OKTARINA SAFAR NIDAG141200521. Data advertising (X) dan sales(Y) dengan menghilangkan data ke-5 dan data ke-2YtXtYt-Yt-1Xt-Xt-1Yt-1Xt-1

1215

2118931215

15.527-5.592118

23.54982215.527

24.5211-2823.549

21.322-3.2124.521

23.5282.2621.322

28364.5823.528

2440-442836

15.53-8.5-372440

17.3211.81815.53

25.3298817.321

2562-0.33325.329

36.56511.532562

36.5460-1936.565

29.644-6.9-236.546

30.5330.9-1129.644

2862-2.52930.533

2622-2-402862

21.512-4.5-102622

19.724-1.81221.512

193-0.7-2119.724

165-32193

20.7144.79165

26.5365.82220.714

30.6404.1426.536

32.3491.7930.640

29.57-2.8-4232.349

28.352-1.24529.57

31.36531328.352

32.2170.9-4831.365

26.45-5.8-1232.217

23.417-31226.45

16.41-7-1623.417

Regression Analysis: Yt versus XtThe regression equation isYt = 18.8 + 0.202 Xt

Predictor Coef SE Coef T PConstant 18.754 1.522 12.32 0.000Xt 0.20167 0.04385 4.60 0.000

S = 4.82712 R-Sq = 39.8% R-Sq(adj) = 37.9%

Analysis of Variance

Source DF SS MS F PRegression 1 492.73 492.73 21.15 0.000Residual Error 32 745.64 23.30Total 33 1238.37

Durbin-Watson statistic = 1.31993H0:=0H1:0N=34, p=1DL=1.3929 DU=1.5136

DWDU dan 4-DW>DU sehingga Terima H0, Galat tidak berkorelasi serial

3. yt= 0+ 1yt-1+ 2(xt-xt-1) + t

Regression Analysis: Yt versus Yt-1, Xt-Xt-1 The regression equation isYt = 8.36 + 0.670 Yt-1 + 0.0423 Xt-Xt-1

Predictor Coef SE Coef T PConstant 8.356 3.440 2.43 0.021Yt-1 0.6702 0.1350 4.97 0.000Xt-Xt-1 0.04234 0.03717 1.14 0.264

S = 4.43023 R-Sq = 45.2% R-Sq(adj) = 41.5%

Analysis of Variance

Source DF SS MS F PRegression 2 485.30 242.65 12.36 0.000Residual Error 30 588.81 19.63Total 32 1074.11

Source DF Seq SSYt-1 1 459.83Xt-Xt-1 1 25.47

Durbin-Watson statistic = 1.77651H0:=0H1:0N=33, p=2DL=1.3212DU=1.5770

DW>DU dan 4-DW>DU sehingga Terima H0, Galat tidak berkorelasi serial

4. yt= 0+ 1yt-1+ 2xt+ t Regression Analysis: Yt versus Yt-1, Xt

The regression equation isYt = 8.26 + 0.499 Yt-1 + 0.147 Xt

Predictor Coef SE Coef T PConstant 8.256 2.663 3.10 0.004Yt-1 0.4989 0.1077 4.63 0.000Xt 0.14697 0.03374 4.36 0.000

S = 3.54161 R-Sq = 65.0% R-Sq(adj) = 62.6%

Analysis of Variance

Source DF SS MS F PRegression 2 697.82 348.91 27.82 0.000Residual Error 30 376.29 12.54Total 32 1074.11

Source DF Seq SSYt-1 1 459.83Xt 1 237.99

Durbin-Watson statistic = 2.28975H0:=0H1:0N=33, p=2DL=1.3212DU=1.5770

DW>DU dan 4-DW>DU sehingga Terima H0, Galat tidak berkorelasi serial

5. Model manakah yang paling baik dari keempat model diatas?

ModelR-sqs

yt= 0+ 0xt+ t39.8%4.82712

yt-yt-1= 0+ 1(xt-xt-1) + t9.1%4.77235

yt= 0+ 1yt-1+ 2(xt-xt-1) + t45.2%4.43023

yt= 0+ 1yt-1+ 2xt+ t65.0%3.54161

RESI1RESI2RESI3RESI4

-9.77942

-1.384428.6348814.4749864.111512

-8.69941-6.27126-7.31066-7.20135

-5.136076.3487773.8248970.309411

1.5105852.7332611.5804741.433171

-1.89108-3.42974-3.51757-2.4127

-0.901081.6318120.6153030.502012

1.9855913.7964323.5562292.728692

-2.82107-4.43281-3.29021-4.10426

-3.85942-6.15753-7.37355-5.17091

-5.689410.419536-2.20574-1.77556

0.6972557.2964325.0113374.15068

-6.25773-2.69581-1.7086-4.99044

4.63726811.1348811.262666.218338

8.4689281.1240544.4870893.273232

1.97226-6.92667-3.13269-3.33284

5.090591.4825362.7726012.626247

-3.25773-4.62505-2.02417-4.58476

2.8089190.5455370.5727510.541118

0.325582-3.98515-3.8571-1.49141

-3.89441-2.77433-3.57277-2.80991

-0.359420.559433-1.669230.474396

-3.76275-3.29743-5.17393-2.4703

-0.877753.9287431.240232.40374

0.4855914.1487773.3399682.625636

3.7789253.6671914.3150533.244106

3.6639290.9287433.055621.575895

9.333914-0.119081.2756634.10029

-0.94107-4.40809-1.73143-2.3162

-0.562731.9579843.42767-0.62806

10.017253.9870544.8998825.829547

6.637246-5.14977-3.027521.344112

1.217251-3.97433-3.15665-0.5258

-2.55609-6.07902-6.96059-3.67763

ModelMADMAPEMSE

yt= 0+ 0xt+ t3.49947716.510%21.93047

yt-yt-1= 0+ 1(xt-xt-1) + t3.898549*21.39501

yt= 0+ 1yt-1+ 2(xt-xt-1) + t3.61893415.737%17.84271

yt= 0+ 1yt-1+ 2xt+ t2.87831112.301%11.40276

Melalui R-sq yang paling besar dan s yang paling kecil maka model yt= 0+ 1yt-1+ 2xt+ t merupakan model yang paling baik. Selain itu, melalui nilai MAD, MAPE, dan MSE model yt= 0+ 1yt-1+ 2xt+ t juga mempunyai nilai error yang paling kecil.