Which activity best describes responsible data cleaning before analysis?
Correct Answer
A) Investigating and documenting errors, inconsistencies and transaction circumstances
Cleaning checks and documents missing fields, duplicates, inconsistent definitions, possible recording errors and transaction circumstances. Correct or otherwise treat problems based on supported findings. A family relationship or foreclosure label does not alone prove the sale lacks usable market information. Deleting all prices below the median is biased selection, and arbitrary rounding or a recent-only window is not a complete cleaning procedure.
Why This Is the Correct Answer
Option A is correct because responsible cleaning investigates and documents errors, inconsistencies and the circumstances of each transaction. Problems are then corrected or otherwise treated based on supported findings; correctable errors such as data-entry mistakes can be fixed and the sale kept in the sample.
Why the Other Options Are Wrong
Option B: Removing each sale priced below the median before any review of the data
Option B removes each sale priced below the median before any review. Deleting half the data by price is biased selection and discards valid sales without examining them.
Option C: Rounding all values to whole thousands before reviewing any recording errors
Option C rounds all values to whole thousands before reviewing any recording errors. Arbitrary rounding loses information and does nothing to find or fix errors in the data.
Option D: Keeping the latest quarter’s sales and dropping earlier ones by default, without review
Option D keeps the latest quarter's sales and drops earlier ones by default, without review. A recent-only window applied without review is not a cleaning procedure and may discard useful, valid sales.
Check, document, then treat
Clean data the way a careful detective works: check each problem, write down what you found, and only then decide what to do.
How to use: Use this when a question asks which data-handling step is appropriate before analysis.
Exam Tip
Prefer answers that investigate and document over answers that delete, round or filter data by a fixed rule before any review.
Common Mistakes to Avoid
- -Deleting sales based on price alone
- -Treating rounding or date filters as data cleaning
- -Removing a sale with a correctable error instead of fixing it
Concept Deep Dive
Analysis
This question tests what responsible data cleaning involves. Cleaning means checking for and documenting problems such as errors, duplicates and unusual transaction circumstances, then treating them based on what the review finds. Mechanical rules that remove or alter data without review introduce bias.
Background Knowledge
Data cleaning checks for missing fields, duplicates, inconsistent definitions, possible recording errors and transaction circumstances. The preferred handling of an outlier is further scrutiny to determine whether it is a non-market sale or contains an error in fact, and correctable errors should be fixed so the sale stays in the sample. Labels such as a related-party or foreclosure sale call for review but do not by themselves prove the sale lacks usable information.
Real-World Application
Before running a market analysis, an appraiser finds a duplicate MLS entry, a sale with square footage entered in the wrong field, and a sale between relatives. The appraiser removes the duplicate, corrects the entry error, verifies the related-party sale's terms, and notes each step in the workfile.
More Statistics Questions
Sale prices are $270,000, $290,000, $300,000, $310,000, $330,000. Treat the listed observations as the complete population and divide by n when computing the standard deviation. Report to the nearest dollar.
A sales sampling frame systematically excludes a relevant part of the target market. What is the principal risk?
An appraiser estimates a market-wide price trend using only sales data from one brokerage that specializes in luxury homes, without checking how other segments are represented. What is the principal concern?
Compared with analyzing one matched sale pair for one feature, what can a suitably specified multiple regression do?
The base-period median sale price is $400,000 and the current-period median is $460,000. Compute the price relative defined as current ÷ base, as a decimal to three places.
The assessment-to-sale ratios are 0.8, 0.85, 0.9, 0.92, 0.95, 0.98, 1.02, 1.05, 1.08, 1.1, 1.15, 1.2. Calculate COD as [average absolute deviation from the median ÷ median]×100. For even n, average the two middle ratios. Report the COD as a percentage to two decimals.
Assessment-to-sale ratios are 0.94, 0.96, 0.98, 1, 1.01. Compute the arithmetic mean. Report as a decimal ratio to three places.
Market data show a first quartile sale price of $405,000 and a third quartile sale price of $495,000. What is the interquartile range, to the nearest dollar?
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Given first quartile $400,000, median $440,000 and third quartile $500,000, compute the trimean (Q1+2×median+Q3)/4. Report to the nearest dollar.
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