When is it inappropriate to use linear regression for measuring the association between two variables?

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In order to find the or measuring the assosiation between two variables , when one variable is dependent variable and other one is independent we use the linear regression. But if there is no cause and effect relation between these two variables then we can not use linear regression, since there is such relationship, in such cases the linear regression become inappropriate.

firstoff all we plot the scatter plot of the data, such that independent variable on X axis and dependent variable on Y axis, if we can not find a linear pattern then the linear regression is inappropriate.

there are two type regression, linear and non-linear regression,

if linear regression is inappropriate then we must go for to check whether non linear is appropriate or not. For checking this we use residual plot, residual = observed value - predicted value

A residual plot is a graph that shows the residuals on the vertical axis and the independent variable on the horizontal axis.If the points in a residual plot are randomly dispersed around the horizontal axis, a linear regression model is appropriate for the data; otherwise, a nonlinear model is more appropriate.

orchestra answered 1 year ago

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