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I am analyzing the number of donors cancelling commitments as a monthly time series which varies significantly with economic indicators, some according to internal data, and maybe according to season. I want to see the variance after adjusting for the economy and season (assuming the remainder of variance is due to the non-profit's behavior). Is a good way to do this to fit a linear regression model in R pseudo-code:

fit <- lm(cancels ~ economy + season)

And then use the residuals?

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    $\begingroup$ lm rather than ln $\endgroup$
    – Henry
    Nov 8, 2012 at 21:10

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If your residual is high enough I believe you have explained the variance of the dependent variable by the independent variables. However, if the variance isn't explained sufficient by the independent variables you might want to consider to do a little further analysis with the help of some of the time series analysis packages that are available in R. Decomposing or STL might give you an insight into the components of the time series (trend, seasonality and randomness).

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