Statistics 100 Fall 2017 Sections
Karle Flanagan
Section L1 TR 12:30pm1:50pm Lincoln Hall Theater
Section L2 TR 3:30pm4:50pm Lincoln Hall Theater

Ellen Fireman
Online Section
Danielle Sass
Section S1 MWF 12:00pm12:50pm 1027 Lincoln Hall

Outline of Course Content
Experimental Design  Why randomized controls are key.
What the possible confounders in observational studies are.
Descriptive Statistics  mean, median, SD, histograms, normal curve, etc.
Linear Regression  correlation coefficient, regression equation, etc.
Probability
Statistics for Chance Numbers  expected value and Standard error of chance processes,
probability histograms and convergence to normal curve. Focus is on developing simple chance models
box models drawing numbers at random from a box) that more complicated sampling processes
can be translated into.
Sampling and Statistical Inference  Using sample means and percents to estimate
population means and proportions, and attaching margins of errors to our estimates
by computing confidence intervals. Why randomized sampling is key.
Hypothesis Testsone sample and two sample Ztests, ttests and chisquare tests
for goodness of fit and independence. Focus is on understanding how these tests depend on
chance models.
Limits of Significance Tests understanding what the Pvalue means and under what
circumstances it is valid. (For example, hypotheses must be stated before looking at the data,
the total number of experiments before significant results were found must be reported, etc.)

Why everyone needs to know basic statistics:
Statistics is a tool to make sense of large amounts of information.
Common sense can only handle limited amounts of information.
Until recently common sense was sufficient for most people because
daily life didn't involve processing a large amount of data.
But in the past 30 years or so, with the advent of personal computers,
large stores of information have become readily available.
You can either choose to ignore the information available
or you can choose to make sense of it, which means learning statistics.
Why most people think statistics is boring or worse:
Most people think statistics is boring for a good reasonit's not about anything!
Art is about beauty, science is about nature, history is about people...
and statistics is about nothing. It's just a tool, but it's such a difficult tool
for most people to learn how to use that it becomes worse than boring. It becomes tedious,
confusing and frustrating.
Why Stat 100 is not too boring or frustrating:
Statistics is to data, what grammar is to words. And like grammar,
it's only interesting if it's used to understand something interesting.
In Stat 100, we use statistics to research a topic we're all interested inourselves.
We'll collect data on ourselves through anonymous surveys.
If we can come up with interesting questions that we can only answer through learning statistics,
the process will be less painful and more productive.
Students tell me that after Stat 100 they:
Read the newspaper in a new way, without their eyes glazing over when they see
quantitative information.
Know what questions to ask in evaluating studies and surveys.
Understand what questions can and cannot be answered by statistical arguments.
Appreciate how much of what matters to them can be better understood with statistics.
Feel much more confident applying both logical reasoning and common sense to
quantitative topics but are very aware that their intuition can sometimes be so wrong that
it's shocking.
But what's most surprising is they ACTUALLY LIKE STATISTICS!!!!!!


Meditations on the Statistical Method
Plato despair!
We prove by norms
How numbers bear
Empiric forms,
How random wrongs
Will average right
If time be long
And error slight;
But in our hearts
Hyperbole
Curves and departs
To infinity.
Error is boundless.
Nor hope nor doubt,
Though both be groundless,
Will average out.
JV Cunningham
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