estimate of parameters

简明释义

参数估计

英英释义

例句

1.The software provided a quick estimate of parameters 参数的估计 for the statistical model.

该软件为统计模型提供了快速的参数的估计 estimate of parameters。

2.An accurate estimate of parameters 参数的估计 is crucial for effective decision-making.

准确的参数的估计 estimate of parameters 对于有效的决策至关重要。

3.The estimate of parameters 参数的估计 helps in predicting future trends in the data.

参数的估计estimate of parameters 有助于预测数据中的未来趋势。

4.We used regression analysis to find the estimate of parameters 参数的估计 for our model.

我们使用回归分析来找到我们的模型的参数的估计 estimate of parameters。

5.In our study, the estimate of parameters 参数的估计 was based on a sample size of 500 participants.

在我们的研究中，参数的估计 estimate of parameters 是基于500名参与者的样本大小。

作文

In the field of statistics and data analysis, the concept of estimate of parameters plays a crucial role. It refers to the process of using sample data to infer the values of parameters in a statistical model. Parameters are numerical characteristics of a population, such as the mean or variance. Since it is often impractical or impossible to collect data from an entire population, statisticians rely on samples to make educated guesses about these parameters. The estimate of parameters is fundamental in various applications, including hypothesis testing, regression analysis, and predictive modeling.To understand the importance of estimate of parameters, consider a simple example involving a survey. Imagine a researcher wants to know the average height of adult men in a city. It would be unfeasible to measure the height of every adult male. Instead, the researcher selects a random sample of men and measures their heights. From this sample, the researcher calculates the sample mean height, which serves as an estimate of parameters for the population mean height.The reliability of the estimate of parameters depends on several factors, including the sample size and the method of sampling. A larger sample size generally provides a more accurate estimate because it reduces the margin of error. Additionally, if the sample is not representative of the population, the estimate of parameters may be biased, leading to incorrect conclusions.There are various methods for estimating parameters, including point estimation and interval estimation. Point estimation provides a single value as the estimate, while interval estimation gives a range of values within which the parameter is expected to lie. For instance, after analyzing the height data, the researcher might conclude that the average height of adult men in the city is approximately 175 cm, with a confidence interval of 172 to 178 cm. This interval reflects the uncertainty associated with the estimate of parameters.Another important aspect of estimate of parameters is the use of statistical models. When building a model, researchers specify a functional form that relates the dependent variable to one or more independent variables. The parameters in this model need to be estimated based on observed data. For example, in a linear regression model, the slope and intercept are parameters that need to be estimated. The quality of the model heavily relies on how accurately these parameters are estimated.Moreover, the estimate of parameters can also be influenced by the choice of estimation techniques. Common methods include Maximum Likelihood Estimation (MLE) and Least Squares Estimation (LSE). MLE seeks to find the parameter values that maximize the likelihood of observing the given sample data, while LSE minimizes the sum of the squared differences between observed and predicted values. Each method has its advantages and disadvantages, and the choice depends on the specific context of the analysis.In conclusion, the estimate of parameters is a foundational concept in statistics that enables researchers to draw conclusions about populations based on sample data. By understanding how to properly estimate parameters, statisticians can make informed decisions, conduct hypothesis tests, and build predictive models. As data continues to grow in complexity and volume, mastering the art of parameter estimation will remain an essential skill for anyone involved in data analysis.

在统计学和数据分析领域，参数估计的概念起着至关重要的作用。它指的是使用样本数据推断统计模型中参数值的过程。参数是人口的数值特征，例如均值或方差。由于收集整个人口的数据通常不切实际或不可能，统计学家依赖样本对这些参数做出有根据的猜测。参数估计在各种应用中都是基础，包括假设检验、回归分析和预测建模。要理解参数估计的重要性，可以考虑一个简单的例子，涉及一项调查。想象一下，一个研究者想知道某个城市成年男性的平均身高。测量每个成年男性的身高是不切实际的。相反，研究者选择一个随机样本并测量他们的身高。通过这个样本，研究者计算样本均值身高，这作为对总体均值身高的参数估计。参数估计的可靠性取决于几个因素，包括样本大小和抽样方法。一般来说，较大的样本大小提供更准确的估计，因为它减少了误差范围。此外，如果样本没有代表性，则参数估计可能会产生偏差，从而导致错误的结论。有多种方法可以估计参数，包括点估计和区间估计。点估计提供一个单一值作为估计，而区间估计则给出一个值的范围，其中该参数预计会落入。例如，在分析身高数据后，研究者可能会得出结论，城市成年男性的平均身高大约为175厘米，置信区间为172到178厘米。这个区间反映了与参数估计相关的不确定性。参数估计的另一个重要方面是使用统计模型。当建立模型时，研究人员指定一个功能形式，将因变量与一个或多个自变量联系起来。这个模型中的参数需要根据观察到的数据进行估计。例如，在线性回归模型中，斜率和截距是需要估计的参数。模型的质量在很大程度上依赖于这些参数的准确估计。此外，参数估计也可能受到估计技术选择的影响。常见的方法包括最大似然估计（MLE）和最小二乘估计（LSE）。MLE试图找到最大化观察到的样本数据可能性的参数值，而LSE则最小化观察值与预测值之间平方差的总和。每种方法都有其优缺点，选择取决于分析的具体背景。总之，参数估计是统计学中的基础概念，使研究人员能够根据样本数据得出关于总体的结论。通过理解如何正确估计参数，统计学家可以做出明智的决策，进行假设检验，并建立预测模型。随着数据的复杂性和体量不断增长，掌握参数估计的艺术将继续成为任何参与数据分析的人的基本技能。