Related Experiment Video
Updated: Aug 29, 2026

Designing and Implementing Nervous System Simulations on LEGO Robots
Published on: May 25, 2013
Estimation of leg power: a two-variable model
1Department of Physical Therapy, Hampton University, Hampton, VA 23668, USA.
Abstract:
Leg power is an essential component for success in sports and athletic performance. Therefore, the leg power measurement may help athletes, coaches, athletic trainers, and rehabilitation specialists in selecting, treating, and training athletes for a specific sport. Using a conventional 'jump and reach' test, one can accurately predict the leg power and success in anaerobic-type sports. Nineteen untrained male subjects performed 'jump and reach' vertical jumps on a force platform. Power values were calculated from the force versus time data obtained from the force platform. A regression equation was obtained to predict the power values using the weight of an individual and the 'jump and reach' height as independent variables. The regression equation is given by p = -666.3 + 14.74 [Mass (kg)] + 1925.72 [Height (m)]; [R-square = 0.69, p < 0.05].
More Related Videos
Related Concept Videos
Indeterminate Structure
Transformers with Off-Nominal Turns Ratios
Design Example: Frog Muscle Response
When the switch connecting the RL circuit is closed, a brief muscle contraction is observed. This is because, at a steady state, the inductor acts like a short circuit,...
Estimation of the Physical Quantities
Multiple Regression
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
Regression Analysis
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:

