This morning, after my course on extreme values, some students did show me a question they got from practicals they were suppose to work on, with undergraduate students :

To be more specific, they wanted some feedback about point B. Now, let’s make it clear : I have no idea what “**precision**” and “**variation**” could mean… But let’s try and see if we can get something usefull, that might help to understand the question. In order to illustrate, consider the following regression model,

> plot(cars,pch=19,col="black",cex=.8) > abline(lm(dist~speed,data=cars),lty=2)

Here is the summary table of the linear regression model

> summary(lm(dist~speed,data=cars)) Estimate Std. Error t value Pr(>|t|) (Intercept) -17.5791 6.7584 -2.601 0.0123 * speed 3.9324 0.4155 9.464 1.49e-12 ***

My first idea was that “**variation of the X’s**” should be related to the “variance” of the explanatory variable. But it is stupid. For instance, If we transform the explanatory variable, say with a multiplicative factor of 100, then the variance of X will be 10,000 times larger. And the regression will be the same

> cars100=cars > cars100$speed=100*cars$speed > plot(cars100,pch=19,col="black",cex=.8) > abline(lm(dist~speed,data=cars100),lty=2)

in the sense that

> summary(lm(dist~speed,data=cars10)) Estimate Std. Error t value Pr(>|t|) (Intercept) -17.57909 6.75844 -2.601 0.0123 * speed 0.39324 0.04155 9.464 1.49e-12 ***

And similarly, divide by 100. So, I guess using some affine transformation of the explanatory variable is clearly not the way we should get a variable with more “variability”. Let us try something else. And keep in mind the following quantities,

> var(cars$speed) [1] 27.95918 > sd(cars$speed)/mean(cars$speed) [1] 0.3433535

with the variance, and the coefficient of variation. Consider the following modified dataset,

> carsg=cars > carsg$speed[12]=8 > carsg$speed[23]=25 > carsg$speed[34]=24 > carsg$speed[39]=12

Four values were changed, here. Observe that, somehow, there is more variability

> var(carsg$speed) [1] 31.84694 > sd(carsg$speed)/mean(carsg$speed) [1] 0.3640845

But if we consider the output of the regression model, we get

> summary(lm(dist~speed,data=carsg)) Estimate Std. Error t value Pr(>|t|) (Intercept) -18.5681 5.3621 -3.463 0.00113 ** speed 3.9708 0.3254 12.201 2.55e-16 ***

It look like we got here a more precision on the slope, with a smaller variance, and a larger Student-t-value. But what if we consider the following transformation,

> carsg=cars > carsg$speed[11]=5 > carsg$speed[21]=25 > carsg$speed[31]=25 > carsg$speed[50]=7

Again, we have more variability here, on the explanatory variable,

> var(carsg$speed) [1] 32.9898 > sd(carsg$speed)/mean(carsg$speed) [1] 0.3754036

But this time,

> summary(lm(dist~speed,data=carsg)) Estimate Std. Error t value Pr(>|t|) (Intercept) -1.5078 8.0498 -0.187 0.852 speed 2.9077 0.4932 5.896 3.61e-07 ***

the estimator of the slope has more variance, and we have a smaller Student-t-value. So here, if we increase the “variability” of X, we get get… almost anything. The intuition about those two transformations is relatively simple. In the first case, I have moved observations that were far away from the regression line – but in the center of the distribution, and I put them closer to the regression line, but more on the border of the sample (to increase the variance)

(I would not call them *outliers* since *outliers* are defined as observations far away from the model, but on Y, not on X). In the second case, I did exactly the opposite.

I am not sure if I understood correctly this sentence. But it looks like it is incorrect. Since there is only *one* false statement here, I will go for this one. What do you think?

Okay. In effect, the first idea (the one to observe what happens when all the observations of the explanatory variable double or still are multiplied by 100) is not correct one sees that it leads directly that B is false … since the value of the t-student has not changed (it means that the variance of the estimator of the slope does not change, since the variance of residuals is constant). In the other two cases, it can be seen that the variance of the estimator of the slope has changed.

Because the values of the t-student changed. But, in the first case the variance of the estimator of the slope increased (the t-student becomes weak), whereas in the second case the variance of the estimator of the slope decreased (the t-student becomes bigger compared with the first case).

So, if we say that B is false… I believe that we are saying : the precision of the estimator of the slope is given by the significativity level of the explanatory variable in the model. If we say rather that B is true… We would be saying : the precision of the estimator of slope is given by its variance (if the variance becomes bigger – the precision decrease. If the variance becomes weak – the precision increase)??

Sorry for my english