In previous lessons, students have been exposed to language like, “As the independent variable increases, the dependent variable tends to decrease.” In this lesson they focus on this language in earnest (MP6). Students also interpret the slopes of fitted lines in context (MP2) and identify positive and negative associations of scatter plots without a fitted line shown (MP7).
- Describe (orally and in writing) the relationship between two variables using a line fit to data on a scatter plot.
- Interpret (orally and in writing) points on the scatter plot, including points that do and do not lie on a line fit to the data.
- Interpret (orally and in writing) the slope of a line fit to data in context.
Let's look at how changing one variable changes another.
- I can use the slope of a line fit to data in a scatter plot to say how the variables are connected in real-world situations.
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.