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 effect does an outlier have on the mean of a data set?

• It increases the mean.

• It makes the mean equal to the median.

• It decreases the mean.

• It has no effect on the mean.

How is an outlier determined using the interquartile range (IQR)?

• By adding 1.5 times the IQR to the mean.

• By subtracting 1.5 times the IQR from the median.

• By multiplying the IQR by 1.5 and adjusting quartiles.

• By dividing the IQR by 1.5 and adjusting quartiles.

Why might median and IQR be preferred over mean and standard deviation in certain data sets?

• Because they provide more detailed information.

• Because they are not affected by outliers.

• Because they are easier to calculate.

• Because they are always larger than mean and standard deviation.

What does a larger IQR indicate about a data set?

• The data has no outliers.

• The data is more spread out.

• The data is skewed left.

• The data is more compact.

In the context of recycling data, what does a smaller IQR for aluminum suggest?

• Aluminum data is skewed right.

• Aluminum data has more outliers.

• Aluminum data is more spread out.

• Aluminum data is more compact.