MGEB12H3 : least square --coefficient correlation
Document Summary
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.