In this Grade 6 lesson from Pengi Math Chapter 7, students learn to identify outliers as extreme data values and analyze how they affect the mean of a data set. Students compare the mean and median when outliers are present, discovering why the median is resistant to extreme values. Real-world situations are used to help students interpret and apply these concepts in context.
Section 1
Identifying Outliers
Outliers are values that are significantly different from the rest of the data.
These are data points that stand far apart from the main group of values.
We can identify potential outliers by looking for values that seem unusually high or low compared to the other data points in the set.
Section 2
Analyzing the Effect of Outliers on the Mean
An outlier is an extreme value that is much higher or lower than the other values in a data set.
Outliers have a strong effect on the mean because the mean uses all values in its calculation.
When an outlier is present, it pulls the mean toward the extreme value, making the mean less representative of the typical values in the data set.
To calculate the mean:
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Section 1
Identifying Outliers
Outliers are values that are significantly different from the rest of the data.
These are data points that stand far apart from the main group of values.
We can identify potential outliers by looking for values that seem unusually high or low compared to the other data points in the set.
Section 2
Analyzing the Effect of Outliers on the Mean
An outlier is an extreme value that is much higher or lower than the other values in a data set.
Outliers have a strong effect on the mean because the mean uses all values in its calculation.
When an outlier is present, it pulls the mean toward the extreme value, making the mean less representative of the typical values in the data set.
To calculate the mean:
Book overview
Jump across lessons in the current chapter without opening the full course modal.
Continue this chapter