Answer:
0.7000cm
Explanation:
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Answer:The train travels 105 meters after applying the brakes
Explanation:If he decelerates 1.5 every minute, then he went from 28,5 m/s, to 27.0 m/s, to 25.5 m/s, to 24.0 m/s, after 4 seconds. Add all this together and youll get 105 meters moved in 4 seconds after he hit the brakes, I dont have a notebook on me though sorry :/
Answer:
5235.84 kg
Explanation:
There is one theorem - whose proof I will never remember without having to drag calculus in there - that says that the variation of momentum is equal to the force applied times the time the application last.
As long as the engine isn't ejecting mass - at this point it's a whole new can of worm - we know the force, we know the variation in speed, time to find the mass. But first, let's convert the variation of speed in meters per second. The ship gains 250 kmh,
;

Complete Question
The complete question is shown on the first uploaded image
Compare the percent error between your value and the accepted value of 1.26 times 10-6 T · m/A. (Use the accepted value given to three significant figures in your calculation.). 100% % error = %
Answer:
The permeability of free space is 
The percentage error is % error = 5.25%
Explanation:
From the question we are told that
The slope is
, and
Generally 
So 
From the relation given in the question
![\mu_0 = \frac{2R}{N} [\frac{B}{I} ]](https://tex.z-dn.net/?f=%5Cmu_0%20%3D%20%5Cfrac%7B2R%7D%7BN%7D%20%5B%5Cfrac%7BB%7D%7BI%7D%20%5D)
Where R is the radius of the coil 
N is the number of loops of the coil = 10
Now from the question we are told that

substituting into the equation above

Substituting values


Generally the % error is mathematically represented as
%
Given that the accepted value is
Hence substituting values
%

The difference between the observed points and the regression line points is equal to the correlation.
The strength and direction of a relationship between two or more variables are described by the statistical measure of correlation, which is given as a number. However, a correlation between two variables does not necessarily imply that a change in one variable is the reason for a change in the values of the other.
Regression expresses the relationship as an equation, whereas correlation assesses the strength of the linear link between two variables. The square of the correlation coefficient, also known as Pearson's r, between the observed and predicted values in a regression is sometimes referred to as R2.
Learn more about correlation here;
brainly.com/question/6563788
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