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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 computationWed, 20 Nov 2013 14:10:57 -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/20/t1384974686joihbse1sez8kh7.htm/, Retrieved Wed, 01 May 2024 18:02:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=226717, Retrieved Wed, 01 May 2024 18:02:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact67

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R P     [Multiple Regression] [WS 7] [2013-11-20 19:10:57] [6b92c6b72208b98f35c650918bf39b5d] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53	32
39	32	16	11	18	11	83	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	9	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	9.5	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	12	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	31	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	10	7	16	8	71	44
29	31	10	9	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	15	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	35	14	6	15	14.5	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	80	48
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	12.5	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	85	52
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	67	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
36	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	85	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	11	9	13	12	69	41
32	34	16	8	15	10	54	33
30	37	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
38	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	38	12	8	12	18	84	50
34	36	13	8	13	16	69	46
27	26	13	10	10	24	66	43
31	26	12	8	14	14	81	47
38	33	17	14	16	20	82	50
34	39	15	10	10	18	72	43
24	30	10	8	11	23	54	33
30	33	14	11	14	12	78	48
26	25	11	12	12	14	74	44
34	38	13	12	9	16	82	50
27	37	16	12	9	18	73	41
37	31	12	5	11	20	55	34
36	37	16	12	16	12	72	44
41	35	12	10	9	12	78	47
29	25	9	7	13	17	59	35
36	28	12	12	16	13	72	44
32	35	15	11	13	9	78	44
37	33	12	8	9	16	68	43
30	30	12	9	12	18	69	41
31	31	14	10	16	10	67	41
38	37	12	9	11	14	74	42
36	36	16	12	14	11	54	33
35	30	11	6	13	9	67	41
31	36	19	15	15	11	70	44
38	32	15	12	14	10	80	48
22	28	8	12	16	11	89	55
32	36	16	12	13	19	76	44
36	34	17	11	14	14	74	43
39	31	12	7	15	12	87	52
28	28	11	7	13	14	54	30
32	36	11	5	11	21	61	39
32	36	14	12	11	13	38	11
38	40	16	12	14	10	75	44
32	33	12	3	15	15	69	42
35	37	16	11	11	16	62	41
32	32	13	10	15	14	72	44
37	38	15	12	12	12	70	44
34	31	16	9	14	19	79	48
33	37	16	12	14	15	87	53
33	33	14	9	8	19	62	37
26	32	16	12	13	13	77	44
30	30	16	12	9	17	69	44
24	30	14	10	15	12	69	40
34	31	11	9	17	11	75	42
34	32	12	12	13	14	54	35
33	34	15	8	15	11	72	43
34	36	15	11	15	13	74	45
35	37	16	11	14	12	85	55
35	36	16	12	16	15	52	31
36	33	11	10	13	14	70	44
34	33	15	10	16	12	84	50
34	33	12	12	9	17	64	40
41	44	12	12	16	11	84	53
32	39	15	11	11	18	87	54
30	32	15	8	10	13	79	49
35	35	16	12	11	17	67	40
28	25	14	10	15	13	65	41
33	35	17	11	17	11	85	52
39	34	14	10	14	12	83	52
36	35	13	8	8	22	61	36
36	39	15	12	15	14	82	52
35	33	13	12	11	12	76	46
38	36	14	10	16	12	58	31
33	32	15	12	10	17	72	44
31	32	12	9	15	9	72	44
34	36	13	9	9	21	38	11
32	36	8	6	16	10	78	46
31	32	14	10	19	11	54	33
33	34	14	9	12	12	63	34
34	33	11	9	8	23	66	42
34	35	12	9	11	13	70	43
34	30	13	6	14	12	71	43
33	38	10	10	9	16	67	44
32	34	16	6	15	9	58	36
41	33	18	14	13	17	72	46
34	32	13	10	16	9	72	44
36	31	11	10	11	14	70	43
37	30	4	6	12	17	76	50
36	27	13	12	13	13	50	33
29	31	16	12	10	11	72	43
37	30	10	7	11	12	72	44
27	32	12	8	12	10	88	53
35	35	12	11	8	19	53	34
28	28	10	3	12	16	58	35
35	33	13	6	12	16	66	40
37	31	15	10	15	14	82	53
29	35	12	8	11	20	69	42
32	35	14	9	13	15	68	43
36	32	10	9	14	23	44	29
19	21	12	8	10	20	56	36
21	20	12	9	12	16	53	30
31	34	11	7	15	14	70	42
33	32	10	7	13	17	78	47
36	34	12	6	13	11	71	44
33	32	16	9	13	13	72	45
37	33	12	10	12	17	68	44
34	33	14	11	12	15	67	43
35	37	16	12	9	21	75	43
31	32	14	8	9	18	62	40
37	34	13	11	15	15	67	41
35	30	4	3	10	8	83	52
27	30	15	11	14	12	64	38
34	38	11	12	15	12	68	41
40	36	11	7	7	22	62	39
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 time14 seconds
R Server'George Udny Yule' @ yule.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 & 14 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&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]14 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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 time14 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Belonging[t] = + 13.1507084450508 -0.0618720549305274Connected[t] -0.00145710919204592Separate[t] + 0.0432478808620867Learning[t] + 0.0385218197353962Software[t] + 0.0156046959869428Happiness[t] -0.185419321745514Depression[t] + 1.41465286364024Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Belonging[t] =  +  13.1507084450508 -0.0618720549305274Connected[t] -0.00145710919204592Separate[t] +  0.0432478808620867Learning[t] +  0.0385218197353962Software[t] +  0.0156046959869428Happiness[t] -0.185419321745514Depression[t] +  1.41465286364024Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Belonging[t] =  +  13.1507084450508 -0.0618720549305274Connected[t] -0.00145710919204592Separate[t] +  0.0432478808620867Learning[t] +  0.0385218197353962Software[t] +  0.0156046959869428Happiness[t] -0.185419321745514Depression[t] +  1.41465286364024Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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
Belonging[t] = + 13.1507084450508 -0.0618720549305274Connected[t] -0.00145710919204592Separate[t] + 0.0432478808620867Learning[t] + 0.0385218197353962Software[t] + 0.0156046959869428Happiness[t] -0.185419321745514Depression[t] + 1.41465286364024Belonging_Final[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.15070844505083.0822764.26662.8e-051.4e-05
Connected-0.06187205493052740.057508-1.07590.2829910.141495
Separate-0.001457109192045920.059015-0.02470.9803210.490161
Learning0.04324788086208670.1031520.41930.6753760.337688
Software0.03852181973539620.1060630.36320.7167570.358378
Happiness0.01560469598694280.0962320.16220.871310.435655
Depression-0.1854193217455140.069419-2.6710.0080470.004023
Belonging_Final1.414652863640240.02951247.935500

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 13.1507084450508 & 3.082276 & 4.2666 & 2.8e-05 & 1.4e-05 \tabularnewline
Connected & -0.0618720549305274 & 0.057508 & -1.0759 & 0.282991 & 0.141495 \tabularnewline
Separate & -0.00145710919204592 & 0.059015 & -0.0247 & 0.980321 & 0.490161 \tabularnewline
Learning & 0.0432478808620867 & 0.103152 & 0.4193 & 0.675376 & 0.337688 \tabularnewline
Software & 0.0385218197353962 & 0.106063 & 0.3632 & 0.716757 & 0.358378 \tabularnewline
Happiness & 0.0156046959869428 & 0.096232 & 0.1622 & 0.87131 & 0.435655 \tabularnewline
Depression & -0.185419321745514 & 0.069419 & -2.671 & 0.008047 & 0.004023 \tabularnewline
Belonging_Final & 1.41465286364024 & 0.029512 & 47.9355 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]13.1507084450508[/C][C]3.082276[/C][C]4.2666[/C][C]2.8e-05[/C][C]1.4e-05[/C][/ROW]
[ROW][C]Connected[/C][C]-0.0618720549305274[/C][C]0.057508[/C][C]-1.0759[/C][C]0.282991[/C][C]0.141495[/C][/ROW]
[ROW][C]Separate[/C][C]-0.00145710919204592[/C][C]0.059015[/C][C]-0.0247[/C][C]0.980321[/C][C]0.490161[/C][/ROW]
[ROW][C]Learning[/C][C]0.0432478808620867[/C][C]0.103152[/C][C]0.4193[/C][C]0.675376[/C][C]0.337688[/C][/ROW]
[ROW][C]Software[/C][C]0.0385218197353962[/C][C]0.106063[/C][C]0.3632[/C][C]0.716757[/C][C]0.358378[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0156046959869428[/C][C]0.096232[/C][C]0.1622[/C][C]0.87131[/C][C]0.435655[/C][/ROW]
[ROW][C]Depression[/C][C]-0.185419321745514[/C][C]0.069419[/C][C]-2.671[/C][C]0.008047[/C][C]0.004023[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]1.41465286364024[/C][C]0.029512[/C][C]47.9355[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&T=2

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


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.15070844505083.0822764.26662.8e-051.4e-05
Connected-0.06187205493052740.057508-1.07590.2829910.141495
Separate-0.001457109192045920.059015-0.02470.9803210.490161
Learning0.04324788086208670.1031520.41930.6753760.337688
Software0.03852181973539620.1060630.36320.7167570.358378
Happiness0.01560469598694280.0962320.16220.871310.435655
Depression-0.1854193217455140.069419-2.6710.0080470.004023
Belonging_Final1.414652863640240.02951247.935500






Multiple Linear Regression - Regression Statistics
Multiple R0.955412016971565
R-squared0.912812122173675
Adjusted R-squared0.910428078639361
F-TEST (value)382.88399898557
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11125322250405
Sum Squared Residuals2478.0535333227

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.955412016971565 \tabularnewline
R-squared & 0.912812122173675 \tabularnewline
Adjusted R-squared & 0.910428078639361 \tabularnewline
F-TEST (value) & 382.88399898557 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 256 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.11125322250405 \tabularnewline
Sum Squared Residuals & 2478.0535333227 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.955412016971565[/C][/ROW]
[ROW][C]R-squared[/C][C]0.912812122173675[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.910428078639361[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]382.88399898557[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]256[/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]3.11125322250405[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2478.0535333227[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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 R0.955412016971565
R-squared0.912812122173675
Adjusted R-squared0.910428078639361
F-TEST (value)382.88399898557
F-TEST (DF numerator)7
F-TEST (DF denominator)256
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11125322250405
Sum Squared Residuals2478.0535333227






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15354.8453938509924-1.84539385099237
28382.19534495371430.804655046285673
36669.6342864321334-3.63428643213344
46768.0274311703578-1.02743117035782
57673.52930063252312.4706993674769
67876.43490826238441.56509173761558
75360.4578961111162-7.45789611111617
88079.67946985871460.32053014128541
97473.97930390088420.0206960991157908
107676.1137907020774-0.113790702077387
117979.5606611965796-0.560661196579581
125457.5246798355563-3.5246798355563
136768.6929768731643-1.69297687316431
145456.1353833958272-2.13538339582722
158785.77135194616691.22864805383307
165860.7837964641213-2.7837964641213
177573.19259748688261.80740251311736
188886.68834159161011.31165840838989
196468.2495699616815-4.24956996168152
205761.0451301678948-4.04513016789484
216668.7585925752518-2.7585925752518
226872.1622394394591-4.16223943945913
235456.6817999294014-2.68179992940142
245661.8538630671094-5.85386306710944
258683.67616282196512.32383717803489
268076.76367728923373.23632271076629
277670.98963567009855.01036432990147
286971.9249431992698-2.92494319926982
297873.5126527588024.487347241198
306771.995588976834-4.99558897683401
318080.1973759654606-0.197375965460552
325456.365800317785-2.36580031778495
337171.0481203079445-0.048120307944471
348486.733086005102-2.73308600510203
357469.29451230456134.70548769543872
367172.8358961927028-1.83589619270281
376360.86463543701282.1353645629872
387170.78207374849680.217926251503215
397675.87355110003360.126448899966407
406969.7783743135576-0.778374313557593
417474.1166746175287-0.116674617528715
427571.9652367744943.034763225506
435457.645592838773-3.64559283877305
445253.3734150688217-1.37341506882173
456969.3593380032304-0.359338003230438
466866.09574713598191.90425286401811
476571.2443667684554-6.24436676845539
487575.4003367778893-0.400336777889257
497468.88306070762245.11693929237757
507573.64330240487991.35669759512011
517272.9869120265718-0.986912026571846
526766.32648772592550.673512274074464
536361.28021431023241.7197856897676
546274.8498557533261-12.8498557533261
556360.78116212325572.21883787674429
567675.6812197069670.318780293033023
577473.34098733775920.659012662240753
586770.4418218871389-3.4418218871389
597371.0445809826471.95541901735299
607070.2488687632913-0.248868763291277
615353.7277178177236-0.727717817723627
627773.54146462647453.45853537352554
638078.07728857542221.92271142457784
645253.687720281869-1.68772028186904
655456.6632798803182-2.66327988031815
668079.60169585263310.398304147366946
676668.6250264367162-2.62502643671625
687368.42039862170394.57960137829614
696363.24107841769-0.24107841768997
706969.139121621252-0.13912162125203
716771.5405083545774-4.54050835457745
725456.3584447518018-2.35844475180176
738178.03495536258042.96504463741959
746967.02358798058881.9764120194112
758481.05783625048762.94216374951237
768077.75817493632342.24182506367655
777070.685180908486-0.685180908486015
786971.4130528816695-2.4130528816695
797776.70990865827850.290091341721531
805457.1939600473029-3.19396004730292
817975.47919226010263.52080773989735
827174.3101768209003-3.31017682090033
837370.77905427041532.22094572958471
847272.4474837837938-0.447483783793783
857776.43764369075440.562356309245644
867573.17790792981221.82209207018776
876969.6224710061933-0.622471006193308
885456.5691350103138-2.56913501031383
897069.03190700879980.968092991200231
907374.974750135792-1.97475013579202
915456.2948439658684-2.29484396586841
927774.97732396079032.02267603920967
938277.58149705201894.41850294798106
948076.71099398143893.28900601856114
958076.46117385067593.53882614932412
966970.0538136816989-1.05381368169885
977875.23156354100872.76843645899131
988178.0951333780192.90486662198099
997675.56294423558370.437055764416277
1007674.0488514751.95114852500004
1017372.80607576950860.193924230491454
1028583.89877852485651.10122147514355
1036669.7499212494633-3.74992124946331
1047975.92385536770383.07614463229621
1056866.27192560168391.72807439831611
1067676.6447945351313-0.644794535131331
1077169.8087892934341.19121070656601
1085456.0463233905278-2.04632339052778
1094649.3632079526495-3.36320795264947
1108583.59938185988321.40061814011676
1117472.05094953880031.94905046119968
1128888.2391712109879-0.239171210987949
1133825.107037969910112.8929620300899
1147676.260630725701-0.260630725701048
1158685.46614412182250.533855878177539
1165456.463039992799-2.46303999279903
1176771.9551596609797-4.95515966097972
1186970.0090063999161-1.00900639991615
1199087.14442577524322.85557422475679
1205456.1304910863229-2.13049108632289
1217675.0827095112040.917290488795971
1228986.35293992440962.64706007559043
1237677.276744290344-1.27674429034403
1247373.8814827165044-0.881482716504362
1257977.10631662841241.89368337158761
1269088.31283997692741.68716002307258
1277472.18627737585631.81372262414369
1288185.2853677494639-4.28536774946386
1297271.94816176386250.051838236137528
1307169.77857115660371.22142884339634
1316665.24605261314280.753947386857196
1327775.07596280920081.92403719079916
1336566.4180594328308-1.41805943283082
1347474.9568747385268-0.956874738526759
1358584.86993297474750.130067025252481
1365454.8857050894344-0.885705089434367
1376369.1065334979822-6.10653349798222
1385458.822285597587-4.82228559758703
1396466.5433663843943-2.54336638439429
1406967.98269558371851.01730441628149
1415457.184823348898-3.18482334889803
1428482.03714902458551.96285097541447
1438685.52662490190380.473375098096202
1447775.39793764797941.60206235202061
1458987.38727932618421.61272067381577
1467676.9240341171028-0.924034117102832
1476063.4133521161641-3.41335211616405
1487575.5015357730326-0.501535773032578
1497373.5389847353316-0.53898473533156
1508584.44611969374750.55388030625249
1517975.18708813660243.81291186339756
1527168.61201954885862.38798045114141
1537272.0584067672662-0.0584067672662491
1546969.1112218223618-0.111221822361844
1557880.8170229159575-2.81702291595751
1565456.2948439658684-2.29484396586841
1576970.3629113926983-1.36291139269827
1588185.2853677494639-4.28536774946386
1598482.79752039192331.20247960807672
1608479.52493340864074.4750665913593
1616974.1751832829428-5.17518328294282
1626668.9257751460021-2.92577514600209
1638176.1332188619114.86678113808901
1648279.29993708819732.70006291180268
1657269.67274003418912.32725996581094
1665454.9532709732985-0.953270973298456
1677878.1724438805588-0.172443880558833
1687472.27970766094061.72029233905945
1698279.92254901411352.07745098588648
1707367.38413973415235.61586026584771
1715556.0893182814043-1.08931828140432
1727272.29299863308-0.292998633079962
1737875.87124313290442.12875686709559
1745958.5424575937360.457542406263979
1757271.94770177061450.0522982293854869
1767872.97107524786445.02892475213561
1776869.6443131899968-1.64431318999679
1786966.96698043901132.03301956098868
1796768.5744422142602-1.5744422142602
1807468.60252968985815.39747031014186
1815456.887484172001-2.88748417200095
1826768.183185415987-1.18318541598698
1837073.0189397444757-3.01893974447567
1848078.13153289439521.86846710560483
1858988.57293915972730.427060840272732
1867671.19719462181474.80280537818531
1877470.48539512267763.51460487732237
1888783.05214271445033.94785728554966
1895452.16944772984991.83055227015009
1906163.2359901256903-2.23599012569035
1913825.508460898461712.4915391015383
1927572.50451244715992.49548755284009
1936968.62545899999940.374541000000632
1946267.2526895104376-5.25268951043759
1957271.95454177722730.0454582227727241
1967072.1240028041475-2.12400280414753
1977976.63938674925562.36061325074441
1988784.62302321342332.37697678657668
1996260.95703914811351.04296085188649
2007772.68677131863914.31322868136088
2016971.6381012463713-2.63810124637128
2026967.20790750484771.79209249515227
2037569.46539882502865.53460117497136
2045459.1015082612388-5.10150826123885
2057271.04081272776230.959187272237663
2067473.55005899744340.449941002556634
2078587.8463209763439-2.8463209763439
2085253.4095826046428-1.40958260464282
2097071.5878912946151-1.58789129461506
2108480.79019684121783.20980315878221
2116465.5546387210637-1.55463872106371
2128484.7177421651424-0.717742165142366
2138785.41179215981511.58820784018486
2147979.2683981693537-0.268398169353733
2156765.69405336317131.30594663682868
2166568.1969383729806-3.19693837298058
2178584.00450200423670.995497995763263
2188383.2342279118176-0.234227911817621
2196158.71582823546362.28417176453643
2208283.1776161034778-1.17761610347777
2217674.98223772953821.01776227046181
2225853.61668500389284.38331499610718
2237271.42192767832050.578072321679541
2247272.8617407402879-0.861740740287894
2253823.711339482594314.2886605174057
2267875.16497436745662.83502563254344
2275457.1171569621615-3.11715696216153
2286358.07197748416714.92802251583289
2296667.0970104818159-1.09701048181589
2307070.4530043133501-0.453004313350101
2317170.62020569067260.379794309327429
2326771.2897166051455-4.28971660514553
2335861.5371576220935-3.53715762209348
2347274.0084012269826-2.00840122698256
2357272.7734989720807-0.773498972080735
2367070.144943257385-0.144943257385025
2377678.9896226429025-2.98962264290246
2385056.3844111726651-6.38441117266513
2397271.41198395492950.588016045070456
2407271.71120647870950.28879352129046
2418885.57034950333052.42965049666954
2425356.5769701066943-3.57697010669426
2435858.6589335487695-0.658933548769483
2446665.53711703828920.46288296171077
2458284.4650101462531-2.46501014625306
2466968.01125465240850.988745347591502
2476870.3236149334182-2.32361493341819
2484448.6346165488833-4.63461654888328
2495660.146852852574-4.14685285257401
2505352.34805716875490.651942831245122
2517068.98213266556291.01786733443714
2527875.30385185421462.6961481457854
2537172.0318527525801-1.03185275258006
2547273.5527543385595-1.55275433855949
2556870.9974044593231-2.99740445932314
2566770.2642239854251-3.26422398542507
2577569.1622110567525.83778894324798
2586265.4887011560844-3.48870115608441
2596767.2514111912597-0.251411191259698
2608383.4646715245736-0.464671524573558
2616464.2591506173862-0.259150617386196
2626867.93948294253090.0605170574691377
2636262.5702192200236-0.570219220023636
2647271.08546508700940.914534912990609

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 53 & 54.8453938509924 & -1.84539385099237 \tabularnewline
2 & 83 & 82.1953449537143 & 0.804655046285673 \tabularnewline
3 & 66 & 69.6342864321334 & -3.63428643213344 \tabularnewline
4 & 67 & 68.0274311703578 & -1.02743117035782 \tabularnewline
5 & 76 & 73.5293006325231 & 2.4706993674769 \tabularnewline
6 & 78 & 76.4349082623844 & 1.56509173761558 \tabularnewline
7 & 53 & 60.4578961111162 & -7.45789611111617 \tabularnewline
8 & 80 & 79.6794698587146 & 0.32053014128541 \tabularnewline
9 & 74 & 73.9793039008842 & 0.0206960991157908 \tabularnewline
10 & 76 & 76.1137907020774 & -0.113790702077387 \tabularnewline
11 & 79 & 79.5606611965796 & -0.560661196579581 \tabularnewline
12 & 54 & 57.5246798355563 & -3.5246798355563 \tabularnewline
13 & 67 & 68.6929768731643 & -1.69297687316431 \tabularnewline
14 & 54 & 56.1353833958272 & -2.13538339582722 \tabularnewline
15 & 87 & 85.7713519461669 & 1.22864805383307 \tabularnewline
16 & 58 & 60.7837964641213 & -2.7837964641213 \tabularnewline
17 & 75 & 73.1925974868826 & 1.80740251311736 \tabularnewline
18 & 88 & 86.6883415916101 & 1.31165840838989 \tabularnewline
19 & 64 & 68.2495699616815 & -4.24956996168152 \tabularnewline
20 & 57 & 61.0451301678948 & -4.04513016789484 \tabularnewline
21 & 66 & 68.7585925752518 & -2.7585925752518 \tabularnewline
22 & 68 & 72.1622394394591 & -4.16223943945913 \tabularnewline
23 & 54 & 56.6817999294014 & -2.68179992940142 \tabularnewline
24 & 56 & 61.8538630671094 & -5.85386306710944 \tabularnewline
25 & 86 & 83.6761628219651 & 2.32383717803489 \tabularnewline
26 & 80 & 76.7636772892337 & 3.23632271076629 \tabularnewline
27 & 76 & 70.9896356700985 & 5.01036432990147 \tabularnewline
28 & 69 & 71.9249431992698 & -2.92494319926982 \tabularnewline
29 & 78 & 73.512652758802 & 4.487347241198 \tabularnewline
30 & 67 & 71.995588976834 & -4.99558897683401 \tabularnewline
31 & 80 & 80.1973759654606 & -0.197375965460552 \tabularnewline
32 & 54 & 56.365800317785 & -2.36580031778495 \tabularnewline
33 & 71 & 71.0481203079445 & -0.048120307944471 \tabularnewline
34 & 84 & 86.733086005102 & -2.73308600510203 \tabularnewline
35 & 74 & 69.2945123045613 & 4.70548769543872 \tabularnewline
36 & 71 & 72.8358961927028 & -1.83589619270281 \tabularnewline
37 & 63 & 60.8646354370128 & 2.1353645629872 \tabularnewline
38 & 71 & 70.7820737484968 & 0.217926251503215 \tabularnewline
39 & 76 & 75.8735511000336 & 0.126448899966407 \tabularnewline
40 & 69 & 69.7783743135576 & -0.778374313557593 \tabularnewline
41 & 74 & 74.1166746175287 & -0.116674617528715 \tabularnewline
42 & 75 & 71.965236774494 & 3.034763225506 \tabularnewline
43 & 54 & 57.645592838773 & -3.64559283877305 \tabularnewline
44 & 52 & 53.3734150688217 & -1.37341506882173 \tabularnewline
45 & 69 & 69.3593380032304 & -0.359338003230438 \tabularnewline
46 & 68 & 66.0957471359819 & 1.90425286401811 \tabularnewline
47 & 65 & 71.2443667684554 & -6.24436676845539 \tabularnewline
48 & 75 & 75.4003367778893 & -0.400336777889257 \tabularnewline
49 & 74 & 68.8830607076224 & 5.11693929237757 \tabularnewline
50 & 75 & 73.6433024048799 & 1.35669759512011 \tabularnewline
51 & 72 & 72.9869120265718 & -0.986912026571846 \tabularnewline
52 & 67 & 66.3264877259255 & 0.673512274074464 \tabularnewline
53 & 63 & 61.2802143102324 & 1.7197856897676 \tabularnewline
54 & 62 & 74.8498557533261 & -12.8498557533261 \tabularnewline
55 & 63 & 60.7811621232557 & 2.21883787674429 \tabularnewline
56 & 76 & 75.681219706967 & 0.318780293033023 \tabularnewline
57 & 74 & 73.3409873377592 & 0.659012662240753 \tabularnewline
58 & 67 & 70.4418218871389 & -3.4418218871389 \tabularnewline
59 & 73 & 71.044580982647 & 1.95541901735299 \tabularnewline
60 & 70 & 70.2488687632913 & -0.248868763291277 \tabularnewline
61 & 53 & 53.7277178177236 & -0.727717817723627 \tabularnewline
62 & 77 & 73.5414646264745 & 3.45853537352554 \tabularnewline
63 & 80 & 78.0772885754222 & 1.92271142457784 \tabularnewline
64 & 52 & 53.687720281869 & -1.68772028186904 \tabularnewline
65 & 54 & 56.6632798803182 & -2.66327988031815 \tabularnewline
66 & 80 & 79.6016958526331 & 0.398304147366946 \tabularnewline
67 & 66 & 68.6250264367162 & -2.62502643671625 \tabularnewline
68 & 73 & 68.4203986217039 & 4.57960137829614 \tabularnewline
69 & 63 & 63.24107841769 & -0.24107841768997 \tabularnewline
70 & 69 & 69.139121621252 & -0.13912162125203 \tabularnewline
71 & 67 & 71.5405083545774 & -4.54050835457745 \tabularnewline
72 & 54 & 56.3584447518018 & -2.35844475180176 \tabularnewline
73 & 81 & 78.0349553625804 & 2.96504463741959 \tabularnewline
74 & 69 & 67.0235879805888 & 1.9764120194112 \tabularnewline
75 & 84 & 81.0578362504876 & 2.94216374951237 \tabularnewline
76 & 80 & 77.7581749363234 & 2.24182506367655 \tabularnewline
77 & 70 & 70.685180908486 & -0.685180908486015 \tabularnewline
78 & 69 & 71.4130528816695 & -2.4130528816695 \tabularnewline
79 & 77 & 76.7099086582785 & 0.290091341721531 \tabularnewline
80 & 54 & 57.1939600473029 & -3.19396004730292 \tabularnewline
81 & 79 & 75.4791922601026 & 3.52080773989735 \tabularnewline
82 & 71 & 74.3101768209003 & -3.31017682090033 \tabularnewline
83 & 73 & 70.7790542704153 & 2.22094572958471 \tabularnewline
84 & 72 & 72.4474837837938 & -0.447483783793783 \tabularnewline
85 & 77 & 76.4376436907544 & 0.562356309245644 \tabularnewline
86 & 75 & 73.1779079298122 & 1.82209207018776 \tabularnewline
87 & 69 & 69.6224710061933 & -0.622471006193308 \tabularnewline
88 & 54 & 56.5691350103138 & -2.56913501031383 \tabularnewline
89 & 70 & 69.0319070087998 & 0.968092991200231 \tabularnewline
90 & 73 & 74.974750135792 & -1.97475013579202 \tabularnewline
91 & 54 & 56.2948439658684 & -2.29484396586841 \tabularnewline
92 & 77 & 74.9773239607903 & 2.02267603920967 \tabularnewline
93 & 82 & 77.5814970520189 & 4.41850294798106 \tabularnewline
94 & 80 & 76.7109939814389 & 3.28900601856114 \tabularnewline
95 & 80 & 76.4611738506759 & 3.53882614932412 \tabularnewline
96 & 69 & 70.0538136816989 & -1.05381368169885 \tabularnewline
97 & 78 & 75.2315635410087 & 2.76843645899131 \tabularnewline
98 & 81 & 78.095133378019 & 2.90486662198099 \tabularnewline
99 & 76 & 75.5629442355837 & 0.437055764416277 \tabularnewline
100 & 76 & 74.048851475 & 1.95114852500004 \tabularnewline
101 & 73 & 72.8060757695086 & 0.193924230491454 \tabularnewline
102 & 85 & 83.8987785248565 & 1.10122147514355 \tabularnewline
103 & 66 & 69.7499212494633 & -3.74992124946331 \tabularnewline
104 & 79 & 75.9238553677038 & 3.07614463229621 \tabularnewline
105 & 68 & 66.2719256016839 & 1.72807439831611 \tabularnewline
106 & 76 & 76.6447945351313 & -0.644794535131331 \tabularnewline
107 & 71 & 69.808789293434 & 1.19121070656601 \tabularnewline
108 & 54 & 56.0463233905278 & -2.04632339052778 \tabularnewline
109 & 46 & 49.3632079526495 & -3.36320795264947 \tabularnewline
110 & 85 & 83.5993818598832 & 1.40061814011676 \tabularnewline
111 & 74 & 72.0509495388003 & 1.94905046119968 \tabularnewline
112 & 88 & 88.2391712109879 & -0.239171210987949 \tabularnewline
113 & 38 & 25.1070379699101 & 12.8929620300899 \tabularnewline
114 & 76 & 76.260630725701 & -0.260630725701048 \tabularnewline
115 & 86 & 85.4661441218225 & 0.533855878177539 \tabularnewline
116 & 54 & 56.463039992799 & -2.46303999279903 \tabularnewline
117 & 67 & 71.9551596609797 & -4.95515966097972 \tabularnewline
118 & 69 & 70.0090063999161 & -1.00900639991615 \tabularnewline
119 & 90 & 87.1444257752432 & 2.85557422475679 \tabularnewline
120 & 54 & 56.1304910863229 & -2.13049108632289 \tabularnewline
121 & 76 & 75.082709511204 & 0.917290488795971 \tabularnewline
122 & 89 & 86.3529399244096 & 2.64706007559043 \tabularnewline
123 & 76 & 77.276744290344 & -1.27674429034403 \tabularnewline
124 & 73 & 73.8814827165044 & -0.881482716504362 \tabularnewline
125 & 79 & 77.1063166284124 & 1.89368337158761 \tabularnewline
126 & 90 & 88.3128399769274 & 1.68716002307258 \tabularnewline
127 & 74 & 72.1862773758563 & 1.81372262414369 \tabularnewline
128 & 81 & 85.2853677494639 & -4.28536774946386 \tabularnewline
129 & 72 & 71.9481617638625 & 0.051838236137528 \tabularnewline
130 & 71 & 69.7785711566037 & 1.22142884339634 \tabularnewline
131 & 66 & 65.2460526131428 & 0.753947386857196 \tabularnewline
132 & 77 & 75.0759628092008 & 1.92403719079916 \tabularnewline
133 & 65 & 66.4180594328308 & -1.41805943283082 \tabularnewline
134 & 74 & 74.9568747385268 & -0.956874738526759 \tabularnewline
135 & 85 & 84.8699329747475 & 0.130067025252481 \tabularnewline
136 & 54 & 54.8857050894344 & -0.885705089434367 \tabularnewline
137 & 63 & 69.1065334979822 & -6.10653349798222 \tabularnewline
138 & 54 & 58.822285597587 & -4.82228559758703 \tabularnewline
139 & 64 & 66.5433663843943 & -2.54336638439429 \tabularnewline
140 & 69 & 67.9826955837185 & 1.01730441628149 \tabularnewline
141 & 54 & 57.184823348898 & -3.18482334889803 \tabularnewline
142 & 84 & 82.0371490245855 & 1.96285097541447 \tabularnewline
143 & 86 & 85.5266249019038 & 0.473375098096202 \tabularnewline
144 & 77 & 75.3979376479794 & 1.60206235202061 \tabularnewline
145 & 89 & 87.3872793261842 & 1.61272067381577 \tabularnewline
146 & 76 & 76.9240341171028 & -0.924034117102832 \tabularnewline
147 & 60 & 63.4133521161641 & -3.41335211616405 \tabularnewline
148 & 75 & 75.5015357730326 & -0.501535773032578 \tabularnewline
149 & 73 & 73.5389847353316 & -0.53898473533156 \tabularnewline
150 & 85 & 84.4461196937475 & 0.55388030625249 \tabularnewline
151 & 79 & 75.1870881366024 & 3.81291186339756 \tabularnewline
152 & 71 & 68.6120195488586 & 2.38798045114141 \tabularnewline
153 & 72 & 72.0584067672662 & -0.0584067672662491 \tabularnewline
154 & 69 & 69.1112218223618 & -0.111221822361844 \tabularnewline
155 & 78 & 80.8170229159575 & -2.81702291595751 \tabularnewline
156 & 54 & 56.2948439658684 & -2.29484396586841 \tabularnewline
157 & 69 & 70.3629113926983 & -1.36291139269827 \tabularnewline
158 & 81 & 85.2853677494639 & -4.28536774946386 \tabularnewline
159 & 84 & 82.7975203919233 & 1.20247960807672 \tabularnewline
160 & 84 & 79.5249334086407 & 4.4750665913593 \tabularnewline
161 & 69 & 74.1751832829428 & -5.17518328294282 \tabularnewline
162 & 66 & 68.9257751460021 & -2.92577514600209 \tabularnewline
163 & 81 & 76.133218861911 & 4.86678113808901 \tabularnewline
164 & 82 & 79.2999370881973 & 2.70006291180268 \tabularnewline
165 & 72 & 69.6727400341891 & 2.32725996581094 \tabularnewline
166 & 54 & 54.9532709732985 & -0.953270973298456 \tabularnewline
167 & 78 & 78.1724438805588 & -0.172443880558833 \tabularnewline
168 & 74 & 72.2797076609406 & 1.72029233905945 \tabularnewline
169 & 82 & 79.9225490141135 & 2.07745098588648 \tabularnewline
170 & 73 & 67.3841397341523 & 5.61586026584771 \tabularnewline
171 & 55 & 56.0893182814043 & -1.08931828140432 \tabularnewline
172 & 72 & 72.29299863308 & -0.292998633079962 \tabularnewline
173 & 78 & 75.8712431329044 & 2.12875686709559 \tabularnewline
174 & 59 & 58.542457593736 & 0.457542406263979 \tabularnewline
175 & 72 & 71.9477017706145 & 0.0522982293854869 \tabularnewline
176 & 78 & 72.9710752478644 & 5.02892475213561 \tabularnewline
177 & 68 & 69.6443131899968 & -1.64431318999679 \tabularnewline
178 & 69 & 66.9669804390113 & 2.03301956098868 \tabularnewline
179 & 67 & 68.5744422142602 & -1.5744422142602 \tabularnewline
180 & 74 & 68.6025296898581 & 5.39747031014186 \tabularnewline
181 & 54 & 56.887484172001 & -2.88748417200095 \tabularnewline
182 & 67 & 68.183185415987 & -1.18318541598698 \tabularnewline
183 & 70 & 73.0189397444757 & -3.01893974447567 \tabularnewline
184 & 80 & 78.1315328943952 & 1.86846710560483 \tabularnewline
185 & 89 & 88.5729391597273 & 0.427060840272732 \tabularnewline
186 & 76 & 71.1971946218147 & 4.80280537818531 \tabularnewline
187 & 74 & 70.4853951226776 & 3.51460487732237 \tabularnewline
188 & 87 & 83.0521427144503 & 3.94785728554966 \tabularnewline
189 & 54 & 52.1694477298499 & 1.83055227015009 \tabularnewline
190 & 61 & 63.2359901256903 & -2.23599012569035 \tabularnewline
191 & 38 & 25.5084608984617 & 12.4915391015383 \tabularnewline
192 & 75 & 72.5045124471599 & 2.49548755284009 \tabularnewline
193 & 69 & 68.6254589999994 & 0.374541000000632 \tabularnewline
194 & 62 & 67.2526895104376 & -5.25268951043759 \tabularnewline
195 & 72 & 71.9545417772273 & 0.0454582227727241 \tabularnewline
196 & 70 & 72.1240028041475 & -2.12400280414753 \tabularnewline
197 & 79 & 76.6393867492556 & 2.36061325074441 \tabularnewline
198 & 87 & 84.6230232134233 & 2.37697678657668 \tabularnewline
199 & 62 & 60.9570391481135 & 1.04296085188649 \tabularnewline
200 & 77 & 72.6867713186391 & 4.31322868136088 \tabularnewline
201 & 69 & 71.6381012463713 & -2.63810124637128 \tabularnewline
202 & 69 & 67.2079075048477 & 1.79209249515227 \tabularnewline
203 & 75 & 69.4653988250286 & 5.53460117497136 \tabularnewline
204 & 54 & 59.1015082612388 & -5.10150826123885 \tabularnewline
205 & 72 & 71.0408127277623 & 0.959187272237663 \tabularnewline
206 & 74 & 73.5500589974434 & 0.449941002556634 \tabularnewline
207 & 85 & 87.8463209763439 & -2.8463209763439 \tabularnewline
208 & 52 & 53.4095826046428 & -1.40958260464282 \tabularnewline
209 & 70 & 71.5878912946151 & -1.58789129461506 \tabularnewline
210 & 84 & 80.7901968412178 & 3.20980315878221 \tabularnewline
211 & 64 & 65.5546387210637 & -1.55463872106371 \tabularnewline
212 & 84 & 84.7177421651424 & -0.717742165142366 \tabularnewline
213 & 87 & 85.4117921598151 & 1.58820784018486 \tabularnewline
214 & 79 & 79.2683981693537 & -0.268398169353733 \tabularnewline
215 & 67 & 65.6940533631713 & 1.30594663682868 \tabularnewline
216 & 65 & 68.1969383729806 & -3.19693837298058 \tabularnewline
217 & 85 & 84.0045020042367 & 0.995497995763263 \tabularnewline
218 & 83 & 83.2342279118176 & -0.234227911817621 \tabularnewline
219 & 61 & 58.7158282354636 & 2.28417176453643 \tabularnewline
220 & 82 & 83.1776161034778 & -1.17761610347777 \tabularnewline
221 & 76 & 74.9822377295382 & 1.01776227046181 \tabularnewline
222 & 58 & 53.6166850038928 & 4.38331499610718 \tabularnewline
223 & 72 & 71.4219276783205 & 0.578072321679541 \tabularnewline
224 & 72 & 72.8617407402879 & -0.861740740287894 \tabularnewline
225 & 38 & 23.7113394825943 & 14.2886605174057 \tabularnewline
226 & 78 & 75.1649743674566 & 2.83502563254344 \tabularnewline
227 & 54 & 57.1171569621615 & -3.11715696216153 \tabularnewline
228 & 63 & 58.0719774841671 & 4.92802251583289 \tabularnewline
229 & 66 & 67.0970104818159 & -1.09701048181589 \tabularnewline
230 & 70 & 70.4530043133501 & -0.453004313350101 \tabularnewline
231 & 71 & 70.6202056906726 & 0.379794309327429 \tabularnewline
232 & 67 & 71.2897166051455 & -4.28971660514553 \tabularnewline
233 & 58 & 61.5371576220935 & -3.53715762209348 \tabularnewline
234 & 72 & 74.0084012269826 & -2.00840122698256 \tabularnewline
235 & 72 & 72.7734989720807 & -0.773498972080735 \tabularnewline
236 & 70 & 70.144943257385 & -0.144943257385025 \tabularnewline
237 & 76 & 78.9896226429025 & -2.98962264290246 \tabularnewline
238 & 50 & 56.3844111726651 & -6.38441117266513 \tabularnewline
239 & 72 & 71.4119839549295 & 0.588016045070456 \tabularnewline
240 & 72 & 71.7112064787095 & 0.28879352129046 \tabularnewline
241 & 88 & 85.5703495033305 & 2.42965049666954 \tabularnewline
242 & 53 & 56.5769701066943 & -3.57697010669426 \tabularnewline
243 & 58 & 58.6589335487695 & -0.658933548769483 \tabularnewline
244 & 66 & 65.5371170382892 & 0.46288296171077 \tabularnewline
245 & 82 & 84.4650101462531 & -2.46501014625306 \tabularnewline
246 & 69 & 68.0112546524085 & 0.988745347591502 \tabularnewline
247 & 68 & 70.3236149334182 & -2.32361493341819 \tabularnewline
248 & 44 & 48.6346165488833 & -4.63461654888328 \tabularnewline
249 & 56 & 60.146852852574 & -4.14685285257401 \tabularnewline
250 & 53 & 52.3480571687549 & 0.651942831245122 \tabularnewline
251 & 70 & 68.9821326655629 & 1.01786733443714 \tabularnewline
252 & 78 & 75.3038518542146 & 2.6961481457854 \tabularnewline
253 & 71 & 72.0318527525801 & -1.03185275258006 \tabularnewline
254 & 72 & 73.5527543385595 & -1.55275433855949 \tabularnewline
255 & 68 & 70.9974044593231 & -2.99740445932314 \tabularnewline
256 & 67 & 70.2642239854251 & -3.26422398542507 \tabularnewline
257 & 75 & 69.162211056752 & 5.83778894324798 \tabularnewline
258 & 62 & 65.4887011560844 & -3.48870115608441 \tabularnewline
259 & 67 & 67.2514111912597 & -0.251411191259698 \tabularnewline
260 & 83 & 83.4646715245736 & -0.464671524573558 \tabularnewline
261 & 64 & 64.2591506173862 & -0.259150617386196 \tabularnewline
262 & 68 & 67.9394829425309 & 0.0605170574691377 \tabularnewline
263 & 62 & 62.5702192200236 & -0.570219220023636 \tabularnewline
264 & 72 & 71.0854650870094 & 0.914534912990609 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&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]53[/C][C]54.8453938509924[/C][C]-1.84539385099237[/C][/ROW]
[ROW][C]2[/C][C]83[/C][C]82.1953449537143[/C][C]0.804655046285673[/C][/ROW]
[ROW][C]3[/C][C]66[/C][C]69.6342864321334[/C][C]-3.63428643213344[/C][/ROW]
[ROW][C]4[/C][C]67[/C][C]68.0274311703578[/C][C]-1.02743117035782[/C][/ROW]
[ROW][C]5[/C][C]76[/C][C]73.5293006325231[/C][C]2.4706993674769[/C][/ROW]
[ROW][C]6[/C][C]78[/C][C]76.4349082623844[/C][C]1.56509173761558[/C][/ROW]
[ROW][C]7[/C][C]53[/C][C]60.4578961111162[/C][C]-7.45789611111617[/C][/ROW]
[ROW][C]8[/C][C]80[/C][C]79.6794698587146[/C][C]0.32053014128541[/C][/ROW]
[ROW][C]9[/C][C]74[/C][C]73.9793039008842[/C][C]0.0206960991157908[/C][/ROW]
[ROW][C]10[/C][C]76[/C][C]76.1137907020774[/C][C]-0.113790702077387[/C][/ROW]
[ROW][C]11[/C][C]79[/C][C]79.5606611965796[/C][C]-0.560661196579581[/C][/ROW]
[ROW][C]12[/C][C]54[/C][C]57.5246798355563[/C][C]-3.5246798355563[/C][/ROW]
[ROW][C]13[/C][C]67[/C][C]68.6929768731643[/C][C]-1.69297687316431[/C][/ROW]
[ROW][C]14[/C][C]54[/C][C]56.1353833958272[/C][C]-2.13538339582722[/C][/ROW]
[ROW][C]15[/C][C]87[/C][C]85.7713519461669[/C][C]1.22864805383307[/C][/ROW]
[ROW][C]16[/C][C]58[/C][C]60.7837964641213[/C][C]-2.7837964641213[/C][/ROW]
[ROW][C]17[/C][C]75[/C][C]73.1925974868826[/C][C]1.80740251311736[/C][/ROW]
[ROW][C]18[/C][C]88[/C][C]86.6883415916101[/C][C]1.31165840838989[/C][/ROW]
[ROW][C]19[/C][C]64[/C][C]68.2495699616815[/C][C]-4.24956996168152[/C][/ROW]
[ROW][C]20[/C][C]57[/C][C]61.0451301678948[/C][C]-4.04513016789484[/C][/ROW]
[ROW][C]21[/C][C]66[/C][C]68.7585925752518[/C][C]-2.7585925752518[/C][/ROW]
[ROW][C]22[/C][C]68[/C][C]72.1622394394591[/C][C]-4.16223943945913[/C][/ROW]
[ROW][C]23[/C][C]54[/C][C]56.6817999294014[/C][C]-2.68179992940142[/C][/ROW]
[ROW][C]24[/C][C]56[/C][C]61.8538630671094[/C][C]-5.85386306710944[/C][/ROW]
[ROW][C]25[/C][C]86[/C][C]83.6761628219651[/C][C]2.32383717803489[/C][/ROW]
[ROW][C]26[/C][C]80[/C][C]76.7636772892337[/C][C]3.23632271076629[/C][/ROW]
[ROW][C]27[/C][C]76[/C][C]70.9896356700985[/C][C]5.01036432990147[/C][/ROW]
[ROW][C]28[/C][C]69[/C][C]71.9249431992698[/C][C]-2.92494319926982[/C][/ROW]
[ROW][C]29[/C][C]78[/C][C]73.512652758802[/C][C]4.487347241198[/C][/ROW]
[ROW][C]30[/C][C]67[/C][C]71.995588976834[/C][C]-4.99558897683401[/C][/ROW]
[ROW][C]31[/C][C]80[/C][C]80.1973759654606[/C][C]-0.197375965460552[/C][/ROW]
[ROW][C]32[/C][C]54[/C][C]56.365800317785[/C][C]-2.36580031778495[/C][/ROW]
[ROW][C]33[/C][C]71[/C][C]71.0481203079445[/C][C]-0.048120307944471[/C][/ROW]
[ROW][C]34[/C][C]84[/C][C]86.733086005102[/C][C]-2.73308600510203[/C][/ROW]
[ROW][C]35[/C][C]74[/C][C]69.2945123045613[/C][C]4.70548769543872[/C][/ROW]
[ROW][C]36[/C][C]71[/C][C]72.8358961927028[/C][C]-1.83589619270281[/C][/ROW]
[ROW][C]37[/C][C]63[/C][C]60.8646354370128[/C][C]2.1353645629872[/C][/ROW]
[ROW][C]38[/C][C]71[/C][C]70.7820737484968[/C][C]0.217926251503215[/C][/ROW]
[ROW][C]39[/C][C]76[/C][C]75.8735511000336[/C][C]0.126448899966407[/C][/ROW]
[ROW][C]40[/C][C]69[/C][C]69.7783743135576[/C][C]-0.778374313557593[/C][/ROW]
[ROW][C]41[/C][C]74[/C][C]74.1166746175287[/C][C]-0.116674617528715[/C][/ROW]
[ROW][C]42[/C][C]75[/C][C]71.965236774494[/C][C]3.034763225506[/C][/ROW]
[ROW][C]43[/C][C]54[/C][C]57.645592838773[/C][C]-3.64559283877305[/C][/ROW]
[ROW][C]44[/C][C]52[/C][C]53.3734150688217[/C][C]-1.37341506882173[/C][/ROW]
[ROW][C]45[/C][C]69[/C][C]69.3593380032304[/C][C]-0.359338003230438[/C][/ROW]
[ROW][C]46[/C][C]68[/C][C]66.0957471359819[/C][C]1.90425286401811[/C][/ROW]
[ROW][C]47[/C][C]65[/C][C]71.2443667684554[/C][C]-6.24436676845539[/C][/ROW]
[ROW][C]48[/C][C]75[/C][C]75.4003367778893[/C][C]-0.400336777889257[/C][/ROW]
[ROW][C]49[/C][C]74[/C][C]68.8830607076224[/C][C]5.11693929237757[/C][/ROW]
[ROW][C]50[/C][C]75[/C][C]73.6433024048799[/C][C]1.35669759512011[/C][/ROW]
[ROW][C]51[/C][C]72[/C][C]72.9869120265718[/C][C]-0.986912026571846[/C][/ROW]
[ROW][C]52[/C][C]67[/C][C]66.3264877259255[/C][C]0.673512274074464[/C][/ROW]
[ROW][C]53[/C][C]63[/C][C]61.2802143102324[/C][C]1.7197856897676[/C][/ROW]
[ROW][C]54[/C][C]62[/C][C]74.8498557533261[/C][C]-12.8498557533261[/C][/ROW]
[ROW][C]55[/C][C]63[/C][C]60.7811621232557[/C][C]2.21883787674429[/C][/ROW]
[ROW][C]56[/C][C]76[/C][C]75.681219706967[/C][C]0.318780293033023[/C][/ROW]
[ROW][C]57[/C][C]74[/C][C]73.3409873377592[/C][C]0.659012662240753[/C][/ROW]
[ROW][C]58[/C][C]67[/C][C]70.4418218871389[/C][C]-3.4418218871389[/C][/ROW]
[ROW][C]59[/C][C]73[/C][C]71.044580982647[/C][C]1.95541901735299[/C][/ROW]
[ROW][C]60[/C][C]70[/C][C]70.2488687632913[/C][C]-0.248868763291277[/C][/ROW]
[ROW][C]61[/C][C]53[/C][C]53.7277178177236[/C][C]-0.727717817723627[/C][/ROW]
[ROW][C]62[/C][C]77[/C][C]73.5414646264745[/C][C]3.45853537352554[/C][/ROW]
[ROW][C]63[/C][C]80[/C][C]78.0772885754222[/C][C]1.92271142457784[/C][/ROW]
[ROW][C]64[/C][C]52[/C][C]53.687720281869[/C][C]-1.68772028186904[/C][/ROW]
[ROW][C]65[/C][C]54[/C][C]56.6632798803182[/C][C]-2.66327988031815[/C][/ROW]
[ROW][C]66[/C][C]80[/C][C]79.6016958526331[/C][C]0.398304147366946[/C][/ROW]
[ROW][C]67[/C][C]66[/C][C]68.6250264367162[/C][C]-2.62502643671625[/C][/ROW]
[ROW][C]68[/C][C]73[/C][C]68.4203986217039[/C][C]4.57960137829614[/C][/ROW]
[ROW][C]69[/C][C]63[/C][C]63.24107841769[/C][C]-0.24107841768997[/C][/ROW]
[ROW][C]70[/C][C]69[/C][C]69.139121621252[/C][C]-0.13912162125203[/C][/ROW]
[ROW][C]71[/C][C]67[/C][C]71.5405083545774[/C][C]-4.54050835457745[/C][/ROW]
[ROW][C]72[/C][C]54[/C][C]56.3584447518018[/C][C]-2.35844475180176[/C][/ROW]
[ROW][C]73[/C][C]81[/C][C]78.0349553625804[/C][C]2.96504463741959[/C][/ROW]
[ROW][C]74[/C][C]69[/C][C]67.0235879805888[/C][C]1.9764120194112[/C][/ROW]
[ROW][C]75[/C][C]84[/C][C]81.0578362504876[/C][C]2.94216374951237[/C][/ROW]
[ROW][C]76[/C][C]80[/C][C]77.7581749363234[/C][C]2.24182506367655[/C][/ROW]
[ROW][C]77[/C][C]70[/C][C]70.685180908486[/C][C]-0.685180908486015[/C][/ROW]
[ROW][C]78[/C][C]69[/C][C]71.4130528816695[/C][C]-2.4130528816695[/C][/ROW]
[ROW][C]79[/C][C]77[/C][C]76.7099086582785[/C][C]0.290091341721531[/C][/ROW]
[ROW][C]80[/C][C]54[/C][C]57.1939600473029[/C][C]-3.19396004730292[/C][/ROW]
[ROW][C]81[/C][C]79[/C][C]75.4791922601026[/C][C]3.52080773989735[/C][/ROW]
[ROW][C]82[/C][C]71[/C][C]74.3101768209003[/C][C]-3.31017682090033[/C][/ROW]
[ROW][C]83[/C][C]73[/C][C]70.7790542704153[/C][C]2.22094572958471[/C][/ROW]
[ROW][C]84[/C][C]72[/C][C]72.4474837837938[/C][C]-0.447483783793783[/C][/ROW]
[ROW][C]85[/C][C]77[/C][C]76.4376436907544[/C][C]0.562356309245644[/C][/ROW]
[ROW][C]86[/C][C]75[/C][C]73.1779079298122[/C][C]1.82209207018776[/C][/ROW]
[ROW][C]87[/C][C]69[/C][C]69.6224710061933[/C][C]-0.622471006193308[/C][/ROW]
[ROW][C]88[/C][C]54[/C][C]56.5691350103138[/C][C]-2.56913501031383[/C][/ROW]
[ROW][C]89[/C][C]70[/C][C]69.0319070087998[/C][C]0.968092991200231[/C][/ROW]
[ROW][C]90[/C][C]73[/C][C]74.974750135792[/C][C]-1.97475013579202[/C][/ROW]
[ROW][C]91[/C][C]54[/C][C]56.2948439658684[/C][C]-2.29484396586841[/C][/ROW]
[ROW][C]92[/C][C]77[/C][C]74.9773239607903[/C][C]2.02267603920967[/C][/ROW]
[ROW][C]93[/C][C]82[/C][C]77.5814970520189[/C][C]4.41850294798106[/C][/ROW]
[ROW][C]94[/C][C]80[/C][C]76.7109939814389[/C][C]3.28900601856114[/C][/ROW]
[ROW][C]95[/C][C]80[/C][C]76.4611738506759[/C][C]3.53882614932412[/C][/ROW]
[ROW][C]96[/C][C]69[/C][C]70.0538136816989[/C][C]-1.05381368169885[/C][/ROW]
[ROW][C]97[/C][C]78[/C][C]75.2315635410087[/C][C]2.76843645899131[/C][/ROW]
[ROW][C]98[/C][C]81[/C][C]78.095133378019[/C][C]2.90486662198099[/C][/ROW]
[ROW][C]99[/C][C]76[/C][C]75.5629442355837[/C][C]0.437055764416277[/C][/ROW]
[ROW][C]100[/C][C]76[/C][C]74.048851475[/C][C]1.95114852500004[/C][/ROW]
[ROW][C]101[/C][C]73[/C][C]72.8060757695086[/C][C]0.193924230491454[/C][/ROW]
[ROW][C]102[/C][C]85[/C][C]83.8987785248565[/C][C]1.10122147514355[/C][/ROW]
[ROW][C]103[/C][C]66[/C][C]69.7499212494633[/C][C]-3.74992124946331[/C][/ROW]
[ROW][C]104[/C][C]79[/C][C]75.9238553677038[/C][C]3.07614463229621[/C][/ROW]
[ROW][C]105[/C][C]68[/C][C]66.2719256016839[/C][C]1.72807439831611[/C][/ROW]
[ROW][C]106[/C][C]76[/C][C]76.6447945351313[/C][C]-0.644794535131331[/C][/ROW]
[ROW][C]107[/C][C]71[/C][C]69.808789293434[/C][C]1.19121070656601[/C][/ROW]
[ROW][C]108[/C][C]54[/C][C]56.0463233905278[/C][C]-2.04632339052778[/C][/ROW]
[ROW][C]109[/C][C]46[/C][C]49.3632079526495[/C][C]-3.36320795264947[/C][/ROW]
[ROW][C]110[/C][C]85[/C][C]83.5993818598832[/C][C]1.40061814011676[/C][/ROW]
[ROW][C]111[/C][C]74[/C][C]72.0509495388003[/C][C]1.94905046119968[/C][/ROW]
[ROW][C]112[/C][C]88[/C][C]88.2391712109879[/C][C]-0.239171210987949[/C][/ROW]
[ROW][C]113[/C][C]38[/C][C]25.1070379699101[/C][C]12.8929620300899[/C][/ROW]
[ROW][C]114[/C][C]76[/C][C]76.260630725701[/C][C]-0.260630725701048[/C][/ROW]
[ROW][C]115[/C][C]86[/C][C]85.4661441218225[/C][C]0.533855878177539[/C][/ROW]
[ROW][C]116[/C][C]54[/C][C]56.463039992799[/C][C]-2.46303999279903[/C][/ROW]
[ROW][C]117[/C][C]67[/C][C]71.9551596609797[/C][C]-4.95515966097972[/C][/ROW]
[ROW][C]118[/C][C]69[/C][C]70.0090063999161[/C][C]-1.00900639991615[/C][/ROW]
[ROW][C]119[/C][C]90[/C][C]87.1444257752432[/C][C]2.85557422475679[/C][/ROW]
[ROW][C]120[/C][C]54[/C][C]56.1304910863229[/C][C]-2.13049108632289[/C][/ROW]
[ROW][C]121[/C][C]76[/C][C]75.082709511204[/C][C]0.917290488795971[/C][/ROW]
[ROW][C]122[/C][C]89[/C][C]86.3529399244096[/C][C]2.64706007559043[/C][/ROW]
[ROW][C]123[/C][C]76[/C][C]77.276744290344[/C][C]-1.27674429034403[/C][/ROW]
[ROW][C]124[/C][C]73[/C][C]73.8814827165044[/C][C]-0.881482716504362[/C][/ROW]
[ROW][C]125[/C][C]79[/C][C]77.1063166284124[/C][C]1.89368337158761[/C][/ROW]
[ROW][C]126[/C][C]90[/C][C]88.3128399769274[/C][C]1.68716002307258[/C][/ROW]
[ROW][C]127[/C][C]74[/C][C]72.1862773758563[/C][C]1.81372262414369[/C][/ROW]
[ROW][C]128[/C][C]81[/C][C]85.2853677494639[/C][C]-4.28536774946386[/C][/ROW]
[ROW][C]129[/C][C]72[/C][C]71.9481617638625[/C][C]0.051838236137528[/C][/ROW]
[ROW][C]130[/C][C]71[/C][C]69.7785711566037[/C][C]1.22142884339634[/C][/ROW]
[ROW][C]131[/C][C]66[/C][C]65.2460526131428[/C][C]0.753947386857196[/C][/ROW]
[ROW][C]132[/C][C]77[/C][C]75.0759628092008[/C][C]1.92403719079916[/C][/ROW]
[ROW][C]133[/C][C]65[/C][C]66.4180594328308[/C][C]-1.41805943283082[/C][/ROW]
[ROW][C]134[/C][C]74[/C][C]74.9568747385268[/C][C]-0.956874738526759[/C][/ROW]
[ROW][C]135[/C][C]85[/C][C]84.8699329747475[/C][C]0.130067025252481[/C][/ROW]
[ROW][C]136[/C][C]54[/C][C]54.8857050894344[/C][C]-0.885705089434367[/C][/ROW]
[ROW][C]137[/C][C]63[/C][C]69.1065334979822[/C][C]-6.10653349798222[/C][/ROW]
[ROW][C]138[/C][C]54[/C][C]58.822285597587[/C][C]-4.82228559758703[/C][/ROW]
[ROW][C]139[/C][C]64[/C][C]66.5433663843943[/C][C]-2.54336638439429[/C][/ROW]
[ROW][C]140[/C][C]69[/C][C]67.9826955837185[/C][C]1.01730441628149[/C][/ROW]
[ROW][C]141[/C][C]54[/C][C]57.184823348898[/C][C]-3.18482334889803[/C][/ROW]
[ROW][C]142[/C][C]84[/C][C]82.0371490245855[/C][C]1.96285097541447[/C][/ROW]
[ROW][C]143[/C][C]86[/C][C]85.5266249019038[/C][C]0.473375098096202[/C][/ROW]
[ROW][C]144[/C][C]77[/C][C]75.3979376479794[/C][C]1.60206235202061[/C][/ROW]
[ROW][C]145[/C][C]89[/C][C]87.3872793261842[/C][C]1.61272067381577[/C][/ROW]
[ROW][C]146[/C][C]76[/C][C]76.9240341171028[/C][C]-0.924034117102832[/C][/ROW]
[ROW][C]147[/C][C]60[/C][C]63.4133521161641[/C][C]-3.41335211616405[/C][/ROW]
[ROW][C]148[/C][C]75[/C][C]75.5015357730326[/C][C]-0.501535773032578[/C][/ROW]
[ROW][C]149[/C][C]73[/C][C]73.5389847353316[/C][C]-0.53898473533156[/C][/ROW]
[ROW][C]150[/C][C]85[/C][C]84.4461196937475[/C][C]0.55388030625249[/C][/ROW]
[ROW][C]151[/C][C]79[/C][C]75.1870881366024[/C][C]3.81291186339756[/C][/ROW]
[ROW][C]152[/C][C]71[/C][C]68.6120195488586[/C][C]2.38798045114141[/C][/ROW]
[ROW][C]153[/C][C]72[/C][C]72.0584067672662[/C][C]-0.0584067672662491[/C][/ROW]
[ROW][C]154[/C][C]69[/C][C]69.1112218223618[/C][C]-0.111221822361844[/C][/ROW]
[ROW][C]155[/C][C]78[/C][C]80.8170229159575[/C][C]-2.81702291595751[/C][/ROW]
[ROW][C]156[/C][C]54[/C][C]56.2948439658684[/C][C]-2.29484396586841[/C][/ROW]
[ROW][C]157[/C][C]69[/C][C]70.3629113926983[/C][C]-1.36291139269827[/C][/ROW]
[ROW][C]158[/C][C]81[/C][C]85.2853677494639[/C][C]-4.28536774946386[/C][/ROW]
[ROW][C]159[/C][C]84[/C][C]82.7975203919233[/C][C]1.20247960807672[/C][/ROW]
[ROW][C]160[/C][C]84[/C][C]79.5249334086407[/C][C]4.4750665913593[/C][/ROW]
[ROW][C]161[/C][C]69[/C][C]74.1751832829428[/C][C]-5.17518328294282[/C][/ROW]
[ROW][C]162[/C][C]66[/C][C]68.9257751460021[/C][C]-2.92577514600209[/C][/ROW]
[ROW][C]163[/C][C]81[/C][C]76.133218861911[/C][C]4.86678113808901[/C][/ROW]
[ROW][C]164[/C][C]82[/C][C]79.2999370881973[/C][C]2.70006291180268[/C][/ROW]
[ROW][C]165[/C][C]72[/C][C]69.6727400341891[/C][C]2.32725996581094[/C][/ROW]
[ROW][C]166[/C][C]54[/C][C]54.9532709732985[/C][C]-0.953270973298456[/C][/ROW]
[ROW][C]167[/C][C]78[/C][C]78.1724438805588[/C][C]-0.172443880558833[/C][/ROW]
[ROW][C]168[/C][C]74[/C][C]72.2797076609406[/C][C]1.72029233905945[/C][/ROW]
[ROW][C]169[/C][C]82[/C][C]79.9225490141135[/C][C]2.07745098588648[/C][/ROW]
[ROW][C]170[/C][C]73[/C][C]67.3841397341523[/C][C]5.61586026584771[/C][/ROW]
[ROW][C]171[/C][C]55[/C][C]56.0893182814043[/C][C]-1.08931828140432[/C][/ROW]
[ROW][C]172[/C][C]72[/C][C]72.29299863308[/C][C]-0.292998633079962[/C][/ROW]
[ROW][C]173[/C][C]78[/C][C]75.8712431329044[/C][C]2.12875686709559[/C][/ROW]
[ROW][C]174[/C][C]59[/C][C]58.542457593736[/C][C]0.457542406263979[/C][/ROW]
[ROW][C]175[/C][C]72[/C][C]71.9477017706145[/C][C]0.0522982293854869[/C][/ROW]
[ROW][C]176[/C][C]78[/C][C]72.9710752478644[/C][C]5.02892475213561[/C][/ROW]
[ROW][C]177[/C][C]68[/C][C]69.6443131899968[/C][C]-1.64431318999679[/C][/ROW]
[ROW][C]178[/C][C]69[/C][C]66.9669804390113[/C][C]2.03301956098868[/C][/ROW]
[ROW][C]179[/C][C]67[/C][C]68.5744422142602[/C][C]-1.5744422142602[/C][/ROW]
[ROW][C]180[/C][C]74[/C][C]68.6025296898581[/C][C]5.39747031014186[/C][/ROW]
[ROW][C]181[/C][C]54[/C][C]56.887484172001[/C][C]-2.88748417200095[/C][/ROW]
[ROW][C]182[/C][C]67[/C][C]68.183185415987[/C][C]-1.18318541598698[/C][/ROW]
[ROW][C]183[/C][C]70[/C][C]73.0189397444757[/C][C]-3.01893974447567[/C][/ROW]
[ROW][C]184[/C][C]80[/C][C]78.1315328943952[/C][C]1.86846710560483[/C][/ROW]
[ROW][C]185[/C][C]89[/C][C]88.5729391597273[/C][C]0.427060840272732[/C][/ROW]
[ROW][C]186[/C][C]76[/C][C]71.1971946218147[/C][C]4.80280537818531[/C][/ROW]
[ROW][C]187[/C][C]74[/C][C]70.4853951226776[/C][C]3.51460487732237[/C][/ROW]
[ROW][C]188[/C][C]87[/C][C]83.0521427144503[/C][C]3.94785728554966[/C][/ROW]
[ROW][C]189[/C][C]54[/C][C]52.1694477298499[/C][C]1.83055227015009[/C][/ROW]
[ROW][C]190[/C][C]61[/C][C]63.2359901256903[/C][C]-2.23599012569035[/C][/ROW]
[ROW][C]191[/C][C]38[/C][C]25.5084608984617[/C][C]12.4915391015383[/C][/ROW]
[ROW][C]192[/C][C]75[/C][C]72.5045124471599[/C][C]2.49548755284009[/C][/ROW]
[ROW][C]193[/C][C]69[/C][C]68.6254589999994[/C][C]0.374541000000632[/C][/ROW]
[ROW][C]194[/C][C]62[/C][C]67.2526895104376[/C][C]-5.25268951043759[/C][/ROW]
[ROW][C]195[/C][C]72[/C][C]71.9545417772273[/C][C]0.0454582227727241[/C][/ROW]
[ROW][C]196[/C][C]70[/C][C]72.1240028041475[/C][C]-2.12400280414753[/C][/ROW]
[ROW][C]197[/C][C]79[/C][C]76.6393867492556[/C][C]2.36061325074441[/C][/ROW]
[ROW][C]198[/C][C]87[/C][C]84.6230232134233[/C][C]2.37697678657668[/C][/ROW]
[ROW][C]199[/C][C]62[/C][C]60.9570391481135[/C][C]1.04296085188649[/C][/ROW]
[ROW][C]200[/C][C]77[/C][C]72.6867713186391[/C][C]4.31322868136088[/C][/ROW]
[ROW][C]201[/C][C]69[/C][C]71.6381012463713[/C][C]-2.63810124637128[/C][/ROW]
[ROW][C]202[/C][C]69[/C][C]67.2079075048477[/C][C]1.79209249515227[/C][/ROW]
[ROW][C]203[/C][C]75[/C][C]69.4653988250286[/C][C]5.53460117497136[/C][/ROW]
[ROW][C]204[/C][C]54[/C][C]59.1015082612388[/C][C]-5.10150826123885[/C][/ROW]
[ROW][C]205[/C][C]72[/C][C]71.0408127277623[/C][C]0.959187272237663[/C][/ROW]
[ROW][C]206[/C][C]74[/C][C]73.5500589974434[/C][C]0.449941002556634[/C][/ROW]
[ROW][C]207[/C][C]85[/C][C]87.8463209763439[/C][C]-2.8463209763439[/C][/ROW]
[ROW][C]208[/C][C]52[/C][C]53.4095826046428[/C][C]-1.40958260464282[/C][/ROW]
[ROW][C]209[/C][C]70[/C][C]71.5878912946151[/C][C]-1.58789129461506[/C][/ROW]
[ROW][C]210[/C][C]84[/C][C]80.7901968412178[/C][C]3.20980315878221[/C][/ROW]
[ROW][C]211[/C][C]64[/C][C]65.5546387210637[/C][C]-1.55463872106371[/C][/ROW]
[ROW][C]212[/C][C]84[/C][C]84.7177421651424[/C][C]-0.717742165142366[/C][/ROW]
[ROW][C]213[/C][C]87[/C][C]85.4117921598151[/C][C]1.58820784018486[/C][/ROW]
[ROW][C]214[/C][C]79[/C][C]79.2683981693537[/C][C]-0.268398169353733[/C][/ROW]
[ROW][C]215[/C][C]67[/C][C]65.6940533631713[/C][C]1.30594663682868[/C][/ROW]
[ROW][C]216[/C][C]65[/C][C]68.1969383729806[/C][C]-3.19693837298058[/C][/ROW]
[ROW][C]217[/C][C]85[/C][C]84.0045020042367[/C][C]0.995497995763263[/C][/ROW]
[ROW][C]218[/C][C]83[/C][C]83.2342279118176[/C][C]-0.234227911817621[/C][/ROW]
[ROW][C]219[/C][C]61[/C][C]58.7158282354636[/C][C]2.28417176453643[/C][/ROW]
[ROW][C]220[/C][C]82[/C][C]83.1776161034778[/C][C]-1.17761610347777[/C][/ROW]
[ROW][C]221[/C][C]76[/C][C]74.9822377295382[/C][C]1.01776227046181[/C][/ROW]
[ROW][C]222[/C][C]58[/C][C]53.6166850038928[/C][C]4.38331499610718[/C][/ROW]
[ROW][C]223[/C][C]72[/C][C]71.4219276783205[/C][C]0.578072321679541[/C][/ROW]
[ROW][C]224[/C][C]72[/C][C]72.8617407402879[/C][C]-0.861740740287894[/C][/ROW]
[ROW][C]225[/C][C]38[/C][C]23.7113394825943[/C][C]14.2886605174057[/C][/ROW]
[ROW][C]226[/C][C]78[/C][C]75.1649743674566[/C][C]2.83502563254344[/C][/ROW]
[ROW][C]227[/C][C]54[/C][C]57.1171569621615[/C][C]-3.11715696216153[/C][/ROW]
[ROW][C]228[/C][C]63[/C][C]58.0719774841671[/C][C]4.92802251583289[/C][/ROW]
[ROW][C]229[/C][C]66[/C][C]67.0970104818159[/C][C]-1.09701048181589[/C][/ROW]
[ROW][C]230[/C][C]70[/C][C]70.4530043133501[/C][C]-0.453004313350101[/C][/ROW]
[ROW][C]231[/C][C]71[/C][C]70.6202056906726[/C][C]0.379794309327429[/C][/ROW]
[ROW][C]232[/C][C]67[/C][C]71.2897166051455[/C][C]-4.28971660514553[/C][/ROW]
[ROW][C]233[/C][C]58[/C][C]61.5371576220935[/C][C]-3.53715762209348[/C][/ROW]
[ROW][C]234[/C][C]72[/C][C]74.0084012269826[/C][C]-2.00840122698256[/C][/ROW]
[ROW][C]235[/C][C]72[/C][C]72.7734989720807[/C][C]-0.773498972080735[/C][/ROW]
[ROW][C]236[/C][C]70[/C][C]70.144943257385[/C][C]-0.144943257385025[/C][/ROW]
[ROW][C]237[/C][C]76[/C][C]78.9896226429025[/C][C]-2.98962264290246[/C][/ROW]
[ROW][C]238[/C][C]50[/C][C]56.3844111726651[/C][C]-6.38441117266513[/C][/ROW]
[ROW][C]239[/C][C]72[/C][C]71.4119839549295[/C][C]0.588016045070456[/C][/ROW]
[ROW][C]240[/C][C]72[/C][C]71.7112064787095[/C][C]0.28879352129046[/C][/ROW]
[ROW][C]241[/C][C]88[/C][C]85.5703495033305[/C][C]2.42965049666954[/C][/ROW]
[ROW][C]242[/C][C]53[/C][C]56.5769701066943[/C][C]-3.57697010669426[/C][/ROW]
[ROW][C]243[/C][C]58[/C][C]58.6589335487695[/C][C]-0.658933548769483[/C][/ROW]
[ROW][C]244[/C][C]66[/C][C]65.5371170382892[/C][C]0.46288296171077[/C][/ROW]
[ROW][C]245[/C][C]82[/C][C]84.4650101462531[/C][C]-2.46501014625306[/C][/ROW]
[ROW][C]246[/C][C]69[/C][C]68.0112546524085[/C][C]0.988745347591502[/C][/ROW]
[ROW][C]247[/C][C]68[/C][C]70.3236149334182[/C][C]-2.32361493341819[/C][/ROW]
[ROW][C]248[/C][C]44[/C][C]48.6346165488833[/C][C]-4.63461654888328[/C][/ROW]
[ROW][C]249[/C][C]56[/C][C]60.146852852574[/C][C]-4.14685285257401[/C][/ROW]
[ROW][C]250[/C][C]53[/C][C]52.3480571687549[/C][C]0.651942831245122[/C][/ROW]
[ROW][C]251[/C][C]70[/C][C]68.9821326655629[/C][C]1.01786733443714[/C][/ROW]
[ROW][C]252[/C][C]78[/C][C]75.3038518542146[/C][C]2.6961481457854[/C][/ROW]
[ROW][C]253[/C][C]71[/C][C]72.0318527525801[/C][C]-1.03185275258006[/C][/ROW]
[ROW][C]254[/C][C]72[/C][C]73.5527543385595[/C][C]-1.55275433855949[/C][/ROW]
[ROW][C]255[/C][C]68[/C][C]70.9974044593231[/C][C]-2.99740445932314[/C][/ROW]
[ROW][C]256[/C][C]67[/C][C]70.2642239854251[/C][C]-3.26422398542507[/C][/ROW]
[ROW][C]257[/C][C]75[/C][C]69.162211056752[/C][C]5.83778894324798[/C][/ROW]
[ROW][C]258[/C][C]62[/C][C]65.4887011560844[/C][C]-3.48870115608441[/C][/ROW]
[ROW][C]259[/C][C]67[/C][C]67.2514111912597[/C][C]-0.251411191259698[/C][/ROW]
[ROW][C]260[/C][C]83[/C][C]83.4646715245736[/C][C]-0.464671524573558[/C][/ROW]
[ROW][C]261[/C][C]64[/C][C]64.2591506173862[/C][C]-0.259150617386196[/C][/ROW]
[ROW][C]262[/C][C]68[/C][C]67.9394829425309[/C][C]0.0605170574691377[/C][/ROW]
[ROW][C]263[/C][C]62[/C][C]62.5702192200236[/C][C]-0.570219220023636[/C][/ROW]
[ROW][C]264[/C][C]72[/C][C]71.0854650870094[/C][C]0.914534912990609[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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
15354.8453938509924-1.84539385099237
28382.19534495371430.804655046285673
36669.6342864321334-3.63428643213344
46768.0274311703578-1.02743117035782
57673.52930063252312.4706993674769
67876.43490826238441.56509173761558
75360.4578961111162-7.45789611111617
88079.67946985871460.32053014128541
97473.97930390088420.0206960991157908
107676.1137907020774-0.113790702077387
117979.5606611965796-0.560661196579581
125457.5246798355563-3.5246798355563
136768.6929768731643-1.69297687316431
145456.1353833958272-2.13538339582722
158785.77135194616691.22864805383307
165860.7837964641213-2.7837964641213
177573.19259748688261.80740251311736
188886.68834159161011.31165840838989
196468.2495699616815-4.24956996168152
205761.0451301678948-4.04513016789484
216668.7585925752518-2.7585925752518
226872.1622394394591-4.16223943945913
235456.6817999294014-2.68179992940142
245661.8538630671094-5.85386306710944
258683.67616282196512.32383717803489
268076.76367728923373.23632271076629
277670.98963567009855.01036432990147
286971.9249431992698-2.92494319926982
297873.5126527588024.487347241198
306771.995588976834-4.99558897683401
318080.1973759654606-0.197375965460552
325456.365800317785-2.36580031778495
337171.0481203079445-0.048120307944471
348486.733086005102-2.73308600510203
357469.29451230456134.70548769543872
367172.8358961927028-1.83589619270281
376360.86463543701282.1353645629872
387170.78207374849680.217926251503215
397675.87355110003360.126448899966407
406969.7783743135576-0.778374313557593
417474.1166746175287-0.116674617528715
427571.9652367744943.034763225506
435457.645592838773-3.64559283877305
445253.3734150688217-1.37341506882173
456969.3593380032304-0.359338003230438
466866.09574713598191.90425286401811
476571.2443667684554-6.24436676845539
487575.4003367778893-0.400336777889257
497468.88306070762245.11693929237757
507573.64330240487991.35669759512011
517272.9869120265718-0.986912026571846
526766.32648772592550.673512274074464
536361.28021431023241.7197856897676
546274.8498557533261-12.8498557533261
556360.78116212325572.21883787674429
567675.6812197069670.318780293033023
577473.34098733775920.659012662240753
586770.4418218871389-3.4418218871389
597371.0445809826471.95541901735299
607070.2488687632913-0.248868763291277
615353.7277178177236-0.727717817723627
627773.54146462647453.45853537352554
638078.07728857542221.92271142457784
645253.687720281869-1.68772028186904
655456.6632798803182-2.66327988031815
668079.60169585263310.398304147366946
676668.6250264367162-2.62502643671625
687368.42039862170394.57960137829614
696363.24107841769-0.24107841768997
706969.139121621252-0.13912162125203
716771.5405083545774-4.54050835457745
725456.3584447518018-2.35844475180176
738178.03495536258042.96504463741959
746967.02358798058881.9764120194112
758481.05783625048762.94216374951237
768077.75817493632342.24182506367655
777070.685180908486-0.685180908486015
786971.4130528816695-2.4130528816695
797776.70990865827850.290091341721531
805457.1939600473029-3.19396004730292
817975.47919226010263.52080773989735
827174.3101768209003-3.31017682090033
837370.77905427041532.22094572958471
847272.4474837837938-0.447483783793783
857776.43764369075440.562356309245644
867573.17790792981221.82209207018776
876969.6224710061933-0.622471006193308
885456.5691350103138-2.56913501031383
897069.03190700879980.968092991200231
907374.974750135792-1.97475013579202
915456.2948439658684-2.29484396586841
927774.97732396079032.02267603920967
938277.58149705201894.41850294798106
948076.71099398143893.28900601856114
958076.46117385067593.53882614932412
966970.0538136816989-1.05381368169885
977875.23156354100872.76843645899131
988178.0951333780192.90486662198099
997675.56294423558370.437055764416277
1007674.0488514751.95114852500004
1017372.80607576950860.193924230491454
1028583.89877852485651.10122147514355
1036669.7499212494633-3.74992124946331
1047975.92385536770383.07614463229621
1056866.27192560168391.72807439831611
1067676.6447945351313-0.644794535131331
1077169.8087892934341.19121070656601
1085456.0463233905278-2.04632339052778
1094649.3632079526495-3.36320795264947
1108583.59938185988321.40061814011676
1117472.05094953880031.94905046119968
1128888.2391712109879-0.239171210987949
1133825.107037969910112.8929620300899
1147676.260630725701-0.260630725701048
1158685.46614412182250.533855878177539
1165456.463039992799-2.46303999279903
1176771.9551596609797-4.95515966097972
1186970.0090063999161-1.00900639991615
1199087.14442577524322.85557422475679
1205456.1304910863229-2.13049108632289
1217675.0827095112040.917290488795971
1228986.35293992440962.64706007559043
1237677.276744290344-1.27674429034403
1247373.8814827165044-0.881482716504362
1257977.10631662841241.89368337158761
1269088.31283997692741.68716002307258
1277472.18627737585631.81372262414369
1288185.2853677494639-4.28536774946386
1297271.94816176386250.051838236137528
1307169.77857115660371.22142884339634
1316665.24605261314280.753947386857196
1327775.07596280920081.92403719079916
1336566.4180594328308-1.41805943283082
1347474.9568747385268-0.956874738526759
1358584.86993297474750.130067025252481
1365454.8857050894344-0.885705089434367
1376369.1065334979822-6.10653349798222
1385458.822285597587-4.82228559758703
1396466.5433663843943-2.54336638439429
1406967.98269558371851.01730441628149
1415457.184823348898-3.18482334889803
1428482.03714902458551.96285097541447
1438685.52662490190380.473375098096202
1447775.39793764797941.60206235202061
1458987.38727932618421.61272067381577
1467676.9240341171028-0.924034117102832
1476063.4133521161641-3.41335211616405
1487575.5015357730326-0.501535773032578
1497373.5389847353316-0.53898473533156
1508584.44611969374750.55388030625249
1517975.18708813660243.81291186339756
1527168.61201954885862.38798045114141
1537272.0584067672662-0.0584067672662491
1546969.1112218223618-0.111221822361844
1557880.8170229159575-2.81702291595751
1565456.2948439658684-2.29484396586841
1576970.3629113926983-1.36291139269827
1588185.2853677494639-4.28536774946386
1598482.79752039192331.20247960807672
1608479.52493340864074.4750665913593
1616974.1751832829428-5.17518328294282
1626668.9257751460021-2.92577514600209
1638176.1332188619114.86678113808901
1648279.29993708819732.70006291180268
1657269.67274003418912.32725996581094
1665454.9532709732985-0.953270973298456
1677878.1724438805588-0.172443880558833
1687472.27970766094061.72029233905945
1698279.92254901411352.07745098588648
1707367.38413973415235.61586026584771
1715556.0893182814043-1.08931828140432
1727272.29299863308-0.292998633079962
1737875.87124313290442.12875686709559
1745958.5424575937360.457542406263979
1757271.94770177061450.0522982293854869
1767872.97107524786445.02892475213561
1776869.6443131899968-1.64431318999679
1786966.96698043901132.03301956098868
1796768.5744422142602-1.5744422142602
1807468.60252968985815.39747031014186
1815456.887484172001-2.88748417200095
1826768.183185415987-1.18318541598698
1837073.0189397444757-3.01893974447567
1848078.13153289439521.86846710560483
1858988.57293915972730.427060840272732
1867671.19719462181474.80280537818531
1877470.48539512267763.51460487732237
1888783.05214271445033.94785728554966
1895452.16944772984991.83055227015009
1906163.2359901256903-2.23599012569035
1913825.508460898461712.4915391015383
1927572.50451244715992.49548755284009
1936968.62545899999940.374541000000632
1946267.2526895104376-5.25268951043759
1957271.95454177722730.0454582227727241
1967072.1240028041475-2.12400280414753
1977976.63938674925562.36061325074441
1988784.62302321342332.37697678657668
1996260.95703914811351.04296085188649
2007772.68677131863914.31322868136088
2016971.6381012463713-2.63810124637128
2026967.20790750484771.79209249515227
2037569.46539882502865.53460117497136
2045459.1015082612388-5.10150826123885
2057271.04081272776230.959187272237663
2067473.55005899744340.449941002556634
2078587.8463209763439-2.8463209763439
2085253.4095826046428-1.40958260464282
2097071.5878912946151-1.58789129461506
2108480.79019684121783.20980315878221
2116465.5546387210637-1.55463872106371
2128484.7177421651424-0.717742165142366
2138785.41179215981511.58820784018486
2147979.2683981693537-0.268398169353733
2156765.69405336317131.30594663682868
2166568.1969383729806-3.19693837298058
2178584.00450200423670.995497995763263
2188383.2342279118176-0.234227911817621
2196158.71582823546362.28417176453643
2208283.1776161034778-1.17761610347777
2217674.98223772953821.01776227046181
2225853.61668500389284.38331499610718
2237271.42192767832050.578072321679541
2247272.8617407402879-0.861740740287894
2253823.711339482594314.2886605174057
2267875.16497436745662.83502563254344
2275457.1171569621615-3.11715696216153
2286358.07197748416714.92802251583289
2296667.0970104818159-1.09701048181589
2307070.4530043133501-0.453004313350101
2317170.62020569067260.379794309327429
2326771.2897166051455-4.28971660514553
2335861.5371576220935-3.53715762209348
2347274.0084012269826-2.00840122698256
2357272.7734989720807-0.773498972080735
2367070.144943257385-0.144943257385025
2377678.9896226429025-2.98962264290246
2385056.3844111726651-6.38441117266513
2397271.41198395492950.588016045070456
2407271.71120647870950.28879352129046
2418885.57034950333052.42965049666954
2425356.5769701066943-3.57697010669426
2435858.6589335487695-0.658933548769483
2446665.53711703828920.46288296171077
2458284.4650101462531-2.46501014625306
2466968.01125465240850.988745347591502
2476870.3236149334182-2.32361493341819
2484448.6346165488833-4.63461654888328
2495660.146852852574-4.14685285257401
2505352.34805716875490.651942831245122
2517068.98213266556291.01786733443714
2527875.30385185421462.6961481457854
2537172.0318527525801-1.03185275258006
2547273.5527543385595-1.55275433855949
2556870.9974044593231-2.99740445932314
2566770.2642239854251-3.26422398542507
2577569.1622110567525.83778894324798
2586265.4887011560844-3.48870115608441
2596767.2514111912597-0.251411191259698
2608383.4646715245736-0.464671524573558
2616464.2591506173862-0.259150617386196
2626867.93948294253090.0605170574691377
2636262.5702192200236-0.570219220023636
2647271.08546508700940.914534912990609






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.01927633632503580.03855267265007160.980723663674964
120.01286159517531870.02572319035063730.987138404824681
130.007021887471935230.01404377494387050.992978112528065
140.004945717575800440.009891435151600880.9950542824242
150.001459518478204970.002919036956409940.998540481521795
160.0004723617555488250.000944723511097650.999527638244451
170.001609973632795650.003219947265591290.998390026367204
180.001470618880122450.00294123776024490.998529381119878
190.001066547843687770.002133095687375540.998933452156312
200.0004797195846178380.0009594391692356770.999520280415382
210.0002533638996279360.0005067277992558710.999746636100372
220.000240100426060730.0004802008521214590.999759899573939
230.000615137057782380.001230274115564760.999384862942218
240.002441586930927580.004883173861855170.997558413069072
250.001909091735851640.003818183471703270.998090908264148
260.005285096162987110.01057019232597420.994714903837013
270.04874184978088260.09748369956176510.951258150219117
280.04219367304753280.08438734609506560.957806326952467
290.1196380907838150.239276181567630.880361909216185
300.1862155370510150.372431074102030.813784462948985
310.1491097247718810.2982194495437620.850890275228119
320.1252926663617440.2505853327234880.874707333638256
330.09590868930212420.1918173786042480.904091310697876
340.1350836221994640.2701672443989280.864916377800536
350.2186041098139220.4372082196278430.781395890186078
360.2195013808144470.4390027616288950.780498619185553
370.2302851974238610.4605703948477220.769714802576139
380.1888286157095730.3776572314191450.811171384290427
390.1577227027626980.3154454055253950.842277297237302
400.1282745409241450.256549081848290.871725459075855
410.1012535368588440.2025070737176870.898746463141156
420.09779705823345220.1955941164669040.902202941766548
430.08157510217526230.1631502043505250.918424897824738
440.0681424242678940.1362848485357880.931857575732106
450.05540986897586630.1108197379517330.944590131024134
460.04380174270552960.08760348541105910.95619825729447
470.1000709300892880.2001418601785760.899929069910712
480.07937886337675440.1587577267535090.920621136623246
490.1720964952535210.3441929905070420.827903504746479
500.1443179266031510.2886358532063020.855682073396849
510.1199044014559510.2398088029119010.880095598544049
520.09972082419276960.1994416483855390.90027917580723
530.0888621989167480.1777243978334960.911137801083252
540.6288757184676580.7422485630646830.371124281532342
550.6053472377170230.7893055245659540.394652762282977
560.5624924894785890.8750150210428220.437507510521411
570.5180271339867260.9639457320265480.481972866013274
580.4926482178035330.9852964356070660.507351782196467
590.5004374627891880.9991250744216230.499562537210812
600.4610191745447060.9220383490894130.538980825455294
610.418079116923760.836158233847520.58192088307624
620.441444706539540.882889413079080.55855529346046
630.4328487889807740.8656975779615480.567151211019226
640.4039411019667060.8078822039334130.596058898033294
650.3753789377514690.7507578755029370.624621062248531
660.3494528491454780.6989056982909560.650547150854522
670.3228650906779860.6457301813559710.677134909322014
680.4137663284840010.8275326569680010.586233671515999
690.3779513860707590.7559027721415180.622048613929241
700.347280082860790.694560165721580.65271991713921
710.4369866618706760.8739733237413530.563013338129324
720.4115720862980240.8231441725960470.588427913701976
730.4486367635596090.8972735271192190.551363236440391
740.4921193782062290.9842387564124580.507880621793771
750.466208969584150.9324179391683010.53379103041585
760.4363636258306970.8727272516613940.563636374169303
770.3989540764280660.7979081528561320.601045923571934
780.3897552284172480.7795104568344960.610244771582752
790.353392102491780.7067842049835590.64660789750822
800.3482417880630910.6964835761261820.651758211936909
810.3705568356740280.7411136713480560.629443164325972
820.3695886646152420.7391773292304830.630411335384758
830.3490500017010080.6981000034020150.650949998298992
840.3141592140565740.6283184281131490.685840785943426
850.2804565954941090.5609131909882170.719543404505891
860.2630706874582070.5261413749164150.736929312541793
870.2339670490185350.467934098037070.766032950981465
880.2171305150376940.4342610300753870.782869484962306
890.1933820262262080.3867640524524150.806617973773792
900.1828396707499210.3656793414998420.817160329250079
910.1685550973799010.3371101947598020.831444902620099
920.1647799073414980.3295598146829950.835220092658502
930.1728974590070340.3457949180140670.827102540992966
940.1660067745361630.3320135490723270.833993225463837
950.1717865583284270.3435731166568530.828213441671574
960.1514518417326770.3029036834653530.848548158267323
970.1438955249011110.2877910498022220.856104475098889
980.1352456699726950.270491339945390.864754330027305
990.1161246390214390.2322492780428770.883875360978561
1000.1046325616108450.2092651232216890.895367438389155
1010.08854953852419640.1770990770483930.911450461475804
1020.07523745997787730.1504749199557550.924762540022123
1030.08503650890721410.1700730178144280.914963491092786
1040.08634006164076730.1726801232815350.913659938359233
1050.07503888440898640.1500777688179730.924961115591014
1060.06408655617292320.1281731123458460.935913443827077
1070.05480803880599660.1096160776119930.945191961194003
1080.04960080796966610.09920161593933230.950399192030334
1090.04830376380482350.09660752760964690.951696236195177
1100.04073690154660960.08147380309321920.95926309845339
1110.0366994299560370.0733988599120740.963300570043963
1120.03005823890645020.06011647781290040.96994176109355
1130.5939062594508050.8121874810983890.406093740549195
1140.565782735484850.86843452903030.43421726451515
1150.5305183937380040.9389632125239920.469481606261996
1160.5169743082864270.9660513834271460.483025691713573
1170.5643641412246640.8712717175506710.435635858775336
1180.5333582261261860.9332835477476270.466641773873814
1190.5232142113683340.9535715772633320.476785788631666
1200.5072740030926640.9854519938146710.492725996907336
1210.4761350294620540.9522700589241090.523864970537946
1220.4606243685222730.9212487370445450.539375631477727
1230.4329459557588260.8658919115176520.567054044241174
1240.4052329787046020.8104659574092040.594767021295398
1250.3809425450495370.7618850900990740.619057454950463
1260.3578847331082440.7157694662164870.642115266891756
1270.3390530183798910.6781060367597830.660946981620109
1280.3768056795578630.7536113591157260.623194320442137
1290.3450833390994820.6901666781989650.654916660900517
1300.3215138093130110.6430276186260210.678486190686989
1310.2906310854188140.5812621708376290.709368914581186
1320.276021131068710.552042262137420.72397886893129
1330.2563516714653640.5127033429307290.743648328534636
1340.2321045794508890.4642091589017780.767895420549111
1350.2112898968995040.4225797937990070.788710103100496
1360.1909574293189660.3819148586379310.809042570681034
1370.272635508891590.545271017783180.72736449110841
1380.3256961790869970.6513923581739940.674303820913003
1390.3181253189463150.6362506378926310.681874681053685
1400.2911545237198850.5823090474397690.708845476280115
1410.3005159757515750.6010319515031490.699484024248425
1420.2831109854683210.5662219709366410.716889014531679
1430.253689006084350.5073780121687010.74631099391565
1440.2414193370251250.482838674050250.758580662974875
1450.2337804405797430.4675608811594870.766219559420257
1460.2116569847204540.4233139694409080.788343015279546
1470.2289199044504440.4578398089008890.771080095549555
1480.2029886921748420.4059773843496850.797011307825158
1490.1790590660419120.3581181320838250.820940933958088
1500.156890747604940.313781495209880.84310925239506
1510.1689160747287110.3378321494574220.831083925271289
1520.1640994725237850.3281989450475710.835900527476214
1530.1487639222168140.2975278444336270.851236077783186
1540.1286568971190070.2573137942380140.871343102880993
1550.1272686313750270.2545372627500540.872731368624973
1560.1211623723833330.2423247447666660.878837627616667
1570.1099727633203840.2199455266407680.890027236679616
1580.1292675646879680.2585351293759370.870732435312032
1590.1141822863861880.2283645727723760.885817713613812
1600.1330402968088740.2660805936177480.866959703191126
1610.1771450978729190.3542901957458380.822854902127081
1620.1680970975713440.3361941951426880.831902902428656
1630.2292338082796750.458467616559350.770766191720325
1640.2470620253557350.4941240507114690.752937974644265
1650.2346635683859950.469327136771990.765336431614005
1660.2133960730813260.4267921461626530.786603926918674
1670.1875002518596360.3750005037192730.812499748140364
1680.1796468006888480.3592936013776960.820353199311152
1690.1637722941986330.3275445883972660.836227705801367
1700.2102419924971140.4204839849942290.789758007502886
1710.1871361765850760.3742723531701510.812863823414924
1720.1644025769786220.3288051539572440.835597423021378
1730.1541051415656220.3082102831312440.845894858434378
1740.1350836010516450.270167202103290.864916398948355
1750.1235259143682290.2470518287364570.876474085631771
1760.1490322193255530.2980644386511060.850967780674447
1770.1329008850698330.2658017701396660.867099114930167
1780.124618111871880.249236223743760.87538188812812
1790.1109478414680420.2218956829360830.889052158531958
1800.1430415889206360.2860831778412720.856958411079364
1810.1601108674846840.3202217349693680.839889132515316
1820.1413423832896820.2826847665793650.858657616710318
1830.1526758297951580.3053516595903160.847324170204842
1840.1467170234876560.2934340469753110.853282976512344
1850.1416470786184910.2832941572369820.858352921381509
1860.1758155265787580.3516310531575160.824184473421242
1870.1829770696771260.3659541393542520.817022930322874
1880.2536735664861070.5073471329722140.746326433513893
1890.2313091218045020.4626182436090030.768690878195498
1900.2392887423015070.4785774846030130.760711257698493
1910.6040597009774030.7918805980451940.395940299022597
1920.5787621844537210.8424756310925580.421237815546279
1930.5457304190059940.9085391619880130.454269580994006
1940.6611339614869920.6777320770260160.338866038513008
1950.6235005653764310.7529988692471390.376499434623569
1960.6129345076365130.7741309847269730.387065492363487
1970.6244030965148260.7511938069703480.375596903485174
1980.6135628655522480.7728742688955030.386437134447752
1990.5734759435674920.8530481128650160.426524056432508
2000.6155093550636920.7689812898726170.384490644936308
2010.587345047852990.825309904294020.41265495214701
2020.5594772806312340.8810454387375320.440522719368766
2030.7264946006333420.5470107987333150.273505399366657
2040.7722031934868820.4555936130262350.227796806513118
2050.7378447584406360.5243104831187280.262155241559364
2060.7005318490783870.5989363018432260.299468150921613
2070.6869423245368820.6261153509262350.313057675463118
2080.6760001773910740.6479996452178510.323999822608926
2090.6373674794028930.7252650411942130.362632520597107
2100.6937096281004510.6125807437990990.306290371899549
2110.660684829784590.6786303404308210.33931517021541
2120.624820104686640.750359790626720.37517989531336
2130.5877934815071720.8244130369856570.412206518492828
2140.5407811984633980.9184376030732040.459218801536602
2150.496875658431320.9937513168626410.50312434156868
2160.4599062575109470.9198125150218950.540093742489053
2170.4402676528325960.8805353056651920.559732347167404
2180.4113686799995280.8227373599990570.588631320000472
2190.3713252915665390.7426505831330780.628674708433461
2200.3259516010341290.6519032020682580.674048398965871
2210.2973228897486160.5946457794972330.702677110251384
2220.3021345636344980.6042691272689960.697865436365502
2230.2720480165752920.5440960331505830.727951983424708
2240.2309280994410870.4618561988821740.769071900558913
2250.9294098460832250.1411803078335510.0705901539167753
2260.9229448387690540.1541103224618920.077055161230946
2270.907388590409520.1852228191809610.0926114095904803
2280.9722831739437490.05543365211250250.0277168260562512
2290.9611810044474710.07763799110505710.0388189955525285
2300.9461729570208120.1076540859583770.0538270429791883
2310.9321823584165020.1356352831669950.0678176415834977
2320.9704148978873820.05917020422523630.0295851021126181
2330.9705570790002870.05888584199942510.0294429209997126
2340.9580791753392480.08384164932150380.0419208246607519
2350.940312094275480.1193758114490390.0596879057245195
2360.9284855749327890.1430288501344210.0715144250672107
2370.9096094588171340.1807810823657330.0903905411828664
2380.897377752241590.205244495516820.10262224775841
2390.8620038542725030.2759922914549940.137996145727497
2400.849529764706540.300940470586920.15047023529346
2410.8008479515016820.3983040969966350.199152048498318
2420.7858609255824450.4282781488351110.214139074417555
2430.7243269750075870.5513460499848260.275673024992413
2440.6875655821290870.6248688357418260.312434417870913
2450.618473089164180.7630538216716390.38152691083582
2460.5279207787310460.9441584425379090.472079221268954
2470.5104545758818160.9790908482363680.489545424118184
2480.4720646161047490.9441292322094970.527935383895251
2490.6418320111809320.7163359776381360.358167988819068
2500.6870762896262290.6258474207475430.312923710373771
2510.5722246743560840.8555506512878320.427775325643916
2520.4287774901981890.8575549803963770.571222509801811
2530.2808834340412710.5617668680825420.719116565958729

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0192763363250358 & 0.0385526726500716 & 0.980723663674964 \tabularnewline
12 & 0.0128615951753187 & 0.0257231903506373 & 0.987138404824681 \tabularnewline
13 & 0.00702188747193523 & 0.0140437749438705 & 0.992978112528065 \tabularnewline
14 & 0.00494571757580044 & 0.00989143515160088 & 0.9950542824242 \tabularnewline
15 & 0.00145951847820497 & 0.00291903695640994 & 0.998540481521795 \tabularnewline
16 & 0.000472361755548825 & 0.00094472351109765 & 0.999527638244451 \tabularnewline
17 & 0.00160997363279565 & 0.00321994726559129 & 0.998390026367204 \tabularnewline
18 & 0.00147061888012245 & 0.0029412377602449 & 0.998529381119878 \tabularnewline
19 & 0.00106654784368777 & 0.00213309568737554 & 0.998933452156312 \tabularnewline
20 & 0.000479719584617838 & 0.000959439169235677 & 0.999520280415382 \tabularnewline
21 & 0.000253363899627936 & 0.000506727799255871 & 0.999746636100372 \tabularnewline
22 & 0.00024010042606073 & 0.000480200852121459 & 0.999759899573939 \tabularnewline
23 & 0.00061513705778238 & 0.00123027411556476 & 0.999384862942218 \tabularnewline
24 & 0.00244158693092758 & 0.00488317386185517 & 0.997558413069072 \tabularnewline
25 & 0.00190909173585164 & 0.00381818347170327 & 0.998090908264148 \tabularnewline
26 & 0.00528509616298711 & 0.0105701923259742 & 0.994714903837013 \tabularnewline
27 & 0.0487418497808826 & 0.0974836995617651 & 0.951258150219117 \tabularnewline
28 & 0.0421936730475328 & 0.0843873460950656 & 0.957806326952467 \tabularnewline
29 & 0.119638090783815 & 0.23927618156763 & 0.880361909216185 \tabularnewline
30 & 0.186215537051015 & 0.37243107410203 & 0.813784462948985 \tabularnewline
31 & 0.149109724771881 & 0.298219449543762 & 0.850890275228119 \tabularnewline
32 & 0.125292666361744 & 0.250585332723488 & 0.874707333638256 \tabularnewline
33 & 0.0959086893021242 & 0.191817378604248 & 0.904091310697876 \tabularnewline
34 & 0.135083622199464 & 0.270167244398928 & 0.864916377800536 \tabularnewline
35 & 0.218604109813922 & 0.437208219627843 & 0.781395890186078 \tabularnewline
36 & 0.219501380814447 & 0.439002761628895 & 0.780498619185553 \tabularnewline
37 & 0.230285197423861 & 0.460570394847722 & 0.769714802576139 \tabularnewline
38 & 0.188828615709573 & 0.377657231419145 & 0.811171384290427 \tabularnewline
39 & 0.157722702762698 & 0.315445405525395 & 0.842277297237302 \tabularnewline
40 & 0.128274540924145 & 0.25654908184829 & 0.871725459075855 \tabularnewline
41 & 0.101253536858844 & 0.202507073717687 & 0.898746463141156 \tabularnewline
42 & 0.0977970582334522 & 0.195594116466904 & 0.902202941766548 \tabularnewline
43 & 0.0815751021752623 & 0.163150204350525 & 0.918424897824738 \tabularnewline
44 & 0.068142424267894 & 0.136284848535788 & 0.931857575732106 \tabularnewline
45 & 0.0554098689758663 & 0.110819737951733 & 0.944590131024134 \tabularnewline
46 & 0.0438017427055296 & 0.0876034854110591 & 0.95619825729447 \tabularnewline
47 & 0.100070930089288 & 0.200141860178576 & 0.899929069910712 \tabularnewline
48 & 0.0793788633767544 & 0.158757726753509 & 0.920621136623246 \tabularnewline
49 & 0.172096495253521 & 0.344192990507042 & 0.827903504746479 \tabularnewline
50 & 0.144317926603151 & 0.288635853206302 & 0.855682073396849 \tabularnewline
51 & 0.119904401455951 & 0.239808802911901 & 0.880095598544049 \tabularnewline
52 & 0.0997208241927696 & 0.199441648385539 & 0.90027917580723 \tabularnewline
53 & 0.088862198916748 & 0.177724397833496 & 0.911137801083252 \tabularnewline
54 & 0.628875718467658 & 0.742248563064683 & 0.371124281532342 \tabularnewline
55 & 0.605347237717023 & 0.789305524565954 & 0.394652762282977 \tabularnewline
56 & 0.562492489478589 & 0.875015021042822 & 0.437507510521411 \tabularnewline
57 & 0.518027133986726 & 0.963945732026548 & 0.481972866013274 \tabularnewline
58 & 0.492648217803533 & 0.985296435607066 & 0.507351782196467 \tabularnewline
59 & 0.500437462789188 & 0.999125074421623 & 0.499562537210812 \tabularnewline
60 & 0.461019174544706 & 0.922038349089413 & 0.538980825455294 \tabularnewline
61 & 0.41807911692376 & 0.83615823384752 & 0.58192088307624 \tabularnewline
62 & 0.44144470653954 & 0.88288941307908 & 0.55855529346046 \tabularnewline
63 & 0.432848788980774 & 0.865697577961548 & 0.567151211019226 \tabularnewline
64 & 0.403941101966706 & 0.807882203933413 & 0.596058898033294 \tabularnewline
65 & 0.375378937751469 & 0.750757875502937 & 0.624621062248531 \tabularnewline
66 & 0.349452849145478 & 0.698905698290956 & 0.650547150854522 \tabularnewline
67 & 0.322865090677986 & 0.645730181355971 & 0.677134909322014 \tabularnewline
68 & 0.413766328484001 & 0.827532656968001 & 0.586233671515999 \tabularnewline
69 & 0.377951386070759 & 0.755902772141518 & 0.622048613929241 \tabularnewline
70 & 0.34728008286079 & 0.69456016572158 & 0.65271991713921 \tabularnewline
71 & 0.436986661870676 & 0.873973323741353 & 0.563013338129324 \tabularnewline
72 & 0.411572086298024 & 0.823144172596047 & 0.588427913701976 \tabularnewline
73 & 0.448636763559609 & 0.897273527119219 & 0.551363236440391 \tabularnewline
74 & 0.492119378206229 & 0.984238756412458 & 0.507880621793771 \tabularnewline
75 & 0.46620896958415 & 0.932417939168301 & 0.53379103041585 \tabularnewline
76 & 0.436363625830697 & 0.872727251661394 & 0.563636374169303 \tabularnewline
77 & 0.398954076428066 & 0.797908152856132 & 0.601045923571934 \tabularnewline
78 & 0.389755228417248 & 0.779510456834496 & 0.610244771582752 \tabularnewline
79 & 0.35339210249178 & 0.706784204983559 & 0.64660789750822 \tabularnewline
80 & 0.348241788063091 & 0.696483576126182 & 0.651758211936909 \tabularnewline
81 & 0.370556835674028 & 0.741113671348056 & 0.629443164325972 \tabularnewline
82 & 0.369588664615242 & 0.739177329230483 & 0.630411335384758 \tabularnewline
83 & 0.349050001701008 & 0.698100003402015 & 0.650949998298992 \tabularnewline
84 & 0.314159214056574 & 0.628318428113149 & 0.685840785943426 \tabularnewline
85 & 0.280456595494109 & 0.560913190988217 & 0.719543404505891 \tabularnewline
86 & 0.263070687458207 & 0.526141374916415 & 0.736929312541793 \tabularnewline
87 & 0.233967049018535 & 0.46793409803707 & 0.766032950981465 \tabularnewline
88 & 0.217130515037694 & 0.434261030075387 & 0.782869484962306 \tabularnewline
89 & 0.193382026226208 & 0.386764052452415 & 0.806617973773792 \tabularnewline
90 & 0.182839670749921 & 0.365679341499842 & 0.817160329250079 \tabularnewline
91 & 0.168555097379901 & 0.337110194759802 & 0.831444902620099 \tabularnewline
92 & 0.164779907341498 & 0.329559814682995 & 0.835220092658502 \tabularnewline
93 & 0.172897459007034 & 0.345794918014067 & 0.827102540992966 \tabularnewline
94 & 0.166006774536163 & 0.332013549072327 & 0.833993225463837 \tabularnewline
95 & 0.171786558328427 & 0.343573116656853 & 0.828213441671574 \tabularnewline
96 & 0.151451841732677 & 0.302903683465353 & 0.848548158267323 \tabularnewline
97 & 0.143895524901111 & 0.287791049802222 & 0.856104475098889 \tabularnewline
98 & 0.135245669972695 & 0.27049133994539 & 0.864754330027305 \tabularnewline
99 & 0.116124639021439 & 0.232249278042877 & 0.883875360978561 \tabularnewline
100 & 0.104632561610845 & 0.209265123221689 & 0.895367438389155 \tabularnewline
101 & 0.0885495385241964 & 0.177099077048393 & 0.911450461475804 \tabularnewline
102 & 0.0752374599778773 & 0.150474919955755 & 0.924762540022123 \tabularnewline
103 & 0.0850365089072141 & 0.170073017814428 & 0.914963491092786 \tabularnewline
104 & 0.0863400616407673 & 0.172680123281535 & 0.913659938359233 \tabularnewline
105 & 0.0750388844089864 & 0.150077768817973 & 0.924961115591014 \tabularnewline
106 & 0.0640865561729232 & 0.128173112345846 & 0.935913443827077 \tabularnewline
107 & 0.0548080388059966 & 0.109616077611993 & 0.945191961194003 \tabularnewline
108 & 0.0496008079696661 & 0.0992016159393323 & 0.950399192030334 \tabularnewline
109 & 0.0483037638048235 & 0.0966075276096469 & 0.951696236195177 \tabularnewline
110 & 0.0407369015466096 & 0.0814738030932192 & 0.95926309845339 \tabularnewline
111 & 0.036699429956037 & 0.073398859912074 & 0.963300570043963 \tabularnewline
112 & 0.0300582389064502 & 0.0601164778129004 & 0.96994176109355 \tabularnewline
113 & 0.593906259450805 & 0.812187481098389 & 0.406093740549195 \tabularnewline
114 & 0.56578273548485 & 0.8684345290303 & 0.43421726451515 \tabularnewline
115 & 0.530518393738004 & 0.938963212523992 & 0.469481606261996 \tabularnewline
116 & 0.516974308286427 & 0.966051383427146 & 0.483025691713573 \tabularnewline
117 & 0.564364141224664 & 0.871271717550671 & 0.435635858775336 \tabularnewline
118 & 0.533358226126186 & 0.933283547747627 & 0.466641773873814 \tabularnewline
119 & 0.523214211368334 & 0.953571577263332 & 0.476785788631666 \tabularnewline
120 & 0.507274003092664 & 0.985451993814671 & 0.492725996907336 \tabularnewline
121 & 0.476135029462054 & 0.952270058924109 & 0.523864970537946 \tabularnewline
122 & 0.460624368522273 & 0.921248737044545 & 0.539375631477727 \tabularnewline
123 & 0.432945955758826 & 0.865891911517652 & 0.567054044241174 \tabularnewline
124 & 0.405232978704602 & 0.810465957409204 & 0.594767021295398 \tabularnewline
125 & 0.380942545049537 & 0.761885090099074 & 0.619057454950463 \tabularnewline
126 & 0.357884733108244 & 0.715769466216487 & 0.642115266891756 \tabularnewline
127 & 0.339053018379891 & 0.678106036759783 & 0.660946981620109 \tabularnewline
128 & 0.376805679557863 & 0.753611359115726 & 0.623194320442137 \tabularnewline
129 & 0.345083339099482 & 0.690166678198965 & 0.654916660900517 \tabularnewline
130 & 0.321513809313011 & 0.643027618626021 & 0.678486190686989 \tabularnewline
131 & 0.290631085418814 & 0.581262170837629 & 0.709368914581186 \tabularnewline
132 & 0.27602113106871 & 0.55204226213742 & 0.72397886893129 \tabularnewline
133 & 0.256351671465364 & 0.512703342930729 & 0.743648328534636 \tabularnewline
134 & 0.232104579450889 & 0.464209158901778 & 0.767895420549111 \tabularnewline
135 & 0.211289896899504 & 0.422579793799007 & 0.788710103100496 \tabularnewline
136 & 0.190957429318966 & 0.381914858637931 & 0.809042570681034 \tabularnewline
137 & 0.27263550889159 & 0.54527101778318 & 0.72736449110841 \tabularnewline
138 & 0.325696179086997 & 0.651392358173994 & 0.674303820913003 \tabularnewline
139 & 0.318125318946315 & 0.636250637892631 & 0.681874681053685 \tabularnewline
140 & 0.291154523719885 & 0.582309047439769 & 0.708845476280115 \tabularnewline
141 & 0.300515975751575 & 0.601031951503149 & 0.699484024248425 \tabularnewline
142 & 0.283110985468321 & 0.566221970936641 & 0.716889014531679 \tabularnewline
143 & 0.25368900608435 & 0.507378012168701 & 0.74631099391565 \tabularnewline
144 & 0.241419337025125 & 0.48283867405025 & 0.758580662974875 \tabularnewline
145 & 0.233780440579743 & 0.467560881159487 & 0.766219559420257 \tabularnewline
146 & 0.211656984720454 & 0.423313969440908 & 0.788343015279546 \tabularnewline
147 & 0.228919904450444 & 0.457839808900889 & 0.771080095549555 \tabularnewline
148 & 0.202988692174842 & 0.405977384349685 & 0.797011307825158 \tabularnewline
149 & 0.179059066041912 & 0.358118132083825 & 0.820940933958088 \tabularnewline
150 & 0.15689074760494 & 0.31378149520988 & 0.84310925239506 \tabularnewline
151 & 0.168916074728711 & 0.337832149457422 & 0.831083925271289 \tabularnewline
152 & 0.164099472523785 & 0.328198945047571 & 0.835900527476214 \tabularnewline
153 & 0.148763922216814 & 0.297527844433627 & 0.851236077783186 \tabularnewline
154 & 0.128656897119007 & 0.257313794238014 & 0.871343102880993 \tabularnewline
155 & 0.127268631375027 & 0.254537262750054 & 0.872731368624973 \tabularnewline
156 & 0.121162372383333 & 0.242324744766666 & 0.878837627616667 \tabularnewline
157 & 0.109972763320384 & 0.219945526640768 & 0.890027236679616 \tabularnewline
158 & 0.129267564687968 & 0.258535129375937 & 0.870732435312032 \tabularnewline
159 & 0.114182286386188 & 0.228364572772376 & 0.885817713613812 \tabularnewline
160 & 0.133040296808874 & 0.266080593617748 & 0.866959703191126 \tabularnewline
161 & 0.177145097872919 & 0.354290195745838 & 0.822854902127081 \tabularnewline
162 & 0.168097097571344 & 0.336194195142688 & 0.831902902428656 \tabularnewline
163 & 0.229233808279675 & 0.45846761655935 & 0.770766191720325 \tabularnewline
164 & 0.247062025355735 & 0.494124050711469 & 0.752937974644265 \tabularnewline
165 & 0.234663568385995 & 0.46932713677199 & 0.765336431614005 \tabularnewline
166 & 0.213396073081326 & 0.426792146162653 & 0.786603926918674 \tabularnewline
167 & 0.187500251859636 & 0.375000503719273 & 0.812499748140364 \tabularnewline
168 & 0.179646800688848 & 0.359293601377696 & 0.820353199311152 \tabularnewline
169 & 0.163772294198633 & 0.327544588397266 & 0.836227705801367 \tabularnewline
170 & 0.210241992497114 & 0.420483984994229 & 0.789758007502886 \tabularnewline
171 & 0.187136176585076 & 0.374272353170151 & 0.812863823414924 \tabularnewline
172 & 0.164402576978622 & 0.328805153957244 & 0.835597423021378 \tabularnewline
173 & 0.154105141565622 & 0.308210283131244 & 0.845894858434378 \tabularnewline
174 & 0.135083601051645 & 0.27016720210329 & 0.864916398948355 \tabularnewline
175 & 0.123525914368229 & 0.247051828736457 & 0.876474085631771 \tabularnewline
176 & 0.149032219325553 & 0.298064438651106 & 0.850967780674447 \tabularnewline
177 & 0.132900885069833 & 0.265801770139666 & 0.867099114930167 \tabularnewline
178 & 0.12461811187188 & 0.24923622374376 & 0.87538188812812 \tabularnewline
179 & 0.110947841468042 & 0.221895682936083 & 0.889052158531958 \tabularnewline
180 & 0.143041588920636 & 0.286083177841272 & 0.856958411079364 \tabularnewline
181 & 0.160110867484684 & 0.320221734969368 & 0.839889132515316 \tabularnewline
182 & 0.141342383289682 & 0.282684766579365 & 0.858657616710318 \tabularnewline
183 & 0.152675829795158 & 0.305351659590316 & 0.847324170204842 \tabularnewline
184 & 0.146717023487656 & 0.293434046975311 & 0.853282976512344 \tabularnewline
185 & 0.141647078618491 & 0.283294157236982 & 0.858352921381509 \tabularnewline
186 & 0.175815526578758 & 0.351631053157516 & 0.824184473421242 \tabularnewline
187 & 0.182977069677126 & 0.365954139354252 & 0.817022930322874 \tabularnewline
188 & 0.253673566486107 & 0.507347132972214 & 0.746326433513893 \tabularnewline
189 & 0.231309121804502 & 0.462618243609003 & 0.768690878195498 \tabularnewline
190 & 0.239288742301507 & 0.478577484603013 & 0.760711257698493 \tabularnewline
191 & 0.604059700977403 & 0.791880598045194 & 0.395940299022597 \tabularnewline
192 & 0.578762184453721 & 0.842475631092558 & 0.421237815546279 \tabularnewline
193 & 0.545730419005994 & 0.908539161988013 & 0.454269580994006 \tabularnewline
194 & 0.661133961486992 & 0.677732077026016 & 0.338866038513008 \tabularnewline
195 & 0.623500565376431 & 0.752998869247139 & 0.376499434623569 \tabularnewline
196 & 0.612934507636513 & 0.774130984726973 & 0.387065492363487 \tabularnewline
197 & 0.624403096514826 & 0.751193806970348 & 0.375596903485174 \tabularnewline
198 & 0.613562865552248 & 0.772874268895503 & 0.386437134447752 \tabularnewline
199 & 0.573475943567492 & 0.853048112865016 & 0.426524056432508 \tabularnewline
200 & 0.615509355063692 & 0.768981289872617 & 0.384490644936308 \tabularnewline
201 & 0.58734504785299 & 0.82530990429402 & 0.41265495214701 \tabularnewline
202 & 0.559477280631234 & 0.881045438737532 & 0.440522719368766 \tabularnewline
203 & 0.726494600633342 & 0.547010798733315 & 0.273505399366657 \tabularnewline
204 & 0.772203193486882 & 0.455593613026235 & 0.227796806513118 \tabularnewline
205 & 0.737844758440636 & 0.524310483118728 & 0.262155241559364 \tabularnewline
206 & 0.700531849078387 & 0.598936301843226 & 0.299468150921613 \tabularnewline
207 & 0.686942324536882 & 0.626115350926235 & 0.313057675463118 \tabularnewline
208 & 0.676000177391074 & 0.647999645217851 & 0.323999822608926 \tabularnewline
209 & 0.637367479402893 & 0.725265041194213 & 0.362632520597107 \tabularnewline
210 & 0.693709628100451 & 0.612580743799099 & 0.306290371899549 \tabularnewline
211 & 0.66068482978459 & 0.678630340430821 & 0.33931517021541 \tabularnewline
212 & 0.62482010468664 & 0.75035979062672 & 0.37517989531336 \tabularnewline
213 & 0.587793481507172 & 0.824413036985657 & 0.412206518492828 \tabularnewline
214 & 0.540781198463398 & 0.918437603073204 & 0.459218801536602 \tabularnewline
215 & 0.49687565843132 & 0.993751316862641 & 0.50312434156868 \tabularnewline
216 & 0.459906257510947 & 0.919812515021895 & 0.540093742489053 \tabularnewline
217 & 0.440267652832596 & 0.880535305665192 & 0.559732347167404 \tabularnewline
218 & 0.411368679999528 & 0.822737359999057 & 0.588631320000472 \tabularnewline
219 & 0.371325291566539 & 0.742650583133078 & 0.628674708433461 \tabularnewline
220 & 0.325951601034129 & 0.651903202068258 & 0.674048398965871 \tabularnewline
221 & 0.297322889748616 & 0.594645779497233 & 0.702677110251384 \tabularnewline
222 & 0.302134563634498 & 0.604269127268996 & 0.697865436365502 \tabularnewline
223 & 0.272048016575292 & 0.544096033150583 & 0.727951983424708 \tabularnewline
224 & 0.230928099441087 & 0.461856198882174 & 0.769071900558913 \tabularnewline
225 & 0.929409846083225 & 0.141180307833551 & 0.0705901539167753 \tabularnewline
226 & 0.922944838769054 & 0.154110322461892 & 0.077055161230946 \tabularnewline
227 & 0.90738859040952 & 0.185222819180961 & 0.0926114095904803 \tabularnewline
228 & 0.972283173943749 & 0.0554336521125025 & 0.0277168260562512 \tabularnewline
229 & 0.961181004447471 & 0.0776379911050571 & 0.0388189955525285 \tabularnewline
230 & 0.946172957020812 & 0.107654085958377 & 0.0538270429791883 \tabularnewline
231 & 0.932182358416502 & 0.135635283166995 & 0.0678176415834977 \tabularnewline
232 & 0.970414897887382 & 0.0591702042252363 & 0.0295851021126181 \tabularnewline
233 & 0.970557079000287 & 0.0588858419994251 & 0.0294429209997126 \tabularnewline
234 & 0.958079175339248 & 0.0838416493215038 & 0.0419208246607519 \tabularnewline
235 & 0.94031209427548 & 0.119375811449039 & 0.0596879057245195 \tabularnewline
236 & 0.928485574932789 & 0.143028850134421 & 0.0715144250672107 \tabularnewline
237 & 0.909609458817134 & 0.180781082365733 & 0.0903905411828664 \tabularnewline
238 & 0.89737775224159 & 0.20524449551682 & 0.10262224775841 \tabularnewline
239 & 0.862003854272503 & 0.275992291454994 & 0.137996145727497 \tabularnewline
240 & 0.84952976470654 & 0.30094047058692 & 0.15047023529346 \tabularnewline
241 & 0.800847951501682 & 0.398304096996635 & 0.199152048498318 \tabularnewline
242 & 0.785860925582445 & 0.428278148835111 & 0.214139074417555 \tabularnewline
243 & 0.724326975007587 & 0.551346049984826 & 0.275673024992413 \tabularnewline
244 & 0.687565582129087 & 0.624868835741826 & 0.312434417870913 \tabularnewline
245 & 0.61847308916418 & 0.763053821671639 & 0.38152691083582 \tabularnewline
246 & 0.527920778731046 & 0.944158442537909 & 0.472079221268954 \tabularnewline
247 & 0.510454575881816 & 0.979090848236368 & 0.489545424118184 \tabularnewline
248 & 0.472064616104749 & 0.944129232209497 & 0.527935383895251 \tabularnewline
249 & 0.641832011180932 & 0.716335977638136 & 0.358167988819068 \tabularnewline
250 & 0.687076289626229 & 0.625847420747543 & 0.312923710373771 \tabularnewline
251 & 0.572224674356084 & 0.855550651287832 & 0.427775325643916 \tabularnewline
252 & 0.428777490198189 & 0.857554980396377 & 0.571222509801811 \tabularnewline
253 & 0.280883434041271 & 0.561766868082542 & 0.719116565958729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&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]11[/C][C]0.0192763363250358[/C][C]0.0385526726500716[/C][C]0.980723663674964[/C][/ROW]
[ROW][C]12[/C][C]0.0128615951753187[/C][C]0.0257231903506373[/C][C]0.987138404824681[/C][/ROW]
[ROW][C]13[/C][C]0.00702188747193523[/C][C]0.0140437749438705[/C][C]0.992978112528065[/C][/ROW]
[ROW][C]14[/C][C]0.00494571757580044[/C][C]0.00989143515160088[/C][C]0.9950542824242[/C][/ROW]
[ROW][C]15[/C][C]0.00145951847820497[/C][C]0.00291903695640994[/C][C]0.998540481521795[/C][/ROW]
[ROW][C]16[/C][C]0.000472361755548825[/C][C]0.00094472351109765[/C][C]0.999527638244451[/C][/ROW]
[ROW][C]17[/C][C]0.00160997363279565[/C][C]0.00321994726559129[/C][C]0.998390026367204[/C][/ROW]
[ROW][C]18[/C][C]0.00147061888012245[/C][C]0.0029412377602449[/C][C]0.998529381119878[/C][/ROW]
[ROW][C]19[/C][C]0.00106654784368777[/C][C]0.00213309568737554[/C][C]0.998933452156312[/C][/ROW]
[ROW][C]20[/C][C]0.000479719584617838[/C][C]0.000959439169235677[/C][C]0.999520280415382[/C][/ROW]
[ROW][C]21[/C][C]0.000253363899627936[/C][C]0.000506727799255871[/C][C]0.999746636100372[/C][/ROW]
[ROW][C]22[/C][C]0.00024010042606073[/C][C]0.000480200852121459[/C][C]0.999759899573939[/C][/ROW]
[ROW][C]23[/C][C]0.00061513705778238[/C][C]0.00123027411556476[/C][C]0.999384862942218[/C][/ROW]
[ROW][C]24[/C][C]0.00244158693092758[/C][C]0.00488317386185517[/C][C]0.997558413069072[/C][/ROW]
[ROW][C]25[/C][C]0.00190909173585164[/C][C]0.00381818347170327[/C][C]0.998090908264148[/C][/ROW]
[ROW][C]26[/C][C]0.00528509616298711[/C][C]0.0105701923259742[/C][C]0.994714903837013[/C][/ROW]
[ROW][C]27[/C][C]0.0487418497808826[/C][C]0.0974836995617651[/C][C]0.951258150219117[/C][/ROW]
[ROW][C]28[/C][C]0.0421936730475328[/C][C]0.0843873460950656[/C][C]0.957806326952467[/C][/ROW]
[ROW][C]29[/C][C]0.119638090783815[/C][C]0.23927618156763[/C][C]0.880361909216185[/C][/ROW]
[ROW][C]30[/C][C]0.186215537051015[/C][C]0.37243107410203[/C][C]0.813784462948985[/C][/ROW]
[ROW][C]31[/C][C]0.149109724771881[/C][C]0.298219449543762[/C][C]0.850890275228119[/C][/ROW]
[ROW][C]32[/C][C]0.125292666361744[/C][C]0.250585332723488[/C][C]0.874707333638256[/C][/ROW]
[ROW][C]33[/C][C]0.0959086893021242[/C][C]0.191817378604248[/C][C]0.904091310697876[/C][/ROW]
[ROW][C]34[/C][C]0.135083622199464[/C][C]0.270167244398928[/C][C]0.864916377800536[/C][/ROW]
[ROW][C]35[/C][C]0.218604109813922[/C][C]0.437208219627843[/C][C]0.781395890186078[/C][/ROW]
[ROW][C]36[/C][C]0.219501380814447[/C][C]0.439002761628895[/C][C]0.780498619185553[/C][/ROW]
[ROW][C]37[/C][C]0.230285197423861[/C][C]0.460570394847722[/C][C]0.769714802576139[/C][/ROW]
[ROW][C]38[/C][C]0.188828615709573[/C][C]0.377657231419145[/C][C]0.811171384290427[/C][/ROW]
[ROW][C]39[/C][C]0.157722702762698[/C][C]0.315445405525395[/C][C]0.842277297237302[/C][/ROW]
[ROW][C]40[/C][C]0.128274540924145[/C][C]0.25654908184829[/C][C]0.871725459075855[/C][/ROW]
[ROW][C]41[/C][C]0.101253536858844[/C][C]0.202507073717687[/C][C]0.898746463141156[/C][/ROW]
[ROW][C]42[/C][C]0.0977970582334522[/C][C]0.195594116466904[/C][C]0.902202941766548[/C][/ROW]
[ROW][C]43[/C][C]0.0815751021752623[/C][C]0.163150204350525[/C][C]0.918424897824738[/C][/ROW]
[ROW][C]44[/C][C]0.068142424267894[/C][C]0.136284848535788[/C][C]0.931857575732106[/C][/ROW]
[ROW][C]45[/C][C]0.0554098689758663[/C][C]0.110819737951733[/C][C]0.944590131024134[/C][/ROW]
[ROW][C]46[/C][C]0.0438017427055296[/C][C]0.0876034854110591[/C][C]0.95619825729447[/C][/ROW]
[ROW][C]47[/C][C]0.100070930089288[/C][C]0.200141860178576[/C][C]0.899929069910712[/C][/ROW]
[ROW][C]48[/C][C]0.0793788633767544[/C][C]0.158757726753509[/C][C]0.920621136623246[/C][/ROW]
[ROW][C]49[/C][C]0.172096495253521[/C][C]0.344192990507042[/C][C]0.827903504746479[/C][/ROW]
[ROW][C]50[/C][C]0.144317926603151[/C][C]0.288635853206302[/C][C]0.855682073396849[/C][/ROW]
[ROW][C]51[/C][C]0.119904401455951[/C][C]0.239808802911901[/C][C]0.880095598544049[/C][/ROW]
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[ROW][C]196[/C][C]0.612934507636513[/C][C]0.774130984726973[/C][C]0.387065492363487[/C][/ROW]
[ROW][C]197[/C][C]0.624403096514826[/C][C]0.751193806970348[/C][C]0.375596903485174[/C][/ROW]
[ROW][C]198[/C][C]0.613562865552248[/C][C]0.772874268895503[/C][C]0.386437134447752[/C][/ROW]
[ROW][C]199[/C][C]0.573475943567492[/C][C]0.853048112865016[/C][C]0.426524056432508[/C][/ROW]
[ROW][C]200[/C][C]0.615509355063692[/C][C]0.768981289872617[/C][C]0.384490644936308[/C][/ROW]
[ROW][C]201[/C][C]0.58734504785299[/C][C]0.82530990429402[/C][C]0.41265495214701[/C][/ROW]
[ROW][C]202[/C][C]0.559477280631234[/C][C]0.881045438737532[/C][C]0.440522719368766[/C][/ROW]
[ROW][C]203[/C][C]0.726494600633342[/C][C]0.547010798733315[/C][C]0.273505399366657[/C][/ROW]
[ROW][C]204[/C][C]0.772203193486882[/C][C]0.455593613026235[/C][C]0.227796806513118[/C][/ROW]
[ROW][C]205[/C][C]0.737844758440636[/C][C]0.524310483118728[/C][C]0.262155241559364[/C][/ROW]
[ROW][C]206[/C][C]0.700531849078387[/C][C]0.598936301843226[/C][C]0.299468150921613[/C][/ROW]
[ROW][C]207[/C][C]0.686942324536882[/C][C]0.626115350926235[/C][C]0.313057675463118[/C][/ROW]
[ROW][C]208[/C][C]0.676000177391074[/C][C]0.647999645217851[/C][C]0.323999822608926[/C][/ROW]
[ROW][C]209[/C][C]0.637367479402893[/C][C]0.725265041194213[/C][C]0.362632520597107[/C][/ROW]
[ROW][C]210[/C][C]0.693709628100451[/C][C]0.612580743799099[/C][C]0.306290371899549[/C][/ROW]
[ROW][C]211[/C][C]0.66068482978459[/C][C]0.678630340430821[/C][C]0.33931517021541[/C][/ROW]
[ROW][C]212[/C][C]0.62482010468664[/C][C]0.75035979062672[/C][C]0.37517989531336[/C][/ROW]
[ROW][C]213[/C][C]0.587793481507172[/C][C]0.824413036985657[/C][C]0.412206518492828[/C][/ROW]
[ROW][C]214[/C][C]0.540781198463398[/C][C]0.918437603073204[/C][C]0.459218801536602[/C][/ROW]
[ROW][C]215[/C][C]0.49687565843132[/C][C]0.993751316862641[/C][C]0.50312434156868[/C][/ROW]
[ROW][C]216[/C][C]0.459906257510947[/C][C]0.919812515021895[/C][C]0.540093742489053[/C][/ROW]
[ROW][C]217[/C][C]0.440267652832596[/C][C]0.880535305665192[/C][C]0.559732347167404[/C][/ROW]
[ROW][C]218[/C][C]0.411368679999528[/C][C]0.822737359999057[/C][C]0.588631320000472[/C][/ROW]
[ROW][C]219[/C][C]0.371325291566539[/C][C]0.742650583133078[/C][C]0.628674708433461[/C][/ROW]
[ROW][C]220[/C][C]0.325951601034129[/C][C]0.651903202068258[/C][C]0.674048398965871[/C][/ROW]
[ROW][C]221[/C][C]0.297322889748616[/C][C]0.594645779497233[/C][C]0.702677110251384[/C][/ROW]
[ROW][C]222[/C][C]0.302134563634498[/C][C]0.604269127268996[/C][C]0.697865436365502[/C][/ROW]
[ROW][C]223[/C][C]0.272048016575292[/C][C]0.544096033150583[/C][C]0.727951983424708[/C][/ROW]
[ROW][C]224[/C][C]0.230928099441087[/C][C]0.461856198882174[/C][C]0.769071900558913[/C][/ROW]
[ROW][C]225[/C][C]0.929409846083225[/C][C]0.141180307833551[/C][C]0.0705901539167753[/C][/ROW]
[ROW][C]226[/C][C]0.922944838769054[/C][C]0.154110322461892[/C][C]0.077055161230946[/C][/ROW]
[ROW][C]227[/C][C]0.90738859040952[/C][C]0.185222819180961[/C][C]0.0926114095904803[/C][/ROW]
[ROW][C]228[/C][C]0.972283173943749[/C][C]0.0554336521125025[/C][C]0.0277168260562512[/C][/ROW]
[ROW][C]229[/C][C]0.961181004447471[/C][C]0.0776379911050571[/C][C]0.0388189955525285[/C][/ROW]
[ROW][C]230[/C][C]0.946172957020812[/C][C]0.107654085958377[/C][C]0.0538270429791883[/C][/ROW]
[ROW][C]231[/C][C]0.932182358416502[/C][C]0.135635283166995[/C][C]0.0678176415834977[/C][/ROW]
[ROW][C]232[/C][C]0.970414897887382[/C][C]0.0591702042252363[/C][C]0.0295851021126181[/C][/ROW]
[ROW][C]233[/C][C]0.970557079000287[/C][C]0.0588858419994251[/C][C]0.0294429209997126[/C][/ROW]
[ROW][C]234[/C][C]0.958079175339248[/C][C]0.0838416493215038[/C][C]0.0419208246607519[/C][/ROW]
[ROW][C]235[/C][C]0.94031209427548[/C][C]0.119375811449039[/C][C]0.0596879057245195[/C][/ROW]
[ROW][C]236[/C][C]0.928485574932789[/C][C]0.143028850134421[/C][C]0.0715144250672107[/C][/ROW]
[ROW][C]237[/C][C]0.909609458817134[/C][C]0.180781082365733[/C][C]0.0903905411828664[/C][/ROW]
[ROW][C]238[/C][C]0.89737775224159[/C][C]0.20524449551682[/C][C]0.10262224775841[/C][/ROW]
[ROW][C]239[/C][C]0.862003854272503[/C][C]0.275992291454994[/C][C]0.137996145727497[/C][/ROW]
[ROW][C]240[/C][C]0.84952976470654[/C][C]0.30094047058692[/C][C]0.15047023529346[/C][/ROW]
[ROW][C]241[/C][C]0.800847951501682[/C][C]0.398304096996635[/C][C]0.199152048498318[/C][/ROW]
[ROW][C]242[/C][C]0.785860925582445[/C][C]0.428278148835111[/C][C]0.214139074417555[/C][/ROW]
[ROW][C]243[/C][C]0.724326975007587[/C][C]0.551346049984826[/C][C]0.275673024992413[/C][/ROW]
[ROW][C]244[/C][C]0.687565582129087[/C][C]0.624868835741826[/C][C]0.312434417870913[/C][/ROW]
[ROW][C]245[/C][C]0.61847308916418[/C][C]0.763053821671639[/C][C]0.38152691083582[/C][/ROW]
[ROW][C]246[/C][C]0.527920778731046[/C][C]0.944158442537909[/C][C]0.472079221268954[/C][/ROW]
[ROW][C]247[/C][C]0.510454575881816[/C][C]0.979090848236368[/C][C]0.489545424118184[/C][/ROW]
[ROW][C]248[/C][C]0.472064616104749[/C][C]0.944129232209497[/C][C]0.527935383895251[/C][/ROW]
[ROW][C]249[/C][C]0.641832011180932[/C][C]0.716335977638136[/C][C]0.358167988819068[/C][/ROW]
[ROW][C]250[/C][C]0.687076289626229[/C][C]0.625847420747543[/C][C]0.312923710373771[/C][/ROW]
[ROW][C]251[/C][C]0.572224674356084[/C][C]0.855550651287832[/C][C]0.427775325643916[/C][/ROW]
[ROW][C]252[/C][C]0.428777490198189[/C][C]0.857554980396377[/C][C]0.571222509801811[/C][/ROW]
[ROW][C]253[/C][C]0.280883434041271[/C][C]0.561766868082542[/C][C]0.719116565958729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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
110.01927633632503580.03855267265007160.980723663674964
120.01286159517531870.02572319035063730.987138404824681
130.007021887471935230.01404377494387050.992978112528065
140.004945717575800440.009891435151600880.9950542824242
150.001459518478204970.002919036956409940.998540481521795
160.0004723617555488250.000944723511097650.999527638244451
170.001609973632795650.003219947265591290.998390026367204
180.001470618880122450.00294123776024490.998529381119878
190.001066547843687770.002133095687375540.998933452156312
200.0004797195846178380.0009594391692356770.999520280415382
210.0002533638996279360.0005067277992558710.999746636100372
220.000240100426060730.0004802008521214590.999759899573939
230.000615137057782380.001230274115564760.999384862942218
240.002441586930927580.004883173861855170.997558413069072
250.001909091735851640.003818183471703270.998090908264148
260.005285096162987110.01057019232597420.994714903837013
270.04874184978088260.09748369956176510.951258150219117
280.04219367304753280.08438734609506560.957806326952467
290.1196380907838150.239276181567630.880361909216185
300.1862155370510150.372431074102030.813784462948985
310.1491097247718810.2982194495437620.850890275228119
320.1252926663617440.2505853327234880.874707333638256
330.09590868930212420.1918173786042480.904091310697876
340.1350836221994640.2701672443989280.864916377800536
350.2186041098139220.4372082196278430.781395890186078
360.2195013808144470.4390027616288950.780498619185553
370.2302851974238610.4605703948477220.769714802576139
380.1888286157095730.3776572314191450.811171384290427
390.1577227027626980.3154454055253950.842277297237302
400.1282745409241450.256549081848290.871725459075855
410.1012535368588440.2025070737176870.898746463141156
420.09779705823345220.1955941164669040.902202941766548
430.08157510217526230.1631502043505250.918424897824738
440.0681424242678940.1362848485357880.931857575732106
450.05540986897586630.1108197379517330.944590131024134
460.04380174270552960.08760348541105910.95619825729447
470.1000709300892880.2001418601785760.899929069910712
480.07937886337675440.1587577267535090.920621136623246
490.1720964952535210.3441929905070420.827903504746479
500.1443179266031510.2886358532063020.855682073396849
510.1199044014559510.2398088029119010.880095598544049
520.09972082419276960.1994416483855390.90027917580723
530.0888621989167480.1777243978334960.911137801083252
540.6288757184676580.7422485630646830.371124281532342
550.6053472377170230.7893055245659540.394652762282977
560.5624924894785890.8750150210428220.437507510521411
570.5180271339867260.9639457320265480.481972866013274
580.4926482178035330.9852964356070660.507351782196467
590.5004374627891880.9991250744216230.499562537210812
600.4610191745447060.9220383490894130.538980825455294
610.418079116923760.836158233847520.58192088307624
620.441444706539540.882889413079080.55855529346046
630.4328487889807740.8656975779615480.567151211019226
640.4039411019667060.8078822039334130.596058898033294
650.3753789377514690.7507578755029370.624621062248531
660.3494528491454780.6989056982909560.650547150854522
670.3228650906779860.6457301813559710.677134909322014
680.4137663284840010.8275326569680010.586233671515999
690.3779513860707590.7559027721415180.622048613929241
700.347280082860790.694560165721580.65271991713921
710.4369866618706760.8739733237413530.563013338129324
720.4115720862980240.8231441725960470.588427913701976
730.4486367635596090.8972735271192190.551363236440391
740.4921193782062290.9842387564124580.507880621793771
750.466208969584150.9324179391683010.53379103041585
760.4363636258306970.8727272516613940.563636374169303
770.3989540764280660.7979081528561320.601045923571934
780.3897552284172480.7795104568344960.610244771582752
790.353392102491780.7067842049835590.64660789750822
800.3482417880630910.6964835761261820.651758211936909
810.3705568356740280.7411136713480560.629443164325972
820.3695886646152420.7391773292304830.630411335384758
830.3490500017010080.6981000034020150.650949998298992
840.3141592140565740.6283184281131490.685840785943426
850.2804565954941090.5609131909882170.719543404505891
860.2630706874582070.5261413749164150.736929312541793
870.2339670490185350.467934098037070.766032950981465
880.2171305150376940.4342610300753870.782869484962306
890.1933820262262080.3867640524524150.806617973773792
900.1828396707499210.3656793414998420.817160329250079
910.1685550973799010.3371101947598020.831444902620099
920.1647799073414980.3295598146829950.835220092658502
930.1728974590070340.3457949180140670.827102540992966
940.1660067745361630.3320135490723270.833993225463837
950.1717865583284270.3435731166568530.828213441671574
960.1514518417326770.3029036834653530.848548158267323
970.1438955249011110.2877910498022220.856104475098889
980.1352456699726950.270491339945390.864754330027305
990.1161246390214390.2322492780428770.883875360978561
1000.1046325616108450.2092651232216890.895367438389155
1010.08854953852419640.1770990770483930.911450461475804
1020.07523745997787730.1504749199557550.924762540022123
1030.08503650890721410.1700730178144280.914963491092786
1040.08634006164076730.1726801232815350.913659938359233
1050.07503888440898640.1500777688179730.924961115591014
1060.06408655617292320.1281731123458460.935913443827077
1070.05480803880599660.1096160776119930.945191961194003
1080.04960080796966610.09920161593933230.950399192030334
1090.04830376380482350.09660752760964690.951696236195177
1100.04073690154660960.08147380309321920.95926309845339
1110.0366994299560370.0733988599120740.963300570043963
1120.03005823890645020.06011647781290040.96994176109355
1130.5939062594508050.8121874810983890.406093740549195
1140.565782735484850.86843452903030.43421726451515
1150.5305183937380040.9389632125239920.469481606261996
1160.5169743082864270.9660513834271460.483025691713573
1170.5643641412246640.8712717175506710.435635858775336
1180.5333582261261860.9332835477476270.466641773873814
1190.5232142113683340.9535715772633320.476785788631666
1200.5072740030926640.9854519938146710.492725996907336
1210.4761350294620540.9522700589241090.523864970537946
1220.4606243685222730.9212487370445450.539375631477727
1230.4329459557588260.8658919115176520.567054044241174
1240.4052329787046020.8104659574092040.594767021295398
1250.3809425450495370.7618850900990740.619057454950463
1260.3578847331082440.7157694662164870.642115266891756
1270.3390530183798910.6781060367597830.660946981620109
1280.3768056795578630.7536113591157260.623194320442137
1290.3450833390994820.6901666781989650.654916660900517
1300.3215138093130110.6430276186260210.678486190686989
1310.2906310854188140.5812621708376290.709368914581186
1320.276021131068710.552042262137420.72397886893129
1330.2563516714653640.5127033429307290.743648328534636
1340.2321045794508890.4642091589017780.767895420549111
1350.2112898968995040.4225797937990070.788710103100496
1360.1909574293189660.3819148586379310.809042570681034
1370.272635508891590.545271017783180.72736449110841
1380.3256961790869970.6513923581739940.674303820913003
1390.3181253189463150.6362506378926310.681874681053685
1400.2911545237198850.5823090474397690.708845476280115
1410.3005159757515750.6010319515031490.699484024248425
1420.2831109854683210.5662219709366410.716889014531679
1430.253689006084350.5073780121687010.74631099391565
1440.2414193370251250.482838674050250.758580662974875
1450.2337804405797430.4675608811594870.766219559420257
1460.2116569847204540.4233139694409080.788343015279546
1470.2289199044504440.4578398089008890.771080095549555
1480.2029886921748420.4059773843496850.797011307825158
1490.1790590660419120.3581181320838250.820940933958088
1500.156890747604940.313781495209880.84310925239506
1510.1689160747287110.3378321494574220.831083925271289
1520.1640994725237850.3281989450475710.835900527476214
1530.1487639222168140.2975278444336270.851236077783186
1540.1286568971190070.2573137942380140.871343102880993
1550.1272686313750270.2545372627500540.872731368624973
1560.1211623723833330.2423247447666660.878837627616667
1570.1099727633203840.2199455266407680.890027236679616
1580.1292675646879680.2585351293759370.870732435312032
1590.1141822863861880.2283645727723760.885817713613812
1600.1330402968088740.2660805936177480.866959703191126
1610.1771450978729190.3542901957458380.822854902127081
1620.1680970975713440.3361941951426880.831902902428656
1630.2292338082796750.458467616559350.770766191720325
1640.2470620253557350.4941240507114690.752937974644265
1650.2346635683859950.469327136771990.765336431614005
1660.2133960730813260.4267921461626530.786603926918674
1670.1875002518596360.3750005037192730.812499748140364
1680.1796468006888480.3592936013776960.820353199311152
1690.1637722941986330.3275445883972660.836227705801367
1700.2102419924971140.4204839849942290.789758007502886
1710.1871361765850760.3742723531701510.812863823414924
1720.1644025769786220.3288051539572440.835597423021378
1730.1541051415656220.3082102831312440.845894858434378
1740.1350836010516450.270167202103290.864916398948355
1750.1235259143682290.2470518287364570.876474085631771
1760.1490322193255530.2980644386511060.850967780674447
1770.1329008850698330.2658017701396660.867099114930167
1780.124618111871880.249236223743760.87538188812812
1790.1109478414680420.2218956829360830.889052158531958
1800.1430415889206360.2860831778412720.856958411079364
1810.1601108674846840.3202217349693680.839889132515316
1820.1413423832896820.2826847665793650.858657616710318
1830.1526758297951580.3053516595903160.847324170204842
1840.1467170234876560.2934340469753110.853282976512344
1850.1416470786184910.2832941572369820.858352921381509
1860.1758155265787580.3516310531575160.824184473421242
1870.1829770696771260.3659541393542520.817022930322874
1880.2536735664861070.5073471329722140.746326433513893
1890.2313091218045020.4626182436090030.768690878195498
1900.2392887423015070.4785774846030130.760711257698493
1910.6040597009774030.7918805980451940.395940299022597
1920.5787621844537210.8424756310925580.421237815546279
1930.5457304190059940.9085391619880130.454269580994006
1940.6611339614869920.6777320770260160.338866038513008
1950.6235005653764310.7529988692471390.376499434623569
1960.6129345076365130.7741309847269730.387065492363487
1970.6244030965148260.7511938069703480.375596903485174
1980.6135628655522480.7728742688955030.386437134447752
1990.5734759435674920.8530481128650160.426524056432508
2000.6155093550636920.7689812898726170.384490644936308
2010.587345047852990.825309904294020.41265495214701
2020.5594772806312340.8810454387375320.440522719368766
2030.7264946006333420.5470107987333150.273505399366657
2040.7722031934868820.4555936130262350.227796806513118
2050.7378447584406360.5243104831187280.262155241559364
2060.7005318490783870.5989363018432260.299468150921613
2070.6869423245368820.6261153509262350.313057675463118
2080.6760001773910740.6479996452178510.323999822608926
2090.6373674794028930.7252650411942130.362632520597107
2100.6937096281004510.6125807437990990.306290371899549
2110.660684829784590.6786303404308210.33931517021541
2120.624820104686640.750359790626720.37517989531336
2130.5877934815071720.8244130369856570.412206518492828
2140.5407811984633980.9184376030732040.459218801536602
2150.496875658431320.9937513168626410.50312434156868
2160.4599062575109470.9198125150218950.540093742489053
2170.4402676528325960.8805353056651920.559732347167404
2180.4113686799995280.8227373599990570.588631320000472
2190.3713252915665390.7426505831330780.628674708433461
2200.3259516010341290.6519032020682580.674048398965871
2210.2973228897486160.5946457794972330.702677110251384
2220.3021345636344980.6042691272689960.697865436365502
2230.2720480165752920.5440960331505830.727951983424708
2240.2309280994410870.4618561988821740.769071900558913
2250.9294098460832250.1411803078335510.0705901539167753
2260.9229448387690540.1541103224618920.077055161230946
2270.907388590409520.1852228191809610.0926114095904803
2280.9722831739437490.05543365211250250.0277168260562512
2290.9611810044474710.07763799110505710.0388189955525285
2300.9461729570208120.1076540859583770.0538270429791883
2310.9321823584165020.1356352831669950.0678176415834977
2320.9704148978873820.05917020422523630.0295851021126181
2330.9705570790002870.05888584199942510.0294429209997126
2340.9580791753392480.08384164932150380.0419208246607519
2350.940312094275480.1193758114490390.0596879057245195
2360.9284855749327890.1430288501344210.0715144250672107
2370.9096094588171340.1807810823657330.0903905411828664
2380.897377752241590.205244495516820.10262224775841
2390.8620038542725030.2759922914549940.137996145727497
2400.849529764706540.300940470586920.15047023529346
2410.8008479515016820.3983040969966350.199152048498318
2420.7858609255824450.4282781488351110.214139074417555
2430.7243269750075870.5513460499848260.275673024992413
2440.6875655821290870.6248688357418260.312434417870913
2450.618473089164180.7630538216716390.38152691083582
2460.5279207787310460.9441584425379090.472079221268954
2470.5104545758818160.9790908482363680.489545424118184
2480.4720646161047490.9441292322094970.527935383895251
2490.6418320111809320.7163359776381360.358167988819068
2500.6870762896262290.6258474207475430.312923710373771
2510.5722246743560840.8555506512878320.427775325643916
2520.4287774901981890.8575549803963770.571222509801811
2530.2808834340412710.5617668680825420.719116565958729






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.0493827160493827NOK
5% type I error level160.065843621399177NOK
10% type I error level290.119341563786008NOK

\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 & 12 & 0.0493827160493827 & NOK \tabularnewline
5% type I error level & 16 & 0.065843621399177 & NOK \tabularnewline
10% type I error level & 29 & 0.119341563786008 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=226717&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]12[/C][C]0.0493827160493827[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]16[/C][C]0.065843621399177[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]29[/C][C]0.119341563786008[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=226717&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=226717&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 level120.0493827160493827NOK
5% type I error level160.065843621399177NOK
10% type I error level290.119341563786008NOK


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')
}