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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 08:55:18 +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/14/t1418547654l6bduggfavuas5t.htm/, Retrieved Thu, 16 May 2024 10:01:15 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267339, Retrieved Thu, 16 May 2024 10:01:15 +0000

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Estimated Impact

119

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

-       [Multiple Regression] [] [2014-12-14 08:55:18] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]

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Dataseries X:

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12.9 0 0 21 21 149 18 68 96
7.4 0 0 23 26 152 7 55 75
12.2 0 1 22 22 139 31 39 70
12.8 0 0 21 22 148 39 32 88
7.4 0 1 21 18 158 46 62 114
6.7 0 1 21 23 128 31 33 69
12.6 0 1 21 12 224 67 52 176
14.8 0 0 21 20 159 35 62 114
13.3 0 1 23 22 105 52 77 121
11.1 0 1 22 21 159 77 76 110
8.2 0 1 25 19 167 37 41 158
11.4 0 1 21 22 165 32 48 116
6.4 0 1 23 15 159 36 63 181
10.6 0 1 22 20 119 38 30 77
12.0 0 0 21 19 176 69 78 141
6.3 0 0 21 18 54 21 19 35
11.3 1 0 25 15 91 26 31 80
11.9 0 1 21 20 163 54 66 152
9.3 0 0 21 21 124 36 35 97
9.6 1 1 20 21 137 42 42 99
10.0 0 0 24 15 121 23 45 84
6.4 0 1 23 16 153 34 21 68
13.8 0 1 21 23 148 112 25 101
10.8 0 0 24 21 221 35 44 107
13.8 0 1 23 18 188 47 69 88
11.7 0 1 21 25 149 47 54 112
10.9 0 1 22 9 244 37 74 171
16.1 1 1 20 30 148 109 80 137
13.4 1 0 18 20 92 24 42 77
9.9 0 1 21 23 150 20 61 66
11.5 0 0 22 16 153 22 41 93
8.3 0 0 22 16 94 23 46 105
11.7 0 0 21 19 156 32 39 131
6.1 0 1 23 25 146 7 63 89
9.0 0 1 21 25 132 30 34 102
9.7 0 1 25 18 161 92 51 161
10.8 0 1 22 23 105 43 42 120
10.3 0 1 22 21 97 55 31 127
10.4 0 0 20 10 151 16 39 77
12.7 1 1 21 14 131 49 20 108
9.3 0 1 21 22 166 71 49 85
11.8 0 0 21 26 157 43 53 168
5.9 0 1 22 23 111 29 31 48
11.4 0 1 21 23 145 56 39 152
13.0 0 1 24 24 162 46 54 75
10.8 0 1 22 24 163 19 49 107
12.3 1 1 22 18 59 23 34 62
11.3 0 0 21 23 187 59 46 121
11.8 0 1 22 15 109 30 55 124
7.9 1 1 19 19 90 61 42 72
12.7 0 0 22 16 105 7 50 40
12.3 1 1 23 25 83 38 13 58
11.6 1 1 20 23 116 32 37 97
6.7 1 1 20 17 42 16 25 88
10.9 0 1 23 19 148 19 30 126
12.1 1 1 20 21 155 22 28 104
13.3 0 1 23 18 125 48 45 148
10.1 0 1 21 27 116 23 35 146
5.7 1 0 22 21 128 26 28 80
14.3 0 1 21 13 138 33 41 97
8.0 1 0 21 8 49 9 6 25
13.3 1 1 19 29 96 24 45 99
9.3 0 1 22 28 164 34 73 118
12.5 0 0 21 23 162 48 17 58
7.6 0 0 21 21 99 18 40 63
15.9 0 1 21 19 202 43 64 139
9.2 0 0 21 19 186 33 37 50
9.1 1 1 21 20 66 28 25 60
11.1 0 0 21 18 183 71 65 152
13.0 0 1 22 19 214 26 100 142
14.5 0 1 22 17 188 67 28 94
12.2 1 0 18 19 104 34 35 66
12.3 0 0 21 25 177 80 56 127
11.4 0 0 23 19 126 29 29 67
8.8 1 0 19 22 76 16 43 90
14.6 1 1 19 23 99 59 59 75
7.3 0 1 23 26 157 58 52 96
12.6 0 0 21 14 139 32 50 128
13.0 0 0 21 16 162 43 59 146
12.6 1 1 21 24 108 38 27 69
13.2 0 0 20 20 159 29 61 186
9.9 1 0 19 12 74 36 28 81
7.7 0 1 21 24 110 32 51 85
10.5 1 0 19 22 96 35 35 54
13.4 1 0 19 12 116 21 29 46
10.9 1 0 19 22 87 29 48 106
4.3 1 1 20 20 97 12 25 34
10.3 1 0 19 10 127 37 44 60
11.8 1 1 19 23 106 37 64 95
11.2 1 1 19 17 80 47 32 57
11.4 1 0 20 22 74 51 20 62
8.6 1 0 19 24 91 32 28 36
13.2 1 0 18 18 133 21 34 56
12.6 1 1 19 21 74 13 31 54
5.6 1 1 21 20 114 14 26 64
9.9 1 1 18 20 140 -2 58 76
8.8 1 0 18 22 95 20 23 98
7.7 1 1 19 19 98 24 21 88
9.0 1 0 21 20 121 11 21 35
7.3 1 1 20 26 126 23 33 102
11.4 1 1 24 23 98 24 16 61
13.6 1 1 22 24 95 14 20 80
7.9 1 1 21 21 110 52 37 49
10.7 1 1 21 21 70 15 35 78
10.3 1 0 19 19 102 23 33 90
8.3 1 1 19 8 86 19 27 45
9.6 1 1 20 17 130 35 41 55
14.2 1 1 18 20 96 24 40 96
8.5 1 0 19 11 102 39 35 43
13.5 1 0 19 8 100 29 28 52
4.9 1 0 20 15 94 13 32 60
6.4 1 0 21 18 52 8 22 54
9.6 1 0 18 18 98 18 44 51
11.6 1 0 19 19 118 24 27 51
11.1 1 1 19 19 99 19 17 38

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	'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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&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]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.9084 + 0.662904programma[t] -0.304561gender[t] -0.204688age[t] -0.00663443NUMERACYTOT[t] + 0.0100884LFM[t] + 0.0325331PRH[t] + 0.0170445CH[t] + 0.00918063Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.9084 +  0.662904programma[t] -0.304561gender[t] -0.204688age[t] -0.00663443NUMERACYTOT[t] +  0.0100884LFM[t] +  0.0325331PRH[t] +  0.0170445CH[t] +  0.00918063Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.9084 +  0.662904programma[t] -0.304561gender[t] -0.204688age[t] -0.00663443NUMERACYTOT[t] +  0.0100884LFM[t] +  0.0325331PRH[t] +  0.0170445CH[t] +  0.00918063Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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] = + 10.9084 + 0.662904programma[t] -0.304561gender[t] -0.204688age[t] -0.00663443NUMERACYTOT[t] + 0.0100884LFM[t] + 0.0325331PRH[t] + 0.0170445CH[t] + 0.00918063Blogs[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)	10.9084	4.17212	2.615	0.0102343	0.00511717
programma	0.662904	0.700586	0.9462	0.346191	0.173096
gender	-0.304561	0.481853	-0.6321	0.528707	0.264354
age	-0.204688	0.175708	-1.165	0.24666	0.12333
NUMERACYTOT	-0.00663443	0.0518658	-0.1279	0.898458	0.449229
LFM	0.0100884	0.00843049	1.197	0.234112	0.117056
PRH	0.0325331	0.0126513	2.572	0.0115111	0.00575556
CH	0.0170445	0.0176555	0.9654	0.336546	0.168273
Blogs	0.00918063	0.00848728	1.082	0.281844	0.140922

\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) & 10.9084 & 4.17212 & 2.615 & 0.0102343 & 0.00511717 \tabularnewline
programma & 0.662904 & 0.700586 & 0.9462 & 0.346191 & 0.173096 \tabularnewline
gender & -0.304561 & 0.481853 & -0.6321 & 0.528707 & 0.264354 \tabularnewline
age & -0.204688 & 0.175708 & -1.165 & 0.24666 & 0.12333 \tabularnewline
NUMERACYTOT & -0.00663443 & 0.0518658 & -0.1279 & 0.898458 & 0.449229 \tabularnewline
LFM & 0.0100884 & 0.00843049 & 1.197 & 0.234112 & 0.117056 \tabularnewline
PRH & 0.0325331 & 0.0126513 & 2.572 & 0.0115111 & 0.00575556 \tabularnewline
CH & 0.0170445 & 0.0176555 & 0.9654 & 0.336546 & 0.168273 \tabularnewline
Blogs & 0.00918063 & 0.00848728 & 1.082 & 0.281844 & 0.140922 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&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]10.9084[/C][C]4.17212[/C][C]2.615[/C][C]0.0102343[/C][C]0.00511717[/C][/ROW]
[ROW][C]programma[/C][C]0.662904[/C][C]0.700586[/C][C]0.9462[/C][C]0.346191[/C][C]0.173096[/C][/ROW]
[ROW][C]gender[/C][C]-0.304561[/C][C]0.481853[/C][C]-0.6321[/C][C]0.528707[/C][C]0.264354[/C][/ROW]
[ROW][C]age[/C][C]-0.204688[/C][C]0.175708[/C][C]-1.165[/C][C]0.24666[/C][C]0.12333[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00663443[/C][C]0.0518658[/C][C]-0.1279[/C][C]0.898458[/C][C]0.449229[/C][/ROW]
[ROW][C]LFM[/C][C]0.0100884[/C][C]0.00843049[/C][C]1.197[/C][C]0.234112[/C][C]0.117056[/C][/ROW]
[ROW][C]PRH[/C][C]0.0325331[/C][C]0.0126513[/C][C]2.572[/C][C]0.0115111[/C][C]0.00575556[/C][/ROW]
[ROW][C]CH[/C][C]0.0170445[/C][C]0.0176555[/C][C]0.9654[/C][C]0.336546[/C][C]0.168273[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00918063[/C][C]0.00848728[/C][C]1.082[/C][C]0.281844[/C][C]0.140922[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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)	10.9084	4.17212	2.615	0.0102343	0.00511717
programma	0.662904	0.700586	0.9462	0.346191	0.173096
gender	-0.304561	0.481853	-0.6321	0.528707	0.264354
age	-0.204688	0.175708	-1.165	0.24666	0.12333
NUMERACYTOT	-0.00663443	0.0518658	-0.1279	0.898458	0.449229
LFM	0.0100884	0.00843049	1.197	0.234112	0.117056
PRH	0.0325331	0.0126513	2.572	0.0115111	0.00575556
CH	0.0170445	0.0176555	0.9654	0.336546	0.168273
Blogs	0.00918063	0.00848728	1.082	0.281844	0.140922

Multiple Linear Regression - Regression Statistics
Multiple R	0.443605
R-squared	0.196785
Adjusted R-squared	0.136166
F-TEST (value)	3.24622
F-TEST (DF numerator)	8
F-TEST (DF denominator)	106
p-value	0.00241103
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33606
Sum Squared Residuals	578.459

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.443605 \tabularnewline
R-squared & 0.196785 \tabularnewline
Adjusted R-squared & 0.136166 \tabularnewline
F-TEST (value) & 3.24622 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.00241103 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.33606 \tabularnewline
Sum Squared Residuals & 578.459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.443605[/C][/ROW]
[ROW][C]R-squared[/C][C]0.196785[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.136166[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.24622[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.00241103[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.33606[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]578.459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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	0.443605
R-squared	0.196785
Adjusted R-squared	0.136166
F-TEST (value)	3.24622
F-TEST (DF numerator)	8
F-TEST (DF denominator)	106
p-value	0.00241103
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.33606
Sum Squared Residuals	578.459

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	12.9	10.5997	2.30029
2	7.4	9.41519	-2.01519
3	12.2	9.67289	2.52711
4	12.8	10.5791	2.22086
5	7.4	11.3798	-3.97976
6	6.7	9.64852	-2.94852
7	12.6	13.1673	-0.567349
8	14.8	11.3233	3.47672
9	13.3	10.9243	2.37571
10	11.1	12.3757	-1.27568
11	8.2	10.3984	-2.19839
12	11.4	10.7481	0.651883
13	6.4	11.3072	-4.90719
14	10.6	9.62299	0.977013
15	12	13.1281	-1.12813
16	6.3	8.36362	-2.06362
17	11.3	9.38128	1.91872
18	11.9	12.0942	-0.194241
19	9.3	10.3798	-1.07981
20	9.6	11.4069	-1.80686
21	10	9.40346	0.596544
22	6.4	9.42168	-3.02168
23	13.8	12.6429	1.1571
24	10.8	10.957	-0.156993
25	13.8	11.1862	2.61382
26	11.7	11.1203	0.579659
27	10.9	12.5374	-1.63741
28	16.1	14.6344	1.4656
29	13.4	10.8859	2.51411
30	9.9	9.96231	-0.0623072
31	11.5	10.1109	1.38906
32	8.3	9.74365	-1.44365
33	11.7	10.9661	0.733905
34	6.1	9.32162	-3.22162
35	9	9.96308	-0.96308
36	9.7	12.3318	-2.6318
37	10.8	10.2238	0.576187
38	10.3	10.4235	-0.123547
39	10.4	10.1638	0.236232
40	12.7	11.1235	1.57654
41	9.3	11.7594	-2.45944
42	11.8	11.8659	-0.0659127
43	5.9	8.98039	-3.08039
44	11.4	11.4976	-0.0976121
45	13	10.2718	2.72816
46	10.8	10.0215	0.778528
47	12.3	9.13633	3.16367
48	11.3	12.1582	-0.858196
49	11.8	10.1526	1.64739
50	7.9	11.5209	-3.62092
51	12.7	8.80553	3.89447
52	12.3	9.22066	3.07934
53	11.6	10.7528	0.847176
54	6.7	9.2384	-2.5384
55	10.9	9.54922	1.35078
56	12.1	10.7451	1.35493
57	13.3	10.7249	2.57508
58	10.1	9.98166	0.118342
59	5.7	10.2777	-4.57767
60	14.3	10.2742	4.02577
61	8	8.33865	-0.338648
62	13.3	10.6104	2.68961
63	9.3	11.0031	-1.70307
64	12.5	10.4755	2.02455
65	7.6	9.31509	-1.71509
66	15.9	11.983	3.91698
67	9.2	10.5236	-1.32356
68	9.1	9.38927	-0.289272
69	11.1	13.1499	-2.04986
70	13	11.9875	1.01253
71	14.5	11.4044	3.09557
72	12.2	11.1186	1.08138
73	12.3	12.9528	-0.652767
74	11.4	9.39846	2.00154
75	8.8	10.3826	-1.58265
76	14.6	11.8374	2.76259
77	7.3	10.9619	-3.66192
78	12.6	10.9877	1.61229
79	13	11.883	1.11701
80	12.6	10.2285	2.37151
81	13.2	11.9767	1.22327
82	9.9	10.7412	-0.841182
83	7.7	9.94652	-2.24652
84	10.5	10.7357	-0.235684
85	13.4	10.3726	3.02738
86	10.9	11.1487	-0.24866
87	4.3	9.14747	-4.84747
88	10.3	11.4016	-1.10159
89	11.8	11.4611	0.338866
90	11.2	10.6697	0.530314
91	11.4	10.6474	0.752641
92	8.6	10.2898	-1.68981
93	13.2	10.886	2.31397
94	12.6	9.43191	3.16809
95	5.6	9.47182	-3.87182
96	9.9	10.4832	-0.583239
97	8.8	10.6417	-1.8417
98	7.7	10.1869	-2.48686
99	9	9.39794	-0.397937
100	7.3	10.5187	-3.21873
101	11.4	8.80378	2.59622
102	13.6	9.09354	4.50646
103	7.9	10.7109	-2.81087
104	10.7	9.33576	1.36424
105	10.3	10.7221	-0.422133
106	8.3	9.68361	-1.38361
107	9.6	10.7141	-1.11406
108	14.2	10.762	3.43798
109	8.5	10.8983	-2.39834
110	13.5	10.536	2.96395
111	4.9	9.84548	-4.94548
112	6.4	8.80899	-2.40899
113	9.6	10.5599	-0.959882
114	11.6	10.4558	1.14423
115	11.1	9.50707	1.59293

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12.9 & 10.5997 & 2.30029 \tabularnewline
2 & 7.4 & 9.41519 & -2.01519 \tabularnewline
3 & 12.2 & 9.67289 & 2.52711 \tabularnewline
4 & 12.8 & 10.5791 & 2.22086 \tabularnewline
5 & 7.4 & 11.3798 & -3.97976 \tabularnewline
6 & 6.7 & 9.64852 & -2.94852 \tabularnewline
7 & 12.6 & 13.1673 & -0.567349 \tabularnewline
8 & 14.8 & 11.3233 & 3.47672 \tabularnewline
9 & 13.3 & 10.9243 & 2.37571 \tabularnewline
10 & 11.1 & 12.3757 & -1.27568 \tabularnewline
11 & 8.2 & 10.3984 & -2.19839 \tabularnewline
12 & 11.4 & 10.7481 & 0.651883 \tabularnewline
13 & 6.4 & 11.3072 & -4.90719 \tabularnewline
14 & 10.6 & 9.62299 & 0.977013 \tabularnewline
15 & 12 & 13.1281 & -1.12813 \tabularnewline
16 & 6.3 & 8.36362 & -2.06362 \tabularnewline
17 & 11.3 & 9.38128 & 1.91872 \tabularnewline
18 & 11.9 & 12.0942 & -0.194241 \tabularnewline
19 & 9.3 & 10.3798 & -1.07981 \tabularnewline
20 & 9.6 & 11.4069 & -1.80686 \tabularnewline
21 & 10 & 9.40346 & 0.596544 \tabularnewline
22 & 6.4 & 9.42168 & -3.02168 \tabularnewline
23 & 13.8 & 12.6429 & 1.1571 \tabularnewline
24 & 10.8 & 10.957 & -0.156993 \tabularnewline
25 & 13.8 & 11.1862 & 2.61382 \tabularnewline
26 & 11.7 & 11.1203 & 0.579659 \tabularnewline
27 & 10.9 & 12.5374 & -1.63741 \tabularnewline
28 & 16.1 & 14.6344 & 1.4656 \tabularnewline
29 & 13.4 & 10.8859 & 2.51411 \tabularnewline
30 & 9.9 & 9.96231 & -0.0623072 \tabularnewline
31 & 11.5 & 10.1109 & 1.38906 \tabularnewline
32 & 8.3 & 9.74365 & -1.44365 \tabularnewline
33 & 11.7 & 10.9661 & 0.733905 \tabularnewline
34 & 6.1 & 9.32162 & -3.22162 \tabularnewline
35 & 9 & 9.96308 & -0.96308 \tabularnewline
36 & 9.7 & 12.3318 & -2.6318 \tabularnewline
37 & 10.8 & 10.2238 & 0.576187 \tabularnewline
38 & 10.3 & 10.4235 & -0.123547 \tabularnewline
39 & 10.4 & 10.1638 & 0.236232 \tabularnewline
40 & 12.7 & 11.1235 & 1.57654 \tabularnewline
41 & 9.3 & 11.7594 & -2.45944 \tabularnewline
42 & 11.8 & 11.8659 & -0.0659127 \tabularnewline
43 & 5.9 & 8.98039 & -3.08039 \tabularnewline
44 & 11.4 & 11.4976 & -0.0976121 \tabularnewline
45 & 13 & 10.2718 & 2.72816 \tabularnewline
46 & 10.8 & 10.0215 & 0.778528 \tabularnewline
47 & 12.3 & 9.13633 & 3.16367 \tabularnewline
48 & 11.3 & 12.1582 & -0.858196 \tabularnewline
49 & 11.8 & 10.1526 & 1.64739 \tabularnewline
50 & 7.9 & 11.5209 & -3.62092 \tabularnewline
51 & 12.7 & 8.80553 & 3.89447 \tabularnewline
52 & 12.3 & 9.22066 & 3.07934 \tabularnewline
53 & 11.6 & 10.7528 & 0.847176 \tabularnewline
54 & 6.7 & 9.2384 & -2.5384 \tabularnewline
55 & 10.9 & 9.54922 & 1.35078 \tabularnewline
56 & 12.1 & 10.7451 & 1.35493 \tabularnewline
57 & 13.3 & 10.7249 & 2.57508 \tabularnewline
58 & 10.1 & 9.98166 & 0.118342 \tabularnewline
59 & 5.7 & 10.2777 & -4.57767 \tabularnewline
60 & 14.3 & 10.2742 & 4.02577 \tabularnewline
61 & 8 & 8.33865 & -0.338648 \tabularnewline
62 & 13.3 & 10.6104 & 2.68961 \tabularnewline
63 & 9.3 & 11.0031 & -1.70307 \tabularnewline
64 & 12.5 & 10.4755 & 2.02455 \tabularnewline
65 & 7.6 & 9.31509 & -1.71509 \tabularnewline
66 & 15.9 & 11.983 & 3.91698 \tabularnewline
67 & 9.2 & 10.5236 & -1.32356 \tabularnewline
68 & 9.1 & 9.38927 & -0.289272 \tabularnewline
69 & 11.1 & 13.1499 & -2.04986 \tabularnewline
70 & 13 & 11.9875 & 1.01253 \tabularnewline
71 & 14.5 & 11.4044 & 3.09557 \tabularnewline
72 & 12.2 & 11.1186 & 1.08138 \tabularnewline
73 & 12.3 & 12.9528 & -0.652767 \tabularnewline
74 & 11.4 & 9.39846 & 2.00154 \tabularnewline
75 & 8.8 & 10.3826 & -1.58265 \tabularnewline
76 & 14.6 & 11.8374 & 2.76259 \tabularnewline
77 & 7.3 & 10.9619 & -3.66192 \tabularnewline
78 & 12.6 & 10.9877 & 1.61229 \tabularnewline
79 & 13 & 11.883 & 1.11701 \tabularnewline
80 & 12.6 & 10.2285 & 2.37151 \tabularnewline
81 & 13.2 & 11.9767 & 1.22327 \tabularnewline
82 & 9.9 & 10.7412 & -0.841182 \tabularnewline
83 & 7.7 & 9.94652 & -2.24652 \tabularnewline
84 & 10.5 & 10.7357 & -0.235684 \tabularnewline
85 & 13.4 & 10.3726 & 3.02738 \tabularnewline
86 & 10.9 & 11.1487 & -0.24866 \tabularnewline
87 & 4.3 & 9.14747 & -4.84747 \tabularnewline
88 & 10.3 & 11.4016 & -1.10159 \tabularnewline
89 & 11.8 & 11.4611 & 0.338866 \tabularnewline
90 & 11.2 & 10.6697 & 0.530314 \tabularnewline
91 & 11.4 & 10.6474 & 0.752641 \tabularnewline
92 & 8.6 & 10.2898 & -1.68981 \tabularnewline
93 & 13.2 & 10.886 & 2.31397 \tabularnewline
94 & 12.6 & 9.43191 & 3.16809 \tabularnewline
95 & 5.6 & 9.47182 & -3.87182 \tabularnewline
96 & 9.9 & 10.4832 & -0.583239 \tabularnewline
97 & 8.8 & 10.6417 & -1.8417 \tabularnewline
98 & 7.7 & 10.1869 & -2.48686 \tabularnewline
99 & 9 & 9.39794 & -0.397937 \tabularnewline
100 & 7.3 & 10.5187 & -3.21873 \tabularnewline
101 & 11.4 & 8.80378 & 2.59622 \tabularnewline
102 & 13.6 & 9.09354 & 4.50646 \tabularnewline
103 & 7.9 & 10.7109 & -2.81087 \tabularnewline
104 & 10.7 & 9.33576 & 1.36424 \tabularnewline
105 & 10.3 & 10.7221 & -0.422133 \tabularnewline
106 & 8.3 & 9.68361 & -1.38361 \tabularnewline
107 & 9.6 & 10.7141 & -1.11406 \tabularnewline
108 & 14.2 & 10.762 & 3.43798 \tabularnewline
109 & 8.5 & 10.8983 & -2.39834 \tabularnewline
110 & 13.5 & 10.536 & 2.96395 \tabularnewline
111 & 4.9 & 9.84548 & -4.94548 \tabularnewline
112 & 6.4 & 8.80899 & -2.40899 \tabularnewline
113 & 9.6 & 10.5599 & -0.959882 \tabularnewline
114 & 11.6 & 10.4558 & 1.14423 \tabularnewline
115 & 11.1 & 9.50707 & 1.59293 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&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]10.5997[/C][C]2.30029[/C][/ROW]
[ROW][C]2[/C][C]7.4[/C][C]9.41519[/C][C]-2.01519[/C][/ROW]
[ROW][C]3[/C][C]12.2[/C][C]9.67289[/C][C]2.52711[/C][/ROW]
[ROW][C]4[/C][C]12.8[/C][C]10.5791[/C][C]2.22086[/C][/ROW]
[ROW][C]5[/C][C]7.4[/C][C]11.3798[/C][C]-3.97976[/C][/ROW]
[ROW][C]6[/C][C]6.7[/C][C]9.64852[/C][C]-2.94852[/C][/ROW]
[ROW][C]7[/C][C]12.6[/C][C]13.1673[/C][C]-0.567349[/C][/ROW]
[ROW][C]8[/C][C]14.8[/C][C]11.3233[/C][C]3.47672[/C][/ROW]
[ROW][C]9[/C][C]13.3[/C][C]10.9243[/C][C]2.37571[/C][/ROW]
[ROW][C]10[/C][C]11.1[/C][C]12.3757[/C][C]-1.27568[/C][/ROW]
[ROW][C]11[/C][C]8.2[/C][C]10.3984[/C][C]-2.19839[/C][/ROW]
[ROW][C]12[/C][C]11.4[/C][C]10.7481[/C][C]0.651883[/C][/ROW]
[ROW][C]13[/C][C]6.4[/C][C]11.3072[/C][C]-4.90719[/C][/ROW]
[ROW][C]14[/C][C]10.6[/C][C]9.62299[/C][C]0.977013[/C][/ROW]
[ROW][C]15[/C][C]12[/C][C]13.1281[/C][C]-1.12813[/C][/ROW]
[ROW][C]16[/C][C]6.3[/C][C]8.36362[/C][C]-2.06362[/C][/ROW]
[ROW][C]17[/C][C]11.3[/C][C]9.38128[/C][C]1.91872[/C][/ROW]
[ROW][C]18[/C][C]11.9[/C][C]12.0942[/C][C]-0.194241[/C][/ROW]
[ROW][C]19[/C][C]9.3[/C][C]10.3798[/C][C]-1.07981[/C][/ROW]
[ROW][C]20[/C][C]9.6[/C][C]11.4069[/C][C]-1.80686[/C][/ROW]
[ROW][C]21[/C][C]10[/C][C]9.40346[/C][C]0.596544[/C][/ROW]
[ROW][C]22[/C][C]6.4[/C][C]9.42168[/C][C]-3.02168[/C][/ROW]
[ROW][C]23[/C][C]13.8[/C][C]12.6429[/C][C]1.1571[/C][/ROW]
[ROW][C]24[/C][C]10.8[/C][C]10.957[/C][C]-0.156993[/C][/ROW]
[ROW][C]25[/C][C]13.8[/C][C]11.1862[/C][C]2.61382[/C][/ROW]
[ROW][C]26[/C][C]11.7[/C][C]11.1203[/C][C]0.579659[/C][/ROW]
[ROW][C]27[/C][C]10.9[/C][C]12.5374[/C][C]-1.63741[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]14.6344[/C][C]1.4656[/C][/ROW]
[ROW][C]29[/C][C]13.4[/C][C]10.8859[/C][C]2.51411[/C][/ROW]
[ROW][C]30[/C][C]9.9[/C][C]9.96231[/C][C]-0.0623072[/C][/ROW]
[ROW][C]31[/C][C]11.5[/C][C]10.1109[/C][C]1.38906[/C][/ROW]
[ROW][C]32[/C][C]8.3[/C][C]9.74365[/C][C]-1.44365[/C][/ROW]
[ROW][C]33[/C][C]11.7[/C][C]10.9661[/C][C]0.733905[/C][/ROW]
[ROW][C]34[/C][C]6.1[/C][C]9.32162[/C][C]-3.22162[/C][/ROW]
[ROW][C]35[/C][C]9[/C][C]9.96308[/C][C]-0.96308[/C][/ROW]
[ROW][C]36[/C][C]9.7[/C][C]12.3318[/C][C]-2.6318[/C][/ROW]
[ROW][C]37[/C][C]10.8[/C][C]10.2238[/C][C]0.576187[/C][/ROW]
[ROW][C]38[/C][C]10.3[/C][C]10.4235[/C][C]-0.123547[/C][/ROW]
[ROW][C]39[/C][C]10.4[/C][C]10.1638[/C][C]0.236232[/C][/ROW]
[ROW][C]40[/C][C]12.7[/C][C]11.1235[/C][C]1.57654[/C][/ROW]
[ROW][C]41[/C][C]9.3[/C][C]11.7594[/C][C]-2.45944[/C][/ROW]
[ROW][C]42[/C][C]11.8[/C][C]11.8659[/C][C]-0.0659127[/C][/ROW]
[ROW][C]43[/C][C]5.9[/C][C]8.98039[/C][C]-3.08039[/C][/ROW]
[ROW][C]44[/C][C]11.4[/C][C]11.4976[/C][C]-0.0976121[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]10.2718[/C][C]2.72816[/C][/ROW]
[ROW][C]46[/C][C]10.8[/C][C]10.0215[/C][C]0.778528[/C][/ROW]
[ROW][C]47[/C][C]12.3[/C][C]9.13633[/C][C]3.16367[/C][/ROW]
[ROW][C]48[/C][C]11.3[/C][C]12.1582[/C][C]-0.858196[/C][/ROW]
[ROW][C]49[/C][C]11.8[/C][C]10.1526[/C][C]1.64739[/C][/ROW]
[ROW][C]50[/C][C]7.9[/C][C]11.5209[/C][C]-3.62092[/C][/ROW]
[ROW][C]51[/C][C]12.7[/C][C]8.80553[/C][C]3.89447[/C][/ROW]
[ROW][C]52[/C][C]12.3[/C][C]9.22066[/C][C]3.07934[/C][/ROW]
[ROW][C]53[/C][C]11.6[/C][C]10.7528[/C][C]0.847176[/C][/ROW]
[ROW][C]54[/C][C]6.7[/C][C]9.2384[/C][C]-2.5384[/C][/ROW]
[ROW][C]55[/C][C]10.9[/C][C]9.54922[/C][C]1.35078[/C][/ROW]
[ROW][C]56[/C][C]12.1[/C][C]10.7451[/C][C]1.35493[/C][/ROW]
[ROW][C]57[/C][C]13.3[/C][C]10.7249[/C][C]2.57508[/C][/ROW]
[ROW][C]58[/C][C]10.1[/C][C]9.98166[/C][C]0.118342[/C][/ROW]
[ROW][C]59[/C][C]5.7[/C][C]10.2777[/C][C]-4.57767[/C][/ROW]
[ROW][C]60[/C][C]14.3[/C][C]10.2742[/C][C]4.02577[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]8.33865[/C][C]-0.338648[/C][/ROW]
[ROW][C]62[/C][C]13.3[/C][C]10.6104[/C][C]2.68961[/C][/ROW]
[ROW][C]63[/C][C]9.3[/C][C]11.0031[/C][C]-1.70307[/C][/ROW]
[ROW][C]64[/C][C]12.5[/C][C]10.4755[/C][C]2.02455[/C][/ROW]
[ROW][C]65[/C][C]7.6[/C][C]9.31509[/C][C]-1.71509[/C][/ROW]
[ROW][C]66[/C][C]15.9[/C][C]11.983[/C][C]3.91698[/C][/ROW]
[ROW][C]67[/C][C]9.2[/C][C]10.5236[/C][C]-1.32356[/C][/ROW]
[ROW][C]68[/C][C]9.1[/C][C]9.38927[/C][C]-0.289272[/C][/ROW]
[ROW][C]69[/C][C]11.1[/C][C]13.1499[/C][C]-2.04986[/C][/ROW]
[ROW][C]70[/C][C]13[/C][C]11.9875[/C][C]1.01253[/C][/ROW]
[ROW][C]71[/C][C]14.5[/C][C]11.4044[/C][C]3.09557[/C][/ROW]
[ROW][C]72[/C][C]12.2[/C][C]11.1186[/C][C]1.08138[/C][/ROW]
[ROW][C]73[/C][C]12.3[/C][C]12.9528[/C][C]-0.652767[/C][/ROW]
[ROW][C]74[/C][C]11.4[/C][C]9.39846[/C][C]2.00154[/C][/ROW]
[ROW][C]75[/C][C]8.8[/C][C]10.3826[/C][C]-1.58265[/C][/ROW]
[ROW][C]76[/C][C]14.6[/C][C]11.8374[/C][C]2.76259[/C][/ROW]
[ROW][C]77[/C][C]7.3[/C][C]10.9619[/C][C]-3.66192[/C][/ROW]
[ROW][C]78[/C][C]12.6[/C][C]10.9877[/C][C]1.61229[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]11.883[/C][C]1.11701[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]10.2285[/C][C]2.37151[/C][/ROW]
[ROW][C]81[/C][C]13.2[/C][C]11.9767[/C][C]1.22327[/C][/ROW]
[ROW][C]82[/C][C]9.9[/C][C]10.7412[/C][C]-0.841182[/C][/ROW]
[ROW][C]83[/C][C]7.7[/C][C]9.94652[/C][C]-2.24652[/C][/ROW]
[ROW][C]84[/C][C]10.5[/C][C]10.7357[/C][C]-0.235684[/C][/ROW]
[ROW][C]85[/C][C]13.4[/C][C]10.3726[/C][C]3.02738[/C][/ROW]
[ROW][C]86[/C][C]10.9[/C][C]11.1487[/C][C]-0.24866[/C][/ROW]
[ROW][C]87[/C][C]4.3[/C][C]9.14747[/C][C]-4.84747[/C][/ROW]
[ROW][C]88[/C][C]10.3[/C][C]11.4016[/C][C]-1.10159[/C][/ROW]
[ROW][C]89[/C][C]11.8[/C][C]11.4611[/C][C]0.338866[/C][/ROW]
[ROW][C]90[/C][C]11.2[/C][C]10.6697[/C][C]0.530314[/C][/ROW]
[ROW][C]91[/C][C]11.4[/C][C]10.6474[/C][C]0.752641[/C][/ROW]
[ROW][C]92[/C][C]8.6[/C][C]10.2898[/C][C]-1.68981[/C][/ROW]
[ROW][C]93[/C][C]13.2[/C][C]10.886[/C][C]2.31397[/C][/ROW]
[ROW][C]94[/C][C]12.6[/C][C]9.43191[/C][C]3.16809[/C][/ROW]
[ROW][C]95[/C][C]5.6[/C][C]9.47182[/C][C]-3.87182[/C][/ROW]
[ROW][C]96[/C][C]9.9[/C][C]10.4832[/C][C]-0.583239[/C][/ROW]
[ROW][C]97[/C][C]8.8[/C][C]10.6417[/C][C]-1.8417[/C][/ROW]
[ROW][C]98[/C][C]7.7[/C][C]10.1869[/C][C]-2.48686[/C][/ROW]
[ROW][C]99[/C][C]9[/C][C]9.39794[/C][C]-0.397937[/C][/ROW]
[ROW][C]100[/C][C]7.3[/C][C]10.5187[/C][C]-3.21873[/C][/ROW]
[ROW][C]101[/C][C]11.4[/C][C]8.80378[/C][C]2.59622[/C][/ROW]
[ROW][C]102[/C][C]13.6[/C][C]9.09354[/C][C]4.50646[/C][/ROW]
[ROW][C]103[/C][C]7.9[/C][C]10.7109[/C][C]-2.81087[/C][/ROW]
[ROW][C]104[/C][C]10.7[/C][C]9.33576[/C][C]1.36424[/C][/ROW]
[ROW][C]105[/C][C]10.3[/C][C]10.7221[/C][C]-0.422133[/C][/ROW]
[ROW][C]106[/C][C]8.3[/C][C]9.68361[/C][C]-1.38361[/C][/ROW]
[ROW][C]107[/C][C]9.6[/C][C]10.7141[/C][C]-1.11406[/C][/ROW]
[ROW][C]108[/C][C]14.2[/C][C]10.762[/C][C]3.43798[/C][/ROW]
[ROW][C]109[/C][C]8.5[/C][C]10.8983[/C][C]-2.39834[/C][/ROW]
[ROW][C]110[/C][C]13.5[/C][C]10.536[/C][C]2.96395[/C][/ROW]
[ROW][C]111[/C][C]4.9[/C][C]9.84548[/C][C]-4.94548[/C][/ROW]
[ROW][C]112[/C][C]6.4[/C][C]8.80899[/C][C]-2.40899[/C][/ROW]
[ROW][C]113[/C][C]9.6[/C][C]10.5599[/C][C]-0.959882[/C][/ROW]
[ROW][C]114[/C][C]11.6[/C][C]10.4558[/C][C]1.14423[/C][/ROW]
[ROW][C]115[/C][C]11.1[/C][C]9.50707[/C][C]1.59293[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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	10.5997	2.30029
2	7.4	9.41519	-2.01519
3	12.2	9.67289	2.52711
4	12.8	10.5791	2.22086
5	7.4	11.3798	-3.97976
6	6.7	9.64852	-2.94852
7	12.6	13.1673	-0.567349
8	14.8	11.3233	3.47672
9	13.3	10.9243	2.37571
10	11.1	12.3757	-1.27568
11	8.2	10.3984	-2.19839
12	11.4	10.7481	0.651883
13	6.4	11.3072	-4.90719
14	10.6	9.62299	0.977013
15	12	13.1281	-1.12813
16	6.3	8.36362	-2.06362
17	11.3	9.38128	1.91872
18	11.9	12.0942	-0.194241
19	9.3	10.3798	-1.07981
20	9.6	11.4069	-1.80686
21	10	9.40346	0.596544
22	6.4	9.42168	-3.02168
23	13.8	12.6429	1.1571
24	10.8	10.957	-0.156993
25	13.8	11.1862	2.61382
26	11.7	11.1203	0.579659
27	10.9	12.5374	-1.63741
28	16.1	14.6344	1.4656
29	13.4	10.8859	2.51411
30	9.9	9.96231	-0.0623072
31	11.5	10.1109	1.38906
32	8.3	9.74365	-1.44365
33	11.7	10.9661	0.733905
34	6.1	9.32162	-3.22162
35	9	9.96308	-0.96308
36	9.7	12.3318	-2.6318
37	10.8	10.2238	0.576187
38	10.3	10.4235	-0.123547
39	10.4	10.1638	0.236232
40	12.7	11.1235	1.57654
41	9.3	11.7594	-2.45944
42	11.8	11.8659	-0.0659127
43	5.9	8.98039	-3.08039
44	11.4	11.4976	-0.0976121
45	13	10.2718	2.72816
46	10.8	10.0215	0.778528
47	12.3	9.13633	3.16367
48	11.3	12.1582	-0.858196
49	11.8	10.1526	1.64739
50	7.9	11.5209	-3.62092
51	12.7	8.80553	3.89447
52	12.3	9.22066	3.07934
53	11.6	10.7528	0.847176
54	6.7	9.2384	-2.5384
55	10.9	9.54922	1.35078
56	12.1	10.7451	1.35493
57	13.3	10.7249	2.57508
58	10.1	9.98166	0.118342
59	5.7	10.2777	-4.57767
60	14.3	10.2742	4.02577
61	8	8.33865	-0.338648
62	13.3	10.6104	2.68961
63	9.3	11.0031	-1.70307
64	12.5	10.4755	2.02455
65	7.6	9.31509	-1.71509
66	15.9	11.983	3.91698
67	9.2	10.5236	-1.32356
68	9.1	9.38927	-0.289272
69	11.1	13.1499	-2.04986
70	13	11.9875	1.01253
71	14.5	11.4044	3.09557
72	12.2	11.1186	1.08138
73	12.3	12.9528	-0.652767
74	11.4	9.39846	2.00154
75	8.8	10.3826	-1.58265
76	14.6	11.8374	2.76259
77	7.3	10.9619	-3.66192
78	12.6	10.9877	1.61229
79	13	11.883	1.11701
80	12.6	10.2285	2.37151
81	13.2	11.9767	1.22327
82	9.9	10.7412	-0.841182
83	7.7	9.94652	-2.24652
84	10.5	10.7357	-0.235684
85	13.4	10.3726	3.02738
86	10.9	11.1487	-0.24866
87	4.3	9.14747	-4.84747
88	10.3	11.4016	-1.10159
89	11.8	11.4611	0.338866
90	11.2	10.6697	0.530314
91	11.4	10.6474	0.752641
92	8.6	10.2898	-1.68981
93	13.2	10.886	2.31397
94	12.6	9.43191	3.16809
95	5.6	9.47182	-3.87182
96	9.9	10.4832	-0.583239
97	8.8	10.6417	-1.8417
98	7.7	10.1869	-2.48686
99	9	9.39794	-0.397937
100	7.3	10.5187	-3.21873
101	11.4	8.80378	2.59622
102	13.6	9.09354	4.50646
103	7.9	10.7109	-2.81087
104	10.7	9.33576	1.36424
105	10.3	10.7221	-0.422133
106	8.3	9.68361	-1.38361
107	9.6	10.7141	-1.11406
108	14.2	10.762	3.43798
109	8.5	10.8983	-2.39834
110	13.5	10.536	2.96395
111	4.9	9.84548	-4.94548
112	6.4	8.80899	-2.40899
113	9.6	10.5599	-0.959882
114	11.6	10.4558	1.14423
115	11.1	9.50707	1.59293

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.953585	0.0928295	0.0464147
13	0.972193	0.0556143	0.0278071
14	0.948683	0.102635	0.0513175
15	0.945638	0.108724	0.0543619
16	0.950773	0.0984536	0.0492268
17	0.92152	0.15696	0.0784798
18	0.88049	0.23902	0.11951
19	0.848453	0.303093	0.151547
20	0.854565	0.29087	0.145435
21	0.800386	0.399227	0.199614
22	0.789068	0.421864	0.210932
23	0.727702	0.544595	0.272298
24	0.664019	0.671962	0.335981
25	0.667373	0.665253	0.332627
26	0.604191	0.791618	0.395809
27	0.552895	0.894211	0.447105
28	0.499752	0.999503	0.500248
29	0.489411	0.978823	0.510589
30	0.419187	0.838375	0.580813
31	0.372565	0.745129	0.627435
32	0.323034	0.646068	0.676966
33	0.276848	0.553696	0.723152
34	0.296145	0.592289	0.703855
35	0.247595	0.49519	0.752405
36	0.240186	0.480372	0.759814
37	0.22414	0.448279	0.77586
38	0.190705	0.38141	0.809295
39	0.150373	0.300747	0.849627
40	0.13253	0.265061	0.86747
41	0.148255	0.296509	0.851745
42	0.115018	0.230035	0.884982
43	0.134012	0.268024	0.865988
44	0.109929	0.219858	0.890071
45	0.133423	0.266845	0.866577
46	0.11468	0.229361	0.88532
47	0.131185	0.26237	0.868815
48	0.111036	0.222073	0.888964
49	0.107689	0.215378	0.892311
50	0.199782	0.399564	0.800218
51	0.28415	0.568299	0.71585
52	0.305647	0.611295	0.694353
53	0.257462	0.514925	0.742538
54	0.284	0.568	0.716
55	0.26699	0.533979	0.73301
56	0.227558	0.455116	0.772442
57	0.242959	0.485918	0.757041
58	0.210684	0.421368	0.789316
59	0.375982	0.751965	0.624018
60	0.456977	0.913954	0.543023
61	0.406152	0.812304	0.593848
62	0.411043	0.822086	0.588957
63	0.378007	0.756013	0.621993
64	0.35682	0.713639	0.64318
65	0.325121	0.650242	0.674879
66	0.387019	0.774038	0.612981
67	0.345485	0.69097	0.654515
68	0.294783	0.589567	0.705217
69	0.297753	0.595506	0.702247
70	0.260914	0.521827	0.739086
71	0.265552	0.531105	0.734448
72	0.22655	0.4531	0.77345
73	0.187584	0.375168	0.812416
74	0.196699	0.393399	0.803301
75	0.173176	0.346352	0.826824
76	0.184678	0.369357	0.815322
77	0.215503	0.431006	0.784497
78	0.187337	0.374674	0.812663
79	0.154715	0.309429	0.845285
80	0.146131	0.292261	0.853869
81	0.124752	0.249504	0.875248
82	0.100451	0.200901	0.899549
83	0.0809054	0.161811	0.919095
84	0.0598142	0.119628	0.940186
85	0.0756299	0.15126	0.92437
86	0.0546232	0.109246	0.945377
87	0.133152	0.266303	0.866848
88	0.104466	0.208933	0.895534
89	0.0801272	0.160254	0.919873
90	0.0564089	0.112818	0.943591
91	0.0416247	0.0832493	0.958375
92	0.0330058	0.0660116	0.966994
93	0.0379661	0.0759323	0.962034
94	0.0349126	0.0698252	0.965087
95	0.0660193	0.132039	0.933981
96	0.0427929	0.0855857	0.957207
97	0.0300175	0.060035	0.969983
98	0.0595198	0.11904	0.94048
99	0.0365851	0.0731702	0.963415
100	0.193276	0.386553	0.806724
101	0.145757	0.291513	0.854243
102	0.171058	0.342117	0.828942
103	0.136138	0.272277	0.863862

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.953585 & 0.0928295 & 0.0464147 \tabularnewline
13 & 0.972193 & 0.0556143 & 0.0278071 \tabularnewline
14 & 0.948683 & 0.102635 & 0.0513175 \tabularnewline
15 & 0.945638 & 0.108724 & 0.0543619 \tabularnewline
16 & 0.950773 & 0.0984536 & 0.0492268 \tabularnewline
17 & 0.92152 & 0.15696 & 0.0784798 \tabularnewline
18 & 0.88049 & 0.23902 & 0.11951 \tabularnewline
19 & 0.848453 & 0.303093 & 0.151547 \tabularnewline
20 & 0.854565 & 0.29087 & 0.145435 \tabularnewline
21 & 0.800386 & 0.399227 & 0.199614 \tabularnewline
22 & 0.789068 & 0.421864 & 0.210932 \tabularnewline
23 & 0.727702 & 0.544595 & 0.272298 \tabularnewline
24 & 0.664019 & 0.671962 & 0.335981 \tabularnewline
25 & 0.667373 & 0.665253 & 0.332627 \tabularnewline
26 & 0.604191 & 0.791618 & 0.395809 \tabularnewline
27 & 0.552895 & 0.894211 & 0.447105 \tabularnewline
28 & 0.499752 & 0.999503 & 0.500248 \tabularnewline
29 & 0.489411 & 0.978823 & 0.510589 \tabularnewline
30 & 0.419187 & 0.838375 & 0.580813 \tabularnewline
31 & 0.372565 & 0.745129 & 0.627435 \tabularnewline
32 & 0.323034 & 0.646068 & 0.676966 \tabularnewline
33 & 0.276848 & 0.553696 & 0.723152 \tabularnewline
34 & 0.296145 & 0.592289 & 0.703855 \tabularnewline
35 & 0.247595 & 0.49519 & 0.752405 \tabularnewline
36 & 0.240186 & 0.480372 & 0.759814 \tabularnewline
37 & 0.22414 & 0.448279 & 0.77586 \tabularnewline
38 & 0.190705 & 0.38141 & 0.809295 \tabularnewline
39 & 0.150373 & 0.300747 & 0.849627 \tabularnewline
40 & 0.13253 & 0.265061 & 0.86747 \tabularnewline
41 & 0.148255 & 0.296509 & 0.851745 \tabularnewline
42 & 0.115018 & 0.230035 & 0.884982 \tabularnewline
43 & 0.134012 & 0.268024 & 0.865988 \tabularnewline
44 & 0.109929 & 0.219858 & 0.890071 \tabularnewline
45 & 0.133423 & 0.266845 & 0.866577 \tabularnewline
46 & 0.11468 & 0.229361 & 0.88532 \tabularnewline
47 & 0.131185 & 0.26237 & 0.868815 \tabularnewline
48 & 0.111036 & 0.222073 & 0.888964 \tabularnewline
49 & 0.107689 & 0.215378 & 0.892311 \tabularnewline
50 & 0.199782 & 0.399564 & 0.800218 \tabularnewline
51 & 0.28415 & 0.568299 & 0.71585 \tabularnewline
52 & 0.305647 & 0.611295 & 0.694353 \tabularnewline
53 & 0.257462 & 0.514925 & 0.742538 \tabularnewline
54 & 0.284 & 0.568 & 0.716 \tabularnewline
55 & 0.26699 & 0.533979 & 0.73301 \tabularnewline
56 & 0.227558 & 0.455116 & 0.772442 \tabularnewline
57 & 0.242959 & 0.485918 & 0.757041 \tabularnewline
58 & 0.210684 & 0.421368 & 0.789316 \tabularnewline
59 & 0.375982 & 0.751965 & 0.624018 \tabularnewline
60 & 0.456977 & 0.913954 & 0.543023 \tabularnewline
61 & 0.406152 & 0.812304 & 0.593848 \tabularnewline
62 & 0.411043 & 0.822086 & 0.588957 \tabularnewline
63 & 0.378007 & 0.756013 & 0.621993 \tabularnewline
64 & 0.35682 & 0.713639 & 0.64318 \tabularnewline
65 & 0.325121 & 0.650242 & 0.674879 \tabularnewline
66 & 0.387019 & 0.774038 & 0.612981 \tabularnewline
67 & 0.345485 & 0.69097 & 0.654515 \tabularnewline
68 & 0.294783 & 0.589567 & 0.705217 \tabularnewline
69 & 0.297753 & 0.595506 & 0.702247 \tabularnewline
70 & 0.260914 & 0.521827 & 0.739086 \tabularnewline
71 & 0.265552 & 0.531105 & 0.734448 \tabularnewline
72 & 0.22655 & 0.4531 & 0.77345 \tabularnewline
73 & 0.187584 & 0.375168 & 0.812416 \tabularnewline
74 & 0.196699 & 0.393399 & 0.803301 \tabularnewline
75 & 0.173176 & 0.346352 & 0.826824 \tabularnewline
76 & 0.184678 & 0.369357 & 0.815322 \tabularnewline
77 & 0.215503 & 0.431006 & 0.784497 \tabularnewline
78 & 0.187337 & 0.374674 & 0.812663 \tabularnewline
79 & 0.154715 & 0.309429 & 0.845285 \tabularnewline
80 & 0.146131 & 0.292261 & 0.853869 \tabularnewline
81 & 0.124752 & 0.249504 & 0.875248 \tabularnewline
82 & 0.100451 & 0.200901 & 0.899549 \tabularnewline
83 & 0.0809054 & 0.161811 & 0.919095 \tabularnewline
84 & 0.0598142 & 0.119628 & 0.940186 \tabularnewline
85 & 0.0756299 & 0.15126 & 0.92437 \tabularnewline
86 & 0.0546232 & 0.109246 & 0.945377 \tabularnewline
87 & 0.133152 & 0.266303 & 0.866848 \tabularnewline
88 & 0.104466 & 0.208933 & 0.895534 \tabularnewline
89 & 0.0801272 & 0.160254 & 0.919873 \tabularnewline
90 & 0.0564089 & 0.112818 & 0.943591 \tabularnewline
91 & 0.0416247 & 0.0832493 & 0.958375 \tabularnewline
92 & 0.0330058 & 0.0660116 & 0.966994 \tabularnewline
93 & 0.0379661 & 0.0759323 & 0.962034 \tabularnewline
94 & 0.0349126 & 0.0698252 & 0.965087 \tabularnewline
95 & 0.0660193 & 0.132039 & 0.933981 \tabularnewline
96 & 0.0427929 & 0.0855857 & 0.957207 \tabularnewline
97 & 0.0300175 & 0.060035 & 0.969983 \tabularnewline
98 & 0.0595198 & 0.11904 & 0.94048 \tabularnewline
99 & 0.0365851 & 0.0731702 & 0.963415 \tabularnewline
100 & 0.193276 & 0.386553 & 0.806724 \tabularnewline
101 & 0.145757 & 0.291513 & 0.854243 \tabularnewline
102 & 0.171058 & 0.342117 & 0.828942 \tabularnewline
103 & 0.136138 & 0.272277 & 0.863862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&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]12[/C][C]0.953585[/C][C]0.0928295[/C][C]0.0464147[/C][/ROW]
[ROW][C]13[/C][C]0.972193[/C][C]0.0556143[/C][C]0.0278071[/C][/ROW]
[ROW][C]14[/C][C]0.948683[/C][C]0.102635[/C][C]0.0513175[/C][/ROW]
[ROW][C]15[/C][C]0.945638[/C][C]0.108724[/C][C]0.0543619[/C][/ROW]
[ROW][C]16[/C][C]0.950773[/C][C]0.0984536[/C][C]0.0492268[/C][/ROW]
[ROW][C]17[/C][C]0.92152[/C][C]0.15696[/C][C]0.0784798[/C][/ROW]
[ROW][C]18[/C][C]0.88049[/C][C]0.23902[/C][C]0.11951[/C][/ROW]
[ROW][C]19[/C][C]0.848453[/C][C]0.303093[/C][C]0.151547[/C][/ROW]
[ROW][C]20[/C][C]0.854565[/C][C]0.29087[/C][C]0.145435[/C][/ROW]
[ROW][C]21[/C][C]0.800386[/C][C]0.399227[/C][C]0.199614[/C][/ROW]
[ROW][C]22[/C][C]0.789068[/C][C]0.421864[/C][C]0.210932[/C][/ROW]
[ROW][C]23[/C][C]0.727702[/C][C]0.544595[/C][C]0.272298[/C][/ROW]
[ROW][C]24[/C][C]0.664019[/C][C]0.671962[/C][C]0.335981[/C][/ROW]
[ROW][C]25[/C][C]0.667373[/C][C]0.665253[/C][C]0.332627[/C][/ROW]
[ROW][C]26[/C][C]0.604191[/C][C]0.791618[/C][C]0.395809[/C][/ROW]
[ROW][C]27[/C][C]0.552895[/C][C]0.894211[/C][C]0.447105[/C][/ROW]
[ROW][C]28[/C][C]0.499752[/C][C]0.999503[/C][C]0.500248[/C][/ROW]
[ROW][C]29[/C][C]0.489411[/C][C]0.978823[/C][C]0.510589[/C][/ROW]
[ROW][C]30[/C][C]0.419187[/C][C]0.838375[/C][C]0.580813[/C][/ROW]
[ROW][C]31[/C][C]0.372565[/C][C]0.745129[/C][C]0.627435[/C][/ROW]
[ROW][C]32[/C][C]0.323034[/C][C]0.646068[/C][C]0.676966[/C][/ROW]
[ROW][C]33[/C][C]0.276848[/C][C]0.553696[/C][C]0.723152[/C][/ROW]
[ROW][C]34[/C][C]0.296145[/C][C]0.592289[/C][C]0.703855[/C][/ROW]
[ROW][C]35[/C][C]0.247595[/C][C]0.49519[/C][C]0.752405[/C][/ROW]
[ROW][C]36[/C][C]0.240186[/C][C]0.480372[/C][C]0.759814[/C][/ROW]
[ROW][C]37[/C][C]0.22414[/C][C]0.448279[/C][C]0.77586[/C][/ROW]
[ROW][C]38[/C][C]0.190705[/C][C]0.38141[/C][C]0.809295[/C][/ROW]
[ROW][C]39[/C][C]0.150373[/C][C]0.300747[/C][C]0.849627[/C][/ROW]
[ROW][C]40[/C][C]0.13253[/C][C]0.265061[/C][C]0.86747[/C][/ROW]
[ROW][C]41[/C][C]0.148255[/C][C]0.296509[/C][C]0.851745[/C][/ROW]
[ROW][C]42[/C][C]0.115018[/C][C]0.230035[/C][C]0.884982[/C][/ROW]
[ROW][C]43[/C][C]0.134012[/C][C]0.268024[/C][C]0.865988[/C][/ROW]
[ROW][C]44[/C][C]0.109929[/C][C]0.219858[/C][C]0.890071[/C][/ROW]
[ROW][C]45[/C][C]0.133423[/C][C]0.266845[/C][C]0.866577[/C][/ROW]
[ROW][C]46[/C][C]0.11468[/C][C]0.229361[/C][C]0.88532[/C][/ROW]
[ROW][C]47[/C][C]0.131185[/C][C]0.26237[/C][C]0.868815[/C][/ROW]
[ROW][C]48[/C][C]0.111036[/C][C]0.222073[/C][C]0.888964[/C][/ROW]
[ROW][C]49[/C][C]0.107689[/C][C]0.215378[/C][C]0.892311[/C][/ROW]
[ROW][C]50[/C][C]0.199782[/C][C]0.399564[/C][C]0.800218[/C][/ROW]
[ROW][C]51[/C][C]0.28415[/C][C]0.568299[/C][C]0.71585[/C][/ROW]
[ROW][C]52[/C][C]0.305647[/C][C]0.611295[/C][C]0.694353[/C][/ROW]
[ROW][C]53[/C][C]0.257462[/C][C]0.514925[/C][C]0.742538[/C][/ROW]
[ROW][C]54[/C][C]0.284[/C][C]0.568[/C][C]0.716[/C][/ROW]
[ROW][C]55[/C][C]0.26699[/C][C]0.533979[/C][C]0.73301[/C][/ROW]
[ROW][C]56[/C][C]0.227558[/C][C]0.455116[/C][C]0.772442[/C][/ROW]
[ROW][C]57[/C][C]0.242959[/C][C]0.485918[/C][C]0.757041[/C][/ROW]
[ROW][C]58[/C][C]0.210684[/C][C]0.421368[/C][C]0.789316[/C][/ROW]
[ROW][C]59[/C][C]0.375982[/C][C]0.751965[/C][C]0.624018[/C][/ROW]
[ROW][C]60[/C][C]0.456977[/C][C]0.913954[/C][C]0.543023[/C][/ROW]
[ROW][C]61[/C][C]0.406152[/C][C]0.812304[/C][C]0.593848[/C][/ROW]
[ROW][C]62[/C][C]0.411043[/C][C]0.822086[/C][C]0.588957[/C][/ROW]
[ROW][C]63[/C][C]0.378007[/C][C]0.756013[/C][C]0.621993[/C][/ROW]
[ROW][C]64[/C][C]0.35682[/C][C]0.713639[/C][C]0.64318[/C][/ROW]
[ROW][C]65[/C][C]0.325121[/C][C]0.650242[/C][C]0.674879[/C][/ROW]
[ROW][C]66[/C][C]0.387019[/C][C]0.774038[/C][C]0.612981[/C][/ROW]
[ROW][C]67[/C][C]0.345485[/C][C]0.69097[/C][C]0.654515[/C][/ROW]
[ROW][C]68[/C][C]0.294783[/C][C]0.589567[/C][C]0.705217[/C][/ROW]
[ROW][C]69[/C][C]0.297753[/C][C]0.595506[/C][C]0.702247[/C][/ROW]
[ROW][C]70[/C][C]0.260914[/C][C]0.521827[/C][C]0.739086[/C][/ROW]
[ROW][C]71[/C][C]0.265552[/C][C]0.531105[/C][C]0.734448[/C][/ROW]
[ROW][C]72[/C][C]0.22655[/C][C]0.4531[/C][C]0.77345[/C][/ROW]
[ROW][C]73[/C][C]0.187584[/C][C]0.375168[/C][C]0.812416[/C][/ROW]
[ROW][C]74[/C][C]0.196699[/C][C]0.393399[/C][C]0.803301[/C][/ROW]
[ROW][C]75[/C][C]0.173176[/C][C]0.346352[/C][C]0.826824[/C][/ROW]
[ROW][C]76[/C][C]0.184678[/C][C]0.369357[/C][C]0.815322[/C][/ROW]
[ROW][C]77[/C][C]0.215503[/C][C]0.431006[/C][C]0.784497[/C][/ROW]
[ROW][C]78[/C][C]0.187337[/C][C]0.374674[/C][C]0.812663[/C][/ROW]
[ROW][C]79[/C][C]0.154715[/C][C]0.309429[/C][C]0.845285[/C][/ROW]
[ROW][C]80[/C][C]0.146131[/C][C]0.292261[/C][C]0.853869[/C][/ROW]
[ROW][C]81[/C][C]0.124752[/C][C]0.249504[/C][C]0.875248[/C][/ROW]
[ROW][C]82[/C][C]0.100451[/C][C]0.200901[/C][C]0.899549[/C][/ROW]
[ROW][C]83[/C][C]0.0809054[/C][C]0.161811[/C][C]0.919095[/C][/ROW]
[ROW][C]84[/C][C]0.0598142[/C][C]0.119628[/C][C]0.940186[/C][/ROW]
[ROW][C]85[/C][C]0.0756299[/C][C]0.15126[/C][C]0.92437[/C][/ROW]
[ROW][C]86[/C][C]0.0546232[/C][C]0.109246[/C][C]0.945377[/C][/ROW]
[ROW][C]87[/C][C]0.133152[/C][C]0.266303[/C][C]0.866848[/C][/ROW]
[ROW][C]88[/C][C]0.104466[/C][C]0.208933[/C][C]0.895534[/C][/ROW]
[ROW][C]89[/C][C]0.0801272[/C][C]0.160254[/C][C]0.919873[/C][/ROW]
[ROW][C]90[/C][C]0.0564089[/C][C]0.112818[/C][C]0.943591[/C][/ROW]
[ROW][C]91[/C][C]0.0416247[/C][C]0.0832493[/C][C]0.958375[/C][/ROW]
[ROW][C]92[/C][C]0.0330058[/C][C]0.0660116[/C][C]0.966994[/C][/ROW]
[ROW][C]93[/C][C]0.0379661[/C][C]0.0759323[/C][C]0.962034[/C][/ROW]
[ROW][C]94[/C][C]0.0349126[/C][C]0.0698252[/C][C]0.965087[/C][/ROW]
[ROW][C]95[/C][C]0.0660193[/C][C]0.132039[/C][C]0.933981[/C][/ROW]
[ROW][C]96[/C][C]0.0427929[/C][C]0.0855857[/C][C]0.957207[/C][/ROW]
[ROW][C]97[/C][C]0.0300175[/C][C]0.060035[/C][C]0.969983[/C][/ROW]
[ROW][C]98[/C][C]0.0595198[/C][C]0.11904[/C][C]0.94048[/C][/ROW]
[ROW][C]99[/C][C]0.0365851[/C][C]0.0731702[/C][C]0.963415[/C][/ROW]
[ROW][C]100[/C][C]0.193276[/C][C]0.386553[/C][C]0.806724[/C][/ROW]
[ROW][C]101[/C][C]0.145757[/C][C]0.291513[/C][C]0.854243[/C][/ROW]
[ROW][C]102[/C][C]0.171058[/C][C]0.342117[/C][C]0.828942[/C][/ROW]
[ROW][C]103[/C][C]0.136138[/C][C]0.272277[/C][C]0.863862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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
12	0.953585	0.0928295	0.0464147
13	0.972193	0.0556143	0.0278071
14	0.948683	0.102635	0.0513175
15	0.945638	0.108724	0.0543619
16	0.950773	0.0984536	0.0492268
17	0.92152	0.15696	0.0784798
18	0.88049	0.23902	0.11951
19	0.848453	0.303093	0.151547
20	0.854565	0.29087	0.145435
21	0.800386	0.399227	0.199614
22	0.789068	0.421864	0.210932
23	0.727702	0.544595	0.272298
24	0.664019	0.671962	0.335981
25	0.667373	0.665253	0.332627
26	0.604191	0.791618	0.395809
27	0.552895	0.894211	0.447105
28	0.499752	0.999503	0.500248
29	0.489411	0.978823	0.510589
30	0.419187	0.838375	0.580813
31	0.372565	0.745129	0.627435
32	0.323034	0.646068	0.676966
33	0.276848	0.553696	0.723152
34	0.296145	0.592289	0.703855
35	0.247595	0.49519	0.752405
36	0.240186	0.480372	0.759814
37	0.22414	0.448279	0.77586
38	0.190705	0.38141	0.809295
39	0.150373	0.300747	0.849627
40	0.13253	0.265061	0.86747
41	0.148255	0.296509	0.851745
42	0.115018	0.230035	0.884982
43	0.134012	0.268024	0.865988
44	0.109929	0.219858	0.890071
45	0.133423	0.266845	0.866577
46	0.11468	0.229361	0.88532
47	0.131185	0.26237	0.868815
48	0.111036	0.222073	0.888964
49	0.107689	0.215378	0.892311
50	0.199782	0.399564	0.800218
51	0.28415	0.568299	0.71585
52	0.305647	0.611295	0.694353
53	0.257462	0.514925	0.742538
54	0.284	0.568	0.716
55	0.26699	0.533979	0.73301
56	0.227558	0.455116	0.772442
57	0.242959	0.485918	0.757041
58	0.210684	0.421368	0.789316
59	0.375982	0.751965	0.624018
60	0.456977	0.913954	0.543023
61	0.406152	0.812304	0.593848
62	0.411043	0.822086	0.588957
63	0.378007	0.756013	0.621993
64	0.35682	0.713639	0.64318
65	0.325121	0.650242	0.674879
66	0.387019	0.774038	0.612981
67	0.345485	0.69097	0.654515
68	0.294783	0.589567	0.705217
69	0.297753	0.595506	0.702247
70	0.260914	0.521827	0.739086
71	0.265552	0.531105	0.734448
72	0.22655	0.4531	0.77345
73	0.187584	0.375168	0.812416
74	0.196699	0.393399	0.803301
75	0.173176	0.346352	0.826824
76	0.184678	0.369357	0.815322
77	0.215503	0.431006	0.784497
78	0.187337	0.374674	0.812663
79	0.154715	0.309429	0.845285
80	0.146131	0.292261	0.853869
81	0.124752	0.249504	0.875248
82	0.100451	0.200901	0.899549
83	0.0809054	0.161811	0.919095
84	0.0598142	0.119628	0.940186
85	0.0756299	0.15126	0.92437
86	0.0546232	0.109246	0.945377
87	0.133152	0.266303	0.866848
88	0.104466	0.208933	0.895534
89	0.0801272	0.160254	0.919873
90	0.0564089	0.112818	0.943591
91	0.0416247	0.0832493	0.958375
92	0.0330058	0.0660116	0.966994
93	0.0379661	0.0759323	0.962034
94	0.0349126	0.0698252	0.965087
95	0.0660193	0.132039	0.933981
96	0.0427929	0.0855857	0.957207
97	0.0300175	0.060035	0.969983
98	0.0595198	0.11904	0.94048
99	0.0365851	0.0731702	0.963415
100	0.193276	0.386553	0.806724
101	0.145757	0.291513	0.854243
102	0.171058	0.342117	0.828942
103	0.136138	0.272277	0.863862

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	10	0.108696	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 10 & 0.108696 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267339&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]10[/C][C]0.108696[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267339&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267339&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	10	0.108696	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