Some thoughts for the course:
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There are three kinds of lies: lies, damned lies, and statistics
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- There are certain goals that we must strive to achieve everyday (FACE):
- Focus
- Attitude
- Creativity
- Effort
- Name (the name that you want me to call you in class)
- Hometown
- What is your calling? ("a calling has to do with one's larger purpose, personhood, deepest values, and the gift one wishes to give the world.... A calling is about the use one makes of a career." David Orr, Earth in Mind)
- What is your dream job?
- What is something special about you?
- What is your height?
- Syllabus
- Tentative Schedule (with assignments!)
- An important and useful resource: the Math/Stat lab (MEP 457)
The Math & Stat Lab will open soon (if not already). The lab is generally open M-R from 9-3, and F 9-12.
- Class agreement -- you need to
sign on, if you want to stay in my section.
- Great news! This course is cheap. We're using resources created,
for the most part, by our chair, Prof. Brooke Buckley.
You'll need to print out the packets. These are found off the homepage, linked to packets. Just print them off as needed (because we may be making changes along the way).
For today, I'm hoping that you've printed off Packet01-NormalDistributions.pdf and brought it to class. We'll be filling it out together as we go along.
- What do we mean by "Statistics"? What is/are Statistics?
- Statistics are numbers ("descriptive statistics")
First Definition of Statistics: Statistics are numbers calculated from a collection of data
"Data": items of information, either numerical or non-numerical (quantitative versus qualitative)
Examples of statistics:
- baseball "averages"
- percent, proportion of NKU students that graduated from the same high school as one of their parents
- average height of NKU students
- percentages of students preferring each type of soft drinks offered on NKU campus
- Statistics is also a science ("inferential statistics")
Second Definition of Statistics: Statistics is a science that deals with the collection and summary of information that is then used to make interpretations, decisions, estimates, predictions, etc.
Examples:
- Polio Vaccine trials
- Calling elections before some voters have even voted (and
certainly before most votes have been counted)
- What makes this easy?
- Why did the 2000 Presidential Election take so long to call?
- A more mundane example: Heights of NKU students:
How might we use the students in this class to estimate the average height of an NKU student?
- Let's try some inferential statistics. Inferential Statistics
is tied up with probability. We may hear someone make a statement,
and then we say "Nonsense!", or "That seems reasonable...."
But we may want to do more: we may want to test an assertion, to see what we can detect or infer from our examination of the data. And for that we'll need probabilities.
Here's a simple probability example:
- What's the probability of a 5 appearing on a throw of a die?
- What's the probability of an odd number appearing?
(Of course we must always be on the lookout for anything funky about the setup.)
How might we determine if a die is fair?
We would look at a distribution of many tosses of the die, and what would we expect to see?
If what we expect to see and what we actually see are radically different, then we may reject the fairness of the die.
And that's how a lot of statistical tests work....
- Statistics are numbers ("descriptive statistics")
- So let's get into it here, with Packet 1: Normal Distributions.