negative residual

简明释义

负余差;

英英释义

例句

1.The negative residual 负残差 in the financial forecast pointed to an underestimation of expenses.

财务预测中的负残差 负残差 指向了对费用的低估。

2.In quality control, a negative residual 负残差 may indicate that a product is performing better than expected.

在质量控制中，负残差 负残差 可能表明产品的表现超出了预期。

3.After adjusting the parameters, the model showed fewer negative residuals 负残差, indicating improved accuracy.

调整参数后，模型显示出更少的负残差 负残差，这表明准确性有所提高。

4.When evaluating the model's performance, we found several instances of negative residuals 负残差 that suggested it was overfitting the data.

在评估模型性能时，我们发现多个负残差 负残差 的实例，这表明模型对数据进行了过拟合。

5.In a regression analysis, the presence of a negative residual 负残差 indicates that the predicted value is greater than the actual value.

在回归分析中，存在一个负残差 负残差 表示预测值大于实际值。

作文

In the realm of statistics and data analysis, the concept of residuals plays a crucial role in understanding the fit of a model. A residual is defined as the difference between the observed value and the predicted value provided by a statistical model. When we talk about a negative residual, we refer to instances where the observed value is less than the predicted value. This phenomenon can provide insightful information about the performance of a model and its accuracy in making predictions.To illustrate this concept, let’s consider a simple linear regression model that predicts the prices of houses based on their sizes. Suppose we have a dataset containing various houses with their corresponding sizes and prices. After fitting a linear regression model to this dataset, we generate predictions for the prices of these houses. However, not all predictions will be accurate. Some predicted prices may be higher than the actual selling prices, resulting in negative residuals.For example, if a house with a size of 1,500 square feet is predicted to sell for $300,000, but it actually sells for $280,000, we can calculate the residual as follows:Residual = Actual Price - Predicted Price = $280,000 - $300,000 = -$20,000.In this case, the residual is negative, indicating that the model overestimated the price of this particular house. Analyzing these negative residuals can help us identify patterns or trends in the data that the model may not have captured effectively.Understanding the implications of negative residuals is essential for improving our predictive models. When a model consistently produces negative residuals for certain types of data, it suggests that the model may be biased or that the underlying assumptions of the model do not hold true for those observations. For instance, if larger houses tend to have negative residuals, it could indicate that the model does not account for certain features that influence the pricing of larger homes, such as location or amenities.Moreover, negative residuals can also serve as a diagnostic tool for model evaluation. By plotting the residuals against the predicted values, analysts can visually assess whether there are patterns or systematic errors in the predictions. If the residuals appear randomly scattered around zero, it suggests that the model is performing well. However, if there is a noticeable pattern, especially with clusters of negative residuals, it signals that further refinement of the model is necessary.In conclusion, negative residuals are an important aspect of statistical modeling that provides valuable insights into the accuracy and reliability of predictions. By analyzing these residuals, we can identify potential weaknesses in our models and make informed decisions on how to improve them. As data analysts or statisticians, recognizing the significance of negative residuals enables us to enhance our understanding of complex datasets and ultimately leads to better predictive performance in our models.

在统计和数据分析领域，残差的概念在理解模型的拟合度方面发挥着至关重要的作用。残差被定义为观察值与统计模型提供的预测值之间的差异。当我们谈论负残差时，我们指的是观察值小于预测值的情况。这种现象可以提供有关模型性能及其预测准确性的重要信息。为了说明这一概念，让我们考虑一个简单的线性回归模型，该模型根据房屋的大小预测价格。假设我们有一个包含各种房屋及其对应大小和价格的数据集。在这个数据集上拟合线性回归模型后，我们生成这些房屋的价格预测。然而，并非所有的预测都是准确的。一些预测价格可能高于实际销售价格，从而导致负残差。例如，如果一栋1500平方英尺的房子被预测以30万美元的价格出售，但实际以28万美元出售，我们可以如下计算残差：残差 = 实际价格 - 预测价格 = 280,000美元 - 300,000美元 = -20,000美元。在这种情况下，残差为负，表明模型高估了这栋特定房子的价格。分析这些负残差可以帮助我们识别模型可能未有效捕捉到的数据中的模式或趋势。理解负残差的含义对于改善我们的预测模型至关重要。当模型对某些类型的数据持续产生负残差时，这表明模型可能存在偏差，或者模型的基本假设不适用于这些观测值。例如，如果较大的房屋往往具有负残差，这可能表明该模型未考虑影响较大房屋定价的某些特征，例如位置或设施。此外，负残差还可以作为模型评估的诊断工具。通过将残差绘制与预测值进行比较，分析师可以直观地评估预测中是否存在模式或系统误差。如果残差随机分布在零周围，这表明模型表现良好。然而，如果出现明显的模式，特别是聚集的负残差，则表明需要进一步完善模型。总之，负残差是统计建模的重要方面，提供了有关预测准确性和可靠性的宝贵见解。通过分析这些残差，我们可以识别模型中的潜在弱点，并就如何改进它们做出明智的决策。作为数据分析师或统计学家，认识到负残差的重要性使我们能够增强对复杂数据集的理解，并最终提高我们模型的预测性能。