Why is there no Multicollinearity assumption?
No Multicollinearity—Multiple regression assumes that the independent variables are not highly correlated with each other. This assumption is tested using Variance Inflation Factor (VIF) values.
Is it possible to model a non linear relationship between an IV and a DV in a linear model?
If your data are not linear, then you can usually make it linear by transforming IVs or the DV so that there is a linear relationship between them. Sometimes transforming one variable won’t work; the IV and DV are just not linearly related.
Are there any assumptions about the distribution of independent variables?
There are NO assumptions in any linear model about the distribution of the independent variables. Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables.
Are there any assumptions in a linear model?
There are NO assumptions in any linear model about the distribution of the independent variables. Yes, you only get meaningful parameter estimates from nominal (unordered categories) or numerical (continuous or discrete) independent variables. But no, the model makes no assumptions about them.
What does violation of normal distribution assumption mean?
Also, a significant violation of the normal distribution assumption is often a “red flag” indicating that there is some other problem with the model assumptions and/or that there are a few unusual data points that should be studied closely and/or that a better model is still waiting out there somewhere.
How many independent variables do you need for a multiple linear regression?
Multiple linear regression requires at least two independent variables, which can be nominal, ordinal, or interval/ratio level variables. A rule of thumb for the sample size is that regression analysis requires at least 20 cases per independent variable in the analysis. Learn more about sample size here. Multiple Linear Regression Assumptions