FORCE-physical power or strength possessed by a living being:
He used all his force in opening the window.
MOTION-the action or process of moving or of changing place or position; movement.
Inertia is when a object in motion will stay in motion or in a standing still state unless acted upon by a unbalancing force.
Friction is when a object slows down because it is rubbing against another object.
If a object is sliding across a surface, theoretically, it would not stop but because it is on a flat surface it would experience friction, this will disperse some of the kinetic energy that it has thus slowing the object down eventually, after some time, to a stop.
Hope this helps! :)
Sorry most of speak english on this website so i cannot help you
Answer:
(D) 4
Explanation:
The percentage error in each of the contributors to the calculation is 1%. The maximum error in the calculation is approximately the sum of the errors of each contributor, multiplied by the number of times it is a factor in the calculation.
density = mass/volume
density = mass/(π(radius^2)(length))
So, mass and length are each a factor once, and radius is a factor twice. Then the total percentage error is approximately 1% +1% +2×1% = 4%.
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If you look at the maximum and minimum density, you find they are ...
{0.0611718, 0.0662668} g/(mm²·cm)
The ratio of the maximum value to the mean of these values is about 1.03998. So, the maximum is 3.998% higher than the "nominal" density.
The error is about 4%.
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<em>Additional comment</em>
If you work through the details of the math, you will see that the above-described sum of error percentages is <em>just an approximation</em>. If you need a more exact error estimate, it is best to work with the ranges of the numbers involved, and/or their distributions.
Using numbers with uniformly distributed errors will give different results than with normally distributed errors. When such distributions are involved, you need to carefully define what you mean by a maximum error. (By definition, normal distributions extend to infinity in both directions.) While the central limit theorem tends to apply, the actual shape of the error distribution may not be precisely normal.
