# Individual slopes for many zip codes over time

I have a dataset where I am interested in calculating a slope for each observation / row.

I have dependent variable $Y$ that is continuous. Every $Y$ is unique to a zipcode. and my independent / predictor variables are measured over time for every $Y$, say $x_1$ to $x_{20}$. I am looking for a slope for each $Y$ for each zip code for each row. I am not sure if I can do this by linear regression because I only have one observation for $y$.

I cannot average $x_1$ to $x_{20}$, because I will lose the information on the slope.

The goal here is to measure change in $x$ ($x_1$ to $x_{20}$) on $Y$, and since $x_1$ to $x_{20}$ is measured over time I like to retain the slope information and not simply take the average. I am trying to answer the question 'how severe is the change in $x$ on a zipcode which has a value $Y$'.

Your question is rather vague, but I'm guessing you will actually want to take your $x$'s as the dependent variable and $Y$ as the predictor variable. ${\rm Time}$ will also be a predictor variable and your data will be nested within zipcodes.