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We might measure these impacts in terms of changes in the variables after they have been scaled to take into account the different sample variations in the data. This suggests an alternative way of thinking about the "impacts" of x 2 and x 4 on y. Now which coefficient estimate do you think is the "larger" - that of β 2 or that of β 4? Ceteris paribus, a change of half a standard deviation in x 2 will lead to a 2 standard deviation change in y while a change of 50 standard deviations in x 4 will lead to a 4 standard deviation change in y. If the y variable has a sample standard deviation of 1.0, then the interpretation of the OLS estimates (2.0 and 4.0) of β 2 and β 4 is as follows. On the other hand, a one-unit change in x 4 is quite substantial, in the sense that it's a change of roughly 50 standard deviations! So, a one-unit change in x 2 is a relatively modest change, in the sense that it's a change that's equivalent to approximately half a standard deviation. Suppose that the sample size is n = 6, and that the sample values for x 2 and x 4 are x 2: $.The sample averages and standard deviations are 3.575 and 1.9959 for x 2, and 0.03575 and 0.019959 for x 4. It's true that a one-unit (dollar) change x 4 leads to a change in the dollar value of y that is twice the size of the dollar change in y that occurs when x 2 changes by one unit. Specifically, we have to decide what sort of "change" in the variables we're talking about when we use the term "impact".
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To answer this question, first we have to decide what it actually means! However, can we say that "the impact of x 4 on y is twice as big as the impact of x 2 on y"? I know that it's tempting to do so!