The importance of graphing the data: Anscombe s regression examples

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1 The importance of graphing the data: Anscombe s regression examples Bruce Weaver Northern Health Research Conference Nipissing University, North Bay May 30-31, 2008 B. Weaver, NHRC

2 The Objective To demonstrate that good graphs are an essential part of linear regression analysis. B. Weaver, NHRC

3 Not this kind of regression analysis B. Weaver, NHRC

4 This kind of regression analysis B. Weaver, NHRC

5 A very brief primer on simple linear regression B. Weaver, NHRC

6 Simple linear regression A model in which X is used to predict Y. Y is a continuous variable with interval scale properties. In the prototypical case, X is also a continuous variable with interval-scale properties. Example: Y = distance in a 6-minute walk test X = FEV1 B. Weaver, NHRC

In the prototypical case, X is also a continuous variable with

7 Back to high school Equation for a straight line Y = bx + a SLOPE INTERCEPT b = slope of the line = the rise over the run a = the value of Y when X = 0 B. Weaver, NHRC

8 Example of a straight line Gym membership Annual fee = $100 Fee per visit = $2 Let X = the number of visits to the gym Let Y = the total cost Y = 2X Let X = 200 visits to the gym Total cost = 2(200) = $500 B. Weaver, NHRC

Let Y = the total cost Y = 2X + 100 Let X = 200 visits to

9 What if the relationship is imperfect? Straight line for a perfect relationship: Y = bx + a Straight line for an imperfect relationship: Y = bx + a Y = bx + a Two different symbols for the predicted value of Y B. Weaver, NHRC

10 R-squared R-squared = the proportion of variability in Y that is accounted for by explanatory variables in the model. For a simple linear regression model (i.e., one predictor variable), R-squared = the proportion of the variability in Y that can be accounted for by the linear relationship between X and Y The adjusted R-squared corrects for upward bias in R-squared B. Weaver, NHRC

11 Anscombe s examples (1973) Frank Anscombe devised 4 sets of X-Y pairs He performed simple linear regression for each data set Here are the results B. Weaver, NHRC

12 Means & Standard Deviations X Y Data Set N Mean SD Mean SD The means and SDs for the 4 data sets are identical to two decimals. B. Weaver, NHRC

13 Correlations between X and Y Data Set Pearson r R-squared Adj. R-sq SE Correlations, R-squared, adjusted R- squared, and standard errors are all identical to two decimals. B. Weaver, NHRC

14 ANOVA Summary Tables Data Set Source SS df MS F p Regression Residual Total Regression Residual Total Regression Residual Total Regression Residual Total B. Weaver, NHRC

756 9 1.528 Total 41.226 10 Regression 27.500 1 27.500 17.966 0.002 3 Residual 13.776 9 1.

15 The Regression Coefficients Data Set B SE t p 95% CI Lower Upper Constant X Constant X Constant X Constant X For all 4 models, Y = 0.5(X) + 3 B. Weaver, NHRC

67 0.026 0.455 5.547 X 0.50 0.118 4.24 0.002 0.233 0.767 Constant 3.00 1.125 2.67 0.026 0.456 5.544 X 0.50 0.118 4.24 0.002 0.233 0.767 For all 4 models, Y = 0.

16 Which Model is Best? Judging by everything we ve just seen, it appears that the models are all equally good But if that were true, I wouldn t be doing this talk! It is well known that good graphs are an essential part of data analysis (Tukey, 1977; Tufte, 1997) Let s look at some graphs that show the relationship between X and Y B. Weaver, NHRC

But if that were true, I wouldn t be doing this talk!

17 Scatter-plot for Data Set 1 10 data points Influential point Not a good model B. Weaver, NHRC

18 Scatter-plot for Data Set 2 Perfect linear relationship except for one outlier Better model than for Data Set 1, but still not great. B. Weaver, NHRC

19 Scatter-plot for Data Set 3 Wrong model! The relationship between X and Y is curvilinear, not linear! The model should include both X and X 2 as predictors. B. Weaver, NHRC

20 Scatter-plot for Data Set 4 This is a good looking plot. No influential points; straight line provides a good fit. B. Weaver, NHRC

21 Summary The usual summary statistics for the 4 regression models were virtually identical Scatter-plots revealed that only one of the 4 data sets gave us a good model Appropriate graphs are an essential part of data analysis B. Weaver, NHRC

22 What about multivariable models? Scatter-plots are useful for simple linear regression models (i.e., only one predictor variable) But often, we have multiple, or multivariable regression models (i.e., 2 or more predictor variables) In that case, it is more common to assess the fit of the model by looking at residual plots B. Weaver, NHRC

23 What is a residual? In linear regression, a residual is an error in prediction Residual = (Y Y ) = (actual score predicted score) B. Weaver, NHRC

24 Set 1: Scatter-plot vs. Residual Plot Scatter-plot Residual Plot Y Residual X Predicted value of Y B. Weaver, NHRC

25 Set 2: Scatter-plot vs. Residual Plot Scatter-plot Residual Plot Residual Predicted value of Y B. Weaver, NHRC

26 Set 3: Scatter-plot vs. Residual Plot Scatter-plot Residual Plot Residual Predicted value of Y Runs of same-sign residuals B. Weaver, NHRC

27 Set 4: Scatter-plot vs. Residual Plot Scatter-plot Residual Plot Residual Predicted value of Y B. Weaver, NHRC

28 Summary The usual summary statistics for the 4 regression models were virtually identical Scatter-plots revealed that only one of the 4 data sets gave us a good model Residual plots reveal the same thing, and have the advantage of being applicable to multivariable regression models Appropriate graphs are an essential part of data analysis B. Weaver, NHRC

29 Questions? I think you should be more explicit here in step 2. B. Weaver, NHRC

30 References Anscombe FJ. (1973). Graphs in statistical analysis. The American Statistician, 27, Tufte ER. (1997). Visual Explanations, Images and Quantities, Evidence and Narrative (3rd Ed.). Graphics Press: Cheshire. Tukey JW. (1977). Exploratory data analysis. Addison-Wesley: Reading, Mass. B. Weaver, NHRC

31 Extra Slides B. Weaver, NHRC

32 Just as one would expect! The experimentalist comes running excitedly into the theorist's office, waving a graph taken off his latest experiment. "Hmmm," says the theorist, "That's exactly where you'd expect to see that peak. Here's the reason (long logical explanation follows)." In the middle of it, the experimentalist says "Wait a minute", studies the chart for a second, and says, "Oops, this is upside down." He fixes it. "Hmmm," says the theorist, "you'd expect to see a dip in exactly that position. Here's the reason...". B. Weaver, NHRC

33 Best-fitting line: Least squares criterion Many lines could be placed on the scatter-plot, but only one of them is considered the best-fitting line. The most common criterion for best-fitting is that the sum of the squared errors in prediction is minimized. This is called the least-squares criterion. B. Weaver, NHRC

34 Illustration of Least Squares Error in prediction B. Weaver, NHRC

35 Illustration of Least Squares Squared error in prediction Error = 0 for this point, so no square Squared error in prediction B. Weaver, NHRC

36 Illustration of Least Squares Sum of squared errors = the sum of the areas of all these squares For any other regression line, the sum of the squared errors would be greater. B. Weaver, NHRC

37 What is a residual plot? Scatter-plot with: X = the fitted (or predicted) value of Y Y = the residual (i.e., the error in prediction) Residuals should be independent of the fitted value of Y There should be no serial correlation in the residuals (e.g., long runs of same-sign residuals) Both of these problems (plus some others) can be detected via residual plots Advantage of residual plots: they can be used in multivariable (i.e., multi-predictor) regression models B. Weaver, NHRC

38 Examples of residual plots Curvilinear relationship Residual Predicted Y Outlier Heteroscedasticity B. Weaver, NHRC

39 Example of a good residual plot B. Weaver, NHRC

40 Example of a zig-zag pattern You do not want to see this kind of zig-zag pattern in the residual plot. B. Weaver, NHRC

41 Simple linear regression & correlation Pearson r = the correlation It measures of the direction and strength of the linear association between X and Y It ranges from -1 to +1 B. Weaver, NHRC

42 Direction of the linear relationship Positive relationship Negative relationship As X increases, Y increases As X increases, Y decreases B. Weaver, NHRC

43 Perfect vs. Imperfect Relationship Perfect relationship Imperfect relationship B. Weaver, NHRC

44 r-squared The square of Pearson r is a measure of how well the regression model fits the observed data It gives the proportion of variability in Y that is accounted for the linear relationship between X and Y. E.g., let r = 0.6 (or -0.6) r 2 = 0.36 So 36% of the variability in the Y-scores is accounted for by the linear relationship between X and Y B. Weaver, NHRC

2. Simple Linear Regression

Research methods - II 3 2. Simple Linear Regression Simple linear regression is a technique in parametric statistics that is commonly used for analyzing mean response of a variable Y which changes according