anscombe {datasets} | R Documentation

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

### Description

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

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

### Source

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

### References

Anscombe, Francis J. (1973) Graphs in statistical analysis. American Statistician, 27, 17–21.

### Examples

    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)

[Package datasets version 3.3.0 Index]