1. Chapter 3 Association: Contingency, Correlation, and Regression
Learn ….
How to examine links
between two variables

2. How Can We Explore the Association Between Two Quantitative Variables?
Section 3.2
How Can We Explore the Association Between Two Quantitative Variables?

3. Scatterplot Graphical display of two quantitative variables:
Horizontal Axis: Explanatory variable, x
Vertical Axis: Response variable, y

4. Example: Internet Usage and Gross National Product (GDP)

5. Positive Association
Two quantitative variables, x and y, are said to have a positive association when high values of x tend to occur with high values of y, and when low values of x tend to occur with low values of y

6. Negative Association
Two quantitative variables, x and y, are said to have a negative association when high values of x tend to occur with low values of y, and when low values of x tend to occur with high values of y

7. Example: Did the Butterfly Ballot Cost Al Gore the 2000 Presidential Election?

8. Linear Correlation: r
Measures the strength of the linear association between x and y
A positive r-value indicates a positive association
A negative r-value indicates a negative association
An r-value close to +1 or -1 indicates a strong linear association
An r-value close to 0 indicates a weak association

9. Calculating the correlation, r

10. Example: 100 cars on the lot of a used-car dealership
Would you expect a positive association, a
negative association or no association between
the age of the car and the mileage on the
odometer?
Positive association
Negative association
No association

11. How Can We Predict the Outcome of a Variable?
Section 3.3
How Can We Predict the Outcome of a Variable?

12. Regression Line
Predicts the value for the response variable, y, as a straight-line function of the value of the explanatory variable, x

13. Example: How Can Anthropologists Predict Height Using Human Remains?
Regression Equation:
is the predicted height and is the length of a femur (thighbone), measured in centimeters

14. Example: How Can Anthropologists Predict Height Using Human Remains?
Use the regression equation to predict the height of a person whose femur length was 50 centimeters

15. Interpreting the y-Intercept
the predicted value for y when x = 0
helps in plotting the line
May not have any interpretative value if no observations had x values near 0

16. Interpreting the Slope
Slope: measures the change in the predicted variable for every unit change in the explanatory variable
Example: A 1 cm increase in femur length results in a 2.4 cm increase in predicted height

17. Slope Values: Positive, Negative, Equal to 0

18. Residuals Measure the size of the prediction errors
Each observation has a residual
Calculation for each residual:

19. Residuals A large residual indicates an unusual observation
Large residuals can easily be found by constructing a histogram of the residuals

20. “Least Squares Method” Yields the Regression Line
Residual sum of squares:
The optimal line through the data is the line that minimizes the residual sum of squares

21. Regression Formulas for y-Intercept and Slope

22. The Slope and the Correlation
Describes the strength of the association between 2 variables
Does not change when the units of measurement change
It is not necessary to identify which variable is the response and which is the explanatory

23. The Slope and the Correlation
Numerical value depends on the units used to measure the variables
Does not tell us whether the association is strong or weak
The two variables must be identified as response and explanatory variables
The regression equation can be used to predict the response variable

24. What Are Some Cautions in Analyzing Associations?
Section 3.4
What Are Some Cautions in Analyzing Associations?

25. Extrapolation
Extrapolation: Using a regression line to predict y-values for x-values outside the observed range of the data
Riskier the farther we move from the range of the given x-values
There is no guarantee that the relationship will have the same trend outside the range of x-values

26. Regression Outliers Construct a scatterplot
Search for data points that are well removed from the trend that the rest of the data points follow

27. Influential Observation
An observation that has a large effect on the regression analysis
Two conditions must hold for an observation to be influential:
Its x-value is relatively low or high compared to the rest of the data
It is a regression outlier, falling quite far from the trend that the rest of the data follow

28. Which Regression Outlier is Influential?

29. Example: Does More Education Cause More Crime?

30. Correlation does not Imply Causation
A correlation between x and y means that there is a linear trend that exists between the two variables
A correlation between x and y, does not mean that x causes y

31. Lurking Variable
A lurking variable is a variable, usually unobserved, that influences the association between the variables of primary interest

32. Simpson’s Paradox
The direction of an association between two variables can change after we include a third variable and analyze the data at separate levels of that variable

33. Example: Is Smoking Actually Beneficial to Your Health?

34. Example: Is Smoking Actually Beneficial to Your Health?

35. Example: Is Smoking Actually Beneficial to Your Health?

36. Example: Is Smoking Actually Beneficial to Your Health?

37. Example: Is Smoking Actually Beneficial to Your Health?
An association can look quite different after adjusting for the effect of a third variable by grouping the data according to the values of the third variable

38. Would you expect the correlation to be negative, zero, or positive?
Data are available for all fires in Chicago last year on x = number of firefighters at the fires and y = cost of damages due to fire
Would you expect the correlation to be negative, zero, or positive?
Negative
Zero
Positive

39. Data are available for all fires in Chicago last year on x = number of firefighters at the fires and y = cost of damages due to fire
If the correlation is positive, does this mean that having more firefighters at a fire causes the damages to be worse?
Yes No

40. Distance from the fire station Intensity of the fire
Data are available for all fires in Chicago last year on x = number of firefighters at the fires and y = cost of damages due to fire
Identify a third variable that could be considered a common cause of x and y:
Distance from the fire station
Intensity of the fire
Time of day that the fire was discovered