The correct answer is 19,200. Hope this helps. :)
Answer:
Bias is the difference between the average prediction of our model and the correct value which we are trying to predict and variance is the variability of model prediction for a given data p[oint or a value which tells us the spread of our data the variance perform very well on training data but has high error rates on test data on the other hand if our model has small training sets then it's going to have smaller variance & & high bias and its contribute more to the overall error than bias. If our model is too simple and has very few parameters then it may have high bias and low variable. As the model go this is conceptually trivial and is much simpler than what people commonly envision when they think of modelling but it helps us to clearly illustrate the difference bewteen bias & variance.
Answer:
<em>The ball will roll upto </em><em>1 m.</em>
Step-by-step explanation:
A 1 kg ball has 10 joules of kinetic energy and starts to roll up a hill.
As along the hill the ball rises up, it loses its kinetic energy. The kinetic energy is converted to potential energy.
According to the law of conservation of energy, the kinetic energy plus the potential energy equals a constant.
Here given kinetic energy as 10 J, so this energy will get converted to potential energy.
We know that, potential energy is
where,
m is the mass, g is the acceleration due to gravity and h is the height.
Putting the values,
Y = k/x for some fixed number constant k
7 = k/9
k = 63
so Y = 63/x
14 = 63/x
14 x = 63
x = 63/14 = 9/2
