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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 13 Dec 2011 11:26:06 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/Dec/13/t1323793600thk5tkaz0xgkf79.htm/, Retrieved Thu, 02 May 2024 16:07:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=154497, Retrieved Thu, 02 May 2024 16:07:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Kendall tau Correlation Matrix] [] [2010-12-05 18:04:16] [b98453cac15ba1066b407e146608df68]
- RMPD  [Multiple Regression] [Multiple Regression] [2011-12-12 20:14:09] [f2faabc3a2466a29562900bc59f67898]
- R P     [Multiple Regression] [Multiple Regression] [2011-12-13 16:22:12] [74be16979710d4c4e7c6647856088456]
-   P       [Multiple Regression] [Multiple Regression] [2011-12-13 16:24:33] [74be16979710d4c4e7c6647856088456]
-   P           [Multiple Regression] [Multiple Regression] [2011-12-13 16:26:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-   P             [Multiple Regression] [Multiple Regression] [2011-12-13 16:28:51] [74be16979710d4c4e7c6647856088456]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	14	12
2	39	32	18	11
2	30	35	11	14
1	31	33	12	12
2	34	37	16	21
2	35	29	18	12
2	39	31	14	22
2	34	36	14	11
2	36	35	15	10
2	37	38	15	13
1	38	31	17	10
2	36	34	19	8
1	38	35	10	15
2	39	38	16	14
2	33	37	18	10
1	32	33	14	14
1	36	32	14	14
2	38	38	17	11
1	39	38	14	10
2	32	32	16	13
1	32	33	18	7
2	31	31	11	14
2	39	38	14	12
2	37	39	12	14
1	39	32	17	11
2	41	32	9	9
1	36	35	16	11
2	33	37	14	15
2	33	33	15	14
1	34	33	11	13
2	31	28	16	9
1	27	32	13	15
2	37	31	17	10
2	34	37	15	11
1	34	30	14	13
1	32	33	16	8
1	29	31	9	20
1	36	33	15	12
2	29	31	17	10
1	35	33	13	10
1	37	32	15	9
2	34	33	16	14
1	38	32	16	8
1	35	33	12	14
2	38	28	12	11
2	37	35	11	13
2	38	39	15	9
2	33	34	15	11
2	36	38	17	15
1	38	32	13	11
2	32	38	16	10
1	32	30	14	14
1	32	33	11	18
2	34	38	12	14
1	32	32	12	11
2	37	32	15	12
2	39	34	16	13
2	29	34	15	9
1	37	36	12	10
2	35	34	12	15
1	30	28	8	20
1	38	34	13	12
2	34	35	11	12
2	31	35	14	14
2	34	31	15	13
1	35	37	10	11
2	36	35	11	17
1	30	27	12	12
2	39	40	15	13
1	35	37	15	14
1	38	36	14	13
2	31	38	16	15
2	34	39	15	13
1	38	41	15	10
1	34	27	13	11
2	39	30	12	19
2	37	37	17	13
2	34	31	13	17
1	28	31	15	13
1	37	27	13	9
1	33	36	15	11
1	37	38	16	10
2	35	37	15	9
1	37	33	16	12
2	32	34	15	12
2	33	31	14	13
1	38	39	15	13
2	33	34	14	12
2	29	32	13	15
2	33	33	7	22
2	31	36	17	13
2	36	32	13	15
2	35	41	15	13
2	32	28	14	15
2	29	30	13	10
2	39	36	16	11
2	37	35	12	16
2	35	31	14	11
1	37	34	17	11
1	32	36	15	10
2	38	36	17	10
1	37	35	12	16
2	36	37	16	12
1	32	28	11	11
2	33	39	15	16
1	40	32	9	19
2	38	35	16	11
1	41	39	15	16
1	36	35	10	15
2	43	42	10	24
2	30	34	15	14
2	31	33	11	15
2	32	41	13	11
1	32	33	14	15
2	37	34	18	12
1	37	32	16	10
2	33	40	14	14
2	34	40	14	13
2	33	35	14	9
2	38	36	14	15
2	33	37	12	15
2	31	27	14	14
2	38	39	15	11
2	37	38	15	8
2	33	31	15	11
2	31	33	13	11
1	39	32	17	8
2	44	39	17	10
2	33	36	19	11
2	35	33	15	13
1	32	33	13	11
1	28	32	9	20
2	40	37	15	10
1	27	30	15	15
1	37	38	15	12
2	32	29	16	14
1	28	22	11	23
1	34	35	14	14
2	30	35	11	16
2	35	34	15	11
1	31	35	13	12
2	32	34	15	10
1	30	34	16	14
2	30	35	14	12
1	31	23	15	12
2	40	31	16	11
2	32	27	16	12
1	36	36	11	13
1	32	31	12	11
1	35	32	9	19
2	38	39	16	12
2	42	37	13	17
1	34	38	16	9
2	35	39	12	12
2	35	34	9	19
2	33	31	13	18
2	36	32	13	15
2	32	37	14	14
2	33	36	19	11
2	34	32	13	9
2	32	35	12	18
2	34	36	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 8 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=0

As an alternative you can also use a QR Code:  
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 time8 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net






Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.5161790562666 + 0.921967037106414Gender[t] + 0.0328499179784167Connected[t] + 0.0313861616355641Separate[t] -0.4011941225649Depression[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Happiness[t] =  +  15.5161790562666 +  0.921967037106414Gender[t] +  0.0328499179784167Connected[t] +  0.0313861616355641Separate[t] -0.4011941225649Depression[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Happiness[t] =  +  15.5161790562666 +  0.921967037106414Gender[t] +  0.0328499179784167Connected[t] +  0.0313861616355641Separate[t] -0.4011941225649Depression[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
Happiness[t] = + 15.5161790562666 + 0.921967037106414Gender[t] + 0.0328499179784167Connected[t] + 0.0313861616355641Separate[t] -0.4011941225649Depression[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.51617905626662.0119157.712100
Gender0.9219670371064140.3208312.87370.0046190.00231
Connected0.03284991797841670.0483720.67910.4980690.249034
Separate0.03138616163556410.0470790.66670.5059640.252982
Depression-0.40119412256490.048178-8.327300

\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) & 15.5161790562666 & 2.011915 & 7.7121 & 0 & 0 \tabularnewline
Gender & 0.921967037106414 & 0.320831 & 2.8737 & 0.004619 & 0.00231 \tabularnewline
Connected & 0.0328499179784167 & 0.048372 & 0.6791 & 0.498069 & 0.249034 \tabularnewline
Separate & 0.0313861616355641 & 0.047079 & 0.6667 & 0.505964 & 0.252982 \tabularnewline
Depression & -0.4011941225649 & 0.048178 & -8.3273 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&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]15.5161790562666[/C][C]2.011915[/C][C]7.7121[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Gender[/C][C]0.921967037106414[/C][C]0.320831[/C][C]2.8737[/C][C]0.004619[/C][C]0.00231[/C][/ROW]
[ROW][C]Connected[/C][C]0.0328499179784167[/C][C]0.048372[/C][C]0.6791[/C][C]0.498069[/C][C]0.249034[/C][/ROW]
[ROW][C]Separate[/C][C]0.0313861616355641[/C][C]0.047079[/C][C]0.6667[/C][C]0.505964[/C][C]0.252982[/C][/ROW]
[ROW][C]Depression[/C][C]-0.4011941225649[/C][C]0.048178[/C][C]-8.3273[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=2

As an alternative you can also use a QR Code:  
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)15.51617905626662.0119157.712100
Gender0.9219670371064140.3208312.87370.0046190.00231
Connected0.03284991797841670.0483720.67910.4980690.249034
Separate0.03138616163556410.0470790.66670.5059640.252982
Depression-0.40119412256490.048178-8.327300






Multiple Linear Regression - Regression Statistics
Multiple R0.587100754320178
R-squared0.344687295723323
Adjusted R-squared0.32799143064621
F-TEST (value)20.6450695505338
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.0946799022804e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.91628880101747
Sum Squared Residuals576.529554718081

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.587100754320178 \tabularnewline
R-squared & 0.344687295723323 \tabularnewline
Adjusted R-squared & 0.32799143064621 \tabularnewline
F-TEST (value) & 20.6450695505338 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 1.0946799022804e-13 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.91628880101747 \tabularnewline
Sum Squared Residuals & 576.529554718081 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.587100754320178[/C][/ROW]
[ROW][C]R-squared[/C][C]0.344687295723323[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.32799143064621[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]20.6450695505338[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/C][/ROW]
[ROW][C]p-value[/C][C]1.0946799022804e-13[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.91628880101747[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]576.529554718081[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.587100754320178
R-squared0.344687295723323
Adjusted R-squared0.32799143064621
F-TEST (value)20.6450695505338
F-TEST (DF numerator)4
F-TEST (DF denominator)157
p-value1.0946799022804e-13
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.91628880101747
Sum Squared Residuals576.529554718081






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11415.0853044389672-1.08530443896719
21815.23248175576182.76751824423817
31113.8274086111681-2.82740861116807
41213.6779074138987-1.67790741389875
51611.21322174839864.78677825160143
61814.60572947637663.39427052362343
71410.78796024591243.21203975408763
81415.193776812412-1.193776812412
91515.6292846092982-0.629284609298171
101514.55271064448860.44728935551142
111714.64747276160632.35252723839367
121916.40028669279242.59971330720759
131012.7670467953241-2.76704679532409
141614.21721635788051.78278364211949
151815.5935071786342.40649282136595
161412.90836908674741.09163091325264
171413.00838259702550.991617402974535
181715.38794880759681.6120511924032
191414.9000258110337-0.9000258110337
201614.20014408478311.79985591521689
211815.71672794470172.28327205529834
221113.7347138826042-2.73471388260423
231415.0196046030103-1.01960460301031
241214.1829026835592-2.18290268355924
251714.31051471865542.68948528134459
26916.1005698368485-7.10056983684846
271614.30612344962691.69387655037314
281413.58753656580960.41246343419045
291513.86318604183221.13681395816781
301113.3752630452691-2.3752630452691
311615.6465260105220.353473989477962
321312.31153921265480.688460787345185
331715.53658988073431.46341011926567
341515.2251629740476-0.225162974047566
351413.28110456036240.718895439637597
361615.31553382213680.684466177863238
37910.3398822741516-1.33988227415159
381513.84215700379081.15784299620917
391715.2737905369071.726209463093
401314.6116953309422-1.61169533094221
411515.0472031278284-0.0472031278283813
421613.89603595981062.10396404018939
431615.48124716837170.518752831628302
441213.0069188406826-1.00691884068261
451215.0740871912412-3.07408719124116
461114.4585521595819-3.45855215958189
471516.2217232143622-1.22172321436216
481515.0981545711625-0.0981545711624569
491713.71747248138043.28252751861964
501314.277664800677-1.277664800677
511615.59204342229120.407956577708803
521412.81421060184071.18578939815933
531111.3035925964878-0.303592596487763
541214.0529667679884-2.05296676798843
551214.0805652928065-2.0805652928065
561514.76558779724010.234412202759904
571614.49286583390321.50713416609684
581515.7691431443786-0.76914314437859
591214.7715536518057-2.77155365180574
601213.5590779168597-1.55907791685969
61810.2785737072233-2.27857370722331
621313.9392430013832-0.939243001383227
631114.7611965282115-3.76119652821154
641413.86025852914650.139741470853512
651514.23445775910440.765542240895619
661014.3360458549196-4.33604585491957
671112.8209257513439-1.82092575134387
681213.4567405261069-1.45674052610694
691514.68118280371650.318817196283458
701513.13246348722491.86753651277513
711413.60082120208950.399178797910545
721613.55322289148832.44677710851172
731514.48554705218890.514452947811105
741514.9613343779620.0386656220380246
751313.9893343205855-0.989334320585511
761211.96015645197150.0398435480284988
771714.5213244828532.47867551714698
781312.62968126884480.370318731155218
791513.11539121412751.88460878587253
801314.8902723196506-1.89027231965056
811514.23895985732720.761040142672828
821614.83432597507691.16567402492313
831516.0604011371558-1.06040113715578
841613.87500692176922.12499307823075
851514.66411053061910.33588946938086
861414.201607841126-0.201607841125965
871513.69497968699611.30502031300385
881414.6969604485976-0.696960448597557
891313.2992060857181-0.299206085718062
90710.653633061313-3.65363306131299
911714.2928388133472.70716118665305
921313.529155511567-0.529155511566979
931514.58116929343840.41883070656156
941413.27221119311110.727788806888944
951315.2424043752714-2.24240437527143
961615.35802640230410.641973597695914
971213.2549697918872-1.25496979188719
981415.0696959222126-1.0696959222126
991714.30758720596972.69241279403029
1001514.60730406191370.392695938086345
1011715.72637060689061.27362939310943
1021212.3330027547808-0.333002754780774
1031614.88966868743951.1103313125605
1041113.9550206462642-2.95502064626424
1051513.24911476651581.75088523348422
106911.1338116561146-2.13381165611463
1071615.29379032269010.706209677309895
1081512.58994707323672.4100529267633
1091012.7013469593673-2.70134695936726
1101010.4622194506874-0.462219450687438
1111513.79602244953251.20397755046749
1121113.3962920833105-2.39629208331046
1131315.285007784633-2.28500778463299
1141412.50717496418251.49282503581754
1151814.82836012051123.17163987948878
1161614.64600900526351.35399099473652
1171414.0828891732811-0.0828891732811422
1181414.5169332138245-0.516933213824459
1191415.9319289779278-1.93192897792782
1201413.72039999406610.279600005933931
1211213.5875365658095-1.58753656580955
1221413.6091692360620.390830763938025
1231515.4193349692324-0.419334969232361
1241516.5586812573131-1.55868125731308
1251515.0039960862558-0.00399608625576461
1261315.0010685735701-2.00106857357006
1271715.51409708635011.48590291364989
1281716.01762859966780.982371400332239
1291915.16092689443363.83907310556642
1301514.33008000035390.669919999646073
1311314.1119514544421-1.11195145444206
132910.3384185178087-1.33841851780873
1331515.823456604483-0.823456604482966
1341512.24876688938372.75123311061631
1351514.03193772994710.968062270052934
1361613.70479147731152.29520852268848
137118.820974533758392.17902546624161
1381413.03684124597530.963158754024676
1391113.0250203660383-2.02502036603827
1401515.1638544071193-0.163854407119291
1411313.7406797371699-0.740679737169874
1421515.4664987757489-0.46649877574894
1431612.87405541242613.12594458757391
1441414.6297968562979-0.629796856297871
1451513.36404579754311.6359542024569
1461615.23394551210470.766054487895318
1471614.44440739917021.55559260082981
1481113.5351213661326-2.53512136613262
1491214.0491791311709-2.04917913117093
150910.9695620662225-1.96956206622255
1511615.01814084666750.981859153332539
1521313.0807975824855-0.0807975824855003
1531615.13697034370650.863029656293483
1541214.9195910927322-2.91959109273221
155911.9543014266001-2.95430142660009
1561312.19563722830150.804362771698535
1571313.529155511567-0.529155511566979
1581413.9558807703960.0441192296039669
1591915.16092689443363.83907310556642
1601315.8706204109995-2.87062041099955
1611212.2883319568653-0.288331956865305
1621313.1878061995875-0.187806199587502

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 14 & 15.0853044389672 & -1.08530443896719 \tabularnewline
2 & 18 & 15.2324817557618 & 2.76751824423817 \tabularnewline
3 & 11 & 13.8274086111681 & -2.82740861116807 \tabularnewline
4 & 12 & 13.6779074138987 & -1.67790741389875 \tabularnewline
5 & 16 & 11.2132217483986 & 4.78677825160143 \tabularnewline
6 & 18 & 14.6057294763766 & 3.39427052362343 \tabularnewline
7 & 14 & 10.7879602459124 & 3.21203975408763 \tabularnewline
8 & 14 & 15.193776812412 & -1.193776812412 \tabularnewline
9 & 15 & 15.6292846092982 & -0.629284609298171 \tabularnewline
10 & 15 & 14.5527106444886 & 0.44728935551142 \tabularnewline
11 & 17 & 14.6474727616063 & 2.35252723839367 \tabularnewline
12 & 19 & 16.4002866927924 & 2.59971330720759 \tabularnewline
13 & 10 & 12.7670467953241 & -2.76704679532409 \tabularnewline
14 & 16 & 14.2172163578805 & 1.78278364211949 \tabularnewline
15 & 18 & 15.593507178634 & 2.40649282136595 \tabularnewline
16 & 14 & 12.9083690867474 & 1.09163091325264 \tabularnewline
17 & 14 & 13.0083825970255 & 0.991617402974535 \tabularnewline
18 & 17 & 15.3879488075968 & 1.6120511924032 \tabularnewline
19 & 14 & 14.9000258110337 & -0.9000258110337 \tabularnewline
20 & 16 & 14.2001440847831 & 1.79985591521689 \tabularnewline
21 & 18 & 15.7167279447017 & 2.28327205529834 \tabularnewline
22 & 11 & 13.7347138826042 & -2.73471388260423 \tabularnewline
23 & 14 & 15.0196046030103 & -1.01960460301031 \tabularnewline
24 & 12 & 14.1829026835592 & -2.18290268355924 \tabularnewline
25 & 17 & 14.3105147186554 & 2.68948528134459 \tabularnewline
26 & 9 & 16.1005698368485 & -7.10056983684846 \tabularnewline
27 & 16 & 14.3061234496269 & 1.69387655037314 \tabularnewline
28 & 14 & 13.5875365658096 & 0.41246343419045 \tabularnewline
29 & 15 & 13.8631860418322 & 1.13681395816781 \tabularnewline
30 & 11 & 13.3752630452691 & -2.3752630452691 \tabularnewline
31 & 16 & 15.646526010522 & 0.353473989477962 \tabularnewline
32 & 13 & 12.3115392126548 & 0.688460787345185 \tabularnewline
33 & 17 & 15.5365898807343 & 1.46341011926567 \tabularnewline
34 & 15 & 15.2251629740476 & -0.225162974047566 \tabularnewline
35 & 14 & 13.2811045603624 & 0.718895439637597 \tabularnewline
36 & 16 & 15.3155338221368 & 0.684466177863238 \tabularnewline
37 & 9 & 10.3398822741516 & -1.33988227415159 \tabularnewline
38 & 15 & 13.8421570037908 & 1.15784299620917 \tabularnewline
39 & 17 & 15.273790536907 & 1.726209463093 \tabularnewline
40 & 13 & 14.6116953309422 & -1.61169533094221 \tabularnewline
41 & 15 & 15.0472031278284 & -0.0472031278283813 \tabularnewline
42 & 16 & 13.8960359598106 & 2.10396404018939 \tabularnewline
43 & 16 & 15.4812471683717 & 0.518752831628302 \tabularnewline
44 & 12 & 13.0069188406826 & -1.00691884068261 \tabularnewline
45 & 12 & 15.0740871912412 & -3.07408719124116 \tabularnewline
46 & 11 & 14.4585521595819 & -3.45855215958189 \tabularnewline
47 & 15 & 16.2217232143622 & -1.22172321436216 \tabularnewline
48 & 15 & 15.0981545711625 & -0.0981545711624569 \tabularnewline
49 & 17 & 13.7174724813804 & 3.28252751861964 \tabularnewline
50 & 13 & 14.277664800677 & -1.277664800677 \tabularnewline
51 & 16 & 15.5920434222912 & 0.407956577708803 \tabularnewline
52 & 14 & 12.8142106018407 & 1.18578939815933 \tabularnewline
53 & 11 & 11.3035925964878 & -0.303592596487763 \tabularnewline
54 & 12 & 14.0529667679884 & -2.05296676798843 \tabularnewline
55 & 12 & 14.0805652928065 & -2.0805652928065 \tabularnewline
56 & 15 & 14.7655877972401 & 0.234412202759904 \tabularnewline
57 & 16 & 14.4928658339032 & 1.50713416609684 \tabularnewline
58 & 15 & 15.7691431443786 & -0.76914314437859 \tabularnewline
59 & 12 & 14.7715536518057 & -2.77155365180574 \tabularnewline
60 & 12 & 13.5590779168597 & -1.55907791685969 \tabularnewline
61 & 8 & 10.2785737072233 & -2.27857370722331 \tabularnewline
62 & 13 & 13.9392430013832 & -0.939243001383227 \tabularnewline
63 & 11 & 14.7611965282115 & -3.76119652821154 \tabularnewline
64 & 14 & 13.8602585291465 & 0.139741470853512 \tabularnewline
65 & 15 & 14.2344577591044 & 0.765542240895619 \tabularnewline
66 & 10 & 14.3360458549196 & -4.33604585491957 \tabularnewline
67 & 11 & 12.8209257513439 & -1.82092575134387 \tabularnewline
68 & 12 & 13.4567405261069 & -1.45674052610694 \tabularnewline
69 & 15 & 14.6811828037165 & 0.318817196283458 \tabularnewline
70 & 15 & 13.1324634872249 & 1.86753651277513 \tabularnewline
71 & 14 & 13.6008212020895 & 0.399178797910545 \tabularnewline
72 & 16 & 13.5532228914883 & 2.44677710851172 \tabularnewline
73 & 15 & 14.4855470521889 & 0.514452947811105 \tabularnewline
74 & 15 & 14.961334377962 & 0.0386656220380246 \tabularnewline
75 & 13 & 13.9893343205855 & -0.989334320585511 \tabularnewline
76 & 12 & 11.9601564519715 & 0.0398435480284988 \tabularnewline
77 & 17 & 14.521324482853 & 2.47867551714698 \tabularnewline
78 & 13 & 12.6296812688448 & 0.370318731155218 \tabularnewline
79 & 15 & 13.1153912141275 & 1.88460878587253 \tabularnewline
80 & 13 & 14.8902723196506 & -1.89027231965056 \tabularnewline
81 & 15 & 14.2389598573272 & 0.761040142672828 \tabularnewline
82 & 16 & 14.8343259750769 & 1.16567402492313 \tabularnewline
83 & 15 & 16.0604011371558 & -1.06040113715578 \tabularnewline
84 & 16 & 13.8750069217692 & 2.12499307823075 \tabularnewline
85 & 15 & 14.6641105306191 & 0.33588946938086 \tabularnewline
86 & 14 & 14.201607841126 & -0.201607841125965 \tabularnewline
87 & 15 & 13.6949796869961 & 1.30502031300385 \tabularnewline
88 & 14 & 14.6969604485976 & -0.696960448597557 \tabularnewline
89 & 13 & 13.2992060857181 & -0.299206085718062 \tabularnewline
90 & 7 & 10.653633061313 & -3.65363306131299 \tabularnewline
91 & 17 & 14.292838813347 & 2.70716118665305 \tabularnewline
92 & 13 & 13.529155511567 & -0.529155511566979 \tabularnewline
93 & 15 & 14.5811692934384 & 0.41883070656156 \tabularnewline
94 & 14 & 13.2722111931111 & 0.727788806888944 \tabularnewline
95 & 13 & 15.2424043752714 & -2.24240437527143 \tabularnewline
96 & 16 & 15.3580264023041 & 0.641973597695914 \tabularnewline
97 & 12 & 13.2549697918872 & -1.25496979188719 \tabularnewline
98 & 14 & 15.0696959222126 & -1.0696959222126 \tabularnewline
99 & 17 & 14.3075872059697 & 2.69241279403029 \tabularnewline
100 & 15 & 14.6073040619137 & 0.392695938086345 \tabularnewline
101 & 17 & 15.7263706068906 & 1.27362939310943 \tabularnewline
102 & 12 & 12.3330027547808 & -0.333002754780774 \tabularnewline
103 & 16 & 14.8896686874395 & 1.1103313125605 \tabularnewline
104 & 11 & 13.9550206462642 & -2.95502064626424 \tabularnewline
105 & 15 & 13.2491147665158 & 1.75088523348422 \tabularnewline
106 & 9 & 11.1338116561146 & -2.13381165611463 \tabularnewline
107 & 16 & 15.2937903226901 & 0.706209677309895 \tabularnewline
108 & 15 & 12.5899470732367 & 2.4100529267633 \tabularnewline
109 & 10 & 12.7013469593673 & -2.70134695936726 \tabularnewline
110 & 10 & 10.4622194506874 & -0.462219450687438 \tabularnewline
111 & 15 & 13.7960224495325 & 1.20397755046749 \tabularnewline
112 & 11 & 13.3962920833105 & -2.39629208331046 \tabularnewline
113 & 13 & 15.285007784633 & -2.28500778463299 \tabularnewline
114 & 14 & 12.5071749641825 & 1.49282503581754 \tabularnewline
115 & 18 & 14.8283601205112 & 3.17163987948878 \tabularnewline
116 & 16 & 14.6460090052635 & 1.35399099473652 \tabularnewline
117 & 14 & 14.0828891732811 & -0.0828891732811422 \tabularnewline
118 & 14 & 14.5169332138245 & -0.516933213824459 \tabularnewline
119 & 14 & 15.9319289779278 & -1.93192897792782 \tabularnewline
120 & 14 & 13.7203999940661 & 0.279600005933931 \tabularnewline
121 & 12 & 13.5875365658095 & -1.58753656580955 \tabularnewline
122 & 14 & 13.609169236062 & 0.390830763938025 \tabularnewline
123 & 15 & 15.4193349692324 & -0.419334969232361 \tabularnewline
124 & 15 & 16.5586812573131 & -1.55868125731308 \tabularnewline
125 & 15 & 15.0039960862558 & -0.00399608625576461 \tabularnewline
126 & 13 & 15.0010685735701 & -2.00106857357006 \tabularnewline
127 & 17 & 15.5140970863501 & 1.48590291364989 \tabularnewline
128 & 17 & 16.0176285996678 & 0.982371400332239 \tabularnewline
129 & 19 & 15.1609268944336 & 3.83907310556642 \tabularnewline
130 & 15 & 14.3300800003539 & 0.669919999646073 \tabularnewline
131 & 13 & 14.1119514544421 & -1.11195145444206 \tabularnewline
132 & 9 & 10.3384185178087 & -1.33841851780873 \tabularnewline
133 & 15 & 15.823456604483 & -0.823456604482966 \tabularnewline
134 & 15 & 12.2487668893837 & 2.75123311061631 \tabularnewline
135 & 15 & 14.0319377299471 & 0.968062270052934 \tabularnewline
136 & 16 & 13.7047914773115 & 2.29520852268848 \tabularnewline
137 & 11 & 8.82097453375839 & 2.17902546624161 \tabularnewline
138 & 14 & 13.0368412459753 & 0.963158754024676 \tabularnewline
139 & 11 & 13.0250203660383 & -2.02502036603827 \tabularnewline
140 & 15 & 15.1638544071193 & -0.163854407119291 \tabularnewline
141 & 13 & 13.7406797371699 & -0.740679737169874 \tabularnewline
142 & 15 & 15.4664987757489 & -0.46649877574894 \tabularnewline
143 & 16 & 12.8740554124261 & 3.12594458757391 \tabularnewline
144 & 14 & 14.6297968562979 & -0.629796856297871 \tabularnewline
145 & 15 & 13.3640457975431 & 1.6359542024569 \tabularnewline
146 & 16 & 15.2339455121047 & 0.766054487895318 \tabularnewline
147 & 16 & 14.4444073991702 & 1.55559260082981 \tabularnewline
148 & 11 & 13.5351213661326 & -2.53512136613262 \tabularnewline
149 & 12 & 14.0491791311709 & -2.04917913117093 \tabularnewline
150 & 9 & 10.9695620662225 & -1.96956206622255 \tabularnewline
151 & 16 & 15.0181408466675 & 0.981859153332539 \tabularnewline
152 & 13 & 13.0807975824855 & -0.0807975824855003 \tabularnewline
153 & 16 & 15.1369703437065 & 0.863029656293483 \tabularnewline
154 & 12 & 14.9195910927322 & -2.91959109273221 \tabularnewline
155 & 9 & 11.9543014266001 & -2.95430142660009 \tabularnewline
156 & 13 & 12.1956372283015 & 0.804362771698535 \tabularnewline
157 & 13 & 13.529155511567 & -0.529155511566979 \tabularnewline
158 & 14 & 13.955880770396 & 0.0441192296039669 \tabularnewline
159 & 19 & 15.1609268944336 & 3.83907310556642 \tabularnewline
160 & 13 & 15.8706204109995 & -2.87062041099955 \tabularnewline
161 & 12 & 12.2883319568653 & -0.288331956865305 \tabularnewline
162 & 13 & 13.1878061995875 & -0.187806199587502 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&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]14[/C][C]15.0853044389672[/C][C]-1.08530443896719[/C][/ROW]
[ROW][C]2[/C][C]18[/C][C]15.2324817557618[/C][C]2.76751824423817[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]13.8274086111681[/C][C]-2.82740861116807[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]13.6779074138987[/C][C]-1.67790741389875[/C][/ROW]
[ROW][C]5[/C][C]16[/C][C]11.2132217483986[/C][C]4.78677825160143[/C][/ROW]
[ROW][C]6[/C][C]18[/C][C]14.6057294763766[/C][C]3.39427052362343[/C][/ROW]
[ROW][C]7[/C][C]14[/C][C]10.7879602459124[/C][C]3.21203975408763[/C][/ROW]
[ROW][C]8[/C][C]14[/C][C]15.193776812412[/C][C]-1.193776812412[/C][/ROW]
[ROW][C]9[/C][C]15[/C][C]15.6292846092982[/C][C]-0.629284609298171[/C][/ROW]
[ROW][C]10[/C][C]15[/C][C]14.5527106444886[/C][C]0.44728935551142[/C][/ROW]
[ROW][C]11[/C][C]17[/C][C]14.6474727616063[/C][C]2.35252723839367[/C][/ROW]
[ROW][C]12[/C][C]19[/C][C]16.4002866927924[/C][C]2.59971330720759[/C][/ROW]
[ROW][C]13[/C][C]10[/C][C]12.7670467953241[/C][C]-2.76704679532409[/C][/ROW]
[ROW][C]14[/C][C]16[/C][C]14.2172163578805[/C][C]1.78278364211949[/C][/ROW]
[ROW][C]15[/C][C]18[/C][C]15.593507178634[/C][C]2.40649282136595[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9083690867474[/C][C]1.09163091325264[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]13.0083825970255[/C][C]0.991617402974535[/C][/ROW]
[ROW][C]18[/C][C]17[/C][C]15.3879488075968[/C][C]1.6120511924032[/C][/ROW]
[ROW][C]19[/C][C]14[/C][C]14.9000258110337[/C][C]-0.9000258110337[/C][/ROW]
[ROW][C]20[/C][C]16[/C][C]14.2001440847831[/C][C]1.79985591521689[/C][/ROW]
[ROW][C]21[/C][C]18[/C][C]15.7167279447017[/C][C]2.28327205529834[/C][/ROW]
[ROW][C]22[/C][C]11[/C][C]13.7347138826042[/C][C]-2.73471388260423[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]15.0196046030103[/C][C]-1.01960460301031[/C][/ROW]
[ROW][C]24[/C][C]12[/C][C]14.1829026835592[/C][C]-2.18290268355924[/C][/ROW]
[ROW][C]25[/C][C]17[/C][C]14.3105147186554[/C][C]2.68948528134459[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]16.1005698368485[/C][C]-7.10056983684846[/C][/ROW]
[ROW][C]27[/C][C]16[/C][C]14.3061234496269[/C][C]1.69387655037314[/C][/ROW]
[ROW][C]28[/C][C]14[/C][C]13.5875365658096[/C][C]0.41246343419045[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]13.8631860418322[/C][C]1.13681395816781[/C][/ROW]
[ROW][C]30[/C][C]11[/C][C]13.3752630452691[/C][C]-2.3752630452691[/C][/ROW]
[ROW][C]31[/C][C]16[/C][C]15.646526010522[/C][C]0.353473989477962[/C][/ROW]
[ROW][C]32[/C][C]13[/C][C]12.3115392126548[/C][C]0.688460787345185[/C][/ROW]
[ROW][C]33[/C][C]17[/C][C]15.5365898807343[/C][C]1.46341011926567[/C][/ROW]
[ROW][C]34[/C][C]15[/C][C]15.2251629740476[/C][C]-0.225162974047566[/C][/ROW]
[ROW][C]35[/C][C]14[/C][C]13.2811045603624[/C][C]0.718895439637597[/C][/ROW]
[ROW][C]36[/C][C]16[/C][C]15.3155338221368[/C][C]0.684466177863238[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]10.3398822741516[/C][C]-1.33988227415159[/C][/ROW]
[ROW][C]38[/C][C]15[/C][C]13.8421570037908[/C][C]1.15784299620917[/C][/ROW]
[ROW][C]39[/C][C]17[/C][C]15.273790536907[/C][C]1.726209463093[/C][/ROW]
[ROW][C]40[/C][C]13[/C][C]14.6116953309422[/C][C]-1.61169533094221[/C][/ROW]
[ROW][C]41[/C][C]15[/C][C]15.0472031278284[/C][C]-0.0472031278283813[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]13.8960359598106[/C][C]2.10396404018939[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]15.4812471683717[/C][C]0.518752831628302[/C][/ROW]
[ROW][C]44[/C][C]12[/C][C]13.0069188406826[/C][C]-1.00691884068261[/C][/ROW]
[ROW][C]45[/C][C]12[/C][C]15.0740871912412[/C][C]-3.07408719124116[/C][/ROW]
[ROW][C]46[/C][C]11[/C][C]14.4585521595819[/C][C]-3.45855215958189[/C][/ROW]
[ROW][C]47[/C][C]15[/C][C]16.2217232143622[/C][C]-1.22172321436216[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]15.0981545711625[/C][C]-0.0981545711624569[/C][/ROW]
[ROW][C]49[/C][C]17[/C][C]13.7174724813804[/C][C]3.28252751861964[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]14.277664800677[/C][C]-1.277664800677[/C][/ROW]
[ROW][C]51[/C][C]16[/C][C]15.5920434222912[/C][C]0.407956577708803[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]12.8142106018407[/C][C]1.18578939815933[/C][/ROW]
[ROW][C]53[/C][C]11[/C][C]11.3035925964878[/C][C]-0.303592596487763[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]14.0529667679884[/C][C]-2.05296676798843[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]14.0805652928065[/C][C]-2.0805652928065[/C][/ROW]
[ROW][C]56[/C][C]15[/C][C]14.7655877972401[/C][C]0.234412202759904[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]14.4928658339032[/C][C]1.50713416609684[/C][/ROW]
[ROW][C]58[/C][C]15[/C][C]15.7691431443786[/C][C]-0.76914314437859[/C][/ROW]
[ROW][C]59[/C][C]12[/C][C]14.7715536518057[/C][C]-2.77155365180574[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]13.5590779168597[/C][C]-1.55907791685969[/C][/ROW]
[ROW][C]61[/C][C]8[/C][C]10.2785737072233[/C][C]-2.27857370722331[/C][/ROW]
[ROW][C]62[/C][C]13[/C][C]13.9392430013832[/C][C]-0.939243001383227[/C][/ROW]
[ROW][C]63[/C][C]11[/C][C]14.7611965282115[/C][C]-3.76119652821154[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.8602585291465[/C][C]0.139741470853512[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]14.2344577591044[/C][C]0.765542240895619[/C][/ROW]
[ROW][C]66[/C][C]10[/C][C]14.3360458549196[/C][C]-4.33604585491957[/C][/ROW]
[ROW][C]67[/C][C]11[/C][C]12.8209257513439[/C][C]-1.82092575134387[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.4567405261069[/C][C]-1.45674052610694[/C][/ROW]
[ROW][C]69[/C][C]15[/C][C]14.6811828037165[/C][C]0.318817196283458[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]13.1324634872249[/C][C]1.86753651277513[/C][/ROW]
[ROW][C]71[/C][C]14[/C][C]13.6008212020895[/C][C]0.399178797910545[/C][/ROW]
[ROW][C]72[/C][C]16[/C][C]13.5532228914883[/C][C]2.44677710851172[/C][/ROW]
[ROW][C]73[/C][C]15[/C][C]14.4855470521889[/C][C]0.514452947811105[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.961334377962[/C][C]0.0386656220380246[/C][/ROW]
[ROW][C]75[/C][C]13[/C][C]13.9893343205855[/C][C]-0.989334320585511[/C][/ROW]
[ROW][C]76[/C][C]12[/C][C]11.9601564519715[/C][C]0.0398435480284988[/C][/ROW]
[ROW][C]77[/C][C]17[/C][C]14.521324482853[/C][C]2.47867551714698[/C][/ROW]
[ROW][C]78[/C][C]13[/C][C]12.6296812688448[/C][C]0.370318731155218[/C][/ROW]
[ROW][C]79[/C][C]15[/C][C]13.1153912141275[/C][C]1.88460878587253[/C][/ROW]
[ROW][C]80[/C][C]13[/C][C]14.8902723196506[/C][C]-1.89027231965056[/C][/ROW]
[ROW][C]81[/C][C]15[/C][C]14.2389598573272[/C][C]0.761040142672828[/C][/ROW]
[ROW][C]82[/C][C]16[/C][C]14.8343259750769[/C][C]1.16567402492313[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]16.0604011371558[/C][C]-1.06040113715578[/C][/ROW]
[ROW][C]84[/C][C]16[/C][C]13.8750069217692[/C][C]2.12499307823075[/C][/ROW]
[ROW][C]85[/C][C]15[/C][C]14.6641105306191[/C][C]0.33588946938086[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]14.201607841126[/C][C]-0.201607841125965[/C][/ROW]
[ROW][C]87[/C][C]15[/C][C]13.6949796869961[/C][C]1.30502031300385[/C][/ROW]
[ROW][C]88[/C][C]14[/C][C]14.6969604485976[/C][C]-0.696960448597557[/C][/ROW]
[ROW][C]89[/C][C]13[/C][C]13.2992060857181[/C][C]-0.299206085718062[/C][/ROW]
[ROW][C]90[/C][C]7[/C][C]10.653633061313[/C][C]-3.65363306131299[/C][/ROW]
[ROW][C]91[/C][C]17[/C][C]14.292838813347[/C][C]2.70716118665305[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]13.529155511567[/C][C]-0.529155511566979[/C][/ROW]
[ROW][C]93[/C][C]15[/C][C]14.5811692934384[/C][C]0.41883070656156[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.2722111931111[/C][C]0.727788806888944[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]15.2424043752714[/C][C]-2.24240437527143[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]15.3580264023041[/C][C]0.641973597695914[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]13.2549697918872[/C][C]-1.25496979188719[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]15.0696959222126[/C][C]-1.0696959222126[/C][/ROW]
[ROW][C]99[/C][C]17[/C][C]14.3075872059697[/C][C]2.69241279403029[/C][/ROW]
[ROW][C]100[/C][C]15[/C][C]14.6073040619137[/C][C]0.392695938086345[/C][/ROW]
[ROW][C]101[/C][C]17[/C][C]15.7263706068906[/C][C]1.27362939310943[/C][/ROW]
[ROW][C]102[/C][C]12[/C][C]12.3330027547808[/C][C]-0.333002754780774[/C][/ROW]
[ROW][C]103[/C][C]16[/C][C]14.8896686874395[/C][C]1.1103313125605[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]13.9550206462642[/C][C]-2.95502064626424[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]13.2491147665158[/C][C]1.75088523348422[/C][/ROW]
[ROW][C]106[/C][C]9[/C][C]11.1338116561146[/C][C]-2.13381165611463[/C][/ROW]
[ROW][C]107[/C][C]16[/C][C]15.2937903226901[/C][C]0.706209677309895[/C][/ROW]
[ROW][C]108[/C][C]15[/C][C]12.5899470732367[/C][C]2.4100529267633[/C][/ROW]
[ROW][C]109[/C][C]10[/C][C]12.7013469593673[/C][C]-2.70134695936726[/C][/ROW]
[ROW][C]110[/C][C]10[/C][C]10.4622194506874[/C][C]-0.462219450687438[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]13.7960224495325[/C][C]1.20397755046749[/C][/ROW]
[ROW][C]112[/C][C]11[/C][C]13.3962920833105[/C][C]-2.39629208331046[/C][/ROW]
[ROW][C]113[/C][C]13[/C][C]15.285007784633[/C][C]-2.28500778463299[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]12.5071749641825[/C][C]1.49282503581754[/C][/ROW]
[ROW][C]115[/C][C]18[/C][C]14.8283601205112[/C][C]3.17163987948878[/C][/ROW]
[ROW][C]116[/C][C]16[/C][C]14.6460090052635[/C][C]1.35399099473652[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]14.0828891732811[/C][C]-0.0828891732811422[/C][/ROW]
[ROW][C]118[/C][C]14[/C][C]14.5169332138245[/C][C]-0.516933213824459[/C][/ROW]
[ROW][C]119[/C][C]14[/C][C]15.9319289779278[/C][C]-1.93192897792782[/C][/ROW]
[ROW][C]120[/C][C]14[/C][C]13.7203999940661[/C][C]0.279600005933931[/C][/ROW]
[ROW][C]121[/C][C]12[/C][C]13.5875365658095[/C][C]-1.58753656580955[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]13.609169236062[/C][C]0.390830763938025[/C][/ROW]
[ROW][C]123[/C][C]15[/C][C]15.4193349692324[/C][C]-0.419334969232361[/C][/ROW]
[ROW][C]124[/C][C]15[/C][C]16.5586812573131[/C][C]-1.55868125731308[/C][/ROW]
[ROW][C]125[/C][C]15[/C][C]15.0039960862558[/C][C]-0.00399608625576461[/C][/ROW]
[ROW][C]126[/C][C]13[/C][C]15.0010685735701[/C][C]-2.00106857357006[/C][/ROW]
[ROW][C]127[/C][C]17[/C][C]15.5140970863501[/C][C]1.48590291364989[/C][/ROW]
[ROW][C]128[/C][C]17[/C][C]16.0176285996678[/C][C]0.982371400332239[/C][/ROW]
[ROW][C]129[/C][C]19[/C][C]15.1609268944336[/C][C]3.83907310556642[/C][/ROW]
[ROW][C]130[/C][C]15[/C][C]14.3300800003539[/C][C]0.669919999646073[/C][/ROW]
[ROW][C]131[/C][C]13[/C][C]14.1119514544421[/C][C]-1.11195145444206[/C][/ROW]
[ROW][C]132[/C][C]9[/C][C]10.3384185178087[/C][C]-1.33841851780873[/C][/ROW]
[ROW][C]133[/C][C]15[/C][C]15.823456604483[/C][C]-0.823456604482966[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.2487668893837[/C][C]2.75123311061631[/C][/ROW]
[ROW][C]135[/C][C]15[/C][C]14.0319377299471[/C][C]0.968062270052934[/C][/ROW]
[ROW][C]136[/C][C]16[/C][C]13.7047914773115[/C][C]2.29520852268848[/C][/ROW]
[ROW][C]137[/C][C]11[/C][C]8.82097453375839[/C][C]2.17902546624161[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]13.0368412459753[/C][C]0.963158754024676[/C][/ROW]
[ROW][C]139[/C][C]11[/C][C]13.0250203660383[/C][C]-2.02502036603827[/C][/ROW]
[ROW][C]140[/C][C]15[/C][C]15.1638544071193[/C][C]-0.163854407119291[/C][/ROW]
[ROW][C]141[/C][C]13[/C][C]13.7406797371699[/C][C]-0.740679737169874[/C][/ROW]
[ROW][C]142[/C][C]15[/C][C]15.4664987757489[/C][C]-0.46649877574894[/C][/ROW]
[ROW][C]143[/C][C]16[/C][C]12.8740554124261[/C][C]3.12594458757391[/C][/ROW]
[ROW][C]144[/C][C]14[/C][C]14.6297968562979[/C][C]-0.629796856297871[/C][/ROW]
[ROW][C]145[/C][C]15[/C][C]13.3640457975431[/C][C]1.6359542024569[/C][/ROW]
[ROW][C]146[/C][C]16[/C][C]15.2339455121047[/C][C]0.766054487895318[/C][/ROW]
[ROW][C]147[/C][C]16[/C][C]14.4444073991702[/C][C]1.55559260082981[/C][/ROW]
[ROW][C]148[/C][C]11[/C][C]13.5351213661326[/C][C]-2.53512136613262[/C][/ROW]
[ROW][C]149[/C][C]12[/C][C]14.0491791311709[/C][C]-2.04917913117093[/C][/ROW]
[ROW][C]150[/C][C]9[/C][C]10.9695620662225[/C][C]-1.96956206622255[/C][/ROW]
[ROW][C]151[/C][C]16[/C][C]15.0181408466675[/C][C]0.981859153332539[/C][/ROW]
[ROW][C]152[/C][C]13[/C][C]13.0807975824855[/C][C]-0.0807975824855003[/C][/ROW]
[ROW][C]153[/C][C]16[/C][C]15.1369703437065[/C][C]0.863029656293483[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.9195910927322[/C][C]-2.91959109273221[/C][/ROW]
[ROW][C]155[/C][C]9[/C][C]11.9543014266001[/C][C]-2.95430142660009[/C][/ROW]
[ROW][C]156[/C][C]13[/C][C]12.1956372283015[/C][C]0.804362771698535[/C][/ROW]
[ROW][C]157[/C][C]13[/C][C]13.529155511567[/C][C]-0.529155511566979[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.955880770396[/C][C]0.0441192296039669[/C][/ROW]
[ROW][C]159[/C][C]19[/C][C]15.1609268944336[/C][C]3.83907310556642[/C][/ROW]
[ROW][C]160[/C][C]13[/C][C]15.8706204109995[/C][C]-2.87062041099955[/C][/ROW]
[ROW][C]161[/C][C]12[/C][C]12.2883319568653[/C][C]-0.288331956865305[/C][/ROW]
[ROW][C]162[/C][C]13[/C][C]13.1878061995875[/C][C]-0.187806199587502[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11415.0853044389672-1.08530443896719
21815.23248175576182.76751824423817
31113.8274086111681-2.82740861116807
41213.6779074138987-1.67790741389875
51611.21322174839864.78677825160143
61814.60572947637663.39427052362343
71410.78796024591243.21203975408763
81415.193776812412-1.193776812412
91515.6292846092982-0.629284609298171
101514.55271064448860.44728935551142
111714.64747276160632.35252723839367
121916.40028669279242.59971330720759
131012.7670467953241-2.76704679532409
141614.21721635788051.78278364211949
151815.5935071786342.40649282136595
161412.90836908674741.09163091325264
171413.00838259702550.991617402974535
181715.38794880759681.6120511924032
191414.9000258110337-0.9000258110337
201614.20014408478311.79985591521689
211815.71672794470172.28327205529834
221113.7347138826042-2.73471388260423
231415.0196046030103-1.01960460301031
241214.1829026835592-2.18290268355924
251714.31051471865542.68948528134459
26916.1005698368485-7.10056983684846
271614.30612344962691.69387655037314
281413.58753656580960.41246343419045
291513.86318604183221.13681395816781
301113.3752630452691-2.3752630452691
311615.6465260105220.353473989477962
321312.31153921265480.688460787345185
331715.53658988073431.46341011926567
341515.2251629740476-0.225162974047566
351413.28110456036240.718895439637597
361615.31553382213680.684466177863238
37910.3398822741516-1.33988227415159
381513.84215700379081.15784299620917
391715.2737905369071.726209463093
401314.6116953309422-1.61169533094221
411515.0472031278284-0.0472031278283813
421613.89603595981062.10396404018939
431615.48124716837170.518752831628302
441213.0069188406826-1.00691884068261
451215.0740871912412-3.07408719124116
461114.4585521595819-3.45855215958189
471516.2217232143622-1.22172321436216
481515.0981545711625-0.0981545711624569
491713.71747248138043.28252751861964
501314.277664800677-1.277664800677
511615.59204342229120.407956577708803
521412.81421060184071.18578939815933
531111.3035925964878-0.303592596487763
541214.0529667679884-2.05296676798843
551214.0805652928065-2.0805652928065
561514.76558779724010.234412202759904
571614.49286583390321.50713416609684
581515.7691431443786-0.76914314437859
591214.7715536518057-2.77155365180574
601213.5590779168597-1.55907791685969
61810.2785737072233-2.27857370722331
621313.9392430013832-0.939243001383227
631114.7611965282115-3.76119652821154
641413.86025852914650.139741470853512
651514.23445775910440.765542240895619
661014.3360458549196-4.33604585491957
671112.8209257513439-1.82092575134387
681213.4567405261069-1.45674052610694
691514.68118280371650.318817196283458
701513.13246348722491.86753651277513
711413.60082120208950.399178797910545
721613.55322289148832.44677710851172
731514.48554705218890.514452947811105
741514.9613343779620.0386656220380246
751313.9893343205855-0.989334320585511
761211.96015645197150.0398435480284988
771714.5213244828532.47867551714698
781312.62968126884480.370318731155218
791513.11539121412751.88460878587253
801314.8902723196506-1.89027231965056
811514.23895985732720.761040142672828
821614.83432597507691.16567402492313
831516.0604011371558-1.06040113715578
841613.87500692176922.12499307823075
851514.66411053061910.33588946938086
861414.201607841126-0.201607841125965
871513.69497968699611.30502031300385
881414.6969604485976-0.696960448597557
891313.2992060857181-0.299206085718062
90710.653633061313-3.65363306131299
911714.2928388133472.70716118665305
921313.529155511567-0.529155511566979
931514.58116929343840.41883070656156
941413.27221119311110.727788806888944
951315.2424043752714-2.24240437527143
961615.35802640230410.641973597695914
971213.2549697918872-1.25496979188719
981415.0696959222126-1.0696959222126
991714.30758720596972.69241279403029
1001514.60730406191370.392695938086345
1011715.72637060689061.27362939310943
1021212.3330027547808-0.333002754780774
1031614.88966868743951.1103313125605
1041113.9550206462642-2.95502064626424
1051513.24911476651581.75088523348422
106911.1338116561146-2.13381165611463
1071615.29379032269010.706209677309895
1081512.58994707323672.4100529267633
1091012.7013469593673-2.70134695936726
1101010.4622194506874-0.462219450687438
1111513.79602244953251.20397755046749
1121113.3962920833105-2.39629208331046
1131315.285007784633-2.28500778463299
1141412.50717496418251.49282503581754
1151814.82836012051123.17163987948878
1161614.64600900526351.35399099473652
1171414.0828891732811-0.0828891732811422
1181414.5169332138245-0.516933213824459
1191415.9319289779278-1.93192897792782
1201413.72039999406610.279600005933931
1211213.5875365658095-1.58753656580955
1221413.6091692360620.390830763938025
1231515.4193349692324-0.419334969232361
1241516.5586812573131-1.55868125731308
1251515.0039960862558-0.00399608625576461
1261315.0010685735701-2.00106857357006
1271715.51409708635011.48590291364989
1281716.01762859966780.982371400332239
1291915.16092689443363.83907310556642
1301514.33008000035390.669919999646073
1311314.1119514544421-1.11195145444206
132910.3384185178087-1.33841851780873
1331515.823456604483-0.823456604482966
1341512.24876688938372.75123311061631
1351514.03193772994710.968062270052934
1361613.70479147731152.29520852268848
137118.820974533758392.17902546624161
1381413.03684124597530.963158754024676
1391113.0250203660383-2.02502036603827
1401515.1638544071193-0.163854407119291
1411313.7406797371699-0.740679737169874
1421515.4664987757489-0.46649877574894
1431612.87405541242613.12594458757391
1441414.6297968562979-0.629796856297871
1451513.36404579754311.6359542024569
1461615.23394551210470.766054487895318
1471614.44440739917021.55559260082981
1481113.5351213661326-2.53512136613262
1491214.0491791311709-2.04917913117093
150910.9695620662225-1.96956206622255
1511615.01814084666750.981859153332539
1521313.0807975824855-0.0807975824855003
1531615.13697034370650.863029656293483
1541214.9195910927322-2.91959109273221
155911.9543014266001-2.95430142660009
1561312.19563722830150.804362771698535
1571313.529155511567-0.529155511566979
1581413.9558807703960.0441192296039669
1591915.16092689443363.83907310556642
1601315.8706204109995-2.87062041099955
1611212.2883319568653-0.288331956865305
1621313.1878061995875-0.187806199587502






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9208072308721570.1583855382556860.0791927691278432
90.8511140925127780.2977718149744440.148885907487222
100.7768568449897840.4462863100204320.223143155010216
110.7415895568545190.5168208862909610.258410443145481
120.8137327404649250.372534519070150.186267259535075
130.8600925884415250.279814823116950.139907411558475
140.8315059631798780.3369880736402440.168494036820122
150.885822128759170.228355742481660.11417787124083
160.8724329988719940.2551340022560120.127567001128006
170.8279482839354970.3441034321290050.172051716064503
180.7988062282957540.4023875434084920.201193771704246
190.7436413456879050.5127173086241910.256358654312095
200.6854888658687440.6290222682625120.314511134131256
210.7535777081449830.4928445837100330.246422291855017
220.9018762540234190.1962474919531630.0981237459765813
230.8908522171348650.218295565730270.109147782865135
240.8937068332617480.2125863334765040.106293166738252
250.8839911220094780.2320177559810450.116008877990522
260.9995091137962130.0009817724075741070.000490886203787054
270.9993543351181060.001291329763787160.000645664881893582
280.9989384039670730.002123192065854820.00106159603292741
290.9983772157513730.003245568497253970.00162278424862699
300.9989209508014270.002158098397145490.00107904919857275
310.9982943595729680.003411280854064770.00170564042703238
320.9974004809592180.005199038081563160.00259951904078158
330.9965547295059420.006890540988115580.00344527049405779
340.9948284533895250.01034309322094990.00517154661047494
350.9925598567987080.01488028640258390.00744014320129194
360.9897583433344870.02048331333102590.0102416566655129
370.9904101521434620.01917969571307520.00958984785653759
380.9874134656785730.02517306864285440.0125865343214272
390.9851224944305090.02975501113898130.0148775055694906
400.9834603877615250.03307922447695050.0165396122384753
410.9773026416556570.04539471668868670.0226973583443434
420.975412756345890.04917448730821960.0245872436541098
430.9675617529948210.06487649401035880.0324382470051794
440.9611429093064150.07771418138717070.0388570906935854
450.978815743734780.04236851253043910.0211842562652196
460.9892434707312510.02151305853749850.0107565292687492
470.9863917410055930.02721651798881470.0136082589944074
480.981497684803750.03700463039249920.0185023151962496
490.988330082632010.02333983473598030.0116699173679902
500.9858633444091720.02827331118165570.0141366555908278
510.9810085401689710.03798291966205750.0189914598310288
520.9763448226178340.04731035476433250.0236551773821662
530.9700838919697420.05983221606051620.0299161080302581
540.9717794247489930.05644115050201360.0282205752510068
550.9729053792209060.05418924155818760.0270946207790938
560.9645690568989180.07086188620216340.0354309431010817
570.9598020133749570.0803959732500870.0401979866250435
580.9504129213285980.09917415734280330.0495870786714017
590.9590876036025120.08182479279497610.0409123963974881
600.9571093636758890.08578127264822280.0428906363241114
610.9642989715231840.07140205695363240.0357010284768162
620.956376625868310.08724674826337940.0436233741316897
630.9787877290691040.04242454186179130.0212122709308957
640.9721285403568460.05574291928630710.0278714596431536
650.9648998851581640.07020022968367240.0351001148418362
660.9876330779963990.0247338440072030.0123669220036015
670.9873623540775950.02527529184480910.0126376459224046
680.986064560165840.02787087966832020.0139354398341601
690.9816475812147630.03670483757047480.0183524187852374
700.9817848461234780.03643030775304390.018215153876522
710.9764201127162070.04715977456758540.0235798872837927
720.9802276406410980.03954471871780310.0197723593589016
730.97452269496690.05095461006620.0254773050331
740.9674718205569190.06505635888616190.0325281794430809
750.9617607129349710.07647857413005890.0382392870650294
760.9515426327079780.09691473458404350.0484573672920217
770.9586447656616770.08271046867664690.0413552343383235
780.9482416756550340.1035166486899320.0517583243449661
790.9468719614412390.1062560771175220.0531280385587611
800.9509923474045680.09801530519086470.0490076525954323
810.9399872750999380.1200254498001250.0600127249000624
820.9300241412804230.1399517174391550.0699758587195773
830.9191809113479240.1616381773041530.0808190886520763
840.9202571951778220.1594856096443550.0797428048221775
850.9023226018648870.1953547962702250.0976773981351126
860.8811067851818940.2377864296362120.118893214818106
870.866173412769670.267653174460660.13382658723033
880.8432623232764530.3134753534470930.156737676723547
890.8147969722811670.3704060554376660.185203027718833
900.8808328201025970.2383343597948060.119167179897403
910.9052316416405190.1895367167189630.0947683583594813
920.8857583242212050.228483351557590.114241675778795
930.8645503538747850.270899292250430.135449646125215
940.8412180185488490.3175639629023020.158781981451151
950.8522485579805980.2955028840388040.147751442019402
960.8259565081986790.3480869836026420.174043491801321
970.8075614161345310.3848771677309370.192438583865468
980.7879891989094730.4240216021810540.212010801090527
990.813886790083070.372226419833860.18611320991693
1000.7818126690454980.4363746619090050.218187330954503
1010.7599690476753970.4800619046492070.240030952324603
1020.7221536099472020.5556927801055960.277846390052798
1030.6958500410625250.608299917874950.304149958937475
1040.7721958094451040.4556083811097910.227804190554896
1050.783458394580030.433083210839940.21654160541997
1060.8100974339924340.3798051320151320.189902566007566
1070.7782851627523220.4434296744953570.221714837247678
1080.8020824562481610.3958350875036780.197917543751839
1090.842713777927450.31457244414510.15728622207255
1100.8158077803571360.3683844392857290.184192219642864
1110.8019597652582960.3960804694834070.198040234741704
1120.8181960849557990.3636078300884020.181803915044201
1130.8167834383496140.3664331233007720.183216561650386
1140.8001802766591880.3996394466816250.199819723340812
1150.8589381551616030.2821236896767940.141061844838397
1160.8363732055378330.3272535889243340.163626794462167
1170.8067368478775550.3865263042448890.193263152122445
1180.7707221556566940.4585556886866110.229277844343306
1190.7732424903399860.4535150193200290.226757509660014
1200.7351977966496010.5296044067007980.264802203350399
1210.7079367095471270.5841265809057460.292063290452873
1220.6614738257359420.6770523485281160.338526174264058
1230.6103990602751680.7792018794496640.389600939724832
1240.5893322861024070.8213354277951870.410667713897593
1250.5372867462623870.9254265074752260.462713253737613
1260.567232734923040.865534530153920.43276726507696
1270.5262361858250980.9475276283498040.473763814174902
1280.5072466529650930.9855066940698140.492753347034907
1290.6789007001251490.6421985997497020.321099299874851
1300.6300790455491750.739841908901650.369920954450825
1310.6053107913102860.7893784173794280.394689208689714
1320.5866906633377560.8266186733244880.413309336662244
1330.5269221228239410.9461557543521180.473077877176059
1340.5365159523684320.9269680952631350.463484047631568
1350.5114943657444130.9770112685111750.488505634255587
1360.5088798457318040.9822403085363920.491120154268196
1370.4954249717734080.9908499435468160.504575028226592
1380.4607117073908210.9214234147816420.539288292609179
1390.4514270598991470.9028541197982930.548572940100853
1400.3812054173116310.7624108346232610.618794582688369
1410.3199479693520880.6398959387041760.680052030647912
1420.2674807130926110.5349614261852220.732519286907389
1430.428363012500690.8567260250013810.571636987499309
1440.3634944723231890.7269889446463780.636505527676811
1450.357904642805980.7158092856119590.64209535719402
1460.2951048488415840.5902096976831670.704895151158416
1470.2965805386284440.5931610772568870.703419461371557
1480.2636818558577120.5273637117154240.736318144142288
1490.2076272852274890.4152545704549780.792372714772511
1500.1596356858521940.3192713717043890.840364314147806
1510.1228050540279910.2456101080559830.877194945972009
1520.1740071937506150.3480143875012290.825992806249385
1530.1029259066411130.2058518132822250.897074093358887
1540.08298703013866280.1659740602773260.917012969861337

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.920807230872157 & 0.158385538255686 & 0.0791927691278432 \tabularnewline
9 & 0.851114092512778 & 0.297771814974444 & 0.148885907487222 \tabularnewline
10 & 0.776856844989784 & 0.446286310020432 & 0.223143155010216 \tabularnewline
11 & 0.741589556854519 & 0.516820886290961 & 0.258410443145481 \tabularnewline
12 & 0.813732740464925 & 0.37253451907015 & 0.186267259535075 \tabularnewline
13 & 0.860092588441525 & 0.27981482311695 & 0.139907411558475 \tabularnewline
14 & 0.831505963179878 & 0.336988073640244 & 0.168494036820122 \tabularnewline
15 & 0.88582212875917 & 0.22835574248166 & 0.11417787124083 \tabularnewline
16 & 0.872432998871994 & 0.255134002256012 & 0.127567001128006 \tabularnewline
17 & 0.827948283935497 & 0.344103432129005 & 0.172051716064503 \tabularnewline
18 & 0.798806228295754 & 0.402387543408492 & 0.201193771704246 \tabularnewline
19 & 0.743641345687905 & 0.512717308624191 & 0.256358654312095 \tabularnewline
20 & 0.685488865868744 & 0.629022268262512 & 0.314511134131256 \tabularnewline
21 & 0.753577708144983 & 0.492844583710033 & 0.246422291855017 \tabularnewline
22 & 0.901876254023419 & 0.196247491953163 & 0.0981237459765813 \tabularnewline
23 & 0.890852217134865 & 0.21829556573027 & 0.109147782865135 \tabularnewline
24 & 0.893706833261748 & 0.212586333476504 & 0.106293166738252 \tabularnewline
25 & 0.883991122009478 & 0.232017755981045 & 0.116008877990522 \tabularnewline
26 & 0.999509113796213 & 0.000981772407574107 & 0.000490886203787054 \tabularnewline
27 & 0.999354335118106 & 0.00129132976378716 & 0.000645664881893582 \tabularnewline
28 & 0.998938403967073 & 0.00212319206585482 & 0.00106159603292741 \tabularnewline
29 & 0.998377215751373 & 0.00324556849725397 & 0.00162278424862699 \tabularnewline
30 & 0.998920950801427 & 0.00215809839714549 & 0.00107904919857275 \tabularnewline
31 & 0.998294359572968 & 0.00341128085406477 & 0.00170564042703238 \tabularnewline
32 & 0.997400480959218 & 0.00519903808156316 & 0.00259951904078158 \tabularnewline
33 & 0.996554729505942 & 0.00689054098811558 & 0.00344527049405779 \tabularnewline
34 & 0.994828453389525 & 0.0103430932209499 & 0.00517154661047494 \tabularnewline
35 & 0.992559856798708 & 0.0148802864025839 & 0.00744014320129194 \tabularnewline
36 & 0.989758343334487 & 0.0204833133310259 & 0.0102416566655129 \tabularnewline
37 & 0.990410152143462 & 0.0191796957130752 & 0.00958984785653759 \tabularnewline
38 & 0.987413465678573 & 0.0251730686428544 & 0.0125865343214272 \tabularnewline
39 & 0.985122494430509 & 0.0297550111389813 & 0.0148775055694906 \tabularnewline
40 & 0.983460387761525 & 0.0330792244769505 & 0.0165396122384753 \tabularnewline
41 & 0.977302641655657 & 0.0453947166886867 & 0.0226973583443434 \tabularnewline
42 & 0.97541275634589 & 0.0491744873082196 & 0.0245872436541098 \tabularnewline
43 & 0.967561752994821 & 0.0648764940103588 & 0.0324382470051794 \tabularnewline
44 & 0.961142909306415 & 0.0777141813871707 & 0.0388570906935854 \tabularnewline
45 & 0.97881574373478 & 0.0423685125304391 & 0.0211842562652196 \tabularnewline
46 & 0.989243470731251 & 0.0215130585374985 & 0.0107565292687492 \tabularnewline
47 & 0.986391741005593 & 0.0272165179888147 & 0.0136082589944074 \tabularnewline
48 & 0.98149768480375 & 0.0370046303924992 & 0.0185023151962496 \tabularnewline
49 & 0.98833008263201 & 0.0233398347359803 & 0.0116699173679902 \tabularnewline
50 & 0.985863344409172 & 0.0282733111816557 & 0.0141366555908278 \tabularnewline
51 & 0.981008540168971 & 0.0379829196620575 & 0.0189914598310288 \tabularnewline
52 & 0.976344822617834 & 0.0473103547643325 & 0.0236551773821662 \tabularnewline
53 & 0.970083891969742 & 0.0598322160605162 & 0.0299161080302581 \tabularnewline
54 & 0.971779424748993 & 0.0564411505020136 & 0.0282205752510068 \tabularnewline
55 & 0.972905379220906 & 0.0541892415581876 & 0.0270946207790938 \tabularnewline
56 & 0.964569056898918 & 0.0708618862021634 & 0.0354309431010817 \tabularnewline
57 & 0.959802013374957 & 0.080395973250087 & 0.0401979866250435 \tabularnewline
58 & 0.950412921328598 & 0.0991741573428033 & 0.0495870786714017 \tabularnewline
59 & 0.959087603602512 & 0.0818247927949761 & 0.0409123963974881 \tabularnewline
60 & 0.957109363675889 & 0.0857812726482228 & 0.0428906363241114 \tabularnewline
61 & 0.964298971523184 & 0.0714020569536324 & 0.0357010284768162 \tabularnewline
62 & 0.95637662586831 & 0.0872467482633794 & 0.0436233741316897 \tabularnewline
63 & 0.978787729069104 & 0.0424245418617913 & 0.0212122709308957 \tabularnewline
64 & 0.972128540356846 & 0.0557429192863071 & 0.0278714596431536 \tabularnewline
65 & 0.964899885158164 & 0.0702002296836724 & 0.0351001148418362 \tabularnewline
66 & 0.987633077996399 & 0.024733844007203 & 0.0123669220036015 \tabularnewline
67 & 0.987362354077595 & 0.0252752918448091 & 0.0126376459224046 \tabularnewline
68 & 0.98606456016584 & 0.0278708796683202 & 0.0139354398341601 \tabularnewline
69 & 0.981647581214763 & 0.0367048375704748 & 0.0183524187852374 \tabularnewline
70 & 0.981784846123478 & 0.0364303077530439 & 0.018215153876522 \tabularnewline
71 & 0.976420112716207 & 0.0471597745675854 & 0.0235798872837927 \tabularnewline
72 & 0.980227640641098 & 0.0395447187178031 & 0.0197723593589016 \tabularnewline
73 & 0.9745226949669 & 0.0509546100662 & 0.0254773050331 \tabularnewline
74 & 0.967471820556919 & 0.0650563588861619 & 0.0325281794430809 \tabularnewline
75 & 0.961760712934971 & 0.0764785741300589 & 0.0382392870650294 \tabularnewline
76 & 0.951542632707978 & 0.0969147345840435 & 0.0484573672920217 \tabularnewline
77 & 0.958644765661677 & 0.0827104686766469 & 0.0413552343383235 \tabularnewline
78 & 0.948241675655034 & 0.103516648689932 & 0.0517583243449661 \tabularnewline
79 & 0.946871961441239 & 0.106256077117522 & 0.0531280385587611 \tabularnewline
80 & 0.950992347404568 & 0.0980153051908647 & 0.0490076525954323 \tabularnewline
81 & 0.939987275099938 & 0.120025449800125 & 0.0600127249000624 \tabularnewline
82 & 0.930024141280423 & 0.139951717439155 & 0.0699758587195773 \tabularnewline
83 & 0.919180911347924 & 0.161638177304153 & 0.0808190886520763 \tabularnewline
84 & 0.920257195177822 & 0.159485609644355 & 0.0797428048221775 \tabularnewline
85 & 0.902322601864887 & 0.195354796270225 & 0.0976773981351126 \tabularnewline
86 & 0.881106785181894 & 0.237786429636212 & 0.118893214818106 \tabularnewline
87 & 0.86617341276967 & 0.26765317446066 & 0.13382658723033 \tabularnewline
88 & 0.843262323276453 & 0.313475353447093 & 0.156737676723547 \tabularnewline
89 & 0.814796972281167 & 0.370406055437666 & 0.185203027718833 \tabularnewline
90 & 0.880832820102597 & 0.238334359794806 & 0.119167179897403 \tabularnewline
91 & 0.905231641640519 & 0.189536716718963 & 0.0947683583594813 \tabularnewline
92 & 0.885758324221205 & 0.22848335155759 & 0.114241675778795 \tabularnewline
93 & 0.864550353874785 & 0.27089929225043 & 0.135449646125215 \tabularnewline
94 & 0.841218018548849 & 0.317563962902302 & 0.158781981451151 \tabularnewline
95 & 0.852248557980598 & 0.295502884038804 & 0.147751442019402 \tabularnewline
96 & 0.825956508198679 & 0.348086983602642 & 0.174043491801321 \tabularnewline
97 & 0.807561416134531 & 0.384877167730937 & 0.192438583865468 \tabularnewline
98 & 0.787989198909473 & 0.424021602181054 & 0.212010801090527 \tabularnewline
99 & 0.81388679008307 & 0.37222641983386 & 0.18611320991693 \tabularnewline
100 & 0.781812669045498 & 0.436374661909005 & 0.218187330954503 \tabularnewline
101 & 0.759969047675397 & 0.480061904649207 & 0.240030952324603 \tabularnewline
102 & 0.722153609947202 & 0.555692780105596 & 0.277846390052798 \tabularnewline
103 & 0.695850041062525 & 0.60829991787495 & 0.304149958937475 \tabularnewline
104 & 0.772195809445104 & 0.455608381109791 & 0.227804190554896 \tabularnewline
105 & 0.78345839458003 & 0.43308321083994 & 0.21654160541997 \tabularnewline
106 & 0.810097433992434 & 0.379805132015132 & 0.189902566007566 \tabularnewline
107 & 0.778285162752322 & 0.443429674495357 & 0.221714837247678 \tabularnewline
108 & 0.802082456248161 & 0.395835087503678 & 0.197917543751839 \tabularnewline
109 & 0.84271377792745 & 0.3145724441451 & 0.15728622207255 \tabularnewline
110 & 0.815807780357136 & 0.368384439285729 & 0.184192219642864 \tabularnewline
111 & 0.801959765258296 & 0.396080469483407 & 0.198040234741704 \tabularnewline
112 & 0.818196084955799 & 0.363607830088402 & 0.181803915044201 \tabularnewline
113 & 0.816783438349614 & 0.366433123300772 & 0.183216561650386 \tabularnewline
114 & 0.800180276659188 & 0.399639446681625 & 0.199819723340812 \tabularnewline
115 & 0.858938155161603 & 0.282123689676794 & 0.141061844838397 \tabularnewline
116 & 0.836373205537833 & 0.327253588924334 & 0.163626794462167 \tabularnewline
117 & 0.806736847877555 & 0.386526304244889 & 0.193263152122445 \tabularnewline
118 & 0.770722155656694 & 0.458555688686611 & 0.229277844343306 \tabularnewline
119 & 0.773242490339986 & 0.453515019320029 & 0.226757509660014 \tabularnewline
120 & 0.735197796649601 & 0.529604406700798 & 0.264802203350399 \tabularnewline
121 & 0.707936709547127 & 0.584126580905746 & 0.292063290452873 \tabularnewline
122 & 0.661473825735942 & 0.677052348528116 & 0.338526174264058 \tabularnewline
123 & 0.610399060275168 & 0.779201879449664 & 0.389600939724832 \tabularnewline
124 & 0.589332286102407 & 0.821335427795187 & 0.410667713897593 \tabularnewline
125 & 0.537286746262387 & 0.925426507475226 & 0.462713253737613 \tabularnewline
126 & 0.56723273492304 & 0.86553453015392 & 0.43276726507696 \tabularnewline
127 & 0.526236185825098 & 0.947527628349804 & 0.473763814174902 \tabularnewline
128 & 0.507246652965093 & 0.985506694069814 & 0.492753347034907 \tabularnewline
129 & 0.678900700125149 & 0.642198599749702 & 0.321099299874851 \tabularnewline
130 & 0.630079045549175 & 0.73984190890165 & 0.369920954450825 \tabularnewline
131 & 0.605310791310286 & 0.789378417379428 & 0.394689208689714 \tabularnewline
132 & 0.586690663337756 & 0.826618673324488 & 0.413309336662244 \tabularnewline
133 & 0.526922122823941 & 0.946155754352118 & 0.473077877176059 \tabularnewline
134 & 0.536515952368432 & 0.926968095263135 & 0.463484047631568 \tabularnewline
135 & 0.511494365744413 & 0.977011268511175 & 0.488505634255587 \tabularnewline
136 & 0.508879845731804 & 0.982240308536392 & 0.491120154268196 \tabularnewline
137 & 0.495424971773408 & 0.990849943546816 & 0.504575028226592 \tabularnewline
138 & 0.460711707390821 & 0.921423414781642 & 0.539288292609179 \tabularnewline
139 & 0.451427059899147 & 0.902854119798293 & 0.548572940100853 \tabularnewline
140 & 0.381205417311631 & 0.762410834623261 & 0.618794582688369 \tabularnewline
141 & 0.319947969352088 & 0.639895938704176 & 0.680052030647912 \tabularnewline
142 & 0.267480713092611 & 0.534961426185222 & 0.732519286907389 \tabularnewline
143 & 0.42836301250069 & 0.856726025001381 & 0.571636987499309 \tabularnewline
144 & 0.363494472323189 & 0.726988944646378 & 0.636505527676811 \tabularnewline
145 & 0.35790464280598 & 0.715809285611959 & 0.64209535719402 \tabularnewline
146 & 0.295104848841584 & 0.590209697683167 & 0.704895151158416 \tabularnewline
147 & 0.296580538628444 & 0.593161077256887 & 0.703419461371557 \tabularnewline
148 & 0.263681855857712 & 0.527363711715424 & 0.736318144142288 \tabularnewline
149 & 0.207627285227489 & 0.415254570454978 & 0.792372714772511 \tabularnewline
150 & 0.159635685852194 & 0.319271371704389 & 0.840364314147806 \tabularnewline
151 & 0.122805054027991 & 0.245610108055983 & 0.877194945972009 \tabularnewline
152 & 0.174007193750615 & 0.348014387501229 & 0.825992806249385 \tabularnewline
153 & 0.102925906641113 & 0.205851813282225 & 0.897074093358887 \tabularnewline
154 & 0.0829870301386628 & 0.165974060277326 & 0.917012969861337 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]8[/C][C]0.920807230872157[/C][C]0.158385538255686[/C][C]0.0791927691278432[/C][/ROW]
[ROW][C]9[/C][C]0.851114092512778[/C][C]0.297771814974444[/C][C]0.148885907487222[/C][/ROW]
[ROW][C]10[/C][C]0.776856844989784[/C][C]0.446286310020432[/C][C]0.223143155010216[/C][/ROW]
[ROW][C]11[/C][C]0.741589556854519[/C][C]0.516820886290961[/C][C]0.258410443145481[/C][/ROW]
[ROW][C]12[/C][C]0.813732740464925[/C][C]0.37253451907015[/C][C]0.186267259535075[/C][/ROW]
[ROW][C]13[/C][C]0.860092588441525[/C][C]0.27981482311695[/C][C]0.139907411558475[/C][/ROW]
[ROW][C]14[/C][C]0.831505963179878[/C][C]0.336988073640244[/C][C]0.168494036820122[/C][/ROW]
[ROW][C]15[/C][C]0.88582212875917[/C][C]0.22835574248166[/C][C]0.11417787124083[/C][/ROW]
[ROW][C]16[/C][C]0.872432998871994[/C][C]0.255134002256012[/C][C]0.127567001128006[/C][/ROW]
[ROW][C]17[/C][C]0.827948283935497[/C][C]0.344103432129005[/C][C]0.172051716064503[/C][/ROW]
[ROW][C]18[/C][C]0.798806228295754[/C][C]0.402387543408492[/C][C]0.201193771704246[/C][/ROW]
[ROW][C]19[/C][C]0.743641345687905[/C][C]0.512717308624191[/C][C]0.256358654312095[/C][/ROW]
[ROW][C]20[/C][C]0.685488865868744[/C][C]0.629022268262512[/C][C]0.314511134131256[/C][/ROW]
[ROW][C]21[/C][C]0.753577708144983[/C][C]0.492844583710033[/C][C]0.246422291855017[/C][/ROW]
[ROW][C]22[/C][C]0.901876254023419[/C][C]0.196247491953163[/C][C]0.0981237459765813[/C][/ROW]
[ROW][C]23[/C][C]0.890852217134865[/C][C]0.21829556573027[/C][C]0.109147782865135[/C][/ROW]
[ROW][C]24[/C][C]0.893706833261748[/C][C]0.212586333476504[/C][C]0.106293166738252[/C][/ROW]
[ROW][C]25[/C][C]0.883991122009478[/C][C]0.232017755981045[/C][C]0.116008877990522[/C][/ROW]
[ROW][C]26[/C][C]0.999509113796213[/C][C]0.000981772407574107[/C][C]0.000490886203787054[/C][/ROW]
[ROW][C]27[/C][C]0.999354335118106[/C][C]0.00129132976378716[/C][C]0.000645664881893582[/C][/ROW]
[ROW][C]28[/C][C]0.998938403967073[/C][C]0.00212319206585482[/C][C]0.00106159603292741[/C][/ROW]
[ROW][C]29[/C][C]0.998377215751373[/C][C]0.00324556849725397[/C][C]0.00162278424862699[/C][/ROW]
[ROW][C]30[/C][C]0.998920950801427[/C][C]0.00215809839714549[/C][C]0.00107904919857275[/C][/ROW]
[ROW][C]31[/C][C]0.998294359572968[/C][C]0.00341128085406477[/C][C]0.00170564042703238[/C][/ROW]
[ROW][C]32[/C][C]0.997400480959218[/C][C]0.00519903808156316[/C][C]0.00259951904078158[/C][/ROW]
[ROW][C]33[/C][C]0.996554729505942[/C][C]0.00689054098811558[/C][C]0.00344527049405779[/C][/ROW]
[ROW][C]34[/C][C]0.994828453389525[/C][C]0.0103430932209499[/C][C]0.00517154661047494[/C][/ROW]
[ROW][C]35[/C][C]0.992559856798708[/C][C]0.0148802864025839[/C][C]0.00744014320129194[/C][/ROW]
[ROW][C]36[/C][C]0.989758343334487[/C][C]0.0204833133310259[/C][C]0.0102416566655129[/C][/ROW]
[ROW][C]37[/C][C]0.990410152143462[/C][C]0.0191796957130752[/C][C]0.00958984785653759[/C][/ROW]
[ROW][C]38[/C][C]0.987413465678573[/C][C]0.0251730686428544[/C][C]0.0125865343214272[/C][/ROW]
[ROW][C]39[/C][C]0.985122494430509[/C][C]0.0297550111389813[/C][C]0.0148775055694906[/C][/ROW]
[ROW][C]40[/C][C]0.983460387761525[/C][C]0.0330792244769505[/C][C]0.0165396122384753[/C][/ROW]
[ROW][C]41[/C][C]0.977302641655657[/C][C]0.0453947166886867[/C][C]0.0226973583443434[/C][/ROW]
[ROW][C]42[/C][C]0.97541275634589[/C][C]0.0491744873082196[/C][C]0.0245872436541098[/C][/ROW]
[ROW][C]43[/C][C]0.967561752994821[/C][C]0.0648764940103588[/C][C]0.0324382470051794[/C][/ROW]
[ROW][C]44[/C][C]0.961142909306415[/C][C]0.0777141813871707[/C][C]0.0388570906935854[/C][/ROW]
[ROW][C]45[/C][C]0.97881574373478[/C][C]0.0423685125304391[/C][C]0.0211842562652196[/C][/ROW]
[ROW][C]46[/C][C]0.989243470731251[/C][C]0.0215130585374985[/C][C]0.0107565292687492[/C][/ROW]
[ROW][C]47[/C][C]0.986391741005593[/C][C]0.0272165179888147[/C][C]0.0136082589944074[/C][/ROW]
[ROW][C]48[/C][C]0.98149768480375[/C][C]0.0370046303924992[/C][C]0.0185023151962496[/C][/ROW]
[ROW][C]49[/C][C]0.98833008263201[/C][C]0.0233398347359803[/C][C]0.0116699173679902[/C][/ROW]
[ROW][C]50[/C][C]0.985863344409172[/C][C]0.0282733111816557[/C][C]0.0141366555908278[/C][/ROW]
[ROW][C]51[/C][C]0.981008540168971[/C][C]0.0379829196620575[/C][C]0.0189914598310288[/C][/ROW]
[ROW][C]52[/C][C]0.976344822617834[/C][C]0.0473103547643325[/C][C]0.0236551773821662[/C][/ROW]
[ROW][C]53[/C][C]0.970083891969742[/C][C]0.0598322160605162[/C][C]0.0299161080302581[/C][/ROW]
[ROW][C]54[/C][C]0.971779424748993[/C][C]0.0564411505020136[/C][C]0.0282205752510068[/C][/ROW]
[ROW][C]55[/C][C]0.972905379220906[/C][C]0.0541892415581876[/C][C]0.0270946207790938[/C][/ROW]
[ROW][C]56[/C][C]0.964569056898918[/C][C]0.0708618862021634[/C][C]0.0354309431010817[/C][/ROW]
[ROW][C]57[/C][C]0.959802013374957[/C][C]0.080395973250087[/C][C]0.0401979866250435[/C][/ROW]
[ROW][C]58[/C][C]0.950412921328598[/C][C]0.0991741573428033[/C][C]0.0495870786714017[/C][/ROW]
[ROW][C]59[/C][C]0.959087603602512[/C][C]0.0818247927949761[/C][C]0.0409123963974881[/C][/ROW]
[ROW][C]60[/C][C]0.957109363675889[/C][C]0.0857812726482228[/C][C]0.0428906363241114[/C][/ROW]
[ROW][C]61[/C][C]0.964298971523184[/C][C]0.0714020569536324[/C][C]0.0357010284768162[/C][/ROW]
[ROW][C]62[/C][C]0.95637662586831[/C][C]0.0872467482633794[/C][C]0.0436233741316897[/C][/ROW]
[ROW][C]63[/C][C]0.978787729069104[/C][C]0.0424245418617913[/C][C]0.0212122709308957[/C][/ROW]
[ROW][C]64[/C][C]0.972128540356846[/C][C]0.0557429192863071[/C][C]0.0278714596431536[/C][/ROW]
[ROW][C]65[/C][C]0.964899885158164[/C][C]0.0702002296836724[/C][C]0.0351001148418362[/C][/ROW]
[ROW][C]66[/C][C]0.987633077996399[/C][C]0.024733844007203[/C][C]0.0123669220036015[/C][/ROW]
[ROW][C]67[/C][C]0.987362354077595[/C][C]0.0252752918448091[/C][C]0.0126376459224046[/C][/ROW]
[ROW][C]68[/C][C]0.98606456016584[/C][C]0.0278708796683202[/C][C]0.0139354398341601[/C][/ROW]
[ROW][C]69[/C][C]0.981647581214763[/C][C]0.0367048375704748[/C][C]0.0183524187852374[/C][/ROW]
[ROW][C]70[/C][C]0.981784846123478[/C][C]0.0364303077530439[/C][C]0.018215153876522[/C][/ROW]
[ROW][C]71[/C][C]0.976420112716207[/C][C]0.0471597745675854[/C][C]0.0235798872837927[/C][/ROW]
[ROW][C]72[/C][C]0.980227640641098[/C][C]0.0395447187178031[/C][C]0.0197723593589016[/C][/ROW]
[ROW][C]73[/C][C]0.9745226949669[/C][C]0.0509546100662[/C][C]0.0254773050331[/C][/ROW]
[ROW][C]74[/C][C]0.967471820556919[/C][C]0.0650563588861619[/C][C]0.0325281794430809[/C][/ROW]
[ROW][C]75[/C][C]0.961760712934971[/C][C]0.0764785741300589[/C][C]0.0382392870650294[/C][/ROW]
[ROW][C]76[/C][C]0.951542632707978[/C][C]0.0969147345840435[/C][C]0.0484573672920217[/C][/ROW]
[ROW][C]77[/C][C]0.958644765661677[/C][C]0.0827104686766469[/C][C]0.0413552343383235[/C][/ROW]
[ROW][C]78[/C][C]0.948241675655034[/C][C]0.103516648689932[/C][C]0.0517583243449661[/C][/ROW]
[ROW][C]79[/C][C]0.946871961441239[/C][C]0.106256077117522[/C][C]0.0531280385587611[/C][/ROW]
[ROW][C]80[/C][C]0.950992347404568[/C][C]0.0980153051908647[/C][C]0.0490076525954323[/C][/ROW]
[ROW][C]81[/C][C]0.939987275099938[/C][C]0.120025449800125[/C][C]0.0600127249000624[/C][/ROW]
[ROW][C]82[/C][C]0.930024141280423[/C][C]0.139951717439155[/C][C]0.0699758587195773[/C][/ROW]
[ROW][C]83[/C][C]0.919180911347924[/C][C]0.161638177304153[/C][C]0.0808190886520763[/C][/ROW]
[ROW][C]84[/C][C]0.920257195177822[/C][C]0.159485609644355[/C][C]0.0797428048221775[/C][/ROW]
[ROW][C]85[/C][C]0.902322601864887[/C][C]0.195354796270225[/C][C]0.0976773981351126[/C][/ROW]
[ROW][C]86[/C][C]0.881106785181894[/C][C]0.237786429636212[/C][C]0.118893214818106[/C][/ROW]
[ROW][C]87[/C][C]0.86617341276967[/C][C]0.26765317446066[/C][C]0.13382658723033[/C][/ROW]
[ROW][C]88[/C][C]0.843262323276453[/C][C]0.313475353447093[/C][C]0.156737676723547[/C][/ROW]
[ROW][C]89[/C][C]0.814796972281167[/C][C]0.370406055437666[/C][C]0.185203027718833[/C][/ROW]
[ROW][C]90[/C][C]0.880832820102597[/C][C]0.238334359794806[/C][C]0.119167179897403[/C][/ROW]
[ROW][C]91[/C][C]0.905231641640519[/C][C]0.189536716718963[/C][C]0.0947683583594813[/C][/ROW]
[ROW][C]92[/C][C]0.885758324221205[/C][C]0.22848335155759[/C][C]0.114241675778795[/C][/ROW]
[ROW][C]93[/C][C]0.864550353874785[/C][C]0.27089929225043[/C][C]0.135449646125215[/C][/ROW]
[ROW][C]94[/C][C]0.841218018548849[/C][C]0.317563962902302[/C][C]0.158781981451151[/C][/ROW]
[ROW][C]95[/C][C]0.852248557980598[/C][C]0.295502884038804[/C][C]0.147751442019402[/C][/ROW]
[ROW][C]96[/C][C]0.825956508198679[/C][C]0.348086983602642[/C][C]0.174043491801321[/C][/ROW]
[ROW][C]97[/C][C]0.807561416134531[/C][C]0.384877167730937[/C][C]0.192438583865468[/C][/ROW]
[ROW][C]98[/C][C]0.787989198909473[/C][C]0.424021602181054[/C][C]0.212010801090527[/C][/ROW]
[ROW][C]99[/C][C]0.81388679008307[/C][C]0.37222641983386[/C][C]0.18611320991693[/C][/ROW]
[ROW][C]100[/C][C]0.781812669045498[/C][C]0.436374661909005[/C][C]0.218187330954503[/C][/ROW]
[ROW][C]101[/C][C]0.759969047675397[/C][C]0.480061904649207[/C][C]0.240030952324603[/C][/ROW]
[ROW][C]102[/C][C]0.722153609947202[/C][C]0.555692780105596[/C][C]0.277846390052798[/C][/ROW]
[ROW][C]103[/C][C]0.695850041062525[/C][C]0.60829991787495[/C][C]0.304149958937475[/C][/ROW]
[ROW][C]104[/C][C]0.772195809445104[/C][C]0.455608381109791[/C][C]0.227804190554896[/C][/ROW]
[ROW][C]105[/C][C]0.78345839458003[/C][C]0.43308321083994[/C][C]0.21654160541997[/C][/ROW]
[ROW][C]106[/C][C]0.810097433992434[/C][C]0.379805132015132[/C][C]0.189902566007566[/C][/ROW]
[ROW][C]107[/C][C]0.778285162752322[/C][C]0.443429674495357[/C][C]0.221714837247678[/C][/ROW]
[ROW][C]108[/C][C]0.802082456248161[/C][C]0.395835087503678[/C][C]0.197917543751839[/C][/ROW]
[ROW][C]109[/C][C]0.84271377792745[/C][C]0.3145724441451[/C][C]0.15728622207255[/C][/ROW]
[ROW][C]110[/C][C]0.815807780357136[/C][C]0.368384439285729[/C][C]0.184192219642864[/C][/ROW]
[ROW][C]111[/C][C]0.801959765258296[/C][C]0.396080469483407[/C][C]0.198040234741704[/C][/ROW]
[ROW][C]112[/C][C]0.818196084955799[/C][C]0.363607830088402[/C][C]0.181803915044201[/C][/ROW]
[ROW][C]113[/C][C]0.816783438349614[/C][C]0.366433123300772[/C][C]0.183216561650386[/C][/ROW]
[ROW][C]114[/C][C]0.800180276659188[/C][C]0.399639446681625[/C][C]0.199819723340812[/C][/ROW]
[ROW][C]115[/C][C]0.858938155161603[/C][C]0.282123689676794[/C][C]0.141061844838397[/C][/ROW]
[ROW][C]116[/C][C]0.836373205537833[/C][C]0.327253588924334[/C][C]0.163626794462167[/C][/ROW]
[ROW][C]117[/C][C]0.806736847877555[/C][C]0.386526304244889[/C][C]0.193263152122445[/C][/ROW]
[ROW][C]118[/C][C]0.770722155656694[/C][C]0.458555688686611[/C][C]0.229277844343306[/C][/ROW]
[ROW][C]119[/C][C]0.773242490339986[/C][C]0.453515019320029[/C][C]0.226757509660014[/C][/ROW]
[ROW][C]120[/C][C]0.735197796649601[/C][C]0.529604406700798[/C][C]0.264802203350399[/C][/ROW]
[ROW][C]121[/C][C]0.707936709547127[/C][C]0.584126580905746[/C][C]0.292063290452873[/C][/ROW]
[ROW][C]122[/C][C]0.661473825735942[/C][C]0.677052348528116[/C][C]0.338526174264058[/C][/ROW]
[ROW][C]123[/C][C]0.610399060275168[/C][C]0.779201879449664[/C][C]0.389600939724832[/C][/ROW]
[ROW][C]124[/C][C]0.589332286102407[/C][C]0.821335427795187[/C][C]0.410667713897593[/C][/ROW]
[ROW][C]125[/C][C]0.537286746262387[/C][C]0.925426507475226[/C][C]0.462713253737613[/C][/ROW]
[ROW][C]126[/C][C]0.56723273492304[/C][C]0.86553453015392[/C][C]0.43276726507696[/C][/ROW]
[ROW][C]127[/C][C]0.526236185825098[/C][C]0.947527628349804[/C][C]0.473763814174902[/C][/ROW]
[ROW][C]128[/C][C]0.507246652965093[/C][C]0.985506694069814[/C][C]0.492753347034907[/C][/ROW]
[ROW][C]129[/C][C]0.678900700125149[/C][C]0.642198599749702[/C][C]0.321099299874851[/C][/ROW]
[ROW][C]130[/C][C]0.630079045549175[/C][C]0.73984190890165[/C][C]0.369920954450825[/C][/ROW]
[ROW][C]131[/C][C]0.605310791310286[/C][C]0.789378417379428[/C][C]0.394689208689714[/C][/ROW]
[ROW][C]132[/C][C]0.586690663337756[/C][C]0.826618673324488[/C][C]0.413309336662244[/C][/ROW]
[ROW][C]133[/C][C]0.526922122823941[/C][C]0.946155754352118[/C][C]0.473077877176059[/C][/ROW]
[ROW][C]134[/C][C]0.536515952368432[/C][C]0.926968095263135[/C][C]0.463484047631568[/C][/ROW]
[ROW][C]135[/C][C]0.511494365744413[/C][C]0.977011268511175[/C][C]0.488505634255587[/C][/ROW]
[ROW][C]136[/C][C]0.508879845731804[/C][C]0.982240308536392[/C][C]0.491120154268196[/C][/ROW]
[ROW][C]137[/C][C]0.495424971773408[/C][C]0.990849943546816[/C][C]0.504575028226592[/C][/ROW]
[ROW][C]138[/C][C]0.460711707390821[/C][C]0.921423414781642[/C][C]0.539288292609179[/C][/ROW]
[ROW][C]139[/C][C]0.451427059899147[/C][C]0.902854119798293[/C][C]0.548572940100853[/C][/ROW]
[ROW][C]140[/C][C]0.381205417311631[/C][C]0.762410834623261[/C][C]0.618794582688369[/C][/ROW]
[ROW][C]141[/C][C]0.319947969352088[/C][C]0.639895938704176[/C][C]0.680052030647912[/C][/ROW]
[ROW][C]142[/C][C]0.267480713092611[/C][C]0.534961426185222[/C][C]0.732519286907389[/C][/ROW]
[ROW][C]143[/C][C]0.42836301250069[/C][C]0.856726025001381[/C][C]0.571636987499309[/C][/ROW]
[ROW][C]144[/C][C]0.363494472323189[/C][C]0.726988944646378[/C][C]0.636505527676811[/C][/ROW]
[ROW][C]145[/C][C]0.35790464280598[/C][C]0.715809285611959[/C][C]0.64209535719402[/C][/ROW]
[ROW][C]146[/C][C]0.295104848841584[/C][C]0.590209697683167[/C][C]0.704895151158416[/C][/ROW]
[ROW][C]147[/C][C]0.296580538628444[/C][C]0.593161077256887[/C][C]0.703419461371557[/C][/ROW]
[ROW][C]148[/C][C]0.263681855857712[/C][C]0.527363711715424[/C][C]0.736318144142288[/C][/ROW]
[ROW][C]149[/C][C]0.207627285227489[/C][C]0.415254570454978[/C][C]0.792372714772511[/C][/ROW]
[ROW][C]150[/C][C]0.159635685852194[/C][C]0.319271371704389[/C][C]0.840364314147806[/C][/ROW]
[ROW][C]151[/C][C]0.122805054027991[/C][C]0.245610108055983[/C][C]0.877194945972009[/C][/ROW]
[ROW][C]152[/C][C]0.174007193750615[/C][C]0.348014387501229[/C][C]0.825992806249385[/C][/ROW]
[ROW][C]153[/C][C]0.102925906641113[/C][C]0.205851813282225[/C][C]0.897074093358887[/C][/ROW]
[ROW][C]154[/C][C]0.0829870301386628[/C][C]0.165974060277326[/C][C]0.917012969861337[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.9208072308721570.1583855382556860.0791927691278432
90.8511140925127780.2977718149744440.148885907487222
100.7768568449897840.4462863100204320.223143155010216
110.7415895568545190.5168208862909610.258410443145481
120.8137327404649250.372534519070150.186267259535075
130.8600925884415250.279814823116950.139907411558475
140.8315059631798780.3369880736402440.168494036820122
150.885822128759170.228355742481660.11417787124083
160.8724329988719940.2551340022560120.127567001128006
170.8279482839354970.3441034321290050.172051716064503
180.7988062282957540.4023875434084920.201193771704246
190.7436413456879050.5127173086241910.256358654312095
200.6854888658687440.6290222682625120.314511134131256
210.7535777081449830.4928445837100330.246422291855017
220.9018762540234190.1962474919531630.0981237459765813
230.8908522171348650.218295565730270.109147782865135
240.8937068332617480.2125863334765040.106293166738252
250.8839911220094780.2320177559810450.116008877990522
260.9995091137962130.0009817724075741070.000490886203787054
270.9993543351181060.001291329763787160.000645664881893582
280.9989384039670730.002123192065854820.00106159603292741
290.9983772157513730.003245568497253970.00162278424862699
300.9989209508014270.002158098397145490.00107904919857275
310.9982943595729680.003411280854064770.00170564042703238
320.9974004809592180.005199038081563160.00259951904078158
330.9965547295059420.006890540988115580.00344527049405779
340.9948284533895250.01034309322094990.00517154661047494
350.9925598567987080.01488028640258390.00744014320129194
360.9897583433344870.02048331333102590.0102416566655129
370.9904101521434620.01917969571307520.00958984785653759
380.9874134656785730.02517306864285440.0125865343214272
390.9851224944305090.02975501113898130.0148775055694906
400.9834603877615250.03307922447695050.0165396122384753
410.9773026416556570.04539471668868670.0226973583443434
420.975412756345890.04917448730821960.0245872436541098
430.9675617529948210.06487649401035880.0324382470051794
440.9611429093064150.07771418138717070.0388570906935854
450.978815743734780.04236851253043910.0211842562652196
460.9892434707312510.02151305853749850.0107565292687492
470.9863917410055930.02721651798881470.0136082589944074
480.981497684803750.03700463039249920.0185023151962496
490.988330082632010.02333983473598030.0116699173679902
500.9858633444091720.02827331118165570.0141366555908278
510.9810085401689710.03798291966205750.0189914598310288
520.9763448226178340.04731035476433250.0236551773821662
530.9700838919697420.05983221606051620.0299161080302581
540.9717794247489930.05644115050201360.0282205752510068
550.9729053792209060.05418924155818760.0270946207790938
560.9645690568989180.07086188620216340.0354309431010817
570.9598020133749570.0803959732500870.0401979866250435
580.9504129213285980.09917415734280330.0495870786714017
590.9590876036025120.08182479279497610.0409123963974881
600.9571093636758890.08578127264822280.0428906363241114
610.9642989715231840.07140205695363240.0357010284768162
620.956376625868310.08724674826337940.0436233741316897
630.9787877290691040.04242454186179130.0212122709308957
640.9721285403568460.05574291928630710.0278714596431536
650.9648998851581640.07020022968367240.0351001148418362
660.9876330779963990.0247338440072030.0123669220036015
670.9873623540775950.02527529184480910.0126376459224046
680.986064560165840.02787087966832020.0139354398341601
690.9816475812147630.03670483757047480.0183524187852374
700.9817848461234780.03643030775304390.018215153876522
710.9764201127162070.04715977456758540.0235798872837927
720.9802276406410980.03954471871780310.0197723593589016
730.97452269496690.05095461006620.0254773050331
740.9674718205569190.06505635888616190.0325281794430809
750.9617607129349710.07647857413005890.0382392870650294
760.9515426327079780.09691473458404350.0484573672920217
770.9586447656616770.08271046867664690.0413552343383235
780.9482416756550340.1035166486899320.0517583243449661
790.9468719614412390.1062560771175220.0531280385587611
800.9509923474045680.09801530519086470.0490076525954323
810.9399872750999380.1200254498001250.0600127249000624
820.9300241412804230.1399517174391550.0699758587195773
830.9191809113479240.1616381773041530.0808190886520763
840.9202571951778220.1594856096443550.0797428048221775
850.9023226018648870.1953547962702250.0976773981351126
860.8811067851818940.2377864296362120.118893214818106
870.866173412769670.267653174460660.13382658723033
880.8432623232764530.3134753534470930.156737676723547
890.8147969722811670.3704060554376660.185203027718833
900.8808328201025970.2383343597948060.119167179897403
910.9052316416405190.1895367167189630.0947683583594813
920.8857583242212050.228483351557590.114241675778795
930.8645503538747850.270899292250430.135449646125215
940.8412180185488490.3175639629023020.158781981451151
950.8522485579805980.2955028840388040.147751442019402
960.8259565081986790.3480869836026420.174043491801321
970.8075614161345310.3848771677309370.192438583865468
980.7879891989094730.4240216021810540.212010801090527
990.813886790083070.372226419833860.18611320991693
1000.7818126690454980.4363746619090050.218187330954503
1010.7599690476753970.4800619046492070.240030952324603
1020.7221536099472020.5556927801055960.277846390052798
1030.6958500410625250.608299917874950.304149958937475
1040.7721958094451040.4556083811097910.227804190554896
1050.783458394580030.433083210839940.21654160541997
1060.8100974339924340.3798051320151320.189902566007566
1070.7782851627523220.4434296744953570.221714837247678
1080.8020824562481610.3958350875036780.197917543751839
1090.842713777927450.31457244414510.15728622207255
1100.8158077803571360.3683844392857290.184192219642864
1110.8019597652582960.3960804694834070.198040234741704
1120.8181960849557990.3636078300884020.181803915044201
1130.8167834383496140.3664331233007720.183216561650386
1140.8001802766591880.3996394466816250.199819723340812
1150.8589381551616030.2821236896767940.141061844838397
1160.8363732055378330.3272535889243340.163626794462167
1170.8067368478775550.3865263042448890.193263152122445
1180.7707221556566940.4585556886866110.229277844343306
1190.7732424903399860.4535150193200290.226757509660014
1200.7351977966496010.5296044067007980.264802203350399
1210.7079367095471270.5841265809057460.292063290452873
1220.6614738257359420.6770523485281160.338526174264058
1230.6103990602751680.7792018794496640.389600939724832
1240.5893322861024070.8213354277951870.410667713897593
1250.5372867462623870.9254265074752260.462713253737613
1260.567232734923040.865534530153920.43276726507696
1270.5262361858250980.9475276283498040.473763814174902
1280.5072466529650930.9855066940698140.492753347034907
1290.6789007001251490.6421985997497020.321099299874851
1300.6300790455491750.739841908901650.369920954450825
1310.6053107913102860.7893784173794280.394689208689714
1320.5866906633377560.8266186733244880.413309336662244
1330.5269221228239410.9461557543521180.473077877176059
1340.5365159523684320.9269680952631350.463484047631568
1350.5114943657444130.9770112685111750.488505634255587
1360.5088798457318040.9822403085363920.491120154268196
1370.4954249717734080.9908499435468160.504575028226592
1380.4607117073908210.9214234147816420.539288292609179
1390.4514270598991470.9028541197982930.548572940100853
1400.3812054173116310.7624108346232610.618794582688369
1410.3199479693520880.6398959387041760.680052030647912
1420.2674807130926110.5349614261852220.732519286907389
1430.428363012500690.8567260250013810.571636987499309
1440.3634944723231890.7269889446463780.636505527676811
1450.357904642805980.7158092856119590.64209535719402
1460.2951048488415840.5902096976831670.704895151158416
1470.2965805386284440.5931610772568870.703419461371557
1480.2636818558577120.5273637117154240.736318144142288
1490.2076272852274890.4152545704549780.792372714772511
1500.1596356858521940.3192713717043890.840364314147806
1510.1228050540279910.2456101080559830.877194945972009
1520.1740071937506150.3480143875012290.825992806249385
1530.1029259066411130.2058518132822250.897074093358887
1540.08298703013866280.1659740602773260.917012969861337






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.054421768707483NOK
5% type I error level330.224489795918367NOK
10% type I error level530.360544217687075NOK

\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 & 8 & 0.054421768707483 & NOK \tabularnewline
5% type I error level & 33 & 0.224489795918367 & NOK \tabularnewline
10% type I error level & 53 & 0.360544217687075 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=154497&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]8[/C][C]0.054421768707483[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]33[/C][C]0.224489795918367[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]53[/C][C]0.360544217687075[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=154497&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=154497&T=6

As an alternative you can also use a QR Code:  
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 level80.054421768707483NOK
5% type I error level330.224489795918367NOK
10% type I error level530.360544217687075NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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Figure 5
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Figure 6
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Figure 7
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Figure 8
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Figure 9
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Figure 10
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Input Parameters & R Code

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