Materi Regresi Terapan Tugas Regresi Dummy

(1)

Tugas Regresi Dummy

Tugas kelompok 1

Kelompok 1 : Nisa dan Jeky

 Modelkan data berikut dengan model regresi yang sesuai  Uji Asumsi dan atasi masalahnya

 Interpretasikan

 Tulis dalam makalah dan dipresentasikan

Obs liquidity(Y) Age(X1) Region(X2)

1 0.825 4 Athens

2 1.039 21 Athens

3 0.854 2 Larissa

4 1.065 15 Athens

5 1.442 43 Ahaia

6 1.620 59 Athens

7 1.477 30 Kalamata

8 1.108 20 Piraeus

9 3.224 12 Salonica

10 1.160 8 Komotini

11 1.711 12 Athens

12 0.936 24 Creta

13 1.076 11 Creta

14 1.829 30 Ioannina

15 1.270 2 Athens

16 1.505 48 Athens

17 1.035 2 Serres

18 1.075 13 Athens

19 1.033 4 Kilkis

20 1.468 38 Salonica

21 2.521 40 Athens

22 1.218 26 Athens

23 0.803 18 athens

24 1.039 30 Fthiotis

25 2.768 9 salonica

26 0.593 38 athens

27 3.364 9 athens

28 1.101 1 athenw

29 1.155 2 creta

30 1.242 2 athens

31 1.383 6 salonica

32 1.478 9 athens

33 1.350 32 athenw

34 3.386 2 athens

(2)

36 2.333 3 salonica

37 0.654 1 kastoria

38 2.031 19 athens

39 1.485 13 athens

40 1.142 1 salonica

41 2.067 25 athens

42 1.408 12 salonica

43 1.280 8 athens

44 3.291 2 athens

45 2.308 8 kastoria

46 3.180 11 athens

47 0.941 29 viotia

48 0.603 18 lamia

49 4.388 25 athens

50 1.281 78 Trikala

51 2.768 21 serres

52 1.362 16 athens

54 0.511 10 athens

56 1.458 8 athens

57 4.812 6 athens

58 1.194 18 athens

59 1.089 2 athens

60 1.501 14 piraeus

61 3.484 8 athens

62 2.082 3 athens

63 1.187 2 athens

64 3.167 18 attica

65 2.498 58 athens

66 1.549 7 athens

67 3.176 22 athens

68 3.837 9 athens

69 4.174 2 athens

70 1.244 13 athens

71 1.283 2 piraeus

72 0.205 3 naousa

73 1.137 1 athens

74 1.225 19 athens

77 1.477 19 larissa

78 2.635 29 athens

79 1.742 28 athens

80 1.164 10 athens

81 1.461 11 athens

82 1.097 11 athens

83 1.292 24 athens

84 2.194 9 athens

(3)

86 0.661 4 Korinth

87 5.200 2 athens

88 1.575 4 athens

89 1.144 19 athens

90 0.939 13 attica

91 1.992 58 attica

92 1.558 26 attica

93 1.286 26 attica

94 1.286 4 serres

95 0.509 13 lamia

96 5.200 11 creta

97 5.200 2 kozani

98 1.646 30 volos

99 2.940 6 thassos

100 1.495 5 lamia

101 4.222 19 athens

102 1.056 14 attica

103 3.459 15 salonica

104 1.561 19 attica

105 1.925 29 larissa

106 0.933 10 larissa

107 1.196 13 athens

108 1.176 13 creta

110 0.488 18 attica

111 1.566 1 komotini

112 1.967 6 attica

113 0.961 7 attica

114 0.865 1 piraeus

115 1.192 49 piraeus

116 1.259 6 attica

118 1.231 35 attica

119 1.151 1 attica

121 2.066 1 amficlia

122 1.954 19 athens

123 1.577 5 kilkis

124 2.645 40 athens

125 1.183 19 salonica

126 1.254 2 athens

127 1.552 2 athens

128 0.562 10 attica

129 1.090 22 attica

130 4.628 3 viotia

131 1.830 14 koropi

132 2.440 2 halkida

133 4.650 2 trikala

134 2.875 7 viotia

(4)

137 2.283 1 attica

138 1.599 2 attica

139 5.200 1 attica

140 1.099 9 athens

141 1.237 16 athens

143 1.338 11 patras

Tugas Kelompok 2

Kelompok 2 : Meilinda, Ika, Danang

 Modelkan data berikut dengan model regresi yang sesuai  Uji Asumsi dan atasi masalahnya

 Interpretasikan

 Tulis dalam makalah dan dipresentasikan

Liquidity

Proxy (Y)

Jumlah Karyawan(X1) Sector(X2)

1.994 8 food

1.038 48 food

1.095 30 food

0.941 42 food

1.363 35 food

0.838 17 food

2.051 20 food

1.492 60 food

1.376 47 food

5.200 40 food

4.763 50 drinks

0.688 45 drinks

0.924 35 drinks

2.711 60 drinks

1.367 5 textiles

0.955 5 textiles

1.407 7 textiles

1.367 18 textiles

1.901 30 textiles

1.580 50 textiles

2.130 7 textiles

1.887 38 textiles

1.675 8 textiles

1.673 70 textiles

1.087 85 textiles

2.064 110 textiles

1.209 13 textiles

2.021 6 garments

(5)

1.614 30 garments

0.975 50 garments

1.122 25 garments

2.125 48 garments

1.450 25 garments

1.046 25 garments

1.518 25 garments

5.200 10 garments

1.347 13 garments

1.361 65 garments

1.598 65 garments

1.010 10 garments

0.929 53 garments

1.677 30 garments

4.070 3 leather

4.739 70 leather

1.027 65 leather

1.790 12 leather

1.815 14 wood

5.200 20 wood

1.523 34 wood

3.533 30 furniture

1.301 45 furniture

0.924 43 furniture

1.233 50 furniture

1.047 13 paper

1.477 17 paper

0.825 32 paper

1.116 72 paper

0.756 40 paper

1.285 23 printing

0.369 9 printing

1.248 19 printing

1.173 33 printing

1.056 28 printing

1.426 14 printing

1.028 45 printing

1.810 35 printing

1.208 65 printing

1.028 17 printing

3.836 8 plastics

0.980 19 plastics

2.022 9 plastics

1.267 10 plastics

5.200 45 plastics

5.187 20 plastics

1.278 45 plastics

1.205 37 plastics

1.208 30 plastics

(6)

1.069 52 plastics

1.138 26 chemicals

1.435 4 chemicals

1.127 25 chemicals

1.438 22 chemicals

1.681 27 chemicals

1.757 35 chemicals

1.472 26 chemicals

1.254 10 chemicals

1.400 67 chemicals

1.131 40 chemicals

1.653 25 chemicals

4.901 30 petroleum

4.814 49 petroleum

1.631 14 metalicnon

1.820 20

non metalic

1.088 28 metalicnon

0.956 25 metalicnon

2.195 40 metalicnon

2.361 65

non metalic

1.075 70 metalicnon

0.851 37 metalicnon

0.992 50 metalicnon

1.056 37

non metalic

0.745 34 metalicnon

2.470 70 metalicnon

5.200 30 metalbasic

2.180 30

basic metal

1.837 24 metal prod

1.989 21 metal prod

1.421 25 metal prod

1.135 17 metal prod

0.956 40 metal prod

1.213 50 metal prod

0.979 35 metal prod

1.724 25 metal prod

2.921 43 metal prod

(7)

1.318 5 machinery

1.038 18 machinery

1.474 4 machinery

1.135 30 machinery

2.350 40 machinery

1.731 105 machinery

1.507 47 machinery

2.000 20 machinery

0.980 40 machinery

0.957 5 machinery

2.216 13 machinery

2.856 35 machinery

0.672 20 machinery

1.800 50 machinery

1.287 45 machinery

1.449 3 machinery

1.020 30 machinery

1.338 27 machinery

1.288 11 sundry

0.746 35 sundry

1.810 35 sundry

5.200 25 sundry

0.173 15 sundry

2.155 20 sundry

1.197 5 sundry