determining the effect of the change in an independent variable using regression I'm creating a regression model that predicts a customer's spending based on their income, while adjusting for age, gender , and region. The model looks as follows:
lm(spending ~ income + age + gender + region, data = tab.ordercustomers)

I want to see what the effect of an extra $10,000 of income is on spending. Would I do this by multiplying the income coefficient by 10,000?

Your idea is correct. Maybe some points to consider are:

*

*You should be sure that the relation between your variables is indeed approximately linear in the range that is relevant for you.

*The R function lm presumes a couple of properties, like the independence of your measurements; check out the documentation and references therein.

*The variables age, gender, and region are confounders of income and spending, so controlling for those is the right thing to do. But you should think about whether there are further relevant confounders, i.e. variables that causally affect both income and spending.

