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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 computationWed, 14 Dec 2011 12:55:39 -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/Dec/14/t1323885364y4k1matl53eem58.htm/, Retrieved Wed, 01 May 2024 22:31:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155158, Retrieved Wed, 01 May 2024 22:31:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMP   [Kendall tau Correlation Matrix] [WS 10 Kendall's t...] [2011-12-14 16:01:30] [a98cda5652e91ff8cb6c2e418e82d3de]
- RMPD      [Multiple Regression] [WS 10 Multiple Re...] [2011-12-14 17:55:39] [312c935a345ba403c8b6fcfc5a5ec794] [Current]
- RMP         [Kendall tau Correlation Matrix] [WS 10 Kendall] [2011-12-14 17:58:35] [a98cda5652e91ff8cb6c2e418e82d3de]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13	53	41	7	2
16	86	39	5	2
19	66	30	5	2
15	67	31	5	1
14	76	34	8	2
13	78	35	6	2
19	53	39	5	2
15	80	34	6	2
14	74	36	5	2
15	76	37	4	2
16	79	38	6	1
16	54	36	5	2
16	67	38	5	1
16	54	39	6	2
17	87	33	7	2
15	58	32	6	1
15	75	36	7	1
20	88	38	6	2
18	64	39	8	1
16	57	32	7	2
16	66	32	5	1
16	68	31	5	2
19	54	39	7	2
16	56	37	7	2
17	86	39	5	1
17	80	41	4	2
16	76	36	10	1
15	69	33	6	2
16	78	33	5	2
14	67	34	5	1
15	80	31	5	2
12	54	27	5	1
14	71	37	6	2
16	84	34	5	2
14	74	34	5	1
7	71	32	5	1
10	63	29	5	1
14	71	36	5	1
16	76	29	5	2
16	69	35	5	1
16	74	37	5	1
14	75	34	7	2
20	54	38	5	1
14	52	35	6	1
14	69	38	7	2
11	68	37	7	2
14	65	38	5	2
15	75	33	5	2
16	74	36	4	2
14	75	38	5	1
16	72	32	4	2
14	67	32	5	1
12	63	32	5	1
16	62	34	7	2
9	63	32	5	1
14	76	37	5	2
16	74	39	6	2
16	67	29	4	2
15	73	37	6	1
16	70	35	6	2
12	53	30	5	1
16	77	38	7	1
16	77	34	6	2
14	52	31	8	2
16	54	34	7	2
17	80	35	5	1
18	66	36	6	2
18	73	30	6	1
12	63	39	5	2
16	69	35	5	1
10	67	38	5	1
14	54	31	5	2
18	81	34	4	2
18	69	38	6	1
16	84	34	6	1
17	80	39	6	2
16	70	37	6	2
16	69	34	7	2
13	77	28	5	1
16	54	37	7	1
16	79	33	6	1
20	30	37	5	1
16	71	35	5	2
15	73	37	4	1
15	72	32	8	2
16	77	33	8	2
14	75	38	5	1
16	69	33	5	2
16	54	29	6	2
15	70	33	4	2
12	73	31	5	2
17	54	36	5	2
16	77	35	5	2
15	82	32	5	2
13	80	29	6	2
16	80	39	6	2
16	69	37	5	2
16	78	35	6	2
16	81	37	5	1
14	76	32	7	1
16	76	38	5	2
16	73	37	6	1
20	85	36	6	2
15	66	32	6	1
16	79	33	4	2
13	68	40	5	1
17	76	38	5	2
16	71	41	7	1
16	54	36	6	1
12	46	43	9	2
16	82	30	6	2
16	74	31	6	2
17	88	32	5	2
13	38	32	6	1
12	76	37	5	2
18	86	37	8	1
14	54	33	7	2
14	70	34	5	2
13	69	33	7	2
16	90	38	6	2
13	54	33	6	2
16	76	31	9	2
13	89	38	7	2
16	76	37	6	2
15	73	33	5	2
16	79	31	5	2
15	90	39	6	1
17	74	44	6	2
15	81	33	7	2
12	72	35	5	2
16	71	32	5	1
10	66	28	5	1
16	77	40	6	2
12	65	27	4	1
14	74	37	5	1
15	82	32	7	2
13	54	28	5	1
15	63	34	7	1
11	54	30	7	2
12	64	35	6	2
8	69	31	5	1
16	54	32	8	2
15	84	30	5	1
17	86	30	5	2
16	77	31	5	1
10	89	40	6	2
18	76	32	4	2
13	60	36	5	1
16	75	32	5	1
13	73	35	7	1
10	85	38	6	2
15	79	42	7	2
16	71	34	10	1
16	72	35	6	2
14	69	35	8	2
10	78	33	4	2
17	54	36	5	2
13	69	32	6	2
15	81	33	7	2
16	84	34	7	2
12	84	32	6	2
13	69	34	6	2

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'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&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]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&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'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.50282042919936 + 0.0126911512935991Belonging[t] + 0.133736377698481Connected[t] + 0.0340105765377038Age[t] + 0.44538665056419Gender[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  8.50282042919936 +  0.0126911512935991Belonging[t] +  0.133736377698481Connected[t] +  0.0340105765377038Age[t] +  0.44538665056419Gender[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  8.50282042919936 +  0.0126911512935991Belonging[t] +  0.133736377698481Connected[t] +  0.0340105765377038Age[t] +  0.44538665056419Gender[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&T=1

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


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

Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 8.50282042919936 + 0.0126911512935991Belonging[t] + 0.133736377698481Connected[t] + 0.0340105765377038Age[t] + 0.44538665056419Gender[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.502820429199362.1948983.87390.0001577.8e-05
Belonging0.01269115129359910.016560.76640.4446060.222303
Connected0.1337363776984810.0525542.54470.0119010.00595
Age0.03401057653770380.153090.22220.8244770.412239
Gender0.445386650564190.3646621.22140.2237770.111888

\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) & 8.50282042919936 & 2.194898 & 3.8739 & 0.000157 & 7.8e-05 \tabularnewline
Belonging & 0.0126911512935991 & 0.01656 & 0.7664 & 0.444606 & 0.222303 \tabularnewline
Connected & 0.133736377698481 & 0.052554 & 2.5447 & 0.011901 & 0.00595 \tabularnewline
Age & 0.0340105765377038 & 0.15309 & 0.2222 & 0.824477 & 0.412239 \tabularnewline
Gender & 0.44538665056419 & 0.364662 & 1.2214 & 0.223777 & 0.111888 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&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]8.50282042919936[/C][C]2.194898[/C][C]3.8739[/C][C]0.000157[/C][C]7.8e-05[/C][/ROW]
[ROW][C]Belonging[/C][C]0.0126911512935991[/C][C]0.01656[/C][C]0.7664[/C][C]0.444606[/C][C]0.222303[/C][/ROW]
[ROW][C]Connected[/C][C]0.133736377698481[/C][C]0.052554[/C][C]2.5447[/C][C]0.011901[/C][C]0.00595[/C][/ROW]
[ROW][C]Age[/C][C]0.0340105765377038[/C][C]0.15309[/C][C]0.2222[/C][C]0.824477[/C][C]0.412239[/C][/ROW]
[ROW][C]Gender[/C][C]0.44538665056419[/C][C]0.364662[/C][C]1.2214[/C][C]0.223777[/C][C]0.111888[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&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)8.502820429199362.1948983.87390.0001577.8e-05
Belonging0.01269115129359910.016560.76640.4446060.222303
Connected0.1337363776984810.0525542.54470.0119010.00595
Age0.03401057653770380.153090.22220.8244770.412239
Gender0.445386650564190.3646621.22140.2237770.111888






Multiple Linear Regression - Regression Statistics
Multiple R0.245778157669621
R-squared0.060406902787473
Adjusted R-squared0.0364682251514851
F-TEST (value)2.52340182302555
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.0431354634466263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21473804354757
Sum Squared Residuals770.0951424413

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.245778157669621 \tabularnewline
R-squared & 0.060406902787473 \tabularnewline
Adjusted R-squared & 0.0364682251514851 \tabularnewline
F-TEST (value) & 2.52340182302555 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0.0431354634466263 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.21473804354757 \tabularnewline
Sum Squared Residuals & 770.0951424413 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.245778157669621[/C][/ROW]
[ROW][C]R-squared[/C][C]0.060406902787473[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0364682251514851[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.52340182302555[/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]0.0431354634466263[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.21473804354757[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]770.0951424413[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&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 R0.245778157669621
R-squared0.060406902787473
Adjusted R-squared0.0364682251514851
F-TEST (value)2.52340182302555
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value0.0431354634466263
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.21473804354757
Sum Squared Residuals770.0951424413






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11315.7874902702901-2.78749027029015
21615.87080435450650.129195645493473
31914.41335392934824.58664607065178
41514.11439480777610.885605192223893
51415.1772426826912-1.17724268269124
61315.2683402099015-2.26834020990151
71915.45199636181783.54800363818224
81515.1599861347902-0.159986134790232
91415.3173014058879-1.3173014058879
101515.4424095096359-0.44240950963587
111615.23685384372640.763146156273635
121615.06347838001590.936521619984086
131615.05054945166550.949450548334527
141615.49869808964910.501301910350941
151715.14909839268461.85090160731535
161514.16792140036990.8320785996301
171514.95262705969270.0473729403072886
182015.79646085593294.20353914406705
191815.24824410509632.75175589490373
201614.63462747617821.3653725238218
211614.2354400341811.76455996581901
221614.57247260963391.4275273903661
231915.53270866618683.46729133381324
241615.2906182133770.709381786623
251715.42541770394231.57458229605766
261716.02811962560420.97188037439581
271615.06734994059940.932650059400578
281514.88664709286220.113352907137839
291614.96685687796681.03314312203315
301414.5156039408716-0.515603940871549
311514.72476642515710.275233574842914
321213.4144643301654-1.4144643301654
331415.4469749062433-1.44697490624328
341615.17674016342690.823259836573075
351414.6044419999267-0.604441999926743
36714.298895790649-7.29889579064898
371013.7961574472047-3.79615744720475
381414.8338413014429-0.833841301442907
391614.40652906458571.59347093541427
401614.67472262115721.32527737884277
411615.00565113302220.994348866977814
421415.1305409548599-1.13054095485994
432014.88556448484875.11443551515132
441414.4929836257037-0.492983625703748
451415.5893395578923-1.58933955789227
461115.4429120289002-4.44291202890019
471415.4705537996425-1.47055379964246
481514.92878342408610.0712165759139481
491615.28329082935020.716709170649809
501415.1520786620143-1.15207866201426
511614.72296301596911.27703698403093
521414.2481311854746-0.248131185474588
531214.1973665803002-2.19736658030019
541614.96555598804321.03444401195685
55914.1973665803002-5.19736658030019
561415.4764200861736-1.47642008617357
571615.7525211155210.247478884478959
581614.25829812640561.74170187359437
591515.0269705582663-0.0269705582662902
601615.16681099955270.833189000447278
611213.8029823119672-1.80298231196724
621615.24548211767690.754517882323129
631615.12191268090940.878087319090565
641414.4714459185494-0.471445918549423
651614.86402677769441.13597322230564
661714.81432528538682.18567471461318
671815.24978277207682.75021722792319
681814.09081591437693.90918408562308
691215.5789078747537-3.57890787475375
701614.67472262115721.32527737884277
711015.0505494516655-5.05054945166547
721414.3947964915235-0.39479649152351
731815.10465613300842.89534386699158
741815.10994233079042.89005766920963
751614.76536408940041.23463591059956
761715.82866802328261.17133197671736
771615.43428375494970.565716245050317
781615.05439404709830.945605952901654
791313.8400971876167-0.840097187616656
801614.81984926022561.18015073977439
811614.5681719552341.43182804476604
822014.44724047610385.55275952389617
831615.14549157430860.854508425691383
841514.95894940519090.0410505948091176
851514.85900532211990.140994677880115
861615.05619745628640.943802543713638
871415.1520786620143-1.15207866201426
881614.85263651632451.14736348367554
891614.16133431266431.83866568733575
901514.83131709108040.168682908919647
911214.6359283661019-2.63592836610189
921715.06347838001591.93652161998409
931615.22163848207020.778361517929788
941514.88388510544280.116114894557235
951314.4913042462978-1.49130424629783
961615.82866802328260.171331976717364
971615.38758202711840.61241797288162
981615.26834020990150.731659790098485
991615.09448919207740.905510807922621
1001414.4303727001924-0.430372700192387
1011615.61015646387210.389843536127945
1021615.02697055826630.97302944173371
1032015.49091464665524.50908535334481
1041514.26945061071870.730549389281307
1051614.94553745272271.05446254727726
1061315.330713358356-2.33071335835603
1071715.61015646387211.38984353612795
1081615.57054434301070.429455656989281
1091614.65210230598941.34789769401057
1101216.0341461197073-4.0341461197073
1111614.65042292658351.34957707341649
1121614.68263009393321.3173699060668
1131714.96003201320442.03996798679564
1141313.9140983744979-0.914098374497919
1151215.4764200861736-3.47642008617357
1161815.25997667815852.74002332184151
1171414.7302903999959-0.730290399995879
1181414.9990640453165-0.999064045316537
1191314.9206576693999-1.92065766939987
1201615.82184315852010.178156841479854
1211314.6962798234582-1.69627982345817
1221614.81004412613351.18995587386649
1231315.8431625837643-2.84316258376425
1241615.51043066271130.489569337288722
1251514.90340112149890.0965988785011463
1261614.71207527386351.28792472613651
1271515.5101928856544-0.510192885654436
1281716.42120300401340.578796995986555
1291515.0729514849231-0.0729514849230541
1301215.1581827256022-3.15818272560222
1311614.2988957906491.70110420935102
1321013.7004945233871-3.70049452338707
1331615.92433094710030.0756690528996806
1341213.5200564178573-1.52005641785728
1351415.0056511330222-1.00565113302219
1361514.95190625851820.0480937414818276
1371313.5482007078639-0.548200707863877
1381514.53286048877260.467139511227439
1391114.3290812669004-3.32908126690044
1401215.0906640917911-3.09066409179113
141814.1397771103633-6.1397771103633
1421614.63056459883511.3694354011649
1431514.19640800206880.803591997931189
1441714.66717695522022.3328230447798
1451614.24130632071211.7586936792879
1461016.0766247626235-6.07662476262351
1471814.77372762114353.22627237885653
1481314.6942386372133-1.69423863721332
1491614.34966039582341.65033960417662
1501314.793508379407-1.79350837940703
1511015.7583874020522-5.75838740205215
1521516.2511965816222-1.25119658162218
1531614.73642142873451.26357857126553
1541615.19219330213990.80780669786008
1551415.2221410013345-1.22214100133453
1561014.9328463014291-4.93284630142915
1571715.06347838001591.93652161998409
1581314.7529107151637-1.75291071516368
1591515.0729514849231-0.0729514849230541
1601615.24476131650230.755238683497668
1611214.9432779845677-2.94327798456767
1621315.0203834705606-2.02038347056064

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 15.7874902702901 & -2.78749027029015 \tabularnewline
2 & 16 & 15.8708043545065 & 0.129195645493473 \tabularnewline
3 & 19 & 14.4133539293482 & 4.58664607065178 \tabularnewline
4 & 15 & 14.1143948077761 & 0.885605192223893 \tabularnewline
5 & 14 & 15.1772426826912 & -1.17724268269124 \tabularnewline
6 & 13 & 15.2683402099015 & -2.26834020990151 \tabularnewline
7 & 19 & 15.4519963618178 & 3.54800363818224 \tabularnewline
8 & 15 & 15.1599861347902 & -0.159986134790232 \tabularnewline
9 & 14 & 15.3173014058879 & -1.3173014058879 \tabularnewline
10 & 15 & 15.4424095096359 & -0.44240950963587 \tabularnewline
11 & 16 & 15.2368538437264 & 0.763146156273635 \tabularnewline
12 & 16 & 15.0634783800159 & 0.936521619984086 \tabularnewline
13 & 16 & 15.0505494516655 & 0.949450548334527 \tabularnewline
14 & 16 & 15.4986980896491 & 0.501301910350941 \tabularnewline
15 & 17 & 15.1490983926846 & 1.85090160731535 \tabularnewline
16 & 15 & 14.1679214003699 & 0.8320785996301 \tabularnewline
17 & 15 & 14.9526270596927 & 0.0473729403072886 \tabularnewline
18 & 20 & 15.7964608559329 & 4.20353914406705 \tabularnewline
19 & 18 & 15.2482441050963 & 2.75175589490373 \tabularnewline
20 & 16 & 14.6346274761782 & 1.3653725238218 \tabularnewline
21 & 16 & 14.235440034181 & 1.76455996581901 \tabularnewline
22 & 16 & 14.5724726096339 & 1.4275273903661 \tabularnewline
23 & 19 & 15.5327086661868 & 3.46729133381324 \tabularnewline
24 & 16 & 15.290618213377 & 0.709381786623 \tabularnewline
25 & 17 & 15.4254177039423 & 1.57458229605766 \tabularnewline
26 & 17 & 16.0281196256042 & 0.97188037439581 \tabularnewline
27 & 16 & 15.0673499405994 & 0.932650059400578 \tabularnewline
28 & 15 & 14.8866470928622 & 0.113352907137839 \tabularnewline
29 & 16 & 14.9668568779668 & 1.03314312203315 \tabularnewline
30 & 14 & 14.5156039408716 & -0.515603940871549 \tabularnewline
31 & 15 & 14.7247664251571 & 0.275233574842914 \tabularnewline
32 & 12 & 13.4144643301654 & -1.4144643301654 \tabularnewline
33 & 14 & 15.4469749062433 & -1.44697490624328 \tabularnewline
34 & 16 & 15.1767401634269 & 0.823259836573075 \tabularnewline
35 & 14 & 14.6044419999267 & -0.604441999926743 \tabularnewline
36 & 7 & 14.298895790649 & -7.29889579064898 \tabularnewline
37 & 10 & 13.7961574472047 & -3.79615744720475 \tabularnewline
38 & 14 & 14.8338413014429 & -0.833841301442907 \tabularnewline
39 & 16 & 14.4065290645857 & 1.59347093541427 \tabularnewline
40 & 16 & 14.6747226211572 & 1.32527737884277 \tabularnewline
41 & 16 & 15.0056511330222 & 0.994348866977814 \tabularnewline
42 & 14 & 15.1305409548599 & -1.13054095485994 \tabularnewline
43 & 20 & 14.8855644848487 & 5.11443551515132 \tabularnewline
44 & 14 & 14.4929836257037 & -0.492983625703748 \tabularnewline
45 & 14 & 15.5893395578923 & -1.58933955789227 \tabularnewline
46 & 11 & 15.4429120289002 & -4.44291202890019 \tabularnewline
47 & 14 & 15.4705537996425 & -1.47055379964246 \tabularnewline
48 & 15 & 14.9287834240861 & 0.0712165759139481 \tabularnewline
49 & 16 & 15.2832908293502 & 0.716709170649809 \tabularnewline
50 & 14 & 15.1520786620143 & -1.15207866201426 \tabularnewline
51 & 16 & 14.7229630159691 & 1.27703698403093 \tabularnewline
52 & 14 & 14.2481311854746 & -0.248131185474588 \tabularnewline
53 & 12 & 14.1973665803002 & -2.19736658030019 \tabularnewline
54 & 16 & 14.9655559880432 & 1.03444401195685 \tabularnewline
55 & 9 & 14.1973665803002 & -5.19736658030019 \tabularnewline
56 & 14 & 15.4764200861736 & -1.47642008617357 \tabularnewline
57 & 16 & 15.752521115521 & 0.247478884478959 \tabularnewline
58 & 16 & 14.2582981264056 & 1.74170187359437 \tabularnewline
59 & 15 & 15.0269705582663 & -0.0269705582662902 \tabularnewline
60 & 16 & 15.1668109995527 & 0.833189000447278 \tabularnewline
61 & 12 & 13.8029823119672 & -1.80298231196724 \tabularnewline
62 & 16 & 15.2454821176769 & 0.754517882323129 \tabularnewline
63 & 16 & 15.1219126809094 & 0.878087319090565 \tabularnewline
64 & 14 & 14.4714459185494 & -0.471445918549423 \tabularnewline
65 & 16 & 14.8640267776944 & 1.13597322230564 \tabularnewline
66 & 17 & 14.8143252853868 & 2.18567471461318 \tabularnewline
67 & 18 & 15.2497827720768 & 2.75021722792319 \tabularnewline
68 & 18 & 14.0908159143769 & 3.90918408562308 \tabularnewline
69 & 12 & 15.5789078747537 & -3.57890787475375 \tabularnewline
70 & 16 & 14.6747226211572 & 1.32527737884277 \tabularnewline
71 & 10 & 15.0505494516655 & -5.05054945166547 \tabularnewline
72 & 14 & 14.3947964915235 & -0.39479649152351 \tabularnewline
73 & 18 & 15.1046561330084 & 2.89534386699158 \tabularnewline
74 & 18 & 15.1099423307904 & 2.89005766920963 \tabularnewline
75 & 16 & 14.7653640894004 & 1.23463591059956 \tabularnewline
76 & 17 & 15.8286680232826 & 1.17133197671736 \tabularnewline
77 & 16 & 15.4342837549497 & 0.565716245050317 \tabularnewline
78 & 16 & 15.0543940470983 & 0.945605952901654 \tabularnewline
79 & 13 & 13.8400971876167 & -0.840097187616656 \tabularnewline
80 & 16 & 14.8198492602256 & 1.18015073977439 \tabularnewline
81 & 16 & 14.568171955234 & 1.43182804476604 \tabularnewline
82 & 20 & 14.4472404761038 & 5.55275952389617 \tabularnewline
83 & 16 & 15.1454915743086 & 0.854508425691383 \tabularnewline
84 & 15 & 14.9589494051909 & 0.0410505948091176 \tabularnewline
85 & 15 & 14.8590053221199 & 0.140994677880115 \tabularnewline
86 & 16 & 15.0561974562864 & 0.943802543713638 \tabularnewline
87 & 14 & 15.1520786620143 & -1.15207866201426 \tabularnewline
88 & 16 & 14.8526365163245 & 1.14736348367554 \tabularnewline
89 & 16 & 14.1613343126643 & 1.83866568733575 \tabularnewline
90 & 15 & 14.8313170910804 & 0.168682908919647 \tabularnewline
91 & 12 & 14.6359283661019 & -2.63592836610189 \tabularnewline
92 & 17 & 15.0634783800159 & 1.93652161998409 \tabularnewline
93 & 16 & 15.2216384820702 & 0.778361517929788 \tabularnewline
94 & 15 & 14.8838851054428 & 0.116114894557235 \tabularnewline
95 & 13 & 14.4913042462978 & -1.49130424629783 \tabularnewline
96 & 16 & 15.8286680232826 & 0.171331976717364 \tabularnewline
97 & 16 & 15.3875820271184 & 0.61241797288162 \tabularnewline
98 & 16 & 15.2683402099015 & 0.731659790098485 \tabularnewline
99 & 16 & 15.0944891920774 & 0.905510807922621 \tabularnewline
100 & 14 & 14.4303727001924 & -0.430372700192387 \tabularnewline
101 & 16 & 15.6101564638721 & 0.389843536127945 \tabularnewline
102 & 16 & 15.0269705582663 & 0.97302944173371 \tabularnewline
103 & 20 & 15.4909146466552 & 4.50908535334481 \tabularnewline
104 & 15 & 14.2694506107187 & 0.730549389281307 \tabularnewline
105 & 16 & 14.9455374527227 & 1.05446254727726 \tabularnewline
106 & 13 & 15.330713358356 & -2.33071335835603 \tabularnewline
107 & 17 & 15.6101564638721 & 1.38984353612795 \tabularnewline
108 & 16 & 15.5705443430107 & 0.429455656989281 \tabularnewline
109 & 16 & 14.6521023059894 & 1.34789769401057 \tabularnewline
110 & 12 & 16.0341461197073 & -4.0341461197073 \tabularnewline
111 & 16 & 14.6504229265835 & 1.34957707341649 \tabularnewline
112 & 16 & 14.6826300939332 & 1.3173699060668 \tabularnewline
113 & 17 & 14.9600320132044 & 2.03996798679564 \tabularnewline
114 & 13 & 13.9140983744979 & -0.914098374497919 \tabularnewline
115 & 12 & 15.4764200861736 & -3.47642008617357 \tabularnewline
116 & 18 & 15.2599766781585 & 2.74002332184151 \tabularnewline
117 & 14 & 14.7302903999959 & -0.730290399995879 \tabularnewline
118 & 14 & 14.9990640453165 & -0.999064045316537 \tabularnewline
119 & 13 & 14.9206576693999 & -1.92065766939987 \tabularnewline
120 & 16 & 15.8218431585201 & 0.178156841479854 \tabularnewline
121 & 13 & 14.6962798234582 & -1.69627982345817 \tabularnewline
122 & 16 & 14.8100441261335 & 1.18995587386649 \tabularnewline
123 & 13 & 15.8431625837643 & -2.84316258376425 \tabularnewline
124 & 16 & 15.5104306627113 & 0.489569337288722 \tabularnewline
125 & 15 & 14.9034011214989 & 0.0965988785011463 \tabularnewline
126 & 16 & 14.7120752738635 & 1.28792472613651 \tabularnewline
127 & 15 & 15.5101928856544 & -0.510192885654436 \tabularnewline
128 & 17 & 16.4212030040134 & 0.578796995986555 \tabularnewline
129 & 15 & 15.0729514849231 & -0.0729514849230541 \tabularnewline
130 & 12 & 15.1581827256022 & -3.15818272560222 \tabularnewline
131 & 16 & 14.298895790649 & 1.70110420935102 \tabularnewline
132 & 10 & 13.7004945233871 & -3.70049452338707 \tabularnewline
133 & 16 & 15.9243309471003 & 0.0756690528996806 \tabularnewline
134 & 12 & 13.5200564178573 & -1.52005641785728 \tabularnewline
135 & 14 & 15.0056511330222 & -1.00565113302219 \tabularnewline
136 & 15 & 14.9519062585182 & 0.0480937414818276 \tabularnewline
137 & 13 & 13.5482007078639 & -0.548200707863877 \tabularnewline
138 & 15 & 14.5328604887726 & 0.467139511227439 \tabularnewline
139 & 11 & 14.3290812669004 & -3.32908126690044 \tabularnewline
140 & 12 & 15.0906640917911 & -3.09066409179113 \tabularnewline
141 & 8 & 14.1397771103633 & -6.1397771103633 \tabularnewline
142 & 16 & 14.6305645988351 & 1.3694354011649 \tabularnewline
143 & 15 & 14.1964080020688 & 0.803591997931189 \tabularnewline
144 & 17 & 14.6671769552202 & 2.3328230447798 \tabularnewline
145 & 16 & 14.2413063207121 & 1.7586936792879 \tabularnewline
146 & 10 & 16.0766247626235 & -6.07662476262351 \tabularnewline
147 & 18 & 14.7737276211435 & 3.22627237885653 \tabularnewline
148 & 13 & 14.6942386372133 & -1.69423863721332 \tabularnewline
149 & 16 & 14.3496603958234 & 1.65033960417662 \tabularnewline
150 & 13 & 14.793508379407 & -1.79350837940703 \tabularnewline
151 & 10 & 15.7583874020522 & -5.75838740205215 \tabularnewline
152 & 15 & 16.2511965816222 & -1.25119658162218 \tabularnewline
153 & 16 & 14.7364214287345 & 1.26357857126553 \tabularnewline
154 & 16 & 15.1921933021399 & 0.80780669786008 \tabularnewline
155 & 14 & 15.2221410013345 & -1.22214100133453 \tabularnewline
156 & 10 & 14.9328463014291 & -4.93284630142915 \tabularnewline
157 & 17 & 15.0634783800159 & 1.93652161998409 \tabularnewline
158 & 13 & 14.7529107151637 & -1.75291071516368 \tabularnewline
159 & 15 & 15.0729514849231 & -0.0729514849230541 \tabularnewline
160 & 16 & 15.2447613165023 & 0.755238683497668 \tabularnewline
161 & 12 & 14.9432779845677 & -2.94327798456767 \tabularnewline
162 & 13 & 15.0203834705606 & -2.02038347056064 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]15.7874902702901[/C][C]-2.78749027029015[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.8708043545065[/C][C]0.129195645493473[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]14.4133539293482[/C][C]4.58664607065178[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]14.1143948077761[/C][C]0.885605192223893[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]15.1772426826912[/C][C]-1.17724268269124[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.2683402099015[/C][C]-2.26834020990151[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]15.4519963618178[/C][C]3.54800363818224[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]15.1599861347902[/C][C]-0.159986134790232[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.3173014058879[/C][C]-1.3173014058879[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]15.4424095096359[/C][C]-0.44240950963587[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.2368538437264[/C][C]0.763146156273635[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]15.0634783800159[/C][C]0.936521619984086[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.0505494516655[/C][C]0.949450548334527[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.4986980896491[/C][C]0.501301910350941[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]15.1490983926846[/C][C]1.85090160731535[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]14.1679214003699[/C][C]0.8320785996301[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.9526270596927[/C][C]0.0473729403072886[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]15.7964608559329[/C][C]4.20353914406705[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.2482441050963[/C][C]2.75175589490373[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.6346274761782[/C][C]1.3653725238218[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]14.235440034181[/C][C]1.76455996581901[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.5724726096339[/C][C]1.4275273903661[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]15.5327086661868[/C][C]3.46729133381324[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]15.290618213377[/C][C]0.709381786623[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]15.4254177039423[/C][C]1.57458229605766[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.0281196256042[/C][C]0.97188037439581[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]15.0673499405994[/C][C]0.932650059400578[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]14.8866470928622[/C][C]0.113352907137839[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]14.9668568779668[/C][C]1.03314312203315[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]14.5156039408716[/C][C]-0.515603940871549[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]14.7247664251571[/C][C]0.275233574842914[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.4144643301654[/C][C]-1.4144643301654[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.4469749062433[/C][C]-1.44697490624328[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.1767401634269[/C][C]0.823259836573075[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]14.6044419999267[/C][C]-0.604441999926743[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]14.298895790649[/C][C]-7.29889579064898[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]13.7961574472047[/C][C]-3.79615744720475[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.8338413014429[/C][C]-0.833841301442907[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.4065290645857[/C][C]1.59347093541427[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.6747226211572[/C][C]1.32527737884277[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]15.0056511330222[/C][C]0.994348866977814[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.1305409548599[/C][C]-1.13054095485994[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]14.8855644848487[/C][C]5.11443551515132[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.4929836257037[/C][C]-0.492983625703748[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]15.5893395578923[/C][C]-1.58933955789227[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.4429120289002[/C][C]-4.44291202890019[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]15.4705537996425[/C][C]-1.47055379964246[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.9287834240861[/C][C]0.0712165759139481[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.2832908293502[/C][C]0.716709170649809[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.1520786620143[/C][C]-1.15207866201426[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]14.7229630159691[/C][C]1.27703698403093[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.2481311854746[/C][C]-0.248131185474588[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.1973665803002[/C][C]-2.19736658030019[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]14.9655559880432[/C][C]1.03444401195685[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]14.1973665803002[/C][C]-5.19736658030019[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]15.4764200861736[/C][C]-1.47642008617357[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]15.752521115521[/C][C]0.247478884478959[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.2582981264056[/C][C]1.74170187359437[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]15.0269705582663[/C][C]-0.0269705582662902[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]15.1668109995527[/C][C]0.833189000447278[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]13.8029823119672[/C][C]-1.80298231196724[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.2454821176769[/C][C]0.754517882323129[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]15.1219126809094[/C][C]0.878087319090565[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4714459185494[/C][C]-0.471445918549423[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]14.8640267776944[/C][C]1.13597322230564[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]14.8143252853868[/C][C]2.18567471461318[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]15.2497827720768[/C][C]2.75021722792319[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.0908159143769[/C][C]3.90918408562308[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.5789078747537[/C][C]-3.57890787475375[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]14.6747226211572[/C][C]1.32527737884277[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]15.0505494516655[/C][C]-5.05054945166547[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.3947964915235[/C][C]-0.39479649152351[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]15.1046561330084[/C][C]2.89534386699158[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]15.1099423307904[/C][C]2.89005766920963[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]14.7653640894004[/C][C]1.23463591059956[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]15.8286680232826[/C][C]1.17133197671736[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]15.4342837549497[/C][C]0.565716245050317[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]15.0543940470983[/C][C]0.945605952901654[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]13.8400971876167[/C][C]-0.840097187616656[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]14.8198492602256[/C][C]1.18015073977439[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]14.568171955234[/C][C]1.43182804476604[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]14.4472404761038[/C][C]5.55275952389617[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.1454915743086[/C][C]0.854508425691383[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]14.9589494051909[/C][C]0.0410505948091176[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.8590053221199[/C][C]0.140994677880115[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]15.0561974562864[/C][C]0.943802543713638[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]15.1520786620143[/C][C]-1.15207866201426[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]14.8526365163245[/C][C]1.14736348367554[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.1613343126643[/C][C]1.83866568733575[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.8313170910804[/C][C]0.168682908919647[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]14.6359283661019[/C][C]-2.63592836610189[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]15.0634783800159[/C][C]1.93652161998409[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.2216384820702[/C][C]0.778361517929788[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]14.8838851054428[/C][C]0.116114894557235[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.4913042462978[/C][C]-1.49130424629783[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.8286680232826[/C][C]0.171331976717364[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]15.3875820271184[/C][C]0.61241797288162[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]15.2683402099015[/C][C]0.731659790098485[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]15.0944891920774[/C][C]0.905510807922621[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.4303727001924[/C][C]-0.430372700192387[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]15.6101564638721[/C][C]0.389843536127945[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]15.0269705582663[/C][C]0.97302944173371[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]15.4909146466552[/C][C]4.50908535334481[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.2694506107187[/C][C]0.730549389281307[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.9455374527227[/C][C]1.05446254727726[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.330713358356[/C][C]-2.33071335835603[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]15.6101564638721[/C][C]1.38984353612795[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]15.5705443430107[/C][C]0.429455656989281[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.6521023059894[/C][C]1.34789769401057[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]16.0341461197073[/C][C]-4.0341461197073[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]14.6504229265835[/C][C]1.34957707341649[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]14.6826300939332[/C][C]1.3173699060668[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.9600320132044[/C][C]2.03996798679564[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]13.9140983744979[/C][C]-0.914098374497919[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.4764200861736[/C][C]-3.47642008617357[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]15.2599766781585[/C][C]2.74002332184151[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.7302903999959[/C][C]-0.730290399995879[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.9990640453165[/C][C]-0.999064045316537[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.9206576693999[/C][C]-1.92065766939987[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.8218431585201[/C][C]0.178156841479854[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.6962798234582[/C][C]-1.69627982345817[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]14.8100441261335[/C][C]1.18995587386649[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8431625837643[/C][C]-2.84316258376425[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]15.5104306627113[/C][C]0.489569337288722[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]14.9034011214989[/C][C]0.0965988785011463[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]14.7120752738635[/C][C]1.28792472613651[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.5101928856544[/C][C]-0.510192885654436[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.4212030040134[/C][C]0.578796995986555[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]15.0729514849231[/C][C]-0.0729514849230541[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.1581827256022[/C][C]-3.15818272560222[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]14.298895790649[/C][C]1.70110420935102[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.7004945233871[/C][C]-3.70049452338707[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]15.9243309471003[/C][C]0.0756690528996806[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]13.5200564178573[/C][C]-1.52005641785728[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.0056511330222[/C][C]-1.00565113302219[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]14.9519062585182[/C][C]0.0480937414818276[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]13.5482007078639[/C][C]-0.548200707863877[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.5328604887726[/C][C]0.467139511227439[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]14.3290812669004[/C][C]-3.32908126690044[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]15.0906640917911[/C][C]-3.09066409179113[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]14.1397771103633[/C][C]-6.1397771103633[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]14.6305645988351[/C][C]1.3694354011649[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]14.1964080020688[/C][C]0.803591997931189[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]14.6671769552202[/C][C]2.3328230447798[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.2413063207121[/C][C]1.7586936792879[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]16.0766247626235[/C][C]-6.07662476262351[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]14.7737276211435[/C][C]3.22627237885653[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]14.6942386372133[/C][C]-1.69423863721332[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.3496603958234[/C][C]1.65033960417662[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]14.793508379407[/C][C]-1.79350837940703[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]15.7583874020522[/C][C]-5.75838740205215[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.2511965816222[/C][C]-1.25119658162218[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]14.7364214287345[/C][C]1.26357857126553[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]15.1921933021399[/C][C]0.80780669786008[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]15.2221410013345[/C][C]-1.22214100133453[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]14.9328463014291[/C][C]-4.93284630142915[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]15.0634783800159[/C][C]1.93652161998409[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]14.7529107151637[/C][C]-1.75291071516368[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]15.0729514849231[/C][C]-0.0729514849230541[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]15.2447613165023[/C][C]0.755238683497668[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]14.9432779845677[/C][C]-2.94327798456767[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]15.0203834705606[/C][C]-2.02038347056064[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&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
11315.7874902702901-2.78749027029015
21615.87080435450650.129195645493473
31914.41335392934824.58664607065178
41514.11439480777610.885605192223893
51415.1772426826912-1.17724268269124
61315.2683402099015-2.26834020990151
71915.45199636181783.54800363818224
81515.1599861347902-0.159986134790232
91415.3173014058879-1.3173014058879
101515.4424095096359-0.44240950963587
111615.23685384372640.763146156273635
121615.06347838001590.936521619984086
131615.05054945166550.949450548334527
141615.49869808964910.501301910350941
151715.14909839268461.85090160731535
161514.16792140036990.8320785996301
171514.95262705969270.0473729403072886
182015.79646085593294.20353914406705
191815.24824410509632.75175589490373
201614.63462747617821.3653725238218
211614.2354400341811.76455996581901
221614.57247260963391.4275273903661
231915.53270866618683.46729133381324
241615.2906182133770.709381786623
251715.42541770394231.57458229605766
261716.02811962560420.97188037439581
271615.06734994059940.932650059400578
281514.88664709286220.113352907137839
291614.96685687796681.03314312203315
301414.5156039408716-0.515603940871549
311514.72476642515710.275233574842914
321213.4144643301654-1.4144643301654
331415.4469749062433-1.44697490624328
341615.17674016342690.823259836573075
351414.6044419999267-0.604441999926743
36714.298895790649-7.29889579064898
371013.7961574472047-3.79615744720475
381414.8338413014429-0.833841301442907
391614.40652906458571.59347093541427
401614.67472262115721.32527737884277
411615.00565113302220.994348866977814
421415.1305409548599-1.13054095485994
432014.88556448484875.11443551515132
441414.4929836257037-0.492983625703748
451415.5893395578923-1.58933955789227
461115.4429120289002-4.44291202890019
471415.4705537996425-1.47055379964246
481514.92878342408610.0712165759139481
491615.28329082935020.716709170649809
501415.1520786620143-1.15207866201426
511614.72296301596911.27703698403093
521414.2481311854746-0.248131185474588
531214.1973665803002-2.19736658030019
541614.96555598804321.03444401195685
55914.1973665803002-5.19736658030019
561415.4764200861736-1.47642008617357
571615.7525211155210.247478884478959
581614.25829812640561.74170187359437
591515.0269705582663-0.0269705582662902
601615.16681099955270.833189000447278
611213.8029823119672-1.80298231196724
621615.24548211767690.754517882323129
631615.12191268090940.878087319090565
641414.4714459185494-0.471445918549423
651614.86402677769441.13597322230564
661714.81432528538682.18567471461318
671815.24978277207682.75021722792319
681814.09081591437693.90918408562308
691215.5789078747537-3.57890787475375
701614.67472262115721.32527737884277
711015.0505494516655-5.05054945166547
721414.3947964915235-0.39479649152351
731815.10465613300842.89534386699158
741815.10994233079042.89005766920963
751614.76536408940041.23463591059956
761715.82866802328261.17133197671736
771615.43428375494970.565716245050317
781615.05439404709830.945605952901654
791313.8400971876167-0.840097187616656
801614.81984926022561.18015073977439
811614.5681719552341.43182804476604
822014.44724047610385.55275952389617
831615.14549157430860.854508425691383
841514.95894940519090.0410505948091176
851514.85900532211990.140994677880115
861615.05619745628640.943802543713638
871415.1520786620143-1.15207866201426
881614.85263651632451.14736348367554
891614.16133431266431.83866568733575
901514.83131709108040.168682908919647
911214.6359283661019-2.63592836610189
921715.06347838001591.93652161998409
931615.22163848207020.778361517929788
941514.88388510544280.116114894557235
951314.4913042462978-1.49130424629783
961615.82866802328260.171331976717364
971615.38758202711840.61241797288162
981615.26834020990150.731659790098485
991615.09448919207740.905510807922621
1001414.4303727001924-0.430372700192387
1011615.61015646387210.389843536127945
1021615.02697055826630.97302944173371
1032015.49091464665524.50908535334481
1041514.26945061071870.730549389281307
1051614.94553745272271.05446254727726
1061315.330713358356-2.33071335835603
1071715.61015646387211.38984353612795
1081615.57054434301070.429455656989281
1091614.65210230598941.34789769401057
1101216.0341461197073-4.0341461197073
1111614.65042292658351.34957707341649
1121614.68263009393321.3173699060668
1131714.96003201320442.03996798679564
1141313.9140983744979-0.914098374497919
1151215.4764200861736-3.47642008617357
1161815.25997667815852.74002332184151
1171414.7302903999959-0.730290399995879
1181414.9990640453165-0.999064045316537
1191314.9206576693999-1.92065766939987
1201615.82184315852010.178156841479854
1211314.6962798234582-1.69627982345817
1221614.81004412613351.18995587386649
1231315.8431625837643-2.84316258376425
1241615.51043066271130.489569337288722
1251514.90340112149890.0965988785011463
1261614.71207527386351.28792472613651
1271515.5101928856544-0.510192885654436
1281716.42120300401340.578796995986555
1291515.0729514849231-0.0729514849230541
1301215.1581827256022-3.15818272560222
1311614.2988957906491.70110420935102
1321013.7004945233871-3.70049452338707
1331615.92433094710030.0756690528996806
1341213.5200564178573-1.52005641785728
1351415.0056511330222-1.00565113302219
1361514.95190625851820.0480937414818276
1371313.5482007078639-0.548200707863877
1381514.53286048877260.467139511227439
1391114.3290812669004-3.32908126690044
1401215.0906640917911-3.09066409179113
141814.1397771103633-6.1397771103633
1421614.63056459883511.3694354011649
1431514.19640800206880.803591997931189
1441714.66717695522022.3328230447798
1451614.24130632071211.7586936792879
1461016.0766247626235-6.07662476262351
1471814.77372762114353.22627237885653
1481314.6942386372133-1.69423863721332
1491614.34966039582341.65033960417662
1501314.793508379407-1.79350837940703
1511015.7583874020522-5.75838740205215
1521516.2511965816222-1.25119658162218
1531614.73642142873451.26357857126553
1541615.19219330213990.80780669786008
1551415.2221410013345-1.22214100133453
1561014.9328463014291-4.93284630142915
1571715.06347838001591.93652161998409
1581314.7529107151637-1.75291071516368
1591515.0729514849231-0.0729514849230541
1601615.24476131650230.755238683497668
1611214.9432779845677-2.94327798456767
1621315.0203834705606-2.02038347056064






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4733981620275270.9467963240550540.526601837972473
90.5270786574056540.9458426851886930.472921342594346
100.4639166460946430.9278332921892860.536083353905357
110.5570639646086130.8858720707827740.442936035391387
120.453286251516440.9065725030328810.54671374848356
130.3451773221968630.6903546443937270.654822677803137
140.2545490750552890.5090981501105790.745450924944711
150.3111304518437410.6222609036874820.688869548156259
160.2465006031216070.4930012062432140.753499396878393
170.1834960444743010.3669920889486020.816503955525699
180.4510614592524360.9021229185048710.548938540747564
190.5291115319118450.941776936176310.470888468088155
200.4564060287558250.912812057511650.543593971244176
210.38519599833940.77039199667880.6148040016606
220.3175712563649750.6351425127299490.682428743635025
230.384356140885710.7687122817714190.61564385911429
240.3192315240330330.6384630480660650.680768475966967
250.2707606771222340.5415213542444690.729239322877766
260.2187859816126320.4375719632252650.781214018387368
270.1736114825361510.3472229650723030.826388517463849
280.1392796649770720.2785593299541440.860720335022928
290.1064191860017830.2128383720035660.893580813998217
300.09821261012292160.1964252202458430.901787389877078
310.0747565798846130.1495131597692260.925243420115387
320.08294017432032810.1658803486406560.917059825679672
330.08472886277118820.1694577255423760.915271137228812
340.06368616125844520.127372322516890.936313838741555
350.05232239654699330.1046447930939870.947677603453007
360.508830577196740.982338845606520.49116942280326
370.580657230258640.8386855394827210.419342769741361
380.5304399305044510.9391201389910980.469560069495549
390.5015676807420960.9968646385158080.498432319257904
400.4724054129509770.9448108259019540.527594587049023
410.4291846858597720.8583693717195450.570815314140228
420.4065838342369270.8131676684738540.593416165763073
430.5980142658219860.8039714683560290.401985734178014
440.5536950615949850.892609876810030.446304938405015
450.5551290927338450.889741814532310.444870907266155
460.7251015056929140.5497969886141730.274898494307086
470.7147151324776660.5705697350446670.285284867522334
480.6699704064188950.6600591871622090.330029593581105
490.6260036280722390.7479927438555230.373996371927761
500.5963557518323350.807288496335330.403644248167665
510.5607362305725450.878527538854910.439263769427455
520.5114549497637980.9770901004724050.488545050236202
530.5069784352691590.9860431294616810.493021564730841
540.4664358249921380.9328716499842760.533564175007862
550.6603629939983920.6792740120032170.339637006001608
560.642717373425860.7145652531482810.357282626574141
570.5988351881972210.8023296236055590.401164811802779
580.5823571302533190.8352857394933620.417642869746681
590.5348469962318550.930306007536290.465153003768145
600.4923779002908190.9847558005816380.507622099709181
610.4701100309333470.9402200618666930.529889969066653
620.428688569589520.857377139179040.57131143041048
630.3886230384805830.7772460769611670.611376961519416
640.3455935466931710.6911870933863420.654406453306829
650.3126999443682830.6253998887365650.687300055631717
660.3140585327507770.6281170655015540.685941467249223
670.3310076536449960.6620153072899930.668992346355004
680.4364969922210870.8729939844421740.563503007778913
690.5185425238104380.9629149523791250.481457476189562
700.4884877980779360.9769755961558720.511512201922064
710.6684702744627080.6630594510745840.331529725537292
720.6265564510309730.7468870979380540.373443548969027
730.6512078344229190.6975843311541620.348792165577081
740.6792570854735520.6414858290528950.320742914526448
750.6475965515778950.7048068968442110.352403448422106
760.616535231760090.7669295364798190.383464768239909
770.575850594291760.848298811416480.42414940570824
780.5381784234512350.923643153097530.461821576548765
790.5002869375433940.9994261249132110.499713062456606
800.4689630797367310.9379261594734610.531036920263269
810.4398329945029620.8796659890059250.560167005497038
820.7075571707686850.5848856584626310.292442829231315
830.6762509923869950.647498015226010.323749007613005
840.6356495755101870.7287008489796270.364350424489813
850.5918208884275210.8163582231449580.408179111572479
860.5536173659946630.8927652680106740.446382634005337
870.5184797808613810.9630404382772380.481520219138619
880.4885847671545770.9771695343091540.511415232845423
890.4829359889043760.9658719778087510.517064011095624
900.4403558815898990.8807117631797970.559644118410101
910.4558103273414360.9116206546828710.544189672658564
920.4733828441389140.9467656882778270.526617155861086
930.4377687396642130.8755374793284260.562231260335787
940.3926998654944870.7853997309889730.607300134505513
950.3705314906043360.7410629812086720.629468509395664
960.3312911721868840.6625823443737690.668708827813116
970.3043771630669880.6087543261339750.695622836933012
980.2718506510355060.5437013020710110.728149348964494
990.2424775140276880.4849550280553760.757522485972312
1000.2098011860471320.4196023720942640.790198813952868
1010.1856918431331590.3713836862663190.81430815686684
1020.1646484405664810.3292968811329610.83535155943352
1030.2904215155557950.580843031111590.709578484444205
1040.2569218164558580.5138436329117150.743078183544142
1050.2392244925427820.4784489850855630.760775507457218
1060.2289966464351370.4579932928702740.771003353564863
1070.2332950312494940.4665900624989880.766704968750506
1080.2075471708444990.4150943416889980.792452829155501
1090.2103057851241670.4206115702483340.789694214875833
1100.2542404259140110.5084808518280220.745759574085989
1110.2287935566943690.4575871133887380.771206443305631
1120.210827156657920.4216543133158390.78917284334208
1130.2195381261052650.4390762522105290.780461873894736
1140.1858232083985920.3716464167971840.814176791601408
1150.2064448450476150.4128896900952310.793555154952385
1160.2255853420931870.4511706841863750.774414657906813
1170.1909728649644290.3819457299288590.80902713503557
1180.1615258620643480.3230517241286950.838474137935652
1190.1474185019991880.2948370039983760.852581498000812
1200.1261909843446550.2523819686893090.873809015655346
1210.1080106461436950.216021292287390.891989353856305
1220.09017192695543550.1803438539108710.909828073044564
1230.09209292121331110.1841858424266220.907907078786689
1240.0785073983869190.1570147967738380.921492601613081
1250.06362739458175520.127254789163510.936372605418245
1260.06002743313878880.1200548662775780.93997256686121
1270.04624875352342650.09249750704685290.953751246476573
1280.04685601556121420.09371203112242850.953143984438786
1290.03528182707804910.07056365415609820.964718172921951
1300.03433555090016150.0686711018003230.965664449099839
1310.03500821483366340.07001642966732680.964991785166337
1320.05177114354969370.1035422870993870.948228856450306
1330.04954087014723390.09908174029446770.950459129852766
1340.04279928763863580.08559857527727150.957200712361364
1350.0325805721023570.0651611442047140.967419427897643
1360.02303162122888010.04606324245776020.97696837877112
1370.0170929714583630.0341859429167260.982907028541637
1380.01206243404005250.02412486808010490.987937565959948
1390.0275102632051090.0550205264102180.972489736794891
1400.02868533647248490.05737067294496980.971314663527515
1410.263957545061520.5279150901230410.73604245493848
1420.2152644958062760.4305289916125530.784735504193724
1430.1649685429309330.3299370858618670.835031457069067
1440.1831310376559350.3662620753118710.816868962344065
1450.163696500602070.3273930012041390.83630349939793
1460.1778391454650970.3556782909301950.822160854534903
1470.4282922259550630.8565844519101260.571707774044937
1480.3957538457986480.7915076915972960.604246154201352
1490.5698243368015970.8603513263968070.430175663198403
1500.4631716693787920.9263433387575840.536828330621208
1510.5787396584829640.8425206830340720.421260341517036
1520.6197444931811010.7605110136377980.380255506818899
1530.4738069406049980.9476138812099960.526193059395002
1540.3410352833556780.6820705667113560.658964716644322

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.473398162027527 & 0.946796324055054 & 0.526601837972473 \tabularnewline
9 & 0.527078657405654 & 0.945842685188693 & 0.472921342594346 \tabularnewline
10 & 0.463916646094643 & 0.927833292189286 & 0.536083353905357 \tabularnewline
11 & 0.557063964608613 & 0.885872070782774 & 0.442936035391387 \tabularnewline
12 & 0.45328625151644 & 0.906572503032881 & 0.54671374848356 \tabularnewline
13 & 0.345177322196863 & 0.690354644393727 & 0.654822677803137 \tabularnewline
14 & 0.254549075055289 & 0.509098150110579 & 0.745450924944711 \tabularnewline
15 & 0.311130451843741 & 0.622260903687482 & 0.688869548156259 \tabularnewline
16 & 0.246500603121607 & 0.493001206243214 & 0.753499396878393 \tabularnewline
17 & 0.183496044474301 & 0.366992088948602 & 0.816503955525699 \tabularnewline
18 & 0.451061459252436 & 0.902122918504871 & 0.548938540747564 \tabularnewline
19 & 0.529111531911845 & 0.94177693617631 & 0.470888468088155 \tabularnewline
20 & 0.456406028755825 & 0.91281205751165 & 0.543593971244176 \tabularnewline
21 & 0.3851959983394 & 0.7703919966788 & 0.6148040016606 \tabularnewline
22 & 0.317571256364975 & 0.635142512729949 & 0.682428743635025 \tabularnewline
23 & 0.38435614088571 & 0.768712281771419 & 0.61564385911429 \tabularnewline
24 & 0.319231524033033 & 0.638463048066065 & 0.680768475966967 \tabularnewline
25 & 0.270760677122234 & 0.541521354244469 & 0.729239322877766 \tabularnewline
26 & 0.218785981612632 & 0.437571963225265 & 0.781214018387368 \tabularnewline
27 & 0.173611482536151 & 0.347222965072303 & 0.826388517463849 \tabularnewline
28 & 0.139279664977072 & 0.278559329954144 & 0.860720335022928 \tabularnewline
29 & 0.106419186001783 & 0.212838372003566 & 0.893580813998217 \tabularnewline
30 & 0.0982126101229216 & 0.196425220245843 & 0.901787389877078 \tabularnewline
31 & 0.074756579884613 & 0.149513159769226 & 0.925243420115387 \tabularnewline
32 & 0.0829401743203281 & 0.165880348640656 & 0.917059825679672 \tabularnewline
33 & 0.0847288627711882 & 0.169457725542376 & 0.915271137228812 \tabularnewline
34 & 0.0636861612584452 & 0.12737232251689 & 0.936313838741555 \tabularnewline
35 & 0.0523223965469933 & 0.104644793093987 & 0.947677603453007 \tabularnewline
36 & 0.50883057719674 & 0.98233884560652 & 0.49116942280326 \tabularnewline
37 & 0.58065723025864 & 0.838685539482721 & 0.419342769741361 \tabularnewline
38 & 0.530439930504451 & 0.939120138991098 & 0.469560069495549 \tabularnewline
39 & 0.501567680742096 & 0.996864638515808 & 0.498432319257904 \tabularnewline
40 & 0.472405412950977 & 0.944810825901954 & 0.527594587049023 \tabularnewline
41 & 0.429184685859772 & 0.858369371719545 & 0.570815314140228 \tabularnewline
42 & 0.406583834236927 & 0.813167668473854 & 0.593416165763073 \tabularnewline
43 & 0.598014265821986 & 0.803971468356029 & 0.401985734178014 \tabularnewline
44 & 0.553695061594985 & 0.89260987681003 & 0.446304938405015 \tabularnewline
45 & 0.555129092733845 & 0.88974181453231 & 0.444870907266155 \tabularnewline
46 & 0.725101505692914 & 0.549796988614173 & 0.274898494307086 \tabularnewline
47 & 0.714715132477666 & 0.570569735044667 & 0.285284867522334 \tabularnewline
48 & 0.669970406418895 & 0.660059187162209 & 0.330029593581105 \tabularnewline
49 & 0.626003628072239 & 0.747992743855523 & 0.373996371927761 \tabularnewline
50 & 0.596355751832335 & 0.80728849633533 & 0.403644248167665 \tabularnewline
51 & 0.560736230572545 & 0.87852753885491 & 0.439263769427455 \tabularnewline
52 & 0.511454949763798 & 0.977090100472405 & 0.488545050236202 \tabularnewline
53 & 0.506978435269159 & 0.986043129461681 & 0.493021564730841 \tabularnewline
54 & 0.466435824992138 & 0.932871649984276 & 0.533564175007862 \tabularnewline
55 & 0.660362993998392 & 0.679274012003217 & 0.339637006001608 \tabularnewline
56 & 0.64271737342586 & 0.714565253148281 & 0.357282626574141 \tabularnewline
57 & 0.598835188197221 & 0.802329623605559 & 0.401164811802779 \tabularnewline
58 & 0.582357130253319 & 0.835285739493362 & 0.417642869746681 \tabularnewline
59 & 0.534846996231855 & 0.93030600753629 & 0.465153003768145 \tabularnewline
60 & 0.492377900290819 & 0.984755800581638 & 0.507622099709181 \tabularnewline
61 & 0.470110030933347 & 0.940220061866693 & 0.529889969066653 \tabularnewline
62 & 0.42868856958952 & 0.85737713917904 & 0.57131143041048 \tabularnewline
63 & 0.388623038480583 & 0.777246076961167 & 0.611376961519416 \tabularnewline
64 & 0.345593546693171 & 0.691187093386342 & 0.654406453306829 \tabularnewline
65 & 0.312699944368283 & 0.625399888736565 & 0.687300055631717 \tabularnewline
66 & 0.314058532750777 & 0.628117065501554 & 0.685941467249223 \tabularnewline
67 & 0.331007653644996 & 0.662015307289993 & 0.668992346355004 \tabularnewline
68 & 0.436496992221087 & 0.872993984442174 & 0.563503007778913 \tabularnewline
69 & 0.518542523810438 & 0.962914952379125 & 0.481457476189562 \tabularnewline
70 & 0.488487798077936 & 0.976975596155872 & 0.511512201922064 \tabularnewline
71 & 0.668470274462708 & 0.663059451074584 & 0.331529725537292 \tabularnewline
72 & 0.626556451030973 & 0.746887097938054 & 0.373443548969027 \tabularnewline
73 & 0.651207834422919 & 0.697584331154162 & 0.348792165577081 \tabularnewline
74 & 0.679257085473552 & 0.641485829052895 & 0.320742914526448 \tabularnewline
75 & 0.647596551577895 & 0.704806896844211 & 0.352403448422106 \tabularnewline
76 & 0.61653523176009 & 0.766929536479819 & 0.383464768239909 \tabularnewline
77 & 0.57585059429176 & 0.84829881141648 & 0.42414940570824 \tabularnewline
78 & 0.538178423451235 & 0.92364315309753 & 0.461821576548765 \tabularnewline
79 & 0.500286937543394 & 0.999426124913211 & 0.499713062456606 \tabularnewline
80 & 0.468963079736731 & 0.937926159473461 & 0.531036920263269 \tabularnewline
81 & 0.439832994502962 & 0.879665989005925 & 0.560167005497038 \tabularnewline
82 & 0.707557170768685 & 0.584885658462631 & 0.292442829231315 \tabularnewline
83 & 0.676250992386995 & 0.64749801522601 & 0.323749007613005 \tabularnewline
84 & 0.635649575510187 & 0.728700848979627 & 0.364350424489813 \tabularnewline
85 & 0.591820888427521 & 0.816358223144958 & 0.408179111572479 \tabularnewline
86 & 0.553617365994663 & 0.892765268010674 & 0.446382634005337 \tabularnewline
87 & 0.518479780861381 & 0.963040438277238 & 0.481520219138619 \tabularnewline
88 & 0.488584767154577 & 0.977169534309154 & 0.511415232845423 \tabularnewline
89 & 0.482935988904376 & 0.965871977808751 & 0.517064011095624 \tabularnewline
90 & 0.440355881589899 & 0.880711763179797 & 0.559644118410101 \tabularnewline
91 & 0.455810327341436 & 0.911620654682871 & 0.544189672658564 \tabularnewline
92 & 0.473382844138914 & 0.946765688277827 & 0.526617155861086 \tabularnewline
93 & 0.437768739664213 & 0.875537479328426 & 0.562231260335787 \tabularnewline
94 & 0.392699865494487 & 0.785399730988973 & 0.607300134505513 \tabularnewline
95 & 0.370531490604336 & 0.741062981208672 & 0.629468509395664 \tabularnewline
96 & 0.331291172186884 & 0.662582344373769 & 0.668708827813116 \tabularnewline
97 & 0.304377163066988 & 0.608754326133975 & 0.695622836933012 \tabularnewline
98 & 0.271850651035506 & 0.543701302071011 & 0.728149348964494 \tabularnewline
99 & 0.242477514027688 & 0.484955028055376 & 0.757522485972312 \tabularnewline
100 & 0.209801186047132 & 0.419602372094264 & 0.790198813952868 \tabularnewline
101 & 0.185691843133159 & 0.371383686266319 & 0.81430815686684 \tabularnewline
102 & 0.164648440566481 & 0.329296881132961 & 0.83535155943352 \tabularnewline
103 & 0.290421515555795 & 0.58084303111159 & 0.709578484444205 \tabularnewline
104 & 0.256921816455858 & 0.513843632911715 & 0.743078183544142 \tabularnewline
105 & 0.239224492542782 & 0.478448985085563 & 0.760775507457218 \tabularnewline
106 & 0.228996646435137 & 0.457993292870274 & 0.771003353564863 \tabularnewline
107 & 0.233295031249494 & 0.466590062498988 & 0.766704968750506 \tabularnewline
108 & 0.207547170844499 & 0.415094341688998 & 0.792452829155501 \tabularnewline
109 & 0.210305785124167 & 0.420611570248334 & 0.789694214875833 \tabularnewline
110 & 0.254240425914011 & 0.508480851828022 & 0.745759574085989 \tabularnewline
111 & 0.228793556694369 & 0.457587113388738 & 0.771206443305631 \tabularnewline
112 & 0.21082715665792 & 0.421654313315839 & 0.78917284334208 \tabularnewline
113 & 0.219538126105265 & 0.439076252210529 & 0.780461873894736 \tabularnewline
114 & 0.185823208398592 & 0.371646416797184 & 0.814176791601408 \tabularnewline
115 & 0.206444845047615 & 0.412889690095231 & 0.793555154952385 \tabularnewline
116 & 0.225585342093187 & 0.451170684186375 & 0.774414657906813 \tabularnewline
117 & 0.190972864964429 & 0.381945729928859 & 0.80902713503557 \tabularnewline
118 & 0.161525862064348 & 0.323051724128695 & 0.838474137935652 \tabularnewline
119 & 0.147418501999188 & 0.294837003998376 & 0.852581498000812 \tabularnewline
120 & 0.126190984344655 & 0.252381968689309 & 0.873809015655346 \tabularnewline
121 & 0.108010646143695 & 0.21602129228739 & 0.891989353856305 \tabularnewline
122 & 0.0901719269554355 & 0.180343853910871 & 0.909828073044564 \tabularnewline
123 & 0.0920929212133111 & 0.184185842426622 & 0.907907078786689 \tabularnewline
124 & 0.078507398386919 & 0.157014796773838 & 0.921492601613081 \tabularnewline
125 & 0.0636273945817552 & 0.12725478916351 & 0.936372605418245 \tabularnewline
126 & 0.0600274331387888 & 0.120054866277578 & 0.93997256686121 \tabularnewline
127 & 0.0462487535234265 & 0.0924975070468529 & 0.953751246476573 \tabularnewline
128 & 0.0468560155612142 & 0.0937120311224285 & 0.953143984438786 \tabularnewline
129 & 0.0352818270780491 & 0.0705636541560982 & 0.964718172921951 \tabularnewline
130 & 0.0343355509001615 & 0.068671101800323 & 0.965664449099839 \tabularnewline
131 & 0.0350082148336634 & 0.0700164296673268 & 0.964991785166337 \tabularnewline
132 & 0.0517711435496937 & 0.103542287099387 & 0.948228856450306 \tabularnewline
133 & 0.0495408701472339 & 0.0990817402944677 & 0.950459129852766 \tabularnewline
134 & 0.0427992876386358 & 0.0855985752772715 & 0.957200712361364 \tabularnewline
135 & 0.032580572102357 & 0.065161144204714 & 0.967419427897643 \tabularnewline
136 & 0.0230316212288801 & 0.0460632424577602 & 0.97696837877112 \tabularnewline
137 & 0.017092971458363 & 0.034185942916726 & 0.982907028541637 \tabularnewline
138 & 0.0120624340400525 & 0.0241248680801049 & 0.987937565959948 \tabularnewline
139 & 0.027510263205109 & 0.055020526410218 & 0.972489736794891 \tabularnewline
140 & 0.0286853364724849 & 0.0573706729449698 & 0.971314663527515 \tabularnewline
141 & 0.26395754506152 & 0.527915090123041 & 0.73604245493848 \tabularnewline
142 & 0.215264495806276 & 0.430528991612553 & 0.784735504193724 \tabularnewline
143 & 0.164968542930933 & 0.329937085861867 & 0.835031457069067 \tabularnewline
144 & 0.183131037655935 & 0.366262075311871 & 0.816868962344065 \tabularnewline
145 & 0.16369650060207 & 0.327393001204139 & 0.83630349939793 \tabularnewline
146 & 0.177839145465097 & 0.355678290930195 & 0.822160854534903 \tabularnewline
147 & 0.428292225955063 & 0.856584451910126 & 0.571707774044937 \tabularnewline
148 & 0.395753845798648 & 0.791507691597296 & 0.604246154201352 \tabularnewline
149 & 0.569824336801597 & 0.860351326396807 & 0.430175663198403 \tabularnewline
150 & 0.463171669378792 & 0.926343338757584 & 0.536828330621208 \tabularnewline
151 & 0.578739658482964 & 0.842520683034072 & 0.421260341517036 \tabularnewline
152 & 0.619744493181101 & 0.760511013637798 & 0.380255506818899 \tabularnewline
153 & 0.473806940604998 & 0.947613881209996 & 0.526193059395002 \tabularnewline
154 & 0.341035283355678 & 0.682070566711356 & 0.658964716644322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&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]0.473398162027527[/C][C]0.946796324055054[/C][C]0.526601837972473[/C][/ROW]
[ROW][C]9[/C][C]0.527078657405654[/C][C]0.945842685188693[/C][C]0.472921342594346[/C][/ROW]
[ROW][C]10[/C][C]0.463916646094643[/C][C]0.927833292189286[/C][C]0.536083353905357[/C][/ROW]
[ROW][C]11[/C][C]0.557063964608613[/C][C]0.885872070782774[/C][C]0.442936035391387[/C][/ROW]
[ROW][C]12[/C][C]0.45328625151644[/C][C]0.906572503032881[/C][C]0.54671374848356[/C][/ROW]
[ROW][C]13[/C][C]0.345177322196863[/C][C]0.690354644393727[/C][C]0.654822677803137[/C][/ROW]
[ROW][C]14[/C][C]0.254549075055289[/C][C]0.509098150110579[/C][C]0.745450924944711[/C][/ROW]
[ROW][C]15[/C][C]0.311130451843741[/C][C]0.622260903687482[/C][C]0.688869548156259[/C][/ROW]
[ROW][C]16[/C][C]0.246500603121607[/C][C]0.493001206243214[/C][C]0.753499396878393[/C][/ROW]
[ROW][C]17[/C][C]0.183496044474301[/C][C]0.366992088948602[/C][C]0.816503955525699[/C][/ROW]
[ROW][C]18[/C][C]0.451061459252436[/C][C]0.902122918504871[/C][C]0.548938540747564[/C][/ROW]
[ROW][C]19[/C][C]0.529111531911845[/C][C]0.94177693617631[/C][C]0.470888468088155[/C][/ROW]
[ROW][C]20[/C][C]0.456406028755825[/C][C]0.91281205751165[/C][C]0.543593971244176[/C][/ROW]
[ROW][C]21[/C][C]0.3851959983394[/C][C]0.7703919966788[/C][C]0.6148040016606[/C][/ROW]
[ROW][C]22[/C][C]0.317571256364975[/C][C]0.635142512729949[/C][C]0.682428743635025[/C][/ROW]
[ROW][C]23[/C][C]0.38435614088571[/C][C]0.768712281771419[/C][C]0.61564385911429[/C][/ROW]
[ROW][C]24[/C][C]0.319231524033033[/C][C]0.638463048066065[/C][C]0.680768475966967[/C][/ROW]
[ROW][C]25[/C][C]0.270760677122234[/C][C]0.541521354244469[/C][C]0.729239322877766[/C][/ROW]
[ROW][C]26[/C][C]0.218785981612632[/C][C]0.437571963225265[/C][C]0.781214018387368[/C][/ROW]
[ROW][C]27[/C][C]0.173611482536151[/C][C]0.347222965072303[/C][C]0.826388517463849[/C][/ROW]
[ROW][C]28[/C][C]0.139279664977072[/C][C]0.278559329954144[/C][C]0.860720335022928[/C][/ROW]
[ROW][C]29[/C][C]0.106419186001783[/C][C]0.212838372003566[/C][C]0.893580813998217[/C][/ROW]
[ROW][C]30[/C][C]0.0982126101229216[/C][C]0.196425220245843[/C][C]0.901787389877078[/C][/ROW]
[ROW][C]31[/C][C]0.074756579884613[/C][C]0.149513159769226[/C][C]0.925243420115387[/C][/ROW]
[ROW][C]32[/C][C]0.0829401743203281[/C][C]0.165880348640656[/C][C]0.917059825679672[/C][/ROW]
[ROW][C]33[/C][C]0.0847288627711882[/C][C]0.169457725542376[/C][C]0.915271137228812[/C][/ROW]
[ROW][C]34[/C][C]0.0636861612584452[/C][C]0.12737232251689[/C][C]0.936313838741555[/C][/ROW]
[ROW][C]35[/C][C]0.0523223965469933[/C][C]0.104644793093987[/C][C]0.947677603453007[/C][/ROW]
[ROW][C]36[/C][C]0.50883057719674[/C][C]0.98233884560652[/C][C]0.49116942280326[/C][/ROW]
[ROW][C]37[/C][C]0.58065723025864[/C][C]0.838685539482721[/C][C]0.419342769741361[/C][/ROW]
[ROW][C]38[/C][C]0.530439930504451[/C][C]0.939120138991098[/C][C]0.469560069495549[/C][/ROW]
[ROW][C]39[/C][C]0.501567680742096[/C][C]0.996864638515808[/C][C]0.498432319257904[/C][/ROW]
[ROW][C]40[/C][C]0.472405412950977[/C][C]0.944810825901954[/C][C]0.527594587049023[/C][/ROW]
[ROW][C]41[/C][C]0.429184685859772[/C][C]0.858369371719545[/C][C]0.570815314140228[/C][/ROW]
[ROW][C]42[/C][C]0.406583834236927[/C][C]0.813167668473854[/C][C]0.593416165763073[/C][/ROW]
[ROW][C]43[/C][C]0.598014265821986[/C][C]0.803971468356029[/C][C]0.401985734178014[/C][/ROW]
[ROW][C]44[/C][C]0.553695061594985[/C][C]0.89260987681003[/C][C]0.446304938405015[/C][/ROW]
[ROW][C]45[/C][C]0.555129092733845[/C][C]0.88974181453231[/C][C]0.444870907266155[/C][/ROW]
[ROW][C]46[/C][C]0.725101505692914[/C][C]0.549796988614173[/C][C]0.274898494307086[/C][/ROW]
[ROW][C]47[/C][C]0.714715132477666[/C][C]0.570569735044667[/C][C]0.285284867522334[/C][/ROW]
[ROW][C]48[/C][C]0.669970406418895[/C][C]0.660059187162209[/C][C]0.330029593581105[/C][/ROW]
[ROW][C]49[/C][C]0.626003628072239[/C][C]0.747992743855523[/C][C]0.373996371927761[/C][/ROW]
[ROW][C]50[/C][C]0.596355751832335[/C][C]0.80728849633533[/C][C]0.403644248167665[/C][/ROW]
[ROW][C]51[/C][C]0.560736230572545[/C][C]0.87852753885491[/C][C]0.439263769427455[/C][/ROW]
[ROW][C]52[/C][C]0.511454949763798[/C][C]0.977090100472405[/C][C]0.488545050236202[/C][/ROW]
[ROW][C]53[/C][C]0.506978435269159[/C][C]0.986043129461681[/C][C]0.493021564730841[/C][/ROW]
[ROW][C]54[/C][C]0.466435824992138[/C][C]0.932871649984276[/C][C]0.533564175007862[/C][/ROW]
[ROW][C]55[/C][C]0.660362993998392[/C][C]0.679274012003217[/C][C]0.339637006001608[/C][/ROW]
[ROW][C]56[/C][C]0.64271737342586[/C][C]0.714565253148281[/C][C]0.357282626574141[/C][/ROW]
[ROW][C]57[/C][C]0.598835188197221[/C][C]0.802329623605559[/C][C]0.401164811802779[/C][/ROW]
[ROW][C]58[/C][C]0.582357130253319[/C][C]0.835285739493362[/C][C]0.417642869746681[/C][/ROW]
[ROW][C]59[/C][C]0.534846996231855[/C][C]0.93030600753629[/C][C]0.465153003768145[/C][/ROW]
[ROW][C]60[/C][C]0.492377900290819[/C][C]0.984755800581638[/C][C]0.507622099709181[/C][/ROW]
[ROW][C]61[/C][C]0.470110030933347[/C][C]0.940220061866693[/C][C]0.529889969066653[/C][/ROW]
[ROW][C]62[/C][C]0.42868856958952[/C][C]0.85737713917904[/C][C]0.57131143041048[/C][/ROW]
[ROW][C]63[/C][C]0.388623038480583[/C][C]0.777246076961167[/C][C]0.611376961519416[/C][/ROW]
[ROW][C]64[/C][C]0.345593546693171[/C][C]0.691187093386342[/C][C]0.654406453306829[/C][/ROW]
[ROW][C]65[/C][C]0.312699944368283[/C][C]0.625399888736565[/C][C]0.687300055631717[/C][/ROW]
[ROW][C]66[/C][C]0.314058532750777[/C][C]0.628117065501554[/C][C]0.685941467249223[/C][/ROW]
[ROW][C]67[/C][C]0.331007653644996[/C][C]0.662015307289993[/C][C]0.668992346355004[/C][/ROW]
[ROW][C]68[/C][C]0.436496992221087[/C][C]0.872993984442174[/C][C]0.563503007778913[/C][/ROW]
[ROW][C]69[/C][C]0.518542523810438[/C][C]0.962914952379125[/C][C]0.481457476189562[/C][/ROW]
[ROW][C]70[/C][C]0.488487798077936[/C][C]0.976975596155872[/C][C]0.511512201922064[/C][/ROW]
[ROW][C]71[/C][C]0.668470274462708[/C][C]0.663059451074584[/C][C]0.331529725537292[/C][/ROW]
[ROW][C]72[/C][C]0.626556451030973[/C][C]0.746887097938054[/C][C]0.373443548969027[/C][/ROW]
[ROW][C]73[/C][C]0.651207834422919[/C][C]0.697584331154162[/C][C]0.348792165577081[/C][/ROW]
[ROW][C]74[/C][C]0.679257085473552[/C][C]0.641485829052895[/C][C]0.320742914526448[/C][/ROW]
[ROW][C]75[/C][C]0.647596551577895[/C][C]0.704806896844211[/C][C]0.352403448422106[/C][/ROW]
[ROW][C]76[/C][C]0.61653523176009[/C][C]0.766929536479819[/C][C]0.383464768239909[/C][/ROW]
[ROW][C]77[/C][C]0.57585059429176[/C][C]0.84829881141648[/C][C]0.42414940570824[/C][/ROW]
[ROW][C]78[/C][C]0.538178423451235[/C][C]0.92364315309753[/C][C]0.461821576548765[/C][/ROW]
[ROW][C]79[/C][C]0.500286937543394[/C][C]0.999426124913211[/C][C]0.499713062456606[/C][/ROW]
[ROW][C]80[/C][C]0.468963079736731[/C][C]0.937926159473461[/C][C]0.531036920263269[/C][/ROW]
[ROW][C]81[/C][C]0.439832994502962[/C][C]0.879665989005925[/C][C]0.560167005497038[/C][/ROW]
[ROW][C]82[/C][C]0.707557170768685[/C][C]0.584885658462631[/C][C]0.292442829231315[/C][/ROW]
[ROW][C]83[/C][C]0.676250992386995[/C][C]0.64749801522601[/C][C]0.323749007613005[/C][/ROW]
[ROW][C]84[/C][C]0.635649575510187[/C][C]0.728700848979627[/C][C]0.364350424489813[/C][/ROW]
[ROW][C]85[/C][C]0.591820888427521[/C][C]0.816358223144958[/C][C]0.408179111572479[/C][/ROW]
[ROW][C]86[/C][C]0.553617365994663[/C][C]0.892765268010674[/C][C]0.446382634005337[/C][/ROW]
[ROW][C]87[/C][C]0.518479780861381[/C][C]0.963040438277238[/C][C]0.481520219138619[/C][/ROW]
[ROW][C]88[/C][C]0.488584767154577[/C][C]0.977169534309154[/C][C]0.511415232845423[/C][/ROW]
[ROW][C]89[/C][C]0.482935988904376[/C][C]0.965871977808751[/C][C]0.517064011095624[/C][/ROW]
[ROW][C]90[/C][C]0.440355881589899[/C][C]0.880711763179797[/C][C]0.559644118410101[/C][/ROW]
[ROW][C]91[/C][C]0.455810327341436[/C][C]0.911620654682871[/C][C]0.544189672658564[/C][/ROW]
[ROW][C]92[/C][C]0.473382844138914[/C][C]0.946765688277827[/C][C]0.526617155861086[/C][/ROW]
[ROW][C]93[/C][C]0.437768739664213[/C][C]0.875537479328426[/C][C]0.562231260335787[/C][/ROW]
[ROW][C]94[/C][C]0.392699865494487[/C][C]0.785399730988973[/C][C]0.607300134505513[/C][/ROW]
[ROW][C]95[/C][C]0.370531490604336[/C][C]0.741062981208672[/C][C]0.629468509395664[/C][/ROW]
[ROW][C]96[/C][C]0.331291172186884[/C][C]0.662582344373769[/C][C]0.668708827813116[/C][/ROW]
[ROW][C]97[/C][C]0.304377163066988[/C][C]0.608754326133975[/C][C]0.695622836933012[/C][/ROW]
[ROW][C]98[/C][C]0.271850651035506[/C][C]0.543701302071011[/C][C]0.728149348964494[/C][/ROW]
[ROW][C]99[/C][C]0.242477514027688[/C][C]0.484955028055376[/C][C]0.757522485972312[/C][/ROW]
[ROW][C]100[/C][C]0.209801186047132[/C][C]0.419602372094264[/C][C]0.790198813952868[/C][/ROW]
[ROW][C]101[/C][C]0.185691843133159[/C][C]0.371383686266319[/C][C]0.81430815686684[/C][/ROW]
[ROW][C]102[/C][C]0.164648440566481[/C][C]0.329296881132961[/C][C]0.83535155943352[/C][/ROW]
[ROW][C]103[/C][C]0.290421515555795[/C][C]0.58084303111159[/C][C]0.709578484444205[/C][/ROW]
[ROW][C]104[/C][C]0.256921816455858[/C][C]0.513843632911715[/C][C]0.743078183544142[/C][/ROW]
[ROW][C]105[/C][C]0.239224492542782[/C][C]0.478448985085563[/C][C]0.760775507457218[/C][/ROW]
[ROW][C]106[/C][C]0.228996646435137[/C][C]0.457993292870274[/C][C]0.771003353564863[/C][/ROW]
[ROW][C]107[/C][C]0.233295031249494[/C][C]0.466590062498988[/C][C]0.766704968750506[/C][/ROW]
[ROW][C]108[/C][C]0.207547170844499[/C][C]0.415094341688998[/C][C]0.792452829155501[/C][/ROW]
[ROW][C]109[/C][C]0.210305785124167[/C][C]0.420611570248334[/C][C]0.789694214875833[/C][/ROW]
[ROW][C]110[/C][C]0.254240425914011[/C][C]0.508480851828022[/C][C]0.745759574085989[/C][/ROW]
[ROW][C]111[/C][C]0.228793556694369[/C][C]0.457587113388738[/C][C]0.771206443305631[/C][/ROW]
[ROW][C]112[/C][C]0.21082715665792[/C][C]0.421654313315839[/C][C]0.78917284334208[/C][/ROW]
[ROW][C]113[/C][C]0.219538126105265[/C][C]0.439076252210529[/C][C]0.780461873894736[/C][/ROW]
[ROW][C]114[/C][C]0.185823208398592[/C][C]0.371646416797184[/C][C]0.814176791601408[/C][/ROW]
[ROW][C]115[/C][C]0.206444845047615[/C][C]0.412889690095231[/C][C]0.793555154952385[/C][/ROW]
[ROW][C]116[/C][C]0.225585342093187[/C][C]0.451170684186375[/C][C]0.774414657906813[/C][/ROW]
[ROW][C]117[/C][C]0.190972864964429[/C][C]0.381945729928859[/C][C]0.80902713503557[/C][/ROW]
[ROW][C]118[/C][C]0.161525862064348[/C][C]0.323051724128695[/C][C]0.838474137935652[/C][/ROW]
[ROW][C]119[/C][C]0.147418501999188[/C][C]0.294837003998376[/C][C]0.852581498000812[/C][/ROW]
[ROW][C]120[/C][C]0.126190984344655[/C][C]0.252381968689309[/C][C]0.873809015655346[/C][/ROW]
[ROW][C]121[/C][C]0.108010646143695[/C][C]0.21602129228739[/C][C]0.891989353856305[/C][/ROW]
[ROW][C]122[/C][C]0.0901719269554355[/C][C]0.180343853910871[/C][C]0.909828073044564[/C][/ROW]
[ROW][C]123[/C][C]0.0920929212133111[/C][C]0.184185842426622[/C][C]0.907907078786689[/C][/ROW]
[ROW][C]124[/C][C]0.078507398386919[/C][C]0.157014796773838[/C][C]0.921492601613081[/C][/ROW]
[ROW][C]125[/C][C]0.0636273945817552[/C][C]0.12725478916351[/C][C]0.936372605418245[/C][/ROW]
[ROW][C]126[/C][C]0.0600274331387888[/C][C]0.120054866277578[/C][C]0.93997256686121[/C][/ROW]
[ROW][C]127[/C][C]0.0462487535234265[/C][C]0.0924975070468529[/C][C]0.953751246476573[/C][/ROW]
[ROW][C]128[/C][C]0.0468560155612142[/C][C]0.0937120311224285[/C][C]0.953143984438786[/C][/ROW]
[ROW][C]129[/C][C]0.0352818270780491[/C][C]0.0705636541560982[/C][C]0.964718172921951[/C][/ROW]
[ROW][C]130[/C][C]0.0343355509001615[/C][C]0.068671101800323[/C][C]0.965664449099839[/C][/ROW]
[ROW][C]131[/C][C]0.0350082148336634[/C][C]0.0700164296673268[/C][C]0.964991785166337[/C][/ROW]
[ROW][C]132[/C][C]0.0517711435496937[/C][C]0.103542287099387[/C][C]0.948228856450306[/C][/ROW]
[ROW][C]133[/C][C]0.0495408701472339[/C][C]0.0990817402944677[/C][C]0.950459129852766[/C][/ROW]
[ROW][C]134[/C][C]0.0427992876386358[/C][C]0.0855985752772715[/C][C]0.957200712361364[/C][/ROW]
[ROW][C]135[/C][C]0.032580572102357[/C][C]0.065161144204714[/C][C]0.967419427897643[/C][/ROW]
[ROW][C]136[/C][C]0.0230316212288801[/C][C]0.0460632424577602[/C][C]0.97696837877112[/C][/ROW]
[ROW][C]137[/C][C]0.017092971458363[/C][C]0.034185942916726[/C][C]0.982907028541637[/C][/ROW]
[ROW][C]138[/C][C]0.0120624340400525[/C][C]0.0241248680801049[/C][C]0.987937565959948[/C][/ROW]
[ROW][C]139[/C][C]0.027510263205109[/C][C]0.055020526410218[/C][C]0.972489736794891[/C][/ROW]
[ROW][C]140[/C][C]0.0286853364724849[/C][C]0.0573706729449698[/C][C]0.971314663527515[/C][/ROW]
[ROW][C]141[/C][C]0.26395754506152[/C][C]0.527915090123041[/C][C]0.73604245493848[/C][/ROW]
[ROW][C]142[/C][C]0.215264495806276[/C][C]0.430528991612553[/C][C]0.784735504193724[/C][/ROW]
[ROW][C]143[/C][C]0.164968542930933[/C][C]0.329937085861867[/C][C]0.835031457069067[/C][/ROW]
[ROW][C]144[/C][C]0.183131037655935[/C][C]0.366262075311871[/C][C]0.816868962344065[/C][/ROW]
[ROW][C]145[/C][C]0.16369650060207[/C][C]0.327393001204139[/C][C]0.83630349939793[/C][/ROW]
[ROW][C]146[/C][C]0.177839145465097[/C][C]0.355678290930195[/C][C]0.822160854534903[/C][/ROW]
[ROW][C]147[/C][C]0.428292225955063[/C][C]0.856584451910126[/C][C]0.571707774044937[/C][/ROW]
[ROW][C]148[/C][C]0.395753845798648[/C][C]0.791507691597296[/C][C]0.604246154201352[/C][/ROW]
[ROW][C]149[/C][C]0.569824336801597[/C][C]0.860351326396807[/C][C]0.430175663198403[/C][/ROW]
[ROW][C]150[/C][C]0.463171669378792[/C][C]0.926343338757584[/C][C]0.536828330621208[/C][/ROW]
[ROW][C]151[/C][C]0.578739658482964[/C][C]0.842520683034072[/C][C]0.421260341517036[/C][/ROW]
[ROW][C]152[/C][C]0.619744493181101[/C][C]0.760511013637798[/C][C]0.380255506818899[/C][/ROW]
[ROW][C]153[/C][C]0.473806940604998[/C][C]0.947613881209996[/C][C]0.526193059395002[/C][/ROW]
[ROW][C]154[/C][C]0.341035283355678[/C][C]0.682070566711356[/C][C]0.658964716644322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&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
80.4733981620275270.9467963240550540.526601837972473
90.5270786574056540.9458426851886930.472921342594346
100.4639166460946430.9278332921892860.536083353905357
110.5570639646086130.8858720707827740.442936035391387
120.453286251516440.9065725030328810.54671374848356
130.3451773221968630.6903546443937270.654822677803137
140.2545490750552890.5090981501105790.745450924944711
150.3111304518437410.6222609036874820.688869548156259
160.2465006031216070.4930012062432140.753499396878393
170.1834960444743010.3669920889486020.816503955525699
180.4510614592524360.9021229185048710.548938540747564
190.5291115319118450.941776936176310.470888468088155
200.4564060287558250.912812057511650.543593971244176
210.38519599833940.77039199667880.6148040016606
220.3175712563649750.6351425127299490.682428743635025
230.384356140885710.7687122817714190.61564385911429
240.3192315240330330.6384630480660650.680768475966967
250.2707606771222340.5415213542444690.729239322877766
260.2187859816126320.4375719632252650.781214018387368
270.1736114825361510.3472229650723030.826388517463849
280.1392796649770720.2785593299541440.860720335022928
290.1064191860017830.2128383720035660.893580813998217
300.09821261012292160.1964252202458430.901787389877078
310.0747565798846130.1495131597692260.925243420115387
320.08294017432032810.1658803486406560.917059825679672
330.08472886277118820.1694577255423760.915271137228812
340.06368616125844520.127372322516890.936313838741555
350.05232239654699330.1046447930939870.947677603453007
360.508830577196740.982338845606520.49116942280326
370.580657230258640.8386855394827210.419342769741361
380.5304399305044510.9391201389910980.469560069495549
390.5015676807420960.9968646385158080.498432319257904
400.4724054129509770.9448108259019540.527594587049023
410.4291846858597720.8583693717195450.570815314140228
420.4065838342369270.8131676684738540.593416165763073
430.5980142658219860.8039714683560290.401985734178014
440.5536950615949850.892609876810030.446304938405015
450.5551290927338450.889741814532310.444870907266155
460.7251015056929140.5497969886141730.274898494307086
470.7147151324776660.5705697350446670.285284867522334
480.6699704064188950.6600591871622090.330029593581105
490.6260036280722390.7479927438555230.373996371927761
500.5963557518323350.807288496335330.403644248167665
510.5607362305725450.878527538854910.439263769427455
520.5114549497637980.9770901004724050.488545050236202
530.5069784352691590.9860431294616810.493021564730841
540.4664358249921380.9328716499842760.533564175007862
550.6603629939983920.6792740120032170.339637006001608
560.642717373425860.7145652531482810.357282626574141
570.5988351881972210.8023296236055590.401164811802779
580.5823571302533190.8352857394933620.417642869746681
590.5348469962318550.930306007536290.465153003768145
600.4923779002908190.9847558005816380.507622099709181
610.4701100309333470.9402200618666930.529889969066653
620.428688569589520.857377139179040.57131143041048
630.3886230384805830.7772460769611670.611376961519416
640.3455935466931710.6911870933863420.654406453306829
650.3126999443682830.6253998887365650.687300055631717
660.3140585327507770.6281170655015540.685941467249223
670.3310076536449960.6620153072899930.668992346355004
680.4364969922210870.8729939844421740.563503007778913
690.5185425238104380.9629149523791250.481457476189562
700.4884877980779360.9769755961558720.511512201922064
710.6684702744627080.6630594510745840.331529725537292
720.6265564510309730.7468870979380540.373443548969027
730.6512078344229190.6975843311541620.348792165577081
740.6792570854735520.6414858290528950.320742914526448
750.6475965515778950.7048068968442110.352403448422106
760.616535231760090.7669295364798190.383464768239909
770.575850594291760.848298811416480.42414940570824
780.5381784234512350.923643153097530.461821576548765
790.5002869375433940.9994261249132110.499713062456606
800.4689630797367310.9379261594734610.531036920263269
810.4398329945029620.8796659890059250.560167005497038
820.7075571707686850.5848856584626310.292442829231315
830.6762509923869950.647498015226010.323749007613005
840.6356495755101870.7287008489796270.364350424489813
850.5918208884275210.8163582231449580.408179111572479
860.5536173659946630.8927652680106740.446382634005337
870.5184797808613810.9630404382772380.481520219138619
880.4885847671545770.9771695343091540.511415232845423
890.4829359889043760.9658719778087510.517064011095624
900.4403558815898990.8807117631797970.559644118410101
910.4558103273414360.9116206546828710.544189672658564
920.4733828441389140.9467656882778270.526617155861086
930.4377687396642130.8755374793284260.562231260335787
940.3926998654944870.7853997309889730.607300134505513
950.3705314906043360.7410629812086720.629468509395664
960.3312911721868840.6625823443737690.668708827813116
970.3043771630669880.6087543261339750.695622836933012
980.2718506510355060.5437013020710110.728149348964494
990.2424775140276880.4849550280553760.757522485972312
1000.2098011860471320.4196023720942640.790198813952868
1010.1856918431331590.3713836862663190.81430815686684
1020.1646484405664810.3292968811329610.83535155943352
1030.2904215155557950.580843031111590.709578484444205
1040.2569218164558580.5138436329117150.743078183544142
1050.2392244925427820.4784489850855630.760775507457218
1060.2289966464351370.4579932928702740.771003353564863
1070.2332950312494940.4665900624989880.766704968750506
1080.2075471708444990.4150943416889980.792452829155501
1090.2103057851241670.4206115702483340.789694214875833
1100.2542404259140110.5084808518280220.745759574085989
1110.2287935566943690.4575871133887380.771206443305631
1120.210827156657920.4216543133158390.78917284334208
1130.2195381261052650.4390762522105290.780461873894736
1140.1858232083985920.3716464167971840.814176791601408
1150.2064448450476150.4128896900952310.793555154952385
1160.2255853420931870.4511706841863750.774414657906813
1170.1909728649644290.3819457299288590.80902713503557
1180.1615258620643480.3230517241286950.838474137935652
1190.1474185019991880.2948370039983760.852581498000812
1200.1261909843446550.2523819686893090.873809015655346
1210.1080106461436950.216021292287390.891989353856305
1220.09017192695543550.1803438539108710.909828073044564
1230.09209292121331110.1841858424266220.907907078786689
1240.0785073983869190.1570147967738380.921492601613081
1250.06362739458175520.127254789163510.936372605418245
1260.06002743313878880.1200548662775780.93997256686121
1270.04624875352342650.09249750704685290.953751246476573
1280.04685601556121420.09371203112242850.953143984438786
1290.03528182707804910.07056365415609820.964718172921951
1300.03433555090016150.0686711018003230.965664449099839
1310.03500821483366340.07001642966732680.964991785166337
1320.05177114354969370.1035422870993870.948228856450306
1330.04954087014723390.09908174029446770.950459129852766
1340.04279928763863580.08559857527727150.957200712361364
1350.0325805721023570.0651611442047140.967419427897643
1360.02303162122888010.04606324245776020.97696837877112
1370.0170929714583630.0341859429167260.982907028541637
1380.01206243404005250.02412486808010490.987937565959948
1390.0275102632051090.0550205264102180.972489736794891
1400.02868533647248490.05737067294496980.971314663527515
1410.263957545061520.5279150901230410.73604245493848
1420.2152644958062760.4305289916125530.784735504193724
1430.1649685429309330.3299370858618670.835031457069067
1440.1831310376559350.3662620753118710.816868962344065
1450.163696500602070.3273930012041390.83630349939793
1460.1778391454650970.3556782909301950.822160854534903
1470.4282922259550630.8565844519101260.571707774044937
1480.3957538457986480.7915076915972960.604246154201352
1490.5698243368015970.8603513263968070.430175663198403
1500.4631716693787920.9263433387575840.536828330621208
1510.5787396584829640.8425206830340720.421260341517036
1520.6197444931811010.7605110136377980.380255506818899
1530.4738069406049980.9476138812099960.526193059395002
1540.3410352833556780.6820705667113560.658964716644322






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0204081632653061OK
10% type I error level130.0884353741496599OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.0204081632653061 & OK \tabularnewline
10% type I error level & 13 & 0.0884353741496599 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155158&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.0204081632653061[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.0884353741496599[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155158&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155158&T=6

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


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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level30.0204081632653061OK
10% type I error level130.0884353741496599OK


Figures (Output of Computation)

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

Parameters (Session):
par1 = 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')
}