dependent variable; induced variable

简明释义

因变量，因变数

英英释义

例句

1.In a study on plant growth, the height of the plants is considered the dependent variable 因变量, while the amount of sunlight is the induced variable 诱导变量.

在一项关于植物生长的研究中，植物的高度被视为因变量，而阳光的数量是诱导变量。

2.In marketing research, customer satisfaction is the dependent variable 因变量, while the marketing strategies implemented are the induced variable 诱导变量.

在市场研究中，客户满意度是因变量，而实施的营销策略是诱导变量。

3.When analyzing test scores, the average score can be seen as the dependent variable 因变量, influenced by the hours of study, which acts as the induced variable 诱导变量.

在分析考试成绩时，平均分数可以视为因变量，受学习时间影响，而学习时间则作为诱导变量。

4.The rate of photosynthesis in plants serves as the dependent variable 因变量, depending on the concentration of carbon dioxide, which is the induced variable 诱导变量.

植物中的光合作用速率作为因变量，取决于二氧化碳浓度，而二氧化碳浓度是诱导变量。

5.In a clinical trial, the effectiveness of a new drug is the dependent variable 因变量, while the dosage given to patients is the induced variable 诱导变量.

在临床试验中，新药的有效性是因变量，而给患者的剂量是诱导变量。

作文

In the realm of scientific research and data analysis, understanding the relationship between variables is crucial. One of the fundamental concepts in statistics is that of the dependent variable, which is the outcome or effect that researchers are trying to explain or predict. It is called 'dependent' because its value depends on changes in another variable, known as the independent variable. For instance, in a study examining how study hours affect test scores, the test score would be the dependent variable as it relies on the number of hours spent studying. This relationship helps researchers identify patterns and draw conclusions based on their findings.Another important concept related to the dependent variable is the induced variable. An induced variable refers to a variable that is influenced or affected by changes in the dependent variable. In our previous example, if we consider that increased test scores may lead to higher self-esteem in students, then self-esteem could be considered an induced variable. It is crucial to distinguish between these terms because they highlight different aspects of the causal relationships being studied.When conducting experiments or observational studies, researchers meticulously manipulate the independent variable to observe how it impacts the dependent variable. By controlling other factors, they can isolate the effects of the independent variable and determine whether it indeed causes changes in the dependent variable. This process often involves statistical methods that help validate the findings and ensure that the observed changes are not due to random chance.Moreover, understanding these concepts is essential not only in academic settings but also in real-world applications. For example, policymakers might use research findings to understand how economic factors (independent variables) affect employment rates (dependent variables). By analyzing this data, they can make informed decisions that aim to induce positive changes in the economy.In conclusion, grasping the concepts of dependent variable and induced variable is vital for anyone involved in research or data analysis. These terms help define the relationships between different variables and facilitate a deeper understanding of how changes in one aspect can lead to changes in another. As researchers continue to explore various fields, the significance of these variables will remain a cornerstone of analytical studies, providing clarity and insight into complex phenomena. Understanding how to identify and analyze dependent variables and induced variables empowers researchers to uncover truths that can lead to advancements in knowledge and practical applications across numerous disciplines.

在科学研究和数据分析的领域中，理解变量之间的关系至关重要。统计学中的一个基本概念是因变量，它是研究人员试图解释或预测的结果或影响。之所以称其为“因变量”，是因为它的值依赖于另一个变量的变化，这个变量被称为自变量。例如，在一项研究中，考察学习时间如何影响考试成绩，考试成绩将是因变量，因为它取决于学习的小时数。这种关系帮助研究人员识别模式并根据他们的发现得出结论。与因变量相关的另一个重要概念是诱导变量。诱导变量指的是受因变量变化影响的变量。在我们之前的例子中，如果我们考虑到提高的考试成绩可能会导致学生自尊心的提高，那么自尊心可以被视为诱导变量。区分这些术语至关重要，因为它们突出了正在研究的因果关系的不同方面。在进行实验或观察性研究时，研究人员仔细操纵自变量，以观察它对因变量的影响。通过控制其他因素，他们可以孤立自变量的影响，并确定它是否确实引起了因变量的变化。这个过程通常涉及统计方法，这些方法帮助验证发现，并确保观察到的变化不是由于随机机会造成的。此外，理解这些概念不仅在学术环境中至关重要，在现实世界的应用中也同样重要。例如，政策制定者可能利用研究结果来了解经济因素（自变量）如何影响就业率（因变量）。通过分析这些数据，他们可以做出明智的决策，旨在对经济产生积极的影响。总之，掌握因变量和诱导变量的概念对于任何参与研究或数据分析的人来说都是至关重要的。这些术语帮助定义不同变量之间的关系，并促进对一个方面的变化如何导致另一个方面变化的深入理解。随着研究人员继续探索各个领域，这些变量的重要性将仍然是分析研究的基石，为复杂现象提供清晰度和洞察力。理解如何识别和分析因变量和诱导变量使研究人员能够揭示真相，从而推动知识和实践应用的进步。