# anscombe

Attachment | Size |
---|---|

dataset-68467.csv | 364 bytes |

Documentation | ||||||
---|---|---|---|---|---|---|

## Anscombe's Quartet of ‘Identical’ Simple Linear RegressionsFour ## Usageanscombe ## FormatA data frame with 11 observations on 8 variables.
## SourceTufte, Edward R. (1989)
## ReferencesAnscombe, Francis J. (1973) Graphs in statistical analysis.
## Examplesrequire(stats); require(graphics) summary(anscombe) ##-- now some "magic" to do the 4 regressions in a loop: ff <- y ~ x mods <- setNames(as.list(1:4), paste0("lm", 1:4)) for(i in 1:4) { ff[2:3] <- lapply(paste0(c("y","x"), i), as.name) ## or ff[[2]] <- as.name(paste0("y", i)) ## ff[[3]] <- as.name(paste0("x", i)) mods[[i]] <- lmi <- lm(ff, data = anscombe) print(anova(lmi)) } ## See how close they are (numerically!) sapply(mods, coef) lapply(mods, function(fm) coef(summary(fm))) ## Now, do what you should have done in the first place: PLOTS op <- par(mfrow = c(2, 2), mar = 0.1+c(4,4,1,1), oma = c(0, 0, 2, 0)) for(i in 1:4) { ff[2:3] <- lapply(paste0(c("y","x"), i), as.name) plot(ff, data = anscombe, col = "red", pch = 21, bg = "orange", cex = 1.2, xlim = c(3, 19), ylim = c(3, 13)) abline(mods[[i]], col = "blue") } mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex = 1.5) par(op) |

Title | Authored on | Content type |
---|---|---|

OpenIntro Statistics Dataset - dream | August 9, 2020 - 12:25 PM | Dataset |

OpenIntro Statistics Dataset - winery_cars | August 9, 2020 - 2:38 PM | Dataset |

R Dataset / Package HSAUR / toothpaste | March 9, 2018 - 1:06 PM | Dataset |

R Dataset / Package HSAUR / pottery | March 9, 2018 - 1:06 PM | Dataset |

R Dataset / Package HistData / Guerry | March 9, 2018 - 1:06 PM | Dataset |