In this lesson, students bring everything they have studied in the unit so far to analyze and interpret bivariate data in context (MP4). They create a scatter plot, identify outliers, fit a line, and determine and interpret the slope of the line. They compare actual and predicted values. They reflect on what they have learned about modeling bivariate data.
- Create a scatter plot and draw a line to fit bivariate data, and identify (orally and in writing) outliers that appear in the data.
- Interpret (orally and in writing) features of a scatter plot with a line of fit, including outliers, slope of the line, and clustering.
Let’s analyze data like a pro.
Class data (or data from another group if it fits the classroom culture better) from the second lesson in the unit on arm span and height.
- I can analyze a set of data to determine associations between two variables.
A negative association is a relationship between two quantities where one tends to decrease as the other increases. In a scatter plot, the data points tend to cluster around a line with negative slope.
Different stores across the country sell a book for different prices.
The scatter plot shows that there is a negative association between the the price of the book in dollars and the number of books sold at that price.
An outlier is a data value that is far from the other values in the data set.
Here is a scatter plot that shows lengths and widths of 20 different left feet. The foot whose length is 24.5 cm and width is 7.8 cm is an outlier.
A positive association is a relationship between two quantities where one tends to increase as the other increases. In a scatter plot, the data points tend to cluster around a line with positive slope.
The relationship between height and weight for 25 dogs is shown in the scatter plot. There is a positive association between dog height and dog weight.