coefficients

coefficients 的音标是 [ˌkəʊɪəˈfɪʃnz]，翻译为“系数；乘数”

速记技巧：可以结合数学中的系数来记忆这个单词

请注意，以上信息仅供参考，因为词汇的含义会根据语境而变化。如需获取最准确的信息，请咨询专业人士。

Coefficients这个词的英文词源可以追溯到拉丁语coefficientis，意为“相关的，起作用的”。这个词后来被引入英语，并演变为“coefficient”。

变化形式：复数形式为coefficients，过去式为coefficiented，现在分词为coefficienting。

相关单词：

1. coefficient of friction（摩擦系数）：这是一个常用的物理术语，用来描述两个表面之间的摩擦力与正压力之比。

2. coefficient of performance（性能系数）：这个术语常用于描述某种能源转换设备的效率，例如空调系统或燃料电池。

3. coefficient of restitution（恢复系数）：在碰撞力学中，这个系数描述了两个物体在碰撞后恢复原状的能力。

4. characteristic coefficient（特征系数）：在数学和物理学中，这个系数用于描述某种系统的特性或行为。

5. coefficient of variation（变异性系数）：这个术语用于描述数据变异性的一种方式，通常用于统计学中。

6. regression coefficient（回归系数）：在统计学中，回归系数描述了因变量和自变量之间的线性关系强度。

7. sensitivity coefficient（灵敏度系数）：这个术语用于描述某种因素对系统或结果的影响程度。

8. activity coefficient（活度系数）：这个术语用于描述物质在溶液中的活度，即实际浓度与理想浓度的比值。

9. impedance coefficient（阻抗系数）：这个术语用于描述电路或系统中的阻抗特性，通常用于电气工程中。

10. correlation coefficient（相关系数）：这个术语用于描述两个变量之间相关性的强度，通常用于统计学中。

常用短语：

1. coefficient of determination (R²)

2. coefficient of correlation (r)

3. regression coefficient

4. constant coefficient

5. proportional coefficient

6. interaction coefficient

7. sensitivity coefficient

双语例句：

1. The coefficient of determination (R²) measures the proportion of variance explained by a regression model. Example: The coefficient of determination for our model is 0.95, indicating that 95% of the variation in Y is explained by X.

2. The coefficient of correlation (r) indicates the strength and direction of the relationship between two variables. Example: The correlation between income and education is strong, indicating that higher income is associated with greater education.

英文小作文：

Title: The Importance of Coefficients in Modeling Data

When analyzing data, we often use mathematical models to predict or explain patterns in the data. Coefficients are an essential part of these models, playing a crucial role in explaining relationships between variables. From determining the strength of a correlation to measuring the proportion of variance explained by a regression model, coefficients provide valuable insights into the data.

For instance, in regression analysis, we use regression coefficients to understand how each variable impacts the other. By examining these coefficients, we can identify patterns and trends in the data that would otherwise be overlooked. Additionally, coefficients can help us identify potential biases or flaws in our data or models, providing valuable feedback for future analysis.

Ultimately, coefficients serve as a valuable tool for analyzing data and understanding relationships between variables. By using appropriate coefficients and carefully interpreting their implications, we can gain a deeper understanding of the data and develop more accurate predictions and explanations.