numerical estimate

简明释义

数值估计值

英英释义

例句

1.A numerical estimate of the time required to complete the task was given as three hours.

完成该任务所需时间的数值估算为三小时。

2.During the meeting, we discussed a numerical estimate of the sales for the next quarter.

在会议上，我们讨论了下个季度的销售额的数值估算。

3.The engineer provided a numerical estimate for the project's cost, which was around $500,000.

工程师提供了一个数值估算，项目成本大约为50万美元。

4.In the report, we included a numerical estimate of the population growth over the next decade.

在报告中，我们包含了对未来十年人口增长的数值估算。

5.The scientist made a numerical estimate of the sample size needed for the experiment.

科学家对实验所需的样本大小进行了数值估算。

作文

In today's data-driven world, the ability to make informed decisions is more important than ever. Businesses, researchers, and policymakers rely heavily on various forms of data analysis to guide their actions. One crucial aspect of this process is the concept of a numerical estimate, which refers to an approximation of a value based on calculations or statistical methods. Understanding how to create and interpret these estimates can significantly enhance decision-making processes across various fields.To begin with, a numerical estimate is often derived from a sample of data rather than the entire population. For instance, in market research, companies may survey a small group of consumers to infer trends about the larger market. By applying statistical techniques, they can produce a numerical estimate of consumer preferences, which helps them tailor their products effectively. This method of estimation allows organizations to save time and resources while still obtaining valuable insights.Moreover, the accuracy of a numerical estimate can vary depending on several factors, including sample size and methodology. A larger sample size typically leads to more reliable estimates, as it reduces the margin of error. For example, if a political poll aims to predict election outcomes, a sample of 1,000 voters may yield a more accurate numerical estimate than a sample of just 100 voters. Therefore, understanding the principles behind sampling and estimation is essential for anyone involved in data analysis.In addition to its application in business and politics, the concept of a numerical estimate extends to scientific research as well. Researchers often need to estimate parameters such as population sizes, average measurements, or even probabilities of certain events occurring. For example, ecologists may use numerical estimates to gauge the population of endangered species based on limited field observations. These estimates are crucial for developing conservation strategies and ensuring that resources are allocated effectively.However, it is also important to recognize the limitations of numerical estimates. While they provide valuable information, they are not infallible. Estimates can be influenced by biases in data collection or flawed methodologies. Therefore, it is vital for analysts to communicate the uncertainty associated with their numerical estimates. This transparency helps stakeholders understand the potential risks involved in decision-making based on these approximations.Furthermore, technological advancements have enhanced our ability to generate numerical estimates with greater precision. With the rise of big data and sophisticated analytical tools, organizations can now process vast amounts of information to produce more accurate estimates. Machine learning algorithms, for instance, can identify patterns in data that human analysts might overlook, leading to improved numerical estimates in various domains, from finance to healthcare.In conclusion, the concept of a numerical estimate plays a vital role in modern decision-making across numerous fields. By providing approximations based on data analysis, these estimates enable businesses, researchers, and policymakers to make informed choices. However, it is crucial to understand both the power and the limitations of numerical estimates. As we continue to navigate an increasingly complex world, mastering the art of estimation will remain an essential skill for effective problem-solving and strategic planning.

在当今数据驱动的世界中，做出明智决策的能力比以往任何时候都更加重要。企业、研究人员和决策者在很大程度上依赖各种形式的数据分析来指导他们的行动。这一过程中的一个关键方面是数值估计的概念，它指的是基于计算或统计方法对某个值的近似理解。理解如何创建和解释这些估计可以显著增强各个领域的决策过程。首先，数值估计通常是从样本数据中得出的，而不是整个总体。例如，在市场调查中，公司可能会对一小部分消费者进行调查，以推断出更大市场的趋势。通过应用统计技术，他们可以生成消费者偏好的数值估计，这有助于他们有效地调整产品。这种估计方法使组织能够节省时间和资源，同时仍然获得有价值的见解。此外，数值估计的准确性可能会因多个因素而异，包括样本大小和方法。较大的样本量通常会导致更可靠的估计，因为它减少了误差范围。例如，如果一项政治民调旨在预测选举结果，1,000名选民的样本可能会比仅100名选民的样本产生更准确的数值估计。因此，理解采样和估计背后的原则对于任何参与数据分析的人都是至关重要的。除了在商业和政治中的应用，数值估计的概念还扩展到科学研究中。研究人员常常需要估计参数，例如种群数量、平均测量值，甚至某些事件发生的概率。例如，生态学家可能会利用数值估计根据有限的实地观察来评估濒危物种的数量。这些估计对于制定保护策略和确保资源的有效分配至关重要。然而，也必须认识到数值估计的局限性。虽然它们提供了有价值的信息，但并非万无一失。估计可能会受到数据收集中的偏见或方法不当的影响。因此，分析师必须沟通与其数值估计相关的不确定性。这种透明度帮助利益相关者理解基于这些近似做出决策时潜在的风险。此外，技术进步增强了我们以更高精度生成数值估计的能力。随着大数据和复杂分析工具的兴起，组织现在可以处理大量信息，以生成更准确的估计。例如，机器学习算法可以识别数据中的模式，而人类分析师可能会忽视，从而提高金融、医疗等各个领域的数值估计。总之，数值估计的概念在现代决策中扮演着至关重要的角色，涉及众多领域。通过提供基于数据分析的近似，这些估计使企业、研究人员和决策者能够做出明智的选择。然而，理解数值估计的力量和局限性至关重要。随着我们继续在日益复杂的世界中航行，掌握估计的艺术将始终是有效解决问题和战略规划的重要技能。