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回帰分析
Sign in to saveAlso known as regression method, regression
set of statistical processes for estimating the relationships among variables
Regression analysis is a statistical method used to understand and estimate how different variables relate to and influence each other. It matters because it helps researchers and analysts predict outcomes and identify patterns in data across many fields, from business to medicine to social science.
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Research
1,149,449 papers- Linear regression analysis: part 14 of a series on evaluation of scientific publications.Deutsches Arzteblatt international · 2010
- Segmented regression analysis of interrupted time series studies in medication use research.Journal of clinical pharmacy and therapeutics · 2002
- Regression analysis.Practical neurology · 2007
- Exploring heterogeneity in meta-analysis: Meta-regression analysis.American journal of orthodontics and dentofacial orthopedics : official publication of the American Association of Orthodontists, its constituent societies, and the American Board of Orthodontics · 2020
- Regression analysis and multivariate analysis.Seminars in reproductive endocrinology · 1996
via PubMed
Wikidata facts
- Subclass of
- data analysis
- Image
- Linear regression.svg
- Has use
- supervised learning
Show 9 more facts
- NCI Thesaurus ID
- C53237
- ACM Classification Code (2012)
- 10003691
- discoverer or inventor
- Francis Galton
- Commons category
- Regression analysis
- topic's main category
- Category:Regression analysis
- Stack Exchange tag
- stats.stackexchange.com/tags/regression
- facet of
- statistics
- topic has template
- Template:Regression bar
- on focus list of Wikimedia project
- Wikipedia:Vital articles/Level/4
via Wikidata · CC0
Article · 日本語
回帰（かいき、（英: regression）とは、統計学において、Y が連続値の時にデータに Y = f(X) というモデル(「定量的な関係の構造」)を当てはめること。別の言い方では、連続尺度の従属変数（目的変数）Y と独立変数（説明変数）X の間にモデルを当てはめること。X が1次元ならば単回帰、X が2次元以上ならば重回帰と言う。Y が離散の場合は分類と言う。 回帰分析（かいきぶんせき、（英: regression analysis）とは、回帰により分析すること。 回帰で使われる、最も基本的なモデルは という形式の線形回帰である。
Abstract from DBpedia / Wikipedia · CC BY-SA