Devry MATH221 all discussions

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?

Post-Class Topic: Social Media’s Use of Data (graded)
This century is already being
characterized as the era of “big data.” You are probably active or at
least knowledgeable about the proliferation of various social media
outlets, like Facebook, Twitter, LinkedIn and flickr. Do you feel like
too much personal data is retained forever? Do you have any concerns
about how your personal data is used? Or, are you satisfied that most
studies based on personal data collected by large companies maintain
sufficient controls and respect an individual’s privacy by only
publishing aggregate figures (or “statistics”) which summarize trends?
(There is no correct answer, just informed opinions.)

Week 2Regression (graded)
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?Post-Class Topic: Correlation and Causation (graded)
If two variables are strongly correlated, does it necessarily
always follow that there is a direct cause-and-effect relationship
between the two variables? Can you think of two variables which are
often associated with each other and are highly correlated, but there is
no direct
cause-and-effect relationship between them? For example, do you think it
is a correct conclusion that watching soap operas gives girls eating
disorders like anorexia if a study showed that “girls who watch soap
operas are more likely to have eating disorders.”

Week 3Statistics 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
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?Post-Class Topic: Interpreting the “Most Likely” outcome of a Binomial (graded)

Do
you think that the “most likely” outcome in a binomial distribution is
the outcome that will occur most of the time?” For example, what is the
“most likely” composition of a four-member committee chosen randomly
from a large population that is 50% women and 50% men? What is the
probability of the committee composed by two mean and two women? What is
the probability of the committee containing one man and three women?
What is the probability of the committee containing three men and one
woman?
Week 5Interpreting 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?Post-Class Topic: Central Limit Theorem (graded)
Explain what property associated
with the Central Limit Theorem you consider the most important
contribution, enabling the use of the normal distribution for sample
means with large sample size.
Week 6

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.
Post-Class Topic: Confidence Intervals and Hypothesis Testing (graded)
The EPA will grant a tax credit if the city-highway mileage estimate is at least 31 mpg.Construct
the 95% and 99% confidence intervals for the mean mpg [miles per
gallon] if we have a data sample with 49 observations of mileage of a
new car model, with x-bar = 31.5531 mpg and known std. dev. sigma = 0.8
mpg. Which CI is wider and why? Would the EPA grant a tax credit if the
99% CI is (31.26, 31.85) and why? This is an example of hypothesis
testing using CIs. If the EPA minimum qualifying mileage were 33 mpg,
instead of 31 mpg, would the EPA grant a tax credit with the same 99%
CI?
Week 7Rejection Region (graded)
How is the rejection region defined and how is that
related to the z-score 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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