Artificial data created by Anscombe to illustrate the necessity for studying residuals. Each of the four data sets has the same fitted regression line, y=3+0.5x, and the same summary ANOVA table (with the same regression sum of squares, total sum of squares, and value for R2).
x
y
x
y
x
y
x
y
10
8.04
10
9.14
10
7.46
8
6.58
8
6.95
8
8.14
8
6.77
8
5.76
13
7.58
13
8.74
13
12.74
8
7.71
9
8.81
9
8.77
9
7.11
8
8.84
11
8.33
11
9.26
11
7.81
8
8.47
14
9.96
14
8.10
14
8.84
8
7.04
6
7.24
6
6.13
6
6.08
8
5.25
4
4.26
4
3.10
4
5.39
19
12.50
12
10.84
12
9.13
12
8.15
8
5.56
7
4.82
7
7.26
7
6.42
8
7.91
5
5.68
5
4.74
5
5.73
8
6.89
Anscombe's regression data. Each data set has the same mean and variance for x, the same mean and variance for y, the same fitted regression line, and the same residual sum of squares—Anscombe created these data sets to emphasize the need for the statistician to look carefully at data.
- Malmquist bias
- malnutrition
- malonic acid
- Malpighian body
- Malpighian layer
- Malpighian tubule
- Malpighi, Marcello
- Malplaquet, Battle of (11 September 1709)
- MALT
- malt
- Malta
- maltase
- Malthusianism
- Malthusian problem
- Malthus, Thomas Robert (1766–1834)
- maltose
- malt sugar
- Malus, Etienne Louis
- Malus’s law
- Malvernian
- malvertising
- Malvinas
- malware
- mamba
- Mameluke