I'm taking a graduate course in regression analysis and I'm suck on a particular homework question that *should* be very simple to me!

I have the following model:

`E(y) = B0 + B1x1 + B2x2 + B3x3 + B4x1x3 + B5x2x3 `

`x3`

is coded as `1`

if "smoker" and `0`

if "non-smoker".

Therefore the regression equations are:

`x3 = 1: E(y) = (B3 + B0) + (B1 + B4)x1 + (B2 + B5)x2 x3 = 0: E(y*) = B0 + B1x1 + B2x2 `

Now I know how to test for parallelism if `x2`

is absent in the models:

`H0: B4 = 0 H1: B4 != 0 `

But I'm lost as to what to do with the inclusion of the `x2`

variable. Parallelism is obviously testing for slope, but I'm not sure where to find the "slope" coefficient.

I was thinking about using an `F-Test`

but then I realized I don't actually want to test the *whole* model, just the parallelism.

Could someone please point me into the right direction? Even hints would be sufficient.

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#### Best Answer

I am bit unsure what exactly you mean by 'parallelism' but perhaps you mean that you want to test if the interaction terms are significant or not in which case you would do a joint test that B4=0 and B5=0.

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