anscombe | R Documentation |

Four *x*-*y* datasets which have the same traditional
statistical properties (mean, variance, correlation, regression line,
etc.), yet are quite different.

anscombe

A data frame with 11 observations on 8 variables.

x1 == x2 == x3 | the integers 4:14, specially arranged |

x4 | values 8 and 19 |

y1, y2, y3, y4 | numbers in (3, 12.5) with mean 7.5 and sdev 2.03 |

Tufte, Edward R. (1989).
*The Visual Display of Quantitative Information*, 13–14.
Graphics Press.

Anscombe, Francis J. (1973).
Graphs in statistical analysis.
*The American Statistician*, **27**, 17–21.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.2307/2682899")}.

require(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)

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