在 英语 中使用 Regression models 的示例及其翻译为 中文
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Test R2 identifies how well the PLS regression model predicts your test data.
检验R2可确定PLS回归模型预测检验数据的优度。On regression model was used.
采用的回归模型。Multiple linear regression model is as follows.
多元线性回归模型如下：.A multinomial logistic regression model was applied to a subset of 10 classes.
多项逻辑回归模型应用于10个类的子集。Such kind of model is known as a multivariate or multiple linear regression model.
这样的模型被称为多元线性回归模型。This model is known as a multiple linear regression model.
这样的模型被称为多元线性回归模型。Thus we specified a multilevel multinomial regression model as follows.
由此，我们建立多元线性回归模型，具体如下：.So now, we have introduced our simple linear regression model.
现在，我们来构建一个简单的线性回归模型。Geographically Weight Regression Model.
地理加权回归模型.Smooth transition regression modelling.
平滑转移回归模型.Regression modelling is one of the most widely utilized approaches in epidemiological analyses.
Logistic回归模型是流行病学病因分析中最为普遍的方法。The average prediction of the optimal least squares regression model is equal to the average label on the training data.
最优的最小二乘回归模型的平均预测结果等于训练数据的平均标签。When you ask this question, what you really want to know is whether your regression model can meet your objectives?
当你问这个问题时,你真正想知道的是：回归模型是否能达到您的目标？模型是否能够满足你的要求?A random effects logistic regression model was used to test whether any pre-marketing characteristics were associated with either post-marketing safety action.
随机效应Logistic回归模型用于检验任何营销前特征与上市后安全行为是否相关。Adding independent variables to a multiple linear regression model will always increase the amount of explained variance in the dependent variable(typically expressed as R²).
将自变量添加到线性回归模型将总是增加模型的解释方差（通常表示为R2）。This violates one of the assumptions required for fitting a simple linear regression model.
这违反了拟合简单线性回归模型需满足的假设之一。Later, you tried a time series regression model and got higher accuracy than decision tree model. .
后来，你尝试了时间序列回归模型，并得到了比决策树模型更高的精度。As the basis of the combination forecasting model, the linear regression and Logistic regression model are established, respectively at first.
作为组合预测模型建立的基础，本文首先分别建立线性回归和Logistic回归模型。In that case these redundant X columns should be omitted from the regression model.
在这种情况下，这些多余的X列应该从回归模型中删除。These additional factors are known as the Fama-French factors, named after the professors who developed the multiple linear regression model to better explain asset returns.
这些额外的因素被称为Fama-French因素，以发展多元线性回归模型的教授命名，以更好地解释资产回报。
