Describe or give an example that uses discrete probabilities or distributions. Provide an example that follows either the binomial probabilities or any discrete probability distribution, and explain why that example follows that distribution. In your response, make up numbers for the example provided by that other person, and ask a related probability question. Then show the work (or describe the technology steps) and solve that probability example.
Solution:
In binomial distribution there are more than 2 trials held on a single experiment which has only two alternatives and for all trials they have same probablity for any event happening.
Consider an experiment where we toss coins 32 times. The coin is biased and hence the probability of head occuring is 0.60. Now consider a random variable X the number of times we get red in these 32 trials.
Now clearly this random variable X has a binomial distribution. There are only two outcomes of a coin toss (head or tail). The same experiment is carried out 32 times. Each time the probability of head occuring is 0.60.
The probability distribution function of X will be given by,
Describe or give an example that uses discrete probabilities or distributions. Provide an example that follows...
Describe an example that uses discrete probabilities or distributions. Provide an example that follows either the binomial probabilities or any discrete probability distribution, and explain why that example follows that distribution.
In your short presentation, you will be describing an example that uses discrete probabilities or distributions. Provide an example that follows either the binomial probabilities or any discrete probability distribution, and explain why that example follows that distribution. *Can we please not use a coin example if possible
This is an example of a binomial experiment: data collected from website shows that 39% of visitors use internet explorer. Randomly select 6 visitors and record how many use internet explorer. To respond to this make up numbers for the example, and ask a related probability question. Then show the work (or describe the technology steps) and solve that probability example.
Provide an example that follows either the binomial or Poisson distribution, and explain why that example follows that particular distribution.
1. What makes the binomial distribution unique? What are its characteristics? Give a real-world example of a distribution of data that would be considered binomial. 2. Solve the following problem: About 30% of adults in United States have college degree. (probability that a person has college degree is p = 0.30). If N adults are randomly selected, find probabilities that 1) exactly X out of selected N adults have college degree 2) less than X out of selected N adults...
Give the probability distribution for the indicated random variable. HINT [See Example 3.] (Enter your probabilities as fractions.) A red die and a green die are rolled, and X is the sum of the numbers facing up. x 2 3 4 5 6 7 8 9 10 11 12 P(X = x) Calculate P(X ≠ 11). (Enter your probability as a fraction.) P(X ≠ 11) =
The second photo is an example of the equation I am expected to use. It says use technology but I have no idea how to do that. Coumes Assignments&Homework P Do Homeswork Mathew McC Determine whether youcan u x https:/www.mathxl.com/Student/PlayerHomework.aspx?homeworkd-5305724008questiond-5&ushedasecid-55113768centerwinyes Elementary Statistics: Picturing the World 7/e-Internet-Summer 1 2019-Mays 7/4/19730 AM Mathew McCarley & Homework: CH 5.5 Normal Approximations to Binomial Distribut Save Score: 0 of 1 pt 7 of 76 complete) HW Score: 69 27 % , 5.54 of 8...
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R studio #Exercise : Calculate the following probabilities : #1. Probability that a normal random variable with mean 22 and variance 25 #(i)lies between 16.2 and 27.5 #(ii) is greater than 29 #(iii) is less than 17 #(iv)is less than 15 or greater than 25 #2.Probability that in 60 tosses of a fair coin the head comes up #(i) 20,25 or 30 times #(ii) less than 20 times #(iii) between 20 and 30 times #3.A random variable X has Poisson...
This course is actually Quantitative Methods and Analysis In Unit 2, you have learned about three different types of distributions: Normal, binomial, and Poisson. You can take data that you collect and plot it out onto graphs to see a visual representation of the data. By simply looking at data on a graph, you can tell a lot about how related your observed data are and if they fit into a normal distribution. For this submission, you will be given...