Honor code:
This community of learners is a rare and fragile thing. Trust is
the foundation of its structure. Betraying the trust damages the
whole community. Please do not betray my or your fellows' trust,
and I will do my best to reciprocate. The flip side of this is that
if you do betray our trust, I will definitely pursue it in Community Court.
| Hand in (all from Moore text unless otherwise noted).
p. 20, 1.14 (hurricane hist) 1.17 (cf. age dist.) p. 17 1.9 (stem: SSHA) p. 22ff, 1.18 &19 (back to back HR) 1.24 (pop of states) |
Read, to discuss
1.15 batting, 1.16 coins |
Optional
1.26 teachers' salaries |
Distribution of one
variable: what values, how many (or what proportion) of each.
Graphical summaries of data: Area
represents proportion.
Categorical:
Bar or pie graph (Bar chart ordered by size = "Pareto
chart"--not in text)
Quantitative: Histogram,
Stemplot (Stem-and-leaf), Dotplot
(I will only require
you to read, not make histograms by hand. You'll
Make
stemplots
and dotplots by hand)
Stemplots
(Stem-and-Leaf)
are a powerful hand tool. Handout
Unordered first, then ordered if necessary. By tens, then split?
Back to back, comparing two groups.
Choosing a display (by hand): Note bottom of p. 38, fig.
1.12, use of a to display a data set of
size n = 7.
A dot plot is
most useful for n = 3 to about 15-20, or when the data only fall on a few
values (just stack the dots up).
A stemplot is
good for continuous data, smeared around; you can do 100 values in 3-5
minutes.
Describing: Pattern-- and deviations
from it
Shape (symmetric, skewed (think smeared, or sliding) right or left),
(Humps: uni- or bi- modal (multi-) Two humps = two "causes"?)
center,
spread--outliers?
What do we see?
What can we infer?
(Introduction)
Data source? Lurking variables?
Variability happens.
Things settle down on average, BUT conclusions are never certain.
Statistics gives us a language
for talking about uncertainty.
| Sievers home | Math151-/Fall02/Day-2.htm | 4pm | 9/1/01 |