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Topics: Regression

Topics: Regression. Simple Linear Regression: one dependent variable and one independent variable Multiple Regression: one dependent variable and two or more independent variables. Correlation. A correlation describes a relationship between two variables

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Topics: Regression

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  1. Topics: Regression • Simple Linear Regression: one dependent variable and one independent variable • Multiple Regression: one dependent variable and two or more independent variables.

  2. Correlation • A correlation describes a relationship between two variables • Correlation tries to answer the following questions: • What is the relationship between variable X and variable Y? • How are the scores on one measure associated with scores on another measure? • To what extent do the high scores on one variable go with the high scores on the second variable?

  3. Simple Linear Regression • Understanding relationships between variables: • Prediction • Explanation

  4. Design Requirements and Assumptions • Two continuous variables • Variables are linearly related • Random Sampling • Independence • Bivariate Normality • N >= 30

  5. Example • You are the admissions committee in the Sociology department of a large west coast University. You are trying to make decisions about who to admit to the Master’s program. You would like to be able to predict how well the applicants you are deciding about will do at your school. • Your department has been analyzing the performance of it’s graduate students over the years. One thing it has been looking at it is relationship between undergraduate GPA and graduate GPA. • From regression analyses done over the years, you are able to make some educated guesses about how applicants will perform once admitted.

  6. How Used in Making Predictions

  7. The Regression Coefficient? What Slope? What Altitude?

  8. Fitting the Regression Line: The Best Fit (Least Squares) • Y'= a + byX • The predicted value of Y(Y') for a value of X is computed by: • Multiplying a score (X) by the regression coefficient (by) • Adding the regression constant (a) to this product • The prediction of Y from X based on linear relationship of X and Y so that errors are minimized

  9. Least Squares Fit: Visual * * Where the average squared distance of the points from the regression line is minimized

  10. Minimizing Prediction Error: What that Means (For Math Types)

  11. The Regression Coefficient: Close Your Eyes if You Don’t Want the Derivation • by = rxy (sy/sx) • by = regression coefficient • r = correlation between X and Y • sy = standard deviation of Y • sx = standard deviation of X • Compute by: divide the standard deviation of Y (sy) by the standard deviation of X (sx) then multiply by the Pearson correlation (rxy)between X and Y

  12. The Constant (a): More Math • Regression Constant (a): the altitude of the regression line; the value where the regression line intercepts Y where X = 0 (the Y intercept) • a = Y - byX • a = the regression constant • Y = mean of Y • by = regression coefficient • X = mean of X • Compute a: multiply X (mean of X) by the regression coefficient (by) and then subtract that product from Y (mean of Y)

  13. Plotting Regression Line • Need compute two predicted scores: • For X (undergrad GPA) = 2.75 • Y’ = a + byX = 2.93+.24(2.75) = 3.59 • For X (undergrad GPA) = 3.60 • Y’ = a + byX = 2.93+.24(3.60) = 3.79 • Draw regression line through scatter plot using these two points

  14. Plotting the Regression Line: Visual * *

  15. Errors of Prediction

  16. Standard Error of Estimate • The magnitude of the error made in estimating Y from X: a measure of dispersion around the regression line • The average error of prediction

  17. The Standard Error of Estimate: A Visual Representation 4.00 3.75 3.75 Graduate GPA 3.50 3.25 3.25 3.00 3.00 3.25 3.50 3.75 4.00 Undergraduate GPA

  18. Standard Error of Estimate: Another Visual Representation Y

  19. Is the prediction worth pursuing? • Standard error • Amount of variance explained by X • Testing the regression coefficient (b) for significance

  20. Explaining Variance: How much? Predicted Variance Total Variance Y Unpredicted Variance

  21. Assessing Prediction Accuracy: Explaining Variance • Total Variance: = Predicted variance + Residual (unexplained) variance • Coefficient of Determination (r2):Proportion of total variance in Y that has been predicted by variable X (r2 = s2y’/s2y) • Our example: r = .56, so r2 = .3136 • Coefficient of Non-Determination (1-r2): : Proportion of total variance in Y that is not predicted by X • Our example: 1- r2 = 1- .31 = .69

  22. Proportion of Explained (Predicted) and Unexplained (Residual) Variance rxy = .56 X Y (1-r2)=.69 (69%) Unexplained variance r2=.31 (31%) Explained variance

  23. t-Test for Individual Regression Coefficients (by) • H0:  = 0 (where  is the population regression coefficient) • H1:  not= 0 • Compute a t statistic: • T = (b - )/sb = b/sb (how many standard error points b is from the hypothesized population parameter under the null hypothesis,  = 0 )

  24. t-Test of b: Our Example • t = .24/.12 = 2.00 • Set alpha at .05 (two-tailed) • Figure out df (N-2): 8 • t critical (05/2,8) = 2.306 • Decision: tobserved (2.00) < tcritical (2.306) so do not reject the null hypothesis • Conclusion: cannot conclude that the slope is significantly different from 0 in the population.

  25. Our Conclusion: Do not reject the null hypothesis * *

  26. Warnings • Simple regression assumes a straight line relationship • Outliers can control regression results • Assumes random samples for making proper generalizations • Regression is correlational and does not show a causal link between x causes y

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