Discrete Distributions and Continuous Distributions

This project is based on Discrete Distributions and Continuous Distributions. Question # 7 does not have to be an elaborate study, just something basic, a study that you might conduct at work.

1. What are the characteristics of a normal distribution?

2. Determine the probability or area for the portions of the normal distribution described.
A. z > 1.96
B. - 1.46 < = 2.84

3. Toms Associates reports that the mean clear height for a Class A warehouse in the United States is 22 feet. Suppose clear heights are normally distributed and that the standard deviation is 4 feet. A Class A warehouse in the United States is randomly selected.
A. What is the probability that the clear height is greater than 17 feet?
B. What is the probability that the clear height is less than 13 feet?
C. What is the probability that the clear height is between 25 and 31 feet?

4. Why would we take a sample instead of a census?

5. Give an example of when you would use stratified random sampling instead of simple random sampling.

6. When would you use non-random sampling like convenience sampling vs random sampling?

7. Design a study you could conduct at work - tell the following:
a. What would the purpose of your study be?
b. What type of data would you collect?
c. What would your frame be?
d. What type of sample would you collect?
e. What type of analysis could you do with your
data?

8. Explain the central limit theorem in your own words (use a diagram if it helps). Why is this theorem important to us?

What is a probability distribution and its purpose?
Distinguish between discreteandcontinuous probability distributions. Give an example of each.
What is randomness? Assume you have an Excel worksheet of the dimension, A1:E101. The first row contains data labels. How would you randomize the data file and to select a

Question 1:
When is the mean the best measure of central tendency? When is the median the best measure of central tendency? Explain.
Questions 2:
A researcher has determined that the distribution of annual salaries of NBA players is bell-shaped and symmetrical about the mean salary, do you think that introducing Michael

Please choose the correct answer and write briefly why.
A property of continuousdistributions is that:
a. As with discrete random variables, the probability distribution can be approximated by a smooth curve
b. Probabilities for continuous variables can be approximated using discrete random variables
c. Unlike discret

See attached file - Formulas for Discrete Probability Distributions
A discrete random variable can have the values, x = 3, x = 8, or x = 10, and the respective probabilities are 0.2, 0.7, and 0.1. Determine the mean, variance, and standard deviation of x.
(6.5)

See attached files.
See attached Practice Business Statistics for Management and Economics ch 7 & 8. The answers are highlighted in yellow. I have difficulty setting up the steps in Excel to come up with the correct answers. I need to see how the answers are derived in Excel.
I have found the data required for problems

Define probability distributions. Describe two common probability distributions.
Looking for a good original (yet cited) response to this definition. And if you can describe two common ones with an example for each that would be great.

Give an example representing a discrete probability distribution and another example representing a continuous probability distribution. Explain why your choices are discreteandcontinuous.
Please provide me an insightful analysis of the question is lengthy in response and include specific examples.

(a) Explain the difference between mutually exclusive and independent events. Can a pair of events be both mutually exclusive and independent? Give examples.
(b) Discuss the problems inherent in using words such as "likely," "possibly," or "probably" to convey degree of belief.
(c) One way a discrete probability distribu

Share 1 real-world binomial distribution situation and 1 real-world Poisson distribution situation. Be sure to explain why each example is defined as binomial or Poisson. How would you characterize the difference between the two types of distributions?