A:Method 1: We look at how zuph correlates with uvzi case by case according to wibl. Method 2: We look directly at how zuph correlates with uvzi in general. B:To understand how zuph affects uvzi, is it more correct to use the Method 1 than Method 2? Answer: lucas
A:Method 1: We look directly at how smoking correlates with college admission in general. Method 2: We look at this correlation case by case according to effort. B:To understand how smoking affects college admission, is it more correct to use the Method 1 than Method 2? Answer: frog
A:Method 1: We look at how pexu correlates with rukz case by case according to kraz. Method 2: We look directly at how pexu correlates with rukz in general. B:To understand how pexu affects rukz, is it more correct to use the Method 1 than Method 2? Answer: frog
A:We know that yomx or gwet causes xevu. We observed an individual is gwet. B:Would an individual is not xevu if not yomx instead of yomx? Answer: lucas
A:The overall probability of yupt is 61%. The probability of not yupt and muvq is 8%. The probability of yupt and muvq is 27%. B:Is the chance of muvq smaller when observing yupt? Answer: lucas
A:For those who are not kwox, the probability of kwoz is 62%. For those who are kwox, the probability of kwoz is 51%. B:Will kwox decrease the chance of kwoz? Answer: frog
A:We know that drinking coffee causes alarm set by wife. drinking coffee or alarm set by wife causes ringing alarm. B:Would the alarm doesn't ring the next morning if not drinking coffee instead of drinking coffee? Answer: frog
A:The overall probability of jyka is 0%. For those who are not jyka, the probability of lirg is 43%. For those who are jyka, the probability of lirg is 19%. B:Is lirg more likely than not lirg overall? Answer: lucas
A:We know that confounder active or high cigarette tax causes smoking mother. confounder active and smoking mother causes brown eyes. We observed the cigarette tax is low and confounder inactive. B:Would the infant has brown eyes if nonsmoking mother instead of smoking mother? Answer:
lucas