What is skewness in data and why is it important?

What is skewness in data and why is it important?

Skewness measures the asymmetry of a data distribution. Positive skew means a tail on the right, while negative skew has a tail on the left. It's important because it affects statistical analyses and interpretations.

How can I calculate skewness for my dataset?

You can calculate skewness using statistical software or programming languages like Python or R. In Python, you can use the scipy.stats library's skew() function.

What does it mean if my data is positively skewed?

If your data is positively skewed, it indicates that most values are concentrated on the left, with a few larger values stretching the tail to the right. This can affect the mean, making it higher than the median.

Can skewness affect my regression analysis?

Yes, skewness can impact regression analysis by violating the assumption of normality. It may lead to biased estimates and affect the validity of your results.

What are some ways to handle skewed data?

You can handle skewed data by transforming it (e.g., log transformation), using non-parametric methods, or applying robust statistical techniques.