📊📈🔍 Hey guys, let’s talk about measures of variation in distributions! 📊📈🔍

Measures of variation tell us how spread out the values are around the central value. If the values are tightly clustered around the center, then there is low variation. But if the values are spread out over a wider range, then there is high variation.

There are several ways to measure the variability of a distribution. Here are a few common ones:

📈 **Range**: This is the difference between the maximum and minimum values in the distribution. It gives us a sense of how wide the spread of values is.

📈** Interquartile range (IQR)**: This is the range between the first quartile (25th percentile) and the third quartile (75th percentile) of the distribution. It tells us how spread out the middle 50% of the values are.

📈 **Variance**: This is a measure of how far each value in the distribution is from the mean. It tells us how spread out the values are from the average.

📈 **Mean absolute deviation (MAD)**: This is similar to variance, but instead of squaring the differences from the mean, it takes the absolute value. This makes it easier to interpret, but it can be less precise than variance.

📈 **Standard deviation**: This is the square root of the variance. It tells us how much the values deviate from the mean, and it’s one of the most commonly used measures of variation.

💡 By using these measures of variation, we can get a better sense of how spread out the values are in a distribution and how they relate to the central value. 💡