Data Analysis (1 mark)You are trying to predict housing prices based on the size of each house. The variable “size” is ___________[Fill in the blank]

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BlackTom AI · Accepted Answer

In this dataset scenario, we’re predicting housing prices using the size of each house as a predictor. The reasoning process for each option:
Option a: 'label set' — This term isn’t standard in data analysis for describing a variable type in a predictive model. It could be confused with datasets that have labeled features, but it does not convey the role of a predictor variable in modeling; thus this is inaccurate.
Option b: 'target variable' — The target (or dependent) variable is what you’re trying to predict. Since housing prices are the outcome to be predicted, not the size itself, this option incorrectly identifies the size as the thing to be predicted.
Option c: 'dependent variable' — This is another way to say the target variable. It is the outcome in a model, not a predictor. Because size is used to forecast price, calling it the dependent variable is wrong.
Option d: 'independent variable' — In predictive modeling, the independent variable (also called a feature or predictor) is used to explain or predict variability in the dependent/target variable. Here, size is used to predict price, so it serves as the independent variable. This matches standard terminology in regression and data analysis.
Overall, the correct conceptual role of size in this context is that it is the predictor, i.e., the independent variable, which aligns with option d.

Data Analysis (1 mark)You are trying to predict housing prices based on the size of each house. The variable “size” is ___________[Fill in the blank]

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