reanalyze

简明释义

英[ˌriːəˈnælɪzaɪz]美[ˌriːəˈnæləˌzaɪz]

v. 再分析

英英释义

单词用法

同义词

反义词

例句

1.If the standard falls out of control, prepare a new standard at this level and reanalyze.

如果该标准失去控制，在这一水平制定新的标准再分析。

2.CONCLUSION: it provides a new way for stu - dying the mechanisms of cancer to reanalyze the publicly accessible datasets of gene expression profiling with new methods.

结论：用新的分析方法研究已公开的表达谱芯片资料为研究肿瘤的发病机制提供了新的手段。

3.Click Back to change the configuration or click Reanalyze to try again.

单击“上一步”以更改配置或单击“重新分析”再试一次。

4.We systematically varied calcium oscillation parameters in Arabidopsis guard cells, and then reanalyze the data from previous literatures.

采用具有不同钙指纹参数的人工钙信号诱导拟南芥保卫细胞，比较分析由此引发的气孔关闭情况；

5.If the new standard with the new liner is in control, reanalyze the 220 PPM standard and the 700ppm standard.

如果新的标准与新的衬垫可以控制，再次分析220含量标准和700含量标准。

6.CONCLUSION: it provides a new way for stu - dying the mechanisms of cancer to reanalyze the publicly accessible datasets of gene expression profiling with new methods.

结论：用新的分析方法研究已公开的表达谱芯片资料为研究肿瘤的发病机制提供了新的手段。

7.Mr. Herr, the former chemical company lawyer, used e-discovery software to reanalyze work his company's lawyers did in the 1980s and ’90s.

曾担任化学公司的律师的Herr先生使用电子证据发现软件重新分析它的公司律师在1980年代和1990年代的工作。

8.If necessary, reanalyze or redesign the system.

如果有必要，重新分析或重新设计系统。

9.Before publishing the paper, the authors decided to reanalyze 重新分析 the statistical data.

在发表论文之前，作者决定<再分析>统计数据。

10.Due to unexpected outcomes, we need to reanalyze 重新分析 the experiment's parameters.

由于意外结果，我们需要<再分析>实验参数。

11.The researchers will reanalyze 重新分析 the samples to validate their findings.

研究人员将<再分析>样本以验证他们的发现。

12.The audit revealed discrepancies, prompting the accountants to reanalyze 重新分析 the financial statements.

审计揭示了差异，促使会计师<再分析>财务报表。

13.After reviewing the initial results, the team decided to reanalyze 重新分析 the data to ensure accuracy.

在审查初步结果后，团队决定<再分析>数据以确保准确性。

作文

In the field of research, the ability to reanalyze data is crucial for drawing accurate conclusions. Often, initial analyses may not reveal the full picture due to various biases or limitations in methodology. Therefore, researchers must be willing to revisit their data and reanalyze it with fresh perspectives and improved techniques. This process not only enhances the reliability of their findings but also contributes to the overall advancement of knowledge in their respective fields.For instance, consider a medical study that investigates the effectiveness of a new drug. Initially, the researchers might have analyzed the data using standard statistical methods. However, as more sophisticated analytical tools become available, they may choose to reanalyze the data to uncover deeper insights. By applying advanced techniques such as machine learning algorithms, they could identify patterns and correlations that were previously overlooked. This reexamination can lead to new hypotheses, better treatment protocols, and ultimately, improved patient outcomes.Moreover, reanalyzing data is not limited to just academic research; it is also essential in business contexts. Companies often collect vast amounts of data regarding customer behavior, sales trends, and market dynamics. Initially, this data may be analyzed to inform immediate business decisions. However, as market conditions change, businesses may find it necessary to reanalyze their data to stay competitive. For example, a company might discover through reanalysis that certain products are trending in unexpected ways, prompting them to adjust their marketing strategies accordingly.The importance of reanalyzing data extends to policy-making as well. Policymakers rely on data to design effective programs and regulations. If initial analyses suggest a particular policy is working, it might be tempting to assume success without further investigation. However, ongoing evaluation and reanalyzing of the data can reveal unintended consequences or areas for improvement. For example, a city may implement a new public transportation system based on initial ridership data. Over time, reanalyzing this data could show that certain routes are underutilized, leading to adjustments that better serve the community.In conclusion, the act of reanalyze is vital across various domains, from scientific research to business strategy and public policy. It encourages critical thinking and adaptability, ensuring that decisions are based on the most accurate and comprehensive understanding of the data available. By embracing the practice of reanalyzing, individuals and organizations can foster innovation, improve outcomes, and remain responsive to changing circumstances. The ability to look back at data with a fresh lens not only enhances the quality of our conclusions but also enriches the overall discourse within any field of study or practice.

在研究领域，重新分析数据的能力对于得出准确结论至关重要。通常，初步分析可能由于各种偏见或方法论的局限性而未能揭示完整的图景。因此，研究人员必须愿意重新审视他们的数据，并以新的视角和改进的技术进行重新分析。这一过程不仅增强了他们发现的可靠性，还促进了各自领域知识的整体进步。例如，考虑一项调查新药有效性的医学研究。最初，研究人员可能使用标准统计方法分析数据。然而，随着越来越多的复杂分析工具的出现，他们可能选择重新分析数据，以揭示更深层次的见解。通过应用机器学习算法等先进技术，他们可以识别出之前被忽视的模式和相关性。这种重新审查可以导致新的假设、更好的治疗方案，最终改善患者的结果。此外，重新分析数据不仅限于学术研究；在商业环境中同样至关重要。公司通常会收集大量关于客户行为、销售趋势和市场动态的数据。最初，这些数据可能会被分析以通知即时的商业决策。然而，随着市场条件的变化，企业可能会发现有必要重新分析他们的数据以保持竞争力。例如，一家公司可能通过重新分析发现某些产品以意想不到的方式流行，从而促使他们相应地调整营销策略。重新分析数据的重要性还延伸到政策制定中。政策制定者依赖数据来设计有效的项目和法规。如果初步分析表明某项政策有效，那么在没有进一步调查的情况下假设成功可能是诱人的。然而，持续评估和重新分析数据可以揭示意想不到的后果或改进的领域。例如，一个城市可能根据最初的乘客数据实施新的公共交通系统。随着时间的推移，重新分析这些数据可能显示某些路线利用率低，从而导致调整，更好地服务于社区。总之，重新分析在科学研究、商业战略和公共政策等各个领域都是至关重要的。它鼓励批判性思维和适应能力，确保决策基于对可用数据的最准确和全面的理解。通过接受重新分析的实践，个人和组织可以促进创新、改善结果，并保持对变化情况的响应能力。以新的视角回顾数据的能力不仅提高了我们结论的质量，还丰富了任何研究或实践领域的整体讨论。