. Simplify the expression: 4 2 + 8 ÷ 2.

Mathematics

2 answers:

zhannawk [14.2K]3 years ago

7 0

Answer:

42+4

Step-by-step explanation:

stealth61 [152]3 years ago

6 0

Answer:

46

Step-by-step explanation:

42 + 8 divided by 2

Step 1. Divide 8 by 2

42 + 4

Step 2. Add

42 + 4 = 46

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Kanye runs a small business with three employees. She pays one employee $2300 a month, another $1700 a month, and the third $140

Molodets [167]

$2300 + $1700 + $1400 = $5400 a month total
$5400 × 12 months in a year = $64,800 a year total

If you want to know how much she pays EACH employee a year, do the same thing for each amount:
$2400 × 12 months a year = $28,800
$1700 × 12 months a year = $20,400
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3 years ago

A record store owner finds that 20% of customers entering her store make a purchase. One morning 180 people, who can be regarded

katen-ka-za [31]

Answer:

a) 0.2

b) 0.0009

c) 0.0298

d) 0.0465 = 4.65% probability that the sample proportion is less than 0.15.

Step-by-step explanation:

To solve this question, we need to understand the normal probability distribution and the central limit theorem.

Normal Probability Distribution

Problems of normal distributions can be solved using the z-score formula.

In a set with mean $\mu$ and standard deviation $\sigma$ , the z-score of a measure X is given by:

$Z = \frac{X - \mu}{\sigma}$

The Z-score measures how many standard deviations the measure is from the mean. After finding the Z-score, we look at the z-score table and find the p-value associated with this z-score. This p-value is the probability that the value of the measure is smaller than X, that is, the percentile of X. Subtracting 1 by the p-value, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem establishes that, for a normally distributed random variable X, with mean $\mu$ and standard deviation $\sigma$ , the sampling distribution of the sample means with size n can be approximated to a normal distribution with mean $\mu$ and standard deviation $s = \frac{\sigma}{\sqrt{n}}$ .

For a skewed variable, the Central Limit Theorem can also be applied, as long as n is at least 30.

For a proportion p in a sample of size n, the sampling distribution of the sample proportion will be approximately normal with mean $\mu = p$ and standard deviation $s = \sqrt{\frac{p(1-p)}{n}}$