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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 computationThu, 24 Nov 2011 15:54:35 -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/t1322168128taq2hiinlzg4y4f.htm/, Retrieved Fri, 01 Nov 2024 00:02:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147205, Retrieved Fri, 01 Nov 2024 00:02:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142

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] [WS7_trends] [2011-11-24 20:54:35] [de50302416ae5d0bdedd77e4c0468c33] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46

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'Gertrude Mary Cox' @ cox.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 & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 13.5418192607468 + 0.0236875343095792Connected[t] + 0.0599170464092312Separate[t] + 0.100158690643845Learning[t] -0.0796049946788367Software[t] -0.34016858129956Depression[t] + 0.0523382523886976Belonging[t] -0.0402494950926348Belonging_Final[t] -1.04308022934069M1[t] -0.675853126651141M2[t] -0.00863759431825111M3[t] -1.12540140962164M4[t] -0.0047466453498684M5[t] -1.16617735315638M6[t] + 0.174722807443339M7[t] -0.796081789230753M8[t] + 0.228190847148109M9[t] -1.10780459080302M10[t] -1.1127438087348M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  13.5418192607468 +  0.0236875343095792Connected[t] +  0.0599170464092312Separate[t] +  0.100158690643845Learning[t] -0.0796049946788367Software[t] -0.34016858129956Depression[t] +  0.0523382523886976Belonging[t] -0.0402494950926348Belonging_Final[t] -1.04308022934069M1[t] -0.675853126651141M2[t] -0.00863759431825111M3[t] -1.12540140962164M4[t] -0.0047466453498684M5[t] -1.16617735315638M6[t] +  0.174722807443339M7[t] -0.796081789230753M8[t] +  0.228190847148109M9[t] -1.10780459080302M10[t] -1.1127438087348M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  13.5418192607468 +  0.0236875343095792Connected[t] +  0.0599170464092312Separate[t] +  0.100158690643845Learning[t] -0.0796049946788367Software[t] -0.34016858129956Depression[t] +  0.0523382523886976Belonging[t] -0.0402494950926348Belonging_Final[t] -1.04308022934069M1[t] -0.675853126651141M2[t] -0.00863759431825111M3[t] -1.12540140962164M4[t] -0.0047466453498684M5[t] -1.16617735315638M6[t] +  0.174722807443339M7[t] -0.796081789230753M8[t] +  0.228190847148109M9[t] -1.10780459080302M10[t] -1.1127438087348M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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
Happiness[t] = + 13.5418192607468 + 0.0236875343095792Connected[t] + 0.0599170464092312Separate[t] + 0.100158690643845Learning[t] -0.0796049946788367Software[t] -0.34016858129956Depression[t] + 0.0523382523886976Belonging[t] -0.0402494950926348Belonging_Final[t] -1.04308022934069M1[t] -0.675853126651141M2[t] -0.00863759431825111M3[t] -1.12540140962164M4[t] -0.0047466453498684M5[t] -1.16617735315638M6[t] + 0.174722807443339M7[t] -0.796081789230753M8[t] + 0.228190847148109M9[t] -1.10780459080302M10[t] -1.1127438087348M11[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.54181926074682.6881485.03761e-061e-06
Connected0.02368753430957920.0525770.45050.6530090.326504
Separate0.05991704640923120.048851.22660.2220050.111003
Learning0.1001586906438450.0859771.1650.2459780.122989
Software-0.07960499467883670.086997-0.9150.3617180.180859
Depression-0.340168581299560.05544-6.135800
Belonging0.05233825238869760.0479411.09170.2767920.138396
Belonging_Final-0.04024949509263480.068906-0.58410.560060.28003
M1-1.043080229340690.760201-1.37210.1721770.086089
M2-0.6758531266511410.760575-0.88860.3757060.187853
M3-0.008637594318251110.769145-0.01120.9910550.495528
M4-1.125401409621640.756017-1.48860.1387970.069398
M5-0.00474664534986840.759216-0.00630.995020.49751
M6-1.166177353156380.755106-1.54440.1247040.062352
M70.1747228074433390.7677090.22760.820290.410145
M8-0.7960817892307530.784562-1.01470.3119710.155985
M90.2281908471481090.7688820.29680.7670630.383531
M10-1.107804590803020.775446-1.42860.1552990.077649
M11-1.11274380873480.768804-1.44740.1499820.074991

\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) & 13.5418192607468 & 2.688148 & 5.0376 & 1e-06 & 1e-06 \tabularnewline
Connected & 0.0236875343095792 & 0.052577 & 0.4505 & 0.653009 & 0.326504 \tabularnewline
Separate & 0.0599170464092312 & 0.04885 & 1.2266 & 0.222005 & 0.111003 \tabularnewline
Learning & 0.100158690643845 & 0.085977 & 1.165 & 0.245978 & 0.122989 \tabularnewline
Software & -0.0796049946788367 & 0.086997 & -0.915 & 0.361718 & 0.180859 \tabularnewline
Depression & -0.34016858129956 & 0.05544 & -6.1358 & 0 & 0 \tabularnewline
Belonging & 0.0523382523886976 & 0.047941 & 1.0917 & 0.276792 & 0.138396 \tabularnewline
Belonging_Final & -0.0402494950926348 & 0.068906 & -0.5841 & 0.56006 & 0.28003 \tabularnewline
M1 & -1.04308022934069 & 0.760201 & -1.3721 & 0.172177 & 0.086089 \tabularnewline
M2 & -0.675853126651141 & 0.760575 & -0.8886 & 0.375706 & 0.187853 \tabularnewline
M3 & -0.00863759431825111 & 0.769145 & -0.0112 & 0.991055 & 0.495528 \tabularnewline
M4 & -1.12540140962164 & 0.756017 & -1.4886 & 0.138797 & 0.069398 \tabularnewline
M5 & -0.0047466453498684 & 0.759216 & -0.0063 & 0.99502 & 0.49751 \tabularnewline
M6 & -1.16617735315638 & 0.755106 & -1.5444 & 0.124704 & 0.062352 \tabularnewline
M7 & 0.174722807443339 & 0.767709 & 0.2276 & 0.82029 & 0.410145 \tabularnewline
M8 & -0.796081789230753 & 0.784562 & -1.0147 & 0.311971 & 0.155985 \tabularnewline
M9 & 0.228190847148109 & 0.768882 & 0.2968 & 0.767063 & 0.383531 \tabularnewline
M10 & -1.10780459080302 & 0.775446 & -1.4286 & 0.155299 & 0.077649 \tabularnewline
M11 & -1.1127438087348 & 0.768804 & -1.4474 & 0.149982 & 0.074991 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&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]13.5418192607468[/C][C]2.688148[/C][C]5.0376[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Connected[/C][C]0.0236875343095792[/C][C]0.052577[/C][C]0.4505[/C][C]0.653009[/C][C]0.326504[/C][/ROW]
[ROW][C]Separate[/C][C]0.0599170464092312[/C][C]0.04885[/C][C]1.2266[/C][C]0.222005[/C][C]0.111003[/C][/ROW]
[ROW][C]Learning[/C][C]0.100158690643845[/C][C]0.085977[/C][C]1.165[/C][C]0.245978[/C][C]0.122989[/C][/ROW]
[ROW][C]Software[/C][C]-0.0796049946788367[/C][C]0.086997[/C][C]-0.915[/C][C]0.361718[/C][C]0.180859[/C][/ROW]
[ROW][C]Depression[/C][C]-0.34016858129956[/C][C]0.05544[/C][C]-6.1358[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0523382523886976[/C][C]0.047941[/C][C]1.0917[/C][C]0.276792[/C][C]0.138396[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0402494950926348[/C][C]0.068906[/C][C]-0.5841[/C][C]0.56006[/C][C]0.28003[/C][/ROW]
[ROW][C]M1[/C][C]-1.04308022934069[/C][C]0.760201[/C][C]-1.3721[/C][C]0.172177[/C][C]0.086089[/C][/ROW]
[ROW][C]M2[/C][C]-0.675853126651141[/C][C]0.760575[/C][C]-0.8886[/C][C]0.375706[/C][C]0.187853[/C][/ROW]
[ROW][C]M3[/C][C]-0.00863759431825111[/C][C]0.769145[/C][C]-0.0112[/C][C]0.991055[/C][C]0.495528[/C][/ROW]
[ROW][C]M4[/C][C]-1.12540140962164[/C][C]0.756017[/C][C]-1.4886[/C][C]0.138797[/C][C]0.069398[/C][/ROW]
[ROW][C]M5[/C][C]-0.0047466453498684[/C][C]0.759216[/C][C]-0.0063[/C][C]0.99502[/C][C]0.49751[/C][/ROW]
[ROW][C]M6[/C][C]-1.16617735315638[/C][C]0.755106[/C][C]-1.5444[/C][C]0.124704[/C][C]0.062352[/C][/ROW]
[ROW][C]M7[/C][C]0.174722807443339[/C][C]0.767709[/C][C]0.2276[/C][C]0.82029[/C][C]0.410145[/C][/ROW]
[ROW][C]M8[/C][C]-0.796081789230753[/C][C]0.784562[/C][C]-1.0147[/C][C]0.311971[/C][C]0.155985[/C][/ROW]
[ROW][C]M9[/C][C]0.228190847148109[/C][C]0.768882[/C][C]0.2968[/C][C]0.767063[/C][C]0.383531[/C][/ROW]
[ROW][C]M10[/C][C]-1.10780459080302[/C][C]0.775446[/C][C]-1.4286[/C][C]0.155299[/C][C]0.077649[/C][/ROW]
[ROW][C]M11[/C][C]-1.1127438087348[/C][C]0.768804[/C][C]-1.4474[/C][C]0.149982[/C][C]0.074991[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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)13.54181926074682.6881485.03761e-061e-06
Connected0.02368753430957920.0525770.45050.6530090.326504
Separate0.05991704640923120.048851.22660.2220050.111003
Learning0.1001586906438450.0859771.1650.2459780.122989
Software-0.07960499467883670.086997-0.9150.3617180.180859
Depression-0.340168581299560.05544-6.135800
Belonging0.05233825238869760.0479411.09170.2767920.138396
Belonging_Final-0.04024949509263480.068906-0.58410.560060.28003
M1-1.043080229340690.760201-1.37210.1721770.086089
M2-0.6758531266511410.760575-0.88860.3757060.187853
M3-0.008637594318251110.769145-0.01120.9910550.495528
M4-1.125401409621640.756017-1.48860.1387970.069398
M5-0.00474664534986840.759216-0.00630.995020.49751
M6-1.166177353156380.755106-1.54440.1247040.062352
M70.1747228074433390.7677090.22760.820290.410145
M8-0.7960817892307530.784562-1.01470.3119710.155985
M90.2281908471481090.7688820.29680.7670630.383531
M10-1.107804590803020.775446-1.42860.1552990.077649
M11-1.11274380873480.768804-1.44740.1499820.074991






Multiple Linear Regression - Regression Statistics
Multiple R0.623835595610296
R-squared0.389170850350452
Adjusted R-squared0.31228326507988
F-TEST (value)5.06155641357359
F-TEST (DF numerator)18
F-TEST (DF denominator)143
p-value8.5893293588768e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93855600480879
Sum Squared Residuals537.393911880569

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.623835595610296 \tabularnewline
R-squared & 0.389170850350452 \tabularnewline
Adjusted R-squared & 0.31228326507988 \tabularnewline
F-TEST (value) & 5.06155641357359 \tabularnewline
F-TEST (DF numerator) & 18 \tabularnewline
F-TEST (DF denominator) & 143 \tabularnewline
p-value & 8.5893293588768e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.93855600480879 \tabularnewline
Sum Squared Residuals & 537.393911880569 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.623835595610296[/C][/ROW]
[ROW][C]R-squared[/C][C]0.389170850350452[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.31228326507988[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.06155641357359[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]18[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]143[/C][/ROW]
[ROW][C]p-value[/C][C]8.5893293588768e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.93855600480879[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]537.393911880569[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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 R0.623835595610296
R-squared0.389170850350452
Adjusted R-squared0.31228326507988
F-TEST (value)5.06155641357359
F-TEST (DF numerator)18
F-TEST (DF denominator)143
p-value8.5893293588768e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.93855600480879
Sum Squared Residuals537.393911880569






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.49749930191560.502500698084415
21815.14052062750742.85947937249255
31114.0513302476591-3.05133024765908
41213.9271549734614-1.92715497346142
51611.90942643724224.09057356275779
61813.55694742724494.44305257275511
71411.24652680062012.7534731993799
81414.5690182801896-0.569018280189591
91515.8269372848301-0.826937284830122
101514.27584094982660.724159050173419
111714.75393016414782.24606983585224
121915.85571282849733.14428717150275
131012.8568964703074-2.85689647030739
141613.52918399729332.47081600270674
151816.05894274146051.9410572585395
161412.52307858106381.47692141893624
171414.3652812602964-0.365281260296428
181715.2909689325441.70903106745596
191416.1421382010196-2.14213820101956
201613.18047017460012.81952982539989
211816.65507313589951.34492686410049
221112.6986990265441-1.69869902654407
231413.91389656038260.0861034396174205
241214.1612576261413-2.16125762614132
251714.99241270972682.00758729027322
26915.6561500260502-6.65615002605025
271615.79464312767220.205356872327771
281412.54078054588761.45921945411238
291514.45209916222920.547900837770814
301113.0779458969188-2.07794589691875
311615.82897105461790.171028945382111
321312.26321428470490.736785715295122
331715.69365085587961.30634914412037
341514.58468673911970.415313260880307
351413.23928506966110.760714930338873
361615.64464824703820.355351752961835
37910.5717698595048-1.57176985950479
381514.04620628863250.953793711367533
391715.60858229788991.39141770211009
401314.5484079949121-1.54840799491211
411516.137632139359-1.13763213935895
421612.99727334537113.0027266546289
431616.1197963902802-0.11979639028016
441212.8697300642282-0.869730064228191
451215.1329916599772-3.13299165997719
461113.0808655264894-2.08086552648935
471514.6430641885110.356935811488987
481515.3194506496632-0.319450649663228
491713.43524530231563.56475469768436
501314.5026869115115-1.50268691151147
511615.65179022759840.348209772401606
521412.71242503167761.28757496832237
531112.2040247150566-1.2040247150566
541212.6566746927878-0.656674692787771
551215.0017655764505-3.00176557645045
561514.46807136359920.531928636400828
571615.01789447873860.982105521261361
581514.5600787414470.439921258553019
591214.7775316327853-2.77753163278528
601214.0449723017412-2.04497230174125
61810.2142835927717-2.21428359277173
621314.5873463090955-1.58734630909546
631115.0605187612416-4.06051876124159
641412.48588940473861.51411059526138
651513.92299706880271.07700293119726
661014.5624496729493-4.56244967294929
671113.3357564154145-2.33575641541446
681214.0897653939569-2.08976539395687
691514.68278768399140.317212316008584
701513.28578868481171.71421131518825
711413.16445594458860.835544055411354
721613.47505088688052.52494911311949
731514.21449763612110.785502363878908
741515.5107516448428-0.510751644842808
751315.2456812518827-2.24568125188269
761211.87562775717660.124372242823387
771714.70887404280082.29112595719916
781311.82282637028831.1771736297117
791514.30015211745010.699847882549907
801314.3237306674119-1.3237306674119
811515.8973822951496-0.897382295149599
821614.80367683834521.19632316165479
831514.96562039287730.0343796071227446
841614.95058214501441.04941785498557
851513.83599853773261.16400146226743
861414.0276999161927-0.0276999161927164
871515.1477995901464-0.147799590146373
881413.72140282074540.278597179254605
891313.184139281065-0.184139281064952
90710.130954084458-3.13095408445804
911714.64035273135552.3596472686445
921312.41996058395850.580039416041543
931515.4593289284384-0.459328928438398
941412.67404570275381.32595429724622
951314.1539764902371-1.15397649023708
961615.98261540051270.0173845994872993
971212.4778623697042-0.477862369704175
981414.9276048895712-0.927604889571163
991715.65964741269521.34035258730478
1001514.66214893627370.337851063726275
1011715.84707580406791.15292419593205
1021212.6428292546157-0.642829254615677
1031615.86907764579850.130922354201517
1041113.8301327565398-2.83013275653977
1051514.41564602746570.584353972534306
106911.0912333885881-2.09123338858809
1071614.51786270279051.48213729720953
1081514.03964242137630.960357578623659
1091012.5709745711477-2.57097457114771
1101010.0817441894812-0.0817441894811905
1111514.48541768592550.514582314074452
1121112.6155872442189-1.61558724421887
1131316.2289050013754-3.22890500137544
1141411.98089399916252.01910600083749
1151815.03997269308872.96002730691131
1161615.2736938793530.72630612064702
1171414.0514094874081-0.0514094874081292
1181413.87196273066050.128037269339494
1191414.5936121346874-0.593612134687446
1201414.6404301147931-0.640430114793107
1211212.3192699891059-0.319269989105903
1221413.14958433930450.850415660695499
1231515.8596531556732-0.859653155673191
1241515.4224057707189-0.422405770718911
1251514.91131586604320.0886841339567667
1261313.9968234078691-0.996823407869108
1271717.0394036387198-0.0394036387198468
1281715.65216367026781.34783632973218
1291915.85896862767123.14103137232879
1301513.14177507955871.85822492044134
1311314.2545109163481-1.25451091634805
132911.2893208994391-2.28932089943914
1331515.4057530604746-0.405753060474594
1341512.31876818105672.68123181894325
1351515.0729608592093-0.0729608592092801
1361612.85528703618083.14471296381922
137119.778271544371561.22172845562844
1381412.74931108503311.25068891496686
1391112.8047963720491-1.80479637204905
1401514.17485903543670.825140964563255
1411314.5653320092836-1.5653320092836
1421514.29124091657210.708759083427851
1431613.54414527949382.45585472050621
1441415.1440082250437-1.14400822504374
1451513.43457445532351.56542554467648
1461614.73817073237681.26182926762321
1471614.68089245474111.31910754525886
1481112.9616085721338-1.96160857213376
1491215.1318190267411-3.13181902674109
150911.2136817252037-2.21368172520365
1511615.63129036313570.368709636864285
1521312.88518984575350.114810154246494
1531616.7425975252669-0.742597525266862
1541214.6401056752832-2.64010567528317
155911.4781085234895-2.4781085234895
1561312.45230825385880.547691746141177
1571312.17296214384850.827037856151482
1581413.78358194708370.216418052916265
1591915.62214018620493.37785981379515
1601315.1481953308108-2.14819533081078
1611213.2181386505488-1.21813865054881
1621312.32042010555370.679579894446278

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.4974993019156 & 0.502500698084415 \tabularnewline
2 & 18 & 15.1405206275074 & 2.85947937249255 \tabularnewline
3 & 11 & 14.0513302476591 & -3.05133024765908 \tabularnewline
4 & 12 & 13.9271549734614 & -1.92715497346142 \tabularnewline
5 & 16 & 11.9094264372422 & 4.09057356275779 \tabularnewline
6 & 18 & 13.5569474272449 & 4.44305257275511 \tabularnewline
7 & 14 & 11.2465268006201 & 2.7534731993799 \tabularnewline
8 & 14 & 14.5690182801896 & -0.569018280189591 \tabularnewline
9 & 15 & 15.8269372848301 & -0.826937284830122 \tabularnewline
10 & 15 & 14.2758409498266 & 0.724159050173419 \tabularnewline
11 & 17 & 14.7539301641478 & 2.24606983585224 \tabularnewline
12 & 19 & 15.8557128284973 & 3.14428717150275 \tabularnewline
13 & 10 & 12.8568964703074 & -2.85689647030739 \tabularnewline
14 & 16 & 13.5291839972933 & 2.47081600270674 \tabularnewline
15 & 18 & 16.0589427414605 & 1.9410572585395 \tabularnewline
16 & 14 & 12.5230785810638 & 1.47692141893624 \tabularnewline
17 & 14 & 14.3652812602964 & -0.365281260296428 \tabularnewline
18 & 17 & 15.290968932544 & 1.70903106745596 \tabularnewline
19 & 14 & 16.1421382010196 & -2.14213820101956 \tabularnewline
20 & 16 & 13.1804701746001 & 2.81952982539989 \tabularnewline
21 & 18 & 16.6550731358995 & 1.34492686410049 \tabularnewline
22 & 11 & 12.6986990265441 & -1.69869902654407 \tabularnewline
23 & 14 & 13.9138965603826 & 0.0861034396174205 \tabularnewline
24 & 12 & 14.1612576261413 & -2.16125762614132 \tabularnewline
25 & 17 & 14.9924127097268 & 2.00758729027322 \tabularnewline
26 & 9 & 15.6561500260502 & -6.65615002605025 \tabularnewline
27 & 16 & 15.7946431276722 & 0.205356872327771 \tabularnewline
28 & 14 & 12.5407805458876 & 1.45921945411238 \tabularnewline
29 & 15 & 14.4520991622292 & 0.547900837770814 \tabularnewline
30 & 11 & 13.0779458969188 & -2.07794589691875 \tabularnewline
31 & 16 & 15.8289710546179 & 0.171028945382111 \tabularnewline
32 & 13 & 12.2632142847049 & 0.736785715295122 \tabularnewline
33 & 17 & 15.6936508558796 & 1.30634914412037 \tabularnewline
34 & 15 & 14.5846867391197 & 0.415313260880307 \tabularnewline
35 & 14 & 13.2392850696611 & 0.760714930338873 \tabularnewline
36 & 16 & 15.6446482470382 & 0.355351752961835 \tabularnewline
37 & 9 & 10.5717698595048 & -1.57176985950479 \tabularnewline
38 & 15 & 14.0462062886325 & 0.953793711367533 \tabularnewline
39 & 17 & 15.6085822978899 & 1.39141770211009 \tabularnewline
40 & 13 & 14.5484079949121 & -1.54840799491211 \tabularnewline
41 & 15 & 16.137632139359 & -1.13763213935895 \tabularnewline
42 & 16 & 12.9972733453711 & 3.0027266546289 \tabularnewline
43 & 16 & 16.1197963902802 & -0.11979639028016 \tabularnewline
44 & 12 & 12.8697300642282 & -0.869730064228191 \tabularnewline
45 & 12 & 15.1329916599772 & -3.13299165997719 \tabularnewline
46 & 11 & 13.0808655264894 & -2.08086552648935 \tabularnewline
47 & 15 & 14.643064188511 & 0.356935811488987 \tabularnewline
48 & 15 & 15.3194506496632 & -0.319450649663228 \tabularnewline
49 & 17 & 13.4352453023156 & 3.56475469768436 \tabularnewline
50 & 13 & 14.5026869115115 & -1.50268691151147 \tabularnewline
51 & 16 & 15.6517902275984 & 0.348209772401606 \tabularnewline
52 & 14 & 12.7124250316776 & 1.28757496832237 \tabularnewline
53 & 11 & 12.2040247150566 & -1.2040247150566 \tabularnewline
54 & 12 & 12.6566746927878 & -0.656674692787771 \tabularnewline
55 & 12 & 15.0017655764505 & -3.00176557645045 \tabularnewline
56 & 15 & 14.4680713635992 & 0.531928636400828 \tabularnewline
57 & 16 & 15.0178944787386 & 0.982105521261361 \tabularnewline
58 & 15 & 14.560078741447 & 0.439921258553019 \tabularnewline
59 & 12 & 14.7775316327853 & -2.77753163278528 \tabularnewline
60 & 12 & 14.0449723017412 & -2.04497230174125 \tabularnewline
61 & 8 & 10.2142835927717 & -2.21428359277173 \tabularnewline
62 & 13 & 14.5873463090955 & -1.58734630909546 \tabularnewline
63 & 11 & 15.0605187612416 & -4.06051876124159 \tabularnewline
64 & 14 & 12.4858894047386 & 1.51411059526138 \tabularnewline
65 & 15 & 13.9229970688027 & 1.07700293119726 \tabularnewline
66 & 10 & 14.5624496729493 & -4.56244967294929 \tabularnewline
67 & 11 & 13.3357564154145 & -2.33575641541446 \tabularnewline
68 & 12 & 14.0897653939569 & -2.08976539395687 \tabularnewline
69 & 15 & 14.6827876839914 & 0.317212316008584 \tabularnewline
70 & 15 & 13.2857886848117 & 1.71421131518825 \tabularnewline
71 & 14 & 13.1644559445886 & 0.835544055411354 \tabularnewline
72 & 16 & 13.4750508868805 & 2.52494911311949 \tabularnewline
73 & 15 & 14.2144976361211 & 0.785502363878908 \tabularnewline
74 & 15 & 15.5107516448428 & -0.510751644842808 \tabularnewline
75 & 13 & 15.2456812518827 & -2.24568125188269 \tabularnewline
76 & 12 & 11.8756277571766 & 0.124372242823387 \tabularnewline
77 & 17 & 14.7088740428008 & 2.29112595719916 \tabularnewline
78 & 13 & 11.8228263702883 & 1.1771736297117 \tabularnewline
79 & 15 & 14.3001521174501 & 0.699847882549907 \tabularnewline
80 & 13 & 14.3237306674119 & -1.3237306674119 \tabularnewline
81 & 15 & 15.8973822951496 & -0.897382295149599 \tabularnewline
82 & 16 & 14.8036768383452 & 1.19632316165479 \tabularnewline
83 & 15 & 14.9656203928773 & 0.0343796071227446 \tabularnewline
84 & 16 & 14.9505821450144 & 1.04941785498557 \tabularnewline
85 & 15 & 13.8359985377326 & 1.16400146226743 \tabularnewline
86 & 14 & 14.0276999161927 & -0.0276999161927164 \tabularnewline
87 & 15 & 15.1477995901464 & -0.147799590146373 \tabularnewline
88 & 14 & 13.7214028207454 & 0.278597179254605 \tabularnewline
89 & 13 & 13.184139281065 & -0.184139281064952 \tabularnewline
90 & 7 & 10.130954084458 & -3.13095408445804 \tabularnewline
91 & 17 & 14.6403527313555 & 2.3596472686445 \tabularnewline
92 & 13 & 12.4199605839585 & 0.580039416041543 \tabularnewline
93 & 15 & 15.4593289284384 & -0.459328928438398 \tabularnewline
94 & 14 & 12.6740457027538 & 1.32595429724622 \tabularnewline
95 & 13 & 14.1539764902371 & -1.15397649023708 \tabularnewline
96 & 16 & 15.9826154005127 & 0.0173845994872993 \tabularnewline
97 & 12 & 12.4778623697042 & -0.477862369704175 \tabularnewline
98 & 14 & 14.9276048895712 & -0.927604889571163 \tabularnewline
99 & 17 & 15.6596474126952 & 1.34035258730478 \tabularnewline
100 & 15 & 14.6621489362737 & 0.337851063726275 \tabularnewline
101 & 17 & 15.8470758040679 & 1.15292419593205 \tabularnewline
102 & 12 & 12.6428292546157 & -0.642829254615677 \tabularnewline
103 & 16 & 15.8690776457985 & 0.130922354201517 \tabularnewline
104 & 11 & 13.8301327565398 & -2.83013275653977 \tabularnewline
105 & 15 & 14.4156460274657 & 0.584353972534306 \tabularnewline
106 & 9 & 11.0912333885881 & -2.09123338858809 \tabularnewline
107 & 16 & 14.5178627027905 & 1.48213729720953 \tabularnewline
108 & 15 & 14.0396424213763 & 0.960357578623659 \tabularnewline
109 & 10 & 12.5709745711477 & -2.57097457114771 \tabularnewline
110 & 10 & 10.0817441894812 & -0.0817441894811905 \tabularnewline
111 & 15 & 14.4854176859255 & 0.514582314074452 \tabularnewline
112 & 11 & 12.6155872442189 & -1.61558724421887 \tabularnewline
113 & 13 & 16.2289050013754 & -3.22890500137544 \tabularnewline
114 & 14 & 11.9808939991625 & 2.01910600083749 \tabularnewline
115 & 18 & 15.0399726930887 & 2.96002730691131 \tabularnewline
116 & 16 & 15.273693879353 & 0.72630612064702 \tabularnewline
117 & 14 & 14.0514094874081 & -0.0514094874081292 \tabularnewline
118 & 14 & 13.8719627306605 & 0.128037269339494 \tabularnewline
119 & 14 & 14.5936121346874 & -0.593612134687446 \tabularnewline
120 & 14 & 14.6404301147931 & -0.640430114793107 \tabularnewline
121 & 12 & 12.3192699891059 & -0.319269989105903 \tabularnewline
122 & 14 & 13.1495843393045 & 0.850415660695499 \tabularnewline
123 & 15 & 15.8596531556732 & -0.859653155673191 \tabularnewline
124 & 15 & 15.4224057707189 & -0.422405770718911 \tabularnewline
125 & 15 & 14.9113158660432 & 0.0886841339567667 \tabularnewline
126 & 13 & 13.9968234078691 & -0.996823407869108 \tabularnewline
127 & 17 & 17.0394036387198 & -0.0394036387198468 \tabularnewline
128 & 17 & 15.6521636702678 & 1.34783632973218 \tabularnewline
129 & 19 & 15.8589686276712 & 3.14103137232879 \tabularnewline
130 & 15 & 13.1417750795587 & 1.85822492044134 \tabularnewline
131 & 13 & 14.2545109163481 & -1.25451091634805 \tabularnewline
132 & 9 & 11.2893208994391 & -2.28932089943914 \tabularnewline
133 & 15 & 15.4057530604746 & -0.405753060474594 \tabularnewline
134 & 15 & 12.3187681810567 & 2.68123181894325 \tabularnewline
135 & 15 & 15.0729608592093 & -0.0729608592092801 \tabularnewline
136 & 16 & 12.8552870361808 & 3.14471296381922 \tabularnewline
137 & 11 & 9.77827154437156 & 1.22172845562844 \tabularnewline
138 & 14 & 12.7493110850331 & 1.25068891496686 \tabularnewline
139 & 11 & 12.8047963720491 & -1.80479637204905 \tabularnewline
140 & 15 & 14.1748590354367 & 0.825140964563255 \tabularnewline
141 & 13 & 14.5653320092836 & -1.5653320092836 \tabularnewline
142 & 15 & 14.2912409165721 & 0.708759083427851 \tabularnewline
143 & 16 & 13.5441452794938 & 2.45585472050621 \tabularnewline
144 & 14 & 15.1440082250437 & -1.14400822504374 \tabularnewline
145 & 15 & 13.4345744553235 & 1.56542554467648 \tabularnewline
146 & 16 & 14.7381707323768 & 1.26182926762321 \tabularnewline
147 & 16 & 14.6808924547411 & 1.31910754525886 \tabularnewline
148 & 11 & 12.9616085721338 & -1.96160857213376 \tabularnewline
149 & 12 & 15.1318190267411 & -3.13181902674109 \tabularnewline
150 & 9 & 11.2136817252037 & -2.21368172520365 \tabularnewline
151 & 16 & 15.6312903631357 & 0.368709636864285 \tabularnewline
152 & 13 & 12.8851898457535 & 0.114810154246494 \tabularnewline
153 & 16 & 16.7425975252669 & -0.742597525266862 \tabularnewline
154 & 12 & 14.6401056752832 & -2.64010567528317 \tabularnewline
155 & 9 & 11.4781085234895 & -2.4781085234895 \tabularnewline
156 & 13 & 12.4523082538588 & 0.547691746141177 \tabularnewline
157 & 13 & 12.1729621438485 & 0.827037856151482 \tabularnewline
158 & 14 & 13.7835819470837 & 0.216418052916265 \tabularnewline
159 & 19 & 15.6221401862049 & 3.37785981379515 \tabularnewline
160 & 13 & 15.1481953308108 & -2.14819533081078 \tabularnewline
161 & 12 & 13.2181386505488 & -1.21813865054881 \tabularnewline
162 & 13 & 12.3204201055537 & 0.679579894446278 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&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]14[/C][C]13.4974993019156[/C][C]0.502500698084415[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.1405206275074[/C][C]2.85947937249255[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]14.0513302476591[/C][C]-3.05133024765908[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.9271549734614[/C][C]-1.92715497346142[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.9094264372422[/C][C]4.09057356275779[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]13.5569474272449[/C][C]4.44305257275511[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]11.2465268006201[/C][C]2.7534731993799[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.5690182801896[/C][C]-0.569018280189591[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.8269372848301[/C][C]-0.826937284830122[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.2758409498266[/C][C]0.724159050173419[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.7539301641478[/C][C]2.24606983585224[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.8557128284973[/C][C]3.14428717150275[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.8568964703074[/C][C]-2.85689647030739[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.5291839972933[/C][C]2.47081600270674[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]16.0589427414605[/C][C]1.9410572585395[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.5230785810638[/C][C]1.47692141893624[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]14.3652812602964[/C][C]-0.365281260296428[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.290968932544[/C][C]1.70903106745596[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]16.1421382010196[/C][C]-2.14213820101956[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.1804701746001[/C][C]2.81952982539989[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]16.6550731358995[/C][C]1.34492686410049[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]12.6986990265441[/C][C]-1.69869902654407[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.9138965603826[/C][C]0.0861034396174205[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.1612576261413[/C][C]-2.16125762614132[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9924127097268[/C][C]2.00758729027322[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.6561500260502[/C][C]-6.65615002605025[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.7946431276722[/C][C]0.205356872327771[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]12.5407805458876[/C][C]1.45921945411238[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.4520991622292[/C][C]0.547900837770814[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.0779458969188[/C][C]-2.07794589691875[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.8289710546179[/C][C]0.171028945382111[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.2632142847049[/C][C]0.736785715295122[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.6936508558796[/C][C]1.30634914412037[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]14.5846867391197[/C][C]0.415313260880307[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.2392850696611[/C][C]0.760714930338873[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.6446482470382[/C][C]0.355351752961835[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.5717698595048[/C][C]-1.57176985950479[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.0462062886325[/C][C]0.953793711367533[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.6085822978899[/C][C]1.39141770211009[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.5484079949121[/C][C]-1.54840799491211[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]16.137632139359[/C][C]-1.13763213935895[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]12.9972733453711[/C][C]3.0027266546289[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]16.1197963902802[/C][C]-0.11979639028016[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.8697300642282[/C][C]-0.869730064228191[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]15.1329916599772[/C][C]-3.13299165997719[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.0808655264894[/C][C]-2.08086552648935[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]14.643064188511[/C][C]0.356935811488987[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.3194506496632[/C][C]-0.319450649663228[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.4352453023156[/C][C]3.56475469768436[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.5026869115115[/C][C]-1.50268691151147[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.6517902275984[/C][C]0.348209772401606[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.7124250316776[/C][C]1.28757496832237[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]12.2040247150566[/C][C]-1.2040247150566[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]12.6566746927878[/C][C]-0.656674692787771[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]15.0017655764505[/C][C]-3.00176557645045[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.4680713635992[/C][C]0.531928636400828[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.0178944787386[/C][C]0.982105521261361[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]14.560078741447[/C][C]0.439921258553019[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.7775316327853[/C][C]-2.77753163278528[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.0449723017412[/C][C]-2.04497230174125[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.2142835927717[/C][C]-2.21428359277173[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.5873463090955[/C][C]-1.58734630909546[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]15.0605187612416[/C][C]-4.06051876124159[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.4858894047386[/C][C]1.51411059526138[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.9229970688027[/C][C]1.07700293119726[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.5624496729493[/C][C]-4.56244967294929[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]13.3357564154145[/C][C]-2.33575641541446[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.0897653939569[/C][C]-2.08976539395687[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.6827876839914[/C][C]0.317212316008584[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.2857886848117[/C][C]1.71421131518825[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.1644559445886[/C][C]0.835544055411354[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.4750508868805[/C][C]2.52494911311949[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.2144976361211[/C][C]0.785502363878908[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.5107516448428[/C][C]-0.510751644842808[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.2456812518827[/C][C]-2.24568125188269[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.8756277571766[/C][C]0.124372242823387[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.7088740428008[/C][C]2.29112595719916[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]11.8228263702883[/C][C]1.1771736297117[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]14.3001521174501[/C][C]0.699847882549907[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.3237306674119[/C][C]-1.3237306674119[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.8973822951496[/C][C]-0.897382295149599[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.8036768383452[/C][C]1.19632316165479[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]14.9656203928773[/C][C]0.0343796071227446[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.9505821450144[/C][C]1.04941785498557[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]13.8359985377326[/C][C]1.16400146226743[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0276999161927[/C][C]-0.0276999161927164[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]15.1477995901464[/C][C]-0.147799590146373[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]13.7214028207454[/C][C]0.278597179254605[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.184139281065[/C][C]-0.184139281064952[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.130954084458[/C][C]-3.13095408445804[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.6403527313555[/C][C]2.3596472686445[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.4199605839585[/C][C]0.580039416041543[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.4593289284384[/C][C]-0.459328928438398[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]12.6740457027538[/C][C]1.32595429724622[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.1539764902371[/C][C]-1.15397649023708[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.9826154005127[/C][C]0.0173845994872993[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.4778623697042[/C][C]-0.477862369704175[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.9276048895712[/C][C]-0.927604889571163[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.6596474126952[/C][C]1.34035258730478[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.6621489362737[/C][C]0.337851063726275[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.8470758040679[/C][C]1.15292419593205[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.6428292546157[/C][C]-0.642829254615677[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.8690776457985[/C][C]0.130922354201517[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.8301327565398[/C][C]-2.83013275653977[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]14.4156460274657[/C][C]0.584353972534306[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.0912333885881[/C][C]-2.09123338858809[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]14.5178627027905[/C][C]1.48213729720953[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]14.0396424213763[/C][C]0.960357578623659[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.5709745711477[/C][C]-2.57097457114771[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.0817441894812[/C][C]-0.0817441894811905[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.4854176859255[/C][C]0.514582314074452[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]12.6155872442189[/C][C]-1.61558724421887[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]16.2289050013754[/C][C]-3.22890500137544[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.9808939991625[/C][C]2.01910600083749[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]15.0399726930887[/C][C]2.96002730691131[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.273693879353[/C][C]0.72630612064702[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0514094874081[/C][C]-0.0514094874081292[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.8719627306605[/C][C]0.128037269339494[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.5936121346874[/C][C]-0.593612134687446[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.6404301147931[/C][C]-0.640430114793107[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.3192699891059[/C][C]-0.319269989105903[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.1495843393045[/C][C]0.850415660695499[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.8596531556732[/C][C]-0.859653155673191[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]15.4224057707189[/C][C]-0.422405770718911[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.9113158660432[/C][C]0.0886841339567667[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]13.9968234078691[/C][C]-0.996823407869108[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]17.0394036387198[/C][C]-0.0394036387198468[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.6521636702678[/C][C]1.34783632973218[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.8589686276712[/C][C]3.14103137232879[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.1417750795587[/C][C]1.85822492044134[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.2545109163481[/C][C]-1.25451091634805[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]11.2893208994391[/C][C]-2.28932089943914[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.4057530604746[/C][C]-0.405753060474594[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.3187681810567[/C][C]2.68123181894325[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.0729608592093[/C][C]-0.0729608592092801[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]12.8552870361808[/C][C]3.14471296381922[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]9.77827154437156[/C][C]1.22172845562844[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.7493110850331[/C][C]1.25068891496686[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.8047963720491[/C][C]-1.80479637204905[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.1748590354367[/C][C]0.825140964563255[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]14.5653320092836[/C][C]-1.5653320092836[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.2912409165721[/C][C]0.708759083427851[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.5441452794938[/C][C]2.45585472050621[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]15.1440082250437[/C][C]-1.14400822504374[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.4345744553235[/C][C]1.56542554467648[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.7381707323768[/C][C]1.26182926762321[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.6808924547411[/C][C]1.31910754525886[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]12.9616085721338[/C][C]-1.96160857213376[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]15.1318190267411[/C][C]-3.13181902674109[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.2136817252037[/C][C]-2.21368172520365[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.6312903631357[/C][C]0.368709636864285[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]12.8851898457535[/C][C]0.114810154246494[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]16.7425975252669[/C][C]-0.742597525266862[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.6401056752832[/C][C]-2.64010567528317[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.4781085234895[/C][C]-2.4781085234895[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.4523082538588[/C][C]0.547691746141177[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.1729621438485[/C][C]0.827037856151482[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.7835819470837[/C][C]0.216418052916265[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.6221401862049[/C][C]3.37785981379515[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.1481953308108[/C][C]-2.14819533081078[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]13.2181386505488[/C][C]-1.21813865054881[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.3204201055537[/C][C]0.679579894446278[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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
11413.49749930191560.502500698084415
21815.14052062750742.85947937249255
31114.0513302476591-3.05133024765908
41213.9271549734614-1.92715497346142
51611.90942643724224.09057356275779
61813.55694742724494.44305257275511
71411.24652680062012.7534731993799
81414.5690182801896-0.569018280189591
91515.8269372848301-0.826937284830122
101514.27584094982660.724159050173419
111714.75393016414782.24606983585224
121915.85571282849733.14428717150275
131012.8568964703074-2.85689647030739
141613.52918399729332.47081600270674
151816.05894274146051.9410572585395
161412.52307858106381.47692141893624
171414.3652812602964-0.365281260296428
181715.2909689325441.70903106745596
191416.1421382010196-2.14213820101956
201613.18047017460012.81952982539989
211816.65507313589951.34492686410049
221112.6986990265441-1.69869902654407
231413.91389656038260.0861034396174205
241214.1612576261413-2.16125762614132
251714.99241270972682.00758729027322
26915.6561500260502-6.65615002605025
271615.79464312767220.205356872327771
281412.54078054588761.45921945411238
291514.45209916222920.547900837770814
301113.0779458969188-2.07794589691875
311615.82897105461790.171028945382111
321312.26321428470490.736785715295122
331715.69365085587961.30634914412037
341514.58468673911970.415313260880307
351413.23928506966110.760714930338873
361615.64464824703820.355351752961835
37910.5717698595048-1.57176985950479
381514.04620628863250.953793711367533
391715.60858229788991.39141770211009
401314.5484079949121-1.54840799491211
411516.137632139359-1.13763213935895
421612.99727334537113.0027266546289
431616.1197963902802-0.11979639028016
441212.8697300642282-0.869730064228191
451215.1329916599772-3.13299165997719
461113.0808655264894-2.08086552648935
471514.6430641885110.356935811488987
481515.3194506496632-0.319450649663228
491713.43524530231563.56475469768436
501314.5026869115115-1.50268691151147
511615.65179022759840.348209772401606
521412.71242503167761.28757496832237
531112.2040247150566-1.2040247150566
541212.6566746927878-0.656674692787771
551215.0017655764505-3.00176557645045
561514.46807136359920.531928636400828
571615.01789447873860.982105521261361
581514.5600787414470.439921258553019
591214.7775316327853-2.77753163278528
601214.0449723017412-2.04497230174125
61810.2142835927717-2.21428359277173
621314.5873463090955-1.58734630909546
631115.0605187612416-4.06051876124159
641412.48588940473861.51411059526138
651513.92299706880271.07700293119726
661014.5624496729493-4.56244967294929
671113.3357564154145-2.33575641541446
681214.0897653939569-2.08976539395687
691514.68278768399140.317212316008584
701513.28578868481171.71421131518825
711413.16445594458860.835544055411354
721613.47505088688052.52494911311949
731514.21449763612110.785502363878908
741515.5107516448428-0.510751644842808
751315.2456812518827-2.24568125188269
761211.87562775717660.124372242823387
771714.70887404280082.29112595719916
781311.82282637028831.1771736297117
791514.30015211745010.699847882549907
801314.3237306674119-1.3237306674119
811515.8973822951496-0.897382295149599
821614.80367683834521.19632316165479
831514.96562039287730.0343796071227446
841614.95058214501441.04941785498557
851513.83599853773261.16400146226743
861414.0276999161927-0.0276999161927164
871515.1477995901464-0.147799590146373
881413.72140282074540.278597179254605
891313.184139281065-0.184139281064952
90710.130954084458-3.13095408445804
911714.64035273135552.3596472686445
921312.41996058395850.580039416041543
931515.4593289284384-0.459328928438398
941412.67404570275381.32595429724622
951314.1539764902371-1.15397649023708
961615.98261540051270.0173845994872993
971212.4778623697042-0.477862369704175
981414.9276048895712-0.927604889571163
991715.65964741269521.34035258730478
1001514.66214893627370.337851063726275
1011715.84707580406791.15292419593205
1021212.6428292546157-0.642829254615677
1031615.86907764579850.130922354201517
1041113.8301327565398-2.83013275653977
1051514.41564602746570.584353972534306
106911.0912333885881-2.09123338858809
1071614.51786270279051.48213729720953
1081514.03964242137630.960357578623659
1091012.5709745711477-2.57097457114771
1101010.0817441894812-0.0817441894811905
1111514.48541768592550.514582314074452
1121112.6155872442189-1.61558724421887
1131316.2289050013754-3.22890500137544
1141411.98089399916252.01910600083749
1151815.03997269308872.96002730691131
1161615.2736938793530.72630612064702
1171414.0514094874081-0.0514094874081292
1181413.87196273066050.128037269339494
1191414.5936121346874-0.593612134687446
1201414.6404301147931-0.640430114793107
1211212.3192699891059-0.319269989105903
1221413.14958433930450.850415660695499
1231515.8596531556732-0.859653155673191
1241515.4224057707189-0.422405770718911
1251514.91131586604320.0886841339567667
1261313.9968234078691-0.996823407869108
1271717.0394036387198-0.0394036387198468
1281715.65216367026781.34783632973218
1291915.85896862767123.14103137232879
1301513.14177507955871.85822492044134
1311314.2545109163481-1.25451091634805
132911.2893208994391-2.28932089943914
1331515.4057530604746-0.405753060474594
1341512.31876818105672.68123181894325
1351515.0729608592093-0.0729608592092801
1361612.85528703618083.14471296381922
137119.778271544371561.22172845562844
1381412.74931108503311.25068891496686
1391112.8047963720491-1.80479637204905
1401514.17485903543670.825140964563255
1411314.5653320092836-1.5653320092836
1421514.29124091657210.708759083427851
1431613.54414527949382.45585472050621
1441415.1440082250437-1.14400822504374
1451513.43457445532351.56542554467648
1461614.73817073237681.26182926762321
1471614.68089245474111.31910754525886
1481112.9616085721338-1.96160857213376
1491215.1318190267411-3.13181902674109
150911.2136817252037-2.21368172520365
1511615.63129036313570.368709636864285
1521312.88518984575350.114810154246494
1531616.7425975252669-0.742597525266862
1541214.6401056752832-2.64010567528317
155911.4781085234895-2.4781085234895
1561312.45230825385880.547691746141177
1571312.17296214384850.827037856151482
1581413.78358194708370.216418052916265
1591915.62214018620493.37785981379515
1601315.1481953308108-2.14819533081078
1611213.2181386505488-1.21813865054881
1621312.32042010555370.679579894446278






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
220.9846745037510120.03065099249797670.0153254962489884
230.9801336907030590.03973261859388260.0198663092969413
240.9847560782391960.03048784352160830.0152439217608041
250.9823523290357220.03529534192855560.0176476709642778
260.9999334410000110.0001331179999789316.65589999894656e-05
270.9999244985179480.0001510029641035357.55014820517675e-05
280.9999205224886370.000158955022726537.94775113632648e-05
290.9998439791408670.0003120417182664820.000156020859133241
300.9999117228066680.0001765543866642578.82771933321285e-05
310.9998625433720480.0002749132559037050.000137456627951853
320.9998191196949920.0003617606100158950.000180880305007948
330.9996724514842820.0006550970314368290.000327548515718415
340.9995382416228930.0009235167542132930.000461758377106646
350.999530190386110.0009396192277796850.000469809613889843
360.9991638366393570.001672326721286120.000836163360643058
370.9990007545661830.001998490867633640.000999245433816821
380.9986035958218950.002792808356210650.00139640417810532
390.9983307618426720.003338476314656160.00166923815732808
400.9978819616674440.004236076665112760.00211803833255638
410.9968698321410090.00626033571798230.00313016785899115
420.9968251391472210.006349721705558110.00317486085277905
430.9950557655071670.009888468985665140.00494423449283257
440.9942053133631220.01158937327375650.00579468663687825
450.997031492757290.005937014485420490.00296850724271024
460.9968501859416570.006299628116686020.00314981405834301
470.9951902061906310.00961958761873760.0048097938093688
480.9934892171209610.01302156575807870.00651078287903934
490.9960406677398450.007918664520310140.00395933226015507
500.9950216690581310.009956661883737850.00497833094186893
510.9926800818671590.0146398362656820.00731991813284098
520.9905529218903530.01889415621929430.00944707810964716
530.9889600375265770.02207992494684590.011039962473423
540.9847979109876810.03040417802463830.0152020890123192
550.9897926214305660.02041475713886850.0102073785694342
560.9857842443130180.02843151137396440.0142157556869822
570.980510968807880.03897806238423980.0194890311921199
580.9750010418328880.04999791633422360.0249989581671118
590.9838284630159310.03234307396813840.0161715369840692
600.9876602695384510.02467946092309710.0123397304615485
610.9880916782824930.0238166434350140.011908321717507
620.98664493498730.02671013002540060.0133550650127003
630.9954154798031890.00916904039362260.0045845201968113
640.9944303365157010.01113932696859710.00556966348429856
650.9927557524590260.01448849508194840.00724424754097421
660.9989493910869210.002101217826158350.00105060891307917
670.999183565868450.001632868263099740.000816434131549871
680.9992339158255330.001532168348934940.000766084174467471
690.9988207647421170.002358470515765630.00117923525788281
700.9986831692179370.002633661564125990.001316830782063
710.9981454274132860.003709145173427970.00185457258671398
720.9984695356369730.003060928726054150.00153046436302707
730.9979226459181150.004154708163769750.00207735408188487
740.9970762700128680.005847459974263550.00292372998713177
750.9979837651043780.004032469791244770.00201623489562238
760.9970237991199860.005952401760027150.00297620088001358
770.9977161300411620.004567739917675370.00228386995883768
780.9970477600280490.00590447994390110.00295223997195055
790.99585703255720.008285934885599240.00414296744279962
800.9958700169818870.008259966036226570.00412998301811329
810.9948568267033980.01028634659320350.00514317329660177
820.9931584699587290.01368306008254220.00684153004127109
830.9903105994271330.01937880114573390.00968940057286696
840.9871840401013460.02563191979730730.0128159598986537
850.9850282256258710.02994354874825760.0149717743741288
860.9803289377978510.0393421244042980.019671062202149
870.9741255226420010.05174895471599830.0258744773579991
880.9659963200807440.06800735983851160.0340036799192558
890.9563730623060660.08725387538786750.0436269376939338
900.9707821328754680.05843573424906350.0292178671245317
910.9738897428755210.05222051424895820.0261102571244791
920.9655972245968120.06880555080637660.0344027754031883
930.9556971811032150.08860563779357060.0443028188967853
940.9485403369471370.1029193261057270.0514596630528634
950.9385359713554320.1229280572891360.0614640286445682
960.9209082332787360.1581835334425280.0790917667212638
970.899865555856320.200268888287360.10013444414368
980.8991779400038470.2016441199923060.100822059996153
990.8814945221038260.2370109557923480.118505477896174
1000.8603046016408090.2793907967183830.139695398359191
1010.8581851720534560.2836296558930880.141814827946544
1020.8309920637480990.3380158725038020.169007936251901
1030.7938286845349250.412342630930150.206171315465075
1040.885536446351480.228927107297040.11446355364852
1050.8629885339339390.2740229321321210.137011466066061
1060.870941600287170.258116799425660.12905839971283
1070.8578638488582580.2842723022834840.142136151141742
1080.860782463921480.2784350721570390.13921753607852
1090.8781834276586190.2436331446827610.121816572341381
1100.8458809221221690.3082381557556630.154119077877831
1110.8094829144566360.3810341710867290.190517085543364
1120.7844323556236440.4311352887527120.215567644376356
1130.7907772911221690.4184454177556620.209222708877831
1140.9692585918436280.06148281631274420.0307414081563721
1150.9868055732451430.0263888535097150.0131944267548575
1160.9890139098506150.02197218029876980.0109860901493849
1170.9838085617146960.03238287657060720.0161914382853036
1180.9758451889241360.04830962215172740.0241548110758637
1190.9645662727107310.07086745457853710.0354337272892686
1200.9565152513277350.08696949734452910.0434847486722646
1210.9375784311158130.1248431377683730.0624215688841867
1220.9266317144931930.1467365710136140.0733682855068072
1230.9011395116541530.1977209766916940.0988604883458468
1240.875068921861880.249862156276240.12493107813812
1250.8354763869221780.3290472261556430.164523613077822
1260.7862436009501170.4275127980997660.213756399049883
1270.726223692077430.547552615845140.27377630792257
1280.7556589195663190.4886821608673630.244341080433681
1290.7111542727831430.5776914544337140.288845727216857
1300.6800407323328750.639918535334250.319959267667125
1310.604861932455470.7902761350890610.39513806754453
1320.5265597999955740.9468804000088520.473440200004426
1330.4422659391413950.8845318782827890.557734060858605
1340.3762874594650750.7525749189301490.623712540534925
1350.2896907670002160.5793815340004310.710309232999784
1360.2537543889851070.5075087779702150.746245611014893
1370.4532066095231810.9064132190463610.546793390476819
1380.3889964597428790.7779929194857570.611003540257121
1390.2683286998454350.536657399690870.731671300154565
1400.6604705402216290.6790589195567420.339529459778371

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
22 & 0.984674503751012 & 0.0306509924979767 & 0.0153254962489884 \tabularnewline
23 & 0.980133690703059 & 0.0397326185938826 & 0.0198663092969413 \tabularnewline
24 & 0.984756078239196 & 0.0304878435216083 & 0.0152439217608041 \tabularnewline
25 & 0.982352329035722 & 0.0352953419285556 & 0.0176476709642778 \tabularnewline
26 & 0.999933441000011 & 0.000133117999978931 & 6.65589999894656e-05 \tabularnewline
27 & 0.999924498517948 & 0.000151002964103535 & 7.55014820517675e-05 \tabularnewline
28 & 0.999920522488637 & 0.00015895502272653 & 7.94775113632648e-05 \tabularnewline
29 & 0.999843979140867 & 0.000312041718266482 & 0.000156020859133241 \tabularnewline
30 & 0.999911722806668 & 0.000176554386664257 & 8.82771933321285e-05 \tabularnewline
31 & 0.999862543372048 & 0.000274913255903705 & 0.000137456627951853 \tabularnewline
32 & 0.999819119694992 & 0.000361760610015895 & 0.000180880305007948 \tabularnewline
33 & 0.999672451484282 & 0.000655097031436829 & 0.000327548515718415 \tabularnewline
34 & 0.999538241622893 & 0.000923516754213293 & 0.000461758377106646 \tabularnewline
35 & 0.99953019038611 & 0.000939619227779685 & 0.000469809613889843 \tabularnewline
36 & 0.999163836639357 & 0.00167232672128612 & 0.000836163360643058 \tabularnewline
37 & 0.999000754566183 & 0.00199849086763364 & 0.000999245433816821 \tabularnewline
38 & 0.998603595821895 & 0.00279280835621065 & 0.00139640417810532 \tabularnewline
39 & 0.998330761842672 & 0.00333847631465616 & 0.00166923815732808 \tabularnewline
40 & 0.997881961667444 & 0.00423607666511276 & 0.00211803833255638 \tabularnewline
41 & 0.996869832141009 & 0.0062603357179823 & 0.00313016785899115 \tabularnewline
42 & 0.996825139147221 & 0.00634972170555811 & 0.00317486085277905 \tabularnewline
43 & 0.995055765507167 & 0.00988846898566514 & 0.00494423449283257 \tabularnewline
44 & 0.994205313363122 & 0.0115893732737565 & 0.00579468663687825 \tabularnewline
45 & 0.99703149275729 & 0.00593701448542049 & 0.00296850724271024 \tabularnewline
46 & 0.996850185941657 & 0.00629962811668602 & 0.00314981405834301 \tabularnewline
47 & 0.995190206190631 & 0.0096195876187376 & 0.0048097938093688 \tabularnewline
48 & 0.993489217120961 & 0.0130215657580787 & 0.00651078287903934 \tabularnewline
49 & 0.996040667739845 & 0.00791866452031014 & 0.00395933226015507 \tabularnewline
50 & 0.995021669058131 & 0.00995666188373785 & 0.00497833094186893 \tabularnewline
51 & 0.992680081867159 & 0.014639836265682 & 0.00731991813284098 \tabularnewline
52 & 0.990552921890353 & 0.0188941562192943 & 0.00944707810964716 \tabularnewline
53 & 0.988960037526577 & 0.0220799249468459 & 0.011039962473423 \tabularnewline
54 & 0.984797910987681 & 0.0304041780246383 & 0.0152020890123192 \tabularnewline
55 & 0.989792621430566 & 0.0204147571388685 & 0.0102073785694342 \tabularnewline
56 & 0.985784244313018 & 0.0284315113739644 & 0.0142157556869822 \tabularnewline
57 & 0.98051096880788 & 0.0389780623842398 & 0.0194890311921199 \tabularnewline
58 & 0.975001041832888 & 0.0499979163342236 & 0.0249989581671118 \tabularnewline
59 & 0.983828463015931 & 0.0323430739681384 & 0.0161715369840692 \tabularnewline
60 & 0.987660269538451 & 0.0246794609230971 & 0.0123397304615485 \tabularnewline
61 & 0.988091678282493 & 0.023816643435014 & 0.011908321717507 \tabularnewline
62 & 0.9866449349873 & 0.0267101300254006 & 0.0133550650127003 \tabularnewline
63 & 0.995415479803189 & 0.0091690403936226 & 0.0045845201968113 \tabularnewline
64 & 0.994430336515701 & 0.0111393269685971 & 0.00556966348429856 \tabularnewline
65 & 0.992755752459026 & 0.0144884950819484 & 0.00724424754097421 \tabularnewline
66 & 0.998949391086921 & 0.00210121782615835 & 0.00105060891307917 \tabularnewline
67 & 0.99918356586845 & 0.00163286826309974 & 0.000816434131549871 \tabularnewline
68 & 0.999233915825533 & 0.00153216834893494 & 0.000766084174467471 \tabularnewline
69 & 0.998820764742117 & 0.00235847051576563 & 0.00117923525788281 \tabularnewline
70 & 0.998683169217937 & 0.00263366156412599 & 0.001316830782063 \tabularnewline
71 & 0.998145427413286 & 0.00370914517342797 & 0.00185457258671398 \tabularnewline
72 & 0.998469535636973 & 0.00306092872605415 & 0.00153046436302707 \tabularnewline
73 & 0.997922645918115 & 0.00415470816376975 & 0.00207735408188487 \tabularnewline
74 & 0.997076270012868 & 0.00584745997426355 & 0.00292372998713177 \tabularnewline
75 & 0.997983765104378 & 0.00403246979124477 & 0.00201623489562238 \tabularnewline
76 & 0.997023799119986 & 0.00595240176002715 & 0.00297620088001358 \tabularnewline
77 & 0.997716130041162 & 0.00456773991767537 & 0.00228386995883768 \tabularnewline
78 & 0.997047760028049 & 0.0059044799439011 & 0.00295223997195055 \tabularnewline
79 & 0.9958570325572 & 0.00828593488559924 & 0.00414296744279962 \tabularnewline
80 & 0.995870016981887 & 0.00825996603622657 & 0.00412998301811329 \tabularnewline
81 & 0.994856826703398 & 0.0102863465932035 & 0.00514317329660177 \tabularnewline
82 & 0.993158469958729 & 0.0136830600825422 & 0.00684153004127109 \tabularnewline
83 & 0.990310599427133 & 0.0193788011457339 & 0.00968940057286696 \tabularnewline
84 & 0.987184040101346 & 0.0256319197973073 & 0.0128159598986537 \tabularnewline
85 & 0.985028225625871 & 0.0299435487482576 & 0.0149717743741288 \tabularnewline
86 & 0.980328937797851 & 0.039342124404298 & 0.019671062202149 \tabularnewline
87 & 0.974125522642001 & 0.0517489547159983 & 0.0258744773579991 \tabularnewline
88 & 0.965996320080744 & 0.0680073598385116 & 0.0340036799192558 \tabularnewline
89 & 0.956373062306066 & 0.0872538753878675 & 0.0436269376939338 \tabularnewline
90 & 0.970782132875468 & 0.0584357342490635 & 0.0292178671245317 \tabularnewline
91 & 0.973889742875521 & 0.0522205142489582 & 0.0261102571244791 \tabularnewline
92 & 0.965597224596812 & 0.0688055508063766 & 0.0344027754031883 \tabularnewline
93 & 0.955697181103215 & 0.0886056377935706 & 0.0443028188967853 \tabularnewline
94 & 0.948540336947137 & 0.102919326105727 & 0.0514596630528634 \tabularnewline
95 & 0.938535971355432 & 0.122928057289136 & 0.0614640286445682 \tabularnewline
96 & 0.920908233278736 & 0.158183533442528 & 0.0790917667212638 \tabularnewline
97 & 0.89986555585632 & 0.20026888828736 & 0.10013444414368 \tabularnewline
98 & 0.899177940003847 & 0.201644119992306 & 0.100822059996153 \tabularnewline
99 & 0.881494522103826 & 0.237010955792348 & 0.118505477896174 \tabularnewline
100 & 0.860304601640809 & 0.279390796718383 & 0.139695398359191 \tabularnewline
101 & 0.858185172053456 & 0.283629655893088 & 0.141814827946544 \tabularnewline
102 & 0.830992063748099 & 0.338015872503802 & 0.169007936251901 \tabularnewline
103 & 0.793828684534925 & 0.41234263093015 & 0.206171315465075 \tabularnewline
104 & 0.88553644635148 & 0.22892710729704 & 0.11446355364852 \tabularnewline
105 & 0.862988533933939 & 0.274022932132121 & 0.137011466066061 \tabularnewline
106 & 0.87094160028717 & 0.25811679942566 & 0.12905839971283 \tabularnewline
107 & 0.857863848858258 & 0.284272302283484 & 0.142136151141742 \tabularnewline
108 & 0.86078246392148 & 0.278435072157039 & 0.13921753607852 \tabularnewline
109 & 0.878183427658619 & 0.243633144682761 & 0.121816572341381 \tabularnewline
110 & 0.845880922122169 & 0.308238155755663 & 0.154119077877831 \tabularnewline
111 & 0.809482914456636 & 0.381034171086729 & 0.190517085543364 \tabularnewline
112 & 0.784432355623644 & 0.431135288752712 & 0.215567644376356 \tabularnewline
113 & 0.790777291122169 & 0.418445417755662 & 0.209222708877831 \tabularnewline
114 & 0.969258591843628 & 0.0614828163127442 & 0.0307414081563721 \tabularnewline
115 & 0.986805573245143 & 0.026388853509715 & 0.0131944267548575 \tabularnewline
116 & 0.989013909850615 & 0.0219721802987698 & 0.0109860901493849 \tabularnewline
117 & 0.983808561714696 & 0.0323828765706072 & 0.0161914382853036 \tabularnewline
118 & 0.975845188924136 & 0.0483096221517274 & 0.0241548110758637 \tabularnewline
119 & 0.964566272710731 & 0.0708674545785371 & 0.0354337272892686 \tabularnewline
120 & 0.956515251327735 & 0.0869694973445291 & 0.0434847486722646 \tabularnewline
121 & 0.937578431115813 & 0.124843137768373 & 0.0624215688841867 \tabularnewline
122 & 0.926631714493193 & 0.146736571013614 & 0.0733682855068072 \tabularnewline
123 & 0.901139511654153 & 0.197720976691694 & 0.0988604883458468 \tabularnewline
124 & 0.87506892186188 & 0.24986215627624 & 0.12493107813812 \tabularnewline
125 & 0.835476386922178 & 0.329047226155643 & 0.164523613077822 \tabularnewline
126 & 0.786243600950117 & 0.427512798099766 & 0.213756399049883 \tabularnewline
127 & 0.72622369207743 & 0.54755261584514 & 0.27377630792257 \tabularnewline
128 & 0.755658919566319 & 0.488682160867363 & 0.244341080433681 \tabularnewline
129 & 0.711154272783143 & 0.577691454433714 & 0.288845727216857 \tabularnewline
130 & 0.680040732332875 & 0.63991853533425 & 0.319959267667125 \tabularnewline
131 & 0.60486193245547 & 0.790276135089061 & 0.39513806754453 \tabularnewline
132 & 0.526559799995574 & 0.946880400008852 & 0.473440200004426 \tabularnewline
133 & 0.442265939141395 & 0.884531878282789 & 0.557734060858605 \tabularnewline
134 & 0.376287459465075 & 0.752574918930149 & 0.623712540534925 \tabularnewline
135 & 0.289690767000216 & 0.579381534000431 & 0.710309232999784 \tabularnewline
136 & 0.253754388985107 & 0.507508777970215 & 0.746245611014893 \tabularnewline
137 & 0.453206609523181 & 0.906413219046361 & 0.546793390476819 \tabularnewline
138 & 0.388996459742879 & 0.777992919485757 & 0.611003540257121 \tabularnewline
139 & 0.268328699845435 & 0.53665739969087 & 0.731671300154565 \tabularnewline
140 & 0.660470540221629 & 0.679058919556742 & 0.339529459778371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&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]22[/C][C]0.984674503751012[/C][C]0.0306509924979767[/C][C]0.0153254962489884[/C][/ROW]
[ROW][C]23[/C][C]0.980133690703059[/C][C]0.0397326185938826[/C][C]0.0198663092969413[/C][/ROW]
[ROW][C]24[/C][C]0.984756078239196[/C][C]0.0304878435216083[/C][C]0.0152439217608041[/C][/ROW]
[ROW][C]25[/C][C]0.982352329035722[/C][C]0.0352953419285556[/C][C]0.0176476709642778[/C][/ROW]
[ROW][C]26[/C][C]0.999933441000011[/C][C]0.000133117999978931[/C][C]6.65589999894656e-05[/C][/ROW]
[ROW][C]27[/C][C]0.999924498517948[/C][C]0.000151002964103535[/C][C]7.55014820517675e-05[/C][/ROW]
[ROW][C]28[/C][C]0.999920522488637[/C][C]0.00015895502272653[/C][C]7.94775113632648e-05[/C][/ROW]
[ROW][C]29[/C][C]0.999843979140867[/C][C]0.000312041718266482[/C][C]0.000156020859133241[/C][/ROW]
[ROW][C]30[/C][C]0.999911722806668[/C][C]0.000176554386664257[/C][C]8.82771933321285e-05[/C][/ROW]
[ROW][C]31[/C][C]0.999862543372048[/C][C]0.000274913255903705[/C][C]0.000137456627951853[/C][/ROW]
[ROW][C]32[/C][C]0.999819119694992[/C][C]0.000361760610015895[/C][C]0.000180880305007948[/C][/ROW]
[ROW][C]33[/C][C]0.999672451484282[/C][C]0.000655097031436829[/C][C]0.000327548515718415[/C][/ROW]
[ROW][C]34[/C][C]0.999538241622893[/C][C]0.000923516754213293[/C][C]0.000461758377106646[/C][/ROW]
[ROW][C]35[/C][C]0.99953019038611[/C][C]0.000939619227779685[/C][C]0.000469809613889843[/C][/ROW]
[ROW][C]36[/C][C]0.999163836639357[/C][C]0.00167232672128612[/C][C]0.000836163360643058[/C][/ROW]
[ROW][C]37[/C][C]0.999000754566183[/C][C]0.00199849086763364[/C][C]0.000999245433816821[/C][/ROW]
[ROW][C]38[/C][C]0.998603595821895[/C][C]0.00279280835621065[/C][C]0.00139640417810532[/C][/ROW]
[ROW][C]39[/C][C]0.998330761842672[/C][C]0.00333847631465616[/C][C]0.00166923815732808[/C][/ROW]
[ROW][C]40[/C][C]0.997881961667444[/C][C]0.00423607666511276[/C][C]0.00211803833255638[/C][/ROW]
[ROW][C]41[/C][C]0.996869832141009[/C][C]0.0062603357179823[/C][C]0.00313016785899115[/C][/ROW]
[ROW][C]42[/C][C]0.996825139147221[/C][C]0.00634972170555811[/C][C]0.00317486085277905[/C][/ROW]
[ROW][C]43[/C][C]0.995055765507167[/C][C]0.00988846898566514[/C][C]0.00494423449283257[/C][/ROW]
[ROW][C]44[/C][C]0.994205313363122[/C][C]0.0115893732737565[/C][C]0.00579468663687825[/C][/ROW]
[ROW][C]45[/C][C]0.99703149275729[/C][C]0.00593701448542049[/C][C]0.00296850724271024[/C][/ROW]
[ROW][C]46[/C][C]0.996850185941657[/C][C]0.00629962811668602[/C][C]0.00314981405834301[/C][/ROW]
[ROW][C]47[/C][C]0.995190206190631[/C][C]0.0096195876187376[/C][C]0.0048097938093688[/C][/ROW]
[ROW][C]48[/C][C]0.993489217120961[/C][C]0.0130215657580787[/C][C]0.00651078287903934[/C][/ROW]
[ROW][C]49[/C][C]0.996040667739845[/C][C]0.00791866452031014[/C][C]0.00395933226015507[/C][/ROW]
[ROW][C]50[/C][C]0.995021669058131[/C][C]0.00995666188373785[/C][C]0.00497833094186893[/C][/ROW]
[ROW][C]51[/C][C]0.992680081867159[/C][C]0.014639836265682[/C][C]0.00731991813284098[/C][/ROW]
[ROW][C]52[/C][C]0.990552921890353[/C][C]0.0188941562192943[/C][C]0.00944707810964716[/C][/ROW]
[ROW][C]53[/C][C]0.988960037526577[/C][C]0.0220799249468459[/C][C]0.011039962473423[/C][/ROW]
[ROW][C]54[/C][C]0.984797910987681[/C][C]0.0304041780246383[/C][C]0.0152020890123192[/C][/ROW]
[ROW][C]55[/C][C]0.989792621430566[/C][C]0.0204147571388685[/C][C]0.0102073785694342[/C][/ROW]
[ROW][C]56[/C][C]0.985784244313018[/C][C]0.0284315113739644[/C][C]0.0142157556869822[/C][/ROW]
[ROW][C]57[/C][C]0.98051096880788[/C][C]0.0389780623842398[/C][C]0.0194890311921199[/C][/ROW]
[ROW][C]58[/C][C]0.975001041832888[/C][C]0.0499979163342236[/C][C]0.0249989581671118[/C][/ROW]
[ROW][C]59[/C][C]0.983828463015931[/C][C]0.0323430739681384[/C][C]0.0161715369840692[/C][/ROW]
[ROW][C]60[/C][C]0.987660269538451[/C][C]0.0246794609230971[/C][C]0.0123397304615485[/C][/ROW]
[ROW][C]61[/C][C]0.988091678282493[/C][C]0.023816643435014[/C][C]0.011908321717507[/C][/ROW]
[ROW][C]62[/C][C]0.9866449349873[/C][C]0.0267101300254006[/C][C]0.0133550650127003[/C][/ROW]
[ROW][C]63[/C][C]0.995415479803189[/C][C]0.0091690403936226[/C][C]0.0045845201968113[/C][/ROW]
[ROW][C]64[/C][C]0.994430336515701[/C][C]0.0111393269685971[/C][C]0.00556966348429856[/C][/ROW]
[ROW][C]65[/C][C]0.992755752459026[/C][C]0.0144884950819484[/C][C]0.00724424754097421[/C][/ROW]
[ROW][C]66[/C][C]0.998949391086921[/C][C]0.00210121782615835[/C][C]0.00105060891307917[/C][/ROW]
[ROW][C]67[/C][C]0.99918356586845[/C][C]0.00163286826309974[/C][C]0.000816434131549871[/C][/ROW]
[ROW][C]68[/C][C]0.999233915825533[/C][C]0.00153216834893494[/C][C]0.000766084174467471[/C][/ROW]
[ROW][C]69[/C][C]0.998820764742117[/C][C]0.00235847051576563[/C][C]0.00117923525788281[/C][/ROW]
[ROW][C]70[/C][C]0.998683169217937[/C][C]0.00263366156412599[/C][C]0.001316830782063[/C][/ROW]
[ROW][C]71[/C][C]0.998145427413286[/C][C]0.00370914517342797[/C][C]0.00185457258671398[/C][/ROW]
[ROW][C]72[/C][C]0.998469535636973[/C][C]0.00306092872605415[/C][C]0.00153046436302707[/C][/ROW]
[ROW][C]73[/C][C]0.997922645918115[/C][C]0.00415470816376975[/C][C]0.00207735408188487[/C][/ROW]
[ROW][C]74[/C][C]0.997076270012868[/C][C]0.00584745997426355[/C][C]0.00292372998713177[/C][/ROW]
[ROW][C]75[/C][C]0.997983765104378[/C][C]0.00403246979124477[/C][C]0.00201623489562238[/C][/ROW]
[ROW][C]76[/C][C]0.997023799119986[/C][C]0.00595240176002715[/C][C]0.00297620088001358[/C][/ROW]
[ROW][C]77[/C][C]0.997716130041162[/C][C]0.00456773991767537[/C][C]0.00228386995883768[/C][/ROW]
[ROW][C]78[/C][C]0.997047760028049[/C][C]0.0059044799439011[/C][C]0.00295223997195055[/C][/ROW]
[ROW][C]79[/C][C]0.9958570325572[/C][C]0.00828593488559924[/C][C]0.00414296744279962[/C][/ROW]
[ROW][C]80[/C][C]0.995870016981887[/C][C]0.00825996603622657[/C][C]0.00412998301811329[/C][/ROW]
[ROW][C]81[/C][C]0.994856826703398[/C][C]0.0102863465932035[/C][C]0.00514317329660177[/C][/ROW]
[ROW][C]82[/C][C]0.993158469958729[/C][C]0.0136830600825422[/C][C]0.00684153004127109[/C][/ROW]
[ROW][C]83[/C][C]0.990310599427133[/C][C]0.0193788011457339[/C][C]0.00968940057286696[/C][/ROW]
[ROW][C]84[/C][C]0.987184040101346[/C][C]0.0256319197973073[/C][C]0.0128159598986537[/C][/ROW]
[ROW][C]85[/C][C]0.985028225625871[/C][C]0.0299435487482576[/C][C]0.0149717743741288[/C][/ROW]
[ROW][C]86[/C][C]0.980328937797851[/C][C]0.039342124404298[/C][C]0.019671062202149[/C][/ROW]
[ROW][C]87[/C][C]0.974125522642001[/C][C]0.0517489547159983[/C][C]0.0258744773579991[/C][/ROW]
[ROW][C]88[/C][C]0.965996320080744[/C][C]0.0680073598385116[/C][C]0.0340036799192558[/C][/ROW]
[ROW][C]89[/C][C]0.956373062306066[/C][C]0.0872538753878675[/C][C]0.0436269376939338[/C][/ROW]
[ROW][C]90[/C][C]0.970782132875468[/C][C]0.0584357342490635[/C][C]0.0292178671245317[/C][/ROW]
[ROW][C]91[/C][C]0.973889742875521[/C][C]0.0522205142489582[/C][C]0.0261102571244791[/C][/ROW]
[ROW][C]92[/C][C]0.965597224596812[/C][C]0.0688055508063766[/C][C]0.0344027754031883[/C][/ROW]
[ROW][C]93[/C][C]0.955697181103215[/C][C]0.0886056377935706[/C][C]0.0443028188967853[/C][/ROW]
[ROW][C]94[/C][C]0.948540336947137[/C][C]0.102919326105727[/C][C]0.0514596630528634[/C][/ROW]
[ROW][C]95[/C][C]0.938535971355432[/C][C]0.122928057289136[/C][C]0.0614640286445682[/C][/ROW]
[ROW][C]96[/C][C]0.920908233278736[/C][C]0.158183533442528[/C][C]0.0790917667212638[/C][/ROW]
[ROW][C]97[/C][C]0.89986555585632[/C][C]0.20026888828736[/C][C]0.10013444414368[/C][/ROW]
[ROW][C]98[/C][C]0.899177940003847[/C][C]0.201644119992306[/C][C]0.100822059996153[/C][/ROW]
[ROW][C]99[/C][C]0.881494522103826[/C][C]0.237010955792348[/C][C]0.118505477896174[/C][/ROW]
[ROW][C]100[/C][C]0.860304601640809[/C][C]0.279390796718383[/C][C]0.139695398359191[/C][/ROW]
[ROW][C]101[/C][C]0.858185172053456[/C][C]0.283629655893088[/C][C]0.141814827946544[/C][/ROW]
[ROW][C]102[/C][C]0.830992063748099[/C][C]0.338015872503802[/C][C]0.169007936251901[/C][/ROW]
[ROW][C]103[/C][C]0.793828684534925[/C][C]0.41234263093015[/C][C]0.206171315465075[/C][/ROW]
[ROW][C]104[/C][C]0.88553644635148[/C][C]0.22892710729704[/C][C]0.11446355364852[/C][/ROW]
[ROW][C]105[/C][C]0.862988533933939[/C][C]0.274022932132121[/C][C]0.137011466066061[/C][/ROW]
[ROW][C]106[/C][C]0.87094160028717[/C][C]0.25811679942566[/C][C]0.12905839971283[/C][/ROW]
[ROW][C]107[/C][C]0.857863848858258[/C][C]0.284272302283484[/C][C]0.142136151141742[/C][/ROW]
[ROW][C]108[/C][C]0.86078246392148[/C][C]0.278435072157039[/C][C]0.13921753607852[/C][/ROW]
[ROW][C]109[/C][C]0.878183427658619[/C][C]0.243633144682761[/C][C]0.121816572341381[/C][/ROW]
[ROW][C]110[/C][C]0.845880922122169[/C][C]0.308238155755663[/C][C]0.154119077877831[/C][/ROW]
[ROW][C]111[/C][C]0.809482914456636[/C][C]0.381034171086729[/C][C]0.190517085543364[/C][/ROW]
[ROW][C]112[/C][C]0.784432355623644[/C][C]0.431135288752712[/C][C]0.215567644376356[/C][/ROW]
[ROW][C]113[/C][C]0.790777291122169[/C][C]0.418445417755662[/C][C]0.209222708877831[/C][/ROW]
[ROW][C]114[/C][C]0.969258591843628[/C][C]0.0614828163127442[/C][C]0.0307414081563721[/C][/ROW]
[ROW][C]115[/C][C]0.986805573245143[/C][C]0.026388853509715[/C][C]0.0131944267548575[/C][/ROW]
[ROW][C]116[/C][C]0.989013909850615[/C][C]0.0219721802987698[/C][C]0.0109860901493849[/C][/ROW]
[ROW][C]117[/C][C]0.983808561714696[/C][C]0.0323828765706072[/C][C]0.0161914382853036[/C][/ROW]
[ROW][C]118[/C][C]0.975845188924136[/C][C]0.0483096221517274[/C][C]0.0241548110758637[/C][/ROW]
[ROW][C]119[/C][C]0.964566272710731[/C][C]0.0708674545785371[/C][C]0.0354337272892686[/C][/ROW]
[ROW][C]120[/C][C]0.956515251327735[/C][C]0.0869694973445291[/C][C]0.0434847486722646[/C][/ROW]
[ROW][C]121[/C][C]0.937578431115813[/C][C]0.124843137768373[/C][C]0.0624215688841867[/C][/ROW]
[ROW][C]122[/C][C]0.926631714493193[/C][C]0.146736571013614[/C][C]0.0733682855068072[/C][/ROW]
[ROW][C]123[/C][C]0.901139511654153[/C][C]0.197720976691694[/C][C]0.0988604883458468[/C][/ROW]
[ROW][C]124[/C][C]0.87506892186188[/C][C]0.24986215627624[/C][C]0.12493107813812[/C][/ROW]
[ROW][C]125[/C][C]0.835476386922178[/C][C]0.329047226155643[/C][C]0.164523613077822[/C][/ROW]
[ROW][C]126[/C][C]0.786243600950117[/C][C]0.427512798099766[/C][C]0.213756399049883[/C][/ROW]
[ROW][C]127[/C][C]0.72622369207743[/C][C]0.54755261584514[/C][C]0.27377630792257[/C][/ROW]
[ROW][C]128[/C][C]0.755658919566319[/C][C]0.488682160867363[/C][C]0.244341080433681[/C][/ROW]
[ROW][C]129[/C][C]0.711154272783143[/C][C]0.577691454433714[/C][C]0.288845727216857[/C][/ROW]
[ROW][C]130[/C][C]0.680040732332875[/C][C]0.63991853533425[/C][C]0.319959267667125[/C][/ROW]
[ROW][C]131[/C][C]0.60486193245547[/C][C]0.790276135089061[/C][C]0.39513806754453[/C][/ROW]
[ROW][C]132[/C][C]0.526559799995574[/C][C]0.946880400008852[/C][C]0.473440200004426[/C][/ROW]
[ROW][C]133[/C][C]0.442265939141395[/C][C]0.884531878282789[/C][C]0.557734060858605[/C][/ROW]
[ROW][C]134[/C][C]0.376287459465075[/C][C]0.752574918930149[/C][C]0.623712540534925[/C][/ROW]
[ROW][C]135[/C][C]0.289690767000216[/C][C]0.579381534000431[/C][C]0.710309232999784[/C][/ROW]
[ROW][C]136[/C][C]0.253754388985107[/C][C]0.507508777970215[/C][C]0.746245611014893[/C][/ROW]
[ROW][C]137[/C][C]0.453206609523181[/C][C]0.906413219046361[/C][C]0.546793390476819[/C][/ROW]
[ROW][C]138[/C][C]0.388996459742879[/C][C]0.777992919485757[/C][C]0.611003540257121[/C][/ROW]
[ROW][C]139[/C][C]0.268328699845435[/C][C]0.53665739969087[/C][C]0.731671300154565[/C][/ROW]
[ROW][C]140[/C][C]0.660470540221629[/C][C]0.679058919556742[/C][C]0.339529459778371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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
220.9846745037510120.03065099249797670.0153254962489884
230.9801336907030590.03973261859388260.0198663092969413
240.9847560782391960.03048784352160830.0152439217608041
250.9823523290357220.03529534192855560.0176476709642778
260.9999334410000110.0001331179999789316.65589999894656e-05
270.9999244985179480.0001510029641035357.55014820517675e-05
280.9999205224886370.000158955022726537.94775113632648e-05
290.9998439791408670.0003120417182664820.000156020859133241
300.9999117228066680.0001765543866642578.82771933321285e-05
310.9998625433720480.0002749132559037050.000137456627951853
320.9998191196949920.0003617606100158950.000180880305007948
330.9996724514842820.0006550970314368290.000327548515718415
340.9995382416228930.0009235167542132930.000461758377106646
350.999530190386110.0009396192277796850.000469809613889843
360.9991638366393570.001672326721286120.000836163360643058
370.9990007545661830.001998490867633640.000999245433816821
380.9986035958218950.002792808356210650.00139640417810532
390.9983307618426720.003338476314656160.00166923815732808
400.9978819616674440.004236076665112760.00211803833255638
410.9968698321410090.00626033571798230.00313016785899115
420.9968251391472210.006349721705558110.00317486085277905
430.9950557655071670.009888468985665140.00494423449283257
440.9942053133631220.01158937327375650.00579468663687825
450.997031492757290.005937014485420490.00296850724271024
460.9968501859416570.006299628116686020.00314981405834301
470.9951902061906310.00961958761873760.0048097938093688
480.9934892171209610.01302156575807870.00651078287903934
490.9960406677398450.007918664520310140.00395933226015507
500.9950216690581310.009956661883737850.00497833094186893
510.9926800818671590.0146398362656820.00731991813284098
520.9905529218903530.01889415621929430.00944707810964716
530.9889600375265770.02207992494684590.011039962473423
540.9847979109876810.03040417802463830.0152020890123192
550.9897926214305660.02041475713886850.0102073785694342
560.9857842443130180.02843151137396440.0142157556869822
570.980510968807880.03897806238423980.0194890311921199
580.9750010418328880.04999791633422360.0249989581671118
590.9838284630159310.03234307396813840.0161715369840692
600.9876602695384510.02467946092309710.0123397304615485
610.9880916782824930.0238166434350140.011908321717507
620.98664493498730.02671013002540060.0133550650127003
630.9954154798031890.00916904039362260.0045845201968113
640.9944303365157010.01113932696859710.00556966348429856
650.9927557524590260.01448849508194840.00724424754097421
660.9989493910869210.002101217826158350.00105060891307917
670.999183565868450.001632868263099740.000816434131549871
680.9992339158255330.001532168348934940.000766084174467471
690.9988207647421170.002358470515765630.00117923525788281
700.9986831692179370.002633661564125990.001316830782063
710.9981454274132860.003709145173427970.00185457258671398
720.9984695356369730.003060928726054150.00153046436302707
730.9979226459181150.004154708163769750.00207735408188487
740.9970762700128680.005847459974263550.00292372998713177
750.9979837651043780.004032469791244770.00201623489562238
760.9970237991199860.005952401760027150.00297620088001358
770.9977161300411620.004567739917675370.00228386995883768
780.9970477600280490.00590447994390110.00295223997195055
790.99585703255720.008285934885599240.00414296744279962
800.9958700169818870.008259966036226570.00412998301811329
810.9948568267033980.01028634659320350.00514317329660177
820.9931584699587290.01368306008254220.00684153004127109
830.9903105994271330.01937880114573390.00968940057286696
840.9871840401013460.02563191979730730.0128159598986537
850.9850282256258710.02994354874825760.0149717743741288
860.9803289377978510.0393421244042980.019671062202149
870.9741255226420010.05174895471599830.0258744773579991
880.9659963200807440.06800735983851160.0340036799192558
890.9563730623060660.08725387538786750.0436269376939338
900.9707821328754680.05843573424906350.0292178671245317
910.9738897428755210.05222051424895820.0261102571244791
920.9655972245968120.06880555080637660.0344027754031883
930.9556971811032150.08860563779357060.0443028188967853
940.9485403369471370.1029193261057270.0514596630528634
950.9385359713554320.1229280572891360.0614640286445682
960.9209082332787360.1581835334425280.0790917667212638
970.899865555856320.200268888287360.10013444414368
980.8991779400038470.2016441199923060.100822059996153
990.8814945221038260.2370109557923480.118505477896174
1000.8603046016408090.2793907967183830.139695398359191
1010.8581851720534560.2836296558930880.141814827946544
1020.8309920637480990.3380158725038020.169007936251901
1030.7938286845349250.412342630930150.206171315465075
1040.885536446351480.228927107297040.11446355364852
1050.8629885339339390.2740229321321210.137011466066061
1060.870941600287170.258116799425660.12905839971283
1070.8578638488582580.2842723022834840.142136151141742
1080.860782463921480.2784350721570390.13921753607852
1090.8781834276586190.2436331446827610.121816572341381
1100.8458809221221690.3082381557556630.154119077877831
1110.8094829144566360.3810341710867290.190517085543364
1120.7844323556236440.4311352887527120.215567644376356
1130.7907772911221690.4184454177556620.209222708877831
1140.9692585918436280.06148281631274420.0307414081563721
1150.9868055732451430.0263888535097150.0131944267548575
1160.9890139098506150.02197218029876980.0109860901493849
1170.9838085617146960.03238287657060720.0161914382853036
1180.9758451889241360.04830962215172740.0241548110758637
1190.9645662727107310.07086745457853710.0354337272892686
1200.9565152513277350.08696949734452910.0434847486722646
1210.9375784311158130.1248431377683730.0624215688841867
1220.9266317144931930.1467365710136140.0733682855068072
1230.9011395116541530.1977209766916940.0988604883458468
1240.875068921861880.249862156276240.12493107813812
1250.8354763869221780.3290472261556430.164523613077822
1260.7862436009501170.4275127980997660.213756399049883
1270.726223692077430.547552615845140.27377630792257
1280.7556589195663190.4886821608673630.244341080433681
1290.7111542727831430.5776914544337140.288845727216857
1300.6800407323328750.639918535334250.319959267667125
1310.604861932455470.7902761350890610.39513806754453
1320.5265597999955740.9468804000088520.473440200004426
1330.4422659391413950.8845318782827890.557734060858605
1340.3762874594650750.7525749189301490.623712540534925
1350.2896907670002160.5793815340004310.710309232999784
1360.2537543889851070.5075087779702150.746245611014893
1370.4532066095231810.9064132190463610.546793390476819
1380.3889964597428790.7779929194857570.611003540257121
1390.2683286998454350.536657399690870.731671300154565
1400.6604705402216290.6790589195567420.339529459778371






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level390.327731092436975NOK
5% type I error level690.579831932773109NOK
10% type I error level790.663865546218487NOK

\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 & 39 & 0.327731092436975 & NOK \tabularnewline
5% type I error level & 69 & 0.579831932773109 & NOK \tabularnewline
10% type I error level & 79 & 0.663865546218487 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147205&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]39[/C][C]0.327731092436975[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]69[/C][C]0.579831932773109[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]79[/C][C]0.663865546218487[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147205&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147205&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 level390.327731092436975NOK
5% type I error level690.579831932773109NOK
10% type I error level790.663865546218487NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 5 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 5 ; par2 = Include Monthly 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')
}