首页 \ 问答 \ 如何在点击li标签内的锚标签时激活特定的Li标签？(How to make particular Li tag active on click on anchor tag inside of that li tag?)

如何在点击li标签内的锚标签时激活特定的Li标签？(How to make particular Li tag active on click on anchor tag inside of that li tag?)

 我想在点击li标签内的特定锚标签时激活我的Li标签。  
 这是我的代码：  
<ul class="list-unstyled detail-sidemenu mrb15">
    <li>
        <a onclick="Display1()" href="#"><span class="glyphicon glyphicon-link"></span> ABC</a>
    </li>
    <li>
        <a href="#" onclick="Display2()"><span class="glyphicon glyphicon-link"></span>PQR</a>
    </li>
</ul>

.detail-sidemenu li a:hover {
    background-color: #ed1b23;
    color: #ffffff;
}
 
 现在我想在Li标签上应用这个悬停类css：  
 function Display1()
    {
        //active ABC
    }

 function Display2()
    {
         //Active PQR and remove active from ABC
    }

I want to make my Li tag active when particular anchor tag inside li tag is clicked. 
This is my Code: 
<ul class="list-unstyled detail-sidemenu mrb15">
    <li>
        <a onclick="Display1()" href="#"><span class="glyphicon glyphicon-link"></span> ABC</a>
    </li>
    <li>
        <a href="#" onclick="Display2()"><span class="glyphicon glyphicon-link"></span>PQR</a>
    </li>
</ul>

.detail-sidemenu li a:hover {
    background-color: #ed1b23;
    color: #ffffff;
}
 
Now i want to apply this hover class css on Li tag: 
 function Display1()
    {
        //active ABC
    }

 function Display2()
    {
         //Active PQR and remove active from ABC
    }

原文：https://stackoverflow.com/questions/33074661

更新时间：2022-04-28 22:04

最满意答案

 在大多数情况下你几乎都是正确的。 为了确保我们用相同的术语，一点点背景说话：  
 线性回归使用某些结果变量y和自变量x1, x2, ..并尝试找到最佳预测y的x1, x2, ..的线性组合。 一旦建立了这种“最佳线性组合”，您就可以通过多种方式评估拟合的质量（即模型的质量）。 您提到的六点都是回归方程质量的关键指标。  
 运行回归可以为您提供多种“成分”。 例如，每次观察都会得到结果变量的预测值 。 y的观测值与预测值之间的差异称为残差或误差。 残差可以是负数（如果y被高估）和正数（如果y被低估）。 残差越接近零越好。 但是，什么是“接近”？ 您提供的指标应该提供一个洞察力。  
 
  平均绝对误差 ：取残差的绝对值并取其平均值。  
  均方根误差 ：是残差的标准差。 这将有助于您了解残差的差价有多大。 残差是平方的，因此，高残差将计入超过小残差。 低RMSE是好的。  
  相对绝对误差 ：绝对误差作为结果变量y的实际值的一部分。 在您的情况下，预测平均比y的实际值高75％/更低。  
  相对平方误差 ：平方误差（ residual^2 ）作为实际值的一部分。  
  测定系数 ：几乎正确。 其范围在0和1之间，可以解释为解释y自变量的解释力。 实际上，在您的情况下，自变量可以模拟y 38,15％的变化。 此外，如果您只有一个自变量，则此系数等于平方相关系数。  
 
 均方根误差和确定系数是几乎所有情况下最重要的指标。 说实话，我从未真正看到其他指标被报道。 

You are almost correct on most points. To make sure we are talking in the same terms, a little bit of background: 
A linear regression uses data on some outcome variable y and independent variables x1, x2, .. and tries to find the linear combination of x1, x2, .. that best predicts y. Once this "best linear combination" is established, you can assess the quality of the fit (i.e. quality of the model) in multiple ways. The six points you mention are all key metrics for the quality of a regression equation.  
Running a regression gives you multiple "ingredients". For example, every observation will get a predicted value for the outcome variable. The difference between the observed value of y and the predicted value is called the residual or error. Residuals can be negative (if the y is overestimated) and positive (if y is underestimated). The closer the residuals are to zero, the better. But, what is "close"? The metrics you present are supposed to give an insight in this. 
 
 Mean absolute error: takes the absolute value of the residuals and takes the mean of that.  
 Root Mean Square Error: is the standard deviation of your residuals. This will help you see, how large the spread is of your residuals. The residuals are squared and therefore, high residuals will count in more than small residuals. A low RMSE is good.  
 Relative Absolute Error: The absolute error as a fraction of the real value of the outcome variable y. In your case, the predictions are on average 75% higher/lower than the actual value of y. 
 Relative Squared Error: The squared error (residual^2) as a fraction of the real value.  
 Coefficient of Determination: Almost correct. This ranges between 0 and 1 and can be interpreted as the explanatory power of the independent variables in explaining y. In fact, in your case the independent variables can model 38,15% of the variation in y. Also, if you have only one independent variable, this coefficient is equal to the squared correlation coefficient.  
 
Root Mean Squared Error and Coefficient of Determination are the most important metrics in nearly all situations. To be honest, I've never really seen the other metrics being reported.

如何在点击li标签内的锚标签时激活特定的Li标签？(How to make particular Li tag active on click on anchor tag inside of that li tag?)

最满意答案

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