# How to address control, extraneous and confounding variables?

I was going through a tutorial here and it has the below info

"Controlling for a variable” means modelling control variable data along with independent and dependent variable data in regression analyses and ANCOVAs. That way, you can isolate the control variable’s effects from the relationship between the variables of interest.

Example: Statistical control. you collect data on your main variables of interest, income and happiness, and on your control variables of age, marital status, and health. In a multiple linear regression analysis, you add all control variables along with the independent variable as predictors. The results tell you how much happiness can be predicted by income, while holding age, marital status, and health fixed."

What I didn't understand is how do they say that age, marital status and health are fixed?

In my dataset, I have information for subjects' income, happiness, age, marital_status and health.

While I understand we are trying to study the association of income with happiness, may I know how can age, marital_status and health be constant?

All my subjects have varying values for age, marital_status and health.

How come collecting data about control variables and putting them in regression equation makes them fixed or controlled?

can help me with an example pleasE?>