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rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 07:51:00 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258729323mq8mbz48cv8xay3.htm/, Retrieved Sat, 20 Apr 2024 07:20:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58243, Retrieved Sat, 20 Apr 2024 07:20:05 +0000

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Estimated Impact

101

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [ws7] [2009-11-20 14:51:00] [557d56ec4b06cd0135c259898de8ce95] [Current]

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Boxplots

15335,63636	13845,66667	12823,61538	12792	10284,5
11188,5	15335,63636	13845,66667	12823,61538	12792
13633,25	11188,5	15335,63636	13845,66667	12823,61538
12298,46667	13633,25	11188,5	15335,63636	13845,66667
15353,63636	12298,46667	13633,25	11188,5	15335,63636
12696,15385	15353,63636	12298,46667	13633,25	11188,5
12213,93333	12696,15385	15353,63636	12298,46667	13633,25
13683,72727	12213,93333	12696,15385	15353,63636	12298,46667
11214,14286	13683,72727	12213,93333	12696,15385	15353,63636
13950,23077	11214,14286	13683,72727	12213,93333	12696,15385
11179,13333	13950,23077	11214,14286	13683,72727	12213,93333
11801,875	11179,13333	13950,23077	11214,14286	13683,72727
11188,82353	11801,875	11179,13333	13950,23077	11214,14286
16456,27273	11188,82353	11801,875	11179,13333	13950,23077
11110,0625	16456,27273	11188,82353	11801,875	11179,13333
16530,69231	11110,0625	16456,27273	11188,82353	11801,875
10038,41176	16530,69231	11110,0625	16456,27273	11188,82353
11681,25	10038,41176	16530,69231	11110,0625	16456,27273
11148,88235	11681,25	10038,41176	16530,69231	11110,0625
8631	11148,88235	11681,25	10038,41176	16530,69231
9386,444444	8631	11148,88235	11681,25	10038,41176
9764,736842	9386,444444	8631	11148,88235	11681,25
12043,75	9764,736842	9386,444444	8631	11148,88235
12948,06667	12043,75	9764,736842	9386,444444	8631
10987,125	12948,06667	12043,75	9764,736842	9386,444444
11648,3125	10987,125	12948,06667	12043,75	9764,736842
10633,35294	11648,3125	10987,125	12948,06667	12043,75
10219,3	10633,35294	11648,3125	10987,125	12948,06667
9037,6	10219,3	10633,35294	11648,3125	10987,125
10296,31579	9037,6	10219,3	10633,35294	11648,3125
11705,41176	10296,31579	9037,6	10219,3	10633,35294
10681,94444	11705,41176	10296,31579	9037,6	10219,3
9362,947368	10681,94444	11705,41176	10296,31579	9037,6
11306,35294	9362,947368	10681,94444	11705,41176	10296,31579
10984,45	11306,35294	9362,947368	10681,94444	11705,41176
10062,61905	10984,45	11306,35294	9362,947368	10681,94444
8118,583333	10062,61905	10984,45	11306,35294	9362,947368
8867,48	8118,583333	10062,61905	10984,45	11306,35294
8346,72	8867,48	8118,583333	10062,61905	10984,45
8529,307692	8346,72	8867,48	8118,583333	10062,61905
10697,18182	8529,307692	8346,72	8867,48	8118,583333
8591,84	10697,18182	8529,307692	8346,72	8867,48
8695,607143	8591,84	10697,18182	8529,307692	8346,72
8125,571429	8695,607143	8591,84	10697,18182	8529,307692
7009,758621	8125,571429	8695,607143	8591,84	10697,18182
7883,466667	7009,758621	8125,571429	8695,607143	8591,84
7527,645161	7883,466667	7009,758621	8125,571429	8695,607143
6763,758621	7527,645161	7883,466667	7009,758621	8125,571429
6682,333333	6763,758621	7527,645161	7883,466667	7009,758621
7855,681818	6682,333333	6763,758621	7527,645161	7883,466667
6738,88	7855,681818	6682,333333	6763,758621	7527,645161
7895,434783	6738,88	7855,681818	6682,333333	6763,758621
6361,884615	7895,434783	6738,88	7855,681818	6682,333333
6935,956522	6361,884615	7895,434783	6738,88	7855,681818
8344,454545	6935,956522	6361,884615	7895,434783	6738,88
9107,944444	8344,454545	6935,956522	6361,884615	7895,434783

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Yt[t] = + 17424.9461206477 -0.0769106839892755`Yt-1`[t] + 0.289791286320848`Yt-2`[t] -0.229658583665953`Yt-3`[t] -0.248195965086348`Yt-4`[t] -482.673839824678M1[t] + 682.241579160312M2[t] -52.7929730074228M3[t] + 704.745486669194M4[t] + 478.005060254843M5[t] -46.9907665014013M6[t] + 655.375475998909M7[t] + 604.825547340014M8[t] -1081.26087721467M9[t] + 581.745707803682M10[t] + 738.588003595692M11[t] -155.411865696257t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Yt[t] =  +  17424.9461206477 -0.0769106839892755`Yt-1`[t] +  0.289791286320848`Yt-2`[t] -0.229658583665953`Yt-3`[t] -0.248195965086348`Yt-4`[t] -482.673839824678M1[t] +  682.241579160312M2[t] -52.7929730074228M3[t] +  704.745486669194M4[t] +  478.005060254843M5[t] -46.9907665014013M6[t] +  655.375475998909M7[t] +  604.825547340014M8[t] -1081.26087721467M9[t] +  581.745707803682M10[t] +  738.588003595692M11[t] -155.411865696257t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Yt[t] =  +  17424.9461206477 -0.0769106839892755`Yt-1`[t] +  0.289791286320848`Yt-2`[t] -0.229658583665953`Yt-3`[t] -0.248195965086348`Yt-4`[t] -482.673839824678M1[t] +  682.241579160312M2[t] -52.7929730074228M3[t] +  704.745486669194M4[t] +  478.005060254843M5[t] -46.9907665014013M6[t] +  655.375475998909M7[t] +  604.825547340014M8[t] -1081.26087721467M9[t] +  581.745707803682M10[t] +  738.588003595692M11[t] -155.411865696257t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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
Yt[t] = + 17424.9461206477 -0.0769106839892755`Yt-1`[t] + 0.289791286320848`Yt-2`[t] -0.229658583665953`Yt-3`[t] -0.248195965086348`Yt-4`[t] -482.673839824678M1[t] + 682.241579160312M2[t] -52.7929730074228M3[t] + 704.745486669194M4[t] + 478.005060254843M5[t] -46.9907665014013M6[t] + 655.375475998909M7[t] + 604.825547340014M8[t] -1081.26087721467M9[t] + 581.745707803682M10[t] + 738.588003595692M11[t] -155.411865696257t + 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)	17424.9461206477	4246.586402	4.1033	0.000201	1e-04
`Yt-1`	-0.0769106839892755	0.15908	-0.4835	0.631464	0.315732
`Yt-2`	0.289791286320848	0.15753	1.8396	0.073448	0.036724
`Yt-3`	-0.229658583665953	0.153446	-1.4967	0.142529	0.071264
`Yt-4`	-0.248195965086348	0.154441	-1.6071	0.11611	0.058055
M1	-482.673839824678	1029.309839	-0.4689	0.641731	0.320865
M2	682.241579160312	1012.524968	0.6738	0.504411	0.252206
M3	-52.7929730074228	1017.625576	-0.0519	0.95889	0.479445
M4	704.745486669194	1009.209135	0.6983	0.489124	0.244562
M5	478.005060254843	1029.194885	0.4644	0.644911	0.322455
M6	-46.9907665014013	1006.65	-0.0467	0.963006	0.481503
M7	655.375475998909	1037.091521	0.6319	0.531116	0.265558
M8	604.825547340014	1024.984216	0.5901	0.558539	0.279269
M9	-1081.26087721467	1068.706898	-1.0117	0.317895	0.158948
M10	581.745707803682	1106.005417	0.526	0.601875	0.300937
M11	738.588003595692	1086.066772	0.6801	0.500485	0.250243
t	-155.411865696257	39.28188	-3.9563	0.000312	0.000156

\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) & 17424.9461206477 & 4246.586402 & 4.1033 & 0.000201 & 1e-04 \tabularnewline
`Yt-1` & -0.0769106839892755 & 0.15908 & -0.4835 & 0.631464 & 0.315732 \tabularnewline
`Yt-2` & 0.289791286320848 & 0.15753 & 1.8396 & 0.073448 & 0.036724 \tabularnewline
`Yt-3` & -0.229658583665953 & 0.153446 & -1.4967 & 0.142529 & 0.071264 \tabularnewline
`Yt-4` & -0.248195965086348 & 0.154441 & -1.6071 & 0.11611 & 0.058055 \tabularnewline
M1 & -482.673839824678 & 1029.309839 & -0.4689 & 0.641731 & 0.320865 \tabularnewline
M2 & 682.241579160312 & 1012.524968 & 0.6738 & 0.504411 & 0.252206 \tabularnewline
M3 & -52.7929730074228 & 1017.625576 & -0.0519 & 0.95889 & 0.479445 \tabularnewline
M4 & 704.745486669194 & 1009.209135 & 0.6983 & 0.489124 & 0.244562 \tabularnewline
M5 & 478.005060254843 & 1029.194885 & 0.4644 & 0.644911 & 0.322455 \tabularnewline
M6 & -46.9907665014013 & 1006.65 & -0.0467 & 0.963006 & 0.481503 \tabularnewline
M7 & 655.375475998909 & 1037.091521 & 0.6319 & 0.531116 & 0.265558 \tabularnewline
M8 & 604.825547340014 & 1024.984216 & 0.5901 & 0.558539 & 0.279269 \tabularnewline
M9 & -1081.26087721467 & 1068.706898 & -1.0117 & 0.317895 & 0.158948 \tabularnewline
M10 & 581.745707803682 & 1106.005417 & 0.526 & 0.601875 & 0.300937 \tabularnewline
M11 & 738.588003595692 & 1086.066772 & 0.6801 & 0.500485 & 0.250243 \tabularnewline
t & -155.411865696257 & 39.28188 & -3.9563 & 0.000312 & 0.000156 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&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]17424.9461206477[/C][C]4246.586402[/C][C]4.1033[/C][C]0.000201[/C][C]1e-04[/C][/ROW]
[ROW][C]`Yt-1`[/C][C]-0.0769106839892755[/C][C]0.15908[/C][C]-0.4835[/C][C]0.631464[/C][C]0.315732[/C][/ROW]
[ROW][C]`Yt-2`[/C][C]0.289791286320848[/C][C]0.15753[/C][C]1.8396[/C][C]0.073448[/C][C]0.036724[/C][/ROW]
[ROW][C]`Yt-3`[/C][C]-0.229658583665953[/C][C]0.153446[/C][C]-1.4967[/C][C]0.142529[/C][C]0.071264[/C][/ROW]
[ROW][C]`Yt-4`[/C][C]-0.248195965086348[/C][C]0.154441[/C][C]-1.6071[/C][C]0.11611[/C][C]0.058055[/C][/ROW]
[ROW][C]M1[/C][C]-482.673839824678[/C][C]1029.309839[/C][C]-0.4689[/C][C]0.641731[/C][C]0.320865[/C][/ROW]
[ROW][C]M2[/C][C]682.241579160312[/C][C]1012.524968[/C][C]0.6738[/C][C]0.504411[/C][C]0.252206[/C][/ROW]
[ROW][C]M3[/C][C]-52.7929730074228[/C][C]1017.625576[/C][C]-0.0519[/C][C]0.95889[/C][C]0.479445[/C][/ROW]
[ROW][C]M4[/C][C]704.745486669194[/C][C]1009.209135[/C][C]0.6983[/C][C]0.489124[/C][C]0.244562[/C][/ROW]
[ROW][C]M5[/C][C]478.005060254843[/C][C]1029.194885[/C][C]0.4644[/C][C]0.644911[/C][C]0.322455[/C][/ROW]
[ROW][C]M6[/C][C]-46.9907665014013[/C][C]1006.65[/C][C]-0.0467[/C][C]0.963006[/C][C]0.481503[/C][/ROW]
[ROW][C]M7[/C][C]655.375475998909[/C][C]1037.091521[/C][C]0.6319[/C][C]0.531116[/C][C]0.265558[/C][/ROW]
[ROW][C]M8[/C][C]604.825547340014[/C][C]1024.984216[/C][C]0.5901[/C][C]0.558539[/C][C]0.279269[/C][/ROW]
[ROW][C]M9[/C][C]-1081.26087721467[/C][C]1068.706898[/C][C]-1.0117[/C][C]0.317895[/C][C]0.158948[/C][/ROW]
[ROW][C]M10[/C][C]581.745707803682[/C][C]1106.005417[/C][C]0.526[/C][C]0.601875[/C][C]0.300937[/C][/ROW]
[ROW][C]M11[/C][C]738.588003595692[/C][C]1086.066772[/C][C]0.6801[/C][C]0.500485[/C][C]0.250243[/C][/ROW]
[ROW][C]t[/C][C]-155.411865696257[/C][C]39.28188[/C][C]-3.9563[/C][C]0.000312[/C][C]0.000156[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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)	17424.9461206477	4246.586402	4.1033	0.000201	1e-04
`Yt-1`	-0.0769106839892755	0.15908	-0.4835	0.631464	0.315732
`Yt-2`	0.289791286320848	0.15753	1.8396	0.073448	0.036724
`Yt-3`	-0.229658583665953	0.153446	-1.4967	0.142529	0.071264
`Yt-4`	-0.248195965086348	0.154441	-1.6071	0.11611	0.058055
M1	-482.673839824678	1029.309839	-0.4689	0.641731	0.320865
M2	682.241579160312	1012.524968	0.6738	0.504411	0.252206
M3	-52.7929730074228	1017.625576	-0.0519	0.95889	0.479445
M4	704.745486669194	1009.209135	0.6983	0.489124	0.244562
M5	478.005060254843	1029.194885	0.4644	0.644911	0.322455
M6	-46.9907665014013	1006.65	-0.0467	0.963006	0.481503
M7	655.375475998909	1037.091521	0.6319	0.531116	0.265558
M8	604.825547340014	1024.984216	0.5901	0.558539	0.279269
M9	-1081.26087721467	1068.706898	-1.0117	0.317895	0.158948
M10	581.745707803682	1106.005417	0.526	0.601875	0.300937
M11	738.588003595692	1086.066772	0.6801	0.500485	0.250243
t	-155.411865696257	39.28188	-3.9563	0.000312	0.000156

Multiple Linear Regression - Regression Statistics
Multiple R	0.866131590033882
R-squared	0.75018393125462
Adjusted R-squared	0.647695287666772
F-TEST (value)	7.31967860040609
F-TEST (DF numerator)	16
F-TEST (DF denominator)	39
p-value	2.06504179978140e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1472.27954328209
Sum Squared Residuals	84536675.0891104

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.866131590033882 \tabularnewline
R-squared & 0.75018393125462 \tabularnewline
Adjusted R-squared & 0.647695287666772 \tabularnewline
F-TEST (value) & 7.31967860040609 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 39 \tabularnewline
p-value & 2.06504179978140e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1472.27954328209 \tabularnewline
Sum Squared Residuals & 84536675.0891104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.866131590033882[/C][/ROW]
[ROW][C]R-squared[/C][C]0.75018393125462[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.647695287666772[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.31967860040609[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]39[/C][/ROW]
[ROW][C]p-value[/C][C]2.06504179978140e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1472.27954328209[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]84536675.0891104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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	0.866131590033882
R-squared	0.75018393125462
Adjusted R-squared	0.647695287666772
F-TEST (value)	7.31967860040609
F-TEST (DF numerator)	16
F-TEST (DF denominator)	39
p-value	2.06504179978140e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1472.27954328209
Sum Squared Residuals	84536675.0891104

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	15335.63636	13947.7887123181	1387.84764768191
2	11188.5	14509.2671097938	-3320.7671097938
3	13633.25	14126.9903575724	-493.740357572352
4	12298.46667	12743.4322415577	-444.965571557676
5	15353.63636	13755.0272931160	1598.60906688404
6	12696.15385	12920.6805207395	-224.526670739477
7	12213.93333	14257.1526128946	-2043.21928289462
8	13683.72727	12947.8053465242	735.921923475761
9	11214.14286	10705.5537738950	508.589086104975
10	13950.23077	13599.3419137661	350.888856233935
11	11179.13333	12456.8083012018	-1277.67497120176
12	11801.875	12771.1941987747	-969.319198774715
13	11188.82353	11266.7479256135	-77.9243956134569
14	16456.27273	12461.1871302626	3995.08559973738
15	11110.0625	11532.7178510228	-422.655351022818
16	16530.69231	14058.7165723701	2471.97573762993
17	10038.41176	10652.8167728662	-614.405012866218
18	11681.25	11963.0295351384	-281.77953513844
19	11148.88235	10584.2394103273	564.642939672658
20	8631	11040.9320916604	-2409.93209166037
21	9386.444444	10472.8762823912	-1086.43183839125
22	9764.736842	10907.2258658294	-1142.48902382937
23	12043.75	11808.8675830287	234.882416971303
24	12948.06667	11300.647032341	1647.41963765900
25	10987.125	10979.0715086024	8.0534913975518
26	11648.3125	11784.1699372059	-135.857437205851
27	10633.35294	9501.28137171024	1132.07156828976
28	10219.3	10598.9749137253	-379.67491372535
29	9037.6	10289.3917054579	-1251.79170545792
30	10296.31579	9648.8705395503	647.445250449693
31	11705.41176	10103.5695402330	1601.84221976705
32	10681.94444	10528.1518962732	153.792543726773
33	9362.947368	9177.93119771314	185.016170286857
34	11306.35294	9854.35980786439	1451.99313213561
35	10984.45	9209.4038489669	1775.04615103311
36	10062.61905	9460.28321415828	602.335835841715
37	8118.583333	8680.86147054834	-562.278137548336
38	8867.48	9164.32591554183	-296.84591554183
39	8346.72	7944.51813291235	402.201867087648
40	8529.307692	9478.97967478225	-949.671982782253
41	10697.18182	9242.38400121523	1454.79781878477
42	8591.84	8381.8798219205	209.960178079504
43	8695.607143	9806.3062103056	-1110.69906730559
44	8125.571429	8439.06546961245	-313.494040612452
45	7009.758621	6616.93203900059	392.826581999415
46	7883.466667	8543.85963154018	-660.392964540184
47	7527.645161	8259.89875780266	-732.253596802657
48	6763.758621	8044.194895726	-1280.43627472600
49	6682.333333	7438.03193891767	-755.698605917668
50	7855.681818	8097.29695519589	-241.615137195890
51	6738.88	7356.75772678224	-617.877726782237
52	7895.434783	8593.09805256466	-697.663269564656
53	6361.884615	7549.09478234467	-1187.21016734467
54	6935.956522	7287.05574465128	-351.099222651279
55	8344.454545	7357.0213542395	987.433190760505
56	9107.944444	7274.23277892971	1833.71166507029

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 15335.63636 & 13947.7887123181 & 1387.84764768191 \tabularnewline
2 & 11188.5 & 14509.2671097938 & -3320.7671097938 \tabularnewline
3 & 13633.25 & 14126.9903575724 & -493.740357572352 \tabularnewline
4 & 12298.46667 & 12743.4322415577 & -444.965571557676 \tabularnewline
5 & 15353.63636 & 13755.0272931160 & 1598.60906688404 \tabularnewline
6 & 12696.15385 & 12920.6805207395 & -224.526670739477 \tabularnewline
7 & 12213.93333 & 14257.1526128946 & -2043.21928289462 \tabularnewline
8 & 13683.72727 & 12947.8053465242 & 735.921923475761 \tabularnewline
9 & 11214.14286 & 10705.5537738950 & 508.589086104975 \tabularnewline
10 & 13950.23077 & 13599.3419137661 & 350.888856233935 \tabularnewline
11 & 11179.13333 & 12456.8083012018 & -1277.67497120176 \tabularnewline
12 & 11801.875 & 12771.1941987747 & -969.319198774715 \tabularnewline
13 & 11188.82353 & 11266.7479256135 & -77.9243956134569 \tabularnewline
14 & 16456.27273 & 12461.1871302626 & 3995.08559973738 \tabularnewline
15 & 11110.0625 & 11532.7178510228 & -422.655351022818 \tabularnewline
16 & 16530.69231 & 14058.7165723701 & 2471.97573762993 \tabularnewline
17 & 10038.41176 & 10652.8167728662 & -614.405012866218 \tabularnewline
18 & 11681.25 & 11963.0295351384 & -281.77953513844 \tabularnewline
19 & 11148.88235 & 10584.2394103273 & 564.642939672658 \tabularnewline
20 & 8631 & 11040.9320916604 & -2409.93209166037 \tabularnewline
21 & 9386.444444 & 10472.8762823912 & -1086.43183839125 \tabularnewline
22 & 9764.736842 & 10907.2258658294 & -1142.48902382937 \tabularnewline
23 & 12043.75 & 11808.8675830287 & 234.882416971303 \tabularnewline
24 & 12948.06667 & 11300.647032341 & 1647.41963765900 \tabularnewline
25 & 10987.125 & 10979.0715086024 & 8.0534913975518 \tabularnewline
26 & 11648.3125 & 11784.1699372059 & -135.857437205851 \tabularnewline
27 & 10633.35294 & 9501.28137171024 & 1132.07156828976 \tabularnewline
28 & 10219.3 & 10598.9749137253 & -379.67491372535 \tabularnewline
29 & 9037.6 & 10289.3917054579 & -1251.79170545792 \tabularnewline
30 & 10296.31579 & 9648.8705395503 & 647.445250449693 \tabularnewline
31 & 11705.41176 & 10103.5695402330 & 1601.84221976705 \tabularnewline
32 & 10681.94444 & 10528.1518962732 & 153.792543726773 \tabularnewline
33 & 9362.947368 & 9177.93119771314 & 185.016170286857 \tabularnewline
34 & 11306.35294 & 9854.35980786439 & 1451.99313213561 \tabularnewline
35 & 10984.45 & 9209.4038489669 & 1775.04615103311 \tabularnewline
36 & 10062.61905 & 9460.28321415828 & 602.335835841715 \tabularnewline
37 & 8118.583333 & 8680.86147054834 & -562.278137548336 \tabularnewline
38 & 8867.48 & 9164.32591554183 & -296.84591554183 \tabularnewline
39 & 8346.72 & 7944.51813291235 & 402.201867087648 \tabularnewline
40 & 8529.307692 & 9478.97967478225 & -949.671982782253 \tabularnewline
41 & 10697.18182 & 9242.38400121523 & 1454.79781878477 \tabularnewline
42 & 8591.84 & 8381.8798219205 & 209.960178079504 \tabularnewline
43 & 8695.607143 & 9806.3062103056 & -1110.69906730559 \tabularnewline
44 & 8125.571429 & 8439.06546961245 & -313.494040612452 \tabularnewline
45 & 7009.758621 & 6616.93203900059 & 392.826581999415 \tabularnewline
46 & 7883.466667 & 8543.85963154018 & -660.392964540184 \tabularnewline
47 & 7527.645161 & 8259.89875780266 & -732.253596802657 \tabularnewline
48 & 6763.758621 & 8044.194895726 & -1280.43627472600 \tabularnewline
49 & 6682.333333 & 7438.03193891767 & -755.698605917668 \tabularnewline
50 & 7855.681818 & 8097.29695519589 & -241.615137195890 \tabularnewline
51 & 6738.88 & 7356.75772678224 & -617.877726782237 \tabularnewline
52 & 7895.434783 & 8593.09805256466 & -697.663269564656 \tabularnewline
53 & 6361.884615 & 7549.09478234467 & -1187.21016734467 \tabularnewline
54 & 6935.956522 & 7287.05574465128 & -351.099222651279 \tabularnewline
55 & 8344.454545 & 7357.0213542395 & 987.433190760505 \tabularnewline
56 & 9107.944444 & 7274.23277892971 & 1833.71166507029 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&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]15335.63636[/C][C]13947.7887123181[/C][C]1387.84764768191[/C][/ROW]
[ROW][C]2[/C][C]11188.5[/C][C]14509.2671097938[/C][C]-3320.7671097938[/C][/ROW]
[ROW][C]3[/C][C]13633.25[/C][C]14126.9903575724[/C][C]-493.740357572352[/C][/ROW]
[ROW][C]4[/C][C]12298.46667[/C][C]12743.4322415577[/C][C]-444.965571557676[/C][/ROW]
[ROW][C]5[/C][C]15353.63636[/C][C]13755.0272931160[/C][C]1598.60906688404[/C][/ROW]
[ROW][C]6[/C][C]12696.15385[/C][C]12920.6805207395[/C][C]-224.526670739477[/C][/ROW]
[ROW][C]7[/C][C]12213.93333[/C][C]14257.1526128946[/C][C]-2043.21928289462[/C][/ROW]
[ROW][C]8[/C][C]13683.72727[/C][C]12947.8053465242[/C][C]735.921923475761[/C][/ROW]
[ROW][C]9[/C][C]11214.14286[/C][C]10705.5537738950[/C][C]508.589086104975[/C][/ROW]
[ROW][C]10[/C][C]13950.23077[/C][C]13599.3419137661[/C][C]350.888856233935[/C][/ROW]
[ROW][C]11[/C][C]11179.13333[/C][C]12456.8083012018[/C][C]-1277.67497120176[/C][/ROW]
[ROW][C]12[/C][C]11801.875[/C][C]12771.1941987747[/C][C]-969.319198774715[/C][/ROW]
[ROW][C]13[/C][C]11188.82353[/C][C]11266.7479256135[/C][C]-77.9243956134569[/C][/ROW]
[ROW][C]14[/C][C]16456.27273[/C][C]12461.1871302626[/C][C]3995.08559973738[/C][/ROW]
[ROW][C]15[/C][C]11110.0625[/C][C]11532.7178510228[/C][C]-422.655351022818[/C][/ROW]
[ROW][C]16[/C][C]16530.69231[/C][C]14058.7165723701[/C][C]2471.97573762993[/C][/ROW]
[ROW][C]17[/C][C]10038.41176[/C][C]10652.8167728662[/C][C]-614.405012866218[/C][/ROW]
[ROW][C]18[/C][C]11681.25[/C][C]11963.0295351384[/C][C]-281.77953513844[/C][/ROW]
[ROW][C]19[/C][C]11148.88235[/C][C]10584.2394103273[/C][C]564.642939672658[/C][/ROW]
[ROW][C]20[/C][C]8631[/C][C]11040.9320916604[/C][C]-2409.93209166037[/C][/ROW]
[ROW][C]21[/C][C]9386.444444[/C][C]10472.8762823912[/C][C]-1086.43183839125[/C][/ROW]
[ROW][C]22[/C][C]9764.736842[/C][C]10907.2258658294[/C][C]-1142.48902382937[/C][/ROW]
[ROW][C]23[/C][C]12043.75[/C][C]11808.8675830287[/C][C]234.882416971303[/C][/ROW]
[ROW][C]24[/C][C]12948.06667[/C][C]11300.647032341[/C][C]1647.41963765900[/C][/ROW]
[ROW][C]25[/C][C]10987.125[/C][C]10979.0715086024[/C][C]8.0534913975518[/C][/ROW]
[ROW][C]26[/C][C]11648.3125[/C][C]11784.1699372059[/C][C]-135.857437205851[/C][/ROW]
[ROW][C]27[/C][C]10633.35294[/C][C]9501.28137171024[/C][C]1132.07156828976[/C][/ROW]
[ROW][C]28[/C][C]10219.3[/C][C]10598.9749137253[/C][C]-379.67491372535[/C][/ROW]
[ROW][C]29[/C][C]9037.6[/C][C]10289.3917054579[/C][C]-1251.79170545792[/C][/ROW]
[ROW][C]30[/C][C]10296.31579[/C][C]9648.8705395503[/C][C]647.445250449693[/C][/ROW]
[ROW][C]31[/C][C]11705.41176[/C][C]10103.5695402330[/C][C]1601.84221976705[/C][/ROW]
[ROW][C]32[/C][C]10681.94444[/C][C]10528.1518962732[/C][C]153.792543726773[/C][/ROW]
[ROW][C]33[/C][C]9362.947368[/C][C]9177.93119771314[/C][C]185.016170286857[/C][/ROW]
[ROW][C]34[/C][C]11306.35294[/C][C]9854.35980786439[/C][C]1451.99313213561[/C][/ROW]
[ROW][C]35[/C][C]10984.45[/C][C]9209.4038489669[/C][C]1775.04615103311[/C][/ROW]
[ROW][C]36[/C][C]10062.61905[/C][C]9460.28321415828[/C][C]602.335835841715[/C][/ROW]
[ROW][C]37[/C][C]8118.583333[/C][C]8680.86147054834[/C][C]-562.278137548336[/C][/ROW]
[ROW][C]38[/C][C]8867.48[/C][C]9164.32591554183[/C][C]-296.84591554183[/C][/ROW]
[ROW][C]39[/C][C]8346.72[/C][C]7944.51813291235[/C][C]402.201867087648[/C][/ROW]
[ROW][C]40[/C][C]8529.307692[/C][C]9478.97967478225[/C][C]-949.671982782253[/C][/ROW]
[ROW][C]41[/C][C]10697.18182[/C][C]9242.38400121523[/C][C]1454.79781878477[/C][/ROW]
[ROW][C]42[/C][C]8591.84[/C][C]8381.8798219205[/C][C]209.960178079504[/C][/ROW]
[ROW][C]43[/C][C]8695.607143[/C][C]9806.3062103056[/C][C]-1110.69906730559[/C][/ROW]
[ROW][C]44[/C][C]8125.571429[/C][C]8439.06546961245[/C][C]-313.494040612452[/C][/ROW]
[ROW][C]45[/C][C]7009.758621[/C][C]6616.93203900059[/C][C]392.826581999415[/C][/ROW]
[ROW][C]46[/C][C]7883.466667[/C][C]8543.85963154018[/C][C]-660.392964540184[/C][/ROW]
[ROW][C]47[/C][C]7527.645161[/C][C]8259.89875780266[/C][C]-732.253596802657[/C][/ROW]
[ROW][C]48[/C][C]6763.758621[/C][C]8044.194895726[/C][C]-1280.43627472600[/C][/ROW]
[ROW][C]49[/C][C]6682.333333[/C][C]7438.03193891767[/C][C]-755.698605917668[/C][/ROW]
[ROW][C]50[/C][C]7855.681818[/C][C]8097.29695519589[/C][C]-241.615137195890[/C][/ROW]
[ROW][C]51[/C][C]6738.88[/C][C]7356.75772678224[/C][C]-617.877726782237[/C][/ROW]
[ROW][C]52[/C][C]7895.434783[/C][C]8593.09805256466[/C][C]-697.663269564656[/C][/ROW]
[ROW][C]53[/C][C]6361.884615[/C][C]7549.09478234467[/C][C]-1187.21016734467[/C][/ROW]
[ROW][C]54[/C][C]6935.956522[/C][C]7287.05574465128[/C][C]-351.099222651279[/C][/ROW]
[ROW][C]55[/C][C]8344.454545[/C][C]7357.0213542395[/C][C]987.433190760505[/C][/ROW]
[ROW][C]56[/C][C]9107.944444[/C][C]7274.23277892971[/C][C]1833.71166507029[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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	15335.63636	13947.7887123181	1387.84764768191
2	11188.5	14509.2671097938	-3320.7671097938
3	13633.25	14126.9903575724	-493.740357572352
4	12298.46667	12743.4322415577	-444.965571557676
5	15353.63636	13755.0272931160	1598.60906688404
6	12696.15385	12920.6805207395	-224.526670739477
7	12213.93333	14257.1526128946	-2043.21928289462
8	13683.72727	12947.8053465242	735.921923475761
9	11214.14286	10705.5537738950	508.589086104975
10	13950.23077	13599.3419137661	350.888856233935
11	11179.13333	12456.8083012018	-1277.67497120176
12	11801.875	12771.1941987747	-969.319198774715
13	11188.82353	11266.7479256135	-77.9243956134569
14	16456.27273	12461.1871302626	3995.08559973738
15	11110.0625	11532.7178510228	-422.655351022818
16	16530.69231	14058.7165723701	2471.97573762993
17	10038.41176	10652.8167728662	-614.405012866218
18	11681.25	11963.0295351384	-281.77953513844
19	11148.88235	10584.2394103273	564.642939672658
20	8631	11040.9320916604	-2409.93209166037
21	9386.444444	10472.8762823912	-1086.43183839125
22	9764.736842	10907.2258658294	-1142.48902382937
23	12043.75	11808.8675830287	234.882416971303
24	12948.06667	11300.647032341	1647.41963765900
25	10987.125	10979.0715086024	8.0534913975518
26	11648.3125	11784.1699372059	-135.857437205851
27	10633.35294	9501.28137171024	1132.07156828976
28	10219.3	10598.9749137253	-379.67491372535
29	9037.6	10289.3917054579	-1251.79170545792
30	10296.31579	9648.8705395503	647.445250449693
31	11705.41176	10103.5695402330	1601.84221976705
32	10681.94444	10528.1518962732	153.792543726773
33	9362.947368	9177.93119771314	185.016170286857
34	11306.35294	9854.35980786439	1451.99313213561
35	10984.45	9209.4038489669	1775.04615103311
36	10062.61905	9460.28321415828	602.335835841715
37	8118.583333	8680.86147054834	-562.278137548336
38	8867.48	9164.32591554183	-296.84591554183
39	8346.72	7944.51813291235	402.201867087648
40	8529.307692	9478.97967478225	-949.671982782253
41	10697.18182	9242.38400121523	1454.79781878477
42	8591.84	8381.8798219205	209.960178079504
43	8695.607143	9806.3062103056	-1110.69906730559
44	8125.571429	8439.06546961245	-313.494040612452
45	7009.758621	6616.93203900059	392.826581999415
46	7883.466667	8543.85963154018	-660.392964540184
47	7527.645161	8259.89875780266	-732.253596802657
48	6763.758621	8044.194895726	-1280.43627472600
49	6682.333333	7438.03193891767	-755.698605917668
50	7855.681818	8097.29695519589	-241.615137195890
51	6738.88	7356.75772678224	-617.877726782237
52	7895.434783	8593.09805256466	-697.663269564656
53	6361.884615	7549.09478234467	-1187.21016734467
54	6935.956522	7287.05574465128	-351.099222651279
55	8344.454545	7357.0213542395	987.433190760505
56	9107.944444	7274.23277892971	1833.71166507029

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
20	0.97612878428926	0.0477424314214779	0.0238712157107390
21	0.999620903367533	0.000758193264934088	0.000379096632467044
22	0.999737789650786	0.000524420698427547	0.000262210349213774
23	0.99915982928248	0.00168034143503955	0.000840170717519775
24	0.998472940966378	0.00305411806724405	0.00152705903362203
25	0.99604498485283	0.0079100302943398	0.0039550151471699
26	0.991336782343626	0.0173264353127479	0.00866321765637397
27	0.983390571312652	0.0332188573746952	0.0166094286873476
28	0.97057254211588	0.0588549157682404	0.0294274578841202
29	0.973959512124225	0.0520809757515499	0.0260404878757749
30	0.948895757802278	0.102208484395445	0.0511042421977225
31	0.915054702655165	0.169890594689670	0.0849452973448348
32	0.87294780381496	0.254104392370081	0.127052196185040
33	0.788129886328093	0.423740227343813	0.211870113671907
34	0.764206198261504	0.471587603476992	0.235793801738496
35	0.685356244109053	0.629287511781894	0.314643755890947
36	0.603496369793614	0.793007260412773	0.396503630206386

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.97612878428926 & 0.0477424314214779 & 0.0238712157107390 \tabularnewline
21 & 0.999620903367533 & 0.000758193264934088 & 0.000379096632467044 \tabularnewline
22 & 0.999737789650786 & 0.000524420698427547 & 0.000262210349213774 \tabularnewline
23 & 0.99915982928248 & 0.00168034143503955 & 0.000840170717519775 \tabularnewline
24 & 0.998472940966378 & 0.00305411806724405 & 0.00152705903362203 \tabularnewline
25 & 0.99604498485283 & 0.0079100302943398 & 0.0039550151471699 \tabularnewline
26 & 0.991336782343626 & 0.0173264353127479 & 0.00866321765637397 \tabularnewline
27 & 0.983390571312652 & 0.0332188573746952 & 0.0166094286873476 \tabularnewline
28 & 0.97057254211588 & 0.0588549157682404 & 0.0294274578841202 \tabularnewline
29 & 0.973959512124225 & 0.0520809757515499 & 0.0260404878757749 \tabularnewline
30 & 0.948895757802278 & 0.102208484395445 & 0.0511042421977225 \tabularnewline
31 & 0.915054702655165 & 0.169890594689670 & 0.0849452973448348 \tabularnewline
32 & 0.87294780381496 & 0.254104392370081 & 0.127052196185040 \tabularnewline
33 & 0.788129886328093 & 0.423740227343813 & 0.211870113671907 \tabularnewline
34 & 0.764206198261504 & 0.471587603476992 & 0.235793801738496 \tabularnewline
35 & 0.685356244109053 & 0.629287511781894 & 0.314643755890947 \tabularnewline
36 & 0.603496369793614 & 0.793007260412773 & 0.396503630206386 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&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]20[/C][C]0.97612878428926[/C][C]0.0477424314214779[/C][C]0.0238712157107390[/C][/ROW]
[ROW][C]21[/C][C]0.999620903367533[/C][C]0.000758193264934088[/C][C]0.000379096632467044[/C][/ROW]
[ROW][C]22[/C][C]0.999737789650786[/C][C]0.000524420698427547[/C][C]0.000262210349213774[/C][/ROW]
[ROW][C]23[/C][C]0.99915982928248[/C][C]0.00168034143503955[/C][C]0.000840170717519775[/C][/ROW]
[ROW][C]24[/C][C]0.998472940966378[/C][C]0.00305411806724405[/C][C]0.00152705903362203[/C][/ROW]
[ROW][C]25[/C][C]0.99604498485283[/C][C]0.0079100302943398[/C][C]0.0039550151471699[/C][/ROW]
[ROW][C]26[/C][C]0.991336782343626[/C][C]0.0173264353127479[/C][C]0.00866321765637397[/C][/ROW]
[ROW][C]27[/C][C]0.983390571312652[/C][C]0.0332188573746952[/C][C]0.0166094286873476[/C][/ROW]
[ROW][C]28[/C][C]0.97057254211588[/C][C]0.0588549157682404[/C][C]0.0294274578841202[/C][/ROW]
[ROW][C]29[/C][C]0.973959512124225[/C][C]0.0520809757515499[/C][C]0.0260404878757749[/C][/ROW]
[ROW][C]30[/C][C]0.948895757802278[/C][C]0.102208484395445[/C][C]0.0511042421977225[/C][/ROW]
[ROW][C]31[/C][C]0.915054702655165[/C][C]0.169890594689670[/C][C]0.0849452973448348[/C][/ROW]
[ROW][C]32[/C][C]0.87294780381496[/C][C]0.254104392370081[/C][C]0.127052196185040[/C][/ROW]
[ROW][C]33[/C][C]0.788129886328093[/C][C]0.423740227343813[/C][C]0.211870113671907[/C][/ROW]
[ROW][C]34[/C][C]0.764206198261504[/C][C]0.471587603476992[/C][C]0.235793801738496[/C][/ROW]
[ROW][C]35[/C][C]0.685356244109053[/C][C]0.629287511781894[/C][C]0.314643755890947[/C][/ROW]
[ROW][C]36[/C][C]0.603496369793614[/C][C]0.793007260412773[/C][C]0.396503630206386[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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
20	0.97612878428926	0.0477424314214779	0.0238712157107390
21	0.999620903367533	0.000758193264934088	0.000379096632467044
22	0.999737789650786	0.000524420698427547	0.000262210349213774
23	0.99915982928248	0.00168034143503955	0.000840170717519775
24	0.998472940966378	0.00305411806724405	0.00152705903362203
25	0.99604498485283	0.0079100302943398	0.0039550151471699
26	0.991336782343626	0.0173264353127479	0.00866321765637397
27	0.983390571312652	0.0332188573746952	0.0166094286873476
28	0.97057254211588	0.0588549157682404	0.0294274578841202
29	0.973959512124225	0.0520809757515499	0.0260404878757749
30	0.948895757802278	0.102208484395445	0.0511042421977225
31	0.915054702655165	0.169890594689670	0.0849452973448348
32	0.87294780381496	0.254104392370081	0.127052196185040
33	0.788129886328093	0.423740227343813	0.211870113671907
34	0.764206198261504	0.471587603476992	0.235793801738496
35	0.685356244109053	0.629287511781894	0.314643755890947
36	0.603496369793614	0.793007260412773	0.396503630206386

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.294117647058824	NOK
5% type I error level	8	0.470588235294118	NOK
10% type I error level	10	0.588235294117647	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 & 0.294117647058824 & NOK \tabularnewline
5% type I error level & 8 & 0.470588235294118 & NOK \tabularnewline
10% type I error level & 10 & 0.588235294117647 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58243&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]5[/C][C]0.294117647058824[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.470588235294118[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.588235294117647[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58243&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58243&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	5	0.294117647058824	NOK
5% type I error level	8	0.470588235294118	NOK
10% type I error level	10	0.588235294117647	NOK

Figure 1

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code