
Week 1 discussion
Descriptive Statistics (graded)

If you were given a large data set
such as the sales over the last year of our top 1,000 customers, what might you
be able to do with this data? What might be the benefits of describing the
data?
Week 2 discussion
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?
Week 3 discussion
Statistics in the News (graded)

Keep your eyes and ears open as
you read or listen to the news this week. Find/discover an example of
statistics in the news to discuss the following statement that represents one
of the objectives of statistics analysis: “Statistics helps us make decisions
based on data analysis.” Briefly discuss how the news item or article meets
this objective. Cite your references.
Week 4 discussions
Discrete Probability Variables
(graded)

What are examples of variables
that follow a binomial probability distribution? What are examples of variables
that follow a Poisson distribution? When might you use a geometric probability?
Week 5 discussion
Interpreting Normal Distributions
(graded)

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?
Week 6 discussion
Confidence Interval Concepts (graded)

Consider the formula used for any
confidence interval and the elements included in that formula. What happens to
the confidence interval if you (a) increase the confidence level, (b) increase
the sample size, or (c) increase the margin of error? Only consider one of
these changes at a time. Explain your answer with words and by referencing the
formula.
Week 7 discussion
Rejection Region (graded)

How is the rejection region defined and how is that
related to the zscore and 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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