Suppose we have data $\{x_i,y_i\}_{i=1}^n$ where $x_i \in \mathbb{R}$ and $y_i \in \mathbb{R}$ and we model $$ y_i=\beta x_i + \varepsilon_i $$ The ordinary least squares estimate of $\beta$ is $$ \widehat \beta = \sum_{i=1}^n w_i y_i $$ where $w_i={x_i}/{\sum_{j=1}^nx_j^2}$ can be viewed as "weights" on each $y_i$.

I've been thinking about what these "weights" mean and why they make sense but it seems to put more weight on larger values of $x_i$, which I don't quite understand why this makes sense.

Can someone help me with the intuition for why the "weights" on $y_i$ make sense? Thanks.

Note: I'm not interested in the derivation of $\widehat \beta$ or that these weights happen to minimize the sum of least squares. I'm interested in the intuition i.e. how would you explain this to the layman without math.

  • 2
    $\begingroup$ If we have $w_i=\frac{x_i}{\sum x_j^2}$ then $w_i$ is only increasing in $x_i$ holding the denominator fixed. If not, then it is not necessarily the case that $w_i$ is increasing in $x_i$. $\endgroup$
    – Greg
    Apr 14, 2015 at 5:18

1 Answer 1


The problem of weights in regression is a really vaste domain.

The traditional problem is to minimize $$SSQ=\sum_{i=1}^n \Big(y_i^{(calc)}-y_i^{(exp)}\Big)^2$$ and, as you know, this gives a large influence to the largest values of the $y_i^{(exp)}$. This corresponds to the sum of squares of the absolute errors of the $y$'s $(w_i=1)$.

If instead you consider $$SSQ=\sum_{i=1}^n \Big(\frac{y_i^{(calc)}-y_i^{(exp)}}{y_i^{(exp)}}\Big)^2$$ This corresponds to the sum of squares of the relative errors of the $y$'s $(w_i=\frac 1 {y_i^2})$.

But there is another situation where the weights can be important. Suppose that the model is $y=Ae^{Bx}$ which is nonlinear. You can linearize it taking logarithms $\log(y)=\alpha+\beta x$ but ordinary least squares can lead to very different results compared to nonlinear regression since the transform gives greater weights to small $y$ values. For this very specific case, it is been found that $$SSQ=\sum_{i=1}^n y_i\Big(\alpha+\beta x_i-\log(y_i)\Big)^2$$ is very acceptable.

I suggest you have a look at http://www.itl.nist.gov/div898/handbook/pmd/section1/pmd143.htm


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