- Announcements:
- For today: you were to read section 7.5: Collecting Data Rather than Dust (this is the "lying" part!;)
- Section 7.6: Mindscapes #5, 6, 9, 23, 26 (type up #22)
Due Monday, 10/27
- Section 7.6: What the Average American Has
- Heights of our class
- Constructing a histogram (using heights)
- Interpreting measures of central tendency from the histogram
- mean is the "center of mass" of the histogram
- median is the middle value of the histogram
- Heights of people tend to be
- Normal distributions arise quite naturally (hexstat)
- Cicada population data
- Let's generate our own "normal data",
- What's not in a mean or median? Spread, or
variation
- How do we quantify variation?
- "The mean difference from the mean" is one way....
- Standard deviation is another. This is a
"typical deviation" from the mean, or a "standard
deviation"...
In any event, these measures help us to appreciate
about by how much an individual measurement typically
varies from the mean.
- Interpreting measures of variation (from the histogram)
- Section 7.5: Collecting Data rather than dust (the power and
pitfalls of statistics)
- Assignment: pp. 581-: #1, 3, 4, 8, 12. These will be attached to your upcoming exam, coming up 11/3, and constitute 10% of the exam.
- We have an election coming up!
- Sampling
- Samples versus Populations
- What is statistical bias?
- Cicada population data
- Avoiding bias. Mindscape #5, p. 581.
- When is enough enough? Data, that is.
- Data we collect should be
- accurate
- unbiased
- "sufficient in number to reflect the population's
variability"
- How do we get honest answers to life's tough questions?
- Are you rich?
- Do you cheat on your spouse?
- Do you have AIDS?
- Sampling with randomness -- sound like a good idea? Let's
try....
- Let's answer an embarrassing question: are you male or
female?
- Let's calculate the true rates of maleness and
femaleness in class...
- Each person will flip a coin:
- If it's heads, answer "Male"
- If it's tails, answer truthfully.
- How do we now estimate the true rate?
- Guess that half the "Male" answers are
from heads; the other half are truthful
answers.
- An example: Mindscape #2, p. 581.
- How could we adapt this process to lose less of our data
- with a die
- with two tosses of a coin?
- Now: what embarrassing question should we now attempt to
answer? Who's got something they really want to know?
Website maintained by Andy Long.
Comments appreciated.