Free Statistics

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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 22 Nov 2011 04:41:25 -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/22/t132195489806jf3ac4iun7o5z.htm/, Retrieved Fri, 29 Mar 2024 09:52:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=146103, Retrieved Fri, 29 Mar 2024 09:52:19 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact116
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] [] [2011-11-22 09:41:25] [8a7cc4cdacda1fa59fc196ea0735b051] [Current]
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Dataseries X:
170650	334	95556
86621	223	54565
122154	395	63016
152521	876	79774
86318	189	31258
37257	111	52491
316392	1317	91256
32750	102	22807
116502	580	77411
130554	421	48821
173368	539	52295
128294	359	63262
111635	463	50466
193105	694	62932
143900	396	38439
262290	1184	70817
181110	486	105965
202871	788	73795
113853	338	82043
159968	486	74349
174585	476	82204
291865	828	55709
96067	280	37137
110856	334	70780
146342	850	55027
146853	710	56699
166051	721	65911
172853	408	56316
106888	406	26982
193791	591	54628
189408	596	96750
136021	411	53009
215469	541	64664
75339	207	36990
247979	859	85224
167255	669	37048
266277	753	59635
122024	368	42051
80964	216	26998
214975	782	63717
225469	1094	55071
123008	464	40001
140190	474	54506
81106	300	35838
93125	836	50838
318604	1443	86997
78800	330	33032
158835	477	61704
228655	1042	117986
131108	646	56733
128744	343	55064
24188	218	5950
265554	598	84607
65029	255	32551
98066	434	31701
173587	654	71170
180360	482	101773
197266	753	101653
212179	693	81493
146062	478	55901
250805	843	109104
209228	832	114425
145696	710	36311
182854	613	70027
142064	390	73713
122105	332	40671
118675	421	89041
145285	636	57231
155015	576	68608
96487	410	59155
118807	468	55827
69471	364	22618
126630	449	58425
146344	530	65724
110652	373	56979
197814	618	72369
206788	640	79194
112604	283	202316
91921	297	44970
124190	457	49319
103304	453	36252
287032	883	75741
132798	570	38417
143272	355	64102
80953	437	56622
109237	641	15430
98875	252	72571
226212	944	67271
175829	559	43460
118217	736	99501
140433	465	28340
152193	450	76013
121798	429	37361
169631	736	48204
187732	586	76168
130533	387	85168
142339	397	125410
202077	575	123328
209419	728	83038
252260	943	120087
163066	447	91939
190562	617	103646
106351	341	29467
43287	214	43750
127493	507	34497
130005	451	66477
149006	616	71181
197727	694	74482
74112	215	174949
94968	360	46765
191351	422	90257
145048	442	51370
22938	154	1168
125927	474	51360
61857	192	25162
98969	340	21067
259571	832	58233
21054	146	855
174409	597	85903
31414	200	14116
193178	777	57637
137544	388	94137
77166	248	62147
82724	371	62832
38214	276	8773
90961	298	63785
197612	624	65196
137107	313	73087
251103	1040	72631
209835	609	86281
263825	819	162365
139144	344	56530
76470	312	35606
197114	642	70111
291962	1074	92046
51025	226	63989
254843	1176	104911
104554	510	43448
168059	391	60029
136745	534	38650
84092	256	47261
251448	1159	73586
152366	446	83042
173260	716	37238
204966	594	63958
83932	414	78956
139409	678	99518
185455	537	111436
0	0	0
14688	85	6023
98	0	0
455	0	0
0	0	0
0	0	0
137891	450	42564
185368	582	38885
0	0	0
203	0	0
7199	74	1644
46660	259	6179
17547	69	3926
73567	187	23238
969	0	0
105477	341	49288




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
Characters[t] = + 20240.366590051 + 0.384236167698985Time_RFC[t] -26.9449573592826Compendium_Views[t] -12.0324869408992t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Characters[t] =  +  20240.366590051 +  0.384236167698985Time_RFC[t] -26.9449573592826Compendium_Views[t] -12.0324869408992t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Characters[t] =  +  20240.366590051 +  0.384236167698985Time_RFC[t] -26.9449573592826Compendium_Views[t] -12.0324869408992t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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
Characters[t] = + 20240.366590051 + 0.384236167698985Time_RFC[t] -26.9449573592826Compendium_Views[t] -12.0324869408992t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)20240.3665900516574.9200923.07840.0024490.001225
Time_RFC0.3842361676989850.0619266.204700
Compendium_Views-26.944957359282616.021807-1.68180.0945650.047282
t-12.032486940899244.715818-0.26910.7882090.394104

\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) & 20240.366590051 & 6574.920092 & 3.0784 & 0.002449 & 0.001225 \tabularnewline
Time_RFC & 0.384236167698985 & 0.061926 & 6.2047 & 0 & 0 \tabularnewline
Compendium_Views & -26.9449573592826 & 16.021807 & -1.6818 & 0.094565 & 0.047282 \tabularnewline
t & -12.0324869408992 & 44.715818 & -0.2691 & 0.788209 & 0.394104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&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]20240.366590051[/C][C]6574.920092[/C][C]3.0784[/C][C]0.002449[/C][C]0.001225[/C][/ROW]
[ROW][C]Time_RFC[/C][C]0.384236167698985[/C][C]0.061926[/C][C]6.2047[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Compendium_Views[/C][C]-26.9449573592826[/C][C]16.021807[/C][C]-1.6818[/C][C]0.094565[/C][C]0.047282[/C][/ROW]
[ROW][C]t[/C][C]-12.0324869408992[/C][C]44.715818[/C][C]-0.2691[/C][C]0.788209[/C][C]0.394104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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)20240.3665900516574.9200923.07840.0024490.001225
Time_RFC0.3842361676989850.0619266.204700
Compendium_Views-26.944957359282616.021807-1.68180.0945650.047282
t-12.032486940899244.715818-0.26910.7882090.394104







Multiple Linear Regression - Regression Statistics
Multiple R0.635323297061139
R-squared0.403635691788637
Adjusted R-squared0.392453861009674
F-TEST (value)36.097460225209
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26115.7712086713
Sum Squared Residuals109125360931.787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.635323297061139 \tabularnewline
R-squared & 0.403635691788637 \tabularnewline
Adjusted R-squared & 0.392453861009674 \tabularnewline
F-TEST (value) & 36.097460225209 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 160 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 26115.7712086713 \tabularnewline
Sum Squared Residuals & 109125360931.787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.635323297061139[/C][/ROW]
[ROW][C]R-squared[/C][C]0.403635691788637[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.392453861009674[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]36.097460225209[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]160[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]26115.7712086713[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]109125360931.787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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 R0.635323297061139
R-squared0.403635691788637
Adjusted R-squared0.392453861009674
F-TEST (value)36.097460225209
F-TEST (DF numerator)3
F-TEST (DF denominator)160
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation26115.7712086713
Sum Squared Residuals109125360931.787







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19555676798.620362941518757.3796370585
25456547490.49720730317074.50279269694
36301656496.99580141366519.00419858643
47977455192.538529172824581.4614708272
53125848254.1047378831-16996.1047378831
65249131492.768301486420998.2316985136
791256106238.879909907-14982.8799099069
82280729979.4555360188-7172.45553601878
97741149268.280948466228142.7190515338
104882158939.7833101574-10118.7833101574
115229572198.9331386855-19903.9331386855
126326259717.93195355143544.06804644859
135046650502.6335835477-36.6335835477239
146293275570.0365290489-12638.0365290489
153843964681.2607035456-26242.2607035456
167081788926.3217113729-18109.3217113729
1710596576529.577367407629435.4226325924
187379576741.531003261-2946.531003261
198204354650.79415176927392.205848231
207434968369.9588490935979.04115090702
218220474243.7559990017960.24400099903
2255709109810.31626933-54101.3162693296
233713749331.4472521496-12194.4472521496
247078053546.855751907817233.1442480922
255502753266.22991454331760.77008545675
265669957222.8361395961-523.836139596096
276591164290.97506918821620.02493081179
285631675326.2886483913-19010.2886483913
292698250022.0072739054-23040.0072739054
305462878416.4333570421-23788.4333570421
319675076585.568960280120164.4310397199
325300961045.1372998608-8036.13729986079
336466488057.0554075621-23393.0554075621
343699043201.6244989628-6211.62449896281
358522491956.0118053225-6732.01180532247
363704866046.4408153124-28998.4408153124
3759635101818.865708081-42183.8657080807
384205156753.4219053818-14702.4219053818
392699845060.2858913316-18062.2858913316
406371781289.2806085454-17572.2806085454
415507176902.5957693415-21831.5957693415
424000154496.6644401429-14495.6644401429
435450660817.1282130132-6311.12821301316
443583842791.3085742606-6953.30857426059
455083832954.913442318317883.0865576817
4686997103224.478694892-16227.4786948924
473303241060.8137899456-8028.81378994555
486170467840.2142529784-6136.2142529784
4911798679431.65008678638554.349913214
505673352608.73526358814124.26473641192
515506459852.6905560694-4788.6905560694
52595023034.5809891037-17084.5809891037
5384607105525.011558469-20918.0115584687
543255137706.1419179227-5155.14191792273
553170145564.9723359416-13863.9723359416
567117068642.94885075362527.05114924639
5710177375867.880593434525905.1194065655
5810165375049.661313247126603.3386867529
598149382384.4402367581-891.440236758133
605590162761.0308823092-6860.03088230918
6110910493160.13787252515943.8621274751
6211442577469.112772115436955.8872278845
633631156333.0728767551-20022.0728767551
647002773212.1487730235-3185.1487730235
657371363535.84849676110177.151503239
664067157417.6538655545-16746.6538655545
678904153689.590118429935351.4098815701
685723158108.9162217132-877.916221713231
696860863452.19908804045155.80091195959
705915545424.455099654213730.5449003458
715582752425.76634891633401.23365108372
722261836259.3338577436-13641.3338577436
735842555919.535104772505.46489522999
746572461299.7928817454424.20711825498
755697951803.96140269935175.0385973007
767236978681.2072117131-6312.20721171312
777919481524.5210317987-2330.52103179869
7820231654942.9391035605147373.06089644
794497046606.5205570715-1636.52055707149
804931954682.2117881239-5363.21178812393
813625246752.8025320592-10500.8025320592
8275741105749.380999626-30008.3809996259
833841754908.8390772552-16491.8390772552
846410264714.4620430392-612.462043039192
855662238547.729317804118074.2706821959
861543043906.6612967676-28476.6612967676
877257150394.762052890822176.2379471092
886727180664.299959612-13393.299959612
894346071667.1052188169-28207.1052188169
909950144749.201185809154751.7988141909
912834060575.4428448344-32235.4428448344
927601365486.202050422810526.7979495772
933736154361.1553508162-17000.1553508162
944820464456.1895641211-16252.1895641211
957616875440.9595525919727.040447408095
968516858813.04902393426354.950976066
9712541063067.859159254562342.1408407455
9812332881213.124448363342114.8755516367
998303879899.57542869813138.42457130193
10012008790555.438769903629531.5612300964
1019193969636.544391423622302.4556085764
10210364675608.82682045628037.173179544
1032946750676.6906465778-21209.6906465778
1044375029855.19806449713894.801935503
1053449754303.2838085471-19806.2838085471
1066647756765.37018698589711.62981301416
1077118159608.291158211711572.7088417883
1087448276214.922323709-1732.92232370905
10917494941612.1705417544133336.829458246
1104676545706.74875124761058.25124875238
1119025781057.96345936259199.03654063751
1125137062715.7445522698-11345.7445522698
113116823544.7813470792-22376.7813470792
1145136054482.4611803187-3122.46118031869
1152516237450.8954042215-12288.8954042215
1162106747710.7818837515-26643.7818837515
1175823396150.927380836-37917.927380836
11885522976.2776313041-22121.2776313041
1198590369736.606872804616166.3931271954
1201411625477.8716573825-11361.8716573825
1215763772074.1782057942-14437.1782057942
1229413761167.139177848932969.8608221511
1236214741727.989387878220419.0106121218
1246283240537.311765816522294.6882341835
125877325982.6984037257-17209.6984037257
1266378545645.181992498918139.8180075011
1276519677828.2649276964-12632.2649276964
1287308762947.904852865210139.0951471347
1297263187148.2745387394-14517.2745387394
1308628182892.86050504763388.1394949524
13116236597967.297666725664397.7023332744
1325653062847.1703005667-6317.17030056673
1333560639615.7588747567-4009.75887475667
1347011177067.6786751289-6956.67867512889
13592046101859.456642891-9813.45664289127
1366398932120.038459731631868.9615402684
13710491184824.54370954420086.456290456
1384344845011.3834165725-1563.38341657252
1396002972606.7186851103-12577.7186851103
1403865056709.585940466-18059.585940466
1414726143957.0646615513303.93533844903
1427358683917.9637606093-10331.9637606093
1438304265046.79790288617995.202097114
1443723865787.8574168414-28549.8574168414
1456395881245.701660797-17287.701660797
1467895639578.12117724839377.878822752
1479951853768.889822893145749.1101771069
14811143675248.634901478536187.3650985215
149018447.5260358571-18447.5260358571
150602321788.8330045398-15765.8330045398
151018461.1162064098-18461.1162064098
152018586.2560313374-18586.2560313374
153018399.3960880935-18399.3960880935
154018387.3636011526-18387.3636011526
1554256459232.8097027153-16668.8097027153
1563888573906.4233781938-35021.4233781938
157018351.2661403299-18351.2661403299
158018417.2335954319-18417.2335954319
159164419099.3904931261-17455.3904931261
160617929264.8843082876-23085.8843082876
161392623186.1261693899-19260.1261693899
1622323841519.4988285508-18281.4988285508
163018651.3960651848-18651.3960651848
1644928849606.8865326141-318.886532614063

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 95556 & 76798.6203629415 & 18757.3796370585 \tabularnewline
2 & 54565 & 47490.4972073031 & 7074.50279269694 \tabularnewline
3 & 63016 & 56496.9958014136 & 6519.00419858643 \tabularnewline
4 & 79774 & 55192.5385291728 & 24581.4614708272 \tabularnewline
5 & 31258 & 48254.1047378831 & -16996.1047378831 \tabularnewline
6 & 52491 & 31492.7683014864 & 20998.2316985136 \tabularnewline
7 & 91256 & 106238.879909907 & -14982.8799099069 \tabularnewline
8 & 22807 & 29979.4555360188 & -7172.45553601878 \tabularnewline
9 & 77411 & 49268.2809484662 & 28142.7190515338 \tabularnewline
10 & 48821 & 58939.7833101574 & -10118.7833101574 \tabularnewline
11 & 52295 & 72198.9331386855 & -19903.9331386855 \tabularnewline
12 & 63262 & 59717.9319535514 & 3544.06804644859 \tabularnewline
13 & 50466 & 50502.6335835477 & -36.6335835477239 \tabularnewline
14 & 62932 & 75570.0365290489 & -12638.0365290489 \tabularnewline
15 & 38439 & 64681.2607035456 & -26242.2607035456 \tabularnewline
16 & 70817 & 88926.3217113729 & -18109.3217113729 \tabularnewline
17 & 105965 & 76529.5773674076 & 29435.4226325924 \tabularnewline
18 & 73795 & 76741.531003261 & -2946.531003261 \tabularnewline
19 & 82043 & 54650.794151769 & 27392.205848231 \tabularnewline
20 & 74349 & 68369.958849093 & 5979.04115090702 \tabularnewline
21 & 82204 & 74243.755999001 & 7960.24400099903 \tabularnewline
22 & 55709 & 109810.31626933 & -54101.3162693296 \tabularnewline
23 & 37137 & 49331.4472521496 & -12194.4472521496 \tabularnewline
24 & 70780 & 53546.8557519078 & 17233.1442480922 \tabularnewline
25 & 55027 & 53266.2299145433 & 1760.77008545675 \tabularnewline
26 & 56699 & 57222.8361395961 & -523.836139596096 \tabularnewline
27 & 65911 & 64290.9750691882 & 1620.02493081179 \tabularnewline
28 & 56316 & 75326.2886483913 & -19010.2886483913 \tabularnewline
29 & 26982 & 50022.0072739054 & -23040.0072739054 \tabularnewline
30 & 54628 & 78416.4333570421 & -23788.4333570421 \tabularnewline
31 & 96750 & 76585.5689602801 & 20164.4310397199 \tabularnewline
32 & 53009 & 61045.1372998608 & -8036.13729986079 \tabularnewline
33 & 64664 & 88057.0554075621 & -23393.0554075621 \tabularnewline
34 & 36990 & 43201.6244989628 & -6211.62449896281 \tabularnewline
35 & 85224 & 91956.0118053225 & -6732.01180532247 \tabularnewline
36 & 37048 & 66046.4408153124 & -28998.4408153124 \tabularnewline
37 & 59635 & 101818.865708081 & -42183.8657080807 \tabularnewline
38 & 42051 & 56753.4219053818 & -14702.4219053818 \tabularnewline
39 & 26998 & 45060.2858913316 & -18062.2858913316 \tabularnewline
40 & 63717 & 81289.2806085454 & -17572.2806085454 \tabularnewline
41 & 55071 & 76902.5957693415 & -21831.5957693415 \tabularnewline
42 & 40001 & 54496.6644401429 & -14495.6644401429 \tabularnewline
43 & 54506 & 60817.1282130132 & -6311.12821301316 \tabularnewline
44 & 35838 & 42791.3085742606 & -6953.30857426059 \tabularnewline
45 & 50838 & 32954.9134423183 & 17883.0865576817 \tabularnewline
46 & 86997 & 103224.478694892 & -16227.4786948924 \tabularnewline
47 & 33032 & 41060.8137899456 & -8028.81378994555 \tabularnewline
48 & 61704 & 67840.2142529784 & -6136.2142529784 \tabularnewline
49 & 117986 & 79431.650086786 & 38554.349913214 \tabularnewline
50 & 56733 & 52608.7352635881 & 4124.26473641192 \tabularnewline
51 & 55064 & 59852.6905560694 & -4788.6905560694 \tabularnewline
52 & 5950 & 23034.5809891037 & -17084.5809891037 \tabularnewline
53 & 84607 & 105525.011558469 & -20918.0115584687 \tabularnewline
54 & 32551 & 37706.1419179227 & -5155.14191792273 \tabularnewline
55 & 31701 & 45564.9723359416 & -13863.9723359416 \tabularnewline
56 & 71170 & 68642.9488507536 & 2527.05114924639 \tabularnewline
57 & 101773 & 75867.8805934345 & 25905.1194065655 \tabularnewline
58 & 101653 & 75049.6613132471 & 26603.3386867529 \tabularnewline
59 & 81493 & 82384.4402367581 & -891.440236758133 \tabularnewline
60 & 55901 & 62761.0308823092 & -6860.03088230918 \tabularnewline
61 & 109104 & 93160.137872525 & 15943.8621274751 \tabularnewline
62 & 114425 & 77469.1127721154 & 36955.8872278845 \tabularnewline
63 & 36311 & 56333.0728767551 & -20022.0728767551 \tabularnewline
64 & 70027 & 73212.1487730235 & -3185.1487730235 \tabularnewline
65 & 73713 & 63535.848496761 & 10177.151503239 \tabularnewline
66 & 40671 & 57417.6538655545 & -16746.6538655545 \tabularnewline
67 & 89041 & 53689.5901184299 & 35351.4098815701 \tabularnewline
68 & 57231 & 58108.9162217132 & -877.916221713231 \tabularnewline
69 & 68608 & 63452.1990880404 & 5155.80091195959 \tabularnewline
70 & 59155 & 45424.4550996542 & 13730.5449003458 \tabularnewline
71 & 55827 & 52425.7663489163 & 3401.23365108372 \tabularnewline
72 & 22618 & 36259.3338577436 & -13641.3338577436 \tabularnewline
73 & 58425 & 55919.53510477 & 2505.46489522999 \tabularnewline
74 & 65724 & 61299.792881745 & 4424.20711825498 \tabularnewline
75 & 56979 & 51803.9614026993 & 5175.0385973007 \tabularnewline
76 & 72369 & 78681.2072117131 & -6312.20721171312 \tabularnewline
77 & 79194 & 81524.5210317987 & -2330.52103179869 \tabularnewline
78 & 202316 & 54942.9391035605 & 147373.06089644 \tabularnewline
79 & 44970 & 46606.5205570715 & -1636.52055707149 \tabularnewline
80 & 49319 & 54682.2117881239 & -5363.21178812393 \tabularnewline
81 & 36252 & 46752.8025320592 & -10500.8025320592 \tabularnewline
82 & 75741 & 105749.380999626 & -30008.3809996259 \tabularnewline
83 & 38417 & 54908.8390772552 & -16491.8390772552 \tabularnewline
84 & 64102 & 64714.4620430392 & -612.462043039192 \tabularnewline
85 & 56622 & 38547.7293178041 & 18074.2706821959 \tabularnewline
86 & 15430 & 43906.6612967676 & -28476.6612967676 \tabularnewline
87 & 72571 & 50394.7620528908 & 22176.2379471092 \tabularnewline
88 & 67271 & 80664.299959612 & -13393.299959612 \tabularnewline
89 & 43460 & 71667.1052188169 & -28207.1052188169 \tabularnewline
90 & 99501 & 44749.2011858091 & 54751.7988141909 \tabularnewline
91 & 28340 & 60575.4428448344 & -32235.4428448344 \tabularnewline
92 & 76013 & 65486.2020504228 & 10526.7979495772 \tabularnewline
93 & 37361 & 54361.1553508162 & -17000.1553508162 \tabularnewline
94 & 48204 & 64456.1895641211 & -16252.1895641211 \tabularnewline
95 & 76168 & 75440.9595525919 & 727.040447408095 \tabularnewline
96 & 85168 & 58813.049023934 & 26354.950976066 \tabularnewline
97 & 125410 & 63067.8591592545 & 62342.1408407455 \tabularnewline
98 & 123328 & 81213.1244483633 & 42114.8755516367 \tabularnewline
99 & 83038 & 79899.5754286981 & 3138.42457130193 \tabularnewline
100 & 120087 & 90555.4387699036 & 29531.5612300964 \tabularnewline
101 & 91939 & 69636.5443914236 & 22302.4556085764 \tabularnewline
102 & 103646 & 75608.826820456 & 28037.173179544 \tabularnewline
103 & 29467 & 50676.6906465778 & -21209.6906465778 \tabularnewline
104 & 43750 & 29855.198064497 & 13894.801935503 \tabularnewline
105 & 34497 & 54303.2838085471 & -19806.2838085471 \tabularnewline
106 & 66477 & 56765.3701869858 & 9711.62981301416 \tabularnewline
107 & 71181 & 59608.2911582117 & 11572.7088417883 \tabularnewline
108 & 74482 & 76214.922323709 & -1732.92232370905 \tabularnewline
109 & 174949 & 41612.1705417544 & 133336.829458246 \tabularnewline
110 & 46765 & 45706.7487512476 & 1058.25124875238 \tabularnewline
111 & 90257 & 81057.9634593625 & 9199.03654063751 \tabularnewline
112 & 51370 & 62715.7445522698 & -11345.7445522698 \tabularnewline
113 & 1168 & 23544.7813470792 & -22376.7813470792 \tabularnewline
114 & 51360 & 54482.4611803187 & -3122.46118031869 \tabularnewline
115 & 25162 & 37450.8954042215 & -12288.8954042215 \tabularnewline
116 & 21067 & 47710.7818837515 & -26643.7818837515 \tabularnewline
117 & 58233 & 96150.927380836 & -37917.927380836 \tabularnewline
118 & 855 & 22976.2776313041 & -22121.2776313041 \tabularnewline
119 & 85903 & 69736.6068728046 & 16166.3931271954 \tabularnewline
120 & 14116 & 25477.8716573825 & -11361.8716573825 \tabularnewline
121 & 57637 & 72074.1782057942 & -14437.1782057942 \tabularnewline
122 & 94137 & 61167.1391778489 & 32969.8608221511 \tabularnewline
123 & 62147 & 41727.9893878782 & 20419.0106121218 \tabularnewline
124 & 62832 & 40537.3117658165 & 22294.6882341835 \tabularnewline
125 & 8773 & 25982.6984037257 & -17209.6984037257 \tabularnewline
126 & 63785 & 45645.1819924989 & 18139.8180075011 \tabularnewline
127 & 65196 & 77828.2649276964 & -12632.2649276964 \tabularnewline
128 & 73087 & 62947.9048528652 & 10139.0951471347 \tabularnewline
129 & 72631 & 87148.2745387394 & -14517.2745387394 \tabularnewline
130 & 86281 & 82892.8605050476 & 3388.1394949524 \tabularnewline
131 & 162365 & 97967.2976667256 & 64397.7023332744 \tabularnewline
132 & 56530 & 62847.1703005667 & -6317.17030056673 \tabularnewline
133 & 35606 & 39615.7588747567 & -4009.75887475667 \tabularnewline
134 & 70111 & 77067.6786751289 & -6956.67867512889 \tabularnewline
135 & 92046 & 101859.456642891 & -9813.45664289127 \tabularnewline
136 & 63989 & 32120.0384597316 & 31868.9615402684 \tabularnewline
137 & 104911 & 84824.543709544 & 20086.456290456 \tabularnewline
138 & 43448 & 45011.3834165725 & -1563.38341657252 \tabularnewline
139 & 60029 & 72606.7186851103 & -12577.7186851103 \tabularnewline
140 & 38650 & 56709.585940466 & -18059.585940466 \tabularnewline
141 & 47261 & 43957.064661551 & 3303.93533844903 \tabularnewline
142 & 73586 & 83917.9637606093 & -10331.9637606093 \tabularnewline
143 & 83042 & 65046.797902886 & 17995.202097114 \tabularnewline
144 & 37238 & 65787.8574168414 & -28549.8574168414 \tabularnewline
145 & 63958 & 81245.701660797 & -17287.701660797 \tabularnewline
146 & 78956 & 39578.121177248 & 39377.878822752 \tabularnewline
147 & 99518 & 53768.8898228931 & 45749.1101771069 \tabularnewline
148 & 111436 & 75248.6349014785 & 36187.3650985215 \tabularnewline
149 & 0 & 18447.5260358571 & -18447.5260358571 \tabularnewline
150 & 6023 & 21788.8330045398 & -15765.8330045398 \tabularnewline
151 & 0 & 18461.1162064098 & -18461.1162064098 \tabularnewline
152 & 0 & 18586.2560313374 & -18586.2560313374 \tabularnewline
153 & 0 & 18399.3960880935 & -18399.3960880935 \tabularnewline
154 & 0 & 18387.3636011526 & -18387.3636011526 \tabularnewline
155 & 42564 & 59232.8097027153 & -16668.8097027153 \tabularnewline
156 & 38885 & 73906.4233781938 & -35021.4233781938 \tabularnewline
157 & 0 & 18351.2661403299 & -18351.2661403299 \tabularnewline
158 & 0 & 18417.2335954319 & -18417.2335954319 \tabularnewline
159 & 1644 & 19099.3904931261 & -17455.3904931261 \tabularnewline
160 & 6179 & 29264.8843082876 & -23085.8843082876 \tabularnewline
161 & 3926 & 23186.1261693899 & -19260.1261693899 \tabularnewline
162 & 23238 & 41519.4988285508 & -18281.4988285508 \tabularnewline
163 & 0 & 18651.3960651848 & -18651.3960651848 \tabularnewline
164 & 49288 & 49606.8865326141 & -318.886532614063 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&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]95556[/C][C]76798.6203629415[/C][C]18757.3796370585[/C][/ROW]
[ROW][C]2[/C][C]54565[/C][C]47490.4972073031[/C][C]7074.50279269694[/C][/ROW]
[ROW][C]3[/C][C]63016[/C][C]56496.9958014136[/C][C]6519.00419858643[/C][/ROW]
[ROW][C]4[/C][C]79774[/C][C]55192.5385291728[/C][C]24581.4614708272[/C][/ROW]
[ROW][C]5[/C][C]31258[/C][C]48254.1047378831[/C][C]-16996.1047378831[/C][/ROW]
[ROW][C]6[/C][C]52491[/C][C]31492.7683014864[/C][C]20998.2316985136[/C][/ROW]
[ROW][C]7[/C][C]91256[/C][C]106238.879909907[/C][C]-14982.8799099069[/C][/ROW]
[ROW][C]8[/C][C]22807[/C][C]29979.4555360188[/C][C]-7172.45553601878[/C][/ROW]
[ROW][C]9[/C][C]77411[/C][C]49268.2809484662[/C][C]28142.7190515338[/C][/ROW]
[ROW][C]10[/C][C]48821[/C][C]58939.7833101574[/C][C]-10118.7833101574[/C][/ROW]
[ROW][C]11[/C][C]52295[/C][C]72198.9331386855[/C][C]-19903.9331386855[/C][/ROW]
[ROW][C]12[/C][C]63262[/C][C]59717.9319535514[/C][C]3544.06804644859[/C][/ROW]
[ROW][C]13[/C][C]50466[/C][C]50502.6335835477[/C][C]-36.6335835477239[/C][/ROW]
[ROW][C]14[/C][C]62932[/C][C]75570.0365290489[/C][C]-12638.0365290489[/C][/ROW]
[ROW][C]15[/C][C]38439[/C][C]64681.2607035456[/C][C]-26242.2607035456[/C][/ROW]
[ROW][C]16[/C][C]70817[/C][C]88926.3217113729[/C][C]-18109.3217113729[/C][/ROW]
[ROW][C]17[/C][C]105965[/C][C]76529.5773674076[/C][C]29435.4226325924[/C][/ROW]
[ROW][C]18[/C][C]73795[/C][C]76741.531003261[/C][C]-2946.531003261[/C][/ROW]
[ROW][C]19[/C][C]82043[/C][C]54650.794151769[/C][C]27392.205848231[/C][/ROW]
[ROW][C]20[/C][C]74349[/C][C]68369.958849093[/C][C]5979.04115090702[/C][/ROW]
[ROW][C]21[/C][C]82204[/C][C]74243.755999001[/C][C]7960.24400099903[/C][/ROW]
[ROW][C]22[/C][C]55709[/C][C]109810.31626933[/C][C]-54101.3162693296[/C][/ROW]
[ROW][C]23[/C][C]37137[/C][C]49331.4472521496[/C][C]-12194.4472521496[/C][/ROW]
[ROW][C]24[/C][C]70780[/C][C]53546.8557519078[/C][C]17233.1442480922[/C][/ROW]
[ROW][C]25[/C][C]55027[/C][C]53266.2299145433[/C][C]1760.77008545675[/C][/ROW]
[ROW][C]26[/C][C]56699[/C][C]57222.8361395961[/C][C]-523.836139596096[/C][/ROW]
[ROW][C]27[/C][C]65911[/C][C]64290.9750691882[/C][C]1620.02493081179[/C][/ROW]
[ROW][C]28[/C][C]56316[/C][C]75326.2886483913[/C][C]-19010.2886483913[/C][/ROW]
[ROW][C]29[/C][C]26982[/C][C]50022.0072739054[/C][C]-23040.0072739054[/C][/ROW]
[ROW][C]30[/C][C]54628[/C][C]78416.4333570421[/C][C]-23788.4333570421[/C][/ROW]
[ROW][C]31[/C][C]96750[/C][C]76585.5689602801[/C][C]20164.4310397199[/C][/ROW]
[ROW][C]32[/C][C]53009[/C][C]61045.1372998608[/C][C]-8036.13729986079[/C][/ROW]
[ROW][C]33[/C][C]64664[/C][C]88057.0554075621[/C][C]-23393.0554075621[/C][/ROW]
[ROW][C]34[/C][C]36990[/C][C]43201.6244989628[/C][C]-6211.62449896281[/C][/ROW]
[ROW][C]35[/C][C]85224[/C][C]91956.0118053225[/C][C]-6732.01180532247[/C][/ROW]
[ROW][C]36[/C][C]37048[/C][C]66046.4408153124[/C][C]-28998.4408153124[/C][/ROW]
[ROW][C]37[/C][C]59635[/C][C]101818.865708081[/C][C]-42183.8657080807[/C][/ROW]
[ROW][C]38[/C][C]42051[/C][C]56753.4219053818[/C][C]-14702.4219053818[/C][/ROW]
[ROW][C]39[/C][C]26998[/C][C]45060.2858913316[/C][C]-18062.2858913316[/C][/ROW]
[ROW][C]40[/C][C]63717[/C][C]81289.2806085454[/C][C]-17572.2806085454[/C][/ROW]
[ROW][C]41[/C][C]55071[/C][C]76902.5957693415[/C][C]-21831.5957693415[/C][/ROW]
[ROW][C]42[/C][C]40001[/C][C]54496.6644401429[/C][C]-14495.6644401429[/C][/ROW]
[ROW][C]43[/C][C]54506[/C][C]60817.1282130132[/C][C]-6311.12821301316[/C][/ROW]
[ROW][C]44[/C][C]35838[/C][C]42791.3085742606[/C][C]-6953.30857426059[/C][/ROW]
[ROW][C]45[/C][C]50838[/C][C]32954.9134423183[/C][C]17883.0865576817[/C][/ROW]
[ROW][C]46[/C][C]86997[/C][C]103224.478694892[/C][C]-16227.4786948924[/C][/ROW]
[ROW][C]47[/C][C]33032[/C][C]41060.8137899456[/C][C]-8028.81378994555[/C][/ROW]
[ROW][C]48[/C][C]61704[/C][C]67840.2142529784[/C][C]-6136.2142529784[/C][/ROW]
[ROW][C]49[/C][C]117986[/C][C]79431.650086786[/C][C]38554.349913214[/C][/ROW]
[ROW][C]50[/C][C]56733[/C][C]52608.7352635881[/C][C]4124.26473641192[/C][/ROW]
[ROW][C]51[/C][C]55064[/C][C]59852.6905560694[/C][C]-4788.6905560694[/C][/ROW]
[ROW][C]52[/C][C]5950[/C][C]23034.5809891037[/C][C]-17084.5809891037[/C][/ROW]
[ROW][C]53[/C][C]84607[/C][C]105525.011558469[/C][C]-20918.0115584687[/C][/ROW]
[ROW][C]54[/C][C]32551[/C][C]37706.1419179227[/C][C]-5155.14191792273[/C][/ROW]
[ROW][C]55[/C][C]31701[/C][C]45564.9723359416[/C][C]-13863.9723359416[/C][/ROW]
[ROW][C]56[/C][C]71170[/C][C]68642.9488507536[/C][C]2527.05114924639[/C][/ROW]
[ROW][C]57[/C][C]101773[/C][C]75867.8805934345[/C][C]25905.1194065655[/C][/ROW]
[ROW][C]58[/C][C]101653[/C][C]75049.6613132471[/C][C]26603.3386867529[/C][/ROW]
[ROW][C]59[/C][C]81493[/C][C]82384.4402367581[/C][C]-891.440236758133[/C][/ROW]
[ROW][C]60[/C][C]55901[/C][C]62761.0308823092[/C][C]-6860.03088230918[/C][/ROW]
[ROW][C]61[/C][C]109104[/C][C]93160.137872525[/C][C]15943.8621274751[/C][/ROW]
[ROW][C]62[/C][C]114425[/C][C]77469.1127721154[/C][C]36955.8872278845[/C][/ROW]
[ROW][C]63[/C][C]36311[/C][C]56333.0728767551[/C][C]-20022.0728767551[/C][/ROW]
[ROW][C]64[/C][C]70027[/C][C]73212.1487730235[/C][C]-3185.1487730235[/C][/ROW]
[ROW][C]65[/C][C]73713[/C][C]63535.848496761[/C][C]10177.151503239[/C][/ROW]
[ROW][C]66[/C][C]40671[/C][C]57417.6538655545[/C][C]-16746.6538655545[/C][/ROW]
[ROW][C]67[/C][C]89041[/C][C]53689.5901184299[/C][C]35351.4098815701[/C][/ROW]
[ROW][C]68[/C][C]57231[/C][C]58108.9162217132[/C][C]-877.916221713231[/C][/ROW]
[ROW][C]69[/C][C]68608[/C][C]63452.1990880404[/C][C]5155.80091195959[/C][/ROW]
[ROW][C]70[/C][C]59155[/C][C]45424.4550996542[/C][C]13730.5449003458[/C][/ROW]
[ROW][C]71[/C][C]55827[/C][C]52425.7663489163[/C][C]3401.23365108372[/C][/ROW]
[ROW][C]72[/C][C]22618[/C][C]36259.3338577436[/C][C]-13641.3338577436[/C][/ROW]
[ROW][C]73[/C][C]58425[/C][C]55919.53510477[/C][C]2505.46489522999[/C][/ROW]
[ROW][C]74[/C][C]65724[/C][C]61299.792881745[/C][C]4424.20711825498[/C][/ROW]
[ROW][C]75[/C][C]56979[/C][C]51803.9614026993[/C][C]5175.0385973007[/C][/ROW]
[ROW][C]76[/C][C]72369[/C][C]78681.2072117131[/C][C]-6312.20721171312[/C][/ROW]
[ROW][C]77[/C][C]79194[/C][C]81524.5210317987[/C][C]-2330.52103179869[/C][/ROW]
[ROW][C]78[/C][C]202316[/C][C]54942.9391035605[/C][C]147373.06089644[/C][/ROW]
[ROW][C]79[/C][C]44970[/C][C]46606.5205570715[/C][C]-1636.52055707149[/C][/ROW]
[ROW][C]80[/C][C]49319[/C][C]54682.2117881239[/C][C]-5363.21178812393[/C][/ROW]
[ROW][C]81[/C][C]36252[/C][C]46752.8025320592[/C][C]-10500.8025320592[/C][/ROW]
[ROW][C]82[/C][C]75741[/C][C]105749.380999626[/C][C]-30008.3809996259[/C][/ROW]
[ROW][C]83[/C][C]38417[/C][C]54908.8390772552[/C][C]-16491.8390772552[/C][/ROW]
[ROW][C]84[/C][C]64102[/C][C]64714.4620430392[/C][C]-612.462043039192[/C][/ROW]
[ROW][C]85[/C][C]56622[/C][C]38547.7293178041[/C][C]18074.2706821959[/C][/ROW]
[ROW][C]86[/C][C]15430[/C][C]43906.6612967676[/C][C]-28476.6612967676[/C][/ROW]
[ROW][C]87[/C][C]72571[/C][C]50394.7620528908[/C][C]22176.2379471092[/C][/ROW]
[ROW][C]88[/C][C]67271[/C][C]80664.299959612[/C][C]-13393.299959612[/C][/ROW]
[ROW][C]89[/C][C]43460[/C][C]71667.1052188169[/C][C]-28207.1052188169[/C][/ROW]
[ROW][C]90[/C][C]99501[/C][C]44749.2011858091[/C][C]54751.7988141909[/C][/ROW]
[ROW][C]91[/C][C]28340[/C][C]60575.4428448344[/C][C]-32235.4428448344[/C][/ROW]
[ROW][C]92[/C][C]76013[/C][C]65486.2020504228[/C][C]10526.7979495772[/C][/ROW]
[ROW][C]93[/C][C]37361[/C][C]54361.1553508162[/C][C]-17000.1553508162[/C][/ROW]
[ROW][C]94[/C][C]48204[/C][C]64456.1895641211[/C][C]-16252.1895641211[/C][/ROW]
[ROW][C]95[/C][C]76168[/C][C]75440.9595525919[/C][C]727.040447408095[/C][/ROW]
[ROW][C]96[/C][C]85168[/C][C]58813.049023934[/C][C]26354.950976066[/C][/ROW]
[ROW][C]97[/C][C]125410[/C][C]63067.8591592545[/C][C]62342.1408407455[/C][/ROW]
[ROW][C]98[/C][C]123328[/C][C]81213.1244483633[/C][C]42114.8755516367[/C][/ROW]
[ROW][C]99[/C][C]83038[/C][C]79899.5754286981[/C][C]3138.42457130193[/C][/ROW]
[ROW][C]100[/C][C]120087[/C][C]90555.4387699036[/C][C]29531.5612300964[/C][/ROW]
[ROW][C]101[/C][C]91939[/C][C]69636.5443914236[/C][C]22302.4556085764[/C][/ROW]
[ROW][C]102[/C][C]103646[/C][C]75608.826820456[/C][C]28037.173179544[/C][/ROW]
[ROW][C]103[/C][C]29467[/C][C]50676.6906465778[/C][C]-21209.6906465778[/C][/ROW]
[ROW][C]104[/C][C]43750[/C][C]29855.198064497[/C][C]13894.801935503[/C][/ROW]
[ROW][C]105[/C][C]34497[/C][C]54303.2838085471[/C][C]-19806.2838085471[/C][/ROW]
[ROW][C]106[/C][C]66477[/C][C]56765.3701869858[/C][C]9711.62981301416[/C][/ROW]
[ROW][C]107[/C][C]71181[/C][C]59608.2911582117[/C][C]11572.7088417883[/C][/ROW]
[ROW][C]108[/C][C]74482[/C][C]76214.922323709[/C][C]-1732.92232370905[/C][/ROW]
[ROW][C]109[/C][C]174949[/C][C]41612.1705417544[/C][C]133336.829458246[/C][/ROW]
[ROW][C]110[/C][C]46765[/C][C]45706.7487512476[/C][C]1058.25124875238[/C][/ROW]
[ROW][C]111[/C][C]90257[/C][C]81057.9634593625[/C][C]9199.03654063751[/C][/ROW]
[ROW][C]112[/C][C]51370[/C][C]62715.7445522698[/C][C]-11345.7445522698[/C][/ROW]
[ROW][C]113[/C][C]1168[/C][C]23544.7813470792[/C][C]-22376.7813470792[/C][/ROW]
[ROW][C]114[/C][C]51360[/C][C]54482.4611803187[/C][C]-3122.46118031869[/C][/ROW]
[ROW][C]115[/C][C]25162[/C][C]37450.8954042215[/C][C]-12288.8954042215[/C][/ROW]
[ROW][C]116[/C][C]21067[/C][C]47710.7818837515[/C][C]-26643.7818837515[/C][/ROW]
[ROW][C]117[/C][C]58233[/C][C]96150.927380836[/C][C]-37917.927380836[/C][/ROW]
[ROW][C]118[/C][C]855[/C][C]22976.2776313041[/C][C]-22121.2776313041[/C][/ROW]
[ROW][C]119[/C][C]85903[/C][C]69736.6068728046[/C][C]16166.3931271954[/C][/ROW]
[ROW][C]120[/C][C]14116[/C][C]25477.8716573825[/C][C]-11361.8716573825[/C][/ROW]
[ROW][C]121[/C][C]57637[/C][C]72074.1782057942[/C][C]-14437.1782057942[/C][/ROW]
[ROW][C]122[/C][C]94137[/C][C]61167.1391778489[/C][C]32969.8608221511[/C][/ROW]
[ROW][C]123[/C][C]62147[/C][C]41727.9893878782[/C][C]20419.0106121218[/C][/ROW]
[ROW][C]124[/C][C]62832[/C][C]40537.3117658165[/C][C]22294.6882341835[/C][/ROW]
[ROW][C]125[/C][C]8773[/C][C]25982.6984037257[/C][C]-17209.6984037257[/C][/ROW]
[ROW][C]126[/C][C]63785[/C][C]45645.1819924989[/C][C]18139.8180075011[/C][/ROW]
[ROW][C]127[/C][C]65196[/C][C]77828.2649276964[/C][C]-12632.2649276964[/C][/ROW]
[ROW][C]128[/C][C]73087[/C][C]62947.9048528652[/C][C]10139.0951471347[/C][/ROW]
[ROW][C]129[/C][C]72631[/C][C]87148.2745387394[/C][C]-14517.2745387394[/C][/ROW]
[ROW][C]130[/C][C]86281[/C][C]82892.8605050476[/C][C]3388.1394949524[/C][/ROW]
[ROW][C]131[/C][C]162365[/C][C]97967.2976667256[/C][C]64397.7023332744[/C][/ROW]
[ROW][C]132[/C][C]56530[/C][C]62847.1703005667[/C][C]-6317.17030056673[/C][/ROW]
[ROW][C]133[/C][C]35606[/C][C]39615.7588747567[/C][C]-4009.75887475667[/C][/ROW]
[ROW][C]134[/C][C]70111[/C][C]77067.6786751289[/C][C]-6956.67867512889[/C][/ROW]
[ROW][C]135[/C][C]92046[/C][C]101859.456642891[/C][C]-9813.45664289127[/C][/ROW]
[ROW][C]136[/C][C]63989[/C][C]32120.0384597316[/C][C]31868.9615402684[/C][/ROW]
[ROW][C]137[/C][C]104911[/C][C]84824.543709544[/C][C]20086.456290456[/C][/ROW]
[ROW][C]138[/C][C]43448[/C][C]45011.3834165725[/C][C]-1563.38341657252[/C][/ROW]
[ROW][C]139[/C][C]60029[/C][C]72606.7186851103[/C][C]-12577.7186851103[/C][/ROW]
[ROW][C]140[/C][C]38650[/C][C]56709.585940466[/C][C]-18059.585940466[/C][/ROW]
[ROW][C]141[/C][C]47261[/C][C]43957.064661551[/C][C]3303.93533844903[/C][/ROW]
[ROW][C]142[/C][C]73586[/C][C]83917.9637606093[/C][C]-10331.9637606093[/C][/ROW]
[ROW][C]143[/C][C]83042[/C][C]65046.797902886[/C][C]17995.202097114[/C][/ROW]
[ROW][C]144[/C][C]37238[/C][C]65787.8574168414[/C][C]-28549.8574168414[/C][/ROW]
[ROW][C]145[/C][C]63958[/C][C]81245.701660797[/C][C]-17287.701660797[/C][/ROW]
[ROW][C]146[/C][C]78956[/C][C]39578.121177248[/C][C]39377.878822752[/C][/ROW]
[ROW][C]147[/C][C]99518[/C][C]53768.8898228931[/C][C]45749.1101771069[/C][/ROW]
[ROW][C]148[/C][C]111436[/C][C]75248.6349014785[/C][C]36187.3650985215[/C][/ROW]
[ROW][C]149[/C][C]0[/C][C]18447.5260358571[/C][C]-18447.5260358571[/C][/ROW]
[ROW][C]150[/C][C]6023[/C][C]21788.8330045398[/C][C]-15765.8330045398[/C][/ROW]
[ROW][C]151[/C][C]0[/C][C]18461.1162064098[/C][C]-18461.1162064098[/C][/ROW]
[ROW][C]152[/C][C]0[/C][C]18586.2560313374[/C][C]-18586.2560313374[/C][/ROW]
[ROW][C]153[/C][C]0[/C][C]18399.3960880935[/C][C]-18399.3960880935[/C][/ROW]
[ROW][C]154[/C][C]0[/C][C]18387.3636011526[/C][C]-18387.3636011526[/C][/ROW]
[ROW][C]155[/C][C]42564[/C][C]59232.8097027153[/C][C]-16668.8097027153[/C][/ROW]
[ROW][C]156[/C][C]38885[/C][C]73906.4233781938[/C][C]-35021.4233781938[/C][/ROW]
[ROW][C]157[/C][C]0[/C][C]18351.2661403299[/C][C]-18351.2661403299[/C][/ROW]
[ROW][C]158[/C][C]0[/C][C]18417.2335954319[/C][C]-18417.2335954319[/C][/ROW]
[ROW][C]159[/C][C]1644[/C][C]19099.3904931261[/C][C]-17455.3904931261[/C][/ROW]
[ROW][C]160[/C][C]6179[/C][C]29264.8843082876[/C][C]-23085.8843082876[/C][/ROW]
[ROW][C]161[/C][C]3926[/C][C]23186.1261693899[/C][C]-19260.1261693899[/C][/ROW]
[ROW][C]162[/C][C]23238[/C][C]41519.4988285508[/C][C]-18281.4988285508[/C][/ROW]
[ROW][C]163[/C][C]0[/C][C]18651.3960651848[/C][C]-18651.3960651848[/C][/ROW]
[ROW][C]164[/C][C]49288[/C][C]49606.8865326141[/C][C]-318.886532614063[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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
19555676798.620362941518757.3796370585
25456547490.49720730317074.50279269694
36301656496.99580141366519.00419858643
47977455192.538529172824581.4614708272
53125848254.1047378831-16996.1047378831
65249131492.768301486420998.2316985136
791256106238.879909907-14982.8799099069
82280729979.4555360188-7172.45553601878
97741149268.280948466228142.7190515338
104882158939.7833101574-10118.7833101574
115229572198.9331386855-19903.9331386855
126326259717.93195355143544.06804644859
135046650502.6335835477-36.6335835477239
146293275570.0365290489-12638.0365290489
153843964681.2607035456-26242.2607035456
167081788926.3217113729-18109.3217113729
1710596576529.577367407629435.4226325924
187379576741.531003261-2946.531003261
198204354650.79415176927392.205848231
207434968369.9588490935979.04115090702
218220474243.7559990017960.24400099903
2255709109810.31626933-54101.3162693296
233713749331.4472521496-12194.4472521496
247078053546.855751907817233.1442480922
255502753266.22991454331760.77008545675
265669957222.8361395961-523.836139596096
276591164290.97506918821620.02493081179
285631675326.2886483913-19010.2886483913
292698250022.0072739054-23040.0072739054
305462878416.4333570421-23788.4333570421
319675076585.568960280120164.4310397199
325300961045.1372998608-8036.13729986079
336466488057.0554075621-23393.0554075621
343699043201.6244989628-6211.62449896281
358522491956.0118053225-6732.01180532247
363704866046.4408153124-28998.4408153124
3759635101818.865708081-42183.8657080807
384205156753.4219053818-14702.4219053818
392699845060.2858913316-18062.2858913316
406371781289.2806085454-17572.2806085454
415507176902.5957693415-21831.5957693415
424000154496.6644401429-14495.6644401429
435450660817.1282130132-6311.12821301316
443583842791.3085742606-6953.30857426059
455083832954.913442318317883.0865576817
4686997103224.478694892-16227.4786948924
473303241060.8137899456-8028.81378994555
486170467840.2142529784-6136.2142529784
4911798679431.65008678638554.349913214
505673352608.73526358814124.26473641192
515506459852.6905560694-4788.6905560694
52595023034.5809891037-17084.5809891037
5384607105525.011558469-20918.0115584687
543255137706.1419179227-5155.14191792273
553170145564.9723359416-13863.9723359416
567117068642.94885075362527.05114924639
5710177375867.880593434525905.1194065655
5810165375049.661313247126603.3386867529
598149382384.4402367581-891.440236758133
605590162761.0308823092-6860.03088230918
6110910493160.13787252515943.8621274751
6211442577469.112772115436955.8872278845
633631156333.0728767551-20022.0728767551
647002773212.1487730235-3185.1487730235
657371363535.84849676110177.151503239
664067157417.6538655545-16746.6538655545
678904153689.590118429935351.4098815701
685723158108.9162217132-877.916221713231
696860863452.19908804045155.80091195959
705915545424.455099654213730.5449003458
715582752425.76634891633401.23365108372
722261836259.3338577436-13641.3338577436
735842555919.535104772505.46489522999
746572461299.7928817454424.20711825498
755697951803.96140269935175.0385973007
767236978681.2072117131-6312.20721171312
777919481524.5210317987-2330.52103179869
7820231654942.9391035605147373.06089644
794497046606.5205570715-1636.52055707149
804931954682.2117881239-5363.21178812393
813625246752.8025320592-10500.8025320592
8275741105749.380999626-30008.3809996259
833841754908.8390772552-16491.8390772552
846410264714.4620430392-612.462043039192
855662238547.729317804118074.2706821959
861543043906.6612967676-28476.6612967676
877257150394.762052890822176.2379471092
886727180664.299959612-13393.299959612
894346071667.1052188169-28207.1052188169
909950144749.201185809154751.7988141909
912834060575.4428448344-32235.4428448344
927601365486.202050422810526.7979495772
933736154361.1553508162-17000.1553508162
944820464456.1895641211-16252.1895641211
957616875440.9595525919727.040447408095
968516858813.04902393426354.950976066
9712541063067.859159254562342.1408407455
9812332881213.124448363342114.8755516367
998303879899.57542869813138.42457130193
10012008790555.438769903629531.5612300964
1019193969636.544391423622302.4556085764
10210364675608.82682045628037.173179544
1032946750676.6906465778-21209.6906465778
1044375029855.19806449713894.801935503
1053449754303.2838085471-19806.2838085471
1066647756765.37018698589711.62981301416
1077118159608.291158211711572.7088417883
1087448276214.922323709-1732.92232370905
10917494941612.1705417544133336.829458246
1104676545706.74875124761058.25124875238
1119025781057.96345936259199.03654063751
1125137062715.7445522698-11345.7445522698
113116823544.7813470792-22376.7813470792
1145136054482.4611803187-3122.46118031869
1152516237450.8954042215-12288.8954042215
1162106747710.7818837515-26643.7818837515
1175823396150.927380836-37917.927380836
11885522976.2776313041-22121.2776313041
1198590369736.606872804616166.3931271954
1201411625477.8716573825-11361.8716573825
1215763772074.1782057942-14437.1782057942
1229413761167.139177848932969.8608221511
1236214741727.989387878220419.0106121218
1246283240537.311765816522294.6882341835
125877325982.6984037257-17209.6984037257
1266378545645.181992498918139.8180075011
1276519677828.2649276964-12632.2649276964
1287308762947.904852865210139.0951471347
1297263187148.2745387394-14517.2745387394
1308628182892.86050504763388.1394949524
13116236597967.297666725664397.7023332744
1325653062847.1703005667-6317.17030056673
1333560639615.7588747567-4009.75887475667
1347011177067.6786751289-6956.67867512889
13592046101859.456642891-9813.45664289127
1366398932120.038459731631868.9615402684
13710491184824.54370954420086.456290456
1384344845011.3834165725-1563.38341657252
1396002972606.7186851103-12577.7186851103
1403865056709.585940466-18059.585940466
1414726143957.0646615513303.93533844903
1427358683917.9637606093-10331.9637606093
1438304265046.79790288617995.202097114
1443723865787.8574168414-28549.8574168414
1456395881245.701660797-17287.701660797
1467895639578.12117724839377.878822752
1479951853768.889822893145749.1101771069
14811143675248.634901478536187.3650985215
149018447.5260358571-18447.5260358571
150602321788.8330045398-15765.8330045398
151018461.1162064098-18461.1162064098
152018586.2560313374-18586.2560313374
153018399.3960880935-18399.3960880935
154018387.3636011526-18387.3636011526
1554256459232.8097027153-16668.8097027153
1563888573906.4233781938-35021.4233781938
157018351.2661403299-18351.2661403299
158018417.2335954319-18417.2335954319
159164419099.3904931261-17455.3904931261
160617929264.8843082876-23085.8843082876
161392623186.1261693899-19260.1261693899
1622323841519.4988285508-18281.4988285508
163018651.3960651848-18651.3960651848
1644928849606.8865326141-318.886532614063







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2611215428982810.5222430857965620.738878457101719
80.1313431331676260.2626862663352520.868656866832374
90.2265975043177670.4531950086355340.773402495682233
100.1330721252188290.2661442504376590.866927874781171
110.0740943823979740.1481887647959480.925905617602026
120.06413355444206570.1282671088841310.935866445557934
130.03526680243633810.07053360487267620.964733197563662
140.01825570463970660.03651140927941310.981744295360293
150.01036271349652730.02072542699305460.989637286503473
160.00512877432464080.01025754864928160.994871225675359
170.07052128982397520.141042579647950.929478710176025
180.04631700417108270.09263400834216530.953682995828917
190.06243364940637190.1248672988127440.937566350593628
200.04226210353370560.08452420706741120.957737896466294
210.02895100601166210.05790201202332420.971048993988338
220.05778576053461430.1155715210692290.942214239465386
230.04600979364035860.09201958728071730.953990206359641
240.03813679445814810.07627358891629620.961863205541852
250.0256941890922650.05138837818452990.974305810907735
260.01664113381349450.03328226762698910.983358866186505
270.01061441791724210.02122883583448430.989385582082758
280.007079836829769040.01415967365953810.992920163170231
290.00739556774139980.01479113548279960.9926044322586
300.005039909755434370.01007981951086870.994960090244566
310.009130303312541850.01826060662508370.990869696687458
320.005847743030680220.01169548606136040.99415225696932
330.003946090288505010.007892180577010030.996053909711495
340.002506014987550010.005012029975100010.99749398501245
350.00176240641771540.003524812835430790.998237593582285
360.00174069689004020.00348139378008040.99825930310996
370.001688023806431680.003376047612863360.998311976193568
380.001083724646080450.00216744929216090.99891627535392
390.0007632139917599450.001526427983519890.99923678600824
400.0004915350477426010.0009830700954852010.999508464952257
410.0003238871588928630.0006477743177857270.999676112841107
420.0001987305888504420.0003974611777008830.99980126941115
430.0001297590478304280.0002595180956608560.99987024095217
447.54201228930624e-050.0001508402457861250.999924579877107
455.7279356227968e-050.0001145587124559360.999942720643772
463.7566061216372e-057.5132122432744e-050.999962433938784
472.1284482932428e-054.2568965864856e-050.999978715517068
481.51884377133979e-053.03768754267957e-050.999984811562287
490.0002149417188593880.0004298834377187760.999785058281141
500.0001352317429374630.0002704634858749250.999864768257062
518.9302606220932e-050.0001786052124418640.999910697393779
528.57866228631547e-050.0001715732457263090.999914213377137
537.83931359857016e-050.0001567862719714030.999921606864014
544.7349813455573e-059.4699626911146e-050.999952650186544
553.1476035359521e-056.29520707190419e-050.999968523964641
562.41540407827636e-054.83080815655273e-050.999975845959217
578.89804458648015e-050.0001779608917296030.999911019554135
580.0001765374980921950.000353074996184390.999823462501908
590.0001250920417201960.0002501840834403910.99987490795828
608.14678430346914e-050.0001629356860693830.999918532156965
619.69617632391493e-050.0001939235264782990.999903038236761
620.0002487671823885830.0004975343647771650.999751232817611
630.000242080016617380.000484160033234760.999757919983383
640.0001602888472712810.0003205776945425630.999839711152729
650.0001187773044763810.0002375546089527620.999881222695524
660.0001007568707836250.000201513741567250.999899243129216
670.0001716966411703750.000343393282340750.99982830335883
680.0001095682374270150.0002191364748540290.999890431762573
696.99965307765621e-050.0001399930615531240.999930003469223
704.61411955374892e-059.22823910749783e-050.999953858804463
712.81900685223287e-055.63801370446575e-050.999971809931478
722.49190090687725e-054.98380181375451e-050.999975080990931
731.52888239637851e-053.05776479275701e-050.999984711176036
749.32851371463753e-061.86570274292751e-050.999990671486285
755.60898461498376e-061.12179692299675e-050.999994391015385
763.66116114299874e-067.32232228599748e-060.999996338838857
772.31765876814743e-064.63531753629486e-060.999997682341232
780.1787747007677960.3575494015355920.821225299232204
790.1555977542784230.3111955085568460.844402245721577
800.1366904687117840.2733809374235670.863309531288216
810.1257140339143890.2514280678287780.874285966085611
820.1479319199476620.2958638398953250.852068080052338
830.1448871770927950.289774354185590.855112822907205
840.1245880901928750.2491761803857490.875411909807125
850.1054960275141950.2109920550283910.894503972485804
860.1275018353683250.2550036707366510.872498164631675
870.1109791749782520.2219583499565030.889020825021748
880.1033989357914210.2067978715828430.896601064208579
890.1267903740240.2535807480480.873209625976
900.1940838884633960.3881677769267930.805916111536604
910.2496011943184060.4992023886368110.750398805681594
920.2181837425584540.4363674851169070.781816257441546
930.2231783206684450.4463566413368890.776821679331555
940.2221832613803760.4443665227607520.777816738619624
950.200383076736750.40076615347350.79961692326325
960.1831655232433150.366331046486630.816834476756685
970.2920563481752620.5841126963505250.707943651824738
980.3179908561665170.6359817123330350.682009143833483
990.2824294781967960.5648589563935930.717570521803204
1000.2720138696325540.5440277392651090.727986130367446
1010.2423622100460510.4847244200921020.757637789953949
1020.22490954502390.4498190900477990.7750904549761
1030.2433120988582520.4866241977165030.756687901141748
1040.2094617108673140.4189234217346270.790538289132686
1050.2199426729121660.4398853458243310.780057327087834
1060.1865970608086590.3731941216173190.813402939191341
1070.1566289929317950.313257985863590.843371007068205
1080.1351158757869690.2702317515739390.864884124213031
1090.9646790754162310.07064184916753840.0353209245837692
1100.9551287383013230.08974252339735470.0448712616986774
1110.9424254245693790.1151491508612410.0575745754306205
1120.9337713526578180.1324572946843630.0662286473421816
1130.9362945607187080.1274108785625850.0637054392812923
1140.9210206743112440.1579586513775110.0789793256887556
1150.9106691098971090.1786617802057810.0893308901028907
1160.9247536593872220.1504926812255560.0752463406127778
1170.9681002132143140.06379957357137230.0318997867856862
1180.9726757573205210.05464848535895760.0273242426794788
1190.9636304789862530.07273904202749410.036369521013747
1200.960575309390310.07884938121938080.0394246906096904
1210.9645029290917020.07099414181659630.0354970709082982
1220.9621722865562360.07565542688752870.0378277134437644
1230.9523470922329880.0953058155340250.0476529077670125
1240.9431326744907810.1137346510184370.0568673255092187
1250.9445208679546670.1109582640906660.0554791320453329
1260.9298475557514660.1403048884970670.0701524442485335
1270.9269621059326180.1460757881347640.0730378940673818
1280.9045195462221880.1909609075556230.0954804537778116
1290.9149201677043380.1701596645913230.0850798322956616
1300.8918517144580390.2162965710839210.108148285541961
1310.9775225101007850.04495497979842920.0224774898992146
1320.968809634853310.06238073029337970.0311903651466898
1330.9590644295681730.0818711408636550.0409355704318275
1340.9472492758741610.1055014482516790.0527507241258394
1350.941206068290910.117587863418180.0587939317090902
1360.9486526653728780.1026946692542440.0513473346271222
1370.9308261921336440.1383476157327120.0691738078663561
1380.906602918304930.186794163390140.0933970816950699
1390.8835755700271920.2328488599456160.116424429972808
1400.882637080610.2347258387799990.11736291939
1410.8428749090707670.3142501818584650.157125090929233
1420.8787146807560480.2425706384879050.121285319243952
1430.8657834837953620.2684330324092760.134216516204638
1440.979222389726410.04155522054717910.0207776102735896
1450.9830213212176290.03395735756474120.0169786787823706
1460.9825634446583520.03487311068329580.0174365553416479
1470.9947821707595570.01043565848088630.00521782924044313
1480.9999949667509151.00664981706796e-055.03324908533981e-06
1490.999981818721453.63625571005388e-051.81812785502694e-05
1500.9999534818942389.30362115248371e-054.65181057624186e-05
1510.9998438600204570.000312279959085560.00015613997954278
1520.9994930772528470.001013845494305180.00050692274715259
1530.9984679794526380.003064041094724020.00153202054736201
1540.9958688035910080.008262392817983160.00413119640899158
1550.9946511452389210.0106977095221580.00534885476107901
1560.9932569148656970.01348617026860520.0067430851343026
1570.9728803007994290.05423939840114290.0271196992005715

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.261121542898281 & 0.522243085796562 & 0.738878457101719 \tabularnewline
8 & 0.131343133167626 & 0.262686266335252 & 0.868656866832374 \tabularnewline
9 & 0.226597504317767 & 0.453195008635534 & 0.773402495682233 \tabularnewline
10 & 0.133072125218829 & 0.266144250437659 & 0.866927874781171 \tabularnewline
11 & 0.074094382397974 & 0.148188764795948 & 0.925905617602026 \tabularnewline
12 & 0.0641335544420657 & 0.128267108884131 & 0.935866445557934 \tabularnewline
13 & 0.0352668024363381 & 0.0705336048726762 & 0.964733197563662 \tabularnewline
14 & 0.0182557046397066 & 0.0365114092794131 & 0.981744295360293 \tabularnewline
15 & 0.0103627134965273 & 0.0207254269930546 & 0.989637286503473 \tabularnewline
16 & 0.0051287743246408 & 0.0102575486492816 & 0.994871225675359 \tabularnewline
17 & 0.0705212898239752 & 0.14104257964795 & 0.929478710176025 \tabularnewline
18 & 0.0463170041710827 & 0.0926340083421653 & 0.953682995828917 \tabularnewline
19 & 0.0624336494063719 & 0.124867298812744 & 0.937566350593628 \tabularnewline
20 & 0.0422621035337056 & 0.0845242070674112 & 0.957737896466294 \tabularnewline
21 & 0.0289510060116621 & 0.0579020120233242 & 0.971048993988338 \tabularnewline
22 & 0.0577857605346143 & 0.115571521069229 & 0.942214239465386 \tabularnewline
23 & 0.0460097936403586 & 0.0920195872807173 & 0.953990206359641 \tabularnewline
24 & 0.0381367944581481 & 0.0762735889162962 & 0.961863205541852 \tabularnewline
25 & 0.025694189092265 & 0.0513883781845299 & 0.974305810907735 \tabularnewline
26 & 0.0166411338134945 & 0.0332822676269891 & 0.983358866186505 \tabularnewline
27 & 0.0106144179172421 & 0.0212288358344843 & 0.989385582082758 \tabularnewline
28 & 0.00707983682976904 & 0.0141596736595381 & 0.992920163170231 \tabularnewline
29 & 0.0073955677413998 & 0.0147911354827996 & 0.9926044322586 \tabularnewline
30 & 0.00503990975543437 & 0.0100798195108687 & 0.994960090244566 \tabularnewline
31 & 0.00913030331254185 & 0.0182606066250837 & 0.990869696687458 \tabularnewline
32 & 0.00584774303068022 & 0.0116954860613604 & 0.99415225696932 \tabularnewline
33 & 0.00394609028850501 & 0.00789218057701003 & 0.996053909711495 \tabularnewline
34 & 0.00250601498755001 & 0.00501202997510001 & 0.99749398501245 \tabularnewline
35 & 0.0017624064177154 & 0.00352481283543079 & 0.998237593582285 \tabularnewline
36 & 0.0017406968900402 & 0.0034813937800804 & 0.99825930310996 \tabularnewline
37 & 0.00168802380643168 & 0.00337604761286336 & 0.998311976193568 \tabularnewline
38 & 0.00108372464608045 & 0.0021674492921609 & 0.99891627535392 \tabularnewline
39 & 0.000763213991759945 & 0.00152642798351989 & 0.99923678600824 \tabularnewline
40 & 0.000491535047742601 & 0.000983070095485201 & 0.999508464952257 \tabularnewline
41 & 0.000323887158892863 & 0.000647774317785727 & 0.999676112841107 \tabularnewline
42 & 0.000198730588850442 & 0.000397461177700883 & 0.99980126941115 \tabularnewline
43 & 0.000129759047830428 & 0.000259518095660856 & 0.99987024095217 \tabularnewline
44 & 7.54201228930624e-05 & 0.000150840245786125 & 0.999924579877107 \tabularnewline
45 & 5.7279356227968e-05 & 0.000114558712455936 & 0.999942720643772 \tabularnewline
46 & 3.7566061216372e-05 & 7.5132122432744e-05 & 0.999962433938784 \tabularnewline
47 & 2.1284482932428e-05 & 4.2568965864856e-05 & 0.999978715517068 \tabularnewline
48 & 1.51884377133979e-05 & 3.03768754267957e-05 & 0.999984811562287 \tabularnewline
49 & 0.000214941718859388 & 0.000429883437718776 & 0.999785058281141 \tabularnewline
50 & 0.000135231742937463 & 0.000270463485874925 & 0.999864768257062 \tabularnewline
51 & 8.9302606220932e-05 & 0.000178605212441864 & 0.999910697393779 \tabularnewline
52 & 8.57866228631547e-05 & 0.000171573245726309 & 0.999914213377137 \tabularnewline
53 & 7.83931359857016e-05 & 0.000156786271971403 & 0.999921606864014 \tabularnewline
54 & 4.7349813455573e-05 & 9.4699626911146e-05 & 0.999952650186544 \tabularnewline
55 & 3.1476035359521e-05 & 6.29520707190419e-05 & 0.999968523964641 \tabularnewline
56 & 2.41540407827636e-05 & 4.83080815655273e-05 & 0.999975845959217 \tabularnewline
57 & 8.89804458648015e-05 & 0.000177960891729603 & 0.999911019554135 \tabularnewline
58 & 0.000176537498092195 & 0.00035307499618439 & 0.999823462501908 \tabularnewline
59 & 0.000125092041720196 & 0.000250184083440391 & 0.99987490795828 \tabularnewline
60 & 8.14678430346914e-05 & 0.000162935686069383 & 0.999918532156965 \tabularnewline
61 & 9.69617632391493e-05 & 0.000193923526478299 & 0.999903038236761 \tabularnewline
62 & 0.000248767182388583 & 0.000497534364777165 & 0.999751232817611 \tabularnewline
63 & 0.00024208001661738 & 0.00048416003323476 & 0.999757919983383 \tabularnewline
64 & 0.000160288847271281 & 0.000320577694542563 & 0.999839711152729 \tabularnewline
65 & 0.000118777304476381 & 0.000237554608952762 & 0.999881222695524 \tabularnewline
66 & 0.000100756870783625 & 0.00020151374156725 & 0.999899243129216 \tabularnewline
67 & 0.000171696641170375 & 0.00034339328234075 & 0.99982830335883 \tabularnewline
68 & 0.000109568237427015 & 0.000219136474854029 & 0.999890431762573 \tabularnewline
69 & 6.99965307765621e-05 & 0.000139993061553124 & 0.999930003469223 \tabularnewline
70 & 4.61411955374892e-05 & 9.22823910749783e-05 & 0.999953858804463 \tabularnewline
71 & 2.81900685223287e-05 & 5.63801370446575e-05 & 0.999971809931478 \tabularnewline
72 & 2.49190090687725e-05 & 4.98380181375451e-05 & 0.999975080990931 \tabularnewline
73 & 1.52888239637851e-05 & 3.05776479275701e-05 & 0.999984711176036 \tabularnewline
74 & 9.32851371463753e-06 & 1.86570274292751e-05 & 0.999990671486285 \tabularnewline
75 & 5.60898461498376e-06 & 1.12179692299675e-05 & 0.999994391015385 \tabularnewline
76 & 3.66116114299874e-06 & 7.32232228599748e-06 & 0.999996338838857 \tabularnewline
77 & 2.31765876814743e-06 & 4.63531753629486e-06 & 0.999997682341232 \tabularnewline
78 & 0.178774700767796 & 0.357549401535592 & 0.821225299232204 \tabularnewline
79 & 0.155597754278423 & 0.311195508556846 & 0.844402245721577 \tabularnewline
80 & 0.136690468711784 & 0.273380937423567 & 0.863309531288216 \tabularnewline
81 & 0.125714033914389 & 0.251428067828778 & 0.874285966085611 \tabularnewline
82 & 0.147931919947662 & 0.295863839895325 & 0.852068080052338 \tabularnewline
83 & 0.144887177092795 & 0.28977435418559 & 0.855112822907205 \tabularnewline
84 & 0.124588090192875 & 0.249176180385749 & 0.875411909807125 \tabularnewline
85 & 0.105496027514195 & 0.210992055028391 & 0.894503972485804 \tabularnewline
86 & 0.127501835368325 & 0.255003670736651 & 0.872498164631675 \tabularnewline
87 & 0.110979174978252 & 0.221958349956503 & 0.889020825021748 \tabularnewline
88 & 0.103398935791421 & 0.206797871582843 & 0.896601064208579 \tabularnewline
89 & 0.126790374024 & 0.253580748048 & 0.873209625976 \tabularnewline
90 & 0.194083888463396 & 0.388167776926793 & 0.805916111536604 \tabularnewline
91 & 0.249601194318406 & 0.499202388636811 & 0.750398805681594 \tabularnewline
92 & 0.218183742558454 & 0.436367485116907 & 0.781816257441546 \tabularnewline
93 & 0.223178320668445 & 0.446356641336889 & 0.776821679331555 \tabularnewline
94 & 0.222183261380376 & 0.444366522760752 & 0.777816738619624 \tabularnewline
95 & 0.20038307673675 & 0.4007661534735 & 0.79961692326325 \tabularnewline
96 & 0.183165523243315 & 0.36633104648663 & 0.816834476756685 \tabularnewline
97 & 0.292056348175262 & 0.584112696350525 & 0.707943651824738 \tabularnewline
98 & 0.317990856166517 & 0.635981712333035 & 0.682009143833483 \tabularnewline
99 & 0.282429478196796 & 0.564858956393593 & 0.717570521803204 \tabularnewline
100 & 0.272013869632554 & 0.544027739265109 & 0.727986130367446 \tabularnewline
101 & 0.242362210046051 & 0.484724420092102 & 0.757637789953949 \tabularnewline
102 & 0.2249095450239 & 0.449819090047799 & 0.7750904549761 \tabularnewline
103 & 0.243312098858252 & 0.486624197716503 & 0.756687901141748 \tabularnewline
104 & 0.209461710867314 & 0.418923421734627 & 0.790538289132686 \tabularnewline
105 & 0.219942672912166 & 0.439885345824331 & 0.780057327087834 \tabularnewline
106 & 0.186597060808659 & 0.373194121617319 & 0.813402939191341 \tabularnewline
107 & 0.156628992931795 & 0.31325798586359 & 0.843371007068205 \tabularnewline
108 & 0.135115875786969 & 0.270231751573939 & 0.864884124213031 \tabularnewline
109 & 0.964679075416231 & 0.0706418491675384 & 0.0353209245837692 \tabularnewline
110 & 0.955128738301323 & 0.0897425233973547 & 0.0448712616986774 \tabularnewline
111 & 0.942425424569379 & 0.115149150861241 & 0.0575745754306205 \tabularnewline
112 & 0.933771352657818 & 0.132457294684363 & 0.0662286473421816 \tabularnewline
113 & 0.936294560718708 & 0.127410878562585 & 0.0637054392812923 \tabularnewline
114 & 0.921020674311244 & 0.157958651377511 & 0.0789793256887556 \tabularnewline
115 & 0.910669109897109 & 0.178661780205781 & 0.0893308901028907 \tabularnewline
116 & 0.924753659387222 & 0.150492681225556 & 0.0752463406127778 \tabularnewline
117 & 0.968100213214314 & 0.0637995735713723 & 0.0318997867856862 \tabularnewline
118 & 0.972675757320521 & 0.0546484853589576 & 0.0273242426794788 \tabularnewline
119 & 0.963630478986253 & 0.0727390420274941 & 0.036369521013747 \tabularnewline
120 & 0.96057530939031 & 0.0788493812193808 & 0.0394246906096904 \tabularnewline
121 & 0.964502929091702 & 0.0709941418165963 & 0.0354970709082982 \tabularnewline
122 & 0.962172286556236 & 0.0756554268875287 & 0.0378277134437644 \tabularnewline
123 & 0.952347092232988 & 0.095305815534025 & 0.0476529077670125 \tabularnewline
124 & 0.943132674490781 & 0.113734651018437 & 0.0568673255092187 \tabularnewline
125 & 0.944520867954667 & 0.110958264090666 & 0.0554791320453329 \tabularnewline
126 & 0.929847555751466 & 0.140304888497067 & 0.0701524442485335 \tabularnewline
127 & 0.926962105932618 & 0.146075788134764 & 0.0730378940673818 \tabularnewline
128 & 0.904519546222188 & 0.190960907555623 & 0.0954804537778116 \tabularnewline
129 & 0.914920167704338 & 0.170159664591323 & 0.0850798322956616 \tabularnewline
130 & 0.891851714458039 & 0.216296571083921 & 0.108148285541961 \tabularnewline
131 & 0.977522510100785 & 0.0449549797984292 & 0.0224774898992146 \tabularnewline
132 & 0.96880963485331 & 0.0623807302933797 & 0.0311903651466898 \tabularnewline
133 & 0.959064429568173 & 0.081871140863655 & 0.0409355704318275 \tabularnewline
134 & 0.947249275874161 & 0.105501448251679 & 0.0527507241258394 \tabularnewline
135 & 0.94120606829091 & 0.11758786341818 & 0.0587939317090902 \tabularnewline
136 & 0.948652665372878 & 0.102694669254244 & 0.0513473346271222 \tabularnewline
137 & 0.930826192133644 & 0.138347615732712 & 0.0691738078663561 \tabularnewline
138 & 0.90660291830493 & 0.18679416339014 & 0.0933970816950699 \tabularnewline
139 & 0.883575570027192 & 0.232848859945616 & 0.116424429972808 \tabularnewline
140 & 0.88263708061 & 0.234725838779999 & 0.11736291939 \tabularnewline
141 & 0.842874909070767 & 0.314250181858465 & 0.157125090929233 \tabularnewline
142 & 0.878714680756048 & 0.242570638487905 & 0.121285319243952 \tabularnewline
143 & 0.865783483795362 & 0.268433032409276 & 0.134216516204638 \tabularnewline
144 & 0.97922238972641 & 0.0415552205471791 & 0.0207776102735896 \tabularnewline
145 & 0.983021321217629 & 0.0339573575647412 & 0.0169786787823706 \tabularnewline
146 & 0.982563444658352 & 0.0348731106832958 & 0.0174365553416479 \tabularnewline
147 & 0.994782170759557 & 0.0104356584808863 & 0.00521782924044313 \tabularnewline
148 & 0.999994966750915 & 1.00664981706796e-05 & 5.03324908533981e-06 \tabularnewline
149 & 0.99998181872145 & 3.63625571005388e-05 & 1.81812785502694e-05 \tabularnewline
150 & 0.999953481894238 & 9.30362115248371e-05 & 4.65181057624186e-05 \tabularnewline
151 & 0.999843860020457 & 0.00031227995908556 & 0.00015613997954278 \tabularnewline
152 & 0.999493077252847 & 0.00101384549430518 & 0.00050692274715259 \tabularnewline
153 & 0.998467979452638 & 0.00306404109472402 & 0.00153202054736201 \tabularnewline
154 & 0.995868803591008 & 0.00826239281798316 & 0.00413119640899158 \tabularnewline
155 & 0.994651145238921 & 0.010697709522158 & 0.00534885476107901 \tabularnewline
156 & 0.993256914865697 & 0.0134861702686052 & 0.0067430851343026 \tabularnewline
157 & 0.972880300799429 & 0.0542393984011429 & 0.0271196992005715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C]0.261121542898281[/C][C]0.522243085796562[/C][C]0.738878457101719[/C][/ROW]
[ROW][C]8[/C][C]0.131343133167626[/C][C]0.262686266335252[/C][C]0.868656866832374[/C][/ROW]
[ROW][C]9[/C][C]0.226597504317767[/C][C]0.453195008635534[/C][C]0.773402495682233[/C][/ROW]
[ROW][C]10[/C][C]0.133072125218829[/C][C]0.266144250437659[/C][C]0.866927874781171[/C][/ROW]
[ROW][C]11[/C][C]0.074094382397974[/C][C]0.148188764795948[/C][C]0.925905617602026[/C][/ROW]
[ROW][C]12[/C][C]0.0641335544420657[/C][C]0.128267108884131[/C][C]0.935866445557934[/C][/ROW]
[ROW][C]13[/C][C]0.0352668024363381[/C][C]0.0705336048726762[/C][C]0.964733197563662[/C][/ROW]
[ROW][C]14[/C][C]0.0182557046397066[/C][C]0.0365114092794131[/C][C]0.981744295360293[/C][/ROW]
[ROW][C]15[/C][C]0.0103627134965273[/C][C]0.0207254269930546[/C][C]0.989637286503473[/C][/ROW]
[ROW][C]16[/C][C]0.0051287743246408[/C][C]0.0102575486492816[/C][C]0.994871225675359[/C][/ROW]
[ROW][C]17[/C][C]0.0705212898239752[/C][C]0.14104257964795[/C][C]0.929478710176025[/C][/ROW]
[ROW][C]18[/C][C]0.0463170041710827[/C][C]0.0926340083421653[/C][C]0.953682995828917[/C][/ROW]
[ROW][C]19[/C][C]0.0624336494063719[/C][C]0.124867298812744[/C][C]0.937566350593628[/C][/ROW]
[ROW][C]20[/C][C]0.0422621035337056[/C][C]0.0845242070674112[/C][C]0.957737896466294[/C][/ROW]
[ROW][C]21[/C][C]0.0289510060116621[/C][C]0.0579020120233242[/C][C]0.971048993988338[/C][/ROW]
[ROW][C]22[/C][C]0.0577857605346143[/C][C]0.115571521069229[/C][C]0.942214239465386[/C][/ROW]
[ROW][C]23[/C][C]0.0460097936403586[/C][C]0.0920195872807173[/C][C]0.953990206359641[/C][/ROW]
[ROW][C]24[/C][C]0.0381367944581481[/C][C]0.0762735889162962[/C][C]0.961863205541852[/C][/ROW]
[ROW][C]25[/C][C]0.025694189092265[/C][C]0.0513883781845299[/C][C]0.974305810907735[/C][/ROW]
[ROW][C]26[/C][C]0.0166411338134945[/C][C]0.0332822676269891[/C][C]0.983358866186505[/C][/ROW]
[ROW][C]27[/C][C]0.0106144179172421[/C][C]0.0212288358344843[/C][C]0.989385582082758[/C][/ROW]
[ROW][C]28[/C][C]0.00707983682976904[/C][C]0.0141596736595381[/C][C]0.992920163170231[/C][/ROW]
[ROW][C]29[/C][C]0.0073955677413998[/C][C]0.0147911354827996[/C][C]0.9926044322586[/C][/ROW]
[ROW][C]30[/C][C]0.00503990975543437[/C][C]0.0100798195108687[/C][C]0.994960090244566[/C][/ROW]
[ROW][C]31[/C][C]0.00913030331254185[/C][C]0.0182606066250837[/C][C]0.990869696687458[/C][/ROW]
[ROW][C]32[/C][C]0.00584774303068022[/C][C]0.0116954860613604[/C][C]0.99415225696932[/C][/ROW]
[ROW][C]33[/C][C]0.00394609028850501[/C][C]0.00789218057701003[/C][C]0.996053909711495[/C][/ROW]
[ROW][C]34[/C][C]0.00250601498755001[/C][C]0.00501202997510001[/C][C]0.99749398501245[/C][/ROW]
[ROW][C]35[/C][C]0.0017624064177154[/C][C]0.00352481283543079[/C][C]0.998237593582285[/C][/ROW]
[ROW][C]36[/C][C]0.0017406968900402[/C][C]0.0034813937800804[/C][C]0.99825930310996[/C][/ROW]
[ROW][C]37[/C][C]0.00168802380643168[/C][C]0.00337604761286336[/C][C]0.998311976193568[/C][/ROW]
[ROW][C]38[/C][C]0.00108372464608045[/C][C]0.0021674492921609[/C][C]0.99891627535392[/C][/ROW]
[ROW][C]39[/C][C]0.000763213991759945[/C][C]0.00152642798351989[/C][C]0.99923678600824[/C][/ROW]
[ROW][C]40[/C][C]0.000491535047742601[/C][C]0.000983070095485201[/C][C]0.999508464952257[/C][/ROW]
[ROW][C]41[/C][C]0.000323887158892863[/C][C]0.000647774317785727[/C][C]0.999676112841107[/C][/ROW]
[ROW][C]42[/C][C]0.000198730588850442[/C][C]0.000397461177700883[/C][C]0.99980126941115[/C][/ROW]
[ROW][C]43[/C][C]0.000129759047830428[/C][C]0.000259518095660856[/C][C]0.99987024095217[/C][/ROW]
[ROW][C]44[/C][C]7.54201228930624e-05[/C][C]0.000150840245786125[/C][C]0.999924579877107[/C][/ROW]
[ROW][C]45[/C][C]5.7279356227968e-05[/C][C]0.000114558712455936[/C][C]0.999942720643772[/C][/ROW]
[ROW][C]46[/C][C]3.7566061216372e-05[/C][C]7.5132122432744e-05[/C][C]0.999962433938784[/C][/ROW]
[ROW][C]47[/C][C]2.1284482932428e-05[/C][C]4.2568965864856e-05[/C][C]0.999978715517068[/C][/ROW]
[ROW][C]48[/C][C]1.51884377133979e-05[/C][C]3.03768754267957e-05[/C][C]0.999984811562287[/C][/ROW]
[ROW][C]49[/C][C]0.000214941718859388[/C][C]0.000429883437718776[/C][C]0.999785058281141[/C][/ROW]
[ROW][C]50[/C][C]0.000135231742937463[/C][C]0.000270463485874925[/C][C]0.999864768257062[/C][/ROW]
[ROW][C]51[/C][C]8.9302606220932e-05[/C][C]0.000178605212441864[/C][C]0.999910697393779[/C][/ROW]
[ROW][C]52[/C][C]8.57866228631547e-05[/C][C]0.000171573245726309[/C][C]0.999914213377137[/C][/ROW]
[ROW][C]53[/C][C]7.83931359857016e-05[/C][C]0.000156786271971403[/C][C]0.999921606864014[/C][/ROW]
[ROW][C]54[/C][C]4.7349813455573e-05[/C][C]9.4699626911146e-05[/C][C]0.999952650186544[/C][/ROW]
[ROW][C]55[/C][C]3.1476035359521e-05[/C][C]6.29520707190419e-05[/C][C]0.999968523964641[/C][/ROW]
[ROW][C]56[/C][C]2.41540407827636e-05[/C][C]4.83080815655273e-05[/C][C]0.999975845959217[/C][/ROW]
[ROW][C]57[/C][C]8.89804458648015e-05[/C][C]0.000177960891729603[/C][C]0.999911019554135[/C][/ROW]
[ROW][C]58[/C][C]0.000176537498092195[/C][C]0.00035307499618439[/C][C]0.999823462501908[/C][/ROW]
[ROW][C]59[/C][C]0.000125092041720196[/C][C]0.000250184083440391[/C][C]0.99987490795828[/C][/ROW]
[ROW][C]60[/C][C]8.14678430346914e-05[/C][C]0.000162935686069383[/C][C]0.999918532156965[/C][/ROW]
[ROW][C]61[/C][C]9.69617632391493e-05[/C][C]0.000193923526478299[/C][C]0.999903038236761[/C][/ROW]
[ROW][C]62[/C][C]0.000248767182388583[/C][C]0.000497534364777165[/C][C]0.999751232817611[/C][/ROW]
[ROW][C]63[/C][C]0.00024208001661738[/C][C]0.00048416003323476[/C][C]0.999757919983383[/C][/ROW]
[ROW][C]64[/C][C]0.000160288847271281[/C][C]0.000320577694542563[/C][C]0.999839711152729[/C][/ROW]
[ROW][C]65[/C][C]0.000118777304476381[/C][C]0.000237554608952762[/C][C]0.999881222695524[/C][/ROW]
[ROW][C]66[/C][C]0.000100756870783625[/C][C]0.00020151374156725[/C][C]0.999899243129216[/C][/ROW]
[ROW][C]67[/C][C]0.000171696641170375[/C][C]0.00034339328234075[/C][C]0.99982830335883[/C][/ROW]
[ROW][C]68[/C][C]0.000109568237427015[/C][C]0.000219136474854029[/C][C]0.999890431762573[/C][/ROW]
[ROW][C]69[/C][C]6.99965307765621e-05[/C][C]0.000139993061553124[/C][C]0.999930003469223[/C][/ROW]
[ROW][C]70[/C][C]4.61411955374892e-05[/C][C]9.22823910749783e-05[/C][C]0.999953858804463[/C][/ROW]
[ROW][C]71[/C][C]2.81900685223287e-05[/C][C]5.63801370446575e-05[/C][C]0.999971809931478[/C][/ROW]
[ROW][C]72[/C][C]2.49190090687725e-05[/C][C]4.98380181375451e-05[/C][C]0.999975080990931[/C][/ROW]
[ROW][C]73[/C][C]1.52888239637851e-05[/C][C]3.05776479275701e-05[/C][C]0.999984711176036[/C][/ROW]
[ROW][C]74[/C][C]9.32851371463753e-06[/C][C]1.86570274292751e-05[/C][C]0.999990671486285[/C][/ROW]
[ROW][C]75[/C][C]5.60898461498376e-06[/C][C]1.12179692299675e-05[/C][C]0.999994391015385[/C][/ROW]
[ROW][C]76[/C][C]3.66116114299874e-06[/C][C]7.32232228599748e-06[/C][C]0.999996338838857[/C][/ROW]
[ROW][C]77[/C][C]2.31765876814743e-06[/C][C]4.63531753629486e-06[/C][C]0.999997682341232[/C][/ROW]
[ROW][C]78[/C][C]0.178774700767796[/C][C]0.357549401535592[/C][C]0.821225299232204[/C][/ROW]
[ROW][C]79[/C][C]0.155597754278423[/C][C]0.311195508556846[/C][C]0.844402245721577[/C][/ROW]
[ROW][C]80[/C][C]0.136690468711784[/C][C]0.273380937423567[/C][C]0.863309531288216[/C][/ROW]
[ROW][C]81[/C][C]0.125714033914389[/C][C]0.251428067828778[/C][C]0.874285966085611[/C][/ROW]
[ROW][C]82[/C][C]0.147931919947662[/C][C]0.295863839895325[/C][C]0.852068080052338[/C][/ROW]
[ROW][C]83[/C][C]0.144887177092795[/C][C]0.28977435418559[/C][C]0.855112822907205[/C][/ROW]
[ROW][C]84[/C][C]0.124588090192875[/C][C]0.249176180385749[/C][C]0.875411909807125[/C][/ROW]
[ROW][C]85[/C][C]0.105496027514195[/C][C]0.210992055028391[/C][C]0.894503972485804[/C][/ROW]
[ROW][C]86[/C][C]0.127501835368325[/C][C]0.255003670736651[/C][C]0.872498164631675[/C][/ROW]
[ROW][C]87[/C][C]0.110979174978252[/C][C]0.221958349956503[/C][C]0.889020825021748[/C][/ROW]
[ROW][C]88[/C][C]0.103398935791421[/C][C]0.206797871582843[/C][C]0.896601064208579[/C][/ROW]
[ROW][C]89[/C][C]0.126790374024[/C][C]0.253580748048[/C][C]0.873209625976[/C][/ROW]
[ROW][C]90[/C][C]0.194083888463396[/C][C]0.388167776926793[/C][C]0.805916111536604[/C][/ROW]
[ROW][C]91[/C][C]0.249601194318406[/C][C]0.499202388636811[/C][C]0.750398805681594[/C][/ROW]
[ROW][C]92[/C][C]0.218183742558454[/C][C]0.436367485116907[/C][C]0.781816257441546[/C][/ROW]
[ROW][C]93[/C][C]0.223178320668445[/C][C]0.446356641336889[/C][C]0.776821679331555[/C][/ROW]
[ROW][C]94[/C][C]0.222183261380376[/C][C]0.444366522760752[/C][C]0.777816738619624[/C][/ROW]
[ROW][C]95[/C][C]0.20038307673675[/C][C]0.4007661534735[/C][C]0.79961692326325[/C][/ROW]
[ROW][C]96[/C][C]0.183165523243315[/C][C]0.36633104648663[/C][C]0.816834476756685[/C][/ROW]
[ROW][C]97[/C][C]0.292056348175262[/C][C]0.584112696350525[/C][C]0.707943651824738[/C][/ROW]
[ROW][C]98[/C][C]0.317990856166517[/C][C]0.635981712333035[/C][C]0.682009143833483[/C][/ROW]
[ROW][C]99[/C][C]0.282429478196796[/C][C]0.564858956393593[/C][C]0.717570521803204[/C][/ROW]
[ROW][C]100[/C][C]0.272013869632554[/C][C]0.544027739265109[/C][C]0.727986130367446[/C][/ROW]
[ROW][C]101[/C][C]0.242362210046051[/C][C]0.484724420092102[/C][C]0.757637789953949[/C][/ROW]
[ROW][C]102[/C][C]0.2249095450239[/C][C]0.449819090047799[/C][C]0.7750904549761[/C][/ROW]
[ROW][C]103[/C][C]0.243312098858252[/C][C]0.486624197716503[/C][C]0.756687901141748[/C][/ROW]
[ROW][C]104[/C][C]0.209461710867314[/C][C]0.418923421734627[/C][C]0.790538289132686[/C][/ROW]
[ROW][C]105[/C][C]0.219942672912166[/C][C]0.439885345824331[/C][C]0.780057327087834[/C][/ROW]
[ROW][C]106[/C][C]0.186597060808659[/C][C]0.373194121617319[/C][C]0.813402939191341[/C][/ROW]
[ROW][C]107[/C][C]0.156628992931795[/C][C]0.31325798586359[/C][C]0.843371007068205[/C][/ROW]
[ROW][C]108[/C][C]0.135115875786969[/C][C]0.270231751573939[/C][C]0.864884124213031[/C][/ROW]
[ROW][C]109[/C][C]0.964679075416231[/C][C]0.0706418491675384[/C][C]0.0353209245837692[/C][/ROW]
[ROW][C]110[/C][C]0.955128738301323[/C][C]0.0897425233973547[/C][C]0.0448712616986774[/C][/ROW]
[ROW][C]111[/C][C]0.942425424569379[/C][C]0.115149150861241[/C][C]0.0575745754306205[/C][/ROW]
[ROW][C]112[/C][C]0.933771352657818[/C][C]0.132457294684363[/C][C]0.0662286473421816[/C][/ROW]
[ROW][C]113[/C][C]0.936294560718708[/C][C]0.127410878562585[/C][C]0.0637054392812923[/C][/ROW]
[ROW][C]114[/C][C]0.921020674311244[/C][C]0.157958651377511[/C][C]0.0789793256887556[/C][/ROW]
[ROW][C]115[/C][C]0.910669109897109[/C][C]0.178661780205781[/C][C]0.0893308901028907[/C][/ROW]
[ROW][C]116[/C][C]0.924753659387222[/C][C]0.150492681225556[/C][C]0.0752463406127778[/C][/ROW]
[ROW][C]117[/C][C]0.968100213214314[/C][C]0.0637995735713723[/C][C]0.0318997867856862[/C][/ROW]
[ROW][C]118[/C][C]0.972675757320521[/C][C]0.0546484853589576[/C][C]0.0273242426794788[/C][/ROW]
[ROW][C]119[/C][C]0.963630478986253[/C][C]0.0727390420274941[/C][C]0.036369521013747[/C][/ROW]
[ROW][C]120[/C][C]0.96057530939031[/C][C]0.0788493812193808[/C][C]0.0394246906096904[/C][/ROW]
[ROW][C]121[/C][C]0.964502929091702[/C][C]0.0709941418165963[/C][C]0.0354970709082982[/C][/ROW]
[ROW][C]122[/C][C]0.962172286556236[/C][C]0.0756554268875287[/C][C]0.0378277134437644[/C][/ROW]
[ROW][C]123[/C][C]0.952347092232988[/C][C]0.095305815534025[/C][C]0.0476529077670125[/C][/ROW]
[ROW][C]124[/C][C]0.943132674490781[/C][C]0.113734651018437[/C][C]0.0568673255092187[/C][/ROW]
[ROW][C]125[/C][C]0.944520867954667[/C][C]0.110958264090666[/C][C]0.0554791320453329[/C][/ROW]
[ROW][C]126[/C][C]0.929847555751466[/C][C]0.140304888497067[/C][C]0.0701524442485335[/C][/ROW]
[ROW][C]127[/C][C]0.926962105932618[/C][C]0.146075788134764[/C][C]0.0730378940673818[/C][/ROW]
[ROW][C]128[/C][C]0.904519546222188[/C][C]0.190960907555623[/C][C]0.0954804537778116[/C][/ROW]
[ROW][C]129[/C][C]0.914920167704338[/C][C]0.170159664591323[/C][C]0.0850798322956616[/C][/ROW]
[ROW][C]130[/C][C]0.891851714458039[/C][C]0.216296571083921[/C][C]0.108148285541961[/C][/ROW]
[ROW][C]131[/C][C]0.977522510100785[/C][C]0.0449549797984292[/C][C]0.0224774898992146[/C][/ROW]
[ROW][C]132[/C][C]0.96880963485331[/C][C]0.0623807302933797[/C][C]0.0311903651466898[/C][/ROW]
[ROW][C]133[/C][C]0.959064429568173[/C][C]0.081871140863655[/C][C]0.0409355704318275[/C][/ROW]
[ROW][C]134[/C][C]0.947249275874161[/C][C]0.105501448251679[/C][C]0.0527507241258394[/C][/ROW]
[ROW][C]135[/C][C]0.94120606829091[/C][C]0.11758786341818[/C][C]0.0587939317090902[/C][/ROW]
[ROW][C]136[/C][C]0.948652665372878[/C][C]0.102694669254244[/C][C]0.0513473346271222[/C][/ROW]
[ROW][C]137[/C][C]0.930826192133644[/C][C]0.138347615732712[/C][C]0.0691738078663561[/C][/ROW]
[ROW][C]138[/C][C]0.90660291830493[/C][C]0.18679416339014[/C][C]0.0933970816950699[/C][/ROW]
[ROW][C]139[/C][C]0.883575570027192[/C][C]0.232848859945616[/C][C]0.116424429972808[/C][/ROW]
[ROW][C]140[/C][C]0.88263708061[/C][C]0.234725838779999[/C][C]0.11736291939[/C][/ROW]
[ROW][C]141[/C][C]0.842874909070767[/C][C]0.314250181858465[/C][C]0.157125090929233[/C][/ROW]
[ROW][C]142[/C][C]0.878714680756048[/C][C]0.242570638487905[/C][C]0.121285319243952[/C][/ROW]
[ROW][C]143[/C][C]0.865783483795362[/C][C]0.268433032409276[/C][C]0.134216516204638[/C][/ROW]
[ROW][C]144[/C][C]0.97922238972641[/C][C]0.0415552205471791[/C][C]0.0207776102735896[/C][/ROW]
[ROW][C]145[/C][C]0.983021321217629[/C][C]0.0339573575647412[/C][C]0.0169786787823706[/C][/ROW]
[ROW][C]146[/C][C]0.982563444658352[/C][C]0.0348731106832958[/C][C]0.0174365553416479[/C][/ROW]
[ROW][C]147[/C][C]0.994782170759557[/C][C]0.0104356584808863[/C][C]0.00521782924044313[/C][/ROW]
[ROW][C]148[/C][C]0.999994966750915[/C][C]1.00664981706796e-05[/C][C]5.03324908533981e-06[/C][/ROW]
[ROW][C]149[/C][C]0.99998181872145[/C][C]3.63625571005388e-05[/C][C]1.81812785502694e-05[/C][/ROW]
[ROW][C]150[/C][C]0.999953481894238[/C][C]9.30362115248371e-05[/C][C]4.65181057624186e-05[/C][/ROW]
[ROW][C]151[/C][C]0.999843860020457[/C][C]0.00031227995908556[/C][C]0.00015613997954278[/C][/ROW]
[ROW][C]152[/C][C]0.999493077252847[/C][C]0.00101384549430518[/C][C]0.00050692274715259[/C][/ROW]
[ROW][C]153[/C][C]0.998467979452638[/C][C]0.00306404109472402[/C][C]0.00153202054736201[/C][/ROW]
[ROW][C]154[/C][C]0.995868803591008[/C][C]0.00826239281798316[/C][C]0.00413119640899158[/C][/ROW]
[ROW][C]155[/C][C]0.994651145238921[/C][C]0.010697709522158[/C][C]0.00534885476107901[/C][/ROW]
[ROW][C]156[/C][C]0.993256914865697[/C][C]0.0134861702686052[/C][C]0.0067430851343026[/C][/ROW]
[ROW][C]157[/C][C]0.972880300799429[/C][C]0.0542393984011429[/C][C]0.0271196992005715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.2611215428982810.5222430857965620.738878457101719
80.1313431331676260.2626862663352520.868656866832374
90.2265975043177670.4531950086355340.773402495682233
100.1330721252188290.2661442504376590.866927874781171
110.0740943823979740.1481887647959480.925905617602026
120.06413355444206570.1282671088841310.935866445557934
130.03526680243633810.07053360487267620.964733197563662
140.01825570463970660.03651140927941310.981744295360293
150.01036271349652730.02072542699305460.989637286503473
160.00512877432464080.01025754864928160.994871225675359
170.07052128982397520.141042579647950.929478710176025
180.04631700417108270.09263400834216530.953682995828917
190.06243364940637190.1248672988127440.937566350593628
200.04226210353370560.08452420706741120.957737896466294
210.02895100601166210.05790201202332420.971048993988338
220.05778576053461430.1155715210692290.942214239465386
230.04600979364035860.09201958728071730.953990206359641
240.03813679445814810.07627358891629620.961863205541852
250.0256941890922650.05138837818452990.974305810907735
260.01664113381349450.03328226762698910.983358866186505
270.01061441791724210.02122883583448430.989385582082758
280.007079836829769040.01415967365953810.992920163170231
290.00739556774139980.01479113548279960.9926044322586
300.005039909755434370.01007981951086870.994960090244566
310.009130303312541850.01826060662508370.990869696687458
320.005847743030680220.01169548606136040.99415225696932
330.003946090288505010.007892180577010030.996053909711495
340.002506014987550010.005012029975100010.99749398501245
350.00176240641771540.003524812835430790.998237593582285
360.00174069689004020.00348139378008040.99825930310996
370.001688023806431680.003376047612863360.998311976193568
380.001083724646080450.00216744929216090.99891627535392
390.0007632139917599450.001526427983519890.99923678600824
400.0004915350477426010.0009830700954852010.999508464952257
410.0003238871588928630.0006477743177857270.999676112841107
420.0001987305888504420.0003974611777008830.99980126941115
430.0001297590478304280.0002595180956608560.99987024095217
447.54201228930624e-050.0001508402457861250.999924579877107
455.7279356227968e-050.0001145587124559360.999942720643772
463.7566061216372e-057.5132122432744e-050.999962433938784
472.1284482932428e-054.2568965864856e-050.999978715517068
481.51884377133979e-053.03768754267957e-050.999984811562287
490.0002149417188593880.0004298834377187760.999785058281141
500.0001352317429374630.0002704634858749250.999864768257062
518.9302606220932e-050.0001786052124418640.999910697393779
528.57866228631547e-050.0001715732457263090.999914213377137
537.83931359857016e-050.0001567862719714030.999921606864014
544.7349813455573e-059.4699626911146e-050.999952650186544
553.1476035359521e-056.29520707190419e-050.999968523964641
562.41540407827636e-054.83080815655273e-050.999975845959217
578.89804458648015e-050.0001779608917296030.999911019554135
580.0001765374980921950.000353074996184390.999823462501908
590.0001250920417201960.0002501840834403910.99987490795828
608.14678430346914e-050.0001629356860693830.999918532156965
619.69617632391493e-050.0001939235264782990.999903038236761
620.0002487671823885830.0004975343647771650.999751232817611
630.000242080016617380.000484160033234760.999757919983383
640.0001602888472712810.0003205776945425630.999839711152729
650.0001187773044763810.0002375546089527620.999881222695524
660.0001007568707836250.000201513741567250.999899243129216
670.0001716966411703750.000343393282340750.99982830335883
680.0001095682374270150.0002191364748540290.999890431762573
696.99965307765621e-050.0001399930615531240.999930003469223
704.61411955374892e-059.22823910749783e-050.999953858804463
712.81900685223287e-055.63801370446575e-050.999971809931478
722.49190090687725e-054.98380181375451e-050.999975080990931
731.52888239637851e-053.05776479275701e-050.999984711176036
749.32851371463753e-061.86570274292751e-050.999990671486285
755.60898461498376e-061.12179692299675e-050.999994391015385
763.66116114299874e-067.32232228599748e-060.999996338838857
772.31765876814743e-064.63531753629486e-060.999997682341232
780.1787747007677960.3575494015355920.821225299232204
790.1555977542784230.3111955085568460.844402245721577
800.1366904687117840.2733809374235670.863309531288216
810.1257140339143890.2514280678287780.874285966085611
820.1479319199476620.2958638398953250.852068080052338
830.1448871770927950.289774354185590.855112822907205
840.1245880901928750.2491761803857490.875411909807125
850.1054960275141950.2109920550283910.894503972485804
860.1275018353683250.2550036707366510.872498164631675
870.1109791749782520.2219583499565030.889020825021748
880.1033989357914210.2067978715828430.896601064208579
890.1267903740240.2535807480480.873209625976
900.1940838884633960.3881677769267930.805916111536604
910.2496011943184060.4992023886368110.750398805681594
920.2181837425584540.4363674851169070.781816257441546
930.2231783206684450.4463566413368890.776821679331555
940.2221832613803760.4443665227607520.777816738619624
950.200383076736750.40076615347350.79961692326325
960.1831655232433150.366331046486630.816834476756685
970.2920563481752620.5841126963505250.707943651824738
980.3179908561665170.6359817123330350.682009143833483
990.2824294781967960.5648589563935930.717570521803204
1000.2720138696325540.5440277392651090.727986130367446
1010.2423622100460510.4847244200921020.757637789953949
1020.22490954502390.4498190900477990.7750904549761
1030.2433120988582520.4866241977165030.756687901141748
1040.2094617108673140.4189234217346270.790538289132686
1050.2199426729121660.4398853458243310.780057327087834
1060.1865970608086590.3731941216173190.813402939191341
1070.1566289929317950.313257985863590.843371007068205
1080.1351158757869690.2702317515739390.864884124213031
1090.9646790754162310.07064184916753840.0353209245837692
1100.9551287383013230.08974252339735470.0448712616986774
1110.9424254245693790.1151491508612410.0575745754306205
1120.9337713526578180.1324572946843630.0662286473421816
1130.9362945607187080.1274108785625850.0637054392812923
1140.9210206743112440.1579586513775110.0789793256887556
1150.9106691098971090.1786617802057810.0893308901028907
1160.9247536593872220.1504926812255560.0752463406127778
1170.9681002132143140.06379957357137230.0318997867856862
1180.9726757573205210.05464848535895760.0273242426794788
1190.9636304789862530.07273904202749410.036369521013747
1200.960575309390310.07884938121938080.0394246906096904
1210.9645029290917020.07099414181659630.0354970709082982
1220.9621722865562360.07565542688752870.0378277134437644
1230.9523470922329880.0953058155340250.0476529077670125
1240.9431326744907810.1137346510184370.0568673255092187
1250.9445208679546670.1109582640906660.0554791320453329
1260.9298475557514660.1403048884970670.0701524442485335
1270.9269621059326180.1460757881347640.0730378940673818
1280.9045195462221880.1909609075556230.0954804537778116
1290.9149201677043380.1701596645913230.0850798322956616
1300.8918517144580390.2162965710839210.108148285541961
1310.9775225101007850.04495497979842920.0224774898992146
1320.968809634853310.06238073029337970.0311903651466898
1330.9590644295681730.0818711408636550.0409355704318275
1340.9472492758741610.1055014482516790.0527507241258394
1350.941206068290910.117587863418180.0587939317090902
1360.9486526653728780.1026946692542440.0513473346271222
1370.9308261921336440.1383476157327120.0691738078663561
1380.906602918304930.186794163390140.0933970816950699
1390.8835755700271920.2328488599456160.116424429972808
1400.882637080610.2347258387799990.11736291939
1410.8428749090707670.3142501818584650.157125090929233
1420.8787146807560480.2425706384879050.121285319243952
1430.8657834837953620.2684330324092760.134216516204638
1440.979222389726410.04155522054717910.0207776102735896
1450.9830213212176290.03395735756474120.0169786787823706
1460.9825634446583520.03487311068329580.0174365553416479
1470.9947821707595570.01043565848088630.00521782924044313
1480.9999949667509151.00664981706796e-055.03324908533981e-06
1490.999981818721453.63625571005388e-051.81812785502694e-05
1500.9999534818942389.30362115248371e-054.65181057624186e-05
1510.9998438600204570.000312279959085560.00015613997954278
1520.9994930772528470.001013845494305180.00050692274715259
1530.9984679794526380.003064041094724020.00153202054736201
1540.9958688035910080.008262392817983160.00413119640899158
1550.9946511452389210.0106977095221580.00534885476107901
1560.9932569148656970.01348617026860520.0067430851343026
1570.9728803007994290.05423939840114290.0271196992005715







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level520.344370860927152NOK
5% type I error level690.456953642384106NOK
10% type I error level880.582781456953642NOK

\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 & 52 & 0.344370860927152 & NOK \tabularnewline
5% type I error level & 69 & 0.456953642384106 & NOK \tabularnewline
10% type I error level & 88 & 0.582781456953642 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=146103&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]52[/C][C]0.344370860927152[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]69[/C][C]0.456953642384106[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]88[/C][C]0.582781456953642[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=146103&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=146103&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 level520.344370860927152NOK
5% type I error level690.456953642384106NOK
10% type I error level880.582781456953642NOK



Parameters (Session):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = 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')
}