4. Let X, Y be two random variables. We want to investigate if there is a linear relationship between them. If there is, we should be able to approximate Y witlh a + bX for some constants a,b. To...


4. Let X, Y be two random variables. We want to investigate if there is a linear<br>relationship between them. If there is, we should be able to approximate Y witlh<br>a + bX for some constants a,b. To evaluate the fit of such approximation, we<br>measure the error by considering the average squared distance between Y and<br>a + bX, that is,<br>expanding the RHS<br>A small E<br>means a good fit<br>.<br>(a) Determine the critical values of a, b by setting the partial derivatives<br>(a, b)<br>(b) With the critical values from parta), the minimum value of E(a, ó) (after lots<br>of cancellation) is (1-r2 y) Var(y), where ρxy s the correlation coefficient<br>between X, Y. With this conclusion, determine the range of ρχ.y<br>

Extracted text: 4. Let X, Y be two random variables. We want to investigate if there is a linear relationship between them. If there is, we should be able to approximate Y witlh a + bX for some constants a,b. To evaluate the fit of such approximation, we measure the error by considering the average squared distance between Y and a + bX, that is, expanding the RHS A small E means a good fit . (a) Determine the critical values of a, b by setting the partial derivatives (a, b) (b) With the critical values from parta), the minimum value of E(a, ó) (after lots of cancellation) is (1-r2 y) Var(y), where ρxy s the correlation coefficient between X, Y. With this conclusion, determine the range of ρχ.y

Jun 01, 2022
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