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多元模型回归与分析-2 matlab 程序-multiple regression model analysis with Matlab procedures -2
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对matlab工具箱中的多元逐步回归算法参数表现更明确-Matlab toolbox to the stepwise multiple regression algorithm performance parameters more clearly
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多元逐步回归算法,在matlab软件中的运用,简单实用 -multiple stepwise regression algorithm in Matlab software on the use of simple and practical
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用DELPHI写的多元非线性回归代码,对生物统计学和生物信息学有较好参考意义.-With the DELPHI code written in multiple non-linear regression of biological statistics and bioinformatics have good reference value.
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Performs hierarchical clustering of data using specified method and
seraches for optimal cutoff empoying VIF criterion suggested in "Okada Y. et al - Detection of Cluster Boundary in Microarray Data by Reference to MIPS Functional Catalogue Databa
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实现多输入多输出数据的线性降维，实现多元统计分析与回归分析的综合能力。降维效果很好！-Realize multiple input multiple output data linear dimensional reduction, realization of multivariate statistical analysis and regression analysis of the comprehensive ability. Dimension reduction effect is
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多元非线性回归——岭回归方法，比常规的最小二乘更适合数据量较大的优化问题-Multiple nonlinear regression- ridge regression method is more suitable than the conventional least squares optimization problem of the large amount of data
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多元线性回归VB源码，是用VB实现的！有兴趣的-Multiple linear regression VB
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逐步回归分析的fortran程序，（I为变换次数，矩阵为R(I)，Q为剩余平方和，SD为剩余标准差，F为显著性检验值，R为复相关系数，B为回归系数）-Stepwise regression analysis fortran program, (I was converted number matrix R (I), Q is the residual sum of squares, SD for the residual standard deviation, F is significant
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多元线性回归模型,用C++语言编写而成，可运行-Multiple linear regression model，written by visual C++
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非常好的偏最小二乘分析的程序，包含了典型相关分析、主成分分析和多元线性回归。可以根据具体的需要更改代码变成非线性回归，建立多个自变量和多个因变量之间的关系应用很广。包含计算程序和画图表示结果的程序两个m文件-Very good partial least squares analysis of programs, including the canonical correlation analysis, principal component analysis and multiple line
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机器学习相关内容，基于matlab仿真，实现了线性回归分类器的功能-Files included in this exercise
ex1.m- Octave scr ipt that will help step you through the exercise
ex1 multi.m- Octave scr ipt for the later parts of the exercise
ex1data1.txt- Dataset for linear regression wit
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