## How do you know when to use a linear model?

The general guideline is to use linear regression first to determine whether it can fit the particular type of curve in your data.

If you can’t obtain an adequate fit using linear regression, that’s when you might need to choose nonlinear regression..

## What makes a linear model?

A model is linear when each term is either a constant or the product of a parameter and a predictor variable. A linear equation is constructed by adding the results for each term.

## When should we use multiple linear regression?

Multiple regression is an extension of simple linear regression. It is used when we want to predict the value of a variable based on the value of two or more other variables. The variable we want to predict is called the dependent variable (or sometimes, the outcome, target or criterion variable).

## Can I use linear regression for time series?

With time series data, this is often not the case. If there are autocorrelated residues, then linear regression will not be able to “capture all the trends” in the data. … Econometrics has invented error corrections to linear regression (OLS) which allows you to use OLS even for time series when few assumptions are met.