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| Mixed Effects Model× | 일원 분산 분석× | |
|---|---|---|
| 분야 | 통계학 | 통계학 |
| 계열≠ | Regression model | Hypothesis test |
| 기원 연도≠ | 1982 | 1925 |
| 창시자≠ | Laird & Ware | Ronald A. Fisher |
| 유형≠ | Mixed effects regression | Parametric mean comparison |
| 원전≠ | Laird, N. M., & Ware, J. H. (1982). Random-effects models for longitudinal data. Biometrics, 38(4), 963–974. DOI ↗ | Fisher, R. A. (1925). Statistical Methods for Research Workers. Edinburgh: Oliver and Boyd. link ↗ |
| 별칭 | LME, LMM, mixed model, random effects model | one-factor ANOVA, single-factor ANOVA, analysis of variance, tek yönlü ANOVA |
| 관련 | 4 | 4 |
| 요약≠ | A mixed effects model (or linear mixed model) extends ordinary regression by including both fixed effects — population-level parameters shared by all observations — and random effects that capture subject-, group-, or cluster-level variability. It is the standard tool for repeated-measures, longitudinal, and multilevel data where observations within the same unit are correlated. | One-way ANOVA is a parametric hypothesis test that compares the means of three or more independent groups on a single continuous outcome to decide whether at least one group mean differs. It rests on the variance-partitioning framework introduced by Ronald A. Fisher in 1925. |
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