# How Are Regression Residuals Calculated - Specific Example

I am trying to figure out how regression residuals are calculated using the specific example in the attached graphic.

Would I simply B-A (Red letters in graphic) to get C so: 22-30 = - 8 in this case? Would I do this for all data points and add the + and - values to get a residual value?

Additionally, for D, if I had another data set would I compute the residual for all data points for 2 predictors and the line of best fit?

• When you add up the residuals you will get $0$ (up to floating point error). With multiple predictors there is no "line" of best fit: you are fitting an affine space (a surface with two predictors, a hypersurface with more predictors). – whuber Oct 16 '14 at 16:16
• Sorry whuber, can you dumb down the language for me? I do not understand: floating point error? affine space? hypersurface? I am left with more questions. – I Heart Beats Oct 16 '14 at 16:18
• These things are easy to learn about on Wikipedia. – whuber Oct 16 '14 at 16:30
• I am looking for a from first principles answer to my question. How do I use equations to calculate the residuals. That is not on wiki hence the "how to" website I was on in the first place. – I Heart Beats Oct 16 '14 at 16:42
• I was not answering your question. Comments are for requesting clarification or for posting relevant but tangential observations. To help keep you from going astray, I have noted that (1) you accomplish nothing by summing the residuals, as suggested in the question; and (2) you might be misled by the characterization of the fit as a "line" when there is more than one predictor, so be careful. I referred you to Wikipedia for information about floating point error and hypersurfaces because the 500 characters available for comments are too few to explain these. – whuber Oct 16 '14 at 17:01

EDIT: The regression plane is defined as $$z_i =\beta_0+\beta_1x_{i} +\beta_2y_{i}+\epsilon_i$$ and the residual is for given parameters $\beta_0,\beta_1,\beta_2$ and given data record $(z_i,y_i,x_i)$ calculated as $$\epsilon_i=z_i -(\beta_0+\beta_1x_{i} +\beta_2y_{i})$$ Similarly also with higher dimensions.
• (+1) A warning for those familiar with regression who might be reading this: the material referenced in the question uses betas (without hats) to refer to the estimated coefficients rather than the model parameters and $\epsilon_i$ to refer to the residuals rather than the random components of the model. In that sense this answer is perfectly correct--but we ought to look askance at the reference material for so abusing a conventional notation! – whuber Oct 16 '14 at 17:05
• @IHeartBeats, you might do better to ask that as a new question. Briefly, Greek letters are often used to represent the true values of the parameters, whereas adding hats (eg, $\beta$ vs $\hat\beta$) suggests the estimate of the parameter values that you got by analyzing your data. – gung Oct 16 '14 at 21:10