approximate analysis

简明释义

近似分析

英英释义

例句

1.Before the detailed calculations, we relied on approximate analysis for our initial estimates.

在详细计算之前，我们依赖于近似分析来进行初步估算。

2.In order to save time, we performed an approximate analysis of the market trends.

为了节省时间，我们对市场趋势进行了近似分析。

3.The engineer conducted an approximate analysis to estimate the load-bearing capacity of the bridge.

工程师进行了近似分析以估算桥梁的承载能力。

4.The financial team provided an approximate analysis of the company's expenses for the last quarter.

财务团队提供了公司上个季度支出的近似分析。

5.The scientist used approximate analysis to predict the outcomes of the experiment.

科学家使用近似分析来预测实验的结果。

作文

In the field of research and data interpretation, the term approximate analysis refers to a method of evaluating data or phenomena without requiring exact measurements or calculations. This approach is particularly useful in situations where precise data may be difficult to obtain or unnecessary for the purposes of understanding a broader trend or pattern. For instance, in the social sciences, researchers often rely on approximate analysis to gauge public opinion through surveys that may not capture every individual's viewpoint but can still provide valuable insights into general trends.One significant advantage of using approximate analysis is its efficiency. In many cases, gathering exact data can be time-consuming and resource-intensive. By employing approximate methods, researchers can quickly arrive at conclusions that are 'good enough' for the purpose of their study. This is especially relevant in fields such as economics, where models often rely on simplified assumptions to predict market behavior. For example, economists might use approximate analysis to estimate the impact of a policy change on employment rates without needing to account for every variable involved.Moreover, approximate analysis allows for flexibility when dealing with complex systems. In natural sciences, researchers may face scenarios where numerous factors interact in unpredictable ways. Instead of attempting to model every interaction precisely, scientists can use approximate methods to identify dominant trends and relationships. This can lead to more generalizable findings that still hold value even if they do not capture every nuance of the system being studied.However, it is crucial to recognize the limitations of approximate analysis. While this method can yield useful insights, the conclusions drawn from approximate data should always be interpreted with caution. There is a risk of oversimplification, which can lead to misleading results if the approximations made are too far removed from reality. Therefore, researchers must be transparent about the methods used and the potential implications of relying on approximate rather than exact data.In conclusion, approximate analysis serves as a vital tool in various fields, allowing researchers to make informed decisions and draw meaningful conclusions without the need for exhaustive data collection. It strikes a balance between practicality and accuracy, enabling studies to progress even when exact figures are unattainable. Nevertheless, careful consideration must be given to the limitations inherent in this approach, ensuring that the findings remain credible and applicable to real-world situations. As we continue to navigate an increasingly complex world, the role of approximate analysis will undoubtedly grow, offering a pathway toward understanding amidst uncertainty.

在研究和数据解释领域，术语approximate analysis指的是一种评估数据或现象的方法，该方法不需要精确的测量或计算。这种方法在获取精确数据可能困难或不必要以理解更广泛的趋势或模式的情况下特别有用。例如，在社会科学中，研究人员通常依赖于approximate analysis通过调查来评估公众舆论，这些调查可能无法捕捉每个人的观点，但仍然可以提供有关一般趋势的宝贵见解。使用approximate analysis的一个显著优势是其效率。在许多情况下，收集准确的数据可能耗时且资源密集。通过采用近似方法，研究人员可以快速得出“足够好的”结论，以满足他们研究的目的。这在经济学等领域尤为相关，因为模型通常依赖于简化的假设来预测市场行为。例如，经济学家可能会使用approximate analysis来估计政策变化对就业率的影响，而无需考虑涉及的每个变量。此外，approximate analysis在处理复杂系统时允许灵活性。在自然科学中，研究人员可能面临多个因素以不可预测的方式相互作用的场景。科学家可以使用近似方法来识别主导趋势和关系，而不是试图精确建模每个相互作用。这可以导致更具普遍性的发现，即使它们没有捕捉到被研究系统的每一个细微差别，仍然具有价值。然而，至关重要的是要认识到approximate analysis的局限性。虽然这种方法可以产生有用的见解，但从近似数据得出的结论应始终谨慎解读。过度简化的风险可能导致误导性结果，尤其是在所作的近似与现实相距甚远时。因此，研究人员必须对所使用的方法及依赖近似而非精确数据的潜在影响保持透明。总之，approximate analysis作为各种领域的重要工具，使研究人员能够做出明智的决策，并在不需要详尽的数据收集的情况下得出有意义的结论。它在实用性和准确性之间取得了平衡，使得即使在无法获得准确数字的情况下，研究也能得以推进。然而，必须仔细考虑这种方法固有的局限性，确保研究结果保持可信，并适用于现实世界的情况。随着我们继续在日益复杂的世界中航行，approximate analysis的作用无疑将增长，为在不确定性中理解提供了一条途径。