Seatbelts
Attachment | Size |
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dataset-75797.csv | 8.65 KB |
Documentation |
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Road Casualties in Great Britain 1969–84
UsageUKDriverDeaths Seatbelts Format
SourceHarvey, A.C. (1989) Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press, pp. 519–523. Durbin, J. and Koopman, S. J. (2001) Time Series Analysis by State Space Methods. Oxford University Press. http://www.ssfpack.com/dkbook/ ReferencesHarvey, A. C. and Durbin, J. (1986) The effects of seat belt legislation on British road casualties: A case study in structural time series modelling. Journal of the Royal Statistical Society series B, 149, 187–227. Examplesrequire(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)))) 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) |
Picostat Manual |
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R Output | Date |
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Picostat Output - Bivariate Plot | Jul 22, 2022 |
Picostat Output - Bivariate Plot | Jul 22, 2022 |
Picostat Output - Bivariate Plot | Jul 22, 2022 |
Picostat Output - Bivariate Plot | Jul 22, 2022 |
Picostat Output - Bivariate Plot | Jul 22, 2022 |
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