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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 computationSun, 04 Nov 2012 11:09:09 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Nov/04/t13520456796pliqg4xjzwtz7e.htm/, Retrieved Fri, 03 May 2024 03:36:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185852, Retrieved Fri, 03 May 2024 03:36:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact96

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-    D  [Multiple Regression] [WS7 0299787] [2012-11-04 10:59:16] [a2dcdd13e1df6929c5c71aa45a46cc8e]
-   PD      [Multiple Regression] [WS7 0299787] [2012-11-04 16:09:09] [b443327ccd50424d9f9aaa9d8bba1a6f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12	53
39	32	16	11	18	11	83
30	35	19	15	11	14	66
31	33	15	6	12	12	67
34	37	14	13	16	21	76
35	29	13	10	18	12	78
39	31	19	12	14	22	53
34	36	15	14	14	11	80
36	35	14	12	15	10	74
37	38	15	9	15	13	76
38	31	16	10	17	10	79
36	34	16	12	19	8	54
38	35	16	12	10	15	67
39	38	16	11	16	14	54
33	37	17	15	18	10	87
32	33	15	12	14	14	58
36	32	15	10	14	14	75
38	38	20	12	17	11	88
39	38	18	11	14	10	64
32	32	16	12	16	13	57
32	33	16	11	18	9.5	66
31	31	16	12	11	14	68
39	38	19	13	14	12	54
37	39	16	11	12	14	56
39	32	17	12	17	11	86
41	32	17	13	9	9	80
36	35	16	10	16	11	76
33	37	15	14	14	15	69
33	33	16	12	15	14	78
34	33	14	10	11	13	67
31	31	15	12	16	9	80
27	32	12	8	13	15	54
37	31	14	10	17	10	71
34	37	16	12	15	11	84
34	30	14	12	14	13	74
32	33	10	7	16	8	71
29	31	10	9	9	20	63
36	33	14	12	15	12	71
29	31	16	10	17	10	76
35	33	16	10	13	10	69
37	32	16	10	15	9	74
34	33	14	12	16	14	75
38	32	20	15	16	8	54
35	33	14	10	12	14	52
38	28	14	10	15	11	69
37	35	11	12	11	13	68
38	39	14	13	15	9	65
33	34	15	11	15	11	75
36	38	16	11	17	15	74
38	32	14	12	13	11	75
32	38	16	14	16	10	72
32	30	14	10	14	14	67
32	33	12	12	11	18	63
34	38	16	13	12	14	62
32	32	9	5	12	11	63
37	35	14	6	15	14.5	76
39	34	16	12	16	13	74
29	34	16	12	15	9	67
37	36	15	11	12	10	73
35	34	16	10	12	15	70
30	28	12	7	8	20	53
38	34	16	12	13	12	77
34	35	16	14	11	12	80
31	35	14	11	14	14	52
34	31	16	12	15	13	54
35	37	17	13	10	11	80
36	35	18	14	11	17	66
30	27	18	11	12	12	73
39	40	12	12	15	13	63
35	37	16	12	15	14	69
38	36	10	8	14	13	67
31	38	14	11	16	15	54
34	39	18	14	15	13	81
38	41	18	14	15	10	69
34	27	16	12	13	11	84
39	30	17	9	12	19	80
37	37	16	13	17	13	70
34	31	16	11	13	17	69
28	31	13	12	15	13	77
37	27	16	12	13	9	54
33	36	16	12	15	11	79
35	37	16	12	15	9	71
37	33	15	12	16	12	73
32	34	15	11	15	12	72
33	31	16	10	14	13	77
38	39	14	9	15	13	75
33	34	16	12	14	12	69
29	32	16	12	13	15	54
33	33	15	12	7	22	70
31	36	12	9	17	13	73
36	32	17	15	13	15	54
35	41	16	12	15	13	77
32	28	15	12	14	15	82
29	30	13	12	13	12.5	80
39	36	16	10	16	11	80
37	35	16	13	12	16	69
35	31	16	9	14	11	78
37	34	16	12	17	11	81
32	36	14	10	15	10	76
38	36	16	14	17	10	76
37	35	16	11	12	16	73
36	37	20	15	16	12	85
32	28	15	11	11	11	66
33	39	16	11	15	16	79
40	32	13	12	9	19	68
38	35	17	12	16	11	76
41	39	16	12	15	16	71
36	35	16	11	10	15	54
43	42	12	7	10	24	46
30	34	16	12	15	14	85
31	33	16	14	11	15	74
32	41	17	11	13	11	88
32	33	13	11	14	15	38
37	34	12	10	18	12	76
37	32	18	13	16	10	86
33	40	14	13	14	14	54
34	40	14	8	14	13	67
33	35	13	11	14	9	69
38	36	16	12	14	15	90
33	37	13	11	12	15	54
31	27	16	13	14	14	76
38	39	13	12	15	11	89
37	38	16	14	15	8	76
36	31	15	13	15	11	73
31	33	16	15	13	11	79
39	32	15	10	17	8	90
44	39	17	11	17	10	74
33	36	15	9	19	11	81
35	33	12	11	15	13	72
32	33	16	10	13	11	71
28	32	10	11	9	20	66
40	37	16	8	15	10	77
27	30	12	11	15	15	65
37	38	14	12	15	12	74
32	29	15	12	16	14	85
28	22	13	9	11	23	54
34	35	15	11	14	14	63
30	35	11	10	11	16	54
35	34	12	8	15	11	64
31	35	11	9	13	12	69
32	34	16	8	15	10	54
30	37	15	9	16	14	84
30	35	17	15	14	12	86
31	23	16	11	15	12	77
40	31	10	8	16	11	89
32	27	18	13	16	12	76
36	36	13	12	11	13	60
32	31	16	12	12	11	75
35	32	13	9	9	19	73
38	39	10	7	16	12	85
42	37	15	13	13	17	79
34	38	16	9	16	9	71
35	39	16	6	12	12	72
38	34	14	8	9	19	69
33	31	10	8	13	18	78
36	32	17	15	13	15	54
32	37	13	6	14	14	69
33	36	15	9	19	11	81
34	32	16	11	13	9	84
32	38	12	8	12	18	84
34	36	13	8	13	16	69

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 10 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]10 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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 time10 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 6.53434064382781 + 0.0873193945853505Connected[t] -0.014045738475813Separate[t] + 0.519348248914638Software[t] + 0.0304668915436922Happiness[t] -0.0835180370613452Depression[t] + 0.0153373237416681Belonging[t] -0.00399108905256656t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  6.53434064382781 +  0.0873193945853505Connected[t] -0.014045738475813Separate[t] +  0.519348248914638Software[t] +  0.0304668915436922Happiness[t] -0.0835180370613452Depression[t] +  0.0153373237416681Belonging[t] -0.00399108905256656t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  6.53434064382781 +  0.0873193945853505Connected[t] -0.014045738475813Separate[t] +  0.519348248914638Software[t] +  0.0304668915436922Happiness[t] -0.0835180370613452Depression[t] +  0.0153373237416681Belonging[t] -0.00399108905256656t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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
Learning[t] = + 6.53434064382781 + 0.0873193945853505Connected[t] -0.014045738475813Separate[t] + 0.519348248914638Software[t] + 0.0304668915436922Happiness[t] -0.0835180370613452Depression[t] + 0.0153373237416681Belonging[t] -0.00399108905256656t + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.534340643827812.4444012.67320.0083290.004164
Connected0.08731939458535050.0433562.0140.0457620.022881
Separate-0.0140457384758130.041291-0.34020.7341970.367099
Software0.5193482489146380.0671237.737300
Happiness0.03046689154369220.0714690.42630.670490.335245
Depression-0.08351803706134520.052935-1.57770.1166910.058346
Belonging0.01533732374166810.0145041.05750.2919680.145984
t-0.003991089052566560.003081-1.29560.1970690.098535

\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) & 6.53434064382781 & 2.444401 & 2.6732 & 0.008329 & 0.004164 \tabularnewline
Connected & 0.0873193945853505 & 0.043356 & 2.014 & 0.045762 & 0.022881 \tabularnewline
Separate & -0.014045738475813 & 0.041291 & -0.3402 & 0.734197 & 0.367099 \tabularnewline
Software & 0.519348248914638 & 0.067123 & 7.7373 & 0 & 0 \tabularnewline
Happiness & 0.0304668915436922 & 0.071469 & 0.4263 & 0.67049 & 0.335245 \tabularnewline
Depression & -0.0835180370613452 & 0.052935 & -1.5777 & 0.116691 & 0.058346 \tabularnewline
Belonging & 0.0153373237416681 & 0.014504 & 1.0575 & 0.291968 & 0.145984 \tabularnewline
t & -0.00399108905256656 & 0.003081 & -1.2956 & 0.197069 & 0.098535 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&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]6.53434064382781[/C][C]2.444401[/C][C]2.6732[/C][C]0.008329[/C][C]0.004164[/C][/ROW]
[ROW][C]Connected[/C][C]0.0873193945853505[/C][C]0.043356[/C][C]2.014[/C][C]0.045762[/C][C]0.022881[/C][/ROW]
[ROW][C]Separate[/C][C]-0.014045738475813[/C][C]0.041291[/C][C]-0.3402[/C][C]0.734197[/C][C]0.367099[/C][/ROW]
[ROW][C]Software[/C][C]0.519348248914638[/C][C]0.067123[/C][C]7.7373[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.0304668915436922[/C][C]0.071469[/C][C]0.4263[/C][C]0.67049[/C][C]0.335245[/C][/ROW]
[ROW][C]Depression[/C][C]-0.0835180370613452[/C][C]0.052935[/C][C]-1.5777[/C][C]0.116691[/C][C]0.058346[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0153373237416681[/C][C]0.014504[/C][C]1.0575[/C][C]0.291968[/C][C]0.145984[/C][/ROW]
[ROW][C]t[/C][C]-0.00399108905256656[/C][C]0.003081[/C][C]-1.2956[/C][C]0.197069[/C][C]0.098535[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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)6.534340643827812.4444012.67320.0083290.004164
Connected0.08731939458535050.0433562.0140.0457620.022881
Separate-0.0140457384758130.041291-0.34020.7341970.367099
Software0.5193482489146380.0671237.737300
Happiness0.03046689154369220.0714690.42630.670490.335245
Depression-0.08351803706134520.052935-1.57770.1166910.058346
Belonging0.01533732374166810.0145041.05750.2919680.145984
t-0.003991089052566560.003081-1.29560.1970690.098535






Multiple Linear Regression - Regression Statistics
Multiple R0.613208100987728
R-squared0.376024175116976
Adjusted R-squared0.347476261560236
F-TEST (value)13.1716867633643
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value3.1907809727727e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71678491065182
Sum Squared Residuals450.944615704593

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.613208100987728 \tabularnewline
R-squared & 0.376024175116976 \tabularnewline
Adjusted R-squared & 0.347476261560236 \tabularnewline
F-TEST (value) & 13.1716867633643 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 153 \tabularnewline
p-value & 3.1907809727727e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.71678491065182 \tabularnewline
Sum Squared Residuals & 450.944615704593 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.613208100987728[/C][/ROW]
[ROW][C]R-squared[/C][C]0.376024175116976[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.347476261560236[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.1716867633643[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]153[/C][/ROW]
[ROW][C]p-value[/C][C]3.1907809727727e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.71678491065182[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]450.944615704593[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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 R0.613208100987728
R-squared0.376024175116976
Adjusted R-squared0.347476261560236
F-TEST (value)13.1716867633643
F-TEST (DF numerator)7
F-TEST (DF denominator)153
p-value3.1907809727727e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.71678491065182
Sum Squared Residuals450.944615704593






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.0460838528533-3.04608385285334
21616.0978854720565-0.0978854720564648
31916.61871875636862.38128124363138
41512.26884458802932.73115541197066
51415.6143076175297-1.61430761752971
61315.0952278482479-2.09522784824791
71915.11063832808453.88936167191552
81516.9713242202552-1.9713242202552
91416.1392821471749-2.1392821471749
101514.40254902683560.597450973164367
111615.46104561611020.538954383889829
121616.1235117839571-0.123511783957113
131615.62067067091850.379329329081526
141615.2094476897910.790552310209007
151717.6741165821685-0.674116582168503
161515.1802222027614-0.180222202761398
171514.76159243630510.238407563694872
182016.42800219785453.57199780214554
191815.61600384710282.38399615289716
201615.3074180814340.692581918565993
211615.26131583146810.738684168531906
221615.15902031359790.840979686402121
231916.31832667718252.68167332281752
241614.88965935292741.11034064707262
251716.54098375244340.459016247556615
261717.0622567007993-0.0622567007993019
271615.00636954836510.993630451634889
281516.287354596739-1.28735459673898
291615.55287080604040.447129193959562
301414.3904425234722-0.390442523472172
311515.87707304005-0.877073040049984
321212.4410863242391-0.44108632423913
331415.163223672435-1.16322367243501
341615.90662985509370.0933701449062522
351415.6500827322888-1.65008273228881
361013.265086391234-3.26508639123402
371012.727741817731-2.72774181773097
381415.8385839962544-1.8385839962544
391614.51740860014511.48259139985485
401614.78001356928661.21998643071339
411615.18584544673760.81415455326237
421415.5727609632611-1.57276096326111
432017.66916236156272.33083763843728
441414.1387756696789-0.138775669678916
451415.0696607461849-1.06966074618494
461115.6144856270065-4.61448562700648
471416.5709069707458-2.57090697074579
481515.1481882666103-0.148188266610253
491615.06149671851080.938503281489179
501416.0633090042108-2.06330900421075
511616.6187303550879-0.618730355087899
521414.1780196281422-0.178019628142177
531214.6837657036483-2.68376570364832
541615.65273467634940.347265323650569
55911.6694846725896-2.66948467258964
561412.57777434350911.42222565649092
571616.0036265752432-0.00362657524322894
581615.32268553084720.677314469152831
591515.3869151033687-0.386915103368729
601614.25342629665071.74657370334928
611211.53887566369250.46112433630749
621615.93448106905030.0655189309496812
631616.5909413491474-0.590941349147434
641414.2618668653366-0.26186686533658
651615.24002473894630.759975261053671
661716.17189889622650.828101103773526
671816.11730306441781.88269693558221
681814.69913511195783.3008648880422
691215.6722816230555-3.67228162305551
701615.36965607647760.630343923522355
711013.5866524120327-3.58665241203271
721414.195891330998-0.195891330997984
731816.54853435757361.45146564242639
741816.93223559619481.06776440380516
751615.82251880560930.177481194390736
761713.8949822443113.10501775568903
771616.1954946350854-0.195494635085416
781614.50384625714061.49615374285942
791315.0129915707567-2.01299157075666
801615.77143790597510.22856209402487
811615.56908839480520.430911605194758
821615.77002784063690.229972159363091
831515.8074459225013-0.807445922501287
841514.78765965784620.212340342153843
851614.3564786199951.64352138000496
861414.1571625912084-0.157162591208444
871615.30587517141970.69412482858025
881614.46961712212471.53038287787534
891514.27882744411280.7211725558872
901213.6023588239322-1.60235882393221
911716.62692436380830.373075636191673
921615.43188579041260.568114209587381
931515.2277147708315-0.227714770831527
941315.0813275746976-2.08132757469762
951615.04423723303750.955762766962504
961615.72952952739410.270470472605887
971614.14624948948471.85375051051533
981615.97021736677540.0297826332246418
991414.4087389652808-0.408738965280757
1001617.0669910224862-1.06699102248623
1011614.73222687926871.26777312073132
1022017.33020551365792.66979448634215
1031514.66572992513880.334270074861162
1041614.49821769694741.50178230305259
1051315.1210647666331-2.12106476663309
1061715.90440880021231.09559119978767
1071615.58144924545380.418550754546195
1081614.34814496419761.65185503580237
1091211.90531554876780.0946844512321837
1101615.06094993674240.939050063257641
1111615.82292431418580.17707568581423
1121714.84557042888432.15442957111575
1131313.8834738038531-0.883473803853096
1141214.737925679879-2.73792567987902
1151816.5795463429741.42045365702602
1161415.2281114767074-1.22811147670737
1171412.997601783371.00239821663001
1181314.8993115345838-1.89931153458377
1191615.65819550510370.341804494896259
1201314.0711360179465-1.07113601794653
1211615.55353296477610.446467035223899
1221315.953286738566-2.95328673856601
1231616.9658873937755-0.96588739377553
1241516.1569827481446-1.15698274814463
1251616.7580898664056-0.758089866405582
1261515.4110906664556-0.411090666455593
1271715.85229137592431.14770862407565
1281513.97600467624881.0239953237512
1291214.8005465356512-2.80054653565118
1301614.10601398122161.89398601877844
1311013.3359227827828-3.33592278278283
1321613.93818327066542.06181672933456
1331213.8537668978712-1.85376689787118
1341415.5185421206393-1.5185421206393
1351515.2365070835217-0.236507083521653
1361312.04406001145220.955939988547819
1371514.40118610941360.598813890586378
1381113.1320965306762-2.13209653067624
1391213.2328826440951-1.23288264409513
1401113.3171512856996-2.3171512856996
1411612.89308708187863.10691291812139
1421513.34818269269091.6518173073091
1431716.62514951259640.374850487403569
1441614.69206466204911.3079353379509
1451014.1015702832193-4.10157028321932
1461815.76904499025742.23095500974264
1471314.9873219097027-1.98732190970275
1481615.13184475647920.868155243520752
1491313.0275017473578-0.0275017473578297
1501013.1303945600366-3.13039456003663
1511516.0188472173771-1.01884721737711
1521613.86170861869592.13829138130414
1531612.01586208539183.98413791460822
1541412.66071546501811.33928453498189
1551012.6056861353774-2.60568613537736
1561716.36750357539150.6324964246085
1571311.61391676011681.38608323988322
1581513.85627200467181.14372799532819
1591615.06472645802270.93527354197734
1601212.4616481771048-0.461648177104801
1611312.62783046371590.372169536284078

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.0460838528533 & -3.04608385285334 \tabularnewline
2 & 16 & 16.0978854720565 & -0.0978854720564648 \tabularnewline
3 & 19 & 16.6187187563686 & 2.38128124363138 \tabularnewline
4 & 15 & 12.2688445880293 & 2.73115541197066 \tabularnewline
5 & 14 & 15.6143076175297 & -1.61430761752971 \tabularnewline
6 & 13 & 15.0952278482479 & -2.09522784824791 \tabularnewline
7 & 19 & 15.1106383280845 & 3.88936167191552 \tabularnewline
8 & 15 & 16.9713242202552 & -1.9713242202552 \tabularnewline
9 & 14 & 16.1392821471749 & -2.1392821471749 \tabularnewline
10 & 15 & 14.4025490268356 & 0.597450973164367 \tabularnewline
11 & 16 & 15.4610456161102 & 0.538954383889829 \tabularnewline
12 & 16 & 16.1235117839571 & -0.123511783957113 \tabularnewline
13 & 16 & 15.6206706709185 & 0.379329329081526 \tabularnewline
14 & 16 & 15.209447689791 & 0.790552310209007 \tabularnewline
15 & 17 & 17.6741165821685 & -0.674116582168503 \tabularnewline
16 & 15 & 15.1802222027614 & -0.180222202761398 \tabularnewline
17 & 15 & 14.7615924363051 & 0.238407563694872 \tabularnewline
18 & 20 & 16.4280021978545 & 3.57199780214554 \tabularnewline
19 & 18 & 15.6160038471028 & 2.38399615289716 \tabularnewline
20 & 16 & 15.307418081434 & 0.692581918565993 \tabularnewline
21 & 16 & 15.2613158314681 & 0.738684168531906 \tabularnewline
22 & 16 & 15.1590203135979 & 0.840979686402121 \tabularnewline
23 & 19 & 16.3183266771825 & 2.68167332281752 \tabularnewline
24 & 16 & 14.8896593529274 & 1.11034064707262 \tabularnewline
25 & 17 & 16.5409837524434 & 0.459016247556615 \tabularnewline
26 & 17 & 17.0622567007993 & -0.0622567007993019 \tabularnewline
27 & 16 & 15.0063695483651 & 0.993630451634889 \tabularnewline
28 & 15 & 16.287354596739 & -1.28735459673898 \tabularnewline
29 & 16 & 15.5528708060404 & 0.447129193959562 \tabularnewline
30 & 14 & 14.3904425234722 & -0.390442523472172 \tabularnewline
31 & 15 & 15.87707304005 & -0.877073040049984 \tabularnewline
32 & 12 & 12.4410863242391 & -0.44108632423913 \tabularnewline
33 & 14 & 15.163223672435 & -1.16322367243501 \tabularnewline
34 & 16 & 15.9066298550937 & 0.0933701449062522 \tabularnewline
35 & 14 & 15.6500827322888 & -1.65008273228881 \tabularnewline
36 & 10 & 13.265086391234 & -3.26508639123402 \tabularnewline
37 & 10 & 12.727741817731 & -2.72774181773097 \tabularnewline
38 & 14 & 15.8385839962544 & -1.8385839962544 \tabularnewline
39 & 16 & 14.5174086001451 & 1.48259139985485 \tabularnewline
40 & 16 & 14.7800135692866 & 1.21998643071339 \tabularnewline
41 & 16 & 15.1858454467376 & 0.81415455326237 \tabularnewline
42 & 14 & 15.5727609632611 & -1.57276096326111 \tabularnewline
43 & 20 & 17.6691623615627 & 2.33083763843728 \tabularnewline
44 & 14 & 14.1387756696789 & -0.138775669678916 \tabularnewline
45 & 14 & 15.0696607461849 & -1.06966074618494 \tabularnewline
46 & 11 & 15.6144856270065 & -4.61448562700648 \tabularnewline
47 & 14 & 16.5709069707458 & -2.57090697074579 \tabularnewline
48 & 15 & 15.1481882666103 & -0.148188266610253 \tabularnewline
49 & 16 & 15.0614967185108 & 0.938503281489179 \tabularnewline
50 & 14 & 16.0633090042108 & -2.06330900421075 \tabularnewline
51 & 16 & 16.6187303550879 & -0.618730355087899 \tabularnewline
52 & 14 & 14.1780196281422 & -0.178019628142177 \tabularnewline
53 & 12 & 14.6837657036483 & -2.68376570364832 \tabularnewline
54 & 16 & 15.6527346763494 & 0.347265323650569 \tabularnewline
55 & 9 & 11.6694846725896 & -2.66948467258964 \tabularnewline
56 & 14 & 12.5777743435091 & 1.42222565649092 \tabularnewline
57 & 16 & 16.0036265752432 & -0.00362657524322894 \tabularnewline
58 & 16 & 15.3226855308472 & 0.677314469152831 \tabularnewline
59 & 15 & 15.3869151033687 & -0.386915103368729 \tabularnewline
60 & 16 & 14.2534262966507 & 1.74657370334928 \tabularnewline
61 & 12 & 11.5388756636925 & 0.46112433630749 \tabularnewline
62 & 16 & 15.9344810690503 & 0.0655189309496812 \tabularnewline
63 & 16 & 16.5909413491474 & -0.590941349147434 \tabularnewline
64 & 14 & 14.2618668653366 & -0.26186686533658 \tabularnewline
65 & 16 & 15.2400247389463 & 0.759975261053671 \tabularnewline
66 & 17 & 16.1718988962265 & 0.828101103773526 \tabularnewline
67 & 18 & 16.1173030644178 & 1.88269693558221 \tabularnewline
68 & 18 & 14.6991351119578 & 3.3008648880422 \tabularnewline
69 & 12 & 15.6722816230555 & -3.67228162305551 \tabularnewline
70 & 16 & 15.3696560764776 & 0.630343923522355 \tabularnewline
71 & 10 & 13.5866524120327 & -3.58665241203271 \tabularnewline
72 & 14 & 14.195891330998 & -0.195891330997984 \tabularnewline
73 & 18 & 16.5485343575736 & 1.45146564242639 \tabularnewline
74 & 18 & 16.9322355961948 & 1.06776440380516 \tabularnewline
75 & 16 & 15.8225188056093 & 0.177481194390736 \tabularnewline
76 & 17 & 13.894982244311 & 3.10501775568903 \tabularnewline
77 & 16 & 16.1954946350854 & -0.195494635085416 \tabularnewline
78 & 16 & 14.5038462571406 & 1.49615374285942 \tabularnewline
79 & 13 & 15.0129915707567 & -2.01299157075666 \tabularnewline
80 & 16 & 15.7714379059751 & 0.22856209402487 \tabularnewline
81 & 16 & 15.5690883948052 & 0.430911605194758 \tabularnewline
82 & 16 & 15.7700278406369 & 0.229972159363091 \tabularnewline
83 & 15 & 15.8074459225013 & -0.807445922501287 \tabularnewline
84 & 15 & 14.7876596578462 & 0.212340342153843 \tabularnewline
85 & 16 & 14.356478619995 & 1.64352138000496 \tabularnewline
86 & 14 & 14.1571625912084 & -0.157162591208444 \tabularnewline
87 & 16 & 15.3058751714197 & 0.69412482858025 \tabularnewline
88 & 16 & 14.4696171221247 & 1.53038287787534 \tabularnewline
89 & 15 & 14.2788274441128 & 0.7211725558872 \tabularnewline
90 & 12 & 13.6023588239322 & -1.60235882393221 \tabularnewline
91 & 17 & 16.6269243638083 & 0.373075636191673 \tabularnewline
92 & 16 & 15.4318857904126 & 0.568114209587381 \tabularnewline
93 & 15 & 15.2277147708315 & -0.227714770831527 \tabularnewline
94 & 13 & 15.0813275746976 & -2.08132757469762 \tabularnewline
95 & 16 & 15.0442372330375 & 0.955762766962504 \tabularnewline
96 & 16 & 15.7295295273941 & 0.270470472605887 \tabularnewline
97 & 16 & 14.1462494894847 & 1.85375051051533 \tabularnewline
98 & 16 & 15.9702173667754 & 0.0297826332246418 \tabularnewline
99 & 14 & 14.4087389652808 & -0.408738965280757 \tabularnewline
100 & 16 & 17.0669910224862 & -1.06699102248623 \tabularnewline
101 & 16 & 14.7322268792687 & 1.26777312073132 \tabularnewline
102 & 20 & 17.3302055136579 & 2.66979448634215 \tabularnewline
103 & 15 & 14.6657299251388 & 0.334270074861162 \tabularnewline
104 & 16 & 14.4982176969474 & 1.50178230305259 \tabularnewline
105 & 13 & 15.1210647666331 & -2.12106476663309 \tabularnewline
106 & 17 & 15.9044088002123 & 1.09559119978767 \tabularnewline
107 & 16 & 15.5814492454538 & 0.418550754546195 \tabularnewline
108 & 16 & 14.3481449641976 & 1.65185503580237 \tabularnewline
109 & 12 & 11.9053155487678 & 0.0946844512321837 \tabularnewline
110 & 16 & 15.0609499367424 & 0.939050063257641 \tabularnewline
111 & 16 & 15.8229243141858 & 0.17707568581423 \tabularnewline
112 & 17 & 14.8455704288843 & 2.15442957111575 \tabularnewline
113 & 13 & 13.8834738038531 & -0.883473803853096 \tabularnewline
114 & 12 & 14.737925679879 & -2.73792567987902 \tabularnewline
115 & 18 & 16.579546342974 & 1.42045365702602 \tabularnewline
116 & 14 & 15.2281114767074 & -1.22811147670737 \tabularnewline
117 & 14 & 12.99760178337 & 1.00239821663001 \tabularnewline
118 & 13 & 14.8993115345838 & -1.89931153458377 \tabularnewline
119 & 16 & 15.6581955051037 & 0.341804494896259 \tabularnewline
120 & 13 & 14.0711360179465 & -1.07113601794653 \tabularnewline
121 & 16 & 15.5535329647761 & 0.446467035223899 \tabularnewline
122 & 13 & 15.953286738566 & -2.95328673856601 \tabularnewline
123 & 16 & 16.9658873937755 & -0.96588739377553 \tabularnewline
124 & 15 & 16.1569827481446 & -1.15698274814463 \tabularnewline
125 & 16 & 16.7580898664056 & -0.758089866405582 \tabularnewline
126 & 15 & 15.4110906664556 & -0.411090666455593 \tabularnewline
127 & 17 & 15.8522913759243 & 1.14770862407565 \tabularnewline
128 & 15 & 13.9760046762488 & 1.0239953237512 \tabularnewline
129 & 12 & 14.8005465356512 & -2.80054653565118 \tabularnewline
130 & 16 & 14.1060139812216 & 1.89398601877844 \tabularnewline
131 & 10 & 13.3359227827828 & -3.33592278278283 \tabularnewline
132 & 16 & 13.9381832706654 & 2.06181672933456 \tabularnewline
133 & 12 & 13.8537668978712 & -1.85376689787118 \tabularnewline
134 & 14 & 15.5185421206393 & -1.5185421206393 \tabularnewline
135 & 15 & 15.2365070835217 & -0.236507083521653 \tabularnewline
136 & 13 & 12.0440600114522 & 0.955939988547819 \tabularnewline
137 & 15 & 14.4011861094136 & 0.598813890586378 \tabularnewline
138 & 11 & 13.1320965306762 & -2.13209653067624 \tabularnewline
139 & 12 & 13.2328826440951 & -1.23288264409513 \tabularnewline
140 & 11 & 13.3171512856996 & -2.3171512856996 \tabularnewline
141 & 16 & 12.8930870818786 & 3.10691291812139 \tabularnewline
142 & 15 & 13.3481826926909 & 1.6518173073091 \tabularnewline
143 & 17 & 16.6251495125964 & 0.374850487403569 \tabularnewline
144 & 16 & 14.6920646620491 & 1.3079353379509 \tabularnewline
145 & 10 & 14.1015702832193 & -4.10157028321932 \tabularnewline
146 & 18 & 15.7690449902574 & 2.23095500974264 \tabularnewline
147 & 13 & 14.9873219097027 & -1.98732190970275 \tabularnewline
148 & 16 & 15.1318447564792 & 0.868155243520752 \tabularnewline
149 & 13 & 13.0275017473578 & -0.0275017473578297 \tabularnewline
150 & 10 & 13.1303945600366 & -3.13039456003663 \tabularnewline
151 & 15 & 16.0188472173771 & -1.01884721737711 \tabularnewline
152 & 16 & 13.8617086186959 & 2.13829138130414 \tabularnewline
153 & 16 & 12.0158620853918 & 3.98413791460822 \tabularnewline
154 & 14 & 12.6607154650181 & 1.33928453498189 \tabularnewline
155 & 10 & 12.6056861353774 & -2.60568613537736 \tabularnewline
156 & 17 & 16.3675035753915 & 0.6324964246085 \tabularnewline
157 & 13 & 11.6139167601168 & 1.38608323988322 \tabularnewline
158 & 15 & 13.8562720046718 & 1.14372799532819 \tabularnewline
159 & 16 & 15.0647264580227 & 0.93527354197734 \tabularnewline
160 & 12 & 12.4616481771048 & -0.461648177104801 \tabularnewline
161 & 13 & 12.6278304637159 & 0.372169536284078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.0460838528533[/C][C]-3.04608385285334[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]16.0978854720565[/C][C]-0.0978854720564648[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.6187187563686[/C][C]2.38128124363138[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]12.2688445880293[/C][C]2.73115541197066[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.6143076175297[/C][C]-1.61430761752971[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0952278482479[/C][C]-2.09522784824791[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.1106383280845[/C][C]3.88936167191552[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.9713242202552[/C][C]-1.9713242202552[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]16.1392821471749[/C][C]-2.1392821471749[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.4025490268356[/C][C]0.597450973164367[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.4610456161102[/C][C]0.538954383889829[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.1235117839571[/C][C]-0.123511783957113[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.6206706709185[/C][C]0.379329329081526[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.209447689791[/C][C]0.790552310209007[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.6741165821685[/C][C]-0.674116582168503[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1802222027614[/C][C]-0.180222202761398[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.7615924363051[/C][C]0.238407563694872[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.4280021978545[/C][C]3.57199780214554[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.6160038471028[/C][C]2.38399615289716[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.307418081434[/C][C]0.692581918565993[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.2613158314681[/C][C]0.738684168531906[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]15.1590203135979[/C][C]0.840979686402121[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.3183266771825[/C][C]2.68167332281752[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8896593529274[/C][C]1.11034064707262[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]16.5409837524434[/C][C]0.459016247556615[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]17.0622567007993[/C][C]-0.0622567007993019[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0063695483651[/C][C]0.993630451634889[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.287354596739[/C][C]-1.28735459673898[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.5528708060404[/C][C]0.447129193959562[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.3904425234722[/C][C]-0.390442523472172[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.87707304005[/C][C]-0.877073040049984[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.4410863242391[/C][C]-0.44108632423913[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.163223672435[/C][C]-1.16322367243501[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.9066298550937[/C][C]0.0933701449062522[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.6500827322888[/C][C]-1.65008273228881[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.265086391234[/C][C]-3.26508639123402[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]12.727741817731[/C][C]-2.72774181773097[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.8385839962544[/C][C]-1.8385839962544[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.5174086001451[/C][C]1.48259139985485[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.7800135692866[/C][C]1.21998643071339[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.1858454467376[/C][C]0.81415455326237[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.5727609632611[/C][C]-1.57276096326111[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.6691623615627[/C][C]2.33083763843728[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.1387756696789[/C][C]-0.138775669678916[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.0696607461849[/C][C]-1.06966074618494[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.6144856270065[/C][C]-4.61448562700648[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.5709069707458[/C][C]-2.57090697074579[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.1481882666103[/C][C]-0.148188266610253[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.0614967185108[/C][C]0.938503281489179[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]16.0633090042108[/C][C]-2.06330900421075[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.6187303550879[/C][C]-0.618730355087899[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1780196281422[/C][C]-0.178019628142177[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.6837657036483[/C][C]-2.68376570364832[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.6527346763494[/C][C]0.347265323650569[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.6694846725896[/C][C]-2.66948467258964[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.5777743435091[/C][C]1.42222565649092[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.0036265752432[/C][C]-0.00362657524322894[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]15.3226855308472[/C][C]0.677314469152831[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.3869151033687[/C][C]-0.386915103368729[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.2534262966507[/C][C]1.74657370334928[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5388756636925[/C][C]0.46112433630749[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.9344810690503[/C][C]0.0655189309496812[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.5909413491474[/C][C]-0.590941349147434[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.2618668653366[/C][C]-0.26186686533658[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.2400247389463[/C][C]0.759975261053671[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]16.1718988962265[/C][C]0.828101103773526[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.1173030644178[/C][C]1.88269693558221[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.6991351119578[/C][C]3.3008648880422[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.6722816230555[/C][C]-3.67228162305551[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.3696560764776[/C][C]0.630343923522355[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5866524120327[/C][C]-3.58665241203271[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.195891330998[/C][C]-0.195891330997984[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5485343575736[/C][C]1.45146564242639[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.9322355961948[/C][C]1.06776440380516[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.8225188056093[/C][C]0.177481194390736[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]13.894982244311[/C][C]3.10501775568903[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.1954946350854[/C][C]-0.195494635085416[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.5038462571406[/C][C]1.49615374285942[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]15.0129915707567[/C][C]-2.01299157075666[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.7714379059751[/C][C]0.22856209402487[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.5690883948052[/C][C]0.430911605194758[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]15.7700278406369[/C][C]0.229972159363091[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.8074459225013[/C][C]-0.807445922501287[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.7876596578462[/C][C]0.212340342153843[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]14.356478619995[/C][C]1.64352138000496[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1571625912084[/C][C]-0.157162591208444[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.3058751714197[/C][C]0.69412482858025[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.4696171221247[/C][C]1.53038287787534[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]14.2788274441128[/C][C]0.7211725558872[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.6023588239322[/C][C]-1.60235882393221[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]16.6269243638083[/C][C]0.373075636191673[/C][/ROW]
[ROW][C]92[/C][C]16[/C][C]15.4318857904126[/C][C]0.568114209587381[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]15.2277147708315[/C][C]-0.227714770831527[/C][/ROW]
[ROW][C]94[/C][C]13[/C][C]15.0813275746976[/C][C]-2.08132757469762[/C][/ROW]
[ROW][C]95[/C][C]16[/C][C]15.0442372330375[/C][C]0.955762766962504[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.7295295273941[/C][C]0.270470472605887[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.1462494894847[/C][C]1.85375051051533[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.9702173667754[/C][C]0.0297826332246418[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]14.4087389652808[/C][C]-0.408738965280757[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]17.0669910224862[/C][C]-1.06699102248623[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]14.7322268792687[/C][C]1.26777312073132[/C][/ROW]
[ROW][C]102[/C][C]20[/C][C]17.3302055136579[/C][C]2.66979448634215[/C][/ROW]
[ROW][C]103[/C][C]15[/C][C]14.6657299251388[/C][C]0.334270074861162[/C][/ROW]
[ROW][C]104[/C][C]16[/C][C]14.4982176969474[/C][C]1.50178230305259[/C][/ROW]
[ROW][C]105[/C][C]13[/C][C]15.1210647666331[/C][C]-2.12106476663309[/C][/ROW]
[ROW][C]106[/C][C]17[/C][C]15.9044088002123[/C][C]1.09559119978767[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.5814492454538[/C][C]0.418550754546195[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]14.3481449641976[/C][C]1.65185503580237[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]11.9053155487678[/C][C]0.0946844512321837[/C][/ROW]
[ROW][C]110[/C][C]16[/C][C]15.0609499367424[/C][C]0.939050063257641[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.8229243141858[/C][C]0.17707568581423[/C][/ROW]
[ROW][C]112[/C][C]17[/C][C]14.8455704288843[/C][C]2.15442957111575[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]13.8834738038531[/C][C]-0.883473803853096[/C][/ROW]
[ROW][C]114[/C][C]12[/C][C]14.737925679879[/C][C]-2.73792567987902[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]16.579546342974[/C][C]1.42045365702602[/C][/ROW]
[ROW][C]116[/C][C]14[/C][C]15.2281114767074[/C][C]-1.22811147670737[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]12.99760178337[/C][C]1.00239821663001[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]14.8993115345838[/C][C]-1.89931153458377[/C][/ROW]
[ROW][C]119[/C][C]16[/C][C]15.6581955051037[/C][C]0.341804494896259[/C][/ROW]
[ROW][C]120[/C][C]13[/C][C]14.0711360179465[/C][C]-1.07113601794653[/C][/ROW]
[ROW][C]121[/C][C]16[/C][C]15.5535329647761[/C][C]0.446467035223899[/C][/ROW]
[ROW][C]122[/C][C]13[/C][C]15.953286738566[/C][C]-2.95328673856601[/C][/ROW]
[ROW][C]123[/C][C]16[/C][C]16.9658873937755[/C][C]-0.96588739377553[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1569827481446[/C][C]-1.15698274814463[/C][/ROW]
[ROW][C]125[/C][C]16[/C][C]16.7580898664056[/C][C]-0.758089866405582[/C][/ROW]
[ROW][C]126[/C][C]15[/C][C]15.4110906664556[/C][C]-0.411090666455593[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.8522913759243[/C][C]1.14770862407565[/C][/ROW]
[ROW][C]128[/C][C]15[/C][C]13.9760046762488[/C][C]1.0239953237512[/C][/ROW]
[ROW][C]129[/C][C]12[/C][C]14.8005465356512[/C][C]-2.80054653565118[/C][/ROW]
[ROW][C]130[/C][C]16[/C][C]14.1060139812216[/C][C]1.89398601877844[/C][/ROW]
[ROW][C]131[/C][C]10[/C][C]13.3359227827828[/C][C]-3.33592278278283[/C][/ROW]
[ROW][C]132[/C][C]16[/C][C]13.9381832706654[/C][C]2.06181672933456[/C][/ROW]
[ROW][C]133[/C][C]12[/C][C]13.8537668978712[/C][C]-1.85376689787118[/C][/ROW]
[ROW][C]134[/C][C]14[/C][C]15.5185421206393[/C][C]-1.5185421206393[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]15.2365070835217[/C][C]-0.236507083521653[/C][/ROW]
[ROW][C]136[/C][C]13[/C][C]12.0440600114522[/C][C]0.955939988547819[/C][/ROW]
[ROW][C]137[/C][C]15[/C][C]14.4011861094136[/C][C]0.598813890586378[/C][/ROW]
[ROW][C]138[/C][C]11[/C][C]13.1320965306762[/C][C]-2.13209653067624[/C][/ROW]
[ROW][C]139[/C][C]12[/C][C]13.2328826440951[/C][C]-1.23288264409513[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]13.3171512856996[/C][C]-2.3171512856996[/C][/ROW]
[ROW][C]141[/C][C]16[/C][C]12.8930870818786[/C][C]3.10691291812139[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]13.3481826926909[/C][C]1.6518173073091[/C][/ROW]
[ROW][C]143[/C][C]17[/C][C]16.6251495125964[/C][C]0.374850487403569[/C][/ROW]
[ROW][C]144[/C][C]16[/C][C]14.6920646620491[/C][C]1.3079353379509[/C][/ROW]
[ROW][C]145[/C][C]10[/C][C]14.1015702832193[/C][C]-4.10157028321932[/C][/ROW]
[ROW][C]146[/C][C]18[/C][C]15.7690449902574[/C][C]2.23095500974264[/C][/ROW]
[ROW][C]147[/C][C]13[/C][C]14.9873219097027[/C][C]-1.98732190970275[/C][/ROW]
[ROW][C]148[/C][C]16[/C][C]15.1318447564792[/C][C]0.868155243520752[/C][/ROW]
[ROW][C]149[/C][C]13[/C][C]13.0275017473578[/C][C]-0.0275017473578297[/C][/ROW]
[ROW][C]150[/C][C]10[/C][C]13.1303945600366[/C][C]-3.13039456003663[/C][/ROW]
[ROW][C]151[/C][C]15[/C][C]16.0188472173771[/C][C]-1.01884721737711[/C][/ROW]
[ROW][C]152[/C][C]16[/C][C]13.8617086186959[/C][C]2.13829138130414[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]12.0158620853918[/C][C]3.98413791460822[/C][/ROW]
[ROW][C]154[/C][C]14[/C][C]12.6607154650181[/C][C]1.33928453498189[/C][/ROW]
[ROW][C]155[/C][C]10[/C][C]12.6056861353774[/C][C]-2.60568613537736[/C][/ROW]
[ROW][C]156[/C][C]17[/C][C]16.3675035753915[/C][C]0.6324964246085[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]11.6139167601168[/C][C]1.38608323988322[/C][/ROW]
[ROW][C]158[/C][C]15[/C][C]13.8562720046718[/C][C]1.14372799532819[/C][/ROW]
[ROW][C]159[/C][C]16[/C][C]15.0647264580227[/C][C]0.93527354197734[/C][/ROW]
[ROW][C]160[/C][C]12[/C][C]12.4616481771048[/C][C]-0.461648177104801[/C][/ROW]
[ROW][C]161[/C][C]13[/C][C]12.6278304637159[/C][C]0.372169536284078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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
11316.0460838528533-3.04608385285334
21616.0978854720565-0.0978854720564648
31916.61871875636862.38128124363138
41512.26884458802932.73115541197066
51415.6143076175297-1.61430761752971
61315.0952278482479-2.09522784824791
71915.11063832808453.88936167191552
81516.9713242202552-1.9713242202552
91416.1392821471749-2.1392821471749
101514.40254902683560.597450973164367
111615.46104561611020.538954383889829
121616.1235117839571-0.123511783957113
131615.62067067091850.379329329081526
141615.2094476897910.790552310209007
151717.6741165821685-0.674116582168503
161515.1802222027614-0.180222202761398
171514.76159243630510.238407563694872
182016.42800219785453.57199780214554
191815.61600384710282.38399615289716
201615.3074180814340.692581918565993
211615.26131583146810.738684168531906
221615.15902031359790.840979686402121
231916.31832667718252.68167332281752
241614.88965935292741.11034064707262
251716.54098375244340.459016247556615
261717.0622567007993-0.0622567007993019
271615.00636954836510.993630451634889
281516.287354596739-1.28735459673898
291615.55287080604040.447129193959562
301414.3904425234722-0.390442523472172
311515.87707304005-0.877073040049984
321212.4410863242391-0.44108632423913
331415.163223672435-1.16322367243501
341615.90662985509370.0933701449062522
351415.6500827322888-1.65008273228881
361013.265086391234-3.26508639123402
371012.727741817731-2.72774181773097
381415.8385839962544-1.8385839962544
391614.51740860014511.48259139985485
401614.78001356928661.21998643071339
411615.18584544673760.81415455326237
421415.5727609632611-1.57276096326111
432017.66916236156272.33083763843728
441414.1387756696789-0.138775669678916
451415.0696607461849-1.06966074618494
461115.6144856270065-4.61448562700648
471416.5709069707458-2.57090697074579
481515.1481882666103-0.148188266610253
491615.06149671851080.938503281489179
501416.0633090042108-2.06330900421075
511616.6187303550879-0.618730355087899
521414.1780196281422-0.178019628142177
531214.6837657036483-2.68376570364832
541615.65273467634940.347265323650569
55911.6694846725896-2.66948467258964
561412.57777434350911.42222565649092
571616.0036265752432-0.00362657524322894
581615.32268553084720.677314469152831
591515.3869151033687-0.386915103368729
601614.25342629665071.74657370334928
611211.53887566369250.46112433630749
621615.93448106905030.0655189309496812
631616.5909413491474-0.590941349147434
641414.2618668653366-0.26186686533658
651615.24002473894630.759975261053671
661716.17189889622650.828101103773526
671816.11730306441781.88269693558221
681814.69913511195783.3008648880422
691215.6722816230555-3.67228162305551
701615.36965607647760.630343923522355
711013.5866524120327-3.58665241203271
721414.195891330998-0.195891330997984
731816.54853435757361.45146564242639
741816.93223559619481.06776440380516
751615.82251880560930.177481194390736
761713.8949822443113.10501775568903
771616.1954946350854-0.195494635085416
781614.50384625714061.49615374285942
791315.0129915707567-2.01299157075666
801615.77143790597510.22856209402487
811615.56908839480520.430911605194758
821615.77002784063690.229972159363091
831515.8074459225013-0.807445922501287
841514.78765965784620.212340342153843
851614.3564786199951.64352138000496
861414.1571625912084-0.157162591208444
871615.30587517141970.69412482858025
881614.46961712212471.53038287787534
891514.27882744411280.7211725558872
901213.6023588239322-1.60235882393221
911716.62692436380830.373075636191673
921615.43188579041260.568114209587381
931515.2277147708315-0.227714770831527
941315.0813275746976-2.08132757469762
951615.04423723303750.955762766962504
961615.72952952739410.270470472605887
971614.14624948948471.85375051051533
981615.97021736677540.0297826332246418
991414.4087389652808-0.408738965280757
1001617.0669910224862-1.06699102248623
1011614.73222687926871.26777312073132
1022017.33020551365792.66979448634215
1031514.66572992513880.334270074861162
1041614.49821769694741.50178230305259
1051315.1210647666331-2.12106476663309
1061715.90440880021231.09559119978767
1071615.58144924545380.418550754546195
1081614.34814496419761.65185503580237
1091211.90531554876780.0946844512321837
1101615.06094993674240.939050063257641
1111615.82292431418580.17707568581423
1121714.84557042888432.15442957111575
1131313.8834738038531-0.883473803853096
1141214.737925679879-2.73792567987902
1151816.5795463429741.42045365702602
1161415.2281114767074-1.22811147670737
1171412.997601783371.00239821663001
1181314.8993115345838-1.89931153458377
1191615.65819550510370.341804494896259
1201314.0711360179465-1.07113601794653
1211615.55353296477610.446467035223899
1221315.953286738566-2.95328673856601
1231616.9658873937755-0.96588739377553
1241516.1569827481446-1.15698274814463
1251616.7580898664056-0.758089866405582
1261515.4110906664556-0.411090666455593
1271715.85229137592431.14770862407565
1281513.97600467624881.0239953237512
1291214.8005465356512-2.80054653565118
1301614.10601398122161.89398601877844
1311013.3359227827828-3.33592278278283
1321613.93818327066542.06181672933456
1331213.8537668978712-1.85376689787118
1341415.5185421206393-1.5185421206393
1351515.2365070835217-0.236507083521653
1361312.04406001145220.955939988547819
1371514.40118610941360.598813890586378
1381113.1320965306762-2.13209653067624
1391213.2328826440951-1.23288264409513
1401113.3171512856996-2.3171512856996
1411612.89308708187863.10691291812139
1421513.34818269269091.6518173073091
1431716.62514951259640.374850487403569
1441614.69206466204911.3079353379509
1451014.1015702832193-4.10157028321932
1461815.76904499025742.23095500974264
1471314.9873219097027-1.98732190970275
1481615.13184475647920.868155243520752
1491313.0275017473578-0.0275017473578297
1501013.1303945600366-3.13039456003663
1511516.0188472173771-1.01884721737711
1521613.86170861869592.13829138130414
1531612.01586208539183.98413791460822
1541412.66071546501811.33928453498189
1551012.6056861353774-2.60568613537736
1561716.36750357539150.6324964246085
1571311.61391676011681.38608323988322
1581513.85627200467181.14372799532819
1591615.06472645802270.93527354197734
1601212.4616481771048-0.461648177104801
1611312.62783046371590.372169536284078






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
110.4814978873607280.9629957747214550.518502112639272
120.8717056526012170.2565886947975660.128294347398783
130.8463058875263280.3073882249473440.153694112473672
140.7800460132178140.4399079735643730.219953986782186
150.7374000873845050.525199825230990.262599912615495
160.7956394677158750.408721064568250.204360532284125
170.7375216363912950.524956727217410.262478363608705
180.9339584796992220.1320830406015550.0660415203007775
190.9222386308789520.1555227382420970.0777613691210485
200.892907610484220.214184779031560.10709238951578
210.8521137286225420.2957725427549160.147886271377458
220.8272531211899250.345493757620150.172746878810075
230.8120981863779820.3758036272440360.187901813622018
240.7939460350635770.4121079298728450.206053964936423
250.7450864834737250.509827033052550.254913516526275
260.7026268824308820.5947462351382360.297373117569118
270.6522559589798770.6954880820402450.347744041020123
280.7008385448379890.5983229103240230.299161455162011
290.6475374738570940.7049250522858110.352462526142906
300.6565705620220480.6868588759559030.343429437977952
310.6100972870017270.7798054259965470.389902712998273
320.604495823862170.7910083522756610.39550417613783
330.5858613849850960.8282772300298070.414138615014904
340.5250143744748280.9499712510503440.474985625525172
350.5151431248211590.9697137503576820.484856875178841
360.6403429162973040.7193141674053920.359657083702696
370.7199385505951050.5601228988097910.280061449404895
380.7039380964361580.5921238071276840.296061903563842
390.7437064650012610.5125870699974780.256293534998739
400.7274970982230110.5450058035539780.272502901776989
410.694602102994270.610795794011460.30539789700573
420.6691975244879460.6616049510241080.330802475512054
430.697283584139780.605432831720440.30271641586022
440.6516758262093650.696648347581270.348324173790635
450.6132976046141540.7734047907716910.386702395385846
460.8352846061298690.3294307877402620.164715393870131
470.852854064849460.2942918703010790.14714593515054
480.8272175973145110.3455648053709790.172782402685489
490.818937078192980.3621258436140390.18106292180702
500.8122932427766620.3754135144466760.187706757223338
510.7783884176894860.4432231646210270.221611582310514
520.7434983648185190.5130032703629630.256501635181481
530.7629939725408440.4740120549183120.237006027459156
540.735326374678160.5293472506436810.264673625321841
550.7709392400502760.4581215198994480.229060759949724
560.7780139875433510.4439720249132980.221986012456649
570.742259220104440.515481559791120.25774077989556
580.724895713179820.550208573640360.27510428682018
590.689992804552940.6200143908941210.310007195447061
600.7110758473301280.5778483053397440.288924152669872
610.6815587002897720.6368825994204570.318441299710228
620.6423168738254470.7153662523491060.357683126174553
630.6068366500528080.7863266998943840.393163349947192
640.5609875460920040.8780249078159910.439012453907996
650.5253879341605390.9492241316789220.474612065839461
660.5031482247479660.9937035505040680.496851775252034
670.512572912681960.974854174636080.48742708731804
680.6474540489322110.7050919021355780.352545951067789
690.7751113952936790.4497772094126420.224888604706321
700.7456447868124390.5087104263751230.254355213187561
710.8486399605567290.3027200788865430.151360039443271
720.8203822612550120.3592354774899760.179617738744988
730.8160408316592210.3679183366815570.183959168340779
740.7981027741458490.4037944517083030.201897225854152
750.7645552761505720.4708894476988570.235444723849428
760.8275377961700850.344924407659830.172462203829915
770.7960221211021080.4079557577957850.203977878897892
780.7841387205341250.4317225589317490.215861279465874
790.7984780975751640.4030438048496730.201521902424837
800.7638499286844080.4723001426311850.236150071315592
810.7296648581772860.5406702836454280.270335141822714
820.6923242509231230.6153514981537530.307675749076877
830.6607076542883080.6785846914233830.339292345711692
840.6183052649660070.7633894700679860.381694735033993
850.6067762891493590.7864474217012810.393223710850641
860.5623831143819680.8752337712360640.437616885618032
870.5205994095908520.9588011808182950.479400590409148
880.5032638178071860.9934723643856280.496736182192814
890.4667563947100360.9335127894200730.533243605289964
900.462679790689180.9253595813783610.53732020931082
910.4217298866974710.8434597733949420.578270113302529
920.3811786344442450.7623572688884910.618821365555755
930.3392379300725730.6784758601451470.660762069927427
940.3684757737477230.7369515474954470.631524226252277
950.3348241139861980.6696482279723950.665175886013802
960.2947408982984730.5894817965969470.705259101701527
970.2913857180883590.5827714361767190.708614281911641
980.2514389012317780.5028778024635570.748561098768222
990.2214379620769270.4428759241538540.778562037923073
1000.1999551575437720.3999103150875450.800044842456228
1010.184533705614110.369067411228220.81546629438589
1020.2382286576741940.4764573153483870.761771342325806
1030.2021748735642250.4043497471284510.797825126435775
1040.1998190284075030.3996380568150050.800180971592497
1050.2110543099185740.4221086198371480.788945690081426
1060.1933247689370780.3866495378741550.806675231062922
1070.1757576803812810.3515153607625630.824242319618719
1080.1768205148055390.3536410296110770.823179485194461
1090.1762786741433350.352557348286670.823721325856665
1100.1629073307552020.3258146615104040.837092669244798
1110.1417371043605210.2834742087210410.858262895639479
1120.1851886194733620.3703772389467230.814811380526638
1130.1565656564613290.3131313129226580.843434343538671
1140.1818834892495990.3637669784991980.818116510750401
1150.1948749879995130.3897499759990260.805125012000487
1160.1667519326824090.3335038653648190.833248067317591
1170.1626119906740670.3252239813481340.837388009325933
1180.1593224429816460.3186448859632930.840677557018354
1190.1876609765086430.3753219530172860.812339023491357
1200.156138609664680.312277219329360.84386139033532
1210.1441514107566520.2883028215133040.855848589243348
1220.1475893363909190.2951786727818390.852410663609081
1230.1199002307330850.2398004614661710.880099769266915
1240.0980827771079540.1961655542159080.901917222892046
1250.07653571118508920.1530714223701780.923464288814911
1260.05778064175850670.1155612835170130.942219358241493
1270.06211624505005910.1242324901001180.937883754949941
1280.0607259701100880.1214519402201760.939274029889912
1290.06455419217050280.1291083843410060.935445807829497
1300.07100933331905460.1420186666381090.928990666680945
1310.08551393680401010.171027873608020.91448606319599
1320.1718583282294270.3437166564588540.828141671770573
1330.1920765862930870.3841531725861730.807923413706913
1340.154644114583680.3092882291673590.84535588541632
1350.1243968148722710.2487936297445420.875603185127729
1360.1022647741657960.2045295483315930.897735225834204
1370.09055271030419770.1811054206083950.909447289695802
1380.1208688313862810.2417376627725630.879131168613719
1390.09938368785885680.1987673757177140.900616312141143
1400.2341036065881750.468207213176350.765896393411825
1410.2027724491372420.4055448982744850.797227550862758
1420.1806633541543580.3613267083087160.819336645845642
1430.1292396266897840.2584792533795680.870760373310216
1440.099648081596270.199296163192540.90035191840373
1450.1364238410185820.2728476820371630.863576158981418
1460.2349086326888580.4698172653777160.765091367311142
1470.53999887459010.9200022508198010.4600011254099
1480.4125192843924450.8250385687848890.587480715607555
1490.3036589504402570.6073179008805140.696341049559743
1500.7441921296219060.5116157407561890.255807870378095

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.481497887360728 & 0.962995774721455 & 0.518502112639272 \tabularnewline
12 & 0.871705652601217 & 0.256588694797566 & 0.128294347398783 \tabularnewline
13 & 0.846305887526328 & 0.307388224947344 & 0.153694112473672 \tabularnewline
14 & 0.780046013217814 & 0.439907973564373 & 0.219953986782186 \tabularnewline
15 & 0.737400087384505 & 0.52519982523099 & 0.262599912615495 \tabularnewline
16 & 0.795639467715875 & 0.40872106456825 & 0.204360532284125 \tabularnewline
17 & 0.737521636391295 & 0.52495672721741 & 0.262478363608705 \tabularnewline
18 & 0.933958479699222 & 0.132083040601555 & 0.0660415203007775 \tabularnewline
19 & 0.922238630878952 & 0.155522738242097 & 0.0777613691210485 \tabularnewline
20 & 0.89290761048422 & 0.21418477903156 & 0.10709238951578 \tabularnewline
21 & 0.852113728622542 & 0.295772542754916 & 0.147886271377458 \tabularnewline
22 & 0.827253121189925 & 0.34549375762015 & 0.172746878810075 \tabularnewline
23 & 0.812098186377982 & 0.375803627244036 & 0.187901813622018 \tabularnewline
24 & 0.793946035063577 & 0.412107929872845 & 0.206053964936423 \tabularnewline
25 & 0.745086483473725 & 0.50982703305255 & 0.254913516526275 \tabularnewline
26 & 0.702626882430882 & 0.594746235138236 & 0.297373117569118 \tabularnewline
27 & 0.652255958979877 & 0.695488082040245 & 0.347744041020123 \tabularnewline
28 & 0.700838544837989 & 0.598322910324023 & 0.299161455162011 \tabularnewline
29 & 0.647537473857094 & 0.704925052285811 & 0.352462526142906 \tabularnewline
30 & 0.656570562022048 & 0.686858875955903 & 0.343429437977952 \tabularnewline
31 & 0.610097287001727 & 0.779805425996547 & 0.389902712998273 \tabularnewline
32 & 0.60449582386217 & 0.791008352275661 & 0.39550417613783 \tabularnewline
33 & 0.585861384985096 & 0.828277230029807 & 0.414138615014904 \tabularnewline
34 & 0.525014374474828 & 0.949971251050344 & 0.474985625525172 \tabularnewline
35 & 0.515143124821159 & 0.969713750357682 & 0.484856875178841 \tabularnewline
36 & 0.640342916297304 & 0.719314167405392 & 0.359657083702696 \tabularnewline
37 & 0.719938550595105 & 0.560122898809791 & 0.280061449404895 \tabularnewline
38 & 0.703938096436158 & 0.592123807127684 & 0.296061903563842 \tabularnewline
39 & 0.743706465001261 & 0.512587069997478 & 0.256293534998739 \tabularnewline
40 & 0.727497098223011 & 0.545005803553978 & 0.272502901776989 \tabularnewline
41 & 0.69460210299427 & 0.61079579401146 & 0.30539789700573 \tabularnewline
42 & 0.669197524487946 & 0.661604951024108 & 0.330802475512054 \tabularnewline
43 & 0.69728358413978 & 0.60543283172044 & 0.30271641586022 \tabularnewline
44 & 0.651675826209365 & 0.69664834758127 & 0.348324173790635 \tabularnewline
45 & 0.613297604614154 & 0.773404790771691 & 0.386702395385846 \tabularnewline
46 & 0.835284606129869 & 0.329430787740262 & 0.164715393870131 \tabularnewline
47 & 0.85285406484946 & 0.294291870301079 & 0.14714593515054 \tabularnewline
48 & 0.827217597314511 & 0.345564805370979 & 0.172782402685489 \tabularnewline
49 & 0.81893707819298 & 0.362125843614039 & 0.18106292180702 \tabularnewline
50 & 0.812293242776662 & 0.375413514446676 & 0.187706757223338 \tabularnewline
51 & 0.778388417689486 & 0.443223164621027 & 0.221611582310514 \tabularnewline
52 & 0.743498364818519 & 0.513003270362963 & 0.256501635181481 \tabularnewline
53 & 0.762993972540844 & 0.474012054918312 & 0.237006027459156 \tabularnewline
54 & 0.73532637467816 & 0.529347250643681 & 0.264673625321841 \tabularnewline
55 & 0.770939240050276 & 0.458121519899448 & 0.229060759949724 \tabularnewline
56 & 0.778013987543351 & 0.443972024913298 & 0.221986012456649 \tabularnewline
57 & 0.74225922010444 & 0.51548155979112 & 0.25774077989556 \tabularnewline
58 & 0.72489571317982 & 0.55020857364036 & 0.27510428682018 \tabularnewline
59 & 0.68999280455294 & 0.620014390894121 & 0.310007195447061 \tabularnewline
60 & 0.711075847330128 & 0.577848305339744 & 0.288924152669872 \tabularnewline
61 & 0.681558700289772 & 0.636882599420457 & 0.318441299710228 \tabularnewline
62 & 0.642316873825447 & 0.715366252349106 & 0.357683126174553 \tabularnewline
63 & 0.606836650052808 & 0.786326699894384 & 0.393163349947192 \tabularnewline
64 & 0.560987546092004 & 0.878024907815991 & 0.439012453907996 \tabularnewline
65 & 0.525387934160539 & 0.949224131678922 & 0.474612065839461 \tabularnewline
66 & 0.503148224747966 & 0.993703550504068 & 0.496851775252034 \tabularnewline
67 & 0.51257291268196 & 0.97485417463608 & 0.48742708731804 \tabularnewline
68 & 0.647454048932211 & 0.705091902135578 & 0.352545951067789 \tabularnewline
69 & 0.775111395293679 & 0.449777209412642 & 0.224888604706321 \tabularnewline
70 & 0.745644786812439 & 0.508710426375123 & 0.254355213187561 \tabularnewline
71 & 0.848639960556729 & 0.302720078886543 & 0.151360039443271 \tabularnewline
72 & 0.820382261255012 & 0.359235477489976 & 0.179617738744988 \tabularnewline
73 & 0.816040831659221 & 0.367918336681557 & 0.183959168340779 \tabularnewline
74 & 0.798102774145849 & 0.403794451708303 & 0.201897225854152 \tabularnewline
75 & 0.764555276150572 & 0.470889447698857 & 0.235444723849428 \tabularnewline
76 & 0.827537796170085 & 0.34492440765983 & 0.172462203829915 \tabularnewline
77 & 0.796022121102108 & 0.407955757795785 & 0.203977878897892 \tabularnewline
78 & 0.784138720534125 & 0.431722558931749 & 0.215861279465874 \tabularnewline
79 & 0.798478097575164 & 0.403043804849673 & 0.201521902424837 \tabularnewline
80 & 0.763849928684408 & 0.472300142631185 & 0.236150071315592 \tabularnewline
81 & 0.729664858177286 & 0.540670283645428 & 0.270335141822714 \tabularnewline
82 & 0.692324250923123 & 0.615351498153753 & 0.307675749076877 \tabularnewline
83 & 0.660707654288308 & 0.678584691423383 & 0.339292345711692 \tabularnewline
84 & 0.618305264966007 & 0.763389470067986 & 0.381694735033993 \tabularnewline
85 & 0.606776289149359 & 0.786447421701281 & 0.393223710850641 \tabularnewline
86 & 0.562383114381968 & 0.875233771236064 & 0.437616885618032 \tabularnewline
87 & 0.520599409590852 & 0.958801180818295 & 0.479400590409148 \tabularnewline
88 & 0.503263817807186 & 0.993472364385628 & 0.496736182192814 \tabularnewline
89 & 0.466756394710036 & 0.933512789420073 & 0.533243605289964 \tabularnewline
90 & 0.46267979068918 & 0.925359581378361 & 0.53732020931082 \tabularnewline
91 & 0.421729886697471 & 0.843459773394942 & 0.578270113302529 \tabularnewline
92 & 0.381178634444245 & 0.762357268888491 & 0.618821365555755 \tabularnewline
93 & 0.339237930072573 & 0.678475860145147 & 0.660762069927427 \tabularnewline
94 & 0.368475773747723 & 0.736951547495447 & 0.631524226252277 \tabularnewline
95 & 0.334824113986198 & 0.669648227972395 & 0.665175886013802 \tabularnewline
96 & 0.294740898298473 & 0.589481796596947 & 0.705259101701527 \tabularnewline
97 & 0.291385718088359 & 0.582771436176719 & 0.708614281911641 \tabularnewline
98 & 0.251438901231778 & 0.502877802463557 & 0.748561098768222 \tabularnewline
99 & 0.221437962076927 & 0.442875924153854 & 0.778562037923073 \tabularnewline
100 & 0.199955157543772 & 0.399910315087545 & 0.800044842456228 \tabularnewline
101 & 0.18453370561411 & 0.36906741122822 & 0.81546629438589 \tabularnewline
102 & 0.238228657674194 & 0.476457315348387 & 0.761771342325806 \tabularnewline
103 & 0.202174873564225 & 0.404349747128451 & 0.797825126435775 \tabularnewline
104 & 0.199819028407503 & 0.399638056815005 & 0.800180971592497 \tabularnewline
105 & 0.211054309918574 & 0.422108619837148 & 0.788945690081426 \tabularnewline
106 & 0.193324768937078 & 0.386649537874155 & 0.806675231062922 \tabularnewline
107 & 0.175757680381281 & 0.351515360762563 & 0.824242319618719 \tabularnewline
108 & 0.176820514805539 & 0.353641029611077 & 0.823179485194461 \tabularnewline
109 & 0.176278674143335 & 0.35255734828667 & 0.823721325856665 \tabularnewline
110 & 0.162907330755202 & 0.325814661510404 & 0.837092669244798 \tabularnewline
111 & 0.141737104360521 & 0.283474208721041 & 0.858262895639479 \tabularnewline
112 & 0.185188619473362 & 0.370377238946723 & 0.814811380526638 \tabularnewline
113 & 0.156565656461329 & 0.313131312922658 & 0.843434343538671 \tabularnewline
114 & 0.181883489249599 & 0.363766978499198 & 0.818116510750401 \tabularnewline
115 & 0.194874987999513 & 0.389749975999026 & 0.805125012000487 \tabularnewline
116 & 0.166751932682409 & 0.333503865364819 & 0.833248067317591 \tabularnewline
117 & 0.162611990674067 & 0.325223981348134 & 0.837388009325933 \tabularnewline
118 & 0.159322442981646 & 0.318644885963293 & 0.840677557018354 \tabularnewline
119 & 0.187660976508643 & 0.375321953017286 & 0.812339023491357 \tabularnewline
120 & 0.15613860966468 & 0.31227721932936 & 0.84386139033532 \tabularnewline
121 & 0.144151410756652 & 0.288302821513304 & 0.855848589243348 \tabularnewline
122 & 0.147589336390919 & 0.295178672781839 & 0.852410663609081 \tabularnewline
123 & 0.119900230733085 & 0.239800461466171 & 0.880099769266915 \tabularnewline
124 & 0.098082777107954 & 0.196165554215908 & 0.901917222892046 \tabularnewline
125 & 0.0765357111850892 & 0.153071422370178 & 0.923464288814911 \tabularnewline
126 & 0.0577806417585067 & 0.115561283517013 & 0.942219358241493 \tabularnewline
127 & 0.0621162450500591 & 0.124232490100118 & 0.937883754949941 \tabularnewline
128 & 0.060725970110088 & 0.121451940220176 & 0.939274029889912 \tabularnewline
129 & 0.0645541921705028 & 0.129108384341006 & 0.935445807829497 \tabularnewline
130 & 0.0710093333190546 & 0.142018666638109 & 0.928990666680945 \tabularnewline
131 & 0.0855139368040101 & 0.17102787360802 & 0.91448606319599 \tabularnewline
132 & 0.171858328229427 & 0.343716656458854 & 0.828141671770573 \tabularnewline
133 & 0.192076586293087 & 0.384153172586173 & 0.807923413706913 \tabularnewline
134 & 0.15464411458368 & 0.309288229167359 & 0.84535588541632 \tabularnewline
135 & 0.124396814872271 & 0.248793629744542 & 0.875603185127729 \tabularnewline
136 & 0.102264774165796 & 0.204529548331593 & 0.897735225834204 \tabularnewline
137 & 0.0905527103041977 & 0.181105420608395 & 0.909447289695802 \tabularnewline
138 & 0.120868831386281 & 0.241737662772563 & 0.879131168613719 \tabularnewline
139 & 0.0993836878588568 & 0.198767375717714 & 0.900616312141143 \tabularnewline
140 & 0.234103606588175 & 0.46820721317635 & 0.765896393411825 \tabularnewline
141 & 0.202772449137242 & 0.405544898274485 & 0.797227550862758 \tabularnewline
142 & 0.180663354154358 & 0.361326708308716 & 0.819336645845642 \tabularnewline
143 & 0.129239626689784 & 0.258479253379568 & 0.870760373310216 \tabularnewline
144 & 0.09964808159627 & 0.19929616319254 & 0.90035191840373 \tabularnewline
145 & 0.136423841018582 & 0.272847682037163 & 0.863576158981418 \tabularnewline
146 & 0.234908632688858 & 0.469817265377716 & 0.765091367311142 \tabularnewline
147 & 0.5399988745901 & 0.920002250819801 & 0.4600011254099 \tabularnewline
148 & 0.412519284392445 & 0.825038568784889 & 0.587480715607555 \tabularnewline
149 & 0.303658950440257 & 0.607317900880514 & 0.696341049559743 \tabularnewline
150 & 0.744192129621906 & 0.511615740756189 & 0.255807870378095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&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]0.481497887360728[/C][C]0.962995774721455[/C][C]0.518502112639272[/C][/ROW]
[ROW][C]12[/C][C]0.871705652601217[/C][C]0.256588694797566[/C][C]0.128294347398783[/C][/ROW]
[ROW][C]13[/C][C]0.846305887526328[/C][C]0.307388224947344[/C][C]0.153694112473672[/C][/ROW]
[ROW][C]14[/C][C]0.780046013217814[/C][C]0.439907973564373[/C][C]0.219953986782186[/C][/ROW]
[ROW][C]15[/C][C]0.737400087384505[/C][C]0.52519982523099[/C][C]0.262599912615495[/C][/ROW]
[ROW][C]16[/C][C]0.795639467715875[/C][C]0.40872106456825[/C][C]0.204360532284125[/C][/ROW]
[ROW][C]17[/C][C]0.737521636391295[/C][C]0.52495672721741[/C][C]0.262478363608705[/C][/ROW]
[ROW][C]18[/C][C]0.933958479699222[/C][C]0.132083040601555[/C][C]0.0660415203007775[/C][/ROW]
[ROW][C]19[/C][C]0.922238630878952[/C][C]0.155522738242097[/C][C]0.0777613691210485[/C][/ROW]
[ROW][C]20[/C][C]0.89290761048422[/C][C]0.21418477903156[/C][C]0.10709238951578[/C][/ROW]
[ROW][C]21[/C][C]0.852113728622542[/C][C]0.295772542754916[/C][C]0.147886271377458[/C][/ROW]
[ROW][C]22[/C][C]0.827253121189925[/C][C]0.34549375762015[/C][C]0.172746878810075[/C][/ROW]
[ROW][C]23[/C][C]0.812098186377982[/C][C]0.375803627244036[/C][C]0.187901813622018[/C][/ROW]
[ROW][C]24[/C][C]0.793946035063577[/C][C]0.412107929872845[/C][C]0.206053964936423[/C][/ROW]
[ROW][C]25[/C][C]0.745086483473725[/C][C]0.50982703305255[/C][C]0.254913516526275[/C][/ROW]
[ROW][C]26[/C][C]0.702626882430882[/C][C]0.594746235138236[/C][C]0.297373117569118[/C][/ROW]
[ROW][C]27[/C][C]0.652255958979877[/C][C]0.695488082040245[/C][C]0.347744041020123[/C][/ROW]
[ROW][C]28[/C][C]0.700838544837989[/C][C]0.598322910324023[/C][C]0.299161455162011[/C][/ROW]
[ROW][C]29[/C][C]0.647537473857094[/C][C]0.704925052285811[/C][C]0.352462526142906[/C][/ROW]
[ROW][C]30[/C][C]0.656570562022048[/C][C]0.686858875955903[/C][C]0.343429437977952[/C][/ROW]
[ROW][C]31[/C][C]0.610097287001727[/C][C]0.779805425996547[/C][C]0.389902712998273[/C][/ROW]
[ROW][C]32[/C][C]0.60449582386217[/C][C]0.791008352275661[/C][C]0.39550417613783[/C][/ROW]
[ROW][C]33[/C][C]0.585861384985096[/C][C]0.828277230029807[/C][C]0.414138615014904[/C][/ROW]
[ROW][C]34[/C][C]0.525014374474828[/C][C]0.949971251050344[/C][C]0.474985625525172[/C][/ROW]
[ROW][C]35[/C][C]0.515143124821159[/C][C]0.969713750357682[/C][C]0.484856875178841[/C][/ROW]
[ROW][C]36[/C][C]0.640342916297304[/C][C]0.719314167405392[/C][C]0.359657083702696[/C][/ROW]
[ROW][C]37[/C][C]0.719938550595105[/C][C]0.560122898809791[/C][C]0.280061449404895[/C][/ROW]
[ROW][C]38[/C][C]0.703938096436158[/C][C]0.592123807127684[/C][C]0.296061903563842[/C][/ROW]
[ROW][C]39[/C][C]0.743706465001261[/C][C]0.512587069997478[/C][C]0.256293534998739[/C][/ROW]
[ROW][C]40[/C][C]0.727497098223011[/C][C]0.545005803553978[/C][C]0.272502901776989[/C][/ROW]
[ROW][C]41[/C][C]0.69460210299427[/C][C]0.61079579401146[/C][C]0.30539789700573[/C][/ROW]
[ROW][C]42[/C][C]0.669197524487946[/C][C]0.661604951024108[/C][C]0.330802475512054[/C][/ROW]
[ROW][C]43[/C][C]0.69728358413978[/C][C]0.60543283172044[/C][C]0.30271641586022[/C][/ROW]
[ROW][C]44[/C][C]0.651675826209365[/C][C]0.69664834758127[/C][C]0.348324173790635[/C][/ROW]
[ROW][C]45[/C][C]0.613297604614154[/C][C]0.773404790771691[/C][C]0.386702395385846[/C][/ROW]
[ROW][C]46[/C][C]0.835284606129869[/C][C]0.329430787740262[/C][C]0.164715393870131[/C][/ROW]
[ROW][C]47[/C][C]0.85285406484946[/C][C]0.294291870301079[/C][C]0.14714593515054[/C][/ROW]
[ROW][C]48[/C][C]0.827217597314511[/C][C]0.345564805370979[/C][C]0.172782402685489[/C][/ROW]
[ROW][C]49[/C][C]0.81893707819298[/C][C]0.362125843614039[/C][C]0.18106292180702[/C][/ROW]
[ROW][C]50[/C][C]0.812293242776662[/C][C]0.375413514446676[/C][C]0.187706757223338[/C][/ROW]
[ROW][C]51[/C][C]0.778388417689486[/C][C]0.443223164621027[/C][C]0.221611582310514[/C][/ROW]
[ROW][C]52[/C][C]0.743498364818519[/C][C]0.513003270362963[/C][C]0.256501635181481[/C][/ROW]
[ROW][C]53[/C][C]0.762993972540844[/C][C]0.474012054918312[/C][C]0.237006027459156[/C][/ROW]
[ROW][C]54[/C][C]0.73532637467816[/C][C]0.529347250643681[/C][C]0.264673625321841[/C][/ROW]
[ROW][C]55[/C][C]0.770939240050276[/C][C]0.458121519899448[/C][C]0.229060759949724[/C][/ROW]
[ROW][C]56[/C][C]0.778013987543351[/C][C]0.443972024913298[/C][C]0.221986012456649[/C][/ROW]
[ROW][C]57[/C][C]0.74225922010444[/C][C]0.51548155979112[/C][C]0.25774077989556[/C][/ROW]
[ROW][C]58[/C][C]0.72489571317982[/C][C]0.55020857364036[/C][C]0.27510428682018[/C][/ROW]
[ROW][C]59[/C][C]0.68999280455294[/C][C]0.620014390894121[/C][C]0.310007195447061[/C][/ROW]
[ROW][C]60[/C][C]0.711075847330128[/C][C]0.577848305339744[/C][C]0.288924152669872[/C][/ROW]
[ROW][C]61[/C][C]0.681558700289772[/C][C]0.636882599420457[/C][C]0.318441299710228[/C][/ROW]
[ROW][C]62[/C][C]0.642316873825447[/C][C]0.715366252349106[/C][C]0.357683126174553[/C][/ROW]
[ROW][C]63[/C][C]0.606836650052808[/C][C]0.786326699894384[/C][C]0.393163349947192[/C][/ROW]
[ROW][C]64[/C][C]0.560987546092004[/C][C]0.878024907815991[/C][C]0.439012453907996[/C][/ROW]
[ROW][C]65[/C][C]0.525387934160539[/C][C]0.949224131678922[/C][C]0.474612065839461[/C][/ROW]
[ROW][C]66[/C][C]0.503148224747966[/C][C]0.993703550504068[/C][C]0.496851775252034[/C][/ROW]
[ROW][C]67[/C][C]0.51257291268196[/C][C]0.97485417463608[/C][C]0.48742708731804[/C][/ROW]
[ROW][C]68[/C][C]0.647454048932211[/C][C]0.705091902135578[/C][C]0.352545951067789[/C][/ROW]
[ROW][C]69[/C][C]0.775111395293679[/C][C]0.449777209412642[/C][C]0.224888604706321[/C][/ROW]
[ROW][C]70[/C][C]0.745644786812439[/C][C]0.508710426375123[/C][C]0.254355213187561[/C][/ROW]
[ROW][C]71[/C][C]0.848639960556729[/C][C]0.302720078886543[/C][C]0.151360039443271[/C][/ROW]
[ROW][C]72[/C][C]0.820382261255012[/C][C]0.359235477489976[/C][C]0.179617738744988[/C][/ROW]
[ROW][C]73[/C][C]0.816040831659221[/C][C]0.367918336681557[/C][C]0.183959168340779[/C][/ROW]
[ROW][C]74[/C][C]0.798102774145849[/C][C]0.403794451708303[/C][C]0.201897225854152[/C][/ROW]
[ROW][C]75[/C][C]0.764555276150572[/C][C]0.470889447698857[/C][C]0.235444723849428[/C][/ROW]
[ROW][C]76[/C][C]0.827537796170085[/C][C]0.34492440765983[/C][C]0.172462203829915[/C][/ROW]
[ROW][C]77[/C][C]0.796022121102108[/C][C]0.407955757795785[/C][C]0.203977878897892[/C][/ROW]
[ROW][C]78[/C][C]0.784138720534125[/C][C]0.431722558931749[/C][C]0.215861279465874[/C][/ROW]
[ROW][C]79[/C][C]0.798478097575164[/C][C]0.403043804849673[/C][C]0.201521902424837[/C][/ROW]
[ROW][C]80[/C][C]0.763849928684408[/C][C]0.472300142631185[/C][C]0.236150071315592[/C][/ROW]
[ROW][C]81[/C][C]0.729664858177286[/C][C]0.540670283645428[/C][C]0.270335141822714[/C][/ROW]
[ROW][C]82[/C][C]0.692324250923123[/C][C]0.615351498153753[/C][C]0.307675749076877[/C][/ROW]
[ROW][C]83[/C][C]0.660707654288308[/C][C]0.678584691423383[/C][C]0.339292345711692[/C][/ROW]
[ROW][C]84[/C][C]0.618305264966007[/C][C]0.763389470067986[/C][C]0.381694735033993[/C][/ROW]
[ROW][C]85[/C][C]0.606776289149359[/C][C]0.786447421701281[/C][C]0.393223710850641[/C][/ROW]
[ROW][C]86[/C][C]0.562383114381968[/C][C]0.875233771236064[/C][C]0.437616885618032[/C][/ROW]
[ROW][C]87[/C][C]0.520599409590852[/C][C]0.958801180818295[/C][C]0.479400590409148[/C][/ROW]
[ROW][C]88[/C][C]0.503263817807186[/C][C]0.993472364385628[/C][C]0.496736182192814[/C][/ROW]
[ROW][C]89[/C][C]0.466756394710036[/C][C]0.933512789420073[/C][C]0.533243605289964[/C][/ROW]
[ROW][C]90[/C][C]0.46267979068918[/C][C]0.925359581378361[/C][C]0.53732020931082[/C][/ROW]
[ROW][C]91[/C][C]0.421729886697471[/C][C]0.843459773394942[/C][C]0.578270113302529[/C][/ROW]
[ROW][C]92[/C][C]0.381178634444245[/C][C]0.762357268888491[/C][C]0.618821365555755[/C][/ROW]
[ROW][C]93[/C][C]0.339237930072573[/C][C]0.678475860145147[/C][C]0.660762069927427[/C][/ROW]
[ROW][C]94[/C][C]0.368475773747723[/C][C]0.736951547495447[/C][C]0.631524226252277[/C][/ROW]
[ROW][C]95[/C][C]0.334824113986198[/C][C]0.669648227972395[/C][C]0.665175886013802[/C][/ROW]
[ROW][C]96[/C][C]0.294740898298473[/C][C]0.589481796596947[/C][C]0.705259101701527[/C][/ROW]
[ROW][C]97[/C][C]0.291385718088359[/C][C]0.582771436176719[/C][C]0.708614281911641[/C][/ROW]
[ROW][C]98[/C][C]0.251438901231778[/C][C]0.502877802463557[/C][C]0.748561098768222[/C][/ROW]
[ROW][C]99[/C][C]0.221437962076927[/C][C]0.442875924153854[/C][C]0.778562037923073[/C][/ROW]
[ROW][C]100[/C][C]0.199955157543772[/C][C]0.399910315087545[/C][C]0.800044842456228[/C][/ROW]
[ROW][C]101[/C][C]0.18453370561411[/C][C]0.36906741122822[/C][C]0.81546629438589[/C][/ROW]
[ROW][C]102[/C][C]0.238228657674194[/C][C]0.476457315348387[/C][C]0.761771342325806[/C][/ROW]
[ROW][C]103[/C][C]0.202174873564225[/C][C]0.404349747128451[/C][C]0.797825126435775[/C][/ROW]
[ROW][C]104[/C][C]0.199819028407503[/C][C]0.399638056815005[/C][C]0.800180971592497[/C][/ROW]
[ROW][C]105[/C][C]0.211054309918574[/C][C]0.422108619837148[/C][C]0.788945690081426[/C][/ROW]
[ROW][C]106[/C][C]0.193324768937078[/C][C]0.386649537874155[/C][C]0.806675231062922[/C][/ROW]
[ROW][C]107[/C][C]0.175757680381281[/C][C]0.351515360762563[/C][C]0.824242319618719[/C][/ROW]
[ROW][C]108[/C][C]0.176820514805539[/C][C]0.353641029611077[/C][C]0.823179485194461[/C][/ROW]
[ROW][C]109[/C][C]0.176278674143335[/C][C]0.35255734828667[/C][C]0.823721325856665[/C][/ROW]
[ROW][C]110[/C][C]0.162907330755202[/C][C]0.325814661510404[/C][C]0.837092669244798[/C][/ROW]
[ROW][C]111[/C][C]0.141737104360521[/C][C]0.283474208721041[/C][C]0.858262895639479[/C][/ROW]
[ROW][C]112[/C][C]0.185188619473362[/C][C]0.370377238946723[/C][C]0.814811380526638[/C][/ROW]
[ROW][C]113[/C][C]0.156565656461329[/C][C]0.313131312922658[/C][C]0.843434343538671[/C][/ROW]
[ROW][C]114[/C][C]0.181883489249599[/C][C]0.363766978499198[/C][C]0.818116510750401[/C][/ROW]
[ROW][C]115[/C][C]0.194874987999513[/C][C]0.389749975999026[/C][C]0.805125012000487[/C][/ROW]
[ROW][C]116[/C][C]0.166751932682409[/C][C]0.333503865364819[/C][C]0.833248067317591[/C][/ROW]
[ROW][C]117[/C][C]0.162611990674067[/C][C]0.325223981348134[/C][C]0.837388009325933[/C][/ROW]
[ROW][C]118[/C][C]0.159322442981646[/C][C]0.318644885963293[/C][C]0.840677557018354[/C][/ROW]
[ROW][C]119[/C][C]0.187660976508643[/C][C]0.375321953017286[/C][C]0.812339023491357[/C][/ROW]
[ROW][C]120[/C][C]0.15613860966468[/C][C]0.31227721932936[/C][C]0.84386139033532[/C][/ROW]
[ROW][C]121[/C][C]0.144151410756652[/C][C]0.288302821513304[/C][C]0.855848589243348[/C][/ROW]
[ROW][C]122[/C][C]0.147589336390919[/C][C]0.295178672781839[/C][C]0.852410663609081[/C][/ROW]
[ROW][C]123[/C][C]0.119900230733085[/C][C]0.239800461466171[/C][C]0.880099769266915[/C][/ROW]
[ROW][C]124[/C][C]0.098082777107954[/C][C]0.196165554215908[/C][C]0.901917222892046[/C][/ROW]
[ROW][C]125[/C][C]0.0765357111850892[/C][C]0.153071422370178[/C][C]0.923464288814911[/C][/ROW]
[ROW][C]126[/C][C]0.0577806417585067[/C][C]0.115561283517013[/C][C]0.942219358241493[/C][/ROW]
[ROW][C]127[/C][C]0.0621162450500591[/C][C]0.124232490100118[/C][C]0.937883754949941[/C][/ROW]
[ROW][C]128[/C][C]0.060725970110088[/C][C]0.121451940220176[/C][C]0.939274029889912[/C][/ROW]
[ROW][C]129[/C][C]0.0645541921705028[/C][C]0.129108384341006[/C][C]0.935445807829497[/C][/ROW]
[ROW][C]130[/C][C]0.0710093333190546[/C][C]0.142018666638109[/C][C]0.928990666680945[/C][/ROW]
[ROW][C]131[/C][C]0.0855139368040101[/C][C]0.17102787360802[/C][C]0.91448606319599[/C][/ROW]
[ROW][C]132[/C][C]0.171858328229427[/C][C]0.343716656458854[/C][C]0.828141671770573[/C][/ROW]
[ROW][C]133[/C][C]0.192076586293087[/C][C]0.384153172586173[/C][C]0.807923413706913[/C][/ROW]
[ROW][C]134[/C][C]0.15464411458368[/C][C]0.309288229167359[/C][C]0.84535588541632[/C][/ROW]
[ROW][C]135[/C][C]0.124396814872271[/C][C]0.248793629744542[/C][C]0.875603185127729[/C][/ROW]
[ROW][C]136[/C][C]0.102264774165796[/C][C]0.204529548331593[/C][C]0.897735225834204[/C][/ROW]
[ROW][C]137[/C][C]0.0905527103041977[/C][C]0.181105420608395[/C][C]0.909447289695802[/C][/ROW]
[ROW][C]138[/C][C]0.120868831386281[/C][C]0.241737662772563[/C][C]0.879131168613719[/C][/ROW]
[ROW][C]139[/C][C]0.0993836878588568[/C][C]0.198767375717714[/C][C]0.900616312141143[/C][/ROW]
[ROW][C]140[/C][C]0.234103606588175[/C][C]0.46820721317635[/C][C]0.765896393411825[/C][/ROW]
[ROW][C]141[/C][C]0.202772449137242[/C][C]0.405544898274485[/C][C]0.797227550862758[/C][/ROW]
[ROW][C]142[/C][C]0.180663354154358[/C][C]0.361326708308716[/C][C]0.819336645845642[/C][/ROW]
[ROW][C]143[/C][C]0.129239626689784[/C][C]0.258479253379568[/C][C]0.870760373310216[/C][/ROW]
[ROW][C]144[/C][C]0.09964808159627[/C][C]0.19929616319254[/C][C]0.90035191840373[/C][/ROW]
[ROW][C]145[/C][C]0.136423841018582[/C][C]0.272847682037163[/C][C]0.863576158981418[/C][/ROW]
[ROW][C]146[/C][C]0.234908632688858[/C][C]0.469817265377716[/C][C]0.765091367311142[/C][/ROW]
[ROW][C]147[/C][C]0.5399988745901[/C][C]0.920002250819801[/C][C]0.4600011254099[/C][/ROW]
[ROW][C]148[/C][C]0.412519284392445[/C][C]0.825038568784889[/C][C]0.587480715607555[/C][/ROW]
[ROW][C]149[/C][C]0.303658950440257[/C][C]0.607317900880514[/C][C]0.696341049559743[/C][/ROW]
[ROW][C]150[/C][C]0.744192129621906[/C][C]0.511615740756189[/C][C]0.255807870378095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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
110.4814978873607280.9629957747214550.518502112639272
120.8717056526012170.2565886947975660.128294347398783
130.8463058875263280.3073882249473440.153694112473672
140.7800460132178140.4399079735643730.219953986782186
150.7374000873845050.525199825230990.262599912615495
160.7956394677158750.408721064568250.204360532284125
170.7375216363912950.524956727217410.262478363608705
180.9339584796992220.1320830406015550.0660415203007775
190.9222386308789520.1555227382420970.0777613691210485
200.892907610484220.214184779031560.10709238951578
210.8521137286225420.2957725427549160.147886271377458
220.8272531211899250.345493757620150.172746878810075
230.8120981863779820.3758036272440360.187901813622018
240.7939460350635770.4121079298728450.206053964936423
250.7450864834737250.509827033052550.254913516526275
260.7026268824308820.5947462351382360.297373117569118
270.6522559589798770.6954880820402450.347744041020123
280.7008385448379890.5983229103240230.299161455162011
290.6475374738570940.7049250522858110.352462526142906
300.6565705620220480.6868588759559030.343429437977952
310.6100972870017270.7798054259965470.389902712998273
320.604495823862170.7910083522756610.39550417613783
330.5858613849850960.8282772300298070.414138615014904
340.5250143744748280.9499712510503440.474985625525172
350.5151431248211590.9697137503576820.484856875178841
360.6403429162973040.7193141674053920.359657083702696
370.7199385505951050.5601228988097910.280061449404895
380.7039380964361580.5921238071276840.296061903563842
390.7437064650012610.5125870699974780.256293534998739
400.7274970982230110.5450058035539780.272502901776989
410.694602102994270.610795794011460.30539789700573
420.6691975244879460.6616049510241080.330802475512054
430.697283584139780.605432831720440.30271641586022
440.6516758262093650.696648347581270.348324173790635
450.6132976046141540.7734047907716910.386702395385846
460.8352846061298690.3294307877402620.164715393870131
470.852854064849460.2942918703010790.14714593515054
480.8272175973145110.3455648053709790.172782402685489
490.818937078192980.3621258436140390.18106292180702
500.8122932427766620.3754135144466760.187706757223338
510.7783884176894860.4432231646210270.221611582310514
520.7434983648185190.5130032703629630.256501635181481
530.7629939725408440.4740120549183120.237006027459156
540.735326374678160.5293472506436810.264673625321841
550.7709392400502760.4581215198994480.229060759949724
560.7780139875433510.4439720249132980.221986012456649
570.742259220104440.515481559791120.25774077989556
580.724895713179820.550208573640360.27510428682018
590.689992804552940.6200143908941210.310007195447061
600.7110758473301280.5778483053397440.288924152669872
610.6815587002897720.6368825994204570.318441299710228
620.6423168738254470.7153662523491060.357683126174553
630.6068366500528080.7863266998943840.393163349947192
640.5609875460920040.8780249078159910.439012453907996
650.5253879341605390.9492241316789220.474612065839461
660.5031482247479660.9937035505040680.496851775252034
670.512572912681960.974854174636080.48742708731804
680.6474540489322110.7050919021355780.352545951067789
690.7751113952936790.4497772094126420.224888604706321
700.7456447868124390.5087104263751230.254355213187561
710.8486399605567290.3027200788865430.151360039443271
720.8203822612550120.3592354774899760.179617738744988
730.8160408316592210.3679183366815570.183959168340779
740.7981027741458490.4037944517083030.201897225854152
750.7645552761505720.4708894476988570.235444723849428
760.8275377961700850.344924407659830.172462203829915
770.7960221211021080.4079557577957850.203977878897892
780.7841387205341250.4317225589317490.215861279465874
790.7984780975751640.4030438048496730.201521902424837
800.7638499286844080.4723001426311850.236150071315592
810.7296648581772860.5406702836454280.270335141822714
820.6923242509231230.6153514981537530.307675749076877
830.6607076542883080.6785846914233830.339292345711692
840.6183052649660070.7633894700679860.381694735033993
850.6067762891493590.7864474217012810.393223710850641
860.5623831143819680.8752337712360640.437616885618032
870.5205994095908520.9588011808182950.479400590409148
880.5032638178071860.9934723643856280.496736182192814
890.4667563947100360.9335127894200730.533243605289964
900.462679790689180.9253595813783610.53732020931082
910.4217298866974710.8434597733949420.578270113302529
920.3811786344442450.7623572688884910.618821365555755
930.3392379300725730.6784758601451470.660762069927427
940.3684757737477230.7369515474954470.631524226252277
950.3348241139861980.6696482279723950.665175886013802
960.2947408982984730.5894817965969470.705259101701527
970.2913857180883590.5827714361767190.708614281911641
980.2514389012317780.5028778024635570.748561098768222
990.2214379620769270.4428759241538540.778562037923073
1000.1999551575437720.3999103150875450.800044842456228
1010.184533705614110.369067411228220.81546629438589
1020.2382286576741940.4764573153483870.761771342325806
1030.2021748735642250.4043497471284510.797825126435775
1040.1998190284075030.3996380568150050.800180971592497
1050.2110543099185740.4221086198371480.788945690081426
1060.1933247689370780.3866495378741550.806675231062922
1070.1757576803812810.3515153607625630.824242319618719
1080.1768205148055390.3536410296110770.823179485194461
1090.1762786741433350.352557348286670.823721325856665
1100.1629073307552020.3258146615104040.837092669244798
1110.1417371043605210.2834742087210410.858262895639479
1120.1851886194733620.3703772389467230.814811380526638
1130.1565656564613290.3131313129226580.843434343538671
1140.1818834892495990.3637669784991980.818116510750401
1150.1948749879995130.3897499759990260.805125012000487
1160.1667519326824090.3335038653648190.833248067317591
1170.1626119906740670.3252239813481340.837388009325933
1180.1593224429816460.3186448859632930.840677557018354
1190.1876609765086430.3753219530172860.812339023491357
1200.156138609664680.312277219329360.84386139033532
1210.1441514107566520.2883028215133040.855848589243348
1220.1475893363909190.2951786727818390.852410663609081
1230.1199002307330850.2398004614661710.880099769266915
1240.0980827771079540.1961655542159080.901917222892046
1250.07653571118508920.1530714223701780.923464288814911
1260.05778064175850670.1155612835170130.942219358241493
1270.06211624505005910.1242324901001180.937883754949941
1280.0607259701100880.1214519402201760.939274029889912
1290.06455419217050280.1291083843410060.935445807829497
1300.07100933331905460.1420186666381090.928990666680945
1310.08551393680401010.171027873608020.91448606319599
1320.1718583282294270.3437166564588540.828141671770573
1330.1920765862930870.3841531725861730.807923413706913
1340.154644114583680.3092882291673590.84535588541632
1350.1243968148722710.2487936297445420.875603185127729
1360.1022647741657960.2045295483315930.897735225834204
1370.09055271030419770.1811054206083950.909447289695802
1380.1208688313862810.2417376627725630.879131168613719
1390.09938368785885680.1987673757177140.900616312141143
1400.2341036065881750.468207213176350.765896393411825
1410.2027724491372420.4055448982744850.797227550862758
1420.1806633541543580.3613267083087160.819336645845642
1430.1292396266897840.2584792533795680.870760373310216
1440.099648081596270.199296163192540.90035191840373
1450.1364238410185820.2728476820371630.863576158981418
1460.2349086326888580.4698172653777160.765091367311142
1470.53999887459010.9200022508198010.4600011254099
1480.4125192843924450.8250385687848890.587480715607555
1490.3036589504402570.6073179008805140.696341049559743
1500.7441921296219060.5116157407561890.255807870378095






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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185852&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185852&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185852&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 level00OK


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 ;
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')
}