MGEB12H3 : least square --coefficient correlation
15 Dec 2011
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In chapter 3, we learned how to regress the output of wheat against the amount of fertilizer per acre. In this chapter we will answer these questions: independent vs. In ordinary mathematics the following five equations are identical: y = a + bx, y a = bx. X = -10/3 +(1/3)y: x = a + b y where. However this is not true in regression as the least squares technique will give different answers depending on whether we have by looking back at figure 3:6 (in the previous chapter). When we regressed y against x, we minimized the. ; that is, we minimized the sum of the squared vertical distances between the line and the observation (3:6b). However, when we regress x against y, we minimize minimizing the sum of the squared horizontal distances between the line and the observations (3:6d). The least squares equation is we get y = 6 - 4 which is definitely not the same as.
