A financial analyst theorizes that commute × increase as the percentage of land availability for homes in a city decreases. To test this theory, the analyst uses a regression analysis. Which analysis result is supportive of this analyst's theory?
Land availability represents the independent variable in the regression analysis.
In this context, the independent variable is the one that is manipulated or changed to observe its effect on the dependent variable, which in this case is commute time. The analyst is examining how variations in land availability influence commute times, making land availability the factor that is being tested.
The dependent variable is the outcome that is measured in a study, which is affected by changes in the independent variable. In this case, commute time is the dependent variable because it is what the analyst is trying to predict based on changes in land availability.
Land availability is the independent variable because it is the factor that the financial analyst is changing to determine its impact on commute times. The relationship being analyzed is how different percentages of land availability affect the length of commute times, making it the predictor in the regression analysis.
The term "target variable" typically refers to the outcome variable in the context of predictive modeling, which aligns with the dependent variable. However, in regression analysis, the more precise term for what is being manipulated is the independent variable, which is land availability in this case.
A control variable is one that is kept constant to eliminate its effects on the dependent variable, ensuring that any observed changes are solely due to the independent variable. Land availability is not being controlled; it is the variable being tested for its effect on commute time.
In regression analysis, the independent variable is the one that is hypothesized to affect the dependent variable. In this scenario, land availability serves as the independent variable that the financial analyst is investigating to see how it impacts commute times. Understanding this distinction is crucial for accurately interpreting regression results and their implications in urban planning and development.
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