分位数回归是给定 回归变量X，估计响应变量Y条件分位数的一个基本方法。
Quantile regression is a basic tool for estimating conditional quantiles of a response variable Y given a vector of regressors X.
使用逻辑回归调整了混杂变量分析。
Analyses were adjusted for confounding variables using logistic regression.
最后，本文探讨了第一个数据挖掘模型：回归模型(特别是线性回归多变量模型)，另外还展示了如何在WEKA中使用它。
Finally, this article discussed the first data-mining model, the regression model (specifically, the linear regression multi-variable model), and showed how to use it in WEKA.
请不要选择该选项，因为简单回归只能有一个变量，而我们有六个变量。
Do not choose this because simple regression only looks at one variable, and we have six.
在深入学习更高级的技术(如多次回归或多变量方差分析)之前，对于简单线性回归的透彻理解将使您受益匪浅。
Before you plunge into learning more advanced techniques, like multiple regression or manova, you could benefit from having a solid understanding of simple linear regression.
然后用回归模型根据给定的这些自变量的值预测一个未知的因变量的结果。
The regression model is then used to predict the result of an unknown dependent variable, given the values of the independent variables.
在应用回归分析时，有时人们所确定的两个变量之间的关系，事实上并不存在。
When applying regression analysis people sometimes find a relationship between two variables that in fact have no common bond.
此外，本文提出的LQ定理使我们能用相关分析法，通过变量变换，把单因素非线性回归问题，化成线性形成来处理。
Besides, the LQ theorem presented in this paper can be used to change a nonlinear single regression problem to a linear one by means of transformation of variables.
回归纳入经济/金融变量以及线性样条在时间变量的设置进行测试的外部系列。
Regressions incorporating the economic/financial variables as well as a linear spline in time variable are set up for testing the externality series.
摘要：数据收集的66个国家和运行一个多变量线性回归分析。
Abstract: Data was gathered for 66 countries and a linear multi-variate regression was run.
在多变量逻辑回归模型中，开发基于回归系数预测程序的一个简单的临床评分体系。
A simple clinical scoring system was developed on the basis of regression coefficients of predictors in a multivariable logistic regression model.
随着电子计算技术的飞速发展和实验技术的不断提高，医学资料中经常出现包含较多自变量的大型回归问题。
With the development of the computer technology and the improvement of the experiment technology, there often are the regression problems which include more independent variables in medical data.
外向性、谨慎性和开放性三个变量进入回归方程。
The three variables in extroversion, caution and opening go into regression equation.
在实际工作中，建立离散变量的回归预报方程是一个不可回避的问题。
It is an inevitable problem to set up a predict equation of discrete variables regression in the practice work.
目的放宽经典线性模型中的多个解释变量的线性假定和探讨多维样条回归分析模型。
Objective to relax linear assumption of explanatory variables in general linear model and explore more-dimensional spline regression analysis model.
特别地，当回归函数线性时，这类集合就是解释变量空间中的超平面。
In particular, when regression functions are linear, these sets become hyperplanes in explanatory variables Spaces.
结果表明，软件可以实现数据的不同方式拟合、多变量数据回归和未知实验数据点预测。
The data simulation in different manners, regression of multiple variables, and prediction of experimental data can be achieved by using of this software.
结果：正确使用哑变量，扩大回归模型的应用范围。
Results: By using dummy variable, we can broaden the application of regression analysis.
但这些变量的回归结果均不显著。
But the regression results of the variables are not significant.
回归式反映了响应变量和预测变量间的线性关系。
Regression expression reflects the linear relation between responding variable and predictive variable.
当回归中加入另外的解释变量时，R2通常会上升。
R2 generally increases when a regressor is added to a regression.
目的探讨边际回归模型在医学研究领域多变量相关分析中的应用。
Objective To explore the application of marginal regression models on multivariate correlation analysis in medical research.
选择变量，建立回归模型，代入样本数据进行回归分析。
Changing variable, set up a regression model, take regression analyse using sample data.
因此在第二章，本人用半参数方法提出了协变量测量有误差时的中值回归模型，用它来估计寿命变量的中值。
In the second chapter, I propose semi-parametric estimate to get median regression models with the measured error margin and use it to estimate the mediant of survival data.
目的放宽经典线性模型中的解释变量的线性假定和探讨半参数回归分析模型。
Objective To relax linear assumption of explanatory variable in classical linear model and explore semiparametric regression model.
将虚拟变量引入修正模型，再次进行回归检验。
So, it introduced a mute variable into the revised model to examine again.
数据处理方法包括单因素多变量方差分析、多因素线性回归分析和t检验。
The data were treated with univariate and multivariate analysis of variance, multiple linear regression analysis and t test.
将模糊集理论引入多元线性回归中，通过模糊控制变量，可以得出更符合实际，也更容易为人所理解的回归模型。
By introducing fuzzy control variable, the paper proposes a fuzzy multiple linear regression model which can get more effective and more understandable fuzzy regression expression.
结论：在协方差分析中使用哑变量使对模型的理解变的简单，在方差分析中使用哑变量使得可以从回归的角度来理解模型。
Conclusion: It is easy to understand the analysis of covariance and analysis of variance can also be considered in regression analysis setting by defining appropriate dummy variables.
结论：在协方差分析中使用哑变量使对模型的理解变的简单，在方差分析中使用哑变量使得可以从回归的角度来理解模型。
Conclusion: It is easy to understand the analysis of covariance and analysis of variance can also be considered in regression analysis setting by defining appropriate dummy variables.
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