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Quantile Regression Caught the House Price OLS Missed by $55K

A quantile regression approach on 88 house sales produced a 90% prediction interval spanning $136K to $227K for a home that ordinary least squares (OLS) priced at $164K, while the actual sale price was $219K, according to a Towards Data Science post. The OLS estimate missed the sale price by $55K, whereas the interval built from 5th and 95th percentile models contained it. The comparison illustrates how percentile-based models can quantify uncertainty that a single point estimate from OLS hides.

read1 min views1 publishedNov 9, 2026
Quantile Regression Caught the House Price OLS Missed by $55K
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On 88 house sales, OLS predicted $164K for a $219K home, while a 90% interval from 5th and 95th percentile models spanned $136K to $227K. The post Quantile Regression Caught the House Price OLS Missed by $55K appeared first on Towards Data Science.

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source & further reading
ainexusdaily.vercel.app — original article How to Estimate the Recall of AI Agents with Capture-Recapture How to Build Multi-Agent Systems with the OpenAI Agents SDK Getting Started with Hermes Agent: Memory, Skills and What It Actually Costs
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