How would you best assess the degree oflinear association between two continuous variables?a) Scatter plotb) Odds ratioc) Bar chartd) Correlation
Answer:
A scatter plot would display graphically the association between two continuous variables. The x axisdisplays values for one variable, and the y axis values for the other. Each point represents one paired measurement of each variable.This is a useful way to display the data and would show U shaped and other non-linear associations as well as linear associations. However, a scatter plot does not measure an association. To put a number to it requires an assessment of correlation. Pearson’s correlation coefficient or Spearman’s rank correlation coefficient may be used to assess linear correlation. The correlation coefficient may take any value between 1 and −1: 0 represents no linear association, 1 represents a perfect straight line with y values increasing with increasing x values, −1 represents a perfect straight linewith y values decreasing with increasing x values. Like other parametric techniques, Pearson’s correlation coefficient is sensitive to outlying values and may generate misleading P values when the y axis valuesare not normally distributed. Spearman’s rank correlation coefficient, like other non-parametric techniques, is less sensitive and more robust. Odds ratios can be used to compare only dichotomous values. Odds express the probability of something happening divided by the probability of it not happening. A bar chart is used to plot the frequency of a nominal variable(such as eye colour) or an ordinal variable (such as agreement with a survey question: “disagree strongly, disagree, agree, agree strongly. 中文解释：散点图：将以图形方式显示两个连续变量之间的关联。x轴显示一个变量的值，y轴显示另一个变量的值。每个点代表每个变量的一对测量值。这是一种显示数据的有用方法，可以显示U形和其他非线性关联以及线性关联。但是，散点图无法准确衡量关联性。要衡量关联性，一般需要相关系数。 相关系数：关联度衡量两个变量线性相关的程度。皮尔逊相关系数或斯皮尔曼等级相关系数可用于评估线性相关。相关系数可以取介于1到-1之间的任何值：0表示没有线性关联，1表示y值随x值增加而增加的理想直线，-1表示y值随x值增加而减少的理想直线。与其他参数技术一样，Pearson的相关系数对异常值值敏感，并且当y轴值不呈正态分布时，可能会产生误导的P值。与其他非参数技术一样，Spearman的秩相关系数也不那么敏感，而且更可靠。 比值比（OR值）：可用于仅比较二分类数值，表示某事发生的概率除以某事未发生的概率。 条形图：用于绘制名义变量（例如眼睛的颜色）或有序变量（例如与调查问题的同意：“强烈不同意，不同意，同意，强烈同意”的频率）。
