MGT 3332 Forecasting Case Analysis

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Business Finance

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Spring 2019

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Forecasting Dataset Spring 2019 Month/Yr. June 2016 Jan. 2017 Jan. 2018 Jan. 2019 PERIOD PRICE 1 6.1 2 5.75 3 5.7 4 5.7 5 5.6 6 5.6 7 5.6 8 6.3 9 6.4 10 6.2 11 5.9 12 5.9 13 5.7 14 5.75 15 5.75 16 5.8 17 5.7 18 5.8 19 5.7 20 5.8 21 5.8 22 5.75 23 5.7 24 5.55 25 5.6 26 5.65 27 5.7 28 5.75 29 5.8 30 5.3 31 5.4 32 5.7 AIP 5.8 6 6.3 5.7 5.85 5.8 5.75 5.85 5.65 6 6.1 6 6.1 6.2 6.1 6.1 6.2 6.3 6.1 5.75 5.75 5.65 5.9 5.65 6.1 6.25 5.65 5.75 5.85 6.25 6.3 6.4 DIFF -0.3 0.25 0.6 0 0.25 0.2 0.15 -0.45 -0.75 -0.2 0.2 0.1 0.4 0.45 0.35 0.3 0.5 0.5 0.4 -0.05 -0.05 -0.1 0.2 0.1 0.5 0.6 -0.05 0 0.05 0.95 0.9 0.7 Feb. 2019 Mar-19 Apr-19 May-19 33 34 35 36 5.9 6.5 0.6 ADV 5.3 6.75 7.25 7.3 7.2 6.5 6.75 6.89 5.8 5.5 6.5 6.25 7 6.9 6.8 6.8 7.1 7 6.8 6.5 8.1 7.7 7.3 7.5 8.1 8.3 8.7 9.2 8.4 8.8 9.5 9.3 DEMAND 14.4 15.3 16.5 16.1 16 15.5 15.2 13.9 13.3 13.12 13.8 14.8 15.3 16.3 17.5 17.4 17.1 16.8 16.5 16 15.2 15.3 15.9 16.2 17.5 18.4 19.4 19.1 18.7 18.2 18.4 17.5 9.1 17.1 GRAPHS Price vs. Periods 8 4 Price Price 6 y = -0.0089x + 5.9193 R² = 0.1474 2 0 0 5 10 15 20 25 30 35 Periods AIP vs. Periods y = -4E-07x5 + 5E-05x4 - 0.0022x3 + 0.0367x2 - 0.2178x + 6.2272 R² = 0.4507 7 AIP 6.5 6 5.5 0 5 10 15 20 25 30 35 Periods DIFF vs. Periods 1.5 y = -5E-07x6 + 5E-05x5 - 0.0019x4 + 0.0348x3 - 0.3009x2 + 1.0748x - 0.9937 R² = 0.5042 1 DIFF 0.5 0 -0.5 -1 10 0 5 10 15 20 25 30 35 Periods ADV vs. Periods 6 4 y = 0.0916x + 5.8024 ADV ADV 8 y = 0.0916x + 5.8024 R² = 0.663 ADV ADV 4 2 0 0 5 10 15 20 25 30 35 Periods Demand vs. Periods y = 0.1173x + 14.3 R² = 0.4775 25 Demand Demand 20 15 10 5 0 0 5 10 15 20 Periods 25 30 35 QUESTION 2 GRAPHS ANALYSIS Price vs. Periods y = -7E-07x5 + 7E-05x4 - 0.0022x3 + 0.0299x2 - 0.1461x + 6.0139 R² = 0.2598 Price 6.5 6 5.5 5 0 5 10 15 20 25 30 35 Studying the first paragraph it seems that there is no ev the price has changed considerably during the periods u Visually, we would say that the price is almost constant d but once we calculate the R-squared we see that the li poor (R-squared < 0.5). Trying to obtain a better fit w describe the data with a polinomical equation of 5th de we still got a r-squared < 0.5 (poor fit) Periods In the other hand, the average industry price has chan time from 5.6 to almost 6.5. Visually there is not a defin However, if we fit the data to a polinomical of 5th degr almost a good fit, obtaining an R-squared of 0.4 Also, the price difference has varied duing time from alm Visually there is not a define pattern that describes the results. However, if we fit the data to a polinomical of 5 we get almost a good fit, obtaining an R-squared of 12 10 ADV vs. Periods ADV 8 6 4 y = 0.0046x2 - 0.066x + 6.7221 Something different happens in the case of ADV. Even behaviour is not totally linear, the linear fit is good, obta squared > 0.5 (0.63). However, if we want to be even m a polinomical fit (2 degrees) is even better (R-squared ADV y = 0.0046x2 - 0.066x + 6.7221 R² = 0.7858 4 Something different happens in the case of ADV. Even behaviour is not totally linear, the linear fit is good, obta squared > 0.5 (0.63). However, if we want to be even m a polinomical fit (2 degrees) is even better (R-squared 2 0 0 5 10 15 20 25 30 35 Periods Demand vs. Periods Demand 4 3 2 25y = -3E-05x + 0.0015x - 0.0134x - 0.0378x + 15.501 R² = 0.5723 20 Finally, let's talk about demand. A linear fit means an R0.4775 (relatively poor, rsquared
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Explanation & Answer

Attached.

Forecasting Dataset
Spring 2019

PERIOD
PRICE
AIP
DIFF
ADV
DEMAND

PERIOD

PRICE

1
-0.38396
0.290259
0.426187
0.814258
0.691026

AIP

DIFF

1
-0.23374
1
-0.76244 0.807343
1
-0.55717 0.299438 0.537413
-0.64098 0.299191 0.588114

ADV

1
0.783047

DEMAND

1

Forecasting Dataset
Spring 2019
Month/Yr.
June 2016

Jan. 2017

Jan. 2018

Jan. 2019

PERIOD
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

PRICE
6.1
5.75
5.7
5.7
5.6
5.6
5.6
6.3
6.4
6.2
5.9
5.9
5.7
5.75
5.75
5.8
5.7
5.8
5.7
5.8
5.8
5.75
5.7
5.55
5.6
5.65
5.7
5.75
5.8
5.3
5.4
5.7

AIP
5.8
6
6.3
5.7
5.85
5.8
5.75
5.85
5.65
6
6.1
6
6.1
6.2
6.1
6.1
6.2
6.3
6.1
5.75
5.75
5.65
5.9
5.65
6.1
6.25
5.65
5.75
5.85
6.25
6.3
6.4

33
34
35
36

5.9

6.5

Time Series Plot of
25

Demand

20
15
10
5
0
0

5

Time Series Plot of
10

Demand

Feb. 2019
Mar-19
Apr-19
May-19

8
6
4
2
0
0

5

Time Series Plo

Demand

Time Series Plo
1.2
1
0.8
0.6
0.4
0.2
0
-0.2 0
-0.4
-0.6
-0.8
-1

5

Demand
Demand Vs.
Vs. Adv
Adv
18
18
16
16
14
14
12
12
10
10
0
0

1
1

2
2

3
3

4
4

5
5

Advertising
Advertising

Demand Vs. DIff
25
20

Demand

Demand
Demand

22
22
20
20

15

Demand

15
10
5
0

-1

-0.8

-0.6

-0.4

-0.2

0

0.2

Diff

DIFF
-0.3
0.25
0.6
0
0.25
0.2
0.15
-0.45
-0.75
-0.2
0.2
0.1
0.4
0.45
0.35
0.3
0.5
0.5
0.4
-0.05
-0.05
-0.1
0.2
0.1
0.5
0.6
-0.05
0
0.05
0.95
0.9
0.7

ADV
5.3
6.75
7.25
7.3
7.2
6.5
6.75
6.89
5.8
5.5
6.5
6.25
7
6.9
6.8
6.8
7.1
7
6.8
6.5
8.1
7.7
7.3
7.5
8.1
8.3
8.7
9.2
8.4
8.8
9.5
9.3

DEMAND
14.4
15.3
16.5
16.1
16
15.5
15.2
13.9
13.3
13.12
13.8
14.8
15.3
16.3
17.5
17.4
17.1
16.8
16.5
16
15.2
15.3
15.9
16.2
17.5
18.4
19.4
19.1
18.7
18.2
18.4
17.5

0.6

9.1

17.1

Time Series Plot of DEMAND

y = 0.1173x + 14.3
R² = 0.4775
6.7

Demand

6.5
6.3
6.1
5.9
5.7
5.5
10

15

20

25

30

0

35

5

Time Period

Time Series Plot of Advertising

Demand

y = 0.0916x + 5.8024
R² = 0.663

10

15

20

25

30

35

Time Period

Time Series Plot of Diff

7
6
5
4
3
2
1
0
0

y = 0.0163x - 0.0552

5

y = 0.0163x - 0.0552
R² = 0.1816

Time Series Plot of Diff

c
10

15

20

25

30

35

Time Period

Vs.
Vs. Adv
Adv

Demand Vs. Price

Demand

20
18
16
14
12
6
6

7
7

8
8

9
9

10
10

5

5.2

5.4

5.6

5.8

Price

y = 2.6166x + 15.716
R² = 0.3459

Demand Vs. AIP
25
20

Demand

nd Vs. DIff

yy == 1.181x
1.181x ++ 7.6018
7.6018

R² == 0.6132
0.6132

15

Demand

15
10
5
0
5.5

0.2

0.4

0.6

0.8

1

5.7

1.2

c

5.9

y = 0.0074x + 5.8641
R² = 0.0843

Time Series Plot of AIP

5

10

15

20

25

30

35

Time Period

c

Time Series Plot of Price

5

10

15

20

Time Period

y = -0.0089x + 5.9193
R² = 0.1474

25

30

35

y = -4.6991x + 43.4
R² = 0.4109

Demand Vs. Price

5.8

6

6.2

6.4

6.6

Price

Demand Vs. AIP

y = 2.0003x + 4.3142
R² = 0.0895

6.1

AIP

6.3

6.5

6.7

Month/Yr.
June 2015

Jan. 2016

Jan. 2017

Jan. 2018
Feb. 2018
Mar-18

PERIOD PRICE
1
6.1
2
5.75
3
5.7
4
5.7
5
5.6
6
5.6
7
5.6
8
6.3
9
6.4
10
6.2
11
5.9
12
5.9
13
5.7
14
5.75
15
5.75
16
5.8
17
5.7
18
5.8
19
5.7
20
5.8
21
5.8
22
5.75
23
5.7
24
5.55
25
5.6
26
5.65
27
5.7
28
5.75
29
5.8
30
5.3
31
5.4
32
5.7
33
5.9

AIP
5.8
6
6.3
5.7
5.85
5.8
5.75
5.85
5.65
6
6.1
6
6.1
6.2
6.1
6.1
6.2
6.3
6.1
5.75
5.75
5.65
5.9
5.65
6.1
6.25
5.65
5.75
5.85
6.25
6.3
6.4
6.5

DIFF
-0.3
0.25
0.6
0
0.25
0.2
0.15
-0.45
-0.75
-0.2
0.2
0.1
0.4
0.45
0.35
0.3
0.5
0.5
0.4
-0.05
-0.05
-0.1
0.2
0.1
0.5
0.6
-0.05
0
0.05
0.95
0.9
0.7
0.6

Ft=f(f-1

Month/Yr.
June 2016

Jan. 2017

Jan. 2018

PERIOD PRICE
1
6.1
2
5.75
3
5.7
4
5.7
5
5.6
6
5.6
7
5.6
8
6.3
9
6.4
10
6.2
11
5.9
12
5.9
13
5.7
14
5.75
15
5.75
16
5.8
17
5.7
18
5.8
19
5.7
20
5.8
21
5.8
22
5.75
23
5.7
24
5.55
25
5.6
26
5.65
27
5.7

AIP
5.8
6
6.3
5.7
5.85
5.8
5.75
5.85
5.65
6
6.1
6
6.1
6.2
6.1
6.1
6.2
6.3
6.1
5.75
5.75
5.65
5.9
5.65
6.1
6.25
5.65

DIFF
-0.3
0.25
0.6
0
0.25
0.2
0.15
-0.45
-0.75
-0.2
0.2
0.1
0.4
0.45
0.35
0.3
0.5
0.5
0.4
-0.05
-0.05
-0.1
0.2
0.1
0.5
0.6
-0.05

Jan. 2019
Feb. 2019

28
29
30
31
32
33

5.75
5.8
5.3
5.4
5.7
5.9

5.75
5.85
6.25
6.3
6.4
6.5

0
0.05
0.95
0.9
0.7
0.6

2017
13.9
13.3
13.12
13.8
14.8
15.3
16.3
17.5
17.4
17.1
16.8
16.5

2018
16
15.2
15.3
15.9
16.2
17.5
18.4
19.4
19.1
18.7
18.2
18.4

2019
17.5
17.1

Mar-18

Month

2016

Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec

14.4
15.3
16.5
16.1
16
15.5
15.2

Grand Avg =

ADV
5.3
6.75
7.25
7.3
7.2
6.5
6.75
6.89
5.8
5.5
6.5
6.25
7
6.9
6.8
6.8
7.1
7
6.8
6.5
8.1
7.7
7.3
7.5
8.1
8.3
8.7
9.2
8.4
8.8
9.5
9.3
9.1

DEMAND
14.4
15.3
16.5
16.1
16
15.5
15.2
13.9
13.3
13.12
13.8
14.8
15.3
16.3
17.5
17.4
17.1
16.8
16.5
16
15.2
15.3
15.9
16.2
17.5
18.4
19.4
19.1
18.7
18.2
18.4
17.5
17.1
3m MA Forc.

3m MA

15.4
15.96666667
16.2

Abs. Dev.

6m MA

0.7
0.033333333
0.7

15.86666667

0.666666667

15.63333333

15.56666667

1.666666667

15.76666667

14.86666667

1.566666667

15.53333333

14.13333333

1.013333333

15

0.36

14.50333333

13.40666667

1.393333333

14.13666667

13.90666667

1.393333333

14.02

14.63333333

1.666666667

14.03666667

15.46666667

2.033333333

14.43666667

16.36666667

1.033333333

15.13666667

17.06666667

0.033333333

15.85

17.33333333

0.533333333

16.4

17.1

0.6

16.73333333

16.8

0.8

16.93333333

1.233333333

16.88333333

15.9

0.6

16.5

15.5

0.4

16.15

0.733333333

15.95

1.7

15.85

16.53333333

1.866666667

16.01666667

17.36666667

2.033333333

16.41666667

18.43333333

0.666666667

17.11666667

18.96666667

0.266666667

17.75

19.06666667

0.866666667

18.21666667

18.66666667

0.266666667

18.55

18.43333333

0.933333333

18.7

18.03333333
17.66666667

0.933333333

18.55
18.16666667

13.44

16.43333333

15.46666667
15.8

6m MA Forc.

MAD =

Alpha =
MAD =

ADV
5.3
6.75
7.25
7.3
7.2
6.5
6.75
6.89
5.8
5.5
6.5
6.25
7
6.9
6.8
6.8
7.1
7
6.8
6.5
8.1
7.7
7.3
7.5
8.1
8.3
8.7

DEMAND
14.4
15.3
16.5
16.1
16
15.5
15.2
13.9
13.3
13.12
13.8
14.8
15.3
16.3
17.5
17.4
17.1
16.8
16.5
16
15.2
15.3
15.9
16.2
17.5
18.4
19.4

0.956444444

MAD =

0.3
0.946974412

Exp. Forecast
14.4

Abs. Dev.
0

14.4

0.9

14.67

1.83

15.219

0.881

15.4833

0.5167

15.63831

0.13831

15.596817

0.396817

15.4777719

1.5777719

15.00444033

1.70444033

14.49310823

1.373108231

14.08117576

0.281175762

13.99682303

0.803176967

14.23777612

1.062223877

14.55644329

1.743556714

15.0795103

2.4204897

15.80565721

1.59434279

16.28396005

0.816039953

16.52877203

0.271227967

16.61014042

0.110140423

16.5770983

0.577098296

16.40396881

1.203968807

16.04277817

0.742778165

15.81994472

0.080055284

15.8439613

0.356038699

15.95077291

1.549227089

16.41554104

1.984458963

17.01087873

2.389121274

1.319135802

9.2
8.4
8.8
9.5
9.3
9.1

19.1
18.7
18.2
18.4
17.5
17.1
Forecast

Monthly Avg.

Seas index

15.8

0.976849626

15.2

0.93975407

14.21

0.878546404

14.85

0.918114996

15.5

0.958301848

15.73333333

0.972727897

16.66666667

1.030432095

17.8

1.100501477

17.53333333

1.084014563

17.26666667

1.06752765

16.83333333

1.040736415

16.7

1.032492959

16.17444444
12

17.72761511

1.372384892

18.13933058

0.560669424

18.3075314

0.107531403

18.27527198

0.124728018

18.31269039

0.812690388

18.06888327
17.77821829

0.968883271

Abs. Dev.

0.433333333
1.866666667
2.233333333
1.88
0.703333333
0.663333333
1.28
2.263333333
3.063333333
2.263333333
1.25
0.4
0.233333333
0.933333333
1.683333333
1.2
0.25
0.25
1.65
2.383333333
2.983333333
1.983333333
0.95
0.016666667
0.15
1.2
1.45

Alpha
0.1
0.2

Forecast
17.20939938
17.73848457

MAD
1.225115131
1.043501296

0.3

17.77821829

0.946974412

0.4

17.67187061

0.872781154

0.5

17.53410079

0.812573061

0.6

17.40670466

0.749029652

0.7

17.30053463

0.690568869

0.8

17.21533139

0.643282505

0.9

17.14885419

0.604965484

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations

0.94405524
0.891240296
0.887731919
0.422078124
33

ANOVA
df
Regression
Residual
Total

1
31
32

SS
MS
F
45.25579318 45.25579318 254.0320383
5.522648238 0.178149943
50.77844141

Coefficients
Standard Error
t Stat
14.08367946
0.150353411 93.67050208
0.122986175
0.007716352 15.93838255
Demand = 14.08 + .1229(Period)

Intercept
PERIOD

RESIDUAL OUTPUT

MAD =

Observation
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22

Predicted Des. Demand
14.20666564
14.32965181
14.45263799
14.57562417
14.69861034
14.82159652
14.94458269
15.06756887
15.19055504
15.31354122
15.43652739
15.55951357
15.68249974
15.80548592
15.92847209
16.05145827
16.17444444
16.29743062
16.4204168
16.54340297
16.66638915
16.78937532

Residuals
0.597063174
0.518488186
0.540526804
0.276574821
0.289291847
0.071703814
-0.222932658
-0.838152551
-1.037916153
-0.379782518
-0.405730535
-0.115527905
0.046462121
0.013120748
-0.026630646
-1.34022E-05
-0.156123981
-0.155014778
-0.439678272
-0.164218723
-0.491944701
0.625754869

0.312893159
Abs. Res.
0.597063174
0.518488186
0.540526804
0.276574821
0.289291847
0.071703814
0.222932658
0.838152551
1.037916153
0.379782518
0.405730535
0.115527905
0.046462121
0.013120748
0.026630646
1.34022E-05
0.156123981
0.155014778
0.439678272
0.164218723
0.491944701
0.625754869

P-value
1.33707E-39
1.7435E-16

23
24
25
26
27
28
29
30
31
32
33

16.9123615
17.03534767
17.15833385
17.28132002
17.4043062
17.52729237
17.65027855
17.77326472
17.8962509
18.01923707
18.14222325

0.405730535
-0.130444446
0.832308808
0.575266644
0.224020893
0.092397107
-0.133167866
-0.285647562
-0.075306122
-0.104504304
0.05402675

0.405730535
0.130444446
0.832308808
0.575266644
0.224020893
0.092397107
0.133167866
0.285647562
0.075306122
0.104504304
0.05402675

Significance F
1.7435E-16

Lower 95%
Upper 95% Lower 95.0% Upper 95.0%
13.77703166 14.39032727 13.77703166 14.39032727
0.107248571 0.13872378 0.107248571
0.13872378
Ses. For.
Mar Demand =
18.26520943 0.878546404 16.04683405
Apr Demand =
18.3881956 0.918114996 16.88247813
May Demand = 18.51118178 0.958301848
17.7392997

SUMMARY OUTPUT
Regression Statistics
Multiple R
R Square
Adjusted R Square
Standard Error
Observations

0.830062691
0.689004071
0.678971945
0.713733675
33

ANOVA
df
Regression
Residual
Total

1
3...


Anonymous
Really helped me to better understand my coursework. Super recommended.

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