1. Chapter 4 Scatterplots and Correlation

2. Explanatory and Response Variables u Interested in studying the relationship between two variables by measuring both variables on the same individuals. –a response variable measures an outcome of a study –an explanatory variable explains or influences changes in a response variable –sometimes there is no distinction

3. Question In a study to determine whether surgery or chemotherapy results in higher survival rates for a certain type of cancer, whether or not the patient survived is one variable, and whether they received surgery or chemotherapy is the other. Which is the explanatory variable and which is the response variable?

4. u Graphs the relationship between two quantitative (numerical) variables measured on the same individuals. u If a distinction exists, plot the explanatory variable on the horizontal (x) axis and plot the response variable on the vertical (y) axis. Scatterplot

5. Relationship between mean SAT verbal score and percent of high school grads taking SAT Scatterplot

6. u Look for overall pattern and deviations from this pattern ( linear, curved, clusters, no pattern) u Describe pattern by form, direction, and strength of the relationship u Look for outliers Scatterplot

7. Linear Relationship Some relationships are such that the points of a scatterplot tend to fall along a straight line -- linear relationship

8. Direction u Positive association –above-average values of one variable tend to accompany above-average values of the other variable, and below-average values tend to occur together u Negative association –above-average values of one variable tend to accompany below-average values of the other variable, and vice versa

9. Positive association: High values of one variable tend to occur together with high values of the other variable. Negative association: High values of one variable tend to occur together with low values of the other variable.

10. One way to remember this: The equation for this line is y = 5. x is not involved. No relationship: x and y vary independently. Knowing x tells you nothing about y.

11. Examples From a scatterplot of college students, there is a positive association between verbal SAT score and GPA. For used cars, there is a negative association between the age of the car and the selling price.

12. Examples of Relationships

13. Measuring Strength & Direction of a Linear Relationship u How closely does a non-horizontal straight line fit the points of a scatterplot? u The correlation coefficient (often referred to as just correlation): r –measure of the strength of the relationship: the stronger the relationship, the larger the magnitude of r. –measure of the direction of the relationship: positive r indicates a positive relationship, negative r indicates a negative relationship.

14. Correlation Coefficient u special values for r :  a perfect positive linear relationship would have r = +1  a perfect negative linear relationship would have r = -1  if there is no linear relationship, or if the scatterplot points are best fit by a horizontal line, then r = 0  Note: r must be between -1 and +1, inclusive u both variables must be quantitative; no distinction between response and explanatory variables u r has no units; does not change when measurement units are changed (ex: ft. or in.)

15. Examples of Correlations

16. u Husband’s versus Wife’s ages v r =.94 u Husband’s versus Wife’s heights v r =.36 u Professional Golfer’s Putting Success: Distance of putt in feet versus percent success v r = -.94

17. Not all Relationships are Linear Miles per Gallon versus Speed u Linear relationship? u Correlation is close to zero.

18. Not all Relationships are Linear Miles per Gallon versus Speed u Curved relationship. u Correlation is misleading.

19. Problems with Correlations u Outliers can inflate or deflate correlations (see next slide) u Groups combined inappropriately may mask relationships (a third variable) –groups may have different relationships when separated

20. Outliers and Correlation For each scatterplot above, how does the outlier affect the correlation? AB A: outlier decreases the correlation B: outlier increases the correlation

21. Example: IQ score and grade point average a)Describe what this plot shows in words. b)Describe the direction, shape, and strength. Are there outliers? c)What is the deal with these people?

22. Correlation Calculation u Suppose we have data on variables X and Y for n individuals: x 1, x 2, …, x n and y 1, y 2, …, y n u Each variable has a mean and std dev:

23. Case Study Per Capita Gross Domestic Product and Average Life Expectancy for Countries in Western Europe

24. Case Study CountryPer Capita GDP (x)Life Expectancy (y) Austria21.477.48 Belgium23.277.53 Finland20.077.32 France22.778.63 Germany20.877.17 Ireland18.676.39 Italy21.578.51 Netherlands22.078.15 Switzerland23.878.99 United Kingdom21.277.37

25. Case Study xy 21.477.48-0.078-0.3450.027 23.277.531.097-0.282-0.309 20.077.32-0.992-0.5460.542 22.778.630.7701.1020.849 20.877.17-0.470-0.7350.345 18.676.39-1.906-1.7163.271 21.578.51-0.0130.951-0.012 22.078.150.3130.4980.156 23.878.991.4891.5552.315 21.277.37-0.209-0.4830.101 = 21.52 = 77.754 sum = 7.285 s x =1.532s y =0.795

26. Case Study

27. “r” doesn’t distinguish explanatory and response variables The correlation coefficient, r, treats x and y symmetrically. “Time to swim” is the explanatory variable here and belongs on the x axis. However, in either plot r is the same (r = −0.75). r = -0.75

28. Changing the units of variables does not change the correlation coefficient “r,” because we get rid of all our units when we standardize (get z-scores). “r” has no unit