Kelompok 1
Fiqih
Hendry
Imam
Tsabitah
Bint
Kelompok 2
Kris
M. Zainul
Afrian
Muslikah
Ria
Kelompok 3
Novita
Ermita
Bikriyah
M. Yudithia
Lina
Kelompok 4
Fadh
Widyaningsih
Sumaya
Yeni
No. Absen 36
Kelompok 5
Dian
M. Luqman
Presiani
No. Absen 29
No. Absen 37
Kelompok 6
Natasya
Shahril
Ayu
Agung
Izzatul
Kelompok 7
Suci
Kelompok 8
Yashinta
Shulby
Rizky
Anggraeni
Tugas Kelompok 1
Modelkan Data berikut menggunakan Regresi Data Panel
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
Variabel Y
Belgium
Denmar
k
German
y
Greece
Spain
1977
0.01352
1
0.00511
-
0.00327
0.01244
5
0.00482
3
1978
0.01839
2
0.00191
4
0.00215
8
0.01467
0.00573
8
1979
0.02167
6
0.00230
5
0.00368
7
0.01585
1
0.01001
9
1980
0.02368
5
0.00207
4
0.00078
8
0.02657
5
0.01308
7
1981
0.01334
0.00169
2
0.00043
7
0.01150
6
0.00882
3
1982
0.01612
7
0.00235
4
0.00113
9
0.00925
2
0.00950
9
1983
0.01489
2
0.00105
6
0.00262
5
0.01027
1
0.00987
5
1984
0.00469
5
-0.00027
0.00086
4
0.01167
0.01067
1985
0.01212
0.00186
2
0.00079
0.01091
2
0.01131
1
1986
0.00603
7
0.00193
5
0.00114
4
0.00971
6
0.01428
1
1987
0.01559
6
0.00080
5
0.00163
8
0.01210
6
0.01491
7
1988
0.03173
5
0.00452
7
0.00085
0.01384
5
0.01948
5
1989
0.04203
2
0.01009
2
0.00604
2
0.01107
0.02121
5
1990
0.03853
0.00846
7
0.00168
2
0.01193
6
0.02714
7
1991
0.04370
2
0.01155
9
0.00243
4
0.01254
8
0.02259
1992
0.04694
4
0.00689
5
0.00130
3
0.01144
2
0.02196
5
1993
0.04702
9
0.01234
6
0.00099
4
0.01046
4
0.01937
7
1994
0.03416
2
0.03288
3
0.00092
2
0.00977
5
0.01819
8
1995
0.03643
3
0.02296
8
0.00487
3
0.00895
4
0.01077
2
Variabel X
Belgium
Denmar
k
German
y
Greece
Spain
1977
0.6
1.6
2.8
2.9
2.8
1978
2.8
1.5
3
7.2
1.5
1979
2.3
3.5
4.2
3.3
0
1980
4.4
-0.4
1
0.7
1.3
1981
-1.2
-0.9
0.1
-1.6
-0.2
1982
1.4
3
-0.9
-1.1
1.6
1983
0
2.5
1.8
-1.1
2.2
1984
2.5
4.4
2.8
2
1.5
1985
1
4.3
2
2.5
2.6
1986
1.5
3.6
2.3
0.5
3.2
1987
2.4
0.3
1.5
-2.3
5.6
1988
4.7
1.2
3.7
4.3
5.2
1989
3.6
0.3
3.6
3.8
4.7
1990
2.7
1.2
5.7
0
3.7
1991
2
1.4
5
3.1
2.3
1992
1.6
1.3
2.2
0.7
0.7
1993
-1.5
0.8
-1.1
-1.6
-1.2
1994
3
5.8
2.3
2
2.3
1995
2.5
3.7
1.7
2.1
2.7
1996
1
2.8
0.8
2.4
2.3
Tugas Kelompok 2
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
Tahun
Y
X1
X2
X3
X4
1972
274246
39534
5.91
8.48
38156
1973
291872
45236
10.19
12.82
46544
1974
289086
52183
10.06
11.3
51671
1975
291249
64207
5.87
10.93
57943
1976
290001
74675
5.06
14.09
64538
1977
284092
85270
7.19
6.39
74074
1978
305163
98328
11.69
11.91
85090
1979
322410
116686
14.5
16.49
97339
1980
327732
135454
17.75
13.45
114149
1981
326175
150391
13.63
15.35
137837
1982
325339
164694
9.25
9.96
154865
1983
333192
181047
9.94
9.04
175385
1984
345191
193935
8.69
9.33
199158
1985
356994
211950
7.94
11.49
225144
1986
372426
235071
6.31
10.94
258208
1987
385240
258136
7.38
8.38
304728
1988
405462
291627
9.25
12.91
357765
1990
438935
347247
7.53
13.5
476690
1991
445552
368232
4.22
10.45
503826
1992
461964
387312
3.37
6.44
517579
1993
475850
412398
3.31
4.95
543008
1994
481924
433829
6.44
6
565728
1995
494574
454171
5.54
6.31
621847
1996
505392
485418
5.5
6.26
681903
1997
523699
516793
5.69
7.13
719869
Tugas Kelompok 3
Modelkan data berikut dengan model regresi yang sesuai
JK = 1, Laki – laki
Ras =1, Putih
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
WAGE
Jenis
Kelamin
RAS
115
1
0
200
1
1
233
1
0
260
1
1
265
1
0
289
1
0
300
1
1
310
0
0
318
1
1
325
1
1
325
1
1
340
1
1
345
1
1
346
1
1
350
1
0
350
1
0
350
0
1
350
1
1
357
1
1
357
1
1
360
0
0
369
0
0
370
1
1
375
1
1
375
0
0
375
1
1
377
0
1
380
1
1
390
1
1
390
1
1
400
1
0
400
1
0
400
1
0
400
1
1
400
1
1
400
0
1
402
1
0
403
1
1
409
0
1
Tugas Kelompok 4
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
liquidity
structure
asset
inventory turnover
net profit/sales
0.825
0.511
66.1
0.028
1.039
0.697
66.5
0.017
0.854
0.730
52.0
-0.121
1.065
0.351
68.4
-0.102
1.442
0.725
54.6
0.022
1.620
0.390
29.5
0.003
1.477
0.206
63.0
0.000
1.108
0.101
105.1
-0.019
3.224
0.649
40.0
-0.028
1.160
0.418
72.7
0.036
1.711
0.378
75.0
0.084
0.936
0.634
36.6
0.026
1.076
0.358
49.5
0.018
1.829
0.541
30.8
0.167
1.270
0.211
86.5
-0.021
1.505
0.174
77.7
0.056
1.035
0.387
48.6
0.009
1.075
0.237
90.2
0.047
1.033
0.436
99.6
-0.020
1.468
0.272
104.3
0.020
2.521
0.013
45.9
0.009
1.218
0.145
58.6
0.036
0.803
0.386
41.0
0.087
1.039
0.282
78.2
0.022
2.768
0.609
48.5
0.226
0.593
0.248
37.9
0.180
3.364
0.187
57.0
0.078
1.101
0.071
54.0
0.008
1.242
0.116
77.3
0.054
1.383
0.219
32.6
0.093
1.478
0.236
81.9
0.032
1.350
0.245
59.7
-0.078
3.386
0.581
96.8
0.002
4.016
0.048
59.7
0.165
2.333
0.109
81.5
0.196
0.654
0.349
62.4
0.031
2.031
0.171
93.2
0.217
1.485
0.225
71.5
0.055
1.142
0.022
53.1
0.013
2.067
0.096
60.8
-0.111
1.408
0.863
79.9
-0.023
1.280
0.033
11.5
0.009
3.291
0.226
73.3
0.134
2.308
0.257
39.7
0.121
3.180
0.232
50.7
-0.023
0.941
0.360
100.2
-0.027
0.603
0.093
48.1
0.020
4.388
0.522
85.2
-0.009
1.281
0.425
92.6
0.073
2.768
0.561
88.8
0.008
1.362
0.675
46.8
0.025
3.191
0.410
81.9
0.084
0.511
0.206
46.3
-0.200
2.706
0.402
48.9
0.036
1.458
0.301
41.5
0.060
4.812
0.735
96.0
0.011
1.194
0.216
25.0
0.189
1.089
0.630
26.9
0.016
1.501
0.135
16.8
0.044
3.484
0.098
15.0
0.055
2.082
0.134
30.3
0.167
1.187
0.014
1.2
0.115
3.167
0.536
6.8
0.113
2.498
0.015
7.0
0.080
1.549
0.434
18.6
0.048
3.176
0.978
15.1
0.112
3.837
0.116
15.2
0.116
4.174
0.611
32.7
0.096
1.244
0.150
28.1
0.041
1.283
0.012
45.0
0.013
0.205
0.561
6.5
0.015
1.137
0.201
12.9
-0.024
1.225
0.296
7.7
0.083
1.168
0.437
27.8
0.058
1.439
0.509
5.8
0.011
1.477
0.359
38.6
0.086
2.635
0.493
4.8
0.074
1.164
0.469
27.3
0.146
1.461
0.064
33.9
0.034
1.097
0.328
23.7
0.220
1.292
0.027
37.4
0.018
2.194
0.148
12.0
0.022
0.774
0.275
0.2
-0.005
0.661
0.327
11.6
0.001
5.200
0.586
3.0
0.035
1.575
0.393
20.6
0.087
1.144
0.307
13.2
0.021
0.939
0.321
27.1
0.006
1.992
0.418
23.8
0.139
1.558
0.671
31.1
0.176
1.286
0.406
31.6
0.136
1.286
0.824
30.0
0.088
0.509
0.204
15.1
0.050
5.200
0.465
135.5
-0.200
5.200
0.403
142.4
0.154
1.646
0.516
124.8
0.076
2.940
0.399
168.2
0.048
1.495
0.329
198.8
0.279
4.222
0.836
183.5
0.042
1.056
0.443
330.0
0.051
3.459
0.463
251.2
0.001
1.561
0.326
212.6
0.070
1.925
0.371
192.7
0.069
0.933
0.096
190.8
0.001
1.196
0.378
215.4
0.040
1.176
0.489
302.1
0.054
1.264
0.226
143.9
0.052
0.488
0.437
330.0
-0.200
1.566
0.743
150.8
0.001
1.967
0.596
330.0
0.001
0.961
0.419
165.6
0.004
0.865
0.107
120.5
0.017
1.192
0.323
119.5
-0.108
1.259
0.152
149.7
0.033
1.163
0.615
127.1
-0.033
1.231
0.629
155.1
0.034
1.151
0.352
243.7
0.061
4.478
0.159
127.6
0.084
2.066
0.735
193.5
-0.093
1.954
0.309
189.7
0.047
1.577
0.516
140.2
0.050
2.645
0.413
138.7
0.008
1.183
0.573
228.4
-0.034
1.254
0.248
198.5
0.052
1.552
0.110
137.5
0.164
0.562
0.284
120.0
-0.211
4.628
0.432
122.1
0.020
1.830
0.495
161.6
0.004
2.440
0.131
330.0
0.218
4.650
0.166
118.7
0.067
2.875
0.295
175.0
0.123
1.130
0.306
214.0
-0.200
0.721
0.655
195.9
-0.204
2.283
0.330
151.9
-0.082
1.599
0.120
203.7
0.005
5.200
0.465
199.4
0.028
1.099
0.582
156.9
0.040
1.237
0.440
265.6
0.013
0.800
0.297
330.0
0.078
1.338
0.646
114.8
0.014
Tugas Kelompok 5
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
Obs
liquidit
y (Y)
Sector
(X1)
Region(X2)
1
0.825
food
Athens
2
1.039
food
Athens
3
0.854
food
Larissa
4
1.065
food
Athens
5
1.442
food
Ahaia
6
1.620
food
Athens
7
1.477
food
Kalamata
8
1.108
food
Piraeus
9
3.224
food
Salonica
10
1.160
food
Komotini
11
1.711
drinks
Athens
12
0.936
drinks
Creta
13
1.076
drinks
Creta
14
1.829
drinks
Ioannina
15
1.270
textiles
Athens
16
1.505
textiles
Athens
17
1.035
textiles
Serres
18
1.075
textiles
Athens
19
1.033
textiles
Kilkis
20
1.468
textiles
Salonica
21
2.521
textiles
Athens
22
1.218
textiles
Athens
23
0.803
textiles
athens
24
1.039
textiles
Fthiotis
25
2.768
textiles
salonica
27
3.364
textiles
athens
28
1.101
garments
athenw
29
1.155
garments
creta
30
1.242
garments
athens
31
1.383
garments
salonica
32
1.478
garments
athens
33
1.350
garments
athenw
34
3.386
garments
athens
35
4.016
garments
,athens
36
2.333
garments
salonica
37
0.654
garments
kastoria
38
2.031
garments
athens
39
1.485
garments
athens
40
1.142
garments
salonica
41
2.067
garments
athens
42
1.408
garments
salonica
43
1.280
garments
athens
44
3.291
leather
athens
45
2.308
leather
kastoria
46
3.180
leather
athens
47
0.941
leather
viotia
48
0.603
wood
lamia
49
4.388
wood
athens
50
1.281
wood
Trikala
51
2.768
furniture
serres
52
1.362
furniture
athens
53
3.191
furniture
salonica
54
0.511
furniture
athens
55
2.706
paper
salonica
56
1.458
paper
athens
57
4.812
paper
athens
58
1.194
paper
athens
59
1.089
paper
athens
60
1.501
printing
piraeus
61
3.484
printing
athens
62
2.082
printing
athens
63
1.187
printing
athens
64
3.167
printing
attica
65
2.498
printing
athens
66
1.549
printing
athens
67
3.176
printing
athens
68
3.837
printing
athens
69
4.174
printing
athens
70
1.244
plastics
athens
71
1.283
plastics
piraeus
72
0.205
plastics
naousa
73
1.137
plastics
athens
74
1.225
plastics
athens
75
1.168
plastics
salonica
77
1.477
plastics
larissa
78
2.635
plastics
athens
79
1.742
plastics
athens
80
1.164
plastics
athens
81
1.461
chemicals
athens
82
1.097
chemicals
athens
83
1.292
chemicals
athens
84
2.194
chemicals
athens
85
0.774
chemicals
attica
86
0.661
chemicals
Korinth
87
5.200
chemicals
athens
88
1.575
chemicals
athens
89
1.144
chemicals
athens
90
0.939
chemicals
attica
91
1.992
chemicals
attica
92
1.558
petroleum
attica
93
1.286
petroleum
attica
94
1.286
non
metalic
serres
95
0.509
metalic
non
lamia
96
5.200
metalic
non
creta
97
5.200
metalic
non
kozani
98
1.646
non
metalic
volos
99
2.940
metalic
non
thassos
100
1.495
metalic
non
lamia
101
4.222
metalic
non
athens
102
1.056
non
metalic
attica
103
3.459
metalic
non
salonica
104
1.561
metalic
non
attica
105
1.925
metalic
non
larissa
106
0.933
basic
metal
larissa
107
1.196
basic
metal
athens
108
1.176
metal
prod
creta
109
1.264
metal
prod
salonica
110
0.488
metal
prod
attica
112
1.967
metal
prod
attica
113
0.961
metal
prod
attica
114
0.865
metal
prod
piraeus
115
1.192
metal
prod
piraeus
116
1.259
metal
prod
attica
117
1.163
machiner
y
salonica
118
1.231
machiner
y
attica
119
1.151
machiner
y
attica
120
4.478
machiner
y
salonica
121
2.066
machiner
y
amficlia
122
1.954
machiner
y
athens
123
1.577
machiner
y
kilkis
124
2.645
machiner
y
athens
125
1.183
machiner
y
salonica
126
1.254
machiner
y
athens
127
1.552
machiner
y
athens
128
0.562
machiner
y
attica
129
1.090
machiner
y
attica
130
4.628
machiner
y
viotia
131
1.830
machiner
y
koropi
132
2.440
machiner
y
halkida
133
4.650
machiner
y
trikala
134
2.875
machiner
y
viotia
135
1.130
machiner
y
piraeus
136
0.721
sundry
salonica
137
2.283
sundry
attica
138
1.599
sundry
attica
139
5.200
sundry
attica
140
1.099
sundry
athens
142
0.800
sundry
salonica
143
1.338
sundry
patras
Tugas Kelompok 6
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
Obs
liquidit
y (Y)
Sector
(X1)
No empl.
(X2)
1
0.825
food
15
2
1.039
food
48
3
0.854
food
30
4
1.065
food
32
5
1.442
food
38
6
1.620
food
24
7
1.477
food
20
8
1.108
food
60
9
3.224
food
60
10
1.160
food
22
11
1.711
drinks
40
12
0.936
drinks
30
13
1.076
drinks
50
14
1.829
drinks
60
15
1.270
textiles
3
16
1.505
textiles
10
17
1.035
textiles
5
18
1.075
textiles
38
19
1.033
textiles
25
20
1.468
textiles
75
21
2.521
textiles
10
22
1.218
textiles
37
23
0.803
textiles
8
24
1.039
textiles
70
25
2.768
textiles
75
26
0.593
textiles
140
27
3.364
textiles
15
28
1.101
garments
18
29
1.155
garments
33
30
1.242
garments
40
31
1.383
garments
55
32
1.478
garments
29
33
1.350
garments
45
34
3.386
garments
40
35
4.016
garments
14
36
2.333
garments
40
37
0.654
garments
8
39
1.485
garments
65
40
1.142
garments
80
41
2.067
garments
85
42
1.408
garments
50
43
1.280
garments
33
44
3.291
leather
15
45
2.308
leather
60
46
3.180
leather
65
47
0.941
leather
11
48
0.603
wood
14
49
4.388
wood
20
50
1.281
wood
46
51
2.768
furniture
36
52
1.362
furniture
45
53
3.191
furniture
42
54
0.511
furniture
45
55
2.706
paper
18
56
1.458
paper
18
57
4.812
paper
35
58
1.194
paper
60
59
1.089
paper
40
60
1.501
printing
18
61
3.484
printing
9
62
2.082
printing
19
63
1.187
printing
35
64
3.167
printing
28
65
2.498
printing
12
66
1.549
printing
47
67
3.176
printing
32
68
3.837
printing
70
69
4.174
printing
12
70
1.244
plastics
10
71
1.283
plastics
11
72
0.205
plastics
9
73
1.137
plastics
12
74
1.225
plastics
32
75
1.168
plastics
18
76
1.439
plastics
32
77
1.477
plastics
40
78
2.635
plastics
40
79
1.742
plastics
55
80
1.164
plastics
65
81
1.461
chemicals
26
82
1.097
chemicals
4
83
1.292
chemicals
38
84
2.194
chemicals
24
85
0.774
chemicals
28
86
0.661
chemicals
35
87
5.200
chemicals
25
89
1.144
chemicals
60
90
0.939
chemicals
70
91
1.992
chemicals
30
92
1.558
petroleum
20
93
1.286
petroleum
49
94
1.286
metalic
non
14
95
0.509
metalic
non
25
96
5.200
non
metalic
30
97
5.200
metalic
non
25
98
1.646
metalic
non
40
99
2.940
metalic
non
70
100
1.495
non
metalic
40
101
4.222
metalic
non
32
102
1.056
metalic
non
50
103
3.459
metalic
non
25
104
1.561
non
metalic
38
105
1.925
metalic
non
70
106
0.933
basic
metal
30
107
1.196
basic
metal
35
108
1.176
metal
prod
25
109
1.264
metal
prod
27
110
0.488
metal
prod
25
111
1.566
metal
prod
16
112
1.967
metal
prod
27
113
0.961
metal
prod
35
114
0.865
metal
prod
35
115
1.192
metal
prod
20
116
1.259
metal
prod
30
117
1.163
machiner
y
8
y
119
1.151
machiner
y
13
120
4.478
machiner
y
20
121
2.066
machiner
y
17
122
1.954
machiner
y
50
123
1.577
machiner
y
50
124
2.645
machiner
y
50
125
1.183
machiner
y
30
126
1.254
machiner
y
35
127
1.552
machiner
y
10
128
0.562
machiner
y
16
129
1.090
machiner
y
30
130
4.628
machiner
y
43
131
1.830
machiner
y
55
132
2.440
machiner
y
50
133
4.650
machiner
y
6
134
2.875
machiner
y
24
135
1.130
machiner
y
30
136
0.721
sundry
15
137
2.283
sundry
34
138
1.599
sundry
50
139
5.200
sundry
21
140
1.099
sundry
12
141
1.237
sundry
20
142
0.800
sundry
20
143
1.338
sundry
36
Tugas Kelompok 7
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Gunakan Excell
(Y)
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
35
4.016
2
,athens
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
50
1.281
78
Trikala
51
2.768
21
serres
52
1.362
16
athens
53
3.191
12
salonica
54
0.511
10
athens
55
2.706
11
salonica
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
75
1.168
13
salonica
76
1.439
21
salonica
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
85
0.774
19
attica
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
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
109
1.264
7
salonica
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
117
1.163
6
salonica
118
1.231
35
attica
119
1.151
1
attica
120
4.478
7
salonica
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
135
1.130
40
piraeus
136
0.721
9
salonica
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
142
0.800
2
salonica
143
1.338
11
patras
Tugas Kelompok 8
Modelkan Data Berikut menggunakan regresi berganda yang sesuai
Uji Asumsi dan jika ada yang tdak memenuhi, atasi masalah tersebut
Tulis dalam bentuk Makalah dan Presentasikan
Obs
liquidity
(Y)
Net Profit / Total
Assets (X1)
Region(X2)
1
0.825
0.039
Athens
2
1.039
0.008
Athens
3
0.854
-0.078
Larissa
4
1.065
-0.141
Athens
5
1.442
0.036
Ahaia
6
1.620
0.009
Athens
7
1.477
0.000
Kalamata
8
1.108
-0.029
Piraeus
9
3.224
-0.033
Salonica
10
1.160
0.031
Komotini
11
1.711
0.108
Athens
12
0.936
0.039
Creta
13
1.076
0.031
Creta
14
1.829
0.291
Ioannina
15
1.270
-0.039
Athens
16
1.505
0.064
Athens
17
1.035
0.017
Serres
18
1.075
0.052
Athens
19
1.033
-0.021
Kilkis
20
1.468
0.031
Salonica
21
2.521
0.013
Athens
22
1.218
0.065
Athens
23
0.803
0.188
athens
24
1.039
0.034
Fthiotis
25
2.768
0.209
salonica
26
0.593
0.119
athens
27
3.364
0.250
athens
28
1.101
0.011
athenw
29
1.155
0.092
creta
30
1.242
0.139
athens
31
1.383
0.191
salonica
32
1.478
0.042
athens
33
1.350
-0.093
athenw
34
3.386
0.001
athens
35
4.016
0.166
,athens
36
2.333
0.224
salonica
37
0.654
0.038
kastoria
38
2.031
0.287
athens
39
1.485
0.094
athens
40
1.142
0.021
salonica
41
2.067
-0.188
athens
42
1.408
-0.025
salonica
43
1.280
0.064
athens
44
3.291
0.165
athens
45
2.308
0.307
kastoria
46
3.180
-0.024
athens
48
0.603
0.017
lamia
49
4.388
-0.009
athens
50
1.281
0.057
Trikala
51
2.768
0.015
serres
52
1.362
0.043
athens
53
3.191
0.085
salonica
54
0.511
-0.250
athens
55
2.706
0.027
salonica
56
1.458
0.074
athens
57
4.812
0.010
athens
58
1.194
0.213
athens
59
1.089
0.033
athens
60
1.501
0.080
piraeus
61
3.484
0.056
athens
62
2.082
0.163
athens
63
1.187
0.207
athens
64
3.167
0.266
attica
65
2.498
0.201
athens
66
1.549
0.123
athens
67
3.176
0.182
athens
68
3.837
0.169
athens
69
4.174
0.129
athens
70
1.244
0.111
athens
71
1.283
0.055
piraeus
72
0.205
0.019
naousa
73
1.137
-0.027
athens
74
1.225
0.186
athens
75
1.168
0.097
salonica
76
1.439
0.019
salonica
77
1.477
0.143
larissa
78
2.635
0.168
athens
79
1.742
0.233
athens
80
1.164
0.165
athens
81
1.461
0.079
athens
82
1.097
0.241
athens
83
1.292
0.034
athens
84
2.194
0.045
athens
85
0.774
-0.010
attica
86
0.661
0.003
Korinth
87
5.200
0.036
athens
88
1.575
0.060
athens
89
1.144
0.071
athens
90
0.939
0.011
attica
91
1.992
0.198
attica
92
1.558
0.360
attica
93
1.286
0.205
attica
94
1.286
0.230
serres
95
0.509
0.101
lamia
96
5.200
-0.169
creta
98
1.646
0.055
volos
99
2.940
0.047
thassos
100
1.495
0.158
lamia
101
4.222
0.033
athens
102
1.056
0.027
attica
103
3.459
0.001
salonica
104
1.561
0.064
attica
105
1.925
0.084
larissa
106
0.933
0.001
larissa
107
1.196
0.043
athens
108
1.176
0.033
creta
109
1.264
0.036
salonica
110
0.488
-0.078
attica
111
1.566
0.001
komotini
112
1.967
0.000
attica
113
0.961
0.001
attica
114
0.865
0.016
piraeus
115
1.192
-0.121
piraeus
116
1.259
0.032
attica
117
1.163
-0.064
salonica
118
1.231
0.033
attica
119
1.151
0.045
attica
120
4.478
0.133
salonica
121
2.066
-0.074
amficlia
122
1.954
0.029
athens
123
1.577
0.099
kilkis
124
2.645
0.009
athens
125
1.183
-0.009
salonica
126
1.254
0.029
athens
127
1.552
0.164
athens
128
0.562
-0.075
attica
129
1.090
-0.071
attica
130
4.628
0.009
viotia
131
1.830
0.002
koropi
132
2.440
0.046
halkida
133
4.650
0.060
trikala
134
2.875
0.067
viotia
135
1.130
-0.145
piraeus
136
0.721
-0.182
salonica
137
2.283
-0.065
attica
138
1.599
0.005
attica
139
5.200
0.006
attica
140
1.099
0.029
athens
141
1.237
0.010
athens
142
0.800
0.028
salonica
