Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Module--
Title produced by softwareMultiple Regression
Date of computationFri, 07 Dec 2012 09:09:13 -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/2012/Dec/07/t1354889393uh4n5rt7ya1tf9p.htm/, Retrieved Thu, 31 Oct 2024 23:38:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=197387, Retrieved Thu, 31 Oct 2024 23:38:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-12-11 14:35:40] [b4c8fd31b0af00c33711722ddf8d2c4c]
-   PD    [Multiple Regression] [] [2011-12-12 10:13:09] [74be16979710d4c4e7c6647856088456]
-  M          [Multiple Regression] [ws 10] [2012-12-07 14:09:13] [f4c84c9faf29e2061c3a475b218c0eb5] [Current]
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Dataseries X:
0	0	264530	165119
0	0	135248	107269
0	0	207253	93497
0	0	202898	100269
0	0	145249	91627
0	0	65295	47552
0	0	439387	233933
0	0	33186	6853
0	0	183696	104380
0	0	190673	98431
0	0	287239	156949
0	0	205260	81817
0	0	141987	59238
0	0	322679	101138
0	0	199717	107158
0	0	349227	155499
0	0	276709	156274
0	0	273576	121777
0	0	157448	105037
0	0	242782	118661
0	0	256814	131187
0	0	405874	145026
0	0	161189	107016
0	0	156189	87242
0	0	200181	91699
0	0	192645	110087
0	0	249893	145447
0	0	241171	143307
0	0	143182	61678
0	0	285266	210080
0	0	243048	165005
0	0	176062	97806
0	0	305210	184471
0	0	87995	27786
0	0	343613	184458
0	0	264159	98765
0	0	394976	178441
0	0	192718	100619
0	0	114673	58391
0	0	310108	151672
0	0	292891	124437
0	0	157518	79929
0	0	180362	123064
0	0	146175	50466
0	0	140319	100991
0	0	405267	79367
0	0	78800	56968
0	0	201970	106257
0	0	305322	178412
0	0	164733	98520
0	1	199186	153670
0	1	24188	15049
0	1	346142	174478
0	1	65029	25109
0	1	101097	45824
0	1	255082	116772
0	1	287314	189150
1	1	308944	194404
1	1	280943	185881
1	1	225816	67508
1	1	348943	188597
1	1	283283	203618
1	1	199642	87232
1	1	232791	110875
1	1	212262	144756
1	1	201345	129825
1	1	180424	92189
1	1	204450	121158
1	1	197813	96219
1	1	138731	84128
1	1	216153	97960
1	1	73566	23824
1	1	219392	103515
1	1	181728	91313
1	1	150006	85407
1	1	325723	95871
1	1	265348	143846
1	1	202410	155387
1	1	173420	74429
1	1	162366	74004
1	1	136341	71987
1	1	390163	150629
1	1	145905	68580
1	1	238921	119855
1	1	80953	55792
1	1	133301	25157
1	1	138630	90895
1	1	334082	117510
1	1	277542	144774
1	1	170849	77529
1	1	236398	103123
1	1	207178	104669
1	1	157125	82414
1	1	242395	82390
1	1	273632	128446
1	1	178489	111542
1	1	207720	136048
1	1	268066	197257
1	1	349934	162079
1	1	368833	206286
1	1	247804	109858
1	1	265849	182125
1	1	174311	74168
1	1	43287	19630
1	1	176724	88634
1	1	189021	128321
1	1	237531	118936
1	1	279589	127044
1	1	106655	178377
1	1	135798	69581
1	1	290495	168019
1	1	266805	113598
1	1	23623	5841
1	1	174970	93116
1	1	61857	24610
1	1	147760	60611
1	1	358662	226620
1	1	21054	6622
1	1	230091	121996
1	1	31414	13155
1	1	284519	154158
1	1	209481	78489
1	1	161691	22007
1	1	137093	72530
1	1	38214	13983
1	1	166059	73397
1	1	319346	143878
1	1	186273	119956
1	1	374212	181558
1	1	275578	208236
1	1	368863	237085
1	1	179928	110297
1	1	94381	61394
1	1	251253	81420
1	1	382564	191154
1	1	118033	11798
1	1	370878	135724
1	1	147989	68614
1	1	236370	139926
1	1	193220	105203
1	1	189020	80338
1	1	341992	121376
1	1	224936	124922
1	1	173260	10901
1	1	286161	135471
1	1	130908	66395
1	1	209639	134041
1	1	262412	153554
1	1	1	0
1	1	14688	7953
1	1	98	0
1	1	455	0
1	1	0	0
1	1	0	0
1	1	195822	98922
1	1	347930	165395
1	1	0	0
1	1	203	0
1	1	7199	4245
1	1	46660	21509
1	1	17547	7670
1	1	107465	15167
1	1	969	0
1	1	179994	63891




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time10 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 & 10 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&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]10 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=197387&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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 time10 seconds
R Server'George Udny Yule' @ yule.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Time_RFC_sec[t] = + 48827.3436391444 + 17275.6764601015Pop[t] -23157.0959244618Gender[t] + 1.52537075933019Compendium_writing_time_sec[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Time_RFC_sec[t] =  +  48827.3436391444 +  17275.6764601015Pop[t] -23157.0959244618Gender[t] +  1.52537075933019Compendium_writing_time_sec[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Time_RFC_sec[t] =  +  48827.3436391444 +  17275.6764601015Pop[t] -23157.0959244618Gender[t] +  1.52537075933019Compendium_writing_time_sec[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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
Time_RFC_sec[t] = + 48827.3436391444 + 17275.6764601015Pop[t] -23157.0959244618Gender[t] + 1.52537075933019Compendium_writing_time_sec[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)48827.343639144411111.7124244.39422e-051e-05
Pop17275.676460101520538.7028040.84110.4015320.200766
Gender-23157.095924461821253.494797-1.08960.2775420.138771
Compendium_writing_time_sec1.525370759330190.07248821.04300

\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) & 48827.3436391444 & 11111.712424 & 4.3942 & 2e-05 & 1e-05 \tabularnewline
Pop & 17275.6764601015 & 20538.702804 & 0.8411 & 0.401532 & 0.200766 \tabularnewline
Gender & -23157.0959244618 & 21253.494797 & -1.0896 & 0.277542 & 0.138771 \tabularnewline
Compendium_writing_time_sec & 1.52537075933019 & 0.072488 & 21.043 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&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]48827.3436391444[/C][C]11111.712424[/C][C]4.3942[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]Pop[/C][C]17275.6764601015[/C][C]20538.702804[/C][C]0.8411[/C][C]0.401532[/C][C]0.200766[/C][/ROW]
[ROW][C]Gender[/C][C]-23157.0959244618[/C][C]21253.494797[/C][C]-1.0896[/C][C]0.277542[/C][C]0.138771[/C][/ROW]
[ROW][C]Compendium_writing_time_sec[/C][C]1.52537075933019[/C][C]0.072488[/C][C]21.043[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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)48827.343639144411111.7124244.39422e-051e-05
Pop17275.676460101520538.7028040.84110.4015320.200766
Gender-23157.095924461821253.494797-1.08960.2775420.138771
Compendium_writing_time_sec1.525370759330190.07248821.04300







Multiple Linear Regression - Regression Statistics
Multiple R0.860703455173917
R-squared0.740810437748319
Adjusted R-squared0.7359506334561
F-TEST (value)152.43626969391
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation52628.8020674177
Sum Squared Residuals443166529128.228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.860703455173917 \tabularnewline
R-squared & 0.740810437748319 \tabularnewline
Adjusted R-squared & 0.7359506334561 \tabularnewline
F-TEST (value) & 152.43626969391 \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 & 52628.8020674177 \tabularnewline
Sum Squared Residuals & 443166529128.228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.860703455173917[/C][/ROW]
[ROW][C]R-squared[/C][C]0.740810437748319[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.7359506334561[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]152.43626969391[/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]52628.8020674177[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]443166529128.228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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 R0.860703455173917
R-squared0.740810437748319
Adjusted R-squared0.7359506334561
F-TEST (value)152.43626969391
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation52628.8020674177
Sum Squared Residuals443166529128.228







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1264530300695.038048986-36165.0380489857
2135248212452.339621735-77204.3396217345
3207253191444.93352423915808.0664757609
4202898201774.7443064231123.25569357675
5145249188592.490204292-43343.4902042917
665295121361.773986814-56066.7739868136
7439387405661.90148153433725.0985184664
83318659280.7094528342-26094.7094528342
9183696208045.54349803-24349.5434980296
10190673198971.112850774-8298.11285077429
11287239288232.758945258-993.758945258312
12205260173628.60305526331631.3969447375
13141987139187.2566803462799.74331965383
14322679203100.291496281119578.708503719
15199717212283.023467449-12566.0234674488
16349227286020.9713442363206.0286557705
17276709287203.13368271-10494.1336827104
18273576234582.41859809738993.5814019031
19157448209047.71208691-51599.7120869095
20242782229829.36331202412952.636687976
21256814248936.1574433947877.84255660603
22405874270045.763381764135828.236618236
23161189212066.420819624-50877.420819624
24156189181903.739424629-25714.7394246288
25200181188702.31689896311478.6831010365
26192645216750.834421527-24105.834421527
27249893270687.944471442-20794.9444714425
28241171267423.651046476-26252.6510464759
29143182142909.161333112272.838666888178
30285266369277.232759231-84011.2327592306
31243048300521.145782422-57473.1457824223
32176062198017.756126193-21955.7561261929
33305210330214.012983544-25004.0129835438
348799591211.295557893-3216.29555789304
35343613330194.18316367313418.8168363275
36264159199480.58668439164678.4133156094
37394976321016.02730478373959.9726952172
38192718202308.624072189-9590.62407218874
39114673137895.267647193-23222.2676471935
40310108280183.37744827329924.6225517271
41292891238639.90481791554251.0951820848
42157518170748.703061647-13230.7030616471
43180362236545.570765355-56183.5707653548
44146175125806.70437950220368.2956204983
45140319202876.06199466-62557.0619946596
46405267169891.444694904235375.555305096
4778800135724.665056667-56924.6650566666
48201970210908.664413292-8938.66441329235
49305322320971.791552762-15649.7915527622
50164733199106.870848355-34373.8708483547
51199186260073.972300953-60887.9723009528
522418848625.5522718426-24437.5522718426
53346142291813.88706109554328.1129389046
546502963970.78211070431058.21788929567
5510109795568.83739022925528.16260977078
56255082203790.84202318751291.1579768125
57287314314194.126841988-26880.126841988
58308944339484.10127161-30540.1012716102
59280943326483.366289839-45540.366289839
60225816145920.65339564779895.3466043535
61348943330626.2732721818316.7267278201
62283283353538.867448079-70255.8674480786
63199642176007.06625267523634.9337473248
64232791212071.40711551920719.5928844811
65212262263752.493812385-51490.493812385
66201345240977.183004826-39632.1830048259
67180424183568.329106675-3144.32910667493
68204450227756.794633711-23306.7946337112
69197813189715.5732667768097.42673322441
70138731171272.315415714-32541.3154157143
71216153192371.24375876923781.7562412305
727356679286.3571450665-5720.35714506651
73219392200844.67832684918547.3216731513
74181728182232.104321502-504.104321501682
75150006173223.264616898-23217.2646168976
76325723189184.744242529136538.255757471
77265348262364.4064213942983.59357860548
78202410279968.710354824-77558.7103548243
79173420156477.74442097116942.2555790292
80162366155829.4618482556536.53815174456
81136341152752.789026686-16411.7890266864
82390163272710.996281931117452.003718069
83145905147555.850849648-1650.85084964848
84238921225769.23653430413151.763465696
8580953128049.409579334-47096.409579334
8613330181319.676367253651981.3236327463
87138630181594.499344102-42964.4993441017
88334082222192.242103675111889.757896325
89277542263779.95048605313762.0495139471
90170849161206.3937748949642.60622510564
91236398200246.73298919136151.2670108088
92207178202604.9561831164573.0438168843
93157125168657.829934222-11532.8299342223
94242395168621.22103599873773.7789640016
95273632238873.6967277134758.3032722904
96178489213088.829411992-34599.8294119921
97207720250469.565240138-42749.5652401377
98268066343835.984047979-75769.9840479793
99349934290176.49147626259757.5085237381
100368833357608.55663397211224.4433660284
101247804210520.1050532837283.89494672
102265849320754.073717795-54905.0737177948
103174311156079.62265278618231.3773472144
1044328772888.9521804357-29601.9521804357
105176724178145.636057256-1421.63605725611
106189021238683.025382793-49662.0253827933
107237531224367.4208064813163.5791935205
108279589236735.12692312942853.8730768713
109106655315036.984111825-208381.984111825
110135798149082.746979738-13284.746979738
111290495299237.193786683-8742.19378668319
112266805216224.99169317550580.0083068251
1132362351855.6147800317-28232.6147800317
114174970184982.347800574-10012.347800574
1156185780485.2985619001-18628.2985619001
116147760135400.17126854612359.8287314538
117358662388625.445654192-29963.4456541917
1182105453046.9293430686-31992.9293430686
119230091229035.055330031055.94466997011
1203141463012.1765137728-31598.1765137728
121284519278094.0296916076424.97030839257
122209481162670.74970385146810.2502961487
12316169176514.758475363685176.2415246364
124137093153581.065349003-16488.0653490027
1253821464275.1835024982-26061.1835024982
126166059154903.56179734211155.438202658
127319346262413.21828569356932.7817143069
128186273225923.298980996-39650.2989809963
129374212319889.18849725554322.8115027453
130275578360583.029614665-85005.0296146654
131368863404588.450650582-35725.4506505821
132179928211189.742816626-31261.742816626
13394381136594.536573102-42213.5365731017
134251253167141.61139944884111.3886005519
135382564334526.64630378748037.3536962128
13611803360942.248393361757090.7516066383
137370878249975.345114115120902.654885885
138147989147607.713455466381.286544534287
139236370256384.95304482-20014.9530448202
140193220203419.504168598-10199.504168598
141189020165491.16023785323528.8397621471
142341992228089.325459245113902.674540755
143224936233498.29017183-8562.29017183003
14417326059573.9908222425113686.009177758
145286161249589.42631200436571.5736879958
146130908144222.915740512-13314.915740512
147209639247408.146126162-37769.146126162
148262412277172.705752972-14760.705752972
149142945.9241747841-42944.9241747841
1501468855077.1978237371-40389.1978237371
1519842945.9241747841-42847.9241747841
15245542945.9241747841-42490.9241747841
153042945.9241747841-42945.9241747841
154042945.9241747841-42945.9241747841
155195822193838.6504292451983.3495707549
156347930295234.62091420152695.3790857992
157042945.9241747841-42945.9241747841
15820342945.9241747841-42742.9241747841
159719949421.1230481408-42222.1230481408
1604666075755.1238372172-29095.1238372172
1611754754645.5178988467-37098.5178988467
16210746566081.222481545141383.7775184549
16396942945.9241747841-41976.9241747841
164179994140403.38735914939590.6126408508

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 264530 & 300695.038048986 & -36165.0380489857 \tabularnewline
2 & 135248 & 212452.339621735 & -77204.3396217345 \tabularnewline
3 & 207253 & 191444.933524239 & 15808.0664757609 \tabularnewline
4 & 202898 & 201774.744306423 & 1123.25569357675 \tabularnewline
5 & 145249 & 188592.490204292 & -43343.4902042917 \tabularnewline
6 & 65295 & 121361.773986814 & -56066.7739868136 \tabularnewline
7 & 439387 & 405661.901481534 & 33725.0985184664 \tabularnewline
8 & 33186 & 59280.7094528342 & -26094.7094528342 \tabularnewline
9 & 183696 & 208045.54349803 & -24349.5434980296 \tabularnewline
10 & 190673 & 198971.112850774 & -8298.11285077429 \tabularnewline
11 & 287239 & 288232.758945258 & -993.758945258312 \tabularnewline
12 & 205260 & 173628.603055263 & 31631.3969447375 \tabularnewline
13 & 141987 & 139187.256680346 & 2799.74331965383 \tabularnewline
14 & 322679 & 203100.291496281 & 119578.708503719 \tabularnewline
15 & 199717 & 212283.023467449 & -12566.0234674488 \tabularnewline
16 & 349227 & 286020.97134423 & 63206.0286557705 \tabularnewline
17 & 276709 & 287203.13368271 & -10494.1336827104 \tabularnewline
18 & 273576 & 234582.418598097 & 38993.5814019031 \tabularnewline
19 & 157448 & 209047.71208691 & -51599.7120869095 \tabularnewline
20 & 242782 & 229829.363312024 & 12952.636687976 \tabularnewline
21 & 256814 & 248936.157443394 & 7877.84255660603 \tabularnewline
22 & 405874 & 270045.763381764 & 135828.236618236 \tabularnewline
23 & 161189 & 212066.420819624 & -50877.420819624 \tabularnewline
24 & 156189 & 181903.739424629 & -25714.7394246288 \tabularnewline
25 & 200181 & 188702.316898963 & 11478.6831010365 \tabularnewline
26 & 192645 & 216750.834421527 & -24105.834421527 \tabularnewline
27 & 249893 & 270687.944471442 & -20794.9444714425 \tabularnewline
28 & 241171 & 267423.651046476 & -26252.6510464759 \tabularnewline
29 & 143182 & 142909.161333112 & 272.838666888178 \tabularnewline
30 & 285266 & 369277.232759231 & -84011.2327592306 \tabularnewline
31 & 243048 & 300521.145782422 & -57473.1457824223 \tabularnewline
32 & 176062 & 198017.756126193 & -21955.7561261929 \tabularnewline
33 & 305210 & 330214.012983544 & -25004.0129835438 \tabularnewline
34 & 87995 & 91211.295557893 & -3216.29555789304 \tabularnewline
35 & 343613 & 330194.183163673 & 13418.8168363275 \tabularnewline
36 & 264159 & 199480.586684391 & 64678.4133156094 \tabularnewline
37 & 394976 & 321016.027304783 & 73959.9726952172 \tabularnewline
38 & 192718 & 202308.624072189 & -9590.62407218874 \tabularnewline
39 & 114673 & 137895.267647193 & -23222.2676471935 \tabularnewline
40 & 310108 & 280183.377448273 & 29924.6225517271 \tabularnewline
41 & 292891 & 238639.904817915 & 54251.0951820848 \tabularnewline
42 & 157518 & 170748.703061647 & -13230.7030616471 \tabularnewline
43 & 180362 & 236545.570765355 & -56183.5707653548 \tabularnewline
44 & 146175 & 125806.704379502 & 20368.2956204983 \tabularnewline
45 & 140319 & 202876.06199466 & -62557.0619946596 \tabularnewline
46 & 405267 & 169891.444694904 & 235375.555305096 \tabularnewline
47 & 78800 & 135724.665056667 & -56924.6650566666 \tabularnewline
48 & 201970 & 210908.664413292 & -8938.66441329235 \tabularnewline
49 & 305322 & 320971.791552762 & -15649.7915527622 \tabularnewline
50 & 164733 & 199106.870848355 & -34373.8708483547 \tabularnewline
51 & 199186 & 260073.972300953 & -60887.9723009528 \tabularnewline
52 & 24188 & 48625.5522718426 & -24437.5522718426 \tabularnewline
53 & 346142 & 291813.887061095 & 54328.1129389046 \tabularnewline
54 & 65029 & 63970.7821107043 & 1058.21788929567 \tabularnewline
55 & 101097 & 95568.8373902292 & 5528.16260977078 \tabularnewline
56 & 255082 & 203790.842023187 & 51291.1579768125 \tabularnewline
57 & 287314 & 314194.126841988 & -26880.126841988 \tabularnewline
58 & 308944 & 339484.10127161 & -30540.1012716102 \tabularnewline
59 & 280943 & 326483.366289839 & -45540.366289839 \tabularnewline
60 & 225816 & 145920.653395647 & 79895.3466043535 \tabularnewline
61 & 348943 & 330626.27327218 & 18316.7267278201 \tabularnewline
62 & 283283 & 353538.867448079 & -70255.8674480786 \tabularnewline
63 & 199642 & 176007.066252675 & 23634.9337473248 \tabularnewline
64 & 232791 & 212071.407115519 & 20719.5928844811 \tabularnewline
65 & 212262 & 263752.493812385 & -51490.493812385 \tabularnewline
66 & 201345 & 240977.183004826 & -39632.1830048259 \tabularnewline
67 & 180424 & 183568.329106675 & -3144.32910667493 \tabularnewline
68 & 204450 & 227756.794633711 & -23306.7946337112 \tabularnewline
69 & 197813 & 189715.573266776 & 8097.42673322441 \tabularnewline
70 & 138731 & 171272.315415714 & -32541.3154157143 \tabularnewline
71 & 216153 & 192371.243758769 & 23781.7562412305 \tabularnewline
72 & 73566 & 79286.3571450665 & -5720.35714506651 \tabularnewline
73 & 219392 & 200844.678326849 & 18547.3216731513 \tabularnewline
74 & 181728 & 182232.104321502 & -504.104321501682 \tabularnewline
75 & 150006 & 173223.264616898 & -23217.2646168976 \tabularnewline
76 & 325723 & 189184.744242529 & 136538.255757471 \tabularnewline
77 & 265348 & 262364.406421394 & 2983.59357860548 \tabularnewline
78 & 202410 & 279968.710354824 & -77558.7103548243 \tabularnewline
79 & 173420 & 156477.744420971 & 16942.2555790292 \tabularnewline
80 & 162366 & 155829.461848255 & 6536.53815174456 \tabularnewline
81 & 136341 & 152752.789026686 & -16411.7890266864 \tabularnewline
82 & 390163 & 272710.996281931 & 117452.003718069 \tabularnewline
83 & 145905 & 147555.850849648 & -1650.85084964848 \tabularnewline
84 & 238921 & 225769.236534304 & 13151.763465696 \tabularnewline
85 & 80953 & 128049.409579334 & -47096.409579334 \tabularnewline
86 & 133301 & 81319.6763672536 & 51981.3236327463 \tabularnewline
87 & 138630 & 181594.499344102 & -42964.4993441017 \tabularnewline
88 & 334082 & 222192.242103675 & 111889.757896325 \tabularnewline
89 & 277542 & 263779.950486053 & 13762.0495139471 \tabularnewline
90 & 170849 & 161206.393774894 & 9642.60622510564 \tabularnewline
91 & 236398 & 200246.732989191 & 36151.2670108088 \tabularnewline
92 & 207178 & 202604.956183116 & 4573.0438168843 \tabularnewline
93 & 157125 & 168657.829934222 & -11532.8299342223 \tabularnewline
94 & 242395 & 168621.221035998 & 73773.7789640016 \tabularnewline
95 & 273632 & 238873.69672771 & 34758.3032722904 \tabularnewline
96 & 178489 & 213088.829411992 & -34599.8294119921 \tabularnewline
97 & 207720 & 250469.565240138 & -42749.5652401377 \tabularnewline
98 & 268066 & 343835.984047979 & -75769.9840479793 \tabularnewline
99 & 349934 & 290176.491476262 & 59757.5085237381 \tabularnewline
100 & 368833 & 357608.556633972 & 11224.4433660284 \tabularnewline
101 & 247804 & 210520.10505328 & 37283.89494672 \tabularnewline
102 & 265849 & 320754.073717795 & -54905.0737177948 \tabularnewline
103 & 174311 & 156079.622652786 & 18231.3773472144 \tabularnewline
104 & 43287 & 72888.9521804357 & -29601.9521804357 \tabularnewline
105 & 176724 & 178145.636057256 & -1421.63605725611 \tabularnewline
106 & 189021 & 238683.025382793 & -49662.0253827933 \tabularnewline
107 & 237531 & 224367.42080648 & 13163.5791935205 \tabularnewline
108 & 279589 & 236735.126923129 & 42853.8730768713 \tabularnewline
109 & 106655 & 315036.984111825 & -208381.984111825 \tabularnewline
110 & 135798 & 149082.746979738 & -13284.746979738 \tabularnewline
111 & 290495 & 299237.193786683 & -8742.19378668319 \tabularnewline
112 & 266805 & 216224.991693175 & 50580.0083068251 \tabularnewline
113 & 23623 & 51855.6147800317 & -28232.6147800317 \tabularnewline
114 & 174970 & 184982.347800574 & -10012.347800574 \tabularnewline
115 & 61857 & 80485.2985619001 & -18628.2985619001 \tabularnewline
116 & 147760 & 135400.171268546 & 12359.8287314538 \tabularnewline
117 & 358662 & 388625.445654192 & -29963.4456541917 \tabularnewline
118 & 21054 & 53046.9293430686 & -31992.9293430686 \tabularnewline
119 & 230091 & 229035.05533003 & 1055.94466997011 \tabularnewline
120 & 31414 & 63012.1765137728 & -31598.1765137728 \tabularnewline
121 & 284519 & 278094.029691607 & 6424.97030839257 \tabularnewline
122 & 209481 & 162670.749703851 & 46810.2502961487 \tabularnewline
123 & 161691 & 76514.7584753636 & 85176.2415246364 \tabularnewline
124 & 137093 & 153581.065349003 & -16488.0653490027 \tabularnewline
125 & 38214 & 64275.1835024982 & -26061.1835024982 \tabularnewline
126 & 166059 & 154903.561797342 & 11155.438202658 \tabularnewline
127 & 319346 & 262413.218285693 & 56932.7817143069 \tabularnewline
128 & 186273 & 225923.298980996 & -39650.2989809963 \tabularnewline
129 & 374212 & 319889.188497255 & 54322.8115027453 \tabularnewline
130 & 275578 & 360583.029614665 & -85005.0296146654 \tabularnewline
131 & 368863 & 404588.450650582 & -35725.4506505821 \tabularnewline
132 & 179928 & 211189.742816626 & -31261.742816626 \tabularnewline
133 & 94381 & 136594.536573102 & -42213.5365731017 \tabularnewline
134 & 251253 & 167141.611399448 & 84111.3886005519 \tabularnewline
135 & 382564 & 334526.646303787 & 48037.3536962128 \tabularnewline
136 & 118033 & 60942.2483933617 & 57090.7516066383 \tabularnewline
137 & 370878 & 249975.345114115 & 120902.654885885 \tabularnewline
138 & 147989 & 147607.713455466 & 381.286544534287 \tabularnewline
139 & 236370 & 256384.95304482 & -20014.9530448202 \tabularnewline
140 & 193220 & 203419.504168598 & -10199.504168598 \tabularnewline
141 & 189020 & 165491.160237853 & 23528.8397621471 \tabularnewline
142 & 341992 & 228089.325459245 & 113902.674540755 \tabularnewline
143 & 224936 & 233498.29017183 & -8562.29017183003 \tabularnewline
144 & 173260 & 59573.9908222425 & 113686.009177758 \tabularnewline
145 & 286161 & 249589.426312004 & 36571.5736879958 \tabularnewline
146 & 130908 & 144222.915740512 & -13314.915740512 \tabularnewline
147 & 209639 & 247408.146126162 & -37769.146126162 \tabularnewline
148 & 262412 & 277172.705752972 & -14760.705752972 \tabularnewline
149 & 1 & 42945.9241747841 & -42944.9241747841 \tabularnewline
150 & 14688 & 55077.1978237371 & -40389.1978237371 \tabularnewline
151 & 98 & 42945.9241747841 & -42847.9241747841 \tabularnewline
152 & 455 & 42945.9241747841 & -42490.9241747841 \tabularnewline
153 & 0 & 42945.9241747841 & -42945.9241747841 \tabularnewline
154 & 0 & 42945.9241747841 & -42945.9241747841 \tabularnewline
155 & 195822 & 193838.650429245 & 1983.3495707549 \tabularnewline
156 & 347930 & 295234.620914201 & 52695.3790857992 \tabularnewline
157 & 0 & 42945.9241747841 & -42945.9241747841 \tabularnewline
158 & 203 & 42945.9241747841 & -42742.9241747841 \tabularnewline
159 & 7199 & 49421.1230481408 & -42222.1230481408 \tabularnewline
160 & 46660 & 75755.1238372172 & -29095.1238372172 \tabularnewline
161 & 17547 & 54645.5178988467 & -37098.5178988467 \tabularnewline
162 & 107465 & 66081.2224815451 & 41383.7775184549 \tabularnewline
163 & 969 & 42945.9241747841 & -41976.9241747841 \tabularnewline
164 & 179994 & 140403.387359149 & 39590.6126408508 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&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]264530[/C][C]300695.038048986[/C][C]-36165.0380489857[/C][/ROW]
[ROW][C]2[/C][C]135248[/C][C]212452.339621735[/C][C]-77204.3396217345[/C][/ROW]
[ROW][C]3[/C][C]207253[/C][C]191444.933524239[/C][C]15808.0664757609[/C][/ROW]
[ROW][C]4[/C][C]202898[/C][C]201774.744306423[/C][C]1123.25569357675[/C][/ROW]
[ROW][C]5[/C][C]145249[/C][C]188592.490204292[/C][C]-43343.4902042917[/C][/ROW]
[ROW][C]6[/C][C]65295[/C][C]121361.773986814[/C][C]-56066.7739868136[/C][/ROW]
[ROW][C]7[/C][C]439387[/C][C]405661.901481534[/C][C]33725.0985184664[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]59280.7094528342[/C][C]-26094.7094528342[/C][/ROW]
[ROW][C]9[/C][C]183696[/C][C]208045.54349803[/C][C]-24349.5434980296[/C][/ROW]
[ROW][C]10[/C][C]190673[/C][C]198971.112850774[/C][C]-8298.11285077429[/C][/ROW]
[ROW][C]11[/C][C]287239[/C][C]288232.758945258[/C][C]-993.758945258312[/C][/ROW]
[ROW][C]12[/C][C]205260[/C][C]173628.603055263[/C][C]31631.3969447375[/C][/ROW]
[ROW][C]13[/C][C]141987[/C][C]139187.256680346[/C][C]2799.74331965383[/C][/ROW]
[ROW][C]14[/C][C]322679[/C][C]203100.291496281[/C][C]119578.708503719[/C][/ROW]
[ROW][C]15[/C][C]199717[/C][C]212283.023467449[/C][C]-12566.0234674488[/C][/ROW]
[ROW][C]16[/C][C]349227[/C][C]286020.97134423[/C][C]63206.0286557705[/C][/ROW]
[ROW][C]17[/C][C]276709[/C][C]287203.13368271[/C][C]-10494.1336827104[/C][/ROW]
[ROW][C]18[/C][C]273576[/C][C]234582.418598097[/C][C]38993.5814019031[/C][/ROW]
[ROW][C]19[/C][C]157448[/C][C]209047.71208691[/C][C]-51599.7120869095[/C][/ROW]
[ROW][C]20[/C][C]242782[/C][C]229829.363312024[/C][C]12952.636687976[/C][/ROW]
[ROW][C]21[/C][C]256814[/C][C]248936.157443394[/C][C]7877.84255660603[/C][/ROW]
[ROW][C]22[/C][C]405874[/C][C]270045.763381764[/C][C]135828.236618236[/C][/ROW]
[ROW][C]23[/C][C]161189[/C][C]212066.420819624[/C][C]-50877.420819624[/C][/ROW]
[ROW][C]24[/C][C]156189[/C][C]181903.739424629[/C][C]-25714.7394246288[/C][/ROW]
[ROW][C]25[/C][C]200181[/C][C]188702.316898963[/C][C]11478.6831010365[/C][/ROW]
[ROW][C]26[/C][C]192645[/C][C]216750.834421527[/C][C]-24105.834421527[/C][/ROW]
[ROW][C]27[/C][C]249893[/C][C]270687.944471442[/C][C]-20794.9444714425[/C][/ROW]
[ROW][C]28[/C][C]241171[/C][C]267423.651046476[/C][C]-26252.6510464759[/C][/ROW]
[ROW][C]29[/C][C]143182[/C][C]142909.161333112[/C][C]272.838666888178[/C][/ROW]
[ROW][C]30[/C][C]285266[/C][C]369277.232759231[/C][C]-84011.2327592306[/C][/ROW]
[ROW][C]31[/C][C]243048[/C][C]300521.145782422[/C][C]-57473.1457824223[/C][/ROW]
[ROW][C]32[/C][C]176062[/C][C]198017.756126193[/C][C]-21955.7561261929[/C][/ROW]
[ROW][C]33[/C][C]305210[/C][C]330214.012983544[/C][C]-25004.0129835438[/C][/ROW]
[ROW][C]34[/C][C]87995[/C][C]91211.295557893[/C][C]-3216.29555789304[/C][/ROW]
[ROW][C]35[/C][C]343613[/C][C]330194.183163673[/C][C]13418.8168363275[/C][/ROW]
[ROW][C]36[/C][C]264159[/C][C]199480.586684391[/C][C]64678.4133156094[/C][/ROW]
[ROW][C]37[/C][C]394976[/C][C]321016.027304783[/C][C]73959.9726952172[/C][/ROW]
[ROW][C]38[/C][C]192718[/C][C]202308.624072189[/C][C]-9590.62407218874[/C][/ROW]
[ROW][C]39[/C][C]114673[/C][C]137895.267647193[/C][C]-23222.2676471935[/C][/ROW]
[ROW][C]40[/C][C]310108[/C][C]280183.377448273[/C][C]29924.6225517271[/C][/ROW]
[ROW][C]41[/C][C]292891[/C][C]238639.904817915[/C][C]54251.0951820848[/C][/ROW]
[ROW][C]42[/C][C]157518[/C][C]170748.703061647[/C][C]-13230.7030616471[/C][/ROW]
[ROW][C]43[/C][C]180362[/C][C]236545.570765355[/C][C]-56183.5707653548[/C][/ROW]
[ROW][C]44[/C][C]146175[/C][C]125806.704379502[/C][C]20368.2956204983[/C][/ROW]
[ROW][C]45[/C][C]140319[/C][C]202876.06199466[/C][C]-62557.0619946596[/C][/ROW]
[ROW][C]46[/C][C]405267[/C][C]169891.444694904[/C][C]235375.555305096[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]135724.665056667[/C][C]-56924.6650566666[/C][/ROW]
[ROW][C]48[/C][C]201970[/C][C]210908.664413292[/C][C]-8938.66441329235[/C][/ROW]
[ROW][C]49[/C][C]305322[/C][C]320971.791552762[/C][C]-15649.7915527622[/C][/ROW]
[ROW][C]50[/C][C]164733[/C][C]199106.870848355[/C][C]-34373.8708483547[/C][/ROW]
[ROW][C]51[/C][C]199186[/C][C]260073.972300953[/C][C]-60887.9723009528[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]48625.5522718426[/C][C]-24437.5522718426[/C][/ROW]
[ROW][C]53[/C][C]346142[/C][C]291813.887061095[/C][C]54328.1129389046[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]63970.7821107043[/C][C]1058.21788929567[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]95568.8373902292[/C][C]5528.16260977078[/C][/ROW]
[ROW][C]56[/C][C]255082[/C][C]203790.842023187[/C][C]51291.1579768125[/C][/ROW]
[ROW][C]57[/C][C]287314[/C][C]314194.126841988[/C][C]-26880.126841988[/C][/ROW]
[ROW][C]58[/C][C]308944[/C][C]339484.10127161[/C][C]-30540.1012716102[/C][/ROW]
[ROW][C]59[/C][C]280943[/C][C]326483.366289839[/C][C]-45540.366289839[/C][/ROW]
[ROW][C]60[/C][C]225816[/C][C]145920.653395647[/C][C]79895.3466043535[/C][/ROW]
[ROW][C]61[/C][C]348943[/C][C]330626.27327218[/C][C]18316.7267278201[/C][/ROW]
[ROW][C]62[/C][C]283283[/C][C]353538.867448079[/C][C]-70255.8674480786[/C][/ROW]
[ROW][C]63[/C][C]199642[/C][C]176007.066252675[/C][C]23634.9337473248[/C][/ROW]
[ROW][C]64[/C][C]232791[/C][C]212071.407115519[/C][C]20719.5928844811[/C][/ROW]
[ROW][C]65[/C][C]212262[/C][C]263752.493812385[/C][C]-51490.493812385[/C][/ROW]
[ROW][C]66[/C][C]201345[/C][C]240977.183004826[/C][C]-39632.1830048259[/C][/ROW]
[ROW][C]67[/C][C]180424[/C][C]183568.329106675[/C][C]-3144.32910667493[/C][/ROW]
[ROW][C]68[/C][C]204450[/C][C]227756.794633711[/C][C]-23306.7946337112[/C][/ROW]
[ROW][C]69[/C][C]197813[/C][C]189715.573266776[/C][C]8097.42673322441[/C][/ROW]
[ROW][C]70[/C][C]138731[/C][C]171272.315415714[/C][C]-32541.3154157143[/C][/ROW]
[ROW][C]71[/C][C]216153[/C][C]192371.243758769[/C][C]23781.7562412305[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]79286.3571450665[/C][C]-5720.35714506651[/C][/ROW]
[ROW][C]73[/C][C]219392[/C][C]200844.678326849[/C][C]18547.3216731513[/C][/ROW]
[ROW][C]74[/C][C]181728[/C][C]182232.104321502[/C][C]-504.104321501682[/C][/ROW]
[ROW][C]75[/C][C]150006[/C][C]173223.264616898[/C][C]-23217.2646168976[/C][/ROW]
[ROW][C]76[/C][C]325723[/C][C]189184.744242529[/C][C]136538.255757471[/C][/ROW]
[ROW][C]77[/C][C]265348[/C][C]262364.406421394[/C][C]2983.59357860548[/C][/ROW]
[ROW][C]78[/C][C]202410[/C][C]279968.710354824[/C][C]-77558.7103548243[/C][/ROW]
[ROW][C]79[/C][C]173420[/C][C]156477.744420971[/C][C]16942.2555790292[/C][/ROW]
[ROW][C]80[/C][C]162366[/C][C]155829.461848255[/C][C]6536.53815174456[/C][/ROW]
[ROW][C]81[/C][C]136341[/C][C]152752.789026686[/C][C]-16411.7890266864[/C][/ROW]
[ROW][C]82[/C][C]390163[/C][C]272710.996281931[/C][C]117452.003718069[/C][/ROW]
[ROW][C]83[/C][C]145905[/C][C]147555.850849648[/C][C]-1650.85084964848[/C][/ROW]
[ROW][C]84[/C][C]238921[/C][C]225769.236534304[/C][C]13151.763465696[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]128049.409579334[/C][C]-47096.409579334[/C][/ROW]
[ROW][C]86[/C][C]133301[/C][C]81319.6763672536[/C][C]51981.3236327463[/C][/ROW]
[ROW][C]87[/C][C]138630[/C][C]181594.499344102[/C][C]-42964.4993441017[/C][/ROW]
[ROW][C]88[/C][C]334082[/C][C]222192.242103675[/C][C]111889.757896325[/C][/ROW]
[ROW][C]89[/C][C]277542[/C][C]263779.950486053[/C][C]13762.0495139471[/C][/ROW]
[ROW][C]90[/C][C]170849[/C][C]161206.393774894[/C][C]9642.60622510564[/C][/ROW]
[ROW][C]91[/C][C]236398[/C][C]200246.732989191[/C][C]36151.2670108088[/C][/ROW]
[ROW][C]92[/C][C]207178[/C][C]202604.956183116[/C][C]4573.0438168843[/C][/ROW]
[ROW][C]93[/C][C]157125[/C][C]168657.829934222[/C][C]-11532.8299342223[/C][/ROW]
[ROW][C]94[/C][C]242395[/C][C]168621.221035998[/C][C]73773.7789640016[/C][/ROW]
[ROW][C]95[/C][C]273632[/C][C]238873.69672771[/C][C]34758.3032722904[/C][/ROW]
[ROW][C]96[/C][C]178489[/C][C]213088.829411992[/C][C]-34599.8294119921[/C][/ROW]
[ROW][C]97[/C][C]207720[/C][C]250469.565240138[/C][C]-42749.5652401377[/C][/ROW]
[ROW][C]98[/C][C]268066[/C][C]343835.984047979[/C][C]-75769.9840479793[/C][/ROW]
[ROW][C]99[/C][C]349934[/C][C]290176.491476262[/C][C]59757.5085237381[/C][/ROW]
[ROW][C]100[/C][C]368833[/C][C]357608.556633972[/C][C]11224.4433660284[/C][/ROW]
[ROW][C]101[/C][C]247804[/C][C]210520.10505328[/C][C]37283.89494672[/C][/ROW]
[ROW][C]102[/C][C]265849[/C][C]320754.073717795[/C][C]-54905.0737177948[/C][/ROW]
[ROW][C]103[/C][C]174311[/C][C]156079.622652786[/C][C]18231.3773472144[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]72888.9521804357[/C][C]-29601.9521804357[/C][/ROW]
[ROW][C]105[/C][C]176724[/C][C]178145.636057256[/C][C]-1421.63605725611[/C][/ROW]
[ROW][C]106[/C][C]189021[/C][C]238683.025382793[/C][C]-49662.0253827933[/C][/ROW]
[ROW][C]107[/C][C]237531[/C][C]224367.42080648[/C][C]13163.5791935205[/C][/ROW]
[ROW][C]108[/C][C]279589[/C][C]236735.126923129[/C][C]42853.8730768713[/C][/ROW]
[ROW][C]109[/C][C]106655[/C][C]315036.984111825[/C][C]-208381.984111825[/C][/ROW]
[ROW][C]110[/C][C]135798[/C][C]149082.746979738[/C][C]-13284.746979738[/C][/ROW]
[ROW][C]111[/C][C]290495[/C][C]299237.193786683[/C][C]-8742.19378668319[/C][/ROW]
[ROW][C]112[/C][C]266805[/C][C]216224.991693175[/C][C]50580.0083068251[/C][/ROW]
[ROW][C]113[/C][C]23623[/C][C]51855.6147800317[/C][C]-28232.6147800317[/C][/ROW]
[ROW][C]114[/C][C]174970[/C][C]184982.347800574[/C][C]-10012.347800574[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]80485.2985619001[/C][C]-18628.2985619001[/C][/ROW]
[ROW][C]116[/C][C]147760[/C][C]135400.171268546[/C][C]12359.8287314538[/C][/ROW]
[ROW][C]117[/C][C]358662[/C][C]388625.445654192[/C][C]-29963.4456541917[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]53046.9293430686[/C][C]-31992.9293430686[/C][/ROW]
[ROW][C]119[/C][C]230091[/C][C]229035.05533003[/C][C]1055.94466997011[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]63012.1765137728[/C][C]-31598.1765137728[/C][/ROW]
[ROW][C]121[/C][C]284519[/C][C]278094.029691607[/C][C]6424.97030839257[/C][/ROW]
[ROW][C]122[/C][C]209481[/C][C]162670.749703851[/C][C]46810.2502961487[/C][/ROW]
[ROW][C]123[/C][C]161691[/C][C]76514.7584753636[/C][C]85176.2415246364[/C][/ROW]
[ROW][C]124[/C][C]137093[/C][C]153581.065349003[/C][C]-16488.0653490027[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]64275.1835024982[/C][C]-26061.1835024982[/C][/ROW]
[ROW][C]126[/C][C]166059[/C][C]154903.561797342[/C][C]11155.438202658[/C][/ROW]
[ROW][C]127[/C][C]319346[/C][C]262413.218285693[/C][C]56932.7817143069[/C][/ROW]
[ROW][C]128[/C][C]186273[/C][C]225923.298980996[/C][C]-39650.2989809963[/C][/ROW]
[ROW][C]129[/C][C]374212[/C][C]319889.188497255[/C][C]54322.8115027453[/C][/ROW]
[ROW][C]130[/C][C]275578[/C][C]360583.029614665[/C][C]-85005.0296146654[/C][/ROW]
[ROW][C]131[/C][C]368863[/C][C]404588.450650582[/C][C]-35725.4506505821[/C][/ROW]
[ROW][C]132[/C][C]179928[/C][C]211189.742816626[/C][C]-31261.742816626[/C][/ROW]
[ROW][C]133[/C][C]94381[/C][C]136594.536573102[/C][C]-42213.5365731017[/C][/ROW]
[ROW][C]134[/C][C]251253[/C][C]167141.611399448[/C][C]84111.3886005519[/C][/ROW]
[ROW][C]135[/C][C]382564[/C][C]334526.646303787[/C][C]48037.3536962128[/C][/ROW]
[ROW][C]136[/C][C]118033[/C][C]60942.2483933617[/C][C]57090.7516066383[/C][/ROW]
[ROW][C]137[/C][C]370878[/C][C]249975.345114115[/C][C]120902.654885885[/C][/ROW]
[ROW][C]138[/C][C]147989[/C][C]147607.713455466[/C][C]381.286544534287[/C][/ROW]
[ROW][C]139[/C][C]236370[/C][C]256384.95304482[/C][C]-20014.9530448202[/C][/ROW]
[ROW][C]140[/C][C]193220[/C][C]203419.504168598[/C][C]-10199.504168598[/C][/ROW]
[ROW][C]141[/C][C]189020[/C][C]165491.160237853[/C][C]23528.8397621471[/C][/ROW]
[ROW][C]142[/C][C]341992[/C][C]228089.325459245[/C][C]113902.674540755[/C][/ROW]
[ROW][C]143[/C][C]224936[/C][C]233498.29017183[/C][C]-8562.29017183003[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]59573.9908222425[/C][C]113686.009177758[/C][/ROW]
[ROW][C]145[/C][C]286161[/C][C]249589.426312004[/C][C]36571.5736879958[/C][/ROW]
[ROW][C]146[/C][C]130908[/C][C]144222.915740512[/C][C]-13314.915740512[/C][/ROW]
[ROW][C]147[/C][C]209639[/C][C]247408.146126162[/C][C]-37769.146126162[/C][/ROW]
[ROW][C]148[/C][C]262412[/C][C]277172.705752972[/C][C]-14760.705752972[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]42945.9241747841[/C][C]-42944.9241747841[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]55077.1978237371[/C][C]-40389.1978237371[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]42945.9241747841[/C][C]-42847.9241747841[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]42945.9241747841[/C][C]-42490.9241747841[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]42945.9241747841[/C][C]-42945.9241747841[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]42945.9241747841[/C][C]-42945.9241747841[/C][/ROW]
[ROW][C]155[/C][C]195822[/C][C]193838.650429245[/C][C]1983.3495707549[/C][/ROW]
[ROW][C]156[/C][C]347930[/C][C]295234.620914201[/C][C]52695.3790857992[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]42945.9241747841[/C][C]-42945.9241747841[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]42945.9241747841[/C][C]-42742.9241747841[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]49421.1230481408[/C][C]-42222.1230481408[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]75755.1238372172[/C][C]-29095.1238372172[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]54645.5178988467[/C][C]-37098.5178988467[/C][/ROW]
[ROW][C]162[/C][C]107465[/C][C]66081.2224815451[/C][C]41383.7775184549[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]42945.9241747841[/C][C]-41976.9241747841[/C][/ROW]
[ROW][C]164[/C][C]179994[/C][C]140403.387359149[/C][C]39590.6126408508[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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
1264530300695.038048986-36165.0380489857
2135248212452.339621735-77204.3396217345
3207253191444.93352423915808.0664757609
4202898201774.7443064231123.25569357675
5145249188592.490204292-43343.4902042917
665295121361.773986814-56066.7739868136
7439387405661.90148153433725.0985184664
83318659280.7094528342-26094.7094528342
9183696208045.54349803-24349.5434980296
10190673198971.112850774-8298.11285077429
11287239288232.758945258-993.758945258312
12205260173628.60305526331631.3969447375
13141987139187.2566803462799.74331965383
14322679203100.291496281119578.708503719
15199717212283.023467449-12566.0234674488
16349227286020.9713442363206.0286557705
17276709287203.13368271-10494.1336827104
18273576234582.41859809738993.5814019031
19157448209047.71208691-51599.7120869095
20242782229829.36331202412952.636687976
21256814248936.1574433947877.84255660603
22405874270045.763381764135828.236618236
23161189212066.420819624-50877.420819624
24156189181903.739424629-25714.7394246288
25200181188702.31689896311478.6831010365
26192645216750.834421527-24105.834421527
27249893270687.944471442-20794.9444714425
28241171267423.651046476-26252.6510464759
29143182142909.161333112272.838666888178
30285266369277.232759231-84011.2327592306
31243048300521.145782422-57473.1457824223
32176062198017.756126193-21955.7561261929
33305210330214.012983544-25004.0129835438
348799591211.295557893-3216.29555789304
35343613330194.18316367313418.8168363275
36264159199480.58668439164678.4133156094
37394976321016.02730478373959.9726952172
38192718202308.624072189-9590.62407218874
39114673137895.267647193-23222.2676471935
40310108280183.37744827329924.6225517271
41292891238639.90481791554251.0951820848
42157518170748.703061647-13230.7030616471
43180362236545.570765355-56183.5707653548
44146175125806.70437950220368.2956204983
45140319202876.06199466-62557.0619946596
46405267169891.444694904235375.555305096
4778800135724.665056667-56924.6650566666
48201970210908.664413292-8938.66441329235
49305322320971.791552762-15649.7915527622
50164733199106.870848355-34373.8708483547
51199186260073.972300953-60887.9723009528
522418848625.5522718426-24437.5522718426
53346142291813.88706109554328.1129389046
546502963970.78211070431058.21788929567
5510109795568.83739022925528.16260977078
56255082203790.84202318751291.1579768125
57287314314194.126841988-26880.126841988
58308944339484.10127161-30540.1012716102
59280943326483.366289839-45540.366289839
60225816145920.65339564779895.3466043535
61348943330626.2732721818316.7267278201
62283283353538.867448079-70255.8674480786
63199642176007.06625267523634.9337473248
64232791212071.40711551920719.5928844811
65212262263752.493812385-51490.493812385
66201345240977.183004826-39632.1830048259
67180424183568.329106675-3144.32910667493
68204450227756.794633711-23306.7946337112
69197813189715.5732667768097.42673322441
70138731171272.315415714-32541.3154157143
71216153192371.24375876923781.7562412305
727356679286.3571450665-5720.35714506651
73219392200844.67832684918547.3216731513
74181728182232.104321502-504.104321501682
75150006173223.264616898-23217.2646168976
76325723189184.744242529136538.255757471
77265348262364.4064213942983.59357860548
78202410279968.710354824-77558.7103548243
79173420156477.74442097116942.2555790292
80162366155829.4618482556536.53815174456
81136341152752.789026686-16411.7890266864
82390163272710.996281931117452.003718069
83145905147555.850849648-1650.85084964848
84238921225769.23653430413151.763465696
8580953128049.409579334-47096.409579334
8613330181319.676367253651981.3236327463
87138630181594.499344102-42964.4993441017
88334082222192.242103675111889.757896325
89277542263779.95048605313762.0495139471
90170849161206.3937748949642.60622510564
91236398200246.73298919136151.2670108088
92207178202604.9561831164573.0438168843
93157125168657.829934222-11532.8299342223
94242395168621.22103599873773.7789640016
95273632238873.6967277134758.3032722904
96178489213088.829411992-34599.8294119921
97207720250469.565240138-42749.5652401377
98268066343835.984047979-75769.9840479793
99349934290176.49147626259757.5085237381
100368833357608.55663397211224.4433660284
101247804210520.1050532837283.89494672
102265849320754.073717795-54905.0737177948
103174311156079.62265278618231.3773472144
1044328772888.9521804357-29601.9521804357
105176724178145.636057256-1421.63605725611
106189021238683.025382793-49662.0253827933
107237531224367.4208064813163.5791935205
108279589236735.12692312942853.8730768713
109106655315036.984111825-208381.984111825
110135798149082.746979738-13284.746979738
111290495299237.193786683-8742.19378668319
112266805216224.99169317550580.0083068251
1132362351855.6147800317-28232.6147800317
114174970184982.347800574-10012.347800574
1156185780485.2985619001-18628.2985619001
116147760135400.17126854612359.8287314538
117358662388625.445654192-29963.4456541917
1182105453046.9293430686-31992.9293430686
119230091229035.055330031055.94466997011
1203141463012.1765137728-31598.1765137728
121284519278094.0296916076424.97030839257
122209481162670.74970385146810.2502961487
12316169176514.758475363685176.2415246364
124137093153581.065349003-16488.0653490027
1253821464275.1835024982-26061.1835024982
126166059154903.56179734211155.438202658
127319346262413.21828569356932.7817143069
128186273225923.298980996-39650.2989809963
129374212319889.18849725554322.8115027453
130275578360583.029614665-85005.0296146654
131368863404588.450650582-35725.4506505821
132179928211189.742816626-31261.742816626
13394381136594.536573102-42213.5365731017
134251253167141.61139944884111.3886005519
135382564334526.64630378748037.3536962128
13611803360942.248393361757090.7516066383
137370878249975.345114115120902.654885885
138147989147607.713455466381.286544534287
139236370256384.95304482-20014.9530448202
140193220203419.504168598-10199.504168598
141189020165491.16023785323528.8397621471
142341992228089.325459245113902.674540755
143224936233498.29017183-8562.29017183003
14417326059573.9908222425113686.009177758
145286161249589.42631200436571.5736879958
146130908144222.915740512-13314.915740512
147209639247408.146126162-37769.146126162
148262412277172.705752972-14760.705752972
149142945.9241747841-42944.9241747841
1501468855077.1978237371-40389.1978237371
1519842945.9241747841-42847.9241747841
15245542945.9241747841-42490.9241747841
153042945.9241747841-42945.9241747841
154042945.9241747841-42945.9241747841
155195822193838.6504292451983.3495707549
156347930295234.62091420152695.3790857992
157042945.9241747841-42945.9241747841
15820342945.9241747841-42742.9241747841
159719949421.1230481408-42222.1230481408
1604666075755.1238372172-29095.1238372172
1611754754645.5178988467-37098.5178988467
16210746566081.222481545141383.7775184549
16396942945.9241747841-41976.9241747841
164179994140403.38735914939590.6126408508







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.5245296853875010.9509406292249970.475470314612499
80.4061374353941020.8122748707882030.593862564605898
90.2668682587256690.5337365174513370.733131741274331
100.1748653903924260.3497307807848520.825134609607574
110.1041178895875440.2082357791750890.895882110412456
120.1389170806219730.2778341612439450.861082919378028
130.1018669566512420.2037339133024840.898133043348758
140.5705283200320440.8589433599359120.429471679967956
150.4803202324163910.9606404648327820.519679767583609
160.4914321857122070.9828643714244140.508567814287793
170.4177231533142760.8354463066285510.582276846685724
180.3775596807241810.7551193614483620.622440319275819
190.3714623053937120.7429246107874240.628537694606288
200.3035966530227890.6071933060455780.696403346977211
210.2399122958361580.4798245916723160.760087704163842
220.5730180767336490.8539638465327030.426981923266351
230.5693221685804390.8613556628391210.430677831419561
240.5101862181219240.9796275637561520.489813781878076
250.4483579957751320.8967159915502640.551642004224868
260.3974810232781640.7949620465563270.602518976721836
270.3570595782445050.7141191564890090.642940421755495
280.3233149206151170.6466298412302340.676685079384883
290.2720398210924650.544079642184930.727960178907535
300.418522141723410.837044283446820.58147785827659
310.4315138878515510.8630277757031020.568486112148449
320.3803223062152620.7606446124305230.619677693784738
330.3373028460404120.6746056920808240.662697153959588
340.2863150080692410.5726300161384830.713684991930759
350.2423757998841050.484751599768210.757624200115895
360.2753071033996370.5506142067992740.724692896600363
370.3201636979279410.6403273958558820.679836302072059
380.2726710204976430.5453420409952870.727328979502357
390.2341690053757180.4683380107514360.765830994624282
400.2046618760237160.4093237520474320.795338123976284
410.2078774597698160.4157549195396330.792122540230184
420.1723457843736930.3446915687473870.827654215626307
430.1804444962585970.3608889925171930.819555503741403
440.155265443927750.3105308878554990.84473455607225
450.1730972897610330.3461945795220660.826902710238967
460.9261855754117370.1476288491765260.0738144245882631
470.9230413822209750.153917235558050.0769586177790249
480.9043042583377520.1913914833244950.0956957416622477
490.8840177223952410.2319645552095180.115982277604759
500.865023660113630.2699526797727410.13497633988637
510.8515266822548730.2969466354902530.148473317745127
520.8325414678442210.3349170643115580.167458532155779
530.8467219540631460.3065560918737080.153278045936854
540.8181185842403580.3637628315192850.181881415759642
550.7862422337243410.4275155325513190.213757766275659
560.7827517642617330.4344964714765340.217248235738267
570.7551694397497040.4896611205005930.244830560250296
580.7208317154915430.5583365690169130.279168284508457
590.6922016808422180.6155966383155640.307798319157782
600.7632905723623290.4734188552753420.236709427637671
610.7291297969995220.5417404060009560.270870203000478
620.7530562919173710.4938874161652580.246943708082629
630.723438709585070.553122580829860.27656129041493
640.6890136650266060.6219726699467870.310986334973394
650.6838680850540930.6322638298918150.316131914945907
660.6612388081848580.6775223836302840.338761191815142
670.6181509498105890.7636981003788210.381849050189411
680.5795464897295630.8409070205408740.420453510270437
690.5366049473192360.9267901053615270.463395052680763
700.5042021113916280.9915957772167440.495797888608372
710.471069831313610.942139662627220.52893016868639
720.4258212349814220.8516424699628430.574178765018578
730.3888381650111520.7776763300223040.611161834988848
740.3458455310587210.6916910621174410.654154468941279
750.3109353220805570.6218706441611140.689064677919443
760.5596182135304720.8807635729390560.440381786469528
770.5145172012451670.9709655975096670.485482798754833
780.5630098992260790.8739802015478430.436990100773921
790.5221341513047050.955731697390590.477865848695295
800.4772746840988140.9545493681976270.522725315901186
810.4367465421655540.8734930843311080.563253457834446
820.6101120006119320.7797759987761350.389887999388067
830.5661688288861010.8676623422277980.433831171113899
840.523394225352910.9532115492941790.47660577464709
850.5156154343889940.9687691312220120.484384565611006
860.5107776272417740.9784447455164520.489222372758226
870.4961031665731020.9922063331462040.503896833426898
880.6443400450968230.7113199098063550.355659954903177
890.6035208137505090.7929583724989830.396479186249491
900.5601933685644770.8796132628710460.439806631435523
910.5344121141081510.9311757717836980.465587885891849
920.4891419286517410.9782838573034820.510858071348259
930.4465597813931320.8931195627862650.553440218606868
940.4854666828679390.9709333657358780.514533317132061
950.4585209031217050.917041806243410.541479096878295
960.433525054823750.86705010964750.56647494517625
970.4180178623123160.8360357246246310.581982137687684
980.464734291536860.929468583073720.53526570846314
990.4755341385626590.9510682771253170.524465861437341
1000.4314090444296950.8628180888593910.568590955570305
1010.4075266145041360.8150532290082710.592473385495864
1020.4105424008901580.8210848017803170.589457599109842
1030.3706905186352570.7413810372705150.629309481364743
1040.3421111976331820.6842223952663640.657888802366818
1050.3000609355190040.6001218710380080.699939064480996
1060.294927021966990.5898540439339790.70507297803301
1070.2572757840913890.5145515681827770.742724215908611
1080.2427681402799640.4855362805599280.757231859720036
1090.8722583659146390.2554832681707220.127741634085361
1100.8468296438679850.3063407122640290.153170356132015
1110.8198302876681980.3603394246636050.180169712331802
1120.8138647177410470.3722705645179050.186135282258953
1130.7880849567740480.4238300864519050.211915043225952
1140.7519022178552640.4961955642894710.248097782144736
1150.7150019657374280.5699960685251440.284998034262572
1160.6729016423236320.6541967153527360.327098357676368
1170.6625513239013250.6748973521973490.337448676098675
1180.6288067138583260.7423865722833490.371193286141674
1190.5797537895782060.8404924208435870.420246210421794
1200.5432841041385220.9134317917229550.456715895861478
1210.4925284502567380.9850569005134760.507471549743262
1220.4768837444510030.9537674889020070.523116255548997
1230.5776513631907030.8446972736185930.422348636809297
1240.5295145776085530.9409708447828940.470485422391447
1250.4839892789138160.9679785578276320.516010721086184
1260.4318278566460670.8636557132921340.568172143353933
1270.4247621732534480.8495243465068950.575237826746552
1280.4080751743257940.8161503486515880.591924825674206
1290.3900434279191420.7800868558382840.609956572080858
1300.5552159048821420.8895681902357150.444784095117858
1310.6318862247570690.7362275504858620.368113775242931
1320.6243026051488580.7513947897022830.375697394851142
1330.6116941260927640.7766117478144720.388305873907236
1340.6785713368500790.6428573262998420.321428663149921
1350.6309145165805860.7381709668388270.369085483419414
1360.6889728852377410.6220542295245190.311027114762259
1370.8401480572048090.3197038855903820.159851942795191
1380.7956067797752040.4087864404495910.204393220224796
1390.7785288497929930.4429423004140150.221471150207007
1400.7349527711356560.5300944577286880.265047228864344
1410.6822804084798030.6354391830403930.317719591520197
1420.8563653678026240.2872692643947520.143634632197376
1430.8153685663471480.3692628673057040.184631433652852
1440.9966489557462460.0067020885075080.003351044253754
1450.9951446248432970.00971075031340520.0048553751567026
1460.9907756259724070.01844874805518540.0092243740275927
1470.9944333268021190.01113334639576120.00556667319788061
1480.9977865070359110.004426985928177740.00221349296408887
1490.9953122721567490.009375455686501160.00468772784325058
1500.9905305609874380.01893887802512510.00946943901256253
1510.9811782635569260.03764347288614730.0188217364430736
1520.963887387179790.07222522564042020.0361126128202101
1530.9339240266437950.132151946712410.066075973356205
1540.8847762260715330.2304475478569350.115223773928467
1550.8258636738419610.3482726523160790.174136326158039
1560.8291103651298120.3417792697403770.170889634870188
1570.6889763414834270.6220473170331460.311023658516573

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.524529685387501 & 0.950940629224997 & 0.475470314612499 \tabularnewline
8 & 0.406137435394102 & 0.812274870788203 & 0.593862564605898 \tabularnewline
9 & 0.266868258725669 & 0.533736517451337 & 0.733131741274331 \tabularnewline
10 & 0.174865390392426 & 0.349730780784852 & 0.825134609607574 \tabularnewline
11 & 0.104117889587544 & 0.208235779175089 & 0.895882110412456 \tabularnewline
12 & 0.138917080621973 & 0.277834161243945 & 0.861082919378028 \tabularnewline
13 & 0.101866956651242 & 0.203733913302484 & 0.898133043348758 \tabularnewline
14 & 0.570528320032044 & 0.858943359935912 & 0.429471679967956 \tabularnewline
15 & 0.480320232416391 & 0.960640464832782 & 0.519679767583609 \tabularnewline
16 & 0.491432185712207 & 0.982864371424414 & 0.508567814287793 \tabularnewline
17 & 0.417723153314276 & 0.835446306628551 & 0.582276846685724 \tabularnewline
18 & 0.377559680724181 & 0.755119361448362 & 0.622440319275819 \tabularnewline
19 & 0.371462305393712 & 0.742924610787424 & 0.628537694606288 \tabularnewline
20 & 0.303596653022789 & 0.607193306045578 & 0.696403346977211 \tabularnewline
21 & 0.239912295836158 & 0.479824591672316 & 0.760087704163842 \tabularnewline
22 & 0.573018076733649 & 0.853963846532703 & 0.426981923266351 \tabularnewline
23 & 0.569322168580439 & 0.861355662839121 & 0.430677831419561 \tabularnewline
24 & 0.510186218121924 & 0.979627563756152 & 0.489813781878076 \tabularnewline
25 & 0.448357995775132 & 0.896715991550264 & 0.551642004224868 \tabularnewline
26 & 0.397481023278164 & 0.794962046556327 & 0.602518976721836 \tabularnewline
27 & 0.357059578244505 & 0.714119156489009 & 0.642940421755495 \tabularnewline
28 & 0.323314920615117 & 0.646629841230234 & 0.676685079384883 \tabularnewline
29 & 0.272039821092465 & 0.54407964218493 & 0.727960178907535 \tabularnewline
30 & 0.41852214172341 & 0.83704428344682 & 0.58147785827659 \tabularnewline
31 & 0.431513887851551 & 0.863027775703102 & 0.568486112148449 \tabularnewline
32 & 0.380322306215262 & 0.760644612430523 & 0.619677693784738 \tabularnewline
33 & 0.337302846040412 & 0.674605692080824 & 0.662697153959588 \tabularnewline
34 & 0.286315008069241 & 0.572630016138483 & 0.713684991930759 \tabularnewline
35 & 0.242375799884105 & 0.48475159976821 & 0.757624200115895 \tabularnewline
36 & 0.275307103399637 & 0.550614206799274 & 0.724692896600363 \tabularnewline
37 & 0.320163697927941 & 0.640327395855882 & 0.679836302072059 \tabularnewline
38 & 0.272671020497643 & 0.545342040995287 & 0.727328979502357 \tabularnewline
39 & 0.234169005375718 & 0.468338010751436 & 0.765830994624282 \tabularnewline
40 & 0.204661876023716 & 0.409323752047432 & 0.795338123976284 \tabularnewline
41 & 0.207877459769816 & 0.415754919539633 & 0.792122540230184 \tabularnewline
42 & 0.172345784373693 & 0.344691568747387 & 0.827654215626307 \tabularnewline
43 & 0.180444496258597 & 0.360888992517193 & 0.819555503741403 \tabularnewline
44 & 0.15526544392775 & 0.310530887855499 & 0.84473455607225 \tabularnewline
45 & 0.173097289761033 & 0.346194579522066 & 0.826902710238967 \tabularnewline
46 & 0.926185575411737 & 0.147628849176526 & 0.0738144245882631 \tabularnewline
47 & 0.923041382220975 & 0.15391723555805 & 0.0769586177790249 \tabularnewline
48 & 0.904304258337752 & 0.191391483324495 & 0.0956957416622477 \tabularnewline
49 & 0.884017722395241 & 0.231964555209518 & 0.115982277604759 \tabularnewline
50 & 0.86502366011363 & 0.269952679772741 & 0.13497633988637 \tabularnewline
51 & 0.851526682254873 & 0.296946635490253 & 0.148473317745127 \tabularnewline
52 & 0.832541467844221 & 0.334917064311558 & 0.167458532155779 \tabularnewline
53 & 0.846721954063146 & 0.306556091873708 & 0.153278045936854 \tabularnewline
54 & 0.818118584240358 & 0.363762831519285 & 0.181881415759642 \tabularnewline
55 & 0.786242233724341 & 0.427515532551319 & 0.213757766275659 \tabularnewline
56 & 0.782751764261733 & 0.434496471476534 & 0.217248235738267 \tabularnewline
57 & 0.755169439749704 & 0.489661120500593 & 0.244830560250296 \tabularnewline
58 & 0.720831715491543 & 0.558336569016913 & 0.279168284508457 \tabularnewline
59 & 0.692201680842218 & 0.615596638315564 & 0.307798319157782 \tabularnewline
60 & 0.763290572362329 & 0.473418855275342 & 0.236709427637671 \tabularnewline
61 & 0.729129796999522 & 0.541740406000956 & 0.270870203000478 \tabularnewline
62 & 0.753056291917371 & 0.493887416165258 & 0.246943708082629 \tabularnewline
63 & 0.72343870958507 & 0.55312258082986 & 0.27656129041493 \tabularnewline
64 & 0.689013665026606 & 0.621972669946787 & 0.310986334973394 \tabularnewline
65 & 0.683868085054093 & 0.632263829891815 & 0.316131914945907 \tabularnewline
66 & 0.661238808184858 & 0.677522383630284 & 0.338761191815142 \tabularnewline
67 & 0.618150949810589 & 0.763698100378821 & 0.381849050189411 \tabularnewline
68 & 0.579546489729563 & 0.840907020540874 & 0.420453510270437 \tabularnewline
69 & 0.536604947319236 & 0.926790105361527 & 0.463395052680763 \tabularnewline
70 & 0.504202111391628 & 0.991595777216744 & 0.495797888608372 \tabularnewline
71 & 0.47106983131361 & 0.94213966262722 & 0.52893016868639 \tabularnewline
72 & 0.425821234981422 & 0.851642469962843 & 0.574178765018578 \tabularnewline
73 & 0.388838165011152 & 0.777676330022304 & 0.611161834988848 \tabularnewline
74 & 0.345845531058721 & 0.691691062117441 & 0.654154468941279 \tabularnewline
75 & 0.310935322080557 & 0.621870644161114 & 0.689064677919443 \tabularnewline
76 & 0.559618213530472 & 0.880763572939056 & 0.440381786469528 \tabularnewline
77 & 0.514517201245167 & 0.970965597509667 & 0.485482798754833 \tabularnewline
78 & 0.563009899226079 & 0.873980201547843 & 0.436990100773921 \tabularnewline
79 & 0.522134151304705 & 0.95573169739059 & 0.477865848695295 \tabularnewline
80 & 0.477274684098814 & 0.954549368197627 & 0.522725315901186 \tabularnewline
81 & 0.436746542165554 & 0.873493084331108 & 0.563253457834446 \tabularnewline
82 & 0.610112000611932 & 0.779775998776135 & 0.389887999388067 \tabularnewline
83 & 0.566168828886101 & 0.867662342227798 & 0.433831171113899 \tabularnewline
84 & 0.52339422535291 & 0.953211549294179 & 0.47660577464709 \tabularnewline
85 & 0.515615434388994 & 0.968769131222012 & 0.484384565611006 \tabularnewline
86 & 0.510777627241774 & 0.978444745516452 & 0.489222372758226 \tabularnewline
87 & 0.496103166573102 & 0.992206333146204 & 0.503896833426898 \tabularnewline
88 & 0.644340045096823 & 0.711319909806355 & 0.355659954903177 \tabularnewline
89 & 0.603520813750509 & 0.792958372498983 & 0.396479186249491 \tabularnewline
90 & 0.560193368564477 & 0.879613262871046 & 0.439806631435523 \tabularnewline
91 & 0.534412114108151 & 0.931175771783698 & 0.465587885891849 \tabularnewline
92 & 0.489141928651741 & 0.978283857303482 & 0.510858071348259 \tabularnewline
93 & 0.446559781393132 & 0.893119562786265 & 0.553440218606868 \tabularnewline
94 & 0.485466682867939 & 0.970933365735878 & 0.514533317132061 \tabularnewline
95 & 0.458520903121705 & 0.91704180624341 & 0.541479096878295 \tabularnewline
96 & 0.43352505482375 & 0.8670501096475 & 0.56647494517625 \tabularnewline
97 & 0.418017862312316 & 0.836035724624631 & 0.581982137687684 \tabularnewline
98 & 0.46473429153686 & 0.92946858307372 & 0.53526570846314 \tabularnewline
99 & 0.475534138562659 & 0.951068277125317 & 0.524465861437341 \tabularnewline
100 & 0.431409044429695 & 0.862818088859391 & 0.568590955570305 \tabularnewline
101 & 0.407526614504136 & 0.815053229008271 & 0.592473385495864 \tabularnewline
102 & 0.410542400890158 & 0.821084801780317 & 0.589457599109842 \tabularnewline
103 & 0.370690518635257 & 0.741381037270515 & 0.629309481364743 \tabularnewline
104 & 0.342111197633182 & 0.684222395266364 & 0.657888802366818 \tabularnewline
105 & 0.300060935519004 & 0.600121871038008 & 0.699939064480996 \tabularnewline
106 & 0.29492702196699 & 0.589854043933979 & 0.70507297803301 \tabularnewline
107 & 0.257275784091389 & 0.514551568182777 & 0.742724215908611 \tabularnewline
108 & 0.242768140279964 & 0.485536280559928 & 0.757231859720036 \tabularnewline
109 & 0.872258365914639 & 0.255483268170722 & 0.127741634085361 \tabularnewline
110 & 0.846829643867985 & 0.306340712264029 & 0.153170356132015 \tabularnewline
111 & 0.819830287668198 & 0.360339424663605 & 0.180169712331802 \tabularnewline
112 & 0.813864717741047 & 0.372270564517905 & 0.186135282258953 \tabularnewline
113 & 0.788084956774048 & 0.423830086451905 & 0.211915043225952 \tabularnewline
114 & 0.751902217855264 & 0.496195564289471 & 0.248097782144736 \tabularnewline
115 & 0.715001965737428 & 0.569996068525144 & 0.284998034262572 \tabularnewline
116 & 0.672901642323632 & 0.654196715352736 & 0.327098357676368 \tabularnewline
117 & 0.662551323901325 & 0.674897352197349 & 0.337448676098675 \tabularnewline
118 & 0.628806713858326 & 0.742386572283349 & 0.371193286141674 \tabularnewline
119 & 0.579753789578206 & 0.840492420843587 & 0.420246210421794 \tabularnewline
120 & 0.543284104138522 & 0.913431791722955 & 0.456715895861478 \tabularnewline
121 & 0.492528450256738 & 0.985056900513476 & 0.507471549743262 \tabularnewline
122 & 0.476883744451003 & 0.953767488902007 & 0.523116255548997 \tabularnewline
123 & 0.577651363190703 & 0.844697273618593 & 0.422348636809297 \tabularnewline
124 & 0.529514577608553 & 0.940970844782894 & 0.470485422391447 \tabularnewline
125 & 0.483989278913816 & 0.967978557827632 & 0.516010721086184 \tabularnewline
126 & 0.431827856646067 & 0.863655713292134 & 0.568172143353933 \tabularnewline
127 & 0.424762173253448 & 0.849524346506895 & 0.575237826746552 \tabularnewline
128 & 0.408075174325794 & 0.816150348651588 & 0.591924825674206 \tabularnewline
129 & 0.390043427919142 & 0.780086855838284 & 0.609956572080858 \tabularnewline
130 & 0.555215904882142 & 0.889568190235715 & 0.444784095117858 \tabularnewline
131 & 0.631886224757069 & 0.736227550485862 & 0.368113775242931 \tabularnewline
132 & 0.624302605148858 & 0.751394789702283 & 0.375697394851142 \tabularnewline
133 & 0.611694126092764 & 0.776611747814472 & 0.388305873907236 \tabularnewline
134 & 0.678571336850079 & 0.642857326299842 & 0.321428663149921 \tabularnewline
135 & 0.630914516580586 & 0.738170966838827 & 0.369085483419414 \tabularnewline
136 & 0.688972885237741 & 0.622054229524519 & 0.311027114762259 \tabularnewline
137 & 0.840148057204809 & 0.319703885590382 & 0.159851942795191 \tabularnewline
138 & 0.795606779775204 & 0.408786440449591 & 0.204393220224796 \tabularnewline
139 & 0.778528849792993 & 0.442942300414015 & 0.221471150207007 \tabularnewline
140 & 0.734952771135656 & 0.530094457728688 & 0.265047228864344 \tabularnewline
141 & 0.682280408479803 & 0.635439183040393 & 0.317719591520197 \tabularnewline
142 & 0.856365367802624 & 0.287269264394752 & 0.143634632197376 \tabularnewline
143 & 0.815368566347148 & 0.369262867305704 & 0.184631433652852 \tabularnewline
144 & 0.996648955746246 & 0.006702088507508 & 0.003351044253754 \tabularnewline
145 & 0.995144624843297 & 0.0097107503134052 & 0.0048553751567026 \tabularnewline
146 & 0.990775625972407 & 0.0184487480551854 & 0.0092243740275927 \tabularnewline
147 & 0.994433326802119 & 0.0111333463957612 & 0.00556667319788061 \tabularnewline
148 & 0.997786507035911 & 0.00442698592817774 & 0.00221349296408887 \tabularnewline
149 & 0.995312272156749 & 0.00937545568650116 & 0.00468772784325058 \tabularnewline
150 & 0.990530560987438 & 0.0189388780251251 & 0.00946943901256253 \tabularnewline
151 & 0.981178263556926 & 0.0376434728861473 & 0.0188217364430736 \tabularnewline
152 & 0.96388738717979 & 0.0722252256404202 & 0.0361126128202101 \tabularnewline
153 & 0.933924026643795 & 0.13215194671241 & 0.066075973356205 \tabularnewline
154 & 0.884776226071533 & 0.230447547856935 & 0.115223773928467 \tabularnewline
155 & 0.825863673841961 & 0.348272652316079 & 0.174136326158039 \tabularnewline
156 & 0.829110365129812 & 0.341779269740377 & 0.170889634870188 \tabularnewline
157 & 0.688976341483427 & 0.622047317033146 & 0.311023658516573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&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]0.524529685387501[/C][C]0.950940629224997[/C][C]0.475470314612499[/C][/ROW]
[ROW][C]8[/C][C]0.406137435394102[/C][C]0.812274870788203[/C][C]0.593862564605898[/C][/ROW]
[ROW][C]9[/C][C]0.266868258725669[/C][C]0.533736517451337[/C][C]0.733131741274331[/C][/ROW]
[ROW][C]10[/C][C]0.174865390392426[/C][C]0.349730780784852[/C][C]0.825134609607574[/C][/ROW]
[ROW][C]11[/C][C]0.104117889587544[/C][C]0.208235779175089[/C][C]0.895882110412456[/C][/ROW]
[ROW][C]12[/C][C]0.138917080621973[/C][C]0.277834161243945[/C][C]0.861082919378028[/C][/ROW]
[ROW][C]13[/C][C]0.101866956651242[/C][C]0.203733913302484[/C][C]0.898133043348758[/C][/ROW]
[ROW][C]14[/C][C]0.570528320032044[/C][C]0.858943359935912[/C][C]0.429471679967956[/C][/ROW]
[ROW][C]15[/C][C]0.480320232416391[/C][C]0.960640464832782[/C][C]0.519679767583609[/C][/ROW]
[ROW][C]16[/C][C]0.491432185712207[/C][C]0.982864371424414[/C][C]0.508567814287793[/C][/ROW]
[ROW][C]17[/C][C]0.417723153314276[/C][C]0.835446306628551[/C][C]0.582276846685724[/C][/ROW]
[ROW][C]18[/C][C]0.377559680724181[/C][C]0.755119361448362[/C][C]0.622440319275819[/C][/ROW]
[ROW][C]19[/C][C]0.371462305393712[/C][C]0.742924610787424[/C][C]0.628537694606288[/C][/ROW]
[ROW][C]20[/C][C]0.303596653022789[/C][C]0.607193306045578[/C][C]0.696403346977211[/C][/ROW]
[ROW][C]21[/C][C]0.239912295836158[/C][C]0.479824591672316[/C][C]0.760087704163842[/C][/ROW]
[ROW][C]22[/C][C]0.573018076733649[/C][C]0.853963846532703[/C][C]0.426981923266351[/C][/ROW]
[ROW][C]23[/C][C]0.569322168580439[/C][C]0.861355662839121[/C][C]0.430677831419561[/C][/ROW]
[ROW][C]24[/C][C]0.510186218121924[/C][C]0.979627563756152[/C][C]0.489813781878076[/C][/ROW]
[ROW][C]25[/C][C]0.448357995775132[/C][C]0.896715991550264[/C][C]0.551642004224868[/C][/ROW]
[ROW][C]26[/C][C]0.397481023278164[/C][C]0.794962046556327[/C][C]0.602518976721836[/C][/ROW]
[ROW][C]27[/C][C]0.357059578244505[/C][C]0.714119156489009[/C][C]0.642940421755495[/C][/ROW]
[ROW][C]28[/C][C]0.323314920615117[/C][C]0.646629841230234[/C][C]0.676685079384883[/C][/ROW]
[ROW][C]29[/C][C]0.272039821092465[/C][C]0.54407964218493[/C][C]0.727960178907535[/C][/ROW]
[ROW][C]30[/C][C]0.41852214172341[/C][C]0.83704428344682[/C][C]0.58147785827659[/C][/ROW]
[ROW][C]31[/C][C]0.431513887851551[/C][C]0.863027775703102[/C][C]0.568486112148449[/C][/ROW]
[ROW][C]32[/C][C]0.380322306215262[/C][C]0.760644612430523[/C][C]0.619677693784738[/C][/ROW]
[ROW][C]33[/C][C]0.337302846040412[/C][C]0.674605692080824[/C][C]0.662697153959588[/C][/ROW]
[ROW][C]34[/C][C]0.286315008069241[/C][C]0.572630016138483[/C][C]0.713684991930759[/C][/ROW]
[ROW][C]35[/C][C]0.242375799884105[/C][C]0.48475159976821[/C][C]0.757624200115895[/C][/ROW]
[ROW][C]36[/C][C]0.275307103399637[/C][C]0.550614206799274[/C][C]0.724692896600363[/C][/ROW]
[ROW][C]37[/C][C]0.320163697927941[/C][C]0.640327395855882[/C][C]0.679836302072059[/C][/ROW]
[ROW][C]38[/C][C]0.272671020497643[/C][C]0.545342040995287[/C][C]0.727328979502357[/C][/ROW]
[ROW][C]39[/C][C]0.234169005375718[/C][C]0.468338010751436[/C][C]0.765830994624282[/C][/ROW]
[ROW][C]40[/C][C]0.204661876023716[/C][C]0.409323752047432[/C][C]0.795338123976284[/C][/ROW]
[ROW][C]41[/C][C]0.207877459769816[/C][C]0.415754919539633[/C][C]0.792122540230184[/C][/ROW]
[ROW][C]42[/C][C]0.172345784373693[/C][C]0.344691568747387[/C][C]0.827654215626307[/C][/ROW]
[ROW][C]43[/C][C]0.180444496258597[/C][C]0.360888992517193[/C][C]0.819555503741403[/C][/ROW]
[ROW][C]44[/C][C]0.15526544392775[/C][C]0.310530887855499[/C][C]0.84473455607225[/C][/ROW]
[ROW][C]45[/C][C]0.173097289761033[/C][C]0.346194579522066[/C][C]0.826902710238967[/C][/ROW]
[ROW][C]46[/C][C]0.926185575411737[/C][C]0.147628849176526[/C][C]0.0738144245882631[/C][/ROW]
[ROW][C]47[/C][C]0.923041382220975[/C][C]0.15391723555805[/C][C]0.0769586177790249[/C][/ROW]
[ROW][C]48[/C][C]0.904304258337752[/C][C]0.191391483324495[/C][C]0.0956957416622477[/C][/ROW]
[ROW][C]49[/C][C]0.884017722395241[/C][C]0.231964555209518[/C][C]0.115982277604759[/C][/ROW]
[ROW][C]50[/C][C]0.86502366011363[/C][C]0.269952679772741[/C][C]0.13497633988637[/C][/ROW]
[ROW][C]51[/C][C]0.851526682254873[/C][C]0.296946635490253[/C][C]0.148473317745127[/C][/ROW]
[ROW][C]52[/C][C]0.832541467844221[/C][C]0.334917064311558[/C][C]0.167458532155779[/C][/ROW]
[ROW][C]53[/C][C]0.846721954063146[/C][C]0.306556091873708[/C][C]0.153278045936854[/C][/ROW]
[ROW][C]54[/C][C]0.818118584240358[/C][C]0.363762831519285[/C][C]0.181881415759642[/C][/ROW]
[ROW][C]55[/C][C]0.786242233724341[/C][C]0.427515532551319[/C][C]0.213757766275659[/C][/ROW]
[ROW][C]56[/C][C]0.782751764261733[/C][C]0.434496471476534[/C][C]0.217248235738267[/C][/ROW]
[ROW][C]57[/C][C]0.755169439749704[/C][C]0.489661120500593[/C][C]0.244830560250296[/C][/ROW]
[ROW][C]58[/C][C]0.720831715491543[/C][C]0.558336569016913[/C][C]0.279168284508457[/C][/ROW]
[ROW][C]59[/C][C]0.692201680842218[/C][C]0.615596638315564[/C][C]0.307798319157782[/C][/ROW]
[ROW][C]60[/C][C]0.763290572362329[/C][C]0.473418855275342[/C][C]0.236709427637671[/C][/ROW]
[ROW][C]61[/C][C]0.729129796999522[/C][C]0.541740406000956[/C][C]0.270870203000478[/C][/ROW]
[ROW][C]62[/C][C]0.753056291917371[/C][C]0.493887416165258[/C][C]0.246943708082629[/C][/ROW]
[ROW][C]63[/C][C]0.72343870958507[/C][C]0.55312258082986[/C][C]0.27656129041493[/C][/ROW]
[ROW][C]64[/C][C]0.689013665026606[/C][C]0.621972669946787[/C][C]0.310986334973394[/C][/ROW]
[ROW][C]65[/C][C]0.683868085054093[/C][C]0.632263829891815[/C][C]0.316131914945907[/C][/ROW]
[ROW][C]66[/C][C]0.661238808184858[/C][C]0.677522383630284[/C][C]0.338761191815142[/C][/ROW]
[ROW][C]67[/C][C]0.618150949810589[/C][C]0.763698100378821[/C][C]0.381849050189411[/C][/ROW]
[ROW][C]68[/C][C]0.579546489729563[/C][C]0.840907020540874[/C][C]0.420453510270437[/C][/ROW]
[ROW][C]69[/C][C]0.536604947319236[/C][C]0.926790105361527[/C][C]0.463395052680763[/C][/ROW]
[ROW][C]70[/C][C]0.504202111391628[/C][C]0.991595777216744[/C][C]0.495797888608372[/C][/ROW]
[ROW][C]71[/C][C]0.47106983131361[/C][C]0.94213966262722[/C][C]0.52893016868639[/C][/ROW]
[ROW][C]72[/C][C]0.425821234981422[/C][C]0.851642469962843[/C][C]0.574178765018578[/C][/ROW]
[ROW][C]73[/C][C]0.388838165011152[/C][C]0.777676330022304[/C][C]0.611161834988848[/C][/ROW]
[ROW][C]74[/C][C]0.345845531058721[/C][C]0.691691062117441[/C][C]0.654154468941279[/C][/ROW]
[ROW][C]75[/C][C]0.310935322080557[/C][C]0.621870644161114[/C][C]0.689064677919443[/C][/ROW]
[ROW][C]76[/C][C]0.559618213530472[/C][C]0.880763572939056[/C][C]0.440381786469528[/C][/ROW]
[ROW][C]77[/C][C]0.514517201245167[/C][C]0.970965597509667[/C][C]0.485482798754833[/C][/ROW]
[ROW][C]78[/C][C]0.563009899226079[/C][C]0.873980201547843[/C][C]0.436990100773921[/C][/ROW]
[ROW][C]79[/C][C]0.522134151304705[/C][C]0.95573169739059[/C][C]0.477865848695295[/C][/ROW]
[ROW][C]80[/C][C]0.477274684098814[/C][C]0.954549368197627[/C][C]0.522725315901186[/C][/ROW]
[ROW][C]81[/C][C]0.436746542165554[/C][C]0.873493084331108[/C][C]0.563253457834446[/C][/ROW]
[ROW][C]82[/C][C]0.610112000611932[/C][C]0.779775998776135[/C][C]0.389887999388067[/C][/ROW]
[ROW][C]83[/C][C]0.566168828886101[/C][C]0.867662342227798[/C][C]0.433831171113899[/C][/ROW]
[ROW][C]84[/C][C]0.52339422535291[/C][C]0.953211549294179[/C][C]0.47660577464709[/C][/ROW]
[ROW][C]85[/C][C]0.515615434388994[/C][C]0.968769131222012[/C][C]0.484384565611006[/C][/ROW]
[ROW][C]86[/C][C]0.510777627241774[/C][C]0.978444745516452[/C][C]0.489222372758226[/C][/ROW]
[ROW][C]87[/C][C]0.496103166573102[/C][C]0.992206333146204[/C][C]0.503896833426898[/C][/ROW]
[ROW][C]88[/C][C]0.644340045096823[/C][C]0.711319909806355[/C][C]0.355659954903177[/C][/ROW]
[ROW][C]89[/C][C]0.603520813750509[/C][C]0.792958372498983[/C][C]0.396479186249491[/C][/ROW]
[ROW][C]90[/C][C]0.560193368564477[/C][C]0.879613262871046[/C][C]0.439806631435523[/C][/ROW]
[ROW][C]91[/C][C]0.534412114108151[/C][C]0.931175771783698[/C][C]0.465587885891849[/C][/ROW]
[ROW][C]92[/C][C]0.489141928651741[/C][C]0.978283857303482[/C][C]0.510858071348259[/C][/ROW]
[ROW][C]93[/C][C]0.446559781393132[/C][C]0.893119562786265[/C][C]0.553440218606868[/C][/ROW]
[ROW][C]94[/C][C]0.485466682867939[/C][C]0.970933365735878[/C][C]0.514533317132061[/C][/ROW]
[ROW][C]95[/C][C]0.458520903121705[/C][C]0.91704180624341[/C][C]0.541479096878295[/C][/ROW]
[ROW][C]96[/C][C]0.43352505482375[/C][C]0.8670501096475[/C][C]0.56647494517625[/C][/ROW]
[ROW][C]97[/C][C]0.418017862312316[/C][C]0.836035724624631[/C][C]0.581982137687684[/C][/ROW]
[ROW][C]98[/C][C]0.46473429153686[/C][C]0.92946858307372[/C][C]0.53526570846314[/C][/ROW]
[ROW][C]99[/C][C]0.475534138562659[/C][C]0.951068277125317[/C][C]0.524465861437341[/C][/ROW]
[ROW][C]100[/C][C]0.431409044429695[/C][C]0.862818088859391[/C][C]0.568590955570305[/C][/ROW]
[ROW][C]101[/C][C]0.407526614504136[/C][C]0.815053229008271[/C][C]0.592473385495864[/C][/ROW]
[ROW][C]102[/C][C]0.410542400890158[/C][C]0.821084801780317[/C][C]0.589457599109842[/C][/ROW]
[ROW][C]103[/C][C]0.370690518635257[/C][C]0.741381037270515[/C][C]0.629309481364743[/C][/ROW]
[ROW][C]104[/C][C]0.342111197633182[/C][C]0.684222395266364[/C][C]0.657888802366818[/C][/ROW]
[ROW][C]105[/C][C]0.300060935519004[/C][C]0.600121871038008[/C][C]0.699939064480996[/C][/ROW]
[ROW][C]106[/C][C]0.29492702196699[/C][C]0.589854043933979[/C][C]0.70507297803301[/C][/ROW]
[ROW][C]107[/C][C]0.257275784091389[/C][C]0.514551568182777[/C][C]0.742724215908611[/C][/ROW]
[ROW][C]108[/C][C]0.242768140279964[/C][C]0.485536280559928[/C][C]0.757231859720036[/C][/ROW]
[ROW][C]109[/C][C]0.872258365914639[/C][C]0.255483268170722[/C][C]0.127741634085361[/C][/ROW]
[ROW][C]110[/C][C]0.846829643867985[/C][C]0.306340712264029[/C][C]0.153170356132015[/C][/ROW]
[ROW][C]111[/C][C]0.819830287668198[/C][C]0.360339424663605[/C][C]0.180169712331802[/C][/ROW]
[ROW][C]112[/C][C]0.813864717741047[/C][C]0.372270564517905[/C][C]0.186135282258953[/C][/ROW]
[ROW][C]113[/C][C]0.788084956774048[/C][C]0.423830086451905[/C][C]0.211915043225952[/C][/ROW]
[ROW][C]114[/C][C]0.751902217855264[/C][C]0.496195564289471[/C][C]0.248097782144736[/C][/ROW]
[ROW][C]115[/C][C]0.715001965737428[/C][C]0.569996068525144[/C][C]0.284998034262572[/C][/ROW]
[ROW][C]116[/C][C]0.672901642323632[/C][C]0.654196715352736[/C][C]0.327098357676368[/C][/ROW]
[ROW][C]117[/C][C]0.662551323901325[/C][C]0.674897352197349[/C][C]0.337448676098675[/C][/ROW]
[ROW][C]118[/C][C]0.628806713858326[/C][C]0.742386572283349[/C][C]0.371193286141674[/C][/ROW]
[ROW][C]119[/C][C]0.579753789578206[/C][C]0.840492420843587[/C][C]0.420246210421794[/C][/ROW]
[ROW][C]120[/C][C]0.543284104138522[/C][C]0.913431791722955[/C][C]0.456715895861478[/C][/ROW]
[ROW][C]121[/C][C]0.492528450256738[/C][C]0.985056900513476[/C][C]0.507471549743262[/C][/ROW]
[ROW][C]122[/C][C]0.476883744451003[/C][C]0.953767488902007[/C][C]0.523116255548997[/C][/ROW]
[ROW][C]123[/C][C]0.577651363190703[/C][C]0.844697273618593[/C][C]0.422348636809297[/C][/ROW]
[ROW][C]124[/C][C]0.529514577608553[/C][C]0.940970844782894[/C][C]0.470485422391447[/C][/ROW]
[ROW][C]125[/C][C]0.483989278913816[/C][C]0.967978557827632[/C][C]0.516010721086184[/C][/ROW]
[ROW][C]126[/C][C]0.431827856646067[/C][C]0.863655713292134[/C][C]0.568172143353933[/C][/ROW]
[ROW][C]127[/C][C]0.424762173253448[/C][C]0.849524346506895[/C][C]0.575237826746552[/C][/ROW]
[ROW][C]128[/C][C]0.408075174325794[/C][C]0.816150348651588[/C][C]0.591924825674206[/C][/ROW]
[ROW][C]129[/C][C]0.390043427919142[/C][C]0.780086855838284[/C][C]0.609956572080858[/C][/ROW]
[ROW][C]130[/C][C]0.555215904882142[/C][C]0.889568190235715[/C][C]0.444784095117858[/C][/ROW]
[ROW][C]131[/C][C]0.631886224757069[/C][C]0.736227550485862[/C][C]0.368113775242931[/C][/ROW]
[ROW][C]132[/C][C]0.624302605148858[/C][C]0.751394789702283[/C][C]0.375697394851142[/C][/ROW]
[ROW][C]133[/C][C]0.611694126092764[/C][C]0.776611747814472[/C][C]0.388305873907236[/C][/ROW]
[ROW][C]134[/C][C]0.678571336850079[/C][C]0.642857326299842[/C][C]0.321428663149921[/C][/ROW]
[ROW][C]135[/C][C]0.630914516580586[/C][C]0.738170966838827[/C][C]0.369085483419414[/C][/ROW]
[ROW][C]136[/C][C]0.688972885237741[/C][C]0.622054229524519[/C][C]0.311027114762259[/C][/ROW]
[ROW][C]137[/C][C]0.840148057204809[/C][C]0.319703885590382[/C][C]0.159851942795191[/C][/ROW]
[ROW][C]138[/C][C]0.795606779775204[/C][C]0.408786440449591[/C][C]0.204393220224796[/C][/ROW]
[ROW][C]139[/C][C]0.778528849792993[/C][C]0.442942300414015[/C][C]0.221471150207007[/C][/ROW]
[ROW][C]140[/C][C]0.734952771135656[/C][C]0.530094457728688[/C][C]0.265047228864344[/C][/ROW]
[ROW][C]141[/C][C]0.682280408479803[/C][C]0.635439183040393[/C][C]0.317719591520197[/C][/ROW]
[ROW][C]142[/C][C]0.856365367802624[/C][C]0.287269264394752[/C][C]0.143634632197376[/C][/ROW]
[ROW][C]143[/C][C]0.815368566347148[/C][C]0.369262867305704[/C][C]0.184631433652852[/C][/ROW]
[ROW][C]144[/C][C]0.996648955746246[/C][C]0.006702088507508[/C][C]0.003351044253754[/C][/ROW]
[ROW][C]145[/C][C]0.995144624843297[/C][C]0.0097107503134052[/C][C]0.0048553751567026[/C][/ROW]
[ROW][C]146[/C][C]0.990775625972407[/C][C]0.0184487480551854[/C][C]0.0092243740275927[/C][/ROW]
[ROW][C]147[/C][C]0.994433326802119[/C][C]0.0111333463957612[/C][C]0.00556667319788061[/C][/ROW]
[ROW][C]148[/C][C]0.997786507035911[/C][C]0.00442698592817774[/C][C]0.00221349296408887[/C][/ROW]
[ROW][C]149[/C][C]0.995312272156749[/C][C]0.00937545568650116[/C][C]0.00468772784325058[/C][/ROW]
[ROW][C]150[/C][C]0.990530560987438[/C][C]0.0189388780251251[/C][C]0.00946943901256253[/C][/ROW]
[ROW][C]151[/C][C]0.981178263556926[/C][C]0.0376434728861473[/C][C]0.0188217364430736[/C][/ROW]
[ROW][C]152[/C][C]0.96388738717979[/C][C]0.0722252256404202[/C][C]0.0361126128202101[/C][/ROW]
[ROW][C]153[/C][C]0.933924026643795[/C][C]0.13215194671241[/C][C]0.066075973356205[/C][/ROW]
[ROW][C]154[/C][C]0.884776226071533[/C][C]0.230447547856935[/C][C]0.115223773928467[/C][/ROW]
[ROW][C]155[/C][C]0.825863673841961[/C][C]0.348272652316079[/C][C]0.174136326158039[/C][/ROW]
[ROW][C]156[/C][C]0.829110365129812[/C][C]0.341779269740377[/C][C]0.170889634870188[/C][/ROW]
[ROW][C]157[/C][C]0.688976341483427[/C][C]0.622047317033146[/C][C]0.311023658516573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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
70.5245296853875010.9509406292249970.475470314612499
80.4061374353941020.8122748707882030.593862564605898
90.2668682587256690.5337365174513370.733131741274331
100.1748653903924260.3497307807848520.825134609607574
110.1041178895875440.2082357791750890.895882110412456
120.1389170806219730.2778341612439450.861082919378028
130.1018669566512420.2037339133024840.898133043348758
140.5705283200320440.8589433599359120.429471679967956
150.4803202324163910.9606404648327820.519679767583609
160.4914321857122070.9828643714244140.508567814287793
170.4177231533142760.8354463066285510.582276846685724
180.3775596807241810.7551193614483620.622440319275819
190.3714623053937120.7429246107874240.628537694606288
200.3035966530227890.6071933060455780.696403346977211
210.2399122958361580.4798245916723160.760087704163842
220.5730180767336490.8539638465327030.426981923266351
230.5693221685804390.8613556628391210.430677831419561
240.5101862181219240.9796275637561520.489813781878076
250.4483579957751320.8967159915502640.551642004224868
260.3974810232781640.7949620465563270.602518976721836
270.3570595782445050.7141191564890090.642940421755495
280.3233149206151170.6466298412302340.676685079384883
290.2720398210924650.544079642184930.727960178907535
300.418522141723410.837044283446820.58147785827659
310.4315138878515510.8630277757031020.568486112148449
320.3803223062152620.7606446124305230.619677693784738
330.3373028460404120.6746056920808240.662697153959588
340.2863150080692410.5726300161384830.713684991930759
350.2423757998841050.484751599768210.757624200115895
360.2753071033996370.5506142067992740.724692896600363
370.3201636979279410.6403273958558820.679836302072059
380.2726710204976430.5453420409952870.727328979502357
390.2341690053757180.4683380107514360.765830994624282
400.2046618760237160.4093237520474320.795338123976284
410.2078774597698160.4157549195396330.792122540230184
420.1723457843736930.3446915687473870.827654215626307
430.1804444962585970.3608889925171930.819555503741403
440.155265443927750.3105308878554990.84473455607225
450.1730972897610330.3461945795220660.826902710238967
460.9261855754117370.1476288491765260.0738144245882631
470.9230413822209750.153917235558050.0769586177790249
480.9043042583377520.1913914833244950.0956957416622477
490.8840177223952410.2319645552095180.115982277604759
500.865023660113630.2699526797727410.13497633988637
510.8515266822548730.2969466354902530.148473317745127
520.8325414678442210.3349170643115580.167458532155779
530.8467219540631460.3065560918737080.153278045936854
540.8181185842403580.3637628315192850.181881415759642
550.7862422337243410.4275155325513190.213757766275659
560.7827517642617330.4344964714765340.217248235738267
570.7551694397497040.4896611205005930.244830560250296
580.7208317154915430.5583365690169130.279168284508457
590.6922016808422180.6155966383155640.307798319157782
600.7632905723623290.4734188552753420.236709427637671
610.7291297969995220.5417404060009560.270870203000478
620.7530562919173710.4938874161652580.246943708082629
630.723438709585070.553122580829860.27656129041493
640.6890136650266060.6219726699467870.310986334973394
650.6838680850540930.6322638298918150.316131914945907
660.6612388081848580.6775223836302840.338761191815142
670.6181509498105890.7636981003788210.381849050189411
680.5795464897295630.8409070205408740.420453510270437
690.5366049473192360.9267901053615270.463395052680763
700.5042021113916280.9915957772167440.495797888608372
710.471069831313610.942139662627220.52893016868639
720.4258212349814220.8516424699628430.574178765018578
730.3888381650111520.7776763300223040.611161834988848
740.3458455310587210.6916910621174410.654154468941279
750.3109353220805570.6218706441611140.689064677919443
760.5596182135304720.8807635729390560.440381786469528
770.5145172012451670.9709655975096670.485482798754833
780.5630098992260790.8739802015478430.436990100773921
790.5221341513047050.955731697390590.477865848695295
800.4772746840988140.9545493681976270.522725315901186
810.4367465421655540.8734930843311080.563253457834446
820.6101120006119320.7797759987761350.389887999388067
830.5661688288861010.8676623422277980.433831171113899
840.523394225352910.9532115492941790.47660577464709
850.5156154343889940.9687691312220120.484384565611006
860.5107776272417740.9784447455164520.489222372758226
870.4961031665731020.9922063331462040.503896833426898
880.6443400450968230.7113199098063550.355659954903177
890.6035208137505090.7929583724989830.396479186249491
900.5601933685644770.8796132628710460.439806631435523
910.5344121141081510.9311757717836980.465587885891849
920.4891419286517410.9782838573034820.510858071348259
930.4465597813931320.8931195627862650.553440218606868
940.4854666828679390.9709333657358780.514533317132061
950.4585209031217050.917041806243410.541479096878295
960.433525054823750.86705010964750.56647494517625
970.4180178623123160.8360357246246310.581982137687684
980.464734291536860.929468583073720.53526570846314
990.4755341385626590.9510682771253170.524465861437341
1000.4314090444296950.8628180888593910.568590955570305
1010.4075266145041360.8150532290082710.592473385495864
1020.4105424008901580.8210848017803170.589457599109842
1030.3706905186352570.7413810372705150.629309481364743
1040.3421111976331820.6842223952663640.657888802366818
1050.3000609355190040.6001218710380080.699939064480996
1060.294927021966990.5898540439339790.70507297803301
1070.2572757840913890.5145515681827770.742724215908611
1080.2427681402799640.4855362805599280.757231859720036
1090.8722583659146390.2554832681707220.127741634085361
1100.8468296438679850.3063407122640290.153170356132015
1110.8198302876681980.3603394246636050.180169712331802
1120.8138647177410470.3722705645179050.186135282258953
1130.7880849567740480.4238300864519050.211915043225952
1140.7519022178552640.4961955642894710.248097782144736
1150.7150019657374280.5699960685251440.284998034262572
1160.6729016423236320.6541967153527360.327098357676368
1170.6625513239013250.6748973521973490.337448676098675
1180.6288067138583260.7423865722833490.371193286141674
1190.5797537895782060.8404924208435870.420246210421794
1200.5432841041385220.9134317917229550.456715895861478
1210.4925284502567380.9850569005134760.507471549743262
1220.4768837444510030.9537674889020070.523116255548997
1230.5776513631907030.8446972736185930.422348636809297
1240.5295145776085530.9409708447828940.470485422391447
1250.4839892789138160.9679785578276320.516010721086184
1260.4318278566460670.8636557132921340.568172143353933
1270.4247621732534480.8495243465068950.575237826746552
1280.4080751743257940.8161503486515880.591924825674206
1290.3900434279191420.7800868558382840.609956572080858
1300.5552159048821420.8895681902357150.444784095117858
1310.6318862247570690.7362275504858620.368113775242931
1320.6243026051488580.7513947897022830.375697394851142
1330.6116941260927640.7766117478144720.388305873907236
1340.6785713368500790.6428573262998420.321428663149921
1350.6309145165805860.7381709668388270.369085483419414
1360.6889728852377410.6220542295245190.311027114762259
1370.8401480572048090.3197038855903820.159851942795191
1380.7956067797752040.4087864404495910.204393220224796
1390.7785288497929930.4429423004140150.221471150207007
1400.7349527711356560.5300944577286880.265047228864344
1410.6822804084798030.6354391830403930.317719591520197
1420.8563653678026240.2872692643947520.143634632197376
1430.8153685663471480.3692628673057040.184631433652852
1440.9966489557462460.0067020885075080.003351044253754
1450.9951446248432970.00971075031340520.0048553751567026
1460.9907756259724070.01844874805518540.0092243740275927
1470.9944333268021190.01113334639576120.00556667319788061
1480.9977865070359110.004426985928177740.00221349296408887
1490.9953122721567490.009375455686501160.00468772784325058
1500.9905305609874380.01893887802512510.00946943901256253
1510.9811782635569260.03764347288614730.0188217364430736
1520.963887387179790.07222522564042020.0361126128202101
1530.9339240266437950.132151946712410.066075973356205
1540.8847762260715330.2304475478569350.115223773928467
1550.8258636738419610.3482726523160790.174136326158039
1560.8291103651298120.3417792697403770.170889634870188
1570.6889763414834270.6220473170331460.311023658516573







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.0264900662251656NOK
5% type I error level80.0529801324503311NOK
10% type I error level90.0596026490066225OK

\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 & 4 & 0.0264900662251656 & NOK \tabularnewline
5% type I error level & 8 & 0.0529801324503311 & NOK \tabularnewline
10% type I error level & 9 & 0.0596026490066225 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=197387&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]4[/C][C]0.0264900662251656[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]8[/C][C]0.0529801324503311[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.0596026490066225[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=197387&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=197387&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 level40.0264900662251656NOK
5% type I error level80.0529801324503311NOK
10% type I error level90.0596026490066225OK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = ; par7 = ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')
}