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Which of the following is not an assumption for the simple linear regression model?
Question: Which of the following is not an assumption for the simple linear regression model? Answer The individual error terms are statistically independent. The distribution of the error terms will be skewed left or right depending on the shape of the dependent variable.
What is difference between simple linear and multiple linear regression?
What is difference between simple linear and multiple linear regressions? Simple linear regression has only one x and one y variable. Multiple linear regression has one y and two or more x variables. For instance, when we predict rent based on square feet alone that is simple linear regression.
When does a dependent variable not meet the assumptions of a linear model?
When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, your model will not meet the assumptions of linear model s.
Do you have to normalize dependent variables in linear regression?
One of the most common questions asked by a researcher who wants to analyse their data through a linear regression model is: must variables, both dependent and predictors, be distributed normally to have a correct model? So if they are not, should I normalize them through a transformation, for example the logarithmic one?
What do you call a binary dependent variable?
1i +β2X2i +⋯ +βkXki+ui Y i = β 0 + β 1 X 1 i + β 2 X 2 i + ⋯ + β k X k i + u i with a binary dependent variable Y i Y i is called the linear probability model.
Is the dependent variable e ( y ) normality distributed?
Based on theory, the expectation of dependent variable E ( Y) would be also normality distributed. However, in reality it’s hard to see a perfectly bell-shaped distribution for the dependent variable Y. Hence, in my opinion, it’s more important to ensure that the assumptions on the errors terms based on the fitted model is not violated.