If Q is greater than or equal to the limit, the pattern may have originated from random chance.If Q is less than the limit, the two variables are related.Look up the limit for N on the trend test table:.Locate the smaller sum and the total of points in all quadrants, and add the diagonally opposite quadrants:Ī = points in upper left + points in lower rightī = points in upper right + points in lower left.If the number of points is odd, draw a line through the middle point.Count X/2 points from left to right and draw a vertical line.Count X/2 points from top to bottom and draw a horizontal line. ![]() Divide points on the graph into four equal sections.If regression or correlation analysis is not needed, complete steps four through seven below. Use regression or correlation analysis, if necessary.Stop if the data forms a line or a curve, as the variables are considered correlated.Review the pattern of points to determine if a relationship is present:.If two dots fall together, place them side by side so they are touching and both are visible.Place a dot or a symbol where the x-axis value intersects the y-axis value.Place the dependent variable on the vertical (Y) axis.Place the independent variable on the horizontal (X) axis.Draw a graph in the shape of an “L,” and make the scale even multiples (i.e., 10, 20).Collect sets of data where a relationship is present.In other words, more people are in the water on hot days when shark attacks occur, and more people are buying ice cream. ![]() The two may be correlated, but ice cream does not cause shark attacks–the heat of the day does. The example often used is shark attacks and ice cream sales. If you are looking for a way to do a graphical analysis of discrete data, you might try attribute charts. I suppose you also *could* put discrete data that comes out like pass/fail as one of two bands, but it would depend on whether or not you got any useful information out of the data. For the discrete data, you’d have to put it into some kind of quantified band–like say 1-10 on a customer satisfaction score. You could use discrete data on one scatter plot axis and continuous data on the other. Continuous data lets you measure things deeply on an infinite set and is generally make use in scatter analysis. ( See notes on the different data types here.)ĭiscrete data is best at pass/ fail measurements. Scatter analysis generally makes use of continuous data. What Kind of Data Should You Use for Scatter Analysis?
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