1. DO NOW 1.Given the scatterplot below, what are the key elements that make up a scatterplot? 2.List as many facts / conclusions that you can draw from this scatterplot.

2. Slide 7 - 2 INTRO TO SCATTERPLOTS Scatterplots may be the most common and most effective display for data. Scatterplots may be the most common and most effective display for data. In a scatterplot, you can see patterns, trends, relationships, and even the occasional extraordinary value sitting apart from the others. In a scatterplot, you can see patterns, trends, relationships, and even the occasional extraordinary value sitting apart from the others. Scatterplots are the best way to start observing the relationship and the ideal way to picture associations between two quantitative variables. Scatterplots are the best way to start observing the relationship and the ideal way to picture associations between two quantitative variables.

3. Slide 7 - 3 DESCRIBING SCATTERPLOTS When looking at scatterplots, we will look for direction, form, strength, and unusual features. When looking at scatterplots, we will look for direction, form, strength, and unusual features. Direction: Direction: A pattern that runs from the upper left to the lower right is said to have a negative direction. A pattern that runs from the upper left to the lower right is said to have a negative direction. A trend running the other way has a positive direction. A trend running the other way has a positive direction.

4. Slide 7 - 4 LOOKING AT SCATTERPLOTS The figure shows a negative direction between the year since 1970 and the and the prediction errors made by NOAA. As the years have passed, the predictions have improved (errors have decreased). Can the NOAA predict where a hurricane will go?

5. Slide 7 - 5 LOOKING AT SCATTERPLOTS (CONT.) The example in the text shows a negative association between central pressure and maximum wind speed As the central pressure increases, the maximum wind speed decreases.

6. Slide 7 - 6 FORM Form: Form: If there is a straight line (linear) relationship, it will appear as a cloud or swarm of points stretched out in a generally consistent, straight form. If there is a straight line (linear) relationship, it will appear as a cloud or swarm of points stretched out in a generally consistent, straight form.

7. Slide 7 - 7 FORM Form: Form: If the relationship isn’t straight, but curves gently, while still increasing or decreasing steadily, If the relationship isn’t straight, but curves gently, while still increasing or decreasing steadily, we can often find ways to make it more nearly straight.

8. Slide 7 - 8 FORM Form: Form: If the relationship curves sharply, If the relationship curves sharply, the methods of this book cannot really help us. the methods of this book cannot really help us.

9. Slide 7 - 9 STRENGTH Strength: Strength: At one extreme, the points appear to follow a single stream At one extreme, the points appear to follow a single stream (whether straight, curved, or bending all over the place). (whether straight, curved, or bending all over the place).

10. Slide 7 - 10 STRENGTH Strength: Strength: At the other extreme, the points appear as a vague cloud with no discernable trend or pattern: At the other extreme, the points appear as a vague cloud with no discernable trend or pattern: Note: we will quantify the amount of scatter soon. Note: we will quantify the amount of scatter soon.

12. Slide 7 - 12 UNUSUAL FEATURES Unusual features: Unusual features: Look for the unexpected. Look for the unexpected. Often the most interesting thing to see in a scatterplot is the thing you never thought to look for. Often the most interesting thing to see in a scatterplot is the thing you never thought to look for. One example of such a surprise is an outlier standing away from the overall pattern of the scatterplot. One example of such a surprise is an outlier standing away from the overall pattern of the scatterplot. Clusters or subgroups should also raise questions. Clusters or subgroups should also raise questions.

13. Slide 7 - 13 ROLES FOR VARIABLES It is important to determine which of the two quantitative variables goes on the x-axis and which on the y-axis. It is important to determine which of the two quantitative variables goes on the x-axis and which on the y-axis. This determination is made based on the roles played by the variables. This determination is made based on the roles played by the variables. When the roles are clear, the explanatory or predictor variable goes on the x-axis, and the response variable (variable of interest) goes on the y-axis. When the roles are clear, the explanatory or predictor variable goes on the x-axis, and the response variable (variable of interest) goes on the y-axis.

14. ROLES FOR VARIABLES Explanatory or predictor variable goes on the x-axis Explanatory or predictor variable goes on the x-axis Response variable (variable of interest) goes on the y-axis. Response variable (variable of interest) goes on the y-axis.

15. Slide 7 - 15 ROLES FOR VARIABLES (CONT.) The roles that we choose for variables are more about how we think about them rather than about the variables themselves. The roles that we choose for variables are more about how we think about them rather than about the variables themselves. Just placing a variable on the x-axis doesn’t necessarily mean that it explains or predicts anything. And the variable on the y-axis may not respond to it in any way. Just placing a variable on the x-axis doesn’t necessarily mean that it explains or predicts anything. And the variable on the y-axis may not respond to it in any way.

16. Slide 7 - 16 Data collected from students in Statistics classes included their heights (in inches) and weights (in pounds): Data collected from students in Statistics classes included their heights (in inches) and weights (in pounds): Here we see a positive association and a fairly straight form, although there seems to be a high outlier. Here we see a positive association and a fairly straight form, although there seems to be a high outlier. LET’S TRY ONE

17. LET’S MAKE OUR OWN!

18. HOMEWORK – DUE TUESDAY 3 sets of variables that you think correlate with each other. 3 sets of variables that you think correlate with each other. Must be quantitative (measurable). Must be quantitative (measurable). 1.Name the explanatory & response variable for each set. 2.Predict the Correlation (Positive, Negative, None)