Analyzing Data: Comparing Sets with Outliers

This lesson covers the use of appropriate measures of center and spread in data analysis, focusing on the influence of outliers and skewness. It explains how to identify outliers using the interquartile range (IQR) and demonstrates the impact of outliers on the mean and median. The lesson compares data sets for aluminum and plastic recycling, highlighting the importance of choosing the right measures for data analysis. The presence of outliers suggests using the median and IQR for comparison. The lesson concludes with a recap of the key points discussed.

What is the effect of outliers on the mean and median of a data set?

How do you determine if a value is an outlier using the IQR?

Explain the difference between using the mean and standard deviation versus the median and IQR for data analysis.

What conclusions can be drawn from comparing the median and IQR of aluminum and plastic recycling data?

Why is it important to inspect data for outliers before choosing measures of center and spread?