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Ghella [55]
3 years ago
5

52 thousands in scientific notation.

Mathematics
2 answers:
Stella [2.4K]3 years ago
6 0

Answer:

5.2 x 10^{3}

Step-by-step explanation:

mafiozo [28]3 years ago
5 0

Answer:

5.2*10^{4}

Step-by-step explanation:

For scientific notation you multiply 10 to any power by a number which is more than one and less than  10.

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Step-by-step explanation:

We multiply the fraction by 100:

= 100 * 18/25

25 divides into 100 giving 4 so we have:

4 * 18

= 72%.

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Name a number whose square root and cube root are whole numbers
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4 years ago
A real estate agent has 17 properties that she shows. She feels that there is a 60% chance of selling any one property during a
Zarrin [17]

Answer:

P(X\leq 5)

And we can find the individual probabilities like this:

P(X=0)=(17C0)(0.6)^0 (1-0.6)^{17-0}=0.000000171  

P(X=1)=(17C1)(0.6)^1 (1-0.6)^{17-1}=0.00000439  

P(X=2)=(17C2)(0.6)^2 (1-0.6)^{17-2}=0.0000526  

P(X=3)=(17C3)(0.6)^3 (1-0.6)^{17-3}=0.000394  

P(X=4)=(17C4)(0.6)^4 (1-0.6)^{17-4}=0.00207  

P(X=5)=(17C5)(0.6)^5 (1-0.6)^{17-5}=0.000807  

And adding we got:

P(X\leq 5) = 0.0106

Step-by-step explanation:

Previous concepts

A Bernoulli trial is "a random experiment with exactly two possible outcomes, "success" and "failure", in which the probability of success is the same every time the experiment is conducted". And this experiment is a particular case of the binomial experiment.

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".

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!}  

The complement rule is a theorem that provides a connection between the probability of an event and the probability of the complement of the event. Lat A the event of interest and A' the complement. The rule is defined by: P(A)+P(A') =1

Solution to the problem

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

X \sim Binom(n=17, p=0.6)

And we want this probability:

P(X\leq 5)

And we can find the individual probabilities like this:

P(X=0)=(17C0)(0.6)^0 (1-0.6)^{17-0}=0.000000171  

P(X=1)=(17C1)(0.6)^1 (1-0.6)^{17-1}=0.00000439  

P(X=2)=(17C2)(0.6)^2 (1-0.6)^{17-2}=0.0000526  

P(X=3)=(17C3)(0.6)^3 (1-0.6)^{17-3}=0.000394  

P(X=4)=(17C4)(0.6)^4 (1-0.6)^{17-4}=0.00207  

P(X=5)=(17C5)(0.6)^5 (1-0.6)^{17-5}=0.000807  

And adding we got:

P(X\leq 5) = 0.0106

5 0
3 years ago
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