Statistics Question

Write a 500 to 700 words executive summary of their findings on the data. Including two recommendations to the management of the company. The word count is a guideline, there is no strict limit for this assignment. If students’ analysis merits writing more that is fine. Just don’t be verbose. Final Assessment [Individual] || Given the shortage of
workers in the retail industry, shall we just replace
them with machines?
•
•
•
•
This is a timed and individual assessment.
The dataset will be released on the last day of class.
Students will have 48 hours to complete the analytics, and write a business
report.
Students are expected to write a 500 to 700 words executive summary of
their findings on the data. Including two recommendations to the
management of the company. The word count is a guideline, there is no strict
limit for this assignment. If students’ analysis merits writing more that is fine. Just
don’t be verbose.
====================
The “unexplainable” high unemployment rates while businesses can’t hire
According to the Bureau of Statistics, there were 7.4 million unemployed people in the
U.S. by the end of October 2021. On the other hand, the service industry reports the
highest rates of attrition, according to a report from CNBC, not only in the U.S. but
around the world. “Sectors particularly affected by workers quitting their jobs were
accommodation and food services, retail trade, and state and local government
education.” (Ellyatt, 2021).
The uncertainty of short-term job security, the nature of the hourly wage, and the lack of
health insurance are some of the reasons why workers don’t want to resume their parttime jobs in those sectors. And who can blame them? We have seen that if the
economy goes bad again, those job positions are the first ones to be furloughed, if not
permanently terminated.
How is this affecting Old Navy, one of the brands of the parent company Gap Inc?
This has exacerbated the problem in fast-fashion retailers like Old Navy. Who simply
can’t get enough people to work on their floors. As a result, their lines at the checkouts
are long, even when there are not that many shoppers in the store. And the look and
feel of the product and shelves are below ideal.
Because of the sluggish economy and high inflation rates, value brands like Old Navy
have seen sales growth they haven’t experienced in years. Year-to-date sales for Old
Navy by July 2021 stand at 162 index vs the same period a year ago. Granted 2020
was one of the worst years for the fashion industry. But compared to 2019, Old Navy
grew by +24% in 2021. And this is before the Holiday comes in, which is expected to
bring accelerated growth. (Gap Inc, 2021 & 2020)
Given the difficulty in hiring workers in the retail industry, should Old Navy just
replace them with machines?
Nancy Green Links to an external site., the President of Old Navy at the time, was
wondering if they should adopt the self-checkout technology that pharmacies and
supermarkets had been embracing for years. This technology is not cheap. With
margins as low as 6% (IDEM) Old Navy has not had the financial means to invest in
such technology. But these are different times, the Company is enjoying additional profit
and a steady cash inflow. Net cash increased by 7% in Q3 2021 (IDEM). Investing in
self-checkout technology would free time for the people behind the checkout counters to
focus on re-stocking and re-organizing the shelves.
The Store Operations and the HR Team are hesitant about this technology. Their
mantra for years has been about investing in retention and training programs for the
Store Associates and the Store Managers. But Nancy is doubtful that those efforts had
paid off. She thinks that given the state of the economy and the reluctance of low-skill
workers to join these types of jobs, Old Navy is better off by decreasing reliance on
them. Operating the checkout machine and representing the Old Navy brand, requires
weeks and weeks of onboarding and training.
“The non-cash and cash costs of bringing new people to learn the Old Navy way, just to
leave a few months after they joined, are reducing our profitability”. Nancy says.
However, Nancy thinks that the Ops and HR teams may be somewhat correct. Training
and retaining talent is a good idea for the Store Managers, not for the Associates and
Cashiers. Somebody needs to lead the store and do the hiring of temp associates. Also,
those managers need to understand how the new self-checkouts would work and how
to troubleshoot when they fail. That initial deployment of the self-checkouts and the
learning curve will be costly. Nancy thinks investing in manager retention is important.
But again, she is unsure if it has been in the past. Therefore, she tasks the Ops/HR
team to analyze the historical performance of the stores and cross reference against the
length of employment of the associates and the managers in those stores.
The Ops/HR Team, in turn, delegates the task to a recent analytics grad from a certain
business school in the Boston area. They provide the grad with the data attached.
Download the data attached The data analytics grad runs regression analyses. The
grad finds a model that is able to explain north of 50% of what drives store financial
performance… before their computer crashes. Since the data analytics grad did not turn
on the Auto-Save button. All the work is gone.
The Ops/HR team is meeting Nancy in three hours. You need to help them to recreate
the model quickly. We know there is a certain combination of dummy and interaction
variables that can produce a ~50% explanatory power model.
Guiding questions:
•
•
•
•
Is the old saying in retail about “Location, location, location” also true for the
Old Navy stores?
o How much explanatory power do the Associate and Managers
working in a store bring?
o Are the location factors more important than the people factors?
Is Nancy right that OId Navy shouldn’t be concerned about attrition in the
associate store levels? Are the experience of the managers driving financial
performance for the stores?
Are these effects ever-lasting? i.e. should Nancy and the Ops/HR team
develop programs that keep managers for as long as possible in their roles?
Discuss, outside of the regression analysis, the pros and cons of installing
self-checkouts in the fast-fashion industry as drugstores and supermarkets
have done in the past.
Deliverables:
•
•
•
Word document with your analysis and two recommendations derived from
your findings.
o Don’t structure your paper as Q&A. The questions above are meant
to guide you in your analysis. If you find other patterns and insights
from your analysis outside of those guiding questions, feel free to
include those.
Upload your Excel with the regression analyses and any other work you
conducted.
Read the rubric to understand what are the expectations from you for this
assignment.
RUBRIC:
OldNavyStoreId
20KKw
21A6A
21kVL
21T4T
21Tfh
2268h
22ksG
23DMN
23EQA
23L03
23VYV
240zv
249SS
24s2n
24YJ9
25AkT
25La4
25Ukz
25Z38
26bsY
26fYW
26jDD
26Krz
26r2D
280Xk
281gj
285TW
28ayX
28VRV
29H4f
A06Xy
A0PQc
A0VE9
A25W7
A2E8V
A3AUG
A3d8C
A3Kr4
A3M8M
A42nf
A4c81
A5H0k
Store Sales in 000s
Gap Store in the same area/mall
3409.7 Yes
5192.5 Yes
3525.8 Yes
3371.1 Yes
3944.1 Yes
5475.6 No
5814.9 Yes
4437.3 Yes
6790.4 Yes
3468.1 No
5084.7 Yes
4652.7 No
2749.4 Yes
3763.4 Yes
3221.4 Yes
2838.9 No
3511.3 Yes
3608.8 Yes
4251.8 Yes
3987.2 Yes
4592.5 Yes
4352.3 Yes
3448.1 No
4132.7 Yes
2880 Yes
3161 Yes
3663.1 No
2968.1 Yes
6008.4 Yes
3203.8 Yes
2669.6 No
4392.2 Yes
5004.1 Yes
3281.7 Yes
3870.2 Yes
3187.2 Yes
3132.2 Yes
3339.2 Yes
2389.9 Yes
4081.7 Yes
4149 Yes
5793.1 Yes
A63kY
A6k2u
A6TYy
A73Rb
A74Uc
A8K0C
A95Km
A995s
A9cE4
A9FFb
Aa2HE
Aa2jY
Aa8Nn
Aa9sC
AaAv2
AaHYb
AaM1j
AaWag
AaYPQ
AaZta
Ab0gk
Abw2y
Ac22T
Ac2pH
Ac2YX
AcgT0
AcjBe
AcTSh
AcU6e
AcuuY
AddJW
AdfQ9
AdKMK
AdvXM
Ae1Y2
Ae8g4
AeNz6
Aeprn
Aes6X
AeVKc
AexpY
Af2pv
Af5HX
5146.3 Yes
4311.3 Yes
5361.3 Yes
4004.3 Yes
3170.8 Yes
3003.7 No
3309.1 Yes
3459.5 Yes
4360.2 Yes
2610.8 Yes
2961.2 Yes
2954.6 No
2257.9 Yes
3170.5 Yes
4287.5 Yes
4598.1 Yes
2302.4 Yes
3038.3 No
3368.5 Yes
3110.6 Yes
4335.8 Yes
2834.1 Yes
3955.8 Yes
3276.7 Yes
5064.3 Yes
3148.4 Yes
2735.3 Yes
3596.6 Yes
1400.2 No
4246.7 Yes
3392.7 Yes
5214.5 Yes
3518.8 Yes
3390.1 Yes
3951.1 Yes
5794.9 Yes
4432.3 Yes
6784.4 Yes
3465.1 No
5089.7 Yes
4653.7 No
2748.4 Yes
3751.4 Yes
Af6fA
Af76W
AfBYv
AfFs0
AfGL4
Afgs1
AfRQF
AgJr9
AgNsK
AgXT2
AgYzm
Agz7E
Ahcj1
AheSc
AhF5x
AjL2Y
AjL5v
Ak4bY
Ak4WV
AkQUV
AkY42
AQ49C
AS1Y
AT17V
AT21A
AU38H
AU99R
AV93N
AX18G
AX44K
AX7C
AX81F
AY10P
AY35B
AZ41K
AZ81J
AZ94K
AzAs3
Azg7a
AzJPY
Azm13
AzU4b
B1cQY
3232.4 Yes
2848.9 No
3509.3 Yes
5464 Yes
3634.8 Yes
4240.8 Yes
3976.2 Yes
4591.5 Yes
4354.3 Yes
3451.1 No
4133.7 Yes
2897 Yes
3171 Yes
3664.1 No
2970.1 Yes
6007.4 Yes
3193.8 Yes
2654.6 No
4396.2 Yes
4989.1 Yes
4627.9 Yes
3280.7 Yes
3850.2 Yes
3195.2 Yes
3153.2 Yes
3364.2 Yes
2383.9 Yes
4079.7 Yes
4155 Yes
5804.1 Yes
5131.3 Yes
4291.3 Yes
5349.3 Yes
3985.3 Yes
3151.8 Yes
3009.7 No
3307.1 Yes
3445.5 Yes
4340.2 Yes
2592.8 Yes
2962.2 Yes
2956.6 No
2244.9 Yes
B2rxS
B3mWn
B4AYD
B4r2Y
B6G9Y
B864p
B8a23
B8fP8
B8Ycr
B9h1e
B9t0Q
BaDfY
BaGG9
Bb2vX
BbEvm
BbG4L
BbkY8
3165.5 Yes
4294.5 Yes
4608.1 Yes
2297.4 Yes
3029.3 No
3368.5 Yes
3107.6 Yes
4344.8 Yes
2822.1 Yes
3936.8 Yes
5042.3 Yes
3879.9 No
3127.4 Yes
2725.3 Yes
3582.6 Yes
5110.6 No
4248.7 Yes
Manager Employement Length: Average number of months of the manager(s) in that particular store
0
85.5
23.8
0
4.1
148.7
64.4
0
110.4
31.7
46.1
277.6
0.5
87.1
0
23.8
31.7
3.5
65.4
24.2
18.5
12.3
44
0
0.5
51.1
23.7
19.5
72.7
0
36.1
51.2
29
41.6
23.3
15.5
34.7
121.1
14.9
2.5
179
183.8
47.6
6.1
13
31.5
54.7
12.8
3.6
23.9
57.6
6.9
6.6
4.9
24.5
4.4
13.4
33.4
21.6
12
8.4
0
149
116.7
5.2
17.3
44.6
43.8
124.6
41.4
170.3
2.2
0
88.2
24
0
3.7
63
0
107.5
31.4
44.7
278.2
0.6
87.2
0
23.8
31.9
241.5
3.5
64.3
24.2
18.4
12.4
46
0
0.7
48.5
24.1
19.7
72.4
0
36.1
50.6
29.1
38
41.8
23.3
15.6
34.7
124.7
15.1
2.4
176.8
184.1
47.9
6.4
13
31.6
55.8
12.9
3.3
23.9
56.6
6.8
6.8
4.6
24.4
4.5
13.5
33.4
22
12.2
8.1
0
149.6
113.4
5.3
43.6
15.1
43.4
125.6
41.3
0
2.3
Associate Employement Length: Average number of months of the associates and entry level positions in that pa
24.8
6.6
5
5.4
6.9
11.4
7.3
56.8
6.1
23.2
2
6.6
1.6
3
8.5
4.7
3.6
17
5.9
7.2
26
3.4
3.4
10.3
20.4
17.4
17
23.5
23.4
10.8
6.6
3.9
19.7
20.9
1.3
1.6
5.5
16.8
11.9
86.1
5.5
114.2
9.2
5.3
6.6
8.2
14.7
16.1
7.1
3.4
8.2
3.9
18.4
2.7
3
4.1
13.8
6.4
13.3
6.9
6.9
0.9
2340
3.9
3.4
2.3
26.8
38.4
27.4
6.4
29.5
8.7
24.8
6.6
5
5.4
6.9
7.3
56.8
6.1
23.2
2
6.6
1.6
3
8.5
4.7
3.6
33.8
17
5.9
7.2
26
3.4
3.4
10.3
20.4
17.4
17
23.5
23.4
10.8
6.6
3.9
19.7
14.8
20.9
1.3
1.6
5.5
16.8
11.9
86.1
5.5
114.2
9.2
5.3
6.6
8.2
14.7
16.1
7.1
3.4
8.2
3.9
18.4
2.7
3
4.1
13.8
6.4
13.3
6.9
6.9
0.9
23.4
3.9
3.4
26.8
3.9
38.4
27.4
6.4
29.5
8.7
Mall traffic. Average # of shoppers entering the mall per week
7611
8717
9502
2714
20742
17434
18110
21033
27050
16218
22197
11384
14470
7105
8945
6735
14043
4810
14916
14163
8363
14211
8797
6122
9999
8398
14820
11577
1094
2604
9988
8096
10596
3283
8870
6361
8985
2572
9410
2106
3495
20831
17808
9100
23151
8407
1901
15093
3033
15305
7048
3822
10427
13740
3533
8477
6356
9022
3075
9919
7430
17110
1086
1046
11668
9018
5101
3331
3088
6276
11023
8877
7762
8544
9986
2714
20946
17222
21241
27315
16054
21335
10937
13903
6830
8858
6804
14188
3807
4716
15224
14443
8363
13384
8979
6369
10299
8317
14527
11237
1128
2553
9601
8014
10596
13931
3154
8870
6361
8896
2597
9604
2106
3391
20212
18343
9193
24096
8576
1826
14800
3126
15157
6909
3747
10743
13878
3679
8477
6045
8934
2984
9722
7430
17453
1054
1046
11437
5202
20404
3233
3246
6402
10804
9146
Number of Stores Similar to Old Navy within walking distance of this particular store. For example, H&M or a Tar
2
5
4
5
1
4
4
3
3
1
3
5
4
5
3
4
2
3
5
5
4
4
4
5
4
4
4
5
4
4
5
5
3
3
4
5
4
12
4
2
4
5
2
3
1
6
5
1
1
6
3
4
6
5
3
3
4
4
4
3
4
1
3
8
5
5
4
4
4
4
2
3
4
4
4
5
1
3
2
3
1
3
6
4
5
3
3
2
5
4
3
5
4
5
3
4
4
4
5
5
3
4
5
2
3
3
4
3
5
11
4
2
4
4
2
3
1
4
3
1
2
6
3
5
7
5
4
5
2
2
3
3
3
2
2
6
4
4
2
4
4
5
2
2
Prime Location Score. Five being the highest ranking. For example a Five would mean the store is right by the foo
3
4
3
4
2
3
2
4
2
4
2
4
3
4
3
3
3
2
3
3
4
3
2
3
3
3
3
3
3
2
3
3
4
3
3
3
2
3
2
3
4
3
5
4
2
3
4
3
2
3
2
3
4
2
3
4
3
3
3
3
3
4
3
2
3
4
3
2
2
4
3
4
3
4
3
4
2
2
4
2
4
2
4
3
4
3
3
3
5
2
3
3
4
3
2
3
3
3
3
3
3
2
3
3
4
4
3
3
3
2
3
2
3
4
3
5
4
2
3
4
3
2
3
2
3
4
2
3
4
3
3
3
3
3
4
3
2
3
3
3
2
2
4
3
4
Mall Traffic Rating
Type of Mall
3 Strip mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
5 Strip mall
4 Inside a mall
5 Inside a mall
3 Inside a mall
4 Inside a mall
3 Inside a mall
5 Inside a mall
4 Inside a mall
2 Inside a mall
2 Inside a mall
4 Inside a mall
3 Inside a mall
4 Inside a mall
3 Inside a mall
2 Inside a mall
3 Inside a mall
3 Inside a mall
4 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
2 Inside a mall
4 Inside a mall
2 Inside a mall
2 Inside a mall
2 Inside a mall
3 Inside a mall
2 Inside a mall
4 Inside a mall
2
3 Inside a mall
2 Inside a mall
4 Inside a mall
4 Inside a mall
3 Inside a mall
2 Inside a mall
3 Inside a mall
4 Strip mall
Store Profit in 000s
664.7
1037.2
548.3
503.6
775.9
1261.6
1228.6
949.4
1240.3
741.3
957.1
775.8
356.8
667.2
476.2
471.4
672
685.5
598.7
675.2
941.7
710.3
722.6
747.7
575.5
592.7
707.7
713.6
798.7
532.6
330.5
772.3
963.1
527.2
445.4
477.8
534.9
563.6
363.2
662.5
846.7
1166
3 Inside a mall
3 Inside a mall
5 Strip mall
2 Inside a mall
1 Inside a mall
4 Inside a mall
4 Inside a mall
3 Inside a mall
2 Inside a mall
2 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
2 Inside a mall
3 Inside a mall
4 Inside a mall
1 Inside a mall
4 Inside a mall
3 Inside a mall
3 Inside a mall
1 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
3 Inside a mall
1 Inside a mall
1 Inside a mall
3 Inside a mall
4 Inside a mall
4 Strip mall
3 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
5 Strip mall
5 Inside a mall
3 Inside a mall
4 Inside a mall
3 Inside a mall
5 Inside a mall
4 Inside a mall
2 Inside a mall
2 Inside a mall
1029.1
766.5
1001.8
739.1
512.4
638
645.6
403.7
956
357.6
336.1
424.1
306.4
608.6
819.1
879.9
467.5
540.5
639
513.6
708.7
334.6
1004.1
629.5
959.4
497.7
462.3
756.4
5736.6
766.7
705.5
1109.4
585.9
536.9
824.7
1259.2
988.3
1191.7
733.7
983.5
780.6
389.7
650
4 Inside a mall
3 Inside a mall
4 Strip mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
3 Inside a mall
3 Strip mall
4 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
2 Inside a mall
4 Inside a mall
2 Inside a mall
2 Inside a mall
2 Inside a mall
3 Inside a mall
2 Inside a mall
4 Inside a mall
3 Inside a mall
2 Inside a mall
3 Inside a mall
2 Inside a mall
4 Inside a mall
4 Inside a mall
3
2 Strip mall
3 Inside a mall
4 Strip mall
3 Inside a mall
3 Inside a mall
5 Inside a mall
2 Inside a mall
1 Inside a mall
4 Inside a mall
4 Inside a mall
3 Inside a mall
2 Inside a mall
2 Inside a mall
3 Inside a mall
3 Inside a mall
2 Inside a mall
516.6
510.1
633
1028
701.4
639.6
681.3
886.4
614.8
716.3
673.4
515.2
559.7
652.9
699.2
714.5
559.7
371.1
663.1
930.3
858.7
589.3
412.3
472.6
522.8
584.7
383.7
690.7
843.7
1075.4
1123.6
827.3
1010.2
699.8
452.6
684.4
608.8
371
912.5
363
321.4
465.8
273.2
2 Inside a mall
3 Inside a mall
4 Strip mall
1 Inside a mall
4 Inside a mall
3 Inside a mall
3 Inside a mall
1 Inside a mall
3 Inside a mall
3 Inside a mall
3 Inside a mall
4 Inside a mall
1 Inside a mall
1 Inside a mall
3 Inside a mall
4 Inside a mall
4 Strip mall
541.2
738.8
951
455
457.1
632.2
510
705.8
307.8
1011.1
1010.7
650.2
472.5
498.7
757
1139.6
750.1
Respondent Price Product1 Product2 Product3 Product4 Product5 Product6 Channel1 Channel2
1
5
4
4
4
4
4
5
3
3
2
5
4
4
4
4
3
5
3
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3
3
4
4
3
4
3
4
4
4
4
4
4
3
3
4
3
3
3
4
4
4
4
4
4
3
4
4
3
3
4
3
4
4
4
4
4
4
4
4
4
4
4
3
4
4
3
3
4
4
4
4
4
3
4
4
4
4
4
4
4
3
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
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4
4
4
4
4
4
4
4
3
4
4
3
4
3
3
3
3
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3
3
3
3
3
3
3
3
3
4
4
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4
5
5
5
5
5
4
5
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5
5
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5
5
5
5
5
5
5
5
3
4
4
4
4
3
3
4
3
3
3
3
3
4
4
3
3
4
3
5
4
5
5
5
5
5
5
3
3
3
3
3
3
5
5
5
5
3
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
3
4
4
4
4
4
3
3
3
3
4
3
3
3
4
3
Promotion1 Promotion2 Promotion3 Future_Prefer
4
3
3
4
4
3
3
4
4
3
3
4
4
3
3
4
4
3
3
4
4
3
3
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3
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3
3
3
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3
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3
3
3
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3
3
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3
3
4
4
3
3
4
3
3
3
4
4
3
3
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4
3
3
4
4
3
3
4
3
3
3
4
4
3
3
4
3
3
3
4
3
3
3
4
3
3
3
4
3
3
3
4
3
3
3
4
4
4
3
4
4
4
3
4
4
3
3
4
3
3
3
4
4
3
3
4
4
3
3
4
4
3
3
4
3
3
3
4
4
3
3
4
3
4
3
3
3
3
3
4
4
4
3
4
4
3
4
4
4
4
4
4
4
3
3
3
3
3
4
4
4
3
3
3
4
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4
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4
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4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
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4
3
4
3
3
4
3
3
3
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3
3
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3
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3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
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4
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4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
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4
4
4
4
4
4
4
4
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4
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4
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4
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4
4
3
3
3
3
3
3
3
3
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3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
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4
4
4
4
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4
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4
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4
4
4
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4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
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4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
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5
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5
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5
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3
3
3
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3
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3
3
3
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3
3
4
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3
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3
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3
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3
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5
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3
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3
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3
3
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3
3
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4
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3
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3
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3
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3
4
3
3
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3
4
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3
3
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3
3
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3
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3
3
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3
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3
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3
4
3
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3
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4
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3
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3
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3
3
4
5
5
5
4
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4
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4
5
4
4
5
5
4
5
4
4
5
4
4
4
4
4
4
4
4
5
4
5
5
5
5
4
4
5
4
5
4
4
4
4
4
4
5
5
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
3
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
4
4
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Positioning Elements Used in the Study
Codes
1. Please rate your perception about the following image attributes of HC as developed using its positioning elements:
Price offered by HC is
Price
Wet dog food without preservatives as offered for pets by HC is
Product1
Dry kibbles for pets
Product2
Vegetarian diet offered by HC for pets
Product3
Availability of variety under HC
Product4
Overall quality of products offered by HC is
Product5
Overall perception about the brand name of HC
Product6
Buying pet food directly from HC (retailer in the present case)
Channel1
Buying pet food online using from other retailing sites i.e., Amazon Channel2
Print media in creating awareness about HC
Promotion1
Social media in creating awareness about HC
Promotion2
Sales promotion in creating awareness about HC
Promotion3
Packaging offered by HC
Packaging
2. Future preference of consumers for HC’s products
Future_Prefer
Note: HC = Harley’s Corner
ioning elements:
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.67%
Standard Error
0.12
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Channel1
Channel2
Promotion1
Promotion2
Promotion3
Packaging
Coefficients P-value Lower 95%
0% 0.22
0.19
0% 0.14
0.09
0% 1.15
1.07
0% 0.16
0.11
0% 0.53
0.49
1%
0.02
0.05
0% 0.05
0.03
88%
0.05
0.00
12% 0.01
0.04
7% 0.10
0.05
0%
0.11
0.15
9% 0.01
0.04
5%
0.00
0.03
Upper 95%
0.16
0.05
0.99
0.05
0.45
0.09
0.01
0.05
0.08
0.00
0.19
0.08
0.07
Revisit the product line and check the ingredients that make up the products. They all have negative correlation
quality is good bcos product 5 is positive
they should focus on promotion 3 ?marketing budget on social media promotion/use more content on Twitter/ engage the a
customers are looking for convenience/expand the reach on the online. Technically market not in India
the online will lead to cost savings on logistics, storage,
when taken together, channels are the least significant but when taken separately, they are significant
to be more against big giants, focus on online marketing and run promos for markets outside Mumbia to come to us
Retain packaging
Product_ID
Product Description
prod2
Dry kibbles for pets
prod4
Availability of variety under HC
price
Price offered by HC
promo2
Social media in creating awareness about HC
Base_Adjusted R Revised_Adjusted R Diff
94%
56.83% 38%
94%
68.89% 26%
94%
89.43% 5%
94%
92.83% 2%
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.61%
Standard Error
0.12
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Channel1
Channel2
Promotion1
Promotion2
Promotion3
Coefficients
-0.19
-0.10
-1.05
-0.10
-0.48
0.05
-0.03
0.00
0.04
-0.05
0.16
0.04
0.000%
P-value
0%
0%
0%
0%
0%
1%
1%
90%
6%
9%
0%
7%
Lower 95%
Upper 95%
-0.22
-0.17
-0.15
-0.05
-1.13
-0.97
-0.16
-0.05
-0.52
-0.45
0.01
0.09
-0.05
-0.01
-0.05
0.05
0.00
0.09
-0.10
0.01
0.12
0.20
0.00
0.08
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.59%
Standard Error
0.12
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Promotion1
Promotion2
Promotion3
Coefficients
0.20
0.11
1.04
0.10
0.47
0.06
0.03
0.05
0.16
0.05
P-value
Lower 95%
Upper 95%
0% 0.23 0.18
0% 0.16 0.06
0% 1.11 0.97
0% 0.16 0.05
0% 0.50 0.44
0%
0.02
0.10
1% 0.05 0.01
7% 0.10
0.00
0%
0.12
0.20
2%
0.01
0.09
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.54%
Standard Error
0.12
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Promotion2
Promotion3
Coefficients
0.20
0.11
1.08
0.11
0.49
0.05
0.04
0.17
0.05
P-value
Lower 95%
Upper 95%
0% 0.22 0.17
0% 0.16 0.06
0% 1.14 1.03
0% 0.16 0.06
0% 0.51 0.46
0%
0.02
0.09
0% 0.06 0.02
0%
0.13
0.21
2%
0.01
0.09
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.45% becomes base R square
Standard Error
0.12
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Promotion2
Coefficients
-0.21
-0.11
-1.06
-0.12
-0.47
0.06
-0.04
0.18
P-value
0.00%
0.00%
0.00%
0.00%
0.00%
0.02%
0.01%
0.00%
Lower 95%
Upper 95%
-0.23
-0.18
-0.15
-0.06
-1.10
-1.01
-0.18
-0.07
-0.50
-0.45
0.03
0.10
-0.06
-0.02
0.14
0.22
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
92.83%
Standard Error
0.13
Observations
300
Price
Product1
Product2
Product3
Product4
Product5
Product6
Coefficients
-0.23
-0.05
-0.97
-0.16
-0.44
0.16
-0.04
P-value
0.00%
6.26%
0.00%
0.00%
0.00%
0.00%
0.05%
Lower 95%
Upper 95%
-0.26
-0.21
-0.10
0.00
-1.02
-0.92
-0.22
-0.10
-0.46
-0.41
0.13
0.19
-0.06
-0.02
Keep
it is relevant @92.83% compared with 94.45% adjusted R
with a p-value of 0.00%
Respondent Price Product1 Product2 Product3 Product4 Product5 Product6 Promotion2 Future_Prefer
1
5
4
4
4
4
4
5
3
4
2
5
4
4
4
4
3
5
3
4
3
5
4
4
4
4
4
5
3
4
4
5
4
4
4
4
3
5
3
4
5
5
4
4
4
4
4
5
3
4
6
5
4
4
4
4
4
5
3
4
7
5
4
4
4
4
3
3
3
4
8
5
4
4
4
4
3
3
3
4
9
5
4
4
4
4
3
3
3
4
10
5
4
4
4
4
3
3
3
4
11
5
4
4
4
4
3
3
3
4
12
5
4
4
4
4
4
3
3
4
13
5
4
4
4
4
4
5
3
4
14
5
4
4
4
4
3
5
3
4
15
5
4
4
4
4
3
5
3
4
16
5
4
4
4
4
4
5
3
4
17
5
4
4
4
4
3
3
3
4
18
5
4
4
4
4
3
5
3
4
19
5
4
4
4
4
3
3
3
4
20
5
4
4
4
4
3
3
3
4
21
5
4
4
4
4
3
3
3
4
22
5
4
4
4
4
4
3
3
4
23
5
4
4
4
4
3
3
3
4
24
5
4
4
4
4
4
3
3
4
25
5
4
4
4
4
4
5
3
4
26
5
4
4
4
4
3
5
3
4
27
5
4
4
4
4
3
3
3
4
28
5
4
4
4
4
3
5
3
4
29
5
4
4
4
4
3
5
3
4
30
5
4
4
4
4
4
5
3
4
31
5
4
4
4
4
3
3
3
4
32
5
4
4
4
4
3
5
3
4
33
5
4
4
4
4
3
3
3
4
34
5
4
4
4
4
3
3
3
4
35
5
4
4
4
4
3
3
3
4
36
5
4
4
4
4
4
3
3
4
37
5
4
4
4
4
3
3
3
4
38
5
4
4
4
4
4
3
4
4
39
5
4
4
4
4
4
5
4
4
40
5
4
4
4
4
3
5
3
4
41
5
4
4
4
4
3
3
3
4
42
5
4
4
4
4
3
5
3
4
43
5
4
4
4
4
3
5
3
4
44
5
4
4
4
4
4
5
3
4
45
5
4
4
4
4
3
3
3
4
46
5
4
4
4
4
4
3
3
4
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
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5
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3
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5
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3
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3
3
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5
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94
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4
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3
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141
142
143
144
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159
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181
182
183
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187
4
4
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3
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3
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3
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3
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3
3
3
3
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3
4
3
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3
3
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3
5
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3
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3
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3
4
3
3
3
3
4
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3
3
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3
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3
3
3
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3
3
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3
4
3
4
3
3
4
4
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3
3
3
3
3
3
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3
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3
3
3
3
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3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
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214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
4
4
3
4
4
4
3
4
3
4
4
4
3
4
4
4
5
4
5
4
5
4
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4
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5
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4
3
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3
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3
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3
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4
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3
3
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3
4
3
3
3
4
4
5
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4
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3
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3
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3
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3
3
4
3
3
4
4
3
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3
3
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3
4
3
3
3
3
4
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4
4
3
3
4
3
4
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4
4
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4
4
3
3
3
3
3
3
3
3
3
3
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3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
4
3
4
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
5
4
4
4
5
4
5
5
5
5
5
4
4
4
5
5
5
4
4
5
4
5
4
5
5
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
5
4
5
5
4
5
4
5
5
5
4
5
4
4
5
4
5
4
4
4
4
5
3
4
4
4
3
4
4
4
3
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
5
4
4
4
5
4
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
4
3
3
3
4
4
4
4
4
4
3
4
4
3
3
4
3
4
4
3
4
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
3
4
3
4
4
4
4
4
4
3
3
4
4
4
4
4
4
4
4
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4
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4
4
4
3
3
3
4
4
4
4
4
4
3
4
4
3
3
4
3
4
4
3
4
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
3
4
3
4
4
4
4
4
4
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
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4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
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4
5
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5
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5
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5
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5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
3
3
3
4
4
4
4
4
4
3
4
4
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
3
4
4
3
4
3
4
4
4
4
4
4
3
3
4
3
3
3
4
4
5
5
5
5
3
5
4
3
3
4
5
4
4
5
5
4
4
4
5
4
5
5
5
4
3
3
5
5
5
5
5
5
5
5
5
5
4
4
4
5
5
4
3
3
3
4
4
4
4
4
4
3
4
4
3
3
4
4
4
4
3
4
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
4
4
3
4
4
4
4
4
4
3
3
4
5
5
5
4
4
4
4
4
4
5
4
4
5
5
4
5
4
4
5
4
4
4
4
4
4
4
4
5
4
5
5
5
5
4
4
5
4
5
4
4
4
4
4
4
5
5
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
4
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
3
4
4
4
4
3
3
4
3
3
3
3
3
4
4
3
3
4
3
5
4
5
5
5
5
5
5
3
3
3
3
3
3
5
5
5
5
3
3
4
4
3
4
3
4
3
3
3
3
3
3
3
3
3
3
3
3
5
4
4
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
Positioning Elements Used in the Study
Codes
1. Please rate your perception about the following image attributes of HC as developed using its positi
Price offered by HC is
Price
Wet dog food without preservatives as offered for pets by HC is
Product1
Dry kibbles for pets
Product2
Vegetarian diet offered by HC for pets
Product3
Availability of variety under HC
Product4
Overall quality of products offered by HC is
Product5
Overall perception about the brand name of HC
Product6
Buying pet food directly from HC (retailer in the present case)
Channel1
Buying pet food online using from other retailing sites i.e., Amazon Channel2
Print media in creating awareness about HC
Promotion1
Social media in creating awareness about HC
Promotion2
Sales promotion in creating awareness about HC
Promotion3
Packaging offered by HC
Packaging
2. Future preference of consumers for HC’s products
Future_Prefer
Note: HC = Harley’s Corner
eveloped using its positioning elements:
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
Standard Error
Observations
Price
Product2
Product3
Product4
Product5
Product6
Promotion2
94.11%
0.12
300
Coefficients
-0.22
-1.02
-0.14
-0.47
0.05
-0.04
0.16
P-value
0.00%
0.00%
0.00%
0.00%
0.41%
0.00%
0.00%
Lower 95%
-0.25
-1.07
-0.19
-0.50
0.02
-0.06
0.12
Upper 95%
-0.20
-0.98
-0.09
-0.44
0.08
-0.02
0.20
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
92.83%
Standard Error
0.13
Observations
300
Price
Product2
Product3
Product4
Product5
Product6
Product1
Coefficients
-0.23
-0.97
-0.16
-0.44
0.16
-0.04
-0.05
P-value
0.00%
0.00%
0.00%
0.00%
0.00%
0.05%
6.26%
Lower 95%
-0.26
-1.02
-0.22
-0.46
0.13
-0.06
-0.10
Upper 95%
-0.21
-0.92
-0.10
-0.41
0.19
-0.02
0.00
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
89.43%
Standard Error
0.16
Observations
300
Intercept
Product2
Product3
Product4
Product5
Product6
Product1
Promotion2
Coefficients
11.94
-1.25
-0.19
-0.47
0.03
-0.04
-0.22
0.27
5.02%
P-value
0%
0%
0%
0%
18%
0%
0%
0%
Lower 95%
11.59
-1.30
-0.26
-0.51
-0.01
-0.07
-0.29
0.21
Upper 95%
12.30
-1.19
-0.12
-0.44
0.08
-0.02
-0.16
0.32
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
56.83%
Standard Error
0.33
Observations
300
Intercept
Price
Product3
Product4
Product5
Product6
Product1
Promotion2
Coefficients
7.75
-0.48
-0.23
-0.20
0.11
-0.08
0.23
-0.17
37.62%
P-value
0%
0%
0%
0%
2%
1%
0%
0%
Lower 95%
Upper 95%
7.19
8.31
-0.54
-0.42
-0.37
-0.08
-0.26
-0.14
0.02
0.21
-0.13
-0.02
0.11
0.36
-0.27
-0.07
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.06%
Standard Error
0.12
Observations
300
Intercept
Price
Product2
Product4
Product5
Product6
Product1
Promotion2
Coefficients
11.26
-0.21
-1.07
-0.51
0.07
-0.04
-0.13
0.20
0.39%
P-value
0.00%
0.00%
0.00%
0.00%
0.02%
0.01%
0.00%
0.00%
Lower 95%
Upper 95%
11.01
11.51
-0.24
-0.19
-1.11
-1.02
-0.53
-0.49
0.03
0.10
-0.06
-0.02
-0.17
-0.08
0.16
0.24
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
68.89%
Standard Error
0.28
Observations
300
Intercept
Price
Product2
Product3
Product5
Product6
Product1
Promotion2
Coefficients
10.51
-0.20
-0.64
-0.67
0.13
-0.11
-0.03
-0.05
25.56%
P-value
0%
0%
0%
0%
0%
0%
66%
29%
Lower 95%
Upper 95%
9.90
11.11
-0.25
-0.14
-0.73
-0.54
-0.78
-0.57
0.05
0.21
-0.15
-0.06
-0.14
0.09
-0.13
0.04
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.20%
Standard Error
0.12
Observations
300
Intercept
Price
Product2
Product3
Product4
Product6
Product1
Promotion2
Coefficients
11.50570331
-0.199430058
-1.062081398
-0.127090668
-0.479407362
-0.027972866
-0.088965114
0.226705859
0.25%
P-value
Lower 95%
2.959E-208 11.23984102
2.30412E-40 -0.224573361
1.6251E-130 -1.109744405
3.76794E-06 -0.180153003
1.0535E-110 -0.50528749
0.003714584 -0.046794707
0.000326322 -0.137111275
7.88247E-34 0.194460124
Upper 95%
11.7715656
-0.17428676
-1.01441839
-0.07402833
-0.45352723
-0.00915103
-0.04081895
0.258951593
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94.18%
Standard Error
0.12
Observations
300
Intercept
Price
Product2
Product3
Product4
Product5
Product1
Promotion2
Coefficients
11.54841375
-0.206540052
-1.06435521
-0.126837712
-0.484107939
0.045567442
-0.117652899
0.184078811
0.28%
P-value
0.00%
0.00%
0.00%
0.00%
0.00%
0.74%
0.00%
0.00%
Lower 95%
Upper 95%
11.28
11.81
-0.23
-0.18
-1.11
-1.02
-0.18
-0.07
-0.51
-0.46
0.01
0.08
-0.17
-0.07
0.14
0.22
Price
Mean
Standard Deviation
Normal Std deviation
Respondent
Price
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4.096666667
0.689618834
2.887197477
B Price
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
Product 1
Mean
3.946666667
Standard Deviation
0.361836022
Normal Std deviation
0.571263672
Product1
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
B Product1
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
3
4
3
4
4
4
3
3
4
4
3
4
3
4
4
4
4
3
4
4
3
3
4
3
3
4
3
3
4
4
4
3
4
4
4
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-1.59025046
-1.59025046
-1.59025046
-1.59025046
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-1.59025046
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-1.59025046
-1.59025046
-0.14017405
-1.59025046
-1.59025046
-0.14017405
-1.59025046
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
4
4
4
4
4
4
3
4
3
4
3
4
3
4
4
3
4
4
3
3
3
3
4
3
3
4
3
4
3
3
3
3
4
4
3
4
4
3
3
4
3
3
4
4
3
4
4
4
3
3
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
-2.616286411
0.147396418
-2.616286411
0.147396418
-2.616286411
0.147396418
-2.616286411
0.147396418
0.147396418
-2.616286411
0.147396418
0.147396418
-2.616286411
-2.616286411
-2.616286411
-2.616286411
0.147396418
-2.616286411
-2.616286411
0.147396418
-2.616286411
0.147396418
-2.616286411
-2.616286411
-2.616286411
-2.616286411
0.147396418
0.147396418
-2.616286411
0.147396418
0.147396418
-2.616286411
-2.616286411
0.147396418
-2.616286411
-2.616286411
0.147396418
0.147396418
-2.616286411
0.147396418
0.147396418
0.147396418
-2.616286411
-2.616286411
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
3
4
3
4
4
4
3
4
4
4
5
4
5
4
5
4
5
5
5
5
5
4
4
5
4
5
4
4
5
5
4
3
4
4
4
3
4
4
4
3
4
4
3
3
3
4
4
4
4
4
-1.59025046
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
1.309902353
-0.14017405
1.309902353
-0.14017405
1.309902353
-0.14017405
1.309902353
1.309902353
1.309902353
1.309902353
1.309902353
-0.14017405
-0.14017405
1.309902353
-0.14017405
1.309902353
-0.14017405
-0.14017405
1.309902353
1.309902353
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-1.59025046
-0.14017405
-0.14017405
-1.59025046
-1.59025046
-1.59025046
-0.14017405
-0.14017405
-0.14017405
-0.14017405
-0.14017405
4
3
4
3
3
3
4
4
5
5
5
5
4
4
5
4
5
5
4
5
5
4
4
5
4
5
4
4
4
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.147396418
-2.616286411
0.147396418
-2.616286411
-2.616286411
-2.616286411
0.147396418
0.147396418
2.911079247
2.911079247
2.911079247
2.911079247
0.147396418
0.147396418
2.911079247
0.147396418
2.911079247
2.911079247
0.147396418
2.911079247
2.911079247
0.147396418
0.147396418
2.911079247
0.147396418
2.911079247
0.147396418
0.147396418
0.147396418
2.911079247
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
4
3
4
4
3
3
4
3
4
4
3
4
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
3
4
3
4
4
4
4
4
4
3
3
3
4
4
3
4
3
3
3
3
3
3
3
-0.14017405
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-0.14017405
-0.14017405
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4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
294
295
296
297
298
299
300
3
3
3
3
3
3
3
-1.59025046
-1.59025046
-1.59025046
-1.59025046
-1.59025046
-1.59025046
-1.59025046
4
4
4
4
4
4
4
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
0.147396418
Product 2
Mean
3.7533333
Standard Deviation
0.4317913
Normal Std deviation
0.5712637
Product 3
Mean
3.863333333
Standard Deviation
0.344069035
Normal Std deviation
Product 4
Mean
Standard Deviation
Normal Std deviation
Product2 B Product 2
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
Product3
Product4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
B Product 3
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
3 -1.7446701
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.397207109
0.397207109
0.397207109
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0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
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0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
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0.397207109
0.397207109
0.397207109
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0.397207109
0.397207109
0.397207109
0.397207109
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0.397207109
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
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3 -1.7446701
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3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
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4 0.5712637
4
4
4
4
4
4
4
3
3
4
3
4
3
3
4
4
4
3
4
3
3
4
3
3
4
3
3
3
3
3
3
3
4
3
4
4
4
4
4
4
3
3
4
3
4
3
3
3
3
4
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
-2.509186374
-2.509186374
0.397207109
-2.509186374
0.397207109
-2.509186374
-2.509186374
0.397207109
0.397207109
0.397207109
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0.397207109
-2.509186374
-2.509186374
0.397207109
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0.397207109
-2.509186374
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-2.509186374
-2.509186374
-2.509186374
-2.509186374
0.397207109
-2.509186374
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
-2.509186374
-2.509186374
0.397207109
-2.509186374
0.397207109
-2.509186374
-2.509186374
-2.509186374
-2.509186374
0.397207109
5
5
5
5
5
5
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
3 -1.7446701
3 -1.7446701
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3
4
4
3
3
4
3
3
4
4
3
4
3
3
4
3
4
3
3
3
3
4
4
4
4
3
3
4
3
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
-2.509186374
0.397207109
0.397207109
-2.509186374
-2.509186374
0.397207109
-2.509186374
-2.509186374
0.397207109
0.397207109
-2.509186374
0.397207109
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0.397207109
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0.397207109
-2.509186374
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0.397207109
0.397207109
0.397207109
0.397207109
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0.397207109
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0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
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0.397207109
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
3 -1.7446701
3 -1.7446701
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
3 -1.7446701
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
4 0.5712637
3 -1.7446701
3 -1.7446701
3 -1.7446701
4 0.5712637
4 0.5712637
3 -1.7446701
4 0.5712637
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
5
5
5
5
5
5
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
3 -1.7446701
4
4
4
4
4
4
4
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
0.397207109
5
5
5
5
5
5
5
Product 4
4.243333333
0.828498797
Normal Std deviation
Product 5
Mean
3.6066667
Standard Deviation
0.6324203
Normal Std deviation
Product 6
Mean
Standard Deviation
Normal Std deviation
B Product 4
-0.293703907
-0.293703907
-0.293703907
-0.293703907
-0.293703907
-0.293703907
-0.293703907
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-0.293703907
-0.293703907
-0.293703907
-0.293703907
-0.293703907
-0.293703907
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3
Product 6
3.886666667
0.83835056
B Product 6
1.328004521
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Promotion2
Mean
Standard Deviation
Normal Std deviation
Promotion2
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
3
3
3
3
3.423333333
0.564373351
B Promo2
Future_Prefer
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-1.057632343
-1.057632343
0.135186089
0.135186089
0.135186089
0.135186089
-1.057632343
-1.057632343
0.135186089
-1.057632343
-1.057632343
0.135186089
0.135186089
0.135186089
-1.057632343
0.135186089
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
4
4
4
4
4
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
-1.057632343
-1.057632343
0.135186089
0.135186089
0.135186089
0.135186089
0.135186089
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
4
4
4
4
4
4
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
3
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
-1.057632343
0.135186089
0.135186089
0.135186089
1.328004521
0.135186089
0.135186089
0.135186089
1.328004521
0.135186089
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
0.135186089
0.135186089
0.135186089
1.328004521
1.328004521
1.328004521
0.135186089
0.135186089
1.328004521
0.135186089
1.328004521
0.135186089
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
0.135186089
-1.057632343
-1.057632343
-1.057632343
0.135186089
0.135186089
1.328004521
1.328004521
1.328004521
3
3
3
3
3
3
3
4
4
5
4
5
5
4
5
4
5
5
5
4
5
4
4
5
4
5
4
4
4
4
5
3
4
4
4
3
4
4
4
3
4
4
3
3
3
4
4
4
4
4
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
1.021782239
1.021782239
2.793658954
1.021782239
2.793658954
2.793658954
1.021782239
2.793658954
1.021782239
2.793658954
2.793658954
2.793658954
1.021782239
2.793658954
1.021782239
1.021782239
2.793658954
1.021782239
2.793658954
1.021782239
1.021782239
1.021782239
1.021782239
2.793658954
-0.750094476
1.021782239
1.021782239
1.021782239
-0.750094476
1.021782239
1.021782239
1.021782239
-0.750094476
1.021782239
1.021782239
-0.750094476
-0.750094476
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
5
4
4
4
5
4
4
5
5
5
4
4
4
4
4
1.328004521
-1.057632343
1.328004521
0.135186089
-1.057632343
-1.057632343
0.135186089
1.328004521
0.135186089
0.135186089
1.328004521
1.328004521
0.135186089
0.135186089
0.135186089
1.328004521
0.135186089
1.328004521
1.328004521
1.328004521
0.135186089
-1.057632343
-1.057632343
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
0.135186089
0.135186089
0.135186089
1.328004521
1.328004521
1.328004521
0.135186089
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
1.328004521
-1.057632343
-1.057632343
-1.057632343
-1.057632343
4
3
4
4
3
3
4
4
4
4
3
4
4
4
4
4
4
4
4
3
4
3
3
3
3
4
4
4
4
3
4
4
4
4
4
4
3
3
3
4
4
3
4
3
4
3
3
3
3
3
1.021782239
-0.750094476
1.021782239
1.021782239
-0.750094476
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
1.021782239
-0.750094476
-0.750094476
-0.750094476
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
1.021782239
-0.750094476
-0.750094476
-0.750094476
1.021782239
1.021782239
-0.750094476
1.021782239
-0.750094476
1.021782239
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
4
5
4
4
5
5
4
5
4
4
5
4
4
4
4
4
4
4
4
5
4
5
5
5
5
4
4
5
4
5
4
4
4
4
4
4
5
5
5
4
4
5
4
5
5
5
5
5
5
5
-1.057632343
-1.057632343
1.328004521
1.328004521
1.328004521
1.328004521
-1.057632343
3
3
3
3
3
3
3
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
-0.750094476
5
5
5
5
5
5
5
Positioning Elements Used in the Study
Codes
1. Please rate your perception about the following image attributes of HC as developed using its positioning
Price offered by HC is
Price
Wet dog food without preservatives as offered for pets by HC is
Product1
Dry kibbles for pets
Product2
Vegetarian diet offered by HC for pets
Product3
Availability of variety under HC
Product4
Overall quality of products offered by HC is
Product5
Overall perception about the brand name of HC
Product6
Buying pet food directly from HC (retailer in the present case)
Channel1
Buying pet food online using from other retailing sites i.e., Amazon Channel2
Print media in creating awareness about HC
Promotion1
Social media in creating awareness about HC
Promotion2
Sales promotion in creating awareness about HC
Promotion3
Packaging offered by HC
Packaging
2. Future preference of consumers for HC’s products
Future_Prefer
Note: HC = Harley’s Corner
tributes of HC as developed using its positioning elements:
SUMMARY OUTPUT
Regression Statistics
Adjusted R Square
94%
Standard Error
0.12
Observations
300
B Price
B Product1
B Product 2
B Product 3
B Product 4
B Product 5
B Product 6
B Promo2
Coefficients P-value Lower 95%
Upper 95%
0.14
0% 0.16 0.12
0.04
0% 0.06 0.02
0.46
0% 0.48 0.44
0.04
0% 0.06 0.02
0.39
0% 0.41 0.37
0.04
0%
0.02
0.06
0.03
0% 0.05 0.02
0.10
0%
0.08
0.13
B Price B Product1 B Product 2 B Product 3 B Product 4 B Product 5 B Product 6 B Promo2
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06
1.02
1.31
0.15
0.57
0.40 0.29
0.62
1.33
1.02
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96
1.33 0.75
1.31
0.15
0.57
0.40 0.29
0.62
1.33 0.75
1.31
0.15
0.57
0.40 0.29 0.96 1.06 0.75
1.31
0.15
0.57
0.40 0.29
0.62 1.06 0.75
–
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
1.31
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15 0.15 0.15 0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
1.74
1.74
1.74
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40 0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.29 0.29 0.29 0.29 0.29 0.29
0.29 0.29
0.29
0.29 0.29 0.29
0.29
0.29
0.29 0.29
0.29 0.29
0.29 0.29
0.29
0.29 0.29 0.29 0.29 0.29 0.29
0.29
0.29 0.29 0.91
0.91 0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.96 0.96
0.96 0.96 0.96 0.62 0.96 0.62 0.62
0.96
0.96 0.62 0.62
0.62
0.96 0.62
0.96
0.62
0.96
0.62
0.62
0.96 0.96 0.96 0.96 0.96 0.62 0.62
0.96
0.96
0.62
0.96 0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
1.06 1.33 1.06 1.06 1.06 1.06 1.06 1.06 1.33 1.33 1.06 1.06 1.33 1.33 1.06 1.33 1.33 1.33 1.33 1.33 1.33 1.06 1.06 1.06 1.06 1.06 1.06 1.33 1.33 0.14 0.14 1.06 0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
–
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 –
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91 0.91 0.91 0.91
0.91 0.91
0.91
0.91 0.91
0.91
0.91 0.91
0.91 0.91 0.91
0.91
0.91
0.91
0.91
0.91 0.91 0.91
0.91 0.91
0.91 0.91 –
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.96 0.96 0.96 0.62 0.96 0.62
0.62
0.96
0.62 0.62 0.96 0.62
0.96 0.96 0.62 0.62
0.62
0.62
0.62
0.96 0.96 0.62
0.96 0.62 0.96
0.96
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
1.06 1.06 1.06 1.06 1.06 0.14 0.14 0.14 1.06 1.06 1.06 0.14 1.06 1.06 1.06 0.14 0.14 0.14 0.14 1.06 1.06 0.14 1.06 1.06 0.14
0.14
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
1.02
1.02
–
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14 1.59
0.14 1.59
1.59 1.59
1.59 0.14
0.14
1.59 0.14
1.59
0.14 0.14 0.14 1.59 1.59
0.14 0.14 1.59
0.14 1.59
0.14 0.14 0.14 0.14 1.59
0.14
0.14 1.59
1.59
0.14 1.59 1.59
0.14 1.59 1.59
0.14
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 2.62
0.15
2.62
0.15
2.62
0.15
2.62
0.15
0.15
2.62
0.15
0.15
2.62
2.62
2.62
2.62
0.15
2.62
2.62
0.15
2.62
0.15
2.62
2.62
2.62
2.62
0.15
0.15
2.62
0.15
0.15
2.62
2.62
0.15
2.62
2.62
0.15
0.15
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
0.57
0.57 0.57 0.57
0.57 0.57
0.57 0.57 0.57
0.57
0.57
0.57 0.57
0.57 0.57 0.57
0.57 0.57 0.57
0.57 0.57 0.57 0.57 0.57 0.57 0.57 0.57
0.57 0.57
0.57
0.57
0.57
0.57
0.57
0.57 0.57 0.57
0.57 –
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40 2.51 2.51 0.40 2.51 0.40 2.51 2.51 0.40 0.40 0.40 2.51 0.40 2.51 2.51 0.40 2.51 2.51 0.40 2.51 2.51 2.51 2.51 2.51 2.51 2.51 0.40 2.51 0.40 0.40 0.40 0.40 0.40 0.40 2.51 2.51 0.40 2.51 –
0.91
0.91 0.91 0.91 0.91 0.91
0.91
0.91
0.91
1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 –
0.62
0.96 0.96
0.96 0.96 0.62
0.62
0.62
0.62
0.96
0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 –
0.14
1.06
0.14
1.06
1.06
0.14
0.14
0.14
0.14
0.14 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 –
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
–
–
–
0.14 0.14
1.59
0.14
0.14 0.14 1.59
0.14 1.59
0.14 0.14 0.14 1.59
0.14
0.14
0.14
1.31
0.14
1.31
0.14
1.31
0.14
1.31
1.31
1.31
1.31
1.31
0.14
0.14
1.31
0.14
1.31
0.14
0.14
1.31
1.31
0.14
1.59
0.14
0.14
0.14
1.59
0.14
0.14
0.14
1.59
0.14
2.62
0.15
0.15
0.15
2.62
2.62
0.15
2.62
0.15
2.62
2.62
2.62
0.15
0.15
2.91
2.91
2.91
2.91
0.15
0.15
2.91
0.15
2.91
2.91
0.15
2.91
2.91
0.15
0.15
2.91
0.15
2.91
0.15
0.15
0.15
2.91
0.15
0.15 0.15
0.15
0.15
0.15 0.15
0.15
0.15
0.15 0.15
0.57
0.57 0.57 0.57 0.57 0.57
0.57 0.57
0.57
0.57 0.57 0.57
0.57 0.57 0.57
0.57
0.57 0.57
0.57 0.57 0.57
0.57 0.57
0.57 0.57 0.57 0.57 0.57
0.57
0.57
0.57
0.57 0.57 0.57
0.57 0.57
0.57
1.74
0.57
0.57
0.57
1.74
0.57
0.57
0.57
1.74
0.57
0.40 2.51 2.51 2.51 2.51 0.40 2.51 0.40 0.40 2.51 2.51 0.40 2.51 2.51 0.40 0.40 2.51 0.40 2.51 2.51 0.40 2.51 0.40 2.51 2.51 2.51 2.51 0.40 0.40 0.40 0.40 2.51 2.51 0.40 2.51 0.40 0.40 0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50 1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
1.50
0.91
0.91
0.91
0.91
0.91 0.91
0.91
0.91
0.91 0.91
0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 0.96 2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
2.20
0.62
0.62
0.62
0.62
0.96
0.62
0.62
0.62
0.96
0.62
1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 1.06 0.14
0.14
0.14
1.33
0.14
0.14
0.14
1.33
0.14
1.33
1.33
1.33
1.33
1.33
0.14
0.14
0.14
1.33
1.33
1.33
0.14
0.14
1.33
0.14
1.33 0.14
1.33
1.33
1.33 1.33
1.33
1.33
1.33 1.33
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
1.02
1.02
2.79
1.02
2.79
2.79
1.02
2.79
1.02
2.79
2.79
2.79
1.02
2.79
1.02
1.02
2.79
1.02
2.79
1.02
1.02
1.02
1.02
2.79
0.75
1.02
1.02
1.02
0.75
1.02
1.02
1.02
0.75
1.02
–
0.14
1.59
1.59
1.59
0.14
0.14
0.14
0.14
0.14
0.14
1.59
0.14
0.14
1.59
1.59
0.14
1.59
0.14
0.14
1.59
0.14
0.14
0.14
0.14
0.14
0.14
0.14
0.14
1.59
0.14
1.59
1.59
1.59
1.59
0.14
0.14
1.59
0.14
1.59
0.14
0.14
0.14
0.14
0.14
0.14
1.59
1.59
0.15
0.15 0.15 0.15 0.15
0.15
0.15
0.15
0.15
0.15
0.15 0.15
0.15
0.15 0.15 0.15
0.15 0.15
0.15
0.15 0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15
0.15 0.15
0.15 0.15 0.15 0.15 0.15
0.15
0.15 0.15
0.15 0.15
0.15
0.15
0.15
0.15
0.15
0.15 0.15 –
0.57
1.74
1.74
1.74
0.57
0.57
0.57
0.57
0.57
0.57
1.74
0.57
0.57
1.74
1.74
0.57
1.74
0.57
0.57
1.74
0.57
0.57
0.57
0.57
0.57
0.57
0.57
0.57
1.74
0.57
1.74
1.74
1.74
1.74
0.57
0.57
1.74
0.57
1.74
0.57
0.57
0.57
0.57
0.57
0.57
1.74
1.74
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.91
0.91 0.91 0.91 0.91
0.91
0.91
0.91
0.91
0.91
0.91 0.91
0.91
0.91 0.91 0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91
0.91 0.91
0.91 0.91
0.91
0.91 0.91
0.91 0.91
0.91
0.91
0.91
0.91
0.91
0.91 0.91 –
0.62
0.96 0.96 0.96 0.62
0.62
0.62
0.62
0.62
0.62
0.96 0.62
0.62
0.96 0.96 0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62
0.62 0.96 0.62
0.96
0.62
0.62
0.96
0.62
0.96
0.62
0.62
0.62
0.62
0.62
0.62
0.96
0.96
0.14
1.06 1.06 1.06 0.14
0.14
1.33
1.33
1.33
1.33
1.06 1.33
0.14
1.06 1.06 0.14
1.33
0.14
0.14
1.33 1.33
0.14
0.14
0.14
1.33
0.14
1.33
1.33
1.33 0.14
1.06 1.06 1.33 1.33 1.33
1.33
1.33
1.33
1.33 1.33
1.33
1.33
0.14
0.14
0.14
1.33 1.33 –
1.02
0.75
0.75
0.75
1.02
1.02
1.02
1.02
1.02
1.02
0.75
1.02
1.02
0.75
0.75
1.02
1.02
1.02
1.02
0.75
1.02
1.02
1.02
1.02
1.02
1.02
1.02
1.02
0.75
1.02
0.75
0.75
0.75
0.75
1.02
1.02
1.02
1.02
0.75
1.02
1.02
1.02
1.02
1.02
1.02
0.75
0.75
–
1.59
0.14
0.14
1.59
0.14
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
1.59
0.15 0.15
0.15
0.15 0.15
0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 0.15 –
1.74
0.57
0.57
1.74
0.57
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
1.74
0.40
0.40
0.40
0.40
0.40
0.40 0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.40
0.91 0.91
0.91
0.91
0.91
0.29 0.91 0.91
0.91 0.91 0.91 0.91 0.91 0.91
0.91
0.91 0.91 0.91
0.91 –
0.96
0.62
0.62
0.62
0.62
0.96
0.96
0.62
0.96 0.96 0.96 0.96 0.96 0.62 0.62
0.96
0.96
0.62
0.96 –
1.33 0.14
1.33
1.33 1.33
1.33 1.33
1.33 1.06 1.06 1.06 1.06 1.06 1.06 1.33 1.33 1.33 1.33 1.06 –
0.75
1.02
1.02
0.75
1.02
0.75
1.02
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
0.75
Future_Prefer
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
5
4
4
4
5
4
4
4
5
4
4
5
5
5
4
4
4
4
4
4
5
4
4
5
5
4
5
4
4
5
4
4
4
4
4
4
4
4
5
4
5
5
5
5
4
4
5
4
5
4
4
4
4
4
4
5
5
5
4
4
5
4
5
5
5
5
5
5
5
5
5
5
5
5
5
5
A
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
Month
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
B
C
Units
Cost
601 $ 45,623.00
738 $ 46,507.00
686 $ 43,343.00
736 $ 46,495.00
756 $ 47,317.00
498 $ 41,172.00
828 $ 43,974.00
671 $ 44,290.00
305 $ 29,297.00
637 $ 47,244.00
499 $ 43,185.00
578 $ 42,658.00
641 $ 39,178.00
452 $ 41,198.00
674 $ 43,505.00
475 $ 35,805.00
536 $ 39,181.00
527 $ 40,248.00
275 $ 28,157.00
495 $ 34,761.00
568 $ 45,148.00
418 $ 33,447.00
694 $ 45,686.00
653 $ 45,296.00
471 $ 37,179.00
669 $ 41,199.00
298 $ 31,259.00
399 $ 37,705.00
549 $ 42,757.00
863 $ 47,332.00
764 $ 44,914.00
800 $ 46,105.00
609 $ 45,972.00
667 $ 46,295.00
705 $ 45,218.00
637 $ 45,357.00
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
A
B
C
Month
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Units
601
738
686
736
756
498
828
671
305
637
499
578
641
452
674
475
536
527
275
495
568
418
694
653
471
669
298
399
549
863
764
800
609
667
705
637
Cost
$ 45,623.00
$ 46,507.00
$ 43,343.00
$ 46,495.00
$ 47,317.00
$ 41,172.00
$ 43,974.00
$ 44,290.00
$ 29,297.00
$ 47,244.00
$ 43,185.00
$ 42,658.00
$ 39,178.00
$ 41,198.00
$ 43,505.00
$ 35,805.00
$ 39,181.00
$ 40,248.00
$ 28,157.00
$ 34,761.00
$ 45,148.00
$ 33,447.00
$ 45,686.00
$ 45,296.00
$ 37,179.00
$ 41,199.00
$ 31,259.00
$ 37,705.00
$ 42,757.00
$ 47,332.00
$ 44,914.00
$ 46,105.00
$ 45,972.00
$ 46,295.00
$ 45,218.00
$ 45,357.00
D
E
F
G
H
I
J
K
L
M
N
O
Cost
$50,000.00
$45,000.00
$40,000.00
$35,000.00
$30,000.00
$25,000.00
Cost
$20,000.00
$15,000.00
$10,000.00
$5,000.00
$0
100
200
300
400
500
600
700
800
900
1000
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.86
R Square
73.6%
Adjusted R Square
72.8%
Standard Error
$ 2,733.74
Observations
36
ANOVA
df
Regression
Residual
Total
Intercept
Units
SS
MS
1 $ 708,085,273.77 $ 708,085,273.77
34 $ 254,093,815.21 $
7,473,347.51
35 $ 962,179,088.97
Coefficients
Standard Error
$ 23,651.49 $
1,917.14
$
30.53 $
3.14
t Stat
12.34
9.73
F
94.75
Significance F
0.00
P-value
Lower 95%
0.0% $ 19,755.40
0.0% $
24.16
Upper 95%
$ 27,547.58
$
36.91
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.86
R Square
73.6%
Adjusted R Square
72.8%
Standard Error
$
2,733.74
Observations
36
ANOVA
df
Regression
Residual
Total
Intercept
Units
1 $
34 $
35 $
$
$
SS
708,085,274 $
254,093,815 $
962,179,089
Coefficients
Standard Error
23,651.49 $
1,917.14
30.53 $
3.14
MS
708,085,274
7,473,348
Significance F
0.00
t Stat
P-value
Lower 95%
12.33687986
0.0% $ 19,755.40
9.733862174
0.0% $
24.16
RESIDUAL OUTPUT
$6,000.00
Predicted Cost
1 $
42,001.88
2 $
46,184.92
3 $
44,597.20
4 $
46,123.85
5 $
46,734.51
6 $
38,856.97
7 $
48,932.90
8 $
44,139.20
9 $
32,964.08
10 $
43,101.07
11 $
38,887.51
12 $
41,299.62
13 $
43,223.21
14 $
37,452.45
15 $
44,230.80
16 $
38,154.71
17 $
40,017.23
18 $
39,742.43
19 $
32,048.09
20 $
38,765.37
21 $
40,994.29
22 $
36,414.32
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
Residuals
3,621.12
322.08
(1,254.20)
371.15
582.49
2,315.03
(4,958.90)
150.80
(3,667.08)
4,142.93
4,297.49
1,358.38
(4,045.21)
3,745.55
(725.80)
(2,349.71)
(836.23)
505.57
(3,891.09)
(4,004.37)
4,153.71
(2,967.32)
$4,000.00
$2,000.00
Residuals
Observation
F
94.75
$0
$(2,000.00)
$(4,000.00)
$(6,000.00)
100
200
23 $
24 $
25 $
26 $
27 $
28 $
29 $
30 $
31 $
32 $
33 $
34 $
35 $
36 $
44,841.46
43,589.60
38,032.58
44,078.13
32,750.35
35,834.20
40,414.16
50,001.55
46,978.78
48,077.97
42,246.15
44,017.07
45,177.32
43,101.07
$
$
$
$
$
$
$
$
$
$
$
$
$
$
844.54
1,706.40
(853.58)
(2,879.13)
(1,491.35)
1,870.80
2,342.84
(2,669.55)
(2,064.78)
(1,972.97)
3,725.85
2,277.93
40.68
2,255.93
Residual is the difference of the predicted cost with the regression equation, and the actual original value
Units
Predicted Value Original Value
Residual
601 $
42,001.88 $ 45,623.00 $
3,621.12
Upper 95%
$ 27,547.58
$
36.91
Units Residual Plot
200
300
400
500
Units
600
700
800
900
1000
d the actual original value
The far extreme left and far
extreme right have all negative
residuals, this implies a pattern.
When the residual scatterplot
has recognizible patterns, it
strongly indicates that the
relationship is not linear
In a perfect linear relationship,
there is no pattern in the
residuals scatterplot. The dots
are randomly plotted all across
the chart.
Month
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
Units
Units^2
Cost
601
361201 $ 45,623.00
738
544644 $ 46,507.00
686
470596 $ 43,343.00
736
541696 $ 46,495.00
756
571536 $ 47,317.00
498
248004 $ 41,172.00
828
685584 $ 43,974.00
671
450241 $ 44,290.00
305
93025 $ 29,297.00
637
405769 $ 47,244.00
499
249001 $ 43,185.00
578
334084 $ 42,658.00
641
410881 $ 39,178.00
452
204304 $ 41,198.00
674
454276 $ 43,505.00
475
225625 $ 35,805.00
536
287296 $ 39,181.00
527
277729 $ 40,248.00
275
75625 $ 28,157.00
495
245025 $ 34,761.00
568
322624 $ 45,148.00
418
174724 $ 33,447.00
694
481636 $ 45,686.00
653
426409 $ 45,296.00
471
221841 $ 37,179.00
669
447561 $ 41,199.00
298
88804 $ 31,259.00
399
159201 $ 37,705.00
549
301401 $ 42,757.00
863
744769 $ 47,332.00
764
583696 $ 44,914.00
800
640000 $ 46,105.00
609
370881 $ 45,972.00
667
444889 $ 46,295.00
705
497025 $ 45,218.00
637
405769 $ 45,357.00
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.91
R Square
82.2%
Adjusted R Square
81.1% Improvement of +8.3% in the R-Square (72.8% in the original regression)
Standard Error
$
2,280.80
Observations
36
ANOVA
df
Regression
Residual
Total
Intercept
Units
Units^2
$
$
$
SS
MS
2 $ 790,511,518 $ 395,255,759
33 $ 171,667,571 $ 5,202,048
35 $ 962,179,089
Coefficients
Standard Error
5,792.80 $
4,763.06
98.35 $
17.24
(0.06) $
0.02
t Stat
1.22
5.71
(3.98)
F
75.98
Significance F
0.00
P-value
Lower 95%
23.3% $
(3,897.72)
0.0% $
63.28
0.0% $
(0.09)
RESIDUAL OUTPUT
Observation
Predicted Cost
1 $
43,239.10
2 $
45,711.49
3 $
45,038.15
4 $
45,691.59
5 $
45,869.01
6 $
39,897.77
7 $
46,110.44
8 $
44,783.64
9 $
30,210.69
10 $
44,106.84
11 $
39,936.32
12 $
42,603.33
13 $
44,193.66
14 $
37,994.47
15 $
44,836.70
16 $
38,977.84
17 $
41,278.63
18 $
40,967.23
19 $
28,303.70
20 $
39,781.37
21 $
42,307.11
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
Residuals
2,383.90
795.51
(1,695.15)
803.41
1,447.99
1,274.23
(2,136.44)
(493.64)
(913.69)
3,137.16
3,248.68
54.67
(5,015.66)
3,203.53
(1,331.70)
(3,172.84)
(2,097.63)
(719.23)
(146.70)
(5,020.37)
2,840.89
Now the scatter plots of the
residuals look to be random,
there is no recognizable pattern
in any of them
22 $
23 $
24 $
25 $
26 $
27 $
28 $
29 $
30 $
31 $
32 $
33 $
34 $
35 $
36 $
36,424.55
45,162.85
44,442.61
38,811.38
44,747.67
29,775.38
35,486.85
41,711.26
46,003.21
45,926.54
46,090.44
43,445.37
44,711.21
45,321.78
44,106.84
$
$
$
$
$
$
$
$
$
$
$
$
$
$
$
(2,977.55)
523.15
853.39
(1,632.38)
(3,548.67)
1,483.62
2,218.15
1,045.74
1,328.79
(1,012.54)
14.56
2,526.63
1,583.79
(103.78)
1,250.16
Units Residual Plot
$4,000.00
$2,000.00
Residuals
riginal regression)
$0
100
200
300
400
500
600
700
800
900
$(2,000.00)
$(4,000.00)
$(6,000.00)
Upper 95%
$ 15,483.31
$
133.42
$
(0.03)
the scatter plots of the
uals look to be random,
e is no recognizable pattern
y of them
Units
Units^2 Residual Plot
$4,000.00
Residuals
$2,000.00
$$(2,000.00)
0
100000 200000 300000 400000 500000 600000 700000 800000
$(4,000.00)
$(6,000.00)
Units^2
1000
800000
Albright et. all, “Data Analysis,
Optimization and Simulation Modeling”,
4th Edition, Cengage Learning, pg. 571