Construct 95% confidence interval for regression model


I need some help here. I've got the following table explaining workplace turnover:

..............B(hat) se
Gender -0.01 0.55
Age -0.01 0.03
Job satisfaction -0.12 0.08

So, workplace turnover is coded as 1 for those who change jobs, and job satisfaction is measured on an interval scale coded from 0 = very unsatisfied to 10 = very satisfied.

I am supposed to make a 95% confidence interval for job satisfaction. However, I am not quite sure what to do.
I have made this so far:

c(-0.12 - qnorm(0.975) * 0.08, -0.012 + qnorm(0.975) * 0.08)

From this I get the following result:
[1] -0.2767971 0.1447971

I am not quite sure whether this is the correct way.

Does anyone have any input that can help me? :slight_smile:

Thanks in advance!

That is basically correct. However, if you have a small number of observations (say less than 120 or so) you should probably use the t-distribution rather than the normal distribution.

Oh, perfect. Thanks a lot for your answer!

If I were to use the t-distribution, how would I do that?

Use the qt() function in place of qnorm().

As a side note, you might want to retrieve the estimated coefficient and standard error as R variables and then use those variables in the formula rather than retyping the values. You'll get very slightly more accurate answers.

Great! Thank you very much for your help :slight_smile:

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