my interest in urbanization and economic development, the data I pulled from Gapminder are all quantitative. As I previously mentioned, I do not like categorizing quantitative data because I believe it introduces too much subjectivity. Unless the data is qualitative to begin with, it makes little sense to categorize data. Compared to the other types of correlation tests, Pearson’s Correlation was relatively easy to perform in both Python and SAS. I looked at the relationships between urbanization rate, as measured by both urban population growth rate and percentage of population in large cities with over 1 …

Chi-Square Testing…*Warning: It’s Painful*

Continuing with Data Analysis Tools… If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. For this assignment, I had to run Chi-Square tests on my variables. As always, both my Python and SAS codes are posted. Since all my data are quantitative, I had to first categorize them. Since I found a relationship between the absolute measure of urbanization (population in cities with over 1 million people) and GDP Growth rate, I decided to categorize GDP growth rate. Additionally, I wanted to see if there is a relationship between urbanization with the absolute measure of GDP  (GDP per capita). To categorize GDP per capita, I used cut-offs of 5000, 10000, and 100000 to produce three distinctive ranks whereby a country is poor if its GDP per …

A Revelation…Through ANOVA

Now that I finished the first course in the Data Analysis and Interpretations Specialization, this is the start of the second called Data Analysis Tools.  If you have not read my previous posts, I am currently enrolled in a Data Analysis Specialization with Wesleyan University through Coursera. With data from Gapminder, I am exploring a broad and basic question: does urbanization drive economic growth? For those of you interested in reading my literature review to gain a background on this project, please visit this page. Since I have been presenting my SAS work in the previous course, I will be presenting my Python work for this course. I actually enjoy working with Python, it seems to have more flexibility and I am more used to its language, having some experience with R during graduate school. As always, I am also including the other set of code for reference, so please see my SAS code at the very bottom of the post. There will be two parts to this presentation. The first part will be a discussion of …