statistical parameter

简明释义

统计参数

英英释义

例句

1.Standard deviation is another important statistical parameter that measures the dispersion of data points.

标准差是另一个重要的统计参数，用于衡量数据点的离散程度。

2.When conducting a survey, it's essential to understand how statistical parameters like variance can affect your results.

在进行调查时，了解像方差这样的统计参数如何影响结果是至关重要的。

3.In regression analysis, the coefficients are statistical parameters that indicate the relationship between variables.

在回归分析中，系数是表示变量之间关系的统计参数。

4.The mean is a common statistical parameter that summarizes the average of a data set.

均值是一个常见的统计参数，用于总结数据集的平均值。

5.The median is a statistical parameter that provides insight into the central tendency of a data set.

中位数是一个统计参数，提供了对数据集中心趋势的洞察。

作文

In the field of statistics, understanding the concept of a statistical parameter is crucial for both researchers and data analysts. A statistical parameter is a numerical characteristic or measure that describes a specific aspect of a population. This could include measures such as the mean, median, variance, or standard deviation. Each of these parameters helps to summarize the data in a way that highlights key features and trends. For example, the mean provides an average value, while the variance indicates how much the values in a dataset differ from the mean.To illustrate the importance of statistical parameters, consider a simple example involving a classroom of students. If a teacher wants to understand the overall performance of the class on a recent exam, she might calculate the mean score of all students. This statistical parameter gives her a quick snapshot of how the class performed collectively. However, if she also calculates the variance, she can determine whether the scores were closely clustered around the mean or widely spread out. This additional insight can help the teacher identify which students may need more support.Moreover, statistical parameters are not just limited to educational contexts; they are essential in various fields including economics, psychology, and healthcare. For instance, in healthcare, a statistical parameter like the average blood pressure of a population can inform public health policies and initiatives. By analyzing this data, health officials can identify trends and risk factors associated with high blood pressure, ultimately leading to better health outcomes.When conducting research, it is important to distinguish between statistical parameters and sample statistics. While statistical parameters refer to the entire population, sample statistics are derived from a subset of that population. For example, if a researcher conducts a survey of 100 individuals to estimate the average height of all adults in a city, the mean height calculated from the sample is a statistic. However, the true average height of all adults in the city is the statistical parameter. Understanding this distinction is vital because sample statistics can vary due to sampling error, whereas statistical parameters provide a definitive measure.Furthermore, the accuracy of statistical parameters relies heavily on the quality of data collected. Poorly designed surveys or biased samples can lead to inaccurate estimates of statistical parameters, which can misinform decision-making processes. Therefore, researchers must employ rigorous methods to ensure that their data collection is valid and reliable.In conclusion, statistical parameters play an integral role in the analysis and interpretation of data across various disciplines. They serve as foundational tools that enable researchers and analysts to summarize complex information into meaningful insights. Whether in education, healthcare, or any other field, the ability to accurately calculate and interpret statistical parameters is essential for informed decision-making. As we continue to generate and analyze vast amounts of data, a solid understanding of statistical parameters will remain critical for making sense of the world around us.

在统计学领域，理解统计参数的概念对于研究人员和数据分析师来说至关重要。统计参数是描述特定人群某一方面的数值特征或测量。这可以包括均值、中位数、方差或标准差等测量。每个这些参数都有助于以突显关键特征和趋势的方式总结数据。例如，均值提供了一个平均值，而方差则表示数据集中值与均值的差异程度。为了说明统计参数的重要性，考虑一个涉及教室学生的简单例子。如果一位老师想了解班级在最近一次考试中的整体表现，她可能会计算所有学生的平均分。这一统计参数为她提供了班级整体表现的快速快照。然而，如果她还计算了方差，她就可以确定分数是紧密集中在均值附近还是广泛分散。这一额外的见解可以帮助老师识别出哪些学生可能需要更多支持。此外，统计参数不仅限于教育背景；它们在经济学、心理学和医疗保健等各个领域都是必不可少的。例如，在医疗保健中，像某个人群的平均血压这样的统计参数可以为公共卫生政策和倡议提供信息。通过分析这些数据，卫生官员可以识别与高血压相关的趋势和风险因素，最终导致更好的健康结果。在进行研究时，区分统计参数和样本统计量非常重要。虽然统计参数是指整个群体，但样本统计量是从该群体的子集得出的。例如，如果一位研究人员对100名个体进行调查，以估算城市中所有成人的平均身高，则从样本计算出的平均身高就是一个统计量。然而，城市中所有成人的真实平均身高是统计参数。理解这一区别至关重要，因为样本统计量可能由于抽样误差而变化，而统计参数则提供了明确的测量。此外，统计参数的准确性在很大程度上依赖于收集的数据质量。设计不良的调查或有偏见的样本可能导致统计参数的估计不准确，这可能会误导决策过程。因此，研究人员必须采用严格的方法，以确保他们的数据收集是有效和可靠的。总之，统计参数在各个学科的数据分析和解释中发挥着重要作用。它们作为基础工具，使研究人员和分析师能够将复杂信息总结为有意义的见解。无论是在教育、医疗保健还是其他任何领域，准确计算和解释统计参数的能力对于明智的决策至关重要。随着我们继续生成和分析大量数据，对统计参数的扎实理解将始终是理解我们周围世界的关键。