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)
summary(anscombe)

##-- now some "magic" to do the 4 regressions in a loop:
ff <- y ~ x
for(i in 1:4) {
ff[2:3] <- lapply(paste(c("y","x"), i, sep=""), as.name)
## or   ff[[2]] <- as.name(paste("y", i, sep=""))
##      ff[[3]] <- as.name(paste("x", i, sep=""))
assign(paste("lm.",i,sep=""), lmi <- lm(ff, data= anscombe))
print(anova(lmi))
}

## See how close they are (numerically!)
sapply(objects(pat="lm\.[1-4]\$"), function(n) coef(get(n)))
lapply(objects(pat="lm\.[1-4]\$"), function(n) summary(get(n))\$coef)

## Now, do what you should have done in the first place: PLOTS
op <- par(mfrow=c(2,2), mar=.1+c(4,4,1,1), oma= c(0,0,2,0))
for(i in 1:4) {
ff[2:3] <- lapply(paste(c("y","x"), i, sep=""), as.name)
plot(ff, data =anscombe, col="red", pch=21, bg = "orange", cex = 1.2,
xlim=c(3,19), ylim=c(3,13))
abline(get(paste("lm.",i,sep="")), col="blue")
}
mtext("Anscombe's 4 Regression data sets", outer = TRUE, cex=1.5)
par(op)
```

[Package datasets version 2.1.0 Index]