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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 08:40:43 -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/t1352123104ot0lvgpzkzrvf37.htm/, Retrieved Sun, 05 Feb 2023 23:20:45 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186055, Retrieved Sun, 05 Feb 2023 23:20:45 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact83
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [Monthly dummies M...] [2012-11-05 13:40:43] [b7b610b08ce09537f4b16b68ce5f31b7] [Current]
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Dataseries X:
9	41	38	13	12	14	12	53	32
9	39	32	16	11	18	11	86	51
9	30	35	19	15	11	14	66	42
9	31	33	15	6	12	12	67	41
9	34	37	14	13	16	21	76	46
9	35	29	13	10	18	12	78	47
9	39	31	19	12	14	22	53	37
9	34	36	15	14	14	11	80	49
9	36	35	14	12	15	10	74	45
9	37	38	15	6	15	13	76	47
9	38	31	16	10	17	10	79	49
9	36	34	16	12	19	8	54	33
9	38	35	16	12	10	15	67	42
9	39	38	16	11	16	14	54	33
9	33	37	17	15	18	10	87	53
9	32	33	15	12	14	14	58	36
9	36	32	15	10	14	14	75	45
9	38	38	20	12	17	11	88	54
9	39	38	18	11	14	10	64	41
9	32	32	16	12	16	13	57	36
9	32	33	16	11	18	7	66	41
9	31	31	16	12	11	14	68	44
9	39	38	19	13	14	12	54	33
9	37	39	16	11	12	14	56	37
9	39	32	17	9	17	11	86	52
9	41	32	17	13	9	9	80	47
9	36	35	16	10	16	11	76	43
9	33	37	15	14	14	15	69	44
9	33	33	16	12	15	14	78	45
9	34	33	14	10	11	13	67	44
9	31	28	15	12	16	9	80	49
9	27	32	12	8	13	15	54	33
9	37	31	14	10	17	10	71	43
9	34	37	16	12	15	11	84	54
9	34	30	14	12	14	13	74	42
9	32	33	7	7	16	8	71	44
9	29	31	10	6	9	20	63	37
9	36	33	14	12	15	12	71	43
9	29	31	16	10	17	10	76	46
9	35	33	16	10	13	10	69	42
9	37	32	16	10	15	9	74	45
9	34	33	14	12	16	14	75	44
9	38	32	20	15	16	8	54	33
9	35	33	14	10	12	14	52	31
9	38	28	14	10	12	11	69	42
9	37	35	11	12	11	13	68	40
9	38	39	14	13	15	9	65	43
9	33	34	15	11	15	11	75	46
9	36	38	16	11	17	15	74	42
9	38	32	14	12	13	11	75	45
9	32	38	16	14	16	10	72	44
9	32	30	14	10	14	14	67	40
9	32	33	12	12	11	18	63	37
9	34	38	16	13	12	14	62	46
10	32	32	9	5	12	11	63	36
10	37	32	14	6	15	12	76	47
10	39	34	16	12	16	13	74	45
10	29	34	16	12	15	9	67	42
10	37	36	15	11	12	10	73	43
10	35	34	16	10	12	15	70	43
10	30	28	12	7	8	20	53	32
10	38	34	16	12	13	12	77	45
10	34	35	16	14	11	12	77	45
10	31	35	14	11	14	14	52	31
10	34	31	16	12	15	13	54	33
10	35	37	17	13	10	11	80	49
10	36	35	18	14	11	17	66	42
10	30	27	18	11	12	12	73	41
10	39	40	12	12	15	13	63	38
10	35	37	16	12	15	14	69	42
10	38	36	10	8	14	13	67	44
10	31	38	14	11	16	15	54	33
10	34	39	18	14	15	13	81	48
10	38	41	18	14	15	10	69	40
10	34	27	16	12	13	11	84	50
10	39	30	17	9	12	19	80	49
10	37	37	16	13	17	13	70	43
10	34	31	16	11	13	17	69	44
10	28	31	13	12	15	13	77	47
10	37	27	16	12	13	9	54	33
10	33	36	16	12	15	11	79	46
10	37	38	20	12	16	10	30	0
10	35	37	16	12	15	9	71	45
10	37	33	15	12	16	12	73	43
10	32	34	15	11	15	12	72	44
10	33	31	16	10	14	13	77	47
10	38	39	14	9	15	13	75	45
10	33	34	16	12	14	12	69	42
10	29	32	16	12	13	15	54	33
10	33	33	15	12	7	22	70	43
10	31	36	12	9	17	13	73	46
10	36	32	17	15	13	15	54	33
10	35	41	16	12	15	13	77	46
10	32	28	15	12	14	15	82	48
10	29	30	13	12	13	10	80	47
10	39	36	16	10	16	11	80	47
10	37	35	16	13	12	16	69	43
10	35	31	16	9	14	11	78	46
10	37	34	16	12	17	11	81	48
10	32	36	14	10	15	10	76	46
10	38	36	16	14	17	10	76	45
10	37	35	16	11	12	16	73	45
10	36	37	20	15	16	12	85	52
10	32	28	15	11	11	11	66	42
10	33	39	16	11	15	16	79	47
10	40	32	13	12	9	19	68	41
10	38	35	17	12	16	11	76	47
10	41	39	16	12	15	16	71	43
11	36	35	16	11	10	15	54	33
11	43	42	12	7	10	24	46	30
11	30	34	16	12	15	14	82	49
11	31	33	16	14	11	15	74	44
11	32	41	17	11	13	11	88	55
11	32	33	13	11	14	15	38	11
11	37	34	12	10	18	12	76	47
11	37	32	18	13	16	10	86	53
11	33	40	14	13	14	14	54	33
11	34	40	14	8	14	13	70	44
11	33	35	13	11	14	9	69	42
11	38	36	16	12	14	15	90	55
11	33	37	13	11	12	15	54	33
11	31	27	16	13	14	14	76	46
11	38	39	13	12	15	11	89	54
11	37	38	16	14	15	8	76	47
11	33	31	15	13	15	11	73	45
11	31	33	16	15	13	11	79	47
11	39	32	15	10	17	8	90	55
11	44	39	17	11	17	10	74	44
11	33	36	15	9	19	11	81	53
11	35	33	12	11	15	13	72	44
11	32	33	16	10	13	11	71	42
11	28	32	10	11	9	20	66	40
11	40	37	16	8	15	10	77	46
11	27	30	12	11	15	15	65	40
11	37	38	14	12	15	12	74	46
11	32	29	15	12	16	14	82	53
11	28	22	13	9	11	23	54	33
11	34	35	15	11	14	14	63	42
11	30	35	11	10	11	16	54	35
11	35	34	12	8	15	11	64	40
11	31	35	8	9	13	12	69	41
11	32	34	16	8	15	10	54	33
11	30	34	15	9	16	14	84	51
11	30	35	17	15	14	12	86	53
11	31	23	16	11	15	12	77	46
11	40	31	10	8	16	11	89	55
11	32	27	18	13	16	12	76	47
11	36	36	13	12	11	13	60	38
11	32	31	16	12	12	11	75	46
11	35	32	13	9	9	19	73	46
11	38	39	10	7	16	12	85	53
11	42	37	15	13	13	17	79	47
11	34	38	16	9	16	9	71	41
11	35	39	16	6	12	12	72	44
11	35	34	14	8	9	19	69	43
11	33	31	10	8	13	18	78	51
11	36	32	17	15	13	15	54	33
11	32	37	13	6	14	14	69	43
11	33	36	15	9	19	11	81	53
11	34	32	16	11	13	9	84	51
11	32	35	12	8	12	18	84	50
11	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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&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]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&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 time11 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 7.41086418819751 -0.197416427817063month[t] + 0.107182174445822Connected[t] -0.0145956286528018Separate[t] + 0.532997096646545Software[t] + 0.0542272646368851Happiness[t] -0.0647808927226989Depression[t] + 0.0413062256139078Belonging[t] -0.0565867207479564Belonging_Final[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  7.41086418819751 -0.197416427817063month[t] +  0.107182174445822Connected[t] -0.0145956286528018Separate[t] +  0.532997096646545Software[t] +  0.0542272646368851Happiness[t] -0.0647808927226989Depression[t] +  0.0413062256139078Belonging[t] -0.0565867207479564Belonging_Final[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  7.41086418819751 -0.197416427817063month[t] +  0.107182174445822Connected[t] -0.0145956286528018Separate[t] +  0.532997096646545Software[t] +  0.0542272646368851Happiness[t] -0.0647808927226989Depression[t] +  0.0413062256139078Belonging[t] -0.0565867207479564Belonging_Final[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&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
Learning[t] = + 7.41086418819751 -0.197416427817063month[t] + 0.107182174445822Connected[t] -0.0145956286528018Separate[t] + 0.532997096646545Software[t] + 0.0542272646368851Happiness[t] -0.0647808927226989Depression[t] + 0.0413062256139078Belonging[t] -0.0565867207479564Belonging_Final[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7.410864188197513.1530852.35040.0200320.010016
month-0.1974164278170630.185899-1.0620.2899290.144964
Connected0.1071821744458220.0473152.26530.0248980.012449
Separate-0.01459562865280180.045156-0.32320.7469630.373482
Software0.5329970966465450.0695097.66800
Happiness0.05422726463688510.0765240.70860.4796310.239815
Depression-0.06478089272269890.056637-1.14380.2544930.127247
Belonging0.04130622561390780.0448170.92170.3581580.179079
Belonging_Final-0.05658672074795640.064063-0.88330.3784590.189229

\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) & 7.41086418819751 & 3.153085 & 2.3504 & 0.020032 & 0.010016 \tabularnewline
month & -0.197416427817063 & 0.185899 & -1.062 & 0.289929 & 0.144964 \tabularnewline
Connected & 0.107182174445822 & 0.047315 & 2.2653 & 0.024898 & 0.012449 \tabularnewline
Separate & -0.0145956286528018 & 0.045156 & -0.3232 & 0.746963 & 0.373482 \tabularnewline
Software & 0.532997096646545 & 0.069509 & 7.668 & 0 & 0 \tabularnewline
Happiness & 0.0542272646368851 & 0.076524 & 0.7086 & 0.479631 & 0.239815 \tabularnewline
Depression & -0.0647808927226989 & 0.056637 & -1.1438 & 0.254493 & 0.127247 \tabularnewline
Belonging & 0.0413062256139078 & 0.044817 & 0.9217 & 0.358158 & 0.179079 \tabularnewline
Belonging_Final & -0.0565867207479564 & 0.064063 & -0.8833 & 0.378459 & 0.189229 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&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]7.41086418819751[/C][C]3.153085[/C][C]2.3504[/C][C]0.020032[/C][C]0.010016[/C][/ROW]
[ROW][C]month[/C][C]-0.197416427817063[/C][C]0.185899[/C][C]-1.062[/C][C]0.289929[/C][C]0.144964[/C][/ROW]
[ROW][C]Connected[/C][C]0.107182174445822[/C][C]0.047315[/C][C]2.2653[/C][C]0.024898[/C][C]0.012449[/C][/ROW]
[ROW][C]Separate[/C][C]-0.0145956286528018[/C][C]0.045156[/C][C]-0.3232[/C][C]0.746963[/C][C]0.373482[/C][/ROW]
[ROW][C]Software[/C][C]0.532997096646545[/C][C]0.069509[/C][C]7.668[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0542272646368851[/C][C]0.076524[/C][C]0.7086[/C][C]0.479631[/C][C]0.239815[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0647808927226989[/C][C]0.056637[/C][C]-1.1438[/C][C]0.254493[/C][C]0.127247[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0413062256139078[/C][C]0.044817[/C][C]0.9217[/C][C]0.358158[/C][C]0.179079[/C][/ROW]
[ROW][C]Belonging_Final[/C][C]-0.0565867207479564[/C][C]0.064063[/C][C]-0.8833[/C][C]0.378459[/C][C]0.189229[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&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)7.410864188197513.1530852.35040.0200320.010016
month-0.1974164278170630.185899-1.0620.2899290.144964
Connected0.1071821744458220.0473152.26530.0248980.012449
Separate-0.01459562865280180.045156-0.32320.7469630.373482
Software0.5329970966465450.0695097.66800
Happiness0.05422726463688510.0765240.70860.4796310.239815
Depression-0.06478089272269890.056637-1.14380.2544930.127247
Belonging0.04130622561390780.0448170.92170.3581580.179079
Belonging_Final-0.05658672074795640.064063-0.88330.3784590.189229







Multiple Linear Regression - Regression Statistics
Multiple R0.601179730636677
R-squared0.361417068528387
Adjusted R-squared0.328027111327257
F-TEST (value)10.8241249412609
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.12323516943525e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84954635690498
Sum Squared Residuals523.385724130092

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.601179730636677 \tabularnewline
R-squared & 0.361417068528387 \tabularnewline
Adjusted R-squared & 0.328027111327257 \tabularnewline
F-TEST (value) & 10.8241249412609 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 5.12323516943525e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84954635690498 \tabularnewline
Sum Squared Residuals & 523.385724130092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.601179730636677[/C][/ROW]
[ROW][C]R-squared[/C][C]0.361417068528387[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.328027111327257[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.8241249412609[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]5.12323516943525e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84954635690498[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]523.385724130092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&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 R0.601179730636677
R-squared0.361417068528387
Adjusted R-squared0.328027111327257
F-TEST (value)10.8241249412609
F-TEST (DF numerator)8
F-TEST (DF denominator)153
p-value5.12323516943525e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84954635690498
Sum Squared Residuals523.385724130092







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.2301826469213-3.23018264692127
21616.1400426756179-0.140042675617893
31916.37282705006042.62717294993958
41511.99390860843713.00609139156291
51415.7107557445178-1.7107557445178
61315.0532199525043-2.05321995250433
71915.18724516763243.81275483236756
81516.7931676079821-1.79316760798213
91416.0536510789015-2.05365107890145
101512.69416011907342.30583988092659
111615.34904252346270.650957476537302
121616.2676336882445-0.267633688244512
131615.55359122394170.446408776058309
141615.44643345007740.553566549922621
151717.5488735491058-0.548873549105821
161515.1891436798052-0.189143679805167
171514.7593991616530.240600838347005
182016.33690885024893.66309114975114
191815.55747098184982.44252901815017
201615.33566850484050.664331495159469
211615.37403809193650.625961908063464
221614.90883945890971.09116054109028
231916.53353489954212.46646510045791
241614.85682998222141.14317001777862
251714.963234496942.03676550306004
261717.0404271508244-0.0404271508244363
271615.17288915024630.827110849753723
281516.2408313559795-1.24083135597951
291615.66739714443440.332602855565576
301414.1586751987573-0.158675198757285
311515.7604082352932-0.760408235293158
321212.4213671520713-0.421367152071332
331415.2509308685934-1.25093086859335
341615.6490963493890.350903650611025
351415.8334550927127-1.8334550927127
36713.1055852491796-6.10558524917956
371011.188928561695-1.188928561695
381415.9425353154158-1.94253531541585
391614.43024443885241.56975556114756
401614.76444047336871.2355595266313
411615.20340683873530.79659316126471
421415.7614746274234-1.76147462742336
432017.94749879047072.05250120952926
441414.3713377306321-0.37133773063211
451415.0399569826106-1.0399569826106
461115.784677766688-4.78467776668797
471416.5488283135219-2.54882831352187
481515.1336416997135-0.133641699713494
491615.43117732420060.568822675799353
501416.1798731173689-2.17987311736894
511616.6753312226099-0.675331222609872
521414.3123455200038-0.312345520003828
531214.9172827223253-2.9172827223253
541615.35443014778160.645569852218355
55911.567762481079-2.56776248107896
561412.64909835589591.35090164410415
571616.0522613895434-0.0522613895434465
581615.06595253428560.934047465714354
591515.3250095222022-0.325009522202214
601614.15901619351441.84098380648559
611211.49112237389250.508877626107472
621615.97109699075140.0289030092486086
631616.4853123283346-0.485312328334619
641414.3574529736465-0.357452973646468
651615.35882627533320.641173724666825
661715.9384315707641.061568429236
671816.09116389410371.90883610589635
681814.68970661500723.31029338499281
691215.8521989164725-3.85219891647246
701615.42396668261650.576033317383503
711013.4428881833827-3.44288818338267
721414.326778733971-0.326778733971037
731816.574522719761.42547728023998
741817.12542189702260.874578102977444
751615.7155285618180.284471438182014
761713.92754867002293.07245132997709
771616.3292830550171-0.329283055017107
781614.45539053450321.54460946549684
791314.8735623273067-1.87356232730673
801615.88942435489890.110575645101125
811615.6052560136930.394743986306997
822016.70278571085483.29721428914521
831615.66072343521390.339276564786062
841515.9891407779093-0.989140777909307
851514.75351696938210.246483030617901
861614.28925174160591.71074825839413
871414.260188742871-0.260188742871004
881615.32872374049180.671276259508229
891614.57030345973211.42969654026791
901514.30063909432530.699360905674661
911213.5229557650066-1.52295576500665
921716.91956997079250.0804300292075153
931615.53446798264740.465532017352574
941515.3122332682877-0.312233268287726
951315.2051469561414-2.2051469561414
961615.22130163657770.778698363422314
971615.85168908535630.148310914643719
981614.19807372565821.80192627434177
991616.1410695077876-0.14106950778757
1001414.3729418618351-0.372941861835067
1011617.3130645451179-1.31306454511791
1021614.83774635302291.16225364697709
1032017.40896159942722.5910384005728
1041514.55429866328650.445701336713466
1051614.64798084672661.3520191532734
1061315.3988681418089-2.39886814180886
1071716.02948438162210.970515618377876
1081615.93433241702030.0656675829797099
1091614.38369647652021.61630352347982
1101212.1560962333135-0.156096233313464
1111614.87530015627381.12469984372617
1121615.73386600022380.266133999776172
1131714.44870318603942.55129681396064
1141314.7850763412226-1.78507634122255
1151214.71716085127-2.71716085127002
1161816.43999258633821.56000741366177
1171415.336855954482-1.33685595448203
1181412.88227922001281.11772077998716
1191314.7780572655435-1.77805726554346
1201615.57548761759880.424512382401249
1211314.1414132251509-1.14141322515088
1221615.48534437185940.514655628140648
1231315.8603320623021-2.86033206230208
1241616.8872085002251-0.887208500225133
1251515.822564192851-0.822564192851004
1261616.6712121628606-0.671212162860609
1271515.2912061563322-0.291206156332239
1281716.08993725759790.910062742402119
1291513.7122627604761.28773723952403
1301214.8274618008326-2.82746180083261
1311614.06589265290221.93410734709777
1321013.2914619007928-3.29146190079283
1331613.99369923325262.0063007667474
1341213.8214328094735-1.82143280947351
1351415.5360650055614-1.53606500556139
1361514.99052303007450.0094769699254514
1371312.18596818300220.814031816997769
1381514.31349762269230.686502377307661
1391113.083879263617-2.08387926361698
1401213.2393337457661-1.23933374576613
141813.3057155013017-5.3057155013017
1421612.9656129042483.03438709575202
1431513.2999751407031.70002485929701
1441716.4739083578330.526091642167015
1451614.70282796887371.29717203112634
1461014.0571135977189-4.05711359771888
1471815.77395614027642.22604385972361
1481315.0507907445399-2.05079074453987
1491615.04572885832780.954271141672159
1501312.99014707615280.00985292384718957
1511013.0761547692759-3.07615476927586
1521516.3371540175242-1.33715401752417
1531614.02311206198731.97688793801267
1541611.97700164449514.02299835550488
1551412.43249398198891.5675060180111
1561012.4626687348674-2.46266873486742
1571716.72215354297540.277846457024578
1581311.59620716619791.40379283380214
1591513.7122627604761.28773723952403
1601614.98511195878781.01488804121219
1611212.5473008556049-0.547300855604908
1621312.53761212470920.462387875290758

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.2301826469213 & -3.23018264692127 \tabularnewline
2 & 16 & 16.1400426756179 & -0.140042675617893 \tabularnewline
3 & 19 & 16.3728270500604 & 2.62717294993958 \tabularnewline
4 & 15 & 11.9939086084371 & 3.00609139156291 \tabularnewline
5 & 14 & 15.7107557445178 & -1.7107557445178 \tabularnewline
6 & 13 & 15.0532199525043 & -2.05321995250433 \tabularnewline
7 & 19 & 15.1872451676324 & 3.81275483236756 \tabularnewline
8 & 15 & 16.7931676079821 & -1.79316760798213 \tabularnewline
9 & 14 & 16.0536510789015 & -2.05365107890145 \tabularnewline
10 & 15 & 12.6941601190734 & 2.30583988092659 \tabularnewline
11 & 16 & 15.3490425234627 & 0.650957476537302 \tabularnewline
12 & 16 & 16.2676336882445 & -0.267633688244512 \tabularnewline
13 & 16 & 15.5535912239417 & 0.446408776058309 \tabularnewline
14 & 16 & 15.4464334500774 & 0.553566549922621 \tabularnewline
15 & 17 & 17.5488735491058 & -0.548873549105821 \tabularnewline
16 & 15 & 15.1891436798052 & -0.189143679805167 \tabularnewline
17 & 15 & 14.759399161653 & 0.240600838347005 \tabularnewline
18 & 20 & 16.3369088502489 & 3.66309114975114 \tabularnewline
19 & 18 & 15.5574709818498 & 2.44252901815017 \tabularnewline
20 & 16 & 15.3356685048405 & 0.664331495159469 \tabularnewline
21 & 16 & 15.3740380919365 & 0.625961908063464 \tabularnewline
22 & 16 & 14.9088394589097 & 1.09116054109028 \tabularnewline
23 & 19 & 16.5335348995421 & 2.46646510045791 \tabularnewline
24 & 16 & 14.8568299822214 & 1.14317001777862 \tabularnewline
25 & 17 & 14.96323449694 & 2.03676550306004 \tabularnewline
26 & 17 & 17.0404271508244 & -0.0404271508244363 \tabularnewline
27 & 16 & 15.1728891502463 & 0.827110849753723 \tabularnewline
28 & 15 & 16.2408313559795 & -1.24083135597951 \tabularnewline
29 & 16 & 15.6673971444344 & 0.332602855565576 \tabularnewline
30 & 14 & 14.1586751987573 & -0.158675198757285 \tabularnewline
31 & 15 & 15.7604082352932 & -0.760408235293158 \tabularnewline
32 & 12 & 12.4213671520713 & -0.421367152071332 \tabularnewline
33 & 14 & 15.2509308685934 & -1.25093086859335 \tabularnewline
34 & 16 & 15.649096349389 & 0.350903650611025 \tabularnewline
35 & 14 & 15.8334550927127 & -1.8334550927127 \tabularnewline
36 & 7 & 13.1055852491796 & -6.10558524917956 \tabularnewline
37 & 10 & 11.188928561695 & -1.188928561695 \tabularnewline
38 & 14 & 15.9425353154158 & -1.94253531541585 \tabularnewline
39 & 16 & 14.4302444388524 & 1.56975556114756 \tabularnewline
40 & 16 & 14.7644404733687 & 1.2355595266313 \tabularnewline
41 & 16 & 15.2034068387353 & 0.79659316126471 \tabularnewline
42 & 14 & 15.7614746274234 & -1.76147462742336 \tabularnewline
43 & 20 & 17.9474987904707 & 2.05250120952926 \tabularnewline
44 & 14 & 14.3713377306321 & -0.37133773063211 \tabularnewline
45 & 14 & 15.0399569826106 & -1.0399569826106 \tabularnewline
46 & 11 & 15.784677766688 & -4.78467776668797 \tabularnewline
47 & 14 & 16.5488283135219 & -2.54882831352187 \tabularnewline
48 & 15 & 15.1336416997135 & -0.133641699713494 \tabularnewline
49 & 16 & 15.4311773242006 & 0.568822675799353 \tabularnewline
50 & 14 & 16.1798731173689 & -2.17987311736894 \tabularnewline
51 & 16 & 16.6753312226099 & -0.675331222609872 \tabularnewline
52 & 14 & 14.3123455200038 & -0.312345520003828 \tabularnewline
53 & 12 & 14.9172827223253 & -2.9172827223253 \tabularnewline
54 & 16 & 15.3544301477816 & 0.645569852218355 \tabularnewline
55 & 9 & 11.567762481079 & -2.56776248107896 \tabularnewline
56 & 14 & 12.6490983558959 & 1.35090164410415 \tabularnewline
57 & 16 & 16.0522613895434 & -0.0522613895434465 \tabularnewline
58 & 16 & 15.0659525342856 & 0.934047465714354 \tabularnewline
59 & 15 & 15.3250095222022 & -0.325009522202214 \tabularnewline
60 & 16 & 14.1590161935144 & 1.84098380648559 \tabularnewline
61 & 12 & 11.4911223738925 & 0.508877626107472 \tabularnewline
62 & 16 & 15.9710969907514 & 0.0289030092486086 \tabularnewline
63 & 16 & 16.4853123283346 & -0.485312328334619 \tabularnewline
64 & 14 & 14.3574529736465 & -0.357452973646468 \tabularnewline
65 & 16 & 15.3588262753332 & 0.641173724666825 \tabularnewline
66 & 17 & 15.938431570764 & 1.061568429236 \tabularnewline
67 & 18 & 16.0911638941037 & 1.90883610589635 \tabularnewline
68 & 18 & 14.6897066150072 & 3.31029338499281 \tabularnewline
69 & 12 & 15.8521989164725 & -3.85219891647246 \tabularnewline
70 & 16 & 15.4239666826165 & 0.576033317383503 \tabularnewline
71 & 10 & 13.4428881833827 & -3.44288818338267 \tabularnewline
72 & 14 & 14.326778733971 & -0.326778733971037 \tabularnewline
73 & 18 & 16.57452271976 & 1.42547728023998 \tabularnewline
74 & 18 & 17.1254218970226 & 0.874578102977444 \tabularnewline
75 & 16 & 15.715528561818 & 0.284471438182014 \tabularnewline
76 & 17 & 13.9275486700229 & 3.07245132997709 \tabularnewline
77 & 16 & 16.3292830550171 & -0.329283055017107 \tabularnewline
78 & 16 & 14.4553905345032 & 1.54460946549684 \tabularnewline
79 & 13 & 14.8735623273067 & -1.87356232730673 \tabularnewline
80 & 16 & 15.8894243548989 & 0.110575645101125 \tabularnewline
81 & 16 & 15.605256013693 & 0.394743986306997 \tabularnewline
82 & 20 & 16.7027857108548 & 3.29721428914521 \tabularnewline
83 & 16 & 15.6607234352139 & 0.339276564786062 \tabularnewline
84 & 15 & 15.9891407779093 & -0.989140777909307 \tabularnewline
85 & 15 & 14.7535169693821 & 0.246483030617901 \tabularnewline
86 & 16 & 14.2892517416059 & 1.71074825839413 \tabularnewline
87 & 14 & 14.260188742871 & -0.260188742871004 \tabularnewline
88 & 16 & 15.3287237404918 & 0.671276259508229 \tabularnewline
89 & 16 & 14.5703034597321 & 1.42969654026791 \tabularnewline
90 & 15 & 14.3006390943253 & 0.699360905674661 \tabularnewline
91 & 12 & 13.5229557650066 & -1.52295576500665 \tabularnewline
92 & 17 & 16.9195699707925 & 0.0804300292075153 \tabularnewline
93 & 16 & 15.5344679826474 & 0.465532017352574 \tabularnewline
94 & 15 & 15.3122332682877 & -0.312233268287726 \tabularnewline
95 & 13 & 15.2051469561414 & -2.2051469561414 \tabularnewline
96 & 16 & 15.2213016365777 & 0.778698363422314 \tabularnewline
97 & 16 & 15.8516890853563 & 0.148310914643719 \tabularnewline
98 & 16 & 14.1980737256582 & 1.80192627434177 \tabularnewline
99 & 16 & 16.1410695077876 & -0.14106950778757 \tabularnewline
100 & 14 & 14.3729418618351 & -0.372941861835067 \tabularnewline
101 & 16 & 17.3130645451179 & -1.31306454511791 \tabularnewline
102 & 16 & 14.8377463530229 & 1.16225364697709 \tabularnewline
103 & 20 & 17.4089615994272 & 2.5910384005728 \tabularnewline
104 & 15 & 14.5542986632865 & 0.445701336713466 \tabularnewline
105 & 16 & 14.6479808467266 & 1.3520191532734 \tabularnewline
106 & 13 & 15.3988681418089 & -2.39886814180886 \tabularnewline
107 & 17 & 16.0294843816221 & 0.970515618377876 \tabularnewline
108 & 16 & 15.9343324170203 & 0.0656675829797099 \tabularnewline
109 & 16 & 14.3836964765202 & 1.61630352347982 \tabularnewline
110 & 12 & 12.1560962333135 & -0.156096233313464 \tabularnewline
111 & 16 & 14.8753001562738 & 1.12469984372617 \tabularnewline
112 & 16 & 15.7338660002238 & 0.266133999776172 \tabularnewline
113 & 17 & 14.4487031860394 & 2.55129681396064 \tabularnewline
114 & 13 & 14.7850763412226 & -1.78507634122255 \tabularnewline
115 & 12 & 14.71716085127 & -2.71716085127002 \tabularnewline
116 & 18 & 16.4399925863382 & 1.56000741366177 \tabularnewline
117 & 14 & 15.336855954482 & -1.33685595448203 \tabularnewline
118 & 14 & 12.8822792200128 & 1.11772077998716 \tabularnewline
119 & 13 & 14.7780572655435 & -1.77805726554346 \tabularnewline
120 & 16 & 15.5754876175988 & 0.424512382401249 \tabularnewline
121 & 13 & 14.1414132251509 & -1.14141322515088 \tabularnewline
122 & 16 & 15.4853443718594 & 0.514655628140648 \tabularnewline
123 & 13 & 15.8603320623021 & -2.86033206230208 \tabularnewline
124 & 16 & 16.8872085002251 & -0.887208500225133 \tabularnewline
125 & 15 & 15.822564192851 & -0.822564192851004 \tabularnewline
126 & 16 & 16.6712121628606 & -0.671212162860609 \tabularnewline
127 & 15 & 15.2912061563322 & -0.291206156332239 \tabularnewline
128 & 17 & 16.0899372575979 & 0.910062742402119 \tabularnewline
129 & 15 & 13.712262760476 & 1.28773723952403 \tabularnewline
130 & 12 & 14.8274618008326 & -2.82746180083261 \tabularnewline
131 & 16 & 14.0658926529022 & 1.93410734709777 \tabularnewline
132 & 10 & 13.2914619007928 & -3.29146190079283 \tabularnewline
133 & 16 & 13.9936992332526 & 2.0063007667474 \tabularnewline
134 & 12 & 13.8214328094735 & -1.82143280947351 \tabularnewline
135 & 14 & 15.5360650055614 & -1.53606500556139 \tabularnewline
136 & 15 & 14.9905230300745 & 0.0094769699254514 \tabularnewline
137 & 13 & 12.1859681830022 & 0.814031816997769 \tabularnewline
138 & 15 & 14.3134976226923 & 0.686502377307661 \tabularnewline
139 & 11 & 13.083879263617 & -2.08387926361698 \tabularnewline
140 & 12 & 13.2393337457661 & -1.23933374576613 \tabularnewline
141 & 8 & 13.3057155013017 & -5.3057155013017 \tabularnewline
142 & 16 & 12.965612904248 & 3.03438709575202 \tabularnewline
143 & 15 & 13.299975140703 & 1.70002485929701 \tabularnewline
144 & 17 & 16.473908357833 & 0.526091642167015 \tabularnewline
145 & 16 & 14.7028279688737 & 1.29717203112634 \tabularnewline
146 & 10 & 14.0571135977189 & -4.05711359771888 \tabularnewline
147 & 18 & 15.7739561402764 & 2.22604385972361 \tabularnewline
148 & 13 & 15.0507907445399 & -2.05079074453987 \tabularnewline
149 & 16 & 15.0457288583278 & 0.954271141672159 \tabularnewline
150 & 13 & 12.9901470761528 & 0.00985292384718957 \tabularnewline
151 & 10 & 13.0761547692759 & -3.07615476927586 \tabularnewline
152 & 15 & 16.3371540175242 & -1.33715401752417 \tabularnewline
153 & 16 & 14.0231120619873 & 1.97688793801267 \tabularnewline
154 & 16 & 11.9770016444951 & 4.02299835550488 \tabularnewline
155 & 14 & 12.4324939819889 & 1.5675060180111 \tabularnewline
156 & 10 & 12.4626687348674 & -2.46266873486742 \tabularnewline
157 & 17 & 16.7221535429754 & 0.277846457024578 \tabularnewline
158 & 13 & 11.5962071661979 & 1.40379283380214 \tabularnewline
159 & 15 & 13.712262760476 & 1.28773723952403 \tabularnewline
160 & 16 & 14.9851119587878 & 1.01488804121219 \tabularnewline
161 & 12 & 12.5473008556049 & -0.547300855604908 \tabularnewline
162 & 13 & 12.5376121247092 & 0.462387875290758 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&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]13[/C][C]16.2301826469213[/C][C]-3.23018264692127[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.1400426756179[/C][C]-0.140042675617893[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.3728270500604[/C][C]2.62717294993958[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.9939086084371[/C][C]3.00609139156291[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7107557445178[/C][C]-1.7107557445178[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0532199525043[/C][C]-2.05321995250433[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1872451676324[/C][C]3.81275483236756[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.7931676079821[/C][C]-1.79316760798213[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.0536510789015[/C][C]-2.05365107890145[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.6941601190734[/C][C]2.30583988092659[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.3490425234627[/C][C]0.650957476537302[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2676336882445[/C][C]-0.267633688244512[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.5535912239417[/C][C]0.446408776058309[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4464334500774[/C][C]0.553566549922621[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5488735491058[/C][C]-0.548873549105821[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1891436798052[/C][C]-0.189143679805167[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.759399161653[/C][C]0.240600838347005[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3369088502489[/C][C]3.66309114975114[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.5574709818498[/C][C]2.44252901815017[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3356685048405[/C][C]0.664331495159469[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.3740380919365[/C][C]0.625961908063464[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9088394589097[/C][C]1.09116054109028[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.5335348995421[/C][C]2.46646510045791[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8568299822214[/C][C]1.14317001777862[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.96323449694[/C][C]2.03676550306004[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0404271508244[/C][C]-0.0404271508244363[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1728891502463[/C][C]0.827110849753723[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.2408313559795[/C][C]-1.24083135597951[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6673971444344[/C][C]0.332602855565576[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1586751987573[/C][C]-0.158675198757285[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7604082352932[/C][C]-0.760408235293158[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4213671520713[/C][C]-0.421367152071332[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.2509308685934[/C][C]-1.25093086859335[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.649096349389[/C][C]0.350903650611025[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.8334550927127[/C][C]-1.8334550927127[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1055852491796[/C][C]-6.10558524917956[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.188928561695[/C][C]-1.188928561695[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.9425353154158[/C][C]-1.94253531541585[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4302444388524[/C][C]1.56975556114756[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7644404733687[/C][C]1.2355595266313[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.2034068387353[/C][C]0.79659316126471[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.7614746274234[/C][C]-1.76147462742336[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.9474987904707[/C][C]2.05250120952926[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.3713377306321[/C][C]-0.37133773063211[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0399569826106[/C][C]-1.0399569826106[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.784677766688[/C][C]-4.78467776668797[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5488283135219[/C][C]-2.54882831352187[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1336416997135[/C][C]-0.133641699713494[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.4311773242006[/C][C]0.568822675799353[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.1798731173689[/C][C]-2.17987311736894[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6753312226099[/C][C]-0.675331222609872[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.3123455200038[/C][C]-0.312345520003828[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.9172827223253[/C][C]-2.9172827223253[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.3544301477816[/C][C]0.645569852218355[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.567762481079[/C][C]-2.56776248107896[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6490983558959[/C][C]1.35090164410415[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0522613895434[/C][C]-0.0522613895434465[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0659525342856[/C][C]0.934047465714354[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3250095222022[/C][C]-0.325009522202214[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1590161935144[/C][C]1.84098380648559[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4911223738925[/C][C]0.508877626107472[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9710969907514[/C][C]0.0289030092486086[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.4853123283346[/C][C]-0.485312328334619[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.3574529736465[/C][C]-0.357452973646468[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.3588262753332[/C][C]0.641173724666825[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.938431570764[/C][C]1.061568429236[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.0911638941037[/C][C]1.90883610589635[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6897066150072[/C][C]3.31029338499281[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.8521989164725[/C][C]-3.85219891647246[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.4239666826165[/C][C]0.576033317383503[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.4428881833827[/C][C]-3.44288818338267[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.326778733971[/C][C]-0.326778733971037[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.57452271976[/C][C]1.42547728023998[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.1254218970226[/C][C]0.874578102977444[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.715528561818[/C][C]0.284471438182014[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.9275486700229[/C][C]3.07245132997709[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.3292830550171[/C][C]-0.329283055017107[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.4553905345032[/C][C]1.54460946549684[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8735623273067[/C][C]-1.87356232730673[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8894243548989[/C][C]0.110575645101125[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.605256013693[/C][C]0.394743986306997[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7027857108548[/C][C]3.29721428914521[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.6607234352139[/C][C]0.339276564786062[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9891407779093[/C][C]-0.989140777909307[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7535169693821[/C][C]0.246483030617901[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2892517416059[/C][C]1.71074825839413[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.260188742871[/C][C]-0.260188742871004[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.3287237404918[/C][C]0.671276259508229[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.5703034597321[/C][C]1.42969654026791[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.3006390943253[/C][C]0.699360905674661[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.5229557650066[/C][C]-1.52295576500665[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9195699707925[/C][C]0.0804300292075153[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5344679826474[/C][C]0.465532017352574[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3122332682877[/C][C]-0.312233268287726[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2051469561414[/C][C]-2.2051469561414[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2213016365777[/C][C]0.778698363422314[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8516890853563[/C][C]0.148310914643719[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.1980737256582[/C][C]1.80192627434177[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1410695077876[/C][C]-0.14106950778757[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.3729418618351[/C][C]-0.372941861835067[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3130645451179[/C][C]-1.31306454511791[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8377463530229[/C][C]1.16225364697709[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4089615994272[/C][C]2.5910384005728[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.5542986632865[/C][C]0.445701336713466[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.6479808467266[/C][C]1.3520191532734[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3988681418089[/C][C]-2.39886814180886[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.0294843816221[/C][C]0.970515618377876[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.9343324170203[/C][C]0.0656675829797099[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.3836964765202[/C][C]1.61630352347982[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.1560962333135[/C][C]-0.156096233313464[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.8753001562738[/C][C]1.12469984372617[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.7338660002238[/C][C]0.266133999776172[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.4487031860394[/C][C]2.55129681396064[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.7850763412226[/C][C]-1.78507634122255[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.71716085127[/C][C]-2.71716085127002[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.4399925863382[/C][C]1.56000741366177[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.336855954482[/C][C]-1.33685595448203[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]12.8822792200128[/C][C]1.11772077998716[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.7780572655435[/C][C]-1.77805726554346[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.5754876175988[/C][C]0.424512382401249[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1414132251509[/C][C]-1.14141322515088[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.4853443718594[/C][C]0.514655628140648[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8603320623021[/C][C]-2.86033206230208[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8872085002251[/C][C]-0.887208500225133[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.822564192851[/C][C]-0.822564192851004[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6712121628606[/C][C]-0.671212162860609[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2912061563322[/C][C]-0.291206156332239[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0899372575979[/C][C]0.910062742402119[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.712262760476[/C][C]1.28773723952403[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.8274618008326[/C][C]-2.82746180083261[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0658926529022[/C][C]1.93410734709777[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2914619007928[/C][C]-3.29146190079283[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9936992332526[/C][C]2.0063007667474[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.8214328094735[/C][C]-1.82143280947351[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.5360650055614[/C][C]-1.53606500556139[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9905230300745[/C][C]0.0094769699254514[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1859681830022[/C][C]0.814031816997769[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.3134976226923[/C][C]0.686502377307661[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.083879263617[/C][C]-2.08387926361698[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.2393337457661[/C][C]-1.23933374576613[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.3057155013017[/C][C]-5.3057155013017[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.965612904248[/C][C]3.03438709575202[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.299975140703[/C][C]1.70002485929701[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.473908357833[/C][C]0.526091642167015[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.7028279688737[/C][C]1.29717203112634[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.0571135977189[/C][C]-4.05711359771888[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.7739561402764[/C][C]2.22604385972361[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.0507907445399[/C][C]-2.05079074453987[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]15.0457288583278[/C][C]0.954271141672159[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.9901470761528[/C][C]0.00985292384718957[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.0761547692759[/C][C]-3.07615476927586[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.3371540175242[/C][C]-1.33715401752417[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.0231120619873[/C][C]1.97688793801267[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.9770016444951[/C][C]4.02299835550488[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.4324939819889[/C][C]1.5675060180111[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.4626687348674[/C][C]-2.46266873486742[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.7221535429754[/C][C]0.277846457024578[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.5962071661979[/C][C]1.40379283380214[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.712262760476[/C][C]1.28773723952403[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.9851119587878[/C][C]1.01488804121219[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.5473008556049[/C][C]-0.547300855604908[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.5376121247092[/C][C]0.462387875290758[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&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
11316.2301826469213-3.23018264692127
21616.1400426756179-0.140042675617893
31916.37282705006042.62717294993958
41511.99390860843713.00609139156291
51415.7107557445178-1.7107557445178
61315.0532199525043-2.05321995250433
71915.18724516763243.81275483236756
81516.7931676079821-1.79316760798213
91416.0536510789015-2.05365107890145
101512.69416011907342.30583988092659
111615.34904252346270.650957476537302
121616.2676336882445-0.267633688244512
131615.55359122394170.446408776058309
141615.44643345007740.553566549922621
151717.5488735491058-0.548873549105821
161515.1891436798052-0.189143679805167
171514.7593991616530.240600838347005
182016.33690885024893.66309114975114
191815.55747098184982.44252901815017
201615.33566850484050.664331495159469
211615.37403809193650.625961908063464
221614.90883945890971.09116054109028
231916.53353489954212.46646510045791
241614.85682998222141.14317001777862
251714.963234496942.03676550306004
261717.0404271508244-0.0404271508244363
271615.17288915024630.827110849753723
281516.2408313559795-1.24083135597951
291615.66739714443440.332602855565576
301414.1586751987573-0.158675198757285
311515.7604082352932-0.760408235293158
321212.4213671520713-0.421367152071332
331415.2509308685934-1.25093086859335
341615.6490963493890.350903650611025
351415.8334550927127-1.8334550927127
36713.1055852491796-6.10558524917956
371011.188928561695-1.188928561695
381415.9425353154158-1.94253531541585
391614.43024443885241.56975556114756
401614.76444047336871.2355595266313
411615.20340683873530.79659316126471
421415.7614746274234-1.76147462742336
432017.94749879047072.05250120952926
441414.3713377306321-0.37133773063211
451415.0399569826106-1.0399569826106
461115.784677766688-4.78467776668797
471416.5488283135219-2.54882831352187
481515.1336416997135-0.133641699713494
491615.43117732420060.568822675799353
501416.1798731173689-2.17987311736894
511616.6753312226099-0.675331222609872
521414.3123455200038-0.312345520003828
531214.9172827223253-2.9172827223253
541615.35443014778160.645569852218355
55911.567762481079-2.56776248107896
561412.64909835589591.35090164410415
571616.0522613895434-0.0522613895434465
581615.06595253428560.934047465714354
591515.3250095222022-0.325009522202214
601614.15901619351441.84098380648559
611211.49112237389250.508877626107472
621615.97109699075140.0289030092486086
631616.4853123283346-0.485312328334619
641414.3574529736465-0.357452973646468
651615.35882627533320.641173724666825
661715.9384315707641.061568429236
671816.09116389410371.90883610589635
681814.68970661500723.31029338499281
691215.8521989164725-3.85219891647246
701615.42396668261650.576033317383503
711013.4428881833827-3.44288818338267
721414.326778733971-0.326778733971037
731816.574522719761.42547728023998
741817.12542189702260.874578102977444
751615.7155285618180.284471438182014
761713.92754867002293.07245132997709
771616.3292830550171-0.329283055017107
781614.45539053450321.54460946549684
791314.8735623273067-1.87356232730673
801615.88942435489890.110575645101125
811615.6052560136930.394743986306997
822016.70278571085483.29721428914521
831615.66072343521390.339276564786062
841515.9891407779093-0.989140777909307
851514.75351696938210.246483030617901
861614.28925174160591.71074825839413
871414.260188742871-0.260188742871004
881615.32872374049180.671276259508229
891614.57030345973211.42969654026791
901514.30063909432530.699360905674661
911213.5229557650066-1.52295576500665
921716.91956997079250.0804300292075153
931615.53446798264740.465532017352574
941515.3122332682877-0.312233268287726
951315.2051469561414-2.2051469561414
961615.22130163657770.778698363422314
971615.85168908535630.148310914643719
981614.19807372565821.80192627434177
991616.1410695077876-0.14106950778757
1001414.3729418618351-0.372941861835067
1011617.3130645451179-1.31306454511791
1021614.83774635302291.16225364697709
1032017.40896159942722.5910384005728
1041514.55429866328650.445701336713466
1051614.64798084672661.3520191532734
1061315.3988681418089-2.39886814180886
1071716.02948438162210.970515618377876
1081615.93433241702030.0656675829797099
1091614.38369647652021.61630352347982
1101212.1560962333135-0.156096233313464
1111614.87530015627381.12469984372617
1121615.73386600022380.266133999776172
1131714.44870318603942.55129681396064
1141314.7850763412226-1.78507634122255
1151214.71716085127-2.71716085127002
1161816.43999258633821.56000741366177
1171415.336855954482-1.33685595448203
1181412.88227922001281.11772077998716
1191314.7780572655435-1.77805726554346
1201615.57548761759880.424512382401249
1211314.1414132251509-1.14141322515088
1221615.48534437185940.514655628140648
1231315.8603320623021-2.86033206230208
1241616.8872085002251-0.887208500225133
1251515.822564192851-0.822564192851004
1261616.6712121628606-0.671212162860609
1271515.2912061563322-0.291206156332239
1281716.08993725759790.910062742402119
1291513.7122627604761.28773723952403
1301214.8274618008326-2.82746180083261
1311614.06589265290221.93410734709777
1321013.2914619007928-3.29146190079283
1331613.99369923325262.0063007667474
1341213.8214328094735-1.82143280947351
1351415.5360650055614-1.53606500556139
1361514.99052303007450.0094769699254514
1371312.18596818300220.814031816997769
1381514.31349762269230.686502377307661
1391113.083879263617-2.08387926361698
1401213.2393337457661-1.23933374576613
141813.3057155013017-5.3057155013017
1421612.9656129042483.03438709575202
1431513.2999751407031.70002485929701
1441716.4739083578330.526091642167015
1451614.70282796887371.29717203112634
1461014.0571135977189-4.05711359771888
1471815.77395614027642.22604385972361
1481315.0507907445399-2.05079074453987
1491615.04572885832780.954271141672159
1501312.99014707615280.00985292384718957
1511013.0761547692759-3.07615476927586
1521516.3371540175242-1.33715401752417
1531614.02311206198731.97688793801267
1541611.97700164449514.02299835550488
1551412.43249398198891.5675060180111
1561012.4626687348674-2.46266873486742
1571716.72215354297540.277846457024578
1581311.59620716619791.40379283380214
1591513.7122627604761.28773723952403
1601614.98511195878781.01488804121219
1611212.5473008556049-0.547300855604908
1621312.53761212470920.462387875290758







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7609569464516930.4780861070966140.239043053548307
130.6241437559894160.7517124880211670.375856244010584
140.5841200061548240.8317599876903520.415879993845176
150.4644177490555510.9288354981111020.535582250944449
160.3640891890912660.7281783781825310.635910810908734
170.3007954981394290.6015909962788570.699204501860571
180.5072760051844790.9854479896310420.492723994815521
190.4222326786845350.8444653573690690.577767321315465
200.332734570887510.665469141775020.66726542911249
210.2625093611827450.5250187223654890.737490638817255
220.2388599530143450.477719906028690.761140046985655
230.4341266034868960.8682532069737920.565873396513104
240.4681639952472390.9363279904944770.531836004752761
250.4549161175228810.9098322350457620.545083882477119
260.4140382081589480.8280764163178960.585961791841052
270.4729730841243650.945946168248730.527026915875635
280.4766027302480960.9532054604961910.523397269751904
290.4559525271223580.9119050542447160.544047472877642
300.4964876407826350.9929752815652690.503512359217365
310.4359748684803850.8719497369607690.564025131519615
320.3867552370024620.7735104740049240.613244762997538
330.3607124908773750.7214249817547490.639287509122626
340.325264459762460.650528919524920.67473554023754
350.2799236088141970.5598472176283950.720076391185803
360.8505581909800590.2988836180398830.149441809019941
370.8264183859638470.3471632280723070.173581614036153
380.8213862718294560.3572274563410880.178613728170544
390.8390564323512540.3218871352974930.160943567648746
400.8205832444113070.3588335111773870.179416755588693
410.7888291630412810.4223416739174380.211170836958719
420.7654760244821450.469047951035710.234523975517855
430.7876873763114920.4246252473770160.212312623688508
440.7464375145516050.507124970896790.253562485448395
450.7186855378133060.5626289243733880.281314462186694
460.8782550709426810.2434898581146380.121744929057319
470.9232834893965290.1534330212069410.0767165106034707
480.9029087226547220.1941825546905560.0970912773452778
490.8871687717860330.2256624564279330.112831228213967
500.8905360492858370.2189279014283260.109463950714163
510.8672451548054730.2655096903890530.132754845194527
520.8397065850684250.320586829863150.160293414931575
530.868414075399760.2631718492004790.13158592460024
540.8516157184692310.2967685630615380.148384281530769
550.8600263981323450.2799472037353110.139973601867655
560.8453057521271010.3093884957457980.154694247872899
570.8138081092122520.3723837815754960.186191890787748
580.7960695529601530.4078608940796950.203930447039847
590.764438723118740.4711225537625190.23556127688126
600.7518091870807530.4963816258384940.248190812919247
610.7133192859844350.573361428031130.286680714015565
620.6715621030453750.656875793909250.328437896954625
630.6336358561915280.7327282876169440.366364143808472
640.5892312358909950.8215375282180090.410768764109005
650.5444190650696220.9111618698607560.455580934930378
660.50557760082450.9888447983509990.4944223991755
670.4880641177237970.9761282354475940.511935882276203
680.60275205383390.79449589233220.3972479461661
690.7580986821330440.4838026357339130.241901317866956
700.7205398216316670.5589203567366660.279460178368333
710.8516251488795370.2967497022409260.148374851120463
720.8239636580611310.3520726838777390.176036341938869
730.809754386245910.380491227508180.19024561375409
740.7879490614285970.4241018771428050.212050938571403
750.7530624970575780.4938750058848430.246937502942422
760.7818264179980870.4363471640038250.218173582001913
770.7498193655097530.5003612689804940.250180634490247
780.7271558138658030.5456883722683950.272844186134197
790.7487718976667830.5024562046664350.251228102333217
800.7102903718483610.5794192563032780.289709628151639
810.6713940639276170.6572118721447660.328605936072383
820.7865246970530920.4269506058938170.213475302946908
830.7515748819247560.4968502361504880.248425118075244
840.729737936652730.5405241266945390.27026206334727
850.6896705663502240.6206588672995510.310329433649776
860.6696535845197290.6606928309605410.330346415480271
870.6286574614355470.7426850771289060.371342538564453
880.5844668830831250.831066233833750.415533116916875
890.5539920306511310.8920159386977390.446007969348869
900.5133316441670260.9733367116659480.486668355832974
910.5152289704276450.969542059144710.484771029572355
920.4731265484159150.946253096831830.526873451584085
930.4267936131838610.8535872263677220.573206386816139
940.3848484352802180.7696968705604350.615151564719782
950.4338587441214290.8677174882428570.566141255878571
960.3896557512080660.7793115024161310.610344248791934
970.3452940361601920.6905880723203840.654705963839808
980.3246862528435650.6493725056871310.675313747156435
990.2841933292553290.5683866585106590.715806670744671
1000.2607328339176180.5214656678352360.739267166082382
1010.2543964391593990.5087928783187980.745603560840601
1020.2214641615365140.4429283230730280.778535838463486
1030.2381904775308680.4763809550617360.761809522469132
1040.2041316176318550.408263235263710.795868382368145
1050.1829250374706910.3658500749413810.817074962529309
1060.2143985396830960.4287970793661920.785601460316904
1070.1812185953405840.3624371906811680.818781404659416
1080.1507858495186610.3015716990373220.849214150481339
1090.1426621376227280.2853242752454570.857337862377272
1100.1349341936703460.2698683873406930.865065806329654
1110.1173452526785060.2346905053570110.882654747321494
1120.09715202454381040.1943040490876210.90284797545619
1130.1107213074645510.2214426149291030.889278692535449
1140.1032777712722850.2065555425445690.896722228727715
1150.1355287810338610.2710575620677210.864471218966139
1160.1278697651787280.2557395303574570.872130234821272
1170.1106297234956410.2212594469912830.889370276504359
1180.09493630229722730.1898726045944550.905063697702773
1190.09734986727090890.1946997345418180.902650132729091
1200.08973204314797650.1794640862959530.910267956852024
1210.07504750147052050.1500950029410410.924952498529479
1220.05910150276453070.1182030055290610.940898497235469
1230.06620428002141190.1324085600428240.933795719978588
1240.05189115874366560.1037823174873310.948108841256334
1250.04083565009691580.08167130019383160.959164349903084
1260.03047535952150060.06095071904300120.969524640478499
1270.02238400447151660.04476800894303330.977615995528483
1280.01754118446737680.03508236893475360.982458815532623
1290.01395045642280440.02790091284560870.986049543577196
1300.01897927619544010.03795855239088020.98102072380456
1310.01580494655916460.03160989311832910.984195053440835
1320.02443309635483250.04886619270966490.975566903645168
1330.02553237596429370.05106475192858730.974467624035706
1340.03386110576731530.06772221153463050.966138894232685
1350.02592778733494580.05185557466989170.974072212665054
1360.01729698909296880.03459397818593770.982703010907031
1370.01136263218611840.02272526437223670.988637367813882
1380.00774408542196420.01548817084392840.992255914578036
1390.01250082918184910.02500165836369830.987499170818151
1400.0100693841914130.0201387683828260.989930615808587
1410.5293728835685660.9412542328628680.470627116431434
1420.4693236852058590.9386473704117180.530676314794141
1430.385784512828370.771569025656740.61421548717163
1440.3078257505273210.6156515010546420.692174249472679
1450.2285471592920550.457094318584110.771452840707945
1460.2586557104222560.5173114208445120.741344289577744
1470.2200317946073790.4400635892147590.779968205392621
1480.5992078555615170.8015842888769660.400792144438483
1490.538583677917950.92283264416410.46141632208205
1500.3759534631999940.7519069263999870.624046536800006

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.760956946451693 & 0.478086107096614 & 0.239043053548307 \tabularnewline
13 & 0.624143755989416 & 0.751712488021167 & 0.375856244010584 \tabularnewline
14 & 0.584120006154824 & 0.831759987690352 & 0.415879993845176 \tabularnewline
15 & 0.464417749055551 & 0.928835498111102 & 0.535582250944449 \tabularnewline
16 & 0.364089189091266 & 0.728178378182531 & 0.635910810908734 \tabularnewline
17 & 0.300795498139429 & 0.601590996278857 & 0.699204501860571 \tabularnewline
18 & 0.507276005184479 & 0.985447989631042 & 0.492723994815521 \tabularnewline
19 & 0.422232678684535 & 0.844465357369069 & 0.577767321315465 \tabularnewline
20 & 0.33273457088751 & 0.66546914177502 & 0.66726542911249 \tabularnewline
21 & 0.262509361182745 & 0.525018722365489 & 0.737490638817255 \tabularnewline
22 & 0.238859953014345 & 0.47771990602869 & 0.761140046985655 \tabularnewline
23 & 0.434126603486896 & 0.868253206973792 & 0.565873396513104 \tabularnewline
24 & 0.468163995247239 & 0.936327990494477 & 0.531836004752761 \tabularnewline
25 & 0.454916117522881 & 0.909832235045762 & 0.545083882477119 \tabularnewline
26 & 0.414038208158948 & 0.828076416317896 & 0.585961791841052 \tabularnewline
27 & 0.472973084124365 & 0.94594616824873 & 0.527026915875635 \tabularnewline
28 & 0.476602730248096 & 0.953205460496191 & 0.523397269751904 \tabularnewline
29 & 0.455952527122358 & 0.911905054244716 & 0.544047472877642 \tabularnewline
30 & 0.496487640782635 & 0.992975281565269 & 0.503512359217365 \tabularnewline
31 & 0.435974868480385 & 0.871949736960769 & 0.564025131519615 \tabularnewline
32 & 0.386755237002462 & 0.773510474004924 & 0.613244762997538 \tabularnewline
33 & 0.360712490877375 & 0.721424981754749 & 0.639287509122626 \tabularnewline
34 & 0.32526445976246 & 0.65052891952492 & 0.67473554023754 \tabularnewline
35 & 0.279923608814197 & 0.559847217628395 & 0.720076391185803 \tabularnewline
36 & 0.850558190980059 & 0.298883618039883 & 0.149441809019941 \tabularnewline
37 & 0.826418385963847 & 0.347163228072307 & 0.173581614036153 \tabularnewline
38 & 0.821386271829456 & 0.357227456341088 & 0.178613728170544 \tabularnewline
39 & 0.839056432351254 & 0.321887135297493 & 0.160943567648746 \tabularnewline
40 & 0.820583244411307 & 0.358833511177387 & 0.179416755588693 \tabularnewline
41 & 0.788829163041281 & 0.422341673917438 & 0.211170836958719 \tabularnewline
42 & 0.765476024482145 & 0.46904795103571 & 0.234523975517855 \tabularnewline
43 & 0.787687376311492 & 0.424625247377016 & 0.212312623688508 \tabularnewline
44 & 0.746437514551605 & 0.50712497089679 & 0.253562485448395 \tabularnewline
45 & 0.718685537813306 & 0.562628924373388 & 0.281314462186694 \tabularnewline
46 & 0.878255070942681 & 0.243489858114638 & 0.121744929057319 \tabularnewline
47 & 0.923283489396529 & 0.153433021206941 & 0.0767165106034707 \tabularnewline
48 & 0.902908722654722 & 0.194182554690556 & 0.0970912773452778 \tabularnewline
49 & 0.887168771786033 & 0.225662456427933 & 0.112831228213967 \tabularnewline
50 & 0.890536049285837 & 0.218927901428326 & 0.109463950714163 \tabularnewline
51 & 0.867245154805473 & 0.265509690389053 & 0.132754845194527 \tabularnewline
52 & 0.839706585068425 & 0.32058682986315 & 0.160293414931575 \tabularnewline
53 & 0.86841407539976 & 0.263171849200479 & 0.13158592460024 \tabularnewline
54 & 0.851615718469231 & 0.296768563061538 & 0.148384281530769 \tabularnewline
55 & 0.860026398132345 & 0.279947203735311 & 0.139973601867655 \tabularnewline
56 & 0.845305752127101 & 0.309388495745798 & 0.154694247872899 \tabularnewline
57 & 0.813808109212252 & 0.372383781575496 & 0.186191890787748 \tabularnewline
58 & 0.796069552960153 & 0.407860894079695 & 0.203930447039847 \tabularnewline
59 & 0.76443872311874 & 0.471122553762519 & 0.23556127688126 \tabularnewline
60 & 0.751809187080753 & 0.496381625838494 & 0.248190812919247 \tabularnewline
61 & 0.713319285984435 & 0.57336142803113 & 0.286680714015565 \tabularnewline
62 & 0.671562103045375 & 0.65687579390925 & 0.328437896954625 \tabularnewline
63 & 0.633635856191528 & 0.732728287616944 & 0.366364143808472 \tabularnewline
64 & 0.589231235890995 & 0.821537528218009 & 0.410768764109005 \tabularnewline
65 & 0.544419065069622 & 0.911161869860756 & 0.455580934930378 \tabularnewline
66 & 0.5055776008245 & 0.988844798350999 & 0.4944223991755 \tabularnewline
67 & 0.488064117723797 & 0.976128235447594 & 0.511935882276203 \tabularnewline
68 & 0.6027520538339 & 0.7944958923322 & 0.3972479461661 \tabularnewline
69 & 0.758098682133044 & 0.483802635733913 & 0.241901317866956 \tabularnewline
70 & 0.720539821631667 & 0.558920356736666 & 0.279460178368333 \tabularnewline
71 & 0.851625148879537 & 0.296749702240926 & 0.148374851120463 \tabularnewline
72 & 0.823963658061131 & 0.352072683877739 & 0.176036341938869 \tabularnewline
73 & 0.80975438624591 & 0.38049122750818 & 0.19024561375409 \tabularnewline
74 & 0.787949061428597 & 0.424101877142805 & 0.212050938571403 \tabularnewline
75 & 0.753062497057578 & 0.493875005884843 & 0.246937502942422 \tabularnewline
76 & 0.781826417998087 & 0.436347164003825 & 0.218173582001913 \tabularnewline
77 & 0.749819365509753 & 0.500361268980494 & 0.250180634490247 \tabularnewline
78 & 0.727155813865803 & 0.545688372268395 & 0.272844186134197 \tabularnewline
79 & 0.748771897666783 & 0.502456204666435 & 0.251228102333217 \tabularnewline
80 & 0.710290371848361 & 0.579419256303278 & 0.289709628151639 \tabularnewline
81 & 0.671394063927617 & 0.657211872144766 & 0.328605936072383 \tabularnewline
82 & 0.786524697053092 & 0.426950605893817 & 0.213475302946908 \tabularnewline
83 & 0.751574881924756 & 0.496850236150488 & 0.248425118075244 \tabularnewline
84 & 0.72973793665273 & 0.540524126694539 & 0.27026206334727 \tabularnewline
85 & 0.689670566350224 & 0.620658867299551 & 0.310329433649776 \tabularnewline
86 & 0.669653584519729 & 0.660692830960541 & 0.330346415480271 \tabularnewline
87 & 0.628657461435547 & 0.742685077128906 & 0.371342538564453 \tabularnewline
88 & 0.584466883083125 & 0.83106623383375 & 0.415533116916875 \tabularnewline
89 & 0.553992030651131 & 0.892015938697739 & 0.446007969348869 \tabularnewline
90 & 0.513331644167026 & 0.973336711665948 & 0.486668355832974 \tabularnewline
91 & 0.515228970427645 & 0.96954205914471 & 0.484771029572355 \tabularnewline
92 & 0.473126548415915 & 0.94625309683183 & 0.526873451584085 \tabularnewline
93 & 0.426793613183861 & 0.853587226367722 & 0.573206386816139 \tabularnewline
94 & 0.384848435280218 & 0.769696870560435 & 0.615151564719782 \tabularnewline
95 & 0.433858744121429 & 0.867717488242857 & 0.566141255878571 \tabularnewline
96 & 0.389655751208066 & 0.779311502416131 & 0.610344248791934 \tabularnewline
97 & 0.345294036160192 & 0.690588072320384 & 0.654705963839808 \tabularnewline
98 & 0.324686252843565 & 0.649372505687131 & 0.675313747156435 \tabularnewline
99 & 0.284193329255329 & 0.568386658510659 & 0.715806670744671 \tabularnewline
100 & 0.260732833917618 & 0.521465667835236 & 0.739267166082382 \tabularnewline
101 & 0.254396439159399 & 0.508792878318798 & 0.745603560840601 \tabularnewline
102 & 0.221464161536514 & 0.442928323073028 & 0.778535838463486 \tabularnewline
103 & 0.238190477530868 & 0.476380955061736 & 0.761809522469132 \tabularnewline
104 & 0.204131617631855 & 0.40826323526371 & 0.795868382368145 \tabularnewline
105 & 0.182925037470691 & 0.365850074941381 & 0.817074962529309 \tabularnewline
106 & 0.214398539683096 & 0.428797079366192 & 0.785601460316904 \tabularnewline
107 & 0.181218595340584 & 0.362437190681168 & 0.818781404659416 \tabularnewline
108 & 0.150785849518661 & 0.301571699037322 & 0.849214150481339 \tabularnewline
109 & 0.142662137622728 & 0.285324275245457 & 0.857337862377272 \tabularnewline
110 & 0.134934193670346 & 0.269868387340693 & 0.865065806329654 \tabularnewline
111 & 0.117345252678506 & 0.234690505357011 & 0.882654747321494 \tabularnewline
112 & 0.0971520245438104 & 0.194304049087621 & 0.90284797545619 \tabularnewline
113 & 0.110721307464551 & 0.221442614929103 & 0.889278692535449 \tabularnewline
114 & 0.103277771272285 & 0.206555542544569 & 0.896722228727715 \tabularnewline
115 & 0.135528781033861 & 0.271057562067721 & 0.864471218966139 \tabularnewline
116 & 0.127869765178728 & 0.255739530357457 & 0.872130234821272 \tabularnewline
117 & 0.110629723495641 & 0.221259446991283 & 0.889370276504359 \tabularnewline
118 & 0.0949363022972273 & 0.189872604594455 & 0.905063697702773 \tabularnewline
119 & 0.0973498672709089 & 0.194699734541818 & 0.902650132729091 \tabularnewline
120 & 0.0897320431479765 & 0.179464086295953 & 0.910267956852024 \tabularnewline
121 & 0.0750475014705205 & 0.150095002941041 & 0.924952498529479 \tabularnewline
122 & 0.0591015027645307 & 0.118203005529061 & 0.940898497235469 \tabularnewline
123 & 0.0662042800214119 & 0.132408560042824 & 0.933795719978588 \tabularnewline
124 & 0.0518911587436656 & 0.103782317487331 & 0.948108841256334 \tabularnewline
125 & 0.0408356500969158 & 0.0816713001938316 & 0.959164349903084 \tabularnewline
126 & 0.0304753595215006 & 0.0609507190430012 & 0.969524640478499 \tabularnewline
127 & 0.0223840044715166 & 0.0447680089430333 & 0.977615995528483 \tabularnewline
128 & 0.0175411844673768 & 0.0350823689347536 & 0.982458815532623 \tabularnewline
129 & 0.0139504564228044 & 0.0279009128456087 & 0.986049543577196 \tabularnewline
130 & 0.0189792761954401 & 0.0379585523908802 & 0.98102072380456 \tabularnewline
131 & 0.0158049465591646 & 0.0316098931183291 & 0.984195053440835 \tabularnewline
132 & 0.0244330963548325 & 0.0488661927096649 & 0.975566903645168 \tabularnewline
133 & 0.0255323759642937 & 0.0510647519285873 & 0.974467624035706 \tabularnewline
134 & 0.0338611057673153 & 0.0677222115346305 & 0.966138894232685 \tabularnewline
135 & 0.0259277873349458 & 0.0518555746698917 & 0.974072212665054 \tabularnewline
136 & 0.0172969890929688 & 0.0345939781859377 & 0.982703010907031 \tabularnewline
137 & 0.0113626321861184 & 0.0227252643722367 & 0.988637367813882 \tabularnewline
138 & 0.0077440854219642 & 0.0154881708439284 & 0.992255914578036 \tabularnewline
139 & 0.0125008291818491 & 0.0250016583636983 & 0.987499170818151 \tabularnewline
140 & 0.010069384191413 & 0.020138768382826 & 0.989930615808587 \tabularnewline
141 & 0.529372883568566 & 0.941254232862868 & 0.470627116431434 \tabularnewline
142 & 0.469323685205859 & 0.938647370411718 & 0.530676314794141 \tabularnewline
143 & 0.38578451282837 & 0.77156902565674 & 0.61421548717163 \tabularnewline
144 & 0.307825750527321 & 0.615651501054642 & 0.692174249472679 \tabularnewline
145 & 0.228547159292055 & 0.45709431858411 & 0.771452840707945 \tabularnewline
146 & 0.258655710422256 & 0.517311420844512 & 0.741344289577744 \tabularnewline
147 & 0.220031794607379 & 0.440063589214759 & 0.779968205392621 \tabularnewline
148 & 0.599207855561517 & 0.801584288876966 & 0.400792144438483 \tabularnewline
149 & 0.53858367791795 & 0.9228326441641 & 0.46141632208205 \tabularnewline
150 & 0.375953463199994 & 0.751906926399987 & 0.624046536800006 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.760956946451693[/C][C]0.478086107096614[/C][C]0.239043053548307[/C][/ROW]
[ROW][C]13[/C][C]0.624143755989416[/C][C]0.751712488021167[/C][C]0.375856244010584[/C][/ROW]
[ROW][C]14[/C][C]0.584120006154824[/C][C]0.831759987690352[/C][C]0.415879993845176[/C][/ROW]
[ROW][C]15[/C][C]0.464417749055551[/C][C]0.928835498111102[/C][C]0.535582250944449[/C][/ROW]
[ROW][C]16[/C][C]0.364089189091266[/C][C]0.728178378182531[/C][C]0.635910810908734[/C][/ROW]
[ROW][C]17[/C][C]0.300795498139429[/C][C]0.601590996278857[/C][C]0.699204501860571[/C][/ROW]
[ROW][C]18[/C][C]0.507276005184479[/C][C]0.985447989631042[/C][C]0.492723994815521[/C][/ROW]
[ROW][C]19[/C][C]0.422232678684535[/C][C]0.844465357369069[/C][C]0.577767321315465[/C][/ROW]
[ROW][C]20[/C][C]0.33273457088751[/C][C]0.66546914177502[/C][C]0.66726542911249[/C][/ROW]
[ROW][C]21[/C][C]0.262509361182745[/C][C]0.525018722365489[/C][C]0.737490638817255[/C][/ROW]
[ROW][C]22[/C][C]0.238859953014345[/C][C]0.47771990602869[/C][C]0.761140046985655[/C][/ROW]
[ROW][C]23[/C][C]0.434126603486896[/C][C]0.868253206973792[/C][C]0.565873396513104[/C][/ROW]
[ROW][C]24[/C][C]0.468163995247239[/C][C]0.936327990494477[/C][C]0.531836004752761[/C][/ROW]
[ROW][C]25[/C][C]0.454916117522881[/C][C]0.909832235045762[/C][C]0.545083882477119[/C][/ROW]
[ROW][C]26[/C][C]0.414038208158948[/C][C]0.828076416317896[/C][C]0.585961791841052[/C][/ROW]
[ROW][C]27[/C][C]0.472973084124365[/C][C]0.94594616824873[/C][C]0.527026915875635[/C][/ROW]
[ROW][C]28[/C][C]0.476602730248096[/C][C]0.953205460496191[/C][C]0.523397269751904[/C][/ROW]
[ROW][C]29[/C][C]0.455952527122358[/C][C]0.911905054244716[/C][C]0.544047472877642[/C][/ROW]
[ROW][C]30[/C][C]0.496487640782635[/C][C]0.992975281565269[/C][C]0.503512359217365[/C][/ROW]
[ROW][C]31[/C][C]0.435974868480385[/C][C]0.871949736960769[/C][C]0.564025131519615[/C][/ROW]
[ROW][C]32[/C][C]0.386755237002462[/C][C]0.773510474004924[/C][C]0.613244762997538[/C][/ROW]
[ROW][C]33[/C][C]0.360712490877375[/C][C]0.721424981754749[/C][C]0.639287509122626[/C][/ROW]
[ROW][C]34[/C][C]0.32526445976246[/C][C]0.65052891952492[/C][C]0.67473554023754[/C][/ROW]
[ROW][C]35[/C][C]0.279923608814197[/C][C]0.559847217628395[/C][C]0.720076391185803[/C][/ROW]
[ROW][C]36[/C][C]0.850558190980059[/C][C]0.298883618039883[/C][C]0.149441809019941[/C][/ROW]
[ROW][C]37[/C][C]0.826418385963847[/C][C]0.347163228072307[/C][C]0.173581614036153[/C][/ROW]
[ROW][C]38[/C][C]0.821386271829456[/C][C]0.357227456341088[/C][C]0.178613728170544[/C][/ROW]
[ROW][C]39[/C][C]0.839056432351254[/C][C]0.321887135297493[/C][C]0.160943567648746[/C][/ROW]
[ROW][C]40[/C][C]0.820583244411307[/C][C]0.358833511177387[/C][C]0.179416755588693[/C][/ROW]
[ROW][C]41[/C][C]0.788829163041281[/C][C]0.422341673917438[/C][C]0.211170836958719[/C][/ROW]
[ROW][C]42[/C][C]0.765476024482145[/C][C]0.46904795103571[/C][C]0.234523975517855[/C][/ROW]
[ROW][C]43[/C][C]0.787687376311492[/C][C]0.424625247377016[/C][C]0.212312623688508[/C][/ROW]
[ROW][C]44[/C][C]0.746437514551605[/C][C]0.50712497089679[/C][C]0.253562485448395[/C][/ROW]
[ROW][C]45[/C][C]0.718685537813306[/C][C]0.562628924373388[/C][C]0.281314462186694[/C][/ROW]
[ROW][C]46[/C][C]0.878255070942681[/C][C]0.243489858114638[/C][C]0.121744929057319[/C][/ROW]
[ROW][C]47[/C][C]0.923283489396529[/C][C]0.153433021206941[/C][C]0.0767165106034707[/C][/ROW]
[ROW][C]48[/C][C]0.902908722654722[/C][C]0.194182554690556[/C][C]0.0970912773452778[/C][/ROW]
[ROW][C]49[/C][C]0.887168771786033[/C][C]0.225662456427933[/C][C]0.112831228213967[/C][/ROW]
[ROW][C]50[/C][C]0.890536049285837[/C][C]0.218927901428326[/C][C]0.109463950714163[/C][/ROW]
[ROW][C]51[/C][C]0.867245154805473[/C][C]0.265509690389053[/C][C]0.132754845194527[/C][/ROW]
[ROW][C]52[/C][C]0.839706585068425[/C][C]0.32058682986315[/C][C]0.160293414931575[/C][/ROW]
[ROW][C]53[/C][C]0.86841407539976[/C][C]0.263171849200479[/C][C]0.13158592460024[/C][/ROW]
[ROW][C]54[/C][C]0.851615718469231[/C][C]0.296768563061538[/C][C]0.148384281530769[/C][/ROW]
[ROW][C]55[/C][C]0.860026398132345[/C][C]0.279947203735311[/C][C]0.139973601867655[/C][/ROW]
[ROW][C]56[/C][C]0.845305752127101[/C][C]0.309388495745798[/C][C]0.154694247872899[/C][/ROW]
[ROW][C]57[/C][C]0.813808109212252[/C][C]0.372383781575496[/C][C]0.186191890787748[/C][/ROW]
[ROW][C]58[/C][C]0.796069552960153[/C][C]0.407860894079695[/C][C]0.203930447039847[/C][/ROW]
[ROW][C]59[/C][C]0.76443872311874[/C][C]0.471122553762519[/C][C]0.23556127688126[/C][/ROW]
[ROW][C]60[/C][C]0.751809187080753[/C][C]0.496381625838494[/C][C]0.248190812919247[/C][/ROW]
[ROW][C]61[/C][C]0.713319285984435[/C][C]0.57336142803113[/C][C]0.286680714015565[/C][/ROW]
[ROW][C]62[/C][C]0.671562103045375[/C][C]0.65687579390925[/C][C]0.328437896954625[/C][/ROW]
[ROW][C]63[/C][C]0.633635856191528[/C][C]0.732728287616944[/C][C]0.366364143808472[/C][/ROW]
[ROW][C]64[/C][C]0.589231235890995[/C][C]0.821537528218009[/C][C]0.410768764109005[/C][/ROW]
[ROW][C]65[/C][C]0.544419065069622[/C][C]0.911161869860756[/C][C]0.455580934930378[/C][/ROW]
[ROW][C]66[/C][C]0.5055776008245[/C][C]0.988844798350999[/C][C]0.4944223991755[/C][/ROW]
[ROW][C]67[/C][C]0.488064117723797[/C][C]0.976128235447594[/C][C]0.511935882276203[/C][/ROW]
[ROW][C]68[/C][C]0.6027520538339[/C][C]0.7944958923322[/C][C]0.3972479461661[/C][/ROW]
[ROW][C]69[/C][C]0.758098682133044[/C][C]0.483802635733913[/C][C]0.241901317866956[/C][/ROW]
[ROW][C]70[/C][C]0.720539821631667[/C][C]0.558920356736666[/C][C]0.279460178368333[/C][/ROW]
[ROW][C]71[/C][C]0.851625148879537[/C][C]0.296749702240926[/C][C]0.148374851120463[/C][/ROW]
[ROW][C]72[/C][C]0.823963658061131[/C][C]0.352072683877739[/C][C]0.176036341938869[/C][/ROW]
[ROW][C]73[/C][C]0.80975438624591[/C][C]0.38049122750818[/C][C]0.19024561375409[/C][/ROW]
[ROW][C]74[/C][C]0.787949061428597[/C][C]0.424101877142805[/C][C]0.212050938571403[/C][/ROW]
[ROW][C]75[/C][C]0.753062497057578[/C][C]0.493875005884843[/C][C]0.246937502942422[/C][/ROW]
[ROW][C]76[/C][C]0.781826417998087[/C][C]0.436347164003825[/C][C]0.218173582001913[/C][/ROW]
[ROW][C]77[/C][C]0.749819365509753[/C][C]0.500361268980494[/C][C]0.250180634490247[/C][/ROW]
[ROW][C]78[/C][C]0.727155813865803[/C][C]0.545688372268395[/C][C]0.272844186134197[/C][/ROW]
[ROW][C]79[/C][C]0.748771897666783[/C][C]0.502456204666435[/C][C]0.251228102333217[/C][/ROW]
[ROW][C]80[/C][C]0.710290371848361[/C][C]0.579419256303278[/C][C]0.289709628151639[/C][/ROW]
[ROW][C]81[/C][C]0.671394063927617[/C][C]0.657211872144766[/C][C]0.328605936072383[/C][/ROW]
[ROW][C]82[/C][C]0.786524697053092[/C][C]0.426950605893817[/C][C]0.213475302946908[/C][/ROW]
[ROW][C]83[/C][C]0.751574881924756[/C][C]0.496850236150488[/C][C]0.248425118075244[/C][/ROW]
[ROW][C]84[/C][C]0.72973793665273[/C][C]0.540524126694539[/C][C]0.27026206334727[/C][/ROW]
[ROW][C]85[/C][C]0.689670566350224[/C][C]0.620658867299551[/C][C]0.310329433649776[/C][/ROW]
[ROW][C]86[/C][C]0.669653584519729[/C][C]0.660692830960541[/C][C]0.330346415480271[/C][/ROW]
[ROW][C]87[/C][C]0.628657461435547[/C][C]0.742685077128906[/C][C]0.371342538564453[/C][/ROW]
[ROW][C]88[/C][C]0.584466883083125[/C][C]0.83106623383375[/C][C]0.415533116916875[/C][/ROW]
[ROW][C]89[/C][C]0.553992030651131[/C][C]0.892015938697739[/C][C]0.446007969348869[/C][/ROW]
[ROW][C]90[/C][C]0.513331644167026[/C][C]0.973336711665948[/C][C]0.486668355832974[/C][/ROW]
[ROW][C]91[/C][C]0.515228970427645[/C][C]0.96954205914471[/C][C]0.484771029572355[/C][/ROW]
[ROW][C]92[/C][C]0.473126548415915[/C][C]0.94625309683183[/C][C]0.526873451584085[/C][/ROW]
[ROW][C]93[/C][C]0.426793613183861[/C][C]0.853587226367722[/C][C]0.573206386816139[/C][/ROW]
[ROW][C]94[/C][C]0.384848435280218[/C][C]0.769696870560435[/C][C]0.615151564719782[/C][/ROW]
[ROW][C]95[/C][C]0.433858744121429[/C][C]0.867717488242857[/C][C]0.566141255878571[/C][/ROW]
[ROW][C]96[/C][C]0.389655751208066[/C][C]0.779311502416131[/C][C]0.610344248791934[/C][/ROW]
[ROW][C]97[/C][C]0.345294036160192[/C][C]0.690588072320384[/C][C]0.654705963839808[/C][/ROW]
[ROW][C]98[/C][C]0.324686252843565[/C][C]0.649372505687131[/C][C]0.675313747156435[/C][/ROW]
[ROW][C]99[/C][C]0.284193329255329[/C][C]0.568386658510659[/C][C]0.715806670744671[/C][/ROW]
[ROW][C]100[/C][C]0.260732833917618[/C][C]0.521465667835236[/C][C]0.739267166082382[/C][/ROW]
[ROW][C]101[/C][C]0.254396439159399[/C][C]0.508792878318798[/C][C]0.745603560840601[/C][/ROW]
[ROW][C]102[/C][C]0.221464161536514[/C][C]0.442928323073028[/C][C]0.778535838463486[/C][/ROW]
[ROW][C]103[/C][C]0.238190477530868[/C][C]0.476380955061736[/C][C]0.761809522469132[/C][/ROW]
[ROW][C]104[/C][C]0.204131617631855[/C][C]0.40826323526371[/C][C]0.795868382368145[/C][/ROW]
[ROW][C]105[/C][C]0.182925037470691[/C][C]0.365850074941381[/C][C]0.817074962529309[/C][/ROW]
[ROW][C]106[/C][C]0.214398539683096[/C][C]0.428797079366192[/C][C]0.785601460316904[/C][/ROW]
[ROW][C]107[/C][C]0.181218595340584[/C][C]0.362437190681168[/C][C]0.818781404659416[/C][/ROW]
[ROW][C]108[/C][C]0.150785849518661[/C][C]0.301571699037322[/C][C]0.849214150481339[/C][/ROW]
[ROW][C]109[/C][C]0.142662137622728[/C][C]0.285324275245457[/C][C]0.857337862377272[/C][/ROW]
[ROW][C]110[/C][C]0.134934193670346[/C][C]0.269868387340693[/C][C]0.865065806329654[/C][/ROW]
[ROW][C]111[/C][C]0.117345252678506[/C][C]0.234690505357011[/C][C]0.882654747321494[/C][/ROW]
[ROW][C]112[/C][C]0.0971520245438104[/C][C]0.194304049087621[/C][C]0.90284797545619[/C][/ROW]
[ROW][C]113[/C][C]0.110721307464551[/C][C]0.221442614929103[/C][C]0.889278692535449[/C][/ROW]
[ROW][C]114[/C][C]0.103277771272285[/C][C]0.206555542544569[/C][C]0.896722228727715[/C][/ROW]
[ROW][C]115[/C][C]0.135528781033861[/C][C]0.271057562067721[/C][C]0.864471218966139[/C][/ROW]
[ROW][C]116[/C][C]0.127869765178728[/C][C]0.255739530357457[/C][C]0.872130234821272[/C][/ROW]
[ROW][C]117[/C][C]0.110629723495641[/C][C]0.221259446991283[/C][C]0.889370276504359[/C][/ROW]
[ROW][C]118[/C][C]0.0949363022972273[/C][C]0.189872604594455[/C][C]0.905063697702773[/C][/ROW]
[ROW][C]119[/C][C]0.0973498672709089[/C][C]0.194699734541818[/C][C]0.902650132729091[/C][/ROW]
[ROW][C]120[/C][C]0.0897320431479765[/C][C]0.179464086295953[/C][C]0.910267956852024[/C][/ROW]
[ROW][C]121[/C][C]0.0750475014705205[/C][C]0.150095002941041[/C][C]0.924952498529479[/C][/ROW]
[ROW][C]122[/C][C]0.0591015027645307[/C][C]0.118203005529061[/C][C]0.940898497235469[/C][/ROW]
[ROW][C]123[/C][C]0.0662042800214119[/C][C]0.132408560042824[/C][C]0.933795719978588[/C][/ROW]
[ROW][C]124[/C][C]0.0518911587436656[/C][C]0.103782317487331[/C][C]0.948108841256334[/C][/ROW]
[ROW][C]125[/C][C]0.0408356500969158[/C][C]0.0816713001938316[/C][C]0.959164349903084[/C][/ROW]
[ROW][C]126[/C][C]0.0304753595215006[/C][C]0.0609507190430012[/C][C]0.969524640478499[/C][/ROW]
[ROW][C]127[/C][C]0.0223840044715166[/C][C]0.0447680089430333[/C][C]0.977615995528483[/C][/ROW]
[ROW][C]128[/C][C]0.0175411844673768[/C][C]0.0350823689347536[/C][C]0.982458815532623[/C][/ROW]
[ROW][C]129[/C][C]0.0139504564228044[/C][C]0.0279009128456087[/C][C]0.986049543577196[/C][/ROW]
[ROW][C]130[/C][C]0.0189792761954401[/C][C]0.0379585523908802[/C][C]0.98102072380456[/C][/ROW]
[ROW][C]131[/C][C]0.0158049465591646[/C][C]0.0316098931183291[/C][C]0.984195053440835[/C][/ROW]
[ROW][C]132[/C][C]0.0244330963548325[/C][C]0.0488661927096649[/C][C]0.975566903645168[/C][/ROW]
[ROW][C]133[/C][C]0.0255323759642937[/C][C]0.0510647519285873[/C][C]0.974467624035706[/C][/ROW]
[ROW][C]134[/C][C]0.0338611057673153[/C][C]0.0677222115346305[/C][C]0.966138894232685[/C][/ROW]
[ROW][C]135[/C][C]0.0259277873349458[/C][C]0.0518555746698917[/C][C]0.974072212665054[/C][/ROW]
[ROW][C]136[/C][C]0.0172969890929688[/C][C]0.0345939781859377[/C][C]0.982703010907031[/C][/ROW]
[ROW][C]137[/C][C]0.0113626321861184[/C][C]0.0227252643722367[/C][C]0.988637367813882[/C][/ROW]
[ROW][C]138[/C][C]0.0077440854219642[/C][C]0.0154881708439284[/C][C]0.992255914578036[/C][/ROW]
[ROW][C]139[/C][C]0.0125008291818491[/C][C]0.0250016583636983[/C][C]0.987499170818151[/C][/ROW]
[ROW][C]140[/C][C]0.010069384191413[/C][C]0.020138768382826[/C][C]0.989930615808587[/C][/ROW]
[ROW][C]141[/C][C]0.529372883568566[/C][C]0.941254232862868[/C][C]0.470627116431434[/C][/ROW]
[ROW][C]142[/C][C]0.469323685205859[/C][C]0.938647370411718[/C][C]0.530676314794141[/C][/ROW]
[ROW][C]143[/C][C]0.38578451282837[/C][C]0.77156902565674[/C][C]0.61421548717163[/C][/ROW]
[ROW][C]144[/C][C]0.307825750527321[/C][C]0.615651501054642[/C][C]0.692174249472679[/C][/ROW]
[ROW][C]145[/C][C]0.228547159292055[/C][C]0.45709431858411[/C][C]0.771452840707945[/C][/ROW]
[ROW][C]146[/C][C]0.258655710422256[/C][C]0.517311420844512[/C][C]0.741344289577744[/C][/ROW]
[ROW][C]147[/C][C]0.220031794607379[/C][C]0.440063589214759[/C][C]0.779968205392621[/C][/ROW]
[ROW][C]148[/C][C]0.599207855561517[/C][C]0.801584288876966[/C][C]0.400792144438483[/C][/ROW]
[ROW][C]149[/C][C]0.53858367791795[/C][C]0.9228326441641[/C][C]0.46141632208205[/C][/ROW]
[ROW][C]150[/C][C]0.375953463199994[/C][C]0.751906926399987[/C][C]0.624046536800006[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.7609569464516930.4780861070966140.239043053548307
130.6241437559894160.7517124880211670.375856244010584
140.5841200061548240.8317599876903520.415879993845176
150.4644177490555510.9288354981111020.535582250944449
160.3640891890912660.7281783781825310.635910810908734
170.3007954981394290.6015909962788570.699204501860571
180.5072760051844790.9854479896310420.492723994815521
190.4222326786845350.8444653573690690.577767321315465
200.332734570887510.665469141775020.66726542911249
210.2625093611827450.5250187223654890.737490638817255
220.2388599530143450.477719906028690.761140046985655
230.4341266034868960.8682532069737920.565873396513104
240.4681639952472390.9363279904944770.531836004752761
250.4549161175228810.9098322350457620.545083882477119
260.4140382081589480.8280764163178960.585961791841052
270.4729730841243650.945946168248730.527026915875635
280.4766027302480960.9532054604961910.523397269751904
290.4559525271223580.9119050542447160.544047472877642
300.4964876407826350.9929752815652690.503512359217365
310.4359748684803850.8719497369607690.564025131519615
320.3867552370024620.7735104740049240.613244762997538
330.3607124908773750.7214249817547490.639287509122626
340.325264459762460.650528919524920.67473554023754
350.2799236088141970.5598472176283950.720076391185803
360.8505581909800590.2988836180398830.149441809019941
370.8264183859638470.3471632280723070.173581614036153
380.8213862718294560.3572274563410880.178613728170544
390.8390564323512540.3218871352974930.160943567648746
400.8205832444113070.3588335111773870.179416755588693
410.7888291630412810.4223416739174380.211170836958719
420.7654760244821450.469047951035710.234523975517855
430.7876873763114920.4246252473770160.212312623688508
440.7464375145516050.507124970896790.253562485448395
450.7186855378133060.5626289243733880.281314462186694
460.8782550709426810.2434898581146380.121744929057319
470.9232834893965290.1534330212069410.0767165106034707
480.9029087226547220.1941825546905560.0970912773452778
490.8871687717860330.2256624564279330.112831228213967
500.8905360492858370.2189279014283260.109463950714163
510.8672451548054730.2655096903890530.132754845194527
520.8397065850684250.320586829863150.160293414931575
530.868414075399760.2631718492004790.13158592460024
540.8516157184692310.2967685630615380.148384281530769
550.8600263981323450.2799472037353110.139973601867655
560.8453057521271010.3093884957457980.154694247872899
570.8138081092122520.3723837815754960.186191890787748
580.7960695529601530.4078608940796950.203930447039847
590.764438723118740.4711225537625190.23556127688126
600.7518091870807530.4963816258384940.248190812919247
610.7133192859844350.573361428031130.286680714015565
620.6715621030453750.656875793909250.328437896954625
630.6336358561915280.7327282876169440.366364143808472
640.5892312358909950.8215375282180090.410768764109005
650.5444190650696220.9111618698607560.455580934930378
660.50557760082450.9888447983509990.4944223991755
670.4880641177237970.9761282354475940.511935882276203
680.60275205383390.79449589233220.3972479461661
690.7580986821330440.4838026357339130.241901317866956
700.7205398216316670.5589203567366660.279460178368333
710.8516251488795370.2967497022409260.148374851120463
720.8239636580611310.3520726838777390.176036341938869
730.809754386245910.380491227508180.19024561375409
740.7879490614285970.4241018771428050.212050938571403
750.7530624970575780.4938750058848430.246937502942422
760.7818264179980870.4363471640038250.218173582001913
770.7498193655097530.5003612689804940.250180634490247
780.7271558138658030.5456883722683950.272844186134197
790.7487718976667830.5024562046664350.251228102333217
800.7102903718483610.5794192563032780.289709628151639
810.6713940639276170.6572118721447660.328605936072383
820.7865246970530920.4269506058938170.213475302946908
830.7515748819247560.4968502361504880.248425118075244
840.729737936652730.5405241266945390.27026206334727
850.6896705663502240.6206588672995510.310329433649776
860.6696535845197290.6606928309605410.330346415480271
870.6286574614355470.7426850771289060.371342538564453
880.5844668830831250.831066233833750.415533116916875
890.5539920306511310.8920159386977390.446007969348869
900.5133316441670260.9733367116659480.486668355832974
910.5152289704276450.969542059144710.484771029572355
920.4731265484159150.946253096831830.526873451584085
930.4267936131838610.8535872263677220.573206386816139
940.3848484352802180.7696968705604350.615151564719782
950.4338587441214290.8677174882428570.566141255878571
960.3896557512080660.7793115024161310.610344248791934
970.3452940361601920.6905880723203840.654705963839808
980.3246862528435650.6493725056871310.675313747156435
990.2841933292553290.5683866585106590.715806670744671
1000.2607328339176180.5214656678352360.739267166082382
1010.2543964391593990.5087928783187980.745603560840601
1020.2214641615365140.4429283230730280.778535838463486
1030.2381904775308680.4763809550617360.761809522469132
1040.2041316176318550.408263235263710.795868382368145
1050.1829250374706910.3658500749413810.817074962529309
1060.2143985396830960.4287970793661920.785601460316904
1070.1812185953405840.3624371906811680.818781404659416
1080.1507858495186610.3015716990373220.849214150481339
1090.1426621376227280.2853242752454570.857337862377272
1100.1349341936703460.2698683873406930.865065806329654
1110.1173452526785060.2346905053570110.882654747321494
1120.09715202454381040.1943040490876210.90284797545619
1130.1107213074645510.2214426149291030.889278692535449
1140.1032777712722850.2065555425445690.896722228727715
1150.1355287810338610.2710575620677210.864471218966139
1160.1278697651787280.2557395303574570.872130234821272
1170.1106297234956410.2212594469912830.889370276504359
1180.09493630229722730.1898726045944550.905063697702773
1190.09734986727090890.1946997345418180.902650132729091
1200.08973204314797650.1794640862959530.910267956852024
1210.07504750147052050.1500950029410410.924952498529479
1220.05910150276453070.1182030055290610.940898497235469
1230.06620428002141190.1324085600428240.933795719978588
1240.05189115874366560.1037823174873310.948108841256334
1250.04083565009691580.08167130019383160.959164349903084
1260.03047535952150060.06095071904300120.969524640478499
1270.02238400447151660.04476800894303330.977615995528483
1280.01754118446737680.03508236893475360.982458815532623
1290.01395045642280440.02790091284560870.986049543577196
1300.01897927619544010.03795855239088020.98102072380456
1310.01580494655916460.03160989311832910.984195053440835
1320.02443309635483250.04886619270966490.975566903645168
1330.02553237596429370.05106475192858730.974467624035706
1340.03386110576731530.06772221153463050.966138894232685
1350.02592778733494580.05185557466989170.974072212665054
1360.01729698909296880.03459397818593770.982703010907031
1370.01136263218611840.02272526437223670.988637367813882
1380.00774408542196420.01548817084392840.992255914578036
1390.01250082918184910.02500165836369830.987499170818151
1400.0100693841914130.0201387683828260.989930615808587
1410.5293728835685660.9412542328628680.470627116431434
1420.4693236852058590.9386473704117180.530676314794141
1430.385784512828370.771569025656740.61421548717163
1440.3078257505273210.6156515010546420.692174249472679
1450.2285471592920550.457094318584110.771452840707945
1460.2586557104222560.5173114208445120.741344289577744
1470.2200317946073790.4400635892147590.779968205392621
1480.5992078555615170.8015842888769660.400792144438483
1490.538583677917950.92283264416410.46141632208205
1500.3759534631999940.7519069263999870.624046536800006







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level110.079136690647482NOK
10% type I error level160.115107913669065NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 11 & 0.079136690647482 & NOK \tabularnewline
10% type I error level & 16 & 0.115107913669065 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186055&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.079136690647482[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.115107913669065[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186055&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186055&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level110.079136690647482NOK
10% type I error level160.115107913669065NOK



Parameters (Session):
par1 = 4 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 4 ; 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')
}