indirect determination

简明释义

间接测定

英英释义

例句

1.The company's profits were assessed through an indirect determination of sales data over the past year.

公司的利润通过对过去一年销售数据的间接评估进行评估。

2.The teacher used an indirect determination of student engagement by analyzing their participation in group discussions.

老师通过分析学生在小组讨论中的参与情况，使用了间接评估学生参与度。

3.In scientific research, an indirect determination of a substance's concentration can be achieved through calibration curves.

在科学研究中，通过校准曲线可以实现物质浓度的间接测定。

4.The indirect determination of air quality was made using satellite imagery to monitor pollution levels.

空气质量的间接测定是通过卫星图像监测污染水平来进行的。

5.The survey results provided an indirect determination of customer satisfaction, as they were based on feedback rather than direct observation.

调查结果提供了客户满意度的间接判断，因为它们是基于反馈而非直接观察。

作文

In the realm of scientific research and data analysis, the concept of indirect determination plays a crucial role in deriving conclusions from observations that are not directly measurable. This approach is particularly useful in fields such as environmental science, psychology, and economics, where direct measurements may be difficult or impossible to obtain. 间接测定 refers to a method of inferring the value of a variable based on the relationships and correlations observed in other measurable variables. For instance, consider a researcher studying the impact of pollution on public health. The researcher may not be able to measure the health effects of every individual directly due to practical limitations. Instead, they can use indirect determination by analyzing data on pollution levels and correlating them with health statistics gathered from hospitals. By establishing a statistical relationship between these two sets of data, the researcher can indirectly determine the potential health risks posed by pollution.Moreover, indirect determination is also prevalent in psychological studies. Psychologists often rely on surveys and questionnaires to gauge emotions, thoughts, and behaviors, which are inherently subjective and difficult to measure directly. Through careful design of these tools, researchers can collect data that allows them to make 间接测定 of underlying psychological states. For example, a study on anxiety might not measure anxiety directly but instead assess related factors such as sleep patterns, social interactions, and physical symptoms. The results can then be analyzed to draw conclusions about the prevalence and impact of anxiety within the population.In economics, indirect determination is frequently used to assess the health of an economy. Economists may look at indicators such as unemployment rates, inflation, and consumer spending to infer the overall economic conditions. These indicators serve as proxies for more complex underlying factors like consumer confidence and market stability. By analyzing these relationships, economists can make predictions and recommendations that guide policy decisions. In this context, 间接测定 becomes an invaluable tool for understanding economic dynamics without needing to measure every aspect directly.While indirect determination offers many advantages, it is essential to recognize its limitations. The accuracy of indirect measurements depends heavily on the strength of the correlations established between the variables. If the relationships are weak or influenced by external factors, the conclusions drawn may be misleading. Therefore, researchers must exercise caution and ensure that their methodologies are robust and well-founded.In conclusion, indirect determination is a powerful technique that allows researchers across various disciplines to derive meaningful insights from data that cannot be measured directly. By leveraging correlations and relationships among observable variables, scientists and analysts can make informed decisions and contribute to our understanding of complex systems. As we continue to advance in technology and data analysis methods, the importance of mastering 间接测定 will only grow, enabling us to tackle increasingly intricate questions in research and real-world applications.

在科学研究和数据分析的领域中，间接测定的概念在从无法直接测量的观察中得出结论方面发挥着至关重要的作用。这种方法在环境科学、心理学和经济学等领域尤为有用，因为在这些领域中，直接测量可能很困难或不可能获得。例如，考虑一个研究污染对公共健康影响的研究者。由于实际限制，研究者可能无法直接测量每个个体的健康影响。相反，他们可以通过分析污染水平的数据并将其与医院收集的健康统计数据相关联来使用间接测定。通过建立这两组数据之间的统计关系，研究者可以间接确定污染所带来的潜在健康风险。此外，间接测定在心理学研究中也很普遍。心理学家通常依赖调查和问卷来评估情感、思想和行为，这些都是固有的主观且难以直接测量。通过精心设计这些工具，研究人员可以收集数据，从而使他们能够对潜在的心理状态进行间接测定。例如，一项关于焦虑的研究可能不会直接测量焦虑，而是评估与之相关的因素，如睡眠模式、社交互动和身体症状。然后可以分析结果，以得出关于焦虑在该人群中流行程度和影响的结论。在经济学中，间接测定常用于评估经济的健康状况。经济学家可能会查看失业率、通货膨胀和消费者支出等指标，以推断整体经济状况。这些指标作为更复杂的潜在因素（如消费者信心和市场稳定性）的代理。通过分析这些关系，经济学家可以做出预测和建议，指导政策决策。在这种情况下，间接测定成为理解经济动态的宝贵工具，而无需直接测量每个方面。虽然间接测定提供了许多优势，但必须认识到其局限性。间接测量的准确性在很大程度上取决于变量之间建立的关联的强度。如果关系较弱或受到外部因素的影响，则得出的结论可能会误导。因此，研究人员必须谨慎行事，并确保他们的方法论是稳健和基于事实的。总之，间接测定是一种强大的技术，使各个学科的研究人员能够从无法直接测量的数据中得出有意义的见解。通过利用可观察变量之间的相关性和关系，科学家和分析师可以做出明智的决策，并为我们理解复杂系统做出贡献。随着我们在技术和数据分析方法上的不断进步，掌握间接测定的重要性只会增加，使我们能够解决研究和现实应用中越来越复杂的问题。