variance of estimate

简明释义

估计值的方差

英英释义

例句

1.The variance of estimate 估计的方差 was significantly reduced after implementing new measurement techniques.

在实施新测量技术后，估计的方差显著减少。

2.When analyzing the data, it's crucial to account for the variance of estimate 估计的方差 to ensure accurate predictions.

在分析数据时，考虑估计的方差是至关重要的，以确保预测的准确性。

3.A low variance of estimate 估计的方差 suggests that the model is reliable and consistent across different datasets.

低估计的方差表明模型在不同数据集之间是可靠和一致的。

4.Understanding the variance of estimate 估计的方差 helps researchers identify potential biases in their study.

理解估计的方差有助于研究人员识别他们研究中的潜在偏见。

5.The researcher noted a high variance of estimate 估计的方差 in the survey results, indicating a lack of consensus among participants.

研究人员注意到调查结果中有很高的估计的方差，这表明参与者之间缺乏共识。

作文

In the field of statistics, understanding the concept of variance of estimate is crucial for anyone looking to analyze data effectively. The variance of estimate refers to the measure of how much the estimates of a particular parameter vary from one sample to another. This concept is essential because it provides insight into the reliability and accuracy of our statistical estimates. When we collect data and compute estimates, such as the mean or proportion, these estimates can fluctuate based on the sample we select. The variance of estimate quantifies this fluctuation, allowing researchers to gauge the stability of their findings.For instance, consider a scenario where a researcher wants to estimate the average height of adult males in a city. If the researcher takes one sample of 100 men and finds an average height of 175 cm, and then takes another sample of 100 men and finds an average height of 178 cm, the difference in these estimates illustrates the concept of variance of estimate. The larger the difference between the estimates from different samples, the higher the variance of estimate, indicating less reliability in the estimation process.The variance of estimate is not just a theoretical concept; it has practical implications in various fields, including economics, healthcare, and social sciences. For example, in public health studies, understanding the variance of estimate can help determine the effectiveness of a new drug by analyzing the variation in response among different patient groups. If the estimates of drug efficacy vary widely across samples, it may suggest that the drug works well for some individuals but not for others, prompting further investigation.Moreover, the variance of estimate plays a significant role in constructing confidence intervals. A confidence interval provides a range within which we expect the true parameter value to lie, and its width is directly influenced by the variance of estimate. A high variance of estimate leads to wider confidence intervals, indicating greater uncertainty about the true parameter value. Conversely, a low variance of estimate results in narrower confidence intervals, suggesting more precision in our estimates.Understanding the variance of estimate also aids in making informed decisions based on statistical analyses. For instance, businesses often rely on market research to make strategic decisions. If the estimates derived from consumer surveys exhibit high variance of estimate, companies might reconsider their marketing strategies, recognizing that their initial conclusions could be unreliable. On the other hand, consistent estimates with low variance of estimate can provide a strong foundation for decision-making.In conclusion, the variance of estimate is a fundamental concept in statistics that measures the reliability of our estimates. By comprehending this concept, researchers and practitioners can better interpret their findings, construct meaningful confidence intervals, and make sound decisions based on data. As we delve deeper into the world of statistics, it becomes increasingly clear that recognizing and addressing the variance of estimate is essential for achieving accurate and trustworthy results. Ultimately, mastering the variance of estimate empowers us to navigate the complexities of data analysis with greater confidence and clarity.

在统计学领域，理解估计的方差这一概念对任何想要有效分析数据的人来说都是至关重要的。估计的方差是指特定参数的估计值在不同样本之间变化的程度。这一概念至关重要，因为它提供了我们统计估计的可靠性和准确性的洞察。当我们收集数据并计算估计值（如均值或比例）时，这些估计值可能会根据我们选择的样本而波动。估计的方差量化了这种波动，使研究人员能够评估他们发现的稳定性。例如，考虑一个研究者想要估计一个城市成年男性的平均身高的场景。如果研究者取一个100名男性的样本，发现平均身高为175厘米；然后再取另一个100名男性的样本，发现平均身高为178厘米，这两个样本估计的差异说明了估计的方差的概念。来自不同样本的估计值之间的差异越大，估计的方差就越高，表明估计过程的可靠性较低。估计的方差不仅是一个理论概念；它在经济学、医疗保健和社会科学等多个领域具有实际意义。例如，在公共卫生研究中，理解估计的方差可以通过分析不同患者群体的反应差异来帮助确定新药的有效性。如果药物疗效的估计在样本之间变化很大，这可能表明该药物对某些个体有效，但对其他个体无效，从而促使进一步调查。此外，估计的方差在构建置信区间中也发挥着重要作用。置信区间提供了一个范围，我们期望真实参数值位于其中，而其宽度直接受估计的方差的影响。高的估计的方差导致更宽的置信区间，表明我们对真实参数值的不确定性更大。相反，低的估计的方差则导致更窄的置信区间，表明我们的估计更精确。理解估计的方差还有助于基于统计分析做出明智的决策。例如，企业通常依赖市场研究来制定战略决策。如果消费者调查得出的估计值显示出高的估计的方差，公司可能会重新考虑他们的营销策略，认识到他们的初步结论可能不可靠。另一方面，一致的、低估计的方差的估计可以为决策提供坚实的基础。总之，估计的方差是统计学中的一个基本概念，它测量我们估计的可靠性。通过理解这一概念，研究人员和从业者可以更好地解读他们的发现，构建有意义的置信区间，并根据数据做出合理的决策。当我们深入探讨统计学的世界时，越来越清楚的是，认识和解决估计的方差对于实现准确和可靠的结果至关重要。最终，掌握估计的方差使我们能够更自信、更清晰地驾驭数据分析的复杂性。