```r
# Read a txt file, named "boxoffice.txt"
dta <- read.delim("C:/Users/ASUS/Desktop/data/boxoffice.txt")
#
m0 <- lm(GrossBoxOffice ~ year, data=dta)
summary(m0)
##
## Call:
## lm(formula = GrossBoxOffice ~ year, data = dta)
##
## Residuals:
## Min 1Q Median 3Q Max
## -116.382 -79.197 6.083 62.260 121.697
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -58386.485 2952.825 -19.77 <2e-16 ***
## year 29.534 1.483 19.92 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 77.44 on 30 degrees of freedom
## Multiple R-squared: 0.9297, Adjusted R-squared: 0.9274
## F-statistic: 396.8 on 1 and 30 DF, p-value: < 2.2e-16
library(lmtest)
## Loading required package: zoo
##
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
##
## as.Date, as.Date.numeric
dwtest(data=dta,GrossBoxOffice ~ year, alternative="two.sided")
##
## Durbin-Watson test
##
## data: GrossBoxOffice ~ year
## DW = 0.24809, p-value = 4.689e-13
## alternative hypothesis: true autocorrelation is not 0
library(nlme)
m1 <- gls(GrossBoxOffice ~ year,data=dta, corr = corAR1(0.87821 , form = ~ 1 | year), method="ML")
summary(m1)
## Generalized least squares fit by maximum likelihood
## Model: GrossBoxOffice ~ year
## Data: dta
## AIC BIC logLik
## 375.1165 380.9794 -183.5582
##
## Correlation Structure: AR(1)
## Formula: ~1 | year
## Parameter estimate(s):
## Phi
## 0.87821
##
## Coefficients:
## Value Std.Error t-value p-value
## (Intercept) -58386.48 2952.8247 -19.7731 0
## year 29.53 1.4827 19.9194 0
##
## Correlation:
## (Intr)
## year -1
##
## Standardized residuals:
## Min Q1 Med Q3 Max
## -1.55212595 -1.05620690 0.08113172 0.83032067 1.62299772
##
## Residual standard error: 74.9826
## Degrees of freedom: 32 total; 30 residual
knitr::spin("C:/Users/ASUS/Desktop/Boxoffice.R", knit=FALSE)
## [1] "C:/Users/ASUS/Desktop/Boxoffice.Rmd"