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Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Nov 2012 05:47:31 -0500

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/07/t1352285304wumilcemp4iykh9.htm/, Retrieved Thu, 02 May 2024 14:55:10 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186533, Retrieved Thu, 02 May 2024 14:55:10 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

152

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [Multiple linear r...] [2012-11-07 10:47:31] [447cab31e466d1c88f957d20e303ed40] [Current]

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Dataseries X:

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Histogram

Boxplots

94	77	76	86
115	84	84	116
121	95	95	131
99	82	82	105
112	89	89	131
108	87	87	114
69	72	72	40
88	79	79	112
115	89	89	121
116	90	90	123
110	89	89	112
98	83	83	91
102	91	91	89
108	89	89	108
121	99	99	111
102	87	87	92
108	94	93	115
114	97	97	116
71	81	80	53
93	84	84	109
115	93	93	105
118	97	97	120
105	92	92	102
95	84	84	69
98	90	90	92
103	88	88	95
112	97	97	113
105	93	93	106
98	91	91	107
104	94	94	110
75	84	83	49
82	81	80	95
116	98	98	115
115	102	102	118
92	94	94	103
90	87	87	67
91	93	92	85
100	93	93	96
100	100	100	114
94	97	97	107
84	92	92	100
101	98	98	112
75	89	89	57
77	82	82	90
106	102	102	122
110	105	105	119
92	94	94	95
88	97	97	77
88	91	91	89
99	93	93	100
117	108	108	122
101	98	98	105
84	92	92	97
111	107	107	121
67	90	89	48
86	88	88	103
112	105	105	120
106	104	104	114
104	100	100	100
98	101	101	86
91	94	94	79
104	96	96	96
111	108	108	103
102	103	103	118
95	96	96	103
109	109	109	124
69	88	87	42
87	90	90	108
118	109	109	124
102	102	102	109
109	104	104	107
101	101	101	87
99	97	97	86
106	98	98	99
120	111	112	122
95	94	94	103
101	105	105	117
117	110	110	124
67	90	89	44
92	93	93	111
120	109	109	124
116	111	111	116
112	107	107	113
102	101	101	84
99	107	107	96
109	105	105	107
119	114	114	123
103	103	103	103
105	108	108	108
122	114	114	120
76	100	100	49
103	100	100	111
121	109	109	113
125	120	120	126
110	112	112	110
100	100	100	74
97	111	112	107
109	113	113	115
109	114	114	94
117	117	117	126
107	109	109	109
119	117	117	117
85	104	104	57
85	95	95	97
130	116	117	123
128	117	117	116
103	99	99	96
104	98	98	78
96	90	90	86
106	93	93	101
115	100	100	117
102	94	94	100
91	91	91	99
109	103	103	114
76	91	91	53
91	90	89	94
121	108	108	118
117	108	108	118
106	100	100	87
101	95	95	69
97	94	94	74
105	100	100	84
118	114	114	121
105	105	106	98
97	101	101	89
116	115	115	116
73	94	94	49
91	96	95	99
124	113	114	115
110	109	109	109
103	107	107	91
109	103	103	59
92	105	105	82
108	108	109	105
116	128	128	122
98	108	109	92
105	116	116	114
106	112	112	104
74	94	94	43
101	106	106	100
123	118	119	118
109	111	111	104
100	111	111	103
106	104	104	80
105	105	105	87
111	103	103	98
118	114	114	146
93	102	102	108
105	106	106	108
114	110	110	112
75	97	97	49
94	98	98	104

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
meubelvervaardiging[t] = + 22.324717718473 -3.16507805240116winningrondstoffen[t] + 3.56780470551802industrie[t] + 0.399823372588894bouw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
meubelvervaardiging[t] =  +  22.324717718473 -3.16507805240116winningrondstoffen[t] +  3.56780470551802industrie[t] +  0.399823372588894bouw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]meubelvervaardiging[t] =  +  22.324717718473 -3.16507805240116winningrondstoffen[t] +  3.56780470551802industrie[t] +  0.399823372588894bouw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
meubelvervaardiging[t] = + 22.324717718473 -3.16507805240116winningrondstoffen[t] + 3.56780470551802industrie[t] + 0.399823372588894bouw[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	22.324717718473	6.38	3.4992	0.000617	0.000308
winningrondstoffen	-3.16507805240116	1.845771	-1.7148	0.08848	0.04424
industrie	3.56780470551802	1.822639	1.9575	0.052171	0.026085
bouw	0.399823372588894	0.03112	12.848	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 22.324717718473 & 6.38 & 3.4992 & 0.000617 & 0.000308 \tabularnewline
winningrondstoffen & -3.16507805240116 & 1.845771 & -1.7148 & 0.08848 & 0.04424 \tabularnewline
industrie & 3.56780470551802 & 1.822639 & 1.9575 & 0.052171 & 0.026085 \tabularnewline
bouw & 0.399823372588894 & 0.03112 & 12.848 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]22.324717718473[/C][C]6.38[/C][C]3.4992[/C][C]0.000617[/C][C]0.000308[/C][/ROW]
[ROW][C]winningrondstoffen[/C][C]-3.16507805240116[/C][C]1.845771[/C][C]-1.7148[/C][C]0.08848[/C][C]0.04424[/C][/ROW]
[ROW][C]industrie[/C][C]3.56780470551802[/C][C]1.822639[/C][C]1.9575[/C][C]0.052171[/C][C]0.026085[/C][/ROW]
[ROW][C]bouw[/C][C]0.399823372588894[/C][C]0.03112[/C][C]12.848[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	22.324717718473	6.38	3.4992	0.000617	0.000308
winningrondstoffen	-3.16507805240116	1.845771	-1.7148	0.08848	0.04424
industrie	3.56780470551802	1.822639	1.9575	0.052171	0.026085
bouw	0.399823372588894	0.03112	12.848	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.854075009067884
R-squared	0.729444121114306
Adjusted R-squared	0.723959880326083
F-TEST (value)	133.007311181643
F-TEST (DF numerator)	3
F-TEST (DF denominator)	148
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.10953283396925
Sum Squared Residuals	7480.72765335844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.854075009067884 \tabularnewline
R-squared & 0.729444121114306 \tabularnewline
Adjusted R-squared & 0.723959880326083 \tabularnewline
F-TEST (value) & 133.007311181643 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.10953283396925 \tabularnewline
Sum Squared Residuals & 7480.72765335844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.854075009067884[/C][/ROW]
[ROW][C]R-squared[/C][C]0.729444121114306[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.723959880326083[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]133.007311181643[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.10953283396925[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]7480.72765335844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R	0.854075009067884
R-squared	0.729444121114306
Adjusted R-squared	0.723959880326083
F-TEST (value)	133.007311181643
F-TEST (DF numerator)	3
F-TEST (DF denominator)	148
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.10953283396925
Sum Squared Residuals	7480.72765335844

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	94	84.151675345598	9.848324654402
2	115	102.533267800601	12.4667321993986
3	121	112.96061157372	8.03938842627963
4	99	97.3297573958899	1.67024260411013
5	112	110.544251655019	1.45574834498084
6	108	102.941801014774	5.05819898522576
7	69	67.3139716464431	1.68602835355689
8	88	98.9203410446615	-10.9203410446615
9	115	106.54601792913	8.45398207086977
10	116	107.748391327425	8.25160867257511
11	110	102.94760757583	7.05239242416982
12	98	92.1349568327622	5.86504316723778
13	102	94.5571233125194	7.44287668748063
14	108	101.348314085475	6.65168591452539
15	121	106.57505073441	14.42494926559
16	102	94.1456868178186	7.85431318218142
17	108	102.592906253663	5.40709374633682
18	114	107.768714291121	6.2312857088793
19	71	72.5684106626325	-1.56841066263251
20	93	99.7345041924792	-6.73450419247917
21	115	101.759750580175	13.2402494198246
22	118	109.368007781476	8.63199221852373
23	105	100.157553809292	4.84244619070815
24	95	83.7415692889234	11.2584307110766
25	98	95.3538667771692	2.64613322283082
26	103	95.7478835887021	7.25211641129787
27	112	106.569244173354	5.43075582664598
28	105	102.159573952764	2.8404260472357
29	98	101.753944019119	-3.75394401911945
30	104	104.161594096237	-0.161594096236736
31	75	72.1772971316275	2.82270286837248
32	82	89.360992311366	-7.36099231136605
33	116	107.771617571649	8.22838242835133
34	115	110.581994301883	4.41800569811717
35	92	101.362830488114	-9.36283048811448
36	90	84.1501025030962	5.84989749690376
37	91	90.1954784228795	0.804521577120496
38	100	98.1613402268754	1.83865977312464
39	100	108.177247505294	-8.17724750529351
40	94	104.170303937821	-10.1703039378207
41	84	99.3579070641141	-15.3579070641141
42	101	106.572147453882	-5.57214745388199
43	75	80.957322083441	-5.95732208344103
44	77	91.3324068070565	-14.3324068070565
45	106	112.181287792238	-6.1812877922384
46	110	112.189997633822	-2.18999763382232
47	92	98.1642435074033	-6.16424350740333
48	88	92.1756027601539	-4.17560276015385
49	88	94.5571233125194	-6.55712331251937
50	99	99.7606337172309	-0.760633717230933
51	117	114.59764771094	2.4023522890604
52	101	103.77338384576	-2.77338384575974
53	84	98.1584369463474	-14.1584369463474
54	111	113.795097685234	-2.79509768523384
55	67	74.1938336777398	-7.19383367773984
56	86	98.9464705694133	-12.9464705694133
57	112	112.589821006411	-0.589821006411213
58	106	109.788154117761	-3.78815411776098
59	104	102.579720289049	1.420279710951
60	98	97.3849197259214	0.615080274078642
61	91	91.767069545981	-0.767069545981035
62	104	99.369520186226	4.63047981377404
63	111	107.001003631751	3.99899636824938
64	102	110.984720955	-8.98472095499969
65	95	102.168283794348	-7.16828379434821
66	109	115.800021109234	-6.80002110923425
67	69	70.9894401359727	-1.98944013597274
68	87	101.751040738591	-14.7510407385915
69	118	115.800021109234	2.19997889076575
70	102	106.983583948583	-4.98358394858278
71	109	106.989390509639	2.01060949036127
72	101	97.7847430985102	3.21525690148975
73	99	95.7740131134539	3.22598688654611
74	106	101.374443610226	4.62555638977363
75	120	119.373632375808	0.626367624191774
76	95	101.362830488114	-6.36283048811448
77	101	111.390350888645	-10.3903508886445
78	117	116.202747762351	0.797252237648882
79	67	72.5945401873843	-5.59454018738426
80	92	104.158690815709	-12.1586908157088
81	120	115.800021109234	4.19997889076575
82	116	113.406887434757	2.59311256524316
83	112	110.596510704523	1.40348929547731
84	102	96.5852729807436	5.41472701925643
85	99	103.799513370512	-4.7995133705115
86	109	107.392117162756	1.60788283724441
87	119	117.41383100223	1.5861689977703
88	103	104.987370366166	-1.98737036616629
89	105	109.000120494695	-4.00012049469509
90	122	116.214360884463	5.78563911553698
91	76	82.1887282870154	-6.18872828701542
92	103	106.977777387527	-3.97777738752683
93	121	111.401964010756	9.59803598924358
94	125	121.029661038698	3.97033896130241
95	110	111.41067385234	-1.41067385234034
96	100	92.1843126017378	7.81568739826223
97	97	113.376281786975	-16.3762817869748
98	109	113.812517368402	-4.81251736840168
99	109	105.818953197152	3.18104680284821
100	117	119.821481079347	-2.82148107934699
101	107	109.802670520401	-2.80267052040085
102	119	116.223070726047	2.77692927395305
103	85	86.9982218801941	-1.99822188019405
104	85	99.366616905698	-14.366616905698
105	130	121.787089013981	8.21291098601854
106	128	115.823247353458	12.1767526465419
107	103	100.577700145577	2.42229985442344
108	104	92.9781527858596	11.0218472141404
109	96	92.9549265416358	3.04507345836418
110	106	100.16045708982	5.83954291018017
111	115	109.37671762306	5.6232823769398
112	102	100.163360370348	1.8366396296522
113	91	98.5553570384083	-7.5553570384083
114	109	109.385427464644	-0.385427464644114
115	76	80.1634818993192	-4.16348189931919
116	91	92.5857088168289	-1.58570881682895
117	121	112.998354220584	8.00164577941598
118	117	112.998354220584	4.00164577941597
119	106	97.3820164453934	8.61798355460661
120	101	88.171562473209	12.828437526791
121	97	89.7679526830366	7.23204731696344
122	105	96.1825463276267	8.8174536723733
123	118	116.614184257052	1.38581574294808
124	105	107.361511514974	-2.36151151497358
125	97	98.584389843688	-1.58438984368804
126	116	115.017794047224	0.982205952775678
127	73	79.7723683683142	-6.77236836831422
128	91	97.0011855984746	-6.00118559847462
129	124	117.38032207392	6.61967792608029
130	110	109.802670520401	0.197329479599152
131	103	101.800396507567	1.19960349243297
132	109	87.395141972255	21.604858027745
133	92	97.3965328480333	-5.39653284803325
134	108	111.368455082446	-3.36845508244643
135	116	122.652180773277	-6.65218077327696
136	98	106.170751238791	-8.17075123879082
137	105	114.620873955163	-9.6208739551634
138	106	109.011733616807	-3.01173361680698
139	74	77.3734281327809	-3.37342813278086
140	101	104.99608020775	-3.9960802077502
141	123	120.593425457271	2.40657454272926
142	109	108.60900696369	0.390993036309884
143	100	108.209183591101	-8.20918359110122
144	106	96.1941594497386	9.8058405502614
145	105	99.3956497109777	5.60435028902228
146	111	102.988253503222	8.01174649677818
147	118	126.609768571774	-8.60976857177426
148	93	106.583760575994	-13.5837605759939
149	105	108.194667188461	-3.19466718846135
150	114	111.404867291284	2.59513270871561
151	75	80.9805483276648	-5.98054832766482
152	94	103.373560473171	-9.37356047317084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 94 & 84.151675345598 & 9.848324654402 \tabularnewline
2 & 115 & 102.533267800601 & 12.4667321993986 \tabularnewline
3 & 121 & 112.96061157372 & 8.03938842627963 \tabularnewline
4 & 99 & 97.3297573958899 & 1.67024260411013 \tabularnewline
5 & 112 & 110.544251655019 & 1.45574834498084 \tabularnewline
6 & 108 & 102.941801014774 & 5.05819898522576 \tabularnewline
7 & 69 & 67.3139716464431 & 1.68602835355689 \tabularnewline
8 & 88 & 98.9203410446615 & -10.9203410446615 \tabularnewline
9 & 115 & 106.54601792913 & 8.45398207086977 \tabularnewline
10 & 116 & 107.748391327425 & 8.25160867257511 \tabularnewline
11 & 110 & 102.94760757583 & 7.05239242416982 \tabularnewline
12 & 98 & 92.1349568327622 & 5.86504316723778 \tabularnewline
13 & 102 & 94.5571233125194 & 7.44287668748063 \tabularnewline
14 & 108 & 101.348314085475 & 6.65168591452539 \tabularnewline
15 & 121 & 106.57505073441 & 14.42494926559 \tabularnewline
16 & 102 & 94.1456868178186 & 7.85431318218142 \tabularnewline
17 & 108 & 102.592906253663 & 5.40709374633682 \tabularnewline
18 & 114 & 107.768714291121 & 6.2312857088793 \tabularnewline
19 & 71 & 72.5684106626325 & -1.56841066263251 \tabularnewline
20 & 93 & 99.7345041924792 & -6.73450419247917 \tabularnewline
21 & 115 & 101.759750580175 & 13.2402494198246 \tabularnewline
22 & 118 & 109.368007781476 & 8.63199221852373 \tabularnewline
23 & 105 & 100.157553809292 & 4.84244619070815 \tabularnewline
24 & 95 & 83.7415692889234 & 11.2584307110766 \tabularnewline
25 & 98 & 95.3538667771692 & 2.64613322283082 \tabularnewline
26 & 103 & 95.7478835887021 & 7.25211641129787 \tabularnewline
27 & 112 & 106.569244173354 & 5.43075582664598 \tabularnewline
28 & 105 & 102.159573952764 & 2.8404260472357 \tabularnewline
29 & 98 & 101.753944019119 & -3.75394401911945 \tabularnewline
30 & 104 & 104.161594096237 & -0.161594096236736 \tabularnewline
31 & 75 & 72.1772971316275 & 2.82270286837248 \tabularnewline
32 & 82 & 89.360992311366 & -7.36099231136605 \tabularnewline
33 & 116 & 107.771617571649 & 8.22838242835133 \tabularnewline
34 & 115 & 110.581994301883 & 4.41800569811717 \tabularnewline
35 & 92 & 101.362830488114 & -9.36283048811448 \tabularnewline
36 & 90 & 84.1501025030962 & 5.84989749690376 \tabularnewline
37 & 91 & 90.1954784228795 & 0.804521577120496 \tabularnewline
38 & 100 & 98.1613402268754 & 1.83865977312464 \tabularnewline
39 & 100 & 108.177247505294 & -8.17724750529351 \tabularnewline
40 & 94 & 104.170303937821 & -10.1703039378207 \tabularnewline
41 & 84 & 99.3579070641141 & -15.3579070641141 \tabularnewline
42 & 101 & 106.572147453882 & -5.57214745388199 \tabularnewline
43 & 75 & 80.957322083441 & -5.95732208344103 \tabularnewline
44 & 77 & 91.3324068070565 & -14.3324068070565 \tabularnewline
45 & 106 & 112.181287792238 & -6.1812877922384 \tabularnewline
46 & 110 & 112.189997633822 & -2.18999763382232 \tabularnewline
47 & 92 & 98.1642435074033 & -6.16424350740333 \tabularnewline
48 & 88 & 92.1756027601539 & -4.17560276015385 \tabularnewline
49 & 88 & 94.5571233125194 & -6.55712331251937 \tabularnewline
50 & 99 & 99.7606337172309 & -0.760633717230933 \tabularnewline
51 & 117 & 114.59764771094 & 2.4023522890604 \tabularnewline
52 & 101 & 103.77338384576 & -2.77338384575974 \tabularnewline
53 & 84 & 98.1584369463474 & -14.1584369463474 \tabularnewline
54 & 111 & 113.795097685234 & -2.79509768523384 \tabularnewline
55 & 67 & 74.1938336777398 & -7.19383367773984 \tabularnewline
56 & 86 & 98.9464705694133 & -12.9464705694133 \tabularnewline
57 & 112 & 112.589821006411 & -0.589821006411213 \tabularnewline
58 & 106 & 109.788154117761 & -3.78815411776098 \tabularnewline
59 & 104 & 102.579720289049 & 1.420279710951 \tabularnewline
60 & 98 & 97.3849197259214 & 0.615080274078642 \tabularnewline
61 & 91 & 91.767069545981 & -0.767069545981035 \tabularnewline
62 & 104 & 99.369520186226 & 4.63047981377404 \tabularnewline
63 & 111 & 107.001003631751 & 3.99899636824938 \tabularnewline
64 & 102 & 110.984720955 & -8.98472095499969 \tabularnewline
65 & 95 & 102.168283794348 & -7.16828379434821 \tabularnewline
66 & 109 & 115.800021109234 & -6.80002110923425 \tabularnewline
67 & 69 & 70.9894401359727 & -1.98944013597274 \tabularnewline
68 & 87 & 101.751040738591 & -14.7510407385915 \tabularnewline
69 & 118 & 115.800021109234 & 2.19997889076575 \tabularnewline
70 & 102 & 106.983583948583 & -4.98358394858278 \tabularnewline
71 & 109 & 106.989390509639 & 2.01060949036127 \tabularnewline
72 & 101 & 97.7847430985102 & 3.21525690148975 \tabularnewline
73 & 99 & 95.7740131134539 & 3.22598688654611 \tabularnewline
74 & 106 & 101.374443610226 & 4.62555638977363 \tabularnewline
75 & 120 & 119.373632375808 & 0.626367624191774 \tabularnewline
76 & 95 & 101.362830488114 & -6.36283048811448 \tabularnewline
77 & 101 & 111.390350888645 & -10.3903508886445 \tabularnewline
78 & 117 & 116.202747762351 & 0.797252237648882 \tabularnewline
79 & 67 & 72.5945401873843 & -5.59454018738426 \tabularnewline
80 & 92 & 104.158690815709 & -12.1586908157088 \tabularnewline
81 & 120 & 115.800021109234 & 4.19997889076575 \tabularnewline
82 & 116 & 113.406887434757 & 2.59311256524316 \tabularnewline
83 & 112 & 110.596510704523 & 1.40348929547731 \tabularnewline
84 & 102 & 96.5852729807436 & 5.41472701925643 \tabularnewline
85 & 99 & 103.799513370512 & -4.7995133705115 \tabularnewline
86 & 109 & 107.392117162756 & 1.60788283724441 \tabularnewline
87 & 119 & 117.41383100223 & 1.5861689977703 \tabularnewline
88 & 103 & 104.987370366166 & -1.98737036616629 \tabularnewline
89 & 105 & 109.000120494695 & -4.00012049469509 \tabularnewline
90 & 122 & 116.214360884463 & 5.78563911553698 \tabularnewline
91 & 76 & 82.1887282870154 & -6.18872828701542 \tabularnewline
92 & 103 & 106.977777387527 & -3.97777738752683 \tabularnewline
93 & 121 & 111.401964010756 & 9.59803598924358 \tabularnewline
94 & 125 & 121.029661038698 & 3.97033896130241 \tabularnewline
95 & 110 & 111.41067385234 & -1.41067385234034 \tabularnewline
96 & 100 & 92.1843126017378 & 7.81568739826223 \tabularnewline
97 & 97 & 113.376281786975 & -16.3762817869748 \tabularnewline
98 & 109 & 113.812517368402 & -4.81251736840168 \tabularnewline
99 & 109 & 105.818953197152 & 3.18104680284821 \tabularnewline
100 & 117 & 119.821481079347 & -2.82148107934699 \tabularnewline
101 & 107 & 109.802670520401 & -2.80267052040085 \tabularnewline
102 & 119 & 116.223070726047 & 2.77692927395305 \tabularnewline
103 & 85 & 86.9982218801941 & -1.99822188019405 \tabularnewline
104 & 85 & 99.366616905698 & -14.366616905698 \tabularnewline
105 & 130 & 121.787089013981 & 8.21291098601854 \tabularnewline
106 & 128 & 115.823247353458 & 12.1767526465419 \tabularnewline
107 & 103 & 100.577700145577 & 2.42229985442344 \tabularnewline
108 & 104 & 92.9781527858596 & 11.0218472141404 \tabularnewline
109 & 96 & 92.9549265416358 & 3.04507345836418 \tabularnewline
110 & 106 & 100.16045708982 & 5.83954291018017 \tabularnewline
111 & 115 & 109.37671762306 & 5.6232823769398 \tabularnewline
112 & 102 & 100.163360370348 & 1.8366396296522 \tabularnewline
113 & 91 & 98.5553570384083 & -7.5553570384083 \tabularnewline
114 & 109 & 109.385427464644 & -0.385427464644114 \tabularnewline
115 & 76 & 80.1634818993192 & -4.16348189931919 \tabularnewline
116 & 91 & 92.5857088168289 & -1.58570881682895 \tabularnewline
117 & 121 & 112.998354220584 & 8.00164577941598 \tabularnewline
118 & 117 & 112.998354220584 & 4.00164577941597 \tabularnewline
119 & 106 & 97.3820164453934 & 8.61798355460661 \tabularnewline
120 & 101 & 88.171562473209 & 12.828437526791 \tabularnewline
121 & 97 & 89.7679526830366 & 7.23204731696344 \tabularnewline
122 & 105 & 96.1825463276267 & 8.8174536723733 \tabularnewline
123 & 118 & 116.614184257052 & 1.38581574294808 \tabularnewline
124 & 105 & 107.361511514974 & -2.36151151497358 \tabularnewline
125 & 97 & 98.584389843688 & -1.58438984368804 \tabularnewline
126 & 116 & 115.017794047224 & 0.982205952775678 \tabularnewline
127 & 73 & 79.7723683683142 & -6.77236836831422 \tabularnewline
128 & 91 & 97.0011855984746 & -6.00118559847462 \tabularnewline
129 & 124 & 117.38032207392 & 6.61967792608029 \tabularnewline
130 & 110 & 109.802670520401 & 0.197329479599152 \tabularnewline
131 & 103 & 101.800396507567 & 1.19960349243297 \tabularnewline
132 & 109 & 87.395141972255 & 21.604858027745 \tabularnewline
133 & 92 & 97.3965328480333 & -5.39653284803325 \tabularnewline
134 & 108 & 111.368455082446 & -3.36845508244643 \tabularnewline
135 & 116 & 122.652180773277 & -6.65218077327696 \tabularnewline
136 & 98 & 106.170751238791 & -8.17075123879082 \tabularnewline
137 & 105 & 114.620873955163 & -9.6208739551634 \tabularnewline
138 & 106 & 109.011733616807 & -3.01173361680698 \tabularnewline
139 & 74 & 77.3734281327809 & -3.37342813278086 \tabularnewline
140 & 101 & 104.99608020775 & -3.9960802077502 \tabularnewline
141 & 123 & 120.593425457271 & 2.40657454272926 \tabularnewline
142 & 109 & 108.60900696369 & 0.390993036309884 \tabularnewline
143 & 100 & 108.209183591101 & -8.20918359110122 \tabularnewline
144 & 106 & 96.1941594497386 & 9.8058405502614 \tabularnewline
145 & 105 & 99.3956497109777 & 5.60435028902228 \tabularnewline
146 & 111 & 102.988253503222 & 8.01174649677818 \tabularnewline
147 & 118 & 126.609768571774 & -8.60976857177426 \tabularnewline
148 & 93 & 106.583760575994 & -13.5837605759939 \tabularnewline
149 & 105 & 108.194667188461 & -3.19466718846135 \tabularnewline
150 & 114 & 111.404867291284 & 2.59513270871561 \tabularnewline
151 & 75 & 80.9805483276648 & -5.98054832766482 \tabularnewline
152 & 94 & 103.373560473171 & -9.37356047317084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]94[/C][C]84.151675345598[/C][C]9.848324654402[/C][/ROW]
[ROW][C]2[/C][C]115[/C][C]102.533267800601[/C][C]12.4667321993986[/C][/ROW]
[ROW][C]3[/C][C]121[/C][C]112.96061157372[/C][C]8.03938842627963[/C][/ROW]
[ROW][C]4[/C][C]99[/C][C]97.3297573958899[/C][C]1.67024260411013[/C][/ROW]
[ROW][C]5[/C][C]112[/C][C]110.544251655019[/C][C]1.45574834498084[/C][/ROW]
[ROW][C]6[/C][C]108[/C][C]102.941801014774[/C][C]5.05819898522576[/C][/ROW]
[ROW][C]7[/C][C]69[/C][C]67.3139716464431[/C][C]1.68602835355689[/C][/ROW]
[ROW][C]8[/C][C]88[/C][C]98.9203410446615[/C][C]-10.9203410446615[/C][/ROW]
[ROW][C]9[/C][C]115[/C][C]106.54601792913[/C][C]8.45398207086977[/C][/ROW]
[ROW][C]10[/C][C]116[/C][C]107.748391327425[/C][C]8.25160867257511[/C][/ROW]
[ROW][C]11[/C][C]110[/C][C]102.94760757583[/C][C]7.05239242416982[/C][/ROW]
[ROW][C]12[/C][C]98[/C][C]92.1349568327622[/C][C]5.86504316723778[/C][/ROW]
[ROW][C]13[/C][C]102[/C][C]94.5571233125194[/C][C]7.44287668748063[/C][/ROW]
[ROW][C]14[/C][C]108[/C][C]101.348314085475[/C][C]6.65168591452539[/C][/ROW]
[ROW][C]15[/C][C]121[/C][C]106.57505073441[/C][C]14.42494926559[/C][/ROW]
[ROW][C]16[/C][C]102[/C][C]94.1456868178186[/C][C]7.85431318218142[/C][/ROW]
[ROW][C]17[/C][C]108[/C][C]102.592906253663[/C][C]5.40709374633682[/C][/ROW]
[ROW][C]18[/C][C]114[/C][C]107.768714291121[/C][C]6.2312857088793[/C][/ROW]
[ROW][C]19[/C][C]71[/C][C]72.5684106626325[/C][C]-1.56841066263251[/C][/ROW]
[ROW][C]20[/C][C]93[/C][C]99.7345041924792[/C][C]-6.73450419247917[/C][/ROW]
[ROW][C]21[/C][C]115[/C][C]101.759750580175[/C][C]13.2402494198246[/C][/ROW]
[ROW][C]22[/C][C]118[/C][C]109.368007781476[/C][C]8.63199221852373[/C][/ROW]
[ROW][C]23[/C][C]105[/C][C]100.157553809292[/C][C]4.84244619070815[/C][/ROW]
[ROW][C]24[/C][C]95[/C][C]83.7415692889234[/C][C]11.2584307110766[/C][/ROW]
[ROW][C]25[/C][C]98[/C][C]95.3538667771692[/C][C]2.64613322283082[/C][/ROW]
[ROW][C]26[/C][C]103[/C][C]95.7478835887021[/C][C]7.25211641129787[/C][/ROW]
[ROW][C]27[/C][C]112[/C][C]106.569244173354[/C][C]5.43075582664598[/C][/ROW]
[ROW][C]28[/C][C]105[/C][C]102.159573952764[/C][C]2.8404260472357[/C][/ROW]
[ROW][C]29[/C][C]98[/C][C]101.753944019119[/C][C]-3.75394401911945[/C][/ROW]
[ROW][C]30[/C][C]104[/C][C]104.161594096237[/C][C]-0.161594096236736[/C][/ROW]
[ROW][C]31[/C][C]75[/C][C]72.1772971316275[/C][C]2.82270286837248[/C][/ROW]
[ROW][C]32[/C][C]82[/C][C]89.360992311366[/C][C]-7.36099231136605[/C][/ROW]
[ROW][C]33[/C][C]116[/C][C]107.771617571649[/C][C]8.22838242835133[/C][/ROW]
[ROW][C]34[/C][C]115[/C][C]110.581994301883[/C][C]4.41800569811717[/C][/ROW]
[ROW][C]35[/C][C]92[/C][C]101.362830488114[/C][C]-9.36283048811448[/C][/ROW]
[ROW][C]36[/C][C]90[/C][C]84.1501025030962[/C][C]5.84989749690376[/C][/ROW]
[ROW][C]37[/C][C]91[/C][C]90.1954784228795[/C][C]0.804521577120496[/C][/ROW]
[ROW][C]38[/C][C]100[/C][C]98.1613402268754[/C][C]1.83865977312464[/C][/ROW]
[ROW][C]39[/C][C]100[/C][C]108.177247505294[/C][C]-8.17724750529351[/C][/ROW]
[ROW][C]40[/C][C]94[/C][C]104.170303937821[/C][C]-10.1703039378207[/C][/ROW]
[ROW][C]41[/C][C]84[/C][C]99.3579070641141[/C][C]-15.3579070641141[/C][/ROW]
[ROW][C]42[/C][C]101[/C][C]106.572147453882[/C][C]-5.57214745388199[/C][/ROW]
[ROW][C]43[/C][C]75[/C][C]80.957322083441[/C][C]-5.95732208344103[/C][/ROW]
[ROW][C]44[/C][C]77[/C][C]91.3324068070565[/C][C]-14.3324068070565[/C][/ROW]
[ROW][C]45[/C][C]106[/C][C]112.181287792238[/C][C]-6.1812877922384[/C][/ROW]
[ROW][C]46[/C][C]110[/C][C]112.189997633822[/C][C]-2.18999763382232[/C][/ROW]
[ROW][C]47[/C][C]92[/C][C]98.1642435074033[/C][C]-6.16424350740333[/C][/ROW]
[ROW][C]48[/C][C]88[/C][C]92.1756027601539[/C][C]-4.17560276015385[/C][/ROW]
[ROW][C]49[/C][C]88[/C][C]94.5571233125194[/C][C]-6.55712331251937[/C][/ROW]
[ROW][C]50[/C][C]99[/C][C]99.7606337172309[/C][C]-0.760633717230933[/C][/ROW]
[ROW][C]51[/C][C]117[/C][C]114.59764771094[/C][C]2.4023522890604[/C][/ROW]
[ROW][C]52[/C][C]101[/C][C]103.77338384576[/C][C]-2.77338384575974[/C][/ROW]
[ROW][C]53[/C][C]84[/C][C]98.1584369463474[/C][C]-14.1584369463474[/C][/ROW]
[ROW][C]54[/C][C]111[/C][C]113.795097685234[/C][C]-2.79509768523384[/C][/ROW]
[ROW][C]55[/C][C]67[/C][C]74.1938336777398[/C][C]-7.19383367773984[/C][/ROW]
[ROW][C]56[/C][C]86[/C][C]98.9464705694133[/C][C]-12.9464705694133[/C][/ROW]
[ROW][C]57[/C][C]112[/C][C]112.589821006411[/C][C]-0.589821006411213[/C][/ROW]
[ROW][C]58[/C][C]106[/C][C]109.788154117761[/C][C]-3.78815411776098[/C][/ROW]
[ROW][C]59[/C][C]104[/C][C]102.579720289049[/C][C]1.420279710951[/C][/ROW]
[ROW][C]60[/C][C]98[/C][C]97.3849197259214[/C][C]0.615080274078642[/C][/ROW]
[ROW][C]61[/C][C]91[/C][C]91.767069545981[/C][C]-0.767069545981035[/C][/ROW]
[ROW][C]62[/C][C]104[/C][C]99.369520186226[/C][C]4.63047981377404[/C][/ROW]
[ROW][C]63[/C][C]111[/C][C]107.001003631751[/C][C]3.99899636824938[/C][/ROW]
[ROW][C]64[/C][C]102[/C][C]110.984720955[/C][C]-8.98472095499969[/C][/ROW]
[ROW][C]65[/C][C]95[/C][C]102.168283794348[/C][C]-7.16828379434821[/C][/ROW]
[ROW][C]66[/C][C]109[/C][C]115.800021109234[/C][C]-6.80002110923425[/C][/ROW]
[ROW][C]67[/C][C]69[/C][C]70.9894401359727[/C][C]-1.98944013597274[/C][/ROW]
[ROW][C]68[/C][C]87[/C][C]101.751040738591[/C][C]-14.7510407385915[/C][/ROW]
[ROW][C]69[/C][C]118[/C][C]115.800021109234[/C][C]2.19997889076575[/C][/ROW]
[ROW][C]70[/C][C]102[/C][C]106.983583948583[/C][C]-4.98358394858278[/C][/ROW]
[ROW][C]71[/C][C]109[/C][C]106.989390509639[/C][C]2.01060949036127[/C][/ROW]
[ROW][C]72[/C][C]101[/C][C]97.7847430985102[/C][C]3.21525690148975[/C][/ROW]
[ROW][C]73[/C][C]99[/C][C]95.7740131134539[/C][C]3.22598688654611[/C][/ROW]
[ROW][C]74[/C][C]106[/C][C]101.374443610226[/C][C]4.62555638977363[/C][/ROW]
[ROW][C]75[/C][C]120[/C][C]119.373632375808[/C][C]0.626367624191774[/C][/ROW]
[ROW][C]76[/C][C]95[/C][C]101.362830488114[/C][C]-6.36283048811448[/C][/ROW]
[ROW][C]77[/C][C]101[/C][C]111.390350888645[/C][C]-10.3903508886445[/C][/ROW]
[ROW][C]78[/C][C]117[/C][C]116.202747762351[/C][C]0.797252237648882[/C][/ROW]
[ROW][C]79[/C][C]67[/C][C]72.5945401873843[/C][C]-5.59454018738426[/C][/ROW]
[ROW][C]80[/C][C]92[/C][C]104.158690815709[/C][C]-12.1586908157088[/C][/ROW]
[ROW][C]81[/C][C]120[/C][C]115.800021109234[/C][C]4.19997889076575[/C][/ROW]
[ROW][C]82[/C][C]116[/C][C]113.406887434757[/C][C]2.59311256524316[/C][/ROW]
[ROW][C]83[/C][C]112[/C][C]110.596510704523[/C][C]1.40348929547731[/C][/ROW]
[ROW][C]84[/C][C]102[/C][C]96.5852729807436[/C][C]5.41472701925643[/C][/ROW]
[ROW][C]85[/C][C]99[/C][C]103.799513370512[/C][C]-4.7995133705115[/C][/ROW]
[ROW][C]86[/C][C]109[/C][C]107.392117162756[/C][C]1.60788283724441[/C][/ROW]
[ROW][C]87[/C][C]119[/C][C]117.41383100223[/C][C]1.5861689977703[/C][/ROW]
[ROW][C]88[/C][C]103[/C][C]104.987370366166[/C][C]-1.98737036616629[/C][/ROW]
[ROW][C]89[/C][C]105[/C][C]109.000120494695[/C][C]-4.00012049469509[/C][/ROW]
[ROW][C]90[/C][C]122[/C][C]116.214360884463[/C][C]5.78563911553698[/C][/ROW]
[ROW][C]91[/C][C]76[/C][C]82.1887282870154[/C][C]-6.18872828701542[/C][/ROW]
[ROW][C]92[/C][C]103[/C][C]106.977777387527[/C][C]-3.97777738752683[/C][/ROW]
[ROW][C]93[/C][C]121[/C][C]111.401964010756[/C][C]9.59803598924358[/C][/ROW]
[ROW][C]94[/C][C]125[/C][C]121.029661038698[/C][C]3.97033896130241[/C][/ROW]
[ROW][C]95[/C][C]110[/C][C]111.41067385234[/C][C]-1.41067385234034[/C][/ROW]
[ROW][C]96[/C][C]100[/C][C]92.1843126017378[/C][C]7.81568739826223[/C][/ROW]
[ROW][C]97[/C][C]97[/C][C]113.376281786975[/C][C]-16.3762817869748[/C][/ROW]
[ROW][C]98[/C][C]109[/C][C]113.812517368402[/C][C]-4.81251736840168[/C][/ROW]
[ROW][C]99[/C][C]109[/C][C]105.818953197152[/C][C]3.18104680284821[/C][/ROW]
[ROW][C]100[/C][C]117[/C][C]119.821481079347[/C][C]-2.82148107934699[/C][/ROW]
[ROW][C]101[/C][C]107[/C][C]109.802670520401[/C][C]-2.80267052040085[/C][/ROW]
[ROW][C]102[/C][C]119[/C][C]116.223070726047[/C][C]2.77692927395305[/C][/ROW]
[ROW][C]103[/C][C]85[/C][C]86.9982218801941[/C][C]-1.99822188019405[/C][/ROW]
[ROW][C]104[/C][C]85[/C][C]99.366616905698[/C][C]-14.366616905698[/C][/ROW]
[ROW][C]105[/C][C]130[/C][C]121.787089013981[/C][C]8.21291098601854[/C][/ROW]
[ROW][C]106[/C][C]128[/C][C]115.823247353458[/C][C]12.1767526465419[/C][/ROW]
[ROW][C]107[/C][C]103[/C][C]100.577700145577[/C][C]2.42229985442344[/C][/ROW]
[ROW][C]108[/C][C]104[/C][C]92.9781527858596[/C][C]11.0218472141404[/C][/ROW]
[ROW][C]109[/C][C]96[/C][C]92.9549265416358[/C][C]3.04507345836418[/C][/ROW]
[ROW][C]110[/C][C]106[/C][C]100.16045708982[/C][C]5.83954291018017[/C][/ROW]
[ROW][C]111[/C][C]115[/C][C]109.37671762306[/C][C]5.6232823769398[/C][/ROW]
[ROW][C]112[/C][C]102[/C][C]100.163360370348[/C][C]1.8366396296522[/C][/ROW]
[ROW][C]113[/C][C]91[/C][C]98.5553570384083[/C][C]-7.5553570384083[/C][/ROW]
[ROW][C]114[/C][C]109[/C][C]109.385427464644[/C][C]-0.385427464644114[/C][/ROW]
[ROW][C]115[/C][C]76[/C][C]80.1634818993192[/C][C]-4.16348189931919[/C][/ROW]
[ROW][C]116[/C][C]91[/C][C]92.5857088168289[/C][C]-1.58570881682895[/C][/ROW]
[ROW][C]117[/C][C]121[/C][C]112.998354220584[/C][C]8.00164577941598[/C][/ROW]
[ROW][C]118[/C][C]117[/C][C]112.998354220584[/C][C]4.00164577941597[/C][/ROW]
[ROW][C]119[/C][C]106[/C][C]97.3820164453934[/C][C]8.61798355460661[/C][/ROW]
[ROW][C]120[/C][C]101[/C][C]88.171562473209[/C][C]12.828437526791[/C][/ROW]
[ROW][C]121[/C][C]97[/C][C]89.7679526830366[/C][C]7.23204731696344[/C][/ROW]
[ROW][C]122[/C][C]105[/C][C]96.1825463276267[/C][C]8.8174536723733[/C][/ROW]
[ROW][C]123[/C][C]118[/C][C]116.614184257052[/C][C]1.38581574294808[/C][/ROW]
[ROW][C]124[/C][C]105[/C][C]107.361511514974[/C][C]-2.36151151497358[/C][/ROW]
[ROW][C]125[/C][C]97[/C][C]98.584389843688[/C][C]-1.58438984368804[/C][/ROW]
[ROW][C]126[/C][C]116[/C][C]115.017794047224[/C][C]0.982205952775678[/C][/ROW]
[ROW][C]127[/C][C]73[/C][C]79.7723683683142[/C][C]-6.77236836831422[/C][/ROW]
[ROW][C]128[/C][C]91[/C][C]97.0011855984746[/C][C]-6.00118559847462[/C][/ROW]
[ROW][C]129[/C][C]124[/C][C]117.38032207392[/C][C]6.61967792608029[/C][/ROW]
[ROW][C]130[/C][C]110[/C][C]109.802670520401[/C][C]0.197329479599152[/C][/ROW]
[ROW][C]131[/C][C]103[/C][C]101.800396507567[/C][C]1.19960349243297[/C][/ROW]
[ROW][C]132[/C][C]109[/C][C]87.395141972255[/C][C]21.604858027745[/C][/ROW]
[ROW][C]133[/C][C]92[/C][C]97.3965328480333[/C][C]-5.39653284803325[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]111.368455082446[/C][C]-3.36845508244643[/C][/ROW]
[ROW][C]135[/C][C]116[/C][C]122.652180773277[/C][C]-6.65218077327696[/C][/ROW]
[ROW][C]136[/C][C]98[/C][C]106.170751238791[/C][C]-8.17075123879082[/C][/ROW]
[ROW][C]137[/C][C]105[/C][C]114.620873955163[/C][C]-9.6208739551634[/C][/ROW]
[ROW][C]138[/C][C]106[/C][C]109.011733616807[/C][C]-3.01173361680698[/C][/ROW]
[ROW][C]139[/C][C]74[/C][C]77.3734281327809[/C][C]-3.37342813278086[/C][/ROW]
[ROW][C]140[/C][C]101[/C][C]104.99608020775[/C][C]-3.9960802077502[/C][/ROW]
[ROW][C]141[/C][C]123[/C][C]120.593425457271[/C][C]2.40657454272926[/C][/ROW]
[ROW][C]142[/C][C]109[/C][C]108.60900696369[/C][C]0.390993036309884[/C][/ROW]
[ROW][C]143[/C][C]100[/C][C]108.209183591101[/C][C]-8.20918359110122[/C][/ROW]
[ROW][C]144[/C][C]106[/C][C]96.1941594497386[/C][C]9.8058405502614[/C][/ROW]
[ROW][C]145[/C][C]105[/C][C]99.3956497109777[/C][C]5.60435028902228[/C][/ROW]
[ROW][C]146[/C][C]111[/C][C]102.988253503222[/C][C]8.01174649677818[/C][/ROW]
[ROW][C]147[/C][C]118[/C][C]126.609768571774[/C][C]-8.60976857177426[/C][/ROW]
[ROW][C]148[/C][C]93[/C][C]106.583760575994[/C][C]-13.5837605759939[/C][/ROW]
[ROW][C]149[/C][C]105[/C][C]108.194667188461[/C][C]-3.19466718846135[/C][/ROW]
[ROW][C]150[/C][C]114[/C][C]111.404867291284[/C][C]2.59513270871561[/C][/ROW]
[ROW][C]151[/C][C]75[/C][C]80.9805483276648[/C][C]-5.98054832766482[/C][/ROW]
[ROW][C]152[/C][C]94[/C][C]103.373560473171[/C][C]-9.37356047317084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=4

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	94	84.151675345598	9.848324654402
2	115	102.533267800601	12.4667321993986
3	121	112.96061157372	8.03938842627963
4	99	97.3297573958899	1.67024260411013
5	112	110.544251655019	1.45574834498084
6	108	102.941801014774	5.05819898522576
7	69	67.3139716464431	1.68602835355689
8	88	98.9203410446615	-10.9203410446615
9	115	106.54601792913	8.45398207086977
10	116	107.748391327425	8.25160867257511
11	110	102.94760757583	7.05239242416982
12	98	92.1349568327622	5.86504316723778
13	102	94.5571233125194	7.44287668748063
14	108	101.348314085475	6.65168591452539
15	121	106.57505073441	14.42494926559
16	102	94.1456868178186	7.85431318218142
17	108	102.592906253663	5.40709374633682
18	114	107.768714291121	6.2312857088793
19	71	72.5684106626325	-1.56841066263251
20	93	99.7345041924792	-6.73450419247917
21	115	101.759750580175	13.2402494198246
22	118	109.368007781476	8.63199221852373
23	105	100.157553809292	4.84244619070815
24	95	83.7415692889234	11.2584307110766
25	98	95.3538667771692	2.64613322283082
26	103	95.7478835887021	7.25211641129787
27	112	106.569244173354	5.43075582664598
28	105	102.159573952764	2.8404260472357
29	98	101.753944019119	-3.75394401911945
30	104	104.161594096237	-0.161594096236736
31	75	72.1772971316275	2.82270286837248
32	82	89.360992311366	-7.36099231136605
33	116	107.771617571649	8.22838242835133
34	115	110.581994301883	4.41800569811717
35	92	101.362830488114	-9.36283048811448
36	90	84.1501025030962	5.84989749690376
37	91	90.1954784228795	0.804521577120496
38	100	98.1613402268754	1.83865977312464
39	100	108.177247505294	-8.17724750529351
40	94	104.170303937821	-10.1703039378207
41	84	99.3579070641141	-15.3579070641141
42	101	106.572147453882	-5.57214745388199
43	75	80.957322083441	-5.95732208344103
44	77	91.3324068070565	-14.3324068070565
45	106	112.181287792238	-6.1812877922384
46	110	112.189997633822	-2.18999763382232
47	92	98.1642435074033	-6.16424350740333
48	88	92.1756027601539	-4.17560276015385
49	88	94.5571233125194	-6.55712331251937
50	99	99.7606337172309	-0.760633717230933
51	117	114.59764771094	2.4023522890604
52	101	103.77338384576	-2.77338384575974
53	84	98.1584369463474	-14.1584369463474
54	111	113.795097685234	-2.79509768523384
55	67	74.1938336777398	-7.19383367773984
56	86	98.9464705694133	-12.9464705694133
57	112	112.589821006411	-0.589821006411213
58	106	109.788154117761	-3.78815411776098
59	104	102.579720289049	1.420279710951
60	98	97.3849197259214	0.615080274078642
61	91	91.767069545981	-0.767069545981035
62	104	99.369520186226	4.63047981377404
63	111	107.001003631751	3.99899636824938
64	102	110.984720955	-8.98472095499969
65	95	102.168283794348	-7.16828379434821
66	109	115.800021109234	-6.80002110923425
67	69	70.9894401359727	-1.98944013597274
68	87	101.751040738591	-14.7510407385915
69	118	115.800021109234	2.19997889076575
70	102	106.983583948583	-4.98358394858278
71	109	106.989390509639	2.01060949036127
72	101	97.7847430985102	3.21525690148975
73	99	95.7740131134539	3.22598688654611
74	106	101.374443610226	4.62555638977363
75	120	119.373632375808	0.626367624191774
76	95	101.362830488114	-6.36283048811448
77	101	111.390350888645	-10.3903508886445
78	117	116.202747762351	0.797252237648882
79	67	72.5945401873843	-5.59454018738426
80	92	104.158690815709	-12.1586908157088
81	120	115.800021109234	4.19997889076575
82	116	113.406887434757	2.59311256524316
83	112	110.596510704523	1.40348929547731
84	102	96.5852729807436	5.41472701925643
85	99	103.799513370512	-4.7995133705115
86	109	107.392117162756	1.60788283724441
87	119	117.41383100223	1.5861689977703
88	103	104.987370366166	-1.98737036616629
89	105	109.000120494695	-4.00012049469509
90	122	116.214360884463	5.78563911553698
91	76	82.1887282870154	-6.18872828701542
92	103	106.977777387527	-3.97777738752683
93	121	111.401964010756	9.59803598924358
94	125	121.029661038698	3.97033896130241
95	110	111.41067385234	-1.41067385234034
96	100	92.1843126017378	7.81568739826223
97	97	113.376281786975	-16.3762817869748
98	109	113.812517368402	-4.81251736840168
99	109	105.818953197152	3.18104680284821
100	117	119.821481079347	-2.82148107934699
101	107	109.802670520401	-2.80267052040085
102	119	116.223070726047	2.77692927395305
103	85	86.9982218801941	-1.99822188019405
104	85	99.366616905698	-14.366616905698
105	130	121.787089013981	8.21291098601854
106	128	115.823247353458	12.1767526465419
107	103	100.577700145577	2.42229985442344
108	104	92.9781527858596	11.0218472141404
109	96	92.9549265416358	3.04507345836418
110	106	100.16045708982	5.83954291018017
111	115	109.37671762306	5.6232823769398
112	102	100.163360370348	1.8366396296522
113	91	98.5553570384083	-7.5553570384083
114	109	109.385427464644	-0.385427464644114
115	76	80.1634818993192	-4.16348189931919
116	91	92.5857088168289	-1.58570881682895
117	121	112.998354220584	8.00164577941598
118	117	112.998354220584	4.00164577941597
119	106	97.3820164453934	8.61798355460661
120	101	88.171562473209	12.828437526791
121	97	89.7679526830366	7.23204731696344
122	105	96.1825463276267	8.8174536723733
123	118	116.614184257052	1.38581574294808
124	105	107.361511514974	-2.36151151497358
125	97	98.584389843688	-1.58438984368804
126	116	115.017794047224	0.982205952775678
127	73	79.7723683683142	-6.77236836831422
128	91	97.0011855984746	-6.00118559847462
129	124	117.38032207392	6.61967792608029
130	110	109.802670520401	0.197329479599152
131	103	101.800396507567	1.19960349243297
132	109	87.395141972255	21.604858027745
133	92	97.3965328480333	-5.39653284803325
134	108	111.368455082446	-3.36845508244643
135	116	122.652180773277	-6.65218077327696
136	98	106.170751238791	-8.17075123879082
137	105	114.620873955163	-9.6208739551634
138	106	109.011733616807	-3.01173361680698
139	74	77.3734281327809	-3.37342813278086
140	101	104.99608020775	-3.9960802077502
141	123	120.593425457271	2.40657454272926
142	109	108.60900696369	0.390993036309884
143	100	108.209183591101	-8.20918359110122
144	106	96.1941594497386	9.8058405502614
145	105	99.3956497109777	5.60435028902228
146	111	102.988253503222	8.01174649677818
147	118	126.609768571774	-8.60976857177426
148	93	106.583760575994	-13.5837605759939
149	105	108.194667188461	-3.19466718846135
150	114	111.404867291284	2.59513270871561
151	75	80.9805483276648	-5.98054832766482
152	94	103.373560473171	-9.37356047317084

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.363442356984703	0.726884713969405	0.636557643015297
8	0.573254170362085	0.85349165927583	0.426745829637915
9	0.450863333678313	0.901726667356625	0.549136666321687
10	0.332326032328549	0.664652064657098	0.667673967671451
11	0.235246785866632	0.470493571733263	0.764753214133368
12	0.157580487882073	0.315160975764147	0.842419512117927
13	0.152359259339067	0.304718518678133	0.847640740660933
14	0.101976462859836	0.203952925719673	0.898023537140164
15	0.072901173174405	0.14580234634881	0.927098826825595
16	0.0474906982956795	0.0949813965913589	0.952509301704321
17	0.105304016675551	0.210608033351101	0.894695983324449
18	0.0902741565800568	0.180548313160114	0.909725843419943
19	0.12081566741664	0.241631334833279	0.87918433258336
20	0.197630451876609	0.395260903753219	0.802369548123391
21	0.201434131480854	0.402868262961708	0.798565868519146
22	0.169599881086097	0.339199762172194	0.830400118913903
23	0.143643672590637	0.287287345181274	0.856356327409363
24	0.153689539011777	0.307379078023554	0.846310460988223
25	0.14493830992162	0.28987661984324	0.85506169007838
26	0.123874327807241	0.247748655614483	0.876125672192759
27	0.115407702211074	0.230815404422149	0.884592297788926
28	0.109511788896321	0.219023577792642	0.890488211103679
29	0.166678858077167	0.333357716154334	0.833321141922833
30	0.180721271564292	0.361442543128584	0.819278728435708
31	0.152899478882586	0.305798957765172	0.847100521117414
32	0.17536062709909	0.350721254198181	0.82463937290091
33	0.162028587634911	0.324057175269823	0.837971412365089
34	0.158359937200648	0.316719874401295	0.841640062799352
35	0.373350317979464	0.746700635958928	0.626649682020536
36	0.344019490216134	0.688038980432268	0.655980509783866
37	0.307518732669486	0.615037465338972	0.692481267330514
38	0.285510229950567	0.571020459901134	0.714489770049433
39	0.452576793683727	0.905153587367454	0.547423206316273
40	0.62342487156852	0.75315025686296	0.37657512843148
41	0.84865126357566	0.302697472848681	0.15134873642434
42	0.853242802295167	0.293514395409665	0.146757197704833
43	0.853280332267048	0.293439335465905	0.146719667732952
44	0.925575908991278	0.148848182017443	0.0744240910087216
45	0.927777339157616	0.144445321684769	0.0722226608423845
46	0.913174275213089	0.173651449573822	0.0868257247869109
47	0.908409632005739	0.183180735988522	0.091590367994261
48	0.892653636244334	0.214692727511333	0.107346363755666
49	0.886351050384322	0.227297899231356	0.113648949615678
50	0.863405682304649	0.273188635390702	0.136594317695351
51	0.838537244221463	0.322925511557075	0.161462755778537
52	0.811617685568395	0.37676462886321	0.188382314431605
53	0.88428934865522	0.231421302689559	0.11571065134478
54	0.861728691242761	0.276542617514479	0.138271308757239
55	0.85553965535727	0.28892068928546	0.14446034464273
56	0.902019642118508	0.195960715762984	0.0979803578814918
57	0.879710306312734	0.240579387374532	0.120289693687266
58	0.858381853662674	0.283236292674652	0.141618146337326
59	0.833228762052972	0.333542475894056	0.166771237947028
60	0.803890215714846	0.392219568570309	0.196109784285154
61	0.768999241991234	0.462001516017531	0.231000758008766
62	0.756466273356786	0.487067453286427	0.243533726643214
63	0.733783729027724	0.532432541944552	0.266216270972276
64	0.748487584846563	0.503024830306874	0.251512415153437
65	0.739292075716672	0.521415848566656	0.260707924283328
66	0.727920463247179	0.544159073505643	0.272079536752821
67	0.690100351578869	0.619799296842262	0.309899648421131
68	0.793897922846982	0.412204154306037	0.206102077153018
69	0.76468313023841	0.470633739523181	0.23531686976159
70	0.739590177438607	0.520819645122786	0.260409822561393
71	0.706153439385162	0.587693121229675	0.293846560614838
72	0.678105519586254	0.643788960827491	0.321894480413746
73	0.648520097478088	0.702959805043823	0.351479902521912
74	0.629989425113301	0.740021149773398	0.370010574886699
75	0.586773793264121	0.826452413471758	0.413226206735879
76	0.567453405850836	0.865093188298327	0.432546594149164
77	0.605405317616416	0.789189364767167	0.394594682383584
78	0.56105329424472	0.877893411510559	0.43894670575528
79	0.546103445178455	0.907793109643089	0.453896554821545
80	0.617149122275552	0.765701755448896	0.382850877724448
81	0.59147404131458	0.81705191737084	0.40852595868542
82	0.553336606658094	0.893326786683812	0.446663393341906
83	0.509127152867555	0.98174569426489	0.490872847132445
84	0.491828910419134	0.983657820838269	0.508171089580866
85	0.464249415292943	0.928498830585886	0.535750584707057
86	0.420700783770427	0.841401567540854	0.579299216229573
87	0.377650901366783	0.755301802733566	0.622349098633217
88	0.33529111759671	0.670582235193421	0.66470888240329
89	0.303944438123307	0.607888876246613	0.696055561876693
90	0.291086247127573	0.582172494255146	0.708913752872427
91	0.289839068003369	0.579678136006739	0.710160931996631
92	0.258431330721944	0.516862661443888	0.741568669278056
93	0.294521416041734	0.589042832083468	0.705478583958266
94	0.267146856286213	0.534293712572427	0.732853143713787
95	0.229750484927688	0.459500969855377	0.770249515072312
96	0.233956643858421	0.467913287716843	0.766043356141579
97	0.399498939399535	0.79899787879907	0.600501060600465
98	0.371280013515491	0.742560027030981	0.628719986484509
99	0.334124290876179	0.668248581752357	0.665875709123821
100	0.295502680172113	0.591005360344226	0.704497319827887
101	0.259197566201498	0.518395132402995	0.740802433798502
102	0.225849157491359	0.451698314982719	0.774150842508641
103	0.199961980548319	0.399923961096638	0.800038019451681
104	0.313313855826893	0.626627711653787	0.686686144173107
105	0.33328164194766	0.66656328389532	0.66671835805234
106	0.435641325720113	0.871282651440227	0.564358674279887
107	0.389944299792989	0.779888599585977	0.610055700207011
108	0.446300180149598	0.892600360299196	0.553699819850402
109	0.401848893162384	0.803697786324767	0.598151106837616
110	0.389109092385289	0.778218184770578	0.610890907614711
111	0.388083301585689	0.776166603171378	0.611916698414311
112	0.347949417089498	0.695898834178996	0.652050582910502
113	0.334254827774485	0.66850965554897	0.665745172225515
114	0.287136585223088	0.574273170446176	0.712863414776912
115	0.2735528141889	0.547105628377801	0.7264471858111
116	0.230374229017359	0.460748458034719	0.769625770982641
117	0.26280267212898	0.52560534425796	0.73719732787102
118	0.250215758936222	0.500431517872445	0.749784241063778
119	0.272482316392896	0.544964632785792	0.727517683607104
120	0.373880096974696	0.747760193949392	0.626119903025304
121	0.392127388251416	0.784254776502832	0.607872611748584
122	0.455091462194818	0.910182924389637	0.544908537805182
123	0.412357067037385	0.824714134074769	0.587642932962615
124	0.353845223660264	0.707690447320529	0.646154776339736
125	0.298140920029428	0.596281840058856	0.701859079970572
126	0.252974406150154	0.505948812300308	0.747025593849846
127	0.270079191500731	0.540158383001462	0.729920808499269
128	0.22545220150302	0.450904403006039	0.77454779849698
129	0.251636441148906	0.503272882297813	0.748363558851094
130	0.21012298132459	0.420245962649181	0.78987701867541
131	0.165716309643088	0.331432619286176	0.834283690356912
132	0.59960498042898	0.80079003914204	0.40039501957102
133	0.544663989140912	0.910672021718176	0.455336010859088
134	0.467545790539707	0.935091581079414	0.532454209460293
135	0.456132264482861	0.912264528965723	0.543867735517139
136	0.461279361131123	0.922558722262245	0.538720638868877
137	0.525842854175163	0.948314291649675	0.474157145824837
138	0.462461777632887	0.924923555265775	0.537538222367113
139	0.392359899172178	0.784719798344356	0.607640100827822
140	0.307828560209163	0.615657120418325	0.692171439790837
141	0.2218812210859	0.443762442171801	0.7781187789141
142	0.14857551603342	0.29715103206684	0.85142448396658
143	0.240579378426068	0.481158756852137	0.759420621573932
144	0.221446644092179	0.442893288184357	0.778553355907821
145	0.158824381488465	0.317648762976931	0.841175618511535

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.363442356984703 & 0.726884713969405 & 0.636557643015297 \tabularnewline
8 & 0.573254170362085 & 0.85349165927583 & 0.426745829637915 \tabularnewline
9 & 0.450863333678313 & 0.901726667356625 & 0.549136666321687 \tabularnewline
10 & 0.332326032328549 & 0.664652064657098 & 0.667673967671451 \tabularnewline
11 & 0.235246785866632 & 0.470493571733263 & 0.764753214133368 \tabularnewline
12 & 0.157580487882073 & 0.315160975764147 & 0.842419512117927 \tabularnewline
13 & 0.152359259339067 & 0.304718518678133 & 0.847640740660933 \tabularnewline
14 & 0.101976462859836 & 0.203952925719673 & 0.898023537140164 \tabularnewline
15 & 0.072901173174405 & 0.14580234634881 & 0.927098826825595 \tabularnewline
16 & 0.0474906982956795 & 0.0949813965913589 & 0.952509301704321 \tabularnewline
17 & 0.105304016675551 & 0.210608033351101 & 0.894695983324449 \tabularnewline
18 & 0.0902741565800568 & 0.180548313160114 & 0.909725843419943 \tabularnewline
19 & 0.12081566741664 & 0.241631334833279 & 0.87918433258336 \tabularnewline
20 & 0.197630451876609 & 0.395260903753219 & 0.802369548123391 \tabularnewline
21 & 0.201434131480854 & 0.402868262961708 & 0.798565868519146 \tabularnewline
22 & 0.169599881086097 & 0.339199762172194 & 0.830400118913903 \tabularnewline
23 & 0.143643672590637 & 0.287287345181274 & 0.856356327409363 \tabularnewline
24 & 0.153689539011777 & 0.307379078023554 & 0.846310460988223 \tabularnewline
25 & 0.14493830992162 & 0.28987661984324 & 0.85506169007838 \tabularnewline
26 & 0.123874327807241 & 0.247748655614483 & 0.876125672192759 \tabularnewline
27 & 0.115407702211074 & 0.230815404422149 & 0.884592297788926 \tabularnewline
28 & 0.109511788896321 & 0.219023577792642 & 0.890488211103679 \tabularnewline
29 & 0.166678858077167 & 0.333357716154334 & 0.833321141922833 \tabularnewline
30 & 0.180721271564292 & 0.361442543128584 & 0.819278728435708 \tabularnewline
31 & 0.152899478882586 & 0.305798957765172 & 0.847100521117414 \tabularnewline
32 & 0.17536062709909 & 0.350721254198181 & 0.82463937290091 \tabularnewline
33 & 0.162028587634911 & 0.324057175269823 & 0.837971412365089 \tabularnewline
34 & 0.158359937200648 & 0.316719874401295 & 0.841640062799352 \tabularnewline
35 & 0.373350317979464 & 0.746700635958928 & 0.626649682020536 \tabularnewline
36 & 0.344019490216134 & 0.688038980432268 & 0.655980509783866 \tabularnewline
37 & 0.307518732669486 & 0.615037465338972 & 0.692481267330514 \tabularnewline
38 & 0.285510229950567 & 0.571020459901134 & 0.714489770049433 \tabularnewline
39 & 0.452576793683727 & 0.905153587367454 & 0.547423206316273 \tabularnewline
40 & 0.62342487156852 & 0.75315025686296 & 0.37657512843148 \tabularnewline
41 & 0.84865126357566 & 0.302697472848681 & 0.15134873642434 \tabularnewline
42 & 0.853242802295167 & 0.293514395409665 & 0.146757197704833 \tabularnewline
43 & 0.853280332267048 & 0.293439335465905 & 0.146719667732952 \tabularnewline
44 & 0.925575908991278 & 0.148848182017443 & 0.0744240910087216 \tabularnewline
45 & 0.927777339157616 & 0.144445321684769 & 0.0722226608423845 \tabularnewline
46 & 0.913174275213089 & 0.173651449573822 & 0.0868257247869109 \tabularnewline
47 & 0.908409632005739 & 0.183180735988522 & 0.091590367994261 \tabularnewline
48 & 0.892653636244334 & 0.214692727511333 & 0.107346363755666 \tabularnewline
49 & 0.886351050384322 & 0.227297899231356 & 0.113648949615678 \tabularnewline
50 & 0.863405682304649 & 0.273188635390702 & 0.136594317695351 \tabularnewline
51 & 0.838537244221463 & 0.322925511557075 & 0.161462755778537 \tabularnewline
52 & 0.811617685568395 & 0.37676462886321 & 0.188382314431605 \tabularnewline
53 & 0.88428934865522 & 0.231421302689559 & 0.11571065134478 \tabularnewline
54 & 0.861728691242761 & 0.276542617514479 & 0.138271308757239 \tabularnewline
55 & 0.85553965535727 & 0.28892068928546 & 0.14446034464273 \tabularnewline
56 & 0.902019642118508 & 0.195960715762984 & 0.0979803578814918 \tabularnewline
57 & 0.879710306312734 & 0.240579387374532 & 0.120289693687266 \tabularnewline
58 & 0.858381853662674 & 0.283236292674652 & 0.141618146337326 \tabularnewline
59 & 0.833228762052972 & 0.333542475894056 & 0.166771237947028 \tabularnewline
60 & 0.803890215714846 & 0.392219568570309 & 0.196109784285154 \tabularnewline
61 & 0.768999241991234 & 0.462001516017531 & 0.231000758008766 \tabularnewline
62 & 0.756466273356786 & 0.487067453286427 & 0.243533726643214 \tabularnewline
63 & 0.733783729027724 & 0.532432541944552 & 0.266216270972276 \tabularnewline
64 & 0.748487584846563 & 0.503024830306874 & 0.251512415153437 \tabularnewline
65 & 0.739292075716672 & 0.521415848566656 & 0.260707924283328 \tabularnewline
66 & 0.727920463247179 & 0.544159073505643 & 0.272079536752821 \tabularnewline
67 & 0.690100351578869 & 0.619799296842262 & 0.309899648421131 \tabularnewline
68 & 0.793897922846982 & 0.412204154306037 & 0.206102077153018 \tabularnewline
69 & 0.76468313023841 & 0.470633739523181 & 0.23531686976159 \tabularnewline
70 & 0.739590177438607 & 0.520819645122786 & 0.260409822561393 \tabularnewline
71 & 0.706153439385162 & 0.587693121229675 & 0.293846560614838 \tabularnewline
72 & 0.678105519586254 & 0.643788960827491 & 0.321894480413746 \tabularnewline
73 & 0.648520097478088 & 0.702959805043823 & 0.351479902521912 \tabularnewline
74 & 0.629989425113301 & 0.740021149773398 & 0.370010574886699 \tabularnewline
75 & 0.586773793264121 & 0.826452413471758 & 0.413226206735879 \tabularnewline
76 & 0.567453405850836 & 0.865093188298327 & 0.432546594149164 \tabularnewline
77 & 0.605405317616416 & 0.789189364767167 & 0.394594682383584 \tabularnewline
78 & 0.56105329424472 & 0.877893411510559 & 0.43894670575528 \tabularnewline
79 & 0.546103445178455 & 0.907793109643089 & 0.453896554821545 \tabularnewline
80 & 0.617149122275552 & 0.765701755448896 & 0.382850877724448 \tabularnewline
81 & 0.59147404131458 & 0.81705191737084 & 0.40852595868542 \tabularnewline
82 & 0.553336606658094 & 0.893326786683812 & 0.446663393341906 \tabularnewline
83 & 0.509127152867555 & 0.98174569426489 & 0.490872847132445 \tabularnewline
84 & 0.491828910419134 & 0.983657820838269 & 0.508171089580866 \tabularnewline
85 & 0.464249415292943 & 0.928498830585886 & 0.535750584707057 \tabularnewline
86 & 0.420700783770427 & 0.841401567540854 & 0.579299216229573 \tabularnewline
87 & 0.377650901366783 & 0.755301802733566 & 0.622349098633217 \tabularnewline
88 & 0.33529111759671 & 0.670582235193421 & 0.66470888240329 \tabularnewline
89 & 0.303944438123307 & 0.607888876246613 & 0.696055561876693 \tabularnewline
90 & 0.291086247127573 & 0.582172494255146 & 0.708913752872427 \tabularnewline
91 & 0.289839068003369 & 0.579678136006739 & 0.710160931996631 \tabularnewline
92 & 0.258431330721944 & 0.516862661443888 & 0.741568669278056 \tabularnewline
93 & 0.294521416041734 & 0.589042832083468 & 0.705478583958266 \tabularnewline
94 & 0.267146856286213 & 0.534293712572427 & 0.732853143713787 \tabularnewline
95 & 0.229750484927688 & 0.459500969855377 & 0.770249515072312 \tabularnewline
96 & 0.233956643858421 & 0.467913287716843 & 0.766043356141579 \tabularnewline
97 & 0.399498939399535 & 0.79899787879907 & 0.600501060600465 \tabularnewline
98 & 0.371280013515491 & 0.742560027030981 & 0.628719986484509 \tabularnewline
99 & 0.334124290876179 & 0.668248581752357 & 0.665875709123821 \tabularnewline
100 & 0.295502680172113 & 0.591005360344226 & 0.704497319827887 \tabularnewline
101 & 0.259197566201498 & 0.518395132402995 & 0.740802433798502 \tabularnewline
102 & 0.225849157491359 & 0.451698314982719 & 0.774150842508641 \tabularnewline
103 & 0.199961980548319 & 0.399923961096638 & 0.800038019451681 \tabularnewline
104 & 0.313313855826893 & 0.626627711653787 & 0.686686144173107 \tabularnewline
105 & 0.33328164194766 & 0.66656328389532 & 0.66671835805234 \tabularnewline
106 & 0.435641325720113 & 0.871282651440227 & 0.564358674279887 \tabularnewline
107 & 0.389944299792989 & 0.779888599585977 & 0.610055700207011 \tabularnewline
108 & 0.446300180149598 & 0.892600360299196 & 0.553699819850402 \tabularnewline
109 & 0.401848893162384 & 0.803697786324767 & 0.598151106837616 \tabularnewline
110 & 0.389109092385289 & 0.778218184770578 & 0.610890907614711 \tabularnewline
111 & 0.388083301585689 & 0.776166603171378 & 0.611916698414311 \tabularnewline
112 & 0.347949417089498 & 0.695898834178996 & 0.652050582910502 \tabularnewline
113 & 0.334254827774485 & 0.66850965554897 & 0.665745172225515 \tabularnewline
114 & 0.287136585223088 & 0.574273170446176 & 0.712863414776912 \tabularnewline
115 & 0.2735528141889 & 0.547105628377801 & 0.7264471858111 \tabularnewline
116 & 0.230374229017359 & 0.460748458034719 & 0.769625770982641 \tabularnewline
117 & 0.26280267212898 & 0.52560534425796 & 0.73719732787102 \tabularnewline
118 & 0.250215758936222 & 0.500431517872445 & 0.749784241063778 \tabularnewline
119 & 0.272482316392896 & 0.544964632785792 & 0.727517683607104 \tabularnewline
120 & 0.373880096974696 & 0.747760193949392 & 0.626119903025304 \tabularnewline
121 & 0.392127388251416 & 0.784254776502832 & 0.607872611748584 \tabularnewline
122 & 0.455091462194818 & 0.910182924389637 & 0.544908537805182 \tabularnewline
123 & 0.412357067037385 & 0.824714134074769 & 0.587642932962615 \tabularnewline
124 & 0.353845223660264 & 0.707690447320529 & 0.646154776339736 \tabularnewline
125 & 0.298140920029428 & 0.596281840058856 & 0.701859079970572 \tabularnewline
126 & 0.252974406150154 & 0.505948812300308 & 0.747025593849846 \tabularnewline
127 & 0.270079191500731 & 0.540158383001462 & 0.729920808499269 \tabularnewline
128 & 0.22545220150302 & 0.450904403006039 & 0.77454779849698 \tabularnewline
129 & 0.251636441148906 & 0.503272882297813 & 0.748363558851094 \tabularnewline
130 & 0.21012298132459 & 0.420245962649181 & 0.78987701867541 \tabularnewline
131 & 0.165716309643088 & 0.331432619286176 & 0.834283690356912 \tabularnewline
132 & 0.59960498042898 & 0.80079003914204 & 0.40039501957102 \tabularnewline
133 & 0.544663989140912 & 0.910672021718176 & 0.455336010859088 \tabularnewline
134 & 0.467545790539707 & 0.935091581079414 & 0.532454209460293 \tabularnewline
135 & 0.456132264482861 & 0.912264528965723 & 0.543867735517139 \tabularnewline
136 & 0.461279361131123 & 0.922558722262245 & 0.538720638868877 \tabularnewline
137 & 0.525842854175163 & 0.948314291649675 & 0.474157145824837 \tabularnewline
138 & 0.462461777632887 & 0.924923555265775 & 0.537538222367113 \tabularnewline
139 & 0.392359899172178 & 0.784719798344356 & 0.607640100827822 \tabularnewline
140 & 0.307828560209163 & 0.615657120418325 & 0.692171439790837 \tabularnewline
141 & 0.2218812210859 & 0.443762442171801 & 0.7781187789141 \tabularnewline
142 & 0.14857551603342 & 0.29715103206684 & 0.85142448396658 \tabularnewline
143 & 0.240579378426068 & 0.481158756852137 & 0.759420621573932 \tabularnewline
144 & 0.221446644092179 & 0.442893288184357 & 0.778553355907821 \tabularnewline
145 & 0.158824381488465 & 0.317648762976931 & 0.841175618511535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.363442356984703[/C][C]0.726884713969405[/C][C]0.636557643015297[/C][/ROW]
[ROW][C]8[/C][C]0.573254170362085[/C][C]0.85349165927583[/C][C]0.426745829637915[/C][/ROW]
[ROW][C]9[/C][C]0.450863333678313[/C][C]0.901726667356625[/C][C]0.549136666321687[/C][/ROW]
[ROW][C]10[/C][C]0.332326032328549[/C][C]0.664652064657098[/C][C]0.667673967671451[/C][/ROW]
[ROW][C]11[/C][C]0.235246785866632[/C][C]0.470493571733263[/C][C]0.764753214133368[/C][/ROW]
[ROW][C]12[/C][C]0.157580487882073[/C][C]0.315160975764147[/C][C]0.842419512117927[/C][/ROW]
[ROW][C]13[/C][C]0.152359259339067[/C][C]0.304718518678133[/C][C]0.847640740660933[/C][/ROW]
[ROW][C]14[/C][C]0.101976462859836[/C][C]0.203952925719673[/C][C]0.898023537140164[/C][/ROW]
[ROW][C]15[/C][C]0.072901173174405[/C][C]0.14580234634881[/C][C]0.927098826825595[/C][/ROW]
[ROW][C]16[/C][C]0.0474906982956795[/C][C]0.0949813965913589[/C][C]0.952509301704321[/C][/ROW]
[ROW][C]17[/C][C]0.105304016675551[/C][C]0.210608033351101[/C][C]0.894695983324449[/C][/ROW]
[ROW][C]18[/C][C]0.0902741565800568[/C][C]0.180548313160114[/C][C]0.909725843419943[/C][/ROW]
[ROW][C]19[/C][C]0.12081566741664[/C][C]0.241631334833279[/C][C]0.87918433258336[/C][/ROW]
[ROW][C]20[/C][C]0.197630451876609[/C][C]0.395260903753219[/C][C]0.802369548123391[/C][/ROW]
[ROW][C]21[/C][C]0.201434131480854[/C][C]0.402868262961708[/C][C]0.798565868519146[/C][/ROW]
[ROW][C]22[/C][C]0.169599881086097[/C][C]0.339199762172194[/C][C]0.830400118913903[/C][/ROW]
[ROW][C]23[/C][C]0.143643672590637[/C][C]0.287287345181274[/C][C]0.856356327409363[/C][/ROW]
[ROW][C]24[/C][C]0.153689539011777[/C][C]0.307379078023554[/C][C]0.846310460988223[/C][/ROW]
[ROW][C]25[/C][C]0.14493830992162[/C][C]0.28987661984324[/C][C]0.85506169007838[/C][/ROW]
[ROW][C]26[/C][C]0.123874327807241[/C][C]0.247748655614483[/C][C]0.876125672192759[/C][/ROW]
[ROW][C]27[/C][C]0.115407702211074[/C][C]0.230815404422149[/C][C]0.884592297788926[/C][/ROW]
[ROW][C]28[/C][C]0.109511788896321[/C][C]0.219023577792642[/C][C]0.890488211103679[/C][/ROW]
[ROW][C]29[/C][C]0.166678858077167[/C][C]0.333357716154334[/C][C]0.833321141922833[/C][/ROW]
[ROW][C]30[/C][C]0.180721271564292[/C][C]0.361442543128584[/C][C]0.819278728435708[/C][/ROW]
[ROW][C]31[/C][C]0.152899478882586[/C][C]0.305798957765172[/C][C]0.847100521117414[/C][/ROW]
[ROW][C]32[/C][C]0.17536062709909[/C][C]0.350721254198181[/C][C]0.82463937290091[/C][/ROW]
[ROW][C]33[/C][C]0.162028587634911[/C][C]0.324057175269823[/C][C]0.837971412365089[/C][/ROW]
[ROW][C]34[/C][C]0.158359937200648[/C][C]0.316719874401295[/C][C]0.841640062799352[/C][/ROW]
[ROW][C]35[/C][C]0.373350317979464[/C][C]0.746700635958928[/C][C]0.626649682020536[/C][/ROW]
[ROW][C]36[/C][C]0.344019490216134[/C][C]0.688038980432268[/C][C]0.655980509783866[/C][/ROW]
[ROW][C]37[/C][C]0.307518732669486[/C][C]0.615037465338972[/C][C]0.692481267330514[/C][/ROW]
[ROW][C]38[/C][C]0.285510229950567[/C][C]0.571020459901134[/C][C]0.714489770049433[/C][/ROW]
[ROW][C]39[/C][C]0.452576793683727[/C][C]0.905153587367454[/C][C]0.547423206316273[/C][/ROW]
[ROW][C]40[/C][C]0.62342487156852[/C][C]0.75315025686296[/C][C]0.37657512843148[/C][/ROW]
[ROW][C]41[/C][C]0.84865126357566[/C][C]0.302697472848681[/C][C]0.15134873642434[/C][/ROW]
[ROW][C]42[/C][C]0.853242802295167[/C][C]0.293514395409665[/C][C]0.146757197704833[/C][/ROW]
[ROW][C]43[/C][C]0.853280332267048[/C][C]0.293439335465905[/C][C]0.146719667732952[/C][/ROW]
[ROW][C]44[/C][C]0.925575908991278[/C][C]0.148848182017443[/C][C]0.0744240910087216[/C][/ROW]
[ROW][C]45[/C][C]0.927777339157616[/C][C]0.144445321684769[/C][C]0.0722226608423845[/C][/ROW]
[ROW][C]46[/C][C]0.913174275213089[/C][C]0.173651449573822[/C][C]0.0868257247869109[/C][/ROW]
[ROW][C]47[/C][C]0.908409632005739[/C][C]0.183180735988522[/C][C]0.091590367994261[/C][/ROW]
[ROW][C]48[/C][C]0.892653636244334[/C][C]0.214692727511333[/C][C]0.107346363755666[/C][/ROW]
[ROW][C]49[/C][C]0.886351050384322[/C][C]0.227297899231356[/C][C]0.113648949615678[/C][/ROW]
[ROW][C]50[/C][C]0.863405682304649[/C][C]0.273188635390702[/C][C]0.136594317695351[/C][/ROW]
[ROW][C]51[/C][C]0.838537244221463[/C][C]0.322925511557075[/C][C]0.161462755778537[/C][/ROW]
[ROW][C]52[/C][C]0.811617685568395[/C][C]0.37676462886321[/C][C]0.188382314431605[/C][/ROW]
[ROW][C]53[/C][C]0.88428934865522[/C][C]0.231421302689559[/C][C]0.11571065134478[/C][/ROW]
[ROW][C]54[/C][C]0.861728691242761[/C][C]0.276542617514479[/C][C]0.138271308757239[/C][/ROW]
[ROW][C]55[/C][C]0.85553965535727[/C][C]0.28892068928546[/C][C]0.14446034464273[/C][/ROW]
[ROW][C]56[/C][C]0.902019642118508[/C][C]0.195960715762984[/C][C]0.0979803578814918[/C][/ROW]
[ROW][C]57[/C][C]0.879710306312734[/C][C]0.240579387374532[/C][C]0.120289693687266[/C][/ROW]
[ROW][C]58[/C][C]0.858381853662674[/C][C]0.283236292674652[/C][C]0.141618146337326[/C][/ROW]
[ROW][C]59[/C][C]0.833228762052972[/C][C]0.333542475894056[/C][C]0.166771237947028[/C][/ROW]
[ROW][C]60[/C][C]0.803890215714846[/C][C]0.392219568570309[/C][C]0.196109784285154[/C][/ROW]
[ROW][C]61[/C][C]0.768999241991234[/C][C]0.462001516017531[/C][C]0.231000758008766[/C][/ROW]
[ROW][C]62[/C][C]0.756466273356786[/C][C]0.487067453286427[/C][C]0.243533726643214[/C][/ROW]
[ROW][C]63[/C][C]0.733783729027724[/C][C]0.532432541944552[/C][C]0.266216270972276[/C][/ROW]
[ROW][C]64[/C][C]0.748487584846563[/C][C]0.503024830306874[/C][C]0.251512415153437[/C][/ROW]
[ROW][C]65[/C][C]0.739292075716672[/C][C]0.521415848566656[/C][C]0.260707924283328[/C][/ROW]
[ROW][C]66[/C][C]0.727920463247179[/C][C]0.544159073505643[/C][C]0.272079536752821[/C][/ROW]
[ROW][C]67[/C][C]0.690100351578869[/C][C]0.619799296842262[/C][C]0.309899648421131[/C][/ROW]
[ROW][C]68[/C][C]0.793897922846982[/C][C]0.412204154306037[/C][C]0.206102077153018[/C][/ROW]
[ROW][C]69[/C][C]0.76468313023841[/C][C]0.470633739523181[/C][C]0.23531686976159[/C][/ROW]
[ROW][C]70[/C][C]0.739590177438607[/C][C]0.520819645122786[/C][C]0.260409822561393[/C][/ROW]
[ROW][C]71[/C][C]0.706153439385162[/C][C]0.587693121229675[/C][C]0.293846560614838[/C][/ROW]
[ROW][C]72[/C][C]0.678105519586254[/C][C]0.643788960827491[/C][C]0.321894480413746[/C][/ROW]
[ROW][C]73[/C][C]0.648520097478088[/C][C]0.702959805043823[/C][C]0.351479902521912[/C][/ROW]
[ROW][C]74[/C][C]0.629989425113301[/C][C]0.740021149773398[/C][C]0.370010574886699[/C][/ROW]
[ROW][C]75[/C][C]0.586773793264121[/C][C]0.826452413471758[/C][C]0.413226206735879[/C][/ROW]
[ROW][C]76[/C][C]0.567453405850836[/C][C]0.865093188298327[/C][C]0.432546594149164[/C][/ROW]
[ROW][C]77[/C][C]0.605405317616416[/C][C]0.789189364767167[/C][C]0.394594682383584[/C][/ROW]
[ROW][C]78[/C][C]0.56105329424472[/C][C]0.877893411510559[/C][C]0.43894670575528[/C][/ROW]
[ROW][C]79[/C][C]0.546103445178455[/C][C]0.907793109643089[/C][C]0.453896554821545[/C][/ROW]
[ROW][C]80[/C][C]0.617149122275552[/C][C]0.765701755448896[/C][C]0.382850877724448[/C][/ROW]
[ROW][C]81[/C][C]0.59147404131458[/C][C]0.81705191737084[/C][C]0.40852595868542[/C][/ROW]
[ROW][C]82[/C][C]0.553336606658094[/C][C]0.893326786683812[/C][C]0.446663393341906[/C][/ROW]
[ROW][C]83[/C][C]0.509127152867555[/C][C]0.98174569426489[/C][C]0.490872847132445[/C][/ROW]
[ROW][C]84[/C][C]0.491828910419134[/C][C]0.983657820838269[/C][C]0.508171089580866[/C][/ROW]
[ROW][C]85[/C][C]0.464249415292943[/C][C]0.928498830585886[/C][C]0.535750584707057[/C][/ROW]
[ROW][C]86[/C][C]0.420700783770427[/C][C]0.841401567540854[/C][C]0.579299216229573[/C][/ROW]
[ROW][C]87[/C][C]0.377650901366783[/C][C]0.755301802733566[/C][C]0.622349098633217[/C][/ROW]
[ROW][C]88[/C][C]0.33529111759671[/C][C]0.670582235193421[/C][C]0.66470888240329[/C][/ROW]
[ROW][C]89[/C][C]0.303944438123307[/C][C]0.607888876246613[/C][C]0.696055561876693[/C][/ROW]
[ROW][C]90[/C][C]0.291086247127573[/C][C]0.582172494255146[/C][C]0.708913752872427[/C][/ROW]
[ROW][C]91[/C][C]0.289839068003369[/C][C]0.579678136006739[/C][C]0.710160931996631[/C][/ROW]
[ROW][C]92[/C][C]0.258431330721944[/C][C]0.516862661443888[/C][C]0.741568669278056[/C][/ROW]
[ROW][C]93[/C][C]0.294521416041734[/C][C]0.589042832083468[/C][C]0.705478583958266[/C][/ROW]
[ROW][C]94[/C][C]0.267146856286213[/C][C]0.534293712572427[/C][C]0.732853143713787[/C][/ROW]
[ROW][C]95[/C][C]0.229750484927688[/C][C]0.459500969855377[/C][C]0.770249515072312[/C][/ROW]
[ROW][C]96[/C][C]0.233956643858421[/C][C]0.467913287716843[/C][C]0.766043356141579[/C][/ROW]
[ROW][C]97[/C][C]0.399498939399535[/C][C]0.79899787879907[/C][C]0.600501060600465[/C][/ROW]
[ROW][C]98[/C][C]0.371280013515491[/C][C]0.742560027030981[/C][C]0.628719986484509[/C][/ROW]
[ROW][C]99[/C][C]0.334124290876179[/C][C]0.668248581752357[/C][C]0.665875709123821[/C][/ROW]
[ROW][C]100[/C][C]0.295502680172113[/C][C]0.591005360344226[/C][C]0.704497319827887[/C][/ROW]
[ROW][C]101[/C][C]0.259197566201498[/C][C]0.518395132402995[/C][C]0.740802433798502[/C][/ROW]
[ROW][C]102[/C][C]0.225849157491359[/C][C]0.451698314982719[/C][C]0.774150842508641[/C][/ROW]
[ROW][C]103[/C][C]0.199961980548319[/C][C]0.399923961096638[/C][C]0.800038019451681[/C][/ROW]
[ROW][C]104[/C][C]0.313313855826893[/C][C]0.626627711653787[/C][C]0.686686144173107[/C][/ROW]
[ROW][C]105[/C][C]0.33328164194766[/C][C]0.66656328389532[/C][C]0.66671835805234[/C][/ROW]
[ROW][C]106[/C][C]0.435641325720113[/C][C]0.871282651440227[/C][C]0.564358674279887[/C][/ROW]
[ROW][C]107[/C][C]0.389944299792989[/C][C]0.779888599585977[/C][C]0.610055700207011[/C][/ROW]
[ROW][C]108[/C][C]0.446300180149598[/C][C]0.892600360299196[/C][C]0.553699819850402[/C][/ROW]
[ROW][C]109[/C][C]0.401848893162384[/C][C]0.803697786324767[/C][C]0.598151106837616[/C][/ROW]
[ROW][C]110[/C][C]0.389109092385289[/C][C]0.778218184770578[/C][C]0.610890907614711[/C][/ROW]
[ROW][C]111[/C][C]0.388083301585689[/C][C]0.776166603171378[/C][C]0.611916698414311[/C][/ROW]
[ROW][C]112[/C][C]0.347949417089498[/C][C]0.695898834178996[/C][C]0.652050582910502[/C][/ROW]
[ROW][C]113[/C][C]0.334254827774485[/C][C]0.66850965554897[/C][C]0.665745172225515[/C][/ROW]
[ROW][C]114[/C][C]0.287136585223088[/C][C]0.574273170446176[/C][C]0.712863414776912[/C][/ROW]
[ROW][C]115[/C][C]0.2735528141889[/C][C]0.547105628377801[/C][C]0.7264471858111[/C][/ROW]
[ROW][C]116[/C][C]0.230374229017359[/C][C]0.460748458034719[/C][C]0.769625770982641[/C][/ROW]
[ROW][C]117[/C][C]0.26280267212898[/C][C]0.52560534425796[/C][C]0.73719732787102[/C][/ROW]
[ROW][C]118[/C][C]0.250215758936222[/C][C]0.500431517872445[/C][C]0.749784241063778[/C][/ROW]
[ROW][C]119[/C][C]0.272482316392896[/C][C]0.544964632785792[/C][C]0.727517683607104[/C][/ROW]
[ROW][C]120[/C][C]0.373880096974696[/C][C]0.747760193949392[/C][C]0.626119903025304[/C][/ROW]
[ROW][C]121[/C][C]0.392127388251416[/C][C]0.784254776502832[/C][C]0.607872611748584[/C][/ROW]
[ROW][C]122[/C][C]0.455091462194818[/C][C]0.910182924389637[/C][C]0.544908537805182[/C][/ROW]
[ROW][C]123[/C][C]0.412357067037385[/C][C]0.824714134074769[/C][C]0.587642932962615[/C][/ROW]
[ROW][C]124[/C][C]0.353845223660264[/C][C]0.707690447320529[/C][C]0.646154776339736[/C][/ROW]
[ROW][C]125[/C][C]0.298140920029428[/C][C]0.596281840058856[/C][C]0.701859079970572[/C][/ROW]
[ROW][C]126[/C][C]0.252974406150154[/C][C]0.505948812300308[/C][C]0.747025593849846[/C][/ROW]
[ROW][C]127[/C][C]0.270079191500731[/C][C]0.540158383001462[/C][C]0.729920808499269[/C][/ROW]
[ROW][C]128[/C][C]0.22545220150302[/C][C]0.450904403006039[/C][C]0.77454779849698[/C][/ROW]
[ROW][C]129[/C][C]0.251636441148906[/C][C]0.503272882297813[/C][C]0.748363558851094[/C][/ROW]
[ROW][C]130[/C][C]0.21012298132459[/C][C]0.420245962649181[/C][C]0.78987701867541[/C][/ROW]
[ROW][C]131[/C][C]0.165716309643088[/C][C]0.331432619286176[/C][C]0.834283690356912[/C][/ROW]
[ROW][C]132[/C][C]0.59960498042898[/C][C]0.80079003914204[/C][C]0.40039501957102[/C][/ROW]
[ROW][C]133[/C][C]0.544663989140912[/C][C]0.910672021718176[/C][C]0.455336010859088[/C][/ROW]
[ROW][C]134[/C][C]0.467545790539707[/C][C]0.935091581079414[/C][C]0.532454209460293[/C][/ROW]
[ROW][C]135[/C][C]0.456132264482861[/C][C]0.912264528965723[/C][C]0.543867735517139[/C][/ROW]
[ROW][C]136[/C][C]0.461279361131123[/C][C]0.922558722262245[/C][C]0.538720638868877[/C][/ROW]
[ROW][C]137[/C][C]0.525842854175163[/C][C]0.948314291649675[/C][C]0.474157145824837[/C][/ROW]
[ROW][C]138[/C][C]0.462461777632887[/C][C]0.924923555265775[/C][C]0.537538222367113[/C][/ROW]
[ROW][C]139[/C][C]0.392359899172178[/C][C]0.784719798344356[/C][C]0.607640100827822[/C][/ROW]
[ROW][C]140[/C][C]0.307828560209163[/C][C]0.615657120418325[/C][C]0.692171439790837[/C][/ROW]
[ROW][C]141[/C][C]0.2218812210859[/C][C]0.443762442171801[/C][C]0.7781187789141[/C][/ROW]
[ROW][C]142[/C][C]0.14857551603342[/C][C]0.29715103206684[/C][C]0.85142448396658[/C][/ROW]
[ROW][C]143[/C][C]0.240579378426068[/C][C]0.481158756852137[/C][C]0.759420621573932[/C][/ROW]
[ROW][C]144[/C][C]0.221446644092179[/C][C]0.442893288184357[/C][C]0.778553355907821[/C][/ROW]
[ROW][C]145[/C][C]0.158824381488465[/C][C]0.317648762976931[/C][C]0.841175618511535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
7	0.363442356984703	0.726884713969405	0.636557643015297
8	0.573254170362085	0.85349165927583	0.426745829637915
9	0.450863333678313	0.901726667356625	0.549136666321687
10	0.332326032328549	0.664652064657098	0.667673967671451
11	0.235246785866632	0.470493571733263	0.764753214133368
12	0.157580487882073	0.315160975764147	0.842419512117927
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47	0.908409632005739	0.183180735988522	0.091590367994261
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49	0.886351050384322	0.227297899231356	0.113648949615678
50	0.863405682304649	0.273188635390702	0.136594317695351
51	0.838537244221463	0.322925511557075	0.161462755778537
52	0.811617685568395	0.37676462886321	0.188382314431605
53	0.88428934865522	0.231421302689559	0.11571065134478
54	0.861728691242761	0.276542617514479	0.138271308757239
55	0.85553965535727	0.28892068928546	0.14446034464273
56	0.902019642118508	0.195960715762984	0.0979803578814918
57	0.879710306312734	0.240579387374532	0.120289693687266
58	0.858381853662674	0.283236292674652	0.141618146337326
59	0.833228762052972	0.333542475894056	0.166771237947028
60	0.803890215714846	0.392219568570309	0.196109784285154
61	0.768999241991234	0.462001516017531	0.231000758008766
62	0.756466273356786	0.487067453286427	0.243533726643214
63	0.733783729027724	0.532432541944552	0.266216270972276
64	0.748487584846563	0.503024830306874	0.251512415153437
65	0.739292075716672	0.521415848566656	0.260707924283328
66	0.727920463247179	0.544159073505643	0.272079536752821
67	0.690100351578869	0.619799296842262	0.309899648421131
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69	0.76468313023841	0.470633739523181	0.23531686976159
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71	0.706153439385162	0.587693121229675	0.293846560614838
72	0.678105519586254	0.643788960827491	0.321894480413746
73	0.648520097478088	0.702959805043823	0.351479902521912
74	0.629989425113301	0.740021149773398	0.370010574886699
75	0.586773793264121	0.826452413471758	0.413226206735879
76	0.567453405850836	0.865093188298327	0.432546594149164
77	0.605405317616416	0.789189364767167	0.394594682383584
78	0.56105329424472	0.877893411510559	0.43894670575528
79	0.546103445178455	0.907793109643089	0.453896554821545
80	0.617149122275552	0.765701755448896	0.382850877724448
81	0.59147404131458	0.81705191737084	0.40852595868542
82	0.553336606658094	0.893326786683812	0.446663393341906
83	0.509127152867555	0.98174569426489	0.490872847132445
84	0.491828910419134	0.983657820838269	0.508171089580866
85	0.464249415292943	0.928498830585886	0.535750584707057
86	0.420700783770427	0.841401567540854	0.579299216229573
87	0.377650901366783	0.755301802733566	0.622349098633217
88	0.33529111759671	0.670582235193421	0.66470888240329
89	0.303944438123307	0.607888876246613	0.696055561876693
90	0.291086247127573	0.582172494255146	0.708913752872427
91	0.289839068003369	0.579678136006739	0.710160931996631
92	0.258431330721944	0.516862661443888	0.741568669278056
93	0.294521416041734	0.589042832083468	0.705478583958266
94	0.267146856286213	0.534293712572427	0.732853143713787
95	0.229750484927688	0.459500969855377	0.770249515072312
96	0.233956643858421	0.467913287716843	0.766043356141579
97	0.399498939399535	0.79899787879907	0.600501060600465
98	0.371280013515491	0.742560027030981	0.628719986484509
99	0.334124290876179	0.668248581752357	0.665875709123821
100	0.295502680172113	0.591005360344226	0.704497319827887
101	0.259197566201498	0.518395132402995	0.740802433798502
102	0.225849157491359	0.451698314982719	0.774150842508641
103	0.199961980548319	0.399923961096638	0.800038019451681
104	0.313313855826893	0.626627711653787	0.686686144173107
105	0.33328164194766	0.66656328389532	0.66671835805234
106	0.435641325720113	0.871282651440227	0.564358674279887
107	0.389944299792989	0.779888599585977	0.610055700207011
108	0.446300180149598	0.892600360299196	0.553699819850402
109	0.401848893162384	0.803697786324767	0.598151106837616
110	0.389109092385289	0.778218184770578	0.610890907614711
111	0.388083301585689	0.776166603171378	0.611916698414311
112	0.347949417089498	0.695898834178996	0.652050582910502
113	0.334254827774485	0.66850965554897	0.665745172225515
114	0.287136585223088	0.574273170446176	0.712863414776912
115	0.2735528141889	0.547105628377801	0.7264471858111
116	0.230374229017359	0.460748458034719	0.769625770982641
117	0.26280267212898	0.52560534425796	0.73719732787102
118	0.250215758936222	0.500431517872445	0.749784241063778
119	0.272482316392896	0.544964632785792	0.727517683607104
120	0.373880096974696	0.747760193949392	0.626119903025304
121	0.392127388251416	0.784254776502832	0.607872611748584
122	0.455091462194818	0.910182924389637	0.544908537805182
123	0.412357067037385	0.824714134074769	0.587642932962615
124	0.353845223660264	0.707690447320529	0.646154776339736
125	0.298140920029428	0.596281840058856	0.701859079970572
126	0.252974406150154	0.505948812300308	0.747025593849846
127	0.270079191500731	0.540158383001462	0.729920808499269
128	0.22545220150302	0.450904403006039	0.77454779849698
129	0.251636441148906	0.503272882297813	0.748363558851094
130	0.21012298132459	0.420245962649181	0.78987701867541
131	0.165716309643088	0.331432619286176	0.834283690356912
132	0.59960498042898	0.80079003914204	0.40039501957102
133	0.544663989140912	0.910672021718176	0.455336010859088
134	0.467545790539707	0.935091581079414	0.532454209460293
135	0.456132264482861	0.912264528965723	0.543867735517139
136	0.461279361131123	0.922558722262245	0.538720638868877
137	0.525842854175163	0.948314291649675	0.474157145824837
138	0.462461777632887	0.924923555265775	0.537538222367113
139	0.392359899172178	0.784719798344356	0.607640100827822
140	0.307828560209163	0.615657120418325	0.692171439790837
141	0.2218812210859	0.443762442171801	0.7781187789141
142	0.14857551603342	0.29715103206684	0.85142448396658
143	0.240579378426068	0.481158756852137	0.759420621573932
144	0.221446644092179	0.442893288184357	0.778553355907821
145	0.158824381488465	0.317648762976931	0.841175618511535

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.00719424460431655	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 1 & 0.00719424460431655 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186533&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]1[/C][C]0.00719424460431655[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186533&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186533&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	1	0.00719424460431655	OK

Figure 1

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code