I’m fitting a regression model and was wondering about some of the predictors – using R [closed]

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When adding new predictors to a regression model, does the intercept value still represent your dependant variable, or does it represent a predictor?

For example,

Image of model:

image of model

The intercept here is cityBristol, but adding the ‘gender’ predictor only comes up with genderMale. How would I account for genderFemale in my analysis?

I’m also unsure regarding Pr – is that value more important that intercept? I’m aware < 2e-16 represents a number close to zero; would this make it correspond more with genderMale than cityLondon?

What I’m most interested in is how to analyse the intercept.

Thank you for literally any help. I think R is just not for me.

  • 1

    Hi frank m, welcome to SO. Don’t be disheartened, R truly is your friend. It just takes some time to get used to some of its foibles. Especially if you’re used to SAS etc. To help you, it’ll be easier if you provide a minimal, reproducible example. Run dput(my_data) where “my_data” is your dataset and paste the result into your question. Also, add the regression code you are using. Thanks

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  • Does this answer your question? How to show reference level from lm output?

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  • It is accounted for. eg the predictor for female living in bristol is 1045.5975, then if female living in london its 1045.5975-473671. Male in Bristol is 1045.5975 – 88.2308 and male in london is 1045.5975 – 47.3671 – 88.2308… hope this helps

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  • this is a duplicate of stackoverflow.com/questions/41032858/…

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