provisional estimate; impute; imputation

简明释义

估算

英英释义

例句

1.To fill in the gaps in our survey, we had to impute 推断 responses based on demographic trends.

为了填补我们调查中的空白，我们不得不根据人口趋势impute 推断回答。

2.The imputation 插补 of missing data is crucial for accurate statistical analysis.

缺失数据的imputation 插补对准确的统计分析至关重要。

3.After reviewing the project, we decided that the provisional estimate 临时估算 needed to be revised to reflect current market conditions.

在审查项目后，我们决定需要修订provisional estimate 临时估算以反映当前市场状况。

4.In the absence of complete data, we had to impute 推断 the missing values in our dataset.

在缺乏完整数据的情况下，我们不得不impute 推断数据集中的缺失值。

5.The accountant provided a provisional estimate 临时估算 of the company's annual revenue based on last quarter's performance.

会计根据上个季度的表现提供了公司的provisional estimate 临时估算。

作文

In the field of economics and finance, accurate data is crucial for making informed decisions. However, there are often instances where complete data is not available, leading analysts to rely on a provisional estimate（暂定估算）. A provisional estimate is an approximation based on the best available information at the time, which may be subject to revision as more data becomes accessible. For example, when businesses forecast their quarterly earnings, they might use a provisional estimate due to incomplete sales figures. This practice allows them to make preliminary assessments while acknowledging that the final numbers may differ significantly from these initial figures.Furthermore, the process of analyzing data often involves the need to impute（推测）missing values. Data sets can be incomplete for various reasons, such as errors in data collection or reporting. In such cases, analysts may choose to impute missing data points using statistical methods. This could include techniques like mean substitution, regression analysis, or more complex algorithms. The goal of imputing is to create a more complete data set that can lead to more accurate conclusions and forecasts. For instance, if a survey on consumer spending has several unanswered questions, researchers might impute those responses based on the average spending patterns of similar demographics.The concept of imputation（归因）is closely related to impute. It refers to the act of attributing a value to something that is not directly observed. In statistics, imputation is essential for ensuring that the data analysis is robust and reliable. When researchers perform imputation, they must be cautious about the methods they choose, as different approaches can yield varying results. For example, using a simplistic method like mean imputation might not capture the underlying variability within the data, potentially leading to biased outcomes.Moreover, the implications of provisional estimates, impute, and imputation extend beyond mere data analysis; they also play a significant role in policy-making and strategic planning. Decision-makers often depend on these estimates and imputations to allocate resources, set budgets, and formulate strategies. If the underlying data is flawed or the methods of imputation are inappropriate, the resulting policies could be ineffective or even detrimental.In conclusion, the use of provisional estimates, impute, and imputation is a common yet critical practice in data analysis across various fields. Understanding these concepts is essential for anyone involved in research, finance, or policy-making. As we continue to navigate an increasingly data-driven world, the importance of accurately interpreting and applying these terms will only grow. Analysts must strive for precision in their estimates and be transparent about the limitations of their data, ensuring that any imputation made is justifiable and well-documented. Only then can we hope to make sound decisions based on the best available information.

在经济和金融领域，准确的数据对于做出明智的决策至关重要。然而，通常会出现数据不完整的情况，这使得分析师不得不依赖于provisional estimate（暂定估算）。provisional estimate是基于当时最佳可用信息的近似值，可能会随着更多数据的获取而修正。例如，当企业预测季度收益时，可能会由于销售数据不完整而使用provisional estimate。这种做法使他们能够在承认最终数字可能与这些初始数字大相径庭的情况下，进行初步评估。此外，数据分析过程中常常需要对缺失值进行impute（推测）。数据集可能因各种原因而不完整，例如数据收集或报告中的错误。在这种情况下，分析师可能会选择使用统计方法来impute缺失的数据点。这可能包括均值替代、回归分析或更复杂的算法。impute的目标是创建一个更完整的数据集，从而导致更准确的结论和预测。例如，如果一项关于消费者支出的调查有几个未回答的问题，研究人员可能会根据类似人群的平均消费模式来impute这些回答。imputation（归因）的概念与impute密切相关。它指的是将一个值归因于未直接观察到的事物。在统计学中，imputation对于确保数据分析的稳健性和可靠性至关重要。当研究人员进行imputation时，必须小心选择所采用的方法，因为不同的方法可能会产生不同的结果。例如，使用简单的均值插补方法可能无法捕捉数据内在的变异性，可能导致偏倚的结果。此外，provisional estimates、impute和imputation的影响不仅限于数据分析，它们在政策制定和战略规划中也发挥着重要作用。决策者往往依赖这些估算和插补来分配资源、设定预算和制定策略。如果基础数据存在缺陷或imputation方法不当，最终的政策可能会无效甚至有害。总之，provisional estimates、impute和imputation的使用是各个领域数据分析中常见但至关重要的实践。理解这些概念对于任何参与研究、金融或政策制定的人来说都是必不可少的。随着我们继续在一个日益数据驱动的世界中航行，准确解读和应用这些术语的重要性只会增加。分析师必须努力确保其估算的精确性，并对数据的局限性保持透明，确保任何进行的imputation都是合理且有据可依的。只有这样，我们才能希望基于最佳可用信息做出合理的决策。