Bootstrap Resampling in Gompertz Growth Model with Levenberg–Marquardt Iteration
نویسندگان
چکیده
Soybean plants have limited growth with a planting period of 12 weeks, which causes the observed sample to be very small. A small soybean plant observations can bias in conclusion prediction results on growth. The purpose this study is apply bootstrap resampling technique Gompertz model overcomes residual distribution samples, research data was taken from four varieties spacing treatments, five replications and twelve weeks (long period). uses nonlinear least squares method estimating parameters Levenberg–Marquardt iteration. value after has no significant difference. adjusted R2 0.96 close 1. This means that total diversity heights explained by 96 percent. Judging graph predictions before coincide each other it also seen initial values 14, 05 14.18, maximum are 55.13 55.60. Bootsrap overcome normality model, but does not change information data.
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the bmj | BMJ 2015;350:h2622 | doi: 10.1136/bmj.h2622 1Department of Health Sciences, University of York, York YO10 5DD, UK 2Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Correspondence to: J M Bland [email protected] Cite this as: BMJ 2015;350:h2622 doi: 10.1136/bmj.h2622 Statis...
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ژورنال
عنوان ژورنال: JTAM (Jurnal Teori dan Aplikasi Matematika)
سال: 2022
ISSN: ['2597-7512', '2614-1175']
DOI: https://doi.org/10.31764/jtam.v6i4.8617