disaggregation 基础释义
分解；细化；拆散。
disaggregation的发音：英 [?d?s??gr?d??]、美 [?d?s??gr?d??]。
disaggregation的英语范文：
I think it is important to disaggregate data before making decisions. We need to understand the different components of the data and how they are related to each other before we can make informed decisions. For example, if we are analyzing sales data, we need to disaggregate it by product category, region, and time period to get a full picture of the trends and patterns in the data.
disaggregation的英语作文音标和基础释义：
[dis??gr?d??]：分解；细化；拆散。
例句：The government has disaggregated its poverty statistics to show the extent of the problem.
翻译：政府对贫困统计数据进行了分解，以显示问题的严重程度。
在写作中，我们可以通过使用不同的词汇和语法结构来表达不同的意思，使文章更加生动和有趣。同时，我们也可以通过使用不同的句式和修辞手法来吸引读者的注意力，使文章更加有说服力和感染力。
Disaggregation
Disaggregation is a process of breaking down complex data into its component parts. It allows us to better understand the data and identify patterns and trends that might otherwise be overlooked.
In the world of business, disaggregation is essential for analyzing data and identifying opportunities for growth and improvement. By disaggregating data, companies can gain a deeper understanding of their operations and identify areas where improvements can be made. This can lead to more effective decision-making, increased productivity, and better outcomes for both employees and customers.
Another area where disaggregation is important is in the field of environmental monitoring. By disaggregating data on the environment, we can better understand the impact of various factors, such as climate change, on our environment and take action to mitigate those impacts.
Overall, disaggregation is a key tool in the toolbox of effective data analysis and decision-making. It allows us to break down complex data into its component parts, identify patterns and trends, and make informed decisions that can have a positive impact on our lives and the world around us.
Disaggregation
Disaggregation is a process of breaking down complex data into its basic components. It helps us understand the data more clearly and make better decisions based on the information.
In the field of climate change, disaggregation is essential. Climate data is often complex and difficult to understand, but by disaggregating it into temperature, precipitation, wind speed, and other basic components, we can better understand the patterns and trends in the environment. This helps us develop effective strategies to adapt to the changes brought about by climate change.
Another example is in the field of economics. When analyzing economic data, disaggregation is crucial to identify trends and patterns that affect the overall economy. By disaggregating data into sectors such as manufacturing, services, and agriculture, we can better understand the impact of policies and market conditions on different parts of the economy.
In terms of social issues, disaggregation is also important. For example, when studying health outcomes, disaggregating data by race, ethnicity, gender, and socioeconomic status can help us identify patterns and trends in health outcomes that might otherwise be overlooked. This helps us develop effective interventions to improve health outcomes for all populations.
In conclusion, disaggregation is a fundamental tool for effective data analysis and decision-making. It helps us understand complex data more clearly and develop effective strategies based on the information. Whether in climate change, economics, or social issues, disaggregation is essential for making informed decisions that benefit everyone.
