A factory hiring people for tasks on its assembly line gives applicants a test of manual dexterity. This test counts how many oddly shaped parts the applicant can install on a model engine in a one-minute period. Assume that these tested applicants represent simple random samples of men and women who apply for these jobs. Complete parts (a) through (f). Click the icon to view the table of gender and number of parts installed. (a) Find the 95% confidence interval for the expected number of parts that men and women can install during a one-minute period. The 95% confidence interval for the expected number of parts that men can install is (21.81,26.19). The 95% confidence interval for the expected number of parts that women can install is (25.11, 32.30). (Round to two decimal places as needed.) (b) These data are counts, and hence cannot be negative or fractions. How can we use the normal model in this situation? A. Normality describes the sampling distribution of the average and not the individual. OB. The normal model can be used because the sample size is less than 30. OC. The normal model cannot be used in this situation. (c) Your intervals in part a should overlap. What does it mean that the intervals overlap? A. The population means could be the same. OB. Women install more parts than men. OC. Men install more parts than women. OD. The population means cannot be equal. (d) Find the 95% confidence interval for the difference men women (-9.7.0.3) (Round to one decimal place as needed.) Table of gender and number of parts installed. Gender Parts Installed Gender Parts Installed Gender Parts Installed Male Male Female Male Male Male Male Male Male Male Male Male Male Male Male Male Male Female Female Female Female Female Female Female Female Female Female Female Female Female Female Female Male Male Male Male Male Male Male Male Male Male Male Male SANNSINSI282872N 35 30 23 35 17 35 17 36 24 26 18 Male Male Male Female 15 23 28 25 28 23 26 18 19 19 12 23 25 23 21 36 30 36 25 18 34 41 28 21 29 32 24 31 27 30 13 33 Full data s