Explain linear fit for independent variable observed

Reference no: EM13151916

eject null hypothesis (H0 : p1 = p1,0 , . . . , pk,0 ) if 2 > 2 ,k-p-1 where for the degrees of freedom, k is the number of categories and p is the number of parameters that are estimated to calculate expected frequencies. In the simplest case, we would like to fit the equation of line yi = b0 + b1 xi + i to a set of n data points ^ where i are the residuals (difference between yi , the independent variable observed, and y , the independent variable predicted) and are assumed to be normally distributed with mean = 0 and 2 variance HOW GOOD IS THE LINEAR FIT?

Reference no: EM13151916

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