bivariate的音标是[ˌbaɪəˈvɪərɪə] ,基本翻译是“双变量的”,速记技巧是:bi(双)+ vari(变化)+ ate(的)=> 双变量的。
Bivariate这个词源自拉丁语,意为“二元”或“双变量”。它的变化形式包括bi-(表示“两个”)和-vari-(表示“变化”)组合而成。
相关单词:
1. Bi-polar:双极的,表示具有两个极端或两个极性的事物。这个词常用于描述气候、情绪、行为等方面的极端情况。
2. Bivariate analysis:二元分析,是一种统计方法,用于研究两个变量之间的关系。
3. Multivariate:多元的,表示涉及多个变量的。这个词常用于统计学和数据分析中。
4. Bivariate distribution:二元分布,是一种描述两个变量之间关系的概率分布。
5. Bivariate regression:二元回归,是一种统计方法,用于研究两个变量之间的线性关系。
6. Bivariate correlation:二元相关,是一种统计指标,用于衡量两个变量之间的线性关系强度。
7. Bivariate plot:二元图,是一种可视化工具,用于展示两个变量之间的关系。
8. Bivariate distribution function:二元分布函数,是一种数学函数,用于计算二元概率分布的概率密度。
9. Bivariate model:二元模型,是一种数学模型,用于描述两个变量之间的相互关系。
10. Bivariate copula:二元共轭分布0维泊松分布,是一种统计模型,用于描述两个变量之间的依赖关系。它通常用于风险评估和金融建模。
常用短语:
1. bivariate analysis
2. correlation analysis
3. scatter plot
4. regression analysis
5. co-occurrence
6. joint probability
7. correlation coefficient
双语例句:
1. Bivariate analysis showed a strong correlation between age and education level. (双变量分析显示年龄和教育水平之间有很强的相关性。)
2. The scatter plot shows a clear relationship between the two variables. (散点图显示了这两个变量之间有明显的关系。)
3. Regression analysis revealed that income is significantly correlated with education. (回归分析显示收入与教育程度有显著的相关性。)
4. Co-occurrence of the two variables is high, indicating a strong relationship between them. (这两个变量出现频率高,表明它们之间有很强的关系。)
5. Joint probability analysis suggests that there is a strong correlation between the two variables. (联合概率分析表明这两个变量之间有很强的相关性。)
6. The correlation coefficient between the two variables is 0.7, indicating a strong positive correlation. (两个变量之间的相关系数为0.7,表明有很强的正相关性。)
7. The data shows a significant bivariate correlation between the two variables, indicating that they are related to each other.
英文小作文:
Bivariate Analysis of Two Variables
Bivariate analysis is a statistical method that examines the relationship between two variables simultaneously. It helps us understand how one variable depends on another, and how they are related to each other. In this example, we will examine the relationship between age and education level using bivariate analysis.
From the data, we can see that there is a significant correlation between age and education level. This means that people with higher levels of education tend to be older, and vice versa. This relationship can be explained by various factors, such as social norms, economic conditions, and life expectancy. However, bivariate analysis does not provide any causal explanations, but rather highlights the relationship between the two variables. Therefore, it is important to consider other factors that may influence the relationship between age and education level, such as income, occupation, and family background.