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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 03 Dec 2014 17:34:51 +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/03/t14176286074xt8b6b0usfap7s.htm/, Retrieved Thu, 16 May 2024 23:19:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=263008, Retrieved Thu, 16 May 2024 23:19:30 +0000

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-       [Multiple Regression] [2011 en numeracy] [2014-12-03 17:34:51] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]

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'12.9' 0 0 21 21 149 18 68
'7.4' 0 0 23 26 152 7 55
'12.2' 0 1 22 22 139 31 39
'12.8' 0 0 21 22 148 39 32
'7.4' 0 1 21 18 158 46 62
'6.7' 0 1 21 23 128 31 33
'12.6' 0 1 21 12 224 67 52
'14.8' 0 0 21 20 159 35 62
'13.3' 0 1 23 22 105 52 77
'11.1' 0 1 22 21 159 77 76
'8.2' 0 1 25 19 167 37 41
'11.4' 0 1 21 22 165 32 48
'6.4' 0 1 23 15 159 36 63
'10.6' 0 1 22 20 119 38 30
'12.0' 0 0 21 19 176 69 78
'6.3' 0 0 21 18 54 21 19
'11.3' 1 0 25 15 91 26 31
'11.9' 0 1 21 20 163 54 66
'9.3' 0 0 21 21 124 36 35
'9.6' 1 1 20 21 137 42 42
'10.0' 0 0 24 15 121 23 45
'6.4' 0 1 23 16 153 34 21
'13.8' 0 1 21 23 148 112 25
'10.8' 0 0 24 21 221 35 44
'13.8' 0 1 23 18 188 47 69
'11.7' 0 1 21 25 149 47 54
'10.9' 0 1 22 9 244 37 74
'16.1' 1 1 20 30 148 109 80
'13.4' 1 0 18 20 92 24 42
'9.9' 0 1 21 23 150 20 61
'11.5' 0 0 22 16 153 22 41
'8.3' 0 0 22 16 94 23 46
'11.7' 0 0 21 19 156 32 39
'6.1' 0 1 23 25 146 7 63
'9.0' 0 1 21 25 132 30 34
'9.7' 0 1 25 18 161 92 51
'10.8' 0 1 22 23 105 43 42
'10.3' 0 1 22 21 97 55 31
'10.4' 0 0 20 10 151 16 39
'12.7' 1 1 21 14 131 49 20
'9.3' 0 1 21 22 166 71 49
'11.8' 0 0 21 26 157 43 53
'5.9' 0 1 22 23 111 29 31
'11.4' 0 1 21 23 145 56 39
'13.0' 0 1 24 24 162 46 54
'10.8' 0 1 22 24 163 19 49
'12.3' 1 1 22 18 59 23 34
'11.3' 0 0 21 23 187 59 46
'11.8' 0 1 22 15 109 30 55
'7.9' 1 1 19 19 90 61 42
'12.7' 0 0 22 16 105 7 50
'12.3' 1 1 23 25 83 38 13
'11.6' 1 1 20 23 116 32 37
'6.7' 1 1 20 17 42 16 25
'10.9' 0 1 23 19 148 19 30
'12.1' 1 1 20 21 155 22 28
'13.3' 0 1 23 18 125 48 45
'10.1' 0 1 21 27 116 23 35
'5.7' 1 0 22 21 128 26 28
'14.3' 0 1 21 13 138 33 41
'8.0' 1 0 21 8 49 9 6
'13.3' 1 1 19 29 96 24 45
'9.3' 0 1 22 28 164 34 73
'12.5' 0 0 21 23 162 48 17
'7.6' 0 0 21 21 99 18 40
'15.9' 0 1 21 19 202 43 64
'9.2' 0 0 21 19 186 33 37
'9.1' 1 1 21 20 66 28 25
'11.1' 0 0 21 18 183 71 65
'13.0' 0 1 22 19 214 26 100
'14.5' 0 1 22 17 188 67 28
'12.2' 1 0 18 19 104 34 35
'12.3' 0 0 21 25 177 80 56
'11.4' 0 0 23 19 126 29 29
'8.8' 1 0 19 22 76 16 43
'14.6' 1 1 19 23 99 59 59
'7.3' 0 1 23 26 157 58 52
'12.6' 0 0 21 14 139 32 50
'13.0' 0 0 21 16 162 43 59
'12.6' 1 1 21 24 108 38 27
'13.2' 0 0 20 20 159 29 61
'9.9' 1 0 19 12 74 36 28
'7.7' 0 1 21 24 110 32 51
'10.5' 1 0 19 22 96 35 35
'13.4' 1 0 19 12 116 21 29
'10.9' 1 0 19 22 87 29 48
'4.3' 1 1 20 20 97 12 25
'10.3' 1 0 19 10 127 37 44
'11.8' 1 1 19 23 106 37 64
'11.2' 1 1 19 17 80 47 32
'11.4' 1 0 20 22 74 51 20
'8.6' 1 0 19 24 91 32 28
'13.2' 1 0 18 18 133 21 34
'12.6' 1 1 19 21 74 13 31
'5.6' 1 1 21 20 114 14 26
'9.9' 1 1 18 20 140 -2 58
'8.8' 1 0 18 22 95 20 23
'7.7' 1 1 19 19 98 24 21
'9.0' 1 0 21 20 121 11 21
'7.3' 1 1 20 26 126 23 33
'11.4' 1 1 24 23 98 24 16
'13.6' 1 1 22 24 95 14 20
'7.9' 1 1 21 21 110 52 37
'10.7' 1 1 21 21 70 15 35
'10.3' 1 0 19 19 102 23 33
'8.3' 1 1 19 8 86 19 27
'9.6' 1 1 20 17 130 35 41
'14.2' 1 1 18 20 96 24 40
'8.5' 1 0 19 11 102 39 35
'13.5' 1 0 19 8 100 29 28
'4.9' 1 0 20 15 94 13 32
'6.4' 1 0 21 18 52 8 22
'9.6' 1 0 18 18 98 18 44
'11.6' 1 0 19 19 118 24 27
'11.1' 1 1 19 19 99 19 17

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&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]5 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=263008&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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	5 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 11.0509 + 0.545912programma[t] -0.247343gender[t] -0.198229age[t] -0.00747823NUMERACYTOT[t] + 0.0118266LFM[t] + 0.0356133PRH[t] + 0.0235348CH[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  11.0509 +  0.545912programma[t] -0.247343gender[t] -0.198229age[t] -0.00747823NUMERACYTOT[t] +  0.0118266LFM[t] +  0.0356133PRH[t] +  0.0235348CH[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  11.0509 +  0.545912programma[t] -0.247343gender[t] -0.198229age[t] -0.00747823NUMERACYTOT[t] +  0.0118266LFM[t] +  0.0356133PRH[t] +  0.0235348CH[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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
TOT[t] = + 11.0509 + 0.545912programma[t] -0.247343gender[t] -0.198229age[t] -0.00747823NUMERACYTOT[t] + 0.0118266LFM[t] + 0.0356133PRH[t] + 0.0235348CH[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)	11.0509	4.17335	2.648	0.00932208	0.00466104
programma	0.545912	0.692737	0.7881	0.432409	0.216205
gender	-0.247343	0.479321	-0.516	0.606899	0.30345
age	-0.198229	0.175745	-1.128	0.261872	0.130936
NUMERACYTOT	-0.00747823	0.0519011	-0.1441	0.885704	0.442852
LFM	0.0118266	0.0082825	1.428	0.156233	0.0781167
PRH	0.0356133	0.0123364	2.887	0.00470868	0.00235434
CH	0.0235348	0.0166179	1.416	0.159612	0.079806

\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) & 11.0509 & 4.17335 & 2.648 & 0.00932208 & 0.00466104 \tabularnewline
programma & 0.545912 & 0.692737 & 0.7881 & 0.432409 & 0.216205 \tabularnewline
gender & -0.247343 & 0.479321 & -0.516 & 0.606899 & 0.30345 \tabularnewline
age & -0.198229 & 0.175745 & -1.128 & 0.261872 & 0.130936 \tabularnewline
NUMERACYTOT & -0.00747823 & 0.0519011 & -0.1441 & 0.885704 & 0.442852 \tabularnewline
LFM & 0.0118266 & 0.0082825 & 1.428 & 0.156233 & 0.0781167 \tabularnewline
PRH & 0.0356133 & 0.0123364 & 2.887 & 0.00470868 & 0.00235434 \tabularnewline
CH & 0.0235348 & 0.0166179 & 1.416 & 0.159612 & 0.079806 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&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]11.0509[/C][C]4.17335[/C][C]2.648[/C][C]0.00932208[/C][C]0.00466104[/C][/ROW]
[ROW][C]programma[/C][C]0.545912[/C][C]0.692737[/C][C]0.7881[/C][C]0.432409[/C][C]0.216205[/C][/ROW]
[ROW][C]gender[/C][C]-0.247343[/C][C]0.479321[/C][C]-0.516[/C][C]0.606899[/C][C]0.30345[/C][/ROW]
[ROW][C]age[/C][C]-0.198229[/C][C]0.175745[/C][C]-1.128[/C][C]0.261872[/C][C]0.130936[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00747823[/C][C]0.0519011[/C][C]-0.1441[/C][C]0.885704[/C][C]0.442852[/C][/ROW]
[ROW][C]LFM[/C][C]0.0118266[/C][C]0.0082825[/C][C]1.428[/C][C]0.156233[/C][C]0.0781167[/C][/ROW]
[ROW][C]PRH[/C][C]0.0356133[/C][C]0.0123364[/C][C]2.887[/C][C]0.00470868[/C][C]0.00235434[/C][/ROW]
[ROW][C]CH[/C][C]0.0235348[/C][C]0.0166179[/C][C]1.416[/C][C]0.159612[/C][C]0.079806[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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)	11.0509	4.17335	2.648	0.00932208	0.00466104
programma	0.545912	0.692737	0.7881	0.432409	0.216205
gender	-0.247343	0.479321	-0.516	0.606899	0.30345
age	-0.198229	0.175745	-1.128	0.261872	0.130936
NUMERACYTOT	-0.00747823	0.0519011	-0.1441	0.885704	0.442852
LFM	0.0118266	0.0082825	1.428	0.156233	0.0781167
PRH	0.0356133	0.0123364	2.887	0.00470868	0.00235434
CH	0.0235348	0.0166179	1.416	0.159612	0.079806

Multiple Linear Regression - Regression Statistics
Multiple R	0.433497
R-squared	0.187919
Adjusted R-squared	0.134793
F-TEST (value)	3.53719
F-TEST (DF numerator)	7
F-TEST (DF denominator)	107
p-value	0.0018613
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33791
Sum Squared Residuals	584.844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.433497 \tabularnewline
R-squared & 0.187919 \tabularnewline
Adjusted R-squared & 0.134793 \tabularnewline
F-TEST (value) & 3.53719 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 107 \tabularnewline
p-value & 0.0018613 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33791 \tabularnewline
Sum Squared Residuals & 584.844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.433497[/C][/ROW]
[ROW][C]R-squared[/C][C]0.187919[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.134793[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.53719[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]107[/C][/ROW]
[ROW][C]p-value[/C][C]0.0018613[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33791[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]584.844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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	0.433497
R-squared	0.187919
Adjusted R-squared	0.134793
F-TEST (value)	3.53719
F-TEST (DF numerator)	7
F-TEST (DF denominator)	107
p-value	0.0018613
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33791
Sum Squared Residuals	584.844

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	10.7346	2.1654
2	7.4	9.63854	-2.23854
3	12.2	9.94375	2.25625
4	12.8	10.6159	2.18407
5	7.4	11.4721	-4.0721
6	6.7	9.8632	-3.1632
7	12.6	12.8101	-0.210054
8	14.8	11.3246	3.47543
9	13.3	10.9856	2.31438
10	11.1	12.6968	-1.59676
11	8.2	9.96339	-1.76339
12	11.4	10.6969	0.703101
13	6.4	10.7773	-4.3773
14	10.6	9.75966	0.840342
15	12	13.1205	-1.12051
16	6.3	8.58715	-2.28715
17	11.3	9.26065	2.03935
18	11.9	11.8953	0.00467874
19	9.3	10.3033	-1.00333
20	9.6	11.3323	-1.7323
21	10	9.49041	0.509589
22	6.4	9.63918	-3.23918
23	13.8	12.7961	1.00387
24	10.8	11.032	-0.232023
25	13.8	11.6308	2.1692
26	11.7	11.1606	0.539352
27	10.9	12.3202	-1.42016
28	16.1	14.6755	1.4245
29	13.4	10.8103	2.58966
30	9.9	10.3906	-0.490615
31	11.5	10.1281	1.37191
32	8.3	9.58361	-1.28361
33	11.7	10.6484	1.05158
34	6.1	9.51599	-3.41599
35	9	9.88347	-0.883474
36	9.7	12.094	-2.39399
37	10.8	10.0321	0.767864
38	10.3	10.121	0.179043
39	10.4	10.285	0.114988
40	12.7	10.847	1.85301
41	9.3	12.1212	-2.82118
42	11.8	11.3291	0.470863
43	5.9	9.34563	-3.44563
44	11.4	11.0958	0.304204
45	13	10.6916	2.30843
46	10.8	10.0206	0.779378
47	12.3	9.17087	3.12913
48	11.3	12.1114	-0.811439
49	11.8	9.98225	1.81775
50	7.9	11.6663	-3.76629
51	12.7	9.23803	3.46197
52	12.3	9.2441	3.0559
53	11.6	10.5952	1.00482
54	6.7	8.91265	-2.21265
55	10.9	9.23523	1.66477
56	12.1	10.5034	1.59658
57	13.3	10.3565	2.9435
58	10.1	9.45353	0.646466
59	5.7	10.1774	-4.47745
60	14.3	10.3158	3.98424
61	8	8.4154	-0.415401
62	13.3	10.4154	2.88462
63	9.3	11.1016	-1.80157
64	12.5	10.7415	1.75848
65	7.6	9.4843	-1.8843
66	15.9	11.9252	3.97478
67	9.2	10.9918	-1.79177
68	9.1	9.40318	-0.303184
69	11.1	12.976	-1.87604
70	13	12.1107	0.889265
71	14.5	11.5838	2.91616
72	12.2	11.1511	1.04887
73	12.3	12.9614	-0.661444
74	11.4	9.55498	1.84502
75	8.8	10.1466	-1.34656
76	14.6	12.0717	2.52832
77	7.3	11.196	-3.896
78	12.6	10.7436	1.85635
79	13	11.6043	1.39574
80	12.6	10.2732	2.32681
81	13.2	11.2856	1.91442
82	9.9	10.5569	-0.656934
83	7.7	10.1021	-2.40209
84	10.5	10.8715	-0.371466
85	13.4	10.543	2.85701
86	10.9	10.8573	0.0427005
87	4.3	9.39822	-5.09822
88	10.3	11.6109	-1.31087
89	11.8	11.4886	0.311354
90	11.2	10.829	0.370955
91	11.4	10.6298	0.770156
92	8.6	10.5258	-1.92579
93	13.2	11.0151	2.18493
94	12.6	9.49378	3.10622
95	5.6	9.49581	-3.89581
96	9.9	10.5813	-0.681285
97	8.8	10.2413	-1.44125
98	7.7	9.94898	-2.24898
99	9	9.60142	-0.601423
100	7.3	10.2763	-2.97635
101	11.4	8.81025	2.58975
102	13.6	8.90175	4.69825
103	7.9	11.0532	-3.15321
104	10.7	9.21539	1.48461
105	10.3	10.4904	-0.190431
106	8.3	9.85246	-1.55246
107	9.6	11.0066	-1.4066
108	14.2	10.5632	3.63676
109	8.5	11.1671	-2.66714
110	13.5	10.645	2.85496
111	4.9	9.84783	-4.94783
112	6.4	8.71704	-2.31704
113	9.6	10.7296	-1.12965
114	11.6	10.5741	1.02594
115	11.1	9.6886	1.4114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.7346 & 2.1654 \tabularnewline
2 & 7.4 & 9.63854 & -2.23854 \tabularnewline
3 & 12.2 & 9.94375 & 2.25625 \tabularnewline
4 & 12.8 & 10.6159 & 2.18407 \tabularnewline
5 & 7.4 & 11.4721 & -4.0721 \tabularnewline
6 & 6.7 & 9.8632 & -3.1632 \tabularnewline
7 & 12.6 & 12.8101 & -0.210054 \tabularnewline
8 & 14.8 & 11.3246 & 3.47543 \tabularnewline
9 & 13.3 & 10.9856 & 2.31438 \tabularnewline
10 & 11.1 & 12.6968 & -1.59676 \tabularnewline
11 & 8.2 & 9.96339 & -1.76339 \tabularnewline
12 & 11.4 & 10.6969 & 0.703101 \tabularnewline
13 & 6.4 & 10.7773 & -4.3773 \tabularnewline
14 & 10.6 & 9.75966 & 0.840342 \tabularnewline
15 & 12 & 13.1205 & -1.12051 \tabularnewline
16 & 6.3 & 8.58715 & -2.28715 \tabularnewline
17 & 11.3 & 9.26065 & 2.03935 \tabularnewline
18 & 11.9 & 11.8953 & 0.00467874 \tabularnewline
19 & 9.3 & 10.3033 & -1.00333 \tabularnewline
20 & 9.6 & 11.3323 & -1.7323 \tabularnewline
21 & 10 & 9.49041 & 0.509589 \tabularnewline
22 & 6.4 & 9.63918 & -3.23918 \tabularnewline
23 & 13.8 & 12.7961 & 1.00387 \tabularnewline
24 & 10.8 & 11.032 & -0.232023 \tabularnewline
25 & 13.8 & 11.6308 & 2.1692 \tabularnewline
26 & 11.7 & 11.1606 & 0.539352 \tabularnewline
27 & 10.9 & 12.3202 & -1.42016 \tabularnewline
28 & 16.1 & 14.6755 & 1.4245 \tabularnewline
29 & 13.4 & 10.8103 & 2.58966 \tabularnewline
30 & 9.9 & 10.3906 & -0.490615 \tabularnewline
31 & 11.5 & 10.1281 & 1.37191 \tabularnewline
32 & 8.3 & 9.58361 & -1.28361 \tabularnewline
33 & 11.7 & 10.6484 & 1.05158 \tabularnewline
34 & 6.1 & 9.51599 & -3.41599 \tabularnewline
35 & 9 & 9.88347 & -0.883474 \tabularnewline
36 & 9.7 & 12.094 & -2.39399 \tabularnewline
37 & 10.8 & 10.0321 & 0.767864 \tabularnewline
38 & 10.3 & 10.121 & 0.179043 \tabularnewline
39 & 10.4 & 10.285 & 0.114988 \tabularnewline
40 & 12.7 & 10.847 & 1.85301 \tabularnewline
41 & 9.3 & 12.1212 & -2.82118 \tabularnewline
42 & 11.8 & 11.3291 & 0.470863 \tabularnewline
43 & 5.9 & 9.34563 & -3.44563 \tabularnewline
44 & 11.4 & 11.0958 & 0.304204 \tabularnewline
45 & 13 & 10.6916 & 2.30843 \tabularnewline
46 & 10.8 & 10.0206 & 0.779378 \tabularnewline
47 & 12.3 & 9.17087 & 3.12913 \tabularnewline
48 & 11.3 & 12.1114 & -0.811439 \tabularnewline
49 & 11.8 & 9.98225 & 1.81775 \tabularnewline
50 & 7.9 & 11.6663 & -3.76629 \tabularnewline
51 & 12.7 & 9.23803 & 3.46197 \tabularnewline
52 & 12.3 & 9.2441 & 3.0559 \tabularnewline
53 & 11.6 & 10.5952 & 1.00482 \tabularnewline
54 & 6.7 & 8.91265 & -2.21265 \tabularnewline
55 & 10.9 & 9.23523 & 1.66477 \tabularnewline
56 & 12.1 & 10.5034 & 1.59658 \tabularnewline
57 & 13.3 & 10.3565 & 2.9435 \tabularnewline
58 & 10.1 & 9.45353 & 0.646466 \tabularnewline
59 & 5.7 & 10.1774 & -4.47745 \tabularnewline
60 & 14.3 & 10.3158 & 3.98424 \tabularnewline
61 & 8 & 8.4154 & -0.415401 \tabularnewline
62 & 13.3 & 10.4154 & 2.88462 \tabularnewline
63 & 9.3 & 11.1016 & -1.80157 \tabularnewline
64 & 12.5 & 10.7415 & 1.75848 \tabularnewline
65 & 7.6 & 9.4843 & -1.8843 \tabularnewline
66 & 15.9 & 11.9252 & 3.97478 \tabularnewline
67 & 9.2 & 10.9918 & -1.79177 \tabularnewline
68 & 9.1 & 9.40318 & -0.303184 \tabularnewline
69 & 11.1 & 12.976 & -1.87604 \tabularnewline
70 & 13 & 12.1107 & 0.889265 \tabularnewline
71 & 14.5 & 11.5838 & 2.91616 \tabularnewline
72 & 12.2 & 11.1511 & 1.04887 \tabularnewline
73 & 12.3 & 12.9614 & -0.661444 \tabularnewline
74 & 11.4 & 9.55498 & 1.84502 \tabularnewline
75 & 8.8 & 10.1466 & -1.34656 \tabularnewline
76 & 14.6 & 12.0717 & 2.52832 \tabularnewline
77 & 7.3 & 11.196 & -3.896 \tabularnewline
78 & 12.6 & 10.7436 & 1.85635 \tabularnewline
79 & 13 & 11.6043 & 1.39574 \tabularnewline
80 & 12.6 & 10.2732 & 2.32681 \tabularnewline
81 & 13.2 & 11.2856 & 1.91442 \tabularnewline
82 & 9.9 & 10.5569 & -0.656934 \tabularnewline
83 & 7.7 & 10.1021 & -2.40209 \tabularnewline
84 & 10.5 & 10.8715 & -0.371466 \tabularnewline
85 & 13.4 & 10.543 & 2.85701 \tabularnewline
86 & 10.9 & 10.8573 & 0.0427005 \tabularnewline
87 & 4.3 & 9.39822 & -5.09822 \tabularnewline
88 & 10.3 & 11.6109 & -1.31087 \tabularnewline
89 & 11.8 & 11.4886 & 0.311354 \tabularnewline
90 & 11.2 & 10.829 & 0.370955 \tabularnewline
91 & 11.4 & 10.6298 & 0.770156 \tabularnewline
92 & 8.6 & 10.5258 & -1.92579 \tabularnewline
93 & 13.2 & 11.0151 & 2.18493 \tabularnewline
94 & 12.6 & 9.49378 & 3.10622 \tabularnewline
95 & 5.6 & 9.49581 & -3.89581 \tabularnewline
96 & 9.9 & 10.5813 & -0.681285 \tabularnewline
97 & 8.8 & 10.2413 & -1.44125 \tabularnewline
98 & 7.7 & 9.94898 & -2.24898 \tabularnewline
99 & 9 & 9.60142 & -0.601423 \tabularnewline
100 & 7.3 & 10.2763 & -2.97635 \tabularnewline
101 & 11.4 & 8.81025 & 2.58975 \tabularnewline
102 & 13.6 & 8.90175 & 4.69825 \tabularnewline
103 & 7.9 & 11.0532 & -3.15321 \tabularnewline
104 & 10.7 & 9.21539 & 1.48461 \tabularnewline
105 & 10.3 & 10.4904 & -0.190431 \tabularnewline
106 & 8.3 & 9.85246 & -1.55246 \tabularnewline
107 & 9.6 & 11.0066 & -1.4066 \tabularnewline
108 & 14.2 & 10.5632 & 3.63676 \tabularnewline
109 & 8.5 & 11.1671 & -2.66714 \tabularnewline
110 & 13.5 & 10.645 & 2.85496 \tabularnewline
111 & 4.9 & 9.84783 & -4.94783 \tabularnewline
112 & 6.4 & 8.71704 & -2.31704 \tabularnewline
113 & 9.6 & 10.7296 & -1.12965 \tabularnewline
114 & 11.6 & 10.5741 & 1.02594 \tabularnewline
115 & 11.1 & 9.6886 & 1.4114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&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]12.9[/C][C]10.7346[/C][C]2.1654[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]9.63854[/C][C]-2.23854[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]9.94375[/C][C]2.25625[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]10.6159[/C][C]2.18407[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]11.4721[/C][C]-4.0721[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]9.8632[/C][C]-3.1632[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]12.8101[/C][C]-0.210054[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]11.3246[/C][C]3.47543[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]10.9856[/C][C]2.31438[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]12.6968[/C][C]-1.59676[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]9.96339[/C][C]-1.76339[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]10.6969[/C][C]0.703101[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]10.7773[/C][C]-4.3773[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]9.75966[/C][C]0.840342[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]13.1205[/C][C]-1.12051[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]8.58715[/C][C]-2.28715[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]9.26065[/C][C]2.03935[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]11.8953[/C][C]0.00467874[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]10.3033[/C][C]-1.00333[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]11.3323[/C][C]-1.7323[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]9.49041[/C][C]0.509589[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]9.63918[/C][C]-3.23918[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]12.7961[/C][C]1.00387[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]11.032[/C][C]-0.232023[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]11.6308[/C][C]2.1692[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.1606[/C][C]0.539352[/C][/ROW]
[ROW][C]27[/C][C]10.9[/C][C]12.3202[/C][C]-1.42016[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.6755[/C][C]1.4245[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]10.8103[/C][C]2.58966[/C][/ROW]
[ROW][C]30[/C][C]9.9[/C][C]10.3906[/C][C]-0.490615[/C][/ROW]
[ROW][C]31[/C][C]11.5[/C][C]10.1281[/C][C]1.37191[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]9.58361[/C][C]-1.28361[/C][/ROW]
[ROW][C]33[/C][C]11.7[/C][C]10.6484[/C][C]1.05158[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]9.51599[/C][C]-3.41599[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.88347[/C][C]-0.883474[/C][/ROW]
[ROW][C]36[/C][C]9.7[/C][C]12.094[/C][C]-2.39399[/C][/ROW]
[ROW][C]37[/C][C]10.8[/C][C]10.0321[/C][C]0.767864[/C][/ROW]
[ROW][C]38[/C][C]10.3[/C][C]10.121[/C][C]0.179043[/C][/ROW]
[ROW][C]39[/C][C]10.4[/C][C]10.285[/C][C]0.114988[/C][/ROW]
[ROW][C]40[/C][C]12.7[/C][C]10.847[/C][C]1.85301[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]12.1212[/C][C]-2.82118[/C][/ROW]
[ROW][C]42[/C][C]11.8[/C][C]11.3291[/C][C]0.470863[/C][/ROW]
[ROW][C]43[/C][C]5.9[/C][C]9.34563[/C][C]-3.44563[/C][/ROW]
[ROW][C]44[/C][C]11.4[/C][C]11.0958[/C][C]0.304204[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]10.6916[/C][C]2.30843[/C][/ROW]
[ROW][C]46[/C][C]10.8[/C][C]10.0206[/C][C]0.779378[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]9.17087[/C][C]3.12913[/C][/ROW]
[ROW][C]48[/C][C]11.3[/C][C]12.1114[/C][C]-0.811439[/C][/ROW]
[ROW][C]49[/C][C]11.8[/C][C]9.98225[/C][C]1.81775[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]11.6663[/C][C]-3.76629[/C][/ROW]
[ROW][C]51[/C][C]12.7[/C][C]9.23803[/C][C]3.46197[/C][/ROW]
[ROW][C]52[/C][C]12.3[/C][C]9.2441[/C][C]3.0559[/C][/ROW]
[ROW][C]53[/C][C]11.6[/C][C]10.5952[/C][C]1.00482[/C][/ROW]
[ROW][C]54[/C][C]6.7[/C][C]8.91265[/C][C]-2.21265[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]9.23523[/C][C]1.66477[/C][/ROW]
[ROW][C]56[/C][C]12.1[/C][C]10.5034[/C][C]1.59658[/C][/ROW]
[ROW][C]57[/C][C]13.3[/C][C]10.3565[/C][C]2.9435[/C][/ROW]
[ROW][C]58[/C][C]10.1[/C][C]9.45353[/C][C]0.646466[/C][/ROW]
[ROW][C]59[/C][C]5.7[/C][C]10.1774[/C][C]-4.47745[/C][/ROW]
[ROW][C]60[/C][C]14.3[/C][C]10.3158[/C][C]3.98424[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.4154[/C][C]-0.415401[/C][/ROW]
[ROW][C]62[/C][C]13.3[/C][C]10.4154[/C][C]2.88462[/C][/ROW]
[ROW][C]63[/C][C]9.3[/C][C]11.1016[/C][C]-1.80157[/C][/ROW]
[ROW][C]64[/C][C]12.5[/C][C]10.7415[/C][C]1.75848[/C][/ROW]
[ROW][C]65[/C][C]7.6[/C][C]9.4843[/C][C]-1.8843[/C][/ROW]
[ROW][C]66[/C][C]15.9[/C][C]11.9252[/C][C]3.97478[/C][/ROW]
[ROW][C]67[/C][C]9.2[/C][C]10.9918[/C][C]-1.79177[/C][/ROW]
[ROW][C]68[/C][C]9.1[/C][C]9.40318[/C][C]-0.303184[/C][/ROW]
[ROW][C]69[/C][C]11.1[/C][C]12.976[/C][C]-1.87604[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]12.1107[/C][C]0.889265[/C][/ROW]
[ROW][C]71[/C][C]14.5[/C][C]11.5838[/C][C]2.91616[/C][/ROW]
[ROW][C]72[/C][C]12.2[/C][C]11.1511[/C][C]1.04887[/C][/ROW]
[ROW][C]73[/C][C]12.3[/C][C]12.9614[/C][C]-0.661444[/C][/ROW]
[ROW][C]74[/C][C]11.4[/C][C]9.55498[/C][C]1.84502[/C][/ROW]
[ROW][C]75[/C][C]8.8[/C][C]10.1466[/C][C]-1.34656[/C][/ROW]
[ROW][C]76[/C][C]14.6[/C][C]12.0717[/C][C]2.52832[/C][/ROW]
[ROW][C]77[/C][C]7.3[/C][C]11.196[/C][C]-3.896[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.7436[/C][C]1.85635[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.6043[/C][C]1.39574[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]10.2732[/C][C]2.32681[/C][/ROW]
[ROW][C]81[/C][C]13.2[/C][C]11.2856[/C][C]1.91442[/C][/ROW]
[ROW][C]82[/C][C]9.9[/C][C]10.5569[/C][C]-0.656934[/C][/ROW]
[ROW][C]83[/C][C]7.7[/C][C]10.1021[/C][C]-2.40209[/C][/ROW]
[ROW][C]84[/C][C]10.5[/C][C]10.8715[/C][C]-0.371466[/C][/ROW]
[ROW][C]85[/C][C]13.4[/C][C]10.543[/C][C]2.85701[/C][/ROW]
[ROW][C]86[/C][C]10.9[/C][C]10.8573[/C][C]0.0427005[/C][/ROW]
[ROW][C]87[/C][C]4.3[/C][C]9.39822[/C][C]-5.09822[/C][/ROW]
[ROW][C]88[/C][C]10.3[/C][C]11.6109[/C][C]-1.31087[/C][/ROW]
[ROW][C]89[/C][C]11.8[/C][C]11.4886[/C][C]0.311354[/C][/ROW]
[ROW][C]90[/C][C]11.2[/C][C]10.829[/C][C]0.370955[/C][/ROW]
[ROW][C]91[/C][C]11.4[/C][C]10.6298[/C][C]0.770156[/C][/ROW]
[ROW][C]92[/C][C]8.6[/C][C]10.5258[/C][C]-1.92579[/C][/ROW]
[ROW][C]93[/C][C]13.2[/C][C]11.0151[/C][C]2.18493[/C][/ROW]
[ROW][C]94[/C][C]12.6[/C][C]9.49378[/C][C]3.10622[/C][/ROW]
[ROW][C]95[/C][C]5.6[/C][C]9.49581[/C][C]-3.89581[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]10.5813[/C][C]-0.681285[/C][/ROW]
[ROW][C]97[/C][C]8.8[/C][C]10.2413[/C][C]-1.44125[/C][/ROW]
[ROW][C]98[/C][C]7.7[/C][C]9.94898[/C][C]-2.24898[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]9.60142[/C][C]-0.601423[/C][/ROW]
[ROW][C]100[/C][C]7.3[/C][C]10.2763[/C][C]-2.97635[/C][/ROW]
[ROW][C]101[/C][C]11.4[/C][C]8.81025[/C][C]2.58975[/C][/ROW]
[ROW][C]102[/C][C]13.6[/C][C]8.90175[/C][C]4.69825[/C][/ROW]
[ROW][C]103[/C][C]7.9[/C][C]11.0532[/C][C]-3.15321[/C][/ROW]
[ROW][C]104[/C][C]10.7[/C][C]9.21539[/C][C]1.48461[/C][/ROW]
[ROW][C]105[/C][C]10.3[/C][C]10.4904[/C][C]-0.190431[/C][/ROW]
[ROW][C]106[/C][C]8.3[/C][C]9.85246[/C][C]-1.55246[/C][/ROW]
[ROW][C]107[/C][C]9.6[/C][C]11.0066[/C][C]-1.4066[/C][/ROW]
[ROW][C]108[/C][C]14.2[/C][C]10.5632[/C][C]3.63676[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]11.1671[/C][C]-2.66714[/C][/ROW]
[ROW][C]110[/C][C]13.5[/C][C]10.645[/C][C]2.85496[/C][/ROW]
[ROW][C]111[/C][C]4.9[/C][C]9.84783[/C][C]-4.94783[/C][/ROW]
[ROW][C]112[/C][C]6.4[/C][C]8.71704[/C][C]-2.31704[/C][/ROW]
[ROW][C]113[/C][C]9.6[/C][C]10.7296[/C][C]-1.12965[/C][/ROW]
[ROW][C]114[/C][C]11.6[/C][C]10.5741[/C][C]1.02594[/C][/ROW]
[ROW][C]115[/C][C]11.1[/C][C]9.6886[/C][C]1.4114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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	12.9	10.7346	2.1654
2	7.4	9.63854	-2.23854
3	12.2	9.94375	2.25625
4	12.8	10.6159	2.18407
5	7.4	11.4721	-4.0721
6	6.7	9.8632	-3.1632
7	12.6	12.8101	-0.210054
8	14.8	11.3246	3.47543
9	13.3	10.9856	2.31438
10	11.1	12.6968	-1.59676
11	8.2	9.96339	-1.76339
12	11.4	10.6969	0.703101
13	6.4	10.7773	-4.3773
14	10.6	9.75966	0.840342
15	12	13.1205	-1.12051
16	6.3	8.58715	-2.28715
17	11.3	9.26065	2.03935
18	11.9	11.8953	0.00467874
19	9.3	10.3033	-1.00333
20	9.6	11.3323	-1.7323
21	10	9.49041	0.509589
22	6.4	9.63918	-3.23918
23	13.8	12.7961	1.00387
24	10.8	11.032	-0.232023
25	13.8	11.6308	2.1692
26	11.7	11.1606	0.539352
27	10.9	12.3202	-1.42016
28	16.1	14.6755	1.4245
29	13.4	10.8103	2.58966
30	9.9	10.3906	-0.490615
31	11.5	10.1281	1.37191
32	8.3	9.58361	-1.28361
33	11.7	10.6484	1.05158
34	6.1	9.51599	-3.41599
35	9	9.88347	-0.883474
36	9.7	12.094	-2.39399
37	10.8	10.0321	0.767864
38	10.3	10.121	0.179043
39	10.4	10.285	0.114988
40	12.7	10.847	1.85301
41	9.3	12.1212	-2.82118
42	11.8	11.3291	0.470863
43	5.9	9.34563	-3.44563
44	11.4	11.0958	0.304204
45	13	10.6916	2.30843
46	10.8	10.0206	0.779378
47	12.3	9.17087	3.12913
48	11.3	12.1114	-0.811439
49	11.8	9.98225	1.81775
50	7.9	11.6663	-3.76629
51	12.7	9.23803	3.46197
52	12.3	9.2441	3.0559
53	11.6	10.5952	1.00482
54	6.7	8.91265	-2.21265
55	10.9	9.23523	1.66477
56	12.1	10.5034	1.59658
57	13.3	10.3565	2.9435
58	10.1	9.45353	0.646466
59	5.7	10.1774	-4.47745
60	14.3	10.3158	3.98424
61	8	8.4154	-0.415401
62	13.3	10.4154	2.88462
63	9.3	11.1016	-1.80157
64	12.5	10.7415	1.75848
65	7.6	9.4843	-1.8843
66	15.9	11.9252	3.97478
67	9.2	10.9918	-1.79177
68	9.1	9.40318	-0.303184
69	11.1	12.976	-1.87604
70	13	12.1107	0.889265
71	14.5	11.5838	2.91616
72	12.2	11.1511	1.04887
73	12.3	12.9614	-0.661444
74	11.4	9.55498	1.84502
75	8.8	10.1466	-1.34656
76	14.6	12.0717	2.52832
77	7.3	11.196	-3.896
78	12.6	10.7436	1.85635
79	13	11.6043	1.39574
80	12.6	10.2732	2.32681
81	13.2	11.2856	1.91442
82	9.9	10.5569	-0.656934
83	7.7	10.1021	-2.40209
84	10.5	10.8715	-0.371466
85	13.4	10.543	2.85701
86	10.9	10.8573	0.0427005
87	4.3	9.39822	-5.09822
88	10.3	11.6109	-1.31087
89	11.8	11.4886	0.311354
90	11.2	10.829	0.370955
91	11.4	10.6298	0.770156
92	8.6	10.5258	-1.92579
93	13.2	11.0151	2.18493
94	12.6	9.49378	3.10622
95	5.6	9.49581	-3.89581
96	9.9	10.5813	-0.681285
97	8.8	10.2413	-1.44125
98	7.7	9.94898	-2.24898
99	9	9.60142	-0.601423
100	7.3	10.2763	-2.97635
101	11.4	8.81025	2.58975
102	13.6	8.90175	4.69825
103	7.9	11.0532	-3.15321
104	10.7	9.21539	1.48461
105	10.3	10.4904	-0.190431
106	8.3	9.85246	-1.55246
107	9.6	11.0066	-1.4066
108	14.2	10.5632	3.63676
109	8.5	11.1671	-2.66714
110	13.5	10.645	2.85496
111	4.9	9.84783	-4.94783
112	6.4	8.71704	-2.31704
113	9.6	10.7296	-1.12965
114	11.6	10.5741	1.02594
115	11.1	9.6886	1.4114

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.891328	0.217344	0.108672
12	0.929552	0.140896	0.0704482
13	0.946588	0.106824	0.0534121
14	0.908822	0.182356	0.091178
15	0.905341	0.189319	0.0946593
16	0.921739	0.156521	0.0782607
17	0.884105	0.23179	0.115895
18	0.83241	0.335181	0.16759
19	0.78758	0.424839	0.21242
20	0.802945	0.394109	0.197055
21	0.743245	0.513511	0.256755
22	0.724248	0.551503	0.275752
23	0.655563	0.688875	0.344437
24	0.587654	0.824692	0.412346
25	0.615913	0.768174	0.384087
26	0.547717	0.904566	0.452283
27	0.489478	0.978955	0.510522
28	0.437334	0.874668	0.562666
29	0.42579	0.851581	0.57421
30	0.36017	0.720341	0.63983
31	0.317968	0.635935	0.682032
32	0.277407	0.554814	0.722593
33	0.228735	0.457471	0.771265
34	0.254827	0.509655	0.745173
35	0.209359	0.418717	0.790641
36	0.199832	0.399664	0.800168
37	0.175499	0.350998	0.824501
38	0.142352	0.284704	0.857648
39	0.110352	0.220705	0.889648
40	0.0980272	0.196054	0.901973
41	0.112567	0.225134	0.887433
42	0.086533	0.173066	0.913467
43	0.110998	0.221996	0.889002
44	0.089126	0.178252	0.910874
45	0.106823	0.213645	0.893177
46	0.0909907	0.181981	0.909009
47	0.104854	0.209708	0.895146
48	0.0875001	0.175	0.9125
49	0.0849804	0.169961	0.91502
50	0.169045	0.338091	0.830955
51	0.210778	0.421557	0.789222
52	0.228362	0.456725	0.771638
53	0.189599	0.379199	0.810401
54	0.200948	0.401896	0.799052
55	0.186878	0.373757	0.813122
56	0.159621	0.319242	0.840379
57	0.184121	0.368242	0.815879
58	0.151801	0.303601	0.848199
59	0.27811	0.55622	0.72189
60	0.353856	0.707712	0.646144
61	0.308068	0.616135	0.691932
62	0.322554	0.645108	0.677446
63	0.298901	0.597802	0.701099
64	0.269535	0.53907	0.730465
65	0.251938	0.503876	0.748062
66	0.321883	0.643767	0.678117
67	0.304322	0.608644	0.695678
68	0.257231	0.514461	0.742769
69	0.243529	0.487059	0.756471
70	0.207068	0.414135	0.792932
71	0.213198	0.426396	0.786802
72	0.177885	0.355769	0.822115
73	0.144527	0.289053	0.855473
74	0.13158	0.26316	0.86842
75	0.111933	0.223865	0.888067
76	0.115355	0.23071	0.884645
77	0.166261	0.332523	0.833739
78	0.143513	0.287025	0.856487
79	0.120039	0.240077	0.879961
80	0.114696	0.229393	0.885304
81	0.126233	0.252466	0.873767
82	0.0996309	0.199262	0.900369
83	0.081002	0.162004	0.918998
84	0.0601688	0.120338	0.939831
85	0.0753234	0.150647	0.924677
86	0.0550651	0.11013	0.944935
87	0.158116	0.316232	0.841884
88	0.127995	0.255991	0.872005
89	0.100445	0.200891	0.899555
90	0.0725076	0.145015	0.927492
91	0.0548661	0.109732	0.945134
92	0.0437754	0.0875508	0.956225
93	0.051576	0.103152	0.948424
94	0.047828	0.0956561	0.952172
95	0.0882319	0.176464	0.911768
96	0.0597409	0.119482	0.940259
97	0.0416911	0.0833822	0.958309
98	0.0603032	0.120606	0.939697
99	0.037133	0.0742659	0.962867
100	0.0799434	0.159887	0.920057
101	0.0612183	0.122437	0.938782
102	0.141516	0.283033	0.858484
103	0.150326	0.300653	0.849674
104	0.163418	0.326836	0.836582

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.891328 & 0.217344 & 0.108672 \tabularnewline
12 & 0.929552 & 0.140896 & 0.0704482 \tabularnewline
13 & 0.946588 & 0.106824 & 0.0534121 \tabularnewline
14 & 0.908822 & 0.182356 & 0.091178 \tabularnewline
15 & 0.905341 & 0.189319 & 0.0946593 \tabularnewline
16 & 0.921739 & 0.156521 & 0.0782607 \tabularnewline
17 & 0.884105 & 0.23179 & 0.115895 \tabularnewline
18 & 0.83241 & 0.335181 & 0.16759 \tabularnewline
19 & 0.78758 & 0.424839 & 0.21242 \tabularnewline
20 & 0.802945 & 0.394109 & 0.197055 \tabularnewline
21 & 0.743245 & 0.513511 & 0.256755 \tabularnewline
22 & 0.724248 & 0.551503 & 0.275752 \tabularnewline
23 & 0.655563 & 0.688875 & 0.344437 \tabularnewline
24 & 0.587654 & 0.824692 & 0.412346 \tabularnewline
25 & 0.615913 & 0.768174 & 0.384087 \tabularnewline
26 & 0.547717 & 0.904566 & 0.452283 \tabularnewline
27 & 0.489478 & 0.978955 & 0.510522 \tabularnewline
28 & 0.437334 & 0.874668 & 0.562666 \tabularnewline
29 & 0.42579 & 0.851581 & 0.57421 \tabularnewline
30 & 0.36017 & 0.720341 & 0.63983 \tabularnewline
31 & 0.317968 & 0.635935 & 0.682032 \tabularnewline
32 & 0.277407 & 0.554814 & 0.722593 \tabularnewline
33 & 0.228735 & 0.457471 & 0.771265 \tabularnewline
34 & 0.254827 & 0.509655 & 0.745173 \tabularnewline
35 & 0.209359 & 0.418717 & 0.790641 \tabularnewline
36 & 0.199832 & 0.399664 & 0.800168 \tabularnewline
37 & 0.175499 & 0.350998 & 0.824501 \tabularnewline
38 & 0.142352 & 0.284704 & 0.857648 \tabularnewline
39 & 0.110352 & 0.220705 & 0.889648 \tabularnewline
40 & 0.0980272 & 0.196054 & 0.901973 \tabularnewline
41 & 0.112567 & 0.225134 & 0.887433 \tabularnewline
42 & 0.086533 & 0.173066 & 0.913467 \tabularnewline
43 & 0.110998 & 0.221996 & 0.889002 \tabularnewline
44 & 0.089126 & 0.178252 & 0.910874 \tabularnewline
45 & 0.106823 & 0.213645 & 0.893177 \tabularnewline
46 & 0.0909907 & 0.181981 & 0.909009 \tabularnewline
47 & 0.104854 & 0.209708 & 0.895146 \tabularnewline
48 & 0.0875001 & 0.175 & 0.9125 \tabularnewline
49 & 0.0849804 & 0.169961 & 0.91502 \tabularnewline
50 & 0.169045 & 0.338091 & 0.830955 \tabularnewline
51 & 0.210778 & 0.421557 & 0.789222 \tabularnewline
52 & 0.228362 & 0.456725 & 0.771638 \tabularnewline
53 & 0.189599 & 0.379199 & 0.810401 \tabularnewline
54 & 0.200948 & 0.401896 & 0.799052 \tabularnewline
55 & 0.186878 & 0.373757 & 0.813122 \tabularnewline
56 & 0.159621 & 0.319242 & 0.840379 \tabularnewline
57 & 0.184121 & 0.368242 & 0.815879 \tabularnewline
58 & 0.151801 & 0.303601 & 0.848199 \tabularnewline
59 & 0.27811 & 0.55622 & 0.72189 \tabularnewline
60 & 0.353856 & 0.707712 & 0.646144 \tabularnewline
61 & 0.308068 & 0.616135 & 0.691932 \tabularnewline
62 & 0.322554 & 0.645108 & 0.677446 \tabularnewline
63 & 0.298901 & 0.597802 & 0.701099 \tabularnewline
64 & 0.269535 & 0.53907 & 0.730465 \tabularnewline
65 & 0.251938 & 0.503876 & 0.748062 \tabularnewline
66 & 0.321883 & 0.643767 & 0.678117 \tabularnewline
67 & 0.304322 & 0.608644 & 0.695678 \tabularnewline
68 & 0.257231 & 0.514461 & 0.742769 \tabularnewline
69 & 0.243529 & 0.487059 & 0.756471 \tabularnewline
70 & 0.207068 & 0.414135 & 0.792932 \tabularnewline
71 & 0.213198 & 0.426396 & 0.786802 \tabularnewline
72 & 0.177885 & 0.355769 & 0.822115 \tabularnewline
73 & 0.144527 & 0.289053 & 0.855473 \tabularnewline
74 & 0.13158 & 0.26316 & 0.86842 \tabularnewline
75 & 0.111933 & 0.223865 & 0.888067 \tabularnewline
76 & 0.115355 & 0.23071 & 0.884645 \tabularnewline
77 & 0.166261 & 0.332523 & 0.833739 \tabularnewline
78 & 0.143513 & 0.287025 & 0.856487 \tabularnewline
79 & 0.120039 & 0.240077 & 0.879961 \tabularnewline
80 & 0.114696 & 0.229393 & 0.885304 \tabularnewline
81 & 0.126233 & 0.252466 & 0.873767 \tabularnewline
82 & 0.0996309 & 0.199262 & 0.900369 \tabularnewline
83 & 0.081002 & 0.162004 & 0.918998 \tabularnewline
84 & 0.0601688 & 0.120338 & 0.939831 \tabularnewline
85 & 0.0753234 & 0.150647 & 0.924677 \tabularnewline
86 & 0.0550651 & 0.11013 & 0.944935 \tabularnewline
87 & 0.158116 & 0.316232 & 0.841884 \tabularnewline
88 & 0.127995 & 0.255991 & 0.872005 \tabularnewline
89 & 0.100445 & 0.200891 & 0.899555 \tabularnewline
90 & 0.0725076 & 0.145015 & 0.927492 \tabularnewline
91 & 0.0548661 & 0.109732 & 0.945134 \tabularnewline
92 & 0.0437754 & 0.0875508 & 0.956225 \tabularnewline
93 & 0.051576 & 0.103152 & 0.948424 \tabularnewline
94 & 0.047828 & 0.0956561 & 0.952172 \tabularnewline
95 & 0.0882319 & 0.176464 & 0.911768 \tabularnewline
96 & 0.0597409 & 0.119482 & 0.940259 \tabularnewline
97 & 0.0416911 & 0.0833822 & 0.958309 \tabularnewline
98 & 0.0603032 & 0.120606 & 0.939697 \tabularnewline
99 & 0.037133 & 0.0742659 & 0.962867 \tabularnewline
100 & 0.0799434 & 0.159887 & 0.920057 \tabularnewline
101 & 0.0612183 & 0.122437 & 0.938782 \tabularnewline
102 & 0.141516 & 0.283033 & 0.858484 \tabularnewline
103 & 0.150326 & 0.300653 & 0.849674 \tabularnewline
104 & 0.163418 & 0.326836 & 0.836582 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&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]11[/C][C]0.891328[/C][C]0.217344[/C][C]0.108672[/C][/ROW]
[ROW][C]12[/C][C]0.929552[/C][C]0.140896[/C][C]0.0704482[/C][/ROW]
[ROW][C]13[/C][C]0.946588[/C][C]0.106824[/C][C]0.0534121[/C][/ROW]
[ROW][C]14[/C][C]0.908822[/C][C]0.182356[/C][C]0.091178[/C][/ROW]
[ROW][C]15[/C][C]0.905341[/C][C]0.189319[/C][C]0.0946593[/C][/ROW]
[ROW][C]16[/C][C]0.921739[/C][C]0.156521[/C][C]0.0782607[/C][/ROW]
[ROW][C]17[/C][C]0.884105[/C][C]0.23179[/C][C]0.115895[/C][/ROW]
[ROW][C]18[/C][C]0.83241[/C][C]0.335181[/C][C]0.16759[/C][/ROW]
[ROW][C]19[/C][C]0.78758[/C][C]0.424839[/C][C]0.21242[/C][/ROW]
[ROW][C]20[/C][C]0.802945[/C][C]0.394109[/C][C]0.197055[/C][/ROW]
[ROW][C]21[/C][C]0.743245[/C][C]0.513511[/C][C]0.256755[/C][/ROW]
[ROW][C]22[/C][C]0.724248[/C][C]0.551503[/C][C]0.275752[/C][/ROW]
[ROW][C]23[/C][C]0.655563[/C][C]0.688875[/C][C]0.344437[/C][/ROW]
[ROW][C]24[/C][C]0.587654[/C][C]0.824692[/C][C]0.412346[/C][/ROW]
[ROW][C]25[/C][C]0.615913[/C][C]0.768174[/C][C]0.384087[/C][/ROW]
[ROW][C]26[/C][C]0.547717[/C][C]0.904566[/C][C]0.452283[/C][/ROW]
[ROW][C]27[/C][C]0.489478[/C][C]0.978955[/C][C]0.510522[/C][/ROW]
[ROW][C]28[/C][C]0.437334[/C][C]0.874668[/C][C]0.562666[/C][/ROW]
[ROW][C]29[/C][C]0.42579[/C][C]0.851581[/C][C]0.57421[/C][/ROW]
[ROW][C]30[/C][C]0.36017[/C][C]0.720341[/C][C]0.63983[/C][/ROW]
[ROW][C]31[/C][C]0.317968[/C][C]0.635935[/C][C]0.682032[/C][/ROW]
[ROW][C]32[/C][C]0.277407[/C][C]0.554814[/C][C]0.722593[/C][/ROW]
[ROW][C]33[/C][C]0.228735[/C][C]0.457471[/C][C]0.771265[/C][/ROW]
[ROW][C]34[/C][C]0.254827[/C][C]0.509655[/C][C]0.745173[/C][/ROW]
[ROW][C]35[/C][C]0.209359[/C][C]0.418717[/C][C]0.790641[/C][/ROW]
[ROW][C]36[/C][C]0.199832[/C][C]0.399664[/C][C]0.800168[/C][/ROW]
[ROW][C]37[/C][C]0.175499[/C][C]0.350998[/C][C]0.824501[/C][/ROW]
[ROW][C]38[/C][C]0.142352[/C][C]0.284704[/C][C]0.857648[/C][/ROW]
[ROW][C]39[/C][C]0.110352[/C][C]0.220705[/C][C]0.889648[/C][/ROW]
[ROW][C]40[/C][C]0.0980272[/C][C]0.196054[/C][C]0.901973[/C][/ROW]
[ROW][C]41[/C][C]0.112567[/C][C]0.225134[/C][C]0.887433[/C][/ROW]
[ROW][C]42[/C][C]0.086533[/C][C]0.173066[/C][C]0.913467[/C][/ROW]
[ROW][C]43[/C][C]0.110998[/C][C]0.221996[/C][C]0.889002[/C][/ROW]
[ROW][C]44[/C][C]0.089126[/C][C]0.178252[/C][C]0.910874[/C][/ROW]
[ROW][C]45[/C][C]0.106823[/C][C]0.213645[/C][C]0.893177[/C][/ROW]
[ROW][C]46[/C][C]0.0909907[/C][C]0.181981[/C][C]0.909009[/C][/ROW]
[ROW][C]47[/C][C]0.104854[/C][C]0.209708[/C][C]0.895146[/C][/ROW]
[ROW][C]48[/C][C]0.0875001[/C][C]0.175[/C][C]0.9125[/C][/ROW]
[ROW][C]49[/C][C]0.0849804[/C][C]0.169961[/C][C]0.91502[/C][/ROW]
[ROW][C]50[/C][C]0.169045[/C][C]0.338091[/C][C]0.830955[/C][/ROW]
[ROW][C]51[/C][C]0.210778[/C][C]0.421557[/C][C]0.789222[/C][/ROW]
[ROW][C]52[/C][C]0.228362[/C][C]0.456725[/C][C]0.771638[/C][/ROW]
[ROW][C]53[/C][C]0.189599[/C][C]0.379199[/C][C]0.810401[/C][/ROW]
[ROW][C]54[/C][C]0.200948[/C][C]0.401896[/C][C]0.799052[/C][/ROW]
[ROW][C]55[/C][C]0.186878[/C][C]0.373757[/C][C]0.813122[/C][/ROW]
[ROW][C]56[/C][C]0.159621[/C][C]0.319242[/C][C]0.840379[/C][/ROW]
[ROW][C]57[/C][C]0.184121[/C][C]0.368242[/C][C]0.815879[/C][/ROW]
[ROW][C]58[/C][C]0.151801[/C][C]0.303601[/C][C]0.848199[/C][/ROW]
[ROW][C]59[/C][C]0.27811[/C][C]0.55622[/C][C]0.72189[/C][/ROW]
[ROW][C]60[/C][C]0.353856[/C][C]0.707712[/C][C]0.646144[/C][/ROW]
[ROW][C]61[/C][C]0.308068[/C][C]0.616135[/C][C]0.691932[/C][/ROW]
[ROW][C]62[/C][C]0.322554[/C][C]0.645108[/C][C]0.677446[/C][/ROW]
[ROW][C]63[/C][C]0.298901[/C][C]0.597802[/C][C]0.701099[/C][/ROW]
[ROW][C]64[/C][C]0.269535[/C][C]0.53907[/C][C]0.730465[/C][/ROW]
[ROW][C]65[/C][C]0.251938[/C][C]0.503876[/C][C]0.748062[/C][/ROW]
[ROW][C]66[/C][C]0.321883[/C][C]0.643767[/C][C]0.678117[/C][/ROW]
[ROW][C]67[/C][C]0.304322[/C][C]0.608644[/C][C]0.695678[/C][/ROW]
[ROW][C]68[/C][C]0.257231[/C][C]0.514461[/C][C]0.742769[/C][/ROW]
[ROW][C]69[/C][C]0.243529[/C][C]0.487059[/C][C]0.756471[/C][/ROW]
[ROW][C]70[/C][C]0.207068[/C][C]0.414135[/C][C]0.792932[/C][/ROW]
[ROW][C]71[/C][C]0.213198[/C][C]0.426396[/C][C]0.786802[/C][/ROW]
[ROW][C]72[/C][C]0.177885[/C][C]0.355769[/C][C]0.822115[/C][/ROW]
[ROW][C]73[/C][C]0.144527[/C][C]0.289053[/C][C]0.855473[/C][/ROW]
[ROW][C]74[/C][C]0.13158[/C][C]0.26316[/C][C]0.86842[/C][/ROW]
[ROW][C]75[/C][C]0.111933[/C][C]0.223865[/C][C]0.888067[/C][/ROW]
[ROW][C]76[/C][C]0.115355[/C][C]0.23071[/C][C]0.884645[/C][/ROW]
[ROW][C]77[/C][C]0.166261[/C][C]0.332523[/C][C]0.833739[/C][/ROW]
[ROW][C]78[/C][C]0.143513[/C][C]0.287025[/C][C]0.856487[/C][/ROW]
[ROW][C]79[/C][C]0.120039[/C][C]0.240077[/C][C]0.879961[/C][/ROW]
[ROW][C]80[/C][C]0.114696[/C][C]0.229393[/C][C]0.885304[/C][/ROW]
[ROW][C]81[/C][C]0.126233[/C][C]0.252466[/C][C]0.873767[/C][/ROW]
[ROW][C]82[/C][C]0.0996309[/C][C]0.199262[/C][C]0.900369[/C][/ROW]
[ROW][C]83[/C][C]0.081002[/C][C]0.162004[/C][C]0.918998[/C][/ROW]
[ROW][C]84[/C][C]0.0601688[/C][C]0.120338[/C][C]0.939831[/C][/ROW]
[ROW][C]85[/C][C]0.0753234[/C][C]0.150647[/C][C]0.924677[/C][/ROW]
[ROW][C]86[/C][C]0.0550651[/C][C]0.11013[/C][C]0.944935[/C][/ROW]
[ROW][C]87[/C][C]0.158116[/C][C]0.316232[/C][C]0.841884[/C][/ROW]
[ROW][C]88[/C][C]0.127995[/C][C]0.255991[/C][C]0.872005[/C][/ROW]
[ROW][C]89[/C][C]0.100445[/C][C]0.200891[/C][C]0.899555[/C][/ROW]
[ROW][C]90[/C][C]0.0725076[/C][C]0.145015[/C][C]0.927492[/C][/ROW]
[ROW][C]91[/C][C]0.0548661[/C][C]0.109732[/C][C]0.945134[/C][/ROW]
[ROW][C]92[/C][C]0.0437754[/C][C]0.0875508[/C][C]0.956225[/C][/ROW]
[ROW][C]93[/C][C]0.051576[/C][C]0.103152[/C][C]0.948424[/C][/ROW]
[ROW][C]94[/C][C]0.047828[/C][C]0.0956561[/C][C]0.952172[/C][/ROW]
[ROW][C]95[/C][C]0.0882319[/C][C]0.176464[/C][C]0.911768[/C][/ROW]
[ROW][C]96[/C][C]0.0597409[/C][C]0.119482[/C][C]0.940259[/C][/ROW]
[ROW][C]97[/C][C]0.0416911[/C][C]0.0833822[/C][C]0.958309[/C][/ROW]
[ROW][C]98[/C][C]0.0603032[/C][C]0.120606[/C][C]0.939697[/C][/ROW]
[ROW][C]99[/C][C]0.037133[/C][C]0.0742659[/C][C]0.962867[/C][/ROW]
[ROW][C]100[/C][C]0.0799434[/C][C]0.159887[/C][C]0.920057[/C][/ROW]
[ROW][C]101[/C][C]0.0612183[/C][C]0.122437[/C][C]0.938782[/C][/ROW]
[ROW][C]102[/C][C]0.141516[/C][C]0.283033[/C][C]0.858484[/C][/ROW]
[ROW][C]103[/C][C]0.150326[/C][C]0.300653[/C][C]0.849674[/C][/ROW]
[ROW][C]104[/C][C]0.163418[/C][C]0.326836[/C][C]0.836582[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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
11	0.891328	0.217344	0.108672
12	0.929552	0.140896	0.0704482
13	0.946588	0.106824	0.0534121
14	0.908822	0.182356	0.091178
15	0.905341	0.189319	0.0946593
16	0.921739	0.156521	0.0782607
17	0.884105	0.23179	0.115895
18	0.83241	0.335181	0.16759
19	0.78758	0.424839	0.21242
20	0.802945	0.394109	0.197055
21	0.743245	0.513511	0.256755
22	0.724248	0.551503	0.275752
23	0.655563	0.688875	0.344437
24	0.587654	0.824692	0.412346
25	0.615913	0.768174	0.384087
26	0.547717	0.904566	0.452283
27	0.489478	0.978955	0.510522
28	0.437334	0.874668	0.562666
29	0.42579	0.851581	0.57421
30	0.36017	0.720341	0.63983
31	0.317968	0.635935	0.682032
32	0.277407	0.554814	0.722593
33	0.228735	0.457471	0.771265
34	0.254827	0.509655	0.745173
35	0.209359	0.418717	0.790641
36	0.199832	0.399664	0.800168
37	0.175499	0.350998	0.824501
38	0.142352	0.284704	0.857648
39	0.110352	0.220705	0.889648
40	0.0980272	0.196054	0.901973
41	0.112567	0.225134	0.887433
42	0.086533	0.173066	0.913467
43	0.110998	0.221996	0.889002
44	0.089126	0.178252	0.910874
45	0.106823	0.213645	0.893177
46	0.0909907	0.181981	0.909009
47	0.104854	0.209708	0.895146
48	0.0875001	0.175	0.9125
49	0.0849804	0.169961	0.91502
50	0.169045	0.338091	0.830955
51	0.210778	0.421557	0.789222
52	0.228362	0.456725	0.771638
53	0.189599	0.379199	0.810401
54	0.200948	0.401896	0.799052
55	0.186878	0.373757	0.813122
56	0.159621	0.319242	0.840379
57	0.184121	0.368242	0.815879
58	0.151801	0.303601	0.848199
59	0.27811	0.55622	0.72189
60	0.353856	0.707712	0.646144
61	0.308068	0.616135	0.691932
62	0.322554	0.645108	0.677446
63	0.298901	0.597802	0.701099
64	0.269535	0.53907	0.730465
65	0.251938	0.503876	0.748062
66	0.321883	0.643767	0.678117
67	0.304322	0.608644	0.695678
68	0.257231	0.514461	0.742769
69	0.243529	0.487059	0.756471
70	0.207068	0.414135	0.792932
71	0.213198	0.426396	0.786802
72	0.177885	0.355769	0.822115
73	0.144527	0.289053	0.855473
74	0.13158	0.26316	0.86842
75	0.111933	0.223865	0.888067
76	0.115355	0.23071	0.884645
77	0.166261	0.332523	0.833739
78	0.143513	0.287025	0.856487
79	0.120039	0.240077	0.879961
80	0.114696	0.229393	0.885304
81	0.126233	0.252466	0.873767
82	0.0996309	0.199262	0.900369
83	0.081002	0.162004	0.918998
84	0.0601688	0.120338	0.939831
85	0.0753234	0.150647	0.924677
86	0.0550651	0.11013	0.944935
87	0.158116	0.316232	0.841884
88	0.127995	0.255991	0.872005
89	0.100445	0.200891	0.899555
90	0.0725076	0.145015	0.927492
91	0.0548661	0.109732	0.945134
92	0.0437754	0.0875508	0.956225
93	0.051576	0.103152	0.948424
94	0.047828	0.0956561	0.952172
95	0.0882319	0.176464	0.911768
96	0.0597409	0.119482	0.940259
97	0.0416911	0.0833822	0.958309
98	0.0603032	0.120606	0.939697
99	0.037133	0.0742659	0.962867
100	0.0799434	0.159887	0.920057
101	0.0612183	0.122437	0.938782
102	0.141516	0.283033	0.858484
103	0.150326	0.300653	0.849674
104	0.163418	0.326836	0.836582

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	4	0.0425532	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 & 4 & 0.0425532 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=263008&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]4[/C][C]0.0425532[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=263008&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=263008&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	4	0.0425532	OK

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

PNG link

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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