mean-per-unit estimation

简明释义

估计每件平均值

英英释义

例句

1.To improve efficiency, the company relied on mean-per-unit estimation 单位均值估计 for their inventory management.

为了提高效率，公司依赖于单位均值估计进行库存管理。

2.In market research, we often use mean-per-unit estimation 单位均值估计 to determine the average spending of consumers on a product.

在市场研究中，我们经常使用单位均值估计来确定消费者在某一产品上的平均消费。

3.The financial analyst applied mean-per-unit estimation 单位均值估计 to forecast the revenue for the upcoming quarter.

金融分析师应用单位均值估计来预测即将到来的季度的收入。

4.The report included a section on mean-per-unit estimation 单位均值估计 to help stakeholders understand pricing strategies.

报告包含了一部分关于单位均值估计的内容，以帮助利益相关者理解定价策略。

5.Using mean-per-unit estimation 单位均值估计, the team calculated the average cost of production per item.

团队使用单位均值估计计算每件产品的平均生产成本。

作文

In the field of statistics and data analysis, various methods are employed to estimate parameters and make predictions. One such method is mean-per-unit estimation, which plays a crucial role in understanding averages within a dataset. This technique is particularly useful when dealing with large populations or samples where calculating the overall mean directly may be impractical or impossible. The concept behind mean-per-unit estimation is straightforward: it involves taking the average of a specific characteristic per unit of observation. For instance, if we want to estimate the average height of students in a school, we might measure the height of a sample of students and then use that data to infer the average height of the entire student population.The process begins by selecting a representative sample from the population. This sample should ideally reflect the diversity of the entire group to ensure that the estimates made are accurate and reliable. Once the sample is collected, the next step involves calculating the mean of the observed values. In the case of our previous example, we would sum the heights of the sampled students and divide that total by the number of students in the sample. The result gives us the mean-per-unit estimation of the height for the sampled group.One of the advantages of using mean-per-unit estimation is its efficiency. Instead of needing to gather data from every single individual in the population, researchers can obtain a manageable amount of data from a smaller group. This not only saves time and resources but also allows for quicker decision-making based on the findings. Additionally, this method can be applied across various fields, including economics, healthcare, and social sciences, making it a versatile tool for analysts.However, it is important to note that the accuracy of mean-per-unit estimation heavily relies on the quality of the sample chosen. If the sample is biased or not representative of the population, the resulting estimates may lead to incorrect conclusions. Therefore, researchers must take great care in the sampling process, ensuring that it is random and includes diverse elements from the population.Moreover, understanding the limitations of mean-per-unit estimation is essential. While it provides a valuable estimate, it does not account for variability within the data. For instance, two groups may have the same mean height, but the distribution of heights within each group could be vastly different. This lack of detail means that while the method can provide a general idea of averages, it may overlook important nuances present in the data.In conclusion, mean-per-unit estimation is a fundamental statistical technique that enables researchers to make informed estimates about a population based on a sample. By understanding how to effectively implement this method and recognizing its strengths and weaknesses, analysts can enhance their research capabilities. Ultimately, the goal of employing mean-per-unit estimation is to gain insights that can drive better decision-making and improve outcomes across various domains, from education to public policy. As data continues to play an increasingly vital role in our lives, mastering techniques like mean-per-unit estimation will be essential for anyone looking to navigate the complex world of statistics and data analysis.

在统计学和数据分析领域，使用各种方法来估计参数和进行预测。其中一种方法是均值每单位估计，它在理解数据集中的平均值方面发挥着至关重要的作用。当处理大型人群或样本时，直接计算总体均值可能不切实际或不可能，此技术尤其有用。均值每单位估计的概念很简单：它涉及到对观察单位的特定特征的平均值进行计算。例如，如果我们想估计一所学校学生的平均身高，我们可能会测量一部分学生的身高，然后利用这些数据推断整个学生群体的平均身高。这个过程首先从人口中选择一个具有代表性的样本开始。该样本理想情况下应该反映整个群体的多样性，以确保所做的估计准确可靠。一旦样本收集完成，下一步就是计算观察值的均值。在我们之前的例子中，我们将采样学生的身高相加，并将总和除以样本中学生的数量。结果给出了被采样组的均值每单位估计。使用均值每单位估计的一个优点是其高效性。研究人员无需从每个个体那里收集数据，只需从较小的群体中获得可管理的数据量。这不仅节省了时间和资源，还允许基于发现更快地做出决策。此外，这种方法可以应用于经济、医疗保健和社会科学等各个领域，使其成为分析师的一种多功能工具。然而，重要的是要注意，均值每单位估计的准确性严重依赖于所选择样本的质量。如果样本存在偏差或不代表总体，则结果估计可能导致错误结论。因此，研究人员在抽样过程中必须非常谨慎，确保样本的随机性并包含来自总体的多样元素。此外，了解均值每单位估计的局限性也至关重要。虽然它提供了有价值的估计，但它并未考虑数据中的变异性。例如，两个组的平均身高可能相同，但每个组内的身高分布可能截然不同。这种缺乏细节意味着，尽管该方法可以提供对平均值的一般了解，但可能会忽略数据中存在的重要细微差别。总之，均值每单位估计是一种基本的统计技术，使研究人员能够根据样本对总体做出明智的估计。通过理解如何有效实施此方法，并认识其优缺点，分析师可以提升他们的研究能力。最终，使用均值每单位估计的目标是获得可以推动更好决策和改善各个领域结果的洞察，从教育到公共政策。随着数据在我们生活中扮演越来越重要的角色，掌握像均值每单位估计这样的技术对于任何希望驾驭复杂统计和数据分析世界的人来说都将至关重要。