FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 15 Dec 2011 16:08:07 -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/15/t1323983322vtar0av1h4s5n3f.htm/, Retrieved Wed, 08 May 2024 04:39:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=155718, Retrieved Wed, 08 May 2024 04:39:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2011-12-15 21:08:07] [3449e2fa27dd268b99e5e23334e3fafb] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	7	41	38	13	12	14
2	5	39	32	16	11	18
2	5	30	35	19	15	11
1	5	31	33	15	6	12
2	8	34	37	14	13	16
2	6	35	29	13	10	18
2	5	39	31	19	12	14
2	6	34	36	15	14	14
2	5	36	35	14	12	15
2	4	37	38	15	6	15
1	6	38	31	16	10	17
2	5	36	34	16	12	19
1	5	38	35	16	12	10
2	6	39	38	16	11	16
2	7	33	37	17	15	18
1	6	32	33	15	12	14
1	7	36	32	15	10	14
2	6	38	38	20	12	17
1	8	39	38	18	11	14
2	7	32	32	16	12	16
1	5	32	33	16	11	18
2	5	31	31	16	12	11
2	7	39	38	19	13	14
2	7	37	39	16	11	12
1	5	39	32	17	9	17
2	4	41	32	17	13	9
1	10	36	35	16	10	16
2	6	33	37	15	14	14
2	5	33	33	16	12	15
1	5	34	33	14	10	11
2	5	31	28	15	12	16
1	5	27	32	12	8	13
2	6	37	31	14	10	17
2	5	34	37	16	12	15
1	5	34	30	14	12	14
1	5	32	33	7	7	16
1	5	29	31	10	6	9
1	5	36	33	14	12	15
2	5	29	31	16	10	17
1	5	35	33	16	10	13
1	5	37	32	16	10	15
2	7	34	33	14	12	16
1	5	38	32	20	15	16
1	6	35	33	14	10	12
2	7	38	28	14	10	12
2	7	37	35	11	12	11
2	5	38	39	14	13	15
2	5	33	34	15	11	15
2	4	36	38	16	11	17
1	5	38	32	14	12	13
2	4	32	38	16	14	16
1	5	32	30	14	10	14
1	5	32	33	12	12	11
2	7	34	38	16	13	12
1	5	32	32	9	5	12
2	5	37	32	14	6	15
2	6	39	34	16	12	16
2	4	29	34	16	12	15
1	6	37	36	15	11	12
2	6	35	34	16	10	12
1	5	30	28	12	7	8
1	7	38	34	16	12	13
2	6	34	35	16	14	11
2	8	31	35	14	11	14
2	7	34	31	16	12	15
1	5	35	37	17	13	10
2	6	36	35	18	14	11
1	6	30	27	18	11	12
2	5	39	40	12	12	15
1	5	35	37	16	12	15
1	5	38	36	10	8	14
2	5	31	38	14	11	16
2	4	34	39	18	14	15
1	6	38	41	18	14	15
1	6	34	27	16	12	13
2	6	39	30	17	9	12
2	6	37	37	16	13	17
2	7	34	31	16	11	13
1	5	28	31	13	12	15
1	7	37	27	16	12	13
1	6	33	36	16	12	15
1	5	37	38	20	12	16
2	5	35	37	16	12	15
1	4	37	33	15	12	16
2	8	32	34	15	11	15
2	8	33	31	16	10	14
1	5	38	39	14	9	15
2	5	33	34	16	12	14
2	6	29	32	16	12	13
2	4	33	33	15	12	7
2	5	31	36	12	9	17
2	5	36	32	17	15	13
2	5	35	41	16	12	15
2	5	32	28	15	12	14
2	6	29	30	13	12	13
2	6	39	36	16	10	16
2	5	37	35	16	13	12
2	6	35	31	16	9	14
1	5	37	34	16	12	17
1	7	32	36	14	10	15
2	5	38	36	16	14	17
1	6	37	35	16	11	12
2	6	36	37	20	15	16
1	6	32	28	15	11	11
2	4	33	39	16	11	15
1	5	40	32	13	12	9
2	5	38	35	17	12	16
1	7	41	39	16	12	15
1	6	36	35	16	11	10
2	9	43	42	12	7	10
2	6	30	34	16	12	15
2	6	31	33	16	14	11
2	5	32	41	17	11	13
1	6	32	33	13	11	14
2	5	37	34	12	10	18
1	8	37	32	18	13	16
2	7	33	40	14	13	14
2	5	34	40	14	8	14
2	7	33	35	13	11	14
2	6	38	36	16	12	14
2	6	33	37	13	11	12
2	9	31	27	16	13	14
2	7	38	39	13	12	15
2	6	37	38	16	14	15
2	5	33	31	15	13	15
2	5	31	33	16	15	13
1	6	39	32	15	10	17
2	6	44	39	17	11	17
2	7	33	36	15	9	19
2	5	35	33	12	11	15
1	5	32	33	16	10	13
1	5	28	32	10	11	9
2	6	40	37	16	8	15
1	4	27	30	12	11	15
1	5	37	38	14	12	15
2	7	32	29	15	12	16
1	5	28	22	13	9	11
1	7	34	35	15	11	14
2	7	30	35	11	10	11
2	6	35	34	12	8	15
1	5	31	35	8	9	13
2	8	32	34	16	8	15
1	5	30	34	15	9	16
2	5	30	35	17	15	14
1	5	31	23	16	11	15
2	6	40	31	10	8	16
2	4	32	27	18	13	16
1	5	36	36	13	12	11
1	5	32	31	16	12	12
1	7	35	32	13	9	9
2	6	38	39	10	7	16
2	7	42	37	15	13	13
1	10	34	38	16	9	16
2	6	35	39	16	6	12
2	8	35	34	14	8	9
2	4	33	31	10	8	13
2	5	36	32	17	15	13
2	6	32	37	13	6	14
2	7	33	36	15	9	19
2	7	34	32	16	11	13
2	6	32	35	12	8	12
2	6	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 time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&T=0

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


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Gender[t] = -0.0858742395574343 + 0.0294352942056614Age[t] -0.00836764635503905Connected[t] + 0.0303151989609395Separate[t] + 0.00136559608326025Learning[t] + 0.0322637897323517Software[t] + 0.0297769060084614Hapiness[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Gender[t] =  -0.0858742395574343 +  0.0294352942056614Age[t] -0.00836764635503905Connected[t] +  0.0303151989609395Separate[t] +  0.00136559608326025Learning[t] +  0.0322637897323517Software[t] +  0.0297769060084614Hapiness[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Gender[t] =  -0.0858742395574343 +  0.0294352942056614Age[t] -0.00836764635503905Connected[t] +  0.0303151989609395Separate[t] +  0.00136559608326025Learning[t] +  0.0322637897323517Software[t] +  0.0297769060084614Hapiness[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&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
Gender[t] = -0.0858742395574343 + 0.0294352942056614Age[t] -0.00836764635503905Connected[t] + 0.0303151989609395Separate[t] + 0.00136559608326025Learning[t] + 0.0322637897323517Software[t] + 0.0297769060084614Hapiness[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-0.08587423955743430.512623-0.16750.867180.43359
Age0.02943529420566140.03240.90850.365020.18251
Connected-0.008367646355039050.012137-0.68940.4915980.245799
Separate0.03031519896093950.0112952.6840.0080660.004033
Learning0.001365596083260250.020290.06730.9464270.473214
Software0.03226378973235170.0207711.55330.122390.061195
Hapiness0.02977690600846140.0162751.82960.0692360.034618

\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) & -0.0858742395574343 & 0.512623 & -0.1675 & 0.86718 & 0.43359 \tabularnewline
Age & 0.0294352942056614 & 0.0324 & 0.9085 & 0.36502 & 0.18251 \tabularnewline
Connected & -0.00836764635503905 & 0.012137 & -0.6894 & 0.491598 & 0.245799 \tabularnewline
Separate & 0.0303151989609395 & 0.011295 & 2.684 & 0.008066 & 0.004033 \tabularnewline
Learning & 0.00136559608326025 & 0.02029 & 0.0673 & 0.946427 & 0.473214 \tabularnewline
Software & 0.0322637897323517 & 0.020771 & 1.5533 & 0.12239 & 0.061195 \tabularnewline
Hapiness & 0.0297769060084614 & 0.016275 & 1.8296 & 0.069236 & 0.034618 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&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]-0.0858742395574343[/C][C]0.512623[/C][C]-0.1675[/C][C]0.86718[/C][C]0.43359[/C][/ROW]
[ROW][C]Age[/C][C]0.0294352942056614[/C][C]0.0324[/C][C]0.9085[/C][C]0.36502[/C][C]0.18251[/C][/ROW]
[ROW][C]Connected[/C][C]-0.00836764635503905[/C][C]0.012137[/C][C]-0.6894[/C][C]0.491598[/C][C]0.245799[/C][/ROW]
[ROW][C]Separate[/C][C]0.0303151989609395[/C][C]0.011295[/C][C]2.684[/C][C]0.008066[/C][C]0.004033[/C][/ROW]
[ROW][C]Learning[/C][C]0.00136559608326025[/C][C]0.02029[/C][C]0.0673[/C][C]0.946427[/C][C]0.473214[/C][/ROW]
[ROW][C]Software[/C][C]0.0322637897323517[/C][C]0.020771[/C][C]1.5533[/C][C]0.12239[/C][C]0.061195[/C][/ROW]
[ROW][C]Hapiness[/C][C]0.0297769060084614[/C][C]0.016275[/C][C]1.8296[/C][C]0.069236[/C][C]0.034618[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&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)-0.08587423955743430.512623-0.16750.867180.43359
Age0.02943529420566140.03240.90850.365020.18251
Connected-0.008367646355039050.012137-0.68940.4915980.245799
Separate0.03031519896093950.0112952.6840.0080660.004033
Learning0.001365596083260250.020290.06730.9464270.473214
Software0.03226378973235170.0207711.55330.122390.061195
Hapiness0.02977690600846140.0162751.82960.0692360.034618






Multiple Linear Regression - Regression Statistics
Multiple R0.324006691573127
R-squared0.104980336184163
Adjusted R-squared0.0703344137138728
F-TEST (value)3.03009210605335
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.00788944518462198
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.468617536388649
Sum Squared Residuals34.0383712886998

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.324006691573127 \tabularnewline
R-squared & 0.104980336184163 \tabularnewline
Adjusted R-squared & 0.0703344137138728 \tabularnewline
F-TEST (value) & 3.03009210605335 \tabularnewline
F-TEST (DF numerator) & 6 \tabularnewline
F-TEST (DF denominator) & 155 \tabularnewline
p-value & 0.00788944518462198 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.468617536388649 \tabularnewline
Sum Squared Residuals & 34.0383712886998 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.324006691573127[/C][/ROW]
[ROW][C]R-squared[/C][C]0.104980336184163[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0703344137138728[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.03009210605335[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]6[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]155[/C][/ROW]
[ROW][C]p-value[/C][C]0.00788944518462198[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.468617536388649[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]34.0383712886998[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&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 R0.324006691573127
R-squared0.104980336184163
Adjusted R-squared0.0703344137138728
F-TEST (value)3.03009210605335
F-TEST (DF numerator)6
F-TEST (DF denominator)155
p-value0.00788944518462198
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.468617536388649
Sum Squared Residuals34.0383712886998






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
121.750871789830360.249128210169642
221.617785922914750.38221407708525
321.708753942112880.291246057887123
411.37369631192021-0.373696311920215
521.901748607392890.0982513926071134
621.553385627675620.446614372324383
721.50472367790210.495276322097903
821.786638393819310.213361606180687
921.674036338403130.325963661596868
1021.53496185241440.465038147585599
1111.5632329687737-0.563232968773698
1221.765559955642560.234440044357441
1311.51114770781727-0.511147707817268
1421.769558598869130.230441401130875
1522.00885913927367-0.00885913927367101
1611.64790051018187-0.64790051018187
1711.54902244054173-0.549022440541732
1821.845429325298020.154570674701982
1911.77160656743005-0.771606567430046
2021.707940013526770.292059986473225
2111.70667464636096-0.706674646360962
2221.478237342467240.521762657532755
2321.808064448772350.191935551227652
2421.726936760711960.273063239288041
2511.52484703352485-0.524847033524845
2621.369516357470820.630483642529179
2711.78919332814172-0.789193328141718
2821.825321239135290.174678760864708
2921.641240071712890.358759928287109
3011.44650602969278-0.446506029692783
3121.534810679543470.465189320456527
3211.46705939560282-0.467059395602815
3321.568869422962220.431130577037784
3421.754133221201610.24586677879839
3511.50941873030005-0.509418730300052
3611.50577531066529-0.505775310665291
3711.23364250826673-0.233642508266729
3811.61340594048125-0.613405940481253
3921.609106491763390.390893508236612
4011.50042338752119-0.500423387521187
4111.51292670786709-0.512926707867092
4221.718788727611120.281211272388884
4311.70111730051531-0.701117300515314
4411.49735058355187-0.497350583551866
4521.350106943887710.649893056112287
4621.601334868175790.39866513182421
4721.810825631269160.189174368730836
4821.637925884858220.362074115141782
4921.765567855531380.234432144468619
5011.50680163679331-0.506801636793313
5121.866052904140130.133947095859869
5211.46162644354543-0.461626443545426
5311.52503770970104-0.525037709701044
5421.786252080280840.21374791971916
5511.29455610037232-0.294556100372323
5621.381140356771160.618859643228835
5721.680561592757720.319438407242281
5821.675590561888330.324409438111675
5911.60519027354022-0.605190273540219
6021.530396974679330.469603025320674
6111.13954734091928-0.139547340919282
6211.62903381529304-0.629033815293035
6321.668358072916250.33164192708375
6421.74213975705450.257860242945501
6521.63111261584730.368887384152704
6611.63051043061988-0.630510430619876
6721.654353972372690.345646027627307
6811.39502379562682-0.395023795626817
6921.797778201976190.202221798023808
7011.74576557484657-0.745765574846571
7111.52332179538308-0.523321795383085
7221.804333283337260.195666716662744
7321.852587096549050.147412903450949
7411.9386174974621-0.938617497462097
7511.42086271378095-0.420862713780954
7621.344767399766320.655232600233679
7721.850283178091430.149716821908572
7821.539295014098020.460704985901979
7911.61835111731643-0.618351117316427
8011.4251950689215-0.425195068921498
8111.76162096280137-0.761620962801371
8211.79458477143893-0.794584771438935
8321.745765574846570.254234425153429
8411.60674550201227-0.606745502012274
8521.734599413830240.265400586169758
8621.574611070934830.425388929065168
8711.68177047233976-0.681770472339757
8821.641778364665370.358221635334631
8921.614276940360850.385723059639153
9021.372223933356280.627776066643722
9121.706221019792610.293778980207386
9221.624425086950230.375574913049773
9321.867026370690330.132973629309671
9421.466889221171510.533110778828489
9521.549549754189190.450450245810813
9621.676664411214890.323335588785105
9721.611332955921580.388667044078419
9821.466741400081080.533258599918921
9911.6976384972706-0.697638497270597
10011.73216513173085-0.732165131730848
10121.814428828302140.18557117169786
10211.57624067066254-0.576240670662539
10321.898863882235750.101136117764249
10411.37473000761944-0.374730007619437
10521.761432181540510.238567818459485
10611.36959312396613-0.369593123966129
10721.69117473995130.308825260048704
10811.81506068304954-0.815060683049538
10911.52505450500066-0.525054505000656
11021.63247571259650.367524287403505
11121.726093503944610.273906496055391
11221.632830614059490.367169385940512
11321.801677304089430.198322695910568
11411.612905528283-0.612905528282998
11521.657425439481310.342574560518686
11611.73053205785611-0.730532057856113
11721.912072744408160.0879272555918401
11821.683515560980040.31648443901996
11921.69460357405550.305396425944501
12021.690005825017710.309994174982286
12121.666244865754790.333755134245206
12221.596312231203870.403687768796132
12321.836066833864870.163933166135125
12421.85330835476780.146691645232203
12521.61150786744010.388492132559897
12621.69521292160310.304787078396899
12711.58381492529634-0.583814925296338
12821.789178068146590.210821931853409
12921.812006915760560.187993084239438
13021.586778604937420.41322139506258
13111.5255263265863-0.525526326586304
13211.43364430224447-0.433644302244465
13321.604307478347630.39569252165237
13411.53333888468925-0.533338884689253
13511.75661428893091-0.756614288930912
13621.61562882056070.384371179439304
13711.12961533343707-0.129615333437071
13811.68896711986698-0.68896711986698
13921.595380813196360.40461918680364
14021.549737728906970.450262271093034
14111.55133581246479-0.551335812464788
14221.639173640716450.360826359283553
14311.62827815046709-0.628278150467094
14421.795353467971740.204646532028259
14511.32255958508122-0.322559585081223
14621.443999614090890.556000385909107
14721.503053118003970.496946881996035
14811.58387831724697-0.583878317246966
14911.49964660212067-0.499646602120667
15011.37351057495559-0.373510574955592
15121.670992708756140.329007291243864
15221.717407020404790.282592979595211
15311.86461042800226-0.864610428002263
15421.552917810554620.447082189445383
15521.432678073434040.567321926565959
15621.354371832139460.64562816786054
15721.624425086950230.375574913049773
15821.5728473754650.427152624535003
15921.812006915760560.187993084239438
16021.569610213058960.430389786941039
16121.515825148907640.484174851092361
16221.560547557250220.439452442749778

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2 & 1.75087178983036 & 0.249128210169642 \tabularnewline
2 & 2 & 1.61778592291475 & 0.38221407708525 \tabularnewline
3 & 2 & 1.70875394211288 & 0.291246057887123 \tabularnewline
4 & 1 & 1.37369631192021 & -0.373696311920215 \tabularnewline
5 & 2 & 1.90174860739289 & 0.0982513926071134 \tabularnewline
6 & 2 & 1.55338562767562 & 0.446614372324383 \tabularnewline
7 & 2 & 1.5047236779021 & 0.495276322097903 \tabularnewline
8 & 2 & 1.78663839381931 & 0.213361606180687 \tabularnewline
9 & 2 & 1.67403633840313 & 0.325963661596868 \tabularnewline
10 & 2 & 1.5349618524144 & 0.465038147585599 \tabularnewline
11 & 1 & 1.5632329687737 & -0.563232968773698 \tabularnewline
12 & 2 & 1.76555995564256 & 0.234440044357441 \tabularnewline
13 & 1 & 1.51114770781727 & -0.511147707817268 \tabularnewline
14 & 2 & 1.76955859886913 & 0.230441401130875 \tabularnewline
15 & 2 & 2.00885913927367 & -0.00885913927367101 \tabularnewline
16 & 1 & 1.64790051018187 & -0.64790051018187 \tabularnewline
17 & 1 & 1.54902244054173 & -0.549022440541732 \tabularnewline
18 & 2 & 1.84542932529802 & 0.154570674701982 \tabularnewline
19 & 1 & 1.77160656743005 & -0.771606567430046 \tabularnewline
20 & 2 & 1.70794001352677 & 0.292059986473225 \tabularnewline
21 & 1 & 1.70667464636096 & -0.706674646360962 \tabularnewline
22 & 2 & 1.47823734246724 & 0.521762657532755 \tabularnewline
23 & 2 & 1.80806444877235 & 0.191935551227652 \tabularnewline
24 & 2 & 1.72693676071196 & 0.273063239288041 \tabularnewline
25 & 1 & 1.52484703352485 & -0.524847033524845 \tabularnewline
26 & 2 & 1.36951635747082 & 0.630483642529179 \tabularnewline
27 & 1 & 1.78919332814172 & -0.789193328141718 \tabularnewline
28 & 2 & 1.82532123913529 & 0.174678760864708 \tabularnewline
29 & 2 & 1.64124007171289 & 0.358759928287109 \tabularnewline
30 & 1 & 1.44650602969278 & -0.446506029692783 \tabularnewline
31 & 2 & 1.53481067954347 & 0.465189320456527 \tabularnewline
32 & 1 & 1.46705939560282 & -0.467059395602815 \tabularnewline
33 & 2 & 1.56886942296222 & 0.431130577037784 \tabularnewline
34 & 2 & 1.75413322120161 & 0.24586677879839 \tabularnewline
35 & 1 & 1.50941873030005 & -0.509418730300052 \tabularnewline
36 & 1 & 1.50577531066529 & -0.505775310665291 \tabularnewline
37 & 1 & 1.23364250826673 & -0.233642508266729 \tabularnewline
38 & 1 & 1.61340594048125 & -0.613405940481253 \tabularnewline
39 & 2 & 1.60910649176339 & 0.390893508236612 \tabularnewline
40 & 1 & 1.50042338752119 & -0.500423387521187 \tabularnewline
41 & 1 & 1.51292670786709 & -0.512926707867092 \tabularnewline
42 & 2 & 1.71878872761112 & 0.281211272388884 \tabularnewline
43 & 1 & 1.70111730051531 & -0.701117300515314 \tabularnewline
44 & 1 & 1.49735058355187 & -0.497350583551866 \tabularnewline
45 & 2 & 1.35010694388771 & 0.649893056112287 \tabularnewline
46 & 2 & 1.60133486817579 & 0.39866513182421 \tabularnewline
47 & 2 & 1.81082563126916 & 0.189174368730836 \tabularnewline
48 & 2 & 1.63792588485822 & 0.362074115141782 \tabularnewline
49 & 2 & 1.76556785553138 & 0.234432144468619 \tabularnewline
50 & 1 & 1.50680163679331 & -0.506801636793313 \tabularnewline
51 & 2 & 1.86605290414013 & 0.133947095859869 \tabularnewline
52 & 1 & 1.46162644354543 & -0.461626443545426 \tabularnewline
53 & 1 & 1.52503770970104 & -0.525037709701044 \tabularnewline
54 & 2 & 1.78625208028084 & 0.21374791971916 \tabularnewline
55 & 1 & 1.29455610037232 & -0.294556100372323 \tabularnewline
56 & 2 & 1.38114035677116 & 0.618859643228835 \tabularnewline
57 & 2 & 1.68056159275772 & 0.319438407242281 \tabularnewline
58 & 2 & 1.67559056188833 & 0.324409438111675 \tabularnewline
59 & 1 & 1.60519027354022 & -0.605190273540219 \tabularnewline
60 & 2 & 1.53039697467933 & 0.469603025320674 \tabularnewline
61 & 1 & 1.13954734091928 & -0.139547340919282 \tabularnewline
62 & 1 & 1.62903381529304 & -0.629033815293035 \tabularnewline
63 & 2 & 1.66835807291625 & 0.33164192708375 \tabularnewline
64 & 2 & 1.7421397570545 & 0.257860242945501 \tabularnewline
65 & 2 & 1.6311126158473 & 0.368887384152704 \tabularnewline
66 & 1 & 1.63051043061988 & -0.630510430619876 \tabularnewline
67 & 2 & 1.65435397237269 & 0.345646027627307 \tabularnewline
68 & 1 & 1.39502379562682 & -0.395023795626817 \tabularnewline
69 & 2 & 1.79777820197619 & 0.202221798023808 \tabularnewline
70 & 1 & 1.74576557484657 & -0.745765574846571 \tabularnewline
71 & 1 & 1.52332179538308 & -0.523321795383085 \tabularnewline
72 & 2 & 1.80433328333726 & 0.195666716662744 \tabularnewline
73 & 2 & 1.85258709654905 & 0.147412903450949 \tabularnewline
74 & 1 & 1.9386174974621 & -0.938617497462097 \tabularnewline
75 & 1 & 1.42086271378095 & -0.420862713780954 \tabularnewline
76 & 2 & 1.34476739976632 & 0.655232600233679 \tabularnewline
77 & 2 & 1.85028317809143 & 0.149716821908572 \tabularnewline
78 & 2 & 1.53929501409802 & 0.460704985901979 \tabularnewline
79 & 1 & 1.61835111731643 & -0.618351117316427 \tabularnewline
80 & 1 & 1.4251950689215 & -0.425195068921498 \tabularnewline
81 & 1 & 1.76162096280137 & -0.761620962801371 \tabularnewline
82 & 1 & 1.79458477143893 & -0.794584771438935 \tabularnewline
83 & 2 & 1.74576557484657 & 0.254234425153429 \tabularnewline
84 & 1 & 1.60674550201227 & -0.606745502012274 \tabularnewline
85 & 2 & 1.73459941383024 & 0.265400586169758 \tabularnewline
86 & 2 & 1.57461107093483 & 0.425388929065168 \tabularnewline
87 & 1 & 1.68177047233976 & -0.681770472339757 \tabularnewline
88 & 2 & 1.64177836466537 & 0.358221635334631 \tabularnewline
89 & 2 & 1.61427694036085 & 0.385723059639153 \tabularnewline
90 & 2 & 1.37222393335628 & 0.627776066643722 \tabularnewline
91 & 2 & 1.70622101979261 & 0.293778980207386 \tabularnewline
92 & 2 & 1.62442508695023 & 0.375574913049773 \tabularnewline
93 & 2 & 1.86702637069033 & 0.132973629309671 \tabularnewline
94 & 2 & 1.46688922117151 & 0.533110778828489 \tabularnewline
95 & 2 & 1.54954975418919 & 0.450450245810813 \tabularnewline
96 & 2 & 1.67666441121489 & 0.323335588785105 \tabularnewline
97 & 2 & 1.61133295592158 & 0.388667044078419 \tabularnewline
98 & 2 & 1.46674140008108 & 0.533258599918921 \tabularnewline
99 & 1 & 1.6976384972706 & -0.697638497270597 \tabularnewline
100 & 1 & 1.73216513173085 & -0.732165131730848 \tabularnewline
101 & 2 & 1.81442882830214 & 0.18557117169786 \tabularnewline
102 & 1 & 1.57624067066254 & -0.576240670662539 \tabularnewline
103 & 2 & 1.89886388223575 & 0.101136117764249 \tabularnewline
104 & 1 & 1.37473000761944 & -0.374730007619437 \tabularnewline
105 & 2 & 1.76143218154051 & 0.238567818459485 \tabularnewline
106 & 1 & 1.36959312396613 & -0.369593123966129 \tabularnewline
107 & 2 & 1.6911747399513 & 0.308825260048704 \tabularnewline
108 & 1 & 1.81506068304954 & -0.815060683049538 \tabularnewline
109 & 1 & 1.52505450500066 & -0.525054505000656 \tabularnewline
110 & 2 & 1.6324757125965 & 0.367524287403505 \tabularnewline
111 & 2 & 1.72609350394461 & 0.273906496055391 \tabularnewline
112 & 2 & 1.63283061405949 & 0.367169385940512 \tabularnewline
113 & 2 & 1.80167730408943 & 0.198322695910568 \tabularnewline
114 & 1 & 1.612905528283 & -0.612905528282998 \tabularnewline
115 & 2 & 1.65742543948131 & 0.342574560518686 \tabularnewline
116 & 1 & 1.73053205785611 & -0.730532057856113 \tabularnewline
117 & 2 & 1.91207274440816 & 0.0879272555918401 \tabularnewline
118 & 2 & 1.68351556098004 & 0.31648443901996 \tabularnewline
119 & 2 & 1.6946035740555 & 0.305396425944501 \tabularnewline
120 & 2 & 1.69000582501771 & 0.309994174982286 \tabularnewline
121 & 2 & 1.66624486575479 & 0.333755134245206 \tabularnewline
122 & 2 & 1.59631223120387 & 0.403687768796132 \tabularnewline
123 & 2 & 1.83606683386487 & 0.163933166135125 \tabularnewline
124 & 2 & 1.8533083547678 & 0.146691645232203 \tabularnewline
125 & 2 & 1.6115078674401 & 0.388492132559897 \tabularnewline
126 & 2 & 1.6952129216031 & 0.304787078396899 \tabularnewline
127 & 1 & 1.58381492529634 & -0.583814925296338 \tabularnewline
128 & 2 & 1.78917806814659 & 0.210821931853409 \tabularnewline
129 & 2 & 1.81200691576056 & 0.187993084239438 \tabularnewline
130 & 2 & 1.58677860493742 & 0.41322139506258 \tabularnewline
131 & 1 & 1.5255263265863 & -0.525526326586304 \tabularnewline
132 & 1 & 1.43364430224447 & -0.433644302244465 \tabularnewline
133 & 2 & 1.60430747834763 & 0.39569252165237 \tabularnewline
134 & 1 & 1.53333888468925 & -0.533338884689253 \tabularnewline
135 & 1 & 1.75661428893091 & -0.756614288930912 \tabularnewline
136 & 2 & 1.6156288205607 & 0.384371179439304 \tabularnewline
137 & 1 & 1.12961533343707 & -0.129615333437071 \tabularnewline
138 & 1 & 1.68896711986698 & -0.68896711986698 \tabularnewline
139 & 2 & 1.59538081319636 & 0.40461918680364 \tabularnewline
140 & 2 & 1.54973772890697 & 0.450262271093034 \tabularnewline
141 & 1 & 1.55133581246479 & -0.551335812464788 \tabularnewline
142 & 2 & 1.63917364071645 & 0.360826359283553 \tabularnewline
143 & 1 & 1.62827815046709 & -0.628278150467094 \tabularnewline
144 & 2 & 1.79535346797174 & 0.204646532028259 \tabularnewline
145 & 1 & 1.32255958508122 & -0.322559585081223 \tabularnewline
146 & 2 & 1.44399961409089 & 0.556000385909107 \tabularnewline
147 & 2 & 1.50305311800397 & 0.496946881996035 \tabularnewline
148 & 1 & 1.58387831724697 & -0.583878317246966 \tabularnewline
149 & 1 & 1.49964660212067 & -0.499646602120667 \tabularnewline
150 & 1 & 1.37351057495559 & -0.373510574955592 \tabularnewline
151 & 2 & 1.67099270875614 & 0.329007291243864 \tabularnewline
152 & 2 & 1.71740702040479 & 0.282592979595211 \tabularnewline
153 & 1 & 1.86461042800226 & -0.864610428002263 \tabularnewline
154 & 2 & 1.55291781055462 & 0.447082189445383 \tabularnewline
155 & 2 & 1.43267807343404 & 0.567321926565959 \tabularnewline
156 & 2 & 1.35437183213946 & 0.64562816786054 \tabularnewline
157 & 2 & 1.62442508695023 & 0.375574913049773 \tabularnewline
158 & 2 & 1.572847375465 & 0.427152624535003 \tabularnewline
159 & 2 & 1.81200691576056 & 0.187993084239438 \tabularnewline
160 & 2 & 1.56961021305896 & 0.430389786941039 \tabularnewline
161 & 2 & 1.51582514890764 & 0.484174851092361 \tabularnewline
162 & 2 & 1.56054755725022 & 0.439452442749778 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&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]2[/C][C]1.75087178983036[/C][C]0.249128210169642[/C][/ROW]
[ROW][C]2[/C][C]2[/C][C]1.61778592291475[/C][C]0.38221407708525[/C][/ROW]
[ROW][C]3[/C][C]2[/C][C]1.70875394211288[/C][C]0.291246057887123[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]1.37369631192021[/C][C]-0.373696311920215[/C][/ROW]
[ROW][C]5[/C][C]2[/C][C]1.90174860739289[/C][C]0.0982513926071134[/C][/ROW]
[ROW][C]6[/C][C]2[/C][C]1.55338562767562[/C][C]0.446614372324383[/C][/ROW]
[ROW][C]7[/C][C]2[/C][C]1.5047236779021[/C][C]0.495276322097903[/C][/ROW]
[ROW][C]8[/C][C]2[/C][C]1.78663839381931[/C][C]0.213361606180687[/C][/ROW]
[ROW][C]9[/C][C]2[/C][C]1.67403633840313[/C][C]0.325963661596868[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]1.5349618524144[/C][C]0.465038147585599[/C][/ROW]
[ROW][C]11[/C][C]1[/C][C]1.5632329687737[/C][C]-0.563232968773698[/C][/ROW]
[ROW][C]12[/C][C]2[/C][C]1.76555995564256[/C][C]0.234440044357441[/C][/ROW]
[ROW][C]13[/C][C]1[/C][C]1.51114770781727[/C][C]-0.511147707817268[/C][/ROW]
[ROW][C]14[/C][C]2[/C][C]1.76955859886913[/C][C]0.230441401130875[/C][/ROW]
[ROW][C]15[/C][C]2[/C][C]2.00885913927367[/C][C]-0.00885913927367101[/C][/ROW]
[ROW][C]16[/C][C]1[/C][C]1.64790051018187[/C][C]-0.64790051018187[/C][/ROW]
[ROW][C]17[/C][C]1[/C][C]1.54902244054173[/C][C]-0.549022440541732[/C][/ROW]
[ROW][C]18[/C][C]2[/C][C]1.84542932529802[/C][C]0.154570674701982[/C][/ROW]
[ROW][C]19[/C][C]1[/C][C]1.77160656743005[/C][C]-0.771606567430046[/C][/ROW]
[ROW][C]20[/C][C]2[/C][C]1.70794001352677[/C][C]0.292059986473225[/C][/ROW]
[ROW][C]21[/C][C]1[/C][C]1.70667464636096[/C][C]-0.706674646360962[/C][/ROW]
[ROW][C]22[/C][C]2[/C][C]1.47823734246724[/C][C]0.521762657532755[/C][/ROW]
[ROW][C]23[/C][C]2[/C][C]1.80806444877235[/C][C]0.191935551227652[/C][/ROW]
[ROW][C]24[/C][C]2[/C][C]1.72693676071196[/C][C]0.273063239288041[/C][/ROW]
[ROW][C]25[/C][C]1[/C][C]1.52484703352485[/C][C]-0.524847033524845[/C][/ROW]
[ROW][C]26[/C][C]2[/C][C]1.36951635747082[/C][C]0.630483642529179[/C][/ROW]
[ROW][C]27[/C][C]1[/C][C]1.78919332814172[/C][C]-0.789193328141718[/C][/ROW]
[ROW][C]28[/C][C]2[/C][C]1.82532123913529[/C][C]0.174678760864708[/C][/ROW]
[ROW][C]29[/C][C]2[/C][C]1.64124007171289[/C][C]0.358759928287109[/C][/ROW]
[ROW][C]30[/C][C]1[/C][C]1.44650602969278[/C][C]-0.446506029692783[/C][/ROW]
[ROW][C]31[/C][C]2[/C][C]1.53481067954347[/C][C]0.465189320456527[/C][/ROW]
[ROW][C]32[/C][C]1[/C][C]1.46705939560282[/C][C]-0.467059395602815[/C][/ROW]
[ROW][C]33[/C][C]2[/C][C]1.56886942296222[/C][C]0.431130577037784[/C][/ROW]
[ROW][C]34[/C][C]2[/C][C]1.75413322120161[/C][C]0.24586677879839[/C][/ROW]
[ROW][C]35[/C][C]1[/C][C]1.50941873030005[/C][C]-0.509418730300052[/C][/ROW]
[ROW][C]36[/C][C]1[/C][C]1.50577531066529[/C][C]-0.505775310665291[/C][/ROW]
[ROW][C]37[/C][C]1[/C][C]1.23364250826673[/C][C]-0.233642508266729[/C][/ROW]
[ROW][C]38[/C][C]1[/C][C]1.61340594048125[/C][C]-0.613405940481253[/C][/ROW]
[ROW][C]39[/C][C]2[/C][C]1.60910649176339[/C][C]0.390893508236612[/C][/ROW]
[ROW][C]40[/C][C]1[/C][C]1.50042338752119[/C][C]-0.500423387521187[/C][/ROW]
[ROW][C]41[/C][C]1[/C][C]1.51292670786709[/C][C]-0.512926707867092[/C][/ROW]
[ROW][C]42[/C][C]2[/C][C]1.71878872761112[/C][C]0.281211272388884[/C][/ROW]
[ROW][C]43[/C][C]1[/C][C]1.70111730051531[/C][C]-0.701117300515314[/C][/ROW]
[ROW][C]44[/C][C]1[/C][C]1.49735058355187[/C][C]-0.497350583551866[/C][/ROW]
[ROW][C]45[/C][C]2[/C][C]1.35010694388771[/C][C]0.649893056112287[/C][/ROW]
[ROW][C]46[/C][C]2[/C][C]1.60133486817579[/C][C]0.39866513182421[/C][/ROW]
[ROW][C]47[/C][C]2[/C][C]1.81082563126916[/C][C]0.189174368730836[/C][/ROW]
[ROW][C]48[/C][C]2[/C][C]1.63792588485822[/C][C]0.362074115141782[/C][/ROW]
[ROW][C]49[/C][C]2[/C][C]1.76556785553138[/C][C]0.234432144468619[/C][/ROW]
[ROW][C]50[/C][C]1[/C][C]1.50680163679331[/C][C]-0.506801636793313[/C][/ROW]
[ROW][C]51[/C][C]2[/C][C]1.86605290414013[/C][C]0.133947095859869[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]1.46162644354543[/C][C]-0.461626443545426[/C][/ROW]
[ROW][C]53[/C][C]1[/C][C]1.52503770970104[/C][C]-0.525037709701044[/C][/ROW]
[ROW][C]54[/C][C]2[/C][C]1.78625208028084[/C][C]0.21374791971916[/C][/ROW]
[ROW][C]55[/C][C]1[/C][C]1.29455610037232[/C][C]-0.294556100372323[/C][/ROW]
[ROW][C]56[/C][C]2[/C][C]1.38114035677116[/C][C]0.618859643228835[/C][/ROW]
[ROW][C]57[/C][C]2[/C][C]1.68056159275772[/C][C]0.319438407242281[/C][/ROW]
[ROW][C]58[/C][C]2[/C][C]1.67559056188833[/C][C]0.324409438111675[/C][/ROW]
[ROW][C]59[/C][C]1[/C][C]1.60519027354022[/C][C]-0.605190273540219[/C][/ROW]
[ROW][C]60[/C][C]2[/C][C]1.53039697467933[/C][C]0.469603025320674[/C][/ROW]
[ROW][C]61[/C][C]1[/C][C]1.13954734091928[/C][C]-0.139547340919282[/C][/ROW]
[ROW][C]62[/C][C]1[/C][C]1.62903381529304[/C][C]-0.629033815293035[/C][/ROW]
[ROW][C]63[/C][C]2[/C][C]1.66835807291625[/C][C]0.33164192708375[/C][/ROW]
[ROW][C]64[/C][C]2[/C][C]1.7421397570545[/C][C]0.257860242945501[/C][/ROW]
[ROW][C]65[/C][C]2[/C][C]1.6311126158473[/C][C]0.368887384152704[/C][/ROW]
[ROW][C]66[/C][C]1[/C][C]1.63051043061988[/C][C]-0.630510430619876[/C][/ROW]
[ROW][C]67[/C][C]2[/C][C]1.65435397237269[/C][C]0.345646027627307[/C][/ROW]
[ROW][C]68[/C][C]1[/C][C]1.39502379562682[/C][C]-0.395023795626817[/C][/ROW]
[ROW][C]69[/C][C]2[/C][C]1.79777820197619[/C][C]0.202221798023808[/C][/ROW]
[ROW][C]70[/C][C]1[/C][C]1.74576557484657[/C][C]-0.745765574846571[/C][/ROW]
[ROW][C]71[/C][C]1[/C][C]1.52332179538308[/C][C]-0.523321795383085[/C][/ROW]
[ROW][C]72[/C][C]2[/C][C]1.80433328333726[/C][C]0.195666716662744[/C][/ROW]
[ROW][C]73[/C][C]2[/C][C]1.85258709654905[/C][C]0.147412903450949[/C][/ROW]
[ROW][C]74[/C][C]1[/C][C]1.9386174974621[/C][C]-0.938617497462097[/C][/ROW]
[ROW][C]75[/C][C]1[/C][C]1.42086271378095[/C][C]-0.420862713780954[/C][/ROW]
[ROW][C]76[/C][C]2[/C][C]1.34476739976632[/C][C]0.655232600233679[/C][/ROW]
[ROW][C]77[/C][C]2[/C][C]1.85028317809143[/C][C]0.149716821908572[/C][/ROW]
[ROW][C]78[/C][C]2[/C][C]1.53929501409802[/C][C]0.460704985901979[/C][/ROW]
[ROW][C]79[/C][C]1[/C][C]1.61835111731643[/C][C]-0.618351117316427[/C][/ROW]
[ROW][C]80[/C][C]1[/C][C]1.4251950689215[/C][C]-0.425195068921498[/C][/ROW]
[ROW][C]81[/C][C]1[/C][C]1.76162096280137[/C][C]-0.761620962801371[/C][/ROW]
[ROW][C]82[/C][C]1[/C][C]1.79458477143893[/C][C]-0.794584771438935[/C][/ROW]
[ROW][C]83[/C][C]2[/C][C]1.74576557484657[/C][C]0.254234425153429[/C][/ROW]
[ROW][C]84[/C][C]1[/C][C]1.60674550201227[/C][C]-0.606745502012274[/C][/ROW]
[ROW][C]85[/C][C]2[/C][C]1.73459941383024[/C][C]0.265400586169758[/C][/ROW]
[ROW][C]86[/C][C]2[/C][C]1.57461107093483[/C][C]0.425388929065168[/C][/ROW]
[ROW][C]87[/C][C]1[/C][C]1.68177047233976[/C][C]-0.681770472339757[/C][/ROW]
[ROW][C]88[/C][C]2[/C][C]1.64177836466537[/C][C]0.358221635334631[/C][/ROW]
[ROW][C]89[/C][C]2[/C][C]1.61427694036085[/C][C]0.385723059639153[/C][/ROW]
[ROW][C]90[/C][C]2[/C][C]1.37222393335628[/C][C]0.627776066643722[/C][/ROW]
[ROW][C]91[/C][C]2[/C][C]1.70622101979261[/C][C]0.293778980207386[/C][/ROW]
[ROW][C]92[/C][C]2[/C][C]1.62442508695023[/C][C]0.375574913049773[/C][/ROW]
[ROW][C]93[/C][C]2[/C][C]1.86702637069033[/C][C]0.132973629309671[/C][/ROW]
[ROW][C]94[/C][C]2[/C][C]1.46688922117151[/C][C]0.533110778828489[/C][/ROW]
[ROW][C]95[/C][C]2[/C][C]1.54954975418919[/C][C]0.450450245810813[/C][/ROW]
[ROW][C]96[/C][C]2[/C][C]1.67666441121489[/C][C]0.323335588785105[/C][/ROW]
[ROW][C]97[/C][C]2[/C][C]1.61133295592158[/C][C]0.388667044078419[/C][/ROW]
[ROW][C]98[/C][C]2[/C][C]1.46674140008108[/C][C]0.533258599918921[/C][/ROW]
[ROW][C]99[/C][C]1[/C][C]1.6976384972706[/C][C]-0.697638497270597[/C][/ROW]
[ROW][C]100[/C][C]1[/C][C]1.73216513173085[/C][C]-0.732165131730848[/C][/ROW]
[ROW][C]101[/C][C]2[/C][C]1.81442882830214[/C][C]0.18557117169786[/C][/ROW]
[ROW][C]102[/C][C]1[/C][C]1.57624067066254[/C][C]-0.576240670662539[/C][/ROW]
[ROW][C]103[/C][C]2[/C][C]1.89886388223575[/C][C]0.101136117764249[/C][/ROW]
[ROW][C]104[/C][C]1[/C][C]1.37473000761944[/C][C]-0.374730007619437[/C][/ROW]
[ROW][C]105[/C][C]2[/C][C]1.76143218154051[/C][C]0.238567818459485[/C][/ROW]
[ROW][C]106[/C][C]1[/C][C]1.36959312396613[/C][C]-0.369593123966129[/C][/ROW]
[ROW][C]107[/C][C]2[/C][C]1.6911747399513[/C][C]0.308825260048704[/C][/ROW]
[ROW][C]108[/C][C]1[/C][C]1.81506068304954[/C][C]-0.815060683049538[/C][/ROW]
[ROW][C]109[/C][C]1[/C][C]1.52505450500066[/C][C]-0.525054505000656[/C][/ROW]
[ROW][C]110[/C][C]2[/C][C]1.6324757125965[/C][C]0.367524287403505[/C][/ROW]
[ROW][C]111[/C][C]2[/C][C]1.72609350394461[/C][C]0.273906496055391[/C][/ROW]
[ROW][C]112[/C][C]2[/C][C]1.63283061405949[/C][C]0.367169385940512[/C][/ROW]
[ROW][C]113[/C][C]2[/C][C]1.80167730408943[/C][C]0.198322695910568[/C][/ROW]
[ROW][C]114[/C][C]1[/C][C]1.612905528283[/C][C]-0.612905528282998[/C][/ROW]
[ROW][C]115[/C][C]2[/C][C]1.65742543948131[/C][C]0.342574560518686[/C][/ROW]
[ROW][C]116[/C][C]1[/C][C]1.73053205785611[/C][C]-0.730532057856113[/C][/ROW]
[ROW][C]117[/C][C]2[/C][C]1.91207274440816[/C][C]0.0879272555918401[/C][/ROW]
[ROW][C]118[/C][C]2[/C][C]1.68351556098004[/C][C]0.31648443901996[/C][/ROW]
[ROW][C]119[/C][C]2[/C][C]1.6946035740555[/C][C]0.305396425944501[/C][/ROW]
[ROW][C]120[/C][C]2[/C][C]1.69000582501771[/C][C]0.309994174982286[/C][/ROW]
[ROW][C]121[/C][C]2[/C][C]1.66624486575479[/C][C]0.333755134245206[/C][/ROW]
[ROW][C]122[/C][C]2[/C][C]1.59631223120387[/C][C]0.403687768796132[/C][/ROW]
[ROW][C]123[/C][C]2[/C][C]1.83606683386487[/C][C]0.163933166135125[/C][/ROW]
[ROW][C]124[/C][C]2[/C][C]1.8533083547678[/C][C]0.146691645232203[/C][/ROW]
[ROW][C]125[/C][C]2[/C][C]1.6115078674401[/C][C]0.388492132559897[/C][/ROW]
[ROW][C]126[/C][C]2[/C][C]1.6952129216031[/C][C]0.304787078396899[/C][/ROW]
[ROW][C]127[/C][C]1[/C][C]1.58381492529634[/C][C]-0.583814925296338[/C][/ROW]
[ROW][C]128[/C][C]2[/C][C]1.78917806814659[/C][C]0.210821931853409[/C][/ROW]
[ROW][C]129[/C][C]2[/C][C]1.81200691576056[/C][C]0.187993084239438[/C][/ROW]
[ROW][C]130[/C][C]2[/C][C]1.58677860493742[/C][C]0.41322139506258[/C][/ROW]
[ROW][C]131[/C][C]1[/C][C]1.5255263265863[/C][C]-0.525526326586304[/C][/ROW]
[ROW][C]132[/C][C]1[/C][C]1.43364430224447[/C][C]-0.433644302244465[/C][/ROW]
[ROW][C]133[/C][C]2[/C][C]1.60430747834763[/C][C]0.39569252165237[/C][/ROW]
[ROW][C]134[/C][C]1[/C][C]1.53333888468925[/C][C]-0.533338884689253[/C][/ROW]
[ROW][C]135[/C][C]1[/C][C]1.75661428893091[/C][C]-0.756614288930912[/C][/ROW]
[ROW][C]136[/C][C]2[/C][C]1.6156288205607[/C][C]0.384371179439304[/C][/ROW]
[ROW][C]137[/C][C]1[/C][C]1.12961533343707[/C][C]-0.129615333437071[/C][/ROW]
[ROW][C]138[/C][C]1[/C][C]1.68896711986698[/C][C]-0.68896711986698[/C][/ROW]
[ROW][C]139[/C][C]2[/C][C]1.59538081319636[/C][C]0.40461918680364[/C][/ROW]
[ROW][C]140[/C][C]2[/C][C]1.54973772890697[/C][C]0.450262271093034[/C][/ROW]
[ROW][C]141[/C][C]1[/C][C]1.55133581246479[/C][C]-0.551335812464788[/C][/ROW]
[ROW][C]142[/C][C]2[/C][C]1.63917364071645[/C][C]0.360826359283553[/C][/ROW]
[ROW][C]143[/C][C]1[/C][C]1.62827815046709[/C][C]-0.628278150467094[/C][/ROW]
[ROW][C]144[/C][C]2[/C][C]1.79535346797174[/C][C]0.204646532028259[/C][/ROW]
[ROW][C]145[/C][C]1[/C][C]1.32255958508122[/C][C]-0.322559585081223[/C][/ROW]
[ROW][C]146[/C][C]2[/C][C]1.44399961409089[/C][C]0.556000385909107[/C][/ROW]
[ROW][C]147[/C][C]2[/C][C]1.50305311800397[/C][C]0.496946881996035[/C][/ROW]
[ROW][C]148[/C][C]1[/C][C]1.58387831724697[/C][C]-0.583878317246966[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]1.49964660212067[/C][C]-0.499646602120667[/C][/ROW]
[ROW][C]150[/C][C]1[/C][C]1.37351057495559[/C][C]-0.373510574955592[/C][/ROW]
[ROW][C]151[/C][C]2[/C][C]1.67099270875614[/C][C]0.329007291243864[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]1.71740702040479[/C][C]0.282592979595211[/C][/ROW]
[ROW][C]153[/C][C]1[/C][C]1.86461042800226[/C][C]-0.864610428002263[/C][/ROW]
[ROW][C]154[/C][C]2[/C][C]1.55291781055462[/C][C]0.447082189445383[/C][/ROW]
[ROW][C]155[/C][C]2[/C][C]1.43267807343404[/C][C]0.567321926565959[/C][/ROW]
[ROW][C]156[/C][C]2[/C][C]1.35437183213946[/C][C]0.64562816786054[/C][/ROW]
[ROW][C]157[/C][C]2[/C][C]1.62442508695023[/C][C]0.375574913049773[/C][/ROW]
[ROW][C]158[/C][C]2[/C][C]1.572847375465[/C][C]0.427152624535003[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]1.81200691576056[/C][C]0.187993084239438[/C][/ROW]
[ROW][C]160[/C][C]2[/C][C]1.56961021305896[/C][C]0.430389786941039[/C][/ROW]
[ROW][C]161[/C][C]2[/C][C]1.51582514890764[/C][C]0.484174851092361[/C][/ROW]
[ROW][C]162[/C][C]2[/C][C]1.56054755725022[/C][C]0.439452442749778[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&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
121.750871789830360.249128210169642
221.617785922914750.38221407708525
321.708753942112880.291246057887123
411.37369631192021-0.373696311920215
521.901748607392890.0982513926071134
621.553385627675620.446614372324383
721.50472367790210.495276322097903
821.786638393819310.213361606180687
921.674036338403130.325963661596868
1021.53496185241440.465038147585599
1111.5632329687737-0.563232968773698
1221.765559955642560.234440044357441
1311.51114770781727-0.511147707817268
1421.769558598869130.230441401130875
1522.00885913927367-0.00885913927367101
1611.64790051018187-0.64790051018187
1711.54902244054173-0.549022440541732
1821.845429325298020.154570674701982
1911.77160656743005-0.771606567430046
2021.707940013526770.292059986473225
2111.70667464636096-0.706674646360962
2221.478237342467240.521762657532755
2321.808064448772350.191935551227652
2421.726936760711960.273063239288041
2511.52484703352485-0.524847033524845
2621.369516357470820.630483642529179
2711.78919332814172-0.789193328141718
2821.825321239135290.174678760864708
2921.641240071712890.358759928287109
3011.44650602969278-0.446506029692783
3121.534810679543470.465189320456527
3211.46705939560282-0.467059395602815
3321.568869422962220.431130577037784
3421.754133221201610.24586677879839
3511.50941873030005-0.509418730300052
3611.50577531066529-0.505775310665291
3711.23364250826673-0.233642508266729
3811.61340594048125-0.613405940481253
3921.609106491763390.390893508236612
4011.50042338752119-0.500423387521187
4111.51292670786709-0.512926707867092
4221.718788727611120.281211272388884
4311.70111730051531-0.701117300515314
4411.49735058355187-0.497350583551866
4521.350106943887710.649893056112287
4621.601334868175790.39866513182421
4721.810825631269160.189174368730836
4821.637925884858220.362074115141782
4921.765567855531380.234432144468619
5011.50680163679331-0.506801636793313
5121.866052904140130.133947095859869
5211.46162644354543-0.461626443545426
5311.52503770970104-0.525037709701044
5421.786252080280840.21374791971916
5511.29455610037232-0.294556100372323
5621.381140356771160.618859643228835
5721.680561592757720.319438407242281
5821.675590561888330.324409438111675
5911.60519027354022-0.605190273540219
6021.530396974679330.469603025320674
6111.13954734091928-0.139547340919282
6211.62903381529304-0.629033815293035
6321.668358072916250.33164192708375
6421.74213975705450.257860242945501
6521.63111261584730.368887384152704
6611.63051043061988-0.630510430619876
6721.654353972372690.345646027627307
6811.39502379562682-0.395023795626817
6921.797778201976190.202221798023808
7011.74576557484657-0.745765574846571
7111.52332179538308-0.523321795383085
7221.804333283337260.195666716662744
7321.852587096549050.147412903450949
7411.9386174974621-0.938617497462097
7511.42086271378095-0.420862713780954
7621.344767399766320.655232600233679
7721.850283178091430.149716821908572
7821.539295014098020.460704985901979
7911.61835111731643-0.618351117316427
8011.4251950689215-0.425195068921498
8111.76162096280137-0.761620962801371
8211.79458477143893-0.794584771438935
8321.745765574846570.254234425153429
8411.60674550201227-0.606745502012274
8521.734599413830240.265400586169758
8621.574611070934830.425388929065168
8711.68177047233976-0.681770472339757
8821.641778364665370.358221635334631
8921.614276940360850.385723059639153
9021.372223933356280.627776066643722
9121.706221019792610.293778980207386
9221.624425086950230.375574913049773
9321.867026370690330.132973629309671
9421.466889221171510.533110778828489
9521.549549754189190.450450245810813
9621.676664411214890.323335588785105
9721.611332955921580.388667044078419
9821.466741400081080.533258599918921
9911.6976384972706-0.697638497270597
10011.73216513173085-0.732165131730848
10121.814428828302140.18557117169786
10211.57624067066254-0.576240670662539
10321.898863882235750.101136117764249
10411.37473000761944-0.374730007619437
10521.761432181540510.238567818459485
10611.36959312396613-0.369593123966129
10721.69117473995130.308825260048704
10811.81506068304954-0.815060683049538
10911.52505450500066-0.525054505000656
11021.63247571259650.367524287403505
11121.726093503944610.273906496055391
11221.632830614059490.367169385940512
11321.801677304089430.198322695910568
11411.612905528283-0.612905528282998
11521.657425439481310.342574560518686
11611.73053205785611-0.730532057856113
11721.912072744408160.0879272555918401
11821.683515560980040.31648443901996
11921.69460357405550.305396425944501
12021.690005825017710.309994174982286
12121.666244865754790.333755134245206
12221.596312231203870.403687768796132
12321.836066833864870.163933166135125
12421.85330835476780.146691645232203
12521.61150786744010.388492132559897
12621.69521292160310.304787078396899
12711.58381492529634-0.583814925296338
12821.789178068146590.210821931853409
12921.812006915760560.187993084239438
13021.586778604937420.41322139506258
13111.5255263265863-0.525526326586304
13211.43364430224447-0.433644302244465
13321.604307478347630.39569252165237
13411.53333888468925-0.533338884689253
13511.75661428893091-0.756614288930912
13621.61562882056070.384371179439304
13711.12961533343707-0.129615333437071
13811.68896711986698-0.68896711986698
13921.595380813196360.40461918680364
14021.549737728906970.450262271093034
14111.55133581246479-0.551335812464788
14221.639173640716450.360826359283553
14311.62827815046709-0.628278150467094
14421.795353467971740.204646532028259
14511.32255958508122-0.322559585081223
14621.443999614090890.556000385909107
14721.503053118003970.496946881996035
14811.58387831724697-0.583878317246966
14911.49964660212067-0.499646602120667
15011.37351057495559-0.373510574955592
15121.670992708756140.329007291243864
15221.717407020404790.282592979595211
15311.86461042800226-0.864610428002263
15421.552917810554620.447082189445383
15521.432678073434040.567321926565959
15621.354371832139460.64562816786054
15721.624425086950230.375574913049773
15821.5728473754650.427152624535003
15921.812006915760560.187993084239438
16021.569610213058960.430389786941039
16121.515825148907640.484174851092361
16221.560547557250220.439452442749778






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
100.1261505163235350.252301032647070.873849483676465
110.4408719497460940.8817438994921880.559128050253906
120.3245056191181830.6490112382363660.675494380881817
130.4455851324435050.8911702648870090.554414867556495
140.3293578849837520.6587157699675030.670642115016248
150.2780705238944090.5561410477888180.721929476105591
160.3474018641039780.6948037282079560.652598135896022
170.2718869942400450.5437739884800890.728113005759955
180.1978479134797650.3956958269595290.802152086520236
190.1647097944438350.3294195888876690.835290205556165
200.1994439316383850.3988878632767690.800556068361615
210.4440251307916360.8880502615832720.555974869208364
220.4899257436793370.9798514873586740.510074256320663
230.4444064669723610.8888129339447210.555593533027639
240.4256393136239180.8512786272478360.574360686376082
250.4360229252038960.8720458504077920.563977074796104
260.394228849139960.788457698279920.60577115086004
270.3585533624652730.7171067249305460.641446637534727
280.2992256702621920.5984513405243840.700774329737808
290.2584974797354330.5169949594708660.741502520264567
300.2672846116945260.5345692233890510.732715388305474
310.2574780382829210.5149560765658410.742521961717079
320.2262581581210960.4525163162421910.773741841878904
330.2266199412122040.4532398824244070.773380058787796
340.1844556181149550.368911236229910.815544381885045
350.2397083265240510.4794166530481010.760291673475949
360.2260991852047050.452198370409410.773900814795295
370.2025500864617950.4051001729235910.797449913538204
380.288576873438880.5771537468777610.71142312656112
390.2842802378939840.5685604757879690.715719762106016
400.2901425240346710.5802850480693420.709857475965329
410.3014506173575020.6029012347150030.698549382642499
420.2864554380460570.5729108760921150.713544561953943
430.4355414462358830.8710828924717660.564458553764117
440.4154331322309120.8308662644618240.584566867769088
450.5421952692517760.9156094614964490.457804730748224
460.5220568367493880.9558863265012240.477943163250612
470.473646387011220.9472927740224390.52635361298878
480.4522916692374160.9045833384748310.547708330762584
490.4075387909715860.8150775819431720.592461209028414
500.437224169263010.8744483385260210.562775830736989
510.3918080538454460.7836161076908930.608191946154554
520.3809992846583370.7619985693166750.619000715341663
530.3969061234262780.7938122468525560.603093876573722
540.3612613343743020.7225226687486040.638738665625698
550.3283156401036890.6566312802073780.671684359896311
560.3936073577444490.7872147154888980.606392642255551
570.3645758162412890.7291516324825790.63542418375871
580.3382659993698010.6765319987396010.661734000630199
590.3628210557110670.7256421114221350.637178944288933
600.3767616774063180.7535233548126360.623238322593682
610.3395541666222960.6791083332445920.660445833377704
620.3651021241806840.7302042483613670.634897875819316
630.3428523390216390.6857046780432770.657147660978361
640.3258359193108470.6516718386216930.674164080689153
650.3152807980957510.6305615961915010.684719201904249
660.3530551239762230.7061102479524450.646944876023777
670.3327840092976330.6655680185952660.667215990702367
680.3159299064550770.6318598129101540.684070093544923
690.2815457622858620.5630915245717240.718454237714138
700.3506481909221350.7012963818442710.649351809077865
710.3581016086016830.7162032172033650.641898391398317
720.3226496041046920.6452992082093840.677350395895308
730.2861831694888430.5723663389776860.713816830511157
740.4091108982002050.818221796400410.590889101799795
750.4023502997557720.8047005995115450.597649700244228
760.4483657901344460.8967315802688920.551634209865554
770.4079132473074540.8158264946149090.592086752692546
780.4086232169151540.8172464338303090.591376783084846
790.4396550403393460.8793100806786930.560344959660653
800.432069625148340.864139250296680.56793037485166
810.4990739536328820.9981479072657640.500926046367118
820.5848492145057930.8303015709884130.415150785494207
830.5523103687846540.8953792624306920.447689631215346
840.5903319237334370.8193361525331250.409668076266562
850.563037033167970.873925933664060.43696296683203
860.5552062616140670.8895874767718660.444793738385933
870.6111085936048790.7777828127902420.388891406395121
880.5894157063558380.8211685872883240.410584293644162
890.5732998926903350.8534002146193290.426700107309665
900.5998221218300480.8003557563399050.400177878169952
910.5726087632992560.8547824734014880.427391236700744
920.5527473525794050.8945052948411890.447252647420595
930.5102017864041780.9795964271916430.489798213595822
940.5157776635863960.9684446728272080.484222336413604
950.5128439453706010.9743121092587990.487156054629399
960.4870773929943790.9741547859887580.512922607005621
970.4714870548968340.9429741097936680.528512945103166
980.4806390239832130.9612780479664260.519360976016787
990.5435964680804760.9128070638390480.456403531919524
1000.6108312171646960.7783375656706080.389168782835304
1010.569128254336220.8617434913275590.43087174566378
1020.5913263960630330.8173472078739350.408673603936967
1030.5458003035062640.9083993929874720.454199696493736
1040.5275810611270850.9448378777458290.472418938872915
1050.4886731828568720.9773463657137440.511326817143128
1060.4728302740221630.9456605480443250.527169725977837
1070.4402697799910280.8805395599820560.559730220008972
1080.549137096895040.9017258062099190.45086290310496
1090.5778648736670130.8442702526659740.422135126332987
1100.5616913778708460.8766172442583090.438308622129154
1110.5315844153782560.9368311692434890.468415584621744
1120.5159459718758280.9681080562483430.484054028124172
1130.4737193827750270.9474387655500530.526280617224973
1140.5091611459972960.9816777080054090.490838854002704
1150.4753761268865630.9507522537731250.524623873113437
1160.5715181133764890.8569637732470230.428481886623511
1170.5218418786200070.9563162427599860.478158121379993
1180.4903019967133050.9806039934266110.509698003286695
1190.4565369343173260.9130738686346530.543463065682674
1200.415759308613370.831518617226740.58424069138663
1210.3915779636033340.7831559272066670.608422036396666
1220.3756895339935410.7513790679870830.624310466006459
1230.3288929644995610.6577859289991210.671107035500439
1240.2856303592038160.5712607184076310.714369640796184
1250.273880466106470.5477609322129410.72611953389353
1260.2818753088704030.5637506177408060.718124691129597
1270.380104642937460.760209285874920.61989535706254
1280.3369619367654040.6739238735308090.663038063234596
1290.2909505413746170.5819010827492340.709049458625383
1300.2715768476350060.5431536952700120.728423152364994
1310.2905444070643180.5810888141286370.709455592935682
1320.2510615591497590.5021231182995180.748938440850241
1330.2130021019474490.4260042038948980.786997898052551
1340.1927459562880830.3854919125761660.807254043711917
1350.2851475154852940.5702950309705880.714852484514706
1360.2947441398264230.5894882796528450.705255860173577
1370.2432510664855090.4865021329710180.756748933514491
1380.2910258035468970.5820516070937940.708974196453103
1390.3784902857635180.7569805715270360.621509714236482
1400.3308526125692260.6617052251384530.669147387430774
1410.2985824980121520.5971649960243040.701417501987848
1420.2832285130110120.5664570260220230.716771486988988
1430.412322998192710.824645996385420.58767700180729
1440.4059384462247080.8118768924494160.594061553775292
1450.4910621907572170.9821243815144340.508937809242783
1460.4784229482522230.9568458965044450.521577051747777
1470.4037168441626190.8074336883252380.596283155837381
1480.4059511397783030.8119022795566050.594048860221697
1490.6521083372717560.6957833254564890.347891662728244
1500.9036264853347970.1927470293304060.0963735146652028
1510.8138559857001180.3722880285997650.186144014299882
1520.7508781671871020.4982436656257960.249121832812898

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
10 & 0.126150516323535 & 0.25230103264707 & 0.873849483676465 \tabularnewline
11 & 0.440871949746094 & 0.881743899492188 & 0.559128050253906 \tabularnewline
12 & 0.324505619118183 & 0.649011238236366 & 0.675494380881817 \tabularnewline
13 & 0.445585132443505 & 0.891170264887009 & 0.554414867556495 \tabularnewline
14 & 0.329357884983752 & 0.658715769967503 & 0.670642115016248 \tabularnewline
15 & 0.278070523894409 & 0.556141047788818 & 0.721929476105591 \tabularnewline
16 & 0.347401864103978 & 0.694803728207956 & 0.652598135896022 \tabularnewline
17 & 0.271886994240045 & 0.543773988480089 & 0.728113005759955 \tabularnewline
18 & 0.197847913479765 & 0.395695826959529 & 0.802152086520236 \tabularnewline
19 & 0.164709794443835 & 0.329419588887669 & 0.835290205556165 \tabularnewline
20 & 0.199443931638385 & 0.398887863276769 & 0.800556068361615 \tabularnewline
21 & 0.444025130791636 & 0.888050261583272 & 0.555974869208364 \tabularnewline
22 & 0.489925743679337 & 0.979851487358674 & 0.510074256320663 \tabularnewline
23 & 0.444406466972361 & 0.888812933944721 & 0.555593533027639 \tabularnewline
24 & 0.425639313623918 & 0.851278627247836 & 0.574360686376082 \tabularnewline
25 & 0.436022925203896 & 0.872045850407792 & 0.563977074796104 \tabularnewline
26 & 0.39422884913996 & 0.78845769827992 & 0.60577115086004 \tabularnewline
27 & 0.358553362465273 & 0.717106724930546 & 0.641446637534727 \tabularnewline
28 & 0.299225670262192 & 0.598451340524384 & 0.700774329737808 \tabularnewline
29 & 0.258497479735433 & 0.516994959470866 & 0.741502520264567 \tabularnewline
30 & 0.267284611694526 & 0.534569223389051 & 0.732715388305474 \tabularnewline
31 & 0.257478038282921 & 0.514956076565841 & 0.742521961717079 \tabularnewline
32 & 0.226258158121096 & 0.452516316242191 & 0.773741841878904 \tabularnewline
33 & 0.226619941212204 & 0.453239882424407 & 0.773380058787796 \tabularnewline
34 & 0.184455618114955 & 0.36891123622991 & 0.815544381885045 \tabularnewline
35 & 0.239708326524051 & 0.479416653048101 & 0.760291673475949 \tabularnewline
36 & 0.226099185204705 & 0.45219837040941 & 0.773900814795295 \tabularnewline
37 & 0.202550086461795 & 0.405100172923591 & 0.797449913538204 \tabularnewline
38 & 0.28857687343888 & 0.577153746877761 & 0.71142312656112 \tabularnewline
39 & 0.284280237893984 & 0.568560475787969 & 0.715719762106016 \tabularnewline
40 & 0.290142524034671 & 0.580285048069342 & 0.709857475965329 \tabularnewline
41 & 0.301450617357502 & 0.602901234715003 & 0.698549382642499 \tabularnewline
42 & 0.286455438046057 & 0.572910876092115 & 0.713544561953943 \tabularnewline
43 & 0.435541446235883 & 0.871082892471766 & 0.564458553764117 \tabularnewline
44 & 0.415433132230912 & 0.830866264461824 & 0.584566867769088 \tabularnewline
45 & 0.542195269251776 & 0.915609461496449 & 0.457804730748224 \tabularnewline
46 & 0.522056836749388 & 0.955886326501224 & 0.477943163250612 \tabularnewline
47 & 0.47364638701122 & 0.947292774022439 & 0.52635361298878 \tabularnewline
48 & 0.452291669237416 & 0.904583338474831 & 0.547708330762584 \tabularnewline
49 & 0.407538790971586 & 0.815077581943172 & 0.592461209028414 \tabularnewline
50 & 0.43722416926301 & 0.874448338526021 & 0.562775830736989 \tabularnewline
51 & 0.391808053845446 & 0.783616107690893 & 0.608191946154554 \tabularnewline
52 & 0.380999284658337 & 0.761998569316675 & 0.619000715341663 \tabularnewline
53 & 0.396906123426278 & 0.793812246852556 & 0.603093876573722 \tabularnewline
54 & 0.361261334374302 & 0.722522668748604 & 0.638738665625698 \tabularnewline
55 & 0.328315640103689 & 0.656631280207378 & 0.671684359896311 \tabularnewline
56 & 0.393607357744449 & 0.787214715488898 & 0.606392642255551 \tabularnewline
57 & 0.364575816241289 & 0.729151632482579 & 0.63542418375871 \tabularnewline
58 & 0.338265999369801 & 0.676531998739601 & 0.661734000630199 \tabularnewline
59 & 0.362821055711067 & 0.725642111422135 & 0.637178944288933 \tabularnewline
60 & 0.376761677406318 & 0.753523354812636 & 0.623238322593682 \tabularnewline
61 & 0.339554166622296 & 0.679108333244592 & 0.660445833377704 \tabularnewline
62 & 0.365102124180684 & 0.730204248361367 & 0.634897875819316 \tabularnewline
63 & 0.342852339021639 & 0.685704678043277 & 0.657147660978361 \tabularnewline
64 & 0.325835919310847 & 0.651671838621693 & 0.674164080689153 \tabularnewline
65 & 0.315280798095751 & 0.630561596191501 & 0.684719201904249 \tabularnewline
66 & 0.353055123976223 & 0.706110247952445 & 0.646944876023777 \tabularnewline
67 & 0.332784009297633 & 0.665568018595266 & 0.667215990702367 \tabularnewline
68 & 0.315929906455077 & 0.631859812910154 & 0.684070093544923 \tabularnewline
69 & 0.281545762285862 & 0.563091524571724 & 0.718454237714138 \tabularnewline
70 & 0.350648190922135 & 0.701296381844271 & 0.649351809077865 \tabularnewline
71 & 0.358101608601683 & 0.716203217203365 & 0.641898391398317 \tabularnewline
72 & 0.322649604104692 & 0.645299208209384 & 0.677350395895308 \tabularnewline
73 & 0.286183169488843 & 0.572366338977686 & 0.713816830511157 \tabularnewline
74 & 0.409110898200205 & 0.81822179640041 & 0.590889101799795 \tabularnewline
75 & 0.402350299755772 & 0.804700599511545 & 0.597649700244228 \tabularnewline
76 & 0.448365790134446 & 0.896731580268892 & 0.551634209865554 \tabularnewline
77 & 0.407913247307454 & 0.815826494614909 & 0.592086752692546 \tabularnewline
78 & 0.408623216915154 & 0.817246433830309 & 0.591376783084846 \tabularnewline
79 & 0.439655040339346 & 0.879310080678693 & 0.560344959660653 \tabularnewline
80 & 0.43206962514834 & 0.86413925029668 & 0.56793037485166 \tabularnewline
81 & 0.499073953632882 & 0.998147907265764 & 0.500926046367118 \tabularnewline
82 & 0.584849214505793 & 0.830301570988413 & 0.415150785494207 \tabularnewline
83 & 0.552310368784654 & 0.895379262430692 & 0.447689631215346 \tabularnewline
84 & 0.590331923733437 & 0.819336152533125 & 0.409668076266562 \tabularnewline
85 & 0.56303703316797 & 0.87392593366406 & 0.43696296683203 \tabularnewline
86 & 0.555206261614067 & 0.889587476771866 & 0.444793738385933 \tabularnewline
87 & 0.611108593604879 & 0.777782812790242 & 0.388891406395121 \tabularnewline
88 & 0.589415706355838 & 0.821168587288324 & 0.410584293644162 \tabularnewline
89 & 0.573299892690335 & 0.853400214619329 & 0.426700107309665 \tabularnewline
90 & 0.599822121830048 & 0.800355756339905 & 0.400177878169952 \tabularnewline
91 & 0.572608763299256 & 0.854782473401488 & 0.427391236700744 \tabularnewline
92 & 0.552747352579405 & 0.894505294841189 & 0.447252647420595 \tabularnewline
93 & 0.510201786404178 & 0.979596427191643 & 0.489798213595822 \tabularnewline
94 & 0.515777663586396 & 0.968444672827208 & 0.484222336413604 \tabularnewline
95 & 0.512843945370601 & 0.974312109258799 & 0.487156054629399 \tabularnewline
96 & 0.487077392994379 & 0.974154785988758 & 0.512922607005621 \tabularnewline
97 & 0.471487054896834 & 0.942974109793668 & 0.528512945103166 \tabularnewline
98 & 0.480639023983213 & 0.961278047966426 & 0.519360976016787 \tabularnewline
99 & 0.543596468080476 & 0.912807063839048 & 0.456403531919524 \tabularnewline
100 & 0.610831217164696 & 0.778337565670608 & 0.389168782835304 \tabularnewline
101 & 0.56912825433622 & 0.861743491327559 & 0.43087174566378 \tabularnewline
102 & 0.591326396063033 & 0.817347207873935 & 0.408673603936967 \tabularnewline
103 & 0.545800303506264 & 0.908399392987472 & 0.454199696493736 \tabularnewline
104 & 0.527581061127085 & 0.944837877745829 & 0.472418938872915 \tabularnewline
105 & 0.488673182856872 & 0.977346365713744 & 0.511326817143128 \tabularnewline
106 & 0.472830274022163 & 0.945660548044325 & 0.527169725977837 \tabularnewline
107 & 0.440269779991028 & 0.880539559982056 & 0.559730220008972 \tabularnewline
108 & 0.54913709689504 & 0.901725806209919 & 0.45086290310496 \tabularnewline
109 & 0.577864873667013 & 0.844270252665974 & 0.422135126332987 \tabularnewline
110 & 0.561691377870846 & 0.876617244258309 & 0.438308622129154 \tabularnewline
111 & 0.531584415378256 & 0.936831169243489 & 0.468415584621744 \tabularnewline
112 & 0.515945971875828 & 0.968108056248343 & 0.484054028124172 \tabularnewline
113 & 0.473719382775027 & 0.947438765550053 & 0.526280617224973 \tabularnewline
114 & 0.509161145997296 & 0.981677708005409 & 0.490838854002704 \tabularnewline
115 & 0.475376126886563 & 0.950752253773125 & 0.524623873113437 \tabularnewline
116 & 0.571518113376489 & 0.856963773247023 & 0.428481886623511 \tabularnewline
117 & 0.521841878620007 & 0.956316242759986 & 0.478158121379993 \tabularnewline
118 & 0.490301996713305 & 0.980603993426611 & 0.509698003286695 \tabularnewline
119 & 0.456536934317326 & 0.913073868634653 & 0.543463065682674 \tabularnewline
120 & 0.41575930861337 & 0.83151861722674 & 0.58424069138663 \tabularnewline
121 & 0.391577963603334 & 0.783155927206667 & 0.608422036396666 \tabularnewline
122 & 0.375689533993541 & 0.751379067987083 & 0.624310466006459 \tabularnewline
123 & 0.328892964499561 & 0.657785928999121 & 0.671107035500439 \tabularnewline
124 & 0.285630359203816 & 0.571260718407631 & 0.714369640796184 \tabularnewline
125 & 0.27388046610647 & 0.547760932212941 & 0.72611953389353 \tabularnewline
126 & 0.281875308870403 & 0.563750617740806 & 0.718124691129597 \tabularnewline
127 & 0.38010464293746 & 0.76020928587492 & 0.61989535706254 \tabularnewline
128 & 0.336961936765404 & 0.673923873530809 & 0.663038063234596 \tabularnewline
129 & 0.290950541374617 & 0.581901082749234 & 0.709049458625383 \tabularnewline
130 & 0.271576847635006 & 0.543153695270012 & 0.728423152364994 \tabularnewline
131 & 0.290544407064318 & 0.581088814128637 & 0.709455592935682 \tabularnewline
132 & 0.251061559149759 & 0.502123118299518 & 0.748938440850241 \tabularnewline
133 & 0.213002101947449 & 0.426004203894898 & 0.786997898052551 \tabularnewline
134 & 0.192745956288083 & 0.385491912576166 & 0.807254043711917 \tabularnewline
135 & 0.285147515485294 & 0.570295030970588 & 0.714852484514706 \tabularnewline
136 & 0.294744139826423 & 0.589488279652845 & 0.705255860173577 \tabularnewline
137 & 0.243251066485509 & 0.486502132971018 & 0.756748933514491 \tabularnewline
138 & 0.291025803546897 & 0.582051607093794 & 0.708974196453103 \tabularnewline
139 & 0.378490285763518 & 0.756980571527036 & 0.621509714236482 \tabularnewline
140 & 0.330852612569226 & 0.661705225138453 & 0.669147387430774 \tabularnewline
141 & 0.298582498012152 & 0.597164996024304 & 0.701417501987848 \tabularnewline
142 & 0.283228513011012 & 0.566457026022023 & 0.716771486988988 \tabularnewline
143 & 0.41232299819271 & 0.82464599638542 & 0.58767700180729 \tabularnewline
144 & 0.405938446224708 & 0.811876892449416 & 0.594061553775292 \tabularnewline
145 & 0.491062190757217 & 0.982124381514434 & 0.508937809242783 \tabularnewline
146 & 0.478422948252223 & 0.956845896504445 & 0.521577051747777 \tabularnewline
147 & 0.403716844162619 & 0.807433688325238 & 0.596283155837381 \tabularnewline
148 & 0.405951139778303 & 0.811902279556605 & 0.594048860221697 \tabularnewline
149 & 0.652108337271756 & 0.695783325456489 & 0.347891662728244 \tabularnewline
150 & 0.903626485334797 & 0.192747029330406 & 0.0963735146652028 \tabularnewline
151 & 0.813855985700118 & 0.372288028599765 & 0.186144014299882 \tabularnewline
152 & 0.750878167187102 & 0.498243665625796 & 0.249121832812898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&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]10[/C][C]0.126150516323535[/C][C]0.25230103264707[/C][C]0.873849483676465[/C][/ROW]
[ROW][C]11[/C][C]0.440871949746094[/C][C]0.881743899492188[/C][C]0.559128050253906[/C][/ROW]
[ROW][C]12[/C][C]0.324505619118183[/C][C]0.649011238236366[/C][C]0.675494380881817[/C][/ROW]
[ROW][C]13[/C][C]0.445585132443505[/C][C]0.891170264887009[/C][C]0.554414867556495[/C][/ROW]
[ROW][C]14[/C][C]0.329357884983752[/C][C]0.658715769967503[/C][C]0.670642115016248[/C][/ROW]
[ROW][C]15[/C][C]0.278070523894409[/C][C]0.556141047788818[/C][C]0.721929476105591[/C][/ROW]
[ROW][C]16[/C][C]0.347401864103978[/C][C]0.694803728207956[/C][C]0.652598135896022[/C][/ROW]
[ROW][C]17[/C][C]0.271886994240045[/C][C]0.543773988480089[/C][C]0.728113005759955[/C][/ROW]
[ROW][C]18[/C][C]0.197847913479765[/C][C]0.395695826959529[/C][C]0.802152086520236[/C][/ROW]
[ROW][C]19[/C][C]0.164709794443835[/C][C]0.329419588887669[/C][C]0.835290205556165[/C][/ROW]
[ROW][C]20[/C][C]0.199443931638385[/C][C]0.398887863276769[/C][C]0.800556068361615[/C][/ROW]
[ROW][C]21[/C][C]0.444025130791636[/C][C]0.888050261583272[/C][C]0.555974869208364[/C][/ROW]
[ROW][C]22[/C][C]0.489925743679337[/C][C]0.979851487358674[/C][C]0.510074256320663[/C][/ROW]
[ROW][C]23[/C][C]0.444406466972361[/C][C]0.888812933944721[/C][C]0.555593533027639[/C][/ROW]
[ROW][C]24[/C][C]0.425639313623918[/C][C]0.851278627247836[/C][C]0.574360686376082[/C][/ROW]
[ROW][C]25[/C][C]0.436022925203896[/C][C]0.872045850407792[/C][C]0.563977074796104[/C][/ROW]
[ROW][C]26[/C][C]0.39422884913996[/C][C]0.78845769827992[/C][C]0.60577115086004[/C][/ROW]
[ROW][C]27[/C][C]0.358553362465273[/C][C]0.717106724930546[/C][C]0.641446637534727[/C][/ROW]
[ROW][C]28[/C][C]0.299225670262192[/C][C]0.598451340524384[/C][C]0.700774329737808[/C][/ROW]
[ROW][C]29[/C][C]0.258497479735433[/C][C]0.516994959470866[/C][C]0.741502520264567[/C][/ROW]
[ROW][C]30[/C][C]0.267284611694526[/C][C]0.534569223389051[/C][C]0.732715388305474[/C][/ROW]
[ROW][C]31[/C][C]0.257478038282921[/C][C]0.514956076565841[/C][C]0.742521961717079[/C][/ROW]
[ROW][C]32[/C][C]0.226258158121096[/C][C]0.452516316242191[/C][C]0.773741841878904[/C][/ROW]
[ROW][C]33[/C][C]0.226619941212204[/C][C]0.453239882424407[/C][C]0.773380058787796[/C][/ROW]
[ROW][C]34[/C][C]0.184455618114955[/C][C]0.36891123622991[/C][C]0.815544381885045[/C][/ROW]
[ROW][C]35[/C][C]0.239708326524051[/C][C]0.479416653048101[/C][C]0.760291673475949[/C][/ROW]
[ROW][C]36[/C][C]0.226099185204705[/C][C]0.45219837040941[/C][C]0.773900814795295[/C][/ROW]
[ROW][C]37[/C][C]0.202550086461795[/C][C]0.405100172923591[/C][C]0.797449913538204[/C][/ROW]
[ROW][C]38[/C][C]0.28857687343888[/C][C]0.577153746877761[/C][C]0.71142312656112[/C][/ROW]
[ROW][C]39[/C][C]0.284280237893984[/C][C]0.568560475787969[/C][C]0.715719762106016[/C][/ROW]
[ROW][C]40[/C][C]0.290142524034671[/C][C]0.580285048069342[/C][C]0.709857475965329[/C][/ROW]
[ROW][C]41[/C][C]0.301450617357502[/C][C]0.602901234715003[/C][C]0.698549382642499[/C][/ROW]
[ROW][C]42[/C][C]0.286455438046057[/C][C]0.572910876092115[/C][C]0.713544561953943[/C][/ROW]
[ROW][C]43[/C][C]0.435541446235883[/C][C]0.871082892471766[/C][C]0.564458553764117[/C][/ROW]
[ROW][C]44[/C][C]0.415433132230912[/C][C]0.830866264461824[/C][C]0.584566867769088[/C][/ROW]
[ROW][C]45[/C][C]0.542195269251776[/C][C]0.915609461496449[/C][C]0.457804730748224[/C][/ROW]
[ROW][C]46[/C][C]0.522056836749388[/C][C]0.955886326501224[/C][C]0.477943163250612[/C][/ROW]
[ROW][C]47[/C][C]0.47364638701122[/C][C]0.947292774022439[/C][C]0.52635361298878[/C][/ROW]
[ROW][C]48[/C][C]0.452291669237416[/C][C]0.904583338474831[/C][C]0.547708330762584[/C][/ROW]
[ROW][C]49[/C][C]0.407538790971586[/C][C]0.815077581943172[/C][C]0.592461209028414[/C][/ROW]
[ROW][C]50[/C][C]0.43722416926301[/C][C]0.874448338526021[/C][C]0.562775830736989[/C][/ROW]
[ROW][C]51[/C][C]0.391808053845446[/C][C]0.783616107690893[/C][C]0.608191946154554[/C][/ROW]
[ROW][C]52[/C][C]0.380999284658337[/C][C]0.761998569316675[/C][C]0.619000715341663[/C][/ROW]
[ROW][C]53[/C][C]0.396906123426278[/C][C]0.793812246852556[/C][C]0.603093876573722[/C][/ROW]
[ROW][C]54[/C][C]0.361261334374302[/C][C]0.722522668748604[/C][C]0.638738665625698[/C][/ROW]
[ROW][C]55[/C][C]0.328315640103689[/C][C]0.656631280207378[/C][C]0.671684359896311[/C][/ROW]
[ROW][C]56[/C][C]0.393607357744449[/C][C]0.787214715488898[/C][C]0.606392642255551[/C][/ROW]
[ROW][C]57[/C][C]0.364575816241289[/C][C]0.729151632482579[/C][C]0.63542418375871[/C][/ROW]
[ROW][C]58[/C][C]0.338265999369801[/C][C]0.676531998739601[/C][C]0.661734000630199[/C][/ROW]
[ROW][C]59[/C][C]0.362821055711067[/C][C]0.725642111422135[/C][C]0.637178944288933[/C][/ROW]
[ROW][C]60[/C][C]0.376761677406318[/C][C]0.753523354812636[/C][C]0.623238322593682[/C][/ROW]
[ROW][C]61[/C][C]0.339554166622296[/C][C]0.679108333244592[/C][C]0.660445833377704[/C][/ROW]
[ROW][C]62[/C][C]0.365102124180684[/C][C]0.730204248361367[/C][C]0.634897875819316[/C][/ROW]
[ROW][C]63[/C][C]0.342852339021639[/C][C]0.685704678043277[/C][C]0.657147660978361[/C][/ROW]
[ROW][C]64[/C][C]0.325835919310847[/C][C]0.651671838621693[/C][C]0.674164080689153[/C][/ROW]
[ROW][C]65[/C][C]0.315280798095751[/C][C]0.630561596191501[/C][C]0.684719201904249[/C][/ROW]
[ROW][C]66[/C][C]0.353055123976223[/C][C]0.706110247952445[/C][C]0.646944876023777[/C][/ROW]
[ROW][C]67[/C][C]0.332784009297633[/C][C]0.665568018595266[/C][C]0.667215990702367[/C][/ROW]
[ROW][C]68[/C][C]0.315929906455077[/C][C]0.631859812910154[/C][C]0.684070093544923[/C][/ROW]
[ROW][C]69[/C][C]0.281545762285862[/C][C]0.563091524571724[/C][C]0.718454237714138[/C][/ROW]
[ROW][C]70[/C][C]0.350648190922135[/C][C]0.701296381844271[/C][C]0.649351809077865[/C][/ROW]
[ROW][C]71[/C][C]0.358101608601683[/C][C]0.716203217203365[/C][C]0.641898391398317[/C][/ROW]
[ROW][C]72[/C][C]0.322649604104692[/C][C]0.645299208209384[/C][C]0.677350395895308[/C][/ROW]
[ROW][C]73[/C][C]0.286183169488843[/C][C]0.572366338977686[/C][C]0.713816830511157[/C][/ROW]
[ROW][C]74[/C][C]0.409110898200205[/C][C]0.81822179640041[/C][C]0.590889101799795[/C][/ROW]
[ROW][C]75[/C][C]0.402350299755772[/C][C]0.804700599511545[/C][C]0.597649700244228[/C][/ROW]
[ROW][C]76[/C][C]0.448365790134446[/C][C]0.896731580268892[/C][C]0.551634209865554[/C][/ROW]
[ROW][C]77[/C][C]0.407913247307454[/C][C]0.815826494614909[/C][C]0.592086752692546[/C][/ROW]
[ROW][C]78[/C][C]0.408623216915154[/C][C]0.817246433830309[/C][C]0.591376783084846[/C][/ROW]
[ROW][C]79[/C][C]0.439655040339346[/C][C]0.879310080678693[/C][C]0.560344959660653[/C][/ROW]
[ROW][C]80[/C][C]0.43206962514834[/C][C]0.86413925029668[/C][C]0.56793037485166[/C][/ROW]
[ROW][C]81[/C][C]0.499073953632882[/C][C]0.998147907265764[/C][C]0.500926046367118[/C][/ROW]
[ROW][C]82[/C][C]0.584849214505793[/C][C]0.830301570988413[/C][C]0.415150785494207[/C][/ROW]
[ROW][C]83[/C][C]0.552310368784654[/C][C]0.895379262430692[/C][C]0.447689631215346[/C][/ROW]
[ROW][C]84[/C][C]0.590331923733437[/C][C]0.819336152533125[/C][C]0.409668076266562[/C][/ROW]
[ROW][C]85[/C][C]0.56303703316797[/C][C]0.87392593366406[/C][C]0.43696296683203[/C][/ROW]
[ROW][C]86[/C][C]0.555206261614067[/C][C]0.889587476771866[/C][C]0.444793738385933[/C][/ROW]
[ROW][C]87[/C][C]0.611108593604879[/C][C]0.777782812790242[/C][C]0.388891406395121[/C][/ROW]
[ROW][C]88[/C][C]0.589415706355838[/C][C]0.821168587288324[/C][C]0.410584293644162[/C][/ROW]
[ROW][C]89[/C][C]0.573299892690335[/C][C]0.853400214619329[/C][C]0.426700107309665[/C][/ROW]
[ROW][C]90[/C][C]0.599822121830048[/C][C]0.800355756339905[/C][C]0.400177878169952[/C][/ROW]
[ROW][C]91[/C][C]0.572608763299256[/C][C]0.854782473401488[/C][C]0.427391236700744[/C][/ROW]
[ROW][C]92[/C][C]0.552747352579405[/C][C]0.894505294841189[/C][C]0.447252647420595[/C][/ROW]
[ROW][C]93[/C][C]0.510201786404178[/C][C]0.979596427191643[/C][C]0.489798213595822[/C][/ROW]
[ROW][C]94[/C][C]0.515777663586396[/C][C]0.968444672827208[/C][C]0.484222336413604[/C][/ROW]
[ROW][C]95[/C][C]0.512843945370601[/C][C]0.974312109258799[/C][C]0.487156054629399[/C][/ROW]
[ROW][C]96[/C][C]0.487077392994379[/C][C]0.974154785988758[/C][C]0.512922607005621[/C][/ROW]
[ROW][C]97[/C][C]0.471487054896834[/C][C]0.942974109793668[/C][C]0.528512945103166[/C][/ROW]
[ROW][C]98[/C][C]0.480639023983213[/C][C]0.961278047966426[/C][C]0.519360976016787[/C][/ROW]
[ROW][C]99[/C][C]0.543596468080476[/C][C]0.912807063839048[/C][C]0.456403531919524[/C][/ROW]
[ROW][C]100[/C][C]0.610831217164696[/C][C]0.778337565670608[/C][C]0.389168782835304[/C][/ROW]
[ROW][C]101[/C][C]0.56912825433622[/C][C]0.861743491327559[/C][C]0.43087174566378[/C][/ROW]
[ROW][C]102[/C][C]0.591326396063033[/C][C]0.817347207873935[/C][C]0.408673603936967[/C][/ROW]
[ROW][C]103[/C][C]0.545800303506264[/C][C]0.908399392987472[/C][C]0.454199696493736[/C][/ROW]
[ROW][C]104[/C][C]0.527581061127085[/C][C]0.944837877745829[/C][C]0.472418938872915[/C][/ROW]
[ROW][C]105[/C][C]0.488673182856872[/C][C]0.977346365713744[/C][C]0.511326817143128[/C][/ROW]
[ROW][C]106[/C][C]0.472830274022163[/C][C]0.945660548044325[/C][C]0.527169725977837[/C][/ROW]
[ROW][C]107[/C][C]0.440269779991028[/C][C]0.880539559982056[/C][C]0.559730220008972[/C][/ROW]
[ROW][C]108[/C][C]0.54913709689504[/C][C]0.901725806209919[/C][C]0.45086290310496[/C][/ROW]
[ROW][C]109[/C][C]0.577864873667013[/C][C]0.844270252665974[/C][C]0.422135126332987[/C][/ROW]
[ROW][C]110[/C][C]0.561691377870846[/C][C]0.876617244258309[/C][C]0.438308622129154[/C][/ROW]
[ROW][C]111[/C][C]0.531584415378256[/C][C]0.936831169243489[/C][C]0.468415584621744[/C][/ROW]
[ROW][C]112[/C][C]0.515945971875828[/C][C]0.968108056248343[/C][C]0.484054028124172[/C][/ROW]
[ROW][C]113[/C][C]0.473719382775027[/C][C]0.947438765550053[/C][C]0.526280617224973[/C][/ROW]
[ROW][C]114[/C][C]0.509161145997296[/C][C]0.981677708005409[/C][C]0.490838854002704[/C][/ROW]
[ROW][C]115[/C][C]0.475376126886563[/C][C]0.950752253773125[/C][C]0.524623873113437[/C][/ROW]
[ROW][C]116[/C][C]0.571518113376489[/C][C]0.856963773247023[/C][C]0.428481886623511[/C][/ROW]
[ROW][C]117[/C][C]0.521841878620007[/C][C]0.956316242759986[/C][C]0.478158121379993[/C][/ROW]
[ROW][C]118[/C][C]0.490301996713305[/C][C]0.980603993426611[/C][C]0.509698003286695[/C][/ROW]
[ROW][C]119[/C][C]0.456536934317326[/C][C]0.913073868634653[/C][C]0.543463065682674[/C][/ROW]
[ROW][C]120[/C][C]0.41575930861337[/C][C]0.83151861722674[/C][C]0.58424069138663[/C][/ROW]
[ROW][C]121[/C][C]0.391577963603334[/C][C]0.783155927206667[/C][C]0.608422036396666[/C][/ROW]
[ROW][C]122[/C][C]0.375689533993541[/C][C]0.751379067987083[/C][C]0.624310466006459[/C][/ROW]
[ROW][C]123[/C][C]0.328892964499561[/C][C]0.657785928999121[/C][C]0.671107035500439[/C][/ROW]
[ROW][C]124[/C][C]0.285630359203816[/C][C]0.571260718407631[/C][C]0.714369640796184[/C][/ROW]
[ROW][C]125[/C][C]0.27388046610647[/C][C]0.547760932212941[/C][C]0.72611953389353[/C][/ROW]
[ROW][C]126[/C][C]0.281875308870403[/C][C]0.563750617740806[/C][C]0.718124691129597[/C][/ROW]
[ROW][C]127[/C][C]0.38010464293746[/C][C]0.76020928587492[/C][C]0.61989535706254[/C][/ROW]
[ROW][C]128[/C][C]0.336961936765404[/C][C]0.673923873530809[/C][C]0.663038063234596[/C][/ROW]
[ROW][C]129[/C][C]0.290950541374617[/C][C]0.581901082749234[/C][C]0.709049458625383[/C][/ROW]
[ROW][C]130[/C][C]0.271576847635006[/C][C]0.543153695270012[/C][C]0.728423152364994[/C][/ROW]
[ROW][C]131[/C][C]0.290544407064318[/C][C]0.581088814128637[/C][C]0.709455592935682[/C][/ROW]
[ROW][C]132[/C][C]0.251061559149759[/C][C]0.502123118299518[/C][C]0.748938440850241[/C][/ROW]
[ROW][C]133[/C][C]0.213002101947449[/C][C]0.426004203894898[/C][C]0.786997898052551[/C][/ROW]
[ROW][C]134[/C][C]0.192745956288083[/C][C]0.385491912576166[/C][C]0.807254043711917[/C][/ROW]
[ROW][C]135[/C][C]0.285147515485294[/C][C]0.570295030970588[/C][C]0.714852484514706[/C][/ROW]
[ROW][C]136[/C][C]0.294744139826423[/C][C]0.589488279652845[/C][C]0.705255860173577[/C][/ROW]
[ROW][C]137[/C][C]0.243251066485509[/C][C]0.486502132971018[/C][C]0.756748933514491[/C][/ROW]
[ROW][C]138[/C][C]0.291025803546897[/C][C]0.582051607093794[/C][C]0.708974196453103[/C][/ROW]
[ROW][C]139[/C][C]0.378490285763518[/C][C]0.756980571527036[/C][C]0.621509714236482[/C][/ROW]
[ROW][C]140[/C][C]0.330852612569226[/C][C]0.661705225138453[/C][C]0.669147387430774[/C][/ROW]
[ROW][C]141[/C][C]0.298582498012152[/C][C]0.597164996024304[/C][C]0.701417501987848[/C][/ROW]
[ROW][C]142[/C][C]0.283228513011012[/C][C]0.566457026022023[/C][C]0.716771486988988[/C][/ROW]
[ROW][C]143[/C][C]0.41232299819271[/C][C]0.82464599638542[/C][C]0.58767700180729[/C][/ROW]
[ROW][C]144[/C][C]0.405938446224708[/C][C]0.811876892449416[/C][C]0.594061553775292[/C][/ROW]
[ROW][C]145[/C][C]0.491062190757217[/C][C]0.982124381514434[/C][C]0.508937809242783[/C][/ROW]
[ROW][C]146[/C][C]0.478422948252223[/C][C]0.956845896504445[/C][C]0.521577051747777[/C][/ROW]
[ROW][C]147[/C][C]0.403716844162619[/C][C]0.807433688325238[/C][C]0.596283155837381[/C][/ROW]
[ROW][C]148[/C][C]0.405951139778303[/C][C]0.811902279556605[/C][C]0.594048860221697[/C][/ROW]
[ROW][C]149[/C][C]0.652108337271756[/C][C]0.695783325456489[/C][C]0.347891662728244[/C][/ROW]
[ROW][C]150[/C][C]0.903626485334797[/C][C]0.192747029330406[/C][C]0.0963735146652028[/C][/ROW]
[ROW][C]151[/C][C]0.813855985700118[/C][C]0.372288028599765[/C][C]0.186144014299882[/C][/ROW]
[ROW][C]152[/C][C]0.750878167187102[/C][C]0.498243665625796[/C][C]0.249121832812898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&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
100.1261505163235350.252301032647070.873849483676465
110.4408719497460940.8817438994921880.559128050253906
120.3245056191181830.6490112382363660.675494380881817
130.4455851324435050.8911702648870090.554414867556495
140.3293578849837520.6587157699675030.670642115016248
150.2780705238944090.5561410477888180.721929476105591
160.3474018641039780.6948037282079560.652598135896022
170.2718869942400450.5437739884800890.728113005759955
180.1978479134797650.3956958269595290.802152086520236
190.1647097944438350.3294195888876690.835290205556165
200.1994439316383850.3988878632767690.800556068361615
210.4440251307916360.8880502615832720.555974869208364
220.4899257436793370.9798514873586740.510074256320663
230.4444064669723610.8888129339447210.555593533027639
240.4256393136239180.8512786272478360.574360686376082
250.4360229252038960.8720458504077920.563977074796104
260.394228849139960.788457698279920.60577115086004
270.3585533624652730.7171067249305460.641446637534727
280.2992256702621920.5984513405243840.700774329737808
290.2584974797354330.5169949594708660.741502520264567
300.2672846116945260.5345692233890510.732715388305474
310.2574780382829210.5149560765658410.742521961717079
320.2262581581210960.4525163162421910.773741841878904
330.2266199412122040.4532398824244070.773380058787796
340.1844556181149550.368911236229910.815544381885045
350.2397083265240510.4794166530481010.760291673475949
360.2260991852047050.452198370409410.773900814795295
370.2025500864617950.4051001729235910.797449913538204
380.288576873438880.5771537468777610.71142312656112
390.2842802378939840.5685604757879690.715719762106016
400.2901425240346710.5802850480693420.709857475965329
410.3014506173575020.6029012347150030.698549382642499
420.2864554380460570.5729108760921150.713544561953943
430.4355414462358830.8710828924717660.564458553764117
440.4154331322309120.8308662644618240.584566867769088
450.5421952692517760.9156094614964490.457804730748224
460.5220568367493880.9558863265012240.477943163250612
470.473646387011220.9472927740224390.52635361298878
480.4522916692374160.9045833384748310.547708330762584
490.4075387909715860.8150775819431720.592461209028414
500.437224169263010.8744483385260210.562775830736989
510.3918080538454460.7836161076908930.608191946154554
520.3809992846583370.7619985693166750.619000715341663
530.3969061234262780.7938122468525560.603093876573722
540.3612613343743020.7225226687486040.638738665625698
550.3283156401036890.6566312802073780.671684359896311
560.3936073577444490.7872147154888980.606392642255551
570.3645758162412890.7291516324825790.63542418375871
580.3382659993698010.6765319987396010.661734000630199
590.3628210557110670.7256421114221350.637178944288933
600.3767616774063180.7535233548126360.623238322593682
610.3395541666222960.6791083332445920.660445833377704
620.3651021241806840.7302042483613670.634897875819316
630.3428523390216390.6857046780432770.657147660978361
640.3258359193108470.6516718386216930.674164080689153
650.3152807980957510.6305615961915010.684719201904249
660.3530551239762230.7061102479524450.646944876023777
670.3327840092976330.6655680185952660.667215990702367
680.3159299064550770.6318598129101540.684070093544923
690.2815457622858620.5630915245717240.718454237714138
700.3506481909221350.7012963818442710.649351809077865
710.3581016086016830.7162032172033650.641898391398317
720.3226496041046920.6452992082093840.677350395895308
730.2861831694888430.5723663389776860.713816830511157
740.4091108982002050.818221796400410.590889101799795
750.4023502997557720.8047005995115450.597649700244228
760.4483657901344460.8967315802688920.551634209865554
770.4079132473074540.8158264946149090.592086752692546
780.4086232169151540.8172464338303090.591376783084846
790.4396550403393460.8793100806786930.560344959660653
800.432069625148340.864139250296680.56793037485166
810.4990739536328820.9981479072657640.500926046367118
820.5848492145057930.8303015709884130.415150785494207
830.5523103687846540.8953792624306920.447689631215346
840.5903319237334370.8193361525331250.409668076266562
850.563037033167970.873925933664060.43696296683203
860.5552062616140670.8895874767718660.444793738385933
870.6111085936048790.7777828127902420.388891406395121
880.5894157063558380.8211685872883240.410584293644162
890.5732998926903350.8534002146193290.426700107309665
900.5998221218300480.8003557563399050.400177878169952
910.5726087632992560.8547824734014880.427391236700744
920.5527473525794050.8945052948411890.447252647420595
930.5102017864041780.9795964271916430.489798213595822
940.5157776635863960.9684446728272080.484222336413604
950.5128439453706010.9743121092587990.487156054629399
960.4870773929943790.9741547859887580.512922607005621
970.4714870548968340.9429741097936680.528512945103166
980.4806390239832130.9612780479664260.519360976016787
990.5435964680804760.9128070638390480.456403531919524
1000.6108312171646960.7783375656706080.389168782835304
1010.569128254336220.8617434913275590.43087174566378
1020.5913263960630330.8173472078739350.408673603936967
1030.5458003035062640.9083993929874720.454199696493736
1040.5275810611270850.9448378777458290.472418938872915
1050.4886731828568720.9773463657137440.511326817143128
1060.4728302740221630.9456605480443250.527169725977837
1070.4402697799910280.8805395599820560.559730220008972
1080.549137096895040.9017258062099190.45086290310496
1090.5778648736670130.8442702526659740.422135126332987
1100.5616913778708460.8766172442583090.438308622129154
1110.5315844153782560.9368311692434890.468415584621744
1120.5159459718758280.9681080562483430.484054028124172
1130.4737193827750270.9474387655500530.526280617224973
1140.5091611459972960.9816777080054090.490838854002704
1150.4753761268865630.9507522537731250.524623873113437
1160.5715181133764890.8569637732470230.428481886623511
1170.5218418786200070.9563162427599860.478158121379993
1180.4903019967133050.9806039934266110.509698003286695
1190.4565369343173260.9130738686346530.543463065682674
1200.415759308613370.831518617226740.58424069138663
1210.3915779636033340.7831559272066670.608422036396666
1220.3756895339935410.7513790679870830.624310466006459
1230.3288929644995610.6577859289991210.671107035500439
1240.2856303592038160.5712607184076310.714369640796184
1250.273880466106470.5477609322129410.72611953389353
1260.2818753088704030.5637506177408060.718124691129597
1270.380104642937460.760209285874920.61989535706254
1280.3369619367654040.6739238735308090.663038063234596
1290.2909505413746170.5819010827492340.709049458625383
1300.2715768476350060.5431536952700120.728423152364994
1310.2905444070643180.5810888141286370.709455592935682
1320.2510615591497590.5021231182995180.748938440850241
1330.2130021019474490.4260042038948980.786997898052551
1340.1927459562880830.3854919125761660.807254043711917
1350.2851475154852940.5702950309705880.714852484514706
1360.2947441398264230.5894882796528450.705255860173577
1370.2432510664855090.4865021329710180.756748933514491
1380.2910258035468970.5820516070937940.708974196453103
1390.3784902857635180.7569805715270360.621509714236482
1400.3308526125692260.6617052251384530.669147387430774
1410.2985824980121520.5971649960243040.701417501987848
1420.2832285130110120.5664570260220230.716771486988988
1430.412322998192710.824645996385420.58767700180729
1440.4059384462247080.8118768924494160.594061553775292
1450.4910621907572170.9821243815144340.508937809242783
1460.4784229482522230.9568458965044450.521577051747777
1470.4037168441626190.8074336883252380.596283155837381
1480.4059511397783030.8119022795566050.594048860221697
1490.6521083372717560.6957833254564890.347891662728244
1500.9036264853347970.1927470293304060.0963735146652028
1510.8138559857001180.3722880285997650.186144014299882
1520.7508781671871020.4982436656257960.249121832812898






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

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=155718&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=155718&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=155718&T=6

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


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

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


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

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