absolute error 是一个在统计学中常用的术语，它指的是实际值与估计值之间的绝对差异。
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英语范文：
Absolute Error in Estimation
In statistics, the absolute error is a measure of the difference between the actual value and the estimated value. It is expressed as the absolute value of the difference between the two. When estimating a quantity, it is important to consider the absolute error to ensure that the estimate is accurate.
Sometimes, the absolute error may be large, but it does not necessarily mean that the estimate is inaccurate. On the other hand, if the absolute error is consistently large, it may indicate that there are systematic errors in the data or that the estimate is not reliable.
In order to reduce the absolute error, it is necessary to collect reliable data, use appropriate methods for estimating quantities, and carefully analyze the results to identify any systematic errors.
In conclusion, the absolute error is an important concept in statistics that should be taken into account when estimating quantities. Understanding the concept and how to reduce the error will help us to produce more accurate results.
英语作文音标和基础释义：
在统计学中，绝对误差是实际值和估计值之间差异的度量。它通过两者之间的差值的绝对值来表示。在估算一个量时，考虑绝对误差以确保估算的准确性是很重要的。
有时，绝对误差可能很大，但这并不一定意味着估算不准确。另一方面，如果绝对误差一直很大，这可能表明数据中存在系统性误差或者估算不可靠。
为了减少绝对误差，我们需要收集可靠的数据，使用适当的估算方法，并仔细分析结果以识别任何系统性误差。
总的来说，绝对误差是统计学中的一个重要概念，在估算量时应予以考虑。理解这个概念以及如何减少误差将有助于我们获得更准确的结果。
Absolute Error
Absolute error is a fundamental concept in many fields of science and engineering. It refers to the difference between the actual value and the estimated value of a quantity. In numerical analysis, for example, absolute error is used to measure the accuracy of a numerical solution.
In a numerical experiment, for instance, we may estimate the height of a mountain from satellite data. If the actual height is 5,000 meters and our estimated height is 4,990 meters, then the absolute error is 10 meters. This error can be attributed to various factors, such as inaccurate satellite data or atmospheric conditions.
Another example is in machine learning, where absolute error is used to measure the accuracy of a machine learning algorithm. If our algorithm predicts a label incorrectly, the absolute error is the difference between the actual label and our prediction. This error can be used to improve our algorithm or adjust our parameters.
In summary, absolute error is a fundamental concept that we need to understand and use effectively in various fields of science and engineering. It helps us understand the accuracy of our results and identify areas for improvement.
Absolute Error
Absolute error is a measure of the difference between a real value and its estimated value. It is expressed as the absolute value of the difference. For example, if the real value is 5 and the estimated value is 3, the absolute error is 2.
In science, engineering, and other fields, absolute error is an important concept that helps us evaluate the accuracy of our calculations and predictions. It can indicate whether our estimates are close to the real values or far away.
To reduce absolute error, we can take several measures. First, we can use more accurate data and methods to make our estimates. Second, we can check and validate our methods and results by using different sources of data and methods. Third, we can use more sophisticated statistical techniques to analyze our data and results.
In my opinion, absolute error is an essential concept that we should always keep in mind when we are making decisions and predictions. It helps us to avoid over-confidence and to be more cautious and objective in our work.
Finally, I would like to share an example of how absolute error can be used in real life. Suppose we are designing a new product and need to estimate its performance based on limited information. Using absolute error, we can compare our estimates with actual test results to determine whether our product design is suitable for the market. If the absolute error is low, we can be more confident in our product's success.
In conclusion, absolute error is a fundamental concept that we should always keep in mind when making decisions and predictions. It helps us to evaluate the accuracy of our work and to make better decisions.
