Change the following parimutuel payoffs from modern to classic or vice versa.
a) -125
b) +144
c) 7:5
d) 4:3
Fill in the Bayesian contingency table for a trait that shows up in 1 in 50 people in the population and a test for the trait that has an error rate of 1 in 200. Find the following probabilities.
p(error, given test -)
p(error, given test +)
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a) -125
100:125 reduces to
4:5
b) +144
144:100 reduces to
36:25
c) 7:5
big/small*100 = 7/5*100 = 140
Since profit > risk the answer is
+140
d) 4:3
big/small*100 = 4/3*100 = 133.33...
round to 133
Since profit > risk the answer is
+133
Fill in the Bayesian contingency table for a trait that shows up in 1 in 50 people in the population and a test for the trait that has an error rate of 1 in 200. Find the following probabilities.
p(error, given test -)
p(error, given test +)
Step 1:
grand total = 50*200 = 10000
________don't___have____row total
test + __________________________
test - __________________________
col._____________________10000 grand total
Step 2:
grand_total * 1/50 = 200 have
10000 - 200 = 9800 don't
________don't___have____row total
test + __________________________
test - __________________________
col.____9800____200______10000 grand total
Step 3:
have column
have_total * 1/200 = 1 test -
200 - 1 = 199 = test +
________don't___have____row total
test + _________199______________
test - ___________1______________
col.____9800____200______10000 grand total
Step 4:
don't have column
don't_total * 1/200 = 49 test +
9800 - 49 = 9751 = test -
________don't___have____row total
test + ___49____199______________
test - _9751______1______________
col.____9800____200______10000 grand total
Step 5:
row totals
49 + 199 = 248 = test + total
9751 + 1 = 9752 = test - total
________don't___have____row total
test + ___49____199________248___
test - _9751______1_______9752___
col.____9800____200______10000 grand total
Step 6
p(error, given test -) = 1/9752 ~= 0.01%
p(error, given test +) = 49/248 ~= 19.76%
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