This is from an assignment I need to finish but I need some advice on where/what to investigate further please (absolutely new to stats):

You use regression to generate a function that operates on two variables (X1 and X2) to predict a label (Y).

The regression output shows the following coefficients:

Intercept: 12.576
X1: 2.32
X2: -1.3

Which formulae describes the regression function?

Y = 12.567 + 2.32 + -1.3
Y = 12.567 + (X1 x 2.32) + (X2 x -1.3)
Y = 12.567 + (X1 + 2.32) + (X2 + -1.3)

My thinking :

sample regression line is: ŷ = b0 + b1x.

thus : y = 12.576 + (regression coefficient)

and regression coefficient = y = mx + b

I am out of my depth - please advise enter image description here

  • $\begingroup$ Is this a multiple choice question? For the three choices you give, are you sure you copied these down correctly? Or have you maybe switched some plus and multiplication signs? $\endgroup$
    – 5ayat
    Commented Oct 12, 2016 at 8:42
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    $\begingroup$ You should read the self-study tag wiki for details about the sort of information we might need to help you. $\endgroup$
    – mdewey
    Commented Oct 12, 2016 at 9:21
  • $\begingroup$ Thanks 5ayat1, I did have the last mcp incorrect. I fixed it and added screenshot. @mdewey I am really respectful to self-study - not asking for the answer as this will mess the next course up for me - just asking where I should be looking please $\endgroup$ Commented Oct 12, 2016 at 9:57
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    $\begingroup$ You need to decide first of all whether you think the coefficients multiply the variables or are added to them. So when you write the notation bx, does that mean b times x or b plus x? $\endgroup$
    – mdewey
    Commented Oct 12, 2016 at 11:11
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    $\begingroup$ Each variable has its own coefficient. Does that help? $\endgroup$
    – mdewey
    Commented Oct 12, 2016 at 13:09

1 Answer 1


The model is: y = b0 + b1 x1 + b2 x2 + epsilon The estimated model is: y= b0.hat + b1.hat x1 + b2.hat X2 with b0.hat=12.576, b1.hat=2.32, b2.hat=-1.3. So: y = 12.576 + 2.32 x1 -1.3 x2


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