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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 19 Nov 2010 14:12:16 +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/Nov/19/t1290176147jdc3sx8eqhmvuyx.htm/, Retrieved Fri, 03 May 2024 22:28:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97995, Retrieved Fri, 03 May 2024 22:28:54 +0000

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No (this computation is public)

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Estimated Impact

145

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

-     [Multiple Regression] [] [2010-11-17 09:14:55] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-11-19 12:14:29] [7789b9488494790f41ddb7f073cada1b]
-    D      [Multiple Regression] [] [2010-11-19 14:12:16] [c05c5ae4ce2db58f67fd725429d7f25c] [Current]

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6	101,82	107,34	93,63	101,76
6	101,68	107,34	93,63	102,37
6	101,68	107,34	93,63	102,38
6	102,45	107,34	96,13	102,86
6	102,45	107,34	96,13	102,87
6	102,45	107,34	96,13	102,92
6	102,45	107,34	96,13	102,95
6	102,45	107,34	96,13	103,02
6	102,45	112,60	96,13	104,08
6	102,52	112,60	96,13	104,16
6	102,52	112,60	96,13	104,24
6	102,85	112,60	96,13	104,33
7	102,85	112,61	96,13	104,73
7	102,85	112,61	96,13	104,86
7	103,25	112,61	96,13	105,03
7	103,25	112,61	98,73	105,62
7	103,25	112,61	98,73	105,63
7	103,25	112,61	98,73	105,63
7	104,45	112,61	98,73	105,94
7	104,45	112,61	98,73	106,61
7	104,45	118,65	98,73	107,69
7	104,80	118,65	98,73	107,78
7	104,80	118,65	98,73	107,93
7	105,29	118,65	98,73	108,48
8	105,29	114,29	98,73	108,14
8	105,29	114,29	98,73	108,48
8	105,29	114,29	98,73	108,48
8	106,04	114,29	101,67	108,89
8	105,94	114,29	101,67	108,93
8	105,94	114,29	101,67	109,21
8	105,94	114,29	101,67	109,47
8	106,28	114,29	101,67	109,80
8	106,48	123,33	101,67	111,73
8	107,19	123,33	101,67	111,85
8	108,14	123,33	101,67	112,12
8	108,22	123,33	101,67	112,15
9	108,22	123,33	101,67	112,17
9	108,61	123,33	101,67	112,67
9	108,61	123,33	101,67	112,80
9	108,61	123,33	107,94	113,44
9	108,61	123,33	107,94	113,53
9	109,06	123,33	107,94	114,53
9	109,06	123,33	107,94	114,51
9	112,93	123,33	107,94	115,05
9	115,84	129,03	107,94	116,67
9	118,57	128,76	107,94	117,07
9	118,57	128,76	107,94	116,92
9	118,86	128,76	107,94	117,00
10	118,98	128,76	107,94	117,02
10	119,27	128,76	107,94	117,35
10	119,39	128,76	107,94	117,36
10	119,49	128,76	110,30	117,82
10	119,59	128,76	110,30	117,88
10	120,12	128,76	110,30	118,24
10	120,14	128,76	110,30	118,50
10	120,14	128,76	110,30	118,80
10	120,14	132,63	110,30	119,76
10	120,14	132,63	110,30	120,09

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Cultuuruitgaves[t] = + 60.4450364106322 + 0.0764799565278573Jaar[t] + 0.103348458934701Bioscoop[t] + 0.171281216338491Schouwburg[t] + 0.123399646201346Eendagattractie[t] + 0.170625237603976t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cultuuruitgaves[t] =  +  60.4450364106322 +  0.0764799565278573Jaar[t] +  0.103348458934701Bioscoop[t] +  0.171281216338491Schouwburg[t] +  0.123399646201346Eendagattractie[t] +  0.170625237603976t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cultuuruitgaves[t] =  +  60.4450364106322 +  0.0764799565278573Jaar[t] +  0.103348458934701Bioscoop[t] +  0.171281216338491Schouwburg[t] +  0.123399646201346Eendagattractie[t] +  0.170625237603976t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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
Cultuuruitgaves[t] = + 60.4450364106322 + 0.0764799565278573Jaar[t] + 0.103348458934701Bioscoop[t] + 0.171281216338491Schouwburg[t] + 0.123399646201346Eendagattractie[t] + 0.170625237603976t + 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)	60.4450364106322	3.901815	15.4915	0	0
Jaar	0.0764799565278573	0.154562	0.4948	0.622812	0.311406
Bioscoop	0.103348458934701	0.018262	5.6592	1e-06	0
Schouwburg	0.171281216338491	0.020692	8.2777	0	0
Eendagattractie	0.123399646201346	0.032198	3.8326	0.000344	0.000172
t	0.170625237603976	0.02084	8.1872	0	0

\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) & 60.4450364106322 & 3.901815 & 15.4915 & 0 & 0 \tabularnewline
Jaar & 0.0764799565278573 & 0.154562 & 0.4948 & 0.622812 & 0.311406 \tabularnewline
Bioscoop & 0.103348458934701 & 0.018262 & 5.6592 & 1e-06 & 0 \tabularnewline
Schouwburg & 0.171281216338491 & 0.020692 & 8.2777 & 0 & 0 \tabularnewline
Eendagattractie & 0.123399646201346 & 0.032198 & 3.8326 & 0.000344 & 0.000172 \tabularnewline
t & 0.170625237603976 & 0.02084 & 8.1872 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&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]60.4450364106322[/C][C]3.901815[/C][C]15.4915[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Jaar[/C][C]0.0764799565278573[/C][C]0.154562[/C][C]0.4948[/C][C]0.622812[/C][C]0.311406[/C][/ROW]
[ROW][C]Bioscoop[/C][C]0.103348458934701[/C][C]0.018262[/C][C]5.6592[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Schouwburg[/C][C]0.171281216338491[/C][C]0.020692[/C][C]8.2777[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Eendagattractie[/C][C]0.123399646201346[/C][C]0.032198[/C][C]3.8326[/C][C]0.000344[/C][C]0.000172[/C][/ROW]
[ROW][C]t[/C][C]0.170625237603976[/C][C]0.02084[/C][C]8.1872[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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)	60.4450364106322	3.901815	15.4915	0	0
Jaar	0.0764799565278573	0.154562	0.4948	0.622812	0.311406
Bioscoop	0.103348458934701	0.018262	5.6592	1e-06	0
Schouwburg	0.171281216338491	0.020692	8.2777	0	0
Eendagattractie	0.123399646201346	0.032198	3.8326	0.000344	0.000172
t	0.170625237603976	0.02084	8.1872	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.998655916446072
R-squared	0.997313639452744
Adjusted R-squared	0.99705533555397
F-TEST (value)	3861.00885113983
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302520033540805
Sum Squared Residuals	4.75895527606356

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.998655916446072 \tabularnewline
R-squared & 0.997313639452744 \tabularnewline
Adjusted R-squared & 0.99705533555397 \tabularnewline
F-TEST (value) & 3861.00885113983 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.302520033540805 \tabularnewline
Sum Squared Residuals & 4.75895527606356 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.998655916446072[/C][/ROW]
[ROW][C]R-squared[/C][C]0.997313639452744[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99705533555397[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3861.00885113983[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/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]0.302520033540805[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]4.75895527606356[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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	0.998655916446072
R-squared	0.997313639452744
Adjusted R-squared	0.99705533555397
F-TEST (value)	3861.00885113983
F-TEST (DF numerator)	5
F-TEST (DF denominator)	52
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.302520033540805
Sum Squared Residuals	4.75895527606356

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.76	101.536716111741	0.223283888259383
2	102.37	101.692872565093	0.677127434906529
3	102.38	101.863497802697	0.516502197302551
4	102.86	102.422200469185	0.437799530815496
5	102.87	102.592825706788	0.277174293211525
6	102.92	102.763450944392	0.156549055607549
7	102.95	102.934076181996	0.0159238180035727
8	103.02	103.104701419600	-0.0847014196004097
9	104.08	104.176265855145	-0.0962658551448465
10	104.16	104.354125484874	-0.194125484874252
11	104.24	104.524750722478	-0.284750722478229
12	104.33	104.729480951531	-0.399480951530652
13	104.73	104.978298957826	-0.248298957825866
14	104.86	105.148924195430	-0.288924195429846
15	105.03	105.360888816608	-0.3308888166077
16	105.62	105.852353134335	-0.232353134335175
17	105.63	106.022978371939	-0.392978371939159
18	105.63	106.193603609543	-0.563603609543134
19	105.94	106.488246997869	-0.54824699786875
20	106.61	106.658872235473	-0.0488722354727233
21	107.69	107.864036019761	-0.17403601976119
22	107.78	108.070833217992	-0.290833217992307
23	107.93	108.241458455596	-0.311458455596277
24	108.48	108.462724438078	0.0172755619217406
25	108.14	107.963043528974	0.176956471025727
26	108.48	108.133668766578	0.346331233421755
27	108.48	108.304294004182	0.175705995817780
28	108.89	108.915225545819	-0.0252255458191837
29	108.93	109.075515937530	-0.145515937529682
30	109.21	109.246141175134	-0.0361411751336705
31	109.47	109.416766412738	0.053233587262359
32	109.8	109.622530126379	0.177469873620583
33	111.73	111.362207251470	0.367792748529712
34	111.85	111.606209894918	0.24379010508209
35	112.12	111.875016168510	0.244983831490159
36	112.15	112.053909282829	0.096090717171408
37	112.17	112.301014476960	-0.131014476960428
38	112.67	112.511945613549	0.158054386451063
39	112.8	112.682570851153	0.117429148847083
40	113.44	113.626911870439	-0.186911870439335
41	113.53	113.797537108043	-0.267537108043307
42	114.53	114.014669152168	0.515330847832102
43	114.51	114.185294389772	0.324705610228131
44	115.05	114.755878163453	0.294121836546853
45	116.67	116.203550349686	0.4664496503135
46	117.07	116.610070951771	0.459929048229178
47	116.92	116.780696189375	0.139303810625211
48	117	116.981292480070	0.0187075199301697
49	117.02	117.240799489274	-0.220799489273832
50	117.35	117.441395779969	-0.0913957799688716
51	117.36	117.624422832645	-0.264422832645006
52	117.82	118.096606081178	-0.276606081177636
53	117.88	118.277566164675	-0.39756616467508
54	118.24	118.502966085514	-0.262966085514448
55	118.5	118.675658292297	-0.175658292297111
56	118.8	118.846283529901	-0.0462835299010896
57	119.76	119.679767074735	0.08023292526498
58	120.09	119.850392312339	0.239607687661003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.76 & 101.536716111741 & 0.223283888259383 \tabularnewline
2 & 102.37 & 101.692872565093 & 0.677127434906529 \tabularnewline
3 & 102.38 & 101.863497802697 & 0.516502197302551 \tabularnewline
4 & 102.86 & 102.422200469185 & 0.437799530815496 \tabularnewline
5 & 102.87 & 102.592825706788 & 0.277174293211525 \tabularnewline
6 & 102.92 & 102.763450944392 & 0.156549055607549 \tabularnewline
7 & 102.95 & 102.934076181996 & 0.0159238180035727 \tabularnewline
8 & 103.02 & 103.104701419600 & -0.0847014196004097 \tabularnewline
9 & 104.08 & 104.176265855145 & -0.0962658551448465 \tabularnewline
10 & 104.16 & 104.354125484874 & -0.194125484874252 \tabularnewline
11 & 104.24 & 104.524750722478 & -0.284750722478229 \tabularnewline
12 & 104.33 & 104.729480951531 & -0.399480951530652 \tabularnewline
13 & 104.73 & 104.978298957826 & -0.248298957825866 \tabularnewline
14 & 104.86 & 105.148924195430 & -0.288924195429846 \tabularnewline
15 & 105.03 & 105.360888816608 & -0.3308888166077 \tabularnewline
16 & 105.62 & 105.852353134335 & -0.232353134335175 \tabularnewline
17 & 105.63 & 106.022978371939 & -0.392978371939159 \tabularnewline
18 & 105.63 & 106.193603609543 & -0.563603609543134 \tabularnewline
19 & 105.94 & 106.488246997869 & -0.54824699786875 \tabularnewline
20 & 106.61 & 106.658872235473 & -0.0488722354727233 \tabularnewline
21 & 107.69 & 107.864036019761 & -0.17403601976119 \tabularnewline
22 & 107.78 & 108.070833217992 & -0.290833217992307 \tabularnewline
23 & 107.93 & 108.241458455596 & -0.311458455596277 \tabularnewline
24 & 108.48 & 108.462724438078 & 0.0172755619217406 \tabularnewline
25 & 108.14 & 107.963043528974 & 0.176956471025727 \tabularnewline
26 & 108.48 & 108.133668766578 & 0.346331233421755 \tabularnewline
27 & 108.48 & 108.304294004182 & 0.175705995817780 \tabularnewline
28 & 108.89 & 108.915225545819 & -0.0252255458191837 \tabularnewline
29 & 108.93 & 109.075515937530 & -0.145515937529682 \tabularnewline
30 & 109.21 & 109.246141175134 & -0.0361411751336705 \tabularnewline
31 & 109.47 & 109.416766412738 & 0.053233587262359 \tabularnewline
32 & 109.8 & 109.622530126379 & 0.177469873620583 \tabularnewline
33 & 111.73 & 111.362207251470 & 0.367792748529712 \tabularnewline
34 & 111.85 & 111.606209894918 & 0.24379010508209 \tabularnewline
35 & 112.12 & 111.875016168510 & 0.244983831490159 \tabularnewline
36 & 112.15 & 112.053909282829 & 0.096090717171408 \tabularnewline
37 & 112.17 & 112.301014476960 & -0.131014476960428 \tabularnewline
38 & 112.67 & 112.511945613549 & 0.158054386451063 \tabularnewline
39 & 112.8 & 112.682570851153 & 0.117429148847083 \tabularnewline
40 & 113.44 & 113.626911870439 & -0.186911870439335 \tabularnewline
41 & 113.53 & 113.797537108043 & -0.267537108043307 \tabularnewline
42 & 114.53 & 114.014669152168 & 0.515330847832102 \tabularnewline
43 & 114.51 & 114.185294389772 & 0.324705610228131 \tabularnewline
44 & 115.05 & 114.755878163453 & 0.294121836546853 \tabularnewline
45 & 116.67 & 116.203550349686 & 0.4664496503135 \tabularnewline
46 & 117.07 & 116.610070951771 & 0.459929048229178 \tabularnewline
47 & 116.92 & 116.780696189375 & 0.139303810625211 \tabularnewline
48 & 117 & 116.981292480070 & 0.0187075199301697 \tabularnewline
49 & 117.02 & 117.240799489274 & -0.220799489273832 \tabularnewline
50 & 117.35 & 117.441395779969 & -0.0913957799688716 \tabularnewline
51 & 117.36 & 117.624422832645 & -0.264422832645006 \tabularnewline
52 & 117.82 & 118.096606081178 & -0.276606081177636 \tabularnewline
53 & 117.88 & 118.277566164675 & -0.39756616467508 \tabularnewline
54 & 118.24 & 118.502966085514 & -0.262966085514448 \tabularnewline
55 & 118.5 & 118.675658292297 & -0.175658292297111 \tabularnewline
56 & 118.8 & 118.846283529901 & -0.0462835299010896 \tabularnewline
57 & 119.76 & 119.679767074735 & 0.08023292526498 \tabularnewline
58 & 120.09 & 119.850392312339 & 0.239607687661003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&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]101.76[/C][C]101.536716111741[/C][C]0.223283888259383[/C][/ROW]
[ROW][C]2[/C][C]102.37[/C][C]101.692872565093[/C][C]0.677127434906529[/C][/ROW]
[ROW][C]3[/C][C]102.38[/C][C]101.863497802697[/C][C]0.516502197302551[/C][/ROW]
[ROW][C]4[/C][C]102.86[/C][C]102.422200469185[/C][C]0.437799530815496[/C][/ROW]
[ROW][C]5[/C][C]102.87[/C][C]102.592825706788[/C][C]0.277174293211525[/C][/ROW]
[ROW][C]6[/C][C]102.92[/C][C]102.763450944392[/C][C]0.156549055607549[/C][/ROW]
[ROW][C]7[/C][C]102.95[/C][C]102.934076181996[/C][C]0.0159238180035727[/C][/ROW]
[ROW][C]8[/C][C]103.02[/C][C]103.104701419600[/C][C]-0.0847014196004097[/C][/ROW]
[ROW][C]9[/C][C]104.08[/C][C]104.176265855145[/C][C]-0.0962658551448465[/C][/ROW]
[ROW][C]10[/C][C]104.16[/C][C]104.354125484874[/C][C]-0.194125484874252[/C][/ROW]
[ROW][C]11[/C][C]104.24[/C][C]104.524750722478[/C][C]-0.284750722478229[/C][/ROW]
[ROW][C]12[/C][C]104.33[/C][C]104.729480951531[/C][C]-0.399480951530652[/C][/ROW]
[ROW][C]13[/C][C]104.73[/C][C]104.978298957826[/C][C]-0.248298957825866[/C][/ROW]
[ROW][C]14[/C][C]104.86[/C][C]105.148924195430[/C][C]-0.288924195429846[/C][/ROW]
[ROW][C]15[/C][C]105.03[/C][C]105.360888816608[/C][C]-0.3308888166077[/C][/ROW]
[ROW][C]16[/C][C]105.62[/C][C]105.852353134335[/C][C]-0.232353134335175[/C][/ROW]
[ROW][C]17[/C][C]105.63[/C][C]106.022978371939[/C][C]-0.392978371939159[/C][/ROW]
[ROW][C]18[/C][C]105.63[/C][C]106.193603609543[/C][C]-0.563603609543134[/C][/ROW]
[ROW][C]19[/C][C]105.94[/C][C]106.488246997869[/C][C]-0.54824699786875[/C][/ROW]
[ROW][C]20[/C][C]106.61[/C][C]106.658872235473[/C][C]-0.0488722354727233[/C][/ROW]
[ROW][C]21[/C][C]107.69[/C][C]107.864036019761[/C][C]-0.17403601976119[/C][/ROW]
[ROW][C]22[/C][C]107.78[/C][C]108.070833217992[/C][C]-0.290833217992307[/C][/ROW]
[ROW][C]23[/C][C]107.93[/C][C]108.241458455596[/C][C]-0.311458455596277[/C][/ROW]
[ROW][C]24[/C][C]108.48[/C][C]108.462724438078[/C][C]0.0172755619217406[/C][/ROW]
[ROW][C]25[/C][C]108.14[/C][C]107.963043528974[/C][C]0.176956471025727[/C][/ROW]
[ROW][C]26[/C][C]108.48[/C][C]108.133668766578[/C][C]0.346331233421755[/C][/ROW]
[ROW][C]27[/C][C]108.48[/C][C]108.304294004182[/C][C]0.175705995817780[/C][/ROW]
[ROW][C]28[/C][C]108.89[/C][C]108.915225545819[/C][C]-0.0252255458191837[/C][/ROW]
[ROW][C]29[/C][C]108.93[/C][C]109.075515937530[/C][C]-0.145515937529682[/C][/ROW]
[ROW][C]30[/C][C]109.21[/C][C]109.246141175134[/C][C]-0.0361411751336705[/C][/ROW]
[ROW][C]31[/C][C]109.47[/C][C]109.416766412738[/C][C]0.053233587262359[/C][/ROW]
[ROW][C]32[/C][C]109.8[/C][C]109.622530126379[/C][C]0.177469873620583[/C][/ROW]
[ROW][C]33[/C][C]111.73[/C][C]111.362207251470[/C][C]0.367792748529712[/C][/ROW]
[ROW][C]34[/C][C]111.85[/C][C]111.606209894918[/C][C]0.24379010508209[/C][/ROW]
[ROW][C]35[/C][C]112.12[/C][C]111.875016168510[/C][C]0.244983831490159[/C][/ROW]
[ROW][C]36[/C][C]112.15[/C][C]112.053909282829[/C][C]0.096090717171408[/C][/ROW]
[ROW][C]37[/C][C]112.17[/C][C]112.301014476960[/C][C]-0.131014476960428[/C][/ROW]
[ROW][C]38[/C][C]112.67[/C][C]112.511945613549[/C][C]0.158054386451063[/C][/ROW]
[ROW][C]39[/C][C]112.8[/C][C]112.682570851153[/C][C]0.117429148847083[/C][/ROW]
[ROW][C]40[/C][C]113.44[/C][C]113.626911870439[/C][C]-0.186911870439335[/C][/ROW]
[ROW][C]41[/C][C]113.53[/C][C]113.797537108043[/C][C]-0.267537108043307[/C][/ROW]
[ROW][C]42[/C][C]114.53[/C][C]114.014669152168[/C][C]0.515330847832102[/C][/ROW]
[ROW][C]43[/C][C]114.51[/C][C]114.185294389772[/C][C]0.324705610228131[/C][/ROW]
[ROW][C]44[/C][C]115.05[/C][C]114.755878163453[/C][C]0.294121836546853[/C][/ROW]
[ROW][C]45[/C][C]116.67[/C][C]116.203550349686[/C][C]0.4664496503135[/C][/ROW]
[ROW][C]46[/C][C]117.07[/C][C]116.610070951771[/C][C]0.459929048229178[/C][/ROW]
[ROW][C]47[/C][C]116.92[/C][C]116.780696189375[/C][C]0.139303810625211[/C][/ROW]
[ROW][C]48[/C][C]117[/C][C]116.981292480070[/C][C]0.0187075199301697[/C][/ROW]
[ROW][C]49[/C][C]117.02[/C][C]117.240799489274[/C][C]-0.220799489273832[/C][/ROW]
[ROW][C]50[/C][C]117.35[/C][C]117.441395779969[/C][C]-0.0913957799688716[/C][/ROW]
[ROW][C]51[/C][C]117.36[/C][C]117.624422832645[/C][C]-0.264422832645006[/C][/ROW]
[ROW][C]52[/C][C]117.82[/C][C]118.096606081178[/C][C]-0.276606081177636[/C][/ROW]
[ROW][C]53[/C][C]117.88[/C][C]118.277566164675[/C][C]-0.39756616467508[/C][/ROW]
[ROW][C]54[/C][C]118.24[/C][C]118.502966085514[/C][C]-0.262966085514448[/C][/ROW]
[ROW][C]55[/C][C]118.5[/C][C]118.675658292297[/C][C]-0.175658292297111[/C][/ROW]
[ROW][C]56[/C][C]118.8[/C][C]118.846283529901[/C][C]-0.0462835299010896[/C][/ROW]
[ROW][C]57[/C][C]119.76[/C][C]119.679767074735[/C][C]0.08023292526498[/C][/ROW]
[ROW][C]58[/C][C]120.09[/C][C]119.850392312339[/C][C]0.239607687661003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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	101.76	101.536716111741	0.223283888259383
2	102.37	101.692872565093	0.677127434906529
3	102.38	101.863497802697	0.516502197302551
4	102.86	102.422200469185	0.437799530815496
5	102.87	102.592825706788	0.277174293211525
6	102.92	102.763450944392	0.156549055607549
7	102.95	102.934076181996	0.0159238180035727
8	103.02	103.104701419600	-0.0847014196004097
9	104.08	104.176265855145	-0.0962658551448465
10	104.16	104.354125484874	-0.194125484874252
11	104.24	104.524750722478	-0.284750722478229
12	104.33	104.729480951531	-0.399480951530652
13	104.73	104.978298957826	-0.248298957825866
14	104.86	105.148924195430	-0.288924195429846
15	105.03	105.360888816608	-0.3308888166077
16	105.62	105.852353134335	-0.232353134335175
17	105.63	106.022978371939	-0.392978371939159
18	105.63	106.193603609543	-0.563603609543134
19	105.94	106.488246997869	-0.54824699786875
20	106.61	106.658872235473	-0.0488722354727233
21	107.69	107.864036019761	-0.17403601976119
22	107.78	108.070833217992	-0.290833217992307
23	107.93	108.241458455596	-0.311458455596277
24	108.48	108.462724438078	0.0172755619217406
25	108.14	107.963043528974	0.176956471025727
26	108.48	108.133668766578	0.346331233421755
27	108.48	108.304294004182	0.175705995817780
28	108.89	108.915225545819	-0.0252255458191837
29	108.93	109.075515937530	-0.145515937529682
30	109.21	109.246141175134	-0.0361411751336705
31	109.47	109.416766412738	0.053233587262359
32	109.8	109.622530126379	0.177469873620583
33	111.73	111.362207251470	0.367792748529712
34	111.85	111.606209894918	0.24379010508209
35	112.12	111.875016168510	0.244983831490159
36	112.15	112.053909282829	0.096090717171408
37	112.17	112.301014476960	-0.131014476960428
38	112.67	112.511945613549	0.158054386451063
39	112.8	112.682570851153	0.117429148847083
40	113.44	113.626911870439	-0.186911870439335
41	113.53	113.797537108043	-0.267537108043307
42	114.53	114.014669152168	0.515330847832102
43	114.51	114.185294389772	0.324705610228131
44	115.05	114.755878163453	0.294121836546853
45	116.67	116.203550349686	0.4664496503135
46	117.07	116.610070951771	0.459929048229178
47	116.92	116.780696189375	0.139303810625211
48	117	116.981292480070	0.0187075199301697
49	117.02	117.240799489274	-0.220799489273832
50	117.35	117.441395779969	-0.0913957799688716
51	117.36	117.624422832645	-0.264422832645006
52	117.82	118.096606081178	-0.276606081177636
53	117.88	118.277566164675	-0.39756616467508
54	118.24	118.502966085514	-0.262966085514448
55	118.5	118.675658292297	-0.175658292297111
56	118.8	118.846283529901	-0.0462835299010896
57	119.76	119.679767074735	0.08023292526498
58	120.09	119.850392312339	0.239607687661003

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.00109593735626365	0.00219187471252729	0.998904062643736
10	0.0553500914830609	0.110700182966122	0.94464990851694
11	0.0331771019266029	0.0663542038532058	0.966822898073397
12	0.183718036959414	0.367436073918829	0.816281963040586
13	0.109250944504299	0.218501889008599	0.8907490554957
14	0.0605769691129721	0.121153938225944	0.939423030887028
15	0.0498720843265786	0.0997441686531572	0.950127915673421
16	0.0290554067796489	0.0581108135592978	0.97094459322035
17	0.0150913922250498	0.0301827844500997	0.98490860777495
18	0.0114012686273410	0.0228025372546821	0.98859873137266
19	0.0119224336388372	0.0238448672776743	0.988077566361163
20	0.127630714012922	0.255261428025844	0.872369285987078
21	0.160502101770877	0.321004203541754	0.839497898229123
22	0.149828076179372	0.299656152358744	0.850171923820628
23	0.226253080228868	0.452506160457736	0.773746919771132
24	0.357585726756749	0.715171453513499	0.642414273243251
25	0.396793864266613	0.793587728533225	0.603206135733387
26	0.592131030882523	0.815737938234954	0.407868969117477
27	0.597196025363354	0.805607949273291	0.402803974636646
28	0.515568231568687	0.968863536862625	0.484431768431313
29	0.444367753427589	0.888735506855179	0.555632246572411
30	0.393306430740601	0.786612861481201	0.6066935692594
31	0.383066266836415	0.76613253367283	0.616933733163585
32	0.369814098952136	0.739628197904271	0.630185901047864
33	0.575548341902918	0.848903316194164	0.424451658097082
34	0.503987930705755	0.99202413858849	0.496012069294245
35	0.484630818052315	0.96926163610463	0.515369181947685
36	0.644299460364117	0.711401079271767	0.355700539635883
37	0.737512866913324	0.524974266173351	0.262487133086676
38	0.67258861186163	0.65482277627674	0.32741138813837
39	0.661045021888071	0.677909956223857	0.338954978111929
40	0.654183117502891	0.691633764994218	0.345816882497109
41	0.95565568059338	0.0886886388132386	0.0443443194066193
42	0.957213445447128	0.0855731091057442	0.0427865545528721
43	0.955302179180063	0.089395641639874	0.044697820819937
44	0.946516296615344	0.106967406769312	0.0534837033846558
45	0.919096359337591	0.161807281324818	0.0809036406624088
46	0.989023991396503	0.0219520172069931	0.0109760086034966
47	0.98304120821809	0.0339175835638194	0.0169587917819097
48	0.954322767284872	0.0913544654302553	0.0456772327151276
49	0.897117813399803	0.205764373200393	0.102882186600197

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.00109593735626365 & 0.00219187471252729 & 0.998904062643736 \tabularnewline
10 & 0.0553500914830609 & 0.110700182966122 & 0.94464990851694 \tabularnewline
11 & 0.0331771019266029 & 0.0663542038532058 & 0.966822898073397 \tabularnewline
12 & 0.183718036959414 & 0.367436073918829 & 0.816281963040586 \tabularnewline
13 & 0.109250944504299 & 0.218501889008599 & 0.8907490554957 \tabularnewline
14 & 0.0605769691129721 & 0.121153938225944 & 0.939423030887028 \tabularnewline
15 & 0.0498720843265786 & 0.0997441686531572 & 0.950127915673421 \tabularnewline
16 & 0.0290554067796489 & 0.0581108135592978 & 0.97094459322035 \tabularnewline
17 & 0.0150913922250498 & 0.0301827844500997 & 0.98490860777495 \tabularnewline
18 & 0.0114012686273410 & 0.0228025372546821 & 0.98859873137266 \tabularnewline
19 & 0.0119224336388372 & 0.0238448672776743 & 0.988077566361163 \tabularnewline
20 & 0.127630714012922 & 0.255261428025844 & 0.872369285987078 \tabularnewline
21 & 0.160502101770877 & 0.321004203541754 & 0.839497898229123 \tabularnewline
22 & 0.149828076179372 & 0.299656152358744 & 0.850171923820628 \tabularnewline
23 & 0.226253080228868 & 0.452506160457736 & 0.773746919771132 \tabularnewline
24 & 0.357585726756749 & 0.715171453513499 & 0.642414273243251 \tabularnewline
25 & 0.396793864266613 & 0.793587728533225 & 0.603206135733387 \tabularnewline
26 & 0.592131030882523 & 0.815737938234954 & 0.407868969117477 \tabularnewline
27 & 0.597196025363354 & 0.805607949273291 & 0.402803974636646 \tabularnewline
28 & 0.515568231568687 & 0.968863536862625 & 0.484431768431313 \tabularnewline
29 & 0.444367753427589 & 0.888735506855179 & 0.555632246572411 \tabularnewline
30 & 0.393306430740601 & 0.786612861481201 & 0.6066935692594 \tabularnewline
31 & 0.383066266836415 & 0.76613253367283 & 0.616933733163585 \tabularnewline
32 & 0.369814098952136 & 0.739628197904271 & 0.630185901047864 \tabularnewline
33 & 0.575548341902918 & 0.848903316194164 & 0.424451658097082 \tabularnewline
34 & 0.503987930705755 & 0.99202413858849 & 0.496012069294245 \tabularnewline
35 & 0.484630818052315 & 0.96926163610463 & 0.515369181947685 \tabularnewline
36 & 0.644299460364117 & 0.711401079271767 & 0.355700539635883 \tabularnewline
37 & 0.737512866913324 & 0.524974266173351 & 0.262487133086676 \tabularnewline
38 & 0.67258861186163 & 0.65482277627674 & 0.32741138813837 \tabularnewline
39 & 0.661045021888071 & 0.677909956223857 & 0.338954978111929 \tabularnewline
40 & 0.654183117502891 & 0.691633764994218 & 0.345816882497109 \tabularnewline
41 & 0.95565568059338 & 0.0886886388132386 & 0.0443443194066193 \tabularnewline
42 & 0.957213445447128 & 0.0855731091057442 & 0.0427865545528721 \tabularnewline
43 & 0.955302179180063 & 0.089395641639874 & 0.044697820819937 \tabularnewline
44 & 0.946516296615344 & 0.106967406769312 & 0.0534837033846558 \tabularnewline
45 & 0.919096359337591 & 0.161807281324818 & 0.0809036406624088 \tabularnewline
46 & 0.989023991396503 & 0.0219520172069931 & 0.0109760086034966 \tabularnewline
47 & 0.98304120821809 & 0.0339175835638194 & 0.0169587917819097 \tabularnewline
48 & 0.954322767284872 & 0.0913544654302553 & 0.0456772327151276 \tabularnewline
49 & 0.897117813399803 & 0.205764373200393 & 0.102882186600197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&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]9[/C][C]0.00109593735626365[/C][C]0.00219187471252729[/C][C]0.998904062643736[/C][/ROW]
[ROW][C]10[/C][C]0.0553500914830609[/C][C]0.110700182966122[/C][C]0.94464990851694[/C][/ROW]
[ROW][C]11[/C][C]0.0331771019266029[/C][C]0.0663542038532058[/C][C]0.966822898073397[/C][/ROW]
[ROW][C]12[/C][C]0.183718036959414[/C][C]0.367436073918829[/C][C]0.816281963040586[/C][/ROW]
[ROW][C]13[/C][C]0.109250944504299[/C][C]0.218501889008599[/C][C]0.8907490554957[/C][/ROW]
[ROW][C]14[/C][C]0.0605769691129721[/C][C]0.121153938225944[/C][C]0.939423030887028[/C][/ROW]
[ROW][C]15[/C][C]0.0498720843265786[/C][C]0.0997441686531572[/C][C]0.950127915673421[/C][/ROW]
[ROW][C]16[/C][C]0.0290554067796489[/C][C]0.0581108135592978[/C][C]0.97094459322035[/C][/ROW]
[ROW][C]17[/C][C]0.0150913922250498[/C][C]0.0301827844500997[/C][C]0.98490860777495[/C][/ROW]
[ROW][C]18[/C][C]0.0114012686273410[/C][C]0.0228025372546821[/C][C]0.98859873137266[/C][/ROW]
[ROW][C]19[/C][C]0.0119224336388372[/C][C]0.0238448672776743[/C][C]0.988077566361163[/C][/ROW]
[ROW][C]20[/C][C]0.127630714012922[/C][C]0.255261428025844[/C][C]0.872369285987078[/C][/ROW]
[ROW][C]21[/C][C]0.160502101770877[/C][C]0.321004203541754[/C][C]0.839497898229123[/C][/ROW]
[ROW][C]22[/C][C]0.149828076179372[/C][C]0.299656152358744[/C][C]0.850171923820628[/C][/ROW]
[ROW][C]23[/C][C]0.226253080228868[/C][C]0.452506160457736[/C][C]0.773746919771132[/C][/ROW]
[ROW][C]24[/C][C]0.357585726756749[/C][C]0.715171453513499[/C][C]0.642414273243251[/C][/ROW]
[ROW][C]25[/C][C]0.396793864266613[/C][C]0.793587728533225[/C][C]0.603206135733387[/C][/ROW]
[ROW][C]26[/C][C]0.592131030882523[/C][C]0.815737938234954[/C][C]0.407868969117477[/C][/ROW]
[ROW][C]27[/C][C]0.597196025363354[/C][C]0.805607949273291[/C][C]0.402803974636646[/C][/ROW]
[ROW][C]28[/C][C]0.515568231568687[/C][C]0.968863536862625[/C][C]0.484431768431313[/C][/ROW]
[ROW][C]29[/C][C]0.444367753427589[/C][C]0.888735506855179[/C][C]0.555632246572411[/C][/ROW]
[ROW][C]30[/C][C]0.393306430740601[/C][C]0.786612861481201[/C][C]0.6066935692594[/C][/ROW]
[ROW][C]31[/C][C]0.383066266836415[/C][C]0.76613253367283[/C][C]0.616933733163585[/C][/ROW]
[ROW][C]32[/C][C]0.369814098952136[/C][C]0.739628197904271[/C][C]0.630185901047864[/C][/ROW]
[ROW][C]33[/C][C]0.575548341902918[/C][C]0.848903316194164[/C][C]0.424451658097082[/C][/ROW]
[ROW][C]34[/C][C]0.503987930705755[/C][C]0.99202413858849[/C][C]0.496012069294245[/C][/ROW]
[ROW][C]35[/C][C]0.484630818052315[/C][C]0.96926163610463[/C][C]0.515369181947685[/C][/ROW]
[ROW][C]36[/C][C]0.644299460364117[/C][C]0.711401079271767[/C][C]0.355700539635883[/C][/ROW]
[ROW][C]37[/C][C]0.737512866913324[/C][C]0.524974266173351[/C][C]0.262487133086676[/C][/ROW]
[ROW][C]38[/C][C]0.67258861186163[/C][C]0.65482277627674[/C][C]0.32741138813837[/C][/ROW]
[ROW][C]39[/C][C]0.661045021888071[/C][C]0.677909956223857[/C][C]0.338954978111929[/C][/ROW]
[ROW][C]40[/C][C]0.654183117502891[/C][C]0.691633764994218[/C][C]0.345816882497109[/C][/ROW]
[ROW][C]41[/C][C]0.95565568059338[/C][C]0.0886886388132386[/C][C]0.0443443194066193[/C][/ROW]
[ROW][C]42[/C][C]0.957213445447128[/C][C]0.0855731091057442[/C][C]0.0427865545528721[/C][/ROW]
[ROW][C]43[/C][C]0.955302179180063[/C][C]0.089395641639874[/C][C]0.044697820819937[/C][/ROW]
[ROW][C]44[/C][C]0.946516296615344[/C][C]0.106967406769312[/C][C]0.0534837033846558[/C][/ROW]
[ROW][C]45[/C][C]0.919096359337591[/C][C]0.161807281324818[/C][C]0.0809036406624088[/C][/ROW]
[ROW][C]46[/C][C]0.989023991396503[/C][C]0.0219520172069931[/C][C]0.0109760086034966[/C][/ROW]
[ROW][C]47[/C][C]0.98304120821809[/C][C]0.0339175835638194[/C][C]0.0169587917819097[/C][/ROW]
[ROW][C]48[/C][C]0.954322767284872[/C][C]0.0913544654302553[/C][C]0.0456772327151276[/C][/ROW]
[ROW][C]49[/C][C]0.897117813399803[/C][C]0.205764373200393[/C][C]0.102882186600197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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
9	0.00109593735626365	0.00219187471252729	0.998904062643736
10	0.0553500914830609	0.110700182966122	0.94464990851694
11	0.0331771019266029	0.0663542038532058	0.966822898073397
12	0.183718036959414	0.367436073918829	0.816281963040586
13	0.109250944504299	0.218501889008599	0.8907490554957
14	0.0605769691129721	0.121153938225944	0.939423030887028
15	0.0498720843265786	0.0997441686531572	0.950127915673421
16	0.0290554067796489	0.0581108135592978	0.97094459322035
17	0.0150913922250498	0.0301827844500997	0.98490860777495
18	0.0114012686273410	0.0228025372546821	0.98859873137266
19	0.0119224336388372	0.0238448672776743	0.988077566361163
20	0.127630714012922	0.255261428025844	0.872369285987078
21	0.160502101770877	0.321004203541754	0.839497898229123
22	0.149828076179372	0.299656152358744	0.850171923820628
23	0.226253080228868	0.452506160457736	0.773746919771132
24	0.357585726756749	0.715171453513499	0.642414273243251
25	0.396793864266613	0.793587728533225	0.603206135733387
26	0.592131030882523	0.815737938234954	0.407868969117477
27	0.597196025363354	0.805607949273291	0.402803974636646
28	0.515568231568687	0.968863536862625	0.484431768431313
29	0.444367753427589	0.888735506855179	0.555632246572411
30	0.393306430740601	0.786612861481201	0.6066935692594
31	0.383066266836415	0.76613253367283	0.616933733163585
32	0.369814098952136	0.739628197904271	0.630185901047864
33	0.575548341902918	0.848903316194164	0.424451658097082
34	0.503987930705755	0.99202413858849	0.496012069294245
35	0.484630818052315	0.96926163610463	0.515369181947685
36	0.644299460364117	0.711401079271767	0.355700539635883
37	0.737512866913324	0.524974266173351	0.262487133086676
38	0.67258861186163	0.65482277627674	0.32741138813837
39	0.661045021888071	0.677909956223857	0.338954978111929
40	0.654183117502891	0.691633764994218	0.345816882497109
41	0.95565568059338	0.0886886388132386	0.0443443194066193
42	0.957213445447128	0.0855731091057442	0.0427865545528721
43	0.955302179180063	0.089395641639874	0.044697820819937
44	0.946516296615344	0.106967406769312	0.0534837033846558
45	0.919096359337591	0.161807281324818	0.0809036406624088
46	0.989023991396503	0.0219520172069931	0.0109760086034966
47	0.98304120821809	0.0339175835638194	0.0169587917819097
48	0.954322767284872	0.0913544654302553	0.0456772327151276
49	0.897117813399803	0.205764373200393	0.102882186600197

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	1	0.024390243902439	NOK
5% type I error level	6	0.146341463414634	NOK
10% type I error level	13	0.317073170731707	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 & 1 & 0.024390243902439 & NOK \tabularnewline
5% type I error level & 6 & 0.146341463414634 & NOK \tabularnewline
10% type I error level & 13 & 0.317073170731707 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97995&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]1[/C][C]0.024390243902439[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.146341463414634[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.317073170731707[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97995&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97995&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	1	0.024390243902439	NOK
5% type I error level	6	0.146341463414634	NOK
10% type I error level	13	0.317073170731707	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 5 ; par2 = Do not include Seasonal Dummies ; par3 = 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