T test and F test

[myb747]

I am still confuse on when you used the T test and F test,
can anyone shed some light when do I have to use the T test and the F test?
Thanks

 

[SeesFA]

Let’s assume this linear model we are regressing:
y = Bo + B1x1 + B2x2 + ei
Least squares are run and we get an equation of the estimates of the predictors, B_hats. Can’t really type it out, but same equation as above, by y and B’s get a hat on them (“^”), and there error term disappears since the expected value is zero.
The t-test looks at the significance of one B (with a hat). that is that null hypothesis, Ho: B1 = 0. If we reject, then that means B1 is significantly different than zero. Therefore, we would keep that factor in our model, it’s helping to explain the estimate we want, y.
The F-test looks at the significance of ALL the B’s at once. that is that the null hypothersis, Ho: B1 = B2 = 0. ALL of them are equal to zero. If we reject, ALL the estimates produce a non-significant (not zero) explanation of dependent variable y_hat.
In short, t-test is significance of one parameter, Beta; F-test is the entire set of B’s.
Sorry if I overexplained, I tend to do that.

 

[smileyface]

T - Test - for testing individual variables.
F - Test - for testing the model as a whole (whether it is significant or not)

 

[SeesFA]

That’s a shorter way of saying it!
To be precise, though, t-test is testing parameter estimates, not variables. You use it to test equal means M1 = M2. Or B = 0 in regressions.
F-test is for equal variance testing, or b1 = b2 = … = bn = 0 in regression.

 

[smileyface]

The equal means / equal variance bit was new Thanks.

 

[myb747]

Thanks to all who answer my question, the answer was crystal clear!