The regression model is assumption of ols assumes that there is no correlation between the error and the independent variables
What is the Assumption of Linearity?
Linearity Assumption (OLS Assumption 1) - The model will be inaccurate and therefore unreliable if it is fitted to data that is not linearly connected. Extrapolating from the model is likely to produce inaccurate results. As a result, you must always draw a graph of the observed and anticipated values. The linearity assumption is confirmed if the graph is symmetrically distributed over the 45-degree line. By applying non-linear transformations to the independent variables, you can change the functional form of the regression if the linearity requirements are violated.
The OLS assumption of no multi-collinearity says that there should be no linear relationship between the independent variables.
Hence, The regression model is the assumption of ols assumes that there is no correlation between the error and the independent variables.
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