Financial Risk Manager Part 1

Multiple linear regression analysis is often preferred over single linear regression because it reduces the omitted variable bias. Omitted variable bias occurs when a regression model leaves out one or more independent variables that have a significant impact on the dependent variable. This omission can lead to an overstatement of the explanatory power of the regression analysis, as it fails to account for all the factors that influence the dependent variable. By including multiple independent variables, multiple linear regression analysis can capture a more comprehensive picture of the relationships at play, thereby reducing the omitted variable bias.

Choice A is incorrect. While modern software and computer programming have made it easier to perform multiple linear regression analysis, this does not inherently make it a preferred method over single linear regression. The choice of method depends on the nature of the data and the research question at hand, not on the ease of use of software or programming.

Choice C is incorrect. Multiple linear regression may not necessarily be easier to model than single linear regression. In fact, multiple linear regression can be more complex due to the inclusion of multiple independent variables and potential interactions between them. Furthermore, establishing relationships between dependent and independent variables is a feature common to both single and multiple regressions.

Choice D is incorrect. While it is true that multiple linear regression requires more data inputs (i.e., multiple independent variables), this requirement is a characteristic of the method rather than a rationale for its preference.