Statistic analysis of an exporting apple company Essay

Statistic analysis of an merchandise apple association - Essay ExampleStatistic analysis of an exporting apple companyThis is statistically world-shaking for this indicates that in promoting slow moving dog products, these items will be placed on the waist level shelves. This also applies for goods that need to be sold immediately the like old stocks and products approaching expiration dates. Through this, inventory and the First-In-First-Out products will be controlled.An apple exporting company is currently retrenching and would like to reduce the number of packers in one of their processing plants from 3 packers to only 2. In finding out the most efficient packers, they conducted a 8 hour study for 6 geezerhood based on their speed in packing apples. Below are six study results for the lead packers indicating the number of boxes packed in 8 hours. Which packer is best? An industrial psychologist is interested in cerebrate among ag groups as a means of solving complex proble ms and she decides to manipulate two types of problem sets or attitudes. She selects 6 groups of four people to participate in the experiment. Three of the groups are given problem set 1 and three of the groups are given problem set 2. In addition, however, two of the participants in each group are males and two are females. She measures number of problems solved by each individual after group discussions at the end of each of three sessions (max = 30). Examine all interesting effects, present important data, and suppose problems in the analysis. TotalProblem set 1G11MalesS18S27FemalesS327S424G12MalesS520S624FemalesS727S828G13MalesS914S1018FemalesS1127S1226Problem set 2G24MalesS1326S1430FemalesS154S168G25MalesS1726S1829FemalesS1915S2018G26MalesS2128S2228FemalesS238S24121) sH0 AProblemSet 1 = 2 G/A 1 = 2 = 3 = 4 = 5 = 6 B sex activity M = F (A)B 1M = 2M = 1F = 2F sHa Not sH0 2) Between Subjects Hierarchical S2(G3B2/A2) 2-tailed (A) (1,4) = 7.71 (G/A) (4,12) = 3.26 (B) (1,4) = 7.71 (AB) (1,4) = 7.71 (GB/A) (4,12) = 3.26 3) = .05 4) Final Source turn off Source DF Sum of Squares Mean Square F-Value F-crit A Problem Set 1 13.50 13.50 .29 7.71 G/A Groups 4 187.83 46.95 10.25* 3.26 B Gender 1 48.17 48.17 1.36 7.71 AB Problem Set*Gender 1 1204.17 1204.17 34.12* 7.71 (GB/A) 4 141.17 35.29 7.70* 3.26 S(GB/A) 12 55.00 4.58 T 23 1649.83 A Problem Set, B Gender, and AB Problem Set*Gender F values are different from SAS output. Why 1 - First, have to test to determine good error term to use Fcrit (4, 12) = 3.26 , = .05 G/A / S(GB/A) = 46.96 / 4.58 = 10.25* so must use G/A to test A. F ratio for A = 13.50 / 46.95 = .29, NS Fcrit (4, 12) = 3.26 , = .05 GB/A / S(GB/A) = 35.29 / 4.58 = 7.71* so must use GB/A to test B and AB F ratio for B = 48.17 / 35.29 = 1.36, NS F ratio for AB = 1204.17 / 35.29 = 7.70* significant (Didnt really need to do this because the group error terms were significant at .05 and cannot be pooled) Subsequent Tests LSDAB = 2.78 2(35.29) / 6 = 9.53 M Female-P1 - M Female-P2 = 26.50 - 10.83 = 15.67* M Male-P1 - M Male-P2 = 15.17 - 27.83 = -12.66* 5) The data indicate there was no significant main effect for Problem Set, F(1,4) = 0.29, MSe = 46.95, or for Gender, F(1,4) = 1.36,