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 âbig data.â 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 individualâs privacy by only

publishing aggregate figures (or âstatisticsâ) 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 âgirls who watch soap

operas are more likely to have eating disorders.â

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: âStatistics helps us make decisions based on

data analysis.â 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 âMost Likelyâ outcome of a Binomial (graded)

Do

you think that the âmost likelyâ outcome in a binomial distribution is

the outcome that will occur most of the time?â For example, what is the

âmost likelyâ 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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