A linear model was applied to this data and the following output was obtained: >

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A linear model was applied to this data and the following output was obtained: > model <- lm(temperatures ~ years) > summary(model) Call: lm(formula = temperatures ~ years)
Residuals: Min 1Q Median 3Q Max -6.145 -1.148 0.192 1.355 4.251
Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -5.28798 1.90781 -2.77 0.0059 ** years 0.00464 0.00104 4.46 0.000011 *** --- Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.94 on 345 degrees of freedom Multiple R-squared: 0.0546, Adjusted R-squared: 0.0519 F-statistic: 19.9 on 1 and 345 DF, p-value: 0.0000109
(e) Given that the mean and the corrected sum of squares for all the years modelled are ar{x}=1832 and S(x,x) = 3481798 respectievely, what does the model estimate the average temperature in January was in the year 2000? Answer the question with a 95% confidence interval to 3 decimal places. Lower bound: Answer Upper bound: Answer (f) If an autocorrelation plot of the residuals of this fitted model was to be produced, where should horizontal lines marking a 95% confidence band be placed? Enter your answers to 3 decimal places. Lower bound: Answer Upper bound: Answer The Central England Temperature data consist of temperatures recorded in Central England from 1659-2005. It is the longest instrumental record of temperature in the world. The measurements are average values from several recording stations within a triangle in Central England (roughly between London, Manchester and Bristol). 9 Data for the month of January from 1659 to 2005 is shown in the following plot. January 1650 1700 17508001850 1900 19502000 Year

Explanation / Answer

In this we need data to this two part but I will give simple R cammands to solve this two problem

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