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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 01 Nov 2012 11:54:54 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/01/t1351785431hyuvlhk9c207e0f.htm/, Retrieved Thu, 28 Mar 2024 17:58:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185547, Retrieved Thu, 28 Mar 2024 17:58:43 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact66
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7 4] [2012-11-01 15:54:54] [b65d2c16eb5ddb762daa9974bc4b1f13] [Current]
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Dataseries X:
41	38	13	12	14	12	53	32
39	32	16	11	18	11	86	51
30	35	19	15	11	14	66	42
31	33	15	6	12	12	67	41
34	37	14	13	16	21	76	46
35	29	13	10	18	12	78	47
39	31	19	12	14	22	53	37
34	36	15	14	14	11	80	49
36	35	14	12	15	10	74	45
37	38	15	6	15	13	76	47
38	31	16	10	17	10	79	49
36	34	16	12	19	8	54	33
38	35	16	12	10	15	67	42
39	38	16	11	16	14	54	33
33	37	17	15	18	10	87	53
32	33	15	12	14	14	58	36
36	32	15	10	14	14	75	45
38	38	20	12	17	11	88	54
39	38	18	11	14	10	64	41
32	32	16	12	16	13	57	36
32	33	16	11	18	7	66	41
31	31	16	12	11	14	68	44
39	38	19	13	14	12	54	33
37	39	16	11	12	14	56	37
39	32	17	9	17	11	86	52
41	32	17	13	9	9	80	47
36	35	16	10	16	11	76	43
33	37	15	14	14	15	69	44
33	33	16	12	15	14	78	45
34	33	14	10	11	13	67	44
31	28	15	12	16	9	80	49
27	32	12	8	13	15	54	33
37	31	14	10	17	10	71	43
34	37	16	12	15	11	84	54
34	30	14	12	14	13	74	42
32	33	7	7	16	8	71	44
29	31	10	6	9	20	63	37
36	33	14	12	15	12	71	43
29	31	16	10	17	10	76	46
35	33	16	10	13	10	69	42
37	32	16	10	15	9	74	45
34	33	14	12	16	14	75	44
38	32	20	15	16	8	54	33
35	33	14	10	12	14	52	31
38	28	14	10	12	11	69	42
37	35	11	12	11	13	68	40
38	39	14	13	15	9	65	43
33	34	15	11	15	11	75	46
36	38	16	11	17	15	74	42
38	32	14	12	13	11	75	45
32	38	16	14	16	10	72	44
32	30	14	10	14	14	67	40
32	33	12	12	11	18	63	37
34	38	16	13	12	14	62	46
32	32	9	5	12	11	63	36
37	32	14	6	15	12	76	47
39	34	16	12	16	13	74	45
29	34	16	12	15	9	67	42
37	36	15	11	12	10	73	43
35	34	16	10	12	15	70	43
30	28	12	7	8	20	53	32
38	34	16	12	13	12	77	45
34	35	16	14	11	12	77	45
31	35	14	11	14	14	52	31
34	31	16	12	15	13	54	33
35	37	17	13	10	11	80	49
36	35	18	14	11	17	66	42
30	27	18	11	12	12	73	41
39	40	12	12	15	13	63	38
35	37	16	12	15	14	69	42
38	36	10	8	14	13	67	44
31	38	14	11	16	15	54	33
34	39	18	14	15	13	81	48
38	41	18	14	15	10	69	40
34	27	16	12	13	11	84	50
39	30	17	9	12	19	80	49
37	37	16	13	17	13	70	43
34	31	16	11	13	17	69	44
28	31	13	12	15	13	77	47
37	27	16	12	13	9	54	33
33	36	16	12	15	11	79	46
37	38	20	12	16	10	30	0
35	37	16	12	15	9	71	45
37	33	15	12	16	12	73	43
32	34	15	11	15	12	72	44
33	31	16	10	14	13	77	47
38	39	14	9	15	13	75	45
33	34	16	12	14	12	69	42
29	32	16	12	13	15	54	33
33	33	15	12	7	22	70	43
31	36	12	9	17	13	73	46
36	32	17	15	13	15	54	33
35	41	16	12	15	13	77	46
32	28	15	12	14	15	82	48
29	30	13	12	13	10	80	47
39	36	16	10	16	11	80	47
37	35	16	13	12	16	69	43
35	31	16	9	14	11	78	46
37	34	16	12	17	11	81	48
32	36	14	10	15	10	76	46
38	36	16	14	17	10	76	45
37	35	16	11	12	16	73	45
36	37	20	15	16	12	85	52
32	28	15	11	11	11	66	42
33	39	16	11	15	16	79	47
40	32	13	12	9	19	68	41
38	35	17	12	16	11	76	47
41	39	16	12	15	16	71	43
36	35	16	11	10	15	54	33
43	42	12	7	10	24	46	30
30	34	16	12	15	14	82	49
31	33	16	14	11	15	74	44
32	41	17	11	13	11	88	55
32	33	13	11	14	15	38	11
37	34	12	10	18	12	76	47
37	32	18	13	16	10	86	53
33	40	14	13	14	14	54	33
34	40	14	8	14	13	70	44
33	35	13	11	14	9	69	42
38	36	16	12	14	15	90	55
33	37	13	11	12	15	54	33
31	27	16	13	14	14	76	46
38	39	13	12	15	11	89	54
37	38	16	14	15	8	76	47
33	31	15	13	15	11	73	45
31	33	16	15	13	11	79	47
39	32	15	10	17	8	90	55
44	39	17	11	17	10	74	44
33	36	15	9	19	11	81	53
35	33	12	11	15	13	72	44
32	33	16	10	13	11	71	42
28	32	10	11	9	20	66	40
40	37	16	8	15	10	77	46
27	30	12	11	15	15	65	40
37	38	14	12	15	12	74	46
32	29	15	12	16	14	82	53
28	22	13	9	11	23	54	33
34	35	15	11	14	14	63	42
30	35	11	10	11	16	54	35
35	34	12	8	15	11	64	40
31	35	8	9	13	12	69	41
32	34	16	8	15	10	54	33
30	34	15	9	16	14	84	51
30	35	17	15	14	12	86	53
31	23	16	11	15	12	77	46
40	31	10	8	16	11	89	55
32	27	18	13	16	12	76	47
36	36	13	12	11	13	60	38
32	31	16	12	12	11	75	46
35	32	13	9	9	19	73	46
38	39	10	7	16	12	85	53
42	37	15	13	13	17	79	47
34	38	16	9	16	9	71	41
35	39	16	6	12	12	72	44
35	34	14	8	9	19	69	43
33	31	10	8	13	18	78	51
36	32	17	15	13	15	54	33
32	37	13	6	14	14	69	43
33	36	15	9	19	11	81	53
34	32	16	11	13	9	84	51
32	35	12	8	12	18	84	50
34	36	13	8	13	16	69	46




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&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]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.646799329877 + 0.342546856180977Separate[t] + 0.300042917488464Learning[t] -0.149104202235988Software[t] + 0.0222601861825917Happiness[t] -0.0100150122535526Depression[t] + 0.047025961909131Belonging[t] -0.0327808023550451Belonging_Final[t] -0.00779785476410572t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Connected[t] =  +  18.646799329877 +  0.342546856180977Separate[t] +  0.300042917488464Learning[t] -0.149104202235988Software[t] +  0.0222601861825917Happiness[t] -0.0100150122535526Depression[t] +  0.047025961909131Belonging[t] -0.0327808023550451Belonging_Final[t] -0.00779785476410572t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Connected[t] =  +  18.646799329877 +  0.342546856180977Separate[t] +  0.300042917488464Learning[t] -0.149104202235988Software[t] +  0.0222601861825917Happiness[t] -0.0100150122535526Depression[t] +  0.047025961909131Belonging[t] -0.0327808023550451Belonging_Final[t] -0.00779785476410572t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Connected[t] = + 18.646799329877 + 0.342546856180977Separate[t] + 0.300042917488464Learning[t] -0.149104202235988Software[t] + 0.0222601861825917Happiness[t] -0.0100150122535526Depression[t] + 0.047025961909131Belonging[t] -0.0327808023550451Belonging_Final[t] -0.00779785476410572t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.6467993298774.1870234.45351.6e-058e-06
Separate0.3425468561809770.0705244.85713e-061e-06
Learning0.3000429174884640.1340142.23890.0266050.013303
Software-0.1491042022359880.136957-1.08870.2780020.139001
Happiness0.02226018618259170.1289350.17260.8631580.431579
Depression-0.01001501225355260.095599-0.10480.9167030.458351
Belonging0.0470259619091310.0754350.62340.5339510.266976
Belonging_Final-0.03278080235504510.107857-0.30390.7615950.380798
t-0.007797854764105720.005477-1.42380.1565370.078268

\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) & 18.646799329877 & 4.187023 & 4.4535 & 1.6e-05 & 8e-06 \tabularnewline
Separate & 0.342546856180977 & 0.070524 & 4.8571 & 3e-06 & 1e-06 \tabularnewline
Learning & 0.300042917488464 & 0.134014 & 2.2389 & 0.026605 & 0.013303 \tabularnewline
Software & -0.149104202235988 & 0.136957 & -1.0887 & 0.278002 & 0.139001 \tabularnewline
Happiness & 0.0222601861825917 & 0.128935 & 0.1726 & 0.863158 & 0.431579 \tabularnewline
Depression & -0.0100150122535526 & 0.095599 & -0.1048 & 0.916703 & 0.458351 \tabularnewline
Belonging & 0.047025961909131 & 0.075435 & 0.6234 & 0.533951 & 0.266976 \tabularnewline
Belonging_Final & -0.0327808023550451 & 0.107857 & -0.3039 & 0.761595 & 0.380798 \tabularnewline
t & -0.00779785476410572 & 0.005477 & -1.4238 & 0.156537 & 0.078268 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&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]18.646799329877[/C][C]4.187023[/C][C]4.4535[/C][C]1.6e-05[/C][C]8e-06[/C][/ROW]
[ROW][C]Separate[/C][C]0.342546856180977[/C][C]0.070524[/C][C]4.8571[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Learning[/C][C]0.300042917488464[/C][C]0.134014[/C][C]2.2389[/C][C]0.026605[/C][C]0.013303[/C][/ROW]
[ROW][C]Software[/C][C]-0.149104202235988[/C][C]0.136957[/C][C]-1.0887[/C][C]0.278002[/C][C]0.139001[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0222601861825917[/C][C]0.128935[/C][C]0.1726[/C][C]0.863158[/C][C]0.431579[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0100150122535526[/C][C]0.095599[/C][C]-0.1048[/C][C]0.916703[/C][C]0.458351[/C][/ROW]
[ROW][C]Belonging[/C][C]0.047025961909131[/C][C]0.075435[/C][C]0.6234[/C][C]0.533951[/C][C]0.266976[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0327808023550451[/C][C]0.107857[/C][C]-0.3039[/C][C]0.761595[/C][C]0.380798[/C][/ROW]
[ROW][C]t[/C][C]-0.00779785476410572[/C][C]0.005477[/C][C]-1.4238[/C][C]0.156537[/C][C]0.078268[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)18.6467993298774.1870234.45351.6e-058e-06
Separate0.3425468561809770.0705244.85713e-061e-06
Learning0.3000429174884640.1340142.23890.0266050.013303
Software-0.1491042022359880.136957-1.08870.2780020.139001
Happiness0.02226018618259170.1289350.17260.8631580.431579
Depression-0.01001501225355260.095599-0.10480.9167030.458351
Belonging0.0470259619091310.0754350.62340.5339510.266976
Belonging_Final-0.03278080235504510.107857-0.30390.7615950.380798
t-0.007797854764105720.005477-1.42380.1565370.078268







Multiple Linear Regression - Regression Statistics
Multiple R0.439391781523899
R-squared0.193065137670746
Adjusted R-squared0.150872465130654
F-TEST (value)4.57579778781006
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.11089487551519e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11011783702619
Sum Squared Residuals1479.94344290884

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.439391781523899 \tabularnewline
R-squared & 0.193065137670746 \tabularnewline
Adjusted R-squared & 0.150872465130654 \tabularnewline
F-TEST (value) & 4.57579778781006 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.11089487551519e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.11011783702619 \tabularnewline
Sum Squared Residuals & 1479.94344290884 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.439391781523899[/C][/ROW]
[ROW][C]R-squared[/C][C]0.193065137670746[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.150872465130654[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.57579778781006[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]5.11089487551519e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.11011783702619[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1479.94344290884[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.439391781523899
R-squared0.193065137670746
Adjusted R-squared0.150872465130654
F-TEST (value)4.57579778781006
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.11089487551519e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11011783702619
Sum Squared Residuals1479.94344290884







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.40194227584445.59805772415559
23935.41617349393513.58382650606486
33035.9083697942094-5.90836979420938
43135.4793413522072-4.47934135220724
53435.756193868882-1.75619386888201
63533.3512174581.64878254200004
73934.49353052461294.5064694753871
83435.6865833544029-1.68658335440292
93635.21564676684290.784353233157112
103737.4286028938736-0.42860289387362
113834.87668484453033.12331515546967
123635.01171334066190.988286659338085
133835.39232586428392.60767413571611
143936.38853862602452.61146137397549
153336.7226411409037-3.72264114090375
163234.2563025840732-2.25630258407317
173634.70858040885991.29141959114012
183838.3712057530837-0.371205753083706
193937.15318806408091.84681193591912
203234.1901166490338-2.19011664903384
213235.0379099439801-3.03790994398015
223133.9656973023182-2.96569730231822
233936.89578793328292.10421206671711
243736.52699490423210.473005095767857
253935.78003316778353.21996683221649
264134.89951527944236.10048472055774
273636.1454383229953-0.145438322995281
283335.4797314970624-2.47973149706236
293335.1227255927981-2.12272559279806
303434.2495197964068-0.249519796406796
313133.1296166467249-2.12961664672494
322733.3632414691375-6.36324146913753
333733.92552332360033.0744766763997
343436.4710973307215-2.47109733072148
353433.34620544619310.653794553806857
363232.8992246919281-0.899224691928128
372932.8328225503949-3.83282255039494
383634.20886896079751.79113103920254
392934.6156094324731-5.61560943247311
403535.0058060213969-0.00580602139685898
413734.8467840975512.15321590244897
423434.335230748698-0.335230748698004
433834.77096463531083.2290353646892
443533.8733560056171.12664399438302
453832.62172143325845.37827856674163
463733.7896598469353.210340153065
473835.79275446807632.20724553192375
483335.0223608418868-2.02236084188677
493636.7733509001971-0.773350900197061
503833.8607847302624.13921526973798
513236.1786439305177-4.1786439305177
523233.2382151787679-1.23821517876794
533233.1631616049645-1.16316160496449
543435.6394325509154-1.6394325509154
553233.0737657767543-1.07376577675433
563734.7245925324042.27540746759601
573935.09110386638373.9088961336163
582934.8702665481524-5.87026654815243
593735.56920308879621.43079691120378
603535.1343056943995-0.134305694399452
613031.7403993067557-1.74039930675574
623835.13642693199632.86357306800366
633435.1284471565761-1.12844715657605
643134.297908792826-3.29790879282601
653433.43167066362330.568329336376746
663536.2370039267483-1.23700392674831
673635.22832333729350.771676662706513
683033.3617610229589-3.36176102295885
693935.54255892564873.45744107435128
703535.8483097220766-0.84830972207659
713834.12226561268733.87773438731271
723135.3261622024759-4.3261622024759
733437.189529041684-3.18952904168403
743837.59480481197330.405195188026675
753432.81251956063841.18748043936163
763934.32201446912114.67798553087886
773735.71340108066471.28659891933532
783433.73962293527790.260377064722058
792833.0450378353998-5.04503783539976
803731.94005509408685.05994490591321
813335.7891679099222-2.78916790992224
823736.90255541069470.0974445893052583
833535.7927221881641-0.792722188164105
843734.26652266913792.73347733086208
853234.648308922344-2.64830892234401
863334.1665298228058-1.1665298228058
873836.4418950518231.55810494817701
883334.6780776061043-1.67807760610427
892933.5225186085934-4.5225186085934
903333.778165856647-0.778165856647038
913134.7006648754386-3.70066487543858
923633.35185535508162.64814464491841
933537.2942459853325-2.29424598533248
943232.6605740768731-0.660574076873088
952932.704327853116-3.70432785311596
963936.00691383866932.99308616133069
973734.68397834343792.31602165656213
983534.32189655664380.678103443356201
993735.03672350327971.96327649672026
1003235.2080683654254-3.20806836542544
1013835.28124071141452.71875928858546
1023735.06573971701581.93426028298425
1033636.8107371557923-0.810737155792331
1043232.1492486455186-0.149248645518629
1053336.6959083027398-3.69590830273981
1064032.75684257928117.24315742071889
1073835.39232124546312.60767875453687
1084136.27832605035864.72167394964136
1093634.47652572554241.52347427445757
1104335.89480060454547.10519939545462
1113034.8828289965387-4.88282899653871
1123133.9229164406399-2.92291644063991
1133237.7852040217218-5.78520402172178
1143232.9101169928898-0.910116992889824
1153733.81989072831063.1801092716894
1163734.72902851651032.27097148348969
1173335.3276386858695-2.32763868586949
1183436.4672034191795-2.46720341917947
1193334.0579114511292-1.05791145112923
1203835.64498969873032.35501030126965
1213334.2124368006349-1.21243680063491
1223132.0440468480585-1.04404684805848
1233835.79718302615832.20281697384171
1243735.69693181163391.30306818836611
1253333.0348059305725-0.03480593057251
1263133.8860100955664-2.88601009556636
1273934.35526842196394.64473157803614
1284436.78442360405987.21557639594018
1293335.515767622528-2.51576762252798
1303533.04471483705931.95528516294067
1313234.3802381494279-2.38023814942795
1322831.7317876714682-3.73178767146819
1334036.2386062157493.76139378425104
1342731.7677943010091-4.76779430100913
1353735.20794680824631.79205319175365
1363232.5662424058051-0.566242405805134
1372829.1452964019555-1.14529640195553
1383434.1776072128656-0.177607212865595
1393032.8381632666319-2.83816326663188
1403533.53154129096141.46845870903855
1413132.6648280427604-1.66482804276035
1423234.4853382610347-2.48533826103465
1433034.8313178385976-4.8313178385976
1443034.8755274328255-4.87552743282555
1453130.88203334083090.117966659169051
1464032.56322495744227.43677504255785
1473232.48095590845-0.48095590845001
1483633.62626526159122.37373473840884
1493233.2912950988739-1.29129509887387
1503532.93227537413892.06772462586115
1513835.28115748012542.71884251987456
1524234.99152870988577.00847129011429
1533436.1901152131684-2.19011521316842
1543536.8017745946462-1.80177459464623
1553533.93776549183331.06223450816666
1563331.96219839389821.03780160610181
1573632.84499479541473.15500520458529
1583235.1015539752482-3.10155397524819
1593335.2818319796048-2.28183197960481
1603433.99878961098230.00121038901768139
1613234.1861587674057-2.18615876740573
1623434.288974677784-0.288974677783981

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 41 & 35.4019422758444 & 5.59805772415559 \tabularnewline
2 & 39 & 35.4161734939351 & 3.58382650606486 \tabularnewline
3 & 30 & 35.9083697942094 & -5.90836979420938 \tabularnewline
4 & 31 & 35.4793413522072 & -4.47934135220724 \tabularnewline
5 & 34 & 35.756193868882 & -1.75619386888201 \tabularnewline
6 & 35 & 33.351217458 & 1.64878254200004 \tabularnewline
7 & 39 & 34.4935305246129 & 4.5064694753871 \tabularnewline
8 & 34 & 35.6865833544029 & -1.68658335440292 \tabularnewline
9 & 36 & 35.2156467668429 & 0.784353233157112 \tabularnewline
10 & 37 & 37.4286028938736 & -0.42860289387362 \tabularnewline
11 & 38 & 34.8766848445303 & 3.12331515546967 \tabularnewline
12 & 36 & 35.0117133406619 & 0.988286659338085 \tabularnewline
13 & 38 & 35.3923258642839 & 2.60767413571611 \tabularnewline
14 & 39 & 36.3885386260245 & 2.61146137397549 \tabularnewline
15 & 33 & 36.7226411409037 & -3.72264114090375 \tabularnewline
16 & 32 & 34.2563025840732 & -2.25630258407317 \tabularnewline
17 & 36 & 34.7085804088599 & 1.29141959114012 \tabularnewline
18 & 38 & 38.3712057530837 & -0.371205753083706 \tabularnewline
19 & 39 & 37.1531880640809 & 1.84681193591912 \tabularnewline
20 & 32 & 34.1901166490338 & -2.19011664903384 \tabularnewline
21 & 32 & 35.0379099439801 & -3.03790994398015 \tabularnewline
22 & 31 & 33.9656973023182 & -2.96569730231822 \tabularnewline
23 & 39 & 36.8957879332829 & 2.10421206671711 \tabularnewline
24 & 37 & 36.5269949042321 & 0.473005095767857 \tabularnewline
25 & 39 & 35.7800331677835 & 3.21996683221649 \tabularnewline
26 & 41 & 34.8995152794423 & 6.10048472055774 \tabularnewline
27 & 36 & 36.1454383229953 & -0.145438322995281 \tabularnewline
28 & 33 & 35.4797314970624 & -2.47973149706236 \tabularnewline
29 & 33 & 35.1227255927981 & -2.12272559279806 \tabularnewline
30 & 34 & 34.2495197964068 & -0.249519796406796 \tabularnewline
31 & 31 & 33.1296166467249 & -2.12961664672494 \tabularnewline
32 & 27 & 33.3632414691375 & -6.36324146913753 \tabularnewline
33 & 37 & 33.9255233236003 & 3.0744766763997 \tabularnewline
34 & 34 & 36.4710973307215 & -2.47109733072148 \tabularnewline
35 & 34 & 33.3462054461931 & 0.653794553806857 \tabularnewline
36 & 32 & 32.8992246919281 & -0.899224691928128 \tabularnewline
37 & 29 & 32.8328225503949 & -3.83282255039494 \tabularnewline
38 & 36 & 34.2088689607975 & 1.79113103920254 \tabularnewline
39 & 29 & 34.6156094324731 & -5.61560943247311 \tabularnewline
40 & 35 & 35.0058060213969 & -0.00580602139685898 \tabularnewline
41 & 37 & 34.846784097551 & 2.15321590244897 \tabularnewline
42 & 34 & 34.335230748698 & -0.335230748698004 \tabularnewline
43 & 38 & 34.7709646353108 & 3.2290353646892 \tabularnewline
44 & 35 & 33.873356005617 & 1.12664399438302 \tabularnewline
45 & 38 & 32.6217214332584 & 5.37827856674163 \tabularnewline
46 & 37 & 33.789659846935 & 3.210340153065 \tabularnewline
47 & 38 & 35.7927544680763 & 2.20724553192375 \tabularnewline
48 & 33 & 35.0223608418868 & -2.02236084188677 \tabularnewline
49 & 36 & 36.7733509001971 & -0.773350900197061 \tabularnewline
50 & 38 & 33.860784730262 & 4.13921526973798 \tabularnewline
51 & 32 & 36.1786439305177 & -4.1786439305177 \tabularnewline
52 & 32 & 33.2382151787679 & -1.23821517876794 \tabularnewline
53 & 32 & 33.1631616049645 & -1.16316160496449 \tabularnewline
54 & 34 & 35.6394325509154 & -1.6394325509154 \tabularnewline
55 & 32 & 33.0737657767543 & -1.07376577675433 \tabularnewline
56 & 37 & 34.724592532404 & 2.27540746759601 \tabularnewline
57 & 39 & 35.0911038663837 & 3.9088961336163 \tabularnewline
58 & 29 & 34.8702665481524 & -5.87026654815243 \tabularnewline
59 & 37 & 35.5692030887962 & 1.43079691120378 \tabularnewline
60 & 35 & 35.1343056943995 & -0.134305694399452 \tabularnewline
61 & 30 & 31.7403993067557 & -1.74039930675574 \tabularnewline
62 & 38 & 35.1364269319963 & 2.86357306800366 \tabularnewline
63 & 34 & 35.1284471565761 & -1.12844715657605 \tabularnewline
64 & 31 & 34.297908792826 & -3.29790879282601 \tabularnewline
65 & 34 & 33.4316706636233 & 0.568329336376746 \tabularnewline
66 & 35 & 36.2370039267483 & -1.23700392674831 \tabularnewline
67 & 36 & 35.2283233372935 & 0.771676662706513 \tabularnewline
68 & 30 & 33.3617610229589 & -3.36176102295885 \tabularnewline
69 & 39 & 35.5425589256487 & 3.45744107435128 \tabularnewline
70 & 35 & 35.8483097220766 & -0.84830972207659 \tabularnewline
71 & 38 & 34.1222656126873 & 3.87773438731271 \tabularnewline
72 & 31 & 35.3261622024759 & -4.3261622024759 \tabularnewline
73 & 34 & 37.189529041684 & -3.18952904168403 \tabularnewline
74 & 38 & 37.5948048119733 & 0.405195188026675 \tabularnewline
75 & 34 & 32.8125195606384 & 1.18748043936163 \tabularnewline
76 & 39 & 34.3220144691211 & 4.67798553087886 \tabularnewline
77 & 37 & 35.7134010806647 & 1.28659891933532 \tabularnewline
78 & 34 & 33.7396229352779 & 0.260377064722058 \tabularnewline
79 & 28 & 33.0450378353998 & -5.04503783539976 \tabularnewline
80 & 37 & 31.9400550940868 & 5.05994490591321 \tabularnewline
81 & 33 & 35.7891679099222 & -2.78916790992224 \tabularnewline
82 & 37 & 36.9025554106947 & 0.0974445893052583 \tabularnewline
83 & 35 & 35.7927221881641 & -0.792722188164105 \tabularnewline
84 & 37 & 34.2665226691379 & 2.73347733086208 \tabularnewline
85 & 32 & 34.648308922344 & -2.64830892234401 \tabularnewline
86 & 33 & 34.1665298228058 & -1.1665298228058 \tabularnewline
87 & 38 & 36.441895051823 & 1.55810494817701 \tabularnewline
88 & 33 & 34.6780776061043 & -1.67807760610427 \tabularnewline
89 & 29 & 33.5225186085934 & -4.5225186085934 \tabularnewline
90 & 33 & 33.778165856647 & -0.778165856647038 \tabularnewline
91 & 31 & 34.7006648754386 & -3.70066487543858 \tabularnewline
92 & 36 & 33.3518553550816 & 2.64814464491841 \tabularnewline
93 & 35 & 37.2942459853325 & -2.29424598533248 \tabularnewline
94 & 32 & 32.6605740768731 & -0.660574076873088 \tabularnewline
95 & 29 & 32.704327853116 & -3.70432785311596 \tabularnewline
96 & 39 & 36.0069138386693 & 2.99308616133069 \tabularnewline
97 & 37 & 34.6839783434379 & 2.31602165656213 \tabularnewline
98 & 35 & 34.3218965566438 & 0.678103443356201 \tabularnewline
99 & 37 & 35.0367235032797 & 1.96327649672026 \tabularnewline
100 & 32 & 35.2080683654254 & -3.20806836542544 \tabularnewline
101 & 38 & 35.2812407114145 & 2.71875928858546 \tabularnewline
102 & 37 & 35.0657397170158 & 1.93426028298425 \tabularnewline
103 & 36 & 36.8107371557923 & -0.810737155792331 \tabularnewline
104 & 32 & 32.1492486455186 & -0.149248645518629 \tabularnewline
105 & 33 & 36.6959083027398 & -3.69590830273981 \tabularnewline
106 & 40 & 32.7568425792811 & 7.24315742071889 \tabularnewline
107 & 38 & 35.3923212454631 & 2.60767875453687 \tabularnewline
108 & 41 & 36.2783260503586 & 4.72167394964136 \tabularnewline
109 & 36 & 34.4765257255424 & 1.52347427445757 \tabularnewline
110 & 43 & 35.8948006045454 & 7.10519939545462 \tabularnewline
111 & 30 & 34.8828289965387 & -4.88282899653871 \tabularnewline
112 & 31 & 33.9229164406399 & -2.92291644063991 \tabularnewline
113 & 32 & 37.7852040217218 & -5.78520402172178 \tabularnewline
114 & 32 & 32.9101169928898 & -0.910116992889824 \tabularnewline
115 & 37 & 33.8198907283106 & 3.1801092716894 \tabularnewline
116 & 37 & 34.7290285165103 & 2.27097148348969 \tabularnewline
117 & 33 & 35.3276386858695 & -2.32763868586949 \tabularnewline
118 & 34 & 36.4672034191795 & -2.46720341917947 \tabularnewline
119 & 33 & 34.0579114511292 & -1.05791145112923 \tabularnewline
120 & 38 & 35.6449896987303 & 2.35501030126965 \tabularnewline
121 & 33 & 34.2124368006349 & -1.21243680063491 \tabularnewline
122 & 31 & 32.0440468480585 & -1.04404684805848 \tabularnewline
123 & 38 & 35.7971830261583 & 2.20281697384171 \tabularnewline
124 & 37 & 35.6969318116339 & 1.30306818836611 \tabularnewline
125 & 33 & 33.0348059305725 & -0.03480593057251 \tabularnewline
126 & 31 & 33.8860100955664 & -2.88601009556636 \tabularnewline
127 & 39 & 34.3552684219639 & 4.64473157803614 \tabularnewline
128 & 44 & 36.7844236040598 & 7.21557639594018 \tabularnewline
129 & 33 & 35.515767622528 & -2.51576762252798 \tabularnewline
130 & 35 & 33.0447148370593 & 1.95528516294067 \tabularnewline
131 & 32 & 34.3802381494279 & -2.38023814942795 \tabularnewline
132 & 28 & 31.7317876714682 & -3.73178767146819 \tabularnewline
133 & 40 & 36.238606215749 & 3.76139378425104 \tabularnewline
134 & 27 & 31.7677943010091 & -4.76779430100913 \tabularnewline
135 & 37 & 35.2079468082463 & 1.79205319175365 \tabularnewline
136 & 32 & 32.5662424058051 & -0.566242405805134 \tabularnewline
137 & 28 & 29.1452964019555 & -1.14529640195553 \tabularnewline
138 & 34 & 34.1776072128656 & -0.177607212865595 \tabularnewline
139 & 30 & 32.8381632666319 & -2.83816326663188 \tabularnewline
140 & 35 & 33.5315412909614 & 1.46845870903855 \tabularnewline
141 & 31 & 32.6648280427604 & -1.66482804276035 \tabularnewline
142 & 32 & 34.4853382610347 & -2.48533826103465 \tabularnewline
143 & 30 & 34.8313178385976 & -4.8313178385976 \tabularnewline
144 & 30 & 34.8755274328255 & -4.87552743282555 \tabularnewline
145 & 31 & 30.8820333408309 & 0.117966659169051 \tabularnewline
146 & 40 & 32.5632249574422 & 7.43677504255785 \tabularnewline
147 & 32 & 32.48095590845 & -0.48095590845001 \tabularnewline
148 & 36 & 33.6262652615912 & 2.37373473840884 \tabularnewline
149 & 32 & 33.2912950988739 & -1.29129509887387 \tabularnewline
150 & 35 & 32.9322753741389 & 2.06772462586115 \tabularnewline
151 & 38 & 35.2811574801254 & 2.71884251987456 \tabularnewline
152 & 42 & 34.9915287098857 & 7.00847129011429 \tabularnewline
153 & 34 & 36.1901152131684 & -2.19011521316842 \tabularnewline
154 & 35 & 36.8017745946462 & -1.80177459464623 \tabularnewline
155 & 35 & 33.9377654918333 & 1.06223450816666 \tabularnewline
156 & 33 & 31.9621983938982 & 1.03780160610181 \tabularnewline
157 & 36 & 32.8449947954147 & 3.15500520458529 \tabularnewline
158 & 32 & 35.1015539752482 & -3.10155397524819 \tabularnewline
159 & 33 & 35.2818319796048 & -2.28183197960481 \tabularnewline
160 & 34 & 33.9987896109823 & 0.00121038901768139 \tabularnewline
161 & 32 & 34.1861587674057 & -2.18615876740573 \tabularnewline
162 & 34 & 34.288974677784 & -0.288974677783981 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&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]41[/C][C]35.4019422758444[/C][C]5.59805772415559[/C][/ROW]
[ROW][C]2[/C][C]39[/C][C]35.4161734939351[/C][C]3.58382650606486[/C][/ROW]
[ROW][C]3[/C][C]30[/C][C]35.9083697942094[/C][C]-5.90836979420938[/C][/ROW]
[ROW][C]4[/C][C]31[/C][C]35.4793413522072[/C][C]-4.47934135220724[/C][/ROW]
[ROW][C]5[/C][C]34[/C][C]35.756193868882[/C][C]-1.75619386888201[/C][/ROW]
[ROW][C]6[/C][C]35[/C][C]33.351217458[/C][C]1.64878254200004[/C][/ROW]
[ROW][C]7[/C][C]39[/C][C]34.4935305246129[/C][C]4.5064694753871[/C][/ROW]
[ROW][C]8[/C][C]34[/C][C]35.6865833544029[/C][C]-1.68658335440292[/C][/ROW]
[ROW][C]9[/C][C]36[/C][C]35.2156467668429[/C][C]0.784353233157112[/C][/ROW]
[ROW][C]10[/C][C]37[/C][C]37.4286028938736[/C][C]-0.42860289387362[/C][/ROW]
[ROW][C]11[/C][C]38[/C][C]34.8766848445303[/C][C]3.12331515546967[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]35.0117133406619[/C][C]0.988286659338085[/C][/ROW]
[ROW][C]13[/C][C]38[/C][C]35.3923258642839[/C][C]2.60767413571611[/C][/ROW]
[ROW][C]14[/C][C]39[/C][C]36.3885386260245[/C][C]2.61146137397549[/C][/ROW]
[ROW][C]15[/C][C]33[/C][C]36.7226411409037[/C][C]-3.72264114090375[/C][/ROW]
[ROW][C]16[/C][C]32[/C][C]34.2563025840732[/C][C]-2.25630258407317[/C][/ROW]
[ROW][C]17[/C][C]36[/C][C]34.7085804088599[/C][C]1.29141959114012[/C][/ROW]
[ROW][C]18[/C][C]38[/C][C]38.3712057530837[/C][C]-0.371205753083706[/C][/ROW]
[ROW][C]19[/C][C]39[/C][C]37.1531880640809[/C][C]1.84681193591912[/C][/ROW]
[ROW][C]20[/C][C]32[/C][C]34.1901166490338[/C][C]-2.19011664903384[/C][/ROW]
[ROW][C]21[/C][C]32[/C][C]35.0379099439801[/C][C]-3.03790994398015[/C][/ROW]
[ROW][C]22[/C][C]31[/C][C]33.9656973023182[/C][C]-2.96569730231822[/C][/ROW]
[ROW][C]23[/C][C]39[/C][C]36.8957879332829[/C][C]2.10421206671711[/C][/ROW]
[ROW][C]24[/C][C]37[/C][C]36.5269949042321[/C][C]0.473005095767857[/C][/ROW]
[ROW][C]25[/C][C]39[/C][C]35.7800331677835[/C][C]3.21996683221649[/C][/ROW]
[ROW][C]26[/C][C]41[/C][C]34.8995152794423[/C][C]6.10048472055774[/C][/ROW]
[ROW][C]27[/C][C]36[/C][C]36.1454383229953[/C][C]-0.145438322995281[/C][/ROW]
[ROW][C]28[/C][C]33[/C][C]35.4797314970624[/C][C]-2.47973149706236[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]35.1227255927981[/C][C]-2.12272559279806[/C][/ROW]
[ROW][C]30[/C][C]34[/C][C]34.2495197964068[/C][C]-0.249519796406796[/C][/ROW]
[ROW][C]31[/C][C]31[/C][C]33.1296166467249[/C][C]-2.12961664672494[/C][/ROW]
[ROW][C]32[/C][C]27[/C][C]33.3632414691375[/C][C]-6.36324146913753[/C][/ROW]
[ROW][C]33[/C][C]37[/C][C]33.9255233236003[/C][C]3.0744766763997[/C][/ROW]
[ROW][C]34[/C][C]34[/C][C]36.4710973307215[/C][C]-2.47109733072148[/C][/ROW]
[ROW][C]35[/C][C]34[/C][C]33.3462054461931[/C][C]0.653794553806857[/C][/ROW]
[ROW][C]36[/C][C]32[/C][C]32.8992246919281[/C][C]-0.899224691928128[/C][/ROW]
[ROW][C]37[/C][C]29[/C][C]32.8328225503949[/C][C]-3.83282255039494[/C][/ROW]
[ROW][C]38[/C][C]36[/C][C]34.2088689607975[/C][C]1.79113103920254[/C][/ROW]
[ROW][C]39[/C][C]29[/C][C]34.6156094324731[/C][C]-5.61560943247311[/C][/ROW]
[ROW][C]40[/C][C]35[/C][C]35.0058060213969[/C][C]-0.00580602139685898[/C][/ROW]
[ROW][C]41[/C][C]37[/C][C]34.846784097551[/C][C]2.15321590244897[/C][/ROW]
[ROW][C]42[/C][C]34[/C][C]34.335230748698[/C][C]-0.335230748698004[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]34.7709646353108[/C][C]3.2290353646892[/C][/ROW]
[ROW][C]44[/C][C]35[/C][C]33.873356005617[/C][C]1.12664399438302[/C][/ROW]
[ROW][C]45[/C][C]38[/C][C]32.6217214332584[/C][C]5.37827856674163[/C][/ROW]
[ROW][C]46[/C][C]37[/C][C]33.789659846935[/C][C]3.210340153065[/C][/ROW]
[ROW][C]47[/C][C]38[/C][C]35.7927544680763[/C][C]2.20724553192375[/C][/ROW]
[ROW][C]48[/C][C]33[/C][C]35.0223608418868[/C][C]-2.02236084188677[/C][/ROW]
[ROW][C]49[/C][C]36[/C][C]36.7733509001971[/C][C]-0.773350900197061[/C][/ROW]
[ROW][C]50[/C][C]38[/C][C]33.860784730262[/C][C]4.13921526973798[/C][/ROW]
[ROW][C]51[/C][C]32[/C][C]36.1786439305177[/C][C]-4.1786439305177[/C][/ROW]
[ROW][C]52[/C][C]32[/C][C]33.2382151787679[/C][C]-1.23821517876794[/C][/ROW]
[ROW][C]53[/C][C]32[/C][C]33.1631616049645[/C][C]-1.16316160496449[/C][/ROW]
[ROW][C]54[/C][C]34[/C][C]35.6394325509154[/C][C]-1.6394325509154[/C][/ROW]
[ROW][C]55[/C][C]32[/C][C]33.0737657767543[/C][C]-1.07376577675433[/C][/ROW]
[ROW][C]56[/C][C]37[/C][C]34.724592532404[/C][C]2.27540746759601[/C][/ROW]
[ROW][C]57[/C][C]39[/C][C]35.0911038663837[/C][C]3.9088961336163[/C][/ROW]
[ROW][C]58[/C][C]29[/C][C]34.8702665481524[/C][C]-5.87026654815243[/C][/ROW]
[ROW][C]59[/C][C]37[/C][C]35.5692030887962[/C][C]1.43079691120378[/C][/ROW]
[ROW][C]60[/C][C]35[/C][C]35.1343056943995[/C][C]-0.134305694399452[/C][/ROW]
[ROW][C]61[/C][C]30[/C][C]31.7403993067557[/C][C]-1.74039930675574[/C][/ROW]
[ROW][C]62[/C][C]38[/C][C]35.1364269319963[/C][C]2.86357306800366[/C][/ROW]
[ROW][C]63[/C][C]34[/C][C]35.1284471565761[/C][C]-1.12844715657605[/C][/ROW]
[ROW][C]64[/C][C]31[/C][C]34.297908792826[/C][C]-3.29790879282601[/C][/ROW]
[ROW][C]65[/C][C]34[/C][C]33.4316706636233[/C][C]0.568329336376746[/C][/ROW]
[ROW][C]66[/C][C]35[/C][C]36.2370039267483[/C][C]-1.23700392674831[/C][/ROW]
[ROW][C]67[/C][C]36[/C][C]35.2283233372935[/C][C]0.771676662706513[/C][/ROW]
[ROW][C]68[/C][C]30[/C][C]33.3617610229589[/C][C]-3.36176102295885[/C][/ROW]
[ROW][C]69[/C][C]39[/C][C]35.5425589256487[/C][C]3.45744107435128[/C][/ROW]
[ROW][C]70[/C][C]35[/C][C]35.8483097220766[/C][C]-0.84830972207659[/C][/ROW]
[ROW][C]71[/C][C]38[/C][C]34.1222656126873[/C][C]3.87773438731271[/C][/ROW]
[ROW][C]72[/C][C]31[/C][C]35.3261622024759[/C][C]-4.3261622024759[/C][/ROW]
[ROW][C]73[/C][C]34[/C][C]37.189529041684[/C][C]-3.18952904168403[/C][/ROW]
[ROW][C]74[/C][C]38[/C][C]37.5948048119733[/C][C]0.405195188026675[/C][/ROW]
[ROW][C]75[/C][C]34[/C][C]32.8125195606384[/C][C]1.18748043936163[/C][/ROW]
[ROW][C]76[/C][C]39[/C][C]34.3220144691211[/C][C]4.67798553087886[/C][/ROW]
[ROW][C]77[/C][C]37[/C][C]35.7134010806647[/C][C]1.28659891933532[/C][/ROW]
[ROW][C]78[/C][C]34[/C][C]33.7396229352779[/C][C]0.260377064722058[/C][/ROW]
[ROW][C]79[/C][C]28[/C][C]33.0450378353998[/C][C]-5.04503783539976[/C][/ROW]
[ROW][C]80[/C][C]37[/C][C]31.9400550940868[/C][C]5.05994490591321[/C][/ROW]
[ROW][C]81[/C][C]33[/C][C]35.7891679099222[/C][C]-2.78916790992224[/C][/ROW]
[ROW][C]82[/C][C]37[/C][C]36.9025554106947[/C][C]0.0974445893052583[/C][/ROW]
[ROW][C]83[/C][C]35[/C][C]35.7927221881641[/C][C]-0.792722188164105[/C][/ROW]
[ROW][C]84[/C][C]37[/C][C]34.2665226691379[/C][C]2.73347733086208[/C][/ROW]
[ROW][C]85[/C][C]32[/C][C]34.648308922344[/C][C]-2.64830892234401[/C][/ROW]
[ROW][C]86[/C][C]33[/C][C]34.1665298228058[/C][C]-1.1665298228058[/C][/ROW]
[ROW][C]87[/C][C]38[/C][C]36.441895051823[/C][C]1.55810494817701[/C][/ROW]
[ROW][C]88[/C][C]33[/C][C]34.6780776061043[/C][C]-1.67807760610427[/C][/ROW]
[ROW][C]89[/C][C]29[/C][C]33.5225186085934[/C][C]-4.5225186085934[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]33.778165856647[/C][C]-0.778165856647038[/C][/ROW]
[ROW][C]91[/C][C]31[/C][C]34.7006648754386[/C][C]-3.70066487543858[/C][/ROW]
[ROW][C]92[/C][C]36[/C][C]33.3518553550816[/C][C]2.64814464491841[/C][/ROW]
[ROW][C]93[/C][C]35[/C][C]37.2942459853325[/C][C]-2.29424598533248[/C][/ROW]
[ROW][C]94[/C][C]32[/C][C]32.6605740768731[/C][C]-0.660574076873088[/C][/ROW]
[ROW][C]95[/C][C]29[/C][C]32.704327853116[/C][C]-3.70432785311596[/C][/ROW]
[ROW][C]96[/C][C]39[/C][C]36.0069138386693[/C][C]2.99308616133069[/C][/ROW]
[ROW][C]97[/C][C]37[/C][C]34.6839783434379[/C][C]2.31602165656213[/C][/ROW]
[ROW][C]98[/C][C]35[/C][C]34.3218965566438[/C][C]0.678103443356201[/C][/ROW]
[ROW][C]99[/C][C]37[/C][C]35.0367235032797[/C][C]1.96327649672026[/C][/ROW]
[ROW][C]100[/C][C]32[/C][C]35.2080683654254[/C][C]-3.20806836542544[/C][/ROW]
[ROW][C]101[/C][C]38[/C][C]35.2812407114145[/C][C]2.71875928858546[/C][/ROW]
[ROW][C]102[/C][C]37[/C][C]35.0657397170158[/C][C]1.93426028298425[/C][/ROW]
[ROW][C]103[/C][C]36[/C][C]36.8107371557923[/C][C]-0.810737155792331[/C][/ROW]
[ROW][C]104[/C][C]32[/C][C]32.1492486455186[/C][C]-0.149248645518629[/C][/ROW]
[ROW][C]105[/C][C]33[/C][C]36.6959083027398[/C][C]-3.69590830273981[/C][/ROW]
[ROW][C]106[/C][C]40[/C][C]32.7568425792811[/C][C]7.24315742071889[/C][/ROW]
[ROW][C]107[/C][C]38[/C][C]35.3923212454631[/C][C]2.60767875453687[/C][/ROW]
[ROW][C]108[/C][C]41[/C][C]36.2783260503586[/C][C]4.72167394964136[/C][/ROW]
[ROW][C]109[/C][C]36[/C][C]34.4765257255424[/C][C]1.52347427445757[/C][/ROW]
[ROW][C]110[/C][C]43[/C][C]35.8948006045454[/C][C]7.10519939545462[/C][/ROW]
[ROW][C]111[/C][C]30[/C][C]34.8828289965387[/C][C]-4.88282899653871[/C][/ROW]
[ROW][C]112[/C][C]31[/C][C]33.9229164406399[/C][C]-2.92291644063991[/C][/ROW]
[ROW][C]113[/C][C]32[/C][C]37.7852040217218[/C][C]-5.78520402172178[/C][/ROW]
[ROW][C]114[/C][C]32[/C][C]32.9101169928898[/C][C]-0.910116992889824[/C][/ROW]
[ROW][C]115[/C][C]37[/C][C]33.8198907283106[/C][C]3.1801092716894[/C][/ROW]
[ROW][C]116[/C][C]37[/C][C]34.7290285165103[/C][C]2.27097148348969[/C][/ROW]
[ROW][C]117[/C][C]33[/C][C]35.3276386858695[/C][C]-2.32763868586949[/C][/ROW]
[ROW][C]118[/C][C]34[/C][C]36.4672034191795[/C][C]-2.46720341917947[/C][/ROW]
[ROW][C]119[/C][C]33[/C][C]34.0579114511292[/C][C]-1.05791145112923[/C][/ROW]
[ROW][C]120[/C][C]38[/C][C]35.6449896987303[/C][C]2.35501030126965[/C][/ROW]
[ROW][C]121[/C][C]33[/C][C]34.2124368006349[/C][C]-1.21243680063491[/C][/ROW]
[ROW][C]122[/C][C]31[/C][C]32.0440468480585[/C][C]-1.04404684805848[/C][/ROW]
[ROW][C]123[/C][C]38[/C][C]35.7971830261583[/C][C]2.20281697384171[/C][/ROW]
[ROW][C]124[/C][C]37[/C][C]35.6969318116339[/C][C]1.30306818836611[/C][/ROW]
[ROW][C]125[/C][C]33[/C][C]33.0348059305725[/C][C]-0.03480593057251[/C][/ROW]
[ROW][C]126[/C][C]31[/C][C]33.8860100955664[/C][C]-2.88601009556636[/C][/ROW]
[ROW][C]127[/C][C]39[/C][C]34.3552684219639[/C][C]4.64473157803614[/C][/ROW]
[ROW][C]128[/C][C]44[/C][C]36.7844236040598[/C][C]7.21557639594018[/C][/ROW]
[ROW][C]129[/C][C]33[/C][C]35.515767622528[/C][C]-2.51576762252798[/C][/ROW]
[ROW][C]130[/C][C]35[/C][C]33.0447148370593[/C][C]1.95528516294067[/C][/ROW]
[ROW][C]131[/C][C]32[/C][C]34.3802381494279[/C][C]-2.38023814942795[/C][/ROW]
[ROW][C]132[/C][C]28[/C][C]31.7317876714682[/C][C]-3.73178767146819[/C][/ROW]
[ROW][C]133[/C][C]40[/C][C]36.238606215749[/C][C]3.76139378425104[/C][/ROW]
[ROW][C]134[/C][C]27[/C][C]31.7677943010091[/C][C]-4.76779430100913[/C][/ROW]
[ROW][C]135[/C][C]37[/C][C]35.2079468082463[/C][C]1.79205319175365[/C][/ROW]
[ROW][C]136[/C][C]32[/C][C]32.5662424058051[/C][C]-0.566242405805134[/C][/ROW]
[ROW][C]137[/C][C]28[/C][C]29.1452964019555[/C][C]-1.14529640195553[/C][/ROW]
[ROW][C]138[/C][C]34[/C][C]34.1776072128656[/C][C]-0.177607212865595[/C][/ROW]
[ROW][C]139[/C][C]30[/C][C]32.8381632666319[/C][C]-2.83816326663188[/C][/ROW]
[ROW][C]140[/C][C]35[/C][C]33.5315412909614[/C][C]1.46845870903855[/C][/ROW]
[ROW][C]141[/C][C]31[/C][C]32.6648280427604[/C][C]-1.66482804276035[/C][/ROW]
[ROW][C]142[/C][C]32[/C][C]34.4853382610347[/C][C]-2.48533826103465[/C][/ROW]
[ROW][C]143[/C][C]30[/C][C]34.8313178385976[/C][C]-4.8313178385976[/C][/ROW]
[ROW][C]144[/C][C]30[/C][C]34.8755274328255[/C][C]-4.87552743282555[/C][/ROW]
[ROW][C]145[/C][C]31[/C][C]30.8820333408309[/C][C]0.117966659169051[/C][/ROW]
[ROW][C]146[/C][C]40[/C][C]32.5632249574422[/C][C]7.43677504255785[/C][/ROW]
[ROW][C]147[/C][C]32[/C][C]32.48095590845[/C][C]-0.48095590845001[/C][/ROW]
[ROW][C]148[/C][C]36[/C][C]33.6262652615912[/C][C]2.37373473840884[/C][/ROW]
[ROW][C]149[/C][C]32[/C][C]33.2912950988739[/C][C]-1.29129509887387[/C][/ROW]
[ROW][C]150[/C][C]35[/C][C]32.9322753741389[/C][C]2.06772462586115[/C][/ROW]
[ROW][C]151[/C][C]38[/C][C]35.2811574801254[/C][C]2.71884251987456[/C][/ROW]
[ROW][C]152[/C][C]42[/C][C]34.9915287098857[/C][C]7.00847129011429[/C][/ROW]
[ROW][C]153[/C][C]34[/C][C]36.1901152131684[/C][C]-2.19011521316842[/C][/ROW]
[ROW][C]154[/C][C]35[/C][C]36.8017745946462[/C][C]-1.80177459464623[/C][/ROW]
[ROW][C]155[/C][C]35[/C][C]33.9377654918333[/C][C]1.06223450816666[/C][/ROW]
[ROW][C]156[/C][C]33[/C][C]31.9621983938982[/C][C]1.03780160610181[/C][/ROW]
[ROW][C]157[/C][C]36[/C][C]32.8449947954147[/C][C]3.15500520458529[/C][/ROW]
[ROW][C]158[/C][C]32[/C][C]35.1015539752482[/C][C]-3.10155397524819[/C][/ROW]
[ROW][C]159[/C][C]33[/C][C]35.2818319796048[/C][C]-2.28183197960481[/C][/ROW]
[ROW][C]160[/C][C]34[/C][C]33.9987896109823[/C][C]0.00121038901768139[/C][/ROW]
[ROW][C]161[/C][C]32[/C][C]34.1861587674057[/C][C]-2.18615876740573[/C][/ROW]
[ROW][C]162[/C][C]34[/C][C]34.288974677784[/C][C]-0.288974677783981[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14135.40194227584445.59805772415559
23935.41617349393513.58382650606486
33035.9083697942094-5.90836979420938
43135.4793413522072-4.47934135220724
53435.756193868882-1.75619386888201
63533.3512174581.64878254200004
73934.49353052461294.5064694753871
83435.6865833544029-1.68658335440292
93635.21564676684290.784353233157112
103737.4286028938736-0.42860289387362
113834.87668484453033.12331515546967
123635.01171334066190.988286659338085
133835.39232586428392.60767413571611
143936.38853862602452.61146137397549
153336.7226411409037-3.72264114090375
163234.2563025840732-2.25630258407317
173634.70858040885991.29141959114012
183838.3712057530837-0.371205753083706
193937.15318806408091.84681193591912
203234.1901166490338-2.19011664903384
213235.0379099439801-3.03790994398015
223133.9656973023182-2.96569730231822
233936.89578793328292.10421206671711
243736.52699490423210.473005095767857
253935.78003316778353.21996683221649
264134.89951527944236.10048472055774
273636.1454383229953-0.145438322995281
283335.4797314970624-2.47973149706236
293335.1227255927981-2.12272559279806
303434.2495197964068-0.249519796406796
313133.1296166467249-2.12961664672494
322733.3632414691375-6.36324146913753
333733.92552332360033.0744766763997
343436.4710973307215-2.47109733072148
353433.34620544619310.653794553806857
363232.8992246919281-0.899224691928128
372932.8328225503949-3.83282255039494
383634.20886896079751.79113103920254
392934.6156094324731-5.61560943247311
403535.0058060213969-0.00580602139685898
413734.8467840975512.15321590244897
423434.335230748698-0.335230748698004
433834.77096463531083.2290353646892
443533.8733560056171.12664399438302
453832.62172143325845.37827856674163
463733.7896598469353.210340153065
473835.79275446807632.20724553192375
483335.0223608418868-2.02236084188677
493636.7733509001971-0.773350900197061
503833.8607847302624.13921526973798
513236.1786439305177-4.1786439305177
523233.2382151787679-1.23821517876794
533233.1631616049645-1.16316160496449
543435.6394325509154-1.6394325509154
553233.0737657767543-1.07376577675433
563734.7245925324042.27540746759601
573935.09110386638373.9088961336163
582934.8702665481524-5.87026654815243
593735.56920308879621.43079691120378
603535.1343056943995-0.134305694399452
613031.7403993067557-1.74039930675574
623835.13642693199632.86357306800366
633435.1284471565761-1.12844715657605
643134.297908792826-3.29790879282601
653433.43167066362330.568329336376746
663536.2370039267483-1.23700392674831
673635.22832333729350.771676662706513
683033.3617610229589-3.36176102295885
693935.54255892564873.45744107435128
703535.8483097220766-0.84830972207659
713834.12226561268733.87773438731271
723135.3261622024759-4.3261622024759
733437.189529041684-3.18952904168403
743837.59480481197330.405195188026675
753432.81251956063841.18748043936163
763934.32201446912114.67798553087886
773735.71340108066471.28659891933532
783433.73962293527790.260377064722058
792833.0450378353998-5.04503783539976
803731.94005509408685.05994490591321
813335.7891679099222-2.78916790992224
823736.90255541069470.0974445893052583
833535.7927221881641-0.792722188164105
843734.26652266913792.73347733086208
853234.648308922344-2.64830892234401
863334.1665298228058-1.1665298228058
873836.4418950518231.55810494817701
883334.6780776061043-1.67807760610427
892933.5225186085934-4.5225186085934
903333.778165856647-0.778165856647038
913134.7006648754386-3.70066487543858
923633.35185535508162.64814464491841
933537.2942459853325-2.29424598533248
943232.6605740768731-0.660574076873088
952932.704327853116-3.70432785311596
963936.00691383866932.99308616133069
973734.68397834343792.31602165656213
983534.32189655664380.678103443356201
993735.03672350327971.96327649672026
1003235.2080683654254-3.20806836542544
1013835.28124071141452.71875928858546
1023735.06573971701581.93426028298425
1033636.8107371557923-0.810737155792331
1043232.1492486455186-0.149248645518629
1053336.6959083027398-3.69590830273981
1064032.75684257928117.24315742071889
1073835.39232124546312.60767875453687
1084136.27832605035864.72167394964136
1093634.47652572554241.52347427445757
1104335.89480060454547.10519939545462
1113034.8828289965387-4.88282899653871
1123133.9229164406399-2.92291644063991
1133237.7852040217218-5.78520402172178
1143232.9101169928898-0.910116992889824
1153733.81989072831063.1801092716894
1163734.72902851651032.27097148348969
1173335.3276386858695-2.32763868586949
1183436.4672034191795-2.46720341917947
1193334.0579114511292-1.05791145112923
1203835.64498969873032.35501030126965
1213334.2124368006349-1.21243680063491
1223132.0440468480585-1.04404684805848
1233835.79718302615832.20281697384171
1243735.69693181163391.30306818836611
1253333.0348059305725-0.03480593057251
1263133.8860100955664-2.88601009556636
1273934.35526842196394.64473157803614
1284436.78442360405987.21557639594018
1293335.515767622528-2.51576762252798
1303533.04471483705931.95528516294067
1313234.3802381494279-2.38023814942795
1322831.7317876714682-3.73178767146819
1334036.2386062157493.76139378425104
1342731.7677943010091-4.76779430100913
1353735.20794680824631.79205319175365
1363232.5662424058051-0.566242405805134
1372829.1452964019555-1.14529640195553
1383434.1776072128656-0.177607212865595
1393032.8381632666319-2.83816326663188
1403533.53154129096141.46845870903855
1413132.6648280427604-1.66482804276035
1423234.4853382610347-2.48533826103465
1433034.8313178385976-4.8313178385976
1443034.8755274328255-4.87552743282555
1453130.88203334083090.117966659169051
1464032.56322495744227.43677504255785
1473232.48095590845-0.48095590845001
1483633.62626526159122.37373473840884
1493233.2912950988739-1.29129509887387
1503532.93227537413892.06772462586115
1513835.28115748012542.71884251987456
1524234.99152870988577.00847129011429
1533436.1901152131684-2.19011521316842
1543536.8017745946462-1.80177459464623
1553533.93776549183331.06223450816666
1563331.96219839389821.03780160610181
1573632.84499479541473.15500520458529
1583235.1015539752482-3.10155397524819
1593335.2818319796048-2.28183197960481
1603433.99878961098230.00121038901768139
1613234.1861587674057-2.18615876740573
1623434.288974677784-0.288974677783981







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3737384931473790.7474769862947580.626261506852621
130.9541481874520090.09170362509598110.0458518125479906
140.9207487430257620.1585025139484760.0792512569742379
150.8933829420385780.2132341159228440.106617057961422
160.8974544909100410.2050910181799170.102545509089959
170.8714528444457030.2570943111085940.128547155554297
180.8442641077571670.3114717844856660.155735892242833
190.801903017822160.396193964355680.19809698217784
200.8142060231926740.3715879536146520.185793976807326
210.8151967798591790.3696064402816420.184803220140821
220.7632467605443390.4735064789113220.236753239455661
230.7370418589554320.5259162820891360.262958141044568
240.6749536377351680.6500927245296650.325046362264832
250.6862243416156860.6275513167686280.313775658384314
260.8132930014590220.3734139970819560.186706998540978
270.8101223589867210.3797552820265580.189877641013279
280.768422690334820.4631546193303610.23157730966518
290.764882673700350.4702346525993010.23511732629965
300.7140992617898640.5718014764202720.285900738210136
310.6694981740795590.6610036518408810.330501825920441
320.7792112520161390.4415774959677210.220788747983861
330.8012146952133260.3975706095733480.198785304786674
340.7607525208190610.4784949583618780.239247479180939
350.7141403604613370.5717192790773270.285859639538663
360.6738794019470570.6522411961058860.326120598052943
370.6546684304970920.6906631390058160.345331569502908
380.637579733578370.7248405328432590.36242026642163
390.7207914715581980.5584170568836040.279208528441802
400.6733700781220870.6532598437558260.326629921877913
410.6566461377498250.686707724500350.343353862250175
420.6075050463891220.7849899072217560.392494953610878
430.5854067639335580.8291864721328840.414593236066442
440.5409125238865370.9181749522269270.459087476113463
450.6453998397413840.7092003205172330.354600160258616
460.6584405923029230.6831188153941550.341559407697077
470.646992633224380.706014733551240.35300736677562
480.6124018084155880.7751963831688250.387598191584412
490.5629522285590440.8740955428819120.437047771440956
500.5921487697301080.8157024605397840.407851230269892
510.6259787666225980.7480424667548030.374021233377402
520.5819954375863350.836009124827330.418004562413665
530.5351578540076350.929684291984730.464842145992365
540.4880036993141490.9760073986282990.511996300685851
550.4408531950507930.8817063901015850.559146804949208
560.4395092295288820.8790184590577640.560490770471118
570.4744914778456510.9489829556913020.525508522154349
580.5972357761915380.8055284476169240.402764223808462
590.5580512986517830.8838974026964340.441948701348217
600.508870194320970.9822596113580590.49112980567903
610.4706946684719620.9413893369439230.529305331528038
620.4578891386029820.9157782772059650.542110861397018
630.419878870425250.83975774085050.58012112957475
640.417695567677020.835391135354040.58230443232298
650.3722807117579640.7445614235159270.627719288242036
660.3323855264071620.6647710528143240.667614473592838
670.295883260958420.591766521916840.70411673904158
680.3184584158539480.6369168317078950.681541584146052
690.3396181086448730.6792362172897470.660381891355127
700.2983528990087950.5967057980175890.701647100991205
710.3373368027210130.6746736054420250.662663197278987
720.3671140376474940.7342280752949870.632885962352506
730.3598551273975450.719710254795090.640144872602455
740.3177336190404950.635467238080990.682266380959505
750.2846563532665070.5693127065330130.715343646733493
760.3507108442389920.7014216884779830.649289155761008
770.3158493963382140.6316987926764280.684150603661786
780.2754223479733360.5508446959466720.724577652026664
790.3319076858446920.6638153716893840.668092314155308
800.4104368083325140.8208736166650270.589563191667486
810.3949963702394360.7899927404788710.605003629760565
820.3504810119340870.7009620238681740.649518988065913
830.3088000999212880.6176001998425760.691199900078712
840.3018456988178840.6036913976357680.698154301182116
850.2838078734071420.5676157468142840.716192126592858
860.2478486811708040.4956973623416080.752151318829196
870.2254232224455020.4508464448910040.774576777554498
880.19798713426520.39597426853040.8020128657348
890.2270067717681020.4540135435362050.772993228231898
900.1962332251373020.3924664502746040.803766774862698
910.2104455695043980.4208911390087970.789554430495602
920.1992460549551720.3984921099103430.800753945044828
930.187432948247560.374865896495120.81256705175244
940.1591842696192530.3183685392385060.840815730380747
950.1695706979194550.339141395838910.830429302080545
960.1667367169808660.3334734339617310.833263283019134
970.1529906549060690.3059813098121380.847009345093931
980.1276964908771530.2553929817543060.872303509122847
990.1121013565561670.2242027131123340.887898643443833
1000.1136427184677990.2272854369355990.886357281532201
1010.105538435132770.211076870265540.89446156486723
1020.09177800333626640.1835560066725330.908221996663734
1030.07458892781791970.1491778556358390.92541107218208
1040.05901162547961710.1180232509592340.940988374520383
1050.07111166183347540.1422233236669510.928888338166524
1060.1654474316154980.3308948632309950.834552568384502
1070.1541342585794410.3082685171588810.845865741420559
1080.1792659571130180.3585319142260360.820734042886982
1090.1670920427395060.3341840854790120.832907957260494
1100.4010802772805430.8021605545610870.598919722719457
1110.4535303865093830.9070607730187650.546469613490617
1120.4274341134379450.8548682268758890.572565886562055
1130.5363428089743820.9273143820512370.463657191025618
1140.4875059344114170.9750118688228340.512494065588583
1150.4775964776339110.9551929552678210.522403522366089
1160.4462176954473260.8924353908946510.553782304552674
1170.412678335721010.8253566714420190.58732166427899
1180.3839129044231350.767825808846270.616087095576865
1190.3382336062788240.6764672125576480.661766393721176
1200.3043032386199790.6086064772399590.695696761380021
1210.2602093179466690.5204186358933370.739790682053331
1220.2197472083908590.4394944167817180.780252791609141
1230.1881148804911350.376229760982270.811885119508865
1240.1544595378417720.3089190756835430.845540462158228
1250.1225183228092180.2450366456184350.877481677190783
1260.1314860476218080.2629720952436160.868513952378192
1270.1468726796327240.2937453592654490.853127320367276
1280.3347912281527460.6695824563054930.665208771847253
1290.294554129679660.589108259359320.70544587032034
1300.2659605872628650.5319211745257290.734039412737135
1310.2266198278989330.4532396557978660.773380172101067
1320.2541114998017120.5082229996034240.745888500198288
1330.3684387151878360.7368774303756730.631561284812164
1340.4537871635945640.9075743271891270.546212836405436
1350.421230428147130.8424608562942610.57876957185287
1360.3523546780258550.7047093560517110.647645321974145
1370.296652483234660.593304966469320.70334751676534
1380.2517353242481850.5034706484963690.748264675751815
1390.2615044314888610.5230088629777220.738495568511139
1400.219506972665610.439013945331220.78049302733439
1410.4088977940273270.8177955880546540.591102205972673
1420.3380942186314020.6761884372628040.661905781368598
1430.3638771811966320.7277543623932640.636122818803368
1440.7761956435245920.4476087129508170.223804356475408
1450.6863754371365630.6272491257268750.313624562863437
1460.9450979427914990.1098041144170030.0549020572085014
1470.896339982334740.207320035330520.10366001766526
1480.9218561480868890.1562877038262220.0781438519131109
1490.9873141373246090.0253717253507820.012685862675391
1500.9941668222321530.01166635553569470.00583317776784737

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.373738493147379 & 0.747476986294758 & 0.626261506852621 \tabularnewline
13 & 0.954148187452009 & 0.0917036250959811 & 0.0458518125479906 \tabularnewline
14 & 0.920748743025762 & 0.158502513948476 & 0.0792512569742379 \tabularnewline
15 & 0.893382942038578 & 0.213234115922844 & 0.106617057961422 \tabularnewline
16 & 0.897454490910041 & 0.205091018179917 & 0.102545509089959 \tabularnewline
17 & 0.871452844445703 & 0.257094311108594 & 0.128547155554297 \tabularnewline
18 & 0.844264107757167 & 0.311471784485666 & 0.155735892242833 \tabularnewline
19 & 0.80190301782216 & 0.39619396435568 & 0.19809698217784 \tabularnewline
20 & 0.814206023192674 & 0.371587953614652 & 0.185793976807326 \tabularnewline
21 & 0.815196779859179 & 0.369606440281642 & 0.184803220140821 \tabularnewline
22 & 0.763246760544339 & 0.473506478911322 & 0.236753239455661 \tabularnewline
23 & 0.737041858955432 & 0.525916282089136 & 0.262958141044568 \tabularnewline
24 & 0.674953637735168 & 0.650092724529665 & 0.325046362264832 \tabularnewline
25 & 0.686224341615686 & 0.627551316768628 & 0.313775658384314 \tabularnewline
26 & 0.813293001459022 & 0.373413997081956 & 0.186706998540978 \tabularnewline
27 & 0.810122358986721 & 0.379755282026558 & 0.189877641013279 \tabularnewline
28 & 0.76842269033482 & 0.463154619330361 & 0.23157730966518 \tabularnewline
29 & 0.76488267370035 & 0.470234652599301 & 0.23511732629965 \tabularnewline
30 & 0.714099261789864 & 0.571801476420272 & 0.285900738210136 \tabularnewline
31 & 0.669498174079559 & 0.661003651840881 & 0.330501825920441 \tabularnewline
32 & 0.779211252016139 & 0.441577495967721 & 0.220788747983861 \tabularnewline
33 & 0.801214695213326 & 0.397570609573348 & 0.198785304786674 \tabularnewline
34 & 0.760752520819061 & 0.478494958361878 & 0.239247479180939 \tabularnewline
35 & 0.714140360461337 & 0.571719279077327 & 0.285859639538663 \tabularnewline
36 & 0.673879401947057 & 0.652241196105886 & 0.326120598052943 \tabularnewline
37 & 0.654668430497092 & 0.690663139005816 & 0.345331569502908 \tabularnewline
38 & 0.63757973357837 & 0.724840532843259 & 0.36242026642163 \tabularnewline
39 & 0.720791471558198 & 0.558417056883604 & 0.279208528441802 \tabularnewline
40 & 0.673370078122087 & 0.653259843755826 & 0.326629921877913 \tabularnewline
41 & 0.656646137749825 & 0.68670772450035 & 0.343353862250175 \tabularnewline
42 & 0.607505046389122 & 0.784989907221756 & 0.392494953610878 \tabularnewline
43 & 0.585406763933558 & 0.829186472132884 & 0.414593236066442 \tabularnewline
44 & 0.540912523886537 & 0.918174952226927 & 0.459087476113463 \tabularnewline
45 & 0.645399839741384 & 0.709200320517233 & 0.354600160258616 \tabularnewline
46 & 0.658440592302923 & 0.683118815394155 & 0.341559407697077 \tabularnewline
47 & 0.64699263322438 & 0.70601473355124 & 0.35300736677562 \tabularnewline
48 & 0.612401808415588 & 0.775196383168825 & 0.387598191584412 \tabularnewline
49 & 0.562952228559044 & 0.874095542881912 & 0.437047771440956 \tabularnewline
50 & 0.592148769730108 & 0.815702460539784 & 0.407851230269892 \tabularnewline
51 & 0.625978766622598 & 0.748042466754803 & 0.374021233377402 \tabularnewline
52 & 0.581995437586335 & 0.83600912482733 & 0.418004562413665 \tabularnewline
53 & 0.535157854007635 & 0.92968429198473 & 0.464842145992365 \tabularnewline
54 & 0.488003699314149 & 0.976007398628299 & 0.511996300685851 \tabularnewline
55 & 0.440853195050793 & 0.881706390101585 & 0.559146804949208 \tabularnewline
56 & 0.439509229528882 & 0.879018459057764 & 0.560490770471118 \tabularnewline
57 & 0.474491477845651 & 0.948982955691302 & 0.525508522154349 \tabularnewline
58 & 0.597235776191538 & 0.805528447616924 & 0.402764223808462 \tabularnewline
59 & 0.558051298651783 & 0.883897402696434 & 0.441948701348217 \tabularnewline
60 & 0.50887019432097 & 0.982259611358059 & 0.49112980567903 \tabularnewline
61 & 0.470694668471962 & 0.941389336943923 & 0.529305331528038 \tabularnewline
62 & 0.457889138602982 & 0.915778277205965 & 0.542110861397018 \tabularnewline
63 & 0.41987887042525 & 0.8397577408505 & 0.58012112957475 \tabularnewline
64 & 0.41769556767702 & 0.83539113535404 & 0.58230443232298 \tabularnewline
65 & 0.372280711757964 & 0.744561423515927 & 0.627719288242036 \tabularnewline
66 & 0.332385526407162 & 0.664771052814324 & 0.667614473592838 \tabularnewline
67 & 0.29588326095842 & 0.59176652191684 & 0.70411673904158 \tabularnewline
68 & 0.318458415853948 & 0.636916831707895 & 0.681541584146052 \tabularnewline
69 & 0.339618108644873 & 0.679236217289747 & 0.660381891355127 \tabularnewline
70 & 0.298352899008795 & 0.596705798017589 & 0.701647100991205 \tabularnewline
71 & 0.337336802721013 & 0.674673605442025 & 0.662663197278987 \tabularnewline
72 & 0.367114037647494 & 0.734228075294987 & 0.632885962352506 \tabularnewline
73 & 0.359855127397545 & 0.71971025479509 & 0.640144872602455 \tabularnewline
74 & 0.317733619040495 & 0.63546723808099 & 0.682266380959505 \tabularnewline
75 & 0.284656353266507 & 0.569312706533013 & 0.715343646733493 \tabularnewline
76 & 0.350710844238992 & 0.701421688477983 & 0.649289155761008 \tabularnewline
77 & 0.315849396338214 & 0.631698792676428 & 0.684150603661786 \tabularnewline
78 & 0.275422347973336 & 0.550844695946672 & 0.724577652026664 \tabularnewline
79 & 0.331907685844692 & 0.663815371689384 & 0.668092314155308 \tabularnewline
80 & 0.410436808332514 & 0.820873616665027 & 0.589563191667486 \tabularnewline
81 & 0.394996370239436 & 0.789992740478871 & 0.605003629760565 \tabularnewline
82 & 0.350481011934087 & 0.700962023868174 & 0.649518988065913 \tabularnewline
83 & 0.308800099921288 & 0.617600199842576 & 0.691199900078712 \tabularnewline
84 & 0.301845698817884 & 0.603691397635768 & 0.698154301182116 \tabularnewline
85 & 0.283807873407142 & 0.567615746814284 & 0.716192126592858 \tabularnewline
86 & 0.247848681170804 & 0.495697362341608 & 0.752151318829196 \tabularnewline
87 & 0.225423222445502 & 0.450846444891004 & 0.774576777554498 \tabularnewline
88 & 0.1979871342652 & 0.3959742685304 & 0.8020128657348 \tabularnewline
89 & 0.227006771768102 & 0.454013543536205 & 0.772993228231898 \tabularnewline
90 & 0.196233225137302 & 0.392466450274604 & 0.803766774862698 \tabularnewline
91 & 0.210445569504398 & 0.420891139008797 & 0.789554430495602 \tabularnewline
92 & 0.199246054955172 & 0.398492109910343 & 0.800753945044828 \tabularnewline
93 & 0.18743294824756 & 0.37486589649512 & 0.81256705175244 \tabularnewline
94 & 0.159184269619253 & 0.318368539238506 & 0.840815730380747 \tabularnewline
95 & 0.169570697919455 & 0.33914139583891 & 0.830429302080545 \tabularnewline
96 & 0.166736716980866 & 0.333473433961731 & 0.833263283019134 \tabularnewline
97 & 0.152990654906069 & 0.305981309812138 & 0.847009345093931 \tabularnewline
98 & 0.127696490877153 & 0.255392981754306 & 0.872303509122847 \tabularnewline
99 & 0.112101356556167 & 0.224202713112334 & 0.887898643443833 \tabularnewline
100 & 0.113642718467799 & 0.227285436935599 & 0.886357281532201 \tabularnewline
101 & 0.10553843513277 & 0.21107687026554 & 0.89446156486723 \tabularnewline
102 & 0.0917780033362664 & 0.183556006672533 & 0.908221996663734 \tabularnewline
103 & 0.0745889278179197 & 0.149177855635839 & 0.92541107218208 \tabularnewline
104 & 0.0590116254796171 & 0.118023250959234 & 0.940988374520383 \tabularnewline
105 & 0.0711116618334754 & 0.142223323666951 & 0.928888338166524 \tabularnewline
106 & 0.165447431615498 & 0.330894863230995 & 0.834552568384502 \tabularnewline
107 & 0.154134258579441 & 0.308268517158881 & 0.845865741420559 \tabularnewline
108 & 0.179265957113018 & 0.358531914226036 & 0.820734042886982 \tabularnewline
109 & 0.167092042739506 & 0.334184085479012 & 0.832907957260494 \tabularnewline
110 & 0.401080277280543 & 0.802160554561087 & 0.598919722719457 \tabularnewline
111 & 0.453530386509383 & 0.907060773018765 & 0.546469613490617 \tabularnewline
112 & 0.427434113437945 & 0.854868226875889 & 0.572565886562055 \tabularnewline
113 & 0.536342808974382 & 0.927314382051237 & 0.463657191025618 \tabularnewline
114 & 0.487505934411417 & 0.975011868822834 & 0.512494065588583 \tabularnewline
115 & 0.477596477633911 & 0.955192955267821 & 0.522403522366089 \tabularnewline
116 & 0.446217695447326 & 0.892435390894651 & 0.553782304552674 \tabularnewline
117 & 0.41267833572101 & 0.825356671442019 & 0.58732166427899 \tabularnewline
118 & 0.383912904423135 & 0.76782580884627 & 0.616087095576865 \tabularnewline
119 & 0.338233606278824 & 0.676467212557648 & 0.661766393721176 \tabularnewline
120 & 0.304303238619979 & 0.608606477239959 & 0.695696761380021 \tabularnewline
121 & 0.260209317946669 & 0.520418635893337 & 0.739790682053331 \tabularnewline
122 & 0.219747208390859 & 0.439494416781718 & 0.780252791609141 \tabularnewline
123 & 0.188114880491135 & 0.37622976098227 & 0.811885119508865 \tabularnewline
124 & 0.154459537841772 & 0.308919075683543 & 0.845540462158228 \tabularnewline
125 & 0.122518322809218 & 0.245036645618435 & 0.877481677190783 \tabularnewline
126 & 0.131486047621808 & 0.262972095243616 & 0.868513952378192 \tabularnewline
127 & 0.146872679632724 & 0.293745359265449 & 0.853127320367276 \tabularnewline
128 & 0.334791228152746 & 0.669582456305493 & 0.665208771847253 \tabularnewline
129 & 0.29455412967966 & 0.58910825935932 & 0.70544587032034 \tabularnewline
130 & 0.265960587262865 & 0.531921174525729 & 0.734039412737135 \tabularnewline
131 & 0.226619827898933 & 0.453239655797866 & 0.773380172101067 \tabularnewline
132 & 0.254111499801712 & 0.508222999603424 & 0.745888500198288 \tabularnewline
133 & 0.368438715187836 & 0.736877430375673 & 0.631561284812164 \tabularnewline
134 & 0.453787163594564 & 0.907574327189127 & 0.546212836405436 \tabularnewline
135 & 0.42123042814713 & 0.842460856294261 & 0.57876957185287 \tabularnewline
136 & 0.352354678025855 & 0.704709356051711 & 0.647645321974145 \tabularnewline
137 & 0.29665248323466 & 0.59330496646932 & 0.70334751676534 \tabularnewline
138 & 0.251735324248185 & 0.503470648496369 & 0.748264675751815 \tabularnewline
139 & 0.261504431488861 & 0.523008862977722 & 0.738495568511139 \tabularnewline
140 & 0.21950697266561 & 0.43901394533122 & 0.78049302733439 \tabularnewline
141 & 0.408897794027327 & 0.817795588054654 & 0.591102205972673 \tabularnewline
142 & 0.338094218631402 & 0.676188437262804 & 0.661905781368598 \tabularnewline
143 & 0.363877181196632 & 0.727754362393264 & 0.636122818803368 \tabularnewline
144 & 0.776195643524592 & 0.447608712950817 & 0.223804356475408 \tabularnewline
145 & 0.686375437136563 & 0.627249125726875 & 0.313624562863437 \tabularnewline
146 & 0.945097942791499 & 0.109804114417003 & 0.0549020572085014 \tabularnewline
147 & 0.89633998233474 & 0.20732003533052 & 0.10366001766526 \tabularnewline
148 & 0.921856148086889 & 0.156287703826222 & 0.0781438519131109 \tabularnewline
149 & 0.987314137324609 & 0.025371725350782 & 0.012685862675391 \tabularnewline
150 & 0.994166822232153 & 0.0116663555356947 & 0.00583317776784737 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.373738493147379[/C][C]0.747476986294758[/C][C]0.626261506852621[/C][/ROW]
[ROW][C]13[/C][C]0.954148187452009[/C][C]0.0917036250959811[/C][C]0.0458518125479906[/C][/ROW]
[ROW][C]14[/C][C]0.920748743025762[/C][C]0.158502513948476[/C][C]0.0792512569742379[/C][/ROW]
[ROW][C]15[/C][C]0.893382942038578[/C][C]0.213234115922844[/C][C]0.106617057961422[/C][/ROW]
[ROW][C]16[/C][C]0.897454490910041[/C][C]0.205091018179917[/C][C]0.102545509089959[/C][/ROW]
[ROW][C]17[/C][C]0.871452844445703[/C][C]0.257094311108594[/C][C]0.128547155554297[/C][/ROW]
[ROW][C]18[/C][C]0.844264107757167[/C][C]0.311471784485666[/C][C]0.155735892242833[/C][/ROW]
[ROW][C]19[/C][C]0.80190301782216[/C][C]0.39619396435568[/C][C]0.19809698217784[/C][/ROW]
[ROW][C]20[/C][C]0.814206023192674[/C][C]0.371587953614652[/C][C]0.185793976807326[/C][/ROW]
[ROW][C]21[/C][C]0.815196779859179[/C][C]0.369606440281642[/C][C]0.184803220140821[/C][/ROW]
[ROW][C]22[/C][C]0.763246760544339[/C][C]0.473506478911322[/C][C]0.236753239455661[/C][/ROW]
[ROW][C]23[/C][C]0.737041858955432[/C][C]0.525916282089136[/C][C]0.262958141044568[/C][/ROW]
[ROW][C]24[/C][C]0.674953637735168[/C][C]0.650092724529665[/C][C]0.325046362264832[/C][/ROW]
[ROW][C]25[/C][C]0.686224341615686[/C][C]0.627551316768628[/C][C]0.313775658384314[/C][/ROW]
[ROW][C]26[/C][C]0.813293001459022[/C][C]0.373413997081956[/C][C]0.186706998540978[/C][/ROW]
[ROW][C]27[/C][C]0.810122358986721[/C][C]0.379755282026558[/C][C]0.189877641013279[/C][/ROW]
[ROW][C]28[/C][C]0.76842269033482[/C][C]0.463154619330361[/C][C]0.23157730966518[/C][/ROW]
[ROW][C]29[/C][C]0.76488267370035[/C][C]0.470234652599301[/C][C]0.23511732629965[/C][/ROW]
[ROW][C]30[/C][C]0.714099261789864[/C][C]0.571801476420272[/C][C]0.285900738210136[/C][/ROW]
[ROW][C]31[/C][C]0.669498174079559[/C][C]0.661003651840881[/C][C]0.330501825920441[/C][/ROW]
[ROW][C]32[/C][C]0.779211252016139[/C][C]0.441577495967721[/C][C]0.220788747983861[/C][/ROW]
[ROW][C]33[/C][C]0.801214695213326[/C][C]0.397570609573348[/C][C]0.198785304786674[/C][/ROW]
[ROW][C]34[/C][C]0.760752520819061[/C][C]0.478494958361878[/C][C]0.239247479180939[/C][/ROW]
[ROW][C]35[/C][C]0.714140360461337[/C][C]0.571719279077327[/C][C]0.285859639538663[/C][/ROW]
[ROW][C]36[/C][C]0.673879401947057[/C][C]0.652241196105886[/C][C]0.326120598052943[/C][/ROW]
[ROW][C]37[/C][C]0.654668430497092[/C][C]0.690663139005816[/C][C]0.345331569502908[/C][/ROW]
[ROW][C]38[/C][C]0.63757973357837[/C][C]0.724840532843259[/C][C]0.36242026642163[/C][/ROW]
[ROW][C]39[/C][C]0.720791471558198[/C][C]0.558417056883604[/C][C]0.279208528441802[/C][/ROW]
[ROW][C]40[/C][C]0.673370078122087[/C][C]0.653259843755826[/C][C]0.326629921877913[/C][/ROW]
[ROW][C]41[/C][C]0.656646137749825[/C][C]0.68670772450035[/C][C]0.343353862250175[/C][/ROW]
[ROW][C]42[/C][C]0.607505046389122[/C][C]0.784989907221756[/C][C]0.392494953610878[/C][/ROW]
[ROW][C]43[/C][C]0.585406763933558[/C][C]0.829186472132884[/C][C]0.414593236066442[/C][/ROW]
[ROW][C]44[/C][C]0.540912523886537[/C][C]0.918174952226927[/C][C]0.459087476113463[/C][/ROW]
[ROW][C]45[/C][C]0.645399839741384[/C][C]0.709200320517233[/C][C]0.354600160258616[/C][/ROW]
[ROW][C]46[/C][C]0.658440592302923[/C][C]0.683118815394155[/C][C]0.341559407697077[/C][/ROW]
[ROW][C]47[/C][C]0.64699263322438[/C][C]0.70601473355124[/C][C]0.35300736677562[/C][/ROW]
[ROW][C]48[/C][C]0.612401808415588[/C][C]0.775196383168825[/C][C]0.387598191584412[/C][/ROW]
[ROW][C]49[/C][C]0.562952228559044[/C][C]0.874095542881912[/C][C]0.437047771440956[/C][/ROW]
[ROW][C]50[/C][C]0.592148769730108[/C][C]0.815702460539784[/C][C]0.407851230269892[/C][/ROW]
[ROW][C]51[/C][C]0.625978766622598[/C][C]0.748042466754803[/C][C]0.374021233377402[/C][/ROW]
[ROW][C]52[/C][C]0.581995437586335[/C][C]0.83600912482733[/C][C]0.418004562413665[/C][/ROW]
[ROW][C]53[/C][C]0.535157854007635[/C][C]0.92968429198473[/C][C]0.464842145992365[/C][/ROW]
[ROW][C]54[/C][C]0.488003699314149[/C][C]0.976007398628299[/C][C]0.511996300685851[/C][/ROW]
[ROW][C]55[/C][C]0.440853195050793[/C][C]0.881706390101585[/C][C]0.559146804949208[/C][/ROW]
[ROW][C]56[/C][C]0.439509229528882[/C][C]0.879018459057764[/C][C]0.560490770471118[/C][/ROW]
[ROW][C]57[/C][C]0.474491477845651[/C][C]0.948982955691302[/C][C]0.525508522154349[/C][/ROW]
[ROW][C]58[/C][C]0.597235776191538[/C][C]0.805528447616924[/C][C]0.402764223808462[/C][/ROW]
[ROW][C]59[/C][C]0.558051298651783[/C][C]0.883897402696434[/C][C]0.441948701348217[/C][/ROW]
[ROW][C]60[/C][C]0.50887019432097[/C][C]0.982259611358059[/C][C]0.49112980567903[/C][/ROW]
[ROW][C]61[/C][C]0.470694668471962[/C][C]0.941389336943923[/C][C]0.529305331528038[/C][/ROW]
[ROW][C]62[/C][C]0.457889138602982[/C][C]0.915778277205965[/C][C]0.542110861397018[/C][/ROW]
[ROW][C]63[/C][C]0.41987887042525[/C][C]0.8397577408505[/C][C]0.58012112957475[/C][/ROW]
[ROW][C]64[/C][C]0.41769556767702[/C][C]0.83539113535404[/C][C]0.58230443232298[/C][/ROW]
[ROW][C]65[/C][C]0.372280711757964[/C][C]0.744561423515927[/C][C]0.627719288242036[/C][/ROW]
[ROW][C]66[/C][C]0.332385526407162[/C][C]0.664771052814324[/C][C]0.667614473592838[/C][/ROW]
[ROW][C]67[/C][C]0.29588326095842[/C][C]0.59176652191684[/C][C]0.70411673904158[/C][/ROW]
[ROW][C]68[/C][C]0.318458415853948[/C][C]0.636916831707895[/C][C]0.681541584146052[/C][/ROW]
[ROW][C]69[/C][C]0.339618108644873[/C][C]0.679236217289747[/C][C]0.660381891355127[/C][/ROW]
[ROW][C]70[/C][C]0.298352899008795[/C][C]0.596705798017589[/C][C]0.701647100991205[/C][/ROW]
[ROW][C]71[/C][C]0.337336802721013[/C][C]0.674673605442025[/C][C]0.662663197278987[/C][/ROW]
[ROW][C]72[/C][C]0.367114037647494[/C][C]0.734228075294987[/C][C]0.632885962352506[/C][/ROW]
[ROW][C]73[/C][C]0.359855127397545[/C][C]0.71971025479509[/C][C]0.640144872602455[/C][/ROW]
[ROW][C]74[/C][C]0.317733619040495[/C][C]0.63546723808099[/C][C]0.682266380959505[/C][/ROW]
[ROW][C]75[/C][C]0.284656353266507[/C][C]0.569312706533013[/C][C]0.715343646733493[/C][/ROW]
[ROW][C]76[/C][C]0.350710844238992[/C][C]0.701421688477983[/C][C]0.649289155761008[/C][/ROW]
[ROW][C]77[/C][C]0.315849396338214[/C][C]0.631698792676428[/C][C]0.684150603661786[/C][/ROW]
[ROW][C]78[/C][C]0.275422347973336[/C][C]0.550844695946672[/C][C]0.724577652026664[/C][/ROW]
[ROW][C]79[/C][C]0.331907685844692[/C][C]0.663815371689384[/C][C]0.668092314155308[/C][/ROW]
[ROW][C]80[/C][C]0.410436808332514[/C][C]0.820873616665027[/C][C]0.589563191667486[/C][/ROW]
[ROW][C]81[/C][C]0.394996370239436[/C][C]0.789992740478871[/C][C]0.605003629760565[/C][/ROW]
[ROW][C]82[/C][C]0.350481011934087[/C][C]0.700962023868174[/C][C]0.649518988065913[/C][/ROW]
[ROW][C]83[/C][C]0.308800099921288[/C][C]0.617600199842576[/C][C]0.691199900078712[/C][/ROW]
[ROW][C]84[/C][C]0.301845698817884[/C][C]0.603691397635768[/C][C]0.698154301182116[/C][/ROW]
[ROW][C]85[/C][C]0.283807873407142[/C][C]0.567615746814284[/C][C]0.716192126592858[/C][/ROW]
[ROW][C]86[/C][C]0.247848681170804[/C][C]0.495697362341608[/C][C]0.752151318829196[/C][/ROW]
[ROW][C]87[/C][C]0.225423222445502[/C][C]0.450846444891004[/C][C]0.774576777554498[/C][/ROW]
[ROW][C]88[/C][C]0.1979871342652[/C][C]0.3959742685304[/C][C]0.8020128657348[/C][/ROW]
[ROW][C]89[/C][C]0.227006771768102[/C][C]0.454013543536205[/C][C]0.772993228231898[/C][/ROW]
[ROW][C]90[/C][C]0.196233225137302[/C][C]0.392466450274604[/C][C]0.803766774862698[/C][/ROW]
[ROW][C]91[/C][C]0.210445569504398[/C][C]0.420891139008797[/C][C]0.789554430495602[/C][/ROW]
[ROW][C]92[/C][C]0.199246054955172[/C][C]0.398492109910343[/C][C]0.800753945044828[/C][/ROW]
[ROW][C]93[/C][C]0.18743294824756[/C][C]0.37486589649512[/C][C]0.81256705175244[/C][/ROW]
[ROW][C]94[/C][C]0.159184269619253[/C][C]0.318368539238506[/C][C]0.840815730380747[/C][/ROW]
[ROW][C]95[/C][C]0.169570697919455[/C][C]0.33914139583891[/C][C]0.830429302080545[/C][/ROW]
[ROW][C]96[/C][C]0.166736716980866[/C][C]0.333473433961731[/C][C]0.833263283019134[/C][/ROW]
[ROW][C]97[/C][C]0.152990654906069[/C][C]0.305981309812138[/C][C]0.847009345093931[/C][/ROW]
[ROW][C]98[/C][C]0.127696490877153[/C][C]0.255392981754306[/C][C]0.872303509122847[/C][/ROW]
[ROW][C]99[/C][C]0.112101356556167[/C][C]0.224202713112334[/C][C]0.887898643443833[/C][/ROW]
[ROW][C]100[/C][C]0.113642718467799[/C][C]0.227285436935599[/C][C]0.886357281532201[/C][/ROW]
[ROW][C]101[/C][C]0.10553843513277[/C][C]0.21107687026554[/C][C]0.89446156486723[/C][/ROW]
[ROW][C]102[/C][C]0.0917780033362664[/C][C]0.183556006672533[/C][C]0.908221996663734[/C][/ROW]
[ROW][C]103[/C][C]0.0745889278179197[/C][C]0.149177855635839[/C][C]0.92541107218208[/C][/ROW]
[ROW][C]104[/C][C]0.0590116254796171[/C][C]0.118023250959234[/C][C]0.940988374520383[/C][/ROW]
[ROW][C]105[/C][C]0.0711116618334754[/C][C]0.142223323666951[/C][C]0.928888338166524[/C][/ROW]
[ROW][C]106[/C][C]0.165447431615498[/C][C]0.330894863230995[/C][C]0.834552568384502[/C][/ROW]
[ROW][C]107[/C][C]0.154134258579441[/C][C]0.308268517158881[/C][C]0.845865741420559[/C][/ROW]
[ROW][C]108[/C][C]0.179265957113018[/C][C]0.358531914226036[/C][C]0.820734042886982[/C][/ROW]
[ROW][C]109[/C][C]0.167092042739506[/C][C]0.334184085479012[/C][C]0.832907957260494[/C][/ROW]
[ROW][C]110[/C][C]0.401080277280543[/C][C]0.802160554561087[/C][C]0.598919722719457[/C][/ROW]
[ROW][C]111[/C][C]0.453530386509383[/C][C]0.907060773018765[/C][C]0.546469613490617[/C][/ROW]
[ROW][C]112[/C][C]0.427434113437945[/C][C]0.854868226875889[/C][C]0.572565886562055[/C][/ROW]
[ROW][C]113[/C][C]0.536342808974382[/C][C]0.927314382051237[/C][C]0.463657191025618[/C][/ROW]
[ROW][C]114[/C][C]0.487505934411417[/C][C]0.975011868822834[/C][C]0.512494065588583[/C][/ROW]
[ROW][C]115[/C][C]0.477596477633911[/C][C]0.955192955267821[/C][C]0.522403522366089[/C][/ROW]
[ROW][C]116[/C][C]0.446217695447326[/C][C]0.892435390894651[/C][C]0.553782304552674[/C][/ROW]
[ROW][C]117[/C][C]0.41267833572101[/C][C]0.825356671442019[/C][C]0.58732166427899[/C][/ROW]
[ROW][C]118[/C][C]0.383912904423135[/C][C]0.76782580884627[/C][C]0.616087095576865[/C][/ROW]
[ROW][C]119[/C][C]0.338233606278824[/C][C]0.676467212557648[/C][C]0.661766393721176[/C][/ROW]
[ROW][C]120[/C][C]0.304303238619979[/C][C]0.608606477239959[/C][C]0.695696761380021[/C][/ROW]
[ROW][C]121[/C][C]0.260209317946669[/C][C]0.520418635893337[/C][C]0.739790682053331[/C][/ROW]
[ROW][C]122[/C][C]0.219747208390859[/C][C]0.439494416781718[/C][C]0.780252791609141[/C][/ROW]
[ROW][C]123[/C][C]0.188114880491135[/C][C]0.37622976098227[/C][C]0.811885119508865[/C][/ROW]
[ROW][C]124[/C][C]0.154459537841772[/C][C]0.308919075683543[/C][C]0.845540462158228[/C][/ROW]
[ROW][C]125[/C][C]0.122518322809218[/C][C]0.245036645618435[/C][C]0.877481677190783[/C][/ROW]
[ROW][C]126[/C][C]0.131486047621808[/C][C]0.262972095243616[/C][C]0.868513952378192[/C][/ROW]
[ROW][C]127[/C][C]0.146872679632724[/C][C]0.293745359265449[/C][C]0.853127320367276[/C][/ROW]
[ROW][C]128[/C][C]0.334791228152746[/C][C]0.669582456305493[/C][C]0.665208771847253[/C][/ROW]
[ROW][C]129[/C][C]0.29455412967966[/C][C]0.58910825935932[/C][C]0.70544587032034[/C][/ROW]
[ROW][C]130[/C][C]0.265960587262865[/C][C]0.531921174525729[/C][C]0.734039412737135[/C][/ROW]
[ROW][C]131[/C][C]0.226619827898933[/C][C]0.453239655797866[/C][C]0.773380172101067[/C][/ROW]
[ROW][C]132[/C][C]0.254111499801712[/C][C]0.508222999603424[/C][C]0.745888500198288[/C][/ROW]
[ROW][C]133[/C][C]0.368438715187836[/C][C]0.736877430375673[/C][C]0.631561284812164[/C][/ROW]
[ROW][C]134[/C][C]0.453787163594564[/C][C]0.907574327189127[/C][C]0.546212836405436[/C][/ROW]
[ROW][C]135[/C][C]0.42123042814713[/C][C]0.842460856294261[/C][C]0.57876957185287[/C][/ROW]
[ROW][C]136[/C][C]0.352354678025855[/C][C]0.704709356051711[/C][C]0.647645321974145[/C][/ROW]
[ROW][C]137[/C][C]0.29665248323466[/C][C]0.59330496646932[/C][C]0.70334751676534[/C][/ROW]
[ROW][C]138[/C][C]0.251735324248185[/C][C]0.503470648496369[/C][C]0.748264675751815[/C][/ROW]
[ROW][C]139[/C][C]0.261504431488861[/C][C]0.523008862977722[/C][C]0.738495568511139[/C][/ROW]
[ROW][C]140[/C][C]0.21950697266561[/C][C]0.43901394533122[/C][C]0.78049302733439[/C][/ROW]
[ROW][C]141[/C][C]0.408897794027327[/C][C]0.817795588054654[/C][C]0.591102205972673[/C][/ROW]
[ROW][C]142[/C][C]0.338094218631402[/C][C]0.676188437262804[/C][C]0.661905781368598[/C][/ROW]
[ROW][C]143[/C][C]0.363877181196632[/C][C]0.727754362393264[/C][C]0.636122818803368[/C][/ROW]
[ROW][C]144[/C][C]0.776195643524592[/C][C]0.447608712950817[/C][C]0.223804356475408[/C][/ROW]
[ROW][C]145[/C][C]0.686375437136563[/C][C]0.627249125726875[/C][C]0.313624562863437[/C][/ROW]
[ROW][C]146[/C][C]0.945097942791499[/C][C]0.109804114417003[/C][C]0.0549020572085014[/C][/ROW]
[ROW][C]147[/C][C]0.89633998233474[/C][C]0.20732003533052[/C][C]0.10366001766526[/C][/ROW]
[ROW][C]148[/C][C]0.921856148086889[/C][C]0.156287703826222[/C][C]0.0781438519131109[/C][/ROW]
[ROW][C]149[/C][C]0.987314137324609[/C][C]0.025371725350782[/C][C]0.012685862675391[/C][/ROW]
[ROW][C]150[/C][C]0.994166822232153[/C][C]0.0116663555356947[/C][C]0.00583317776784737[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.3737384931473790.7474769862947580.626261506852621
130.9541481874520090.09170362509598110.0458518125479906
140.9207487430257620.1585025139484760.0792512569742379
150.8933829420385780.2132341159228440.106617057961422
160.8974544909100410.2050910181799170.102545509089959
170.8714528444457030.2570943111085940.128547155554297
180.8442641077571670.3114717844856660.155735892242833
190.801903017822160.396193964355680.19809698217784
200.8142060231926740.3715879536146520.185793976807326
210.8151967798591790.3696064402816420.184803220140821
220.7632467605443390.4735064789113220.236753239455661
230.7370418589554320.5259162820891360.262958141044568
240.6749536377351680.6500927245296650.325046362264832
250.6862243416156860.6275513167686280.313775658384314
260.8132930014590220.3734139970819560.186706998540978
270.8101223589867210.3797552820265580.189877641013279
280.768422690334820.4631546193303610.23157730966518
290.764882673700350.4702346525993010.23511732629965
300.7140992617898640.5718014764202720.285900738210136
310.6694981740795590.6610036518408810.330501825920441
320.7792112520161390.4415774959677210.220788747983861
330.8012146952133260.3975706095733480.198785304786674
340.7607525208190610.4784949583618780.239247479180939
350.7141403604613370.5717192790773270.285859639538663
360.6738794019470570.6522411961058860.326120598052943
370.6546684304970920.6906631390058160.345331569502908
380.637579733578370.7248405328432590.36242026642163
390.7207914715581980.5584170568836040.279208528441802
400.6733700781220870.6532598437558260.326629921877913
410.6566461377498250.686707724500350.343353862250175
420.6075050463891220.7849899072217560.392494953610878
430.5854067639335580.8291864721328840.414593236066442
440.5409125238865370.9181749522269270.459087476113463
450.6453998397413840.7092003205172330.354600160258616
460.6584405923029230.6831188153941550.341559407697077
470.646992633224380.706014733551240.35300736677562
480.6124018084155880.7751963831688250.387598191584412
490.5629522285590440.8740955428819120.437047771440956
500.5921487697301080.8157024605397840.407851230269892
510.6259787666225980.7480424667548030.374021233377402
520.5819954375863350.836009124827330.418004562413665
530.5351578540076350.929684291984730.464842145992365
540.4880036993141490.9760073986282990.511996300685851
550.4408531950507930.8817063901015850.559146804949208
560.4395092295288820.8790184590577640.560490770471118
570.4744914778456510.9489829556913020.525508522154349
580.5972357761915380.8055284476169240.402764223808462
590.5580512986517830.8838974026964340.441948701348217
600.508870194320970.9822596113580590.49112980567903
610.4706946684719620.9413893369439230.529305331528038
620.4578891386029820.9157782772059650.542110861397018
630.419878870425250.83975774085050.58012112957475
640.417695567677020.835391135354040.58230443232298
650.3722807117579640.7445614235159270.627719288242036
660.3323855264071620.6647710528143240.667614473592838
670.295883260958420.591766521916840.70411673904158
680.3184584158539480.6369168317078950.681541584146052
690.3396181086448730.6792362172897470.660381891355127
700.2983528990087950.5967057980175890.701647100991205
710.3373368027210130.6746736054420250.662663197278987
720.3671140376474940.7342280752949870.632885962352506
730.3598551273975450.719710254795090.640144872602455
740.3177336190404950.635467238080990.682266380959505
750.2846563532665070.5693127065330130.715343646733493
760.3507108442389920.7014216884779830.649289155761008
770.3158493963382140.6316987926764280.684150603661786
780.2754223479733360.5508446959466720.724577652026664
790.3319076858446920.6638153716893840.668092314155308
800.4104368083325140.8208736166650270.589563191667486
810.3949963702394360.7899927404788710.605003629760565
820.3504810119340870.7009620238681740.649518988065913
830.3088000999212880.6176001998425760.691199900078712
840.3018456988178840.6036913976357680.698154301182116
850.2838078734071420.5676157468142840.716192126592858
860.2478486811708040.4956973623416080.752151318829196
870.2254232224455020.4508464448910040.774576777554498
880.19798713426520.39597426853040.8020128657348
890.2270067717681020.4540135435362050.772993228231898
900.1962332251373020.3924664502746040.803766774862698
910.2104455695043980.4208911390087970.789554430495602
920.1992460549551720.3984921099103430.800753945044828
930.187432948247560.374865896495120.81256705175244
940.1591842696192530.3183685392385060.840815730380747
950.1695706979194550.339141395838910.830429302080545
960.1667367169808660.3334734339617310.833263283019134
970.1529906549060690.3059813098121380.847009345093931
980.1276964908771530.2553929817543060.872303509122847
990.1121013565561670.2242027131123340.887898643443833
1000.1136427184677990.2272854369355990.886357281532201
1010.105538435132770.211076870265540.89446156486723
1020.09177800333626640.1835560066725330.908221996663734
1030.07458892781791970.1491778556358390.92541107218208
1040.05901162547961710.1180232509592340.940988374520383
1050.07111166183347540.1422233236669510.928888338166524
1060.1654474316154980.3308948632309950.834552568384502
1070.1541342585794410.3082685171588810.845865741420559
1080.1792659571130180.3585319142260360.820734042886982
1090.1670920427395060.3341840854790120.832907957260494
1100.4010802772805430.8021605545610870.598919722719457
1110.4535303865093830.9070607730187650.546469613490617
1120.4274341134379450.8548682268758890.572565886562055
1130.5363428089743820.9273143820512370.463657191025618
1140.4875059344114170.9750118688228340.512494065588583
1150.4775964776339110.9551929552678210.522403522366089
1160.4462176954473260.8924353908946510.553782304552674
1170.412678335721010.8253566714420190.58732166427899
1180.3839129044231350.767825808846270.616087095576865
1190.3382336062788240.6764672125576480.661766393721176
1200.3043032386199790.6086064772399590.695696761380021
1210.2602093179466690.5204186358933370.739790682053331
1220.2197472083908590.4394944167817180.780252791609141
1230.1881148804911350.376229760982270.811885119508865
1240.1544595378417720.3089190756835430.845540462158228
1250.1225183228092180.2450366456184350.877481677190783
1260.1314860476218080.2629720952436160.868513952378192
1270.1468726796327240.2937453592654490.853127320367276
1280.3347912281527460.6695824563054930.665208771847253
1290.294554129679660.589108259359320.70544587032034
1300.2659605872628650.5319211745257290.734039412737135
1310.2266198278989330.4532396557978660.773380172101067
1320.2541114998017120.5082229996034240.745888500198288
1330.3684387151878360.7368774303756730.631561284812164
1340.4537871635945640.9075743271891270.546212836405436
1350.421230428147130.8424608562942610.57876957185287
1360.3523546780258550.7047093560517110.647645321974145
1370.296652483234660.593304966469320.70334751676534
1380.2517353242481850.5034706484963690.748264675751815
1390.2615044314888610.5230088629777220.738495568511139
1400.219506972665610.439013945331220.78049302733439
1410.4088977940273270.8177955880546540.591102205972673
1420.3380942186314020.6761884372628040.661905781368598
1430.3638771811966320.7277543623932640.636122818803368
1440.7761956435245920.4476087129508170.223804356475408
1450.6863754371365630.6272491257268750.313624562863437
1460.9450979427914990.1098041144170030.0549020572085014
1470.896339982334740.207320035330520.10366001766526
1480.9218561480868890.1562877038262220.0781438519131109
1490.9873141373246090.0253717253507820.012685862675391
1500.9941668222321530.01166635553569470.00583317776784737







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0143884892086331OK
10% type I error level30.0215827338129496OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 2 & 0.0143884892086331 & OK \tabularnewline
10% type I error level & 3 & 0.0215827338129496 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185547&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]2[/C][C]0.0143884892086331[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0215827338129496[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185547&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185547&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0143884892086331OK
10% type I error level30.0215827338129496OK



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