We combined information from three different sets: one for inactive individuals, another for people with diabetes, and a third for those dealing with obesity.

Histogram Plot for Inactive and Diabetics Data: When we looked at the data for inactive individuals and those with diabetes, the histogram plot showed a “normal distribution.” This means that the data forms a bell-shaped curve, and the mean and standard deviation can fully describe the characteristics of this distribution. It’s like how most people’s heights cluster around an average height, with some variability.

Histogram for Obesity Data: However, when we examined the data for obesity, the histogram looked different. It was a “left-skewed histogram,” meaning it tilted more towards the left side. The mean was typically less than the median. The longer tail on the left side of the graph indicated that there were some unusually low values in the data, which is common in obesity data.

By understanding these different distributions, we can analyze data more accurately, especially when the data follows a normal distribution.