In this Grade 8 enVision Mathematics lesson from Chapter 4, students learn how to construct scatter plots by plotting paired data as ordered pairs on a coordinate plane and how to interpret the resulting graphs to identify positive association, negative association, or no association between two data sets. Students also practice recognizing clusters, gaps, and outliers within scatter plots. The lesson builds foundational skills for analyzing bivariate data using real-world contexts such as social media campaigns, sleep and test scores, and basketball statistics.
Section 1
Procedure: Plotting Points on a Scatter Plot
A scatter plot is constructed by converting two-variable data into ordered pairs where represents the first variable and represents the second variable, then plotting these points on a coordinate plane with appropriate scales.
Section 2
Interpreting Scatter Plots: Association
Positive Association: As -values increase, -values tend to increase (upward trend)
Negative Association: As -values increase, -values tend to decrease (downward trend)
Section 3
Clustering and outliers
In general, clustering refers to a set of data points that are in close proximity to each other.
Outliers are data points that notably deviate or “stand out” from the general behavior of the data set.
Once outliers have been identified graphically, the researcher must give justification to treat them as outliers in terms of the context.
Outliers are simply data points that “stand apart from the general trend”, regardless of the reason.
Clustering is when data points group together, showing a common trend. An outlier is a point that sits far away from this main group. It's crucial to investigate an outlier, as it could be a mistake or a very important, unique piece of data.
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Section 1
Procedure: Plotting Points on a Scatter Plot
A scatter plot is constructed by converting two-variable data into ordered pairs where represents the first variable and represents the second variable, then plotting these points on a coordinate plane with appropriate scales.
Section 2
Interpreting Scatter Plots: Association
Positive Association: As -values increase, -values tend to increase (upward trend)
Negative Association: As -values increase, -values tend to decrease (downward trend)
Section 3
Clustering and outliers
In general, clustering refers to a set of data points that are in close proximity to each other.
Outliers are data points that notably deviate or “stand out” from the general behavior of the data set.
Once outliers have been identified graphically, the researcher must give justification to treat them as outliers in terms of the context.
Outliers are simply data points that “stand apart from the general trend”, regardless of the reason.
Clustering is when data points group together, showing a common trend. An outlier is a point that sits far away from this main group. It's crucial to investigate an outlier, as it could be a mistake or a very important, unique piece of data.
Book overview
Jump across lessons in the current chapter without opening the full course modal.
Continue this chapter