Data Analysis: Logistic Regression and Olympic Insights with R

Data Analysis and Visualization: Logistic Regression of Civil Wars and Olympic Insights using R

In this data-driven journey, we dive into the world of data analysis and visualization, all while highlighting our expertise in R Programming assignment. Our exploration begins with a comprehensive investigation of logistic regression, where we analyze the intricate relationships between various factors and the occurrence of civil wars. This analysis includes identifying significant coefficients and predicting the probability of civil wars. Shifting our focus, we leverage a rich dataset spanning 120 years of Olympic history, unraveling the dynamics between athlete physique, medal achievements, and more. Throughout this process, we showcase our proficiency in R programming and data interpretation, providing valuable insights into two fascinating realms of data analytics.

Problem Description

Explore the world of data analysis with a focus on logistic regression and data visualization. In the first part, delve into logistic regression by examining the influence of various factors on civil wars. Identify significant coefficients and predict the likelihood of a civil war in Ethiopia. In the second part, leverage a dataset containing 120 years of Olympic results to create insightful visualizations relating to athlete physique, medal achievements, and more. Mastery of R programming and data interpretation is essential for this comprehensive analysis.

Part 1: Logistic Regression

Problem 1

In this part, we conducted a logistic regression analysis to understand the factors affecting the occurrence of civil wars. We used various independent variables, except for country and year, and included a quadratic term for exports. The coefficients, standard errors, and p-values were reported. Significant coefficients at the 5% level were identified.

Results:

Variable	Coefficients	Std. Error	p-value
(Intercept)	-13.070	2.795	0.0000
exports	18.940	5.865	0.0012
I(exports^2)	-29.440	11.780	0.0124
schooling	-0.032	0.010	0.0013
growth	-0.115	0.043	0.0075
peace	-0.004	0.001	0.0007
concentration	-2.487	1.005	0.0134
lnpop	0.768	0.166	0.0000
fractionalization	0.000	0.000	0.0190
dominance	0.670	0.354	0.0579

Table 1: Report of coefficients, standard errors, and p-values

All coefficients, except for "dominance," are significant.

Problem 2

We calculated the model's predicted probability for a civil war in Ethiopia starting in 1970. Additionally, we predicted the probabilities for a country like Ethiopia in 1970, but with adjusted values for male secondary school enrollment rate and the ratio of commodity exports to GDP.

Predicted Probabilities:

Exports	Schooling	Growth	Peace	Concentration	Inpop	Fractionalization	dominance
0.065	6	0.41	292	0.639	17.1806	4347	1

Table 2: Data for Ethiopia in 1970

Ethiopia in 1970: 0.12522
Ethiopia-like country with higher schooling: 0.07076
Ethiopia-like country with higher exports: 0.47508

Part II: Data Visualization

Problem 3

Using the dataset "athlete_events.csv," which contains 120 years of Olympic results, we were tasked with creating a visualization that explores the relationship between athlete physique (height and weight) and sport over time.

Problem 4

For this problem, we had the freedom to create and interpret a visualization of our choice. In our analysis, we presented a scatterplot that shows a positive correlation between height and weight.

Fig 1: Scatterplot between height and weight

These analyses demonstrate our proficiency in using logistic regression and data visualization techniques, with a specific focus on Olympic medal winners and the programming language R.