On Mon, 4 Feb 2013 12:55:36 -0800 (PST), email@example.com wrote:
>Hi- >I haven't taken stats in a few years and recently there have been a lot thrown around my work place, including the attached graph (and raw data). I realize that low R2 mean that the linear regression is not a good fit, but it produces a p-value 0.025. I can't formulate a solid argument because I don't understand the material well enough. Am I incorrect in saying this is a poor fit? Even visually to me it looks like a poor fit. Additionally, he says things like: "FC Count at Samish River/Thomas Road: N = 498, r2 = 0.01, p = 0.025, meaning it is significant at 97.5% confidence" I know you can't use P-values to describe stats like this. I need help explaining why this data isn't showing a significant declining trend with a linear regression (in less of course I am incorrect.) > >Thanks for clarification and help. > >Data and Graph: http://dl.dropbox.com/u/18470470/Copy%20of%20Regression%20Correlation%20info.xlsx >
Oh, there's a declining trend, of sorts. I'm tempted to say that Nobody calls R^2 of 0.01 a large effect -- but that's not really true. Context rules.
The highest 5 outcome scores are all in the first half of the graph, and the very highest one is near the beginning. Does that seem important? That's most of the effect.
On the other hand, very few people would say that any time series is properly tested by a simply linear regression when there are autocorrelation effects... which there almost always are.
As to the size of the effect, and how few cases it depends on -- I'm "pretty sure" that the trend becomes n.s. if you remove the top 5 points; "probably" for the top 3, and "maybe" for removing the top one alone.