3.9 Table B.6 in Appendix B contains data on the global mean surface air tempera

Question

3.9 Table B.6 in Appendix B contains data on the global mean surface air temperature anomaly and the global CO2 concentration. Fit a regression model to these data, using the global CO2 concentration as the predictor. Analyze the residuals from this model. Is there evidence of autocorrelation in these data? If so, use one iteration of the Cochrane–Orcutt method to estimate the parameters.

v-20109221287783697918 9013468912 2333333344 (p 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 cp122 nal 25 15 07 02 09 00 04 10 05 18 06 02 21 16 07 13 27 40 10 34 0586216737 1010021012 0 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 78888 2V-8150 .4 8 3 8 2 7 2 6 0 4 8 0 2 3 44 (p 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 158125241 100011011 00200 ma no ( 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 67890123456789 2 2 iv 7 2 7 1 6 0 3 6 8 0 2 3 5 6 7 8 9 0 25 1122333344444444555 1-y 922751 254333212 110 Glo re-0-0-0-0-0-0-0-0-00-0-0-0-0-0-0-0-0-0-0 56789 6 9 88888800 8 8 8 8 8 8 8 8 8 8 8 8 8 3

Explanation / Answer

Solution :

global <- read.csv('Global.csv',header = T,sep = ',',stringsAsFactors = F) #reading the dataset

glo<- lm(global$X..C. ~ global$X.ppmv.,data=global) #running a simple linear regression method on the data

summary(glo)

To Check for autocorrelation we need to use

Cochrane–Orcutt method :

glocoh <- cochrane.orcutt(glo)

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