Answer:
(A)The water will freeze.
Explanation:
Heat is the energy that when transferred causes change in temperature. If heat is added to a body by some process the bodies temperature increases and if heat is removed from a body by some process the temperature reduces.
In this case if enough heat is removed from the room the internal movement of the water particles will slow down causing the water to form crystal lattice thus forming ice. So, the water will freeze.
Answer:
-22/15
Explanation:
the least common denominator is 15 so first you multiply -2/3 by 5 in both the numerator and denominator making it -10/15
Then you do the same to -4/5 except you multiply the numerator and denominator by 3 giving you -12/15
If you add -10/15+ -12/15 you get -22/15
Answer:
C.
Explanation:
Visual constancy is a key mechanism that allows the perception to remain.the same even as images change
#<em><u>CARRYONLEARNING</u></em>
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.
