CAS MA 416 Lecture 2: MA416 Class 2
14 Feb 2017
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Ma416 class 2: the regression model: (cid:1851)=(cid:2868)+(cid:2869)(cid:1850)+ The assumptions of the regression model: independent, random sample from underlying population, linearity, the means of y|x fall on a straight line, homoscedasticity, the variance of y|x is the same for all x (or equivalently, the variance of. Ei is the same for all x: normality, the distribution of y|x follows a normal distribution for all x (or equivalently, ei follows a normal distribution), existence, the model holds for valid values of x. Three parameters to the model: intercept, slope, standard deviation about the regression line. 0 , 1 are the regression coefficients or regression parameters. 0 is the intercept from the regression equation. 1 is the slope from the regression equation. Ei are the error terms or the residuals. The logic of least squares: minimize the sum of the squared errors: (cid:4666)(cid:1877) (cid:1877) (cid:4667)(cid:2870).
