Test 3 Study Guide: Consumer Statistics and Probability
The purpose of this test experience is to help you understand the material and make connections. Research has shown that the effort you expend in studying for tests and clearly explaining your work solidifies your learning.
At the Exam
One 8.5*11 sheet with writing on both sides allowed.
You can put anything you like on your sheet.
One scientific calculator or graphing calculator mandatory (but no cell phone nor other
calculators bundled in combination with additional technologies)
Bring your stock graph, and a straight edge/ruler like your school i.d.
You may have out food, hydration, ear plugs, or similar if they will
help you (however any ear plugs must be stand alone--no cell phone, internet or other technological connections)
Partial credit will be given, so (if you have time) showing your reasoning or thoughts on questions you are unsure of can help your grade.
On the test,
your grade will be based on the quality and depth of your
responses in the timed environment (the test must be turned in when time is
Questions will mainly consist of
computations and short answer or essay types. There is NO need to write in full sentences -
bullet points, etc will be fine.
The test will be a mixture of computational
questions as well as critical reasoning and reflection involving
the "big picture."
You will be expected to answer questions about
activities from class or lab and the homework readings.
Here is a partial sample test
so that you can see and have some practice with some diverse examples of the formatting and style of questions.
The actual test will differ and will be about 5 pages long.
The main web page and class highlights page
links to homework readings and class activities and clickers, which also serve as a good
Review Questions for Test 3 and glossary/wiki, also serve as a good review.
Be sure you read through the following overview of topics and that you could respond to questions about these topics on the exam. I want you to understand the material and I am happy to help in office hours or on the ASULearn forum!
- mean and median
- bar charts, histograms (see ASULearn questions and glossary)
- the scale balancing idea
(see also lab and clickers)
- box plots (see also ASUlearn and
clicker questions) including
- haircut costs
- Nielsen boxplots
- 1969 Vietnam draft
- reaction time with cell phone usage
- stock boxplots
- armspan and height boxplots
- confidence intervals and margin of error (see also ASULearn questions and clickers) including
- probabilities (see also ASUlearn questions and clickers) including
- Fisher's experiment of a lady tasting tea
- HIV testing
- personal coincidence probability of a one-in-a-hundred chance event not happening (or happening) over time
- expected average or number of yards (by using a weighted average of expectations times the corresponding probabilities)
- price of a life
- linear regression
(see also lab, clickers, ASULearn questions, and class activities) including
- what kind of predictor something is via the r2 precentage
(and put this chart on your reference sheet),
- what prediction the regression line
gives for x-values not on the graph (for example
homework on police tickets
and be able to do similar calculations using the line),
- whether or not the prediction makes sense given the context of the
(see also egg bungee and linear regression lab worksheets, class notes, ASULearn questions, Buchanan votes class analysis...),
- and the impact of adding or removing outliers on the slope of the line
and the r2 value.
- How to use your stock graph to answer questions like we did in the
labs (bring your stock graph to the test).
- Be able to critique media representations (like the youth vote in the
2008 election, exposure to A or F, or the worst graph we explored in class, etc)
- All the statistics labs, especially the critical analysis questions,
as you can expect some similar test questions
- Review the clicker questions on the class highlights page,
the ASULearn review questions, and the ASULearn glossary/Wiki
Other Directed Questions and Short Essay Questions
Know big picture ideas, but it is NOT necessary to know the
calculation details, ie I won't ask you to do a calculation, but
I do expect you to be able to discuss the highlights and point of our
homework readings and class discussions on
Creating various statistical
representations of data:
- stock market issues
- golden mean (1+sqrt(5))/2 and statistics of nature
- census and sampling issues, including chicken soup
- election issues [Landon and Roosevelt, Bush and Obama, and
Buchanan and Gore].
- stereotype vulnerability and success in mathematics
- Leonardo da Vinci's assertion about the relationship between
Armspan and Height
- correlation versus causation, including underlying variables and
Bradford Hill dimensions
- meta studies and consensus to obtain reasonable certainty
- The cigarette controversy
- misleading advertising and labeling
- birthday problem
Analyzing and critiquing various statistical
representations of data:
Big Picture Connections
For this test you will be asked to look at examples from the statistics
segment and discuss the similarities. Ideas from the rest of this
study guide work well here. For example:
Make sure that you understand
what kind of information you can get from statistical
representations where you are not given the underlying data (like the
ASULearn Review Questions for Test 3. ).
- Make sure that you
understand how to give various spins on data and statistical
representations (as if you were
in advertising and wanted to use the stats to say something positive or
negative about the situation, like "Here's Good News... SAT scores
are declining at a slower rate").
- Make sure that you understand statistical common sense
in the context of the real life problem that we are working on
in order to critique statistical methodology of data collection,
interpretations, conclusions and predictions
- problematic instructions like those before the Mental Rotation Test
may affect the results,
- it doesn't make sense
to predict someone's ability when they haven't slept for 2000
hours because a human can't stay awake that long
- the stock market
is based on emotion and thus not predictable.
If the r2 value was 100% in an increasing stock, we anticipate
something fishy is going on, since
our stocks should fluctuate instead of following a linear trend. So we might
be just as safe shorting the stock (betting it will decline) as investing in
it. Many people invest in the long term and diversify their stocks
(like with mutual funds) with the idea that those stocks who stay in
business will do much better than a corresponding savings account at a
bank, and can balance out those that go out of business. In the short term anything can happen. In fact if a stock is completely linear, it probably
means it isn't being actively traded.
- It is problematic to use data to predict long in the future
except in very specialized circumstances like the egg bungee where we
had good reason to expect a linear trend to continue because
the slope, the change in distance stretched per change in
rubber bands, was approximately a constant
since each rubber band stretched about the same amount...
- In order to trust a causality relationship like
(does cell phone use cause brain cancer?)
we would like to see lots of different types of studies by different
people to try and isolate hidden, underlying variables that satisfy the
Bradford Hill criteria.
Understanding what the possible underlying variables are and conducting
sufficiently controlled experiments with alternative hypotheses are the
key to examining coincidence versus causation, like in our class discussion
on birds flying south, or the GE experiment on turning up or down the lights
and measuring productivity, or in the Bradford Hill criteria for examing
smoking and cancer.
- Impossibility of checking all the cases, but finding a solution by
shifting our viewpoint (Jeff Weeks' research).
It is impossible to
conduct a census of everyone for most data collection situations, so
we have to carefully shift our view to sampling issues (elaborate on
random samples and Gallup). Note that this can also connect to local to global connections as well as
truth and consequences-what is truth? When are we convinced? What
are the consequences of certain truths? What is the role of chance and probability?.
- Viewing objects that are impossible to see by managing small pieces at
a time (Jeff Weeks' research).
When we examined the Vietnam Draft info, we obtained a scatterplot
that looked random to the naked eye, but it was hard to tell because there
were so many points. The regression line had a negative
slope, indicating that as the birthday occurred later in the year,
the draft number was lower, but the r2 value was very small,
indicating a very weak correlation. It was impossible to see any patterns
via the complete picture, so instead we broke the data up into smaller pieces -
by month, via 12 boxplots. Here it was easy to see the pattern.
November and December birthdays had a 75% chance of being drafted because
q3 was under the 196 draft number, while
earlier birthdays only had a 50% chance, since the median was near the 196
draft number. It turned out that the later birthdays had been thrown in
on top, and not mixed well enough, so indeed it was not a random sample.
Note that this is also local to global connections.
- Impossibility of constructing a solution but finding a non-constructivist
[You might explore the notion that there is no proof in statistics,
but that we can still eventually be convinced that smoking causes cancer,
via the Bradford Hill criteria for example, which also relates to
truth and consequences-what is truth? When are we convinced?
What are the consequences of certain truths? What is the role of chance and probability?].
- Think about ethical connections such as twisting or misrepresenting statistics and the
price of life
- people and their successes: Florence Nightingale,
George Gallup, Nate Silver, Leonardo da Vinci, Ronald Fisher,
Austin Bradford Hill, psychics/stock whiz..., David Blackwell
- critiquing pros and cons of representations
- how we connected statistics to each of the first 2 segments
[geometry of the earth and universe [sum of squares Pythagorean theorem
compared to least squares regression for instance], and personal finance and beyond].
I want you to succeed!