Hello, fellow statisticians!

I hope you’re all diving deep into the intricacies of our course, Advanced Mathematical Statistics. As we journey through this subject, I wanted to share some insights and experiences I had while working on our recent project involving CDC’s data on US county rates of diabetes, obesity, and inactivity.

**Getting a Grip on the Data**

The first step in any data analysis is understanding the dataset at hand. We were given a comprehensive dataset by the CDC that provided statistics on diabetes, obesity, and inactivity rates across various US counties for the year 2018. Before applying any advanced statistical techniques, it’s crucial to visualize and understand the basic structure and distribution of our data.

**Histograms: A Peek into Distribution**

Histograms are our first line of defense when it comes to visualizing the distribution of numeric data. Using Python’s Matplotlib library, I plotted histograms for our primary metrics: obesity rates, inactivity rates, and diabetes rates.

For instance, while plotting the distribution of obesity rates, the code snippet I used was:

python

import matplotlib.pyplot as plt

# Assuming ‘data’ is our dataset
plt.hist(data[‘obesity_rate’], bins=20, color=’blue’, alpha=0.7)
plt.xlabel(‘Obesity Rate’)
plt.ylabel(‘Frequency’)
plt.title(‘Distribution of Obesity Rates’)
plt.show()
“`

The histogram provided an immediate understanding of the distribution, showing where most of the data points were concentrated.

**Box Plots: Highlighting Outliers and Summarizing Data**

Box plots, on the other hand, gave me a concise summary of our data, emphasizing outliers. Using Seaborn, another Python plotting library, I created box plots for the obesity rates categorized by states:

“`python
import seaborn as sns
import matplotlib.pyplot as plt

# Assuming ‘data’ is our dataset
sns.boxplot(x=’state’, y=’obesity_rate’, data=data)
plt.xlabel(‘State’)
plt.ylabel(‘Obesity Rate’)
plt.title(‘Box Plot of Obesity Rates by State’)
plt.xticks(rotation=90) # Rotate x-axis labels for better readability
plt.show()
“`

This visualization highlighted states with unusually high or low obesity rates and helped pinpoint potential outliers in our dataset.

**Wrapping Up**

These primary visualizations laid the foundation for the more advanced statistical analyses we’ll be venturing into as the course progresses. Remember, a good visual can communicate complex data points more efficiently than rows of numbers.

I’d love to hear about your insights and the methodologies you’ve adopted in this project. Let’s keep the discussion alive and learn from each other’s experiences. Until next time, keep crunching those numbers!

Best,

Aditya Domala