# Why does the y-intercept of a linear model disappear when I standardize variables?

I was fiddling with my independent variables in a linear model and I note how the y-intercept becomes 0 when all the variables become standardized. Intuitively I understand that I'm shifting the line by changing with the mean of the normal distributed variables, but I was wondering if there is some more rigorous/theoretical way of explaining/deriving this?

$$y = \text{X}b + e$$

where $y$ is a vector of the dependent variable, $\text X$ is the design matrix and $b$ is the coefficient matrix, and we subtract from every random variable its respective sample mean and divide by the respective sample standard deviation. How do we see that the design matrix gets all zeroes in its first column?

• – miura Nov 7 '12 at 11:18

$$y_j = \text{X}_{j} b + \epsilon_j = \sum_{i=0}^px_{ij}b_i + \epsilon_j$$

Let $b_0$ be the intercept, so every $x_{0j} = 1$

$$y_j = b_0 + \sum_{i=1}^px_{ij}b_i + \epsilon_j$$

The average then becomes:

$$\mathbf E[y]=\hat y = \frac{1}{n}\sum_{j=1}^n y_j = \frac{1}{n}\sum_{j=1}^n \left( b_0 + \sum_{i=1}^px_{ij}b_i + \epsilon_j\right)=\\ =\frac{1}{n} n \cdot b_0 + \frac{1}{n}\sum_{j=1}^n \left(\sum_{i=1}^px_{ij}b_i\right) + \frac{1}{n}\sum_{j=1}^n\epsilon_j=\\ =b_0 + \sum_{i=1}^p \left(\frac{1}{n}\sum_{j=1}^nx_{ij}\right)b_i$$

As we made the average of each column of $\mathbf X$ equal to $0$, we get:

$$\hat y = b_0$$

If you standardize $y$ as well, then:

$$\hat y = b_0 = 0$$

QED.

See it only depends on the centering, not on the scale of the variables.

This can easily be shown in R. Compare the three fits, and specially fit2 with mean(y)

x = iris$Petal.Width y = iris$Petal.Length
fit1 = lm(y ~ x)
fit2 = lm(y ~ I(scale(x)))
mean(y)
fit3 = lm(I(scale(y)) ~ I(scale(x)))


As implied in the link given by @miura in the comments, the least squares regression line/surface/etc. always goes through the point ($\bar x$, $\bar y$). Intutively this makes sense since if you are predicting $y$ when all the $x$'s are at their mean value then should not the prediction for $y$ be at its mean value.

Since you standardized all the variables the means are all equal to 0 so the equation needs to go through the origin to meet the condition of going through the point defined by the means.