Mathematics of Data Management - Probability distributiono

In summary, the probability of the arrow on a spinner stopping on a prime number is calculated by dividing the number of prime numbers (4 or 3, depending on whether 1 is included) by the total number of outcomes (8). This results in a probability of 1/2 or 3/8, depending on whether 1 is considered a prime number. The formula used is P(x) = 1/n, where n is the total number of possible outcomes.
  • #1
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Homework Statement


A spinner has eight equally-sized sectors, numbered 1 through 8. What is the probability that the arrow on the spinner will stop on a prime number?


Homework Equations


P(x) = 1 / n, out come of a uniform probability distribution
P(x) = Probability distribution
n = Total # of possible outcomes


The Attempt at a Solution


n = 8, since the total number of possible outcome range from 1 through 8
P(x) = 1/n
Prime numbers are 1, 3, 5, 7
*Note that some textbooks don't include 1 as a prime number

P(x) = 1/n
= 1/8
** This is wrong but that's how it seems like it is suppose to be done

P(x) = 4/8
4 = total number of primes
P(x) = 1/2 or 3/8 ( if you don't consider 1 as a prime number)
** this is the correct answer

The second way of solving it is the correct answer but what i don't understand is that the formula is P(x) = 1/n, why did 1 become a 4, how would i know that I'm suppose to change the 1? and yes i have checked the 1 is not an L or an I...
 
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  • #2
the probability of any single number coming up is 1/8. As you have 4 (or 3) potential outcomes, the probability is 4/8 (or 3/8)
 
  • #3
But why is it 4/8 or 3/8? the formula is p(x) = 1/n
 
  • #4
not sure if I'm understanding you

say you have n, distinct evenly distributed outcomes (labelled 1 to n)

the probability of getting an outcome x is p(x) = 1/n

the probabilty of getting one of m outcomes is m/n

If you want to break it right down, you know
[tex] p(x)=\frac{1}{8} [/tex]

So
[tex] p(1)=p(3)=p(5)=p(7)=\frac{1}{8} [/tex]

you also know only a single number can appear at a time, they are mutually exclusive events
[tex] p(i\cap j)=0 [/tex]

then, as the intersection is zero (mutually exclusive)
[tex] p(1\cup3 \cup 5\cup7)=p(1)+p(3)+p(5)+p(7)= \frac{1}{8}+\frac{1}{8}+\frac{1}{8}+\frac{1}{8}= \frac{4}{8}[/tex]
 
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  • #5
My lord! Thank you that is just perfect!<3
 

FAQ: Mathematics of Data Management - Probability distributiono

1. What is a probability distribution?

A probability distribution is a mathematical function that describes the likelihood of different outcomes occurring in a random experiment or process. It maps all possible outcomes to their respective probabilities, and the total area under the curve is equal to 1.

2. What are the types of probability distributions?

There are many types of probability distributions, but the most commonly used ones include the normal distribution, binomial distribution, Poisson distribution, and exponential distribution. Each distribution has its own set of parameters and characteristics.

3. How is probability distribution used in data management?

Probability distribution is used in data management to understand and analyze the likelihood of events or outcomes occurring. It helps in making informed decisions based on the available data and can also be used to predict future outcomes.

4. What is the difference between discrete and continuous probability distributions?

Discrete probability distributions are used for countable and finite outcomes, such as the number of heads in a coin toss. Continuous probability distributions, on the other hand, are used for outcomes that can take on any value within a given range, such as the height of individuals in a population.

5. How is the mean, variance, and standard deviation calculated for a probability distribution?

The mean of a probability distribution is calculated by summing the products of each possible outcome and its corresponding probability. The variance is calculated by subtracting the mean from each outcome, squaring the differences, multiplying them by their respective probabilities, and then summing them. The standard deviation is the square root of the variance.

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