1. 4.1 Scatterplots  Explanatory and Response Variables  Scatterplots  Interpreting Scatterplots  Categorical Variables in Scatterplots 1

2. 2 Many interesting examples of the use of statistics involve relationships between pairs of variables. Two variables measured on the same cases are associated if knowing the value of one of the variables tells you something about the values of the other variable that you would not know without this information. 2 Associations Between Variables A response variable measures an outcome of a study. An explanatory variable explains or causes changes in the response variable. When you examine the relationship between two variables, a new question becomes important: Is your purpose simply to explore the nature of the relationship, or do you wish to show that one of the variables can explain variation in the other?

3. 3 The most useful graph for displaying the relationship between two quantitative variables is a scatterplot. A scatterplot shows the relationship between two quantitative variables measured on the same individuals. The values of one variable appear on the horizontal axis, and the values of the other variable appear on the vertical axis. Each individual in the data appears as a point on the graph. 1.Decide which variable should go on each axis. If a distinction exists, plot the explanatory variable on the x- axis and the response variable on the y-axis. 2.Label and scale your axes. 3.Plot individual (x,y) data pairs of values. 1.Decide which variable should go on each axis. If a distinction exists, plot the explanatory variable on the x- axis and the response variable on the y-axis. 2.Label and scale your axes. 3.Plot individual (x,y) data pairs of values. How to Make a Scatterplot 3 Scatterplot

4. Figure 4.13 Fat gain after 8 weeks of overeating plotted against the increase in nonexercise activity over the same period, for Example 4.16. David S. Moore, George P. McCabe and Bruce A. Craig : EXPLORING the PRACTICE of STATISTICS, First Edition Copyright © 2014 by W.H. Freeman and Company From section 4.3, Example 4.16

5. 5 To interpret a scatterplot, follow the basic strategy of data analysis from Chapter 1. Look for patterns and important departures from those patterns. As in any graph of data, look for the overall pattern and for striking deviations from that pattern.  You can describe the overall pattern of a scatterplot by the direction, form, and strength of the relationship.  An important kind of departure is an outlier, an individual value that falls outside the overall pattern of the relationship. As in any graph of data, look for the overall pattern and for striking deviations from that pattern.  You can describe the overall pattern of a scatterplot by the direction, form, and strength of the relationship.  An important kind of departure is an outlier, an individual value that falls outside the overall pattern of the relationship. How to Examine a Scatterplot Interpreting Scatterplots

6. 6 Two variables are positively associated when above-average values of one tend to accompany above-average values of the other, and when below-average values also tend to occur together. Two variables are negatively associated when above-average values of one tend to accompany below-average values of the other, and vice-versa. Two variables are positively associated when above-average values of one tend to accompany above-average values of the other, and when below-average values also tend to occur together. Two variables are negatively associated when above-average values of one tend to accompany below-average values of the other, and vice-versa. Interpreting Scatterplots

7. 7 Direction Form Strength Outlier There is one possible outlier―the hiker with the body weight of 187 pounds seems to be carrying relatively less weight than are the other group members. There is a moderately strong, positive, linear relationship between body weight and backpack weight. It appears that lighter hikers are carrying lighter backpacks. Interpreting Scatterplots

8. 8 Consider the relationship between mean SAT verbal score and percent of high school grads taking the SAT for each state. To add a categorical variable, use a different plot color or symbol for each category. Southern states highlighted Adding Categorical Variables

9. HW: Read section 4.1, be able to use the terminology to describe the association between two quantitative variables (direction, strength, and form) Work on exercises # 4.13, 4.14, 4.20-4.22, 4.24-4.26, 4.29, and especially 4.31 (use JMP)