A pre-loaded example dataset in R

Main page: https://www.rdocumentation.org/packages/datasets/versions/3.6.2/topics/Seatbelts

head(Seatbelts)
##      DriversKilled drivers front rear   kms PetrolPrice VanKilled law
## [1,]           107    1687   867  269  9059   0.1029718        12   0
## [2,]            97    1508   825  265  7685   0.1023630         6   0
## [3,]           102    1507   806  319  9963   0.1020625        12   0
## [4,]            87    1385   814  407 10955   0.1008733         8   0
## [5,]           119    1632   991  454 11823   0.1010197        10   0
## [6,]           106    1511   945  427 12391   0.1005812        13   0
require(stats); require(graphics)
## work with pre-seatbelt period to identify a model, use logs
work <- window(log10(UKDriverDeaths), end = 1982+11/12)
par(mfrow = c(3, 1))
plot(work); acf(work); pacf(work)

par(mfrow = c(1, 1))
(fit <- arima(work, c(1, 0, 0), seasonal = list(order = c(1, 0, 0))))
## 
## Call:
## arima(x = work, order = c(1, 0, 0), seasonal = list(order = c(1, 0, 0)))
## 
## Coefficients:
##          ar1    sar1  intercept
##       0.4378  0.6281     3.2274
## s.e.  0.0764  0.0637     0.0131
## 
## sigma^2 estimated as 0.00157:  log likelihood = 300.85,  aic = -593.7
z <- predict(fit, n.ahead = 24)
ts.plot(log10(UKDriverDeaths), z$pred, z$pred+2*z$se, z$pred-2*z$se,
        lty = c(1, 3, 2, 2), col = c("black", "red", "blue", "blue"))

## now see the effect of the explanatory variables
X <- Seatbelts[, c("kms", "PetrolPrice", "law")]
X[, 1] <- log10(X[, 1]) - 4
arima(log10(Seatbelts[, "drivers"]), c(1, 0, 0),
      seasonal = list(order = c(1, 0, 0)), xreg = X)
## 
## Call:
## arima(x = log10(Seatbelts[, "drivers"]), order = c(1, 0, 0), seasonal = list(order = c(1, 
##     0, 0)), xreg = X)
## 
## Coefficients:
##          ar1    sar1  intercept     kms  PetrolPrice      law
##       0.3348  0.6672     3.3539  0.0082      -1.2224  -0.0963
## s.e.  0.0775  0.0612     0.0441  0.0902       0.3839   0.0166
## 
## sigma^2 estimated as 0.001476:  log likelihood = 349.73,  aic = -685.46