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Regression of deviance residuals

Hi I’m trying to look up an issue I’m having in an NDA. I’ve built a linear regression to predict Y values from X.
the model is $Y = b_0 + b_1*X_1 + b_2*X_2$ and the residuals of the regressions are $\Delta Y = y_i-b_0-b_1x_i-b_2x_i$, where $i = 1, 2…n$ is the number of observations. The residuals are plotted on the right:

The new question is if I calculate the residuals squared and look up if there is a linear relationship between residuals and my primary terms, $X_1$ and $X_2$, am I looking for a linear relationship? How would I do this?

Let $y_i=\beta_0+\beta_1x_{i1}+\beta_2x_{i2}+e_i$, where $\beta_0, \beta_1, \beta_2$ are parameters and $e_i$ is the error.
$\Delta y_i=(y_i-\beta_0-\beta_1x_{i1}-\beta_2x_{i2})$
$(\Delta y_i)^2=\beta_0^2+\beta_1^2x_{i1}^2+\beta_2^2x_{i2}^2+2(\beta_0+\beta_1x_{i1}+\beta_2x_{i2})(e_i)$
The $y$ values can be regarded as measurements from an ‘unobserved’ variable $X$ by the linear regression model. $\Delta y_i$ are the measurement errors.
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