File: Open Data Table:  storks.txt
Basic: Bivariate:  storks are "X", people are "Y"
  - note correlation
 Hot spot: Fit Line
  - note b0, b1 (and interpret meanings)
  - do more storks bring more people?  correlation is not causation!
  - predict people for 160 storks:  y-hat = 36.55+0.149*160 = 60.4 (thousand)
  - fitted value for 240 storks is  y-hat = 36.55+0.149*240 = 72.3
  - error (residual) at 240 is  e = 69.2-72.3 = -3.1


File: Open Data Table:  quiz.txt
Basic: Bivariate:  QuizTotal is "X", Midterm.Grade is "Y"
  - weak but noticeable correlation
 Hot spot: Fit Line
  - note b0, b1
  - predict midterm score for quiz total of 30:  y-hat = 53.7+.96*30 = 82.5


File: Open Data Table:  o-ring.txt
Basic: Bivariate:  Temperature is "X", Damage is "Y"
  - does there look like a relationship between temperature and damage? 
 Hot spot: Fit Line
  - note b0, b1
  - how do you feel about predicting for a temp of 31?
  - "extrapolation" (does the model even hold?)
  - predicted damage for temp of 31:  y-hat = 18.036-0.24*31 = 10.6
      should we launch?