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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 15:37:56 -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/t13521479404pow0wvfybzvl8z.htm/, Retrieved Wed, 01 Feb 2023 16:13:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=186285, Retrieved Wed, 01 Feb 2023 16:13:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact42
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Vermindering van ...] [2012-11-05 19:01:54] [86dcce9422b96d4554cb918e531c1d5d]
- R PD      [Multiple Regression] [Variabele t_ inge...] [2012-11-05 20:37:56] [5f6cd87c5735ffe37dbfae854ce1e663] [Current]
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Dataseries X:
9	13	12	14	12	53	32	41	38	1
9	16	11	18	11	86	51	39	32	2
9	19	15	11	14	66	42	30	35	3
9	15	6	12	12	67	41	31	33	4
9	14	13	16	21	76	46	34	37	5
9	13	10	18	12	78	47	35	29	6
9	19	12	14	22	53	37	39	31	7
9	15	14	14	11	80	49	34	36	8
9	14	12	15	10	74	45	36	35	9
9	15	6	15	13	76	47	37	38	10
9	16	10	17	10	79	49	38	31	11
9	16	12	19	8	54	33	36	34	12
9	16	12	10	15	67	42	38	35	13
9	16	11	16	14	54	33	39	38	14
9	17	15	18	10	87	53	33	37	15
9	15	12	14	14	58	36	32	33	16
9	15	10	14	14	75	45	36	32	17
9	20	12	17	11	88	54	38	38	18
9	18	11	14	10	64	41	39	38	19
9	16	12	16	13	57	36	32	32	20
9	16	11	18	7	66	41	32	33	21
9	16	12	11	14	68	44	31	31	22
9	19	13	14	12	54	33	39	38	23
9	16	11	12	14	56	37	37	39	24
9	17	9	17	11	86	52	39	32	25
9	17	13	9	9	80	47	41	32	26
9	16	10	16	11	76	43	36	35	27
9	15	14	14	15	69	44	33	37	28
9	16	12	15	14	78	45	33	33	29
9	14	10	11	13	67	44	34	33	30
9	15	12	16	9	80	49	31	28	31
9	12	8	13	15	54	33	27	32	32
9	14	10	17	10	71	43	37	31	33
9	16	12	15	11	84	54	34	37	34
9	14	12	14	13	74	42	34	30	35
9	7	7	16	8	71	44	32	33	36
9	10	6	9	20	63	37	29	31	37
9	14	12	15	12	71	43	36	33	38
9	16	10	17	10	76	46	29	31	39
9	16	10	13	10	69	42	35	33	40
9	16	10	15	9	74	45	37	32	41
9	14	12	16	14	75	44	34	33	42
9	20	15	16	8	54	33	38	32	43
9	14	10	12	14	52	31	35	33	44
9	14	10	12	11	69	42	38	28	45
9	11	12	11	13	68	40	37	35	46
9	14	13	15	9	65	43	38	39	47
9	15	11	15	11	75	46	33	34	48
9	16	11	17	15	74	42	36	38	49
9	14	12	13	11	75	45	38	32	50
9	16	14	16	10	72	44	32	38	51
9	14	10	14	14	67	40	32	30	52
9	12	12	11	18	63	37	32	33	53
9	16	13	12	14	62	46	34	38	54
9	9	5	12	11	63	36	32	32	55
9	14	6	15	12	76	47	37	32	56
9	16	12	16	13	74	45	39	34	57
9	16	12	15	9	67	42	29	34	58
9	15	11	12	10	73	43	37	36	59
9	16	10	12	15	70	43	35	34	60
9	12	7	8	20	53	32	30	28	61
9	16	12	13	12	77	45	38	34	62
9	16	14	11	12	77	45	34	35	63
9	14	11	14	14	52	31	31	35	64
9	16	12	15	13	54	33	34	31	65
10	17	13	10	11	80	49	35	37	66
10	18	14	11	17	66	42	36	35	67
10	18	11	12	12	73	41	30	27	68
10	12	12	15	13	63	38	39	40	69
10	16	12	15	14	69	42	35	37	70
10	10	8	14	13	67	44	38	36	71
10	14	11	16	15	54	33	31	38	72
10	18	14	15	13	81	48	34	39	73
10	18	14	15	10	69	40	38	41	74
10	16	12	13	11	84	50	34	27	75
10	17	9	12	19	80	49	39	30	76
10	16	13	17	13	70	43	37	37	77
10	16	11	13	17	69	44	34	31	78
10	13	12	15	13	77	47	28	31	79
10	16	12	13	9	54	33	37	27	80
10	16	12	15	11	79	46	33	36	81
10	20	12	16	10	30	0	37	38	82
10	16	12	15	9	71	45	35	37	83
10	15	12	16	12	73	43	37	33	84
10	15	11	15	12	72	44	32	34	85
10	16	10	14	13	77	47	33	31	86
10	14	9	15	13	75	45	38	39	87
10	16	12	14	12	69	42	33	34	88
10	16	12	13	15	54	33	29	32	89
10	15	12	7	22	70	43	33	33	90
10	12	9	17	13	73	46	31	36	91
10	17	15	13	15	54	33	36	32	92
10	16	12	15	13	77	46	35	41	93
10	15	12	14	15	82	48	32	28	94
10	13	12	13	10	80	47	29	30	95
10	16	10	16	11	80	47	39	36	96
10	16	13	12	16	69	43	37	35	97
10	16	9	14	11	78	46	35	31	98
10	16	12	17	11	81	48	37	34	99
10	14	10	15	10	76	46	32	36	100
10	16	14	17	10	76	45	38	36	101
10	16	11	12	16	73	45	37	35	102
10	20	15	16	12	85	52	36	37	103
10	15	11	11	11	66	42	32	28	104
10	16	11	15	16	79	47	33	39	105
10	13	12	9	19	68	41	40	32	106
10	17	12	16	11	76	47	38	35	107
10	16	12	15	16	71	43	41	39	108
10	16	11	10	15	54	33	36	35	109
10	12	7	10	24	46	30	43	42	110
10	16	12	15	14	82	49	30	34	111
10	16	14	11	15	74	44	31	33	112
10	17	11	13	11	88	55	32	41	113
10	13	11	14	15	38	11	32	33	114
10	12	10	18	12	76	47	37	34	115
10	18	13	16	10	86	53	37	32	116
10	14	13	14	14	54	33	33	40	117
10	14	8	14	13	70	44	34	40	118
10	13	11	14	9	69	42	33	35	119
10	16	12	14	15	90	55	38	36	120
10	13	11	12	15	54	33	33	37	121
10	16	13	14	14	76	46	31	27	122
10	13	12	15	11	89	54	38	39	123
10	16	14	15	8	76	47	37	38	124
10	15	13	15	11	73	45	33	31	125
10	16	15	13	11	79	47	31	33	126
10	15	10	17	8	90	55	39	32	127
10	17	11	17	10	74	44	44	39	128
10	15	9	19	11	81	53	33	36	129
10	12	11	15	13	72	44	35	33	130
10	16	10	13	11	71	42	32	33	131
10	10	11	9	20	66	40	28	32	132
10	16	8	15	10	77	46	40	37	133
10	12	11	15	15	65	40	27	30	134
10	14	12	15	12	74	46	37	38	135
10	15	12	16	14	82	53	32	29	136
10	13	9	11	23	54	33	28	22	137
10	15	11	14	14	63	42	34	35	138
10	11	10	11	16	54	35	30	35	139
10	12	8	15	11	64	40	35	34	140
10	8	9	13	12	69	41	31	35	141
10	16	8	15	10	54	33	32	34	142
10	15	9	16	14	84	51	30	34	143
10	17	15	14	12	86	53	30	35	144
10	16	11	15	12	77	46	31	23	145
10	10	8	16	11	89	55	40	31	146
10	18	13	16	12	76	47	32	27	147
10	13	12	11	13	60	38	36	36	148
10	16	12	12	11	75	46	32	31	149
10	13	9	9	19	73	46	35	32	150
10	10	7	16	12	85	53	38	39	151
10	15	13	13	17	79	47	42	37	152
10	16	9	16	9	71	41	34	38	153
9	16	6	12	12	72	44	35	39	154
10	14	8	9	19	69	43	35	34	155
10	10	8	13	18	78	51	33	31	156
10	17	15	13	15	54	33	36	32	157
10	13	6	14	14	69	43	32	37	158
10	15	9	19	11	81	53	33	36	159
10	16	11	13	9	84	51	34	32	160
10	12	8	12	18	84	50	32	35	161
11	13	8	13	16	69	46	34	36	162




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
learning[t] = + 2.48990404372805 + 0.426091612628586month[t] + 0.516843561342474software[t] + 0.045757829853616happiness[t] -0.0709397127583948depression[t] + 0.038589237638697belonging[t] -0.050854282587912belonging_final[t] + 0.104520106029249connected[t] -0.0166444792252037separate[t] -0.00808417941848365t_[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
learning[t] =  +  2.48990404372805 +  0.426091612628586month[t] +  0.516843561342474software[t] +  0.045757829853616happiness[t] -0.0709397127583948depression[t] +  0.038589237638697belonging[t] -0.050854282587912belonging_final[t] +  0.104520106029249connected[t] -0.0166444792252037separate[t] -0.00808417941848365t_[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]learning[t] =  +  2.48990404372805 +  0.426091612628586month[t] +  0.516843561342474software[t] +  0.045757829853616happiness[t] -0.0709397127583948depression[t] +  0.038589237638697belonging[t] -0.050854282587912belonging_final[t] +  0.104520106029249connected[t] -0.0166444792252037separate[t] -0.00808417941848365t_[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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] = + 2.48990404372805 + 0.426091612628586month[t] + 0.516843561342474software[t] + 0.045757829853616happiness[t] -0.0709397127583948depression[t] + 0.038589237638697belonging[t] -0.050854282587912belonging_final[t] + 0.104520106029249connected[t] -0.0166444792252037separate[t] -0.00808417941848365t_[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)2.489904043728054.9556140.50240.6160840.308042
month0.4260916126285860.5448930.7820.4354470.217723
software0.5168435613424740.0716237.216100
happiness0.0457578298536160.0769740.59450.553090.276545
depression-0.07093971275839480.057365-1.23660.2181280.109064
belonging0.0385892376386970.0449980.85760.3924770.196238
belonging_final-0.0508542825879120.064311-0.79080.430320.21516
connected0.1045201060292490.0473092.20930.0286490.014325
separate-0.01664447922520370.045099-0.36910.7125940.356297
t_-0.008084179418483650.005915-1.36670.1737260.086863

\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) & 2.48990404372805 & 4.955614 & 0.5024 & 0.616084 & 0.308042 \tabularnewline
month & 0.426091612628586 & 0.544893 & 0.782 & 0.435447 & 0.217723 \tabularnewline
software & 0.516843561342474 & 0.071623 & 7.2161 & 0 & 0 \tabularnewline
happiness & 0.045757829853616 & 0.076974 & 0.5945 & 0.55309 & 0.276545 \tabularnewline
depression & -0.0709397127583948 & 0.057365 & -1.2366 & 0.218128 & 0.109064 \tabularnewline
belonging & 0.038589237638697 & 0.044998 & 0.8576 & 0.392477 & 0.196238 \tabularnewline
belonging_final & -0.050854282587912 & 0.064311 & -0.7908 & 0.43032 & 0.21516 \tabularnewline
connected & 0.104520106029249 & 0.047309 & 2.2093 & 0.028649 & 0.014325 \tabularnewline
separate & -0.0166444792252037 & 0.045099 & -0.3691 & 0.712594 & 0.356297 \tabularnewline
t_ & -0.00808417941848365 & 0.005915 & -1.3667 & 0.173726 & 0.086863 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&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]2.48990404372805[/C][C]4.955614[/C][C]0.5024[/C][C]0.616084[/C][C]0.308042[/C][/ROW]
[ROW][C]month[/C][C]0.426091612628586[/C][C]0.544893[/C][C]0.782[/C][C]0.435447[/C][C]0.217723[/C][/ROW]
[ROW][C]software[/C][C]0.516843561342474[/C][C]0.071623[/C][C]7.2161[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]happiness[/C][C]0.045757829853616[/C][C]0.076974[/C][C]0.5945[/C][C]0.55309[/C][C]0.276545[/C][/ROW]
[ROW][C]depression[/C][C]-0.0709397127583948[/C][C]0.057365[/C][C]-1.2366[/C][C]0.218128[/C][C]0.109064[/C][/ROW]
[ROW][C]belonging[/C][C]0.038589237638697[/C][C]0.044998[/C][C]0.8576[/C][C]0.392477[/C][C]0.196238[/C][/ROW]
[ROW][C]belonging_final[/C][C]-0.050854282587912[/C][C]0.064311[/C][C]-0.7908[/C][C]0.43032[/C][C]0.21516[/C][/ROW]
[ROW][C]connected[/C][C]0.104520106029249[/C][C]0.047309[/C][C]2.2093[/C][C]0.028649[/C][C]0.014325[/C][/ROW]
[ROW][C]separate[/C][C]-0.0166444792252037[/C][C]0.045099[/C][C]-0.3691[/C][C]0.712594[/C][C]0.356297[/C][/ROW]
[ROW][C]t_[/C][C]-0.00808417941848365[/C][C]0.005915[/C][C]-1.3667[/C][C]0.173726[/C][C]0.086863[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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)2.489904043728054.9556140.50240.6160840.308042
month0.4260916126285860.5448930.7820.4354470.217723
software0.5168435613424740.0716237.216100
happiness0.0457578298536160.0769740.59450.553090.276545
depression-0.07093971275839480.057365-1.23660.2181280.109064
belonging0.0385892376386970.0449980.85760.3924770.196238
belonging_final-0.0508542825879120.064311-0.79080.430320.21516
connected0.1045201060292490.0473092.20930.0286490.014325
separate-0.01664447922520370.045099-0.36910.7125940.356297
t_-0.008084179418483650.005915-1.36670.1737260.086863







Multiple Linear Regression - Regression Statistics
Multiple R0.605198140049454
R-squared0.366264788719319
Adjusted R-squared0.328740993314542
F-TEST (value)9.76086733147174
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.03371755599824e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84856364710096
Sum Squared Residuals519.412508722245

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.605198140049454 \tabularnewline
R-squared & 0.366264788719319 \tabularnewline
Adjusted R-squared & 0.328740993314542 \tabularnewline
F-TEST (value) & 9.76086733147174 \tabularnewline
F-TEST (DF numerator) & 9 \tabularnewline
F-TEST (DF denominator) & 152 \tabularnewline
p-value & 1.03371755599824e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.84856364710096 \tabularnewline
Sum Squared Residuals & 519.412508722245 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.605198140049454[/C][/ROW]
[ROW][C]R-squared[/C][C]0.366264788719319[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.328740993314542[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.76086733147174[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]9[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]152[/C][/ROW]
[ROW][C]p-value[/C][C]1.03371755599824e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.84856364710096[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]519.412508722245[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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 R0.605198140049454
R-squared0.366264788719319
Adjusted R-squared0.328740993314542
F-TEST (value)9.76086733147174
F-TEST (DF numerator)9
F-TEST (DF denominator)152
p-value1.03371755599824e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.84856364710096
Sum Squared Residuals519.412508722245







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.3788268676057-3.37882686760566
21616.3059102952169-0.305910295216947
31916.52736581249632.47263418750373
41512.28257942107222.7174205789278
51415.7769882026356-1.77698820263559
61315.2123465462428-2.21234654624283
71915.27412439308923.72587560691083
81516.9058992756156-1.90589927561558
91416.1783919119273-2.17839191192733
101512.8864838046342.11351619536597
111615.48519927691380.514800723086201
121616.3341612361093-0.334161236109342
131615.65404587754470.345954122455302
141615.48521995100560.514780048994384
151717.5656675610663-0.56566756106631
161515.2477552505162-0.247755250516216
171514.83903734832160.160962651678373
182016.36787780214333.63212219785675
191815.61610036092322.38389963907678
201615.35592914689440.644070853105585
211615.42454258897460.575457411025432
221614.96980365254931.0301963474507
231916.49651322473382.50348677526623
241614.86942349104841.13057650895164
251714.98967493346532.01032506653465
261717.0565579852705-0.0565579852704987
271615.15289471725810.847105282741883
281516.1690820498718-1.16908204987177
291615.60703506344150.392964936558473
301414.1840649292735-0.18406492927352
311515.7392666272434-0.739266627243416
321212.4265864381265-0.426586438126476
331415.1992390180958-1.1992390180958
341615.59122237629370.408777623706298
351415.7363713107492-1.73637131074921
36713.1138336202915-6.11383362029154
371011.184319234823-1.184319234823
381415.8213010939847-1.82130109398466
391614.35495643378071.64504356621934
401614.69096507955361.30903492044643
411615.11140430431420.888595695685838
421415.5873052365559-1.58730523655593
432017.73913803911182.26086196088817
441414.2324917496016-0.23249174960162
451414.9306293540629-0.930629354062905
461115.6106829088904-4.61068290889042
471416.3558440897111-2.35584408971106
481514.96614475669380.0338552433061588
491615.17762767984890.822372320151106
501415.9820480706767-1.98204807067668
511616.4239632744075-0.423963274407492
521414.1168571148381-0.116857114838093
531214.6697001770435-2.66970017704347
541615.13751627485720.862483725142772
55911.6454614697847-2.64546146978468
561412.68541813949341.3145818605066
571615.95349478873150.0465052112685003
581615.02064875449330.979351245506657
591515.271060844441-0.271060844441
601614.09991557336391.90008442663611
611211.48421724052570.515782759474301
621615.85798772172350.14201227827653
631616.3573501019405-0.357350101940502
641414.2277979997142-0.227797999714248
651615.20886306934040.791136930659618
661716.25110922801920.748890771980774
671816.33352787889971.66647212110025
681815.00238355278442.99761644721565
691216.0684499072232-4.06844990722316
701615.64939732408540.350602675914571
711013.7504385390616-3.75043853906156
721414.5353285963698-0.535328596369761
731816.74990771293061.25009228706944
741817.28319754649280.716802453507221
751615.96420889566090.0357911043390888
761714.1614829247982.83851707520201
771616.4688821690734-0.468882169073428
781614.65718373355881.34281626644118
791315.070248043394-2.07024804339396
801616.0860734170066-0.0860734170066234
811615.76337000195930.236629998040679
822016.70519918556723.29480081443276
831615.82361718295080.176382817049226
841516.1029768645233-1.10297686452325
851514.90360276431060.0963972356893793
861614.45681436507231.54318563492774
871414.391619240408-0.391619240407967
881615.4408969158330.55910308416699
891614.66829428132991.33170571867011
901514.39940605471280.600593945287229
911213.6410581200468-1.64105812004682
921716.92621316930660.073786830693395
931615.57311976407580.426880235924186
941515.3714538641445-0.371453864144486
951315.2991369494367-2.29913694943672
961615.2690336090770.730966390922985
971615.86029001397220.139709986027799
981614.28332380850991.71667619149012
991616.1362097248028-0.136209724802822
1001414.4267353641362-0.426735364136238
1011617.2555160085583-1.25551600855829
1021614.83883037957381.1611696204262
1032017.33419242504262.66580757495742
1041514.60795176339820.392048236601774
1051614.59701985051771.40298014948226
1061315.3472092933278-2.34720929332783
1071715.97156218079981.02843781920019
1081615.82047495108080.179525048919168
1091614.5342009465861.46579905341397
1101212.2792634412546-0.279263441254573
1111614.99095898664461.00904101335536
1121615.82931299482270.170687005177315
1131714.59818913281992.40181086718006
1141314.7933863179564-1.79338631795639
1151214.8059019429118-2.80590194291178
1161816.51276785264021.48723214735981
1171415.3604029518822-1.3604029518822
1181413.00159147829110.998408521708891
1191314.8696184515675-1.86961845156752
1201615.6079639246320.39203607536803
1211314.1818571767333-1.1818571767333
1221615.51517762706740.484822372932591
1231315.8755596745373-2.87555967453732
1241616.8804260180872-0.880426018087244
1251515.8270509217703-0.827050921770263
1261616.6486358954769-0.648635895476948
1271515.3226370478173-0.322637047817312
1281716.03757548604220.962424513957788
1291513.72902852242121.27097147757884
1301214.7990797750334-2.79907977503341
1311614.07407480953141.92592519046861
1321013.2686718893059-3.26867188930591
1331613.98437592728882.01562407271118
1341213.7719286881651-1.77192868816507
1351415.4477298780759-1.44772987807591
1361514.9234578088690.0765421911309641
1371312.13261430966360.867385690336352
1381514.23430515902140.765694840978573
1391113.0208209184329-2.02082091843295
1401213.1876454723548-1.18764547235481
141813.2413164840765-5.24131648407655
1421612.89894410991693.1010558900831
1431513.20296230118091.79703769881907
1441716.30512868650320.694871313496786
1451614.58835878766731.41164121233275
1461013.9593488956681-3.95934889566813
1471815.60013405027072.39986594972932
1481314.8840182996455-1.88401829964546
1491614.90071765148361.09928234851643
1501312.85704895999480.142951040005196
1511012.9363002932357-2.93630029323575
1521516.062265080782-1.06226508078198
1531613.83520431458032.16479568541969
1541611.42854939749524.57145060250483
1551412.26470144031851.73529855968155
1561012.2919503967349-2.29195039673492
1571716.40074150710520.599258492894832
1581311.42675573667471.57324426332525
1591513.48650313986661.51349686013335
1601614.76801283055021.23198716944981
1611212.3170633552789-0.317063355278913
1621312.73968234246390.260317657536092

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.3788268676057 & -3.37882686760566 \tabularnewline
2 & 16 & 16.3059102952169 & -0.305910295216947 \tabularnewline
3 & 19 & 16.5273658124963 & 2.47263418750373 \tabularnewline
4 & 15 & 12.2825794210722 & 2.7174205789278 \tabularnewline
5 & 14 & 15.7769882026356 & -1.77698820263559 \tabularnewline
6 & 13 & 15.2123465462428 & -2.21234654624283 \tabularnewline
7 & 19 & 15.2741243930892 & 3.72587560691083 \tabularnewline
8 & 15 & 16.9058992756156 & -1.90589927561558 \tabularnewline
9 & 14 & 16.1783919119273 & -2.17839191192733 \tabularnewline
10 & 15 & 12.886483804634 & 2.11351619536597 \tabularnewline
11 & 16 & 15.4851992769138 & 0.514800723086201 \tabularnewline
12 & 16 & 16.3341612361093 & -0.334161236109342 \tabularnewline
13 & 16 & 15.6540458775447 & 0.345954122455302 \tabularnewline
14 & 16 & 15.4852199510056 & 0.514780048994384 \tabularnewline
15 & 17 & 17.5656675610663 & -0.56566756106631 \tabularnewline
16 & 15 & 15.2477552505162 & -0.247755250516216 \tabularnewline
17 & 15 & 14.8390373483216 & 0.160962651678373 \tabularnewline
18 & 20 & 16.3678778021433 & 3.63212219785675 \tabularnewline
19 & 18 & 15.6161003609232 & 2.38389963907678 \tabularnewline
20 & 16 & 15.3559291468944 & 0.644070853105585 \tabularnewline
21 & 16 & 15.4245425889746 & 0.575457411025432 \tabularnewline
22 & 16 & 14.9698036525493 & 1.0301963474507 \tabularnewline
23 & 19 & 16.4965132247338 & 2.50348677526623 \tabularnewline
24 & 16 & 14.8694234910484 & 1.13057650895164 \tabularnewline
25 & 17 & 14.9896749334653 & 2.01032506653465 \tabularnewline
26 & 17 & 17.0565579852705 & -0.0565579852704987 \tabularnewline
27 & 16 & 15.1528947172581 & 0.847105282741883 \tabularnewline
28 & 15 & 16.1690820498718 & -1.16908204987177 \tabularnewline
29 & 16 & 15.6070350634415 & 0.392964936558473 \tabularnewline
30 & 14 & 14.1840649292735 & -0.18406492927352 \tabularnewline
31 & 15 & 15.7392666272434 & -0.739266627243416 \tabularnewline
32 & 12 & 12.4265864381265 & -0.426586438126476 \tabularnewline
33 & 14 & 15.1992390180958 & -1.1992390180958 \tabularnewline
34 & 16 & 15.5912223762937 & 0.408777623706298 \tabularnewline
35 & 14 & 15.7363713107492 & -1.73637131074921 \tabularnewline
36 & 7 & 13.1138336202915 & -6.11383362029154 \tabularnewline
37 & 10 & 11.184319234823 & -1.184319234823 \tabularnewline
38 & 14 & 15.8213010939847 & -1.82130109398466 \tabularnewline
39 & 16 & 14.3549564337807 & 1.64504356621934 \tabularnewline
40 & 16 & 14.6909650795536 & 1.30903492044643 \tabularnewline
41 & 16 & 15.1114043043142 & 0.888595695685838 \tabularnewline
42 & 14 & 15.5873052365559 & -1.58730523655593 \tabularnewline
43 & 20 & 17.7391380391118 & 2.26086196088817 \tabularnewline
44 & 14 & 14.2324917496016 & -0.23249174960162 \tabularnewline
45 & 14 & 14.9306293540629 & -0.930629354062905 \tabularnewline
46 & 11 & 15.6106829088904 & -4.61068290889042 \tabularnewline
47 & 14 & 16.3558440897111 & -2.35584408971106 \tabularnewline
48 & 15 & 14.9661447566938 & 0.0338552433061588 \tabularnewline
49 & 16 & 15.1776276798489 & 0.822372320151106 \tabularnewline
50 & 14 & 15.9820480706767 & -1.98204807067668 \tabularnewline
51 & 16 & 16.4239632744075 & -0.423963274407492 \tabularnewline
52 & 14 & 14.1168571148381 & -0.116857114838093 \tabularnewline
53 & 12 & 14.6697001770435 & -2.66970017704347 \tabularnewline
54 & 16 & 15.1375162748572 & 0.862483725142772 \tabularnewline
55 & 9 & 11.6454614697847 & -2.64546146978468 \tabularnewline
56 & 14 & 12.6854181394934 & 1.3145818605066 \tabularnewline
57 & 16 & 15.9534947887315 & 0.0465052112685003 \tabularnewline
58 & 16 & 15.0206487544933 & 0.979351245506657 \tabularnewline
59 & 15 & 15.271060844441 & -0.271060844441 \tabularnewline
60 & 16 & 14.0999155733639 & 1.90008442663611 \tabularnewline
61 & 12 & 11.4842172405257 & 0.515782759474301 \tabularnewline
62 & 16 & 15.8579877217235 & 0.14201227827653 \tabularnewline
63 & 16 & 16.3573501019405 & -0.357350101940502 \tabularnewline
64 & 14 & 14.2277979997142 & -0.227797999714248 \tabularnewline
65 & 16 & 15.2088630693404 & 0.791136930659618 \tabularnewline
66 & 17 & 16.2511092280192 & 0.748890771980774 \tabularnewline
67 & 18 & 16.3335278788997 & 1.66647212110025 \tabularnewline
68 & 18 & 15.0023835527844 & 2.99761644721565 \tabularnewline
69 & 12 & 16.0684499072232 & -4.06844990722316 \tabularnewline
70 & 16 & 15.6493973240854 & 0.350602675914571 \tabularnewline
71 & 10 & 13.7504385390616 & -3.75043853906156 \tabularnewline
72 & 14 & 14.5353285963698 & -0.535328596369761 \tabularnewline
73 & 18 & 16.7499077129306 & 1.25009228706944 \tabularnewline
74 & 18 & 17.2831975464928 & 0.716802453507221 \tabularnewline
75 & 16 & 15.9642088956609 & 0.0357911043390888 \tabularnewline
76 & 17 & 14.161482924798 & 2.83851707520201 \tabularnewline
77 & 16 & 16.4688821690734 & -0.468882169073428 \tabularnewline
78 & 16 & 14.6571837335588 & 1.34281626644118 \tabularnewline
79 & 13 & 15.070248043394 & -2.07024804339396 \tabularnewline
80 & 16 & 16.0860734170066 & -0.0860734170066234 \tabularnewline
81 & 16 & 15.7633700019593 & 0.236629998040679 \tabularnewline
82 & 20 & 16.7051991855672 & 3.29480081443276 \tabularnewline
83 & 16 & 15.8236171829508 & 0.176382817049226 \tabularnewline
84 & 15 & 16.1029768645233 & -1.10297686452325 \tabularnewline
85 & 15 & 14.9036027643106 & 0.0963972356893793 \tabularnewline
86 & 16 & 14.4568143650723 & 1.54318563492774 \tabularnewline
87 & 14 & 14.391619240408 & -0.391619240407967 \tabularnewline
88 & 16 & 15.440896915833 & 0.55910308416699 \tabularnewline
89 & 16 & 14.6682942813299 & 1.33170571867011 \tabularnewline
90 & 15 & 14.3994060547128 & 0.600593945287229 \tabularnewline
91 & 12 & 13.6410581200468 & -1.64105812004682 \tabularnewline
92 & 17 & 16.9262131693066 & 0.073786830693395 \tabularnewline
93 & 16 & 15.5731197640758 & 0.426880235924186 \tabularnewline
94 & 15 & 15.3714538641445 & -0.371453864144486 \tabularnewline
95 & 13 & 15.2991369494367 & -2.29913694943672 \tabularnewline
96 & 16 & 15.269033609077 & 0.730966390922985 \tabularnewline
97 & 16 & 15.8602900139722 & 0.139709986027799 \tabularnewline
98 & 16 & 14.2833238085099 & 1.71667619149012 \tabularnewline
99 & 16 & 16.1362097248028 & -0.136209724802822 \tabularnewline
100 & 14 & 14.4267353641362 & -0.426735364136238 \tabularnewline
101 & 16 & 17.2555160085583 & -1.25551600855829 \tabularnewline
102 & 16 & 14.8388303795738 & 1.1611696204262 \tabularnewline
103 & 20 & 17.3341924250426 & 2.66580757495742 \tabularnewline
104 & 15 & 14.6079517633982 & 0.392048236601774 \tabularnewline
105 & 16 & 14.5970198505177 & 1.40298014948226 \tabularnewline
106 & 13 & 15.3472092933278 & -2.34720929332783 \tabularnewline
107 & 17 & 15.9715621807998 & 1.02843781920019 \tabularnewline
108 & 16 & 15.8204749510808 & 0.179525048919168 \tabularnewline
109 & 16 & 14.534200946586 & 1.46579905341397 \tabularnewline
110 & 12 & 12.2792634412546 & -0.279263441254573 \tabularnewline
111 & 16 & 14.9909589866446 & 1.00904101335536 \tabularnewline
112 & 16 & 15.8293129948227 & 0.170687005177315 \tabularnewline
113 & 17 & 14.5981891328199 & 2.40181086718006 \tabularnewline
114 & 13 & 14.7933863179564 & -1.79338631795639 \tabularnewline
115 & 12 & 14.8059019429118 & -2.80590194291178 \tabularnewline
116 & 18 & 16.5127678526402 & 1.48723214735981 \tabularnewline
117 & 14 & 15.3604029518822 & -1.3604029518822 \tabularnewline
118 & 14 & 13.0015914782911 & 0.998408521708891 \tabularnewline
119 & 13 & 14.8696184515675 & -1.86961845156752 \tabularnewline
120 & 16 & 15.607963924632 & 0.39203607536803 \tabularnewline
121 & 13 & 14.1818571767333 & -1.1818571767333 \tabularnewline
122 & 16 & 15.5151776270674 & 0.484822372932591 \tabularnewline
123 & 13 & 15.8755596745373 & -2.87555967453732 \tabularnewline
124 & 16 & 16.8804260180872 & -0.880426018087244 \tabularnewline
125 & 15 & 15.8270509217703 & -0.827050921770263 \tabularnewline
126 & 16 & 16.6486358954769 & -0.648635895476948 \tabularnewline
127 & 15 & 15.3226370478173 & -0.322637047817312 \tabularnewline
128 & 17 & 16.0375754860422 & 0.962424513957788 \tabularnewline
129 & 15 & 13.7290285224212 & 1.27097147757884 \tabularnewline
130 & 12 & 14.7990797750334 & -2.79907977503341 \tabularnewline
131 & 16 & 14.0740748095314 & 1.92592519046861 \tabularnewline
132 & 10 & 13.2686718893059 & -3.26867188930591 \tabularnewline
133 & 16 & 13.9843759272888 & 2.01562407271118 \tabularnewline
134 & 12 & 13.7719286881651 & -1.77192868816507 \tabularnewline
135 & 14 & 15.4477298780759 & -1.44772987807591 \tabularnewline
136 & 15 & 14.923457808869 & 0.0765421911309641 \tabularnewline
137 & 13 & 12.1326143096636 & 0.867385690336352 \tabularnewline
138 & 15 & 14.2343051590214 & 0.765694840978573 \tabularnewline
139 & 11 & 13.0208209184329 & -2.02082091843295 \tabularnewline
140 & 12 & 13.1876454723548 & -1.18764547235481 \tabularnewline
141 & 8 & 13.2413164840765 & -5.24131648407655 \tabularnewline
142 & 16 & 12.8989441099169 & 3.1010558900831 \tabularnewline
143 & 15 & 13.2029623011809 & 1.79703769881907 \tabularnewline
144 & 17 & 16.3051286865032 & 0.694871313496786 \tabularnewline
145 & 16 & 14.5883587876673 & 1.41164121233275 \tabularnewline
146 & 10 & 13.9593488956681 & -3.95934889566813 \tabularnewline
147 & 18 & 15.6001340502707 & 2.39986594972932 \tabularnewline
148 & 13 & 14.8840182996455 & -1.88401829964546 \tabularnewline
149 & 16 & 14.9007176514836 & 1.09928234851643 \tabularnewline
150 & 13 & 12.8570489599948 & 0.142951040005196 \tabularnewline
151 & 10 & 12.9363002932357 & -2.93630029323575 \tabularnewline
152 & 15 & 16.062265080782 & -1.06226508078198 \tabularnewline
153 & 16 & 13.8352043145803 & 2.16479568541969 \tabularnewline
154 & 16 & 11.4285493974952 & 4.57145060250483 \tabularnewline
155 & 14 & 12.2647014403185 & 1.73529855968155 \tabularnewline
156 & 10 & 12.2919503967349 & -2.29195039673492 \tabularnewline
157 & 17 & 16.4007415071052 & 0.599258492894832 \tabularnewline
158 & 13 & 11.4267557366747 & 1.57324426332525 \tabularnewline
159 & 15 & 13.4865031398666 & 1.51349686013335 \tabularnewline
160 & 16 & 14.7680128305502 & 1.23198716944981 \tabularnewline
161 & 12 & 12.3170633552789 & -0.317063355278913 \tabularnewline
162 & 13 & 12.7396823424639 & 0.260317657536092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&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]16.3788268676057[/C][C]-3.37882686760566[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.3059102952169[/C][C]-0.305910295216947[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.5273658124963[/C][C]2.47263418750373[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2825794210722[/C][C]2.7174205789278[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.7769882026356[/C][C]-1.77698820263559[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2123465462428[/C][C]-2.21234654624283[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.2741243930892[/C][C]3.72587560691083[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9058992756156[/C][C]-1.90589927561558[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1783919119273[/C][C]-2.17839191192733[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.886483804634[/C][C]2.11351619536597[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4851992769138[/C][C]0.514800723086201[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.3341612361093[/C][C]-0.334161236109342[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6540458775447[/C][C]0.345954122455302[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4852199510056[/C][C]0.514780048994384[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.5656675610663[/C][C]-0.56566756106631[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.2477552505162[/C][C]-0.247755250516216[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.8390373483216[/C][C]0.160962651678373[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.3678778021433[/C][C]3.63212219785675[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6161003609232[/C][C]2.38389963907678[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.3559291468944[/C][C]0.644070853105585[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.4245425889746[/C][C]0.575457411025432[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.9698036525493[/C][C]1.0301963474507[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4965132247338[/C][C]2.50348677526623[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8694234910484[/C][C]1.13057650895164[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.9896749334653[/C][C]2.01032506653465[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0565579852705[/C][C]-0.0565579852704987[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.1528947172581[/C][C]0.847105282741883[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.1690820498718[/C][C]-1.16908204987177[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.6070350634415[/C][C]0.392964936558473[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.1840649292735[/C][C]-0.18406492927352[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.7392666272434[/C][C]-0.739266627243416[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4265864381265[/C][C]-0.426586438126476[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1992390180958[/C][C]-1.1992390180958[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.5912223762937[/C][C]0.408777623706298[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.7363713107492[/C][C]-1.73637131074921[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]13.1138336202915[/C][C]-6.11383362029154[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]11.184319234823[/C][C]-1.184319234823[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8213010939847[/C][C]-1.82130109398466[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.3549564337807[/C][C]1.64504356621934[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6909650795536[/C][C]1.30903492044643[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1114043043142[/C][C]0.888595695685838[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5873052365559[/C][C]-1.58730523655593[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.7391380391118[/C][C]2.26086196088817[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.2324917496016[/C][C]-0.23249174960162[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.9306293540629[/C][C]-0.930629354062905[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6106829088904[/C][C]-4.61068290889042[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.3558440897111[/C][C]-2.35584408971106[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9661447566938[/C][C]0.0338552433061588[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.1776276798489[/C][C]0.822372320151106[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.9820480706767[/C][C]-1.98204807067668[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.4239632744075[/C][C]-0.423963274407492[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1168571148381[/C][C]-0.116857114838093[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6697001770435[/C][C]-2.66970017704347[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.1375162748572[/C][C]0.862483725142772[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6454614697847[/C][C]-2.64546146978468[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6854181394934[/C][C]1.3145818605066[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.9534947887315[/C][C]0.0465052112685003[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.0206487544933[/C][C]0.979351245506657[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.271060844441[/C][C]-0.271060844441[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.0999155733639[/C][C]1.90008442663611[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.4842172405257[/C][C]0.515782759474301[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.8579877217235[/C][C]0.14201227827653[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.3573501019405[/C][C]-0.357350101940502[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2277979997142[/C][C]-0.227797999714248[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2088630693404[/C][C]0.791136930659618[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.2511092280192[/C][C]0.748890771980774[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3335278788997[/C][C]1.66647212110025[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]15.0023835527844[/C][C]2.99761644721565[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]16.0684499072232[/C][C]-4.06844990722316[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.6493973240854[/C][C]0.350602675914571[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.7504385390616[/C][C]-3.75043853906156[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.5353285963698[/C][C]-0.535328596369761[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.7499077129306[/C][C]1.25009228706944[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]17.2831975464928[/C][C]0.716802453507221[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.9642088956609[/C][C]0.0357911043390888[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.161482924798[/C][C]2.83851707520201[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.4688821690734[/C][C]-0.468882169073428[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.6571837335588[/C][C]1.34281626644118[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.070248043394[/C][C]-2.07024804339396[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]16.0860734170066[/C][C]-0.0860734170066234[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.7633700019593[/C][C]0.236629998040679[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]16.7051991855672[/C][C]3.29480081443276[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.8236171829508[/C][C]0.176382817049226[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]16.1029768645233[/C][C]-1.10297686452325[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.9036027643106[/C][C]0.0963972356893793[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.4568143650723[/C][C]1.54318563492774[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.391619240408[/C][C]-0.391619240407967[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.440896915833[/C][C]0.55910308416699[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.6682942813299[/C][C]1.33170571867011[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.3994060547128[/C][C]0.600593945287229[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.6410581200468[/C][C]-1.64105812004682[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]16.9262131693066[/C][C]0.073786830693395[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.5731197640758[/C][C]0.426880235924186[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3714538641445[/C][C]-0.371453864144486[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2991369494367[/C][C]-2.29913694943672[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.269033609077[/C][C]0.730966390922985[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.8602900139722[/C][C]0.139709986027799[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]14.2833238085099[/C][C]1.71667619149012[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.1362097248028[/C][C]-0.136209724802822[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4267353641362[/C][C]-0.426735364136238[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.2555160085583[/C][C]-1.25551600855829[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.8388303795738[/C][C]1.1611696204262[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.3341924250426[/C][C]2.66580757495742[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.6079517633982[/C][C]0.392048236601774[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.5970198505177[/C][C]1.40298014948226[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.3472092933278[/C][C]-2.34720929332783[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.9715621807998[/C][C]1.02843781920019[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.8204749510808[/C][C]0.179525048919168[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.534200946586[/C][C]1.46579905341397[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]12.2792634412546[/C][C]-0.279263441254573[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.9909589866446[/C][C]1.00904101335536[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8293129948227[/C][C]0.170687005177315[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.5981891328199[/C][C]2.40181086718006[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.7933863179564[/C][C]-1.79338631795639[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]14.8059019429118[/C][C]-2.80590194291178[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5127678526402[/C][C]1.48723214735981[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.3604029518822[/C][C]-1.3604029518822[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.0015914782911[/C][C]0.998408521708891[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.8696184515675[/C][C]-1.86961845156752[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.607963924632[/C][C]0.39203607536803[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.1818571767333[/C][C]-1.1818571767333[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.5151776270674[/C][C]0.484822372932591[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8755596745373[/C][C]-2.87555967453732[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8804260180872[/C][C]-0.880426018087244[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.8270509217703[/C][C]-0.827050921770263[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6486358954769[/C][C]-0.648635895476948[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.3226370478173[/C][C]-0.322637047817312[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0375754860422[/C][C]0.962424513957788[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]13.7290285224212[/C][C]1.27097147757884[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]14.7990797750334[/C][C]-2.79907977503341[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.0740748095314[/C][C]1.92592519046861[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.2686718893059[/C][C]-3.26867188930591[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9843759272888[/C][C]2.01562407271118[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.7719286881651[/C][C]-1.77192868816507[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.4477298780759[/C][C]-1.44772987807591[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.923457808869[/C][C]0.0765421911309641[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.1326143096636[/C][C]0.867385690336352[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.2343051590214[/C][C]0.765694840978573[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.0208209184329[/C][C]-2.02082091843295[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.1876454723548[/C][C]-1.18764547235481[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2413164840765[/C][C]-5.24131648407655[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]12.8989441099169[/C][C]3.1010558900831[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.2029623011809[/C][C]1.79703769881907[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.3051286865032[/C][C]0.694871313496786[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.5883587876673[/C][C]1.41164121233275[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]13.9593488956681[/C][C]-3.95934889566813[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]15.6001340502707[/C][C]2.39986594972932[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.8840182996455[/C][C]-1.88401829964546[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9007176514836[/C][C]1.09928234851643[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]12.8570489599948[/C][C]0.142951040005196[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]12.9363002932357[/C][C]-2.93630029323575[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.062265080782[/C][C]-1.06226508078198[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8352043145803[/C][C]2.16479568541969[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.4285493974952[/C][C]4.57145060250483[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.2647014403185[/C][C]1.73529855968155[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]12.2919503967349[/C][C]-2.29195039673492[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]16.4007415071052[/C][C]0.599258492894832[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.4267557366747[/C][C]1.57324426332525[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]13.4865031398666[/C][C]1.51349686013335[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.7680128305502[/C][C]1.23198716944981[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3170633552789[/C][C]-0.317063355278913[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.7396823424639[/C][C]0.260317657536092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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
11316.3788268676057-3.37882686760566
21616.3059102952169-0.305910295216947
31916.52736581249632.47263418750373
41512.28257942107222.7174205789278
51415.7769882026356-1.77698820263559
61315.2123465462428-2.21234654624283
71915.27412439308923.72587560691083
81516.9058992756156-1.90589927561558
91416.1783919119273-2.17839191192733
101512.8864838046342.11351619536597
111615.48519927691380.514800723086201
121616.3341612361093-0.334161236109342
131615.65404587754470.345954122455302
141615.48521995100560.514780048994384
151717.5656675610663-0.56566756106631
161515.2477552505162-0.247755250516216
171514.83903734832160.160962651678373
182016.36787780214333.63212219785675
191815.61610036092322.38389963907678
201615.35592914689440.644070853105585
211615.42454258897460.575457411025432
221614.96980365254931.0301963474507
231916.49651322473382.50348677526623
241614.86942349104841.13057650895164
251714.98967493346532.01032506653465
261717.0565579852705-0.0565579852704987
271615.15289471725810.847105282741883
281516.1690820498718-1.16908204987177
291615.60703506344150.392964936558473
301414.1840649292735-0.18406492927352
311515.7392666272434-0.739266627243416
321212.4265864381265-0.426586438126476
331415.1992390180958-1.1992390180958
341615.59122237629370.408777623706298
351415.7363713107492-1.73637131074921
36713.1138336202915-6.11383362029154
371011.184319234823-1.184319234823
381415.8213010939847-1.82130109398466
391614.35495643378071.64504356621934
401614.69096507955361.30903492044643
411615.11140430431420.888595695685838
421415.5873052365559-1.58730523655593
432017.73913803911182.26086196088817
441414.2324917496016-0.23249174960162
451414.9306293540629-0.930629354062905
461115.6106829088904-4.61068290889042
471416.3558440897111-2.35584408971106
481514.96614475669380.0338552433061588
491615.17762767984890.822372320151106
501415.9820480706767-1.98204807067668
511616.4239632744075-0.423963274407492
521414.1168571148381-0.116857114838093
531214.6697001770435-2.66970017704347
541615.13751627485720.862483725142772
55911.6454614697847-2.64546146978468
561412.68541813949341.3145818605066
571615.95349478873150.0465052112685003
581615.02064875449330.979351245506657
591515.271060844441-0.271060844441
601614.09991557336391.90008442663611
611211.48421724052570.515782759474301
621615.85798772172350.14201227827653
631616.3573501019405-0.357350101940502
641414.2277979997142-0.227797999714248
651615.20886306934040.791136930659618
661716.25110922801920.748890771980774
671816.33352787889971.66647212110025
681815.00238355278442.99761644721565
691216.0684499072232-4.06844990722316
701615.64939732408540.350602675914571
711013.7504385390616-3.75043853906156
721414.5353285963698-0.535328596369761
731816.74990771293061.25009228706944
741817.28319754649280.716802453507221
751615.96420889566090.0357911043390888
761714.1614829247982.83851707520201
771616.4688821690734-0.468882169073428
781614.65718373355881.34281626644118
791315.070248043394-2.07024804339396
801616.0860734170066-0.0860734170066234
811615.76337000195930.236629998040679
822016.70519918556723.29480081443276
831615.82361718295080.176382817049226
841516.1029768645233-1.10297686452325
851514.90360276431060.0963972356893793
861614.45681436507231.54318563492774
871414.391619240408-0.391619240407967
881615.4408969158330.55910308416699
891614.66829428132991.33170571867011
901514.39940605471280.600593945287229
911213.6410581200468-1.64105812004682
921716.92621316930660.073786830693395
931615.57311976407580.426880235924186
941515.3714538641445-0.371453864144486
951315.2991369494367-2.29913694943672
961615.2690336090770.730966390922985
971615.86029001397220.139709986027799
981614.28332380850991.71667619149012
991616.1362097248028-0.136209724802822
1001414.4267353641362-0.426735364136238
1011617.2555160085583-1.25551600855829
1021614.83883037957381.1611696204262
1032017.33419242504262.66580757495742
1041514.60795176339820.392048236601774
1051614.59701985051771.40298014948226
1061315.3472092933278-2.34720929332783
1071715.97156218079981.02843781920019
1081615.82047495108080.179525048919168
1091614.5342009465861.46579905341397
1101212.2792634412546-0.279263441254573
1111614.99095898664461.00904101335536
1121615.82931299482270.170687005177315
1131714.59818913281992.40181086718006
1141314.7933863179564-1.79338631795639
1151214.8059019429118-2.80590194291178
1161816.51276785264021.48723214735981
1171415.3604029518822-1.3604029518822
1181413.00159147829110.998408521708891
1191314.8696184515675-1.86961845156752
1201615.6079639246320.39203607536803
1211314.1818571767333-1.1818571767333
1221615.51517762706740.484822372932591
1231315.8755596745373-2.87555967453732
1241616.8804260180872-0.880426018087244
1251515.8270509217703-0.827050921770263
1261616.6486358954769-0.648635895476948
1271515.3226370478173-0.322637047817312
1281716.03757548604220.962424513957788
1291513.72902852242121.27097147757884
1301214.7990797750334-2.79907977503341
1311614.07407480953141.92592519046861
1321013.2686718893059-3.26867188930591
1331613.98437592728882.01562407271118
1341213.7719286881651-1.77192868816507
1351415.4477298780759-1.44772987807591
1361514.9234578088690.0765421911309641
1371312.13261430966360.867385690336352
1381514.23430515902140.765694840978573
1391113.0208209184329-2.02082091843295
1401213.1876454723548-1.18764547235481
141813.2413164840765-5.24131648407655
1421612.89894410991693.1010558900831
1431513.20296230118091.79703769881907
1441716.30512868650320.694871313496786
1451614.58835878766731.41164121233275
1461013.9593488956681-3.95934889566813
1471815.60013405027072.39986594972932
1481314.8840182996455-1.88401829964546
1491614.90071765148361.09928234851643
1501312.85704895999480.142951040005196
1511012.9363002932357-2.93630029323575
1521516.062265080782-1.06226508078198
1531613.83520431458032.16479568541969
1541611.42854939749524.57145060250483
1551412.26470144031851.73529855968155
1561012.2919503967349-2.29195039673492
1571716.40074150710520.599258492894832
1581311.42675573667471.57324426332525
1591513.48650313986661.51349686013335
1601614.76801283055021.23198716944981
1611212.3170633552789-0.317063355278913
1621312.73968234246390.260317657536092







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
130.735533505378640.5289329892427210.26446649462136
140.710005394965790.5799892100684190.28999460503421
150.588050605192610.8238987896147810.41194939480739
160.474892767529030.949785535058060.52510723247097
170.3964016414960110.7928032829920210.603598358503989
180.5516918906088760.8966162187822470.448308109391124
190.4657871307780590.9315742615561170.534212869221941
200.377124790505630.7542495810112610.62287520949437
210.3065446028026020.6130892056052040.693455397197398
220.2986371104425480.5972742208850960.701362889557452
230.4251399281801850.850279856360370.574860071819815
240.4976604969113160.9953209938226310.502339503088684
250.4433339366447360.8866678732894710.556666063355264
260.3750269376612030.7500538753224050.624973062338797
270.3461448836053090.6922897672106180.653855116394691
280.4494409732139190.8988819464278390.550559026786081
290.3918253808327790.7836507616655580.608174619167221
300.5014112211954520.9971775576090960.498588778804548
310.4503936386738150.900787277347630.549606361326185
320.4129253482037420.8258506964074830.587074651796258
330.4007730519912960.8015461039825920.599226948008704
340.3690938213379050.738187642675810.630906178662095
350.3204460881372620.6408921762745240.679553911862738
360.8641363822577750.271727235484450.135863617742225
370.8372820494603490.3254359010793020.162717950539651
380.81874510187940.36250979624120.1812548981206
390.8491000681732060.3017998636535880.150899931826794
400.8369836020040220.3260327959919570.163016397995978
410.8099303265657220.3801393468685560.190069673434278
420.7816301199991840.4367397600016320.218369880000816
430.8115826442573380.3768347114853250.188417355742663
440.7722943749117690.4554112501764620.227705625088231
450.7430767634719170.5138464730561670.256923236528083
460.8769931317814550.246013736437090.123006868218545
470.8995319915275260.2009360169449480.100468008472474
480.8776660644307970.2446678711384060.122333935569203
490.8746143818556430.2507712362887150.125385618144357
500.8670230546278140.2659538907443720.132976945372186
510.8396665626981070.3206668746037850.160333437301893
520.8097031511201880.3805936977596230.190296848879812
530.8221550675779630.3556898648440740.177844932422037
540.792152841224330.4156943175513410.20784715877567
550.8116166961333510.3767666077332980.188383303866649
560.7907385012275210.4185229975449570.209261498772479
570.754439376158460.4911212476830790.24556062384154
580.7391863963484020.5216272073031950.260813603651598
590.7107007902620070.5785984194759850.289299209737993
600.7139555932565330.5720888134869330.286044406743467
610.6800055783346290.6399888433307420.319994421665371
620.6458566709972160.7082866580055690.354143329002784
630.6206869665882850.758626066823430.379313033411715
640.5864779465800240.8270441068399520.413522053419976
650.5502715135788010.8994569728423990.449728486421199
660.5026492885403330.9947014229193340.497350711459667
670.4775906825915640.9551813651831270.522409317408436
680.5155177227165320.9689645545669360.484482277283468
690.7377405605526630.5245188788946740.262259439447337
700.6979542356694230.6040915286611530.302045764330576
710.828940174554920.3421196508901590.17105982544508
720.7991085657459240.4017828685081520.200891434254076
730.7874901106934370.4250197786131250.212509889306563
740.7655949498637370.4688101002725260.234405050136263
750.7282560762828020.5434878474343970.271743923717198
760.7600403285998820.4799193428002360.239959671400118
770.7242349842415150.551530031516970.275765015758485
780.7010477977571670.5979044044856660.298952202242833
790.7183254970581010.5633490058837970.281674502941899
800.6770514653868830.6458970692262350.322948534613117
810.6370022816255530.7259954367488940.362997718374447
820.7594136913479290.4811726173041420.240586308652071
830.7220399025181190.5559201949637610.277960097481881
840.6950093186778780.6099813626442440.304990681322122
850.6524719647381260.6950560705237480.347528035261874
860.6354438971953870.7291122056092270.364556102804613
870.590655044841710.818689910316580.40934495515829
880.5469882384382050.9060235231235910.453011761561796
890.5205129403332540.9589741193334910.479487059666746
900.4820665742804140.9641331485608280.517933425719586
910.4724475608413370.9448951216826740.527552439158663
920.4294804735048230.8589609470096470.570519526495177
930.387161079921050.77432215984210.61283892007895
940.3438819328017860.6877638656035720.656118067198214
950.3694995505650840.7389991011301680.630500449434916
960.3336422645228130.6672845290456270.666357735477187
970.292102065928990.584204131857980.70789793407101
980.2873947386017110.5747894772034220.712605261398289
990.2471583630460660.4943167260921310.752841636953934
1000.2135445628999910.4270891257999820.786455437100009
1010.1906669348082410.3813338696164830.809333065191759
1020.1740737106959370.3481474213918730.825926289304063
1030.2174769070496640.4349538140993280.782523092950336
1040.1838046187875890.3676092375751780.816195381212411
1050.1788101285518330.3576202571036650.821189871448167
1060.1865777485104220.3731554970208440.813422251489578
1070.1682083365360450.3364166730720910.831791663463955
1080.1470988040439370.2941976080878730.852901195956063
1090.1455335163188140.2910670326376280.854466483681186
1100.1398040682903460.2796081365806920.860195931709654
1110.12772169155680.25544338311360.8722783084432
1120.109277373127510.2185547462550190.89072262687249
1130.1570879264690380.3141758529380760.842912073530962
1140.1398917737375090.2797835474750190.860108226262491
1150.1556220999184480.3112441998368960.844377900081552
1160.165829764602070.3316595292041390.83417023539793
1170.1397241505762150.279448301152430.860275849423785
1180.145086941919560.290173883839120.85491305808044
1190.1289949500622840.2579899001245690.871005049937716
1200.1460500739840080.2921001479680160.853949926015992
1210.1186253663879210.2372507327758420.881374633612079
1220.1051317210614290.2102634421228570.894868278938571
1230.09731848641399860.1946369728279970.902681513586001
1240.07514021907376530.1502804381475310.924859780926235
1250.05706902327124330.1141380465424870.942930976728757
1260.04280688436283080.08561376872566160.957193115637169
1270.03122417603825150.06244835207650310.968775823961748
1280.03167137674228460.06334275348456920.968328623257715
1290.03403036404971080.06806072809942160.965969635950289
1300.03238921188501120.06477842377002240.967610788114989
1310.04107038971685020.08214077943370040.95892961028315
1320.04022806292886780.08045612585773560.959771937071132
1330.1201763484596820.2403526969193630.879823651540318
1340.1258922131933580.2517844263867160.874107786806642
1350.1007775456756390.2015550913512780.899222454324361
1360.07983241892178050.1596648378435610.920167581078219
1370.05810116791840830.1162023358368170.941898832081592
1380.06214185981476570.1242837196295310.937858140185234
1390.05511692398371750.1102338479674350.944883076016283
1400.03676952909193680.07353905818387360.963230470908063
1410.5318345598758610.9363308802482780.468165440124139
1420.4744710899933190.9489421799866380.525528910006681
1430.4122233510926270.8244467021852530.587776648907373
1440.319893967713640.6397879354272790.68010603228636
1450.2388034548004380.4776069096008760.761196545199562
1460.2395795245431220.4791590490862450.760420475456878
1470.2901787890938830.5803575781877660.709821210906117
1480.6484585831213660.7030828337572690.351541416878634
1490.5985945979525440.8028108040949120.401405402047456

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
13 & 0.73553350537864 & 0.528932989242721 & 0.26446649462136 \tabularnewline
14 & 0.71000539496579 & 0.579989210068419 & 0.28999460503421 \tabularnewline
15 & 0.58805060519261 & 0.823898789614781 & 0.41194939480739 \tabularnewline
16 & 0.47489276752903 & 0.94978553505806 & 0.52510723247097 \tabularnewline
17 & 0.396401641496011 & 0.792803282992021 & 0.603598358503989 \tabularnewline
18 & 0.551691890608876 & 0.896616218782247 & 0.448308109391124 \tabularnewline
19 & 0.465787130778059 & 0.931574261556117 & 0.534212869221941 \tabularnewline
20 & 0.37712479050563 & 0.754249581011261 & 0.62287520949437 \tabularnewline
21 & 0.306544602802602 & 0.613089205605204 & 0.693455397197398 \tabularnewline
22 & 0.298637110442548 & 0.597274220885096 & 0.701362889557452 \tabularnewline
23 & 0.425139928180185 & 0.85027985636037 & 0.574860071819815 \tabularnewline
24 & 0.497660496911316 & 0.995320993822631 & 0.502339503088684 \tabularnewline
25 & 0.443333936644736 & 0.886667873289471 & 0.556666063355264 \tabularnewline
26 & 0.375026937661203 & 0.750053875322405 & 0.624973062338797 \tabularnewline
27 & 0.346144883605309 & 0.692289767210618 & 0.653855116394691 \tabularnewline
28 & 0.449440973213919 & 0.898881946427839 & 0.550559026786081 \tabularnewline
29 & 0.391825380832779 & 0.783650761665558 & 0.608174619167221 \tabularnewline
30 & 0.501411221195452 & 0.997177557609096 & 0.498588778804548 \tabularnewline
31 & 0.450393638673815 & 0.90078727734763 & 0.549606361326185 \tabularnewline
32 & 0.412925348203742 & 0.825850696407483 & 0.587074651796258 \tabularnewline
33 & 0.400773051991296 & 0.801546103982592 & 0.599226948008704 \tabularnewline
34 & 0.369093821337905 & 0.73818764267581 & 0.630906178662095 \tabularnewline
35 & 0.320446088137262 & 0.640892176274524 & 0.679553911862738 \tabularnewline
36 & 0.864136382257775 & 0.27172723548445 & 0.135863617742225 \tabularnewline
37 & 0.837282049460349 & 0.325435901079302 & 0.162717950539651 \tabularnewline
38 & 0.8187451018794 & 0.3625097962412 & 0.1812548981206 \tabularnewline
39 & 0.849100068173206 & 0.301799863653588 & 0.150899931826794 \tabularnewline
40 & 0.836983602004022 & 0.326032795991957 & 0.163016397995978 \tabularnewline
41 & 0.809930326565722 & 0.380139346868556 & 0.190069673434278 \tabularnewline
42 & 0.781630119999184 & 0.436739760001632 & 0.218369880000816 \tabularnewline
43 & 0.811582644257338 & 0.376834711485325 & 0.188417355742663 \tabularnewline
44 & 0.772294374911769 & 0.455411250176462 & 0.227705625088231 \tabularnewline
45 & 0.743076763471917 & 0.513846473056167 & 0.256923236528083 \tabularnewline
46 & 0.876993131781455 & 0.24601373643709 & 0.123006868218545 \tabularnewline
47 & 0.899531991527526 & 0.200936016944948 & 0.100468008472474 \tabularnewline
48 & 0.877666064430797 & 0.244667871138406 & 0.122333935569203 \tabularnewline
49 & 0.874614381855643 & 0.250771236288715 & 0.125385618144357 \tabularnewline
50 & 0.867023054627814 & 0.265953890744372 & 0.132976945372186 \tabularnewline
51 & 0.839666562698107 & 0.320666874603785 & 0.160333437301893 \tabularnewline
52 & 0.809703151120188 & 0.380593697759623 & 0.190296848879812 \tabularnewline
53 & 0.822155067577963 & 0.355689864844074 & 0.177844932422037 \tabularnewline
54 & 0.79215284122433 & 0.415694317551341 & 0.20784715877567 \tabularnewline
55 & 0.811616696133351 & 0.376766607733298 & 0.188383303866649 \tabularnewline
56 & 0.790738501227521 & 0.418522997544957 & 0.209261498772479 \tabularnewline
57 & 0.75443937615846 & 0.491121247683079 & 0.24556062384154 \tabularnewline
58 & 0.739186396348402 & 0.521627207303195 & 0.260813603651598 \tabularnewline
59 & 0.710700790262007 & 0.578598419475985 & 0.289299209737993 \tabularnewline
60 & 0.713955593256533 & 0.572088813486933 & 0.286044406743467 \tabularnewline
61 & 0.680005578334629 & 0.639988843330742 & 0.319994421665371 \tabularnewline
62 & 0.645856670997216 & 0.708286658005569 & 0.354143329002784 \tabularnewline
63 & 0.620686966588285 & 0.75862606682343 & 0.379313033411715 \tabularnewline
64 & 0.586477946580024 & 0.827044106839952 & 0.413522053419976 \tabularnewline
65 & 0.550271513578801 & 0.899456972842399 & 0.449728486421199 \tabularnewline
66 & 0.502649288540333 & 0.994701422919334 & 0.497350711459667 \tabularnewline
67 & 0.477590682591564 & 0.955181365183127 & 0.522409317408436 \tabularnewline
68 & 0.515517722716532 & 0.968964554566936 & 0.484482277283468 \tabularnewline
69 & 0.737740560552663 & 0.524518878894674 & 0.262259439447337 \tabularnewline
70 & 0.697954235669423 & 0.604091528661153 & 0.302045764330576 \tabularnewline
71 & 0.82894017455492 & 0.342119650890159 & 0.17105982544508 \tabularnewline
72 & 0.799108565745924 & 0.401782868508152 & 0.200891434254076 \tabularnewline
73 & 0.787490110693437 & 0.425019778613125 & 0.212509889306563 \tabularnewline
74 & 0.765594949863737 & 0.468810100272526 & 0.234405050136263 \tabularnewline
75 & 0.728256076282802 & 0.543487847434397 & 0.271743923717198 \tabularnewline
76 & 0.760040328599882 & 0.479919342800236 & 0.239959671400118 \tabularnewline
77 & 0.724234984241515 & 0.55153003151697 & 0.275765015758485 \tabularnewline
78 & 0.701047797757167 & 0.597904404485666 & 0.298952202242833 \tabularnewline
79 & 0.718325497058101 & 0.563349005883797 & 0.281674502941899 \tabularnewline
80 & 0.677051465386883 & 0.645897069226235 & 0.322948534613117 \tabularnewline
81 & 0.637002281625553 & 0.725995436748894 & 0.362997718374447 \tabularnewline
82 & 0.759413691347929 & 0.481172617304142 & 0.240586308652071 \tabularnewline
83 & 0.722039902518119 & 0.555920194963761 & 0.277960097481881 \tabularnewline
84 & 0.695009318677878 & 0.609981362644244 & 0.304990681322122 \tabularnewline
85 & 0.652471964738126 & 0.695056070523748 & 0.347528035261874 \tabularnewline
86 & 0.635443897195387 & 0.729112205609227 & 0.364556102804613 \tabularnewline
87 & 0.59065504484171 & 0.81868991031658 & 0.40934495515829 \tabularnewline
88 & 0.546988238438205 & 0.906023523123591 & 0.453011761561796 \tabularnewline
89 & 0.520512940333254 & 0.958974119333491 & 0.479487059666746 \tabularnewline
90 & 0.482066574280414 & 0.964133148560828 & 0.517933425719586 \tabularnewline
91 & 0.472447560841337 & 0.944895121682674 & 0.527552439158663 \tabularnewline
92 & 0.429480473504823 & 0.858960947009647 & 0.570519526495177 \tabularnewline
93 & 0.38716107992105 & 0.7743221598421 & 0.61283892007895 \tabularnewline
94 & 0.343881932801786 & 0.687763865603572 & 0.656118067198214 \tabularnewline
95 & 0.369499550565084 & 0.738999101130168 & 0.630500449434916 \tabularnewline
96 & 0.333642264522813 & 0.667284529045627 & 0.666357735477187 \tabularnewline
97 & 0.29210206592899 & 0.58420413185798 & 0.70789793407101 \tabularnewline
98 & 0.287394738601711 & 0.574789477203422 & 0.712605261398289 \tabularnewline
99 & 0.247158363046066 & 0.494316726092131 & 0.752841636953934 \tabularnewline
100 & 0.213544562899991 & 0.427089125799982 & 0.786455437100009 \tabularnewline
101 & 0.190666934808241 & 0.381333869616483 & 0.809333065191759 \tabularnewline
102 & 0.174073710695937 & 0.348147421391873 & 0.825926289304063 \tabularnewline
103 & 0.217476907049664 & 0.434953814099328 & 0.782523092950336 \tabularnewline
104 & 0.183804618787589 & 0.367609237575178 & 0.816195381212411 \tabularnewline
105 & 0.178810128551833 & 0.357620257103665 & 0.821189871448167 \tabularnewline
106 & 0.186577748510422 & 0.373155497020844 & 0.813422251489578 \tabularnewline
107 & 0.168208336536045 & 0.336416673072091 & 0.831791663463955 \tabularnewline
108 & 0.147098804043937 & 0.294197608087873 & 0.852901195956063 \tabularnewline
109 & 0.145533516318814 & 0.291067032637628 & 0.854466483681186 \tabularnewline
110 & 0.139804068290346 & 0.279608136580692 & 0.860195931709654 \tabularnewline
111 & 0.1277216915568 & 0.2554433831136 & 0.8722783084432 \tabularnewline
112 & 0.10927737312751 & 0.218554746255019 & 0.89072262687249 \tabularnewline
113 & 0.157087926469038 & 0.314175852938076 & 0.842912073530962 \tabularnewline
114 & 0.139891773737509 & 0.279783547475019 & 0.860108226262491 \tabularnewline
115 & 0.155622099918448 & 0.311244199836896 & 0.844377900081552 \tabularnewline
116 & 0.16582976460207 & 0.331659529204139 & 0.83417023539793 \tabularnewline
117 & 0.139724150576215 & 0.27944830115243 & 0.860275849423785 \tabularnewline
118 & 0.14508694191956 & 0.29017388383912 & 0.85491305808044 \tabularnewline
119 & 0.128994950062284 & 0.257989900124569 & 0.871005049937716 \tabularnewline
120 & 0.146050073984008 & 0.292100147968016 & 0.853949926015992 \tabularnewline
121 & 0.118625366387921 & 0.237250732775842 & 0.881374633612079 \tabularnewline
122 & 0.105131721061429 & 0.210263442122857 & 0.894868278938571 \tabularnewline
123 & 0.0973184864139986 & 0.194636972827997 & 0.902681513586001 \tabularnewline
124 & 0.0751402190737653 & 0.150280438147531 & 0.924859780926235 \tabularnewline
125 & 0.0570690232712433 & 0.114138046542487 & 0.942930976728757 \tabularnewline
126 & 0.0428068843628308 & 0.0856137687256616 & 0.957193115637169 \tabularnewline
127 & 0.0312241760382515 & 0.0624483520765031 & 0.968775823961748 \tabularnewline
128 & 0.0316713767422846 & 0.0633427534845692 & 0.968328623257715 \tabularnewline
129 & 0.0340303640497108 & 0.0680607280994216 & 0.965969635950289 \tabularnewline
130 & 0.0323892118850112 & 0.0647784237700224 & 0.967610788114989 \tabularnewline
131 & 0.0410703897168502 & 0.0821407794337004 & 0.95892961028315 \tabularnewline
132 & 0.0402280629288678 & 0.0804561258577356 & 0.959771937071132 \tabularnewline
133 & 0.120176348459682 & 0.240352696919363 & 0.879823651540318 \tabularnewline
134 & 0.125892213193358 & 0.251784426386716 & 0.874107786806642 \tabularnewline
135 & 0.100777545675639 & 0.201555091351278 & 0.899222454324361 \tabularnewline
136 & 0.0798324189217805 & 0.159664837843561 & 0.920167581078219 \tabularnewline
137 & 0.0581011679184083 & 0.116202335836817 & 0.941898832081592 \tabularnewline
138 & 0.0621418598147657 & 0.124283719629531 & 0.937858140185234 \tabularnewline
139 & 0.0551169239837175 & 0.110233847967435 & 0.944883076016283 \tabularnewline
140 & 0.0367695290919368 & 0.0735390581838736 & 0.963230470908063 \tabularnewline
141 & 0.531834559875861 & 0.936330880248278 & 0.468165440124139 \tabularnewline
142 & 0.474471089993319 & 0.948942179986638 & 0.525528910006681 \tabularnewline
143 & 0.412223351092627 & 0.824446702185253 & 0.587776648907373 \tabularnewline
144 & 0.31989396771364 & 0.639787935427279 & 0.68010603228636 \tabularnewline
145 & 0.238803454800438 & 0.477606909600876 & 0.761196545199562 \tabularnewline
146 & 0.239579524543122 & 0.479159049086245 & 0.760420475456878 \tabularnewline
147 & 0.290178789093883 & 0.580357578187766 & 0.709821210906117 \tabularnewline
148 & 0.648458583121366 & 0.703082833757269 & 0.351541416878634 \tabularnewline
149 & 0.598594597952544 & 0.802810804094912 & 0.401405402047456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&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]13[/C][C]0.73553350537864[/C][C]0.528932989242721[/C][C]0.26446649462136[/C][/ROW]
[ROW][C]14[/C][C]0.71000539496579[/C][C]0.579989210068419[/C][C]0.28999460503421[/C][/ROW]
[ROW][C]15[/C][C]0.58805060519261[/C][C]0.823898789614781[/C][C]0.41194939480739[/C][/ROW]
[ROW][C]16[/C][C]0.47489276752903[/C][C]0.94978553505806[/C][C]0.52510723247097[/C][/ROW]
[ROW][C]17[/C][C]0.396401641496011[/C][C]0.792803282992021[/C][C]0.603598358503989[/C][/ROW]
[ROW][C]18[/C][C]0.551691890608876[/C][C]0.896616218782247[/C][C]0.448308109391124[/C][/ROW]
[ROW][C]19[/C][C]0.465787130778059[/C][C]0.931574261556117[/C][C]0.534212869221941[/C][/ROW]
[ROW][C]20[/C][C]0.37712479050563[/C][C]0.754249581011261[/C][C]0.62287520949437[/C][/ROW]
[ROW][C]21[/C][C]0.306544602802602[/C][C]0.613089205605204[/C][C]0.693455397197398[/C][/ROW]
[ROW][C]22[/C][C]0.298637110442548[/C][C]0.597274220885096[/C][C]0.701362889557452[/C][/ROW]
[ROW][C]23[/C][C]0.425139928180185[/C][C]0.85027985636037[/C][C]0.574860071819815[/C][/ROW]
[ROW][C]24[/C][C]0.497660496911316[/C][C]0.995320993822631[/C][C]0.502339503088684[/C][/ROW]
[ROW][C]25[/C][C]0.443333936644736[/C][C]0.886667873289471[/C][C]0.556666063355264[/C][/ROW]
[ROW][C]26[/C][C]0.375026937661203[/C][C]0.750053875322405[/C][C]0.624973062338797[/C][/ROW]
[ROW][C]27[/C][C]0.346144883605309[/C][C]0.692289767210618[/C][C]0.653855116394691[/C][/ROW]
[ROW][C]28[/C][C]0.449440973213919[/C][C]0.898881946427839[/C][C]0.550559026786081[/C][/ROW]
[ROW][C]29[/C][C]0.391825380832779[/C][C]0.783650761665558[/C][C]0.608174619167221[/C][/ROW]
[ROW][C]30[/C][C]0.501411221195452[/C][C]0.997177557609096[/C][C]0.498588778804548[/C][/ROW]
[ROW][C]31[/C][C]0.450393638673815[/C][C]0.90078727734763[/C][C]0.549606361326185[/C][/ROW]
[ROW][C]32[/C][C]0.412925348203742[/C][C]0.825850696407483[/C][C]0.587074651796258[/C][/ROW]
[ROW][C]33[/C][C]0.400773051991296[/C][C]0.801546103982592[/C][C]0.599226948008704[/C][/ROW]
[ROW][C]34[/C][C]0.369093821337905[/C][C]0.73818764267581[/C][C]0.630906178662095[/C][/ROW]
[ROW][C]35[/C][C]0.320446088137262[/C][C]0.640892176274524[/C][C]0.679553911862738[/C][/ROW]
[ROW][C]36[/C][C]0.864136382257775[/C][C]0.27172723548445[/C][C]0.135863617742225[/C][/ROW]
[ROW][C]37[/C][C]0.837282049460349[/C][C]0.325435901079302[/C][C]0.162717950539651[/C][/ROW]
[ROW][C]38[/C][C]0.8187451018794[/C][C]0.3625097962412[/C][C]0.1812548981206[/C][/ROW]
[ROW][C]39[/C][C]0.849100068173206[/C][C]0.301799863653588[/C][C]0.150899931826794[/C][/ROW]
[ROW][C]40[/C][C]0.836983602004022[/C][C]0.326032795991957[/C][C]0.163016397995978[/C][/ROW]
[ROW][C]41[/C][C]0.809930326565722[/C][C]0.380139346868556[/C][C]0.190069673434278[/C][/ROW]
[ROW][C]42[/C][C]0.781630119999184[/C][C]0.436739760001632[/C][C]0.218369880000816[/C][/ROW]
[ROW][C]43[/C][C]0.811582644257338[/C][C]0.376834711485325[/C][C]0.188417355742663[/C][/ROW]
[ROW][C]44[/C][C]0.772294374911769[/C][C]0.455411250176462[/C][C]0.227705625088231[/C][/ROW]
[ROW][C]45[/C][C]0.743076763471917[/C][C]0.513846473056167[/C][C]0.256923236528083[/C][/ROW]
[ROW][C]46[/C][C]0.876993131781455[/C][C]0.24601373643709[/C][C]0.123006868218545[/C][/ROW]
[ROW][C]47[/C][C]0.899531991527526[/C][C]0.200936016944948[/C][C]0.100468008472474[/C][/ROW]
[ROW][C]48[/C][C]0.877666064430797[/C][C]0.244667871138406[/C][C]0.122333935569203[/C][/ROW]
[ROW][C]49[/C][C]0.874614381855643[/C][C]0.250771236288715[/C][C]0.125385618144357[/C][/ROW]
[ROW][C]50[/C][C]0.867023054627814[/C][C]0.265953890744372[/C][C]0.132976945372186[/C][/ROW]
[ROW][C]51[/C][C]0.839666562698107[/C][C]0.320666874603785[/C][C]0.160333437301893[/C][/ROW]
[ROW][C]52[/C][C]0.809703151120188[/C][C]0.380593697759623[/C][C]0.190296848879812[/C][/ROW]
[ROW][C]53[/C][C]0.822155067577963[/C][C]0.355689864844074[/C][C]0.177844932422037[/C][/ROW]
[ROW][C]54[/C][C]0.79215284122433[/C][C]0.415694317551341[/C][C]0.20784715877567[/C][/ROW]
[ROW][C]55[/C][C]0.811616696133351[/C][C]0.376766607733298[/C][C]0.188383303866649[/C][/ROW]
[ROW][C]56[/C][C]0.790738501227521[/C][C]0.418522997544957[/C][C]0.209261498772479[/C][/ROW]
[ROW][C]57[/C][C]0.75443937615846[/C][C]0.491121247683079[/C][C]0.24556062384154[/C][/ROW]
[ROW][C]58[/C][C]0.739186396348402[/C][C]0.521627207303195[/C][C]0.260813603651598[/C][/ROW]
[ROW][C]59[/C][C]0.710700790262007[/C][C]0.578598419475985[/C][C]0.289299209737993[/C][/ROW]
[ROW][C]60[/C][C]0.713955593256533[/C][C]0.572088813486933[/C][C]0.286044406743467[/C][/ROW]
[ROW][C]61[/C][C]0.680005578334629[/C][C]0.639988843330742[/C][C]0.319994421665371[/C][/ROW]
[ROW][C]62[/C][C]0.645856670997216[/C][C]0.708286658005569[/C][C]0.354143329002784[/C][/ROW]
[ROW][C]63[/C][C]0.620686966588285[/C][C]0.75862606682343[/C][C]0.379313033411715[/C][/ROW]
[ROW][C]64[/C][C]0.586477946580024[/C][C]0.827044106839952[/C][C]0.413522053419976[/C][/ROW]
[ROW][C]65[/C][C]0.550271513578801[/C][C]0.899456972842399[/C][C]0.449728486421199[/C][/ROW]
[ROW][C]66[/C][C]0.502649288540333[/C][C]0.994701422919334[/C][C]0.497350711459667[/C][/ROW]
[ROW][C]67[/C][C]0.477590682591564[/C][C]0.955181365183127[/C][C]0.522409317408436[/C][/ROW]
[ROW][C]68[/C][C]0.515517722716532[/C][C]0.968964554566936[/C][C]0.484482277283468[/C][/ROW]
[ROW][C]69[/C][C]0.737740560552663[/C][C]0.524518878894674[/C][C]0.262259439447337[/C][/ROW]
[ROW][C]70[/C][C]0.697954235669423[/C][C]0.604091528661153[/C][C]0.302045764330576[/C][/ROW]
[ROW][C]71[/C][C]0.82894017455492[/C][C]0.342119650890159[/C][C]0.17105982544508[/C][/ROW]
[ROW][C]72[/C][C]0.799108565745924[/C][C]0.401782868508152[/C][C]0.200891434254076[/C][/ROW]
[ROW][C]73[/C][C]0.787490110693437[/C][C]0.425019778613125[/C][C]0.212509889306563[/C][/ROW]
[ROW][C]74[/C][C]0.765594949863737[/C][C]0.468810100272526[/C][C]0.234405050136263[/C][/ROW]
[ROW][C]75[/C][C]0.728256076282802[/C][C]0.543487847434397[/C][C]0.271743923717198[/C][/ROW]
[ROW][C]76[/C][C]0.760040328599882[/C][C]0.479919342800236[/C][C]0.239959671400118[/C][/ROW]
[ROW][C]77[/C][C]0.724234984241515[/C][C]0.55153003151697[/C][C]0.275765015758485[/C][/ROW]
[ROW][C]78[/C][C]0.701047797757167[/C][C]0.597904404485666[/C][C]0.298952202242833[/C][/ROW]
[ROW][C]79[/C][C]0.718325497058101[/C][C]0.563349005883797[/C][C]0.281674502941899[/C][/ROW]
[ROW][C]80[/C][C]0.677051465386883[/C][C]0.645897069226235[/C][C]0.322948534613117[/C][/ROW]
[ROW][C]81[/C][C]0.637002281625553[/C][C]0.725995436748894[/C][C]0.362997718374447[/C][/ROW]
[ROW][C]82[/C][C]0.759413691347929[/C][C]0.481172617304142[/C][C]0.240586308652071[/C][/ROW]
[ROW][C]83[/C][C]0.722039902518119[/C][C]0.555920194963761[/C][C]0.277960097481881[/C][/ROW]
[ROW][C]84[/C][C]0.695009318677878[/C][C]0.609981362644244[/C][C]0.304990681322122[/C][/ROW]
[ROW][C]85[/C][C]0.652471964738126[/C][C]0.695056070523748[/C][C]0.347528035261874[/C][/ROW]
[ROW][C]86[/C][C]0.635443897195387[/C][C]0.729112205609227[/C][C]0.364556102804613[/C][/ROW]
[ROW][C]87[/C][C]0.59065504484171[/C][C]0.81868991031658[/C][C]0.40934495515829[/C][/ROW]
[ROW][C]88[/C][C]0.546988238438205[/C][C]0.906023523123591[/C][C]0.453011761561796[/C][/ROW]
[ROW][C]89[/C][C]0.520512940333254[/C][C]0.958974119333491[/C][C]0.479487059666746[/C][/ROW]
[ROW][C]90[/C][C]0.482066574280414[/C][C]0.964133148560828[/C][C]0.517933425719586[/C][/ROW]
[ROW][C]91[/C][C]0.472447560841337[/C][C]0.944895121682674[/C][C]0.527552439158663[/C][/ROW]
[ROW][C]92[/C][C]0.429480473504823[/C][C]0.858960947009647[/C][C]0.570519526495177[/C][/ROW]
[ROW][C]93[/C][C]0.38716107992105[/C][C]0.7743221598421[/C][C]0.61283892007895[/C][/ROW]
[ROW][C]94[/C][C]0.343881932801786[/C][C]0.687763865603572[/C][C]0.656118067198214[/C][/ROW]
[ROW][C]95[/C][C]0.369499550565084[/C][C]0.738999101130168[/C][C]0.630500449434916[/C][/ROW]
[ROW][C]96[/C][C]0.333642264522813[/C][C]0.667284529045627[/C][C]0.666357735477187[/C][/ROW]
[ROW][C]97[/C][C]0.29210206592899[/C][C]0.58420413185798[/C][C]0.70789793407101[/C][/ROW]
[ROW][C]98[/C][C]0.287394738601711[/C][C]0.574789477203422[/C][C]0.712605261398289[/C][/ROW]
[ROW][C]99[/C][C]0.247158363046066[/C][C]0.494316726092131[/C][C]0.752841636953934[/C][/ROW]
[ROW][C]100[/C][C]0.213544562899991[/C][C]0.427089125799982[/C][C]0.786455437100009[/C][/ROW]
[ROW][C]101[/C][C]0.190666934808241[/C][C]0.381333869616483[/C][C]0.809333065191759[/C][/ROW]
[ROW][C]102[/C][C]0.174073710695937[/C][C]0.348147421391873[/C][C]0.825926289304063[/C][/ROW]
[ROW][C]103[/C][C]0.217476907049664[/C][C]0.434953814099328[/C][C]0.782523092950336[/C][/ROW]
[ROW][C]104[/C][C]0.183804618787589[/C][C]0.367609237575178[/C][C]0.816195381212411[/C][/ROW]
[ROW][C]105[/C][C]0.178810128551833[/C][C]0.357620257103665[/C][C]0.821189871448167[/C][/ROW]
[ROW][C]106[/C][C]0.186577748510422[/C][C]0.373155497020844[/C][C]0.813422251489578[/C][/ROW]
[ROW][C]107[/C][C]0.168208336536045[/C][C]0.336416673072091[/C][C]0.831791663463955[/C][/ROW]
[ROW][C]108[/C][C]0.147098804043937[/C][C]0.294197608087873[/C][C]0.852901195956063[/C][/ROW]
[ROW][C]109[/C][C]0.145533516318814[/C][C]0.291067032637628[/C][C]0.854466483681186[/C][/ROW]
[ROW][C]110[/C][C]0.139804068290346[/C][C]0.279608136580692[/C][C]0.860195931709654[/C][/ROW]
[ROW][C]111[/C][C]0.1277216915568[/C][C]0.2554433831136[/C][C]0.8722783084432[/C][/ROW]
[ROW][C]112[/C][C]0.10927737312751[/C][C]0.218554746255019[/C][C]0.89072262687249[/C][/ROW]
[ROW][C]113[/C][C]0.157087926469038[/C][C]0.314175852938076[/C][C]0.842912073530962[/C][/ROW]
[ROW][C]114[/C][C]0.139891773737509[/C][C]0.279783547475019[/C][C]0.860108226262491[/C][/ROW]
[ROW][C]115[/C][C]0.155622099918448[/C][C]0.311244199836896[/C][C]0.844377900081552[/C][/ROW]
[ROW][C]116[/C][C]0.16582976460207[/C][C]0.331659529204139[/C][C]0.83417023539793[/C][/ROW]
[ROW][C]117[/C][C]0.139724150576215[/C][C]0.27944830115243[/C][C]0.860275849423785[/C][/ROW]
[ROW][C]118[/C][C]0.14508694191956[/C][C]0.29017388383912[/C][C]0.85491305808044[/C][/ROW]
[ROW][C]119[/C][C]0.128994950062284[/C][C]0.257989900124569[/C][C]0.871005049937716[/C][/ROW]
[ROW][C]120[/C][C]0.146050073984008[/C][C]0.292100147968016[/C][C]0.853949926015992[/C][/ROW]
[ROW][C]121[/C][C]0.118625366387921[/C][C]0.237250732775842[/C][C]0.881374633612079[/C][/ROW]
[ROW][C]122[/C][C]0.105131721061429[/C][C]0.210263442122857[/C][C]0.894868278938571[/C][/ROW]
[ROW][C]123[/C][C]0.0973184864139986[/C][C]0.194636972827997[/C][C]0.902681513586001[/C][/ROW]
[ROW][C]124[/C][C]0.0751402190737653[/C][C]0.150280438147531[/C][C]0.924859780926235[/C][/ROW]
[ROW][C]125[/C][C]0.0570690232712433[/C][C]0.114138046542487[/C][C]0.942930976728757[/C][/ROW]
[ROW][C]126[/C][C]0.0428068843628308[/C][C]0.0856137687256616[/C][C]0.957193115637169[/C][/ROW]
[ROW][C]127[/C][C]0.0312241760382515[/C][C]0.0624483520765031[/C][C]0.968775823961748[/C][/ROW]
[ROW][C]128[/C][C]0.0316713767422846[/C][C]0.0633427534845692[/C][C]0.968328623257715[/C][/ROW]
[ROW][C]129[/C][C]0.0340303640497108[/C][C]0.0680607280994216[/C][C]0.965969635950289[/C][/ROW]
[ROW][C]130[/C][C]0.0323892118850112[/C][C]0.0647784237700224[/C][C]0.967610788114989[/C][/ROW]
[ROW][C]131[/C][C]0.0410703897168502[/C][C]0.0821407794337004[/C][C]0.95892961028315[/C][/ROW]
[ROW][C]132[/C][C]0.0402280629288678[/C][C]0.0804561258577356[/C][C]0.959771937071132[/C][/ROW]
[ROW][C]133[/C][C]0.120176348459682[/C][C]0.240352696919363[/C][C]0.879823651540318[/C][/ROW]
[ROW][C]134[/C][C]0.125892213193358[/C][C]0.251784426386716[/C][C]0.874107786806642[/C][/ROW]
[ROW][C]135[/C][C]0.100777545675639[/C][C]0.201555091351278[/C][C]0.899222454324361[/C][/ROW]
[ROW][C]136[/C][C]0.0798324189217805[/C][C]0.159664837843561[/C][C]0.920167581078219[/C][/ROW]
[ROW][C]137[/C][C]0.0581011679184083[/C][C]0.116202335836817[/C][C]0.941898832081592[/C][/ROW]
[ROW][C]138[/C][C]0.0621418598147657[/C][C]0.124283719629531[/C][C]0.937858140185234[/C][/ROW]
[ROW][C]139[/C][C]0.0551169239837175[/C][C]0.110233847967435[/C][C]0.944883076016283[/C][/ROW]
[ROW][C]140[/C][C]0.0367695290919368[/C][C]0.0735390581838736[/C][C]0.963230470908063[/C][/ROW]
[ROW][C]141[/C][C]0.531834559875861[/C][C]0.936330880248278[/C][C]0.468165440124139[/C][/ROW]
[ROW][C]142[/C][C]0.474471089993319[/C][C]0.948942179986638[/C][C]0.525528910006681[/C][/ROW]
[ROW][C]143[/C][C]0.412223351092627[/C][C]0.824446702185253[/C][C]0.587776648907373[/C][/ROW]
[ROW][C]144[/C][C]0.31989396771364[/C][C]0.639787935427279[/C][C]0.68010603228636[/C][/ROW]
[ROW][C]145[/C][C]0.238803454800438[/C][C]0.477606909600876[/C][C]0.761196545199562[/C][/ROW]
[ROW][C]146[/C][C]0.239579524543122[/C][C]0.479159049086245[/C][C]0.760420475456878[/C][/ROW]
[ROW][C]147[/C][C]0.290178789093883[/C][C]0.580357578187766[/C][C]0.709821210906117[/C][/ROW]
[ROW][C]148[/C][C]0.648458583121366[/C][C]0.703082833757269[/C][C]0.351541416878634[/C][/ROW]
[ROW][C]149[/C][C]0.598594597952544[/C][C]0.802810804094912[/C][C]0.401405402047456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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
130.735533505378640.5289329892427210.26446649462136
140.710005394965790.5799892100684190.28999460503421
150.588050605192610.8238987896147810.41194939480739
160.474892767529030.949785535058060.52510723247097
170.3964016414960110.7928032829920210.603598358503989
180.5516918906088760.8966162187822470.448308109391124
190.4657871307780590.9315742615561170.534212869221941
200.377124790505630.7542495810112610.62287520949437
210.3065446028026020.6130892056052040.693455397197398
220.2986371104425480.5972742208850960.701362889557452
230.4251399281801850.850279856360370.574860071819815
240.4976604969113160.9953209938226310.502339503088684
250.4433339366447360.8866678732894710.556666063355264
260.3750269376612030.7500538753224050.624973062338797
270.3461448836053090.6922897672106180.653855116394691
280.4494409732139190.8988819464278390.550559026786081
290.3918253808327790.7836507616655580.608174619167221
300.5014112211954520.9971775576090960.498588778804548
310.4503936386738150.900787277347630.549606361326185
320.4129253482037420.8258506964074830.587074651796258
330.4007730519912960.8015461039825920.599226948008704
340.3690938213379050.738187642675810.630906178662095
350.3204460881372620.6408921762745240.679553911862738
360.8641363822577750.271727235484450.135863617742225
370.8372820494603490.3254359010793020.162717950539651
380.81874510187940.36250979624120.1812548981206
390.8491000681732060.3017998636535880.150899931826794
400.8369836020040220.3260327959919570.163016397995978
410.8099303265657220.3801393468685560.190069673434278
420.7816301199991840.4367397600016320.218369880000816
430.8115826442573380.3768347114853250.188417355742663
440.7722943749117690.4554112501764620.227705625088231
450.7430767634719170.5138464730561670.256923236528083
460.8769931317814550.246013736437090.123006868218545
470.8995319915275260.2009360169449480.100468008472474
480.8776660644307970.2446678711384060.122333935569203
490.8746143818556430.2507712362887150.125385618144357
500.8670230546278140.2659538907443720.132976945372186
510.8396665626981070.3206668746037850.160333437301893
520.8097031511201880.3805936977596230.190296848879812
530.8221550675779630.3556898648440740.177844932422037
540.792152841224330.4156943175513410.20784715877567
550.8116166961333510.3767666077332980.188383303866649
560.7907385012275210.4185229975449570.209261498772479
570.754439376158460.4911212476830790.24556062384154
580.7391863963484020.5216272073031950.260813603651598
590.7107007902620070.5785984194759850.289299209737993
600.7139555932565330.5720888134869330.286044406743467
610.6800055783346290.6399888433307420.319994421665371
620.6458566709972160.7082866580055690.354143329002784
630.6206869665882850.758626066823430.379313033411715
640.5864779465800240.8270441068399520.413522053419976
650.5502715135788010.8994569728423990.449728486421199
660.5026492885403330.9947014229193340.497350711459667
670.4775906825915640.9551813651831270.522409317408436
680.5155177227165320.9689645545669360.484482277283468
690.7377405605526630.5245188788946740.262259439447337
700.6979542356694230.6040915286611530.302045764330576
710.828940174554920.3421196508901590.17105982544508
720.7991085657459240.4017828685081520.200891434254076
730.7874901106934370.4250197786131250.212509889306563
740.7655949498637370.4688101002725260.234405050136263
750.7282560762828020.5434878474343970.271743923717198
760.7600403285998820.4799193428002360.239959671400118
770.7242349842415150.551530031516970.275765015758485
780.7010477977571670.5979044044856660.298952202242833
790.7183254970581010.5633490058837970.281674502941899
800.6770514653868830.6458970692262350.322948534613117
810.6370022816255530.7259954367488940.362997718374447
820.7594136913479290.4811726173041420.240586308652071
830.7220399025181190.5559201949637610.277960097481881
840.6950093186778780.6099813626442440.304990681322122
850.6524719647381260.6950560705237480.347528035261874
860.6354438971953870.7291122056092270.364556102804613
870.590655044841710.818689910316580.40934495515829
880.5469882384382050.9060235231235910.453011761561796
890.5205129403332540.9589741193334910.479487059666746
900.4820665742804140.9641331485608280.517933425719586
910.4724475608413370.9448951216826740.527552439158663
920.4294804735048230.8589609470096470.570519526495177
930.387161079921050.77432215984210.61283892007895
940.3438819328017860.6877638656035720.656118067198214
950.3694995505650840.7389991011301680.630500449434916
960.3336422645228130.6672845290456270.666357735477187
970.292102065928990.584204131857980.70789793407101
980.2873947386017110.5747894772034220.712605261398289
990.2471583630460660.4943167260921310.752841636953934
1000.2135445628999910.4270891257999820.786455437100009
1010.1906669348082410.3813338696164830.809333065191759
1020.1740737106959370.3481474213918730.825926289304063
1030.2174769070496640.4349538140993280.782523092950336
1040.1838046187875890.3676092375751780.816195381212411
1050.1788101285518330.3576202571036650.821189871448167
1060.1865777485104220.3731554970208440.813422251489578
1070.1682083365360450.3364166730720910.831791663463955
1080.1470988040439370.2941976080878730.852901195956063
1090.1455335163188140.2910670326376280.854466483681186
1100.1398040682903460.2796081365806920.860195931709654
1110.12772169155680.25544338311360.8722783084432
1120.109277373127510.2185547462550190.89072262687249
1130.1570879264690380.3141758529380760.842912073530962
1140.1398917737375090.2797835474750190.860108226262491
1150.1556220999184480.3112441998368960.844377900081552
1160.165829764602070.3316595292041390.83417023539793
1170.1397241505762150.279448301152430.860275849423785
1180.145086941919560.290173883839120.85491305808044
1190.1289949500622840.2579899001245690.871005049937716
1200.1460500739840080.2921001479680160.853949926015992
1210.1186253663879210.2372507327758420.881374633612079
1220.1051317210614290.2102634421228570.894868278938571
1230.09731848641399860.1946369728279970.902681513586001
1240.07514021907376530.1502804381475310.924859780926235
1250.05706902327124330.1141380465424870.942930976728757
1260.04280688436283080.08561376872566160.957193115637169
1270.03122417603825150.06244835207650310.968775823961748
1280.03167137674228460.06334275348456920.968328623257715
1290.03403036404971080.06806072809942160.965969635950289
1300.03238921188501120.06477842377002240.967610788114989
1310.04107038971685020.08214077943370040.95892961028315
1320.04022806292886780.08045612585773560.959771937071132
1330.1201763484596820.2403526969193630.879823651540318
1340.1258922131933580.2517844263867160.874107786806642
1350.1007775456756390.2015550913512780.899222454324361
1360.07983241892178050.1596648378435610.920167581078219
1370.05810116791840830.1162023358368170.941898832081592
1380.06214185981476570.1242837196295310.937858140185234
1390.05511692398371750.1102338479674350.944883076016283
1400.03676952909193680.07353905818387360.963230470908063
1410.5318345598758610.9363308802482780.468165440124139
1420.4744710899933190.9489421799866380.525528910006681
1430.4122233510926270.8244467021852530.587776648907373
1440.319893967713640.6397879354272790.68010603228636
1450.2388034548004380.4776069096008760.761196545199562
1460.2395795245431220.4791590490862450.760420475456878
1470.2901787890938830.5803575781877660.709821210906117
1480.6484585831213660.7030828337572690.351541416878634
1490.5985945979525440.8028108040949120.401405402047456







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level80.0583941605839416OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 8 & 0.0583941605839416 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=186285&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]8[/C][C]0.0583941605839416[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=186285&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=186285&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 level00OK
10% type I error level80.0583941605839416OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}