Free Statistics

of Irreproducible Research!

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, 24 Nov 2011 14:48:49 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/24/t1322164153xecexa7vaox79bq.htm/, Retrieved Thu, 31 Oct 2024 23:24:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147174, Retrieved Thu, 31 Oct 2024 23:24:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact122
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [multiple regression] [2011-11-24 19:48:49] [1a4698f17d8e7f554418314cf0e4bd67] [Current]
Feedback Forum

Post a new message
Dataseries X:
-14	-20	36	-2	3
-7	-8	24	1	5
-9	-15	22	-1	4
-9	-13	17	-1	-4
-4	-6	8	-2	-1
-3	0	12	-1	3
1	5	5	1	2
-1	-1	6	0	2
-2	-5	5	-2	2
1	4	8	3	6
-3	-3	15	0	6
-2	3	16	0	6
0	8	17	2	6
-2	3	23	3	7
-4	3	24	1	4
-4	7	27	1	3
-7	4	31	0	0
-9	-4	40	1	6
-13	-6	47	-1	3
-8	8	43	2	1
-13	2	60	2	6
-15	-1	64	0	5
-15	-2	65	1	7
-15	0	65	1	4
-10	10	55	3	3
-12	3	57	3	6
-11	6	57	1	6
-11	7	57	1	5
-17	-4	65	-2	2
-18	-5	69	1	3
-19	-7	70	1	-2
-22	-10	71	-1	-4
-24	-21	71	-4	0
-24	-22	73	-2	1
-20	-16	68	-1	4
-25	-25	65	-5	-3
-22	-22	57	-4	-3
-17	-22	41	-5	0
-9	-19	21	0	6
-11	-21	21	-2	-1
-13	-31	17	-4	0
-11	-28	9	-6	-1
-9	-23	11	-2	1
-7	-17	6	-2	-4
-3	-12	-2	-2	-1
-3	-14	0	1	-1
-6	-18	5	-2	0
-4	-16	3	0	3
-8	-22	7	-1	0
-1	-9	4	2	8
-2	-10	8	3	8
-2	-10	9	2	8
-1	0	14	3	8
1	3	12	4	11
2	2	12	5	13
2	4	7	5	5
-1	-3	15	4	12
1	0	14	5	13
-1	-1	19	6	9
-8	-7	39	4	11
1	2	12	6	7
2	3	11	6	12
-2	-3	17	3	11
-2	-5	16	5	10
-2	0	25	5	13
-2	-3	24	5	14
-6	-7	28	3	10
-4	-7	25	5	13
-5	-7	31	5	12
-2	-4	24	6	13
-1	-3	24	6	17
-5	-6	33	5	15
-9	-10	37	4	6




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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 time4 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net







Multiple Linear Regression - Estimated Regression Equation
ALGEMENEECONOMISCHSITUATIE[t] = -0.23850311485394 + 3.67658759515122CONSUMENTENVERTROUWEN[t] + 0.93126921464805`WERKLOOSHEIDINBELGIË`[t] -0.753614192485316`FINANCIËLESITUATIEVANDEGEZINNEN`[t] -0.821393675927843SPAARVERMOGENVANDEGEZINNEN[t] -0.0264087506488414t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
ALGEMENEECONOMISCHSITUATIE[t] =  -0.23850311485394 +  3.67658759515122CONSUMENTENVERTROUWEN[t] +  0.93126921464805`WERKLOOSHEIDINBELGIË`[t] -0.753614192485316`FINANCIËLESITUATIEVANDEGEZINNEN`[t] -0.821393675927843SPAARVERMOGENVANDEGEZINNEN[t] -0.0264087506488414t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]ALGEMENEECONOMISCHSITUATIE[t] =  -0.23850311485394 +  3.67658759515122CONSUMENTENVERTROUWEN[t] +  0.93126921464805`WERKLOOSHEIDINBELGIË`[t] -0.753614192485316`FINANCIËLESITUATIEVANDEGEZINNEN`[t] -0.821393675927843SPAARVERMOGENVANDEGEZINNEN[t] -0.0264087506488414t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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
ALGEMENEECONOMISCHSITUATIE[t] = -0.23850311485394 + 3.67658759515122CONSUMENTENVERTROUWEN[t] + 0.93126921464805`WERKLOOSHEIDINBELGIË`[t] -0.753614192485316`FINANCIËLESITUATIEVANDEGEZINNEN`[t] -0.821393675927843SPAARVERMOGENVANDEGEZINNEN[t] -0.0264087506488414t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.238503114853940.380648-0.62660.5330690.266535
CONSUMENTENVERTROUWEN3.676587595151220.10501835.009200
`WERKLOOSHEIDINBELGIË`0.931269214648050.02635435.336800
`FINANCIËLESITUATIEVANDEGEZINNEN`-0.7536141924853160.145882-5.16592e-061e-06
SPAARVERMOGENVANDEGEZINNEN-0.8213936759278430.055351-14.839700
t-0.02640875064884140.011089-2.38160.0200860.010043

\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) & -0.23850311485394 & 0.380648 & -0.6266 & 0.533069 & 0.266535 \tabularnewline
CONSUMENTENVERTROUWEN & 3.67658759515122 & 0.105018 & 35.0092 & 0 & 0 \tabularnewline
`WERKLOOSHEIDINBELGIË` & 0.93126921464805 & 0.026354 & 35.3368 & 0 & 0 \tabularnewline
`FINANCIËLESITUATIEVANDEGEZINNEN` & -0.753614192485316 & 0.145882 & -5.1659 & 2e-06 & 1e-06 \tabularnewline
SPAARVERMOGENVANDEGEZINNEN & -0.821393675927843 & 0.055351 & -14.8397 & 0 & 0 \tabularnewline
t & -0.0264087506488414 & 0.011089 & -2.3816 & 0.020086 & 0.010043 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&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]-0.23850311485394[/C][C]0.380648[/C][C]-0.6266[/C][C]0.533069[/C][C]0.266535[/C][/ROW]
[ROW][C]CONSUMENTENVERTROUWEN[/C][C]3.67658759515122[/C][C]0.105018[/C][C]35.0092[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`WERKLOOSHEIDINBELGIË`[/C][C]0.93126921464805[/C][C]0.026354[/C][C]35.3368[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`FINANCIËLESITUATIEVANDEGEZINNEN`[/C][C]-0.753614192485316[/C][C]0.145882[/C][C]-5.1659[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]SPAARVERMOGENVANDEGEZINNEN[/C][C]-0.821393675927843[/C][C]0.055351[/C][C]-14.8397[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]t[/C][C]-0.0264087506488414[/C][C]0.011089[/C][C]-2.3816[/C][C]0.020086[/C][C]0.010043[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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)-0.238503114853940.380648-0.62660.5330690.266535
CONSUMENTENVERTROUWEN3.676587595151220.10501835.009200
`WERKLOOSHEIDINBELGIË`0.931269214648050.02635435.336800
`FINANCIËLESITUATIEVANDEGEZINNEN`-0.7536141924853160.145882-5.16592e-061e-06
SPAARVERMOGENVANDEGEZINNEN-0.8213936759278430.055351-14.839700
t-0.02640875064884140.011089-2.38160.0200860.010043







Multiple Linear Regression - Regression Statistics
Multiple R0.993270672110762
R-squared0.986586628075364
Adjusted R-squared0.985585630170541
F-TEST (value)985.603089997735
F-TEST (DF numerator)5
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17731800509428
Sum Squared Residuals92.8672049029844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.993270672110762 \tabularnewline
R-squared & 0.986586628075364 \tabularnewline
Adjusted R-squared & 0.985585630170541 \tabularnewline
F-TEST (value) & 985.603089997735 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 67 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.17731800509428 \tabularnewline
Sum Squared Residuals & 92.8672049029844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.993270672110762[/C][/ROW]
[ROW][C]R-squared[/C][C]0.986586628075364[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.985585630170541[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]985.603089997735[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]67[/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]1.17731800509428[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]92.8672049029844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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 R0.993270672110762
R-squared0.986586628075364
Adjusted R-squared0.985585630170541
F-TEST (value)985.603089997735
F-TEST (DF numerator)5
F-TEST (DF denominator)67
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.17731800509428
Sum Squared Residuals92.8672049029844







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1-20-19.168399113103-0.831600886896995
2-8-8.537555202781560.537555202781556
3-15-15.45105551213040.451055512130416
4-13-13.56266092859680.562660928596787
5-6-5.29812147062017-0.701878529379832
60-1.962054663722271.96205466372227
755.51316775465465-0.513167754654651
8-1-0.181532779163275-0.818467220836725
9-5-3.30856995464076-1.69143004535924
1043.434946057970270.565053942029726
11-3-2.51808599329117-0.481914006708831
1232.063362065859260.936637934140736
1388.81416933519029-0.814169335190291
1435.44719281371414-2.44719281371414
1532.970287500165060.0297124998349408
1676.559080069388210.440919930611791
1742.445780612146741.55421938785326
18-4-2.23435664502447-1.76564335497553
19-6-6.47682186098770.476821860987702
2087.536575279927110.463424720072886
2120.851836822899791.14816317710021
22-1-0.474048198560825-0.525951801439175
23-2-1.96558927890262-0.0344107210973821
2400.472182998232069-0.472182998232069
25108.830185367816061.16981463218394
2630.8489588283773372.15104117162266
2766.00636605785035-0.00636605785035383
2876.801350983129350.198649016870647
29-4-3.10940601600296-0.890593983997044
30-5-6.169561756594611.16956175659461
31-7-4.83432050810741-2.16567949189259
32-10-11.80920709273561.80920709273556
33-21-20.2135231599423-0.786476840057722
34-22-20.7060155421935-1.29398445780651
35-16-13.9002152057465-2.09978479425347
36-25-26.33915707465951.33915707465947
37-22-23.53957094952441.53957094952437
38-22-21.7939159940841-0.206084005915903
39-19-19.72944129447780.729441294477777
40-21-19.8520411189635-1.14795888103646
41-31-30.2708672094642-0.729132790535761
42-28-28.06563242609660.0656324260965574
43-23-23.53357167894380.533571678943797
44-17-16.7561829328912-0.243817067108774
45-12-11.9905760479031-0.00942395209690235
46-14-12.4152889467118-1.58471105328821
47-18-17.3756655080459-0.624334491954053
48-16-15.8828469104426-0.117153089557399
49-22-23.67273396283531.6727339628353
50-9-9.58882917624840.588829176248403
51-10-10.32036285594160.320362855941588
52-10-8.66188819945706-1.33811180054294
530-1.108977474199741.10897747419974
5431.137455315888921.86254468411108
5522.3912326160503-0.391232616050301
5644.27962719958395-0.279627199583949
57-3-4.322532158343751.32253215834375
5800.497957198248652-0.497957198248652
59-10.306679841763666-1.30667984176367
60-7-6.9660167488678-0.033983251132199
6122.73094038008777-0.730940380087767
6231.342881630302881.65711836969712
63-3-4.720025959678761.72002595967876
64-5-6.363538634018441.36353863401844
650-0.4727054806183620.472705480618362
66-3-2.2517771218431-0.748222878156903
67-7-8.466656305822641.46665630582263
68-7-7.905106922867340.905106922867338
69-7-5.19909430485126-1.80090569514874
70-4-2.28963264099594-1.71036735900406
71-3-1.92502850020492-1.07497149979508
72-6-5.87996315528521-0.120036844714789
73-10-8.74148815211085-1.25851184788915

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & -20 & -19.168399113103 & -0.831600886896995 \tabularnewline
2 & -8 & -8.53755520278156 & 0.537555202781556 \tabularnewline
3 & -15 & -15.4510555121304 & 0.451055512130416 \tabularnewline
4 & -13 & -13.5626609285968 & 0.562660928596787 \tabularnewline
5 & -6 & -5.29812147062017 & -0.701878529379832 \tabularnewline
6 & 0 & -1.96205466372227 & 1.96205466372227 \tabularnewline
7 & 5 & 5.51316775465465 & -0.513167754654651 \tabularnewline
8 & -1 & -0.181532779163275 & -0.818467220836725 \tabularnewline
9 & -5 & -3.30856995464076 & -1.69143004535924 \tabularnewline
10 & 4 & 3.43494605797027 & 0.565053942029726 \tabularnewline
11 & -3 & -2.51808599329117 & -0.481914006708831 \tabularnewline
12 & 3 & 2.06336206585926 & 0.936637934140736 \tabularnewline
13 & 8 & 8.81416933519029 & -0.814169335190291 \tabularnewline
14 & 3 & 5.44719281371414 & -2.44719281371414 \tabularnewline
15 & 3 & 2.97028750016506 & 0.0297124998349408 \tabularnewline
16 & 7 & 6.55908006938821 & 0.440919930611791 \tabularnewline
17 & 4 & 2.44578061214674 & 1.55421938785326 \tabularnewline
18 & -4 & -2.23435664502447 & -1.76564335497553 \tabularnewline
19 & -6 & -6.4768218609877 & 0.476821860987702 \tabularnewline
20 & 8 & 7.53657527992711 & 0.463424720072886 \tabularnewline
21 & 2 & 0.85183682289979 & 1.14816317710021 \tabularnewline
22 & -1 & -0.474048198560825 & -0.525951801439175 \tabularnewline
23 & -2 & -1.96558927890262 & -0.0344107210973821 \tabularnewline
24 & 0 & 0.472182998232069 & -0.472182998232069 \tabularnewline
25 & 10 & 8.83018536781606 & 1.16981463218394 \tabularnewline
26 & 3 & 0.848958828377337 & 2.15104117162266 \tabularnewline
27 & 6 & 6.00636605785035 & -0.00636605785035383 \tabularnewline
28 & 7 & 6.80135098312935 & 0.198649016870647 \tabularnewline
29 & -4 & -3.10940601600296 & -0.890593983997044 \tabularnewline
30 & -5 & -6.16956175659461 & 1.16956175659461 \tabularnewline
31 & -7 & -4.83432050810741 & -2.16567949189259 \tabularnewline
32 & -10 & -11.8092070927356 & 1.80920709273556 \tabularnewline
33 & -21 & -20.2135231599423 & -0.786476840057722 \tabularnewline
34 & -22 & -20.7060155421935 & -1.29398445780651 \tabularnewline
35 & -16 & -13.9002152057465 & -2.09978479425347 \tabularnewline
36 & -25 & -26.3391570746595 & 1.33915707465947 \tabularnewline
37 & -22 & -23.5395709495244 & 1.53957094952437 \tabularnewline
38 & -22 & -21.7939159940841 & -0.206084005915903 \tabularnewline
39 & -19 & -19.7294412944778 & 0.729441294477777 \tabularnewline
40 & -21 & -19.8520411189635 & -1.14795888103646 \tabularnewline
41 & -31 & -30.2708672094642 & -0.729132790535761 \tabularnewline
42 & -28 & -28.0656324260966 & 0.0656324260965574 \tabularnewline
43 & -23 & -23.5335716789438 & 0.533571678943797 \tabularnewline
44 & -17 & -16.7561829328912 & -0.243817067108774 \tabularnewline
45 & -12 & -11.9905760479031 & -0.00942395209690235 \tabularnewline
46 & -14 & -12.4152889467118 & -1.58471105328821 \tabularnewline
47 & -18 & -17.3756655080459 & -0.624334491954053 \tabularnewline
48 & -16 & -15.8828469104426 & -0.117153089557399 \tabularnewline
49 & -22 & -23.6727339628353 & 1.6727339628353 \tabularnewline
50 & -9 & -9.5888291762484 & 0.588829176248403 \tabularnewline
51 & -10 & -10.3203628559416 & 0.320362855941588 \tabularnewline
52 & -10 & -8.66188819945706 & -1.33811180054294 \tabularnewline
53 & 0 & -1.10897747419974 & 1.10897747419974 \tabularnewline
54 & 3 & 1.13745531588892 & 1.86254468411108 \tabularnewline
55 & 2 & 2.3912326160503 & -0.391232616050301 \tabularnewline
56 & 4 & 4.27962719958395 & -0.279627199583949 \tabularnewline
57 & -3 & -4.32253215834375 & 1.32253215834375 \tabularnewline
58 & 0 & 0.497957198248652 & -0.497957198248652 \tabularnewline
59 & -1 & 0.306679841763666 & -1.30667984176367 \tabularnewline
60 & -7 & -6.9660167488678 & -0.033983251132199 \tabularnewline
61 & 2 & 2.73094038008777 & -0.730940380087767 \tabularnewline
62 & 3 & 1.34288163030288 & 1.65711836969712 \tabularnewline
63 & -3 & -4.72002595967876 & 1.72002595967876 \tabularnewline
64 & -5 & -6.36353863401844 & 1.36353863401844 \tabularnewline
65 & 0 & -0.472705480618362 & 0.472705480618362 \tabularnewline
66 & -3 & -2.2517771218431 & -0.748222878156903 \tabularnewline
67 & -7 & -8.46665630582264 & 1.46665630582263 \tabularnewline
68 & -7 & -7.90510692286734 & 0.905106922867338 \tabularnewline
69 & -7 & -5.19909430485126 & -1.80090569514874 \tabularnewline
70 & -4 & -2.28963264099594 & -1.71036735900406 \tabularnewline
71 & -3 & -1.92502850020492 & -1.07497149979508 \tabularnewline
72 & -6 & -5.87996315528521 & -0.120036844714789 \tabularnewline
73 & -10 & -8.74148815211085 & -1.25851184788915 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&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]-20[/C][C]-19.168399113103[/C][C]-0.831600886896995[/C][/ROW]
[ROW][C]2[/C][C]-8[/C][C]-8.53755520278156[/C][C]0.537555202781556[/C][/ROW]
[ROW][C]3[/C][C]-15[/C][C]-15.4510555121304[/C][C]0.451055512130416[/C][/ROW]
[ROW][C]4[/C][C]-13[/C][C]-13.5626609285968[/C][C]0.562660928596787[/C][/ROW]
[ROW][C]5[/C][C]-6[/C][C]-5.29812147062017[/C][C]-0.701878529379832[/C][/ROW]
[ROW][C]6[/C][C]0[/C][C]-1.96205466372227[/C][C]1.96205466372227[/C][/ROW]
[ROW][C]7[/C][C]5[/C][C]5.51316775465465[/C][C]-0.513167754654651[/C][/ROW]
[ROW][C]8[/C][C]-1[/C][C]-0.181532779163275[/C][C]-0.818467220836725[/C][/ROW]
[ROW][C]9[/C][C]-5[/C][C]-3.30856995464076[/C][C]-1.69143004535924[/C][/ROW]
[ROW][C]10[/C][C]4[/C][C]3.43494605797027[/C][C]0.565053942029726[/C][/ROW]
[ROW][C]11[/C][C]-3[/C][C]-2.51808599329117[/C][C]-0.481914006708831[/C][/ROW]
[ROW][C]12[/C][C]3[/C][C]2.06336206585926[/C][C]0.936637934140736[/C][/ROW]
[ROW][C]13[/C][C]8[/C][C]8.81416933519029[/C][C]-0.814169335190291[/C][/ROW]
[ROW][C]14[/C][C]3[/C][C]5.44719281371414[/C][C]-2.44719281371414[/C][/ROW]
[ROW][C]15[/C][C]3[/C][C]2.97028750016506[/C][C]0.0297124998349408[/C][/ROW]
[ROW][C]16[/C][C]7[/C][C]6.55908006938821[/C][C]0.440919930611791[/C][/ROW]
[ROW][C]17[/C][C]4[/C][C]2.44578061214674[/C][C]1.55421938785326[/C][/ROW]
[ROW][C]18[/C][C]-4[/C][C]-2.23435664502447[/C][C]-1.76564335497553[/C][/ROW]
[ROW][C]19[/C][C]-6[/C][C]-6.4768218609877[/C][C]0.476821860987702[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]7.53657527992711[/C][C]0.463424720072886[/C][/ROW]
[ROW][C]21[/C][C]2[/C][C]0.85183682289979[/C][C]1.14816317710021[/C][/ROW]
[ROW][C]22[/C][C]-1[/C][C]-0.474048198560825[/C][C]-0.525951801439175[/C][/ROW]
[ROW][C]23[/C][C]-2[/C][C]-1.96558927890262[/C][C]-0.0344107210973821[/C][/ROW]
[ROW][C]24[/C][C]0[/C][C]0.472182998232069[/C][C]-0.472182998232069[/C][/ROW]
[ROW][C]25[/C][C]10[/C][C]8.83018536781606[/C][C]1.16981463218394[/C][/ROW]
[ROW][C]26[/C][C]3[/C][C]0.848958828377337[/C][C]2.15104117162266[/C][/ROW]
[ROW][C]27[/C][C]6[/C][C]6.00636605785035[/C][C]-0.00636605785035383[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]6.80135098312935[/C][C]0.198649016870647[/C][/ROW]
[ROW][C]29[/C][C]-4[/C][C]-3.10940601600296[/C][C]-0.890593983997044[/C][/ROW]
[ROW][C]30[/C][C]-5[/C][C]-6.16956175659461[/C][C]1.16956175659461[/C][/ROW]
[ROW][C]31[/C][C]-7[/C][C]-4.83432050810741[/C][C]-2.16567949189259[/C][/ROW]
[ROW][C]32[/C][C]-10[/C][C]-11.8092070927356[/C][C]1.80920709273556[/C][/ROW]
[ROW][C]33[/C][C]-21[/C][C]-20.2135231599423[/C][C]-0.786476840057722[/C][/ROW]
[ROW][C]34[/C][C]-22[/C][C]-20.7060155421935[/C][C]-1.29398445780651[/C][/ROW]
[ROW][C]35[/C][C]-16[/C][C]-13.9002152057465[/C][C]-2.09978479425347[/C][/ROW]
[ROW][C]36[/C][C]-25[/C][C]-26.3391570746595[/C][C]1.33915707465947[/C][/ROW]
[ROW][C]37[/C][C]-22[/C][C]-23.5395709495244[/C][C]1.53957094952437[/C][/ROW]
[ROW][C]38[/C][C]-22[/C][C]-21.7939159940841[/C][C]-0.206084005915903[/C][/ROW]
[ROW][C]39[/C][C]-19[/C][C]-19.7294412944778[/C][C]0.729441294477777[/C][/ROW]
[ROW][C]40[/C][C]-21[/C][C]-19.8520411189635[/C][C]-1.14795888103646[/C][/ROW]
[ROW][C]41[/C][C]-31[/C][C]-30.2708672094642[/C][C]-0.729132790535761[/C][/ROW]
[ROW][C]42[/C][C]-28[/C][C]-28.0656324260966[/C][C]0.0656324260965574[/C][/ROW]
[ROW][C]43[/C][C]-23[/C][C]-23.5335716789438[/C][C]0.533571678943797[/C][/ROW]
[ROW][C]44[/C][C]-17[/C][C]-16.7561829328912[/C][C]-0.243817067108774[/C][/ROW]
[ROW][C]45[/C][C]-12[/C][C]-11.9905760479031[/C][C]-0.00942395209690235[/C][/ROW]
[ROW][C]46[/C][C]-14[/C][C]-12.4152889467118[/C][C]-1.58471105328821[/C][/ROW]
[ROW][C]47[/C][C]-18[/C][C]-17.3756655080459[/C][C]-0.624334491954053[/C][/ROW]
[ROW][C]48[/C][C]-16[/C][C]-15.8828469104426[/C][C]-0.117153089557399[/C][/ROW]
[ROW][C]49[/C][C]-22[/C][C]-23.6727339628353[/C][C]1.6727339628353[/C][/ROW]
[ROW][C]50[/C][C]-9[/C][C]-9.5888291762484[/C][C]0.588829176248403[/C][/ROW]
[ROW][C]51[/C][C]-10[/C][C]-10.3203628559416[/C][C]0.320362855941588[/C][/ROW]
[ROW][C]52[/C][C]-10[/C][C]-8.66188819945706[/C][C]-1.33811180054294[/C][/ROW]
[ROW][C]53[/C][C]0[/C][C]-1.10897747419974[/C][C]1.10897747419974[/C][/ROW]
[ROW][C]54[/C][C]3[/C][C]1.13745531588892[/C][C]1.86254468411108[/C][/ROW]
[ROW][C]55[/C][C]2[/C][C]2.3912326160503[/C][C]-0.391232616050301[/C][/ROW]
[ROW][C]56[/C][C]4[/C][C]4.27962719958395[/C][C]-0.279627199583949[/C][/ROW]
[ROW][C]57[/C][C]-3[/C][C]-4.32253215834375[/C][C]1.32253215834375[/C][/ROW]
[ROW][C]58[/C][C]0[/C][C]0.497957198248652[/C][C]-0.497957198248652[/C][/ROW]
[ROW][C]59[/C][C]-1[/C][C]0.306679841763666[/C][C]-1.30667984176367[/C][/ROW]
[ROW][C]60[/C][C]-7[/C][C]-6.9660167488678[/C][C]-0.033983251132199[/C][/ROW]
[ROW][C]61[/C][C]2[/C][C]2.73094038008777[/C][C]-0.730940380087767[/C][/ROW]
[ROW][C]62[/C][C]3[/C][C]1.34288163030288[/C][C]1.65711836969712[/C][/ROW]
[ROW][C]63[/C][C]-3[/C][C]-4.72002595967876[/C][C]1.72002595967876[/C][/ROW]
[ROW][C]64[/C][C]-5[/C][C]-6.36353863401844[/C][C]1.36353863401844[/C][/ROW]
[ROW][C]65[/C][C]0[/C][C]-0.472705480618362[/C][C]0.472705480618362[/C][/ROW]
[ROW][C]66[/C][C]-3[/C][C]-2.2517771218431[/C][C]-0.748222878156903[/C][/ROW]
[ROW][C]67[/C][C]-7[/C][C]-8.46665630582264[/C][C]1.46665630582263[/C][/ROW]
[ROW][C]68[/C][C]-7[/C][C]-7.90510692286734[/C][C]0.905106922867338[/C][/ROW]
[ROW][C]69[/C][C]-7[/C][C]-5.19909430485126[/C][C]-1.80090569514874[/C][/ROW]
[ROW][C]70[/C][C]-4[/C][C]-2.28963264099594[/C][C]-1.71036735900406[/C][/ROW]
[ROW][C]71[/C][C]-3[/C][C]-1.92502850020492[/C][C]-1.07497149979508[/C][/ROW]
[ROW][C]72[/C][C]-6[/C][C]-5.87996315528521[/C][C]-0.120036844714789[/C][/ROW]
[ROW][C]73[/C][C]-10[/C][C]-8.74148815211085[/C][C]-1.25851184788915[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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
1-20-19.168399113103-0.831600886896995
2-8-8.537555202781560.537555202781556
3-15-15.45105551213040.451055512130416
4-13-13.56266092859680.562660928596787
5-6-5.29812147062017-0.701878529379832
60-1.962054663722271.96205466372227
755.51316775465465-0.513167754654651
8-1-0.181532779163275-0.818467220836725
9-5-3.30856995464076-1.69143004535924
1043.434946057970270.565053942029726
11-3-2.51808599329117-0.481914006708831
1232.063362065859260.936637934140736
1388.81416933519029-0.814169335190291
1435.44719281371414-2.44719281371414
1532.970287500165060.0297124998349408
1676.559080069388210.440919930611791
1742.445780612146741.55421938785326
18-4-2.23435664502447-1.76564335497553
19-6-6.47682186098770.476821860987702
2087.536575279927110.463424720072886
2120.851836822899791.14816317710021
22-1-0.474048198560825-0.525951801439175
23-2-1.96558927890262-0.0344107210973821
2400.472182998232069-0.472182998232069
25108.830185367816061.16981463218394
2630.8489588283773372.15104117162266
2766.00636605785035-0.00636605785035383
2876.801350983129350.198649016870647
29-4-3.10940601600296-0.890593983997044
30-5-6.169561756594611.16956175659461
31-7-4.83432050810741-2.16567949189259
32-10-11.80920709273561.80920709273556
33-21-20.2135231599423-0.786476840057722
34-22-20.7060155421935-1.29398445780651
35-16-13.9002152057465-2.09978479425347
36-25-26.33915707465951.33915707465947
37-22-23.53957094952441.53957094952437
38-22-21.7939159940841-0.206084005915903
39-19-19.72944129447780.729441294477777
40-21-19.8520411189635-1.14795888103646
41-31-30.2708672094642-0.729132790535761
42-28-28.06563242609660.0656324260965574
43-23-23.53357167894380.533571678943797
44-17-16.7561829328912-0.243817067108774
45-12-11.9905760479031-0.00942395209690235
46-14-12.4152889467118-1.58471105328821
47-18-17.3756655080459-0.624334491954053
48-16-15.8828469104426-0.117153089557399
49-22-23.67273396283531.6727339628353
50-9-9.58882917624840.588829176248403
51-10-10.32036285594160.320362855941588
52-10-8.66188819945706-1.33811180054294
530-1.108977474199741.10897747419974
5431.137455315888921.86254468411108
5522.3912326160503-0.391232616050301
5644.27962719958395-0.279627199583949
57-3-4.322532158343751.32253215834375
5800.497957198248652-0.497957198248652
59-10.306679841763666-1.30667984176367
60-7-6.9660167488678-0.033983251132199
6122.73094038008777-0.730940380087767
6231.342881630302881.65711836969712
63-3-4.720025959678761.72002595967876
64-5-6.363538634018441.36353863401844
650-0.4727054806183620.472705480618362
66-3-2.2517771218431-0.748222878156903
67-7-8.466656305822641.46665630582263
68-7-7.905106922867340.905106922867338
69-7-5.19909430485126-1.80090569514874
70-4-2.28963264099594-1.71036735900406
71-3-1.92502850020492-1.07497149979508
72-6-5.87996315528521-0.120036844714789
73-10-8.74148815211085-1.25851184788915







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.8285979016421470.3428041967157050.171402098357853
100.7326424976550820.5347150046898360.267357502344918
110.6107225845671370.7785548308657260.389277415432863
120.5710735419143140.8578529161713720.428926458085686
130.5528078264115040.8943843471769920.447192173588496
140.6945693700023520.6108612599952970.305430629997648
150.6938616061287220.6122767877425550.306138393871278
160.6593129043756560.6813741912486890.340687095624344
170.673711027759850.6525779444802990.32628897224015
180.727085219819460.545829560361080.27291478018054
190.667956938802960.664086122394080.33204306119704
200.5853462312703980.8293075374592030.414653768729602
210.538353679043650.92329264191270.46164632095635
220.5104875786922730.9790248426154530.489512421307727
230.4318968818183760.8637937636367530.568103118181624
240.3833544222820750.7667088445641490.616645577717925
250.3421605473467690.6843210946935380.657839452653231
260.4709348378866540.9418696757733090.529065162113346
270.3942483226801320.7884966453602640.605751677319868
280.3236436621852070.6472873243704140.676356337814793
290.2939306272825890.5878612545651790.706069372717411
300.2727157044620230.5454314089240450.727284295537977
310.4950285830472640.9900571660945280.504971416952736
320.6395807172276730.7208385655446540.360419282772327
330.5764520231218150.847095953756370.423547976878185
340.5373167349277740.9253665301444520.462683265072226
350.6522754904687870.6954490190624260.347724509531213
360.721397241111280.5572055177774410.27860275888872
370.8124649240207870.3750701519584260.187535075979213
380.7625536076889650.474892784622070.237446392311035
390.7285583018631950.542883396273610.271441698136805
400.7075081463707080.5849837072585850.292491853629292
410.6654428617401250.6691142765197510.334557138259875
420.6209322538192910.7581354923614180.379067746180709
430.5556638506805450.8886722986389110.444336149319455
440.4860388314617160.9720776629234330.513961168538284
450.4191092999027790.8382185998055590.580890700097221
460.4641804322557270.9283608645114540.535819567744273
470.4994930793029980.9989861586059950.500506920697002
480.5058325253721610.9883349492556770.494167474627839
490.4902885513640080.9805771027280170.509711448635992
500.4319237795079390.8638475590158790.568076220492061
510.3563083233846190.7126166467692390.643691676615381
520.9174290230130080.1651419539739850.0825709769869924
530.8938881858796880.2122236282406240.106111814120312
540.8952576331243450.209484733751310.104742366875655
550.8752700748142460.2494598503715080.124729925185754
560.8217117152037120.3565765695925750.178288284796288
570.7806248930305440.4387502139389130.219375106969456
580.8495621152432530.3008757695134930.150437884756747
590.8455170442550440.3089659114899120.154482955744956
600.7709744698990950.458051060201810.229025530100905
610.7066766744479860.5866466511040280.293323325552014
620.7721896200513230.4556207598973550.227810379948677
630.6837327963224610.6325344073550790.316267203677539
640.5382841899046850.923431620190630.461715810095315

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.828597901642147 & 0.342804196715705 & 0.171402098357853 \tabularnewline
10 & 0.732642497655082 & 0.534715004689836 & 0.267357502344918 \tabularnewline
11 & 0.610722584567137 & 0.778554830865726 & 0.389277415432863 \tabularnewline
12 & 0.571073541914314 & 0.857852916171372 & 0.428926458085686 \tabularnewline
13 & 0.552807826411504 & 0.894384347176992 & 0.447192173588496 \tabularnewline
14 & 0.694569370002352 & 0.610861259995297 & 0.305430629997648 \tabularnewline
15 & 0.693861606128722 & 0.612276787742555 & 0.306138393871278 \tabularnewline
16 & 0.659312904375656 & 0.681374191248689 & 0.340687095624344 \tabularnewline
17 & 0.67371102775985 & 0.652577944480299 & 0.32628897224015 \tabularnewline
18 & 0.72708521981946 & 0.54582956036108 & 0.27291478018054 \tabularnewline
19 & 0.66795693880296 & 0.66408612239408 & 0.33204306119704 \tabularnewline
20 & 0.585346231270398 & 0.829307537459203 & 0.414653768729602 \tabularnewline
21 & 0.53835367904365 & 0.9232926419127 & 0.46164632095635 \tabularnewline
22 & 0.510487578692273 & 0.979024842615453 & 0.489512421307727 \tabularnewline
23 & 0.431896881818376 & 0.863793763636753 & 0.568103118181624 \tabularnewline
24 & 0.383354422282075 & 0.766708844564149 & 0.616645577717925 \tabularnewline
25 & 0.342160547346769 & 0.684321094693538 & 0.657839452653231 \tabularnewline
26 & 0.470934837886654 & 0.941869675773309 & 0.529065162113346 \tabularnewline
27 & 0.394248322680132 & 0.788496645360264 & 0.605751677319868 \tabularnewline
28 & 0.323643662185207 & 0.647287324370414 & 0.676356337814793 \tabularnewline
29 & 0.293930627282589 & 0.587861254565179 & 0.706069372717411 \tabularnewline
30 & 0.272715704462023 & 0.545431408924045 & 0.727284295537977 \tabularnewline
31 & 0.495028583047264 & 0.990057166094528 & 0.504971416952736 \tabularnewline
32 & 0.639580717227673 & 0.720838565544654 & 0.360419282772327 \tabularnewline
33 & 0.576452023121815 & 0.84709595375637 & 0.423547976878185 \tabularnewline
34 & 0.537316734927774 & 0.925366530144452 & 0.462683265072226 \tabularnewline
35 & 0.652275490468787 & 0.695449019062426 & 0.347724509531213 \tabularnewline
36 & 0.72139724111128 & 0.557205517777441 & 0.27860275888872 \tabularnewline
37 & 0.812464924020787 & 0.375070151958426 & 0.187535075979213 \tabularnewline
38 & 0.762553607688965 & 0.47489278462207 & 0.237446392311035 \tabularnewline
39 & 0.728558301863195 & 0.54288339627361 & 0.271441698136805 \tabularnewline
40 & 0.707508146370708 & 0.584983707258585 & 0.292491853629292 \tabularnewline
41 & 0.665442861740125 & 0.669114276519751 & 0.334557138259875 \tabularnewline
42 & 0.620932253819291 & 0.758135492361418 & 0.379067746180709 \tabularnewline
43 & 0.555663850680545 & 0.888672298638911 & 0.444336149319455 \tabularnewline
44 & 0.486038831461716 & 0.972077662923433 & 0.513961168538284 \tabularnewline
45 & 0.419109299902779 & 0.838218599805559 & 0.580890700097221 \tabularnewline
46 & 0.464180432255727 & 0.928360864511454 & 0.535819567744273 \tabularnewline
47 & 0.499493079302998 & 0.998986158605995 & 0.500506920697002 \tabularnewline
48 & 0.505832525372161 & 0.988334949255677 & 0.494167474627839 \tabularnewline
49 & 0.490288551364008 & 0.980577102728017 & 0.509711448635992 \tabularnewline
50 & 0.431923779507939 & 0.863847559015879 & 0.568076220492061 \tabularnewline
51 & 0.356308323384619 & 0.712616646769239 & 0.643691676615381 \tabularnewline
52 & 0.917429023013008 & 0.165141953973985 & 0.0825709769869924 \tabularnewline
53 & 0.893888185879688 & 0.212223628240624 & 0.106111814120312 \tabularnewline
54 & 0.895257633124345 & 0.20948473375131 & 0.104742366875655 \tabularnewline
55 & 0.875270074814246 & 0.249459850371508 & 0.124729925185754 \tabularnewline
56 & 0.821711715203712 & 0.356576569592575 & 0.178288284796288 \tabularnewline
57 & 0.780624893030544 & 0.438750213938913 & 0.219375106969456 \tabularnewline
58 & 0.849562115243253 & 0.300875769513493 & 0.150437884756747 \tabularnewline
59 & 0.845517044255044 & 0.308965911489912 & 0.154482955744956 \tabularnewline
60 & 0.770974469899095 & 0.45805106020181 & 0.229025530100905 \tabularnewline
61 & 0.706676674447986 & 0.586646651104028 & 0.293323325552014 \tabularnewline
62 & 0.772189620051323 & 0.455620759897355 & 0.227810379948677 \tabularnewline
63 & 0.683732796322461 & 0.632534407355079 & 0.316267203677539 \tabularnewline
64 & 0.538284189904685 & 0.92343162019063 & 0.461715810095315 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&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]9[/C][C]0.828597901642147[/C][C]0.342804196715705[/C][C]0.171402098357853[/C][/ROW]
[ROW][C]10[/C][C]0.732642497655082[/C][C]0.534715004689836[/C][C]0.267357502344918[/C][/ROW]
[ROW][C]11[/C][C]0.610722584567137[/C][C]0.778554830865726[/C][C]0.389277415432863[/C][/ROW]
[ROW][C]12[/C][C]0.571073541914314[/C][C]0.857852916171372[/C][C]0.428926458085686[/C][/ROW]
[ROW][C]13[/C][C]0.552807826411504[/C][C]0.894384347176992[/C][C]0.447192173588496[/C][/ROW]
[ROW][C]14[/C][C]0.694569370002352[/C][C]0.610861259995297[/C][C]0.305430629997648[/C][/ROW]
[ROW][C]15[/C][C]0.693861606128722[/C][C]0.612276787742555[/C][C]0.306138393871278[/C][/ROW]
[ROW][C]16[/C][C]0.659312904375656[/C][C]0.681374191248689[/C][C]0.340687095624344[/C][/ROW]
[ROW][C]17[/C][C]0.67371102775985[/C][C]0.652577944480299[/C][C]0.32628897224015[/C][/ROW]
[ROW][C]18[/C][C]0.72708521981946[/C][C]0.54582956036108[/C][C]0.27291478018054[/C][/ROW]
[ROW][C]19[/C][C]0.66795693880296[/C][C]0.66408612239408[/C][C]0.33204306119704[/C][/ROW]
[ROW][C]20[/C][C]0.585346231270398[/C][C]0.829307537459203[/C][C]0.414653768729602[/C][/ROW]
[ROW][C]21[/C][C]0.53835367904365[/C][C]0.9232926419127[/C][C]0.46164632095635[/C][/ROW]
[ROW][C]22[/C][C]0.510487578692273[/C][C]0.979024842615453[/C][C]0.489512421307727[/C][/ROW]
[ROW][C]23[/C][C]0.431896881818376[/C][C]0.863793763636753[/C][C]0.568103118181624[/C][/ROW]
[ROW][C]24[/C][C]0.383354422282075[/C][C]0.766708844564149[/C][C]0.616645577717925[/C][/ROW]
[ROW][C]25[/C][C]0.342160547346769[/C][C]0.684321094693538[/C][C]0.657839452653231[/C][/ROW]
[ROW][C]26[/C][C]0.470934837886654[/C][C]0.941869675773309[/C][C]0.529065162113346[/C][/ROW]
[ROW][C]27[/C][C]0.394248322680132[/C][C]0.788496645360264[/C][C]0.605751677319868[/C][/ROW]
[ROW][C]28[/C][C]0.323643662185207[/C][C]0.647287324370414[/C][C]0.676356337814793[/C][/ROW]
[ROW][C]29[/C][C]0.293930627282589[/C][C]0.587861254565179[/C][C]0.706069372717411[/C][/ROW]
[ROW][C]30[/C][C]0.272715704462023[/C][C]0.545431408924045[/C][C]0.727284295537977[/C][/ROW]
[ROW][C]31[/C][C]0.495028583047264[/C][C]0.990057166094528[/C][C]0.504971416952736[/C][/ROW]
[ROW][C]32[/C][C]0.639580717227673[/C][C]0.720838565544654[/C][C]0.360419282772327[/C][/ROW]
[ROW][C]33[/C][C]0.576452023121815[/C][C]0.84709595375637[/C][C]0.423547976878185[/C][/ROW]
[ROW][C]34[/C][C]0.537316734927774[/C][C]0.925366530144452[/C][C]0.462683265072226[/C][/ROW]
[ROW][C]35[/C][C]0.652275490468787[/C][C]0.695449019062426[/C][C]0.347724509531213[/C][/ROW]
[ROW][C]36[/C][C]0.72139724111128[/C][C]0.557205517777441[/C][C]0.27860275888872[/C][/ROW]
[ROW][C]37[/C][C]0.812464924020787[/C][C]0.375070151958426[/C][C]0.187535075979213[/C][/ROW]
[ROW][C]38[/C][C]0.762553607688965[/C][C]0.47489278462207[/C][C]0.237446392311035[/C][/ROW]
[ROW][C]39[/C][C]0.728558301863195[/C][C]0.54288339627361[/C][C]0.271441698136805[/C][/ROW]
[ROW][C]40[/C][C]0.707508146370708[/C][C]0.584983707258585[/C][C]0.292491853629292[/C][/ROW]
[ROW][C]41[/C][C]0.665442861740125[/C][C]0.669114276519751[/C][C]0.334557138259875[/C][/ROW]
[ROW][C]42[/C][C]0.620932253819291[/C][C]0.758135492361418[/C][C]0.379067746180709[/C][/ROW]
[ROW][C]43[/C][C]0.555663850680545[/C][C]0.888672298638911[/C][C]0.444336149319455[/C][/ROW]
[ROW][C]44[/C][C]0.486038831461716[/C][C]0.972077662923433[/C][C]0.513961168538284[/C][/ROW]
[ROW][C]45[/C][C]0.419109299902779[/C][C]0.838218599805559[/C][C]0.580890700097221[/C][/ROW]
[ROW][C]46[/C][C]0.464180432255727[/C][C]0.928360864511454[/C][C]0.535819567744273[/C][/ROW]
[ROW][C]47[/C][C]0.499493079302998[/C][C]0.998986158605995[/C][C]0.500506920697002[/C][/ROW]
[ROW][C]48[/C][C]0.505832525372161[/C][C]0.988334949255677[/C][C]0.494167474627839[/C][/ROW]
[ROW][C]49[/C][C]0.490288551364008[/C][C]0.980577102728017[/C][C]0.509711448635992[/C][/ROW]
[ROW][C]50[/C][C]0.431923779507939[/C][C]0.863847559015879[/C][C]0.568076220492061[/C][/ROW]
[ROW][C]51[/C][C]0.356308323384619[/C][C]0.712616646769239[/C][C]0.643691676615381[/C][/ROW]
[ROW][C]52[/C][C]0.917429023013008[/C][C]0.165141953973985[/C][C]0.0825709769869924[/C][/ROW]
[ROW][C]53[/C][C]0.893888185879688[/C][C]0.212223628240624[/C][C]0.106111814120312[/C][/ROW]
[ROW][C]54[/C][C]0.895257633124345[/C][C]0.20948473375131[/C][C]0.104742366875655[/C][/ROW]
[ROW][C]55[/C][C]0.875270074814246[/C][C]0.249459850371508[/C][C]0.124729925185754[/C][/ROW]
[ROW][C]56[/C][C]0.821711715203712[/C][C]0.356576569592575[/C][C]0.178288284796288[/C][/ROW]
[ROW][C]57[/C][C]0.780624893030544[/C][C]0.438750213938913[/C][C]0.219375106969456[/C][/ROW]
[ROW][C]58[/C][C]0.849562115243253[/C][C]0.300875769513493[/C][C]0.150437884756747[/C][/ROW]
[ROW][C]59[/C][C]0.845517044255044[/C][C]0.308965911489912[/C][C]0.154482955744956[/C][/ROW]
[ROW][C]60[/C][C]0.770974469899095[/C][C]0.45805106020181[/C][C]0.229025530100905[/C][/ROW]
[ROW][C]61[/C][C]0.706676674447986[/C][C]0.586646651104028[/C][C]0.293323325552014[/C][/ROW]
[ROW][C]62[/C][C]0.772189620051323[/C][C]0.455620759897355[/C][C]0.227810379948677[/C][/ROW]
[ROW][C]63[/C][C]0.683732796322461[/C][C]0.632534407355079[/C][C]0.316267203677539[/C][/ROW]
[ROW][C]64[/C][C]0.538284189904685[/C][C]0.92343162019063[/C][C]0.461715810095315[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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
90.8285979016421470.3428041967157050.171402098357853
100.7326424976550820.5347150046898360.267357502344918
110.6107225845671370.7785548308657260.389277415432863
120.5710735419143140.8578529161713720.428926458085686
130.5528078264115040.8943843471769920.447192173588496
140.6945693700023520.6108612599952970.305430629997648
150.6938616061287220.6122767877425550.306138393871278
160.6593129043756560.6813741912486890.340687095624344
170.673711027759850.6525779444802990.32628897224015
180.727085219819460.545829560361080.27291478018054
190.667956938802960.664086122394080.33204306119704
200.5853462312703980.8293075374592030.414653768729602
210.538353679043650.92329264191270.46164632095635
220.5104875786922730.9790248426154530.489512421307727
230.4318968818183760.8637937636367530.568103118181624
240.3833544222820750.7667088445641490.616645577717925
250.3421605473467690.6843210946935380.657839452653231
260.4709348378866540.9418696757733090.529065162113346
270.3942483226801320.7884966453602640.605751677319868
280.3236436621852070.6472873243704140.676356337814793
290.2939306272825890.5878612545651790.706069372717411
300.2727157044620230.5454314089240450.727284295537977
310.4950285830472640.9900571660945280.504971416952736
320.6395807172276730.7208385655446540.360419282772327
330.5764520231218150.847095953756370.423547976878185
340.5373167349277740.9253665301444520.462683265072226
350.6522754904687870.6954490190624260.347724509531213
360.721397241111280.5572055177774410.27860275888872
370.8124649240207870.3750701519584260.187535075979213
380.7625536076889650.474892784622070.237446392311035
390.7285583018631950.542883396273610.271441698136805
400.7075081463707080.5849837072585850.292491853629292
410.6654428617401250.6691142765197510.334557138259875
420.6209322538192910.7581354923614180.379067746180709
430.5556638506805450.8886722986389110.444336149319455
440.4860388314617160.9720776629234330.513961168538284
450.4191092999027790.8382185998055590.580890700097221
460.4641804322557270.9283608645114540.535819567744273
470.4994930793029980.9989861586059950.500506920697002
480.5058325253721610.9883349492556770.494167474627839
490.4902885513640080.9805771027280170.509711448635992
500.4319237795079390.8638475590158790.568076220492061
510.3563083233846190.7126166467692390.643691676615381
520.9174290230130080.1651419539739850.0825709769869924
530.8938881858796880.2122236282406240.106111814120312
540.8952576331243450.209484733751310.104742366875655
550.8752700748142460.2494598503715080.124729925185754
560.8217117152037120.3565765695925750.178288284796288
570.7806248930305440.4387502139389130.219375106969456
580.8495621152432530.3008757695134930.150437884756747
590.8455170442550440.3089659114899120.154482955744956
600.7709744698990950.458051060201810.229025530100905
610.7066766744479860.5866466511040280.293323325552014
620.7721896200513230.4556207598973550.227810379948677
630.6837327963224610.6325344073550790.316267203677539
640.5382841899046850.923431620190630.461715810095315







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147174&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147174&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147174&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 level00OK
5% type I error level00OK
10% type I error level00OK



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