Consider the logit regression log(odds(QualExam) ) = 8, + 8, . ParEduc + 8, . Awards. where QualExam is a binary variable that indicates passing the exam if equal to 1, and falling the exam if 0, ParEduc indicates the parents' education level, and Awards is a binary variable that indicates having experience of obtaining award(s) if equal to 1, and not having experience if 0, Given the parents' average education level unchanged, the odds ratio is expected to be for an individual with awards to pass the exam comparing to those without awards. For an individual without awards and the parents' education level of 4, the estimated probability of passing the exam is approximately Intercept ParEduc Awards -10.53 2.98 0.48 A 0.48: 446. B. 0,48: 80%. C. 1.616; 80%. D. 1.616; 4%.Consider the logit regression log(odds(QualExam) ) = 8, + 8, . ParEduc + 8, . Awards. where QualExam is a binary variable that indicates passing the exam if equal to 1, and falling the exam if 0, ParEduc indicates the parents' education level, and Awards is a binary variable that indicates having experience of obtaining award(s) if equal to 1, and not having experience if 0, Given the parents' average education level unchanged, the odds ratio is expected to be for an individual with awards to pass the exam comparing to those without awards. For an individual without awards and the parents' education level of 4, the estimated probability of passing the exam is approximately Intercept ParEduc Awards -10.53 2.98 0.48 A 0.48: 446. B. 0,48: 80%. C. 1.616; 80%. D. 1.616; 4%