A:We know that high poverty and global water company causes clean water. high poverty or clean water causes cholera contraction. We observed the person is served by a global water company and the region has high poverty. B:Would the person avoids cholera if clean water instead of polluted water? Answer: algorithms
A:For individuals who are not male, the probability of brown eyes is 81%. For individuals who are male, the probability of brown eyes is 77%. B:For individuals who are male, would it be more likely to see brown eyes if the individual had not been male? Answer: aid
A:The overall probability of zuph is 83%. For those who are not zuph, the probability of uvzi is 57%. For those who are zuph, the probability of uvzi is 26%. B:Is uvzi more likely than not uvzi overall? Answer: algorithms
A:We know that hwax causes jyka. hwax or jyka causes lirg. We observed an individual is hwax. B:Would an individual is not lirg if jyka instead of not jyka? Answer: algorithms
A:For those who are not yomx, the probability of xevu is 70%. For those who are yomx, the probability of xevu is 60%. B:Does yomx negatively affect xevu through gwet? Answer: aid
A:For those who are not kwox, the probability of kwoz is 53%. For those who are kwox, the probability of kwoz is 92%. B:For those who are kwox, would it be less likely to see kwoz if the individual was not kwox? Answer: aid
A:For those who are not zuph, the probability of uvzi is 65%. For those who are zuph, the probability of uvzi is 18%. B:Will zuph decrease the chance of uvzi? Answer: aid
A:Method 1: We look at how zuph correlates with glimx case by case according to jyka. Method 2: We look directly at how zuph correlates with glimx in general. B:To understand how zuph affects glimx, is it more correct to use the Method 1 than Method 2? Answer: algorithms
A:For those who are not yomx, the probability of xevu is 9%. For those who are yomx, the probability of xevu is 21%. B:Will yomx decrease the chance of xevu? Answer:
algorithms