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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 computationFri, 02 Nov 2012 15:34:46 -0400
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/02/t1351884908bzqey9z7nen4o75.htm/, Retrieved Mon, 27 Jun 2022 06:28:24 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185665, Retrieved Mon, 27 Jun 2022 06:28:24 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact88
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [MRanalysis model] [2012-11-02 19:34:46] [64435dfec13c3cda39d1733fd4b6eb52] [Current]
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Dataseries X:
14	12	38	41	7	2	53
18	11	32	39	5	2	86
11	14	35	30	5	2	66
12	12	33	31	5	1	67
16	21	37	34	8	2	76
18	12	29	35	6	2	78
14	22	31	39	5	2	53
14	11	36	34	6	2	80
15	10	35	36	5	2	74
15	13	38	37	4	2	76
17	10	31	38	6	1	79
19	8	34	36	5	2	54
10	15	35	38	5	1	67
16	14	38	39	6	2	54
18	10	37	33	7	2	87
14	14	33	32	6	1	58
14	14	32	36	7	1	75
17	11	38	38	6	2	88
14	10	38	39	8	1	64
16	13	32	32	7	2	57
18	7	33	32	5	1	66
11	14	31	31	5	2	68
14	12	38	39	7	2	54
12	14	39	37	7	2	56
17	11	32	39	5	1	86
9	9	32	41	4	2	80
16	11	35	36	10	1	76
14	15	37	33	6	2	69
15	14	33	33	5	2	78
11	13	33	34	5	1	67
16	9	28	31	5	2	80
13	15	32	27	5	1	54
17	10	31	37	6	2	71
15	11	37	34	5	2	84
14	13	30	34	5	1	74
16	8	33	32	5	1	71
9	20	31	29	5	1	63
15	12	33	36	5	1	71
17	10	31	29	5	2	76
13	10	33	35	5	1	69
15	9	32	37	5	1	74
16	14	33	34	7	2	75
16	8	32	38	5	1	54
12	14	33	35	6	1	52
12	11	28	38	7	2	69
11	13	35	37	7	2	68
15	9	39	38	5	2	65
15	11	34	33	5	2	75
17	15	38	36	4	2	74
13	11	32	38	5	1	75
16	10	38	32	4	2	72
14	14	30	32	5	1	67
11	18	33	32	5	1	63
12	14	38	34	7	2	62
12	11	32	32	5	1	63
15	12	32	37	5	2	76
16	13	34	39	6	2	74
15	9	34	29	4	2	67
12	10	36	37	6	1	73
12	15	34	35	6	2	70
8	20	28	30	5	1	53
13	12	34	38	7	1	77
11	12	35	34	6	2	77
14	14	35	31	8	2	52
15	13	31	34	7	2	54
10	11	37	35	5	1	80
11	17	35	36	6	2	66
12	12	27	30	6	1	73
15	13	40	39	5	2	63
15	14	37	35	5	1	69
14	13	36	38	5	1	67
16	15	38	31	5	2	54
15	13	39	34	4	2	81
15	10	41	38	6	1	69
13	11	27	34	6	1	84
12	19	30	39	6	2	80
17	13	37	37	6	2	70
13	17	31	34	7	2	69
15	13	31	28	5	1	77
13	9	27	37	7	1	54
15	11	36	33	6	1	79
16	10	38	37	5	1	30
15	9	37	35	5	2	71
16	12	33	37	4	1	73
15	12	34	32	8	2	72
14	13	31	33	8	2	77
15	13	39	38	5	1	75
14	12	34	33	5	2	69
13	15	32	29	6	2	54
7	22	33	33	4	2	70
17	13	36	31	5	2	73
13	15	32	36	5	2	54
15	13	41	35	5	2	77
14	15	28	32	5	2	82
13	10	30	29	6	2	80
16	11	36	39	6	2	80
12	16	35	37	5	2	69
14	11	31	35	6	2	78
17	11	34	37	5	1	81
15	10	36	32	7	1	76
17	10	36	38	5	2	76
12	16	35	37	6	1	73
16	12	37	36	6	2	85
11	11	28	32	6	1	66
15	16	39	33	4	2	79
9	19	32	40	5	1	68
16	11	35	38	5	2	76
15	16	39	41	7	1	71
10	15	35	36	6	1	54
10	24	42	43	9	2	46
15	14	34	30	6	2	82
11	15	33	31	6	2	74
13	11	41	32	5	2	88
14	15	33	32	6	1	38
18	12	34	37	5	2	76
16	10	32	37	8	1	86
14	14	40	33	7	2	54
14	13	40	34	5	2	70
14	9	35	33	7	2	69
14	15	36	38	6	2	90
12	15	37	33	6	2	54
14	14	27	31	9	2	76
15	11	39	38	7	2	89
15	8	38	37	6	2	76
15	11	31	33	5	2	73
13	11	33	31	5	2	79
17	8	32	39	6	1	90
17	10	39	44	6	2	74
19	11	36	33	7	2	81
15	13	33	35	5	2	72
13	11	33	32	5	1	71
9	20	32	28	5	1	66
15	10	37	40	6	2	77
15	15	30	27	4	1	65
15	12	38	37	5	1	74
16	14	29	32	7	2	82
11	23	22	28	5	1	54
14	14	35	34	7	1	63
11	16	35	30	7	2	54
15	11	34	35	6	2	64
13	12	35	31	5	1	69
15	10	34	32	8	2	54
16	14	34	30	5	1	84
14	12	35	30	5	2	86
15	12	23	31	5	1	77
16	11	31	40	6	2	89
16	12	27	32	4	2	76
11	13	36	36	5	1	60
12	11	31	32	5	1	75
9	19	32	35	7	1	73
16	12	39	38	6	2	85
13	17	37	42	7	2	79
16	9	38	34	10	1	71
12	12	39	35	6	2	72
9	19	34	35	8	2	69
13	18	31	33	4	2	78
13	15	32	36	5	2	54
14	14	37	32	6	2	69
19	11	36	33	7	2	81
13	9	32	34	7	2	84
12	18	35	32	6	2	84
13	16	36	34	6	2	69




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time12 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 12 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&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]12 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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 time12 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y_t[t] = + 13.2907280296004 -0.368425731377247X_1t[t] + 0.0437427998472926X_2t[t] + 0.0174641693390636X_3t[t] + 0.0481096813814307X_4t[t] + 0.81384209306755X_5t[t] + 0.0293493833358802X_6t[t] -0.00324891555912615t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y_t[t] =  +  13.2907280296004 -0.368425731377247X_1t[t] +  0.0437427998472926X_2t[t] +  0.0174641693390636X_3t[t] +  0.0481096813814307X_4t[t] +  0.81384209306755X_5t[t] +  0.0293493833358802X_6t[t] -0.00324891555912615t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y_t[t] =  +  13.2907280296004 -0.368425731377247X_1t[t] +  0.0437427998472926X_2t[t] +  0.0174641693390636X_3t[t] +  0.0481096813814307X_4t[t] +  0.81384209306755X_5t[t] +  0.0293493833358802X_6t[t] -0.00324891555912615t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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
Y_t[t] = + 13.2907280296004 -0.368425731377247X_1t[t] + 0.0437427998472926X_2t[t] + 0.0174641693390636X_3t[t] + 0.0481096813814307X_4t[t] + 0.81384209306755X_5t[t] + 0.0293493833358802X_6t[t] -0.00324891555912615t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13.29072802960042.3901175.560700
X_1t-0.3684257313772470.050782-7.25500
X_2t0.04374279984729260.047380.92320.3573270.178663
X_3t0.01746416933906360.0493580.35380.7239540.361977
X_4t0.04810968138143070.1332850.3610.7186290.359314
X_5t0.813842093067550.3258852.49730.0135650.006782
X_6t0.02934938333588020.0152421.92550.0560070.028004
t-0.003248915559126150.003354-0.96870.3342060.167103

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 13.2907280296004 & 2.390117 & 5.5607 & 0 & 0 \tabularnewline
X_1t & -0.368425731377247 & 0.050782 & -7.255 & 0 & 0 \tabularnewline
X_2t & 0.0437427998472926 & 0.04738 & 0.9232 & 0.357327 & 0.178663 \tabularnewline
X_3t & 0.0174641693390636 & 0.049358 & 0.3538 & 0.723954 & 0.361977 \tabularnewline
X_4t & 0.0481096813814307 & 0.133285 & 0.361 & 0.718629 & 0.359314 \tabularnewline
X_5t & 0.81384209306755 & 0.325885 & 2.4973 & 0.013565 & 0.006782 \tabularnewline
X_6t & 0.0293493833358802 & 0.015242 & 1.9255 & 0.056007 & 0.028004 \tabularnewline
t & -0.00324891555912615 & 0.003354 & -0.9687 & 0.334206 & 0.167103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]13.2907280296004[/C][C]2.390117[/C][C]5.5607[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_1t[/C][C]-0.368425731377247[/C][C]0.050782[/C][C]-7.255[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X_2t[/C][C]0.0437427998472926[/C][C]0.04738[/C][C]0.9232[/C][C]0.357327[/C][C]0.178663[/C][/ROW]
[ROW][C]X_3t[/C][C]0.0174641693390636[/C][C]0.049358[/C][C]0.3538[/C][C]0.723954[/C][C]0.361977[/C][/ROW]
[ROW][C]X_4t[/C][C]0.0481096813814307[/C][C]0.133285[/C][C]0.361[/C][C]0.718629[/C][C]0.359314[/C][/ROW]
[ROW][C]X_5t[/C][C]0.81384209306755[/C][C]0.325885[/C][C]2.4973[/C][C]0.013565[/C][C]0.006782[/C][/ROW]
[ROW][C]X_6t[/C][C]0.0293493833358802[/C][C]0.015242[/C][C]1.9255[/C][C]0.056007[/C][C]0.028004[/C][/ROW]
[ROW][C]t[/C][C]-0.00324891555912615[/C][C]0.003354[/C][C]-0.9687[/C][C]0.334206[/C][C]0.167103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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)13.29072802960042.3901175.560700
X_1t-0.3684257313772470.050782-7.25500
X_2t0.04374279984729260.047380.92320.3573270.178663
X_3t0.01746416933906360.0493580.35380.7239540.361977
X_4t0.04810968138143070.1332850.3610.7186290.359314
X_5t0.813842093067550.3258852.49730.0135650.006782
X_6t0.02934938333588020.0152421.92550.0560070.028004
t-0.003248915559126150.003354-0.96870.3342060.167103







Multiple Linear Regression - Regression Statistics
Multiple R0.601314228778046
R-squared0.361578801730936
Adjusted R-squared0.33255965635507
F-TEST (value)12.4600086269818
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.35669253609194e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.90976435464058
Sum Squared Residuals561.668783099383

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.601314228778046 \tabularnewline
R-squared & 0.361578801730936 \tabularnewline
Adjusted R-squared & 0.33255965635507 \tabularnewline
F-TEST (value) & 12.4600086269818 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 154 \tabularnewline
p-value & 1.35669253609194e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.90976435464058 \tabularnewline
Sum Squared Residuals & 561.668783099383 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.601314228778046[/C][/ROW]
[ROW][C]R-squared[/C][C]0.361578801730936[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.33255965635507[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.4600086269818[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]154[/C][/ROW]
[ROW][C]p-value[/C][C]1.35669253609194e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.90976435464058[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]561.668783099383[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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 R0.601314228778046
R-squared0.361578801730936
Adjusted R-squared0.33255965635507
F-TEST (value)12.4600086269818
F-TEST (DF numerator)7
F-TEST (DF denominator)154
p-value1.35669253609194e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.90976435464058
Sum Squared Residuals561.668783099383







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.7645969472198-0.764596947219812
21815.70469891259722.2953010874028
31113.983236011679-2.98323601167903
41213.8623244187872-1.86232441878721
51611.9929232154744.00707678452601
61814.93550705677973.0644929432203
71410.62349883972053.37650116027948
81415.6448691533024-1.64486915330244
91515.7770255265547-0.777025526554685
101514.82778107103510.17221892896491
111715.01149933971871.98850066028133
121915.87339977606693.1266002239331
131012.9375417696914-2.93754176969136
141613.9318209454732.06817905452704
151816.27038647100661.72961352899337
161412.88791537002081.11208462997923
171413.4578295300620.542170469937996
181716.00451734144910.995482658550939
191414.9651503962404-0.965150396240434
201614.03220503042731.96779496957268
211815.63733629717152.36266370282852
221113.8226983526773-2.82269835267729
231414.6875418495768-0.687541849576753
241214.0149546991041-2.01495469910406
251714.81613176166972.18386823833025
26916.1742987592141-7.17429875921408
271614.83552439562451.16447560437547
281413.80962333042450.190376669575521
291514.21586371549490.78413628450508
301113.4618193908899-2.46181939088987
311615.85655097002010.143449029979929
321312.17093612842990.829063871570053
331715.50160605445931.49839394554066
341515.6734280005745-0.673428000574544
351413.51979209690350.480207903096474
361615.36692374908670.633076250913257
37910.5678928826018-1.56789288260184
381513.95657966981581.04342033018424
391715.441036441691.55896355830996
401314.6107703654412-1.61077036544118
411515.1138796367695-0.113879636769532
421614.19926319532061.80073680467943
431614.906284039651.09371596035001
441212.6732419423672-0.673241942367152
451214.9698400208794-2.96984002087944
461114.4891256888219-3.48912568882192
471515.9677475547295-0.967747554729514
481515.2151061638429-0.215106163842915
491713.88805896546393.11194103453615
501314.3946014866578-1.39460148665785
511615.59513434720380.404865652796181
521412.85576077899181.14423922100815
531111.3926398041221-0.39263980412209
541213.9974482244811-1.99744822448107
551213.9213792927973-1.92137929279727
561514.83240956899020.167590431009791
571614.57255977513621.42744022486378
581515.5667070455814-0.566707045581421
591214.8807049227627-2.88070492276273
601213.6387073550046-1.63870735500457
61810.0826608456212-2.0826608456212
621314.2295924977003-1.22959249770029
631114.9659621163183-3.96596211631832
641413.53595400935340.46404599064663
651513.78914121908981.21085878091016
661014.6556872456105-4.6556872456105
671112.822922919179-1.82292291917903
681213.598678835977-1.59867883597703
691514.42507668943430.574923310565651
701513.21457117254761.78542882745243
711413.52969892986380.470301070136167
721613.18713507557252.81286492442754
731514.76119659931960.238803400680355
741514.95075182460780.0492481753921514
751314.3370620524913-1.33706205249133
761212.3014010918755-0.301401091875453
771714.48648399147392.51351600852607
781312.71344314135040.286556858649583
791513.40384574612251.59615425387747
801314.2776896267724-1.27768962677241
811514.54704267174370.452957328256261
821613.58333229977312.41666770022686
831515.8870047869045-0.887004786904478
841613.83518280872532.16481719127465
851514.76528728157560.234712718424411
861414.4265953211158-0.426595321115803
871513.84373974714671.15626025285327
881414.5406275100853-0.540627510085342
891312.88262805468690.117371945313136
90710.7873692673018-3.78736926730178
911714.33240982639072.66759017360931
921312.94702081200150.0529791879984996
931514.73188020520870.268119794791319
941413.51747783754250.482522162457533
951315.3808615852566-2.38086158525664
961615.44628543079470.553714569205341
971213.1512838217478-1.15128382174777
981415.0925181564119-1.09251815641193
991714.48152235463152.51847764536853
1001514.79633636953230.203663630467684
1011715.61549520031231.38450479968774
1021212.4867043656095-0.486704365609537
1031615.1932144990130.806785500986969
1041113.7233690624-2.72336906239999
1051513.4757911712851.52420882871496
106911.0947390196558-2.09473901965578
1071615.1838331757330.816166824267037
1081512.70144966370992.29855033629012
1091012.2572852353521-2.25728523535211
1101010.0900295922271-0.0900295922270626
1111514.10631014620160.893689853798371
1121113.47356180207-2.47356180206998
1131315.6742040654581-2.67420406545814
1141411.61410824713162.38589175286844
1151814.72820915069643.27179084930365
1161614.99830688263271.00169311736732
1171413.62799290792460.372007092075396
1181414.384004663693-0.38400466369301
1191415.6851504844943-1.68515048449433
1201414.1706381958864-0.170638195886382
1211213.0672334333875-1.06723343338754
1221413.75006938958830.249930610411732
1231515.7845830723054-0.784583072305419
1241516.3957527169438-1.3957527169438
1251514.77501249957660.224987500423436
1261315.0004171450492-2.00041714504918
1271715.75552678349561.24447321650443
1281715.75319881050181.24680118949822
1291915.31174526602643.68825473397357
1301514.11498101406330.885018985936728
1311313.952999576838-0.952999576838019
132910.3735726850007-1.37357268500072
1331515.667660105663-0.667660105662953
1341512.02679467701772.97320532298225
1351513.96566117916371.03433882083631
1361613.88941127804662.11058872195343
137118.462430314569872.53756968543013
1381412.80881820824091.19118179175907
1391112.5485587956157-1.54855879561568
1401514.67640073576820.323599264231814
1411313.5634073535533-0.563407353553271
1421514.7886616574140.211338342585951
1431613.19909184053242.80090815946763
1441414.8489780473143-0.848978047314341
1451513.26029315983631.7397068401637
1461615.34673427296390.653265727036127
1471614.18261372579651.81738627420348
1481113.0391584097818-2.03915840978183
1491213.9244310304227-1.92443103042268
150911.1074321678012-2.10743216780117
1511615.15968049054770.840319509452312
1521313.1686873771301-0.168687377130148
1531615.11256564211350.88743435788652
1541214.7159992524867-2.71599925248668
155911.9232274308056-2.92322743080558
1561312.19395323290090.806046767099104
1571312.73584130065830.264158699341699
1581413.73822586977630.261774130223738
1591915.21427779925263.78572220074736
1601315.8784214664055-2.87842146640554
1611212.6075313479335-0.607531347933514
1621312.97956428361610.0204357163839008

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.7645969472198 & -0.764596947219812 \tabularnewline
2 & 18 & 15.7046989125972 & 2.2953010874028 \tabularnewline
3 & 11 & 13.983236011679 & -2.98323601167903 \tabularnewline
4 & 12 & 13.8623244187872 & -1.86232441878721 \tabularnewline
5 & 16 & 11.992923215474 & 4.00707678452601 \tabularnewline
6 & 18 & 14.9355070567797 & 3.0644929432203 \tabularnewline
7 & 14 & 10.6234988397205 & 3.37650116027948 \tabularnewline
8 & 14 & 15.6448691533024 & -1.64486915330244 \tabularnewline
9 & 15 & 15.7770255265547 & -0.777025526554685 \tabularnewline
10 & 15 & 14.8277810710351 & 0.17221892896491 \tabularnewline
11 & 17 & 15.0114993397187 & 1.98850066028133 \tabularnewline
12 & 19 & 15.8733997760669 & 3.1266002239331 \tabularnewline
13 & 10 & 12.9375417696914 & -2.93754176969136 \tabularnewline
14 & 16 & 13.931820945473 & 2.06817905452704 \tabularnewline
15 & 18 & 16.2703864710066 & 1.72961352899337 \tabularnewline
16 & 14 & 12.8879153700208 & 1.11208462997923 \tabularnewline
17 & 14 & 13.457829530062 & 0.542170469937996 \tabularnewline
18 & 17 & 16.0045173414491 & 0.995482658550939 \tabularnewline
19 & 14 & 14.9651503962404 & -0.965150396240434 \tabularnewline
20 & 16 & 14.0322050304273 & 1.96779496957268 \tabularnewline
21 & 18 & 15.6373362971715 & 2.36266370282852 \tabularnewline
22 & 11 & 13.8226983526773 & -2.82269835267729 \tabularnewline
23 & 14 & 14.6875418495768 & -0.687541849576753 \tabularnewline
24 & 12 & 14.0149546991041 & -2.01495469910406 \tabularnewline
25 & 17 & 14.8161317616697 & 2.18386823833025 \tabularnewline
26 & 9 & 16.1742987592141 & -7.17429875921408 \tabularnewline
27 & 16 & 14.8355243956245 & 1.16447560437547 \tabularnewline
28 & 14 & 13.8096233304245 & 0.190376669575521 \tabularnewline
29 & 15 & 14.2158637154949 & 0.78413628450508 \tabularnewline
30 & 11 & 13.4618193908899 & -2.46181939088987 \tabularnewline
31 & 16 & 15.8565509700201 & 0.143449029979929 \tabularnewline
32 & 13 & 12.1709361284299 & 0.829063871570053 \tabularnewline
33 & 17 & 15.5016060544593 & 1.49839394554066 \tabularnewline
34 & 15 & 15.6734280005745 & -0.673428000574544 \tabularnewline
35 & 14 & 13.5197920969035 & 0.480207903096474 \tabularnewline
36 & 16 & 15.3669237490867 & 0.633076250913257 \tabularnewline
37 & 9 & 10.5678928826018 & -1.56789288260184 \tabularnewline
38 & 15 & 13.9565796698158 & 1.04342033018424 \tabularnewline
39 & 17 & 15.44103644169 & 1.55896355830996 \tabularnewline
40 & 13 & 14.6107703654412 & -1.61077036544118 \tabularnewline
41 & 15 & 15.1138796367695 & -0.113879636769532 \tabularnewline
42 & 16 & 14.1992631953206 & 1.80073680467943 \tabularnewline
43 & 16 & 14.90628403965 & 1.09371596035001 \tabularnewline
44 & 12 & 12.6732419423672 & -0.673241942367152 \tabularnewline
45 & 12 & 14.9698400208794 & -2.96984002087944 \tabularnewline
46 & 11 & 14.4891256888219 & -3.48912568882192 \tabularnewline
47 & 15 & 15.9677475547295 & -0.967747554729514 \tabularnewline
48 & 15 & 15.2151061638429 & -0.215106163842915 \tabularnewline
49 & 17 & 13.8880589654639 & 3.11194103453615 \tabularnewline
50 & 13 & 14.3946014866578 & -1.39460148665785 \tabularnewline
51 & 16 & 15.5951343472038 & 0.404865652796181 \tabularnewline
52 & 14 & 12.8557607789918 & 1.14423922100815 \tabularnewline
53 & 11 & 11.3926398041221 & -0.39263980412209 \tabularnewline
54 & 12 & 13.9974482244811 & -1.99744822448107 \tabularnewline
55 & 12 & 13.9213792927973 & -1.92137929279727 \tabularnewline
56 & 15 & 14.8324095689902 & 0.167590431009791 \tabularnewline
57 & 16 & 14.5725597751362 & 1.42744022486378 \tabularnewline
58 & 15 & 15.5667070455814 & -0.566707045581421 \tabularnewline
59 & 12 & 14.8807049227627 & -2.88070492276273 \tabularnewline
60 & 12 & 13.6387073550046 & -1.63870735500457 \tabularnewline
61 & 8 & 10.0826608456212 & -2.0826608456212 \tabularnewline
62 & 13 & 14.2295924977003 & -1.22959249770029 \tabularnewline
63 & 11 & 14.9659621163183 & -3.96596211631832 \tabularnewline
64 & 14 & 13.5359540093534 & 0.46404599064663 \tabularnewline
65 & 15 & 13.7891412190898 & 1.21085878091016 \tabularnewline
66 & 10 & 14.6556872456105 & -4.6556872456105 \tabularnewline
67 & 11 & 12.822922919179 & -1.82292291917903 \tabularnewline
68 & 12 & 13.598678835977 & -1.59867883597703 \tabularnewline
69 & 15 & 14.4250766894343 & 0.574923310565651 \tabularnewline
70 & 15 & 13.2145711725476 & 1.78542882745243 \tabularnewline
71 & 14 & 13.5296989298638 & 0.470301070136167 \tabularnewline
72 & 16 & 13.1871350755725 & 2.81286492442754 \tabularnewline
73 & 15 & 14.7611965993196 & 0.238803400680355 \tabularnewline
74 & 15 & 14.9507518246078 & 0.0492481753921514 \tabularnewline
75 & 13 & 14.3370620524913 & -1.33706205249133 \tabularnewline
76 & 12 & 12.3014010918755 & -0.301401091875453 \tabularnewline
77 & 17 & 14.4864839914739 & 2.51351600852607 \tabularnewline
78 & 13 & 12.7134431413504 & 0.286556858649583 \tabularnewline
79 & 15 & 13.4038457461225 & 1.59615425387747 \tabularnewline
80 & 13 & 14.2776896267724 & -1.27768962677241 \tabularnewline
81 & 15 & 14.5470426717437 & 0.452957328256261 \tabularnewline
82 & 16 & 13.5833322997731 & 2.41666770022686 \tabularnewline
83 & 15 & 15.8870047869045 & -0.887004786904478 \tabularnewline
84 & 16 & 13.8351828087253 & 2.16481719127465 \tabularnewline
85 & 15 & 14.7652872815756 & 0.234712718424411 \tabularnewline
86 & 14 & 14.4265953211158 & -0.426595321115803 \tabularnewline
87 & 15 & 13.8437397471467 & 1.15626025285327 \tabularnewline
88 & 14 & 14.5406275100853 & -0.540627510085342 \tabularnewline
89 & 13 & 12.8826280546869 & 0.117371945313136 \tabularnewline
90 & 7 & 10.7873692673018 & -3.78736926730178 \tabularnewline
91 & 17 & 14.3324098263907 & 2.66759017360931 \tabularnewline
92 & 13 & 12.9470208120015 & 0.0529791879984996 \tabularnewline
93 & 15 & 14.7318802052087 & 0.268119794791319 \tabularnewline
94 & 14 & 13.5174778375425 & 0.482522162457533 \tabularnewline
95 & 13 & 15.3808615852566 & -2.38086158525664 \tabularnewline
96 & 16 & 15.4462854307947 & 0.553714569205341 \tabularnewline
97 & 12 & 13.1512838217478 & -1.15128382174777 \tabularnewline
98 & 14 & 15.0925181564119 & -1.09251815641193 \tabularnewline
99 & 17 & 14.4815223546315 & 2.51847764536853 \tabularnewline
100 & 15 & 14.7963363695323 & 0.203663630467684 \tabularnewline
101 & 17 & 15.6154952003123 & 1.38450479968774 \tabularnewline
102 & 12 & 12.4867043656095 & -0.486704365609537 \tabularnewline
103 & 16 & 15.193214499013 & 0.806785500986969 \tabularnewline
104 & 11 & 13.7233690624 & -2.72336906239999 \tabularnewline
105 & 15 & 13.475791171285 & 1.52420882871496 \tabularnewline
106 & 9 & 11.0947390196558 & -2.09473901965578 \tabularnewline
107 & 16 & 15.183833175733 & 0.816166824267037 \tabularnewline
108 & 15 & 12.7014496637099 & 2.29855033629012 \tabularnewline
109 & 10 & 12.2572852353521 & -2.25728523535211 \tabularnewline
110 & 10 & 10.0900295922271 & -0.0900295922270626 \tabularnewline
111 & 15 & 14.1063101462016 & 0.893689853798371 \tabularnewline
112 & 11 & 13.47356180207 & -2.47356180206998 \tabularnewline
113 & 13 & 15.6742040654581 & -2.67420406545814 \tabularnewline
114 & 14 & 11.6141082471316 & 2.38589175286844 \tabularnewline
115 & 18 & 14.7282091506964 & 3.27179084930365 \tabularnewline
116 & 16 & 14.9983068826327 & 1.00169311736732 \tabularnewline
117 & 14 & 13.6279929079246 & 0.372007092075396 \tabularnewline
118 & 14 & 14.384004663693 & -0.38400466369301 \tabularnewline
119 & 14 & 15.6851504844943 & -1.68515048449433 \tabularnewline
120 & 14 & 14.1706381958864 & -0.170638195886382 \tabularnewline
121 & 12 & 13.0672334333875 & -1.06723343338754 \tabularnewline
122 & 14 & 13.7500693895883 & 0.249930610411732 \tabularnewline
123 & 15 & 15.7845830723054 & -0.784583072305419 \tabularnewline
124 & 15 & 16.3957527169438 & -1.3957527169438 \tabularnewline
125 & 15 & 14.7750124995766 & 0.224987500423436 \tabularnewline
126 & 13 & 15.0004171450492 & -2.00041714504918 \tabularnewline
127 & 17 & 15.7555267834956 & 1.24447321650443 \tabularnewline
128 & 17 & 15.7531988105018 & 1.24680118949822 \tabularnewline
129 & 19 & 15.3117452660264 & 3.68825473397357 \tabularnewline
130 & 15 & 14.1149810140633 & 0.885018985936728 \tabularnewline
131 & 13 & 13.952999576838 & -0.952999576838019 \tabularnewline
132 & 9 & 10.3735726850007 & -1.37357268500072 \tabularnewline
133 & 15 & 15.667660105663 & -0.667660105662953 \tabularnewline
134 & 15 & 12.0267946770177 & 2.97320532298225 \tabularnewline
135 & 15 & 13.9656611791637 & 1.03433882083631 \tabularnewline
136 & 16 & 13.8894112780466 & 2.11058872195343 \tabularnewline
137 & 11 & 8.46243031456987 & 2.53756968543013 \tabularnewline
138 & 14 & 12.8088182082409 & 1.19118179175907 \tabularnewline
139 & 11 & 12.5485587956157 & -1.54855879561568 \tabularnewline
140 & 15 & 14.6764007357682 & 0.323599264231814 \tabularnewline
141 & 13 & 13.5634073535533 & -0.563407353553271 \tabularnewline
142 & 15 & 14.788661657414 & 0.211338342585951 \tabularnewline
143 & 16 & 13.1990918405324 & 2.80090815946763 \tabularnewline
144 & 14 & 14.8489780473143 & -0.848978047314341 \tabularnewline
145 & 15 & 13.2602931598363 & 1.7397068401637 \tabularnewline
146 & 16 & 15.3467342729639 & 0.653265727036127 \tabularnewline
147 & 16 & 14.1826137257965 & 1.81738627420348 \tabularnewline
148 & 11 & 13.0391584097818 & -2.03915840978183 \tabularnewline
149 & 12 & 13.9244310304227 & -1.92443103042268 \tabularnewline
150 & 9 & 11.1074321678012 & -2.10743216780117 \tabularnewline
151 & 16 & 15.1596804905477 & 0.840319509452312 \tabularnewline
152 & 13 & 13.1686873771301 & -0.168687377130148 \tabularnewline
153 & 16 & 15.1125656421135 & 0.88743435788652 \tabularnewline
154 & 12 & 14.7159992524867 & -2.71599925248668 \tabularnewline
155 & 9 & 11.9232274308056 & -2.92322743080558 \tabularnewline
156 & 13 & 12.1939532329009 & 0.806046767099104 \tabularnewline
157 & 13 & 12.7358413006583 & 0.264158699341699 \tabularnewline
158 & 14 & 13.7382258697763 & 0.261774130223738 \tabularnewline
159 & 19 & 15.2142777992526 & 3.78572220074736 \tabularnewline
160 & 13 & 15.8784214664055 & -2.87842146640554 \tabularnewline
161 & 12 & 12.6075313479335 & -0.607531347933514 \tabularnewline
162 & 13 & 12.9795642836161 & 0.0204357163839008 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]14.7645969472198[/C][C]-0.764596947219812[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.7046989125972[/C][C]2.2953010874028[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.983236011679[/C][C]-2.98323601167903[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.8623244187872[/C][C]-1.86232441878721[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.992923215474[/C][C]4.00707678452601[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.9355070567797[/C][C]3.0644929432203[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.6234988397205[/C][C]3.37650116027948[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.6448691533024[/C][C]-1.64486915330244[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.7770255265547[/C][C]-0.777025526554685[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.8277810710351[/C][C]0.17221892896491[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.0114993397187[/C][C]1.98850066028133[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.8733997760669[/C][C]3.1266002239331[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.9375417696914[/C][C]-2.93754176969136[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.931820945473[/C][C]2.06817905452704[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]16.2703864710066[/C][C]1.72961352899337[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.8879153700208[/C][C]1.11208462997923[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.457829530062[/C][C]0.542170469937996[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]16.0045173414491[/C][C]0.995482658550939[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.9651503962404[/C][C]-0.965150396240434[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.0322050304273[/C][C]1.96779496957268[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.6373362971715[/C][C]2.36266370282852[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.8226983526773[/C][C]-2.82269835267729[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.6875418495768[/C][C]-0.687541849576753[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.0149546991041[/C][C]-2.01495469910406[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.8161317616697[/C][C]2.18386823833025[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.1742987592141[/C][C]-7.17429875921408[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.8355243956245[/C][C]1.16447560437547[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.8096233304245[/C][C]0.190376669575521[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.2158637154949[/C][C]0.78413628450508[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.4618193908899[/C][C]-2.46181939088987[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.8565509700201[/C][C]0.143449029979929[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.1709361284299[/C][C]0.829063871570053[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.5016060544593[/C][C]1.49839394554066[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.6734280005745[/C][C]-0.673428000574544[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.5197920969035[/C][C]0.480207903096474[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3669237490867[/C][C]0.633076250913257[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.5678928826018[/C][C]-1.56789288260184[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.9565796698158[/C][C]1.04342033018424[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.44103644169[/C][C]1.55896355830996[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.6107703654412[/C][C]-1.61077036544118[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.1138796367695[/C][C]-0.113879636769532[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.1992631953206[/C][C]1.80073680467943[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]14.90628403965[/C][C]1.09371596035001[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.6732419423672[/C][C]-0.673241942367152[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.9698400208794[/C][C]-2.96984002087944[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.4891256888219[/C][C]-3.48912568882192[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.9677475547295[/C][C]-0.967747554729514[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.2151061638429[/C][C]-0.215106163842915[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.8880589654639[/C][C]3.11194103453615[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.3946014866578[/C][C]-1.39460148665785[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.5951343472038[/C][C]0.404865652796181[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.8557607789918[/C][C]1.14423922100815[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.3926398041221[/C][C]-0.39263980412209[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.9974482244811[/C][C]-1.99744822448107[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]13.9213792927973[/C][C]-1.92137929279727[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.8324095689902[/C][C]0.167590431009791[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.5725597751362[/C][C]1.42744022486378[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.5667070455814[/C][C]-0.566707045581421[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.8807049227627[/C][C]-2.88070492276273[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.6387073550046[/C][C]-1.63870735500457[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.0826608456212[/C][C]-2.0826608456212[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.2295924977003[/C][C]-1.22959249770029[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.9659621163183[/C][C]-3.96596211631832[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.5359540093534[/C][C]0.46404599064663[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.7891412190898[/C][C]1.21085878091016[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.6556872456105[/C][C]-4.6556872456105[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.822922919179[/C][C]-1.82292291917903[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.598678835977[/C][C]-1.59867883597703[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.4250766894343[/C][C]0.574923310565651[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.2145711725476[/C][C]1.78542882745243[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.5296989298638[/C][C]0.470301070136167[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.1871350755725[/C][C]2.81286492442754[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.7611965993196[/C][C]0.238803400680355[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.9507518246078[/C][C]0.0492481753921514[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]14.3370620524913[/C][C]-1.33706205249133[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.3014010918755[/C][C]-0.301401091875453[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.4864839914739[/C][C]2.51351600852607[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.7134431413504[/C][C]0.286556858649583[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.4038457461225[/C][C]1.59615425387747[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.2776896267724[/C][C]-1.27768962677241[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.5470426717437[/C][C]0.452957328256261[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]13.5833322997731[/C][C]2.41666770022686[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.8870047869045[/C][C]-0.887004786904478[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.8351828087253[/C][C]2.16481719127465[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7652872815756[/C][C]0.234712718424411[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.4265953211158[/C][C]-0.426595321115803[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.8437397471467[/C][C]1.15626025285327[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.5406275100853[/C][C]-0.540627510085342[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.8826280546869[/C][C]0.117371945313136[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.7873692673018[/C][C]-3.78736926730178[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.3324098263907[/C][C]2.66759017360931[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.9470208120015[/C][C]0.0529791879984996[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.7318802052087[/C][C]0.268119794791319[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.5174778375425[/C][C]0.482522162457533[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.3808615852566[/C][C]-2.38086158525664[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.4462854307947[/C][C]0.553714569205341[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]13.1512838217478[/C][C]-1.15128382174777[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]15.0925181564119[/C][C]-1.09251815641193[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.4815223546315[/C][C]2.51847764536853[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.7963363695323[/C][C]0.203663630467684[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.6154952003123[/C][C]1.38450479968774[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.4867043656095[/C][C]-0.486704365609537[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.193214499013[/C][C]0.806785500986969[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.7233690624[/C][C]-2.72336906239999[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.475791171285[/C][C]1.52420882871496[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.0947390196558[/C][C]-2.09473901965578[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.183833175733[/C][C]0.816166824267037[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.7014496637099[/C][C]2.29855033629012[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.2572852353521[/C][C]-2.25728523535211[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.0900295922271[/C][C]-0.0900295922270626[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.1063101462016[/C][C]0.893689853798371[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.47356180207[/C][C]-2.47356180206998[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.6742040654581[/C][C]-2.67420406545814[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]11.6141082471316[/C][C]2.38589175286844[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.7282091506964[/C][C]3.27179084930365[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.9983068826327[/C][C]1.00169311736732[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.6279929079246[/C][C]0.372007092075396[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.384004663693[/C][C]-0.38400466369301[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.6851504844943[/C][C]-1.68515048449433[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.1706381958864[/C][C]-0.170638195886382[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.0672334333875[/C][C]-1.06723343338754[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.7500693895883[/C][C]0.249930610411732[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.7845830723054[/C][C]-0.784583072305419[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.3957527169438[/C][C]-1.3957527169438[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.7750124995766[/C][C]0.224987500423436[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]15.0004171450492[/C][C]-2.00041714504918[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.7555267834956[/C][C]1.24447321650443[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.7531988105018[/C][C]1.24680118949822[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.3117452660264[/C][C]3.68825473397357[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.1149810140633[/C][C]0.885018985936728[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]13.952999576838[/C][C]-0.952999576838019[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.3735726850007[/C][C]-1.37357268500072[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.667660105663[/C][C]-0.667660105662953[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.0267946770177[/C][C]2.97320532298225[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]13.9656611791637[/C][C]1.03433882083631[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.8894112780466[/C][C]2.11058872195343[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.46243031456987[/C][C]2.53756968543013[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.8088182082409[/C][C]1.19118179175907[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.5485587956157[/C][C]-1.54855879561568[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.6764007357682[/C][C]0.323599264231814[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.5634073535533[/C][C]-0.563407353553271[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.788661657414[/C][C]0.211338342585951[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.1990918405324[/C][C]2.80090815946763[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.8489780473143[/C][C]-0.848978047314341[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.2602931598363[/C][C]1.7397068401637[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.3467342729639[/C][C]0.653265727036127[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.1826137257965[/C][C]1.81738627420348[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.0391584097818[/C][C]-2.03915840978183[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]13.9244310304227[/C][C]-1.92443103042268[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.1074321678012[/C][C]-2.10743216780117[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.1596804905477[/C][C]0.840319509452312[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.1686873771301[/C][C]-0.168687377130148[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.1125656421135[/C][C]0.88743435788652[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.7159992524867[/C][C]-2.71599925248668[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.9232274308056[/C][C]-2.92322743080558[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.1939532329009[/C][C]0.806046767099104[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.7358413006583[/C][C]0.264158699341699[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.7382258697763[/C][C]0.261774130223738[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.2142777992526[/C][C]3.78572220074736[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.8784214664055[/C][C]-2.87842146640554[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6075313479335[/C][C]-0.607531347933514[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.9795642836161[/C][C]0.0204357163839008[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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
11414.7645969472198-0.764596947219812
21815.70469891259722.2953010874028
31113.983236011679-2.98323601167903
41213.8623244187872-1.86232441878721
51611.9929232154744.00707678452601
61814.93550705677973.0644929432203
71410.62349883972053.37650116027948
81415.6448691533024-1.64486915330244
91515.7770255265547-0.777025526554685
101514.82778107103510.17221892896491
111715.01149933971871.98850066028133
121915.87339977606693.1266002239331
131012.9375417696914-2.93754176969136
141613.9318209454732.06817905452704
151816.27038647100661.72961352899337
161412.88791537002081.11208462997923
171413.4578295300620.542170469937996
181716.00451734144910.995482658550939
191414.9651503962404-0.965150396240434
201614.03220503042731.96779496957268
211815.63733629717152.36266370282852
221113.8226983526773-2.82269835267729
231414.6875418495768-0.687541849576753
241214.0149546991041-2.01495469910406
251714.81613176166972.18386823833025
26916.1742987592141-7.17429875921408
271614.83552439562451.16447560437547
281413.80962333042450.190376669575521
291514.21586371549490.78413628450508
301113.4618193908899-2.46181939088987
311615.85655097002010.143449029979929
321312.17093612842990.829063871570053
331715.50160605445931.49839394554066
341515.6734280005745-0.673428000574544
351413.51979209690350.480207903096474
361615.36692374908670.633076250913257
37910.5678928826018-1.56789288260184
381513.95657966981581.04342033018424
391715.441036441691.55896355830996
401314.6107703654412-1.61077036544118
411515.1138796367695-0.113879636769532
421614.19926319532061.80073680467943
431614.906284039651.09371596035001
441212.6732419423672-0.673241942367152
451214.9698400208794-2.96984002087944
461114.4891256888219-3.48912568882192
471515.9677475547295-0.967747554729514
481515.2151061638429-0.215106163842915
491713.88805896546393.11194103453615
501314.3946014866578-1.39460148665785
511615.59513434720380.404865652796181
521412.85576077899181.14423922100815
531111.3926398041221-0.39263980412209
541213.9974482244811-1.99744822448107
551213.9213792927973-1.92137929279727
561514.83240956899020.167590431009791
571614.57255977513621.42744022486378
581515.5667070455814-0.566707045581421
591214.8807049227627-2.88070492276273
601213.6387073550046-1.63870735500457
61810.0826608456212-2.0826608456212
621314.2295924977003-1.22959249770029
631114.9659621163183-3.96596211631832
641413.53595400935340.46404599064663
651513.78914121908981.21085878091016
661014.6556872456105-4.6556872456105
671112.822922919179-1.82292291917903
681213.598678835977-1.59867883597703
691514.42507668943430.574923310565651
701513.21457117254761.78542882745243
711413.52969892986380.470301070136167
721613.18713507557252.81286492442754
731514.76119659931960.238803400680355
741514.95075182460780.0492481753921514
751314.3370620524913-1.33706205249133
761212.3014010918755-0.301401091875453
771714.48648399147392.51351600852607
781312.71344314135040.286556858649583
791513.40384574612251.59615425387747
801314.2776896267724-1.27768962677241
811514.54704267174370.452957328256261
821613.58333229977312.41666770022686
831515.8870047869045-0.887004786904478
841613.83518280872532.16481719127465
851514.76528728157560.234712718424411
861414.4265953211158-0.426595321115803
871513.84373974714671.15626025285327
881414.5406275100853-0.540627510085342
891312.88262805468690.117371945313136
90710.7873692673018-3.78736926730178
911714.33240982639072.66759017360931
921312.94702081200150.0529791879984996
931514.73188020520870.268119794791319
941413.51747783754250.482522162457533
951315.3808615852566-2.38086158525664
961615.44628543079470.553714569205341
971213.1512838217478-1.15128382174777
981415.0925181564119-1.09251815641193
991714.48152235463152.51847764536853
1001514.79633636953230.203663630467684
1011715.61549520031231.38450479968774
1021212.4867043656095-0.486704365609537
1031615.1932144990130.806785500986969
1041113.7233690624-2.72336906239999
1051513.4757911712851.52420882871496
106911.0947390196558-2.09473901965578
1071615.1838331757330.816166824267037
1081512.70144966370992.29855033629012
1091012.2572852353521-2.25728523535211
1101010.0900295922271-0.0900295922270626
1111514.10631014620160.893689853798371
1121113.47356180207-2.47356180206998
1131315.6742040654581-2.67420406545814
1141411.61410824713162.38589175286844
1151814.72820915069643.27179084930365
1161614.99830688263271.00169311736732
1171413.62799290792460.372007092075396
1181414.384004663693-0.38400466369301
1191415.6851504844943-1.68515048449433
1201414.1706381958864-0.170638195886382
1211213.0672334333875-1.06723343338754
1221413.75006938958830.249930610411732
1231515.7845830723054-0.784583072305419
1241516.3957527169438-1.3957527169438
1251514.77501249957660.224987500423436
1261315.0004171450492-2.00041714504918
1271715.75552678349561.24447321650443
1281715.75319881050181.24680118949822
1291915.31174526602643.68825473397357
1301514.11498101406330.885018985936728
1311313.952999576838-0.952999576838019
132910.3735726850007-1.37357268500072
1331515.667660105663-0.667660105662953
1341512.02679467701772.97320532298225
1351513.96566117916371.03433882083631
1361613.88941127804662.11058872195343
137118.462430314569872.53756968543013
1381412.80881820824091.19118179175907
1391112.5485587956157-1.54855879561568
1401514.67640073576820.323599264231814
1411313.5634073535533-0.563407353553271
1421514.7886616574140.211338342585951
1431613.19909184053242.80090815946763
1441414.8489780473143-0.848978047314341
1451513.26029315983631.7397068401637
1461615.34673427296390.653265727036127
1471614.18261372579651.81738627420348
1481113.0391584097818-2.03915840978183
1491213.9244310304227-1.92443103042268
150911.1074321678012-2.10743216780117
1511615.15968049054770.840319509452312
1521313.1686873771301-0.168687377130148
1531615.11256564211350.88743435788652
1541214.7159992524867-2.71599925248668
155911.9232274308056-2.92322743080558
1561312.19395323290090.806046767099104
1571312.73584130065830.264158699341699
1581413.73822586977630.261774130223738
1591915.21427779925263.78572220074736
1601315.8784214664055-2.87842146640554
1611212.6075313479335-0.607531347933514
1621312.97956428361610.0204357163839008







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1953842725857340.3907685451714690.804615727414266
120.7200767711037290.5598464577925430.279923228896271
130.8279384296948940.3441231406102110.172061570305106
140.7512171719204660.4975656561590690.248782828079534
150.6604491206054570.6791017587890860.339550879394543
160.5656640386161230.8686719227677530.434335961383877
170.5842838392595480.8314323214809040.415716160740452
180.4994660749504020.9989321499008040.500533925049598
190.4338064266640540.8676128533281080.566193573335946
200.4634473410504060.9268946821008120.536552658949594
210.5917495423716840.8165009152566320.408250457628316
220.8912760810415970.2174478379168060.108723918958403
230.8827980803914780.2344038392170440.117201919608522
240.8771993981803710.2456012036392580.122800601819629
250.8578888978885450.2842222042229110.142111102111455
260.9977181640819850.00456367183603090.00228183591801545
270.9970985321635080.005802935672983260.00290146783649163
280.9961186218886150.007762756222769920.00388137811138496
290.9950526805980710.009894638803857480.00494731940192874
300.9939453741977480.01210925160450470.00605462580225234
310.9908343086704030.01833138265919450.00916569132959725
320.9891496307883380.0217007384233240.010850369211662
330.9871127730086010.02577445398279880.0128872269913994
340.982669800454550.03466039909090020.0173301995454501
350.9765572744448710.04688545111025730.0234427255551287
360.9734254068054530.05314918638909430.0265745931945471
370.9688245640103770.06235087197924650.0311754359896233
380.9672259613933830.06554807721323380.0327740386066169
390.9640470325264980.07190593494700320.0359529674735016
400.9547317064825470.09053658703490690.0452682935174535
410.9413029101702910.1173941796594180.0586970898297088
420.9365253549584010.1269492900831980.0634746450415991
430.9307708432907160.1384583134185680.0692291567092838
440.9129414420163520.1741171159672960.0870585579836478
450.9571698440136060.08566031197278730.0428301559863936
460.9691556409864420.0616887180271150.0308443590135575
470.9631843418765870.07363131624682630.0368156581234131
480.9531463407150310.09370731856993810.046853659284969
490.982044764853920.0359104702921590.0179552351460795
500.977263232176080.04547353564783940.0227367678239197
510.9719432002284610.05611359954307740.0280567997715387
520.9676630472032630.06467390559347430.0323369527967372
530.9577466668198210.0845066663603580.042253333180179
540.9533153129752610.09336937404947780.0466846870247389
550.948006746467080.1039865070658390.0519932535329197
560.935262943881510.1294741122369790.0647370561184895
570.933610692971920.1327786140561610.0663893070280804
580.9170870522204310.1658258955591390.0829129477795694
590.9247007917935790.1505984164128410.0752992082064206
600.9139231716527440.1721536566945120.086076828347256
610.9105322601465410.1789354797069180.0894677398534592
620.8928058484546560.2143883030906880.107194151545344
630.9336162068514020.1327675862971950.0663837931485976
640.9217429660114950.1565140679770110.0782570339885054
650.9157790018648030.1684419962703930.0842209981351966
660.9638356308436460.07232873831270850.0361643691563543
670.9588650482429960.08226990351400820.0411349517570041
680.9552636269449530.08947274611009410.044736373055047
690.9541147337661630.09177053246767440.0458852662338372
700.9626668146018780.07466637079624320.0373331853981216
710.9574741867489680.08505162650206320.0425258132510316
720.97265453460870.05469093078260010.0273454653913001
730.9658749774463680.06825004510726480.0341250225536324
740.958473840209850.08305231958030040.0415261597901502
750.9538274010187390.0923451979625230.0461725989812615
760.941521866394740.116956267210520.0584781336052599
770.9550971316417990.08980573671640290.0449028683582014
780.943983685667220.1120326286655590.0560163143327797
790.9418846396563930.1162307206872130.0581153603436067
800.9373222560598570.1253554878802850.0626777439401426
810.9239800371547640.1520399256904730.0760199628452363
820.9328343987896710.1343312024206580.0671656012103289
830.919525384166860.160949231666280.0804746158331399
840.924717664055690.1505646718886190.0752823359443097
850.9075916439695750.184816712060850.092408356030425
860.8866903222605390.2266193554789210.113309677739461
870.8717591451271110.2564817097457770.128240854872889
880.8465585122574360.3068829754851280.153441487742564
890.8182060499294730.3635879001410540.181793950070527
900.8869764041855180.2260471916289650.113023595814482
910.9118791629424210.1762416741151570.0881208370575787
920.8914110818327430.2171778363345140.108588918167257
930.8689094744459540.2621810511080920.131090525554046
940.845217600424970.309564799150060.15478239957503
950.858243339730430.2835133205391390.14175666026957
960.8326409231632210.3347181536735570.167359076836779
970.8121840232121570.3756319535756860.187815976787843
980.7953412366226350.4093175267547310.204658763377365
990.8138029733307590.3723940533384820.186197026669241
1000.7795803013025850.4408393973948290.220419698697415
1010.7602787749825110.4794424500349780.239721225017489
1020.7238636718713680.5522726562572630.276136328128632
1030.6884725362390980.6230549275218050.311527463760902
1040.7579171044799060.4841657910401890.242082895520094
1050.7526952729911240.4946094540177530.247304727008876
1060.7889977187826270.4220045624347460.211002281217373
1070.7549722283582010.4900555432835990.245027771641799
1080.7662480844471750.467503831105650.233751915552825
1090.7996904156577520.4006191686844960.200309584342248
1100.7651861765618680.4696276468762640.234813823438132
1110.735329086278740.5293418274425210.26467091372126
1120.770826758526660.4583464829466810.22917324147334
1130.8016293044169260.3967413911661480.198370695583074
1140.8156371363892060.3687257272215870.184362863610794
1150.867052247917320.265895504165360.13294775208268
1160.8392745880417660.3214508239164670.160725411958234
1170.8186100946276490.3627798107447030.181389905372351
1180.7809748414723280.4380503170553430.219025158527672
1190.7732419539050150.4535160921899710.226758046094985
1200.730093650207210.539812699585580.26990634979279
1210.6872029417183840.6255941165632320.312797058281616
1220.6480213695486160.7039572609027690.351978630451384
1230.6102329369830860.7795341260338280.389767063016914
1240.5972966248064550.805406750387090.402703375193545
1250.5515027821131820.8969944357736360.448497217886818
1260.6706907479838560.6586185040322870.329309252016144
1270.6242297384733150.7515405230533710.375770261526685
1280.5841154392326910.8317691215346190.415884560767309
1290.6539137510018810.6921724979962380.346086248998119
1300.597538039880130.804923920239740.40246196011987
1310.5838875465563840.8322249068872310.416112453443616
1320.6112100239150150.777579952169970.388789976084985
1330.5626935595214160.8746128809571670.437306440478584
1340.558911679881220.882176640237560.44108832011878
1350.5044747779566630.9910504440866730.495525222043337
1360.4513943620974790.9027887241949570.548605637902521
1370.4651871766630140.9303743533260280.534812823336986
1380.4481019497388450.896203899477690.551898050261155
1390.3930934833096560.7861869666193120.606906516690344
1400.3209705167723660.6419410335447330.679029483227634
1410.2589930845337680.5179861690675360.741006915466232
1420.1998482130644310.3996964261288630.800151786935569
1430.2577517682448660.5155035364897330.742248231755134
1440.2201066001149980.4402132002299970.779893399885002
1450.1994627615883250.398925523176650.800537238411675
1460.1401168646568660.2802337293137320.859883135343134
1470.1532929069243790.3065858138487570.846707093075622
1480.1205868057331520.2411736114663030.879413194266848
1490.08510128486336190.1702025697267240.914898715136638
1500.07004018335080570.1400803667016110.929959816649194
1510.0363091576148790.07261831522975790.963690842385121

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.195384272585734 & 0.390768545171469 & 0.804615727414266 \tabularnewline
12 & 0.720076771103729 & 0.559846457792543 & 0.279923228896271 \tabularnewline
13 & 0.827938429694894 & 0.344123140610211 & 0.172061570305106 \tabularnewline
14 & 0.751217171920466 & 0.497565656159069 & 0.248782828079534 \tabularnewline
15 & 0.660449120605457 & 0.679101758789086 & 0.339550879394543 \tabularnewline
16 & 0.565664038616123 & 0.868671922767753 & 0.434335961383877 \tabularnewline
17 & 0.584283839259548 & 0.831432321480904 & 0.415716160740452 \tabularnewline
18 & 0.499466074950402 & 0.998932149900804 & 0.500533925049598 \tabularnewline
19 & 0.433806426664054 & 0.867612853328108 & 0.566193573335946 \tabularnewline
20 & 0.463447341050406 & 0.926894682100812 & 0.536552658949594 \tabularnewline
21 & 0.591749542371684 & 0.816500915256632 & 0.408250457628316 \tabularnewline
22 & 0.891276081041597 & 0.217447837916806 & 0.108723918958403 \tabularnewline
23 & 0.882798080391478 & 0.234403839217044 & 0.117201919608522 \tabularnewline
24 & 0.877199398180371 & 0.245601203639258 & 0.122800601819629 \tabularnewline
25 & 0.857888897888545 & 0.284222204222911 & 0.142111102111455 \tabularnewline
26 & 0.997718164081985 & 0.0045636718360309 & 0.00228183591801545 \tabularnewline
27 & 0.997098532163508 & 0.00580293567298326 & 0.00290146783649163 \tabularnewline
28 & 0.996118621888615 & 0.00776275622276992 & 0.00388137811138496 \tabularnewline
29 & 0.995052680598071 & 0.00989463880385748 & 0.00494731940192874 \tabularnewline
30 & 0.993945374197748 & 0.0121092516045047 & 0.00605462580225234 \tabularnewline
31 & 0.990834308670403 & 0.0183313826591945 & 0.00916569132959725 \tabularnewline
32 & 0.989149630788338 & 0.021700738423324 & 0.010850369211662 \tabularnewline
33 & 0.987112773008601 & 0.0257744539827988 & 0.0128872269913994 \tabularnewline
34 & 0.98266980045455 & 0.0346603990909002 & 0.0173301995454501 \tabularnewline
35 & 0.976557274444871 & 0.0468854511102573 & 0.0234427255551287 \tabularnewline
36 & 0.973425406805453 & 0.0531491863890943 & 0.0265745931945471 \tabularnewline
37 & 0.968824564010377 & 0.0623508719792465 & 0.0311754359896233 \tabularnewline
38 & 0.967225961393383 & 0.0655480772132338 & 0.0327740386066169 \tabularnewline
39 & 0.964047032526498 & 0.0719059349470032 & 0.0359529674735016 \tabularnewline
40 & 0.954731706482547 & 0.0905365870349069 & 0.0452682935174535 \tabularnewline
41 & 0.941302910170291 & 0.117394179659418 & 0.0586970898297088 \tabularnewline
42 & 0.936525354958401 & 0.126949290083198 & 0.0634746450415991 \tabularnewline
43 & 0.930770843290716 & 0.138458313418568 & 0.0692291567092838 \tabularnewline
44 & 0.912941442016352 & 0.174117115967296 & 0.0870585579836478 \tabularnewline
45 & 0.957169844013606 & 0.0856603119727873 & 0.0428301559863936 \tabularnewline
46 & 0.969155640986442 & 0.061688718027115 & 0.0308443590135575 \tabularnewline
47 & 0.963184341876587 & 0.0736313162468263 & 0.0368156581234131 \tabularnewline
48 & 0.953146340715031 & 0.0937073185699381 & 0.046853659284969 \tabularnewline
49 & 0.98204476485392 & 0.035910470292159 & 0.0179552351460795 \tabularnewline
50 & 0.97726323217608 & 0.0454735356478394 & 0.0227367678239197 \tabularnewline
51 & 0.971943200228461 & 0.0561135995430774 & 0.0280567997715387 \tabularnewline
52 & 0.967663047203263 & 0.0646739055934743 & 0.0323369527967372 \tabularnewline
53 & 0.957746666819821 & 0.084506666360358 & 0.042253333180179 \tabularnewline
54 & 0.953315312975261 & 0.0933693740494778 & 0.0466846870247389 \tabularnewline
55 & 0.94800674646708 & 0.103986507065839 & 0.0519932535329197 \tabularnewline
56 & 0.93526294388151 & 0.129474112236979 & 0.0647370561184895 \tabularnewline
57 & 0.93361069297192 & 0.132778614056161 & 0.0663893070280804 \tabularnewline
58 & 0.917087052220431 & 0.165825895559139 & 0.0829129477795694 \tabularnewline
59 & 0.924700791793579 & 0.150598416412841 & 0.0752992082064206 \tabularnewline
60 & 0.913923171652744 & 0.172153656694512 & 0.086076828347256 \tabularnewline
61 & 0.910532260146541 & 0.178935479706918 & 0.0894677398534592 \tabularnewline
62 & 0.892805848454656 & 0.214388303090688 & 0.107194151545344 \tabularnewline
63 & 0.933616206851402 & 0.132767586297195 & 0.0663837931485976 \tabularnewline
64 & 0.921742966011495 & 0.156514067977011 & 0.0782570339885054 \tabularnewline
65 & 0.915779001864803 & 0.168441996270393 & 0.0842209981351966 \tabularnewline
66 & 0.963835630843646 & 0.0723287383127085 & 0.0361643691563543 \tabularnewline
67 & 0.958865048242996 & 0.0822699035140082 & 0.0411349517570041 \tabularnewline
68 & 0.955263626944953 & 0.0894727461100941 & 0.044736373055047 \tabularnewline
69 & 0.954114733766163 & 0.0917705324676744 & 0.0458852662338372 \tabularnewline
70 & 0.962666814601878 & 0.0746663707962432 & 0.0373331853981216 \tabularnewline
71 & 0.957474186748968 & 0.0850516265020632 & 0.0425258132510316 \tabularnewline
72 & 0.9726545346087 & 0.0546909307826001 & 0.0273454653913001 \tabularnewline
73 & 0.965874977446368 & 0.0682500451072648 & 0.0341250225536324 \tabularnewline
74 & 0.95847384020985 & 0.0830523195803004 & 0.0415261597901502 \tabularnewline
75 & 0.953827401018739 & 0.092345197962523 & 0.0461725989812615 \tabularnewline
76 & 0.94152186639474 & 0.11695626721052 & 0.0584781336052599 \tabularnewline
77 & 0.955097131641799 & 0.0898057367164029 & 0.0449028683582014 \tabularnewline
78 & 0.94398368566722 & 0.112032628665559 & 0.0560163143327797 \tabularnewline
79 & 0.941884639656393 & 0.116230720687213 & 0.0581153603436067 \tabularnewline
80 & 0.937322256059857 & 0.125355487880285 & 0.0626777439401426 \tabularnewline
81 & 0.923980037154764 & 0.152039925690473 & 0.0760199628452363 \tabularnewline
82 & 0.932834398789671 & 0.134331202420658 & 0.0671656012103289 \tabularnewline
83 & 0.91952538416686 & 0.16094923166628 & 0.0804746158331399 \tabularnewline
84 & 0.92471766405569 & 0.150564671888619 & 0.0752823359443097 \tabularnewline
85 & 0.907591643969575 & 0.18481671206085 & 0.092408356030425 \tabularnewline
86 & 0.886690322260539 & 0.226619355478921 & 0.113309677739461 \tabularnewline
87 & 0.871759145127111 & 0.256481709745777 & 0.128240854872889 \tabularnewline
88 & 0.846558512257436 & 0.306882975485128 & 0.153441487742564 \tabularnewline
89 & 0.818206049929473 & 0.363587900141054 & 0.181793950070527 \tabularnewline
90 & 0.886976404185518 & 0.226047191628965 & 0.113023595814482 \tabularnewline
91 & 0.911879162942421 & 0.176241674115157 & 0.0881208370575787 \tabularnewline
92 & 0.891411081832743 & 0.217177836334514 & 0.108588918167257 \tabularnewline
93 & 0.868909474445954 & 0.262181051108092 & 0.131090525554046 \tabularnewline
94 & 0.84521760042497 & 0.30956479915006 & 0.15478239957503 \tabularnewline
95 & 0.85824333973043 & 0.283513320539139 & 0.14175666026957 \tabularnewline
96 & 0.832640923163221 & 0.334718153673557 & 0.167359076836779 \tabularnewline
97 & 0.812184023212157 & 0.375631953575686 & 0.187815976787843 \tabularnewline
98 & 0.795341236622635 & 0.409317526754731 & 0.204658763377365 \tabularnewline
99 & 0.813802973330759 & 0.372394053338482 & 0.186197026669241 \tabularnewline
100 & 0.779580301302585 & 0.440839397394829 & 0.220419698697415 \tabularnewline
101 & 0.760278774982511 & 0.479442450034978 & 0.239721225017489 \tabularnewline
102 & 0.723863671871368 & 0.552272656257263 & 0.276136328128632 \tabularnewline
103 & 0.688472536239098 & 0.623054927521805 & 0.311527463760902 \tabularnewline
104 & 0.757917104479906 & 0.484165791040189 & 0.242082895520094 \tabularnewline
105 & 0.752695272991124 & 0.494609454017753 & 0.247304727008876 \tabularnewline
106 & 0.788997718782627 & 0.422004562434746 & 0.211002281217373 \tabularnewline
107 & 0.754972228358201 & 0.490055543283599 & 0.245027771641799 \tabularnewline
108 & 0.766248084447175 & 0.46750383110565 & 0.233751915552825 \tabularnewline
109 & 0.799690415657752 & 0.400619168684496 & 0.200309584342248 \tabularnewline
110 & 0.765186176561868 & 0.469627646876264 & 0.234813823438132 \tabularnewline
111 & 0.73532908627874 & 0.529341827442521 & 0.26467091372126 \tabularnewline
112 & 0.77082675852666 & 0.458346482946681 & 0.22917324147334 \tabularnewline
113 & 0.801629304416926 & 0.396741391166148 & 0.198370695583074 \tabularnewline
114 & 0.815637136389206 & 0.368725727221587 & 0.184362863610794 \tabularnewline
115 & 0.86705224791732 & 0.26589550416536 & 0.13294775208268 \tabularnewline
116 & 0.839274588041766 & 0.321450823916467 & 0.160725411958234 \tabularnewline
117 & 0.818610094627649 & 0.362779810744703 & 0.181389905372351 \tabularnewline
118 & 0.780974841472328 & 0.438050317055343 & 0.219025158527672 \tabularnewline
119 & 0.773241953905015 & 0.453516092189971 & 0.226758046094985 \tabularnewline
120 & 0.73009365020721 & 0.53981269958558 & 0.26990634979279 \tabularnewline
121 & 0.687202941718384 & 0.625594116563232 & 0.312797058281616 \tabularnewline
122 & 0.648021369548616 & 0.703957260902769 & 0.351978630451384 \tabularnewline
123 & 0.610232936983086 & 0.779534126033828 & 0.389767063016914 \tabularnewline
124 & 0.597296624806455 & 0.80540675038709 & 0.402703375193545 \tabularnewline
125 & 0.551502782113182 & 0.896994435773636 & 0.448497217886818 \tabularnewline
126 & 0.670690747983856 & 0.658618504032287 & 0.329309252016144 \tabularnewline
127 & 0.624229738473315 & 0.751540523053371 & 0.375770261526685 \tabularnewline
128 & 0.584115439232691 & 0.831769121534619 & 0.415884560767309 \tabularnewline
129 & 0.653913751001881 & 0.692172497996238 & 0.346086248998119 \tabularnewline
130 & 0.59753803988013 & 0.80492392023974 & 0.40246196011987 \tabularnewline
131 & 0.583887546556384 & 0.832224906887231 & 0.416112453443616 \tabularnewline
132 & 0.611210023915015 & 0.77757995216997 & 0.388789976084985 \tabularnewline
133 & 0.562693559521416 & 0.874612880957167 & 0.437306440478584 \tabularnewline
134 & 0.55891167988122 & 0.88217664023756 & 0.44108832011878 \tabularnewline
135 & 0.504474777956663 & 0.991050444086673 & 0.495525222043337 \tabularnewline
136 & 0.451394362097479 & 0.902788724194957 & 0.548605637902521 \tabularnewline
137 & 0.465187176663014 & 0.930374353326028 & 0.534812823336986 \tabularnewline
138 & 0.448101949738845 & 0.89620389947769 & 0.551898050261155 \tabularnewline
139 & 0.393093483309656 & 0.786186966619312 & 0.606906516690344 \tabularnewline
140 & 0.320970516772366 & 0.641941033544733 & 0.679029483227634 \tabularnewline
141 & 0.258993084533768 & 0.517986169067536 & 0.741006915466232 \tabularnewline
142 & 0.199848213064431 & 0.399696426128863 & 0.800151786935569 \tabularnewline
143 & 0.257751768244866 & 0.515503536489733 & 0.742248231755134 \tabularnewline
144 & 0.220106600114998 & 0.440213200229997 & 0.779893399885002 \tabularnewline
145 & 0.199462761588325 & 0.39892552317665 & 0.800537238411675 \tabularnewline
146 & 0.140116864656866 & 0.280233729313732 & 0.859883135343134 \tabularnewline
147 & 0.153292906924379 & 0.306585813848757 & 0.846707093075622 \tabularnewline
148 & 0.120586805733152 & 0.241173611466303 & 0.879413194266848 \tabularnewline
149 & 0.0851012848633619 & 0.170202569726724 & 0.914898715136638 \tabularnewline
150 & 0.0700401833508057 & 0.140080366701611 & 0.929959816649194 \tabularnewline
151 & 0.036309157614879 & 0.0726183152297579 & 0.963690842385121 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]11[/C][C]0.195384272585734[/C][C]0.390768545171469[/C][C]0.804615727414266[/C][/ROW]
[ROW][C]12[/C][C]0.720076771103729[/C][C]0.559846457792543[/C][C]0.279923228896271[/C][/ROW]
[ROW][C]13[/C][C]0.827938429694894[/C][C]0.344123140610211[/C][C]0.172061570305106[/C][/ROW]
[ROW][C]14[/C][C]0.751217171920466[/C][C]0.497565656159069[/C][C]0.248782828079534[/C][/ROW]
[ROW][C]15[/C][C]0.660449120605457[/C][C]0.679101758789086[/C][C]0.339550879394543[/C][/ROW]
[ROW][C]16[/C][C]0.565664038616123[/C][C]0.868671922767753[/C][C]0.434335961383877[/C][/ROW]
[ROW][C]17[/C][C]0.584283839259548[/C][C]0.831432321480904[/C][C]0.415716160740452[/C][/ROW]
[ROW][C]18[/C][C]0.499466074950402[/C][C]0.998932149900804[/C][C]0.500533925049598[/C][/ROW]
[ROW][C]19[/C][C]0.433806426664054[/C][C]0.867612853328108[/C][C]0.566193573335946[/C][/ROW]
[ROW][C]20[/C][C]0.463447341050406[/C][C]0.926894682100812[/C][C]0.536552658949594[/C][/ROW]
[ROW][C]21[/C][C]0.591749542371684[/C][C]0.816500915256632[/C][C]0.408250457628316[/C][/ROW]
[ROW][C]22[/C][C]0.891276081041597[/C][C]0.217447837916806[/C][C]0.108723918958403[/C][/ROW]
[ROW][C]23[/C][C]0.882798080391478[/C][C]0.234403839217044[/C][C]0.117201919608522[/C][/ROW]
[ROW][C]24[/C][C]0.877199398180371[/C][C]0.245601203639258[/C][C]0.122800601819629[/C][/ROW]
[ROW][C]25[/C][C]0.857888897888545[/C][C]0.284222204222911[/C][C]0.142111102111455[/C][/ROW]
[ROW][C]26[/C][C]0.997718164081985[/C][C]0.0045636718360309[/C][C]0.00228183591801545[/C][/ROW]
[ROW][C]27[/C][C]0.997098532163508[/C][C]0.00580293567298326[/C][C]0.00290146783649163[/C][/ROW]
[ROW][C]28[/C][C]0.996118621888615[/C][C]0.00776275622276992[/C][C]0.00388137811138496[/C][/ROW]
[ROW][C]29[/C][C]0.995052680598071[/C][C]0.00989463880385748[/C][C]0.00494731940192874[/C][/ROW]
[ROW][C]30[/C][C]0.993945374197748[/C][C]0.0121092516045047[/C][C]0.00605462580225234[/C][/ROW]
[ROW][C]31[/C][C]0.990834308670403[/C][C]0.0183313826591945[/C][C]0.00916569132959725[/C][/ROW]
[ROW][C]32[/C][C]0.989149630788338[/C][C]0.021700738423324[/C][C]0.010850369211662[/C][/ROW]
[ROW][C]33[/C][C]0.987112773008601[/C][C]0.0257744539827988[/C][C]0.0128872269913994[/C][/ROW]
[ROW][C]34[/C][C]0.98266980045455[/C][C]0.0346603990909002[/C][C]0.0173301995454501[/C][/ROW]
[ROW][C]35[/C][C]0.976557274444871[/C][C]0.0468854511102573[/C][C]0.0234427255551287[/C][/ROW]
[ROW][C]36[/C][C]0.973425406805453[/C][C]0.0531491863890943[/C][C]0.0265745931945471[/C][/ROW]
[ROW][C]37[/C][C]0.968824564010377[/C][C]0.0623508719792465[/C][C]0.0311754359896233[/C][/ROW]
[ROW][C]38[/C][C]0.967225961393383[/C][C]0.0655480772132338[/C][C]0.0327740386066169[/C][/ROW]
[ROW][C]39[/C][C]0.964047032526498[/C][C]0.0719059349470032[/C][C]0.0359529674735016[/C][/ROW]
[ROW][C]40[/C][C]0.954731706482547[/C][C]0.0905365870349069[/C][C]0.0452682935174535[/C][/ROW]
[ROW][C]41[/C][C]0.941302910170291[/C][C]0.117394179659418[/C][C]0.0586970898297088[/C][/ROW]
[ROW][C]42[/C][C]0.936525354958401[/C][C]0.126949290083198[/C][C]0.0634746450415991[/C][/ROW]
[ROW][C]43[/C][C]0.930770843290716[/C][C]0.138458313418568[/C][C]0.0692291567092838[/C][/ROW]
[ROW][C]44[/C][C]0.912941442016352[/C][C]0.174117115967296[/C][C]0.0870585579836478[/C][/ROW]
[ROW][C]45[/C][C]0.957169844013606[/C][C]0.0856603119727873[/C][C]0.0428301559863936[/C][/ROW]
[ROW][C]46[/C][C]0.969155640986442[/C][C]0.061688718027115[/C][C]0.0308443590135575[/C][/ROW]
[ROW][C]47[/C][C]0.963184341876587[/C][C]0.0736313162468263[/C][C]0.0368156581234131[/C][/ROW]
[ROW][C]48[/C][C]0.953146340715031[/C][C]0.0937073185699381[/C][C]0.046853659284969[/C][/ROW]
[ROW][C]49[/C][C]0.98204476485392[/C][C]0.035910470292159[/C][C]0.0179552351460795[/C][/ROW]
[ROW][C]50[/C][C]0.97726323217608[/C][C]0.0454735356478394[/C][C]0.0227367678239197[/C][/ROW]
[ROW][C]51[/C][C]0.971943200228461[/C][C]0.0561135995430774[/C][C]0.0280567997715387[/C][/ROW]
[ROW][C]52[/C][C]0.967663047203263[/C][C]0.0646739055934743[/C][C]0.0323369527967372[/C][/ROW]
[ROW][C]53[/C][C]0.957746666819821[/C][C]0.084506666360358[/C][C]0.042253333180179[/C][/ROW]
[ROW][C]54[/C][C]0.953315312975261[/C][C]0.0933693740494778[/C][C]0.0466846870247389[/C][/ROW]
[ROW][C]55[/C][C]0.94800674646708[/C][C]0.103986507065839[/C][C]0.0519932535329197[/C][/ROW]
[ROW][C]56[/C][C]0.93526294388151[/C][C]0.129474112236979[/C][C]0.0647370561184895[/C][/ROW]
[ROW][C]57[/C][C]0.93361069297192[/C][C]0.132778614056161[/C][C]0.0663893070280804[/C][/ROW]
[ROW][C]58[/C][C]0.917087052220431[/C][C]0.165825895559139[/C][C]0.0829129477795694[/C][/ROW]
[ROW][C]59[/C][C]0.924700791793579[/C][C]0.150598416412841[/C][C]0.0752992082064206[/C][/ROW]
[ROW][C]60[/C][C]0.913923171652744[/C][C]0.172153656694512[/C][C]0.086076828347256[/C][/ROW]
[ROW][C]61[/C][C]0.910532260146541[/C][C]0.178935479706918[/C][C]0.0894677398534592[/C][/ROW]
[ROW][C]62[/C][C]0.892805848454656[/C][C]0.214388303090688[/C][C]0.107194151545344[/C][/ROW]
[ROW][C]63[/C][C]0.933616206851402[/C][C]0.132767586297195[/C][C]0.0663837931485976[/C][/ROW]
[ROW][C]64[/C][C]0.921742966011495[/C][C]0.156514067977011[/C][C]0.0782570339885054[/C][/ROW]
[ROW][C]65[/C][C]0.915779001864803[/C][C]0.168441996270393[/C][C]0.0842209981351966[/C][/ROW]
[ROW][C]66[/C][C]0.963835630843646[/C][C]0.0723287383127085[/C][C]0.0361643691563543[/C][/ROW]
[ROW][C]67[/C][C]0.958865048242996[/C][C]0.0822699035140082[/C][C]0.0411349517570041[/C][/ROW]
[ROW][C]68[/C][C]0.955263626944953[/C][C]0.0894727461100941[/C][C]0.044736373055047[/C][/ROW]
[ROW][C]69[/C][C]0.954114733766163[/C][C]0.0917705324676744[/C][C]0.0458852662338372[/C][/ROW]
[ROW][C]70[/C][C]0.962666814601878[/C][C]0.0746663707962432[/C][C]0.0373331853981216[/C][/ROW]
[ROW][C]71[/C][C]0.957474186748968[/C][C]0.0850516265020632[/C][C]0.0425258132510316[/C][/ROW]
[ROW][C]72[/C][C]0.9726545346087[/C][C]0.0546909307826001[/C][C]0.0273454653913001[/C][/ROW]
[ROW][C]73[/C][C]0.965874977446368[/C][C]0.0682500451072648[/C][C]0.0341250225536324[/C][/ROW]
[ROW][C]74[/C][C]0.95847384020985[/C][C]0.0830523195803004[/C][C]0.0415261597901502[/C][/ROW]
[ROW][C]75[/C][C]0.953827401018739[/C][C]0.092345197962523[/C][C]0.0461725989812615[/C][/ROW]
[ROW][C]76[/C][C]0.94152186639474[/C][C]0.11695626721052[/C][C]0.0584781336052599[/C][/ROW]
[ROW][C]77[/C][C]0.955097131641799[/C][C]0.0898057367164029[/C][C]0.0449028683582014[/C][/ROW]
[ROW][C]78[/C][C]0.94398368566722[/C][C]0.112032628665559[/C][C]0.0560163143327797[/C][/ROW]
[ROW][C]79[/C][C]0.941884639656393[/C][C]0.116230720687213[/C][C]0.0581153603436067[/C][/ROW]
[ROW][C]80[/C][C]0.937322256059857[/C][C]0.125355487880285[/C][C]0.0626777439401426[/C][/ROW]
[ROW][C]81[/C][C]0.923980037154764[/C][C]0.152039925690473[/C][C]0.0760199628452363[/C][/ROW]
[ROW][C]82[/C][C]0.932834398789671[/C][C]0.134331202420658[/C][C]0.0671656012103289[/C][/ROW]
[ROW][C]83[/C][C]0.91952538416686[/C][C]0.16094923166628[/C][C]0.0804746158331399[/C][/ROW]
[ROW][C]84[/C][C]0.92471766405569[/C][C]0.150564671888619[/C][C]0.0752823359443097[/C][/ROW]
[ROW][C]85[/C][C]0.907591643969575[/C][C]0.18481671206085[/C][C]0.092408356030425[/C][/ROW]
[ROW][C]86[/C][C]0.886690322260539[/C][C]0.226619355478921[/C][C]0.113309677739461[/C][/ROW]
[ROW][C]87[/C][C]0.871759145127111[/C][C]0.256481709745777[/C][C]0.128240854872889[/C][/ROW]
[ROW][C]88[/C][C]0.846558512257436[/C][C]0.306882975485128[/C][C]0.153441487742564[/C][/ROW]
[ROW][C]89[/C][C]0.818206049929473[/C][C]0.363587900141054[/C][C]0.181793950070527[/C][/ROW]
[ROW][C]90[/C][C]0.886976404185518[/C][C]0.226047191628965[/C][C]0.113023595814482[/C][/ROW]
[ROW][C]91[/C][C]0.911879162942421[/C][C]0.176241674115157[/C][C]0.0881208370575787[/C][/ROW]
[ROW][C]92[/C][C]0.891411081832743[/C][C]0.217177836334514[/C][C]0.108588918167257[/C][/ROW]
[ROW][C]93[/C][C]0.868909474445954[/C][C]0.262181051108092[/C][C]0.131090525554046[/C][/ROW]
[ROW][C]94[/C][C]0.84521760042497[/C][C]0.30956479915006[/C][C]0.15478239957503[/C][/ROW]
[ROW][C]95[/C][C]0.85824333973043[/C][C]0.283513320539139[/C][C]0.14175666026957[/C][/ROW]
[ROW][C]96[/C][C]0.832640923163221[/C][C]0.334718153673557[/C][C]0.167359076836779[/C][/ROW]
[ROW][C]97[/C][C]0.812184023212157[/C][C]0.375631953575686[/C][C]0.187815976787843[/C][/ROW]
[ROW][C]98[/C][C]0.795341236622635[/C][C]0.409317526754731[/C][C]0.204658763377365[/C][/ROW]
[ROW][C]99[/C][C]0.813802973330759[/C][C]0.372394053338482[/C][C]0.186197026669241[/C][/ROW]
[ROW][C]100[/C][C]0.779580301302585[/C][C]0.440839397394829[/C][C]0.220419698697415[/C][/ROW]
[ROW][C]101[/C][C]0.760278774982511[/C][C]0.479442450034978[/C][C]0.239721225017489[/C][/ROW]
[ROW][C]102[/C][C]0.723863671871368[/C][C]0.552272656257263[/C][C]0.276136328128632[/C][/ROW]
[ROW][C]103[/C][C]0.688472536239098[/C][C]0.623054927521805[/C][C]0.311527463760902[/C][/ROW]
[ROW][C]104[/C][C]0.757917104479906[/C][C]0.484165791040189[/C][C]0.242082895520094[/C][/ROW]
[ROW][C]105[/C][C]0.752695272991124[/C][C]0.494609454017753[/C][C]0.247304727008876[/C][/ROW]
[ROW][C]106[/C][C]0.788997718782627[/C][C]0.422004562434746[/C][C]0.211002281217373[/C][/ROW]
[ROW][C]107[/C][C]0.754972228358201[/C][C]0.490055543283599[/C][C]0.245027771641799[/C][/ROW]
[ROW][C]108[/C][C]0.766248084447175[/C][C]0.46750383110565[/C][C]0.233751915552825[/C][/ROW]
[ROW][C]109[/C][C]0.799690415657752[/C][C]0.400619168684496[/C][C]0.200309584342248[/C][/ROW]
[ROW][C]110[/C][C]0.765186176561868[/C][C]0.469627646876264[/C][C]0.234813823438132[/C][/ROW]
[ROW][C]111[/C][C]0.73532908627874[/C][C]0.529341827442521[/C][C]0.26467091372126[/C][/ROW]
[ROW][C]112[/C][C]0.77082675852666[/C][C]0.458346482946681[/C][C]0.22917324147334[/C][/ROW]
[ROW][C]113[/C][C]0.801629304416926[/C][C]0.396741391166148[/C][C]0.198370695583074[/C][/ROW]
[ROW][C]114[/C][C]0.815637136389206[/C][C]0.368725727221587[/C][C]0.184362863610794[/C][/ROW]
[ROW][C]115[/C][C]0.86705224791732[/C][C]0.26589550416536[/C][C]0.13294775208268[/C][/ROW]
[ROW][C]116[/C][C]0.839274588041766[/C][C]0.321450823916467[/C][C]0.160725411958234[/C][/ROW]
[ROW][C]117[/C][C]0.818610094627649[/C][C]0.362779810744703[/C][C]0.181389905372351[/C][/ROW]
[ROW][C]118[/C][C]0.780974841472328[/C][C]0.438050317055343[/C][C]0.219025158527672[/C][/ROW]
[ROW][C]119[/C][C]0.773241953905015[/C][C]0.453516092189971[/C][C]0.226758046094985[/C][/ROW]
[ROW][C]120[/C][C]0.73009365020721[/C][C]0.53981269958558[/C][C]0.26990634979279[/C][/ROW]
[ROW][C]121[/C][C]0.687202941718384[/C][C]0.625594116563232[/C][C]0.312797058281616[/C][/ROW]
[ROW][C]122[/C][C]0.648021369548616[/C][C]0.703957260902769[/C][C]0.351978630451384[/C][/ROW]
[ROW][C]123[/C][C]0.610232936983086[/C][C]0.779534126033828[/C][C]0.389767063016914[/C][/ROW]
[ROW][C]124[/C][C]0.597296624806455[/C][C]0.80540675038709[/C][C]0.402703375193545[/C][/ROW]
[ROW][C]125[/C][C]0.551502782113182[/C][C]0.896994435773636[/C][C]0.448497217886818[/C][/ROW]
[ROW][C]126[/C][C]0.670690747983856[/C][C]0.658618504032287[/C][C]0.329309252016144[/C][/ROW]
[ROW][C]127[/C][C]0.624229738473315[/C][C]0.751540523053371[/C][C]0.375770261526685[/C][/ROW]
[ROW][C]128[/C][C]0.584115439232691[/C][C]0.831769121534619[/C][C]0.415884560767309[/C][/ROW]
[ROW][C]129[/C][C]0.653913751001881[/C][C]0.692172497996238[/C][C]0.346086248998119[/C][/ROW]
[ROW][C]130[/C][C]0.59753803988013[/C][C]0.80492392023974[/C][C]0.40246196011987[/C][/ROW]
[ROW][C]131[/C][C]0.583887546556384[/C][C]0.832224906887231[/C][C]0.416112453443616[/C][/ROW]
[ROW][C]132[/C][C]0.611210023915015[/C][C]0.77757995216997[/C][C]0.388789976084985[/C][/ROW]
[ROW][C]133[/C][C]0.562693559521416[/C][C]0.874612880957167[/C][C]0.437306440478584[/C][/ROW]
[ROW][C]134[/C][C]0.55891167988122[/C][C]0.88217664023756[/C][C]0.44108832011878[/C][/ROW]
[ROW][C]135[/C][C]0.504474777956663[/C][C]0.991050444086673[/C][C]0.495525222043337[/C][/ROW]
[ROW][C]136[/C][C]0.451394362097479[/C][C]0.902788724194957[/C][C]0.548605637902521[/C][/ROW]
[ROW][C]137[/C][C]0.465187176663014[/C][C]0.930374353326028[/C][C]0.534812823336986[/C][/ROW]
[ROW][C]138[/C][C]0.448101949738845[/C][C]0.89620389947769[/C][C]0.551898050261155[/C][/ROW]
[ROW][C]139[/C][C]0.393093483309656[/C][C]0.786186966619312[/C][C]0.606906516690344[/C][/ROW]
[ROW][C]140[/C][C]0.320970516772366[/C][C]0.641941033544733[/C][C]0.679029483227634[/C][/ROW]
[ROW][C]141[/C][C]0.258993084533768[/C][C]0.517986169067536[/C][C]0.741006915466232[/C][/ROW]
[ROW][C]142[/C][C]0.199848213064431[/C][C]0.399696426128863[/C][C]0.800151786935569[/C][/ROW]
[ROW][C]143[/C][C]0.257751768244866[/C][C]0.515503536489733[/C][C]0.742248231755134[/C][/ROW]
[ROW][C]144[/C][C]0.220106600114998[/C][C]0.440213200229997[/C][C]0.779893399885002[/C][/ROW]
[ROW][C]145[/C][C]0.199462761588325[/C][C]0.39892552317665[/C][C]0.800537238411675[/C][/ROW]
[ROW][C]146[/C][C]0.140116864656866[/C][C]0.280233729313732[/C][C]0.859883135343134[/C][/ROW]
[ROW][C]147[/C][C]0.153292906924379[/C][C]0.306585813848757[/C][C]0.846707093075622[/C][/ROW]
[ROW][C]148[/C][C]0.120586805733152[/C][C]0.241173611466303[/C][C]0.879413194266848[/C][/ROW]
[ROW][C]149[/C][C]0.0851012848633619[/C][C]0.170202569726724[/C][C]0.914898715136638[/C][/ROW]
[ROW][C]150[/C][C]0.0700401833508057[/C][C]0.140080366701611[/C][C]0.929959816649194[/C][/ROW]
[ROW][C]151[/C][C]0.036309157614879[/C][C]0.0726183152297579[/C][C]0.963690842385121[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.1953842725857340.3907685451714690.804615727414266
120.7200767711037290.5598464577925430.279923228896271
130.8279384296948940.3441231406102110.172061570305106
140.7512171719204660.4975656561590690.248782828079534
150.6604491206054570.6791017587890860.339550879394543
160.5656640386161230.8686719227677530.434335961383877
170.5842838392595480.8314323214809040.415716160740452
180.4994660749504020.9989321499008040.500533925049598
190.4338064266640540.8676128533281080.566193573335946
200.4634473410504060.9268946821008120.536552658949594
210.5917495423716840.8165009152566320.408250457628316
220.8912760810415970.2174478379168060.108723918958403
230.8827980803914780.2344038392170440.117201919608522
240.8771993981803710.2456012036392580.122800601819629
250.8578888978885450.2842222042229110.142111102111455
260.9977181640819850.00456367183603090.00228183591801545
270.9970985321635080.005802935672983260.00290146783649163
280.9961186218886150.007762756222769920.00388137811138496
290.9950526805980710.009894638803857480.00494731940192874
300.9939453741977480.01210925160450470.00605462580225234
310.9908343086704030.01833138265919450.00916569132959725
320.9891496307883380.0217007384233240.010850369211662
330.9871127730086010.02577445398279880.0128872269913994
340.982669800454550.03466039909090020.0173301995454501
350.9765572744448710.04688545111025730.0234427255551287
360.9734254068054530.05314918638909430.0265745931945471
370.9688245640103770.06235087197924650.0311754359896233
380.9672259613933830.06554807721323380.0327740386066169
390.9640470325264980.07190593494700320.0359529674735016
400.9547317064825470.09053658703490690.0452682935174535
410.9413029101702910.1173941796594180.0586970898297088
420.9365253549584010.1269492900831980.0634746450415991
430.9307708432907160.1384583134185680.0692291567092838
440.9129414420163520.1741171159672960.0870585579836478
450.9571698440136060.08566031197278730.0428301559863936
460.9691556409864420.0616887180271150.0308443590135575
470.9631843418765870.07363131624682630.0368156581234131
480.9531463407150310.09370731856993810.046853659284969
490.982044764853920.0359104702921590.0179552351460795
500.977263232176080.04547353564783940.0227367678239197
510.9719432002284610.05611359954307740.0280567997715387
520.9676630472032630.06467390559347430.0323369527967372
530.9577466668198210.0845066663603580.042253333180179
540.9533153129752610.09336937404947780.0466846870247389
550.948006746467080.1039865070658390.0519932535329197
560.935262943881510.1294741122369790.0647370561184895
570.933610692971920.1327786140561610.0663893070280804
580.9170870522204310.1658258955591390.0829129477795694
590.9247007917935790.1505984164128410.0752992082064206
600.9139231716527440.1721536566945120.086076828347256
610.9105322601465410.1789354797069180.0894677398534592
620.8928058484546560.2143883030906880.107194151545344
630.9336162068514020.1327675862971950.0663837931485976
640.9217429660114950.1565140679770110.0782570339885054
650.9157790018648030.1684419962703930.0842209981351966
660.9638356308436460.07232873831270850.0361643691563543
670.9588650482429960.08226990351400820.0411349517570041
680.9552636269449530.08947274611009410.044736373055047
690.9541147337661630.09177053246767440.0458852662338372
700.9626668146018780.07466637079624320.0373331853981216
710.9574741867489680.08505162650206320.0425258132510316
720.97265453460870.05469093078260010.0273454653913001
730.9658749774463680.06825004510726480.0341250225536324
740.958473840209850.08305231958030040.0415261597901502
750.9538274010187390.0923451979625230.0461725989812615
760.941521866394740.116956267210520.0584781336052599
770.9550971316417990.08980573671640290.0449028683582014
780.943983685667220.1120326286655590.0560163143327797
790.9418846396563930.1162307206872130.0581153603436067
800.9373222560598570.1253554878802850.0626777439401426
810.9239800371547640.1520399256904730.0760199628452363
820.9328343987896710.1343312024206580.0671656012103289
830.919525384166860.160949231666280.0804746158331399
840.924717664055690.1505646718886190.0752823359443097
850.9075916439695750.184816712060850.092408356030425
860.8866903222605390.2266193554789210.113309677739461
870.8717591451271110.2564817097457770.128240854872889
880.8465585122574360.3068829754851280.153441487742564
890.8182060499294730.3635879001410540.181793950070527
900.8869764041855180.2260471916289650.113023595814482
910.9118791629424210.1762416741151570.0881208370575787
920.8914110818327430.2171778363345140.108588918167257
930.8689094744459540.2621810511080920.131090525554046
940.845217600424970.309564799150060.15478239957503
950.858243339730430.2835133205391390.14175666026957
960.8326409231632210.3347181536735570.167359076836779
970.8121840232121570.3756319535756860.187815976787843
980.7953412366226350.4093175267547310.204658763377365
990.8138029733307590.3723940533384820.186197026669241
1000.7795803013025850.4408393973948290.220419698697415
1010.7602787749825110.4794424500349780.239721225017489
1020.7238636718713680.5522726562572630.276136328128632
1030.6884725362390980.6230549275218050.311527463760902
1040.7579171044799060.4841657910401890.242082895520094
1050.7526952729911240.4946094540177530.247304727008876
1060.7889977187826270.4220045624347460.211002281217373
1070.7549722283582010.4900555432835990.245027771641799
1080.7662480844471750.467503831105650.233751915552825
1090.7996904156577520.4006191686844960.200309584342248
1100.7651861765618680.4696276468762640.234813823438132
1110.735329086278740.5293418274425210.26467091372126
1120.770826758526660.4583464829466810.22917324147334
1130.8016293044169260.3967413911661480.198370695583074
1140.8156371363892060.3687257272215870.184362863610794
1150.867052247917320.265895504165360.13294775208268
1160.8392745880417660.3214508239164670.160725411958234
1170.8186100946276490.3627798107447030.181389905372351
1180.7809748414723280.4380503170553430.219025158527672
1190.7732419539050150.4535160921899710.226758046094985
1200.730093650207210.539812699585580.26990634979279
1210.6872029417183840.6255941165632320.312797058281616
1220.6480213695486160.7039572609027690.351978630451384
1230.6102329369830860.7795341260338280.389767063016914
1240.5972966248064550.805406750387090.402703375193545
1250.5515027821131820.8969944357736360.448497217886818
1260.6706907479838560.6586185040322870.329309252016144
1270.6242297384733150.7515405230533710.375770261526685
1280.5841154392326910.8317691215346190.415884560767309
1290.6539137510018810.6921724979962380.346086248998119
1300.597538039880130.804923920239740.40246196011987
1310.5838875465563840.8322249068872310.416112453443616
1320.6112100239150150.777579952169970.388789976084985
1330.5626935595214160.8746128809571670.437306440478584
1340.558911679881220.882176640237560.44108832011878
1350.5044747779566630.9910504440866730.495525222043337
1360.4513943620974790.9027887241949570.548605637902521
1370.4651871766630140.9303743533260280.534812823336986
1380.4481019497388450.896203899477690.551898050261155
1390.3930934833096560.7861869666193120.606906516690344
1400.3209705167723660.6419410335447330.679029483227634
1410.2589930845337680.5179861690675360.741006915466232
1420.1998482130644310.3996964261288630.800151786935569
1430.2577517682448660.5155035364897330.742248231755134
1440.2201066001149980.4402132002299970.779893399885002
1450.1994627615883250.398925523176650.800537238411675
1460.1401168646568660.2802337293137320.859883135343134
1470.1532929069243790.3065858138487570.846707093075622
1480.1205868057331520.2411736114663030.879413194266848
1490.08510128486336190.1702025697267240.914898715136638
1500.07004018335080570.1400803667016110.929959816649194
1510.0363091576148790.07261831522975790.963690842385121







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0283687943262411NOK
5% type I error level120.0851063829787234NOK
10% type I error level370.26241134751773NOK

\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 & 4 & 0.0283687943262411 & NOK \tabularnewline
5% type I error level & 12 & 0.0851063829787234 & NOK \tabularnewline
10% type I error level & 37 & 0.26241134751773 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185665&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]4[/C][C]0.0283687943262411[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.0851063829787234[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.26241134751773[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185665&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185665&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 level40.0283687943262411NOK
5% type I error level120.0851063829787234NOK
10% type I error level370.26241134751773NOK



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