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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 computationThu, 24 Nov 2011 10:48:50 -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/2011/Nov/24/t1322149893g3e4aqcjsi3asky.htm/, Retrieved Fri, 26 Apr 2024 21:26:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147006, Retrieved Fri, 26 Apr 2024 21:26:28 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact97

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-11-24 15:48:50] [46e17293cd0520480fa187e99449b207] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 14.2913833810387 + 0.0447596128803711Learning[t] + 0.0438619025707971Connected[t] -0.359814744565595Depression[t] + 0.0312054961791069`Belonging `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  14.2913833810387 +  0.0447596128803711Learning[t] +  0.0438619025707971Connected[t] -0.359814744565595Depression[t] +  0.0312054961791069`Belonging
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  14.2913833810387 +  0.0447596128803711Learning[t] +  0.0438619025707971Connected[t] -0.359814744565595Depression[t] +  0.0312054961791069`Belonging
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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
Happiness[t] = + 14.2913833810387 + 0.0447596128803711Learning[t] + 0.0438619025707971Connected[t] -0.359814744565595Depression[t] + 0.0312054961791069`Belonging `[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)14.29138338103872.2935716.231100
Learning0.04475961288037110.0712940.62780.5310360.265518
Connected0.04386190257079710.0467310.93860.3493790.17469
Depression-0.3598147445655950.051739-6.954500
`Belonging `0.03120549617910690.0149052.09370.0378980.018949

\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) & 14.2913833810387 & 2.293571 & 6.2311 & 0 & 0 \tabularnewline
Learning & 0.0447596128803711 & 0.071294 & 0.6278 & 0.531036 & 0.265518 \tabularnewline
Connected & 0.0438619025707971 & 0.046731 & 0.9386 & 0.349379 & 0.17469 \tabularnewline
Depression & -0.359814744565595 & 0.051739 & -6.9545 & 0 & 0 \tabularnewline
`Belonging
` & 0.0312054961791069 & 0.014905 & 2.0937 & 0.037898 & 0.018949 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&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]14.2913833810387[/C][C]2.293571[/C][C]6.2311[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Learning[/C][C]0.0447596128803711[/C][C]0.071294[/C][C]0.6278[/C][C]0.531036[/C][C]0.265518[/C][/ROW]
[ROW][C]Connected[/C][C]0.0438619025707971[/C][C]0.046731[/C][C]0.9386[/C][C]0.349379[/C][C]0.17469[/C][/ROW]
[ROW][C]Depression[/C][C]-0.359814744565595[/C][C]0.051739[/C][C]-6.9545[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Belonging
`[/C][C]0.0312054961791069[/C][C]0.014905[/C][C]2.0937[/C][C]0.037898[/C][C]0.018949[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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)14.29138338103872.2935716.231100
Learning0.04475961288037110.0712940.62780.5310360.265518
Connected0.04386190257079710.0467310.93860.3493790.17469
Depression-0.3598147445655950.051739-6.954500
`Belonging `0.03120549617910690.0149052.09370.0378980.018949






Multiple Linear Regression - Regression Statistics
Multiple R0.568382414414984
R-squared0.323058569016207
Adjusted R-squared0.305811653577129
F-TEST (value)18.731382440957
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.31583632878574e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.94765583064057
Sum Squared Residuals595.558027836631

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.568382414414984 \tabularnewline
R-squared & 0.323058569016207 \tabularnewline
Adjusted R-squared & 0.305811653577129 \tabularnewline
F-TEST (value) & 18.731382440957 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 1.31583632878574e-12 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.94765583064057 \tabularnewline
Sum Squared Residuals & 595.558027836631 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.568382414414984[/C][/ROW]
[ROW][C]R-squared[/C][C]0.323058569016207[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.305811653577129[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]18.731382440957[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]1.31583632878574e-12[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.94765583064057[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]595.558027836631[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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 R0.568382414414984
R-squared0.323058569016207
Adjusted R-squared0.305811653577129
F-TEST (value)18.731382440957
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.31583632878574e-12
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.94765583064057
Sum Squared Residuals595.558027836631






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11414.0077107165917-0.00771071659171896
21815.44386186856732.55613813143268
31113.4798294267923-2.47982942679234
41214.0954878631519-2.09548786315195
51611.22483072250564.77516927749443
61814.52467670564463.47532329435543
71410.59039714307643.40960285692364
81414.9925597657583-0.992559765758324
91515.2081057255105-0.208105725510501
101514.27969399962310.720306000376903
111715.54137623730841.45862376269163
121915.39314451682033.60685548317971
131013.3678365603311-3.36783656033111
141613.36584175713912.63415824286089
151815.61647030676762.38352969323239
161413.13887081097960.861129189020408
171413.84481185630760.155188143692402
181715.64144940987621.35855059012378
191415.2066749229533-1.20667492295331
201613.51223967224652.48776032775355
211815.9519776052522.04802239474801
221113.4518234830802-2.45182348308023
231414.2197500849114-0.219750084911419
241213.3405289443557-1.34052894435573
251715.48862148144771.51137851855231
26916.1087417986458-7.10874179864584
271615.00022119906390.999778800936138
281413.1661784269550.833821573045031
291513.85160225001291.1483977499871
301113.8224992134184-2.82249921341837
311615.58060354717710.419396452822876
321312.30064573020250.69935426979753
331715.1583511395441.84164886045602
341515.1621413633551-0.162141363355123
351414.0409376866721-0.0409376866721208
361615.34535382565860.654646174341414
37910.7806260523673-1.78062605236731
381514.3948597478420.605140252158011
391715.05300262563391.94699737436612
401315.0977355678049-2.09773556780491
411515.7013015984076-0.701301598407636
421613.71232843828562.28767156171437
431615.65990677348340.340093226516626
441213.038463928737-1.03846392873697
451214.779987305191-2.77998730519097
461113.8510115786688-2.85101157866876
471515.3747948096057-0.374794809605728
481514.7926703822920.207329617708007
491713.49855122844333.50144877155673
501314.9672202822656-1.96722028226561
511615.05976634862980.940233651370159
521413.37496066371120.625039336288817
531111.7213604749716-0.721360474971631
541213.396176213718-1.39617621371798
551214.1057848482897-2.10578484828969
561514.59474913130830.405250868691679
571614.34976642528681.65023357471315
581515.1319679045875-0.131967904587511
591215.2655217447826-3.26552174478256
601213.329867341156-1.32986734115604
61810.6019522189078-2.60195221890778
621314.759335755819-1.75933575581897
631114.5838881455358-3.58388814553578
641412.86301631845381.13698368154622
651513.50634698885071.49365301114928
661015.1259408940899-5.12594089408986
671112.61879699564-1.61879699563996
681214.3731377762969-2.3731377762969
691513.82746751579521.17253248420481
701513.65847658954251.34152341045747
711413.81890837218010.181091627819924
721612.56561256624643.4343874337536
731514.43841461144740.561585388552646
741515.318840501278-0.318840501278045
751315.1621413633551-2.16214136335512
761212.4228705478483-0.422870547848288
771714.13722063542882.86277936457117
781312.53517045327490.464829546725053
791513.82662314690431.17337685309571
801315.0771916748255-2.0771916748255
811514.96225197988880.0377480201112086
821614.14748347348281.85251652651718
831515.5199613047287-0.519961304728721
841614.54589225565141.45410774434863
851514.29537724661830.704622753381721
861414.1802114983994-0.180211498399387
871514.24759079313440.752409206865584
881414.2903822735321-0.290382273532127
891312.56740798686550.432592013134452
90710.6786807111749-3.67868071117491
911713.78862725701993.21137274298012
921312.91920091774150.080799082258501
931514.2679353035410.732064696459019
941413.52798797471260.472012025287438
951315.0435457717092-2.04354577170919
961615.25662889149270.74337110850732
971213.0265709055529-1.02657090555293
981415.0187702888513-1.01877028885128
991715.20011058253021.79988941746981
1001515.0950691075855-0.0950691075855265
1011715.44775974877111.55224025122895
1021213.1513928902694-1.15139289026936
1031615.10029437163170.899705628368287
1041114.4679590141092-3.46795901410923
1051513.16317825706081.83682174293919
106911.9132280447483-2.91322804474832
1071615.13270461708580.867295382914173
1081513.26442950819431.73557049180566
1091012.8744413048611-2.87444130486113
110109.514459500812010.485540499187991
1111513.84483852701691.15516147298307
1121113.2792417155893-2.27924171558928
1131315.2439991558103-2.24399915581033
1141412.06542691707111.93457308292888
1151814.50522990554763.49477009445242
1161615.80547203375210.194527966247934
1171413.01315111595360.98684888404641
1181413.91611570195570.0838842980443069
1191415.2355476685878-1.2355476685878
1201414.0855629724506-0.085562972450571
1211212.6085767585076-0.608576758507623
1221413.70146745251310.29853254748691
1231515.3593376158927-0.359337615892732
1241516.1235273353314-1.12352733533144
1251514.73025938993380.269740610066221
1261314.8745281747472-1.8745281747472
1271716.60336847410020.396631525899836
1281715.6932797847181.30672021528201
1291914.97990335936664.02009664063336
1301513.9328693711241.06713062887604
1311314.6687461078851-1.66874610788514
132910.8303806383338-1.83038063833383
1331515.5666890500918-0.566689050091752
1341512.64390618817262.35609381182735
1351514.53233813895010.467661861049893
1361613.88780271927822.11219728072184
137119.510749289128871.48925071087113
1381413.38262209701670.61737790298328
1391112.0276570804689-1.02765708046889
1401514.40285489082230.597145109177703
1411313.8445815653476-0.844581565347564
1421514.49806741740590.501932582594083
1431613.86248990649482.13751009350522
1441414.7340496137449-0.734049613744924
1451514.45230243782340.547697562176612
1461615.31278258239320.687217417606787
1471614.55447806997581.44552193002418
1481113.6470249324258-2.64702493242585
1491214.7935680926016-2.79356809260157
150911.8499460127899-2.84994601278987
1511614.74042204796961.2595779520304
1521313.153361022752-0.153361022752021
1531615.47609940215790.523900597842076
1541214.471722567211-2.47172256721104
155911.7698836409538-2.76988364095381
1561312.14378559446830.85621440553171
1571312.91920091774150.080799082258501
1581413.3926120431890.607387956810975
1591914.97990335936664.02009664063336
1601315.8817708524863-2.88177085248631
1611212.3766758947329-0.376675894732876
1621312.76070635919940.239293640800571

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 14.0077107165917 & -0.00771071659171896 \tabularnewline
2 & 18 & 15.4438618685673 & 2.55613813143268 \tabularnewline
3 & 11 & 13.4798294267923 & -2.47982942679234 \tabularnewline
4 & 12 & 14.0954878631519 & -2.09548786315195 \tabularnewline
5 & 16 & 11.2248307225056 & 4.77516927749443 \tabularnewline
6 & 18 & 14.5246767056446 & 3.47532329435543 \tabularnewline
7 & 14 & 10.5903971430764 & 3.40960285692364 \tabularnewline
8 & 14 & 14.9925597657583 & -0.992559765758324 \tabularnewline
9 & 15 & 15.2081057255105 & -0.208105725510501 \tabularnewline
10 & 15 & 14.2796939996231 & 0.720306000376903 \tabularnewline
11 & 17 & 15.5413762373084 & 1.45862376269163 \tabularnewline
12 & 19 & 15.3931445168203 & 3.60685548317971 \tabularnewline
13 & 10 & 13.3678365603311 & -3.36783656033111 \tabularnewline
14 & 16 & 13.3658417571391 & 2.63415824286089 \tabularnewline
15 & 18 & 15.6164703067676 & 2.38352969323239 \tabularnewline
16 & 14 & 13.1388708109796 & 0.861129189020408 \tabularnewline
17 & 14 & 13.8448118563076 & 0.155188143692402 \tabularnewline
18 & 17 & 15.6414494098762 & 1.35855059012378 \tabularnewline
19 & 14 & 15.2066749229533 & -1.20667492295331 \tabularnewline
20 & 16 & 13.5122396722465 & 2.48776032775355 \tabularnewline
21 & 18 & 15.951977605252 & 2.04802239474801 \tabularnewline
22 & 11 & 13.4518234830802 & -2.45182348308023 \tabularnewline
23 & 14 & 14.2197500849114 & -0.219750084911419 \tabularnewline
24 & 12 & 13.3405289443557 & -1.34052894435573 \tabularnewline
25 & 17 & 15.4886214814477 & 1.51137851855231 \tabularnewline
26 & 9 & 16.1087417986458 & -7.10874179864584 \tabularnewline
27 & 16 & 15.0002211990639 & 0.999778800936138 \tabularnewline
28 & 14 & 13.166178426955 & 0.833821573045031 \tabularnewline
29 & 15 & 13.8516022500129 & 1.1483977499871 \tabularnewline
30 & 11 & 13.8224992134184 & -2.82249921341837 \tabularnewline
31 & 16 & 15.5806035471771 & 0.419396452822876 \tabularnewline
32 & 13 & 12.3006457302025 & 0.69935426979753 \tabularnewline
33 & 17 & 15.158351139544 & 1.84164886045602 \tabularnewline
34 & 15 & 15.1621413633551 & -0.162141363355123 \tabularnewline
35 & 14 & 14.0409376866721 & -0.0409376866721208 \tabularnewline
36 & 16 & 15.3453538256586 & 0.654646174341414 \tabularnewline
37 & 9 & 10.7806260523673 & -1.78062605236731 \tabularnewline
38 & 15 & 14.394859747842 & 0.605140252158011 \tabularnewline
39 & 17 & 15.0530026256339 & 1.94699737436612 \tabularnewline
40 & 13 & 15.0977355678049 & -2.09773556780491 \tabularnewline
41 & 15 & 15.7013015984076 & -0.701301598407636 \tabularnewline
42 & 16 & 13.7123284382856 & 2.28767156171437 \tabularnewline
43 & 16 & 15.6599067734834 & 0.340093226516626 \tabularnewline
44 & 12 & 13.038463928737 & -1.03846392873697 \tabularnewline
45 & 12 & 14.779987305191 & -2.77998730519097 \tabularnewline
46 & 11 & 13.8510115786688 & -2.85101157866876 \tabularnewline
47 & 15 & 15.3747948096057 & -0.374794809605728 \tabularnewline
48 & 15 & 14.792670382292 & 0.207329617708007 \tabularnewline
49 & 17 & 13.4985512284433 & 3.50144877155673 \tabularnewline
50 & 13 & 14.9672202822656 & -1.96722028226561 \tabularnewline
51 & 16 & 15.0597663486298 & 0.940233651370159 \tabularnewline
52 & 14 & 13.3749606637112 & 0.625039336288817 \tabularnewline
53 & 11 & 11.7213604749716 & -0.721360474971631 \tabularnewline
54 & 12 & 13.396176213718 & -1.39617621371798 \tabularnewline
55 & 12 & 14.1057848482897 & -2.10578484828969 \tabularnewline
56 & 15 & 14.5947491313083 & 0.405250868691679 \tabularnewline
57 & 16 & 14.3497664252868 & 1.65023357471315 \tabularnewline
58 & 15 & 15.1319679045875 & -0.131967904587511 \tabularnewline
59 & 12 & 15.2655217447826 & -3.26552174478256 \tabularnewline
60 & 12 & 13.329867341156 & -1.32986734115604 \tabularnewline
61 & 8 & 10.6019522189078 & -2.60195221890778 \tabularnewline
62 & 13 & 14.759335755819 & -1.75933575581897 \tabularnewline
63 & 11 & 14.5838881455358 & -3.58388814553578 \tabularnewline
64 & 14 & 12.8630163184538 & 1.13698368154622 \tabularnewline
65 & 15 & 13.5063469888507 & 1.49365301114928 \tabularnewline
66 & 10 & 15.1259408940899 & -5.12594089408986 \tabularnewline
67 & 11 & 12.61879699564 & -1.61879699563996 \tabularnewline
68 & 12 & 14.3731377762969 & -2.3731377762969 \tabularnewline
69 & 15 & 13.8274675157952 & 1.17253248420481 \tabularnewline
70 & 15 & 13.6584765895425 & 1.34152341045747 \tabularnewline
71 & 14 & 13.8189083721801 & 0.181091627819924 \tabularnewline
72 & 16 & 12.5656125662464 & 3.4343874337536 \tabularnewline
73 & 15 & 14.4384146114474 & 0.561585388552646 \tabularnewline
74 & 15 & 15.318840501278 & -0.318840501278045 \tabularnewline
75 & 13 & 15.1621413633551 & -2.16214136335512 \tabularnewline
76 & 12 & 12.4228705478483 & -0.422870547848288 \tabularnewline
77 & 17 & 14.1372206354288 & 2.86277936457117 \tabularnewline
78 & 13 & 12.5351704532749 & 0.464829546725053 \tabularnewline
79 & 15 & 13.8266231469043 & 1.17337685309571 \tabularnewline
80 & 13 & 15.0771916748255 & -2.0771916748255 \tabularnewline
81 & 15 & 14.9622519798888 & 0.0377480201112086 \tabularnewline
82 & 16 & 14.1474834734828 & 1.85251652651718 \tabularnewline
83 & 15 & 15.5199613047287 & -0.519961304728721 \tabularnewline
84 & 16 & 14.5458922556514 & 1.45410774434863 \tabularnewline
85 & 15 & 14.2953772466183 & 0.704622753381721 \tabularnewline
86 & 14 & 14.1802114983994 & -0.180211498399387 \tabularnewline
87 & 15 & 14.2475907931344 & 0.752409206865584 \tabularnewline
88 & 14 & 14.2903822735321 & -0.290382273532127 \tabularnewline
89 & 13 & 12.5674079868655 & 0.432592013134452 \tabularnewline
90 & 7 & 10.6786807111749 & -3.67868071117491 \tabularnewline
91 & 17 & 13.7886272570199 & 3.21137274298012 \tabularnewline
92 & 13 & 12.9192009177415 & 0.080799082258501 \tabularnewline
93 & 15 & 14.267935303541 & 0.732064696459019 \tabularnewline
94 & 14 & 13.5279879747126 & 0.472012025287438 \tabularnewline
95 & 13 & 15.0435457717092 & -2.04354577170919 \tabularnewline
96 & 16 & 15.2566288914927 & 0.74337110850732 \tabularnewline
97 & 12 & 13.0265709055529 & -1.02657090555293 \tabularnewline
98 & 14 & 15.0187702888513 & -1.01877028885128 \tabularnewline
99 & 17 & 15.2001105825302 & 1.79988941746981 \tabularnewline
100 & 15 & 15.0950691075855 & -0.0950691075855265 \tabularnewline
101 & 17 & 15.4477597487711 & 1.55224025122895 \tabularnewline
102 & 12 & 13.1513928902694 & -1.15139289026936 \tabularnewline
103 & 16 & 15.1002943716317 & 0.899705628368287 \tabularnewline
104 & 11 & 14.4679590141092 & -3.46795901410923 \tabularnewline
105 & 15 & 13.1631782570608 & 1.83682174293919 \tabularnewline
106 & 9 & 11.9132280447483 & -2.91322804474832 \tabularnewline
107 & 16 & 15.1327046170858 & 0.867295382914173 \tabularnewline
108 & 15 & 13.2644295081943 & 1.73557049180566 \tabularnewline
109 & 10 & 12.8744413048611 & -2.87444130486113 \tabularnewline
110 & 10 & 9.51445950081201 & 0.485540499187991 \tabularnewline
111 & 15 & 13.8448385270169 & 1.15516147298307 \tabularnewline
112 & 11 & 13.2792417155893 & -2.27924171558928 \tabularnewline
113 & 13 & 15.2439991558103 & -2.24399915581033 \tabularnewline
114 & 14 & 12.0654269170711 & 1.93457308292888 \tabularnewline
115 & 18 & 14.5052299055476 & 3.49477009445242 \tabularnewline
116 & 16 & 15.8054720337521 & 0.194527966247934 \tabularnewline
117 & 14 & 13.0131511159536 & 0.98684888404641 \tabularnewline
118 & 14 & 13.9161157019557 & 0.0838842980443069 \tabularnewline
119 & 14 & 15.2355476685878 & -1.2355476685878 \tabularnewline
120 & 14 & 14.0855629724506 & -0.085562972450571 \tabularnewline
121 & 12 & 12.6085767585076 & -0.608576758507623 \tabularnewline
122 & 14 & 13.7014674525131 & 0.29853254748691 \tabularnewline
123 & 15 & 15.3593376158927 & -0.359337615892732 \tabularnewline
124 & 15 & 16.1235273353314 & -1.12352733533144 \tabularnewline
125 & 15 & 14.7302593899338 & 0.269740610066221 \tabularnewline
126 & 13 & 14.8745281747472 & -1.8745281747472 \tabularnewline
127 & 17 & 16.6033684741002 & 0.396631525899836 \tabularnewline
128 & 17 & 15.693279784718 & 1.30672021528201 \tabularnewline
129 & 19 & 14.9799033593666 & 4.02009664063336 \tabularnewline
130 & 15 & 13.932869371124 & 1.06713062887604 \tabularnewline
131 & 13 & 14.6687461078851 & -1.66874610788514 \tabularnewline
132 & 9 & 10.8303806383338 & -1.83038063833383 \tabularnewline
133 & 15 & 15.5666890500918 & -0.566689050091752 \tabularnewline
134 & 15 & 12.6439061881726 & 2.35609381182735 \tabularnewline
135 & 15 & 14.5323381389501 & 0.467661861049893 \tabularnewline
136 & 16 & 13.8878027192782 & 2.11219728072184 \tabularnewline
137 & 11 & 9.51074928912887 & 1.48925071087113 \tabularnewline
138 & 14 & 13.3826220970167 & 0.61737790298328 \tabularnewline
139 & 11 & 12.0276570804689 & -1.02765708046889 \tabularnewline
140 & 15 & 14.4028548908223 & 0.597145109177703 \tabularnewline
141 & 13 & 13.8445815653476 & -0.844581565347564 \tabularnewline
142 & 15 & 14.4980674174059 & 0.501932582594083 \tabularnewline
143 & 16 & 13.8624899064948 & 2.13751009350522 \tabularnewline
144 & 14 & 14.7340496137449 & -0.734049613744924 \tabularnewline
145 & 15 & 14.4523024378234 & 0.547697562176612 \tabularnewline
146 & 16 & 15.3127825823932 & 0.687217417606787 \tabularnewline
147 & 16 & 14.5544780699758 & 1.44552193002418 \tabularnewline
148 & 11 & 13.6470249324258 & -2.64702493242585 \tabularnewline
149 & 12 & 14.7935680926016 & -2.79356809260157 \tabularnewline
150 & 9 & 11.8499460127899 & -2.84994601278987 \tabularnewline
151 & 16 & 14.7404220479696 & 1.2595779520304 \tabularnewline
152 & 13 & 13.153361022752 & -0.153361022752021 \tabularnewline
153 & 16 & 15.4760994021579 & 0.523900597842076 \tabularnewline
154 & 12 & 14.471722567211 & -2.47172256721104 \tabularnewline
155 & 9 & 11.7698836409538 & -2.76988364095381 \tabularnewline
156 & 13 & 12.1437855944683 & 0.85621440553171 \tabularnewline
157 & 13 & 12.9192009177415 & 0.080799082258501 \tabularnewline
158 & 14 & 13.392612043189 & 0.607387956810975 \tabularnewline
159 & 19 & 14.9799033593666 & 4.02009664063336 \tabularnewline
160 & 13 & 15.8817708524863 & -2.88177085248631 \tabularnewline
161 & 12 & 12.3766758947329 & -0.376675894732876 \tabularnewline
162 & 13 & 12.7607063591994 & 0.239293640800571 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]14[/C][C]14.0077107165917[/C][C]-0.00771071659171896[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.4438618685673[/C][C]2.55613813143268[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.4798294267923[/C][C]-2.47982942679234[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.0954878631519[/C][C]-2.09548786315195[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.2248307225056[/C][C]4.77516927749443[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.5246767056446[/C][C]3.47532329435543[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.5903971430764[/C][C]3.40960285692364[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]14.9925597657583[/C][C]-0.992559765758324[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.2081057255105[/C][C]-0.208105725510501[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.2796939996231[/C][C]0.720306000376903[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]15.5413762373084[/C][C]1.45862376269163[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]15.3931445168203[/C][C]3.60685548317971[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]13.3678365603311[/C][C]-3.36783656033111[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]13.3658417571391[/C][C]2.63415824286089[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.6164703067676[/C][C]2.38352969323239[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]13.1388708109796[/C][C]0.861129189020408[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.8448118563076[/C][C]0.155188143692402[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.6414494098762[/C][C]1.35855059012378[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]15.2066749229533[/C][C]-1.20667492295331[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]13.5122396722465[/C][C]2.48776032775355[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.951977605252[/C][C]2.04802239474801[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.4518234830802[/C][C]-2.45182348308023[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]14.2197500849114[/C][C]-0.219750084911419[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]13.3405289443557[/C][C]-1.34052894435573[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4886214814477[/C][C]1.51137851855231[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.1087417986458[/C][C]-7.10874179864584[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0002211990639[/C][C]0.999778800936138[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.166178426955[/C][C]0.833821573045031[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.8516022500129[/C][C]1.1483977499871[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.8224992134184[/C][C]-2.82249921341837[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.5806035471771[/C][C]0.419396452822876[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.3006457302025[/C][C]0.69935426979753[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.158351139544[/C][C]1.84164886045602[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.1621413633551[/C][C]-0.162141363355123[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.0409376866721[/C][C]-0.0409376866721208[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3453538256586[/C][C]0.654646174341414[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.7806260523673[/C][C]-1.78062605236731[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]14.394859747842[/C][C]0.605140252158011[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.0530026256339[/C][C]1.94699737436612[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]15.0977355678049[/C][C]-2.09773556780491[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.7013015984076[/C][C]-0.701301598407636[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.7123284382856[/C][C]2.28767156171437[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.6599067734834[/C][C]0.340093226516626[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.038463928737[/C][C]-1.03846392873697[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]14.779987305191[/C][C]-2.77998730519097[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]13.8510115786688[/C][C]-2.85101157866876[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]15.3747948096057[/C][C]-0.374794809605728[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.792670382292[/C][C]0.207329617708007[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.4985512284433[/C][C]3.50144877155673[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.9672202822656[/C][C]-1.96722028226561[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.0597663486298[/C][C]0.940233651370159[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.3749606637112[/C][C]0.625039336288817[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.7213604749716[/C][C]-0.721360474971631[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]13.396176213718[/C][C]-1.39617621371798[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.1057848482897[/C][C]-2.10578484828969[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.5947491313083[/C][C]0.405250868691679[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.3497664252868[/C][C]1.65023357471315[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.1319679045875[/C][C]-0.131967904587511[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]15.2655217447826[/C][C]-3.26552174478256[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.329867341156[/C][C]-1.32986734115604[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.6019522189078[/C][C]-2.60195221890778[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]14.759335755819[/C][C]-1.75933575581897[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.5838881455358[/C][C]-3.58388814553578[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.8630163184538[/C][C]1.13698368154622[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.5063469888507[/C][C]1.49365301114928[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]15.1259408940899[/C][C]-5.12594089408986[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.61879699564[/C][C]-1.61879699563996[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]14.3731377762969[/C][C]-2.3731377762969[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]13.8274675157952[/C][C]1.17253248420481[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.6584765895425[/C][C]1.34152341045747[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.8189083721801[/C][C]0.181091627819924[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]12.5656125662464[/C][C]3.4343874337536[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.4384146114474[/C][C]0.561585388552646[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]15.318840501278[/C][C]-0.318840501278045[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]15.1621413633551[/C][C]-2.16214136335512[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]12.4228705478483[/C][C]-0.422870547848288[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.1372206354288[/C][C]2.86277936457117[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.5351704532749[/C][C]0.464829546725053[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.8266231469043[/C][C]1.17337685309571[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]15.0771916748255[/C][C]-2.0771916748255[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.9622519798888[/C][C]0.0377480201112086[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.1474834734828[/C][C]1.85251652651718[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.5199613047287[/C][C]-0.519961304728721[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]14.5458922556514[/C][C]1.45410774434863[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.2953772466183[/C][C]0.704622753381721[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.1802114983994[/C][C]-0.180211498399387[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]14.2475907931344[/C][C]0.752409206865584[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.2903822735321[/C][C]-0.290382273532127[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]12.5674079868655[/C][C]0.432592013134452[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.6786807111749[/C][C]-3.67868071117491[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]13.7886272570199[/C][C]3.21137274298012[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]12.9192009177415[/C][C]0.080799082258501[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.267935303541[/C][C]0.732064696459019[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.5279879747126[/C][C]0.472012025287438[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.0435457717092[/C][C]-2.04354577170919[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.2566288914927[/C][C]0.74337110850732[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]13.0265709055529[/C][C]-1.02657090555293[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]15.0187702888513[/C][C]-1.01877028885128[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]15.2001105825302[/C][C]1.79988941746981[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]15.0950691075855[/C][C]-0.0950691075855265[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.4477597487711[/C][C]1.55224025122895[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]13.1513928902694[/C][C]-1.15139289026936[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]15.1002943716317[/C][C]0.899705628368287[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.4679590141092[/C][C]-3.46795901410923[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.1631782570608[/C][C]1.83682174293919[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.9132280447483[/C][C]-2.91322804474832[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.1327046170858[/C][C]0.867295382914173[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]13.2644295081943[/C][C]1.73557049180566[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.8744413048611[/C][C]-2.87444130486113[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]9.51445950081201[/C][C]0.485540499187991[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.8448385270169[/C][C]1.15516147298307[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.2792417155893[/C][C]-2.27924171558928[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.2439991558103[/C][C]-2.24399915581033[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.0654269170711[/C][C]1.93457308292888[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.5052299055476[/C][C]3.49477009445242[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]15.8054720337521[/C][C]0.194527966247934[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.0131511159536[/C][C]0.98684888404641[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.9161157019557[/C][C]0.0838842980443069[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.2355476685878[/C][C]-1.2355476685878[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]14.0855629724506[/C][C]-0.085562972450571[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]12.6085767585076[/C][C]-0.608576758507623[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.7014674525131[/C][C]0.29853254748691[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.3593376158927[/C][C]-0.359337615892732[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.1235273353314[/C][C]-1.12352733533144[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.7302593899338[/C][C]0.269740610066221[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]14.8745281747472[/C][C]-1.8745281747472[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]16.6033684741002[/C][C]0.396631525899836[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]15.693279784718[/C][C]1.30672021528201[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]14.9799033593666[/C][C]4.02009664063336[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]13.932869371124[/C][C]1.06713062887604[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.6687461078851[/C][C]-1.66874610788514[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.8303806383338[/C][C]-1.83038063833383[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.5666890500918[/C][C]-0.566689050091752[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.6439061881726[/C][C]2.35609381182735[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.5323381389501[/C][C]0.467661861049893[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.8878027192782[/C][C]2.11219728072184[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]9.51074928912887[/C][C]1.48925071087113[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.3826220970167[/C][C]0.61737790298328[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]12.0276570804689[/C][C]-1.02765708046889[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]14.4028548908223[/C][C]0.597145109177703[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.8445815653476[/C][C]-0.844581565347564[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]14.4980674174059[/C][C]0.501932582594083[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]13.8624899064948[/C][C]2.13751009350522[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.7340496137449[/C][C]-0.734049613744924[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]14.4523024378234[/C][C]0.547697562176612[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.3127825823932[/C][C]0.687217417606787[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.5544780699758[/C][C]1.44552193002418[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.6470249324258[/C][C]-2.64702493242585[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.7935680926016[/C][C]-2.79356809260157[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]11.8499460127899[/C][C]-2.84994601278987[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]14.7404220479696[/C][C]1.2595779520304[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.153361022752[/C][C]-0.153361022752021[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.4760994021579[/C][C]0.523900597842076[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.471722567211[/C][C]-2.47172256721104[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.7698836409538[/C][C]-2.76988364095381[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.1437855944683[/C][C]0.85621440553171[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]12.9192009177415[/C][C]0.080799082258501[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.392612043189[/C][C]0.607387956810975[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]14.9799033593666[/C][C]4.02009664063336[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.8817708524863[/C][C]-2.88177085248631[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.3766758947329[/C][C]-0.376675894732876[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]12.7607063591994[/C][C]0.239293640800571[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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
11414.0077107165917-0.00771071659171896
21815.44386186856732.55613813143268
31113.4798294267923-2.47982942679234
41214.0954878631519-2.09548786315195
51611.22483072250564.77516927749443
61814.52467670564463.47532329435543
71410.59039714307643.40960285692364
81414.9925597657583-0.992559765758324
91515.2081057255105-0.208105725510501
101514.27969399962310.720306000376903
111715.54137623730841.45862376269163
121915.39314451682033.60685548317971
131013.3678365603311-3.36783656033111
141613.36584175713912.63415824286089
151815.61647030676762.38352969323239
161413.13887081097960.861129189020408
171413.84481185630760.155188143692402
181715.64144940987621.35855059012378
191415.2066749229533-1.20667492295331
201613.51223967224652.48776032775355
211815.9519776052522.04802239474801
221113.4518234830802-2.45182348308023
231414.2197500849114-0.219750084911419
241213.3405289443557-1.34052894435573
251715.48862148144771.51137851855231
26916.1087417986458-7.10874179864584
271615.00022119906390.999778800936138
281413.1661784269550.833821573045031
291513.85160225001291.1483977499871
301113.8224992134184-2.82249921341837
311615.58060354717710.419396452822876
321312.30064573020250.69935426979753
331715.1583511395441.84164886045602
341515.1621413633551-0.162141363355123
351414.0409376866721-0.0409376866721208
361615.34535382565860.654646174341414
37910.7806260523673-1.78062605236731
381514.3948597478420.605140252158011
391715.05300262563391.94699737436612
401315.0977355678049-2.09773556780491
411515.7013015984076-0.701301598407636
421613.71232843828562.28767156171437
431615.65990677348340.340093226516626
441213.038463928737-1.03846392873697
451214.779987305191-2.77998730519097
461113.8510115786688-2.85101157866876
471515.3747948096057-0.374794809605728
481514.7926703822920.207329617708007
491713.49855122844333.50144877155673
501314.9672202822656-1.96722028226561
511615.05976634862980.940233651370159
521413.37496066371120.625039336288817
531111.7213604749716-0.721360474971631
541213.396176213718-1.39617621371798
551214.1057848482897-2.10578484828969
561514.59474913130830.405250868691679
571614.34976642528681.65023357471315
581515.1319679045875-0.131967904587511
591215.2655217447826-3.26552174478256
601213.329867341156-1.32986734115604
61810.6019522189078-2.60195221890778
621314.759335755819-1.75933575581897
631114.5838881455358-3.58388814553578
641412.86301631845381.13698368154622
651513.50634698885071.49365301114928
661015.1259408940899-5.12594089408986
671112.61879699564-1.61879699563996
681214.3731377762969-2.3731377762969
691513.82746751579521.17253248420481
701513.65847658954251.34152341045747
711413.81890837218010.181091627819924
721612.56561256624643.4343874337536
731514.43841461144740.561585388552646
741515.318840501278-0.318840501278045
751315.1621413633551-2.16214136335512
761212.4228705478483-0.422870547848288
771714.13722063542882.86277936457117
781312.53517045327490.464829546725053
791513.82662314690431.17337685309571
801315.0771916748255-2.0771916748255
811514.96225197988880.0377480201112086
821614.14748347348281.85251652651718
831515.5199613047287-0.519961304728721
841614.54589225565141.45410774434863
851514.29537724661830.704622753381721
861414.1802114983994-0.180211498399387
871514.24759079313440.752409206865584
881414.2903822735321-0.290382273532127
891312.56740798686550.432592013134452
90710.6786807111749-3.67868071117491
911713.78862725701993.21137274298012
921312.91920091774150.080799082258501
931514.2679353035410.732064696459019
941413.52798797471260.472012025287438
951315.0435457717092-2.04354577170919
961615.25662889149270.74337110850732
971213.0265709055529-1.02657090555293
981415.0187702888513-1.01877028885128
991715.20011058253021.79988941746981
1001515.0950691075855-0.0950691075855265
1011715.44775974877111.55224025122895
1021213.1513928902694-1.15139289026936
1031615.10029437163170.899705628368287
1041114.4679590141092-3.46795901410923
1051513.16317825706081.83682174293919
106911.9132280447483-2.91322804474832
1071615.13270461708580.867295382914173
1081513.26442950819431.73557049180566
1091012.8744413048611-2.87444130486113
110109.514459500812010.485540499187991
1111513.84483852701691.15516147298307
1121113.2792417155893-2.27924171558928
1131315.2439991558103-2.24399915581033
1141412.06542691707111.93457308292888
1151814.50522990554763.49477009445242
1161615.80547203375210.194527966247934
1171413.01315111595360.98684888404641
1181413.91611570195570.0838842980443069
1191415.2355476685878-1.2355476685878
1201414.0855629724506-0.085562972450571
1211212.6085767585076-0.608576758507623
1221413.70146745251310.29853254748691
1231515.3593376158927-0.359337615892732
1241516.1235273353314-1.12352733533144
1251514.73025938993380.269740610066221
1261314.8745281747472-1.8745281747472
1271716.60336847410020.396631525899836
1281715.6932797847181.30672021528201
1291914.97990335936664.02009664063336
1301513.9328693711241.06713062887604
1311314.6687461078851-1.66874610788514
132910.8303806383338-1.83038063833383
1331515.5666890500918-0.566689050091752
1341512.64390618817262.35609381182735
1351514.53233813895010.467661861049893
1361613.88780271927822.11219728072184
137119.510749289128871.48925071087113
1381413.38262209701670.61737790298328
1391112.0276570804689-1.02765708046889
1401514.40285489082230.597145109177703
1411313.8445815653476-0.844581565347564
1421514.49806741740590.501932582594083
1431613.86248990649482.13751009350522
1441414.7340496137449-0.734049613744924
1451514.45230243782340.547697562176612
1461615.31278258239320.687217417606787
1471614.55447806997581.44552193002418
1481113.6470249324258-2.64702493242585
1491214.7935680926016-2.79356809260157
150911.8499460127899-2.84994601278987
1511614.74042204796961.2595779520304
1521313.153361022752-0.153361022752021
1531615.47609940215790.523900597842076
1541214.471722567211-2.47172256721104
155911.7698836409538-2.76988364095381
1561312.14378559446830.85621440553171
1571312.91920091774150.080799082258501
1581413.3926120431890.607387956810975
1591914.97990335936664.02009664063336
1601315.8817708524863-2.88177085248631
1611212.3766758947329-0.376675894732876
1621312.76070635919940.239293640800571






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.3650859650135030.7301719300270070.634914034986497
90.2121652273346820.4243304546693650.787834772665318
100.1544830584147010.3089661168294010.845516941585299
110.1014805307013430.2029610614026860.898519469298657
120.8684484968227690.2631030063544610.13155150317723
130.9819285485746820.03614290285063650.0180714514253182
140.9759132403036210.04817351939275830.0240867596963792
150.9775858779186140.04482824416277150.0224141220813857
160.9661153311624680.06776933767506430.0338846688375321
170.953983533500740.09203293299851910.0460164664992595
180.9337539726496780.1324920547006440.0662460273503222
190.9231999477490310.1536001045019370.0768000522509687
200.9320463955567980.1359072088864040.0679536044432022
210.934275230059310.1314495398813810.0657247699406904
220.9523283317923020.09534333641539520.0476716682076976
230.9350378725455830.1299242549088330.0649621274544166
240.9328368888254880.1343262223490250.0671631111745123
250.9125073158180680.1749853683638640.0874926841819321
260.9987960521849570.002407895630086820.00120394781504341
270.9981600461492480.003679907701503920.00183995385075196
280.9971738975746270.005652204850746750.00282610242537338
290.9958080168015140.008383966396972420.00419198319848621
300.9978947496626960.004210500674607420.00210525033730371
310.9967551088822820.0064897822354350.0032448911177175
320.9952061894782030.009587621043593030.00479381052179652
330.994349826153470.01130034769305980.0056501738465299
340.9918142356344510.01637152873109860.0081857643655493
350.9886424564399840.02271508712003150.0113575435600158
360.9840431500676170.03191369986476590.015956849932383
370.9880884370467380.02382312590652440.0119115629532622
380.9834720176818020.03305596463639540.0165279823181977
390.9822793957077940.03544120858441170.0177206042922059
400.9828831094266180.03423378114676410.017116890573382
410.9774220025599460.04515599488010840.0225779974400542
420.9767131968741280.04657360625174440.0232868031258722
430.9694584162129090.0610831675741820.030541583787091
440.9631799218237120.07364015635257630.0368200781762881
450.9718411454425280.0563177091149440.028158854557472
460.9787978472124860.04240430557502780.0212021527875139
470.9717989144240730.05640217115185460.0282010855759273
480.9627792819764810.07444143604703790.0372207180235189
490.9758041760444290.04839164791114220.0241958239555711
500.9753947994012030.04921040119759480.0246052005987974
510.9688199286109420.06236014277811620.0311800713890581
520.9598371863597430.08032562728051480.0401628136402574
530.9515485089770130.0969029820459740.048451491022987
540.9466292432077260.1067415135845480.0533707567922742
550.9465023312601710.1069953374796590.0534976687398294
560.9325982288014810.1348035423970370.0674017711985187
570.9263077511847550.147384497630490.0736922488152451
580.9083921210640690.1832157578718610.0916078789359307
590.9374184925779770.1251630148440470.0625815074220233
600.931947161082520.1361056778349610.0680528389174803
610.9438079291568310.1123841416863390.0561920708431694
620.9420697183989240.1158605632021530.0579302816010765
630.9690059799768750.06198804004625040.0309940200231252
640.9632969800023560.07340603999528740.0367030199976437
650.9589951080872460.08200978382550880.0410048919127544
660.9923499555437130.01530008891257480.00765004445628738
670.9916668533326220.01666629333475530.00833314666737765
680.99289739152580.01420521694839930.00710260847419963
690.9912719745518170.01745605089636640.00872802544818321
700.9896645276375250.02067094472495080.0103354723624754
710.9861431702514680.02771365949706470.0138568297485323
720.9925187051670690.01496258966586180.00748129483293091
730.9900625279588040.01987494408239240.00993747204119622
740.9866826656446480.02663466871070470.0133173343553524
750.9875587922667530.02488241546649330.0124412077332466
760.9835872474665180.03282550506696310.0164127525334815
770.9883081014854220.02338379702915650.0116918985145782
780.984708688231970.03058262353606070.0152913117680304
790.9816187612941740.03676247741165110.0183812387058255
800.9830524081086960.03389518378260730.0169475918913036
810.9775629276403920.04487414471921550.0224370723596077
820.9770079593999620.04598408120007530.0229920406000377
830.9706575934284030.05868481314319440.0293424065715972
840.9670219261971110.06595614760577810.032978073802889
850.9588739265752180.08225214684956390.0411260734247819
860.9476151809032520.1047696381934960.0523848190967481
870.9360053338733530.1279893322532950.0639946661266473
880.9203862942392750.159227411521450.0796137057607249
890.904414022055980.1911719558880410.0955859779440204
900.9433793429774650.113241314045070.0566206570225348
910.9629915541692480.07401689166150430.0370084458307522
920.9528085996149230.09438280077015380.0471914003850769
930.9421139467761570.1157721064476870.0578860532238433
940.9281058269568040.1437883460863910.0718941730431957
950.9313141251656560.1373717496686890.0686858748343443
960.916809718531180.1663805629376390.0831902814688197
970.9018218525794570.1963562948410870.0981781474205434
980.8861736812910550.227652637417890.113826318708945
990.8824561493159320.2350877013681360.117543850684068
1000.8572640454170430.2854719091659140.142735954582957
1010.8475905324252130.3048189351495730.152409467574787
1020.8267110642313260.3465778715373490.173288935768674
1030.8033342461643910.3933315076712170.196665753835609
1040.8680552884375220.2638894231249560.131944711562478
1050.868885954011710.262228091976580.13111404598829
1060.8977371124827490.2045257750345010.102262887517251
1070.8796251035462760.2407497929074490.120374896453724
1080.8796681589567630.2406636820864740.120331841043237
1090.9029446926312370.1941106147375270.0970553073687633
1100.8824847037338320.2350305925323360.117515296266168
1110.8677758111489560.2644483777020880.132224188851044
1120.8746509823452280.2506980353095440.125349017654772
1130.8859676132865830.2280647734268340.114032386713417
1140.893495910256290.213008179487420.10650408974371
1150.940425063645630.119149872708740.0595749363543698
1160.9229199518947060.1541600962105880.077080048105294
1170.9129969004603340.1740061990793320.087003099539666
1180.8896799334051660.2206401331896680.110320066594834
1190.8756404651911090.2487190696177830.124359534808891
1200.8452657597575470.3094684804849050.154734240242453
1210.8110143927566350.377971214486730.188985607243365
1220.7714149214298460.4571701571403080.228585078570154
1230.7317760531245210.5364478937509570.268223946875479
1240.7034662966170450.5930674067659090.296533703382955
1250.6525667999856420.6948664000287160.347433200014358
1260.6737198860098330.6525602279803330.326280113990167
1270.6210553977196490.7578892045607030.378944602280351
1280.6158417526103480.7683164947793040.384158247389652
1290.7557242432572470.4885515134855070.244275756742753
1300.7246183447482680.5507633105034640.275381655251732
1310.7179552249301010.5640895501397990.282044775069899
1320.732463425662220.5350731486755590.26753657433778
1330.678444309475870.643111381048260.32155569052413
1340.6713829575657260.6572340848685490.328617042434274
1350.6196648313449330.7606703373101340.380335168655067
1360.6155971563065740.7688056873868520.384402843693426
1370.6310064351079610.7379871297840780.368993564892039
1380.5914424599950490.8171150800099020.408557540004951
1390.5241311957039730.9517376085920540.475868804296027
1400.4656817529780220.9313635059560440.534318247021978
1410.4254351026138150.8508702052276310.574564897386185
1420.372952576458620.7459051529172410.62704742354138
1430.3695721450889250.739144290177850.630427854911075
1440.3081524375644440.6163048751288880.691847562435556
1450.2455999598786720.4911999197573450.754400040121328
1460.1841589051131720.3683178102263440.815841094886828
1470.196597921164260.3931958423285210.80340207883574
1480.2682849136699050.536569827339810.731715086330095
1490.2723082224384520.5446164448769030.727691777561548
1500.2660207650544460.5320415301088920.733979234945554
1510.2006216882550760.4012433765101520.799378311744924
1520.2423068842788050.484613768557610.757693115721195
1530.1503314279199780.3006628558399550.849668572080022
1540.1036909290635580.2073818581271160.896309070936442

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.365085965013503 & 0.730171930027007 & 0.634914034986497 \tabularnewline
9 & 0.212165227334682 & 0.424330454669365 & 0.787834772665318 \tabularnewline
10 & 0.154483058414701 & 0.308966116829401 & 0.845516941585299 \tabularnewline
11 & 0.101480530701343 & 0.202961061402686 & 0.898519469298657 \tabularnewline
12 & 0.868448496822769 & 0.263103006354461 & 0.13155150317723 \tabularnewline
13 & 0.981928548574682 & 0.0361429028506365 & 0.0180714514253182 \tabularnewline
14 & 0.975913240303621 & 0.0481735193927583 & 0.0240867596963792 \tabularnewline
15 & 0.977585877918614 & 0.0448282441627715 & 0.0224141220813857 \tabularnewline
16 & 0.966115331162468 & 0.0677693376750643 & 0.0338846688375321 \tabularnewline
17 & 0.95398353350074 & 0.0920329329985191 & 0.0460164664992595 \tabularnewline
18 & 0.933753972649678 & 0.132492054700644 & 0.0662460273503222 \tabularnewline
19 & 0.923199947749031 & 0.153600104501937 & 0.0768000522509687 \tabularnewline
20 & 0.932046395556798 & 0.135907208886404 & 0.0679536044432022 \tabularnewline
21 & 0.93427523005931 & 0.131449539881381 & 0.0657247699406904 \tabularnewline
22 & 0.952328331792302 & 0.0953433364153952 & 0.0476716682076976 \tabularnewline
23 & 0.935037872545583 & 0.129924254908833 & 0.0649621274544166 \tabularnewline
24 & 0.932836888825488 & 0.134326222349025 & 0.0671631111745123 \tabularnewline
25 & 0.912507315818068 & 0.174985368363864 & 0.0874926841819321 \tabularnewline
26 & 0.998796052184957 & 0.00240789563008682 & 0.00120394781504341 \tabularnewline
27 & 0.998160046149248 & 0.00367990770150392 & 0.00183995385075196 \tabularnewline
28 & 0.997173897574627 & 0.00565220485074675 & 0.00282610242537338 \tabularnewline
29 & 0.995808016801514 & 0.00838396639697242 & 0.00419198319848621 \tabularnewline
30 & 0.997894749662696 & 0.00421050067460742 & 0.00210525033730371 \tabularnewline
31 & 0.996755108882282 & 0.006489782235435 & 0.0032448911177175 \tabularnewline
32 & 0.995206189478203 & 0.00958762104359303 & 0.00479381052179652 \tabularnewline
33 & 0.99434982615347 & 0.0113003476930598 & 0.0056501738465299 \tabularnewline
34 & 0.991814235634451 & 0.0163715287310986 & 0.0081857643655493 \tabularnewline
35 & 0.988642456439984 & 0.0227150871200315 & 0.0113575435600158 \tabularnewline
36 & 0.984043150067617 & 0.0319136998647659 & 0.015956849932383 \tabularnewline
37 & 0.988088437046738 & 0.0238231259065244 & 0.0119115629532622 \tabularnewline
38 & 0.983472017681802 & 0.0330559646363954 & 0.0165279823181977 \tabularnewline
39 & 0.982279395707794 & 0.0354412085844117 & 0.0177206042922059 \tabularnewline
40 & 0.982883109426618 & 0.0342337811467641 & 0.017116890573382 \tabularnewline
41 & 0.977422002559946 & 0.0451559948801084 & 0.0225779974400542 \tabularnewline
42 & 0.976713196874128 & 0.0465736062517444 & 0.0232868031258722 \tabularnewline
43 & 0.969458416212909 & 0.061083167574182 & 0.030541583787091 \tabularnewline
44 & 0.963179921823712 & 0.0736401563525763 & 0.0368200781762881 \tabularnewline
45 & 0.971841145442528 & 0.056317709114944 & 0.028158854557472 \tabularnewline
46 & 0.978797847212486 & 0.0424043055750278 & 0.0212021527875139 \tabularnewline
47 & 0.971798914424073 & 0.0564021711518546 & 0.0282010855759273 \tabularnewline
48 & 0.962779281976481 & 0.0744414360470379 & 0.0372207180235189 \tabularnewline
49 & 0.975804176044429 & 0.0483916479111422 & 0.0241958239555711 \tabularnewline
50 & 0.975394799401203 & 0.0492104011975948 & 0.0246052005987974 \tabularnewline
51 & 0.968819928610942 & 0.0623601427781162 & 0.0311800713890581 \tabularnewline
52 & 0.959837186359743 & 0.0803256272805148 & 0.0401628136402574 \tabularnewline
53 & 0.951548508977013 & 0.096902982045974 & 0.048451491022987 \tabularnewline
54 & 0.946629243207726 & 0.106741513584548 & 0.0533707567922742 \tabularnewline
55 & 0.946502331260171 & 0.106995337479659 & 0.0534976687398294 \tabularnewline
56 & 0.932598228801481 & 0.134803542397037 & 0.0674017711985187 \tabularnewline
57 & 0.926307751184755 & 0.14738449763049 & 0.0736922488152451 \tabularnewline
58 & 0.908392121064069 & 0.183215757871861 & 0.0916078789359307 \tabularnewline
59 & 0.937418492577977 & 0.125163014844047 & 0.0625815074220233 \tabularnewline
60 & 0.93194716108252 & 0.136105677834961 & 0.0680528389174803 \tabularnewline
61 & 0.943807929156831 & 0.112384141686339 & 0.0561920708431694 \tabularnewline
62 & 0.942069718398924 & 0.115860563202153 & 0.0579302816010765 \tabularnewline
63 & 0.969005979976875 & 0.0619880400462504 & 0.0309940200231252 \tabularnewline
64 & 0.963296980002356 & 0.0734060399952874 & 0.0367030199976437 \tabularnewline
65 & 0.958995108087246 & 0.0820097838255088 & 0.0410048919127544 \tabularnewline
66 & 0.992349955543713 & 0.0153000889125748 & 0.00765004445628738 \tabularnewline
67 & 0.991666853332622 & 0.0166662933347553 & 0.00833314666737765 \tabularnewline
68 & 0.9928973915258 & 0.0142052169483993 & 0.00710260847419963 \tabularnewline
69 & 0.991271974551817 & 0.0174560508963664 & 0.00872802544818321 \tabularnewline
70 & 0.989664527637525 & 0.0206709447249508 & 0.0103354723624754 \tabularnewline
71 & 0.986143170251468 & 0.0277136594970647 & 0.0138568297485323 \tabularnewline
72 & 0.992518705167069 & 0.0149625896658618 & 0.00748129483293091 \tabularnewline
73 & 0.990062527958804 & 0.0198749440823924 & 0.00993747204119622 \tabularnewline
74 & 0.986682665644648 & 0.0266346687107047 & 0.0133173343553524 \tabularnewline
75 & 0.987558792266753 & 0.0248824154664933 & 0.0124412077332466 \tabularnewline
76 & 0.983587247466518 & 0.0328255050669631 & 0.0164127525334815 \tabularnewline
77 & 0.988308101485422 & 0.0233837970291565 & 0.0116918985145782 \tabularnewline
78 & 0.98470868823197 & 0.0305826235360607 & 0.0152913117680304 \tabularnewline
79 & 0.981618761294174 & 0.0367624774116511 & 0.0183812387058255 \tabularnewline
80 & 0.983052408108696 & 0.0338951837826073 & 0.0169475918913036 \tabularnewline
81 & 0.977562927640392 & 0.0448741447192155 & 0.0224370723596077 \tabularnewline
82 & 0.977007959399962 & 0.0459840812000753 & 0.0229920406000377 \tabularnewline
83 & 0.970657593428403 & 0.0586848131431944 & 0.0293424065715972 \tabularnewline
84 & 0.967021926197111 & 0.0659561476057781 & 0.032978073802889 \tabularnewline
85 & 0.958873926575218 & 0.0822521468495639 & 0.0411260734247819 \tabularnewline
86 & 0.947615180903252 & 0.104769638193496 & 0.0523848190967481 \tabularnewline
87 & 0.936005333873353 & 0.127989332253295 & 0.0639946661266473 \tabularnewline
88 & 0.920386294239275 & 0.15922741152145 & 0.0796137057607249 \tabularnewline
89 & 0.90441402205598 & 0.191171955888041 & 0.0955859779440204 \tabularnewline
90 & 0.943379342977465 & 0.11324131404507 & 0.0566206570225348 \tabularnewline
91 & 0.962991554169248 & 0.0740168916615043 & 0.0370084458307522 \tabularnewline
92 & 0.952808599614923 & 0.0943828007701538 & 0.0471914003850769 \tabularnewline
93 & 0.942113946776157 & 0.115772106447687 & 0.0578860532238433 \tabularnewline
94 & 0.928105826956804 & 0.143788346086391 & 0.0718941730431957 \tabularnewline
95 & 0.931314125165656 & 0.137371749668689 & 0.0686858748343443 \tabularnewline
96 & 0.91680971853118 & 0.166380562937639 & 0.0831902814688197 \tabularnewline
97 & 0.901821852579457 & 0.196356294841087 & 0.0981781474205434 \tabularnewline
98 & 0.886173681291055 & 0.22765263741789 & 0.113826318708945 \tabularnewline
99 & 0.882456149315932 & 0.235087701368136 & 0.117543850684068 \tabularnewline
100 & 0.857264045417043 & 0.285471909165914 & 0.142735954582957 \tabularnewline
101 & 0.847590532425213 & 0.304818935149573 & 0.152409467574787 \tabularnewline
102 & 0.826711064231326 & 0.346577871537349 & 0.173288935768674 \tabularnewline
103 & 0.803334246164391 & 0.393331507671217 & 0.196665753835609 \tabularnewline
104 & 0.868055288437522 & 0.263889423124956 & 0.131944711562478 \tabularnewline
105 & 0.86888595401171 & 0.26222809197658 & 0.13111404598829 \tabularnewline
106 & 0.897737112482749 & 0.204525775034501 & 0.102262887517251 \tabularnewline
107 & 0.879625103546276 & 0.240749792907449 & 0.120374896453724 \tabularnewline
108 & 0.879668158956763 & 0.240663682086474 & 0.120331841043237 \tabularnewline
109 & 0.902944692631237 & 0.194110614737527 & 0.0970553073687633 \tabularnewline
110 & 0.882484703733832 & 0.235030592532336 & 0.117515296266168 \tabularnewline
111 & 0.867775811148956 & 0.264448377702088 & 0.132224188851044 \tabularnewline
112 & 0.874650982345228 & 0.250698035309544 & 0.125349017654772 \tabularnewline
113 & 0.885967613286583 & 0.228064773426834 & 0.114032386713417 \tabularnewline
114 & 0.89349591025629 & 0.21300817948742 & 0.10650408974371 \tabularnewline
115 & 0.94042506364563 & 0.11914987270874 & 0.0595749363543698 \tabularnewline
116 & 0.922919951894706 & 0.154160096210588 & 0.077080048105294 \tabularnewline
117 & 0.912996900460334 & 0.174006199079332 & 0.087003099539666 \tabularnewline
118 & 0.889679933405166 & 0.220640133189668 & 0.110320066594834 \tabularnewline
119 & 0.875640465191109 & 0.248719069617783 & 0.124359534808891 \tabularnewline
120 & 0.845265759757547 & 0.309468480484905 & 0.154734240242453 \tabularnewline
121 & 0.811014392756635 & 0.37797121448673 & 0.188985607243365 \tabularnewline
122 & 0.771414921429846 & 0.457170157140308 & 0.228585078570154 \tabularnewline
123 & 0.731776053124521 & 0.536447893750957 & 0.268223946875479 \tabularnewline
124 & 0.703466296617045 & 0.593067406765909 & 0.296533703382955 \tabularnewline
125 & 0.652566799985642 & 0.694866400028716 & 0.347433200014358 \tabularnewline
126 & 0.673719886009833 & 0.652560227980333 & 0.326280113990167 \tabularnewline
127 & 0.621055397719649 & 0.757889204560703 & 0.378944602280351 \tabularnewline
128 & 0.615841752610348 & 0.768316494779304 & 0.384158247389652 \tabularnewline
129 & 0.755724243257247 & 0.488551513485507 & 0.244275756742753 \tabularnewline
130 & 0.724618344748268 & 0.550763310503464 & 0.275381655251732 \tabularnewline
131 & 0.717955224930101 & 0.564089550139799 & 0.282044775069899 \tabularnewline
132 & 0.73246342566222 & 0.535073148675559 & 0.26753657433778 \tabularnewline
133 & 0.67844430947587 & 0.64311138104826 & 0.32155569052413 \tabularnewline
134 & 0.671382957565726 & 0.657234084868549 & 0.328617042434274 \tabularnewline
135 & 0.619664831344933 & 0.760670337310134 & 0.380335168655067 \tabularnewline
136 & 0.615597156306574 & 0.768805687386852 & 0.384402843693426 \tabularnewline
137 & 0.631006435107961 & 0.737987129784078 & 0.368993564892039 \tabularnewline
138 & 0.591442459995049 & 0.817115080009902 & 0.408557540004951 \tabularnewline
139 & 0.524131195703973 & 0.951737608592054 & 0.475868804296027 \tabularnewline
140 & 0.465681752978022 & 0.931363505956044 & 0.534318247021978 \tabularnewline
141 & 0.425435102613815 & 0.850870205227631 & 0.574564897386185 \tabularnewline
142 & 0.37295257645862 & 0.745905152917241 & 0.62704742354138 \tabularnewline
143 & 0.369572145088925 & 0.73914429017785 & 0.630427854911075 \tabularnewline
144 & 0.308152437564444 & 0.616304875128888 & 0.691847562435556 \tabularnewline
145 & 0.245599959878672 & 0.491199919757345 & 0.754400040121328 \tabularnewline
146 & 0.184158905113172 & 0.368317810226344 & 0.815841094886828 \tabularnewline
147 & 0.19659792116426 & 0.393195842328521 & 0.80340207883574 \tabularnewline
148 & 0.268284913669905 & 0.53656982733981 & 0.731715086330095 \tabularnewline
149 & 0.272308222438452 & 0.544616444876903 & 0.727691777561548 \tabularnewline
150 & 0.266020765054446 & 0.532041530108892 & 0.733979234945554 \tabularnewline
151 & 0.200621688255076 & 0.401243376510152 & 0.799378311744924 \tabularnewline
152 & 0.242306884278805 & 0.48461376855761 & 0.757693115721195 \tabularnewline
153 & 0.150331427919978 & 0.300662855839955 & 0.849668572080022 \tabularnewline
154 & 0.103690929063558 & 0.207381858127116 & 0.896309070936442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&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]8[/C][C]0.365085965013503[/C][C]0.730171930027007[/C][C]0.634914034986497[/C][/ROW]
[ROW][C]9[/C][C]0.212165227334682[/C][C]0.424330454669365[/C][C]0.787834772665318[/C][/ROW]
[ROW][C]10[/C][C]0.154483058414701[/C][C]0.308966116829401[/C][C]0.845516941585299[/C][/ROW]
[ROW][C]11[/C][C]0.101480530701343[/C][C]0.202961061402686[/C][C]0.898519469298657[/C][/ROW]
[ROW][C]12[/C][C]0.868448496822769[/C][C]0.263103006354461[/C][C]0.13155150317723[/C][/ROW]
[ROW][C]13[/C][C]0.981928548574682[/C][C]0.0361429028506365[/C][C]0.0180714514253182[/C][/ROW]
[ROW][C]14[/C][C]0.975913240303621[/C][C]0.0481735193927583[/C][C]0.0240867596963792[/C][/ROW]
[ROW][C]15[/C][C]0.977585877918614[/C][C]0.0448282441627715[/C][C]0.0224141220813857[/C][/ROW]
[ROW][C]16[/C][C]0.966115331162468[/C][C]0.0677693376750643[/C][C]0.0338846688375321[/C][/ROW]
[ROW][C]17[/C][C]0.95398353350074[/C][C]0.0920329329985191[/C][C]0.0460164664992595[/C][/ROW]
[ROW][C]18[/C][C]0.933753972649678[/C][C]0.132492054700644[/C][C]0.0662460273503222[/C][/ROW]
[ROW][C]19[/C][C]0.923199947749031[/C][C]0.153600104501937[/C][C]0.0768000522509687[/C][/ROW]
[ROW][C]20[/C][C]0.932046395556798[/C][C]0.135907208886404[/C][C]0.0679536044432022[/C][/ROW]
[ROW][C]21[/C][C]0.93427523005931[/C][C]0.131449539881381[/C][C]0.0657247699406904[/C][/ROW]
[ROW][C]22[/C][C]0.952328331792302[/C][C]0.0953433364153952[/C][C]0.0476716682076976[/C][/ROW]
[ROW][C]23[/C][C]0.935037872545583[/C][C]0.129924254908833[/C][C]0.0649621274544166[/C][/ROW]
[ROW][C]24[/C][C]0.932836888825488[/C][C]0.134326222349025[/C][C]0.0671631111745123[/C][/ROW]
[ROW][C]25[/C][C]0.912507315818068[/C][C]0.174985368363864[/C][C]0.0874926841819321[/C][/ROW]
[ROW][C]26[/C][C]0.998796052184957[/C][C]0.00240789563008682[/C][C]0.00120394781504341[/C][/ROW]
[ROW][C]27[/C][C]0.998160046149248[/C][C]0.00367990770150392[/C][C]0.00183995385075196[/C][/ROW]
[ROW][C]28[/C][C]0.997173897574627[/C][C]0.00565220485074675[/C][C]0.00282610242537338[/C][/ROW]
[ROW][C]29[/C][C]0.995808016801514[/C][C]0.00838396639697242[/C][C]0.00419198319848621[/C][/ROW]
[ROW][C]30[/C][C]0.997894749662696[/C][C]0.00421050067460742[/C][C]0.00210525033730371[/C][/ROW]
[ROW][C]31[/C][C]0.996755108882282[/C][C]0.006489782235435[/C][C]0.0032448911177175[/C][/ROW]
[ROW][C]32[/C][C]0.995206189478203[/C][C]0.00958762104359303[/C][C]0.00479381052179652[/C][/ROW]
[ROW][C]33[/C][C]0.99434982615347[/C][C]0.0113003476930598[/C][C]0.0056501738465299[/C][/ROW]
[ROW][C]34[/C][C]0.991814235634451[/C][C]0.0163715287310986[/C][C]0.0081857643655493[/C][/ROW]
[ROW][C]35[/C][C]0.988642456439984[/C][C]0.0227150871200315[/C][C]0.0113575435600158[/C][/ROW]
[ROW][C]36[/C][C]0.984043150067617[/C][C]0.0319136998647659[/C][C]0.015956849932383[/C][/ROW]
[ROW][C]37[/C][C]0.988088437046738[/C][C]0.0238231259065244[/C][C]0.0119115629532622[/C][/ROW]
[ROW][C]38[/C][C]0.983472017681802[/C][C]0.0330559646363954[/C][C]0.0165279823181977[/C][/ROW]
[ROW][C]39[/C][C]0.982279395707794[/C][C]0.0354412085844117[/C][C]0.0177206042922059[/C][/ROW]
[ROW][C]40[/C][C]0.982883109426618[/C][C]0.0342337811467641[/C][C]0.017116890573382[/C][/ROW]
[ROW][C]41[/C][C]0.977422002559946[/C][C]0.0451559948801084[/C][C]0.0225779974400542[/C][/ROW]
[ROW][C]42[/C][C]0.976713196874128[/C][C]0.0465736062517444[/C][C]0.0232868031258722[/C][/ROW]
[ROW][C]43[/C][C]0.969458416212909[/C][C]0.061083167574182[/C][C]0.030541583787091[/C][/ROW]
[ROW][C]44[/C][C]0.963179921823712[/C][C]0.0736401563525763[/C][C]0.0368200781762881[/C][/ROW]
[ROW][C]45[/C][C]0.971841145442528[/C][C]0.056317709114944[/C][C]0.028158854557472[/C][/ROW]
[ROW][C]46[/C][C]0.978797847212486[/C][C]0.0424043055750278[/C][C]0.0212021527875139[/C][/ROW]
[ROW][C]47[/C][C]0.971798914424073[/C][C]0.0564021711518546[/C][C]0.0282010855759273[/C][/ROW]
[ROW][C]48[/C][C]0.962779281976481[/C][C]0.0744414360470379[/C][C]0.0372207180235189[/C][/ROW]
[ROW][C]49[/C][C]0.975804176044429[/C][C]0.0483916479111422[/C][C]0.0241958239555711[/C][/ROW]
[ROW][C]50[/C][C]0.975394799401203[/C][C]0.0492104011975948[/C][C]0.0246052005987974[/C][/ROW]
[ROW][C]51[/C][C]0.968819928610942[/C][C]0.0623601427781162[/C][C]0.0311800713890581[/C][/ROW]
[ROW][C]52[/C][C]0.959837186359743[/C][C]0.0803256272805148[/C][C]0.0401628136402574[/C][/ROW]
[ROW][C]53[/C][C]0.951548508977013[/C][C]0.096902982045974[/C][C]0.048451491022987[/C][/ROW]
[ROW][C]54[/C][C]0.946629243207726[/C][C]0.106741513584548[/C][C]0.0533707567922742[/C][/ROW]
[ROW][C]55[/C][C]0.946502331260171[/C][C]0.106995337479659[/C][C]0.0534976687398294[/C][/ROW]
[ROW][C]56[/C][C]0.932598228801481[/C][C]0.134803542397037[/C][C]0.0674017711985187[/C][/ROW]
[ROW][C]57[/C][C]0.926307751184755[/C][C]0.14738449763049[/C][C]0.0736922488152451[/C][/ROW]
[ROW][C]58[/C][C]0.908392121064069[/C][C]0.183215757871861[/C][C]0.0916078789359307[/C][/ROW]
[ROW][C]59[/C][C]0.937418492577977[/C][C]0.125163014844047[/C][C]0.0625815074220233[/C][/ROW]
[ROW][C]60[/C][C]0.93194716108252[/C][C]0.136105677834961[/C][C]0.0680528389174803[/C][/ROW]
[ROW][C]61[/C][C]0.943807929156831[/C][C]0.112384141686339[/C][C]0.0561920708431694[/C][/ROW]
[ROW][C]62[/C][C]0.942069718398924[/C][C]0.115860563202153[/C][C]0.0579302816010765[/C][/ROW]
[ROW][C]63[/C][C]0.969005979976875[/C][C]0.0619880400462504[/C][C]0.0309940200231252[/C][/ROW]
[ROW][C]64[/C][C]0.963296980002356[/C][C]0.0734060399952874[/C][C]0.0367030199976437[/C][/ROW]
[ROW][C]65[/C][C]0.958995108087246[/C][C]0.0820097838255088[/C][C]0.0410048919127544[/C][/ROW]
[ROW][C]66[/C][C]0.992349955543713[/C][C]0.0153000889125748[/C][C]0.00765004445628738[/C][/ROW]
[ROW][C]67[/C][C]0.991666853332622[/C][C]0.0166662933347553[/C][C]0.00833314666737765[/C][/ROW]
[ROW][C]68[/C][C]0.9928973915258[/C][C]0.0142052169483993[/C][C]0.00710260847419963[/C][/ROW]
[ROW][C]69[/C][C]0.991271974551817[/C][C]0.0174560508963664[/C][C]0.00872802544818321[/C][/ROW]
[ROW][C]70[/C][C]0.989664527637525[/C][C]0.0206709447249508[/C][C]0.0103354723624754[/C][/ROW]
[ROW][C]71[/C][C]0.986143170251468[/C][C]0.0277136594970647[/C][C]0.0138568297485323[/C][/ROW]
[ROW][C]72[/C][C]0.992518705167069[/C][C]0.0149625896658618[/C][C]0.00748129483293091[/C][/ROW]
[ROW][C]73[/C][C]0.990062527958804[/C][C]0.0198749440823924[/C][C]0.00993747204119622[/C][/ROW]
[ROW][C]74[/C][C]0.986682665644648[/C][C]0.0266346687107047[/C][C]0.0133173343553524[/C][/ROW]
[ROW][C]75[/C][C]0.987558792266753[/C][C]0.0248824154664933[/C][C]0.0124412077332466[/C][/ROW]
[ROW][C]76[/C][C]0.983587247466518[/C][C]0.0328255050669631[/C][C]0.0164127525334815[/C][/ROW]
[ROW][C]77[/C][C]0.988308101485422[/C][C]0.0233837970291565[/C][C]0.0116918985145782[/C][/ROW]
[ROW][C]78[/C][C]0.98470868823197[/C][C]0.0305826235360607[/C][C]0.0152913117680304[/C][/ROW]
[ROW][C]79[/C][C]0.981618761294174[/C][C]0.0367624774116511[/C][C]0.0183812387058255[/C][/ROW]
[ROW][C]80[/C][C]0.983052408108696[/C][C]0.0338951837826073[/C][C]0.0169475918913036[/C][/ROW]
[ROW][C]81[/C][C]0.977562927640392[/C][C]0.0448741447192155[/C][C]0.0224370723596077[/C][/ROW]
[ROW][C]82[/C][C]0.977007959399962[/C][C]0.0459840812000753[/C][C]0.0229920406000377[/C][/ROW]
[ROW][C]83[/C][C]0.970657593428403[/C][C]0.0586848131431944[/C][C]0.0293424065715972[/C][/ROW]
[ROW][C]84[/C][C]0.967021926197111[/C][C]0.0659561476057781[/C][C]0.032978073802889[/C][/ROW]
[ROW][C]85[/C][C]0.958873926575218[/C][C]0.0822521468495639[/C][C]0.0411260734247819[/C][/ROW]
[ROW][C]86[/C][C]0.947615180903252[/C][C]0.104769638193496[/C][C]0.0523848190967481[/C][/ROW]
[ROW][C]87[/C][C]0.936005333873353[/C][C]0.127989332253295[/C][C]0.0639946661266473[/C][/ROW]
[ROW][C]88[/C][C]0.920386294239275[/C][C]0.15922741152145[/C][C]0.0796137057607249[/C][/ROW]
[ROW][C]89[/C][C]0.90441402205598[/C][C]0.191171955888041[/C][C]0.0955859779440204[/C][/ROW]
[ROW][C]90[/C][C]0.943379342977465[/C][C]0.11324131404507[/C][C]0.0566206570225348[/C][/ROW]
[ROW][C]91[/C][C]0.962991554169248[/C][C]0.0740168916615043[/C][C]0.0370084458307522[/C][/ROW]
[ROW][C]92[/C][C]0.952808599614923[/C][C]0.0943828007701538[/C][C]0.0471914003850769[/C][/ROW]
[ROW][C]93[/C][C]0.942113946776157[/C][C]0.115772106447687[/C][C]0.0578860532238433[/C][/ROW]
[ROW][C]94[/C][C]0.928105826956804[/C][C]0.143788346086391[/C][C]0.0718941730431957[/C][/ROW]
[ROW][C]95[/C][C]0.931314125165656[/C][C]0.137371749668689[/C][C]0.0686858748343443[/C][/ROW]
[ROW][C]96[/C][C]0.91680971853118[/C][C]0.166380562937639[/C][C]0.0831902814688197[/C][/ROW]
[ROW][C]97[/C][C]0.901821852579457[/C][C]0.196356294841087[/C][C]0.0981781474205434[/C][/ROW]
[ROW][C]98[/C][C]0.886173681291055[/C][C]0.22765263741789[/C][C]0.113826318708945[/C][/ROW]
[ROW][C]99[/C][C]0.882456149315932[/C][C]0.235087701368136[/C][C]0.117543850684068[/C][/ROW]
[ROW][C]100[/C][C]0.857264045417043[/C][C]0.285471909165914[/C][C]0.142735954582957[/C][/ROW]
[ROW][C]101[/C][C]0.847590532425213[/C][C]0.304818935149573[/C][C]0.152409467574787[/C][/ROW]
[ROW][C]102[/C][C]0.826711064231326[/C][C]0.346577871537349[/C][C]0.173288935768674[/C][/ROW]
[ROW][C]103[/C][C]0.803334246164391[/C][C]0.393331507671217[/C][C]0.196665753835609[/C][/ROW]
[ROW][C]104[/C][C]0.868055288437522[/C][C]0.263889423124956[/C][C]0.131944711562478[/C][/ROW]
[ROW][C]105[/C][C]0.86888595401171[/C][C]0.26222809197658[/C][C]0.13111404598829[/C][/ROW]
[ROW][C]106[/C][C]0.897737112482749[/C][C]0.204525775034501[/C][C]0.102262887517251[/C][/ROW]
[ROW][C]107[/C][C]0.879625103546276[/C][C]0.240749792907449[/C][C]0.120374896453724[/C][/ROW]
[ROW][C]108[/C][C]0.879668158956763[/C][C]0.240663682086474[/C][C]0.120331841043237[/C][/ROW]
[ROW][C]109[/C][C]0.902944692631237[/C][C]0.194110614737527[/C][C]0.0970553073687633[/C][/ROW]
[ROW][C]110[/C][C]0.882484703733832[/C][C]0.235030592532336[/C][C]0.117515296266168[/C][/ROW]
[ROW][C]111[/C][C]0.867775811148956[/C][C]0.264448377702088[/C][C]0.132224188851044[/C][/ROW]
[ROW][C]112[/C][C]0.874650982345228[/C][C]0.250698035309544[/C][C]0.125349017654772[/C][/ROW]
[ROW][C]113[/C][C]0.885967613286583[/C][C]0.228064773426834[/C][C]0.114032386713417[/C][/ROW]
[ROW][C]114[/C][C]0.89349591025629[/C][C]0.21300817948742[/C][C]0.10650408974371[/C][/ROW]
[ROW][C]115[/C][C]0.94042506364563[/C][C]0.11914987270874[/C][C]0.0595749363543698[/C][/ROW]
[ROW][C]116[/C][C]0.922919951894706[/C][C]0.154160096210588[/C][C]0.077080048105294[/C][/ROW]
[ROW][C]117[/C][C]0.912996900460334[/C][C]0.174006199079332[/C][C]0.087003099539666[/C][/ROW]
[ROW][C]118[/C][C]0.889679933405166[/C][C]0.220640133189668[/C][C]0.110320066594834[/C][/ROW]
[ROW][C]119[/C][C]0.875640465191109[/C][C]0.248719069617783[/C][C]0.124359534808891[/C][/ROW]
[ROW][C]120[/C][C]0.845265759757547[/C][C]0.309468480484905[/C][C]0.154734240242453[/C][/ROW]
[ROW][C]121[/C][C]0.811014392756635[/C][C]0.37797121448673[/C][C]0.188985607243365[/C][/ROW]
[ROW][C]122[/C][C]0.771414921429846[/C][C]0.457170157140308[/C][C]0.228585078570154[/C][/ROW]
[ROW][C]123[/C][C]0.731776053124521[/C][C]0.536447893750957[/C][C]0.268223946875479[/C][/ROW]
[ROW][C]124[/C][C]0.703466296617045[/C][C]0.593067406765909[/C][C]0.296533703382955[/C][/ROW]
[ROW][C]125[/C][C]0.652566799985642[/C][C]0.694866400028716[/C][C]0.347433200014358[/C][/ROW]
[ROW][C]126[/C][C]0.673719886009833[/C][C]0.652560227980333[/C][C]0.326280113990167[/C][/ROW]
[ROW][C]127[/C][C]0.621055397719649[/C][C]0.757889204560703[/C][C]0.378944602280351[/C][/ROW]
[ROW][C]128[/C][C]0.615841752610348[/C][C]0.768316494779304[/C][C]0.384158247389652[/C][/ROW]
[ROW][C]129[/C][C]0.755724243257247[/C][C]0.488551513485507[/C][C]0.244275756742753[/C][/ROW]
[ROW][C]130[/C][C]0.724618344748268[/C][C]0.550763310503464[/C][C]0.275381655251732[/C][/ROW]
[ROW][C]131[/C][C]0.717955224930101[/C][C]0.564089550139799[/C][C]0.282044775069899[/C][/ROW]
[ROW][C]132[/C][C]0.73246342566222[/C][C]0.535073148675559[/C][C]0.26753657433778[/C][/ROW]
[ROW][C]133[/C][C]0.67844430947587[/C][C]0.64311138104826[/C][C]0.32155569052413[/C][/ROW]
[ROW][C]134[/C][C]0.671382957565726[/C][C]0.657234084868549[/C][C]0.328617042434274[/C][/ROW]
[ROW][C]135[/C][C]0.619664831344933[/C][C]0.760670337310134[/C][C]0.380335168655067[/C][/ROW]
[ROW][C]136[/C][C]0.615597156306574[/C][C]0.768805687386852[/C][C]0.384402843693426[/C][/ROW]
[ROW][C]137[/C][C]0.631006435107961[/C][C]0.737987129784078[/C][C]0.368993564892039[/C][/ROW]
[ROW][C]138[/C][C]0.591442459995049[/C][C]0.817115080009902[/C][C]0.408557540004951[/C][/ROW]
[ROW][C]139[/C][C]0.524131195703973[/C][C]0.951737608592054[/C][C]0.475868804296027[/C][/ROW]
[ROW][C]140[/C][C]0.465681752978022[/C][C]0.931363505956044[/C][C]0.534318247021978[/C][/ROW]
[ROW][C]141[/C][C]0.425435102613815[/C][C]0.850870205227631[/C][C]0.574564897386185[/C][/ROW]
[ROW][C]142[/C][C]0.37295257645862[/C][C]0.745905152917241[/C][C]0.62704742354138[/C][/ROW]
[ROW][C]143[/C][C]0.369572145088925[/C][C]0.73914429017785[/C][C]0.630427854911075[/C][/ROW]
[ROW][C]144[/C][C]0.308152437564444[/C][C]0.616304875128888[/C][C]0.691847562435556[/C][/ROW]
[ROW][C]145[/C][C]0.245599959878672[/C][C]0.491199919757345[/C][C]0.754400040121328[/C][/ROW]
[ROW][C]146[/C][C]0.184158905113172[/C][C]0.368317810226344[/C][C]0.815841094886828[/C][/ROW]
[ROW][C]147[/C][C]0.19659792116426[/C][C]0.393195842328521[/C][C]0.80340207883574[/C][/ROW]
[ROW][C]148[/C][C]0.268284913669905[/C][C]0.53656982733981[/C][C]0.731715086330095[/C][/ROW]
[ROW][C]149[/C][C]0.272308222438452[/C][C]0.544616444876903[/C][C]0.727691777561548[/C][/ROW]
[ROW][C]150[/C][C]0.266020765054446[/C][C]0.532041530108892[/C][C]0.733979234945554[/C][/ROW]
[ROW][C]151[/C][C]0.200621688255076[/C][C]0.401243376510152[/C][C]0.799378311744924[/C][/ROW]
[ROW][C]152[/C][C]0.242306884278805[/C][C]0.48461376855761[/C][C]0.757693115721195[/C][/ROW]
[ROW][C]153[/C][C]0.150331427919978[/C][C]0.300662855839955[/C][C]0.849668572080022[/C][/ROW]
[ROW][C]154[/C][C]0.103690929063558[/C][C]0.207381858127116[/C][C]0.896309070936442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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
80.3650859650135030.7301719300270070.634914034986497
90.2121652273346820.4243304546693650.787834772665318
100.1544830584147010.3089661168294010.845516941585299
110.1014805307013430.2029610614026860.898519469298657
120.8684484968227690.2631030063544610.13155150317723
130.9819285485746820.03614290285063650.0180714514253182
140.9759132403036210.04817351939275830.0240867596963792
150.9775858779186140.04482824416277150.0224141220813857
160.9661153311624680.06776933767506430.0338846688375321
170.953983533500740.09203293299851910.0460164664992595
180.9337539726496780.1324920547006440.0662460273503222
190.9231999477490310.1536001045019370.0768000522509687
200.9320463955567980.1359072088864040.0679536044432022
210.934275230059310.1314495398813810.0657247699406904
220.9523283317923020.09534333641539520.0476716682076976
230.9350378725455830.1299242549088330.0649621274544166
240.9328368888254880.1343262223490250.0671631111745123
250.9125073158180680.1749853683638640.0874926841819321
260.9987960521849570.002407895630086820.00120394781504341
270.9981600461492480.003679907701503920.00183995385075196
280.9971738975746270.005652204850746750.00282610242537338
290.9958080168015140.008383966396972420.00419198319848621
300.9978947496626960.004210500674607420.00210525033730371
310.9967551088822820.0064897822354350.0032448911177175
320.9952061894782030.009587621043593030.00479381052179652
330.994349826153470.01130034769305980.0056501738465299
340.9918142356344510.01637152873109860.0081857643655493
350.9886424564399840.02271508712003150.0113575435600158
360.9840431500676170.03191369986476590.015956849932383
370.9880884370467380.02382312590652440.0119115629532622
380.9834720176818020.03305596463639540.0165279823181977
390.9822793957077940.03544120858441170.0177206042922059
400.9828831094266180.03423378114676410.017116890573382
410.9774220025599460.04515599488010840.0225779974400542
420.9767131968741280.04657360625174440.0232868031258722
430.9694584162129090.0610831675741820.030541583787091
440.9631799218237120.07364015635257630.0368200781762881
450.9718411454425280.0563177091149440.028158854557472
460.9787978472124860.04240430557502780.0212021527875139
470.9717989144240730.05640217115185460.0282010855759273
480.9627792819764810.07444143604703790.0372207180235189
490.9758041760444290.04839164791114220.0241958239555711
500.9753947994012030.04921040119759480.0246052005987974
510.9688199286109420.06236014277811620.0311800713890581
520.9598371863597430.08032562728051480.0401628136402574
530.9515485089770130.0969029820459740.048451491022987
540.9466292432077260.1067415135845480.0533707567922742
550.9465023312601710.1069953374796590.0534976687398294
560.9325982288014810.1348035423970370.0674017711985187
570.9263077511847550.147384497630490.0736922488152451
580.9083921210640690.1832157578718610.0916078789359307
590.9374184925779770.1251630148440470.0625815074220233
600.931947161082520.1361056778349610.0680528389174803
610.9438079291568310.1123841416863390.0561920708431694
620.9420697183989240.1158605632021530.0579302816010765
630.9690059799768750.06198804004625040.0309940200231252
640.9632969800023560.07340603999528740.0367030199976437
650.9589951080872460.08200978382550880.0410048919127544
660.9923499555437130.01530008891257480.00765004445628738
670.9916668533326220.01666629333475530.00833314666737765
680.99289739152580.01420521694839930.00710260847419963
690.9912719745518170.01745605089636640.00872802544818321
700.9896645276375250.02067094472495080.0103354723624754
710.9861431702514680.02771365949706470.0138568297485323
720.9925187051670690.01496258966586180.00748129483293091
730.9900625279588040.01987494408239240.00993747204119622
740.9866826656446480.02663466871070470.0133173343553524
750.9875587922667530.02488241546649330.0124412077332466
760.9835872474665180.03282550506696310.0164127525334815
770.9883081014854220.02338379702915650.0116918985145782
780.984708688231970.03058262353606070.0152913117680304
790.9816187612941740.03676247741165110.0183812387058255
800.9830524081086960.03389518378260730.0169475918913036
810.9775629276403920.04487414471921550.0224370723596077
820.9770079593999620.04598408120007530.0229920406000377
830.9706575934284030.05868481314319440.0293424065715972
840.9670219261971110.06595614760577810.032978073802889
850.9588739265752180.08225214684956390.0411260734247819
860.9476151809032520.1047696381934960.0523848190967481
870.9360053338733530.1279893322532950.0639946661266473
880.9203862942392750.159227411521450.0796137057607249
890.904414022055980.1911719558880410.0955859779440204
900.9433793429774650.113241314045070.0566206570225348
910.9629915541692480.07401689166150430.0370084458307522
920.9528085996149230.09438280077015380.0471914003850769
930.9421139467761570.1157721064476870.0578860532238433
940.9281058269568040.1437883460863910.0718941730431957
950.9313141251656560.1373717496686890.0686858748343443
960.916809718531180.1663805629376390.0831902814688197
970.9018218525794570.1963562948410870.0981781474205434
980.8861736812910550.227652637417890.113826318708945
990.8824561493159320.2350877013681360.117543850684068
1000.8572640454170430.2854719091659140.142735954582957
1010.8475905324252130.3048189351495730.152409467574787
1020.8267110642313260.3465778715373490.173288935768674
1030.8033342461643910.3933315076712170.196665753835609
1040.8680552884375220.2638894231249560.131944711562478
1050.868885954011710.262228091976580.13111404598829
1060.8977371124827490.2045257750345010.102262887517251
1070.8796251035462760.2407497929074490.120374896453724
1080.8796681589567630.2406636820864740.120331841043237
1090.9029446926312370.1941106147375270.0970553073687633
1100.8824847037338320.2350305925323360.117515296266168
1110.8677758111489560.2644483777020880.132224188851044
1120.8746509823452280.2506980353095440.125349017654772
1130.8859676132865830.2280647734268340.114032386713417
1140.893495910256290.213008179487420.10650408974371
1150.940425063645630.119149872708740.0595749363543698
1160.9229199518947060.1541600962105880.077080048105294
1170.9129969004603340.1740061990793320.087003099539666
1180.8896799334051660.2206401331896680.110320066594834
1190.8756404651911090.2487190696177830.124359534808891
1200.8452657597575470.3094684804849050.154734240242453
1210.8110143927566350.377971214486730.188985607243365
1220.7714149214298460.4571701571403080.228585078570154
1230.7317760531245210.5364478937509570.268223946875479
1240.7034662966170450.5930674067659090.296533703382955
1250.6525667999856420.6948664000287160.347433200014358
1260.6737198860098330.6525602279803330.326280113990167
1270.6210553977196490.7578892045607030.378944602280351
1280.6158417526103480.7683164947793040.384158247389652
1290.7557242432572470.4885515134855070.244275756742753
1300.7246183447482680.5507633105034640.275381655251732
1310.7179552249301010.5640895501397990.282044775069899
1320.732463425662220.5350731486755590.26753657433778
1330.678444309475870.643111381048260.32155569052413
1340.6713829575657260.6572340848685490.328617042434274
1350.6196648313449330.7606703373101340.380335168655067
1360.6155971563065740.7688056873868520.384402843693426
1370.6310064351079610.7379871297840780.368993564892039
1380.5914424599950490.8171150800099020.408557540004951
1390.5241311957039730.9517376085920540.475868804296027
1400.4656817529780220.9313635059560440.534318247021978
1410.4254351026138150.8508702052276310.574564897386185
1420.372952576458620.7459051529172410.62704742354138
1430.3695721450889250.739144290177850.630427854911075
1440.3081524375644440.6163048751288880.691847562435556
1450.2455999598786720.4911999197573450.754400040121328
1460.1841589051131720.3683178102263440.815841094886828
1470.196597921164260.3931958423285210.80340207883574
1480.2682849136699050.536569827339810.731715086330095
1490.2723082224384520.5446164448769030.727691777561548
1500.2660207650544460.5320415301088920.733979234945554
1510.2006216882550760.4012433765101520.799378311744924
1520.2423068842788050.484613768557610.757693115721195
1530.1503314279199780.3006628558399550.849668572080022
1540.1036909290635580.2073818581271160.896309070936442






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level70.0476190476190476NOK
5% type I error level400.272108843537415NOK
10% type I error level590.401360544217687NOK

\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 & 7 & 0.0476190476190476 & NOK \tabularnewline
5% type I error level & 40 & 0.272108843537415 & NOK \tabularnewline
10% type I error level & 59 & 0.401360544217687 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147006&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]7[/C][C]0.0476190476190476[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]40[/C][C]0.272108843537415[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]59[/C][C]0.401360544217687[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147006&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147006&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 level70.0476190476190476NOK
5% type I error level400.272108843537415NOK
10% type I error level590.401360544217687NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 4
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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}