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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 computationFri, 12 Dec 2014 12:28:06 +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/Dec/12/t1418395920tatc7xwa82nq336.htm/, Retrieved Thu, 16 May 2024 19:20:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=266766, Retrieved Thu, 16 May 2024 19:20:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [zelfvertrouwencursus] [2014-12-12 12:28:06] [4ce2356216df8db4950cd852fec912aa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
13 26 50 4 12.9 13
8 57 62 4 12.2 13
14 37 54 5 12.8 11
16 67 71 4 7.4 14
14 43 54 4 6.7 15
13 52 65 9 12.6 14
15 52 73 8 14.8 11
13 43 52 11 13.3 13
20 84 84 4 11.1 16
17 67 42 4 8.2 14
15 49 66 6 11.4 14
16 70 65 4 6.4 15
12 52 78 8 10.6 15
17 58 73 4 12.0 13
11 68 75 4 6.3 14
16 62 72 11 11.3 11
16 43 66 4 11.9 12
15 56 70 4 9.3 14
13 56 61 6 9.6 13
14 74 81 6 10.0 12
19 65 71 4 6.4 15
16 63 69 8 13.8 15
17 58 71 5 10.8 14
10 57 72 4 13.8 14
15 63 68 9 11.7 12
14 53 70 4 10.9 12
14 57 68 7 16.1 12
16 51 61 10 13.4 15
15 64 67 4 9.9 14
17 53 76 4 11.5 16
14 29 70 7 8.3 12
16 54 60 12 11.7 12
15 58 72 7 9.0 14
16 43 69 5 9.7 16
16 51 71 8 10.8 15
10 53 62 5 10.3 12
8 54 70 4 10.4 14
17 56 64 9 12.7 13
14 61 58 7 9.3 14
10 47 76 4 11.8 16
14 39 52 4 5.9 12
12 48 59 4 11.4 14
16 50 68 4 13.0 15
16 35 76 4 10.8 13
16 30 65 7 12.3 16
8 68 67 4 11.3 16
16 49 59 7 11.8 12
15 61 69 4 7.9 12
8 67 76 4 12.7 16
13 47 63 4 12.3 12
14 56 75 4 11.6 15
13 50 63 8 6.7 12
16 43 60 4 10.9 13
19 67 73 4 12.1 12
19 62 63 4 13.3 14
14 57 70 4 10.1 14
15 41 75 7 5.7 11
13 54 66 12 14.3 10
10 45 63 4 8.0 12
16 48 63 4 13.3 11
15 61 64 4 9.3 16
11 56 70 5 12.5 14
9 41 75 15 7.6 14
16 43 61 5 15.9 15
12 53 60 10 9.2 15
12 44 62 9 9.1 14
14 66 73 8 11.1 13
14 58 61 4 13.0 11
13 46 66 5 14.5 16
15 37 64 4 12.2 12
17 51 59 9 12.3 15
14 51 64 4 11.4 14
11 56 60 10 8.8 15
9 66 56 4 14.6 14
7 37 78 4 12.6 13
15 42 67 7 13.0 12
12 38 59 5 12.6 12
15 66 66 4 13.2 14
14 34 68 4 9.9 14
16 53 71 4 7.7 15
14 49 66 4 10.5 11
13 55 73 4 13.4 13
16 49 72 4 10.9 14
13 59 71 6 4.3 16
16 40 59 10 10.3 13
16 58 64 7 11.8 14
16 60 66 4 11.2 16
10 63 78 4 11.4 11
12 56 68 7 8.6 13
12 54 73 4 13.2 13
12 52 62 8 12.6 15
12 34 65 11 5.6 12
19 69 68 6 9.9 13
14 32 65 14 8.8 12
13 48 60 5 7.7 14
16 67 71 4 9.0 14
15 58 65 8 7.3 16
12 57 68 9 11.4 15
8 42 64 4 13.6 14
10 64 74 4 7.9 13
16 58 69 5 10.7 14
16 66 76 4 10.3 15
10 26 68 5 8.3 14
18 61 72 4 9.6 12
12 52 67 4 14.2 7
16 51 63 7 8.5 12
10 55 59 10 13.5 15
14 50 73 4 4.9 12
12 60 66 5 6.4 13
11 56 62 4 9.6 11
15 63 69 4 11.6 14
7 61 66 4 11.1 13

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 7 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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 time7 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net






Multiple Linear Regression - Estimated Regression Equation
CONFSTATTOT[t] = + 12.2618 + 0.0465486AMS.I[t] -0.0139987AMS.E[t] + 0.0267853AMS.A[t] -0.0330293TOT[t] + 0.0155657STRESSTOT[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CONFSTATTOT[t] =  +  12.2618 +  0.0465486AMS.I[t] -0.0139987AMS.E[t] +  0.0267853AMS.A[t] -0.0330293TOT[t] +  0.0155657STRESSTOT[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CONFSTATTOT[t] =  +  12.2618 +  0.0465486AMS.I[t] -0.0139987AMS.E[t] +  0.0267853AMS.A[t] -0.0330293TOT[t] +  0.0155657STRESSTOT[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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
CONFSTATTOT[t] = + 12.2618 + 0.0465486AMS.I[t] -0.0139987AMS.E[t] + 0.0267853AMS.A[t] -0.0330293TOT[t] + 0.0155657STRESSTOT[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.26183.820843.2090.001761420.000880708
AMS.I0.04654860.02682661.7350.0856170.0428085
AMS.E-0.01399870.0416572-0.3360.73750.36875
AMS.A0.02678530.1082310.24750.8050140.402507
TOT-0.03302930.108692-0.30390.7618150.380908
STRESSTOT0.01556570.1689220.092150.9267550.463378

\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) & 12.2618 & 3.82084 & 3.209 & 0.00176142 & 0.000880708 \tabularnewline
AMS.I & 0.0465486 & 0.0268266 & 1.735 & 0.085617 & 0.0428085 \tabularnewline
AMS.E & -0.0139987 & 0.0416572 & -0.336 & 0.7375 & 0.36875 \tabularnewline
AMS.A & 0.0267853 & 0.108231 & 0.2475 & 0.805014 & 0.402507 \tabularnewline
TOT & -0.0330293 & 0.108692 & -0.3039 & 0.761815 & 0.380908 \tabularnewline
STRESSTOT & 0.0155657 & 0.168922 & 0.09215 & 0.926755 & 0.463378 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&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]12.2618[/C][C]3.82084[/C][C]3.209[/C][C]0.00176142[/C][C]0.000880708[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0465486[/C][C]0.0268266[/C][C]1.735[/C][C]0.085617[/C][C]0.0428085[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0139987[/C][C]0.0416572[/C][C]-0.336[/C][C]0.7375[/C][C]0.36875[/C][/ROW]
[ROW][C]AMS.A[/C][C]0.0267853[/C][C]0.108231[/C][C]0.2475[/C][C]0.805014[/C][C]0.402507[/C][/ROW]
[ROW][C]TOT[/C][C]-0.0330293[/C][C]0.108692[/C][C]-0.3039[/C][C]0.761815[/C][C]0.380908[/C][/ROW]
[ROW][C]STRESSTOT[/C][C]0.0155657[/C][C]0.168922[/C][C]0.09215[/C][C]0.926755[/C][C]0.463378[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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)12.26183.820843.2090.001761420.000880708
AMS.I0.04654860.02682661.7350.0856170.0428085
AMS.E-0.01399870.0416572-0.3360.73750.36875
AMS.A0.02678530.1082310.24750.8050140.402507
TOT-0.03302930.108692-0.30390.7618150.380908
STRESSTOT0.01556570.1689220.092150.9267550.463378






Multiple Linear Regression - Regression Statistics
Multiple R0.173711
R-squared0.0301756
Adjusted R-squared-0.0155709
F-TEST (value)0.659626
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.654844
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.81656
Sum Squared Residuals840.898

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.173711 \tabularnewline
R-squared & 0.0301756 \tabularnewline
Adjusted R-squared & -0.0155709 \tabularnewline
F-TEST (value) & 0.659626 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 106 \tabularnewline
p-value & 0.654844 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.81656 \tabularnewline
Sum Squared Residuals & 840.898 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.173711[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0301756[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.0155709[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.659626[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]106[/C][/ROW]
[ROW][C]p-value[/C][C]0.654844[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.81656[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]840.898[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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 R0.173711
R-squared0.0301756
Adjusted R-squared-0.0155709
F-TEST (value)0.659626
F-TEST (DF numerator)5
F-TEST (DF denominator)106
p-value0.654844
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.81656
Sum Squared Residuals840.898






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.65550.344458
2813.9537-5.95369
31413.11050.889461
41614.46731.53271
51413.62680.373213
61313.8152-0.815227
71513.55711.44291
81313.5932-0.593158
92014.98565.01444
101714.84682.15317
111513.62091.37914
121614.73951.26048
131213.6881-1.68808
141713.85293.14715
151114.4942-3.49418
161614.23251.76747
171613.24042.75965
181513.90651.0935
191314.0606-1.06058
201414.5897-0.589706
211914.42284.57721
221614.22041.77959
231713.96283.03716
241013.7764-3.77642
251514.28390.716139
261413.68290.317126
271413.80570.19433
281613.84062.1594
291514.30110.698934
301713.64133.35867
311412.73191.26806
321614.05731.94273
331514.06190.938137
341613.36012.63993
351613.73292.26708
361013.8415-3.84147
37813.7771-5.77707
381713.99663.00345
391414.3876-0.387582
401013.3521-3.35213
411413.44830.551683
421213.6187-1.61873
431613.54862.45144
441612.77993.22013
451612.77863.22137
46814.4722-6.47215
471613.70132.2987
481514.16830.83165
49814.2534-6.25337
501313.4553-0.455332
511413.77610.223897
521313.8871-0.887083
531613.37292.62706
541914.25294.74708
551914.15174.84834
561413.92660.0733768
571513.29081.70916
581313.8563-0.856269
591013.5043-3.50426
601613.45332.54671
611514.25440.745635
621113.8276-2.82759
63913.4891-4.48906
641613.25172.74829
651214.0864-2.08642
661213.6004-1.60044
671414.3621-0.362111
681413.95670.0433215
691313.3832-0.383171
701512.97912.02085
711713.87813.12185
721413.68840.311614
731114.2393-3.23928
74914.3929-5.39291
75712.7855-5.78552
761513.22381.77617
771213.1093-1.10927
781514.29920.700835
791412.89061.10939
801613.82132.17873
811413.55030.44968
821313.667-0.666967
831613.49982.50019
841314.282-1.28199
851613.42782.57218
861614.08141.91863
871614.11711.88294
881014.0043-4.00429
891214.0224-2.02241
901213.627-1.62702
911213.846-1.846
921213.231-1.231
931914.55784.44218
941413.11260.887439
951313.7537-0.753728
961614.41441.58556
971514.27390.72608
981214.0612-2.06118
99813.1968-5.19678
1001014.2536-4.25357
1011613.99412.00586
1021614.27051.72947
1031012.5979-2.59785
1041814.07023.9298
1051213.4915-1.4915
1061613.84742.15261
1071014.0515-4.05149
1081413.69940.300593
1091214.2557-2.25569
1101113.9619-2.96188
1111514.17040.82963
112714.1202-7.12022

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.6555 & 0.344458 \tabularnewline
2 & 8 & 13.9537 & -5.95369 \tabularnewline
3 & 14 & 13.1105 & 0.889461 \tabularnewline
4 & 16 & 14.4673 & 1.53271 \tabularnewline
5 & 14 & 13.6268 & 0.373213 \tabularnewline
6 & 13 & 13.8152 & -0.815227 \tabularnewline
7 & 15 & 13.5571 & 1.44291 \tabularnewline
8 & 13 & 13.5932 & -0.593158 \tabularnewline
9 & 20 & 14.9856 & 5.01444 \tabularnewline
10 & 17 & 14.8468 & 2.15317 \tabularnewline
11 & 15 & 13.6209 & 1.37914 \tabularnewline
12 & 16 & 14.7395 & 1.26048 \tabularnewline
13 & 12 & 13.6881 & -1.68808 \tabularnewline
14 & 17 & 13.8529 & 3.14715 \tabularnewline
15 & 11 & 14.4942 & -3.49418 \tabularnewline
16 & 16 & 14.2325 & 1.76747 \tabularnewline
17 & 16 & 13.2404 & 2.75965 \tabularnewline
18 & 15 & 13.9065 & 1.0935 \tabularnewline
19 & 13 & 14.0606 & -1.06058 \tabularnewline
20 & 14 & 14.5897 & -0.589706 \tabularnewline
21 & 19 & 14.4228 & 4.57721 \tabularnewline
22 & 16 & 14.2204 & 1.77959 \tabularnewline
23 & 17 & 13.9628 & 3.03716 \tabularnewline
24 & 10 & 13.7764 & -3.77642 \tabularnewline
25 & 15 & 14.2839 & 0.716139 \tabularnewline
26 & 14 & 13.6829 & 0.317126 \tabularnewline
27 & 14 & 13.8057 & 0.19433 \tabularnewline
28 & 16 & 13.8406 & 2.1594 \tabularnewline
29 & 15 & 14.3011 & 0.698934 \tabularnewline
30 & 17 & 13.6413 & 3.35867 \tabularnewline
31 & 14 & 12.7319 & 1.26806 \tabularnewline
32 & 16 & 14.0573 & 1.94273 \tabularnewline
33 & 15 & 14.0619 & 0.938137 \tabularnewline
34 & 16 & 13.3601 & 2.63993 \tabularnewline
35 & 16 & 13.7329 & 2.26708 \tabularnewline
36 & 10 & 13.8415 & -3.84147 \tabularnewline
37 & 8 & 13.7771 & -5.77707 \tabularnewline
38 & 17 & 13.9966 & 3.00345 \tabularnewline
39 & 14 & 14.3876 & -0.387582 \tabularnewline
40 & 10 & 13.3521 & -3.35213 \tabularnewline
41 & 14 & 13.4483 & 0.551683 \tabularnewline
42 & 12 & 13.6187 & -1.61873 \tabularnewline
43 & 16 & 13.5486 & 2.45144 \tabularnewline
44 & 16 & 12.7799 & 3.22013 \tabularnewline
45 & 16 & 12.7786 & 3.22137 \tabularnewline
46 & 8 & 14.4722 & -6.47215 \tabularnewline
47 & 16 & 13.7013 & 2.2987 \tabularnewline
48 & 15 & 14.1683 & 0.83165 \tabularnewline
49 & 8 & 14.2534 & -6.25337 \tabularnewline
50 & 13 & 13.4553 & -0.455332 \tabularnewline
51 & 14 & 13.7761 & 0.223897 \tabularnewline
52 & 13 & 13.8871 & -0.887083 \tabularnewline
53 & 16 & 13.3729 & 2.62706 \tabularnewline
54 & 19 & 14.2529 & 4.74708 \tabularnewline
55 & 19 & 14.1517 & 4.84834 \tabularnewline
56 & 14 & 13.9266 & 0.0733768 \tabularnewline
57 & 15 & 13.2908 & 1.70916 \tabularnewline
58 & 13 & 13.8563 & -0.856269 \tabularnewline
59 & 10 & 13.5043 & -3.50426 \tabularnewline
60 & 16 & 13.4533 & 2.54671 \tabularnewline
61 & 15 & 14.2544 & 0.745635 \tabularnewline
62 & 11 & 13.8276 & -2.82759 \tabularnewline
63 & 9 & 13.4891 & -4.48906 \tabularnewline
64 & 16 & 13.2517 & 2.74829 \tabularnewline
65 & 12 & 14.0864 & -2.08642 \tabularnewline
66 & 12 & 13.6004 & -1.60044 \tabularnewline
67 & 14 & 14.3621 & -0.362111 \tabularnewline
68 & 14 & 13.9567 & 0.0433215 \tabularnewline
69 & 13 & 13.3832 & -0.383171 \tabularnewline
70 & 15 & 12.9791 & 2.02085 \tabularnewline
71 & 17 & 13.8781 & 3.12185 \tabularnewline
72 & 14 & 13.6884 & 0.311614 \tabularnewline
73 & 11 & 14.2393 & -3.23928 \tabularnewline
74 & 9 & 14.3929 & -5.39291 \tabularnewline
75 & 7 & 12.7855 & -5.78552 \tabularnewline
76 & 15 & 13.2238 & 1.77617 \tabularnewline
77 & 12 & 13.1093 & -1.10927 \tabularnewline
78 & 15 & 14.2992 & 0.700835 \tabularnewline
79 & 14 & 12.8906 & 1.10939 \tabularnewline
80 & 16 & 13.8213 & 2.17873 \tabularnewline
81 & 14 & 13.5503 & 0.44968 \tabularnewline
82 & 13 & 13.667 & -0.666967 \tabularnewline
83 & 16 & 13.4998 & 2.50019 \tabularnewline
84 & 13 & 14.282 & -1.28199 \tabularnewline
85 & 16 & 13.4278 & 2.57218 \tabularnewline
86 & 16 & 14.0814 & 1.91863 \tabularnewline
87 & 16 & 14.1171 & 1.88294 \tabularnewline
88 & 10 & 14.0043 & -4.00429 \tabularnewline
89 & 12 & 14.0224 & -2.02241 \tabularnewline
90 & 12 & 13.627 & -1.62702 \tabularnewline
91 & 12 & 13.846 & -1.846 \tabularnewline
92 & 12 & 13.231 & -1.231 \tabularnewline
93 & 19 & 14.5578 & 4.44218 \tabularnewline
94 & 14 & 13.1126 & 0.887439 \tabularnewline
95 & 13 & 13.7537 & -0.753728 \tabularnewline
96 & 16 & 14.4144 & 1.58556 \tabularnewline
97 & 15 & 14.2739 & 0.72608 \tabularnewline
98 & 12 & 14.0612 & -2.06118 \tabularnewline
99 & 8 & 13.1968 & -5.19678 \tabularnewline
100 & 10 & 14.2536 & -4.25357 \tabularnewline
101 & 16 & 13.9941 & 2.00586 \tabularnewline
102 & 16 & 14.2705 & 1.72947 \tabularnewline
103 & 10 & 12.5979 & -2.59785 \tabularnewline
104 & 18 & 14.0702 & 3.9298 \tabularnewline
105 & 12 & 13.4915 & -1.4915 \tabularnewline
106 & 16 & 13.8474 & 2.15261 \tabularnewline
107 & 10 & 14.0515 & -4.05149 \tabularnewline
108 & 14 & 13.6994 & 0.300593 \tabularnewline
109 & 12 & 14.2557 & -2.25569 \tabularnewline
110 & 11 & 13.9619 & -2.96188 \tabularnewline
111 & 15 & 14.1704 & 0.82963 \tabularnewline
112 & 7 & 14.1202 & -7.12022 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&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]13[/C][C]12.6555[/C][C]0.344458[/C][/ROW]
[ROW][C]2[/C][C]8[/C][C]13.9537[/C][C]-5.95369[/C][/ROW]
[ROW][C]3[/C][C]14[/C][C]13.1105[/C][C]0.889461[/C][/ROW]
[ROW][C]4[/C][C]16[/C][C]14.4673[/C][C]1.53271[/C][/ROW]
[ROW][C]5[/C][C]14[/C][C]13.6268[/C][C]0.373213[/C][/ROW]
[ROW][C]6[/C][C]13[/C][C]13.8152[/C][C]-0.815227[/C][/ROW]
[ROW][C]7[/C][C]15[/C][C]13.5571[/C][C]1.44291[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]13.5932[/C][C]-0.593158[/C][/ROW]
[ROW][C]9[/C][C]20[/C][C]14.9856[/C][C]5.01444[/C][/ROW]
[ROW][C]10[/C][C]17[/C][C]14.8468[/C][C]2.15317[/C][/ROW]
[ROW][C]11[/C][C]15[/C][C]13.6209[/C][C]1.37914[/C][/ROW]
[ROW][C]12[/C][C]16[/C][C]14.7395[/C][C]1.26048[/C][/ROW]
[ROW][C]13[/C][C]12[/C][C]13.6881[/C][C]-1.68808[/C][/ROW]
[ROW][C]14[/C][C]17[/C][C]13.8529[/C][C]3.14715[/C][/ROW]
[ROW][C]15[/C][C]11[/C][C]14.4942[/C][C]-3.49418[/C][/ROW]
[ROW][C]16[/C][C]16[/C][C]14.2325[/C][C]1.76747[/C][/ROW]
[ROW][C]17[/C][C]16[/C][C]13.2404[/C][C]2.75965[/C][/ROW]
[ROW][C]18[/C][C]15[/C][C]13.9065[/C][C]1.0935[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]14.0606[/C][C]-1.06058[/C][/ROW]
[ROW][C]20[/C][C]14[/C][C]14.5897[/C][C]-0.589706[/C][/ROW]
[ROW][C]21[/C][C]19[/C][C]14.4228[/C][C]4.57721[/C][/ROW]
[ROW][C]22[/C][C]16[/C][C]14.2204[/C][C]1.77959[/C][/ROW]
[ROW][C]23[/C][C]17[/C][C]13.9628[/C][C]3.03716[/C][/ROW]
[ROW][C]24[/C][C]10[/C][C]13.7764[/C][C]-3.77642[/C][/ROW]
[ROW][C]25[/C][C]15[/C][C]14.2839[/C][C]0.716139[/C][/ROW]
[ROW][C]26[/C][C]14[/C][C]13.6829[/C][C]0.317126[/C][/ROW]
[ROW][C]27[/C][C]14[/C][C]13.8057[/C][C]0.19433[/C][/ROW]
[ROW][C]28[/C][C]16[/C][C]13.8406[/C][C]2.1594[/C][/ROW]
[ROW][C]29[/C][C]15[/C][C]14.3011[/C][C]0.698934[/C][/ROW]
[ROW][C]30[/C][C]17[/C][C]13.6413[/C][C]3.35867[/C][/ROW]
[ROW][C]31[/C][C]14[/C][C]12.7319[/C][C]1.26806[/C][/ROW]
[ROW][C]32[/C][C]16[/C][C]14.0573[/C][C]1.94273[/C][/ROW]
[ROW][C]33[/C][C]15[/C][C]14.0619[/C][C]0.938137[/C][/ROW]
[ROW][C]34[/C][C]16[/C][C]13.3601[/C][C]2.63993[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]13.7329[/C][C]2.26708[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]13.8415[/C][C]-3.84147[/C][/ROW]
[ROW][C]37[/C][C]8[/C][C]13.7771[/C][C]-5.77707[/C][/ROW]
[ROW][C]38[/C][C]17[/C][C]13.9966[/C][C]3.00345[/C][/ROW]
[ROW][C]39[/C][C]14[/C][C]14.3876[/C][C]-0.387582[/C][/ROW]
[ROW][C]40[/C][C]10[/C][C]13.3521[/C][C]-3.35213[/C][/ROW]
[ROW][C]41[/C][C]14[/C][C]13.4483[/C][C]0.551683[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]13.6187[/C][C]-1.61873[/C][/ROW]
[ROW][C]43[/C][C]16[/C][C]13.5486[/C][C]2.45144[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]12.7799[/C][C]3.22013[/C][/ROW]
[ROW][C]45[/C][C]16[/C][C]12.7786[/C][C]3.22137[/C][/ROW]
[ROW][C]46[/C][C]8[/C][C]14.4722[/C][C]-6.47215[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]13.7013[/C][C]2.2987[/C][/ROW]
[ROW][C]48[/C][C]15[/C][C]14.1683[/C][C]0.83165[/C][/ROW]
[ROW][C]49[/C][C]8[/C][C]14.2534[/C][C]-6.25337[/C][/ROW]
[ROW][C]50[/C][C]13[/C][C]13.4553[/C][C]-0.455332[/C][/ROW]
[ROW][C]51[/C][C]14[/C][C]13.7761[/C][C]0.223897[/C][/ROW]
[ROW][C]52[/C][C]13[/C][C]13.8871[/C][C]-0.887083[/C][/ROW]
[ROW][C]53[/C][C]16[/C][C]13.3729[/C][C]2.62706[/C][/ROW]
[ROW][C]54[/C][C]19[/C][C]14.2529[/C][C]4.74708[/C][/ROW]
[ROW][C]55[/C][C]19[/C][C]14.1517[/C][C]4.84834[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.9266[/C][C]0.0733768[/C][/ROW]
[ROW][C]57[/C][C]15[/C][C]13.2908[/C][C]1.70916[/C][/ROW]
[ROW][C]58[/C][C]13[/C][C]13.8563[/C][C]-0.856269[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]13.5043[/C][C]-3.50426[/C][/ROW]
[ROW][C]60[/C][C]16[/C][C]13.4533[/C][C]2.54671[/C][/ROW]
[ROW][C]61[/C][C]15[/C][C]14.2544[/C][C]0.745635[/C][/ROW]
[ROW][C]62[/C][C]11[/C][C]13.8276[/C][C]-2.82759[/C][/ROW]
[ROW][C]63[/C][C]9[/C][C]13.4891[/C][C]-4.48906[/C][/ROW]
[ROW][C]64[/C][C]16[/C][C]13.2517[/C][C]2.74829[/C][/ROW]
[ROW][C]65[/C][C]12[/C][C]14.0864[/C][C]-2.08642[/C][/ROW]
[ROW][C]66[/C][C]12[/C][C]13.6004[/C][C]-1.60044[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]14.3621[/C][C]-0.362111[/C][/ROW]
[ROW][C]68[/C][C]14[/C][C]13.9567[/C][C]0.0433215[/C][/ROW]
[ROW][C]69[/C][C]13[/C][C]13.3832[/C][C]-0.383171[/C][/ROW]
[ROW][C]70[/C][C]15[/C][C]12.9791[/C][C]2.02085[/C][/ROW]
[ROW][C]71[/C][C]17[/C][C]13.8781[/C][C]3.12185[/C][/ROW]
[ROW][C]72[/C][C]14[/C][C]13.6884[/C][C]0.311614[/C][/ROW]
[ROW][C]73[/C][C]11[/C][C]14.2393[/C][C]-3.23928[/C][/ROW]
[ROW][C]74[/C][C]9[/C][C]14.3929[/C][C]-5.39291[/C][/ROW]
[ROW][C]75[/C][C]7[/C][C]12.7855[/C][C]-5.78552[/C][/ROW]
[ROW][C]76[/C][C]15[/C][C]13.2238[/C][C]1.77617[/C][/ROW]
[ROW][C]77[/C][C]12[/C][C]13.1093[/C][C]-1.10927[/C][/ROW]
[ROW][C]78[/C][C]15[/C][C]14.2992[/C][C]0.700835[/C][/ROW]
[ROW][C]79[/C][C]14[/C][C]12.8906[/C][C]1.10939[/C][/ROW]
[ROW][C]80[/C][C]16[/C][C]13.8213[/C][C]2.17873[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]13.5503[/C][C]0.44968[/C][/ROW]
[ROW][C]82[/C][C]13[/C][C]13.667[/C][C]-0.666967[/C][/ROW]
[ROW][C]83[/C][C]16[/C][C]13.4998[/C][C]2.50019[/C][/ROW]
[ROW][C]84[/C][C]13[/C][C]14.282[/C][C]-1.28199[/C][/ROW]
[ROW][C]85[/C][C]16[/C][C]13.4278[/C][C]2.57218[/C][/ROW]
[ROW][C]86[/C][C]16[/C][C]14.0814[/C][C]1.91863[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]14.1171[/C][C]1.88294[/C][/ROW]
[ROW][C]88[/C][C]10[/C][C]14.0043[/C][C]-4.00429[/C][/ROW]
[ROW][C]89[/C][C]12[/C][C]14.0224[/C][C]-2.02241[/C][/ROW]
[ROW][C]90[/C][C]12[/C][C]13.627[/C][C]-1.62702[/C][/ROW]
[ROW][C]91[/C][C]12[/C][C]13.846[/C][C]-1.846[/C][/ROW]
[ROW][C]92[/C][C]12[/C][C]13.231[/C][C]-1.231[/C][/ROW]
[ROW][C]93[/C][C]19[/C][C]14.5578[/C][C]4.44218[/C][/ROW]
[ROW][C]94[/C][C]14[/C][C]13.1126[/C][C]0.887439[/C][/ROW]
[ROW][C]95[/C][C]13[/C][C]13.7537[/C][C]-0.753728[/C][/ROW]
[ROW][C]96[/C][C]16[/C][C]14.4144[/C][C]1.58556[/C][/ROW]
[ROW][C]97[/C][C]15[/C][C]14.2739[/C][C]0.72608[/C][/ROW]
[ROW][C]98[/C][C]12[/C][C]14.0612[/C][C]-2.06118[/C][/ROW]
[ROW][C]99[/C][C]8[/C][C]13.1968[/C][C]-5.19678[/C][/ROW]
[ROW][C]100[/C][C]10[/C][C]14.2536[/C][C]-4.25357[/C][/ROW]
[ROW][C]101[/C][C]16[/C][C]13.9941[/C][C]2.00586[/C][/ROW]
[ROW][C]102[/C][C]16[/C][C]14.2705[/C][C]1.72947[/C][/ROW]
[ROW][C]103[/C][C]10[/C][C]12.5979[/C][C]-2.59785[/C][/ROW]
[ROW][C]104[/C][C]18[/C][C]14.0702[/C][C]3.9298[/C][/ROW]
[ROW][C]105[/C][C]12[/C][C]13.4915[/C][C]-1.4915[/C][/ROW]
[ROW][C]106[/C][C]16[/C][C]13.8474[/C][C]2.15261[/C][/ROW]
[ROW][C]107[/C][C]10[/C][C]14.0515[/C][C]-4.05149[/C][/ROW]
[ROW][C]108[/C][C]14[/C][C]13.6994[/C][C]0.300593[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]14.2557[/C][C]-2.25569[/C][/ROW]
[ROW][C]110[/C][C]11[/C][C]13.9619[/C][C]-2.96188[/C][/ROW]
[ROW][C]111[/C][C]15[/C][C]14.1704[/C][C]0.82963[/C][/ROW]
[ROW][C]112[/C][C]7[/C][C]14.1202[/C][C]-7.12022[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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
11312.65550.344458
2813.9537-5.95369
31413.11050.889461
41614.46731.53271
51413.62680.373213
61313.8152-0.815227
71513.55711.44291
81313.5932-0.593158
92014.98565.01444
101714.84682.15317
111513.62091.37914
121614.73951.26048
131213.6881-1.68808
141713.85293.14715
151114.4942-3.49418
161614.23251.76747
171613.24042.75965
181513.90651.0935
191314.0606-1.06058
201414.5897-0.589706
211914.42284.57721
221614.22041.77959
231713.96283.03716
241013.7764-3.77642
251514.28390.716139
261413.68290.317126
271413.80570.19433
281613.84062.1594
291514.30110.698934
301713.64133.35867
311412.73191.26806
321614.05731.94273
331514.06190.938137
341613.36012.63993
351613.73292.26708
361013.8415-3.84147
37813.7771-5.77707
381713.99663.00345
391414.3876-0.387582
401013.3521-3.35213
411413.44830.551683
421213.6187-1.61873
431613.54862.45144
441612.77993.22013
451612.77863.22137
46814.4722-6.47215
471613.70132.2987
481514.16830.83165
49814.2534-6.25337
501313.4553-0.455332
511413.77610.223897
521313.8871-0.887083
531613.37292.62706
541914.25294.74708
551914.15174.84834
561413.92660.0733768
571513.29081.70916
581313.8563-0.856269
591013.5043-3.50426
601613.45332.54671
611514.25440.745635
621113.8276-2.82759
63913.4891-4.48906
641613.25172.74829
651214.0864-2.08642
661213.6004-1.60044
671414.3621-0.362111
681413.95670.0433215
691313.3832-0.383171
701512.97912.02085
711713.87813.12185
721413.68840.311614
731114.2393-3.23928
74914.3929-5.39291
75712.7855-5.78552
761513.22381.77617
771213.1093-1.10927
781514.29920.700835
791412.89061.10939
801613.82132.17873
811413.55030.44968
821313.667-0.666967
831613.49982.50019
841314.282-1.28199
851613.42782.57218
861614.08141.91863
871614.11711.88294
881014.0043-4.00429
891214.0224-2.02241
901213.627-1.62702
911213.846-1.846
921213.231-1.231
931914.55784.44218
941413.11260.887439
951313.7537-0.753728
961614.41441.58556
971514.27390.72608
981214.0612-2.06118
99813.1968-5.19678
1001014.2536-4.25357
1011613.99412.00586
1021614.27051.72947
1031012.5979-2.59785
1041814.07023.9298
1051213.4915-1.4915
1061613.84742.15261
1071014.0515-4.05149
1081413.69940.300593
1091214.2557-2.25569
1101113.9619-2.96188
1111514.17040.82963
112714.1202-7.12022






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.7942750.411450.205725
100.8644670.2710650.135533
110.7827730.4344530.217227
120.6873160.6253680.312684
130.6465870.7068270.353413
140.5969070.8061870.403093
150.6632170.6735660.336783
160.6269840.7460320.373016
170.6030540.7938930.396946
180.518540.9629210.48146
190.4492540.8985070.550746
200.3895470.7790930.610453
210.4863940.9727890.513606
220.4143740.8287490.585626
230.3850760.7701510.614924
240.5254230.9491540.474577
250.4535670.9071340.546433
260.3826760.7653510.617324
270.3161060.6322120.683894
280.2767250.553450.723275
290.2232770.4465530.776723
300.2153230.4306460.784677
310.175370.3507410.82463
320.1484570.2969130.851543
330.1157240.2314490.884276
340.09714460.1942890.902855
350.07921530.1584310.920785
360.1091790.2183590.890821
370.2826180.5652350.717382
380.2771120.5542230.722888
390.2353380.4706760.764662
400.2840350.568070.715965
410.2376250.475250.762375
420.2081920.4163830.791808
430.1982960.3965920.801704
440.2126980.4253950.787302
450.2089480.4178960.791052
460.4327770.8655530.567223
470.4087710.8175420.591229
480.3567720.7135450.643228
490.5402270.9195450.459773
500.4833330.9666660.516667
510.4275750.8551490.572425
520.4023420.8046850.597658
530.3958360.7916720.604164
540.5151010.9697980.484899
550.6409840.7180330.359016
560.5874860.8250280.412514
570.5554060.8891890.444594
580.5155590.9688830.484441
590.5545450.890910.445455
600.5515070.8969860.448493
610.497370.9947390.50263
620.490420.9808390.50958
630.5676170.8647670.432383
640.5796930.8406130.420307
650.547850.9043010.45215
660.5045890.9908220.495411
670.4475290.8950570.552471
680.3984520.7969040.601548
690.3463110.6926210.653689
700.3423670.6847340.657633
710.3768090.7536180.623191
720.3320340.6640680.667966
730.3433620.6867240.656638
740.4516240.9032470.548376
750.596880.8062390.40312
760.5771220.8457560.422878
770.5224020.9551950.477598
780.4709120.9418240.529088
790.4375220.8750440.562478
800.4177910.8355810.582209
810.3748870.7497740.625113
820.3174280.6348560.682572
830.3497590.6995190.650241
840.3180560.6361120.681944
850.3499810.6999630.650019
860.3415340.6830690.658466
870.3524910.7049810.647509
880.4535520.9071050.546448
890.4243970.8487950.575603
900.3578860.7157730.642114
910.2956280.5912560.704372
920.2496050.4992110.750395
930.319680.6393590.68032
940.2484090.4968170.751591
950.2209790.4419580.779021
960.1824860.3649720.817514
970.1450570.2901140.854943
980.1252780.2505560.874722
990.0955140.1910280.904486
1000.2761150.552230.723885
1010.2533540.5067070.746646
1020.1611050.3222110.838895
1030.09875280.1975060.901247

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.794275 & 0.41145 & 0.205725 \tabularnewline
10 & 0.864467 & 0.271065 & 0.135533 \tabularnewline
11 & 0.782773 & 0.434453 & 0.217227 \tabularnewline
12 & 0.687316 & 0.625368 & 0.312684 \tabularnewline
13 & 0.646587 & 0.706827 & 0.353413 \tabularnewline
14 & 0.596907 & 0.806187 & 0.403093 \tabularnewline
15 & 0.663217 & 0.673566 & 0.336783 \tabularnewline
16 & 0.626984 & 0.746032 & 0.373016 \tabularnewline
17 & 0.603054 & 0.793893 & 0.396946 \tabularnewline
18 & 0.51854 & 0.962921 & 0.48146 \tabularnewline
19 & 0.449254 & 0.898507 & 0.550746 \tabularnewline
20 & 0.389547 & 0.779093 & 0.610453 \tabularnewline
21 & 0.486394 & 0.972789 & 0.513606 \tabularnewline
22 & 0.414374 & 0.828749 & 0.585626 \tabularnewline
23 & 0.385076 & 0.770151 & 0.614924 \tabularnewline
24 & 0.525423 & 0.949154 & 0.474577 \tabularnewline
25 & 0.453567 & 0.907134 & 0.546433 \tabularnewline
26 & 0.382676 & 0.765351 & 0.617324 \tabularnewline
27 & 0.316106 & 0.632212 & 0.683894 \tabularnewline
28 & 0.276725 & 0.55345 & 0.723275 \tabularnewline
29 & 0.223277 & 0.446553 & 0.776723 \tabularnewline
30 & 0.215323 & 0.430646 & 0.784677 \tabularnewline
31 & 0.17537 & 0.350741 & 0.82463 \tabularnewline
32 & 0.148457 & 0.296913 & 0.851543 \tabularnewline
33 & 0.115724 & 0.231449 & 0.884276 \tabularnewline
34 & 0.0971446 & 0.194289 & 0.902855 \tabularnewline
35 & 0.0792153 & 0.158431 & 0.920785 \tabularnewline
36 & 0.109179 & 0.218359 & 0.890821 \tabularnewline
37 & 0.282618 & 0.565235 & 0.717382 \tabularnewline
38 & 0.277112 & 0.554223 & 0.722888 \tabularnewline
39 & 0.235338 & 0.470676 & 0.764662 \tabularnewline
40 & 0.284035 & 0.56807 & 0.715965 \tabularnewline
41 & 0.237625 & 0.47525 & 0.762375 \tabularnewline
42 & 0.208192 & 0.416383 & 0.791808 \tabularnewline
43 & 0.198296 & 0.396592 & 0.801704 \tabularnewline
44 & 0.212698 & 0.425395 & 0.787302 \tabularnewline
45 & 0.208948 & 0.417896 & 0.791052 \tabularnewline
46 & 0.432777 & 0.865553 & 0.567223 \tabularnewline
47 & 0.408771 & 0.817542 & 0.591229 \tabularnewline
48 & 0.356772 & 0.713545 & 0.643228 \tabularnewline
49 & 0.540227 & 0.919545 & 0.459773 \tabularnewline
50 & 0.483333 & 0.966666 & 0.516667 \tabularnewline
51 & 0.427575 & 0.855149 & 0.572425 \tabularnewline
52 & 0.402342 & 0.804685 & 0.597658 \tabularnewline
53 & 0.395836 & 0.791672 & 0.604164 \tabularnewline
54 & 0.515101 & 0.969798 & 0.484899 \tabularnewline
55 & 0.640984 & 0.718033 & 0.359016 \tabularnewline
56 & 0.587486 & 0.825028 & 0.412514 \tabularnewline
57 & 0.555406 & 0.889189 & 0.444594 \tabularnewline
58 & 0.515559 & 0.968883 & 0.484441 \tabularnewline
59 & 0.554545 & 0.89091 & 0.445455 \tabularnewline
60 & 0.551507 & 0.896986 & 0.448493 \tabularnewline
61 & 0.49737 & 0.994739 & 0.50263 \tabularnewline
62 & 0.49042 & 0.980839 & 0.50958 \tabularnewline
63 & 0.567617 & 0.864767 & 0.432383 \tabularnewline
64 & 0.579693 & 0.840613 & 0.420307 \tabularnewline
65 & 0.54785 & 0.904301 & 0.45215 \tabularnewline
66 & 0.504589 & 0.990822 & 0.495411 \tabularnewline
67 & 0.447529 & 0.895057 & 0.552471 \tabularnewline
68 & 0.398452 & 0.796904 & 0.601548 \tabularnewline
69 & 0.346311 & 0.692621 & 0.653689 \tabularnewline
70 & 0.342367 & 0.684734 & 0.657633 \tabularnewline
71 & 0.376809 & 0.753618 & 0.623191 \tabularnewline
72 & 0.332034 & 0.664068 & 0.667966 \tabularnewline
73 & 0.343362 & 0.686724 & 0.656638 \tabularnewline
74 & 0.451624 & 0.903247 & 0.548376 \tabularnewline
75 & 0.59688 & 0.806239 & 0.40312 \tabularnewline
76 & 0.577122 & 0.845756 & 0.422878 \tabularnewline
77 & 0.522402 & 0.955195 & 0.477598 \tabularnewline
78 & 0.470912 & 0.941824 & 0.529088 \tabularnewline
79 & 0.437522 & 0.875044 & 0.562478 \tabularnewline
80 & 0.417791 & 0.835581 & 0.582209 \tabularnewline
81 & 0.374887 & 0.749774 & 0.625113 \tabularnewline
82 & 0.317428 & 0.634856 & 0.682572 \tabularnewline
83 & 0.349759 & 0.699519 & 0.650241 \tabularnewline
84 & 0.318056 & 0.636112 & 0.681944 \tabularnewline
85 & 0.349981 & 0.699963 & 0.650019 \tabularnewline
86 & 0.341534 & 0.683069 & 0.658466 \tabularnewline
87 & 0.352491 & 0.704981 & 0.647509 \tabularnewline
88 & 0.453552 & 0.907105 & 0.546448 \tabularnewline
89 & 0.424397 & 0.848795 & 0.575603 \tabularnewline
90 & 0.357886 & 0.715773 & 0.642114 \tabularnewline
91 & 0.295628 & 0.591256 & 0.704372 \tabularnewline
92 & 0.249605 & 0.499211 & 0.750395 \tabularnewline
93 & 0.31968 & 0.639359 & 0.68032 \tabularnewline
94 & 0.248409 & 0.496817 & 0.751591 \tabularnewline
95 & 0.220979 & 0.441958 & 0.779021 \tabularnewline
96 & 0.182486 & 0.364972 & 0.817514 \tabularnewline
97 & 0.145057 & 0.290114 & 0.854943 \tabularnewline
98 & 0.125278 & 0.250556 & 0.874722 \tabularnewline
99 & 0.095514 & 0.191028 & 0.904486 \tabularnewline
100 & 0.276115 & 0.55223 & 0.723885 \tabularnewline
101 & 0.253354 & 0.506707 & 0.746646 \tabularnewline
102 & 0.161105 & 0.322211 & 0.838895 \tabularnewline
103 & 0.0987528 & 0.197506 & 0.901247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&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]0.794275[/C][C]0.41145[/C][C]0.205725[/C][/ROW]
[ROW][C]10[/C][C]0.864467[/C][C]0.271065[/C][C]0.135533[/C][/ROW]
[ROW][C]11[/C][C]0.782773[/C][C]0.434453[/C][C]0.217227[/C][/ROW]
[ROW][C]12[/C][C]0.687316[/C][C]0.625368[/C][C]0.312684[/C][/ROW]
[ROW][C]13[/C][C]0.646587[/C][C]0.706827[/C][C]0.353413[/C][/ROW]
[ROW][C]14[/C][C]0.596907[/C][C]0.806187[/C][C]0.403093[/C][/ROW]
[ROW][C]15[/C][C]0.663217[/C][C]0.673566[/C][C]0.336783[/C][/ROW]
[ROW][C]16[/C][C]0.626984[/C][C]0.746032[/C][C]0.373016[/C][/ROW]
[ROW][C]17[/C][C]0.603054[/C][C]0.793893[/C][C]0.396946[/C][/ROW]
[ROW][C]18[/C][C]0.51854[/C][C]0.962921[/C][C]0.48146[/C][/ROW]
[ROW][C]19[/C][C]0.449254[/C][C]0.898507[/C][C]0.550746[/C][/ROW]
[ROW][C]20[/C][C]0.389547[/C][C]0.779093[/C][C]0.610453[/C][/ROW]
[ROW][C]21[/C][C]0.486394[/C][C]0.972789[/C][C]0.513606[/C][/ROW]
[ROW][C]22[/C][C]0.414374[/C][C]0.828749[/C][C]0.585626[/C][/ROW]
[ROW][C]23[/C][C]0.385076[/C][C]0.770151[/C][C]0.614924[/C][/ROW]
[ROW][C]24[/C][C]0.525423[/C][C]0.949154[/C][C]0.474577[/C][/ROW]
[ROW][C]25[/C][C]0.453567[/C][C]0.907134[/C][C]0.546433[/C][/ROW]
[ROW][C]26[/C][C]0.382676[/C][C]0.765351[/C][C]0.617324[/C][/ROW]
[ROW][C]27[/C][C]0.316106[/C][C]0.632212[/C][C]0.683894[/C][/ROW]
[ROW][C]28[/C][C]0.276725[/C][C]0.55345[/C][C]0.723275[/C][/ROW]
[ROW][C]29[/C][C]0.223277[/C][C]0.446553[/C][C]0.776723[/C][/ROW]
[ROW][C]30[/C][C]0.215323[/C][C]0.430646[/C][C]0.784677[/C][/ROW]
[ROW][C]31[/C][C]0.17537[/C][C]0.350741[/C][C]0.82463[/C][/ROW]
[ROW][C]32[/C][C]0.148457[/C][C]0.296913[/C][C]0.851543[/C][/ROW]
[ROW][C]33[/C][C]0.115724[/C][C]0.231449[/C][C]0.884276[/C][/ROW]
[ROW][C]34[/C][C]0.0971446[/C][C]0.194289[/C][C]0.902855[/C][/ROW]
[ROW][C]35[/C][C]0.0792153[/C][C]0.158431[/C][C]0.920785[/C][/ROW]
[ROW][C]36[/C][C]0.109179[/C][C]0.218359[/C][C]0.890821[/C][/ROW]
[ROW][C]37[/C][C]0.282618[/C][C]0.565235[/C][C]0.717382[/C][/ROW]
[ROW][C]38[/C][C]0.277112[/C][C]0.554223[/C][C]0.722888[/C][/ROW]
[ROW][C]39[/C][C]0.235338[/C][C]0.470676[/C][C]0.764662[/C][/ROW]
[ROW][C]40[/C][C]0.284035[/C][C]0.56807[/C][C]0.715965[/C][/ROW]
[ROW][C]41[/C][C]0.237625[/C][C]0.47525[/C][C]0.762375[/C][/ROW]
[ROW][C]42[/C][C]0.208192[/C][C]0.416383[/C][C]0.791808[/C][/ROW]
[ROW][C]43[/C][C]0.198296[/C][C]0.396592[/C][C]0.801704[/C][/ROW]
[ROW][C]44[/C][C]0.212698[/C][C]0.425395[/C][C]0.787302[/C][/ROW]
[ROW][C]45[/C][C]0.208948[/C][C]0.417896[/C][C]0.791052[/C][/ROW]
[ROW][C]46[/C][C]0.432777[/C][C]0.865553[/C][C]0.567223[/C][/ROW]
[ROW][C]47[/C][C]0.408771[/C][C]0.817542[/C][C]0.591229[/C][/ROW]
[ROW][C]48[/C][C]0.356772[/C][C]0.713545[/C][C]0.643228[/C][/ROW]
[ROW][C]49[/C][C]0.540227[/C][C]0.919545[/C][C]0.459773[/C][/ROW]
[ROW][C]50[/C][C]0.483333[/C][C]0.966666[/C][C]0.516667[/C][/ROW]
[ROW][C]51[/C][C]0.427575[/C][C]0.855149[/C][C]0.572425[/C][/ROW]
[ROW][C]52[/C][C]0.402342[/C][C]0.804685[/C][C]0.597658[/C][/ROW]
[ROW][C]53[/C][C]0.395836[/C][C]0.791672[/C][C]0.604164[/C][/ROW]
[ROW][C]54[/C][C]0.515101[/C][C]0.969798[/C][C]0.484899[/C][/ROW]
[ROW][C]55[/C][C]0.640984[/C][C]0.718033[/C][C]0.359016[/C][/ROW]
[ROW][C]56[/C][C]0.587486[/C][C]0.825028[/C][C]0.412514[/C][/ROW]
[ROW][C]57[/C][C]0.555406[/C][C]0.889189[/C][C]0.444594[/C][/ROW]
[ROW][C]58[/C][C]0.515559[/C][C]0.968883[/C][C]0.484441[/C][/ROW]
[ROW][C]59[/C][C]0.554545[/C][C]0.89091[/C][C]0.445455[/C][/ROW]
[ROW][C]60[/C][C]0.551507[/C][C]0.896986[/C][C]0.448493[/C][/ROW]
[ROW][C]61[/C][C]0.49737[/C][C]0.994739[/C][C]0.50263[/C][/ROW]
[ROW][C]62[/C][C]0.49042[/C][C]0.980839[/C][C]0.50958[/C][/ROW]
[ROW][C]63[/C][C]0.567617[/C][C]0.864767[/C][C]0.432383[/C][/ROW]
[ROW][C]64[/C][C]0.579693[/C][C]0.840613[/C][C]0.420307[/C][/ROW]
[ROW][C]65[/C][C]0.54785[/C][C]0.904301[/C][C]0.45215[/C][/ROW]
[ROW][C]66[/C][C]0.504589[/C][C]0.990822[/C][C]0.495411[/C][/ROW]
[ROW][C]67[/C][C]0.447529[/C][C]0.895057[/C][C]0.552471[/C][/ROW]
[ROW][C]68[/C][C]0.398452[/C][C]0.796904[/C][C]0.601548[/C][/ROW]
[ROW][C]69[/C][C]0.346311[/C][C]0.692621[/C][C]0.653689[/C][/ROW]
[ROW][C]70[/C][C]0.342367[/C][C]0.684734[/C][C]0.657633[/C][/ROW]
[ROW][C]71[/C][C]0.376809[/C][C]0.753618[/C][C]0.623191[/C][/ROW]
[ROW][C]72[/C][C]0.332034[/C][C]0.664068[/C][C]0.667966[/C][/ROW]
[ROW][C]73[/C][C]0.343362[/C][C]0.686724[/C][C]0.656638[/C][/ROW]
[ROW][C]74[/C][C]0.451624[/C][C]0.903247[/C][C]0.548376[/C][/ROW]
[ROW][C]75[/C][C]0.59688[/C][C]0.806239[/C][C]0.40312[/C][/ROW]
[ROW][C]76[/C][C]0.577122[/C][C]0.845756[/C][C]0.422878[/C][/ROW]
[ROW][C]77[/C][C]0.522402[/C][C]0.955195[/C][C]0.477598[/C][/ROW]
[ROW][C]78[/C][C]0.470912[/C][C]0.941824[/C][C]0.529088[/C][/ROW]
[ROW][C]79[/C][C]0.437522[/C][C]0.875044[/C][C]0.562478[/C][/ROW]
[ROW][C]80[/C][C]0.417791[/C][C]0.835581[/C][C]0.582209[/C][/ROW]
[ROW][C]81[/C][C]0.374887[/C][C]0.749774[/C][C]0.625113[/C][/ROW]
[ROW][C]82[/C][C]0.317428[/C][C]0.634856[/C][C]0.682572[/C][/ROW]
[ROW][C]83[/C][C]0.349759[/C][C]0.699519[/C][C]0.650241[/C][/ROW]
[ROW][C]84[/C][C]0.318056[/C][C]0.636112[/C][C]0.681944[/C][/ROW]
[ROW][C]85[/C][C]0.349981[/C][C]0.699963[/C][C]0.650019[/C][/ROW]
[ROW][C]86[/C][C]0.341534[/C][C]0.683069[/C][C]0.658466[/C][/ROW]
[ROW][C]87[/C][C]0.352491[/C][C]0.704981[/C][C]0.647509[/C][/ROW]
[ROW][C]88[/C][C]0.453552[/C][C]0.907105[/C][C]0.546448[/C][/ROW]
[ROW][C]89[/C][C]0.424397[/C][C]0.848795[/C][C]0.575603[/C][/ROW]
[ROW][C]90[/C][C]0.357886[/C][C]0.715773[/C][C]0.642114[/C][/ROW]
[ROW][C]91[/C][C]0.295628[/C][C]0.591256[/C][C]0.704372[/C][/ROW]
[ROW][C]92[/C][C]0.249605[/C][C]0.499211[/C][C]0.750395[/C][/ROW]
[ROW][C]93[/C][C]0.31968[/C][C]0.639359[/C][C]0.68032[/C][/ROW]
[ROW][C]94[/C][C]0.248409[/C][C]0.496817[/C][C]0.751591[/C][/ROW]
[ROW][C]95[/C][C]0.220979[/C][C]0.441958[/C][C]0.779021[/C][/ROW]
[ROW][C]96[/C][C]0.182486[/C][C]0.364972[/C][C]0.817514[/C][/ROW]
[ROW][C]97[/C][C]0.145057[/C][C]0.290114[/C][C]0.854943[/C][/ROW]
[ROW][C]98[/C][C]0.125278[/C][C]0.250556[/C][C]0.874722[/C][/ROW]
[ROW][C]99[/C][C]0.095514[/C][C]0.191028[/C][C]0.904486[/C][/ROW]
[ROW][C]100[/C][C]0.276115[/C][C]0.55223[/C][C]0.723885[/C][/ROW]
[ROW][C]101[/C][C]0.253354[/C][C]0.506707[/C][C]0.746646[/C][/ROW]
[ROW][C]102[/C][C]0.161105[/C][C]0.322211[/C][C]0.838895[/C][/ROW]
[ROW][C]103[/C][C]0.0987528[/C][C]0.197506[/C][C]0.901247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&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
90.7942750.411450.205725
100.8644670.2710650.135533
110.7827730.4344530.217227
120.6873160.6253680.312684
130.6465870.7068270.353413
140.5969070.8061870.403093
150.6632170.6735660.336783
160.6269840.7460320.373016
170.6030540.7938930.396946
180.518540.9629210.48146
190.4492540.8985070.550746
200.3895470.7790930.610453
210.4863940.9727890.513606
220.4143740.8287490.585626
230.3850760.7701510.614924
240.5254230.9491540.474577
250.4535670.9071340.546433
260.3826760.7653510.617324
270.3161060.6322120.683894
280.2767250.553450.723275
290.2232770.4465530.776723
300.2153230.4306460.784677
310.175370.3507410.82463
320.1484570.2969130.851543
330.1157240.2314490.884276
340.09714460.1942890.902855
350.07921530.1584310.920785
360.1091790.2183590.890821
370.2826180.5652350.717382
380.2771120.5542230.722888
390.2353380.4706760.764662
400.2840350.568070.715965
410.2376250.475250.762375
420.2081920.4163830.791808
430.1982960.3965920.801704
440.2126980.4253950.787302
450.2089480.4178960.791052
460.4327770.8655530.567223
470.4087710.8175420.591229
480.3567720.7135450.643228
490.5402270.9195450.459773
500.4833330.9666660.516667
510.4275750.8551490.572425
520.4023420.8046850.597658
530.3958360.7916720.604164
540.5151010.9697980.484899
550.6409840.7180330.359016
560.5874860.8250280.412514
570.5554060.8891890.444594
580.5155590.9688830.484441
590.5545450.890910.445455
600.5515070.8969860.448493
610.497370.9947390.50263
620.490420.9808390.50958
630.5676170.8647670.432383
640.5796930.8406130.420307
650.547850.9043010.45215
660.5045890.9908220.495411
670.4475290.8950570.552471
680.3984520.7969040.601548
690.3463110.6926210.653689
700.3423670.6847340.657633
710.3768090.7536180.623191
720.3320340.6640680.667966
730.3433620.6867240.656638
740.4516240.9032470.548376
750.596880.8062390.40312
760.5771220.8457560.422878
770.5224020.9551950.477598
780.4709120.9418240.529088
790.4375220.8750440.562478
800.4177910.8355810.582209
810.3748870.7497740.625113
820.3174280.6348560.682572
830.3497590.6995190.650241
840.3180560.6361120.681944
850.3499810.6999630.650019
860.3415340.6830690.658466
870.3524910.7049810.647509
880.4535520.9071050.546448
890.4243970.8487950.575603
900.3578860.7157730.642114
910.2956280.5912560.704372
920.2496050.4992110.750395
930.319680.6393590.68032
940.2484090.4968170.751591
950.2209790.4419580.779021
960.1824860.3649720.817514
970.1450570.2901140.854943
980.1252780.2505560.874722
990.0955140.1910280.904486
1000.2761150.552230.723885
1010.2533540.5067070.746646
1020.1611050.3222110.838895
1030.09875280.1975060.901247






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\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 & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=266766&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=266766&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=266766&T=6

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Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

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Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$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')
}