Graduate School of Business
MKTG 901
ÜLKER CHOCOLATE BARS ASSIGNMENT
28.12.2010
MKTG 901
Group 10
G du t School of Busin ss
BA S ASSIGN
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1) To find the breakeven point, the cost of consumer promotion must be calculated.
Amount of coupons for JAN 2005
Amount of redeemed coupons
Face Value
Redeemed coupon value
Coupon processing cost
Printing & Distribution cost
: 4 X 2 million = 8.000.000 coupons
: 8.000.000 X 10% = 800.000 coupons
: 1TL
: 1 TL X 800.000 = 800.000 TL
: 0,1 TL X 800.000 = 80.000 TL
: (20 TL X 8.000.000)/1000 = 160.000 TL
TOTAL COST = 800.000 TL + 80.000 TL + 160.000 TL =1.040.000 TL
Since gross margin per case is 52,8 TL, to reach the break-even point of this promotion, the amount that Ülker must sell in excess of sales is;
1.040.000 TL / 52.8 TL = 19.697 cas s (appx. 20.000 cases)
§
2) Since we have a data that may have seasonality and trend components, we must build our regression model using dummy variables and trend factor.
COEFFICIENTS
Y
: Expected amount of cases to be sold int : Intercept of regression model
CP
: Consumer promotion coefficient
TP
: Trade promotion coefficient
Jan_D
: January dummy coeffitient
.
.
.
Nov_D
: November dummy coefficient
TRND
: Trend component coefficient
VA IABLES
X1
: Consumer promotion amount
X2
: Trade promotion amount
X3
: January dummy variable
X4
: February dummy variable
.
.
.
X13
: November dummy variable
X14
: Trend component variable
¨
So our regression model should be in below format;
Y=int+(CP. X1)+ (TP. X2)+(Jan_D. X3)+(Feb_D. X4)+...+(Nov_D. X13)+(TRND. X14)
MKTG 901
1
G aduate School of Business
Our current data shows that there are discontinuing valuesbetween the beginning of 1998 and the end of 2001. In order to run a proper regression, discontinuing part of the data (Jan 1998 Dec
2001) is deleted from the data series.
We assume that our sales data is affected by seasonality and trend components, so these issues must be examined by Excel Data Analysis/Regression Tool. According to our iterative calculations including shifted effects of consumer and trade promotions1, all possible regression models show that there is no significant trend effect in existing data. This means that existing positive sales trend exists because of the positive trend in promotions. Therefore, trend variable is excluded from our regression model.
In addition, we calculated both consumer and trend promotions shifting possibilities for 1 and 2 months, but all possible regression models resulted with smaller R2 values. This also shows us that there is no significant shifted effect of consumer and trade promotions on sales amount. These results can be seen in Exhibits1 to 9.
The best possible regression model (R2 = 0,93 - Exhibit 1) has the following equation:
Y
= 225.186,50 + (0,04 x CP) + (0,07 x TP) + (Jan_D x 241.553,38) + (Mar_D x 107.144,37) +
(Apr_D x 125.707,62) + (May_D x 201.766,33) + (Jun_D x 104.040,00) + (Jul_D x 167.069,63)
(Sep_D x 209.899,83) + (Oct_D x 97.483,01) + (Nov_D x 80.466,71)
In our example, we are looking for the sale expectation for January 2005. So our regression model becomes as below;
Y
= 466.739,88 + (0,04 x CP) + (0,07 x TP)
a. The variables that predict sales are amount of consumer promotions (in dollars) and trade promotions (in dollar) and the dummy variable that is used for the month of January.
b. The potential promotion impact will be the delta of the regression model, with or without the consumer promotions in Jan 2005. (Trade promotion is constant, so we can assume that it s zero, and we assume that the exchange rate for USD and TRL is 1,5 TL/USD)
Without consumer promotion;
With consumer promotion;
Difference
Y = 466.740 cases
Y = 466.740 + 0,04 x (1.040.000 / 1,5) = 494.473 cases
= 27.733 cases
Since we have 52,8 TL margin for each case, our potential profit would be;
Potential Profit = 27.733 x 52,8 TL = 1.464.302 TL
c. According to our calculation, Ülker Bar brand manager will spend 1.040.000 TL for promotion for an expected additional profit of 1.464.302 TL. These figures show that he should do the consumer promotion.
1
Shifted effect: Statistically, if consumer or trade promotions have a significant effect on previous months sales, we call this as a shifted effect of promotions to sales.
MKTG 901
2
G aduate School of Business
Exhibit 1 Regression Model Without Shifting Effect
%
Mar_D Apr_D May_D Jun _D Jul _D Sep_D
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$315. 196
$703. 62
$198. 64
$478.880
$457. 172
$709. 480
$45.380
$28. 080
$111.520
$267.200
$354.304
$664. 712
$536.824
$551.560
$150. 080
$580.800
$435. 080
$361. 144
$97.844
$30.372
$150.324
$293. 044
$162. 788
$32.532
$23. 468
$4.503. 456
$500. 904
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$92.252
$4.869. 952
$376.556
$376.556
$552.536
c t _D N o _D
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CP
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$ . 633. 9
$ 3.526
$13. 06
$133. 195
$105. 058
$5.328
$2. 093
$6. 754
$1.807. 920
$589. 949
$72. 662
$26.554
$2. 615. 074
$209. 798
$27.552
$46. 147
$7.234
$65.376
$485. 659
$385. 483
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$440.208
$47.309
$514. 426
$1. 438. 949
$101.846
$754
$62.213
$1. 600. 939
$854. 904
$1.514. 707
$384. 989
$28.512
$176. 731
$1. 125.898
$345. 029
&
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&
Sal es i n CASES
()
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389.320
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386. 986
414.314
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365. 058
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528.210
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551.354
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276. 901
455. 136
247.570
622.204
429.331
453. 156
320. 103
451. 779
249. 482
744.583
421. 186
397.367
269. 096
'
M onth
'
INSIGNIFICAN
PARAME ERS
SIGNIFICAN REGRESSI N M DEL PARAME ERS
RN D
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SUMMA Y OUTPUT
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48
43
Regression
Resi dual
Total
is ics
2
(
Regressi S
Mul i pl R
R Squar j usted R Square
Standard Error se r ations
Q
R
R
8
R
G
FQD
Shifting Effect Assumption: Consumer Prom. and Trade Prom. have no delayed effect
Regression R2 =0,93(Best Possible Model)
MKTG 901
3
Graduate School of Business
Exhibit 2 Regression Model With Shifting Effect
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PARAME ERS
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M onth
REGRESS
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Regression
Multipl e ua e uste ua e ta a se vations
istics
01
0
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0. 9
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N VA
P- alue Lo er 95 er 95 Lo er 95 0
0, 00
276.762,36 348.947, 64 276.762,36
0, 00
120.190, 89 364.983,11 120.190,89
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43.920,26 248.091, 08
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60.316,26 264.487, 08
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31.109,92 235.280, 74
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178.493,59 382.664, 41 178.493,59
n
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t Stat
ignificance
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rrq p
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17.647, 23
59.844, 68
49.913,91
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10,
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m
Coefficients
312. 55, 00
242.587, 00
146.005, 67
162.401, 67
133.195,33
280.579, 00
9. 53. . 9,
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ps
nte cept
Jan_
Ma _
May_
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364.983,11
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264.487,08
235.280,74
382.664,41
u u u u u
t
Shifting Effect Assumption: Consumer Prom. none, Trade Prom.has 1 month delayed effect
Regression R2 =0,65
MKTG 901
4
Graduate School of Business
Exhibit 3 Regression Model With Shifting Effect
T +2
0
315.196
703.624
198.464
478.880
457.172
709.480
45.380
28.080
111.520
267.200
354.304
664.712
536.824
551.560
150.080
580.800
435.080
361.144
97.844
30.372
150.324
293.044
162.788
3 2 .5 3 2
23.468
4.503.456
500.904
0
0
46.104
92.252
4.869.952
376.556
0
0
0
0
0
0
0
0
0
0
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1
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0
0
0
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0
0
0
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Feb _
0
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Au g _
13.406
133.195
105.058
5.328
2.093
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1.807.920
589.949
72.662
26.554
2.615.074
209.798
27.552
46.147
7.234
65.376
485.659
385.483
1.611.686
440.208
47.309
514.426
1.438.949
101.846
754
62.213
1.600.939
854.904
1.514.707
384.989
28.512
176.731
1.125.898
345.029
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4
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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
TR
0
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~
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TP T
v
S MMARY
Jan_
Mar.02 i s.02
May.02
a .02
Tem.02
A u.02
Eyl.02
E i .02
Kas.02
Ara.02 ca.03 ub.03
Mar.03
i s.03
May.03
a .0 3
Tem.03
A u.03
Eyl.03
E i .0 3
Kas.03
Ara.03 ca.04 ub.04
Mar.04
i s.04
May.04
a .0 4
Tem.04
A u.04
Eyl.04
E i .0 4
Kas.04
Ara.04
Sales in
A SES Y
389.320
376.569
444.404
386.986
414.314
253.493
484.365
305.989
315.407
182.784
655.748
270.483
365.058
313.135
528.210
379.856
472.058
254.516
551.354
335.826
320.408
276.901
455.136
247.570
622.204
429.331
453.156
320.103
451.779
249.482
744.583
421.186
397.367
269.096
yz y y
M onth
I SIG IFI A T ARAMETERS
ARAMETERS
~v y } | y y
SIG IFI A T REGRESSIO MO E
Regre i n Stati tics
Mul ti l e R
R S uare j uste R S uare
Sta ar Error
.
O servatio s
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¡
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I ter e t aD Mar D
Ma D ul D
Se D
df
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Resi ual
Total
% L wer , % U pper , %
,
.,
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.,
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«
«
ǻ
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«
© ª
Shifting Effect Assumption: Consumer Prom. none, Trade Prom.has 2 months delayed effect
Regression R2 =0,64
MKTG 901
5
Graduate School of Business
Exhibit 4 Regression Model With Shifting Effect
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
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0
TR ND
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
CP +1
Feb _D Au g _D
2.633.779
253.526
13.406
133.195
105.058
5.328
2.093
6.754
1.807.920
589.949
72.662
26.554
2.615.074
209.798
27.552
46.147
7.234
65.376
485.659
385.483
1.611.686
440.208
47.309
514.426
1.438.949
101.846
754
62.213
1.600.939
854.904
1.514.707
384.989
28.512
176.731
1.125.898
1
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°
°
°
Jan_D Mar _D A r _D Ma _D Jun_D Jul_D Se _D O c t_D Nov_D
TP
°
°
°
°
°
°
315.196
703.624
198.464
478.880
457.172
709.480
45.380
28.080
111.520
267.200
354.304
664.712
536.824
551.560
150.080
580.800
435.080
361.144
97.844
30.372
150.324
293.044
162.788
3 2 .5 3 2
23.468
4.503.456
500.904
0
0
46.104
92.252
4.869.952
376.556
376.556
552.536
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
°
Sales in
CA SES (Y)
263.467
389.320
376.569
444.404
386.986
414.314
253.493
484.365
305.989
315.407
182.784
655.748
270.483
365.058
313.135
528.210
379.856
472.058
254.516
551.354
335.826
320.408
276.901
455.136
247.570
622.204
429.331
453.156
320.103
451.779
249.482
744.583
421.186
397.367
269.096
®
¯
ub.02
Mar.02
Nis.02
May.02
a z.02
Tem.02
A u.02
Eyl.02
Eki.02
Kas.02
Ara.02 ca.03 ub.03
Mar.03
Nis.03
May.03
a z.03
Tem.03
A u.03
Eyl.03
Eki.03
Kas.03
Ara.03 ca.04 ub.04
Mar.04
Nis.04
May.04
a z.04
Tem.04
A u.04
Eyl.04
Eki.04
Kas.04
Ara.04
M onth
IN SIGNIFICAN T PARAMETERS
°
SIGNIFICAN T REGRESSION MODEL PARAMETERS
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Shifting Effect Assumption: Consumer Prom.has 1 month delayed effect, Trade Prom. none
Regression R2 =0,91
MKTG 901
6
Graduate School of Business
Exhibit 5 Regression Model With Shifting Effect
u .0 2
Mar.0 2
N is .0 2
May.0 2
H a .0 2
Tem.0 2
A u .0 2
Eyl .0 2
E i .0 2
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26 3.46 7
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444 .4 0 4
3 86 .9 86
4 1 4 .31 4
25 3.4 93
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30 5 .9 8 9
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313.13 5
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0
0
0
0
0
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1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
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
ct
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
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1
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1
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N
No
0
0
0
0
0
0
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0
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0
R ME ERS
Feb
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
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0
1
0
0
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0
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1
$ 2 .6 33.77 9
$ 25 3.526
$ 13.4 0 6
$ 133.19 5
$ 10 5 .0 58
$ 5 .3 28
$ 2 .093
$ 6 .7 54
$ 1.8 0 7 .9 2 0
$ 58 9.9 4 9
$7 2 .662
$ 26 .554
$ 2 .6 1 5 .0 7 4
$ 2 09.7 9 8
$ 2 7 .552
$ 46 .1 4 7
$7 .2 3 4
$ 65 .3 7 6
$ 485 .65 9
$ 3 85 .48 3
$ 1.6 11.686
$ 44 0.2 0 8
$ 4 7 .309
$ 5 1 4 .426
$ 1.4 3 8 .9 4 9
$ 101.846
$7 54
$ 62 .2 13
$ 1.6 00.939
$ 854 .90 4
$ 1.5 1 4 .7 0 7
$ 3 84 .9 8 9
$ 28 .5 1 2
$ 1 7 6 .7 31
$ 1.1 25 .8 9 8
ãâ ß Ý Ü Ø
ä
Ý Ü á Ý Ü à Ý Ü Û Ý Ü Ø Ý ß Ý Ü Ý Ü Û Ý Ü Þ Ý Ü Ý Ü Û ÚÙ Ø× ë ê êî íì ìè ë êéè è è êéè è è è
M onth
N REGRESS N M
ãâ × Ý Ü ë ê êî ë
S GN F
ug
0
0
0
0
0
0
1
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0
0
0
0
0
0
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$ 31 5 .19 6
$7 03.624
$ 19 8 .464
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$ 45 7 .1 7 2
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$ 26 7 .2 00
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$ 664 .7 1 2
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$ 58 0.8 00
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$ 9 7 .844
$ 30.3 7 2
$ 1 5 0.3 24
$ 2 93.0 44
$ 1 62 .7 88
$ 3 2 .5 3 2
$ 2 3.468
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$ 5 00.90 4
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$0
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$ 9 2 .252
$ 4 .86 9.9 52
$ 3 7 6 .556
$ 3 7 6 .556
OUTP UT
R r ssio t tistics
Mu l ti p l
,8
ua
,65
djusted ua e
0, 5 tand a d o r
80.86 ,78 servati ons
35, 00
ÿ ÿ ù úù
Lo er
276.762, 36
120.190, 89
43.920, 26
60.316, 26
31.109, 92
178.493, 59
lu e
0,00
0,00
0,01
0,00
0,01
0,00
ö© ¨
17,73
4, 05
2, 93
3, 25
2,67
5,62
-
öõ
ttt
er
Lo er
348.947,64 276.762, 36
364.983, 11 120.190, 89
248.091, 08
43.920, 26
264.487, 08
60.316, 26
235.280,74
31.109, 92
382.664,41 178.493, 59
õõ
ú
rro r
17.647, 23
59.844,68
49.913, 91
49.913, 91
49.913, 91
49.913, 91
ific n c
0, 00
ÿ
r
i
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ÿ ÿ ö öõ
§£ £
tn
0,68
ÿ
£
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o efficients
312.855, 00
242.587, 00
146.005,67
162.401,67
133.195, 33
280.579, 00
34 .266.242. 46,40 6 .853.248.42 ,28
18 .657.719.930, 00 6.539.921.376, 90
538.923.962.076,40
ú
5, 00
2 , 00
34, 00
¥
¦
Intercept
Jan _
Mar_
Ma _
Ju l_ e p_
f
ò ö ôó õ
Regressi on
Resi du al
Total
¥
¢¡
ANOVA
er
348.947,64
364.983, 11
248.091, 08
264.487, 08
235.280,74
382.664,41
û
Shifting Effect Assumption:Both Consumer and Trade Promotionshave 1 month delayed effect
Regression R2 =0,65
MKTG 901
7
Graduate School of Business
Exhibit 6 Regression Model With Shifting Effect
%
!$
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0
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0
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A @9
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I SIG IFIC
7
E P R METERS
8
Mar.02
Nis.02
May.02 a z.02
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A u.02
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M
7
M onth
T REGRESSI
88
SIG IFIC
6
4
5
S MMARY O TP T
B
CC
B
B
CC
Reg re io n Sta ti tic
Mu l ti p l R
0 80
R Squ are
0 64
Ad j uste d R Squ are
0 58
Stand ard Error
81.743 63 b servati o ns
34,00
E
E
E
E
D
F
A
t Sta t
17,25
3, 96
2,84
3, 16
2,58
5,50
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0,00
0,00
0,01
0,00
0,02
0,00
Lo er 95
277.882,73
115.937,63
39.864,98
56.260,98
27.054,65
174.438,31
I
Sta n da rd Error
18.278,43
60.622,70
50.610,68
50.610,68
50.610,68
50.610,68
P
C
Co efficien t
315.324,40
240.117,60
143.536,27
159.932,27
130.725,93
278.109,60
R RQ
Interce pt
Jan
Mar_D
May_D
Ju l _D
Se p _D
F
Sig n ifica n ce F
10,05
0,00
SS
MS
335.703.829.762,64 67.140.765.952,53
187.096.586.658,80 6.682.020.952,10
522.800.416.421,44
er 95 Lo er 95 0
352.766,07
277.882,73
364.297,57
115.937,63
247.207,55
39.864,98
263.603,55
56.260, 98
234.397,22
27.054,65
381.780,89
174.438,31
P
5,00
28,00
33, 00
I
df
R RQ P S
Re gre ssi o n
Re si du al
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er 95 0
352.766,07
364.297,57
247.207,55
263.603,55
234.397,22
381.780,89
PS
FG
H
A
UT
Shifting Effect Assumption:Con. Prom. has 1 month, Trade Prom. has 2 months delayed effect
Regression R2 =0,65
MKTG 901
8
Graduate School of Business
Exhibit 7 Regression Model With Shifting Effect
o _D
TR D
0
0
0
0
0
0
0
0
1
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0
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u g _D Feb _D
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x
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y
an_D Mar _D
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q q pqg p g qh r ghs rrp hr r pp sr g g qs gsr gg r r q ps pr qg ssr qp hr g q h sgg rsg ss q s rs hs pr qsh pg hs sg g g hs sqg ghr p hrp ssq rhp h p rs g h sg sqg rhr g r gs r gr r qh qhg r r rrr qs qpg g hg
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Mar.02
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Nis.0
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a .0
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Sales in
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9. 20
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MODE P R METERS
x
M onth
T REGRESSI
SIG IFI
2.
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ut v r r r r rv r t ru r r r r w r g g g g gv g t gu g g g g w g S MMARY O TP T
Regression Statisti s
Mul tiple R
,
R Square
,
Adj usted R Square
,
Standard Error
44. 45, 42
Observati ons
4,
A OVA
14, 83
6,42
8,40
3,39
3,93
7,16
3,56
6,17
8,06
3,45
2,94
P-value Lower 95% pper 95% Lower 95, % pper 95, %
0,00
204.410, 20 270.685, 14 204.410,20 270.685, 14
0,00
0, 04
0,07
0, 04
0, 07
0,00
224.934, 69 372.002, 12 224.934,69 372.002, 12
0,00
44.509, 75 184.194, 17
44.509,75 184.194, 17
0,00
56.734, 44 182.638, 25
56.734,44 182.638, 25
0,00
155.044, 12 281.008, 86 155.044,12 281.008, 86
0,00
45.238, 56 171.150, 71
45.238,56 171.150, 71
0,00
124.765, 58 250.755, 52 124.765,58 250.755, 52
0,00
196.507, 91 332.184, 30 196.507,91 332.184, 30
0,00
41.981, 77 167.874, 93
41.981,77 167.874, 93
0,01
26.484, 68 152.407, 72
26.484,68 152.407, 72
t Stat
Standard Error
16.018, 83
0, 01
35.546, 59
33.762, 09
30.431, 28
30.446, 01
30.433, 30
30.452, 10
32.793, 34
30.428, 71
30.435, 93
Coeffi ients
237.547,67
0, 06
298.468,41
114.351,96
119.686,34
218.026,49
108.194,63
187.760,55
264.346,11
104.928,35
89.446, 20
Intercept
TP
Jan_D
Mar_D
Apr_D
Ma _D
Jun_D
Jul_D
Sep_D
Oct_D
Nov_D
F
Signifi an e F
23,58
0, 00
SS
MS
,
476. . 22.266, 4 47.633. 32.226,68
23, 00 46.462.094. 54, 60 2.020.091.050, 20
33, 00 522.800.416.421, 44
df
Regressi on
Residual
Total
Shifting Effect Assumption: Con. Prom. has 2 months delayed effect, Trade Prom. none
Regression R2 =0,91
MKTG 901
9
Graduate School of Business
Exhibit 8 Regression Model With Shifting Effect
SG
A
ARAME ERS
h
kj f
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
$ 31 5 .19 6
$ 7 03 .6 2 4
$ 19 8 .464
$ 478 .88 0
$ 457 .1 7 2
$ 7 09 .48 0
$ 45 .3 8 0
$ 2 8 .0 8 0
$ 111 .5 20
$ 2 67 .200
$ 3 54 .30 4
$ 664 .7 12
$ 5 3 6 .8 2 4
0 $ 2 .6 33 .77 9
0
$ 2 5 3 .5 2 6
0
$ 13 .4 0 6
0
$ 133 .19 5
0
$ 10 5 .0 58
0
$ 5 .32 8
0
$ 2 .093
0
$ 6 .754
0 $ 1 .8 0 7 .920
0
$ 58 9 .9 4 9
0
$ 7 2 .66 2
1
$ 2 6 .554
0 $ 2 .6 1 5 .0 74
0
0
0
0
0
1
0
0
0
0
0
0
0
313 .13 5
5 2 8 .210
3 7 9 .856
47 2 .0 58
2 54 .5 1 6
55 1 .3 54
33 5 .8 2 6
320 .4 0 8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
1
0
0
14
15
16
17
18
19
20
21
1
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
$ 55 1 .56 0
$ 1 5 0 .0 8 0
$ 58 0 .8 00
$ 4 3 5 .0 8 0
$ 3 6 1 .1 44
$ 9 7 .844
$ 30 .3 7 2
$ 1 5 0 .32 4
0
$ 209 .7 9 8
0
$ 2 7 .55 2
0
$ 46 .1 47
0
$ 7 .23 4
0
$ 65 .3 76
0
$ 485 .65 9
0
$ 3 85 .48 3
0 $ 1 .6 11 .686
0
0
0
0
1
0
0
0
2 76 .901
455 .13 6
2 47 .57 0
6 22 .20 4
4 29 .331
45 3 .1 56
320 .103
45 1 .77 9
2 4 9 .48 2
744 .58 3
4 21 .1 86
39 7 .3 67
2 6 9 .09 6
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
22
23
24
25
26
27
28
29
30
31
32
33
34
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
$ 293 .0 44
0
$ 1 6 2 .788
0
$ 32 .5 32
0
$ 23 .468
0 $ 4 .5 03 .456
0
$ 5 00 .90 4
0
$0
0
$0
0
$ 46 .10 4
0
$ 92 .2 5 2
0 $ 4 .86 9 .9 5 2
1
$ 3 76 .556
0
$ 3 76 .556
0
$ 44 0 .20 8
0
$ 47 .309
1
$ 5 1 4 .4 2 6
0 $ 1 .4 3 8 .9 4 9
0
$ 101 .846
0
$ 754
0
$ 6 2 .213
0 $ 1 .6 00 .939
0
$ 854 .90 4
0 $ 1 .5 1 4 .7 0 7
0
$ 3 84 .9 8 9
0
$ 2 8 .5 12
0
$ 1 76 .7 31
0
0
0
0
0
0
0
0
1
0
0
0
0
ig
ct D
o
e
e
un D
1
eb D
el
e
R D Apr D
2
Aug D
e
e
g
f
ul D Sep D
d
e
e
d
e
e
e
d
q sprpq p t D
0
0
0
0
0
0
0
0
0
0
1
0
0
a n D Mar D May D
kj
qup
Ara.03
O ca.0 4 u .0 4
Mar.0 4
N is.0 4
May.0 4
H a .0 4
Tem.0 4
A u.0 4
Eyl.0 4
E i .0 4
Kas.0 4
Ara.0 4
u
N is.03
May.03
H a .03
Tem.03
A u.03
Eyl.03
E i .03
Kas.03
M DEL ARAME ERS v Mar.02
N is.02
May.02
H a .02
Tem.02
A u.02
Eyl.02
E i .02
Kas.02
Ara.02
O ca.03 u .03
Mar.03
Sa le s in
ASES
3 8 9 .320
3 76 .56 9
444 .4 0 4
3 86 .9 86
4 1 4 .31 4
2 5 3 .4 93
484 .3 65
30 5 .9 8 9
31 5 .4 0 7
1 8 2 .784
655 .748
2 7 0 .48 3
3 65 .0 58
M onth
t
REGRESS
v q sprpq p qp t A
t
SG
m
n
o m n
o m n
SUMMARY OUTPUT
Re ression Statistics
Multiple R
0, 80
R Square
0, 64 dj usted R Square
0, 58
Standard Error
81.743, 63
Observati ons
34, 00 w x
xy
NO
17, 25
3, 96
2, 84
3, 16
2, 58
5, 50
P-v alue
0, 00
0, 00
0, 01
0, 00
0, 02
0, 00
Lower 95
277.882, 73
115.937, 63
39.864, 98
56.260, 98
27.054, 65
174.438, 31 z t Stat
U pper 95
Lower 95 0 U pper 95 0
352.766, 07 277.882, 73 352.766,07
364.297, 57 115.937, 63 364.297,57
247.207, 55
39.864, 98 247.207,55
263.603, 55
56.260, 98 263.603,55
234.397, 22
27.054, 65 234.397,22
381.780, 89 174.438, 31 381.780,89 z Standard Error
18.278, 43
60.622, 70
50.610, 68
50.610, 68
50.610, 68
50.610, 68
z{
Coefficients
315.324, 40
240.117, 60
143.536, 27
159.932, 27
130.725, 93
278.109, 60
F
Si nificance F
10, 05
0, 00
z{
x
ntercept
Jan_D
Mar_D
May_D
Jul _D
Sep_D
SS
MS
5, 00 335.703.829.762, 64 67.140.765.952, 53
28, 00 187.096.586.658, 80 6.682.020.952,10
33, 00 522.800.416.421, 44
w
df
Regressi on
Residual
Total
|
Shifting Effect Assumption: Con. Prom. has 2 months, Trade Prom. has 1 month delayed effect
Regression R2 =0,64
MKTG 901
10
Graduate School of Business
Exhibit 9 Regression Model With Shifting Effect
TP+2
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
22
23
24
25
26
27
28
29
30
31
32
33
34
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
25 3.52 6
1 3.4 0 6
1 33.195
105 .058
5 .3 28
2 .09 3
6.7 5 4
1 .80 7.920
589 .9 4 9
7 2 .66 2
2 6.55 4
2 .6 15 .0 74
209 .7 98
2 7.552
46.1 47
7.2 34
6 5 .376
4 85 .6 59
3 85 .4 8 3
1 .6 11 .6 8 6
44 0 .208
47.3 09
51 4.4 2 6
1 .43 8 .9 4 9
101 .8 46
754
6 2 .21 3
1 .6 00 .9 3 9
85 4.90 4
1 .51 4.7 0 7
3 8 4.989
28 .512
1 76.73 1
0
0
0
0
0
0
0
0
0
1
0
0
0
Feb D Au g D
2 .633.77 9
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
3 15 .19 6
7 0 3.6 2 4
198 .464
47 8 .880
4 5 7.1 7 2
7 09 .4 80
4 5 .3 80
28 .080
111 .520
2 67.200
3 5 4.3 0 4
664.7 12
5 36.82 4
551 .5 6 0
150 .080
580 .800
43 5 .080
36 1 .1 44
9 7.8 44
3 0 .37 2
150 .3 2 4
29 3.0 44
1 6 2 .7 88
3 2 .5 3 2
2 3.46 8
4.50 3.4 5 6
500 .90 4
0
0
46.10 4
92 .252
4.8 6 9 .952
376.55 6
0
0
0
1
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
CP+2
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
~
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
un D O c t D Nov D
0
0
0
0
0
0
0
0
0
0
1
0
0
0
0
0
0
0
0
0
0
~
T RND A r D
D
~
}
2 76.901
4 55 .1 36
2 47.5 7 0
6 22 .20 4
4 29 .33 1
4 5 3.15 6
3 20 .10 3
4 51 .77 9
2 4 9 .4 82
744.58 3
4 21 .18 6
3 9 7.367
2 6 9 .09 6
D ul D Se
Ara.0 3
Oca.0 4 ub.0 4
Mar.0 4
Nis.0 4
May.0 4 az.0 4
Tem.0 4
A u.0 4
Eyl.0 4
Eki.0 4
Kas.0 4
Ara.0 4
D Mar D Ma
Mar.02
Nis.02
May.02 az.02 Tem.02
A u.02
Eyl.02
Eki.02
Kas.02
Ara.02
Oca.0 3 ub.0 3
Mar.0 3
Nis.0 3
May.0 3 az.0 3
Tem.0 3
A u.0 3
Eyl.0 3
Eki.0 3
Kas.0 3
Sa le s in an CA SES ( )
3 89 .3 20
376.5 6 9
444.4 0 4
3 8 6.98 6
4 1 4.3 1 4
25 3.4 9 3
4 8 4.36 5
3 05 .989
3 15 .4 0 7
182 .7 8 4
6 55 .74 8
2 7 0 .4 8 3
36 5 .058
3 1 3.1 3 5
528 .210
37 9 .85 6
47 2 .058
25 4.51 6
551 .3 5 4
33 5 .82 6
3 20 .4 08
M onth
INSIGNIFICANT PA RA METERS
SIGNIFICANT REGRESSION MODEL PA RA METERS
SUMMAR OUTP UT
Reg re ion Sta ti ti
Mu lti p l e R
0, 80
R Square
0, 64
Adjuste d R Square
0, 58
Standard Error
81.743,63
Observati o ns
34, 00
ANOV A
t Sta t
17, 25
3, 96
2, 84
3, 16
2, 58
5, 50
P -v a lu e
0, 00
0, 00
0, 01
0, 00
0, 02
0, 00
Lower 95%
277.882, 73
115.937, 63
39.864, 98
56.260, 98
27.054, 65
174.438, 31
Sta n da rd Erro r
18.278, 43
60.622, 70
50.610, 68
50.610, 68
50.610, 68
50.610, 68
er 95% Lower 95,0%
352.766, 07
277.882,73
364.297, 57
115.937,63
247.207, 55
39.864,98
263.603, 55
56.260,98
234.397, 22
27.054,65
381.780, 89
174.438,31
C oeffi ien t
315.324,40
240.117,60
143.536,27
159.932,27
130.725,93
278.109,60
In terce p t
Jan_D
Mar_D
May_D
Ju l _D
Se p_D
F
Sig n ifi a n e F
10, 05
0, 00
SS
S
5, 00 335.703.829.762, 64 67.140.765.952, 53
28, 00 187.096.586.658, 80 6.682.020.952, 10
33, 00 522.800.416.421, 44
df
Regre ssi o n
Re si dual
To tal
er 95,0%
352.766, 07
364.297, 57
247.207, 55
263.603, 55
234.397, 22
381.780, 89
Shifting Effect Assumption: Both Consumer and Trade Promotions have 2 months delayed effect
Regression R2 =0,64
MKTG 901
11 