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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:23:59 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/19/t1258655090pguan731bdrmkix.htm/, Retrieved Fri, 29 Mar 2024 11:01:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57877, Retrieved Fri, 29 Mar 2024 11:01:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact175
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [workshop 7 bereke...] [2009-11-19 18:23:59] [78d370e6d5f4594e9982a5085e7604c6] [Current]
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Dataseries X:
5246.24	0	5170.09	4920.10	4926.65
5283.61	0	5246.24	5170.09	4920.10
4979.05	0	5283.61	5246.24	5170.09
4825.20	0	4979.05	5283.61	5246.24
4695.12	0	4825.20	4979.05	5283.61
4711.54	0	4695.12	4825.20	4979.05
4727.22	0	4711.54	4695.12	4825.20
4384.96	0	4727.22	4711.54	4695.12
4378.75	0	4384.96	4727.22	4711.54
4472.93	0	4378.75	4384.96	4727.22
4564.07	0	4472.93	4378.75	4384.96
4310.54	0	4564.07	4472.93	4378.75
4171.38	0	4310.54	4564.07	4472.93
4049.38	0	4171.38	4310.54	4564.07
3591.37	0	4049.38	4171.38	4310.54
3720.46	0	3591.37	4049.38	4171.38
4107.23	0	3720.46	3591.37	4049.38
4101.71	0	4107.23	3720.46	3591.37
4162.34	0	4101.71	4107.23	3720.46
4136.22	0	4162.34	4101.71	4107.23
4125.88	0	4136.22	4162.34	4101.71
4031.48	0	4125.88	4136.22	4162.34
3761.36	0	4031.48	4125.88	4136.22
3408.56	0	3761.36	4031.48	4125.88
3228.47	0	3408.56	3761.36	4031.48
3090.45	0	3228.47	3408.56	3761.36
2741.14	0	3090.45	3228.47	3408.56
2980.44	0	2741.14	3090.45	3228.47
3104.33	0	2980.44	2741.14	3090.45
3181.57	0	3104.33	2980.44	2741.14
2863.86	0	3181.57	3104.33	2980.44
2898.01	0	2863.86	3181.57	3104.33
3112.33	0	2898.01	2863.86	3181.57
3254.33	0	3112.33	2898.01	2863.86
3513.47	0	3254.33	3112.33	2898.01
3587.61	0	3513.47	3254.33	3112.33
3727.45	0	3587.61	3513.47	3254.33
3793.34	0	3727.45	3587.61	3513.47
3817.58	0	3793.34	3727.45	3587.61
3845.13	0	3817.58	3793.34	3727.45
3931.86	0	3845.13	3817.58	3793.34
4197.52	0	3931.86	3845.13	3817.58
4307.13	0	4197.52	3931.86	3845.13
4229.43	0	4307.13	4197.52	3931.86
4362.28	0	4229.43	4307.13	4197.52
4217.34	0	4362.28	4229.43	4307.13
4361.28	0	4217.34	4362.28	4229.43
4327.74	0	4361.28	4217.34	4362.28
4417.65	0	4327.74	4361.28	4217.34
4557.68	0	4417.65	4327.74	4361.28
4650.35	0	4557.68	4417.65	4327.74
4967.18	0	4650.35	4557.68	4417.65
5123.42	0	4967.18	4650.35	4557.68
5290.85	0	5123.42	4967.18	4650.35
5535.66	0	5290.85	5123.42	4967.18
5514.06	0	5535.66	5290.85	5123.42
5493.88	0	5514.06	5535.66	5290.85
5694.83	0	5493.88	5514.06	5535.66
5850.41	0	5694.83	5493.88	5514.06
6116.64	0	5850.41	5694.83	5493.88
6175.00	0	6116.64	5850.41	5694.83
6513.58	0	6175.00	6116.64	5850.41
6383.78	0	6513.58	6175.00	6116.64
6673.66	0	6383.78	6513.58	6175.00
6936.61	0	6673.66	6383.78	6513.58
7300.68	0	6936.61	6673.66	6383.78
7392.93	0	7300.68	6936.61	6673.66
7497.31	0	7392.93	7300.68	6936.61
7584.71	0	7497.31	7392.93	7300.68
7160.79	0	7584.71	7497.31	7392.93
7196.19	0	7160.79	7584.71	7497.31
7245.63	0	7196.19	7160.79	7584.71
7347.51	0	7245.63	7196.19	7160.79
7425.75	0	7347.51	7245.63	7196.19
7778.51	0	7425.75	7347.51	7245.63
7822.33	0	7778.51	7425.75	7347.51
8181.22	0	7822.33	7778.51	7425.75
8371.47	0	8181.22	7822.33	7778.51
8347.71	0	8371.47	8181.22	7822.33
8672.11	0	8347.71	8371.47	8181.22
8802.79	0	8672.11	8347.71	8371.47
9138.46	0	8802.79	8672.11	8347.71
9123.29	0	9138.46	8802.79	8672.11
9023.21	1	9123.29	9138.46	8802.79
8850.41	1	9023.21	9123.29	9138.46
8864.58	1	8850.41	9023.21	9123.29
9163.74	1	8864.58	8850.41	9023.21
8516.66	1	9163.74	8864.58	8850.41
8553.44	1	8516.66	9163.74	8864.58
7555.20	1	8553.44	8516.66	9163.74
7851.22	1	7555.20	8553.44	8516.66
7442.00	1	7851.22	7555.20	8553.44
7992.53	1	7442.00	7851.22	7555.20
8264.04	1	7992.53	7442.00	7851.22
7517.39	1	8264.04	7992.53	7442.00
7200.40	1	7517.39	8264.04	7992.53
7193.69	1	7200.40	7517.39	8264.04
6193.58	1	7193.69	7200.40	7517.39
5104.21	1	6193.58	7193.69	7200.40
4800.46	1	5104.21	6193.58	7193.69
4461.61	1	4800.46	5104.21	6193.58
4398.59	1	4461.61	4800.46	5104.21
4243.63	1	4398.59	4461.61	4800.46
4293.82	1	4243.63	4398.59	4461.61




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 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 & 8 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&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]8 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=57877&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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 time8 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = -96.5555117821981 -360.738224795812X[t] + 1.00748531201206Y1[t] + 0.166310440331208Y2[t] -0.188431146174605Y3[t] + 100.779769951900M1[t] + 36.3284441219571M2[t] -83.1206506007616M3[t] + 92.1137994899325M4[t] + 216.725669303820M5[t] + 77.7633337740983M6[t] + 90.034499607901M7[t] + 29.6711390219347M8[t] + 179.016831664903M9[t] + 107.290835612820M10[t] -19.5422145209566M11[t] + 3.32000008834032t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -96.5555117821981 -360.738224795812X[t] +  1.00748531201206Y1[t] +  0.166310440331208Y2[t] -0.188431146174605Y3[t] +  100.779769951900M1[t] +  36.3284441219571M2[t] -83.1206506007616M3[t] +  92.1137994899325M4[t] +  216.725669303820M5[t] +  77.7633337740983M6[t] +  90.034499607901M7[t] +  29.6711390219347M8[t] +  179.016831664903M9[t] +  107.290835612820M10[t] -19.5422145209566M11[t] +  3.32000008834032t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -96.5555117821981 -360.738224795812X[t] +  1.00748531201206Y1[t] +  0.166310440331208Y2[t] -0.188431146174605Y3[t] +  100.779769951900M1[t] +  36.3284441219571M2[t] -83.1206506007616M3[t] +  92.1137994899325M4[t] +  216.725669303820M5[t] +  77.7633337740983M6[t] +  90.034499607901M7[t] +  29.6711390219347M8[t] +  179.016831664903M9[t] +  107.290835612820M10[t] -19.5422145209566M11[t] +  3.32000008834032t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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
Y[t] = -96.5555117821981 -360.738224795812X[t] + 1.00748531201206Y1[t] + 0.166310440331208Y2[t] -0.188431146174605Y3[t] + 100.779769951900M1[t] + 36.3284441219571M2[t] -83.1206506007616M3[t] + 92.1137994899325M4[t] + 216.725669303820M5[t] + 77.7633337740983M6[t] + 90.034499607901M7[t] + 29.6711390219347M8[t] + 179.016831664903M9[t] + 107.290835612820M10[t] -19.5422145209566M11[t] + 3.32000008834032t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-96.5555117821981127.205611-0.75910.4498740.224937
X-360.738224795812107.986594-3.34060.0012330.000617
Y11.007485312012060.1046139.630600
Y20.1663104403312080.1493161.11380.2684250.134213
Y3-0.1884311461746050.106365-1.77160.0799710.039985
M1100.779769951900126.6373130.79580.4283060.214153
M236.3284441219571126.7031620.28670.7750080.387504
M3-83.1206506007616126.351095-0.65790.5123680.256184
M492.1137994899325127.5150340.72240.4720010.236
M5216.725669303820128.4144821.68770.095050.047525
M677.7633337740983127.4481630.61020.5433490.271674
M790.034499607901126.6731350.71080.4791330.239566
M829.6711390219347126.7956520.2340.8155290.407764
M9179.016831664903130.3069571.37380.1730310.086515
M10107.290835612820131.7904320.81410.4178090.208905
M11-19.5422145209566130.385552-0.14990.8812060.440603
t3.320000088340321.5152482.19110.031120.01556

\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) & -96.5555117821981 & 127.205611 & -0.7591 & 0.449874 & 0.224937 \tabularnewline
X & -360.738224795812 & 107.986594 & -3.3406 & 0.001233 & 0.000617 \tabularnewline
Y1 & 1.00748531201206 & 0.104613 & 9.6306 & 0 & 0 \tabularnewline
Y2 & 0.166310440331208 & 0.149316 & 1.1138 & 0.268425 & 0.134213 \tabularnewline
Y3 & -0.188431146174605 & 0.106365 & -1.7716 & 0.079971 & 0.039985 \tabularnewline
M1 & 100.779769951900 & 126.637313 & 0.7958 & 0.428306 & 0.214153 \tabularnewline
M2 & 36.3284441219571 & 126.703162 & 0.2867 & 0.775008 & 0.387504 \tabularnewline
M3 & -83.1206506007616 & 126.351095 & -0.6579 & 0.512368 & 0.256184 \tabularnewline
M4 & 92.1137994899325 & 127.515034 & 0.7224 & 0.472001 & 0.236 \tabularnewline
M5 & 216.725669303820 & 128.414482 & 1.6877 & 0.09505 & 0.047525 \tabularnewline
M6 & 77.7633337740983 & 127.448163 & 0.6102 & 0.543349 & 0.271674 \tabularnewline
M7 & 90.034499607901 & 126.673135 & 0.7108 & 0.479133 & 0.239566 \tabularnewline
M8 & 29.6711390219347 & 126.795652 & 0.234 & 0.815529 & 0.407764 \tabularnewline
M9 & 179.016831664903 & 130.306957 & 1.3738 & 0.173031 & 0.086515 \tabularnewline
M10 & 107.290835612820 & 131.790432 & 0.8141 & 0.417809 & 0.208905 \tabularnewline
M11 & -19.5422145209566 & 130.385552 & -0.1499 & 0.881206 & 0.440603 \tabularnewline
t & 3.32000008834032 & 1.515248 & 2.1911 & 0.03112 & 0.01556 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&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]-96.5555117821981[/C][C]127.205611[/C][C]-0.7591[/C][C]0.449874[/C][C]0.224937[/C][/ROW]
[ROW][C]X[/C][C]-360.738224795812[/C][C]107.986594[/C][C]-3.3406[/C][C]0.001233[/C][C]0.000617[/C][/ROW]
[ROW][C]Y1[/C][C]1.00748531201206[/C][C]0.104613[/C][C]9.6306[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]0.166310440331208[/C][C]0.149316[/C][C]1.1138[/C][C]0.268425[/C][C]0.134213[/C][/ROW]
[ROW][C]Y3[/C][C]-0.188431146174605[/C][C]0.106365[/C][C]-1.7716[/C][C]0.079971[/C][C]0.039985[/C][/ROW]
[ROW][C]M1[/C][C]100.779769951900[/C][C]126.637313[/C][C]0.7958[/C][C]0.428306[/C][C]0.214153[/C][/ROW]
[ROW][C]M2[/C][C]36.3284441219571[/C][C]126.703162[/C][C]0.2867[/C][C]0.775008[/C][C]0.387504[/C][/ROW]
[ROW][C]M3[/C][C]-83.1206506007616[/C][C]126.351095[/C][C]-0.6579[/C][C]0.512368[/C][C]0.256184[/C][/ROW]
[ROW][C]M4[/C][C]92.1137994899325[/C][C]127.515034[/C][C]0.7224[/C][C]0.472001[/C][C]0.236[/C][/ROW]
[ROW][C]M5[/C][C]216.725669303820[/C][C]128.414482[/C][C]1.6877[/C][C]0.09505[/C][C]0.047525[/C][/ROW]
[ROW][C]M6[/C][C]77.7633337740983[/C][C]127.448163[/C][C]0.6102[/C][C]0.543349[/C][C]0.271674[/C][/ROW]
[ROW][C]M7[/C][C]90.034499607901[/C][C]126.673135[/C][C]0.7108[/C][C]0.479133[/C][C]0.239566[/C][/ROW]
[ROW][C]M8[/C][C]29.6711390219347[/C][C]126.795652[/C][C]0.234[/C][C]0.815529[/C][C]0.407764[/C][/ROW]
[ROW][C]M9[/C][C]179.016831664903[/C][C]130.306957[/C][C]1.3738[/C][C]0.173031[/C][C]0.086515[/C][/ROW]
[ROW][C]M10[/C][C]107.290835612820[/C][C]131.790432[/C][C]0.8141[/C][C]0.417809[/C][C]0.208905[/C][/ROW]
[ROW][C]M11[/C][C]-19.5422145209566[/C][C]130.385552[/C][C]-0.1499[/C][C]0.881206[/C][C]0.440603[/C][/ROW]
[ROW][C]t[/C][C]3.32000008834032[/C][C]1.515248[/C][C]2.1911[/C][C]0.03112[/C][C]0.01556[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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)-96.5555117821981127.205611-0.75910.4498740.224937
X-360.738224795812107.986594-3.34060.0012330.000617
Y11.007485312012060.1046139.630600
Y20.1663104403312080.1493161.11380.2684250.134213
Y3-0.1884311461746050.106365-1.77160.0799710.039985
M1100.779769951900126.6373130.79580.4283060.214153
M236.3284441219571126.7031620.28670.7750080.387504
M3-83.1206506007616126.351095-0.65790.5123680.256184
M492.1137994899325127.5150340.72240.4720010.236
M5216.725669303820128.4144821.68770.095050.047525
M677.7633337740983127.4481630.61020.5433490.271674
M790.034499607901126.6731350.71080.4791330.239566
M829.6711390219347126.7956520.2340.8155290.407764
M9179.016831664903130.3069571.37380.1730310.086515
M10107.290835612820131.7904320.81410.4178090.208905
M11-19.5422145209566130.385552-0.14990.8812060.440603
t3.320000088340321.5152482.19110.031120.01556







Multiple Linear Regression - Regression Statistics
Multiple R0.991885608386146
R-squared0.983837060123556
Adjusted R-squared0.980864565433635
F-TEST (value)330.980258252291
F-TEST (DF numerator)16
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation259.673901397968
Sum Squared Residuals5866456.55085003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991885608386146 \tabularnewline
R-squared & 0.983837060123556 \tabularnewline
Adjusted R-squared & 0.980864565433635 \tabularnewline
F-TEST (value) & 330.980258252291 \tabularnewline
F-TEST (DF numerator) & 16 \tabularnewline
F-TEST (DF denominator) & 87 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 259.673901397968 \tabularnewline
Sum Squared Residuals & 5866456.55085003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991885608386146[/C][/ROW]
[ROW][C]R-squared[/C][C]0.983837060123556[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.980864565433635[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]330.980258252291[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]16[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]87[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]259.673901397968[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5866456.55085003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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 R0.991885608386146
R-squared0.983837060123556
Adjusted R-squared0.980864565433635
F-TEST (value)330.980258252291
F-TEST (DF numerator)16
F-TEST (DF denominator)87
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation259.673901397968
Sum Squared Residuals5866456.55085003







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15246.245106.26368621096139.976313789040
25283.615164.66253796491118.947462035086
34979.055051.74180723946-72.6918072394601
44825.24915.32252016608-90.122520166081
54695.124830.55959517543-135.439595175435
64711.544595.66529898151115.874701018495
74727.224635.1558434875792.0641565124338
84384.964621.15179360692-236.191793606921
94378.754428.50927173319-49.7592717331867
104472.934293.97078030207178.959219697929
114564.074328.80235319720235.267646802804
124310.544460.32005383141-149.780053831408
134171.384306.40316090229-135.023160902292
144049.384045.731878541573.64812145843335
153591.373831.31876345487-239.948763454874
163720.463554.36707046051166.092929539488
174107.233759.17197434758348.058025652417
184101.714120.96709703489-19.2570970348935
194162.344170.99625638195-8.6562563819518
204136.224101.2391823150434.9808176849629
214125.884238.71290062076-112.832900620756
224031.484144.12089743679-112.640897436791
233761.363928.70340552247-167.343405522471
243408.563665.67236013525-257.112360135249
253228.473387.19543615425-158.725436154250
263090.453136.85077842823-46.4007784282287
272741.142918.1962222011-177.056222201099
282980.442755.80637618127224.633623818730
293104.333092.7428481309111.5871518690903
303181.573187.53684003621-5.96684003621169
312863.863256.45879863122-392.598798631217
322898.012868.8283633658529.1816366341508
333112.332988.50676777422123.823232225784
343254.333201.5709848693452.7590151306578
353513.473250.32957905954263.140420940459
363587.613517.5030567025370.1069432974656
373727.453712.6382525259914.8117474740144
383793.343755.8938816426237.4461183573824
393817.583715.43456101525102.145438984755
403845.133903.01843858982-57.8884385898188
413931.864050.32216569016-118.462165690162
424197.524002.07331300744195.44668699256
434307.134294.5458533315212.5841466684760
444229.434375.77235615421-146.342356154205
454362.284418.32710921413-56.0471092141354
464217.344450.18927780526-232.849277805261
474361.284217.38674869256143.893251307436
484327.744336.12828612198-8.38828612197622
494417.654457.68693390515-40.0369339051536
504557.684454.43778121747103.242218782527
514650.354500.65980715702149.690192842980
524967.184778.92452780723188.255472192767
535123.425215.08394422091-91.6639442209063
545290.855272.0813364224318.7686635775745
555535.665422.6394712896113.020528710403
565514.065610.64346477198-96.5834647719775
575493.885750.7129068573-256.832906857296
585694.835612.2537228909982.5762771090105
595850.415691.90881436587158.501185634134
606116.645908.73821733236207.901782667641
6161756269.07014147252-94.070141472515
626513.586281.69636934747231.883630652532
636383.786466.2215049058-82.4415049057986
646673.666559.31690878226114.343091217743
656936.616893.9125083037542.6974916962457
667300.687095.85686887262204.823131127381
677392.937467.35212197099-74.4221219709918
687497.317514.24995363125-16.9399536312489
697584.717718.81697396314-134.106973963144
707160.797738.44190479642-577.651904796418
717196.197182.7027707300713.4872292699298
727245.637154.2587613437391.3712386562724
737347.517393.93572628391-46.4257262839081
747425.757436.99892972549-11.2489297254887
757778.517407.323157697371.186842302995
767822.337935.09289022066-112.762890220661
778181.228151.0975845497930.1224154502077
788371.478317.8484051071853.6215948928178
798347.718576.54375274472-228.833752744716
808672.118459.57704845609212.532951543911
818802.798899.27041478213-96.480414782126
829138.469020.95083026867117.509169731327
839123.299196.22675942976-72.9367594297645
849023.218874.26844038391148.941559616092
858850.418811.7654681817338.6445318182745
868864.588562.75483214356301.825167856441
879163.748451.02154940031712.718450599686
888516.668965.89282651934-449.232826519343
898553.448488.98446271364.4555372870072
907555.28226.4102156283-671.210215628304
917851.227364.33616774957486.883832250433
9274427432.580377801239.41962219877333
937992.537410.29365505514582.23634494486
948264.047772.70160163046491.338398369546
957517.398091.39956900253-574.009569002527
967200.47303.44082414884-103.040824148837
977193.696912.84119436321280.848805636791
986193.586932.92301098868-739.343010988684
995104.215867.81262692919-763.602626929185
1004800.464783.7784412728316.6815587271750
1014461.614612.96491686846-151.354916868464
1024398.594290.69062490942107.899375090581
1034243.634243.67173441287-0.0417344128685802
1044293.824083.87745989745209.942540102554

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 5246.24 & 5106.26368621096 & 139.976313789040 \tabularnewline
2 & 5283.61 & 5164.66253796491 & 118.947462035086 \tabularnewline
3 & 4979.05 & 5051.74180723946 & -72.6918072394601 \tabularnewline
4 & 4825.2 & 4915.32252016608 & -90.122520166081 \tabularnewline
5 & 4695.12 & 4830.55959517543 & -135.439595175435 \tabularnewline
6 & 4711.54 & 4595.66529898151 & 115.874701018495 \tabularnewline
7 & 4727.22 & 4635.15584348757 & 92.0641565124338 \tabularnewline
8 & 4384.96 & 4621.15179360692 & -236.191793606921 \tabularnewline
9 & 4378.75 & 4428.50927173319 & -49.7592717331867 \tabularnewline
10 & 4472.93 & 4293.97078030207 & 178.959219697929 \tabularnewline
11 & 4564.07 & 4328.80235319720 & 235.267646802804 \tabularnewline
12 & 4310.54 & 4460.32005383141 & -149.780053831408 \tabularnewline
13 & 4171.38 & 4306.40316090229 & -135.023160902292 \tabularnewline
14 & 4049.38 & 4045.73187854157 & 3.64812145843335 \tabularnewline
15 & 3591.37 & 3831.31876345487 & -239.948763454874 \tabularnewline
16 & 3720.46 & 3554.36707046051 & 166.092929539488 \tabularnewline
17 & 4107.23 & 3759.17197434758 & 348.058025652417 \tabularnewline
18 & 4101.71 & 4120.96709703489 & -19.2570970348935 \tabularnewline
19 & 4162.34 & 4170.99625638195 & -8.6562563819518 \tabularnewline
20 & 4136.22 & 4101.23918231504 & 34.9808176849629 \tabularnewline
21 & 4125.88 & 4238.71290062076 & -112.832900620756 \tabularnewline
22 & 4031.48 & 4144.12089743679 & -112.640897436791 \tabularnewline
23 & 3761.36 & 3928.70340552247 & -167.343405522471 \tabularnewline
24 & 3408.56 & 3665.67236013525 & -257.112360135249 \tabularnewline
25 & 3228.47 & 3387.19543615425 & -158.725436154250 \tabularnewline
26 & 3090.45 & 3136.85077842823 & -46.4007784282287 \tabularnewline
27 & 2741.14 & 2918.1962222011 & -177.056222201099 \tabularnewline
28 & 2980.44 & 2755.80637618127 & 224.633623818730 \tabularnewline
29 & 3104.33 & 3092.74284813091 & 11.5871518690903 \tabularnewline
30 & 3181.57 & 3187.53684003621 & -5.96684003621169 \tabularnewline
31 & 2863.86 & 3256.45879863122 & -392.598798631217 \tabularnewline
32 & 2898.01 & 2868.82836336585 & 29.1816366341508 \tabularnewline
33 & 3112.33 & 2988.50676777422 & 123.823232225784 \tabularnewline
34 & 3254.33 & 3201.57098486934 & 52.7590151306578 \tabularnewline
35 & 3513.47 & 3250.32957905954 & 263.140420940459 \tabularnewline
36 & 3587.61 & 3517.50305670253 & 70.1069432974656 \tabularnewline
37 & 3727.45 & 3712.63825252599 & 14.8117474740144 \tabularnewline
38 & 3793.34 & 3755.89388164262 & 37.4461183573824 \tabularnewline
39 & 3817.58 & 3715.43456101525 & 102.145438984755 \tabularnewline
40 & 3845.13 & 3903.01843858982 & -57.8884385898188 \tabularnewline
41 & 3931.86 & 4050.32216569016 & -118.462165690162 \tabularnewline
42 & 4197.52 & 4002.07331300744 & 195.44668699256 \tabularnewline
43 & 4307.13 & 4294.54585333152 & 12.5841466684760 \tabularnewline
44 & 4229.43 & 4375.77235615421 & -146.342356154205 \tabularnewline
45 & 4362.28 & 4418.32710921413 & -56.0471092141354 \tabularnewline
46 & 4217.34 & 4450.18927780526 & -232.849277805261 \tabularnewline
47 & 4361.28 & 4217.38674869256 & 143.893251307436 \tabularnewline
48 & 4327.74 & 4336.12828612198 & -8.38828612197622 \tabularnewline
49 & 4417.65 & 4457.68693390515 & -40.0369339051536 \tabularnewline
50 & 4557.68 & 4454.43778121747 & 103.242218782527 \tabularnewline
51 & 4650.35 & 4500.65980715702 & 149.690192842980 \tabularnewline
52 & 4967.18 & 4778.92452780723 & 188.255472192767 \tabularnewline
53 & 5123.42 & 5215.08394422091 & -91.6639442209063 \tabularnewline
54 & 5290.85 & 5272.08133642243 & 18.7686635775745 \tabularnewline
55 & 5535.66 & 5422.6394712896 & 113.020528710403 \tabularnewline
56 & 5514.06 & 5610.64346477198 & -96.5834647719775 \tabularnewline
57 & 5493.88 & 5750.7129068573 & -256.832906857296 \tabularnewline
58 & 5694.83 & 5612.25372289099 & 82.5762771090105 \tabularnewline
59 & 5850.41 & 5691.90881436587 & 158.501185634134 \tabularnewline
60 & 6116.64 & 5908.73821733236 & 207.901782667641 \tabularnewline
61 & 6175 & 6269.07014147252 & -94.070141472515 \tabularnewline
62 & 6513.58 & 6281.69636934747 & 231.883630652532 \tabularnewline
63 & 6383.78 & 6466.2215049058 & -82.4415049057986 \tabularnewline
64 & 6673.66 & 6559.31690878226 & 114.343091217743 \tabularnewline
65 & 6936.61 & 6893.91250830375 & 42.6974916962457 \tabularnewline
66 & 7300.68 & 7095.85686887262 & 204.823131127381 \tabularnewline
67 & 7392.93 & 7467.35212197099 & -74.4221219709918 \tabularnewline
68 & 7497.31 & 7514.24995363125 & -16.9399536312489 \tabularnewline
69 & 7584.71 & 7718.81697396314 & -134.106973963144 \tabularnewline
70 & 7160.79 & 7738.44190479642 & -577.651904796418 \tabularnewline
71 & 7196.19 & 7182.70277073007 & 13.4872292699298 \tabularnewline
72 & 7245.63 & 7154.25876134373 & 91.3712386562724 \tabularnewline
73 & 7347.51 & 7393.93572628391 & -46.4257262839081 \tabularnewline
74 & 7425.75 & 7436.99892972549 & -11.2489297254887 \tabularnewline
75 & 7778.51 & 7407.323157697 & 371.186842302995 \tabularnewline
76 & 7822.33 & 7935.09289022066 & -112.762890220661 \tabularnewline
77 & 8181.22 & 8151.09758454979 & 30.1224154502077 \tabularnewline
78 & 8371.47 & 8317.84840510718 & 53.6215948928178 \tabularnewline
79 & 8347.71 & 8576.54375274472 & -228.833752744716 \tabularnewline
80 & 8672.11 & 8459.57704845609 & 212.532951543911 \tabularnewline
81 & 8802.79 & 8899.27041478213 & -96.480414782126 \tabularnewline
82 & 9138.46 & 9020.95083026867 & 117.509169731327 \tabularnewline
83 & 9123.29 & 9196.22675942976 & -72.9367594297645 \tabularnewline
84 & 9023.21 & 8874.26844038391 & 148.941559616092 \tabularnewline
85 & 8850.41 & 8811.76546818173 & 38.6445318182745 \tabularnewline
86 & 8864.58 & 8562.75483214356 & 301.825167856441 \tabularnewline
87 & 9163.74 & 8451.02154940031 & 712.718450599686 \tabularnewline
88 & 8516.66 & 8965.89282651934 & -449.232826519343 \tabularnewline
89 & 8553.44 & 8488.984462713 & 64.4555372870072 \tabularnewline
90 & 7555.2 & 8226.4102156283 & -671.210215628304 \tabularnewline
91 & 7851.22 & 7364.33616774957 & 486.883832250433 \tabularnewline
92 & 7442 & 7432.58037780123 & 9.41962219877333 \tabularnewline
93 & 7992.53 & 7410.29365505514 & 582.23634494486 \tabularnewline
94 & 8264.04 & 7772.70160163046 & 491.338398369546 \tabularnewline
95 & 7517.39 & 8091.39956900253 & -574.009569002527 \tabularnewline
96 & 7200.4 & 7303.44082414884 & -103.040824148837 \tabularnewline
97 & 7193.69 & 6912.84119436321 & 280.848805636791 \tabularnewline
98 & 6193.58 & 6932.92301098868 & -739.343010988684 \tabularnewline
99 & 5104.21 & 5867.81262692919 & -763.602626929185 \tabularnewline
100 & 4800.46 & 4783.77844127283 & 16.6815587271750 \tabularnewline
101 & 4461.61 & 4612.96491686846 & -151.354916868464 \tabularnewline
102 & 4398.59 & 4290.69062490942 & 107.899375090581 \tabularnewline
103 & 4243.63 & 4243.67173441287 & -0.0417344128685802 \tabularnewline
104 & 4293.82 & 4083.87745989745 & 209.942540102554 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&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]5246.24[/C][C]5106.26368621096[/C][C]139.976313789040[/C][/ROW]
[ROW][C]2[/C][C]5283.61[/C][C]5164.66253796491[/C][C]118.947462035086[/C][/ROW]
[ROW][C]3[/C][C]4979.05[/C][C]5051.74180723946[/C][C]-72.6918072394601[/C][/ROW]
[ROW][C]4[/C][C]4825.2[/C][C]4915.32252016608[/C][C]-90.122520166081[/C][/ROW]
[ROW][C]5[/C][C]4695.12[/C][C]4830.55959517543[/C][C]-135.439595175435[/C][/ROW]
[ROW][C]6[/C][C]4711.54[/C][C]4595.66529898151[/C][C]115.874701018495[/C][/ROW]
[ROW][C]7[/C][C]4727.22[/C][C]4635.15584348757[/C][C]92.0641565124338[/C][/ROW]
[ROW][C]8[/C][C]4384.96[/C][C]4621.15179360692[/C][C]-236.191793606921[/C][/ROW]
[ROW][C]9[/C][C]4378.75[/C][C]4428.50927173319[/C][C]-49.7592717331867[/C][/ROW]
[ROW][C]10[/C][C]4472.93[/C][C]4293.97078030207[/C][C]178.959219697929[/C][/ROW]
[ROW][C]11[/C][C]4564.07[/C][C]4328.80235319720[/C][C]235.267646802804[/C][/ROW]
[ROW][C]12[/C][C]4310.54[/C][C]4460.32005383141[/C][C]-149.780053831408[/C][/ROW]
[ROW][C]13[/C][C]4171.38[/C][C]4306.40316090229[/C][C]-135.023160902292[/C][/ROW]
[ROW][C]14[/C][C]4049.38[/C][C]4045.73187854157[/C][C]3.64812145843335[/C][/ROW]
[ROW][C]15[/C][C]3591.37[/C][C]3831.31876345487[/C][C]-239.948763454874[/C][/ROW]
[ROW][C]16[/C][C]3720.46[/C][C]3554.36707046051[/C][C]166.092929539488[/C][/ROW]
[ROW][C]17[/C][C]4107.23[/C][C]3759.17197434758[/C][C]348.058025652417[/C][/ROW]
[ROW][C]18[/C][C]4101.71[/C][C]4120.96709703489[/C][C]-19.2570970348935[/C][/ROW]
[ROW][C]19[/C][C]4162.34[/C][C]4170.99625638195[/C][C]-8.6562563819518[/C][/ROW]
[ROW][C]20[/C][C]4136.22[/C][C]4101.23918231504[/C][C]34.9808176849629[/C][/ROW]
[ROW][C]21[/C][C]4125.88[/C][C]4238.71290062076[/C][C]-112.832900620756[/C][/ROW]
[ROW][C]22[/C][C]4031.48[/C][C]4144.12089743679[/C][C]-112.640897436791[/C][/ROW]
[ROW][C]23[/C][C]3761.36[/C][C]3928.70340552247[/C][C]-167.343405522471[/C][/ROW]
[ROW][C]24[/C][C]3408.56[/C][C]3665.67236013525[/C][C]-257.112360135249[/C][/ROW]
[ROW][C]25[/C][C]3228.47[/C][C]3387.19543615425[/C][C]-158.725436154250[/C][/ROW]
[ROW][C]26[/C][C]3090.45[/C][C]3136.85077842823[/C][C]-46.4007784282287[/C][/ROW]
[ROW][C]27[/C][C]2741.14[/C][C]2918.1962222011[/C][C]-177.056222201099[/C][/ROW]
[ROW][C]28[/C][C]2980.44[/C][C]2755.80637618127[/C][C]224.633623818730[/C][/ROW]
[ROW][C]29[/C][C]3104.33[/C][C]3092.74284813091[/C][C]11.5871518690903[/C][/ROW]
[ROW][C]30[/C][C]3181.57[/C][C]3187.53684003621[/C][C]-5.96684003621169[/C][/ROW]
[ROW][C]31[/C][C]2863.86[/C][C]3256.45879863122[/C][C]-392.598798631217[/C][/ROW]
[ROW][C]32[/C][C]2898.01[/C][C]2868.82836336585[/C][C]29.1816366341508[/C][/ROW]
[ROW][C]33[/C][C]3112.33[/C][C]2988.50676777422[/C][C]123.823232225784[/C][/ROW]
[ROW][C]34[/C][C]3254.33[/C][C]3201.57098486934[/C][C]52.7590151306578[/C][/ROW]
[ROW][C]35[/C][C]3513.47[/C][C]3250.32957905954[/C][C]263.140420940459[/C][/ROW]
[ROW][C]36[/C][C]3587.61[/C][C]3517.50305670253[/C][C]70.1069432974656[/C][/ROW]
[ROW][C]37[/C][C]3727.45[/C][C]3712.63825252599[/C][C]14.8117474740144[/C][/ROW]
[ROW][C]38[/C][C]3793.34[/C][C]3755.89388164262[/C][C]37.4461183573824[/C][/ROW]
[ROW][C]39[/C][C]3817.58[/C][C]3715.43456101525[/C][C]102.145438984755[/C][/ROW]
[ROW][C]40[/C][C]3845.13[/C][C]3903.01843858982[/C][C]-57.8884385898188[/C][/ROW]
[ROW][C]41[/C][C]3931.86[/C][C]4050.32216569016[/C][C]-118.462165690162[/C][/ROW]
[ROW][C]42[/C][C]4197.52[/C][C]4002.07331300744[/C][C]195.44668699256[/C][/ROW]
[ROW][C]43[/C][C]4307.13[/C][C]4294.54585333152[/C][C]12.5841466684760[/C][/ROW]
[ROW][C]44[/C][C]4229.43[/C][C]4375.77235615421[/C][C]-146.342356154205[/C][/ROW]
[ROW][C]45[/C][C]4362.28[/C][C]4418.32710921413[/C][C]-56.0471092141354[/C][/ROW]
[ROW][C]46[/C][C]4217.34[/C][C]4450.18927780526[/C][C]-232.849277805261[/C][/ROW]
[ROW][C]47[/C][C]4361.28[/C][C]4217.38674869256[/C][C]143.893251307436[/C][/ROW]
[ROW][C]48[/C][C]4327.74[/C][C]4336.12828612198[/C][C]-8.38828612197622[/C][/ROW]
[ROW][C]49[/C][C]4417.65[/C][C]4457.68693390515[/C][C]-40.0369339051536[/C][/ROW]
[ROW][C]50[/C][C]4557.68[/C][C]4454.43778121747[/C][C]103.242218782527[/C][/ROW]
[ROW][C]51[/C][C]4650.35[/C][C]4500.65980715702[/C][C]149.690192842980[/C][/ROW]
[ROW][C]52[/C][C]4967.18[/C][C]4778.92452780723[/C][C]188.255472192767[/C][/ROW]
[ROW][C]53[/C][C]5123.42[/C][C]5215.08394422091[/C][C]-91.6639442209063[/C][/ROW]
[ROW][C]54[/C][C]5290.85[/C][C]5272.08133642243[/C][C]18.7686635775745[/C][/ROW]
[ROW][C]55[/C][C]5535.66[/C][C]5422.6394712896[/C][C]113.020528710403[/C][/ROW]
[ROW][C]56[/C][C]5514.06[/C][C]5610.64346477198[/C][C]-96.5834647719775[/C][/ROW]
[ROW][C]57[/C][C]5493.88[/C][C]5750.7129068573[/C][C]-256.832906857296[/C][/ROW]
[ROW][C]58[/C][C]5694.83[/C][C]5612.25372289099[/C][C]82.5762771090105[/C][/ROW]
[ROW][C]59[/C][C]5850.41[/C][C]5691.90881436587[/C][C]158.501185634134[/C][/ROW]
[ROW][C]60[/C][C]6116.64[/C][C]5908.73821733236[/C][C]207.901782667641[/C][/ROW]
[ROW][C]61[/C][C]6175[/C][C]6269.07014147252[/C][C]-94.070141472515[/C][/ROW]
[ROW][C]62[/C][C]6513.58[/C][C]6281.69636934747[/C][C]231.883630652532[/C][/ROW]
[ROW][C]63[/C][C]6383.78[/C][C]6466.2215049058[/C][C]-82.4415049057986[/C][/ROW]
[ROW][C]64[/C][C]6673.66[/C][C]6559.31690878226[/C][C]114.343091217743[/C][/ROW]
[ROW][C]65[/C][C]6936.61[/C][C]6893.91250830375[/C][C]42.6974916962457[/C][/ROW]
[ROW][C]66[/C][C]7300.68[/C][C]7095.85686887262[/C][C]204.823131127381[/C][/ROW]
[ROW][C]67[/C][C]7392.93[/C][C]7467.35212197099[/C][C]-74.4221219709918[/C][/ROW]
[ROW][C]68[/C][C]7497.31[/C][C]7514.24995363125[/C][C]-16.9399536312489[/C][/ROW]
[ROW][C]69[/C][C]7584.71[/C][C]7718.81697396314[/C][C]-134.106973963144[/C][/ROW]
[ROW][C]70[/C][C]7160.79[/C][C]7738.44190479642[/C][C]-577.651904796418[/C][/ROW]
[ROW][C]71[/C][C]7196.19[/C][C]7182.70277073007[/C][C]13.4872292699298[/C][/ROW]
[ROW][C]72[/C][C]7245.63[/C][C]7154.25876134373[/C][C]91.3712386562724[/C][/ROW]
[ROW][C]73[/C][C]7347.51[/C][C]7393.93572628391[/C][C]-46.4257262839081[/C][/ROW]
[ROW][C]74[/C][C]7425.75[/C][C]7436.99892972549[/C][C]-11.2489297254887[/C][/ROW]
[ROW][C]75[/C][C]7778.51[/C][C]7407.323157697[/C][C]371.186842302995[/C][/ROW]
[ROW][C]76[/C][C]7822.33[/C][C]7935.09289022066[/C][C]-112.762890220661[/C][/ROW]
[ROW][C]77[/C][C]8181.22[/C][C]8151.09758454979[/C][C]30.1224154502077[/C][/ROW]
[ROW][C]78[/C][C]8371.47[/C][C]8317.84840510718[/C][C]53.6215948928178[/C][/ROW]
[ROW][C]79[/C][C]8347.71[/C][C]8576.54375274472[/C][C]-228.833752744716[/C][/ROW]
[ROW][C]80[/C][C]8672.11[/C][C]8459.57704845609[/C][C]212.532951543911[/C][/ROW]
[ROW][C]81[/C][C]8802.79[/C][C]8899.27041478213[/C][C]-96.480414782126[/C][/ROW]
[ROW][C]82[/C][C]9138.46[/C][C]9020.95083026867[/C][C]117.509169731327[/C][/ROW]
[ROW][C]83[/C][C]9123.29[/C][C]9196.22675942976[/C][C]-72.9367594297645[/C][/ROW]
[ROW][C]84[/C][C]9023.21[/C][C]8874.26844038391[/C][C]148.941559616092[/C][/ROW]
[ROW][C]85[/C][C]8850.41[/C][C]8811.76546818173[/C][C]38.6445318182745[/C][/ROW]
[ROW][C]86[/C][C]8864.58[/C][C]8562.75483214356[/C][C]301.825167856441[/C][/ROW]
[ROW][C]87[/C][C]9163.74[/C][C]8451.02154940031[/C][C]712.718450599686[/C][/ROW]
[ROW][C]88[/C][C]8516.66[/C][C]8965.89282651934[/C][C]-449.232826519343[/C][/ROW]
[ROW][C]89[/C][C]8553.44[/C][C]8488.984462713[/C][C]64.4555372870072[/C][/ROW]
[ROW][C]90[/C][C]7555.2[/C][C]8226.4102156283[/C][C]-671.210215628304[/C][/ROW]
[ROW][C]91[/C][C]7851.22[/C][C]7364.33616774957[/C][C]486.883832250433[/C][/ROW]
[ROW][C]92[/C][C]7442[/C][C]7432.58037780123[/C][C]9.41962219877333[/C][/ROW]
[ROW][C]93[/C][C]7992.53[/C][C]7410.29365505514[/C][C]582.23634494486[/C][/ROW]
[ROW][C]94[/C][C]8264.04[/C][C]7772.70160163046[/C][C]491.338398369546[/C][/ROW]
[ROW][C]95[/C][C]7517.39[/C][C]8091.39956900253[/C][C]-574.009569002527[/C][/ROW]
[ROW][C]96[/C][C]7200.4[/C][C]7303.44082414884[/C][C]-103.040824148837[/C][/ROW]
[ROW][C]97[/C][C]7193.69[/C][C]6912.84119436321[/C][C]280.848805636791[/C][/ROW]
[ROW][C]98[/C][C]6193.58[/C][C]6932.92301098868[/C][C]-739.343010988684[/C][/ROW]
[ROW][C]99[/C][C]5104.21[/C][C]5867.81262692919[/C][C]-763.602626929185[/C][/ROW]
[ROW][C]100[/C][C]4800.46[/C][C]4783.77844127283[/C][C]16.6815587271750[/C][/ROW]
[ROW][C]101[/C][C]4461.61[/C][C]4612.96491686846[/C][C]-151.354916868464[/C][/ROW]
[ROW][C]102[/C][C]4398.59[/C][C]4290.69062490942[/C][C]107.899375090581[/C][/ROW]
[ROW][C]103[/C][C]4243.63[/C][C]4243.67173441287[/C][C]-0.0417344128685802[/C][/ROW]
[ROW][C]104[/C][C]4293.82[/C][C]4083.87745989745[/C][C]209.942540102554[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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
15246.245106.26368621096139.976313789040
25283.615164.66253796491118.947462035086
34979.055051.74180723946-72.6918072394601
44825.24915.32252016608-90.122520166081
54695.124830.55959517543-135.439595175435
64711.544595.66529898151115.874701018495
74727.224635.1558434875792.0641565124338
84384.964621.15179360692-236.191793606921
94378.754428.50927173319-49.7592717331867
104472.934293.97078030207178.959219697929
114564.074328.80235319720235.267646802804
124310.544460.32005383141-149.780053831408
134171.384306.40316090229-135.023160902292
144049.384045.731878541573.64812145843335
153591.373831.31876345487-239.948763454874
163720.463554.36707046051166.092929539488
174107.233759.17197434758348.058025652417
184101.714120.96709703489-19.2570970348935
194162.344170.99625638195-8.6562563819518
204136.224101.2391823150434.9808176849629
214125.884238.71290062076-112.832900620756
224031.484144.12089743679-112.640897436791
233761.363928.70340552247-167.343405522471
243408.563665.67236013525-257.112360135249
253228.473387.19543615425-158.725436154250
263090.453136.85077842823-46.4007784282287
272741.142918.1962222011-177.056222201099
282980.442755.80637618127224.633623818730
293104.333092.7428481309111.5871518690903
303181.573187.53684003621-5.96684003621169
312863.863256.45879863122-392.598798631217
322898.012868.8283633658529.1816366341508
333112.332988.50676777422123.823232225784
343254.333201.5709848693452.7590151306578
353513.473250.32957905954263.140420940459
363587.613517.5030567025370.1069432974656
373727.453712.6382525259914.8117474740144
383793.343755.8938816426237.4461183573824
393817.583715.43456101525102.145438984755
403845.133903.01843858982-57.8884385898188
413931.864050.32216569016-118.462165690162
424197.524002.07331300744195.44668699256
434307.134294.5458533315212.5841466684760
444229.434375.77235615421-146.342356154205
454362.284418.32710921413-56.0471092141354
464217.344450.18927780526-232.849277805261
474361.284217.38674869256143.893251307436
484327.744336.12828612198-8.38828612197622
494417.654457.68693390515-40.0369339051536
504557.684454.43778121747103.242218782527
514650.354500.65980715702149.690192842980
524967.184778.92452780723188.255472192767
535123.425215.08394422091-91.6639442209063
545290.855272.0813364224318.7686635775745
555535.665422.6394712896113.020528710403
565514.065610.64346477198-96.5834647719775
575493.885750.7129068573-256.832906857296
585694.835612.2537228909982.5762771090105
595850.415691.90881436587158.501185634134
606116.645908.73821733236207.901782667641
6161756269.07014147252-94.070141472515
626513.586281.69636934747231.883630652532
636383.786466.2215049058-82.4415049057986
646673.666559.31690878226114.343091217743
656936.616893.9125083037542.6974916962457
667300.687095.85686887262204.823131127381
677392.937467.35212197099-74.4221219709918
687497.317514.24995363125-16.9399536312489
697584.717718.81697396314-134.106973963144
707160.797738.44190479642-577.651904796418
717196.197182.7027707300713.4872292699298
727245.637154.2587613437391.3712386562724
737347.517393.93572628391-46.4257262839081
747425.757436.99892972549-11.2489297254887
757778.517407.323157697371.186842302995
767822.337935.09289022066-112.762890220661
778181.228151.0975845497930.1224154502077
788371.478317.8484051071853.6215948928178
798347.718576.54375274472-228.833752744716
808672.118459.57704845609212.532951543911
818802.798899.27041478213-96.480414782126
829138.469020.95083026867117.509169731327
839123.299196.22675942976-72.9367594297645
849023.218874.26844038391148.941559616092
858850.418811.7654681817338.6445318182745
868864.588562.75483214356301.825167856441
879163.748451.02154940031712.718450599686
888516.668965.89282651934-449.232826519343
898553.448488.98446271364.4555372870072
907555.28226.4102156283-671.210215628304
917851.227364.33616774957486.883832250433
9274427432.580377801239.41962219877333
937992.537410.29365505514582.23634494486
948264.047772.70160163046491.338398369546
957517.398091.39956900253-574.009569002527
967200.47303.44082414884-103.040824148837
977193.696912.84119436321280.848805636791
986193.586932.92301098868-739.343010988684
995104.215867.81262692919-763.602626929185
1004800.464783.7784412728316.6815587271750
1014461.614612.96491686846-151.354916868464
1024398.594290.69062490942107.899375090581
1034243.634243.67173441287-0.0417344128685802
1044293.824083.87745989745209.942540102554







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
200.4564877642622630.9129755285245260.543512235737737
210.2914223529964070.5828447059928130.708577647003593
220.1733209695493340.3466419390986680.826679030450666
230.1080757960792910.2161515921585810.89192420392071
240.0625615029996620.1251230059993240.937438497000338
250.03223092471063320.06446184942126640.967769075289367
260.01807591019602110.03615182039204230.981924089803979
270.009258549585768180.01851709917153640.990741450414232
280.004791425641655890.009582851283311780.995208574358344
290.003362215133602330.006724430267204650.996637784866398
300.001451672908383170.002903345816766350.998548327091617
310.004850688893057740.009701377786115470.995149311106942
320.003772563910587990.007545127821175970.996227436089412
330.002360927203215940.004721854406431880.997639072796784
340.001153583648382790.002307167296765580.998846416351617
350.001837166956983270.003674333913966550.998162833043017
360.002661772005965480.005323544011930960.997338227994035
370.0020276181427550.004055236285510.997972381857245
380.001127329879020230.002254659758040450.99887267012098
390.001388259501634860.002776519003269720.998611740498365
400.001037222740214540.002074445480429070.998962777259785
410.000539898461966850.00107979692393370.999460101538033
420.0005168591122040570.001033718224408110.999483140887796
430.0002689024825672040.0005378049651344070.999731097517433
440.0001533232769094600.0003066465538189210.99984667672309
457.55477790420018e-050.0001510955580840040.999924452220958
466.92759054298304e-050.0001385518108596610.99993072409457
474.54979766963736e-059.09959533927471e-050.999954502023304
482.36515345451589e-054.73030690903178e-050.999976348465455
491.14716920525279e-052.29433841050558e-050.999988528307947
505.48205944314602e-061.09641188862920e-050.999994517940557
514.31587220125221e-068.63174440250441e-060.9999956841278
522.14771625802605e-064.29543251605209e-060.999997852283742
531.10077700015232e-062.20155400030465e-060.999998899223
544.50160445991516e-079.00320891983031e-070.999999549839554
552.5823181397344e-075.1646362794688e-070.999999741768186
561.37922311723677e-072.75844623447354e-070.999999862077688
571.54505632811504e-073.09011265623009e-070.999999845494367
589.16343385747625e-081.83268677149525e-070.999999908365661
593.97702632624459e-087.95405265248919e-080.999999960229737
604.6070535911162e-089.2141071822324e-080.999999953929464
612.71130369707650e-085.42260739415299e-080.999999972886963
622.19039894187291e-084.38079788374581e-080.99999997809601
631.19248270250692e-082.38496540501385e-080.999999988075173
645.02990076142499e-091.00598015228500e-080.9999999949701
651.76251584175015e-093.52503168350029e-090.999999998237484
661.06802449132289e-092.13604898264578e-090.999999998931975
674.49446103846945e-108.9889220769389e-100.999999999550554
681.76704459993446e-103.53408919986893e-100.999999999823296
691.47227990370749e-102.94455980741498e-100.999999999852772
702.54015830053602e-075.08031660107205e-070.99999974598417
711.83538436984950e-073.67076873969901e-070.999999816461563
721.18684498997847e-072.37368997995694e-070.9999998813155
733.38572684866247e-076.77145369732495e-070.999999661427315
743.86943844592037e-077.73887689184075e-070.999999613056155
759.30987004694379e-071.86197400938876e-060.999999069012995
769.02001882648798e-071.80400376529760e-060.999999097998117
774.26422991027671e-078.52845982055343e-070.99999957357701
782.00195270349507e-074.00390540699014e-070.99999979980473
791.16391849366838e-072.32783698733677e-070.99999988360815
807.98080095064588e-081.59616019012918e-070.99999992019199
815.72677547212754e-081.14535509442551e-070.999999942732245
821.14939752107190e-072.29879504214381e-070.999999885060248
833.78977711450038e-087.57955422900076e-080.999999962102229
842.55721635218840e-085.11443270437679e-080.999999974427837

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
20 & 0.456487764262263 & 0.912975528524526 & 0.543512235737737 \tabularnewline
21 & 0.291422352996407 & 0.582844705992813 & 0.708577647003593 \tabularnewline
22 & 0.173320969549334 & 0.346641939098668 & 0.826679030450666 \tabularnewline
23 & 0.108075796079291 & 0.216151592158581 & 0.89192420392071 \tabularnewline
24 & 0.062561502999662 & 0.125123005999324 & 0.937438497000338 \tabularnewline
25 & 0.0322309247106332 & 0.0644618494212664 & 0.967769075289367 \tabularnewline
26 & 0.0180759101960211 & 0.0361518203920423 & 0.981924089803979 \tabularnewline
27 & 0.00925854958576818 & 0.0185170991715364 & 0.990741450414232 \tabularnewline
28 & 0.00479142564165589 & 0.00958285128331178 & 0.995208574358344 \tabularnewline
29 & 0.00336221513360233 & 0.00672443026720465 & 0.996637784866398 \tabularnewline
30 & 0.00145167290838317 & 0.00290334581676635 & 0.998548327091617 \tabularnewline
31 & 0.00485068889305774 & 0.00970137778611547 & 0.995149311106942 \tabularnewline
32 & 0.00377256391058799 & 0.00754512782117597 & 0.996227436089412 \tabularnewline
33 & 0.00236092720321594 & 0.00472185440643188 & 0.997639072796784 \tabularnewline
34 & 0.00115358364838279 & 0.00230716729676558 & 0.998846416351617 \tabularnewline
35 & 0.00183716695698327 & 0.00367433391396655 & 0.998162833043017 \tabularnewline
36 & 0.00266177200596548 & 0.00532354401193096 & 0.997338227994035 \tabularnewline
37 & 0.002027618142755 & 0.00405523628551 & 0.997972381857245 \tabularnewline
38 & 0.00112732987902023 & 0.00225465975804045 & 0.99887267012098 \tabularnewline
39 & 0.00138825950163486 & 0.00277651900326972 & 0.998611740498365 \tabularnewline
40 & 0.00103722274021454 & 0.00207444548042907 & 0.998962777259785 \tabularnewline
41 & 0.00053989846196685 & 0.0010797969239337 & 0.999460101538033 \tabularnewline
42 & 0.000516859112204057 & 0.00103371822440811 & 0.999483140887796 \tabularnewline
43 & 0.000268902482567204 & 0.000537804965134407 & 0.999731097517433 \tabularnewline
44 & 0.000153323276909460 & 0.000306646553818921 & 0.99984667672309 \tabularnewline
45 & 7.55477790420018e-05 & 0.000151095558084004 & 0.999924452220958 \tabularnewline
46 & 6.92759054298304e-05 & 0.000138551810859661 & 0.99993072409457 \tabularnewline
47 & 4.54979766963736e-05 & 9.09959533927471e-05 & 0.999954502023304 \tabularnewline
48 & 2.36515345451589e-05 & 4.73030690903178e-05 & 0.999976348465455 \tabularnewline
49 & 1.14716920525279e-05 & 2.29433841050558e-05 & 0.999988528307947 \tabularnewline
50 & 5.48205944314602e-06 & 1.09641188862920e-05 & 0.999994517940557 \tabularnewline
51 & 4.31587220125221e-06 & 8.63174440250441e-06 & 0.9999956841278 \tabularnewline
52 & 2.14771625802605e-06 & 4.29543251605209e-06 & 0.999997852283742 \tabularnewline
53 & 1.10077700015232e-06 & 2.20155400030465e-06 & 0.999998899223 \tabularnewline
54 & 4.50160445991516e-07 & 9.00320891983031e-07 & 0.999999549839554 \tabularnewline
55 & 2.5823181397344e-07 & 5.1646362794688e-07 & 0.999999741768186 \tabularnewline
56 & 1.37922311723677e-07 & 2.75844623447354e-07 & 0.999999862077688 \tabularnewline
57 & 1.54505632811504e-07 & 3.09011265623009e-07 & 0.999999845494367 \tabularnewline
58 & 9.16343385747625e-08 & 1.83268677149525e-07 & 0.999999908365661 \tabularnewline
59 & 3.97702632624459e-08 & 7.95405265248919e-08 & 0.999999960229737 \tabularnewline
60 & 4.6070535911162e-08 & 9.2141071822324e-08 & 0.999999953929464 \tabularnewline
61 & 2.71130369707650e-08 & 5.42260739415299e-08 & 0.999999972886963 \tabularnewline
62 & 2.19039894187291e-08 & 4.38079788374581e-08 & 0.99999997809601 \tabularnewline
63 & 1.19248270250692e-08 & 2.38496540501385e-08 & 0.999999988075173 \tabularnewline
64 & 5.02990076142499e-09 & 1.00598015228500e-08 & 0.9999999949701 \tabularnewline
65 & 1.76251584175015e-09 & 3.52503168350029e-09 & 0.999999998237484 \tabularnewline
66 & 1.06802449132289e-09 & 2.13604898264578e-09 & 0.999999998931975 \tabularnewline
67 & 4.49446103846945e-10 & 8.9889220769389e-10 & 0.999999999550554 \tabularnewline
68 & 1.76704459993446e-10 & 3.53408919986893e-10 & 0.999999999823296 \tabularnewline
69 & 1.47227990370749e-10 & 2.94455980741498e-10 & 0.999999999852772 \tabularnewline
70 & 2.54015830053602e-07 & 5.08031660107205e-07 & 0.99999974598417 \tabularnewline
71 & 1.83538436984950e-07 & 3.67076873969901e-07 & 0.999999816461563 \tabularnewline
72 & 1.18684498997847e-07 & 2.37368997995694e-07 & 0.9999998813155 \tabularnewline
73 & 3.38572684866247e-07 & 6.77145369732495e-07 & 0.999999661427315 \tabularnewline
74 & 3.86943844592037e-07 & 7.73887689184075e-07 & 0.999999613056155 \tabularnewline
75 & 9.30987004694379e-07 & 1.86197400938876e-06 & 0.999999069012995 \tabularnewline
76 & 9.02001882648798e-07 & 1.80400376529760e-06 & 0.999999097998117 \tabularnewline
77 & 4.26422991027671e-07 & 8.52845982055343e-07 & 0.99999957357701 \tabularnewline
78 & 2.00195270349507e-07 & 4.00390540699014e-07 & 0.99999979980473 \tabularnewline
79 & 1.16391849366838e-07 & 2.32783698733677e-07 & 0.99999988360815 \tabularnewline
80 & 7.98080095064588e-08 & 1.59616019012918e-07 & 0.99999992019199 \tabularnewline
81 & 5.72677547212754e-08 & 1.14535509442551e-07 & 0.999999942732245 \tabularnewline
82 & 1.14939752107190e-07 & 2.29879504214381e-07 & 0.999999885060248 \tabularnewline
83 & 3.78977711450038e-08 & 7.57955422900076e-08 & 0.999999962102229 \tabularnewline
84 & 2.55721635218840e-08 & 5.11443270437679e-08 & 0.999999974427837 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&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]0.456487764262263[/C][C]0.912975528524526[/C][C]0.543512235737737[/C][/ROW]
[ROW][C]21[/C][C]0.291422352996407[/C][C]0.582844705992813[/C][C]0.708577647003593[/C][/ROW]
[ROW][C]22[/C][C]0.173320969549334[/C][C]0.346641939098668[/C][C]0.826679030450666[/C][/ROW]
[ROW][C]23[/C][C]0.108075796079291[/C][C]0.216151592158581[/C][C]0.89192420392071[/C][/ROW]
[ROW][C]24[/C][C]0.062561502999662[/C][C]0.125123005999324[/C][C]0.937438497000338[/C][/ROW]
[ROW][C]25[/C][C]0.0322309247106332[/C][C]0.0644618494212664[/C][C]0.967769075289367[/C][/ROW]
[ROW][C]26[/C][C]0.0180759101960211[/C][C]0.0361518203920423[/C][C]0.981924089803979[/C][/ROW]
[ROW][C]27[/C][C]0.00925854958576818[/C][C]0.0185170991715364[/C][C]0.990741450414232[/C][/ROW]
[ROW][C]28[/C][C]0.00479142564165589[/C][C]0.00958285128331178[/C][C]0.995208574358344[/C][/ROW]
[ROW][C]29[/C][C]0.00336221513360233[/C][C]0.00672443026720465[/C][C]0.996637784866398[/C][/ROW]
[ROW][C]30[/C][C]0.00145167290838317[/C][C]0.00290334581676635[/C][C]0.998548327091617[/C][/ROW]
[ROW][C]31[/C][C]0.00485068889305774[/C][C]0.00970137778611547[/C][C]0.995149311106942[/C][/ROW]
[ROW][C]32[/C][C]0.00377256391058799[/C][C]0.00754512782117597[/C][C]0.996227436089412[/C][/ROW]
[ROW][C]33[/C][C]0.00236092720321594[/C][C]0.00472185440643188[/C][C]0.997639072796784[/C][/ROW]
[ROW][C]34[/C][C]0.00115358364838279[/C][C]0.00230716729676558[/C][C]0.998846416351617[/C][/ROW]
[ROW][C]35[/C][C]0.00183716695698327[/C][C]0.00367433391396655[/C][C]0.998162833043017[/C][/ROW]
[ROW][C]36[/C][C]0.00266177200596548[/C][C]0.00532354401193096[/C][C]0.997338227994035[/C][/ROW]
[ROW][C]37[/C][C]0.002027618142755[/C][C]0.00405523628551[/C][C]0.997972381857245[/C][/ROW]
[ROW][C]38[/C][C]0.00112732987902023[/C][C]0.00225465975804045[/C][C]0.99887267012098[/C][/ROW]
[ROW][C]39[/C][C]0.00138825950163486[/C][C]0.00277651900326972[/C][C]0.998611740498365[/C][/ROW]
[ROW][C]40[/C][C]0.00103722274021454[/C][C]0.00207444548042907[/C][C]0.998962777259785[/C][/ROW]
[ROW][C]41[/C][C]0.00053989846196685[/C][C]0.0010797969239337[/C][C]0.999460101538033[/C][/ROW]
[ROW][C]42[/C][C]0.000516859112204057[/C][C]0.00103371822440811[/C][C]0.999483140887796[/C][/ROW]
[ROW][C]43[/C][C]0.000268902482567204[/C][C]0.000537804965134407[/C][C]0.999731097517433[/C][/ROW]
[ROW][C]44[/C][C]0.000153323276909460[/C][C]0.000306646553818921[/C][C]0.99984667672309[/C][/ROW]
[ROW][C]45[/C][C]7.55477790420018e-05[/C][C]0.000151095558084004[/C][C]0.999924452220958[/C][/ROW]
[ROW][C]46[/C][C]6.92759054298304e-05[/C][C]0.000138551810859661[/C][C]0.99993072409457[/C][/ROW]
[ROW][C]47[/C][C]4.54979766963736e-05[/C][C]9.09959533927471e-05[/C][C]0.999954502023304[/C][/ROW]
[ROW][C]48[/C][C]2.36515345451589e-05[/C][C]4.73030690903178e-05[/C][C]0.999976348465455[/C][/ROW]
[ROW][C]49[/C][C]1.14716920525279e-05[/C][C]2.29433841050558e-05[/C][C]0.999988528307947[/C][/ROW]
[ROW][C]50[/C][C]5.48205944314602e-06[/C][C]1.09641188862920e-05[/C][C]0.999994517940557[/C][/ROW]
[ROW][C]51[/C][C]4.31587220125221e-06[/C][C]8.63174440250441e-06[/C][C]0.9999956841278[/C][/ROW]
[ROW][C]52[/C][C]2.14771625802605e-06[/C][C]4.29543251605209e-06[/C][C]0.999997852283742[/C][/ROW]
[ROW][C]53[/C][C]1.10077700015232e-06[/C][C]2.20155400030465e-06[/C][C]0.999998899223[/C][/ROW]
[ROW][C]54[/C][C]4.50160445991516e-07[/C][C]9.00320891983031e-07[/C][C]0.999999549839554[/C][/ROW]
[ROW][C]55[/C][C]2.5823181397344e-07[/C][C]5.1646362794688e-07[/C][C]0.999999741768186[/C][/ROW]
[ROW][C]56[/C][C]1.37922311723677e-07[/C][C]2.75844623447354e-07[/C][C]0.999999862077688[/C][/ROW]
[ROW][C]57[/C][C]1.54505632811504e-07[/C][C]3.09011265623009e-07[/C][C]0.999999845494367[/C][/ROW]
[ROW][C]58[/C][C]9.16343385747625e-08[/C][C]1.83268677149525e-07[/C][C]0.999999908365661[/C][/ROW]
[ROW][C]59[/C][C]3.97702632624459e-08[/C][C]7.95405265248919e-08[/C][C]0.999999960229737[/C][/ROW]
[ROW][C]60[/C][C]4.6070535911162e-08[/C][C]9.2141071822324e-08[/C][C]0.999999953929464[/C][/ROW]
[ROW][C]61[/C][C]2.71130369707650e-08[/C][C]5.42260739415299e-08[/C][C]0.999999972886963[/C][/ROW]
[ROW][C]62[/C][C]2.19039894187291e-08[/C][C]4.38079788374581e-08[/C][C]0.99999997809601[/C][/ROW]
[ROW][C]63[/C][C]1.19248270250692e-08[/C][C]2.38496540501385e-08[/C][C]0.999999988075173[/C][/ROW]
[ROW][C]64[/C][C]5.02990076142499e-09[/C][C]1.00598015228500e-08[/C][C]0.9999999949701[/C][/ROW]
[ROW][C]65[/C][C]1.76251584175015e-09[/C][C]3.52503168350029e-09[/C][C]0.999999998237484[/C][/ROW]
[ROW][C]66[/C][C]1.06802449132289e-09[/C][C]2.13604898264578e-09[/C][C]0.999999998931975[/C][/ROW]
[ROW][C]67[/C][C]4.49446103846945e-10[/C][C]8.9889220769389e-10[/C][C]0.999999999550554[/C][/ROW]
[ROW][C]68[/C][C]1.76704459993446e-10[/C][C]3.53408919986893e-10[/C][C]0.999999999823296[/C][/ROW]
[ROW][C]69[/C][C]1.47227990370749e-10[/C][C]2.94455980741498e-10[/C][C]0.999999999852772[/C][/ROW]
[ROW][C]70[/C][C]2.54015830053602e-07[/C][C]5.08031660107205e-07[/C][C]0.99999974598417[/C][/ROW]
[ROW][C]71[/C][C]1.83538436984950e-07[/C][C]3.67076873969901e-07[/C][C]0.999999816461563[/C][/ROW]
[ROW][C]72[/C][C]1.18684498997847e-07[/C][C]2.37368997995694e-07[/C][C]0.9999998813155[/C][/ROW]
[ROW][C]73[/C][C]3.38572684866247e-07[/C][C]6.77145369732495e-07[/C][C]0.999999661427315[/C][/ROW]
[ROW][C]74[/C][C]3.86943844592037e-07[/C][C]7.73887689184075e-07[/C][C]0.999999613056155[/C][/ROW]
[ROW][C]75[/C][C]9.30987004694379e-07[/C][C]1.86197400938876e-06[/C][C]0.999999069012995[/C][/ROW]
[ROW][C]76[/C][C]9.02001882648798e-07[/C][C]1.80400376529760e-06[/C][C]0.999999097998117[/C][/ROW]
[ROW][C]77[/C][C]4.26422991027671e-07[/C][C]8.52845982055343e-07[/C][C]0.99999957357701[/C][/ROW]
[ROW][C]78[/C][C]2.00195270349507e-07[/C][C]4.00390540699014e-07[/C][C]0.99999979980473[/C][/ROW]
[ROW][C]79[/C][C]1.16391849366838e-07[/C][C]2.32783698733677e-07[/C][C]0.99999988360815[/C][/ROW]
[ROW][C]80[/C][C]7.98080095064588e-08[/C][C]1.59616019012918e-07[/C][C]0.99999992019199[/C][/ROW]
[ROW][C]81[/C][C]5.72677547212754e-08[/C][C]1.14535509442551e-07[/C][C]0.999999942732245[/C][/ROW]
[ROW][C]82[/C][C]1.14939752107190e-07[/C][C]2.29879504214381e-07[/C][C]0.999999885060248[/C][/ROW]
[ROW][C]83[/C][C]3.78977711450038e-08[/C][C]7.57955422900076e-08[/C][C]0.999999962102229[/C][/ROW]
[ROW][C]84[/C][C]2.55721635218840e-08[/C][C]5.11443270437679e-08[/C][C]0.999999974427837[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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
200.4564877642622630.9129755285245260.543512235737737
210.2914223529964070.5828447059928130.708577647003593
220.1733209695493340.3466419390986680.826679030450666
230.1080757960792910.2161515921585810.89192420392071
240.0625615029996620.1251230059993240.937438497000338
250.03223092471063320.06446184942126640.967769075289367
260.01807591019602110.03615182039204230.981924089803979
270.009258549585768180.01851709917153640.990741450414232
280.004791425641655890.009582851283311780.995208574358344
290.003362215133602330.006724430267204650.996637784866398
300.001451672908383170.002903345816766350.998548327091617
310.004850688893057740.009701377786115470.995149311106942
320.003772563910587990.007545127821175970.996227436089412
330.002360927203215940.004721854406431880.997639072796784
340.001153583648382790.002307167296765580.998846416351617
350.001837166956983270.003674333913966550.998162833043017
360.002661772005965480.005323544011930960.997338227994035
370.0020276181427550.004055236285510.997972381857245
380.001127329879020230.002254659758040450.99887267012098
390.001388259501634860.002776519003269720.998611740498365
400.001037222740214540.002074445480429070.998962777259785
410.000539898461966850.00107979692393370.999460101538033
420.0005168591122040570.001033718224408110.999483140887796
430.0002689024825672040.0005378049651344070.999731097517433
440.0001533232769094600.0003066465538189210.99984667672309
457.55477790420018e-050.0001510955580840040.999924452220958
466.92759054298304e-050.0001385518108596610.99993072409457
474.54979766963736e-059.09959533927471e-050.999954502023304
482.36515345451589e-054.73030690903178e-050.999976348465455
491.14716920525279e-052.29433841050558e-050.999988528307947
505.48205944314602e-061.09641188862920e-050.999994517940557
514.31587220125221e-068.63174440250441e-060.9999956841278
522.14771625802605e-064.29543251605209e-060.999997852283742
531.10077700015232e-062.20155400030465e-060.999998899223
544.50160445991516e-079.00320891983031e-070.999999549839554
552.5823181397344e-075.1646362794688e-070.999999741768186
561.37922311723677e-072.75844623447354e-070.999999862077688
571.54505632811504e-073.09011265623009e-070.999999845494367
589.16343385747625e-081.83268677149525e-070.999999908365661
593.97702632624459e-087.95405265248919e-080.999999960229737
604.6070535911162e-089.2141071822324e-080.999999953929464
612.71130369707650e-085.42260739415299e-080.999999972886963
622.19039894187291e-084.38079788374581e-080.99999997809601
631.19248270250692e-082.38496540501385e-080.999999988075173
645.02990076142499e-091.00598015228500e-080.9999999949701
651.76251584175015e-093.52503168350029e-090.999999998237484
661.06802449132289e-092.13604898264578e-090.999999998931975
674.49446103846945e-108.9889220769389e-100.999999999550554
681.76704459993446e-103.53408919986893e-100.999999999823296
691.47227990370749e-102.94455980741498e-100.999999999852772
702.54015830053602e-075.08031660107205e-070.99999974598417
711.83538436984950e-073.67076873969901e-070.999999816461563
721.18684498997847e-072.37368997995694e-070.9999998813155
733.38572684866247e-076.77145369732495e-070.999999661427315
743.86943844592037e-077.73887689184075e-070.999999613056155
759.30987004694379e-071.86197400938876e-060.999999069012995
769.02001882648798e-071.80400376529760e-060.999999097998117
774.26422991027671e-078.52845982055343e-070.99999957357701
782.00195270349507e-074.00390540699014e-070.99999979980473
791.16391849366838e-072.32783698733677e-070.99999988360815
807.98080095064588e-081.59616019012918e-070.99999992019199
815.72677547212754e-081.14535509442551e-070.999999942732245
821.14939752107190e-072.29879504214381e-070.999999885060248
833.78977711450038e-087.57955422900076e-080.999999962102229
842.55721635218840e-085.11443270437679e-080.999999974427837







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level570.876923076923077NOK
5% type I error level590.907692307692308NOK
10% type I error level600.923076923076923NOK

\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 & 57 & 0.876923076923077 & NOK \tabularnewline
5% type I error level & 59 & 0.907692307692308 & NOK \tabularnewline
10% type I error level & 60 & 0.923076923076923 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57877&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]57[/C][C]0.876923076923077[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]59[/C][C]0.907692307692308[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]60[/C][C]0.923076923076923[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57877&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57877&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 level570.876923076923077NOK
5% type I error level590.907692307692308NOK
10% type I error level600.923076923076923NOK



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')
}