I have the following linear model:

$$w^\*=\text{arg min}\_w\sum\_{i=1}^N \bigg(Y\_i-\sum\_{j=1}^M X\_{i,j}\times w\_j\bigg)^2$$

Let $T \in N^\*$ and $e\_i=|Y\_i-\sum\_{j=1}^M X\_{i,j}\times w\_j|$. 

It's possible using logistic regression to predict which errors will be less than $T$ (i.e., $e\_i<T$) and greater or equal with $T$ (i.e., $e\_i \ge T$)?

Here is more information to make the question clearer:

$N$ represent the number of observations. My data has the following property: the histogram of errors using multiple linear regression has a Laplace distribution. My data come from digital images represented on 8 bits. The $Y\_i$ are current pixels and $X\_{ij}$ are neighborhoods pixels. I want to predict which pixels produce errors less than $T$. I want to know what R functions can I use to make a test? $T$ is not very large, it has the values between 1 and 15 in general.