Answer:
25%
Step-by-step explanation:
George is 33
% (
%) richer than Pete. Let Pete's percentage of wealth be 100%.
Thus George percentage of wealth = 100% + %
= 
%
= 133%
Pete's percent poorer than George can be determined by;
= 
÷ 
× 100
= × 
×100
= 0.25 × 100
= 25%
Pete is 25% poorer than George.
Answer:
24 ft
Step-by-step explanation:
Use proportions! The old tent has sides of 10ft, and a base of 15ft. The new tent has sides of 16ft and a base of ____ ft.
So, 10/15 = 16/___. Cross multiple and you get 24ft!
Based on this question, one thing that we would seriously consider would be the fact of first, doing
first. By doing this, it would then give us our answer as 16. By us understanding this point of view, we would then consider that this would then be your answer. That would then include that there would then be 16 pairs of the "enantiomeric pairs", and that would then be the possible estimate.
<span>a.2
b.4
c.8
d.16</span>
The probability of multiple events happening is found by multiplying the probabilities of each event together.
So yes, 1/10 is the answer :)
Answer:
NO
Step-by-step explanation:
The changeability of a sampling distribution is measured by its variance or its standard deviation. The changeability of a sampling distribution depends on three factors:
- N: The number of observations in the population.
- n: The number of observations in the sample.
- The way that the random sample is chosen.
We know the following about the sampling distribution of the mean. The mean of the sampling distribution (μ_x) is equal to the mean of the population (μ). And the standard error of the sampling distribution (σ_x) is determined by the standard deviation of the population (σ), the population size (N), and the sample size (n). That is
μ_x=p
σ_x== [ σ / sqrt(n) ] * sqrt[ (N - n ) / (N - 1) ]
In the standard error formula, the factor sqrt[ (N - n ) / (N - 1) ] is called the finite population correction. When the population size is very large relative to the sample size, the finite population correction is approximately equal to one; and the standard error formula can be approximated by:
σ_x = σ / sqrt(n).
