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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 14 Dec 2014 09:14:20 +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/t1418548659f7iborkshigvwha.htm/, Retrieved Thu, 16 May 2024 08:12:38 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267346, Retrieved Thu, 16 May 2024 08:12:38 +0000

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Estimated Impact

136

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

-       [Multiple Regression] [] [2014-12-14 09:14:20] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]

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Dataseries X:

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1.5 0 0 21 21 149 18 68 96
1.8 0 0 23 26 152 7 55 75
2.1 0 1 22 22 139 31 39 70
2.1 0 0 21 22 148 39 32 88
1.9 0 1 21 18 158 46 62 114
1.6 0 1 21 23 128 31 33 69
2.1 0 1 21 12 224 67 52 176
2.1 0 0 21 20 159 35 62 114
2.2 0 1 23 22 105 52 77 121
1.5 0 1 22 21 159 77 76 110
1.9 0 1 25 19 167 37 41 158
2.2 0 1 21 22 165 32 48 116
1.6 0 1 23 15 159 36 63 181
1.5 0 1 22 20 119 38 30 77
1.9 0 0 21 19 176 69 78 141
0.1 0 0 21 18 54 21 19 35
2.2 1 0 25 15 91 26 31 80
1.8 0 1 21 20 163 54 66 152
1.6 0 0 21 21 124 36 35 97
2.2 1 1 20 21 137 42 42 99
2.1 0 0 24 15 121 23 45 84
1.9 0 1 23 16 153 34 21 68
1.6 0 1 21 23 148 112 25 101
1.9 0 0 24 21 221 35 44 107
2.2 0 1 23 18 188 47 69 88
1.8 0 1 21 25 149 47 54 112
2.4 0 1 22 9 244 37 74 171
2.4 1 1 20 30 148 109 80 137
2.5 1 0 18 20 92 24 42 77
1.9 0 1 21 23 150 20 61 66
2.1 0 0 22 16 153 22 41 93
1.9 0 0 22 16 94 23 46 105
2.1 0 0 21 19 156 32 39 131
1.9 0 1 23 25 146 7 63 89
1.5 0 1 21 25 132 30 34 102
1.9 0 1 25 18 161 92 51 161
2.1 0 1 22 23 105 43 42 120
1.5 0 1 22 21 97 55 31 127
2.1 0 0 20 10 151 16 39 77
2.1 1 1 21 14 131 49 20 108
1.8 0 1 21 22 166 71 49 85
2.4 0 0 21 26 157 43 53 168
2.1 0 1 22 23 111 29 31 48
1.9 0 1 21 23 145 56 39 152
2.1 0 1 24 24 162 46 54 75
1.9 0 1 22 24 163 19 49 107
2.4 1 1 22 18 59 23 34 62
2.1 0 0 21 23 187 59 46 121
2.2 0 1 22 15 109 30 55 124
2.2 1 1 19 19 90 61 42 72
1.8 0 0 22 16 105 7 50 40
2.1 1 1 23 25 83 38 13 58
2.4 1 1 20 23 116 32 37 97
2.2 1 1 20 17 42 16 25 88
2.1 0 1 23 19 148 19 30 126
1.5 1 1 20 21 155 22 28 104
1.9 0 1 23 18 125 48 45 148
1.8 0 1 21 27 116 23 35 146
1.8 1 0 22 21 128 26 28 80
1.6 0 1 21 13 138 33 41 97
1.2 1 0 21 8 49 9 6 25
1.8 1 1 19 29 96 24 45 99
1.5 0 1 22 28 164 34 73 118
2.1 0 0 21 23 162 48 17 58
2.4 0 0 21 21 99 18 40 63
2.4 0 1 21 19 202 43 64 139
1.5 0 0 21 19 186 33 37 50
1.8 1 1 21 20 66 28 25 60
2.1 0 0 21 18 183 71 65 152
2.2 0 1 22 19 214 26 100 142
2.1 0 1 22 17 188 67 28 94
1.9 1 0 18 19 104 34 35 66
2.1 0 0 21 25 177 80 56 127
1.9 0 0 23 19 126 29 29 67
1.6 1 0 19 22 76 16 43 90
2.4 1 1 19 23 99 59 59 75
1.9 0 1 23 26 157 58 52 96
1.9 0 0 21 14 139 32 50 128
2.1 0 0 21 16 162 43 59 146
1.8 1 1 21 24 108 38 27 69
2.1 0 0 20 20 159 29 61 186
2.4 1 0 19 12 74 36 28 81
2.1 0 1 21 24 110 32 51 85
2.2 1 0 19 22 96 35 35 54
2.1 1 0 19 12 116 21 29 46
2.2 1 0 19 22 87 29 48 106
1.6 1 1 20 20 97 12 25 34
2.4 1 0 19 10 127 37 44 60
2.1 1 1 19 23 106 37 64 95
1.9 1 1 19 17 80 47 32 57
2.4 1 0 20 22 74 51 20 62
2.1 1 0 19 24 91 32 28 36
1.8 1 0 18 18 133 21 34 56
2.1 1 1 19 21 74 13 31 54
1.8 1 1 21 20 114 14 26 64
1.9 1 1 18 20 140 -2 58 76
1.9 1 0 18 22 95 20 23 98
2.4 1 1 19 19 98 24 21 88
1.8 1 0 21 20 121 11 21 35
1.8 1 1 20 26 126 23 33 102
2.1 1 1 24 23 98 24 16 61
2.1 1 1 22 24 95 14 20 80
2.4 1 1 21 21 110 52 37 49
1.9 1 1 21 21 70 15 35 78
1.8 1 0 19 19 102 23 33 90
1.8 1 1 19 8 86 19 27 45
2.2 1 1 20 17 130 35 41 55
2.4 1 1 18 20 96 24 40 96
1.8 1 0 19 11 102 39 35 43
2.4 1 0 19 8 100 29 28 52
1.8 1 0 20 15 94 13 32 60
1.9 1 0 21 18 52 8 22 54
2.4 1 0 18 18 98 18 44 51
2.1 1 0 19 19 118 24 27 51
1.9 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	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.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 & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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	6 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
PA[t] = + 1.20359 + 0.345333programma[t] -0.0297883gender[t] + 0.00938839age[t] -0.00363078NUMERACYTOT[t] + 0.00138745LFM[t] + 0.00137072PRH[t] + 0.00404821CH[t] + 0.00122842Blogs[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
PA[t] =  +  1.20359 +  0.345333programma[t] -0.0297883gender[t] +  0.00938839age[t] -0.00363078NUMERACYTOT[t] +  0.00138745LFM[t] +  0.00137072PRH[t] +  0.00404821CH[t] +  0.00122842Blogs[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]PA[t] =  +  1.20359 +  0.345333programma[t] -0.0297883gender[t] +  0.00938839age[t] -0.00363078NUMERACYTOT[t] +  0.00138745LFM[t] +  0.00137072PRH[t] +  0.00404821CH[t] +  0.00122842Blogs[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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
PA[t] = + 1.20359 + 0.345333programma[t] -0.0297883gender[t] + 0.00938839age[t] -0.00363078NUMERACYTOT[t] + 0.00138745LFM[t] + 0.00137072PRH[t] + 0.00404821CH[t] + 0.00122842Blogs[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)	1.20359	0.539595	2.231	0.027818	0.013909
programma	0.345333	0.0906092	3.811	0.000232364	0.000116182
gender	-0.0297883	0.0623197	-0.478	0.633641	0.31682
age	0.00938839	0.0227249	0.4131	0.680344	0.340172
NUMERACYTOT	-0.00363078	0.00670799	-0.5413	0.589463	0.294731
LFM	0.00138745	0.00109035	1.272	0.205985	0.102992
PRH	0.00137072	0.00163623	0.8377	0.404067	0.202033
CH	0.00404821	0.00228345	1.773	0.0791266	0.0395633
Blogs	0.00122842	0.00109769	1.119	0.26563	0.132815

\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) & 1.20359 & 0.539595 & 2.231 & 0.027818 & 0.013909 \tabularnewline
programma & 0.345333 & 0.0906092 & 3.811 & 0.000232364 & 0.000116182 \tabularnewline
gender & -0.0297883 & 0.0623197 & -0.478 & 0.633641 & 0.31682 \tabularnewline
age & 0.00938839 & 0.0227249 & 0.4131 & 0.680344 & 0.340172 \tabularnewline
NUMERACYTOT & -0.00363078 & 0.00670799 & -0.5413 & 0.589463 & 0.294731 \tabularnewline
LFM & 0.00138745 & 0.00109035 & 1.272 & 0.205985 & 0.102992 \tabularnewline
PRH & 0.00137072 & 0.00163623 & 0.8377 & 0.404067 & 0.202033 \tabularnewline
CH & 0.00404821 & 0.00228345 & 1.773 & 0.0791266 & 0.0395633 \tabularnewline
Blogs & 0.00122842 & 0.00109769 & 1.119 & 0.26563 & 0.132815 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]1.20359[/C][C]0.539595[/C][C]2.231[/C][C]0.027818[/C][C]0.013909[/C][/ROW]
[ROW][C]programma[/C][C]0.345333[/C][C]0.0906092[/C][C]3.811[/C][C]0.000232364[/C][C]0.000116182[/C][/ROW]
[ROW][C]gender[/C][C]-0.0297883[/C][C]0.0623197[/C][C]-0.478[/C][C]0.633641[/C][C]0.31682[/C][/ROW]
[ROW][C]age[/C][C]0.00938839[/C][C]0.0227249[/C][C]0.4131[/C][C]0.680344[/C][C]0.340172[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]-0.00363078[/C][C]0.00670799[/C][C]-0.5413[/C][C]0.589463[/C][C]0.294731[/C][/ROW]
[ROW][C]LFM[/C][C]0.00138745[/C][C]0.00109035[/C][C]1.272[/C][C]0.205985[/C][C]0.102992[/C][/ROW]
[ROW][C]PRH[/C][C]0.00137072[/C][C]0.00163623[/C][C]0.8377[/C][C]0.404067[/C][C]0.202033[/C][/ROW]
[ROW][C]CH[/C][C]0.00404821[/C][C]0.00228345[/C][C]1.773[/C][C]0.0791266[/C][C]0.0395633[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00122842[/C][C]0.00109769[/C][C]1.119[/C][C]0.26563[/C][C]0.132815[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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)	1.20359	0.539595	2.231	0.027818	0.013909
programma	0.345333	0.0906092	3.811	0.000232364	0.000116182
gender	-0.0297883	0.0623197	-0.478	0.633641	0.31682
age	0.00938839	0.0227249	0.4131	0.680344	0.340172
NUMERACYTOT	-0.00363078	0.00670799	-0.5413	0.589463	0.294731
LFM	0.00138745	0.00109035	1.272	0.205985	0.102992
PRH	0.00137072	0.00163623	0.8377	0.404067	0.202033
CH	0.00404821	0.00228345	1.773	0.0791266	0.0395633
Blogs	0.00122842	0.00109769	1.119	0.26563	0.132815

Multiple Linear Regression - Regression Statistics
Multiple R	0.424794
R-squared	0.18045
Adjusted R-squared	0.118597
F-TEST (value)	2.9174
F-TEST (DF numerator)	8
F-TEST (DF denominator)	106
p-value	0.00555181
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.30213
Sum Squared Residuals	9.67597

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.424794 \tabularnewline
R-squared & 0.18045 \tabularnewline
Adjusted R-squared & 0.118597 \tabularnewline
F-TEST (value) & 2.9174 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.00555181 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.30213 \tabularnewline
Sum Squared Residuals & 9.67597 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.424794[/C][/ROW]
[ROW][C]R-squared[/C][C]0.18045[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.118597[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.9174[/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]0.00555181[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.30213[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]9.67597[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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	0.424794
R-squared	0.18045
Adjusted R-squared	0.118597
F-TEST (value)	2.9174
F-TEST (DF numerator)	8
F-TEST (DF denominator)	106
p-value	0.00555181
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.30213
Sum Squared Residuals	9.67597

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.5	1.94911	-0.44911
2	1.8	1.86039	-0.0603941
3	2.1	1.77969	0.320312
4	2.1	1.81731	0.282686
5	1.9	1.9789	-0.0789039
6	1.6	1.72589	-0.125889
7	2.1	2.15673	-0.0567253
8	2.1	1.98774	0.11226
9	2.2	1.98717	0.212831
10	1.5	2.07304	-0.573041
11	1.9	1.98202	-0.0820153
12	2.2	1.90068	0.299315
13	1.6	2.08261	-0.482605
14	1.5	1.74096	-0.24096
15	1.9	2.1595	-0.259501
16	0.1	1.55901	-1.45901
17	2.2	2.11484	0.0851627
18	1.8	2.05242	-0.252418
19	1.6	1.80673	-0.206735
20	2.2	2.16995	0.0300532
21	2.1	1.85922	0.240785
22	1.9	1.75907	0.140927
23	1.6	1.87159	-0.27159
24	1.9	2.01683	-0.11683
25	2.2	2.03707	0.162926
26	1.8	1.90753	-0.10753
27	2.4	2.24655	0.153448
28	2.4	2.44488	-0.044882
29	2.5	2.07046	0.429544
30	1.9	1.851	0.0490006
31	2.1	1.8747	0.225302
32	1.9	1.82919	0.0708083
33	2.1	1.91087	0.189129
34	1.9	1.8755	0.0245046
35	1.5	1.76739	-0.267392
36	1.9	2.09688	-0.196878
37	2.1	1.8189	0.281102
38	1.5	1.79558	-0.295577
39	2.1	1.83896	0.261044
40	2.1	2.12802	-0.0280162
41	1.8	1.9215	-0.121498
42	2.4	2.00405	0.395953
43	2.1	1.67506	0.424944
44	1.9	1.90999	-0.00999153
45	2.1	1.91054	0.18946
46	1.9	1.87521	0.0247902
47	2.4	1.98751	0.412486
48	2.1	1.99242	0.107579
49	2.2	1.89321	0.306785
50	2.2	2.09549	0.104514
51	1.8	1.75887	0.0411323
52	2.1	1.93542	0.164579
53	2.4	2.09714	0.302856
54	2.2	1.93469	0.265309
55	2.1	1.82836	0.271636
56	1.5	2.11697	-0.616974
57	1.9	1.92758	-0.027583
58	1.8	1.78644	0.0135648
59	1.8	2.10408	-0.304078
60	1.6	1.84559	-0.245594
61	1.2	1.85236	-0.652356
62	1.8	2.0621	-0.262098
63	1.5	1.9933	-0.493305
64	2.1	1.74787	0.352131
65	2.4	1.72585	0.67415
66	2.4	2.07102	0.328984
67	1.5	1.84627	-0.346267
68	1.8	1.94854	-0.148539
69	2.1	2.13647	-0.036471
70	2.2	2.22317	-0.0231718
71	2.1	1.90012	0.199876
72	1.9	2.06259	-0.162593
73	2.1	2.04792	0.0520769
74	1.9	1.76481	0.135189
75	1.6	2.05944	-0.459435
76	2.4	2.16321	0.236786
77	1.9	1.9211	-0.0211022
78	1.9	1.94628	-0.046283
79	2.1	2.04456	0.0554439
80	1.8	2.02515	-0.225148
81	2.1	2.05453	0.0454748
82	2.4	2.0486	0.351396
83	2.1	1.79118	0.308823
84	2.2	2.03662	0.163381
85	2.1	2.04737	0.0526307
86	2.2	2.13241	0.0675878
87	1.6	1.92829	-0.328291
88	2.4	2.16975	0.230255
89	2.1	2.18758	-0.0875792
90	1.9	2.01077	-0.110775
91	2.4	1.98652	0.41348
92	2.1	1.96786	0.132141
93	1.8	2.07231	-0.272308
94	2.1	1.93359	0.166411
95	1.8	2.00491	-0.204908
96	1.9	2.13517	-0.235169
97	1.9	2.01075	-0.110754
98	2.4	1.99051	0.409488
99	1.8	1.98443	-0.184432
100	1.8	2.07774	-0.277739
101	2.1	1.96952	0.130478
102	2.1	1.96878	0.131222
103	2.4	2.07392	0.326081
104	1.9	1.99523	-0.0952324
105	1.8	2.07551	-0.275514
106	1.8	1.97841	-0.178414
107	2.2	2.10706	0.0929358
108	2.4	2.06146	0.338539
109	1.8	2.07685	-0.276853
110	2.4	2.05398	0.346019
111	1.8	2.03372	-0.233718
112	1.9	1.91924	-0.0192351
113	2.4	2.05398	0.346025
114	2.1	2.02689	0.0731134
115	1.9	1.90743	-0.00743165

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.5 & 1.94911 & -0.44911 \tabularnewline
2 & 1.8 & 1.86039 & -0.0603941 \tabularnewline
3 & 2.1 & 1.77969 & 0.320312 \tabularnewline
4 & 2.1 & 1.81731 & 0.282686 \tabularnewline
5 & 1.9 & 1.9789 & -0.0789039 \tabularnewline
6 & 1.6 & 1.72589 & -0.125889 \tabularnewline
7 & 2.1 & 2.15673 & -0.0567253 \tabularnewline
8 & 2.1 & 1.98774 & 0.11226 \tabularnewline
9 & 2.2 & 1.98717 & 0.212831 \tabularnewline
10 & 1.5 & 2.07304 & -0.573041 \tabularnewline
11 & 1.9 & 1.98202 & -0.0820153 \tabularnewline
12 & 2.2 & 1.90068 & 0.299315 \tabularnewline
13 & 1.6 & 2.08261 & -0.482605 \tabularnewline
14 & 1.5 & 1.74096 & -0.24096 \tabularnewline
15 & 1.9 & 2.1595 & -0.259501 \tabularnewline
16 & 0.1 & 1.55901 & -1.45901 \tabularnewline
17 & 2.2 & 2.11484 & 0.0851627 \tabularnewline
18 & 1.8 & 2.05242 & -0.252418 \tabularnewline
19 & 1.6 & 1.80673 & -0.206735 \tabularnewline
20 & 2.2 & 2.16995 & 0.0300532 \tabularnewline
21 & 2.1 & 1.85922 & 0.240785 \tabularnewline
22 & 1.9 & 1.75907 & 0.140927 \tabularnewline
23 & 1.6 & 1.87159 & -0.27159 \tabularnewline
24 & 1.9 & 2.01683 & -0.11683 \tabularnewline
25 & 2.2 & 2.03707 & 0.162926 \tabularnewline
26 & 1.8 & 1.90753 & -0.10753 \tabularnewline
27 & 2.4 & 2.24655 & 0.153448 \tabularnewline
28 & 2.4 & 2.44488 & -0.044882 \tabularnewline
29 & 2.5 & 2.07046 & 0.429544 \tabularnewline
30 & 1.9 & 1.851 & 0.0490006 \tabularnewline
31 & 2.1 & 1.8747 & 0.225302 \tabularnewline
32 & 1.9 & 1.82919 & 0.0708083 \tabularnewline
33 & 2.1 & 1.91087 & 0.189129 \tabularnewline
34 & 1.9 & 1.8755 & 0.0245046 \tabularnewline
35 & 1.5 & 1.76739 & -0.267392 \tabularnewline
36 & 1.9 & 2.09688 & -0.196878 \tabularnewline
37 & 2.1 & 1.8189 & 0.281102 \tabularnewline
38 & 1.5 & 1.79558 & -0.295577 \tabularnewline
39 & 2.1 & 1.83896 & 0.261044 \tabularnewline
40 & 2.1 & 2.12802 & -0.0280162 \tabularnewline
41 & 1.8 & 1.9215 & -0.121498 \tabularnewline
42 & 2.4 & 2.00405 & 0.395953 \tabularnewline
43 & 2.1 & 1.67506 & 0.424944 \tabularnewline
44 & 1.9 & 1.90999 & -0.00999153 \tabularnewline
45 & 2.1 & 1.91054 & 0.18946 \tabularnewline
46 & 1.9 & 1.87521 & 0.0247902 \tabularnewline
47 & 2.4 & 1.98751 & 0.412486 \tabularnewline
48 & 2.1 & 1.99242 & 0.107579 \tabularnewline
49 & 2.2 & 1.89321 & 0.306785 \tabularnewline
50 & 2.2 & 2.09549 & 0.104514 \tabularnewline
51 & 1.8 & 1.75887 & 0.0411323 \tabularnewline
52 & 2.1 & 1.93542 & 0.164579 \tabularnewline
53 & 2.4 & 2.09714 & 0.302856 \tabularnewline
54 & 2.2 & 1.93469 & 0.265309 \tabularnewline
55 & 2.1 & 1.82836 & 0.271636 \tabularnewline
56 & 1.5 & 2.11697 & -0.616974 \tabularnewline
57 & 1.9 & 1.92758 & -0.027583 \tabularnewline
58 & 1.8 & 1.78644 & 0.0135648 \tabularnewline
59 & 1.8 & 2.10408 & -0.304078 \tabularnewline
60 & 1.6 & 1.84559 & -0.245594 \tabularnewline
61 & 1.2 & 1.85236 & -0.652356 \tabularnewline
62 & 1.8 & 2.0621 & -0.262098 \tabularnewline
63 & 1.5 & 1.9933 & -0.493305 \tabularnewline
64 & 2.1 & 1.74787 & 0.352131 \tabularnewline
65 & 2.4 & 1.72585 & 0.67415 \tabularnewline
66 & 2.4 & 2.07102 & 0.328984 \tabularnewline
67 & 1.5 & 1.84627 & -0.346267 \tabularnewline
68 & 1.8 & 1.94854 & -0.148539 \tabularnewline
69 & 2.1 & 2.13647 & -0.036471 \tabularnewline
70 & 2.2 & 2.22317 & -0.0231718 \tabularnewline
71 & 2.1 & 1.90012 & 0.199876 \tabularnewline
72 & 1.9 & 2.06259 & -0.162593 \tabularnewline
73 & 2.1 & 2.04792 & 0.0520769 \tabularnewline
74 & 1.9 & 1.76481 & 0.135189 \tabularnewline
75 & 1.6 & 2.05944 & -0.459435 \tabularnewline
76 & 2.4 & 2.16321 & 0.236786 \tabularnewline
77 & 1.9 & 1.9211 & -0.0211022 \tabularnewline
78 & 1.9 & 1.94628 & -0.046283 \tabularnewline
79 & 2.1 & 2.04456 & 0.0554439 \tabularnewline
80 & 1.8 & 2.02515 & -0.225148 \tabularnewline
81 & 2.1 & 2.05453 & 0.0454748 \tabularnewline
82 & 2.4 & 2.0486 & 0.351396 \tabularnewline
83 & 2.1 & 1.79118 & 0.308823 \tabularnewline
84 & 2.2 & 2.03662 & 0.163381 \tabularnewline
85 & 2.1 & 2.04737 & 0.0526307 \tabularnewline
86 & 2.2 & 2.13241 & 0.0675878 \tabularnewline
87 & 1.6 & 1.92829 & -0.328291 \tabularnewline
88 & 2.4 & 2.16975 & 0.230255 \tabularnewline
89 & 2.1 & 2.18758 & -0.0875792 \tabularnewline
90 & 1.9 & 2.01077 & -0.110775 \tabularnewline
91 & 2.4 & 1.98652 & 0.41348 \tabularnewline
92 & 2.1 & 1.96786 & 0.132141 \tabularnewline
93 & 1.8 & 2.07231 & -0.272308 \tabularnewline
94 & 2.1 & 1.93359 & 0.166411 \tabularnewline
95 & 1.8 & 2.00491 & -0.204908 \tabularnewline
96 & 1.9 & 2.13517 & -0.235169 \tabularnewline
97 & 1.9 & 2.01075 & -0.110754 \tabularnewline
98 & 2.4 & 1.99051 & 0.409488 \tabularnewline
99 & 1.8 & 1.98443 & -0.184432 \tabularnewline
100 & 1.8 & 2.07774 & -0.277739 \tabularnewline
101 & 2.1 & 1.96952 & 0.130478 \tabularnewline
102 & 2.1 & 1.96878 & 0.131222 \tabularnewline
103 & 2.4 & 2.07392 & 0.326081 \tabularnewline
104 & 1.9 & 1.99523 & -0.0952324 \tabularnewline
105 & 1.8 & 2.07551 & -0.275514 \tabularnewline
106 & 1.8 & 1.97841 & -0.178414 \tabularnewline
107 & 2.2 & 2.10706 & 0.0929358 \tabularnewline
108 & 2.4 & 2.06146 & 0.338539 \tabularnewline
109 & 1.8 & 2.07685 & -0.276853 \tabularnewline
110 & 2.4 & 2.05398 & 0.346019 \tabularnewline
111 & 1.8 & 2.03372 & -0.233718 \tabularnewline
112 & 1.9 & 1.91924 & -0.0192351 \tabularnewline
113 & 2.4 & 2.05398 & 0.346025 \tabularnewline
114 & 2.1 & 2.02689 & 0.0731134 \tabularnewline
115 & 1.9 & 1.90743 & -0.00743165 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]1.5[/C][C]1.94911[/C][C]-0.44911[/C][/ROW]
[ROW][C]2[/C][C]1.8[/C][C]1.86039[/C][C]-0.0603941[/C][/ROW]
[ROW][C]3[/C][C]2.1[/C][C]1.77969[/C][C]0.320312[/C][/ROW]
[ROW][C]4[/C][C]2.1[/C][C]1.81731[/C][C]0.282686[/C][/ROW]
[ROW][C]5[/C][C]1.9[/C][C]1.9789[/C][C]-0.0789039[/C][/ROW]
[ROW][C]6[/C][C]1.6[/C][C]1.72589[/C][C]-0.125889[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.15673[/C][C]-0.0567253[/C][/ROW]
[ROW][C]8[/C][C]2.1[/C][C]1.98774[/C][C]0.11226[/C][/ROW]
[ROW][C]9[/C][C]2.2[/C][C]1.98717[/C][C]0.212831[/C][/ROW]
[ROW][C]10[/C][C]1.5[/C][C]2.07304[/C][C]-0.573041[/C][/ROW]
[ROW][C]11[/C][C]1.9[/C][C]1.98202[/C][C]-0.0820153[/C][/ROW]
[ROW][C]12[/C][C]2.2[/C][C]1.90068[/C][C]0.299315[/C][/ROW]
[ROW][C]13[/C][C]1.6[/C][C]2.08261[/C][C]-0.482605[/C][/ROW]
[ROW][C]14[/C][C]1.5[/C][C]1.74096[/C][C]-0.24096[/C][/ROW]
[ROW][C]15[/C][C]1.9[/C][C]2.1595[/C][C]-0.259501[/C][/ROW]
[ROW][C]16[/C][C]0.1[/C][C]1.55901[/C][C]-1.45901[/C][/ROW]
[ROW][C]17[/C][C]2.2[/C][C]2.11484[/C][C]0.0851627[/C][/ROW]
[ROW][C]18[/C][C]1.8[/C][C]2.05242[/C][C]-0.252418[/C][/ROW]
[ROW][C]19[/C][C]1.6[/C][C]1.80673[/C][C]-0.206735[/C][/ROW]
[ROW][C]20[/C][C]2.2[/C][C]2.16995[/C][C]0.0300532[/C][/ROW]
[ROW][C]21[/C][C]2.1[/C][C]1.85922[/C][C]0.240785[/C][/ROW]
[ROW][C]22[/C][C]1.9[/C][C]1.75907[/C][C]0.140927[/C][/ROW]
[ROW][C]23[/C][C]1.6[/C][C]1.87159[/C][C]-0.27159[/C][/ROW]
[ROW][C]24[/C][C]1.9[/C][C]2.01683[/C][C]-0.11683[/C][/ROW]
[ROW][C]25[/C][C]2.2[/C][C]2.03707[/C][C]0.162926[/C][/ROW]
[ROW][C]26[/C][C]1.8[/C][C]1.90753[/C][C]-0.10753[/C][/ROW]
[ROW][C]27[/C][C]2.4[/C][C]2.24655[/C][C]0.153448[/C][/ROW]
[ROW][C]28[/C][C]2.4[/C][C]2.44488[/C][C]-0.044882[/C][/ROW]
[ROW][C]29[/C][C]2.5[/C][C]2.07046[/C][C]0.429544[/C][/ROW]
[ROW][C]30[/C][C]1.9[/C][C]1.851[/C][C]0.0490006[/C][/ROW]
[ROW][C]31[/C][C]2.1[/C][C]1.8747[/C][C]0.225302[/C][/ROW]
[ROW][C]32[/C][C]1.9[/C][C]1.82919[/C][C]0.0708083[/C][/ROW]
[ROW][C]33[/C][C]2.1[/C][C]1.91087[/C][C]0.189129[/C][/ROW]
[ROW][C]34[/C][C]1.9[/C][C]1.8755[/C][C]0.0245046[/C][/ROW]
[ROW][C]35[/C][C]1.5[/C][C]1.76739[/C][C]-0.267392[/C][/ROW]
[ROW][C]36[/C][C]1.9[/C][C]2.09688[/C][C]-0.196878[/C][/ROW]
[ROW][C]37[/C][C]2.1[/C][C]1.8189[/C][C]0.281102[/C][/ROW]
[ROW][C]38[/C][C]1.5[/C][C]1.79558[/C][C]-0.295577[/C][/ROW]
[ROW][C]39[/C][C]2.1[/C][C]1.83896[/C][C]0.261044[/C][/ROW]
[ROW][C]40[/C][C]2.1[/C][C]2.12802[/C][C]-0.0280162[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.9215[/C][C]-0.121498[/C][/ROW]
[ROW][C]42[/C][C]2.4[/C][C]2.00405[/C][C]0.395953[/C][/ROW]
[ROW][C]43[/C][C]2.1[/C][C]1.67506[/C][C]0.424944[/C][/ROW]
[ROW][C]44[/C][C]1.9[/C][C]1.90999[/C][C]-0.00999153[/C][/ROW]
[ROW][C]45[/C][C]2.1[/C][C]1.91054[/C][C]0.18946[/C][/ROW]
[ROW][C]46[/C][C]1.9[/C][C]1.87521[/C][C]0.0247902[/C][/ROW]
[ROW][C]47[/C][C]2.4[/C][C]1.98751[/C][C]0.412486[/C][/ROW]
[ROW][C]48[/C][C]2.1[/C][C]1.99242[/C][C]0.107579[/C][/ROW]
[ROW][C]49[/C][C]2.2[/C][C]1.89321[/C][C]0.306785[/C][/ROW]
[ROW][C]50[/C][C]2.2[/C][C]2.09549[/C][C]0.104514[/C][/ROW]
[ROW][C]51[/C][C]1.8[/C][C]1.75887[/C][C]0.0411323[/C][/ROW]
[ROW][C]52[/C][C]2.1[/C][C]1.93542[/C][C]0.164579[/C][/ROW]
[ROW][C]53[/C][C]2.4[/C][C]2.09714[/C][C]0.302856[/C][/ROW]
[ROW][C]54[/C][C]2.2[/C][C]1.93469[/C][C]0.265309[/C][/ROW]
[ROW][C]55[/C][C]2.1[/C][C]1.82836[/C][C]0.271636[/C][/ROW]
[ROW][C]56[/C][C]1.5[/C][C]2.11697[/C][C]-0.616974[/C][/ROW]
[ROW][C]57[/C][C]1.9[/C][C]1.92758[/C][C]-0.027583[/C][/ROW]
[ROW][C]58[/C][C]1.8[/C][C]1.78644[/C][C]0.0135648[/C][/ROW]
[ROW][C]59[/C][C]1.8[/C][C]2.10408[/C][C]-0.304078[/C][/ROW]
[ROW][C]60[/C][C]1.6[/C][C]1.84559[/C][C]-0.245594[/C][/ROW]
[ROW][C]61[/C][C]1.2[/C][C]1.85236[/C][C]-0.652356[/C][/ROW]
[ROW][C]62[/C][C]1.8[/C][C]2.0621[/C][C]-0.262098[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]1.9933[/C][C]-0.493305[/C][/ROW]
[ROW][C]64[/C][C]2.1[/C][C]1.74787[/C][C]0.352131[/C][/ROW]
[ROW][C]65[/C][C]2.4[/C][C]1.72585[/C][C]0.67415[/C][/ROW]
[ROW][C]66[/C][C]2.4[/C][C]2.07102[/C][C]0.328984[/C][/ROW]
[ROW][C]67[/C][C]1.5[/C][C]1.84627[/C][C]-0.346267[/C][/ROW]
[ROW][C]68[/C][C]1.8[/C][C]1.94854[/C][C]-0.148539[/C][/ROW]
[ROW][C]69[/C][C]2.1[/C][C]2.13647[/C][C]-0.036471[/C][/ROW]
[ROW][C]70[/C][C]2.2[/C][C]2.22317[/C][C]-0.0231718[/C][/ROW]
[ROW][C]71[/C][C]2.1[/C][C]1.90012[/C][C]0.199876[/C][/ROW]
[ROW][C]72[/C][C]1.9[/C][C]2.06259[/C][C]-0.162593[/C][/ROW]
[ROW][C]73[/C][C]2.1[/C][C]2.04792[/C][C]0.0520769[/C][/ROW]
[ROW][C]74[/C][C]1.9[/C][C]1.76481[/C][C]0.135189[/C][/ROW]
[ROW][C]75[/C][C]1.6[/C][C]2.05944[/C][C]-0.459435[/C][/ROW]
[ROW][C]76[/C][C]2.4[/C][C]2.16321[/C][C]0.236786[/C][/ROW]
[ROW][C]77[/C][C]1.9[/C][C]1.9211[/C][C]-0.0211022[/C][/ROW]
[ROW][C]78[/C][C]1.9[/C][C]1.94628[/C][C]-0.046283[/C][/ROW]
[ROW][C]79[/C][C]2.1[/C][C]2.04456[/C][C]0.0554439[/C][/ROW]
[ROW][C]80[/C][C]1.8[/C][C]2.02515[/C][C]-0.225148[/C][/ROW]
[ROW][C]81[/C][C]2.1[/C][C]2.05453[/C][C]0.0454748[/C][/ROW]
[ROW][C]82[/C][C]2.4[/C][C]2.0486[/C][C]0.351396[/C][/ROW]
[ROW][C]83[/C][C]2.1[/C][C]1.79118[/C][C]0.308823[/C][/ROW]
[ROW][C]84[/C][C]2.2[/C][C]2.03662[/C][C]0.163381[/C][/ROW]
[ROW][C]85[/C][C]2.1[/C][C]2.04737[/C][C]0.0526307[/C][/ROW]
[ROW][C]86[/C][C]2.2[/C][C]2.13241[/C][C]0.0675878[/C][/ROW]
[ROW][C]87[/C][C]1.6[/C][C]1.92829[/C][C]-0.328291[/C][/ROW]
[ROW][C]88[/C][C]2.4[/C][C]2.16975[/C][C]0.230255[/C][/ROW]
[ROW][C]89[/C][C]2.1[/C][C]2.18758[/C][C]-0.0875792[/C][/ROW]
[ROW][C]90[/C][C]1.9[/C][C]2.01077[/C][C]-0.110775[/C][/ROW]
[ROW][C]91[/C][C]2.4[/C][C]1.98652[/C][C]0.41348[/C][/ROW]
[ROW][C]92[/C][C]2.1[/C][C]1.96786[/C][C]0.132141[/C][/ROW]
[ROW][C]93[/C][C]1.8[/C][C]2.07231[/C][C]-0.272308[/C][/ROW]
[ROW][C]94[/C][C]2.1[/C][C]1.93359[/C][C]0.166411[/C][/ROW]
[ROW][C]95[/C][C]1.8[/C][C]2.00491[/C][C]-0.204908[/C][/ROW]
[ROW][C]96[/C][C]1.9[/C][C]2.13517[/C][C]-0.235169[/C][/ROW]
[ROW][C]97[/C][C]1.9[/C][C]2.01075[/C][C]-0.110754[/C][/ROW]
[ROW][C]98[/C][C]2.4[/C][C]1.99051[/C][C]0.409488[/C][/ROW]
[ROW][C]99[/C][C]1.8[/C][C]1.98443[/C][C]-0.184432[/C][/ROW]
[ROW][C]100[/C][C]1.8[/C][C]2.07774[/C][C]-0.277739[/C][/ROW]
[ROW][C]101[/C][C]2.1[/C][C]1.96952[/C][C]0.130478[/C][/ROW]
[ROW][C]102[/C][C]2.1[/C][C]1.96878[/C][C]0.131222[/C][/ROW]
[ROW][C]103[/C][C]2.4[/C][C]2.07392[/C][C]0.326081[/C][/ROW]
[ROW][C]104[/C][C]1.9[/C][C]1.99523[/C][C]-0.0952324[/C][/ROW]
[ROW][C]105[/C][C]1.8[/C][C]2.07551[/C][C]-0.275514[/C][/ROW]
[ROW][C]106[/C][C]1.8[/C][C]1.97841[/C][C]-0.178414[/C][/ROW]
[ROW][C]107[/C][C]2.2[/C][C]2.10706[/C][C]0.0929358[/C][/ROW]
[ROW][C]108[/C][C]2.4[/C][C]2.06146[/C][C]0.338539[/C][/ROW]
[ROW][C]109[/C][C]1.8[/C][C]2.07685[/C][C]-0.276853[/C][/ROW]
[ROW][C]110[/C][C]2.4[/C][C]2.05398[/C][C]0.346019[/C][/ROW]
[ROW][C]111[/C][C]1.8[/C][C]2.03372[/C][C]-0.233718[/C][/ROW]
[ROW][C]112[/C][C]1.9[/C][C]1.91924[/C][C]-0.0192351[/C][/ROW]
[ROW][C]113[/C][C]2.4[/C][C]2.05398[/C][C]0.346025[/C][/ROW]
[ROW][C]114[/C][C]2.1[/C][C]2.02689[/C][C]0.0731134[/C][/ROW]
[ROW][C]115[/C][C]1.9[/C][C]1.90743[/C][C]-0.00743165[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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	1.5	1.94911	-0.44911
2	1.8	1.86039	-0.0603941
3	2.1	1.77969	0.320312
4	2.1	1.81731	0.282686
5	1.9	1.9789	-0.0789039
6	1.6	1.72589	-0.125889
7	2.1	2.15673	-0.0567253
8	2.1	1.98774	0.11226
9	2.2	1.98717	0.212831
10	1.5	2.07304	-0.573041
11	1.9	1.98202	-0.0820153
12	2.2	1.90068	0.299315
13	1.6	2.08261	-0.482605
14	1.5	1.74096	-0.24096
15	1.9	2.1595	-0.259501
16	0.1	1.55901	-1.45901
17	2.2	2.11484	0.0851627
18	1.8	2.05242	-0.252418
19	1.6	1.80673	-0.206735
20	2.2	2.16995	0.0300532
21	2.1	1.85922	0.240785
22	1.9	1.75907	0.140927
23	1.6	1.87159	-0.27159
24	1.9	2.01683	-0.11683
25	2.2	2.03707	0.162926
26	1.8	1.90753	-0.10753
27	2.4	2.24655	0.153448
28	2.4	2.44488	-0.044882
29	2.5	2.07046	0.429544
30	1.9	1.851	0.0490006
31	2.1	1.8747	0.225302
32	1.9	1.82919	0.0708083
33	2.1	1.91087	0.189129
34	1.9	1.8755	0.0245046
35	1.5	1.76739	-0.267392
36	1.9	2.09688	-0.196878
37	2.1	1.8189	0.281102
38	1.5	1.79558	-0.295577
39	2.1	1.83896	0.261044
40	2.1	2.12802	-0.0280162
41	1.8	1.9215	-0.121498
42	2.4	2.00405	0.395953
43	2.1	1.67506	0.424944
44	1.9	1.90999	-0.00999153
45	2.1	1.91054	0.18946
46	1.9	1.87521	0.0247902
47	2.4	1.98751	0.412486
48	2.1	1.99242	0.107579
49	2.2	1.89321	0.306785
50	2.2	2.09549	0.104514
51	1.8	1.75887	0.0411323
52	2.1	1.93542	0.164579
53	2.4	2.09714	0.302856
54	2.2	1.93469	0.265309
55	2.1	1.82836	0.271636
56	1.5	2.11697	-0.616974
57	1.9	1.92758	-0.027583
58	1.8	1.78644	0.0135648
59	1.8	2.10408	-0.304078
60	1.6	1.84559	-0.245594
61	1.2	1.85236	-0.652356
62	1.8	2.0621	-0.262098
63	1.5	1.9933	-0.493305
64	2.1	1.74787	0.352131
65	2.4	1.72585	0.67415
66	2.4	2.07102	0.328984
67	1.5	1.84627	-0.346267
68	1.8	1.94854	-0.148539
69	2.1	2.13647	-0.036471
70	2.2	2.22317	-0.0231718
71	2.1	1.90012	0.199876
72	1.9	2.06259	-0.162593
73	2.1	2.04792	0.0520769
74	1.9	1.76481	0.135189
75	1.6	2.05944	-0.459435
76	2.4	2.16321	0.236786
77	1.9	1.9211	-0.0211022
78	1.9	1.94628	-0.046283
79	2.1	2.04456	0.0554439
80	1.8	2.02515	-0.225148
81	2.1	2.05453	0.0454748
82	2.4	2.0486	0.351396
83	2.1	1.79118	0.308823
84	2.2	2.03662	0.163381
85	2.1	2.04737	0.0526307
86	2.2	2.13241	0.0675878
87	1.6	1.92829	-0.328291
88	2.4	2.16975	0.230255
89	2.1	2.18758	-0.0875792
90	1.9	2.01077	-0.110775
91	2.4	1.98652	0.41348
92	2.1	1.96786	0.132141
93	1.8	2.07231	-0.272308
94	2.1	1.93359	0.166411
95	1.8	2.00491	-0.204908
96	1.9	2.13517	-0.235169
97	1.9	2.01075	-0.110754
98	2.4	1.99051	0.409488
99	1.8	1.98443	-0.184432
100	1.8	2.07774	-0.277739
101	2.1	1.96952	0.130478
102	2.1	1.96878	0.131222
103	2.4	2.07392	0.326081
104	1.9	1.99523	-0.0952324
105	1.8	2.07551	-0.275514
106	1.8	1.97841	-0.178414
107	2.2	2.10706	0.0929358
108	2.4	2.06146	0.338539
109	1.8	2.07685	-0.276853
110	2.4	2.05398	0.346019
111	1.8	2.03372	-0.233718
112	1.9	1.91924	-0.0192351
113	2.4	2.05398	0.346025
114	2.1	2.02689	0.0731134
115	1.9	1.90743	-0.00743165

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.859497	0.281005	0.140503
13	0.884409	0.231183	0.115591
14	0.844771	0.310458	0.155229
15	0.778227	0.443546	0.221773
16	0.959334	0.081332	0.040666
17	0.932251	0.135497	0.0677485
18	0.965384	0.0692316	0.0346158
19	0.949033	0.101933	0.0509667
20	0.981454	0.0370921	0.018546
21	0.993722	0.0125559	0.00627794
22	0.989553	0.0208933	0.0104466
23	0.986744	0.0265117	0.0132558
24	0.993803	0.0123937	0.00619686
25	0.990052	0.0198959	0.00994793
26	0.985248	0.0295047	0.0147524
27	0.980264	0.0394714	0.0197357
28	0.971684	0.0566328	0.0283164
29	0.986218	0.027564	0.013782
30	0.979228	0.0415437	0.0207719
31	0.978662	0.0426753	0.0213376
32	0.980503	0.0389946	0.0194973
33	0.977141	0.0457184	0.0228592
34	0.96745	0.0651005	0.0325503
35	0.965542	0.0689156	0.0344578
36	0.957791	0.0844177	0.0422088
37	0.96605	0.0678998	0.0339499
38	0.972272	0.0554553	0.0277276
39	0.971337	0.0573267	0.0286634
40	0.962977	0.0740455	0.0370228
41	0.958347	0.0833064	0.0416532
42	0.965001	0.0699975	0.0349987
43	0.975046	0.0499075	0.0249538
44	0.967595	0.0648092	0.0324046
45	0.958929	0.0821411	0.0410706
46	0.946367	0.107266	0.053633
47	0.953495	0.0930108	0.0465054
48	0.939038	0.121923	0.0609617
49	0.941857	0.116285	0.0581426
50	0.925154	0.149692	0.0748459
51	0.903585	0.192831	0.0964154
52	0.880253	0.239493	0.119747
53	0.875436	0.249129	0.124564
54	0.854765	0.290471	0.145235
55	0.857098	0.285804	0.142902
56	0.955137	0.0897257	0.0448628
57	0.940912	0.118176	0.0590882
58	0.922414	0.155173	0.0775864
59	0.91946	0.161079	0.0805397
60	0.92335	0.1533	0.0766499
61	0.980905	0.0381907	0.0190953
62	0.980014	0.039972	0.019986
63	0.991446	0.0171074	0.00855368
64	0.991637	0.0167257	0.00836286
65	0.998311	0.00337889	0.00168944
66	0.998604	0.00279242	0.00139621
67	0.998671	0.00265851	0.00132926
68	0.998453	0.00309308	0.00154654
69	0.997823	0.00435339	0.0021767
70	0.997179	0.00564294	0.00282147
71	0.995811	0.00837805	0.00418902
72	0.994773	0.0104531	0.00522655
73	0.993057	0.0138865	0.00694324
74	0.989692	0.0206154	0.0103077
75	0.995313	0.00937346	0.00468673
76	0.993311	0.0133778	0.00668889
77	0.990546	0.0189078	0.00945392
78	0.987027	0.0259452	0.0129726
79	0.981143	0.0377147	0.0188573
80	0.982488	0.0350233	0.0175117
81	0.974924	0.0501513	0.0250756
82	0.970912	0.0581768	0.0290884
83	0.959742	0.080517	0.0402585
84	0.943581	0.112837	0.0564187
85	0.922576	0.154847	0.0774237
86	0.891893	0.216213	0.108107
87	0.893526	0.212948	0.106474
88	0.895061	0.209877	0.104939
89	0.863315	0.273371	0.136685
90	0.911125	0.177751	0.0888753
91	0.884448	0.231103	0.115552
92	0.839816	0.320368	0.160184
93	0.804275	0.391449	0.195725
94	0.737265	0.525469	0.262735
95	0.673416	0.653167	0.326584
96	0.585894	0.828212	0.414106
97	0.514947	0.970107	0.485053
98	0.532555	0.93489	0.467445
99	0.432477	0.864954	0.567523
100	0.433409	0.866817	0.566591
101	0.333258	0.666515	0.666742
102	0.27663	0.553261	0.72337
103	0.204142	0.408283	0.795858

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.859497 & 0.281005 & 0.140503 \tabularnewline
13 & 0.884409 & 0.231183 & 0.115591 \tabularnewline
14 & 0.844771 & 0.310458 & 0.155229 \tabularnewline
15 & 0.778227 & 0.443546 & 0.221773 \tabularnewline
16 & 0.959334 & 0.081332 & 0.040666 \tabularnewline
17 & 0.932251 & 0.135497 & 0.0677485 \tabularnewline
18 & 0.965384 & 0.0692316 & 0.0346158 \tabularnewline
19 & 0.949033 & 0.101933 & 0.0509667 \tabularnewline
20 & 0.981454 & 0.0370921 & 0.018546 \tabularnewline
21 & 0.993722 & 0.0125559 & 0.00627794 \tabularnewline
22 & 0.989553 & 0.0208933 & 0.0104466 \tabularnewline
23 & 0.986744 & 0.0265117 & 0.0132558 \tabularnewline
24 & 0.993803 & 0.0123937 & 0.00619686 \tabularnewline
25 & 0.990052 & 0.0198959 & 0.00994793 \tabularnewline
26 & 0.985248 & 0.0295047 & 0.0147524 \tabularnewline
27 & 0.980264 & 0.0394714 & 0.0197357 \tabularnewline
28 & 0.971684 & 0.0566328 & 0.0283164 \tabularnewline
29 & 0.986218 & 0.027564 & 0.013782 \tabularnewline
30 & 0.979228 & 0.0415437 & 0.0207719 \tabularnewline
31 & 0.978662 & 0.0426753 & 0.0213376 \tabularnewline
32 & 0.980503 & 0.0389946 & 0.0194973 \tabularnewline
33 & 0.977141 & 0.0457184 & 0.0228592 \tabularnewline
34 & 0.96745 & 0.0651005 & 0.0325503 \tabularnewline
35 & 0.965542 & 0.0689156 & 0.0344578 \tabularnewline
36 & 0.957791 & 0.0844177 & 0.0422088 \tabularnewline
37 & 0.96605 & 0.0678998 & 0.0339499 \tabularnewline
38 & 0.972272 & 0.0554553 & 0.0277276 \tabularnewline
39 & 0.971337 & 0.0573267 & 0.0286634 \tabularnewline
40 & 0.962977 & 0.0740455 & 0.0370228 \tabularnewline
41 & 0.958347 & 0.0833064 & 0.0416532 \tabularnewline
42 & 0.965001 & 0.0699975 & 0.0349987 \tabularnewline
43 & 0.975046 & 0.0499075 & 0.0249538 \tabularnewline
44 & 0.967595 & 0.0648092 & 0.0324046 \tabularnewline
45 & 0.958929 & 0.0821411 & 0.0410706 \tabularnewline
46 & 0.946367 & 0.107266 & 0.053633 \tabularnewline
47 & 0.953495 & 0.0930108 & 0.0465054 \tabularnewline
48 & 0.939038 & 0.121923 & 0.0609617 \tabularnewline
49 & 0.941857 & 0.116285 & 0.0581426 \tabularnewline
50 & 0.925154 & 0.149692 & 0.0748459 \tabularnewline
51 & 0.903585 & 0.192831 & 0.0964154 \tabularnewline
52 & 0.880253 & 0.239493 & 0.119747 \tabularnewline
53 & 0.875436 & 0.249129 & 0.124564 \tabularnewline
54 & 0.854765 & 0.290471 & 0.145235 \tabularnewline
55 & 0.857098 & 0.285804 & 0.142902 \tabularnewline
56 & 0.955137 & 0.0897257 & 0.0448628 \tabularnewline
57 & 0.940912 & 0.118176 & 0.0590882 \tabularnewline
58 & 0.922414 & 0.155173 & 0.0775864 \tabularnewline
59 & 0.91946 & 0.161079 & 0.0805397 \tabularnewline
60 & 0.92335 & 0.1533 & 0.0766499 \tabularnewline
61 & 0.980905 & 0.0381907 & 0.0190953 \tabularnewline
62 & 0.980014 & 0.039972 & 0.019986 \tabularnewline
63 & 0.991446 & 0.0171074 & 0.00855368 \tabularnewline
64 & 0.991637 & 0.0167257 & 0.00836286 \tabularnewline
65 & 0.998311 & 0.00337889 & 0.00168944 \tabularnewline
66 & 0.998604 & 0.00279242 & 0.00139621 \tabularnewline
67 & 0.998671 & 0.00265851 & 0.00132926 \tabularnewline
68 & 0.998453 & 0.00309308 & 0.00154654 \tabularnewline
69 & 0.997823 & 0.00435339 & 0.0021767 \tabularnewline
70 & 0.997179 & 0.00564294 & 0.00282147 \tabularnewline
71 & 0.995811 & 0.00837805 & 0.00418902 \tabularnewline
72 & 0.994773 & 0.0104531 & 0.00522655 \tabularnewline
73 & 0.993057 & 0.0138865 & 0.00694324 \tabularnewline
74 & 0.989692 & 0.0206154 & 0.0103077 \tabularnewline
75 & 0.995313 & 0.00937346 & 0.00468673 \tabularnewline
76 & 0.993311 & 0.0133778 & 0.00668889 \tabularnewline
77 & 0.990546 & 0.0189078 & 0.00945392 \tabularnewline
78 & 0.987027 & 0.0259452 & 0.0129726 \tabularnewline
79 & 0.981143 & 0.0377147 & 0.0188573 \tabularnewline
80 & 0.982488 & 0.0350233 & 0.0175117 \tabularnewline
81 & 0.974924 & 0.0501513 & 0.0250756 \tabularnewline
82 & 0.970912 & 0.0581768 & 0.0290884 \tabularnewline
83 & 0.959742 & 0.080517 & 0.0402585 \tabularnewline
84 & 0.943581 & 0.112837 & 0.0564187 \tabularnewline
85 & 0.922576 & 0.154847 & 0.0774237 \tabularnewline
86 & 0.891893 & 0.216213 & 0.108107 \tabularnewline
87 & 0.893526 & 0.212948 & 0.106474 \tabularnewline
88 & 0.895061 & 0.209877 & 0.104939 \tabularnewline
89 & 0.863315 & 0.273371 & 0.136685 \tabularnewline
90 & 0.911125 & 0.177751 & 0.0888753 \tabularnewline
91 & 0.884448 & 0.231103 & 0.115552 \tabularnewline
92 & 0.839816 & 0.320368 & 0.160184 \tabularnewline
93 & 0.804275 & 0.391449 & 0.195725 \tabularnewline
94 & 0.737265 & 0.525469 & 0.262735 \tabularnewline
95 & 0.673416 & 0.653167 & 0.326584 \tabularnewline
96 & 0.585894 & 0.828212 & 0.414106 \tabularnewline
97 & 0.514947 & 0.970107 & 0.485053 \tabularnewline
98 & 0.532555 & 0.93489 & 0.467445 \tabularnewline
99 & 0.432477 & 0.864954 & 0.567523 \tabularnewline
100 & 0.433409 & 0.866817 & 0.566591 \tabularnewline
101 & 0.333258 & 0.666515 & 0.666742 \tabularnewline
102 & 0.27663 & 0.553261 & 0.72337 \tabularnewline
103 & 0.204142 & 0.408283 & 0.795858 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]0.859497[/C][C]0.281005[/C][C]0.140503[/C][/ROW]
[ROW][C]13[/C][C]0.884409[/C][C]0.231183[/C][C]0.115591[/C][/ROW]
[ROW][C]14[/C][C]0.844771[/C][C]0.310458[/C][C]0.155229[/C][/ROW]
[ROW][C]15[/C][C]0.778227[/C][C]0.443546[/C][C]0.221773[/C][/ROW]
[ROW][C]16[/C][C]0.959334[/C][C]0.081332[/C][C]0.040666[/C][/ROW]
[ROW][C]17[/C][C]0.932251[/C][C]0.135497[/C][C]0.0677485[/C][/ROW]
[ROW][C]18[/C][C]0.965384[/C][C]0.0692316[/C][C]0.0346158[/C][/ROW]
[ROW][C]19[/C][C]0.949033[/C][C]0.101933[/C][C]0.0509667[/C][/ROW]
[ROW][C]20[/C][C]0.981454[/C][C]0.0370921[/C][C]0.018546[/C][/ROW]
[ROW][C]21[/C][C]0.993722[/C][C]0.0125559[/C][C]0.00627794[/C][/ROW]
[ROW][C]22[/C][C]0.989553[/C][C]0.0208933[/C][C]0.0104466[/C][/ROW]
[ROW][C]23[/C][C]0.986744[/C][C]0.0265117[/C][C]0.0132558[/C][/ROW]
[ROW][C]24[/C][C]0.993803[/C][C]0.0123937[/C][C]0.00619686[/C][/ROW]
[ROW][C]25[/C][C]0.990052[/C][C]0.0198959[/C][C]0.00994793[/C][/ROW]
[ROW][C]26[/C][C]0.985248[/C][C]0.0295047[/C][C]0.0147524[/C][/ROW]
[ROW][C]27[/C][C]0.980264[/C][C]0.0394714[/C][C]0.0197357[/C][/ROW]
[ROW][C]28[/C][C]0.971684[/C][C]0.0566328[/C][C]0.0283164[/C][/ROW]
[ROW][C]29[/C][C]0.986218[/C][C]0.027564[/C][C]0.013782[/C][/ROW]
[ROW][C]30[/C][C]0.979228[/C][C]0.0415437[/C][C]0.0207719[/C][/ROW]
[ROW][C]31[/C][C]0.978662[/C][C]0.0426753[/C][C]0.0213376[/C][/ROW]
[ROW][C]32[/C][C]0.980503[/C][C]0.0389946[/C][C]0.0194973[/C][/ROW]
[ROW][C]33[/C][C]0.977141[/C][C]0.0457184[/C][C]0.0228592[/C][/ROW]
[ROW][C]34[/C][C]0.96745[/C][C]0.0651005[/C][C]0.0325503[/C][/ROW]
[ROW][C]35[/C][C]0.965542[/C][C]0.0689156[/C][C]0.0344578[/C][/ROW]
[ROW][C]36[/C][C]0.957791[/C][C]0.0844177[/C][C]0.0422088[/C][/ROW]
[ROW][C]37[/C][C]0.96605[/C][C]0.0678998[/C][C]0.0339499[/C][/ROW]
[ROW][C]38[/C][C]0.972272[/C][C]0.0554553[/C][C]0.0277276[/C][/ROW]
[ROW][C]39[/C][C]0.971337[/C][C]0.0573267[/C][C]0.0286634[/C][/ROW]
[ROW][C]40[/C][C]0.962977[/C][C]0.0740455[/C][C]0.0370228[/C][/ROW]
[ROW][C]41[/C][C]0.958347[/C][C]0.0833064[/C][C]0.0416532[/C][/ROW]
[ROW][C]42[/C][C]0.965001[/C][C]0.0699975[/C][C]0.0349987[/C][/ROW]
[ROW][C]43[/C][C]0.975046[/C][C]0.0499075[/C][C]0.0249538[/C][/ROW]
[ROW][C]44[/C][C]0.967595[/C][C]0.0648092[/C][C]0.0324046[/C][/ROW]
[ROW][C]45[/C][C]0.958929[/C][C]0.0821411[/C][C]0.0410706[/C][/ROW]
[ROW][C]46[/C][C]0.946367[/C][C]0.107266[/C][C]0.053633[/C][/ROW]
[ROW][C]47[/C][C]0.953495[/C][C]0.0930108[/C][C]0.0465054[/C][/ROW]
[ROW][C]48[/C][C]0.939038[/C][C]0.121923[/C][C]0.0609617[/C][/ROW]
[ROW][C]49[/C][C]0.941857[/C][C]0.116285[/C][C]0.0581426[/C][/ROW]
[ROW][C]50[/C][C]0.925154[/C][C]0.149692[/C][C]0.0748459[/C][/ROW]
[ROW][C]51[/C][C]0.903585[/C][C]0.192831[/C][C]0.0964154[/C][/ROW]
[ROW][C]52[/C][C]0.880253[/C][C]0.239493[/C][C]0.119747[/C][/ROW]
[ROW][C]53[/C][C]0.875436[/C][C]0.249129[/C][C]0.124564[/C][/ROW]
[ROW][C]54[/C][C]0.854765[/C][C]0.290471[/C][C]0.145235[/C][/ROW]
[ROW][C]55[/C][C]0.857098[/C][C]0.285804[/C][C]0.142902[/C][/ROW]
[ROW][C]56[/C][C]0.955137[/C][C]0.0897257[/C][C]0.0448628[/C][/ROW]
[ROW][C]57[/C][C]0.940912[/C][C]0.118176[/C][C]0.0590882[/C][/ROW]
[ROW][C]58[/C][C]0.922414[/C][C]0.155173[/C][C]0.0775864[/C][/ROW]
[ROW][C]59[/C][C]0.91946[/C][C]0.161079[/C][C]0.0805397[/C][/ROW]
[ROW][C]60[/C][C]0.92335[/C][C]0.1533[/C][C]0.0766499[/C][/ROW]
[ROW][C]61[/C][C]0.980905[/C][C]0.0381907[/C][C]0.0190953[/C][/ROW]
[ROW][C]62[/C][C]0.980014[/C][C]0.039972[/C][C]0.019986[/C][/ROW]
[ROW][C]63[/C][C]0.991446[/C][C]0.0171074[/C][C]0.00855368[/C][/ROW]
[ROW][C]64[/C][C]0.991637[/C][C]0.0167257[/C][C]0.00836286[/C][/ROW]
[ROW][C]65[/C][C]0.998311[/C][C]0.00337889[/C][C]0.00168944[/C][/ROW]
[ROW][C]66[/C][C]0.998604[/C][C]0.00279242[/C][C]0.00139621[/C][/ROW]
[ROW][C]67[/C][C]0.998671[/C][C]0.00265851[/C][C]0.00132926[/C][/ROW]
[ROW][C]68[/C][C]0.998453[/C][C]0.00309308[/C][C]0.00154654[/C][/ROW]
[ROW][C]69[/C][C]0.997823[/C][C]0.00435339[/C][C]0.0021767[/C][/ROW]
[ROW][C]70[/C][C]0.997179[/C][C]0.00564294[/C][C]0.00282147[/C][/ROW]
[ROW][C]71[/C][C]0.995811[/C][C]0.00837805[/C][C]0.00418902[/C][/ROW]
[ROW][C]72[/C][C]0.994773[/C][C]0.0104531[/C][C]0.00522655[/C][/ROW]
[ROW][C]73[/C][C]0.993057[/C][C]0.0138865[/C][C]0.00694324[/C][/ROW]
[ROW][C]74[/C][C]0.989692[/C][C]0.0206154[/C][C]0.0103077[/C][/ROW]
[ROW][C]75[/C][C]0.995313[/C][C]0.00937346[/C][C]0.00468673[/C][/ROW]
[ROW][C]76[/C][C]0.993311[/C][C]0.0133778[/C][C]0.00668889[/C][/ROW]
[ROW][C]77[/C][C]0.990546[/C][C]0.0189078[/C][C]0.00945392[/C][/ROW]
[ROW][C]78[/C][C]0.987027[/C][C]0.0259452[/C][C]0.0129726[/C][/ROW]
[ROW][C]79[/C][C]0.981143[/C][C]0.0377147[/C][C]0.0188573[/C][/ROW]
[ROW][C]80[/C][C]0.982488[/C][C]0.0350233[/C][C]0.0175117[/C][/ROW]
[ROW][C]81[/C][C]0.974924[/C][C]0.0501513[/C][C]0.0250756[/C][/ROW]
[ROW][C]82[/C][C]0.970912[/C][C]0.0581768[/C][C]0.0290884[/C][/ROW]
[ROW][C]83[/C][C]0.959742[/C][C]0.080517[/C][C]0.0402585[/C][/ROW]
[ROW][C]84[/C][C]0.943581[/C][C]0.112837[/C][C]0.0564187[/C][/ROW]
[ROW][C]85[/C][C]0.922576[/C][C]0.154847[/C][C]0.0774237[/C][/ROW]
[ROW][C]86[/C][C]0.891893[/C][C]0.216213[/C][C]0.108107[/C][/ROW]
[ROW][C]87[/C][C]0.893526[/C][C]0.212948[/C][C]0.106474[/C][/ROW]
[ROW][C]88[/C][C]0.895061[/C][C]0.209877[/C][C]0.104939[/C][/ROW]
[ROW][C]89[/C][C]0.863315[/C][C]0.273371[/C][C]0.136685[/C][/ROW]
[ROW][C]90[/C][C]0.911125[/C][C]0.177751[/C][C]0.0888753[/C][/ROW]
[ROW][C]91[/C][C]0.884448[/C][C]0.231103[/C][C]0.115552[/C][/ROW]
[ROW][C]92[/C][C]0.839816[/C][C]0.320368[/C][C]0.160184[/C][/ROW]
[ROW][C]93[/C][C]0.804275[/C][C]0.391449[/C][C]0.195725[/C][/ROW]
[ROW][C]94[/C][C]0.737265[/C][C]0.525469[/C][C]0.262735[/C][/ROW]
[ROW][C]95[/C][C]0.673416[/C][C]0.653167[/C][C]0.326584[/C][/ROW]
[ROW][C]96[/C][C]0.585894[/C][C]0.828212[/C][C]0.414106[/C][/ROW]
[ROW][C]97[/C][C]0.514947[/C][C]0.970107[/C][C]0.485053[/C][/ROW]
[ROW][C]98[/C][C]0.532555[/C][C]0.93489[/C][C]0.467445[/C][/ROW]
[ROW][C]99[/C][C]0.432477[/C][C]0.864954[/C][C]0.567523[/C][/ROW]
[ROW][C]100[/C][C]0.433409[/C][C]0.866817[/C][C]0.566591[/C][/ROW]
[ROW][C]101[/C][C]0.333258[/C][C]0.666515[/C][C]0.666742[/C][/ROW]
[ROW][C]102[/C][C]0.27663[/C][C]0.553261[/C][C]0.72337[/C][/ROW]
[ROW][C]103[/C][C]0.204142[/C][C]0.408283[/C][C]0.795858[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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	0.859497	0.281005	0.140503
13	0.884409	0.231183	0.115591
14	0.844771	0.310458	0.155229
15	0.778227	0.443546	0.221773
16	0.959334	0.081332	0.040666
17	0.932251	0.135497	0.0677485
18	0.965384	0.0692316	0.0346158
19	0.949033	0.101933	0.0509667
20	0.981454	0.0370921	0.018546
21	0.993722	0.0125559	0.00627794
22	0.989553	0.0208933	0.0104466
23	0.986744	0.0265117	0.0132558
24	0.993803	0.0123937	0.00619686
25	0.990052	0.0198959	0.00994793
26	0.985248	0.0295047	0.0147524
27	0.980264	0.0394714	0.0197357
28	0.971684	0.0566328	0.0283164
29	0.986218	0.027564	0.013782
30	0.979228	0.0415437	0.0207719
31	0.978662	0.0426753	0.0213376
32	0.980503	0.0389946	0.0194973
33	0.977141	0.0457184	0.0228592
34	0.96745	0.0651005	0.0325503
35	0.965542	0.0689156	0.0344578
36	0.957791	0.0844177	0.0422088
37	0.96605	0.0678998	0.0339499
38	0.972272	0.0554553	0.0277276
39	0.971337	0.0573267	0.0286634
40	0.962977	0.0740455	0.0370228
41	0.958347	0.0833064	0.0416532
42	0.965001	0.0699975	0.0349987
43	0.975046	0.0499075	0.0249538
44	0.967595	0.0648092	0.0324046
45	0.958929	0.0821411	0.0410706
46	0.946367	0.107266	0.053633
47	0.953495	0.0930108	0.0465054
48	0.939038	0.121923	0.0609617
49	0.941857	0.116285	0.0581426
50	0.925154	0.149692	0.0748459
51	0.903585	0.192831	0.0964154
52	0.880253	0.239493	0.119747
53	0.875436	0.249129	0.124564
54	0.854765	0.290471	0.145235
55	0.857098	0.285804	0.142902
56	0.955137	0.0897257	0.0448628
57	0.940912	0.118176	0.0590882
58	0.922414	0.155173	0.0775864
59	0.91946	0.161079	0.0805397
60	0.92335	0.1533	0.0766499
61	0.980905	0.0381907	0.0190953
62	0.980014	0.039972	0.019986
63	0.991446	0.0171074	0.00855368
64	0.991637	0.0167257	0.00836286
65	0.998311	0.00337889	0.00168944
66	0.998604	0.00279242	0.00139621
67	0.998671	0.00265851	0.00132926
68	0.998453	0.00309308	0.00154654
69	0.997823	0.00435339	0.0021767
70	0.997179	0.00564294	0.00282147
71	0.995811	0.00837805	0.00418902
72	0.994773	0.0104531	0.00522655
73	0.993057	0.0138865	0.00694324
74	0.989692	0.0206154	0.0103077
75	0.995313	0.00937346	0.00468673
76	0.993311	0.0133778	0.00668889
77	0.990546	0.0189078	0.00945392
78	0.987027	0.0259452	0.0129726
79	0.981143	0.0377147	0.0188573
80	0.982488	0.0350233	0.0175117
81	0.974924	0.0501513	0.0250756
82	0.970912	0.0581768	0.0290884
83	0.959742	0.080517	0.0402585
84	0.943581	0.112837	0.0564187
85	0.922576	0.154847	0.0774237
86	0.891893	0.216213	0.108107
87	0.893526	0.212948	0.106474
88	0.895061	0.209877	0.104939
89	0.863315	0.273371	0.136685
90	0.911125	0.177751	0.0888753
91	0.884448	0.231103	0.115552
92	0.839816	0.320368	0.160184
93	0.804275	0.391449	0.195725
94	0.737265	0.525469	0.262735
95	0.673416	0.653167	0.326584
96	0.585894	0.828212	0.414106
97	0.514947	0.970107	0.485053
98	0.532555	0.93489	0.467445
99	0.432477	0.864954	0.567523
100	0.433409	0.866817	0.566591
101	0.333258	0.666515	0.666742
102	0.27663	0.553261	0.72337
103	0.204142	0.408283	0.795858

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	8	0.0869565	NOK
5% type I error level	34	0.369565	NOK
10% type I error level	53	0.576087	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 & 8 & 0.0869565 & NOK \tabularnewline
5% type I error level & 34 & 0.369565 & NOK \tabularnewline
10% type I error level & 53 & 0.576087 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267346&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]8[/C][C]0.0869565[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]34[/C][C]0.369565[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.576087[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267346&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267346&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	8	0.0869565	NOK
5% type I error level	34	0.369565	NOK
10% type I error level	53	0.576087	NOK

Figure 1

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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