Comparing Variability: Medians and Interquartile Ranges

This lesson teaches how to compare two data sets by examining their medians and interquartile ranges (IQRs). It explains the importance of medians and IQRs in understanding data variability, using the example of soccer and baseball player weights. The lesson includes visual data representations like histograms and box plots to illustrate the concepts. It concludes with a summary of findings, highlighting that while baseball players are generally heavier, soccer players show more variability in weight.

What is the interquartile range (IQR) used for in data analysis?

• To find the average of a data set

• To calculate the total range of data

• To determine the spread of the middle 50% of data

• To identify the highest value in a data set

In the example provided, what is the median weight of soccer players?

• 175 pounds

• 150 pounds

• 190 pounds

• 200 pounds

Which sport showed a greater range in player weights according to the example?

• Soccer

• Baseball

• Both have the same range

• Neither, range is not discussed

What does a similar IQR between two data sets suggest about their data distribution?

• The data sets have no variability

• The data sets have the same range

• The data sets have identical medians

• The data sets have similar variability in the middle 50%

What conclusion can be drawn about the weights of soccer and baseball players from the lesson?

• Both sports have identical weight distributions

• Soccer players have more variability in their weights

• Baseball players have a wider range of weights

• Soccer players are generally heavier than baseball players