Statistic analysis of an exporting apple company Essay

Statistic analysis of an exporting apple company - Essay Example Statistic analysis of an exporting apple company This is statistically significant 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 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 days based on their speed in packing apples. Below are six 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 means of solving complex problems and she decides to manipulate two types of problem â€Å"sets† or attitudes. She selects 6 groups of four people to participate in the experiment. Three of the groups are given problem â€Å"set† 1 and three of the groups are given problem â€Å"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 consider problems in the analysis. Total Problem "set" 1 G11 Males S1 8 S2 7 Females S3 27 S4 24 G12 Males S5 20 S6 24 Females S7 27 S8 28 G13 Males S9 14 S10 18 Females S11 27 S12 26 Problem "set" 2 G24 Males S13 26 S14 30 Females S15 4 S16 8 G25 Males S17 26 S18 29 Females S19 15 S20 18 G26 Males S21 28 S22 28 Females S23 8 S24 12 1) 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 test 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! (Didn't 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,