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photoshop1234 [79]
3 years ago
14

Four is no less than the quotient of a number x and 2.1. how do I write that as and inequality

Mathematics
1 answer:
GarryVolchara [31]3 years ago
6 0

Answer:

4>=>x/2.1

4 is greater than or equal to x/2.1

Pls mark brainiest

Step-by-step explanation:

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What is the maximum number of​ x-intercepts that a polynomial of degree 7 can​ have
Daniel [21]
Polynomial of degree seven can have the maximum 7 real solutions,
so maximum number of x-intercepts is also 7.
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3 years ago
Round 30,944 to the nearest ten thousand
Arte-miy333 [17]
Actually none of the answers is correct - it's neither 30,900 nor 40,000.

30,944 rounded to the nearest ten thousand will give us 30,000. It's because 30,944 is closer to 30,000 than to 40,000.
6 0
3 years ago
Use a model to divide. 1/2÷7 . What is the answer 2/7 1/14 14 3 1/2?
shutvik [7]

Answer:

1/14 i think

Step-by-step explanation:

7 0
3 years ago
The projected world population (in billions of people) t years after
MA_775_DIABLO [31]

Answer:

y = 5 e^r * t

Let y be the population in billions and t the value of elapsed years

7 =   5 e^r * t  is the equation being used where  t = 15

7/5 = e^r * t

ln 7/5 = r * t     taking ln of both sides

r = .336 / 15 = .0224

y = 5 e^(.0224 t)    is then our equation

Check  - suppose you want y at 2020

y = 5 e^(.0224 * 20)    would be the equation

y = 5 e^.449 = 7.83  billion - seems to be a reasonable answer

4 0
3 years ago
Read 2 more answers
Suppose a batch of metal shafts produced in a manufacturing company have a population standard deviation of 1.3 and a mean diame
lbvjy [14]

Answer:

54.86% probability that the mean diameter of the sample shafts would differ from the population mean by more than 0.1 inches

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 normally distributed samples are solved using the z-score formula.

In a set with mean \mu and standard deviation \sigma, the zscore 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 pvalue, we get the probability that the value of the measure is greater than X.

Central Limit Theorem

The Central Limit Theorem estabilishes 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.

In this problem, we have that:

\mu = 208, \sigma = 1.3, n = 60, s = \frac{1.3}{\sqrt{60}} = 0.1678

What is the probability that the mean diameter of the sample shafts would differ from the population mean by more than 0.1 inches

Lesser than 208 - 0.1 = 207.9 or greater than 208 + 0.1 = 208.1. Since the normal distribution is symmetric, these probabilities are equal, so we find one of them and multiply by 2.

Lesser than 207.9.

pvalue of Z when X = 207.9. So

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

By the Central Limit Theorem

Z = \frac{207.9 - 208}{0.1678}

Z = -0.6

Z = -0.6 has a pvalue of 0.2743

2*0.2743 = 0.5486

54.86% probability that the mean diameter of the sample shafts would differ from the population mean by more than 0.1 inches

6 0
3 years ago
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