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      <image:title>Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out</image:title>
      <image:caption>Each panel shows one school’s applicants from this dataset. Notice how cleanly the blue dots separate from the red X’s in some schools (JMU, Georgetown), and how scrambled they are in others (Penn State, Northeastern). That visual gap is the whole story.</image:caption>
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      <image:title>Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out</image:title>
      <image:caption>Each bar shows how much of an admissions decision at that school is statistically explained by GPA, test score, and decision type (early vs. regular). The gray block is everything the numbers can’t explain: essays, extracurriculars, recommendations, and the intangibles of holistic review.</image:caption>
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      <image:title>Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out</image:title>
      <image:caption>Left: JMU, where the dashed line cleanly separates blue (accepted) from red (denied). Right: Northeastern, where blue and red are scattered all over each other and no line can really separate them. Background shading shows the model’s predicted probability of admission: warm cream means low, cool blue means high.</image:caption>
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      <image:title>Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out</image:title>
      <image:caption>Several Northeastern applicants with SATs of 1500+ were denied or waitlisted. The pattern is consistent with what some college counselors call yield protection, but I want to be clear that this is one possible explanation among several, and the sample here is small.</image:caption>
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