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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 20 Nov 2011 05:21:37 -0500

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/20/t1321784616vidjl2c5g5l6j0g.htm/, Retrieved Wed, 24 Apr 2024 07:44:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145560, Retrieved Wed, 24 Apr 2024 07:44:18 +0000

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Estimated Impact

152

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

-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD    [Multiple Regression] [] [2011-11-20 10:21:37] [43f1c1fe5c2aaa4d7bd6f731e1a494da] [Current]

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Dataseries X:

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96	6.08	54.7	1914	1005	2
89	5.73	54.2	1684	963	2
87	6.22	53	1902	1035	2
87	5.8	52.9	1860	1027	2
101	7.99	57.8	2264	1281	2
103	8.42	56.9	2216	1272	2
103	7.44	56.6	1866	1051	2
96	6.84	55.3	1850	1079	2
127	6.48	53.1	1743	1034	2
126	6.43	54.8	1709	1070	2
101	7.99	57.2	1689	1173	1
96	8.76	57.2	1806	1079	1
93	6.32	57.2	2136	1067	1
88	6.32	57.2	2018	1104	1
94	7.6	55.8	1966	1347	1
85	7.62	57.2	2154	1439	1
97	6.03	57.2	1767	1029	1
114	6.59	56.5	1827	1100	1
113	7.52	59.2	1773	1204	1
124	7.67	58.5	1971	1160	1
129	7.57	57.3	2067	1401	1
110	6.45	53.7	2221	1142	1
102	7.99	56.6	2151	1288	1
134	8.43	57.5	2018	979	1
119	7.02	55.5	1677	1104	2
139	5.21	55.7	2126	956	2
75	6.21	53.1	1841	1153	1
138	5.39	55.9	2062	1001	2
132	5.59	57.8	1809	1230	1
122	7.72	59	2326	1014	2
102	6.69	58.4	1871	1287	1
78	5.96	55.4	2108	1198	2
119	8.49	59.5	1753	1125	2
136	6.64	53	1942	1142	1
109	5.23	54.6	1661	1379	2
85	6.2	58.4	1782	1148	2
119	7.36	58.2	2206	1318	2
136	6.67	53.2	1710	1041	2
72	6.36	54.2	2114	1253	2
125	7.43	53.8	2339	1264	1
87	8.41	53.8	2310	953	1
106	7.15	57.3	2012	1049	1
99	5.36	53	2028	1392	2
123	7.39	52.1	1728	1135	1
99	5.63	52.7	2107	1450	1
88	8.47	55.5	1965	958	1
97	7.75	57.8	2113	1209	2
119	8.33	55.4	1917	1441	2
77	6	57.9	2036	994	1
128	5.45	55.2	1869	1149	1
100	8.28	58.5	1858	1204	1
116	5.6	53.4	2145	1414	2
76	7.38	58.6	2009	1339	2
76	7.99	53.5	2249	1255	1
100	6.83	53.3	1949	1189	2
105	5.64	53.4	2058	1298	2
120	8.43	57.2	1953	1167	2
97	7.38	54.2	2089	1290	2
95	6.55	55.7	2254	1057	2
101	5.71	59.2	1973	1018	1

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	15 seconds
R Server	'AstonUniversity' @ aston.wessa.net
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 & 15 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \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=145560&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]15 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/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=145560&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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	15 seconds
R Server	'AstonUniversity' @ aston.wessa.net
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
IQ[t] = + 118.482164017296 + 0.181842599683854CCMIDSA[t] + 0.2880589720523HC[t] -0.0136404465839835TOTSA[t] -0.00461933057322705TOTVOL[t] + 1.09946804277519`SEX `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
IQ[t] =  +  118.482164017296 +  0.181842599683854CCMIDSA[t] +  0.2880589720523HC[t] -0.0136404465839835TOTSA[t] -0.00461933057322705TOTVOL[t] +  1.09946804277519`SEX
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]IQ[t] =  +  118.482164017296 +  0.181842599683854CCMIDSA[t] +  0.2880589720523HC[t] -0.0136404465839835TOTSA[t] -0.00461933057322705TOTVOL[t] +  1.09946804277519`SEX
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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
IQ[t] = + 118.482164017296 + 0.181842599683854CCMIDSA[t] + 0.2880589720523HC[t] -0.0136404465839835TOTSA[t] -0.00461933057322705TOTVOL[t] + 1.09946804277519`SEX `[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)	118.482164017296	75.511825	1.5691	0.122477	0.061239
CCMIDSA	0.181842599683854	2.619733	0.0694	0.944918	0.472459
HC	0.2880589720523	1.267239	0.2273	0.821039	0.41052
TOTSA	-0.0136404465839835	0.013631	-1.0007	0.321437	0.160718
TOTVOL	-0.00461933057322705	0.01772	-0.2607	0.795325	0.397663
`SEX `	1.09946804277519	4.935794	0.2228	0.824567	0.412284

\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) & 118.482164017296 & 75.511825 & 1.5691 & 0.122477 & 0.061239 \tabularnewline
CCMIDSA & 0.181842599683854 & 2.619733 & 0.0694 & 0.944918 & 0.472459 \tabularnewline
HC & 0.2880589720523 & 1.267239 & 0.2273 & 0.821039 & 0.41052 \tabularnewline
TOTSA & -0.0136404465839835 & 0.013631 & -1.0007 & 0.321437 & 0.160718 \tabularnewline
TOTVOL & -0.00461933057322705 & 0.01772 & -0.2607 & 0.795325 & 0.397663 \tabularnewline
`SEX
` & 1.09946804277519 & 4.935794 & 0.2228 & 0.824567 & 0.412284 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&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]118.482164017296[/C][C]75.511825[/C][C]1.5691[/C][C]0.122477[/C][C]0.061239[/C][/ROW]
[ROW][C]CCMIDSA[/C][C]0.181842599683854[/C][C]2.619733[/C][C]0.0694[/C][C]0.944918[/C][C]0.472459[/C][/ROW]
[ROW][C]HC[/C][C]0.2880589720523[/C][C]1.267239[/C][C]0.2273[/C][C]0.821039[/C][C]0.41052[/C][/ROW]
[ROW][C]TOTSA[/C][C]-0.0136404465839835[/C][C]0.013631[/C][C]-1.0007[/C][C]0.321437[/C][C]0.160718[/C][/ROW]
[ROW][C]TOTVOL[/C][C]-0.00461933057322705[/C][C]0.01772[/C][C]-0.2607[/C][C]0.795325[/C][C]0.397663[/C][/ROW]
[ROW][C]`SEX
`[/C][C]1.09946804277519[/C][C]4.935794[/C][C]0.2228[/C][C]0.824567[/C][C]0.412284[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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)	118.482164017296	75.511825	1.5691	0.122477	0.061239
CCMIDSA	0.181842599683854	2.619733	0.0694	0.944918	0.472459
HC	0.2880589720523	1.267239	0.2273	0.821039	0.41052
TOTSA	-0.0136404465839835	0.013631	-1.0007	0.321437	0.160718
TOTVOL	-0.00461933057322705	0.01772	-0.2607	0.795325	0.397663
`SEX `	1.09946804277519	4.935794	0.2228	0.824567	0.412284

Multiple Linear Regression - Regression Statistics
Multiple R	0.156351358846663
R-squared	0.0244457474131979
Adjusted R-squared	-0.065883350048543
F-TEST (value)	0.270629820291871
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0.927229767110673
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	18.6447987549234
Sum Squared Residuals	18771.9401130261

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.156351358846663 \tabularnewline
R-squared & 0.0244457474131979 \tabularnewline
Adjusted R-squared & -0.065883350048543 \tabularnewline
F-TEST (value) & 0.270629820291871 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0.927229767110673 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 18.6447987549234 \tabularnewline
Sum Squared Residuals & 18771.9401130261 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.156351358846663[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0244457474131979[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.065883350048543[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.270629820291871[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/C][/ROW]
[ROW][C]p-value[/C][C]0.927229767110673[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]18.6447987549234[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]18771.9401130261[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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	0.156351358846663
R-squared	0.0244457474131979
Adjusted R-squared	-0.065883350048543
F-TEST (value)	0.270629820291871
F-TEST (DF numerator)	5
F-TEST (DF denominator)	54
p-value	0.927229767110673
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	18.6447987549234
Sum Squared Residuals	18771.9401130261

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	96	106.793286892347	-10.7932868923473
2	89	109.916927094823	-20.9169270948234
3	87	106.354150045625	-19.354150045625
4	87	106.858823657666	-19.8588236576656
5	101	101.984497528501	-0.984497528500557
6	103	102.499752182708	0.500247817292202
7	103	108.030157104479	-5.03015710447932
8	96	107.635480770294	-11.6354807702944
9	127	108.603685356175	18.3963146438254
10	126	109.381772761899	16.6182272381014
11	101	109.054338590193	-8.054338590193
12	96	108.032642215507	-12.0326422155068
13	93	103.143030866442	-10.1430308664424
14	88	104.581688332143	-16.5816883321431
15	94	103.997970191938	-9.99797019193814
16	85	101.415507234279	-16.4155072342793
17	97	108.299155863807	-11.2991558638066
18	114	107.052947173455	6.94705282654515
19	113	108.255993751622	4.74400624837847
20	124	105.584070982731	18.4159290172692
21	129	102.797474416089	26.2025255839105
22	110	100.652576249588	9.34742375041235
23	102	102.04839386924	-0.0483938692401484
24	134	105.629210230745	28.3707897692549
25	119	109.970138227346	9.0298617726536
26	139	104.257715324958	34.7422846750419
27	75	105.56865570804	-30.5686557080404
28	138	105.013177492891	32.9868225071086
29	132	106.890596301431	25.1094036985688
30	122	102.668724367893	19.3312756321067
31	102	106.154449013434	-4.15444901343387
32	78	103.435329622896	-25.4353296228961
33	119	110.25600285467	8.74399714532964
34	136	104.291169660022	31.7088303399777
35	109	108.333318136772	0.666681863228466
36	85	109.020900878017	-24.0209008780171
37	119	102.605390950182	16.3946090498177
38	136	109.084840770579	26.9151592294214
39	72	102.826490035275	-30.8264900352755
40	125	98.6864568676393	26.3135431323607
41	87	100.696847374539	-13.6968473745386
42	106	105.097329448117	0.902670551882716
43	99	102.829968125673	-3.82996812567288
44	123	107.119689417923	15.8803105820768
45	99	100.347663439815	-1.34766343981477
46	88	105.880315601617	-17.8803156016167
47	97	104.33315454003	-7.33315454003028
48	119	105.349124552393	13.6508754476065
49	77	104.987738305224	-27.9877383052241
50	128	105.671923991532	22.3280760084682
51	100	107.033114887306	-7.03311488730606
52	116	101.291276415481	14.7087235845191
53	76	105.314413426004	-29.3144134260039
54	76	99.971085199564	-23.9710851995641
55	100	105.199013825324	-5.19901382532362
56	105	103.021111318769	1.97888868123088
57	120	106.660455462097	13.3395445379032
58	97	103.182065420343	-6.18206542034319
59	95	102.288854857889	-7.28885485788868
60	101	106.057964816017	-5.0579648160173

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 96 & 106.793286892347 & -10.7932868923473 \tabularnewline
2 & 89 & 109.916927094823 & -20.9169270948234 \tabularnewline
3 & 87 & 106.354150045625 & -19.354150045625 \tabularnewline
4 & 87 & 106.858823657666 & -19.8588236576656 \tabularnewline
5 & 101 & 101.984497528501 & -0.984497528500557 \tabularnewline
6 & 103 & 102.499752182708 & 0.500247817292202 \tabularnewline
7 & 103 & 108.030157104479 & -5.03015710447932 \tabularnewline
8 & 96 & 107.635480770294 & -11.6354807702944 \tabularnewline
9 & 127 & 108.603685356175 & 18.3963146438254 \tabularnewline
10 & 126 & 109.381772761899 & 16.6182272381014 \tabularnewline
11 & 101 & 109.054338590193 & -8.054338590193 \tabularnewline
12 & 96 & 108.032642215507 & -12.0326422155068 \tabularnewline
13 & 93 & 103.143030866442 & -10.1430308664424 \tabularnewline
14 & 88 & 104.581688332143 & -16.5816883321431 \tabularnewline
15 & 94 & 103.997970191938 & -9.99797019193814 \tabularnewline
16 & 85 & 101.415507234279 & -16.4155072342793 \tabularnewline
17 & 97 & 108.299155863807 & -11.2991558638066 \tabularnewline
18 & 114 & 107.052947173455 & 6.94705282654515 \tabularnewline
19 & 113 & 108.255993751622 & 4.74400624837847 \tabularnewline
20 & 124 & 105.584070982731 & 18.4159290172692 \tabularnewline
21 & 129 & 102.797474416089 & 26.2025255839105 \tabularnewline
22 & 110 & 100.652576249588 & 9.34742375041235 \tabularnewline
23 & 102 & 102.04839386924 & -0.0483938692401484 \tabularnewline
24 & 134 & 105.629210230745 & 28.3707897692549 \tabularnewline
25 & 119 & 109.970138227346 & 9.0298617726536 \tabularnewline
26 & 139 & 104.257715324958 & 34.7422846750419 \tabularnewline
27 & 75 & 105.56865570804 & -30.5686557080404 \tabularnewline
28 & 138 & 105.013177492891 & 32.9868225071086 \tabularnewline
29 & 132 & 106.890596301431 & 25.1094036985688 \tabularnewline
30 & 122 & 102.668724367893 & 19.3312756321067 \tabularnewline
31 & 102 & 106.154449013434 & -4.15444901343387 \tabularnewline
32 & 78 & 103.435329622896 & -25.4353296228961 \tabularnewline
33 & 119 & 110.25600285467 & 8.74399714532964 \tabularnewline
34 & 136 & 104.291169660022 & 31.7088303399777 \tabularnewline
35 & 109 & 108.333318136772 & 0.666681863228466 \tabularnewline
36 & 85 & 109.020900878017 & -24.0209008780171 \tabularnewline
37 & 119 & 102.605390950182 & 16.3946090498177 \tabularnewline
38 & 136 & 109.084840770579 & 26.9151592294214 \tabularnewline
39 & 72 & 102.826490035275 & -30.8264900352755 \tabularnewline
40 & 125 & 98.6864568676393 & 26.3135431323607 \tabularnewline
41 & 87 & 100.696847374539 & -13.6968473745386 \tabularnewline
42 & 106 & 105.097329448117 & 0.902670551882716 \tabularnewline
43 & 99 & 102.829968125673 & -3.82996812567288 \tabularnewline
44 & 123 & 107.119689417923 & 15.8803105820768 \tabularnewline
45 & 99 & 100.347663439815 & -1.34766343981477 \tabularnewline
46 & 88 & 105.880315601617 & -17.8803156016167 \tabularnewline
47 & 97 & 104.33315454003 & -7.33315454003028 \tabularnewline
48 & 119 & 105.349124552393 & 13.6508754476065 \tabularnewline
49 & 77 & 104.987738305224 & -27.9877383052241 \tabularnewline
50 & 128 & 105.671923991532 & 22.3280760084682 \tabularnewline
51 & 100 & 107.033114887306 & -7.03311488730606 \tabularnewline
52 & 116 & 101.291276415481 & 14.7087235845191 \tabularnewline
53 & 76 & 105.314413426004 & -29.3144134260039 \tabularnewline
54 & 76 & 99.971085199564 & -23.9710851995641 \tabularnewline
55 & 100 & 105.199013825324 & -5.19901382532362 \tabularnewline
56 & 105 & 103.021111318769 & 1.97888868123088 \tabularnewline
57 & 120 & 106.660455462097 & 13.3395445379032 \tabularnewline
58 & 97 & 103.182065420343 & -6.18206542034319 \tabularnewline
59 & 95 & 102.288854857889 & -7.28885485788868 \tabularnewline
60 & 101 & 106.057964816017 & -5.0579648160173 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&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]96[/C][C]106.793286892347[/C][C]-10.7932868923473[/C][/ROW]
[ROW][C]2[/C][C]89[/C][C]109.916927094823[/C][C]-20.9169270948234[/C][/ROW]
[ROW][C]3[/C][C]87[/C][C]106.354150045625[/C][C]-19.354150045625[/C][/ROW]
[ROW][C]4[/C][C]87[/C][C]106.858823657666[/C][C]-19.8588236576656[/C][/ROW]
[ROW][C]5[/C][C]101[/C][C]101.984497528501[/C][C]-0.984497528500557[/C][/ROW]
[ROW][C]6[/C][C]103[/C][C]102.499752182708[/C][C]0.500247817292202[/C][/ROW]
[ROW][C]7[/C][C]103[/C][C]108.030157104479[/C][C]-5.03015710447932[/C][/ROW]
[ROW][C]8[/C][C]96[/C][C]107.635480770294[/C][C]-11.6354807702944[/C][/ROW]
[ROW][C]9[/C][C]127[/C][C]108.603685356175[/C][C]18.3963146438254[/C][/ROW]
[ROW][C]10[/C][C]126[/C][C]109.381772761899[/C][C]16.6182272381014[/C][/ROW]
[ROW][C]11[/C][C]101[/C][C]109.054338590193[/C][C]-8.054338590193[/C][/ROW]
[ROW][C]12[/C][C]96[/C][C]108.032642215507[/C][C]-12.0326422155068[/C][/ROW]
[ROW][C]13[/C][C]93[/C][C]103.143030866442[/C][C]-10.1430308664424[/C][/ROW]
[ROW][C]14[/C][C]88[/C][C]104.581688332143[/C][C]-16.5816883321431[/C][/ROW]
[ROW][C]15[/C][C]94[/C][C]103.997970191938[/C][C]-9.99797019193814[/C][/ROW]
[ROW][C]16[/C][C]85[/C][C]101.415507234279[/C][C]-16.4155072342793[/C][/ROW]
[ROW][C]17[/C][C]97[/C][C]108.299155863807[/C][C]-11.2991558638066[/C][/ROW]
[ROW][C]18[/C][C]114[/C][C]107.052947173455[/C][C]6.94705282654515[/C][/ROW]
[ROW][C]19[/C][C]113[/C][C]108.255993751622[/C][C]4.74400624837847[/C][/ROW]
[ROW][C]20[/C][C]124[/C][C]105.584070982731[/C][C]18.4159290172692[/C][/ROW]
[ROW][C]21[/C][C]129[/C][C]102.797474416089[/C][C]26.2025255839105[/C][/ROW]
[ROW][C]22[/C][C]110[/C][C]100.652576249588[/C][C]9.34742375041235[/C][/ROW]
[ROW][C]23[/C][C]102[/C][C]102.04839386924[/C][C]-0.0483938692401484[/C][/ROW]
[ROW][C]24[/C][C]134[/C][C]105.629210230745[/C][C]28.3707897692549[/C][/ROW]
[ROW][C]25[/C][C]119[/C][C]109.970138227346[/C][C]9.0298617726536[/C][/ROW]
[ROW][C]26[/C][C]139[/C][C]104.257715324958[/C][C]34.7422846750419[/C][/ROW]
[ROW][C]27[/C][C]75[/C][C]105.56865570804[/C][C]-30.5686557080404[/C][/ROW]
[ROW][C]28[/C][C]138[/C][C]105.013177492891[/C][C]32.9868225071086[/C][/ROW]
[ROW][C]29[/C][C]132[/C][C]106.890596301431[/C][C]25.1094036985688[/C][/ROW]
[ROW][C]30[/C][C]122[/C][C]102.668724367893[/C][C]19.3312756321067[/C][/ROW]
[ROW][C]31[/C][C]102[/C][C]106.154449013434[/C][C]-4.15444901343387[/C][/ROW]
[ROW][C]32[/C][C]78[/C][C]103.435329622896[/C][C]-25.4353296228961[/C][/ROW]
[ROW][C]33[/C][C]119[/C][C]110.25600285467[/C][C]8.74399714532964[/C][/ROW]
[ROW][C]34[/C][C]136[/C][C]104.291169660022[/C][C]31.7088303399777[/C][/ROW]
[ROW][C]35[/C][C]109[/C][C]108.333318136772[/C][C]0.666681863228466[/C][/ROW]
[ROW][C]36[/C][C]85[/C][C]109.020900878017[/C][C]-24.0209008780171[/C][/ROW]
[ROW][C]37[/C][C]119[/C][C]102.605390950182[/C][C]16.3946090498177[/C][/ROW]
[ROW][C]38[/C][C]136[/C][C]109.084840770579[/C][C]26.9151592294214[/C][/ROW]
[ROW][C]39[/C][C]72[/C][C]102.826490035275[/C][C]-30.8264900352755[/C][/ROW]
[ROW][C]40[/C][C]125[/C][C]98.6864568676393[/C][C]26.3135431323607[/C][/ROW]
[ROW][C]41[/C][C]87[/C][C]100.696847374539[/C][C]-13.6968473745386[/C][/ROW]
[ROW][C]42[/C][C]106[/C][C]105.097329448117[/C][C]0.902670551882716[/C][/ROW]
[ROW][C]43[/C][C]99[/C][C]102.829968125673[/C][C]-3.82996812567288[/C][/ROW]
[ROW][C]44[/C][C]123[/C][C]107.119689417923[/C][C]15.8803105820768[/C][/ROW]
[ROW][C]45[/C][C]99[/C][C]100.347663439815[/C][C]-1.34766343981477[/C][/ROW]
[ROW][C]46[/C][C]88[/C][C]105.880315601617[/C][C]-17.8803156016167[/C][/ROW]
[ROW][C]47[/C][C]97[/C][C]104.33315454003[/C][C]-7.33315454003028[/C][/ROW]
[ROW][C]48[/C][C]119[/C][C]105.349124552393[/C][C]13.6508754476065[/C][/ROW]
[ROW][C]49[/C][C]77[/C][C]104.987738305224[/C][C]-27.9877383052241[/C][/ROW]
[ROW][C]50[/C][C]128[/C][C]105.671923991532[/C][C]22.3280760084682[/C][/ROW]
[ROW][C]51[/C][C]100[/C][C]107.033114887306[/C][C]-7.03311488730606[/C][/ROW]
[ROW][C]52[/C][C]116[/C][C]101.291276415481[/C][C]14.7087235845191[/C][/ROW]
[ROW][C]53[/C][C]76[/C][C]105.314413426004[/C][C]-29.3144134260039[/C][/ROW]
[ROW][C]54[/C][C]76[/C][C]99.971085199564[/C][C]-23.9710851995641[/C][/ROW]
[ROW][C]55[/C][C]100[/C][C]105.199013825324[/C][C]-5.19901382532362[/C][/ROW]
[ROW][C]56[/C][C]105[/C][C]103.021111318769[/C][C]1.97888868123088[/C][/ROW]
[ROW][C]57[/C][C]120[/C][C]106.660455462097[/C][C]13.3395445379032[/C][/ROW]
[ROW][C]58[/C][C]97[/C][C]103.182065420343[/C][C]-6.18206542034319[/C][/ROW]
[ROW][C]59[/C][C]95[/C][C]102.288854857889[/C][C]-7.28885485788868[/C][/ROW]
[ROW][C]60[/C][C]101[/C][C]106.057964816017[/C][C]-5.0579648160173[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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	96	106.793286892347	-10.7932868923473
2	89	109.916927094823	-20.9169270948234
3	87	106.354150045625	-19.354150045625
4	87	106.858823657666	-19.8588236576656
5	101	101.984497528501	-0.984497528500557
6	103	102.499752182708	0.500247817292202
7	103	108.030157104479	-5.03015710447932
8	96	107.635480770294	-11.6354807702944
9	127	108.603685356175	18.3963146438254
10	126	109.381772761899	16.6182272381014
11	101	109.054338590193	-8.054338590193
12	96	108.032642215507	-12.0326422155068
13	93	103.143030866442	-10.1430308664424
14	88	104.581688332143	-16.5816883321431
15	94	103.997970191938	-9.99797019193814
16	85	101.415507234279	-16.4155072342793
17	97	108.299155863807	-11.2991558638066
18	114	107.052947173455	6.94705282654515
19	113	108.255993751622	4.74400624837847
20	124	105.584070982731	18.4159290172692
21	129	102.797474416089	26.2025255839105
22	110	100.652576249588	9.34742375041235
23	102	102.04839386924	-0.0483938692401484
24	134	105.629210230745	28.3707897692549
25	119	109.970138227346	9.0298617726536
26	139	104.257715324958	34.7422846750419
27	75	105.56865570804	-30.5686557080404
28	138	105.013177492891	32.9868225071086
29	132	106.890596301431	25.1094036985688
30	122	102.668724367893	19.3312756321067
31	102	106.154449013434	-4.15444901343387
32	78	103.435329622896	-25.4353296228961
33	119	110.25600285467	8.74399714532964
34	136	104.291169660022	31.7088303399777
35	109	108.333318136772	0.666681863228466
36	85	109.020900878017	-24.0209008780171
37	119	102.605390950182	16.3946090498177
38	136	109.084840770579	26.9151592294214
39	72	102.826490035275	-30.8264900352755
40	125	98.6864568676393	26.3135431323607
41	87	100.696847374539	-13.6968473745386
42	106	105.097329448117	0.902670551882716
43	99	102.829968125673	-3.82996812567288
44	123	107.119689417923	15.8803105820768
45	99	100.347663439815	-1.34766343981477
46	88	105.880315601617	-17.8803156016167
47	97	104.33315454003	-7.33315454003028
48	119	105.349124552393	13.6508754476065
49	77	104.987738305224	-27.9877383052241
50	128	105.671923991532	22.3280760084682
51	100	107.033114887306	-7.03311488730606
52	116	101.291276415481	14.7087235845191
53	76	105.314413426004	-29.3144134260039
54	76	99.971085199564	-23.9710851995641
55	100	105.199013825324	-5.19901382532362
56	105	103.021111318769	1.97888868123088
57	120	106.660455462097	13.3395445379032
58	97	103.182065420343	-6.18206542034319
59	95	102.288854857889	-7.28885485788868
60	101	106.057964816017	-5.0579648160173

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.377475459957822	0.754950919915644	0.622524540042178
10	0.275387570542074	0.550775141084149	0.724612429457926
11	0.157240981882068	0.314481963764136	0.842759018117932
12	0.0982970068560934	0.196594013712187	0.901702993143907
13	0.19932004249951	0.39864008499902	0.80067995750049
14	0.131520918625056	0.263041837250112	0.868479081374944
15	0.0883047705498836	0.176609541099767	0.911695229450116
16	0.0607962169143201	0.12159243382864	0.93920378308568
17	0.0366618523088635	0.073323704617727	0.963338147691137
18	0.0416130421479261	0.0832260842958523	0.958386957852074
19	0.0238347534708576	0.0476695069417152	0.976165246529142
20	0.03988365577381	0.07976731154762	0.96011634422619
21	0.0864602460265231	0.172920492053046	0.913539753973477
22	0.101714070097258	0.203428140194517	0.898285929902742
23	0.0673491471971966	0.134698294394393	0.932650852802803
24	0.106523808108599	0.213047616217197	0.893476191891401
25	0.0832389259263462	0.166477851852692	0.916761074073654
26	0.201441231543728	0.402882463087455	0.798558768456272
27	0.253837755930518	0.507675511861037	0.746162244069482
28	0.354111492085601	0.708222984171202	0.645888507914399
29	0.402763066459423	0.805526132918845	0.597236933540577
30	0.477678009765792	0.955356019531585	0.522321990234208
31	0.410193023616915	0.820386047233831	0.589806976383085
32	0.468128763504093	0.936257527008187	0.531871236495907
33	0.412742240735192	0.825484481470384	0.587257759264808
34	0.602490117735477	0.795019764529046	0.397509882264523
35	0.541558533474338	0.916882933051323	0.458441466525662
36	0.591889626975473	0.816220746049055	0.408110373024528
37	0.658131640096466	0.683736719807068	0.341868359903534
38	0.698844239288067	0.602311521423866	0.301155760711933
39	0.798859047755091	0.402281904489818	0.201140952244909
40	0.945243038704513	0.109513922590973	0.0547569612954867
41	0.933863941805388	0.132272116389225	0.0661360581946124
42	0.917665427543703	0.164669144912595	0.0823345724562974
43	0.9071263105852	0.185747378829601	0.0928736894148005
44	0.863916271838551	0.272167456322898	0.136083728161449
45	0.793919206897294	0.412161586205413	0.206080793102706
46	0.736045742781816	0.527908514436367	0.263954257218184
47	0.652630252431305	0.694739495137389	0.347369747568695
48	0.573010451999894	0.853979096000212	0.426989548000106
49	0.624877902119496	0.750244195761008	0.375122097880504
50	0.54058889516143	0.91882220967714	0.45941110483857
51	0.390085266582523	0.780170533165046	0.609914733417477

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.377475459957822 & 0.754950919915644 & 0.622524540042178 \tabularnewline
10 & 0.275387570542074 & 0.550775141084149 & 0.724612429457926 \tabularnewline
11 & 0.157240981882068 & 0.314481963764136 & 0.842759018117932 \tabularnewline
12 & 0.0982970068560934 & 0.196594013712187 & 0.901702993143907 \tabularnewline
13 & 0.19932004249951 & 0.39864008499902 & 0.80067995750049 \tabularnewline
14 & 0.131520918625056 & 0.263041837250112 & 0.868479081374944 \tabularnewline
15 & 0.0883047705498836 & 0.176609541099767 & 0.911695229450116 \tabularnewline
16 & 0.0607962169143201 & 0.12159243382864 & 0.93920378308568 \tabularnewline
17 & 0.0366618523088635 & 0.073323704617727 & 0.963338147691137 \tabularnewline
18 & 0.0416130421479261 & 0.0832260842958523 & 0.958386957852074 \tabularnewline
19 & 0.0238347534708576 & 0.0476695069417152 & 0.976165246529142 \tabularnewline
20 & 0.03988365577381 & 0.07976731154762 & 0.96011634422619 \tabularnewline
21 & 0.0864602460265231 & 0.172920492053046 & 0.913539753973477 \tabularnewline
22 & 0.101714070097258 & 0.203428140194517 & 0.898285929902742 \tabularnewline
23 & 0.0673491471971966 & 0.134698294394393 & 0.932650852802803 \tabularnewline
24 & 0.106523808108599 & 0.213047616217197 & 0.893476191891401 \tabularnewline
25 & 0.0832389259263462 & 0.166477851852692 & 0.916761074073654 \tabularnewline
26 & 0.201441231543728 & 0.402882463087455 & 0.798558768456272 \tabularnewline
27 & 0.253837755930518 & 0.507675511861037 & 0.746162244069482 \tabularnewline
28 & 0.354111492085601 & 0.708222984171202 & 0.645888507914399 \tabularnewline
29 & 0.402763066459423 & 0.805526132918845 & 0.597236933540577 \tabularnewline
30 & 0.477678009765792 & 0.955356019531585 & 0.522321990234208 \tabularnewline
31 & 0.410193023616915 & 0.820386047233831 & 0.589806976383085 \tabularnewline
32 & 0.468128763504093 & 0.936257527008187 & 0.531871236495907 \tabularnewline
33 & 0.412742240735192 & 0.825484481470384 & 0.587257759264808 \tabularnewline
34 & 0.602490117735477 & 0.795019764529046 & 0.397509882264523 \tabularnewline
35 & 0.541558533474338 & 0.916882933051323 & 0.458441466525662 \tabularnewline
36 & 0.591889626975473 & 0.816220746049055 & 0.408110373024528 \tabularnewline
37 & 0.658131640096466 & 0.683736719807068 & 0.341868359903534 \tabularnewline
38 & 0.698844239288067 & 0.602311521423866 & 0.301155760711933 \tabularnewline
39 & 0.798859047755091 & 0.402281904489818 & 0.201140952244909 \tabularnewline
40 & 0.945243038704513 & 0.109513922590973 & 0.0547569612954867 \tabularnewline
41 & 0.933863941805388 & 0.132272116389225 & 0.0661360581946124 \tabularnewline
42 & 0.917665427543703 & 0.164669144912595 & 0.0823345724562974 \tabularnewline
43 & 0.9071263105852 & 0.185747378829601 & 0.0928736894148005 \tabularnewline
44 & 0.863916271838551 & 0.272167456322898 & 0.136083728161449 \tabularnewline
45 & 0.793919206897294 & 0.412161586205413 & 0.206080793102706 \tabularnewline
46 & 0.736045742781816 & 0.527908514436367 & 0.263954257218184 \tabularnewline
47 & 0.652630252431305 & 0.694739495137389 & 0.347369747568695 \tabularnewline
48 & 0.573010451999894 & 0.853979096000212 & 0.426989548000106 \tabularnewline
49 & 0.624877902119496 & 0.750244195761008 & 0.375122097880504 \tabularnewline
50 & 0.54058889516143 & 0.91882220967714 & 0.45941110483857 \tabularnewline
51 & 0.390085266582523 & 0.780170533165046 & 0.609914733417477 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&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]0.377475459957822[/C][C]0.754950919915644[/C][C]0.622524540042178[/C][/ROW]
[ROW][C]10[/C][C]0.275387570542074[/C][C]0.550775141084149[/C][C]0.724612429457926[/C][/ROW]
[ROW][C]11[/C][C]0.157240981882068[/C][C]0.314481963764136[/C][C]0.842759018117932[/C][/ROW]
[ROW][C]12[/C][C]0.0982970068560934[/C][C]0.196594013712187[/C][C]0.901702993143907[/C][/ROW]
[ROW][C]13[/C][C]0.19932004249951[/C][C]0.39864008499902[/C][C]0.80067995750049[/C][/ROW]
[ROW][C]14[/C][C]0.131520918625056[/C][C]0.263041837250112[/C][C]0.868479081374944[/C][/ROW]
[ROW][C]15[/C][C]0.0883047705498836[/C][C]0.176609541099767[/C][C]0.911695229450116[/C][/ROW]
[ROW][C]16[/C][C]0.0607962169143201[/C][C]0.12159243382864[/C][C]0.93920378308568[/C][/ROW]
[ROW][C]17[/C][C]0.0366618523088635[/C][C]0.073323704617727[/C][C]0.963338147691137[/C][/ROW]
[ROW][C]18[/C][C]0.0416130421479261[/C][C]0.0832260842958523[/C][C]0.958386957852074[/C][/ROW]
[ROW][C]19[/C][C]0.0238347534708576[/C][C]0.0476695069417152[/C][C]0.976165246529142[/C][/ROW]
[ROW][C]20[/C][C]0.03988365577381[/C][C]0.07976731154762[/C][C]0.96011634422619[/C][/ROW]
[ROW][C]21[/C][C]0.0864602460265231[/C][C]0.172920492053046[/C][C]0.913539753973477[/C][/ROW]
[ROW][C]22[/C][C]0.101714070097258[/C][C]0.203428140194517[/C][C]0.898285929902742[/C][/ROW]
[ROW][C]23[/C][C]0.0673491471971966[/C][C]0.134698294394393[/C][C]0.932650852802803[/C][/ROW]
[ROW][C]24[/C][C]0.106523808108599[/C][C]0.213047616217197[/C][C]0.893476191891401[/C][/ROW]
[ROW][C]25[/C][C]0.0832389259263462[/C][C]0.166477851852692[/C][C]0.916761074073654[/C][/ROW]
[ROW][C]26[/C][C]0.201441231543728[/C][C]0.402882463087455[/C][C]0.798558768456272[/C][/ROW]
[ROW][C]27[/C][C]0.253837755930518[/C][C]0.507675511861037[/C][C]0.746162244069482[/C][/ROW]
[ROW][C]28[/C][C]0.354111492085601[/C][C]0.708222984171202[/C][C]0.645888507914399[/C][/ROW]
[ROW][C]29[/C][C]0.402763066459423[/C][C]0.805526132918845[/C][C]0.597236933540577[/C][/ROW]
[ROW][C]30[/C][C]0.477678009765792[/C][C]0.955356019531585[/C][C]0.522321990234208[/C][/ROW]
[ROW][C]31[/C][C]0.410193023616915[/C][C]0.820386047233831[/C][C]0.589806976383085[/C][/ROW]
[ROW][C]32[/C][C]0.468128763504093[/C][C]0.936257527008187[/C][C]0.531871236495907[/C][/ROW]
[ROW][C]33[/C][C]0.412742240735192[/C][C]0.825484481470384[/C][C]0.587257759264808[/C][/ROW]
[ROW][C]34[/C][C]0.602490117735477[/C][C]0.795019764529046[/C][C]0.397509882264523[/C][/ROW]
[ROW][C]35[/C][C]0.541558533474338[/C][C]0.916882933051323[/C][C]0.458441466525662[/C][/ROW]
[ROW][C]36[/C][C]0.591889626975473[/C][C]0.816220746049055[/C][C]0.408110373024528[/C][/ROW]
[ROW][C]37[/C][C]0.658131640096466[/C][C]0.683736719807068[/C][C]0.341868359903534[/C][/ROW]
[ROW][C]38[/C][C]0.698844239288067[/C][C]0.602311521423866[/C][C]0.301155760711933[/C][/ROW]
[ROW][C]39[/C][C]0.798859047755091[/C][C]0.402281904489818[/C][C]0.201140952244909[/C][/ROW]
[ROW][C]40[/C][C]0.945243038704513[/C][C]0.109513922590973[/C][C]0.0547569612954867[/C][/ROW]
[ROW][C]41[/C][C]0.933863941805388[/C][C]0.132272116389225[/C][C]0.0661360581946124[/C][/ROW]
[ROW][C]42[/C][C]0.917665427543703[/C][C]0.164669144912595[/C][C]0.0823345724562974[/C][/ROW]
[ROW][C]43[/C][C]0.9071263105852[/C][C]0.185747378829601[/C][C]0.0928736894148005[/C][/ROW]
[ROW][C]44[/C][C]0.863916271838551[/C][C]0.272167456322898[/C][C]0.136083728161449[/C][/ROW]
[ROW][C]45[/C][C]0.793919206897294[/C][C]0.412161586205413[/C][C]0.206080793102706[/C][/ROW]
[ROW][C]46[/C][C]0.736045742781816[/C][C]0.527908514436367[/C][C]0.263954257218184[/C][/ROW]
[ROW][C]47[/C][C]0.652630252431305[/C][C]0.694739495137389[/C][C]0.347369747568695[/C][/ROW]
[ROW][C]48[/C][C]0.573010451999894[/C][C]0.853979096000212[/C][C]0.426989548000106[/C][/ROW]
[ROW][C]49[/C][C]0.624877902119496[/C][C]0.750244195761008[/C][C]0.375122097880504[/C][/ROW]
[ROW][C]50[/C][C]0.54058889516143[/C][C]0.91882220967714[/C][C]0.45941110483857[/C][/ROW]
[ROW][C]51[/C][C]0.390085266582523[/C][C]0.780170533165046[/C][C]0.609914733417477[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&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	0.377475459957822	0.754950919915644	0.622524540042178
10	0.275387570542074	0.550775141084149	0.724612429457926
11	0.157240981882068	0.314481963764136	0.842759018117932
12	0.0982970068560934	0.196594013712187	0.901702993143907
13	0.19932004249951	0.39864008499902	0.80067995750049
14	0.131520918625056	0.263041837250112	0.868479081374944
15	0.0883047705498836	0.176609541099767	0.911695229450116
16	0.0607962169143201	0.12159243382864	0.93920378308568
17	0.0366618523088635	0.073323704617727	0.963338147691137
18	0.0416130421479261	0.0832260842958523	0.958386957852074
19	0.0238347534708576	0.0476695069417152	0.976165246529142
20	0.03988365577381	0.07976731154762	0.96011634422619
21	0.0864602460265231	0.172920492053046	0.913539753973477
22	0.101714070097258	0.203428140194517	0.898285929902742
23	0.0673491471971966	0.134698294394393	0.932650852802803
24	0.106523808108599	0.213047616217197	0.893476191891401
25	0.0832389259263462	0.166477851852692	0.916761074073654
26	0.201441231543728	0.402882463087455	0.798558768456272
27	0.253837755930518	0.507675511861037	0.746162244069482
28	0.354111492085601	0.708222984171202	0.645888507914399
29	0.402763066459423	0.805526132918845	0.597236933540577
30	0.477678009765792	0.955356019531585	0.522321990234208
31	0.410193023616915	0.820386047233831	0.589806976383085
32	0.468128763504093	0.936257527008187	0.531871236495907
33	0.412742240735192	0.825484481470384	0.587257759264808
34	0.602490117735477	0.795019764529046	0.397509882264523
35	0.541558533474338	0.916882933051323	0.458441466525662
36	0.591889626975473	0.816220746049055	0.408110373024528
37	0.658131640096466	0.683736719807068	0.341868359903534
38	0.698844239288067	0.602311521423866	0.301155760711933
39	0.798859047755091	0.402281904489818	0.201140952244909
40	0.945243038704513	0.109513922590973	0.0547569612954867
41	0.933863941805388	0.132272116389225	0.0661360581946124
42	0.917665427543703	0.164669144912595	0.0823345724562974
43	0.9071263105852	0.185747378829601	0.0928736894148005
44	0.863916271838551	0.272167456322898	0.136083728161449
45	0.793919206897294	0.412161586205413	0.206080793102706
46	0.736045742781816	0.527908514436367	0.263954257218184
47	0.652630252431305	0.694739495137389	0.347369747568695
48	0.573010451999894	0.853979096000212	0.426989548000106
49	0.624877902119496	0.750244195761008	0.375122097880504
50	0.54058889516143	0.91882220967714	0.45941110483857
51	0.390085266582523	0.780170533165046	0.609914733417477

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0232558139534884	OK
10% type I error level	4	0.0930232558139535	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 1 & 0.0232558139534884 & OK \tabularnewline
10% type I error level & 4 & 0.0930232558139535 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145560&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]1[/C][C]0.0232558139534884[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.0930232558139535[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145560&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145560&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	1	0.0232558139534884	OK
10% type I error level	4	0.0930232558139535	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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