Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 21 Nov 2011 13:59:33 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Nov/21/t1321901984yfjedpdnwc1mok0.htm/, Retrieved Thu, 28 Mar 2024 17:16:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=145918, Retrieved Thu, 28 Mar 2024 17:16:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact73
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Competence to learn] [2010-11-17 07:43:53] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [] [2011-11-21 17:40:13] [77e355412ccdb651b3c7eae41c3da865]
-         [Multiple Regression] [] [2011-11-21 18:45:57] [aefb5c2d4042694c5b6b82f93ac1885a]
-             [Multiple Regression] [] [2011-11-21 18:59:33] [5f7ae77ad4c15dc18491c39fdf8bddde] [Current]
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Dataseries X:
170588	65	26	84	95556
86621	54	20	72	54565
113514	58	24	37	63016
152510	99	25	85	79774
86206	41	15	30	31258
37257	0	16	53	52491
306055	111	20	74	91256
32750	1	18	22	22807
116502	37	19	68	77411
130539	60	20	47	48821
161876	64	30	102	52295
128274	71	37	123	63262
104367	38	23	69	50466
193024	76	36	108	62932
141574	62	29	59	38439
253559	126	35	122	70817
181110	85	24	91	105965
198432	74	22	45	73795
113853	78	19	53	82043
159940	100	30	112	74349
166822	79	27	82	82204
286675	76	26	92	55709
91657	40	15	51	37137
108278	81	30	120	70780
146342	103	28	99	55027
145142	70	24	86	56699
161740	75	21	59	65911
160905	93	27	98	56316
106888	42	21	71	26982
188150	95	30	100	54628
189401	87	30	113	96750
129484	44	33	92	53009
204030	88	30	107	64664
68538	29	20	75	36990
243625	89	27	100	85224
167255	71	25	69	37048
264528	70	30	106	59635
122024	50	20	51	42051
80964	30	8	18	26998
209795	87	24	91	63717
224205	78	25	75	55071
115971	48	25	63	40001
138191	57	21	72	54506
81106	31	21	59	35838
93125	30	21	29	50838
305756	70	26	85	86997
78800	20	26	66	33032
158835	84	30	106	61704
223590	81	34	113	117986
131108	79	30	101	56733
128734	72	18	65	55064
24188	8	4	7	5950
257662	67	31	111	84607
65029	21	18	61	32551
98066	30	14	41	31701
173587	70	20	70	71170
180042	87	36	136	101773
197266	87	24	87	101653
212060	116	26	90	81493
141582	54	22	76	55901
245107	96	31	101	109104
206879	94	21	57	114425
145696	51	31	61	36311
173535	51	26	92	70027
142064	38	24	80	73713
117926	65	15	35	40671
113461	64	19	72	89041
145285	66	28	88	57231
150999	98	24	80	68608
91812	100	18	62	59155
118807	56	25	81	55827
69471	22	20	63	22618
126630	51	25	91	58425
145908	61	24	65	65724
98393	94	23	79	56979
190926	98	25	85	72369
198797	76	20	75	79194
106193	57	23	70	202316
89318	75	22	78	44970
120362	48	25	75	49319
98791	48	18	55	36252
274953	109	30	80	75741
132798	27	22	83	38417
135251	85	25	38	64102
80953	49	8	27	56622
109237	24	21	62	15430
96634	46	22	82	72571
226191	44	24	88	67271
171286	49	30	59	43460
117815	108	27	92	99501
133561	42	24	40	28340
152193	110	25	91	76013
112004	28	21	63	37361
169613	79	24	88	48204
187483	49	24	85	76168
130533	64	20	76	85168
142339	75	20	67	125410
189764	118	24	69	123328
201744	95	40	150	83038
246834	106	22	77	120087
155947	73	31	103	91939
182581	108	26	81	103646
106351	30	20	37	29467
43287	13	19	64	43750
127493	69	15	22	34497
127930	75	21	35	66477
149006	82	22	61	71181
187714	108	24	80	74482
74112	28	19	54	174949
94006	83	24	76	46765
176625	51	23	87	90257
141933	90	27	75	51370
22938	12	1	0	1168
125927	87	24	61	51360
61857	23	11	30	25162
91290	57	27	66	21067
255100	93	22	56	58233
21054	4	0	0	855
169102	56	17	32	85903
31414	18	8	9	14116
188701	86	24	82	57637
137544	40	31	110	94137
77166	16	24	71	62147
74567	18	20	50	62832
38214	16	8	21	8773
90961	42	22	78	63785
194652	78	33	118	65196
135261	31	33	102	73087
244272	104	31	109	72631
201748	121	33	104	86281
256402	111	35	124	162365
139144	57	21	76	56530
76470	28	20	57	35606
193518	56	24	91	70111
280334	82	29	101	92046
50999	2	20	66	63989
253274	91	27	98	104911
103239	41	24	63	43448
168059	84	26	85	60029
128768	55	26	74	38650
78256	3	12	19	47261
249232	68	21	57	73586
152366	93	24	74	83042
173260	41	21	78	37238
197197	94	30	91	63958
68388	105	32	112	78956
139409	70	24	79	99518
185366	114	29	100	111436
0	0	0	0	0
14688	4	0	0	6023
98	0	0	0	0
455	0	0	0	0
0	0	0	0	0
0	0	0	0	0
137885	42	20	48	42564
185288	97	27	55	38885
0	0	0	0	0
203	0	0	0	0
7199	7	0	0	1644
46660	12	5	13	6179
17547	0	1	4	3926
73567	37	23	31	23238
969	0	0	0	0
105477	39	16	29	49288




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=145918&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=145918&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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 time6 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







Multiple Linear Regression - Estimated Regression Equation
Total_Time_RFC[t] = + 7842.47988328901 + 982.346728580461Blogged_Computations[t] + 1960.02729529599Reviewed_Compendiums[t] + 153.962528491684Long_feedback_messages[t] + 0.276221966533435`number_characters_compendium `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Total_Time_RFC[t] =  +  7842.47988328901 +  982.346728580461Blogged_Computations[t] +  1960.02729529599Reviewed_Compendiums[t] +  153.962528491684Long_feedback_messages[t] +  0.276221966533435`number_characters_compendium

`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Total_Time_RFC[t] =  +  7842.47988328901 +  982.346728580461Blogged_Computations[t] +  1960.02729529599Reviewed_Compendiums[t] +  153.962528491684Long_feedback_messages[t] +  0.276221966533435`number_characters_compendium

`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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
Total_Time_RFC[t] = + 7842.47988328901 + 982.346728580461Blogged_Computations[t] + 1960.02729529599Reviewed_Compendiums[t] + 153.962528491684Long_feedback_messages[t] + 0.276221966533435`number_characters_compendium `[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)7842.479883289018742.2905020.89710.3710360.185518
Blogged_Computations982.346728580461146.0001536.728400
Reviewed_Compendiums1960.02729529599889.9740082.20230.0290810.01454
Long_feedback_messages153.962528491684241.0475080.63870.5239220.261961
`number_characters_compendium `0.2762219665334350.1302282.12110.0354670.017734

\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) & 7842.47988328901 & 8742.290502 & 0.8971 & 0.371036 & 0.185518 \tabularnewline
Blogged_Computations & 982.346728580461 & 146.000153 & 6.7284 & 0 & 0 \tabularnewline
Reviewed_Compendiums & 1960.02729529599 & 889.974008 & 2.2023 & 0.029081 & 0.01454 \tabularnewline
Long_feedback_messages & 153.962528491684 & 241.047508 & 0.6387 & 0.523922 & 0.261961 \tabularnewline
`number_characters_compendium

` & 0.276221966533435 & 0.130228 & 2.1211 & 0.035467 & 0.017734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&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]7842.47988328901[/C][C]8742.290502[/C][C]0.8971[/C][C]0.371036[/C][C]0.185518[/C][/ROW]
[ROW][C]Blogged_Computations[/C][C]982.346728580461[/C][C]146.000153[/C][C]6.7284[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Reviewed_Compendiums[/C][C]1960.02729529599[/C][C]889.974008[/C][C]2.2023[/C][C]0.029081[/C][C]0.01454[/C][/ROW]
[ROW][C]Long_feedback_messages[/C][C]153.962528491684[/C][C]241.047508[/C][C]0.6387[/C][C]0.523922[/C][C]0.261961[/C][/ROW]
[ROW][C]`number_characters_compendium

`[/C][C]0.276221966533435[/C][C]0.130228[/C][C]2.1211[/C][C]0.035467[/C][C]0.017734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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)7842.479883289018742.2905020.89710.3710360.185518
Blogged_Computations982.346728580461146.0001536.728400
Reviewed_Compendiums1960.02729529599889.9740082.20230.0290810.01454
Long_feedback_messages153.962528491684241.0475080.63870.5239220.261961
`number_characters_compendium `0.2762219665334350.1302282.12110.0354670.017734







Multiple Linear Regression - Regression Statistics
Multiple R0.819969689009304
R-squared0.672350290894014
Adjusted R-squared0.664107530916505
F-TEST (value)81.5685877944483
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40272.3493441558
Sum Squared Residuals257876077349.938

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.819969689009304 \tabularnewline
R-squared & 0.672350290894014 \tabularnewline
Adjusted R-squared & 0.664107530916505 \tabularnewline
F-TEST (value) & 81.5685877944483 \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 & 40272.3493441558 \tabularnewline
Sum Squared Residuals & 257876077349.938 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.819969689009304[/C][/ROW]
[ROW][C]R-squared[/C][C]0.672350290894014[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.664107530916505[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]81.5685877944483[/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]40272.3493441558[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]257876077349.938[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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 R0.819969689009304
R-squared0.672350290894014
Adjusted R-squared0.664107530916505
F-TEST (value)81.5685877944483
F-TEST (DF numerator)4
F-TEST (DF denominator)159
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation40272.3493441558
Sum Squared Residuals257876077349.938







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1170588161983.2455460858604.75445391489
286621126247.102787852-39626.1027878519
3113514134962.262225323-21448.2622253227
4152510189217.634475186-36707.6344751858
58620690772.1272691804-4566.12726918039
63725761862.0978633906-24605.0978633906
7306055192683.651548113371.348452
83275053792.2879447424-21042.2879447424
9116502113281.8980401443220.10195985588
10130539126705.5009712743833.49902872556
11161876159662.6950173362213.30498266405
12128274186521.852589769-58247.8525897686
13104367114815.515590157-10448.5155901568
14193024187072.9677610445951.03223895634
15141574145290.253971449-3716.25397144934
16253559238563.7624997714995.2375002295
17181110181663.057676191-553.05767619064
18198432150968.85209721647463.1479027845
19113853152528.136133551-38675.1361335506
20159940202658.601781078-42718.6017810778
21166822173700.08628737-6878.08628736973
22286675163014.143087946123660.856912054
239165794646.9025801754-2989.90258017536
24108278184239.877967425-75961.8779674246
25146342194346.913668476-48004.9136684763
26145142152549.692701789-7407.69270178909
27161740149968.91294523411771.087054766
28160905182765.506673746-21860.5066737456
29106888108645.976308799-1757.97630879886
30188150190451.944394269-2301.94439426939
31189401196229.705110339-6828.70511033894
32129484144553.439530803-15069.4395308027
33204030187425.41864977816604.5813502225
346853897295.7210969902-28757.7210969902
35243625187129.06942495656495.9305750444
36167255147446.66587695819808.3341230416
37264528168200.09473714196327.9052628591
38122024115627.8610860056396.13891399479
398096463221.866268390717742.1337316093
40209795171957.92549124737837.074508753
41224205160225.21665080463979.7833491962
42115971124744.599415831-8773.5994158309
43138191131137.8731728647053.12682713627
448110698438.8336881337-17332.8336881337
459312596980.9406028042-3855.94060280421
46305756164684.757905919141071.242094081
477880097735.8150115775-18935.8150115775
48158835182524.452186025-23689.452186025
49223590204041.58360134419548.4163986559
50131108175469.806505027-44361.8065050266
51128734139069.386373567-10335.3863735666
522418826262.6212934324-2074.6212934324
53257662174880.70943742782781.2905625732
546502982135.2679694291-17106.2679694291
559806679822.240104082218243.7598959178
56173587146243.39114244427343.6088575565
57180042212918.469975321-32876.469975321
58197266181820.83189969315445.1681003073
59212060209122.1943592792937.80564072099
60141582131152.04003969910429.9599603008
61245107208595.74879551336511.2512044871
62206879181726.20821568225152.7917843176
63145696138124.6192598567571.38074014354
64173535142410.4209902631124.57900974
65142064124890.46275486417173.5372451359
66117926117718.338768549207.661231450896
67113461143633.571296567-30172.5712965672
68145285156915.29011183-11630.2901118304
69150999182421.153330539-31422.1533305386
7091812167243.231253433-75431.2312534327
71118807139746.187599683-20939.187599683
726947184601.8815520083-15130.8815520083
73126630137091.703910751-10461.7039107514
74145908142968.2622942042939.73770579618
7598393173165.591343612-74772.5913436117
76190926186189.8640844254736.13591557476
77198797155123.68921584943673.310784151
78106193175578.371579779-69385.3715797794
7989318149069.864080695-59751.8640806953
80120362129165.98604189-8803.98604188966
8198791108757.151968292-9966.15196829166
82274953206957.42240398367995.5775960174
83132798100876.95120459831921.048795402
84135251163899.590776438-28648.5907764382
858095391454.9164044311-10501.9164044312
8610923786387.156280531222849.8437194688
8796634128821.661564118-32187.661564118
88226191130236.82144587295954.178554128
89171286135866.68428916435419.3157108363
90117815208505.578056249-90690.5780562488
91133561110128.32924199723432.670758003
92152193199908.352844389-47715.3528443887
9311200496528.329671389515475.6703286105
94169613159352.23271029510260.7672897049
95187483137144.21433954750338.7856604528
96130533145139.641029446-14606.6410294459
97142339165675.516664644-23336.5166646443
98189764215489.366097449-25725.3660974488
99201744225597.809841028-23853.8098410284
100246834200117.6155982946716.38440171
101155947181568.349039599-25621.3490395993
102182581205996.902998825-23415.9029988253
10310635190349.473888655716001.5261113443
1044328779791.8188247644-36504.8188247644
105127493117940.8183911029552.18160889837
106127930146430.153894491-18500.1538944915
107149006160568.982161208-11562.9821612078
108187714193867.148447761-6153.14844776059
10974112129227.440255775-55115.4402557746
11094006161036.585872875-67030.5858728751
111176625141348.49684488535276.503155115
112141933174911.134486221-32978.1344862211
1132293821913.29517846161024.7048215384
114125927163925.774796043-37998.7747960428
1156185763565.9278655603-1708.92786556032
11691290132737.675434778-41447.675434778
117255100167028.46151045988071.5384895405
1182105412008.0365789979045.96342100303
119169102124829.45720668244272.5427933178
1203141446489.7513961164-15075.7513961164
121188701167910.48644971820790.5135502819
122137544150835.780578326-13291.7805783263
1237716698698.3887047431-21532.3887047431
1247456789778.9719294701-15211.9719294701
1253821444896.7543136675-6682.75431366751
12690961121849.538337867-30888.5383378666
127194652185322.5711494659329.42885053484
128135261138868.541988232-3607.54198823187
129244272207611.57906671636660.4209332839
130201748231232.145243899-29484.1452438989
131256402249424.055220256977.94477975018
132139144132312.7965470946831.20345290586
1337647093159.7576538772-16689.7576538772
134193518143271.34015926750246.6598407325
135280334186211.04569966794122.9543003328
1365099976844.4135433288-25845.4135433288
137253274194223.81968027759050.1803197231
138103239116860.282139112-13621.2821391124
139168059170988.458112572-2929.45811257218
140128768134901.465747812-6133.46574781197
1417825650289.662014260927966.3379857391
142249232144904.564381331104327.435618669
143152366180572.63238163-28206.6323816298
144173260111574.29976842761685.7002315729
145197197190661.0858570216535.91414297928
14668388212762.944614392-144374.944614392
147139409163299.503387342-23890.5033873425
148185366222848.122416834-37482.1224168335
14907842.47988328901-7842.47988328901
1501468813435.55170204181252.44829795824
151987842.47988328901-7744.47988328901
1524557842.47988328901-7387.47988328901
15307842.47988328901-7842.47988328901
15407842.47988328901-7842.47988328901
155137885107448.90154071830436.0984592819
156185288175259.67976428110028.3202357193
15707842.47988328901-7842.47988328901
1582037842.47988328901-7639.47988328901
159719915173.0158963332-7974.01589633323
1604666033139.065504336513520.9344956635
1611754711502.8047331626044.19526683798
16273567100461.62107412-26894.62107412
1639697842.47988328901-6873.47988328901
16410547795593.78063542169883.2193645784

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 170588 & 161983.245546085 & 8604.75445391489 \tabularnewline
2 & 86621 & 126247.102787852 & -39626.1027878519 \tabularnewline
3 & 113514 & 134962.262225323 & -21448.2622253227 \tabularnewline
4 & 152510 & 189217.634475186 & -36707.6344751858 \tabularnewline
5 & 86206 & 90772.1272691804 & -4566.12726918039 \tabularnewline
6 & 37257 & 61862.0978633906 & -24605.0978633906 \tabularnewline
7 & 306055 & 192683.651548 & 113371.348452 \tabularnewline
8 & 32750 & 53792.2879447424 & -21042.2879447424 \tabularnewline
9 & 116502 & 113281.898040144 & 3220.10195985588 \tabularnewline
10 & 130539 & 126705.500971274 & 3833.49902872556 \tabularnewline
11 & 161876 & 159662.695017336 & 2213.30498266405 \tabularnewline
12 & 128274 & 186521.852589769 & -58247.8525897686 \tabularnewline
13 & 104367 & 114815.515590157 & -10448.5155901568 \tabularnewline
14 & 193024 & 187072.967761044 & 5951.03223895634 \tabularnewline
15 & 141574 & 145290.253971449 & -3716.25397144934 \tabularnewline
16 & 253559 & 238563.76249977 & 14995.2375002295 \tabularnewline
17 & 181110 & 181663.057676191 & -553.05767619064 \tabularnewline
18 & 198432 & 150968.852097216 & 47463.1479027845 \tabularnewline
19 & 113853 & 152528.136133551 & -38675.1361335506 \tabularnewline
20 & 159940 & 202658.601781078 & -42718.6017810778 \tabularnewline
21 & 166822 & 173700.08628737 & -6878.08628736973 \tabularnewline
22 & 286675 & 163014.143087946 & 123660.856912054 \tabularnewline
23 & 91657 & 94646.9025801754 & -2989.90258017536 \tabularnewline
24 & 108278 & 184239.877967425 & -75961.8779674246 \tabularnewline
25 & 146342 & 194346.913668476 & -48004.9136684763 \tabularnewline
26 & 145142 & 152549.692701789 & -7407.69270178909 \tabularnewline
27 & 161740 & 149968.912945234 & 11771.087054766 \tabularnewline
28 & 160905 & 182765.506673746 & -21860.5066737456 \tabularnewline
29 & 106888 & 108645.976308799 & -1757.97630879886 \tabularnewline
30 & 188150 & 190451.944394269 & -2301.94439426939 \tabularnewline
31 & 189401 & 196229.705110339 & -6828.70511033894 \tabularnewline
32 & 129484 & 144553.439530803 & -15069.4395308027 \tabularnewline
33 & 204030 & 187425.418649778 & 16604.5813502225 \tabularnewline
34 & 68538 & 97295.7210969902 & -28757.7210969902 \tabularnewline
35 & 243625 & 187129.069424956 & 56495.9305750444 \tabularnewline
36 & 167255 & 147446.665876958 & 19808.3341230416 \tabularnewline
37 & 264528 & 168200.094737141 & 96327.9052628591 \tabularnewline
38 & 122024 & 115627.861086005 & 6396.13891399479 \tabularnewline
39 & 80964 & 63221.8662683907 & 17742.1337316093 \tabularnewline
40 & 209795 & 171957.925491247 & 37837.074508753 \tabularnewline
41 & 224205 & 160225.216650804 & 63979.7833491962 \tabularnewline
42 & 115971 & 124744.599415831 & -8773.5994158309 \tabularnewline
43 & 138191 & 131137.873172864 & 7053.12682713627 \tabularnewline
44 & 81106 & 98438.8336881337 & -17332.8336881337 \tabularnewline
45 & 93125 & 96980.9406028042 & -3855.94060280421 \tabularnewline
46 & 305756 & 164684.757905919 & 141071.242094081 \tabularnewline
47 & 78800 & 97735.8150115775 & -18935.8150115775 \tabularnewline
48 & 158835 & 182524.452186025 & -23689.452186025 \tabularnewline
49 & 223590 & 204041.583601344 & 19548.4163986559 \tabularnewline
50 & 131108 & 175469.806505027 & -44361.8065050266 \tabularnewline
51 & 128734 & 139069.386373567 & -10335.3863735666 \tabularnewline
52 & 24188 & 26262.6212934324 & -2074.6212934324 \tabularnewline
53 & 257662 & 174880.709437427 & 82781.2905625732 \tabularnewline
54 & 65029 & 82135.2679694291 & -17106.2679694291 \tabularnewline
55 & 98066 & 79822.2401040822 & 18243.7598959178 \tabularnewline
56 & 173587 & 146243.391142444 & 27343.6088575565 \tabularnewline
57 & 180042 & 212918.469975321 & -32876.469975321 \tabularnewline
58 & 197266 & 181820.831899693 & 15445.1681003073 \tabularnewline
59 & 212060 & 209122.194359279 & 2937.80564072099 \tabularnewline
60 & 141582 & 131152.040039699 & 10429.9599603008 \tabularnewline
61 & 245107 & 208595.748795513 & 36511.2512044871 \tabularnewline
62 & 206879 & 181726.208215682 & 25152.7917843176 \tabularnewline
63 & 145696 & 138124.619259856 & 7571.38074014354 \tabularnewline
64 & 173535 & 142410.42099026 & 31124.57900974 \tabularnewline
65 & 142064 & 124890.462754864 & 17173.5372451359 \tabularnewline
66 & 117926 & 117718.338768549 & 207.661231450896 \tabularnewline
67 & 113461 & 143633.571296567 & -30172.5712965672 \tabularnewline
68 & 145285 & 156915.29011183 & -11630.2901118304 \tabularnewline
69 & 150999 & 182421.153330539 & -31422.1533305386 \tabularnewline
70 & 91812 & 167243.231253433 & -75431.2312534327 \tabularnewline
71 & 118807 & 139746.187599683 & -20939.187599683 \tabularnewline
72 & 69471 & 84601.8815520083 & -15130.8815520083 \tabularnewline
73 & 126630 & 137091.703910751 & -10461.7039107514 \tabularnewline
74 & 145908 & 142968.262294204 & 2939.73770579618 \tabularnewline
75 & 98393 & 173165.591343612 & -74772.5913436117 \tabularnewline
76 & 190926 & 186189.864084425 & 4736.13591557476 \tabularnewline
77 & 198797 & 155123.689215849 & 43673.310784151 \tabularnewline
78 & 106193 & 175578.371579779 & -69385.3715797794 \tabularnewline
79 & 89318 & 149069.864080695 & -59751.8640806953 \tabularnewline
80 & 120362 & 129165.98604189 & -8803.98604188966 \tabularnewline
81 & 98791 & 108757.151968292 & -9966.15196829166 \tabularnewline
82 & 274953 & 206957.422403983 & 67995.5775960174 \tabularnewline
83 & 132798 & 100876.951204598 & 31921.048795402 \tabularnewline
84 & 135251 & 163899.590776438 & -28648.5907764382 \tabularnewline
85 & 80953 & 91454.9164044311 & -10501.9164044312 \tabularnewline
86 & 109237 & 86387.1562805312 & 22849.8437194688 \tabularnewline
87 & 96634 & 128821.661564118 & -32187.661564118 \tabularnewline
88 & 226191 & 130236.821445872 & 95954.178554128 \tabularnewline
89 & 171286 & 135866.684289164 & 35419.3157108363 \tabularnewline
90 & 117815 & 208505.578056249 & -90690.5780562488 \tabularnewline
91 & 133561 & 110128.329241997 & 23432.670758003 \tabularnewline
92 & 152193 & 199908.352844389 & -47715.3528443887 \tabularnewline
93 & 112004 & 96528.3296713895 & 15475.6703286105 \tabularnewline
94 & 169613 & 159352.232710295 & 10260.7672897049 \tabularnewline
95 & 187483 & 137144.214339547 & 50338.7856604528 \tabularnewline
96 & 130533 & 145139.641029446 & -14606.6410294459 \tabularnewline
97 & 142339 & 165675.516664644 & -23336.5166646443 \tabularnewline
98 & 189764 & 215489.366097449 & -25725.3660974488 \tabularnewline
99 & 201744 & 225597.809841028 & -23853.8098410284 \tabularnewline
100 & 246834 & 200117.61559829 & 46716.38440171 \tabularnewline
101 & 155947 & 181568.349039599 & -25621.3490395993 \tabularnewline
102 & 182581 & 205996.902998825 & -23415.9029988253 \tabularnewline
103 & 106351 & 90349.4738886557 & 16001.5261113443 \tabularnewline
104 & 43287 & 79791.8188247644 & -36504.8188247644 \tabularnewline
105 & 127493 & 117940.818391102 & 9552.18160889837 \tabularnewline
106 & 127930 & 146430.153894491 & -18500.1538944915 \tabularnewline
107 & 149006 & 160568.982161208 & -11562.9821612078 \tabularnewline
108 & 187714 & 193867.148447761 & -6153.14844776059 \tabularnewline
109 & 74112 & 129227.440255775 & -55115.4402557746 \tabularnewline
110 & 94006 & 161036.585872875 & -67030.5858728751 \tabularnewline
111 & 176625 & 141348.496844885 & 35276.503155115 \tabularnewline
112 & 141933 & 174911.134486221 & -32978.1344862211 \tabularnewline
113 & 22938 & 21913.2951784616 & 1024.7048215384 \tabularnewline
114 & 125927 & 163925.774796043 & -37998.7747960428 \tabularnewline
115 & 61857 & 63565.9278655603 & -1708.92786556032 \tabularnewline
116 & 91290 & 132737.675434778 & -41447.675434778 \tabularnewline
117 & 255100 & 167028.461510459 & 88071.5384895405 \tabularnewline
118 & 21054 & 12008.036578997 & 9045.96342100303 \tabularnewline
119 & 169102 & 124829.457206682 & 44272.5427933178 \tabularnewline
120 & 31414 & 46489.7513961164 & -15075.7513961164 \tabularnewline
121 & 188701 & 167910.486449718 & 20790.5135502819 \tabularnewline
122 & 137544 & 150835.780578326 & -13291.7805783263 \tabularnewline
123 & 77166 & 98698.3887047431 & -21532.3887047431 \tabularnewline
124 & 74567 & 89778.9719294701 & -15211.9719294701 \tabularnewline
125 & 38214 & 44896.7543136675 & -6682.75431366751 \tabularnewline
126 & 90961 & 121849.538337867 & -30888.5383378666 \tabularnewline
127 & 194652 & 185322.571149465 & 9329.42885053484 \tabularnewline
128 & 135261 & 138868.541988232 & -3607.54198823187 \tabularnewline
129 & 244272 & 207611.579066716 & 36660.4209332839 \tabularnewline
130 & 201748 & 231232.145243899 & -29484.1452438989 \tabularnewline
131 & 256402 & 249424.05522025 & 6977.94477975018 \tabularnewline
132 & 139144 & 132312.796547094 & 6831.20345290586 \tabularnewline
133 & 76470 & 93159.7576538772 & -16689.7576538772 \tabularnewline
134 & 193518 & 143271.340159267 & 50246.6598407325 \tabularnewline
135 & 280334 & 186211.045699667 & 94122.9543003328 \tabularnewline
136 & 50999 & 76844.4135433288 & -25845.4135433288 \tabularnewline
137 & 253274 & 194223.819680277 & 59050.1803197231 \tabularnewline
138 & 103239 & 116860.282139112 & -13621.2821391124 \tabularnewline
139 & 168059 & 170988.458112572 & -2929.45811257218 \tabularnewline
140 & 128768 & 134901.465747812 & -6133.46574781197 \tabularnewline
141 & 78256 & 50289.6620142609 & 27966.3379857391 \tabularnewline
142 & 249232 & 144904.564381331 & 104327.435618669 \tabularnewline
143 & 152366 & 180572.63238163 & -28206.6323816298 \tabularnewline
144 & 173260 & 111574.299768427 & 61685.7002315729 \tabularnewline
145 & 197197 & 190661.085857021 & 6535.91414297928 \tabularnewline
146 & 68388 & 212762.944614392 & -144374.944614392 \tabularnewline
147 & 139409 & 163299.503387342 & -23890.5033873425 \tabularnewline
148 & 185366 & 222848.122416834 & -37482.1224168335 \tabularnewline
149 & 0 & 7842.47988328901 & -7842.47988328901 \tabularnewline
150 & 14688 & 13435.5517020418 & 1252.44829795824 \tabularnewline
151 & 98 & 7842.47988328901 & -7744.47988328901 \tabularnewline
152 & 455 & 7842.47988328901 & -7387.47988328901 \tabularnewline
153 & 0 & 7842.47988328901 & -7842.47988328901 \tabularnewline
154 & 0 & 7842.47988328901 & -7842.47988328901 \tabularnewline
155 & 137885 & 107448.901540718 & 30436.0984592819 \tabularnewline
156 & 185288 & 175259.679764281 & 10028.3202357193 \tabularnewline
157 & 0 & 7842.47988328901 & -7842.47988328901 \tabularnewline
158 & 203 & 7842.47988328901 & -7639.47988328901 \tabularnewline
159 & 7199 & 15173.0158963332 & -7974.01589633323 \tabularnewline
160 & 46660 & 33139.0655043365 & 13520.9344956635 \tabularnewline
161 & 17547 & 11502.804733162 & 6044.19526683798 \tabularnewline
162 & 73567 & 100461.62107412 & -26894.62107412 \tabularnewline
163 & 969 & 7842.47988328901 & -6873.47988328901 \tabularnewline
164 & 105477 & 95593.7806354216 & 9883.2193645784 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&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]170588[/C][C]161983.245546085[/C][C]8604.75445391489[/C][/ROW]
[ROW][C]2[/C][C]86621[/C][C]126247.102787852[/C][C]-39626.1027878519[/C][/ROW]
[ROW][C]3[/C][C]113514[/C][C]134962.262225323[/C][C]-21448.2622253227[/C][/ROW]
[ROW][C]4[/C][C]152510[/C][C]189217.634475186[/C][C]-36707.6344751858[/C][/ROW]
[ROW][C]5[/C][C]86206[/C][C]90772.1272691804[/C][C]-4566.12726918039[/C][/ROW]
[ROW][C]6[/C][C]37257[/C][C]61862.0978633906[/C][C]-24605.0978633906[/C][/ROW]
[ROW][C]7[/C][C]306055[/C][C]192683.651548[/C][C]113371.348452[/C][/ROW]
[ROW][C]8[/C][C]32750[/C][C]53792.2879447424[/C][C]-21042.2879447424[/C][/ROW]
[ROW][C]9[/C][C]116502[/C][C]113281.898040144[/C][C]3220.10195985588[/C][/ROW]
[ROW][C]10[/C][C]130539[/C][C]126705.500971274[/C][C]3833.49902872556[/C][/ROW]
[ROW][C]11[/C][C]161876[/C][C]159662.695017336[/C][C]2213.30498266405[/C][/ROW]
[ROW][C]12[/C][C]128274[/C][C]186521.852589769[/C][C]-58247.8525897686[/C][/ROW]
[ROW][C]13[/C][C]104367[/C][C]114815.515590157[/C][C]-10448.5155901568[/C][/ROW]
[ROW][C]14[/C][C]193024[/C][C]187072.967761044[/C][C]5951.03223895634[/C][/ROW]
[ROW][C]15[/C][C]141574[/C][C]145290.253971449[/C][C]-3716.25397144934[/C][/ROW]
[ROW][C]16[/C][C]253559[/C][C]238563.76249977[/C][C]14995.2375002295[/C][/ROW]
[ROW][C]17[/C][C]181110[/C][C]181663.057676191[/C][C]-553.05767619064[/C][/ROW]
[ROW][C]18[/C][C]198432[/C][C]150968.852097216[/C][C]47463.1479027845[/C][/ROW]
[ROW][C]19[/C][C]113853[/C][C]152528.136133551[/C][C]-38675.1361335506[/C][/ROW]
[ROW][C]20[/C][C]159940[/C][C]202658.601781078[/C][C]-42718.6017810778[/C][/ROW]
[ROW][C]21[/C][C]166822[/C][C]173700.08628737[/C][C]-6878.08628736973[/C][/ROW]
[ROW][C]22[/C][C]286675[/C][C]163014.143087946[/C][C]123660.856912054[/C][/ROW]
[ROW][C]23[/C][C]91657[/C][C]94646.9025801754[/C][C]-2989.90258017536[/C][/ROW]
[ROW][C]24[/C][C]108278[/C][C]184239.877967425[/C][C]-75961.8779674246[/C][/ROW]
[ROW][C]25[/C][C]146342[/C][C]194346.913668476[/C][C]-48004.9136684763[/C][/ROW]
[ROW][C]26[/C][C]145142[/C][C]152549.692701789[/C][C]-7407.69270178909[/C][/ROW]
[ROW][C]27[/C][C]161740[/C][C]149968.912945234[/C][C]11771.087054766[/C][/ROW]
[ROW][C]28[/C][C]160905[/C][C]182765.506673746[/C][C]-21860.5066737456[/C][/ROW]
[ROW][C]29[/C][C]106888[/C][C]108645.976308799[/C][C]-1757.97630879886[/C][/ROW]
[ROW][C]30[/C][C]188150[/C][C]190451.944394269[/C][C]-2301.94439426939[/C][/ROW]
[ROW][C]31[/C][C]189401[/C][C]196229.705110339[/C][C]-6828.70511033894[/C][/ROW]
[ROW][C]32[/C][C]129484[/C][C]144553.439530803[/C][C]-15069.4395308027[/C][/ROW]
[ROW][C]33[/C][C]204030[/C][C]187425.418649778[/C][C]16604.5813502225[/C][/ROW]
[ROW][C]34[/C][C]68538[/C][C]97295.7210969902[/C][C]-28757.7210969902[/C][/ROW]
[ROW][C]35[/C][C]243625[/C][C]187129.069424956[/C][C]56495.9305750444[/C][/ROW]
[ROW][C]36[/C][C]167255[/C][C]147446.665876958[/C][C]19808.3341230416[/C][/ROW]
[ROW][C]37[/C][C]264528[/C][C]168200.094737141[/C][C]96327.9052628591[/C][/ROW]
[ROW][C]38[/C][C]122024[/C][C]115627.861086005[/C][C]6396.13891399479[/C][/ROW]
[ROW][C]39[/C][C]80964[/C][C]63221.8662683907[/C][C]17742.1337316093[/C][/ROW]
[ROW][C]40[/C][C]209795[/C][C]171957.925491247[/C][C]37837.074508753[/C][/ROW]
[ROW][C]41[/C][C]224205[/C][C]160225.216650804[/C][C]63979.7833491962[/C][/ROW]
[ROW][C]42[/C][C]115971[/C][C]124744.599415831[/C][C]-8773.5994158309[/C][/ROW]
[ROW][C]43[/C][C]138191[/C][C]131137.873172864[/C][C]7053.12682713627[/C][/ROW]
[ROW][C]44[/C][C]81106[/C][C]98438.8336881337[/C][C]-17332.8336881337[/C][/ROW]
[ROW][C]45[/C][C]93125[/C][C]96980.9406028042[/C][C]-3855.94060280421[/C][/ROW]
[ROW][C]46[/C][C]305756[/C][C]164684.757905919[/C][C]141071.242094081[/C][/ROW]
[ROW][C]47[/C][C]78800[/C][C]97735.8150115775[/C][C]-18935.8150115775[/C][/ROW]
[ROW][C]48[/C][C]158835[/C][C]182524.452186025[/C][C]-23689.452186025[/C][/ROW]
[ROW][C]49[/C][C]223590[/C][C]204041.583601344[/C][C]19548.4163986559[/C][/ROW]
[ROW][C]50[/C][C]131108[/C][C]175469.806505027[/C][C]-44361.8065050266[/C][/ROW]
[ROW][C]51[/C][C]128734[/C][C]139069.386373567[/C][C]-10335.3863735666[/C][/ROW]
[ROW][C]52[/C][C]24188[/C][C]26262.6212934324[/C][C]-2074.6212934324[/C][/ROW]
[ROW][C]53[/C][C]257662[/C][C]174880.709437427[/C][C]82781.2905625732[/C][/ROW]
[ROW][C]54[/C][C]65029[/C][C]82135.2679694291[/C][C]-17106.2679694291[/C][/ROW]
[ROW][C]55[/C][C]98066[/C][C]79822.2401040822[/C][C]18243.7598959178[/C][/ROW]
[ROW][C]56[/C][C]173587[/C][C]146243.391142444[/C][C]27343.6088575565[/C][/ROW]
[ROW][C]57[/C][C]180042[/C][C]212918.469975321[/C][C]-32876.469975321[/C][/ROW]
[ROW][C]58[/C][C]197266[/C][C]181820.831899693[/C][C]15445.1681003073[/C][/ROW]
[ROW][C]59[/C][C]212060[/C][C]209122.194359279[/C][C]2937.80564072099[/C][/ROW]
[ROW][C]60[/C][C]141582[/C][C]131152.040039699[/C][C]10429.9599603008[/C][/ROW]
[ROW][C]61[/C][C]245107[/C][C]208595.748795513[/C][C]36511.2512044871[/C][/ROW]
[ROW][C]62[/C][C]206879[/C][C]181726.208215682[/C][C]25152.7917843176[/C][/ROW]
[ROW][C]63[/C][C]145696[/C][C]138124.619259856[/C][C]7571.38074014354[/C][/ROW]
[ROW][C]64[/C][C]173535[/C][C]142410.42099026[/C][C]31124.57900974[/C][/ROW]
[ROW][C]65[/C][C]142064[/C][C]124890.462754864[/C][C]17173.5372451359[/C][/ROW]
[ROW][C]66[/C][C]117926[/C][C]117718.338768549[/C][C]207.661231450896[/C][/ROW]
[ROW][C]67[/C][C]113461[/C][C]143633.571296567[/C][C]-30172.5712965672[/C][/ROW]
[ROW][C]68[/C][C]145285[/C][C]156915.29011183[/C][C]-11630.2901118304[/C][/ROW]
[ROW][C]69[/C][C]150999[/C][C]182421.153330539[/C][C]-31422.1533305386[/C][/ROW]
[ROW][C]70[/C][C]91812[/C][C]167243.231253433[/C][C]-75431.2312534327[/C][/ROW]
[ROW][C]71[/C][C]118807[/C][C]139746.187599683[/C][C]-20939.187599683[/C][/ROW]
[ROW][C]72[/C][C]69471[/C][C]84601.8815520083[/C][C]-15130.8815520083[/C][/ROW]
[ROW][C]73[/C][C]126630[/C][C]137091.703910751[/C][C]-10461.7039107514[/C][/ROW]
[ROW][C]74[/C][C]145908[/C][C]142968.262294204[/C][C]2939.73770579618[/C][/ROW]
[ROW][C]75[/C][C]98393[/C][C]173165.591343612[/C][C]-74772.5913436117[/C][/ROW]
[ROW][C]76[/C][C]190926[/C][C]186189.864084425[/C][C]4736.13591557476[/C][/ROW]
[ROW][C]77[/C][C]198797[/C][C]155123.689215849[/C][C]43673.310784151[/C][/ROW]
[ROW][C]78[/C][C]106193[/C][C]175578.371579779[/C][C]-69385.3715797794[/C][/ROW]
[ROW][C]79[/C][C]89318[/C][C]149069.864080695[/C][C]-59751.8640806953[/C][/ROW]
[ROW][C]80[/C][C]120362[/C][C]129165.98604189[/C][C]-8803.98604188966[/C][/ROW]
[ROW][C]81[/C][C]98791[/C][C]108757.151968292[/C][C]-9966.15196829166[/C][/ROW]
[ROW][C]82[/C][C]274953[/C][C]206957.422403983[/C][C]67995.5775960174[/C][/ROW]
[ROW][C]83[/C][C]132798[/C][C]100876.951204598[/C][C]31921.048795402[/C][/ROW]
[ROW][C]84[/C][C]135251[/C][C]163899.590776438[/C][C]-28648.5907764382[/C][/ROW]
[ROW][C]85[/C][C]80953[/C][C]91454.9164044311[/C][C]-10501.9164044312[/C][/ROW]
[ROW][C]86[/C][C]109237[/C][C]86387.1562805312[/C][C]22849.8437194688[/C][/ROW]
[ROW][C]87[/C][C]96634[/C][C]128821.661564118[/C][C]-32187.661564118[/C][/ROW]
[ROW][C]88[/C][C]226191[/C][C]130236.821445872[/C][C]95954.178554128[/C][/ROW]
[ROW][C]89[/C][C]171286[/C][C]135866.684289164[/C][C]35419.3157108363[/C][/ROW]
[ROW][C]90[/C][C]117815[/C][C]208505.578056249[/C][C]-90690.5780562488[/C][/ROW]
[ROW][C]91[/C][C]133561[/C][C]110128.329241997[/C][C]23432.670758003[/C][/ROW]
[ROW][C]92[/C][C]152193[/C][C]199908.352844389[/C][C]-47715.3528443887[/C][/ROW]
[ROW][C]93[/C][C]112004[/C][C]96528.3296713895[/C][C]15475.6703286105[/C][/ROW]
[ROW][C]94[/C][C]169613[/C][C]159352.232710295[/C][C]10260.7672897049[/C][/ROW]
[ROW][C]95[/C][C]187483[/C][C]137144.214339547[/C][C]50338.7856604528[/C][/ROW]
[ROW][C]96[/C][C]130533[/C][C]145139.641029446[/C][C]-14606.6410294459[/C][/ROW]
[ROW][C]97[/C][C]142339[/C][C]165675.516664644[/C][C]-23336.5166646443[/C][/ROW]
[ROW][C]98[/C][C]189764[/C][C]215489.366097449[/C][C]-25725.3660974488[/C][/ROW]
[ROW][C]99[/C][C]201744[/C][C]225597.809841028[/C][C]-23853.8098410284[/C][/ROW]
[ROW][C]100[/C][C]246834[/C][C]200117.61559829[/C][C]46716.38440171[/C][/ROW]
[ROW][C]101[/C][C]155947[/C][C]181568.349039599[/C][C]-25621.3490395993[/C][/ROW]
[ROW][C]102[/C][C]182581[/C][C]205996.902998825[/C][C]-23415.9029988253[/C][/ROW]
[ROW][C]103[/C][C]106351[/C][C]90349.4738886557[/C][C]16001.5261113443[/C][/ROW]
[ROW][C]104[/C][C]43287[/C][C]79791.8188247644[/C][C]-36504.8188247644[/C][/ROW]
[ROW][C]105[/C][C]127493[/C][C]117940.818391102[/C][C]9552.18160889837[/C][/ROW]
[ROW][C]106[/C][C]127930[/C][C]146430.153894491[/C][C]-18500.1538944915[/C][/ROW]
[ROW][C]107[/C][C]149006[/C][C]160568.982161208[/C][C]-11562.9821612078[/C][/ROW]
[ROW][C]108[/C][C]187714[/C][C]193867.148447761[/C][C]-6153.14844776059[/C][/ROW]
[ROW][C]109[/C][C]74112[/C][C]129227.440255775[/C][C]-55115.4402557746[/C][/ROW]
[ROW][C]110[/C][C]94006[/C][C]161036.585872875[/C][C]-67030.5858728751[/C][/ROW]
[ROW][C]111[/C][C]176625[/C][C]141348.496844885[/C][C]35276.503155115[/C][/ROW]
[ROW][C]112[/C][C]141933[/C][C]174911.134486221[/C][C]-32978.1344862211[/C][/ROW]
[ROW][C]113[/C][C]22938[/C][C]21913.2951784616[/C][C]1024.7048215384[/C][/ROW]
[ROW][C]114[/C][C]125927[/C][C]163925.774796043[/C][C]-37998.7747960428[/C][/ROW]
[ROW][C]115[/C][C]61857[/C][C]63565.9278655603[/C][C]-1708.92786556032[/C][/ROW]
[ROW][C]116[/C][C]91290[/C][C]132737.675434778[/C][C]-41447.675434778[/C][/ROW]
[ROW][C]117[/C][C]255100[/C][C]167028.461510459[/C][C]88071.5384895405[/C][/ROW]
[ROW][C]118[/C][C]21054[/C][C]12008.036578997[/C][C]9045.96342100303[/C][/ROW]
[ROW][C]119[/C][C]169102[/C][C]124829.457206682[/C][C]44272.5427933178[/C][/ROW]
[ROW][C]120[/C][C]31414[/C][C]46489.7513961164[/C][C]-15075.7513961164[/C][/ROW]
[ROW][C]121[/C][C]188701[/C][C]167910.486449718[/C][C]20790.5135502819[/C][/ROW]
[ROW][C]122[/C][C]137544[/C][C]150835.780578326[/C][C]-13291.7805783263[/C][/ROW]
[ROW][C]123[/C][C]77166[/C][C]98698.3887047431[/C][C]-21532.3887047431[/C][/ROW]
[ROW][C]124[/C][C]74567[/C][C]89778.9719294701[/C][C]-15211.9719294701[/C][/ROW]
[ROW][C]125[/C][C]38214[/C][C]44896.7543136675[/C][C]-6682.75431366751[/C][/ROW]
[ROW][C]126[/C][C]90961[/C][C]121849.538337867[/C][C]-30888.5383378666[/C][/ROW]
[ROW][C]127[/C][C]194652[/C][C]185322.571149465[/C][C]9329.42885053484[/C][/ROW]
[ROW][C]128[/C][C]135261[/C][C]138868.541988232[/C][C]-3607.54198823187[/C][/ROW]
[ROW][C]129[/C][C]244272[/C][C]207611.579066716[/C][C]36660.4209332839[/C][/ROW]
[ROW][C]130[/C][C]201748[/C][C]231232.145243899[/C][C]-29484.1452438989[/C][/ROW]
[ROW][C]131[/C][C]256402[/C][C]249424.05522025[/C][C]6977.94477975018[/C][/ROW]
[ROW][C]132[/C][C]139144[/C][C]132312.796547094[/C][C]6831.20345290586[/C][/ROW]
[ROW][C]133[/C][C]76470[/C][C]93159.7576538772[/C][C]-16689.7576538772[/C][/ROW]
[ROW][C]134[/C][C]193518[/C][C]143271.340159267[/C][C]50246.6598407325[/C][/ROW]
[ROW][C]135[/C][C]280334[/C][C]186211.045699667[/C][C]94122.9543003328[/C][/ROW]
[ROW][C]136[/C][C]50999[/C][C]76844.4135433288[/C][C]-25845.4135433288[/C][/ROW]
[ROW][C]137[/C][C]253274[/C][C]194223.819680277[/C][C]59050.1803197231[/C][/ROW]
[ROW][C]138[/C][C]103239[/C][C]116860.282139112[/C][C]-13621.2821391124[/C][/ROW]
[ROW][C]139[/C][C]168059[/C][C]170988.458112572[/C][C]-2929.45811257218[/C][/ROW]
[ROW][C]140[/C][C]128768[/C][C]134901.465747812[/C][C]-6133.46574781197[/C][/ROW]
[ROW][C]141[/C][C]78256[/C][C]50289.6620142609[/C][C]27966.3379857391[/C][/ROW]
[ROW][C]142[/C][C]249232[/C][C]144904.564381331[/C][C]104327.435618669[/C][/ROW]
[ROW][C]143[/C][C]152366[/C][C]180572.63238163[/C][C]-28206.6323816298[/C][/ROW]
[ROW][C]144[/C][C]173260[/C][C]111574.299768427[/C][C]61685.7002315729[/C][/ROW]
[ROW][C]145[/C][C]197197[/C][C]190661.085857021[/C][C]6535.91414297928[/C][/ROW]
[ROW][C]146[/C][C]68388[/C][C]212762.944614392[/C][C]-144374.944614392[/C][/ROW]
[ROW][C]147[/C][C]139409[/C][C]163299.503387342[/C][C]-23890.5033873425[/C][/ROW]
[ROW][C]148[/C][C]185366[/C][C]222848.122416834[/C][C]-37482.1224168335[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]7842.47988328901[/C][C]-7842.47988328901[/C][/ROW]
[ROW][C]150[/C][C]14688[/C][C]13435.5517020418[/C][C]1252.44829795824[/C][/ROW]
[ROW][C]151[/C][C]98[/C][C]7842.47988328901[/C][C]-7744.47988328901[/C][/ROW]
[ROW][C]152[/C][C]455[/C][C]7842.47988328901[/C][C]-7387.47988328901[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]7842.47988328901[/C][C]-7842.47988328901[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]7842.47988328901[/C][C]-7842.47988328901[/C][/ROW]
[ROW][C]155[/C][C]137885[/C][C]107448.901540718[/C][C]30436.0984592819[/C][/ROW]
[ROW][C]156[/C][C]185288[/C][C]175259.679764281[/C][C]10028.3202357193[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]7842.47988328901[/C][C]-7842.47988328901[/C][/ROW]
[ROW][C]158[/C][C]203[/C][C]7842.47988328901[/C][C]-7639.47988328901[/C][/ROW]
[ROW][C]159[/C][C]7199[/C][C]15173.0158963332[/C][C]-7974.01589633323[/C][/ROW]
[ROW][C]160[/C][C]46660[/C][C]33139.0655043365[/C][C]13520.9344956635[/C][/ROW]
[ROW][C]161[/C][C]17547[/C][C]11502.804733162[/C][C]6044.19526683798[/C][/ROW]
[ROW][C]162[/C][C]73567[/C][C]100461.62107412[/C][C]-26894.62107412[/C][/ROW]
[ROW][C]163[/C][C]969[/C][C]7842.47988328901[/C][C]-6873.47988328901[/C][/ROW]
[ROW][C]164[/C][C]105477[/C][C]95593.7806354216[/C][C]9883.2193645784[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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
1170588161983.2455460858604.75445391489
286621126247.102787852-39626.1027878519
3113514134962.262225323-21448.2622253227
4152510189217.634475186-36707.6344751858
58620690772.1272691804-4566.12726918039
63725761862.0978633906-24605.0978633906
7306055192683.651548113371.348452
83275053792.2879447424-21042.2879447424
9116502113281.8980401443220.10195985588
10130539126705.5009712743833.49902872556
11161876159662.6950173362213.30498266405
12128274186521.852589769-58247.8525897686
13104367114815.515590157-10448.5155901568
14193024187072.9677610445951.03223895634
15141574145290.253971449-3716.25397144934
16253559238563.7624997714995.2375002295
17181110181663.057676191-553.05767619064
18198432150968.85209721647463.1479027845
19113853152528.136133551-38675.1361335506
20159940202658.601781078-42718.6017810778
21166822173700.08628737-6878.08628736973
22286675163014.143087946123660.856912054
239165794646.9025801754-2989.90258017536
24108278184239.877967425-75961.8779674246
25146342194346.913668476-48004.9136684763
26145142152549.692701789-7407.69270178909
27161740149968.91294523411771.087054766
28160905182765.506673746-21860.5066737456
29106888108645.976308799-1757.97630879886
30188150190451.944394269-2301.94439426939
31189401196229.705110339-6828.70511033894
32129484144553.439530803-15069.4395308027
33204030187425.41864977816604.5813502225
346853897295.7210969902-28757.7210969902
35243625187129.06942495656495.9305750444
36167255147446.66587695819808.3341230416
37264528168200.09473714196327.9052628591
38122024115627.8610860056396.13891399479
398096463221.866268390717742.1337316093
40209795171957.92549124737837.074508753
41224205160225.21665080463979.7833491962
42115971124744.599415831-8773.5994158309
43138191131137.8731728647053.12682713627
448110698438.8336881337-17332.8336881337
459312596980.9406028042-3855.94060280421
46305756164684.757905919141071.242094081
477880097735.8150115775-18935.8150115775
48158835182524.452186025-23689.452186025
49223590204041.58360134419548.4163986559
50131108175469.806505027-44361.8065050266
51128734139069.386373567-10335.3863735666
522418826262.6212934324-2074.6212934324
53257662174880.70943742782781.2905625732
546502982135.2679694291-17106.2679694291
559806679822.240104082218243.7598959178
56173587146243.39114244427343.6088575565
57180042212918.469975321-32876.469975321
58197266181820.83189969315445.1681003073
59212060209122.1943592792937.80564072099
60141582131152.04003969910429.9599603008
61245107208595.74879551336511.2512044871
62206879181726.20821568225152.7917843176
63145696138124.6192598567571.38074014354
64173535142410.4209902631124.57900974
65142064124890.46275486417173.5372451359
66117926117718.338768549207.661231450896
67113461143633.571296567-30172.5712965672
68145285156915.29011183-11630.2901118304
69150999182421.153330539-31422.1533305386
7091812167243.231253433-75431.2312534327
71118807139746.187599683-20939.187599683
726947184601.8815520083-15130.8815520083
73126630137091.703910751-10461.7039107514
74145908142968.2622942042939.73770579618
7598393173165.591343612-74772.5913436117
76190926186189.8640844254736.13591557476
77198797155123.68921584943673.310784151
78106193175578.371579779-69385.3715797794
7989318149069.864080695-59751.8640806953
80120362129165.98604189-8803.98604188966
8198791108757.151968292-9966.15196829166
82274953206957.42240398367995.5775960174
83132798100876.95120459831921.048795402
84135251163899.590776438-28648.5907764382
858095391454.9164044311-10501.9164044312
8610923786387.156280531222849.8437194688
8796634128821.661564118-32187.661564118
88226191130236.82144587295954.178554128
89171286135866.68428916435419.3157108363
90117815208505.578056249-90690.5780562488
91133561110128.32924199723432.670758003
92152193199908.352844389-47715.3528443887
9311200496528.329671389515475.6703286105
94169613159352.23271029510260.7672897049
95187483137144.21433954750338.7856604528
96130533145139.641029446-14606.6410294459
97142339165675.516664644-23336.5166646443
98189764215489.366097449-25725.3660974488
99201744225597.809841028-23853.8098410284
100246834200117.6155982946716.38440171
101155947181568.349039599-25621.3490395993
102182581205996.902998825-23415.9029988253
10310635190349.473888655716001.5261113443
1044328779791.8188247644-36504.8188247644
105127493117940.8183911029552.18160889837
106127930146430.153894491-18500.1538944915
107149006160568.982161208-11562.9821612078
108187714193867.148447761-6153.14844776059
10974112129227.440255775-55115.4402557746
11094006161036.585872875-67030.5858728751
111176625141348.49684488535276.503155115
112141933174911.134486221-32978.1344862211
1132293821913.29517846161024.7048215384
114125927163925.774796043-37998.7747960428
1156185763565.9278655603-1708.92786556032
11691290132737.675434778-41447.675434778
117255100167028.46151045988071.5384895405
1182105412008.0365789979045.96342100303
119169102124829.45720668244272.5427933178
1203141446489.7513961164-15075.7513961164
121188701167910.48644971820790.5135502819
122137544150835.780578326-13291.7805783263
1237716698698.3887047431-21532.3887047431
1247456789778.9719294701-15211.9719294701
1253821444896.7543136675-6682.75431366751
12690961121849.538337867-30888.5383378666
127194652185322.5711494659329.42885053484
128135261138868.541988232-3607.54198823187
129244272207611.57906671636660.4209332839
130201748231232.145243899-29484.1452438989
131256402249424.055220256977.94477975018
132139144132312.7965470946831.20345290586
1337647093159.7576538772-16689.7576538772
134193518143271.34015926750246.6598407325
135280334186211.04569966794122.9543003328
1365099976844.4135433288-25845.4135433288
137253274194223.81968027759050.1803197231
138103239116860.282139112-13621.2821391124
139168059170988.458112572-2929.45811257218
140128768134901.465747812-6133.46574781197
1417825650289.662014260927966.3379857391
142249232144904.564381331104327.435618669
143152366180572.63238163-28206.6323816298
144173260111574.29976842761685.7002315729
145197197190661.0858570216535.91414297928
14668388212762.944614392-144374.944614392
147139409163299.503387342-23890.5033873425
148185366222848.122416834-37482.1224168335
14907842.47988328901-7842.47988328901
1501468813435.55170204181252.44829795824
151987842.47988328901-7744.47988328901
1524557842.47988328901-7387.47988328901
15307842.47988328901-7842.47988328901
15407842.47988328901-7842.47988328901
155137885107448.90154071830436.0984592819
156185288175259.67976428110028.3202357193
15707842.47988328901-7842.47988328901
1582037842.47988328901-7639.47988328901
159719915173.0158963332-7974.01589633323
1604666033139.065504336513520.9344956635
1611754711502.8047331626044.19526683798
16273567100461.62107412-26894.62107412
1639697842.47988328901-6873.47988328901
16410547795593.78063542169883.2193645784







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.4389095883470560.8778191766941120.561090411652944
90.3251938429306510.6503876858613020.674806157069349
100.2119271588707960.4238543177415930.788072841129204
110.6156005981787590.7687988036424820.384399401821241
120.5126558431728580.9746883136542840.487344156827142
130.4152885590409830.8305771180819660.584711440959017
140.4207865173151280.8415730346302560.579213482684872
150.3415718452542170.6831436905084340.658428154745783
160.258347461978980.5166949239579610.74165253802102
170.2124304071678370.4248608143356740.787569592832163
180.1836945301710.3673890603420010.816305469829
190.3063447141619710.6126894283239430.693655285838029
200.3238446903879110.6476893807758220.676155309612089
210.2582890352677540.5165780705355080.741710964732246
220.8118535666624060.3762928666751880.188146433337594
230.7663151358012050.467369728397590.233684864198795
240.8432964231167020.3134071537665970.156703576883298
250.8686000373048520.2627999253902970.131399962695148
260.8307978430923130.3384043138153740.169202156907687
270.7877298961171120.4245402077657760.212270103882888
280.7499844829413320.5000310341173350.250015517058667
290.7043449507246560.5913100985506870.295655049275344
300.6490567674585310.7018864650829390.350943232541469
310.5917575322899240.8164849354201510.408242467710076
320.5451545660684020.9096908678631960.454845433931598
330.5071348565144650.9857302869710690.492865143485535
340.4572595581658790.9145191163317570.542740441834121
350.5095259227949740.9809481544100520.490474077205026
360.4669448937886950.933889787577390.533055106211305
370.7627454514859330.4745090970281330.237254548514067
380.7187965081434570.5624069837130870.281203491856543
390.6750087855702980.6499824288594040.324991214429702
400.6542793086649360.6914413826701280.345720691335064
410.7035785178159990.5928429643680020.296421482184001
420.6568554046627840.6862891906744320.343144595337216
430.6077560800324770.7844878399350450.392243919967522
440.560822448318170.878355103363660.43917755168183
450.509249208196390.981501583607220.49075079180361
460.9072935666662550.185412866667490.0927064333337449
470.8880064123214960.2239871753570090.111993587678504
480.8713664733803410.2572670532393180.128633526619659
490.8461953913278170.3076092173443660.153804608672183
500.8483793148171930.3032413703656150.151620685182807
510.8249313963951650.3501372072096710.175068603604835
520.7914088223039420.4171823553921150.208591177696058
530.8794514734988730.2410970530022530.120548526501127
540.8558996528157580.2882006943684850.144100347184242
550.8340410297875730.3319179404248550.165958970212427
560.8115756595852510.3768486808294980.188424340414749
570.8061335287179910.3877329425640180.193866471282009
580.7784945486596450.443010902680710.221505451340355
590.7501226754513750.4997546490972510.249877324548625
600.7133888109471620.5732223781056750.286611189052838
610.6938707743365540.6122584513268920.306129225663446
620.6724977590958840.6550044818082310.327502240904115
630.6339989638341140.7320020723317720.366001036165886
640.616274601851040.767450796297920.38372539814896
650.5772336175391990.8455327649216020.422766382460801
660.5336225496352880.9327549007294250.466377450364712
670.5359072302901120.9281855394197760.464092769709888
680.4929479694453530.9858959388907060.507052030554647
690.4865924731568480.9731849463136970.513407526843152
700.6107881938612880.7784236122774240.389211806138712
710.5772590866387830.8454818267224340.422740913361217
720.5365675820779560.9268648358440890.463432417922044
730.4931192758574560.9862385517149120.506880724142544
740.4478605711785870.8957211423571750.552139428821413
750.551100381605580.8977992367888410.44889961839442
760.5062709343610290.9874581312779420.493729065638971
770.5096324953009940.9807350093980120.490367504699006
780.6622158574196440.6755682851607120.337784142580356
790.7063652770144720.5872694459710560.293634722985528
800.6677401147231720.6645197705536550.332259885276828
810.6273619764257610.7452760471484780.372638023574239
820.6955349501199430.6089300997601150.304465049880057
830.6847455831171060.6305088337657870.315254416882894
840.667246587705770.6655068245884610.33275341229423
850.6264584060520570.7470831878958850.373541593947943
860.598046453251440.803907093497120.40195354674856
870.5793918863188910.8412162273622180.420608113681109
880.7695349409978040.4609301180043910.230465059002196
890.7598841973004930.4802316053990140.240115802699507
900.876605487094270.2467890258114590.12339451290573
910.8618863986000250.2762272027999490.138113601399975
920.8720364055444820.2559271889110370.127963594455518
930.8515631312452380.2968737375095250.148436868754762
940.8250423010621490.3499153978757020.174957698937851
950.8440495762364110.3119008475271780.155950423763589
960.81840199977590.3631960004481990.1815980002241
970.7990118052284430.4019763895431130.200988194771557
980.7846279042001920.4307441915996150.215372095799808
990.7586368066199390.4827263867601220.241363193380061
1000.7626550158665350.474689968266930.237344984133465
1010.7381579329941190.5236841340117620.261842067005881
1020.7143491473433510.5713017053132980.285650852656649
1030.6843911110452320.6312177779095360.315608888954768
1040.6705547636314170.6588904727371650.329445236368583
1050.6290521129864440.7418957740271120.370947887013556
1060.5907559056279410.8184881887441180.409244094372059
1070.5475787914008220.9048424171983570.452421208599178
1080.5021055339150770.9957889321698460.497894466084923
1090.6216493424588410.7567013150823180.378350657541159
1100.6869110624731790.6261778750536420.313088937526821
1110.666067455473810.667865089052380.33393254452619
1120.6470688154352050.705862369129590.352931184564795
1130.5994910560685110.8010178878629770.400508943931489
1140.6008219215659080.7983561568681830.399178078434092
1150.5511920359693460.8976159280613080.448807964030654
1160.5371632405040050.9256735189919910.462836759495995
1170.6885658647010050.622868270597990.311434135298995
1180.6431297411453170.7137405177093650.356870258854683
1190.6312799815890460.7374400368219080.368720018410954
1200.5870043291461590.8259913417076820.412995670853841
1210.5488615395826080.9022769208347840.451138460417392
1220.5036458878625040.9927082242749920.496354112137496
1230.4693753541896070.9387507083792140.530624645810393
1240.4320790038373410.8641580076746810.567920996162659
1250.3797925896823660.7595851793647310.620207410317634
1260.3675213587021010.7350427174042010.632478641297899
1270.3194928086252310.6389856172504620.680507191374769
1280.2781917536308660.5563835072617320.721808246369134
1290.2820984516838110.5641969033676210.717901548316189
1300.2487382239943880.4974764479887750.751261776005613
1310.2156058352597270.4312116705194530.784394164740273
1320.1760740534626090.3521481069252180.823925946537391
1330.1474826157556880.2949652315113760.852517384244312
1340.1556263081628370.3112526163256750.844373691837162
1350.3578397820710830.7156795641421660.642160217928917
1360.3640795392307550.7281590784615110.635920460769245
1370.4640843240493350.9281686480986710.535915675950665
1380.4174154009863370.8348308019726750.582584599013663
1390.3685803485058640.7371606970117280.631419651494136
1400.3063339994654480.6126679989308960.693666000534552
1410.260146669877630.520293339755260.73985333012237
1420.7120455848279790.5759088303440430.287954415172021
1430.6528982107737190.6942035784525620.347101789226281
1440.9351288816985550.1297422366028910.0648711183014454
1450.9916430140578740.01671397188425180.00835698594212592
1460.9997983970091880.0004032059816242570.000201602990812128
1470.9994921715941660.001015656811668920.000507828405834459
1480.9999999997336715.32657153635605e-102.66328576817802e-10
1490.9999999974946985.01060462011716e-092.50530231005858e-09
1500.9999999881967882.36064246354537e-081.18032123177268e-08
1510.9999998835232262.32953548502702e-071.16476774251351e-07
1520.999998878279022.24344195940651e-061.12172097970326e-06
1530.9999903303053541.93393892925444e-059.66969464627221e-06
1540.9999228346258320.0001543307483350697.71653741675344e-05
1550.9999910032942041.79934115913923e-058.99670579569614e-06
1560.9999475964751590.0001048070496829155.24035248414576e-05

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.438909588347056 & 0.877819176694112 & 0.561090411652944 \tabularnewline
9 & 0.325193842930651 & 0.650387685861302 & 0.674806157069349 \tabularnewline
10 & 0.211927158870796 & 0.423854317741593 & 0.788072841129204 \tabularnewline
11 & 0.615600598178759 & 0.768798803642482 & 0.384399401821241 \tabularnewline
12 & 0.512655843172858 & 0.974688313654284 & 0.487344156827142 \tabularnewline
13 & 0.415288559040983 & 0.830577118081966 & 0.584711440959017 \tabularnewline
14 & 0.420786517315128 & 0.841573034630256 & 0.579213482684872 \tabularnewline
15 & 0.341571845254217 & 0.683143690508434 & 0.658428154745783 \tabularnewline
16 & 0.25834746197898 & 0.516694923957961 & 0.74165253802102 \tabularnewline
17 & 0.212430407167837 & 0.424860814335674 & 0.787569592832163 \tabularnewline
18 & 0.183694530171 & 0.367389060342001 & 0.816305469829 \tabularnewline
19 & 0.306344714161971 & 0.612689428323943 & 0.693655285838029 \tabularnewline
20 & 0.323844690387911 & 0.647689380775822 & 0.676155309612089 \tabularnewline
21 & 0.258289035267754 & 0.516578070535508 & 0.741710964732246 \tabularnewline
22 & 0.811853566662406 & 0.376292866675188 & 0.188146433337594 \tabularnewline
23 & 0.766315135801205 & 0.46736972839759 & 0.233684864198795 \tabularnewline
24 & 0.843296423116702 & 0.313407153766597 & 0.156703576883298 \tabularnewline
25 & 0.868600037304852 & 0.262799925390297 & 0.131399962695148 \tabularnewline
26 & 0.830797843092313 & 0.338404313815374 & 0.169202156907687 \tabularnewline
27 & 0.787729896117112 & 0.424540207765776 & 0.212270103882888 \tabularnewline
28 & 0.749984482941332 & 0.500031034117335 & 0.250015517058667 \tabularnewline
29 & 0.704344950724656 & 0.591310098550687 & 0.295655049275344 \tabularnewline
30 & 0.649056767458531 & 0.701886465082939 & 0.350943232541469 \tabularnewline
31 & 0.591757532289924 & 0.816484935420151 & 0.408242467710076 \tabularnewline
32 & 0.545154566068402 & 0.909690867863196 & 0.454845433931598 \tabularnewline
33 & 0.507134856514465 & 0.985730286971069 & 0.492865143485535 \tabularnewline
34 & 0.457259558165879 & 0.914519116331757 & 0.542740441834121 \tabularnewline
35 & 0.509525922794974 & 0.980948154410052 & 0.490474077205026 \tabularnewline
36 & 0.466944893788695 & 0.93388978757739 & 0.533055106211305 \tabularnewline
37 & 0.762745451485933 & 0.474509097028133 & 0.237254548514067 \tabularnewline
38 & 0.718796508143457 & 0.562406983713087 & 0.281203491856543 \tabularnewline
39 & 0.675008785570298 & 0.649982428859404 & 0.324991214429702 \tabularnewline
40 & 0.654279308664936 & 0.691441382670128 & 0.345720691335064 \tabularnewline
41 & 0.703578517815999 & 0.592842964368002 & 0.296421482184001 \tabularnewline
42 & 0.656855404662784 & 0.686289190674432 & 0.343144595337216 \tabularnewline
43 & 0.607756080032477 & 0.784487839935045 & 0.392243919967522 \tabularnewline
44 & 0.56082244831817 & 0.87835510336366 & 0.43917755168183 \tabularnewline
45 & 0.50924920819639 & 0.98150158360722 & 0.49075079180361 \tabularnewline
46 & 0.907293566666255 & 0.18541286666749 & 0.0927064333337449 \tabularnewline
47 & 0.888006412321496 & 0.223987175357009 & 0.111993587678504 \tabularnewline
48 & 0.871366473380341 & 0.257267053239318 & 0.128633526619659 \tabularnewline
49 & 0.846195391327817 & 0.307609217344366 & 0.153804608672183 \tabularnewline
50 & 0.848379314817193 & 0.303241370365615 & 0.151620685182807 \tabularnewline
51 & 0.824931396395165 & 0.350137207209671 & 0.175068603604835 \tabularnewline
52 & 0.791408822303942 & 0.417182355392115 & 0.208591177696058 \tabularnewline
53 & 0.879451473498873 & 0.241097053002253 & 0.120548526501127 \tabularnewline
54 & 0.855899652815758 & 0.288200694368485 & 0.144100347184242 \tabularnewline
55 & 0.834041029787573 & 0.331917940424855 & 0.165958970212427 \tabularnewline
56 & 0.811575659585251 & 0.376848680829498 & 0.188424340414749 \tabularnewline
57 & 0.806133528717991 & 0.387732942564018 & 0.193866471282009 \tabularnewline
58 & 0.778494548659645 & 0.44301090268071 & 0.221505451340355 \tabularnewline
59 & 0.750122675451375 & 0.499754649097251 & 0.249877324548625 \tabularnewline
60 & 0.713388810947162 & 0.573222378105675 & 0.286611189052838 \tabularnewline
61 & 0.693870774336554 & 0.612258451326892 & 0.306129225663446 \tabularnewline
62 & 0.672497759095884 & 0.655004481808231 & 0.327502240904115 \tabularnewline
63 & 0.633998963834114 & 0.732002072331772 & 0.366001036165886 \tabularnewline
64 & 0.61627460185104 & 0.76745079629792 & 0.38372539814896 \tabularnewline
65 & 0.577233617539199 & 0.845532764921602 & 0.422766382460801 \tabularnewline
66 & 0.533622549635288 & 0.932754900729425 & 0.466377450364712 \tabularnewline
67 & 0.535907230290112 & 0.928185539419776 & 0.464092769709888 \tabularnewline
68 & 0.492947969445353 & 0.985895938890706 & 0.507052030554647 \tabularnewline
69 & 0.486592473156848 & 0.973184946313697 & 0.513407526843152 \tabularnewline
70 & 0.610788193861288 & 0.778423612277424 & 0.389211806138712 \tabularnewline
71 & 0.577259086638783 & 0.845481826722434 & 0.422740913361217 \tabularnewline
72 & 0.536567582077956 & 0.926864835844089 & 0.463432417922044 \tabularnewline
73 & 0.493119275857456 & 0.986238551714912 & 0.506880724142544 \tabularnewline
74 & 0.447860571178587 & 0.895721142357175 & 0.552139428821413 \tabularnewline
75 & 0.55110038160558 & 0.897799236788841 & 0.44889961839442 \tabularnewline
76 & 0.506270934361029 & 0.987458131277942 & 0.493729065638971 \tabularnewline
77 & 0.509632495300994 & 0.980735009398012 & 0.490367504699006 \tabularnewline
78 & 0.662215857419644 & 0.675568285160712 & 0.337784142580356 \tabularnewline
79 & 0.706365277014472 & 0.587269445971056 & 0.293634722985528 \tabularnewline
80 & 0.667740114723172 & 0.664519770553655 & 0.332259885276828 \tabularnewline
81 & 0.627361976425761 & 0.745276047148478 & 0.372638023574239 \tabularnewline
82 & 0.695534950119943 & 0.608930099760115 & 0.304465049880057 \tabularnewline
83 & 0.684745583117106 & 0.630508833765787 & 0.315254416882894 \tabularnewline
84 & 0.66724658770577 & 0.665506824588461 & 0.33275341229423 \tabularnewline
85 & 0.626458406052057 & 0.747083187895885 & 0.373541593947943 \tabularnewline
86 & 0.59804645325144 & 0.80390709349712 & 0.40195354674856 \tabularnewline
87 & 0.579391886318891 & 0.841216227362218 & 0.420608113681109 \tabularnewline
88 & 0.769534940997804 & 0.460930118004391 & 0.230465059002196 \tabularnewline
89 & 0.759884197300493 & 0.480231605399014 & 0.240115802699507 \tabularnewline
90 & 0.87660548709427 & 0.246789025811459 & 0.12339451290573 \tabularnewline
91 & 0.861886398600025 & 0.276227202799949 & 0.138113601399975 \tabularnewline
92 & 0.872036405544482 & 0.255927188911037 & 0.127963594455518 \tabularnewline
93 & 0.851563131245238 & 0.296873737509525 & 0.148436868754762 \tabularnewline
94 & 0.825042301062149 & 0.349915397875702 & 0.174957698937851 \tabularnewline
95 & 0.844049576236411 & 0.311900847527178 & 0.155950423763589 \tabularnewline
96 & 0.8184019997759 & 0.363196000448199 & 0.1815980002241 \tabularnewline
97 & 0.799011805228443 & 0.401976389543113 & 0.200988194771557 \tabularnewline
98 & 0.784627904200192 & 0.430744191599615 & 0.215372095799808 \tabularnewline
99 & 0.758636806619939 & 0.482726386760122 & 0.241363193380061 \tabularnewline
100 & 0.762655015866535 & 0.47468996826693 & 0.237344984133465 \tabularnewline
101 & 0.738157932994119 & 0.523684134011762 & 0.261842067005881 \tabularnewline
102 & 0.714349147343351 & 0.571301705313298 & 0.285650852656649 \tabularnewline
103 & 0.684391111045232 & 0.631217777909536 & 0.315608888954768 \tabularnewline
104 & 0.670554763631417 & 0.658890472737165 & 0.329445236368583 \tabularnewline
105 & 0.629052112986444 & 0.741895774027112 & 0.370947887013556 \tabularnewline
106 & 0.590755905627941 & 0.818488188744118 & 0.409244094372059 \tabularnewline
107 & 0.547578791400822 & 0.904842417198357 & 0.452421208599178 \tabularnewline
108 & 0.502105533915077 & 0.995788932169846 & 0.497894466084923 \tabularnewline
109 & 0.621649342458841 & 0.756701315082318 & 0.378350657541159 \tabularnewline
110 & 0.686911062473179 & 0.626177875053642 & 0.313088937526821 \tabularnewline
111 & 0.66606745547381 & 0.66786508905238 & 0.33393254452619 \tabularnewline
112 & 0.647068815435205 & 0.70586236912959 & 0.352931184564795 \tabularnewline
113 & 0.599491056068511 & 0.801017887862977 & 0.400508943931489 \tabularnewline
114 & 0.600821921565908 & 0.798356156868183 & 0.399178078434092 \tabularnewline
115 & 0.551192035969346 & 0.897615928061308 & 0.448807964030654 \tabularnewline
116 & 0.537163240504005 & 0.925673518991991 & 0.462836759495995 \tabularnewline
117 & 0.688565864701005 & 0.62286827059799 & 0.311434135298995 \tabularnewline
118 & 0.643129741145317 & 0.713740517709365 & 0.356870258854683 \tabularnewline
119 & 0.631279981589046 & 0.737440036821908 & 0.368720018410954 \tabularnewline
120 & 0.587004329146159 & 0.825991341707682 & 0.412995670853841 \tabularnewline
121 & 0.548861539582608 & 0.902276920834784 & 0.451138460417392 \tabularnewline
122 & 0.503645887862504 & 0.992708224274992 & 0.496354112137496 \tabularnewline
123 & 0.469375354189607 & 0.938750708379214 & 0.530624645810393 \tabularnewline
124 & 0.432079003837341 & 0.864158007674681 & 0.567920996162659 \tabularnewline
125 & 0.379792589682366 & 0.759585179364731 & 0.620207410317634 \tabularnewline
126 & 0.367521358702101 & 0.735042717404201 & 0.632478641297899 \tabularnewline
127 & 0.319492808625231 & 0.638985617250462 & 0.680507191374769 \tabularnewline
128 & 0.278191753630866 & 0.556383507261732 & 0.721808246369134 \tabularnewline
129 & 0.282098451683811 & 0.564196903367621 & 0.717901548316189 \tabularnewline
130 & 0.248738223994388 & 0.497476447988775 & 0.751261776005613 \tabularnewline
131 & 0.215605835259727 & 0.431211670519453 & 0.784394164740273 \tabularnewline
132 & 0.176074053462609 & 0.352148106925218 & 0.823925946537391 \tabularnewline
133 & 0.147482615755688 & 0.294965231511376 & 0.852517384244312 \tabularnewline
134 & 0.155626308162837 & 0.311252616325675 & 0.844373691837162 \tabularnewline
135 & 0.357839782071083 & 0.715679564142166 & 0.642160217928917 \tabularnewline
136 & 0.364079539230755 & 0.728159078461511 & 0.635920460769245 \tabularnewline
137 & 0.464084324049335 & 0.928168648098671 & 0.535915675950665 \tabularnewline
138 & 0.417415400986337 & 0.834830801972675 & 0.582584599013663 \tabularnewline
139 & 0.368580348505864 & 0.737160697011728 & 0.631419651494136 \tabularnewline
140 & 0.306333999465448 & 0.612667998930896 & 0.693666000534552 \tabularnewline
141 & 0.26014666987763 & 0.52029333975526 & 0.73985333012237 \tabularnewline
142 & 0.712045584827979 & 0.575908830344043 & 0.287954415172021 \tabularnewline
143 & 0.652898210773719 & 0.694203578452562 & 0.347101789226281 \tabularnewline
144 & 0.935128881698555 & 0.129742236602891 & 0.0648711183014454 \tabularnewline
145 & 0.991643014057874 & 0.0167139718842518 & 0.00835698594212592 \tabularnewline
146 & 0.999798397009188 & 0.000403205981624257 & 0.000201602990812128 \tabularnewline
147 & 0.999492171594166 & 0.00101565681166892 & 0.000507828405834459 \tabularnewline
148 & 0.999999999733671 & 5.32657153635605e-10 & 2.66328576817802e-10 \tabularnewline
149 & 0.999999997494698 & 5.01060462011716e-09 & 2.50530231005858e-09 \tabularnewline
150 & 0.999999988196788 & 2.36064246354537e-08 & 1.18032123177268e-08 \tabularnewline
151 & 0.999999883523226 & 2.32953548502702e-07 & 1.16476774251351e-07 \tabularnewline
152 & 0.99999887827902 & 2.24344195940651e-06 & 1.12172097970326e-06 \tabularnewline
153 & 0.999990330305354 & 1.93393892925444e-05 & 9.66969464627221e-06 \tabularnewline
154 & 0.999922834625832 & 0.000154330748335069 & 7.71653741675344e-05 \tabularnewline
155 & 0.999991003294204 & 1.79934115913923e-05 & 8.99670579569614e-06 \tabularnewline
156 & 0.999947596475159 & 0.000104807049682915 & 5.24035248414576e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&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]0.438909588347056[/C][C]0.877819176694112[/C][C]0.561090411652944[/C][/ROW]
[ROW][C]9[/C][C]0.325193842930651[/C][C]0.650387685861302[/C][C]0.674806157069349[/C][/ROW]
[ROW][C]10[/C][C]0.211927158870796[/C][C]0.423854317741593[/C][C]0.788072841129204[/C][/ROW]
[ROW][C]11[/C][C]0.615600598178759[/C][C]0.768798803642482[/C][C]0.384399401821241[/C][/ROW]
[ROW][C]12[/C][C]0.512655843172858[/C][C]0.974688313654284[/C][C]0.487344156827142[/C][/ROW]
[ROW][C]13[/C][C]0.415288559040983[/C][C]0.830577118081966[/C][C]0.584711440959017[/C][/ROW]
[ROW][C]14[/C][C]0.420786517315128[/C][C]0.841573034630256[/C][C]0.579213482684872[/C][/ROW]
[ROW][C]15[/C][C]0.341571845254217[/C][C]0.683143690508434[/C][C]0.658428154745783[/C][/ROW]
[ROW][C]16[/C][C]0.25834746197898[/C][C]0.516694923957961[/C][C]0.74165253802102[/C][/ROW]
[ROW][C]17[/C][C]0.212430407167837[/C][C]0.424860814335674[/C][C]0.787569592832163[/C][/ROW]
[ROW][C]18[/C][C]0.183694530171[/C][C]0.367389060342001[/C][C]0.816305469829[/C][/ROW]
[ROW][C]19[/C][C]0.306344714161971[/C][C]0.612689428323943[/C][C]0.693655285838029[/C][/ROW]
[ROW][C]20[/C][C]0.323844690387911[/C][C]0.647689380775822[/C][C]0.676155309612089[/C][/ROW]
[ROW][C]21[/C][C]0.258289035267754[/C][C]0.516578070535508[/C][C]0.741710964732246[/C][/ROW]
[ROW][C]22[/C][C]0.811853566662406[/C][C]0.376292866675188[/C][C]0.188146433337594[/C][/ROW]
[ROW][C]23[/C][C]0.766315135801205[/C][C]0.46736972839759[/C][C]0.233684864198795[/C][/ROW]
[ROW][C]24[/C][C]0.843296423116702[/C][C]0.313407153766597[/C][C]0.156703576883298[/C][/ROW]
[ROW][C]25[/C][C]0.868600037304852[/C][C]0.262799925390297[/C][C]0.131399962695148[/C][/ROW]
[ROW][C]26[/C][C]0.830797843092313[/C][C]0.338404313815374[/C][C]0.169202156907687[/C][/ROW]
[ROW][C]27[/C][C]0.787729896117112[/C][C]0.424540207765776[/C][C]0.212270103882888[/C][/ROW]
[ROW][C]28[/C][C]0.749984482941332[/C][C]0.500031034117335[/C][C]0.250015517058667[/C][/ROW]
[ROW][C]29[/C][C]0.704344950724656[/C][C]0.591310098550687[/C][C]0.295655049275344[/C][/ROW]
[ROW][C]30[/C][C]0.649056767458531[/C][C]0.701886465082939[/C][C]0.350943232541469[/C][/ROW]
[ROW][C]31[/C][C]0.591757532289924[/C][C]0.816484935420151[/C][C]0.408242467710076[/C][/ROW]
[ROW][C]32[/C][C]0.545154566068402[/C][C]0.909690867863196[/C][C]0.454845433931598[/C][/ROW]
[ROW][C]33[/C][C]0.507134856514465[/C][C]0.985730286971069[/C][C]0.492865143485535[/C][/ROW]
[ROW][C]34[/C][C]0.457259558165879[/C][C]0.914519116331757[/C][C]0.542740441834121[/C][/ROW]
[ROW][C]35[/C][C]0.509525922794974[/C][C]0.980948154410052[/C][C]0.490474077205026[/C][/ROW]
[ROW][C]36[/C][C]0.466944893788695[/C][C]0.93388978757739[/C][C]0.533055106211305[/C][/ROW]
[ROW][C]37[/C][C]0.762745451485933[/C][C]0.474509097028133[/C][C]0.237254548514067[/C][/ROW]
[ROW][C]38[/C][C]0.718796508143457[/C][C]0.562406983713087[/C][C]0.281203491856543[/C][/ROW]
[ROW][C]39[/C][C]0.675008785570298[/C][C]0.649982428859404[/C][C]0.324991214429702[/C][/ROW]
[ROW][C]40[/C][C]0.654279308664936[/C][C]0.691441382670128[/C][C]0.345720691335064[/C][/ROW]
[ROW][C]41[/C][C]0.703578517815999[/C][C]0.592842964368002[/C][C]0.296421482184001[/C][/ROW]
[ROW][C]42[/C][C]0.656855404662784[/C][C]0.686289190674432[/C][C]0.343144595337216[/C][/ROW]
[ROW][C]43[/C][C]0.607756080032477[/C][C]0.784487839935045[/C][C]0.392243919967522[/C][/ROW]
[ROW][C]44[/C][C]0.56082244831817[/C][C]0.87835510336366[/C][C]0.43917755168183[/C][/ROW]
[ROW][C]45[/C][C]0.50924920819639[/C][C]0.98150158360722[/C][C]0.49075079180361[/C][/ROW]
[ROW][C]46[/C][C]0.907293566666255[/C][C]0.18541286666749[/C][C]0.0927064333337449[/C][/ROW]
[ROW][C]47[/C][C]0.888006412321496[/C][C]0.223987175357009[/C][C]0.111993587678504[/C][/ROW]
[ROW][C]48[/C][C]0.871366473380341[/C][C]0.257267053239318[/C][C]0.128633526619659[/C][/ROW]
[ROW][C]49[/C][C]0.846195391327817[/C][C]0.307609217344366[/C][C]0.153804608672183[/C][/ROW]
[ROW][C]50[/C][C]0.848379314817193[/C][C]0.303241370365615[/C][C]0.151620685182807[/C][/ROW]
[ROW][C]51[/C][C]0.824931396395165[/C][C]0.350137207209671[/C][C]0.175068603604835[/C][/ROW]
[ROW][C]52[/C][C]0.791408822303942[/C][C]0.417182355392115[/C][C]0.208591177696058[/C][/ROW]
[ROW][C]53[/C][C]0.879451473498873[/C][C]0.241097053002253[/C][C]0.120548526501127[/C][/ROW]
[ROW][C]54[/C][C]0.855899652815758[/C][C]0.288200694368485[/C][C]0.144100347184242[/C][/ROW]
[ROW][C]55[/C][C]0.834041029787573[/C][C]0.331917940424855[/C][C]0.165958970212427[/C][/ROW]
[ROW][C]56[/C][C]0.811575659585251[/C][C]0.376848680829498[/C][C]0.188424340414749[/C][/ROW]
[ROW][C]57[/C][C]0.806133528717991[/C][C]0.387732942564018[/C][C]0.193866471282009[/C][/ROW]
[ROW][C]58[/C][C]0.778494548659645[/C][C]0.44301090268071[/C][C]0.221505451340355[/C][/ROW]
[ROW][C]59[/C][C]0.750122675451375[/C][C]0.499754649097251[/C][C]0.249877324548625[/C][/ROW]
[ROW][C]60[/C][C]0.713388810947162[/C][C]0.573222378105675[/C][C]0.286611189052838[/C][/ROW]
[ROW][C]61[/C][C]0.693870774336554[/C][C]0.612258451326892[/C][C]0.306129225663446[/C][/ROW]
[ROW][C]62[/C][C]0.672497759095884[/C][C]0.655004481808231[/C][C]0.327502240904115[/C][/ROW]
[ROW][C]63[/C][C]0.633998963834114[/C][C]0.732002072331772[/C][C]0.366001036165886[/C][/ROW]
[ROW][C]64[/C][C]0.61627460185104[/C][C]0.76745079629792[/C][C]0.38372539814896[/C][/ROW]
[ROW][C]65[/C][C]0.577233617539199[/C][C]0.845532764921602[/C][C]0.422766382460801[/C][/ROW]
[ROW][C]66[/C][C]0.533622549635288[/C][C]0.932754900729425[/C][C]0.466377450364712[/C][/ROW]
[ROW][C]67[/C][C]0.535907230290112[/C][C]0.928185539419776[/C][C]0.464092769709888[/C][/ROW]
[ROW][C]68[/C][C]0.492947969445353[/C][C]0.985895938890706[/C][C]0.507052030554647[/C][/ROW]
[ROW][C]69[/C][C]0.486592473156848[/C][C]0.973184946313697[/C][C]0.513407526843152[/C][/ROW]
[ROW][C]70[/C][C]0.610788193861288[/C][C]0.778423612277424[/C][C]0.389211806138712[/C][/ROW]
[ROW][C]71[/C][C]0.577259086638783[/C][C]0.845481826722434[/C][C]0.422740913361217[/C][/ROW]
[ROW][C]72[/C][C]0.536567582077956[/C][C]0.926864835844089[/C][C]0.463432417922044[/C][/ROW]
[ROW][C]73[/C][C]0.493119275857456[/C][C]0.986238551714912[/C][C]0.506880724142544[/C][/ROW]
[ROW][C]74[/C][C]0.447860571178587[/C][C]0.895721142357175[/C][C]0.552139428821413[/C][/ROW]
[ROW][C]75[/C][C]0.55110038160558[/C][C]0.897799236788841[/C][C]0.44889961839442[/C][/ROW]
[ROW][C]76[/C][C]0.506270934361029[/C][C]0.987458131277942[/C][C]0.493729065638971[/C][/ROW]
[ROW][C]77[/C][C]0.509632495300994[/C][C]0.980735009398012[/C][C]0.490367504699006[/C][/ROW]
[ROW][C]78[/C][C]0.662215857419644[/C][C]0.675568285160712[/C][C]0.337784142580356[/C][/ROW]
[ROW][C]79[/C][C]0.706365277014472[/C][C]0.587269445971056[/C][C]0.293634722985528[/C][/ROW]
[ROW][C]80[/C][C]0.667740114723172[/C][C]0.664519770553655[/C][C]0.332259885276828[/C][/ROW]
[ROW][C]81[/C][C]0.627361976425761[/C][C]0.745276047148478[/C][C]0.372638023574239[/C][/ROW]
[ROW][C]82[/C][C]0.695534950119943[/C][C]0.608930099760115[/C][C]0.304465049880057[/C][/ROW]
[ROW][C]83[/C][C]0.684745583117106[/C][C]0.630508833765787[/C][C]0.315254416882894[/C][/ROW]
[ROW][C]84[/C][C]0.66724658770577[/C][C]0.665506824588461[/C][C]0.33275341229423[/C][/ROW]
[ROW][C]85[/C][C]0.626458406052057[/C][C]0.747083187895885[/C][C]0.373541593947943[/C][/ROW]
[ROW][C]86[/C][C]0.59804645325144[/C][C]0.80390709349712[/C][C]0.40195354674856[/C][/ROW]
[ROW][C]87[/C][C]0.579391886318891[/C][C]0.841216227362218[/C][C]0.420608113681109[/C][/ROW]
[ROW][C]88[/C][C]0.769534940997804[/C][C]0.460930118004391[/C][C]0.230465059002196[/C][/ROW]
[ROW][C]89[/C][C]0.759884197300493[/C][C]0.480231605399014[/C][C]0.240115802699507[/C][/ROW]
[ROW][C]90[/C][C]0.87660548709427[/C][C]0.246789025811459[/C][C]0.12339451290573[/C][/ROW]
[ROW][C]91[/C][C]0.861886398600025[/C][C]0.276227202799949[/C][C]0.138113601399975[/C][/ROW]
[ROW][C]92[/C][C]0.872036405544482[/C][C]0.255927188911037[/C][C]0.127963594455518[/C][/ROW]
[ROW][C]93[/C][C]0.851563131245238[/C][C]0.296873737509525[/C][C]0.148436868754762[/C][/ROW]
[ROW][C]94[/C][C]0.825042301062149[/C][C]0.349915397875702[/C][C]0.174957698937851[/C][/ROW]
[ROW][C]95[/C][C]0.844049576236411[/C][C]0.311900847527178[/C][C]0.155950423763589[/C][/ROW]
[ROW][C]96[/C][C]0.8184019997759[/C][C]0.363196000448199[/C][C]0.1815980002241[/C][/ROW]
[ROW][C]97[/C][C]0.799011805228443[/C][C]0.401976389543113[/C][C]0.200988194771557[/C][/ROW]
[ROW][C]98[/C][C]0.784627904200192[/C][C]0.430744191599615[/C][C]0.215372095799808[/C][/ROW]
[ROW][C]99[/C][C]0.758636806619939[/C][C]0.482726386760122[/C][C]0.241363193380061[/C][/ROW]
[ROW][C]100[/C][C]0.762655015866535[/C][C]0.47468996826693[/C][C]0.237344984133465[/C][/ROW]
[ROW][C]101[/C][C]0.738157932994119[/C][C]0.523684134011762[/C][C]0.261842067005881[/C][/ROW]
[ROW][C]102[/C][C]0.714349147343351[/C][C]0.571301705313298[/C][C]0.285650852656649[/C][/ROW]
[ROW][C]103[/C][C]0.684391111045232[/C][C]0.631217777909536[/C][C]0.315608888954768[/C][/ROW]
[ROW][C]104[/C][C]0.670554763631417[/C][C]0.658890472737165[/C][C]0.329445236368583[/C][/ROW]
[ROW][C]105[/C][C]0.629052112986444[/C][C]0.741895774027112[/C][C]0.370947887013556[/C][/ROW]
[ROW][C]106[/C][C]0.590755905627941[/C][C]0.818488188744118[/C][C]0.409244094372059[/C][/ROW]
[ROW][C]107[/C][C]0.547578791400822[/C][C]0.904842417198357[/C][C]0.452421208599178[/C][/ROW]
[ROW][C]108[/C][C]0.502105533915077[/C][C]0.995788932169846[/C][C]0.497894466084923[/C][/ROW]
[ROW][C]109[/C][C]0.621649342458841[/C][C]0.756701315082318[/C][C]0.378350657541159[/C][/ROW]
[ROW][C]110[/C][C]0.686911062473179[/C][C]0.626177875053642[/C][C]0.313088937526821[/C][/ROW]
[ROW][C]111[/C][C]0.66606745547381[/C][C]0.66786508905238[/C][C]0.33393254452619[/C][/ROW]
[ROW][C]112[/C][C]0.647068815435205[/C][C]0.70586236912959[/C][C]0.352931184564795[/C][/ROW]
[ROW][C]113[/C][C]0.599491056068511[/C][C]0.801017887862977[/C][C]0.400508943931489[/C][/ROW]
[ROW][C]114[/C][C]0.600821921565908[/C][C]0.798356156868183[/C][C]0.399178078434092[/C][/ROW]
[ROW][C]115[/C][C]0.551192035969346[/C][C]0.897615928061308[/C][C]0.448807964030654[/C][/ROW]
[ROW][C]116[/C][C]0.537163240504005[/C][C]0.925673518991991[/C][C]0.462836759495995[/C][/ROW]
[ROW][C]117[/C][C]0.688565864701005[/C][C]0.62286827059799[/C][C]0.311434135298995[/C][/ROW]
[ROW][C]118[/C][C]0.643129741145317[/C][C]0.713740517709365[/C][C]0.356870258854683[/C][/ROW]
[ROW][C]119[/C][C]0.631279981589046[/C][C]0.737440036821908[/C][C]0.368720018410954[/C][/ROW]
[ROW][C]120[/C][C]0.587004329146159[/C][C]0.825991341707682[/C][C]0.412995670853841[/C][/ROW]
[ROW][C]121[/C][C]0.548861539582608[/C][C]0.902276920834784[/C][C]0.451138460417392[/C][/ROW]
[ROW][C]122[/C][C]0.503645887862504[/C][C]0.992708224274992[/C][C]0.496354112137496[/C][/ROW]
[ROW][C]123[/C][C]0.469375354189607[/C][C]0.938750708379214[/C][C]0.530624645810393[/C][/ROW]
[ROW][C]124[/C][C]0.432079003837341[/C][C]0.864158007674681[/C][C]0.567920996162659[/C][/ROW]
[ROW][C]125[/C][C]0.379792589682366[/C][C]0.759585179364731[/C][C]0.620207410317634[/C][/ROW]
[ROW][C]126[/C][C]0.367521358702101[/C][C]0.735042717404201[/C][C]0.632478641297899[/C][/ROW]
[ROW][C]127[/C][C]0.319492808625231[/C][C]0.638985617250462[/C][C]0.680507191374769[/C][/ROW]
[ROW][C]128[/C][C]0.278191753630866[/C][C]0.556383507261732[/C][C]0.721808246369134[/C][/ROW]
[ROW][C]129[/C][C]0.282098451683811[/C][C]0.564196903367621[/C][C]0.717901548316189[/C][/ROW]
[ROW][C]130[/C][C]0.248738223994388[/C][C]0.497476447988775[/C][C]0.751261776005613[/C][/ROW]
[ROW][C]131[/C][C]0.215605835259727[/C][C]0.431211670519453[/C][C]0.784394164740273[/C][/ROW]
[ROW][C]132[/C][C]0.176074053462609[/C][C]0.352148106925218[/C][C]0.823925946537391[/C][/ROW]
[ROW][C]133[/C][C]0.147482615755688[/C][C]0.294965231511376[/C][C]0.852517384244312[/C][/ROW]
[ROW][C]134[/C][C]0.155626308162837[/C][C]0.311252616325675[/C][C]0.844373691837162[/C][/ROW]
[ROW][C]135[/C][C]0.357839782071083[/C][C]0.715679564142166[/C][C]0.642160217928917[/C][/ROW]
[ROW][C]136[/C][C]0.364079539230755[/C][C]0.728159078461511[/C][C]0.635920460769245[/C][/ROW]
[ROW][C]137[/C][C]0.464084324049335[/C][C]0.928168648098671[/C][C]0.535915675950665[/C][/ROW]
[ROW][C]138[/C][C]0.417415400986337[/C][C]0.834830801972675[/C][C]0.582584599013663[/C][/ROW]
[ROW][C]139[/C][C]0.368580348505864[/C][C]0.737160697011728[/C][C]0.631419651494136[/C][/ROW]
[ROW][C]140[/C][C]0.306333999465448[/C][C]0.612667998930896[/C][C]0.693666000534552[/C][/ROW]
[ROW][C]141[/C][C]0.26014666987763[/C][C]0.52029333975526[/C][C]0.73985333012237[/C][/ROW]
[ROW][C]142[/C][C]0.712045584827979[/C][C]0.575908830344043[/C][C]0.287954415172021[/C][/ROW]
[ROW][C]143[/C][C]0.652898210773719[/C][C]0.694203578452562[/C][C]0.347101789226281[/C][/ROW]
[ROW][C]144[/C][C]0.935128881698555[/C][C]0.129742236602891[/C][C]0.0648711183014454[/C][/ROW]
[ROW][C]145[/C][C]0.991643014057874[/C][C]0.0167139718842518[/C][C]0.00835698594212592[/C][/ROW]
[ROW][C]146[/C][C]0.999798397009188[/C][C]0.000403205981624257[/C][C]0.000201602990812128[/C][/ROW]
[ROW][C]147[/C][C]0.999492171594166[/C][C]0.00101565681166892[/C][C]0.000507828405834459[/C][/ROW]
[ROW][C]148[/C][C]0.999999999733671[/C][C]5.32657153635605e-10[/C][C]2.66328576817802e-10[/C][/ROW]
[ROW][C]149[/C][C]0.999999997494698[/C][C]5.01060462011716e-09[/C][C]2.50530231005858e-09[/C][/ROW]
[ROW][C]150[/C][C]0.999999988196788[/C][C]2.36064246354537e-08[/C][C]1.18032123177268e-08[/C][/ROW]
[ROW][C]151[/C][C]0.999999883523226[/C][C]2.32953548502702e-07[/C][C]1.16476774251351e-07[/C][/ROW]
[ROW][C]152[/C][C]0.99999887827902[/C][C]2.24344195940651e-06[/C][C]1.12172097970326e-06[/C][/ROW]
[ROW][C]153[/C][C]0.999990330305354[/C][C]1.93393892925444e-05[/C][C]9.66969464627221e-06[/C][/ROW]
[ROW][C]154[/C][C]0.999922834625832[/C][C]0.000154330748335069[/C][C]7.71653741675344e-05[/C][/ROW]
[ROW][C]155[/C][C]0.999991003294204[/C][C]1.79934115913923e-05[/C][C]8.99670579569614e-06[/C][/ROW]
[ROW][C]156[/C][C]0.999947596475159[/C][C]0.000104807049682915[/C][C]5.24035248414576e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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
80.4389095883470560.8778191766941120.561090411652944
90.3251938429306510.6503876858613020.674806157069349
100.2119271588707960.4238543177415930.788072841129204
110.6156005981787590.7687988036424820.384399401821241
120.5126558431728580.9746883136542840.487344156827142
130.4152885590409830.8305771180819660.584711440959017
140.4207865173151280.8415730346302560.579213482684872
150.3415718452542170.6831436905084340.658428154745783
160.258347461978980.5166949239579610.74165253802102
170.2124304071678370.4248608143356740.787569592832163
180.1836945301710.3673890603420010.816305469829
190.3063447141619710.6126894283239430.693655285838029
200.3238446903879110.6476893807758220.676155309612089
210.2582890352677540.5165780705355080.741710964732246
220.8118535666624060.3762928666751880.188146433337594
230.7663151358012050.467369728397590.233684864198795
240.8432964231167020.3134071537665970.156703576883298
250.8686000373048520.2627999253902970.131399962695148
260.8307978430923130.3384043138153740.169202156907687
270.7877298961171120.4245402077657760.212270103882888
280.7499844829413320.5000310341173350.250015517058667
290.7043449507246560.5913100985506870.295655049275344
300.6490567674585310.7018864650829390.350943232541469
310.5917575322899240.8164849354201510.408242467710076
320.5451545660684020.9096908678631960.454845433931598
330.5071348565144650.9857302869710690.492865143485535
340.4572595581658790.9145191163317570.542740441834121
350.5095259227949740.9809481544100520.490474077205026
360.4669448937886950.933889787577390.533055106211305
370.7627454514859330.4745090970281330.237254548514067
380.7187965081434570.5624069837130870.281203491856543
390.6750087855702980.6499824288594040.324991214429702
400.6542793086649360.6914413826701280.345720691335064
410.7035785178159990.5928429643680020.296421482184001
420.6568554046627840.6862891906744320.343144595337216
430.6077560800324770.7844878399350450.392243919967522
440.560822448318170.878355103363660.43917755168183
450.509249208196390.981501583607220.49075079180361
460.9072935666662550.185412866667490.0927064333337449
470.8880064123214960.2239871753570090.111993587678504
480.8713664733803410.2572670532393180.128633526619659
490.8461953913278170.3076092173443660.153804608672183
500.8483793148171930.3032413703656150.151620685182807
510.8249313963951650.3501372072096710.175068603604835
520.7914088223039420.4171823553921150.208591177696058
530.8794514734988730.2410970530022530.120548526501127
540.8558996528157580.2882006943684850.144100347184242
550.8340410297875730.3319179404248550.165958970212427
560.8115756595852510.3768486808294980.188424340414749
570.8061335287179910.3877329425640180.193866471282009
580.7784945486596450.443010902680710.221505451340355
590.7501226754513750.4997546490972510.249877324548625
600.7133888109471620.5732223781056750.286611189052838
610.6938707743365540.6122584513268920.306129225663446
620.6724977590958840.6550044818082310.327502240904115
630.6339989638341140.7320020723317720.366001036165886
640.616274601851040.767450796297920.38372539814896
650.5772336175391990.8455327649216020.422766382460801
660.5336225496352880.9327549007294250.466377450364712
670.5359072302901120.9281855394197760.464092769709888
680.4929479694453530.9858959388907060.507052030554647
690.4865924731568480.9731849463136970.513407526843152
700.6107881938612880.7784236122774240.389211806138712
710.5772590866387830.8454818267224340.422740913361217
720.5365675820779560.9268648358440890.463432417922044
730.4931192758574560.9862385517149120.506880724142544
740.4478605711785870.8957211423571750.552139428821413
750.551100381605580.8977992367888410.44889961839442
760.5062709343610290.9874581312779420.493729065638971
770.5096324953009940.9807350093980120.490367504699006
780.6622158574196440.6755682851607120.337784142580356
790.7063652770144720.5872694459710560.293634722985528
800.6677401147231720.6645197705536550.332259885276828
810.6273619764257610.7452760471484780.372638023574239
820.6955349501199430.6089300997601150.304465049880057
830.6847455831171060.6305088337657870.315254416882894
840.667246587705770.6655068245884610.33275341229423
850.6264584060520570.7470831878958850.373541593947943
860.598046453251440.803907093497120.40195354674856
870.5793918863188910.8412162273622180.420608113681109
880.7695349409978040.4609301180043910.230465059002196
890.7598841973004930.4802316053990140.240115802699507
900.876605487094270.2467890258114590.12339451290573
910.8618863986000250.2762272027999490.138113601399975
920.8720364055444820.2559271889110370.127963594455518
930.8515631312452380.2968737375095250.148436868754762
940.8250423010621490.3499153978757020.174957698937851
950.8440495762364110.3119008475271780.155950423763589
960.81840199977590.3631960004481990.1815980002241
970.7990118052284430.4019763895431130.200988194771557
980.7846279042001920.4307441915996150.215372095799808
990.7586368066199390.4827263867601220.241363193380061
1000.7626550158665350.474689968266930.237344984133465
1010.7381579329941190.5236841340117620.261842067005881
1020.7143491473433510.5713017053132980.285650852656649
1030.6843911110452320.6312177779095360.315608888954768
1040.6705547636314170.6588904727371650.329445236368583
1050.6290521129864440.7418957740271120.370947887013556
1060.5907559056279410.8184881887441180.409244094372059
1070.5475787914008220.9048424171983570.452421208599178
1080.5021055339150770.9957889321698460.497894466084923
1090.6216493424588410.7567013150823180.378350657541159
1100.6869110624731790.6261778750536420.313088937526821
1110.666067455473810.667865089052380.33393254452619
1120.6470688154352050.705862369129590.352931184564795
1130.5994910560685110.8010178878629770.400508943931489
1140.6008219215659080.7983561568681830.399178078434092
1150.5511920359693460.8976159280613080.448807964030654
1160.5371632405040050.9256735189919910.462836759495995
1170.6885658647010050.622868270597990.311434135298995
1180.6431297411453170.7137405177093650.356870258854683
1190.6312799815890460.7374400368219080.368720018410954
1200.5870043291461590.8259913417076820.412995670853841
1210.5488615395826080.9022769208347840.451138460417392
1220.5036458878625040.9927082242749920.496354112137496
1230.4693753541896070.9387507083792140.530624645810393
1240.4320790038373410.8641580076746810.567920996162659
1250.3797925896823660.7595851793647310.620207410317634
1260.3675213587021010.7350427174042010.632478641297899
1270.3194928086252310.6389856172504620.680507191374769
1280.2781917536308660.5563835072617320.721808246369134
1290.2820984516838110.5641969033676210.717901548316189
1300.2487382239943880.4974764479887750.751261776005613
1310.2156058352597270.4312116705194530.784394164740273
1320.1760740534626090.3521481069252180.823925946537391
1330.1474826157556880.2949652315113760.852517384244312
1340.1556263081628370.3112526163256750.844373691837162
1350.3578397820710830.7156795641421660.642160217928917
1360.3640795392307550.7281590784615110.635920460769245
1370.4640843240493350.9281686480986710.535915675950665
1380.4174154009863370.8348308019726750.582584599013663
1390.3685803485058640.7371606970117280.631419651494136
1400.3063339994654480.6126679989308960.693666000534552
1410.260146669877630.520293339755260.73985333012237
1420.7120455848279790.5759088303440430.287954415172021
1430.6528982107737190.6942035784525620.347101789226281
1440.9351288816985550.1297422366028910.0648711183014454
1450.9916430140578740.01671397188425180.00835698594212592
1460.9997983970091880.0004032059816242570.000201602990812128
1470.9994921715941660.001015656811668920.000507828405834459
1480.9999999997336715.32657153635605e-102.66328576817802e-10
1490.9999999974946985.01060462011716e-092.50530231005858e-09
1500.9999999881967882.36064246354537e-081.18032123177268e-08
1510.9999998835232262.32953548502702e-071.16476774251351e-07
1520.999998878279022.24344195940651e-061.12172097970326e-06
1530.9999903303053541.93393892925444e-059.66969464627221e-06
1540.9999228346258320.0001543307483350697.71653741675344e-05
1550.9999910032942041.79934115913923e-058.99670579569614e-06
1560.9999475964751590.0001048070496829155.24035248414576e-05







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level110.0738255033557047NOK
5% type I error level120.0805369127516778NOK
10% type I error level120.0805369127516778OK

\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 & 11 & 0.0738255033557047 & NOK \tabularnewline
5% type I error level & 12 & 0.0805369127516778 & NOK \tabularnewline
10% type I error level & 12 & 0.0805369127516778 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=145918&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]11[/C][C]0.0738255033557047[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]12[/C][C]0.0805369127516778[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]12[/C][C]0.0805369127516778[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=145918&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=145918&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 level110.0738255033557047NOK
5% type I error level120.0805369127516778NOK
10% type I error level120.0805369127516778OK



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')
}