Answer:
10,740 money does she ean.
Step-by-step explanation:
Given: Mrs Miller sells a house for 179,000.
She earns a commission of 6%.
we set up a proportions to find how much she earn.
Let x be the money does she earns then;
Simplify:
Multiply both sides by 6 we get;
Simplify:
x = 10,740
Therefore, she earns money is, 10,740
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Move
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to the denominator using the negative exponent rule
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Multiply
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by
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by adding the exponents.
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to the numerator using the negative exponent rule
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Multiply
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by
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by adding the exponents.
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Change the sign of the exponent by rewriting the base as its reciprocal.
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1
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7
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2
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Use the power rule
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to distribute the exponent.
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Simplify the numerator.
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x
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2
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2
Simplify the denominator.
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x
9
y
7
X is the hypotenuse of an isosceles triangle. The length of the sides are the difference of the inner and outer squares divided by 2. Which means that the sides of the triangle is 1.5 inches. Using that information, the length of X can be deduced to be 2.12 inches using Pythagoras theorem
Answer:
any # where -2<=x<7
Step-by-step explanation:
(x-7) would be negative and (x+3) positive, resulting in f(x) being negative.
Answers:
- a) Stratified random sampling, or simply stratified sampling. Each group individually is known as a stratum. The plural is strata. The key here is that each stratum is sampled, though we don't pick everyone from every stratum. We randomly select from each unit to have them represent their unit. Think of it like house of representative members that go to congress. We have members from every state, but Be sure not to mix this up with cluster sampling. Cluster sampling is where we break the population into groups or clusters, then we randomly select a few clusters in which every individual from those clusters is part of the sample.
- b) Simple random sampling (SRS). This is exactly what it sounds like. We're randomly generating numbers to help determine who gets selected. Think of it like a lottery. A computer is useful to make sure this process is quick, efficient and unbiased as possible. Though numbers in a box or a hat work just as well.
For each of the methods mentioned, they aren't biased since they have randomness built into their processes.
