Statistik Regresi dan Korelasi
STATISTIK
NILAI
Mata Kuliah : Statistik
Hari/Tanggal :
Dosen Pengampu
Nama :
Budimanysah
NIM :
G1F113022
Judul Tugas :
Regresi dan Korelasi
Tugas Ke :
02
Tabel 1. Data untuk menentukan persamaan regresi
linear sederhana.
No
X
Y
x.y
x2
y2
ŷ
1
26000
264000
6864000000
676000000
69696000000
219570,1562
2
26962,5
429000
11566912500
726976406,3
184041000000
219536,4446
3
28000
3000
84000000
784000000
9000000
219500,1061
4
29000
6000
174000000
841000000
36000000
219465,081
5
29935
9560
286178600
896104225
91393600
219432,3326
6
6200000
7000
43400000000
38440000000000
49000000
3325,37673
7
19000
11500
218500000
361000000
132250000
219815,3317
8
123111
7268,181818
894793131,8
15156318321
52826466,94
216168,8367
9
18168,82857
5850
106287647,1
330106331,7
34222500
219844,4436
10
22525,91429
2130000
47980197429
507416814,4
4536900000000
219691,8363
11
2394,71694
40150
96147885,16
5734669,224
1612022500
220396,9329
12
39,69410966
114500
4544975,556
1575,622342
13110250000
220479,4178
13
0,247605871
10161,5
2516,047058
0,061308667
103256082,3
220480,7994
14
64472,46449
12195
786241704,5
4156698677
148718025
218222,6555
15
35000
25000
875000000
1225000000
625000000
219254,9306
∑
6624610,366
3075184,682
113336806389
38465666357021
4806640939174
3075184,682
Rerata
441640,6911
205012,3121
7555787093
2564377757135
320442729278
205012,3121
Sumber : Data Olahan
CARA 1 (menggunakan persamaan dengan cara eleminasi)
Ŷ = a + bX..... (1)
(1) x n ∑Y = a.n + b. ∑X
(2) x ∑X ∑XY = a.∑X + b. ∑X2
(1) 3075184,682 = a.15+ b . 6624610,366
x1324943,363 =
(2) 115025517009 = a . 6624610,366 + b . 38465666357021 x3 =
(1) 4074380064109 =
a. 19873831,1+ b. 8777374572210
(2) 340010419166 = a. 19873831,1 + b. 115396999071062 _
3734369644943 =
b. -106619906570791
b =
3734369644943
-106619906570791
b =
-0,0350
a = ӯ– b x̄
=
205012,3121 - (-0,0350 . 441640,6911)
a =
220480,808
CARA 2 (menggunakan rumus yang telah ditetapkan)
b = n ∑XY - ∑X ∑Y
n ∑X2 – (∑X)
2
b = (15 . 113336806389) – (6624610,366. 3075184,682)
(15 . 38465666357021) – (6624610,366) 2
b
= -18671848224715
533099532853957
b = -0,0350
∑Y = an + b∑X
3075184,682 = 15 a + 6624610,366
b
3075184,682 = 15 a + 6624610,366
(-0,0350)
3075184,682 = 15 a + (-6624610,3310)
15 a = 3075184,682
+ (-6624610,3310)
15 a = - 3549425,6492
a = -
3549425,6492
-15
a =
220480,808
ŷ = a + bx
= 220480,808+ (-0,0350)
X
Gambar 1. Grafik Regresi Linear
Berdasarkan data regresi
dari perhitungan di atas setiap peningkatan 1 ppm bahan organik , maka akan mengakibatkan pengurangan sebesar
-0,350 ppm terhadap BOD (Biological Oxygen Demand) di perairan. Peningkatan
atau penurunan jumlah bahan organik diperairan berpengaruh terhadap nilai BOD
diperaian tersebut.
NILAI
|
Dosen Pengampu
|
No
X
Y
x.y
x2
y2
ŷ
1
26000
264000
6864000000
676000000
69696000000
219570,1562
2
26962,5
429000
11566912500
726976406,3
184041000000
219536,4446
3
28000
3000
84000000
784000000
9000000
219500,1061
4
29000
6000
174000000
841000000
36000000
219465,081
5
29935
9560
286178600
896104225
91393600
219432,3326
6
6200000
7000
43400000000
38440000000000
49000000
3325,37673
7
19000
11500
218500000
361000000
132250000
219815,3317
8
123111
7268,181818
894793131,8
15156318321
52826466,94
216168,8367
9
18168,82857
5850
106287647,1
330106331,7
34222500
219844,4436
10
22525,91429
2130000
47980197429
507416814,4
4536900000000
219691,8363
11
2394,71694
40150
96147885,16
5734669,224
1612022500
220396,9329
12
39,69410966
114500
4544975,556
1575,622342
13110250000
220479,4178
13
0,247605871
10161,5
2516,047058
0,061308667
103256082,3
220480,7994
14
64472,46449
12195
786241704,5
4156698677
148718025
218222,6555
15
35000
25000
875000000
1225000000
625000000
219254,9306
∑
6624610,366
3075184,682
113336806389
38465666357021
4806640939174
3075184,682
Rerata
441640,6911
205012,3121
7555787093
2564377757135
320442729278
205012,3121
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