Chapter 8: Linear Regression

1. Chapter 8: Linear Regression A.P. Statistics

2. Linear Model • Making a scatterplot allows you to describe the relationship between the two quantitative variables. • However, sometimes it is much more useful to use that linear relationship to predict or estimate information based on that real data relationship. • We use the Linear Model to make those predictions and estimations.

3. Linear Model Normal Model Linear Model Allow us to make predictions and estimations about the population and future events. It is a model of real data, as long as that data has a linear relationship between two quantitative variables. Allows us to make predictions and estimations about the population and future events. It is a model of real data, as long as that data has a nearly symmetric distribution.

4. Linear Model and the Least Squared Regression Line • To make this model, we need to find a line of best fit. • This line of best fit is the “predictor line” and will be the way we predict or estimate our response variable, given our explanatory variable. • This line has to do with how well it minimizes the residuals.

5. Residuals and the Least Squares Regression Line • The residual is the difference between the observed value and the predicted value. • It tells us how far off the model’s prediction is at that point • Negative residual: predicted value is too big (overestimation) • Positive residual: predicted value is too small (underestimation)

6. Residuals

7. Least Squares Regression Line • The LSRL attempts to find a line where the sum of the squared residuals are the smallest. • Why not just find a line where the sum of the residuals is the smallest? • Sum of residuals will always be zero • By squaring residuals, we get all positive values, which can be added • Emphasizes the large residuals—which have a big impact on the correlation and the regression line

8. Scatterplot of Math and Verbal SAT scores

9. Scatterplot of Math and Verbal SAT scores with incorrect LSRL

10. Scatterplot of Math and Verbal SAT scores with correct LSRL

12. Least-Squares Regression Line We Can Find the LSRL For Three Different Situations • Using z-Scores of Real Data (Standardizing Data) • Using Summary Statistics of Data (mean and standard deviation) • Using Real Data

14. LSRL: Using z-Scores of Real Data • LSRL passes through and • LSRL equation is: “moving one standard deviation from the mean in x, we can expect to move about r standard deviations from the mean in y .”

15. LSRL: Using z-Scores of Real Data (Interpretation) LSRL of scatterplot: For every standard deviation above (below) the mean a sandwich is in protein, we’ll predict that that its fat content is 0.83 standard deviations above (below) the mean.

16. LSRL: Using Summary Statistics of Data ProteinFat LSRL Equation:

17. LSRL: Using Summary Statistics of Data (Interpretation) Slope: One additional gram of protein is associated with an additional 0.97 grams of fat. y-intercept: An item that has zero grams of protein will have 6.8 grams of fat. ALWAYS CHECK TO SEE IF Y-INTERCEPT MAKES SENSE IN THE CONTEXT OF THE PROBLEM AND DATA

18. LSRL: Using Summary Statistics of Data (Interpretation) Use technology to get the LSRL. Making sure you check your conditions, etc.

19. Properties of the LSRL The fact that the Sum of Squared Errors (SSE, same as Least Squared Sum)is as small as possible means that for this line: • The sum and mean of the residuals is 0 • The variation in the residuals is as small as possible • The line contains the point of averages

20. Assumptions and Conditions for using LSRL Quantitative Variable Condition Straight Enough Condition if not—re-express (Chapter 10) Outlier Condition with and without ?

21. Residuals and LSRL • Residuals should be used to see if a linear model is appropriate • Residuals are the part of the data that has not been modeled in our linear model

22. Residuals and LSRL What to Look for in a Residual Plot to Satisfy Straight Enough Condition: No patterns, no interesting features (like direction or shape), should stretch horizontally with about same scatter throughout, no bends or outliers. The distribution of residuals should be symmetric if the original data is straight enough. Looking at a scatterplot of the residuals vs. the x-value is a good way to check the Straight Enough Condition, which determines if a linear model is appropriate.

23. Residuals, again

27. A Complete Linear Regression AnalysisPART I Draw a scatterplot of the data. Comment on what you see. (Satisfy Quantitative Data Condition) • Form, strength, direction • Unusual Points, Deviations • Comment on General Variable Direction

28. A Complete Linear Regression AnalysisPART II Compute r . Comment on what r means in context and if it is appropriate to use (does the relationship seem linear—Straight Enough Condition)

29. A Complete Linear Regression AnalysisPART III Find the LSRL • Check all three conditions • Quantitative Data Condition • Straight Enough Condition • Outlier Condition

30. A Complete Linear Regression AnalysisPART IV Draw a residual plot and interpret it-is the linear model appropriate?

31. A Complete Linear Regression AnalysisPART V Interpret slope in context Interpret the y-intercept in context

32. A Complete Linear Regression AnalysisPART VI Compute R-Squared. Interpret the value and use as a measure for the accuracy of the model. “How well does the model predict?”

33. What is R-Squared This value will determine how accurate the linear model is predicting your y-values from you x-values. It is written as a percent. It is, literally, your r-value squared.

34. R-Squared Interpretation If a Regression analysis has an R-squared value of 97%, that means the model does an excellent job predicting the y-values in your model. How do we interpret that? “97% of the variation is y can be accounted for by the variation is x, on average.”

35. R-Squared Interpretation • There are other ways to write that interpretation. • Also, can be thought of as how much error was eliminated in our predictions if we used the LSRL instead of a guess of .