The answer is sqft_living, as it has the highest absolute standardized coefficient in the regression output. Standardized coefficients, also known as beta weights, allow you to compare the relative strength of independent variables by measuring how many standard deviations the dependent variable changes per one standard deviation change in the predictor, effectively putting all variables on a common scale. On the CompTIA Data+ DA0-001 exam, this concept tests your ability to interpret regression output and identify which predictor has the strongest effect, even when variables are measured in different units. A common trap is to compare unstandardized coefficients directly, which can be misleading due to differing scales, or to mistakenly consider the intercept as an independent variable. To remember this, think: “Beta weights level the playing field—highest absolute beta wins the strongest effect.”
DA0-001 Analyzing and Modeling Data Practice Question
This DA0-001 practice question tests your understanding of analyzing and modeling data. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Exhibit
Refer to the exhibit.
Call:
lm(formula = price ~ sqft_living + bedrooms + bathrooms, data = housing)
Residuals:
Min 1Q Median 3Q Max
-1.2345 -0.3456 -0.0123 0.3456 2.3456
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 0.123456 0.012345 10.000 <2e-16 ***
sqft_living 0.001234 0.000123 10.000 <2e-16 ***
bedrooms -0.056789 0.012345 -4.600 4.23e-06 ***
bathrooms 0.234567 0.045678 5.135 3.45e-07 ***
--
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 0.4567 on 496 degrees of freedom
Multiple R-squared: 0.789, Adjusted R-squared: 0.787
F-statistic: 617.8 on 3 and 496 DF, p-value: < 2.2e-16
Given the linear regression output, which independent variable has the strongest effect on price, based on standardized coefficients?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
sqft_living
Standardized coefficients (beta weights) allow comparison of the relative strength of independent variables by measuring the number of standard deviations the dependent variable changes per one standard deviation change in the predictor. In the regression output, sqft_living has the highest absolute standardized coefficient, indicating it has the strongest effect on price. The intercept is not an independent variable and its coefficient is not standardized for comparison.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
bathrooms
Why it's wrong here
bathrooms has a lower t-value (5.135) than sqft_living.
✓
sqft_living
Why this is correct
sqft_living has the highest absolute t-value (10.0) indicating strong effect.
Related concept
Read the scenario before looking for a memorised answer.
✗
Intercept
Why it's wrong here
The intercept is not an independent variable; it's the baseline.
✗
bedrooms
Why it's wrong here
bedrooms has a negative coefficient and lower t-value magnitude (4.6).
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates mistakenly compare unstandardized coefficients or p-values instead of standardized coefficients, leading them to choose a variable like bathrooms or bedrooms that appears significant but has a weaker standardized effect.
Detailed technical explanation
How to think about this question
Standardized coefficients are computed by multiplying the unstandardized coefficient by the ratio of the standard deviation of the independent variable to the standard deviation of the dependent variable (β_i = b_i * (s_x_i / s_y)). This removes the units of measurement, enabling fair comparison across variables like square footage and room counts. In real estate modeling, sqft_living often dominates because it captures the primary driver of home value, while bedrooms and bathrooms may suffer from multicollinearity, reducing their unique contribution.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the DA0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Analyzing and Modeling Data — This question tests Analyzing and Modeling Data — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: sqft_living — Standardized coefficients (beta weights) allow comparison of the relative strength of independent variables by measuring the number of standard deviations the dependent variable changes per one standard deviation change in the predictor. In the regression output, sqft_living has the highest absolute standardized coefficient, indicating it has the strongest effect on price. The intercept is not an independent variable and its coefficient is not standardized for comparison.
What should I do if I get this DA0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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