residual range

简明释义

剩余射程

英英释义

例句

1.We need to address the residual range 残留范围 in our model to improve its accuracy.

我们需要解决模型中的残留范围 residual range 以提高其准确性。

2.In our study, we found the residual range 残留范围 to be a critical factor in determining the outcome.

在我们的研究中，我们发现残留范围 residual range 是决定结果的一个关键因素。

3.The residual range 残留范围 indicates the variation left after accounting for certain variables.

在考虑某些变量后，残留范围 residual range 表示剩余的变异。

4.The analysis showed that the residual range 残留范围 of the data was significantly lower than expected.

分析显示，数据的残留范围 residual range 明显低于预期。

5.The residual range 残留范围 can provide insights into unexplained variations in the results.

残留范围 residual range 可以为结果中未解释的变异提供洞见。

作文

In the world of statistics and data analysis, understanding various terms is crucial for interpreting results accurately. One such term that often arises in regression analysis is the residual range. The residual range refers to the difference between the predicted values of a dependent variable and the actual observed values. This concept is fundamental in assessing the performance of a statistical model. When we conduct a regression analysis, we aim to create a model that can predict outcomes based on certain independent variables. However, no model is perfect, and discrepancies between predicted and actual values are inevitable. The residual range helps us quantify these discrepancies, providing insights into the model's accuracy and reliability.To illustrate this concept, consider a simple linear regression model where we are trying to predict a person's weight based on their height. After collecting data and applying the regression analysis, we obtain a set of predicted weights for each height measurement. However, when we compare these predicted weights to the actual weights recorded, we notice some differences. These differences represent the residuals, and the residual range encompasses the maximum and minimum values of these residuals.Analyzing the residual range is essential for several reasons. Firstly, it allows us to identify potential outliers in our data. If the residual range is large, it may indicate that certain observations deviate significantly from the predicted values, suggesting that they could be outliers. Outliers can skew our results and lead to incorrect conclusions if not addressed properly.Moreover, understanding the residual range can help us improve our model. By examining the patterns within the residuals, we can identify whether the model is missing key variables or whether the relationship between the independent and dependent variables is more complex than initially thought. For instance, if we observe a systematic pattern in the residuals, such as a funnel shape, it might indicate that the variability of the dependent variable changes at different levels of the independent variable, prompting us to consider a different modeling approach.Furthermore, the residual range plays a vital role in assessing the goodness-of-fit of our model. A smaller residual range typically suggests a better fit, meaning our predictions are closer to the actual values. This can be quantified using various metrics, such as the root mean square error (RMSE), which provides a single value to summarize the overall prediction error. By comparing the residual range across different models, we can determine which model performs best in terms of predictive accuracy.In conclusion, the concept of residual range is integral to the field of statistics and data analysis. It provides valuable insights into the accuracy and reliability of predictive models. By understanding and analyzing the residual range, researchers and analysts can identify outliers, improve their models, and assess the goodness-of-fit, ultimately leading to more accurate and reliable conclusions. As we continue to navigate the complexities of data analysis, mastering terms like residual range will empower us to make informed decisions based on our findings.

在统计学和数据分析的世界中，理解各种术语对于准确解读结果至关重要。一个常见的术语是在回归分析中经常出现的残差范围。残差范围是指依赖变量的预测值与实际观察值之间的差异。这个概念对于评估统计模型的表现是基础。当我们进行回归分析时，我们的目标是创建一个可以根据某些自变量预测结果的模型。然而，没有模型是完美的，预测值与实际值之间的差异是不可避免的。残差范围帮助我们量化这些差异，提供对模型准确性和可靠性的洞察。为了说明这个概念，考虑一个简单的线性回归模型，我们试图根据身高预测一个人的体重。在收集数据并应用回归分析后，我们为每个身高测量获得了一组预测体重。然而，当我们将这些预测体重与实际记录的体重进行比较时，我们注意到一些差异。这些差异代表了残差，而残差范围则包括这些残差的最大值和最小值。分析残差范围至关重要，原因有几个。首先，它使我们能够识别数据中的潜在异常值。如果残差范围很大，可能表明某些观察值与预测值显著偏离，暗示它们可能是异常值。异常值会扭曲我们的结果，如果不加以处理，可能导致错误的结论。此外，理解残差范围可以帮助我们改进模型。通过检查残差中的模式，我们可以识别模型是否遗漏了关键变量，或者自变量与因变量之间的关系是否比最初想象的更复杂。例如，如果我们观察到残差中存在系统性模式，比如漏斗形状，这可能表明因变量的变异性在不同水平的自变量下发生变化，促使我们考虑不同的建模方法。此外，残差范围在评估模型的拟合优度方面发挥着重要作用。较小的残差范围通常表明更好的拟合，这意味着我们的预测更接近实际值。这可以通过各种指标进行量化，例如均方根误差（RMSE），它提供了一个单一值来总结整体预测误差。通过比较不同模型的残差范围，我们可以确定哪个模型在预测准确性方面表现最佳。总之，残差范围的概念是统计学和数据分析领域不可或缺的一部分。它提供了对预测模型准确性和可靠性的宝贵洞察。通过理解和分析残差范围，研究人员和分析师可以识别异常值、改进他们的模型，并评估拟合优度，最终得出更准确和可靠的结论。当我们继续应对数据分析的复杂性时，掌握像残差范围这样的术语将使我们能够基于我们的发现做出明智的决策。