Math 151 , Fall 2002, Day 1 Hit reload to get most current version

Syllabus, class mechanics
Handouts:  Syllabus, Optional software
           Pretest, Student questionnaire (Return when finished)
             Data collection:  Class, height, hair color, shoe size, heart rate (pulse).  Pick a card.  Record on questionnaire.

Activstats:  Very rich.  Prof. Velleman's life work. Email me if you get it--we'll do a "lab" to get started.
Requires a Windows machine, because of SPSS.  Email me if your personal computer is a Macintosh.
You'll need earphones unless you have a single room and speakers on your computer.  Possibly an extension cable.

SPSS instruction: Some is built in to Activstats.  I'll be giving handouts and working in class with it (later).   SPSS for Windows Manual (with new books) is 2 versions behind, but ok for some later stuff.
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HW assignment Day 1

--Leave under my door, Mac 102, by 10:30 Monday!:  Pretest and Student questionnaire, if not finished in class today.

Reading-- MRB: Read, also read ahead:  Intro, xxv-xxxi [very good],   Chapter 1, section 1.1 (Distributions with graphs).
Italicized notes give me a hint which problem it is. [my comments]
    Problems on the same line usually cover similar issues.

Hand in (in class) p.5, 1.2 cat/quant p.8,  1.4 bar/pie

Read, be able to discuss     1.1 indiv/vble 1.3 bar (pie OK?)

Optional  p. 20, 1.11

  MRB:  From David S. Moore, The Basic Practice of Statistics
  ACTS: Activstats
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Data:  Information (usually numbers)  in context:  What, Who (how many), Why?  When and Where? How?
Variable (possible values), individuals (cases)
        Categorical (ordinal--has natural order or nominal--just names) Ordinal/nominal not in text!
    or Quantitative (can add, average--measured on a ruler-type scale) Units?!

Distribution of one variable:  what values, how many (or what proportion) of each.
  Categorical: Bar or pie graph  (Pie: the whole thing, all categories.) Area represents proportion
    Describing:  Pattern-- and deviations from it

What do we see?  What can we infer? (Teaser--inference is 3rd part of course)
    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.