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# Statistic analysis of an exporting apple company - Essay Example

Summary
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…

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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 “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 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, ...Download file to see next pagesRead More
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