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Multiple regression

Multiple regression. Overview. Simple linear regression SPSS output Linearity assumption Multiple regression … in action; 7 steps checking assumptions (and repairing) Presenting multiple regression in a paper. Simple linear regression. Class attendance and language learning

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Multiple regression

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  1. Multiple regression

  2. Overview • Simple linear regression SPSS output Linearity assumption • Multiple regression … in action; 7 steps checking assumptions (and repairing) Presenting multiple regression in a paper

  3. Simple linear regression Class attendance and language learning Bob: 10 classes; 100 words Carol: 15 classes; 150 words Dave: 12 classes; 120 words Ann: 17 classes; 170 words Here’s some data. We expect that the more classes someone attends, the more words they learn.

  4. The straight line is the model for the data. The definition of the line (y = mx + c) summarises the data.

  5. SPSS output for simple regression (1/3) Model Summaryb Model R R Square Adjusted R Square Std. Error of the Estimate 1 .792a .627 .502 25.73131 a. Predictors: (Constant), classes b. Dependent Variable: vocabulary

  6. SPSS output for simple regression (2/3)

  7. SPSS output for simple regression (3/3) Coefficientsa Model Unstandardized Coefficients Standrdzd Coefficients t Sig. B Std. Error Beta 1 (Constant) -19.178 64.837 -.296 .787 classes 10.685 4.762 .792 2.244 .111 a. Dependent Variable: vocabulary

  8. Linearity assumption • Always check that the relationship between each predictor variable and the outcome is linear

  9. Multiple regression More than one predictor e.g. predict vocabulary from classes + homework + L1vocabulary

  10. Multiple regression in action • Bivariate correlations & scatterplots – check for outliers • Analyse / Regression • Overall fit (R2) and its significance (F) • Coefficients for each predictor (‘m’s) • Regression equation • Check mulitcollinearity (Tolerance) • Check residuals are normally distributed

  11. Bivariate outlier

  12. Multivariate outlier Test Mahalanobis distance (In SPSS, click ‘Save’ button in Regression dialog) to test sig., treat as a chi-square value with df = number of predictors

  13. Multicollinearity Tolerance should not be too close to zero T = 1 – R2 where R2 is for prediction of this predictor by the others If it fails, you need to reduce the number of predictors (you don’t need the extra ones anyway)

  14. Failed normality assumption If residuals do not (roughly) follow a normal distribution … it is often because one or more predictors is not normally distributed  May be able to transform predictor

  15. Categorical predictor Typically predictors are continuous variables Categorical predictors e.g. Sex (male, female) can do: code as 0, 1 Compare simple regression with t-test (vocabulary = constant + Sex)

  16. Presenting multiple regression Table is a good idea: Include correlations (bivariate) R2 adjusted Report F (df, df), and its p, for the overall model Report N Coefficient, t, and p (sig.) for each predictor Mention that assumptions of linearity, normality, and absence of multicollinearity were checked, and satisfied

  17. Further reading Tabachnik & Fidell (2001, 2007) Using Multivariate Statistics. Ch5 Multiple regression

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