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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 16:36:08 -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/13/t1323812213wry7ihf55sszx24.htm/, Retrieved Thu, 02 May 2024 22:38:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154739, Retrieved Thu, 02 May 2024 22:38:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact80

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [WS10 MR] [2011-12-13 19:08:35] [43a0606d8103c0ba382f0586f4417c48]
-   P       [Multiple Regression] [WS10 MR] [2011-12-13 21:36:08] [635499bc27d9f41bf7bccae25a54e146] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	13	12	14
2	39	32	16	11	18
2	30	35	19	15	11
1	31	33	15	6	12
2	34	37	14	13	16
2	35	29	13	10	18
2	39	31	19	12	14
2	34	36	15	14	14
2	36	35	14	12	15
2	37	38	15	6	15
1	38	31	16	10	17
2	36	34	16	12	19
1	38	35	16	12	10
2	39	38	16	11	16
2	33	37	17	15	18
1	32	33	15	12	14
1	36	32	15	10	14
2	38	38	20	12	17
1	39	38	18	11	14
2	32	32	16	12	16
1	32	33	16	11	18
2	31	31	16	12	11
2	39	38	19	13	14
2	37	39	16	11	12
1	39	32	17	9	17
2	41	32	17	13	9
1	36	35	16	10	16
2	33	37	15	14	14
2	33	33	16	12	15
1	34	33	14	10	11
2	31	28	15	12	16
1	27	32	12	8	13
2	37	31	14	10	17
2	34	37	16	12	15
1	34	30	14	12	14
1	32	33	7	7	16
1	29	31	10	6	9
1	36	33	14	12	15
2	29	31	16	10	17
1	35	33	16	10	13
1	37	32	16	10	15
2	34	33	14	12	16
1	38	32	20	15	16
1	35	33	14	10	12
2	38	28	14	10	12
2	37	35	11	12	11
2	38	39	14	13	15
2	33	34	15	11	15
2	36	38	16	11	17
1	38	32	14	12	13
2	32	38	16	14	16
1	32	30	14	10	14
1	32	33	12	12	11
2	34	38	16	13	12
1	32	32	9	5	12
2	37	32	14	6	15
2	39	34	16	12	16
2	29	34	16	12	15
1	37	36	15	11	12
2	35	34	16	10	12
1	30	28	12	7	8
1	38	34	16	12	13
2	34	35	16	14	11
2	31	35	14	11	14
2	34	31	16	12	15
1	35	37	17	13	10
2	36	35	18	14	11
1	30	27	18	11	12
2	39	40	12	12	15
1	35	37	16	12	15
1	38	36	10	8	14
2	31	38	14	11	16
2	34	39	18	14	15
1	38	41	18	14	15
1	34	27	16	12	13
2	39	30	17	9	12
2	37	37	16	13	17
2	34	31	16	11	13
1	28	31	13	12	15
1	37	27	16	12	13
1	33	36	16	12	15
1	37	38	20	12	16
2	35	37	16	12	15
1	37	33	15	12	16
2	32	34	15	11	15
2	33	31	16	10	14
1	38	39	14	9	15
2	33	34	16	12	14
2	29	32	16	12	13
2	33	33	15	12	7
2	31	36	12	9	17
2	36	32	17	15	13
2	35	41	16	12	15
2	32	28	15	12	14
2	29	30	13	12	13
2	39	36	16	10	16
2	37	35	16	13	12
2	35	31	16	9	14
1	37	34	16	12	17
1	32	36	14	10	15
2	38	36	16	14	17
1	37	35	16	11	12
2	36	37	20	15	16
1	32	28	15	11	11
2	33	39	16	11	15
1	40	32	13	12	9
2	38	35	17	12	16
1	41	39	16	12	15
1	36	35	16	11	10
2	43	42	12	7	10
2	30	34	16	12	15
2	31	33	16	14	11
2	32	41	17	11	13
1	32	33	13	11	14
2	37	34	12	10	18
1	37	32	18	13	16
2	33	40	14	13	14
2	34	40	14	8	14
2	33	35	13	11	14
2	38	36	16	12	14
2	33	37	13	11	12
2	31	27	16	13	14
2	38	39	13	12	15
2	37	38	16	14	15
2	33	31	15	13	15
2	31	33	16	15	13
1	39	32	15	10	17
2	44	39	17	11	17
2	33	36	15	9	19
2	35	33	12	11	15
1	32	33	16	10	13
1	28	32	10	11	9
2	40	37	16	8	15
1	27	30	12	11	15
1	37	38	14	12	15
2	32	29	15	12	16
1	28	22	13	9	11
1	34	35	15	11	14
2	30	35	11	10	11
2	35	34	12	8	15
1	31	35	8	9	13
2	32	34	16	8	15
1	30	34	15	9	16
2	30	35	17	15	14
1	31	23	16	11	15
2	40	31	10	8	16
2	32	27	18	13	16
1	36	36	13	12	11
1	32	31	16	12	12
1	35	32	13	9	9
2	38	39	10	7	16
2	42	37	15	13	13
1	34	38	16	9	16
2	35	39	16	6	12
2	35	34	14	8	9
2	33	31	10	8	13
2	36	32	17	15	13
2	32	37	13	6	14
2	33	36	15	9	19
2	34	32	16	11	13
2	32	35	12	8	12
2	34	36	13	8	13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 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 & 7 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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 time7 seconds
R Server'AstonUniversity' @ aston.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Learning[t] = + 3.87413342773088 + 0.0425460757646513Gender[t] + 0.119376264216209Connected[t] -0.027338439450035Separate[t] + 0.554918283400906Software[t] + 0.119063701101276Happiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Learning[t] =  +  3.87413342773088 +  0.0425460757646513Gender[t] +  0.119376264216209Connected[t] -0.027338439450035Separate[t] +  0.554918283400906Software[t] +  0.119063701101276Happiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Learning[t] =  +  3.87413342773088 +  0.0425460757646513Gender[t] +  0.119376264216209Connected[t] -0.027338439450035Separate[t] +  0.554918283400906Software[t] +  0.119063701101276Happiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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] = + 3.87413342773088 + 0.0425460757646513Gender[t] + 0.119376264216209Connected[t] -0.027338439450035Separate[t] + 0.554918283400906Software[t] + 0.119063701101276Happiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)3.874133427730881.9430651.99380.0479140.023957
Gender0.04254607576465130.3169190.13420.8933790.446689
Connected0.1193762642162090.0468472.54820.0117930.005897
Separate-0.0273384394500350.045612-0.59940.5497920.274896
Software0.5549182834009060.0693058.00700
Happiness0.1190637011012760.0643551.85010.0661910.033095

\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) & 3.87413342773088 & 1.943065 & 1.9938 & 0.047914 & 0.023957 \tabularnewline
Gender & 0.0425460757646513 & 0.316919 & 0.1342 & 0.893379 & 0.446689 \tabularnewline
Connected & 0.119376264216209 & 0.046847 & 2.5482 & 0.011793 & 0.005897 \tabularnewline
Separate & -0.027338439450035 & 0.045612 & -0.5994 & 0.549792 & 0.274896 \tabularnewline
Software & 0.554918283400906 & 0.069305 & 8.007 & 0 & 0 \tabularnewline
Happiness & 0.119063701101276 & 0.064355 & 1.8501 & 0.066191 & 0.033095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&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]3.87413342773088[/C][C]1.943065[/C][C]1.9938[/C][C]0.047914[/C][C]0.023957[/C][/ROW]
[ROW][C]Gender[/C][C]0.0425460757646513[/C][C]0.316919[/C][C]0.1342[/C][C]0.893379[/C][C]0.446689[/C][/ROW]
[ROW][C]Connected[/C][C]0.119376264216209[/C][C]0.046847[/C][C]2.5482[/C][C]0.011793[/C][C]0.005897[/C][/ROW]
[ROW][C]Separate[/C][C]-0.027338439450035[/C][C]0.045612[/C][C]-0.5994[/C][C]0.549792[/C][C]0.274896[/C][/ROW]
[ROW][C]Software[/C][C]0.554918283400906[/C][C]0.069305[/C][C]8.007[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Happiness[/C][C]0.119063701101276[/C][C]0.064355[/C][C]1.8501[/C][C]0.066191[/C][C]0.033095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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)3.874133427730881.9430651.99380.0479140.023957
Gender0.04254607576465130.3169190.13420.8933790.446689
Connected0.1193762642162090.0468472.54820.0117930.005897
Separate-0.0273384394500350.045612-0.59940.5497920.274896
Software0.5549182834009060.0693058.00700
Happiness0.1190637011012760.0643551.85010.0661910.033095






Multiple Linear Regression - Regression Statistics
Multiple R0.588008219838766
R-squared0.345753666597954
Adjusted R-squared0.324784232835068
F-TEST (value)16.4884598462504
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value4.74620343027254e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85400385462494
Sum Squared Residuals536.223525702408

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.588008219838766 \tabularnewline
R-squared & 0.345753666597954 \tabularnewline
Adjusted R-squared & 0.324784232835068 \tabularnewline
F-TEST (value) & 16.4884598462504 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 4.74620343027254e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.85400385462494 \tabularnewline
Sum Squared Residuals & 536.223525702408 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.588008219838766[/C][/ROW]
[ROW][C]R-squared[/C][C]0.345753666597954[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.324784232835068[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]16.4884598462504[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]4.74620343027254e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.85400385462494[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]536.223525702408[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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 R0.588008219838766
R-squared0.345753666597954
Adjusted R-squared0.324784232835068
F-TEST (value)16.4884598462504
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value4.74620343027254e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.85400385462494
Sum Squared Residuals536.223525702408






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1407029292522-3.14070292925215
21615.98731755852410.0126824414758741
31916.21714308812282.78285691187716
41511.47344930596763.52655069403241
51416.1254532047922-2.12545320479217
61315.0369095366085-2.03690953660849
71916.093319476972.90668052303004
81516.4695825254406-1.46958252544056
91415.7449006276225-1.74490062762247
101512.45275187308312.54724812691686
111615.17875167349110.82124832650888
121616.2484938714776-0.248493871477612
131615.34578857478390.65421142521614
141615.58515951962140.414840480378636
151717.3540409095803-0.354040909580323
161515.1604626727918-0.160462672791782
171514.55546960230480.44453039769516
182016.13976523990733.86023476009266
191815.30448604165422.69551395834584
201615.4684745902090.53152540979098
211615.0817991937960.91820080620402
221614.78111825993651.21888174006353
231916.45686868422062.54313131577938
241614.84281374733381.15718625266619
251714.71587121485642.28412878514361
261716.26433334384690.735666656153128
271614.71158168615731.28841831384271
281516.3228678217743-1.32286782177431
291615.44144871387390.558551286126082
301413.93218753111860.0678124688814402
311515.458452083793-0.458452083792952
321212.2521829564559-0.252182956455876
331415.1019214850396-1.10192148503956
341615.451471220290.548528779710014
351415.4812305195743-1.4812305195743
36712.6239986579898-5.6239986579898
371010.9321825531314-0.932182553131413
381415.7570314307579-1.75703143075789
391614.14691137130991.85308862869011
401614.28969119753731.71030880246268
411614.79390956762231.20609043237768
421415.6798886791914-1.6798886791914
432017.80694094994432.19305905005566
441414.170627496436-0.170627496436044
451414.7079945620995-0.707994562099496
461115.3880220874336-4.38802208743358
471416.4292176816557-2.42921768165566
481514.8591919910230.140808008977023
491615.3460944280740.653905571925986
501415.7849949964378-1.78499499643779
511616.4142805203106-0.414280520310622
521414.1326414243401-0.132641424340075
531214.803271569488-2.80327156948795
541615.6218599609370.378140039062971
55911.0652457262329-2.06524572623292
561412.61678250978341.38321749021665
571616.2494315608224-0.24943156082241
581614.9366052175591.06339478244095
591514.88228298991930.117717010080738
601614.18583513275071.81416486724934
611211.56942871799740.430571282002644
621615.73031811753770.269681882462278
631616.1397298615868-0.139729861586764
641414.4740373220392-0.474037322039249
651615.61550185699020.384498143009804
661715.48790118663611.51209881336393
671816.37848239001921.62151760998082
681814.29269509545613.70730490454388
691215.9663372230209-3.96633722302092
701615.52830140874150.471698591258456
711013.5750318061353-3.57503180613531
721414.6301494058917-0.630149405891696
731816.50663090819171.49336909180827
741816.88691301039181.11308698960816
751615.44418213682310.555817863176867
761714.21777566401472.78222433598527
771616.6026456985421-0.602645698542069
781614.82245617138671.17754382861326
791314.8566981959283-1.85669819592829
801615.80231092947180.197689070528241
811615.31688731975920.683112680240838
822015.85877919882524.1412208011748
831615.57084748450620.429152515493805
841515.9954713960754-0.995471396075376
851514.73981572680680.260184273193232
861614.26722532487091.7327746751291
871414.1669984722874-0.166998472287382
881615.29504657332260.704953426677393
891614.75315469425661.24684530574343
901514.48893910506370.511060894936289
911213.6940534190912-1.69405341909123
921717.2535433939727-0.253543393972744
931615.46149372670610.538506273293945
941515.3397009458066-0.339700945806608
951314.8078315731566-1.80783157315664
961615.08491811512050.915081884879472
971616.0620040719358-0.0620040719357598
981613.95105956990242.04894043009759
991616.0871966577266-0.0871966577266171
1001414.0876744887411-0.0876744887411413
1011617.3042786856092-1.30427868560922
1021614.90962142936931.0903785706307
1032017.47404230002642.5259576999736
1041514.38504548333720.614954516662776
1051614.72249979377281.2775002062272
1061315.5474927204651-2.54749272046511
1071716.10271685715620.897283142843834
1081616.1898821151387-0.189882115138725
1091614.55211776295051.44788223704946
1101213.0192554784748-1.01925547847478
1111615.05598148177530.944018518224743
1121615.83627794783820.163722052161791
1131714.3103192484542.68968075154603
1141314.6055443893909-1.60554438939088
1151215.1389698677907-3.13896986779073
1161816.57772811892631.42227188107368
1171415.6859342200233-1.6859342200233
1181413.0307190672350.969280932765018
1191314.7127898504717-1.71278985047167
1201615.83725101550360.162748984496421
1211314.419985569369-1.41998556936904
1221615.80258140444130.197418595558659
1231315.8742993982548-2.87429939825475
1241616.8920981402904-0.892098140290388
1251516.0510438761749-1.05104387617489
1261616.6293236334417-0.629323633441666
1271515.2707894982573-0.270789498257293
1281716.27376610235360.726233897646353
1291514.17093334972620.8290666502738
1301215.1252829589054-3.12528295890543
1311613.93156240488872.06843759511131
1321013.5600592664697-3.5600592664697
1331613.94805567198362.05194432801639
1341214.2097420877612-2.20974208776121
1351415.7397154977239-1.73971549772393
1361515.5504899085591-0.550489908559125
1371312.96173449637080.0382655036292115
1381514.78962003892320.210379961076777
1391113.4425516711183-2.44255167111831
1401213.4331896692527-1.43318966925268
141813.2025909783715-5.20259097837151
1421613.07506087660412.92493912339595
1431513.46774425690921.53225574309084
1441716.57433419142670.425665808573336
1451614.87861622077631.12138377922371
1461014.2311500097851-4.2311500097851
1471816.16008507086011.8399149291399
1481315.1987613080027-2.19876130800268
1491614.97701214948931.0229878505107
1501313.2858565491813-0.285856549181347
1511013.2187716823515-3.2187716823515
1521516.723272215218-1.72327221521801
1531613.83589555597392.16410444402614
1541611.82946980189694.17053019810314
1551412.7188074626451.28119253735498
1561013.0383250569678-3.03832505696781
1571717.2535433939727-0.253543393972744
1581311.76414529035091.23585470964914
1591514.17093334972620.8290666502738
1601614.79511773193671.2048822680633
1611212.6905313338502-0.690531333850189
1621313.0210091239338-0.0210091239338469

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 16.1407029292522 & -3.14070292925215 \tabularnewline
2 & 16 & 15.9873175585241 & 0.0126824414758741 \tabularnewline
3 & 19 & 16.2171430881228 & 2.78285691187716 \tabularnewline
4 & 15 & 11.4734493059676 & 3.52655069403241 \tabularnewline
5 & 14 & 16.1254532047922 & -2.12545320479217 \tabularnewline
6 & 13 & 15.0369095366085 & -2.03690953660849 \tabularnewline
7 & 19 & 16.09331947697 & 2.90668052303004 \tabularnewline
8 & 15 & 16.4695825254406 & -1.46958252544056 \tabularnewline
9 & 14 & 15.7449006276225 & -1.74490062762247 \tabularnewline
10 & 15 & 12.4527518730831 & 2.54724812691686 \tabularnewline
11 & 16 & 15.1787516734911 & 0.82124832650888 \tabularnewline
12 & 16 & 16.2484938714776 & -0.248493871477612 \tabularnewline
13 & 16 & 15.3457885747839 & 0.65421142521614 \tabularnewline
14 & 16 & 15.5851595196214 & 0.414840480378636 \tabularnewline
15 & 17 & 17.3540409095803 & -0.354040909580323 \tabularnewline
16 & 15 & 15.1604626727918 & -0.160462672791782 \tabularnewline
17 & 15 & 14.5554696023048 & 0.44453039769516 \tabularnewline
18 & 20 & 16.1397652399073 & 3.86023476009266 \tabularnewline
19 & 18 & 15.3044860416542 & 2.69551395834584 \tabularnewline
20 & 16 & 15.468474590209 & 0.53152540979098 \tabularnewline
21 & 16 & 15.081799193796 & 0.91820080620402 \tabularnewline
22 & 16 & 14.7811182599365 & 1.21888174006353 \tabularnewline
23 & 19 & 16.4568686842206 & 2.54313131577938 \tabularnewline
24 & 16 & 14.8428137473338 & 1.15718625266619 \tabularnewline
25 & 17 & 14.7158712148564 & 2.28412878514361 \tabularnewline
26 & 17 & 16.2643333438469 & 0.735666656153128 \tabularnewline
27 & 16 & 14.7115816861573 & 1.28841831384271 \tabularnewline
28 & 15 & 16.3228678217743 & -1.32286782177431 \tabularnewline
29 & 16 & 15.4414487138739 & 0.558551286126082 \tabularnewline
30 & 14 & 13.9321875311186 & 0.0678124688814402 \tabularnewline
31 & 15 & 15.458452083793 & -0.458452083792952 \tabularnewline
32 & 12 & 12.2521829564559 & -0.252182956455876 \tabularnewline
33 & 14 & 15.1019214850396 & -1.10192148503956 \tabularnewline
34 & 16 & 15.45147122029 & 0.548528779710014 \tabularnewline
35 & 14 & 15.4812305195743 & -1.4812305195743 \tabularnewline
36 & 7 & 12.6239986579898 & -5.6239986579898 \tabularnewline
37 & 10 & 10.9321825531314 & -0.932182553131413 \tabularnewline
38 & 14 & 15.7570314307579 & -1.75703143075789 \tabularnewline
39 & 16 & 14.1469113713099 & 1.85308862869011 \tabularnewline
40 & 16 & 14.2896911975373 & 1.71030880246268 \tabularnewline
41 & 16 & 14.7939095676223 & 1.20609043237768 \tabularnewline
42 & 14 & 15.6798886791914 & -1.6798886791914 \tabularnewline
43 & 20 & 17.8069409499443 & 2.19305905005566 \tabularnewline
44 & 14 & 14.170627496436 & -0.170627496436044 \tabularnewline
45 & 14 & 14.7079945620995 & -0.707994562099496 \tabularnewline
46 & 11 & 15.3880220874336 & -4.38802208743358 \tabularnewline
47 & 14 & 16.4292176816557 & -2.42921768165566 \tabularnewline
48 & 15 & 14.859191991023 & 0.140808008977023 \tabularnewline
49 & 16 & 15.346094428074 & 0.653905571925986 \tabularnewline
50 & 14 & 15.7849949964378 & -1.78499499643779 \tabularnewline
51 & 16 & 16.4142805203106 & -0.414280520310622 \tabularnewline
52 & 14 & 14.1326414243401 & -0.132641424340075 \tabularnewline
53 & 12 & 14.803271569488 & -2.80327156948795 \tabularnewline
54 & 16 & 15.621859960937 & 0.378140039062971 \tabularnewline
55 & 9 & 11.0652457262329 & -2.06524572623292 \tabularnewline
56 & 14 & 12.6167825097834 & 1.38321749021665 \tabularnewline
57 & 16 & 16.2494315608224 & -0.24943156082241 \tabularnewline
58 & 16 & 14.936605217559 & 1.06339478244095 \tabularnewline
59 & 15 & 14.8822829899193 & 0.117717010080738 \tabularnewline
60 & 16 & 14.1858351327507 & 1.81416486724934 \tabularnewline
61 & 12 & 11.5694287179974 & 0.430571282002644 \tabularnewline
62 & 16 & 15.7303181175377 & 0.269681882462278 \tabularnewline
63 & 16 & 16.1397298615868 & -0.139729861586764 \tabularnewline
64 & 14 & 14.4740373220392 & -0.474037322039249 \tabularnewline
65 & 16 & 15.6155018569902 & 0.384498143009804 \tabularnewline
66 & 17 & 15.4879011866361 & 1.51209881336393 \tabularnewline
67 & 18 & 16.3784823900192 & 1.62151760998082 \tabularnewline
68 & 18 & 14.2926950954561 & 3.70730490454388 \tabularnewline
69 & 12 & 15.9663372230209 & -3.96633722302092 \tabularnewline
70 & 16 & 15.5283014087415 & 0.471698591258456 \tabularnewline
71 & 10 & 13.5750318061353 & -3.57503180613531 \tabularnewline
72 & 14 & 14.6301494058917 & -0.630149405891696 \tabularnewline
73 & 18 & 16.5066309081917 & 1.49336909180827 \tabularnewline
74 & 18 & 16.8869130103918 & 1.11308698960816 \tabularnewline
75 & 16 & 15.4441821368231 & 0.555817863176867 \tabularnewline
76 & 17 & 14.2177756640147 & 2.78222433598527 \tabularnewline
77 & 16 & 16.6026456985421 & -0.602645698542069 \tabularnewline
78 & 16 & 14.8224561713867 & 1.17754382861326 \tabularnewline
79 & 13 & 14.8566981959283 & -1.85669819592829 \tabularnewline
80 & 16 & 15.8023109294718 & 0.197689070528241 \tabularnewline
81 & 16 & 15.3168873197592 & 0.683112680240838 \tabularnewline
82 & 20 & 15.8587791988252 & 4.1412208011748 \tabularnewline
83 & 16 & 15.5708474845062 & 0.429152515493805 \tabularnewline
84 & 15 & 15.9954713960754 & -0.995471396075376 \tabularnewline
85 & 15 & 14.7398157268068 & 0.260184273193232 \tabularnewline
86 & 16 & 14.2672253248709 & 1.7327746751291 \tabularnewline
87 & 14 & 14.1669984722874 & -0.166998472287382 \tabularnewline
88 & 16 & 15.2950465733226 & 0.704953426677393 \tabularnewline
89 & 16 & 14.7531546942566 & 1.24684530574343 \tabularnewline
90 & 15 & 14.4889391050637 & 0.511060894936289 \tabularnewline
91 & 12 & 13.6940534190912 & -1.69405341909123 \tabularnewline
92 & 17 & 17.2535433939727 & -0.253543393972744 \tabularnewline
93 & 16 & 15.4614937267061 & 0.538506273293945 \tabularnewline
94 & 15 & 15.3397009458066 & -0.339700945806608 \tabularnewline
95 & 13 & 14.8078315731566 & -1.80783157315664 \tabularnewline
96 & 16 & 15.0849181151205 & 0.915081884879472 \tabularnewline
97 & 16 & 16.0620040719358 & -0.0620040719357598 \tabularnewline
98 & 16 & 13.9510595699024 & 2.04894043009759 \tabularnewline
99 & 16 & 16.0871966577266 & -0.0871966577266171 \tabularnewline
100 & 14 & 14.0876744887411 & -0.0876744887411413 \tabularnewline
101 & 16 & 17.3042786856092 & -1.30427868560922 \tabularnewline
102 & 16 & 14.9096214293693 & 1.0903785706307 \tabularnewline
103 & 20 & 17.4740423000264 & 2.5259576999736 \tabularnewline
104 & 15 & 14.3850454833372 & 0.614954516662776 \tabularnewline
105 & 16 & 14.7224997937728 & 1.2775002062272 \tabularnewline
106 & 13 & 15.5474927204651 & -2.54749272046511 \tabularnewline
107 & 17 & 16.1027168571562 & 0.897283142843834 \tabularnewline
108 & 16 & 16.1898821151387 & -0.189882115138725 \tabularnewline
109 & 16 & 14.5521177629505 & 1.44788223704946 \tabularnewline
110 & 12 & 13.0192554784748 & -1.01925547847478 \tabularnewline
111 & 16 & 15.0559814817753 & 0.944018518224743 \tabularnewline
112 & 16 & 15.8362779478382 & 0.163722052161791 \tabularnewline
113 & 17 & 14.310319248454 & 2.68968075154603 \tabularnewline
114 & 13 & 14.6055443893909 & -1.60554438939088 \tabularnewline
115 & 12 & 15.1389698677907 & -3.13896986779073 \tabularnewline
116 & 18 & 16.5777281189263 & 1.42227188107368 \tabularnewline
117 & 14 & 15.6859342200233 & -1.6859342200233 \tabularnewline
118 & 14 & 13.030719067235 & 0.969280932765018 \tabularnewline
119 & 13 & 14.7127898504717 & -1.71278985047167 \tabularnewline
120 & 16 & 15.8372510155036 & 0.162748984496421 \tabularnewline
121 & 13 & 14.419985569369 & -1.41998556936904 \tabularnewline
122 & 16 & 15.8025814044413 & 0.197418595558659 \tabularnewline
123 & 13 & 15.8742993982548 & -2.87429939825475 \tabularnewline
124 & 16 & 16.8920981402904 & -0.892098140290388 \tabularnewline
125 & 15 & 16.0510438761749 & -1.05104387617489 \tabularnewline
126 & 16 & 16.6293236334417 & -0.629323633441666 \tabularnewline
127 & 15 & 15.2707894982573 & -0.270789498257293 \tabularnewline
128 & 17 & 16.2737661023536 & 0.726233897646353 \tabularnewline
129 & 15 & 14.1709333497262 & 0.8290666502738 \tabularnewline
130 & 12 & 15.1252829589054 & -3.12528295890543 \tabularnewline
131 & 16 & 13.9315624048887 & 2.06843759511131 \tabularnewline
132 & 10 & 13.5600592664697 & -3.5600592664697 \tabularnewline
133 & 16 & 13.9480556719836 & 2.05194432801639 \tabularnewline
134 & 12 & 14.2097420877612 & -2.20974208776121 \tabularnewline
135 & 14 & 15.7397154977239 & -1.73971549772393 \tabularnewline
136 & 15 & 15.5504899085591 & -0.550489908559125 \tabularnewline
137 & 13 & 12.9617344963708 & 0.0382655036292115 \tabularnewline
138 & 15 & 14.7896200389232 & 0.210379961076777 \tabularnewline
139 & 11 & 13.4425516711183 & -2.44255167111831 \tabularnewline
140 & 12 & 13.4331896692527 & -1.43318966925268 \tabularnewline
141 & 8 & 13.2025909783715 & -5.20259097837151 \tabularnewline
142 & 16 & 13.0750608766041 & 2.92493912339595 \tabularnewline
143 & 15 & 13.4677442569092 & 1.53225574309084 \tabularnewline
144 & 17 & 16.5743341914267 & 0.425665808573336 \tabularnewline
145 & 16 & 14.8786162207763 & 1.12138377922371 \tabularnewline
146 & 10 & 14.2311500097851 & -4.2311500097851 \tabularnewline
147 & 18 & 16.1600850708601 & 1.8399149291399 \tabularnewline
148 & 13 & 15.1987613080027 & -2.19876130800268 \tabularnewline
149 & 16 & 14.9770121494893 & 1.0229878505107 \tabularnewline
150 & 13 & 13.2858565491813 & -0.285856549181347 \tabularnewline
151 & 10 & 13.2187716823515 & -3.2187716823515 \tabularnewline
152 & 15 & 16.723272215218 & -1.72327221521801 \tabularnewline
153 & 16 & 13.8358955559739 & 2.16410444402614 \tabularnewline
154 & 16 & 11.8294698018969 & 4.17053019810314 \tabularnewline
155 & 14 & 12.718807462645 & 1.28119253735498 \tabularnewline
156 & 10 & 13.0383250569678 & -3.03832505696781 \tabularnewline
157 & 17 & 17.2535433939727 & -0.253543393972744 \tabularnewline
158 & 13 & 11.7641452903509 & 1.23585470964914 \tabularnewline
159 & 15 & 14.1709333497262 & 0.8290666502738 \tabularnewline
160 & 16 & 14.7951177319367 & 1.2048822680633 \tabularnewline
161 & 12 & 12.6905313338502 & -0.690531333850189 \tabularnewline
162 & 13 & 13.0210091239338 & -0.0210091239338469 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C]13[/C][C]16.1407029292522[/C][C]-3.14070292925215[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]15.9873175585241[/C][C]0.0126824414758741[/C][/ROW]
[ROW][C]3[/C][C]19[/C][C]16.2171430881228[/C][C]2.78285691187716[/C][/ROW]
[ROW][C]4[/C][C]15[/C][C]11.4734493059676[/C][C]3.52655069403241[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]16.1254532047922[/C][C]-2.12545320479217[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]15.0369095366085[/C][C]-2.03690953660849[/C][/ROW]
[ROW][C]7[/C][C]19[/C][C]16.09331947697[/C][C]2.90668052303004[/C][/ROW]
[ROW][C]8[/C][C]15[/C][C]16.4695825254406[/C][C]-1.46958252544056[/C][/ROW]
[ROW][C]9[/C][C]14[/C][C]15.7449006276225[/C][C]-1.74490062762247[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]12.4527518730831[/C][C]2.54724812691686[/C][/ROW]
[ROW][C]11[/C][C]16[/C][C]15.1787516734911[/C][C]0.82124832650888[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]16.2484938714776[/C][C]-0.248493871477612[/C][/ROW]
[ROW][C]13[/C][C]16[/C][C]15.3457885747839[/C][C]0.65421142521614[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]15.5851595196214[/C][C]0.414840480378636[/C][/ROW]
[ROW][C]15[/C][C]17[/C][C]17.3540409095803[/C][C]-0.354040909580323[/C][/ROW]
[ROW][C]16[/C][C]15[/C][C]15.1604626727918[/C][C]-0.160462672791782[/C][/ROW]
[ROW][C]17[/C][C]15[/C][C]14.5554696023048[/C][C]0.44453039769516[/C][/ROW]
[ROW][C]18[/C][C]20[/C][C]16.1397652399073[/C][C]3.86023476009266[/C][/ROW]
[ROW][C]19[/C][C]18[/C][C]15.3044860416542[/C][C]2.69551395834584[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]15.468474590209[/C][C]0.53152540979098[/C][/ROW]
[ROW][C]21[/C][C]16[/C][C]15.081799193796[/C][C]0.91820080620402[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.7811182599365[/C][C]1.21888174006353[/C][/ROW]
[ROW][C]23[/C][C]19[/C][C]16.4568686842206[/C][C]2.54313131577938[/C][/ROW]
[ROW][C]24[/C][C]16[/C][C]14.8428137473338[/C][C]1.15718625266619[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.7158712148564[/C][C]2.28412878514361[/C][/ROW]
[ROW][C]26[/C][C]17[/C][C]16.2643333438469[/C][C]0.735666656153128[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.7115816861573[/C][C]1.28841831384271[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]16.3228678217743[/C][C]-1.32286782177431[/C][/ROW]
[ROW][C]29[/C][C]16[/C][C]15.4414487138739[/C][C]0.558551286126082[/C][/ROW]
[ROW][C]30[/C][C]14[/C][C]13.9321875311186[/C][C]0.0678124688814402[/C][/ROW]
[ROW][C]31[/C][C]15[/C][C]15.458452083793[/C][C]-0.458452083792952[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]12.2521829564559[/C][C]-0.252182956455876[/C][/ROW]
[ROW][C]33[/C][C]14[/C][C]15.1019214850396[/C][C]-1.10192148503956[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]15.45147122029[/C][C]0.548528779710014[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]15.4812305195743[/C][C]-1.4812305195743[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]12.6239986579898[/C][C]-5.6239986579898[/C][/ROW]
[ROW][C]37[/C][C]10[/C][C]10.9321825531314[/C][C]-0.932182553131413[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]15.7570314307579[/C][C]-1.75703143075789[/C][/ROW]
[ROW][C]39[/C][C]16[/C][C]14.1469113713099[/C][C]1.85308862869011[/C][/ROW]
[ROW][C]40[/C][C]16[/C][C]14.2896911975373[/C][C]1.71030880246268[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]14.7939095676223[/C][C]1.20609043237768[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]15.6798886791914[/C][C]-1.6798886791914[/C][/ROW]
[ROW][C]43[/C][C]20[/C][C]17.8069409499443[/C][C]2.19305905005566[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.170627496436[/C][C]-0.170627496436044[/C][/ROW]
[ROW][C]45[/C][C]14[/C][C]14.7079945620995[/C][C]-0.707994562099496[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]15.3880220874336[/C][C]-4.38802208743358[/C][/ROW]
[ROW][C]47[/C][C]14[/C][C]16.4292176816557[/C][C]-2.42921768165566[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.859191991023[/C][C]0.140808008977023[/C][/ROW]
[ROW][C]49[/C][C]16[/C][C]15.346094428074[/C][C]0.653905571925986[/C][/ROW]
[ROW][C]50[/C][C]14[/C][C]15.7849949964378[/C][C]-1.78499499643779[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]16.4142805203106[/C][C]-0.414280520310622[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]14.1326414243401[/C][C]-0.132641424340075[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]14.803271569488[/C][C]-2.80327156948795[/C][/ROW]
[ROW][C]54[/C][C]16[/C][C]15.621859960937[/C][C]0.378140039062971[/C][/ROW]
[ROW][C]55[/C][C]9[/C][C]11.0652457262329[/C][C]-2.06524572623292[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]12.6167825097834[/C][C]1.38321749021665[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]16.2494315608224[/C][C]-0.24943156082241[/C][/ROW]
[ROW][C]58[/C][C]16[/C][C]14.936605217559[/C][C]1.06339478244095[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]14.8822829899193[/C][C]0.117717010080738[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]14.1858351327507[/C][C]1.81416486724934[/C][/ROW]
[ROW][C]61[/C][C]12[/C][C]11.5694287179974[/C][C]0.430571282002644[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.7303181175377[/C][C]0.269681882462278[/C][/ROW]
[ROW][C]63[/C][C]16[/C][C]16.1397298615868[/C][C]-0.139729861586764[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]14.4740373220392[/C][C]-0.474037322039249[/C][/ROW]
[ROW][C]65[/C][C]16[/C][C]15.6155018569902[/C][C]0.384498143009804[/C][/ROW]
[ROW][C]66[/C][C]17[/C][C]15.4879011866361[/C][C]1.51209881336393[/C][/ROW]
[ROW][C]67[/C][C]18[/C][C]16.3784823900192[/C][C]1.62151760998082[/C][/ROW]
[ROW][C]68[/C][C]18[/C][C]14.2926950954561[/C][C]3.70730490454388[/C][/ROW]
[ROW][C]69[/C][C]12[/C][C]15.9663372230209[/C][C]-3.96633722302092[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]15.5283014087415[/C][C]0.471698591258456[/C][/ROW]
[ROW][C]71[/C][C]10[/C][C]13.5750318061353[/C][C]-3.57503180613531[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]14.6301494058917[/C][C]-0.630149405891696[/C][/ROW]
[ROW][C]73[/C][C]18[/C][C]16.5066309081917[/C][C]1.49336909180827[/C][/ROW]
[ROW][C]74[/C][C]18[/C][C]16.8869130103918[/C][C]1.11308698960816[/C][/ROW]
[ROW][C]75[/C][C]16[/C][C]15.4441821368231[/C][C]0.555817863176867[/C][/ROW]
[ROW][C]76[/C][C]17[/C][C]14.2177756640147[/C][C]2.78222433598527[/C][/ROW]
[ROW][C]77[/C][C]16[/C][C]16.6026456985421[/C][C]-0.602645698542069[/C][/ROW]
[ROW][C]78[/C][C]16[/C][C]14.8224561713867[/C][C]1.17754382861326[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]14.8566981959283[/C][C]-1.85669819592829[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]15.8023109294718[/C][C]0.197689070528241[/C][/ROW]
[ROW][C]81[/C][C]16[/C][C]15.3168873197592[/C][C]0.683112680240838[/C][/ROW]
[ROW][C]82[/C][C]20[/C][C]15.8587791988252[/C][C]4.1412208011748[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]15.5708474845062[/C][C]0.429152515493805[/C][/ROW]
[ROW][C]84[/C][C]15[/C][C]15.9954713960754[/C][C]-0.995471396075376[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.7398157268068[/C][C]0.260184273193232[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.2672253248709[/C][C]1.7327746751291[/C][/ROW]
[ROW][C]87[/C][C]14[/C][C]14.1669984722874[/C][C]-0.166998472287382[/C][/ROW]
[ROW][C]88[/C][C]16[/C][C]15.2950465733226[/C][C]0.704953426677393[/C][/ROW]
[ROW][C]89[/C][C]16[/C][C]14.7531546942566[/C][C]1.24684530574343[/C][/ROW]
[ROW][C]90[/C][C]15[/C][C]14.4889391050637[/C][C]0.511060894936289[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.6940534190912[/C][C]-1.69405341909123[/C][/ROW]
[ROW][C]92[/C][C]17[/C][C]17.2535433939727[/C][C]-0.253543393972744[/C][/ROW]
[ROW][C]93[/C][C]16[/C][C]15.4614937267061[/C][C]0.538506273293945[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.3397009458066[/C][C]-0.339700945806608[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]14.8078315731566[/C][C]-1.80783157315664[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.0849181151205[/C][C]0.915081884879472[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]16.0620040719358[/C][C]-0.0620040719357598[/C][/ROW]
[ROW][C]98[/C][C]16[/C][C]13.9510595699024[/C][C]2.04894043009759[/C][/ROW]
[ROW][C]99[/C][C]16[/C][C]16.0871966577266[/C][C]-0.0871966577266171[/C][/ROW]
[ROW][C]100[/C][C]14[/C][C]14.0876744887411[/C][C]-0.0876744887411413[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]17.3042786856092[/C][C]-1.30427868560922[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.9096214293693[/C][C]1.0903785706307[/C][/ROW]
[ROW][C]103[/C][C]20[/C][C]17.4740423000264[/C][C]2.5259576999736[/C][/ROW]
[ROW][C]104[/C][C]15[/C][C]14.3850454833372[/C][C]0.614954516662776[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]14.7224997937728[/C][C]1.2775002062272[/C][/ROW]
[ROW][C]106[/C][C]13[/C][C]15.5474927204651[/C][C]-2.54749272046511[/C][/ROW]
[ROW][C]107[/C][C]17[/C][C]16.1027168571562[/C][C]0.897283142843834[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]16.1898821151387[/C][C]-0.189882115138725[/C][/ROW]
[ROW][C]109[/C][C]16[/C][C]14.5521177629505[/C][C]1.44788223704946[/C][/ROW]
[ROW][C]110[/C][C]12[/C][C]13.0192554784748[/C][C]-1.01925547847478[/C][/ROW]
[ROW][C]111[/C][C]16[/C][C]15.0559814817753[/C][C]0.944018518224743[/C][/ROW]
[ROW][C]112[/C][C]16[/C][C]15.8362779478382[/C][C]0.163722052161791[/C][/ROW]
[ROW][C]113[/C][C]17[/C][C]14.310319248454[/C][C]2.68968075154603[/C][/ROW]
[ROW][C]114[/C][C]13[/C][C]14.6055443893909[/C][C]-1.60554438939088[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]15.1389698677907[/C][C]-3.13896986779073[/C][/ROW]
[ROW][C]116[/C][C]18[/C][C]16.5777281189263[/C][C]1.42227188107368[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]15.6859342200233[/C][C]-1.6859342200233[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]13.030719067235[/C][C]0.969280932765018[/C][/ROW]
[ROW][C]119[/C][C]13[/C][C]14.7127898504717[/C][C]-1.71278985047167[/C][/ROW]
[ROW][C]120[/C][C]16[/C][C]15.8372510155036[/C][C]0.162748984496421[/C][/ROW]
[ROW][C]121[/C][C]13[/C][C]14.419985569369[/C][C]-1.41998556936904[/C][/ROW]
[ROW][C]122[/C][C]16[/C][C]15.8025814044413[/C][C]0.197418595558659[/C][/ROW]
[ROW][C]123[/C][C]13[/C][C]15.8742993982548[/C][C]-2.87429939825475[/C][/ROW]
[ROW][C]124[/C][C]16[/C][C]16.8920981402904[/C][C]-0.892098140290388[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]16.0510438761749[/C][C]-1.05104387617489[/C][/ROW]
[ROW][C]126[/C][C]16[/C][C]16.6293236334417[/C][C]-0.629323633441666[/C][/ROW]
[ROW][C]127[/C][C]15[/C][C]15.2707894982573[/C][C]-0.270789498257293[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.2737661023536[/C][C]0.726233897646353[/C][/ROW]
[ROW][C]129[/C][C]15[/C][C]14.1709333497262[/C][C]0.8290666502738[/C][/ROW]
[ROW][C]130[/C][C]12[/C][C]15.1252829589054[/C][C]-3.12528295890543[/C][/ROW]
[ROW][C]131[/C][C]16[/C][C]13.9315624048887[/C][C]2.06843759511131[/C][/ROW]
[ROW][C]132[/C][C]10[/C][C]13.5600592664697[/C][C]-3.5600592664697[/C][/ROW]
[ROW][C]133[/C][C]16[/C][C]13.9480556719836[/C][C]2.05194432801639[/C][/ROW]
[ROW][C]134[/C][C]12[/C][C]14.2097420877612[/C][C]-2.20974208776121[/C][/ROW]
[ROW][C]135[/C][C]14[/C][C]15.7397154977239[/C][C]-1.73971549772393[/C][/ROW]
[ROW][C]136[/C][C]15[/C][C]15.5504899085591[/C][C]-0.550489908559125[/C][/ROW]
[ROW][C]137[/C][C]13[/C][C]12.9617344963708[/C][C]0.0382655036292115[/C][/ROW]
[ROW][C]138[/C][C]15[/C][C]14.7896200389232[/C][C]0.210379961076777[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.4425516711183[/C][C]-2.44255167111831[/C][/ROW]
[ROW][C]140[/C][C]12[/C][C]13.4331896692527[/C][C]-1.43318966925268[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]13.2025909783715[/C][C]-5.20259097837151[/C][/ROW]
[ROW][C]142[/C][C]16[/C][C]13.0750608766041[/C][C]2.92493912339595[/C][/ROW]
[ROW][C]143[/C][C]15[/C][C]13.4677442569092[/C][C]1.53225574309084[/C][/ROW]
[ROW][C]144[/C][C]17[/C][C]16.5743341914267[/C][C]0.425665808573336[/C][/ROW]
[ROW][C]145[/C][C]16[/C][C]14.8786162207763[/C][C]1.12138377922371[/C][/ROW]
[ROW][C]146[/C][C]10[/C][C]14.2311500097851[/C][C]-4.2311500097851[/C][/ROW]
[ROW][C]147[/C][C]18[/C][C]16.1600850708601[/C][C]1.8399149291399[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.1987613080027[/C][C]-2.19876130800268[/C][/ROW]
[ROW][C]149[/C][C]16[/C][C]14.9770121494893[/C][C]1.0229878505107[/C][/ROW]
[ROW][C]150[/C][C]13[/C][C]13.2858565491813[/C][C]-0.285856549181347[/C][/ROW]
[ROW][C]151[/C][C]10[/C][C]13.2187716823515[/C][C]-3.2187716823515[/C][/ROW]
[ROW][C]152[/C][C]15[/C][C]16.723272215218[/C][C]-1.72327221521801[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]13.8358955559739[/C][C]2.16410444402614[/C][/ROW]
[ROW][C]154[/C][C]16[/C][C]11.8294698018969[/C][C]4.17053019810314[/C][/ROW]
[ROW][C]155[/C][C]14[/C][C]12.718807462645[/C][C]1.28119253735498[/C][/ROW]
[ROW][C]156[/C][C]10[/C][C]13.0383250569678[/C][C]-3.03832505696781[/C][/ROW]
[ROW][C]157[/C][C]17[/C][C]17.2535433939727[/C][C]-0.253543393972744[/C][/ROW]
[ROW][C]158[/C][C]13[/C][C]11.7641452903509[/C][C]1.23585470964914[/C][/ROW]
[ROW][C]159[/C][C]15[/C][C]14.1709333497262[/C][C]0.8290666502738[/C][/ROW]
[ROW][C]160[/C][C]16[/C][C]14.7951177319367[/C][C]1.2048822680633[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.6905313338502[/C][C]-0.690531333850189[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.0210091239338[/C][C]-0.0210091239338469[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&T=4

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


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11316.1407029292522-3.14070292925215
21615.98731755852410.0126824414758741
31916.21714308812282.78285691187716
41511.47344930596763.52655069403241
51416.1254532047922-2.12545320479217
61315.0369095366085-2.03690953660849
71916.093319476972.90668052303004
81516.4695825254406-1.46958252544056
91415.7449006276225-1.74490062762247
101512.45275187308312.54724812691686
111615.17875167349110.82124832650888
121616.2484938714776-0.248493871477612
131615.34578857478390.65421142521614
141615.58515951962140.414840480378636
151717.3540409095803-0.354040909580323
161515.1604626727918-0.160462672791782
171514.55546960230480.44453039769516
182016.13976523990733.86023476009266
191815.30448604165422.69551395834584
201615.4684745902090.53152540979098
211615.0817991937960.91820080620402
221614.78111825993651.21888174006353
231916.45686868422062.54313131577938
241614.84281374733381.15718625266619
251714.71587121485642.28412878514361
261716.26433334384690.735666656153128
271614.71158168615731.28841831384271
281516.3228678217743-1.32286782177431
291615.44144871387390.558551286126082
301413.93218753111860.0678124688814402
311515.458452083793-0.458452083792952
321212.2521829564559-0.252182956455876
331415.1019214850396-1.10192148503956
341615.451471220290.548528779710014
351415.4812305195743-1.4812305195743
36712.6239986579898-5.6239986579898
371010.9321825531314-0.932182553131413
381415.7570314307579-1.75703143075789
391614.14691137130991.85308862869011
401614.28969119753731.71030880246268
411614.79390956762231.20609043237768
421415.6798886791914-1.6798886791914
432017.80694094994432.19305905005566
441414.170627496436-0.170627496436044
451414.7079945620995-0.707994562099496
461115.3880220874336-4.38802208743358
471416.4292176816557-2.42921768165566
481514.8591919910230.140808008977023
491615.3460944280740.653905571925986
501415.7849949964378-1.78499499643779
511616.4142805203106-0.414280520310622
521414.1326414243401-0.132641424340075
531214.803271569488-2.80327156948795
541615.6218599609370.378140039062971
55911.0652457262329-2.06524572623292
561412.61678250978341.38321749021665
571616.2494315608224-0.24943156082241
581614.9366052175591.06339478244095
591514.88228298991930.117717010080738
601614.18583513275071.81416486724934
611211.56942871799740.430571282002644
621615.73031811753770.269681882462278
631616.1397298615868-0.139729861586764
641414.4740373220392-0.474037322039249
651615.61550185699020.384498143009804
661715.48790118663611.51209881336393
671816.37848239001921.62151760998082
681814.29269509545613.70730490454388
691215.9663372230209-3.96633722302092
701615.52830140874150.471698591258456
711013.5750318061353-3.57503180613531
721414.6301494058917-0.630149405891696
731816.50663090819171.49336909180827
741816.88691301039181.11308698960816
751615.44418213682310.555817863176867
761714.21777566401472.78222433598527
771616.6026456985421-0.602645698542069
781614.82245617138671.17754382861326
791314.8566981959283-1.85669819592829
801615.80231092947180.197689070528241
811615.31688731975920.683112680240838
822015.85877919882524.1412208011748
831615.57084748450620.429152515493805
841515.9954713960754-0.995471396075376
851514.73981572680680.260184273193232
861614.26722532487091.7327746751291
871414.1669984722874-0.166998472287382
881615.29504657332260.704953426677393
891614.75315469425661.24684530574343
901514.48893910506370.511060894936289
911213.6940534190912-1.69405341909123
921717.2535433939727-0.253543393972744
931615.46149372670610.538506273293945
941515.3397009458066-0.339700945806608
951314.8078315731566-1.80783157315664
961615.08491811512050.915081884879472
971616.0620040719358-0.0620040719357598
981613.95105956990242.04894043009759
991616.0871966577266-0.0871966577266171
1001414.0876744887411-0.0876744887411413
1011617.3042786856092-1.30427868560922
1021614.90962142936931.0903785706307
1032017.47404230002642.5259576999736
1041514.38504548333720.614954516662776
1051614.72249979377281.2775002062272
1061315.5474927204651-2.54749272046511
1071716.10271685715620.897283142843834
1081616.1898821151387-0.189882115138725
1091614.55211776295051.44788223704946
1101213.0192554784748-1.01925547847478
1111615.05598148177530.944018518224743
1121615.83627794783820.163722052161791
1131714.3103192484542.68968075154603
1141314.6055443893909-1.60554438939088
1151215.1389698677907-3.13896986779073
1161816.57772811892631.42227188107368
1171415.6859342200233-1.6859342200233
1181413.0307190672350.969280932765018
1191314.7127898504717-1.71278985047167
1201615.83725101550360.162748984496421
1211314.419985569369-1.41998556936904
1221615.80258140444130.197418595558659
1231315.8742993982548-2.87429939825475
1241616.8920981402904-0.892098140290388
1251516.0510438761749-1.05104387617489
1261616.6293236334417-0.629323633441666
1271515.2707894982573-0.270789498257293
1281716.27376610235360.726233897646353
1291514.17093334972620.8290666502738
1301215.1252829589054-3.12528295890543
1311613.93156240488872.06843759511131
1321013.5600592664697-3.5600592664697
1331613.94805567198362.05194432801639
1341214.2097420877612-2.20974208776121
1351415.7397154977239-1.73971549772393
1361515.5504899085591-0.550489908559125
1371312.96173449637080.0382655036292115
1381514.78962003892320.210379961076777
1391113.4425516711183-2.44255167111831
1401213.4331896692527-1.43318966925268
141813.2025909783715-5.20259097837151
1421613.07506087660412.92493912339595
1431513.46774425690921.53225574309084
1441716.57433419142670.425665808573336
1451614.87861622077631.12138377922371
1461014.2311500097851-4.2311500097851
1471816.16008507086011.8399149291399
1481315.1987613080027-2.19876130800268
1491614.97701214948931.0229878505107
1501313.2858565491813-0.285856549181347
1511013.2187716823515-3.2187716823515
1521516.723272215218-1.72327221521801
1531613.83589555597392.16410444402614
1541611.82946980189694.17053019810314
1551412.7188074626451.28119253735498
1561013.0383250569678-3.03832505696781
1571717.2535433939727-0.253543393972744
1581311.76414529035091.23585470964914
1591514.17093334972620.8290666502738
1601614.79511773193671.2048822680633
1611212.6905313338502-0.690531333850189
1621313.0210091239338-0.0210091239338469






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.3380365273268680.6760730546537350.661963472673132
100.7228543811223830.5542912377552340.277145618877617
110.7596153704689520.4807692590620970.240384629531048
120.7921097472990520.4157805054018960.207890252700948
130.718263374269180.563473251461640.28173662573082
140.6972578830812840.6054842338374320.302742116918716
150.67906901246910.6418619750617990.3209309875309
160.6135541220053580.7728917559892830.386445877994642
170.5278632986594110.9442734026811780.472136701340589
180.851627402338830.2967451953223410.14837259766117
190.8516111054008530.2967777891982950.148388894599147
200.8044016894890940.3911966210218110.195598310510906
210.7503989164436150.499202167112770.249601083556385
220.6911464641038030.6177070717923940.308853535896197
230.715080111020120.569839777959760.28491988897988
240.6586672082093130.6826655835813740.341332791790687
250.6385996656402650.722800668719470.361400334359735
260.5744938674486150.851012265102770.425506132551385
270.5136372327423430.9727255345153140.486362767257657
280.4903552520096740.9807105040193470.509644747990326
290.4276273997477410.8552547994954830.572372600252259
300.4161959078812810.8323918157625620.583804092118719
310.3565042966355940.7130085932711890.643495703364406
320.3473778972836670.6947557945673340.652622102716333
330.320328656103640.640657312207280.67967134389636
340.2686475248872480.5372950497744950.731352475112752
350.262499794276620.524999588553240.73750020572338
360.7948371559348230.4103256881303550.205162844065177
370.7760343004287210.4479313991425580.223965699571279
380.7745443340763020.4509113318473970.225455665923698
390.7914043969076420.4171912061847170.208595603092358
400.7751004086123140.4497991827753710.224899591387686
410.745575678885280.508848642229440.25442432111472
420.7348401920002480.5303196159995050.265159807999752
430.7466219722498420.5067560555003170.253378027750158
440.7087961786263990.5824076427472030.291203821373601
450.6728073877779230.6543852244441540.327192612222077
460.8634683162672410.2730633674655190.136531683732759
470.887211636400380.225576727199240.11278836359962
480.8614239872862330.2771520254275330.138576012713767
490.8342315142997250.3315369714005490.165768485700275
500.835838235295050.3283235294098990.164161764704949
510.8044630728564410.3910738542871180.195536927143559
520.7682876445129240.4634247109741530.231712355487076
530.8067188527862950.3865622944274110.193281147213705
540.7725771334121710.4548457331756580.227422866587829
550.7809904012131240.4380191975737510.219009598786876
560.761228085949980.4775438281000410.238771914050021
570.7234807953271630.5530384093456740.276519204672837
580.6999726473845410.6000547052309190.300027352615459
590.6568161908174620.6863676183650760.343183809182538
600.6519156979191590.6961686041616830.348084302080841
610.610801616011310.7783967679773810.389198383988691
620.564982182069470.870035635861060.43501781793053
630.5176629956432430.9646740087135140.482337004356757
640.4729472123981740.9458944247963490.527052787601826
650.4288228475635580.8576456951271150.571177152436443
660.410106404815090.820212809630180.58989359518491
670.397505412499150.79501082499830.60249458750085
680.5413404889094550.917319022181090.458659511090545
690.6935174975395820.6129650049208360.306482502460418
700.6541355588649440.6917288822701120.345864441135056
710.7514755235402250.497048952919550.248524476459775
720.7168227018892140.5663545962215730.283177298110786
730.7029068856426520.5941862287146950.297093114357348
740.6775602943550970.6448794112898070.322439705644903
750.6388608438246780.7222783123506430.361139156175322
760.6889295137734760.6221409724530480.311070486226524
770.6509606747388870.6980786505222260.349039325261113
780.6245113666786120.7509772666427760.375488633321388
790.6255365383831130.7489269232337730.374463461616887
800.5856385783738120.8287228432523760.414361421626188
810.5464429467105010.9071141065789980.453557053289499
820.7190736802322460.5618526395355080.280926319767754
830.6810054265951790.6379891468096430.318994573404821
840.6494557317644950.7010885364710110.350544268235505
850.6062909216903690.7874181566192620.393709078309631
860.6006500596681840.7986998806636310.399349940331816
870.5553027058642830.8893945882714350.444697294135717
880.5159011541817230.9681976916365540.484098845818277
890.4892937706480820.9785875412961640.510706229351918
900.4519306617968290.9038613235936570.548069338203171
910.446790118150190.8935802363003790.55320988184981
920.4050864735554050.810172947110810.594913526444595
930.3640510505839140.7281021011678280.635948949416086
940.3228681673987550.645736334797510.677131832601245
950.3178142165653280.6356284331306560.682185783434672
960.2881847738158510.5763695476317030.711815226184149
970.2516722915354660.5033445830709310.748327708464534
980.2646755450505350.529351090101070.735324454949465
990.2275947561038260.4551895122076520.772405243896174
1000.1935494538030480.3870989076060960.806450546196952
1010.1747461572080550.349492314416110.825253842791945
1020.1605280962299260.3210561924598520.839471903770074
1030.1913679243839410.3827358487678810.80863207561606
1040.169007515694610.338015031389220.83099248430539
1050.1525882528445530.3051765056891060.847411747155447
1060.1621609828132080.3243219656264160.837839017186792
1070.1445457358046970.2890914716093930.855454264195303
1080.1198749072061580.2397498144123160.880125092793842
1090.1206385760595380.2412771521190770.879361423940461
1100.1014478403556750.2028956807113490.898552159644325
1110.08593150669312140.1718630133862430.914068493306879
1120.07044993616171050.1408998723234210.92955006383829
1130.09569069090530040.1913813818106010.9043093090947
1140.08620084346964670.1724016869392930.913799156530353
1150.1252471436471040.2504942872942070.874752856352896
1160.1229064749750350.245812949950070.877093525024965
1170.1098538578293050.2197077156586090.890146142170695
1180.09351331363374390.1870266272674880.906486686366256
1190.08532932393530880.1706586478706180.914670676064691
1200.06938533101194830.1387706620238970.930614668988052
1210.05819036315944890.1163807263188980.941809636840551
1220.04512807938374240.09025615876748480.954871920616258
1230.05385980770870770.1077196154174150.946140192291292
1240.04158211324944440.08316422649888870.958417886750556
1250.0325992793135370.0651985586270740.967400720686463
1260.02411516715675290.04823033431350580.975884832843247
1270.01729837559538490.03459675119076970.982701624404615
1280.01371192738167280.02742385476334560.986288072618327
1290.009778203965790680.01955640793158140.99022179603421
1300.01489122135839360.02978244271678730.985108778641606
1310.01791380555513230.03582761111026460.982086194444868
1320.02996548370729040.05993096741458070.97003451629271
1330.03897406051959740.07794812103919480.961025939480403
1340.05197382480816680.1039476496163340.948026175191833
1350.04096076551306220.08192153102612440.959039234486938
1360.03034739652118120.06069479304236230.969652603478819
1370.02102509352634660.04205018705269320.978974906473653
1380.01444919326388210.02889838652776420.985550806736118
1390.02947000963324730.05894001926649450.970529990366753
1400.02271536377154020.04543072754308030.97728463622846
1410.3079089698375750.6158179396751490.692091030162425
1420.3270687555084650.6541375110169310.672931244491535
1430.2652300086640220.5304600173280430.734769991335978
1440.2803322044400540.5606644088801070.719667795559946
1450.2600462012386080.5200924024772160.739953798761392
1460.2214632043933550.4429264087867110.778536795606645
1470.2725077271100360.5450154542200720.727492272889964
1480.5628457939005840.8743084121988320.437154206099416
1490.4589921443583250.917984288716650.541007855641675
1500.3456444725946210.6912889451892420.654355527405379
1510.3792928777442850.7585857554885710.620707122255715
1520.6546209907465630.6907580185068750.345379009253437
1530.4888434855814490.9776869711628980.511156514418551

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.338036527326868 & 0.676073054653735 & 0.661963472673132 \tabularnewline
10 & 0.722854381122383 & 0.554291237755234 & 0.277145618877617 \tabularnewline
11 & 0.759615370468952 & 0.480769259062097 & 0.240384629531048 \tabularnewline
12 & 0.792109747299052 & 0.415780505401896 & 0.207890252700948 \tabularnewline
13 & 0.71826337426918 & 0.56347325146164 & 0.28173662573082 \tabularnewline
14 & 0.697257883081284 & 0.605484233837432 & 0.302742116918716 \tabularnewline
15 & 0.6790690124691 & 0.641861975061799 & 0.3209309875309 \tabularnewline
16 & 0.613554122005358 & 0.772891755989283 & 0.386445877994642 \tabularnewline
17 & 0.527863298659411 & 0.944273402681178 & 0.472136701340589 \tabularnewline
18 & 0.85162740233883 & 0.296745195322341 & 0.14837259766117 \tabularnewline
19 & 0.851611105400853 & 0.296777789198295 & 0.148388894599147 \tabularnewline
20 & 0.804401689489094 & 0.391196621021811 & 0.195598310510906 \tabularnewline
21 & 0.750398916443615 & 0.49920216711277 & 0.249601083556385 \tabularnewline
22 & 0.691146464103803 & 0.617707071792394 & 0.308853535896197 \tabularnewline
23 & 0.71508011102012 & 0.56983977795976 & 0.28491988897988 \tabularnewline
24 & 0.658667208209313 & 0.682665583581374 & 0.341332791790687 \tabularnewline
25 & 0.638599665640265 & 0.72280066871947 & 0.361400334359735 \tabularnewline
26 & 0.574493867448615 & 0.85101226510277 & 0.425506132551385 \tabularnewline
27 & 0.513637232742343 & 0.972725534515314 & 0.486362767257657 \tabularnewline
28 & 0.490355252009674 & 0.980710504019347 & 0.509644747990326 \tabularnewline
29 & 0.427627399747741 & 0.855254799495483 & 0.572372600252259 \tabularnewline
30 & 0.416195907881281 & 0.832391815762562 & 0.583804092118719 \tabularnewline
31 & 0.356504296635594 & 0.713008593271189 & 0.643495703364406 \tabularnewline
32 & 0.347377897283667 & 0.694755794567334 & 0.652622102716333 \tabularnewline
33 & 0.32032865610364 & 0.64065731220728 & 0.67967134389636 \tabularnewline
34 & 0.268647524887248 & 0.537295049774495 & 0.731352475112752 \tabularnewline
35 & 0.26249979427662 & 0.52499958855324 & 0.73750020572338 \tabularnewline
36 & 0.794837155934823 & 0.410325688130355 & 0.205162844065177 \tabularnewline
37 & 0.776034300428721 & 0.447931399142558 & 0.223965699571279 \tabularnewline
38 & 0.774544334076302 & 0.450911331847397 & 0.225455665923698 \tabularnewline
39 & 0.791404396907642 & 0.417191206184717 & 0.208595603092358 \tabularnewline
40 & 0.775100408612314 & 0.449799182775371 & 0.224899591387686 \tabularnewline
41 & 0.74557567888528 & 0.50884864222944 & 0.25442432111472 \tabularnewline
42 & 0.734840192000248 & 0.530319615999505 & 0.265159807999752 \tabularnewline
43 & 0.746621972249842 & 0.506756055500317 & 0.253378027750158 \tabularnewline
44 & 0.708796178626399 & 0.582407642747203 & 0.291203821373601 \tabularnewline
45 & 0.672807387777923 & 0.654385224444154 & 0.327192612222077 \tabularnewline
46 & 0.863468316267241 & 0.273063367465519 & 0.136531683732759 \tabularnewline
47 & 0.88721163640038 & 0.22557672719924 & 0.11278836359962 \tabularnewline
48 & 0.861423987286233 & 0.277152025427533 & 0.138576012713767 \tabularnewline
49 & 0.834231514299725 & 0.331536971400549 & 0.165768485700275 \tabularnewline
50 & 0.83583823529505 & 0.328323529409899 & 0.164161764704949 \tabularnewline
51 & 0.804463072856441 & 0.391073854287118 & 0.195536927143559 \tabularnewline
52 & 0.768287644512924 & 0.463424710974153 & 0.231712355487076 \tabularnewline
53 & 0.806718852786295 & 0.386562294427411 & 0.193281147213705 \tabularnewline
54 & 0.772577133412171 & 0.454845733175658 & 0.227422866587829 \tabularnewline
55 & 0.780990401213124 & 0.438019197573751 & 0.219009598786876 \tabularnewline
56 & 0.76122808594998 & 0.477543828100041 & 0.238771914050021 \tabularnewline
57 & 0.723480795327163 & 0.553038409345674 & 0.276519204672837 \tabularnewline
58 & 0.699972647384541 & 0.600054705230919 & 0.300027352615459 \tabularnewline
59 & 0.656816190817462 & 0.686367618365076 & 0.343183809182538 \tabularnewline
60 & 0.651915697919159 & 0.696168604161683 & 0.348084302080841 \tabularnewline
61 & 0.61080161601131 & 0.778396767977381 & 0.389198383988691 \tabularnewline
62 & 0.56498218206947 & 0.87003563586106 & 0.43501781793053 \tabularnewline
63 & 0.517662995643243 & 0.964674008713514 & 0.482337004356757 \tabularnewline
64 & 0.472947212398174 & 0.945894424796349 & 0.527052787601826 \tabularnewline
65 & 0.428822847563558 & 0.857645695127115 & 0.571177152436443 \tabularnewline
66 & 0.41010640481509 & 0.82021280963018 & 0.58989359518491 \tabularnewline
67 & 0.39750541249915 & 0.7950108249983 & 0.60249458750085 \tabularnewline
68 & 0.541340488909455 & 0.91731902218109 & 0.458659511090545 \tabularnewline
69 & 0.693517497539582 & 0.612965004920836 & 0.306482502460418 \tabularnewline
70 & 0.654135558864944 & 0.691728882270112 & 0.345864441135056 \tabularnewline
71 & 0.751475523540225 & 0.49704895291955 & 0.248524476459775 \tabularnewline
72 & 0.716822701889214 & 0.566354596221573 & 0.283177298110786 \tabularnewline
73 & 0.702906885642652 & 0.594186228714695 & 0.297093114357348 \tabularnewline
74 & 0.677560294355097 & 0.644879411289807 & 0.322439705644903 \tabularnewline
75 & 0.638860843824678 & 0.722278312350643 & 0.361139156175322 \tabularnewline
76 & 0.688929513773476 & 0.622140972453048 & 0.311070486226524 \tabularnewline
77 & 0.650960674738887 & 0.698078650522226 & 0.349039325261113 \tabularnewline
78 & 0.624511366678612 & 0.750977266642776 & 0.375488633321388 \tabularnewline
79 & 0.625536538383113 & 0.748926923233773 & 0.374463461616887 \tabularnewline
80 & 0.585638578373812 & 0.828722843252376 & 0.414361421626188 \tabularnewline
81 & 0.546442946710501 & 0.907114106578998 & 0.453557053289499 \tabularnewline
82 & 0.719073680232246 & 0.561852639535508 & 0.280926319767754 \tabularnewline
83 & 0.681005426595179 & 0.637989146809643 & 0.318994573404821 \tabularnewline
84 & 0.649455731764495 & 0.701088536471011 & 0.350544268235505 \tabularnewline
85 & 0.606290921690369 & 0.787418156619262 & 0.393709078309631 \tabularnewline
86 & 0.600650059668184 & 0.798699880663631 & 0.399349940331816 \tabularnewline
87 & 0.555302705864283 & 0.889394588271435 & 0.444697294135717 \tabularnewline
88 & 0.515901154181723 & 0.968197691636554 & 0.484098845818277 \tabularnewline
89 & 0.489293770648082 & 0.978587541296164 & 0.510706229351918 \tabularnewline
90 & 0.451930661796829 & 0.903861323593657 & 0.548069338203171 \tabularnewline
91 & 0.44679011815019 & 0.893580236300379 & 0.55320988184981 \tabularnewline
92 & 0.405086473555405 & 0.81017294711081 & 0.594913526444595 \tabularnewline
93 & 0.364051050583914 & 0.728102101167828 & 0.635948949416086 \tabularnewline
94 & 0.322868167398755 & 0.64573633479751 & 0.677131832601245 \tabularnewline
95 & 0.317814216565328 & 0.635628433130656 & 0.682185783434672 \tabularnewline
96 & 0.288184773815851 & 0.576369547631703 & 0.711815226184149 \tabularnewline
97 & 0.251672291535466 & 0.503344583070931 & 0.748327708464534 \tabularnewline
98 & 0.264675545050535 & 0.52935109010107 & 0.735324454949465 \tabularnewline
99 & 0.227594756103826 & 0.455189512207652 & 0.772405243896174 \tabularnewline
100 & 0.193549453803048 & 0.387098907606096 & 0.806450546196952 \tabularnewline
101 & 0.174746157208055 & 0.34949231441611 & 0.825253842791945 \tabularnewline
102 & 0.160528096229926 & 0.321056192459852 & 0.839471903770074 \tabularnewline
103 & 0.191367924383941 & 0.382735848767881 & 0.80863207561606 \tabularnewline
104 & 0.16900751569461 & 0.33801503138922 & 0.83099248430539 \tabularnewline
105 & 0.152588252844553 & 0.305176505689106 & 0.847411747155447 \tabularnewline
106 & 0.162160982813208 & 0.324321965626416 & 0.837839017186792 \tabularnewline
107 & 0.144545735804697 & 0.289091471609393 & 0.855454264195303 \tabularnewline
108 & 0.119874907206158 & 0.239749814412316 & 0.880125092793842 \tabularnewline
109 & 0.120638576059538 & 0.241277152119077 & 0.879361423940461 \tabularnewline
110 & 0.101447840355675 & 0.202895680711349 & 0.898552159644325 \tabularnewline
111 & 0.0859315066931214 & 0.171863013386243 & 0.914068493306879 \tabularnewline
112 & 0.0704499361617105 & 0.140899872323421 & 0.92955006383829 \tabularnewline
113 & 0.0956906909053004 & 0.191381381810601 & 0.9043093090947 \tabularnewline
114 & 0.0862008434696467 & 0.172401686939293 & 0.913799156530353 \tabularnewline
115 & 0.125247143647104 & 0.250494287294207 & 0.874752856352896 \tabularnewline
116 & 0.122906474975035 & 0.24581294995007 & 0.877093525024965 \tabularnewline
117 & 0.109853857829305 & 0.219707715658609 & 0.890146142170695 \tabularnewline
118 & 0.0935133136337439 & 0.187026627267488 & 0.906486686366256 \tabularnewline
119 & 0.0853293239353088 & 0.170658647870618 & 0.914670676064691 \tabularnewline
120 & 0.0693853310119483 & 0.138770662023897 & 0.930614668988052 \tabularnewline
121 & 0.0581903631594489 & 0.116380726318898 & 0.941809636840551 \tabularnewline
122 & 0.0451280793837424 & 0.0902561587674848 & 0.954871920616258 \tabularnewline
123 & 0.0538598077087077 & 0.107719615417415 & 0.946140192291292 \tabularnewline
124 & 0.0415821132494444 & 0.0831642264988887 & 0.958417886750556 \tabularnewline
125 & 0.032599279313537 & 0.065198558627074 & 0.967400720686463 \tabularnewline
126 & 0.0241151671567529 & 0.0482303343135058 & 0.975884832843247 \tabularnewline
127 & 0.0172983755953849 & 0.0345967511907697 & 0.982701624404615 \tabularnewline
128 & 0.0137119273816728 & 0.0274238547633456 & 0.986288072618327 \tabularnewline
129 & 0.00977820396579068 & 0.0195564079315814 & 0.99022179603421 \tabularnewline
130 & 0.0148912213583936 & 0.0297824427167873 & 0.985108778641606 \tabularnewline
131 & 0.0179138055551323 & 0.0358276111102646 & 0.982086194444868 \tabularnewline
132 & 0.0299654837072904 & 0.0599309674145807 & 0.97003451629271 \tabularnewline
133 & 0.0389740605195974 & 0.0779481210391948 & 0.961025939480403 \tabularnewline
134 & 0.0519738248081668 & 0.103947649616334 & 0.948026175191833 \tabularnewline
135 & 0.0409607655130622 & 0.0819215310261244 & 0.959039234486938 \tabularnewline
136 & 0.0303473965211812 & 0.0606947930423623 & 0.969652603478819 \tabularnewline
137 & 0.0210250935263466 & 0.0420501870526932 & 0.978974906473653 \tabularnewline
138 & 0.0144491932638821 & 0.0288983865277642 & 0.985550806736118 \tabularnewline
139 & 0.0294700096332473 & 0.0589400192664945 & 0.970529990366753 \tabularnewline
140 & 0.0227153637715402 & 0.0454307275430803 & 0.97728463622846 \tabularnewline
141 & 0.307908969837575 & 0.615817939675149 & 0.692091030162425 \tabularnewline
142 & 0.327068755508465 & 0.654137511016931 & 0.672931244491535 \tabularnewline
143 & 0.265230008664022 & 0.530460017328043 & 0.734769991335978 \tabularnewline
144 & 0.280332204440054 & 0.560664408880107 & 0.719667795559946 \tabularnewline
145 & 0.260046201238608 & 0.520092402477216 & 0.739953798761392 \tabularnewline
146 & 0.221463204393355 & 0.442926408786711 & 0.778536795606645 \tabularnewline
147 & 0.272507727110036 & 0.545015454220072 & 0.727492272889964 \tabularnewline
148 & 0.562845793900584 & 0.874308412198832 & 0.437154206099416 \tabularnewline
149 & 0.458992144358325 & 0.91798428871665 & 0.541007855641675 \tabularnewline
150 & 0.345644472594621 & 0.691288945189242 & 0.654355527405379 \tabularnewline
151 & 0.379292877744285 & 0.758585755488571 & 0.620707122255715 \tabularnewline
152 & 0.654620990746563 & 0.690758018506875 & 0.345379009253437 \tabularnewline
153 & 0.488843485581449 & 0.977686971162898 & 0.511156514418551 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&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]9[/C][C]0.338036527326868[/C][C]0.676073054653735[/C][C]0.661963472673132[/C][/ROW]
[ROW][C]10[/C][C]0.722854381122383[/C][C]0.554291237755234[/C][C]0.277145618877617[/C][/ROW]
[ROW][C]11[/C][C]0.759615370468952[/C][C]0.480769259062097[/C][C]0.240384629531048[/C][/ROW]
[ROW][C]12[/C][C]0.792109747299052[/C][C]0.415780505401896[/C][C]0.207890252700948[/C][/ROW]
[ROW][C]13[/C][C]0.71826337426918[/C][C]0.56347325146164[/C][C]0.28173662573082[/C][/ROW]
[ROW][C]14[/C][C]0.697257883081284[/C][C]0.605484233837432[/C][C]0.302742116918716[/C][/ROW]
[ROW][C]15[/C][C]0.6790690124691[/C][C]0.641861975061799[/C][C]0.3209309875309[/C][/ROW]
[ROW][C]16[/C][C]0.613554122005358[/C][C]0.772891755989283[/C][C]0.386445877994642[/C][/ROW]
[ROW][C]17[/C][C]0.527863298659411[/C][C]0.944273402681178[/C][C]0.472136701340589[/C][/ROW]
[ROW][C]18[/C][C]0.85162740233883[/C][C]0.296745195322341[/C][C]0.14837259766117[/C][/ROW]
[ROW][C]19[/C][C]0.851611105400853[/C][C]0.296777789198295[/C][C]0.148388894599147[/C][/ROW]
[ROW][C]20[/C][C]0.804401689489094[/C][C]0.391196621021811[/C][C]0.195598310510906[/C][/ROW]
[ROW][C]21[/C][C]0.750398916443615[/C][C]0.49920216711277[/C][C]0.249601083556385[/C][/ROW]
[ROW][C]22[/C][C]0.691146464103803[/C][C]0.617707071792394[/C][C]0.308853535896197[/C][/ROW]
[ROW][C]23[/C][C]0.71508011102012[/C][C]0.56983977795976[/C][C]0.28491988897988[/C][/ROW]
[ROW][C]24[/C][C]0.658667208209313[/C][C]0.682665583581374[/C][C]0.341332791790687[/C][/ROW]
[ROW][C]25[/C][C]0.638599665640265[/C][C]0.72280066871947[/C][C]0.361400334359735[/C][/ROW]
[ROW][C]26[/C][C]0.574493867448615[/C][C]0.85101226510277[/C][C]0.425506132551385[/C][/ROW]
[ROW][C]27[/C][C]0.513637232742343[/C][C]0.972725534515314[/C][C]0.486362767257657[/C][/ROW]
[ROW][C]28[/C][C]0.490355252009674[/C][C]0.980710504019347[/C][C]0.509644747990326[/C][/ROW]
[ROW][C]29[/C][C]0.427627399747741[/C][C]0.855254799495483[/C][C]0.572372600252259[/C][/ROW]
[ROW][C]30[/C][C]0.416195907881281[/C][C]0.832391815762562[/C][C]0.583804092118719[/C][/ROW]
[ROW][C]31[/C][C]0.356504296635594[/C][C]0.713008593271189[/C][C]0.643495703364406[/C][/ROW]
[ROW][C]32[/C][C]0.347377897283667[/C][C]0.694755794567334[/C][C]0.652622102716333[/C][/ROW]
[ROW][C]33[/C][C]0.32032865610364[/C][C]0.64065731220728[/C][C]0.67967134389636[/C][/ROW]
[ROW][C]34[/C][C]0.268647524887248[/C][C]0.537295049774495[/C][C]0.731352475112752[/C][/ROW]
[ROW][C]35[/C][C]0.26249979427662[/C][C]0.52499958855324[/C][C]0.73750020572338[/C][/ROW]
[ROW][C]36[/C][C]0.794837155934823[/C][C]0.410325688130355[/C][C]0.205162844065177[/C][/ROW]
[ROW][C]37[/C][C]0.776034300428721[/C][C]0.447931399142558[/C][C]0.223965699571279[/C][/ROW]
[ROW][C]38[/C][C]0.774544334076302[/C][C]0.450911331847397[/C][C]0.225455665923698[/C][/ROW]
[ROW][C]39[/C][C]0.791404396907642[/C][C]0.417191206184717[/C][C]0.208595603092358[/C][/ROW]
[ROW][C]40[/C][C]0.775100408612314[/C][C]0.449799182775371[/C][C]0.224899591387686[/C][/ROW]
[ROW][C]41[/C][C]0.74557567888528[/C][C]0.50884864222944[/C][C]0.25442432111472[/C][/ROW]
[ROW][C]42[/C][C]0.734840192000248[/C][C]0.530319615999505[/C][C]0.265159807999752[/C][/ROW]
[ROW][C]43[/C][C]0.746621972249842[/C][C]0.506756055500317[/C][C]0.253378027750158[/C][/ROW]
[ROW][C]44[/C][C]0.708796178626399[/C][C]0.582407642747203[/C][C]0.291203821373601[/C][/ROW]
[ROW][C]45[/C][C]0.672807387777923[/C][C]0.654385224444154[/C][C]0.327192612222077[/C][/ROW]
[ROW][C]46[/C][C]0.863468316267241[/C][C]0.273063367465519[/C][C]0.136531683732759[/C][/ROW]
[ROW][C]47[/C][C]0.88721163640038[/C][C]0.22557672719924[/C][C]0.11278836359962[/C][/ROW]
[ROW][C]48[/C][C]0.861423987286233[/C][C]0.277152025427533[/C][C]0.138576012713767[/C][/ROW]
[ROW][C]49[/C][C]0.834231514299725[/C][C]0.331536971400549[/C][C]0.165768485700275[/C][/ROW]
[ROW][C]50[/C][C]0.83583823529505[/C][C]0.328323529409899[/C][C]0.164161764704949[/C][/ROW]
[ROW][C]51[/C][C]0.804463072856441[/C][C]0.391073854287118[/C][C]0.195536927143559[/C][/ROW]
[ROW][C]52[/C][C]0.768287644512924[/C][C]0.463424710974153[/C][C]0.231712355487076[/C][/ROW]
[ROW][C]53[/C][C]0.806718852786295[/C][C]0.386562294427411[/C][C]0.193281147213705[/C][/ROW]
[ROW][C]54[/C][C]0.772577133412171[/C][C]0.454845733175658[/C][C]0.227422866587829[/C][/ROW]
[ROW][C]55[/C][C]0.780990401213124[/C][C]0.438019197573751[/C][C]0.219009598786876[/C][/ROW]
[ROW][C]56[/C][C]0.76122808594998[/C][C]0.477543828100041[/C][C]0.238771914050021[/C][/ROW]
[ROW][C]57[/C][C]0.723480795327163[/C][C]0.553038409345674[/C][C]0.276519204672837[/C][/ROW]
[ROW][C]58[/C][C]0.699972647384541[/C][C]0.600054705230919[/C][C]0.300027352615459[/C][/ROW]
[ROW][C]59[/C][C]0.656816190817462[/C][C]0.686367618365076[/C][C]0.343183809182538[/C][/ROW]
[ROW][C]60[/C][C]0.651915697919159[/C][C]0.696168604161683[/C][C]0.348084302080841[/C][/ROW]
[ROW][C]61[/C][C]0.61080161601131[/C][C]0.778396767977381[/C][C]0.389198383988691[/C][/ROW]
[ROW][C]62[/C][C]0.56498218206947[/C][C]0.87003563586106[/C][C]0.43501781793053[/C][/ROW]
[ROW][C]63[/C][C]0.517662995643243[/C][C]0.964674008713514[/C][C]0.482337004356757[/C][/ROW]
[ROW][C]64[/C][C]0.472947212398174[/C][C]0.945894424796349[/C][C]0.527052787601826[/C][/ROW]
[ROW][C]65[/C][C]0.428822847563558[/C][C]0.857645695127115[/C][C]0.571177152436443[/C][/ROW]
[ROW][C]66[/C][C]0.41010640481509[/C][C]0.82021280963018[/C][C]0.58989359518491[/C][/ROW]
[ROW][C]67[/C][C]0.39750541249915[/C][C]0.7950108249983[/C][C]0.60249458750085[/C][/ROW]
[ROW][C]68[/C][C]0.541340488909455[/C][C]0.91731902218109[/C][C]0.458659511090545[/C][/ROW]
[ROW][C]69[/C][C]0.693517497539582[/C][C]0.612965004920836[/C][C]0.306482502460418[/C][/ROW]
[ROW][C]70[/C][C]0.654135558864944[/C][C]0.691728882270112[/C][C]0.345864441135056[/C][/ROW]
[ROW][C]71[/C][C]0.751475523540225[/C][C]0.49704895291955[/C][C]0.248524476459775[/C][/ROW]
[ROW][C]72[/C][C]0.716822701889214[/C][C]0.566354596221573[/C][C]0.283177298110786[/C][/ROW]
[ROW][C]73[/C][C]0.702906885642652[/C][C]0.594186228714695[/C][C]0.297093114357348[/C][/ROW]
[ROW][C]74[/C][C]0.677560294355097[/C][C]0.644879411289807[/C][C]0.322439705644903[/C][/ROW]
[ROW][C]75[/C][C]0.638860843824678[/C][C]0.722278312350643[/C][C]0.361139156175322[/C][/ROW]
[ROW][C]76[/C][C]0.688929513773476[/C][C]0.622140972453048[/C][C]0.311070486226524[/C][/ROW]
[ROW][C]77[/C][C]0.650960674738887[/C][C]0.698078650522226[/C][C]0.349039325261113[/C][/ROW]
[ROW][C]78[/C][C]0.624511366678612[/C][C]0.750977266642776[/C][C]0.375488633321388[/C][/ROW]
[ROW][C]79[/C][C]0.625536538383113[/C][C]0.748926923233773[/C][C]0.374463461616887[/C][/ROW]
[ROW][C]80[/C][C]0.585638578373812[/C][C]0.828722843252376[/C][C]0.414361421626188[/C][/ROW]
[ROW][C]81[/C][C]0.546442946710501[/C][C]0.907114106578998[/C][C]0.453557053289499[/C][/ROW]
[ROW][C]82[/C][C]0.719073680232246[/C][C]0.561852639535508[/C][C]0.280926319767754[/C][/ROW]
[ROW][C]83[/C][C]0.681005426595179[/C][C]0.637989146809643[/C][C]0.318994573404821[/C][/ROW]
[ROW][C]84[/C][C]0.649455731764495[/C][C]0.701088536471011[/C][C]0.350544268235505[/C][/ROW]
[ROW][C]85[/C][C]0.606290921690369[/C][C]0.787418156619262[/C][C]0.393709078309631[/C][/ROW]
[ROW][C]86[/C][C]0.600650059668184[/C][C]0.798699880663631[/C][C]0.399349940331816[/C][/ROW]
[ROW][C]87[/C][C]0.555302705864283[/C][C]0.889394588271435[/C][C]0.444697294135717[/C][/ROW]
[ROW][C]88[/C][C]0.515901154181723[/C][C]0.968197691636554[/C][C]0.484098845818277[/C][/ROW]
[ROW][C]89[/C][C]0.489293770648082[/C][C]0.978587541296164[/C][C]0.510706229351918[/C][/ROW]
[ROW][C]90[/C][C]0.451930661796829[/C][C]0.903861323593657[/C][C]0.548069338203171[/C][/ROW]
[ROW][C]91[/C][C]0.44679011815019[/C][C]0.893580236300379[/C][C]0.55320988184981[/C][/ROW]
[ROW][C]92[/C][C]0.405086473555405[/C][C]0.81017294711081[/C][C]0.594913526444595[/C][/ROW]
[ROW][C]93[/C][C]0.364051050583914[/C][C]0.728102101167828[/C][C]0.635948949416086[/C][/ROW]
[ROW][C]94[/C][C]0.322868167398755[/C][C]0.64573633479751[/C][C]0.677131832601245[/C][/ROW]
[ROW][C]95[/C][C]0.317814216565328[/C][C]0.635628433130656[/C][C]0.682185783434672[/C][/ROW]
[ROW][C]96[/C][C]0.288184773815851[/C][C]0.576369547631703[/C][C]0.711815226184149[/C][/ROW]
[ROW][C]97[/C][C]0.251672291535466[/C][C]0.503344583070931[/C][C]0.748327708464534[/C][/ROW]
[ROW][C]98[/C][C]0.264675545050535[/C][C]0.52935109010107[/C][C]0.735324454949465[/C][/ROW]
[ROW][C]99[/C][C]0.227594756103826[/C][C]0.455189512207652[/C][C]0.772405243896174[/C][/ROW]
[ROW][C]100[/C][C]0.193549453803048[/C][C]0.387098907606096[/C][C]0.806450546196952[/C][/ROW]
[ROW][C]101[/C][C]0.174746157208055[/C][C]0.34949231441611[/C][C]0.825253842791945[/C][/ROW]
[ROW][C]102[/C][C]0.160528096229926[/C][C]0.321056192459852[/C][C]0.839471903770074[/C][/ROW]
[ROW][C]103[/C][C]0.191367924383941[/C][C]0.382735848767881[/C][C]0.80863207561606[/C][/ROW]
[ROW][C]104[/C][C]0.16900751569461[/C][C]0.33801503138922[/C][C]0.83099248430539[/C][/ROW]
[ROW][C]105[/C][C]0.152588252844553[/C][C]0.305176505689106[/C][C]0.847411747155447[/C][/ROW]
[ROW][C]106[/C][C]0.162160982813208[/C][C]0.324321965626416[/C][C]0.837839017186792[/C][/ROW]
[ROW][C]107[/C][C]0.144545735804697[/C][C]0.289091471609393[/C][C]0.855454264195303[/C][/ROW]
[ROW][C]108[/C][C]0.119874907206158[/C][C]0.239749814412316[/C][C]0.880125092793842[/C][/ROW]
[ROW][C]109[/C][C]0.120638576059538[/C][C]0.241277152119077[/C][C]0.879361423940461[/C][/ROW]
[ROW][C]110[/C][C]0.101447840355675[/C][C]0.202895680711349[/C][C]0.898552159644325[/C][/ROW]
[ROW][C]111[/C][C]0.0859315066931214[/C][C]0.171863013386243[/C][C]0.914068493306879[/C][/ROW]
[ROW][C]112[/C][C]0.0704499361617105[/C][C]0.140899872323421[/C][C]0.92955006383829[/C][/ROW]
[ROW][C]113[/C][C]0.0956906909053004[/C][C]0.191381381810601[/C][C]0.9043093090947[/C][/ROW]
[ROW][C]114[/C][C]0.0862008434696467[/C][C]0.172401686939293[/C][C]0.913799156530353[/C][/ROW]
[ROW][C]115[/C][C]0.125247143647104[/C][C]0.250494287294207[/C][C]0.874752856352896[/C][/ROW]
[ROW][C]116[/C][C]0.122906474975035[/C][C]0.24581294995007[/C][C]0.877093525024965[/C][/ROW]
[ROW][C]117[/C][C]0.109853857829305[/C][C]0.219707715658609[/C][C]0.890146142170695[/C][/ROW]
[ROW][C]118[/C][C]0.0935133136337439[/C][C]0.187026627267488[/C][C]0.906486686366256[/C][/ROW]
[ROW][C]119[/C][C]0.0853293239353088[/C][C]0.170658647870618[/C][C]0.914670676064691[/C][/ROW]
[ROW][C]120[/C][C]0.0693853310119483[/C][C]0.138770662023897[/C][C]0.930614668988052[/C][/ROW]
[ROW][C]121[/C][C]0.0581903631594489[/C][C]0.116380726318898[/C][C]0.941809636840551[/C][/ROW]
[ROW][C]122[/C][C]0.0451280793837424[/C][C]0.0902561587674848[/C][C]0.954871920616258[/C][/ROW]
[ROW][C]123[/C][C]0.0538598077087077[/C][C]0.107719615417415[/C][C]0.946140192291292[/C][/ROW]
[ROW][C]124[/C][C]0.0415821132494444[/C][C]0.0831642264988887[/C][C]0.958417886750556[/C][/ROW]
[ROW][C]125[/C][C]0.032599279313537[/C][C]0.065198558627074[/C][C]0.967400720686463[/C][/ROW]
[ROW][C]126[/C][C]0.0241151671567529[/C][C]0.0482303343135058[/C][C]0.975884832843247[/C][/ROW]
[ROW][C]127[/C][C]0.0172983755953849[/C][C]0.0345967511907697[/C][C]0.982701624404615[/C][/ROW]
[ROW][C]128[/C][C]0.0137119273816728[/C][C]0.0274238547633456[/C][C]0.986288072618327[/C][/ROW]
[ROW][C]129[/C][C]0.00977820396579068[/C][C]0.0195564079315814[/C][C]0.99022179603421[/C][/ROW]
[ROW][C]130[/C][C]0.0148912213583936[/C][C]0.0297824427167873[/C][C]0.985108778641606[/C][/ROW]
[ROW][C]131[/C][C]0.0179138055551323[/C][C]0.0358276111102646[/C][C]0.982086194444868[/C][/ROW]
[ROW][C]132[/C][C]0.0299654837072904[/C][C]0.0599309674145807[/C][C]0.97003451629271[/C][/ROW]
[ROW][C]133[/C][C]0.0389740605195974[/C][C]0.0779481210391948[/C][C]0.961025939480403[/C][/ROW]
[ROW][C]134[/C][C]0.0519738248081668[/C][C]0.103947649616334[/C][C]0.948026175191833[/C][/ROW]
[ROW][C]135[/C][C]0.0409607655130622[/C][C]0.0819215310261244[/C][C]0.959039234486938[/C][/ROW]
[ROW][C]136[/C][C]0.0303473965211812[/C][C]0.0606947930423623[/C][C]0.969652603478819[/C][/ROW]
[ROW][C]137[/C][C]0.0210250935263466[/C][C]0.0420501870526932[/C][C]0.978974906473653[/C][/ROW]
[ROW][C]138[/C][C]0.0144491932638821[/C][C]0.0288983865277642[/C][C]0.985550806736118[/C][/ROW]
[ROW][C]139[/C][C]0.0294700096332473[/C][C]0.0589400192664945[/C][C]0.970529990366753[/C][/ROW]
[ROW][C]140[/C][C]0.0227153637715402[/C][C]0.0454307275430803[/C][C]0.97728463622846[/C][/ROW]
[ROW][C]141[/C][C]0.307908969837575[/C][C]0.615817939675149[/C][C]0.692091030162425[/C][/ROW]
[ROW][C]142[/C][C]0.327068755508465[/C][C]0.654137511016931[/C][C]0.672931244491535[/C][/ROW]
[ROW][C]143[/C][C]0.265230008664022[/C][C]0.530460017328043[/C][C]0.734769991335978[/C][/ROW]
[ROW][C]144[/C][C]0.280332204440054[/C][C]0.560664408880107[/C][C]0.719667795559946[/C][/ROW]
[ROW][C]145[/C][C]0.260046201238608[/C][C]0.520092402477216[/C][C]0.739953798761392[/C][/ROW]
[ROW][C]146[/C][C]0.221463204393355[/C][C]0.442926408786711[/C][C]0.778536795606645[/C][/ROW]
[ROW][C]147[/C][C]0.272507727110036[/C][C]0.545015454220072[/C][C]0.727492272889964[/C][/ROW]
[ROW][C]148[/C][C]0.562845793900584[/C][C]0.874308412198832[/C][C]0.437154206099416[/C][/ROW]
[ROW][C]149[/C][C]0.458992144358325[/C][C]0.91798428871665[/C][C]0.541007855641675[/C][/ROW]
[ROW][C]150[/C][C]0.345644472594621[/C][C]0.691288945189242[/C][C]0.654355527405379[/C][/ROW]
[ROW][C]151[/C][C]0.379292877744285[/C][C]0.758585755488571[/C][C]0.620707122255715[/C][/ROW]
[ROW][C]152[/C][C]0.654620990746563[/C][C]0.690758018506875[/C][C]0.345379009253437[/C][/ROW]
[ROW][C]153[/C][C]0.488843485581449[/C][C]0.977686971162898[/C][C]0.511156514418551[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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
90.3380365273268680.6760730546537350.661963472673132
100.7228543811223830.5542912377552340.277145618877617
110.7596153704689520.4807692590620970.240384629531048
120.7921097472990520.4157805054018960.207890252700948
130.718263374269180.563473251461640.28173662573082
140.6972578830812840.6054842338374320.302742116918716
150.67906901246910.6418619750617990.3209309875309
160.6135541220053580.7728917559892830.386445877994642
170.5278632986594110.9442734026811780.472136701340589
180.851627402338830.2967451953223410.14837259766117
190.8516111054008530.2967777891982950.148388894599147
200.8044016894890940.3911966210218110.195598310510906
210.7503989164436150.499202167112770.249601083556385
220.6911464641038030.6177070717923940.308853535896197
230.715080111020120.569839777959760.28491988897988
240.6586672082093130.6826655835813740.341332791790687
250.6385996656402650.722800668719470.361400334359735
260.5744938674486150.851012265102770.425506132551385
270.5136372327423430.9727255345153140.486362767257657
280.4903552520096740.9807105040193470.509644747990326
290.4276273997477410.8552547994954830.572372600252259
300.4161959078812810.8323918157625620.583804092118719
310.3565042966355940.7130085932711890.643495703364406
320.3473778972836670.6947557945673340.652622102716333
330.320328656103640.640657312207280.67967134389636
340.2686475248872480.5372950497744950.731352475112752
350.262499794276620.524999588553240.73750020572338
360.7948371559348230.4103256881303550.205162844065177
370.7760343004287210.4479313991425580.223965699571279
380.7745443340763020.4509113318473970.225455665923698
390.7914043969076420.4171912061847170.208595603092358
400.7751004086123140.4497991827753710.224899591387686
410.745575678885280.508848642229440.25442432111472
420.7348401920002480.5303196159995050.265159807999752
430.7466219722498420.5067560555003170.253378027750158
440.7087961786263990.5824076427472030.291203821373601
450.6728073877779230.6543852244441540.327192612222077
460.8634683162672410.2730633674655190.136531683732759
470.887211636400380.225576727199240.11278836359962
480.8614239872862330.2771520254275330.138576012713767
490.8342315142997250.3315369714005490.165768485700275
500.835838235295050.3283235294098990.164161764704949
510.8044630728564410.3910738542871180.195536927143559
520.7682876445129240.4634247109741530.231712355487076
530.8067188527862950.3865622944274110.193281147213705
540.7725771334121710.4548457331756580.227422866587829
550.7809904012131240.4380191975737510.219009598786876
560.761228085949980.4775438281000410.238771914050021
570.7234807953271630.5530384093456740.276519204672837
580.6999726473845410.6000547052309190.300027352615459
590.6568161908174620.6863676183650760.343183809182538
600.6519156979191590.6961686041616830.348084302080841
610.610801616011310.7783967679773810.389198383988691
620.564982182069470.870035635861060.43501781793053
630.5176629956432430.9646740087135140.482337004356757
640.4729472123981740.9458944247963490.527052787601826
650.4288228475635580.8576456951271150.571177152436443
660.410106404815090.820212809630180.58989359518491
670.397505412499150.79501082499830.60249458750085
680.5413404889094550.917319022181090.458659511090545
690.6935174975395820.6129650049208360.306482502460418
700.6541355588649440.6917288822701120.345864441135056
710.7514755235402250.497048952919550.248524476459775
720.7168227018892140.5663545962215730.283177298110786
730.7029068856426520.5941862287146950.297093114357348
740.6775602943550970.6448794112898070.322439705644903
750.6388608438246780.7222783123506430.361139156175322
760.6889295137734760.6221409724530480.311070486226524
770.6509606747388870.6980786505222260.349039325261113
780.6245113666786120.7509772666427760.375488633321388
790.6255365383831130.7489269232337730.374463461616887
800.5856385783738120.8287228432523760.414361421626188
810.5464429467105010.9071141065789980.453557053289499
820.7190736802322460.5618526395355080.280926319767754
830.6810054265951790.6379891468096430.318994573404821
840.6494557317644950.7010885364710110.350544268235505
850.6062909216903690.7874181566192620.393709078309631
860.6006500596681840.7986998806636310.399349940331816
870.5553027058642830.8893945882714350.444697294135717
880.5159011541817230.9681976916365540.484098845818277
890.4892937706480820.9785875412961640.510706229351918
900.4519306617968290.9038613235936570.548069338203171
910.446790118150190.8935802363003790.55320988184981
920.4050864735554050.810172947110810.594913526444595
930.3640510505839140.7281021011678280.635948949416086
940.3228681673987550.645736334797510.677131832601245
950.3178142165653280.6356284331306560.682185783434672
960.2881847738158510.5763695476317030.711815226184149
970.2516722915354660.5033445830709310.748327708464534
980.2646755450505350.529351090101070.735324454949465
990.2275947561038260.4551895122076520.772405243896174
1000.1935494538030480.3870989076060960.806450546196952
1010.1747461572080550.349492314416110.825253842791945
1020.1605280962299260.3210561924598520.839471903770074
1030.1913679243839410.3827358487678810.80863207561606
1040.169007515694610.338015031389220.83099248430539
1050.1525882528445530.3051765056891060.847411747155447
1060.1621609828132080.3243219656264160.837839017186792
1070.1445457358046970.2890914716093930.855454264195303
1080.1198749072061580.2397498144123160.880125092793842
1090.1206385760595380.2412771521190770.879361423940461
1100.1014478403556750.2028956807113490.898552159644325
1110.08593150669312140.1718630133862430.914068493306879
1120.07044993616171050.1408998723234210.92955006383829
1130.09569069090530040.1913813818106010.9043093090947
1140.08620084346964670.1724016869392930.913799156530353
1150.1252471436471040.2504942872942070.874752856352896
1160.1229064749750350.245812949950070.877093525024965
1170.1098538578293050.2197077156586090.890146142170695
1180.09351331363374390.1870266272674880.906486686366256
1190.08532932393530880.1706586478706180.914670676064691
1200.06938533101194830.1387706620238970.930614668988052
1210.05819036315944890.1163807263188980.941809636840551
1220.04512807938374240.09025615876748480.954871920616258
1230.05385980770870770.1077196154174150.946140192291292
1240.04158211324944440.08316422649888870.958417886750556
1250.0325992793135370.0651985586270740.967400720686463
1260.02411516715675290.04823033431350580.975884832843247
1270.01729837559538490.03459675119076970.982701624404615
1280.01371192738167280.02742385476334560.986288072618327
1290.009778203965790680.01955640793158140.99022179603421
1300.01489122135839360.02978244271678730.985108778641606
1310.01791380555513230.03582761111026460.982086194444868
1320.02996548370729040.05993096741458070.97003451629271
1330.03897406051959740.07794812103919480.961025939480403
1340.05197382480816680.1039476496163340.948026175191833
1350.04096076551306220.08192153102612440.959039234486938
1360.03034739652118120.06069479304236230.969652603478819
1370.02102509352634660.04205018705269320.978974906473653
1380.01444919326388210.02889838652776420.985550806736118
1390.02947000963324730.05894001926649450.970529990366753
1400.02271536377154020.04543072754308030.97728463622846
1410.3079089698375750.6158179396751490.692091030162425
1420.3270687555084650.6541375110169310.672931244491535
1430.2652300086640220.5304600173280430.734769991335978
1440.2803322044400540.5606644088801070.719667795559946
1450.2600462012386080.5200924024772160.739953798761392
1460.2214632043933550.4429264087867110.778536795606645
1470.2725077271100360.5450154542200720.727492272889964
1480.5628457939005840.8743084121988320.437154206099416
1490.4589921443583250.917984288716650.541007855641675
1500.3456444725946210.6912889451892420.654355527405379
1510.3792928777442850.7585857554885710.620707122255715
1520.6546209907465630.6907580185068750.345379009253437
1530.4888434855814490.9776869711628980.511156514418551






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level90.0620689655172414NOK
10% type I error level170.117241379310345NOK

\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 & 9 & 0.0620689655172414 & NOK \tabularnewline
10% type I error level & 17 & 0.117241379310345 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154739&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]9[/C][C]0.0620689655172414[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.117241379310345[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154739&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154739&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 level90.0620689655172414NOK
10% type I error level170.117241379310345NOK


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 = pearson ;
Parameters (R input):
par1 = 4 ; 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')
}