Hey folks

How can I calculate the CI 95% in the model:

temp <- c(1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26)

tx <- c(33,19,35,32,3,13,11,2,24,36,40,15,10,29,50,42,8,26,16,7,10,21,47,29,40,8)

dados1 <- data.frame(temp, tx)

m1 <- prais_winsten(tx ~ temp, index = "temp", data = dados1)

summary(m1)

call:

Residuals:

Min 1Q Median 3Q Max

-21.01 -12.91 -0.83 11.72 26.69

AR(1) coefficient rho after 5 iterations: 0.2279

Coefficients:

Estimate Std. Error t value Pr(>|t|)

(Intercept) 22.67389 7.19953 3.149 0.00434 **

temp 0.04233 0.46369 0.091 0.92802

Signif. codes: 0 ‘* ’ 0.001 ‘’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 14.16 on 24 degrees of freedom

Multiple R-squared: 0.02283, Adjusted R-squared: -0.01788

F-statistic: 0.5608 on 1 and 24 DF, p-value: 0.4612

Durbin-Watson statistic (original): 1.5

Durbin-Watson statistic (transformed): 1.789

Can anybody teach me how to script some way to perform it?

Thanks