Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 15:27:04 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t13219073169obcdupn5n8h9sz.htm/, Retrieved Thu, 31 Oct 2024 23:25:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145966, Retrieved Thu, 31 Oct 2024 23:25:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact235
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R  D  [Multiple Regression] [WS7] [2011-11-21 20:04:04] [8501ca4b76170905b8a207a77f626994]
-    D    [Multiple Regression] [WS7 (2)] [2011-11-21 20:07:43] [8501ca4b76170905b8a207a77f626994]
-           [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:16:12] [f722e8e78b9e5c5ebaa2263f273aa636]
-    D          [Multiple Regression] [Workshop7_Tutorial] [2011-11-21 20:27:04] [3e64eea457df40fcb7af8f28e1ee6256] [Current]
- R P             [Multiple Regression] [Paper: Multiple R...] [2011-12-21 16:51:41] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 21:45:13] [f722e8e78b9e5c5ebaa2263f273aa636]
- R PD              [Multiple Regression] [Paper: Multiple R...] [2011-12-22 23:01:32] [f722e8e78b9e5c5ebaa2263f273aa636]
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Dataseries X:
46	26	99	47
48	20	77	24
37	24	90	31
75	25	96	42
31	15	41	24
18	16	64	10
79	20	76	85
16	18	67	9
38	19	72	32
24	20	75	36
65	30	113	45
74	37	139	36
43	23	76	28
42	36	123	54
55	29	110	39
121	35	133	70
42	24	92	50
102	22	83	55
36	19	72	32
50	30	115	44
48	27	99	46
56	26	92	80
19	15	56	25
32	30	120	30
77	28	107	41
90	24	90	40
81	21	78	45
55	27	103	45
34	21	81	30
38	30	114	52
53	30	115	53
48	33	118	36
63	30	113	57
25	20	75	17
56	27	103	68
37	25	93	46
83	30	114	73
50	20	76	34
26	8	27	22
108	24	92	58
55	25	96	62
41	25	92	32
49	21	76	38
31	21	79	23
49	21	57	26
96	26	99	85
42	26	82	22
55	30	113	44
70	34	129	62
39	30	110	36
53	18	78	36
24	4	12	7
209	31	114	72
17	18	67	18
58	14	52	27
27	20	76	48
58	36	138	50
114	24	92	55
75	26	93	59
51	22	83	39
86	31	118	68
77	21	77	57
62	31	122	40
60	26	99	47
39	24	92	39
35	15	58	32
86	19	73	32
102	28	103	40
49	24	92	42
35	18	69	26
33	25	95	33
28	20	76	19
44	25	95	35
37	24	92	41
33	23	88	27
45	25	95	53
57	20	76	55
58	23	87	29
36	22	84	25
42	25	95	33
30	18	69	27
67	30	115	76
53	22	83	37
59	25	47	38
25	8	28	22
39	21	79	30
36	22	83	27
114	24	92	63
54	30	98	48
70	27	103	33
51	24	89	37
49	25	95	42
42	21	78	31
51	24	92	47
51	24	92	52
27	20	76	36
29	20	67	40
54	24	92	53
92	40	151	56
72	22	83	69
63	31	118	43
41	26	98	51
111	20	76	30
14	19	71	12
45	15	57	35
91	21	79	36
29	22	83	41
64	24	92	52
32	19	75	21
65	24	95	26
42	23	88	49
55	27	99	39
10	1	0	6
53	24	91	35
25	11	32	17
33	27	101	25
66	22	84	71
16	0	0	6
35	17	60	47
19	8	25	9
76	24	90	52
35	31	115	38
46	24	92	21
29	20	71	21
34	8	27	11
25	22	83	25
48	33	126	54
38	33	125	38
50	31	119	68
65	33	127	56
72	35	133	71
23	21	79	39
29	20	76	21
194	24	92	53
114	29	109	78
15	20	76	14
86	27	100	70
50	24	87	29
33	26	97	47
50	26	95	36
72	12	48	21
81	21	80	69
54	24	91	42
63	21	79	48
69	30	114	55
39	32	120	19
49	24	89	39
67	29	111	51
0	0	0	0
10	0	0	4
1	0	0	0
2	0	0	0
0	0	0	0
0	0	0	0
58	20	74	38
72	27	107	51
0	0	0	0
4	0	0	0
5	0	0	2
20	5	15	13
5	1	4	5
27	23	82	20
2	0	0	0
33	16	54	29




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.01138573870643 + 0.269255495850061X1[t] + 0.404714263529591X2[t] + 0.176272052812395X3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  1.01138573870643 +  0.269255495850061X1[t] +  0.404714263529591X2[t] +  0.176272052812395X3[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  1.01138573870643 +  0.269255495850061X1[t] +  0.404714263529591X2[t] +  0.176272052812395X3[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 1.01138573870643 + 0.269255495850061X1[t] + 0.404714263529591X2[t] + 0.176272052812395X3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1.011385738706432.4605130.4110.6815880.340794
X10.2692554958500610.035397.608100
X20.4047142635295910.6593380.61380.5402070.270103
X30.1762720528123950.1720191.02470.307040.15352

\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) & 1.01138573870643 & 2.460513 & 0.411 & 0.681588 & 0.340794 \tabularnewline
X1 & 0.269255495850061 & 0.03539 & 7.6081 & 0 & 0 \tabularnewline
X2 & 0.404714263529591 & 0.659338 & 0.6138 & 0.540207 & 0.270103 \tabularnewline
X3 & 0.176272052812395 & 0.172019 & 1.0247 & 0.30704 & 0.15352 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&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]1.01138573870643[/C][C]2.460513[/C][C]0.411[/C][C]0.681588[/C][C]0.340794[/C][/ROW]
[ROW][C]X1[/C][C]0.269255495850061[/C][C]0.03539[/C][C]7.6081[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X2[/C][C]0.404714263529591[/C][C]0.659338[/C][C]0.6138[/C][C]0.540207[/C][C]0.270103[/C][/ROW]
[ROW][C]X3[/C][C]0.176272052812395[/C][C]0.172019[/C][C]1.0247[/C][C]0.30704[/C][C]0.15352[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=2

As an alternative you can also use a 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)1.011385738706432.4605130.4110.6815880.340794
X10.2692554958500610.035397.608100
X20.4047142635295910.6593380.61380.5402070.270103
X30.1762720528123950.1720191.02470.307040.15352







Multiple Linear Regression - Regression Statistics
Multiple R0.80587761427563
R-squared0.64943872919058
Adjusted R-squared0.642865705362904
F-TEST (value)98.8036474865693
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.5445604323912
Sum Squared Residuals21324.3000923411

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.80587761427563 \tabularnewline
R-squared & 0.64943872919058 \tabularnewline
Adjusted R-squared & 0.642865705362904 \tabularnewline
F-TEST (value) & 98.8036474865693 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.5445604323912 \tabularnewline
Sum Squared Residuals & 21324.3000923411 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.80587761427563[/C][/ROW]
[ROW][C]R-squared[/C][C]0.64943872919058[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.642865705362904[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]98.8036474865693[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.5445604323912[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]21324.3000923411[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.80587761427563
R-squared0.64943872919058
Adjusted R-squared0.642865705362904
F-TEST (value)98.8036474865693
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.5445604323912
Sum Squared Residuals21324.3000923411







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14741.37064262800565.62935737199436
22435.6028828766555-11.6028828766555
33136.5514661629844-5.55146616298437
44248.2455215856906-6.24552158569063
52422.65617422831041.34382577168965
61023.6148242604742-13.6148242604742
78543.77353119519541.226468804805
8924.4145579542705-15.4145579542705
93231.62425339056340.375746609436629
103628.78820687062937.2117931293707
114550.5731628426487-5.57316284264868
123660.4125355231286-24.4125355231286
132835.2944761351816-7.29447613518161
145448.57129254739885.42870745260122
153946.9470774621813-7.9470774621813
167071.200482984148-1.20048298414792
175038.250287747859511.7497122521405
185552.00974049649242.99025950350764
193231.08574239886320.91425760113675
204446.8868745105226-2.88687451052256
214642.31386788323543.68613211676464
228042.829293216819537.1707067831805
232522.06918907029552.93081092970446
243042.9216358492834-12.9216358492834
254151.9371679489159-10.9371679489159
264050.8220074430376-10.8220074430376
274545.0693005560495-0.069300556049528
284544.90374456543540.0962554345646405
293032.9431084095339-2.94310840953386
305243.47953650750948.52046349249056
315347.69464099807275.30535900192726
323648.0913224678484-12.0913224678484
335750.03465185094866.96534814905144
341729.0574623664794-12.0574623664794
356845.173000061285422.8269999387146
364637.48499658495118.51500341504886
377355.596033820762217.4039661792378
383435.9651218155433-1.96512181554327
392216.00908816497945.99091183502061
405856.02115047396351.97884952603654
416242.860411668689419.1395883313106
423238.385746515539-6.38574651553899
433836.10058058322281.8994194167772
442331.7827978163589-8.78279781635889
452632.7514115797873-6.7514115797873
468554.833417420508730.1665825794913
472237.2969957467947-15.2969957467947
484447.8806078841481-3.88060788414808
496256.35865022101575.64134977898434
503643.0437037921099-7.04370379210992
513636.3160038816591-0.316003881659058
52711.207639326975-4.20763932697499
537289.9269405613994-17.9269405613994
541824.6838134501205-6.68381345012054
552731.4603509336687-4.46035093366874
564829.772245410991918.2277545890081
575055.5234612731857-5.52346127318567
585557.6366834490638-2.63668344906382
595948.12141969078310.878580309217
603938.27771020813930.722289791860728
616857.513602783091510.4863972169085
625743.816006519836913.1839934801631
634051.7565590939396-11.7565590939396
644745.14021956990651.85978043009351
653937.44252126030931.55747873969072
663226.72982110952135.2701788904787
673244.7247892441787-12.7247892441787
684057.9634671339178-17.9634671339178
694240.13507621880991.86492378119012
702629.8829564810464-3.88295648104642
713336.7605187071757-3.76051870717569
721930.0415009068419-11.0415009068419
733539.7223291615264-4.72232916152636
744136.90401026860924.09598973139085
752734.7171858104297-7.71718581042974
765339.991584657376413.0084153426236
775537.849910286493717.1500897135063
782941.2723011538689-12.2723011538689
792534.4151498232008-9.41514982320076
803339.1838181698262-6.18381816982623
812728.5366790017961-1.53667900179611
827651.464217939973624.5357820600264
833738.8162211998394-1.81622119983939
843835.30010306428232.69989693571768
852215.91610472194176.08389527805828
863033.9368417831594-3.93684178315938
872734.2388777703884-7.23887777038836
886357.63668344906385.36331655093617
894844.96727159611213.0327284038879
903348.9425770031863-15.9425770031863
913740.1447710520728-3.14477105207282
924241.06860664077670.931393359223343
933134.5683362178972-3.56833621789716
944740.673587210516.32641278949
955240.6735872105111.32641278949
963629.77224541099196.22775458900813
974028.724307927380411.2756920726196
985341.481353698060211.5186463019398
995668.5885418727672-12.5885418727672
1006943.932075620990525.0679243790095
1014351.3207263785401-8.32072637854012
1025139.848093095942911.1519069040571
1033052.389707062397-22.389707062397
1041224.9858494373495-12.9858494373495
1053529.24610401520955.75389598479048
1063647.9381275673625-11.9381275673625
1074132.35408929943798.64591070056206
1085244.17390865656087.8260913434392
1092130.5375365739002-9.53753657390019
1102644.971980310848-18.971980310848
1114937.140485273080311.8595147269197
1123944.1986563541858-5.19865635418578
11364.108654960736641.89134503926336
1143541.0358261493977-6.03582614939773
1151717.8353357237801-0.835335723780074
1162538.6275795511092-13.6275795511092
1177142.492814698702628.5071853012974
11865.319473672307410.680526327692589
1194727.891793742205319.1082062577947
120913.7717555884042-4.77175558840418
1215247.05243050113674.94756949886327
1223843.2527563363012-5.25275633630124
1232139.3273097312597-18.3273097312597
1242129.42939613863-8.42939613863002
1251118.1631321317799-7.16313213177988
1262531.2770673160377-6.2770673160377
1275449.50149889034764.49850110965245
1283846.6326718790345-8.63267187903455
1296847.996676985301720.0033230146983
1305654.2551143726111.74488562738902
1317158.00696368749512.993036312505
1323929.62875384955849.3712461504416
1332130.310756402692-9.310756402692
1345379.1771231170687-26.1771231170687
1357862.656879664522515.3431203354775
1361426.5411794607911-12.5411794607911
1377052.7218487783517.2781512216499
1382939.522971450598-10.522971450598
1394737.51777707633019.48222292366993
1403641.7425764001563-5.74257640015631
1412133.7154111372608-12.7154111372608
1426945.421844661674323.5781553383257
1434241.30508164524780.694918354752208
1444840.39897368356087.60102631643917
1455551.82645687886133.17354312113868
1461945.615852847293-26.615852847293
1473939.6062600603727-0.606260060372699
1485150.35441546519440.645584534805574
14901.01138573870644-1.01138573870644
15043.703940697207050.296059302792954
15101.2806412345565-1.2806412345565
15201.54989673040656-1.54989673040656
15301.01138573870644-1.01138573870644
15401.01138573870644-1.01138573870644
1553837.7666216767190.233378323281036
1565150.1861762061360.813823793864031
15701.01138573870644-1.01138573870644
15802.08840772210668-2.08840772210668
15922.35766321795674-0.357663217956742
1601311.06414776554151.93585223445848
16153.467465692735911.53253430726409
1622032.044020518455-12.044020518455
16301.54989673040656-1.54989673040656
1642925.89093617010123.10906382989881

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 47 & 41.3706426280056 & 5.62935737199436 \tabularnewline
2 & 24 & 35.6028828766555 & -11.6028828766555 \tabularnewline
3 & 31 & 36.5514661629844 & -5.55146616298437 \tabularnewline
4 & 42 & 48.2455215856906 & -6.24552158569063 \tabularnewline
5 & 24 & 22.6561742283104 & 1.34382577168965 \tabularnewline
6 & 10 & 23.6148242604742 & -13.6148242604742 \tabularnewline
7 & 85 & 43.773531195195 & 41.226468804805 \tabularnewline
8 & 9 & 24.4145579542705 & -15.4145579542705 \tabularnewline
9 & 32 & 31.6242533905634 & 0.375746609436629 \tabularnewline
10 & 36 & 28.7882068706293 & 7.2117931293707 \tabularnewline
11 & 45 & 50.5731628426487 & -5.57316284264868 \tabularnewline
12 & 36 & 60.4125355231286 & -24.4125355231286 \tabularnewline
13 & 28 & 35.2944761351816 & -7.29447613518161 \tabularnewline
14 & 54 & 48.5712925473988 & 5.42870745260122 \tabularnewline
15 & 39 & 46.9470774621813 & -7.9470774621813 \tabularnewline
16 & 70 & 71.200482984148 & -1.20048298414792 \tabularnewline
17 & 50 & 38.2502877478595 & 11.7497122521405 \tabularnewline
18 & 55 & 52.0097404964924 & 2.99025950350764 \tabularnewline
19 & 32 & 31.0857423988632 & 0.91425760113675 \tabularnewline
20 & 44 & 46.8868745105226 & -2.88687451052256 \tabularnewline
21 & 46 & 42.3138678832354 & 3.68613211676464 \tabularnewline
22 & 80 & 42.8292932168195 & 37.1707067831805 \tabularnewline
23 & 25 & 22.0691890702955 & 2.93081092970446 \tabularnewline
24 & 30 & 42.9216358492834 & -12.9216358492834 \tabularnewline
25 & 41 & 51.9371679489159 & -10.9371679489159 \tabularnewline
26 & 40 & 50.8220074430376 & -10.8220074430376 \tabularnewline
27 & 45 & 45.0693005560495 & -0.069300556049528 \tabularnewline
28 & 45 & 44.9037445654354 & 0.0962554345646405 \tabularnewline
29 & 30 & 32.9431084095339 & -2.94310840953386 \tabularnewline
30 & 52 & 43.4795365075094 & 8.52046349249056 \tabularnewline
31 & 53 & 47.6946409980727 & 5.30535900192726 \tabularnewline
32 & 36 & 48.0913224678484 & -12.0913224678484 \tabularnewline
33 & 57 & 50.0346518509486 & 6.96534814905144 \tabularnewline
34 & 17 & 29.0574623664794 & -12.0574623664794 \tabularnewline
35 & 68 & 45.1730000612854 & 22.8269999387146 \tabularnewline
36 & 46 & 37.4849965849511 & 8.51500341504886 \tabularnewline
37 & 73 & 55.5960338207622 & 17.4039661792378 \tabularnewline
38 & 34 & 35.9651218155433 & -1.96512181554327 \tabularnewline
39 & 22 & 16.0090881649794 & 5.99091183502061 \tabularnewline
40 & 58 & 56.0211504739635 & 1.97884952603654 \tabularnewline
41 & 62 & 42.8604116686894 & 19.1395883313106 \tabularnewline
42 & 32 & 38.385746515539 & -6.38574651553899 \tabularnewline
43 & 38 & 36.1005805832228 & 1.8994194167772 \tabularnewline
44 & 23 & 31.7827978163589 & -8.78279781635889 \tabularnewline
45 & 26 & 32.7514115797873 & -6.7514115797873 \tabularnewline
46 & 85 & 54.8334174205087 & 30.1665825794913 \tabularnewline
47 & 22 & 37.2969957467947 & -15.2969957467947 \tabularnewline
48 & 44 & 47.8806078841481 & -3.88060788414808 \tabularnewline
49 & 62 & 56.3586502210157 & 5.64134977898434 \tabularnewline
50 & 36 & 43.0437037921099 & -7.04370379210992 \tabularnewline
51 & 36 & 36.3160038816591 & -0.316003881659058 \tabularnewline
52 & 7 & 11.207639326975 & -4.20763932697499 \tabularnewline
53 & 72 & 89.9269405613994 & -17.9269405613994 \tabularnewline
54 & 18 & 24.6838134501205 & -6.68381345012054 \tabularnewline
55 & 27 & 31.4603509336687 & -4.46035093366874 \tabularnewline
56 & 48 & 29.7722454109919 & 18.2277545890081 \tabularnewline
57 & 50 & 55.5234612731857 & -5.52346127318567 \tabularnewline
58 & 55 & 57.6366834490638 & -2.63668344906382 \tabularnewline
59 & 59 & 48.121419690783 & 10.878580309217 \tabularnewline
60 & 39 & 38.2777102081393 & 0.722289791860728 \tabularnewline
61 & 68 & 57.5136027830915 & 10.4863972169085 \tabularnewline
62 & 57 & 43.8160065198369 & 13.1839934801631 \tabularnewline
63 & 40 & 51.7565590939396 & -11.7565590939396 \tabularnewline
64 & 47 & 45.1402195699065 & 1.85978043009351 \tabularnewline
65 & 39 & 37.4425212603093 & 1.55747873969072 \tabularnewline
66 & 32 & 26.7298211095213 & 5.2701788904787 \tabularnewline
67 & 32 & 44.7247892441787 & -12.7247892441787 \tabularnewline
68 & 40 & 57.9634671339178 & -17.9634671339178 \tabularnewline
69 & 42 & 40.1350762188099 & 1.86492378119012 \tabularnewline
70 & 26 & 29.8829564810464 & -3.88295648104642 \tabularnewline
71 & 33 & 36.7605187071757 & -3.76051870717569 \tabularnewline
72 & 19 & 30.0415009068419 & -11.0415009068419 \tabularnewline
73 & 35 & 39.7223291615264 & -4.72232916152636 \tabularnewline
74 & 41 & 36.9040102686092 & 4.09598973139085 \tabularnewline
75 & 27 & 34.7171858104297 & -7.71718581042974 \tabularnewline
76 & 53 & 39.9915846573764 & 13.0084153426236 \tabularnewline
77 & 55 & 37.8499102864937 & 17.1500897135063 \tabularnewline
78 & 29 & 41.2723011538689 & -12.2723011538689 \tabularnewline
79 & 25 & 34.4151498232008 & -9.41514982320076 \tabularnewline
80 & 33 & 39.1838181698262 & -6.18381816982623 \tabularnewline
81 & 27 & 28.5366790017961 & -1.53667900179611 \tabularnewline
82 & 76 & 51.4642179399736 & 24.5357820600264 \tabularnewline
83 & 37 & 38.8162211998394 & -1.81622119983939 \tabularnewline
84 & 38 & 35.3001030642823 & 2.69989693571768 \tabularnewline
85 & 22 & 15.9161047219417 & 6.08389527805828 \tabularnewline
86 & 30 & 33.9368417831594 & -3.93684178315938 \tabularnewline
87 & 27 & 34.2388777703884 & -7.23887777038836 \tabularnewline
88 & 63 & 57.6366834490638 & 5.36331655093617 \tabularnewline
89 & 48 & 44.9672715961121 & 3.0327284038879 \tabularnewline
90 & 33 & 48.9425770031863 & -15.9425770031863 \tabularnewline
91 & 37 & 40.1447710520728 & -3.14477105207282 \tabularnewline
92 & 42 & 41.0686066407767 & 0.931393359223343 \tabularnewline
93 & 31 & 34.5683362178972 & -3.56833621789716 \tabularnewline
94 & 47 & 40.67358721051 & 6.32641278949 \tabularnewline
95 & 52 & 40.67358721051 & 11.32641278949 \tabularnewline
96 & 36 & 29.7722454109919 & 6.22775458900813 \tabularnewline
97 & 40 & 28.7243079273804 & 11.2756920726196 \tabularnewline
98 & 53 & 41.4813536980602 & 11.5186463019398 \tabularnewline
99 & 56 & 68.5885418727672 & -12.5885418727672 \tabularnewline
100 & 69 & 43.9320756209905 & 25.0679243790095 \tabularnewline
101 & 43 & 51.3207263785401 & -8.32072637854012 \tabularnewline
102 & 51 & 39.8480930959429 & 11.1519069040571 \tabularnewline
103 & 30 & 52.389707062397 & -22.389707062397 \tabularnewline
104 & 12 & 24.9858494373495 & -12.9858494373495 \tabularnewline
105 & 35 & 29.2461040152095 & 5.75389598479048 \tabularnewline
106 & 36 & 47.9381275673625 & -11.9381275673625 \tabularnewline
107 & 41 & 32.3540892994379 & 8.64591070056206 \tabularnewline
108 & 52 & 44.1739086565608 & 7.8260913434392 \tabularnewline
109 & 21 & 30.5375365739002 & -9.53753657390019 \tabularnewline
110 & 26 & 44.971980310848 & -18.971980310848 \tabularnewline
111 & 49 & 37.1404852730803 & 11.8595147269197 \tabularnewline
112 & 39 & 44.1986563541858 & -5.19865635418578 \tabularnewline
113 & 6 & 4.10865496073664 & 1.89134503926336 \tabularnewline
114 & 35 & 41.0358261493977 & -6.03582614939773 \tabularnewline
115 & 17 & 17.8353357237801 & -0.835335723780074 \tabularnewline
116 & 25 & 38.6275795511092 & -13.6275795511092 \tabularnewline
117 & 71 & 42.4928146987026 & 28.5071853012974 \tabularnewline
118 & 6 & 5.31947367230741 & 0.680526327692589 \tabularnewline
119 & 47 & 27.8917937422053 & 19.1082062577947 \tabularnewline
120 & 9 & 13.7717555884042 & -4.77175558840418 \tabularnewline
121 & 52 & 47.0524305011367 & 4.94756949886327 \tabularnewline
122 & 38 & 43.2527563363012 & -5.25275633630124 \tabularnewline
123 & 21 & 39.3273097312597 & -18.3273097312597 \tabularnewline
124 & 21 & 29.42939613863 & -8.42939613863002 \tabularnewline
125 & 11 & 18.1631321317799 & -7.16313213177988 \tabularnewline
126 & 25 & 31.2770673160377 & -6.2770673160377 \tabularnewline
127 & 54 & 49.5014988903476 & 4.49850110965245 \tabularnewline
128 & 38 & 46.6326718790345 & -8.63267187903455 \tabularnewline
129 & 68 & 47.9966769853017 & 20.0033230146983 \tabularnewline
130 & 56 & 54.255114372611 & 1.74488562738902 \tabularnewline
131 & 71 & 58.006963687495 & 12.993036312505 \tabularnewline
132 & 39 & 29.6287538495584 & 9.3712461504416 \tabularnewline
133 & 21 & 30.310756402692 & -9.310756402692 \tabularnewline
134 & 53 & 79.1771231170687 & -26.1771231170687 \tabularnewline
135 & 78 & 62.6568796645225 & 15.3431203354775 \tabularnewline
136 & 14 & 26.5411794607911 & -12.5411794607911 \tabularnewline
137 & 70 & 52.72184877835 & 17.2781512216499 \tabularnewline
138 & 29 & 39.522971450598 & -10.522971450598 \tabularnewline
139 & 47 & 37.5177770763301 & 9.48222292366993 \tabularnewline
140 & 36 & 41.7425764001563 & -5.74257640015631 \tabularnewline
141 & 21 & 33.7154111372608 & -12.7154111372608 \tabularnewline
142 & 69 & 45.4218446616743 & 23.5781553383257 \tabularnewline
143 & 42 & 41.3050816452478 & 0.694918354752208 \tabularnewline
144 & 48 & 40.3989736835608 & 7.60102631643917 \tabularnewline
145 & 55 & 51.8264568788613 & 3.17354312113868 \tabularnewline
146 & 19 & 45.615852847293 & -26.615852847293 \tabularnewline
147 & 39 & 39.6062600603727 & -0.606260060372699 \tabularnewline
148 & 51 & 50.3544154651944 & 0.645584534805574 \tabularnewline
149 & 0 & 1.01138573870644 & -1.01138573870644 \tabularnewline
150 & 4 & 3.70394069720705 & 0.296059302792954 \tabularnewline
151 & 0 & 1.2806412345565 & -1.2806412345565 \tabularnewline
152 & 0 & 1.54989673040656 & -1.54989673040656 \tabularnewline
153 & 0 & 1.01138573870644 & -1.01138573870644 \tabularnewline
154 & 0 & 1.01138573870644 & -1.01138573870644 \tabularnewline
155 & 38 & 37.766621676719 & 0.233378323281036 \tabularnewline
156 & 51 & 50.186176206136 & 0.813823793864031 \tabularnewline
157 & 0 & 1.01138573870644 & -1.01138573870644 \tabularnewline
158 & 0 & 2.08840772210668 & -2.08840772210668 \tabularnewline
159 & 2 & 2.35766321795674 & -0.357663217956742 \tabularnewline
160 & 13 & 11.0641477655415 & 1.93585223445848 \tabularnewline
161 & 5 & 3.46746569273591 & 1.53253430726409 \tabularnewline
162 & 20 & 32.044020518455 & -12.044020518455 \tabularnewline
163 & 0 & 1.54989673040656 & -1.54989673040656 \tabularnewline
164 & 29 & 25.8909361701012 & 3.10906382989881 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&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]47[/C][C]41.3706426280056[/C][C]5.62935737199436[/C][/ROW]
[ROW][C]2[/C][C]24[/C][C]35.6028828766555[/C][C]-11.6028828766555[/C][/ROW]
[ROW][C]3[/C][C]31[/C][C]36.5514661629844[/C][C]-5.55146616298437[/C][/ROW]
[ROW][C]4[/C][C]42[/C][C]48.2455215856906[/C][C]-6.24552158569063[/C][/ROW]
[ROW][C]5[/C][C]24[/C][C]22.6561742283104[/C][C]1.34382577168965[/C][/ROW]
[ROW][C]6[/C][C]10[/C][C]23.6148242604742[/C][C]-13.6148242604742[/C][/ROW]
[ROW][C]7[/C][C]85[/C][C]43.773531195195[/C][C]41.226468804805[/C][/ROW]
[ROW][C]8[/C][C]9[/C][C]24.4145579542705[/C][C]-15.4145579542705[/C][/ROW]
[ROW][C]9[/C][C]32[/C][C]31.6242533905634[/C][C]0.375746609436629[/C][/ROW]
[ROW][C]10[/C][C]36[/C][C]28.7882068706293[/C][C]7.2117931293707[/C][/ROW]
[ROW][C]11[/C][C]45[/C][C]50.5731628426487[/C][C]-5.57316284264868[/C][/ROW]
[ROW][C]12[/C][C]36[/C][C]60.4125355231286[/C][C]-24.4125355231286[/C][/ROW]
[ROW][C]13[/C][C]28[/C][C]35.2944761351816[/C][C]-7.29447613518161[/C][/ROW]
[ROW][C]14[/C][C]54[/C][C]48.5712925473988[/C][C]5.42870745260122[/C][/ROW]
[ROW][C]15[/C][C]39[/C][C]46.9470774621813[/C][C]-7.9470774621813[/C][/ROW]
[ROW][C]16[/C][C]70[/C][C]71.200482984148[/C][C]-1.20048298414792[/C][/ROW]
[ROW][C]17[/C][C]50[/C][C]38.2502877478595[/C][C]11.7497122521405[/C][/ROW]
[ROW][C]18[/C][C]55[/C][C]52.0097404964924[/C][C]2.99025950350764[/C][/ROW]
[ROW][C]19[/C][C]32[/C][C]31.0857423988632[/C][C]0.91425760113675[/C][/ROW]
[ROW][C]20[/C][C]44[/C][C]46.8868745105226[/C][C]-2.88687451052256[/C][/ROW]
[ROW][C]21[/C][C]46[/C][C]42.3138678832354[/C][C]3.68613211676464[/C][/ROW]
[ROW][C]22[/C][C]80[/C][C]42.8292932168195[/C][C]37.1707067831805[/C][/ROW]
[ROW][C]23[/C][C]25[/C][C]22.0691890702955[/C][C]2.93081092970446[/C][/ROW]
[ROW][C]24[/C][C]30[/C][C]42.9216358492834[/C][C]-12.9216358492834[/C][/ROW]
[ROW][C]25[/C][C]41[/C][C]51.9371679489159[/C][C]-10.9371679489159[/C][/ROW]
[ROW][C]26[/C][C]40[/C][C]50.8220074430376[/C][C]-10.8220074430376[/C][/ROW]
[ROW][C]27[/C][C]45[/C][C]45.0693005560495[/C][C]-0.069300556049528[/C][/ROW]
[ROW][C]28[/C][C]45[/C][C]44.9037445654354[/C][C]0.0962554345646405[/C][/ROW]
[ROW][C]29[/C][C]30[/C][C]32.9431084095339[/C][C]-2.94310840953386[/C][/ROW]
[ROW][C]30[/C][C]52[/C][C]43.4795365075094[/C][C]8.52046349249056[/C][/ROW]
[ROW][C]31[/C][C]53[/C][C]47.6946409980727[/C][C]5.30535900192726[/C][/ROW]
[ROW][C]32[/C][C]36[/C][C]48.0913224678484[/C][C]-12.0913224678484[/C][/ROW]
[ROW][C]33[/C][C]57[/C][C]50.0346518509486[/C][C]6.96534814905144[/C][/ROW]
[ROW][C]34[/C][C]17[/C][C]29.0574623664794[/C][C]-12.0574623664794[/C][/ROW]
[ROW][C]35[/C][C]68[/C][C]45.1730000612854[/C][C]22.8269999387146[/C][/ROW]
[ROW][C]36[/C][C]46[/C][C]37.4849965849511[/C][C]8.51500341504886[/C][/ROW]
[ROW][C]37[/C][C]73[/C][C]55.5960338207622[/C][C]17.4039661792378[/C][/ROW]
[ROW][C]38[/C][C]34[/C][C]35.9651218155433[/C][C]-1.96512181554327[/C][/ROW]
[ROW][C]39[/C][C]22[/C][C]16.0090881649794[/C][C]5.99091183502061[/C][/ROW]
[ROW][C]40[/C][C]58[/C][C]56.0211504739635[/C][C]1.97884952603654[/C][/ROW]
[ROW][C]41[/C][C]62[/C][C]42.8604116686894[/C][C]19.1395883313106[/C][/ROW]
[ROW][C]42[/C][C]32[/C][C]38.385746515539[/C][C]-6.38574651553899[/C][/ROW]
[ROW][C]43[/C][C]38[/C][C]36.1005805832228[/C][C]1.8994194167772[/C][/ROW]
[ROW][C]44[/C][C]23[/C][C]31.7827978163589[/C][C]-8.78279781635889[/C][/ROW]
[ROW][C]45[/C][C]26[/C][C]32.7514115797873[/C][C]-6.7514115797873[/C][/ROW]
[ROW][C]46[/C][C]85[/C][C]54.8334174205087[/C][C]30.1665825794913[/C][/ROW]
[ROW][C]47[/C][C]22[/C][C]37.2969957467947[/C][C]-15.2969957467947[/C][/ROW]
[ROW][C]48[/C][C]44[/C][C]47.8806078841481[/C][C]-3.88060788414808[/C][/ROW]
[ROW][C]49[/C][C]62[/C][C]56.3586502210157[/C][C]5.64134977898434[/C][/ROW]
[ROW][C]50[/C][C]36[/C][C]43.0437037921099[/C][C]-7.04370379210992[/C][/ROW]
[ROW][C]51[/C][C]36[/C][C]36.3160038816591[/C][C]-0.316003881659058[/C][/ROW]
[ROW][C]52[/C][C]7[/C][C]11.207639326975[/C][C]-4.20763932697499[/C][/ROW]
[ROW][C]53[/C][C]72[/C][C]89.9269405613994[/C][C]-17.9269405613994[/C][/ROW]
[ROW][C]54[/C][C]18[/C][C]24.6838134501205[/C][C]-6.68381345012054[/C][/ROW]
[ROW][C]55[/C][C]27[/C][C]31.4603509336687[/C][C]-4.46035093366874[/C][/ROW]
[ROW][C]56[/C][C]48[/C][C]29.7722454109919[/C][C]18.2277545890081[/C][/ROW]
[ROW][C]57[/C][C]50[/C][C]55.5234612731857[/C][C]-5.52346127318567[/C][/ROW]
[ROW][C]58[/C][C]55[/C][C]57.6366834490638[/C][C]-2.63668344906382[/C][/ROW]
[ROW][C]59[/C][C]59[/C][C]48.121419690783[/C][C]10.878580309217[/C][/ROW]
[ROW][C]60[/C][C]39[/C][C]38.2777102081393[/C][C]0.722289791860728[/C][/ROW]
[ROW][C]61[/C][C]68[/C][C]57.5136027830915[/C][C]10.4863972169085[/C][/ROW]
[ROW][C]62[/C][C]57[/C][C]43.8160065198369[/C][C]13.1839934801631[/C][/ROW]
[ROW][C]63[/C][C]40[/C][C]51.7565590939396[/C][C]-11.7565590939396[/C][/ROW]
[ROW][C]64[/C][C]47[/C][C]45.1402195699065[/C][C]1.85978043009351[/C][/ROW]
[ROW][C]65[/C][C]39[/C][C]37.4425212603093[/C][C]1.55747873969072[/C][/ROW]
[ROW][C]66[/C][C]32[/C][C]26.7298211095213[/C][C]5.2701788904787[/C][/ROW]
[ROW][C]67[/C][C]32[/C][C]44.7247892441787[/C][C]-12.7247892441787[/C][/ROW]
[ROW][C]68[/C][C]40[/C][C]57.9634671339178[/C][C]-17.9634671339178[/C][/ROW]
[ROW][C]69[/C][C]42[/C][C]40.1350762188099[/C][C]1.86492378119012[/C][/ROW]
[ROW][C]70[/C][C]26[/C][C]29.8829564810464[/C][C]-3.88295648104642[/C][/ROW]
[ROW][C]71[/C][C]33[/C][C]36.7605187071757[/C][C]-3.76051870717569[/C][/ROW]
[ROW][C]72[/C][C]19[/C][C]30.0415009068419[/C][C]-11.0415009068419[/C][/ROW]
[ROW][C]73[/C][C]35[/C][C]39.7223291615264[/C][C]-4.72232916152636[/C][/ROW]
[ROW][C]74[/C][C]41[/C][C]36.9040102686092[/C][C]4.09598973139085[/C][/ROW]
[ROW][C]75[/C][C]27[/C][C]34.7171858104297[/C][C]-7.71718581042974[/C][/ROW]
[ROW][C]76[/C][C]53[/C][C]39.9915846573764[/C][C]13.0084153426236[/C][/ROW]
[ROW][C]77[/C][C]55[/C][C]37.8499102864937[/C][C]17.1500897135063[/C][/ROW]
[ROW][C]78[/C][C]29[/C][C]41.2723011538689[/C][C]-12.2723011538689[/C][/ROW]
[ROW][C]79[/C][C]25[/C][C]34.4151498232008[/C][C]-9.41514982320076[/C][/ROW]
[ROW][C]80[/C][C]33[/C][C]39.1838181698262[/C][C]-6.18381816982623[/C][/ROW]
[ROW][C]81[/C][C]27[/C][C]28.5366790017961[/C][C]-1.53667900179611[/C][/ROW]
[ROW][C]82[/C][C]76[/C][C]51.4642179399736[/C][C]24.5357820600264[/C][/ROW]
[ROW][C]83[/C][C]37[/C][C]38.8162211998394[/C][C]-1.81622119983939[/C][/ROW]
[ROW][C]84[/C][C]38[/C][C]35.3001030642823[/C][C]2.69989693571768[/C][/ROW]
[ROW][C]85[/C][C]22[/C][C]15.9161047219417[/C][C]6.08389527805828[/C][/ROW]
[ROW][C]86[/C][C]30[/C][C]33.9368417831594[/C][C]-3.93684178315938[/C][/ROW]
[ROW][C]87[/C][C]27[/C][C]34.2388777703884[/C][C]-7.23887777038836[/C][/ROW]
[ROW][C]88[/C][C]63[/C][C]57.6366834490638[/C][C]5.36331655093617[/C][/ROW]
[ROW][C]89[/C][C]48[/C][C]44.9672715961121[/C][C]3.0327284038879[/C][/ROW]
[ROW][C]90[/C][C]33[/C][C]48.9425770031863[/C][C]-15.9425770031863[/C][/ROW]
[ROW][C]91[/C][C]37[/C][C]40.1447710520728[/C][C]-3.14477105207282[/C][/ROW]
[ROW][C]92[/C][C]42[/C][C]41.0686066407767[/C][C]0.931393359223343[/C][/ROW]
[ROW][C]93[/C][C]31[/C][C]34.5683362178972[/C][C]-3.56833621789716[/C][/ROW]
[ROW][C]94[/C][C]47[/C][C]40.67358721051[/C][C]6.32641278949[/C][/ROW]
[ROW][C]95[/C][C]52[/C][C]40.67358721051[/C][C]11.32641278949[/C][/ROW]
[ROW][C]96[/C][C]36[/C][C]29.7722454109919[/C][C]6.22775458900813[/C][/ROW]
[ROW][C]97[/C][C]40[/C][C]28.7243079273804[/C][C]11.2756920726196[/C][/ROW]
[ROW][C]98[/C][C]53[/C][C]41.4813536980602[/C][C]11.5186463019398[/C][/ROW]
[ROW][C]99[/C][C]56[/C][C]68.5885418727672[/C][C]-12.5885418727672[/C][/ROW]
[ROW][C]100[/C][C]69[/C][C]43.9320756209905[/C][C]25.0679243790095[/C][/ROW]
[ROW][C]101[/C][C]43[/C][C]51.3207263785401[/C][C]-8.32072637854012[/C][/ROW]
[ROW][C]102[/C][C]51[/C][C]39.8480930959429[/C][C]11.1519069040571[/C][/ROW]
[ROW][C]103[/C][C]30[/C][C]52.389707062397[/C][C]-22.389707062397[/C][/ROW]
[ROW][C]104[/C][C]12[/C][C]24.9858494373495[/C][C]-12.9858494373495[/C][/ROW]
[ROW][C]105[/C][C]35[/C][C]29.2461040152095[/C][C]5.75389598479048[/C][/ROW]
[ROW][C]106[/C][C]36[/C][C]47.9381275673625[/C][C]-11.9381275673625[/C][/ROW]
[ROW][C]107[/C][C]41[/C][C]32.3540892994379[/C][C]8.64591070056206[/C][/ROW]
[ROW][C]108[/C][C]52[/C][C]44.1739086565608[/C][C]7.8260913434392[/C][/ROW]
[ROW][C]109[/C][C]21[/C][C]30.5375365739002[/C][C]-9.53753657390019[/C][/ROW]
[ROW][C]110[/C][C]26[/C][C]44.971980310848[/C][C]-18.971980310848[/C][/ROW]
[ROW][C]111[/C][C]49[/C][C]37.1404852730803[/C][C]11.8595147269197[/C][/ROW]
[ROW][C]112[/C][C]39[/C][C]44.1986563541858[/C][C]-5.19865635418578[/C][/ROW]
[ROW][C]113[/C][C]6[/C][C]4.10865496073664[/C][C]1.89134503926336[/C][/ROW]
[ROW][C]114[/C][C]35[/C][C]41.0358261493977[/C][C]-6.03582614939773[/C][/ROW]
[ROW][C]115[/C][C]17[/C][C]17.8353357237801[/C][C]-0.835335723780074[/C][/ROW]
[ROW][C]116[/C][C]25[/C][C]38.6275795511092[/C][C]-13.6275795511092[/C][/ROW]
[ROW][C]117[/C][C]71[/C][C]42.4928146987026[/C][C]28.5071853012974[/C][/ROW]
[ROW][C]118[/C][C]6[/C][C]5.31947367230741[/C][C]0.680526327692589[/C][/ROW]
[ROW][C]119[/C][C]47[/C][C]27.8917937422053[/C][C]19.1082062577947[/C][/ROW]
[ROW][C]120[/C][C]9[/C][C]13.7717555884042[/C][C]-4.77175558840418[/C][/ROW]
[ROW][C]121[/C][C]52[/C][C]47.0524305011367[/C][C]4.94756949886327[/C][/ROW]
[ROW][C]122[/C][C]38[/C][C]43.2527563363012[/C][C]-5.25275633630124[/C][/ROW]
[ROW][C]123[/C][C]21[/C][C]39.3273097312597[/C][C]-18.3273097312597[/C][/ROW]
[ROW][C]124[/C][C]21[/C][C]29.42939613863[/C][C]-8.42939613863002[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]18.1631321317799[/C][C]-7.16313213177988[/C][/ROW]
[ROW][C]126[/C][C]25[/C][C]31.2770673160377[/C][C]-6.2770673160377[/C][/ROW]
[ROW][C]127[/C][C]54[/C][C]49.5014988903476[/C][C]4.49850110965245[/C][/ROW]
[ROW][C]128[/C][C]38[/C][C]46.6326718790345[/C][C]-8.63267187903455[/C][/ROW]
[ROW][C]129[/C][C]68[/C][C]47.9966769853017[/C][C]20.0033230146983[/C][/ROW]
[ROW][C]130[/C][C]56[/C][C]54.255114372611[/C][C]1.74488562738902[/C][/ROW]
[ROW][C]131[/C][C]71[/C][C]58.006963687495[/C][C]12.993036312505[/C][/ROW]
[ROW][C]132[/C][C]39[/C][C]29.6287538495584[/C][C]9.3712461504416[/C][/ROW]
[ROW][C]133[/C][C]21[/C][C]30.310756402692[/C][C]-9.310756402692[/C][/ROW]
[ROW][C]134[/C][C]53[/C][C]79.1771231170687[/C][C]-26.1771231170687[/C][/ROW]
[ROW][C]135[/C][C]78[/C][C]62.6568796645225[/C][C]15.3431203354775[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]26.5411794607911[/C][C]-12.5411794607911[/C][/ROW]
[ROW][C]137[/C][C]70[/C][C]52.72184877835[/C][C]17.2781512216499[/C][/ROW]
[ROW][C]138[/C][C]29[/C][C]39.522971450598[/C][C]-10.522971450598[/C][/ROW]
[ROW][C]139[/C][C]47[/C][C]37.5177770763301[/C][C]9.48222292366993[/C][/ROW]
[ROW][C]140[/C][C]36[/C][C]41.7425764001563[/C][C]-5.74257640015631[/C][/ROW]
[ROW][C]141[/C][C]21[/C][C]33.7154111372608[/C][C]-12.7154111372608[/C][/ROW]
[ROW][C]142[/C][C]69[/C][C]45.4218446616743[/C][C]23.5781553383257[/C][/ROW]
[ROW][C]143[/C][C]42[/C][C]41.3050816452478[/C][C]0.694918354752208[/C][/ROW]
[ROW][C]144[/C][C]48[/C][C]40.3989736835608[/C][C]7.60102631643917[/C][/ROW]
[ROW][C]145[/C][C]55[/C][C]51.8264568788613[/C][C]3.17354312113868[/C][/ROW]
[ROW][C]146[/C][C]19[/C][C]45.615852847293[/C][C]-26.615852847293[/C][/ROW]
[ROW][C]147[/C][C]39[/C][C]39.6062600603727[/C][C]-0.606260060372699[/C][/ROW]
[ROW][C]148[/C][C]51[/C][C]50.3544154651944[/C][C]0.645584534805574[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]1.01138573870644[/C][C]-1.01138573870644[/C][/ROW]
[ROW][C]150[/C][C]4[/C][C]3.70394069720705[/C][C]0.296059302792954[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]1.2806412345565[/C][C]-1.2806412345565[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]1.54989673040656[/C][C]-1.54989673040656[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]1.01138573870644[/C][C]-1.01138573870644[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]1.01138573870644[/C][C]-1.01138573870644[/C][/ROW]
[ROW][C]155[/C][C]38[/C][C]37.766621676719[/C][C]0.233378323281036[/C][/ROW]
[ROW][C]156[/C][C]51[/C][C]50.186176206136[/C][C]0.813823793864031[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]1.01138573870644[/C][C]-1.01138573870644[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]2.08840772210668[/C][C]-2.08840772210668[/C][/ROW]
[ROW][C]159[/C][C]2[/C][C]2.35766321795674[/C][C]-0.357663217956742[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]11.0641477655415[/C][C]1.93585223445848[/C][/ROW]
[ROW][C]161[/C][C]5[/C][C]3.46746569273591[/C][C]1.53253430726409[/C][/ROW]
[ROW][C]162[/C][C]20[/C][C]32.044020518455[/C][C]-12.044020518455[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]1.54989673040656[/C][C]-1.54989673040656[/C][/ROW]
[ROW][C]164[/C][C]29[/C][C]25.8909361701012[/C][C]3.10906382989881[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14741.37064262800565.62935737199436
22435.6028828766555-11.6028828766555
33136.5514661629844-5.55146616298437
44248.2455215856906-6.24552158569063
52422.65617422831041.34382577168965
61023.6148242604742-13.6148242604742
78543.77353119519541.226468804805
8924.4145579542705-15.4145579542705
93231.62425339056340.375746609436629
103628.78820687062937.2117931293707
114550.5731628426487-5.57316284264868
123660.4125355231286-24.4125355231286
132835.2944761351816-7.29447613518161
145448.57129254739885.42870745260122
153946.9470774621813-7.9470774621813
167071.200482984148-1.20048298414792
175038.250287747859511.7497122521405
185552.00974049649242.99025950350764
193231.08574239886320.91425760113675
204446.8868745105226-2.88687451052256
214642.31386788323543.68613211676464
228042.829293216819537.1707067831805
232522.06918907029552.93081092970446
243042.9216358492834-12.9216358492834
254151.9371679489159-10.9371679489159
264050.8220074430376-10.8220074430376
274545.0693005560495-0.069300556049528
284544.90374456543540.0962554345646405
293032.9431084095339-2.94310840953386
305243.47953650750948.52046349249056
315347.69464099807275.30535900192726
323648.0913224678484-12.0913224678484
335750.03465185094866.96534814905144
341729.0574623664794-12.0574623664794
356845.173000061285422.8269999387146
364637.48499658495118.51500341504886
377355.596033820762217.4039661792378
383435.9651218155433-1.96512181554327
392216.00908816497945.99091183502061
405856.02115047396351.97884952603654
416242.860411668689419.1395883313106
423238.385746515539-6.38574651553899
433836.10058058322281.8994194167772
442331.7827978163589-8.78279781635889
452632.7514115797873-6.7514115797873
468554.833417420508730.1665825794913
472237.2969957467947-15.2969957467947
484447.8806078841481-3.88060788414808
496256.35865022101575.64134977898434
503643.0437037921099-7.04370379210992
513636.3160038816591-0.316003881659058
52711.207639326975-4.20763932697499
537289.9269405613994-17.9269405613994
541824.6838134501205-6.68381345012054
552731.4603509336687-4.46035093366874
564829.772245410991918.2277545890081
575055.5234612731857-5.52346127318567
585557.6366834490638-2.63668344906382
595948.12141969078310.878580309217
603938.27771020813930.722289791860728
616857.513602783091510.4863972169085
625743.816006519836913.1839934801631
634051.7565590939396-11.7565590939396
644745.14021956990651.85978043009351
653937.44252126030931.55747873969072
663226.72982110952135.2701788904787
673244.7247892441787-12.7247892441787
684057.9634671339178-17.9634671339178
694240.13507621880991.86492378119012
702629.8829564810464-3.88295648104642
713336.7605187071757-3.76051870717569
721930.0415009068419-11.0415009068419
733539.7223291615264-4.72232916152636
744136.90401026860924.09598973139085
752734.7171858104297-7.71718581042974
765339.991584657376413.0084153426236
775537.849910286493717.1500897135063
782941.2723011538689-12.2723011538689
792534.4151498232008-9.41514982320076
803339.1838181698262-6.18381816982623
812728.5366790017961-1.53667900179611
827651.464217939973624.5357820600264
833738.8162211998394-1.81622119983939
843835.30010306428232.69989693571768
852215.91610472194176.08389527805828
863033.9368417831594-3.93684178315938
872734.2388777703884-7.23887777038836
886357.63668344906385.36331655093617
894844.96727159611213.0327284038879
903348.9425770031863-15.9425770031863
913740.1447710520728-3.14477105207282
924241.06860664077670.931393359223343
933134.5683362178972-3.56833621789716
944740.673587210516.32641278949
955240.6735872105111.32641278949
963629.77224541099196.22775458900813
974028.724307927380411.2756920726196
985341.481353698060211.5186463019398
995668.5885418727672-12.5885418727672
1006943.932075620990525.0679243790095
1014351.3207263785401-8.32072637854012
1025139.848093095942911.1519069040571
1033052.389707062397-22.389707062397
1041224.9858494373495-12.9858494373495
1053529.24610401520955.75389598479048
1063647.9381275673625-11.9381275673625
1074132.35408929943798.64591070056206
1085244.17390865656087.8260913434392
1092130.5375365739002-9.53753657390019
1102644.971980310848-18.971980310848
1114937.140485273080311.8595147269197
1123944.1986563541858-5.19865635418578
11364.108654960736641.89134503926336
1143541.0358261493977-6.03582614939773
1151717.8353357237801-0.835335723780074
1162538.6275795511092-13.6275795511092
1177142.492814698702628.5071853012974
11865.319473672307410.680526327692589
1194727.891793742205319.1082062577947
120913.7717555884042-4.77175558840418
1215247.05243050113674.94756949886327
1223843.2527563363012-5.25275633630124
1232139.3273097312597-18.3273097312597
1242129.42939613863-8.42939613863002
1251118.1631321317799-7.16313213177988
1262531.2770673160377-6.2770673160377
1275449.50149889034764.49850110965245
1283846.6326718790345-8.63267187903455
1296847.996676985301720.0033230146983
1305654.2551143726111.74488562738902
1317158.00696368749512.993036312505
1323929.62875384955849.3712461504416
1332130.310756402692-9.310756402692
1345379.1771231170687-26.1771231170687
1357862.656879664522515.3431203354775
1361426.5411794607911-12.5411794607911
1377052.7218487783517.2781512216499
1382939.522971450598-10.522971450598
1394737.51777707633019.48222292366993
1403641.7425764001563-5.74257640015631
1412133.7154111372608-12.7154111372608
1426945.421844661674323.5781553383257
1434241.30508164524780.694918354752208
1444840.39897368356087.60102631643917
1455551.82645687886133.17354312113868
1461945.615852847293-26.615852847293
1473939.6062600603727-0.606260060372699
1485150.35441546519440.645584534805574
14901.01138573870644-1.01138573870644
15043.703940697207050.296059302792954
15101.2806412345565-1.2806412345565
15201.54989673040656-1.54989673040656
15301.01138573870644-1.01138573870644
15401.01138573870644-1.01138573870644
1553837.7666216767190.233378323281036
1565150.1861762061360.813823793864031
15701.01138573870644-1.01138573870644
15802.08840772210668-2.08840772210668
15922.35766321795674-0.357663217956742
1601311.06414776554151.93585223445848
16153.467465692735911.53253430726409
1622032.044020518455-12.044020518455
16301.54989673040656-1.54989673040656
1642925.89093617010123.10906382989881







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.962076926391890.0758461472162210.0379230736081105
80.9254659602912480.1490680794175040.0745340397087521
90.871994076161120.256011847677760.12800592383888
100.9111381571824320.1777236856351360.088861842817568
110.8719447595798760.2561104808402480.128055240420124
120.871438390772440.257123218455120.12856160922756
130.8166513220676850.3666973558646290.183348677932315
140.9392698187879360.1214603624241270.0607301812120635
150.9112233354281720.1775533291436570.0887766645718283
160.89810017588110.2037996482377990.101899824118899
170.9213080431137980.1573839137724040.078691956886202
180.90602042281840.1879591543632010.0939795771816004
190.8712915771301310.2574168457397380.128708422869869
200.8350846947523950.329830610495210.164915305247605
210.801764709594530.3964705808109390.19823529040547
220.9796509152401880.04069816951962450.0203490847598122
230.9705457552493960.0589084895012070.0294542447506035
240.961515296015690.07696940796862050.0384847039843102
250.9596751581734570.08064968365308510.0403248418265426
260.9665035304720150.06699293905597010.0334964695279851
270.9554913199268780.08901736014624370.0445086800731219
280.9404387359176410.1191225281647170.0595612640823585
290.9208994046211150.1582011907577710.0791005953788853
300.9249518370062780.1500963259874440.0750481629937218
310.9136843472250340.1726313055499320.086315652774966
320.9093469550790580.1813060898418840.0906530449209419
330.8968047227519130.2063905544961730.103195277248087
340.8927815999483990.2144368001032020.107218400051601
350.9487882185974430.1024235628051140.0512117814025572
360.9424926751478350.1150146497043290.0575073248521646
370.9536877817792030.09262443644159370.0463122182207969
380.9402528388422620.1194943223154770.0597471611577383
390.9245971455935980.1508057088128030.0754028544064017
400.9068025350220750.1863949299558510.0931974649779254
410.9350982382128210.1298035235743570.0649017617871786
420.9226150712166270.1547698575667470.0773849287833733
430.9025170006657910.1949659986684180.097482999334209
440.8908813010949650.2182373978100690.109118698905035
450.8852042627305880.2295914745388230.114795737269412
460.9560964393436720.08780712131265530.0439035606563276
470.9611218170517310.07775636589653740.0388781829482687
480.9505988678240340.09880226435193170.0494011321759658
490.940002315193260.1199953696134780.0599976848067392
500.9278319128322920.1443361743354160.072168087167708
510.911840602808830.1763187943823390.0881593971911694
520.8964365432964060.2071269134071870.103563456703594
530.9514015193526780.09719696129464430.0485984806473222
540.9428550325762920.1142899348474150.0571449674237075
550.9309138192871010.1381723614257970.0690861807128987
560.9485408986726830.1029182026546350.0514591013273174
570.937785720952070.1244285580958620.0622142790479309
580.9235326626859740.1529346746280520.076467337314026
590.9211054814603370.1577890370793260.0788945185396628
600.9024380463948310.1951239072103380.0975619536051691
610.8977177359799580.2045645280400830.102282264020042
620.9014746732110260.1970506535779470.0985253267889737
630.9019450858647580.1961098282704840.0980549141352422
640.8808676625068780.2382646749862440.119132337493122
650.8565520205327420.2868959589345150.143447979467258
660.8332521114899890.3334957770200230.166747888510011
670.8421762811499860.3156474377000280.157823718850014
680.8752316513548470.2495366972903060.124768348645153
690.8505915961412630.2988168077174730.149408403858737
700.8265194065401540.3469611869196920.173480593459846
710.799060072150420.4018798556991620.200939927849581
720.7967004240372650.406599151925470.203299575962735
730.768670016760470.4626599664790590.23132998323953
740.736736736270460.526526527459080.26326326372954
750.7157824550544060.5684350898911890.284217544945594
760.7241429466993610.5517141066012780.275857053300639
770.763922777166720.4721544456665610.23607722283328
780.7680867398842620.4638265202314760.231913260115738
790.7565229425403680.4869541149192640.243477057459632
800.7303733833888730.5392532332222530.269626616611127
810.6924984075025950.615003184994810.307501592497405
820.8143068707361410.3713862585277180.185693129263859
830.7831401271283160.4337197457433680.216859872871684
840.7533921415511030.4932157168977930.246607858448897
850.7253108543792310.5493782912415380.274689145620769
860.6907263651042710.6185472697914580.309273634895729
870.6656563919409530.6686872161180930.334343608059047
880.6346803825056290.7306392349887420.365319617494371
890.593186357104130.813627285791740.40681364289587
900.6289594813317970.7420810373364060.371040518668203
910.588326305168590.823347389662820.41167369483141
920.5434619290195110.9130761419609780.456538070980489
930.5023510515645230.9952978968709540.497648948435477
940.4700203731056040.9400407462112080.529979626894396
950.4679316173929380.9358632347858770.532068382607062
960.4349446716763480.8698893433526960.565055328323652
970.4282715066166650.856543013233330.571728493383335
980.4280787472222210.8561574944444420.571921252777779
990.4292019659909380.8584039319818750.570798034009062
1000.6015386415758090.7969227168483820.398461358424191
1010.576506577341670.846986845316660.42349342265833
1020.573998603013920.852002793972160.42600139698608
1030.6879768376997850.624046324600430.312023162300215
1040.6985487936537460.6029024126925080.301451206346254
1050.6668334037111810.6663331925776380.333166596288819
1060.6686609867116180.6626780265767640.331339013288382
1070.6499303833375650.700139233324870.350069616662435
1080.6264898144801880.7470203710396240.373510185519812
1090.6092866014650080.7814267970699840.390713398534992
1100.689859814430370.620280371139260.31014018556963
1110.6901191049432720.6197617901134560.309880895056728
1120.653102131085560.693795737828880.34689786891444
1130.6090979743371510.7818040513256980.390902025662849
1140.5742934930868880.8514130138262240.425706506913112
1150.5260342655404850.947931468919030.473965734459515
1160.5453356164794510.9093287670410970.454664383520549
1170.7750646769873660.4498706460252690.224935323012634
1180.7359270934832330.5281458130335340.264072906516767
1190.8202225247596360.3595549504807270.179777475240364
1200.7869196091353920.4261607817292160.213080390864608
1210.7572754579054920.4854490841890160.242724542094508
1220.7213144065838020.5573711868323960.278685593416198
1230.7959998973312290.4080002053375420.204000102668771
1240.771329574978260.457340850043480.22867042502174
1250.738887370176990.5222252596460210.26111262982301
1260.710461430073270.5790771398534610.28953856992673
1270.6639956871604410.6720086256791170.336004312839559
1280.6602463743076090.6795072513847820.339753625692391
1290.7369984358962180.5260031282075630.263001564103782
1300.6874728785705770.6250542428588470.312527121429423
1310.7036753650794090.5926492698411820.296324634920591
1320.6920147447378510.6159705105242980.307985255262149
1330.6671918796602980.6656162406794030.332808120339702
1340.9905809765716550.01883804685669080.00941902342834542
1350.9867875682277880.02642486354442320.0132124317722116
1360.9811467462335440.03770650753291190.018853253766456
1370.9833128477284470.03337430454310610.0166871522715531
1380.9830238124040940.03395237519181230.0169761875959061
1390.994968592581950.01006281483609890.00503140741804943
1400.9918246690206770.01635066195864640.00817533097932318
1410.999999983240843.35183209871494e-081.67591604935747e-08
1420.9999999806788823.86422368929187e-081.93211184464593e-08
1430.999999944605981.10788039574631e-075.53940197873153e-08
1440.9999997849253344.30149332853657e-072.15074666426829e-07
1450.9999997509267224.98146555459615e-072.49073277729808e-07
1460.9999999976031524.79369516161665e-092.39684758080832e-09
1470.9999999914944381.70111233888427e-088.50556169442134e-09
1480.9999999790626474.1874706272619e-082.09373531363095e-08
1490.9999998748701082.50259783162278e-071.25129891581139e-07
1500.9999994458104871.10837902668573e-065.54189513342863e-07
1510.999996662667816.67466437867945e-063.33733218933973e-06
1520.999981777797633.64444047389576e-051.82222023694788e-05
1530.9999039761632520.0001920476734959739.60238367479864e-05
1540.9995270943099060.0009458113801882660.000472905690094133
1550.9992372821587420.001525435682515190.000762717841257597
1560.9959046428898650.008190714220270450.00409535711013523
1570.9883097026666420.02338059466671540.0116902973333577

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.96207692639189 & 0.075846147216221 & 0.0379230736081105 \tabularnewline
8 & 0.925465960291248 & 0.149068079417504 & 0.0745340397087521 \tabularnewline
9 & 0.87199407616112 & 0.25601184767776 & 0.12800592383888 \tabularnewline
10 & 0.911138157182432 & 0.177723685635136 & 0.088861842817568 \tabularnewline
11 & 0.871944759579876 & 0.256110480840248 & 0.128055240420124 \tabularnewline
12 & 0.87143839077244 & 0.25712321845512 & 0.12856160922756 \tabularnewline
13 & 0.816651322067685 & 0.366697355864629 & 0.183348677932315 \tabularnewline
14 & 0.939269818787936 & 0.121460362424127 & 0.0607301812120635 \tabularnewline
15 & 0.911223335428172 & 0.177553329143657 & 0.0887766645718283 \tabularnewline
16 & 0.8981001758811 & 0.203799648237799 & 0.101899824118899 \tabularnewline
17 & 0.921308043113798 & 0.157383913772404 & 0.078691956886202 \tabularnewline
18 & 0.9060204228184 & 0.187959154363201 & 0.0939795771816004 \tabularnewline
19 & 0.871291577130131 & 0.257416845739738 & 0.128708422869869 \tabularnewline
20 & 0.835084694752395 & 0.32983061049521 & 0.164915305247605 \tabularnewline
21 & 0.80176470959453 & 0.396470580810939 & 0.19823529040547 \tabularnewline
22 & 0.979650915240188 & 0.0406981695196245 & 0.0203490847598122 \tabularnewline
23 & 0.970545755249396 & 0.058908489501207 & 0.0294542447506035 \tabularnewline
24 & 0.96151529601569 & 0.0769694079686205 & 0.0384847039843102 \tabularnewline
25 & 0.959675158173457 & 0.0806496836530851 & 0.0403248418265426 \tabularnewline
26 & 0.966503530472015 & 0.0669929390559701 & 0.0334964695279851 \tabularnewline
27 & 0.955491319926878 & 0.0890173601462437 & 0.0445086800731219 \tabularnewline
28 & 0.940438735917641 & 0.119122528164717 & 0.0595612640823585 \tabularnewline
29 & 0.920899404621115 & 0.158201190757771 & 0.0791005953788853 \tabularnewline
30 & 0.924951837006278 & 0.150096325987444 & 0.0750481629937218 \tabularnewline
31 & 0.913684347225034 & 0.172631305549932 & 0.086315652774966 \tabularnewline
32 & 0.909346955079058 & 0.181306089841884 & 0.0906530449209419 \tabularnewline
33 & 0.896804722751913 & 0.206390554496173 & 0.103195277248087 \tabularnewline
34 & 0.892781599948399 & 0.214436800103202 & 0.107218400051601 \tabularnewline
35 & 0.948788218597443 & 0.102423562805114 & 0.0512117814025572 \tabularnewline
36 & 0.942492675147835 & 0.115014649704329 & 0.0575073248521646 \tabularnewline
37 & 0.953687781779203 & 0.0926244364415937 & 0.0463122182207969 \tabularnewline
38 & 0.940252838842262 & 0.119494322315477 & 0.0597471611577383 \tabularnewline
39 & 0.924597145593598 & 0.150805708812803 & 0.0754028544064017 \tabularnewline
40 & 0.906802535022075 & 0.186394929955851 & 0.0931974649779254 \tabularnewline
41 & 0.935098238212821 & 0.129803523574357 & 0.0649017617871786 \tabularnewline
42 & 0.922615071216627 & 0.154769857566747 & 0.0773849287833733 \tabularnewline
43 & 0.902517000665791 & 0.194965998668418 & 0.097482999334209 \tabularnewline
44 & 0.890881301094965 & 0.218237397810069 & 0.109118698905035 \tabularnewline
45 & 0.885204262730588 & 0.229591474538823 & 0.114795737269412 \tabularnewline
46 & 0.956096439343672 & 0.0878071213126553 & 0.0439035606563276 \tabularnewline
47 & 0.961121817051731 & 0.0777563658965374 & 0.0388781829482687 \tabularnewline
48 & 0.950598867824034 & 0.0988022643519317 & 0.0494011321759658 \tabularnewline
49 & 0.94000231519326 & 0.119995369613478 & 0.0599976848067392 \tabularnewline
50 & 0.927831912832292 & 0.144336174335416 & 0.072168087167708 \tabularnewline
51 & 0.91184060280883 & 0.176318794382339 & 0.0881593971911694 \tabularnewline
52 & 0.896436543296406 & 0.207126913407187 & 0.103563456703594 \tabularnewline
53 & 0.951401519352678 & 0.0971969612946443 & 0.0485984806473222 \tabularnewline
54 & 0.942855032576292 & 0.114289934847415 & 0.0571449674237075 \tabularnewline
55 & 0.930913819287101 & 0.138172361425797 & 0.0690861807128987 \tabularnewline
56 & 0.948540898672683 & 0.102918202654635 & 0.0514591013273174 \tabularnewline
57 & 0.93778572095207 & 0.124428558095862 & 0.0622142790479309 \tabularnewline
58 & 0.923532662685974 & 0.152934674628052 & 0.076467337314026 \tabularnewline
59 & 0.921105481460337 & 0.157789037079326 & 0.0788945185396628 \tabularnewline
60 & 0.902438046394831 & 0.195123907210338 & 0.0975619536051691 \tabularnewline
61 & 0.897717735979958 & 0.204564528040083 & 0.102282264020042 \tabularnewline
62 & 0.901474673211026 & 0.197050653577947 & 0.0985253267889737 \tabularnewline
63 & 0.901945085864758 & 0.196109828270484 & 0.0980549141352422 \tabularnewline
64 & 0.880867662506878 & 0.238264674986244 & 0.119132337493122 \tabularnewline
65 & 0.856552020532742 & 0.286895958934515 & 0.143447979467258 \tabularnewline
66 & 0.833252111489989 & 0.333495777020023 & 0.166747888510011 \tabularnewline
67 & 0.842176281149986 & 0.315647437700028 & 0.157823718850014 \tabularnewline
68 & 0.875231651354847 & 0.249536697290306 & 0.124768348645153 \tabularnewline
69 & 0.850591596141263 & 0.298816807717473 & 0.149408403858737 \tabularnewline
70 & 0.826519406540154 & 0.346961186919692 & 0.173480593459846 \tabularnewline
71 & 0.79906007215042 & 0.401879855699162 & 0.200939927849581 \tabularnewline
72 & 0.796700424037265 & 0.40659915192547 & 0.203299575962735 \tabularnewline
73 & 0.76867001676047 & 0.462659966479059 & 0.23132998323953 \tabularnewline
74 & 0.73673673627046 & 0.52652652745908 & 0.26326326372954 \tabularnewline
75 & 0.715782455054406 & 0.568435089891189 & 0.284217544945594 \tabularnewline
76 & 0.724142946699361 & 0.551714106601278 & 0.275857053300639 \tabularnewline
77 & 0.76392277716672 & 0.472154445666561 & 0.23607722283328 \tabularnewline
78 & 0.768086739884262 & 0.463826520231476 & 0.231913260115738 \tabularnewline
79 & 0.756522942540368 & 0.486954114919264 & 0.243477057459632 \tabularnewline
80 & 0.730373383388873 & 0.539253233222253 & 0.269626616611127 \tabularnewline
81 & 0.692498407502595 & 0.61500318499481 & 0.307501592497405 \tabularnewline
82 & 0.814306870736141 & 0.371386258527718 & 0.185693129263859 \tabularnewline
83 & 0.783140127128316 & 0.433719745743368 & 0.216859872871684 \tabularnewline
84 & 0.753392141551103 & 0.493215716897793 & 0.246607858448897 \tabularnewline
85 & 0.725310854379231 & 0.549378291241538 & 0.274689145620769 \tabularnewline
86 & 0.690726365104271 & 0.618547269791458 & 0.309273634895729 \tabularnewline
87 & 0.665656391940953 & 0.668687216118093 & 0.334343608059047 \tabularnewline
88 & 0.634680382505629 & 0.730639234988742 & 0.365319617494371 \tabularnewline
89 & 0.59318635710413 & 0.81362728579174 & 0.40681364289587 \tabularnewline
90 & 0.628959481331797 & 0.742081037336406 & 0.371040518668203 \tabularnewline
91 & 0.58832630516859 & 0.82334738966282 & 0.41167369483141 \tabularnewline
92 & 0.543461929019511 & 0.913076141960978 & 0.456538070980489 \tabularnewline
93 & 0.502351051564523 & 0.995297896870954 & 0.497648948435477 \tabularnewline
94 & 0.470020373105604 & 0.940040746211208 & 0.529979626894396 \tabularnewline
95 & 0.467931617392938 & 0.935863234785877 & 0.532068382607062 \tabularnewline
96 & 0.434944671676348 & 0.869889343352696 & 0.565055328323652 \tabularnewline
97 & 0.428271506616665 & 0.85654301323333 & 0.571728493383335 \tabularnewline
98 & 0.428078747222221 & 0.856157494444442 & 0.571921252777779 \tabularnewline
99 & 0.429201965990938 & 0.858403931981875 & 0.570798034009062 \tabularnewline
100 & 0.601538641575809 & 0.796922716848382 & 0.398461358424191 \tabularnewline
101 & 0.57650657734167 & 0.84698684531666 & 0.42349342265833 \tabularnewline
102 & 0.57399860301392 & 0.85200279397216 & 0.42600139698608 \tabularnewline
103 & 0.687976837699785 & 0.62404632460043 & 0.312023162300215 \tabularnewline
104 & 0.698548793653746 & 0.602902412692508 & 0.301451206346254 \tabularnewline
105 & 0.666833403711181 & 0.666333192577638 & 0.333166596288819 \tabularnewline
106 & 0.668660986711618 & 0.662678026576764 & 0.331339013288382 \tabularnewline
107 & 0.649930383337565 & 0.70013923332487 & 0.350069616662435 \tabularnewline
108 & 0.626489814480188 & 0.747020371039624 & 0.373510185519812 \tabularnewline
109 & 0.609286601465008 & 0.781426797069984 & 0.390713398534992 \tabularnewline
110 & 0.68985981443037 & 0.62028037113926 & 0.31014018556963 \tabularnewline
111 & 0.690119104943272 & 0.619761790113456 & 0.309880895056728 \tabularnewline
112 & 0.65310213108556 & 0.69379573782888 & 0.34689786891444 \tabularnewline
113 & 0.609097974337151 & 0.781804051325698 & 0.390902025662849 \tabularnewline
114 & 0.574293493086888 & 0.851413013826224 & 0.425706506913112 \tabularnewline
115 & 0.526034265540485 & 0.94793146891903 & 0.473965734459515 \tabularnewline
116 & 0.545335616479451 & 0.909328767041097 & 0.454664383520549 \tabularnewline
117 & 0.775064676987366 & 0.449870646025269 & 0.224935323012634 \tabularnewline
118 & 0.735927093483233 & 0.528145813033534 & 0.264072906516767 \tabularnewline
119 & 0.820222524759636 & 0.359554950480727 & 0.179777475240364 \tabularnewline
120 & 0.786919609135392 & 0.426160781729216 & 0.213080390864608 \tabularnewline
121 & 0.757275457905492 & 0.485449084189016 & 0.242724542094508 \tabularnewline
122 & 0.721314406583802 & 0.557371186832396 & 0.278685593416198 \tabularnewline
123 & 0.795999897331229 & 0.408000205337542 & 0.204000102668771 \tabularnewline
124 & 0.77132957497826 & 0.45734085004348 & 0.22867042502174 \tabularnewline
125 & 0.73888737017699 & 0.522225259646021 & 0.26111262982301 \tabularnewline
126 & 0.71046143007327 & 0.579077139853461 & 0.28953856992673 \tabularnewline
127 & 0.663995687160441 & 0.672008625679117 & 0.336004312839559 \tabularnewline
128 & 0.660246374307609 & 0.679507251384782 & 0.339753625692391 \tabularnewline
129 & 0.736998435896218 & 0.526003128207563 & 0.263001564103782 \tabularnewline
130 & 0.687472878570577 & 0.625054242858847 & 0.312527121429423 \tabularnewline
131 & 0.703675365079409 & 0.592649269841182 & 0.296324634920591 \tabularnewline
132 & 0.692014744737851 & 0.615970510524298 & 0.307985255262149 \tabularnewline
133 & 0.667191879660298 & 0.665616240679403 & 0.332808120339702 \tabularnewline
134 & 0.990580976571655 & 0.0188380468566908 & 0.00941902342834542 \tabularnewline
135 & 0.986787568227788 & 0.0264248635444232 & 0.0132124317722116 \tabularnewline
136 & 0.981146746233544 & 0.0377065075329119 & 0.018853253766456 \tabularnewline
137 & 0.983312847728447 & 0.0333743045431061 & 0.0166871522715531 \tabularnewline
138 & 0.983023812404094 & 0.0339523751918123 & 0.0169761875959061 \tabularnewline
139 & 0.99496859258195 & 0.0100628148360989 & 0.00503140741804943 \tabularnewline
140 & 0.991824669020677 & 0.0163506619586464 & 0.00817533097932318 \tabularnewline
141 & 0.99999998324084 & 3.35183209871494e-08 & 1.67591604935747e-08 \tabularnewline
142 & 0.999999980678882 & 3.86422368929187e-08 & 1.93211184464593e-08 \tabularnewline
143 & 0.99999994460598 & 1.10788039574631e-07 & 5.53940197873153e-08 \tabularnewline
144 & 0.999999784925334 & 4.30149332853657e-07 & 2.15074666426829e-07 \tabularnewline
145 & 0.999999750926722 & 4.98146555459615e-07 & 2.49073277729808e-07 \tabularnewline
146 & 0.999999997603152 & 4.79369516161665e-09 & 2.39684758080832e-09 \tabularnewline
147 & 0.999999991494438 & 1.70111233888427e-08 & 8.50556169442134e-09 \tabularnewline
148 & 0.999999979062647 & 4.1874706272619e-08 & 2.09373531363095e-08 \tabularnewline
149 & 0.999999874870108 & 2.50259783162278e-07 & 1.25129891581139e-07 \tabularnewline
150 & 0.999999445810487 & 1.10837902668573e-06 & 5.54189513342863e-07 \tabularnewline
151 & 0.99999666266781 & 6.67466437867945e-06 & 3.33733218933973e-06 \tabularnewline
152 & 0.99998177779763 & 3.64444047389576e-05 & 1.82222023694788e-05 \tabularnewline
153 & 0.999903976163252 & 0.000192047673495973 & 9.60238367479864e-05 \tabularnewline
154 & 0.999527094309906 & 0.000945811380188266 & 0.000472905690094133 \tabularnewline
155 & 0.999237282158742 & 0.00152543568251519 & 0.000762717841257597 \tabularnewline
156 & 0.995904642889865 & 0.00819071422027045 & 0.00409535711013523 \tabularnewline
157 & 0.988309702666642 & 0.0233805946667154 & 0.0116902973333577 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&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]7[/C][C]0.96207692639189[/C][C]0.075846147216221[/C][C]0.0379230736081105[/C][/ROW]
[ROW][C]8[/C][C]0.925465960291248[/C][C]0.149068079417504[/C][C]0.0745340397087521[/C][/ROW]
[ROW][C]9[/C][C]0.87199407616112[/C][C]0.25601184767776[/C][C]0.12800592383888[/C][/ROW]
[ROW][C]10[/C][C]0.911138157182432[/C][C]0.177723685635136[/C][C]0.088861842817568[/C][/ROW]
[ROW][C]11[/C][C]0.871944759579876[/C][C]0.256110480840248[/C][C]0.128055240420124[/C][/ROW]
[ROW][C]12[/C][C]0.87143839077244[/C][C]0.25712321845512[/C][C]0.12856160922756[/C][/ROW]
[ROW][C]13[/C][C]0.816651322067685[/C][C]0.366697355864629[/C][C]0.183348677932315[/C][/ROW]
[ROW][C]14[/C][C]0.939269818787936[/C][C]0.121460362424127[/C][C]0.0607301812120635[/C][/ROW]
[ROW][C]15[/C][C]0.911223335428172[/C][C]0.177553329143657[/C][C]0.0887766645718283[/C][/ROW]
[ROW][C]16[/C][C]0.8981001758811[/C][C]0.203799648237799[/C][C]0.101899824118899[/C][/ROW]
[ROW][C]17[/C][C]0.921308043113798[/C][C]0.157383913772404[/C][C]0.078691956886202[/C][/ROW]
[ROW][C]18[/C][C]0.9060204228184[/C][C]0.187959154363201[/C][C]0.0939795771816004[/C][/ROW]
[ROW][C]19[/C][C]0.871291577130131[/C][C]0.257416845739738[/C][C]0.128708422869869[/C][/ROW]
[ROW][C]20[/C][C]0.835084694752395[/C][C]0.32983061049521[/C][C]0.164915305247605[/C][/ROW]
[ROW][C]21[/C][C]0.80176470959453[/C][C]0.396470580810939[/C][C]0.19823529040547[/C][/ROW]
[ROW][C]22[/C][C]0.979650915240188[/C][C]0.0406981695196245[/C][C]0.0203490847598122[/C][/ROW]
[ROW][C]23[/C][C]0.970545755249396[/C][C]0.058908489501207[/C][C]0.0294542447506035[/C][/ROW]
[ROW][C]24[/C][C]0.96151529601569[/C][C]0.0769694079686205[/C][C]0.0384847039843102[/C][/ROW]
[ROW][C]25[/C][C]0.959675158173457[/C][C]0.0806496836530851[/C][C]0.0403248418265426[/C][/ROW]
[ROW][C]26[/C][C]0.966503530472015[/C][C]0.0669929390559701[/C][C]0.0334964695279851[/C][/ROW]
[ROW][C]27[/C][C]0.955491319926878[/C][C]0.0890173601462437[/C][C]0.0445086800731219[/C][/ROW]
[ROW][C]28[/C][C]0.940438735917641[/C][C]0.119122528164717[/C][C]0.0595612640823585[/C][/ROW]
[ROW][C]29[/C][C]0.920899404621115[/C][C]0.158201190757771[/C][C]0.0791005953788853[/C][/ROW]
[ROW][C]30[/C][C]0.924951837006278[/C][C]0.150096325987444[/C][C]0.0750481629937218[/C][/ROW]
[ROW][C]31[/C][C]0.913684347225034[/C][C]0.172631305549932[/C][C]0.086315652774966[/C][/ROW]
[ROW][C]32[/C][C]0.909346955079058[/C][C]0.181306089841884[/C][C]0.0906530449209419[/C][/ROW]
[ROW][C]33[/C][C]0.896804722751913[/C][C]0.206390554496173[/C][C]0.103195277248087[/C][/ROW]
[ROW][C]34[/C][C]0.892781599948399[/C][C]0.214436800103202[/C][C]0.107218400051601[/C][/ROW]
[ROW][C]35[/C][C]0.948788218597443[/C][C]0.102423562805114[/C][C]0.0512117814025572[/C][/ROW]
[ROW][C]36[/C][C]0.942492675147835[/C][C]0.115014649704329[/C][C]0.0575073248521646[/C][/ROW]
[ROW][C]37[/C][C]0.953687781779203[/C][C]0.0926244364415937[/C][C]0.0463122182207969[/C][/ROW]
[ROW][C]38[/C][C]0.940252838842262[/C][C]0.119494322315477[/C][C]0.0597471611577383[/C][/ROW]
[ROW][C]39[/C][C]0.924597145593598[/C][C]0.150805708812803[/C][C]0.0754028544064017[/C][/ROW]
[ROW][C]40[/C][C]0.906802535022075[/C][C]0.186394929955851[/C][C]0.0931974649779254[/C][/ROW]
[ROW][C]41[/C][C]0.935098238212821[/C][C]0.129803523574357[/C][C]0.0649017617871786[/C][/ROW]
[ROW][C]42[/C][C]0.922615071216627[/C][C]0.154769857566747[/C][C]0.0773849287833733[/C][/ROW]
[ROW][C]43[/C][C]0.902517000665791[/C][C]0.194965998668418[/C][C]0.097482999334209[/C][/ROW]
[ROW][C]44[/C][C]0.890881301094965[/C][C]0.218237397810069[/C][C]0.109118698905035[/C][/ROW]
[ROW][C]45[/C][C]0.885204262730588[/C][C]0.229591474538823[/C][C]0.114795737269412[/C][/ROW]
[ROW][C]46[/C][C]0.956096439343672[/C][C]0.0878071213126553[/C][C]0.0439035606563276[/C][/ROW]
[ROW][C]47[/C][C]0.961121817051731[/C][C]0.0777563658965374[/C][C]0.0388781829482687[/C][/ROW]
[ROW][C]48[/C][C]0.950598867824034[/C][C]0.0988022643519317[/C][C]0.0494011321759658[/C][/ROW]
[ROW][C]49[/C][C]0.94000231519326[/C][C]0.119995369613478[/C][C]0.0599976848067392[/C][/ROW]
[ROW][C]50[/C][C]0.927831912832292[/C][C]0.144336174335416[/C][C]0.072168087167708[/C][/ROW]
[ROW][C]51[/C][C]0.91184060280883[/C][C]0.176318794382339[/C][C]0.0881593971911694[/C][/ROW]
[ROW][C]52[/C][C]0.896436543296406[/C][C]0.207126913407187[/C][C]0.103563456703594[/C][/ROW]
[ROW][C]53[/C][C]0.951401519352678[/C][C]0.0971969612946443[/C][C]0.0485984806473222[/C][/ROW]
[ROW][C]54[/C][C]0.942855032576292[/C][C]0.114289934847415[/C][C]0.0571449674237075[/C][/ROW]
[ROW][C]55[/C][C]0.930913819287101[/C][C]0.138172361425797[/C][C]0.0690861807128987[/C][/ROW]
[ROW][C]56[/C][C]0.948540898672683[/C][C]0.102918202654635[/C][C]0.0514591013273174[/C][/ROW]
[ROW][C]57[/C][C]0.93778572095207[/C][C]0.124428558095862[/C][C]0.0622142790479309[/C][/ROW]
[ROW][C]58[/C][C]0.923532662685974[/C][C]0.152934674628052[/C][C]0.076467337314026[/C][/ROW]
[ROW][C]59[/C][C]0.921105481460337[/C][C]0.157789037079326[/C][C]0.0788945185396628[/C][/ROW]
[ROW][C]60[/C][C]0.902438046394831[/C][C]0.195123907210338[/C][C]0.0975619536051691[/C][/ROW]
[ROW][C]61[/C][C]0.897717735979958[/C][C]0.204564528040083[/C][C]0.102282264020042[/C][/ROW]
[ROW][C]62[/C][C]0.901474673211026[/C][C]0.197050653577947[/C][C]0.0985253267889737[/C][/ROW]
[ROW][C]63[/C][C]0.901945085864758[/C][C]0.196109828270484[/C][C]0.0980549141352422[/C][/ROW]
[ROW][C]64[/C][C]0.880867662506878[/C][C]0.238264674986244[/C][C]0.119132337493122[/C][/ROW]
[ROW][C]65[/C][C]0.856552020532742[/C][C]0.286895958934515[/C][C]0.143447979467258[/C][/ROW]
[ROW][C]66[/C][C]0.833252111489989[/C][C]0.333495777020023[/C][C]0.166747888510011[/C][/ROW]
[ROW][C]67[/C][C]0.842176281149986[/C][C]0.315647437700028[/C][C]0.157823718850014[/C][/ROW]
[ROW][C]68[/C][C]0.875231651354847[/C][C]0.249536697290306[/C][C]0.124768348645153[/C][/ROW]
[ROW][C]69[/C][C]0.850591596141263[/C][C]0.298816807717473[/C][C]0.149408403858737[/C][/ROW]
[ROW][C]70[/C][C]0.826519406540154[/C][C]0.346961186919692[/C][C]0.173480593459846[/C][/ROW]
[ROW][C]71[/C][C]0.79906007215042[/C][C]0.401879855699162[/C][C]0.200939927849581[/C][/ROW]
[ROW][C]72[/C][C]0.796700424037265[/C][C]0.40659915192547[/C][C]0.203299575962735[/C][/ROW]
[ROW][C]73[/C][C]0.76867001676047[/C][C]0.462659966479059[/C][C]0.23132998323953[/C][/ROW]
[ROW][C]74[/C][C]0.73673673627046[/C][C]0.52652652745908[/C][C]0.26326326372954[/C][/ROW]
[ROW][C]75[/C][C]0.715782455054406[/C][C]0.568435089891189[/C][C]0.284217544945594[/C][/ROW]
[ROW][C]76[/C][C]0.724142946699361[/C][C]0.551714106601278[/C][C]0.275857053300639[/C][/ROW]
[ROW][C]77[/C][C]0.76392277716672[/C][C]0.472154445666561[/C][C]0.23607722283328[/C][/ROW]
[ROW][C]78[/C][C]0.768086739884262[/C][C]0.463826520231476[/C][C]0.231913260115738[/C][/ROW]
[ROW][C]79[/C][C]0.756522942540368[/C][C]0.486954114919264[/C][C]0.243477057459632[/C][/ROW]
[ROW][C]80[/C][C]0.730373383388873[/C][C]0.539253233222253[/C][C]0.269626616611127[/C][/ROW]
[ROW][C]81[/C][C]0.692498407502595[/C][C]0.61500318499481[/C][C]0.307501592497405[/C][/ROW]
[ROW][C]82[/C][C]0.814306870736141[/C][C]0.371386258527718[/C][C]0.185693129263859[/C][/ROW]
[ROW][C]83[/C][C]0.783140127128316[/C][C]0.433719745743368[/C][C]0.216859872871684[/C][/ROW]
[ROW][C]84[/C][C]0.753392141551103[/C][C]0.493215716897793[/C][C]0.246607858448897[/C][/ROW]
[ROW][C]85[/C][C]0.725310854379231[/C][C]0.549378291241538[/C][C]0.274689145620769[/C][/ROW]
[ROW][C]86[/C][C]0.690726365104271[/C][C]0.618547269791458[/C][C]0.309273634895729[/C][/ROW]
[ROW][C]87[/C][C]0.665656391940953[/C][C]0.668687216118093[/C][C]0.334343608059047[/C][/ROW]
[ROW][C]88[/C][C]0.634680382505629[/C][C]0.730639234988742[/C][C]0.365319617494371[/C][/ROW]
[ROW][C]89[/C][C]0.59318635710413[/C][C]0.81362728579174[/C][C]0.40681364289587[/C][/ROW]
[ROW][C]90[/C][C]0.628959481331797[/C][C]0.742081037336406[/C][C]0.371040518668203[/C][/ROW]
[ROW][C]91[/C][C]0.58832630516859[/C][C]0.82334738966282[/C][C]0.41167369483141[/C][/ROW]
[ROW][C]92[/C][C]0.543461929019511[/C][C]0.913076141960978[/C][C]0.456538070980489[/C][/ROW]
[ROW][C]93[/C][C]0.502351051564523[/C][C]0.995297896870954[/C][C]0.497648948435477[/C][/ROW]
[ROW][C]94[/C][C]0.470020373105604[/C][C]0.940040746211208[/C][C]0.529979626894396[/C][/ROW]
[ROW][C]95[/C][C]0.467931617392938[/C][C]0.935863234785877[/C][C]0.532068382607062[/C][/ROW]
[ROW][C]96[/C][C]0.434944671676348[/C][C]0.869889343352696[/C][C]0.565055328323652[/C][/ROW]
[ROW][C]97[/C][C]0.428271506616665[/C][C]0.85654301323333[/C][C]0.571728493383335[/C][/ROW]
[ROW][C]98[/C][C]0.428078747222221[/C][C]0.856157494444442[/C][C]0.571921252777779[/C][/ROW]
[ROW][C]99[/C][C]0.429201965990938[/C][C]0.858403931981875[/C][C]0.570798034009062[/C][/ROW]
[ROW][C]100[/C][C]0.601538641575809[/C][C]0.796922716848382[/C][C]0.398461358424191[/C][/ROW]
[ROW][C]101[/C][C]0.57650657734167[/C][C]0.84698684531666[/C][C]0.42349342265833[/C][/ROW]
[ROW][C]102[/C][C]0.57399860301392[/C][C]0.85200279397216[/C][C]0.42600139698608[/C][/ROW]
[ROW][C]103[/C][C]0.687976837699785[/C][C]0.62404632460043[/C][C]0.312023162300215[/C][/ROW]
[ROW][C]104[/C][C]0.698548793653746[/C][C]0.602902412692508[/C][C]0.301451206346254[/C][/ROW]
[ROW][C]105[/C][C]0.666833403711181[/C][C]0.666333192577638[/C][C]0.333166596288819[/C][/ROW]
[ROW][C]106[/C][C]0.668660986711618[/C][C]0.662678026576764[/C][C]0.331339013288382[/C][/ROW]
[ROW][C]107[/C][C]0.649930383337565[/C][C]0.70013923332487[/C][C]0.350069616662435[/C][/ROW]
[ROW][C]108[/C][C]0.626489814480188[/C][C]0.747020371039624[/C][C]0.373510185519812[/C][/ROW]
[ROW][C]109[/C][C]0.609286601465008[/C][C]0.781426797069984[/C][C]0.390713398534992[/C][/ROW]
[ROW][C]110[/C][C]0.68985981443037[/C][C]0.62028037113926[/C][C]0.31014018556963[/C][/ROW]
[ROW][C]111[/C][C]0.690119104943272[/C][C]0.619761790113456[/C][C]0.309880895056728[/C][/ROW]
[ROW][C]112[/C][C]0.65310213108556[/C][C]0.69379573782888[/C][C]0.34689786891444[/C][/ROW]
[ROW][C]113[/C][C]0.609097974337151[/C][C]0.781804051325698[/C][C]0.390902025662849[/C][/ROW]
[ROW][C]114[/C][C]0.574293493086888[/C][C]0.851413013826224[/C][C]0.425706506913112[/C][/ROW]
[ROW][C]115[/C][C]0.526034265540485[/C][C]0.94793146891903[/C][C]0.473965734459515[/C][/ROW]
[ROW][C]116[/C][C]0.545335616479451[/C][C]0.909328767041097[/C][C]0.454664383520549[/C][/ROW]
[ROW][C]117[/C][C]0.775064676987366[/C][C]0.449870646025269[/C][C]0.224935323012634[/C][/ROW]
[ROW][C]118[/C][C]0.735927093483233[/C][C]0.528145813033534[/C][C]0.264072906516767[/C][/ROW]
[ROW][C]119[/C][C]0.820222524759636[/C][C]0.359554950480727[/C][C]0.179777475240364[/C][/ROW]
[ROW][C]120[/C][C]0.786919609135392[/C][C]0.426160781729216[/C][C]0.213080390864608[/C][/ROW]
[ROW][C]121[/C][C]0.757275457905492[/C][C]0.485449084189016[/C][C]0.242724542094508[/C][/ROW]
[ROW][C]122[/C][C]0.721314406583802[/C][C]0.557371186832396[/C][C]0.278685593416198[/C][/ROW]
[ROW][C]123[/C][C]0.795999897331229[/C][C]0.408000205337542[/C][C]0.204000102668771[/C][/ROW]
[ROW][C]124[/C][C]0.77132957497826[/C][C]0.45734085004348[/C][C]0.22867042502174[/C][/ROW]
[ROW][C]125[/C][C]0.73888737017699[/C][C]0.522225259646021[/C][C]0.26111262982301[/C][/ROW]
[ROW][C]126[/C][C]0.71046143007327[/C][C]0.579077139853461[/C][C]0.28953856992673[/C][/ROW]
[ROW][C]127[/C][C]0.663995687160441[/C][C]0.672008625679117[/C][C]0.336004312839559[/C][/ROW]
[ROW][C]128[/C][C]0.660246374307609[/C][C]0.679507251384782[/C][C]0.339753625692391[/C][/ROW]
[ROW][C]129[/C][C]0.736998435896218[/C][C]0.526003128207563[/C][C]0.263001564103782[/C][/ROW]
[ROW][C]130[/C][C]0.687472878570577[/C][C]0.625054242858847[/C][C]0.312527121429423[/C][/ROW]
[ROW][C]131[/C][C]0.703675365079409[/C][C]0.592649269841182[/C][C]0.296324634920591[/C][/ROW]
[ROW][C]132[/C][C]0.692014744737851[/C][C]0.615970510524298[/C][C]0.307985255262149[/C][/ROW]
[ROW][C]133[/C][C]0.667191879660298[/C][C]0.665616240679403[/C][C]0.332808120339702[/C][/ROW]
[ROW][C]134[/C][C]0.990580976571655[/C][C]0.0188380468566908[/C][C]0.00941902342834542[/C][/ROW]
[ROW][C]135[/C][C]0.986787568227788[/C][C]0.0264248635444232[/C][C]0.0132124317722116[/C][/ROW]
[ROW][C]136[/C][C]0.981146746233544[/C][C]0.0377065075329119[/C][C]0.018853253766456[/C][/ROW]
[ROW][C]137[/C][C]0.983312847728447[/C][C]0.0333743045431061[/C][C]0.0166871522715531[/C][/ROW]
[ROW][C]138[/C][C]0.983023812404094[/C][C]0.0339523751918123[/C][C]0.0169761875959061[/C][/ROW]
[ROW][C]139[/C][C]0.99496859258195[/C][C]0.0100628148360989[/C][C]0.00503140741804943[/C][/ROW]
[ROW][C]140[/C][C]0.991824669020677[/C][C]0.0163506619586464[/C][C]0.00817533097932318[/C][/ROW]
[ROW][C]141[/C][C]0.99999998324084[/C][C]3.35183209871494e-08[/C][C]1.67591604935747e-08[/C][/ROW]
[ROW][C]142[/C][C]0.999999980678882[/C][C]3.86422368929187e-08[/C][C]1.93211184464593e-08[/C][/ROW]
[ROW][C]143[/C][C]0.99999994460598[/C][C]1.10788039574631e-07[/C][C]5.53940197873153e-08[/C][/ROW]
[ROW][C]144[/C][C]0.999999784925334[/C][C]4.30149332853657e-07[/C][C]2.15074666426829e-07[/C][/ROW]
[ROW][C]145[/C][C]0.999999750926722[/C][C]4.98146555459615e-07[/C][C]2.49073277729808e-07[/C][/ROW]
[ROW][C]146[/C][C]0.999999997603152[/C][C]4.79369516161665e-09[/C][C]2.39684758080832e-09[/C][/ROW]
[ROW][C]147[/C][C]0.999999991494438[/C][C]1.70111233888427e-08[/C][C]8.50556169442134e-09[/C][/ROW]
[ROW][C]148[/C][C]0.999999979062647[/C][C]4.1874706272619e-08[/C][C]2.09373531363095e-08[/C][/ROW]
[ROW][C]149[/C][C]0.999999874870108[/C][C]2.50259783162278e-07[/C][C]1.25129891581139e-07[/C][/ROW]
[ROW][C]150[/C][C]0.999999445810487[/C][C]1.10837902668573e-06[/C][C]5.54189513342863e-07[/C][/ROW]
[ROW][C]151[/C][C]0.99999666266781[/C][C]6.67466437867945e-06[/C][C]3.33733218933973e-06[/C][/ROW]
[ROW][C]152[/C][C]0.99998177779763[/C][C]3.64444047389576e-05[/C][C]1.82222023694788e-05[/C][/ROW]
[ROW][C]153[/C][C]0.999903976163252[/C][C]0.000192047673495973[/C][C]9.60238367479864e-05[/C][/ROW]
[ROW][C]154[/C][C]0.999527094309906[/C][C]0.000945811380188266[/C][C]0.000472905690094133[/C][/ROW]
[ROW][C]155[/C][C]0.999237282158742[/C][C]0.00152543568251519[/C][C]0.000762717841257597[/C][/ROW]
[ROW][C]156[/C][C]0.995904642889865[/C][C]0.00819071422027045[/C][C]0.00409535711013523[/C][/ROW]
[ROW][C]157[/C][C]0.988309702666642[/C][C]0.0233805946667154[/C][C]0.0116902973333577[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.962076926391890.0758461472162210.0379230736081105
80.9254659602912480.1490680794175040.0745340397087521
90.871994076161120.256011847677760.12800592383888
100.9111381571824320.1777236856351360.088861842817568
110.8719447595798760.2561104808402480.128055240420124
120.871438390772440.257123218455120.12856160922756
130.8166513220676850.3666973558646290.183348677932315
140.9392698187879360.1214603624241270.0607301812120635
150.9112233354281720.1775533291436570.0887766645718283
160.89810017588110.2037996482377990.101899824118899
170.9213080431137980.1573839137724040.078691956886202
180.90602042281840.1879591543632010.0939795771816004
190.8712915771301310.2574168457397380.128708422869869
200.8350846947523950.329830610495210.164915305247605
210.801764709594530.3964705808109390.19823529040547
220.9796509152401880.04069816951962450.0203490847598122
230.9705457552493960.0589084895012070.0294542447506035
240.961515296015690.07696940796862050.0384847039843102
250.9596751581734570.08064968365308510.0403248418265426
260.9665035304720150.06699293905597010.0334964695279851
270.9554913199268780.08901736014624370.0445086800731219
280.9404387359176410.1191225281647170.0595612640823585
290.9208994046211150.1582011907577710.0791005953788853
300.9249518370062780.1500963259874440.0750481629937218
310.9136843472250340.1726313055499320.086315652774966
320.9093469550790580.1813060898418840.0906530449209419
330.8968047227519130.2063905544961730.103195277248087
340.8927815999483990.2144368001032020.107218400051601
350.9487882185974430.1024235628051140.0512117814025572
360.9424926751478350.1150146497043290.0575073248521646
370.9536877817792030.09262443644159370.0463122182207969
380.9402528388422620.1194943223154770.0597471611577383
390.9245971455935980.1508057088128030.0754028544064017
400.9068025350220750.1863949299558510.0931974649779254
410.9350982382128210.1298035235743570.0649017617871786
420.9226150712166270.1547698575667470.0773849287833733
430.9025170006657910.1949659986684180.097482999334209
440.8908813010949650.2182373978100690.109118698905035
450.8852042627305880.2295914745388230.114795737269412
460.9560964393436720.08780712131265530.0439035606563276
470.9611218170517310.07775636589653740.0388781829482687
480.9505988678240340.09880226435193170.0494011321759658
490.940002315193260.1199953696134780.0599976848067392
500.9278319128322920.1443361743354160.072168087167708
510.911840602808830.1763187943823390.0881593971911694
520.8964365432964060.2071269134071870.103563456703594
530.9514015193526780.09719696129464430.0485984806473222
540.9428550325762920.1142899348474150.0571449674237075
550.9309138192871010.1381723614257970.0690861807128987
560.9485408986726830.1029182026546350.0514591013273174
570.937785720952070.1244285580958620.0622142790479309
580.9235326626859740.1529346746280520.076467337314026
590.9211054814603370.1577890370793260.0788945185396628
600.9024380463948310.1951239072103380.0975619536051691
610.8977177359799580.2045645280400830.102282264020042
620.9014746732110260.1970506535779470.0985253267889737
630.9019450858647580.1961098282704840.0980549141352422
640.8808676625068780.2382646749862440.119132337493122
650.8565520205327420.2868959589345150.143447979467258
660.8332521114899890.3334957770200230.166747888510011
670.8421762811499860.3156474377000280.157823718850014
680.8752316513548470.2495366972903060.124768348645153
690.8505915961412630.2988168077174730.149408403858737
700.8265194065401540.3469611869196920.173480593459846
710.799060072150420.4018798556991620.200939927849581
720.7967004240372650.406599151925470.203299575962735
730.768670016760470.4626599664790590.23132998323953
740.736736736270460.526526527459080.26326326372954
750.7157824550544060.5684350898911890.284217544945594
760.7241429466993610.5517141066012780.275857053300639
770.763922777166720.4721544456665610.23607722283328
780.7680867398842620.4638265202314760.231913260115738
790.7565229425403680.4869541149192640.243477057459632
800.7303733833888730.5392532332222530.269626616611127
810.6924984075025950.615003184994810.307501592497405
820.8143068707361410.3713862585277180.185693129263859
830.7831401271283160.4337197457433680.216859872871684
840.7533921415511030.4932157168977930.246607858448897
850.7253108543792310.5493782912415380.274689145620769
860.6907263651042710.6185472697914580.309273634895729
870.6656563919409530.6686872161180930.334343608059047
880.6346803825056290.7306392349887420.365319617494371
890.593186357104130.813627285791740.40681364289587
900.6289594813317970.7420810373364060.371040518668203
910.588326305168590.823347389662820.41167369483141
920.5434619290195110.9130761419609780.456538070980489
930.5023510515645230.9952978968709540.497648948435477
940.4700203731056040.9400407462112080.529979626894396
950.4679316173929380.9358632347858770.532068382607062
960.4349446716763480.8698893433526960.565055328323652
970.4282715066166650.856543013233330.571728493383335
980.4280787472222210.8561574944444420.571921252777779
990.4292019659909380.8584039319818750.570798034009062
1000.6015386415758090.7969227168483820.398461358424191
1010.576506577341670.846986845316660.42349342265833
1020.573998603013920.852002793972160.42600139698608
1030.6879768376997850.624046324600430.312023162300215
1040.6985487936537460.6029024126925080.301451206346254
1050.6668334037111810.6663331925776380.333166596288819
1060.6686609867116180.6626780265767640.331339013288382
1070.6499303833375650.700139233324870.350069616662435
1080.6264898144801880.7470203710396240.373510185519812
1090.6092866014650080.7814267970699840.390713398534992
1100.689859814430370.620280371139260.31014018556963
1110.6901191049432720.6197617901134560.309880895056728
1120.653102131085560.693795737828880.34689786891444
1130.6090979743371510.7818040513256980.390902025662849
1140.5742934930868880.8514130138262240.425706506913112
1150.5260342655404850.947931468919030.473965734459515
1160.5453356164794510.9093287670410970.454664383520549
1170.7750646769873660.4498706460252690.224935323012634
1180.7359270934832330.5281458130335340.264072906516767
1190.8202225247596360.3595549504807270.179777475240364
1200.7869196091353920.4261607817292160.213080390864608
1210.7572754579054920.4854490841890160.242724542094508
1220.7213144065838020.5573711868323960.278685593416198
1230.7959998973312290.4080002053375420.204000102668771
1240.771329574978260.457340850043480.22867042502174
1250.738887370176990.5222252596460210.26111262982301
1260.710461430073270.5790771398534610.28953856992673
1270.6639956871604410.6720086256791170.336004312839559
1280.6602463743076090.6795072513847820.339753625692391
1290.7369984358962180.5260031282075630.263001564103782
1300.6874728785705770.6250542428588470.312527121429423
1310.7036753650794090.5926492698411820.296324634920591
1320.6920147447378510.6159705105242980.307985255262149
1330.6671918796602980.6656162406794030.332808120339702
1340.9905809765716550.01883804685669080.00941902342834542
1350.9867875682277880.02642486354442320.0132124317722116
1360.9811467462335440.03770650753291190.018853253766456
1370.9833128477284470.03337430454310610.0166871522715531
1380.9830238124040940.03395237519181230.0169761875959061
1390.994968592581950.01006281483609890.00503140741804943
1400.9918246690206770.01635066195864640.00817533097932318
1410.999999983240843.35183209871494e-081.67591604935747e-08
1420.9999999806788823.86422368929187e-081.93211184464593e-08
1430.999999944605981.10788039574631e-075.53940197873153e-08
1440.9999997849253344.30149332853657e-072.15074666426829e-07
1450.9999997509267224.98146555459615e-072.49073277729808e-07
1460.9999999976031524.79369516161665e-092.39684758080832e-09
1470.9999999914944381.70111233888427e-088.50556169442134e-09
1480.9999999790626474.1874706272619e-082.09373531363095e-08
1490.9999998748701082.50259783162278e-071.25129891581139e-07
1500.9999994458104871.10837902668573e-065.54189513342863e-07
1510.999996662667816.67466437867945e-063.33733218933973e-06
1520.999981777797633.64444047389576e-051.82222023694788e-05
1530.9999039761632520.0001920476734959739.60238367479864e-05
1540.9995270943099060.0009458113801882660.000472905690094133
1550.9992372821587420.001525435682515190.000762717841257597
1560.9959046428898650.008190714220270450.00409535711013523
1570.9883097026666420.02338059466671540.0116902973333577







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level160.105960264900662NOK
5% type I error level250.165562913907285NOK
10% type I error level360.23841059602649NOK

\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 & 16 & 0.105960264900662 & NOK \tabularnewline
5% type I error level & 25 & 0.165562913907285 & NOK \tabularnewline
10% type I error level & 36 & 0.23841059602649 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145966&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]16[/C][C]0.105960264900662[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]25[/C][C]0.165562913907285[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]36[/C][C]0.23841059602649[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145966&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145966&T=6

As an alternative you can also use a 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 level160.105960264900662NOK
5% type I error level250.165562913907285NOK
10% type I error level360.23841059602649NOK



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