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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 11 Dec 2015 17:02:17 +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/2015/Dec/11/t1449854455ngwwpwplyo6at6p.htm/, Retrieved Thu, 16 May 2024 12:45:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285996, Retrieved Thu, 16 May 2024 12:45:16 +0000

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-       [Multiple Regression] [] [2015-12-11 17:02:17] [5fd2fca6b664199b2dd86155c5786748] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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	4 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
[t] = -7.22506 -1.20557`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.952837`(1-B12)(1-B)Scheidingen(t-2)`[t] -0.372673`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.150294`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0215768`(t-1s)`[t] -0.0112336`(t-2s)`[t] -0.0366865`(t-3s)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
[t] =  -7.22506 -1.20557`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.952837`(1-B12)(1-B)Scheidingen(t-2)`[t] -0.372673`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.150294`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0215768`(t-1s)`[t] -0.0112336`(t-2s)`[t] -0.0366865`(t-3s)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C][t] =  -7.22506 -1.20557`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.952837`(1-B12)(1-B)Scheidingen(t-2)`[t] -0.372673`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.150294`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0215768`(t-1s)`[t] -0.0112336`(t-2s)`[t] -0.0366865`(t-3s)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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
[t] = -7.22506 -1.20557`(1-B12)(1-B)Scheidingen(t-1)`[t] -0.952837`(1-B12)(1-B)Scheidingen(t-2)`[t] -0.372673`(1-B12)(1-B)Scheidingen(t-3)`[t] -0.150294`(1-B12)(1-B)Scheidingen(t-4)`[t] -0.0215768`(t-1s)`[t] -0.0112336`(t-2s)`[t] -0.0366865`(t-3s)`[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)	-7.225	21.74	-3.3230e-01	0.7407	0.3703
`(1-B12)(1-B)Scheidingen(t-1)`	-1.206	0.1195	-1.0090e+01	2.352e-15	1.176e-15
`(1-B12)(1-B)Scheidingen(t-2)`	-0.9528	0.1741	-5.4720e+00	6.31e-07	3.155e-07
`(1-B12)(1-B)Scheidingen(t-3)`	-0.3727	0.1785	-2.0880e+00	0.04041	0.02021
`(1-B12)(1-B)Scheidingen(t-4)`	-0.1503	0.1208	-1.2440e+00	0.2177	0.1089
`(t-1s)`	-0.02158	0.07389	-2.9200e-01	0.7711	0.3856
`(t-2s)`	-0.01123	0.06672	-1.6840e-01	0.8668	0.4334
`(t-3s)`	-0.03669	0.05877	-6.2430e-01	0.5345	0.2672

\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) & -7.225 &  21.74 & -3.3230e-01 &  0.7407 &  0.3703 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-1)` & -1.206 &  0.1195 & -1.0090e+01 &  2.352e-15 &  1.176e-15 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-2)` & -0.9528 &  0.1741 & -5.4720e+00 &  6.31e-07 &  3.155e-07 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-3)` & -0.3727 &  0.1785 & -2.0880e+00 &  0.04041 &  0.02021 \tabularnewline
`(1-B12)(1-B)Scheidingen(t-4)` & -0.1503 &  0.1208 & -1.2440e+00 &  0.2177 &  0.1089 \tabularnewline
`(t-1s)` & -0.02158 &  0.07389 & -2.9200e-01 &  0.7711 &  0.3856 \tabularnewline
`(t-2s)` & -0.01123 &  0.06672 & -1.6840e-01 &  0.8668 &  0.4334 \tabularnewline
`(t-3s)` & -0.03669 &  0.05877 & -6.2430e-01 &  0.5345 &  0.2672 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&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]-7.225[/C][C] 21.74[/C][C]-3.3230e-01[/C][C] 0.7407[/C][C] 0.3703[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-1)`[/C][C]-1.206[/C][C] 0.1195[/C][C]-1.0090e+01[/C][C] 2.352e-15[/C][C] 1.176e-15[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-2)`[/C][C]-0.9528[/C][C] 0.1741[/C][C]-5.4720e+00[/C][C] 6.31e-07[/C][C] 3.155e-07[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-3)`[/C][C]-0.3727[/C][C] 0.1785[/C][C]-2.0880e+00[/C][C] 0.04041[/C][C] 0.02021[/C][/ROW]
[ROW][C]`(1-B12)(1-B)Scheidingen(t-4)`[/C][C]-0.1503[/C][C] 0.1208[/C][C]-1.2440e+00[/C][C] 0.2177[/C][C] 0.1089[/C][/ROW]
[ROW][C]`(t-1s)`[/C][C]-0.02158[/C][C] 0.07389[/C][C]-2.9200e-01[/C][C] 0.7711[/C][C] 0.3856[/C][/ROW]
[ROW][C]`(t-2s)`[/C][C]-0.01123[/C][C] 0.06672[/C][C]-1.6840e-01[/C][C] 0.8668[/C][C] 0.4334[/C][/ROW]
[ROW][C]`(t-3s)`[/C][C]-0.03669[/C][C] 0.05877[/C][C]-6.2430e-01[/C][C] 0.5345[/C][C] 0.2672[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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)	-7.225	21.74	-3.3230e-01	0.7407	0.3703
`(1-B12)(1-B)Scheidingen(t-1)`	-1.206	0.1195	-1.0090e+01	2.352e-15	1.176e-15
`(1-B12)(1-B)Scheidingen(t-2)`	-0.9528	0.1741	-5.4720e+00	6.31e-07	3.155e-07
`(1-B12)(1-B)Scheidingen(t-3)`	-0.3727	0.1785	-2.0880e+00	0.04041	0.02021
`(1-B12)(1-B)Scheidingen(t-4)`	-0.1503	0.1208	-1.2440e+00	0.2177	0.1089
`(t-1s)`	-0.02158	0.07389	-2.9200e-01	0.7711	0.3856
`(t-2s)`	-0.01123	0.06672	-1.6840e-01	0.8668	0.4334
`(t-3s)`	-0.03669	0.05877	-6.2430e-01	0.5345	0.2672

Multiple Linear Regression - Regression Statistics
Multiple R	0.8235
R-squared	0.6782
Adjusted R-squared	0.6465
F-TEST (value)	21.38
F-TEST (DF numerator)	7
F-TEST (DF denominator)	71
p-value	3.331e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	193.2
Sum Squared Residuals	2.65e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8235 \tabularnewline
R-squared &  0.6782 \tabularnewline
Adjusted R-squared &  0.6465 \tabularnewline
F-TEST (value) &  21.38 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 71 \tabularnewline
p-value &  3.331e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  193.2 \tabularnewline
Sum Squared Residuals &  2.65e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8235[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.6782[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.6465[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 21.38[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]71[/C][/ROW]
[ROW][C]p-value[/C][C] 3.331e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 193.2[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 2.65e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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	0.8235
R-squared	0.6782
Adjusted R-squared	0.6465
F-TEST (value)	21.38
F-TEST (DF numerator)	7
F-TEST (DF denominator)	71
p-value	3.331e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	193.2
Sum Squared Residuals	2.65e+06

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	-504	-385.3	-118.7
2	444	402.4	41.61
3	-183	-153.1	-29.87
4	34	-33.13	67.13
5	244	35.19	208.8
6	-227	-322	95.01
7	-173	47.09	-220.1
8	651	325.1	325.9
9	-378	-586.3	208.3
10	-136	-82.28	-53.72
11	347	318.1	28.91
12	-203	-268.1	65.11
13	236	12.08	223.9
14	-259	-191.4	-67.65
15	-111	87.32	-198.3
16	568	330.6	237.4
17	-626	-523.4	-102.6
18	348	279.3	68.67
19	-255	-13.6	-241.4
20	71	117.4	-46.39
21	17	89.02	-72.02
22	83	-16.3	99.3
23	-111	-118.6	7.63
24	113	21.64	91.36
25	-242	-44.08	-197.9
26	334	182.2	151.8
27	-492	-202.4	-289.6
28	272	353.1	-81.07
29	166	33.58	132.4
30	-475	-325	-150
31	641	395.9	245.1
32	-393	-460.2	67.15
33	-136	5.251	-141.3
34	176	367.5	-191.5
35	22	-51.14	73.14
36	-350	-95.93	-254.1
37	759	369.7	389.3
38	-778	-644	-134
39	758	353.2	404.8
40	-561	-423.5	-137.5
41	-39	117.2	-156.2
42	413	423.4	-10.44
43	-84	-377.4	293.4
44	-246	-216.8	-29.17
45	228	237.9	-9.941
46	66	-78.2	144.2
47	-348	-211.7	-136.3
48	190	315.2	-125.2
49	-258	14.12	-272.1
50	-88	265.1	-353.1
51	246	319.4	-73.44
52	119	-164.1	283.1
53	49	-291.6	340.6
54	-201	-274.5	73.48
55	-80	111.1	-191.1
56	9	251.7	-242.7
57	13	121.7	-108.7
58	200	22.1	177.9
59	-259	-240.7	-18.28
60	204	103.9	100.1
61	-112	-76.95	-35.05
62	231	-1.732	232.7
63	38	-211.9	249.9
64	-361	-268.3	-92.69
65	-25	315.8	-340.8
66	137	335.1	-198.1
67	-169	-40.59	-128.4
68	213	146.5	66.46
69	-41	-148.1	107.1
70	-246	-129.9	-116.1
71	296	283.1	12.88
72	68	-140.1	208.1
73	-399	-295.9	-103.1
74	93	360.2	-267.2
75	263	159.6	103.4
76	-475	-247.4	-227.6
77	388	341.6	46.45
78	222	-150.2	372.2
79	-581	-499.4	-81.56

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -504 & -385.3 & -118.7 \tabularnewline
2 &  444 &  402.4 &  41.61 \tabularnewline
3 & -183 & -153.1 & -29.87 \tabularnewline
4 &  34 & -33.13 &  67.13 \tabularnewline
5 &  244 &  35.19 &  208.8 \tabularnewline
6 & -227 & -322 &  95.01 \tabularnewline
7 & -173 &  47.09 & -220.1 \tabularnewline
8 &  651 &  325.1 &  325.9 \tabularnewline
9 & -378 & -586.3 &  208.3 \tabularnewline
10 & -136 & -82.28 & -53.72 \tabularnewline
11 &  347 &  318.1 &  28.91 \tabularnewline
12 & -203 & -268.1 &  65.11 \tabularnewline
13 &  236 &  12.08 &  223.9 \tabularnewline
14 & -259 & -191.4 & -67.65 \tabularnewline
15 & -111 &  87.32 & -198.3 \tabularnewline
16 &  568 &  330.6 &  237.4 \tabularnewline
17 & -626 & -523.4 & -102.6 \tabularnewline
18 &  348 &  279.3 &  68.67 \tabularnewline
19 & -255 & -13.6 & -241.4 \tabularnewline
20 &  71 &  117.4 & -46.39 \tabularnewline
21 &  17 &  89.02 & -72.02 \tabularnewline
22 &  83 & -16.3 &  99.3 \tabularnewline
23 & -111 & -118.6 &  7.63 \tabularnewline
24 &  113 &  21.64 &  91.36 \tabularnewline
25 & -242 & -44.08 & -197.9 \tabularnewline
26 &  334 &  182.2 &  151.8 \tabularnewline
27 & -492 & -202.4 & -289.6 \tabularnewline
28 &  272 &  353.1 & -81.07 \tabularnewline
29 &  166 &  33.58 &  132.4 \tabularnewline
30 & -475 & -325 & -150 \tabularnewline
31 &  641 &  395.9 &  245.1 \tabularnewline
32 & -393 & -460.2 &  67.15 \tabularnewline
33 & -136 &  5.251 & -141.3 \tabularnewline
34 &  176 &  367.5 & -191.5 \tabularnewline
35 &  22 & -51.14 &  73.14 \tabularnewline
36 & -350 & -95.93 & -254.1 \tabularnewline
37 &  759 &  369.7 &  389.3 \tabularnewline
38 & -778 & -644 & -134 \tabularnewline
39 &  758 &  353.2 &  404.8 \tabularnewline
40 & -561 & -423.5 & -137.5 \tabularnewline
41 & -39 &  117.2 & -156.2 \tabularnewline
42 &  413 &  423.4 & -10.44 \tabularnewline
43 & -84 & -377.4 &  293.4 \tabularnewline
44 & -246 & -216.8 & -29.17 \tabularnewline
45 &  228 &  237.9 & -9.941 \tabularnewline
46 &  66 & -78.2 &  144.2 \tabularnewline
47 & -348 & -211.7 & -136.3 \tabularnewline
48 &  190 &  315.2 & -125.2 \tabularnewline
49 & -258 &  14.12 & -272.1 \tabularnewline
50 & -88 &  265.1 & -353.1 \tabularnewline
51 &  246 &  319.4 & -73.44 \tabularnewline
52 &  119 & -164.1 &  283.1 \tabularnewline
53 &  49 & -291.6 &  340.6 \tabularnewline
54 & -201 & -274.5 &  73.48 \tabularnewline
55 & -80 &  111.1 & -191.1 \tabularnewline
56 &  9 &  251.7 & -242.7 \tabularnewline
57 &  13 &  121.7 & -108.7 \tabularnewline
58 &  200 &  22.1 &  177.9 \tabularnewline
59 & -259 & -240.7 & -18.28 \tabularnewline
60 &  204 &  103.9 &  100.1 \tabularnewline
61 & -112 & -76.95 & -35.05 \tabularnewline
62 &  231 & -1.732 &  232.7 \tabularnewline
63 &  38 & -211.9 &  249.9 \tabularnewline
64 & -361 & -268.3 & -92.69 \tabularnewline
65 & -25 &  315.8 & -340.8 \tabularnewline
66 &  137 &  335.1 & -198.1 \tabularnewline
67 & -169 & -40.59 & -128.4 \tabularnewline
68 &  213 &  146.5 &  66.46 \tabularnewline
69 & -41 & -148.1 &  107.1 \tabularnewline
70 & -246 & -129.9 & -116.1 \tabularnewline
71 &  296 &  283.1 &  12.88 \tabularnewline
72 &  68 & -140.1 &  208.1 \tabularnewline
73 & -399 & -295.9 & -103.1 \tabularnewline
74 &  93 &  360.2 & -267.2 \tabularnewline
75 &  263 &  159.6 &  103.4 \tabularnewline
76 & -475 & -247.4 & -227.6 \tabularnewline
77 &  388 &  341.6 &  46.45 \tabularnewline
78 &  222 & -150.2 &  372.2 \tabularnewline
79 & -581 & -499.4 & -81.56 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&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]-504[/C][C]-385.3[/C][C]-118.7[/C][/ROW]
[ROW][C]2[/C][C] 444[/C][C] 402.4[/C][C] 41.61[/C][/ROW]
[ROW][C]3[/C][C]-183[/C][C]-153.1[/C][C]-29.87[/C][/ROW]
[ROW][C]4[/C][C] 34[/C][C]-33.13[/C][C] 67.13[/C][/ROW]
[ROW][C]5[/C][C] 244[/C][C] 35.19[/C][C] 208.8[/C][/ROW]
[ROW][C]6[/C][C]-227[/C][C]-322[/C][C] 95.01[/C][/ROW]
[ROW][C]7[/C][C]-173[/C][C] 47.09[/C][C]-220.1[/C][/ROW]
[ROW][C]8[/C][C] 651[/C][C] 325.1[/C][C] 325.9[/C][/ROW]
[ROW][C]9[/C][C]-378[/C][C]-586.3[/C][C] 208.3[/C][/ROW]
[ROW][C]10[/C][C]-136[/C][C]-82.28[/C][C]-53.72[/C][/ROW]
[ROW][C]11[/C][C] 347[/C][C] 318.1[/C][C] 28.91[/C][/ROW]
[ROW][C]12[/C][C]-203[/C][C]-268.1[/C][C] 65.11[/C][/ROW]
[ROW][C]13[/C][C] 236[/C][C] 12.08[/C][C] 223.9[/C][/ROW]
[ROW][C]14[/C][C]-259[/C][C]-191.4[/C][C]-67.65[/C][/ROW]
[ROW][C]15[/C][C]-111[/C][C] 87.32[/C][C]-198.3[/C][/ROW]
[ROW][C]16[/C][C] 568[/C][C] 330.6[/C][C] 237.4[/C][/ROW]
[ROW][C]17[/C][C]-626[/C][C]-523.4[/C][C]-102.6[/C][/ROW]
[ROW][C]18[/C][C] 348[/C][C] 279.3[/C][C] 68.67[/C][/ROW]
[ROW][C]19[/C][C]-255[/C][C]-13.6[/C][C]-241.4[/C][/ROW]
[ROW][C]20[/C][C] 71[/C][C] 117.4[/C][C]-46.39[/C][/ROW]
[ROW][C]21[/C][C] 17[/C][C] 89.02[/C][C]-72.02[/C][/ROW]
[ROW][C]22[/C][C] 83[/C][C]-16.3[/C][C] 99.3[/C][/ROW]
[ROW][C]23[/C][C]-111[/C][C]-118.6[/C][C] 7.63[/C][/ROW]
[ROW][C]24[/C][C] 113[/C][C] 21.64[/C][C] 91.36[/C][/ROW]
[ROW][C]25[/C][C]-242[/C][C]-44.08[/C][C]-197.9[/C][/ROW]
[ROW][C]26[/C][C] 334[/C][C] 182.2[/C][C] 151.8[/C][/ROW]
[ROW][C]27[/C][C]-492[/C][C]-202.4[/C][C]-289.6[/C][/ROW]
[ROW][C]28[/C][C] 272[/C][C] 353.1[/C][C]-81.07[/C][/ROW]
[ROW][C]29[/C][C] 166[/C][C] 33.58[/C][C] 132.4[/C][/ROW]
[ROW][C]30[/C][C]-475[/C][C]-325[/C][C]-150[/C][/ROW]
[ROW][C]31[/C][C] 641[/C][C] 395.9[/C][C] 245.1[/C][/ROW]
[ROW][C]32[/C][C]-393[/C][C]-460.2[/C][C] 67.15[/C][/ROW]
[ROW][C]33[/C][C]-136[/C][C] 5.251[/C][C]-141.3[/C][/ROW]
[ROW][C]34[/C][C] 176[/C][C] 367.5[/C][C]-191.5[/C][/ROW]
[ROW][C]35[/C][C] 22[/C][C]-51.14[/C][C] 73.14[/C][/ROW]
[ROW][C]36[/C][C]-350[/C][C]-95.93[/C][C]-254.1[/C][/ROW]
[ROW][C]37[/C][C] 759[/C][C] 369.7[/C][C] 389.3[/C][/ROW]
[ROW][C]38[/C][C]-778[/C][C]-644[/C][C]-134[/C][/ROW]
[ROW][C]39[/C][C] 758[/C][C] 353.2[/C][C] 404.8[/C][/ROW]
[ROW][C]40[/C][C]-561[/C][C]-423.5[/C][C]-137.5[/C][/ROW]
[ROW][C]41[/C][C]-39[/C][C] 117.2[/C][C]-156.2[/C][/ROW]
[ROW][C]42[/C][C] 413[/C][C] 423.4[/C][C]-10.44[/C][/ROW]
[ROW][C]43[/C][C]-84[/C][C]-377.4[/C][C] 293.4[/C][/ROW]
[ROW][C]44[/C][C]-246[/C][C]-216.8[/C][C]-29.17[/C][/ROW]
[ROW][C]45[/C][C] 228[/C][C] 237.9[/C][C]-9.941[/C][/ROW]
[ROW][C]46[/C][C] 66[/C][C]-78.2[/C][C] 144.2[/C][/ROW]
[ROW][C]47[/C][C]-348[/C][C]-211.7[/C][C]-136.3[/C][/ROW]
[ROW][C]48[/C][C] 190[/C][C] 315.2[/C][C]-125.2[/C][/ROW]
[ROW][C]49[/C][C]-258[/C][C] 14.12[/C][C]-272.1[/C][/ROW]
[ROW][C]50[/C][C]-88[/C][C] 265.1[/C][C]-353.1[/C][/ROW]
[ROW][C]51[/C][C] 246[/C][C] 319.4[/C][C]-73.44[/C][/ROW]
[ROW][C]52[/C][C] 119[/C][C]-164.1[/C][C] 283.1[/C][/ROW]
[ROW][C]53[/C][C] 49[/C][C]-291.6[/C][C] 340.6[/C][/ROW]
[ROW][C]54[/C][C]-201[/C][C]-274.5[/C][C] 73.48[/C][/ROW]
[ROW][C]55[/C][C]-80[/C][C] 111.1[/C][C]-191.1[/C][/ROW]
[ROW][C]56[/C][C] 9[/C][C] 251.7[/C][C]-242.7[/C][/ROW]
[ROW][C]57[/C][C] 13[/C][C] 121.7[/C][C]-108.7[/C][/ROW]
[ROW][C]58[/C][C] 200[/C][C] 22.1[/C][C] 177.9[/C][/ROW]
[ROW][C]59[/C][C]-259[/C][C]-240.7[/C][C]-18.28[/C][/ROW]
[ROW][C]60[/C][C] 204[/C][C] 103.9[/C][C] 100.1[/C][/ROW]
[ROW][C]61[/C][C]-112[/C][C]-76.95[/C][C]-35.05[/C][/ROW]
[ROW][C]62[/C][C] 231[/C][C]-1.732[/C][C] 232.7[/C][/ROW]
[ROW][C]63[/C][C] 38[/C][C]-211.9[/C][C] 249.9[/C][/ROW]
[ROW][C]64[/C][C]-361[/C][C]-268.3[/C][C]-92.69[/C][/ROW]
[ROW][C]65[/C][C]-25[/C][C] 315.8[/C][C]-340.8[/C][/ROW]
[ROW][C]66[/C][C] 137[/C][C] 335.1[/C][C]-198.1[/C][/ROW]
[ROW][C]67[/C][C]-169[/C][C]-40.59[/C][C]-128.4[/C][/ROW]
[ROW][C]68[/C][C] 213[/C][C] 146.5[/C][C] 66.46[/C][/ROW]
[ROW][C]69[/C][C]-41[/C][C]-148.1[/C][C] 107.1[/C][/ROW]
[ROW][C]70[/C][C]-246[/C][C]-129.9[/C][C]-116.1[/C][/ROW]
[ROW][C]71[/C][C] 296[/C][C] 283.1[/C][C] 12.88[/C][/ROW]
[ROW][C]72[/C][C] 68[/C][C]-140.1[/C][C] 208.1[/C][/ROW]
[ROW][C]73[/C][C]-399[/C][C]-295.9[/C][C]-103.1[/C][/ROW]
[ROW][C]74[/C][C] 93[/C][C] 360.2[/C][C]-267.2[/C][/ROW]
[ROW][C]75[/C][C] 263[/C][C] 159.6[/C][C] 103.4[/C][/ROW]
[ROW][C]76[/C][C]-475[/C][C]-247.4[/C][C]-227.6[/C][/ROW]
[ROW][C]77[/C][C] 388[/C][C] 341.6[/C][C] 46.45[/C][/ROW]
[ROW][C]78[/C][C] 222[/C][C]-150.2[/C][C] 372.2[/C][/ROW]
[ROW][C]79[/C][C]-581[/C][C]-499.4[/C][C]-81.56[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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	-504	-385.3	-118.7
2	444	402.4	41.61
3	-183	-153.1	-29.87
4	34	-33.13	67.13
5	244	35.19	208.8
6	-227	-322	95.01
7	-173	47.09	-220.1
8	651	325.1	325.9
9	-378	-586.3	208.3
10	-136	-82.28	-53.72
11	347	318.1	28.91
12	-203	-268.1	65.11
13	236	12.08	223.9
14	-259	-191.4	-67.65
15	-111	87.32	-198.3
16	568	330.6	237.4
17	-626	-523.4	-102.6
18	348	279.3	68.67
19	-255	-13.6	-241.4
20	71	117.4	-46.39
21	17	89.02	-72.02
22	83	-16.3	99.3
23	-111	-118.6	7.63
24	113	21.64	91.36
25	-242	-44.08	-197.9
26	334	182.2	151.8
27	-492	-202.4	-289.6
28	272	353.1	-81.07
29	166	33.58	132.4
30	-475	-325	-150
31	641	395.9	245.1
32	-393	-460.2	67.15
33	-136	5.251	-141.3
34	176	367.5	-191.5
35	22	-51.14	73.14
36	-350	-95.93	-254.1
37	759	369.7	389.3
38	-778	-644	-134
39	758	353.2	404.8
40	-561	-423.5	-137.5
41	-39	117.2	-156.2
42	413	423.4	-10.44
43	-84	-377.4	293.4
44	-246	-216.8	-29.17
45	228	237.9	-9.941
46	66	-78.2	144.2
47	-348	-211.7	-136.3
48	190	315.2	-125.2
49	-258	14.12	-272.1
50	-88	265.1	-353.1
51	246	319.4	-73.44
52	119	-164.1	283.1
53	49	-291.6	340.6
54	-201	-274.5	73.48
55	-80	111.1	-191.1
56	9	251.7	-242.7
57	13	121.7	-108.7
58	200	22.1	177.9
59	-259	-240.7	-18.28
60	204	103.9	100.1
61	-112	-76.95	-35.05
62	231	-1.732	232.7
63	38	-211.9	249.9
64	-361	-268.3	-92.69
65	-25	315.8	-340.8
66	137	335.1	-198.1
67	-169	-40.59	-128.4
68	213	146.5	66.46
69	-41	-148.1	107.1
70	-246	-129.9	-116.1
71	296	283.1	12.88
72	68	-140.1	208.1
73	-399	-295.9	-103.1
74	93	360.2	-267.2
75	263	159.6	103.4
76	-475	-247.4	-227.6
77	388	341.6	46.45
78	222	-150.2	372.2
79	-581	-499.4	-81.56

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.1795	0.359	0.8205
12	0.08727	0.1745	0.9127
13	0.08569	0.1714	0.9143
14	0.05802	0.116	0.942
15	0.04224	0.08448	0.9578
16	0.03192	0.06383	0.9681
17	0.1014	0.2028	0.8986
18	0.1327	0.2654	0.8673
19	0.2142	0.4285	0.7858
20	0.2325	0.4651	0.7675
21	0.1681	0.3362	0.8319
22	0.122	0.2439	0.878
23	0.08243	0.1649	0.9176
24	0.07516	0.1503	0.9248
25	0.1405	0.281	0.8595
26	0.1216	0.2433	0.8784
27	0.1968	0.3936	0.8032
28	0.1809	0.3618	0.8191
29	0.1472	0.2944	0.8528
30	0.1308	0.2616	0.8692
31	0.1396	0.2792	0.8604
32	0.1131	0.2262	0.8869
33	0.09398	0.188	0.906
34	0.1098	0.2195	0.8902
35	0.0799	0.1598	0.9201
36	0.1028	0.2055	0.8972
37	0.1934	0.3868	0.8066
38	0.171	0.3421	0.829
39	0.3959	0.7919	0.6041
40	0.4928	0.9856	0.5072
41	0.4748	0.9496	0.5252
42	0.4441	0.8882	0.5559
43	0.5872	0.8256	0.4128
44	0.5475	0.905	0.4525
45	0.4783	0.9566	0.5217
46	0.4769	0.9538	0.5231
47	0.461	0.9221	0.539
48	0.4247	0.8493	0.5753
49	0.5171	0.9659	0.4829
50	0.608	0.7841	0.392
51	0.5838	0.8324	0.4162
52	0.7058	0.5883	0.2942
53	0.818	0.364	0.182
54	0.8065	0.3869	0.1935
55	0.7741	0.4518	0.2259
56	0.7826	0.4349	0.2174
57	0.7713	0.4574	0.2287
58	0.7357	0.5286	0.2643
59	0.655	0.69	0.345
60	0.6208	0.7583	0.3792
61	0.5253	0.9493	0.4747
62	0.7848	0.4304	0.2152
63	0.7134	0.5732	0.2866
64	0.6069	0.7861	0.3931
65	0.7205	0.559	0.2795
66	0.6151	0.7699	0.3849
67	0.5612	0.8777	0.4388
68	0.4162	0.8324	0.5838

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.1795 &  0.359 &  0.8205 \tabularnewline
12 &  0.08727 &  0.1745 &  0.9127 \tabularnewline
13 &  0.08569 &  0.1714 &  0.9143 \tabularnewline
14 &  0.05802 &  0.116 &  0.942 \tabularnewline
15 &  0.04224 &  0.08448 &  0.9578 \tabularnewline
16 &  0.03192 &  0.06383 &  0.9681 \tabularnewline
17 &  0.1014 &  0.2028 &  0.8986 \tabularnewline
18 &  0.1327 &  0.2654 &  0.8673 \tabularnewline
19 &  0.2142 &  0.4285 &  0.7858 \tabularnewline
20 &  0.2325 &  0.4651 &  0.7675 \tabularnewline
21 &  0.1681 &  0.3362 &  0.8319 \tabularnewline
22 &  0.122 &  0.2439 &  0.878 \tabularnewline
23 &  0.08243 &  0.1649 &  0.9176 \tabularnewline
24 &  0.07516 &  0.1503 &  0.9248 \tabularnewline
25 &  0.1405 &  0.281 &  0.8595 \tabularnewline
26 &  0.1216 &  0.2433 &  0.8784 \tabularnewline
27 &  0.1968 &  0.3936 &  0.8032 \tabularnewline
28 &  0.1809 &  0.3618 &  0.8191 \tabularnewline
29 &  0.1472 &  0.2944 &  0.8528 \tabularnewline
30 &  0.1308 &  0.2616 &  0.8692 \tabularnewline
31 &  0.1396 &  0.2792 &  0.8604 \tabularnewline
32 &  0.1131 &  0.2262 &  0.8869 \tabularnewline
33 &  0.09398 &  0.188 &  0.906 \tabularnewline
34 &  0.1098 &  0.2195 &  0.8902 \tabularnewline
35 &  0.0799 &  0.1598 &  0.9201 \tabularnewline
36 &  0.1028 &  0.2055 &  0.8972 \tabularnewline
37 &  0.1934 &  0.3868 &  0.8066 \tabularnewline
38 &  0.171 &  0.3421 &  0.829 \tabularnewline
39 &  0.3959 &  0.7919 &  0.6041 \tabularnewline
40 &  0.4928 &  0.9856 &  0.5072 \tabularnewline
41 &  0.4748 &  0.9496 &  0.5252 \tabularnewline
42 &  0.4441 &  0.8882 &  0.5559 \tabularnewline
43 &  0.5872 &  0.8256 &  0.4128 \tabularnewline
44 &  0.5475 &  0.905 &  0.4525 \tabularnewline
45 &  0.4783 &  0.9566 &  0.5217 \tabularnewline
46 &  0.4769 &  0.9538 &  0.5231 \tabularnewline
47 &  0.461 &  0.9221 &  0.539 \tabularnewline
48 &  0.4247 &  0.8493 &  0.5753 \tabularnewline
49 &  0.5171 &  0.9659 &  0.4829 \tabularnewline
50 &  0.608 &  0.7841 &  0.392 \tabularnewline
51 &  0.5838 &  0.8324 &  0.4162 \tabularnewline
52 &  0.7058 &  0.5883 &  0.2942 \tabularnewline
53 &  0.818 &  0.364 &  0.182 \tabularnewline
54 &  0.8065 &  0.3869 &  0.1935 \tabularnewline
55 &  0.7741 &  0.4518 &  0.2259 \tabularnewline
56 &  0.7826 &  0.4349 &  0.2174 \tabularnewline
57 &  0.7713 &  0.4574 &  0.2287 \tabularnewline
58 &  0.7357 &  0.5286 &  0.2643 \tabularnewline
59 &  0.655 &  0.69 &  0.345 \tabularnewline
60 &  0.6208 &  0.7583 &  0.3792 \tabularnewline
61 &  0.5253 &  0.9493 &  0.4747 \tabularnewline
62 &  0.7848 &  0.4304 &  0.2152 \tabularnewline
63 &  0.7134 &  0.5732 &  0.2866 \tabularnewline
64 &  0.6069 &  0.7861 &  0.3931 \tabularnewline
65 &  0.7205 &  0.559 &  0.2795 \tabularnewline
66 &  0.6151 &  0.7699 &  0.3849 \tabularnewline
67 &  0.5612 &  0.8777 &  0.4388 \tabularnewline
68 &  0.4162 &  0.8324 &  0.5838 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&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]11[/C][C] 0.1795[/C][C] 0.359[/C][C] 0.8205[/C][/ROW]
[ROW][C]12[/C][C] 0.08727[/C][C] 0.1745[/C][C] 0.9127[/C][/ROW]
[ROW][C]13[/C][C] 0.08569[/C][C] 0.1714[/C][C] 0.9143[/C][/ROW]
[ROW][C]14[/C][C] 0.05802[/C][C] 0.116[/C][C] 0.942[/C][/ROW]
[ROW][C]15[/C][C] 0.04224[/C][C] 0.08448[/C][C] 0.9578[/C][/ROW]
[ROW][C]16[/C][C] 0.03192[/C][C] 0.06383[/C][C] 0.9681[/C][/ROW]
[ROW][C]17[/C][C] 0.1014[/C][C] 0.2028[/C][C] 0.8986[/C][/ROW]
[ROW][C]18[/C][C] 0.1327[/C][C] 0.2654[/C][C] 0.8673[/C][/ROW]
[ROW][C]19[/C][C] 0.2142[/C][C] 0.4285[/C][C] 0.7858[/C][/ROW]
[ROW][C]20[/C][C] 0.2325[/C][C] 0.4651[/C][C] 0.7675[/C][/ROW]
[ROW][C]21[/C][C] 0.1681[/C][C] 0.3362[/C][C] 0.8319[/C][/ROW]
[ROW][C]22[/C][C] 0.122[/C][C] 0.2439[/C][C] 0.878[/C][/ROW]
[ROW][C]23[/C][C] 0.08243[/C][C] 0.1649[/C][C] 0.9176[/C][/ROW]
[ROW][C]24[/C][C] 0.07516[/C][C] 0.1503[/C][C] 0.9248[/C][/ROW]
[ROW][C]25[/C][C] 0.1405[/C][C] 0.281[/C][C] 0.8595[/C][/ROW]
[ROW][C]26[/C][C] 0.1216[/C][C] 0.2433[/C][C] 0.8784[/C][/ROW]
[ROW][C]27[/C][C] 0.1968[/C][C] 0.3936[/C][C] 0.8032[/C][/ROW]
[ROW][C]28[/C][C] 0.1809[/C][C] 0.3618[/C][C] 0.8191[/C][/ROW]
[ROW][C]29[/C][C] 0.1472[/C][C] 0.2944[/C][C] 0.8528[/C][/ROW]
[ROW][C]30[/C][C] 0.1308[/C][C] 0.2616[/C][C] 0.8692[/C][/ROW]
[ROW][C]31[/C][C] 0.1396[/C][C] 0.2792[/C][C] 0.8604[/C][/ROW]
[ROW][C]32[/C][C] 0.1131[/C][C] 0.2262[/C][C] 0.8869[/C][/ROW]
[ROW][C]33[/C][C] 0.09398[/C][C] 0.188[/C][C] 0.906[/C][/ROW]
[ROW][C]34[/C][C] 0.1098[/C][C] 0.2195[/C][C] 0.8902[/C][/ROW]
[ROW][C]35[/C][C] 0.0799[/C][C] 0.1598[/C][C] 0.9201[/C][/ROW]
[ROW][C]36[/C][C] 0.1028[/C][C] 0.2055[/C][C] 0.8972[/C][/ROW]
[ROW][C]37[/C][C] 0.1934[/C][C] 0.3868[/C][C] 0.8066[/C][/ROW]
[ROW][C]38[/C][C] 0.171[/C][C] 0.3421[/C][C] 0.829[/C][/ROW]
[ROW][C]39[/C][C] 0.3959[/C][C] 0.7919[/C][C] 0.6041[/C][/ROW]
[ROW][C]40[/C][C] 0.4928[/C][C] 0.9856[/C][C] 0.5072[/C][/ROW]
[ROW][C]41[/C][C] 0.4748[/C][C] 0.9496[/C][C] 0.5252[/C][/ROW]
[ROW][C]42[/C][C] 0.4441[/C][C] 0.8882[/C][C] 0.5559[/C][/ROW]
[ROW][C]43[/C][C] 0.5872[/C][C] 0.8256[/C][C] 0.4128[/C][/ROW]
[ROW][C]44[/C][C] 0.5475[/C][C] 0.905[/C][C] 0.4525[/C][/ROW]
[ROW][C]45[/C][C] 0.4783[/C][C] 0.9566[/C][C] 0.5217[/C][/ROW]
[ROW][C]46[/C][C] 0.4769[/C][C] 0.9538[/C][C] 0.5231[/C][/ROW]
[ROW][C]47[/C][C] 0.461[/C][C] 0.9221[/C][C] 0.539[/C][/ROW]
[ROW][C]48[/C][C] 0.4247[/C][C] 0.8493[/C][C] 0.5753[/C][/ROW]
[ROW][C]49[/C][C] 0.5171[/C][C] 0.9659[/C][C] 0.4829[/C][/ROW]
[ROW][C]50[/C][C] 0.608[/C][C] 0.7841[/C][C] 0.392[/C][/ROW]
[ROW][C]51[/C][C] 0.5838[/C][C] 0.8324[/C][C] 0.4162[/C][/ROW]
[ROW][C]52[/C][C] 0.7058[/C][C] 0.5883[/C][C] 0.2942[/C][/ROW]
[ROW][C]53[/C][C] 0.818[/C][C] 0.364[/C][C] 0.182[/C][/ROW]
[ROW][C]54[/C][C] 0.8065[/C][C] 0.3869[/C][C] 0.1935[/C][/ROW]
[ROW][C]55[/C][C] 0.7741[/C][C] 0.4518[/C][C] 0.2259[/C][/ROW]
[ROW][C]56[/C][C] 0.7826[/C][C] 0.4349[/C][C] 0.2174[/C][/ROW]
[ROW][C]57[/C][C] 0.7713[/C][C] 0.4574[/C][C] 0.2287[/C][/ROW]
[ROW][C]58[/C][C] 0.7357[/C][C] 0.5286[/C][C] 0.2643[/C][/ROW]
[ROW][C]59[/C][C] 0.655[/C][C] 0.69[/C][C] 0.345[/C][/ROW]
[ROW][C]60[/C][C] 0.6208[/C][C] 0.7583[/C][C] 0.3792[/C][/ROW]
[ROW][C]61[/C][C] 0.5253[/C][C] 0.9493[/C][C] 0.4747[/C][/ROW]
[ROW][C]62[/C][C] 0.7848[/C][C] 0.4304[/C][C] 0.2152[/C][/ROW]
[ROW][C]63[/C][C] 0.7134[/C][C] 0.5732[/C][C] 0.2866[/C][/ROW]
[ROW][C]64[/C][C] 0.6069[/C][C] 0.7861[/C][C] 0.3931[/C][/ROW]
[ROW][C]65[/C][C] 0.7205[/C][C] 0.559[/C][C] 0.2795[/C][/ROW]
[ROW][C]66[/C][C] 0.6151[/C][C] 0.7699[/C][C] 0.3849[/C][/ROW]
[ROW][C]67[/C][C] 0.5612[/C][C] 0.8777[/C][C] 0.4388[/C][/ROW]
[ROW][C]68[/C][C] 0.4162[/C][C] 0.8324[/C][C] 0.5838[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&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
11	0.1795	0.359	0.8205
12	0.08727	0.1745	0.9127
13	0.08569	0.1714	0.9143
14	0.05802	0.116	0.942
15	0.04224	0.08448	0.9578
16	0.03192	0.06383	0.9681
17	0.1014	0.2028	0.8986
18	0.1327	0.2654	0.8673
19	0.2142	0.4285	0.7858
20	0.2325	0.4651	0.7675
21	0.1681	0.3362	0.8319
22	0.122	0.2439	0.878
23	0.08243	0.1649	0.9176
24	0.07516	0.1503	0.9248
25	0.1405	0.281	0.8595
26	0.1216	0.2433	0.8784
27	0.1968	0.3936	0.8032
28	0.1809	0.3618	0.8191
29	0.1472	0.2944	0.8528
30	0.1308	0.2616	0.8692
31	0.1396	0.2792	0.8604
32	0.1131	0.2262	0.8869
33	0.09398	0.188	0.906
34	0.1098	0.2195	0.8902
35	0.0799	0.1598	0.9201
36	0.1028	0.2055	0.8972
37	0.1934	0.3868	0.8066
38	0.171	0.3421	0.829
39	0.3959	0.7919	0.6041
40	0.4928	0.9856	0.5072
41	0.4748	0.9496	0.5252
42	0.4441	0.8882	0.5559
43	0.5872	0.8256	0.4128
44	0.5475	0.905	0.4525
45	0.4783	0.9566	0.5217
46	0.4769	0.9538	0.5231
47	0.461	0.9221	0.539
48	0.4247	0.8493	0.5753
49	0.5171	0.9659	0.4829
50	0.608	0.7841	0.392
51	0.5838	0.8324	0.4162
52	0.7058	0.5883	0.2942
53	0.818	0.364	0.182
54	0.8065	0.3869	0.1935
55	0.7741	0.4518	0.2259
56	0.7826	0.4349	0.2174
57	0.7713	0.4574	0.2287
58	0.7357	0.5286	0.2643
59	0.655	0.69	0.345
60	0.6208	0.7583	0.3792
61	0.5253	0.9493	0.4747
62	0.7848	0.4304	0.2152
63	0.7134	0.5732	0.2866
64	0.6069	0.7861	0.3931
65	0.7205	0.559	0.2795
66	0.6151	0.7699	0.3849
67	0.5612	0.8777	0.4388
68	0.4162	0.8324	0.5838

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0344828	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 & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0344828 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285996&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0344828[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285996&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285996&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	0	0	OK
10% type I error level	2	0.0344828	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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PDF link

Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 3 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = First and Seasonal Differences (s=12) ; par4 = 4 ; par5 = 3 ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code