Math 151 , Fall 2004, Day 1 Hit reload to get most current version

Take your pulse (heartbeats per minute).  Write it down.
Pick a playing card from the circulating envelope.  Write it down. Return it to the OTHER envelope.
Sign in on the clipboard circulating.

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

SPSS instruction:   I'll be giving handouts and working in class with it (later).   Tutorial built in for basic intro.
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
HW assignment Day 1, August 27
--Leave under my door, Mac 102, by 10 a.m.Wednesday!:  Pretest and Student questionnaire, if not finished in class today.
Reading-- Text: 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 
All from David S. Moore, The Basic Practice of Statistics, ed. 2, unless otherwise noted
~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~ ~
Data:  Information (usually numbers)  in context:  What, Who (how many), Why?  When and Where? How?
       Context for height, hair color, shoe size, pulse rate:

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?  Any problems with the way I collected data?
    Variability happens.  Things settle down on average.  BUT conclusions are never certain.
    Statistics gives us a language for talking about uncertainty.

Sievers home  Math151-Sp04/Dayf1.htm  9pm 8/26/04
This page belongs to Sally Sievers who is solely responsible for its content. Please see our statement of responsibility.