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Hatshy [7]
3 years ago
13

To find the quotient of 3 divided by one-sixth, multiply 3 by what

Mathematics
2 answers:
Reptile [31]3 years ago
7 0
You have to multiply 3 x 6.

iren [92.7K]3 years ago
4 0

Answer:

just do 3x6

Step-by-step explanation:

You might be interested in
Out of 450 applicants for a job, 206 are male and 62 are male and have a graduate degree.
xxMikexx [17]

Answer:

0.3009 is the  probability that the applicant has graduate degree given he is a male.                                                              

Step-by-step explanation:

We are given he following in the question:

M: Applicant is male.

G: Applicant have a graduate degree

Total number of applicants = 450

Number of male applicants = 206

n(M) = 206

Number of applicants that are male and have a graduate degree = 62

n(M\cap G) = 62

\text{Probability} = \displaystyle\frac{\text{Number of favourable outcomes}}{\text{Total number of outcomes}}

P(M) = \dfrac{206}{450} = 0.4578

P(M\cap G) = \dfrac{n(M\cap G)}{n} = \dfrac{62}{450} = 0.1378

We have to find the probability that the applicant has graduate degree given he is a male.

P(G|M) = \dfrac{P(G\cap M)}{P(M)} = \dfrac{\frac{62}{450}}{\frac{206}{450}} = \dfrac{62}{206} = 0.3009

Thus, 0.3009 is the  probability that the applicant has graduate degree given he is a male.

5 0
3 years ago
Order the numbers from least to greatest<br><br>-3,9,0,-11,7,-6
igor_vitrenko [27]

-11 ,-3, -6, 0, 7, 9 I hope im right


7 0
3 years ago
Consider the question of whether the home team wins more than half of its games in the National Basketball Association. Suppose
spayn [35]

Answer:

0.0037 = 0.37% probability that the home team would win 65% or more of its games in a simple random sample of 80 games

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 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.

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}}

The home team therefore wins 50% of its games

This means that p = 0.5

Determine the probability that the home team would win 65% or more of its games in a simple random sample of 80 games

Sample of 80 means that n = 80 and, by the Central Limit Theorem:

\mu = p = 0.65

s = \sqrt{\frac{p(1-p)}{n}} = \sqrt{\frac{0.5*0.5}{80}} = 0.0559

This probability is 1 subtracted by the pvalue of Z when X = 0.65. So

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

By the Central Limit Theorem

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

Z = \frac{0.65 - 0.5}{0.0559}

Z = 2.68

Z = 2.68 has a pvalue of 0.9963

1 - 0.9963 = 0.0037

0.0037 = 0.37% probability that the home team would win 65% or more of its games in a simple random sample of 80 games

7 0
3 years ago
L
d1i1m1o1n [39]

Whole numbers

Natural numbers

Real nunbers

6 0
3 years ago
For skewed data displays, the median is often a better esitmate of the center of distribution than the mean, but
bazaltina [42]

For skewed data displays, the median is often a better estimate of the center of distribution than the mean because the former is unaffected by large numbers.

<h3>What is mean?</h3>

Mean refers to the average of set of two or more numbers.

Mean of a set having 'n'  numbers = \frac{Sum Of 'n' Numbers}{n}

<h3>What is median?</h3>

Median refers to the middle-most value of a list of numbers, arranged either in ascending or descending order.

Median = \left \{ {{\frac{n}{2}^{th}  term, if n = even } \atop( (\frac{n-1}{2}+\frac{n+1}{2})/2)^{th} term, if n = odd   }} \right.

Now,

  • Since it takes the average of all the values in the data set, the mean is the most widely used measure of central tendency.
  • Because it is unaffected by exceptionally big numbers, the median performs better than the mean when analyzing data from skewed distributions.

Hence, For skewed data displays, the median is often a better estimate of the center of distribution than the mean.

To learn more about mean and median, refer to the link:brainly.com/question/6281520

#SPJ4

7 0
2 years ago
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