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[Algorithm] caculation2 DL : 0: 主要介绍逐步回归的方法，并介绍如何利用逐步回归建立最佳线性回归方程。-introduced stepwise regression method, and how to use stepwise regression to establish the best linear regression equation.
Update : 2025-02-19 Size : 277kb Publisher : 梁建军
[assembly language] dyzbhg DL : 0: 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
Update : 2025-02-19 Size : 2kb Publisher : wangqin
[matlab] dynhgj DL : 0: matlab来编写多元逐步回归分析，初学者适合-Matlab to prepare multiple stepwise regression analysis, suitable for beginners
Update : 2025-02-19 Size : 3kb Publisher : 张
[Algorithm] zhubuhuiguifenxi DL : 0: 本程序是一个逐步回归分析的演示程序，通过对众多因子的逐步选择，最终选出影响最大的因子构成线性回归方程。-this procedure is a stepwise regression analysis of the demonstration program, many of the progressive choice factor, ultimately selected the greatest impact factor constitute linear regression equation.
Update : 2025-02-19 Size : 3kb Publisher : 彭晓波
[Algorithm] 05050822222628 DL : 0: !逐步回归分析程序： ! M：输入变量，M=N+1，其中N为自变量的个数；M包括的因变量个数 ! K：输入变量，观测点数； ! F1：引入因子时显著性的F-分布值； ! F2：剔除因子时显著性的F-分布值； ! XX：存放自变量和因变量的平均值； ! B：存放回归系数； ! V：存放偏回归平方和和残差平方和Q； ! S：存放回归系数的标准偏差和估计的标准偏差； ! C：存放复相关系数； ! F：存放F-检验值；-! Stepwise regression analysis procedure:! M: input variables, M = N+ 1, in which N is the number of independent variables M, including the number of the dependent variable! K: input variables, observation points ! F1: when to introduce a significant factor of the F-distribution value ! F2: remove significant factor when the F-distribution value ! XX: storage self-variables and the dependent variable on average ! B: regression coefficient storage ! V: store partial regression sum of squares and residual sum of squares Q ! S: storage of the standard deviation of regression coefficients and the estimated standard deviation ! C: storage of multiple correlation coefficient ! F: storing F-test value
Update : 2025-02-19 Size : 2kb Publisher : wang hanting
[Algorithm] regress DL : 0: 逐步回归算法程序，可以实现中间结果的任意输出-Stepwise regression algorithm procedure can be achieved between the results of any output
Update : 2025-02-19 Size : 15kb Publisher : tangshx
[Algorithm] Stepwise DL : 0: 本人初次使用VB，也是刚开始尝试逐步回归分析算法，如有问题请多指教。-I first use of VB, is just beginning to try stepwise regression analysis algorithm, please advise if there are questions.
Update : 2025-02-19 Size : 3.04mb Publisher : 未来
[Other systems] Regressionanalysis DL : 0: 回归的各种分析实例以及matlab代码，包括各种数学应用，比如线性回归，非线性回归，逐步回归-Examples of regression analysis, as well as a variety of matlab code, including a variety of mathematical applications such as linear regression, nonlinear regression, stepwise regression
Update : 2025-02-19 Size : 843kb Publisher : 吴明岩
[Algorithm] stepwise DL : 0: 这是一个关于逐步回归求多元线性回归的一个VC例程-It is a stepwise regression for multiple linear regression routine of a VC
Update : 2025-02-19 Size : 107kb Publisher : 和君洋
[Other] [42][751e]spring-reference DL : 0: Spring参考手册有众多Spring爱好者共同协作完成，本文档的翻译是在网上协作进行，也会不断根据Spring文档的升级进行逐步更新。提供此文档的目的是为了减缓学习Srping的曲线，更好的让优秀的技术扩大在中文世界的使用。目录：引言 1. 前提 2. Java的学习 3. 目标 4. 联机文档 5. 章节 6. 练习 7. 多媒体CD-ROM 8. 源代码 9. 编码样式 10. Java版本 11. 课程和培训 12. 错误 13. 封面设计 14. 致谢 -Spring reference manual there are many Spring fans together to complete the translation of this document is the online collaboration will continue to update the document according to stepwise Spring update. To provide this document is designed to reduce Srping learning curve, better make excellent technologies to expand the use of the world in Chinese. Contents: Introduction 1. Premise 2. Java learning 3. Goal 4. Online documentation 5. Section 6. Exercise 7. Multimedia CD-ROM 8. The source code 9. Coding style 10. Java version of the 11. Courses and training 12. Error 13. cover design 14. Thanks
Update : 2025-02-19 Size : 995kb Publisher : 齐晓龙
[matlab] fsbox DL : 0: Stepwise forward and backward selection of variables using linear models
Update : 2025-02-19 Size : 4kb Publisher : Tqing
[CSharp] StepwiseRegress DL : 0: 逐步法线性回归 C#源码（vs2005工程），得到回归系数、复相关系数、相关系数矩阵等。-Stepwise linear regression C# source code (vs2005 project), the regression coefficient, multiple correlation coefficient, correlation coefficient matrix.
Update : 2025-02-19 Size : 27kb Publisher : wuyang
[matlab] stepwise DL : 0: 最小二乘法多元线性回归的matlab实现-Least squares linear regression of the matlab implementation
Update : 2025-02-19 Size : 10kb Publisher : 飞
[Delphi VCL] stepwise DL : 0: 大坝安全监测领域的逐步回归分析，已调试，采用的是吴中如院士的分析参数-The field of dam safety monitoring stepwise regression analysis, have been debugging, using Wu' s analysis of parameters such as the Academy of Sciences
Update : 2025-02-19 Size : 4kb Publisher : zhangshuai
[Algorithm] stepwise-regression-analysis- DL : 0: C++做的逐步回归分析程序，用于数据统计分析建模-C++do stepwise regression analysis program, used for data analysis modeling
Update : 2025-02-19 Size : 1kb Publisher : 李海燕
[Algorithm] Stepwise-regression DL : 0: 大坝安全监测逐步回归的经典案例，非常适合初学者进行程序调试，解决实际问题-Classic case of dam safety monitoring stepwise regression, very suitable for beginners program debugging, and solve practical problems
Update : 2025-02-19 Size : 105kb Publisher : 王刚
[matlab] stepwise DL : 0: 多元统计分析中用于判别的逐步判别法，用于筛选变量，剔除多余变量的逐步判别的matlab程序-Multivariate statistical analysis for discriminant stepwise discriminant method for screening variables, stepwise discriminant eliminate redundant variables matlab program
Update : 2025-02-19 Size : 1kb Publisher : 漱寒
[Other] Stepwise-regression-analysis DL : 0: 逐步回归分析，更好的精度预测与模拟，高次运算算法。-Stepwise regression analysis, forecasting and simulation better accuracy, higher computation algorithm.
Update : 2025-02-19 Size : 1kb Publisher : lixiaodong
[Special Effects] 1-stepwise-selection-criteria DL : 0: X. Huo and X. S. Ni (2007). When do stepwise algorithms meet subset selection criteria aos07-X. Huo and X. S. Ni (2007). When do stepwise algorithms meet subset selection criteria aos07
Update : 2025-02-19 Size : 142kb Publisher : fangsm2
[matlab] stepwise DL : 0: 逐步回归的基本思想是将变量逐个引入模型，每引入一个解释变量后都要进行F检验，并对已经选入的解释变量逐个进行t检验，当原来引入的解释变量由于后面解释变量的引入变得不再显著时，则将其删除。以确保每次引入新的变量之前回归方程中只包含显著性变量。这是一个反复的过程，直到既没有显著的解释变量选入回归方程，也没有不显著的解释变量从回归方程中剔除为止。以保证最后所得到的解释变量集是最优的。(In statistics, stepwise regression is a method of fitting regression models in which the choice of predictive variables is carried out by an automatic procedure.[1][2][3][4] In each step, a variable is considered for addition to or subtraction from the set of explanatory variables based on some prespecified criterion. Usually, this takes the form of a sequence of F-tests or t-tests, but other techniques are possible, such as adjusted R2, Akaike information criterion, Bayesian information criterion, Mallows's Cp, PRESS, or false discovery rate.)
Update : 2025-02-19 Size : 2kb Publisher : 宫晓楠

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