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3 years ago

What is the opposite of the number 14

Mathematics

2 answers:

Aleks04 [339]3 years ago

5 0

Answer:

- 14

Step-by-step explanation:

The opposite of the number is the negative of the number, thus the negative of 14 is - 14 and the negative of - 14 is - (- 14) = 14

lidiya [134]3 years ago

3 0

Answer:

The opposite of 14 is -14.

Step-by-step explanation:

Opposites are numbers with the same distance from 0 but one is to the right of 0 and the other is to the left of 0.

The opposite of 0 is itself,0.

So the opposite of 5 is -5 since both 5 and -5 have the same distance from 0 and one is to the left and one is to the right of 0.

The opposite of 14 is -14 since both have the same distance from 0 and one is to the right and one is to the left of 0.

You can also think of this question as what can I add to 14 that will give me 0.

The answer is -14 since -14+14=0.

Another word for opposites is additive inverses. When you add additive inverses you get 0.

Another example: What is the opposite of -16?

The answer is 16 since 16+(-16)=0.

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Assume the random variable X has a binomial distribution with the given probability of obtaining a success. Find the following p

drek231 [11]

Answer:

$P(X=14)=(18C14)(0.8)^{14} (1-0.8)^{18-14}=0.2153$

$P(X=15)=(18C15)(0.8)^{15} (1-0.8)^{18-15}=0.2297$

$P(X=16)=(18C16)(0.8)^{16} (1-0.8)^{18-16}=0.1722$

$P(X=17)=(18C17)(0.8)^{17} (1-0.8)^{18-17}=0.0811$

$P(X=18)=(18C18)(0.8)^{18} (1-0.8)^{18-18}=0.0180$

And adding the values we got:

$P(X \geq 14)= 0.2153 +0.2297+0.1722+0.0811+0.0180=0.7164$

Step-by-step explanation:

Assuming this question: P(X≥14), n=18, p=0.8

Previous concepts

The binomial distribution is a "DISCRETE probability distribution that summarizes the probability that a value will take one of two independent values under a given set of parameters. The assumptions for the binomial distribution are that there is only one outcome for each trial, each trial has the same probability of success, and each trial is mutually exclusive, or independent of each other".

Solution to the problem

Let X the random variable of interest, on this case we now that:

$X \sim Binom(n=18, p=0.8)$

The probability mass function for the Binomial distribution is given as:

$P(X)=(nCx)(p)^x (1-p)^{n-x}$

Where (nCx) means combinatory and it's given by this formula:

$nCx=\frac{n!}{(n-x)! x!}$

And we want this probability:

$P(X \geq 14) = P(X=14) +P(X=15) +P(X=16)+P(X=17)+P(X=18)$

And we can find the individual proabilities using the probability mass function:

$P(X=14)=(18C14)(0.8)^{14} (1-0.8)^{18-14}=0.2153$

$P(X=15)=(18C15)(0.8)^{15} (1-0.8)^{18-15}=0.2297$

$P(X=16)=(18C16)(0.8)^{16} (1-0.8)^{18-16}=0.1722$

$P(X=17)=(18C17)(0.8)^{17} (1-0.8)^{18-17}=0.0811$

$P(X=18)=(18C18)(0.8)^{18} (1-0.8)^{18-18}=0.0180$

And adding the values we got:

$P(X \geq 14)= 0.2153 +0.2297+0.1722+0.0811+0.0180=0.7164$

5 0

3 years ago

What is another way to solve 8 × 6.98

Oksanka [162]

Answer:

8 × 6.98

Step-by-step explanation:

..............

8 0

2 years ago

4 lines are shown. A line with points T, R, W intersects a line with points S, R, V at point R. Another line extends from point

lianna [129]

Answer:

The Answer is B

Step-by-step explanation:

Edgenuity ;)

4 0

3 years ago

At a large Midwestern university, a simple random sample of 100 entering freshmen in 1993 found that 20 of the sampled freshmen

guajiro [1.7K]

Answer:

The 90% confidence interval for the difference of proportions is (0.01775,0.18225).

Step-by-step explanation:

Before building the confidence interval, we need to understand the central limit theorem and subtraction of normal variables.

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 $s = \sqrt{\frac{p(1-p)}{n}}$

Subtraction between normal variables:

When two normal variables are subtracted, the mean is the difference of the means, while the standard deviation is the square root of the sum of the variances.

p1 -> 1993

20 out of 100, so:

$p_1 = \frac{20}{100} = 0.2$