remaining deviation

简明释义

剩余自差

英英释义

例句

1.To finalize the report, we need to address the remaining deviation in our calculations.

为了最终确定报告，我们需要解决计算中的剩余偏差。

2.The engineer calculated the remaining deviation to ensure the project stays within budget.

工程师计算了剩余偏差以确保项目在预算内进行。

3.The remaining deviation in the experiment was attributed to measurement errors.

实验中的剩余偏差归因于测量误差。

4.The team analyzed the remaining deviation from the expected results to improve future forecasts.

团队分析了与预期结果的剩余偏差以改善未来的预测。

5.After several adjustments, the remaining deviation was minimal, allowing for a smooth operation.

经过几次调整，剩余偏差非常小，从而保证了操作的顺利进行。

作文

In the realm of mathematics and statistics, precision is paramount. However, in practical applications, achieving absolute accuracy can be a daunting task. This is where the concept of remaining deviation comes into play. The term remaining deviation (剩余偏差) refers to the difference between the actual value and the expected value after accounting for known variances. It is crucial for understanding how much error remains in a given measurement or calculation. When conducting experiments or analyzing data, scientists and statisticians often start with a theoretical model that predicts certain outcomes. However, real-world conditions can lead to discrepancies between predicted and observed results. These discrepancies are not merely random; they can often be attributed to various factors such as measurement errors, environmental influences, or inherent variability in the system being studied. The remaining deviation (剩余偏差) serves as an indicator of these discrepancies. By calculating the remaining deviation, researchers can assess the reliability of their models and make necessary adjustments. For instance, if a scientist is measuring the growth rate of a plant species under controlled conditions, they may have a theoretical growth rate based on prior studies. However, if their measurements show a different growth rate, the difference can be quantified as the remaining deviation (剩余偏差). Understanding the remaining deviation (剩余偏差) is essential not only for improving accuracy but also for enhancing the credibility of scientific research. When results are published, other researchers often look for the remaining deviation to gauge the validity of the findings. A small remaining deviation (剩余偏差) suggests that the experimental design was robust and that the results can be reliably used for further research or application. Conversely, a large remaining deviation (剩余偏差) raises questions about the methodology and may warrant further investigation. Moreover, the concept of remaining deviation (剩余偏差) extends beyond the confines of science and mathematics. In fields like finance, engineering, and quality control, understanding the remaining deviation (剩余偏差) is vital for risk assessment and decision-making. For example, in financial forecasting, analysts use models to predict future market trends. The remaining deviation (剩余偏差) from these predictions can indicate potential risks and help investors make informed choices. In conclusion, the remaining deviation (剩余偏差) is a critical concept that bridges the gap between theoretical predictions and practical outcomes. Whether in scientific research, financial analysis, or engineering, recognizing and addressing the remaining deviation (剩余偏差) allows professionals to refine their approaches and enhance the accuracy of their work. As we continue to navigate complex systems and data, the importance of understanding remaining deviation (剩余偏差) cannot be overstated. It is a reminder that while striving for precision, we must also acknowledge and manage the uncertainties that inevitably arise in our pursuits.

在数学和统计学的领域，精确性至关重要。然而，在实际应用中，达到绝对准确性可能是一项艰巨的任务。这就是剩余偏差的概念发挥作用的地方。术语剩余偏差（remaining deviation）指的是在考虑已知变量后，实际值与预期值之间的差异。它对于理解给定测量或计算中还剩下多少误差至关重要。在进行实验或分析数据时，科学家和统计学家通常会从一个理论模型开始，该模型预测某些结果。然而，现实世界的条件可能导致预测结果与观察结果之间的差异。这些差异并不仅仅是随机的；它们通常可以归因于各种因素，例如测量误差、环境影响或被研究系统内在的变异性。剩余偏差作为这些差异的指示器至关重要。通过计算剩余偏差，研究人员可以评估其模型的可靠性并进行必要的调整。例如，如果一位科学家在受控条件下测量一种植物物种的生长率，他们可能有基于先前研究的理论生长率。然而，如果他们的测量显示出不同的生长率，这一差异可以量化为剩余偏差。理解剩余偏差不仅对提高准确性至关重要，而且对增强科学研究的可信度也同样重要。当结果被公布时，其他研究人员通常会查看剩余偏差以评估发现的有效性。较小的剩余偏差表明实验设计稳健，结果可以可靠地用于进一步的研究或应用。相反，较大的剩余偏差则引发对方法论的质疑，可能需要进一步调查。此外，剩余偏差的概念超越了科学和数学的范畴。在金融、工程和质量控制等领域，理解剩余偏差对于风险评估和决策至关重要。例如，在金融预测中，分析师使用模型来预测未来市场趋势。这些预测的剩余偏差可能表明潜在风险，并帮助投资者做出明智的选择。总之，剩余偏差是一个关键概念，连接了理论预测和实际结果之间的差距。无论是在科学研究、金融分析还是工程领域，识别和解决剩余偏差都使专业人士能够完善他们的方法，提升工作的准确性。随着我们继续在复杂系统和数据中导航，理解剩余偏差的重要性不容小觑。这提醒我们，在追求精确性的同时，我们必须承认并管理不可避免地出现在我们追求中的不确定性。