50. A financial analyst theorizes that commute × increase as the percentage of land availability for homes in a city decreases. To test this hypothesis, the analyst uses a regression analysis to explore how land availability predicts commute time. What does land availability represent in this regression?
Answer: B
Land availability represents the independent variable in this regression.
In the context of the regression analysis, land availability is the factor being manipulated or assessed to see its effect on commute time. As such, it serves as the independent variable that predicts the dependent variable, which is commute time.
A) It is a control.
This option is incorrect because a control variable is one that is held constant to eliminate its influence on the relationship being studied. Land availability is not controlled but rather is the main focus of analysis in this hypothesis.
B) It is the independent variable.
This option is correct as land availability is the variable that is hypothesized to affect the commute time. In regression analysis, the independent variable is the one that is tested to see how it influences the dependent variable.
C) It is the target variable.
This option is incorrect because the target variable refers to the outcome or result that is being predicted by the independent variable. In this instance, the target variable is the commute time, not land availability.
D) It is the dependent variable.
This option is also incorrect since the dependent variable is the outcome that is expected to change in response to the independent variable. Here, commute time is the dependent variable, while land availability is what is being tested as the independent variable.
Conclusion
Land availability is definitively the independent variable in this regression analysis because it is the factor being examined to determine its effect on commute time. Other options fail because they misclassify the role of land availability in the hypothesis, either as a control or as a dependent variable. Understanding the distinction between independent and dependent variables is crucial for correctly interpreting regression models.