![]() ![]() The data should not show multicollinearity, which occurs when the independent variables (explanatory variables) are highly correlated. The independent variables are not highly correlated with each other If the relationship displayed in the scatterplot is not linear, then the analyst will need to run a non-linear regression or transform the data using statistical software, such as SPSS. The best way to check the linear relationships is to create scatterplots and then visually inspect the scatterplots for linearity. The first assumption of multiple linear regression is that there is a linear relationship between the dependent variable and each of the independent variables. A linear relationship between the dependent and independent variables Multiple linear regression is based on the following assumptions: 1. Assumptions of Multiple Linear Regression However, non-linear regression is usually difficult to execute since it is created from assumptions derived from trial and error. It can also be non-linear, where the dependent and independent variables do not follow a straight line.īoth linear and non-linear regression track a particular response using two or more variables graphically. Multiple regression is a type of regression where the dependent variable shows a linear relationship with two or more independent variables. Linear regression attempts to establish the relationship between the two variables along a straight line. ![]() Simple linear regression enables statisticians to predict the value of one variable using the available information about another variable. ϵ is the model’s random error (residual) term.βp is the slope coefficient for each independent variable.β1 and β2 are the regression coefficients representing the change in y relative to a one-unit change in xi1 and xi2, respectively.β0 is the y-intercept, i.e., the value of y when both xi and x2 are 0. ![]()
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