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multivariate linear regression analysis  (STATA Corporation)


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    STATA Corporation multivariate linear regression analysis
    Multivariate Linear Regression Analysis, supplied by STATA Corporation, used in various techniques. Bioz Stars score: 99/100, based on 35425 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/multivariable+linear+regression+analysis/STATA+16%2E0/10__1089_slash_dia__2024__2525__abstracts-2406-0-15
    Average 99 stars, based on 35425 article reviews
    multivariate linear regression analysis - by Bioz Stars, 2026-10
    99/100 stars

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    Article Title: Astigmatic equivalence of 2.2-mm and 1.8-mm superior clear corneal cataract incision.
    Article Snippet: Purpose To evaluate the astigmatic effects of 2.2-mm and 1.8-mm cataract incisions.. Methods A randomized prospective study of 190 eyes of 151 patients undergoing superior clear corneal incision (SCCI) was divided into three groups: 61 eyes with a control 3.2mm SCCI; 66 eyes with a 2.2-mm SCCI; and 63 eyes with a 1.8-mm SCCI.. The corneal astigmatism was measured with an autokeratometer preoperatively and 1 month after surgery.

    Article Title: Evaluation of anticyclin B1 serum antibody as a diagnostic and prognostic biomarker for lung cancer.
    Article Snippet: The primary aims of this study were to establish a high-throughput assay and analytic procedures that would allow us to assess with accuracy and reproducibility the presence of anti-cyclin B1 antibodies in a large cohort of subjects at-risk for development of lung cancer.. The overall goal is to identify factors significantly associated with immune responses against this lung tumor antigen and to evaluate the relationship between anticyclin B1 antibodies and lung cancer risk in a prospective cohort.. Successful cancer treatment relies on early detection.

    Mann-Whitney U-Test:

    Article Title: Postpartum use of oxytocin and volume of placental transfusion: a randomised controlled trial.
    Article Snippet: To cite: Vain NE, Satragno DS, Gordillo JE, et al. Arch Dis Child Fetal Neonatal Ed Epub ahead of print: [please include Day Month Year]. doi:10.1136/ archdischild-2018-316649 Pediatrics, Hospital Trinidad, University of Buenos Aires, Buenos Aires, Argentina Fundasamin, Buenos Aires, Argentina Neonatology, Hospital de Ninos Ricardo Gutierrez, Buenos Aires, Argentina Neonatology, Instituto de Maternidad y Ginecología Nuestra Señora de Las Mercedes, Tucumán, Argentina Centro Rosarino de Estudios Perinatales, Rosario, Argentina

    Comparison:

    Article Title: Postpartum use of oxytocin and volume of placental transfusion: a randomised controlled trial.
    Article Snippet: To cite: Vain NE, Satragno DS, Gordillo JE, et al. Arch Dis Child Fetal Neonatal Ed Epub ahead of print: [please include Day Month Year]. doi:10.1136/ archdischild-2018-316649 Pediatrics, Hospital Trinidad, University of Buenos Aires, Buenos Aires, Argentina Fundasamin, Buenos Aires, Argentina Neonatology, Hospital de Ninos Ricardo Gutierrez, Buenos Aires, Argentina Neonatology, Instituto de Maternidad y Ginecología Nuestra Señora de Las Mercedes, Tucumán, Argentina Centro Rosarino de Estudios Perinatales, Rosario, Argentina



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    <t>MLRA</t> regression coefficients (SI) in isotropic case (a-i) and gradient case (j-r). The columns refer to the key parameter α, K, T d , T m . The rows refer to the output of interest: a , j) core cell number; b , k) migrated cell number; c , l) number of cells migrated toward East; d , m) number of cells migrated toward South; e , n) number of cells migrated toward West; f , o) core cell percentage; g , p) percentage of cells migrated toward East; h , q) percentage of cells migrated toward South; i , r) percentage of cells migrated toward West. The asterisk indicates which parameter is significance (p<0.05) for the related output.
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    <t>MLRA</t> regression coefficients (SI) in isotropic case (a-i) and gradient case (j-r). The columns refer to the key parameter α, K, T d , T m . The rows refer to the output of interest: a , j) core cell number; b , k) migrated cell number; c , l) number of cells migrated toward East; d , m) number of cells migrated toward South; e , n) number of cells migrated toward West; f , o) core cell percentage; g , p) percentage of cells migrated toward East; h , q) percentage of cells migrated toward South; i , r) percentage of cells migrated toward West. The asterisk indicates which parameter is significance (p<0.05) for the related output.
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    MLRA regression coefficients (SI) in isotropic case (a-i) and gradient case (j-r). The columns refer to the key parameter α, K, T d , T m . The rows refer to the output of interest: a , j) core cell number; b , k) migrated cell number; c , l) number of cells migrated toward East; d , m) number of cells migrated toward South; e , n) number of cells migrated toward West; f , o) core cell percentage; g , p) percentage of cells migrated toward East; h , q) percentage of cells migrated toward South; i , r) percentage of cells migrated toward West. The asterisk indicates which parameter is significance (p<0.05) for the related output.

    Journal: bioRxiv

    Article Title: Hybrid Cellular Automata Modelling Reveals the Effects of Glucose Gradients on Tumour Spheroid Growth

    doi: 10.1101/2023.10.19.563082

    Figure Lengend Snippet: MLRA regression coefficients (SI) in isotropic case (a-i) and gradient case (j-r). The columns refer to the key parameter α, K, T d , T m . The rows refer to the output of interest: a , j) core cell number; b , k) migrated cell number; c , l) number of cells migrated toward East; d , m) number of cells migrated toward South; e , n) number of cells migrated toward West; f , o) core cell percentage; g , p) percentage of cells migrated toward East; h , q) percentage of cells migrated toward South; i , r) percentage of cells migrated toward West. The asterisk indicates which parameter is significance (p<0.05) for the related output.

    Article Snippet: A Multivariate Linear Regression Analysis (MLRA) was performed using the builtin MATLAB function mvregress on whole set of samples to obtain the linear regression coefficients for each parameter.

    Techniques: