extrapolating

简明释义

英[ɪkˈstræpəleɪtɪŋ]美[ɪkˈstræpəleɪtɪŋ]

外推

进行推断（extrapolate 的现在分词形式）

英英释义

单词用法

同义词

反义词

例句

1.Studies on the effects on animals can be more controlled, but there is always a danger in extrapolating the effects on animals to those on man.

在动物上的影响的研究可以更受控制，但根据对动物的影响去推断对人的影响通常都会有危险。

2.I spent hours measuring, comparing, calculating ratios and extrapolating data.

我花了时间，测量，比较，计算比率和外推的数据。

3.She is extrapolating and judging men just as harshly as men judge women.

她是推算和判断男人一样苛刻作为男女法官。

4.The usual caveat about extrapolating from one month of data obviously applies when dealing with erratic Chinese statistics.

对于难以预料的中国统计数据，根据某个月的数据进行推断的缺点明显可见。

5.Extrapolating across the country, 2.3 million cardiovascular deaths in 2005 were related to raised blood pressure, they said.

报告称，从全国推算，2005年有230万死于心血管疾病的人是和高血压相关的。

6.Extrapolating wave field is most important in the whole process.

在整个计算过程中，波场外推是最为重要的环节。

7.The main use of BIM has been to create documents more efficiently, just extrapolating what has been done with CAD for twenty years.

BIM的主要作用是可以更高效地创建文档，这只是从过去二十年CAD的作用就可以推断出来。

8.The scientist is extrapolating 推断 the results of the experiment to predict future outcomes.

科学家正在推断实验的结果以预测未来的结果。

9.By extrapolating 推断 data from previous years, we can estimate this year's sales.

通过推断往年数据，我们可以估算今年的销售额。

10.The analyst is extrapolating 推断 trends from the current market data.

分析师正在推断当前市场数据中的趋势。

11.They are extrapolating 推断 the impact of climate change on future weather patterns.

他们正在推断气候变化对未来天气模式的影响。

12.Using past performance, the coach is extrapolating 推断 the team's potential for the upcoming season.

教练正在利用过去的表现推断球队在即将到来的赛季中的潜力。

作文

In the realm of scientific research and data analysis, the term extrapolating is often used to describe the process of estimating unknown values based on known data. This technique allows researchers to make predictions about future events or trends by extending the patterns observed in their existing data. For instance, when scientists collect temperature data over several years, they may use extrapolating to predict future temperature changes based on past trends. By examining the historical data, they can identify a pattern and extend that pattern into the future, thereby making informed predictions. The importance of extrapolating cannot be overstated. It serves as a critical tool in various fields, including economics, meteorology, and healthcare. For example, economists often rely on extrapolating to forecast economic growth or recession by analyzing historical financial data. Similarly, meteorologists use this method to predict weather patterns, such as the likelihood of rain based on previous weather conditions. However, while extrapolating can provide valuable insights, it also comes with risks. One significant limitation is that it assumes that the future will behave similarly to the past. If there are sudden changes in external factors—such as a natural disaster, a pandemic, or a shift in government policy—the predictions made through extrapolating may become inaccurate. Therefore, it is crucial for researchers and analysts to approach their predictions with caution and to constantly update their models with new data. In addition to its applications in research, extrapolating is also a useful skill in everyday decision-making. For example, when planning for retirement, individuals might look at their current savings and investment growth rates to estimate how much money they will have in the future. By extrapolating from their current financial situation, they can make informed decisions about how much to save and invest now to achieve their long-term goals. Moreover, extrapolating is not limited to quantitative data. It can also be applied to qualitative insights. For instance, if a company receives positive feedback from customers about a new product, they might extrapolate that success to predict future sales trends, informing their marketing strategies and production plans. In conclusion, extrapolating is a powerful analytical tool that enables us to make predictions based on existing data. While it holds great potential for providing insights across various domains, it is essential to recognize its limitations and the need for continuous data updates. Whether in scientific research, economic forecasting, or personal finance, the ability to effectively extrapolating can lead to better decision-making and more accurate predictions. As we continue to gather and analyze data in our increasingly complex world, mastering the art of extrapolating will undoubtedly remain a valuable skill.

在科学研究和数据分析领域，“extrapolating”一词常用于描述根据已知数据估算未知值的过程。这种技术使研究人员能够通过扩展现有数据中观察到的模式来预测未来事件或趋势。例如，当科学家收集多年的温度数据时，他们可能会使用extrapolating来根据过去的趋势预测未来的温度变化。通过检查历史数据，他们可以识别出一个模式，并将该模式延伸到未来，从而做出明智的预测。 extrapolating的重要性不容小觑。它作为一个关键工具在经济学、气象学和医疗保健等多个领域中发挥着作用。例如，经济学家通常依赖于extrapolating来通过分析历史金融数据来预测经济增长或衰退。同样，气象学家利用这种方法预测天气模式，例如基于先前天气条件的降雨可能性。 然而，尽管extrapolating可以提供有价值的见解，但它也存在风险。一个显著的限制是，它假设未来将与过去表现相似。如果外部因素发生突变——例如自然灾害、流行病或政府政策的变化——通过extrapolating所做的预测可能会变得不准确。因此，研究人员和分析师必须谨慎对待他们的预测，并不断用新数据更新他们的模型。 除了在研究中的应用，extrapolating在日常决策中也是一项有用的技能。例如，在计划退休时，个人可能会查看他们当前的储蓄和投资增长率，以估算未来将拥有多少钱。通过从他们当前的财务状况中extrapolating，他们可以做出明智的决定，确定现在需要存多少钱以及投资多少钱以实现长期目标。 此外，extrapolating不仅限于定量数据。它也可以应用于定性见解。例如，如果一家公司收到客户对新产品的积极反馈，他们可能会extrapolate这种成功来预测未来的销售趋势，从而为他们的营销策略和生产计划提供信息。 总之，extrapolating是一个强大的分析工具，使我们能够根据现有数据做出预测。虽然它在各个领域提供见解的潜力很大，但认识到其局限性和持续数据更新的必要性同样重要。无论是在科学研究、经济预测还是个人理财中，有效extrapolating的能力都能导致更好的决策和更准确的预测。随着我们在日益复杂的世界中继续收集和分析数据，掌握extrapolating的艺术无疑将是一项宝贵的技能。