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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 05 Nov 2012 16:22:39 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/05/t1352150583gnno5iyycj04nr5.htm/, Retrieved Mon, 06 Feb 2023 00:36:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186307, Retrieved Mon, 06 Feb 2023 00:36:20 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact82
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD    [Multiple Regression] [] [2012-11-05 21:22:39] [e8c322125b0cf2de4bdab96981906a22] [Current]
-   P       [Multiple Regression] [] [2012-12-20 10:32:02] [43bd65bee76289cab2ce37423d405966]
- R           [Multiple Regression] [] [2012-12-22 21:44:37] [74be16979710d4c4e7c6647856088456]
- RMPD      [Univariate Explorative Data Analysis] [] [2012-12-20 11:48:39] [43bd65bee76289cab2ce37423d405966]
- R           [Univariate Explorative Data Analysis] [] [2012-12-22 22:17:32] [74be16979710d4c4e7c6647856088456]
- RMPD      [Central Tendency] [] [2012-12-20 12:17:02] [43bd65bee76289cab2ce37423d405966]
- R P         [Central Tendency] [] [2012-12-22 22:19:33] [74be16979710d4c4e7c6647856088456]
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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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.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 & 9 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&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]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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 time9 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 12.6547460959361 + 0.0137609572325931Connected[t] + 0.0739108725388224Separate[t] + 0.0661953497728065Learning[t] -0.0462160812507193Software[t] -0.351184559016834Depression[t] + 0.0585099208420599Belonging[t] -0.0393031134505445Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  12.6547460959361 +  0.0137609572325931Connected[t] +  0.0739108725388224Separate[t] +  0.0661953497728065Learning[t] -0.0462160812507193Software[t] -0.351184559016834Depression[t] +  0.0585099208420599Belonging[t] -0.0393031134505445Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  12.6547460959361 +  0.0137609572325931Connected[t] +  0.0739108725388224Separate[t] +  0.0661953497728065Learning[t] -0.0462160812507193Software[t] -0.351184559016834Depression[t] +  0.0585099208420599Belonging[t] -0.0393031134505445Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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
Happiness[t] = + 12.6547460959361 + 0.0137609572325931Connected[t] + 0.0739108725388224Separate[t] + 0.0661953497728065Learning[t] -0.0462160812507193Software[t] -0.351184559016834Depression[t] + 0.0585099208420599Belonging[t] -0.0393031134505445Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.65474609593612.5797224.90552e-061e-06
Connected0.01376095723259310.0502980.27360.7847680.392384
Separate0.07391087253882240.0468381.5780.1166120.058306
Learning0.06619534977280650.0846780.78170.4355760.217788
Software-0.04621608125071930.085884-0.53810.5912710.295635
Depression-0.3511845590168340.052485-6.691200
Belonging0.05850992084205990.0467481.25160.2126110.106305
Belonging_Final-0.03930311345054450.067414-0.5830.5607360.280368

\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) & 12.6547460959361 & 2.579722 & 4.9055 & 2e-06 & 1e-06 \tabularnewline
Connected & 0.0137609572325931 & 0.050298 & 0.2736 & 0.784768 & 0.392384 \tabularnewline
Separate & 0.0739108725388224 & 0.046838 & 1.578 & 0.116612 & 0.058306 \tabularnewline
Learning & 0.0661953497728065 & 0.084678 & 0.7817 & 0.435576 & 0.217788 \tabularnewline
Software & -0.0462160812507193 & 0.085884 & -0.5381 & 0.591271 & 0.295635 \tabularnewline
Depression & -0.351184559016834 & 0.052485 & -6.6912 & 0 & 0 \tabularnewline
Belonging & 0.0585099208420599 & 0.046748 & 1.2516 & 0.212611 & 0.106305 \tabularnewline
Belonging_Final & -0.0393031134505445 & 0.067414 & -0.583 & 0.560736 & 0.280368 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&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]12.6547460959361[/C][C]2.579722[/C][C]4.9055[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]Connected[/C][C]0.0137609572325931[/C][C]0.050298[/C][C]0.2736[/C][C]0.784768[/C][C]0.392384[/C][/ROW]
[ROW][C]Separate[/C][C]0.0739108725388224[/C][C]0.046838[/C][C]1.578[/C][C]0.116612[/C][C]0.058306[/C][/ROW]
[ROW][C]Learning[/C][C]0.0661953497728065[/C][C]0.084678[/C][C]0.7817[/C][C]0.435576[/C][C]0.217788[/C][/ROW]
[ROW][C]Software[/C][C]-0.0462160812507193[/C][C]0.085884[/C][C]-0.5381[/C][C]0.591271[/C][C]0.295635[/C][/ROW]
[ROW][C]Depression[/C][C]-0.351184559016834[/C][C]0.052485[/C][C]-6.6912[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0585099208420599[/C][C]0.046748[/C][C]1.2516[/C][C]0.212611[/C][C]0.106305[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0393031134505445[/C][C]0.067414[/C][C]-0.583[/C][C]0.560736[/C][C]0.280368[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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)12.65474609593612.5797224.90552e-061e-06
Connected0.01376095723259310.0502980.27360.7847680.392384
Separate0.07391087253882240.0468381.5780.1166120.058306
Learning0.06619534977280650.0846780.78170.4355760.217788
Software-0.04621608125071930.085884-0.53810.5912710.295635
Depression-0.3511845590168340.052485-6.691200
Belonging0.05850992084205990.0467481.25160.2126110.106305
Belonging_Final-0.03930311345054450.067414-0.5830.5607360.280368







Multiple Linear Regression - Regression Statistics
Multiple R0.579316084592813
R-squared0.335607125867947
Adjusted R-squared0.305407449771035
F-TEST (value)11.1129379265849
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.443512059358e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.94822277716126
Sum Squared Residuals584.518086375289

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.579316084592813 \tabularnewline
R-squared & 0.335607125867947 \tabularnewline
Adjusted R-squared & 0.305407449771035 \tabularnewline
F-TEST (value) & 11.1129379265849 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 2.443512059358e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.94822277716126 \tabularnewline
Sum Squared Residuals & 584.518086375289 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.579316084592813[/C][/ROW]
[ROW][C]R-squared[/C][C]0.335607125867947[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.305407449771035[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]11.1129379265849[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]2.443512059358e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.94822277716126[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]584.518086375289[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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 R0.579316084592813
R-squared0.335607125867947
Adjusted R-squared0.305407449771035
F-TEST (value)11.1129379265849
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value2.443512059358e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.94822277716126
Sum Squared Residuals584.518086375289







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11413.96261653699540.0373834630046497
21815.27168430911082.7283156908892
31113.5132659631127-2.51326596311267
41214.3305506597591-2.33055065975914
51611.44718179225874.55281820774132
61814.18048642254513.81951357745487
71410.10652945597413.89347054402585
81415.0212261214269-1.02122612142689
91515.1584114638485-0.158411463848536
101514.72225681370720.277743186292752
111715.25044990061361.74955009938639
121915.22069935345433.77930064654567
131013.2707411772324-3.27074117723238
141613.49673444245712.50326555754289
151815.77109220613712.22890779386291
161413.03457229112790.965427708872069
171413.7490780432810.250921956718962
181715.91906440628411.08093559371587
191415.3045376788863-1.30453767888631
201613.31953140653672.68046859346331
211815.8768394347532.12316056524695
221113.2098592394067-2.20985923940674
231414.3052574473078-0.305257447307763
241213.5029307884127-1.50293078841269
251715.39100870793451.6089912920655
26915.7814914576308-6.78149145763084
271615.2276758354640.772324164536037
281413.22954423865580.770455761344155
291513.93099899391961.06900100608037
301113.6516799573128-2.65167995731277
311615.18345754995360.816542450046365
321312.41082000607720.589179993922839
331714.87203755780222.12796244219781
341515.2912886223555-0.291288622355473
351413.82569084999040.17430915000963
361615.28940131664250.710598683357498
37910.9379265496515-1.93792654965154
381514.21129706511210.788702934887867
391715.06898086334571.93101913665428
401315.0470113597267-2.04701135972668
411515.5264472245285-0.526447224528547
421613.6761426025312.32385739746903
431615.22653267818090.773467321819061
441212.9475480177547-0.947548017754705
451214.2351646101679-2.2351646101679
461113.7654887369126-2.76548873691257
471515.3385622855577-0.338562285557675
481514.82365139901010.176348600989858
491713.92073740752883.0792625924712
501314.6715261225225-1.67152612252248
511615.28734206134520.71265793865477
521413.20845332001650.791546679983543
531111.6844944965019-0.684494496501931
541213.2926363856721-1.29263638567211
551214.2331051699681-2.23310516996811
561514.56378078771830.436219212281659
571614.20462048550261.7953795144974
581515.1800890437012-0.180089043701222
591215.3785910307025-3.37859103070251
601213.3842062445729-1.38420624457285
61810.4275458663947-2.42754586639468
621314.717573849813-1.71757384981302
631114.64400873092-3.64400873092004
641412.99410985265131.00589014734874
651513.21552202628851.78447797371149
661015.2875047319946-5.28750473199463
671112.5222987609356-1.52229876093564
681214.1918896354108-2.19188963541079
691514.01481698653550.985183013464535
701513.84548445131321.1545155486868
711413.75610716726980.243892832730181
721612.90307152603393.09692847396608
731514.83698870462070.163011295379289
741515.7057138131789-0.705713813178867
751314.7093923507694-1.70939235076943
761212.2005603060215-0.200560306021458
771714.1971816509362.80281834906399
781312.30231443614680.6976855638532
791513.72985482463431.27014517536572
801314.3658996438983-1.36589964389833
811515.2254920959781-0.225492095978114
821614.98528072555831.0147192744417
831515.6005177477299-0.600517747729854
841614.40827321380171.59172678619835
851514.36178234713560.638217652864376
861414.0896778226171-0.0896778226171078
871514.62518135601270.374818643987272
881414.2985990372652-0.298599037265213
891312.51825899463070.481741005369304
90710.6658540321768-3.66585403217676
911714.01840838308782.98159161691217
921312.54213280127950.457867198720504
931514.80317941341960.196820586580376
941413.24643410821990.75356589178011
951314.8987883489047-1.89878834890474
961615.41969680926660.580303190733374
971212.9372963079658-0.937296307965809
981414.9635979706593-0.963597970659285
991715.17112779461391.82887220538613
1001515.347427398192-0.347427398191999
1011715.41682262958081.58317737041916
1021213.1851619269344-1.1851619269344
1031615.2308752808860.769124719113958
1041113.9970483727683-2.99704837276825
1051513.69821488631061.30178511368941
106911.5710192229879-2.57101922298795
1071615.07174848339830.928251516601661
1081513.45121954998631.54878045001369
1091012.882534394126-2.88253439412603
110109.905489070901240.0945109290987561
1111514.04045422432670.959545775673266
1121113.2651237880185-2.26512378801847
1131315.8665581989868-2.8665581989868
1141412.40959253323861.59040746676138
1151814.39434750824753.6056524917525
1161615.55669926380550.443300736194469
1171413.33716758209210.662832417907851
1181414.4370179901121-0.437017990112142
1191415.2736936187868-1.27369361878684
1201414.1794397542815-0.179439754281502
1211212.7904872181875-0.79048721818748
1221413.25747280783610.742527192163875
1231515.5881177512554-0.588117751255369
1241516.1746463085585-1.17464630855847
1251514.43176989065870.56823010934134
1261314.7982862066938-1.79828620669376
1271716.38208594720530.617914052794726
1281715.84824785588431.15175214411575
1291915.13984303748783.86015696251223
1301513.77938373795931.22061626204067
1311314.7715637706962-1.77156377069619
132910.8246164808787-1.82461648087872
1331515.8147587114807-0.814758711480679
1341512.49283735235622.50716264764379
1351514.65223280721340.347767192786616
1361613.47501397252282.52498602747718
137118.895975034308952.10402496569105
1381413.31286295542850.687137044571486
1391112.0854171971992-1.08541719719924
1401514.38344505934970.616554940650326
1411313.9933765543591-0.9933765543591
1421514.64815073149320.351849268506834
1431614.15132073308911.84867926691088
1441414.8211085504859-0.821108550485934
1451513.8151405190581.184859480942
1461614.97132784764391.02867215235611
1471614.06669046919751.93330953080255
1481113.568556211929-2.56855621192902
1491214.6081370926832-2.60813709268317
150911.7368967175347-2.73689671753472
1511615.0746909792560.925309020743982
1521313.1644296850352-0.164429685035201
1531615.95652836059040.0434716394095803
1541215.0698953375539-3.06989533755392
155911.8809995506193-2.88099955061928
1561311.93031255843741.06968744156258
1571312.54213280127950.457867198720504
1581413.84360890435080.156391095649228
1591915.13984303748783.86015696251223
1601315.7882287992974-2.78822879929738
1611212.7349484294086-0.734948429408634
1621312.88450932539040.115490674609604

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 13.9626165369954 & 0.0373834630046497 \tabularnewline
2 & 18 & 15.2716843091108 & 2.7283156908892 \tabularnewline
3 & 11 & 13.5132659631127 & -2.51326596311267 \tabularnewline
4 & 12 & 14.3305506597591 & -2.33055065975914 \tabularnewline
5 & 16 & 11.4471817922587 & 4.55281820774132 \tabularnewline
6 & 18 & 14.1804864225451 & 3.81951357745487 \tabularnewline
7 & 14 & 10.1065294559741 & 3.89347054402585 \tabularnewline
8 & 14 & 15.0212261214269 & -1.02122612142689 \tabularnewline
9 & 15 & 15.1584114638485 & -0.158411463848536 \tabularnewline
10 & 15 & 14.7222568137072 & 0.277743186292752 \tabularnewline
11 & 17 & 15.2504499006136 & 1.74955009938639 \tabularnewline
12 & 19 & 15.2206993534543 & 3.77930064654567 \tabularnewline
13 & 10 & 13.2707411772324 & -3.27074117723238 \tabularnewline
14 & 16 & 13.4967344424571 & 2.50326555754289 \tabularnewline
15 & 18 & 15.7710922061371 & 2.22890779386291 \tabularnewline
16 & 14 & 13.0345722911279 & 0.965427708872069 \tabularnewline
17 & 14 & 13.749078043281 & 0.250921956718962 \tabularnewline
18 & 17 & 15.9190644062841 & 1.08093559371587 \tabularnewline
19 & 14 & 15.3045376788863 & -1.30453767888631 \tabularnewline
20 & 16 & 13.3195314065367 & 2.68046859346331 \tabularnewline
21 & 18 & 15.876839434753 & 2.12316056524695 \tabularnewline
22 & 11 & 13.2098592394067 & -2.20985923940674 \tabularnewline
23 & 14 & 14.3052574473078 & -0.305257447307763 \tabularnewline
24 & 12 & 13.5029307884127 & -1.50293078841269 \tabularnewline
25 & 17 & 15.3910087079345 & 1.6089912920655 \tabularnewline
26 & 9 & 15.7814914576308 & -6.78149145763084 \tabularnewline
27 & 16 & 15.227675835464 & 0.772324164536037 \tabularnewline
28 & 14 & 13.2295442386558 & 0.770455761344155 \tabularnewline
29 & 15 & 13.9309989939196 & 1.06900100608037 \tabularnewline
30 & 11 & 13.6516799573128 & -2.65167995731277 \tabularnewline
31 & 16 & 15.1834575499536 & 0.816542450046365 \tabularnewline
32 & 13 & 12.4108200060772 & 0.589179993922839 \tabularnewline
33 & 17 & 14.8720375578022 & 2.12796244219781 \tabularnewline
34 & 15 & 15.2912886223555 & -0.291288622355473 \tabularnewline
35 & 14 & 13.8256908499904 & 0.17430915000963 \tabularnewline
36 & 16 & 15.2894013166425 & 0.710598683357498 \tabularnewline
37 & 9 & 10.9379265496515 & -1.93792654965154 \tabularnewline
38 & 15 & 14.2112970651121 & 0.788702934887867 \tabularnewline
39 & 17 & 15.0689808633457 & 1.93101913665428 \tabularnewline
40 & 13 & 15.0470113597267 & -2.04701135972668 \tabularnewline
41 & 15 & 15.5264472245285 & -0.526447224528547 \tabularnewline
42 & 16 & 13.676142602531 & 2.32385739746903 \tabularnewline
43 & 16 & 15.2265326781809 & 0.773467321819061 \tabularnewline
44 & 12 & 12.9475480177547 & -0.947548017754705 \tabularnewline
45 & 12 & 14.2351646101679 & -2.2351646101679 \tabularnewline
46 & 11 & 13.7654887369126 & -2.76548873691257 \tabularnewline
47 & 15 & 15.3385622855577 & -0.338562285557675 \tabularnewline
48 & 15 & 14.8236513990101 & 0.176348600989858 \tabularnewline
49 & 17 & 13.9207374075288 & 3.0792625924712 \tabularnewline
50 & 13 & 14.6715261225225 & -1.67152612252248 \tabularnewline
51 & 16 & 15.2873420613452 & 0.71265793865477 \tabularnewline
52 & 14 & 13.2084533200165 & 0.791546679983543 \tabularnewline
53 & 11 & 11.6844944965019 & -0.684494496501931 \tabularnewline
54 & 12 & 13.2926363856721 & -1.29263638567211 \tabularnewline
55 & 12 & 14.2331051699681 & -2.23310516996811 \tabularnewline
56 & 15 & 14.5637807877183 & 0.436219212281659 \tabularnewline
57 & 16 & 14.2046204855026 & 1.7953795144974 \tabularnewline
58 & 15 & 15.1800890437012 & -0.180089043701222 \tabularnewline
59 & 12 & 15.3785910307025 & -3.37859103070251 \tabularnewline
60 & 12 & 13.3842062445729 & -1.38420624457285 \tabularnewline
61 & 8 & 10.4275458663947 & -2.42754586639468 \tabularnewline
62 & 13 & 14.717573849813 & -1.71757384981302 \tabularnewline
63 & 11 & 14.64400873092 & -3.64400873092004 \tabularnewline
64 & 14 & 12.9941098526513 & 1.00589014734874 \tabularnewline
65 & 15 & 13.2155220262885 & 1.78447797371149 \tabularnewline
66 & 10 & 15.2875047319946 & -5.28750473199463 \tabularnewline
67 & 11 & 12.5222987609356 & -1.52229876093564 \tabularnewline
68 & 12 & 14.1918896354108 & -2.19188963541079 \tabularnewline
69 & 15 & 14.0148169865355 & 0.985183013464535 \tabularnewline
70 & 15 & 13.8454844513132 & 1.1545155486868 \tabularnewline
71 & 14 & 13.7561071672698 & 0.243892832730181 \tabularnewline
72 & 16 & 12.9030715260339 & 3.09692847396608 \tabularnewline
73 & 15 & 14.8369887046207 & 0.163011295379289 \tabularnewline
74 & 15 & 15.7057138131789 & -0.705713813178867 \tabularnewline
75 & 13 & 14.7093923507694 & -1.70939235076943 \tabularnewline
76 & 12 & 12.2005603060215 & -0.200560306021458 \tabularnewline
77 & 17 & 14.197181650936 & 2.80281834906399 \tabularnewline
78 & 13 & 12.3023144361468 & 0.6976855638532 \tabularnewline
79 & 15 & 13.7298548246343 & 1.27014517536572 \tabularnewline
80 & 13 & 14.3658996438983 & -1.36589964389833 \tabularnewline
81 & 15 & 15.2254920959781 & -0.225492095978114 \tabularnewline
82 & 16 & 14.9852807255583 & 1.0147192744417 \tabularnewline
83 & 15 & 15.6005177477299 & -0.600517747729854 \tabularnewline
84 & 16 & 14.4082732138017 & 1.59172678619835 \tabularnewline
85 & 15 & 14.3617823471356 & 0.638217652864376 \tabularnewline
86 & 14 & 14.0896778226171 & -0.0896778226171078 \tabularnewline
87 & 15 & 14.6251813560127 & 0.374818643987272 \tabularnewline
88 & 14 & 14.2985990372652 & -0.298599037265213 \tabularnewline
89 & 13 & 12.5182589946307 & 0.481741005369304 \tabularnewline
90 & 7 & 10.6658540321768 & -3.66585403217676 \tabularnewline
91 & 17 & 14.0184083830878 & 2.98159161691217 \tabularnewline
92 & 13 & 12.5421328012795 & 0.457867198720504 \tabularnewline
93 & 15 & 14.8031794134196 & 0.196820586580376 \tabularnewline
94 & 14 & 13.2464341082199 & 0.75356589178011 \tabularnewline
95 & 13 & 14.8987883489047 & -1.89878834890474 \tabularnewline
96 & 16 & 15.4196968092666 & 0.580303190733374 \tabularnewline
97 & 12 & 12.9372963079658 & -0.937296307965809 \tabularnewline
98 & 14 & 14.9635979706593 & -0.963597970659285 \tabularnewline
99 & 17 & 15.1711277946139 & 1.82887220538613 \tabularnewline
100 & 15 & 15.347427398192 & -0.347427398191999 \tabularnewline
101 & 17 & 15.4168226295808 & 1.58317737041916 \tabularnewline
102 & 12 & 13.1851619269344 & -1.1851619269344 \tabularnewline
103 & 16 & 15.230875280886 & 0.769124719113958 \tabularnewline
104 & 11 & 13.9970483727683 & -2.99704837276825 \tabularnewline
105 & 15 & 13.6982148863106 & 1.30178511368941 \tabularnewline
106 & 9 & 11.5710192229879 & -2.57101922298795 \tabularnewline
107 & 16 & 15.0717484833983 & 0.928251516601661 \tabularnewline
108 & 15 & 13.4512195499863 & 1.54878045001369 \tabularnewline
109 & 10 & 12.882534394126 & -2.88253439412603 \tabularnewline
110 & 10 & 9.90548907090124 & 0.0945109290987561 \tabularnewline
111 & 15 & 14.0404542243267 & 0.959545775673266 \tabularnewline
112 & 11 & 13.2651237880185 & -2.26512378801847 \tabularnewline
113 & 13 & 15.8665581989868 & -2.8665581989868 \tabularnewline
114 & 14 & 12.4095925332386 & 1.59040746676138 \tabularnewline
115 & 18 & 14.3943475082475 & 3.6056524917525 \tabularnewline
116 & 16 & 15.5566992638055 & 0.443300736194469 \tabularnewline
117 & 14 & 13.3371675820921 & 0.662832417907851 \tabularnewline
118 & 14 & 14.4370179901121 & -0.437017990112142 \tabularnewline
119 & 14 & 15.2736936187868 & -1.27369361878684 \tabularnewline
120 & 14 & 14.1794397542815 & -0.179439754281502 \tabularnewline
121 & 12 & 12.7904872181875 & -0.79048721818748 \tabularnewline
122 & 14 & 13.2574728078361 & 0.742527192163875 \tabularnewline
123 & 15 & 15.5881177512554 & -0.588117751255369 \tabularnewline
124 & 15 & 16.1746463085585 & -1.17464630855847 \tabularnewline
125 & 15 & 14.4317698906587 & 0.56823010934134 \tabularnewline
126 & 13 & 14.7982862066938 & -1.79828620669376 \tabularnewline
127 & 17 & 16.3820859472053 & 0.617914052794726 \tabularnewline
128 & 17 & 15.8482478558843 & 1.15175214411575 \tabularnewline
129 & 19 & 15.1398430374878 & 3.86015696251223 \tabularnewline
130 & 15 & 13.7793837379593 & 1.22061626204067 \tabularnewline
131 & 13 & 14.7715637706962 & -1.77156377069619 \tabularnewline
132 & 9 & 10.8246164808787 & -1.82461648087872 \tabularnewline
133 & 15 & 15.8147587114807 & -0.814758711480679 \tabularnewline
134 & 15 & 12.4928373523562 & 2.50716264764379 \tabularnewline
135 & 15 & 14.6522328072134 & 0.347767192786616 \tabularnewline
136 & 16 & 13.4750139725228 & 2.52498602747718 \tabularnewline
137 & 11 & 8.89597503430895 & 2.10402496569105 \tabularnewline
138 & 14 & 13.3128629554285 & 0.687137044571486 \tabularnewline
139 & 11 & 12.0854171971992 & -1.08541719719924 \tabularnewline
140 & 15 & 14.3834450593497 & 0.616554940650326 \tabularnewline
141 & 13 & 13.9933765543591 & -0.9933765543591 \tabularnewline
142 & 15 & 14.6481507314932 & 0.351849268506834 \tabularnewline
143 & 16 & 14.1513207330891 & 1.84867926691088 \tabularnewline
144 & 14 & 14.8211085504859 & -0.821108550485934 \tabularnewline
145 & 15 & 13.815140519058 & 1.184859480942 \tabularnewline
146 & 16 & 14.9713278476439 & 1.02867215235611 \tabularnewline
147 & 16 & 14.0666904691975 & 1.93330953080255 \tabularnewline
148 & 11 & 13.568556211929 & -2.56855621192902 \tabularnewline
149 & 12 & 14.6081370926832 & -2.60813709268317 \tabularnewline
150 & 9 & 11.7368967175347 & -2.73689671753472 \tabularnewline
151 & 16 & 15.074690979256 & 0.925309020743982 \tabularnewline
152 & 13 & 13.1644296850352 & -0.164429685035201 \tabularnewline
153 & 16 & 15.9565283605904 & 0.0434716394095803 \tabularnewline
154 & 12 & 15.0698953375539 & -3.06989533755392 \tabularnewline
155 & 9 & 11.8809995506193 & -2.88099955061928 \tabularnewline
156 & 13 & 11.9303125584374 & 1.06968744156258 \tabularnewline
157 & 13 & 12.5421328012795 & 0.457867198720504 \tabularnewline
158 & 14 & 13.8436089043508 & 0.156391095649228 \tabularnewline
159 & 19 & 15.1398430374878 & 3.86015696251223 \tabularnewline
160 & 13 & 15.7882287992974 & -2.78822879929738 \tabularnewline
161 & 12 & 12.7349484294086 & -0.734948429408634 \tabularnewline
162 & 13 & 12.8845093253904 & 0.115490674609604 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]13.9626165369954[/C][C]0.0373834630046497[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2716843091108[/C][C]2.7283156908892[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.5132659631127[/C][C]-2.51326596311267[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.3305506597591[/C][C]-2.33055065975914[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.4471817922587[/C][C]4.55281820774132[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.1804864225451[/C][C]3.81951357745487[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.1065294559741[/C][C]3.89347054402585[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.0212261214269[/C][C]-1.02122612142689[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.1584114638485[/C][C]-0.158411463848536[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.7222568137072[/C][C]0.277743186292752[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.2504499006136[/C][C]1.74955009938639[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.2206993534543[/C][C]3.77930064654567[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.2707411772324[/C][C]-3.27074117723238[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.4967344424571[/C][C]2.50326555754289[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.7710922061371[/C][C]2.22890779386291[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.0345722911279[/C][C]0.965427708872069[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.749078043281[/C][C]0.250921956718962[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.9190644062841[/C][C]1.08093559371587[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.3045376788863[/C][C]-1.30453767888631[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.3195314065367[/C][C]2.68046859346331[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.876839434753[/C][C]2.12316056524695[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.2098592394067[/C][C]-2.20985923940674[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.3052574473078[/C][C]-0.305257447307763[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.5029307884127[/C][C]-1.50293078841269[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.3910087079345[/C][C]1.6089912920655[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.7814914576308[/C][C]-6.78149145763084[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.227675835464[/C][C]0.772324164536037[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2295442386558[/C][C]0.770455761344155[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.9309989939196[/C][C]1.06900100608037[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.6516799573128[/C][C]-2.65167995731277[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.1834575499536[/C][C]0.816542450046365[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.4108200060772[/C][C]0.589179993922839[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]14.8720375578022[/C][C]2.12796244219781[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.2912886223555[/C][C]-0.291288622355473[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.8256908499904[/C][C]0.17430915000963[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.2894013166425[/C][C]0.710598683357498[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.9379265496515[/C][C]-1.93792654965154[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.2112970651121[/C][C]0.788702934887867[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.0689808633457[/C][C]1.93101913665428[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.0470113597267[/C][C]-2.04701135972668[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.5264472245285[/C][C]-0.526447224528547[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.676142602531[/C][C]2.32385739746903[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.2265326781809[/C][C]0.773467321819061[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.9475480177547[/C][C]-0.947548017754705[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.2351646101679[/C][C]-2.2351646101679[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.7654887369126[/C][C]-2.76548873691257[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.3385622855577[/C][C]-0.338562285557675[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.8236513990101[/C][C]0.176348600989858[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.9207374075288[/C][C]3.0792625924712[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.6715261225225[/C][C]-1.67152612252248[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.2873420613452[/C][C]0.71265793865477[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.2084533200165[/C][C]0.791546679983543[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.6844944965019[/C][C]-0.684494496501931[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.2926363856721[/C][C]-1.29263638567211[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.2331051699681[/C][C]-2.23310516996811[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.5637807877183[/C][C]0.436219212281659[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.2046204855026[/C][C]1.7953795144974[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.1800890437012[/C][C]-0.180089043701222[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.3785910307025[/C][C]-3.37859103070251[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.3842062445729[/C][C]-1.38420624457285[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.4275458663947[/C][C]-2.42754586639468[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.717573849813[/C][C]-1.71757384981302[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.64400873092[/C][C]-3.64400873092004[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.9941098526513[/C][C]1.00589014734874[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.2155220262885[/C][C]1.78447797371149[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.2875047319946[/C][C]-5.28750473199463[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.5222987609356[/C][C]-1.52229876093564[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.1918896354108[/C][C]-2.19188963541079[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.0148169865355[/C][C]0.985183013464535[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.8454844513132[/C][C]1.1545155486868[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.7561071672698[/C][C]0.243892832730181[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.9030715260339[/C][C]3.09692847396608[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.8369887046207[/C][C]0.163011295379289[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.7057138131789[/C][C]-0.705713813178867[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.7093923507694[/C][C]-1.70939235076943[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.2005603060215[/C][C]-0.200560306021458[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.197181650936[/C][C]2.80281834906399[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.3023144361468[/C][C]0.6976855638532[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.7298548246343[/C][C]1.27014517536572[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.3658996438983[/C][C]-1.36589964389833[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]15.2254920959781[/C][C]-0.225492095978114[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.9852807255583[/C][C]1.0147192744417[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6005177477299[/C][C]-0.600517747729854[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.4082732138017[/C][C]1.59172678619835[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.3617823471356[/C][C]0.638217652864376[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.0896778226171[/C][C]-0.0896778226171078[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.6251813560127[/C][C]0.374818643987272[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.2985990372652[/C][C]-0.298599037265213[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.5182589946307[/C][C]0.481741005369304[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.6658540321768[/C][C]-3.66585403217676[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.0184083830878[/C][C]2.98159161691217[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.5421328012795[/C][C]0.457867198720504[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.8031794134196[/C][C]0.196820586580376[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.2464341082199[/C][C]0.75356589178011[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8987883489047[/C][C]-1.89878834890474[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.4196968092666[/C][C]0.580303190733374[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]12.9372963079658[/C][C]-0.937296307965809[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]14.9635979706593[/C][C]-0.963597970659285[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.1711277946139[/C][C]1.82887220538613[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.347427398192[/C][C]-0.347427398191999[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.4168226295808[/C][C]1.58317737041916[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.1851619269344[/C][C]-1.1851619269344[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.230875280886[/C][C]0.769124719113958[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.9970483727683[/C][C]-2.99704837276825[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.6982148863106[/C][C]1.30178511368941[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.5710192229879[/C][C]-2.57101922298795[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.0717484833983[/C][C]0.928251516601661[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.4512195499863[/C][C]1.54878045001369[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.882534394126[/C][C]-2.88253439412603[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.90548907090124[/C][C]0.0945109290987561[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.0404542243267[/C][C]0.959545775673266[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.2651237880185[/C][C]-2.26512378801847[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.8665581989868[/C][C]-2.8665581989868[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.4095925332386[/C][C]1.59040746676138[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.3943475082475[/C][C]3.6056524917525[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.5566992638055[/C][C]0.443300736194469[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.3371675820921[/C][C]0.662832417907851[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.4370179901121[/C][C]-0.437017990112142[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.2736936187868[/C][C]-1.27369361878684[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.1794397542815[/C][C]-0.179439754281502[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.7904872181875[/C][C]-0.79048721818748[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.2574728078361[/C][C]0.742527192163875[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.5881177512554[/C][C]-0.588117751255369[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1746463085585[/C][C]-1.17464630855847[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.4317698906587[/C][C]0.56823010934134[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.7982862066938[/C][C]-1.79828620669376[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]16.3820859472053[/C][C]0.617914052794726[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.8482478558843[/C][C]1.15175214411575[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.1398430374878[/C][C]3.86015696251223[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.7793837379593[/C][C]1.22061626204067[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.7715637706962[/C][C]-1.77156377069619[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.8246164808787[/C][C]-1.82461648087872[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.8147587114807[/C][C]-0.814758711480679[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.4928373523562[/C][C]2.50716264764379[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.6522328072134[/C][C]0.347767192786616[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.4750139725228[/C][C]2.52498602747718[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.89597503430895[/C][C]2.10402496569105[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.3128629554285[/C][C]0.687137044571486[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.0854171971992[/C][C]-1.08541719719924[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.3834450593497[/C][C]0.616554940650326[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.9933765543591[/C][C]-0.9933765543591[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.6481507314932[/C][C]0.351849268506834[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]14.1513207330891[/C][C]1.84867926691088[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.8211085504859[/C][C]-0.821108550485934[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.815140519058[/C][C]1.184859480942[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]14.9713278476439[/C][C]1.02867215235611[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.0666904691975[/C][C]1.93330953080255[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.568556211929[/C][C]-2.56855621192902[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.6081370926832[/C][C]-2.60813709268317[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.7368967175347[/C][C]-2.73689671753472[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.074690979256[/C][C]0.925309020743982[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.1644296850352[/C][C]-0.164429685035201[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.9565283605904[/C][C]0.0434716394095803[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]15.0698953375539[/C][C]-3.06989533755392[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.8809995506193[/C][C]-2.88099955061928[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]11.9303125584374[/C][C]1.06968744156258[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.5421328012795[/C][C]0.457867198720504[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.8436089043508[/C][C]0.156391095649228[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.1398430374878[/C][C]3.86015696251223[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.7882287992974[/C][C]-2.78822879929738[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.7349484294086[/C][C]-0.734948429408634[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.8845093253904[/C][C]0.115490674609604[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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
11413.96261653699540.0373834630046497
21815.27168430911082.7283156908892
31113.5132659631127-2.51326596311267
41214.3305506597591-2.33055065975914
51611.44718179225874.55281820774132
61814.18048642254513.81951357745487
71410.10652945597413.89347054402585
81415.0212261214269-1.02122612142689
91515.1584114638485-0.158411463848536
101514.72225681370720.277743186292752
111715.25044990061361.74955009938639
121915.22069935345433.77930064654567
131013.2707411772324-3.27074117723238
141613.49673444245712.50326555754289
151815.77109220613712.22890779386291
161413.03457229112790.965427708872069
171413.7490780432810.250921956718962
181715.91906440628411.08093559371587
191415.3045376788863-1.30453767888631
201613.31953140653672.68046859346331
211815.8768394347532.12316056524695
221113.2098592394067-2.20985923940674
231414.3052574473078-0.305257447307763
241213.5029307884127-1.50293078841269
251715.39100870793451.6089912920655
26915.7814914576308-6.78149145763084
271615.2276758354640.772324164536037
281413.22954423865580.770455761344155
291513.93099899391961.06900100608037
301113.6516799573128-2.65167995731277
311615.18345754995360.816542450046365
321312.41082000607720.589179993922839
331714.87203755780222.12796244219781
341515.2912886223555-0.291288622355473
351413.82569084999040.17430915000963
361615.28940131664250.710598683357498
37910.9379265496515-1.93792654965154
381514.21129706511210.788702934887867
391715.06898086334571.93101913665428
401315.0470113597267-2.04701135972668
411515.5264472245285-0.526447224528547
421613.6761426025312.32385739746903
431615.22653267818090.773467321819061
441212.9475480177547-0.947548017754705
451214.2351646101679-2.2351646101679
461113.7654887369126-2.76548873691257
471515.3385622855577-0.338562285557675
481514.82365139901010.176348600989858
491713.92073740752883.0792625924712
501314.6715261225225-1.67152612252248
511615.28734206134520.71265793865477
521413.20845332001650.791546679983543
531111.6844944965019-0.684494496501931
541213.2926363856721-1.29263638567211
551214.2331051699681-2.23310516996811
561514.56378078771830.436219212281659
571614.20462048550261.7953795144974
581515.1800890437012-0.180089043701222
591215.3785910307025-3.37859103070251
601213.3842062445729-1.38420624457285
61810.4275458663947-2.42754586639468
621314.717573849813-1.71757384981302
631114.64400873092-3.64400873092004
641412.99410985265131.00589014734874
651513.21552202628851.78447797371149
661015.2875047319946-5.28750473199463
671112.5222987609356-1.52229876093564
681214.1918896354108-2.19188963541079
691514.01481698653550.985183013464535
701513.84548445131321.1545155486868
711413.75610716726980.243892832730181
721612.90307152603393.09692847396608
731514.83698870462070.163011295379289
741515.7057138131789-0.705713813178867
751314.7093923507694-1.70939235076943
761212.2005603060215-0.200560306021458
771714.1971816509362.80281834906399
781312.30231443614680.6976855638532
791513.72985482463431.27014517536572
801314.3658996438983-1.36589964389833
811515.2254920959781-0.225492095978114
821614.98528072555831.0147192744417
831515.6005177477299-0.600517747729854
841614.40827321380171.59172678619835
851514.36178234713560.638217652864376
861414.0896778226171-0.0896778226171078
871514.62518135601270.374818643987272
881414.2985990372652-0.298599037265213
891312.51825899463070.481741005369304
90710.6658540321768-3.66585403217676
911714.01840838308782.98159161691217
921312.54213280127950.457867198720504
931514.80317941341960.196820586580376
941413.24643410821990.75356589178011
951314.8987883489047-1.89878834890474
961615.41969680926660.580303190733374
971212.9372963079658-0.937296307965809
981414.9635979706593-0.963597970659285
991715.17112779461391.82887220538613
1001515.347427398192-0.347427398191999
1011715.41682262958081.58317737041916
1021213.1851619269344-1.1851619269344
1031615.2308752808860.769124719113958
1041113.9970483727683-2.99704837276825
1051513.69821488631061.30178511368941
106911.5710192229879-2.57101922298795
1071615.07174848339830.928251516601661
1081513.45121954998631.54878045001369
1091012.882534394126-2.88253439412603
110109.905489070901240.0945109290987561
1111514.04045422432670.959545775673266
1121113.2651237880185-2.26512378801847
1131315.8665581989868-2.8665581989868
1141412.40959253323861.59040746676138
1151814.39434750824753.6056524917525
1161615.55669926380550.443300736194469
1171413.33716758209210.662832417907851
1181414.4370179901121-0.437017990112142
1191415.2736936187868-1.27369361878684
1201414.1794397542815-0.179439754281502
1211212.7904872181875-0.79048721818748
1221413.25747280783610.742527192163875
1231515.5881177512554-0.588117751255369
1241516.1746463085585-1.17464630855847
1251514.43176989065870.56823010934134
1261314.7982862066938-1.79828620669376
1271716.38208594720530.617914052794726
1281715.84824785588431.15175214411575
1291915.13984303748783.86015696251223
1301513.77938373795931.22061626204067
1311314.7715637706962-1.77156377069619
132910.8246164808787-1.82461648087872
1331515.8147587114807-0.814758711480679
1341512.49283735235622.50716264764379
1351514.65223280721340.347767192786616
1361613.47501397252282.52498602747718
137118.895975034308952.10402496569105
1381413.31286295542850.687137044571486
1391112.0854171971992-1.08541719719924
1401514.38344505934970.616554940650326
1411313.9933765543591-0.9933765543591
1421514.64815073149320.351849268506834
1431614.15132073308911.84867926691088
1441414.8211085504859-0.821108550485934
1451513.8151405190581.184859480942
1461614.97132784764391.02867215235611
1471614.06669046919751.93330953080255
1481113.568556211929-2.56855621192902
1491214.6081370926832-2.60813709268317
150911.7368967175347-2.73689671753472
1511615.0746909792560.925309020743982
1521313.1644296850352-0.164429685035201
1531615.95652836059040.0434716394095803
1541215.0698953375539-3.06989533755392
155911.8809995506193-2.88099955061928
1561311.93031255843741.06968744156258
1571312.54213280127950.457867198720504
1581413.84360890435080.156391095649228
1591915.13984303748783.86015696251223
1601315.7882287992974-2.78822879929738
1611212.7349484294086-0.734948429408634
1621312.88450932539040.115490674609604







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.05944292817294050.1188858563458810.940557071827059
120.8856823922123830.2286352155752340.114317607787617
130.9838844980124870.03223100397502510.0161155019875126
140.9745409330516910.05091813389661870.0254590669483094
150.985648541356850.02870291728629970.0143514586431498
160.9747017686663390.05059646266732180.0252982313336609
170.9757231559243030.04855368815139370.0242768440756968
180.9614815477519620.07703690449607530.0385184522480376
190.9420603974160270.1158792051679460.0579396025839729
200.9428929381120980.1142141237758030.0571070618879016
210.949809527820010.100380944359980.0501904721799902
220.9517488225631120.09650235487377540.0482511774368877
230.9473376307694710.1053247384610590.0526623692305294
240.9264979688018760.1470040623962490.0735020311981243
250.9040566043512820.1918867912974360.095943395648718
260.9996875461238210.0006249077523577350.000312453876178867
270.9994589721027930.001082055794413930.000541027897206964
280.9990950780506050.001809843898789590.000904921949394795
290.9985584956109710.002883008778057860.00144150438902893
300.9990013813428030.001997237314394480.00099861865719724
310.9984254633529680.003149073294063670.00157453664703184
320.9976678865057860.004664226988428290.00233211349421414
330.997293741878360.005412516243279690.00270625812163985
340.9958912072925790.00821758541484110.00410879270742055
350.9947194644801020.01056107103979590.00528053551989793
360.9922805902597520.01543881948049630.00771940974024813
370.9950119970152610.009976005969477130.00498800298473856
380.9928172132728170.01436557345436610.00718278672718307
390.9920785499774320.01584290004513580.00792145002256792
400.9922265967768930.01554680644621440.00777340322310719
410.9892043818444960.02159123631100730.0107956181555036
420.9883822755998930.02323544880021390.011617724400107
430.9842135219331740.03157295613365270.0157864780668264
440.9807283971169190.03854320576616210.019271602883081
450.9834223184215420.0331553631569150.0165776815784575
460.9882780916449480.02344381671010380.0117219083550519
470.9838405499794860.03231890004102840.0161594500205142
480.9779538441880850.04409231162382950.0220461558119148
490.9823888787995560.03522224240088690.0176111212004435
500.9808498119129170.03830037617416560.0191501880870828
510.9747231140422740.05055377191545110.0252768859577256
520.9671798974925130.06564020501497490.0328201025074874
530.9605699722748660.07886005545026770.0394300277251338
540.9519212819752230.09615743604955480.0480787180247774
550.9536275732435160.09274485351296870.0463724267564844
560.941465522036990.117068955926020.0585344779630101
570.9367897070186140.1264205859627720.0632102929813859
580.9204621133276730.1590757733446540.079537886672327
590.9502252285113570.09954954297728610.049774771488643
600.9456852184285190.1086295631429610.0543147815714805
610.9536328056826460.09273438863470740.0463671943173537
620.9526816482190980.09463670356180440.0473183517809022
630.9764909031398580.04701819372028490.0235090968601424
640.9711048479898860.05779030402022790.0288951520101139
650.9688630617104850.06227387657903070.0311369382895154
660.9949852561765220.0100294876469560.00501474382347799
670.9943138300071020.01137233998579580.00568616999289789
680.9950447173214350.009910565357129540.00495528267856477
690.9936369506936610.01272609861267790.00636304930633895
700.9921190235928530.01576195281429420.0078809764071471
710.9892589293167780.02148214136644460.0107410706832223
720.9932750609300970.01344987813980560.00672493906990278
730.9907807620551910.01843847588961690.00921923794480845
740.9878671712567180.02426565748656360.0121328287432818
750.9872344474300190.02553110513996130.0127655525699806
760.9828783948679720.0342432102640550.0171216051320275
770.9877562909342250.02448741813155040.0122437090657752
780.9842480801523260.03150383969534870.0157519198476743
790.9813199361070270.03736012778594550.0186800638929728
800.9793861251611120.04122774967777660.0206138748388883
810.9728456187126340.05430876257473170.0271543812873659
820.9672164065978120.06556718680437560.0327835934021878
830.9586164713548630.08276705729027340.0413835286451367
840.9545379325138180.09092413497236470.0454620674861824
850.9435260306009770.1129479387980470.0564739693990233
860.9288430910310190.1423138179379630.0711569089689814
870.9120350938641210.1759298122717580.0879649061358788
880.8919009282288220.2161981435423560.108099071771178
890.8714626478617650.2570747042764710.128537352138235
900.9200631505240970.1598736989518050.0799368494759025
910.9415258996126010.1169482007747980.0584741003873988
920.9277905566374720.1444188867250560.072209443362528
930.9115365007714150.1769269984571710.0884634992285854
940.893610784128510.212778431742980.10638921587149
950.8940283312753710.2119433374492590.105971668724629
960.8720067947421760.2559864105156470.127993205257824
970.8499377086675790.3001245826648420.150062291332421
980.8306682654461470.3386634691077050.169331734553853
990.8253238028097260.3493523943805470.174676197190274
1000.7929125684386980.4141748631226040.207087431561302
1010.7840777790414270.4318444419171470.215922220958573
1020.7588597548126570.4822804903746860.241140245187343
1030.7318169965242890.5363660069514220.268183003475711
1040.8039796628567620.3920406742864750.196020337143238
1050.8077077142274920.3845845715450160.192292285772508
1060.8402904893227740.3194190213544520.159709510677226
1070.8148112166962670.3703775666074660.185188783303733
1080.8204373099733970.3591253800532060.179562690026603
1090.8565672440311050.2868655119377890.143432755968895
1100.8298464305176530.3403071389646940.170153569482347
1110.8189434722996420.3621130554007160.181056527700358
1120.8147931900893560.3704136198212870.185206809910644
1130.8247499761272150.3505000477455690.175250023872785
1140.9141863852331990.1716272295336010.0858136147668006
1150.9549403882382250.09011922352355050.0450596117617752
1160.9407589465066670.1184821069866660.0592410534933332
1170.9449495643870720.1101008712258560.0550504356129282
1180.9278524271386410.1442951457227190.0721475728613594
1190.9143054977871340.1713890044257330.0856945022128664
1200.8900649477925310.2198701044149390.109935052207469
1210.8642516429851540.2714967140296930.135748357014846
1220.8328483276620230.3343033446759550.167151672337977
1230.7949428792401810.4101142415196370.205057120759819
1240.7643760055148710.4712479889702590.235623994485129
1250.7171862317085180.5656275365829650.282813768291482
1260.6947133293416880.6105733413166240.305286670658312
1270.644883947126470.710232105747060.35511605287353
1280.6742311271782210.6515377456435590.325768872821779
1290.7357874976352170.5284250047295660.264212502364783
1300.7083970305530390.5832059388939220.291602969446961
1310.6815610782140770.6368778435718460.318438921785923
1320.6545370884550960.6909258230898080.345462911544904
1330.5958852096770650.8082295806458710.404114790322935
1340.6076496691058620.7847006617882760.392350330894138
1350.5593800808902260.8812398382195480.440619919109774
1360.5241151056866940.9517697886266110.475884894313306
1370.553168106480280.8936637870394390.44683189351972
1380.4823594173564620.9647188347129250.517640582643538
1390.4102785126311430.8205570252622850.589721487368857
1400.3447164281151090.6894328562302190.655283571884891
1410.2763491725468720.5526983450937440.723650827453128
1420.2340352946941270.4680705893882550.765964705305873
1430.2434225757804640.4868451515609280.756577424219536
1440.2037588962836720.4075177925673440.796241103716328
1450.1947879578625020.3895759157250040.805212042137498
1460.1923508757843870.3847017515687750.807649124215613
1470.3215101593728480.6430203187456960.678489840627152
1480.7688888865429340.4622222269141310.231111113457066
1490.8206648298860790.3586703402278420.179335170113921
1500.7048658316655920.5902683366688170.295134168334408
1510.5780133067572310.8439733864855370.421986693242769

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.0594429281729405 & 0.118885856345881 & 0.940557071827059 \tabularnewline
12 & 0.885682392212383 & 0.228635215575234 & 0.114317607787617 \tabularnewline
13 & 0.983884498012487 & 0.0322310039750251 & 0.0161155019875126 \tabularnewline
14 & 0.974540933051691 & 0.0509181338966187 & 0.0254590669483094 \tabularnewline
15 & 0.98564854135685 & 0.0287029172862997 & 0.0143514586431498 \tabularnewline
16 & 0.974701768666339 & 0.0505964626673218 & 0.0252982313336609 \tabularnewline
17 & 0.975723155924303 & 0.0485536881513937 & 0.0242768440756968 \tabularnewline
18 & 0.961481547751962 & 0.0770369044960753 & 0.0385184522480376 \tabularnewline
19 & 0.942060397416027 & 0.115879205167946 & 0.0579396025839729 \tabularnewline
20 & 0.942892938112098 & 0.114214123775803 & 0.0571070618879016 \tabularnewline
21 & 0.94980952782001 & 0.10038094435998 & 0.0501904721799902 \tabularnewline
22 & 0.951748822563112 & 0.0965023548737754 & 0.0482511774368877 \tabularnewline
23 & 0.947337630769471 & 0.105324738461059 & 0.0526623692305294 \tabularnewline
24 & 0.926497968801876 & 0.147004062396249 & 0.0735020311981243 \tabularnewline
25 & 0.904056604351282 & 0.191886791297436 & 0.095943395648718 \tabularnewline
26 & 0.999687546123821 & 0.000624907752357735 & 0.000312453876178867 \tabularnewline
27 & 0.999458972102793 & 0.00108205579441393 & 0.000541027897206964 \tabularnewline
28 & 0.999095078050605 & 0.00180984389878959 & 0.000904921949394795 \tabularnewline
29 & 0.998558495610971 & 0.00288300877805786 & 0.00144150438902893 \tabularnewline
30 & 0.999001381342803 & 0.00199723731439448 & 0.00099861865719724 \tabularnewline
31 & 0.998425463352968 & 0.00314907329406367 & 0.00157453664703184 \tabularnewline
32 & 0.997667886505786 & 0.00466422698842829 & 0.00233211349421414 \tabularnewline
33 & 0.99729374187836 & 0.00541251624327969 & 0.00270625812163985 \tabularnewline
34 & 0.995891207292579 & 0.0082175854148411 & 0.00410879270742055 \tabularnewline
35 & 0.994719464480102 & 0.0105610710397959 & 0.00528053551989793 \tabularnewline
36 & 0.992280590259752 & 0.0154388194804963 & 0.00771940974024813 \tabularnewline
37 & 0.995011997015261 & 0.00997600596947713 & 0.00498800298473856 \tabularnewline
38 & 0.992817213272817 & 0.0143655734543661 & 0.00718278672718307 \tabularnewline
39 & 0.992078549977432 & 0.0158429000451358 & 0.00792145002256792 \tabularnewline
40 & 0.992226596776893 & 0.0155468064462144 & 0.00777340322310719 \tabularnewline
41 & 0.989204381844496 & 0.0215912363110073 & 0.0107956181555036 \tabularnewline
42 & 0.988382275599893 & 0.0232354488002139 & 0.011617724400107 \tabularnewline
43 & 0.984213521933174 & 0.0315729561336527 & 0.0157864780668264 \tabularnewline
44 & 0.980728397116919 & 0.0385432057661621 & 0.019271602883081 \tabularnewline
45 & 0.983422318421542 & 0.033155363156915 & 0.0165776815784575 \tabularnewline
46 & 0.988278091644948 & 0.0234438167101038 & 0.0117219083550519 \tabularnewline
47 & 0.983840549979486 & 0.0323189000410284 & 0.0161594500205142 \tabularnewline
48 & 0.977953844188085 & 0.0440923116238295 & 0.0220461558119148 \tabularnewline
49 & 0.982388878799556 & 0.0352222424008869 & 0.0176111212004435 \tabularnewline
50 & 0.980849811912917 & 0.0383003761741656 & 0.0191501880870828 \tabularnewline
51 & 0.974723114042274 & 0.0505537719154511 & 0.0252768859577256 \tabularnewline
52 & 0.967179897492513 & 0.0656402050149749 & 0.0328201025074874 \tabularnewline
53 & 0.960569972274866 & 0.0788600554502677 & 0.0394300277251338 \tabularnewline
54 & 0.951921281975223 & 0.0961574360495548 & 0.0480787180247774 \tabularnewline
55 & 0.953627573243516 & 0.0927448535129687 & 0.0463724267564844 \tabularnewline
56 & 0.94146552203699 & 0.11706895592602 & 0.0585344779630101 \tabularnewline
57 & 0.936789707018614 & 0.126420585962772 & 0.0632102929813859 \tabularnewline
58 & 0.920462113327673 & 0.159075773344654 & 0.079537886672327 \tabularnewline
59 & 0.950225228511357 & 0.0995495429772861 & 0.049774771488643 \tabularnewline
60 & 0.945685218428519 & 0.108629563142961 & 0.0543147815714805 \tabularnewline
61 & 0.953632805682646 & 0.0927343886347074 & 0.0463671943173537 \tabularnewline
62 & 0.952681648219098 & 0.0946367035618044 & 0.0473183517809022 \tabularnewline
63 & 0.976490903139858 & 0.0470181937202849 & 0.0235090968601424 \tabularnewline
64 & 0.971104847989886 & 0.0577903040202279 & 0.0288951520101139 \tabularnewline
65 & 0.968863061710485 & 0.0622738765790307 & 0.0311369382895154 \tabularnewline
66 & 0.994985256176522 & 0.010029487646956 & 0.00501474382347799 \tabularnewline
67 & 0.994313830007102 & 0.0113723399857958 & 0.00568616999289789 \tabularnewline
68 & 0.995044717321435 & 0.00991056535712954 & 0.00495528267856477 \tabularnewline
69 & 0.993636950693661 & 0.0127260986126779 & 0.00636304930633895 \tabularnewline
70 & 0.992119023592853 & 0.0157619528142942 & 0.0078809764071471 \tabularnewline
71 & 0.989258929316778 & 0.0214821413664446 & 0.0107410706832223 \tabularnewline
72 & 0.993275060930097 & 0.0134498781398056 & 0.00672493906990278 \tabularnewline
73 & 0.990780762055191 & 0.0184384758896169 & 0.00921923794480845 \tabularnewline
74 & 0.987867171256718 & 0.0242656574865636 & 0.0121328287432818 \tabularnewline
75 & 0.987234447430019 & 0.0255311051399613 & 0.0127655525699806 \tabularnewline
76 & 0.982878394867972 & 0.034243210264055 & 0.0171216051320275 \tabularnewline
77 & 0.987756290934225 & 0.0244874181315504 & 0.0122437090657752 \tabularnewline
78 & 0.984248080152326 & 0.0315038396953487 & 0.0157519198476743 \tabularnewline
79 & 0.981319936107027 & 0.0373601277859455 & 0.0186800638929728 \tabularnewline
80 & 0.979386125161112 & 0.0412277496777766 & 0.0206138748388883 \tabularnewline
81 & 0.972845618712634 & 0.0543087625747317 & 0.0271543812873659 \tabularnewline
82 & 0.967216406597812 & 0.0655671868043756 & 0.0327835934021878 \tabularnewline
83 & 0.958616471354863 & 0.0827670572902734 & 0.0413835286451367 \tabularnewline
84 & 0.954537932513818 & 0.0909241349723647 & 0.0454620674861824 \tabularnewline
85 & 0.943526030600977 & 0.112947938798047 & 0.0564739693990233 \tabularnewline
86 & 0.928843091031019 & 0.142313817937963 & 0.0711569089689814 \tabularnewline
87 & 0.912035093864121 & 0.175929812271758 & 0.0879649061358788 \tabularnewline
88 & 0.891900928228822 & 0.216198143542356 & 0.108099071771178 \tabularnewline
89 & 0.871462647861765 & 0.257074704276471 & 0.128537352138235 \tabularnewline
90 & 0.920063150524097 & 0.159873698951805 & 0.0799368494759025 \tabularnewline
91 & 0.941525899612601 & 0.116948200774798 & 0.0584741003873988 \tabularnewline
92 & 0.927790556637472 & 0.144418886725056 & 0.072209443362528 \tabularnewline
93 & 0.911536500771415 & 0.176926998457171 & 0.0884634992285854 \tabularnewline
94 & 0.89361078412851 & 0.21277843174298 & 0.10638921587149 \tabularnewline
95 & 0.894028331275371 & 0.211943337449259 & 0.105971668724629 \tabularnewline
96 & 0.872006794742176 & 0.255986410515647 & 0.127993205257824 \tabularnewline
97 & 0.849937708667579 & 0.300124582664842 & 0.150062291332421 \tabularnewline
98 & 0.830668265446147 & 0.338663469107705 & 0.169331734553853 \tabularnewline
99 & 0.825323802809726 & 0.349352394380547 & 0.174676197190274 \tabularnewline
100 & 0.792912568438698 & 0.414174863122604 & 0.207087431561302 \tabularnewline
101 & 0.784077779041427 & 0.431844441917147 & 0.215922220958573 \tabularnewline
102 & 0.758859754812657 & 0.482280490374686 & 0.241140245187343 \tabularnewline
103 & 0.731816996524289 & 0.536366006951422 & 0.268183003475711 \tabularnewline
104 & 0.803979662856762 & 0.392040674286475 & 0.196020337143238 \tabularnewline
105 & 0.807707714227492 & 0.384584571545016 & 0.192292285772508 \tabularnewline
106 & 0.840290489322774 & 0.319419021354452 & 0.159709510677226 \tabularnewline
107 & 0.814811216696267 & 0.370377566607466 & 0.185188783303733 \tabularnewline
108 & 0.820437309973397 & 0.359125380053206 & 0.179562690026603 \tabularnewline
109 & 0.856567244031105 & 0.286865511937789 & 0.143432755968895 \tabularnewline
110 & 0.829846430517653 & 0.340307138964694 & 0.170153569482347 \tabularnewline
111 & 0.818943472299642 & 0.362113055400716 & 0.181056527700358 \tabularnewline
112 & 0.814793190089356 & 0.370413619821287 & 0.185206809910644 \tabularnewline
113 & 0.824749976127215 & 0.350500047745569 & 0.175250023872785 \tabularnewline
114 & 0.914186385233199 & 0.171627229533601 & 0.0858136147668006 \tabularnewline
115 & 0.954940388238225 & 0.0901192235235505 & 0.0450596117617752 \tabularnewline
116 & 0.940758946506667 & 0.118482106986666 & 0.0592410534933332 \tabularnewline
117 & 0.944949564387072 & 0.110100871225856 & 0.0550504356129282 \tabularnewline
118 & 0.927852427138641 & 0.144295145722719 & 0.0721475728613594 \tabularnewline
119 & 0.914305497787134 & 0.171389004425733 & 0.0856945022128664 \tabularnewline
120 & 0.890064947792531 & 0.219870104414939 & 0.109935052207469 \tabularnewline
121 & 0.864251642985154 & 0.271496714029693 & 0.135748357014846 \tabularnewline
122 & 0.832848327662023 & 0.334303344675955 & 0.167151672337977 \tabularnewline
123 & 0.794942879240181 & 0.410114241519637 & 0.205057120759819 \tabularnewline
124 & 0.764376005514871 & 0.471247988970259 & 0.235623994485129 \tabularnewline
125 & 0.717186231708518 & 0.565627536582965 & 0.282813768291482 \tabularnewline
126 & 0.694713329341688 & 0.610573341316624 & 0.305286670658312 \tabularnewline
127 & 0.64488394712647 & 0.71023210574706 & 0.35511605287353 \tabularnewline
128 & 0.674231127178221 & 0.651537745643559 & 0.325768872821779 \tabularnewline
129 & 0.735787497635217 & 0.528425004729566 & 0.264212502364783 \tabularnewline
130 & 0.708397030553039 & 0.583205938893922 & 0.291602969446961 \tabularnewline
131 & 0.681561078214077 & 0.636877843571846 & 0.318438921785923 \tabularnewline
132 & 0.654537088455096 & 0.690925823089808 & 0.345462911544904 \tabularnewline
133 & 0.595885209677065 & 0.808229580645871 & 0.404114790322935 \tabularnewline
134 & 0.607649669105862 & 0.784700661788276 & 0.392350330894138 \tabularnewline
135 & 0.559380080890226 & 0.881239838219548 & 0.440619919109774 \tabularnewline
136 & 0.524115105686694 & 0.951769788626611 & 0.475884894313306 \tabularnewline
137 & 0.55316810648028 & 0.893663787039439 & 0.44683189351972 \tabularnewline
138 & 0.482359417356462 & 0.964718834712925 & 0.517640582643538 \tabularnewline
139 & 0.410278512631143 & 0.820557025262285 & 0.589721487368857 \tabularnewline
140 & 0.344716428115109 & 0.689432856230219 & 0.655283571884891 \tabularnewline
141 & 0.276349172546872 & 0.552698345093744 & 0.723650827453128 \tabularnewline
142 & 0.234035294694127 & 0.468070589388255 & 0.765964705305873 \tabularnewline
143 & 0.243422575780464 & 0.486845151560928 & 0.756577424219536 \tabularnewline
144 & 0.203758896283672 & 0.407517792567344 & 0.796241103716328 \tabularnewline
145 & 0.194787957862502 & 0.389575915725004 & 0.805212042137498 \tabularnewline
146 & 0.192350875784387 & 0.384701751568775 & 0.807649124215613 \tabularnewline
147 & 0.321510159372848 & 0.643020318745696 & 0.678489840627152 \tabularnewline
148 & 0.768888886542934 & 0.462222226914131 & 0.231111113457066 \tabularnewline
149 & 0.820664829886079 & 0.358670340227842 & 0.179335170113921 \tabularnewline
150 & 0.704865831665592 & 0.590268336668817 & 0.295134168334408 \tabularnewline
151 & 0.578013306757231 & 0.843973386485537 & 0.421986693242769 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.0594429281729405[/C][C]0.118885856345881[/C][C]0.940557071827059[/C][/ROW]
[ROW][C]12[/C][C]0.885682392212383[/C][C]0.228635215575234[/C][C]0.114317607787617[/C][/ROW]
[ROW][C]13[/C][C]0.983884498012487[/C][C]0.0322310039750251[/C][C]0.0161155019875126[/C][/ROW]
[ROW][C]14[/C][C]0.974540933051691[/C][C]0.0509181338966187[/C][C]0.0254590669483094[/C][/ROW]
[ROW][C]15[/C][C]0.98564854135685[/C][C]0.0287029172862997[/C][C]0.0143514586431498[/C][/ROW]
[ROW][C]16[/C][C]0.974701768666339[/C][C]0.0505964626673218[/C][C]0.0252982313336609[/C][/ROW]
[ROW][C]17[/C][C]0.975723155924303[/C][C]0.0485536881513937[/C][C]0.0242768440756968[/C][/ROW]
[ROW][C]18[/C][C]0.961481547751962[/C][C]0.0770369044960753[/C][C]0.0385184522480376[/C][/ROW]
[ROW][C]19[/C][C]0.942060397416027[/C][C]0.115879205167946[/C][C]0.0579396025839729[/C][/ROW]
[ROW][C]20[/C][C]0.942892938112098[/C][C]0.114214123775803[/C][C]0.0571070618879016[/C][/ROW]
[ROW][C]21[/C][C]0.94980952782001[/C][C]0.10038094435998[/C][C]0.0501904721799902[/C][/ROW]
[ROW][C]22[/C][C]0.951748822563112[/C][C]0.0965023548737754[/C][C]0.0482511774368877[/C][/ROW]
[ROW][C]23[/C][C]0.947337630769471[/C][C]0.105324738461059[/C][C]0.0526623692305294[/C][/ROW]
[ROW][C]24[/C][C]0.926497968801876[/C][C]0.147004062396249[/C][C]0.0735020311981243[/C][/ROW]
[ROW][C]25[/C][C]0.904056604351282[/C][C]0.191886791297436[/C][C]0.095943395648718[/C][/ROW]
[ROW][C]26[/C][C]0.999687546123821[/C][C]0.000624907752357735[/C][C]0.000312453876178867[/C][/ROW]
[ROW][C]27[/C][C]0.999458972102793[/C][C]0.00108205579441393[/C][C]0.000541027897206964[/C][/ROW]
[ROW][C]28[/C][C]0.999095078050605[/C][C]0.00180984389878959[/C][C]0.000904921949394795[/C][/ROW]
[ROW][C]29[/C][C]0.998558495610971[/C][C]0.00288300877805786[/C][C]0.00144150438902893[/C][/ROW]
[ROW][C]30[/C][C]0.999001381342803[/C][C]0.00199723731439448[/C][C]0.00099861865719724[/C][/ROW]
[ROW][C]31[/C][C]0.998425463352968[/C][C]0.00314907329406367[/C][C]0.00157453664703184[/C][/ROW]
[ROW][C]32[/C][C]0.997667886505786[/C][C]0.00466422698842829[/C][C]0.00233211349421414[/C][/ROW]
[ROW][C]33[/C][C]0.99729374187836[/C][C]0.00541251624327969[/C][C]0.00270625812163985[/C][/ROW]
[ROW][C]34[/C][C]0.995891207292579[/C][C]0.0082175854148411[/C][C]0.00410879270742055[/C][/ROW]
[ROW][C]35[/C][C]0.994719464480102[/C][C]0.0105610710397959[/C][C]0.00528053551989793[/C][/ROW]
[ROW][C]36[/C][C]0.992280590259752[/C][C]0.0154388194804963[/C][C]0.00771940974024813[/C][/ROW]
[ROW][C]37[/C][C]0.995011997015261[/C][C]0.00997600596947713[/C][C]0.00498800298473856[/C][/ROW]
[ROW][C]38[/C][C]0.992817213272817[/C][C]0.0143655734543661[/C][C]0.00718278672718307[/C][/ROW]
[ROW][C]39[/C][C]0.992078549977432[/C][C]0.0158429000451358[/C][C]0.00792145002256792[/C][/ROW]
[ROW][C]40[/C][C]0.992226596776893[/C][C]0.0155468064462144[/C][C]0.00777340322310719[/C][/ROW]
[ROW][C]41[/C][C]0.989204381844496[/C][C]0.0215912363110073[/C][C]0.0107956181555036[/C][/ROW]
[ROW][C]42[/C][C]0.988382275599893[/C][C]0.0232354488002139[/C][C]0.011617724400107[/C][/ROW]
[ROW][C]43[/C][C]0.984213521933174[/C][C]0.0315729561336527[/C][C]0.0157864780668264[/C][/ROW]
[ROW][C]44[/C][C]0.980728397116919[/C][C]0.0385432057661621[/C][C]0.019271602883081[/C][/ROW]
[ROW][C]45[/C][C]0.983422318421542[/C][C]0.033155363156915[/C][C]0.0165776815784575[/C][/ROW]
[ROW][C]46[/C][C]0.988278091644948[/C][C]0.0234438167101038[/C][C]0.0117219083550519[/C][/ROW]
[ROW][C]47[/C][C]0.983840549979486[/C][C]0.0323189000410284[/C][C]0.0161594500205142[/C][/ROW]
[ROW][C]48[/C][C]0.977953844188085[/C][C]0.0440923116238295[/C][C]0.0220461558119148[/C][/ROW]
[ROW][C]49[/C][C]0.982388878799556[/C][C]0.0352222424008869[/C][C]0.0176111212004435[/C][/ROW]
[ROW][C]50[/C][C]0.980849811912917[/C][C]0.0383003761741656[/C][C]0.0191501880870828[/C][/ROW]
[ROW][C]51[/C][C]0.974723114042274[/C][C]0.0505537719154511[/C][C]0.0252768859577256[/C][/ROW]
[ROW][C]52[/C][C]0.967179897492513[/C][C]0.0656402050149749[/C][C]0.0328201025074874[/C][/ROW]
[ROW][C]53[/C][C]0.960569972274866[/C][C]0.0788600554502677[/C][C]0.0394300277251338[/C][/ROW]
[ROW][C]54[/C][C]0.951921281975223[/C][C]0.0961574360495548[/C][C]0.0480787180247774[/C][/ROW]
[ROW][C]55[/C][C]0.953627573243516[/C][C]0.0927448535129687[/C][C]0.0463724267564844[/C][/ROW]
[ROW][C]56[/C][C]0.94146552203699[/C][C]0.11706895592602[/C][C]0.0585344779630101[/C][/ROW]
[ROW][C]57[/C][C]0.936789707018614[/C][C]0.126420585962772[/C][C]0.0632102929813859[/C][/ROW]
[ROW][C]58[/C][C]0.920462113327673[/C][C]0.159075773344654[/C][C]0.079537886672327[/C][/ROW]
[ROW][C]59[/C][C]0.950225228511357[/C][C]0.0995495429772861[/C][C]0.049774771488643[/C][/ROW]
[ROW][C]60[/C][C]0.945685218428519[/C][C]0.108629563142961[/C][C]0.0543147815714805[/C][/ROW]
[ROW][C]61[/C][C]0.953632805682646[/C][C]0.0927343886347074[/C][C]0.0463671943173537[/C][/ROW]
[ROW][C]62[/C][C]0.952681648219098[/C][C]0.0946367035618044[/C][C]0.0473183517809022[/C][/ROW]
[ROW][C]63[/C][C]0.976490903139858[/C][C]0.0470181937202849[/C][C]0.0235090968601424[/C][/ROW]
[ROW][C]64[/C][C]0.971104847989886[/C][C]0.0577903040202279[/C][C]0.0288951520101139[/C][/ROW]
[ROW][C]65[/C][C]0.968863061710485[/C][C]0.0622738765790307[/C][C]0.0311369382895154[/C][/ROW]
[ROW][C]66[/C][C]0.994985256176522[/C][C]0.010029487646956[/C][C]0.00501474382347799[/C][/ROW]
[ROW][C]67[/C][C]0.994313830007102[/C][C]0.0113723399857958[/C][C]0.00568616999289789[/C][/ROW]
[ROW][C]68[/C][C]0.995044717321435[/C][C]0.00991056535712954[/C][C]0.00495528267856477[/C][/ROW]
[ROW][C]69[/C][C]0.993636950693661[/C][C]0.0127260986126779[/C][C]0.00636304930633895[/C][/ROW]
[ROW][C]70[/C][C]0.992119023592853[/C][C]0.0157619528142942[/C][C]0.0078809764071471[/C][/ROW]
[ROW][C]71[/C][C]0.989258929316778[/C][C]0.0214821413664446[/C][C]0.0107410706832223[/C][/ROW]
[ROW][C]72[/C][C]0.993275060930097[/C][C]0.0134498781398056[/C][C]0.00672493906990278[/C][/ROW]
[ROW][C]73[/C][C]0.990780762055191[/C][C]0.0184384758896169[/C][C]0.00921923794480845[/C][/ROW]
[ROW][C]74[/C][C]0.987867171256718[/C][C]0.0242656574865636[/C][C]0.0121328287432818[/C][/ROW]
[ROW][C]75[/C][C]0.987234447430019[/C][C]0.0255311051399613[/C][C]0.0127655525699806[/C][/ROW]
[ROW][C]76[/C][C]0.982878394867972[/C][C]0.034243210264055[/C][C]0.0171216051320275[/C][/ROW]
[ROW][C]77[/C][C]0.987756290934225[/C][C]0.0244874181315504[/C][C]0.0122437090657752[/C][/ROW]
[ROW][C]78[/C][C]0.984248080152326[/C][C]0.0315038396953487[/C][C]0.0157519198476743[/C][/ROW]
[ROW][C]79[/C][C]0.981319936107027[/C][C]0.0373601277859455[/C][C]0.0186800638929728[/C][/ROW]
[ROW][C]80[/C][C]0.979386125161112[/C][C]0.0412277496777766[/C][C]0.0206138748388883[/C][/ROW]
[ROW][C]81[/C][C]0.972845618712634[/C][C]0.0543087625747317[/C][C]0.0271543812873659[/C][/ROW]
[ROW][C]82[/C][C]0.967216406597812[/C][C]0.0655671868043756[/C][C]0.0327835934021878[/C][/ROW]
[ROW][C]83[/C][C]0.958616471354863[/C][C]0.0827670572902734[/C][C]0.0413835286451367[/C][/ROW]
[ROW][C]84[/C][C]0.954537932513818[/C][C]0.0909241349723647[/C][C]0.0454620674861824[/C][/ROW]
[ROW][C]85[/C][C]0.943526030600977[/C][C]0.112947938798047[/C][C]0.0564739693990233[/C][/ROW]
[ROW][C]86[/C][C]0.928843091031019[/C][C]0.142313817937963[/C][C]0.0711569089689814[/C][/ROW]
[ROW][C]87[/C][C]0.912035093864121[/C][C]0.175929812271758[/C][C]0.0879649061358788[/C][/ROW]
[ROW][C]88[/C][C]0.891900928228822[/C][C]0.216198143542356[/C][C]0.108099071771178[/C][/ROW]
[ROW][C]89[/C][C]0.871462647861765[/C][C]0.257074704276471[/C][C]0.128537352138235[/C][/ROW]
[ROW][C]90[/C][C]0.920063150524097[/C][C]0.159873698951805[/C][C]0.0799368494759025[/C][/ROW]
[ROW][C]91[/C][C]0.941525899612601[/C][C]0.116948200774798[/C][C]0.0584741003873988[/C][/ROW]
[ROW][C]92[/C][C]0.927790556637472[/C][C]0.144418886725056[/C][C]0.072209443362528[/C][/ROW]
[ROW][C]93[/C][C]0.911536500771415[/C][C]0.176926998457171[/C][C]0.0884634992285854[/C][/ROW]
[ROW][C]94[/C][C]0.89361078412851[/C][C]0.21277843174298[/C][C]0.10638921587149[/C][/ROW]
[ROW][C]95[/C][C]0.894028331275371[/C][C]0.211943337449259[/C][C]0.105971668724629[/C][/ROW]
[ROW][C]96[/C][C]0.872006794742176[/C][C]0.255986410515647[/C][C]0.127993205257824[/C][/ROW]
[ROW][C]97[/C][C]0.849937708667579[/C][C]0.300124582664842[/C][C]0.150062291332421[/C][/ROW]
[ROW][C]98[/C][C]0.830668265446147[/C][C]0.338663469107705[/C][C]0.169331734553853[/C][/ROW]
[ROW][C]99[/C][C]0.825323802809726[/C][C]0.349352394380547[/C][C]0.174676197190274[/C][/ROW]
[ROW][C]100[/C][C]0.792912568438698[/C][C]0.414174863122604[/C][C]0.207087431561302[/C][/ROW]
[ROW][C]101[/C][C]0.784077779041427[/C][C]0.431844441917147[/C][C]0.215922220958573[/C][/ROW]
[ROW][C]102[/C][C]0.758859754812657[/C][C]0.482280490374686[/C][C]0.241140245187343[/C][/ROW]
[ROW][C]103[/C][C]0.731816996524289[/C][C]0.536366006951422[/C][C]0.268183003475711[/C][/ROW]
[ROW][C]104[/C][C]0.803979662856762[/C][C]0.392040674286475[/C][C]0.196020337143238[/C][/ROW]
[ROW][C]105[/C][C]0.807707714227492[/C][C]0.384584571545016[/C][C]0.192292285772508[/C][/ROW]
[ROW][C]106[/C][C]0.840290489322774[/C][C]0.319419021354452[/C][C]0.159709510677226[/C][/ROW]
[ROW][C]107[/C][C]0.814811216696267[/C][C]0.370377566607466[/C][C]0.185188783303733[/C][/ROW]
[ROW][C]108[/C][C]0.820437309973397[/C][C]0.359125380053206[/C][C]0.179562690026603[/C][/ROW]
[ROW][C]109[/C][C]0.856567244031105[/C][C]0.286865511937789[/C][C]0.143432755968895[/C][/ROW]
[ROW][C]110[/C][C]0.829846430517653[/C][C]0.340307138964694[/C][C]0.170153569482347[/C][/ROW]
[ROW][C]111[/C][C]0.818943472299642[/C][C]0.362113055400716[/C][C]0.181056527700358[/C][/ROW]
[ROW][C]112[/C][C]0.814793190089356[/C][C]0.370413619821287[/C][C]0.185206809910644[/C][/ROW]
[ROW][C]113[/C][C]0.824749976127215[/C][C]0.350500047745569[/C][C]0.175250023872785[/C][/ROW]
[ROW][C]114[/C][C]0.914186385233199[/C][C]0.171627229533601[/C][C]0.0858136147668006[/C][/ROW]
[ROW][C]115[/C][C]0.954940388238225[/C][C]0.0901192235235505[/C][C]0.0450596117617752[/C][/ROW]
[ROW][C]116[/C][C]0.940758946506667[/C][C]0.118482106986666[/C][C]0.0592410534933332[/C][/ROW]
[ROW][C]117[/C][C]0.944949564387072[/C][C]0.110100871225856[/C][C]0.0550504356129282[/C][/ROW]
[ROW][C]118[/C][C]0.927852427138641[/C][C]0.144295145722719[/C][C]0.0721475728613594[/C][/ROW]
[ROW][C]119[/C][C]0.914305497787134[/C][C]0.171389004425733[/C][C]0.0856945022128664[/C][/ROW]
[ROW][C]120[/C][C]0.890064947792531[/C][C]0.219870104414939[/C][C]0.109935052207469[/C][/ROW]
[ROW][C]121[/C][C]0.864251642985154[/C][C]0.271496714029693[/C][C]0.135748357014846[/C][/ROW]
[ROW][C]122[/C][C]0.832848327662023[/C][C]0.334303344675955[/C][C]0.167151672337977[/C][/ROW]
[ROW][C]123[/C][C]0.794942879240181[/C][C]0.410114241519637[/C][C]0.205057120759819[/C][/ROW]
[ROW][C]124[/C][C]0.764376005514871[/C][C]0.471247988970259[/C][C]0.235623994485129[/C][/ROW]
[ROW][C]125[/C][C]0.717186231708518[/C][C]0.565627536582965[/C][C]0.282813768291482[/C][/ROW]
[ROW][C]126[/C][C]0.694713329341688[/C][C]0.610573341316624[/C][C]0.305286670658312[/C][/ROW]
[ROW][C]127[/C][C]0.64488394712647[/C][C]0.71023210574706[/C][C]0.35511605287353[/C][/ROW]
[ROW][C]128[/C][C]0.674231127178221[/C][C]0.651537745643559[/C][C]0.325768872821779[/C][/ROW]
[ROW][C]129[/C][C]0.735787497635217[/C][C]0.528425004729566[/C][C]0.264212502364783[/C][/ROW]
[ROW][C]130[/C][C]0.708397030553039[/C][C]0.583205938893922[/C][C]0.291602969446961[/C][/ROW]
[ROW][C]131[/C][C]0.681561078214077[/C][C]0.636877843571846[/C][C]0.318438921785923[/C][/ROW]
[ROW][C]132[/C][C]0.654537088455096[/C][C]0.690925823089808[/C][C]0.345462911544904[/C][/ROW]
[ROW][C]133[/C][C]0.595885209677065[/C][C]0.808229580645871[/C][C]0.404114790322935[/C][/ROW]
[ROW][C]134[/C][C]0.607649669105862[/C][C]0.784700661788276[/C][C]0.392350330894138[/C][/ROW]
[ROW][C]135[/C][C]0.559380080890226[/C][C]0.881239838219548[/C][C]0.440619919109774[/C][/ROW]
[ROW][C]136[/C][C]0.524115105686694[/C][C]0.951769788626611[/C][C]0.475884894313306[/C][/ROW]
[ROW][C]137[/C][C]0.55316810648028[/C][C]0.893663787039439[/C][C]0.44683189351972[/C][/ROW]
[ROW][C]138[/C][C]0.482359417356462[/C][C]0.964718834712925[/C][C]0.517640582643538[/C][/ROW]
[ROW][C]139[/C][C]0.410278512631143[/C][C]0.820557025262285[/C][C]0.589721487368857[/C][/ROW]
[ROW][C]140[/C][C]0.344716428115109[/C][C]0.689432856230219[/C][C]0.655283571884891[/C][/ROW]
[ROW][C]141[/C][C]0.276349172546872[/C][C]0.552698345093744[/C][C]0.723650827453128[/C][/ROW]
[ROW][C]142[/C][C]0.234035294694127[/C][C]0.468070589388255[/C][C]0.765964705305873[/C][/ROW]
[ROW][C]143[/C][C]0.243422575780464[/C][C]0.486845151560928[/C][C]0.756577424219536[/C][/ROW]
[ROW][C]144[/C][C]0.203758896283672[/C][C]0.407517792567344[/C][C]0.796241103716328[/C][/ROW]
[ROW][C]145[/C][C]0.194787957862502[/C][C]0.389575915725004[/C][C]0.805212042137498[/C][/ROW]
[ROW][C]146[/C][C]0.192350875784387[/C][C]0.384701751568775[/C][C]0.807649124215613[/C][/ROW]
[ROW][C]147[/C][C]0.321510159372848[/C][C]0.643020318745696[/C][C]0.678489840627152[/C][/ROW]
[ROW][C]148[/C][C]0.768888886542934[/C][C]0.462222226914131[/C][C]0.231111113457066[/C][/ROW]
[ROW][C]149[/C][C]0.820664829886079[/C][C]0.358670340227842[/C][C]0.179335170113921[/C][/ROW]
[ROW][C]150[/C][C]0.704865831665592[/C][C]0.590268336668817[/C][C]0.295134168334408[/C][/ROW]
[ROW][C]151[/C][C]0.578013306757231[/C][C]0.843973386485537[/C][C]0.421986693242769[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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
110.05944292817294050.1188858563458810.940557071827059
120.8856823922123830.2286352155752340.114317607787617
130.9838844980124870.03223100397502510.0161155019875126
140.9745409330516910.05091813389661870.0254590669483094
150.985648541356850.02870291728629970.0143514586431498
160.9747017686663390.05059646266732180.0252982313336609
170.9757231559243030.04855368815139370.0242768440756968
180.9614815477519620.07703690449607530.0385184522480376
190.9420603974160270.1158792051679460.0579396025839729
200.9428929381120980.1142141237758030.0571070618879016
210.949809527820010.100380944359980.0501904721799902
220.9517488225631120.09650235487377540.0482511774368877
230.9473376307694710.1053247384610590.0526623692305294
240.9264979688018760.1470040623962490.0735020311981243
250.9040566043512820.1918867912974360.095943395648718
260.9996875461238210.0006249077523577350.000312453876178867
270.9994589721027930.001082055794413930.000541027897206964
280.9990950780506050.001809843898789590.000904921949394795
290.9985584956109710.002883008778057860.00144150438902893
300.9990013813428030.001997237314394480.00099861865719724
310.9984254633529680.003149073294063670.00157453664703184
320.9976678865057860.004664226988428290.00233211349421414
330.997293741878360.005412516243279690.00270625812163985
340.9958912072925790.00821758541484110.00410879270742055
350.9947194644801020.01056107103979590.00528053551989793
360.9922805902597520.01543881948049630.00771940974024813
370.9950119970152610.009976005969477130.00498800298473856
380.9928172132728170.01436557345436610.00718278672718307
390.9920785499774320.01584290004513580.00792145002256792
400.9922265967768930.01554680644621440.00777340322310719
410.9892043818444960.02159123631100730.0107956181555036
420.9883822755998930.02323544880021390.011617724400107
430.9842135219331740.03157295613365270.0157864780668264
440.9807283971169190.03854320576616210.019271602883081
450.9834223184215420.0331553631569150.0165776815784575
460.9882780916449480.02344381671010380.0117219083550519
470.9838405499794860.03231890004102840.0161594500205142
480.9779538441880850.04409231162382950.0220461558119148
490.9823888787995560.03522224240088690.0176111212004435
500.9808498119129170.03830037617416560.0191501880870828
510.9747231140422740.05055377191545110.0252768859577256
520.9671798974925130.06564020501497490.0328201025074874
530.9605699722748660.07886005545026770.0394300277251338
540.9519212819752230.09615743604955480.0480787180247774
550.9536275732435160.09274485351296870.0463724267564844
560.941465522036990.117068955926020.0585344779630101
570.9367897070186140.1264205859627720.0632102929813859
580.9204621133276730.1590757733446540.079537886672327
590.9502252285113570.09954954297728610.049774771488643
600.9456852184285190.1086295631429610.0543147815714805
610.9536328056826460.09273438863470740.0463671943173537
620.9526816482190980.09463670356180440.0473183517809022
630.9764909031398580.04701819372028490.0235090968601424
640.9711048479898860.05779030402022790.0288951520101139
650.9688630617104850.06227387657903070.0311369382895154
660.9949852561765220.0100294876469560.00501474382347799
670.9943138300071020.01137233998579580.00568616999289789
680.9950447173214350.009910565357129540.00495528267856477
690.9936369506936610.01272609861267790.00636304930633895
700.9921190235928530.01576195281429420.0078809764071471
710.9892589293167780.02148214136644460.0107410706832223
720.9932750609300970.01344987813980560.00672493906990278
730.9907807620551910.01843847588961690.00921923794480845
740.9878671712567180.02426565748656360.0121328287432818
750.9872344474300190.02553110513996130.0127655525699806
760.9828783948679720.0342432102640550.0171216051320275
770.9877562909342250.02448741813155040.0122437090657752
780.9842480801523260.03150383969534870.0157519198476743
790.9813199361070270.03736012778594550.0186800638929728
800.9793861251611120.04122774967777660.0206138748388883
810.9728456187126340.05430876257473170.0271543812873659
820.9672164065978120.06556718680437560.0327835934021878
830.9586164713548630.08276705729027340.0413835286451367
840.9545379325138180.09092413497236470.0454620674861824
850.9435260306009770.1129479387980470.0564739693990233
860.9288430910310190.1423138179379630.0711569089689814
870.9120350938641210.1759298122717580.0879649061358788
880.8919009282288220.2161981435423560.108099071771178
890.8714626478617650.2570747042764710.128537352138235
900.9200631505240970.1598736989518050.0799368494759025
910.9415258996126010.1169482007747980.0584741003873988
920.9277905566374720.1444188867250560.072209443362528
930.9115365007714150.1769269984571710.0884634992285854
940.893610784128510.212778431742980.10638921587149
950.8940283312753710.2119433374492590.105971668724629
960.8720067947421760.2559864105156470.127993205257824
970.8499377086675790.3001245826648420.150062291332421
980.8306682654461470.3386634691077050.169331734553853
990.8253238028097260.3493523943805470.174676197190274
1000.7929125684386980.4141748631226040.207087431561302
1010.7840777790414270.4318444419171470.215922220958573
1020.7588597548126570.4822804903746860.241140245187343
1030.7318169965242890.5363660069514220.268183003475711
1040.8039796628567620.3920406742864750.196020337143238
1050.8077077142274920.3845845715450160.192292285772508
1060.8402904893227740.3194190213544520.159709510677226
1070.8148112166962670.3703775666074660.185188783303733
1080.8204373099733970.3591253800532060.179562690026603
1090.8565672440311050.2868655119377890.143432755968895
1100.8298464305176530.3403071389646940.170153569482347
1110.8189434722996420.3621130554007160.181056527700358
1120.8147931900893560.3704136198212870.185206809910644
1130.8247499761272150.3505000477455690.175250023872785
1140.9141863852331990.1716272295336010.0858136147668006
1150.9549403882382250.09011922352355050.0450596117617752
1160.9407589465066670.1184821069866660.0592410534933332
1170.9449495643870720.1101008712258560.0550504356129282
1180.9278524271386410.1442951457227190.0721475728613594
1190.9143054977871340.1713890044257330.0856945022128664
1200.8900649477925310.2198701044149390.109935052207469
1210.8642516429851540.2714967140296930.135748357014846
1220.8328483276620230.3343033446759550.167151672337977
1230.7949428792401810.4101142415196370.205057120759819
1240.7643760055148710.4712479889702590.235623994485129
1250.7171862317085180.5656275365829650.282813768291482
1260.6947133293416880.6105733413166240.305286670658312
1270.644883947126470.710232105747060.35511605287353
1280.6742311271782210.6515377456435590.325768872821779
1290.7357874976352170.5284250047295660.264212502364783
1300.7083970305530390.5832059388939220.291602969446961
1310.6815610782140770.6368778435718460.318438921785923
1320.6545370884550960.6909258230898080.345462911544904
1330.5958852096770650.8082295806458710.404114790322935
1340.6076496691058620.7847006617882760.392350330894138
1350.5593800808902260.8812398382195480.440619919109774
1360.5241151056866940.9517697886266110.475884894313306
1370.553168106480280.8936637870394390.44683189351972
1380.4823594173564620.9647188347129250.517640582643538
1390.4102785126311430.8205570252622850.589721487368857
1400.3447164281151090.6894328562302190.655283571884891
1410.2763491725468720.5526983450937440.723650827453128
1420.2340352946941270.4680705893882550.765964705305873
1430.2434225757804640.4868451515609280.756577424219536
1440.2037588962836720.4075177925673440.796241103716328
1450.1947879578625020.3895759157250040.805212042137498
1460.1923508757843870.3847017515687750.807649124215613
1470.3215101593728480.6430203187456960.678489840627152
1480.7688888865429340.4622222269141310.231111113457066
1490.8206648298860790.3586703402278420.179335170113921
1500.7048658316655920.5902683366688170.295134168334408
1510.5780133067572310.8439733864855370.421986693242769







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.0780141843971631NOK
5% type I error level440.312056737588652NOK
10% type I error level630.446808510638298NOK

\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 & 11 & 0.0780141843971631 & NOK \tabularnewline
5% type I error level & 44 & 0.312056737588652 & NOK \tabularnewline
10% type I error level & 63 & 0.446808510638298 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186307&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]11[/C][C]0.0780141843971631[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]44[/C][C]0.312056737588652[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]63[/C][C]0.446808510638298[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186307&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186307&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 level110.0780141843971631NOK
5% type I error level440.312056737588652NOK
10% type I error level630.446808510638298NOK



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