Multiple relationships!

Using the LINEST() function, you explored how one variable related to another. This is a univariate linear regression with a y-intercept and one coefficient. The single coefficient along with the y-intercept demonstrates how the dependent-y variable changes with one unit measure change in the single independent-x variable.

Since you are concerned with many dimensions of a profile and the users' overall site activity, you need multiple linear regression, in which there are many X variable coefficients impacting Y.

This equation calculates the estimated outcome of your multiple linear regression, where you're interested in how the hours since last login is affected by age, income, and hours volunteered.

Hours since last login = (age_coeff * age) + (income_coeff * income) + (volunteer_coeff * volunteerHrs) + y-intercept

Your job is to calculate the coefficients for age, income, and hours volunteered, and then use the above formula to calculate the estimate. Go for it!