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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 17 Nov 2013 08:02:03 -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/2013/Nov/17/t1384693341izx0e2r2hgntbeo.htm/, Retrieved Mon, 29 Apr 2024 04:50:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=225724, Retrieved Mon, 29 Apr 2024 04:50:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R P     [Multiple Regression] [] [2013-11-17 13:02:03] [eed7bd73d5c44ddc39c9fbee8149d07c] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	83	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	9	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	9.5	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	12	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	31	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	10	7	16	8	71	44
9	29	31	10	9	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	15	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
9	32	32	9	5	12	11	63	36
9	37	35	14	6	15	14.5	76	47
9	39	34	16	12	16	13	74	45
9	29	34	16	12	15	9	67	42
9	37	36	15	11	12	10	73	43
9	35	34	16	10	12	15	70	43
9	30	28	12	7	8	20	53	32
9	38	34	16	12	13	12	77	45
9	34	35	16	14	11	12	80	48
9	31	35	14	11	14	14	52	31
9	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	12.5	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
10	36	35	16	11	10	15	54	33
10	43	42	12	7	10	24	46	30
10	30	34	16	12	15	14	85	52
10	31	33	16	14	11	15	74	44
10	32	41	17	11	13	11	88	55
10	32	33	13	11	14	15	38	11
10	37	34	12	10	18	12	76	47
10	37	32	18	13	16	10	86	53
10	33	40	14	13	14	14	54	33
10	34	40	14	8	14	13	67	44
10	33	35	13	11	14	9	69	42
10	38	36	16	12	14	15	90	55
10	33	37	13	11	12	15	54	33
10	31	27	16	13	14	14	76	46
10	38	39	13	12	15	11	89	54
10	37	38	16	14	15	8	76	47
10	36	31	15	13	15	11	73	45
10	31	33	16	15	13	11	79	47
10	39	32	15	10	17	8	90	55
10	44	39	17	11	17	10	74	44
10	33	36	15	9	19	11	81	53
10	35	33	12	11	15	13	72	44
10	32	33	16	10	13	11	71	42
10	28	32	10	11	9	20	66	40
10	40	37	16	8	15	10	77	46
10	27	30	12	11	15	15	65	40
10	37	38	14	12	15	12	74	46
10	32	29	15	12	16	14	85	53
10	28	22	13	9	11	23	54	33
10	34	35	15	11	14	14	63	42
10	30	35	11	10	11	16	54	35
10	35	34	12	8	15	11	64	40
10	31	35	11	9	13	12	69	41
10	32	34	16	8	15	10	54	33
10	30	37	15	9	16	14	84	51
10	30	35	17	15	14	12	86	53
10	31	23	16	11	15	12	77	46
10	40	31	10	8	16	11	89	55
10	32	27	18	13	16	12	76	47
10	36	36	13	12	11	13	60	38
10	32	31	16	12	12	11	75	46
10	35	32	13	9	9	19	73	46
10	38	39	10	7	16	12	85	53
10	42	37	15	13	13	17	79	47
10	34	38	16	9	16	9	71	41
10	35	39	16	6	12	12	72	44
9	38	34	14	8	9	19	69	43
10	33	31	10	8	13	18	78	51
10	36	32	17	15	13	15	54	33
10	32	37	13	6	14	14	69	43
10	33	36	15	9	19	11	81	53
10	34	32	16	11	13	9	84	51
10	32	38	12	8	12	18	84	50
10	34	36	13	8	13	16	69	46
11	27	26	13	10	10	24	66	43
11	31	26	12	8	14	14	81	47
11	38	33	17	14	16	20	82	50
11	34	39	15	10	10	18	72	43
11	24	30	10	8	11	23	54	33
11	30	33	14	11	14	12	78	48
11	26	25	11	12	12	14	74	44
11	34	38	13	12	9	16	82	50
11	27	37	16	12	9	18	73	41
11	37	31	12	5	11	20	55	34
11	36	37	16	12	16	12	72	44
11	41	35	12	10	9	12	78	47
11	29	25	9	7	13	17	59	35
11	36	28	12	12	16	13	72	44
11	32	35	15	11	13	9	78	44
11	37	33	12	8	9	16	68	43
11	30	30	12	9	12	18	69	41
11	31	31	14	10	16	10	67	41
11	38	37	12	9	11	14	74	42
11	36	36	16	12	14	11	54	33
11	35	30	11	6	13	9	67	41
11	31	36	19	15	15	11	70	44
11	38	32	15	12	14	10	80	48
11	22	28	8	12	16	11	89	55
11	32	36	16	12	13	19	76	44
11	36	34	17	11	14	14	74	43
11	39	31	12	7	15	12	87	52
11	28	28	11	7	13	14	54	30
11	32	36	11	5	11	21	61	39
11	32	36	14	12	11	13	38	11
11	38	40	16	12	14	10	75	44
11	32	33	12	3	15	15	69	42
11	35	37	16	11	11	16	62	41
11	32	32	13	10	15	14	72	44
11	37	38	15	12	12	12	70	44
11	34	31	16	9	14	19	79	48
11	33	37	16	12	14	15	87	53
11	33	33	14	9	8	19	62	37
11	26	32	16	12	13	13	77	44
11	30	30	16	12	9	17	69	44
11	24	30	14	10	15	12	69	40
11	34	31	11	9	17	11	75	42
11	34	32	12	12	13	14	54	35
11	33	34	15	8	15	11	72	43
11	34	36	15	11	15	13	74	45
11	35	37	16	11	14	12	85	55
11	35	36	16	12	16	15	52	31
11	36	33	11	10	13	14	70	44
11	34	33	15	10	16	12	84	50
11	34	33	12	12	9	17	64	40
11	41	44	12	12	16	11	84	53
11	32	39	15	11	11	18	87	54
11	30	32	15	8	10	13	79	49
11	35	35	16	12	11	17	67	40
11	28	25	14	10	15	13	65	41
11	33	35	17	11	17	11	85	52
11	39	34	14	10	14	12	83	52
11	36	35	13	8	8	22	61	36
11	36	39	15	12	15	14	82	52
11	35	33	13	12	11	12	76	46
11	38	36	14	10	16	12	58	31
11	33	32	15	12	10	17	72	44
11	31	32	12	9	15	9	72	44
11	34	36	13	9	9	21	38	11
11	32	36	8	6	16	10	78	46
11	31	32	14	10	19	11	54	33
11	33	34	14	9	12	12	63	34
11	34	33	11	9	8	23	66	42
11	34	35	12	9	11	13	70	43
11	34	30	13	6	14	12	71	43
11	33	38	10	10	9	16	67	44
11	32	34	16	6	15	9	58	36
11	41	33	18	14	13	17	72	46
11	34	32	13	10	16	9	72	44
11	36	31	11	10	11	14	70	43
11	37	30	4	6	12	17	76	50
11	36	27	13	12	13	13	50	33
11	29	31	16	12	10	11	72	43
11	37	30	10	7	11	12	72	44
11	27	32	12	8	12	10	88	53
11	35	35	12	11	8	19	53	34
11	28	28	10	3	12	16	58	35
11	35	33	13	6	12	16	66	40
11	37	31	15	10	15	14	82	53
11	29	35	12	8	11	20	69	42
11	32	35	14	9	13	15	68	43
11	36	32	10	9	14	23	44	29
11	19	21	12	8	10	20	56	36
11	21	20	12	9	12	16	53	30
11	31	34	11	7	15	14	70	42
11	33	32	10	7	13	17	78	47
11	36	34	12	6	13	11	71	44
11	33	32	16	9	13	13	72	45
11	37	33	12	10	12	17	68	44
11	34	33	14	11	12	15	67	43
11	35	37	16	12	9	21	75	43
11	31	32	14	8	9	18	62	40
11	37	34	13	11	15	15	67	41
11	35	30	4	3	10	8	83	52
11	27	30	15	11	14	12	64	38
11	34	38	11	12	15	12	68	41
11	40	36	11	7	7	22	62	39
11	29	32	14	9	14	12	72	43

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time20 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 20 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&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]20 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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 time20 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Depression[t] = + 24.4755358925393 + 0.440017585293486month[t] -0.0277949894362898Connected[t] + 0.00335189157837722Separate[t] -0.0567552867204635Learning[t] -0.00534308539381128Software[t] -0.679487245862429Happiness[t] -0.158204693315738Belonging[t] + 0.155997078148968Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Depression[t] =  +  24.4755358925393 +  0.440017585293486month[t] -0.0277949894362898Connected[t] +  0.00335189157837722Separate[t] -0.0567552867204635Learning[t] -0.00534308539381128Software[t] -0.679487245862429Happiness[t] -0.158204693315738Belonging[t] +  0.155997078148968Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Depression[t] =  +  24.4755358925393 +  0.440017585293486month[t] -0.0277949894362898Connected[t] +  0.00335189157837722Separate[t] -0.0567552867204635Learning[t] -0.00534308539381128Software[t] -0.679487245862429Happiness[t] -0.158204693315738Belonging[t] +  0.155997078148968Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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
Depression[t] = + 24.4755358925393 + 0.440017585293486month[t] -0.0277949894362898Connected[t] + 0.00335189157837722Separate[t] -0.0567552867204635Learning[t] -0.00534308539381128Software[t] -0.679487245862429Happiness[t] -0.158204693315738Belonging[t] + 0.155997078148968Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)24.47553589253933.6244576.752900
month0.4400175852934860.2365681.860.0640360.032018
Connected-0.02779498943628980.051321-0.54160.5885770.294288
Separate0.003351891578377220.0521760.06420.9488280.474414
Learning-0.05675528672046350.092273-0.61510.539050.269525
Software-0.005343085393811280.094258-0.05670.954840.47742
Happiness-0.6794872458624290.07429-9.146500
Belonging-0.1582046933157380.054863-2.88360.0042670.002134
Belonging_Final0.1559970781489680.0821971.89790.0588450.029423

\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) & 24.4755358925393 & 3.624457 & 6.7529 & 0 & 0 \tabularnewline
month & 0.440017585293486 & 0.236568 & 1.86 & 0.064036 & 0.032018 \tabularnewline
Connected & -0.0277949894362898 & 0.051321 & -0.5416 & 0.588577 & 0.294288 \tabularnewline
Separate & 0.00335189157837722 & 0.052176 & 0.0642 & 0.948828 & 0.474414 \tabularnewline
Learning & -0.0567552867204635 & 0.092273 & -0.6151 & 0.53905 & 0.269525 \tabularnewline
Software & -0.00534308539381128 & 0.094258 & -0.0567 & 0.95484 & 0.47742 \tabularnewline
Happiness & -0.679487245862429 & 0.07429 & -9.1465 & 0 & 0 \tabularnewline
Belonging & -0.158204693315738 & 0.054863 & -2.8836 & 0.004267 & 0.002134 \tabularnewline
Belonging_Final & 0.155997078148968 & 0.082197 & 1.8979 & 0.058845 & 0.029423 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&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]24.4755358925393[/C][C]3.624457[/C][C]6.7529[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]month[/C][C]0.440017585293486[/C][C]0.236568[/C][C]1.86[/C][C]0.064036[/C][C]0.032018[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0277949894362898[/C][C]0.051321[/C][C]-0.5416[/C][C]0.588577[/C][C]0.294288[/C][/ROW]
[ROW][C]Separate[/C][C]0.00335189157837722[/C][C]0.052176[/C][C]0.0642[/C][C]0.948828[/C][C]0.474414[/C][/ROW]
[ROW][C]Learning[/C][C]-0.0567552867204635[/C][C]0.092273[/C][C]-0.6151[/C][C]0.53905[/C][C]0.269525[/C][/ROW]
[ROW][C]Software[/C][C]-0.00534308539381128[/C][C]0.094258[/C][C]-0.0567[/C][C]0.95484[/C][C]0.47742[/C][/ROW]
[ROW][C]Happiness[/C][C]-0.679487245862429[/C][C]0.07429[/C][C]-9.1465[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]-0.158204693315738[/C][C]0.054863[/C][C]-2.8836[/C][C]0.004267[/C][C]0.002134[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]0.155997078148968[/C][C]0.082197[/C][C]1.8979[/C][C]0.058845[/C][C]0.029423[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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)24.47553589253933.6244576.752900
month0.4400175852934860.2365681.860.0640360.032018
Connected-0.02779498943628980.051321-0.54160.5885770.294288
Separate0.003351891578377220.0521760.06420.9488280.474414
Learning-0.05675528672046350.092273-0.61510.539050.269525
Software-0.005343085393811280.094258-0.05670.954840.47742
Happiness-0.6794872458624290.07429-9.146500
Belonging-0.1582046933157380.054863-2.88360.0042670.002134
Belonging_Final0.1559970781489680.0821971.89790.0588450.029423






Multiple Linear Regression - Regression Statistics
Multiple R0.625294738691167
R-squared0.390993510234855
Adjusted R-squared0.371887424281439
F-TEST (value)20.4643437273422
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.74974956989676
Sum Squared Residuals1928.08628777259

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.625294738691167 \tabularnewline
R-squared & 0.390993510234855 \tabularnewline
Adjusted R-squared & 0.371887424281439 \tabularnewline
F-TEST (value) & 20.4643437273422 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 255 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.74974956989676 \tabularnewline
Sum Squared Residuals & 1928.08628777259 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.625294738691167[/C][/ROW]
[ROW][C]R-squared[/C][C]0.390993510234855[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.371887424281439[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.4643437273422[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]255[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.74974956989676[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1928.08628777259[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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 R0.625294738691167
R-squared0.390993510234855
Adjusted R-squared0.371887424281439
F-TEST (value)20.4643437273422
F-TEST (DF numerator)8
F-TEST (DF denominator)255
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.74974956989676
Sum Squared Residuals1928.08628777259






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.7157720341382-1.71577203413821
2119.08618259068151.9138174093185
31415.1966717726704-1.19667177267044
41214.4435928981764-2.44359289817641
52111.03116335259839.96883664740168
6129.529950973229552.47004902677045
72214.18735244238427.81264755761578
81112.1598600418145-1.15986004181448
91011.8141122303077-1.81411223030773
101311.7512316547341.24876834526599
111010.1162806367607-0.116280636760667
12810.2714297103659-2.27142971036586
131515.6818895260696-0.681889526069634
141412.24525713135161.75474286864841
15108.87075973993041.1292402600696
161413.66862175374920.3313782462508
171412.27926999218651.72073000781355
18119.258179711043031.74182028895697
191013.1566507416699-3.15665074166989
201312.40774477704120.592255222958761
219.510.4136084131918-0.913608413191769
221415.3373491029299-1.33734910292992
231213.4232795921274-1.42327959212744
241415.3297269112167-1.32972691121666
25119.384954462631411.61504553736859
26914.929162134414-5.92916213441404
271110.40339703886260.59660296113741
281513.15127315855741.84872684144258
291411.1444640687562.85553593124403
301315.5430693553221-2.5430693553221
31910.8781972312942-1.87819723129423
321514.84019779576740.159802204232617
331010.58724127727-0.587241277269979
341111.5848221890795-0.584822189079476
351312.0644387627040.935561237296047
36811.8114547346829-3.81145473468287
372016.80751846956753.19248153043251
381211.97002837080030.0299716291997416
391010.3730583871876-0.373058387187584
401013.4143857577906-3.41438575779061
41911.673437163483-2.67343716348301
421410.86930940869643.13069059130357
43812.0045472828605-4.00454728286052
441415.1808955038229-1.18089550382286
451112.0687774133059-1.06877741330592
461314.8437748536321-1.84377485363214
47912.8784348158986-3.87843481589856
481111.8405254905448-0.840525490544805
49159.889034690823995.11096530917601
501112.9492363751091-1.94923637510914
511011.2920761811796-1.29207618117956
521412.92615360927641.07384639072362
531815.24232296306822.75767703693184
541415.8538193606058-1.85381936060582
551114.6111542053965-3.61115420539645
5614.511.81396052290082.68603947709923
571310.93437755111712.06562244888287
58912.5312563101057-3.53125631010572
591013.6229292057283-3.62292920572826
601514.09501728006460.904982719935355
612018.14839219101771.85160780898227
621212.5260201981935-0.526020198193496
631213.982217522954-1.98221752295396
641413.93446166760610.0655383323938942
651313.034912997953-0.0349129979530201
661115.1852160246527-4.18521602465265
671715.53201779346051.46798220653954
681213.7450846764111-1.74508467641105
691312.94928695805550.0507130419444946
701412.49815024688511.50184975311488
711314.0812082376877-1.08120823768774
721513.02114520557631.97885479442367
731311.44597842435461.55402157564543
741011.9919819443633-1.99198194436328
751112.7263070377182-1.72630703771817
761913.71297067570925.28702932429076
771311.07603507572711.92396492427289
781714.18214562026782.81785437973224
791312.35721752784920.642782472150784
80914.737112540349-5.73711254034899
811111.5923297136178-0.592329713617819
82912.6497320947006-3.64973209470055
831211.32959904744310.670400952556914
841212.4709579889239-0.470957988923858
851312.73815013715640.261849862843572
861312.06977196590780.930228034092154
871213.223171796885-1.223171796885
881514.97623191373120.0237680862688101
892218.03077829789733.96922170210268
901311.48122376372331.51877623627674
911514.70888244477530.291117555224736
921311.86990857926861.1300914207314
931512.16693217936072.83306782063929
9412.513.2104310586122-0.710431058612193
951110.75455108675850.245448913241495
961614.62497221519821.37502778480177
971112.373701472213-1.37370147221295
981110.11105625065750.888943749342492
991012.2189355272299-2.21893552722987
1001010.4023111057221-0.402311105722136
1011614.31483376902081.68516623097916
1021210.5765132969611.42348670303901
1031115.8060296264998-4.8060296264998
1041611.76372555189574.23627444810427
1051916.59181279231452.40818720768548
1061111.3443715003713-0.344371500371283
1071612.17767178494163.82232821505837
1081516.8355274853934-1.83552748539339
1092417.71046560092416.2895343990759
1101411.65576520776932.34423479223069
1111514.82415614069810.175843859301886
1121112.9245779148433-1.92457791484327
1131513.4916599104681.50834008953202
1141210.30430271089431.69569728910572
1151010.6539479786946-0.653947978694595
1161414.3205473307977-0.320547330797736
1171313.9787746148646-0.978774614864558
118913.4021326340186-4.40213263401858
1191511.79656408916643.20343591083361
1201515.7369076052955-0.736907605295548
1211412.76651090870081.23348909129917
1221111.2996059933673-0.299605993367257
123812.107778526338-4.10777852633797
1241112.3368285704892-1.33682857048917
1251113.1368063314477-2.13680633144766
12689.7843412533374-1.7843412533374
1271010.365283121783-0.365283121783007
128119.72273543348131.2772645665187
1291312.5544869891980.445513010801998
1301113.6213789247615-2.62137892476149
1312017.26137391958772.73862608041229
1321011.7388984073489-1.73889840734889
1331513.25023577056751.74976422943252
1341212.392387579049-0.392387579049031
1351411.11668089001192.88331910998815
1362316.51577759545146.48422240454861
1371414.2100552310358-0.21005523103579
1381616.9239238514428-0.923923851442766
1391113.2155173708878-2.21551737088784
1401214.1054095248332-2.10540952483317
1411013.5619485784295-3.56194857842946
1421411.06132579471072.93867420528932
1431212.2636121871415-0.263612187141454
1441211.92609757399680.0739024260032187
1451110.88534891589080.114651084109165
1461211.42222793224770.577772067752272
1471316.1550721367273-3.15507213672733
1481114.2746457560125-3.27464575601246
1491916.73577891984362.26422108015644
1501211.29392172974930.70607827025074
1511712.91191047147014.08808952852993
152911.3934326734384-2.39343267343835
1531214.4127543563428-2.41275435634275
1541916.33249548688742.66750451311265
1551814.23463889340943.7653611065906
1561514.70888244477530.291117555224736
1571413.61934391172960.380656088270352
158119.72273543348131.2772645665187
159912.9044066591528-3.90440665915284
1601813.74664855827244.25335144172757
1611614.69719435080041.3028056491996
1622417.33265635866416.66734364133588
1631412.82188678783711.17811321216287
1642011.28576220627298.71423779372713
1651816.11892728979361.88107271020636
1662317.26939921667235.73060078332775
1671213.3742163467959-1.37421634679588
1681414.9913088990735-0.991308899073541
1691616.4078196606164-0.407819660616392
1701816.44863537143161.55136462856842
1712016.81172731315263.1882726868474
1721212.0682656732306-0.0682656732306124
1731216.4354680561514-4.43546805615137
1741715.33775938170761.66224061829242
1751312.26511979590710.734880204092929
176913.3240737976262-4.32407379762615
1771617.5086890220889-1.5086890220889
1781815.1791946008132.82080539918698
1791012.6343582473021-2.63435824730213
1801415.0247587838041-1.02475878380405
1811114.5556048934217-3.55560489342173
182914.7499263373025-5.74992633730252
1831113.0134902449848-2.01349024498476
1841012.7699967809814-2.76999678098144
1851111.9077588681681-0.907758868168088
1861913.58173670372175.41826329627827
1871412.89336582411321.10663417588676
1881211.77289940062050.227100599379505
1891415.2731375482719-1.27313754827193
1902116.85497423579684.14502576420319
1911315.9180965359696-2.91809653596961
1921012.9070917808708-2.90709178087081
1931513.28325414960011.71674585039988
1941616.6128956760833-0.612895676083333
1951413.02312544989530.976874550104672
1961215.1349362321744-3.13493623217436
1971912.99530350992496.00469649007507
1981512.5415284368692.45847156313102
1991918.19374825786240.806251742137632
2001313.5768943807102-0.576894380710221
2011717.4425971697839-0.442597169783927
2021213.0326520628598-1.03265206285977
2031110.93745451030910.0625454896908994
2041415.816289855023-1.81628985502302
2051112.7432127628135-1.74321276281352
2061312.7016770700190.298322929980989
2071213.1196850863196-1.11968508631963
2081513.22884062146671.7711593785333
2091414.7041918355258-0.704191835525806
2101211.21541569240270.784584307597277
2111717.7355291876386-0.73552918763857
2121111.6852924975315-0.685292497531517
2131814.83258439731263.16741560268744
2141316.0458797929614-3.04587979296142
2151715.65382826280511.34617173719489
2161313.6935284986346-0.693528498634643
2171110.60536302328080.394636976719202
2181212.9657212648586-0.965721264858646
2192218.18137305999133.8186269400087
2201412.47714168100461.52285831899538
2211215.3295305688619-3.32953056886191
2221212.320424237492-0.320424237491951
2231716.26856994554260.731430054457368
224913.1130188114459-4.11301881144589
2252117.29426559172383.70573440827622
2261012.0249605419275-2.02496054192748
2271111.4079327892061-0.407932789206072
2281214.8529552382284-2.85295523822839
2292318.48338574606944.51661425393064
2301315.9180508098044-2.91805080980438
2311213.6638988904704-1.66389889047044
2321618.053654611844-2.05365461184395
233913.8478247956945-4.84782479569452
2341714.14214230939232.85785769060767
235912.2880482251603-3.28804822516032
2361415.9004654659449-1.90046546594494
2371715.75124207483471.24875792516532
2381316.0080097485031-3.0080097485031
2391116.16364564684-5.16364564683998
2401215.7816908193497-3.78169081934966
2411014.1407022024611-4.14070220246106
2421919.2035374701946-0.203537470194584
2431616.177919039912-0.177919039911952
2441615.32816629962590.671833700374078
2451412.58921480787791.41078519212209
2462016.06457645754643.93542354245359
2471514.81756511014260.182434889857352
2482315.85681692417397.14318307582606
2492018.09576565988521.90423434011476
2501616.211137823369-0.211137823369017
2511413.19157928244430.808420717555727
2521714.05936314307922.9406368569208
2531114.5139560886432-3.51395608864323
2541314.3453792555653-1.34537925556528
2551715.6155381918631.38446180813704
2561515.5822771165039-0.582277116503859
2572116.22186022560774.77813977439228
2581818.0398334191349-0.0398334191348575
2591513.20854343260861.79145656739139
260816.3863971047013-8.38639710470133
2611214.0456852785798-2.04568527857981
2621213.2552987619624-1.25529876196236
2632219.18167243965282.81832756034715
2641213.578588384591-1.57858838459101

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.7157720341382 & -1.71577203413821 \tabularnewline
2 & 11 & 9.0861825906815 & 1.9138174093185 \tabularnewline
3 & 14 & 15.1966717726704 & -1.19667177267044 \tabularnewline
4 & 12 & 14.4435928981764 & -2.44359289817641 \tabularnewline
5 & 21 & 11.0311633525983 & 9.96883664740168 \tabularnewline
6 & 12 & 9.52995097322955 & 2.47004902677045 \tabularnewline
7 & 22 & 14.1873524423842 & 7.81264755761578 \tabularnewline
8 & 11 & 12.1598600418145 & -1.15986004181448 \tabularnewline
9 & 10 & 11.8141122303077 & -1.81411223030773 \tabularnewline
10 & 13 & 11.751231654734 & 1.24876834526599 \tabularnewline
11 & 10 & 10.1162806367607 & -0.116280636760667 \tabularnewline
12 & 8 & 10.2714297103659 & -2.27142971036586 \tabularnewline
13 & 15 & 15.6818895260696 & -0.681889526069634 \tabularnewline
14 & 14 & 12.2452571313516 & 1.75474286864841 \tabularnewline
15 & 10 & 8.8707597399304 & 1.1292402600696 \tabularnewline
16 & 14 & 13.6686217537492 & 0.3313782462508 \tabularnewline
17 & 14 & 12.2792699921865 & 1.72073000781355 \tabularnewline
18 & 11 & 9.25817971104303 & 1.74182028895697 \tabularnewline
19 & 10 & 13.1566507416699 & -3.15665074166989 \tabularnewline
20 & 13 & 12.4077447770412 & 0.592255222958761 \tabularnewline
21 & 9.5 & 10.4136084131918 & -0.913608413191769 \tabularnewline
22 & 14 & 15.3373491029299 & -1.33734910292992 \tabularnewline
23 & 12 & 13.4232795921274 & -1.42327959212744 \tabularnewline
24 & 14 & 15.3297269112167 & -1.32972691121666 \tabularnewline
25 & 11 & 9.38495446263141 & 1.61504553736859 \tabularnewline
26 & 9 & 14.929162134414 & -5.92916213441404 \tabularnewline
27 & 11 & 10.4033970388626 & 0.59660296113741 \tabularnewline
28 & 15 & 13.1512731585574 & 1.84872684144258 \tabularnewline
29 & 14 & 11.144464068756 & 2.85553593124403 \tabularnewline
30 & 13 & 15.5430693553221 & -2.5430693553221 \tabularnewline
31 & 9 & 10.8781972312942 & -1.87819723129423 \tabularnewline
32 & 15 & 14.8401977957674 & 0.159802204232617 \tabularnewline
33 & 10 & 10.58724127727 & -0.587241277269979 \tabularnewline
34 & 11 & 11.5848221890795 & -0.584822189079476 \tabularnewline
35 & 13 & 12.064438762704 & 0.935561237296047 \tabularnewline
36 & 8 & 11.8114547346829 & -3.81145473468287 \tabularnewline
37 & 20 & 16.8075184695675 & 3.19248153043251 \tabularnewline
38 & 12 & 11.9700283708003 & 0.0299716291997416 \tabularnewline
39 & 10 & 10.3730583871876 & -0.373058387187584 \tabularnewline
40 & 10 & 13.4143857577906 & -3.41438575779061 \tabularnewline
41 & 9 & 11.673437163483 & -2.67343716348301 \tabularnewline
42 & 14 & 10.8693094086964 & 3.13069059130357 \tabularnewline
43 & 8 & 12.0045472828605 & -4.00454728286052 \tabularnewline
44 & 14 & 15.1808955038229 & -1.18089550382286 \tabularnewline
45 & 11 & 12.0687774133059 & -1.06877741330592 \tabularnewline
46 & 13 & 14.8437748536321 & -1.84377485363214 \tabularnewline
47 & 9 & 12.8784348158986 & -3.87843481589856 \tabularnewline
48 & 11 & 11.8405254905448 & -0.840525490544805 \tabularnewline
49 & 15 & 9.88903469082399 & 5.11096530917601 \tabularnewline
50 & 11 & 12.9492363751091 & -1.94923637510914 \tabularnewline
51 & 10 & 11.2920761811796 & -1.29207618117956 \tabularnewline
52 & 14 & 12.9261536092764 & 1.07384639072362 \tabularnewline
53 & 18 & 15.2423229630682 & 2.75767703693184 \tabularnewline
54 & 14 & 15.8538193606058 & -1.85381936060582 \tabularnewline
55 & 11 & 14.6111542053965 & -3.61115420539645 \tabularnewline
56 & 14.5 & 11.8139605229008 & 2.68603947709923 \tabularnewline
57 & 13 & 10.9343775511171 & 2.06562244888287 \tabularnewline
58 & 9 & 12.5312563101057 & -3.53125631010572 \tabularnewline
59 & 10 & 13.6229292057283 & -3.62292920572826 \tabularnewline
60 & 15 & 14.0950172800646 & 0.904982719935355 \tabularnewline
61 & 20 & 18.1483921910177 & 1.85160780898227 \tabularnewline
62 & 12 & 12.5260201981935 & -0.526020198193496 \tabularnewline
63 & 12 & 13.982217522954 & -1.98221752295396 \tabularnewline
64 & 14 & 13.9344616676061 & 0.0655383323938942 \tabularnewline
65 & 13 & 13.034912997953 & -0.0349129979530201 \tabularnewline
66 & 11 & 15.1852160246527 & -4.18521602465265 \tabularnewline
67 & 17 & 15.5320177934605 & 1.46798220653954 \tabularnewline
68 & 12 & 13.7450846764111 & -1.74508467641105 \tabularnewline
69 & 13 & 12.9492869580555 & 0.0507130419444946 \tabularnewline
70 & 14 & 12.4981502468851 & 1.50184975311488 \tabularnewline
71 & 13 & 14.0812082376877 & -1.08120823768774 \tabularnewline
72 & 15 & 13.0211452055763 & 1.97885479442367 \tabularnewline
73 & 13 & 11.4459784243546 & 1.55402157564543 \tabularnewline
74 & 10 & 11.9919819443633 & -1.99198194436328 \tabularnewline
75 & 11 & 12.7263070377182 & -1.72630703771817 \tabularnewline
76 & 19 & 13.7129706757092 & 5.28702932429076 \tabularnewline
77 & 13 & 11.0760350757271 & 1.92396492427289 \tabularnewline
78 & 17 & 14.1821456202678 & 2.81785437973224 \tabularnewline
79 & 13 & 12.3572175278492 & 0.642782472150784 \tabularnewline
80 & 9 & 14.737112540349 & -5.73711254034899 \tabularnewline
81 & 11 & 11.5923297136178 & -0.592329713617819 \tabularnewline
82 & 9 & 12.6497320947006 & -3.64973209470055 \tabularnewline
83 & 12 & 11.3295990474431 & 0.670400952556914 \tabularnewline
84 & 12 & 12.4709579889239 & -0.470957988923858 \tabularnewline
85 & 13 & 12.7381501371564 & 0.261849862843572 \tabularnewline
86 & 13 & 12.0697719659078 & 0.930228034092154 \tabularnewline
87 & 12 & 13.223171796885 & -1.223171796885 \tabularnewline
88 & 15 & 14.9762319137312 & 0.0237680862688101 \tabularnewline
89 & 22 & 18.0307782978973 & 3.96922170210268 \tabularnewline
90 & 13 & 11.4812237637233 & 1.51877623627674 \tabularnewline
91 & 15 & 14.7088824447753 & 0.291117555224736 \tabularnewline
92 & 13 & 11.8699085792686 & 1.1300914207314 \tabularnewline
93 & 15 & 12.1669321793607 & 2.83306782063929 \tabularnewline
94 & 12.5 & 13.2104310586122 & -0.710431058612193 \tabularnewline
95 & 11 & 10.7545510867585 & 0.245448913241495 \tabularnewline
96 & 16 & 14.6249722151982 & 1.37502778480177 \tabularnewline
97 & 11 & 12.373701472213 & -1.37370147221295 \tabularnewline
98 & 11 & 10.1110562506575 & 0.888943749342492 \tabularnewline
99 & 10 & 12.2189355272299 & -2.21893552722987 \tabularnewline
100 & 10 & 10.4023111057221 & -0.402311105722136 \tabularnewline
101 & 16 & 14.3148337690208 & 1.68516623097916 \tabularnewline
102 & 12 & 10.576513296961 & 1.42348670303901 \tabularnewline
103 & 11 & 15.8060296264998 & -4.8060296264998 \tabularnewline
104 & 16 & 11.7637255518957 & 4.23627444810427 \tabularnewline
105 & 19 & 16.5918127923145 & 2.40818720768548 \tabularnewline
106 & 11 & 11.3443715003713 & -0.344371500371283 \tabularnewline
107 & 16 & 12.1776717849416 & 3.82232821505837 \tabularnewline
108 & 15 & 16.8355274853934 & -1.83552748539339 \tabularnewline
109 & 24 & 17.7104656009241 & 6.2895343990759 \tabularnewline
110 & 14 & 11.6557652077693 & 2.34423479223069 \tabularnewline
111 & 15 & 14.8241561406981 & 0.175843859301886 \tabularnewline
112 & 11 & 12.9245779148433 & -1.92457791484327 \tabularnewline
113 & 15 & 13.491659910468 & 1.50834008953202 \tabularnewline
114 & 12 & 10.3043027108943 & 1.69569728910572 \tabularnewline
115 & 10 & 10.6539479786946 & -0.653947978694595 \tabularnewline
116 & 14 & 14.3205473307977 & -0.320547330797736 \tabularnewline
117 & 13 & 13.9787746148646 & -0.978774614864558 \tabularnewline
118 & 9 & 13.4021326340186 & -4.40213263401858 \tabularnewline
119 & 15 & 11.7965640891664 & 3.20343591083361 \tabularnewline
120 & 15 & 15.7369076052955 & -0.736907605295548 \tabularnewline
121 & 14 & 12.7665109087008 & 1.23348909129917 \tabularnewline
122 & 11 & 11.2996059933673 & -0.299605993367257 \tabularnewline
123 & 8 & 12.107778526338 & -4.10777852633797 \tabularnewline
124 & 11 & 12.3368285704892 & -1.33682857048917 \tabularnewline
125 & 11 & 13.1368063314477 & -2.13680633144766 \tabularnewline
126 & 8 & 9.7843412533374 & -1.7843412533374 \tabularnewline
127 & 10 & 10.365283121783 & -0.365283121783007 \tabularnewline
128 & 11 & 9.7227354334813 & 1.2772645665187 \tabularnewline
129 & 13 & 12.554486989198 & 0.445513010801998 \tabularnewline
130 & 11 & 13.6213789247615 & -2.62137892476149 \tabularnewline
131 & 20 & 17.2613739195877 & 2.73862608041229 \tabularnewline
132 & 10 & 11.7388984073489 & -1.73889840734889 \tabularnewline
133 & 15 & 13.2502357705675 & 1.74976422943252 \tabularnewline
134 & 12 & 12.392387579049 & -0.392387579049031 \tabularnewline
135 & 14 & 11.1166808900119 & 2.88331910998815 \tabularnewline
136 & 23 & 16.5157775954514 & 6.48422240454861 \tabularnewline
137 & 14 & 14.2100552310358 & -0.21005523103579 \tabularnewline
138 & 16 & 16.9239238514428 & -0.923923851442766 \tabularnewline
139 & 11 & 13.2155173708878 & -2.21551737088784 \tabularnewline
140 & 12 & 14.1054095248332 & -2.10540952483317 \tabularnewline
141 & 10 & 13.5619485784295 & -3.56194857842946 \tabularnewline
142 & 14 & 11.0613257947107 & 2.93867420528932 \tabularnewline
143 & 12 & 12.2636121871415 & -0.263612187141454 \tabularnewline
144 & 12 & 11.9260975739968 & 0.0739024260032187 \tabularnewline
145 & 11 & 10.8853489158908 & 0.114651084109165 \tabularnewline
146 & 12 & 11.4222279322477 & 0.577772067752272 \tabularnewline
147 & 13 & 16.1550721367273 & -3.15507213672733 \tabularnewline
148 & 11 & 14.2746457560125 & -3.27464575601246 \tabularnewline
149 & 19 & 16.7357789198436 & 2.26422108015644 \tabularnewline
150 & 12 & 11.2939217297493 & 0.70607827025074 \tabularnewline
151 & 17 & 12.9119104714701 & 4.08808952852993 \tabularnewline
152 & 9 & 11.3934326734384 & -2.39343267343835 \tabularnewline
153 & 12 & 14.4127543563428 & -2.41275435634275 \tabularnewline
154 & 19 & 16.3324954868874 & 2.66750451311265 \tabularnewline
155 & 18 & 14.2346388934094 & 3.7653611065906 \tabularnewline
156 & 15 & 14.7088824447753 & 0.291117555224736 \tabularnewline
157 & 14 & 13.6193439117296 & 0.380656088270352 \tabularnewline
158 & 11 & 9.7227354334813 & 1.2772645665187 \tabularnewline
159 & 9 & 12.9044066591528 & -3.90440665915284 \tabularnewline
160 & 18 & 13.7466485582724 & 4.25335144172757 \tabularnewline
161 & 16 & 14.6971943508004 & 1.3028056491996 \tabularnewline
162 & 24 & 17.3326563586641 & 6.66734364133588 \tabularnewline
163 & 14 & 12.8218867878371 & 1.17811321216287 \tabularnewline
164 & 20 & 11.2857622062729 & 8.71423779372713 \tabularnewline
165 & 18 & 16.1189272897936 & 1.88107271020636 \tabularnewline
166 & 23 & 17.2693992166723 & 5.73060078332775 \tabularnewline
167 & 12 & 13.3742163467959 & -1.37421634679588 \tabularnewline
168 & 14 & 14.9913088990735 & -0.991308899073541 \tabularnewline
169 & 16 & 16.4078196606164 & -0.407819660616392 \tabularnewline
170 & 18 & 16.4486353714316 & 1.55136462856842 \tabularnewline
171 & 20 & 16.8117273131526 & 3.1882726868474 \tabularnewline
172 & 12 & 12.0682656732306 & -0.0682656732306124 \tabularnewline
173 & 12 & 16.4354680561514 & -4.43546805615137 \tabularnewline
174 & 17 & 15.3377593817076 & 1.66224061829242 \tabularnewline
175 & 13 & 12.2651197959071 & 0.734880204092929 \tabularnewline
176 & 9 & 13.3240737976262 & -4.32407379762615 \tabularnewline
177 & 16 & 17.5086890220889 & -1.5086890220889 \tabularnewline
178 & 18 & 15.179194600813 & 2.82080539918698 \tabularnewline
179 & 10 & 12.6343582473021 & -2.63435824730213 \tabularnewline
180 & 14 & 15.0247587838041 & -1.02475878380405 \tabularnewline
181 & 11 & 14.5556048934217 & -3.55560489342173 \tabularnewline
182 & 9 & 14.7499263373025 & -5.74992633730252 \tabularnewline
183 & 11 & 13.0134902449848 & -2.01349024498476 \tabularnewline
184 & 10 & 12.7699967809814 & -2.76999678098144 \tabularnewline
185 & 11 & 11.9077588681681 & -0.907758868168088 \tabularnewline
186 & 19 & 13.5817367037217 & 5.41826329627827 \tabularnewline
187 & 14 & 12.8933658241132 & 1.10663417588676 \tabularnewline
188 & 12 & 11.7728994006205 & 0.227100599379505 \tabularnewline
189 & 14 & 15.2731375482719 & -1.27313754827193 \tabularnewline
190 & 21 & 16.8549742357968 & 4.14502576420319 \tabularnewline
191 & 13 & 15.9180965359696 & -2.91809653596961 \tabularnewline
192 & 10 & 12.9070917808708 & -2.90709178087081 \tabularnewline
193 & 15 & 13.2832541496001 & 1.71674585039988 \tabularnewline
194 & 16 & 16.6128956760833 & -0.612895676083333 \tabularnewline
195 & 14 & 13.0231254498953 & 0.976874550104672 \tabularnewline
196 & 12 & 15.1349362321744 & -3.13493623217436 \tabularnewline
197 & 19 & 12.9953035099249 & 6.00469649007507 \tabularnewline
198 & 15 & 12.541528436869 & 2.45847156313102 \tabularnewline
199 & 19 & 18.1937482578624 & 0.806251742137632 \tabularnewline
200 & 13 & 13.5768943807102 & -0.576894380710221 \tabularnewline
201 & 17 & 17.4425971697839 & -0.442597169783927 \tabularnewline
202 & 12 & 13.0326520628598 & -1.03265206285977 \tabularnewline
203 & 11 & 10.9374545103091 & 0.0625454896908994 \tabularnewline
204 & 14 & 15.816289855023 & -1.81628985502302 \tabularnewline
205 & 11 & 12.7432127628135 & -1.74321276281352 \tabularnewline
206 & 13 & 12.701677070019 & 0.298322929980989 \tabularnewline
207 & 12 & 13.1196850863196 & -1.11968508631963 \tabularnewline
208 & 15 & 13.2288406214667 & 1.7711593785333 \tabularnewline
209 & 14 & 14.7041918355258 & -0.704191835525806 \tabularnewline
210 & 12 & 11.2154156924027 & 0.784584307597277 \tabularnewline
211 & 17 & 17.7355291876386 & -0.73552918763857 \tabularnewline
212 & 11 & 11.6852924975315 & -0.685292497531517 \tabularnewline
213 & 18 & 14.8325843973126 & 3.16741560268744 \tabularnewline
214 & 13 & 16.0458797929614 & -3.04587979296142 \tabularnewline
215 & 17 & 15.6538282628051 & 1.34617173719489 \tabularnewline
216 & 13 & 13.6935284986346 & -0.693528498634643 \tabularnewline
217 & 11 & 10.6053630232808 & 0.394636976719202 \tabularnewline
218 & 12 & 12.9657212648586 & -0.965721264858646 \tabularnewline
219 & 22 & 18.1813730599913 & 3.8186269400087 \tabularnewline
220 & 14 & 12.4771416810046 & 1.52285831899538 \tabularnewline
221 & 12 & 15.3295305688619 & -3.32953056886191 \tabularnewline
222 & 12 & 12.320424237492 & -0.320424237491951 \tabularnewline
223 & 17 & 16.2685699455426 & 0.731430054457368 \tabularnewline
224 & 9 & 13.1130188114459 & -4.11301881144589 \tabularnewline
225 & 21 & 17.2942655917238 & 3.70573440827622 \tabularnewline
226 & 10 & 12.0249605419275 & -2.02496054192748 \tabularnewline
227 & 11 & 11.4079327892061 & -0.407932789206072 \tabularnewline
228 & 12 & 14.8529552382284 & -2.85295523822839 \tabularnewline
229 & 23 & 18.4833857460694 & 4.51661425393064 \tabularnewline
230 & 13 & 15.9180508098044 & -2.91805080980438 \tabularnewline
231 & 12 & 13.6638988904704 & -1.66389889047044 \tabularnewline
232 & 16 & 18.053654611844 & -2.05365461184395 \tabularnewline
233 & 9 & 13.8478247956945 & -4.84782479569452 \tabularnewline
234 & 17 & 14.1421423093923 & 2.85785769060767 \tabularnewline
235 & 9 & 12.2880482251603 & -3.28804822516032 \tabularnewline
236 & 14 & 15.9004654659449 & -1.90046546594494 \tabularnewline
237 & 17 & 15.7512420748347 & 1.24875792516532 \tabularnewline
238 & 13 & 16.0080097485031 & -3.0080097485031 \tabularnewline
239 & 11 & 16.16364564684 & -5.16364564683998 \tabularnewline
240 & 12 & 15.7816908193497 & -3.78169081934966 \tabularnewline
241 & 10 & 14.1407022024611 & -4.14070220246106 \tabularnewline
242 & 19 & 19.2035374701946 & -0.203537470194584 \tabularnewline
243 & 16 & 16.177919039912 & -0.177919039911952 \tabularnewline
244 & 16 & 15.3281662996259 & 0.671833700374078 \tabularnewline
245 & 14 & 12.5892148078779 & 1.41078519212209 \tabularnewline
246 & 20 & 16.0645764575464 & 3.93542354245359 \tabularnewline
247 & 15 & 14.8175651101426 & 0.182434889857352 \tabularnewline
248 & 23 & 15.8568169241739 & 7.14318307582606 \tabularnewline
249 & 20 & 18.0957656598852 & 1.90423434011476 \tabularnewline
250 & 16 & 16.211137823369 & -0.211137823369017 \tabularnewline
251 & 14 & 13.1915792824443 & 0.808420717555727 \tabularnewline
252 & 17 & 14.0593631430792 & 2.9406368569208 \tabularnewline
253 & 11 & 14.5139560886432 & -3.51395608864323 \tabularnewline
254 & 13 & 14.3453792555653 & -1.34537925556528 \tabularnewline
255 & 17 & 15.615538191863 & 1.38446180813704 \tabularnewline
256 & 15 & 15.5822771165039 & -0.582277116503859 \tabularnewline
257 & 21 & 16.2218602256077 & 4.77813977439228 \tabularnewline
258 & 18 & 18.0398334191349 & -0.0398334191348575 \tabularnewline
259 & 15 & 13.2085434326086 & 1.79145656739139 \tabularnewline
260 & 8 & 16.3863971047013 & -8.38639710470133 \tabularnewline
261 & 12 & 14.0456852785798 & -2.04568527857981 \tabularnewline
262 & 12 & 13.2552987619624 & -1.25529876196236 \tabularnewline
263 & 22 & 19.1816724396528 & 2.81832756034715 \tabularnewline
264 & 12 & 13.578588384591 & -1.57858838459101 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&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]12[/C][C]13.7157720341382[/C][C]-1.71577203413821[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.0861825906815[/C][C]1.9138174093185[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]15.1966717726704[/C][C]-1.19667177267044[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.4435928981764[/C][C]-2.44359289817641[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.0311633525983[/C][C]9.96883664740168[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]9.52995097322955[/C][C]2.47004902677045[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]14.1873524423842[/C][C]7.81264755761578[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.1598600418145[/C][C]-1.15986004181448[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]11.8141122303077[/C][C]-1.81411223030773[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]11.751231654734[/C][C]1.24876834526599[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.1162806367607[/C][C]-0.116280636760667[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]10.2714297103659[/C][C]-2.27142971036586[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.6818895260696[/C][C]-0.681889526069634[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]12.2452571313516[/C][C]1.75474286864841[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]8.8707597399304[/C][C]1.1292402600696[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.6686217537492[/C][C]0.3313782462508[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.2792699921865[/C][C]1.72073000781355[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.25817971104303[/C][C]1.74182028895697[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]13.1566507416699[/C][C]-3.15665074166989[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]12.4077447770412[/C][C]0.592255222958761[/C][/ROW]
[ROW][C]21[/C][C]9.5[/C][C]10.4136084131918[/C][C]-0.913608413191769[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]15.3373491029299[/C][C]-1.33734910292992[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]13.4232795921274[/C][C]-1.42327959212744[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]15.3297269112167[/C][C]-1.32972691121666[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]9.38495446263141[/C][C]1.61504553736859[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]14.929162134414[/C][C]-5.92916213441404[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]10.4033970388626[/C][C]0.59660296113741[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1512731585574[/C][C]1.84872684144258[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]11.144464068756[/C][C]2.85553593124403[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.5430693553221[/C][C]-2.5430693553221[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]10.8781972312942[/C][C]-1.87819723129423[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.8401977957674[/C][C]0.159802204232617[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.58724127727[/C][C]-0.587241277269979[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]11.5848221890795[/C][C]-0.584822189079476[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.064438762704[/C][C]0.935561237296047[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]11.8114547346829[/C][C]-3.81145473468287[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]16.8075184695675[/C][C]3.19248153043251[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]11.9700283708003[/C][C]0.0299716291997416[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.3730583871876[/C][C]-0.373058387187584[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.4143857577906[/C][C]-3.41438575779061[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]11.673437163483[/C][C]-2.67343716348301[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]10.8693094086964[/C][C]3.13069059130357[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]12.0045472828605[/C][C]-4.00454728286052[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]15.1808955038229[/C][C]-1.18089550382286[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]12.0687774133059[/C][C]-1.06877741330592[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]14.8437748536321[/C][C]-1.84377485363214[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.8784348158986[/C][C]-3.87843481589856[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]11.8405254905448[/C][C]-0.840525490544805[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]9.88903469082399[/C][C]5.11096530917601[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]12.9492363751091[/C][C]-1.94923637510914[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.2920761811796[/C][C]-1.29207618117956[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.9261536092764[/C][C]1.07384639072362[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.2423229630682[/C][C]2.75767703693184[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]15.8538193606058[/C][C]-1.85381936060582[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]14.6111542053965[/C][C]-3.61115420539645[/C][/ROW]
[ROW][C]56[/C][C]14.5[/C][C]11.8139605229008[/C][C]2.68603947709923[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]10.9343775511171[/C][C]2.06562244888287[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.5312563101057[/C][C]-3.53125631010572[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.6229292057283[/C][C]-3.62292920572826[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.0950172800646[/C][C]0.904982719935355[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]18.1483921910177[/C][C]1.85160780898227[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]12.5260201981935[/C][C]-0.526020198193496[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]13.982217522954[/C][C]-1.98221752295396[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.9344616676061[/C][C]0.0655383323938942[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]13.034912997953[/C][C]-0.0349129979530201[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.1852160246527[/C][C]-4.18521602465265[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]15.5320177934605[/C][C]1.46798220653954[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.7450846764111[/C][C]-1.74508467641105[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.9492869580555[/C][C]0.0507130419444946[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.4981502468851[/C][C]1.50184975311488[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]14.0812082376877[/C][C]-1.08120823768774[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]13.0211452055763[/C][C]1.97885479442367[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.4459784243546[/C][C]1.55402157564543[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.9919819443633[/C][C]-1.99198194436328[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]12.7263070377182[/C][C]-1.72630703771817[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.7129706757092[/C][C]5.28702932429076[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]11.0760350757271[/C][C]1.92396492427289[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]14.1821456202678[/C][C]2.81785437973224[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.3572175278492[/C][C]0.642782472150784[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]14.737112540349[/C][C]-5.73711254034899[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]11.5923297136178[/C][C]-0.592329713617819[/C][/ROW]
[ROW][C]82[/C][C]9[/C][C]12.6497320947006[/C][C]-3.64973209470055[/C][/ROW]
[ROW][C]83[/C][C]12[/C][C]11.3295990474431[/C][C]0.670400952556914[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]12.4709579889239[/C][C]-0.470957988923858[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]12.7381501371564[/C][C]0.261849862843572[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.0697719659078[/C][C]0.930228034092154[/C][/ROW]
[ROW][C]87[/C][C]12[/C][C]13.223171796885[/C][C]-1.223171796885[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]14.9762319137312[/C][C]0.0237680862688101[/C][/ROW]
[ROW][C]89[/C][C]22[/C][C]18.0307782978973[/C][C]3.96922170210268[/C][/ROW]
[ROW][C]90[/C][C]13[/C][C]11.4812237637233[/C][C]1.51877623627674[/C][/ROW]
[ROW][C]91[/C][C]15[/C][C]14.7088824447753[/C][C]0.291117555224736[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.8699085792686[/C][C]1.1300914207314[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]12.1669321793607[/C][C]2.83306782063929[/C][/ROW]
[ROW][C]94[/C][C]12.5[/C][C]13.2104310586122[/C][C]-0.710431058612193[/C][/ROW]
[ROW][C]95[/C][C]11[/C][C]10.7545510867585[/C][C]0.245448913241495[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.6249722151982[/C][C]1.37502778480177[/C][/ROW]
[ROW][C]97[/C][C]11[/C][C]12.373701472213[/C][C]-1.37370147221295[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]10.1110562506575[/C][C]0.888943749342492[/C][/ROW]
[ROW][C]99[/C][C]10[/C][C]12.2189355272299[/C][C]-2.21893552722987[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]10.4023111057221[/C][C]-0.402311105722136[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.3148337690208[/C][C]1.68516623097916[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]10.576513296961[/C][C]1.42348670303901[/C][/ROW]
[ROW][C]103[/C][C]11[/C][C]15.8060296264998[/C][C]-4.8060296264998[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]11.7637255518957[/C][C]4.23627444810427[/C][/ROW]
[ROW][C]105[/C][C]19[/C][C]16.5918127923145[/C][C]2.40818720768548[/C][/ROW]
[ROW][C]106[/C][C]11[/C][C]11.3443715003713[/C][C]-0.344371500371283[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]12.1776717849416[/C][C]3.82232821505837[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]16.8355274853934[/C][C]-1.83552748539339[/C][/ROW]
[ROW][C]109[/C][C]24[/C][C]17.7104656009241[/C][C]6.2895343990759[/C][/ROW]
[ROW][C]110[/C][C]14[/C][C]11.6557652077693[/C][C]2.34423479223069[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.8241561406981[/C][C]0.175843859301886[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]12.9245779148433[/C][C]-1.92457791484327[/C][/ROW]
[ROW][C]113[/C][C]15[/C][C]13.491659910468[/C][C]1.50834008953202[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]10.3043027108943[/C][C]1.69569728910572[/C][/ROW]
[ROW][C]115[/C][C]10[/C][C]10.6539479786946[/C][C]-0.653947978694595[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.3205473307977[/C][C]-0.320547330797736[/C][/ROW]
[ROW][C]117[/C][C]13[/C][C]13.9787746148646[/C][C]-0.978774614864558[/C][/ROW]
[ROW][C]118[/C][C]9[/C][C]13.4021326340186[/C][C]-4.40213263401858[/C][/ROW]
[ROW][C]119[/C][C]15[/C][C]11.7965640891664[/C][C]3.20343591083361[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]15.7369076052955[/C][C]-0.736907605295548[/C][/ROW]
[ROW][C]121[/C][C]14[/C][C]12.7665109087008[/C][C]1.23348909129917[/C][/ROW]
[ROW][C]122[/C][C]11[/C][C]11.2996059933673[/C][C]-0.299605993367257[/C][/ROW]
[ROW][C]123[/C][C]8[/C][C]12.107778526338[/C][C]-4.10777852633797[/C][/ROW]
[ROW][C]124[/C][C]11[/C][C]12.3368285704892[/C][C]-1.33682857048917[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]13.1368063314477[/C][C]-2.13680633144766[/C][/ROW]
[ROW][C]126[/C][C]8[/C][C]9.7843412533374[/C][C]-1.7843412533374[/C][/ROW]
[ROW][C]127[/C][C]10[/C][C]10.365283121783[/C][C]-0.365283121783007[/C][/ROW]
[ROW][C]128[/C][C]11[/C][C]9.7227354334813[/C][C]1.2772645665187[/C][/ROW]
[ROW][C]129[/C][C]13[/C][C]12.554486989198[/C][C]0.445513010801998[/C][/ROW]
[ROW][C]130[/C][C]11[/C][C]13.6213789247615[/C][C]-2.62137892476149[/C][/ROW]
[ROW][C]131[/C][C]20[/C][C]17.2613739195877[/C][C]2.73862608041229[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]11.7388984073489[/C][C]-1.73889840734889[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]13.2502357705675[/C][C]1.74976422943252[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]12.392387579049[/C][C]-0.392387579049031[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]11.1166808900119[/C][C]2.88331910998815[/C][/ROW]
[ROW][C]136[/C][C]23[/C][C]16.5157775954514[/C][C]6.48422240454861[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.2100552310358[/C][C]-0.21005523103579[/C][/ROW]
[ROW][C]138[/C][C]16[/C][C]16.9239238514428[/C][C]-0.923923851442766[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.2155173708878[/C][C]-2.21551737088784[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]14.1054095248332[/C][C]-2.10540952483317[/C][/ROW]
[ROW][C]141[/C][C]10[/C][C]13.5619485784295[/C][C]-3.56194857842946[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]11.0613257947107[/C][C]2.93867420528932[/C][/ROW]
[ROW][C]143[/C][C]12[/C][C]12.2636121871415[/C][C]-0.263612187141454[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]11.9260975739968[/C][C]0.0739024260032187[/C][/ROW]
[ROW][C]145[/C][C]11[/C][C]10.8853489158908[/C][C]0.114651084109165[/C][/ROW]
[ROW][C]146[/C][C]12[/C][C]11.4222279322477[/C][C]0.577772067752272[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]16.1550721367273[/C][C]-3.15507213672733[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]14.2746457560125[/C][C]-3.27464575601246[/C][/ROW]
[ROW][C]149[/C][C]19[/C][C]16.7357789198436[/C][C]2.26422108015644[/C][/ROW]
[ROW][C]150[/C][C]12[/C][C]11.2939217297493[/C][C]0.70607827025074[/C][/ROW]
[ROW][C]151[/C][C]17[/C][C]12.9119104714701[/C][C]4.08808952852993[/C][/ROW]
[ROW][C]152[/C][C]9[/C][C]11.3934326734384[/C][C]-2.39343267343835[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]14.4127543563428[/C][C]-2.41275435634275[/C][/ROW]
[ROW][C]154[/C][C]19[/C][C]16.3324954868874[/C][C]2.66750451311265[/C][/ROW]
[ROW][C]155[/C][C]18[/C][C]14.2346388934094[/C][C]3.7653611065906[/C][/ROW]
[ROW][C]156[/C][C]15[/C][C]14.7088824447753[/C][C]0.291117555224736[/C][/ROW]
[ROW][C]157[/C][C]14[/C][C]13.6193439117296[/C][C]0.380656088270352[/C][/ROW]
[ROW][C]158[/C][C]11[/C][C]9.7227354334813[/C][C]1.2772645665187[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]12.9044066591528[/C][C]-3.90440665915284[/C][/ROW]
[ROW][C]160[/C][C]18[/C][C]13.7466485582724[/C][C]4.25335144172757[/C][/ROW]
[ROW][C]161[/C][C]16[/C][C]14.6971943508004[/C][C]1.3028056491996[/C][/ROW]
[ROW][C]162[/C][C]24[/C][C]17.3326563586641[/C][C]6.66734364133588[/C][/ROW]
[ROW][C]163[/C][C]14[/C][C]12.8218867878371[/C][C]1.17811321216287[/C][/ROW]
[ROW][C]164[/C][C]20[/C][C]11.2857622062729[/C][C]8.71423779372713[/C][/ROW]
[ROW][C]165[/C][C]18[/C][C]16.1189272897936[/C][C]1.88107271020636[/C][/ROW]
[ROW][C]166[/C][C]23[/C][C]17.2693992166723[/C][C]5.73060078332775[/C][/ROW]
[ROW][C]167[/C][C]12[/C][C]13.3742163467959[/C][C]-1.37421634679588[/C][/ROW]
[ROW][C]168[/C][C]14[/C][C]14.9913088990735[/C][C]-0.991308899073541[/C][/ROW]
[ROW][C]169[/C][C]16[/C][C]16.4078196606164[/C][C]-0.407819660616392[/C][/ROW]
[ROW][C]170[/C][C]18[/C][C]16.4486353714316[/C][C]1.55136462856842[/C][/ROW]
[ROW][C]171[/C][C]20[/C][C]16.8117273131526[/C][C]3.1882726868474[/C][/ROW]
[ROW][C]172[/C][C]12[/C][C]12.0682656732306[/C][C]-0.0682656732306124[/C][/ROW]
[ROW][C]173[/C][C]12[/C][C]16.4354680561514[/C][C]-4.43546805615137[/C][/ROW]
[ROW][C]174[/C][C]17[/C][C]15.3377593817076[/C][C]1.66224061829242[/C][/ROW]
[ROW][C]175[/C][C]13[/C][C]12.2651197959071[/C][C]0.734880204092929[/C][/ROW]
[ROW][C]176[/C][C]9[/C][C]13.3240737976262[/C][C]-4.32407379762615[/C][/ROW]
[ROW][C]177[/C][C]16[/C][C]17.5086890220889[/C][C]-1.5086890220889[/C][/ROW]
[ROW][C]178[/C][C]18[/C][C]15.179194600813[/C][C]2.82080539918698[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]12.6343582473021[/C][C]-2.63435824730213[/C][/ROW]
[ROW][C]180[/C][C]14[/C][C]15.0247587838041[/C][C]-1.02475878380405[/C][/ROW]
[ROW][C]181[/C][C]11[/C][C]14.5556048934217[/C][C]-3.55560489342173[/C][/ROW]
[ROW][C]182[/C][C]9[/C][C]14.7499263373025[/C][C]-5.74992633730252[/C][/ROW]
[ROW][C]183[/C][C]11[/C][C]13.0134902449848[/C][C]-2.01349024498476[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]12.7699967809814[/C][C]-2.76999678098144[/C][/ROW]
[ROW][C]185[/C][C]11[/C][C]11.9077588681681[/C][C]-0.907758868168088[/C][/ROW]
[ROW][C]186[/C][C]19[/C][C]13.5817367037217[/C][C]5.41826329627827[/C][/ROW]
[ROW][C]187[/C][C]14[/C][C]12.8933658241132[/C][C]1.10663417588676[/C][/ROW]
[ROW][C]188[/C][C]12[/C][C]11.7728994006205[/C][C]0.227100599379505[/C][/ROW]
[ROW][C]189[/C][C]14[/C][C]15.2731375482719[/C][C]-1.27313754827193[/C][/ROW]
[ROW][C]190[/C][C]21[/C][C]16.8549742357968[/C][C]4.14502576420319[/C][/ROW]
[ROW][C]191[/C][C]13[/C][C]15.9180965359696[/C][C]-2.91809653596961[/C][/ROW]
[ROW][C]192[/C][C]10[/C][C]12.9070917808708[/C][C]-2.90709178087081[/C][/ROW]
[ROW][C]193[/C][C]15[/C][C]13.2832541496001[/C][C]1.71674585039988[/C][/ROW]
[ROW][C]194[/C][C]16[/C][C]16.6128956760833[/C][C]-0.612895676083333[/C][/ROW]
[ROW][C]195[/C][C]14[/C][C]13.0231254498953[/C][C]0.976874550104672[/C][/ROW]
[ROW][C]196[/C][C]12[/C][C]15.1349362321744[/C][C]-3.13493623217436[/C][/ROW]
[ROW][C]197[/C][C]19[/C][C]12.9953035099249[/C][C]6.00469649007507[/C][/ROW]
[ROW][C]198[/C][C]15[/C][C]12.541528436869[/C][C]2.45847156313102[/C][/ROW]
[ROW][C]199[/C][C]19[/C][C]18.1937482578624[/C][C]0.806251742137632[/C][/ROW]
[ROW][C]200[/C][C]13[/C][C]13.5768943807102[/C][C]-0.576894380710221[/C][/ROW]
[ROW][C]201[/C][C]17[/C][C]17.4425971697839[/C][C]-0.442597169783927[/C][/ROW]
[ROW][C]202[/C][C]12[/C][C]13.0326520628598[/C][C]-1.03265206285977[/C][/ROW]
[ROW][C]203[/C][C]11[/C][C]10.9374545103091[/C][C]0.0625454896908994[/C][/ROW]
[ROW][C]204[/C][C]14[/C][C]15.816289855023[/C][C]-1.81628985502302[/C][/ROW]
[ROW][C]205[/C][C]11[/C][C]12.7432127628135[/C][C]-1.74321276281352[/C][/ROW]
[ROW][C]206[/C][C]13[/C][C]12.701677070019[/C][C]0.298322929980989[/C][/ROW]
[ROW][C]207[/C][C]12[/C][C]13.1196850863196[/C][C]-1.11968508631963[/C][/ROW]
[ROW][C]208[/C][C]15[/C][C]13.2288406214667[/C][C]1.7711593785333[/C][/ROW]
[ROW][C]209[/C][C]14[/C][C]14.7041918355258[/C][C]-0.704191835525806[/C][/ROW]
[ROW][C]210[/C][C]12[/C][C]11.2154156924027[/C][C]0.784584307597277[/C][/ROW]
[ROW][C]211[/C][C]17[/C][C]17.7355291876386[/C][C]-0.73552918763857[/C][/ROW]
[ROW][C]212[/C][C]11[/C][C]11.6852924975315[/C][C]-0.685292497531517[/C][/ROW]
[ROW][C]213[/C][C]18[/C][C]14.8325843973126[/C][C]3.16741560268744[/C][/ROW]
[ROW][C]214[/C][C]13[/C][C]16.0458797929614[/C][C]-3.04587979296142[/C][/ROW]
[ROW][C]215[/C][C]17[/C][C]15.6538282628051[/C][C]1.34617173719489[/C][/ROW]
[ROW][C]216[/C][C]13[/C][C]13.6935284986346[/C][C]-0.693528498634643[/C][/ROW]
[ROW][C]217[/C][C]11[/C][C]10.6053630232808[/C][C]0.394636976719202[/C][/ROW]
[ROW][C]218[/C][C]12[/C][C]12.9657212648586[/C][C]-0.965721264858646[/C][/ROW]
[ROW][C]219[/C][C]22[/C][C]18.1813730599913[/C][C]3.8186269400087[/C][/ROW]
[ROW][C]220[/C][C]14[/C][C]12.4771416810046[/C][C]1.52285831899538[/C][/ROW]
[ROW][C]221[/C][C]12[/C][C]15.3295305688619[/C][C]-3.32953056886191[/C][/ROW]
[ROW][C]222[/C][C]12[/C][C]12.320424237492[/C][C]-0.320424237491951[/C][/ROW]
[ROW][C]223[/C][C]17[/C][C]16.2685699455426[/C][C]0.731430054457368[/C][/ROW]
[ROW][C]224[/C][C]9[/C][C]13.1130188114459[/C][C]-4.11301881144589[/C][/ROW]
[ROW][C]225[/C][C]21[/C][C]17.2942655917238[/C][C]3.70573440827622[/C][/ROW]
[ROW][C]226[/C][C]10[/C][C]12.0249605419275[/C][C]-2.02496054192748[/C][/ROW]
[ROW][C]227[/C][C]11[/C][C]11.4079327892061[/C][C]-0.407932789206072[/C][/ROW]
[ROW][C]228[/C][C]12[/C][C]14.8529552382284[/C][C]-2.85295523822839[/C][/ROW]
[ROW][C]229[/C][C]23[/C][C]18.4833857460694[/C][C]4.51661425393064[/C][/ROW]
[ROW][C]230[/C][C]13[/C][C]15.9180508098044[/C][C]-2.91805080980438[/C][/ROW]
[ROW][C]231[/C][C]12[/C][C]13.6638988904704[/C][C]-1.66389889047044[/C][/ROW]
[ROW][C]232[/C][C]16[/C][C]18.053654611844[/C][C]-2.05365461184395[/C][/ROW]
[ROW][C]233[/C][C]9[/C][C]13.8478247956945[/C][C]-4.84782479569452[/C][/ROW]
[ROW][C]234[/C][C]17[/C][C]14.1421423093923[/C][C]2.85785769060767[/C][/ROW]
[ROW][C]235[/C][C]9[/C][C]12.2880482251603[/C][C]-3.28804822516032[/C][/ROW]
[ROW][C]236[/C][C]14[/C][C]15.9004654659449[/C][C]-1.90046546594494[/C][/ROW]
[ROW][C]237[/C][C]17[/C][C]15.7512420748347[/C][C]1.24875792516532[/C][/ROW]
[ROW][C]238[/C][C]13[/C][C]16.0080097485031[/C][C]-3.0080097485031[/C][/ROW]
[ROW][C]239[/C][C]11[/C][C]16.16364564684[/C][C]-5.16364564683998[/C][/ROW]
[ROW][C]240[/C][C]12[/C][C]15.7816908193497[/C][C]-3.78169081934966[/C][/ROW]
[ROW][C]241[/C][C]10[/C][C]14.1407022024611[/C][C]-4.14070220246106[/C][/ROW]
[ROW][C]242[/C][C]19[/C][C]19.2035374701946[/C][C]-0.203537470194584[/C][/ROW]
[ROW][C]243[/C][C]16[/C][C]16.177919039912[/C][C]-0.177919039911952[/C][/ROW]
[ROW][C]244[/C][C]16[/C][C]15.3281662996259[/C][C]0.671833700374078[/C][/ROW]
[ROW][C]245[/C][C]14[/C][C]12.5892148078779[/C][C]1.41078519212209[/C][/ROW]
[ROW][C]246[/C][C]20[/C][C]16.0645764575464[/C][C]3.93542354245359[/C][/ROW]
[ROW][C]247[/C][C]15[/C][C]14.8175651101426[/C][C]0.182434889857352[/C][/ROW]
[ROW][C]248[/C][C]23[/C][C]15.8568169241739[/C][C]7.14318307582606[/C][/ROW]
[ROW][C]249[/C][C]20[/C][C]18.0957656598852[/C][C]1.90423434011476[/C][/ROW]
[ROW][C]250[/C][C]16[/C][C]16.211137823369[/C][C]-0.211137823369017[/C][/ROW]
[ROW][C]251[/C][C]14[/C][C]13.1915792824443[/C][C]0.808420717555727[/C][/ROW]
[ROW][C]252[/C][C]17[/C][C]14.0593631430792[/C][C]2.9406368569208[/C][/ROW]
[ROW][C]253[/C][C]11[/C][C]14.5139560886432[/C][C]-3.51395608864323[/C][/ROW]
[ROW][C]254[/C][C]13[/C][C]14.3453792555653[/C][C]-1.34537925556528[/C][/ROW]
[ROW][C]255[/C][C]17[/C][C]15.615538191863[/C][C]1.38446180813704[/C][/ROW]
[ROW][C]256[/C][C]15[/C][C]15.5822771165039[/C][C]-0.582277116503859[/C][/ROW]
[ROW][C]257[/C][C]21[/C][C]16.2218602256077[/C][C]4.77813977439228[/C][/ROW]
[ROW][C]258[/C][C]18[/C][C]18.0398334191349[/C][C]-0.0398334191348575[/C][/ROW]
[ROW][C]259[/C][C]15[/C][C]13.2085434326086[/C][C]1.79145656739139[/C][/ROW]
[ROW][C]260[/C][C]8[/C][C]16.3863971047013[/C][C]-8.38639710470133[/C][/ROW]
[ROW][C]261[/C][C]12[/C][C]14.0456852785798[/C][C]-2.04568527857981[/C][/ROW]
[ROW][C]262[/C][C]12[/C][C]13.2552987619624[/C][C]-1.25529876196236[/C][/ROW]
[ROW][C]263[/C][C]22[/C][C]19.1816724396528[/C][C]2.81832756034715[/C][/ROW]
[ROW][C]264[/C][C]12[/C][C]13.578588384591[/C][C]-1.57858838459101[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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
11213.7157720341382-1.71577203413821
2119.08618259068151.9138174093185
31415.1966717726704-1.19667177267044
41214.4435928981764-2.44359289817641
52111.03116335259839.96883664740168
6129.529950973229552.47004902677045
72214.18735244238427.81264755761578
81112.1598600418145-1.15986004181448
91011.8141122303077-1.81411223030773
101311.7512316547341.24876834526599
111010.1162806367607-0.116280636760667
12810.2714297103659-2.27142971036586
131515.6818895260696-0.681889526069634
141412.24525713135161.75474286864841
15108.87075973993041.1292402600696
161413.66862175374920.3313782462508
171412.27926999218651.72073000781355
18119.258179711043031.74182028895697
191013.1566507416699-3.15665074166989
201312.40774477704120.592255222958761
219.510.4136084131918-0.913608413191769
221415.3373491029299-1.33734910292992
231213.4232795921274-1.42327959212744
241415.3297269112167-1.32972691121666
25119.384954462631411.61504553736859
26914.929162134414-5.92916213441404
271110.40339703886260.59660296113741
281513.15127315855741.84872684144258
291411.1444640687562.85553593124403
301315.5430693553221-2.5430693553221
31910.8781972312942-1.87819723129423
321514.84019779576740.159802204232617
331010.58724127727-0.587241277269979
341111.5848221890795-0.584822189079476
351312.0644387627040.935561237296047
36811.8114547346829-3.81145473468287
372016.80751846956753.19248153043251
381211.97002837080030.0299716291997416
391010.3730583871876-0.373058387187584
401013.4143857577906-3.41438575779061
41911.673437163483-2.67343716348301
421410.86930940869643.13069059130357
43812.0045472828605-4.00454728286052
441415.1808955038229-1.18089550382286
451112.0687774133059-1.06877741330592
461314.8437748536321-1.84377485363214
47912.8784348158986-3.87843481589856
481111.8405254905448-0.840525490544805
49159.889034690823995.11096530917601
501112.9492363751091-1.94923637510914
511011.2920761811796-1.29207618117956
521412.92615360927641.07384639072362
531815.24232296306822.75767703693184
541415.8538193606058-1.85381936060582
551114.6111542053965-3.61115420539645
5614.511.81396052290082.68603947709923
571310.93437755111712.06562244888287
58912.5312563101057-3.53125631010572
591013.6229292057283-3.62292920572826
601514.09501728006460.904982719935355
612018.14839219101771.85160780898227
621212.5260201981935-0.526020198193496
631213.982217522954-1.98221752295396
641413.93446166760610.0655383323938942
651313.034912997953-0.0349129979530201
661115.1852160246527-4.18521602465265
671715.53201779346051.46798220653954
681213.7450846764111-1.74508467641105
691312.94928695805550.0507130419444946
701412.49815024688511.50184975311488
711314.0812082376877-1.08120823768774
721513.02114520557631.97885479442367
731311.44597842435461.55402157564543
741011.9919819443633-1.99198194436328
751112.7263070377182-1.72630703771817
761913.71297067570925.28702932429076
771311.07603507572711.92396492427289
781714.18214562026782.81785437973224
791312.35721752784920.642782472150784
80914.737112540349-5.73711254034899
811111.5923297136178-0.592329713617819
82912.6497320947006-3.64973209470055
831211.32959904744310.670400952556914
841212.4709579889239-0.470957988923858
851312.73815013715640.261849862843572
861312.06977196590780.930228034092154
871213.223171796885-1.223171796885
881514.97623191373120.0237680862688101
892218.03077829789733.96922170210268
901311.48122376372331.51877623627674
911514.70888244477530.291117555224736
921311.86990857926861.1300914207314
931512.16693217936072.83306782063929
9412.513.2104310586122-0.710431058612193
951110.75455108675850.245448913241495
961614.62497221519821.37502778480177
971112.373701472213-1.37370147221295
981110.11105625065750.888943749342492
991012.2189355272299-2.21893552722987
1001010.4023111057221-0.402311105722136
1011614.31483376902081.68516623097916
1021210.5765132969611.42348670303901
1031115.8060296264998-4.8060296264998
1041611.76372555189574.23627444810427
1051916.59181279231452.40818720768548
1061111.3443715003713-0.344371500371283
1071612.17767178494163.82232821505837
1081516.8355274853934-1.83552748539339
1092417.71046560092416.2895343990759
1101411.65576520776932.34423479223069
1111514.82415614069810.175843859301886
1121112.9245779148433-1.92457791484327
1131513.4916599104681.50834008953202
1141210.30430271089431.69569728910572
1151010.6539479786946-0.653947978694595
1161414.3205473307977-0.320547330797736
1171313.9787746148646-0.978774614864558
118913.4021326340186-4.40213263401858
1191511.79656408916643.20343591083361
1201515.7369076052955-0.736907605295548
1211412.76651090870081.23348909129917
1221111.2996059933673-0.299605993367257
123812.107778526338-4.10777852633797
1241112.3368285704892-1.33682857048917
1251113.1368063314477-2.13680633144766
12689.7843412533374-1.7843412533374
1271010.365283121783-0.365283121783007
128119.72273543348131.2772645665187
1291312.5544869891980.445513010801998
1301113.6213789247615-2.62137892476149
1312017.26137391958772.73862608041229
1321011.7388984073489-1.73889840734889
1331513.25023577056751.74976422943252
1341212.392387579049-0.392387579049031
1351411.11668089001192.88331910998815
1362316.51577759545146.48422240454861
1371414.2100552310358-0.21005523103579
1381616.9239238514428-0.923923851442766
1391113.2155173708878-2.21551737088784
1401214.1054095248332-2.10540952483317
1411013.5619485784295-3.56194857842946
1421411.06132579471072.93867420528932
1431212.2636121871415-0.263612187141454
1441211.92609757399680.0739024260032187
1451110.88534891589080.114651084109165
1461211.42222793224770.577772067752272
1471316.1550721367273-3.15507213672733
1481114.2746457560125-3.27464575601246
1491916.73577891984362.26422108015644
1501211.29392172974930.70607827025074
1511712.91191047147014.08808952852993
152911.3934326734384-2.39343267343835
1531214.4127543563428-2.41275435634275
1541916.33249548688742.66750451311265
1551814.23463889340943.7653611065906
1561514.70888244477530.291117555224736
1571413.61934391172960.380656088270352
158119.72273543348131.2772645665187
159912.9044066591528-3.90440665915284
1601813.74664855827244.25335144172757
1611614.69719435080041.3028056491996
1622417.33265635866416.66734364133588
1631412.82188678783711.17811321216287
1642011.28576220627298.71423779372713
1651816.11892728979361.88107271020636
1662317.26939921667235.73060078332775
1671213.3742163467959-1.37421634679588
1681414.9913088990735-0.991308899073541
1691616.4078196606164-0.407819660616392
1701816.44863537143161.55136462856842
1712016.81172731315263.1882726868474
1721212.0682656732306-0.0682656732306124
1731216.4354680561514-4.43546805615137
1741715.33775938170761.66224061829242
1751312.26511979590710.734880204092929
176913.3240737976262-4.32407379762615
1771617.5086890220889-1.5086890220889
1781815.1791946008132.82080539918698
1791012.6343582473021-2.63435824730213
1801415.0247587838041-1.02475878380405
1811114.5556048934217-3.55560489342173
182914.7499263373025-5.74992633730252
1831113.0134902449848-2.01349024498476
1841012.7699967809814-2.76999678098144
1851111.9077588681681-0.907758868168088
1861913.58173670372175.41826329627827
1871412.89336582411321.10663417588676
1881211.77289940062050.227100599379505
1891415.2731375482719-1.27313754827193
1902116.85497423579684.14502576420319
1911315.9180965359696-2.91809653596961
1921012.9070917808708-2.90709178087081
1931513.28325414960011.71674585039988
1941616.6128956760833-0.612895676083333
1951413.02312544989530.976874550104672
1961215.1349362321744-3.13493623217436
1971912.99530350992496.00469649007507
1981512.5415284368692.45847156313102
1991918.19374825786240.806251742137632
2001313.5768943807102-0.576894380710221
2011717.4425971697839-0.442597169783927
2021213.0326520628598-1.03265206285977
2031110.93745451030910.0625454896908994
2041415.816289855023-1.81628985502302
2051112.7432127628135-1.74321276281352
2061312.7016770700190.298322929980989
2071213.1196850863196-1.11968508631963
2081513.22884062146671.7711593785333
2091414.7041918355258-0.704191835525806
2101211.21541569240270.784584307597277
2111717.7355291876386-0.73552918763857
2121111.6852924975315-0.685292497531517
2131814.83258439731263.16741560268744
2141316.0458797929614-3.04587979296142
2151715.65382826280511.34617173719489
2161313.6935284986346-0.693528498634643
2171110.60536302328080.394636976719202
2181212.9657212648586-0.965721264858646
2192218.18137305999133.8186269400087
2201412.47714168100461.52285831899538
2211215.3295305688619-3.32953056886191
2221212.320424237492-0.320424237491951
2231716.26856994554260.731430054457368
224913.1130188114459-4.11301881144589
2252117.29426559172383.70573440827622
2261012.0249605419275-2.02496054192748
2271111.4079327892061-0.407932789206072
2281214.8529552382284-2.85295523822839
2292318.48338574606944.51661425393064
2301315.9180508098044-2.91805080980438
2311213.6638988904704-1.66389889047044
2321618.053654611844-2.05365461184395
233913.8478247956945-4.84782479569452
2341714.14214230939232.85785769060767
235912.2880482251603-3.28804822516032
2361415.9004654659449-1.90046546594494
2371715.75124207483471.24875792516532
2381316.0080097485031-3.0080097485031
2391116.16364564684-5.16364564683998
2401215.7816908193497-3.78169081934966
2411014.1407022024611-4.14070220246106
2421919.2035374701946-0.203537470194584
2431616.177919039912-0.177919039911952
2441615.32816629962590.671833700374078
2451412.58921480787791.41078519212209
2462016.06457645754643.93542354245359
2471514.81756511014260.182434889857352
2482315.85681692417397.14318307582606
2492018.09576565988521.90423434011476
2501616.211137823369-0.211137823369017
2511413.19157928244430.808420717555727
2521714.05936314307922.9406368569208
2531114.5139560886432-3.51395608864323
2541314.3453792555653-1.34537925556528
2551715.6155381918631.38446180813704
2561515.5822771165039-0.582277116503859
2572116.22186022560774.77813977439228
2581818.0398334191349-0.0398334191348575
2591513.20854343260861.79145656739139
260816.3863971047013-8.38639710470133
2611214.0456852785798-2.04568527857981
2621213.2552987619624-1.25529876196236
2632219.18167243965282.81832756034715
2641213.578588384591-1.57858838459101






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.9788352446304670.04232951073906530.0211647553695326
130.9732879131515240.05342417369695250.0267120868484763
140.9796288309315380.04074233813692440.0203711690684622
150.9645491972967510.07090160540649890.0354508027032494
160.940712126382930.118575747234140.0592878736170698
170.9715672777527290.05686544449454270.0284327222472713
180.9594265316770790.08114693664584220.0405734683229211
190.974726571662970.0505468566740590.0252734283370295
200.9614302440466310.07713951190673820.0385697559533691
210.953135759397530.093728481204940.04686424060247
220.9489047962712580.1021904074574850.0510952037287424
230.9280909153684960.1438181692630080.0719090846315038
240.9122970522419710.1754058955160570.0877029477580287
250.8819429605906680.2361140788186630.118057039409332
260.8795609028347530.2408781943304940.120439097165247
270.8879436621300030.2241126757399950.112056337869997
280.8552888031849390.2894223936301220.144711196815061
290.8817253750396040.2365492499207920.118274624960396
300.8718182451980070.2563635096039870.128181754801993
310.875164865125090.249670269749820.12483513487491
320.8482520374330890.3034959251338220.151747962566911
330.82073448879930.3585310224013990.1792655112007
340.7982592304512730.4034815390974540.201740769548727
350.7684483570470280.4631032859059440.231551642952972
360.7950313974838160.4099372050323670.204968602516184
370.8679229693950710.2641540612098590.132077030604929
380.8373312777148410.3253374445703170.162668722285159
390.8069240240031440.3861519519937120.193075975996856
400.8033649021251230.3932701957497540.196635097874877
410.7903767852598870.4192464294802260.209623214740113
420.7800559448283990.4398881103432010.219944055171601
430.825386145188910.349227709622180.17461385481109
440.7920107351707660.4159785296584680.207989264829234
450.7571446365169950.4857107269660090.242855363483005
460.7306360916599370.5387278166801260.269363908340063
470.7797261933601190.4405476132797620.220273806639881
480.7469898896079940.5060202207840120.253010110392006
490.7992990600337260.4014018799325480.200700939966274
500.7734352451339830.4531295097320340.226564754866017
510.7609381464428820.4781237071142350.239061853557118
520.728791367338210.5424172653235790.27120863266179
530.7328851662655760.5342296674688490.267114833734424
540.6996502635216790.6006994729566420.300349736478321
550.7061916637455650.587616672508870.293808336254435
560.7224571099470610.5550857801058790.277542890052939
570.7020183708086520.5959632583826970.297981629191348
580.7336306064072960.5327387871854080.266369393592704
590.7452424354943920.5095151290112150.254757564505608
600.7226943160708280.5546113678583450.277305683929172
610.7343332730758160.5313334538483680.265666726924184
620.6983624946965250.6032750106069510.301637505303475
630.6740116743535690.6519766512928630.325988325646431
640.6344606345502850.7310787308994290.365539365449714
650.5937834050046640.8124331899906710.406216594995336
660.5812498408986870.8375003182026260.418750159101313
670.6042191589503920.7915616820992160.395780841049608
680.5756075126629490.8487849746741030.424392487337051
690.5348281674154830.9303436651690330.465171832584517
700.5064099550654340.9871800898691310.493590044934566
710.4685988547656630.9371977095313260.531401145234337
720.4392401415957940.8784802831915880.560759858404206
730.4068153630057990.8136307260115970.593184636994201
740.3913052829002720.7826105658005440.608694717099728
750.3644556630896330.7289113261792660.635544336910367
760.4976836628496820.9953673256993650.502316337150318
770.4666606711520340.9333213423040680.533339328847966
780.4585927201016780.9171854402033560.541407279898322
790.4205493239349750.8410986478699490.579450676065025
800.5542143269110960.8915713461778080.445785673088904
810.5211137910393710.9577724179212590.478886208960629
820.5577797037958610.8844405924082780.442220296204139
830.5195262990332990.9609474019334030.480473700966701
840.4829725383930410.9659450767860830.517027461606959
850.4440044962398490.8880089924796980.555995503760151
860.4080291413993490.8160582827986990.591970858600651
870.3781583428982170.7563166857964350.621841657101783
880.3432017246995230.6864034493990470.656798275300477
890.4201439543613050.840287908722610.579856045638695
900.3876339140059030.7752678280118050.612366085994097
910.354886432384270.709772864768540.64511356761573
920.3227919108811090.6455838217622180.677208089118891
930.3146293153585510.6292586307171020.685370684641449
940.2872443461218150.5744886922436290.712755653878185
950.2561902611687770.5123805223375540.743809738831223
960.2353590900713380.4707181801426760.764640909928662
970.2181254321200750.4362508642401490.781874567879925
980.1923239660748870.3846479321497750.807676033925113
990.1901410265256660.3802820530513320.809858973474334
1000.1674799167295420.3349598334590840.832520083270458
1010.1537289108282530.3074578216565060.846271089171747
1020.1356922316360460.2713844632720910.864307768363954
1030.1819090900984410.3638181801968810.818090909901559
1040.2064827672520670.4129655345041350.793517232747933
1050.2076527528539070.4153055057078150.792347247146093
1060.1834947065454090.3669894130908180.816505293454591
1070.1998703500830520.3997407001661040.800129649916948
1080.1864896210021650.372979242004330.813510378997835
1090.2884922582310420.5769845164620840.711507741768958
1100.2756536673736950.551307334747390.724346332626305
1110.2463043380879390.4926086761758770.753695661912061
1120.2392214536835640.4784429073671280.760778546316436
1130.2173099531140720.4346199062281440.782690046885928
1140.1982970833947520.3965941667895040.801702916605248
1150.1760890409783710.3521780819567410.823910959021629
1160.1547471425695040.3094942851390080.845252857430496
1170.1394428069551940.2788856139103880.860557193044806
1180.1809491184164730.3618982368329460.819050881583527
1190.1836220145160870.3672440290321740.816377985483913
1200.1628136356740490.3256272713480980.837186364325951
1210.1462517594487220.2925035188974430.853748240551279
1220.1293241723107430.2586483446214860.870675827689257
1230.1595530585513230.3191061171026470.840446941448676
1240.1442210996788310.2884421993576610.855778900321169
1250.1366903149412120.2733806298824240.863309685058788
1260.1298355432544910.2596710865089830.870164456745509
1270.1145886409323950.229177281864790.885411359067605
1280.1004199397413960.2008398794827930.899580060258604
1290.08548338433165650.1709667686633130.914516615668344
1300.08678502359550480.173570047191010.913214976404495
1310.08610564200220410.1722112840044080.913894357997796
1320.08065253118070960.1613050623614190.91934746881929
1330.0717235089935890.1434470179871780.928276491006411
1340.06078615422753480.121572308455070.939213845772465
1350.06023617922234680.1204723584446940.939763820777653
1360.1158023563856050.231604712771210.884197643614395
1370.0995518262999080.1991036525998160.900448173700092
1380.08761902925435070.1752380585087010.912380970745649
1390.08590664858635960.1718132971727190.91409335141364
1400.08275185602297560.1655037120459510.917248143977024
1410.09776265630880710.1955253126176140.902237343691193
1420.09667313827149390.1933462765429880.903326861728506
1430.08252298756740550.1650459751348110.917477012432594
1440.06970751175639310.1394150235127860.930292488243607
1450.05903937312747120.1180787462549420.940960626872529
1460.04923968155795370.09847936311590740.950760318442046
1470.0564600991763280.1129201983526560.943539900823672
1480.0655848198153570.1311696396307140.934415180184643
1490.0597292442374080.1194584884748160.940270755762592
1500.0499608928902690.0999217857805380.950039107109731
1510.05830856541963750.1166171308392750.941691434580362
1520.05783010553116960.1156602110623390.94216989446883
1530.05915678463494730.1183135692698950.940843215365053
1540.05492974018829650.1098594803765930.945070259811703
1550.05845968595561750.1169193719112350.941540314044383
1560.04935380090457610.09870760180915230.950646199095424
1570.04092533459786390.08185066919572770.959074665402136
1580.03410416824064780.06820833648129550.965895831759352
1590.0500854707328970.1001709414657940.949914529267103
1600.0535825936971470.1071651873942940.946417406302853
1610.04490823462644560.08981646925289110.955091765373554
1620.0908886020220480.1817772040440960.909111397977952
1630.08214388748638060.1642877749727610.917856112513619
1640.2918598165186780.5837196330373560.708140183481322
1650.2683408984519980.5366817969039960.731659101548002
1660.3429520828864190.6859041657728380.657047917113581
1670.3245723673513470.6491447347026940.675427632648653
1680.3052586853614540.6105173707229080.694741314638546
1690.2761110618449080.5522221236898160.723888938155092
1700.2527754586791780.5055509173583560.747224541320822
1710.24910918443040.4982183688608010.7508908155696
1720.2217509107973430.4435018215946860.778249089202657
1730.276820800305030.5536416006100590.72317919969497
1740.2644891347018120.5289782694036240.735510865298188
1750.2460163212624550.4920326425249110.753983678737545
1760.2924660777292990.5849321554585980.707533922270701
1770.2744476966489620.5488953932979240.725552303351038
1780.2803491655111240.5606983310222490.719650834488876
1790.2780349329546810.5560698659093630.721965067045319
1800.251612879172910.5032257583458190.74838712082709
1810.2869614762699280.5739229525398570.713038523730072
1820.4010333535286030.8020667070572070.598966646471397
1830.39042132292080.78084264584160.6095786770792
1840.3846870700505660.7693741401011320.615312929949434
1850.3704222549987420.7408445099974840.629577745001258
1860.4863513614262760.9727027228525530.513648638573724
1870.4512649411363160.9025298822726320.548735058863684
1880.4191999742026650.838399948405330.580800025797335
1890.3863076612587960.7726153225175920.613692338741204
1900.4179930852450250.835986170490050.582006914754975
1910.4497168214539890.8994336429079780.550283178546011
1920.471167254746020.9423345094920390.52883274525398
1930.4601629522867530.9203259045735070.539837047713247
1940.4298905781243560.8597811562487130.570109421875644
1950.4012844706326020.8025689412652030.598715529367398
1960.4405206936971820.8810413873943650.559479306302818
1970.6414383287104390.7171233425791220.358561671289561
1980.6467465929711020.7065068140577960.353253407028898
1990.6062977412785250.7874045174429490.393702258721475
2000.5654568630984240.8690862738031520.434543136901576
2010.5242165551726920.9515668896546150.475783444827308
2020.4844087864681710.9688175729363430.515591213531829
2030.4599210960573160.9198421921146310.540078903942685
2040.4583015161820710.9166030323641420.541698483817929
2050.4239639371002970.8479278742005940.576036062899703
2060.3816361978134240.7632723956268480.618363802186576
2070.3436271593227960.6872543186455910.656372840677204
2080.3075724339732470.6151448679464940.692427566026753
2090.2701944456886010.5403888913772020.729805554311399
2100.2626785749151170.5253571498302350.737321425084883
2110.2395780448101110.4791560896202210.760421955189889
2120.2089864289248620.4179728578497230.791013571075138
2130.2313630212441290.4627260424882590.768636978755871
2140.2173904226190880.4347808452381760.782609577380912
2150.1863910172846580.3727820345693160.813608982715342
2160.1586101083269990.3172202166539980.841389891673001
2170.1494953125896170.2989906251792340.850504687410383
2180.1248358076512480.2496716153024960.875164192348752
2190.1256464652317410.2512929304634830.874353534768259
2200.1175229002612340.2350458005224680.882477099738766
2210.1179793836200930.2359587672401860.882020616379907
2220.09582723763233380.1916544752646680.904172762367666
2230.07711799557754140.1542359911550830.922882004422459
2240.07920925870329070.1584185174065810.920790741296709
2250.07043453289716580.1408690657943320.929565467102834
2260.05664095084194170.1132819016838830.943359049158058
2270.04323475059725460.08646950119450930.956765249402745
2280.04068835209132420.08137670418264850.959311647908676
2290.05539355757509650.1107871151501930.944606442424904
2300.05284087994541260.1056817598908250.947159120054587
2310.04006529135165020.08013058270330040.95993470864835
2320.04120583644236480.08241167288472960.958794163557635
2330.09159082793645260.1831816558729050.908409172063547
2340.09583202159849920.1916640431969980.904167978401501
2350.09120932034695460.1824186406939090.908790679653045
2360.06959990607401990.139199812148040.93040009392598
2370.09167430442853250.1833486088570650.908325695571468
2380.1280877747386720.2561755494773430.871912225261328
2390.2152917008065590.4305834016131180.784708299193441
2400.2130200186149040.4260400372298090.786979981385096
2410.1722738408930940.3445476817861880.827726159106906
2420.2114014004608740.4228028009217480.788598599539126
2430.1589773465567440.3179546931134880.841022653443256
2440.1143205500822960.2286411001645930.885679449917704
2450.1734519350500070.3469038701000150.826548064949993
2460.1879505639332160.3759011278664320.812049436066784
2470.1313714837624290.2627429675248580.868628516237571
2480.1613077277477150.3226154554954290.838692272252285
2490.2442379354873910.4884758709747820.755762064512609
2500.1607517339897230.3215034679794460.839248266010277
2510.1756819271847310.3513638543694620.824318072815269
2520.7988203241200770.4023593517598460.201179675879923

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.978835244630467 & 0.0423295107390653 & 0.0211647553695326 \tabularnewline
13 & 0.973287913151524 & 0.0534241736969525 & 0.0267120868484763 \tabularnewline
14 & 0.979628830931538 & 0.0407423381369244 & 0.0203711690684622 \tabularnewline
15 & 0.964549197296751 & 0.0709016054064989 & 0.0354508027032494 \tabularnewline
16 & 0.94071212638293 & 0.11857574723414 & 0.0592878736170698 \tabularnewline
17 & 0.971567277752729 & 0.0568654444945427 & 0.0284327222472713 \tabularnewline
18 & 0.959426531677079 & 0.0811469366458422 & 0.0405734683229211 \tabularnewline
19 & 0.97472657166297 & 0.050546856674059 & 0.0252734283370295 \tabularnewline
20 & 0.961430244046631 & 0.0771395119067382 & 0.0385697559533691 \tabularnewline
21 & 0.95313575939753 & 0.09372848120494 & 0.04686424060247 \tabularnewline
22 & 0.948904796271258 & 0.102190407457485 & 0.0510952037287424 \tabularnewline
23 & 0.928090915368496 & 0.143818169263008 & 0.0719090846315038 \tabularnewline
24 & 0.912297052241971 & 0.175405895516057 & 0.0877029477580287 \tabularnewline
25 & 0.881942960590668 & 0.236114078818663 & 0.118057039409332 \tabularnewline
26 & 0.879560902834753 & 0.240878194330494 & 0.120439097165247 \tabularnewline
27 & 0.887943662130003 & 0.224112675739995 & 0.112056337869997 \tabularnewline
28 & 0.855288803184939 & 0.289422393630122 & 0.144711196815061 \tabularnewline
29 & 0.881725375039604 & 0.236549249920792 & 0.118274624960396 \tabularnewline
30 & 0.871818245198007 & 0.256363509603987 & 0.128181754801993 \tabularnewline
31 & 0.87516486512509 & 0.24967026974982 & 0.12483513487491 \tabularnewline
32 & 0.848252037433089 & 0.303495925133822 & 0.151747962566911 \tabularnewline
33 & 0.8207344887993 & 0.358531022401399 & 0.1792655112007 \tabularnewline
34 & 0.798259230451273 & 0.403481539097454 & 0.201740769548727 \tabularnewline
35 & 0.768448357047028 & 0.463103285905944 & 0.231551642952972 \tabularnewline
36 & 0.795031397483816 & 0.409937205032367 & 0.204968602516184 \tabularnewline
37 & 0.867922969395071 & 0.264154061209859 & 0.132077030604929 \tabularnewline
38 & 0.837331277714841 & 0.325337444570317 & 0.162668722285159 \tabularnewline
39 & 0.806924024003144 & 0.386151951993712 & 0.193075975996856 \tabularnewline
40 & 0.803364902125123 & 0.393270195749754 & 0.196635097874877 \tabularnewline
41 & 0.790376785259887 & 0.419246429480226 & 0.209623214740113 \tabularnewline
42 & 0.780055944828399 & 0.439888110343201 & 0.219944055171601 \tabularnewline
43 & 0.82538614518891 & 0.34922770962218 & 0.17461385481109 \tabularnewline
44 & 0.792010735170766 & 0.415978529658468 & 0.207989264829234 \tabularnewline
45 & 0.757144636516995 & 0.485710726966009 & 0.242855363483005 \tabularnewline
46 & 0.730636091659937 & 0.538727816680126 & 0.269363908340063 \tabularnewline
47 & 0.779726193360119 & 0.440547613279762 & 0.220273806639881 \tabularnewline
48 & 0.746989889607994 & 0.506020220784012 & 0.253010110392006 \tabularnewline
49 & 0.799299060033726 & 0.401401879932548 & 0.200700939966274 \tabularnewline
50 & 0.773435245133983 & 0.453129509732034 & 0.226564754866017 \tabularnewline
51 & 0.760938146442882 & 0.478123707114235 & 0.239061853557118 \tabularnewline
52 & 0.72879136733821 & 0.542417265323579 & 0.27120863266179 \tabularnewline
53 & 0.732885166265576 & 0.534229667468849 & 0.267114833734424 \tabularnewline
54 & 0.699650263521679 & 0.600699472956642 & 0.300349736478321 \tabularnewline
55 & 0.706191663745565 & 0.58761667250887 & 0.293808336254435 \tabularnewline
56 & 0.722457109947061 & 0.555085780105879 & 0.277542890052939 \tabularnewline
57 & 0.702018370808652 & 0.595963258382697 & 0.297981629191348 \tabularnewline
58 & 0.733630606407296 & 0.532738787185408 & 0.266369393592704 \tabularnewline
59 & 0.745242435494392 & 0.509515129011215 & 0.254757564505608 \tabularnewline
60 & 0.722694316070828 & 0.554611367858345 & 0.277305683929172 \tabularnewline
61 & 0.734333273075816 & 0.531333453848368 & 0.265666726924184 \tabularnewline
62 & 0.698362494696525 & 0.603275010606951 & 0.301637505303475 \tabularnewline
63 & 0.674011674353569 & 0.651976651292863 & 0.325988325646431 \tabularnewline
64 & 0.634460634550285 & 0.731078730899429 & 0.365539365449714 \tabularnewline
65 & 0.593783405004664 & 0.812433189990671 & 0.406216594995336 \tabularnewline
66 & 0.581249840898687 & 0.837500318202626 & 0.418750159101313 \tabularnewline
67 & 0.604219158950392 & 0.791561682099216 & 0.395780841049608 \tabularnewline
68 & 0.575607512662949 & 0.848784974674103 & 0.424392487337051 \tabularnewline
69 & 0.534828167415483 & 0.930343665169033 & 0.465171832584517 \tabularnewline
70 & 0.506409955065434 & 0.987180089869131 & 0.493590044934566 \tabularnewline
71 & 0.468598854765663 & 0.937197709531326 & 0.531401145234337 \tabularnewline
72 & 0.439240141595794 & 0.878480283191588 & 0.560759858404206 \tabularnewline
73 & 0.406815363005799 & 0.813630726011597 & 0.593184636994201 \tabularnewline
74 & 0.391305282900272 & 0.782610565800544 & 0.608694717099728 \tabularnewline
75 & 0.364455663089633 & 0.728911326179266 & 0.635544336910367 \tabularnewline
76 & 0.497683662849682 & 0.995367325699365 & 0.502316337150318 \tabularnewline
77 & 0.466660671152034 & 0.933321342304068 & 0.533339328847966 \tabularnewline
78 & 0.458592720101678 & 0.917185440203356 & 0.541407279898322 \tabularnewline
79 & 0.420549323934975 & 0.841098647869949 & 0.579450676065025 \tabularnewline
80 & 0.554214326911096 & 0.891571346177808 & 0.445785673088904 \tabularnewline
81 & 0.521113791039371 & 0.957772417921259 & 0.478886208960629 \tabularnewline
82 & 0.557779703795861 & 0.884440592408278 & 0.442220296204139 \tabularnewline
83 & 0.519526299033299 & 0.960947401933403 & 0.480473700966701 \tabularnewline
84 & 0.482972538393041 & 0.965945076786083 & 0.517027461606959 \tabularnewline
85 & 0.444004496239849 & 0.888008992479698 & 0.555995503760151 \tabularnewline
86 & 0.408029141399349 & 0.816058282798699 & 0.591970858600651 \tabularnewline
87 & 0.378158342898217 & 0.756316685796435 & 0.621841657101783 \tabularnewline
88 & 0.343201724699523 & 0.686403449399047 & 0.656798275300477 \tabularnewline
89 & 0.420143954361305 & 0.84028790872261 & 0.579856045638695 \tabularnewline
90 & 0.387633914005903 & 0.775267828011805 & 0.612366085994097 \tabularnewline
91 & 0.35488643238427 & 0.70977286476854 & 0.64511356761573 \tabularnewline
92 & 0.322791910881109 & 0.645583821762218 & 0.677208089118891 \tabularnewline
93 & 0.314629315358551 & 0.629258630717102 & 0.685370684641449 \tabularnewline
94 & 0.287244346121815 & 0.574488692243629 & 0.712755653878185 \tabularnewline
95 & 0.256190261168777 & 0.512380522337554 & 0.743809738831223 \tabularnewline
96 & 0.235359090071338 & 0.470718180142676 & 0.764640909928662 \tabularnewline
97 & 0.218125432120075 & 0.436250864240149 & 0.781874567879925 \tabularnewline
98 & 0.192323966074887 & 0.384647932149775 & 0.807676033925113 \tabularnewline
99 & 0.190141026525666 & 0.380282053051332 & 0.809858973474334 \tabularnewline
100 & 0.167479916729542 & 0.334959833459084 & 0.832520083270458 \tabularnewline
101 & 0.153728910828253 & 0.307457821656506 & 0.846271089171747 \tabularnewline
102 & 0.135692231636046 & 0.271384463272091 & 0.864307768363954 \tabularnewline
103 & 0.181909090098441 & 0.363818180196881 & 0.818090909901559 \tabularnewline
104 & 0.206482767252067 & 0.412965534504135 & 0.793517232747933 \tabularnewline
105 & 0.207652752853907 & 0.415305505707815 & 0.792347247146093 \tabularnewline
106 & 0.183494706545409 & 0.366989413090818 & 0.816505293454591 \tabularnewline
107 & 0.199870350083052 & 0.399740700166104 & 0.800129649916948 \tabularnewline
108 & 0.186489621002165 & 0.37297924200433 & 0.813510378997835 \tabularnewline
109 & 0.288492258231042 & 0.576984516462084 & 0.711507741768958 \tabularnewline
110 & 0.275653667373695 & 0.55130733474739 & 0.724346332626305 \tabularnewline
111 & 0.246304338087939 & 0.492608676175877 & 0.753695661912061 \tabularnewline
112 & 0.239221453683564 & 0.478442907367128 & 0.760778546316436 \tabularnewline
113 & 0.217309953114072 & 0.434619906228144 & 0.782690046885928 \tabularnewline
114 & 0.198297083394752 & 0.396594166789504 & 0.801702916605248 \tabularnewline
115 & 0.176089040978371 & 0.352178081956741 & 0.823910959021629 \tabularnewline
116 & 0.154747142569504 & 0.309494285139008 & 0.845252857430496 \tabularnewline
117 & 0.139442806955194 & 0.278885613910388 & 0.860557193044806 \tabularnewline
118 & 0.180949118416473 & 0.361898236832946 & 0.819050881583527 \tabularnewline
119 & 0.183622014516087 & 0.367244029032174 & 0.816377985483913 \tabularnewline
120 & 0.162813635674049 & 0.325627271348098 & 0.837186364325951 \tabularnewline
121 & 0.146251759448722 & 0.292503518897443 & 0.853748240551279 \tabularnewline
122 & 0.129324172310743 & 0.258648344621486 & 0.870675827689257 \tabularnewline
123 & 0.159553058551323 & 0.319106117102647 & 0.840446941448676 \tabularnewline
124 & 0.144221099678831 & 0.288442199357661 & 0.855778900321169 \tabularnewline
125 & 0.136690314941212 & 0.273380629882424 & 0.863309685058788 \tabularnewline
126 & 0.129835543254491 & 0.259671086508983 & 0.870164456745509 \tabularnewline
127 & 0.114588640932395 & 0.22917728186479 & 0.885411359067605 \tabularnewline
128 & 0.100419939741396 & 0.200839879482793 & 0.899580060258604 \tabularnewline
129 & 0.0854833843316565 & 0.170966768663313 & 0.914516615668344 \tabularnewline
130 & 0.0867850235955048 & 0.17357004719101 & 0.913214976404495 \tabularnewline
131 & 0.0861056420022041 & 0.172211284004408 & 0.913894357997796 \tabularnewline
132 & 0.0806525311807096 & 0.161305062361419 & 0.91934746881929 \tabularnewline
133 & 0.071723508993589 & 0.143447017987178 & 0.928276491006411 \tabularnewline
134 & 0.0607861542275348 & 0.12157230845507 & 0.939213845772465 \tabularnewline
135 & 0.0602361792223468 & 0.120472358444694 & 0.939763820777653 \tabularnewline
136 & 0.115802356385605 & 0.23160471277121 & 0.884197643614395 \tabularnewline
137 & 0.099551826299908 & 0.199103652599816 & 0.900448173700092 \tabularnewline
138 & 0.0876190292543507 & 0.175238058508701 & 0.912380970745649 \tabularnewline
139 & 0.0859066485863596 & 0.171813297172719 & 0.91409335141364 \tabularnewline
140 & 0.0827518560229756 & 0.165503712045951 & 0.917248143977024 \tabularnewline
141 & 0.0977626563088071 & 0.195525312617614 & 0.902237343691193 \tabularnewline
142 & 0.0966731382714939 & 0.193346276542988 & 0.903326861728506 \tabularnewline
143 & 0.0825229875674055 & 0.165045975134811 & 0.917477012432594 \tabularnewline
144 & 0.0697075117563931 & 0.139415023512786 & 0.930292488243607 \tabularnewline
145 & 0.0590393731274712 & 0.118078746254942 & 0.940960626872529 \tabularnewline
146 & 0.0492396815579537 & 0.0984793631159074 & 0.950760318442046 \tabularnewline
147 & 0.056460099176328 & 0.112920198352656 & 0.943539900823672 \tabularnewline
148 & 0.065584819815357 & 0.131169639630714 & 0.934415180184643 \tabularnewline
149 & 0.059729244237408 & 0.119458488474816 & 0.940270755762592 \tabularnewline
150 & 0.049960892890269 & 0.099921785780538 & 0.950039107109731 \tabularnewline
151 & 0.0583085654196375 & 0.116617130839275 & 0.941691434580362 \tabularnewline
152 & 0.0578301055311696 & 0.115660211062339 & 0.94216989446883 \tabularnewline
153 & 0.0591567846349473 & 0.118313569269895 & 0.940843215365053 \tabularnewline
154 & 0.0549297401882965 & 0.109859480376593 & 0.945070259811703 \tabularnewline
155 & 0.0584596859556175 & 0.116919371911235 & 0.941540314044383 \tabularnewline
156 & 0.0493538009045761 & 0.0987076018091523 & 0.950646199095424 \tabularnewline
157 & 0.0409253345978639 & 0.0818506691957277 & 0.959074665402136 \tabularnewline
158 & 0.0341041682406478 & 0.0682083364812955 & 0.965895831759352 \tabularnewline
159 & 0.050085470732897 & 0.100170941465794 & 0.949914529267103 \tabularnewline
160 & 0.053582593697147 & 0.107165187394294 & 0.946417406302853 \tabularnewline
161 & 0.0449082346264456 & 0.0898164692528911 & 0.955091765373554 \tabularnewline
162 & 0.090888602022048 & 0.181777204044096 & 0.909111397977952 \tabularnewline
163 & 0.0821438874863806 & 0.164287774972761 & 0.917856112513619 \tabularnewline
164 & 0.291859816518678 & 0.583719633037356 & 0.708140183481322 \tabularnewline
165 & 0.268340898451998 & 0.536681796903996 & 0.731659101548002 \tabularnewline
166 & 0.342952082886419 & 0.685904165772838 & 0.657047917113581 \tabularnewline
167 & 0.324572367351347 & 0.649144734702694 & 0.675427632648653 \tabularnewline
168 & 0.305258685361454 & 0.610517370722908 & 0.694741314638546 \tabularnewline
169 & 0.276111061844908 & 0.552222123689816 & 0.723888938155092 \tabularnewline
170 & 0.252775458679178 & 0.505550917358356 & 0.747224541320822 \tabularnewline
171 & 0.2491091844304 & 0.498218368860801 & 0.7508908155696 \tabularnewline
172 & 0.221750910797343 & 0.443501821594686 & 0.778249089202657 \tabularnewline
173 & 0.27682080030503 & 0.553641600610059 & 0.72317919969497 \tabularnewline
174 & 0.264489134701812 & 0.528978269403624 & 0.735510865298188 \tabularnewline
175 & 0.246016321262455 & 0.492032642524911 & 0.753983678737545 \tabularnewline
176 & 0.292466077729299 & 0.584932155458598 & 0.707533922270701 \tabularnewline
177 & 0.274447696648962 & 0.548895393297924 & 0.725552303351038 \tabularnewline
178 & 0.280349165511124 & 0.560698331022249 & 0.719650834488876 \tabularnewline
179 & 0.278034932954681 & 0.556069865909363 & 0.721965067045319 \tabularnewline
180 & 0.25161287917291 & 0.503225758345819 & 0.74838712082709 \tabularnewline
181 & 0.286961476269928 & 0.573922952539857 & 0.713038523730072 \tabularnewline
182 & 0.401033353528603 & 0.802066707057207 & 0.598966646471397 \tabularnewline
183 & 0.3904213229208 & 0.7808426458416 & 0.6095786770792 \tabularnewline
184 & 0.384687070050566 & 0.769374140101132 & 0.615312929949434 \tabularnewline
185 & 0.370422254998742 & 0.740844509997484 & 0.629577745001258 \tabularnewline
186 & 0.486351361426276 & 0.972702722852553 & 0.513648638573724 \tabularnewline
187 & 0.451264941136316 & 0.902529882272632 & 0.548735058863684 \tabularnewline
188 & 0.419199974202665 & 0.83839994840533 & 0.580800025797335 \tabularnewline
189 & 0.386307661258796 & 0.772615322517592 & 0.613692338741204 \tabularnewline
190 & 0.417993085245025 & 0.83598617049005 & 0.582006914754975 \tabularnewline
191 & 0.449716821453989 & 0.899433642907978 & 0.550283178546011 \tabularnewline
192 & 0.47116725474602 & 0.942334509492039 & 0.52883274525398 \tabularnewline
193 & 0.460162952286753 & 0.920325904573507 & 0.539837047713247 \tabularnewline
194 & 0.429890578124356 & 0.859781156248713 & 0.570109421875644 \tabularnewline
195 & 0.401284470632602 & 0.802568941265203 & 0.598715529367398 \tabularnewline
196 & 0.440520693697182 & 0.881041387394365 & 0.559479306302818 \tabularnewline
197 & 0.641438328710439 & 0.717123342579122 & 0.358561671289561 \tabularnewline
198 & 0.646746592971102 & 0.706506814057796 & 0.353253407028898 \tabularnewline
199 & 0.606297741278525 & 0.787404517442949 & 0.393702258721475 \tabularnewline
200 & 0.565456863098424 & 0.869086273803152 & 0.434543136901576 \tabularnewline
201 & 0.524216555172692 & 0.951566889654615 & 0.475783444827308 \tabularnewline
202 & 0.484408786468171 & 0.968817572936343 & 0.515591213531829 \tabularnewline
203 & 0.459921096057316 & 0.919842192114631 & 0.540078903942685 \tabularnewline
204 & 0.458301516182071 & 0.916603032364142 & 0.541698483817929 \tabularnewline
205 & 0.423963937100297 & 0.847927874200594 & 0.576036062899703 \tabularnewline
206 & 0.381636197813424 & 0.763272395626848 & 0.618363802186576 \tabularnewline
207 & 0.343627159322796 & 0.687254318645591 & 0.656372840677204 \tabularnewline
208 & 0.307572433973247 & 0.615144867946494 & 0.692427566026753 \tabularnewline
209 & 0.270194445688601 & 0.540388891377202 & 0.729805554311399 \tabularnewline
210 & 0.262678574915117 & 0.525357149830235 & 0.737321425084883 \tabularnewline
211 & 0.239578044810111 & 0.479156089620221 & 0.760421955189889 \tabularnewline
212 & 0.208986428924862 & 0.417972857849723 & 0.791013571075138 \tabularnewline
213 & 0.231363021244129 & 0.462726042488259 & 0.768636978755871 \tabularnewline
214 & 0.217390422619088 & 0.434780845238176 & 0.782609577380912 \tabularnewline
215 & 0.186391017284658 & 0.372782034569316 & 0.813608982715342 \tabularnewline
216 & 0.158610108326999 & 0.317220216653998 & 0.841389891673001 \tabularnewline
217 & 0.149495312589617 & 0.298990625179234 & 0.850504687410383 \tabularnewline
218 & 0.124835807651248 & 0.249671615302496 & 0.875164192348752 \tabularnewline
219 & 0.125646465231741 & 0.251292930463483 & 0.874353534768259 \tabularnewline
220 & 0.117522900261234 & 0.235045800522468 & 0.882477099738766 \tabularnewline
221 & 0.117979383620093 & 0.235958767240186 & 0.882020616379907 \tabularnewline
222 & 0.0958272376323338 & 0.191654475264668 & 0.904172762367666 \tabularnewline
223 & 0.0771179955775414 & 0.154235991155083 & 0.922882004422459 \tabularnewline
224 & 0.0792092587032907 & 0.158418517406581 & 0.920790741296709 \tabularnewline
225 & 0.0704345328971658 & 0.140869065794332 & 0.929565467102834 \tabularnewline
226 & 0.0566409508419417 & 0.113281901683883 & 0.943359049158058 \tabularnewline
227 & 0.0432347505972546 & 0.0864695011945093 & 0.956765249402745 \tabularnewline
228 & 0.0406883520913242 & 0.0813767041826485 & 0.959311647908676 \tabularnewline
229 & 0.0553935575750965 & 0.110787115150193 & 0.944606442424904 \tabularnewline
230 & 0.0528408799454126 & 0.105681759890825 & 0.947159120054587 \tabularnewline
231 & 0.0400652913516502 & 0.0801305827033004 & 0.95993470864835 \tabularnewline
232 & 0.0412058364423648 & 0.0824116728847296 & 0.958794163557635 \tabularnewline
233 & 0.0915908279364526 & 0.183181655872905 & 0.908409172063547 \tabularnewline
234 & 0.0958320215984992 & 0.191664043196998 & 0.904167978401501 \tabularnewline
235 & 0.0912093203469546 & 0.182418640693909 & 0.908790679653045 \tabularnewline
236 & 0.0695999060740199 & 0.13919981214804 & 0.93040009392598 \tabularnewline
237 & 0.0916743044285325 & 0.183348608857065 & 0.908325695571468 \tabularnewline
238 & 0.128087774738672 & 0.256175549477343 & 0.871912225261328 \tabularnewline
239 & 0.215291700806559 & 0.430583401613118 & 0.784708299193441 \tabularnewline
240 & 0.213020018614904 & 0.426040037229809 & 0.786979981385096 \tabularnewline
241 & 0.172273840893094 & 0.344547681786188 & 0.827726159106906 \tabularnewline
242 & 0.211401400460874 & 0.422802800921748 & 0.788598599539126 \tabularnewline
243 & 0.158977346556744 & 0.317954693113488 & 0.841022653443256 \tabularnewline
244 & 0.114320550082296 & 0.228641100164593 & 0.885679449917704 \tabularnewline
245 & 0.173451935050007 & 0.346903870100015 & 0.826548064949993 \tabularnewline
246 & 0.187950563933216 & 0.375901127866432 & 0.812049436066784 \tabularnewline
247 & 0.131371483762429 & 0.262742967524858 & 0.868628516237571 \tabularnewline
248 & 0.161307727747715 & 0.322615455495429 & 0.838692272252285 \tabularnewline
249 & 0.244237935487391 & 0.488475870974782 & 0.755762064512609 \tabularnewline
250 & 0.160751733989723 & 0.321503467979446 & 0.839248266010277 \tabularnewline
251 & 0.175681927184731 & 0.351363854369462 & 0.824318072815269 \tabularnewline
252 & 0.798820324120077 & 0.402359351759846 & 0.201179675879923 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&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]12[/C][C]0.978835244630467[/C][C]0.0423295107390653[/C][C]0.0211647553695326[/C][/ROW]
[ROW][C]13[/C][C]0.973287913151524[/C][C]0.0534241736969525[/C][C]0.0267120868484763[/C][/ROW]
[ROW][C]14[/C][C]0.979628830931538[/C][C]0.0407423381369244[/C][C]0.0203711690684622[/C][/ROW]
[ROW][C]15[/C][C]0.964549197296751[/C][C]0.0709016054064989[/C][C]0.0354508027032494[/C][/ROW]
[ROW][C]16[/C][C]0.94071212638293[/C][C]0.11857574723414[/C][C]0.0592878736170698[/C][/ROW]
[ROW][C]17[/C][C]0.971567277752729[/C][C]0.0568654444945427[/C][C]0.0284327222472713[/C][/ROW]
[ROW][C]18[/C][C]0.959426531677079[/C][C]0.0811469366458422[/C][C]0.0405734683229211[/C][/ROW]
[ROW][C]19[/C][C]0.97472657166297[/C][C]0.050546856674059[/C][C]0.0252734283370295[/C][/ROW]
[ROW][C]20[/C][C]0.961430244046631[/C][C]0.0771395119067382[/C][C]0.0385697559533691[/C][/ROW]
[ROW][C]21[/C][C]0.95313575939753[/C][C]0.09372848120494[/C][C]0.04686424060247[/C][/ROW]
[ROW][C]22[/C][C]0.948904796271258[/C][C]0.102190407457485[/C][C]0.0510952037287424[/C][/ROW]
[ROW][C]23[/C][C]0.928090915368496[/C][C]0.143818169263008[/C][C]0.0719090846315038[/C][/ROW]
[ROW][C]24[/C][C]0.912297052241971[/C][C]0.175405895516057[/C][C]0.0877029477580287[/C][/ROW]
[ROW][C]25[/C][C]0.881942960590668[/C][C]0.236114078818663[/C][C]0.118057039409332[/C][/ROW]
[ROW][C]26[/C][C]0.879560902834753[/C][C]0.240878194330494[/C][C]0.120439097165247[/C][/ROW]
[ROW][C]27[/C][C]0.887943662130003[/C][C]0.224112675739995[/C][C]0.112056337869997[/C][/ROW]
[ROW][C]28[/C][C]0.855288803184939[/C][C]0.289422393630122[/C][C]0.144711196815061[/C][/ROW]
[ROW][C]29[/C][C]0.881725375039604[/C][C]0.236549249920792[/C][C]0.118274624960396[/C][/ROW]
[ROW][C]30[/C][C]0.871818245198007[/C][C]0.256363509603987[/C][C]0.128181754801993[/C][/ROW]
[ROW][C]31[/C][C]0.87516486512509[/C][C]0.24967026974982[/C][C]0.12483513487491[/C][/ROW]
[ROW][C]32[/C][C]0.848252037433089[/C][C]0.303495925133822[/C][C]0.151747962566911[/C][/ROW]
[ROW][C]33[/C][C]0.8207344887993[/C][C]0.358531022401399[/C][C]0.1792655112007[/C][/ROW]
[ROW][C]34[/C][C]0.798259230451273[/C][C]0.403481539097454[/C][C]0.201740769548727[/C][/ROW]
[ROW][C]35[/C][C]0.768448357047028[/C][C]0.463103285905944[/C][C]0.231551642952972[/C][/ROW]
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[ROW][C]181[/C][C]0.286961476269928[/C][C]0.573922952539857[/C][C]0.713038523730072[/C][/ROW]
[ROW][C]182[/C][C]0.401033353528603[/C][C]0.802066707057207[/C][C]0.598966646471397[/C][/ROW]
[ROW][C]183[/C][C]0.3904213229208[/C][C]0.7808426458416[/C][C]0.6095786770792[/C][/ROW]
[ROW][C]184[/C][C]0.384687070050566[/C][C]0.769374140101132[/C][C]0.615312929949434[/C][/ROW]
[ROW][C]185[/C][C]0.370422254998742[/C][C]0.740844509997484[/C][C]0.629577745001258[/C][/ROW]
[ROW][C]186[/C][C]0.486351361426276[/C][C]0.972702722852553[/C][C]0.513648638573724[/C][/ROW]
[ROW][C]187[/C][C]0.451264941136316[/C][C]0.902529882272632[/C][C]0.548735058863684[/C][/ROW]
[ROW][C]188[/C][C]0.419199974202665[/C][C]0.83839994840533[/C][C]0.580800025797335[/C][/ROW]
[ROW][C]189[/C][C]0.386307661258796[/C][C]0.772615322517592[/C][C]0.613692338741204[/C][/ROW]
[ROW][C]190[/C][C]0.417993085245025[/C][C]0.83598617049005[/C][C]0.582006914754975[/C][/ROW]
[ROW][C]191[/C][C]0.449716821453989[/C][C]0.899433642907978[/C][C]0.550283178546011[/C][/ROW]
[ROW][C]192[/C][C]0.47116725474602[/C][C]0.942334509492039[/C][C]0.52883274525398[/C][/ROW]
[ROW][C]193[/C][C]0.460162952286753[/C][C]0.920325904573507[/C][C]0.539837047713247[/C][/ROW]
[ROW][C]194[/C][C]0.429890578124356[/C][C]0.859781156248713[/C][C]0.570109421875644[/C][/ROW]
[ROW][C]195[/C][C]0.401284470632602[/C][C]0.802568941265203[/C][C]0.598715529367398[/C][/ROW]
[ROW][C]196[/C][C]0.440520693697182[/C][C]0.881041387394365[/C][C]0.559479306302818[/C][/ROW]
[ROW][C]197[/C][C]0.641438328710439[/C][C]0.717123342579122[/C][C]0.358561671289561[/C][/ROW]
[ROW][C]198[/C][C]0.646746592971102[/C][C]0.706506814057796[/C][C]0.353253407028898[/C][/ROW]
[ROW][C]199[/C][C]0.606297741278525[/C][C]0.787404517442949[/C][C]0.393702258721475[/C][/ROW]
[ROW][C]200[/C][C]0.565456863098424[/C][C]0.869086273803152[/C][C]0.434543136901576[/C][/ROW]
[ROW][C]201[/C][C]0.524216555172692[/C][C]0.951566889654615[/C][C]0.475783444827308[/C][/ROW]
[ROW][C]202[/C][C]0.484408786468171[/C][C]0.968817572936343[/C][C]0.515591213531829[/C][/ROW]
[ROW][C]203[/C][C]0.459921096057316[/C][C]0.919842192114631[/C][C]0.540078903942685[/C][/ROW]
[ROW][C]204[/C][C]0.458301516182071[/C][C]0.916603032364142[/C][C]0.541698483817929[/C][/ROW]
[ROW][C]205[/C][C]0.423963937100297[/C][C]0.847927874200594[/C][C]0.576036062899703[/C][/ROW]
[ROW][C]206[/C][C]0.381636197813424[/C][C]0.763272395626848[/C][C]0.618363802186576[/C][/ROW]
[ROW][C]207[/C][C]0.343627159322796[/C][C]0.687254318645591[/C][C]0.656372840677204[/C][/ROW]
[ROW][C]208[/C][C]0.307572433973247[/C][C]0.615144867946494[/C][C]0.692427566026753[/C][/ROW]
[ROW][C]209[/C][C]0.270194445688601[/C][C]0.540388891377202[/C][C]0.729805554311399[/C][/ROW]
[ROW][C]210[/C][C]0.262678574915117[/C][C]0.525357149830235[/C][C]0.737321425084883[/C][/ROW]
[ROW][C]211[/C][C]0.239578044810111[/C][C]0.479156089620221[/C][C]0.760421955189889[/C][/ROW]
[ROW][C]212[/C][C]0.208986428924862[/C][C]0.417972857849723[/C][C]0.791013571075138[/C][/ROW]
[ROW][C]213[/C][C]0.231363021244129[/C][C]0.462726042488259[/C][C]0.768636978755871[/C][/ROW]
[ROW][C]214[/C][C]0.217390422619088[/C][C]0.434780845238176[/C][C]0.782609577380912[/C][/ROW]
[ROW][C]215[/C][C]0.186391017284658[/C][C]0.372782034569316[/C][C]0.813608982715342[/C][/ROW]
[ROW][C]216[/C][C]0.158610108326999[/C][C]0.317220216653998[/C][C]0.841389891673001[/C][/ROW]
[ROW][C]217[/C][C]0.149495312589617[/C][C]0.298990625179234[/C][C]0.850504687410383[/C][/ROW]
[ROW][C]218[/C][C]0.124835807651248[/C][C]0.249671615302496[/C][C]0.875164192348752[/C][/ROW]
[ROW][C]219[/C][C]0.125646465231741[/C][C]0.251292930463483[/C][C]0.874353534768259[/C][/ROW]
[ROW][C]220[/C][C]0.117522900261234[/C][C]0.235045800522468[/C][C]0.882477099738766[/C][/ROW]
[ROW][C]221[/C][C]0.117979383620093[/C][C]0.235958767240186[/C][C]0.882020616379907[/C][/ROW]
[ROW][C]222[/C][C]0.0958272376323338[/C][C]0.191654475264668[/C][C]0.904172762367666[/C][/ROW]
[ROW][C]223[/C][C]0.0771179955775414[/C][C]0.154235991155083[/C][C]0.922882004422459[/C][/ROW]
[ROW][C]224[/C][C]0.0792092587032907[/C][C]0.158418517406581[/C][C]0.920790741296709[/C][/ROW]
[ROW][C]225[/C][C]0.0704345328971658[/C][C]0.140869065794332[/C][C]0.929565467102834[/C][/ROW]
[ROW][C]226[/C][C]0.0566409508419417[/C][C]0.113281901683883[/C][C]0.943359049158058[/C][/ROW]
[ROW][C]227[/C][C]0.0432347505972546[/C][C]0.0864695011945093[/C][C]0.956765249402745[/C][/ROW]
[ROW][C]228[/C][C]0.0406883520913242[/C][C]0.0813767041826485[/C][C]0.959311647908676[/C][/ROW]
[ROW][C]229[/C][C]0.0553935575750965[/C][C]0.110787115150193[/C][C]0.944606442424904[/C][/ROW]
[ROW][C]230[/C][C]0.0528408799454126[/C][C]0.105681759890825[/C][C]0.947159120054587[/C][/ROW]
[ROW][C]231[/C][C]0.0400652913516502[/C][C]0.0801305827033004[/C][C]0.95993470864835[/C][/ROW]
[ROW][C]232[/C][C]0.0412058364423648[/C][C]0.0824116728847296[/C][C]0.958794163557635[/C][/ROW]
[ROW][C]233[/C][C]0.0915908279364526[/C][C]0.183181655872905[/C][C]0.908409172063547[/C][/ROW]
[ROW][C]234[/C][C]0.0958320215984992[/C][C]0.191664043196998[/C][C]0.904167978401501[/C][/ROW]
[ROW][C]235[/C][C]0.0912093203469546[/C][C]0.182418640693909[/C][C]0.908790679653045[/C][/ROW]
[ROW][C]236[/C][C]0.0695999060740199[/C][C]0.13919981214804[/C][C]0.93040009392598[/C][/ROW]
[ROW][C]237[/C][C]0.0916743044285325[/C][C]0.183348608857065[/C][C]0.908325695571468[/C][/ROW]
[ROW][C]238[/C][C]0.128087774738672[/C][C]0.256175549477343[/C][C]0.871912225261328[/C][/ROW]
[ROW][C]239[/C][C]0.215291700806559[/C][C]0.430583401613118[/C][C]0.784708299193441[/C][/ROW]
[ROW][C]240[/C][C]0.213020018614904[/C][C]0.426040037229809[/C][C]0.786979981385096[/C][/ROW]
[ROW][C]241[/C][C]0.172273840893094[/C][C]0.344547681786188[/C][C]0.827726159106906[/C][/ROW]
[ROW][C]242[/C][C]0.211401400460874[/C][C]0.422802800921748[/C][C]0.788598599539126[/C][/ROW]
[ROW][C]243[/C][C]0.158977346556744[/C][C]0.317954693113488[/C][C]0.841022653443256[/C][/ROW]
[ROW][C]244[/C][C]0.114320550082296[/C][C]0.228641100164593[/C][C]0.885679449917704[/C][/ROW]
[ROW][C]245[/C][C]0.173451935050007[/C][C]0.346903870100015[/C][C]0.826548064949993[/C][/ROW]
[ROW][C]246[/C][C]0.187950563933216[/C][C]0.375901127866432[/C][C]0.812049436066784[/C][/ROW]
[ROW][C]247[/C][C]0.131371483762429[/C][C]0.262742967524858[/C][C]0.868628516237571[/C][/ROW]
[ROW][C]248[/C][C]0.161307727747715[/C][C]0.322615455495429[/C][C]0.838692272252285[/C][/ROW]
[ROW][C]249[/C][C]0.244237935487391[/C][C]0.488475870974782[/C][C]0.755762064512609[/C][/ROW]
[ROW][C]250[/C][C]0.160751733989723[/C][C]0.321503467979446[/C][C]0.839248266010277[/C][/ROW]
[ROW][C]251[/C][C]0.175681927184731[/C][C]0.351363854369462[/C][C]0.824318072815269[/C][/ROW]
[ROW][C]252[/C][C]0.798820324120077[/C][C]0.402359351759846[/C][C]0.201179675879923[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&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
120.9788352446304670.04232951073906530.0211647553695326
130.9732879131515240.05342417369695250.0267120868484763
140.9796288309315380.04074233813692440.0203711690684622
150.9645491972967510.07090160540649890.0354508027032494
160.940712126382930.118575747234140.0592878736170698
170.9715672777527290.05686544449454270.0284327222472713
180.9594265316770790.08114693664584220.0405734683229211
190.974726571662970.0505468566740590.0252734283370295
200.9614302440466310.07713951190673820.0385697559533691
210.953135759397530.093728481204940.04686424060247
220.9489047962712580.1021904074574850.0510952037287424
230.9280909153684960.1438181692630080.0719090846315038
240.9122970522419710.1754058955160570.0877029477580287
250.8819429605906680.2361140788186630.118057039409332
260.8795609028347530.2408781943304940.120439097165247
270.8879436621300030.2241126757399950.112056337869997
280.8552888031849390.2894223936301220.144711196815061
290.8817253750396040.2365492499207920.118274624960396
300.8718182451980070.2563635096039870.128181754801993
310.875164865125090.249670269749820.12483513487491
320.8482520374330890.3034959251338220.151747962566911
330.82073448879930.3585310224013990.1792655112007
340.7982592304512730.4034815390974540.201740769548727
350.7684483570470280.4631032859059440.231551642952972
360.7950313974838160.4099372050323670.204968602516184
370.8679229693950710.2641540612098590.132077030604929
380.8373312777148410.3253374445703170.162668722285159
390.8069240240031440.3861519519937120.193075975996856
400.8033649021251230.3932701957497540.196635097874877
410.7903767852598870.4192464294802260.209623214740113
420.7800559448283990.4398881103432010.219944055171601
430.825386145188910.349227709622180.17461385481109
440.7920107351707660.4159785296584680.207989264829234
450.7571446365169950.4857107269660090.242855363483005
460.7306360916599370.5387278166801260.269363908340063
470.7797261933601190.4405476132797620.220273806639881
480.7469898896079940.5060202207840120.253010110392006
490.7992990600337260.4014018799325480.200700939966274
500.7734352451339830.4531295097320340.226564754866017
510.7609381464428820.4781237071142350.239061853557118
520.728791367338210.5424172653235790.27120863266179
530.7328851662655760.5342296674688490.267114833734424
540.6996502635216790.6006994729566420.300349736478321
550.7061916637455650.587616672508870.293808336254435
560.7224571099470610.5550857801058790.277542890052939
570.7020183708086520.5959632583826970.297981629191348
580.7336306064072960.5327387871854080.266369393592704
590.7452424354943920.5095151290112150.254757564505608
600.7226943160708280.5546113678583450.277305683929172
610.7343332730758160.5313334538483680.265666726924184
620.6983624946965250.6032750106069510.301637505303475
630.6740116743535690.6519766512928630.325988325646431
640.6344606345502850.7310787308994290.365539365449714
650.5937834050046640.8124331899906710.406216594995336
660.5812498408986870.8375003182026260.418750159101313
670.6042191589503920.7915616820992160.395780841049608
680.5756075126629490.8487849746741030.424392487337051
690.5348281674154830.9303436651690330.465171832584517
700.5064099550654340.9871800898691310.493590044934566
710.4685988547656630.9371977095313260.531401145234337
720.4392401415957940.8784802831915880.560759858404206
730.4068153630057990.8136307260115970.593184636994201
740.3913052829002720.7826105658005440.608694717099728
750.3644556630896330.7289113261792660.635544336910367
760.4976836628496820.9953673256993650.502316337150318
770.4666606711520340.9333213423040680.533339328847966
780.4585927201016780.9171854402033560.541407279898322
790.4205493239349750.8410986478699490.579450676065025
800.5542143269110960.8915713461778080.445785673088904
810.5211137910393710.9577724179212590.478886208960629
820.5577797037958610.8844405924082780.442220296204139
830.5195262990332990.9609474019334030.480473700966701
840.4829725383930410.9659450767860830.517027461606959
850.4440044962398490.8880089924796980.555995503760151
860.4080291413993490.8160582827986990.591970858600651
870.3781583428982170.7563166857964350.621841657101783
880.3432017246995230.6864034493990470.656798275300477
890.4201439543613050.840287908722610.579856045638695
900.3876339140059030.7752678280118050.612366085994097
910.354886432384270.709772864768540.64511356761573
920.3227919108811090.6455838217622180.677208089118891
930.3146293153585510.6292586307171020.685370684641449
940.2872443461218150.5744886922436290.712755653878185
950.2561902611687770.5123805223375540.743809738831223
960.2353590900713380.4707181801426760.764640909928662
970.2181254321200750.4362508642401490.781874567879925
980.1923239660748870.3846479321497750.807676033925113
990.1901410265256660.3802820530513320.809858973474334
1000.1674799167295420.3349598334590840.832520083270458
1010.1537289108282530.3074578216565060.846271089171747
1020.1356922316360460.2713844632720910.864307768363954
1030.1819090900984410.3638181801968810.818090909901559
1040.2064827672520670.4129655345041350.793517232747933
1050.2076527528539070.4153055057078150.792347247146093
1060.1834947065454090.3669894130908180.816505293454591
1070.1998703500830520.3997407001661040.800129649916948
1080.1864896210021650.372979242004330.813510378997835
1090.2884922582310420.5769845164620840.711507741768958
1100.2756536673736950.551307334747390.724346332626305
1110.2463043380879390.4926086761758770.753695661912061
1120.2392214536835640.4784429073671280.760778546316436
1130.2173099531140720.4346199062281440.782690046885928
1140.1982970833947520.3965941667895040.801702916605248
1150.1760890409783710.3521780819567410.823910959021629
1160.1547471425695040.3094942851390080.845252857430496
1170.1394428069551940.2788856139103880.860557193044806
1180.1809491184164730.3618982368329460.819050881583527
1190.1836220145160870.3672440290321740.816377985483913
1200.1628136356740490.3256272713480980.837186364325951
1210.1462517594487220.2925035188974430.853748240551279
1220.1293241723107430.2586483446214860.870675827689257
1230.1595530585513230.3191061171026470.840446941448676
1240.1442210996788310.2884421993576610.855778900321169
1250.1366903149412120.2733806298824240.863309685058788
1260.1298355432544910.2596710865089830.870164456745509
1270.1145886409323950.229177281864790.885411359067605
1280.1004199397413960.2008398794827930.899580060258604
1290.08548338433165650.1709667686633130.914516615668344
1300.08678502359550480.173570047191010.913214976404495
1310.08610564200220410.1722112840044080.913894357997796
1320.08065253118070960.1613050623614190.91934746881929
1330.0717235089935890.1434470179871780.928276491006411
1340.06078615422753480.121572308455070.939213845772465
1350.06023617922234680.1204723584446940.939763820777653
1360.1158023563856050.231604712771210.884197643614395
1370.0995518262999080.1991036525998160.900448173700092
1380.08761902925435070.1752380585087010.912380970745649
1390.08590664858635960.1718132971727190.91409335141364
1400.08275185602297560.1655037120459510.917248143977024
1410.09776265630880710.1955253126176140.902237343691193
1420.09667313827149390.1933462765429880.903326861728506
1430.08252298756740550.1650459751348110.917477012432594
1440.06970751175639310.1394150235127860.930292488243607
1450.05903937312747120.1180787462549420.940960626872529
1460.04923968155795370.09847936311590740.950760318442046
1470.0564600991763280.1129201983526560.943539900823672
1480.0655848198153570.1311696396307140.934415180184643
1490.0597292442374080.1194584884748160.940270755762592
1500.0499608928902690.0999217857805380.950039107109731
1510.05830856541963750.1166171308392750.941691434580362
1520.05783010553116960.1156602110623390.94216989446883
1530.05915678463494730.1183135692698950.940843215365053
1540.05492974018829650.1098594803765930.945070259811703
1550.05845968595561750.1169193719112350.941540314044383
1560.04935380090457610.09870760180915230.950646199095424
1570.04092533459786390.08185066919572770.959074665402136
1580.03410416824064780.06820833648129550.965895831759352
1590.0500854707328970.1001709414657940.949914529267103
1600.0535825936971470.1071651873942940.946417406302853
1610.04490823462644560.08981646925289110.955091765373554
1620.0908886020220480.1817772040440960.909111397977952
1630.08214388748638060.1642877749727610.917856112513619
1640.2918598165186780.5837196330373560.708140183481322
1650.2683408984519980.5366817969039960.731659101548002
1660.3429520828864190.6859041657728380.657047917113581
1670.3245723673513470.6491447347026940.675427632648653
1680.3052586853614540.6105173707229080.694741314638546
1690.2761110618449080.5522221236898160.723888938155092
1700.2527754586791780.5055509173583560.747224541320822
1710.24910918443040.4982183688608010.7508908155696
1720.2217509107973430.4435018215946860.778249089202657
1730.276820800305030.5536416006100590.72317919969497
1740.2644891347018120.5289782694036240.735510865298188
1750.2460163212624550.4920326425249110.753983678737545
1760.2924660777292990.5849321554585980.707533922270701
1770.2744476966489620.5488953932979240.725552303351038
1780.2803491655111240.5606983310222490.719650834488876
1790.2780349329546810.5560698659093630.721965067045319
1800.251612879172910.5032257583458190.74838712082709
1810.2869614762699280.5739229525398570.713038523730072
1820.4010333535286030.8020667070572070.598966646471397
1830.39042132292080.78084264584160.6095786770792
1840.3846870700505660.7693741401011320.615312929949434
1850.3704222549987420.7408445099974840.629577745001258
1860.4863513614262760.9727027228525530.513648638573724
1870.4512649411363160.9025298822726320.548735058863684
1880.4191999742026650.838399948405330.580800025797335
1890.3863076612587960.7726153225175920.613692338741204
1900.4179930852450250.835986170490050.582006914754975
1910.4497168214539890.8994336429079780.550283178546011
1920.471167254746020.9423345094920390.52883274525398
1930.4601629522867530.9203259045735070.539837047713247
1940.4298905781243560.8597811562487130.570109421875644
1950.4012844706326020.8025689412652030.598715529367398
1960.4405206936971820.8810413873943650.559479306302818
1970.6414383287104390.7171233425791220.358561671289561
1980.6467465929711020.7065068140577960.353253407028898
1990.6062977412785250.7874045174429490.393702258721475
2000.5654568630984240.8690862738031520.434543136901576
2010.5242165551726920.9515668896546150.475783444827308
2020.4844087864681710.9688175729363430.515591213531829
2030.4599210960573160.9198421921146310.540078903942685
2040.4583015161820710.9166030323641420.541698483817929
2050.4239639371002970.8479278742005940.576036062899703
2060.3816361978134240.7632723956268480.618363802186576
2070.3436271593227960.6872543186455910.656372840677204
2080.3075724339732470.6151448679464940.692427566026753
2090.2701944456886010.5403888913772020.729805554311399
2100.2626785749151170.5253571498302350.737321425084883
2110.2395780448101110.4791560896202210.760421955189889
2120.2089864289248620.4179728578497230.791013571075138
2130.2313630212441290.4627260424882590.768636978755871
2140.2173904226190880.4347808452381760.782609577380912
2150.1863910172846580.3727820345693160.813608982715342
2160.1586101083269990.3172202166539980.841389891673001
2170.1494953125896170.2989906251792340.850504687410383
2180.1248358076512480.2496716153024960.875164192348752
2190.1256464652317410.2512929304634830.874353534768259
2200.1175229002612340.2350458005224680.882477099738766
2210.1179793836200930.2359587672401860.882020616379907
2220.09582723763233380.1916544752646680.904172762367666
2230.07711799557754140.1542359911550830.922882004422459
2240.07920925870329070.1584185174065810.920790741296709
2250.07043453289716580.1408690657943320.929565467102834
2260.05664095084194170.1132819016838830.943359049158058
2270.04323475059725460.08646950119450930.956765249402745
2280.04068835209132420.08137670418264850.959311647908676
2290.05539355757509650.1107871151501930.944606442424904
2300.05284087994541260.1056817598908250.947159120054587
2310.04006529135165020.08013058270330040.95993470864835
2320.04120583644236480.08241167288472960.958794163557635
2330.09159082793645260.1831816558729050.908409172063547
2340.09583202159849920.1916640431969980.904167978401501
2350.09120932034695460.1824186406939090.908790679653045
2360.06959990607401990.139199812148040.93040009392598
2370.09167430442853250.1833486088570650.908325695571468
2380.1280877747386720.2561755494773430.871912225261328
2390.2152917008065590.4305834016131180.784708299193441
2400.2130200186149040.4260400372298090.786979981385096
2410.1722738408930940.3445476817861880.827726159106906
2420.2114014004608740.4228028009217480.788598599539126
2430.1589773465567440.3179546931134880.841022653443256
2440.1143205500822960.2286411001645930.885679449917704
2450.1734519350500070.3469038701000150.826548064949993
2460.1879505639332160.3759011278664320.812049436066784
2470.1313714837624290.2627429675248580.868628516237571
2480.1613077277477150.3226154554954290.838692272252285
2490.2442379354873910.4884758709747820.755762064512609
2500.1607517339897230.3215034679794460.839248266010277
2510.1756819271847310.3513638543694620.824318072815269
2520.7988203241200770.4023593517598460.201179675879923






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.00829875518672199OK
10% type I error level190.0788381742738589OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.00829875518672199 & OK \tabularnewline
10% type I error level & 19 & 0.0788381742738589 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=225724&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.00829875518672199[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]19[/C][C]0.0788381742738589[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=225724&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=225724&T=6

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.00829875518672199OK
10% type I error level190.0788381742738589OK


Figures (Output of Computation)

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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 7 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}