Assignment__3_Solutions - Stat 373 (F08) - Assignment #3:...

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Stat 373 (F08) - Assignment #3: Due: Friday, Oct. 31 at 4:00 in MC 4018 Consider the mean monthly temperature time series for Kenora, Ontario (God’s country) from January 1958 to July, 2006 (583months) found in the dataset Kenoratemp in UW-ACE. 1) Plot the time series and correlogram for the data. (Note: if you are having difficulty plotting the time series, you may need to convert the dataset to a matrix using the as.matrix command). Comment on what the correlogram tells you about the process. 1
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Stat 202 (W07) – Assignment #1 2 2) Based on what you know about weather, give a reasonable explanation why you think r 3 is approximately 0 for this data. An r 3 of approx. 0 suggests that there is no consistent correlation in mean monthly temperatures three months apart. This is due to the positive and negative correlations effectively ‘cancelling out’ over the year. For example, one would expect (Oct., Jan.), (Nov.,Feb.), to be positively correlated with respect to mean temperature, whereas one would expect(Jan., April), (Feb., May) to be negatively correlated. Thus, when taking into account the autocorrelation of lag 3, we would expect to see an overall autocorrelation of approx. 0. 3) Fit a linear regression model using a separate indicator variate for each month to account only for the observed seasonal pattern (you will first need to augment the data set with the matrix of indicator variates). Comment on the fit of the model. Bassam : One way you can instruct the students on how to create dummy variables. (Is there an easier way?) > n<-length(t(ktreg)) > jan<-rep(c(1,rep(0,11)),length.out=n) > feb<-c(0,jan[-n])
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Assignment__3_Solutions - Stat 373 (F08) - Assignment #3:...

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