1. Essential Statistics Chapter 41 Scatterplots and Correlation

2. Essential Statistics Chapter 42 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. Essential Statistics Chapter 43 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. Essential Statistics Chapter 44 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. Essential Statistics Chapter 45 Relationship between mean SAT verbal score and percent of high school grads taking SAT Scatterplot

6. Essential Statistics Chapter 46 u Look for overall pattern and deviations from this pattern u Describe pattern by form, direction, and strength of the relationship u Look for outliers Scatterplot

7. Essential Statistics Chapter 47 Linear Relationship Some relationships are such that the points of a scatterplot tend to fall along a straight line -- linear relationship

8. Essential Statistics Chapter 48 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. Essential Statistics Chapter 49 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.

10. Essential Statistics Chapter 410 Examples of Relationships

11. Essential Statistics Chapter 411 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.

12. Essential Statistics Chapter 412 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.)

13. Essential Statistics Chapter 413 Examples of Correlations

14. Essential Statistics Chapter 414 Examples of Correlations 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

15. Essential Statistics Chapter 415 Not all Relationships are Linear Miles per Gallon versus Speed u Linear relationship? u Correlation is close to zero.

16. Essential Statistics Chapter 416 Not all Relationships are Linear Miles per Gallon versus Speed u Curved relationship. u Correlation is misleading.

17. Essential Statistics Chapter 417 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

18. Essential Statistics Chapter 418 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

19. Essential Statistics Chapter 419 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:

20. Essential Statistics Chapter 420 Case Study Per Capita Gross Domestic Product and Average Life Expectancy for Countries in Western Europe

21. Essential Statistics Chapter 421 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

22. Essential Statistics Chapter 422 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

23. Essential Statistics Chapter 423 Case Study