predictor

简明释义

英[prɪˈdɪktə(r)]美[prɪˈdɪktər]

n. <正式>预测器，预示物；预言者

复 数 p r e d i c t o r s

英英释义

单词用法

同义词

反义词

例句

1.Research suggests that working through conflicts is an important predictor of marital satisfaction.

研究表明，解决冲突是婚姻满意度的重要预测指标。

2.No indicator was a good predictor in Ghana or Peru.

没有一个指标可以很好地预测加纳和秘鲁的婴儿死亡率。

3.The city had a large foreign-born population (26.5 percent in 1990), leading to broader diversity, which, the Harvard and Berkeley economists say, is a good predictor of mobility.

这座城市一直以来都有大量的外国出生人口（1990年比例为26.5%），进一步增强了人口多样性。哈佛大学和伯克利大学的经济学家认为，这预示着良好的流动性。

4.In fact, personality is not the best predictor of who does it well.

事实上，性格并不能很好地预测谁会完成得更好。

5.Seligman would argue that this is a powerful predictor of future success.

赛里格曼声称这是预测未来能否成功的有力证据。

6.I see that as a predictor of both short-term and long-term effects.

我认为这就是短期影响和长期影响共同的先兆。

7.The weather forecast is a reliable predictor of rain tomorrow.

天气预报是明天降雨的可靠预测者。

8.In many studies, age is a strong predictor of health outcomes.

在许多研究中，年龄是健康结果的一个重要预测者。

9.The number of hours studied is often a good predictor of exam success.

学习的小时数通常是考试成功的一个很好的预测者。

10.Economic indicators can serve as a predictor of market trends.

经济指标可以作为市场趋势的一个预测者。

11.The model uses historical data as a predictor for future sales.

该模型使用历史数据作为未来销售的一个预测者。

作文

In today's rapidly changing world, the ability to foresee future trends has become increasingly important. One of the most significant tools we have at our disposal is the concept of a predictor, which refers to any factor or variable that can provide insight into future outcomes. Understanding how to identify and utilize predictors can greatly enhance our decision-making processes in various fields, including economics, healthcare, and environmental science. For instance, in the field of economics, certain indicators such as unemployment rates and consumer spending patterns serve as reliable predictors of economic growth or recession. By analyzing these factors, economists can formulate policies that aim to stabilize the economy or stimulate growth. Similarly, in healthcare, specific lifestyle choices and genetic factors are often used as predictors of an individual's health outcomes. This information is crucial for preventative medicine, allowing healthcare providers to target interventions effectively. Moreover, in the realm of environmental science, climate models utilize various predictors to forecast weather patterns and assess the potential impacts of climate change. These models rely on historical data and current observations to predict future scenarios, helping policymakers make informed decisions about resource management and disaster preparedness. However, it is essential to recognize that while predictors can provide valuable insights, they are not foolproof. The complexity of human behavior and the unpredictability of natural events mean that predictors can sometimes lead us astray. For example, during the COVID-19 pandemic, many models initially underestimated the virus's spread, highlighting the limitations of relying solely on predictors without considering real-time data and changing circumstances. To effectively use predictors, one must adopt a critical approach that combines quantitative analysis with qualitative insights. This means not only looking at numbers and statistics but also understanding the broader context in which these predictors operate. Engaging with experts from different fields can provide a more holistic view and improve the accuracy of our forecasts. Furthermore, technological advancements have significantly enhanced our ability to identify and analyze predictors. Big data analytics and machine learning algorithms can process vast amounts of information to uncover hidden patterns and correlations that traditional methods might miss. This has opened new avenues for research and application across various disciplines. In conclusion, the role of a predictor in our understanding of the future cannot be overstated. Whether in economics, healthcare, or environmental science, recognizing and utilizing predictors allows us to make more informed decisions and better prepare for what lies ahead. However, we must remain vigilant and adaptable, acknowledging the limitations of predictors and the need for continuous learning and adjustment in our approaches. By doing so, we can harness the power of predictors to navigate an uncertain future more effectively.

在当今快速变化的世界中，预见未来趋势的能力变得越来越重要。我们手头最重要的工具之一就是‘预测因素’这一概念，它指的是任何可以提供未来结果洞察的因素或变量。理解如何识别和利用预测因素可以大大增强我们在经济、医疗保健和环境科学等各个领域的决策过程。 例如，在经济学领域，失业率和消费者支出模式等特定指标被视为经济增长或衰退的可靠预测因素。通过分析这些因素，经济学家可以制定旨在稳定经济或刺激增长的政策。类似地，在医疗保健中，特定的生活方式选择和遗传因素常常被用作个人健康结果的预测因素。这些信息对于预防医学至关重要，使医疗提供者能够有效地针对干预措施。 此外，在环境科学领域，气候模型利用各种预测因素来预测天气模式并评估气候变化的潜在影响。这些模型依赖历史数据和当前观察来预测未来情景，帮助决策者就资源管理和灾害准备做出明智的决策。 然而，必须认识到，虽然预测因素可以提供有价值的见解，但它们并不是万无一失的。人类行为的复杂性和自然事件的不可预测性意味着预测因素有时会误导我们。例如，在COVID-19大流行期间，许多模型最初低估了病毒的传播，这突显了仅依赖预测因素而不考虑实时数据和变化情况的局限性。 要有效使用预测因素，必须采取一种批判性的方式，将定量分析与定性洞察相结合。这意味着不仅要关注数字和统计数据，还要理解这些预测因素所运作的更广泛背景。与不同领域的专家进行互动可以提供更全面的视角，提高我们的预测准确性。 此外，技术进步显著增强了我们识别和分析预测因素的能力。大数据分析和机器学习算法可以处理大量信息，以发现传统方法可能遗漏的隐藏模式和关联。这为各个学科的研究和应用开辟了新的途径。 总之，预测因素在我们对未来的理解中扮演的角色不容小觑。无论是在经济学、医疗保健还是环境科学中，识别和利用预测因素使我们能够做出更明智的决策，更好地为未来做好准备。然而，我们必须保持警惕和适应能力，承认预测因素的局限性，以及我们方法中持续学习和调整的必要性。通过这样做，我们可以更有效地利用预测因素来应对不确定的未来。