statistical point estimation

简明释义

统计点估计

英英释义

例句

1.In our research, we used statistical point estimation to determine the average height of the students in the class.

在我们的研究中，我们使用统计点估计来确定班级学生的平均身高。

2.Using statistical point estimation, we found that the average income of households in the area is around $50,000.

通过使用统计点估计，我们发现该地区家庭的平均收入约为50,000美元。

3.The accuracy of statistical point estimation can greatly affect the results of our study.

对统计点估计的准确性会极大影响我们研究的结果。

4.In clinical trials, statistical point estimation helps estimate the effectiveness of a new drug.

在临床试验中，统计点估计有助于评估新药的有效性。

5.The statistical point estimation of the population mean is crucial for making informed decisions.

对总体均值的统计点估计对做出明智的决策至关重要。

作文

Statistical point estimation is a fundamental concept in the field of statistics that involves providing a single value, known as a point estimate, to represent an unknown parameter of a population. This method is particularly useful when researchers need to make informed decisions based on sample data. For instance, if a researcher wants to estimate the average height of adult men in a city, they might take a sample of men from that city and calculate the average height of this sample. This calculated average serves as a point estimate for the true average height of all adult men in the city. The process of statistical point estimation (统计点估计) allows for a simplified representation of complex data, making it easier for analysts to draw conclusions and make predictions.One of the key aspects of statistical point estimation (统计点估计) is the selection of an appropriate estimator. An estimator is a rule or a formula that provides a way to compute the point estimate from the sample data. Commonly used estimators include the sample mean, median, and mode, each serving different purposes depending on the nature of the data and the underlying distribution. For example, the sample mean is often used for normally distributed data, while the median may be more appropriate for skewed distributions.Moreover, the accuracy of a point estimate can be evaluated by considering its bias and variance. Bias refers to the difference between the expected value of the estimator and the true value of the parameter being estimated. A point estimate is considered unbiased if, on average, it hits the true parameter value across many samples. Variance, on the other hand, measures how much the point estimates vary from one sample to another. A lower variance indicates that the point estimates are consistently close to each other, which is desirable in statistical inference.In practice, statistical point estimation (统计点估计) plays a crucial role in various fields such as economics, medicine, and social sciences. For instance, in clinical trials, researchers often use point estimates to determine the effectiveness of a new drug based on sample data collected from trial participants. By estimating parameters such as the average treatment effect, researchers can make recommendations about the drug's use in the general population.However, it is important to note that while point estimates provide valuable information, they do not convey the uncertainty associated with the estimation process. To address this limitation, statisticians often complement point estimates with interval estimates, such as confidence intervals. Confidence intervals provide a range of values within which the true parameter is likely to fall, offering a more comprehensive understanding of the estimation's reliability.In conclusion, statistical point estimation (统计点估计) is a vital tool in statistics that aids researchers and analysts in summarizing data and making informed decisions. By providing a single value to represent an unknown parameter, it simplifies complex data analysis. However, understanding the limitations of point estimates, such as their inherent uncertainty, is crucial for drawing accurate conclusions. As statistical methods continue to evolve, the importance of statistical point estimation (统计点估计) will remain significant in helping us navigate the complexities of data interpretation and decision-making.

统计点估计是统计学领域的一个基本概念，涉及提供一个单一值，即点估计，用于表示一个未知的总体参数。当研究人员需要根据样本数据做出明智的决策时，这种方法特别有用。例如，如果研究人员想要估计某城市成年男性的平均身高，他们可能会从该城市中抽取一组男性样本，并计算该样本的平均身高。这个计算得出的平均值作为所有成年男性在该城市中真实平均身高的点估计。统计点估计（统计点估计）这一过程允许对复杂数据进行简化表示，使分析人员更容易得出结论并进行预测。统计点估计（统计点估计）的一个关键方面是选择合适的估计量。估计量是一个规则或公式，提供了一种从样本数据中计算点估计的方法。常用的估计量包括样本均值、中位数和众数，每种估计量根据数据的性质和潜在分布服务不同的目的。例如，样本均值通常用于正态分布数据，而中位数可能更适合于偏斜分布。此外，点估计的准确性可以通过考虑其偏差和方差来评估。偏差指的是估计量的期望值与被估计参数的真实值之间的差异。如果一个点估计在多个样本中平均上能够命中真实参数值，则该点估计被认为是无偏的。另一方面，方差则衡量了点估计在不同样本间的变化程度。较低的方差表明点估计彼此之间的一致性较高，这在统计推断中是可取的。在实践中，统计点估计（统计点估计）在经济学、医学和社会科学等多个领域发挥着至关重要的作用。例如，在临床试验中，研究人员通常使用点估计来根据从试验参与者收集的样本数据确定新药的有效性。通过估计如平均治疗效果等参数，研究人员可以对该药物在一般人群中的使用提出建议。然而，重要的是要注意，尽管点估计提供了有价值的信息，但它们并不传达与估计过程相关的不确定性。为了应对这一局限性，统计学家通常通过区间估计来补充点估计，例如置信区间。置信区间提供了一系列值，在该范围内真实参数很可能落入，从而提供对估计可靠性的更全面理解。总之，统计点估计（统计点估计）是统计学中的一个重要工具，帮助研究人员和分析人员总结数据并做出明智的决策。通过提供一个单一值来表示一个未知参数，它简化了复杂的数据分析。然而，理解点估计的局限性，例如其固有的不确定性，对于得出准确结论至关重要。随着统计方法的不断发展，统计点估计（统计点估计）的重要性将在帮助我们应对数据解释和决策制定的复杂性中继续保持显著。