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stepwise multiple linear regression model  (MathWorks Inc)


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    MathWorks Inc stepwise multiple linear regression model
    Stepwise Multiple Linear Regression Model, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/multiple+stepwise+regression+model/pmc08912927-150-2-22
    Average 90 stars, based on 1 article reviews
    stepwise multiple linear regression model - by Bioz Stars, 2026-09
    90/100 stars

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    Article Title: Comprehensive evaluation of resistance effects of pyramiding lines with different broad-spectrum resistance genes against Magnaporthe oryzae in rice (Oryza sativa L.).
    Article Snippet: .. The relationship between different interaction effect and RF was analyzed using a multiple stepwise regression model, which is an available option in Matlab (V.7.0) software (Xu et al. 2012). .. The interaction effects that were significantly correlated with RF were subjected to clustering by principal component analysis (PCA) in SPSS (V.21) software.

    Article Title: Comprehensive evaluation of resistance effects of pyramiding lines with different broad-spectrum resistance genes against Magnaporthe oryzae in rice ( Oryza sativa L.)
    Article Snippet: .. The relationship between different interaction effect and RF was analyzed using a multiple stepwise regression model, which is an available option in Matlab (V.7.0) software (Xu et al. ). .. The interaction effects that were significantly correlated with RF were subjected to clustering by principal component analysis (PCA) in SPSS (V.21) software.

    Article Title: Combination Patterns of Major R Genes Determine the Level of Resistance to the M . oryzae in Rice ( Oryza sativa L.)
    Article Snippet: .. The relationship between R genes and RF was analyzed using a multiple stepwise regression model, which is an available option in Matlab (V.7.0) software, according to Xu et al. [ ]. .. R genes that were significantly correlated with RF ( P ≤0.01) were subjected to various R gene combination factors according to factor analysis (principal component analysis, PCA) in SPSS (V.21) software.



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