An Introduction to Things to Come!. Administrivia. The details on the class can be found here: www.stanford.edu/class/hrp259 Class participation plus lots of little assignments and 2 exams determines your final grade.
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An Introduction to Things to Come!
Weeks of Gestation
Weight at birth
…and express it with a simple formula like this:
lbs = weeks * something
Outcome = baseline + predictor + predictor
Impact of being a
Weeks * a number
Outcome = baseline + predictor + predictor + error
Impact being a
Weeks * a number
Histogram of actual weights at 40 week births
My model guesses
Histogram of errors at 40 weeks
I guessed way too high rarely
I guessed way too low rarely
0 error if child was 7.5 lbs
Most errors are off by just a bit
If you pretend your variability is normally distributed but your outcome has a limited range, you clearly have problems.
In theory, the variance of count data increases with the mean.
Weight = estimated weight gain each week after conception * number of weeks + weight at 0 weeks
Induced because of HUGE size
From Crawley: Statistical Computing
From Crawley Statistical Computing
Is this better than a flat line at the mean?
Flatten the line, then look up and down to see if you are systematically off.
size = intercept + X * something + X2*something else
size = intercept +
X * something +
X2* something else +
X3 * another thing
poly2 = lm(y~poly(x,2))
poly3 = lm(y~poly(x,3))
baseline + predictor + predictor + error
Tweaked outcome =
baseline + predictor + predictor + not normalerror