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| 다중 선형 회귀× | 기호 데이터 분석× | |
|---|---|---|
| 분야≠ | 통계학 | 소프트 컴퓨팅 |
| 계열≠ | Regression model | Machine learning |
| 기원 연도≠ | 1886 | 2003 |
| 창시자≠ | Francis Galton; formalized by Karl Pearson | Edwin Diday; Lynne Billard |
| 유형≠ | Parametric linear model | Statistical framework for aggregate and set-valued data |
| 원전≠ | Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute of Great Britain and Ireland, 15, 246–263. DOI ↗ | Billard, L., & Diday, E. (2003). From the statistics of data to the statistics of knowledge: symbolic data analysis. Journal of the American Statistical Association, 98(462), 470–487. DOI ↗ |
| 별칭≠ | MLR, OLS regression, multiple regression, linear regression with multiple predictors | SDA, Interval Data Analysis, Distributional Data Analysis, Sembolik Veri Analizi |
| 관련≠ | 8 | 1 |
| 요약≠ | Multiple linear regression (MLR) is a parametric regression model that expresses a continuous outcome as a weighted linear combination of two or more predictor variables plus a random error term. The unknown weights (regression coefficients) are estimated by ordinary least squares (OLS), which minimises the sum of squared residuals. The method traces to Francis Galton's 1886 work on hereditary stature and was placed on firm mathematical footing by Karl Pearson; Draper and Smith's 1966 textbook established it as the standard framework for applied regression. | Symbolic Data Analysis (SDA) is a statistical framework designed to analyze complex, aggregate, or set-valued data — called symbolic data — in which each observation represents a group or concept rather than a single scalar. Introduced in its modern statistical form by Lynne Billard and Edwin Diday in 2003, SDA extends classical statistics to handle interval-valued, histogram-valued, and multi-valued variables, enabling rigorous inference at the level of knowledge rather than raw individual records. |
| ScholarGate데이터셋 ↗ |
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