Data Home | Math Topics | Environment Topics | Topics Matrix | Master List | Help

Download the Data

PDF

Excel File

Text File

Minitab File

Data Set #017

About the Data
View the Data
Help with Using Data
Play with the data on StatCrunch
 

  Go to Top  

About the Data

ABOUT BODY WEIGHT VERSUS BRAIN WEIGHT OF MAMMALS

Biometrics is the quantitative analysis of ontogenetic ("of or relating to life cycle or development") parameters such as height, weight, shape, morphology, age, etc. Biometrics is one key to understanding growth of individuals with time. Biometrics can also be applied to groups of organisms (such as a population of Douglas fir trees, or all butterflies, or all mammals) to gain insights into fundamental principles of growth or behavior in those groups.

Allison and Cicchetti (1976) provide data on body weight (in kilograms) and corresponding brain weight (in grams) for 62 different terrestrial mammals (no whales). Students should question the meaning of these pairs of numbers immediately. For example, there is only one pair of numbers for humans. Is this a single datum of a single human? Is this the mean of many measurements? Old or young, male or female, well fed or malnourished? The "human" in the table weighs 62 kilograms or 136 pounds; is this representative? In addition, these data were not collected by a single investigator, nor were they collected in the same manner, perhaps adding complexity.

The values of body weight range over 6 orders of magnitude. To represent data with such a wide range of values on a single graph requires logarithms. The plot of log body weight versus log brain weight shows a strong positive correlation, as to be expected.

A linear regression through the log-log data has a fairly high correlation coefficient, suggesting a good fit of a power law to the original ("unlogged") data. The slope of this line is less than 1, indicating a variable ratio of body to brain weight as a function of size of the mammal. The exponent less than 1 indicates that small mammals have relatively large brains compared to body size, probably because they have relatively large heads.

Despite taking the log of both variables, a large amount of scatter remains. Thus the best fit power law to these data will not have much predictive value; one would not use this empirical relationship to predict brain weight of a mammal for which only body weight was known. Your prediction might be off by an order of magnitude or more.

Mammals that plot above the best fit line have relatively large brains for their body size; monkeys, chimps, babboons and humans all plot well above the line. And in case you're feeling smug, so do ground squirrels. The opossum falls well below the line.

It would be interesting to see data for a single species, such as coyotes or rabbits.

 

Reference: Allison, T. and Cicchetti, D. V. (1976), Sleep in mammals: ecological and constitutional correlates; Science, v. 194, pp. 732-734.

     
  Go to Top  

View the Data

Allison et al (1976), mammals brain and body weight

body wt kg

brain wt g

log body

log brain

6654.000

5712.00

3.82

3.76

1.000

6.60

0.00

0.82

3.385

44.50

0.53

1.65

0.920

5.70

-0.04

0.76

2547.000

4603.00

3.41

3.66

10.550

179.50

1.02

2.25

0.023

0.30

-1.64

-0.52

160.000

169.00

2.20

2.23

3.300

25.60

0.52

1.41

52.160

440.00

1.72

2.64

0.425

6.40

-0.37

0.81

465.000

423.00

2.67

2.63

0.550

2.40

-0.26

0.38

187.100

419.00

2.27

2.62

0.075

1.20

-1.12

0.08

3.000

25.00

0.48

1.40

0.785

3.50

-0.11

0.54

0.200

5.00

-0.70

0.70

1.410

17.50

0.15

1.24

60.000

81.00

1.78

1.91

529.000

680.00

2.72

2.83

27.660

115.00

1.44

2.06

0.120

1.00

-0.92

0.00

207.000

406.00

2.32

2.61

85.000

325.00

1.93

2.51

36.330

119.50

1.56

2.08

0.101

4.00

-1.00

0.60

1.040

5.50

0.02

0.74

521.000

655.00

2.72

2.82

100.000

157.00

2.00

2.20

35.000

56.00

1.54

1.75

0.005

0.14

-2.30

-0.85

0.010

0.25

-2.00

-0.60

62.000

1320.00

1.79

3.12

0.122

3.00

-0.91

0.48

1.350

8.10

0.13

0.91

0.023

0.40

-1.64

-0.40

0.048

0.33

-1.32

-0.48

1.700

6.30

0.23

0.80

3.500

10.80

0.54

1.03

250.000

490.00

2.40

2.69

0.480

15.50

-0.32

1.19

10.000

115.00

1.00

2.06

1.620

11.40

0.21

1.06

192.000

180.00

2.28

2.26

2.500

12.10

0.40

1.08

4.288

39.20

0.63

1.59

0.280

1.90

-0.55

0.28

4.235

50.40

0.63

1.70

6.800

179.00

0.83

2.25

0.750

12.30

-0.12

1.09

3.600

21.00

0.56

1.32

83.000

98.20

1.92

1.99

55.500

175.00

1.74

2.24

1.400

12.50

0.15

1.10

0.060

1.00

-1.22

0.00

0.900

2.60

-0.05

0.41

2.000

12.30

0.30

1.09

0.104

2.50

-0.98

0.40

4.190

58.00

0.62

1.76

3.500

3.90

0.54

0.59

4.050

17.00

0.61

1.23

 

  Go to Top