## How do you decide whether your linear regression model fits the data?

A well-fitting regression model results in predicted values close to the observed data values.

The mean model, which uses the mean for every predicted value, generally would be used if there were no informative predictor variables..

## What conditions are necessary before using a linear model?

What conditions are necessary before using a linear model for a set of data? See that the data satisfies the straight enough condition by checking to see if the scatterplot looks reasonably straight. (you should also check linearity when examining the residuals).

## What does a general linear model show?

The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only).

## How do you tell if a linear model is a good fit?

In general, a model fits the data well if the differences between the observed values and the model’s predicted values are small and unbiased. Before you look at the statistical measures for goodness-of-fit, you should check the residual plots.

## What is best fit line in linear regression?

Line of best fit refers to a line through a scatter plot of data points that best expresses the relationship between those points. Statisticians typically use the least squares method to arrive at the geometric equation for the line, either though manual calculations or regression analysis software.