Linear Regression model

Hello RStudioCommunity,
I'm currently working on a uni class project, which I can't solve because our Prof. is not teaching us RStudio. I tried to solve this task in R for the last days but I couldn't find a proper solution for it online or maybe my knowledge is not good enough to identify the right solution/code. :frowning: So I'm reaching out to your community now to maybe receive some tips or help <3

I already found out how to do a linear regression model and to summarize it, but that's it. The data we use is coming from the "wooldridge" package, called "wage2".

The code I used until now is:

library(wooldridge)
data("wage2", package="wooldridge")
library(ggplot2)
mlr1= lm(lwage~educ+exper, data=wage2)
summary(mlr1)

The task says:
Extend equation (the linear model I used in the code) to allow the effect of educ to depend on the level of
work experience and estimate the new model by OLS. Let θ be the semielasticity
of wage w.r.t. education after 10 years of work experience. Estimate θ and test
whether it is significantly different from zero at the 1% significance level.

I'm grateful for any help and if you need more information, please let me know. :slight_smile:

Forgetting about R for the moment, what would your regression equation look like in math?

mlr2= lm(lwage~educ + exper + educ*exper, data=wage2)

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