residuals
简明释义
n. [数]残差（residual 的复数）；剩余误差；残留物
英英释义
单词用法
剩余应力 | |
渣油，残油 |
同义词
反义词
| 总数 | 销售的总额超出了我们的预期。 | ||
| 完整 | 在做决定之前，我们需要一份完整的报告。 |
例句
1.The residuals of these observers are applied to failure detection for subsystems.
这些观测器的残差被用于子系统的故障检测。
2.They demanded residual payments for future telecasts and retroactive residuals for films shown on TV between 1948 and 1959.
他们要求未来电影在电视播放时，要得到剩余工资，并且要求拿回1948年到1959年电影在电视屏幕上放映时所应得的剩余工资。
3.Residuals are room-temperature solids made up of molecules with 30 or more carbons.
常温下为固体的残余物是由30或更多的碳组成。
4.Universal Pictures agreed, in principle, to the concept of film residuals.
环球营业原则上同意了电影剩余工资的概念。
5.Actor residuals for all studio films made starting in 1960.
从1960年之后所制作的电影就应该有演员的剩余工资。
6.In this paper, we present a nonparametric approach for checking the residuals of time series models.
提出一种时间序列模型残差诊断捡验的非参数方法。
7.No residuals for any studio films produced before 1948.
1948年之前的电影不计算剩余工资。
8.When you are done, you just remove the files without worrying about an uninstall program leaving residuals in your operating system.
完成使用后，您只需删除文件，而不用担心卸载程序在操作系统中留下残余内容。
9.What is the clue to the operation of our neurotic residuals?
我们的神经症残留的操作线索是什么呢？
10.The residuals from the experiment were used to improve future predictions.
实验中的残差被用来改进未来的预测。
11.The residuals showed a consistent trend that was not accounted for in the model.
这些残差显示出一种在模型中未考虑到的一致趋势。
12.We need to check the residuals for any patterns that might indicate a problem.
我们需要检查残差是否有任何模式，这可能表明存在问题。
13.In statistics, residuals are the differences between observed and predicted values.
在统计学中，残差是观察值与预测值之间的差异。
14.The engineer analyzed the residuals to determine the accuracy of the model.
工程师分析了残差以确定模型的准确性。
作文
In the field of statistics and data analysis, understanding the concept of residuals is crucial for interpreting the results of various models. A residual is defined as the difference between the observed value and the predicted value provided by a regression model. This means that when we fit a line or curve to a set of data points, the residuals represent how far off our predictions are from the actual data. The smaller the residuals, the better our model is at predicting outcomes based on the input data.For instance, consider a simple linear regression where we try to predict the price of houses based on their square footage. After fitting our model, we calculate the predicted prices for each house. The differences between these predicted prices and the actual sale prices are the residuals. If a house sold for $300,000 but our model predicted it would sell for $280,000, the residual for that house would be $20,000. Analyzing these residuals helps us to understand whether our model is performing well or if there are patterns that we need to address.Furthermore, examining the residuals can reveal important insights about the data itself. If the residuals show a random pattern when plotted, it suggests that our model has captured the underlying structure of the data effectively. However, if we notice systematic patterns in the residuals, such as a funnel shape or a trend, it indicates that our model is missing some key information or that the relationship between the variables is more complex than we initially assumed.In addition to their role in regression analysis, residuals are also significant in other areas such as quality control and experimental design. In quality control processes, for example, residuals can help identify variations that may indicate problems in production. By monitoring these residuals, companies can make adjustments to improve their processes and ensure consistent quality.Moreover, in experimental design, researchers often analyze residuals to determine if their experimental conditions are producing valid results. If the residuals indicate that there are significant deviations from what was expected, it may prompt a re-evaluation of the experimental setup or the assumptions made during the analysis.Understanding and analyzing residuals is not only important for statisticians but also for anyone who relies on data-driven decision-making. In business, for instance, managers can use insights from residuals to adjust their strategies based on accurate assessments of performance metrics. In healthcare, analyzing residuals from predictive models can improve patient outcomes by refining treatments based on individual responses.In conclusion, residuals play a vital role in the interpretation of statistical models and the validation of predictions. By carefully examining residuals, analysts can gain deeper insights into their data, leading to better decision-making and improved outcomes across various fields. Thus, mastering the concept of residuals is essential for anyone involved in data analysis, ensuring that they can effectively evaluate the performance of their models and make informed decisions based on their findings.
在统计和数据分析领域，理解残差的概念对于解释各种模型的结果至关重要。残差被定义为观察值与回归模型所提供的预测值之间的差异。这意味着，当我们将一条线或曲线拟合到一组数据点时，残差表示我们的预测与实际数据之间的偏差。残差越小，我们的模型在根据输入数据预测结果的能力就越好。例如，考虑一个简单的线性回归，我们试图根据房屋的平方英尺来预测房价。在拟合我们的模型后，我们计算每栋房屋的预测价格。这些预测价格与实际销售价格之间的差异就是残差。如果一栋房屋以300,000美元出售，但我们的模型预测它会以280,000美元出售，那么该房屋的残差就是20,000美元。分析这些残差有助于我们理解我们的模型是否表现良好，或者是否有需要解决的模式。此外，检查残差可以揭示有关数据本身的重要见解。如果残差在绘图时显示随机模式，这表明我们的模型有效地捕捉到了数据的基本结构。然而，如果我们注意到残差中存在系统性模式，例如漏斗形状或趋势，这表明我们的模型缺少一些关键信息，或者变量之间的关系比我们最初假设的要复杂得多。除了在回归分析中的作用外，残差在质量控制和实验设计等其他领域也具有重要意义。例如，在质量控制过程中，残差可以帮助识别可能表明生产问题的变异。通过监测这些残差，公司可以进行调整，以改善其流程并确保一致的质量。此外，在实验设计中，研究人员通常分析残差以确定他们的实验条件是否产生有效结果。如果残差表明与预期有显著偏差，可能会促使重新评估实验设置或在分析过程中做出的假设。理解和分析残差不仅对统计学家重要，而且对任何依赖数据驱动决策的人来说都是如此。在商业中，例如，管理者可以使用残差的见解根据对绩效指标的准确评估来调整他们的策略。在医疗保健中，分析来自预测模型的残差可以通过根据个体反应细化治疗来改善患者结果。总之，残差在统计模型的解释和预测验证中发挥着至关重要的作用。通过仔细检查残差，分析师可以深入了解他们的数据，从而在各个领域做出更好的决策和改善结果。因此，掌握残差的概念对于任何参与数据分析的人来说都是必不可少的，确保他们能够有效评估模型的性能，并根据研究结果做出明智的决策。
