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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 26 Dec 2010 06:49:21 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/26/t12933461229cy4yrbumhetm8v.htm/, Retrieved Mon, 06 May 2024 12:00:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=115478, Retrieved Mon, 06 May 2024 12:00:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [Workshop 7 multip...] [2010-12-01 09:15:23] [6a528ed37664d761abf4790b0717b23b]
-    D      [Multiple Regression] [MR] [2010-12-26 06:49:21] [fd751bc40fbbb4c72222c10190589d42] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,8	0,8	2,9	1,8	2,3	0,8	2,6
1,7	-0,1	2,9	1,7	2,2	1	2,2
1,4	-1,5	2,9	1,6	2,1	0,6	2,3
1,2	-4,4	1,4	1,8	2,4	0,9	2,4
1	-4,2	1,1	1,6	2,5	0,6	2,1
1,7	3,5	1,9	1,5	2,4	0,6	1,9
2,4	10	2,8	1,5	2,3	0,4	2,2
2	8,6	1,4	1,3	2,1	0,3	1,9
2,1	9,5	0,7	1,4	2,3	0	2,3
2	9,9	-0,8	1,4	2,2	0,3	2,1
1,8	10,4	-3,1	1,3	2,1	0,1	2,2
2,7	16	0,1	1,3	2	0	2,3
2,3	12,7	1	1,2	2,1	0	1,9
1,9	10,2	1,9	1,1	2,1	0	1,7
2	8,9	-0,5	1,4	2,5	-0,2	2,5
2,3	12,6	1,5	1,2	2,2	-0,3	2,1
2,8	13,6	3,9	1,5	2,3	0,1	2,4
2,4	14,8	1,9	1,1	2,3	0,1	1,5
2,3	9,5	2,6	1,3	2,2	0,4	1,9
2,7	13,7	1,7	1,5	2,2	0,4	2,1
2,7	17	1,4	1,1	1,6	-0,5	2,2
2,9	14,7	2,8	1,4	1,8	0,5	2
3	17,4	0,5	1,3	1,7	0,4	2
2,2	9	1	1,5	1,9	0,7	2,2
2,3	9,1	1,5	1,6	1,8	0,8	2,3
2,8	12,2	1,8	1,7	1,9	0,8	2,3
2,8	15,9	2,7	1,1	1,5	0	2
2,8	12,9	3	1,6	1	1,1	2,2
2,2	10,9	-0,3	1,3	0,8	0,9	1,9
2,6	10,6	1,1	1,7	1,1	1,1	2,3
2,8	13,2	1,7	1,6	1,5	1	2,2
2,5	9,6	1,6	1,7	1,7	1,1	2,3
2,4	6,4	3	1,9	2,3	1,5	2,1
2,3	5,8	3,3	1,8	2,4	1	2,4
1,9	-1	6,7	1,9	3	1	2,3
1,7	-0,2	5,6	1,6	3	0,9	1,9
2	2,7	6	1,5	3,2	0,8	1,6
2,1	3,6	4,8	1,6	3,2	0,8	1,8
1,7	-0,9	5,9	1,6	3,2	0,8	1,8
1,8	0,3	4,3	1,7	3,5	0,8	2
1,8	-1,1	3,7	2	4	0,9	2,3
1,8	-2,5	5,6	2	4,3	0,8	2,2
1,3	-3,4	1,7	1,9	4,1	0,7	2,2
1,3	-3,5	3,2	1,7	4	0,6	2
1,3	-3,9	3,6	1,8	4,1	0,6	2
1,2	-4,6	1,7	1,9	4,2	1	1,9
1,4	-0,1	0,5	1,7	4,5	1	1,5
2,2	4,3	2,1	2	5,6	1	1,6
2,9	10,2	1,5	2,1	6,5	1,1	1,5
3,1	8,7	2,7	2,4	7,6	1,1	2
3,5	13,3	1,4	2,5	8,5	1,4	1,5
3,6	15	1,2	2,5	8,7	1,2	1,5
4,4	20,7	2,3	2,6	8,3	1,2	1,9
4,1	20,7	1,6	2,2	8,3	1,3	1,1
5,1	26,4	4,7	2,5	8,5	1,4	1,5
5,8	31,2	3,5	2,8	8,7	1,4	2,1
5,9	31,4	4,4	2,8	8,7	1,1	2,3
5,4	26,6	3,9	2,9	8,5	1,1	2,6
5,5	26,6	3,5	3	7,9	1,3	2,9
4,8	19,2	3	3,1	7	1,5	3,2
3,2	6,5	1,6	2,9	5,8	1,5	3,2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&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]6 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=115478&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=0

As an alternative you can also use a QR Code:  
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 time6 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
HCPI[t] = -0.0999629355292632 + 0.102771874381054ED[t] + 0.0799171884528425NBL[t] + 0.299497088937924IT[t] + 0.0759467616143747BL[t] + 0.254171329601758NEI[t] + 0.258910129983403D[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
HCPI[t] =  -0.0999629355292632 +  0.102771874381054ED[t] +  0.0799171884528425NBL[t] +  0.299497088937924IT[t] +  0.0759467616143747BL[t] +  0.254171329601758NEI[t] +  0.258910129983403D[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]HCPI[t] =  -0.0999629355292632 +  0.102771874381054ED[t] +  0.0799171884528425NBL[t] +  0.299497088937924IT[t] +  0.0759467616143747BL[t] +  0.254171329601758NEI[t] +  0.258910129983403D[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
HCPI[t] = -0.0999629355292632 + 0.102771874381054ED[t] + 0.0799171884528425NBL[t] + 0.299497088937924IT[t] + 0.0759467616143747BL[t] + 0.254171329601758NEI[t] + 0.258910129983403D[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.09996293552926320.040991-2.43860.0180620.009031
ED0.1027718743810540.000902113.885900
NBL0.07991718845284250.00421218.975500
IT0.2994970889379240.2008641.4910.1417680.070884
BL0.07594676161437470.0305162.48870.0159350.007968
NEI0.2541713296017580.0784733.2390.0020540.001027
D0.2589101299834030.0935192.76850.0076990.00385

\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) & -0.0999629355292632 & 0.040991 & -2.4386 & 0.018062 & 0.009031 \tabularnewline
ED & 0.102771874381054 & 0.000902 & 113.8859 & 0 & 0 \tabularnewline
NBL & 0.0799171884528425 & 0.004212 & 18.9755 & 0 & 0 \tabularnewline
IT & 0.299497088937924 & 0.200864 & 1.491 & 0.141768 & 0.070884 \tabularnewline
BL & 0.0759467616143747 & 0.030516 & 2.4887 & 0.015935 & 0.007968 \tabularnewline
NEI & 0.254171329601758 & 0.078473 & 3.239 & 0.002054 & 0.001027 \tabularnewline
D & 0.258910129983403 & 0.093519 & 2.7685 & 0.007699 & 0.00385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&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]-0.0999629355292632[/C][C]0.040991[/C][C]-2.4386[/C][C]0.018062[/C][C]0.009031[/C][/ROW]
[ROW][C]ED[/C][C]0.102771874381054[/C][C]0.000902[/C][C]113.8859[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NBL[/C][C]0.0799171884528425[/C][C]0.004212[/C][C]18.9755[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]IT[/C][C]0.299497088937924[/C][C]0.200864[/C][C]1.491[/C][C]0.141768[/C][C]0.070884[/C][/ROW]
[ROW][C]BL[/C][C]0.0759467616143747[/C][C]0.030516[/C][C]2.4887[/C][C]0.015935[/C][C]0.007968[/C][/ROW]
[ROW][C]NEI[/C][C]0.254171329601758[/C][C]0.078473[/C][C]3.239[/C][C]0.002054[/C][C]0.001027[/C][/ROW]
[ROW][C]D[/C][C]0.258910129983403[/C][C]0.093519[/C][C]2.7685[/C][C]0.007699[/C][C]0.00385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=2

As an alternative you can also use a QR Code:  
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)-0.09996293552926320.040991-2.43860.0180620.009031
ED0.1027718743810540.000902113.885900
NBL0.07991718845284250.00421218.975500
IT0.2994970889379240.2008641.4910.1417680.070884
BL0.07594676161437470.0305162.48870.0159350.007968
NEI0.2541713296017580.0784733.2390.0020540.001027
D0.2589101299834030.0935192.76850.0076990.00385






Multiple Linear Regression - Regression Statistics
Multiple R0.999078733038373
R-squared0.99815831480956
Adjusted R-squared0.997953683121733
F-TEST (value)4877.82867556148
F-TEST (DF numerator)6
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.052813378113236
Sum Squared Residuals0.150619657017508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999078733038373 \tabularnewline
R-squared & 0.99815831480956 \tabularnewline
Adjusted R-squared & 0.997953683121733 \tabularnewline
F-TEST (value) & 4877.82867556148 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 54 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.052813378113236 \tabularnewline
Sum Squared Residuals & 0.150619657017508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999078733038373[/C][/ROW]
[ROW][C]R-squared[/C][C]0.99815831480956[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997953683121733[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4877.82867556148[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]54[/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]0.052813378113236[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.150619657017508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.999078733038373
R-squared0.99815831480956
Adjusted R-squared0.997953683121733
F-TEST (value)4877.82867556148
F-TEST (DF numerator)6
F-TEST (DF denominator)54
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.052813378113236
Sum Squared Residuals0.150619657017508






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.81.8042901239284-0.00429012392840041
21.71.621521265857210.0784787341427856
31.41.364318737826150.0356812621738534
41.21.131230377592590.0687696224074087
510.9215804164312540.0784195835687461
61.71.687531188875730.0124688111242695
72.42.44671793887337-0.0467179388733699
822.01277430884026-0.0127743088402587
92.12.12177867819572-0.0217786781957181
1022.05988634199129-0.0598863419912855
111.81.86497510776303-0.0649751077630332
122.72.689111811222760.0108881887772431
132.32.295971010647120.00402898935287778
141.92.02923505941157-0.129235059411573
1521.980352039822880.0196479601771210
162.32.30877772071303-0.00877772071302889
172.82.88013622105944-0.0801362210594432
182.42.49081014085079-0.0908101408507907
192.32.234181431048230.0658185689517684
202.72.70557927762536-0.00557927762536377
212.72.652521230359950.047478769640048
222.92.835457765726840.0645422342731623
2332.866170775098740.133829224901261
242.22.24596781951198-0.0459678195119769
252.32.36986677986737-0.0698667798673748
262.82.749979132039720.0500208679602761
272.82.711073476172250.0889265238277503
282.82.86987866178533-0.0698786617853303
292.22.167062407209220.0329375927907824
302.62.545096090702080.0549039092979244
312.82.80937412695796-0.00937412695796248
322.52.52785086751607-0.0278508675160681
332.42.46621891393091-0.0662189139309082
342.32.35676328729992-0.0567632872999156
351.91.97925973511249-0.079259735112492
361.71.75473801568429-0.0547380156842941
3721.966892798244370.0331072017556274
382.12.045218593934380.0547814060656172
391.71.670654066517770.0293459334822318
401.81.770628577625270.0293714223747302
411.81.80971031986385-0.00971031986385004
421.81.789148236316570.0108517636834279
431.31.31442032023069-0.0144203202306948
441.31.279325662565970.0206743374340254
451.31.30772817324992-0.0077281732499199
461.21.197267107020370.00273289297962568
471.41.42316047429507-0.0231604742950717
482.22.20250580055179-0.00250580055178534
492.92.858737460636860.0412625393631379
503.13.10332590465758-0.00332590465758297
513.53.51728231005729-0.0172823100572907
523.63.64036614521704-0.0403661452170365
534.44.41720979272857-0.0172097927285728
544.14.059757954209870.040242045790132
555.15.12732058634347-0.0273205863434747
565.85.785109514223410.0148904857765852
575.95.853119985823340.0468800141766634
585.45.4122897901338-0.0122897901337956
595.55.49321187159320.00678812840680105
604.84.782846335303210.0171536646967917
613.23.21472393510501-0.0147239351050119

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.8 & 1.8042901239284 & -0.00429012392840041 \tabularnewline
2 & 1.7 & 1.62152126585721 & 0.0784787341427856 \tabularnewline
3 & 1.4 & 1.36431873782615 & 0.0356812621738534 \tabularnewline
4 & 1.2 & 1.13123037759259 & 0.0687696224074087 \tabularnewline
5 & 1 & 0.921580416431254 & 0.0784195835687461 \tabularnewline
6 & 1.7 & 1.68753118887573 & 0.0124688111242695 \tabularnewline
7 & 2.4 & 2.44671793887337 & -0.0467179388733699 \tabularnewline
8 & 2 & 2.01277430884026 & -0.0127743088402587 \tabularnewline
9 & 2.1 & 2.12177867819572 & -0.0217786781957181 \tabularnewline
10 & 2 & 2.05988634199129 & -0.0598863419912855 \tabularnewline
11 & 1.8 & 1.86497510776303 & -0.0649751077630332 \tabularnewline
12 & 2.7 & 2.68911181122276 & 0.0108881887772431 \tabularnewline
13 & 2.3 & 2.29597101064712 & 0.00402898935287778 \tabularnewline
14 & 1.9 & 2.02923505941157 & -0.129235059411573 \tabularnewline
15 & 2 & 1.98035203982288 & 0.0196479601771210 \tabularnewline
16 & 2.3 & 2.30877772071303 & -0.00877772071302889 \tabularnewline
17 & 2.8 & 2.88013622105944 & -0.0801362210594432 \tabularnewline
18 & 2.4 & 2.49081014085079 & -0.0908101408507907 \tabularnewline
19 & 2.3 & 2.23418143104823 & 0.0658185689517684 \tabularnewline
20 & 2.7 & 2.70557927762536 & -0.00557927762536377 \tabularnewline
21 & 2.7 & 2.65252123035995 & 0.047478769640048 \tabularnewline
22 & 2.9 & 2.83545776572684 & 0.0645422342731623 \tabularnewline
23 & 3 & 2.86617077509874 & 0.133829224901261 \tabularnewline
24 & 2.2 & 2.24596781951198 & -0.0459678195119769 \tabularnewline
25 & 2.3 & 2.36986677986737 & -0.0698667798673748 \tabularnewline
26 & 2.8 & 2.74997913203972 & 0.0500208679602761 \tabularnewline
27 & 2.8 & 2.71107347617225 & 0.0889265238277503 \tabularnewline
28 & 2.8 & 2.86987866178533 & -0.0698786617853303 \tabularnewline
29 & 2.2 & 2.16706240720922 & 0.0329375927907824 \tabularnewline
30 & 2.6 & 2.54509609070208 & 0.0549039092979244 \tabularnewline
31 & 2.8 & 2.80937412695796 & -0.00937412695796248 \tabularnewline
32 & 2.5 & 2.52785086751607 & -0.0278508675160681 \tabularnewline
33 & 2.4 & 2.46621891393091 & -0.0662189139309082 \tabularnewline
34 & 2.3 & 2.35676328729992 & -0.0567632872999156 \tabularnewline
35 & 1.9 & 1.97925973511249 & -0.079259735112492 \tabularnewline
36 & 1.7 & 1.75473801568429 & -0.0547380156842941 \tabularnewline
37 & 2 & 1.96689279824437 & 0.0331072017556274 \tabularnewline
38 & 2.1 & 2.04521859393438 & 0.0547814060656172 \tabularnewline
39 & 1.7 & 1.67065406651777 & 0.0293459334822318 \tabularnewline
40 & 1.8 & 1.77062857762527 & 0.0293714223747302 \tabularnewline
41 & 1.8 & 1.80971031986385 & -0.00971031986385004 \tabularnewline
42 & 1.8 & 1.78914823631657 & 0.0108517636834279 \tabularnewline
43 & 1.3 & 1.31442032023069 & -0.0144203202306948 \tabularnewline
44 & 1.3 & 1.27932566256597 & 0.0206743374340254 \tabularnewline
45 & 1.3 & 1.30772817324992 & -0.0077281732499199 \tabularnewline
46 & 1.2 & 1.19726710702037 & 0.00273289297962568 \tabularnewline
47 & 1.4 & 1.42316047429507 & -0.0231604742950717 \tabularnewline
48 & 2.2 & 2.20250580055179 & -0.00250580055178534 \tabularnewline
49 & 2.9 & 2.85873746063686 & 0.0412625393631379 \tabularnewline
50 & 3.1 & 3.10332590465758 & -0.00332590465758297 \tabularnewline
51 & 3.5 & 3.51728231005729 & -0.0172823100572907 \tabularnewline
52 & 3.6 & 3.64036614521704 & -0.0403661452170365 \tabularnewline
53 & 4.4 & 4.41720979272857 & -0.0172097927285728 \tabularnewline
54 & 4.1 & 4.05975795420987 & 0.040242045790132 \tabularnewline
55 & 5.1 & 5.12732058634347 & -0.0273205863434747 \tabularnewline
56 & 5.8 & 5.78510951422341 & 0.0148904857765852 \tabularnewline
57 & 5.9 & 5.85311998582334 & 0.0468800141766634 \tabularnewline
58 & 5.4 & 5.4122897901338 & -0.0122897901337956 \tabularnewline
59 & 5.5 & 5.4932118715932 & 0.00678812840680105 \tabularnewline
60 & 4.8 & 4.78284633530321 & 0.0171536646967917 \tabularnewline
61 & 3.2 & 3.21472393510501 & -0.0147239351050119 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&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]1.8[/C][C]1.8042901239284[/C][C]-0.00429012392840041[/C][/ROW]
[ROW][C]2[/C][C]1.7[/C][C]1.62152126585721[/C][C]0.0784787341427856[/C][/ROW]
[ROW][C]3[/C][C]1.4[/C][C]1.36431873782615[/C][C]0.0356812621738534[/C][/ROW]
[ROW][C]4[/C][C]1.2[/C][C]1.13123037759259[/C][C]0.0687696224074087[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]0.921580416431254[/C][C]0.0784195835687461[/C][/ROW]
[ROW][C]6[/C][C]1.7[/C][C]1.68753118887573[/C][C]0.0124688111242695[/C][/ROW]
[ROW][C]7[/C][C]2.4[/C][C]2.44671793887337[/C][C]-0.0467179388733699[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]2.01277430884026[/C][C]-0.0127743088402587[/C][/ROW]
[ROW][C]9[/C][C]2.1[/C][C]2.12177867819572[/C][C]-0.0217786781957181[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]2.05988634199129[/C][C]-0.0598863419912855[/C][/ROW]
[ROW][C]11[/C][C]1.8[/C][C]1.86497510776303[/C][C]-0.0649751077630332[/C][/ROW]
[ROW][C]12[/C][C]2.7[/C][C]2.68911181122276[/C][C]0.0108881887772431[/C][/ROW]
[ROW][C]13[/C][C]2.3[/C][C]2.29597101064712[/C][C]0.00402898935287778[/C][/ROW]
[ROW][C]14[/C][C]1.9[/C][C]2.02923505941157[/C][C]-0.129235059411573[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]1.98035203982288[/C][C]0.0196479601771210[/C][/ROW]
[ROW][C]16[/C][C]2.3[/C][C]2.30877772071303[/C][C]-0.00877772071302889[/C][/ROW]
[ROW][C]17[/C][C]2.8[/C][C]2.88013622105944[/C][C]-0.0801362210594432[/C][/ROW]
[ROW][C]18[/C][C]2.4[/C][C]2.49081014085079[/C][C]-0.0908101408507907[/C][/ROW]
[ROW][C]19[/C][C]2.3[/C][C]2.23418143104823[/C][C]0.0658185689517684[/C][/ROW]
[ROW][C]20[/C][C]2.7[/C][C]2.70557927762536[/C][C]-0.00557927762536377[/C][/ROW]
[ROW][C]21[/C][C]2.7[/C][C]2.65252123035995[/C][C]0.047478769640048[/C][/ROW]
[ROW][C]22[/C][C]2.9[/C][C]2.83545776572684[/C][C]0.0645422342731623[/C][/ROW]
[ROW][C]23[/C][C]3[/C][C]2.86617077509874[/C][C]0.133829224901261[/C][/ROW]
[ROW][C]24[/C][C]2.2[/C][C]2.24596781951198[/C][C]-0.0459678195119769[/C][/ROW]
[ROW][C]25[/C][C]2.3[/C][C]2.36986677986737[/C][C]-0.0698667798673748[/C][/ROW]
[ROW][C]26[/C][C]2.8[/C][C]2.74997913203972[/C][C]0.0500208679602761[/C][/ROW]
[ROW][C]27[/C][C]2.8[/C][C]2.71107347617225[/C][C]0.0889265238277503[/C][/ROW]
[ROW][C]28[/C][C]2.8[/C][C]2.86987866178533[/C][C]-0.0698786617853303[/C][/ROW]
[ROW][C]29[/C][C]2.2[/C][C]2.16706240720922[/C][C]0.0329375927907824[/C][/ROW]
[ROW][C]30[/C][C]2.6[/C][C]2.54509609070208[/C][C]0.0549039092979244[/C][/ROW]
[ROW][C]31[/C][C]2.8[/C][C]2.80937412695796[/C][C]-0.00937412695796248[/C][/ROW]
[ROW][C]32[/C][C]2.5[/C][C]2.52785086751607[/C][C]-0.0278508675160681[/C][/ROW]
[ROW][C]33[/C][C]2.4[/C][C]2.46621891393091[/C][C]-0.0662189139309082[/C][/ROW]
[ROW][C]34[/C][C]2.3[/C][C]2.35676328729992[/C][C]-0.0567632872999156[/C][/ROW]
[ROW][C]35[/C][C]1.9[/C][C]1.97925973511249[/C][C]-0.079259735112492[/C][/ROW]
[ROW][C]36[/C][C]1.7[/C][C]1.75473801568429[/C][C]-0.0547380156842941[/C][/ROW]
[ROW][C]37[/C][C]2[/C][C]1.96689279824437[/C][C]0.0331072017556274[/C][/ROW]
[ROW][C]38[/C][C]2.1[/C][C]2.04521859393438[/C][C]0.0547814060656172[/C][/ROW]
[ROW][C]39[/C][C]1.7[/C][C]1.67065406651777[/C][C]0.0293459334822318[/C][/ROW]
[ROW][C]40[/C][C]1.8[/C][C]1.77062857762527[/C][C]0.0293714223747302[/C][/ROW]
[ROW][C]41[/C][C]1.8[/C][C]1.80971031986385[/C][C]-0.00971031986385004[/C][/ROW]
[ROW][C]42[/C][C]1.8[/C][C]1.78914823631657[/C][C]0.0108517636834279[/C][/ROW]
[ROW][C]43[/C][C]1.3[/C][C]1.31442032023069[/C][C]-0.0144203202306948[/C][/ROW]
[ROW][C]44[/C][C]1.3[/C][C]1.27932566256597[/C][C]0.0206743374340254[/C][/ROW]
[ROW][C]45[/C][C]1.3[/C][C]1.30772817324992[/C][C]-0.0077281732499199[/C][/ROW]
[ROW][C]46[/C][C]1.2[/C][C]1.19726710702037[/C][C]0.00273289297962568[/C][/ROW]
[ROW][C]47[/C][C]1.4[/C][C]1.42316047429507[/C][C]-0.0231604742950717[/C][/ROW]
[ROW][C]48[/C][C]2.2[/C][C]2.20250580055179[/C][C]-0.00250580055178534[/C][/ROW]
[ROW][C]49[/C][C]2.9[/C][C]2.85873746063686[/C][C]0.0412625393631379[/C][/ROW]
[ROW][C]50[/C][C]3.1[/C][C]3.10332590465758[/C][C]-0.00332590465758297[/C][/ROW]
[ROW][C]51[/C][C]3.5[/C][C]3.51728231005729[/C][C]-0.0172823100572907[/C][/ROW]
[ROW][C]52[/C][C]3.6[/C][C]3.64036614521704[/C][C]-0.0403661452170365[/C][/ROW]
[ROW][C]53[/C][C]4.4[/C][C]4.41720979272857[/C][C]-0.0172097927285728[/C][/ROW]
[ROW][C]54[/C][C]4.1[/C][C]4.05975795420987[/C][C]0.040242045790132[/C][/ROW]
[ROW][C]55[/C][C]5.1[/C][C]5.12732058634347[/C][C]-0.0273205863434747[/C][/ROW]
[ROW][C]56[/C][C]5.8[/C][C]5.78510951422341[/C][C]0.0148904857765852[/C][/ROW]
[ROW][C]57[/C][C]5.9[/C][C]5.85311998582334[/C][C]0.0468800141766634[/C][/ROW]
[ROW][C]58[/C][C]5.4[/C][C]5.4122897901338[/C][C]-0.0122897901337956[/C][/ROW]
[ROW][C]59[/C][C]5.5[/C][C]5.4932118715932[/C][C]0.00678812840680105[/C][/ROW]
[ROW][C]60[/C][C]4.8[/C][C]4.78284633530321[/C][C]0.0171536646967917[/C][/ROW]
[ROW][C]61[/C][C]3.2[/C][C]3.21472393510501[/C][C]-0.0147239351050119[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11.81.8042901239284-0.00429012392840041
21.71.621521265857210.0784787341427856
31.41.364318737826150.0356812621738534
41.21.131230377592590.0687696224074087
510.9215804164312540.0784195835687461
61.71.687531188875730.0124688111242695
72.42.44671793887337-0.0467179388733699
822.01277430884026-0.0127743088402587
92.12.12177867819572-0.0217786781957181
1022.05988634199129-0.0598863419912855
111.81.86497510776303-0.0649751077630332
122.72.689111811222760.0108881887772431
132.32.295971010647120.00402898935287778
141.92.02923505941157-0.129235059411573
1521.980352039822880.0196479601771210
162.32.30877772071303-0.00877772071302889
172.82.88013622105944-0.0801362210594432
182.42.49081014085079-0.0908101408507907
192.32.234181431048230.0658185689517684
202.72.70557927762536-0.00557927762536377
212.72.652521230359950.047478769640048
222.92.835457765726840.0645422342731623
2332.866170775098740.133829224901261
242.22.24596781951198-0.0459678195119769
252.32.36986677986737-0.0698667798673748
262.82.749979132039720.0500208679602761
272.82.711073476172250.0889265238277503
282.82.86987866178533-0.0698786617853303
292.22.167062407209220.0329375927907824
302.62.545096090702080.0549039092979244
312.82.80937412695796-0.00937412695796248
322.52.52785086751607-0.0278508675160681
332.42.46621891393091-0.0662189139309082
342.32.35676328729992-0.0567632872999156
351.91.97925973511249-0.079259735112492
361.71.75473801568429-0.0547380156842941
3721.966892798244370.0331072017556274
382.12.045218593934380.0547814060656172
391.71.670654066517770.0293459334822318
401.81.770628577625270.0293714223747302
411.81.80971031986385-0.00971031986385004
421.81.789148236316570.0108517636834279
431.31.31442032023069-0.0144203202306948
441.31.279325662565970.0206743374340254
451.31.30772817324992-0.0077281732499199
461.21.197267107020370.00273289297962568
471.41.42316047429507-0.0231604742950717
482.22.20250580055179-0.00250580055178534
492.92.858737460636860.0412625393631379
503.13.10332590465758-0.00332590465758297
513.53.51728231005729-0.0172823100572907
523.63.64036614521704-0.0403661452170365
534.44.41720979272857-0.0172097927285728
544.14.059757954209870.040242045790132
555.15.12732058634347-0.0273205863434747
565.85.785109514223410.0148904857765852
575.95.853119985823340.0468800141766634
585.45.4122897901338-0.0122897901337956
595.55.49321187159320.00678812840680105
604.84.782846335303210.0171536646967917
613.23.21472393510501-0.0147239351050119






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1885609172123670.3771218344247350.811439082787633
110.1008300594501190.2016601189002370.899169940549881
120.4147265132213130.8294530264426260.585273486778687
130.3130560675314530.6261121350629070.686943932468547
140.5786848613802960.8426302772394080.421315138619704
150.6261176415529860.7477647168940280.373882358447014
160.5368465381329470.9263069237341060.463153461867053
170.6594873588353240.6810252823293520.340512641164676
180.7794128960421380.4411742079157240.220587103957862
190.8711724172422340.2576551655155310.128827582757766
200.8852138747505560.2295722504988880.114786125249444
210.93981800667590.12036398664820.0601819933241
220.93594962833980.1281007433204020.0640503716602009
230.980758444795160.03848311040968120.0192415552048406
240.9925346028841020.01493079423179690.00746539711589847
250.9991849541619540.001630091676092430.000815045838046214
260.9986095465212380.002780906957523200.00139045347876160
270.9979618369036290.004076326192742290.00203816309637114
280.9997438568500030.0005122862999940440.000256143149997022
290.999452398101230.001095203797538670.000547601898769335
300.9996584443304750.0006831113390506410.000341555669525321
310.9992708230662520.001458353867495300.000729176933747652
320.9986260327382470.002747934523506610.00137396726175330
330.997627832544360.004744334911281560.00237216745564078
340.999039860893150.001920278213700820.000960139106850412
350.9996893816154450.0006212367691109150.000310618384555457
360.999984109673883.17806522409666e-051.58903261204833e-05
370.999975219796644.95604067179121e-052.47802033589560e-05
380.9999641285616257.17428767509856e-053.58714383754928e-05
390.9999041420367710.0001917159264574199.58579632287096e-05
400.999768294951620.0004634100967607790.000231705048380390
410.9994772926829150.001045414634170130.000522707317085067
420.9989694619894560.002061076021088960.00103053801054448
430.9978776953886840.004244609222632050.00212230461131602
440.995064077995150.009871844009700330.00493592200485017
450.9883802865103520.02323942697929660.0116197134896483
460.9769321301505320.04613573969893550.0230678698494678
470.9950310543380570.009937891323885470.00496894566194274
480.9883814084647730.02323718307045380.0116185915352269
490.9742883412407920.05142331751841530.0257116587592077
500.9329924477145650.1340151045708690.0670075522854345
510.9218785736685330.1562428526629330.0781214263314667

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.188560917212367 & 0.377121834424735 & 0.811439082787633 \tabularnewline
11 & 0.100830059450119 & 0.201660118900237 & 0.899169940549881 \tabularnewline
12 & 0.414726513221313 & 0.829453026442626 & 0.585273486778687 \tabularnewline
13 & 0.313056067531453 & 0.626112135062907 & 0.686943932468547 \tabularnewline
14 & 0.578684861380296 & 0.842630277239408 & 0.421315138619704 \tabularnewline
15 & 0.626117641552986 & 0.747764716894028 & 0.373882358447014 \tabularnewline
16 & 0.536846538132947 & 0.926306923734106 & 0.463153461867053 \tabularnewline
17 & 0.659487358835324 & 0.681025282329352 & 0.340512641164676 \tabularnewline
18 & 0.779412896042138 & 0.441174207915724 & 0.220587103957862 \tabularnewline
19 & 0.871172417242234 & 0.257655165515531 & 0.128827582757766 \tabularnewline
20 & 0.885213874750556 & 0.229572250498888 & 0.114786125249444 \tabularnewline
21 & 0.9398180066759 & 0.1203639866482 & 0.0601819933241 \tabularnewline
22 & 0.9359496283398 & 0.128100743320402 & 0.0640503716602009 \tabularnewline
23 & 0.98075844479516 & 0.0384831104096812 & 0.0192415552048406 \tabularnewline
24 & 0.992534602884102 & 0.0149307942317969 & 0.00746539711589847 \tabularnewline
25 & 0.999184954161954 & 0.00163009167609243 & 0.000815045838046214 \tabularnewline
26 & 0.998609546521238 & 0.00278090695752320 & 0.00139045347876160 \tabularnewline
27 & 0.997961836903629 & 0.00407632619274229 & 0.00203816309637114 \tabularnewline
28 & 0.999743856850003 & 0.000512286299994044 & 0.000256143149997022 \tabularnewline
29 & 0.99945239810123 & 0.00109520379753867 & 0.000547601898769335 \tabularnewline
30 & 0.999658444330475 & 0.000683111339050641 & 0.000341555669525321 \tabularnewline
31 & 0.999270823066252 & 0.00145835386749530 & 0.000729176933747652 \tabularnewline
32 & 0.998626032738247 & 0.00274793452350661 & 0.00137396726175330 \tabularnewline
33 & 0.99762783254436 & 0.00474433491128156 & 0.00237216745564078 \tabularnewline
34 & 0.99903986089315 & 0.00192027821370082 & 0.000960139106850412 \tabularnewline
35 & 0.999689381615445 & 0.000621236769110915 & 0.000310618384555457 \tabularnewline
36 & 0.99998410967388 & 3.17806522409666e-05 & 1.58903261204833e-05 \tabularnewline
37 & 0.99997521979664 & 4.95604067179121e-05 & 2.47802033589560e-05 \tabularnewline
38 & 0.999964128561625 & 7.17428767509856e-05 & 3.58714383754928e-05 \tabularnewline
39 & 0.999904142036771 & 0.000191715926457419 & 9.58579632287096e-05 \tabularnewline
40 & 0.99976829495162 & 0.000463410096760779 & 0.000231705048380390 \tabularnewline
41 & 0.999477292682915 & 0.00104541463417013 & 0.000522707317085067 \tabularnewline
42 & 0.998969461989456 & 0.00206107602108896 & 0.00103053801054448 \tabularnewline
43 & 0.997877695388684 & 0.00424460922263205 & 0.00212230461131602 \tabularnewline
44 & 0.99506407799515 & 0.00987184400970033 & 0.00493592200485017 \tabularnewline
45 & 0.988380286510352 & 0.0232394269792966 & 0.0116197134896483 \tabularnewline
46 & 0.976932130150532 & 0.0461357396989355 & 0.0230678698494678 \tabularnewline
47 & 0.995031054338057 & 0.00993789132388547 & 0.00496894566194274 \tabularnewline
48 & 0.988381408464773 & 0.0232371830704538 & 0.0116185915352269 \tabularnewline
49 & 0.974288341240792 & 0.0514233175184153 & 0.0257116587592077 \tabularnewline
50 & 0.932992447714565 & 0.134015104570869 & 0.0670075522854345 \tabularnewline
51 & 0.921878573668533 & 0.156242852662933 & 0.0781214263314667 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&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]10[/C][C]0.188560917212367[/C][C]0.377121834424735[/C][C]0.811439082787633[/C][/ROW]
[ROW][C]11[/C][C]0.100830059450119[/C][C]0.201660118900237[/C][C]0.899169940549881[/C][/ROW]
[ROW][C]12[/C][C]0.414726513221313[/C][C]0.829453026442626[/C][C]0.585273486778687[/C][/ROW]
[ROW][C]13[/C][C]0.313056067531453[/C][C]0.626112135062907[/C][C]0.686943932468547[/C][/ROW]
[ROW][C]14[/C][C]0.578684861380296[/C][C]0.842630277239408[/C][C]0.421315138619704[/C][/ROW]
[ROW][C]15[/C][C]0.626117641552986[/C][C]0.747764716894028[/C][C]0.373882358447014[/C][/ROW]
[ROW][C]16[/C][C]0.536846538132947[/C][C]0.926306923734106[/C][C]0.463153461867053[/C][/ROW]
[ROW][C]17[/C][C]0.659487358835324[/C][C]0.681025282329352[/C][C]0.340512641164676[/C][/ROW]
[ROW][C]18[/C][C]0.779412896042138[/C][C]0.441174207915724[/C][C]0.220587103957862[/C][/ROW]
[ROW][C]19[/C][C]0.871172417242234[/C][C]0.257655165515531[/C][C]0.128827582757766[/C][/ROW]
[ROW][C]20[/C][C]0.885213874750556[/C][C]0.229572250498888[/C][C]0.114786125249444[/C][/ROW]
[ROW][C]21[/C][C]0.9398180066759[/C][C]0.1203639866482[/C][C]0.0601819933241[/C][/ROW]
[ROW][C]22[/C][C]0.9359496283398[/C][C]0.128100743320402[/C][C]0.0640503716602009[/C][/ROW]
[ROW][C]23[/C][C]0.98075844479516[/C][C]0.0384831104096812[/C][C]0.0192415552048406[/C][/ROW]
[ROW][C]24[/C][C]0.992534602884102[/C][C]0.0149307942317969[/C][C]0.00746539711589847[/C][/ROW]
[ROW][C]25[/C][C]0.999184954161954[/C][C]0.00163009167609243[/C][C]0.000815045838046214[/C][/ROW]
[ROW][C]26[/C][C]0.998609546521238[/C][C]0.00278090695752320[/C][C]0.00139045347876160[/C][/ROW]
[ROW][C]27[/C][C]0.997961836903629[/C][C]0.00407632619274229[/C][C]0.00203816309637114[/C][/ROW]
[ROW][C]28[/C][C]0.999743856850003[/C][C]0.000512286299994044[/C][C]0.000256143149997022[/C][/ROW]
[ROW][C]29[/C][C]0.99945239810123[/C][C]0.00109520379753867[/C][C]0.000547601898769335[/C][/ROW]
[ROW][C]30[/C][C]0.999658444330475[/C][C]0.000683111339050641[/C][C]0.000341555669525321[/C][/ROW]
[ROW][C]31[/C][C]0.999270823066252[/C][C]0.00145835386749530[/C][C]0.000729176933747652[/C][/ROW]
[ROW][C]32[/C][C]0.998626032738247[/C][C]0.00274793452350661[/C][C]0.00137396726175330[/C][/ROW]
[ROW][C]33[/C][C]0.99762783254436[/C][C]0.00474433491128156[/C][C]0.00237216745564078[/C][/ROW]
[ROW][C]34[/C][C]0.99903986089315[/C][C]0.00192027821370082[/C][C]0.000960139106850412[/C][/ROW]
[ROW][C]35[/C][C]0.999689381615445[/C][C]0.000621236769110915[/C][C]0.000310618384555457[/C][/ROW]
[ROW][C]36[/C][C]0.99998410967388[/C][C]3.17806522409666e-05[/C][C]1.58903261204833e-05[/C][/ROW]
[ROW][C]37[/C][C]0.99997521979664[/C][C]4.95604067179121e-05[/C][C]2.47802033589560e-05[/C][/ROW]
[ROW][C]38[/C][C]0.999964128561625[/C][C]7.17428767509856e-05[/C][C]3.58714383754928e-05[/C][/ROW]
[ROW][C]39[/C][C]0.999904142036771[/C][C]0.000191715926457419[/C][C]9.58579632287096e-05[/C][/ROW]
[ROW][C]40[/C][C]0.99976829495162[/C][C]0.000463410096760779[/C][C]0.000231705048380390[/C][/ROW]
[ROW][C]41[/C][C]0.999477292682915[/C][C]0.00104541463417013[/C][C]0.000522707317085067[/C][/ROW]
[ROW][C]42[/C][C]0.998969461989456[/C][C]0.00206107602108896[/C][C]0.00103053801054448[/C][/ROW]
[ROW][C]43[/C][C]0.997877695388684[/C][C]0.00424460922263205[/C][C]0.00212230461131602[/C][/ROW]
[ROW][C]44[/C][C]0.99506407799515[/C][C]0.00987184400970033[/C][C]0.00493592200485017[/C][/ROW]
[ROW][C]45[/C][C]0.988380286510352[/C][C]0.0232394269792966[/C][C]0.0116197134896483[/C][/ROW]
[ROW][C]46[/C][C]0.976932130150532[/C][C]0.0461357396989355[/C][C]0.0230678698494678[/C][/ROW]
[ROW][C]47[/C][C]0.995031054338057[/C][C]0.00993789132388547[/C][C]0.00496894566194274[/C][/ROW]
[ROW][C]48[/C][C]0.988381408464773[/C][C]0.0232371830704538[/C][C]0.0116185915352269[/C][/ROW]
[ROW][C]49[/C][C]0.974288341240792[/C][C]0.0514233175184153[/C][C]0.0257116587592077[/C][/ROW]
[ROW][C]50[/C][C]0.932992447714565[/C][C]0.134015104570869[/C][C]0.0670075522854345[/C][/ROW]
[ROW][C]51[/C][C]0.921878573668533[/C][C]0.156242852662933[/C][C]0.0781214263314667[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1885609172123670.3771218344247350.811439082787633
110.1008300594501190.2016601189002370.899169940549881
120.4147265132213130.8294530264426260.585273486778687
130.3130560675314530.6261121350629070.686943932468547
140.5786848613802960.8426302772394080.421315138619704
150.6261176415529860.7477647168940280.373882358447014
160.5368465381329470.9263069237341060.463153461867053
170.6594873588353240.6810252823293520.340512641164676
180.7794128960421380.4411742079157240.220587103957862
190.8711724172422340.2576551655155310.128827582757766
200.8852138747505560.2295722504988880.114786125249444
210.93981800667590.12036398664820.0601819933241
220.93594962833980.1281007433204020.0640503716602009
230.980758444795160.03848311040968120.0192415552048406
240.9925346028841020.01493079423179690.00746539711589847
250.9991849541619540.001630091676092430.000815045838046214
260.9986095465212380.002780906957523200.00139045347876160
270.9979618369036290.004076326192742290.00203816309637114
280.9997438568500030.0005122862999940440.000256143149997022
290.999452398101230.001095203797538670.000547601898769335
300.9996584443304750.0006831113390506410.000341555669525321
310.9992708230662520.001458353867495300.000729176933747652
320.9986260327382470.002747934523506610.00137396726175330
330.997627832544360.004744334911281560.00237216745564078
340.999039860893150.001920278213700820.000960139106850412
350.9996893816154450.0006212367691109150.000310618384555457
360.999984109673883.17806522409666e-051.58903261204833e-05
370.999975219796644.95604067179121e-052.47802033589560e-05
380.9999641285616257.17428767509856e-053.58714383754928e-05
390.9999041420367710.0001917159264574199.58579632287096e-05
400.999768294951620.0004634100967607790.000231705048380390
410.9994772926829150.001045414634170130.000522707317085067
420.9989694619894560.002061076021088960.00103053801054448
430.9978776953886840.004244609222632050.00212230461131602
440.995064077995150.009871844009700330.00493592200485017
450.9883802865103520.02323942697929660.0116197134896483
460.9769321301505320.04613573969893550.0230678698494678
470.9950310543380570.009937891323885470.00496894566194274
480.9883814084647730.02323718307045380.0116185915352269
490.9742883412407920.05142331751841530.0257116587592077
500.9329924477145650.1340151045708690.0670075522854345
510.9218785736685330.1562428526629330.0781214263314667






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level210.5NOK
5% type I error level260.619047619047619NOK
10% type I error level270.642857142857143NOK

\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 & 21 & 0.5 & NOK \tabularnewline
5% type I error level & 26 & 0.619047619047619 & NOK \tabularnewline
10% type I error level & 27 & 0.642857142857143 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=115478&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]21[/C][C]0.5[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]26[/C][C]0.619047619047619[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.642857142857143[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=115478&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=115478&T=6

As an alternative you can also use a QR Code:  
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 level210.5NOK
5% type I error level260.619047619047619NOK
10% type I error level270.642857142857143NOK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

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')
}