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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 28 Dec 2010 00:44:59 +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/2010/Dec/28/t1293497011gige8uvplb3glud.htm/, Retrieved Sun, 05 May 2024 07:22:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=116206, Retrieved Sun, 05 May 2024 07:22:42 +0000

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No (this computation is public)

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Estimated Impact

215

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

-       [Multiple Regression] [] [2010-12-28 00:44:59] [c984196f1244e05baf3e7c2e52d47a33] [Current]

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Dataseries X:

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99	94.6
106.3	95.9
128.9	104.7
111.1	102.8
102.9	98.1
130	113.9
87	80.9
87.5	95.7
117.6	113.2
103.4	105.9
110.8	108.8
112.6	102.3
102.5	99
112.4	100.7
135.6	115.5
105.1	100.7
127.7	109.9
137	114.6
91	85.4
90.5	100.5
122.4	114.8
123.3	116.5
124.3	112.9
120	102
118.1	106
119	105.3
142.7	118.8
123.6	106.1
129.6	109.3
151.6	117.2
110.4	92.5
99.2	104.2
130.5	112.5
136.2	122.4
129.7	113.3
128	100
121.6	110.7
135.8	112.8
143.8	109.8
147.5	117.3
136.2	109.1
156.6	115.9
123.3	96
104.5	99.8
139.8	116.8
136.5	115.7
112.1	99.4
118.5	94.3
94.4	91
102.3	93.2
111.4	103.1
99.2	94.1
87.8	91.8
115.8	102.7
79.7	82.6
72.7	89.1
104.5	104.5
103	105.1
95.1	95.1
104.2	88.7
78.3	86.3

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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
R Framework error message & The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[ROW][C]R Framework error message[/C][C]The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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	'Sir Ronald Aylmer Fisher' @ 193.190.124.24
R Framework error message	The field 'Names of X columns' contains a hard return which cannot be interpreted. Please, resubmit your request without hard returns in the 'Names of X columns'.

Multiple Linear Regression - Estimated Regression Equation
ipi[t] = -70.5918402602645 + 1.92131992879401`tip `[t] -15.2527580996292M1[t] -9.41583810663133M2[t] -9.00345348001863M3[t] -12.3096963200716M4[t] -11.693757159947M5[t] -8.0483269034278M6[t] + 0.794772889364236M7[t] -26.5485279715176M8[t] -22.3276669390308M9[t] -26.2678700849142M10[t] -18.4759401990215M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ipi[t] =  -70.5918402602645 +  1.92131992879401`tip
`[t] -15.2527580996292M1[t] -9.41583810663133M2[t] -9.00345348001863M3[t] -12.3096963200716M4[t] -11.693757159947M5[t] -8.0483269034278M6[t] +  0.794772889364236M7[t] -26.5485279715176M8[t] -22.3276669390308M9[t] -26.2678700849142M10[t] -18.4759401990215M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ipi[t] =  -70.5918402602645 +  1.92131992879401`tip
`[t] -15.2527580996292M1[t] -9.41583810663133M2[t] -9.00345348001863M3[t] -12.3096963200716M4[t] -11.693757159947M5[t] -8.0483269034278M6[t] +  0.794772889364236M7[t] -26.5485279715176M8[t] -22.3276669390308M9[t] -26.2678700849142M10[t] -18.4759401990215M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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
ipi[t] = -70.5918402602645 + 1.92131992879401`tip `[t] -15.2527580996292M1[t] -9.41583810663133M2[t] -9.00345348001863M3[t] -12.3096963200716M4[t] -11.693757159947M5[t] -8.0483269034278M6[t] + 0.794772889364236M7[t] -26.5485279715176M8[t] -22.3276669390308M9[t] -26.2678700849142M10[t] -18.4759401990215M11[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)	-70.5918402602645	13.335151	-5.2937	3e-06	1e-06
`tip `	1.92131992879401	0.133273	14.4164	0	0
M1	-15.2527580996292	4.089094	-3.7301	0.000506	0.000253
M2	-9.41583810663133	4.305566	-2.1869	0.033655	0.016828
M3	-9.00345348001863	4.604486	-1.9554	0.056375	0.028188
M4	-12.3096963200716	4.36386	-2.8208	0.006945	0.003473
M5	-11.693757159947	4.349111	-2.6888	0.009831	0.004916
M6	-8.0483269034278	4.738012	-1.6987	0.095855	0.047927
M7	0.794772889364236	4.472748	0.1777	0.859712	0.429856
M8	-26.5485279715176	4.270743	-6.2164	0	0
M9	-22.3276669390308	4.70953	-4.741	1.9e-05	1e-05
M10	-26.2678700849142	4.753125	-5.5264	1e-06	1e-06
M11	-18.4759401990215	4.416065	-4.1838	0.000121	6.1e-05

\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) & -70.5918402602645 & 13.335151 & -5.2937 & 3e-06 & 1e-06 \tabularnewline
`tip
` & 1.92131992879401 & 0.133273 & 14.4164 & 0 & 0 \tabularnewline
M1 & -15.2527580996292 & 4.089094 & -3.7301 & 0.000506 & 0.000253 \tabularnewline
M2 & -9.41583810663133 & 4.305566 & -2.1869 & 0.033655 & 0.016828 \tabularnewline
M3 & -9.00345348001863 & 4.604486 & -1.9554 & 0.056375 & 0.028188 \tabularnewline
M4 & -12.3096963200716 & 4.36386 & -2.8208 & 0.006945 & 0.003473 \tabularnewline
M5 & -11.693757159947 & 4.349111 & -2.6888 & 0.009831 & 0.004916 \tabularnewline
M6 & -8.0483269034278 & 4.738012 & -1.6987 & 0.095855 & 0.047927 \tabularnewline
M7 & 0.794772889364236 & 4.472748 & 0.1777 & 0.859712 & 0.429856 \tabularnewline
M8 & -26.5485279715176 & 4.270743 & -6.2164 & 0 & 0 \tabularnewline
M9 & -22.3276669390308 & 4.70953 & -4.741 & 1.9e-05 & 1e-05 \tabularnewline
M10 & -26.2678700849142 & 4.753125 & -5.5264 & 1e-06 & 1e-06 \tabularnewline
M11 & -18.4759401990215 & 4.416065 & -4.1838 & 0.000121 & 6.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&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]-70.5918402602645[/C][C]13.335151[/C][C]-5.2937[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`tip
`[/C][C]1.92131992879401[/C][C]0.133273[/C][C]14.4164[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]-15.2527580996292[/C][C]4.089094[/C][C]-3.7301[/C][C]0.000506[/C][C]0.000253[/C][/ROW]
[ROW][C]M2[/C][C]-9.41583810663133[/C][C]4.305566[/C][C]-2.1869[/C][C]0.033655[/C][C]0.016828[/C][/ROW]
[ROW][C]M3[/C][C]-9.00345348001863[/C][C]4.604486[/C][C]-1.9554[/C][C]0.056375[/C][C]0.028188[/C][/ROW]
[ROW][C]M4[/C][C]-12.3096963200716[/C][C]4.36386[/C][C]-2.8208[/C][C]0.006945[/C][C]0.003473[/C][/ROW]
[ROW][C]M5[/C][C]-11.693757159947[/C][C]4.349111[/C][C]-2.6888[/C][C]0.009831[/C][C]0.004916[/C][/ROW]
[ROW][C]M6[/C][C]-8.0483269034278[/C][C]4.738012[/C][C]-1.6987[/C][C]0.095855[/C][C]0.047927[/C][/ROW]
[ROW][C]M7[/C][C]0.794772889364236[/C][C]4.472748[/C][C]0.1777[/C][C]0.859712[/C][C]0.429856[/C][/ROW]
[ROW][C]M8[/C][C]-26.5485279715176[/C][C]4.270743[/C][C]-6.2164[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-22.3276669390308[/C][C]4.70953[/C][C]-4.741[/C][C]1.9e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M10[/C][C]-26.2678700849142[/C][C]4.753125[/C][C]-5.5264[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M11[/C][C]-18.4759401990215[/C][C]4.416065[/C][C]-4.1838[/C][C]0.000121[/C][C]6.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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)	-70.5918402602645	13.335151	-5.2937	3e-06	1e-06
`tip `	1.92131992879401	0.133273	14.4164	0	0
M1	-15.2527580996292	4.089094	-3.7301	0.000506	0.000253
M2	-9.41583810663133	4.305566	-2.1869	0.033655	0.016828
M3	-9.00345348001863	4.604486	-1.9554	0.056375	0.028188
M4	-12.3096963200716	4.36386	-2.8208	0.006945	0.003473
M5	-11.693757159947	4.349111	-2.6888	0.009831	0.004916
M6	-8.0483269034278	4.738012	-1.6987	0.095855	0.047927
M7	0.794772889364236	4.472748	0.1777	0.859712	0.429856
M8	-26.5485279715176	4.270743	-6.2164	0	0
M9	-22.3276669390308	4.70953	-4.741	1.9e-05	1e-05
M10	-26.2678700849142	4.753125	-5.5264	1e-06	1e-06
M11	-18.4759401990215	4.416065	-4.1838	0.000121	6.1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.94817767238409
R-squared	0.89904089840771
Adjusted R-squared	0.873801123009637
F-TEST (value)	35.6200039116181
F-TEST (DF numerator)	12
F-TEST (DF denominator)	48
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.75211221609483
Sum Squared Residuals	2188.36893017938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.94817767238409 \tabularnewline
R-squared & 0.89904089840771 \tabularnewline
Adjusted R-squared & 0.873801123009637 \tabularnewline
F-TEST (value) & 35.6200039116181 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.75211221609483 \tabularnewline
Sum Squared Residuals & 2188.36893017938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.94817767238409[/C][/ROW]
[ROW][C]R-squared[/C][C]0.89904089840771[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.873801123009637[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]35.6200039116181[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.75211221609483[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2188.36893017938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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	0.94817767238409
R-squared	0.89904089840771
Adjusted R-squared	0.873801123009637
F-TEST (value)	35.6200039116181
F-TEST (DF numerator)	12
F-TEST (DF denominator)	48
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.75211221609483
Sum Squared Residuals	2188.36893017938

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	99	95.91226690402	3.08773309598005
2	106.3	104.24690280445	2.05309719554997
3	128.9	121.56690280445	7.33309719554998
4	111.1	114.610152099688	-3.51015209968838
5	102.9	106.195887594481	-3.29588759448116
6	130	140.198172725946	-10.1981727259458
7	87	85.6377148685354	1.36228513146459
8	87.5	86.729948953805	0.77005104619506
9	117.6	124.573908740187	-6.97390874018698
10	103.4	106.608070114107	-3.20807011410722
11	110.8	119.971827793503	-9.17182779350262
12	112.6	125.959188455363	-13.3591884553630
13	102.5	104.366074590714	-1.86607459071361
14	112.4	113.469238462661	-1.06923846266127
15	135.6	142.317158035425	-6.71715803542535
16	105.1	110.575380249221	-5.47538024922097
17	127.7	128.867462754251	-1.16746275425052
18	137	141.543096676102	-4.54309667610157
19	91	94.2836545481085	-3.28365454810847
20	90.5	95.9522846120162	-5.45228461201619
21	122.4	127.648020626257	-5.24802062625738
22	123.3	126.974061359324	-3.67406135932376
23	124.3	127.849239501558	-3.54923950155809
24	120	125.382792476725	-5.38279247672481
25	118.1	117.815314092272	0.284685907728300
26	119	122.307310135114	-3.30731013511372
27	142.7	148.657513800446	-5.95751380044559
28	123.6	120.950507864709	2.64949213529138
29	129.6	127.714670796974	1.88532920302589
30	151.6	146.538528490966	5.06147150903398
31	110.4	107.925026042546	2.47497395745407
32	99.2	103.061168348554	-3.86116834855404
33	130.5	123.228984790031	7.27101520996883
34	136.2	138.309848939208	-2.10984893920845
35	129.7	128.617767473076	1.08223252692431
36	128	121.540152619137	6.45984738086321
37	121.6	126.845517757604	-5.24551775760356
38	135.8	136.717209601069	-0.917209601068798
39	143.8	131.365634441299	12.4343655587005
40	147.5	142.469291067202	5.03070893279844
41	136.2	127.330406811215	8.8695931887847
42	156.6	144.040812583534	12.5591874164662
43	123.3	114.649645793325	8.65035420667502
44	104.5	94.6073606618604	9.89263933813961
45	139.8	131.490660483845	8.3093395161546
46	136.5	125.437005416289	11.0629945837114
47	112.1	101.911420462839	10.1885795371611
48	118.5	110.588629025011	7.91137097498908
49	94.4	88.9955151603615	5.40448483963849
50	102.3	99.0593389967062	3.24066100329381
51	111.4	118.492790918380	-7.09279091837958
52	99.2	97.8946687191805	1.30533128081953
53	87.8	94.0915720430789	-6.2915720430789
54	115.8	118.679389523453	-2.87938952345284
55	79.7	88.9039587474852	-9.2039587474852
56	72.7	74.0492374237645	-1.34923742376444
57	104.5	107.858425359679	-3.35842535967907
58	103	105.071014171072	-2.07101417107201
59	95.1	93.6497447690247	1.45025523097534
60	104.2	99.8292374237645	4.37076257623554
61	78.3	79.9653114950297	-1.66531149502966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 99 & 95.91226690402 & 3.08773309598005 \tabularnewline
2 & 106.3 & 104.24690280445 & 2.05309719554997 \tabularnewline
3 & 128.9 & 121.56690280445 & 7.33309719554998 \tabularnewline
4 & 111.1 & 114.610152099688 & -3.51015209968838 \tabularnewline
5 & 102.9 & 106.195887594481 & -3.29588759448116 \tabularnewline
6 & 130 & 140.198172725946 & -10.1981727259458 \tabularnewline
7 & 87 & 85.6377148685354 & 1.36228513146459 \tabularnewline
8 & 87.5 & 86.729948953805 & 0.77005104619506 \tabularnewline
9 & 117.6 & 124.573908740187 & -6.97390874018698 \tabularnewline
10 & 103.4 & 106.608070114107 & -3.20807011410722 \tabularnewline
11 & 110.8 & 119.971827793503 & -9.17182779350262 \tabularnewline
12 & 112.6 & 125.959188455363 & -13.3591884553630 \tabularnewline
13 & 102.5 & 104.366074590714 & -1.86607459071361 \tabularnewline
14 & 112.4 & 113.469238462661 & -1.06923846266127 \tabularnewline
15 & 135.6 & 142.317158035425 & -6.71715803542535 \tabularnewline
16 & 105.1 & 110.575380249221 & -5.47538024922097 \tabularnewline
17 & 127.7 & 128.867462754251 & -1.16746275425052 \tabularnewline
18 & 137 & 141.543096676102 & -4.54309667610157 \tabularnewline
19 & 91 & 94.2836545481085 & -3.28365454810847 \tabularnewline
20 & 90.5 & 95.9522846120162 & -5.45228461201619 \tabularnewline
21 & 122.4 & 127.648020626257 & -5.24802062625738 \tabularnewline
22 & 123.3 & 126.974061359324 & -3.67406135932376 \tabularnewline
23 & 124.3 & 127.849239501558 & -3.54923950155809 \tabularnewline
24 & 120 & 125.382792476725 & -5.38279247672481 \tabularnewline
25 & 118.1 & 117.815314092272 & 0.284685907728300 \tabularnewline
26 & 119 & 122.307310135114 & -3.30731013511372 \tabularnewline
27 & 142.7 & 148.657513800446 & -5.95751380044559 \tabularnewline
28 & 123.6 & 120.950507864709 & 2.64949213529138 \tabularnewline
29 & 129.6 & 127.714670796974 & 1.88532920302589 \tabularnewline
30 & 151.6 & 146.538528490966 & 5.06147150903398 \tabularnewline
31 & 110.4 & 107.925026042546 & 2.47497395745407 \tabularnewline
32 & 99.2 & 103.061168348554 & -3.86116834855404 \tabularnewline
33 & 130.5 & 123.228984790031 & 7.27101520996883 \tabularnewline
34 & 136.2 & 138.309848939208 & -2.10984893920845 \tabularnewline
35 & 129.7 & 128.617767473076 & 1.08223252692431 \tabularnewline
36 & 128 & 121.540152619137 & 6.45984738086321 \tabularnewline
37 & 121.6 & 126.845517757604 & -5.24551775760356 \tabularnewline
38 & 135.8 & 136.717209601069 & -0.917209601068798 \tabularnewline
39 & 143.8 & 131.365634441299 & 12.4343655587005 \tabularnewline
40 & 147.5 & 142.469291067202 & 5.03070893279844 \tabularnewline
41 & 136.2 & 127.330406811215 & 8.8695931887847 \tabularnewline
42 & 156.6 & 144.040812583534 & 12.5591874164662 \tabularnewline
43 & 123.3 & 114.649645793325 & 8.65035420667502 \tabularnewline
44 & 104.5 & 94.6073606618604 & 9.89263933813961 \tabularnewline
45 & 139.8 & 131.490660483845 & 8.3093395161546 \tabularnewline
46 & 136.5 & 125.437005416289 & 11.0629945837114 \tabularnewline
47 & 112.1 & 101.911420462839 & 10.1885795371611 \tabularnewline
48 & 118.5 & 110.588629025011 & 7.91137097498908 \tabularnewline
49 & 94.4 & 88.9955151603615 & 5.40448483963849 \tabularnewline
50 & 102.3 & 99.0593389967062 & 3.24066100329381 \tabularnewline
51 & 111.4 & 118.492790918380 & -7.09279091837958 \tabularnewline
52 & 99.2 & 97.8946687191805 & 1.30533128081953 \tabularnewline
53 & 87.8 & 94.0915720430789 & -6.2915720430789 \tabularnewline
54 & 115.8 & 118.679389523453 & -2.87938952345284 \tabularnewline
55 & 79.7 & 88.9039587474852 & -9.2039587474852 \tabularnewline
56 & 72.7 & 74.0492374237645 & -1.34923742376444 \tabularnewline
57 & 104.5 & 107.858425359679 & -3.35842535967907 \tabularnewline
58 & 103 & 105.071014171072 & -2.07101417107201 \tabularnewline
59 & 95.1 & 93.6497447690247 & 1.45025523097534 \tabularnewline
60 & 104.2 & 99.8292374237645 & 4.37076257623554 \tabularnewline
61 & 78.3 & 79.9653114950297 & -1.66531149502966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&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]99[/C][C]95.91226690402[/C][C]3.08773309598005[/C][/ROW]
[ROW][C]2[/C][C]106.3[/C][C]104.24690280445[/C][C]2.05309719554997[/C][/ROW]
[ROW][C]3[/C][C]128.9[/C][C]121.56690280445[/C][C]7.33309719554998[/C][/ROW]
[ROW][C]4[/C][C]111.1[/C][C]114.610152099688[/C][C]-3.51015209968838[/C][/ROW]
[ROW][C]5[/C][C]102.9[/C][C]106.195887594481[/C][C]-3.29588759448116[/C][/ROW]
[ROW][C]6[/C][C]130[/C][C]140.198172725946[/C][C]-10.1981727259458[/C][/ROW]
[ROW][C]7[/C][C]87[/C][C]85.6377148685354[/C][C]1.36228513146459[/C][/ROW]
[ROW][C]8[/C][C]87.5[/C][C]86.729948953805[/C][C]0.77005104619506[/C][/ROW]
[ROW][C]9[/C][C]117.6[/C][C]124.573908740187[/C][C]-6.97390874018698[/C][/ROW]
[ROW][C]10[/C][C]103.4[/C][C]106.608070114107[/C][C]-3.20807011410722[/C][/ROW]
[ROW][C]11[/C][C]110.8[/C][C]119.971827793503[/C][C]-9.17182779350262[/C][/ROW]
[ROW][C]12[/C][C]112.6[/C][C]125.959188455363[/C][C]-13.3591884553630[/C][/ROW]
[ROW][C]13[/C][C]102.5[/C][C]104.366074590714[/C][C]-1.86607459071361[/C][/ROW]
[ROW][C]14[/C][C]112.4[/C][C]113.469238462661[/C][C]-1.06923846266127[/C][/ROW]
[ROW][C]15[/C][C]135.6[/C][C]142.317158035425[/C][C]-6.71715803542535[/C][/ROW]
[ROW][C]16[/C][C]105.1[/C][C]110.575380249221[/C][C]-5.47538024922097[/C][/ROW]
[ROW][C]17[/C][C]127.7[/C][C]128.867462754251[/C][C]-1.16746275425052[/C][/ROW]
[ROW][C]18[/C][C]137[/C][C]141.543096676102[/C][C]-4.54309667610157[/C][/ROW]
[ROW][C]19[/C][C]91[/C][C]94.2836545481085[/C][C]-3.28365454810847[/C][/ROW]
[ROW][C]20[/C][C]90.5[/C][C]95.9522846120162[/C][C]-5.45228461201619[/C][/ROW]
[ROW][C]21[/C][C]122.4[/C][C]127.648020626257[/C][C]-5.24802062625738[/C][/ROW]
[ROW][C]22[/C][C]123.3[/C][C]126.974061359324[/C][C]-3.67406135932376[/C][/ROW]
[ROW][C]23[/C][C]124.3[/C][C]127.849239501558[/C][C]-3.54923950155809[/C][/ROW]
[ROW][C]24[/C][C]120[/C][C]125.382792476725[/C][C]-5.38279247672481[/C][/ROW]
[ROW][C]25[/C][C]118.1[/C][C]117.815314092272[/C][C]0.284685907728300[/C][/ROW]
[ROW][C]26[/C][C]119[/C][C]122.307310135114[/C][C]-3.30731013511372[/C][/ROW]
[ROW][C]27[/C][C]142.7[/C][C]148.657513800446[/C][C]-5.95751380044559[/C][/ROW]
[ROW][C]28[/C][C]123.6[/C][C]120.950507864709[/C][C]2.64949213529138[/C][/ROW]
[ROW][C]29[/C][C]129.6[/C][C]127.714670796974[/C][C]1.88532920302589[/C][/ROW]
[ROW][C]30[/C][C]151.6[/C][C]146.538528490966[/C][C]5.06147150903398[/C][/ROW]
[ROW][C]31[/C][C]110.4[/C][C]107.925026042546[/C][C]2.47497395745407[/C][/ROW]
[ROW][C]32[/C][C]99.2[/C][C]103.061168348554[/C][C]-3.86116834855404[/C][/ROW]
[ROW][C]33[/C][C]130.5[/C][C]123.228984790031[/C][C]7.27101520996883[/C][/ROW]
[ROW][C]34[/C][C]136.2[/C][C]138.309848939208[/C][C]-2.10984893920845[/C][/ROW]
[ROW][C]35[/C][C]129.7[/C][C]128.617767473076[/C][C]1.08223252692431[/C][/ROW]
[ROW][C]36[/C][C]128[/C][C]121.540152619137[/C][C]6.45984738086321[/C][/ROW]
[ROW][C]37[/C][C]121.6[/C][C]126.845517757604[/C][C]-5.24551775760356[/C][/ROW]
[ROW][C]38[/C][C]135.8[/C][C]136.717209601069[/C][C]-0.917209601068798[/C][/ROW]
[ROW][C]39[/C][C]143.8[/C][C]131.365634441299[/C][C]12.4343655587005[/C][/ROW]
[ROW][C]40[/C][C]147.5[/C][C]142.469291067202[/C][C]5.03070893279844[/C][/ROW]
[ROW][C]41[/C][C]136.2[/C][C]127.330406811215[/C][C]8.8695931887847[/C][/ROW]
[ROW][C]42[/C][C]156.6[/C][C]144.040812583534[/C][C]12.5591874164662[/C][/ROW]
[ROW][C]43[/C][C]123.3[/C][C]114.649645793325[/C][C]8.65035420667502[/C][/ROW]
[ROW][C]44[/C][C]104.5[/C][C]94.6073606618604[/C][C]9.89263933813961[/C][/ROW]
[ROW][C]45[/C][C]139.8[/C][C]131.490660483845[/C][C]8.3093395161546[/C][/ROW]
[ROW][C]46[/C][C]136.5[/C][C]125.437005416289[/C][C]11.0629945837114[/C][/ROW]
[ROW][C]47[/C][C]112.1[/C][C]101.911420462839[/C][C]10.1885795371611[/C][/ROW]
[ROW][C]48[/C][C]118.5[/C][C]110.588629025011[/C][C]7.91137097498908[/C][/ROW]
[ROW][C]49[/C][C]94.4[/C][C]88.9955151603615[/C][C]5.40448483963849[/C][/ROW]
[ROW][C]50[/C][C]102.3[/C][C]99.0593389967062[/C][C]3.24066100329381[/C][/ROW]
[ROW][C]51[/C][C]111.4[/C][C]118.492790918380[/C][C]-7.09279091837958[/C][/ROW]
[ROW][C]52[/C][C]99.2[/C][C]97.8946687191805[/C][C]1.30533128081953[/C][/ROW]
[ROW][C]53[/C][C]87.8[/C][C]94.0915720430789[/C][C]-6.2915720430789[/C][/ROW]
[ROW][C]54[/C][C]115.8[/C][C]118.679389523453[/C][C]-2.87938952345284[/C][/ROW]
[ROW][C]55[/C][C]79.7[/C][C]88.9039587474852[/C][C]-9.2039587474852[/C][/ROW]
[ROW][C]56[/C][C]72.7[/C][C]74.0492374237645[/C][C]-1.34923742376444[/C][/ROW]
[ROW][C]57[/C][C]104.5[/C][C]107.858425359679[/C][C]-3.35842535967907[/C][/ROW]
[ROW][C]58[/C][C]103[/C][C]105.071014171072[/C][C]-2.07101417107201[/C][/ROW]
[ROW][C]59[/C][C]95.1[/C][C]93.6497447690247[/C][C]1.45025523097534[/C][/ROW]
[ROW][C]60[/C][C]104.2[/C][C]99.8292374237645[/C][C]4.37076257623554[/C][/ROW]
[ROW][C]61[/C][C]78.3[/C][C]79.9653114950297[/C][C]-1.66531149502966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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	99	95.91226690402	3.08773309598005
2	106.3	104.24690280445	2.05309719554997
3	128.9	121.56690280445	7.33309719554998
4	111.1	114.610152099688	-3.51015209968838
5	102.9	106.195887594481	-3.29588759448116
6	130	140.198172725946	-10.1981727259458
7	87	85.6377148685354	1.36228513146459
8	87.5	86.729948953805	0.77005104619506
9	117.6	124.573908740187	-6.97390874018698
10	103.4	106.608070114107	-3.20807011410722
11	110.8	119.971827793503	-9.17182779350262
12	112.6	125.959188455363	-13.3591884553630
13	102.5	104.366074590714	-1.86607459071361
14	112.4	113.469238462661	-1.06923846266127
15	135.6	142.317158035425	-6.71715803542535
16	105.1	110.575380249221	-5.47538024922097
17	127.7	128.867462754251	-1.16746275425052
18	137	141.543096676102	-4.54309667610157
19	91	94.2836545481085	-3.28365454810847
20	90.5	95.9522846120162	-5.45228461201619
21	122.4	127.648020626257	-5.24802062625738
22	123.3	126.974061359324	-3.67406135932376
23	124.3	127.849239501558	-3.54923950155809
24	120	125.382792476725	-5.38279247672481
25	118.1	117.815314092272	0.284685907728300
26	119	122.307310135114	-3.30731013511372
27	142.7	148.657513800446	-5.95751380044559
28	123.6	120.950507864709	2.64949213529138
29	129.6	127.714670796974	1.88532920302589
30	151.6	146.538528490966	5.06147150903398
31	110.4	107.925026042546	2.47497395745407
32	99.2	103.061168348554	-3.86116834855404
33	130.5	123.228984790031	7.27101520996883
34	136.2	138.309848939208	-2.10984893920845
35	129.7	128.617767473076	1.08223252692431
36	128	121.540152619137	6.45984738086321
37	121.6	126.845517757604	-5.24551775760356
38	135.8	136.717209601069	-0.917209601068798
39	143.8	131.365634441299	12.4343655587005
40	147.5	142.469291067202	5.03070893279844
41	136.2	127.330406811215	8.8695931887847
42	156.6	144.040812583534	12.5591874164662
43	123.3	114.649645793325	8.65035420667502
44	104.5	94.6073606618604	9.89263933813961
45	139.8	131.490660483845	8.3093395161546
46	136.5	125.437005416289	11.0629945837114
47	112.1	101.911420462839	10.1885795371611
48	118.5	110.588629025011	7.91137097498908
49	94.4	88.9955151603615	5.40448483963849
50	102.3	99.0593389967062	3.24066100329381
51	111.4	118.492790918380	-7.09279091837958
52	99.2	97.8946687191805	1.30533128081953
53	87.8	94.0915720430789	-6.2915720430789
54	115.8	118.679389523453	-2.87938952345284
55	79.7	88.9039587474852	-9.2039587474852
56	72.7	74.0492374237645	-1.34923742376444
57	104.5	107.858425359679	-3.35842535967907
58	103	105.071014171072	-2.07101417107201
59	95.1	93.6497447690247	1.45025523097534
60	104.2	99.8292374237645	4.37076257623554
61	78.3	79.9653114950297	-1.66531149502966

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.0416459383256458	0.0832918766512917	0.958354061674354
17	0.208754920828958	0.417509841657916	0.791245079171042
18	0.171437152055514	0.342874304111028	0.828562847944486
19	0.0975249229766727	0.195049845953345	0.902475077023327
20	0.0610022263551397	0.122004452710279	0.93899777364486
21	0.0381365527066469	0.0762731054132939	0.961863447293353
22	0.0276237807804865	0.0552475615609729	0.972376219219514
23	0.0318658801987914	0.0637317603975828	0.968134119801209
24	0.0476127306373149	0.0952254612746297	0.952387269362685
25	0.0289898711285964	0.0579797422571929	0.971010128871404
26	0.0164946287955907	0.0329892575911813	0.98350537120441
27	0.0139705808580587	0.0279411617161173	0.986029419141941
28	0.0206662584328961	0.0413325168657921	0.979333741567104
29	0.0164045903461241	0.0328091806922482	0.983595409653876
30	0.0457404509185833	0.0914809018371666	0.954259549081417
31	0.0327353438341914	0.0654706876683828	0.967264656165809
32	0.0275351325346125	0.0550702650692251	0.972464867465388
33	0.0524827271677833	0.104965454335567	0.947517272832217
34	0.0478790895483947	0.0957581790967894	0.952120910451605
35	0.0602685126401806	0.120537025280361	0.93973148735982
36	0.103066138786688	0.206132277573375	0.896933861213312
37	0.252917453800927	0.505834907601854	0.747082546199073
38	0.496625835210786	0.993251670421571	0.503374164789214
39	0.778144136377215	0.443711727245571	0.221855863622785
40	0.998771992681473	0.00245601463705438	0.00122800731852719
41	0.999054459359756	0.00189108128048877	0.000945540640244383
42	0.99777104506156	0.00445790987687922	0.00222895493843961
43	0.99525383544556	0.00949232910887979	0.00474616455443989
44	0.984881253675234	0.0302374926495327	0.0151187463247663
45	0.964707601808473	0.0705847963830542	0.0352923981915271

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.0416459383256458 & 0.0832918766512917 & 0.958354061674354 \tabularnewline
17 & 0.208754920828958 & 0.417509841657916 & 0.791245079171042 \tabularnewline
18 & 0.171437152055514 & 0.342874304111028 & 0.828562847944486 \tabularnewline
19 & 0.0975249229766727 & 0.195049845953345 & 0.902475077023327 \tabularnewline
20 & 0.0610022263551397 & 0.122004452710279 & 0.93899777364486 \tabularnewline
21 & 0.0381365527066469 & 0.0762731054132939 & 0.961863447293353 \tabularnewline
22 & 0.0276237807804865 & 0.0552475615609729 & 0.972376219219514 \tabularnewline
23 & 0.0318658801987914 & 0.0637317603975828 & 0.968134119801209 \tabularnewline
24 & 0.0476127306373149 & 0.0952254612746297 & 0.952387269362685 \tabularnewline
25 & 0.0289898711285964 & 0.0579797422571929 & 0.971010128871404 \tabularnewline
26 & 0.0164946287955907 & 0.0329892575911813 & 0.98350537120441 \tabularnewline
27 & 0.0139705808580587 & 0.0279411617161173 & 0.986029419141941 \tabularnewline
28 & 0.0206662584328961 & 0.0413325168657921 & 0.979333741567104 \tabularnewline
29 & 0.0164045903461241 & 0.0328091806922482 & 0.983595409653876 \tabularnewline
30 & 0.0457404509185833 & 0.0914809018371666 & 0.954259549081417 \tabularnewline
31 & 0.0327353438341914 & 0.0654706876683828 & 0.967264656165809 \tabularnewline
32 & 0.0275351325346125 & 0.0550702650692251 & 0.972464867465388 \tabularnewline
33 & 0.0524827271677833 & 0.104965454335567 & 0.947517272832217 \tabularnewline
34 & 0.0478790895483947 & 0.0957581790967894 & 0.952120910451605 \tabularnewline
35 & 0.0602685126401806 & 0.120537025280361 & 0.93973148735982 \tabularnewline
36 & 0.103066138786688 & 0.206132277573375 & 0.896933861213312 \tabularnewline
37 & 0.252917453800927 & 0.505834907601854 & 0.747082546199073 \tabularnewline
38 & 0.496625835210786 & 0.993251670421571 & 0.503374164789214 \tabularnewline
39 & 0.778144136377215 & 0.443711727245571 & 0.221855863622785 \tabularnewline
40 & 0.998771992681473 & 0.00245601463705438 & 0.00122800731852719 \tabularnewline
41 & 0.999054459359756 & 0.00189108128048877 & 0.000945540640244383 \tabularnewline
42 & 0.99777104506156 & 0.00445790987687922 & 0.00222895493843961 \tabularnewline
43 & 0.99525383544556 & 0.00949232910887979 & 0.00474616455443989 \tabularnewline
44 & 0.984881253675234 & 0.0302374926495327 & 0.0151187463247663 \tabularnewline
45 & 0.964707601808473 & 0.0705847963830542 & 0.0352923981915271 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&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]16[/C][C]0.0416459383256458[/C][C]0.0832918766512917[/C][C]0.958354061674354[/C][/ROW]
[ROW][C]17[/C][C]0.208754920828958[/C][C]0.417509841657916[/C][C]0.791245079171042[/C][/ROW]
[ROW][C]18[/C][C]0.171437152055514[/C][C]0.342874304111028[/C][C]0.828562847944486[/C][/ROW]
[ROW][C]19[/C][C]0.0975249229766727[/C][C]0.195049845953345[/C][C]0.902475077023327[/C][/ROW]
[ROW][C]20[/C][C]0.0610022263551397[/C][C]0.122004452710279[/C][C]0.93899777364486[/C][/ROW]
[ROW][C]21[/C][C]0.0381365527066469[/C][C]0.0762731054132939[/C][C]0.961863447293353[/C][/ROW]
[ROW][C]22[/C][C]0.0276237807804865[/C][C]0.0552475615609729[/C][C]0.972376219219514[/C][/ROW]
[ROW][C]23[/C][C]0.0318658801987914[/C][C]0.0637317603975828[/C][C]0.968134119801209[/C][/ROW]
[ROW][C]24[/C][C]0.0476127306373149[/C][C]0.0952254612746297[/C][C]0.952387269362685[/C][/ROW]
[ROW][C]25[/C][C]0.0289898711285964[/C][C]0.0579797422571929[/C][C]0.971010128871404[/C][/ROW]
[ROW][C]26[/C][C]0.0164946287955907[/C][C]0.0329892575911813[/C][C]0.98350537120441[/C][/ROW]
[ROW][C]27[/C][C]0.0139705808580587[/C][C]0.0279411617161173[/C][C]0.986029419141941[/C][/ROW]
[ROW][C]28[/C][C]0.0206662584328961[/C][C]0.0413325168657921[/C][C]0.979333741567104[/C][/ROW]
[ROW][C]29[/C][C]0.0164045903461241[/C][C]0.0328091806922482[/C][C]0.983595409653876[/C][/ROW]
[ROW][C]30[/C][C]0.0457404509185833[/C][C]0.0914809018371666[/C][C]0.954259549081417[/C][/ROW]
[ROW][C]31[/C][C]0.0327353438341914[/C][C]0.0654706876683828[/C][C]0.967264656165809[/C][/ROW]
[ROW][C]32[/C][C]0.0275351325346125[/C][C]0.0550702650692251[/C][C]0.972464867465388[/C][/ROW]
[ROW][C]33[/C][C]0.0524827271677833[/C][C]0.104965454335567[/C][C]0.947517272832217[/C][/ROW]
[ROW][C]34[/C][C]0.0478790895483947[/C][C]0.0957581790967894[/C][C]0.952120910451605[/C][/ROW]
[ROW][C]35[/C][C]0.0602685126401806[/C][C]0.120537025280361[/C][C]0.93973148735982[/C][/ROW]
[ROW][C]36[/C][C]0.103066138786688[/C][C]0.206132277573375[/C][C]0.896933861213312[/C][/ROW]
[ROW][C]37[/C][C]0.252917453800927[/C][C]0.505834907601854[/C][C]0.747082546199073[/C][/ROW]
[ROW][C]38[/C][C]0.496625835210786[/C][C]0.993251670421571[/C][C]0.503374164789214[/C][/ROW]
[ROW][C]39[/C][C]0.778144136377215[/C][C]0.443711727245571[/C][C]0.221855863622785[/C][/ROW]
[ROW][C]40[/C][C]0.998771992681473[/C][C]0.00245601463705438[/C][C]0.00122800731852719[/C][/ROW]
[ROW][C]41[/C][C]0.999054459359756[/C][C]0.00189108128048877[/C][C]0.000945540640244383[/C][/ROW]
[ROW][C]42[/C][C]0.99777104506156[/C][C]0.00445790987687922[/C][C]0.00222895493843961[/C][/ROW]
[ROW][C]43[/C][C]0.99525383544556[/C][C]0.00949232910887979[/C][C]0.00474616455443989[/C][/ROW]
[ROW][C]44[/C][C]0.984881253675234[/C][C]0.0302374926495327[/C][C]0.0151187463247663[/C][/ROW]
[ROW][C]45[/C][C]0.964707601808473[/C][C]0.0705847963830542[/C][C]0.0352923981915271[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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
16	0.0416459383256458	0.0832918766512917	0.958354061674354
17	0.208754920828958	0.417509841657916	0.791245079171042
18	0.171437152055514	0.342874304111028	0.828562847944486
19	0.0975249229766727	0.195049845953345	0.902475077023327
20	0.0610022263551397	0.122004452710279	0.93899777364486
21	0.0381365527066469	0.0762731054132939	0.961863447293353
22	0.0276237807804865	0.0552475615609729	0.972376219219514
23	0.0318658801987914	0.0637317603975828	0.968134119801209
24	0.0476127306373149	0.0952254612746297	0.952387269362685
25	0.0289898711285964	0.0579797422571929	0.971010128871404
26	0.0164946287955907	0.0329892575911813	0.98350537120441
27	0.0139705808580587	0.0279411617161173	0.986029419141941
28	0.0206662584328961	0.0413325168657921	0.979333741567104
29	0.0164045903461241	0.0328091806922482	0.983595409653876
30	0.0457404509185833	0.0914809018371666	0.954259549081417
31	0.0327353438341914	0.0654706876683828	0.967264656165809
32	0.0275351325346125	0.0550702650692251	0.972464867465388
33	0.0524827271677833	0.104965454335567	0.947517272832217
34	0.0478790895483947	0.0957581790967894	0.952120910451605
35	0.0602685126401806	0.120537025280361	0.93973148735982
36	0.103066138786688	0.206132277573375	0.896933861213312
37	0.252917453800927	0.505834907601854	0.747082546199073
38	0.496625835210786	0.993251670421571	0.503374164789214
39	0.778144136377215	0.443711727245571	0.221855863622785
40	0.998771992681473	0.00245601463705438	0.00122800731852719
41	0.999054459359756	0.00189108128048877	0.000945540640244383
42	0.99777104506156	0.00445790987687922	0.00222895493843961
43	0.99525383544556	0.00949232910887979	0.00474616455443989
44	0.984881253675234	0.0302374926495327	0.0151187463247663
45	0.964707601808473	0.0705847963830542	0.0352923981915271

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	4	0.133333333333333	NOK
5% type I error level	9	0.3	NOK
10% type I error level	20	0.666666666666667	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 & 4 & 0.133333333333333 & NOK \tabularnewline
5% type I error level & 9 & 0.3 & NOK \tabularnewline
10% type I error level & 20 & 0.666666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=116206&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]4[/C][C]0.133333333333333[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.3[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]20[/C][C]0.666666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=116206&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=116206&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	4	0.133333333333333	NOK
5% type I error level	9	0.3	NOK
10% type I error level	20	0.666666666666667	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 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code