# Multicollinearity in simple linear regression (not multiple)?

I am doing a simple linear regression analysis with 1 independent variable. I am checking data against assumptions. As I am checking against Tolerance and VIF level, I get the their values equal to 1 (both case). Therefore, I guess I shouldn't check against multicollinearity, right?

There is not much reason to expect multicollinearity in simple regression indeed. Multicollinearity arises when some regressor can be written as a linear combination of the other regressors. If the only other regressor is the constant term, the only way this can be the case is if $x_i$ has no variation, i.e. $\sum_i(x_i-\bar{x})^2=0$. In that case, $x_i=c$ for all $i$, so that the regressor is just $c$ times the constant, so you get multicollinearity.
Then, the OLSE of the slope parameter is not well-defined: $$\hat\beta_1=\frac{\sum_i(x_i-\bar{x})(y_i-\bar{y})}{\sum_i(x_i-\bar{x})^2}$$ Such a sample might look as follows: Heuristically, it is quite intuitive that OLS breaks down here - $\hat\beta_1$ is supposed to tell you what happens to $y$ if $x$ changes. If your sample does not contain any variation in $x$, it is just not informative about that question.