MATH399
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Descriptive
Statistics (graded)
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If you were given a large data set
(i.e., sales over the last year of our
top 100 customers), what might you be able to do with these data? What might be
the benefits of describing the data?
Suppose you are given data from a
survey showing the IQ of each person interviewed and the IQ of his or her
mother. That is all the information that you have. Your boss has asked you to put together a report showing
the relationship between these two variables. What could you present and why?
Probability
and Odds (graded)
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The odds of winning a game are given
as 1:25. What is the probability that you will win this game? What
is the probability that you will lose this game? In your follow-up replies,
consider which number in the odds ratio needs to change and how it needs to
change in order to increase the probability of winning. (Note: See page 145 in the text for a discussion on odds.)
Discrete
Probability Variables (graded)
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Provide an example that follows
either the binomial or Poisson distribution, and explain why that example
follows that particular distribution. In your responses to other students, make up numbers for
the example provided by that other student, and ask a related probability
question. Then, show the work (or describe
the technology steps), and solve that probability example.
Interpreting
Normal Distributions (graded)
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Assume that a population is normally
distributed with a mean of 100 and a standard deviation of 15. Would it be unusual for the mean of a sample of 3 to be 115
or more? Why or why not?
Confidence
Interval Concepts (graded)
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Consider the formula used for any
confidence interval and the elements included in that formula. What happens to the confidence interval if you
- increase the confidence level,
- increase the sample size, or
- increase the margin of error? Only consider one of
these changes at a time.
Explain your answer with words and by referencing the formula.
Rejection
Region (graded)
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How is the rejection region defined,
and how is that related to the p value? When do you reject or fail to reject
the null hypothesis? Why do you think statisticians are asked to complete
hypothesis testing? Can you think of examples in courts, in medicine, or in
your area?
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