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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:38:21 -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/t1258656095sulr0ro24v2pqmb.htm/, Retrieved Thu, 28 Mar 2024 11:46:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57886, Retrieved Thu, 28 Mar 2024 11:46:05 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsWs7.1 inflatie -werkloosheid
Estimated Impact145
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 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Ws7.1 inflatie -w...] [2009-11-19 18:38:21] [88e98f4c87ea17c4967db8279bda8533] [Current]
-    D        [Multiple Regression] [Ws7.1 werklooshei...] [2009-11-19 19:11:02] [616e2df490b611f6cb7080068870ecbd]
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Dataseries X:
1.4	8.2
1.2	8.0
1.0	7.5
1.7	6.8
2.4	6.5
2.0	6.6
2.1	7.6
2.0	8.0
1.8	8.1
2.7	7.7
2.3	7.5
1.9	7.6
2.0	7.8
2.3	7.8
2.8	7.8
2.4	7.5
2.3	7.5
2.7	7.1
2.7	7.5
2.9	7.5
3.0	7.6
2.2	7.7
2.3	7.7
2.8	7.9
2.8	8.1
2.8	8.2
2.2	8.2
2.6	8.2
2.8	7.9
2.5	7.3
2.4	6.9
2.3	6.6
1.9	6.7
1.7	6.9
2.0	7.0
2.1	7.1
1.7	7.2
1.8	7.1
1.8	6.9
1.8	7.0
1.3	6.8
1.3	6.4
1.3	6.7
1.2	6.6
1.4	6.4
2.2	6.3
2.9	6.2
3.1	6.5
3.5	6.8
3.6	6.8
4.4	6.4
4.1	6.1
5.1	5.8
5.8	6.1
5.9	7.2
5.4	7.3
5.5	6.9
4.8	6.1
3.2	5.8
2.7	6.2
2.1	7.1
1.9	7.7
0.6	7.9
0.7	7.7




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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=57886&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=57886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 6.7992929704292 -0.595409398402679X[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  6.7992929704292 -0.595409398402679X[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  6.7992929704292 -0.595409398402679X[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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] = + 6.7992929704292 -0.595409398402679X[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.79929297042921.5417654.41014.2e-052.1e-05
X-0.5954093984026790.214263-2.77890.007210.003605

\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) & 6.7992929704292 & 1.541765 & 4.4101 & 4.2e-05 & 2.1e-05 \tabularnewline
X & -0.595409398402679 & 0.214263 & -2.7789 & 0.00721 & 0.003605 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&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]6.7992929704292[/C][C]1.541765[/C][C]4.4101[/C][C]4.2e-05[/C][C]2.1e-05[/C][/ROW]
[ROW][C]X[/C][C]-0.595409398402679[/C][C]0.214263[/C][C]-2.7789[/C][C]0.00721[/C][C]0.003605[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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)6.79929297042921.5417654.41014.2e-052.1e-05
X-0.5954093984026790.214263-2.77890.007210.003605







Multiple Linear Regression - Regression Statistics
Multiple R0.332800503518977
R-squared0.110756175142485
Adjusted R-squared0.0964135328060733
F-TEST (value)7.72215973491225
F-TEST (DF numerator)1
F-TEST (DF denominator)62
p-value0.00720990438056168
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.12614036542989
Sum Squared Residuals78.627911604335

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.332800503518977 \tabularnewline
R-squared & 0.110756175142485 \tabularnewline
Adjusted R-squared & 0.0964135328060733 \tabularnewline
F-TEST (value) & 7.72215973491225 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 62 \tabularnewline
p-value & 0.00720990438056168 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.12614036542989 \tabularnewline
Sum Squared Residuals & 78.627911604335 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.332800503518977[/C][/ROW]
[ROW][C]R-squared[/C][C]0.110756175142485[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0964135328060733[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.72215973491225[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]62[/C][/ROW]
[ROW][C]p-value[/C][C]0.00720990438056168[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.12614036542989[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]78.627911604335[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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 R0.332800503518977
R-squared0.110756175142485
Adjusted R-squared0.0964135328060733
F-TEST (value)7.72215973491225
F-TEST (DF numerator)1
F-TEST (DF denominator)62
p-value0.00720990438056168
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.12614036542989
Sum Squared Residuals78.627911604335







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.41.91693590352723-0.51693590352723
21.22.03601778320776-0.836017783207764
312.33372248240910-1.33372248240910
41.72.75050906129098-1.05050906129098
52.42.92913188081178-0.529131880811783
622.86959094097152-0.869590940971516
72.12.27418154256884-0.174181542568836
822.03601778320776-0.0360177832077644
91.81.97647684336750-0.176476843367497
102.72.214640602728570.485359397271432
112.32.33372248240910-0.0337224824091043
121.92.27418154256884-0.374181542568837
1322.1550996628883-0.155099662888300
142.32.15509966288830.144900337111699
152.82.15509966288830.6449003371117
162.42.333722482409100.0662775175908958
172.32.33372248240910-0.0337224824091043
182.72.571886241770180.128113758229824
192.72.333722482409100.366277517590896
202.92.333722482409100.566277517590896
2132.274181542568840.725818457431164
222.22.21464060272857-0.0146406027285680
232.32.214640602728570.0853593972714317
242.82.095558723048030.704441276951968
252.81.976476843367500.823523156632503
262.81.916935903527230.88306409647277
272.21.916935903527230.283064096472771
282.61.916935903527230.683064096472771
292.82.095558723048030.704441276951968
302.52.452804362089640.047195637910360
312.42.69096812145071-0.290968121450712
322.32.86959094097152-0.569590940971516
331.92.81005000113125-0.910050001131247
341.72.69096812145071-0.990968121450711
3522.63142718161044-0.631427181610444
362.12.57188624177018-0.471886241770176
371.72.51234530192991-0.812345301929908
381.82.57188624177018-0.771886241770176
391.82.69096812145071-0.890968121450711
401.82.63142718161044-0.831427181610444
411.32.75050906129098-1.45050906129098
421.32.98867282065205-1.68867282065205
431.32.81005000113125-1.51005000113125
441.22.86959094097152-1.66959094097152
451.42.98867282065205-1.58867282065205
462.23.04821376049232-0.848213760492319
472.93.10775470033259-0.207754700332587
483.12.929131880811780.170868119188217
493.52.750509061290980.74949093870902
503.62.750509061290980.84949093870902
514.42.988672820652051.41132717934795
524.13.167295640172860.932704359827144
535.13.345918459693661.75408154030634
545.83.167295640172852.63270435982714
555.92.512345301929913.38765469807009
565.42.452804362089642.94719563791036
575.52.690968121450712.80903187854929
584.83.167295640172861.63270435982714
593.23.34591845969366-0.145918459693659
602.73.10775470033259-0.407754700332587
612.12.57188624177018-0.471886241770176
621.92.21464060272857-0.314640602728568
630.62.09555872304803-1.49555872304803
640.72.21464060272857-1.51464060272857

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.4 & 1.91693590352723 & -0.51693590352723 \tabularnewline
2 & 1.2 & 2.03601778320776 & -0.836017783207764 \tabularnewline
3 & 1 & 2.33372248240910 & -1.33372248240910 \tabularnewline
4 & 1.7 & 2.75050906129098 & -1.05050906129098 \tabularnewline
5 & 2.4 & 2.92913188081178 & -0.529131880811783 \tabularnewline
6 & 2 & 2.86959094097152 & -0.869590940971516 \tabularnewline
7 & 2.1 & 2.27418154256884 & -0.174181542568836 \tabularnewline
8 & 2 & 2.03601778320776 & -0.0360177832077644 \tabularnewline
9 & 1.8 & 1.97647684336750 & -0.176476843367497 \tabularnewline
10 & 2.7 & 2.21464060272857 & 0.485359397271432 \tabularnewline
11 & 2.3 & 2.33372248240910 & -0.0337224824091043 \tabularnewline
12 & 1.9 & 2.27418154256884 & -0.374181542568837 \tabularnewline
13 & 2 & 2.1550996628883 & -0.155099662888300 \tabularnewline
14 & 2.3 & 2.1550996628883 & 0.144900337111699 \tabularnewline
15 & 2.8 & 2.1550996628883 & 0.6449003371117 \tabularnewline
16 & 2.4 & 2.33372248240910 & 0.0662775175908958 \tabularnewline
17 & 2.3 & 2.33372248240910 & -0.0337224824091043 \tabularnewline
18 & 2.7 & 2.57188624177018 & 0.128113758229824 \tabularnewline
19 & 2.7 & 2.33372248240910 & 0.366277517590896 \tabularnewline
20 & 2.9 & 2.33372248240910 & 0.566277517590896 \tabularnewline
21 & 3 & 2.27418154256884 & 0.725818457431164 \tabularnewline
22 & 2.2 & 2.21464060272857 & -0.0146406027285680 \tabularnewline
23 & 2.3 & 2.21464060272857 & 0.0853593972714317 \tabularnewline
24 & 2.8 & 2.09555872304803 & 0.704441276951968 \tabularnewline
25 & 2.8 & 1.97647684336750 & 0.823523156632503 \tabularnewline
26 & 2.8 & 1.91693590352723 & 0.88306409647277 \tabularnewline
27 & 2.2 & 1.91693590352723 & 0.283064096472771 \tabularnewline
28 & 2.6 & 1.91693590352723 & 0.683064096472771 \tabularnewline
29 & 2.8 & 2.09555872304803 & 0.704441276951968 \tabularnewline
30 & 2.5 & 2.45280436208964 & 0.047195637910360 \tabularnewline
31 & 2.4 & 2.69096812145071 & -0.290968121450712 \tabularnewline
32 & 2.3 & 2.86959094097152 & -0.569590940971516 \tabularnewline
33 & 1.9 & 2.81005000113125 & -0.910050001131247 \tabularnewline
34 & 1.7 & 2.69096812145071 & -0.990968121450711 \tabularnewline
35 & 2 & 2.63142718161044 & -0.631427181610444 \tabularnewline
36 & 2.1 & 2.57188624177018 & -0.471886241770176 \tabularnewline
37 & 1.7 & 2.51234530192991 & -0.812345301929908 \tabularnewline
38 & 1.8 & 2.57188624177018 & -0.771886241770176 \tabularnewline
39 & 1.8 & 2.69096812145071 & -0.890968121450711 \tabularnewline
40 & 1.8 & 2.63142718161044 & -0.831427181610444 \tabularnewline
41 & 1.3 & 2.75050906129098 & -1.45050906129098 \tabularnewline
42 & 1.3 & 2.98867282065205 & -1.68867282065205 \tabularnewline
43 & 1.3 & 2.81005000113125 & -1.51005000113125 \tabularnewline
44 & 1.2 & 2.86959094097152 & -1.66959094097152 \tabularnewline
45 & 1.4 & 2.98867282065205 & -1.58867282065205 \tabularnewline
46 & 2.2 & 3.04821376049232 & -0.848213760492319 \tabularnewline
47 & 2.9 & 3.10775470033259 & -0.207754700332587 \tabularnewline
48 & 3.1 & 2.92913188081178 & 0.170868119188217 \tabularnewline
49 & 3.5 & 2.75050906129098 & 0.74949093870902 \tabularnewline
50 & 3.6 & 2.75050906129098 & 0.84949093870902 \tabularnewline
51 & 4.4 & 2.98867282065205 & 1.41132717934795 \tabularnewline
52 & 4.1 & 3.16729564017286 & 0.932704359827144 \tabularnewline
53 & 5.1 & 3.34591845969366 & 1.75408154030634 \tabularnewline
54 & 5.8 & 3.16729564017285 & 2.63270435982714 \tabularnewline
55 & 5.9 & 2.51234530192991 & 3.38765469807009 \tabularnewline
56 & 5.4 & 2.45280436208964 & 2.94719563791036 \tabularnewline
57 & 5.5 & 2.69096812145071 & 2.80903187854929 \tabularnewline
58 & 4.8 & 3.16729564017286 & 1.63270435982714 \tabularnewline
59 & 3.2 & 3.34591845969366 & -0.145918459693659 \tabularnewline
60 & 2.7 & 3.10775470033259 & -0.407754700332587 \tabularnewline
61 & 2.1 & 2.57188624177018 & -0.471886241770176 \tabularnewline
62 & 1.9 & 2.21464060272857 & -0.314640602728568 \tabularnewline
63 & 0.6 & 2.09555872304803 & -1.49555872304803 \tabularnewline
64 & 0.7 & 2.21464060272857 & -1.51464060272857 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&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]1.4[/C][C]1.91693590352723[/C][C]-0.51693590352723[/C][/ROW]
[ROW][C]2[/C][C]1.2[/C][C]2.03601778320776[/C][C]-0.836017783207764[/C][/ROW]
[ROW][C]3[/C][C]1[/C][C]2.33372248240910[/C][C]-1.33372248240910[/C][/ROW]
[ROW][C]4[/C][C]1.7[/C][C]2.75050906129098[/C][C]-1.05050906129098[/C][/ROW]
[ROW][C]5[/C][C]2.4[/C][C]2.92913188081178[/C][C]-0.529131880811783[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]2.86959094097152[/C][C]-0.869590940971516[/C][/ROW]
[ROW][C]7[/C][C]2.1[/C][C]2.27418154256884[/C][C]-0.174181542568836[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.03601778320776[/C][C]-0.0360177832077644[/C][/ROW]
[ROW][C]9[/C][C]1.8[/C][C]1.97647684336750[/C][C]-0.176476843367497[/C][/ROW]
[ROW][C]10[/C][C]2.7[/C][C]2.21464060272857[/C][C]0.485359397271432[/C][/ROW]
[ROW][C]11[/C][C]2.3[/C][C]2.33372248240910[/C][C]-0.0337224824091043[/C][/ROW]
[ROW][C]12[/C][C]1.9[/C][C]2.27418154256884[/C][C]-0.374181542568837[/C][/ROW]
[ROW][C]13[/C][C]2[/C][C]2.1550996628883[/C][C]-0.155099662888300[/C][/ROW]
[ROW][C]14[/C][C]2.3[/C][C]2.1550996628883[/C][C]0.144900337111699[/C][/ROW]
[ROW][C]15[/C][C]2.8[/C][C]2.1550996628883[/C][C]0.6449003371117[/C][/ROW]
[ROW][C]16[/C][C]2.4[/C][C]2.33372248240910[/C][C]0.0662775175908958[/C][/ROW]
[ROW][C]17[/C][C]2.3[/C][C]2.33372248240910[/C][C]-0.0337224824091043[/C][/ROW]
[ROW][C]18[/C][C]2.7[/C][C]2.57188624177018[/C][C]0.128113758229824[/C][/ROW]
[ROW][C]19[/C][C]2.7[/C][C]2.33372248240910[/C][C]0.366277517590896[/C][/ROW]
[ROW][C]20[/C][C]2.9[/C][C]2.33372248240910[/C][C]0.566277517590896[/C][/ROW]
[ROW][C]21[/C][C]3[/C][C]2.27418154256884[/C][C]0.725818457431164[/C][/ROW]
[ROW][C]22[/C][C]2.2[/C][C]2.21464060272857[/C][C]-0.0146406027285680[/C][/ROW]
[ROW][C]23[/C][C]2.3[/C][C]2.21464060272857[/C][C]0.0853593972714317[/C][/ROW]
[ROW][C]24[/C][C]2.8[/C][C]2.09555872304803[/C][C]0.704441276951968[/C][/ROW]
[ROW][C]25[/C][C]2.8[/C][C]1.97647684336750[/C][C]0.823523156632503[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]1.91693590352723[/C][C]0.88306409647277[/C][/ROW]
[ROW][C]27[/C][C]2.2[/C][C]1.91693590352723[/C][C]0.283064096472771[/C][/ROW]
[ROW][C]28[/C][C]2.6[/C][C]1.91693590352723[/C][C]0.683064096472771[/C][/ROW]
[ROW][C]29[/C][C]2.8[/C][C]2.09555872304803[/C][C]0.704441276951968[/C][/ROW]
[ROW][C]30[/C][C]2.5[/C][C]2.45280436208964[/C][C]0.047195637910360[/C][/ROW]
[ROW][C]31[/C][C]2.4[/C][C]2.69096812145071[/C][C]-0.290968121450712[/C][/ROW]
[ROW][C]32[/C][C]2.3[/C][C]2.86959094097152[/C][C]-0.569590940971516[/C][/ROW]
[ROW][C]33[/C][C]1.9[/C][C]2.81005000113125[/C][C]-0.910050001131247[/C][/ROW]
[ROW][C]34[/C][C]1.7[/C][C]2.69096812145071[/C][C]-0.990968121450711[/C][/ROW]
[ROW][C]35[/C][C]2[/C][C]2.63142718161044[/C][C]-0.631427181610444[/C][/ROW]
[ROW][C]36[/C][C]2.1[/C][C]2.57188624177018[/C][C]-0.471886241770176[/C][/ROW]
[ROW][C]37[/C][C]1.7[/C][C]2.51234530192991[/C][C]-0.812345301929908[/C][/ROW]
[ROW][C]38[/C][C]1.8[/C][C]2.57188624177018[/C][C]-0.771886241770176[/C][/ROW]
[ROW][C]39[/C][C]1.8[/C][C]2.69096812145071[/C][C]-0.890968121450711[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]2.63142718161044[/C][C]-0.831427181610444[/C][/ROW]
[ROW][C]41[/C][C]1.3[/C][C]2.75050906129098[/C][C]-1.45050906129098[/C][/ROW]
[ROW][C]42[/C][C]1.3[/C][C]2.98867282065205[/C][C]-1.68867282065205[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]2.81005000113125[/C][C]-1.51005000113125[/C][/ROW]
[ROW][C]44[/C][C]1.2[/C][C]2.86959094097152[/C][C]-1.66959094097152[/C][/ROW]
[ROW][C]45[/C][C]1.4[/C][C]2.98867282065205[/C][C]-1.58867282065205[/C][/ROW]
[ROW][C]46[/C][C]2.2[/C][C]3.04821376049232[/C][C]-0.848213760492319[/C][/ROW]
[ROW][C]47[/C][C]2.9[/C][C]3.10775470033259[/C][C]-0.207754700332587[/C][/ROW]
[ROW][C]48[/C][C]3.1[/C][C]2.92913188081178[/C][C]0.170868119188217[/C][/ROW]
[ROW][C]49[/C][C]3.5[/C][C]2.75050906129098[/C][C]0.74949093870902[/C][/ROW]
[ROW][C]50[/C][C]3.6[/C][C]2.75050906129098[/C][C]0.84949093870902[/C][/ROW]
[ROW][C]51[/C][C]4.4[/C][C]2.98867282065205[/C][C]1.41132717934795[/C][/ROW]
[ROW][C]52[/C][C]4.1[/C][C]3.16729564017286[/C][C]0.932704359827144[/C][/ROW]
[ROW][C]53[/C][C]5.1[/C][C]3.34591845969366[/C][C]1.75408154030634[/C][/ROW]
[ROW][C]54[/C][C]5.8[/C][C]3.16729564017285[/C][C]2.63270435982714[/C][/ROW]
[ROW][C]55[/C][C]5.9[/C][C]2.51234530192991[/C][C]3.38765469807009[/C][/ROW]
[ROW][C]56[/C][C]5.4[/C][C]2.45280436208964[/C][C]2.94719563791036[/C][/ROW]
[ROW][C]57[/C][C]5.5[/C][C]2.69096812145071[/C][C]2.80903187854929[/C][/ROW]
[ROW][C]58[/C][C]4.8[/C][C]3.16729564017286[/C][C]1.63270435982714[/C][/ROW]
[ROW][C]59[/C][C]3.2[/C][C]3.34591845969366[/C][C]-0.145918459693659[/C][/ROW]
[ROW][C]60[/C][C]2.7[/C][C]3.10775470033259[/C][C]-0.407754700332587[/C][/ROW]
[ROW][C]61[/C][C]2.1[/C][C]2.57188624177018[/C][C]-0.471886241770176[/C][/ROW]
[ROW][C]62[/C][C]1.9[/C][C]2.21464060272857[/C][C]-0.314640602728568[/C][/ROW]
[ROW][C]63[/C][C]0.6[/C][C]2.09555872304803[/C][C]-1.49555872304803[/C][/ROW]
[ROW][C]64[/C][C]0.7[/C][C]2.21464060272857[/C][C]-1.51464060272857[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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
11.41.91693590352723-0.51693590352723
21.22.03601778320776-0.836017783207764
312.33372248240910-1.33372248240910
41.72.75050906129098-1.05050906129098
52.42.92913188081178-0.529131880811783
622.86959094097152-0.869590940971516
72.12.27418154256884-0.174181542568836
822.03601778320776-0.0360177832077644
91.81.97647684336750-0.176476843367497
102.72.214640602728570.485359397271432
112.32.33372248240910-0.0337224824091043
121.92.27418154256884-0.374181542568837
1322.1550996628883-0.155099662888300
142.32.15509966288830.144900337111699
152.82.15509966288830.6449003371117
162.42.333722482409100.0662775175908958
172.32.33372248240910-0.0337224824091043
182.72.571886241770180.128113758229824
192.72.333722482409100.366277517590896
202.92.333722482409100.566277517590896
2132.274181542568840.725818457431164
222.22.21464060272857-0.0146406027285680
232.32.214640602728570.0853593972714317
242.82.095558723048030.704441276951968
252.81.976476843367500.823523156632503
262.81.916935903527230.88306409647277
272.21.916935903527230.283064096472771
282.61.916935903527230.683064096472771
292.82.095558723048030.704441276951968
302.52.452804362089640.047195637910360
312.42.69096812145071-0.290968121450712
322.32.86959094097152-0.569590940971516
331.92.81005000113125-0.910050001131247
341.72.69096812145071-0.990968121450711
3522.63142718161044-0.631427181610444
362.12.57188624177018-0.471886241770176
371.72.51234530192991-0.812345301929908
381.82.57188624177018-0.771886241770176
391.82.69096812145071-0.890968121450711
401.82.63142718161044-0.831427181610444
411.32.75050906129098-1.45050906129098
421.32.98867282065205-1.68867282065205
431.32.81005000113125-1.51005000113125
441.22.86959094097152-1.66959094097152
451.42.98867282065205-1.58867282065205
462.23.04821376049232-0.848213760492319
472.93.10775470033259-0.207754700332587
483.12.929131880811780.170868119188217
493.52.750509061290980.74949093870902
503.62.750509061290980.84949093870902
514.42.988672820652051.41132717934795
524.13.167295640172860.932704359827144
535.13.345918459693661.75408154030634
545.83.167295640172852.63270435982714
555.92.512345301929913.38765469807009
565.42.452804362089642.94719563791036
575.52.690968121450712.80903187854929
584.83.167295640172861.63270435982714
593.23.34591845969366-0.145918459693659
602.73.10775470033259-0.407754700332587
612.12.57188624177018-0.471886241770176
621.92.21464060272857-0.314640602728568
630.62.09555872304803-1.49555872304803
640.72.21464060272857-1.51464060272857







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.054874764957590.109749529915180.94512523504241
60.01570448521299620.03140897042599240.984295514787004
70.01497702884161000.02995405768322010.98502297115839
80.01104064872666410.02208129745332810.988959351273336
90.004634311735730530.009268623471461060.99536568826427
100.01033830923311280.02067661846622560.989661690766887
110.005665461229261840.01133092245852370.994334538770738
120.002255092983573680.004510185967147360.997744907016426
130.0009032319390079240.001806463878015850.999096768060992
140.0004899149604871340.0009798299209742680.999510085039513
150.0007437636283483650.001487527256696730.999256236371652
160.0003716200366325430.0007432400732650860.999628379963367
170.0001601998311389480.0003203996622778970.999839800168861
180.0001021288588140470.0002042577176280950.999897871141186
197.07488441709346e-050.0001414976883418690.99992925115583
206.85276338702973e-050.0001370552677405950.99993147236613
217.7246112161872e-050.0001544922243237440.999922753887838
223.04108332078833e-056.08216664157665e-050.999969589166792
231.19134891270325e-052.38269782540651e-050.999988086510873
248.93929302917981e-061.78785860583596e-050.99999106070697
256.5612137106992e-061.31224274213984e-050.99999343878629
264.53730720429082e-069.07461440858165e-060.999995462692796
271.74666609901726e-063.49333219803453e-060.9999982533339
288.91585774884334e-071.78317154976867e-060.999999108414225
295.93523608934406e-071.18704721786881e-060.99999940647639
302.29458732742378e-074.58917465484755e-070.999999770541267
318.02944229432409e-081.60588845886482e-070.999999919705577
322.84238599628386e-085.68477199256773e-080.99999997157614
331.33303409033169e-082.66606818066338e-080.99999998666966
347.9033183298663e-091.58066366597326e-080.999999992096682
352.8188469665673e-095.6376939331346e-090.999999997181153
368.95697248669437e-101.79139449733887e-090.999999999104303
374.58176425368841e-109.16352850737683e-100.999999999541824
381.92631040055146e-103.85262080110292e-100.99999999980737
398.34724264386317e-111.66944852877263e-100.999999999916528
403.4904774995334e-116.9809549990668e-110.999999999965095
415.5523985823355e-111.1104797164671e-100.999999999944476
421.05284507464494e-102.10569014928988e-100.999999999894716
431.85088336377084e-103.70176672754168e-100.999999999814912
445.74345941235107e-101.14869188247021e-090.999999999425654
451.72209793056495e-093.44419586112990e-090.999999998277902
463.05692268830710e-096.11384537661421e-090.999999996943077
471.27728054792601e-082.55456109585201e-080.999999987227195
482.92461738331463e-085.84923476662926e-080.999999970753826
497.99498084186314e-081.59899616837263e-070.999999920050192
501.81279966049386e-073.62559932098772e-070.999999818720034
511.85820561814267e-063.71641123628534e-060.999998141794382
523.85187723798283e-067.70375447596566e-060.999996148122762
531.91228242940512e-053.82456485881023e-050.999980877175706
540.0001931126108941950.000386225221788390.999806887389106
550.009351062776783880.01870212555356780.990648937223216
560.1215078838552530.2430157677105050.878492116144747
570.7409952898488670.5180094203022670.259004710151133
580.945339129846410.1093217403071790.0546608701535897
590.8611295364194690.2777409271610620.138870463580531

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.05487476495759 & 0.10974952991518 & 0.94512523504241 \tabularnewline
6 & 0.0157044852129962 & 0.0314089704259924 & 0.984295514787004 \tabularnewline
7 & 0.0149770288416100 & 0.0299540576832201 & 0.98502297115839 \tabularnewline
8 & 0.0110406487266641 & 0.0220812974533281 & 0.988959351273336 \tabularnewline
9 & 0.00463431173573053 & 0.00926862347146106 & 0.99536568826427 \tabularnewline
10 & 0.0103383092331128 & 0.0206766184662256 & 0.989661690766887 \tabularnewline
11 & 0.00566546122926184 & 0.0113309224585237 & 0.994334538770738 \tabularnewline
12 & 0.00225509298357368 & 0.00451018596714736 & 0.997744907016426 \tabularnewline
13 & 0.000903231939007924 & 0.00180646387801585 & 0.999096768060992 \tabularnewline
14 & 0.000489914960487134 & 0.000979829920974268 & 0.999510085039513 \tabularnewline
15 & 0.000743763628348365 & 0.00148752725669673 & 0.999256236371652 \tabularnewline
16 & 0.000371620036632543 & 0.000743240073265086 & 0.999628379963367 \tabularnewline
17 & 0.000160199831138948 & 0.000320399662277897 & 0.999839800168861 \tabularnewline
18 & 0.000102128858814047 & 0.000204257717628095 & 0.999897871141186 \tabularnewline
19 & 7.07488441709346e-05 & 0.000141497688341869 & 0.99992925115583 \tabularnewline
20 & 6.85276338702973e-05 & 0.000137055267740595 & 0.99993147236613 \tabularnewline
21 & 7.7246112161872e-05 & 0.000154492224323744 & 0.999922753887838 \tabularnewline
22 & 3.04108332078833e-05 & 6.08216664157665e-05 & 0.999969589166792 \tabularnewline
23 & 1.19134891270325e-05 & 2.38269782540651e-05 & 0.999988086510873 \tabularnewline
24 & 8.93929302917981e-06 & 1.78785860583596e-05 & 0.99999106070697 \tabularnewline
25 & 6.5612137106992e-06 & 1.31224274213984e-05 & 0.99999343878629 \tabularnewline
26 & 4.53730720429082e-06 & 9.07461440858165e-06 & 0.999995462692796 \tabularnewline
27 & 1.74666609901726e-06 & 3.49333219803453e-06 & 0.9999982533339 \tabularnewline
28 & 8.91585774884334e-07 & 1.78317154976867e-06 & 0.999999108414225 \tabularnewline
29 & 5.93523608934406e-07 & 1.18704721786881e-06 & 0.99999940647639 \tabularnewline
30 & 2.29458732742378e-07 & 4.58917465484755e-07 & 0.999999770541267 \tabularnewline
31 & 8.02944229432409e-08 & 1.60588845886482e-07 & 0.999999919705577 \tabularnewline
32 & 2.84238599628386e-08 & 5.68477199256773e-08 & 0.99999997157614 \tabularnewline
33 & 1.33303409033169e-08 & 2.66606818066338e-08 & 0.99999998666966 \tabularnewline
34 & 7.9033183298663e-09 & 1.58066366597326e-08 & 0.999999992096682 \tabularnewline
35 & 2.8188469665673e-09 & 5.6376939331346e-09 & 0.999999997181153 \tabularnewline
36 & 8.95697248669437e-10 & 1.79139449733887e-09 & 0.999999999104303 \tabularnewline
37 & 4.58176425368841e-10 & 9.16352850737683e-10 & 0.999999999541824 \tabularnewline
38 & 1.92631040055146e-10 & 3.85262080110292e-10 & 0.99999999980737 \tabularnewline
39 & 8.34724264386317e-11 & 1.66944852877263e-10 & 0.999999999916528 \tabularnewline
40 & 3.4904774995334e-11 & 6.9809549990668e-11 & 0.999999999965095 \tabularnewline
41 & 5.5523985823355e-11 & 1.1104797164671e-10 & 0.999999999944476 \tabularnewline
42 & 1.05284507464494e-10 & 2.10569014928988e-10 & 0.999999999894716 \tabularnewline
43 & 1.85088336377084e-10 & 3.70176672754168e-10 & 0.999999999814912 \tabularnewline
44 & 5.74345941235107e-10 & 1.14869188247021e-09 & 0.999999999425654 \tabularnewline
45 & 1.72209793056495e-09 & 3.44419586112990e-09 & 0.999999998277902 \tabularnewline
46 & 3.05692268830710e-09 & 6.11384537661421e-09 & 0.999999996943077 \tabularnewline
47 & 1.27728054792601e-08 & 2.55456109585201e-08 & 0.999999987227195 \tabularnewline
48 & 2.92461738331463e-08 & 5.84923476662926e-08 & 0.999999970753826 \tabularnewline
49 & 7.99498084186314e-08 & 1.59899616837263e-07 & 0.999999920050192 \tabularnewline
50 & 1.81279966049386e-07 & 3.62559932098772e-07 & 0.999999818720034 \tabularnewline
51 & 1.85820561814267e-06 & 3.71641123628534e-06 & 0.999998141794382 \tabularnewline
52 & 3.85187723798283e-06 & 7.70375447596566e-06 & 0.999996148122762 \tabularnewline
53 & 1.91228242940512e-05 & 3.82456485881023e-05 & 0.999980877175706 \tabularnewline
54 & 0.000193112610894195 & 0.00038622522178839 & 0.999806887389106 \tabularnewline
55 & 0.00935106277678388 & 0.0187021255535678 & 0.990648937223216 \tabularnewline
56 & 0.121507883855253 & 0.243015767710505 & 0.878492116144747 \tabularnewline
57 & 0.740995289848867 & 0.518009420302267 & 0.259004710151133 \tabularnewline
58 & 0.94533912984641 & 0.109321740307179 & 0.0546608701535897 \tabularnewline
59 & 0.861129536419469 & 0.277740927161062 & 0.138870463580531 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&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]5[/C][C]0.05487476495759[/C][C]0.10974952991518[/C][C]0.94512523504241[/C][/ROW]
[ROW][C]6[/C][C]0.0157044852129962[/C][C]0.0314089704259924[/C][C]0.984295514787004[/C][/ROW]
[ROW][C]7[/C][C]0.0149770288416100[/C][C]0.0299540576832201[/C][C]0.98502297115839[/C][/ROW]
[ROW][C]8[/C][C]0.0110406487266641[/C][C]0.0220812974533281[/C][C]0.988959351273336[/C][/ROW]
[ROW][C]9[/C][C]0.00463431173573053[/C][C]0.00926862347146106[/C][C]0.99536568826427[/C][/ROW]
[ROW][C]10[/C][C]0.0103383092331128[/C][C]0.0206766184662256[/C][C]0.989661690766887[/C][/ROW]
[ROW][C]11[/C][C]0.00566546122926184[/C][C]0.0113309224585237[/C][C]0.994334538770738[/C][/ROW]
[ROW][C]12[/C][C]0.00225509298357368[/C][C]0.00451018596714736[/C][C]0.997744907016426[/C][/ROW]
[ROW][C]13[/C][C]0.000903231939007924[/C][C]0.00180646387801585[/C][C]0.999096768060992[/C][/ROW]
[ROW][C]14[/C][C]0.000489914960487134[/C][C]0.000979829920974268[/C][C]0.999510085039513[/C][/ROW]
[ROW][C]15[/C][C]0.000743763628348365[/C][C]0.00148752725669673[/C][C]0.999256236371652[/C][/ROW]
[ROW][C]16[/C][C]0.000371620036632543[/C][C]0.000743240073265086[/C][C]0.999628379963367[/C][/ROW]
[ROW][C]17[/C][C]0.000160199831138948[/C][C]0.000320399662277897[/C][C]0.999839800168861[/C][/ROW]
[ROW][C]18[/C][C]0.000102128858814047[/C][C]0.000204257717628095[/C][C]0.999897871141186[/C][/ROW]
[ROW][C]19[/C][C]7.07488441709346e-05[/C][C]0.000141497688341869[/C][C]0.99992925115583[/C][/ROW]
[ROW][C]20[/C][C]6.85276338702973e-05[/C][C]0.000137055267740595[/C][C]0.99993147236613[/C][/ROW]
[ROW][C]21[/C][C]7.7246112161872e-05[/C][C]0.000154492224323744[/C][C]0.999922753887838[/C][/ROW]
[ROW][C]22[/C][C]3.04108332078833e-05[/C][C]6.08216664157665e-05[/C][C]0.999969589166792[/C][/ROW]
[ROW][C]23[/C][C]1.19134891270325e-05[/C][C]2.38269782540651e-05[/C][C]0.999988086510873[/C][/ROW]
[ROW][C]24[/C][C]8.93929302917981e-06[/C][C]1.78785860583596e-05[/C][C]0.99999106070697[/C][/ROW]
[ROW][C]25[/C][C]6.5612137106992e-06[/C][C]1.31224274213984e-05[/C][C]0.99999343878629[/C][/ROW]
[ROW][C]26[/C][C]4.53730720429082e-06[/C][C]9.07461440858165e-06[/C][C]0.999995462692796[/C][/ROW]
[ROW][C]27[/C][C]1.74666609901726e-06[/C][C]3.49333219803453e-06[/C][C]0.9999982533339[/C][/ROW]
[ROW][C]28[/C][C]8.91585774884334e-07[/C][C]1.78317154976867e-06[/C][C]0.999999108414225[/C][/ROW]
[ROW][C]29[/C][C]5.93523608934406e-07[/C][C]1.18704721786881e-06[/C][C]0.99999940647639[/C][/ROW]
[ROW][C]30[/C][C]2.29458732742378e-07[/C][C]4.58917465484755e-07[/C][C]0.999999770541267[/C][/ROW]
[ROW][C]31[/C][C]8.02944229432409e-08[/C][C]1.60588845886482e-07[/C][C]0.999999919705577[/C][/ROW]
[ROW][C]32[/C][C]2.84238599628386e-08[/C][C]5.68477199256773e-08[/C][C]0.99999997157614[/C][/ROW]
[ROW][C]33[/C][C]1.33303409033169e-08[/C][C]2.66606818066338e-08[/C][C]0.99999998666966[/C][/ROW]
[ROW][C]34[/C][C]7.9033183298663e-09[/C][C]1.58066366597326e-08[/C][C]0.999999992096682[/C][/ROW]
[ROW][C]35[/C][C]2.8188469665673e-09[/C][C]5.6376939331346e-09[/C][C]0.999999997181153[/C][/ROW]
[ROW][C]36[/C][C]8.95697248669437e-10[/C][C]1.79139449733887e-09[/C][C]0.999999999104303[/C][/ROW]
[ROW][C]37[/C][C]4.58176425368841e-10[/C][C]9.16352850737683e-10[/C][C]0.999999999541824[/C][/ROW]
[ROW][C]38[/C][C]1.92631040055146e-10[/C][C]3.85262080110292e-10[/C][C]0.99999999980737[/C][/ROW]
[ROW][C]39[/C][C]8.34724264386317e-11[/C][C]1.66944852877263e-10[/C][C]0.999999999916528[/C][/ROW]
[ROW][C]40[/C][C]3.4904774995334e-11[/C][C]6.9809549990668e-11[/C][C]0.999999999965095[/C][/ROW]
[ROW][C]41[/C][C]5.5523985823355e-11[/C][C]1.1104797164671e-10[/C][C]0.999999999944476[/C][/ROW]
[ROW][C]42[/C][C]1.05284507464494e-10[/C][C]2.10569014928988e-10[/C][C]0.999999999894716[/C][/ROW]
[ROW][C]43[/C][C]1.85088336377084e-10[/C][C]3.70176672754168e-10[/C][C]0.999999999814912[/C][/ROW]
[ROW][C]44[/C][C]5.74345941235107e-10[/C][C]1.14869188247021e-09[/C][C]0.999999999425654[/C][/ROW]
[ROW][C]45[/C][C]1.72209793056495e-09[/C][C]3.44419586112990e-09[/C][C]0.999999998277902[/C][/ROW]
[ROW][C]46[/C][C]3.05692268830710e-09[/C][C]6.11384537661421e-09[/C][C]0.999999996943077[/C][/ROW]
[ROW][C]47[/C][C]1.27728054792601e-08[/C][C]2.55456109585201e-08[/C][C]0.999999987227195[/C][/ROW]
[ROW][C]48[/C][C]2.92461738331463e-08[/C][C]5.84923476662926e-08[/C][C]0.999999970753826[/C][/ROW]
[ROW][C]49[/C][C]7.99498084186314e-08[/C][C]1.59899616837263e-07[/C][C]0.999999920050192[/C][/ROW]
[ROW][C]50[/C][C]1.81279966049386e-07[/C][C]3.62559932098772e-07[/C][C]0.999999818720034[/C][/ROW]
[ROW][C]51[/C][C]1.85820561814267e-06[/C][C]3.71641123628534e-06[/C][C]0.999998141794382[/C][/ROW]
[ROW][C]52[/C][C]3.85187723798283e-06[/C][C]7.70375447596566e-06[/C][C]0.999996148122762[/C][/ROW]
[ROW][C]53[/C][C]1.91228242940512e-05[/C][C]3.82456485881023e-05[/C][C]0.999980877175706[/C][/ROW]
[ROW][C]54[/C][C]0.000193112610894195[/C][C]0.00038622522178839[/C][C]0.999806887389106[/C][/ROW]
[ROW][C]55[/C][C]0.00935106277678388[/C][C]0.0187021255535678[/C][C]0.990648937223216[/C][/ROW]
[ROW][C]56[/C][C]0.121507883855253[/C][C]0.243015767710505[/C][C]0.878492116144747[/C][/ROW]
[ROW][C]57[/C][C]0.740995289848867[/C][C]0.518009420302267[/C][C]0.259004710151133[/C][/ROW]
[ROW][C]58[/C][C]0.94533912984641[/C][C]0.109321740307179[/C][C]0.0546608701535897[/C][/ROW]
[ROW][C]59[/C][C]0.861129536419469[/C][C]0.277740927161062[/C][C]0.138870463580531[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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
50.054874764957590.109749529915180.94512523504241
60.01570448521299620.03140897042599240.984295514787004
70.01497702884161000.02995405768322010.98502297115839
80.01104064872666410.02208129745332810.988959351273336
90.004634311735730530.009268623471461060.99536568826427
100.01033830923311280.02067661846622560.989661690766887
110.005665461229261840.01133092245852370.994334538770738
120.002255092983573680.004510185967147360.997744907016426
130.0009032319390079240.001806463878015850.999096768060992
140.0004899149604871340.0009798299209742680.999510085039513
150.0007437636283483650.001487527256696730.999256236371652
160.0003716200366325430.0007432400732650860.999628379963367
170.0001601998311389480.0003203996622778970.999839800168861
180.0001021288588140470.0002042577176280950.999897871141186
197.07488441709346e-050.0001414976883418690.99992925115583
206.85276338702973e-050.0001370552677405950.99993147236613
217.7246112161872e-050.0001544922243237440.999922753887838
223.04108332078833e-056.08216664157665e-050.999969589166792
231.19134891270325e-052.38269782540651e-050.999988086510873
248.93929302917981e-061.78785860583596e-050.99999106070697
256.5612137106992e-061.31224274213984e-050.99999343878629
264.53730720429082e-069.07461440858165e-060.999995462692796
271.74666609901726e-063.49333219803453e-060.9999982533339
288.91585774884334e-071.78317154976867e-060.999999108414225
295.93523608934406e-071.18704721786881e-060.99999940647639
302.29458732742378e-074.58917465484755e-070.999999770541267
318.02944229432409e-081.60588845886482e-070.999999919705577
322.84238599628386e-085.68477199256773e-080.99999997157614
331.33303409033169e-082.66606818066338e-080.99999998666966
347.9033183298663e-091.58066366597326e-080.999999992096682
352.8188469665673e-095.6376939331346e-090.999999997181153
368.95697248669437e-101.79139449733887e-090.999999999104303
374.58176425368841e-109.16352850737683e-100.999999999541824
381.92631040055146e-103.85262080110292e-100.99999999980737
398.34724264386317e-111.66944852877263e-100.999999999916528
403.4904774995334e-116.9809549990668e-110.999999999965095
415.5523985823355e-111.1104797164671e-100.999999999944476
421.05284507464494e-102.10569014928988e-100.999999999894716
431.85088336377084e-103.70176672754168e-100.999999999814912
445.74345941235107e-101.14869188247021e-090.999999999425654
451.72209793056495e-093.44419586112990e-090.999999998277902
463.05692268830710e-096.11384537661421e-090.999999996943077
471.27728054792601e-082.55456109585201e-080.999999987227195
482.92461738331463e-085.84923476662926e-080.999999970753826
497.99498084186314e-081.59899616837263e-070.999999920050192
501.81279966049386e-073.62559932098772e-070.999999818720034
511.85820561814267e-063.71641123628534e-060.999998141794382
523.85187723798283e-067.70375447596566e-060.999996148122762
531.91228242940512e-053.82456485881023e-050.999980877175706
540.0001931126108941950.000386225221788390.999806887389106
550.009351062776783880.01870212555356780.990648937223216
560.1215078838552530.2430157677105050.878492116144747
570.7409952898488670.5180094203022670.259004710151133
580.945339129846410.1093217403071790.0546608701535897
590.8611295364194690.2777409271610620.138870463580531







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level440.8NOK
5% type I error level500.909090909090909NOK
10% type I error level500.909090909090909NOK

\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 & 44 & 0.8 & NOK \tabularnewline
5% type I error level & 50 & 0.909090909090909 & NOK \tabularnewline
10% type I error level & 50 & 0.909090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57886&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]44[/C][C]0.8[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]50[/C][C]0.909090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.909090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57886&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57886&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 level440.8NOK
5% type I error level500.909090909090909NOK
10% type I error level500.909090909090909NOK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}