parametric

简明释义

英[ˌpærəˈmetrɪk]美[ˌpærəˈmetrɪk]

adj. [数][物] 参数的；[数][物] 参量的

英英释义

单词用法

同义词

反义词

例句

1.OK, so we are going to learn about parametric equations of lines.

我们马上就要学习直线的参数方程。

2.In fact, parametric equations of lines always look like that.

实际上，直线的参数方程总是这个样子的。

3.The behavior of good parametric code can be influenced by parameters that can be set at run time.

在运行时，可以通过设置参数来影响具有良好参数代码的行为。

4.Study and design to the parametric loudspeaker system include the contents of two respects: software and hardware.

对参量扬声器系统的研究和设计包含了两个方面的内容：软件和硬件。

5.That is a parametric equation for the sphere.

那是球体的一个参数方程。

6.One thing developers usually ponder when starting a new project is the strategy they need to employ to write good parametric code.

当启动一个新项目时，开发者通常深思的一个问题是他们需要采用用来编写具有良好参数的代码的策略。

7.I am not going to write parametric equations.

我不准备写参数方程。

8.The parametric 参数的 equations allow for a more flexible modeling of curves in geometry.

这些参数的方程允许在几何中对曲线进行更灵活的建模。

9.The software uses parametric 参数的 modeling to generate complex 3D shapes based on user-defined parameters.

该软件使用参数的建模，根据用户定义的参数生成复杂的3D形状。

10.In statistics, a parametric 参数的 test assumes that the data follows a certain distribution.

在统计学中，参数的测试假设数据遵循某种分布。

11.Using parametric 参数的 design, architects can create structures that adapt to environmental conditions.

通过使用参数的设计，建筑师可以创建适应环境条件的结构。

12.In machine learning, parametric 参数的 models make assumptions about the form of the function being learned.

在机器学习中，参数的模型对所学习函数的形式做出假设。

作文

In the realm of statistics and data analysis, the term parametric refers to methods that assume a specific form for the underlying population distribution. This concept is crucial for researchers and analysts who seek to draw conclusions from sample data. The parametric approach typically involves parameters such as the mean and standard deviation, which characterize the population. For instance, when conducting a hypothesis test, one might assume that the data follows a normal distribution, allowing for the application of various parametric tests like the t-test or ANOVA.One of the primary advantages of parametric methods is their efficiency. Because they make strong assumptions about the distribution of the data, they can often yield more powerful results than non-parametric methods, especially when the assumptions hold true. For example, if we are analyzing the heights of adult males in a certain population, and we can reasonably assume that these heights follow a normal distribution, we can use parametric methods to estimate the average height and test hypotheses about it.However, the reliance on the assumption of a specific distribution also poses risks. If the actual data does not conform to the assumed distribution, the results obtained from parametric tests may be misleading. This is particularly important in real-world applications where data can often be skewed or exhibit outliers. Therefore, while parametric methods can provide valuable insights, researchers must exercise caution and verify the underlying assumptions before proceeding with their analyses.In contrast, non-parametric methods do not assume any specific distribution for the data. They are often used when the sample size is small or when the data does not meet the assumptions required for parametric tests. For example, the Mann-Whitney U test is a popular non-parametric alternative to the t-test, which can be employed when comparing two independent groups without assuming a normal distribution.In conclusion, understanding the concept of parametric methods is essential for anyone involved in data analysis. While these methods offer powerful tools for making inferences about populations based on sample data, they come with certain assumptions that must be validated. As the field of statistics continues to evolve, the choice between parametric and non-parametric methods will depend on the nature of the data at hand and the specific goals of the analysis. Ultimately, a solid grasp of parametric techniques, along with an awareness of their limitations, will enable researchers to make informed decisions and derive meaningful conclusions from their data.

在统计学和数据分析领域，术语parametric指的是假设基础总体分布具有特定形式的方法。这个概念对于希望从样本数据中得出结论的研究人员和分析师至关重要。parametric方法通常涉及诸如均值和标准差等参数，这些参数用于表征总体。例如，在进行假设检验时，人们可能会假设数据遵循正态分布，从而可以应用各种parametric检验，如t检验或方差分析（ANOVA）。parametric方法的主要优点之一是其效率。由于它们对数据分布做出了强假设，因此当这些假设成立时，它们通常能够比非parametric方法产生更强的结果。例如，如果我们分析某一人群成年男性的身高，并且可以合理假设这些身高遵循正态分布，我们可以使用parametric方法来估计平均身高并对其进行假设检验。然而，对特定分布假设的依赖也带来了风险。如果实际数据不符合假设的分布，则从parametric检验中获得的结果可能会产生误导。这在现实应用中尤其重要，因为数据往往可能存在偏斜或异常值。因此，尽管parametric方法可以提供有价值的见解，但研究人员必须谨慎行事，并在进行分析之前验证基础假设。相比之下，non-parametric方法不对数据假设任何特定分布。当样本量较小或数据不满足parametric检验所需的假设时，通常会使用这些方法。例如，曼-惠特尼U检验是t检验的一个流行的non-parametric替代方法，可以在比较两个独立组时使用，而无需假设正态分布。总之，理解parametric方法的概念对任何参与数据分析的人来说都是必不可少的。虽然这些方法为基于样本数据对总体进行推断提供了强大的工具，但它们伴随着某些必须验证的假设。随着统计学领域的不断发展，选择parametric和non-parametric方法将取决于手头数据的性质以及分析的具体目标。最终，扎实掌握parametric技术，加上对其局限性的意识，将使研究人员能够做出明智的决策，并从数据中得出有意义的结论。