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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 11 Dec 2014 16:33:49 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/11/t1418315706ybyr2a42mckcqwp.htm/, Retrieved Sun, 19 May 2024 20:27:37 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266190, Retrieved Sun, 19 May 2024 20:27:37 +0000

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

-       [Multiple Regression] [] [2014-12-11 16:33:49] [a97fb05c06a04cb9398859e294d4eb9c] [Current]

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91 62 72 15 57
137 56 61 21 84
148 57 68 30 189
92 51 61 20 66
131 56 64 14 69
59 30 65 18 57
90 61 69 19 103
83 47 63 25 51
116 56 75 23 69
42 50 63 17 41
155 67 73 21 50
128 41 75 21 54
49 45 63 8 15
96 48 63 29 69
66 44 62 20 53
104 37 64 19 69
76 56 60 22 59
99 66 56 23 118
108 38 59 24 65
74 34 68 12 64
96 49 66 22 70
116 55 73 12 50
87 49 72 22 77
97 59 71 20 37
127 40 59 10 81
106 58 64 23 101
80 60 66 17 79
74 63 78 22 71
91 56 68 24 60
133 54 73 18 55
74 52 62 21 44
114 34 65 20 40
140 69 68 20 56
95 32 65 22 43
98 48 60 19 45
121 67 71 20 32
126 58 65 26 56
98 57 68 23 40
95 42 64 24 34
110 64 74 21 89
70 58 69 21 50
102 66 76 19 56
86 26 68 8 46
130 61 72 17 76
96 52 67 20 64
102 51 63 11 74
100 55 59 8 57
94 50 73 15 45
52 60 66 18 30
98 56 62 18 62
118 63 69 19 51
99 61 66 19 36
109 52 57 30 145
68 55 56 17 23
131 72 71 24 160
71 33 56 20 32
68 66 62 25 40
89 66 59 20 58
115 64 57 27 102
78 40 66 18 80
118 46 63 28 97
87 58 69 21 46
162 51 48 27 140
49 50 66 22 78
122 52 73 28 98
96 54 67 25 40
100 66 61 21 80
82 61 68 22 76
100 80 75 28 79
115 51 62 20 87
141 56 69 29 95
110 53 74 20 80
146 47 63 20 79
90 50 58 23 120
121 39 58 18 69
104 58 72 18 72
147 35 62 19 43
110 58 62 25 87
108 60 65 25 52
113 62 69 25 71
115 63 66 24 61
61 53 72 19 51
60 46 62 26 50
109 67 75 10 67
68 59 58 17 30
111 64 66 13 70
77 38 55 17 52
73 50 47 30 75
89 48 62 4 69
78 47 64 16 72
110 66 64 21 79
65 63 50 22 43
117 44 70 20 57
63 43 69 22 69
52 38 48 23 38
62 56 66 16 53
131 45 73 0 90
101 50 74 18 96
42 54 66 25 49
77 55 78 18 40
96 37 60 18 78
57 46 69 24 59
112 51 65 29 96
49 64 78 15 38
56 47 63 22 48
86 62 71 23 91
88 67 80 24 52
48 56 73 22 27
85 65 69 15 62
63 50 84 17 58
102 57 64 20 76
162 47 58 27 140
86 47 59 26 68
114 57 78 23 80
94 50 67 23 70
81 22 60 15 78
110 59 66 26 100
64 56 74 22 51
104 53 72 18 102
105 42 55 15 78
49 52 49 22 78
88 54 74 27 55
95 44 53 10 98
102 62 64 20 76
99 53 65 17 73
63 50 57 23 47
76 36 51 19 45
109 76 80 13 83
117 66 67 27 60
57 62 70 23 48
120 59 74 16 50
73 47 75 25 56
91 55 70 2 77
108 58 69 26 91
105 60 65 20 76
119 57 71 22 74
31 45 65 24 29

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 seconds
R Server	'George Udny Yule' @ yule.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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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	7 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
LFM[t] = + 30.1364 + 0.195961AMS.I[t] + 0.381337AMS.E[t] -0.239789NumeracyTOT[t] + 0.511097HoursRFC[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
LFM[t] =  +  30.1364 +  0.195961AMS.I[t] +  0.381337AMS.E[t] -0.239789NumeracyTOT[t] +  0.511097HoursRFC[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]LFM[t] =  +  30.1364 +  0.195961AMS.I[t] +  0.381337AMS.E[t] -0.239789NumeracyTOT[t] +  0.511097HoursRFC[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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
LFM[t] = + 30.1364 + 0.195961AMS.I[t] + 0.381337AMS.E[t] -0.239789NumeracyTOT[t] + 0.511097HoursRFC[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)	30.1364	21.2916	1.415	0.159303	0.0796516
AMS.I	0.195961	0.216226	0.9063	0.366441	0.18322
AMS.E	0.381337	0.306724	1.243	0.215977	0.107988
NumeracyTOT	-0.239789	0.381328	-0.6288	0.53055	0.265275
HoursRFC	0.511097	0.0769795	6.639	7.4662e-10	3.7331e-10

\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) & 30.1364 & 21.2916 & 1.415 & 0.159303 & 0.0796516 \tabularnewline
AMS.I & 0.195961 & 0.216226 & 0.9063 & 0.366441 & 0.18322 \tabularnewline
AMS.E & 0.381337 & 0.306724 & 1.243 & 0.215977 & 0.107988 \tabularnewline
NumeracyTOT & -0.239789 & 0.381328 & -0.6288 & 0.53055 & 0.265275 \tabularnewline
HoursRFC & 0.511097 & 0.0769795 & 6.639 & 7.4662e-10 & 3.7331e-10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&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]30.1364[/C][C]21.2916[/C][C]1.415[/C][C]0.159303[/C][C]0.0796516[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.195961[/C][C]0.216226[/C][C]0.9063[/C][C]0.366441[/C][C]0.18322[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.381337[/C][C]0.306724[/C][C]1.243[/C][C]0.215977[/C][C]0.107988[/C][/ROW]
[ROW][C]NumeracyTOT[/C][C]-0.239789[/C][C]0.381328[/C][C]-0.6288[/C][C]0.53055[/C][C]0.265275[/C][/ROW]
[ROW][C]HoursRFC[/C][C]0.511097[/C][C]0.0769795[/C][C]6.639[/C][C]7.4662e-10[/C][C]3.7331e-10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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)	30.1364	21.2916	1.415	0.159303	0.0796516
AMS.I	0.195961	0.216226	0.9063	0.366441	0.18322
AMS.E	0.381337	0.306724	1.243	0.215977	0.107988
NumeracyTOT	-0.239789	0.381328	-0.6288	0.53055	0.265275
HoursRFC	0.511097	0.0769795	6.639	7.4662e-10	3.7331e-10

Multiple Linear Regression - Regression Statistics
Multiple R	0.524062
R-squared	0.274641
Adjusted R-squared	0.25266
F-TEST (value)	12.4947
F-TEST (DF numerator)	4
F-TEST (DF denominator)	132
p-value	1.19709e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	23.0306
Sum Squared Residuals	70013.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.524062 \tabularnewline
R-squared & 0.274641 \tabularnewline
Adjusted R-squared & 0.25266 \tabularnewline
F-TEST (value) & 12.4947 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 132 \tabularnewline
p-value & 1.19709e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 23.0306 \tabularnewline
Sum Squared Residuals & 70013.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.524062[/C][/ROW]
[ROW][C]R-squared[/C][C]0.274641[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.25266[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.4947[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]132[/C][/ROW]
[ROW][C]p-value[/C][C]1.19709e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]23.0306[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]70013.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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	0.524062
R-squared	0.274641
Adjusted R-squared	0.25266
F-TEST (value)	12.4947
F-TEST (DF numerator)	4
F-TEST (DF denominator)	132
p-value	1.19709e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	23.0306
Sum Squared Residuals	70013.7

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	91	95.2779	-4.27791
2	137	102.268	34.7317
3	148	156.641	-8.64075
4	92	92.3286	-0.328568
5	131	97.4244	33.5756
6	59	85.6184	-26.6184
7	90	116.489	-26.4893
8	83	83.442	-0.441997
9	116	99.461	16.539
10	42	80.8372	-38.8372
11	155	91.6226	63.3774
12	128	89.3347	38.6653
13	49	68.727	-19.727
14	96	91.8786	4.12145
15	66	84.6939	-18.6939
16	104	92.5022	11.4978
17	76	88.8698	-12.8698
18	99	119.219	-20.219
19	108	87.5482	20.4518
20	74	92.5627	-18.5627
21	96	95.4081	0.591859
22	116	91.4292	24.5708
23	87	101.274	-14.2738
24	97	82.8878	14.1122
25	127	99.4747	27.5253
26	106	112.013	-6.01334
27	80	103.363	-23.3625
28	74	103.239	-29.2387
29	91	91.952	-0.95199
30	133	92.35	40.65
31	74	81.4219	-7.42194
32	114	77.2341	36.7659
33	140	93.4142	46.5858
34	95	77.8959	17.1041
35	98	80.8661	17.1339
36	121	81.9	39.1
37	126	88.6759	37.3241
38	98	82.1658	15.8342
39	95	74.3947	20.6053
40	110	111.349	-1.34888
41	70	88.3336	-18.3336
42	102	96.1168	5.88315
43	86	82.7544	3.24557
44	130	104.313	25.6868
45	96	93.7904	2.20964
46	102	99.3381	2.66188
47	100	90.6273	9.37267
48	94	87.1746	6.82545
49	52	78.079	-26.079
50	98	92.1249	5.8751
51	118	90.3041	27.6959
52	99	81.1017	17.8983
53	109	128.978	-19.978
54	68	69.9479	-1.94792
55	131	147.341	-16.3411
56	71	69.5173	1.48271
57	68	81.1618	-13.1618
58	89	90.4165	-1.41653
59	115	110.072	4.92832
60	78	99.7146	-21.7146
61	118	106.037	11.9629
62	87	86.2893	0.710745
63	162	123.514	38.4861
64	49	99.6929	-50.6929
65	122	111.537	10.4626
66	96	80.717	15.283
67	100	102.184	-2.18355
68	82	101.589	-19.5889
69	100	108.076	-8.07609
70	115	103.443	11.5571
71	141	109.023	31.9772
72	110	104.833	5.16677
73	146	98.9517	47.0483
74	90	117.868	-27.8685
75	121	90.8459	30.1541
76	104	101.441	2.55884
77	147	78.0591	68.9409
78	110	103.616	6.38428
79	108	87.2633	20.7367
80	113	98.8914	14.1086
81	115	93.0721	21.9279
82	61	89.4885	-28.4885
83	60	82.1138	-22.1138
84	109	103.712	5.28836
85	68	75.0721	-7.07211
86	111	100.506	10.4943
87	77	81.0571	-4.05707
88	73	88.9959	-15.9959
89	89	97.4919	-8.49194
90	78	96.7145	-18.7145
91	110	102.816	7.18354
92	65	78.2506	-13.2506
93	117	89.789	27.211
94	63	94.8653	-31.8653
95	52	69.7936	-17.7936
96	62	89.5299	-27.5299
97	131	112.791	18.209
98	101	112.902	-11.9025
99	42	84.9355	-42.9355
100	77	86.7862	-9.78619
101	96	95.8165	0.183474
102	57	89.8626	-32.8626
103	112	107.029	4.97127
104	49	88.247	-39.247
105	56	82.6281	-26.6281
106	86	110.356	-24.3556
107	88	94.5948	-6.59482
108	48	77.472	-29.472
109	85	97.2773	-12.2773
110	63	97.5339	-34.5339
111	102	99.7593	2.24069
112	162	126.543	35.4566
113	86	90.3655	-4.36551
114	114	106.423	7.57695
115	94	95.7456	-1.74565
116	81	93.5965	-12.5965
117	110	111.742	-1.74151
118	64	90.1197	-26.1197
119	104	115.794	-11.7943
120	105	95.609	9.39099
121	49	93.6021	-44.6021
122	88	90.5732	-2.57324
123	95	106.659	-11.6591
124	102	100.739	1.26088
125	99	98.5429	0.457117
126	63	80.177	-17.177
127	76	75.0825	0.917461
128	109	114.84	-5.84016
129	117	92.8109	24.1891
130	57	87.9971	-30.9971
131	120	91.6352	28.3648
132	73	90.5735	-17.5735
133	91	106.483	-15.4827
134	108	108.09	-0.0896821
135	105	100.729	4.27147
136	119	100.927	18.0731
137	31	72.8084	-41.8084

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 91 & 95.2779 & -4.27791 \tabularnewline
2 & 137 & 102.268 & 34.7317 \tabularnewline
3 & 148 & 156.641 & -8.64075 \tabularnewline
4 & 92 & 92.3286 & -0.328568 \tabularnewline
5 & 131 & 97.4244 & 33.5756 \tabularnewline
6 & 59 & 85.6184 & -26.6184 \tabularnewline
7 & 90 & 116.489 & -26.4893 \tabularnewline
8 & 83 & 83.442 & -0.441997 \tabularnewline
9 & 116 & 99.461 & 16.539 \tabularnewline
10 & 42 & 80.8372 & -38.8372 \tabularnewline
11 & 155 & 91.6226 & 63.3774 \tabularnewline
12 & 128 & 89.3347 & 38.6653 \tabularnewline
13 & 49 & 68.727 & -19.727 \tabularnewline
14 & 96 & 91.8786 & 4.12145 \tabularnewline
15 & 66 & 84.6939 & -18.6939 \tabularnewline
16 & 104 & 92.5022 & 11.4978 \tabularnewline
17 & 76 & 88.8698 & -12.8698 \tabularnewline
18 & 99 & 119.219 & -20.219 \tabularnewline
19 & 108 & 87.5482 & 20.4518 \tabularnewline
20 & 74 & 92.5627 & -18.5627 \tabularnewline
21 & 96 & 95.4081 & 0.591859 \tabularnewline
22 & 116 & 91.4292 & 24.5708 \tabularnewline
23 & 87 & 101.274 & -14.2738 \tabularnewline
24 & 97 & 82.8878 & 14.1122 \tabularnewline
25 & 127 & 99.4747 & 27.5253 \tabularnewline
26 & 106 & 112.013 & -6.01334 \tabularnewline
27 & 80 & 103.363 & -23.3625 \tabularnewline
28 & 74 & 103.239 & -29.2387 \tabularnewline
29 & 91 & 91.952 & -0.95199 \tabularnewline
30 & 133 & 92.35 & 40.65 \tabularnewline
31 & 74 & 81.4219 & -7.42194 \tabularnewline
32 & 114 & 77.2341 & 36.7659 \tabularnewline
33 & 140 & 93.4142 & 46.5858 \tabularnewline
34 & 95 & 77.8959 & 17.1041 \tabularnewline
35 & 98 & 80.8661 & 17.1339 \tabularnewline
36 & 121 & 81.9 & 39.1 \tabularnewline
37 & 126 & 88.6759 & 37.3241 \tabularnewline
38 & 98 & 82.1658 & 15.8342 \tabularnewline
39 & 95 & 74.3947 & 20.6053 \tabularnewline
40 & 110 & 111.349 & -1.34888 \tabularnewline
41 & 70 & 88.3336 & -18.3336 \tabularnewline
42 & 102 & 96.1168 & 5.88315 \tabularnewline
43 & 86 & 82.7544 & 3.24557 \tabularnewline
44 & 130 & 104.313 & 25.6868 \tabularnewline
45 & 96 & 93.7904 & 2.20964 \tabularnewline
46 & 102 & 99.3381 & 2.66188 \tabularnewline
47 & 100 & 90.6273 & 9.37267 \tabularnewline
48 & 94 & 87.1746 & 6.82545 \tabularnewline
49 & 52 & 78.079 & -26.079 \tabularnewline
50 & 98 & 92.1249 & 5.8751 \tabularnewline
51 & 118 & 90.3041 & 27.6959 \tabularnewline
52 & 99 & 81.1017 & 17.8983 \tabularnewline
53 & 109 & 128.978 & -19.978 \tabularnewline
54 & 68 & 69.9479 & -1.94792 \tabularnewline
55 & 131 & 147.341 & -16.3411 \tabularnewline
56 & 71 & 69.5173 & 1.48271 \tabularnewline
57 & 68 & 81.1618 & -13.1618 \tabularnewline
58 & 89 & 90.4165 & -1.41653 \tabularnewline
59 & 115 & 110.072 & 4.92832 \tabularnewline
60 & 78 & 99.7146 & -21.7146 \tabularnewline
61 & 118 & 106.037 & 11.9629 \tabularnewline
62 & 87 & 86.2893 & 0.710745 \tabularnewline
63 & 162 & 123.514 & 38.4861 \tabularnewline
64 & 49 & 99.6929 & -50.6929 \tabularnewline
65 & 122 & 111.537 & 10.4626 \tabularnewline
66 & 96 & 80.717 & 15.283 \tabularnewline
67 & 100 & 102.184 & -2.18355 \tabularnewline
68 & 82 & 101.589 & -19.5889 \tabularnewline
69 & 100 & 108.076 & -8.07609 \tabularnewline
70 & 115 & 103.443 & 11.5571 \tabularnewline
71 & 141 & 109.023 & 31.9772 \tabularnewline
72 & 110 & 104.833 & 5.16677 \tabularnewline
73 & 146 & 98.9517 & 47.0483 \tabularnewline
74 & 90 & 117.868 & -27.8685 \tabularnewline
75 & 121 & 90.8459 & 30.1541 \tabularnewline
76 & 104 & 101.441 & 2.55884 \tabularnewline
77 & 147 & 78.0591 & 68.9409 \tabularnewline
78 & 110 & 103.616 & 6.38428 \tabularnewline
79 & 108 & 87.2633 & 20.7367 \tabularnewline
80 & 113 & 98.8914 & 14.1086 \tabularnewline
81 & 115 & 93.0721 & 21.9279 \tabularnewline
82 & 61 & 89.4885 & -28.4885 \tabularnewline
83 & 60 & 82.1138 & -22.1138 \tabularnewline
84 & 109 & 103.712 & 5.28836 \tabularnewline
85 & 68 & 75.0721 & -7.07211 \tabularnewline
86 & 111 & 100.506 & 10.4943 \tabularnewline
87 & 77 & 81.0571 & -4.05707 \tabularnewline
88 & 73 & 88.9959 & -15.9959 \tabularnewline
89 & 89 & 97.4919 & -8.49194 \tabularnewline
90 & 78 & 96.7145 & -18.7145 \tabularnewline
91 & 110 & 102.816 & 7.18354 \tabularnewline
92 & 65 & 78.2506 & -13.2506 \tabularnewline
93 & 117 & 89.789 & 27.211 \tabularnewline
94 & 63 & 94.8653 & -31.8653 \tabularnewline
95 & 52 & 69.7936 & -17.7936 \tabularnewline
96 & 62 & 89.5299 & -27.5299 \tabularnewline
97 & 131 & 112.791 & 18.209 \tabularnewline
98 & 101 & 112.902 & -11.9025 \tabularnewline
99 & 42 & 84.9355 & -42.9355 \tabularnewline
100 & 77 & 86.7862 & -9.78619 \tabularnewline
101 & 96 & 95.8165 & 0.183474 \tabularnewline
102 & 57 & 89.8626 & -32.8626 \tabularnewline
103 & 112 & 107.029 & 4.97127 \tabularnewline
104 & 49 & 88.247 & -39.247 \tabularnewline
105 & 56 & 82.6281 & -26.6281 \tabularnewline
106 & 86 & 110.356 & -24.3556 \tabularnewline
107 & 88 & 94.5948 & -6.59482 \tabularnewline
108 & 48 & 77.472 & -29.472 \tabularnewline
109 & 85 & 97.2773 & -12.2773 \tabularnewline
110 & 63 & 97.5339 & -34.5339 \tabularnewline
111 & 102 & 99.7593 & 2.24069 \tabularnewline
112 & 162 & 126.543 & 35.4566 \tabularnewline
113 & 86 & 90.3655 & -4.36551 \tabularnewline
114 & 114 & 106.423 & 7.57695 \tabularnewline
115 & 94 & 95.7456 & -1.74565 \tabularnewline
116 & 81 & 93.5965 & -12.5965 \tabularnewline
117 & 110 & 111.742 & -1.74151 \tabularnewline
118 & 64 & 90.1197 & -26.1197 \tabularnewline
119 & 104 & 115.794 & -11.7943 \tabularnewline
120 & 105 & 95.609 & 9.39099 \tabularnewline
121 & 49 & 93.6021 & -44.6021 \tabularnewline
122 & 88 & 90.5732 & -2.57324 \tabularnewline
123 & 95 & 106.659 & -11.6591 \tabularnewline
124 & 102 & 100.739 & 1.26088 \tabularnewline
125 & 99 & 98.5429 & 0.457117 \tabularnewline
126 & 63 & 80.177 & -17.177 \tabularnewline
127 & 76 & 75.0825 & 0.917461 \tabularnewline
128 & 109 & 114.84 & -5.84016 \tabularnewline
129 & 117 & 92.8109 & 24.1891 \tabularnewline
130 & 57 & 87.9971 & -30.9971 \tabularnewline
131 & 120 & 91.6352 & 28.3648 \tabularnewline
132 & 73 & 90.5735 & -17.5735 \tabularnewline
133 & 91 & 106.483 & -15.4827 \tabularnewline
134 & 108 & 108.09 & -0.0896821 \tabularnewline
135 & 105 & 100.729 & 4.27147 \tabularnewline
136 & 119 & 100.927 & 18.0731 \tabularnewline
137 & 31 & 72.8084 & -41.8084 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&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]91[/C][C]95.2779[/C][C]-4.27791[/C][/ROW]
[ROW][C]2[/C][C]137[/C][C]102.268[/C][C]34.7317[/C][/ROW]
[ROW][C]3[/C][C]148[/C][C]156.641[/C][C]-8.64075[/C][/ROW]
[ROW][C]4[/C][C]92[/C][C]92.3286[/C][C]-0.328568[/C][/ROW]
[ROW][C]5[/C][C]131[/C][C]97.4244[/C][C]33.5756[/C][/ROW]
[ROW][C]6[/C][C]59[/C][C]85.6184[/C][C]-26.6184[/C][/ROW]
[ROW][C]7[/C][C]90[/C][C]116.489[/C][C]-26.4893[/C][/ROW]
[ROW][C]8[/C][C]83[/C][C]83.442[/C][C]-0.441997[/C][/ROW]
[ROW][C]9[/C][C]116[/C][C]99.461[/C][C]16.539[/C][/ROW]
[ROW][C]10[/C][C]42[/C][C]80.8372[/C][C]-38.8372[/C][/ROW]
[ROW][C]11[/C][C]155[/C][C]91.6226[/C][C]63.3774[/C][/ROW]
[ROW][C]12[/C][C]128[/C][C]89.3347[/C][C]38.6653[/C][/ROW]
[ROW][C]13[/C][C]49[/C][C]68.727[/C][C]-19.727[/C][/ROW]
[ROW][C]14[/C][C]96[/C][C]91.8786[/C][C]4.12145[/C][/ROW]
[ROW][C]15[/C][C]66[/C][C]84.6939[/C][C]-18.6939[/C][/ROW]
[ROW][C]16[/C][C]104[/C][C]92.5022[/C][C]11.4978[/C][/ROW]
[ROW][C]17[/C][C]76[/C][C]88.8698[/C][C]-12.8698[/C][/ROW]
[ROW][C]18[/C][C]99[/C][C]119.219[/C][C]-20.219[/C][/ROW]
[ROW][C]19[/C][C]108[/C][C]87.5482[/C][C]20.4518[/C][/ROW]
[ROW][C]20[/C][C]74[/C][C]92.5627[/C][C]-18.5627[/C][/ROW]
[ROW][C]21[/C][C]96[/C][C]95.4081[/C][C]0.591859[/C][/ROW]
[ROW][C]22[/C][C]116[/C][C]91.4292[/C][C]24.5708[/C][/ROW]
[ROW][C]23[/C][C]87[/C][C]101.274[/C][C]-14.2738[/C][/ROW]
[ROW][C]24[/C][C]97[/C][C]82.8878[/C][C]14.1122[/C][/ROW]
[ROW][C]25[/C][C]127[/C][C]99.4747[/C][C]27.5253[/C][/ROW]
[ROW][C]26[/C][C]106[/C][C]112.013[/C][C]-6.01334[/C][/ROW]
[ROW][C]27[/C][C]80[/C][C]103.363[/C][C]-23.3625[/C][/ROW]
[ROW][C]28[/C][C]74[/C][C]103.239[/C][C]-29.2387[/C][/ROW]
[ROW][C]29[/C][C]91[/C][C]91.952[/C][C]-0.95199[/C][/ROW]
[ROW][C]30[/C][C]133[/C][C]92.35[/C][C]40.65[/C][/ROW]
[ROW][C]31[/C][C]74[/C][C]81.4219[/C][C]-7.42194[/C][/ROW]
[ROW][C]32[/C][C]114[/C][C]77.2341[/C][C]36.7659[/C][/ROW]
[ROW][C]33[/C][C]140[/C][C]93.4142[/C][C]46.5858[/C][/ROW]
[ROW][C]34[/C][C]95[/C][C]77.8959[/C][C]17.1041[/C][/ROW]
[ROW][C]35[/C][C]98[/C][C]80.8661[/C][C]17.1339[/C][/ROW]
[ROW][C]36[/C][C]121[/C][C]81.9[/C][C]39.1[/C][/ROW]
[ROW][C]37[/C][C]126[/C][C]88.6759[/C][C]37.3241[/C][/ROW]
[ROW][C]38[/C][C]98[/C][C]82.1658[/C][C]15.8342[/C][/ROW]
[ROW][C]39[/C][C]95[/C][C]74.3947[/C][C]20.6053[/C][/ROW]
[ROW][C]40[/C][C]110[/C][C]111.349[/C][C]-1.34888[/C][/ROW]
[ROW][C]41[/C][C]70[/C][C]88.3336[/C][C]-18.3336[/C][/ROW]
[ROW][C]42[/C][C]102[/C][C]96.1168[/C][C]5.88315[/C][/ROW]
[ROW][C]43[/C][C]86[/C][C]82.7544[/C][C]3.24557[/C][/ROW]
[ROW][C]44[/C][C]130[/C][C]104.313[/C][C]25.6868[/C][/ROW]
[ROW][C]45[/C][C]96[/C][C]93.7904[/C][C]2.20964[/C][/ROW]
[ROW][C]46[/C][C]102[/C][C]99.3381[/C][C]2.66188[/C][/ROW]
[ROW][C]47[/C][C]100[/C][C]90.6273[/C][C]9.37267[/C][/ROW]
[ROW][C]48[/C][C]94[/C][C]87.1746[/C][C]6.82545[/C][/ROW]
[ROW][C]49[/C][C]52[/C][C]78.079[/C][C]-26.079[/C][/ROW]
[ROW][C]50[/C][C]98[/C][C]92.1249[/C][C]5.8751[/C][/ROW]
[ROW][C]51[/C][C]118[/C][C]90.3041[/C][C]27.6959[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]81.1017[/C][C]17.8983[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]128.978[/C][C]-19.978[/C][/ROW]
[ROW][C]54[/C][C]68[/C][C]69.9479[/C][C]-1.94792[/C][/ROW]
[ROW][C]55[/C][C]131[/C][C]147.341[/C][C]-16.3411[/C][/ROW]
[ROW][C]56[/C][C]71[/C][C]69.5173[/C][C]1.48271[/C][/ROW]
[ROW][C]57[/C][C]68[/C][C]81.1618[/C][C]-13.1618[/C][/ROW]
[ROW][C]58[/C][C]89[/C][C]90.4165[/C][C]-1.41653[/C][/ROW]
[ROW][C]59[/C][C]115[/C][C]110.072[/C][C]4.92832[/C][/ROW]
[ROW][C]60[/C][C]78[/C][C]99.7146[/C][C]-21.7146[/C][/ROW]
[ROW][C]61[/C][C]118[/C][C]106.037[/C][C]11.9629[/C][/ROW]
[ROW][C]62[/C][C]87[/C][C]86.2893[/C][C]0.710745[/C][/ROW]
[ROW][C]63[/C][C]162[/C][C]123.514[/C][C]38.4861[/C][/ROW]
[ROW][C]64[/C][C]49[/C][C]99.6929[/C][C]-50.6929[/C][/ROW]
[ROW][C]65[/C][C]122[/C][C]111.537[/C][C]10.4626[/C][/ROW]
[ROW][C]66[/C][C]96[/C][C]80.717[/C][C]15.283[/C][/ROW]
[ROW][C]67[/C][C]100[/C][C]102.184[/C][C]-2.18355[/C][/ROW]
[ROW][C]68[/C][C]82[/C][C]101.589[/C][C]-19.5889[/C][/ROW]
[ROW][C]69[/C][C]100[/C][C]108.076[/C][C]-8.07609[/C][/ROW]
[ROW][C]70[/C][C]115[/C][C]103.443[/C][C]11.5571[/C][/ROW]
[ROW][C]71[/C][C]141[/C][C]109.023[/C][C]31.9772[/C][/ROW]
[ROW][C]72[/C][C]110[/C][C]104.833[/C][C]5.16677[/C][/ROW]
[ROW][C]73[/C][C]146[/C][C]98.9517[/C][C]47.0483[/C][/ROW]
[ROW][C]74[/C][C]90[/C][C]117.868[/C][C]-27.8685[/C][/ROW]
[ROW][C]75[/C][C]121[/C][C]90.8459[/C][C]30.1541[/C][/ROW]
[ROW][C]76[/C][C]104[/C][C]101.441[/C][C]2.55884[/C][/ROW]
[ROW][C]77[/C][C]147[/C][C]78.0591[/C][C]68.9409[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]103.616[/C][C]6.38428[/C][/ROW]
[ROW][C]79[/C][C]108[/C][C]87.2633[/C][C]20.7367[/C][/ROW]
[ROW][C]80[/C][C]113[/C][C]98.8914[/C][C]14.1086[/C][/ROW]
[ROW][C]81[/C][C]115[/C][C]93.0721[/C][C]21.9279[/C][/ROW]
[ROW][C]82[/C][C]61[/C][C]89.4885[/C][C]-28.4885[/C][/ROW]
[ROW][C]83[/C][C]60[/C][C]82.1138[/C][C]-22.1138[/C][/ROW]
[ROW][C]84[/C][C]109[/C][C]103.712[/C][C]5.28836[/C][/ROW]
[ROW][C]85[/C][C]68[/C][C]75.0721[/C][C]-7.07211[/C][/ROW]
[ROW][C]86[/C][C]111[/C][C]100.506[/C][C]10.4943[/C][/ROW]
[ROW][C]87[/C][C]77[/C][C]81.0571[/C][C]-4.05707[/C][/ROW]
[ROW][C]88[/C][C]73[/C][C]88.9959[/C][C]-15.9959[/C][/ROW]
[ROW][C]89[/C][C]89[/C][C]97.4919[/C][C]-8.49194[/C][/ROW]
[ROW][C]90[/C][C]78[/C][C]96.7145[/C][C]-18.7145[/C][/ROW]
[ROW][C]91[/C][C]110[/C][C]102.816[/C][C]7.18354[/C][/ROW]
[ROW][C]92[/C][C]65[/C][C]78.2506[/C][C]-13.2506[/C][/ROW]
[ROW][C]93[/C][C]117[/C][C]89.789[/C][C]27.211[/C][/ROW]
[ROW][C]94[/C][C]63[/C][C]94.8653[/C][C]-31.8653[/C][/ROW]
[ROW][C]95[/C][C]52[/C][C]69.7936[/C][C]-17.7936[/C][/ROW]
[ROW][C]96[/C][C]62[/C][C]89.5299[/C][C]-27.5299[/C][/ROW]
[ROW][C]97[/C][C]131[/C][C]112.791[/C][C]18.209[/C][/ROW]
[ROW][C]98[/C][C]101[/C][C]112.902[/C][C]-11.9025[/C][/ROW]
[ROW][C]99[/C][C]42[/C][C]84.9355[/C][C]-42.9355[/C][/ROW]
[ROW][C]100[/C][C]77[/C][C]86.7862[/C][C]-9.78619[/C][/ROW]
[ROW][C]101[/C][C]96[/C][C]95.8165[/C][C]0.183474[/C][/ROW]
[ROW][C]102[/C][C]57[/C][C]89.8626[/C][C]-32.8626[/C][/ROW]
[ROW][C]103[/C][C]112[/C][C]107.029[/C][C]4.97127[/C][/ROW]
[ROW][C]104[/C][C]49[/C][C]88.247[/C][C]-39.247[/C][/ROW]
[ROW][C]105[/C][C]56[/C][C]82.6281[/C][C]-26.6281[/C][/ROW]
[ROW][C]106[/C][C]86[/C][C]110.356[/C][C]-24.3556[/C][/ROW]
[ROW][C]107[/C][C]88[/C][C]94.5948[/C][C]-6.59482[/C][/ROW]
[ROW][C]108[/C][C]48[/C][C]77.472[/C][C]-29.472[/C][/ROW]
[ROW][C]109[/C][C]85[/C][C]97.2773[/C][C]-12.2773[/C][/ROW]
[ROW][C]110[/C][C]63[/C][C]97.5339[/C][C]-34.5339[/C][/ROW]
[ROW][C]111[/C][C]102[/C][C]99.7593[/C][C]2.24069[/C][/ROW]
[ROW][C]112[/C][C]162[/C][C]126.543[/C][C]35.4566[/C][/ROW]
[ROW][C]113[/C][C]86[/C][C]90.3655[/C][C]-4.36551[/C][/ROW]
[ROW][C]114[/C][C]114[/C][C]106.423[/C][C]7.57695[/C][/ROW]
[ROW][C]115[/C][C]94[/C][C]95.7456[/C][C]-1.74565[/C][/ROW]
[ROW][C]116[/C][C]81[/C][C]93.5965[/C][C]-12.5965[/C][/ROW]
[ROW][C]117[/C][C]110[/C][C]111.742[/C][C]-1.74151[/C][/ROW]
[ROW][C]118[/C][C]64[/C][C]90.1197[/C][C]-26.1197[/C][/ROW]
[ROW][C]119[/C][C]104[/C][C]115.794[/C][C]-11.7943[/C][/ROW]
[ROW][C]120[/C][C]105[/C][C]95.609[/C][C]9.39099[/C][/ROW]
[ROW][C]121[/C][C]49[/C][C]93.6021[/C][C]-44.6021[/C][/ROW]
[ROW][C]122[/C][C]88[/C][C]90.5732[/C][C]-2.57324[/C][/ROW]
[ROW][C]123[/C][C]95[/C][C]106.659[/C][C]-11.6591[/C][/ROW]
[ROW][C]124[/C][C]102[/C][C]100.739[/C][C]1.26088[/C][/ROW]
[ROW][C]125[/C][C]99[/C][C]98.5429[/C][C]0.457117[/C][/ROW]
[ROW][C]126[/C][C]63[/C][C]80.177[/C][C]-17.177[/C][/ROW]
[ROW][C]127[/C][C]76[/C][C]75.0825[/C][C]0.917461[/C][/ROW]
[ROW][C]128[/C][C]109[/C][C]114.84[/C][C]-5.84016[/C][/ROW]
[ROW][C]129[/C][C]117[/C][C]92.8109[/C][C]24.1891[/C][/ROW]
[ROW][C]130[/C][C]57[/C][C]87.9971[/C][C]-30.9971[/C][/ROW]
[ROW][C]131[/C][C]120[/C][C]91.6352[/C][C]28.3648[/C][/ROW]
[ROW][C]132[/C][C]73[/C][C]90.5735[/C][C]-17.5735[/C][/ROW]
[ROW][C]133[/C][C]91[/C][C]106.483[/C][C]-15.4827[/C][/ROW]
[ROW][C]134[/C][C]108[/C][C]108.09[/C][C]-0.0896821[/C][/ROW]
[ROW][C]135[/C][C]105[/C][C]100.729[/C][C]4.27147[/C][/ROW]
[ROW][C]136[/C][C]119[/C][C]100.927[/C][C]18.0731[/C][/ROW]
[ROW][C]137[/C][C]31[/C][C]72.8084[/C][C]-41.8084[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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	91	95.2779	-4.27791
2	137	102.268	34.7317
3	148	156.641	-8.64075
4	92	92.3286	-0.328568
5	131	97.4244	33.5756
6	59	85.6184	-26.6184
7	90	116.489	-26.4893
8	83	83.442	-0.441997
9	116	99.461	16.539
10	42	80.8372	-38.8372
11	155	91.6226	63.3774
12	128	89.3347	38.6653
13	49	68.727	-19.727
14	96	91.8786	4.12145
15	66	84.6939	-18.6939
16	104	92.5022	11.4978
17	76	88.8698	-12.8698
18	99	119.219	-20.219
19	108	87.5482	20.4518
20	74	92.5627	-18.5627
21	96	95.4081	0.591859
22	116	91.4292	24.5708
23	87	101.274	-14.2738
24	97	82.8878	14.1122
25	127	99.4747	27.5253
26	106	112.013	-6.01334
27	80	103.363	-23.3625
28	74	103.239	-29.2387
29	91	91.952	-0.95199
30	133	92.35	40.65
31	74	81.4219	-7.42194
32	114	77.2341	36.7659
33	140	93.4142	46.5858
34	95	77.8959	17.1041
35	98	80.8661	17.1339
36	121	81.9	39.1
37	126	88.6759	37.3241
38	98	82.1658	15.8342
39	95	74.3947	20.6053
40	110	111.349	-1.34888
41	70	88.3336	-18.3336
42	102	96.1168	5.88315
43	86	82.7544	3.24557
44	130	104.313	25.6868
45	96	93.7904	2.20964
46	102	99.3381	2.66188
47	100	90.6273	9.37267
48	94	87.1746	6.82545
49	52	78.079	-26.079
50	98	92.1249	5.8751
51	118	90.3041	27.6959
52	99	81.1017	17.8983
53	109	128.978	-19.978
54	68	69.9479	-1.94792
55	131	147.341	-16.3411
56	71	69.5173	1.48271
57	68	81.1618	-13.1618
58	89	90.4165	-1.41653
59	115	110.072	4.92832
60	78	99.7146	-21.7146
61	118	106.037	11.9629
62	87	86.2893	0.710745
63	162	123.514	38.4861
64	49	99.6929	-50.6929
65	122	111.537	10.4626
66	96	80.717	15.283
67	100	102.184	-2.18355
68	82	101.589	-19.5889
69	100	108.076	-8.07609
70	115	103.443	11.5571
71	141	109.023	31.9772
72	110	104.833	5.16677
73	146	98.9517	47.0483
74	90	117.868	-27.8685
75	121	90.8459	30.1541
76	104	101.441	2.55884
77	147	78.0591	68.9409
78	110	103.616	6.38428
79	108	87.2633	20.7367
80	113	98.8914	14.1086
81	115	93.0721	21.9279
82	61	89.4885	-28.4885
83	60	82.1138	-22.1138
84	109	103.712	5.28836
85	68	75.0721	-7.07211
86	111	100.506	10.4943
87	77	81.0571	-4.05707
88	73	88.9959	-15.9959
89	89	97.4919	-8.49194
90	78	96.7145	-18.7145
91	110	102.816	7.18354
92	65	78.2506	-13.2506
93	117	89.789	27.211
94	63	94.8653	-31.8653
95	52	69.7936	-17.7936
96	62	89.5299	-27.5299
97	131	112.791	18.209
98	101	112.902	-11.9025
99	42	84.9355	-42.9355
100	77	86.7862	-9.78619
101	96	95.8165	0.183474
102	57	89.8626	-32.8626
103	112	107.029	4.97127
104	49	88.247	-39.247
105	56	82.6281	-26.6281
106	86	110.356	-24.3556
107	88	94.5948	-6.59482
108	48	77.472	-29.472
109	85	97.2773	-12.2773
110	63	97.5339	-34.5339
111	102	99.7593	2.24069
112	162	126.543	35.4566
113	86	90.3655	-4.36551
114	114	106.423	7.57695
115	94	95.7456	-1.74565
116	81	93.5965	-12.5965
117	110	111.742	-1.74151
118	64	90.1197	-26.1197
119	104	115.794	-11.7943
120	105	95.609	9.39099
121	49	93.6021	-44.6021
122	88	90.5732	-2.57324
123	95	106.659	-11.6591
124	102	100.739	1.26088
125	99	98.5429	0.457117
126	63	80.177	-17.177
127	76	75.0825	0.917461
128	109	114.84	-5.84016
129	117	92.8109	24.1891
130	57	87.9971	-30.9971
131	120	91.6352	28.3648
132	73	90.5735	-17.5735
133	91	106.483	-15.4827
134	108	108.09	-0.0896821
135	105	100.729	4.27147
136	119	100.927	18.0731
137	31	72.8084	-41.8084

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.519672	0.960657	0.480328
9	0.704146	0.591709	0.295854
10	0.870068	0.259863	0.129932
11	0.92758	0.14484	0.0724198
12	0.961184	0.0776311	0.0388156
13	0.937811	0.124378	0.062189
14	0.908727	0.182547	0.0912733
15	0.876593	0.246813	0.123407
16	0.882808	0.234384	0.117192
17	0.853285	0.29343	0.146715
18	0.80982	0.38036	0.19018
19	0.832329	0.335343	0.167671
20	0.785385	0.42923	0.214615
21	0.728498	0.543004	0.271502
22	0.698459	0.603083	0.301541
23	0.710855	0.578291	0.289145
24	0.660206	0.679587	0.339794
25	0.804478	0.391043	0.195522
26	0.759307	0.481386	0.240693
27	0.767933	0.464134	0.232067
28	0.844412	0.311177	0.155588
29	0.805253	0.389494	0.194747
30	0.851047	0.297906	0.148953
31	0.817627	0.364745	0.182373
32	0.854556	0.290888	0.145444
33	0.910813	0.178374	0.0891869
34	0.89481	0.21038	0.10519
35	0.878387	0.243225	0.121613
36	0.893637	0.212726	0.106363
37	0.910208	0.179583	0.0897915
38	0.893393	0.213213	0.106607
39	0.881004	0.237993	0.118996
40	0.854875	0.29025	0.145125
41	0.866835	0.266329	0.133165
42	0.841251	0.317498	0.158749
43	0.80871	0.382581	0.19129
44	0.810076	0.379849	0.189924
45	0.773562	0.452876	0.226438
46	0.733435	0.533129	0.266565
47	0.697891	0.604217	0.302109
48	0.658657	0.682686	0.341343
49	0.71316	0.573679	0.28684
50	0.670003	0.659993	0.329997
51	0.680194	0.639611	0.319806
52	0.66121	0.67758	0.33879
53	0.653335	0.693329	0.346665
54	0.613677	0.772646	0.386323
55	0.609461	0.781077	0.390539
56	0.567531	0.864938	0.432469
57	0.552205	0.895589	0.447795
58	0.502757	0.994486	0.497243
59	0.458762	0.917524	0.541238
60	0.455151	0.910303	0.544849
61	0.417264	0.834528	0.582736
62	0.380678	0.761357	0.619322
63	0.494795	0.98959	0.505205
64	0.70229	0.595421	0.29771
65	0.660948	0.678105	0.339052
66	0.648707	0.702587	0.351293
67	0.602315	0.795369	0.397685
68	0.595916	0.808167	0.404084
69	0.561237	0.877526	0.438763
70	0.522516	0.954968	0.477484
71	0.555425	0.889149	0.444575
72	0.5084	0.983199	0.4916
73	0.67572	0.64856	0.32428
74	0.722825	0.55435	0.277175
75	0.767032	0.465937	0.232968
76	0.728969	0.542063	0.271031
77	0.986315	0.0273709	0.0136855
78	0.981249	0.0375026	0.0187513
79	0.985411	0.0291788	0.0145894
80	0.983685	0.0326302	0.0163151
81	0.98761	0.0247803	0.0123902
82	0.988534	0.0229318	0.0114659
83	0.987625	0.0247502	0.0123751
84	0.983711	0.0325781	0.016289
85	0.980563	0.0388735	0.0194367
86	0.977433	0.0451345	0.0225672
87	0.972278	0.0554434	0.0277217
88	0.966651	0.0666979	0.0333489
89	0.955464	0.0890712	0.0445356
90	0.948138	0.103724	0.0518618
91	0.935236	0.129527	0.0647637
92	0.919616	0.160767	0.0803837
93	0.961325	0.0773494	0.0386747
94	0.968342	0.0633165	0.0316583
95	0.960334	0.0793318	0.0396659
96	0.957246	0.085508	0.042754
97	0.959871	0.0802576	0.0401288
98	0.951461	0.0970778	0.0485389
99	0.971135	0.0577307	0.0288653
100	0.965289	0.0694227	0.0347114
101	0.954385	0.0912307	0.0456153
102	0.959298	0.0814044	0.0407022
103	0.943673	0.112654	0.0563272
104	0.950354	0.0992919	0.049646
105	0.942095	0.115809	0.0579047
106	0.956479	0.0870424	0.0435212
107	0.939405	0.121191	0.0605953
108	0.927422	0.145156	0.0725779
109	0.902904	0.194193	0.0970964
110	0.917559	0.164881	0.0824407
111	0.888882	0.222236	0.111118
112	0.89882	0.20236	0.10118
113	0.864394	0.271211	0.135606
114	0.82469	0.35062	0.17531
115	0.773673	0.452654	0.226327
116	0.714281	0.571438	0.285719
117	0.642771	0.714457	0.357229
118	0.631583	0.736835	0.368417
119	0.584932	0.830136	0.415068
120	0.56962	0.860759	0.43038
121	0.731459	0.537082	0.268541
122	0.648128	0.703743	0.351872
123	0.583164	0.833673	0.416836
124	0.484105	0.968211	0.515895
125	0.376261	0.752523	0.623739
126	0.303503	0.607006	0.696497
127	0.236359	0.472719	0.763641
128	0.346597	0.693194	0.653403
129	0.268288	0.536576	0.731712

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.519672 & 0.960657 & 0.480328 \tabularnewline
9 & 0.704146 & 0.591709 & 0.295854 \tabularnewline
10 & 0.870068 & 0.259863 & 0.129932 \tabularnewline
11 & 0.92758 & 0.14484 & 0.0724198 \tabularnewline
12 & 0.961184 & 0.0776311 & 0.0388156 \tabularnewline
13 & 0.937811 & 0.124378 & 0.062189 \tabularnewline
14 & 0.908727 & 0.182547 & 0.0912733 \tabularnewline
15 & 0.876593 & 0.246813 & 0.123407 \tabularnewline
16 & 0.882808 & 0.234384 & 0.117192 \tabularnewline
17 & 0.853285 & 0.29343 & 0.146715 \tabularnewline
18 & 0.80982 & 0.38036 & 0.19018 \tabularnewline
19 & 0.832329 & 0.335343 & 0.167671 \tabularnewline
20 & 0.785385 & 0.42923 & 0.214615 \tabularnewline
21 & 0.728498 & 0.543004 & 0.271502 \tabularnewline
22 & 0.698459 & 0.603083 & 0.301541 \tabularnewline
23 & 0.710855 & 0.578291 & 0.289145 \tabularnewline
24 & 0.660206 & 0.679587 & 0.339794 \tabularnewline
25 & 0.804478 & 0.391043 & 0.195522 \tabularnewline
26 & 0.759307 & 0.481386 & 0.240693 \tabularnewline
27 & 0.767933 & 0.464134 & 0.232067 \tabularnewline
28 & 0.844412 & 0.311177 & 0.155588 \tabularnewline
29 & 0.805253 & 0.389494 & 0.194747 \tabularnewline
30 & 0.851047 & 0.297906 & 0.148953 \tabularnewline
31 & 0.817627 & 0.364745 & 0.182373 \tabularnewline
32 & 0.854556 & 0.290888 & 0.145444 \tabularnewline
33 & 0.910813 & 0.178374 & 0.0891869 \tabularnewline
34 & 0.89481 & 0.21038 & 0.10519 \tabularnewline
35 & 0.878387 & 0.243225 & 0.121613 \tabularnewline
36 & 0.893637 & 0.212726 & 0.106363 \tabularnewline
37 & 0.910208 & 0.179583 & 0.0897915 \tabularnewline
38 & 0.893393 & 0.213213 & 0.106607 \tabularnewline
39 & 0.881004 & 0.237993 & 0.118996 \tabularnewline
40 & 0.854875 & 0.29025 & 0.145125 \tabularnewline
41 & 0.866835 & 0.266329 & 0.133165 \tabularnewline
42 & 0.841251 & 0.317498 & 0.158749 \tabularnewline
43 & 0.80871 & 0.382581 & 0.19129 \tabularnewline
44 & 0.810076 & 0.379849 & 0.189924 \tabularnewline
45 & 0.773562 & 0.452876 & 0.226438 \tabularnewline
46 & 0.733435 & 0.533129 & 0.266565 \tabularnewline
47 & 0.697891 & 0.604217 & 0.302109 \tabularnewline
48 & 0.658657 & 0.682686 & 0.341343 \tabularnewline
49 & 0.71316 & 0.573679 & 0.28684 \tabularnewline
50 & 0.670003 & 0.659993 & 0.329997 \tabularnewline
51 & 0.680194 & 0.639611 & 0.319806 \tabularnewline
52 & 0.66121 & 0.67758 & 0.33879 \tabularnewline
53 & 0.653335 & 0.693329 & 0.346665 \tabularnewline
54 & 0.613677 & 0.772646 & 0.386323 \tabularnewline
55 & 0.609461 & 0.781077 & 0.390539 \tabularnewline
56 & 0.567531 & 0.864938 & 0.432469 \tabularnewline
57 & 0.552205 & 0.895589 & 0.447795 \tabularnewline
58 & 0.502757 & 0.994486 & 0.497243 \tabularnewline
59 & 0.458762 & 0.917524 & 0.541238 \tabularnewline
60 & 0.455151 & 0.910303 & 0.544849 \tabularnewline
61 & 0.417264 & 0.834528 & 0.582736 \tabularnewline
62 & 0.380678 & 0.761357 & 0.619322 \tabularnewline
63 & 0.494795 & 0.98959 & 0.505205 \tabularnewline
64 & 0.70229 & 0.595421 & 0.29771 \tabularnewline
65 & 0.660948 & 0.678105 & 0.339052 \tabularnewline
66 & 0.648707 & 0.702587 & 0.351293 \tabularnewline
67 & 0.602315 & 0.795369 & 0.397685 \tabularnewline
68 & 0.595916 & 0.808167 & 0.404084 \tabularnewline
69 & 0.561237 & 0.877526 & 0.438763 \tabularnewline
70 & 0.522516 & 0.954968 & 0.477484 \tabularnewline
71 & 0.555425 & 0.889149 & 0.444575 \tabularnewline
72 & 0.5084 & 0.983199 & 0.4916 \tabularnewline
73 & 0.67572 & 0.64856 & 0.32428 \tabularnewline
74 & 0.722825 & 0.55435 & 0.277175 \tabularnewline
75 & 0.767032 & 0.465937 & 0.232968 \tabularnewline
76 & 0.728969 & 0.542063 & 0.271031 \tabularnewline
77 & 0.986315 & 0.0273709 & 0.0136855 \tabularnewline
78 & 0.981249 & 0.0375026 & 0.0187513 \tabularnewline
79 & 0.985411 & 0.0291788 & 0.0145894 \tabularnewline
80 & 0.983685 & 0.0326302 & 0.0163151 \tabularnewline
81 & 0.98761 & 0.0247803 & 0.0123902 \tabularnewline
82 & 0.988534 & 0.0229318 & 0.0114659 \tabularnewline
83 & 0.987625 & 0.0247502 & 0.0123751 \tabularnewline
84 & 0.983711 & 0.0325781 & 0.016289 \tabularnewline
85 & 0.980563 & 0.0388735 & 0.0194367 \tabularnewline
86 & 0.977433 & 0.0451345 & 0.0225672 \tabularnewline
87 & 0.972278 & 0.0554434 & 0.0277217 \tabularnewline
88 & 0.966651 & 0.0666979 & 0.0333489 \tabularnewline
89 & 0.955464 & 0.0890712 & 0.0445356 \tabularnewline
90 & 0.948138 & 0.103724 & 0.0518618 \tabularnewline
91 & 0.935236 & 0.129527 & 0.0647637 \tabularnewline
92 & 0.919616 & 0.160767 & 0.0803837 \tabularnewline
93 & 0.961325 & 0.0773494 & 0.0386747 \tabularnewline
94 & 0.968342 & 0.0633165 & 0.0316583 \tabularnewline
95 & 0.960334 & 0.0793318 & 0.0396659 \tabularnewline
96 & 0.957246 & 0.085508 & 0.042754 \tabularnewline
97 & 0.959871 & 0.0802576 & 0.0401288 \tabularnewline
98 & 0.951461 & 0.0970778 & 0.0485389 \tabularnewline
99 & 0.971135 & 0.0577307 & 0.0288653 \tabularnewline
100 & 0.965289 & 0.0694227 & 0.0347114 \tabularnewline
101 & 0.954385 & 0.0912307 & 0.0456153 \tabularnewline
102 & 0.959298 & 0.0814044 & 0.0407022 \tabularnewline
103 & 0.943673 & 0.112654 & 0.0563272 \tabularnewline
104 & 0.950354 & 0.0992919 & 0.049646 \tabularnewline
105 & 0.942095 & 0.115809 & 0.0579047 \tabularnewline
106 & 0.956479 & 0.0870424 & 0.0435212 \tabularnewline
107 & 0.939405 & 0.121191 & 0.0605953 \tabularnewline
108 & 0.927422 & 0.145156 & 0.0725779 \tabularnewline
109 & 0.902904 & 0.194193 & 0.0970964 \tabularnewline
110 & 0.917559 & 0.164881 & 0.0824407 \tabularnewline
111 & 0.888882 & 0.222236 & 0.111118 \tabularnewline
112 & 0.89882 & 0.20236 & 0.10118 \tabularnewline
113 & 0.864394 & 0.271211 & 0.135606 \tabularnewline
114 & 0.82469 & 0.35062 & 0.17531 \tabularnewline
115 & 0.773673 & 0.452654 & 0.226327 \tabularnewline
116 & 0.714281 & 0.571438 & 0.285719 \tabularnewline
117 & 0.642771 & 0.714457 & 0.357229 \tabularnewline
118 & 0.631583 & 0.736835 & 0.368417 \tabularnewline
119 & 0.584932 & 0.830136 & 0.415068 \tabularnewline
120 & 0.56962 & 0.860759 & 0.43038 \tabularnewline
121 & 0.731459 & 0.537082 & 0.268541 \tabularnewline
122 & 0.648128 & 0.703743 & 0.351872 \tabularnewline
123 & 0.583164 & 0.833673 & 0.416836 \tabularnewline
124 & 0.484105 & 0.968211 & 0.515895 \tabularnewline
125 & 0.376261 & 0.752523 & 0.623739 \tabularnewline
126 & 0.303503 & 0.607006 & 0.696497 \tabularnewline
127 & 0.236359 & 0.472719 & 0.763641 \tabularnewline
128 & 0.346597 & 0.693194 & 0.653403 \tabularnewline
129 & 0.268288 & 0.536576 & 0.731712 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&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]8[/C][C]0.519672[/C][C]0.960657[/C][C]0.480328[/C][/ROW]
[ROW][C]9[/C][C]0.704146[/C][C]0.591709[/C][C]0.295854[/C][/ROW]
[ROW][C]10[/C][C]0.870068[/C][C]0.259863[/C][C]0.129932[/C][/ROW]
[ROW][C]11[/C][C]0.92758[/C][C]0.14484[/C][C]0.0724198[/C][/ROW]
[ROW][C]12[/C][C]0.961184[/C][C]0.0776311[/C][C]0.0388156[/C][/ROW]
[ROW][C]13[/C][C]0.937811[/C][C]0.124378[/C][C]0.062189[/C][/ROW]
[ROW][C]14[/C][C]0.908727[/C][C]0.182547[/C][C]0.0912733[/C][/ROW]
[ROW][C]15[/C][C]0.876593[/C][C]0.246813[/C][C]0.123407[/C][/ROW]
[ROW][C]16[/C][C]0.882808[/C][C]0.234384[/C][C]0.117192[/C][/ROW]
[ROW][C]17[/C][C]0.853285[/C][C]0.29343[/C][C]0.146715[/C][/ROW]
[ROW][C]18[/C][C]0.80982[/C][C]0.38036[/C][C]0.19018[/C][/ROW]
[ROW][C]19[/C][C]0.832329[/C][C]0.335343[/C][C]0.167671[/C][/ROW]
[ROW][C]20[/C][C]0.785385[/C][C]0.42923[/C][C]0.214615[/C][/ROW]
[ROW][C]21[/C][C]0.728498[/C][C]0.543004[/C][C]0.271502[/C][/ROW]
[ROW][C]22[/C][C]0.698459[/C][C]0.603083[/C][C]0.301541[/C][/ROW]
[ROW][C]23[/C][C]0.710855[/C][C]0.578291[/C][C]0.289145[/C][/ROW]
[ROW][C]24[/C][C]0.660206[/C][C]0.679587[/C][C]0.339794[/C][/ROW]
[ROW][C]25[/C][C]0.804478[/C][C]0.391043[/C][C]0.195522[/C][/ROW]
[ROW][C]26[/C][C]0.759307[/C][C]0.481386[/C][C]0.240693[/C][/ROW]
[ROW][C]27[/C][C]0.767933[/C][C]0.464134[/C][C]0.232067[/C][/ROW]
[ROW][C]28[/C][C]0.844412[/C][C]0.311177[/C][C]0.155588[/C][/ROW]
[ROW][C]29[/C][C]0.805253[/C][C]0.389494[/C][C]0.194747[/C][/ROW]
[ROW][C]30[/C][C]0.851047[/C][C]0.297906[/C][C]0.148953[/C][/ROW]
[ROW][C]31[/C][C]0.817627[/C][C]0.364745[/C][C]0.182373[/C][/ROW]
[ROW][C]32[/C][C]0.854556[/C][C]0.290888[/C][C]0.145444[/C][/ROW]
[ROW][C]33[/C][C]0.910813[/C][C]0.178374[/C][C]0.0891869[/C][/ROW]
[ROW][C]34[/C][C]0.89481[/C][C]0.21038[/C][C]0.10519[/C][/ROW]
[ROW][C]35[/C][C]0.878387[/C][C]0.243225[/C][C]0.121613[/C][/ROW]
[ROW][C]36[/C][C]0.893637[/C][C]0.212726[/C][C]0.106363[/C][/ROW]
[ROW][C]37[/C][C]0.910208[/C][C]0.179583[/C][C]0.0897915[/C][/ROW]
[ROW][C]38[/C][C]0.893393[/C][C]0.213213[/C][C]0.106607[/C][/ROW]
[ROW][C]39[/C][C]0.881004[/C][C]0.237993[/C][C]0.118996[/C][/ROW]
[ROW][C]40[/C][C]0.854875[/C][C]0.29025[/C][C]0.145125[/C][/ROW]
[ROW][C]41[/C][C]0.866835[/C][C]0.266329[/C][C]0.133165[/C][/ROW]
[ROW][C]42[/C][C]0.841251[/C][C]0.317498[/C][C]0.158749[/C][/ROW]
[ROW][C]43[/C][C]0.80871[/C][C]0.382581[/C][C]0.19129[/C][/ROW]
[ROW][C]44[/C][C]0.810076[/C][C]0.379849[/C][C]0.189924[/C][/ROW]
[ROW][C]45[/C][C]0.773562[/C][C]0.452876[/C][C]0.226438[/C][/ROW]
[ROW][C]46[/C][C]0.733435[/C][C]0.533129[/C][C]0.266565[/C][/ROW]
[ROW][C]47[/C][C]0.697891[/C][C]0.604217[/C][C]0.302109[/C][/ROW]
[ROW][C]48[/C][C]0.658657[/C][C]0.682686[/C][C]0.341343[/C][/ROW]
[ROW][C]49[/C][C]0.71316[/C][C]0.573679[/C][C]0.28684[/C][/ROW]
[ROW][C]50[/C][C]0.670003[/C][C]0.659993[/C][C]0.329997[/C][/ROW]
[ROW][C]51[/C][C]0.680194[/C][C]0.639611[/C][C]0.319806[/C][/ROW]
[ROW][C]52[/C][C]0.66121[/C][C]0.67758[/C][C]0.33879[/C][/ROW]
[ROW][C]53[/C][C]0.653335[/C][C]0.693329[/C][C]0.346665[/C][/ROW]
[ROW][C]54[/C][C]0.613677[/C][C]0.772646[/C][C]0.386323[/C][/ROW]
[ROW][C]55[/C][C]0.609461[/C][C]0.781077[/C][C]0.390539[/C][/ROW]
[ROW][C]56[/C][C]0.567531[/C][C]0.864938[/C][C]0.432469[/C][/ROW]
[ROW][C]57[/C][C]0.552205[/C][C]0.895589[/C][C]0.447795[/C][/ROW]
[ROW][C]58[/C][C]0.502757[/C][C]0.994486[/C][C]0.497243[/C][/ROW]
[ROW][C]59[/C][C]0.458762[/C][C]0.917524[/C][C]0.541238[/C][/ROW]
[ROW][C]60[/C][C]0.455151[/C][C]0.910303[/C][C]0.544849[/C][/ROW]
[ROW][C]61[/C][C]0.417264[/C][C]0.834528[/C][C]0.582736[/C][/ROW]
[ROW][C]62[/C][C]0.380678[/C][C]0.761357[/C][C]0.619322[/C][/ROW]
[ROW][C]63[/C][C]0.494795[/C][C]0.98959[/C][C]0.505205[/C][/ROW]
[ROW][C]64[/C][C]0.70229[/C][C]0.595421[/C][C]0.29771[/C][/ROW]
[ROW][C]65[/C][C]0.660948[/C][C]0.678105[/C][C]0.339052[/C][/ROW]
[ROW][C]66[/C][C]0.648707[/C][C]0.702587[/C][C]0.351293[/C][/ROW]
[ROW][C]67[/C][C]0.602315[/C][C]0.795369[/C][C]0.397685[/C][/ROW]
[ROW][C]68[/C][C]0.595916[/C][C]0.808167[/C][C]0.404084[/C][/ROW]
[ROW][C]69[/C][C]0.561237[/C][C]0.877526[/C][C]0.438763[/C][/ROW]
[ROW][C]70[/C][C]0.522516[/C][C]0.954968[/C][C]0.477484[/C][/ROW]
[ROW][C]71[/C][C]0.555425[/C][C]0.889149[/C][C]0.444575[/C][/ROW]
[ROW][C]72[/C][C]0.5084[/C][C]0.983199[/C][C]0.4916[/C][/ROW]
[ROW][C]73[/C][C]0.67572[/C][C]0.64856[/C][C]0.32428[/C][/ROW]
[ROW][C]74[/C][C]0.722825[/C][C]0.55435[/C][C]0.277175[/C][/ROW]
[ROW][C]75[/C][C]0.767032[/C][C]0.465937[/C][C]0.232968[/C][/ROW]
[ROW][C]76[/C][C]0.728969[/C][C]0.542063[/C][C]0.271031[/C][/ROW]
[ROW][C]77[/C][C]0.986315[/C][C]0.0273709[/C][C]0.0136855[/C][/ROW]
[ROW][C]78[/C][C]0.981249[/C][C]0.0375026[/C][C]0.0187513[/C][/ROW]
[ROW][C]79[/C][C]0.985411[/C][C]0.0291788[/C][C]0.0145894[/C][/ROW]
[ROW][C]80[/C][C]0.983685[/C][C]0.0326302[/C][C]0.0163151[/C][/ROW]
[ROW][C]81[/C][C]0.98761[/C][C]0.0247803[/C][C]0.0123902[/C][/ROW]
[ROW][C]82[/C][C]0.988534[/C][C]0.0229318[/C][C]0.0114659[/C][/ROW]
[ROW][C]83[/C][C]0.987625[/C][C]0.0247502[/C][C]0.0123751[/C][/ROW]
[ROW][C]84[/C][C]0.983711[/C][C]0.0325781[/C][C]0.016289[/C][/ROW]
[ROW][C]85[/C][C]0.980563[/C][C]0.0388735[/C][C]0.0194367[/C][/ROW]
[ROW][C]86[/C][C]0.977433[/C][C]0.0451345[/C][C]0.0225672[/C][/ROW]
[ROW][C]87[/C][C]0.972278[/C][C]0.0554434[/C][C]0.0277217[/C][/ROW]
[ROW][C]88[/C][C]0.966651[/C][C]0.0666979[/C][C]0.0333489[/C][/ROW]
[ROW][C]89[/C][C]0.955464[/C][C]0.0890712[/C][C]0.0445356[/C][/ROW]
[ROW][C]90[/C][C]0.948138[/C][C]0.103724[/C][C]0.0518618[/C][/ROW]
[ROW][C]91[/C][C]0.935236[/C][C]0.129527[/C][C]0.0647637[/C][/ROW]
[ROW][C]92[/C][C]0.919616[/C][C]0.160767[/C][C]0.0803837[/C][/ROW]
[ROW][C]93[/C][C]0.961325[/C][C]0.0773494[/C][C]0.0386747[/C][/ROW]
[ROW][C]94[/C][C]0.968342[/C][C]0.0633165[/C][C]0.0316583[/C][/ROW]
[ROW][C]95[/C][C]0.960334[/C][C]0.0793318[/C][C]0.0396659[/C][/ROW]
[ROW][C]96[/C][C]0.957246[/C][C]0.085508[/C][C]0.042754[/C][/ROW]
[ROW][C]97[/C][C]0.959871[/C][C]0.0802576[/C][C]0.0401288[/C][/ROW]
[ROW][C]98[/C][C]0.951461[/C][C]0.0970778[/C][C]0.0485389[/C][/ROW]
[ROW][C]99[/C][C]0.971135[/C][C]0.0577307[/C][C]0.0288653[/C][/ROW]
[ROW][C]100[/C][C]0.965289[/C][C]0.0694227[/C][C]0.0347114[/C][/ROW]
[ROW][C]101[/C][C]0.954385[/C][C]0.0912307[/C][C]0.0456153[/C][/ROW]
[ROW][C]102[/C][C]0.959298[/C][C]0.0814044[/C][C]0.0407022[/C][/ROW]
[ROW][C]103[/C][C]0.943673[/C][C]0.112654[/C][C]0.0563272[/C][/ROW]
[ROW][C]104[/C][C]0.950354[/C][C]0.0992919[/C][C]0.049646[/C][/ROW]
[ROW][C]105[/C][C]0.942095[/C][C]0.115809[/C][C]0.0579047[/C][/ROW]
[ROW][C]106[/C][C]0.956479[/C][C]0.0870424[/C][C]0.0435212[/C][/ROW]
[ROW][C]107[/C][C]0.939405[/C][C]0.121191[/C][C]0.0605953[/C][/ROW]
[ROW][C]108[/C][C]0.927422[/C][C]0.145156[/C][C]0.0725779[/C][/ROW]
[ROW][C]109[/C][C]0.902904[/C][C]0.194193[/C][C]0.0970964[/C][/ROW]
[ROW][C]110[/C][C]0.917559[/C][C]0.164881[/C][C]0.0824407[/C][/ROW]
[ROW][C]111[/C][C]0.888882[/C][C]0.222236[/C][C]0.111118[/C][/ROW]
[ROW][C]112[/C][C]0.89882[/C][C]0.20236[/C][C]0.10118[/C][/ROW]
[ROW][C]113[/C][C]0.864394[/C][C]0.271211[/C][C]0.135606[/C][/ROW]
[ROW][C]114[/C][C]0.82469[/C][C]0.35062[/C][C]0.17531[/C][/ROW]
[ROW][C]115[/C][C]0.773673[/C][C]0.452654[/C][C]0.226327[/C][/ROW]
[ROW][C]116[/C][C]0.714281[/C][C]0.571438[/C][C]0.285719[/C][/ROW]
[ROW][C]117[/C][C]0.642771[/C][C]0.714457[/C][C]0.357229[/C][/ROW]
[ROW][C]118[/C][C]0.631583[/C][C]0.736835[/C][C]0.368417[/C][/ROW]
[ROW][C]119[/C][C]0.584932[/C][C]0.830136[/C][C]0.415068[/C][/ROW]
[ROW][C]120[/C][C]0.56962[/C][C]0.860759[/C][C]0.43038[/C][/ROW]
[ROW][C]121[/C][C]0.731459[/C][C]0.537082[/C][C]0.268541[/C][/ROW]
[ROW][C]122[/C][C]0.648128[/C][C]0.703743[/C][C]0.351872[/C][/ROW]
[ROW][C]123[/C][C]0.583164[/C][C]0.833673[/C][C]0.416836[/C][/ROW]
[ROW][C]124[/C][C]0.484105[/C][C]0.968211[/C][C]0.515895[/C][/ROW]
[ROW][C]125[/C][C]0.376261[/C][C]0.752523[/C][C]0.623739[/C][/ROW]
[ROW][C]126[/C][C]0.303503[/C][C]0.607006[/C][C]0.696497[/C][/ROW]
[ROW][C]127[/C][C]0.236359[/C][C]0.472719[/C][C]0.763641[/C][/ROW]
[ROW][C]128[/C][C]0.346597[/C][C]0.693194[/C][C]0.653403[/C][/ROW]
[ROW][C]129[/C][C]0.268288[/C][C]0.536576[/C][C]0.731712[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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
8	0.519672	0.960657	0.480328
9	0.704146	0.591709	0.295854
10	0.870068	0.259863	0.129932
11	0.92758	0.14484	0.0724198
12	0.961184	0.0776311	0.0388156
13	0.937811	0.124378	0.062189
14	0.908727	0.182547	0.0912733
15	0.876593	0.246813	0.123407
16	0.882808	0.234384	0.117192
17	0.853285	0.29343	0.146715
18	0.80982	0.38036	0.19018
19	0.832329	0.335343	0.167671
20	0.785385	0.42923	0.214615
21	0.728498	0.543004	0.271502
22	0.698459	0.603083	0.301541
23	0.710855	0.578291	0.289145
24	0.660206	0.679587	0.339794
25	0.804478	0.391043	0.195522
26	0.759307	0.481386	0.240693
27	0.767933	0.464134	0.232067
28	0.844412	0.311177	0.155588
29	0.805253	0.389494	0.194747
30	0.851047	0.297906	0.148953
31	0.817627	0.364745	0.182373
32	0.854556	0.290888	0.145444
33	0.910813	0.178374	0.0891869
34	0.89481	0.21038	0.10519
35	0.878387	0.243225	0.121613
36	0.893637	0.212726	0.106363
37	0.910208	0.179583	0.0897915
38	0.893393	0.213213	0.106607
39	0.881004	0.237993	0.118996
40	0.854875	0.29025	0.145125
41	0.866835	0.266329	0.133165
42	0.841251	0.317498	0.158749
43	0.80871	0.382581	0.19129
44	0.810076	0.379849	0.189924
45	0.773562	0.452876	0.226438
46	0.733435	0.533129	0.266565
47	0.697891	0.604217	0.302109
48	0.658657	0.682686	0.341343
49	0.71316	0.573679	0.28684
50	0.670003	0.659993	0.329997
51	0.680194	0.639611	0.319806
52	0.66121	0.67758	0.33879
53	0.653335	0.693329	0.346665
54	0.613677	0.772646	0.386323
55	0.609461	0.781077	0.390539
56	0.567531	0.864938	0.432469
57	0.552205	0.895589	0.447795
58	0.502757	0.994486	0.497243
59	0.458762	0.917524	0.541238
60	0.455151	0.910303	0.544849
61	0.417264	0.834528	0.582736
62	0.380678	0.761357	0.619322
63	0.494795	0.98959	0.505205
64	0.70229	0.595421	0.29771
65	0.660948	0.678105	0.339052
66	0.648707	0.702587	0.351293
67	0.602315	0.795369	0.397685
68	0.595916	0.808167	0.404084
69	0.561237	0.877526	0.438763
70	0.522516	0.954968	0.477484
71	0.555425	0.889149	0.444575
72	0.5084	0.983199	0.4916
73	0.67572	0.64856	0.32428
74	0.722825	0.55435	0.277175
75	0.767032	0.465937	0.232968
76	0.728969	0.542063	0.271031
77	0.986315	0.0273709	0.0136855
78	0.981249	0.0375026	0.0187513
79	0.985411	0.0291788	0.0145894
80	0.983685	0.0326302	0.0163151
81	0.98761	0.0247803	0.0123902
82	0.988534	0.0229318	0.0114659
83	0.987625	0.0247502	0.0123751
84	0.983711	0.0325781	0.016289
85	0.980563	0.0388735	0.0194367
86	0.977433	0.0451345	0.0225672
87	0.972278	0.0554434	0.0277217
88	0.966651	0.0666979	0.0333489
89	0.955464	0.0890712	0.0445356
90	0.948138	0.103724	0.0518618
91	0.935236	0.129527	0.0647637
92	0.919616	0.160767	0.0803837
93	0.961325	0.0773494	0.0386747
94	0.968342	0.0633165	0.0316583
95	0.960334	0.0793318	0.0396659
96	0.957246	0.085508	0.042754
97	0.959871	0.0802576	0.0401288
98	0.951461	0.0970778	0.0485389
99	0.971135	0.0577307	0.0288653
100	0.965289	0.0694227	0.0347114
101	0.954385	0.0912307	0.0456153
102	0.959298	0.0814044	0.0407022
103	0.943673	0.112654	0.0563272
104	0.950354	0.0992919	0.049646
105	0.942095	0.115809	0.0579047
106	0.956479	0.0870424	0.0435212
107	0.939405	0.121191	0.0605953
108	0.927422	0.145156	0.0725779
109	0.902904	0.194193	0.0970964
110	0.917559	0.164881	0.0824407
111	0.888882	0.222236	0.111118
112	0.89882	0.20236	0.10118
113	0.864394	0.271211	0.135606
114	0.82469	0.35062	0.17531
115	0.773673	0.452654	0.226327
116	0.714281	0.571438	0.285719
117	0.642771	0.714457	0.357229
118	0.631583	0.736835	0.368417
119	0.584932	0.830136	0.415068
120	0.56962	0.860759	0.43038
121	0.731459	0.537082	0.268541
122	0.648128	0.703743	0.351872
123	0.583164	0.833673	0.416836
124	0.484105	0.968211	0.515895
125	0.376261	0.752523	0.623739
126	0.303503	0.607006	0.696497
127	0.236359	0.472719	0.763641
128	0.346597	0.693194	0.653403
129	0.268288	0.536576	0.731712

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	10	0.0819672	NOK
10% type I error level	26	0.213115	NOK

\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 & 10 & 0.0819672 & NOK \tabularnewline
10% type I error level & 26 & 0.213115 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266190&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]10[/C][C]0.0819672[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]26[/C][C]0.213115[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266190&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266190&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	10	0.0819672	NOK
10% type I error level	26	0.213115	NOK

Figure 1

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Figure 2

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Figure 3

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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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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