Stature estimation is obtained from measurements of long bones; namely the humerus, femur, and tibia. If these bones are unavailable, the ulna, radius, and fibula can also provide a good range for the expected height of an individual. As many elements as possible should be used in a regression equation to estimate stature. Incomplete fragments can be used to estimate height, by first estimating the complete length of bone from a regression equation before applying the original formula to estimate stature. Read more below about regression formulas.

Since stature is variable among population and sex, there are different regression formulas for different populations. Therefore, determining which regression formula to use is an important aspect of stature estimation. It is necessary to know the population from which an individual is from and the individual’s sex when selecting a formula.

Age may also be an important data point since the spinal column compresses with age, reducing the person’s expected height.

**Segment 1**is from the most proximal point of the head to lesser trochanter midpoint**Segment 2**is identified as lesser trochanter midpoint to the most proximal extension of the popliteal surface below the linea aspera**Segment 3**is identified as most proximal extension of the popliteal surface below the linea aspera to the proximal point of the intercondylar fossa**Segment 4**is the from the intercondylar fossa proximal point to the medial condyle most distal point

Below are examples of estimating stature from the femur of a European male*:

- Entire femur: (2.38 x the length of the femur) + 61.41 = stature +/- 3.27 cm
- From two shaft segments: (2.71 x segment 2) + (3.06 x segment 3) + 73.0 = stature +/- 4.41 cm
- From shaft and proximal end: (2.89 x segment 1) + (2.31 x segment 2) + (2.62 x segment 3) + 63.88 = stature +/- 3.93 cm.

Additional regressions can be found throughout the literature are listed in this table, as the above formula found in *The Anatomy and Biology of the Human Skeleton* (Steel and Bramblett 1988).

**Segment 1**is identified as the entirety of the humeral head**Segment 2**is identified as between the most distal point of the head and the most proximal margin of the olecranon fossa.**Segment 3**is identified as the entirety of the olecranon fossa, from the most proximal to the most distal margins**Segment 4**is identified as between the most distal margin of the olecranon fossa, and the most distal point of the trochlea

Below are examples of estimating stature from a humerus for a European Male*:

- For entire humerus: (3.08 x length of humerus) + 70.45 = stature +/- 4.05 cm
- From one segment of the shaft: (3.42 x segment 2) + 80394 = stature +/- 5.31 cm
- From two segments of the shaft: (7.17 x segment 1)+(3.04 x segment 2) + 63.94 = stature +/- 5.05 cm

Additional regressions can be found throughout the literature are listed in this table, as the above formula found in *The Anatomy and Biology of the Human Skeleton* (Steel and Bramblett 1988).

Below are examples of estimating stature from the tibia of a European male*:

- For entire tibia: (2.52 x the length of the tibia) + 78.62 = stature +/- 3.37 cm
- From three shaft segments: (segment 2, 3 and 4): (3.52 x segment 2) + (2.89 x segment 3) + (2.23 x segment 4) + 74.55 = stature +/- 4.56 cm

Additional regressions can be found throughout the literature are listed in this table, as the above formula found in *The Anatomy and Biology of the Human Skeleton* (Steel and Bramblett 1988).

## Regression Formulas in stature estimations – the basics

Regression formulas are equations in which the first variable is used to predict the second variable. Another way of saying this is that one variable is dependent on another (as opposed to having two independent variables). Stature estimation from individual elements assumes that height of an individual is dependent upon the length of bones of that individual.

Regression formulas for stature do not predict the exact height of an individual, but rather provide a range in which an individual’s height is expected to fall. The formula provides two numbers that constructs this range. The first number is the mean stature, or average height. The second number is a margin of error, which provides the limits of the range above and below the mean. For example, if the mean equated from the regression is 180 cm and the margin of error is 5 cm then the predicted range of the individual is 180 cm +/- 5 cm. Or more simply put, the individual’s height falls between 175 cm and 185 cm.