In a regression predicting sale price from living area, which is the dependent variable?
Correct Answer
B) The sale price of the property
In the model explicitly predicting sale price from area, price is the response or dependent variable and area is the predictor. Other models can use a different outcome. Age would be a predictor only if included, and observation count is sample metadata rather than the stated response. This naming convention alone establishes neither causal direction nor predictive quality.
Why This Is the Correct Answer
Option B is correct because the model predicts sale price from living area, so sale price is the response or dependent variable. Living area is the predictor, or independent variable.
Why the Other Options Are Wrong
Option A: The living area in square feet
Option A, the living area in square feet, is the predictor (independent variable) in this model, not the outcome. The dependent variable is the sale price being predicted.
Option C: The age of the improvements
Option C, the age of the improvements, is not part of the stated model. Age could be a predictor if added, but it would not be the dependent variable in a model predicting sale price.
Option D: The number of comparable sales
Option D, the number of comparable sales, is the sample size, which describes the data set rather than serving as a variable in the model.
Price depends on features
The dependent variable is what you are trying to predict; it depends on the inputs. If the model predicts price, price is dependent.
How to use: Use this to identify the dependent variable from the phrase predicting X from Y.
Exam Tip
In the phrase predicting X from Y, X is the dependent variable and Y is the independent variable.
Common Mistakes to Avoid
- -Calling the predictor the dependent variable
- -Treating sample size as a model variable
- -Assuming the dependent-variable label proves causation
Concept Deep Dive
Analysis
This question tests identifying the dependent variable in a regression. The dependent (response) variable is the outcome being predicted, and the independent variable is the predictor used to explain it.
Background Knowledge
In regression, the dependent variable is the outcome the model estimates, and independent variables are the factors used to explain it. In appraisal regressions, sale price is commonly the dependent variable and property characteristics are predictors. The naming does not by itself establish causation or model quality.
Real-World Application
An appraiser builds a model estimating sale price from living area for a tract-home neighborhood. Sale price is the dependent variable, and the slope shows the indicated price change per square foot.
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Previous Question
Sale prices are $200,000, $220,000, $240,000, $260,000, $280,000. Define percentile rank as (number strictly below the selected value ÷ total observations) × 100. What is the percentile rank of $240,000?
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The arithmetic mean is $400,000 and sample standard deviation is $20,000. Compute the coefficient of variation s/mean, expressed as a decimal ratio. Report three decimals.