Explain the assumptions underlying multiple linear regression model.

Multiple linear regression (MLR) is a statistical technique used to model the relationship between a dependent variable and two or more independent variables. For the model to be valid and provide reliable results, several assumptions must be met. These assumptions are critical to ensure that the results of the regression analysis are accurate, unbiased, and interpretable. Below are the key assumptions underlying a multiple linear regression model:

1. Linearity: This assumption posits that the relationship between the dependent variable and the independent variables is linear. This means that changes in the independent variables are assumed to result in proportional changes in the dependent variable. The model assumes that a straight-line (or linear) relationship exists between the variables, which can be verified through _________ _____ ___ _________ _________ ___ ___ ________ _________ _____ ______.
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