Answer:
58.5 meters
Explanation:
1. Find your formula. Use distance = speed x time for this problem
2. Plug in the given information. d (for distance) = 9m/s^2 * 6.5 s
3. Multiply number AND units. d = 58.5m
4. Check to make sure units & numbers make sense. In this case check that the answer is a lot bigger than what we stated with and that our units go with distance
Answer:
The current on the water layer = 1.64×10^-3A
Explanation:
Let's assume that the radius given for the string originates from the centre of the string. The equation for determining the current in the water layer is given by:
I = V × pi[(Rwater + Rstring)^2 - (Rstring)^2/ ( Resitivity × L)
I =[ 166×10^6 ×3.142[(0.519×10^-4) + (2.15×10^-3])^2 - ( 2.15×10^-3)^2] / ( 183 × 831)
I =[ 521572000(4.848×10^6)- 4.623×10^-6]/ 154566
I = 252.83 -(4.623×10^-6)/ 154566
I = 252.83/154566
I = 1.64× 10^-3A
Considering conservation of mechanical energy we have:
K+U (start)= K+U (end)
Where:
K=kinetic energy=
U=potential energy=
So:
0.05*0.95*9.81+0 (start) = 0+K (end)
K (end)=0.5 Joules
To answer this question, we will use the law of conservation of momentum which states that:
(m1+m2)Vi = m1V1 + m2V2 where:
m1 is the mass of the woman = 50 kg
m2 is the mass of the cart = 10 kg
Vi is the initial velocity (of woman and cart combined) = 5 m/sec
V1 is the final velocity of the woman = 7 m/sec
V2 is the final velocity of the cart that we need to calculate
Substitute with the givens in the above equation to get the final velocity of the cart as follows:
(50+10)(5) = (50)(7) + (10)V2
10V2 = -50
V2 = -5 m/sec
Note that the negative sign indicates that the cart is moving in an opposite direction to the others.
Answer:
c. Performs better on training data as the training process proceeds, while performing worse on a held-out test data
Explanation:
An over-fitted model is one that will perform best on training but would fail or do worse on a held-out test data.
Such models are optimum for a just a particular set of data but would grossly failed when extrapolated to some other data set not novel to it.
- Over-fitting a model implies that a model closely corresponds to a set of data but would not perform well with others.
- It is usually as a result of a model adapting the noise and other details of a particular data set and thereby incorporates it.
- This makes it difficult for the model to fit into another data set.
