It can be argued that there are various reasons for choosing one of these linear regression equation forms over the other. Statistician will also say that they prefer this method because the variables represent the same context in each formula (that is, a is a constant in the linear equation and in the quadratic equation). ![]() Statisticians prefer this method as it more closely follows the general form of the regression equation: In Statistics, the standard form of a quadratic equation is y = a + bx + cx 2. In advanced multi-variable statistics, equations "get richer" as terms are added at the right (that is, the powers increase moving to the right.) Thus, statisticians prefer to maintain this format by using the form LinReg( a + bx), where a is the y-intercept and b is the slope. (The preferred form is actually y = b 0 + b 1 x.) In Statistics, the preferred equation of a line is represented by y = a + bx, where b is the slope and a is the y-intercept.
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