Regression Analysis for Urban Crime: The Chicago Model

Saradindu Dolui ()
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Saradindu Dolui: Capitol Technology University, 11301 Springfield Rd, Laurel, MD, 20708, USA. Author-2-Name: Leila Halawi Author-2-Workplace-Name: Embry‑Riddle Aeronautical University, 1 Aerospace Boulevard, Daytona Beach, FL, 32114, USA. Author-3-Name: Author-3-Workplace-Name: Author-4-Name: Author-4-Workplace-Name: Author-5-Name: Author-5-Workplace-Name: Author-6-Name: Author-6-Workplace-Name: Author-7-Name: Author-7-Workplace-Name: Author-8-Name: Author-8-Workplace-Name:

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Abstract: " Objective - This study aims to analyze crime dynamics in Chicago by examining the predictive relationship between crime categories and arrest frequencies using regression analysis. Methodology - A quantitative research design was applied using secondary data from the Chicago Data Portal. Regression analysis was performed to evaluate how crime categories predict arrest patterns. Findings - The results reveal a strong, positive, and statistically significant relationship between crime categories and arrest frequencies (R² = 0.613, β = 0.783, p

Keywords: regression analysis; crime; Chicago; prediction model; crime prevention. (search for similar items in EconPapers)
JEL-codes: C12 C35 C55 C80 Z19 Z39 (search for similar items in EconPapers)
Pages: 8
Date: 2026-03-31
New Economics Papers: this item is included in nep-law
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Published in Journal of Business and Economics Review, Volume 10, Issue4

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Persistent link: https://EconPapers.repec.org/RePEc:gtr:gatrjs:jber270

DOI: 10.35609/jber.2026.10.4(3)

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