# anscombe

0th

Percentile

##### Anscombe's Quartet of ‘Identical’ Simple Linear Regressions

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

Keywords
datasets
##### Usage
anscombe
##### Format

A data frame with 11 observations on 8 variables.

 x1 == x2 == x3 the integers 4:14, specially arranged x4 values 8 and 19

##### References

Anscombe, Francis J. (1973). Graphs in statistical analysis. The American Statistician, 27, 17--21. 10.2307/2682899.

• anscombe
##### Examples
library(datasets) # NOT RUN { 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[] <- as.name(paste0("y", i)) ## ff[] <- 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) # } 
Documentation reproduced from package datasets, version 3.6.2, License: Part of R 3.6.2

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