Exploring Mean Absolute Deviation (MAD)

The video tutorial covers the concept of Mean Absolute Deviation (MAD), explaining its importance in understanding data spread and prediction accuracy. It includes a detailed explanation of how to calculate MAD, using two examples: Instagram likes and mile run times. The tutorial also discusses the significance of MAD in data analysis and provides guided practice for students to reinforce their understanding.

What does MAD stand for in the context of this lesson?

• Maximum Allowed Distance

• Mean Absolute Deviation

• Median Allocated Division

• Mean Allocated Deviation

What is the purpose of calculating the Mean Absolute Deviation?

• To identify the maximum value in the data

• To determine the consistency of data

• To find the median of the data

• To calculate the total sum of the data

How is the mean of a data set calculated?

• Subtract smallest value from largest and divide by two

• Add all numbers and divide by the count

• None of the above

• Multiply all numbers and divide by the count

What does a small MAD value indicate about the data set?

• The data points are widely spread out

• The data points are closely packed

• The data set is inconsistent

• The data set contains outliers

In the Instagram likes example, what is the first step to calculate MAD?

• Calculate the total number of likes

• Divide the total likes by the number of data points

• Find the mean of the likes

• Subtract the mean from each data point

What is the mean of the Instagram likes in the example provided?

• 26

• 27

• 28

• 29

What is the final step in calculating MAD for the Instagram likes example?

• None of the above

• Subtract the smallest deviation from the largest

• Multiply the deviations by the number of data points

• Add up the absolute deviations and divide by the number of data points

In the mile run times example, what is the calculated mean time?

• 8 minutes

• 10 minutes

• 9 minutes

• 7 minutes

What does a MAD value of 0.83 in the mile run times example suggest?

• None of the above

• Low variability in run times

• High variability in run times

• Inaccurate data points

How is the absolute value used in calculating MAD?

• To ensure all deviations are counted as positive

• To negate all deviations

• To double the deviations

• To subtract the mean from each data point