Linear regression models

1. Linear regression models

2. Simple Linear Regression

3. History • Developed by Sir Francis Galton (1822-1911) in his article “Regression towards mediocrity in hereditary structure”

4. Purposes: • To describe the linear relationship between two continuous variables, the response variable (y-axis) and a single predictor variable (x-axis) • To determine how much of the variation in Y can be explained by the linear relationship with X and how much of this relationship remains unexplained • To predict new values of Y from new values of X

5. The linear regression model is: • Xi and Yi are paired observations (i = 1 to n) • β0= population intercept (when Xi =0) • β1= population slope (measures the change in Yi per unit change in Xi) • εi= the random or unexplained error associated with the i th observation. The εi are assumed to be independent and distributed as N(0, σ2).

6. Linear relationship Y ß1 1.0 ß0 X

7. Linear models approximate non-linear functions over a limited domain extrapolation extrapolation interpolation

8. For a given value of X, the sampled Y values are independent with normally distributed errors: Yi = βo + β1*Xi+ εi ε ~ N(0,σ2)  E(εi) = 0 E(Yi ) = βo + β1*Xi Y E(Y2) E(Y1) X X1 X2

9. Fitting data to a linear model: Yi Yi – Ŷi = εi (residual) Ŷi Xi

10. The residual The residual sum of squares

11. Estimating Regression Parameters • The “best fit” estimates for the regression population parameters (β0 and β1) are the values that minimize the residual sum of squares (SSresidual) between each observed value and the predicted value of the model:

12. Sum of squares Sum of cross products

13. Least-squares parameter estimates where

14. Sample variance of X: Sample covariance:

15. Solving for the intercept: Thus, our estimated regression equation is:

16. Hypothesis Tests with Regression • Null hypothesis is that there is no linear relationship between X and Y: H0: β1 = 0  Yi = β0 + εi HA: β1 ≠ 0  Yi = β0 + β1 Xi + εi • We can use an F-ratio (i.e., the ratio of variances) to test these hypotheses

17. Variance of the error of regression: NOTE: this is also referred to as residual variance, mean squared error (MSE) or residual mean square (MSresidual)

18. Mean square of regression: The F-ratio is: (MSRegression)/(MSResidual) This ratio follows the F-distribution with (1, n-2) degrees of freedom

19. Variance components and Coefficient of determination

20. Coefficient of determination

21. ANOVA table for regression

22. Product-moment correlation coefficient

23. Parametric Confidence Intervals • If we assume our parameter of interest has a particular sampling distribution and we have estimated its expected value and variance, we can construct a confidence interval for a given percentile. • Example: if we assume Y is a normal random variable with unknown mean μ and variance σ2, then is distributed as a standard normal variable. But, since we don’t know σ, we must divide by the standard error instead: , giving us a t-distribution with (n-1) degrees of freedom. • The 100(1-α)% confidence interval for μ is then given by: • IMPORTANT: this does not mean “There is a 100(1-α)% chance that the true population mean μ occurs inside this interval.” It means that if we were to repeatedly sample the population in the same way, 100(1-α)% of the confidence intervals would contain the true population mean μ.

24. Publication form of ANOVA table for regression

25. Variance of estimated intercept

26. Variance of the slope estimator

27. Variance of the fitted value

28. Variance of the predicted value (Ỹ):

29. Regression

30. Assumptions of regression • The linear model correctly describes the functional relationship between X and Y • The X variable is measured without error • For a given value of X, the sampled Y values are independent with normally distributed errors • Variances are constant along the regression line

31. Residual plot for species-area relationship