Statistic analysis of an exporting apple company Essay

Statistic analysis of an exporting apple company - Essay pillow slipStatistic analysis of an exporting apple companyThis is statistically significant for this indicates that in promoting slow moving dog products, these items will be placed on the stem level shelves. This also applies for goods that need to be sold immediately like old stocks and products approaching expi ration 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 closely efficient packers, they conducted a 8 hour study for 6 days based on their speed in packing apples. Below argon sixsome study results for the three packers indicating the number of boxes packed in 8 hours. Which packer is best? An industrial psychologist is interested in brainstorming among groups as a core of solving complex puzzles an d 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, stick important data, and consider problems in the analysis. TotalProblem set 1G11MalesS18S27FemalesS327S424G12MalesS520S624FemalesS727S828G13MalesS914S1018FemalesS1127S1226Problem set 2G24MalesS1326S1430FemalesS154S168G25MalesS1726S1829FemalesS1915S2018G26MalesS2128S2228FemalesS238S24121) sH0 AProblemSet 1 = 2 G/A 1 = 2 = 3 = 4 = 5 = 6 BGender 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 Table 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 running play to determine proper 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,