# Why linear regression is widely adopted given its (sometimes) poor prediction

Linear regressions are in nearly every paper I've read. The authors always model the response variable as a linear combination of independent variables without regards to how the data is actually generated. It seems like they only use linear regression because it can produce a table with CI of "influence" of each variable ($$\beta$$ in $$y = \alpha + \beta x + \epsilon_i$$).

I want to know reasons social scientists use linear regression this much.

Also, should I use a more opinionated model (with a better fit) if possible?

• Often linear regression is used but the axis of the graph are scaled to turn non linear data into linear data. So you can get a non-linear function if you rescale the axis back to their original but it's nice because it's easy to calculate correlation coefficient and R-squared. – ajax2112 Mar 23 '20 at 8:40