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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 23 Dec 2011 07:48:19 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/23/t1324644675frb1lces3pilty1.htm/, Retrieved Thu, 31 Oct 2024 22:49:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=160360, Retrieved Thu, 31 Oct 2024 22:49:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact152
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] [Multiple Lineair ...] [2011-12-13 17:16:57] [570fce4db58fd7864ac807c4286d6e49]
- R  D      [Multiple Regression] [] [2011-12-23 12:48:19] [204816f6f70a8d342ddc2b9d4f4a80d3] [Current]
- R           [Multiple Regression] [Hall of fame analyse] [2011-12-23 16:00:27] [19d77e37efa419fdc040c74a96874aff]
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Dataseries X:
279055	73	3	96	130
212408	75	4	75	143
233939	83	16	70	118
222117	106	2	134	146
179751	55	1	72	73
70849	28	3	8	89
605767	135	0	173	146
33186	19	0	1	22
227332	62	7	88	132
258874	48	0	98	92
359064	120	0	112	147
264989	131	7	125	203
212638	87	10	57	113
368577	85	4	139	171
269455	88	10	87	87
397992	190	0	176	208
335567	76	8	114	153
428322	172	4	121	97
182016	58	3	103	95
267365	89	8	135	197
279428	73	0	123	160
508849	111	1	99	148
206722	47	5	74	84
200004	58	9	103	227
257139	133	1	158	154
270941	138	0	116	151
324969	134	5	114	142
329962	92	0	150	148
190867	60	0	64	110
393860	79	0	150	149
327660	89	3	143	179
269239	83	6	50	149
391045	105	1	145	187
130446	49	4	56	153
430118	104	4	141	163
273950	56	0	83	127
428077	128	0	112	151
254312	93	2	79	100
120351	35	1	33	46
395643	211	2	152	156
345875	86	10	126	128
216827	82	10	97	111
224524	83	5	84	119
182485	69	6	68	148
157164	85	1	50	65
459455	157	2	101	134
78800	42	2	20	66
217932	84	0	101	201
368086	123	10	150	177
230299	70	3	129	156
244782	81	0	99	158
24188	24	0	8	7
400109	334	8	88	175
65029	17	5	21	61
101097	64	3	30	41
309810	67	1	102	133
369627	90	5	163	228
367127	204	6	132	140
377704	154	0	161	155
280106	90	12	90	141
400971	153	10	160	181
315924	122	12	139	75
291391	124	11	104	97
295075	93	8	103	142
280018	81	3	66	136
267432	71	0	163	87
217181	141	6	93	140
258166	159	10	85	169
260919	87	2	150	129
182961	73	5	143	92
256967	74	13	107	160
73566	32	6	22	67
272362	93	7	85	179
229056	62	2	101	90
229851	70	5	131	144
371391	91	4	140	144
398210	104	3	156	144
220419	111	6	81	134
231884	72	2	137	146
217714	72	0	102	121
200046	53	1	72	112
483074	131	1	161	145
146100	72	5	30	99
295224	109	2	120	96
80953	25	0	49	27
217384	63	0	121	77
179344	62	6	76	137
415550	221	1	85	151
389059	129	4	151	126
180679	106	1	165	159
299505	104	1	89	101
292260	84	3	168	144
199481	68	10	48	102
282361	78	1	149	135
329281	89	4	75	147
234577	48	5	107	155
297995	67	7	116	138
329583	89	0	173	113
416463	163	12	155	248
415683	119	13	165	116
297080	142	9	121	176
318283	70	0	156	140
224033	199	0	86	59
43287	14	4	13	64
238089	87	4	120	40
263322	160	0	117	98
299566	60	0	133	139
321797	95	0	169	135
193926	95	0	39	97
175138	105	0	125	142
354041	78	5	82	155
303273	91	1	148	115
23668	13	0	12	0
196743	79	0	146	103
61857	25	4	23	30
217543	54	0	87	130
440711	128	1	164	102
21054	16	0	4	0
252805	52	5	81	77
31961	22	0	18	9
360436	125	3	118	150
251948	77	7	76	163
187003	96	14	55	148
180842	58	3	62	94
38214	34	0	16	21
280392	56	3	98	151
358276	84	0	137	187
211775	67	0	50	171
447335	90	4	152	170
348017	99	0	163	145
441946	133	3	142	198
215177	43	0	80	152
130177	47	0	59	112
316128	363	4	94	173
466139	198	5	128	177
162279	62	16	63	153
416643	140	6	127	161
178322	86	5	60	115
292443	54	2	118	147
283913	100	1	110	124
244802	126	1	45	57
387072	125	9	96	144
246963	92	1	128	126
173260	63	3	41	78
346748	108	11	146	153
176654	59	5	147	196
268189	95	2	121	130
314070	112	1	185	159
1	0	9	0	0
14688	10	0	4	0
98	1	0	0	0
455	2	0	0	0
0	0	1	0	0
0	0	0	0	0
291650	94	2	85	94
415421	168	3	164	129
0	0	0	0	0
203	4	0	0	0
7199	5	0	7	0
46660	20	0	12	13
17547	5	0	0	4
121550	46	0	37	89
969	2	0	0	0
242774	75	2	62	71




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'AstonUniversity' @ aston.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'AstonUniversity' @ aston.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'AstonUniversity' @ aston.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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 time5 seconds
R Server'AstonUniversity' @ aston.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Tijd_RFC[t] = + 13561.3061256069 + 755.559043810155`#Logins`[t] + 198.842019692495`#Gedeelde_Compendia`[t] + 1240.85716085954`#Blogs`[t] + 424.359080273322`#Reviews+120tekens`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Tijd_RFC[t] =  +  13561.3061256069 +  755.559043810155`#Logins`[t] +  198.842019692495`#Gedeelde_Compendia`[t] +  1240.85716085954`#Blogs`[t] +  424.359080273322`#Reviews+120tekens`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Tijd_RFC[t] =  +  13561.3061256069 +  755.559043810155`#Logins`[t] +  198.842019692495`#Gedeelde_Compendia`[t] +  1240.85716085954`#Blogs`[t] +  424.359080273322`#Reviews+120tekens`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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
Tijd_RFC[t] = + 13561.3061256069 + 755.559043810155`#Logins`[t] + 198.842019692495`#Gedeelde_Compendia`[t] + 1240.85716085954`#Blogs`[t] + 424.359080273322`#Reviews+120tekens`[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)13561.306125606911426.633441.18680.2370710.118535
`#Logins`755.559043810155109.1166736.924300
`#Gedeelde_Compendia`198.8420196924951319.9985910.15060.8804520.440226
`#Blogs`1240.85716085954143.3877478.653900
`#Reviews+120tekens`424.359080273322136.2369093.11490.0021840.001092

\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) & 13561.3061256069 & 11426.63344 & 1.1868 & 0.237071 & 0.118535 \tabularnewline
`#Logins` & 755.559043810155 & 109.116673 & 6.9243 & 0 & 0 \tabularnewline
`#Gedeelde_Compendia` & 198.842019692495 & 1319.998591 & 0.1506 & 0.880452 & 0.440226 \tabularnewline
`#Blogs` & 1240.85716085954 & 143.387747 & 8.6539 & 0 & 0 \tabularnewline
`#Reviews+120tekens` & 424.359080273322 & 136.236909 & 3.1149 & 0.002184 & 0.001092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&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]13561.3061256069[/C][C]11426.63344[/C][C]1.1868[/C][C]0.237071[/C][C]0.118535[/C][/ROW]
[ROW][C]`#Logins`[/C][C]755.559043810155[/C][C]109.116673[/C][C]6.9243[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`#Gedeelde_Compendia`[/C][C]198.842019692495[/C][C]1319.998591[/C][C]0.1506[/C][C]0.880452[/C][C]0.440226[/C][/ROW]
[ROW][C]`#Blogs`[/C][C]1240.85716085954[/C][C]143.387747[/C][C]8.6539[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`#Reviews+120tekens`[/C][C]424.359080273322[/C][C]136.236909[/C][C]3.1149[/C][C]0.002184[/C][C]0.001092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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)13561.306125606911426.633441.18680.2370710.118535
`#Logins`755.559043810155109.1166736.924300
`#Gedeelde_Compendia`198.8420196924951319.9985910.15060.8804520.440226
`#Blogs`1240.85716085954143.3877478.653900
`#Reviews+120tekens`424.359080273322136.2369093.11490.0021840.001092







Multiple Linear Regression - Regression Statistics
Multiple R0.883128498446283
R-squared0.779915944767987
Adjusted R-squared0.774379238975987
F-TEST (value)140.862811582809
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation59706.7511630799
Sum Squared Residuals566818485377.54

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.883128498446283 \tabularnewline
R-squared & 0.779915944767987 \tabularnewline
Adjusted R-squared & 0.774379238975987 \tabularnewline
F-TEST (value) & 140.862811582809 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 159 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 59706.7511630799 \tabularnewline
Sum Squared Residuals & 566818485377.54 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.883128498446283[/C][/ROW]
[ROW][C]R-squared[/C][C]0.779915944767987[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.774379238975987[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]140.862811582809[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]159[/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]59706.7511630799[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]566818485377.54[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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 R0.883128498446283
R-squared0.779915944767987
Adjusted R-squared0.774379238975987
F-TEST (value)140.862811582809
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation59706.7511630799
Sum Squared Residuals566818485377.54







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1279055243602.61026087435452.3897391265
2212408224771.238033689-12363.2380336891
3233939216388.5518093517550.4481906503
4222117322279.534083952-100162.534083952
5179751175635.8239966974115.17600330255
67084983008.3008425707-12159.3008425707
7605767392186.491588584213580.508411416
83318638493.6848848724-5307.68488487243
9227332227008.689731402323.310268597804
10258874210473.17737787548400.8226221249
11359064305585.17819927353478.8218007274
12264989355183.473405512-90194.4734055119
13212638199964.79737389512673.2026261054
14368577323623.74101445544953.2589855455
15269455226912.73515638542542.2648436153
16397992463775.073457667-65783.0734576668
17335567278959.18523252556607.8147674753
18428322335619.3769902492702.6230097596
19182016226102.656920172-44086.6569201718
20267365333511.252712133-66146.2527121333
21279428289239.999953203-9811.99995320344
22508849283277.204813773225571.795186227
23206722177536.38392971229185.6160702882
24200004283311.107634405-83307.1076344053
25257139375656.230749949-118517.230749949
26270941325846.105952387-54905.1059523868
27324969317517.133831437451.86616857029
28329962332006.456165524-2044.45616552416
29190867184989.2058792925877.79412070769
30393860322608.54767626571251.4523237345
31327660334805.436455627-7145.43645562737
32269239202738.11988370766500.8801162932
33391045352373.28408111138671.7159188895
34130446185794.007641027-55348.0076410271
35430118337066.2045263893051.79547362
36273950212757.36012502961192.6398749705
37428077313327.086870847114749.913129153
38254312224689.60497457229622.3950254277
39120351100673.51867959319677.4813204075
40395643428192.253382223-32549.2533822233
41345875291193.76863349354681.2313665073
42216827244972.570428679-28145.5704286789
43224524231997.648925039-7473.64892503911
44182485214071.363085563-31586.3630855631
45157164167608.865129906-10444.8651299056
46459455314772.450046625144682.549953375
477880098517.3125202485-19717.3125202485
48217932287651.014187411-69719.0141874114
49368086369723.62004849-1637.62004849028
50230299293317.555524914-63018.5555249144
51244782264655.182282509-19873.182282509
522418844592.0940258402-20404.0940258402
53400109450967.03211921-50858.0321192098
546502979343.924243565-14314.924243565
55101097117138.048105527-16041.0481055268
56309810247389.79216460562420.2078353951
57369627381569.417689406-11942.4176894061
58367127392091.819652758-24964.8196527582
59377704395471.059213122-17767.059213122
60280106255459.49910072824646.500899272
61400971406496.399292484-5525.39929248363
62315924312431.6900867313492.30991326871
63291391279649.86529058811741.1347094118
64295075273486.31032483621588.6896751641
65280018214967.52226720965050.4777327915
66267432306384.955440012-38952.9554400123
67217181296098.170619196-78917.1706191963
68258166312873.157527599-54707.157527599
69260919320563.522460665-59644.5224606653
70182961286194.935810271-103233.935810271
71256967272726.790679263-15759.7906792634
727356694663.1635629093-21097.1635629093
73272362266653.3253797855708.67462021548
74229056224322.5413526344733.45864736576
75229851291104.644922739-61253.6449227386
76371391317940.25727079553450.7427292047
77398210347417.39739438850792.6026056125
78220419255994.958892937-35575.9588929372
79231884300313.098076985-68429.0980769853
80217714245876.436400683-28162.4364006833
81200046190674.7100397379371.28996026329
82483074374048.452422448109025.547577552
83146100148193.031151246-2093.03115124567
84295224285956.2569496839267.74305031723
8580953104709.978270358-23756.978270358
86217384243980.891530697-26596.8915306971
87179344214041.357182762-34697.3571827618
88415550350289.77662167665260.2233783236
89389059352662.46626011636396.5337398837
90180679366063.932094458-185384.932094459
91299505245634.8431256653870.1568743397
92292260347196.502448499-54936.5024484989
93199481169773.51121075929707.4887892407
94282361314869.946367462-32508.9463674618
95329281237046.50096812592234.4990318753
96234577249369.723981293-14792.7239812928
97297995268076.6399361629918.3600638399
98329583343426.925924297-13843.9259242969
99416463436677.446343985-20214.446343985
100415683360024.86344854855658.1365514524
101297080347471.183115991-50391.1831159907
102318283319434.427524671-1151.42752467143
103224033295668.457413874-71635.4574138744
1044328768224.6250463857-24937.6250463857
105238089245967.533529938-7878.53352993832
106263322321218.230822584-57896.2308225837
107299566282914.76330652716651.2366934729
108321797352332.751309733-30535.7513097327
109193926174895.67534760619030.324652394
110175138308261.140231928-133123.140231928
111354041241015.066274109113025.933725891
112303273314964.175170668-11691.1751706678
1132366838273.8596254534-14605.8596254534
114196743298124.601340254-101381.601340254
1156185774516.1374075999-12659.1374075999
116217543217482.74792166760.2520783326979
117440711357256.90632184383454.093678157
1182105430613.6794700075-9559.6794700075
119252805187029.66571286665775.3342871339
1203196156338.265707362-24377.2657073619
121360436318677.71968337841758.2803166219
122251948236606.92094671315341.079053287
123187003219931.050334803-32928.0503348032
124180842174803.1542446576038.84575534274
1253821468015.5688746446-29801.5688746446
126280392242151.3615235638240.6384764402
127358276326380.84485452831895.1551454716
128211775198792.02283060212982.9771693975
129447335343108.320244406104226.679755594
130348017352153.435322549-4136.43532254934
131441946374871.99974760867074.0002523922
132215177209821.4980797525355.50192024812
133130177169811.370666009-39634.3706660092
134316128478679.301115545-162551.301115545
135466139398097.4806968868041.5193031205
136162279206688.379572886-44409.3795728859
137416643346443.2957303570199.7042696497
138178322202786.317874747-24464.3178747473
139292443263561.10831234528881.8916876554
140283913278430.8661747565482.13382524353
141244802188987.62747963855814.3725203623
142387072290025.75978098397046.2402190168
143246963295570.540880294-48607.5408802937
144173260145733.20380128427526.7961987155
145346748343441.0298410333306.97015896746
146176654324713.882188792-148059.882188792
147268189291047.496226493-22858.4962264931
148314070395414.42957451-81344.4295745103
149115350.8843028393-15349.8843028393
1501468826080.3252071466-11392.3252071466
1519814316.865169417-14218.865169417
15245515072.4242132272-14617.4242132272
153013760.1481452994-13760.1481452994
154013561.3061256069-13561.3061256069
155291650230344.152501961305.8474981002
156415421399334.64728101416086.3527189861
157013561.3061256069-13561.3061256069
15820316583.5423008475-16380.5423008475
159719926025.1014706744-18826.1014706744
1604666049079.4409756777-2419.44097567765
1611754719036.5376657509-1489.53766575092
162121550131996.695237003-10446.6952370027
16396915072.4242132272-14103.4242132272
164242774177688.55712345165085.442876549

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 279055 & 243602.610260874 & 35452.3897391265 \tabularnewline
2 & 212408 & 224771.238033689 & -12363.2380336891 \tabularnewline
3 & 233939 & 216388.55180935 & 17550.4481906503 \tabularnewline
4 & 222117 & 322279.534083952 & -100162.534083952 \tabularnewline
5 & 179751 & 175635.823996697 & 4115.17600330255 \tabularnewline
6 & 70849 & 83008.3008425707 & -12159.3008425707 \tabularnewline
7 & 605767 & 392186.491588584 & 213580.508411416 \tabularnewline
8 & 33186 & 38493.6848848724 & -5307.68488487243 \tabularnewline
9 & 227332 & 227008.689731402 & 323.310268597804 \tabularnewline
10 & 258874 & 210473.177377875 & 48400.8226221249 \tabularnewline
11 & 359064 & 305585.178199273 & 53478.8218007274 \tabularnewline
12 & 264989 & 355183.473405512 & -90194.4734055119 \tabularnewline
13 & 212638 & 199964.797373895 & 12673.2026261054 \tabularnewline
14 & 368577 & 323623.741014455 & 44953.2589855455 \tabularnewline
15 & 269455 & 226912.735156385 & 42542.2648436153 \tabularnewline
16 & 397992 & 463775.073457667 & -65783.0734576668 \tabularnewline
17 & 335567 & 278959.185232525 & 56607.8147674753 \tabularnewline
18 & 428322 & 335619.37699024 & 92702.6230097596 \tabularnewline
19 & 182016 & 226102.656920172 & -44086.6569201718 \tabularnewline
20 & 267365 & 333511.252712133 & -66146.2527121333 \tabularnewline
21 & 279428 & 289239.999953203 & -9811.99995320344 \tabularnewline
22 & 508849 & 283277.204813773 & 225571.795186227 \tabularnewline
23 & 206722 & 177536.383929712 & 29185.6160702882 \tabularnewline
24 & 200004 & 283311.107634405 & -83307.1076344053 \tabularnewline
25 & 257139 & 375656.230749949 & -118517.230749949 \tabularnewline
26 & 270941 & 325846.105952387 & -54905.1059523868 \tabularnewline
27 & 324969 & 317517.13383143 & 7451.86616857029 \tabularnewline
28 & 329962 & 332006.456165524 & -2044.45616552416 \tabularnewline
29 & 190867 & 184989.205879292 & 5877.79412070769 \tabularnewline
30 & 393860 & 322608.547676265 & 71251.4523237345 \tabularnewline
31 & 327660 & 334805.436455627 & -7145.43645562737 \tabularnewline
32 & 269239 & 202738.119883707 & 66500.8801162932 \tabularnewline
33 & 391045 & 352373.284081111 & 38671.7159188895 \tabularnewline
34 & 130446 & 185794.007641027 & -55348.0076410271 \tabularnewline
35 & 430118 & 337066.20452638 & 93051.79547362 \tabularnewline
36 & 273950 & 212757.360125029 & 61192.6398749705 \tabularnewline
37 & 428077 & 313327.086870847 & 114749.913129153 \tabularnewline
38 & 254312 & 224689.604974572 & 29622.3950254277 \tabularnewline
39 & 120351 & 100673.518679593 & 19677.4813204075 \tabularnewline
40 & 395643 & 428192.253382223 & -32549.2533822233 \tabularnewline
41 & 345875 & 291193.768633493 & 54681.2313665073 \tabularnewline
42 & 216827 & 244972.570428679 & -28145.5704286789 \tabularnewline
43 & 224524 & 231997.648925039 & -7473.64892503911 \tabularnewline
44 & 182485 & 214071.363085563 & -31586.3630855631 \tabularnewline
45 & 157164 & 167608.865129906 & -10444.8651299056 \tabularnewline
46 & 459455 & 314772.450046625 & 144682.549953375 \tabularnewline
47 & 78800 & 98517.3125202485 & -19717.3125202485 \tabularnewline
48 & 217932 & 287651.014187411 & -69719.0141874114 \tabularnewline
49 & 368086 & 369723.62004849 & -1637.62004849028 \tabularnewline
50 & 230299 & 293317.555524914 & -63018.5555249144 \tabularnewline
51 & 244782 & 264655.182282509 & -19873.182282509 \tabularnewline
52 & 24188 & 44592.0940258402 & -20404.0940258402 \tabularnewline
53 & 400109 & 450967.03211921 & -50858.0321192098 \tabularnewline
54 & 65029 & 79343.924243565 & -14314.924243565 \tabularnewline
55 & 101097 & 117138.048105527 & -16041.0481055268 \tabularnewline
56 & 309810 & 247389.792164605 & 62420.2078353951 \tabularnewline
57 & 369627 & 381569.417689406 & -11942.4176894061 \tabularnewline
58 & 367127 & 392091.819652758 & -24964.8196527582 \tabularnewline
59 & 377704 & 395471.059213122 & -17767.059213122 \tabularnewline
60 & 280106 & 255459.499100728 & 24646.500899272 \tabularnewline
61 & 400971 & 406496.399292484 & -5525.39929248363 \tabularnewline
62 & 315924 & 312431.690086731 & 3492.30991326871 \tabularnewline
63 & 291391 & 279649.865290588 & 11741.1347094118 \tabularnewline
64 & 295075 & 273486.310324836 & 21588.6896751641 \tabularnewline
65 & 280018 & 214967.522267209 & 65050.4777327915 \tabularnewline
66 & 267432 & 306384.955440012 & -38952.9554400123 \tabularnewline
67 & 217181 & 296098.170619196 & -78917.1706191963 \tabularnewline
68 & 258166 & 312873.157527599 & -54707.157527599 \tabularnewline
69 & 260919 & 320563.522460665 & -59644.5224606653 \tabularnewline
70 & 182961 & 286194.935810271 & -103233.935810271 \tabularnewline
71 & 256967 & 272726.790679263 & -15759.7906792634 \tabularnewline
72 & 73566 & 94663.1635629093 & -21097.1635629093 \tabularnewline
73 & 272362 & 266653.325379785 & 5708.67462021548 \tabularnewline
74 & 229056 & 224322.541352634 & 4733.45864736576 \tabularnewline
75 & 229851 & 291104.644922739 & -61253.6449227386 \tabularnewline
76 & 371391 & 317940.257270795 & 53450.7427292047 \tabularnewline
77 & 398210 & 347417.397394388 & 50792.6026056125 \tabularnewline
78 & 220419 & 255994.958892937 & -35575.9588929372 \tabularnewline
79 & 231884 & 300313.098076985 & -68429.0980769853 \tabularnewline
80 & 217714 & 245876.436400683 & -28162.4364006833 \tabularnewline
81 & 200046 & 190674.710039737 & 9371.28996026329 \tabularnewline
82 & 483074 & 374048.452422448 & 109025.547577552 \tabularnewline
83 & 146100 & 148193.031151246 & -2093.03115124567 \tabularnewline
84 & 295224 & 285956.256949683 & 9267.74305031723 \tabularnewline
85 & 80953 & 104709.978270358 & -23756.978270358 \tabularnewline
86 & 217384 & 243980.891530697 & -26596.8915306971 \tabularnewline
87 & 179344 & 214041.357182762 & -34697.3571827618 \tabularnewline
88 & 415550 & 350289.776621676 & 65260.2233783236 \tabularnewline
89 & 389059 & 352662.466260116 & 36396.5337398837 \tabularnewline
90 & 180679 & 366063.932094458 & -185384.932094459 \tabularnewline
91 & 299505 & 245634.84312566 & 53870.1568743397 \tabularnewline
92 & 292260 & 347196.502448499 & -54936.5024484989 \tabularnewline
93 & 199481 & 169773.511210759 & 29707.4887892407 \tabularnewline
94 & 282361 & 314869.946367462 & -32508.9463674618 \tabularnewline
95 & 329281 & 237046.500968125 & 92234.4990318753 \tabularnewline
96 & 234577 & 249369.723981293 & -14792.7239812928 \tabularnewline
97 & 297995 & 268076.63993616 & 29918.3600638399 \tabularnewline
98 & 329583 & 343426.925924297 & -13843.9259242969 \tabularnewline
99 & 416463 & 436677.446343985 & -20214.446343985 \tabularnewline
100 & 415683 & 360024.863448548 & 55658.1365514524 \tabularnewline
101 & 297080 & 347471.183115991 & -50391.1831159907 \tabularnewline
102 & 318283 & 319434.427524671 & -1151.42752467143 \tabularnewline
103 & 224033 & 295668.457413874 & -71635.4574138744 \tabularnewline
104 & 43287 & 68224.6250463857 & -24937.6250463857 \tabularnewline
105 & 238089 & 245967.533529938 & -7878.53352993832 \tabularnewline
106 & 263322 & 321218.230822584 & -57896.2308225837 \tabularnewline
107 & 299566 & 282914.763306527 & 16651.2366934729 \tabularnewline
108 & 321797 & 352332.751309733 & -30535.7513097327 \tabularnewline
109 & 193926 & 174895.675347606 & 19030.324652394 \tabularnewline
110 & 175138 & 308261.140231928 & -133123.140231928 \tabularnewline
111 & 354041 & 241015.066274109 & 113025.933725891 \tabularnewline
112 & 303273 & 314964.175170668 & -11691.1751706678 \tabularnewline
113 & 23668 & 38273.8596254534 & -14605.8596254534 \tabularnewline
114 & 196743 & 298124.601340254 & -101381.601340254 \tabularnewline
115 & 61857 & 74516.1374075999 & -12659.1374075999 \tabularnewline
116 & 217543 & 217482.747921667 & 60.2520783326979 \tabularnewline
117 & 440711 & 357256.906321843 & 83454.093678157 \tabularnewline
118 & 21054 & 30613.6794700075 & -9559.6794700075 \tabularnewline
119 & 252805 & 187029.665712866 & 65775.3342871339 \tabularnewline
120 & 31961 & 56338.265707362 & -24377.2657073619 \tabularnewline
121 & 360436 & 318677.719683378 & 41758.2803166219 \tabularnewline
122 & 251948 & 236606.920946713 & 15341.079053287 \tabularnewline
123 & 187003 & 219931.050334803 & -32928.0503348032 \tabularnewline
124 & 180842 & 174803.154244657 & 6038.84575534274 \tabularnewline
125 & 38214 & 68015.5688746446 & -29801.5688746446 \tabularnewline
126 & 280392 & 242151.36152356 & 38240.6384764402 \tabularnewline
127 & 358276 & 326380.844854528 & 31895.1551454716 \tabularnewline
128 & 211775 & 198792.022830602 & 12982.9771693975 \tabularnewline
129 & 447335 & 343108.320244406 & 104226.679755594 \tabularnewline
130 & 348017 & 352153.435322549 & -4136.43532254934 \tabularnewline
131 & 441946 & 374871.999747608 & 67074.0002523922 \tabularnewline
132 & 215177 & 209821.498079752 & 5355.50192024812 \tabularnewline
133 & 130177 & 169811.370666009 & -39634.3706660092 \tabularnewline
134 & 316128 & 478679.301115545 & -162551.301115545 \tabularnewline
135 & 466139 & 398097.48069688 & 68041.5193031205 \tabularnewline
136 & 162279 & 206688.379572886 & -44409.3795728859 \tabularnewline
137 & 416643 & 346443.29573035 & 70199.7042696497 \tabularnewline
138 & 178322 & 202786.317874747 & -24464.3178747473 \tabularnewline
139 & 292443 & 263561.108312345 & 28881.8916876554 \tabularnewline
140 & 283913 & 278430.866174756 & 5482.13382524353 \tabularnewline
141 & 244802 & 188987.627479638 & 55814.3725203623 \tabularnewline
142 & 387072 & 290025.759780983 & 97046.2402190168 \tabularnewline
143 & 246963 & 295570.540880294 & -48607.5408802937 \tabularnewline
144 & 173260 & 145733.203801284 & 27526.7961987155 \tabularnewline
145 & 346748 & 343441.029841033 & 3306.97015896746 \tabularnewline
146 & 176654 & 324713.882188792 & -148059.882188792 \tabularnewline
147 & 268189 & 291047.496226493 & -22858.4962264931 \tabularnewline
148 & 314070 & 395414.42957451 & -81344.4295745103 \tabularnewline
149 & 1 & 15350.8843028393 & -15349.8843028393 \tabularnewline
150 & 14688 & 26080.3252071466 & -11392.3252071466 \tabularnewline
151 & 98 & 14316.865169417 & -14218.865169417 \tabularnewline
152 & 455 & 15072.4242132272 & -14617.4242132272 \tabularnewline
153 & 0 & 13760.1481452994 & -13760.1481452994 \tabularnewline
154 & 0 & 13561.3061256069 & -13561.3061256069 \tabularnewline
155 & 291650 & 230344.1525019 & 61305.8474981002 \tabularnewline
156 & 415421 & 399334.647281014 & 16086.3527189861 \tabularnewline
157 & 0 & 13561.3061256069 & -13561.3061256069 \tabularnewline
158 & 203 & 16583.5423008475 & -16380.5423008475 \tabularnewline
159 & 7199 & 26025.1014706744 & -18826.1014706744 \tabularnewline
160 & 46660 & 49079.4409756777 & -2419.44097567765 \tabularnewline
161 & 17547 & 19036.5376657509 & -1489.53766575092 \tabularnewline
162 & 121550 & 131996.695237003 & -10446.6952370027 \tabularnewline
163 & 969 & 15072.4242132272 & -14103.4242132272 \tabularnewline
164 & 242774 & 177688.557123451 & 65085.442876549 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&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]279055[/C][C]243602.610260874[/C][C]35452.3897391265[/C][/ROW]
[ROW][C]2[/C][C]212408[/C][C]224771.238033689[/C][C]-12363.2380336891[/C][/ROW]
[ROW][C]3[/C][C]233939[/C][C]216388.55180935[/C][C]17550.4481906503[/C][/ROW]
[ROW][C]4[/C][C]222117[/C][C]322279.534083952[/C][C]-100162.534083952[/C][/ROW]
[ROW][C]5[/C][C]179751[/C][C]175635.823996697[/C][C]4115.17600330255[/C][/ROW]
[ROW][C]6[/C][C]70849[/C][C]83008.3008425707[/C][C]-12159.3008425707[/C][/ROW]
[ROW][C]7[/C][C]605767[/C][C]392186.491588584[/C][C]213580.508411416[/C][/ROW]
[ROW][C]8[/C][C]33186[/C][C]38493.6848848724[/C][C]-5307.68488487243[/C][/ROW]
[ROW][C]9[/C][C]227332[/C][C]227008.689731402[/C][C]323.310268597804[/C][/ROW]
[ROW][C]10[/C][C]258874[/C][C]210473.177377875[/C][C]48400.8226221249[/C][/ROW]
[ROW][C]11[/C][C]359064[/C][C]305585.178199273[/C][C]53478.8218007274[/C][/ROW]
[ROW][C]12[/C][C]264989[/C][C]355183.473405512[/C][C]-90194.4734055119[/C][/ROW]
[ROW][C]13[/C][C]212638[/C][C]199964.797373895[/C][C]12673.2026261054[/C][/ROW]
[ROW][C]14[/C][C]368577[/C][C]323623.741014455[/C][C]44953.2589855455[/C][/ROW]
[ROW][C]15[/C][C]269455[/C][C]226912.735156385[/C][C]42542.2648436153[/C][/ROW]
[ROW][C]16[/C][C]397992[/C][C]463775.073457667[/C][C]-65783.0734576668[/C][/ROW]
[ROW][C]17[/C][C]335567[/C][C]278959.185232525[/C][C]56607.8147674753[/C][/ROW]
[ROW][C]18[/C][C]428322[/C][C]335619.37699024[/C][C]92702.6230097596[/C][/ROW]
[ROW][C]19[/C][C]182016[/C][C]226102.656920172[/C][C]-44086.6569201718[/C][/ROW]
[ROW][C]20[/C][C]267365[/C][C]333511.252712133[/C][C]-66146.2527121333[/C][/ROW]
[ROW][C]21[/C][C]279428[/C][C]289239.999953203[/C][C]-9811.99995320344[/C][/ROW]
[ROW][C]22[/C][C]508849[/C][C]283277.204813773[/C][C]225571.795186227[/C][/ROW]
[ROW][C]23[/C][C]206722[/C][C]177536.383929712[/C][C]29185.6160702882[/C][/ROW]
[ROW][C]24[/C][C]200004[/C][C]283311.107634405[/C][C]-83307.1076344053[/C][/ROW]
[ROW][C]25[/C][C]257139[/C][C]375656.230749949[/C][C]-118517.230749949[/C][/ROW]
[ROW][C]26[/C][C]270941[/C][C]325846.105952387[/C][C]-54905.1059523868[/C][/ROW]
[ROW][C]27[/C][C]324969[/C][C]317517.13383143[/C][C]7451.86616857029[/C][/ROW]
[ROW][C]28[/C][C]329962[/C][C]332006.456165524[/C][C]-2044.45616552416[/C][/ROW]
[ROW][C]29[/C][C]190867[/C][C]184989.205879292[/C][C]5877.79412070769[/C][/ROW]
[ROW][C]30[/C][C]393860[/C][C]322608.547676265[/C][C]71251.4523237345[/C][/ROW]
[ROW][C]31[/C][C]327660[/C][C]334805.436455627[/C][C]-7145.43645562737[/C][/ROW]
[ROW][C]32[/C][C]269239[/C][C]202738.119883707[/C][C]66500.8801162932[/C][/ROW]
[ROW][C]33[/C][C]391045[/C][C]352373.284081111[/C][C]38671.7159188895[/C][/ROW]
[ROW][C]34[/C][C]130446[/C][C]185794.007641027[/C][C]-55348.0076410271[/C][/ROW]
[ROW][C]35[/C][C]430118[/C][C]337066.20452638[/C][C]93051.79547362[/C][/ROW]
[ROW][C]36[/C][C]273950[/C][C]212757.360125029[/C][C]61192.6398749705[/C][/ROW]
[ROW][C]37[/C][C]428077[/C][C]313327.086870847[/C][C]114749.913129153[/C][/ROW]
[ROW][C]38[/C][C]254312[/C][C]224689.604974572[/C][C]29622.3950254277[/C][/ROW]
[ROW][C]39[/C][C]120351[/C][C]100673.518679593[/C][C]19677.4813204075[/C][/ROW]
[ROW][C]40[/C][C]395643[/C][C]428192.253382223[/C][C]-32549.2533822233[/C][/ROW]
[ROW][C]41[/C][C]345875[/C][C]291193.768633493[/C][C]54681.2313665073[/C][/ROW]
[ROW][C]42[/C][C]216827[/C][C]244972.570428679[/C][C]-28145.5704286789[/C][/ROW]
[ROW][C]43[/C][C]224524[/C][C]231997.648925039[/C][C]-7473.64892503911[/C][/ROW]
[ROW][C]44[/C][C]182485[/C][C]214071.363085563[/C][C]-31586.3630855631[/C][/ROW]
[ROW][C]45[/C][C]157164[/C][C]167608.865129906[/C][C]-10444.8651299056[/C][/ROW]
[ROW][C]46[/C][C]459455[/C][C]314772.450046625[/C][C]144682.549953375[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]98517.3125202485[/C][C]-19717.3125202485[/C][/ROW]
[ROW][C]48[/C][C]217932[/C][C]287651.014187411[/C][C]-69719.0141874114[/C][/ROW]
[ROW][C]49[/C][C]368086[/C][C]369723.62004849[/C][C]-1637.62004849028[/C][/ROW]
[ROW][C]50[/C][C]230299[/C][C]293317.555524914[/C][C]-63018.5555249144[/C][/ROW]
[ROW][C]51[/C][C]244782[/C][C]264655.182282509[/C][C]-19873.182282509[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]44592.0940258402[/C][C]-20404.0940258402[/C][/ROW]
[ROW][C]53[/C][C]400109[/C][C]450967.03211921[/C][C]-50858.0321192098[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]79343.924243565[/C][C]-14314.924243565[/C][/ROW]
[ROW][C]55[/C][C]101097[/C][C]117138.048105527[/C][C]-16041.0481055268[/C][/ROW]
[ROW][C]56[/C][C]309810[/C][C]247389.792164605[/C][C]62420.2078353951[/C][/ROW]
[ROW][C]57[/C][C]369627[/C][C]381569.417689406[/C][C]-11942.4176894061[/C][/ROW]
[ROW][C]58[/C][C]367127[/C][C]392091.819652758[/C][C]-24964.8196527582[/C][/ROW]
[ROW][C]59[/C][C]377704[/C][C]395471.059213122[/C][C]-17767.059213122[/C][/ROW]
[ROW][C]60[/C][C]280106[/C][C]255459.499100728[/C][C]24646.500899272[/C][/ROW]
[ROW][C]61[/C][C]400971[/C][C]406496.399292484[/C][C]-5525.39929248363[/C][/ROW]
[ROW][C]62[/C][C]315924[/C][C]312431.690086731[/C][C]3492.30991326871[/C][/ROW]
[ROW][C]63[/C][C]291391[/C][C]279649.865290588[/C][C]11741.1347094118[/C][/ROW]
[ROW][C]64[/C][C]295075[/C][C]273486.310324836[/C][C]21588.6896751641[/C][/ROW]
[ROW][C]65[/C][C]280018[/C][C]214967.522267209[/C][C]65050.4777327915[/C][/ROW]
[ROW][C]66[/C][C]267432[/C][C]306384.955440012[/C][C]-38952.9554400123[/C][/ROW]
[ROW][C]67[/C][C]217181[/C][C]296098.170619196[/C][C]-78917.1706191963[/C][/ROW]
[ROW][C]68[/C][C]258166[/C][C]312873.157527599[/C][C]-54707.157527599[/C][/ROW]
[ROW][C]69[/C][C]260919[/C][C]320563.522460665[/C][C]-59644.5224606653[/C][/ROW]
[ROW][C]70[/C][C]182961[/C][C]286194.935810271[/C][C]-103233.935810271[/C][/ROW]
[ROW][C]71[/C][C]256967[/C][C]272726.790679263[/C][C]-15759.7906792634[/C][/ROW]
[ROW][C]72[/C][C]73566[/C][C]94663.1635629093[/C][C]-21097.1635629093[/C][/ROW]
[ROW][C]73[/C][C]272362[/C][C]266653.325379785[/C][C]5708.67462021548[/C][/ROW]
[ROW][C]74[/C][C]229056[/C][C]224322.541352634[/C][C]4733.45864736576[/C][/ROW]
[ROW][C]75[/C][C]229851[/C][C]291104.644922739[/C][C]-61253.6449227386[/C][/ROW]
[ROW][C]76[/C][C]371391[/C][C]317940.257270795[/C][C]53450.7427292047[/C][/ROW]
[ROW][C]77[/C][C]398210[/C][C]347417.397394388[/C][C]50792.6026056125[/C][/ROW]
[ROW][C]78[/C][C]220419[/C][C]255994.958892937[/C][C]-35575.9588929372[/C][/ROW]
[ROW][C]79[/C][C]231884[/C][C]300313.098076985[/C][C]-68429.0980769853[/C][/ROW]
[ROW][C]80[/C][C]217714[/C][C]245876.436400683[/C][C]-28162.4364006833[/C][/ROW]
[ROW][C]81[/C][C]200046[/C][C]190674.710039737[/C][C]9371.28996026329[/C][/ROW]
[ROW][C]82[/C][C]483074[/C][C]374048.452422448[/C][C]109025.547577552[/C][/ROW]
[ROW][C]83[/C][C]146100[/C][C]148193.031151246[/C][C]-2093.03115124567[/C][/ROW]
[ROW][C]84[/C][C]295224[/C][C]285956.256949683[/C][C]9267.74305031723[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]104709.978270358[/C][C]-23756.978270358[/C][/ROW]
[ROW][C]86[/C][C]217384[/C][C]243980.891530697[/C][C]-26596.8915306971[/C][/ROW]
[ROW][C]87[/C][C]179344[/C][C]214041.357182762[/C][C]-34697.3571827618[/C][/ROW]
[ROW][C]88[/C][C]415550[/C][C]350289.776621676[/C][C]65260.2233783236[/C][/ROW]
[ROW][C]89[/C][C]389059[/C][C]352662.466260116[/C][C]36396.5337398837[/C][/ROW]
[ROW][C]90[/C][C]180679[/C][C]366063.932094458[/C][C]-185384.932094459[/C][/ROW]
[ROW][C]91[/C][C]299505[/C][C]245634.84312566[/C][C]53870.1568743397[/C][/ROW]
[ROW][C]92[/C][C]292260[/C][C]347196.502448499[/C][C]-54936.5024484989[/C][/ROW]
[ROW][C]93[/C][C]199481[/C][C]169773.511210759[/C][C]29707.4887892407[/C][/ROW]
[ROW][C]94[/C][C]282361[/C][C]314869.946367462[/C][C]-32508.9463674618[/C][/ROW]
[ROW][C]95[/C][C]329281[/C][C]237046.500968125[/C][C]92234.4990318753[/C][/ROW]
[ROW][C]96[/C][C]234577[/C][C]249369.723981293[/C][C]-14792.7239812928[/C][/ROW]
[ROW][C]97[/C][C]297995[/C][C]268076.63993616[/C][C]29918.3600638399[/C][/ROW]
[ROW][C]98[/C][C]329583[/C][C]343426.925924297[/C][C]-13843.9259242969[/C][/ROW]
[ROW][C]99[/C][C]416463[/C][C]436677.446343985[/C][C]-20214.446343985[/C][/ROW]
[ROW][C]100[/C][C]415683[/C][C]360024.863448548[/C][C]55658.1365514524[/C][/ROW]
[ROW][C]101[/C][C]297080[/C][C]347471.183115991[/C][C]-50391.1831159907[/C][/ROW]
[ROW][C]102[/C][C]318283[/C][C]319434.427524671[/C][C]-1151.42752467143[/C][/ROW]
[ROW][C]103[/C][C]224033[/C][C]295668.457413874[/C][C]-71635.4574138744[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]68224.6250463857[/C][C]-24937.6250463857[/C][/ROW]
[ROW][C]105[/C][C]238089[/C][C]245967.533529938[/C][C]-7878.53352993832[/C][/ROW]
[ROW][C]106[/C][C]263322[/C][C]321218.230822584[/C][C]-57896.2308225837[/C][/ROW]
[ROW][C]107[/C][C]299566[/C][C]282914.763306527[/C][C]16651.2366934729[/C][/ROW]
[ROW][C]108[/C][C]321797[/C][C]352332.751309733[/C][C]-30535.7513097327[/C][/ROW]
[ROW][C]109[/C][C]193926[/C][C]174895.675347606[/C][C]19030.324652394[/C][/ROW]
[ROW][C]110[/C][C]175138[/C][C]308261.140231928[/C][C]-133123.140231928[/C][/ROW]
[ROW][C]111[/C][C]354041[/C][C]241015.066274109[/C][C]113025.933725891[/C][/ROW]
[ROW][C]112[/C][C]303273[/C][C]314964.175170668[/C][C]-11691.1751706678[/C][/ROW]
[ROW][C]113[/C][C]23668[/C][C]38273.8596254534[/C][C]-14605.8596254534[/C][/ROW]
[ROW][C]114[/C][C]196743[/C][C]298124.601340254[/C][C]-101381.601340254[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]74516.1374075999[/C][C]-12659.1374075999[/C][/ROW]
[ROW][C]116[/C][C]217543[/C][C]217482.747921667[/C][C]60.2520783326979[/C][/ROW]
[ROW][C]117[/C][C]440711[/C][C]357256.906321843[/C][C]83454.093678157[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]30613.6794700075[/C][C]-9559.6794700075[/C][/ROW]
[ROW][C]119[/C][C]252805[/C][C]187029.665712866[/C][C]65775.3342871339[/C][/ROW]
[ROW][C]120[/C][C]31961[/C][C]56338.265707362[/C][C]-24377.2657073619[/C][/ROW]
[ROW][C]121[/C][C]360436[/C][C]318677.719683378[/C][C]41758.2803166219[/C][/ROW]
[ROW][C]122[/C][C]251948[/C][C]236606.920946713[/C][C]15341.079053287[/C][/ROW]
[ROW][C]123[/C][C]187003[/C][C]219931.050334803[/C][C]-32928.0503348032[/C][/ROW]
[ROW][C]124[/C][C]180842[/C][C]174803.154244657[/C][C]6038.84575534274[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]68015.5688746446[/C][C]-29801.5688746446[/C][/ROW]
[ROW][C]126[/C][C]280392[/C][C]242151.36152356[/C][C]38240.6384764402[/C][/ROW]
[ROW][C]127[/C][C]358276[/C][C]326380.844854528[/C][C]31895.1551454716[/C][/ROW]
[ROW][C]128[/C][C]211775[/C][C]198792.022830602[/C][C]12982.9771693975[/C][/ROW]
[ROW][C]129[/C][C]447335[/C][C]343108.320244406[/C][C]104226.679755594[/C][/ROW]
[ROW][C]130[/C][C]348017[/C][C]352153.435322549[/C][C]-4136.43532254934[/C][/ROW]
[ROW][C]131[/C][C]441946[/C][C]374871.999747608[/C][C]67074.0002523922[/C][/ROW]
[ROW][C]132[/C][C]215177[/C][C]209821.498079752[/C][C]5355.50192024812[/C][/ROW]
[ROW][C]133[/C][C]130177[/C][C]169811.370666009[/C][C]-39634.3706660092[/C][/ROW]
[ROW][C]134[/C][C]316128[/C][C]478679.301115545[/C][C]-162551.301115545[/C][/ROW]
[ROW][C]135[/C][C]466139[/C][C]398097.48069688[/C][C]68041.5193031205[/C][/ROW]
[ROW][C]136[/C][C]162279[/C][C]206688.379572886[/C][C]-44409.3795728859[/C][/ROW]
[ROW][C]137[/C][C]416643[/C][C]346443.29573035[/C][C]70199.7042696497[/C][/ROW]
[ROW][C]138[/C][C]178322[/C][C]202786.317874747[/C][C]-24464.3178747473[/C][/ROW]
[ROW][C]139[/C][C]292443[/C][C]263561.108312345[/C][C]28881.8916876554[/C][/ROW]
[ROW][C]140[/C][C]283913[/C][C]278430.866174756[/C][C]5482.13382524353[/C][/ROW]
[ROW][C]141[/C][C]244802[/C][C]188987.627479638[/C][C]55814.3725203623[/C][/ROW]
[ROW][C]142[/C][C]387072[/C][C]290025.759780983[/C][C]97046.2402190168[/C][/ROW]
[ROW][C]143[/C][C]246963[/C][C]295570.540880294[/C][C]-48607.5408802937[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]145733.203801284[/C][C]27526.7961987155[/C][/ROW]
[ROW][C]145[/C][C]346748[/C][C]343441.029841033[/C][C]3306.97015896746[/C][/ROW]
[ROW][C]146[/C][C]176654[/C][C]324713.882188792[/C][C]-148059.882188792[/C][/ROW]
[ROW][C]147[/C][C]268189[/C][C]291047.496226493[/C][C]-22858.4962264931[/C][/ROW]
[ROW][C]148[/C][C]314070[/C][C]395414.42957451[/C][C]-81344.4295745103[/C][/ROW]
[ROW][C]149[/C][C]1[/C][C]15350.8843028393[/C][C]-15349.8843028393[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]26080.3252071466[/C][C]-11392.3252071466[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]14316.865169417[/C][C]-14218.865169417[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]15072.4242132272[/C][C]-14617.4242132272[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]13760.1481452994[/C][C]-13760.1481452994[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]13561.3061256069[/C][C]-13561.3061256069[/C][/ROW]
[ROW][C]155[/C][C]291650[/C][C]230344.1525019[/C][C]61305.8474981002[/C][/ROW]
[ROW][C]156[/C][C]415421[/C][C]399334.647281014[/C][C]16086.3527189861[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]13561.3061256069[/C][C]-13561.3061256069[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]16583.5423008475[/C][C]-16380.5423008475[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]26025.1014706744[/C][C]-18826.1014706744[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]49079.4409756777[/C][C]-2419.44097567765[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]19036.5376657509[/C][C]-1489.53766575092[/C][/ROW]
[ROW][C]162[/C][C]121550[/C][C]131996.695237003[/C][C]-10446.6952370027[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]15072.4242132272[/C][C]-14103.4242132272[/C][/ROW]
[ROW][C]164[/C][C]242774[/C][C]177688.557123451[/C][C]65085.442876549[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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
1279055243602.61026087435452.3897391265
2212408224771.238033689-12363.2380336891
3233939216388.5518093517550.4481906503
4222117322279.534083952-100162.534083952
5179751175635.8239966974115.17600330255
67084983008.3008425707-12159.3008425707
7605767392186.491588584213580.508411416
83318638493.6848848724-5307.68488487243
9227332227008.689731402323.310268597804
10258874210473.17737787548400.8226221249
11359064305585.17819927353478.8218007274
12264989355183.473405512-90194.4734055119
13212638199964.79737389512673.2026261054
14368577323623.74101445544953.2589855455
15269455226912.73515638542542.2648436153
16397992463775.073457667-65783.0734576668
17335567278959.18523252556607.8147674753
18428322335619.3769902492702.6230097596
19182016226102.656920172-44086.6569201718
20267365333511.252712133-66146.2527121333
21279428289239.999953203-9811.99995320344
22508849283277.204813773225571.795186227
23206722177536.38392971229185.6160702882
24200004283311.107634405-83307.1076344053
25257139375656.230749949-118517.230749949
26270941325846.105952387-54905.1059523868
27324969317517.133831437451.86616857029
28329962332006.456165524-2044.45616552416
29190867184989.2058792925877.79412070769
30393860322608.54767626571251.4523237345
31327660334805.436455627-7145.43645562737
32269239202738.11988370766500.8801162932
33391045352373.28408111138671.7159188895
34130446185794.007641027-55348.0076410271
35430118337066.2045263893051.79547362
36273950212757.36012502961192.6398749705
37428077313327.086870847114749.913129153
38254312224689.60497457229622.3950254277
39120351100673.51867959319677.4813204075
40395643428192.253382223-32549.2533822233
41345875291193.76863349354681.2313665073
42216827244972.570428679-28145.5704286789
43224524231997.648925039-7473.64892503911
44182485214071.363085563-31586.3630855631
45157164167608.865129906-10444.8651299056
46459455314772.450046625144682.549953375
477880098517.3125202485-19717.3125202485
48217932287651.014187411-69719.0141874114
49368086369723.62004849-1637.62004849028
50230299293317.555524914-63018.5555249144
51244782264655.182282509-19873.182282509
522418844592.0940258402-20404.0940258402
53400109450967.03211921-50858.0321192098
546502979343.924243565-14314.924243565
55101097117138.048105527-16041.0481055268
56309810247389.79216460562420.2078353951
57369627381569.417689406-11942.4176894061
58367127392091.819652758-24964.8196527582
59377704395471.059213122-17767.059213122
60280106255459.49910072824646.500899272
61400971406496.399292484-5525.39929248363
62315924312431.6900867313492.30991326871
63291391279649.86529058811741.1347094118
64295075273486.31032483621588.6896751641
65280018214967.52226720965050.4777327915
66267432306384.955440012-38952.9554400123
67217181296098.170619196-78917.1706191963
68258166312873.157527599-54707.157527599
69260919320563.522460665-59644.5224606653
70182961286194.935810271-103233.935810271
71256967272726.790679263-15759.7906792634
727356694663.1635629093-21097.1635629093
73272362266653.3253797855708.67462021548
74229056224322.5413526344733.45864736576
75229851291104.644922739-61253.6449227386
76371391317940.25727079553450.7427292047
77398210347417.39739438850792.6026056125
78220419255994.958892937-35575.9588929372
79231884300313.098076985-68429.0980769853
80217714245876.436400683-28162.4364006833
81200046190674.7100397379371.28996026329
82483074374048.452422448109025.547577552
83146100148193.031151246-2093.03115124567
84295224285956.2569496839267.74305031723
8580953104709.978270358-23756.978270358
86217384243980.891530697-26596.8915306971
87179344214041.357182762-34697.3571827618
88415550350289.77662167665260.2233783236
89389059352662.46626011636396.5337398837
90180679366063.932094458-185384.932094459
91299505245634.8431256653870.1568743397
92292260347196.502448499-54936.5024484989
93199481169773.51121075929707.4887892407
94282361314869.946367462-32508.9463674618
95329281237046.50096812592234.4990318753
96234577249369.723981293-14792.7239812928
97297995268076.6399361629918.3600638399
98329583343426.925924297-13843.9259242969
99416463436677.446343985-20214.446343985
100415683360024.86344854855658.1365514524
101297080347471.183115991-50391.1831159907
102318283319434.427524671-1151.42752467143
103224033295668.457413874-71635.4574138744
1044328768224.6250463857-24937.6250463857
105238089245967.533529938-7878.53352993832
106263322321218.230822584-57896.2308225837
107299566282914.76330652716651.2366934729
108321797352332.751309733-30535.7513097327
109193926174895.67534760619030.324652394
110175138308261.140231928-133123.140231928
111354041241015.066274109113025.933725891
112303273314964.175170668-11691.1751706678
1132366838273.8596254534-14605.8596254534
114196743298124.601340254-101381.601340254
1156185774516.1374075999-12659.1374075999
116217543217482.74792166760.2520783326979
117440711357256.90632184383454.093678157
1182105430613.6794700075-9559.6794700075
119252805187029.66571286665775.3342871339
1203196156338.265707362-24377.2657073619
121360436318677.71968337841758.2803166219
122251948236606.92094671315341.079053287
123187003219931.050334803-32928.0503348032
124180842174803.1542446576038.84575534274
1253821468015.5688746446-29801.5688746446
126280392242151.3615235638240.6384764402
127358276326380.84485452831895.1551454716
128211775198792.02283060212982.9771693975
129447335343108.320244406104226.679755594
130348017352153.435322549-4136.43532254934
131441946374871.99974760867074.0002523922
132215177209821.4980797525355.50192024812
133130177169811.370666009-39634.3706660092
134316128478679.301115545-162551.301115545
135466139398097.4806968868041.5193031205
136162279206688.379572886-44409.3795728859
137416643346443.2957303570199.7042696497
138178322202786.317874747-24464.3178747473
139292443263561.10831234528881.8916876554
140283913278430.8661747565482.13382524353
141244802188987.62747963855814.3725203623
142387072290025.75978098397046.2402190168
143246963295570.540880294-48607.5408802937
144173260145733.20380128427526.7961987155
145346748343441.0298410333306.97015896746
146176654324713.882188792-148059.882188792
147268189291047.496226493-22858.4962264931
148314070395414.42957451-81344.4295745103
149115350.8843028393-15349.8843028393
1501468826080.3252071466-11392.3252071466
1519814316.865169417-14218.865169417
15245515072.4242132272-14617.4242132272
153013760.1481452994-13760.1481452994
154013561.3061256069-13561.3061256069
155291650230344.152501961305.8474981002
156415421399334.64728101416086.3527189861
157013561.3061256069-13561.3061256069
15820316583.5423008475-16380.5423008475
159719926025.1014706744-18826.1014706744
1604666049079.4409756777-2419.44097567765
1611754719036.5376657509-1489.53766575092
162121550131996.695237003-10446.6952370027
16396915072.4242132272-14103.4242132272
164242774177688.55712345165085.442876549







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9822106191181930.03557876176361330.0177893808818067
90.9692660647243620.0614678705512750.0307339352756375
100.9479516207399980.1040967585200040.0520483792600021
110.9114933675672610.1770132648654780.0885066324327388
120.9193184514021870.1613630971956260.0806815485978128
130.8759504314511940.2480991370976120.124049568548806
140.8440891234720630.3118217530558750.155910876527937
150.7926548336401370.4146903327197260.207345166359863
160.8160525933840920.3678948132318170.183947406615908
170.7755417965427870.4489164069144250.224458203457213
180.7411587698947940.5176824602104120.258841230105206
190.8386495990479660.3227008019040680.161350400952034
200.8166377928938370.3667244142123260.183362207106163
210.7633503799520910.4732992400958170.236649620047909
220.9956521988246750.008695602350650760.00434780117532538
230.9931861354501370.01362772909972640.0068138645498632
240.9912844757736490.01743104845270190.00871552422635093
250.9987347498754940.002530500249012160.00126525012450608
260.998752752719240.002494494561521410.0012472472807607
270.9979890833221120.004021833355776610.0020109166778883
280.9968785727940370.006242854411926780.00312142720596339
290.9951679848505320.00966403029893530.00483201514946765
300.9949379833470350.01012403330593070.00506201665296533
310.9924147508888520.01517049822229560.00758524911114778
320.9940218660994190.01195626780116180.0059781339005809
330.9925915901195680.01481681976086430.00740840988043216
340.9908511558987730.01829768820245350.00914884410122676
350.9933390715646580.01332185687068460.0066609284353423
360.9927599444769670.01448011104606660.00724005552303329
370.99609278264780.007814434704401470.00390721735220074
380.9945194382721050.01096112345578990.00548056172789494
390.9924676307957610.01506473840847810.00753236920423904
400.992562295810160.0148754083796810.0074377041898405
410.9910143100488060.01797137990238720.00898568995119358
420.9891476339798450.02170473204031030.0108523660201551
430.9853097758161420.02938044836771570.0146902241838579
440.9809158359166470.03816832816670620.0190841640833531
450.9768764757350360.04624704852992780.0231235242649639
460.9929858593923550.01402828121528990.00701414060764497
470.9910204058686120.01795918826277530.00897959413138763
480.990626433613350.01874713277329940.00937356638664972
490.9870815234515410.02583695309691750.0129184765484587
500.9876040585111840.02479188297763210.0123959414888161
510.9835454297303860.03290914053922840.0164545702696142
520.9815461912700420.03690761745991640.0184538087299582
530.9808392723797020.03832145524059690.0191607276202985
540.9751379902520180.04972401949596370.0248620097479818
550.9696751978828240.06064960423435120.0303248021171756
560.9688783479741460.06224330405170790.031121652025854
570.9598184243883230.08036315122335380.0401815756116769
580.953262236368010.09347552726398150.0467377636319908
590.9460421645030990.1079156709938030.0539578354969014
600.935088946259470.1298221074810610.0649110537405305
610.9193073451752740.1613853096494520.0806926548247262
620.9060506238985510.1878987522028970.0939493761014486
630.8858838164667540.2282323670664910.114116183533246
640.8651637008072970.2696725983854060.134836299192703
650.8715355143084420.2569289713831150.128464485691558
660.8765188665406450.246962266918710.123481133459355
670.892066922028010.2158661559439810.10793307797199
680.8848374132150930.2303251735698130.115162586784907
690.8911922809784110.2176154380431780.108807719021589
700.9332226770919140.1335546458161730.0667773229080864
710.9190252559122940.1619494881754130.0809747440877064
720.9037471372210070.1925057255579860.0962528627789931
730.8836142212028240.2327715575943510.116385778797176
740.8601405641608540.2797188716782920.139859435839146
750.8620241096942540.2759517806114920.137975890305746
760.8555530144736790.2888939710526430.144446985526321
770.8470843591501350.3058312816997290.152915640849865
780.8295587573672430.3408824852655140.170441242632757
790.8395186690795490.3209626618409030.160481330920451
800.8181223636157840.3637552727684330.181877636384216
810.7869012207536150.426197558492770.213098779246385
820.8559792835690560.2880414328618880.144020716430944
830.828696727622490.3426065447550210.171303272377511
840.8004228986964110.3991542026071770.199577101303589
850.7759837871049850.448032425790030.224016212895015
860.7502791244411830.4994417511176340.249720875558817
870.7276051217647040.5447897564705920.272394878235296
880.7411946974047360.5176106051905280.258805302595264
890.719697122443380.5606057551132390.28030287755662
900.9425640732969330.1148718534061340.057435926703067
910.94174748992340.1165050201532010.0582525100766005
920.9404669550368550.119066089926290.0595330449631449
930.9283729789758240.1432540420483520.0716270210241761
940.9168650613925810.1662698772148380.0831349386074188
950.9401263903714910.1197472192570180.0598736096285088
960.9273511946183420.1452976107633170.0726488053816584
970.9135568797506860.1728862404986290.0864431202493144
980.894674175920170.210651648159660.10532582407983
990.877133629605360.245732740789280.12286637039464
1000.8707857661596860.2584284676806270.129214233840314
1010.8658857456806880.2682285086386240.134114254319312
1020.8383762701702770.3232474596594450.161623729829723
1030.8448947403428680.3102105193142640.155105259657132
1040.8222861842497810.3554276315004380.177713815750219
1050.7898609152951040.4202781694097930.210139084704896
1060.780862565036850.4382748699262990.219137434963149
1070.7454187137104780.5091625725790430.254581286289522
1080.7160867686258280.5678264627483440.283913231374172
1090.6808785130871480.6382429738257030.319121486912852
1100.8370070370122260.3259859259755480.162992962987774
1110.9020569915540470.1958860168919050.0979430084459527
1120.8813309566278140.2373380867443710.118669043372186
1130.8558628207291820.2882743585416350.144137179270818
1140.9265791537537250.146841692492550.0734208462462752
1150.9078744629714360.1842510740571270.0921255370285637
1160.8844938951317830.2310122097364330.115506104868217
1170.8936978428600050.2126043142799910.106302157139995
1180.8678622645072310.2642754709855370.132137735492769
1190.8701531855303930.2596936289392140.129846814469607
1200.8442416275631790.3115167448736420.155758372436821
1210.8266444115949880.3467111768100230.173355588405012
1220.7920048114215820.4159903771568370.207995188578419
1230.7623283926749840.4753432146500330.237671607325016
1240.7177051897206580.5645896205586840.282294810279342
1250.6783213312810250.643357337437950.321678668718975
1260.6426862040528920.7146275918942160.357313795947108
1270.6008803164597080.7982393670805850.399119683540292
1280.5568092365958440.8863815268083120.443190763404156
1290.6596368997287180.6807262005425640.340363100271282
1300.6041954394222620.7916091211554760.395804560577738
1310.6545746401325760.6908507197348470.345425359867424
1320.6207707399131410.7584585201737180.379229260086859
1330.5666370519571890.8667258960856230.433362948042811
1340.9992678527086250.001464294582750850.000732147291375423
1350.999014929333220.001970141333559110.000985070666779555
1360.9991470225718260.001705954856347930.000852977428173967
1370.9989066527512820.002186694497436990.00109334724871849
1380.9994742143739360.001051571252128170.000525785626064083
1390.9999934910938131.30178123732208e-056.5089061866104e-06
1400.9999853217295432.93565409129972e-051.46782704564986e-05
1410.9999998745540672.50891866525537e-071.25445933262768e-07
1420.9999996683814996.6323700265612e-073.3161850132806e-07
1430.9999989275818032.14483639419379e-061.0724181970969e-06
1440.9999971272004285.74559914352881e-062.8727995717644e-06
1450.999997052888165.89422368158719e-062.94711184079359e-06
1460.9999945227247441.09545505116725e-055.47727525583623e-06
1470.999980365255873.92694882581834e-051.96347441290917e-05
1480.999981685017683.66299646397067e-051.83149823198534e-05
1490.9999974172467585.16550648375203e-062.58275324187601e-06
1500.999989872542552.02549149e-051.012745745e-05
1510.9999508161759519.83676480972719e-054.91838240486359e-05
1520.9997656194042050.0004687611915890430.000234380595794521
1530.9999722078472635.55843054745736e-052.77921527372868e-05
1540.9997859407433310.0004281185133375860.000214059256668793
1550.999766122457020.0004677550859584730.000233877542979236
1560.998779622540040.002440754919918850.00122037745995943

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.982210619118193 & 0.0355787617636133 & 0.0177893808818067 \tabularnewline
9 & 0.969266064724362 & 0.061467870551275 & 0.0307339352756375 \tabularnewline
10 & 0.947951620739998 & 0.104096758520004 & 0.0520483792600021 \tabularnewline
11 & 0.911493367567261 & 0.177013264865478 & 0.0885066324327388 \tabularnewline
12 & 0.919318451402187 & 0.161363097195626 & 0.0806815485978128 \tabularnewline
13 & 0.875950431451194 & 0.248099137097612 & 0.124049568548806 \tabularnewline
14 & 0.844089123472063 & 0.311821753055875 & 0.155910876527937 \tabularnewline
15 & 0.792654833640137 & 0.414690332719726 & 0.207345166359863 \tabularnewline
16 & 0.816052593384092 & 0.367894813231817 & 0.183947406615908 \tabularnewline
17 & 0.775541796542787 & 0.448916406914425 & 0.224458203457213 \tabularnewline
18 & 0.741158769894794 & 0.517682460210412 & 0.258841230105206 \tabularnewline
19 & 0.838649599047966 & 0.322700801904068 & 0.161350400952034 \tabularnewline
20 & 0.816637792893837 & 0.366724414212326 & 0.183362207106163 \tabularnewline
21 & 0.763350379952091 & 0.473299240095817 & 0.236649620047909 \tabularnewline
22 & 0.995652198824675 & 0.00869560235065076 & 0.00434780117532538 \tabularnewline
23 & 0.993186135450137 & 0.0136277290997264 & 0.0068138645498632 \tabularnewline
24 & 0.991284475773649 & 0.0174310484527019 & 0.00871552422635093 \tabularnewline
25 & 0.998734749875494 & 0.00253050024901216 & 0.00126525012450608 \tabularnewline
26 & 0.99875275271924 & 0.00249449456152141 & 0.0012472472807607 \tabularnewline
27 & 0.997989083322112 & 0.00402183335577661 & 0.0020109166778883 \tabularnewline
28 & 0.996878572794037 & 0.00624285441192678 & 0.00312142720596339 \tabularnewline
29 & 0.995167984850532 & 0.0096640302989353 & 0.00483201514946765 \tabularnewline
30 & 0.994937983347035 & 0.0101240333059307 & 0.00506201665296533 \tabularnewline
31 & 0.992414750888852 & 0.0151704982222956 & 0.00758524911114778 \tabularnewline
32 & 0.994021866099419 & 0.0119562678011618 & 0.0059781339005809 \tabularnewline
33 & 0.992591590119568 & 0.0148168197608643 & 0.00740840988043216 \tabularnewline
34 & 0.990851155898773 & 0.0182976882024535 & 0.00914884410122676 \tabularnewline
35 & 0.993339071564658 & 0.0133218568706846 & 0.0066609284353423 \tabularnewline
36 & 0.992759944476967 & 0.0144801110460666 & 0.00724005552303329 \tabularnewline
37 & 0.9960927826478 & 0.00781443470440147 & 0.00390721735220074 \tabularnewline
38 & 0.994519438272105 & 0.0109611234557899 & 0.00548056172789494 \tabularnewline
39 & 0.992467630795761 & 0.0150647384084781 & 0.00753236920423904 \tabularnewline
40 & 0.99256229581016 & 0.014875408379681 & 0.0074377041898405 \tabularnewline
41 & 0.991014310048806 & 0.0179713799023872 & 0.00898568995119358 \tabularnewline
42 & 0.989147633979845 & 0.0217047320403103 & 0.0108523660201551 \tabularnewline
43 & 0.985309775816142 & 0.0293804483677157 & 0.0146902241838579 \tabularnewline
44 & 0.980915835916647 & 0.0381683281667062 & 0.0190841640833531 \tabularnewline
45 & 0.976876475735036 & 0.0462470485299278 & 0.0231235242649639 \tabularnewline
46 & 0.992985859392355 & 0.0140282812152899 & 0.00701414060764497 \tabularnewline
47 & 0.991020405868612 & 0.0179591882627753 & 0.00897959413138763 \tabularnewline
48 & 0.99062643361335 & 0.0187471327732994 & 0.00937356638664972 \tabularnewline
49 & 0.987081523451541 & 0.0258369530969175 & 0.0129184765484587 \tabularnewline
50 & 0.987604058511184 & 0.0247918829776321 & 0.0123959414888161 \tabularnewline
51 & 0.983545429730386 & 0.0329091405392284 & 0.0164545702696142 \tabularnewline
52 & 0.981546191270042 & 0.0369076174599164 & 0.0184538087299582 \tabularnewline
53 & 0.980839272379702 & 0.0383214552405969 & 0.0191607276202985 \tabularnewline
54 & 0.975137990252018 & 0.0497240194959637 & 0.0248620097479818 \tabularnewline
55 & 0.969675197882824 & 0.0606496042343512 & 0.0303248021171756 \tabularnewline
56 & 0.968878347974146 & 0.0622433040517079 & 0.031121652025854 \tabularnewline
57 & 0.959818424388323 & 0.0803631512233538 & 0.0401815756116769 \tabularnewline
58 & 0.95326223636801 & 0.0934755272639815 & 0.0467377636319908 \tabularnewline
59 & 0.946042164503099 & 0.107915670993803 & 0.0539578354969014 \tabularnewline
60 & 0.93508894625947 & 0.129822107481061 & 0.0649110537405305 \tabularnewline
61 & 0.919307345175274 & 0.161385309649452 & 0.0806926548247262 \tabularnewline
62 & 0.906050623898551 & 0.187898752202897 & 0.0939493761014486 \tabularnewline
63 & 0.885883816466754 & 0.228232367066491 & 0.114116183533246 \tabularnewline
64 & 0.865163700807297 & 0.269672598385406 & 0.134836299192703 \tabularnewline
65 & 0.871535514308442 & 0.256928971383115 & 0.128464485691558 \tabularnewline
66 & 0.876518866540645 & 0.24696226691871 & 0.123481133459355 \tabularnewline
67 & 0.89206692202801 & 0.215866155943981 & 0.10793307797199 \tabularnewline
68 & 0.884837413215093 & 0.230325173569813 & 0.115162586784907 \tabularnewline
69 & 0.891192280978411 & 0.217615438043178 & 0.108807719021589 \tabularnewline
70 & 0.933222677091914 & 0.133554645816173 & 0.0667773229080864 \tabularnewline
71 & 0.919025255912294 & 0.161949488175413 & 0.0809747440877064 \tabularnewline
72 & 0.903747137221007 & 0.192505725557986 & 0.0962528627789931 \tabularnewline
73 & 0.883614221202824 & 0.232771557594351 & 0.116385778797176 \tabularnewline
74 & 0.860140564160854 & 0.279718871678292 & 0.139859435839146 \tabularnewline
75 & 0.862024109694254 & 0.275951780611492 & 0.137975890305746 \tabularnewline
76 & 0.855553014473679 & 0.288893971052643 & 0.144446985526321 \tabularnewline
77 & 0.847084359150135 & 0.305831281699729 & 0.152915640849865 \tabularnewline
78 & 0.829558757367243 & 0.340882485265514 & 0.170441242632757 \tabularnewline
79 & 0.839518669079549 & 0.320962661840903 & 0.160481330920451 \tabularnewline
80 & 0.818122363615784 & 0.363755272768433 & 0.181877636384216 \tabularnewline
81 & 0.786901220753615 & 0.42619755849277 & 0.213098779246385 \tabularnewline
82 & 0.855979283569056 & 0.288041432861888 & 0.144020716430944 \tabularnewline
83 & 0.82869672762249 & 0.342606544755021 & 0.171303272377511 \tabularnewline
84 & 0.800422898696411 & 0.399154202607177 & 0.199577101303589 \tabularnewline
85 & 0.775983787104985 & 0.44803242579003 & 0.224016212895015 \tabularnewline
86 & 0.750279124441183 & 0.499441751117634 & 0.249720875558817 \tabularnewline
87 & 0.727605121764704 & 0.544789756470592 & 0.272394878235296 \tabularnewline
88 & 0.741194697404736 & 0.517610605190528 & 0.258805302595264 \tabularnewline
89 & 0.71969712244338 & 0.560605755113239 & 0.28030287755662 \tabularnewline
90 & 0.942564073296933 & 0.114871853406134 & 0.057435926703067 \tabularnewline
91 & 0.9417474899234 & 0.116505020153201 & 0.0582525100766005 \tabularnewline
92 & 0.940466955036855 & 0.11906608992629 & 0.0595330449631449 \tabularnewline
93 & 0.928372978975824 & 0.143254042048352 & 0.0716270210241761 \tabularnewline
94 & 0.916865061392581 & 0.166269877214838 & 0.0831349386074188 \tabularnewline
95 & 0.940126390371491 & 0.119747219257018 & 0.0598736096285088 \tabularnewline
96 & 0.927351194618342 & 0.145297610763317 & 0.0726488053816584 \tabularnewline
97 & 0.913556879750686 & 0.172886240498629 & 0.0864431202493144 \tabularnewline
98 & 0.89467417592017 & 0.21065164815966 & 0.10532582407983 \tabularnewline
99 & 0.87713362960536 & 0.24573274078928 & 0.12286637039464 \tabularnewline
100 & 0.870785766159686 & 0.258428467680627 & 0.129214233840314 \tabularnewline
101 & 0.865885745680688 & 0.268228508638624 & 0.134114254319312 \tabularnewline
102 & 0.838376270170277 & 0.323247459659445 & 0.161623729829723 \tabularnewline
103 & 0.844894740342868 & 0.310210519314264 & 0.155105259657132 \tabularnewline
104 & 0.822286184249781 & 0.355427631500438 & 0.177713815750219 \tabularnewline
105 & 0.789860915295104 & 0.420278169409793 & 0.210139084704896 \tabularnewline
106 & 0.78086256503685 & 0.438274869926299 & 0.219137434963149 \tabularnewline
107 & 0.745418713710478 & 0.509162572579043 & 0.254581286289522 \tabularnewline
108 & 0.716086768625828 & 0.567826462748344 & 0.283913231374172 \tabularnewline
109 & 0.680878513087148 & 0.638242973825703 & 0.319121486912852 \tabularnewline
110 & 0.837007037012226 & 0.325985925975548 & 0.162992962987774 \tabularnewline
111 & 0.902056991554047 & 0.195886016891905 & 0.0979430084459527 \tabularnewline
112 & 0.881330956627814 & 0.237338086744371 & 0.118669043372186 \tabularnewline
113 & 0.855862820729182 & 0.288274358541635 & 0.144137179270818 \tabularnewline
114 & 0.926579153753725 & 0.14684169249255 & 0.0734208462462752 \tabularnewline
115 & 0.907874462971436 & 0.184251074057127 & 0.0921255370285637 \tabularnewline
116 & 0.884493895131783 & 0.231012209736433 & 0.115506104868217 \tabularnewline
117 & 0.893697842860005 & 0.212604314279991 & 0.106302157139995 \tabularnewline
118 & 0.867862264507231 & 0.264275470985537 & 0.132137735492769 \tabularnewline
119 & 0.870153185530393 & 0.259693628939214 & 0.129846814469607 \tabularnewline
120 & 0.844241627563179 & 0.311516744873642 & 0.155758372436821 \tabularnewline
121 & 0.826644411594988 & 0.346711176810023 & 0.173355588405012 \tabularnewline
122 & 0.792004811421582 & 0.415990377156837 & 0.207995188578419 \tabularnewline
123 & 0.762328392674984 & 0.475343214650033 & 0.237671607325016 \tabularnewline
124 & 0.717705189720658 & 0.564589620558684 & 0.282294810279342 \tabularnewline
125 & 0.678321331281025 & 0.64335733743795 & 0.321678668718975 \tabularnewline
126 & 0.642686204052892 & 0.714627591894216 & 0.357313795947108 \tabularnewline
127 & 0.600880316459708 & 0.798239367080585 & 0.399119683540292 \tabularnewline
128 & 0.556809236595844 & 0.886381526808312 & 0.443190763404156 \tabularnewline
129 & 0.659636899728718 & 0.680726200542564 & 0.340363100271282 \tabularnewline
130 & 0.604195439422262 & 0.791609121155476 & 0.395804560577738 \tabularnewline
131 & 0.654574640132576 & 0.690850719734847 & 0.345425359867424 \tabularnewline
132 & 0.620770739913141 & 0.758458520173718 & 0.379229260086859 \tabularnewline
133 & 0.566637051957189 & 0.866725896085623 & 0.433362948042811 \tabularnewline
134 & 0.999267852708625 & 0.00146429458275085 & 0.000732147291375423 \tabularnewline
135 & 0.99901492933322 & 0.00197014133355911 & 0.000985070666779555 \tabularnewline
136 & 0.999147022571826 & 0.00170595485634793 & 0.000852977428173967 \tabularnewline
137 & 0.998906652751282 & 0.00218669449743699 & 0.00109334724871849 \tabularnewline
138 & 0.999474214373936 & 0.00105157125212817 & 0.000525785626064083 \tabularnewline
139 & 0.999993491093813 & 1.30178123732208e-05 & 6.5089061866104e-06 \tabularnewline
140 & 0.999985321729543 & 2.93565409129972e-05 & 1.46782704564986e-05 \tabularnewline
141 & 0.999999874554067 & 2.50891866525537e-07 & 1.25445933262768e-07 \tabularnewline
142 & 0.999999668381499 & 6.6323700265612e-07 & 3.3161850132806e-07 \tabularnewline
143 & 0.999998927581803 & 2.14483639419379e-06 & 1.0724181970969e-06 \tabularnewline
144 & 0.999997127200428 & 5.74559914352881e-06 & 2.8727995717644e-06 \tabularnewline
145 & 0.99999705288816 & 5.89422368158719e-06 & 2.94711184079359e-06 \tabularnewline
146 & 0.999994522724744 & 1.09545505116725e-05 & 5.47727525583623e-06 \tabularnewline
147 & 0.99998036525587 & 3.92694882581834e-05 & 1.96347441290917e-05 \tabularnewline
148 & 0.99998168501768 & 3.66299646397067e-05 & 1.83149823198534e-05 \tabularnewline
149 & 0.999997417246758 & 5.16550648375203e-06 & 2.58275324187601e-06 \tabularnewline
150 & 0.99998987254255 & 2.02549149e-05 & 1.012745745e-05 \tabularnewline
151 & 0.999950816175951 & 9.83676480972719e-05 & 4.91838240486359e-05 \tabularnewline
152 & 0.999765619404205 & 0.000468761191589043 & 0.000234380595794521 \tabularnewline
153 & 0.999972207847263 & 5.55843054745736e-05 & 2.77921527372868e-05 \tabularnewline
154 & 0.999785940743331 & 0.000428118513337586 & 0.000214059256668793 \tabularnewline
155 & 0.99976612245702 & 0.000467755085958473 & 0.000233877542979236 \tabularnewline
156 & 0.99877962254004 & 0.00244075491991885 & 0.00122037745995943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.982210619118193[/C][C]0.0355787617636133[/C][C]0.0177893808818067[/C][/ROW]
[ROW][C]9[/C][C]0.969266064724362[/C][C]0.061467870551275[/C][C]0.0307339352756375[/C][/ROW]
[ROW][C]10[/C][C]0.947951620739998[/C][C]0.104096758520004[/C][C]0.0520483792600021[/C][/ROW]
[ROW][C]11[/C][C]0.911493367567261[/C][C]0.177013264865478[/C][C]0.0885066324327388[/C][/ROW]
[ROW][C]12[/C][C]0.919318451402187[/C][C]0.161363097195626[/C][C]0.0806815485978128[/C][/ROW]
[ROW][C]13[/C][C]0.875950431451194[/C][C]0.248099137097612[/C][C]0.124049568548806[/C][/ROW]
[ROW][C]14[/C][C]0.844089123472063[/C][C]0.311821753055875[/C][C]0.155910876527937[/C][/ROW]
[ROW][C]15[/C][C]0.792654833640137[/C][C]0.414690332719726[/C][C]0.207345166359863[/C][/ROW]
[ROW][C]16[/C][C]0.816052593384092[/C][C]0.367894813231817[/C][C]0.183947406615908[/C][/ROW]
[ROW][C]17[/C][C]0.775541796542787[/C][C]0.448916406914425[/C][C]0.224458203457213[/C][/ROW]
[ROW][C]18[/C][C]0.741158769894794[/C][C]0.517682460210412[/C][C]0.258841230105206[/C][/ROW]
[ROW][C]19[/C][C]0.838649599047966[/C][C]0.322700801904068[/C][C]0.161350400952034[/C][/ROW]
[ROW][C]20[/C][C]0.816637792893837[/C][C]0.366724414212326[/C][C]0.183362207106163[/C][/ROW]
[ROW][C]21[/C][C]0.763350379952091[/C][C]0.473299240095817[/C][C]0.236649620047909[/C][/ROW]
[ROW][C]22[/C][C]0.995652198824675[/C][C]0.00869560235065076[/C][C]0.00434780117532538[/C][/ROW]
[ROW][C]23[/C][C]0.993186135450137[/C][C]0.0136277290997264[/C][C]0.0068138645498632[/C][/ROW]
[ROW][C]24[/C][C]0.991284475773649[/C][C]0.0174310484527019[/C][C]0.00871552422635093[/C][/ROW]
[ROW][C]25[/C][C]0.998734749875494[/C][C]0.00253050024901216[/C][C]0.00126525012450608[/C][/ROW]
[ROW][C]26[/C][C]0.99875275271924[/C][C]0.00249449456152141[/C][C]0.0012472472807607[/C][/ROW]
[ROW][C]27[/C][C]0.997989083322112[/C][C]0.00402183335577661[/C][C]0.0020109166778883[/C][/ROW]
[ROW][C]28[/C][C]0.996878572794037[/C][C]0.00624285441192678[/C][C]0.00312142720596339[/C][/ROW]
[ROW][C]29[/C][C]0.995167984850532[/C][C]0.0096640302989353[/C][C]0.00483201514946765[/C][/ROW]
[ROW][C]30[/C][C]0.994937983347035[/C][C]0.0101240333059307[/C][C]0.00506201665296533[/C][/ROW]
[ROW][C]31[/C][C]0.992414750888852[/C][C]0.0151704982222956[/C][C]0.00758524911114778[/C][/ROW]
[ROW][C]32[/C][C]0.994021866099419[/C][C]0.0119562678011618[/C][C]0.0059781339005809[/C][/ROW]
[ROW][C]33[/C][C]0.992591590119568[/C][C]0.0148168197608643[/C][C]0.00740840988043216[/C][/ROW]
[ROW][C]34[/C][C]0.990851155898773[/C][C]0.0182976882024535[/C][C]0.00914884410122676[/C][/ROW]
[ROW][C]35[/C][C]0.993339071564658[/C][C]0.0133218568706846[/C][C]0.0066609284353423[/C][/ROW]
[ROW][C]36[/C][C]0.992759944476967[/C][C]0.0144801110460666[/C][C]0.00724005552303329[/C][/ROW]
[ROW][C]37[/C][C]0.9960927826478[/C][C]0.00781443470440147[/C][C]0.00390721735220074[/C][/ROW]
[ROW][C]38[/C][C]0.994519438272105[/C][C]0.0109611234557899[/C][C]0.00548056172789494[/C][/ROW]
[ROW][C]39[/C][C]0.992467630795761[/C][C]0.0150647384084781[/C][C]0.00753236920423904[/C][/ROW]
[ROW][C]40[/C][C]0.99256229581016[/C][C]0.014875408379681[/C][C]0.0074377041898405[/C][/ROW]
[ROW][C]41[/C][C]0.991014310048806[/C][C]0.0179713799023872[/C][C]0.00898568995119358[/C][/ROW]
[ROW][C]42[/C][C]0.989147633979845[/C][C]0.0217047320403103[/C][C]0.0108523660201551[/C][/ROW]
[ROW][C]43[/C][C]0.985309775816142[/C][C]0.0293804483677157[/C][C]0.0146902241838579[/C][/ROW]
[ROW][C]44[/C][C]0.980915835916647[/C][C]0.0381683281667062[/C][C]0.0190841640833531[/C][/ROW]
[ROW][C]45[/C][C]0.976876475735036[/C][C]0.0462470485299278[/C][C]0.0231235242649639[/C][/ROW]
[ROW][C]46[/C][C]0.992985859392355[/C][C]0.0140282812152899[/C][C]0.00701414060764497[/C][/ROW]
[ROW][C]47[/C][C]0.991020405868612[/C][C]0.0179591882627753[/C][C]0.00897959413138763[/C][/ROW]
[ROW][C]48[/C][C]0.99062643361335[/C][C]0.0187471327732994[/C][C]0.00937356638664972[/C][/ROW]
[ROW][C]49[/C][C]0.987081523451541[/C][C]0.0258369530969175[/C][C]0.0129184765484587[/C][/ROW]
[ROW][C]50[/C][C]0.987604058511184[/C][C]0.0247918829776321[/C][C]0.0123959414888161[/C][/ROW]
[ROW][C]51[/C][C]0.983545429730386[/C][C]0.0329091405392284[/C][C]0.0164545702696142[/C][/ROW]
[ROW][C]52[/C][C]0.981546191270042[/C][C]0.0369076174599164[/C][C]0.0184538087299582[/C][/ROW]
[ROW][C]53[/C][C]0.980839272379702[/C][C]0.0383214552405969[/C][C]0.0191607276202985[/C][/ROW]
[ROW][C]54[/C][C]0.975137990252018[/C][C]0.0497240194959637[/C][C]0.0248620097479818[/C][/ROW]
[ROW][C]55[/C][C]0.969675197882824[/C][C]0.0606496042343512[/C][C]0.0303248021171756[/C][/ROW]
[ROW][C]56[/C][C]0.968878347974146[/C][C]0.0622433040517079[/C][C]0.031121652025854[/C][/ROW]
[ROW][C]57[/C][C]0.959818424388323[/C][C]0.0803631512233538[/C][C]0.0401815756116769[/C][/ROW]
[ROW][C]58[/C][C]0.95326223636801[/C][C]0.0934755272639815[/C][C]0.0467377636319908[/C][/ROW]
[ROW][C]59[/C][C]0.946042164503099[/C][C]0.107915670993803[/C][C]0.0539578354969014[/C][/ROW]
[ROW][C]60[/C][C]0.93508894625947[/C][C]0.129822107481061[/C][C]0.0649110537405305[/C][/ROW]
[ROW][C]61[/C][C]0.919307345175274[/C][C]0.161385309649452[/C][C]0.0806926548247262[/C][/ROW]
[ROW][C]62[/C][C]0.906050623898551[/C][C]0.187898752202897[/C][C]0.0939493761014486[/C][/ROW]
[ROW][C]63[/C][C]0.885883816466754[/C][C]0.228232367066491[/C][C]0.114116183533246[/C][/ROW]
[ROW][C]64[/C][C]0.865163700807297[/C][C]0.269672598385406[/C][C]0.134836299192703[/C][/ROW]
[ROW][C]65[/C][C]0.871535514308442[/C][C]0.256928971383115[/C][C]0.128464485691558[/C][/ROW]
[ROW][C]66[/C][C]0.876518866540645[/C][C]0.24696226691871[/C][C]0.123481133459355[/C][/ROW]
[ROW][C]67[/C][C]0.89206692202801[/C][C]0.215866155943981[/C][C]0.10793307797199[/C][/ROW]
[ROW][C]68[/C][C]0.884837413215093[/C][C]0.230325173569813[/C][C]0.115162586784907[/C][/ROW]
[ROW][C]69[/C][C]0.891192280978411[/C][C]0.217615438043178[/C][C]0.108807719021589[/C][/ROW]
[ROW][C]70[/C][C]0.933222677091914[/C][C]0.133554645816173[/C][C]0.0667773229080864[/C][/ROW]
[ROW][C]71[/C][C]0.919025255912294[/C][C]0.161949488175413[/C][C]0.0809747440877064[/C][/ROW]
[ROW][C]72[/C][C]0.903747137221007[/C][C]0.192505725557986[/C][C]0.0962528627789931[/C][/ROW]
[ROW][C]73[/C][C]0.883614221202824[/C][C]0.232771557594351[/C][C]0.116385778797176[/C][/ROW]
[ROW][C]74[/C][C]0.860140564160854[/C][C]0.279718871678292[/C][C]0.139859435839146[/C][/ROW]
[ROW][C]75[/C][C]0.862024109694254[/C][C]0.275951780611492[/C][C]0.137975890305746[/C][/ROW]
[ROW][C]76[/C][C]0.855553014473679[/C][C]0.288893971052643[/C][C]0.144446985526321[/C][/ROW]
[ROW][C]77[/C][C]0.847084359150135[/C][C]0.305831281699729[/C][C]0.152915640849865[/C][/ROW]
[ROW][C]78[/C][C]0.829558757367243[/C][C]0.340882485265514[/C][C]0.170441242632757[/C][/ROW]
[ROW][C]79[/C][C]0.839518669079549[/C][C]0.320962661840903[/C][C]0.160481330920451[/C][/ROW]
[ROW][C]80[/C][C]0.818122363615784[/C][C]0.363755272768433[/C][C]0.181877636384216[/C][/ROW]
[ROW][C]81[/C][C]0.786901220753615[/C][C]0.42619755849277[/C][C]0.213098779246385[/C][/ROW]
[ROW][C]82[/C][C]0.855979283569056[/C][C]0.288041432861888[/C][C]0.144020716430944[/C][/ROW]
[ROW][C]83[/C][C]0.82869672762249[/C][C]0.342606544755021[/C][C]0.171303272377511[/C][/ROW]
[ROW][C]84[/C][C]0.800422898696411[/C][C]0.399154202607177[/C][C]0.199577101303589[/C][/ROW]
[ROW][C]85[/C][C]0.775983787104985[/C][C]0.44803242579003[/C][C]0.224016212895015[/C][/ROW]
[ROW][C]86[/C][C]0.750279124441183[/C][C]0.499441751117634[/C][C]0.249720875558817[/C][/ROW]
[ROW][C]87[/C][C]0.727605121764704[/C][C]0.544789756470592[/C][C]0.272394878235296[/C][/ROW]
[ROW][C]88[/C][C]0.741194697404736[/C][C]0.517610605190528[/C][C]0.258805302595264[/C][/ROW]
[ROW][C]89[/C][C]0.71969712244338[/C][C]0.560605755113239[/C][C]0.28030287755662[/C][/ROW]
[ROW][C]90[/C][C]0.942564073296933[/C][C]0.114871853406134[/C][C]0.057435926703067[/C][/ROW]
[ROW][C]91[/C][C]0.9417474899234[/C][C]0.116505020153201[/C][C]0.0582525100766005[/C][/ROW]
[ROW][C]92[/C][C]0.940466955036855[/C][C]0.11906608992629[/C][C]0.0595330449631449[/C][/ROW]
[ROW][C]93[/C][C]0.928372978975824[/C][C]0.143254042048352[/C][C]0.0716270210241761[/C][/ROW]
[ROW][C]94[/C][C]0.916865061392581[/C][C]0.166269877214838[/C][C]0.0831349386074188[/C][/ROW]
[ROW][C]95[/C][C]0.940126390371491[/C][C]0.119747219257018[/C][C]0.0598736096285088[/C][/ROW]
[ROW][C]96[/C][C]0.927351194618342[/C][C]0.145297610763317[/C][C]0.0726488053816584[/C][/ROW]
[ROW][C]97[/C][C]0.913556879750686[/C][C]0.172886240498629[/C][C]0.0864431202493144[/C][/ROW]
[ROW][C]98[/C][C]0.89467417592017[/C][C]0.21065164815966[/C][C]0.10532582407983[/C][/ROW]
[ROW][C]99[/C][C]0.87713362960536[/C][C]0.24573274078928[/C][C]0.12286637039464[/C][/ROW]
[ROW][C]100[/C][C]0.870785766159686[/C][C]0.258428467680627[/C][C]0.129214233840314[/C][/ROW]
[ROW][C]101[/C][C]0.865885745680688[/C][C]0.268228508638624[/C][C]0.134114254319312[/C][/ROW]
[ROW][C]102[/C][C]0.838376270170277[/C][C]0.323247459659445[/C][C]0.161623729829723[/C][/ROW]
[ROW][C]103[/C][C]0.844894740342868[/C][C]0.310210519314264[/C][C]0.155105259657132[/C][/ROW]
[ROW][C]104[/C][C]0.822286184249781[/C][C]0.355427631500438[/C][C]0.177713815750219[/C][/ROW]
[ROW][C]105[/C][C]0.789860915295104[/C][C]0.420278169409793[/C][C]0.210139084704896[/C][/ROW]
[ROW][C]106[/C][C]0.78086256503685[/C][C]0.438274869926299[/C][C]0.219137434963149[/C][/ROW]
[ROW][C]107[/C][C]0.745418713710478[/C][C]0.509162572579043[/C][C]0.254581286289522[/C][/ROW]
[ROW][C]108[/C][C]0.716086768625828[/C][C]0.567826462748344[/C][C]0.283913231374172[/C][/ROW]
[ROW][C]109[/C][C]0.680878513087148[/C][C]0.638242973825703[/C][C]0.319121486912852[/C][/ROW]
[ROW][C]110[/C][C]0.837007037012226[/C][C]0.325985925975548[/C][C]0.162992962987774[/C][/ROW]
[ROW][C]111[/C][C]0.902056991554047[/C][C]0.195886016891905[/C][C]0.0979430084459527[/C][/ROW]
[ROW][C]112[/C][C]0.881330956627814[/C][C]0.237338086744371[/C][C]0.118669043372186[/C][/ROW]
[ROW][C]113[/C][C]0.855862820729182[/C][C]0.288274358541635[/C][C]0.144137179270818[/C][/ROW]
[ROW][C]114[/C][C]0.926579153753725[/C][C]0.14684169249255[/C][C]0.0734208462462752[/C][/ROW]
[ROW][C]115[/C][C]0.907874462971436[/C][C]0.184251074057127[/C][C]0.0921255370285637[/C][/ROW]
[ROW][C]116[/C][C]0.884493895131783[/C][C]0.231012209736433[/C][C]0.115506104868217[/C][/ROW]
[ROW][C]117[/C][C]0.893697842860005[/C][C]0.212604314279991[/C][C]0.106302157139995[/C][/ROW]
[ROW][C]118[/C][C]0.867862264507231[/C][C]0.264275470985537[/C][C]0.132137735492769[/C][/ROW]
[ROW][C]119[/C][C]0.870153185530393[/C][C]0.259693628939214[/C][C]0.129846814469607[/C][/ROW]
[ROW][C]120[/C][C]0.844241627563179[/C][C]0.311516744873642[/C][C]0.155758372436821[/C][/ROW]
[ROW][C]121[/C][C]0.826644411594988[/C][C]0.346711176810023[/C][C]0.173355588405012[/C][/ROW]
[ROW][C]122[/C][C]0.792004811421582[/C][C]0.415990377156837[/C][C]0.207995188578419[/C][/ROW]
[ROW][C]123[/C][C]0.762328392674984[/C][C]0.475343214650033[/C][C]0.237671607325016[/C][/ROW]
[ROW][C]124[/C][C]0.717705189720658[/C][C]0.564589620558684[/C][C]0.282294810279342[/C][/ROW]
[ROW][C]125[/C][C]0.678321331281025[/C][C]0.64335733743795[/C][C]0.321678668718975[/C][/ROW]
[ROW][C]126[/C][C]0.642686204052892[/C][C]0.714627591894216[/C][C]0.357313795947108[/C][/ROW]
[ROW][C]127[/C][C]0.600880316459708[/C][C]0.798239367080585[/C][C]0.399119683540292[/C][/ROW]
[ROW][C]128[/C][C]0.556809236595844[/C][C]0.886381526808312[/C][C]0.443190763404156[/C][/ROW]
[ROW][C]129[/C][C]0.659636899728718[/C][C]0.680726200542564[/C][C]0.340363100271282[/C][/ROW]
[ROW][C]130[/C][C]0.604195439422262[/C][C]0.791609121155476[/C][C]0.395804560577738[/C][/ROW]
[ROW][C]131[/C][C]0.654574640132576[/C][C]0.690850719734847[/C][C]0.345425359867424[/C][/ROW]
[ROW][C]132[/C][C]0.620770739913141[/C][C]0.758458520173718[/C][C]0.379229260086859[/C][/ROW]
[ROW][C]133[/C][C]0.566637051957189[/C][C]0.866725896085623[/C][C]0.433362948042811[/C][/ROW]
[ROW][C]134[/C][C]0.999267852708625[/C][C]0.00146429458275085[/C][C]0.000732147291375423[/C][/ROW]
[ROW][C]135[/C][C]0.99901492933322[/C][C]0.00197014133355911[/C][C]0.000985070666779555[/C][/ROW]
[ROW][C]136[/C][C]0.999147022571826[/C][C]0.00170595485634793[/C][C]0.000852977428173967[/C][/ROW]
[ROW][C]137[/C][C]0.998906652751282[/C][C]0.00218669449743699[/C][C]0.00109334724871849[/C][/ROW]
[ROW][C]138[/C][C]0.999474214373936[/C][C]0.00105157125212817[/C][C]0.000525785626064083[/C][/ROW]
[ROW][C]139[/C][C]0.999993491093813[/C][C]1.30178123732208e-05[/C][C]6.5089061866104e-06[/C][/ROW]
[ROW][C]140[/C][C]0.999985321729543[/C][C]2.93565409129972e-05[/C][C]1.46782704564986e-05[/C][/ROW]
[ROW][C]141[/C][C]0.999999874554067[/C][C]2.50891866525537e-07[/C][C]1.25445933262768e-07[/C][/ROW]
[ROW][C]142[/C][C]0.999999668381499[/C][C]6.6323700265612e-07[/C][C]3.3161850132806e-07[/C][/ROW]
[ROW][C]143[/C][C]0.999998927581803[/C][C]2.14483639419379e-06[/C][C]1.0724181970969e-06[/C][/ROW]
[ROW][C]144[/C][C]0.999997127200428[/C][C]5.74559914352881e-06[/C][C]2.8727995717644e-06[/C][/ROW]
[ROW][C]145[/C][C]0.99999705288816[/C][C]5.89422368158719e-06[/C][C]2.94711184079359e-06[/C][/ROW]
[ROW][C]146[/C][C]0.999994522724744[/C][C]1.09545505116725e-05[/C][C]5.47727525583623e-06[/C][/ROW]
[ROW][C]147[/C][C]0.99998036525587[/C][C]3.92694882581834e-05[/C][C]1.96347441290917e-05[/C][/ROW]
[ROW][C]148[/C][C]0.99998168501768[/C][C]3.66299646397067e-05[/C][C]1.83149823198534e-05[/C][/ROW]
[ROW][C]149[/C][C]0.999997417246758[/C][C]5.16550648375203e-06[/C][C]2.58275324187601e-06[/C][/ROW]
[ROW][C]150[/C][C]0.99998987254255[/C][C]2.02549149e-05[/C][C]1.012745745e-05[/C][/ROW]
[ROW][C]151[/C][C]0.999950816175951[/C][C]9.83676480972719e-05[/C][C]4.91838240486359e-05[/C][/ROW]
[ROW][C]152[/C][C]0.999765619404205[/C][C]0.000468761191589043[/C][C]0.000234380595794521[/C][/ROW]
[ROW][C]153[/C][C]0.999972207847263[/C][C]5.55843054745736e-05[/C][C]2.77921527372868e-05[/C][/ROW]
[ROW][C]154[/C][C]0.999785940743331[/C][C]0.000428118513337586[/C][C]0.000214059256668793[/C][/ROW]
[ROW][C]155[/C][C]0.99976612245702[/C][C]0.000467755085958473[/C][C]0.000233877542979236[/C][/ROW]
[ROW][C]156[/C][C]0.99877962254004[/C][C]0.00244075491991885[/C][C]0.00122037745995943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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
80.9822106191181930.03557876176361330.0177893808818067
90.9692660647243620.0614678705512750.0307339352756375
100.9479516207399980.1040967585200040.0520483792600021
110.9114933675672610.1770132648654780.0885066324327388
120.9193184514021870.1613630971956260.0806815485978128
130.8759504314511940.2480991370976120.124049568548806
140.8440891234720630.3118217530558750.155910876527937
150.7926548336401370.4146903327197260.207345166359863
160.8160525933840920.3678948132318170.183947406615908
170.7755417965427870.4489164069144250.224458203457213
180.7411587698947940.5176824602104120.258841230105206
190.8386495990479660.3227008019040680.161350400952034
200.8166377928938370.3667244142123260.183362207106163
210.7633503799520910.4732992400958170.236649620047909
220.9956521988246750.008695602350650760.00434780117532538
230.9931861354501370.01362772909972640.0068138645498632
240.9912844757736490.01743104845270190.00871552422635093
250.9987347498754940.002530500249012160.00126525012450608
260.998752752719240.002494494561521410.0012472472807607
270.9979890833221120.004021833355776610.0020109166778883
280.9968785727940370.006242854411926780.00312142720596339
290.9951679848505320.00966403029893530.00483201514946765
300.9949379833470350.01012403330593070.00506201665296533
310.9924147508888520.01517049822229560.00758524911114778
320.9940218660994190.01195626780116180.0059781339005809
330.9925915901195680.01481681976086430.00740840988043216
340.9908511558987730.01829768820245350.00914884410122676
350.9933390715646580.01332185687068460.0066609284353423
360.9927599444769670.01448011104606660.00724005552303329
370.99609278264780.007814434704401470.00390721735220074
380.9945194382721050.01096112345578990.00548056172789494
390.9924676307957610.01506473840847810.00753236920423904
400.992562295810160.0148754083796810.0074377041898405
410.9910143100488060.01797137990238720.00898568995119358
420.9891476339798450.02170473204031030.0108523660201551
430.9853097758161420.02938044836771570.0146902241838579
440.9809158359166470.03816832816670620.0190841640833531
450.9768764757350360.04624704852992780.0231235242649639
460.9929858593923550.01402828121528990.00701414060764497
470.9910204058686120.01795918826277530.00897959413138763
480.990626433613350.01874713277329940.00937356638664972
490.9870815234515410.02583695309691750.0129184765484587
500.9876040585111840.02479188297763210.0123959414888161
510.9835454297303860.03290914053922840.0164545702696142
520.9815461912700420.03690761745991640.0184538087299582
530.9808392723797020.03832145524059690.0191607276202985
540.9751379902520180.04972401949596370.0248620097479818
550.9696751978828240.06064960423435120.0303248021171756
560.9688783479741460.06224330405170790.031121652025854
570.9598184243883230.08036315122335380.0401815756116769
580.953262236368010.09347552726398150.0467377636319908
590.9460421645030990.1079156709938030.0539578354969014
600.935088946259470.1298221074810610.0649110537405305
610.9193073451752740.1613853096494520.0806926548247262
620.9060506238985510.1878987522028970.0939493761014486
630.8858838164667540.2282323670664910.114116183533246
640.8651637008072970.2696725983854060.134836299192703
650.8715355143084420.2569289713831150.128464485691558
660.8765188665406450.246962266918710.123481133459355
670.892066922028010.2158661559439810.10793307797199
680.8848374132150930.2303251735698130.115162586784907
690.8911922809784110.2176154380431780.108807719021589
700.9332226770919140.1335546458161730.0667773229080864
710.9190252559122940.1619494881754130.0809747440877064
720.9037471372210070.1925057255579860.0962528627789931
730.8836142212028240.2327715575943510.116385778797176
740.8601405641608540.2797188716782920.139859435839146
750.8620241096942540.2759517806114920.137975890305746
760.8555530144736790.2888939710526430.144446985526321
770.8470843591501350.3058312816997290.152915640849865
780.8295587573672430.3408824852655140.170441242632757
790.8395186690795490.3209626618409030.160481330920451
800.8181223636157840.3637552727684330.181877636384216
810.7869012207536150.426197558492770.213098779246385
820.8559792835690560.2880414328618880.144020716430944
830.828696727622490.3426065447550210.171303272377511
840.8004228986964110.3991542026071770.199577101303589
850.7759837871049850.448032425790030.224016212895015
860.7502791244411830.4994417511176340.249720875558817
870.7276051217647040.5447897564705920.272394878235296
880.7411946974047360.5176106051905280.258805302595264
890.719697122443380.5606057551132390.28030287755662
900.9425640732969330.1148718534061340.057435926703067
910.94174748992340.1165050201532010.0582525100766005
920.9404669550368550.119066089926290.0595330449631449
930.9283729789758240.1432540420483520.0716270210241761
940.9168650613925810.1662698772148380.0831349386074188
950.9401263903714910.1197472192570180.0598736096285088
960.9273511946183420.1452976107633170.0726488053816584
970.9135568797506860.1728862404986290.0864431202493144
980.894674175920170.210651648159660.10532582407983
990.877133629605360.245732740789280.12286637039464
1000.8707857661596860.2584284676806270.129214233840314
1010.8658857456806880.2682285086386240.134114254319312
1020.8383762701702770.3232474596594450.161623729829723
1030.8448947403428680.3102105193142640.155105259657132
1040.8222861842497810.3554276315004380.177713815750219
1050.7898609152951040.4202781694097930.210139084704896
1060.780862565036850.4382748699262990.219137434963149
1070.7454187137104780.5091625725790430.254581286289522
1080.7160867686258280.5678264627483440.283913231374172
1090.6808785130871480.6382429738257030.319121486912852
1100.8370070370122260.3259859259755480.162992962987774
1110.9020569915540470.1958860168919050.0979430084459527
1120.8813309566278140.2373380867443710.118669043372186
1130.8558628207291820.2882743585416350.144137179270818
1140.9265791537537250.146841692492550.0734208462462752
1150.9078744629714360.1842510740571270.0921255370285637
1160.8844938951317830.2310122097364330.115506104868217
1170.8936978428600050.2126043142799910.106302157139995
1180.8678622645072310.2642754709855370.132137735492769
1190.8701531855303930.2596936289392140.129846814469607
1200.8442416275631790.3115167448736420.155758372436821
1210.8266444115949880.3467111768100230.173355588405012
1220.7920048114215820.4159903771568370.207995188578419
1230.7623283926749840.4753432146500330.237671607325016
1240.7177051897206580.5645896205586840.282294810279342
1250.6783213312810250.643357337437950.321678668718975
1260.6426862040528920.7146275918942160.357313795947108
1270.6008803164597080.7982393670805850.399119683540292
1280.5568092365958440.8863815268083120.443190763404156
1290.6596368997287180.6807262005425640.340363100271282
1300.6041954394222620.7916091211554760.395804560577738
1310.6545746401325760.6908507197348470.345425359867424
1320.6207707399131410.7584585201737180.379229260086859
1330.5666370519571890.8667258960856230.433362948042811
1340.9992678527086250.001464294582750850.000732147291375423
1350.999014929333220.001970141333559110.000985070666779555
1360.9991470225718260.001705954856347930.000852977428173967
1370.9989066527512820.002186694497436990.00109334724871849
1380.9994742143739360.001051571252128170.000525785626064083
1390.9999934910938131.30178123732208e-056.5089061866104e-06
1400.9999853217295432.93565409129972e-051.46782704564986e-05
1410.9999998745540672.50891866525537e-071.25445933262768e-07
1420.9999996683814996.6323700265612e-073.3161850132806e-07
1430.9999989275818032.14483639419379e-061.0724181970969e-06
1440.9999971272004285.74559914352881e-062.8727995717644e-06
1450.999997052888165.89422368158719e-062.94711184079359e-06
1460.9999945227247441.09545505116725e-055.47727525583623e-06
1470.999980365255873.92694882581834e-051.96347441290917e-05
1480.999981685017683.66299646397067e-051.83149823198534e-05
1490.9999974172467585.16550648375203e-062.58275324187601e-06
1500.999989872542552.02549149e-051.012745745e-05
1510.9999508161759519.83676480972719e-054.91838240486359e-05
1520.9997656194042050.0004687611915890430.000234380595794521
1530.9999722078472635.55843054745736e-052.77921527372868e-05
1540.9997859407433310.0004281185133375860.000214059256668793
1550.999766122457020.0004677550859584730.000233877542979236
1560.998779622540040.002440754919918850.00122037745995943







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level300.201342281879195NOK
5% type I error level570.38255033557047NOK
10% type I error level620.416107382550336NOK

\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 & 30 & 0.201342281879195 & NOK \tabularnewline
5% type I error level & 57 & 0.38255033557047 & NOK \tabularnewline
10% type I error level & 62 & 0.416107382550336 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=160360&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]30[/C][C]0.201342281879195[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]57[/C][C]0.38255033557047[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]62[/C][C]0.416107382550336[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=160360&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=160360&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 level300.201342281879195NOK
5% type I error level570.38255033557047NOK
10% type I error level620.416107382550336NOK



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