Q correlation coefficient (diagrams from textbook provided)

In summary, locating the median in a set of data points can help you draw horizontal and vertical lines to create quadrants. The median can be found by counting the points from left to right or from bottom to top. Drawing the lines at the median will generate the four quadrants, with the vertical line passing through any point with an x-coordinate equal to the median, and the horizontal line passing through any point with a y-coordinate equal to the median.
  • #1
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The thing I don't understand is how does finding out the median help you drawing in the horizontal and vertical lines. How do you know if it's going to pass through a few of the data points? Like in the example on that website it has a moderate negative correlation and the median is the 8'th score. How did they put the vertical+horizontal line by knowing the median? I see 6 data points in the B quadrant, 5 in the D quadrant, 1 in both A and C. How did they know all this by just the 8'th score? Thanks.

[PLAIN]http://coburgmaths09.globalteacher.org.au/files/2009/05/qcorrelation1.png
[PLAIN]http://coburgmaths09.globalteacher.org.au/files/2009/05/qcorrelation2.png
[PLAIN]http://coburgmaths09.globalteacher.org.au/files/2009/05/qcorrelation-eg-1.png
[PLAIN]http://coburgmaths09.globalteacher.org.au/files/2009/05/qcorrelation-eg-2.png[/quote]
 
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  • #2
Look at the points in order from left to right. The x-coordinates are in increasing order, so locating the median can be done by counting.

If you look at the points from bottom to top, the y-coordinates are in increasing order, so you know where the median is.

Drawing the two lines will generate the four quadrants. (Note: the vertical line will always pass through any point with an x-coordinate equal to the median, and the horizontal line will always pass through any point with a y-coordinate equal to that median. These may not be the same point.)
 

Related to Q correlation coefficient (diagrams from textbook provided)

1. What is the Q correlation coefficient?

The Q correlation coefficient is a measure of association between two binary variables. It ranges from -1 to 1, with a value of 0 indicating no association, a positive value indicating a positive association, and a negative value indicating a negative association.

2. How is the Q correlation coefficient calculated?

The Q correlation coefficient is calculated by first creating a contingency table with the two binary variables. Then, the formula for Q is used: Q = (ad - bc) / [(a+b)(a+c)(b+d)(c+d)], where a, b, c, and d represent the frequencies in each cell of the contingency table.

3. What does the Q correlation coefficient tell us?

The Q correlation coefficient tells us the strength and direction of the relationship between the two binary variables. A higher absolute value of Q indicates a stronger association, while a positive or negative value indicates the direction of the association.

4. Can the Q correlation coefficient be used for continuous variables?

No, the Q correlation coefficient is only appropriate for binary variables. For continuous variables, other measures such as Pearson correlation coefficient or Spearman's rank correlation coefficient should be used.

5. How is the significance of the Q correlation coefficient determined?

The significance of the Q correlation coefficient can be determined by using a chi-square test. The null hypothesis is that there is no association between the two variables, and if the p-value is less than the chosen significance level (usually 0.05), we can reject the null hypothesis and conclude that there is a significant association between the variables.

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