Contributors to Skeletal Traumatic Injuries - Assignment Example

• fr14lb = number of long bones with traumatic fractures (maximum of 14)
• rpfr = parry fracture of right ulna (0=none, 1=fracture)
• lpfr = parry fracture of left ulna (0=none, 1=fracture)
• ptib = number of tibias with periosteal lesions (0, 1, 2)
• prtib = presence [1]/absence [0] of periosteal lesions on right tibia
• pltib = presence [1]/absence [0] of periosteal lesions on left tibia
• feml = maximum femur length (in millimeters)

Various research has examined skeletal trauma, more especially from war victims with observable trends being recognized. While noting that ways of life were a major Contributor To Skeletal Traumatic Injuries with aging, these researches have also identified that skeletal structures also dictate the vulnerability of traumatic injuries. In research applied to Native American skeletal collections across the United States, most data gathered reveal significant injury trends with specific bone and skeletal structures. The research further noted temporal changes in skeletal health associated with structures had relation with incidence and pattern of traumatic injury.

Regression analysis equation for hypothesis testing
Where  are coefficients and  are the listed variables
The analysis is performed using SPSS software and results tabulated as shown below,

To test the hypothesis,

pcvf

Pearson Correlation

pcvf

1.000

cribs

.

ncan

.

ehyc

.

n14lb

.

fr14lb

.

rpfr

.

lpfr

.

ptib

.

prtib

.

pltib

.

feml

.

Shockingly or unexpectedly, the results have shown no relationship between the listed variables and traumatic injuries. As a matter of fact, the findings show that none of the variables can be used to predict the prevalence of post-traumatic injuries. With R= 0, we reject the hypothesis rightly and conclude that the listed variables are not predictors of traumatic injury prevalence.