FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSat, 08 Nov 2014 09:21:19 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Nov/08/t14154396191eio0xx0lzsvydr.htm/, Retrieved Sun, 19 May 2024 13:37:44 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=253115, Retrieved Sun, 19 May 2024 13:37:44 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact155

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [WS7] [2014-11-08 09:21:19] [c15d474939d69eac0efd26ce7542850f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
41	38	13	12	14	12
39	32	16	11	18	11
30	35	19	15	11	14
31	33	15	6	12	12
34	37	14	13	16	21
35	29	13	10	18	12
39	31	19	12	14	22
34	36	15	14	14	11
36	35	14	12	15	10
37	38	15	6	15	13
38	31	16	10	17	10
36	34	16	12	19	8
38	35	16	12	10	15
39	38	16	11	16	14
33	37	17	15	18	10
32	33	15	12	14	14
36	32	15	10	14	14
38	38	20	12	17	11
39	38	18	11	14	10
32	32	16	12	16	13
32	33	16	11	18	7
31	31	16	12	11	14
39	38	19	13	14	12
37	39	16	11	12	14
39	32	17	9	17	11
41	32	17	13	9	9
36	35	16	10	16	11
33	37	15	14	14	15
33	33	16	12	15	14
34	33	14	10	11	13
31	28	15	12	16	9
27	32	12	8	13	15
37	31	14	10	17	10
34	37	16	12	15	11
34	30	14	12	14	13
32	33	7	7	16	8
29	31	10	6	9	20
36	33	14	12	15	12
29	31	16	10	17	10
35	33	16	10	13	10
37	32	16	10	15	9
34	33	14	12	16	14
38	32	20	15	16	8
35	33	14	10	12	14
38	28	14	10	12	11
37	35	11	12	11	13
38	39	14	13	15	9
33	34	15	11	15	11
36	38	16	11	17	15
38	32	14	12	13	11
32	38	16	14	16	10
32	30	14	10	14	14
32	33	12	12	11	18
34	38	16	13	12	14
32	32	9	5	12	11
37	32	14	6	15	12
39	34	16	12	16	13
29	34	16	12	15	9
37	36	15	11	12	10
35	34	16	10	12	15
30	28	12	7	8	20
38	34	16	12	13	12
34	35	16	14	11	12
31	35	14	11	14	14
34	31	16	12	15	13
35	37	17	13	10	11
36	35	18	14	11	17
30	27	18	11	12	12
39	40	12	12	15	13
35	37	16	12	15	14
38	36	10	8	14	13
31	38	14	11	16	15
34	39	18	14	15	13
38	41	18	14	15	10
34	27	16	12	13	11
39	30	17	9	12	19
37	37	16	13	17	13
34	31	16	11	13	17
28	31	13	12	15	13
37	27	16	12	13	9
33	36	16	12	15	11
37	38	20	12	16	10
35	37	16	12	15	9
37	33	15	12	16	12
32	34	15	11	15	12
33	31	16	10	14	13
38	39	14	9	15	13
33	34	16	12	14	12
29	32	16	12	13	15
33	33	15	12	7	22
31	36	12	9	17	13
36	32	17	15	13	15
35	41	16	12	15	13
32	28	15	12	14	15
29	30	13	12	13	10
39	36	16	10	16	11
37	35	16	13	12	16
35	31	16	9	14	11
37	34	16	12	17	11
32	36	14	10	15	10
38	36	16	14	17	10
37	35	16	11	12	16
36	37	20	15	16	12
32	28	15	11	11	11
33	39	16	11	15	16
40	32	13	12	9	19
38	35	17	12	16	11
41	39	16	12	15	16
36	35	16	11	10	15
43	42	12	7	10	24
30	34	16	12	15	14
31	33	16	14	11	15
32	41	17	11	13	11
32	33	13	11	14	15
37	34	12	10	18	12
37	32	18	13	16	10
33	40	14	13	14	14
34	40	14	8	14	13
33	35	13	11	14	9
38	36	16	12	14	15
33	37	13	11	12	15
31	27	16	13	14	14
38	39	13	12	15	11
37	38	16	14	15	8
33	31	15	13	15	11
31	33	16	15	13	11
39	32	15	10	17	8
44	39	17	11	17	10
33	36	15	9	19	11
35	33	12	11	15	13
32	33	16	10	13	11
28	32	10	11	9	20
40	37	16	8	15	10
27	30	12	11	15	15
37	38	14	12	15	12
32	29	15	12	16	14
28	22	13	9	11	23
34	35	15	11	14	14
30	35	11	10	11	16
35	34	12	8	15	11
31	35	8	9	13	12
32	34	16	8	15	10
30	34	15	9	16	14
30	35	17	15	14	12
31	23	16	11	15	12
40	31	10	8	16	11
32	27	18	13	16	12
36	36	13	12	11	13
32	31	16	12	12	11
35	32	13	9	9	19
38	39	10	7	16	12
42	37	15	13	13	17
34	38	16	9	16	9
35	39	16	6	12	12
35	34	14	8	9	19
33	31	10	8	13	18
36	32	17	15	13	15
32	37	13	6	14	14
33	36	15	9	19	11
34	32	16	11	13	9
32	35	12	8	12	18
34	36	13	8	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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 time6 seconds
R Server'Sir Maurice George Kendall' @ kendall.wessa.net






Multiple Linear Regression - Estimated Regression Equation
"depression"[t] = + 25.9715 -0.0261817`"Connected"`[t] + 0.0208244`"separate"`[t] -0.161722`"learning"`[t] -0.0493223`"Software"`[t] -0.704716`"happiness"`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
"depression"[t] =  +  25.9715 -0.0261817`"Connected"`[t] +  0.0208244`"separate"`[t] -0.161722`"learning"`[t] -0.0493223`"Software"`[t] -0.704716`"happiness"`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]"depression"[t] =  +  25.9715 -0.0261817`"Connected"`[t] +  0.0208244`"separate"`[t] -0.161722`"learning"`[t] -0.0493223`"Software"`[t] -0.704716`"happiness"`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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
"depression"[t] = + 25.9715 -0.0261817`"Connected"`[t] + 0.0208244`"separate"`[t] -0.161722`"learning"`[t] -0.0493223`"Software"`[t] -0.704716`"happiness"`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)25.97152.823229.1992.21599e-161.108e-16
`"Connected"`-0.02618170.0685226-0.38210.7029150.351458
`"separate"`0.02082440.06398870.32540.7452850.372643
`"learning"`-0.1617220.114871-1.4080.1611630.0805813
`"Software"`-0.04932230.117309-0.42040.6747360.337368
`"happiness"`-0.7047160.0923752-7.6292.19009e-121.09505e-12

\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) & 25.9715 & 2.82322 & 9.199 & 2.21599e-16 & 1.108e-16 \tabularnewline
`"Connected"` & -0.0261817 & 0.0685226 & -0.3821 & 0.702915 & 0.351458 \tabularnewline
`"separate"` & 0.0208244 & 0.0639887 & 0.3254 & 0.745285 & 0.372643 \tabularnewline
`"learning"` & -0.161722 & 0.114871 & -1.408 & 0.161163 & 0.0805813 \tabularnewline
`"Software"` & -0.0493223 & 0.117309 & -0.4204 & 0.674736 & 0.337368 \tabularnewline
`"happiness"` & -0.704716 & 0.0923752 & -7.629 & 2.19009e-12 & 1.09505e-12 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&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]25.9715[/C][C]2.82322[/C][C]9.199[/C][C]2.21599e-16[/C][C]1.108e-16[/C][/ROW]
[ROW][C]`"Connected"`[/C][C]-0.0261817[/C][C]0.0685226[/C][C]-0.3821[/C][C]0.702915[/C][C]0.351458[/C][/ROW]
[ROW][C]`"separate"`[/C][C]0.0208244[/C][C]0.0639887[/C][C]0.3254[/C][C]0.745285[/C][C]0.372643[/C][/ROW]
[ROW][C]`"learning"`[/C][C]-0.161722[/C][C]0.114871[/C][C]-1.408[/C][C]0.161163[/C][C]0.0805813[/C][/ROW]
[ROW][C]`"Software"`[/C][C]-0.0493223[/C][C]0.117309[/C][C]-0.4204[/C][C]0.674736[/C][C]0.337368[/C][/ROW]
[ROW][C]`"happiness"`[/C][C]-0.704716[/C][C]0.0923752[/C][C]-7.629[/C][C]2.19009e-12[/C][C]1.09505e-12[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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)25.97152.823229.1992.21599e-161.108e-16
`"Connected"`-0.02618170.0685226-0.38210.7029150.351458
`"separate"`0.02082440.06398870.32540.7452850.372643
`"learning"`-0.1617220.114871-1.4080.1611630.0805813
`"Software"`-0.04932230.117309-0.42040.6747360.337368
`"happiness"`-0.7047160.0923752-7.6292.19009e-121.09505e-12






Multiple Linear Regression - Regression Statistics
Multiple R0.562151
R-squared0.316014
Adjusted R-squared0.294091
F-TEST (value)14.415
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.33604e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66016
Sum Squared Residuals1103.92

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.562151 \tabularnewline
R-squared & 0.316014 \tabularnewline
Adjusted R-squared & 0.294091 \tabularnewline
F-TEST (value) & 14.415 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 156 \tabularnewline
p-value & 1.33604e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.66016 \tabularnewline
Sum Squared Residuals & 1103.92 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.562151[/C][/ROW]
[ROW][C]R-squared[/C][C]0.316014[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.294091[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]14.415[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]156[/C][/ROW]
[ROW][C]p-value[/C][C]1.33604e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.66016[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1103.92[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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 R0.562151
R-squared0.316014
Adjusted R-squared0.294091
F-TEST (value)14.415
F-TEST (DF numerator)5
F-TEST (DF denominator)156
p-value1.33604e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.66016
Sum Squared Residuals1103.92






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11213.1291-1.12915
2119.801861.19814
31414.3505-0.350526
41214.6688-2.66877
52111.67119.32888
61210.37861.6214
72212.06549.93459
81112.8487-1.84868
91012.3311-2.33115
101312.50170.49835
111010.5613-0.561255
1289.16802-1.16802
131515.4789-0.47892
141411.33622.66376
15109.704060.295939
161412.93721.06278
171412.91031.08969
18119.961491.03851
191012.4222-2.42223
201311.34521.65476
21710.006-3.00596
221414.8742-0.874179
231212.1619-0.16186
241414.2283-0.228289
251110.44350.556501
26915.8316-6.83157
271111.4016-0.401632
281512.89572.10431
291412.04461.9554
301315.2594-2.25937
31911.4498-2.44985
321514.43450.565526
331010.9109-0.91088
341112.1017-1.10172
351312.98410.0158956
36813.0682-5.06817
372017.60222.39776
381212.2895-0.289498
391010.7969-0.796891
401013.5003-3.50031
41912.0177-3.01769
421411.63712.36285
43810.3933-2.3933
441414.5285-0.528472
451114.3458-3.34581
461315.609-2.60899
47912.3128-3.31276
481112.2765-1.27647
491510.71014.28993
501113.6257-2.62574
511011.3715-1.37154
521413.13510.864888
531815.53652.46347
541414.1874-0.187366
551115.6414-4.64141
561212.5384-0.538426
571311.20361.79638
58912.1702-3.17015
591014.3275-4.32754
601514.22590.774147
612017.84552.15447
621213.3439-1.34395
631214.7803-2.78029
641413.21610.783906
651311.97681.02323
661115.3881-4.38807
671714.40452.59552
681213.8382-1.83823
691312.68020.319833
701412.07551.92447
711313.8485-0.848499
721511.86913.13086
731311.72131.27872
741011.6582-1.6582
751113.3029-2.3029
761913.92545.07457
771310.56442.43558
781713.43553.56448
791312.6190.380975
80913.2244-4.22436
811112.1071-1.10707
821010.6924-0.692393
83912.0755-3.07553
841211.39690.603121
851212.3027-0.30265
861312.80630.193688
871312.510.489952
881212.7701-0.77014
891513.53791.46207
902217.8444.15595
911311.54491.45514
921513.0451.95503
931312.15880.841168
941512.83312.1669
951013.9815-3.98145
961111.3439-0.343911
971614.04631.95365
981112.8033-1.80327
991110.55130.448734
1001012.5553-2.55534
1011010.4681-0.468088
1021614.1451.85501
1031210.54981.45022
1041114.9966-3.99657
1051612.21893.78113
1061916.5542.44604
1071111.0889-0.0889025
1081611.96014.03991
1091515.5806-0.580606
1102416.38737.61272
1111412.1441.85603
1121514.81720.182817
1131113.5344-2.53441
1141513.311.69002
1151210.59211.40792
1161010.8416-0.841567
1171413.16920.830792
1181313.3896-0.389637
119913.3255-4.32545
1201512.68092.31912
1211514.77650.223467
1221412.62741.37259
1231112.5238-1.5238
124811.9454-3.94535
1251112.1154-1.11535
1261113.3584-2.35843
127810.7176-2.71762
1281010.3597-0.359716
129119.59791.4021
1301312.68840.311555
1311113.5789-2.57886
1322017.40262.59737
1331012.1419-2.14191
1341512.83542.16457
1351212.3674-0.367438
1361411.44452.55551
1372315.39847.60156
1381412.97581.02417
1391615.89090.109089
1401112.8572-1.85724
1411214.9898-2.98978
1421012.2889-2.2889
1431411.74892.25106
1441212.5598-0.559822
1451211.9380.061958
1461112.2826-1.28258
1471210.86841.13165
1481315.3326-2.33256
1491114.1433-3.14328
1501916.83282.16716
1511212.5509-0.550863
1521713.41413.58591
153911.5658-2.56579
1541214.5273-2.52726
1551916.76212.23791
1561814.583.42
1571513.0451.95503
1581413.63990.360106
159119.59791.4021
160913.4563-4.45635
1611815.07082.92925
1621614.17281.82722

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 12 & 13.1291 & -1.12915 \tabularnewline
2 & 11 & 9.80186 & 1.19814 \tabularnewline
3 & 14 & 14.3505 & -0.350526 \tabularnewline
4 & 12 & 14.6688 & -2.66877 \tabularnewline
5 & 21 & 11.6711 & 9.32888 \tabularnewline
6 & 12 & 10.3786 & 1.6214 \tabularnewline
7 & 22 & 12.0654 & 9.93459 \tabularnewline
8 & 11 & 12.8487 & -1.84868 \tabularnewline
9 & 10 & 12.3311 & -2.33115 \tabularnewline
10 & 13 & 12.5017 & 0.49835 \tabularnewline
11 & 10 & 10.5613 & -0.561255 \tabularnewline
12 & 8 & 9.16802 & -1.16802 \tabularnewline
13 & 15 & 15.4789 & -0.47892 \tabularnewline
14 & 14 & 11.3362 & 2.66376 \tabularnewline
15 & 10 & 9.70406 & 0.295939 \tabularnewline
16 & 14 & 12.9372 & 1.06278 \tabularnewline
17 & 14 & 12.9103 & 1.08969 \tabularnewline
18 & 11 & 9.96149 & 1.03851 \tabularnewline
19 & 10 & 12.4222 & -2.42223 \tabularnewline
20 & 13 & 11.3452 & 1.65476 \tabularnewline
21 & 7 & 10.006 & -3.00596 \tabularnewline
22 & 14 & 14.8742 & -0.874179 \tabularnewline
23 & 12 & 12.1619 & -0.16186 \tabularnewline
24 & 14 & 14.2283 & -0.228289 \tabularnewline
25 & 11 & 10.4435 & 0.556501 \tabularnewline
26 & 9 & 15.8316 & -6.83157 \tabularnewline
27 & 11 & 11.4016 & -0.401632 \tabularnewline
28 & 15 & 12.8957 & 2.10431 \tabularnewline
29 & 14 & 12.0446 & 1.9554 \tabularnewline
30 & 13 & 15.2594 & -2.25937 \tabularnewline
31 & 9 & 11.4498 & -2.44985 \tabularnewline
32 & 15 & 14.4345 & 0.565526 \tabularnewline
33 & 10 & 10.9109 & -0.91088 \tabularnewline
34 & 11 & 12.1017 & -1.10172 \tabularnewline
35 & 13 & 12.9841 & 0.0158956 \tabularnewline
36 & 8 & 13.0682 & -5.06817 \tabularnewline
37 & 20 & 17.6022 & 2.39776 \tabularnewline
38 & 12 & 12.2895 & -0.289498 \tabularnewline
39 & 10 & 10.7969 & -0.796891 \tabularnewline
40 & 10 & 13.5003 & -3.50031 \tabularnewline
41 & 9 & 12.0177 & -3.01769 \tabularnewline
42 & 14 & 11.6371 & 2.36285 \tabularnewline
43 & 8 & 10.3933 & -2.3933 \tabularnewline
44 & 14 & 14.5285 & -0.528472 \tabularnewline
45 & 11 & 14.3458 & -3.34581 \tabularnewline
46 & 13 & 15.609 & -2.60899 \tabularnewline
47 & 9 & 12.3128 & -3.31276 \tabularnewline
48 & 11 & 12.2765 & -1.27647 \tabularnewline
49 & 15 & 10.7101 & 4.28993 \tabularnewline
50 & 11 & 13.6257 & -2.62574 \tabularnewline
51 & 10 & 11.3715 & -1.37154 \tabularnewline
52 & 14 & 13.1351 & 0.864888 \tabularnewline
53 & 18 & 15.5365 & 2.46347 \tabularnewline
54 & 14 & 14.1874 & -0.187366 \tabularnewline
55 & 11 & 15.6414 & -4.64141 \tabularnewline
56 & 12 & 12.5384 & -0.538426 \tabularnewline
57 & 13 & 11.2036 & 1.79638 \tabularnewline
58 & 9 & 12.1702 & -3.17015 \tabularnewline
59 & 10 & 14.3275 & -4.32754 \tabularnewline
60 & 15 & 14.2259 & 0.774147 \tabularnewline
61 & 20 & 17.8455 & 2.15447 \tabularnewline
62 & 12 & 13.3439 & -1.34395 \tabularnewline
63 & 12 & 14.7803 & -2.78029 \tabularnewline
64 & 14 & 13.2161 & 0.783906 \tabularnewline
65 & 13 & 11.9768 & 1.02323 \tabularnewline
66 & 11 & 15.3881 & -4.38807 \tabularnewline
67 & 17 & 14.4045 & 2.59552 \tabularnewline
68 & 12 & 13.8382 & -1.83823 \tabularnewline
69 & 13 & 12.6802 & 0.319833 \tabularnewline
70 & 14 & 12.0755 & 1.92447 \tabularnewline
71 & 13 & 13.8485 & -0.848499 \tabularnewline
72 & 15 & 11.8691 & 3.13086 \tabularnewline
73 & 13 & 11.7213 & 1.27872 \tabularnewline
74 & 10 & 11.6582 & -1.6582 \tabularnewline
75 & 11 & 13.3029 & -2.3029 \tabularnewline
76 & 19 & 13.9254 & 5.07457 \tabularnewline
77 & 13 & 10.5644 & 2.43558 \tabularnewline
78 & 17 & 13.4355 & 3.56448 \tabularnewline
79 & 13 & 12.619 & 0.380975 \tabularnewline
80 & 9 & 13.2244 & -4.22436 \tabularnewline
81 & 11 & 12.1071 & -1.10707 \tabularnewline
82 & 10 & 10.6924 & -0.692393 \tabularnewline
83 & 9 & 12.0755 & -3.07553 \tabularnewline
84 & 12 & 11.3969 & 0.603121 \tabularnewline
85 & 12 & 12.3027 & -0.30265 \tabularnewline
86 & 13 & 12.8063 & 0.193688 \tabularnewline
87 & 13 & 12.51 & 0.489952 \tabularnewline
88 & 12 & 12.7701 & -0.77014 \tabularnewline
89 & 15 & 13.5379 & 1.46207 \tabularnewline
90 & 22 & 17.844 & 4.15595 \tabularnewline
91 & 13 & 11.5449 & 1.45514 \tabularnewline
92 & 15 & 13.045 & 1.95503 \tabularnewline
93 & 13 & 12.1588 & 0.841168 \tabularnewline
94 & 15 & 12.8331 & 2.1669 \tabularnewline
95 & 10 & 13.9815 & -3.98145 \tabularnewline
96 & 11 & 11.3439 & -0.343911 \tabularnewline
97 & 16 & 14.0463 & 1.95365 \tabularnewline
98 & 11 & 12.8033 & -1.80327 \tabularnewline
99 & 11 & 10.5513 & 0.448734 \tabularnewline
100 & 10 & 12.5553 & -2.55534 \tabularnewline
101 & 10 & 10.4681 & -0.468088 \tabularnewline
102 & 16 & 14.145 & 1.85501 \tabularnewline
103 & 12 & 10.5498 & 1.45022 \tabularnewline
104 & 11 & 14.9966 & -3.99657 \tabularnewline
105 & 16 & 12.2189 & 3.78113 \tabularnewline
106 & 19 & 16.554 & 2.44604 \tabularnewline
107 & 11 & 11.0889 & -0.0889025 \tabularnewline
108 & 16 & 11.9601 & 4.03991 \tabularnewline
109 & 15 & 15.5806 & -0.580606 \tabularnewline
110 & 24 & 16.3873 & 7.61272 \tabularnewline
111 & 14 & 12.144 & 1.85603 \tabularnewline
112 & 15 & 14.8172 & 0.182817 \tabularnewline
113 & 11 & 13.5344 & -2.53441 \tabularnewline
114 & 15 & 13.31 & 1.69002 \tabularnewline
115 & 12 & 10.5921 & 1.40792 \tabularnewline
116 & 10 & 10.8416 & -0.841567 \tabularnewline
117 & 14 & 13.1692 & 0.830792 \tabularnewline
118 & 13 & 13.3896 & -0.389637 \tabularnewline
119 & 9 & 13.3255 & -4.32545 \tabularnewline
120 & 15 & 12.6809 & 2.31912 \tabularnewline
121 & 15 & 14.7765 & 0.223467 \tabularnewline
122 & 14 & 12.6274 & 1.37259 \tabularnewline
123 & 11 & 12.5238 & -1.5238 \tabularnewline
124 & 8 & 11.9454 & -3.94535 \tabularnewline
125 & 11 & 12.1154 & -1.11535 \tabularnewline
126 & 11 & 13.3584 & -2.35843 \tabularnewline
127 & 8 & 10.7176 & -2.71762 \tabularnewline
128 & 10 & 10.3597 & -0.359716 \tabularnewline
129 & 11 & 9.5979 & 1.4021 \tabularnewline
130 & 13 & 12.6884 & 0.311555 \tabularnewline
131 & 11 & 13.5789 & -2.57886 \tabularnewline
132 & 20 & 17.4026 & 2.59737 \tabularnewline
133 & 10 & 12.1419 & -2.14191 \tabularnewline
134 & 15 & 12.8354 & 2.16457 \tabularnewline
135 & 12 & 12.3674 & -0.367438 \tabularnewline
136 & 14 & 11.4445 & 2.55551 \tabularnewline
137 & 23 & 15.3984 & 7.60156 \tabularnewline
138 & 14 & 12.9758 & 1.02417 \tabularnewline
139 & 16 & 15.8909 & 0.109089 \tabularnewline
140 & 11 & 12.8572 & -1.85724 \tabularnewline
141 & 12 & 14.9898 & -2.98978 \tabularnewline
142 & 10 & 12.2889 & -2.2889 \tabularnewline
143 & 14 & 11.7489 & 2.25106 \tabularnewline
144 & 12 & 12.5598 & -0.559822 \tabularnewline
145 & 12 & 11.938 & 0.061958 \tabularnewline
146 & 11 & 12.2826 & -1.28258 \tabularnewline
147 & 12 & 10.8684 & 1.13165 \tabularnewline
148 & 13 & 15.3326 & -2.33256 \tabularnewline
149 & 11 & 14.1433 & -3.14328 \tabularnewline
150 & 19 & 16.8328 & 2.16716 \tabularnewline
151 & 12 & 12.5509 & -0.550863 \tabularnewline
152 & 17 & 13.4141 & 3.58591 \tabularnewline
153 & 9 & 11.5658 & -2.56579 \tabularnewline
154 & 12 & 14.5273 & -2.52726 \tabularnewline
155 & 19 & 16.7621 & 2.23791 \tabularnewline
156 & 18 & 14.58 & 3.42 \tabularnewline
157 & 15 & 13.045 & 1.95503 \tabularnewline
158 & 14 & 13.6399 & 0.360106 \tabularnewline
159 & 11 & 9.5979 & 1.4021 \tabularnewline
160 & 9 & 13.4563 & -4.45635 \tabularnewline
161 & 18 & 15.0708 & 2.92925 \tabularnewline
162 & 16 & 14.1728 & 1.82722 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&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]12[/C][C]13.1291[/C][C]-1.12915[/C][/ROW]
[ROW][C]2[/C][C]11[/C][C]9.80186[/C][C]1.19814[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]14.3505[/C][C]-0.350526[/C][/ROW]
[ROW][C]4[/C][C]12[/C][C]14.6688[/C][C]-2.66877[/C][/ROW]
[ROW][C]5[/C][C]21[/C][C]11.6711[/C][C]9.32888[/C][/ROW]
[ROW][C]6[/C][C]12[/C][C]10.3786[/C][C]1.6214[/C][/ROW]
[ROW][C]7[/C][C]22[/C][C]12.0654[/C][C]9.93459[/C][/ROW]
[ROW][C]8[/C][C]11[/C][C]12.8487[/C][C]-1.84868[/C][/ROW]
[ROW][C]9[/C][C]10[/C][C]12.3311[/C][C]-2.33115[/C][/ROW]
[ROW][C]10[/C][C]13[/C][C]12.5017[/C][C]0.49835[/C][/ROW]
[ROW][C]11[/C][C]10[/C][C]10.5613[/C][C]-0.561255[/C][/ROW]
[ROW][C]12[/C][C]8[/C][C]9.16802[/C][C]-1.16802[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]15.4789[/C][C]-0.47892[/C][/ROW]
[ROW][C]14[/C][C]14[/C][C]11.3362[/C][C]2.66376[/C][/ROW]
[ROW][C]15[/C][C]10[/C][C]9.70406[/C][C]0.295939[/C][/ROW]
[ROW][C]16[/C][C]14[/C][C]12.9372[/C][C]1.06278[/C][/ROW]
[ROW][C]17[/C][C]14[/C][C]12.9103[/C][C]1.08969[/C][/ROW]
[ROW][C]18[/C][C]11[/C][C]9.96149[/C][C]1.03851[/C][/ROW]
[ROW][C]19[/C][C]10[/C][C]12.4222[/C][C]-2.42223[/C][/ROW]
[ROW][C]20[/C][C]13[/C][C]11.3452[/C][C]1.65476[/C][/ROW]
[ROW][C]21[/C][C]7[/C][C]10.006[/C][C]-3.00596[/C][/ROW]
[ROW][C]22[/C][C]14[/C][C]14.8742[/C][C]-0.874179[/C][/ROW]
[ROW][C]23[/C][C]12[/C][C]12.1619[/C][C]-0.16186[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]14.2283[/C][C]-0.228289[/C][/ROW]
[ROW][C]25[/C][C]11[/C][C]10.4435[/C][C]0.556501[/C][/ROW]
[ROW][C]26[/C][C]9[/C][C]15.8316[/C][C]-6.83157[/C][/ROW]
[ROW][C]27[/C][C]11[/C][C]11.4016[/C][C]-0.401632[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]12.8957[/C][C]2.10431[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]12.0446[/C][C]1.9554[/C][/ROW]
[ROW][C]30[/C][C]13[/C][C]15.2594[/C][C]-2.25937[/C][/ROW]
[ROW][C]31[/C][C]9[/C][C]11.4498[/C][C]-2.44985[/C][/ROW]
[ROW][C]32[/C][C]15[/C][C]14.4345[/C][C]0.565526[/C][/ROW]
[ROW][C]33[/C][C]10[/C][C]10.9109[/C][C]-0.91088[/C][/ROW]
[ROW][C]34[/C][C]11[/C][C]12.1017[/C][C]-1.10172[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]12.9841[/C][C]0.0158956[/C][/ROW]
[ROW][C]36[/C][C]8[/C][C]13.0682[/C][C]-5.06817[/C][/ROW]
[ROW][C]37[/C][C]20[/C][C]17.6022[/C][C]2.39776[/C][/ROW]
[ROW][C]38[/C][C]12[/C][C]12.2895[/C][C]-0.289498[/C][/ROW]
[ROW][C]39[/C][C]10[/C][C]10.7969[/C][C]-0.796891[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.5003[/C][C]-3.50031[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]12.0177[/C][C]-3.01769[/C][/ROW]
[ROW][C]42[/C][C]14[/C][C]11.6371[/C][C]2.36285[/C][/ROW]
[ROW][C]43[/C][C]8[/C][C]10.3933[/C][C]-2.3933[/C][/ROW]
[ROW][C]44[/C][C]14[/C][C]14.5285[/C][C]-0.528472[/C][/ROW]
[ROW][C]45[/C][C]11[/C][C]14.3458[/C][C]-3.34581[/C][/ROW]
[ROW][C]46[/C][C]13[/C][C]15.609[/C][C]-2.60899[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]12.3128[/C][C]-3.31276[/C][/ROW]
[ROW][C]48[/C][C]11[/C][C]12.2765[/C][C]-1.27647[/C][/ROW]
[ROW][C]49[/C][C]15[/C][C]10.7101[/C][C]4.28993[/C][/ROW]
[ROW][C]50[/C][C]11[/C][C]13.6257[/C][C]-2.62574[/C][/ROW]
[ROW][C]51[/C][C]10[/C][C]11.3715[/C][C]-1.37154[/C][/ROW]
[ROW][C]52[/C][C]14[/C][C]13.1351[/C][C]0.864888[/C][/ROW]
[ROW][C]53[/C][C]18[/C][C]15.5365[/C][C]2.46347[/C][/ROW]
[ROW][C]54[/C][C]14[/C][C]14.1874[/C][C]-0.187366[/C][/ROW]
[ROW][C]55[/C][C]11[/C][C]15.6414[/C][C]-4.64141[/C][/ROW]
[ROW][C]56[/C][C]12[/C][C]12.5384[/C][C]-0.538426[/C][/ROW]
[ROW][C]57[/C][C]13[/C][C]11.2036[/C][C]1.79638[/C][/ROW]
[ROW][C]58[/C][C]9[/C][C]12.1702[/C][C]-3.17015[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]14.3275[/C][C]-4.32754[/C][/ROW]
[ROW][C]60[/C][C]15[/C][C]14.2259[/C][C]0.774147[/C][/ROW]
[ROW][C]61[/C][C]20[/C][C]17.8455[/C][C]2.15447[/C][/ROW]
[ROW][C]62[/C][C]12[/C][C]13.3439[/C][C]-1.34395[/C][/ROW]
[ROW][C]63[/C][C]12[/C][C]14.7803[/C][C]-2.78029[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]13.2161[/C][C]0.783906[/C][/ROW]
[ROW][C]65[/C][C]13[/C][C]11.9768[/C][C]1.02323[/C][/ROW]
[ROW][C]66[/C][C]11[/C][C]15.3881[/C][C]-4.38807[/C][/ROW]
[ROW][C]67[/C][C]17[/C][C]14.4045[/C][C]2.59552[/C][/ROW]
[ROW][C]68[/C][C]12[/C][C]13.8382[/C][C]-1.83823[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]12.6802[/C][C]0.319833[/C][/ROW]
[ROW][C]70[/C][C]14[/C][C]12.0755[/C][C]1.92447[/C][/ROW]
[ROW][C]71[/C][C]13[/C][C]13.8485[/C][C]-0.848499[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]11.8691[/C][C]3.13086[/C][/ROW]
[ROW][C]73[/C][C]13[/C][C]11.7213[/C][C]1.27872[/C][/ROW]
[ROW][C]74[/C][C]10[/C][C]11.6582[/C][C]-1.6582[/C][/ROW]
[ROW][C]75[/C][C]11[/C][C]13.3029[/C][C]-2.3029[/C][/ROW]
[ROW][C]76[/C][C]19[/C][C]13.9254[/C][C]5.07457[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]10.5644[/C][C]2.43558[/C][/ROW]
[ROW][C]78[/C][C]17[/C][C]13.4355[/C][C]3.56448[/C][/ROW]
[ROW][C]79[/C][C]13[/C][C]12.619[/C][C]0.380975[/C][/ROW]
[ROW][C]80[/C][C]9[/C][C]13.2244[/C][C]-4.22436[/C][/ROW]
[ROW][C]81[/C][C]11[/C][C]12.1071[/C][C]-1.10707[/C][/ROW]
[ROW][C]82[/C][C]10[/C][C]10.6924[/C][C]-0.692393[/C][/ROW]
[ROW][C]83[/C][C]9[/C][C]12.0755[/C][C]-3.07553[/C][/ROW]
[ROW][C]84[/C][C]12[/C][C]11.3969[/C][C]0.603121[/C][/ROW]
[ROW][C]85[/C][C]12[/C][C]12.3027[/C][C]-0.30265[/C][/ROW]
[ROW][C]86[/C][C]13[/C][C]12.8063[/C][C]0.193688[/C][/ROW]
[ROW][C]87[/C][C]13[/C][C]12.51[/C][C]0.489952[/C][/ROW]
[ROW][C]88[/C][C]12[/C][C]12.7701[/C][C]-0.77014[/C][/ROW]
[ROW][C]89[/C][C]15[/C][C]13.5379[/C][C]1.46207[/C][/ROW]
[ROW][C]90[/C][C]22[/C][C]17.844[/C][C]4.15595[/C][/ROW]
[ROW][C]91[/C][C]13[/C][C]11.5449[/C][C]1.45514[/C][/ROW]
[ROW][C]92[/C][C]15[/C][C]13.045[/C][C]1.95503[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]12.1588[/C][C]0.841168[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]12.8331[/C][C]2.1669[/C][/ROW]
[ROW][C]95[/C][C]10[/C][C]13.9815[/C][C]-3.98145[/C][/ROW]
[ROW][C]96[/C][C]11[/C][C]11.3439[/C][C]-0.343911[/C][/ROW]
[ROW][C]97[/C][C]16[/C][C]14.0463[/C][C]1.95365[/C][/ROW]
[ROW][C]98[/C][C]11[/C][C]12.8033[/C][C]-1.80327[/C][/ROW]
[ROW][C]99[/C][C]11[/C][C]10.5513[/C][C]0.448734[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]12.5553[/C][C]-2.55534[/C][/ROW]
[ROW][C]101[/C][C]10[/C][C]10.4681[/C][C]-0.468088[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.145[/C][C]1.85501[/C][/ROW]
[ROW][C]103[/C][C]12[/C][C]10.5498[/C][C]1.45022[/C][/ROW]
[ROW][C]104[/C][C]11[/C][C]14.9966[/C][C]-3.99657[/C][/ROW]
[ROW][C]105[/C][C]16[/C][C]12.2189[/C][C]3.78113[/C][/ROW]
[ROW][C]106[/C][C]19[/C][C]16.554[/C][C]2.44604[/C][/ROW]
[ROW][C]107[/C][C]11[/C][C]11.0889[/C][C]-0.0889025[/C][/ROW]
[ROW][C]108[/C][C]16[/C][C]11.9601[/C][C]4.03991[/C][/ROW]
[ROW][C]109[/C][C]15[/C][C]15.5806[/C][C]-0.580606[/C][/ROW]
[ROW][C]110[/C][C]24[/C][C]16.3873[/C][C]7.61272[/C][/ROW]
[ROW][C]111[/C][C]14[/C][C]12.144[/C][C]1.85603[/C][/ROW]
[ROW][C]112[/C][C]15[/C][C]14.8172[/C][C]0.182817[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]13.5344[/C][C]-2.53441[/C][/ROW]
[ROW][C]114[/C][C]15[/C][C]13.31[/C][C]1.69002[/C][/ROW]
[ROW][C]115[/C][C]12[/C][C]10.5921[/C][C]1.40792[/C][/ROW]
[ROW][C]116[/C][C]10[/C][C]10.8416[/C][C]-0.841567[/C][/ROW]
[ROW][C]117[/C][C]14[/C][C]13.1692[/C][C]0.830792[/C][/ROW]
[ROW][C]118[/C][C]13[/C][C]13.3896[/C][C]-0.389637[/C][/ROW]
[ROW][C]119[/C][C]9[/C][C]13.3255[/C][C]-4.32545[/C][/ROW]
[ROW][C]120[/C][C]15[/C][C]12.6809[/C][C]2.31912[/C][/ROW]
[ROW][C]121[/C][C]15[/C][C]14.7765[/C][C]0.223467[/C][/ROW]
[ROW][C]122[/C][C]14[/C][C]12.6274[/C][C]1.37259[/C][/ROW]
[ROW][C]123[/C][C]11[/C][C]12.5238[/C][C]-1.5238[/C][/ROW]
[ROW][C]124[/C][C]8[/C][C]11.9454[/C][C]-3.94535[/C][/ROW]
[ROW][C]125[/C][C]11[/C][C]12.1154[/C][C]-1.11535[/C][/ROW]
[ROW][C]126[/C][C]11[/C][C]13.3584[/C][C]-2.35843[/C][/ROW]
[ROW][C]127[/C][C]8[/C][C]10.7176[/C][C]-2.71762[/C][/ROW]
[ROW][C]128[/C][C]10[/C][C]10.3597[/C][C]-0.359716[/C][/ROW]
[ROW][C]129[/C][C]11[/C][C]9.5979[/C][C]1.4021[/C][/ROW]
[ROW][C]130[/C][C]13[/C][C]12.6884[/C][C]0.311555[/C][/ROW]
[ROW][C]131[/C][C]11[/C][C]13.5789[/C][C]-2.57886[/C][/ROW]
[ROW][C]132[/C][C]20[/C][C]17.4026[/C][C]2.59737[/C][/ROW]
[ROW][C]133[/C][C]10[/C][C]12.1419[/C][C]-2.14191[/C][/ROW]
[ROW][C]134[/C][C]15[/C][C]12.8354[/C][C]2.16457[/C][/ROW]
[ROW][C]135[/C][C]12[/C][C]12.3674[/C][C]-0.367438[/C][/ROW]
[ROW][C]136[/C][C]14[/C][C]11.4445[/C][C]2.55551[/C][/ROW]
[ROW][C]137[/C][C]23[/C][C]15.3984[/C][C]7.60156[/C][/ROW]
[ROW][C]138[/C][C]14[/C][C]12.9758[/C][C]1.02417[/C][/ROW]
[ROW][C]139[/C][C]16[/C][C]15.8909[/C][C]0.109089[/C][/ROW]
[ROW][C]140[/C][C]11[/C][C]12.8572[/C][C]-1.85724[/C][/ROW]
[ROW][C]141[/C][C]12[/C][C]14.9898[/C][C]-2.98978[/C][/ROW]
[ROW][C]142[/C][C]10[/C][C]12.2889[/C][C]-2.2889[/C][/ROW]
[ROW][C]143[/C][C]14[/C][C]11.7489[/C][C]2.25106[/C][/ROW]
[ROW][C]144[/C][C]12[/C][C]12.5598[/C][C]-0.559822[/C][/ROW]
[ROW][C]145[/C][C]12[/C][C]11.938[/C][C]0.061958[/C][/ROW]
[ROW][C]146[/C][C]11[/C][C]12.2826[/C][C]-1.28258[/C][/ROW]
[ROW][C]147[/C][C]12[/C][C]10.8684[/C][C]1.13165[/C][/ROW]
[ROW][C]148[/C][C]13[/C][C]15.3326[/C][C]-2.33256[/C][/ROW]
[ROW][C]149[/C][C]11[/C][C]14.1433[/C][C]-3.14328[/C][/ROW]
[ROW][C]150[/C][C]19[/C][C]16.8328[/C][C]2.16716[/C][/ROW]
[ROW][C]151[/C][C]12[/C][C]12.5509[/C][C]-0.550863[/C][/ROW]
[ROW][C]152[/C][C]17[/C][C]13.4141[/C][C]3.58591[/C][/ROW]
[ROW][C]153[/C][C]9[/C][C]11.5658[/C][C]-2.56579[/C][/ROW]
[ROW][C]154[/C][C]12[/C][C]14.5273[/C][C]-2.52726[/C][/ROW]
[ROW][C]155[/C][C]19[/C][C]16.7621[/C][C]2.23791[/C][/ROW]
[ROW][C]156[/C][C]18[/C][C]14.58[/C][C]3.42[/C][/ROW]
[ROW][C]157[/C][C]15[/C][C]13.045[/C][C]1.95503[/C][/ROW]
[ROW][C]158[/C][C]14[/C][C]13.6399[/C][C]0.360106[/C][/ROW]
[ROW][C]159[/C][C]11[/C][C]9.5979[/C][C]1.4021[/C][/ROW]
[ROW][C]160[/C][C]9[/C][C]13.4563[/C][C]-4.45635[/C][/ROW]
[ROW][C]161[/C][C]18[/C][C]15.0708[/C][C]2.92925[/C][/ROW]
[ROW][C]162[/C][C]16[/C][C]14.1728[/C][C]1.82722[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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
11213.1291-1.12915
2119.801861.19814
31414.3505-0.350526
41214.6688-2.66877
52111.67119.32888
61210.37861.6214
72212.06549.93459
81112.8487-1.84868
91012.3311-2.33115
101312.50170.49835
111010.5613-0.561255
1289.16802-1.16802
131515.4789-0.47892
141411.33622.66376
15109.704060.295939
161412.93721.06278
171412.91031.08969
18119.961491.03851
191012.4222-2.42223
201311.34521.65476
21710.006-3.00596
221414.8742-0.874179
231212.1619-0.16186
241414.2283-0.228289
251110.44350.556501
26915.8316-6.83157
271111.4016-0.401632
281512.89572.10431
291412.04461.9554
301315.2594-2.25937
31911.4498-2.44985
321514.43450.565526
331010.9109-0.91088
341112.1017-1.10172
351312.98410.0158956
36813.0682-5.06817
372017.60222.39776
381212.2895-0.289498
391010.7969-0.796891
401013.5003-3.50031
41912.0177-3.01769
421411.63712.36285
43810.3933-2.3933
441414.5285-0.528472
451114.3458-3.34581
461315.609-2.60899
47912.3128-3.31276
481112.2765-1.27647
491510.71014.28993
501113.6257-2.62574
511011.3715-1.37154
521413.13510.864888
531815.53652.46347
541414.1874-0.187366
551115.6414-4.64141
561212.5384-0.538426
571311.20361.79638
58912.1702-3.17015
591014.3275-4.32754
601514.22590.774147
612017.84552.15447
621213.3439-1.34395
631214.7803-2.78029
641413.21610.783906
651311.97681.02323
661115.3881-4.38807
671714.40452.59552
681213.8382-1.83823
691312.68020.319833
701412.07551.92447
711313.8485-0.848499
721511.86913.13086
731311.72131.27872
741011.6582-1.6582
751113.3029-2.3029
761913.92545.07457
771310.56442.43558
781713.43553.56448
791312.6190.380975
80913.2244-4.22436
811112.1071-1.10707
821010.6924-0.692393
83912.0755-3.07553
841211.39690.603121
851212.3027-0.30265
861312.80630.193688
871312.510.489952
881212.7701-0.77014
891513.53791.46207
902217.8444.15595
911311.54491.45514
921513.0451.95503
931312.15880.841168
941512.83312.1669
951013.9815-3.98145
961111.3439-0.343911
971614.04631.95365
981112.8033-1.80327
991110.55130.448734
1001012.5553-2.55534
1011010.4681-0.468088
1021614.1451.85501
1031210.54981.45022
1041114.9966-3.99657
1051612.21893.78113
1061916.5542.44604
1071111.0889-0.0889025
1081611.96014.03991
1091515.5806-0.580606
1102416.38737.61272
1111412.1441.85603
1121514.81720.182817
1131113.5344-2.53441
1141513.311.69002
1151210.59211.40792
1161010.8416-0.841567
1171413.16920.830792
1181313.3896-0.389637
119913.3255-4.32545
1201512.68092.31912
1211514.77650.223467
1221412.62741.37259
1231112.5238-1.5238
124811.9454-3.94535
1251112.1154-1.11535
1261113.3584-2.35843
127810.7176-2.71762
1281010.3597-0.359716
129119.59791.4021
1301312.68840.311555
1311113.5789-2.57886
1322017.40262.59737
1331012.1419-2.14191
1341512.83542.16457
1351212.3674-0.367438
1361411.44452.55551
1372315.39847.60156
1381412.97581.02417
1391615.89090.109089
1401112.8572-1.85724
1411214.9898-2.98978
1421012.2889-2.2889
1431411.74892.25106
1441212.5598-0.559822
1451211.9380.061958
1461112.2826-1.28258
1471210.86841.13165
1481315.3326-2.33256
1491114.1433-3.14328
1501916.83282.16716
1511212.5509-0.550863
1521713.41413.58591
153911.5658-2.56579
1541214.5273-2.52726
1551916.76212.23791
1561814.583.42
1571513.0451.95503
1581413.63990.360106
159119.59791.4021
160913.4563-4.45635
1611815.07082.92925
1621614.17281.82722






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.9996630.0006742390.00033712
100.9992330.001534790.000767396
110.9993590.001281940.00064097
120.999640.0007196110.000359805
130.9992440.001512610.000756304
140.9985450.002910660.00145533
150.9977040.004592070.00229603
160.9960860.007827330.00391367
170.9931720.01365690.00682843
180.9905140.01897140.00948568
190.9912450.01751070.00875536
200.986350.02729920.0136496
210.9878430.0243130.0121565
220.9822620.03547530.0177377
230.975490.04901990.0245099
240.9641520.07169630.0358481
250.9506830.09863460.0493173
260.9919030.01619430.00809716
270.9880480.02390450.0119523
280.984980.03003970.0150199
290.9804110.03917780.0195889
300.9744070.05118590.0255929
310.9740630.05187380.0259369
320.9668090.06638170.0331909
330.956590.08682070.0434104
340.9464140.1071720.0535862
350.9293060.1413880.0706941
360.9450640.1098710.0549356
370.9646450.07071020.0353551
380.9525910.09481740.0474087
390.9417650.1164690.0582347
400.9473930.1052130.0526067
410.9477020.1045960.0522978
420.9432220.1135560.0567779
430.9425420.1149170.0574584
440.9266080.1467840.0733922
450.9237770.1524470.0762233
460.9148140.1703720.0851859
470.9222290.1555410.0777707
480.9066560.1866870.0933436
490.9276370.1447260.072363
500.9209070.1581860.079093
510.9114370.1771260.0885628
520.8946950.210610.105305
530.9026470.1947070.0973535
540.8798610.2402780.120139
550.9093730.1812540.090627
560.8901780.2196450.109822
570.8784450.243110.121555
580.8919340.2161310.108066
590.9175510.1648980.0824492
600.9022720.1954560.0977282
610.9093920.1812170.0906083
620.893350.21330.10665
630.8923750.215250.107625
640.8714270.2571450.128573
650.8490650.301870.150935
660.888290.2234190.11171
670.8900710.2198570.109929
680.8804110.2391790.119589
690.8579270.2841460.142073
700.8450510.3098980.154949
710.8222980.3554030.177702
720.833860.332280.16614
730.8120970.3758060.187903
740.7931270.4137470.206873
750.7870330.4259340.212967
760.8670790.2658430.132921
770.8650390.2699210.134961
780.8843970.2312050.115603
790.8610060.2779890.138994
800.9032460.1935090.0967543
810.8857440.2285120.114256
820.8660480.2679050.133952
830.8737770.2524460.126223
840.8494880.3010230.150512
850.8209490.3581030.179051
860.7889850.4220310.211015
870.7549650.490070.245035
880.7200340.5599320.279966
890.6930090.6139820.306991
900.7491880.5016230.250812
910.7262030.5475940.273797
920.7068570.5862860.293143
930.6738780.6522430.326122
940.6565650.6868710.343435
950.7133430.5733140.286657
960.6722840.6554330.327716
970.6506670.6986660.349333
980.632480.735040.36752
990.5884390.8231210.411561
1000.5812560.8374890.418744
1010.5345390.9309210.465461
1020.5065310.9869380.493469
1030.4899780.9799560.510022
1040.5889850.822030.411015
1050.6741440.6517110.325856
1060.6596180.6807630.340382
1070.6138320.7723360.386168
1080.7026710.5946580.297329
1090.6651630.6696730.334837
1100.917250.16550.0827502
1110.9115640.1768710.0884357
1120.8891540.2216920.110846
1130.8757170.2485660.124283
1140.8572240.2855520.142776
1150.8370830.3258340.162917
1160.8032450.3935110.196755
1170.7915450.4169090.208455
1180.7559780.4880440.244022
1190.8208950.358210.179105
1200.8387920.3224150.161208
1210.8044560.3910890.195544
1220.7697720.4604570.230228
1230.7291940.5416120.270806
1240.7352280.5295440.264772
1250.6998560.6002880.300144
1260.6948390.6103230.305161
1270.7048740.5902520.295126
1280.6677410.6645190.332259
1290.6548270.6903470.345173
1300.5975840.8048330.402416
1310.6037150.792570.396285
1320.5597450.880510.440255
1330.5131060.9737890.486894
1340.4652340.9304670.534766
1350.4036880.8073770.596312
1360.3732020.7464050.626798
1370.6182820.7634370.381718
1380.5611770.8776460.438823
1390.4885660.9771330.511434
1400.4509110.9018220.549089
1410.5491380.9017250.450862
1420.5002320.9995350.499768
1430.4844180.9688360.515582
1440.4040390.8080770.595961
1450.3220470.6440930.677953
1460.4486810.8973610.551319
1470.3610570.7221150.638943
1480.4682620.9365240.531738
1490.5600660.8798680.439934
1500.4456910.8913820.554309
1510.6600240.6799520.339976
1520.612260.775480.38774
1530.6120690.7758630.387931

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.999663 & 0.000674239 & 0.00033712 \tabularnewline
10 & 0.999233 & 0.00153479 & 0.000767396 \tabularnewline
11 & 0.999359 & 0.00128194 & 0.00064097 \tabularnewline
12 & 0.99964 & 0.000719611 & 0.000359805 \tabularnewline
13 & 0.999244 & 0.00151261 & 0.000756304 \tabularnewline
14 & 0.998545 & 0.00291066 & 0.00145533 \tabularnewline
15 & 0.997704 & 0.00459207 & 0.00229603 \tabularnewline
16 & 0.996086 & 0.00782733 & 0.00391367 \tabularnewline
17 & 0.993172 & 0.0136569 & 0.00682843 \tabularnewline
18 & 0.990514 & 0.0189714 & 0.00948568 \tabularnewline
19 & 0.991245 & 0.0175107 & 0.00875536 \tabularnewline
20 & 0.98635 & 0.0272992 & 0.0136496 \tabularnewline
21 & 0.987843 & 0.024313 & 0.0121565 \tabularnewline
22 & 0.982262 & 0.0354753 & 0.0177377 \tabularnewline
23 & 0.97549 & 0.0490199 & 0.0245099 \tabularnewline
24 & 0.964152 & 0.0716963 & 0.0358481 \tabularnewline
25 & 0.950683 & 0.0986346 & 0.0493173 \tabularnewline
26 & 0.991903 & 0.0161943 & 0.00809716 \tabularnewline
27 & 0.988048 & 0.0239045 & 0.0119523 \tabularnewline
28 & 0.98498 & 0.0300397 & 0.0150199 \tabularnewline
29 & 0.980411 & 0.0391778 & 0.0195889 \tabularnewline
30 & 0.974407 & 0.0511859 & 0.0255929 \tabularnewline
31 & 0.974063 & 0.0518738 & 0.0259369 \tabularnewline
32 & 0.966809 & 0.0663817 & 0.0331909 \tabularnewline
33 & 0.95659 & 0.0868207 & 0.0434104 \tabularnewline
34 & 0.946414 & 0.107172 & 0.0535862 \tabularnewline
35 & 0.929306 & 0.141388 & 0.0706941 \tabularnewline
36 & 0.945064 & 0.109871 & 0.0549356 \tabularnewline
37 & 0.964645 & 0.0707102 & 0.0353551 \tabularnewline
38 & 0.952591 & 0.0948174 & 0.0474087 \tabularnewline
39 & 0.941765 & 0.116469 & 0.0582347 \tabularnewline
40 & 0.947393 & 0.105213 & 0.0526067 \tabularnewline
41 & 0.947702 & 0.104596 & 0.0522978 \tabularnewline
42 & 0.943222 & 0.113556 & 0.0567779 \tabularnewline
43 & 0.942542 & 0.114917 & 0.0574584 \tabularnewline
44 & 0.926608 & 0.146784 & 0.0733922 \tabularnewline
45 & 0.923777 & 0.152447 & 0.0762233 \tabularnewline
46 & 0.914814 & 0.170372 & 0.0851859 \tabularnewline
47 & 0.922229 & 0.155541 & 0.0777707 \tabularnewline
48 & 0.906656 & 0.186687 & 0.0933436 \tabularnewline
49 & 0.927637 & 0.144726 & 0.072363 \tabularnewline
50 & 0.920907 & 0.158186 & 0.079093 \tabularnewline
51 & 0.911437 & 0.177126 & 0.0885628 \tabularnewline
52 & 0.894695 & 0.21061 & 0.105305 \tabularnewline
53 & 0.902647 & 0.194707 & 0.0973535 \tabularnewline
54 & 0.879861 & 0.240278 & 0.120139 \tabularnewline
55 & 0.909373 & 0.181254 & 0.090627 \tabularnewline
56 & 0.890178 & 0.219645 & 0.109822 \tabularnewline
57 & 0.878445 & 0.24311 & 0.121555 \tabularnewline
58 & 0.891934 & 0.216131 & 0.108066 \tabularnewline
59 & 0.917551 & 0.164898 & 0.0824492 \tabularnewline
60 & 0.902272 & 0.195456 & 0.0977282 \tabularnewline
61 & 0.909392 & 0.181217 & 0.0906083 \tabularnewline
62 & 0.89335 & 0.2133 & 0.10665 \tabularnewline
63 & 0.892375 & 0.21525 & 0.107625 \tabularnewline
64 & 0.871427 & 0.257145 & 0.128573 \tabularnewline
65 & 0.849065 & 0.30187 & 0.150935 \tabularnewline
66 & 0.88829 & 0.223419 & 0.11171 \tabularnewline
67 & 0.890071 & 0.219857 & 0.109929 \tabularnewline
68 & 0.880411 & 0.239179 & 0.119589 \tabularnewline
69 & 0.857927 & 0.284146 & 0.142073 \tabularnewline
70 & 0.845051 & 0.309898 & 0.154949 \tabularnewline
71 & 0.822298 & 0.355403 & 0.177702 \tabularnewline
72 & 0.83386 & 0.33228 & 0.16614 \tabularnewline
73 & 0.812097 & 0.375806 & 0.187903 \tabularnewline
74 & 0.793127 & 0.413747 & 0.206873 \tabularnewline
75 & 0.787033 & 0.425934 & 0.212967 \tabularnewline
76 & 0.867079 & 0.265843 & 0.132921 \tabularnewline
77 & 0.865039 & 0.269921 & 0.134961 \tabularnewline
78 & 0.884397 & 0.231205 & 0.115603 \tabularnewline
79 & 0.861006 & 0.277989 & 0.138994 \tabularnewline
80 & 0.903246 & 0.193509 & 0.0967543 \tabularnewline
81 & 0.885744 & 0.228512 & 0.114256 \tabularnewline
82 & 0.866048 & 0.267905 & 0.133952 \tabularnewline
83 & 0.873777 & 0.252446 & 0.126223 \tabularnewline
84 & 0.849488 & 0.301023 & 0.150512 \tabularnewline
85 & 0.820949 & 0.358103 & 0.179051 \tabularnewline
86 & 0.788985 & 0.422031 & 0.211015 \tabularnewline
87 & 0.754965 & 0.49007 & 0.245035 \tabularnewline
88 & 0.720034 & 0.559932 & 0.279966 \tabularnewline
89 & 0.693009 & 0.613982 & 0.306991 \tabularnewline
90 & 0.749188 & 0.501623 & 0.250812 \tabularnewline
91 & 0.726203 & 0.547594 & 0.273797 \tabularnewline
92 & 0.706857 & 0.586286 & 0.293143 \tabularnewline
93 & 0.673878 & 0.652243 & 0.326122 \tabularnewline
94 & 0.656565 & 0.686871 & 0.343435 \tabularnewline
95 & 0.713343 & 0.573314 & 0.286657 \tabularnewline
96 & 0.672284 & 0.655433 & 0.327716 \tabularnewline
97 & 0.650667 & 0.698666 & 0.349333 \tabularnewline
98 & 0.63248 & 0.73504 & 0.36752 \tabularnewline
99 & 0.588439 & 0.823121 & 0.411561 \tabularnewline
100 & 0.581256 & 0.837489 & 0.418744 \tabularnewline
101 & 0.534539 & 0.930921 & 0.465461 \tabularnewline
102 & 0.506531 & 0.986938 & 0.493469 \tabularnewline
103 & 0.489978 & 0.979956 & 0.510022 \tabularnewline
104 & 0.588985 & 0.82203 & 0.411015 \tabularnewline
105 & 0.674144 & 0.651711 & 0.325856 \tabularnewline
106 & 0.659618 & 0.680763 & 0.340382 \tabularnewline
107 & 0.613832 & 0.772336 & 0.386168 \tabularnewline
108 & 0.702671 & 0.594658 & 0.297329 \tabularnewline
109 & 0.665163 & 0.669673 & 0.334837 \tabularnewline
110 & 0.91725 & 0.1655 & 0.0827502 \tabularnewline
111 & 0.911564 & 0.176871 & 0.0884357 \tabularnewline
112 & 0.889154 & 0.221692 & 0.110846 \tabularnewline
113 & 0.875717 & 0.248566 & 0.124283 \tabularnewline
114 & 0.857224 & 0.285552 & 0.142776 \tabularnewline
115 & 0.837083 & 0.325834 & 0.162917 \tabularnewline
116 & 0.803245 & 0.393511 & 0.196755 \tabularnewline
117 & 0.791545 & 0.416909 & 0.208455 \tabularnewline
118 & 0.755978 & 0.488044 & 0.244022 \tabularnewline
119 & 0.820895 & 0.35821 & 0.179105 \tabularnewline
120 & 0.838792 & 0.322415 & 0.161208 \tabularnewline
121 & 0.804456 & 0.391089 & 0.195544 \tabularnewline
122 & 0.769772 & 0.460457 & 0.230228 \tabularnewline
123 & 0.729194 & 0.541612 & 0.270806 \tabularnewline
124 & 0.735228 & 0.529544 & 0.264772 \tabularnewline
125 & 0.699856 & 0.600288 & 0.300144 \tabularnewline
126 & 0.694839 & 0.610323 & 0.305161 \tabularnewline
127 & 0.704874 & 0.590252 & 0.295126 \tabularnewline
128 & 0.667741 & 0.664519 & 0.332259 \tabularnewline
129 & 0.654827 & 0.690347 & 0.345173 \tabularnewline
130 & 0.597584 & 0.804833 & 0.402416 \tabularnewline
131 & 0.603715 & 0.79257 & 0.396285 \tabularnewline
132 & 0.559745 & 0.88051 & 0.440255 \tabularnewline
133 & 0.513106 & 0.973789 & 0.486894 \tabularnewline
134 & 0.465234 & 0.930467 & 0.534766 \tabularnewline
135 & 0.403688 & 0.807377 & 0.596312 \tabularnewline
136 & 0.373202 & 0.746405 & 0.626798 \tabularnewline
137 & 0.618282 & 0.763437 & 0.381718 \tabularnewline
138 & 0.561177 & 0.877646 & 0.438823 \tabularnewline
139 & 0.488566 & 0.977133 & 0.511434 \tabularnewline
140 & 0.450911 & 0.901822 & 0.549089 \tabularnewline
141 & 0.549138 & 0.901725 & 0.450862 \tabularnewline
142 & 0.500232 & 0.999535 & 0.499768 \tabularnewline
143 & 0.484418 & 0.968836 & 0.515582 \tabularnewline
144 & 0.404039 & 0.808077 & 0.595961 \tabularnewline
145 & 0.322047 & 0.644093 & 0.677953 \tabularnewline
146 & 0.448681 & 0.897361 & 0.551319 \tabularnewline
147 & 0.361057 & 0.722115 & 0.638943 \tabularnewline
148 & 0.468262 & 0.936524 & 0.531738 \tabularnewline
149 & 0.560066 & 0.879868 & 0.439934 \tabularnewline
150 & 0.445691 & 0.891382 & 0.554309 \tabularnewline
151 & 0.660024 & 0.679952 & 0.339976 \tabularnewline
152 & 0.61226 & 0.77548 & 0.38774 \tabularnewline
153 & 0.612069 & 0.775863 & 0.387931 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&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]9[/C][C]0.999663[/C][C]0.000674239[/C][C]0.00033712[/C][/ROW]
[ROW][C]10[/C][C]0.999233[/C][C]0.00153479[/C][C]0.000767396[/C][/ROW]
[ROW][C]11[/C][C]0.999359[/C][C]0.00128194[/C][C]0.00064097[/C][/ROW]
[ROW][C]12[/C][C]0.99964[/C][C]0.000719611[/C][C]0.000359805[/C][/ROW]
[ROW][C]13[/C][C]0.999244[/C][C]0.00151261[/C][C]0.000756304[/C][/ROW]
[ROW][C]14[/C][C]0.998545[/C][C]0.00291066[/C][C]0.00145533[/C][/ROW]
[ROW][C]15[/C][C]0.997704[/C][C]0.00459207[/C][C]0.00229603[/C][/ROW]
[ROW][C]16[/C][C]0.996086[/C][C]0.00782733[/C][C]0.00391367[/C][/ROW]
[ROW][C]17[/C][C]0.993172[/C][C]0.0136569[/C][C]0.00682843[/C][/ROW]
[ROW][C]18[/C][C]0.990514[/C][C]0.0189714[/C][C]0.00948568[/C][/ROW]
[ROW][C]19[/C][C]0.991245[/C][C]0.0175107[/C][C]0.00875536[/C][/ROW]
[ROW][C]20[/C][C]0.98635[/C][C]0.0272992[/C][C]0.0136496[/C][/ROW]
[ROW][C]21[/C][C]0.987843[/C][C]0.024313[/C][C]0.0121565[/C][/ROW]
[ROW][C]22[/C][C]0.982262[/C][C]0.0354753[/C][C]0.0177377[/C][/ROW]
[ROW][C]23[/C][C]0.97549[/C][C]0.0490199[/C][C]0.0245099[/C][/ROW]
[ROW][C]24[/C][C]0.964152[/C][C]0.0716963[/C][C]0.0358481[/C][/ROW]
[ROW][C]25[/C][C]0.950683[/C][C]0.0986346[/C][C]0.0493173[/C][/ROW]
[ROW][C]26[/C][C]0.991903[/C][C]0.0161943[/C][C]0.00809716[/C][/ROW]
[ROW][C]27[/C][C]0.988048[/C][C]0.0239045[/C][C]0.0119523[/C][/ROW]
[ROW][C]28[/C][C]0.98498[/C][C]0.0300397[/C][C]0.0150199[/C][/ROW]
[ROW][C]29[/C][C]0.980411[/C][C]0.0391778[/C][C]0.0195889[/C][/ROW]
[ROW][C]30[/C][C]0.974407[/C][C]0.0511859[/C][C]0.0255929[/C][/ROW]
[ROW][C]31[/C][C]0.974063[/C][C]0.0518738[/C][C]0.0259369[/C][/ROW]
[ROW][C]32[/C][C]0.966809[/C][C]0.0663817[/C][C]0.0331909[/C][/ROW]
[ROW][C]33[/C][C]0.95659[/C][C]0.0868207[/C][C]0.0434104[/C][/ROW]
[ROW][C]34[/C][C]0.946414[/C][C]0.107172[/C][C]0.0535862[/C][/ROW]
[ROW][C]35[/C][C]0.929306[/C][C]0.141388[/C][C]0.0706941[/C][/ROW]
[ROW][C]36[/C][C]0.945064[/C][C]0.109871[/C][C]0.0549356[/C][/ROW]
[ROW][C]37[/C][C]0.964645[/C][C]0.0707102[/C][C]0.0353551[/C][/ROW]
[ROW][C]38[/C][C]0.952591[/C][C]0.0948174[/C][C]0.0474087[/C][/ROW]
[ROW][C]39[/C][C]0.941765[/C][C]0.116469[/C][C]0.0582347[/C][/ROW]
[ROW][C]40[/C][C]0.947393[/C][C]0.105213[/C][C]0.0526067[/C][/ROW]
[ROW][C]41[/C][C]0.947702[/C][C]0.104596[/C][C]0.0522978[/C][/ROW]
[ROW][C]42[/C][C]0.943222[/C][C]0.113556[/C][C]0.0567779[/C][/ROW]
[ROW][C]43[/C][C]0.942542[/C][C]0.114917[/C][C]0.0574584[/C][/ROW]
[ROW][C]44[/C][C]0.926608[/C][C]0.146784[/C][C]0.0733922[/C][/ROW]
[ROW][C]45[/C][C]0.923777[/C][C]0.152447[/C][C]0.0762233[/C][/ROW]
[ROW][C]46[/C][C]0.914814[/C][C]0.170372[/C][C]0.0851859[/C][/ROW]
[ROW][C]47[/C][C]0.922229[/C][C]0.155541[/C][C]0.0777707[/C][/ROW]
[ROW][C]48[/C][C]0.906656[/C][C]0.186687[/C][C]0.0933436[/C][/ROW]
[ROW][C]49[/C][C]0.927637[/C][C]0.144726[/C][C]0.072363[/C][/ROW]
[ROW][C]50[/C][C]0.920907[/C][C]0.158186[/C][C]0.079093[/C][/ROW]
[ROW][C]51[/C][C]0.911437[/C][C]0.177126[/C][C]0.0885628[/C][/ROW]
[ROW][C]52[/C][C]0.894695[/C][C]0.21061[/C][C]0.105305[/C][/ROW]
[ROW][C]53[/C][C]0.902647[/C][C]0.194707[/C][C]0.0973535[/C][/ROW]
[ROW][C]54[/C][C]0.879861[/C][C]0.240278[/C][C]0.120139[/C][/ROW]
[ROW][C]55[/C][C]0.909373[/C][C]0.181254[/C][C]0.090627[/C][/ROW]
[ROW][C]56[/C][C]0.890178[/C][C]0.219645[/C][C]0.109822[/C][/ROW]
[ROW][C]57[/C][C]0.878445[/C][C]0.24311[/C][C]0.121555[/C][/ROW]
[ROW][C]58[/C][C]0.891934[/C][C]0.216131[/C][C]0.108066[/C][/ROW]
[ROW][C]59[/C][C]0.917551[/C][C]0.164898[/C][C]0.0824492[/C][/ROW]
[ROW][C]60[/C][C]0.902272[/C][C]0.195456[/C][C]0.0977282[/C][/ROW]
[ROW][C]61[/C][C]0.909392[/C][C]0.181217[/C][C]0.0906083[/C][/ROW]
[ROW][C]62[/C][C]0.89335[/C][C]0.2133[/C][C]0.10665[/C][/ROW]
[ROW][C]63[/C][C]0.892375[/C][C]0.21525[/C][C]0.107625[/C][/ROW]
[ROW][C]64[/C][C]0.871427[/C][C]0.257145[/C][C]0.128573[/C][/ROW]
[ROW][C]65[/C][C]0.849065[/C][C]0.30187[/C][C]0.150935[/C][/ROW]
[ROW][C]66[/C][C]0.88829[/C][C]0.223419[/C][C]0.11171[/C][/ROW]
[ROW][C]67[/C][C]0.890071[/C][C]0.219857[/C][C]0.109929[/C][/ROW]
[ROW][C]68[/C][C]0.880411[/C][C]0.239179[/C][C]0.119589[/C][/ROW]
[ROW][C]69[/C][C]0.857927[/C][C]0.284146[/C][C]0.142073[/C][/ROW]
[ROW][C]70[/C][C]0.845051[/C][C]0.309898[/C][C]0.154949[/C][/ROW]
[ROW][C]71[/C][C]0.822298[/C][C]0.355403[/C][C]0.177702[/C][/ROW]
[ROW][C]72[/C][C]0.83386[/C][C]0.33228[/C][C]0.16614[/C][/ROW]
[ROW][C]73[/C][C]0.812097[/C][C]0.375806[/C][C]0.187903[/C][/ROW]
[ROW][C]74[/C][C]0.793127[/C][C]0.413747[/C][C]0.206873[/C][/ROW]
[ROW][C]75[/C][C]0.787033[/C][C]0.425934[/C][C]0.212967[/C][/ROW]
[ROW][C]76[/C][C]0.867079[/C][C]0.265843[/C][C]0.132921[/C][/ROW]
[ROW][C]77[/C][C]0.865039[/C][C]0.269921[/C][C]0.134961[/C][/ROW]
[ROW][C]78[/C][C]0.884397[/C][C]0.231205[/C][C]0.115603[/C][/ROW]
[ROW][C]79[/C][C]0.861006[/C][C]0.277989[/C][C]0.138994[/C][/ROW]
[ROW][C]80[/C][C]0.903246[/C][C]0.193509[/C][C]0.0967543[/C][/ROW]
[ROW][C]81[/C][C]0.885744[/C][C]0.228512[/C][C]0.114256[/C][/ROW]
[ROW][C]82[/C][C]0.866048[/C][C]0.267905[/C][C]0.133952[/C][/ROW]
[ROW][C]83[/C][C]0.873777[/C][C]0.252446[/C][C]0.126223[/C][/ROW]
[ROW][C]84[/C][C]0.849488[/C][C]0.301023[/C][C]0.150512[/C][/ROW]
[ROW][C]85[/C][C]0.820949[/C][C]0.358103[/C][C]0.179051[/C][/ROW]
[ROW][C]86[/C][C]0.788985[/C][C]0.422031[/C][C]0.211015[/C][/ROW]
[ROW][C]87[/C][C]0.754965[/C][C]0.49007[/C][C]0.245035[/C][/ROW]
[ROW][C]88[/C][C]0.720034[/C][C]0.559932[/C][C]0.279966[/C][/ROW]
[ROW][C]89[/C][C]0.693009[/C][C]0.613982[/C][C]0.306991[/C][/ROW]
[ROW][C]90[/C][C]0.749188[/C][C]0.501623[/C][C]0.250812[/C][/ROW]
[ROW][C]91[/C][C]0.726203[/C][C]0.547594[/C][C]0.273797[/C][/ROW]
[ROW][C]92[/C][C]0.706857[/C][C]0.586286[/C][C]0.293143[/C][/ROW]
[ROW][C]93[/C][C]0.673878[/C][C]0.652243[/C][C]0.326122[/C][/ROW]
[ROW][C]94[/C][C]0.656565[/C][C]0.686871[/C][C]0.343435[/C][/ROW]
[ROW][C]95[/C][C]0.713343[/C][C]0.573314[/C][C]0.286657[/C][/ROW]
[ROW][C]96[/C][C]0.672284[/C][C]0.655433[/C][C]0.327716[/C][/ROW]
[ROW][C]97[/C][C]0.650667[/C][C]0.698666[/C][C]0.349333[/C][/ROW]
[ROW][C]98[/C][C]0.63248[/C][C]0.73504[/C][C]0.36752[/C][/ROW]
[ROW][C]99[/C][C]0.588439[/C][C]0.823121[/C][C]0.411561[/C][/ROW]
[ROW][C]100[/C][C]0.581256[/C][C]0.837489[/C][C]0.418744[/C][/ROW]
[ROW][C]101[/C][C]0.534539[/C][C]0.930921[/C][C]0.465461[/C][/ROW]
[ROW][C]102[/C][C]0.506531[/C][C]0.986938[/C][C]0.493469[/C][/ROW]
[ROW][C]103[/C][C]0.489978[/C][C]0.979956[/C][C]0.510022[/C][/ROW]
[ROW][C]104[/C][C]0.588985[/C][C]0.82203[/C][C]0.411015[/C][/ROW]
[ROW][C]105[/C][C]0.674144[/C][C]0.651711[/C][C]0.325856[/C][/ROW]
[ROW][C]106[/C][C]0.659618[/C][C]0.680763[/C][C]0.340382[/C][/ROW]
[ROW][C]107[/C][C]0.613832[/C][C]0.772336[/C][C]0.386168[/C][/ROW]
[ROW][C]108[/C][C]0.702671[/C][C]0.594658[/C][C]0.297329[/C][/ROW]
[ROW][C]109[/C][C]0.665163[/C][C]0.669673[/C][C]0.334837[/C][/ROW]
[ROW][C]110[/C][C]0.91725[/C][C]0.1655[/C][C]0.0827502[/C][/ROW]
[ROW][C]111[/C][C]0.911564[/C][C]0.176871[/C][C]0.0884357[/C][/ROW]
[ROW][C]112[/C][C]0.889154[/C][C]0.221692[/C][C]0.110846[/C][/ROW]
[ROW][C]113[/C][C]0.875717[/C][C]0.248566[/C][C]0.124283[/C][/ROW]
[ROW][C]114[/C][C]0.857224[/C][C]0.285552[/C][C]0.142776[/C][/ROW]
[ROW][C]115[/C][C]0.837083[/C][C]0.325834[/C][C]0.162917[/C][/ROW]
[ROW][C]116[/C][C]0.803245[/C][C]0.393511[/C][C]0.196755[/C][/ROW]
[ROW][C]117[/C][C]0.791545[/C][C]0.416909[/C][C]0.208455[/C][/ROW]
[ROW][C]118[/C][C]0.755978[/C][C]0.488044[/C][C]0.244022[/C][/ROW]
[ROW][C]119[/C][C]0.820895[/C][C]0.35821[/C][C]0.179105[/C][/ROW]
[ROW][C]120[/C][C]0.838792[/C][C]0.322415[/C][C]0.161208[/C][/ROW]
[ROW][C]121[/C][C]0.804456[/C][C]0.391089[/C][C]0.195544[/C][/ROW]
[ROW][C]122[/C][C]0.769772[/C][C]0.460457[/C][C]0.230228[/C][/ROW]
[ROW][C]123[/C][C]0.729194[/C][C]0.541612[/C][C]0.270806[/C][/ROW]
[ROW][C]124[/C][C]0.735228[/C][C]0.529544[/C][C]0.264772[/C][/ROW]
[ROW][C]125[/C][C]0.699856[/C][C]0.600288[/C][C]0.300144[/C][/ROW]
[ROW][C]126[/C][C]0.694839[/C][C]0.610323[/C][C]0.305161[/C][/ROW]
[ROW][C]127[/C][C]0.704874[/C][C]0.590252[/C][C]0.295126[/C][/ROW]
[ROW][C]128[/C][C]0.667741[/C][C]0.664519[/C][C]0.332259[/C][/ROW]
[ROW][C]129[/C][C]0.654827[/C][C]0.690347[/C][C]0.345173[/C][/ROW]
[ROW][C]130[/C][C]0.597584[/C][C]0.804833[/C][C]0.402416[/C][/ROW]
[ROW][C]131[/C][C]0.603715[/C][C]0.79257[/C][C]0.396285[/C][/ROW]
[ROW][C]132[/C][C]0.559745[/C][C]0.88051[/C][C]0.440255[/C][/ROW]
[ROW][C]133[/C][C]0.513106[/C][C]0.973789[/C][C]0.486894[/C][/ROW]
[ROW][C]134[/C][C]0.465234[/C][C]0.930467[/C][C]0.534766[/C][/ROW]
[ROW][C]135[/C][C]0.403688[/C][C]0.807377[/C][C]0.596312[/C][/ROW]
[ROW][C]136[/C][C]0.373202[/C][C]0.746405[/C][C]0.626798[/C][/ROW]
[ROW][C]137[/C][C]0.618282[/C][C]0.763437[/C][C]0.381718[/C][/ROW]
[ROW][C]138[/C][C]0.561177[/C][C]0.877646[/C][C]0.438823[/C][/ROW]
[ROW][C]139[/C][C]0.488566[/C][C]0.977133[/C][C]0.511434[/C][/ROW]
[ROW][C]140[/C][C]0.450911[/C][C]0.901822[/C][C]0.549089[/C][/ROW]
[ROW][C]141[/C][C]0.549138[/C][C]0.901725[/C][C]0.450862[/C][/ROW]
[ROW][C]142[/C][C]0.500232[/C][C]0.999535[/C][C]0.499768[/C][/ROW]
[ROW][C]143[/C][C]0.484418[/C][C]0.968836[/C][C]0.515582[/C][/ROW]
[ROW][C]144[/C][C]0.404039[/C][C]0.808077[/C][C]0.595961[/C][/ROW]
[ROW][C]145[/C][C]0.322047[/C][C]0.644093[/C][C]0.677953[/C][/ROW]
[ROW][C]146[/C][C]0.448681[/C][C]0.897361[/C][C]0.551319[/C][/ROW]
[ROW][C]147[/C][C]0.361057[/C][C]0.722115[/C][C]0.638943[/C][/ROW]
[ROW][C]148[/C][C]0.468262[/C][C]0.936524[/C][C]0.531738[/C][/ROW]
[ROW][C]149[/C][C]0.560066[/C][C]0.879868[/C][C]0.439934[/C][/ROW]
[ROW][C]150[/C][C]0.445691[/C][C]0.891382[/C][C]0.554309[/C][/ROW]
[ROW][C]151[/C][C]0.660024[/C][C]0.679952[/C][C]0.339976[/C][/ROW]
[ROW][C]152[/C][C]0.61226[/C][C]0.77548[/C][C]0.38774[/C][/ROW]
[ROW][C]153[/C][C]0.612069[/C][C]0.775863[/C][C]0.387931[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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
90.9996630.0006742390.00033712
100.9992330.001534790.000767396
110.9993590.001281940.00064097
120.999640.0007196110.000359805
130.9992440.001512610.000756304
140.9985450.002910660.00145533
150.9977040.004592070.00229603
160.9960860.007827330.00391367
170.9931720.01365690.00682843
180.9905140.01897140.00948568
190.9912450.01751070.00875536
200.986350.02729920.0136496
210.9878430.0243130.0121565
220.9822620.03547530.0177377
230.975490.04901990.0245099
240.9641520.07169630.0358481
250.9506830.09863460.0493173
260.9919030.01619430.00809716
270.9880480.02390450.0119523
280.984980.03003970.0150199
290.9804110.03917780.0195889
300.9744070.05118590.0255929
310.9740630.05187380.0259369
320.9668090.06638170.0331909
330.956590.08682070.0434104
340.9464140.1071720.0535862
350.9293060.1413880.0706941
360.9450640.1098710.0549356
370.9646450.07071020.0353551
380.9525910.09481740.0474087
390.9417650.1164690.0582347
400.9473930.1052130.0526067
410.9477020.1045960.0522978
420.9432220.1135560.0567779
430.9425420.1149170.0574584
440.9266080.1467840.0733922
450.9237770.1524470.0762233
460.9148140.1703720.0851859
470.9222290.1555410.0777707
480.9066560.1866870.0933436
490.9276370.1447260.072363
500.9209070.1581860.079093
510.9114370.1771260.0885628
520.8946950.210610.105305
530.9026470.1947070.0973535
540.8798610.2402780.120139
550.9093730.1812540.090627
560.8901780.2196450.109822
570.8784450.243110.121555
580.8919340.2161310.108066
590.9175510.1648980.0824492
600.9022720.1954560.0977282
610.9093920.1812170.0906083
620.893350.21330.10665
630.8923750.215250.107625
640.8714270.2571450.128573
650.8490650.301870.150935
660.888290.2234190.11171
670.8900710.2198570.109929
680.8804110.2391790.119589
690.8579270.2841460.142073
700.8450510.3098980.154949
710.8222980.3554030.177702
720.833860.332280.16614
730.8120970.3758060.187903
740.7931270.4137470.206873
750.7870330.4259340.212967
760.8670790.2658430.132921
770.8650390.2699210.134961
780.8843970.2312050.115603
790.8610060.2779890.138994
800.9032460.1935090.0967543
810.8857440.2285120.114256
820.8660480.2679050.133952
830.8737770.2524460.126223
840.8494880.3010230.150512
850.8209490.3581030.179051
860.7889850.4220310.211015
870.7549650.490070.245035
880.7200340.5599320.279966
890.6930090.6139820.306991
900.7491880.5016230.250812
910.7262030.5475940.273797
920.7068570.5862860.293143
930.6738780.6522430.326122
940.6565650.6868710.343435
950.7133430.5733140.286657
960.6722840.6554330.327716
970.6506670.6986660.349333
980.632480.735040.36752
990.5884390.8231210.411561
1000.5812560.8374890.418744
1010.5345390.9309210.465461
1020.5065310.9869380.493469
1030.4899780.9799560.510022
1040.5889850.822030.411015
1050.6741440.6517110.325856
1060.6596180.6807630.340382
1070.6138320.7723360.386168
1080.7026710.5946580.297329
1090.6651630.6696730.334837
1100.917250.16550.0827502
1110.9115640.1768710.0884357
1120.8891540.2216920.110846
1130.8757170.2485660.124283
1140.8572240.2855520.142776
1150.8370830.3258340.162917
1160.8032450.3935110.196755
1170.7915450.4169090.208455
1180.7559780.4880440.244022
1190.8208950.358210.179105
1200.8387920.3224150.161208
1210.8044560.3910890.195544
1220.7697720.4604570.230228
1230.7291940.5416120.270806
1240.7352280.5295440.264772
1250.6998560.6002880.300144
1260.6948390.6103230.305161
1270.7048740.5902520.295126
1280.6677410.6645190.332259
1290.6548270.6903470.345173
1300.5975840.8048330.402416
1310.6037150.792570.396285
1320.5597450.880510.440255
1330.5131060.9737890.486894
1340.4652340.9304670.534766
1350.4036880.8073770.596312
1360.3732020.7464050.626798
1370.6182820.7634370.381718
1380.5611770.8776460.438823
1390.4885660.9771330.511434
1400.4509110.9018220.549089
1410.5491380.9017250.450862
1420.5002320.9995350.499768
1430.4844180.9688360.515582
1440.4040390.8080770.595961
1450.3220470.6440930.677953
1460.4486810.8973610.551319
1470.3610570.7221150.638943
1480.4682620.9365240.531738
1490.5600660.8798680.439934
1500.4456910.8913820.554309
1510.6600240.6799520.339976
1520.612260.775480.38774
1530.6120690.7758630.387931






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level80.0551724NOK
5% type I error level190.131034NOK
10% type I error level270.186207NOK

\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 & 0.0551724 & NOK \tabularnewline
5% type I error level & 19 & 0.131034 & NOK \tabularnewline
10% type I error level & 27 & 0.186207 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=253115&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]0.0551724[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.131034[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]27[/C][C]0.186207[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=253115&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=253115&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 level80.0551724NOK
5% type I error level190.131034NOK
10% type I error level270.186207NOK


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 10
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 6 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), 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,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
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,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}