**Descriptive Statistics and Visualizations**

**Histograms and Summary Statistics**

I generated histograms for ages within each race category. These visualizations provided a clear overview of the age distribution for different races. Alongside the visualizations, I calculated essential summary statistics, including mean, median, standard deviation, variance, skewness, and kurtosis. These metrics offer insights into the central tendency and the spread of age within each racial group.

**Variance Analysis**

Variance analysis helps us understand the extent of age variation within each race. By comparing variances, we identify which racial group has the most diverse age range among individuals involved in police shootings.

**Confidence Intervals for the Mean Age**

Confidence intervals provide a range in which the true mean age for each race is likely to fall. We compute 95%, 99%, and 99.9% confidence intervals, offering a more comprehensive understanding of the uncertainty associated with the mean age within each racial category.

**Bayesian Probability Analysis**

Using a Bayesian approach, we calculate the probability of an individual’s race given their age. This analysis provides a unique perspective on how race and age are correlated in the context of police shootings.

**Conclusion**

Analyzing police shootings data through statistical techniques sheds light on the intricate relationship between race, age, and law enforcement encounters. By employing various statistical methods, I gained valuable insights into the demographics of individuals involved in these incidents. Understanding these patterns is a significant step toward fostering informed discussions and implementing policies aimed at addressing the complex issues surrounding police shootings.