extrapolated

简明释义

英[ɪkˈstræpəleɪtɪd]美[ɪkˈstræpəleɪtɪd]

adj. 推测的

v. 推测（extrapolate 的过去分词）

英英释义

单词用法

同义词

反义词

例句

1.The physical properties of the Hot fluid have been extrapolated beyond the valid temperature range.

热流体的物理参数已经外推超过有效温度范围。

2.The physical properties of the Hot fluid have been extrapolated beyond the valid temperature range.

热流体的物理参数已经外推超过有效温度范围。

3.In the history of science we sometimes extrapolated and turned out to be absolutely wrong.

科学史上我们有时的推断被证实是彻底错误的。

4.He also pointed out that this model could possibly be extrapolated to other cancers.

他也指出这一模型可能外推到其他癌症。

5.The fourth point is extrapolated from the first three to form a parallelogram.

可从前三个点推断出第四个点以形成一个平行四边形。

6.We have extrapolated these results from research done in other countries.

我们从其他国家所作的研究中推断出这些结果。

7.The scientist extrapolated the results from the small sample size to predict the outcome for the entire population.

科学家推断了小样本的结果，以预测整个群体的结果。

8.Using historical data, the analyst extrapolated future sales trends for the company.

分析师利用历史数据推断了公司的未来销售趋势。

9.From the current climate models, researchers extrapolated potential impacts of global warming over the next century.

研究人员从当前的气候模型中推断了未来一个世纪全球变暖的潜在影响。

10.The engineer extrapolated the performance of the prototype based on initial testing results.

工程师根据初步测试结果推断了原型的性能。

11.In her thesis, she extrapolated the implications of her findings for future research.

在她的论文中，她推断了她的发现对未来研究的影响。

作文

In the realm of scientific research and data analysis, the term extrapolated refers to the process of estimating or predicting values beyond the range of observed data. This technique is particularly valuable when researchers aim to make informed predictions about future trends or behaviors based on existing information. For instance, in climate science, scientists often use historical temperature data to project future climate conditions. By analyzing past patterns, they can identify trends and extrapolated future scenarios that help policymakers understand potential impacts on ecosystems and human societies. One of the most significant applications of extrapolated data is in economics. Economists frequently analyze current and past economic indicators, such as GDP growth rates, unemployment rates, and inflation, to forecast future economic performance. By extrapolated these trends, they can provide insights into how the economy might behave under various circumstances. For example, if unemployment rates have been steadily declining over several years, economists may extrapolated this trend to predict continued improvement in job markets, which could influence government policies and business investments. However, it is essential to approach extrapolated data with caution. While it can provide valuable insights, there are inherent risks involved in making predictions based solely on past data. The assumption that future trends will mirror historical patterns can lead to inaccuracies, especially in rapidly changing environments. For instance, during the COVID-19 pandemic, many traditional economic models that relied on historical data became less reliable as unprecedented factors came into play. Therefore, while extrapolated data can guide decision-making, it should be supplemented with qualitative analysis and real-time data to improve accuracy. Moreover, the context in which data is extrapolated also plays a crucial role. In fields like medicine, researchers must consider various external factors that can influence outcomes. For example, when studying the effectiveness of a new drug, clinical trial results may be extrapolated to a broader population. However, differences in demographics, health conditions, and lifestyle choices must be accounted for to ensure that the predictions are valid across diverse groups. In conclusion, the concept of extrapolated data is a powerful tool in various fields, enabling researchers and analysts to make predictions and inform decisions. Whether in climate science, economics, or medicine, extrapolated data provides a framework for understanding potential future scenarios based on historical trends. However, it is crucial to recognize the limitations and uncertainties associated with this technique. By combining extrapolated data with other analytical methods and contextual considerations, we can enhance our ability to navigate complex issues and make more informed choices for the future.

在科学研究和数据分析领域，术语extrapolated指的是估计或预测超出观察数据范围的值的过程。这种技术在研究人员希望根据现有信息对未来趋势或行为进行明智预测时特别有价值。例如，在气候科学中，科学家们经常使用历史温度数据来预测未来的气候条件。通过分析过去的模式，他们可以识别趋势并extrapolated未来情景，这有助于决策者了解对生态系统和人类社会的潜在影响。 extrapolated数据的一个重要应用是在经济学中。经济学家经常分析当前和过去的经济指标，如GDP增长率、失业率和通货膨胀，以预测未来的经济表现。通过extrapolated这些趋势，他们可以提供对经济在各种情况下可能表现的见解。例如，如果失业率在数年内稳步下降，经济学家可能会extrapolated这一趋势，以预测就业市场的持续改善，这可能会影响政府政策和商业投资。 然而，必须谨慎对待extrapolated数据。虽然它可以提供有价值的见解，但仅仅依赖过去数据进行预测固有风险。假设未来趋势将与历史模式相似可能导致不准确，特别是在快速变化的环境中。例如，在COVID-19大流行期间，许多依赖历史数据的传统经济模型变得不那么可靠，因为前所未有的因素开始发挥作用。因此，虽然extrapolated数据可以指导决策，但应结合定性分析和实时数据以提高准确性。 此外，数据被extrapolated的上下文也起着至关重要的作用。在医学等领域，研究人员必须考虑可能影响结果的各种外部因素。例如，在研究一种新药的有效性时，临床试验结果可能会被extrapolated到更广泛的人群。然而，必须考虑人口统计、健康状况和生活方式选择的差异，以确保预测在不同群体中有效。 总之，extrapolated数据的概念是各个领域中的一个强大工具，使研究人员和分析师能够进行预测并为决策提供信息。无论是在气候科学、经济学还是医学中，extrapolated数据为基于历史趋势理解潜在未来情景提供了框架。然而，认识到与这种技术相关的局限性和不确定性至关重要。通过将extrapolated数据与其他分析方法和上下文考虑相结合，我们可以增强应对复杂问题的能力，并为未来做出更明智的选择。