aggregately

简明释义

英[ˈæɡrɪɡətli]美[ˈæɡrəˌɡeɪtli]

adv. 合计，一共

英英释义

单词用法

同义词

反义词

例句

1.These researches are based on institutional analysis and describe economic phenomena aggregately; as a result, many of them came up with identical Suggestions.

这一研究思路基于制度分析，通过总量描述经济现象，提出的政策建议多有雷同。

2.It might be one of the reasons why BPH has a preference of feeding on leaf sheath aggregately from chemical point of view.

这也许是褐飞虱喜欢聚集在叶鞘部位取食的化学因素之一。

3.These researches are based on institutional analysis and describe economic phenomena aggregately; as a result, many of them came up with identical Suggestions.

这一研究思路基于制度分析，通过总量描述经济现象，提出的政策建议多有雷同。

4.The company aggregately sold 225,000 sedans, SUVs, and pickups in 2009, with a 77% surge year on year.

该公司去年出售可的松225,000轿车，越野车，并以每年77%激增到2009年皮卡。

5.The survey results were presented aggregately to highlight common responses.

调查结果被整体上呈现，以突出常见的回答。

6.The team reported their findings aggregately to provide a comprehensive overview.

团队将他们的发现整体上报告，以提供全面的概述。

7.When considering the results, we must look at them aggregately rather than individually.

在考虑结果时，我们必须整体上看待它们，而不是单独看待。

8.The expenses were calculated aggregately to determine the total budget for the project.

费用被整体上计算，以确定项目的总预算。

9.The data was analyzed aggregately to identify overall trends.

这些数据被整体上分析，以识别整体趋势。

作文

In today's world, we are often faced with a plethora of information and data that can be overwhelming. As individuals and organizations strive to make sense of this abundance, the concept of analyzing data aggregately becomes increasingly important. To analyze data aggregately means to consider it as a whole rather than focusing on individual components. This approach allows for a more comprehensive understanding of trends and patterns that might not be visible when examining isolated pieces of information. For instance, consider a company that collects customer feedback through various channels such as surveys, social media, and direct communication. If the company were to analyze each piece of feedback in isolation, it might miss the overarching sentiment that emerges when the data is considered aggregately. By aggregating the data, the company can identify common themes, recognize areas for improvement, and ultimately enhance customer satisfaction. Moreover, analyzing data aggregately is crucial in fields such as economics and public health. Economists often rely on aggregated data to assess the overall health of an economy. For example, gross domestic product (GDP) is an aggregated measure that reflects the total economic output of a country. It provides a snapshot of economic performance, helping policymakers make informed decisions. Similarly, in public health, aggregated data on disease prevalence can help identify outbreaks and allocate resources effectively. However, while analyzing data aggregately offers numerous benefits, it is essential to acknowledge the potential pitfalls. One significant risk is the loss of nuance and detail. When data is aggregated, individual variations may be masked, leading to conclusions that do not accurately reflect the experiences of specific groups. For example, if a city reports crime statistics aggregately, it may obscure disparities between neighborhoods, making it difficult to address localized issues effectively. Therefore, it is crucial to strike a balance between aggregation and disaggregation. While aggregated data provides valuable insights, it should be complemented by a deeper analysis of specific segments. This dual approach ensures that decision-makers are equipped with a holistic view while also being mindful of the unique contexts that shape individual experiences. In conclusion, the practice of analyzing data aggregately is a powerful tool in our increasingly data-driven world. It enables us to discern patterns and trends that guide decision-making across various domains. However, we must remain vigilant about the limitations of aggregation and strive to incorporate both aggregated and disaggregated analyses in our evaluations. By doing so, we can harness the full potential of data to inform our actions and drive positive change. Ultimately, the ability to analyze data aggregately will continue to play a vital role in shaping our understanding of complex issues, guiding effective strategies, and fostering informed decision-making in an ever-evolving landscape.

在当今世界，我们经常面临大量的信息和数据，这可能会让人感到不知所措。随着个人和组织努力理解这种丰富的内容，分析数据aggregately的概念变得越来越重要。分析数据aggregately意味着将其视为一个整体，而不是关注单个组成部分。这种方法允许对趋势和模式进行更全面的理解，这些趋势和模式在检查孤立的信息片段时可能不可见。例如，考虑一家通过各种渠道（如调查、社交媒体和直接沟通）收集客户反馈的公司。如果该公司孤立地分析每一条反馈，可能会错过在数据被aggregately考虑时出现的总体情感。通过对数据进行聚合，该公司可以识别出共同主题，认识到改进的领域，最终提升客户满意度。此外，在经济学和公共卫生等领域，分析数据aggregately至关重要。经济学家通常依赖聚合数据来评估经济的整体健康状况。例如，国内生产总值（GDP）是一个反映国家总经济产出的聚合指标。它提供了经济表现的快照，帮助政策制定者做出明智的决策。同样，在公共卫生方面，关于疾病流行的聚合数据可以帮助识别疫情并有效分配资源。然而，虽然分析数据aggregately提供了许多好处，但必须承认潜在的陷阱。一个显著的风险是失去细节和细微差别。当数据被聚合时，个体差异可能会被掩盖，导致得出的结论不能准确反映特定群体的经历。例如，如果一个城市以aggregately的方式报告犯罪统计数据，可能会掩盖社区之间的差异，使得有效解决地方问题变得困难。因此，找到聚合与分解之间的平衡至关重要。虽然聚合数据提供有价值的见解，但应补充对特定细分市场的深入分析。这种双重方法确保决策者能够获得全面的视角，同时也关注塑造个体经验的独特背景。总之，分析数据aggregately的实践是在我们日益数据驱动的世界中一种强大的工具。它使我们能够辨别引导各个领域决策的模式和趋势。然而，我们必须时刻关注聚合的局限性，并努力在评估中结合聚合和分解分析。通过这样做，我们可以充分利用数据来指导我们的行动并推动积极变化。最终，分析数据aggregately的能力将在塑造我们对复杂问题的理解、指导有效策略和促进在不断发展的环境中做出明智决策方面继续发挥重要作用。