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This analysis focuses on the relationship between O-ring temperature at liftoff and the occurrence of erosion or blowby issues in previous Shuttle missions. Logistic regression is used to predict the probability of failure or risk based on temperature and launch data.
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Example: Shuttle Missions • O-rings failed in Challenger disaster • Low temperature • Prior flights “erosion” and “blowby” in O-rings • Feature: Temperature at liftoff • Target: problem (1) - erosion or blowby vs. no problem (0)
Data Shuttle; input Atrisk failed temp LCpress launch; *; label temp= "Temperature at Liftoff"; label launch = "Launch number"; label LCpress = "Leak Check pressure (PSI)"; cards; 6 0 66 50 1 6 1 70 50 2 (more data) proc logistic data=shuttle; title3 "Logistic Regression"; model failed/atrisk = temp launch; output out=out1 predicted = p; run; proc genmod data=shuttle; title3 "Logistic Regression"; model failed/atrisk = temp launch/dist=binomial; run;
Chi-Square Contingency Tables Heart Disease No Yes Heart Disease No Yes 100 100 Low BP High BP DEPENDENT INDEPENDENT
c2 Test Statistic • Expect 100(150/200)=75 in upper left if independent (etc. e.g. 100(50/200)=25) Heart Disease No Yes Low BP High BP 100 100 2(400/75)+ 2(400/25) = 42.67 Compare to c2 Tables Significant! 150 50 200 Distribution: Chi-square df=(rows-1)(cols-1)
