We analyzed the distribution of radon concentration levels using a histogram. Here's what we learned:

This visualization helps in identifying patterns or outliers in radon concentration levels, contributing to a better understanding of the data's characteristics.

The correlation between radon concentration levels and temperature was explored, yielding a correlation coefficient of 0.04. This indicates a very weak linear relationship between the two variables, suggesting that changes in temperature have minimal impact on radon concentration levels within the dataset.

The correlation between radon concentration levels and humidity was investigated, resulting in a correlation coefficient of 0.01. This indicates a very weak linear relationship, suggesting that humidity levels have minimal impact on radon concentration levels within the dataset.

We compared radon concentration levels across different offices using a bar chart. Here's what we found:

This visualization is crucial for understanding the distribution of radon levels in different environments and planning appropriate safety measures.

We attempted to test the null hypothesis stating that there is a statistical difference between the radon concentrations in different offices by performing a one-way ANOVA test. However, the test resulted in NaN values for both the test statistic and the p-value, indicating an issue with the data or the test execution.

This outcome suggests that the data might not be suitable for the ANOVA test as performed, possibly due to missing values, non-numeric data, or insufficient data points per group. Further investigation and data preprocessing might be necessary to correctly perform the hypothesis test.