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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 30 Nov 2010 13:44:41 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/30/t1291124610yd9xvvgxhpy4ox5.htm/, Retrieved Mon, 29 Apr 2024 09:07:10 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103432, Retrieved Mon, 29 Apr 2024 09:07:10 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 4 probee...] [2010-11-30 12:02:39] [17d39bb3ec485d4ce196f61215d11ba1]
-           [Multiple Regression] [Workshop 4 ] [2010-11-30 13:44:41] [5d543145eb38bc0730d6bf6b0284f470] [Current]
-   PD        [Multiple Regression] [Workshop 4] [2010-12-02 10:27:01] [caa3ae6143f673f211f626a62938a034]
-    D          [Multiple Regression] [Multiple regressi...] [2010-12-26 17:01:47] [17d39bb3ec485d4ce196f61215d11ba1]
-   PD        [Multiple Regression] [Workshop 4 Multip...] [2010-12-02 10:27:32] [f1bd7399181c649098ca7b814ee0e027]
-   PD          [Multiple Regression] [Workshop 7] [2010-12-24 10:50:01] [74332fba660dac580e4e829dacd99a9c]
-   PD          [Multiple Regression] [] [2010-12-24 14:05:48] [f1052bedf858e5044a431fba108f61db]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	14	13	3	25	55	147
12	8	13	5	158	7	71
10	12	16	6	0	0	0
9	7	12	6	143	10	0
10	10	11	5	67	74	43
12	7	12	3	0	0	0
13	16	18	8	148	138	8
12	11	11	4	28	0	0
12	14	14	4	114	113	34
6	6	9	4	0	0	0
5	16	14	6	123	115	103
12	11	12	6	145	9	0
11	16	11	5	113	114	73
14	12	12	4	152	59	159
14	7	13	6	0	0	0
12	13	11	4	36	114	113
12	11	12	6	0	0	0
11	15	16	6	8	102	44
11	7	9	4	108	0	0
7	9	11	4	112	86	0
9	7	13	2	51	17	41
11	14	15	7	43	45	74
11	15	10	5	120	123	0
12	7	11	4	13	24	0
12	15	13	6	55	5	0
11	17	16	6	103	123	32
11	15	15	7	127	136	126
8	14	14	5	14	4	154
9	14	14	6	135	76	129
12	8	14	4	38	99	98
10	8	8	4	11	98	82
10	14	13	7	43	67	45
12	14	15	7	141	92	8
8	8	13	4	62	13	0
12	11	11	4	62	24	129
11	16	15	6	135	129	31
12	10	15	6	117	117	117
7	8	9	5	82	11	99
11	14	13	6	145	20	55
11	16	16	7	87	91	132
12	13	13	6	76	111	58
9	5	11	3	124	0	0
15	8	12	3	151	58	0
11	10	12	4	131	0	0
11	8	12	6	127	146	101
11	13	14	7	76	129	31
11	15	14	5	25	48	147
15	6	8	4	0	0	0
11	12	13	5	58	111	132
12	16	16	6	115	32	123
12	5	13	6	130	112	39
9	15	11	6	17	51	136
12	12	14	5	102	53	141
12	8	13	4	21	131	0
13	13	13	5	0	0	0
11	14	13	5	14	76	135
9	12	12	4	110	106	118
9	16	16	6	133	26	154
11	10	15	2	83	44	0
11	15	15	8	56	63	116
12	8	12	3	0	0	0
12	16	14	6	44	116	88
9	19	12	6	70	119	25
11	14	15	6	36	18	113
9	6	12	5	5	134	157
12	13	13	5	118	138	26
12	15	12	6	17	41	38
12	7	12	5	79	0	0
12	13	13	6	122	57	53
14	4	5	2	119	101	0
11	14	13	5	36	114	106
12	13	13	5	36	113	106
11	11	14	5	141	122	102
6	14	17	6	0	14	138
10	12	13	6	37	10	142
12	15	13	6	110	27	73
13	14	12	5	10	39	130
8	13	13	5	14	133	86
12	8	14	4	157	42	78
12	6	11	2	59	0	0
12	7	12	4	77	58	0
6	13	12	6	129	133	4
11	13	16	6	125	151	91
10	11	12	5	87	111	132
12	5	12	3	61	139	0
13	12	12	6	146	126	0
11	8	10	4	96	139	0
7	11	15	5	133	138	14
11	14	15	8	47	52	97
11	9	12	4	74	67	45
11	10	16	6	109	97	0
11	13	15	6	30	137	149
12	16	16	7	116	56	57
10	16	13	6	149	3	105
11	11	12	5	19	78	0
12	8	11	4	96	0	0
7	4	13	6	0	0	0
13	7	10	3	21	0	0
8	14	15	5	26	118	128
12	11	13	6	156	39	29
11	17	16	7	53	63	148
12	15	15	7	72	78	93
14	17	18	6	27	26	4
10	5	13	3	66	50	0
10	4	10	2	71	104	158
13	10	16	8	66	54	144
10	11	13	3	40	104	0
11	15	15	8	57	148	122
10	10	14	3	3	30	149
7	9	15	4	12	38	17
10	12	14	5	107	132	91
8	15	13	7	80	132	111
12	7	13	6	98	84	99
12	13	15	6	155	71	40
12	12	16	7	111	125	132
11	14	14	6	81	25	123
12	14	14	6	50	66	54
12	8	16	6	49	86	90
12	15	14	6	96	61	86
11	12	12	4	2	60	152
12	12	13	4	1	144	152
11	16	12	5	22	120	123
11	9	12	4	64	139	100
13	15	14	6	56	131	116
12	15	14	6	144	159	59
12	6	14	5	0	0	0
12	14	16	8	94	18	5
12	15	13	6	25	123	147
8	10	14	5	93	18	139
8	6	4	4	0	0	0
12	14	16	8	48	123	81
11	12	13	6	30	105	3
12	8	16	4	19	0	0
13	11	15	6	0	0	0
12	13	14	6	10	68	37
12	9	13	4	78	157	5
11	15	14	6	93	94	69
12	13	12	3	0	0	0
12	15	15	6	95	87	0
10	14	14	5	50	156	142
11	16	13	4	86	139	17
12	14	14	6	33	145	100
12	14	16	4	152	55	70
10	10	6	4	51	41	0
12	10	13	4	48	25	123
13	4	13	6	97	47	109
12	8	14	5	77	0	0
15	15	15	6	130	143	37
11	16	14	6	8	102	44
12	12	15	8	84	148	98
11	12	13	7	51	153	11
12	15	16	7	33	32	9
11	9	12	4	6	106	0
10	12	15	6	116	63	57
11	14	12	6	88	56	63
11	11	14	2	142	39	66

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 6.68487647585357 + 0.0804780908515764FindingFriends[t] + 0.171089311176096KnowingPeople[t] + 0.554952662162989Celebrity[t] + 0.00330749834696466friendone[t] -0.00236012166624288friendtwo[t] + 0.00334213262966336`friendthree `[t] + 0.00590218756718485t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Liked[t] =  +  6.68487647585357 +  0.0804780908515764FindingFriends[t] +  0.171089311176096KnowingPeople[t] +  0.554952662162989Celebrity[t] +  0.00330749834696466friendone[t] -0.00236012166624288friendtwo[t] +  0.00334213262966336`friendthree
`[t] +  0.00590218756718485t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Liked[t] =  +  6.68487647585357 +  0.0804780908515764FindingFriends[t] +  0.171089311176096KnowingPeople[t] +  0.554952662162989Celebrity[t] +  0.00330749834696466friendone[t] -0.00236012166624288friendtwo[t] +  0.00334213262966336`friendthree
`[t] +  0.00590218756718485t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Liked[t] = + 6.68487647585357 + 0.0804780908515764FindingFriends[t] + 0.171089311176096KnowingPeople[t] + 0.554952662162989Celebrity[t] + 0.00330749834696466friendone[t] -0.00236012166624288friendtwo[t] + 0.00334213262966336`friendthree `[t] + 0.00590218756718485t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.684876475853571.0626456.290800
FindingFriends0.08047809085157640.0814040.98860.324460.16223
KnowingPeople0.1710893111760960.0519013.29650.0012260.000613
Celebrity0.5549526621629890.1229914.51211.3e-056e-06
friendone0.003307498346964660.0030291.0920.2765880.138294
friendtwo-0.002360121666242880.003057-0.77190.4413890.220695
`friendthree `0.003342132629663360.0027621.210.2282110.114105
t0.005902187567184850.0032471.81780.0711130.035557

\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) & 6.68487647585357 & 1.062645 & 6.2908 & 0 & 0 \tabularnewline
FindingFriends & 0.0804780908515764 & 0.081404 & 0.9886 & 0.32446 & 0.16223 \tabularnewline
KnowingPeople & 0.171089311176096 & 0.051901 & 3.2965 & 0.001226 & 0.000613 \tabularnewline
Celebrity & 0.554952662162989 & 0.122991 & 4.5121 & 1.3e-05 & 6e-06 \tabularnewline
friendone & 0.00330749834696466 & 0.003029 & 1.092 & 0.276588 & 0.138294 \tabularnewline
friendtwo & -0.00236012166624288 & 0.003057 & -0.7719 & 0.441389 & 0.220695 \tabularnewline
`friendthree
` & 0.00334213262966336 & 0.002762 & 1.21 & 0.228211 & 0.114105 \tabularnewline
t & 0.00590218756718485 & 0.003247 & 1.8178 & 0.071113 & 0.035557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&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]6.68487647585357[/C][C]1.062645[/C][C]6.2908[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]FindingFriends[/C][C]0.0804780908515764[/C][C]0.081404[/C][C]0.9886[/C][C]0.32446[/C][C]0.16223[/C][/ROW]
[ROW][C]KnowingPeople[/C][C]0.171089311176096[/C][C]0.051901[/C][C]3.2965[/C][C]0.001226[/C][C]0.000613[/C][/ROW]
[ROW][C]Celebrity[/C][C]0.554952662162989[/C][C]0.122991[/C][C]4.5121[/C][C]1.3e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]friendone[/C][C]0.00330749834696466[/C][C]0.003029[/C][C]1.092[/C][C]0.276588[/C][C]0.138294[/C][/ROW]
[ROW][C]friendtwo[/C][C]-0.00236012166624288[/C][C]0.003057[/C][C]-0.7719[/C][C]0.441389[/C][C]0.220695[/C][/ROW]
[ROW][C]`friendthree
`[/C][C]0.00334213262966336[/C][C]0.002762[/C][C]1.21[/C][C]0.228211[/C][C]0.114105[/C][/ROW]
[ROW][C]t[/C][C]0.00590218756718485[/C][C]0.003247[/C][C]1.8178[/C][C]0.071113[/C][C]0.035557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=2

As an alternative you can also use a QR Code:  
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)6.684876475853571.0626456.290800
FindingFriends0.08047809085157640.0814040.98860.324460.16223
KnowingPeople0.1710893111760960.0519013.29650.0012260.000613
Celebrity0.5549526621629890.1229914.51211.3e-056e-06
friendone0.003307498346964660.0030291.0920.2765880.138294
friendtwo-0.002360121666242880.003057-0.77190.4413890.220695
`friendthree `0.003342132629663360.0027621.210.2282110.114105
t0.005902187567184850.0032471.81780.0711130.035557






Multiple Linear Regression - Regression Statistics
Multiple R0.610957784065894
R-squared0.373269413910707
Adjusted R-squared0.343626751055132
F-TEST (value)12.5923037255241
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value1.30406796472471e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76255186325910
Sum Squared Residuals459.775182460365

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.610957784065894 \tabularnewline
R-squared & 0.373269413910707 \tabularnewline
Adjusted R-squared & 0.343626751055132 \tabularnewline
F-TEST (value) & 12.5923037255241 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 148 \tabularnewline
p-value & 1.30406796472471e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.76255186325910 \tabularnewline
Sum Squared Residuals & 459.775182460365 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.610957784065894[/C][/ROW]
[ROW][C]R-squared[/C][C]0.373269413910707[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.343626751055132[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.5923037255241[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]148[/C][/ROW]
[ROW][C]p-value[/C][C]1.30406796472471e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.76255186325910[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]459.775182460365[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.610957784065894
R-squared0.373269413910707
Adjusted R-squared0.343626751055132
F-TEST (value)12.5923037255241
F-TEST (DF numerator)7
F-TEST (DF denominator)148
p-value1.30406796472471e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.76255186325910
Sum Squared Residuals459.775182460365






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.24127645103710.758723548962934
21312.54925104529340.450748954706606
31612.89015165416203.10984834583802
41212.4095002419506-0.409500241950638
51112.1954898338014-1.19548983380136
61210.54850985619721.45149014380275
71815.14000727246242.85999272753762
81111.892234091914-0.892234091914003
91412.54278783197151.45721216802846
10910.4731134123434-1.47311341234342
111413.69898391106000.301016088939982
121213.3914843781074-1.39148437810740
131113.5077253284476-2.50772532844755
141213.0619744150281-1.06197441502812
151312.42744371249400.57255628750597
161112.4166973187797-1.41669731877966
171212.9626491506296-0.962649150629632
181613.50521190457382.49478809542624
19911.4569226873542-2.45692268735425
201111.2933506639583-0.293350663958290
211310.10624352017262.89375647982743
221514.26323736183890.736762638161104
231013.1535936044104-3.15359360441045
241111.1960564530903-0.196056453090276
251313.8643356966233-0.864335696623336
261614.1191522238781.88084777612199
271514.70068729711030.299312702889659
281413.21552903698840.78447096301165
291414.0035872018418-0.00358720184175349
301411.63576622108242.36423377891761
31811.34029577117-3.34029577117001
321314.0929366237416-1.09293662374159
331514.40126788206080.598732117939154
341311.29228403386191.70771596613814
351112.5385402892615-1.53854028926154
361514.09542187284950.90457812715049
371513.41147618011231.58852381988771
38912.1921086960505-3.19210869605046
391314.1414892414009-1.14148924140091
401614.94246338354021.05753661645984
411313.6297203365311-0.62972033653109
421110.58750550873260.4124944912674
431211.54195957366350.458040426336511
441212.1938177720423-0.193817772042343
451212.94719430052-0.947194300519995
461414.0009860746851-0.000986074685144223
471413.64933638459000.350663615410032
48811.4216993575606-3.42169935756063
491313.0582006172276-0.0582006172276327
501614.72878862625701.27121137374303
511312.43277199190040.567228008099584
521114.0025399921850-3.00253999218499
531413.47478363592350.52521636407655
541311.31813835976961.68186164023035
551313.0546363292234-0.0546363292234351
561313.3887952814912-0.388795281491164
571212.5265099394571-0.52650993945706
581614.71187366600211.28812633399790
591510.90983998725514.09016001274485
601515.3544473216950-0.354447321695035
611211.04421948356850.95578051643149
621414.2495576324261-0.249557632426077
631214.3956537173204-2.39565371732038
641514.12709054661430.872909453385653
651211.81911667457170.180883325428266
661313.1905657649825-0.190565764982453
671214.0285792972029-2.02857929720291
681212.2856431790989-0.285643179098895
691314.0578624192014-1.05786241920144
70510.1742054615072-5.17420546150722
711313.3634865890547-0.363486589054693
721313.2811376779636-0.281137677963601
731413.17706085324350.822939146756533
741713.75764582994413.24235417005586
751313.8884682145868-0.888468214586758
761314.5393126769409-1.5393126769409
771213.73108124722-1.73108124722001
781312.80782839040910.192171609590942
791412.38624999754581.61375000245418
801110.45437216530080.545627834699243
811211.66391690197330.336083098026707
821213.3317410554062-1.33174105540617
831613.97508705263172.02491294736828
841213.1091272321134-1.10912723211341
851210.54630453920971.45369546079026
861213.8069869235033-1.80698692350330
871011.6616138613276-1.66161386132758
881512.58535169849902.41464830150104
891515.2872147832097-0.287214783209686
901212.0979694998763-0.0979694998763433
911613.27942914676722.72057085323277
921513.94087979362261.05912020637738
931615.26361917860830.73638082139174
941314.9482687822939-1.94826878229385
951212.6663420064809-0.666342006480869
961112.1232685518916-1.12326855189158
971311.83480852351391.16519147648613
981011.2414466685161-1.24144666851609
991513.31832501609911.68167498390088
1001313.9733775621092-0.973377562109227
1011615.48068872124720.519311278752849
1021515.0685137262809-0.0685137262809203
1031814.69903715273163.30096284726836
1041310.72407824131352.27592175868646
1051010.4210863327862-0.421086332786162
1061615.07935336770460.920646632295382
1071311.55487914407351.44512085592652
1081515.4605022774142-0.460502277414208
1091411.94584353431592.05415646568413
1101511.66389980499243.33610019500760
1111413.31913558173550.680864418264513
1121314.764795042679-1.76479504267901
1131313.3016576607498-0.301657660749802
1141514.35611887766160.643881122338433
1151614.78038412090111.21961587909889
1161414.5997422003543-0.599742200354303
1171414.2562178902544-0.256217890254429
1181613.30539105376112.69460894623890
1191414.7100053530057-0.710005353005711
1201212.9242922224764-0.9242922224764
1211312.80911478258380.190885217416204
1221214.0030273251826-2.00302732518263
1231212.2735552407858-0.273555240785795
1241414.6227499100675-0.622749910067525
1251414.5826488947704-0.582648894770411
1261412.19558837740931.80441162259069
1271615.52019635864490.479803641355146
1281314.5858352055783-1.58583520557829
1291413.30541141320750.694588586792506
130411.3423321021088-7.34233210210876
1311615.39784948985210.602150510147868
1321313.5934505145228-0.593450514522837
1331612.08697211916113.91302788083886
1341513.73368318684181.26631681315817
1351413.99753152337230.00246847662768872
1361312.11708195705790.882918042942112
1371414.5911438736323-0.591143873632276
1381212.3541344821222-0.354134482122223
1391514.47595503652910.524044963470921
1401413.75775608188210.242243918117944
1411313.3727877505990-0.372787750598957
1421414.3148335968670-0.314833596866979
1431613.71656973446892.28343026553106
144612.3421935818361-6.34219358183612
1451312.94797371617400.0520262838259557
1461313.1810783373908-0.181078337390845
1471412.91639031138871.08360968861134
1481514.87776353332020.122236466679822
1491414.4494877870511-0.449487787051067
1501515.2846955848154-0.284695584815386
1511314.2434534268062-1.24345342680624
1521615.0624571248640.93754287513601
1531212.0024567156971-0.00245671569706828
1541514.20686567997230.79313432002769
1551214.5793882744699-2.57938827446993
1561412.08096525680811.91903474319191

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.2412764510371 & 0.758723548962934 \tabularnewline
2 & 13 & 12.5492510452934 & 0.450748954706606 \tabularnewline
3 & 16 & 12.8901516541620 & 3.10984834583802 \tabularnewline
4 & 12 & 12.4095002419506 & -0.409500241950638 \tabularnewline
5 & 11 & 12.1954898338014 & -1.19548983380136 \tabularnewline
6 & 12 & 10.5485098561972 & 1.45149014380275 \tabularnewline
7 & 18 & 15.1400072724624 & 2.85999272753762 \tabularnewline
8 & 11 & 11.892234091914 & -0.892234091914003 \tabularnewline
9 & 14 & 12.5427878319715 & 1.45721216802846 \tabularnewline
10 & 9 & 10.4731134123434 & -1.47311341234342 \tabularnewline
11 & 14 & 13.6989839110600 & 0.301016088939982 \tabularnewline
12 & 12 & 13.3914843781074 & -1.39148437810740 \tabularnewline
13 & 11 & 13.5077253284476 & -2.50772532844755 \tabularnewline
14 & 12 & 13.0619744150281 & -1.06197441502812 \tabularnewline
15 & 13 & 12.4274437124940 & 0.57255628750597 \tabularnewline
16 & 11 & 12.4166973187797 & -1.41669731877966 \tabularnewline
17 & 12 & 12.9626491506296 & -0.962649150629632 \tabularnewline
18 & 16 & 13.5052119045738 & 2.49478809542624 \tabularnewline
19 & 9 & 11.4569226873542 & -2.45692268735425 \tabularnewline
20 & 11 & 11.2933506639583 & -0.293350663958290 \tabularnewline
21 & 13 & 10.1062435201726 & 2.89375647982743 \tabularnewline
22 & 15 & 14.2632373618389 & 0.736762638161104 \tabularnewline
23 & 10 & 13.1535936044104 & -3.15359360441045 \tabularnewline
24 & 11 & 11.1960564530903 & -0.196056453090276 \tabularnewline
25 & 13 & 13.8643356966233 & -0.864335696623336 \tabularnewline
26 & 16 & 14.119152223878 & 1.88084777612199 \tabularnewline
27 & 15 & 14.7006872971103 & 0.299312702889659 \tabularnewline
28 & 14 & 13.2155290369884 & 0.78447096301165 \tabularnewline
29 & 14 & 14.0035872018418 & -0.00358720184175349 \tabularnewline
30 & 14 & 11.6357662210824 & 2.36423377891761 \tabularnewline
31 & 8 & 11.34029577117 & -3.34029577117001 \tabularnewline
32 & 13 & 14.0929366237416 & -1.09293662374159 \tabularnewline
33 & 15 & 14.4012678820608 & 0.598732117939154 \tabularnewline
34 & 13 & 11.2922840338619 & 1.70771596613814 \tabularnewline
35 & 11 & 12.5385402892615 & -1.53854028926154 \tabularnewline
36 & 15 & 14.0954218728495 & 0.90457812715049 \tabularnewline
37 & 15 & 13.4114761801123 & 1.58852381988771 \tabularnewline
38 & 9 & 12.1921086960505 & -3.19210869605046 \tabularnewline
39 & 13 & 14.1414892414009 & -1.14148924140091 \tabularnewline
40 & 16 & 14.9424633835402 & 1.05753661645984 \tabularnewline
41 & 13 & 13.6297203365311 & -0.62972033653109 \tabularnewline
42 & 11 & 10.5875055087326 & 0.4124944912674 \tabularnewline
43 & 12 & 11.5419595736635 & 0.458040426336511 \tabularnewline
44 & 12 & 12.1938177720423 & -0.193817772042343 \tabularnewline
45 & 12 & 12.94719430052 & -0.947194300519995 \tabularnewline
46 & 14 & 14.0009860746851 & -0.000986074685144223 \tabularnewline
47 & 14 & 13.6493363845900 & 0.350663615410032 \tabularnewline
48 & 8 & 11.4216993575606 & -3.42169935756063 \tabularnewline
49 & 13 & 13.0582006172276 & -0.0582006172276327 \tabularnewline
50 & 16 & 14.7287886262570 & 1.27121137374303 \tabularnewline
51 & 13 & 12.4327719919004 & 0.567228008099584 \tabularnewline
52 & 11 & 14.0025399921850 & -3.00253999218499 \tabularnewline
53 & 14 & 13.4747836359235 & 0.52521636407655 \tabularnewline
54 & 13 & 11.3181383597696 & 1.68186164023035 \tabularnewline
55 & 13 & 13.0546363292234 & -0.0546363292234351 \tabularnewline
56 & 13 & 13.3887952814912 & -0.388795281491164 \tabularnewline
57 & 12 & 12.5265099394571 & -0.52650993945706 \tabularnewline
58 & 16 & 14.7118736660021 & 1.28812633399790 \tabularnewline
59 & 15 & 10.9098399872551 & 4.09016001274485 \tabularnewline
60 & 15 & 15.3544473216950 & -0.354447321695035 \tabularnewline
61 & 12 & 11.0442194835685 & 0.95578051643149 \tabularnewline
62 & 14 & 14.2495576324261 & -0.249557632426077 \tabularnewline
63 & 12 & 14.3956537173204 & -2.39565371732038 \tabularnewline
64 & 15 & 14.1270905466143 & 0.872909453385653 \tabularnewline
65 & 12 & 11.8191166745717 & 0.180883325428266 \tabularnewline
66 & 13 & 13.1905657649825 & -0.190565764982453 \tabularnewline
67 & 12 & 14.0285792972029 & -2.02857929720291 \tabularnewline
68 & 12 & 12.2856431790989 & -0.285643179098895 \tabularnewline
69 & 13 & 14.0578624192014 & -1.05786241920144 \tabularnewline
70 & 5 & 10.1742054615072 & -5.17420546150722 \tabularnewline
71 & 13 & 13.3634865890547 & -0.363486589054693 \tabularnewline
72 & 13 & 13.2811376779636 & -0.281137677963601 \tabularnewline
73 & 14 & 13.1770608532435 & 0.822939146756533 \tabularnewline
74 & 17 & 13.7576458299441 & 3.24235417005586 \tabularnewline
75 & 13 & 13.8884682145868 & -0.888468214586758 \tabularnewline
76 & 13 & 14.5393126769409 & -1.5393126769409 \tabularnewline
77 & 12 & 13.73108124722 & -1.73108124722001 \tabularnewline
78 & 13 & 12.8078283904091 & 0.192171609590942 \tabularnewline
79 & 14 & 12.3862499975458 & 1.61375000245418 \tabularnewline
80 & 11 & 10.4543721653008 & 0.545627834699243 \tabularnewline
81 & 12 & 11.6639169019733 & 0.336083098026707 \tabularnewline
82 & 12 & 13.3317410554062 & -1.33174105540617 \tabularnewline
83 & 16 & 13.9750870526317 & 2.02491294736828 \tabularnewline
84 & 12 & 13.1091272321134 & -1.10912723211341 \tabularnewline
85 & 12 & 10.5463045392097 & 1.45369546079026 \tabularnewline
86 & 12 & 13.8069869235033 & -1.80698692350330 \tabularnewline
87 & 10 & 11.6616138613276 & -1.66161386132758 \tabularnewline
88 & 15 & 12.5853516984990 & 2.41464830150104 \tabularnewline
89 & 15 & 15.2872147832097 & -0.287214783209686 \tabularnewline
90 & 12 & 12.0979694998763 & -0.0979694998763433 \tabularnewline
91 & 16 & 13.2794291467672 & 2.72057085323277 \tabularnewline
92 & 15 & 13.9408797936226 & 1.05912020637738 \tabularnewline
93 & 16 & 15.2636191786083 & 0.73638082139174 \tabularnewline
94 & 13 & 14.9482687822939 & -1.94826878229385 \tabularnewline
95 & 12 & 12.6663420064809 & -0.666342006480869 \tabularnewline
96 & 11 & 12.1232685518916 & -1.12326855189158 \tabularnewline
97 & 13 & 11.8348085235139 & 1.16519147648613 \tabularnewline
98 & 10 & 11.2414466685161 & -1.24144666851609 \tabularnewline
99 & 15 & 13.3183250160991 & 1.68167498390088 \tabularnewline
100 & 13 & 13.9733775621092 & -0.973377562109227 \tabularnewline
101 & 16 & 15.4806887212472 & 0.519311278752849 \tabularnewline
102 & 15 & 15.0685137262809 & -0.0685137262809203 \tabularnewline
103 & 18 & 14.6990371527316 & 3.30096284726836 \tabularnewline
104 & 13 & 10.7240782413135 & 2.27592175868646 \tabularnewline
105 & 10 & 10.4210863327862 & -0.421086332786162 \tabularnewline
106 & 16 & 15.0793533677046 & 0.920646632295382 \tabularnewline
107 & 13 & 11.5548791440735 & 1.44512085592652 \tabularnewline
108 & 15 & 15.4605022774142 & -0.460502277414208 \tabularnewline
109 & 14 & 11.9458435343159 & 2.05415646568413 \tabularnewline
110 & 15 & 11.6638998049924 & 3.33610019500760 \tabularnewline
111 & 14 & 13.3191355817355 & 0.680864418264513 \tabularnewline
112 & 13 & 14.764795042679 & -1.76479504267901 \tabularnewline
113 & 13 & 13.3016576607498 & -0.301657660749802 \tabularnewline
114 & 15 & 14.3561188776616 & 0.643881122338433 \tabularnewline
115 & 16 & 14.7803841209011 & 1.21961587909889 \tabularnewline
116 & 14 & 14.5997422003543 & -0.599742200354303 \tabularnewline
117 & 14 & 14.2562178902544 & -0.256217890254429 \tabularnewline
118 & 16 & 13.3053910537611 & 2.69460894623890 \tabularnewline
119 & 14 & 14.7100053530057 & -0.710005353005711 \tabularnewline
120 & 12 & 12.9242922224764 & -0.9242922224764 \tabularnewline
121 & 13 & 12.8091147825838 & 0.190885217416204 \tabularnewline
122 & 12 & 14.0030273251826 & -2.00302732518263 \tabularnewline
123 & 12 & 12.2735552407858 & -0.273555240785795 \tabularnewline
124 & 14 & 14.6227499100675 & -0.622749910067525 \tabularnewline
125 & 14 & 14.5826488947704 & -0.582648894770411 \tabularnewline
126 & 14 & 12.1955883774093 & 1.80441162259069 \tabularnewline
127 & 16 & 15.5201963586449 & 0.479803641355146 \tabularnewline
128 & 13 & 14.5858352055783 & -1.58583520557829 \tabularnewline
129 & 14 & 13.3054114132075 & 0.694588586792506 \tabularnewline
130 & 4 & 11.3423321021088 & -7.34233210210876 \tabularnewline
131 & 16 & 15.3978494898521 & 0.602150510147868 \tabularnewline
132 & 13 & 13.5934505145228 & -0.593450514522837 \tabularnewline
133 & 16 & 12.0869721191611 & 3.91302788083886 \tabularnewline
134 & 15 & 13.7336831868418 & 1.26631681315817 \tabularnewline
135 & 14 & 13.9975315233723 & 0.00246847662768872 \tabularnewline
136 & 13 & 12.1170819570579 & 0.882918042942112 \tabularnewline
137 & 14 & 14.5911438736323 & -0.591143873632276 \tabularnewline
138 & 12 & 12.3541344821222 & -0.354134482122223 \tabularnewline
139 & 15 & 14.4759550365291 & 0.524044963470921 \tabularnewline
140 & 14 & 13.7577560818821 & 0.242243918117944 \tabularnewline
141 & 13 & 13.3727877505990 & -0.372787750598957 \tabularnewline
142 & 14 & 14.3148335968670 & -0.314833596866979 \tabularnewline
143 & 16 & 13.7165697344689 & 2.28343026553106 \tabularnewline
144 & 6 & 12.3421935818361 & -6.34219358183612 \tabularnewline
145 & 13 & 12.9479737161740 & 0.0520262838259557 \tabularnewline
146 & 13 & 13.1810783373908 & -0.181078337390845 \tabularnewline
147 & 14 & 12.9163903113887 & 1.08360968861134 \tabularnewline
148 & 15 & 14.8777635333202 & 0.122236466679822 \tabularnewline
149 & 14 & 14.4494877870511 & -0.449487787051067 \tabularnewline
150 & 15 & 15.2846955848154 & -0.284695584815386 \tabularnewline
151 & 13 & 14.2434534268062 & -1.24345342680624 \tabularnewline
152 & 16 & 15.062457124864 & 0.93754287513601 \tabularnewline
153 & 12 & 12.0024567156971 & -0.00245671569706828 \tabularnewline
154 & 15 & 14.2068656799723 & 0.79313432002769 \tabularnewline
155 & 12 & 14.5793882744699 & -2.57938827446993 \tabularnewline
156 & 14 & 12.0809652568081 & 1.91903474319191 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]12.2412764510371[/C][C]0.758723548962934[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]12.5492510452934[/C][C]0.450748954706606[/C][/ROW]
[ROW][C]3[/C][C]16[/C][C]12.8901516541620[/C][C]3.10984834583802[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]12.4095002419506[/C][C]-0.409500241950638[/C][/ROW]
[ROW][C]5[/C][C]11[/C][C]12.1954898338014[/C][C]-1.19548983380136[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.5485098561972[/C][C]1.45149014380275[/C][/ROW]
[ROW][C]7[/C][C]18[/C][C]15.1400072724624[/C][C]2.85999272753762[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]11.892234091914[/C][C]-0.892234091914003[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]12.5427878319715[/C][C]1.45721216802846[/C][/ROW]
[ROW][C]10[/C][C]9[/C][C]10.4731134123434[/C][C]-1.47311341234342[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.6989839110600[/C][C]0.301016088939982[/C][/ROW]
[ROW][C]12[/C][C]12[/C][C]13.3914843781074[/C][C]-1.39148437810740[/C][/ROW]
[ROW][C]13[/C][C]11[/C][C]13.5077253284476[/C][C]-2.50772532844755[/C][/ROW]
[ROW][C]14[/C][C]12[/C][C]13.0619744150281[/C][C]-1.06197441502812[/C][/ROW]
[ROW][C]15[/C][C]13[/C][C]12.4274437124940[/C][C]0.57255628750597[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]12.4166973187797[/C][C]-1.41669731877966[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]12.9626491506296[/C][C]-0.962649150629632[/C][/ROW]
[ROW][C]18[/C][C]16[/C][C]13.5052119045738[/C][C]2.49478809542624[/C][/ROW]
[ROW][C]19[/C][C]9[/C][C]11.4569226873542[/C][C]-2.45692268735425[/C][/ROW]
[ROW][C]20[/C][C]11[/C][C]11.2933506639583[/C][C]-0.293350663958290[/C][/ROW]
[ROW][C]21[/C][C]13[/C][C]10.1062435201726[/C][C]2.89375647982743[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]14.2632373618389[/C][C]0.736762638161104[/C][/ROW]
[ROW][C]23[/C][C]10[/C][C]13.1535936044104[/C][C]-3.15359360441045[/C][/ROW]
[ROW][C]24[/C][C]11[/C][C]11.1960564530903[/C][C]-0.196056453090276[/C][/ROW]
[ROW][C]25[/C][C]13[/C][C]13.8643356966233[/C][C]-0.864335696623336[/C][/ROW]
[ROW][C]26[/C][C]16[/C][C]14.119152223878[/C][C]1.88084777612199[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]14.7006872971103[/C][C]0.299312702889659[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.2155290369884[/C][C]0.78447096301165[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]14.0035872018418[/C][C]-0.00358720184175349[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]11.6357662210824[/C][C]2.36423377891761[/C][/ROW]
[ROW][C]31[/C][C]8[/C][C]11.34029577117[/C][C]-3.34029577117001[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]14.0929366237416[/C][C]-1.09293662374159[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]14.4012678820608[/C][C]0.598732117939154[/C][/ROW]
[ROW][C]34[/C][C]13[/C][C]11.2922840338619[/C][C]1.70771596613814[/C][/ROW]
[ROW][C]35[/C][C]11[/C][C]12.5385402892615[/C][C]-1.53854028926154[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]14.0954218728495[/C][C]0.90457812715049[/C][/ROW]
[ROW][C]37[/C][C]15[/C][C]13.4114761801123[/C][C]1.58852381988771[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]12.1921086960505[/C][C]-3.19210869605046[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]14.1414892414009[/C][C]-1.14148924140091[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.9424633835402[/C][C]1.05753661645984[/C][/ROW]
[ROW][C]41[/C][C]13[/C][C]13.6297203365311[/C][C]-0.62972033653109[/C][/ROW]
[ROW][C]42[/C][C]11[/C][C]10.5875055087326[/C][C]0.4124944912674[/C][/ROW]
[ROW][C]43[/C][C]12[/C][C]11.5419595736635[/C][C]0.458040426336511[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]12.1938177720423[/C][C]-0.193817772042343[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]12.94719430052[/C][C]-0.947194300519995[/C][/ROW]
[ROW][C]46[/C][C]14[/C][C]14.0009860746851[/C][C]-0.000986074685144223[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]13.6493363845900[/C][C]0.350663615410032[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]11.4216993575606[/C][C]-3.42169935756063[/C][/ROW]
[ROW][C]49[/C][C]13[/C][C]13.0582006172276[/C][C]-0.0582006172276327[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]14.7287886262570[/C][C]1.27121137374303[/C][/ROW]
[ROW][C]51[/C][C]13[/C][C]12.4327719919004[/C][C]0.567228008099584[/C][/ROW]
[ROW][C]52[/C][C]11[/C][C]14.0025399921850[/C][C]-3.00253999218499[/C][/ROW]
[ROW][C]53[/C][C]14[/C][C]13.4747836359235[/C][C]0.52521636407655[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]11.3181383597696[/C][C]1.68186164023035[/C][/ROW]
[ROW][C]55[/C][C]13[/C][C]13.0546363292234[/C][C]-0.0546363292234351[/C][/ROW]
[ROW][C]56[/C][C]13[/C][C]13.3887952814912[/C][C]-0.388795281491164[/C][/ROW]
[ROW][C]57[/C][C]12[/C][C]12.5265099394571[/C][C]-0.52650993945706[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.7118736660021[/C][C]1.28812633399790[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]10.9098399872551[/C][C]4.09016001274485[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]15.3544473216950[/C][C]-0.354447321695035[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.0442194835685[/C][C]0.95578051643149[/C][/ROW]
[ROW][C]62[/C][C]14[/C][C]14.2495576324261[/C][C]-0.249557632426077[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.3956537173204[/C][C]-2.39565371732038[/C][/ROW]
[ROW][C]64[/C][C]15[/C][C]14.1270905466143[/C][C]0.872909453385653[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]11.8191166745717[/C][C]0.180883325428266[/C][/ROW]
[ROW][C]66[/C][C]13[/C][C]13.1905657649825[/C][C]-0.190565764982453[/C][/ROW]
[ROW][C]67[/C][C]12[/C][C]14.0285792972029[/C][C]-2.02857929720291[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]12.2856431790989[/C][C]-0.285643179098895[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]14.0578624192014[/C][C]-1.05786241920144[/C][/ROW]
[ROW][C]70[/C][C]5[/C][C]10.1742054615072[/C][C]-5.17420546150722[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.3634865890547[/C][C]-0.363486589054693[/C][/ROW]
[ROW][C]72[/C][C]13[/C][C]13.2811376779636[/C][C]-0.281137677963601[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]13.1770608532435[/C][C]0.822939146756533[/C][/ROW]
[ROW][C]74[/C][C]17[/C][C]13.7576458299441[/C][C]3.24235417005586[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.8884682145868[/C][C]-0.888468214586758[/C][/ROW]
[ROW][C]76[/C][C]13[/C][C]14.5393126769409[/C][C]-1.5393126769409[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.73108124722[/C][C]-1.73108124722001[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.8078283904091[/C][C]0.192171609590942[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]12.3862499975458[/C][C]1.61375000245418[/C][/ROW]
[ROW][C]80[/C][C]11[/C][C]10.4543721653008[/C][C]0.545627834699243[/C][/ROW]
[ROW][C]81[/C][C]12[/C][C]11.6639169019733[/C][C]0.336083098026707[/C][/ROW]
[ROW][C]82[/C][C]12[/C][C]13.3317410554062[/C][C]-1.33174105540617[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]13.9750870526317[/C][C]2.02491294736828[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]13.1091272321134[/C][C]-1.10912723211341[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]10.5463045392097[/C][C]1.45369546079026[/C][/ROW]
[ROW][C]86[/C][C]12[/C][C]13.8069869235033[/C][C]-1.80698692350330[/C][/ROW]
[ROW][C]87[/C][C]10[/C][C]11.6616138613276[/C][C]-1.66161386132758[/C][/ROW]
[ROW][C]88[/C][C]15[/C][C]12.5853516984990[/C][C]2.41464830150104[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]15.2872147832097[/C][C]-0.287214783209686[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]12.0979694998763[/C][C]-0.0979694998763433[/C][/ROW]
[ROW][C]91[/C][C]16[/C][C]13.2794291467672[/C][C]2.72057085323277[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.9408797936226[/C][C]1.05912020637738[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2636191786083[/C][C]0.73638082139174[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]14.9482687822939[/C][C]-1.94826878229385[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]12.6663420064809[/C][C]-0.666342006480869[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]12.1232685518916[/C][C]-1.12326855189158[/C][/ROW]
[ROW][C]97[/C][C]13[/C][C]11.8348085235139[/C][C]1.16519147648613[/C][/ROW]
[ROW][C]98[/C][C]10[/C][C]11.2414466685161[/C][C]-1.24144666851609[/C][/ROW]
[ROW][C]99[/C][C]15[/C][C]13.3183250160991[/C][C]1.68167498390088[/C][/ROW]
[ROW][C]100[/C][C]13[/C][C]13.9733775621092[/C][C]-0.973377562109227[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.4806887212472[/C][C]0.519311278752849[/C][/ROW]
[ROW][C]102[/C][C]15[/C][C]15.0685137262809[/C][C]-0.0685137262809203[/C][/ROW]
[ROW][C]103[/C][C]18[/C][C]14.6990371527316[/C][C]3.30096284726836[/C][/ROW]
[ROW][C]104[/C][C]13[/C][C]10.7240782413135[/C][C]2.27592175868646[/C][/ROW]
[ROW][C]105[/C][C]10[/C][C]10.4210863327862[/C][C]-0.421086332786162[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]15.0793533677046[/C][C]0.920646632295382[/C][/ROW]
[ROW][C]107[/C][C]13[/C][C]11.5548791440735[/C][C]1.44512085592652[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]15.4605022774142[/C][C]-0.460502277414208[/C][/ROW]
[ROW][C]109[/C][C]14[/C][C]11.9458435343159[/C][C]2.05415646568413[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]11.6638998049924[/C][C]3.33610019500760[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]13.3191355817355[/C][C]0.680864418264513[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]14.764795042679[/C][C]-1.76479504267901[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.3016576607498[/C][C]-0.301657660749802[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]14.3561188776616[/C][C]0.643881122338433[/C][/ROW]
[ROW][C]115[/C][C]16[/C][C]14.7803841209011[/C][C]1.21961587909889[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]14.5997422003543[/C][C]-0.599742200354303[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.2562178902544[/C][C]-0.256217890254429[/C][/ROW]
[ROW][C]118[/C][C]16[/C][C]13.3053910537611[/C][C]2.69460894623890[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]14.7100053530057[/C][C]-0.710005353005711[/C][/ROW]
[ROW][C]120[/C][C]12[/C][C]12.9242922224764[/C][C]-0.9242922224764[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]12.8091147825838[/C][C]0.190885217416204[/C][/ROW]
[ROW][C]122[/C][C]12[/C][C]14.0030273251826[/C][C]-2.00302732518263[/C][/ROW]
[ROW][C]123[/C][C]12[/C][C]12.2735552407858[/C][C]-0.273555240785795[/C][/ROW]
[ROW][C]124[/C][C]14[/C][C]14.6227499100675[/C][C]-0.622749910067525[/C][/ROW]
[ROW][C]125[/C][C]14[/C][C]14.5826488947704[/C][C]-0.582648894770411[/C][/ROW]
[ROW][C]126[/C][C]14[/C][C]12.1955883774093[/C][C]1.80441162259069[/C][/ROW]
[ROW][C]127[/C][C]16[/C][C]15.5201963586449[/C][C]0.479803641355146[/C][/ROW]
[ROW][C]128[/C][C]13[/C][C]14.5858352055783[/C][C]-1.58583520557829[/C][/ROW]
[ROW][C]129[/C][C]14[/C][C]13.3054114132075[/C][C]0.694588586792506[/C][/ROW]
[ROW][C]130[/C][C]4[/C][C]11.3423321021088[/C][C]-7.34233210210876[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]15.3978494898521[/C][C]0.602150510147868[/C][/ROW]
[ROW][C]132[/C][C]13[/C][C]13.5934505145228[/C][C]-0.593450514522837[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]12.0869721191611[/C][C]3.91302788083886[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]13.7336831868418[/C][C]1.26631681315817[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]13.9975315233723[/C][C]0.00246847662768872[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.1170819570579[/C][C]0.882918042942112[/C][/ROW]
[ROW][C]137[/C][C]14[/C][C]14.5911438736323[/C][C]-0.591143873632276[/C][/ROW]
[ROW][C]138[/C][C]12[/C][C]12.3541344821222[/C][C]-0.354134482122223[/C][/ROW]
[ROW][C]139[/C][C]15[/C][C]14.4759550365291[/C][C]0.524044963470921[/C][/ROW]
[ROW][C]140[/C][C]14[/C][C]13.7577560818821[/C][C]0.242243918117944[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.3727877505990[/C][C]-0.372787750598957[/C][/ROW]
[ROW][C]142[/C][C]14[/C][C]14.3148335968670[/C][C]-0.314833596866979[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.7165697344689[/C][C]2.28343026553106[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]12.3421935818361[/C][C]-6.34219358183612[/C][/ROW]
[ROW][C]145[/C][C]13[/C][C]12.9479737161740[/C][C]0.0520262838259557[/C][/ROW]
[ROW][C]146[/C][C]13[/C][C]13.1810783373908[/C][C]-0.181078337390845[/C][/ROW]
[ROW][C]147[/C][C]14[/C][C]12.9163903113887[/C][C]1.08360968861134[/C][/ROW]
[ROW][C]148[/C][C]15[/C][C]14.8777635333202[/C][C]0.122236466679822[/C][/ROW]
[ROW][C]149[/C][C]14[/C][C]14.4494877870511[/C][C]-0.449487787051067[/C][/ROW]
[ROW][C]150[/C][C]15[/C][C]15.2846955848154[/C][C]-0.284695584815386[/C][/ROW]
[ROW][C]151[/C][C]13[/C][C]14.2434534268062[/C][C]-1.24345342680624[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]15.062457124864[/C][C]0.93754287513601[/C][/ROW]
[ROW][C]153[/C][C]12[/C][C]12.0024567156971[/C][C]-0.00245671569706828[/C][/ROW]
[ROW][C]154[/C][C]15[/C][C]14.2068656799723[/C][C]0.79313432002769[/C][/ROW]
[ROW][C]155[/C][C]12[/C][C]14.5793882744699[/C][C]-2.57938827446993[/C][/ROW]
[ROW][C]156[/C][C]14[/C][C]12.0809652568081[/C][C]1.91903474319191[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.24127645103710.758723548962934
21312.54925104529340.450748954706606
31612.89015165416203.10984834583802
41212.4095002419506-0.409500241950638
51112.1954898338014-1.19548983380136
61210.54850985619721.45149014380275
71815.14000727246242.85999272753762
81111.892234091914-0.892234091914003
91412.54278783197151.45721216802846
10910.4731134123434-1.47311341234342
111413.69898391106000.301016088939982
121213.3914843781074-1.39148437810740
131113.5077253284476-2.50772532844755
141213.0619744150281-1.06197441502812
151312.42744371249400.57255628750597
161112.4166973187797-1.41669731877966
171212.9626491506296-0.962649150629632
181613.50521190457382.49478809542624
19911.4569226873542-2.45692268735425
201111.2933506639583-0.293350663958290
211310.10624352017262.89375647982743
221514.26323736183890.736762638161104
231013.1535936044104-3.15359360441045
241111.1960564530903-0.196056453090276
251313.8643356966233-0.864335696623336
261614.1191522238781.88084777612199
271514.70068729711030.299312702889659
281413.21552903698840.78447096301165
291414.0035872018418-0.00358720184175349
301411.63576622108242.36423377891761
31811.34029577117-3.34029577117001
321314.0929366237416-1.09293662374159
331514.40126788206080.598732117939154
341311.29228403386191.70771596613814
351112.5385402892615-1.53854028926154
361514.09542187284950.90457812715049
371513.41147618011231.58852381988771
38912.1921086960505-3.19210869605046
391314.1414892414009-1.14148924140091
401614.94246338354021.05753661645984
411313.6297203365311-0.62972033653109
421110.58750550873260.4124944912674
431211.54195957366350.458040426336511
441212.1938177720423-0.193817772042343
451212.94719430052-0.947194300519995
461414.0009860746851-0.000986074685144223
471413.64933638459000.350663615410032
48811.4216993575606-3.42169935756063
491313.0582006172276-0.0582006172276327
501614.72878862625701.27121137374303
511312.43277199190040.567228008099584
521114.0025399921850-3.00253999218499
531413.47478363592350.52521636407655
541311.31813835976961.68186164023035
551313.0546363292234-0.0546363292234351
561313.3887952814912-0.388795281491164
571212.5265099394571-0.52650993945706
581614.71187366600211.28812633399790
591510.90983998725514.09016001274485
601515.3544473216950-0.354447321695035
611211.04421948356850.95578051643149
621414.2495576324261-0.249557632426077
631214.3956537173204-2.39565371732038
641514.12709054661430.872909453385653
651211.81911667457170.180883325428266
661313.1905657649825-0.190565764982453
671214.0285792972029-2.02857929720291
681212.2856431790989-0.285643179098895
691314.0578624192014-1.05786241920144
70510.1742054615072-5.17420546150722
711313.3634865890547-0.363486589054693
721313.2811376779636-0.281137677963601
731413.17706085324350.822939146756533
741713.75764582994413.24235417005586
751313.8884682145868-0.888468214586758
761314.5393126769409-1.5393126769409
771213.73108124722-1.73108124722001
781312.80782839040910.192171609590942
791412.38624999754581.61375000245418
801110.45437216530080.545627834699243
811211.66391690197330.336083098026707
821213.3317410554062-1.33174105540617
831613.97508705263172.02491294736828
841213.1091272321134-1.10912723211341
851210.54630453920971.45369546079026
861213.8069869235033-1.80698692350330
871011.6616138613276-1.66161386132758
881512.58535169849902.41464830150104
891515.2872147832097-0.287214783209686
901212.0979694998763-0.0979694998763433
911613.27942914676722.72057085323277
921513.94087979362261.05912020637738
931615.26361917860830.73638082139174
941314.9482687822939-1.94826878229385
951212.6663420064809-0.666342006480869
961112.1232685518916-1.12326855189158
971311.83480852351391.16519147648613
981011.2414466685161-1.24144666851609
991513.31832501609911.68167498390088
1001313.9733775621092-0.973377562109227
1011615.48068872124720.519311278752849
1021515.0685137262809-0.0685137262809203
1031814.69903715273163.30096284726836
1041310.72407824131352.27592175868646
1051010.4210863327862-0.421086332786162
1061615.07935336770460.920646632295382
1071311.55487914407351.44512085592652
1081515.4605022774142-0.460502277414208
1091411.94584353431592.05415646568413
1101511.66389980499243.33610019500760
1111413.31913558173550.680864418264513
1121314.764795042679-1.76479504267901
1131313.3016576607498-0.301657660749802
1141514.35611887766160.643881122338433
1151614.78038412090111.21961587909889
1161414.5997422003543-0.599742200354303
1171414.2562178902544-0.256217890254429
1181613.30539105376112.69460894623890
1191414.7100053530057-0.710005353005711
1201212.9242922224764-0.9242922224764
1211312.80911478258380.190885217416204
1221214.0030273251826-2.00302732518263
1231212.2735552407858-0.273555240785795
1241414.6227499100675-0.622749910067525
1251414.5826488947704-0.582648894770411
1261412.19558837740931.80441162259069
1271615.52019635864490.479803641355146
1281314.5858352055783-1.58583520557829
1291413.30541141320750.694588586792506
130411.3423321021088-7.34233210210876
1311615.39784948985210.602150510147868
1321313.5934505145228-0.593450514522837
1331612.08697211916113.91302788083886
1341513.73368318684181.26631681315817
1351413.99753152337230.00246847662768872
1361312.11708195705790.882918042942112
1371414.5911438736323-0.591143873632276
1381212.3541344821222-0.354134482122223
1391514.47595503652910.524044963470921
1401413.75775608188210.242243918117944
1411313.3727877505990-0.372787750598957
1421414.3148335968670-0.314833596866979
1431613.71656973446892.28343026553106
144612.3421935818361-6.34219358183612
1451312.94797371617400.0520262838259557
1461313.1810783373908-0.181078337390845
1471412.91639031138871.08360968861134
1481514.87776353332020.122236466679822
1491414.4494877870511-0.449487787051067
1501515.2846955848154-0.284695584815386
1511314.2434534268062-1.24345342680624
1521615.0624571248640.93754287513601
1531212.0024567156971-0.00245671569706828
1541514.20686567997230.79313432002769
1551214.5793882744699-2.57938827446993
1561412.08096525680811.91903474319191






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.6141832515470040.7716334969059920.385816748452996
120.4570043567957270.9140087135914540.542995643204273
130.4234873032036810.8469746064073620.576512696796319
140.5188393277001920.9623213445996160.481160672299808
150.4089976503370110.8179953006740230.591002349662989
160.3123804121206050.6247608242412090.687619587879395
170.2312124375419040.4624248750838080.768787562458096
180.3123572432976860.6247144865953720.687642756702314
190.2524833725819240.5049667451638480.747516627418076
200.2768249328887650.553649865777530.723175067111235
210.7370282805396880.5259434389206240.262971719460312
220.6893937322984440.6212125354031110.310606267701556
230.759430988653410.4811380226931790.240569011346590
240.6981558084384770.6036883831230460.301844191561523
250.6322579052157960.7354841895684080.367742094784204
260.675566841375880.648866317248240.32443315862412
270.6142765761696190.7714468476607610.385723423830381
280.5637998884179660.8724002231640680.436200111582034
290.5011385585763530.9977228828472940.498861441423647
300.5211919517262150.957616096547570.478808048273785
310.7018397940957510.5963204118084970.298160205904249
320.6650120590034570.6699758819930850.334987940996543
330.6326655350229160.7346689299541670.367334464977083
340.6774999598306840.6450000803386310.322500040169316
350.6418792018114940.7162415963770110.358120798188506
360.6076496475375170.7847007049249660.392350352462483
370.6021297489887180.7957405020225630.397870251011282
380.6612676352830850.677464729433830.338732364716915
390.6131587699373770.7736824601252450.386841230062623
400.5771930418534730.8456139162930540.422806958146527
410.5283737323899660.9432525352200690.471626267610034
420.518678369214610.962643261570780.48132163078539
430.4755258966042250.951051793208450.524474103395775
440.4242050519632350.848410103926470.575794948036765
450.3818843024027400.7637686048054810.61811569759726
460.3314712976470560.6629425952941120.668528702352944
470.2874967748349370.5749935496698740.712503225165063
480.3908779004798210.7817558009596420.609122099520179
490.3419863076045830.6839726152091650.658013692395417
500.3286363724196060.6572727448392120.671363627580394
510.2989909464905150.597981892981030.701009053509485
520.3644615270916210.7289230541832430.635538472908379
530.3312780103593980.6625560207187960.668721989640602
540.3320671483066130.6641342966132270.667932851693387
550.2874614385250940.5749228770501880.712538561474906
560.2462386636860310.4924773273720620.753761336313969
570.2097327438955790.4194654877911590.79026725610442
580.2063249292833670.4126498585667330.793675070716633
590.3810492000119860.7620984000239720.618950799988014
600.3351706182041170.6703412364082350.664829381795883
610.3021122723436550.6042245446873110.697887727656345
620.2639698122119420.5279396244238840.736030187788058
630.3143023896242870.6286047792485740.685697610375713
640.2870368586747570.5740737173495150.712963141325243
650.2498271307859260.4996542615718530.750172869214074
660.2144177445699180.4288354891398350.785582255430082
670.2203073467681240.4406146935362480.779692653231876
680.1864670727100800.3729341454201590.81353292728992
690.1640312206435720.3280624412871430.835968779356429
700.4549328387277640.9098656774555290.545067161272236
710.4098127753178010.8196255506356030.590187224682199
720.366398948058750.73279789611750.63360105194125
730.3375845779407990.6751691558815970.662415422059201
740.4579913207056530.9159826414113060.542008679294347
750.4199773110070810.8399546220141620.580022688992919
760.4079037533099770.8158075066199550.592096246690023
770.4137858039759650.827571607951930.586214196024035
780.3684247886973960.7368495773947920.631575211302604
790.3670352377390520.7340704754781040.632964762260948
800.3318610006749460.6637220013498930.668138999325054
810.2953361204860160.5906722409720320.704663879513984
820.2720978849005020.5441957698010030.727902115099498
830.2813987987446280.5627975974892560.718601201255372
840.2593929434948300.5187858869896600.74060705650517
850.2422194907672590.4844389815345170.757780509232741
860.2576975198155250.515395039631050.742302480184475
870.2759229053415360.5518458106830730.724077094658464
880.3055517357008610.6111034714017230.694448264299139
890.2673575350214090.5347150700428180.732642464978591
900.2345711330842930.4691422661685870.765428866915707
910.2701462509260260.5402925018520510.729853749073974
920.2406335298992040.4812670597984090.759366470100796
930.2080602894003030.4161205788006060.791939710599697
940.2189028089323980.4378056178647960.781097191067602
950.1943394371858190.3886788743716390.80566056281418
960.1990745173839410.3981490347678820.800925482616059
970.1832680959156860.3665361918313710.816731904084314
980.2175153881658980.4350307763317960.782484611834102
990.2284121570919360.4568243141838720.771587842908064
1000.2487705742105440.4975411484210880.751229425789456
1010.2129322831410110.4258645662820210.78706771685899
1020.1850981093304960.3701962186609920.814901890669504
1030.2101226916097420.4202453832194840.789877308390258
1040.2042516494760320.4085032989520650.795748350523968
1050.1824762046973260.3649524093946530.817523795302674
1060.1545476096439450.309095219287890.845452390356055
1070.1345465270783460.2690930541566910.865453472921655
1080.1101167383417920.2202334766835840.889883261658208
1090.1119584482776580.2239168965553170.888041551722342
1100.3401319549696830.6802639099393670.659868045030317
1110.3181595151917050.636319030383410.681840484808295
1120.3105352629725480.6210705259450960.689464737027452
1130.2776594680260990.5553189360521980.722340531973901
1140.2354383262440520.4708766524881050.764561673755948
1150.2041466151176320.4082932302352650.795853384882368
1160.1709740726929540.3419481453859090.829025927307046
1170.1392737783670500.2785475567340990.86072622163295
1180.1713808387705160.3427616775410310.828619161229484
1190.1500197502961780.3000395005923570.849980249703822
1200.1242174709417680.2484349418835360.875782529058232
1210.0979099273722550.195819854744510.902090072627745
1220.09192492101326540.1838498420265310.908075078986735
1230.07057265637101080.1411453127420220.92942734362899
1240.06336890809044040.1267378161808810.93663109190956
1250.05305019689558020.1061003937911600.94694980310442
1260.05266211574110150.1053242314822030.947337884258898
1270.03778446180741920.07556892361483840.962215538192581
1280.0462599951954560.0925199903909120.953740004804544
1290.06020301538457560.1204060307691510.939796984615424
1300.3223482762964550.6446965525929110.677651723703545
1310.2642705696735960.5285411393471920.735729430326404
1320.2095538027270020.4191076054540030.790446197272998
1330.3884943682693110.7769887365386210.611505631730689
1340.3629875015825690.7259750031651380.637012498417431
1350.3036245389101240.6072490778202490.696375461089876
1360.2828059920197360.5656119840394720.717194007980264
1370.2156453921940420.4312907843880850.784354607805958
1380.1580515469983180.3161030939966370.841948453001682
1390.1302112364144360.2604224728288710.869788763585564
1400.1005070980639790.2010141961279580.899492901936021
1410.0727070136492170.1454140272984340.927292986350783
1420.04863361467069850.0972672293413970.951366385329302
1430.1470180091910660.2940360183821320.852981990808934
1440.5918988194022070.8162023611955860.408101180597793
1450.4282472591685190.8564945183370380.571752740831481

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.614183251547004 & 0.771633496905992 & 0.385816748452996 \tabularnewline
12 & 0.457004356795727 & 0.914008713591454 & 0.542995643204273 \tabularnewline
13 & 0.423487303203681 & 0.846974606407362 & 0.576512696796319 \tabularnewline
14 & 0.518839327700192 & 0.962321344599616 & 0.481160672299808 \tabularnewline
15 & 0.408997650337011 & 0.817995300674023 & 0.591002349662989 \tabularnewline
16 & 0.312380412120605 & 0.624760824241209 & 0.687619587879395 \tabularnewline
17 & 0.231212437541904 & 0.462424875083808 & 0.768787562458096 \tabularnewline
18 & 0.312357243297686 & 0.624714486595372 & 0.687642756702314 \tabularnewline
19 & 0.252483372581924 & 0.504966745163848 & 0.747516627418076 \tabularnewline
20 & 0.276824932888765 & 0.55364986577753 & 0.723175067111235 \tabularnewline
21 & 0.737028280539688 & 0.525943438920624 & 0.262971719460312 \tabularnewline
22 & 0.689393732298444 & 0.621212535403111 & 0.310606267701556 \tabularnewline
23 & 0.75943098865341 & 0.481138022693179 & 0.240569011346590 \tabularnewline
24 & 0.698155808438477 & 0.603688383123046 & 0.301844191561523 \tabularnewline
25 & 0.632257905215796 & 0.735484189568408 & 0.367742094784204 \tabularnewline
26 & 0.67556684137588 & 0.64886631724824 & 0.32443315862412 \tabularnewline
27 & 0.614276576169619 & 0.771446847660761 & 0.385723423830381 \tabularnewline
28 & 0.563799888417966 & 0.872400223164068 & 0.436200111582034 \tabularnewline
29 & 0.501138558576353 & 0.997722882847294 & 0.498861441423647 \tabularnewline
30 & 0.521191951726215 & 0.95761609654757 & 0.478808048273785 \tabularnewline
31 & 0.701839794095751 & 0.596320411808497 & 0.298160205904249 \tabularnewline
32 & 0.665012059003457 & 0.669975881993085 & 0.334987940996543 \tabularnewline
33 & 0.632665535022916 & 0.734668929954167 & 0.367334464977083 \tabularnewline
34 & 0.677499959830684 & 0.645000080338631 & 0.322500040169316 \tabularnewline
35 & 0.641879201811494 & 0.716241596377011 & 0.358120798188506 \tabularnewline
36 & 0.607649647537517 & 0.784700704924966 & 0.392350352462483 \tabularnewline
37 & 0.602129748988718 & 0.795740502022563 & 0.397870251011282 \tabularnewline
38 & 0.661267635283085 & 0.67746472943383 & 0.338732364716915 \tabularnewline
39 & 0.613158769937377 & 0.773682460125245 & 0.386841230062623 \tabularnewline
40 & 0.577193041853473 & 0.845613916293054 & 0.422806958146527 \tabularnewline
41 & 0.528373732389966 & 0.943252535220069 & 0.471626267610034 \tabularnewline
42 & 0.51867836921461 & 0.96264326157078 & 0.48132163078539 \tabularnewline
43 & 0.475525896604225 & 0.95105179320845 & 0.524474103395775 \tabularnewline
44 & 0.424205051963235 & 0.84841010392647 & 0.575794948036765 \tabularnewline
45 & 0.381884302402740 & 0.763768604805481 & 0.61811569759726 \tabularnewline
46 & 0.331471297647056 & 0.662942595294112 & 0.668528702352944 \tabularnewline
47 & 0.287496774834937 & 0.574993549669874 & 0.712503225165063 \tabularnewline
48 & 0.390877900479821 & 0.781755800959642 & 0.609122099520179 \tabularnewline
49 & 0.341986307604583 & 0.683972615209165 & 0.658013692395417 \tabularnewline
50 & 0.328636372419606 & 0.657272744839212 & 0.671363627580394 \tabularnewline
51 & 0.298990946490515 & 0.59798189298103 & 0.701009053509485 \tabularnewline
52 & 0.364461527091621 & 0.728923054183243 & 0.635538472908379 \tabularnewline
53 & 0.331278010359398 & 0.662556020718796 & 0.668721989640602 \tabularnewline
54 & 0.332067148306613 & 0.664134296613227 & 0.667932851693387 \tabularnewline
55 & 0.287461438525094 & 0.574922877050188 & 0.712538561474906 \tabularnewline
56 & 0.246238663686031 & 0.492477327372062 & 0.753761336313969 \tabularnewline
57 & 0.209732743895579 & 0.419465487791159 & 0.79026725610442 \tabularnewline
58 & 0.206324929283367 & 0.412649858566733 & 0.793675070716633 \tabularnewline
59 & 0.381049200011986 & 0.762098400023972 & 0.618950799988014 \tabularnewline
60 & 0.335170618204117 & 0.670341236408235 & 0.664829381795883 \tabularnewline
61 & 0.302112272343655 & 0.604224544687311 & 0.697887727656345 \tabularnewline
62 & 0.263969812211942 & 0.527939624423884 & 0.736030187788058 \tabularnewline
63 & 0.314302389624287 & 0.628604779248574 & 0.685697610375713 \tabularnewline
64 & 0.287036858674757 & 0.574073717349515 & 0.712963141325243 \tabularnewline
65 & 0.249827130785926 & 0.499654261571853 & 0.750172869214074 \tabularnewline
66 & 0.214417744569918 & 0.428835489139835 & 0.785582255430082 \tabularnewline
67 & 0.220307346768124 & 0.440614693536248 & 0.779692653231876 \tabularnewline
68 & 0.186467072710080 & 0.372934145420159 & 0.81353292728992 \tabularnewline
69 & 0.164031220643572 & 0.328062441287143 & 0.835968779356429 \tabularnewline
70 & 0.454932838727764 & 0.909865677455529 & 0.545067161272236 \tabularnewline
71 & 0.409812775317801 & 0.819625550635603 & 0.590187224682199 \tabularnewline
72 & 0.36639894805875 & 0.7327978961175 & 0.63360105194125 \tabularnewline
73 & 0.337584577940799 & 0.675169155881597 & 0.662415422059201 \tabularnewline
74 & 0.457991320705653 & 0.915982641411306 & 0.542008679294347 \tabularnewline
75 & 0.419977311007081 & 0.839954622014162 & 0.580022688992919 \tabularnewline
76 & 0.407903753309977 & 0.815807506619955 & 0.592096246690023 \tabularnewline
77 & 0.413785803975965 & 0.82757160795193 & 0.586214196024035 \tabularnewline
78 & 0.368424788697396 & 0.736849577394792 & 0.631575211302604 \tabularnewline
79 & 0.367035237739052 & 0.734070475478104 & 0.632964762260948 \tabularnewline
80 & 0.331861000674946 & 0.663722001349893 & 0.668138999325054 \tabularnewline
81 & 0.295336120486016 & 0.590672240972032 & 0.704663879513984 \tabularnewline
82 & 0.272097884900502 & 0.544195769801003 & 0.727902115099498 \tabularnewline
83 & 0.281398798744628 & 0.562797597489256 & 0.718601201255372 \tabularnewline
84 & 0.259392943494830 & 0.518785886989660 & 0.74060705650517 \tabularnewline
85 & 0.242219490767259 & 0.484438981534517 & 0.757780509232741 \tabularnewline
86 & 0.257697519815525 & 0.51539503963105 & 0.742302480184475 \tabularnewline
87 & 0.275922905341536 & 0.551845810683073 & 0.724077094658464 \tabularnewline
88 & 0.305551735700861 & 0.611103471401723 & 0.694448264299139 \tabularnewline
89 & 0.267357535021409 & 0.534715070042818 & 0.732642464978591 \tabularnewline
90 & 0.234571133084293 & 0.469142266168587 & 0.765428866915707 \tabularnewline
91 & 0.270146250926026 & 0.540292501852051 & 0.729853749073974 \tabularnewline
92 & 0.240633529899204 & 0.481267059798409 & 0.759366470100796 \tabularnewline
93 & 0.208060289400303 & 0.416120578800606 & 0.791939710599697 \tabularnewline
94 & 0.218902808932398 & 0.437805617864796 & 0.781097191067602 \tabularnewline
95 & 0.194339437185819 & 0.388678874371639 & 0.80566056281418 \tabularnewline
96 & 0.199074517383941 & 0.398149034767882 & 0.800925482616059 \tabularnewline
97 & 0.183268095915686 & 0.366536191831371 & 0.816731904084314 \tabularnewline
98 & 0.217515388165898 & 0.435030776331796 & 0.782484611834102 \tabularnewline
99 & 0.228412157091936 & 0.456824314183872 & 0.771587842908064 \tabularnewline
100 & 0.248770574210544 & 0.497541148421088 & 0.751229425789456 \tabularnewline
101 & 0.212932283141011 & 0.425864566282021 & 0.78706771685899 \tabularnewline
102 & 0.185098109330496 & 0.370196218660992 & 0.814901890669504 \tabularnewline
103 & 0.210122691609742 & 0.420245383219484 & 0.789877308390258 \tabularnewline
104 & 0.204251649476032 & 0.408503298952065 & 0.795748350523968 \tabularnewline
105 & 0.182476204697326 & 0.364952409394653 & 0.817523795302674 \tabularnewline
106 & 0.154547609643945 & 0.30909521928789 & 0.845452390356055 \tabularnewline
107 & 0.134546527078346 & 0.269093054156691 & 0.865453472921655 \tabularnewline
108 & 0.110116738341792 & 0.220233476683584 & 0.889883261658208 \tabularnewline
109 & 0.111958448277658 & 0.223916896555317 & 0.888041551722342 \tabularnewline
110 & 0.340131954969683 & 0.680263909939367 & 0.659868045030317 \tabularnewline
111 & 0.318159515191705 & 0.63631903038341 & 0.681840484808295 \tabularnewline
112 & 0.310535262972548 & 0.621070525945096 & 0.689464737027452 \tabularnewline
113 & 0.277659468026099 & 0.555318936052198 & 0.722340531973901 \tabularnewline
114 & 0.235438326244052 & 0.470876652488105 & 0.764561673755948 \tabularnewline
115 & 0.204146615117632 & 0.408293230235265 & 0.795853384882368 \tabularnewline
116 & 0.170974072692954 & 0.341948145385909 & 0.829025927307046 \tabularnewline
117 & 0.139273778367050 & 0.278547556734099 & 0.86072622163295 \tabularnewline
118 & 0.171380838770516 & 0.342761677541031 & 0.828619161229484 \tabularnewline
119 & 0.150019750296178 & 0.300039500592357 & 0.849980249703822 \tabularnewline
120 & 0.124217470941768 & 0.248434941883536 & 0.875782529058232 \tabularnewline
121 & 0.097909927372255 & 0.19581985474451 & 0.902090072627745 \tabularnewline
122 & 0.0919249210132654 & 0.183849842026531 & 0.908075078986735 \tabularnewline
123 & 0.0705726563710108 & 0.141145312742022 & 0.92942734362899 \tabularnewline
124 & 0.0633689080904404 & 0.126737816180881 & 0.93663109190956 \tabularnewline
125 & 0.0530501968955802 & 0.106100393791160 & 0.94694980310442 \tabularnewline
126 & 0.0526621157411015 & 0.105324231482203 & 0.947337884258898 \tabularnewline
127 & 0.0377844618074192 & 0.0755689236148384 & 0.962215538192581 \tabularnewline
128 & 0.046259995195456 & 0.092519990390912 & 0.953740004804544 \tabularnewline
129 & 0.0602030153845756 & 0.120406030769151 & 0.939796984615424 \tabularnewline
130 & 0.322348276296455 & 0.644696552592911 & 0.677651723703545 \tabularnewline
131 & 0.264270569673596 & 0.528541139347192 & 0.735729430326404 \tabularnewline
132 & 0.209553802727002 & 0.419107605454003 & 0.790446197272998 \tabularnewline
133 & 0.388494368269311 & 0.776988736538621 & 0.611505631730689 \tabularnewline
134 & 0.362987501582569 & 0.725975003165138 & 0.637012498417431 \tabularnewline
135 & 0.303624538910124 & 0.607249077820249 & 0.696375461089876 \tabularnewline
136 & 0.282805992019736 & 0.565611984039472 & 0.717194007980264 \tabularnewline
137 & 0.215645392194042 & 0.431290784388085 & 0.784354607805958 \tabularnewline
138 & 0.158051546998318 & 0.316103093996637 & 0.841948453001682 \tabularnewline
139 & 0.130211236414436 & 0.260422472828871 & 0.869788763585564 \tabularnewline
140 & 0.100507098063979 & 0.201014196127958 & 0.899492901936021 \tabularnewline
141 & 0.072707013649217 & 0.145414027298434 & 0.927292986350783 \tabularnewline
142 & 0.0486336146706985 & 0.097267229341397 & 0.951366385329302 \tabularnewline
143 & 0.147018009191066 & 0.294036018382132 & 0.852981990808934 \tabularnewline
144 & 0.591898819402207 & 0.816202361195586 & 0.408101180597793 \tabularnewline
145 & 0.428247259168519 & 0.856494518337038 & 0.571752740831481 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&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]0.614183251547004[/C][C]0.771633496905992[/C][C]0.385816748452996[/C][/ROW]
[ROW][C]12[/C][C]0.457004356795727[/C][C]0.914008713591454[/C][C]0.542995643204273[/C][/ROW]
[ROW][C]13[/C][C]0.423487303203681[/C][C]0.846974606407362[/C][C]0.576512696796319[/C][/ROW]
[ROW][C]14[/C][C]0.518839327700192[/C][C]0.962321344599616[/C][C]0.481160672299808[/C][/ROW]
[ROW][C]15[/C][C]0.408997650337011[/C][C]0.817995300674023[/C][C]0.591002349662989[/C][/ROW]
[ROW][C]16[/C][C]0.312380412120605[/C][C]0.624760824241209[/C][C]0.687619587879395[/C][/ROW]
[ROW][C]17[/C][C]0.231212437541904[/C][C]0.462424875083808[/C][C]0.768787562458096[/C][/ROW]
[ROW][C]18[/C][C]0.312357243297686[/C][C]0.624714486595372[/C][C]0.687642756702314[/C][/ROW]
[ROW][C]19[/C][C]0.252483372581924[/C][C]0.504966745163848[/C][C]0.747516627418076[/C][/ROW]
[ROW][C]20[/C][C]0.276824932888765[/C][C]0.55364986577753[/C][C]0.723175067111235[/C][/ROW]
[ROW][C]21[/C][C]0.737028280539688[/C][C]0.525943438920624[/C][C]0.262971719460312[/C][/ROW]
[ROW][C]22[/C][C]0.689393732298444[/C][C]0.621212535403111[/C][C]0.310606267701556[/C][/ROW]
[ROW][C]23[/C][C]0.75943098865341[/C][C]0.481138022693179[/C][C]0.240569011346590[/C][/ROW]
[ROW][C]24[/C][C]0.698155808438477[/C][C]0.603688383123046[/C][C]0.301844191561523[/C][/ROW]
[ROW][C]25[/C][C]0.632257905215796[/C][C]0.735484189568408[/C][C]0.367742094784204[/C][/ROW]
[ROW][C]26[/C][C]0.67556684137588[/C][C]0.64886631724824[/C][C]0.32443315862412[/C][/ROW]
[ROW][C]27[/C][C]0.614276576169619[/C][C]0.771446847660761[/C][C]0.385723423830381[/C][/ROW]
[ROW][C]28[/C][C]0.563799888417966[/C][C]0.872400223164068[/C][C]0.436200111582034[/C][/ROW]
[ROW][C]29[/C][C]0.501138558576353[/C][C]0.997722882847294[/C][C]0.498861441423647[/C][/ROW]
[ROW][C]30[/C][C]0.521191951726215[/C][C]0.95761609654757[/C][C]0.478808048273785[/C][/ROW]
[ROW][C]31[/C][C]0.701839794095751[/C][C]0.596320411808497[/C][C]0.298160205904249[/C][/ROW]
[ROW][C]32[/C][C]0.665012059003457[/C][C]0.669975881993085[/C][C]0.334987940996543[/C][/ROW]
[ROW][C]33[/C][C]0.632665535022916[/C][C]0.734668929954167[/C][C]0.367334464977083[/C][/ROW]
[ROW][C]34[/C][C]0.677499959830684[/C][C]0.645000080338631[/C][C]0.322500040169316[/C][/ROW]
[ROW][C]35[/C][C]0.641879201811494[/C][C]0.716241596377011[/C][C]0.358120798188506[/C][/ROW]
[ROW][C]36[/C][C]0.607649647537517[/C][C]0.784700704924966[/C][C]0.392350352462483[/C][/ROW]
[ROW][C]37[/C][C]0.602129748988718[/C][C]0.795740502022563[/C][C]0.397870251011282[/C][/ROW]
[ROW][C]38[/C][C]0.661267635283085[/C][C]0.67746472943383[/C][C]0.338732364716915[/C][/ROW]
[ROW][C]39[/C][C]0.613158769937377[/C][C]0.773682460125245[/C][C]0.386841230062623[/C][/ROW]
[ROW][C]40[/C][C]0.577193041853473[/C][C]0.845613916293054[/C][C]0.422806958146527[/C][/ROW]
[ROW][C]41[/C][C]0.528373732389966[/C][C]0.943252535220069[/C][C]0.471626267610034[/C][/ROW]
[ROW][C]42[/C][C]0.51867836921461[/C][C]0.96264326157078[/C][C]0.48132163078539[/C][/ROW]
[ROW][C]43[/C][C]0.475525896604225[/C][C]0.95105179320845[/C][C]0.524474103395775[/C][/ROW]
[ROW][C]44[/C][C]0.424205051963235[/C][C]0.84841010392647[/C][C]0.575794948036765[/C][/ROW]
[ROW][C]45[/C][C]0.381884302402740[/C][C]0.763768604805481[/C][C]0.61811569759726[/C][/ROW]
[ROW][C]46[/C][C]0.331471297647056[/C][C]0.662942595294112[/C][C]0.668528702352944[/C][/ROW]
[ROW][C]47[/C][C]0.287496774834937[/C][C]0.574993549669874[/C][C]0.712503225165063[/C][/ROW]
[ROW][C]48[/C][C]0.390877900479821[/C][C]0.781755800959642[/C][C]0.609122099520179[/C][/ROW]
[ROW][C]49[/C][C]0.341986307604583[/C][C]0.683972615209165[/C][C]0.658013692395417[/C][/ROW]
[ROW][C]50[/C][C]0.328636372419606[/C][C]0.657272744839212[/C][C]0.671363627580394[/C][/ROW]
[ROW][C]51[/C][C]0.298990946490515[/C][C]0.59798189298103[/C][C]0.701009053509485[/C][/ROW]
[ROW][C]52[/C][C]0.364461527091621[/C][C]0.728923054183243[/C][C]0.635538472908379[/C][/ROW]
[ROW][C]53[/C][C]0.331278010359398[/C][C]0.662556020718796[/C][C]0.668721989640602[/C][/ROW]
[ROW][C]54[/C][C]0.332067148306613[/C][C]0.664134296613227[/C][C]0.667932851693387[/C][/ROW]
[ROW][C]55[/C][C]0.287461438525094[/C][C]0.574922877050188[/C][C]0.712538561474906[/C][/ROW]
[ROW][C]56[/C][C]0.246238663686031[/C][C]0.492477327372062[/C][C]0.753761336313969[/C][/ROW]
[ROW][C]57[/C][C]0.209732743895579[/C][C]0.419465487791159[/C][C]0.79026725610442[/C][/ROW]
[ROW][C]58[/C][C]0.206324929283367[/C][C]0.412649858566733[/C][C]0.793675070716633[/C][/ROW]
[ROW][C]59[/C][C]0.381049200011986[/C][C]0.762098400023972[/C][C]0.618950799988014[/C][/ROW]
[ROW][C]60[/C][C]0.335170618204117[/C][C]0.670341236408235[/C][C]0.664829381795883[/C][/ROW]
[ROW][C]61[/C][C]0.302112272343655[/C][C]0.604224544687311[/C][C]0.697887727656345[/C][/ROW]
[ROW][C]62[/C][C]0.263969812211942[/C][C]0.527939624423884[/C][C]0.736030187788058[/C][/ROW]
[ROW][C]63[/C][C]0.314302389624287[/C][C]0.628604779248574[/C][C]0.685697610375713[/C][/ROW]
[ROW][C]64[/C][C]0.287036858674757[/C][C]0.574073717349515[/C][C]0.712963141325243[/C][/ROW]
[ROW][C]65[/C][C]0.249827130785926[/C][C]0.499654261571853[/C][C]0.750172869214074[/C][/ROW]
[ROW][C]66[/C][C]0.214417744569918[/C][C]0.428835489139835[/C][C]0.785582255430082[/C][/ROW]
[ROW][C]67[/C][C]0.220307346768124[/C][C]0.440614693536248[/C][C]0.779692653231876[/C][/ROW]
[ROW][C]68[/C][C]0.186467072710080[/C][C]0.372934145420159[/C][C]0.81353292728992[/C][/ROW]
[ROW][C]69[/C][C]0.164031220643572[/C][C]0.328062441287143[/C][C]0.835968779356429[/C][/ROW]
[ROW][C]70[/C][C]0.454932838727764[/C][C]0.909865677455529[/C][C]0.545067161272236[/C][/ROW]
[ROW][C]71[/C][C]0.409812775317801[/C][C]0.819625550635603[/C][C]0.590187224682199[/C][/ROW]
[ROW][C]72[/C][C]0.36639894805875[/C][C]0.7327978961175[/C][C]0.63360105194125[/C][/ROW]
[ROW][C]73[/C][C]0.337584577940799[/C][C]0.675169155881597[/C][C]0.662415422059201[/C][/ROW]
[ROW][C]74[/C][C]0.457991320705653[/C][C]0.915982641411306[/C][C]0.542008679294347[/C][/ROW]
[ROW][C]75[/C][C]0.419977311007081[/C][C]0.839954622014162[/C][C]0.580022688992919[/C][/ROW]
[ROW][C]76[/C][C]0.407903753309977[/C][C]0.815807506619955[/C][C]0.592096246690023[/C][/ROW]
[ROW][C]77[/C][C]0.413785803975965[/C][C]0.82757160795193[/C][C]0.586214196024035[/C][/ROW]
[ROW][C]78[/C][C]0.368424788697396[/C][C]0.736849577394792[/C][C]0.631575211302604[/C][/ROW]
[ROW][C]79[/C][C]0.367035237739052[/C][C]0.734070475478104[/C][C]0.632964762260948[/C][/ROW]
[ROW][C]80[/C][C]0.331861000674946[/C][C]0.663722001349893[/C][C]0.668138999325054[/C][/ROW]
[ROW][C]81[/C][C]0.295336120486016[/C][C]0.590672240972032[/C][C]0.704663879513984[/C][/ROW]
[ROW][C]82[/C][C]0.272097884900502[/C][C]0.544195769801003[/C][C]0.727902115099498[/C][/ROW]
[ROW][C]83[/C][C]0.281398798744628[/C][C]0.562797597489256[/C][C]0.718601201255372[/C][/ROW]
[ROW][C]84[/C][C]0.259392943494830[/C][C]0.518785886989660[/C][C]0.74060705650517[/C][/ROW]
[ROW][C]85[/C][C]0.242219490767259[/C][C]0.484438981534517[/C][C]0.757780509232741[/C][/ROW]
[ROW][C]86[/C][C]0.257697519815525[/C][C]0.51539503963105[/C][C]0.742302480184475[/C][/ROW]
[ROW][C]87[/C][C]0.275922905341536[/C][C]0.551845810683073[/C][C]0.724077094658464[/C][/ROW]
[ROW][C]88[/C][C]0.305551735700861[/C][C]0.611103471401723[/C][C]0.694448264299139[/C][/ROW]
[ROW][C]89[/C][C]0.267357535021409[/C][C]0.534715070042818[/C][C]0.732642464978591[/C][/ROW]
[ROW][C]90[/C][C]0.234571133084293[/C][C]0.469142266168587[/C][C]0.765428866915707[/C][/ROW]
[ROW][C]91[/C][C]0.270146250926026[/C][C]0.540292501852051[/C][C]0.729853749073974[/C][/ROW]
[ROW][C]92[/C][C]0.240633529899204[/C][C]0.481267059798409[/C][C]0.759366470100796[/C][/ROW]
[ROW][C]93[/C][C]0.208060289400303[/C][C]0.416120578800606[/C][C]0.791939710599697[/C][/ROW]
[ROW][C]94[/C][C]0.218902808932398[/C][C]0.437805617864796[/C][C]0.781097191067602[/C][/ROW]
[ROW][C]95[/C][C]0.194339437185819[/C][C]0.388678874371639[/C][C]0.80566056281418[/C][/ROW]
[ROW][C]96[/C][C]0.199074517383941[/C][C]0.398149034767882[/C][C]0.800925482616059[/C][/ROW]
[ROW][C]97[/C][C]0.183268095915686[/C][C]0.366536191831371[/C][C]0.816731904084314[/C][/ROW]
[ROW][C]98[/C][C]0.217515388165898[/C][C]0.435030776331796[/C][C]0.782484611834102[/C][/ROW]
[ROW][C]99[/C][C]0.228412157091936[/C][C]0.456824314183872[/C][C]0.771587842908064[/C][/ROW]
[ROW][C]100[/C][C]0.248770574210544[/C][C]0.497541148421088[/C][C]0.751229425789456[/C][/ROW]
[ROW][C]101[/C][C]0.212932283141011[/C][C]0.425864566282021[/C][C]0.78706771685899[/C][/ROW]
[ROW][C]102[/C][C]0.185098109330496[/C][C]0.370196218660992[/C][C]0.814901890669504[/C][/ROW]
[ROW][C]103[/C][C]0.210122691609742[/C][C]0.420245383219484[/C][C]0.789877308390258[/C][/ROW]
[ROW][C]104[/C][C]0.204251649476032[/C][C]0.408503298952065[/C][C]0.795748350523968[/C][/ROW]
[ROW][C]105[/C][C]0.182476204697326[/C][C]0.364952409394653[/C][C]0.817523795302674[/C][/ROW]
[ROW][C]106[/C][C]0.154547609643945[/C][C]0.30909521928789[/C][C]0.845452390356055[/C][/ROW]
[ROW][C]107[/C][C]0.134546527078346[/C][C]0.269093054156691[/C][C]0.865453472921655[/C][/ROW]
[ROW][C]108[/C][C]0.110116738341792[/C][C]0.220233476683584[/C][C]0.889883261658208[/C][/ROW]
[ROW][C]109[/C][C]0.111958448277658[/C][C]0.223916896555317[/C][C]0.888041551722342[/C][/ROW]
[ROW][C]110[/C][C]0.340131954969683[/C][C]0.680263909939367[/C][C]0.659868045030317[/C][/ROW]
[ROW][C]111[/C][C]0.318159515191705[/C][C]0.63631903038341[/C][C]0.681840484808295[/C][/ROW]
[ROW][C]112[/C][C]0.310535262972548[/C][C]0.621070525945096[/C][C]0.689464737027452[/C][/ROW]
[ROW][C]113[/C][C]0.277659468026099[/C][C]0.555318936052198[/C][C]0.722340531973901[/C][/ROW]
[ROW][C]114[/C][C]0.235438326244052[/C][C]0.470876652488105[/C][C]0.764561673755948[/C][/ROW]
[ROW][C]115[/C][C]0.204146615117632[/C][C]0.408293230235265[/C][C]0.795853384882368[/C][/ROW]
[ROW][C]116[/C][C]0.170974072692954[/C][C]0.341948145385909[/C][C]0.829025927307046[/C][/ROW]
[ROW][C]117[/C][C]0.139273778367050[/C][C]0.278547556734099[/C][C]0.86072622163295[/C][/ROW]
[ROW][C]118[/C][C]0.171380838770516[/C][C]0.342761677541031[/C][C]0.828619161229484[/C][/ROW]
[ROW][C]119[/C][C]0.150019750296178[/C][C]0.300039500592357[/C][C]0.849980249703822[/C][/ROW]
[ROW][C]120[/C][C]0.124217470941768[/C][C]0.248434941883536[/C][C]0.875782529058232[/C][/ROW]
[ROW][C]121[/C][C]0.097909927372255[/C][C]0.19581985474451[/C][C]0.902090072627745[/C][/ROW]
[ROW][C]122[/C][C]0.0919249210132654[/C][C]0.183849842026531[/C][C]0.908075078986735[/C][/ROW]
[ROW][C]123[/C][C]0.0705726563710108[/C][C]0.141145312742022[/C][C]0.92942734362899[/C][/ROW]
[ROW][C]124[/C][C]0.0633689080904404[/C][C]0.126737816180881[/C][C]0.93663109190956[/C][/ROW]
[ROW][C]125[/C][C]0.0530501968955802[/C][C]0.106100393791160[/C][C]0.94694980310442[/C][/ROW]
[ROW][C]126[/C][C]0.0526621157411015[/C][C]0.105324231482203[/C][C]0.947337884258898[/C][/ROW]
[ROW][C]127[/C][C]0.0377844618074192[/C][C]0.0755689236148384[/C][C]0.962215538192581[/C][/ROW]
[ROW][C]128[/C][C]0.046259995195456[/C][C]0.092519990390912[/C][C]0.953740004804544[/C][/ROW]
[ROW][C]129[/C][C]0.0602030153845756[/C][C]0.120406030769151[/C][C]0.939796984615424[/C][/ROW]
[ROW][C]130[/C][C]0.322348276296455[/C][C]0.644696552592911[/C][C]0.677651723703545[/C][/ROW]
[ROW][C]131[/C][C]0.264270569673596[/C][C]0.528541139347192[/C][C]0.735729430326404[/C][/ROW]
[ROW][C]132[/C][C]0.209553802727002[/C][C]0.419107605454003[/C][C]0.790446197272998[/C][/ROW]
[ROW][C]133[/C][C]0.388494368269311[/C][C]0.776988736538621[/C][C]0.611505631730689[/C][/ROW]
[ROW][C]134[/C][C]0.362987501582569[/C][C]0.725975003165138[/C][C]0.637012498417431[/C][/ROW]
[ROW][C]135[/C][C]0.303624538910124[/C][C]0.607249077820249[/C][C]0.696375461089876[/C][/ROW]
[ROW][C]136[/C][C]0.282805992019736[/C][C]0.565611984039472[/C][C]0.717194007980264[/C][/ROW]
[ROW][C]137[/C][C]0.215645392194042[/C][C]0.431290784388085[/C][C]0.784354607805958[/C][/ROW]
[ROW][C]138[/C][C]0.158051546998318[/C][C]0.316103093996637[/C][C]0.841948453001682[/C][/ROW]
[ROW][C]139[/C][C]0.130211236414436[/C][C]0.260422472828871[/C][C]0.869788763585564[/C][/ROW]
[ROW][C]140[/C][C]0.100507098063979[/C][C]0.201014196127958[/C][C]0.899492901936021[/C][/ROW]
[ROW][C]141[/C][C]0.072707013649217[/C][C]0.145414027298434[/C][C]0.927292986350783[/C][/ROW]
[ROW][C]142[/C][C]0.0486336146706985[/C][C]0.097267229341397[/C][C]0.951366385329302[/C][/ROW]
[ROW][C]143[/C][C]0.147018009191066[/C][C]0.294036018382132[/C][C]0.852981990808934[/C][/ROW]
[ROW][C]144[/C][C]0.591898819402207[/C][C]0.816202361195586[/C][C]0.408101180597793[/C][/ROW]
[ROW][C]145[/C][C]0.428247259168519[/C][C]0.856494518337038[/C][C]0.571752740831481[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.6141832515470040.7716334969059920.385816748452996
120.4570043567957270.9140087135914540.542995643204273
130.4234873032036810.8469746064073620.576512696796319
140.5188393277001920.9623213445996160.481160672299808
150.4089976503370110.8179953006740230.591002349662989
160.3123804121206050.6247608242412090.687619587879395
170.2312124375419040.4624248750838080.768787562458096
180.3123572432976860.6247144865953720.687642756702314
190.2524833725819240.5049667451638480.747516627418076
200.2768249328887650.553649865777530.723175067111235
210.7370282805396880.5259434389206240.262971719460312
220.6893937322984440.6212125354031110.310606267701556
230.759430988653410.4811380226931790.240569011346590
240.6981558084384770.6036883831230460.301844191561523
250.6322579052157960.7354841895684080.367742094784204
260.675566841375880.648866317248240.32443315862412
270.6142765761696190.7714468476607610.385723423830381
280.5637998884179660.8724002231640680.436200111582034
290.5011385585763530.9977228828472940.498861441423647
300.5211919517262150.957616096547570.478808048273785
310.7018397940957510.5963204118084970.298160205904249
320.6650120590034570.6699758819930850.334987940996543
330.6326655350229160.7346689299541670.367334464977083
340.6774999598306840.6450000803386310.322500040169316
350.6418792018114940.7162415963770110.358120798188506
360.6076496475375170.7847007049249660.392350352462483
370.6021297489887180.7957405020225630.397870251011282
380.6612676352830850.677464729433830.338732364716915
390.6131587699373770.7736824601252450.386841230062623
400.5771930418534730.8456139162930540.422806958146527
410.5283737323899660.9432525352200690.471626267610034
420.518678369214610.962643261570780.48132163078539
430.4755258966042250.951051793208450.524474103395775
440.4242050519632350.848410103926470.575794948036765
450.3818843024027400.7637686048054810.61811569759726
460.3314712976470560.6629425952941120.668528702352944
470.2874967748349370.5749935496698740.712503225165063
480.3908779004798210.7817558009596420.609122099520179
490.3419863076045830.6839726152091650.658013692395417
500.3286363724196060.6572727448392120.671363627580394
510.2989909464905150.597981892981030.701009053509485
520.3644615270916210.7289230541832430.635538472908379
530.3312780103593980.6625560207187960.668721989640602
540.3320671483066130.6641342966132270.667932851693387
550.2874614385250940.5749228770501880.712538561474906
560.2462386636860310.4924773273720620.753761336313969
570.2097327438955790.4194654877911590.79026725610442
580.2063249292833670.4126498585667330.793675070716633
590.3810492000119860.7620984000239720.618950799988014
600.3351706182041170.6703412364082350.664829381795883
610.3021122723436550.6042245446873110.697887727656345
620.2639698122119420.5279396244238840.736030187788058
630.3143023896242870.6286047792485740.685697610375713
640.2870368586747570.5740737173495150.712963141325243
650.2498271307859260.4996542615718530.750172869214074
660.2144177445699180.4288354891398350.785582255430082
670.2203073467681240.4406146935362480.779692653231876
680.1864670727100800.3729341454201590.81353292728992
690.1640312206435720.3280624412871430.835968779356429
700.4549328387277640.9098656774555290.545067161272236
710.4098127753178010.8196255506356030.590187224682199
720.366398948058750.73279789611750.63360105194125
730.3375845779407990.6751691558815970.662415422059201
740.4579913207056530.9159826414113060.542008679294347
750.4199773110070810.8399546220141620.580022688992919
760.4079037533099770.8158075066199550.592096246690023
770.4137858039759650.827571607951930.586214196024035
780.3684247886973960.7368495773947920.631575211302604
790.3670352377390520.7340704754781040.632964762260948
800.3318610006749460.6637220013498930.668138999325054
810.2953361204860160.5906722409720320.704663879513984
820.2720978849005020.5441957698010030.727902115099498
830.2813987987446280.5627975974892560.718601201255372
840.2593929434948300.5187858869896600.74060705650517
850.2422194907672590.4844389815345170.757780509232741
860.2576975198155250.515395039631050.742302480184475
870.2759229053415360.5518458106830730.724077094658464
880.3055517357008610.6111034714017230.694448264299139
890.2673575350214090.5347150700428180.732642464978591
900.2345711330842930.4691422661685870.765428866915707
910.2701462509260260.5402925018520510.729853749073974
920.2406335298992040.4812670597984090.759366470100796
930.2080602894003030.4161205788006060.791939710599697
940.2189028089323980.4378056178647960.781097191067602
950.1943394371858190.3886788743716390.80566056281418
960.1990745173839410.3981490347678820.800925482616059
970.1832680959156860.3665361918313710.816731904084314
980.2175153881658980.4350307763317960.782484611834102
990.2284121570919360.4568243141838720.771587842908064
1000.2487705742105440.4975411484210880.751229425789456
1010.2129322831410110.4258645662820210.78706771685899
1020.1850981093304960.3701962186609920.814901890669504
1030.2101226916097420.4202453832194840.789877308390258
1040.2042516494760320.4085032989520650.795748350523968
1050.1824762046973260.3649524093946530.817523795302674
1060.1545476096439450.309095219287890.845452390356055
1070.1345465270783460.2690930541566910.865453472921655
1080.1101167383417920.2202334766835840.889883261658208
1090.1119584482776580.2239168965553170.888041551722342
1100.3401319549696830.6802639099393670.659868045030317
1110.3181595151917050.636319030383410.681840484808295
1120.3105352629725480.6210705259450960.689464737027452
1130.2776594680260990.5553189360521980.722340531973901
1140.2354383262440520.4708766524881050.764561673755948
1150.2041466151176320.4082932302352650.795853384882368
1160.1709740726929540.3419481453859090.829025927307046
1170.1392737783670500.2785475567340990.86072622163295
1180.1713808387705160.3427616775410310.828619161229484
1190.1500197502961780.3000395005923570.849980249703822
1200.1242174709417680.2484349418835360.875782529058232
1210.0979099273722550.195819854744510.902090072627745
1220.09192492101326540.1838498420265310.908075078986735
1230.07057265637101080.1411453127420220.92942734362899
1240.06336890809044040.1267378161808810.93663109190956
1250.05305019689558020.1061003937911600.94694980310442
1260.05266211574110150.1053242314822030.947337884258898
1270.03778446180741920.07556892361483840.962215538192581
1280.0462599951954560.0925199903909120.953740004804544
1290.06020301538457560.1204060307691510.939796984615424
1300.3223482762964550.6446965525929110.677651723703545
1310.2642705696735960.5285411393471920.735729430326404
1320.2095538027270020.4191076054540030.790446197272998
1330.3884943682693110.7769887365386210.611505631730689
1340.3629875015825690.7259750031651380.637012498417431
1350.3036245389101240.6072490778202490.696375461089876
1360.2828059920197360.5656119840394720.717194007980264
1370.2156453921940420.4312907843880850.784354607805958
1380.1580515469983180.3161030939966370.841948453001682
1390.1302112364144360.2604224728288710.869788763585564
1400.1005070980639790.2010141961279580.899492901936021
1410.0727070136492170.1454140272984340.927292986350783
1420.04863361467069850.0972672293413970.951366385329302
1430.1470180091910660.2940360183821320.852981990808934
1440.5918988194022070.8162023611955860.408101180597793
1450.4282472591685190.8564945183370380.571752740831481






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0222222222222222OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 3 & 0.0222222222222222 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103432&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]3[/C][C]0.0222222222222222[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103432&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103432&T=6

As an alternative you can also use a QR Code:  
QR Code


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level30.0222222222222222OK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}