parametric approach

简明释义

历史媒介推算式

英英释义

例句

1.The team decided to use a parametric approach to model the financial projections for the next quarter.

团队决定采用参数化方法来建模下一个季度的财务预测。

2.The software incorporates a parametric approach to adjust the model based on user input.

该软件采用参数化方法根据用户输入调整模型。

3.Using a parametric approach in data analysis can help identify trends more effectively.

在数据分析中使用参数化方法可以更有效地识别趋势。

4.In architecture, a parametric approach allows for more flexibility in design by using algorithms.

在建筑设计中，参数化方法通过使用算法提供了更大的设计灵活性。

5.Researchers employed a parametric approach to estimate the effects of various factors on climate change.

研究人员采用参数化方法来估计各种因素对气候变化的影响。

作文

In the realm of data analysis and modeling, the term parametric approach refers to a statistical technique that assumes a specific form for the underlying distribution of the data. This approach is particularly useful when we have prior knowledge about the population from which our sample is drawn. By making certain assumptions about the parameters of the distribution, we can simplify our analysis and make predictions based on these parameters. For instance, in many cases, we might assume that the data follows a normal distribution, characterized by its mean and standard deviation. This assumption allows us to apply various statistical tests and methods that rely on this normality condition.One of the advantages of using a parametric approach is that it can lead to more efficient estimates when our assumptions are correct. Because we are incorporating prior knowledge into our analysis, we can achieve greater accuracy with fewer data points compared to non-parametric methods, which do not make such assumptions. For example, consider a scenario where we are trying to estimate the average height of adult males in a city. If we assume that the heights are normally distributed, we can use the mean and standard deviation to make inferences about the entire population based on a relatively small sample size.However, the parametric approach does come with its limitations. The primary concern is the validity of the assumptions we make about the data. If the true distribution of the data deviates significantly from our assumed model, the results can be misleading. For instance, if the height data is actually skewed or has outliers, relying on a normal distribution could lead to incorrect conclusions. Therefore, it is crucial to conduct preliminary analyses to check the validity of our assumptions before applying a parametric approach.Moreover, the parametric approach may not be suitable for all types of data. In situations where the data is categorical or ordinal, alternative methods may provide better insights. Non-parametric methods, which do not assume a specific distribution, can be more flexible and robust in such cases. For example, if we were analyzing survey responses that are ranked (like satisfaction levels), a non-parametric test like the Mann-Whitney U test would be more appropriate than a parametric t-test.In conclusion, while the parametric approach offers powerful tools for data analysis, it is essential to understand its assumptions and limitations. By carefully considering the nature of our data and validating our assumptions, we can effectively utilize this approach to draw meaningful conclusions. Whether we choose to adopt a parametric approach or a non-parametric one, the goal remains the same: to uncover insights that can inform decision-making and enhance our understanding of the world around us.

在数据分析和建模的领域中，术语参数方法指的是一种统计技术，它假设数据的潜在分布具有特定形式。当我们对样本所抽取的人群有先前知识时，这种方法尤其有用。通过对分布参数做出某些假设，我们可以简化分析并基于这些参数进行预测。例如，在许多情况下，我们可能会假设数据遵循正态分布，其特征由均值和标准差来描述。这一假设使我们能够应用各种依赖于这一正态条件的统计检验和方法。使用参数方法的一个优点是，当我们的假设正确时，它可以导致更高效的估计。因为我们将先前的知识融入到分析中，所以与不做假设的非参数方法相比，我们可以用更少的数据点实现更高的准确性。例如，考虑一个场景，我们试图估计一个城市成年男性的平均身高。如果我们假设身高呈正态分布，我们可以利用均值和标准差根据相对较小的样本量对整个种群进行推断。然而，参数方法也有其局限性。主要问题是我们对数据做出的假设的有效性。如果数据的真实分布与我们假设的模型显著偏离，结果可能会产生误导。例如，如果身高数据实际上是偏斜的或存在离群值，那么依赖正态分布可能会导致错误的结论。因此，在应用参数方法之前，进行初步分析以检查假设的有效性至关重要。此外，参数方法可能不适用于所有类型的数据。在分类或有序数据的情况下，替代方法可能提供更好的见解。在这种情况下，非参数方法不假设特定分布，可以更加灵活和稳健。例如，如果我们分析的是排名的调查反馈（如满意度水平），那么非参数检验如曼-惠特尼U检验将比参数t检验更为合适。总之，虽然参数方法为数据分析提供了强大的工具，但理解其假设和局限性至关重要。通过仔细考虑数据的性质并验证我们的假设，我们可以有效地利用这种方法得出有意义的结论。无论我们选择采用参数方法还是非参数方法，目标始终是一样的：揭示能够指导决策并增强我们对周围世界理解的见解。