Which of the following is an assumption of regression analysis?

The assumption that independent and dependent variables must exhibit a linear relationship is fundamental to regression analysis. This means that the relationship between the independent variable(s) and the dependent variable can be represented by a straight line, which is the basis for both linear regression and many other types of regression models.

In regression, if the relationship is not linear, the model may not fit the data well, leading to inaccurate predictions and interpretations. This assumption is crucial because it determines the appropriateness of using linear regression techniques. If the relationship is non-linear, transformations or different types of modeling may be necessary to accurately capture the relationship between the variables.

In the context of the other options, while it’s true that not all variables must be dichotomous (as in option A), regression analysis can handle both continuous and categorical variables. Option C is also unfounded because it misrepresents how regression works; the independent variables are meant to explain the variation in the dependent variable, and correlation between them is generally expected. Option D incorrectly states that all variables are strictly assumed to follow a uniform distribution, which is not true; regression does not require all variables to follow a uniform distribution, though certain assumptions about the distribution of the errors may apply.

Thus, the correct understanding