linearity

简明释义

英[ˌlɪniˈærəti]美[ˌlɪniˈærəti]

n. 线性；线性度；直线性

复 数 l i n e a r i t i e s

英英释义

单词用法

同义词

反义词

例句

1.Of course, real world structures have all kinds of measurement problems with respect to noise, linearity, time variability, etc.

当然，真实结构具有关于噪声、线性、时变等等方面的各种测量问题。

2.The linearity analysis of laser scanning measurement has been presented.

对激光扫描测量系统的线性进行了系统的分析；

3.SCR contactor usually used zigzag wave, which linearity is better, but structure is complex and cost is expensive.

SCR触发器常以锯齿波为主，其线性度较好，但结构复杂，成本较高。

4.Conclusion This method has the advantages of accurate results, broad linearity, good repetition, and rapid report for clinical application.

结论本方法结果重复性好，线性范围宽，准确、快速，适合临床应用。

5.It is mainly used to compensate temperature shift and non-linearity error of piezoresistive pressure sensor.

此芯片集成化程度较高，可以补偿硅压阻式压力传感器的温度误差和非线性误差。

6.Data from the regression line itself may be helpful to provide mathematical estimates of the degree of linearity.

来自回归线自身的数据可以用于提供线性程度的数学评估。

7.Analysis of data shows that the experimental results are with high sensitivity and linearity.

数据分析显示，测量方法具有较高灵敏度和线性度。

8.In mathematics, the concept of linearity 线性性 is fundamental to understanding linear equations.

在数学中，线性性 线性性的概念是理解线性方程的基础。

9.The linearity 线性性 of a function can be tested by plotting its graph and checking for a straight line.

通过绘制函数的图形并检查是否为直线，可以测试其线性性 线性性。

10.In statistics, we often assume linearity 线性性 between independent and dependent variables.

在统计学中，我们通常假设自变量和因变量之间存在线性性 线性性。

11.The linearity 线性性 of a system makes it easier to analyze and predict its behavior.

系统的线性性 线性性使得分析和预测其行为变得更加容易。

12.A violation of linearity 线性性 in a regression model can lead to inaccurate predictions.

回归模型中线性性 线性性的违反可能导致不准确的预测。

作文

In mathematics and various fields of science, the concept of linearity plays a crucial role in understanding relationships and behaviors of different variables. Linearity refers to a property of a mathematical relationship that can be graphically represented as a straight line. This means that if one variable changes, the other variable changes in a consistent and proportional manner. In simpler terms, the relationship between the two variables is direct and predictable, which is a fundamental aspect in both theoretical and applied sciences. For example, consider the equation of a straight line, y = mx + b, where m represents the slope and b represents the y-intercept. This equation illustrates how for every unit increase in x, y increases by a constant amount, demonstrating linearity. This concept is not only limited to mathematics; it extends to physics, economics, and many other disciplines. In physics, linear relationships are often observed in phenomena such as Hooke's Law, which states that the force exerted by a spring is directly proportional to its extension or compression. Here, the relationship between force and displacement is linear, illustrating the principle of linearity in action. Moreover, in economics, the concept of linearity is essential when analyzing supply and demand curves. A linear demand curve indicates that as the price of a good decreases, the quantity demanded increases at a constant rate, showing a clear and predictable relationship between price and quantity. This allows economists to make forecasts and decisions based on the assumption of linearity, which simplifies complex market dynamics into more manageable forms. However, it is crucial to recognize that not all relationships exhibit linearity. Many real-world scenarios involve nonlinear relationships, where changes in one variable do not result in proportional changes in another. For instance, the relationship between speed and fuel consumption in a vehicle is typically nonlinear; as speed increases, fuel efficiency may decrease at an accelerating rate. Understanding the limitations of linearity is vital in fields such as data analysis and machine learning, where relying solely on linear models can lead to oversimplified conclusions. In summary, the concept of linearity is fundamental in various scientific disciplines, providing a framework for understanding and predicting relationships between variables. While linear models offer simplicity and clarity, it is essential to remain aware of their limitations and the existence of nonlinear relationships in the real world. By mastering the idea of linearity, we can better analyze data, solve problems, and make informed decisions across different fields of study. The ability to distinguish between linear and nonlinear relationships enhances our analytical skills and allows us to approach complex issues with a more nuanced perspective.

在数学和各种科学领域，线性的概念在理解不同变量的关系和行为中起着至关重要的作用。线性是指一种数学关系的属性，可以用直线在图形上表示。这意味着如果一个变量发生变化，另一个变量以一致和成比例的方式变化。简单来说，两个变量之间的关系是直接和可预测的，这是理论和应用科学中的基本方面。 例如，考虑直线方程y = mx + b，其中m表示斜率，b表示y截距。这个方程说明了每当x增加一个单位时，y就增加一个恒定的量，展示了线性的概念。这一概念不仅限于数学，它还扩展到物理学、经济学和许多其他学科。在物理学中，线性关系通常出现在现象如胡克定律中，该定律指出弹簧所施加的力与其伸长或压缩成正比。在这里，力与位移之间的关系是线性的，体现了线性原理的实际应用。 此外，在经济学中，线性的概念在分析供需曲线时也至关重要。线性需求曲线表明，随着商品价格的下降，需求量以恒定的速度增加，显示了价格与数量之间清晰且可预测的关系。这使得经济学家能够基于线性的假设进行预测和决策，从而将复杂的市场动态简化为更易管理的形式。 然而，必须认识到并非所有关系都表现出线性。许多现实场景涉及非线性关系，其中一个变量的变化不会导致另一个变量的成比例变化。例如，车辆的速度与燃油消耗之间的关系通常是非线性的；随着速度的增加，燃油效率可能以加速的方式下降。理解线性的局限性在数据分析和机器学习等领域至关重要，因为仅依赖线性模型可能会导致过于简单化的结论。 总之，线性的概念在各种科学学科中是基础，为理解和预测变量之间的关系提供了框架。虽然线性模型提供了简单性和清晰性，但重要的是要意识到它们的局限性以及现实世界中存在的非线性关系。通过掌握线性的概念，我们可以更好地分析数据、解决问题，并在不同的研究领域做出明智的决策。区分线性和非线性关系的能力增强了我们的分析技能，使我们能够以更细致的视角来处理复杂问题。