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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 12:17:07 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/22/t13219822460s2fo4c07q4zgir.htm/, Retrieved Thu, 28 Mar 2024 16:59:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146325, Retrieved Thu, 28 Mar 2024 16:59:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact120
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 12:52:07] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D    [Multiple Regression] [WS7 comp 3] [2010-11-23 09:24:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-           [Multiple Regression] [] [2010-12-02 15:15:07] [2e1e44f0ae3cb9513dc28781dfdb387b]
- R  D          [Multiple Regression] [ws7] [2011-11-22 17:17:07] [13dfa60174f50d862e8699db2153bfc5] [Current]
Feedback Forum

Post a new message
Dataseries X:
9	13	2528	80	15.3
9	12	3333	83	19.4
9	54	19611	96	19.5
9	19	3570	56	17
9	37	1722	43	19.3
9	2	583	51	12.9
9	72	4790	91	16.7
9	164	35971	81	13.8
9	18	25440	120	13.7
9	1	2217	46	14.3
9	53	1971	56	22.2
9	16	12620	37	16.8
9	32	19046	120	18
10	21	8612	103	15
10	23	3896	105	18.4
10	18	6298	42	18.2
10	112	27350	65	24.1
10	25	4145	51	23
10	5	1175	57	21.8
10	26	8297	60	19.1
10	8	7814	160	22.6
10	15	1745	48	14.3
10	11	5046	109	19
10	11	18943	50	18.5
10	87	8624	78	21.3
10	33	2225	41	20.5
11	22	12659	65	18
11	98	1967	50	16.8
11	1	1172	73	20.5
11	5	639	26	20.1
11	1	7056	60	24.5
11	38	1934	85	12
11	30	6260	133	21
11	12	424	62	20.2
11	24	3488	44	24
11	6	3330	67	14.9
11	15	2227	54	24
11	38	8115	110	20.5
11	84	1600	56	19.5
12	3	15305	85	17.5
12	18	7121	58	16
12	63	5794	34	17.5
12	239	8636	150	18.1
12	234	4803	93	24.3
12	6	1097	53	13.1
12	76	9765	130	16.9
12	25	4266	68	17
12	8	1507	51	21
12	23	3836	121	18.5
12	16	17419	48	18
12	6	8735	63	20.8
12	100	22550	107	23
13	80	9961	79	21.8
13	28	4706	61	19.7
13	48	4011	52	18.9
13	18	6949	100	18.6
13	36	11405	70	23.6
13	19	904	39	19.2
13	32	3332	73	17.7
13	3	575	33	18
13	106	29708	73	21.4
13	62	2511	60	17.7
13	23	18422	45	20.1
13	2	6311	46	18.5
13	26	1450	60	18.6
14	20	4106	96	15.4
14	38	10274	90	15
14	19	510	82	26.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.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 & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Fish[t] = -63.6423894141477 + 2.31247145920237Month[t] + 0.00179280767392933Acre[t] + 0.374300156306726Depth[t] + 1.90718013651869Temp[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Fish[t] =  -63.6423894141477 +  2.31247145920237Month[t] +  0.00179280767392933Acre[t] +  0.374300156306726Depth[t] +  1.90718013651869Temp[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Fish[t] =  -63.6423894141477 +  2.31247145920237Month[t] +  0.00179280767392933Acre[t] +  0.374300156306726Depth[t] +  1.90718013651869Temp[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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
Fish[t] = -63.6423894141477 + 2.31247145920237Month[t] + 0.00179280767392933Acre[t] + 0.374300156306726Depth[t] + 1.90718013651869Temp[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-63.642389414147749.437503-1.28730.2026890.101344
Month2.312471459202373.5823270.64550.5209330.260467
Acre0.001792807673929330.0007182.49770.0151250.007563
Depth0.3743001563067260.1888681.98180.0518680.025934
Temp1.907180136518691.7275791.1040.2738090.136904

\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) & -63.6423894141477 & 49.437503 & -1.2873 & 0.202689 & 0.101344 \tabularnewline
Month & 2.31247145920237 & 3.582327 & 0.6455 & 0.520933 & 0.260467 \tabularnewline
Acre & 0.00179280767392933 & 0.000718 & 2.4977 & 0.015125 & 0.007563 \tabularnewline
Depth & 0.374300156306726 & 0.188868 & 1.9818 & 0.051868 & 0.025934 \tabularnewline
Temp & 1.90718013651869 & 1.727579 & 1.104 & 0.273809 & 0.136904 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]-63.6423894141477[/C][C]49.437503[/C][C]-1.2873[/C][C]0.202689[/C][C]0.101344[/C][/ROW]
[ROW][C]Month[/C][C]2.31247145920237[/C][C]3.582327[/C][C]0.6455[/C][C]0.520933[/C][C]0.260467[/C][/ROW]
[ROW][C]Acre[/C][C]0.00179280767392933[/C][C]0.000718[/C][C]2.4977[/C][C]0.015125[/C][C]0.007563[/C][/ROW]
[ROW][C]Depth[/C][C]0.374300156306726[/C][C]0.188868[/C][C]1.9818[/C][C]0.051868[/C][C]0.025934[/C][/ROW]
[ROW][C]Temp[/C][C]1.90718013651869[/C][C]1.727579[/C][C]1.104[/C][C]0.273809[/C][C]0.136904[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-63.642389414147749.437503-1.28730.2026890.101344
Month2.312471459202373.5823270.64550.5209330.260467
Acre0.001792807673929330.0007182.49770.0151250.007563
Depth0.3743001563067260.1888681.98180.0518680.025934
Temp1.907180136518691.7275791.1040.2738090.136904







Multiple Linear Regression - Regression Statistics
Multiple R0.44124252320546
R-squared0.194694964284721
Adjusted R-squared0.143564485826608
F-TEST (value)3.80780642301673
F-TEST (DF numerator)4
F-TEST (DF denominator)63
p-value0.00778070011843357
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation43.7633005626207
Sum Squared Residuals120659.267996459

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.44124252320546 \tabularnewline
R-squared & 0.194694964284721 \tabularnewline
Adjusted R-squared & 0.143564485826608 \tabularnewline
F-TEST (value) & 3.80780642301673 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 63 \tabularnewline
p-value & 0.00778070011843357 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 43.7633005626207 \tabularnewline
Sum Squared Residuals & 120659.267996459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.44124252320546[/C][/ROW]
[ROW][C]R-squared[/C][C]0.194694964284721[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.143564485826608[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.80780642301673[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]63[/C][/ROW]
[ROW][C]p-value[/C][C]0.00778070011843357[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]43.7633005626207[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]120659.267996459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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 R0.44124252320546
R-squared0.194694964284721
Adjusted R-squared0.143564485826608
F-TEST (value)3.80780642301673
F-TEST (DF numerator)4
F-TEST (DF denominator)63
p-value0.00778070011843357
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation43.7633005626207
Sum Squared Residuals120659.267996459







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11320.8259401116409-7.82594011164095
21231.2114893178009-19.2114893178009
35465.4514326796618-11.4514326796618
41916.95304818859572.04695181140428
53713.160551889179923.8394481108201
621.906992325308550.0930076746914495
77231.668624980569340.3313750194307
816478.296337102388285.7036628976118
91873.8232675705489-55.8232675705489
1015.63499147410161-4.63499147410161
115324.003685427879928.9963145721201
121625.6848186405246-9.68481864052458
133270.5609298904751-38.5609298904751
142142.0826030131285-21.0826030131285
152340.8607347996548-17.8607347996548
161821.2047129578055-3.20471295780554
1711278.808166509880733.1918334901193
182529.8679640978859-4.86796409788595
19524.5005100803338-19.5005100803338
202633.2424004343782-7.24240043437816
21876.4816204363583-68.4816204363583
22157.849858023822787.15014197617722
231145.5639723318116-34.5639723318116
241147.4413212860513-36.4413212860513
258744.761847657615242.2381523423848
263317.914821459577615.0851785404224
272243.1487015986233-21.1487015986233
289816.076883440547681.9231165594524
29130.3170714399477-29.3170714399477
30511.0065255487197-6.00652554871973
31143.6287703074351-42.6287703074351
323819.963761602753718.0362383972463
333062.850476331563-32.850476331563
341224.2865955395189-12.2865955395189
352430.2896399576883-6.28963995768835
36621.2599406979421-15.2599406979421
371531.7719110439307-16.7719110439307
383856.6136409033879-18.6136409033879
398422.814110330656461.1858896693436
40356.7373552209179-53.7373552209179
411829.0981427924206-11.0981427924206
426320.59665346253342.403346537467
4323970.2549390853316168.745060914668
4423453.872515208093180.127484791907
45610.8959461872325-4.89594618723251
467662.504399659240913.4956003407591
472529.6298585829383-4.62985858293833
48825.9491200994277-17.9491200994277
492351.5576297721832-28.5576297721832
501647.631834928515-31.6318349285149
51643.0176998149659-37.0176998149659
5210088.450341008136611.5496589918634
538055.424136119831924.5758638801681
542835.260450693123-7.26045069312298
554829.120003843766618.8799961562334
561851.7815262515382-33.7815262515382
573658.077173239959-22.077173239959
581919.2560024098364-0.256002409836356
593233.4743745517874-1.47437455178742
60314.1317515834508-11.1317515834508
6110687.818036264466518.1819637355335
626227.13657741950434.863422580496
632354.6246703024375-31.6246703024375
64230.2347885013562-28.2347885013562
652626.9508706003318-0.95087060033181
662041.3968684316728-21.3968684316728
673849.4462331720211-11.4462331720211
681950.8794293632862-31.8794293632862

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 20.8259401116409 & -7.82594011164095 \tabularnewline
2 & 12 & 31.2114893178009 & -19.2114893178009 \tabularnewline
3 & 54 & 65.4514326796618 & -11.4514326796618 \tabularnewline
4 & 19 & 16.9530481885957 & 2.04695181140428 \tabularnewline
5 & 37 & 13.1605518891799 & 23.8394481108201 \tabularnewline
6 & 2 & 1.90699232530855 & 0.0930076746914495 \tabularnewline
7 & 72 & 31.6686249805693 & 40.3313750194307 \tabularnewline
8 & 164 & 78.2963371023882 & 85.7036628976118 \tabularnewline
9 & 18 & 73.8232675705489 & -55.8232675705489 \tabularnewline
10 & 1 & 5.63499147410161 & -4.63499147410161 \tabularnewline
11 & 53 & 24.0036854278799 & 28.9963145721201 \tabularnewline
12 & 16 & 25.6848186405246 & -9.68481864052458 \tabularnewline
13 & 32 & 70.5609298904751 & -38.5609298904751 \tabularnewline
14 & 21 & 42.0826030131285 & -21.0826030131285 \tabularnewline
15 & 23 & 40.8607347996548 & -17.8607347996548 \tabularnewline
16 & 18 & 21.2047129578055 & -3.20471295780554 \tabularnewline
17 & 112 & 78.8081665098807 & 33.1918334901193 \tabularnewline
18 & 25 & 29.8679640978859 & -4.86796409788595 \tabularnewline
19 & 5 & 24.5005100803338 & -19.5005100803338 \tabularnewline
20 & 26 & 33.2424004343782 & -7.24240043437816 \tabularnewline
21 & 8 & 76.4816204363583 & -68.4816204363583 \tabularnewline
22 & 15 & 7.84985802382278 & 7.15014197617722 \tabularnewline
23 & 11 & 45.5639723318116 & -34.5639723318116 \tabularnewline
24 & 11 & 47.4413212860513 & -36.4413212860513 \tabularnewline
25 & 87 & 44.7618476576152 & 42.2381523423848 \tabularnewline
26 & 33 & 17.9148214595776 & 15.0851785404224 \tabularnewline
27 & 22 & 43.1487015986233 & -21.1487015986233 \tabularnewline
28 & 98 & 16.0768834405476 & 81.9231165594524 \tabularnewline
29 & 1 & 30.3170714399477 & -29.3170714399477 \tabularnewline
30 & 5 & 11.0065255487197 & -6.00652554871973 \tabularnewline
31 & 1 & 43.6287703074351 & -42.6287703074351 \tabularnewline
32 & 38 & 19.9637616027537 & 18.0362383972463 \tabularnewline
33 & 30 & 62.850476331563 & -32.850476331563 \tabularnewline
34 & 12 & 24.2865955395189 & -12.2865955395189 \tabularnewline
35 & 24 & 30.2896399576883 & -6.28963995768835 \tabularnewline
36 & 6 & 21.2599406979421 & -15.2599406979421 \tabularnewline
37 & 15 & 31.7719110439307 & -16.7719110439307 \tabularnewline
38 & 38 & 56.6136409033879 & -18.6136409033879 \tabularnewline
39 & 84 & 22.8141103306564 & 61.1858896693436 \tabularnewline
40 & 3 & 56.7373552209179 & -53.7373552209179 \tabularnewline
41 & 18 & 29.0981427924206 & -11.0981427924206 \tabularnewline
42 & 63 & 20.596653462533 & 42.403346537467 \tabularnewline
43 & 239 & 70.2549390853316 & 168.745060914668 \tabularnewline
44 & 234 & 53.872515208093 & 180.127484791907 \tabularnewline
45 & 6 & 10.8959461872325 & -4.89594618723251 \tabularnewline
46 & 76 & 62.5043996592409 & 13.4956003407591 \tabularnewline
47 & 25 & 29.6298585829383 & -4.62985858293833 \tabularnewline
48 & 8 & 25.9491200994277 & -17.9491200994277 \tabularnewline
49 & 23 & 51.5576297721832 & -28.5576297721832 \tabularnewline
50 & 16 & 47.631834928515 & -31.6318349285149 \tabularnewline
51 & 6 & 43.0176998149659 & -37.0176998149659 \tabularnewline
52 & 100 & 88.4503410081366 & 11.5496589918634 \tabularnewline
53 & 80 & 55.4241361198319 & 24.5758638801681 \tabularnewline
54 & 28 & 35.260450693123 & -7.26045069312298 \tabularnewline
55 & 48 & 29.1200038437666 & 18.8799961562334 \tabularnewline
56 & 18 & 51.7815262515382 & -33.7815262515382 \tabularnewline
57 & 36 & 58.077173239959 & -22.077173239959 \tabularnewline
58 & 19 & 19.2560024098364 & -0.256002409836356 \tabularnewline
59 & 32 & 33.4743745517874 & -1.47437455178742 \tabularnewline
60 & 3 & 14.1317515834508 & -11.1317515834508 \tabularnewline
61 & 106 & 87.8180362644665 & 18.1819637355335 \tabularnewline
62 & 62 & 27.136577419504 & 34.863422580496 \tabularnewline
63 & 23 & 54.6246703024375 & -31.6246703024375 \tabularnewline
64 & 2 & 30.2347885013562 & -28.2347885013562 \tabularnewline
65 & 26 & 26.9508706003318 & -0.95087060033181 \tabularnewline
66 & 20 & 41.3968684316728 & -21.3968684316728 \tabularnewline
67 & 38 & 49.4462331720211 & -11.4462331720211 \tabularnewline
68 & 19 & 50.8794293632862 & -31.8794293632862 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]13[/C][C]20.8259401116409[/C][C]-7.82594011164095[/C][/ROW]
[ROW][C]2[/C][C]12[/C][C]31.2114893178009[/C][C]-19.2114893178009[/C][/ROW]
[ROW][C]3[/C][C]54[/C][C]65.4514326796618[/C][C]-11.4514326796618[/C][/ROW]
[ROW][C]4[/C][C]19[/C][C]16.9530481885957[/C][C]2.04695181140428[/C][/ROW]
[ROW][C]5[/C][C]37[/C][C]13.1605518891799[/C][C]23.8394481108201[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.90699232530855[/C][C]0.0930076746914495[/C][/ROW]
[ROW][C]7[/C][C]72[/C][C]31.6686249805693[/C][C]40.3313750194307[/C][/ROW]
[ROW][C]8[/C][C]164[/C][C]78.2963371023882[/C][C]85.7036628976118[/C][/ROW]
[ROW][C]9[/C][C]18[/C][C]73.8232675705489[/C][C]-55.8232675705489[/C][/ROW]
[ROW][C]10[/C][C]1[/C][C]5.63499147410161[/C][C]-4.63499147410161[/C][/ROW]
[ROW][C]11[/C][C]53[/C][C]24.0036854278799[/C][C]28.9963145721201[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]25.6848186405246[/C][C]-9.68481864052458[/C][/ROW]
[ROW][C]13[/C][C]32[/C][C]70.5609298904751[/C][C]-38.5609298904751[/C][/ROW]
[ROW][C]14[/C][C]21[/C][C]42.0826030131285[/C][C]-21.0826030131285[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]40.8607347996548[/C][C]-17.8607347996548[/C][/ROW]
[ROW][C]16[/C][C]18[/C][C]21.2047129578055[/C][C]-3.20471295780554[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]78.8081665098807[/C][C]33.1918334901193[/C][/ROW]
[ROW][C]18[/C][C]25[/C][C]29.8679640978859[/C][C]-4.86796409788595[/C][/ROW]
[ROW][C]19[/C][C]5[/C][C]24.5005100803338[/C][C]-19.5005100803338[/C][/ROW]
[ROW][C]20[/C][C]26[/C][C]33.2424004343782[/C][C]-7.24240043437816[/C][/ROW]
[ROW][C]21[/C][C]8[/C][C]76.4816204363583[/C][C]-68.4816204363583[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]7.84985802382278[/C][C]7.15014197617722[/C][/ROW]
[ROW][C]23[/C][C]11[/C][C]45.5639723318116[/C][C]-34.5639723318116[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]47.4413212860513[/C][C]-36.4413212860513[/C][/ROW]
[ROW][C]25[/C][C]87[/C][C]44.7618476576152[/C][C]42.2381523423848[/C][/ROW]
[ROW][C]26[/C][C]33[/C][C]17.9148214595776[/C][C]15.0851785404224[/C][/ROW]
[ROW][C]27[/C][C]22[/C][C]43.1487015986233[/C][C]-21.1487015986233[/C][/ROW]
[ROW][C]28[/C][C]98[/C][C]16.0768834405476[/C][C]81.9231165594524[/C][/ROW]
[ROW][C]29[/C][C]1[/C][C]30.3170714399477[/C][C]-29.3170714399477[/C][/ROW]
[ROW][C]30[/C][C]5[/C][C]11.0065255487197[/C][C]-6.00652554871973[/C][/ROW]
[ROW][C]31[/C][C]1[/C][C]43.6287703074351[/C][C]-42.6287703074351[/C][/ROW]
[ROW][C]32[/C][C]38[/C][C]19.9637616027537[/C][C]18.0362383972463[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]62.850476331563[/C][C]-32.850476331563[/C][/ROW]
[ROW][C]34[/C][C]12[/C][C]24.2865955395189[/C][C]-12.2865955395189[/C][/ROW]
[ROW][C]35[/C][C]24[/C][C]30.2896399576883[/C][C]-6.28963995768835[/C][/ROW]
[ROW][C]36[/C][C]6[/C][C]21.2599406979421[/C][C]-15.2599406979421[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]31.7719110439307[/C][C]-16.7719110439307[/C][/ROW]
[ROW][C]38[/C][C]38[/C][C]56.6136409033879[/C][C]-18.6136409033879[/C][/ROW]
[ROW][C]39[/C][C]84[/C][C]22.8141103306564[/C][C]61.1858896693436[/C][/ROW]
[ROW][C]40[/C][C]3[/C][C]56.7373552209179[/C][C]-53.7373552209179[/C][/ROW]
[ROW][C]41[/C][C]18[/C][C]29.0981427924206[/C][C]-11.0981427924206[/C][/ROW]
[ROW][C]42[/C][C]63[/C][C]20.596653462533[/C][C]42.403346537467[/C][/ROW]
[ROW][C]43[/C][C]239[/C][C]70.2549390853316[/C][C]168.745060914668[/C][/ROW]
[ROW][C]44[/C][C]234[/C][C]53.872515208093[/C][C]180.127484791907[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]10.8959461872325[/C][C]-4.89594618723251[/C][/ROW]
[ROW][C]46[/C][C]76[/C][C]62.5043996592409[/C][C]13.4956003407591[/C][/ROW]
[ROW][C]47[/C][C]25[/C][C]29.6298585829383[/C][C]-4.62985858293833[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]25.9491200994277[/C][C]-17.9491200994277[/C][/ROW]
[ROW][C]49[/C][C]23[/C][C]51.5576297721832[/C][C]-28.5576297721832[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]47.631834928515[/C][C]-31.6318349285149[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]43.0176998149659[/C][C]-37.0176998149659[/C][/ROW]
[ROW][C]52[/C][C]100[/C][C]88.4503410081366[/C][C]11.5496589918634[/C][/ROW]
[ROW][C]53[/C][C]80[/C][C]55.4241361198319[/C][C]24.5758638801681[/C][/ROW]
[ROW][C]54[/C][C]28[/C][C]35.260450693123[/C][C]-7.26045069312298[/C][/ROW]
[ROW][C]55[/C][C]48[/C][C]29.1200038437666[/C][C]18.8799961562334[/C][/ROW]
[ROW][C]56[/C][C]18[/C][C]51.7815262515382[/C][C]-33.7815262515382[/C][/ROW]
[ROW][C]57[/C][C]36[/C][C]58.077173239959[/C][C]-22.077173239959[/C][/ROW]
[ROW][C]58[/C][C]19[/C][C]19.2560024098364[/C][C]-0.256002409836356[/C][/ROW]
[ROW][C]59[/C][C]32[/C][C]33.4743745517874[/C][C]-1.47437455178742[/C][/ROW]
[ROW][C]60[/C][C]3[/C][C]14.1317515834508[/C][C]-11.1317515834508[/C][/ROW]
[ROW][C]61[/C][C]106[/C][C]87.8180362644665[/C][C]18.1819637355335[/C][/ROW]
[ROW][C]62[/C][C]62[/C][C]27.136577419504[/C][C]34.863422580496[/C][/ROW]
[ROW][C]63[/C][C]23[/C][C]54.6246703024375[/C][C]-31.6246703024375[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]30.2347885013562[/C][C]-28.2347885013562[/C][/ROW]
[ROW][C]65[/C][C]26[/C][C]26.9508706003318[/C][C]-0.95087060033181[/C][/ROW]
[ROW][C]66[/C][C]20[/C][C]41.3968684316728[/C][C]-21.3968684316728[/C][/ROW]
[ROW][C]67[/C][C]38[/C][C]49.4462331720211[/C][C]-11.4462331720211[/C][/ROW]
[ROW][C]68[/C][C]19[/C][C]50.8794293632862[/C][C]-31.8794293632862[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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 IndexActualsInterpolationForecastResidualsPrediction Error
11320.8259401116409-7.82594011164095
21231.2114893178009-19.2114893178009
35465.4514326796618-11.4514326796618
41916.95304818859572.04695181140428
53713.160551889179923.8394481108201
621.906992325308550.0930076746914495
77231.668624980569340.3313750194307
816478.296337102388285.7036628976118
91873.8232675705489-55.8232675705489
1015.63499147410161-4.63499147410161
115324.003685427879928.9963145721201
121625.6848186405246-9.68481864052458
133270.5609298904751-38.5609298904751
142142.0826030131285-21.0826030131285
152340.8607347996548-17.8607347996548
161821.2047129578055-3.20471295780554
1711278.808166509880733.1918334901193
182529.8679640978859-4.86796409788595
19524.5005100803338-19.5005100803338
202633.2424004343782-7.24240043437816
21876.4816204363583-68.4816204363583
22157.849858023822787.15014197617722
231145.5639723318116-34.5639723318116
241147.4413212860513-36.4413212860513
258744.761847657615242.2381523423848
263317.914821459577615.0851785404224
272243.1487015986233-21.1487015986233
289816.076883440547681.9231165594524
29130.3170714399477-29.3170714399477
30511.0065255487197-6.00652554871973
31143.6287703074351-42.6287703074351
323819.963761602753718.0362383972463
333062.850476331563-32.850476331563
341224.2865955395189-12.2865955395189
352430.2896399576883-6.28963995768835
36621.2599406979421-15.2599406979421
371531.7719110439307-16.7719110439307
383856.6136409033879-18.6136409033879
398422.814110330656461.1858896693436
40356.7373552209179-53.7373552209179
411829.0981427924206-11.0981427924206
426320.59665346253342.403346537467
4323970.2549390853316168.745060914668
4423453.872515208093180.127484791907
45610.8959461872325-4.89594618723251
467662.504399659240913.4956003407591
472529.6298585829383-4.62985858293833
48825.9491200994277-17.9491200994277
492351.5576297721832-28.5576297721832
501647.631834928515-31.6318349285149
51643.0176998149659-37.0176998149659
5210088.450341008136611.5496589918634
538055.424136119831924.5758638801681
542835.260450693123-7.26045069312298
554829.120003843766618.8799961562334
561851.7815262515382-33.7815262515382
573658.077173239959-22.077173239959
581919.2560024098364-0.256002409836356
593233.4743745517874-1.47437455178742
60314.1317515834508-11.1317515834508
6110687.818036264466518.1819637355335
626227.13657741950434.863422580496
632354.6246703024375-31.6246703024375
64230.2347885013562-28.2347885013562
652626.9508706003318-0.95087060033181
662041.3968684316728-21.3968684316728
673849.4462331720211-11.4462331720211
681950.8794293632862-31.8794293632862







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4022281879165620.8044563758331240.597771812083438
90.5517185553191810.8965628893616370.448281444680819
100.4480290002490770.8960580004981530.551970999750923
110.3251853446820830.6503706893641660.674814655317917
120.3710010738334020.7420021476668030.628998926166598
130.2974621596271150.5949243192542310.702537840372885
140.2087013990391360.4174027980782720.791298600960864
150.1410635221616440.2821270443232880.858936477838356
160.1047080290313690.2094160580627370.895291970968631
170.07048727278092370.1409745455618470.929512727219076
180.04484545880183410.08969091760366820.955154541198166
190.02867946950129550.0573589390025910.971320530498704
200.01642402876060380.03284805752120760.983575971239396
210.01409546985890860.02819093971781720.985904530141091
220.008104294734435830.01620858946887170.991895705265564
230.005339408557155540.01067881711431110.994660591442844
240.009150744157031960.01830148831406390.990849255842968
250.01515185393143040.03030370786286090.98484814606857
260.009189191256387450.01837838251277490.990810808743613
270.005392695314059360.01078539062811870.99460730468594
280.03777903221984790.07555806443969590.962220967780152
290.02934718660114960.05869437320229910.97065281339885
300.02064996394268980.04129992788537960.97935003605731
310.02045764415734710.04091528831469410.979542355842653
320.01529536264631680.03059072529263350.984704637353683
330.0163487434424660.0326974868849320.983651256557534
340.01085202170214690.02170404340429380.989147978297853
350.006771366595740090.01354273319148020.99322863340426
360.004460155927342370.008920311854684730.995539844072658
370.003444698905566630.006889397811133270.996555301094433
380.004488105822274510.008976211644549010.995511894177725
390.007186770716792810.01437354143358560.992813229283207
400.01170079982918860.02340159965837720.988299200170811
410.007345072568540330.01469014513708070.99265492743146
420.007867066489100350.01573413297820070.9921329335109
430.5116931062426010.9766137875147980.488306893757399
440.9998275492074650.0003449015850699630.000172450792534981
450.9996218009982520.0007563980034950090.000378199001747504
460.9993918615145840.001216276970832880.000608138485416441
470.9987464970779280.002507005844144260.00125350292207213
480.997490286915040.005019426169918940.00250971308495947
490.9957777397815980.008444520436803120.00422226021840156
500.995486061861480.009027876277040630.00451393813852031
510.9973580063987270.005283987202546530.00264199360127326
520.9939891356250120.01202172874997550.00601086437498773
530.993797565371270.01240486925746190.00620243462873094
540.9861183933346150.02776321333077010.013881606665385
550.9800916371785450.03981672564290960.0199083628214548
560.9864225393758650.02715492124827090.0135774606241355
570.9801541282143620.03969174357127650.0198458717856383
580.9595011012270720.08099779754585690.0404988987729284
590.9429903976878580.1140192046242840.057009602312142
600.907757084523880.1844858309522390.0922429154761197

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.402228187916562 & 0.804456375833124 & 0.597771812083438 \tabularnewline
9 & 0.551718555319181 & 0.896562889361637 & 0.448281444680819 \tabularnewline
10 & 0.448029000249077 & 0.896058000498153 & 0.551970999750923 \tabularnewline
11 & 0.325185344682083 & 0.650370689364166 & 0.674814655317917 \tabularnewline
12 & 0.371001073833402 & 0.742002147666803 & 0.628998926166598 \tabularnewline
13 & 0.297462159627115 & 0.594924319254231 & 0.702537840372885 \tabularnewline
14 & 0.208701399039136 & 0.417402798078272 & 0.791298600960864 \tabularnewline
15 & 0.141063522161644 & 0.282127044323288 & 0.858936477838356 \tabularnewline
16 & 0.104708029031369 & 0.209416058062737 & 0.895291970968631 \tabularnewline
17 & 0.0704872727809237 & 0.140974545561847 & 0.929512727219076 \tabularnewline
18 & 0.0448454588018341 & 0.0896909176036682 & 0.955154541198166 \tabularnewline
19 & 0.0286794695012955 & 0.057358939002591 & 0.971320530498704 \tabularnewline
20 & 0.0164240287606038 & 0.0328480575212076 & 0.983575971239396 \tabularnewline
21 & 0.0140954698589086 & 0.0281909397178172 & 0.985904530141091 \tabularnewline
22 & 0.00810429473443583 & 0.0162085894688717 & 0.991895705265564 \tabularnewline
23 & 0.00533940855715554 & 0.0106788171143111 & 0.994660591442844 \tabularnewline
24 & 0.00915074415703196 & 0.0183014883140639 & 0.990849255842968 \tabularnewline
25 & 0.0151518539314304 & 0.0303037078628609 & 0.98484814606857 \tabularnewline
26 & 0.00918919125638745 & 0.0183783825127749 & 0.990810808743613 \tabularnewline
27 & 0.00539269531405936 & 0.0107853906281187 & 0.99460730468594 \tabularnewline
28 & 0.0377790322198479 & 0.0755580644396959 & 0.962220967780152 \tabularnewline
29 & 0.0293471866011496 & 0.0586943732022991 & 0.97065281339885 \tabularnewline
30 & 0.0206499639426898 & 0.0412999278853796 & 0.97935003605731 \tabularnewline
31 & 0.0204576441573471 & 0.0409152883146941 & 0.979542355842653 \tabularnewline
32 & 0.0152953626463168 & 0.0305907252926335 & 0.984704637353683 \tabularnewline
33 & 0.016348743442466 & 0.032697486884932 & 0.983651256557534 \tabularnewline
34 & 0.0108520217021469 & 0.0217040434042938 & 0.989147978297853 \tabularnewline
35 & 0.00677136659574009 & 0.0135427331914802 & 0.99322863340426 \tabularnewline
36 & 0.00446015592734237 & 0.00892031185468473 & 0.995539844072658 \tabularnewline
37 & 0.00344469890556663 & 0.00688939781113327 & 0.996555301094433 \tabularnewline
38 & 0.00448810582227451 & 0.00897621164454901 & 0.995511894177725 \tabularnewline
39 & 0.00718677071679281 & 0.0143735414335856 & 0.992813229283207 \tabularnewline
40 & 0.0117007998291886 & 0.0234015996583772 & 0.988299200170811 \tabularnewline
41 & 0.00734507256854033 & 0.0146901451370807 & 0.99265492743146 \tabularnewline
42 & 0.00786706648910035 & 0.0157341329782007 & 0.9921329335109 \tabularnewline
43 & 0.511693106242601 & 0.976613787514798 & 0.488306893757399 \tabularnewline
44 & 0.999827549207465 & 0.000344901585069963 & 0.000172450792534981 \tabularnewline
45 & 0.999621800998252 & 0.000756398003495009 & 0.000378199001747504 \tabularnewline
46 & 0.999391861514584 & 0.00121627697083288 & 0.000608138485416441 \tabularnewline
47 & 0.998746497077928 & 0.00250700584414426 & 0.00125350292207213 \tabularnewline
48 & 0.99749028691504 & 0.00501942616991894 & 0.00250971308495947 \tabularnewline
49 & 0.995777739781598 & 0.00844452043680312 & 0.00422226021840156 \tabularnewline
50 & 0.99548606186148 & 0.00902787627704063 & 0.00451393813852031 \tabularnewline
51 & 0.997358006398727 & 0.00528398720254653 & 0.00264199360127326 \tabularnewline
52 & 0.993989135625012 & 0.0120217287499755 & 0.00601086437498773 \tabularnewline
53 & 0.99379756537127 & 0.0124048692574619 & 0.00620243462873094 \tabularnewline
54 & 0.986118393334615 & 0.0277632133307701 & 0.013881606665385 \tabularnewline
55 & 0.980091637178545 & 0.0398167256429096 & 0.0199083628214548 \tabularnewline
56 & 0.986422539375865 & 0.0271549212482709 & 0.0135774606241355 \tabularnewline
57 & 0.980154128214362 & 0.0396917435712765 & 0.0198458717856383 \tabularnewline
58 & 0.959501101227072 & 0.0809977975458569 & 0.0404988987729284 \tabularnewline
59 & 0.942990397687858 & 0.114019204624284 & 0.057009602312142 \tabularnewline
60 & 0.90775708452388 & 0.184485830952239 & 0.0922429154761197 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]8[/C][C]0.402228187916562[/C][C]0.804456375833124[/C][C]0.597771812083438[/C][/ROW]
[ROW][C]9[/C][C]0.551718555319181[/C][C]0.896562889361637[/C][C]0.448281444680819[/C][/ROW]
[ROW][C]10[/C][C]0.448029000249077[/C][C]0.896058000498153[/C][C]0.551970999750923[/C][/ROW]
[ROW][C]11[/C][C]0.325185344682083[/C][C]0.650370689364166[/C][C]0.674814655317917[/C][/ROW]
[ROW][C]12[/C][C]0.371001073833402[/C][C]0.742002147666803[/C][C]0.628998926166598[/C][/ROW]
[ROW][C]13[/C][C]0.297462159627115[/C][C]0.594924319254231[/C][C]0.702537840372885[/C][/ROW]
[ROW][C]14[/C][C]0.208701399039136[/C][C]0.417402798078272[/C][C]0.791298600960864[/C][/ROW]
[ROW][C]15[/C][C]0.141063522161644[/C][C]0.282127044323288[/C][C]0.858936477838356[/C][/ROW]
[ROW][C]16[/C][C]0.104708029031369[/C][C]0.209416058062737[/C][C]0.895291970968631[/C][/ROW]
[ROW][C]17[/C][C]0.0704872727809237[/C][C]0.140974545561847[/C][C]0.929512727219076[/C][/ROW]
[ROW][C]18[/C][C]0.0448454588018341[/C][C]0.0896909176036682[/C][C]0.955154541198166[/C][/ROW]
[ROW][C]19[/C][C]0.0286794695012955[/C][C]0.057358939002591[/C][C]0.971320530498704[/C][/ROW]
[ROW][C]20[/C][C]0.0164240287606038[/C][C]0.0328480575212076[/C][C]0.983575971239396[/C][/ROW]
[ROW][C]21[/C][C]0.0140954698589086[/C][C]0.0281909397178172[/C][C]0.985904530141091[/C][/ROW]
[ROW][C]22[/C][C]0.00810429473443583[/C][C]0.0162085894688717[/C][C]0.991895705265564[/C][/ROW]
[ROW][C]23[/C][C]0.00533940855715554[/C][C]0.0106788171143111[/C][C]0.994660591442844[/C][/ROW]
[ROW][C]24[/C][C]0.00915074415703196[/C][C]0.0183014883140639[/C][C]0.990849255842968[/C][/ROW]
[ROW][C]25[/C][C]0.0151518539314304[/C][C]0.0303037078628609[/C][C]0.98484814606857[/C][/ROW]
[ROW][C]26[/C][C]0.00918919125638745[/C][C]0.0183783825127749[/C][C]0.990810808743613[/C][/ROW]
[ROW][C]27[/C][C]0.00539269531405936[/C][C]0.0107853906281187[/C][C]0.99460730468594[/C][/ROW]
[ROW][C]28[/C][C]0.0377790322198479[/C][C]0.0755580644396959[/C][C]0.962220967780152[/C][/ROW]
[ROW][C]29[/C][C]0.0293471866011496[/C][C]0.0586943732022991[/C][C]0.97065281339885[/C][/ROW]
[ROW][C]30[/C][C]0.0206499639426898[/C][C]0.0412999278853796[/C][C]0.97935003605731[/C][/ROW]
[ROW][C]31[/C][C]0.0204576441573471[/C][C]0.0409152883146941[/C][C]0.979542355842653[/C][/ROW]
[ROW][C]32[/C][C]0.0152953626463168[/C][C]0.0305907252926335[/C][C]0.984704637353683[/C][/ROW]
[ROW][C]33[/C][C]0.016348743442466[/C][C]0.032697486884932[/C][C]0.983651256557534[/C][/ROW]
[ROW][C]34[/C][C]0.0108520217021469[/C][C]0.0217040434042938[/C][C]0.989147978297853[/C][/ROW]
[ROW][C]35[/C][C]0.00677136659574009[/C][C]0.0135427331914802[/C][C]0.99322863340426[/C][/ROW]
[ROW][C]36[/C][C]0.00446015592734237[/C][C]0.00892031185468473[/C][C]0.995539844072658[/C][/ROW]
[ROW][C]37[/C][C]0.00344469890556663[/C][C]0.00688939781113327[/C][C]0.996555301094433[/C][/ROW]
[ROW][C]38[/C][C]0.00448810582227451[/C][C]0.00897621164454901[/C][C]0.995511894177725[/C][/ROW]
[ROW][C]39[/C][C]0.00718677071679281[/C][C]0.0143735414335856[/C][C]0.992813229283207[/C][/ROW]
[ROW][C]40[/C][C]0.0117007998291886[/C][C]0.0234015996583772[/C][C]0.988299200170811[/C][/ROW]
[ROW][C]41[/C][C]0.00734507256854033[/C][C]0.0146901451370807[/C][C]0.99265492743146[/C][/ROW]
[ROW][C]42[/C][C]0.00786706648910035[/C][C]0.0157341329782007[/C][C]0.9921329335109[/C][/ROW]
[ROW][C]43[/C][C]0.511693106242601[/C][C]0.976613787514798[/C][C]0.488306893757399[/C][/ROW]
[ROW][C]44[/C][C]0.999827549207465[/C][C]0.000344901585069963[/C][C]0.000172450792534981[/C][/ROW]
[ROW][C]45[/C][C]0.999621800998252[/C][C]0.000756398003495009[/C][C]0.000378199001747504[/C][/ROW]
[ROW][C]46[/C][C]0.999391861514584[/C][C]0.00121627697083288[/C][C]0.000608138485416441[/C][/ROW]
[ROW][C]47[/C][C]0.998746497077928[/C][C]0.00250700584414426[/C][C]0.00125350292207213[/C][/ROW]
[ROW][C]48[/C][C]0.99749028691504[/C][C]0.00501942616991894[/C][C]0.00250971308495947[/C][/ROW]
[ROW][C]49[/C][C]0.995777739781598[/C][C]0.00844452043680312[/C][C]0.00422226021840156[/C][/ROW]
[ROW][C]50[/C][C]0.99548606186148[/C][C]0.00902787627704063[/C][C]0.00451393813852031[/C][/ROW]
[ROW][C]51[/C][C]0.997358006398727[/C][C]0.00528398720254653[/C][C]0.00264199360127326[/C][/ROW]
[ROW][C]52[/C][C]0.993989135625012[/C][C]0.0120217287499755[/C][C]0.00601086437498773[/C][/ROW]
[ROW][C]53[/C][C]0.99379756537127[/C][C]0.0124048692574619[/C][C]0.00620243462873094[/C][/ROW]
[ROW][C]54[/C][C]0.986118393334615[/C][C]0.0277632133307701[/C][C]0.013881606665385[/C][/ROW]
[ROW][C]55[/C][C]0.980091637178545[/C][C]0.0398167256429096[/C][C]0.0199083628214548[/C][/ROW]
[ROW][C]56[/C][C]0.986422539375865[/C][C]0.0271549212482709[/C][C]0.0135774606241355[/C][/ROW]
[ROW][C]57[/C][C]0.980154128214362[/C][C]0.0396917435712765[/C][C]0.0198458717856383[/C][/ROW]
[ROW][C]58[/C][C]0.959501101227072[/C][C]0.0809977975458569[/C][C]0.0404988987729284[/C][/ROW]
[ROW][C]59[/C][C]0.942990397687858[/C][C]0.114019204624284[/C][C]0.057009602312142[/C][/ROW]
[ROW][C]60[/C][C]0.90775708452388[/C][C]0.184485830952239[/C][C]0.0922429154761197[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4022281879165620.8044563758331240.597771812083438
90.5517185553191810.8965628893616370.448281444680819
100.4480290002490770.8960580004981530.551970999750923
110.3251853446820830.6503706893641660.674814655317917
120.3710010738334020.7420021476668030.628998926166598
130.2974621596271150.5949243192542310.702537840372885
140.2087013990391360.4174027980782720.791298600960864
150.1410635221616440.2821270443232880.858936477838356
160.1047080290313690.2094160580627370.895291970968631
170.07048727278092370.1409745455618470.929512727219076
180.04484545880183410.08969091760366820.955154541198166
190.02867946950129550.0573589390025910.971320530498704
200.01642402876060380.03284805752120760.983575971239396
210.01409546985890860.02819093971781720.985904530141091
220.008104294734435830.01620858946887170.991895705265564
230.005339408557155540.01067881711431110.994660591442844
240.009150744157031960.01830148831406390.990849255842968
250.01515185393143040.03030370786286090.98484814606857
260.009189191256387450.01837838251277490.990810808743613
270.005392695314059360.01078539062811870.99460730468594
280.03777903221984790.07555806443969590.962220967780152
290.02934718660114960.05869437320229910.97065281339885
300.02064996394268980.04129992788537960.97935003605731
310.02045764415734710.04091528831469410.979542355842653
320.01529536264631680.03059072529263350.984704637353683
330.0163487434424660.0326974868849320.983651256557534
340.01085202170214690.02170404340429380.989147978297853
350.006771366595740090.01354273319148020.99322863340426
360.004460155927342370.008920311854684730.995539844072658
370.003444698905566630.006889397811133270.996555301094433
380.004488105822274510.008976211644549010.995511894177725
390.007186770716792810.01437354143358560.992813229283207
400.01170079982918860.02340159965837720.988299200170811
410.007345072568540330.01469014513708070.99265492743146
420.007867066489100350.01573413297820070.9921329335109
430.5116931062426010.9766137875147980.488306893757399
440.9998275492074650.0003449015850699630.000172450792534981
450.9996218009982520.0007563980034950090.000378199001747504
460.9993918615145840.001216276970832880.000608138485416441
470.9987464970779280.002507005844144260.00125350292207213
480.997490286915040.005019426169918940.00250971308495947
490.9957777397815980.008444520436803120.00422226021840156
500.995486061861480.009027876277040630.00451393813852031
510.9973580063987270.005283987202546530.00264199360127326
520.9939891356250120.01202172874997550.00601086437498773
530.993797565371270.01240486925746190.00620243462873094
540.9861183933346150.02776321333077010.013881606665385
550.9800916371785450.03981672564290960.0199083628214548
560.9864225393758650.02715492124827090.0135774606241355
570.9801541282143620.03969174357127650.0198458717856383
580.9595011012270720.08099779754585690.0404988987729284
590.9429903976878580.1140192046242840.057009602312142
600.907757084523880.1844858309522390.0922429154761197







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.207547169811321NOK
5% type I error level350.660377358490566NOK
10% type I error level400.754716981132076NOK

\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 & 11 & 0.207547169811321 & NOK \tabularnewline
5% type I error level & 35 & 0.660377358490566 & NOK \tabularnewline
10% type I error level & 40 & 0.754716981132076 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146325&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]11[/C][C]0.207547169811321[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.660377358490566[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]40[/C][C]0.754716981132076[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146325&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146325&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 testsOK/NOK
1% type I error level110.207547169811321NOK
5% type I error level350.660377358490566NOK
10% type I error level400.754716981132076NOK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}