To tackle the CDC 2018 diabetes dataset comprehensively, I plan to consider various factors and approaches, including additional variables like socioeconomic status, household composition, and disability. Here’s how I initially approached this dataset:

 I began by getting to know the dataset inside out. This involved reviewing its structure, understanding column names, and identifying data types. During this process, several key questions emerged:

What is the overall prevalence of diabetes in different U.S. counties, and is there geographic variation?

Are there any correlations between obesity rates and diabetes prevalence in different counties? Does a higher obesity rate correlate with a higher diabetes rate?

How does the level of physical inactivity relate to diabetes and obesity rates? Are areas with higher rates of inactivity more likely to have higher diabetes and obesity rates?

Are there regional patterns in diabetes, obesity, and inactivity rates? Are certain areas of the country more affected than others?

Can we build predictive models to forecast diabetes rates based on obesity and inactivity levels? How accurate are these models?

To gain deeper insights into the dataset, I conducted data exploration. This involved generating summary statistics and creating visualizations, including histograms, box plots, and scatterplots. These techniques helped me understand the distributions of variables such as %diabetes, %obesity, and %inactivity.

 Data visualization played a crucial role in understanding relationships and patterns. I used scatterplots to visualize how %diabetes relates to %obesity and %inactivity. Heatmaps were employed to identify correlations between variables.

Moving forward, I tried linear regression analysis. The goal here was to comprehend how %diabetes is influenced by %obesity and %inactivity.

Additionally, I plan on examining the residuals of the regression models to check for important statistical assumptions:

Normality: Are the residuals normally distributed?

Heteroscedasticity: Does the spread of residuals change as %inactivity levels change?

Outliers: Are there any outlier data points that significantly affect the model?

 If the assumptions of normality, heteroscedasticity, or the presence of outliers are violated, I will consider applying appropriate transformations to the data to improve the reliability of the analysis.