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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 16 Dec 2014 07:48:12 +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/16/t1418716197wb50s28rhug9vdx.htm/, Retrieved Thu, 31 Oct 2024 23:29:13 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269150, Retrieved Thu, 31 Oct 2024 23:29:13 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact149
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-16 07:48:12] [860910a2400ea2aea496b5f7252c36a0] [Current]
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Dataseries X:
52	23
16	22
46	21
56	25
52	30
55	17
50	27
59	23
60	23
52	18
44	18
67	23
52	19
55	15
37	20
54	16
72	24
51	25
48	25
60	19
50	19
63	16
33	19
67	19
46	23
54	21
59	22
61	19
33	20
47	20
69	3
52	23
55	23
41	20
73	15
52	16
50	7
51	24
60	17
56	24
56	24
29	19
66	25
66	20
73	28
55	23
64	27
40	18
46	28
58	21
43	19
61	23
51	27
50	22
52	28
54	25
66	21
61	22
80	28
51	20
56	29
56	25
56	25
53	20
47	20
25	16
47	20
46	20
50	23
39	18
51	25
58	18
35	19
58	25
60	25
62	25
63	24
53	19
46	26
67	10
59	17
64	13
38	17
50	30
48	25
48	4
47	16
66	21
47	23
63	22
58	17
44	20
51	20
43	22
55	16
38	23
45	0
50	18
54	25
57	23
60	12
55	18
56	24
49	11
37	18
59	23
46	24
51	29
58	18
64	15
53	29
48	16
51	19
47	22
59	16
62	23
62	23
51	19
64	4
52	20
67	24
50	20
54	4
58	24
56	22
63	16
31	3
65	15
71	24
50	17
57	20
47	27
47	26
57	23
43	17
41	20
63	22
63	19
56	24
51	19
50	23
22	15
41	27
59	26
56	22
66	22
53	18
42	15
52	22
54	27
44	10
62	20
53	17
50	23
36	19
76	13
66	27
62	23
59	16
47	25
55	2
58	26
60	20
44	23
57	22




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
AMS.I[t] = + 49.9607 + 0.161184Numeracytot[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
AMS.I[t] =  +  49.9607 +  0.161184Numeracytot[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]AMS.I[t] =  +  49.9607 +  0.161184Numeracytot[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
AMS.I[t] = + 49.9607 + 0.161184Numeracytot[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)49.96072.9665816.841.64121e-378.20604e-38
Numeracytot0.1611840.141081.1430.2549190.127459

\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) & 49.9607 & 2.96658 & 16.84 & 1.64121e-37 & 8.20604e-38 \tabularnewline
Numeracytot & 0.161184 & 0.14108 & 1.143 & 0.254919 & 0.127459 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&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]49.9607[/C][C]2.96658[/C][C]16.84[/C][C]1.64121e-37[/C][C]8.20604e-38[/C][/ROW]
[ROW][C]Numeracytot[/C][C]0.161184[/C][C]0.14108[/C][C]1.143[/C][C]0.254919[/C][C]0.127459[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=2

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)49.96072.9665816.841.64121e-378.20604e-38
Numeracytot0.1611840.141081.1430.2549190.127459







Multiple Linear Regression - Regression Statistics
Multiple R0.0891319
R-squared0.0079445
Adjusted R-squared0.00185827
F-TEST (value)1.30532
F-TEST (DF numerator)1
F-TEST (DF denominator)163
p-value0.254919
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.0391
Sum Squared Residuals16427.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.0891319 \tabularnewline
R-squared & 0.0079445 \tabularnewline
Adjusted R-squared & 0.00185827 \tabularnewline
F-TEST (value) & 1.30532 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 163 \tabularnewline
p-value & 0.254919 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 10.0391 \tabularnewline
Sum Squared Residuals & 16427.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.0891319[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0079445[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.00185827[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.30532[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]163[/C][/ROW]
[ROW][C]p-value[/C][C]0.254919[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]10.0391[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]16427.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=3

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Regression Statistics
Multiple R0.0891319
R-squared0.0079445
Adjusted R-squared0.00185827
F-TEST (value)1.30532
F-TEST (DF numerator)1
F-TEST (DF denominator)163
p-value0.254919
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation10.0391
Sum Squared Residuals16427.7







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15253.6679-1.66794
21653.5068-37.5068
34653.3456-7.34557
45653.99032.00969
55254.7962-2.79623
65552.70082.29916
75054.3127-4.31268
85953.66795.33206
96053.66796.33206
105252.862-0.862021
114452.862-8.86202
126753.667913.3321
135253.0232-1.02321
145552.37852.62153
153753.1844-16.1844
165452.53971.46035
177253.829118.1709
185153.9903-2.99031
194853.9903-5.99031
206053.02326.97679
215053.0232-3.02321
226352.539710.4603
233353.0232-20.0232
246753.023213.9768
254653.6679-7.66794
265453.34560.654426
275953.50685.49324
286153.02327.97679
293353.1844-20.1844
304753.1844-6.18439
316950.444318.5557
325253.6679-1.66794
335553.66791.33206
344153.1844-12.1844
357352.378520.6215
365252.5397-0.539652
375051.089-1.08899
385153.8291-2.82913
396052.70087.29916
405653.82912.17087
415653.82912.17087
422953.0232-24.0232
436653.990312.0097
446653.184412.8156
457354.473918.5261
465553.66791.33206
476454.31279.68732
484052.862-12.862
494654.4739-8.47387
505853.34564.65443
514353.0232-10.0232
526153.66797.33206
535154.3127-3.31268
545053.5068-3.50676
555254.4739-2.47387
565453.99030.00968766
576653.345612.6544
586153.50687.49324
598054.473925.5261
605153.1844-2.18439
615654.63511.36495
625653.99032.00969
635653.99032.00969
645353.1844-0.18439
654753.1844-6.18439
662552.5397-27.5397
674753.1844-6.18439
684653.1844-7.18439
695053.6679-3.66794
703952.862-13.862
715153.9903-2.99031
725852.8625.13798
733553.0232-18.0232
745853.99034.00969
756053.99036.00969
766253.99038.00969
776353.82919.17087
785353.0232-0.0232054
794654.1515-8.1515
806751.572515.4275
815952.70086.29916
826452.056111.9439
833852.7008-14.7008
845054.7962-4.79623
854853.9903-5.99031
864850.6054-2.60544
874752.5397-5.53965
886653.345612.6544
894753.6679-6.66794
906353.50689.49324
915852.70085.29916
924453.1844-9.18439
935153.1844-2.18439
944353.5068-10.5068
955552.53972.46035
963853.6679-15.6679
974549.9607-4.9607
985052.862-2.86202
995453.99030.00968766
1005753.66793.33206
1016051.89498.10509
1025552.8622.13798
1035653.82912.17087
1044951.7337-2.73373
1053752.862-15.862
1065953.66795.33206
1074653.8291-7.82913
1085154.6351-3.63505
1095852.8625.13798
1106452.378511.6215
1115354.6351-1.63505
1124852.5397-4.53965
1135153.0232-2.02321
1144753.5068-6.50676
1155952.53976.46035
1166253.66798.33206
1176253.66798.33206
1185153.0232-2.02321
1196450.605413.3946
1205253.1844-1.18439
1216753.829113.1709
1225053.1844-3.18439
1235450.60543.39456
1245853.82914.17087
1255653.50682.49324
1266352.539710.4603
1273150.4443-19.4443
1286552.378512.6215
1297153.829117.1709
1305052.7008-2.70084
1315753.18443.81561
1324754.3127-7.31268
1334754.1515-7.1515
1345753.66793.33206
1354352.7008-9.70084
1364153.1844-12.1844
1376353.50689.49324
1386353.02329.97679
1395653.82912.17087
1405153.0232-2.02321
1415053.6679-3.66794
1422252.3785-30.3785
1434154.3127-13.3127
1445954.15154.8485
1455653.50682.49324
1466653.506812.4932
1475352.8620.137979
1484252.3785-10.3785
1495253.5068-1.50676
1505454.3127-0.312681
1514451.5725-7.57254
1526253.18448.81561
1535352.70080.299164
1545053.6679-3.66794
1553653.0232-17.0232
1567652.056123.9439
1576654.312711.6873
1586253.66798.33206
1595952.53976.46035
1604753.9903-6.99031
1615550.28314.71693
1625854.15153.8485
1636053.18446.81561
1644453.6679-9.66794
1655753.50683.49324

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 52 & 53.6679 & -1.66794 \tabularnewline
2 & 16 & 53.5068 & -37.5068 \tabularnewline
3 & 46 & 53.3456 & -7.34557 \tabularnewline
4 & 56 & 53.9903 & 2.00969 \tabularnewline
5 & 52 & 54.7962 & -2.79623 \tabularnewline
6 & 55 & 52.7008 & 2.29916 \tabularnewline
7 & 50 & 54.3127 & -4.31268 \tabularnewline
8 & 59 & 53.6679 & 5.33206 \tabularnewline
9 & 60 & 53.6679 & 6.33206 \tabularnewline
10 & 52 & 52.862 & -0.862021 \tabularnewline
11 & 44 & 52.862 & -8.86202 \tabularnewline
12 & 67 & 53.6679 & 13.3321 \tabularnewline
13 & 52 & 53.0232 & -1.02321 \tabularnewline
14 & 55 & 52.3785 & 2.62153 \tabularnewline
15 & 37 & 53.1844 & -16.1844 \tabularnewline
16 & 54 & 52.5397 & 1.46035 \tabularnewline
17 & 72 & 53.8291 & 18.1709 \tabularnewline
18 & 51 & 53.9903 & -2.99031 \tabularnewline
19 & 48 & 53.9903 & -5.99031 \tabularnewline
20 & 60 & 53.0232 & 6.97679 \tabularnewline
21 & 50 & 53.0232 & -3.02321 \tabularnewline
22 & 63 & 52.5397 & 10.4603 \tabularnewline
23 & 33 & 53.0232 & -20.0232 \tabularnewline
24 & 67 & 53.0232 & 13.9768 \tabularnewline
25 & 46 & 53.6679 & -7.66794 \tabularnewline
26 & 54 & 53.3456 & 0.654426 \tabularnewline
27 & 59 & 53.5068 & 5.49324 \tabularnewline
28 & 61 & 53.0232 & 7.97679 \tabularnewline
29 & 33 & 53.1844 & -20.1844 \tabularnewline
30 & 47 & 53.1844 & -6.18439 \tabularnewline
31 & 69 & 50.4443 & 18.5557 \tabularnewline
32 & 52 & 53.6679 & -1.66794 \tabularnewline
33 & 55 & 53.6679 & 1.33206 \tabularnewline
34 & 41 & 53.1844 & -12.1844 \tabularnewline
35 & 73 & 52.3785 & 20.6215 \tabularnewline
36 & 52 & 52.5397 & -0.539652 \tabularnewline
37 & 50 & 51.089 & -1.08899 \tabularnewline
38 & 51 & 53.8291 & -2.82913 \tabularnewline
39 & 60 & 52.7008 & 7.29916 \tabularnewline
40 & 56 & 53.8291 & 2.17087 \tabularnewline
41 & 56 & 53.8291 & 2.17087 \tabularnewline
42 & 29 & 53.0232 & -24.0232 \tabularnewline
43 & 66 & 53.9903 & 12.0097 \tabularnewline
44 & 66 & 53.1844 & 12.8156 \tabularnewline
45 & 73 & 54.4739 & 18.5261 \tabularnewline
46 & 55 & 53.6679 & 1.33206 \tabularnewline
47 & 64 & 54.3127 & 9.68732 \tabularnewline
48 & 40 & 52.862 & -12.862 \tabularnewline
49 & 46 & 54.4739 & -8.47387 \tabularnewline
50 & 58 & 53.3456 & 4.65443 \tabularnewline
51 & 43 & 53.0232 & -10.0232 \tabularnewline
52 & 61 & 53.6679 & 7.33206 \tabularnewline
53 & 51 & 54.3127 & -3.31268 \tabularnewline
54 & 50 & 53.5068 & -3.50676 \tabularnewline
55 & 52 & 54.4739 & -2.47387 \tabularnewline
56 & 54 & 53.9903 & 0.00968766 \tabularnewline
57 & 66 & 53.3456 & 12.6544 \tabularnewline
58 & 61 & 53.5068 & 7.49324 \tabularnewline
59 & 80 & 54.4739 & 25.5261 \tabularnewline
60 & 51 & 53.1844 & -2.18439 \tabularnewline
61 & 56 & 54.6351 & 1.36495 \tabularnewline
62 & 56 & 53.9903 & 2.00969 \tabularnewline
63 & 56 & 53.9903 & 2.00969 \tabularnewline
64 & 53 & 53.1844 & -0.18439 \tabularnewline
65 & 47 & 53.1844 & -6.18439 \tabularnewline
66 & 25 & 52.5397 & -27.5397 \tabularnewline
67 & 47 & 53.1844 & -6.18439 \tabularnewline
68 & 46 & 53.1844 & -7.18439 \tabularnewline
69 & 50 & 53.6679 & -3.66794 \tabularnewline
70 & 39 & 52.862 & -13.862 \tabularnewline
71 & 51 & 53.9903 & -2.99031 \tabularnewline
72 & 58 & 52.862 & 5.13798 \tabularnewline
73 & 35 & 53.0232 & -18.0232 \tabularnewline
74 & 58 & 53.9903 & 4.00969 \tabularnewline
75 & 60 & 53.9903 & 6.00969 \tabularnewline
76 & 62 & 53.9903 & 8.00969 \tabularnewline
77 & 63 & 53.8291 & 9.17087 \tabularnewline
78 & 53 & 53.0232 & -0.0232054 \tabularnewline
79 & 46 & 54.1515 & -8.1515 \tabularnewline
80 & 67 & 51.5725 & 15.4275 \tabularnewline
81 & 59 & 52.7008 & 6.29916 \tabularnewline
82 & 64 & 52.0561 & 11.9439 \tabularnewline
83 & 38 & 52.7008 & -14.7008 \tabularnewline
84 & 50 & 54.7962 & -4.79623 \tabularnewline
85 & 48 & 53.9903 & -5.99031 \tabularnewline
86 & 48 & 50.6054 & -2.60544 \tabularnewline
87 & 47 & 52.5397 & -5.53965 \tabularnewline
88 & 66 & 53.3456 & 12.6544 \tabularnewline
89 & 47 & 53.6679 & -6.66794 \tabularnewline
90 & 63 & 53.5068 & 9.49324 \tabularnewline
91 & 58 & 52.7008 & 5.29916 \tabularnewline
92 & 44 & 53.1844 & -9.18439 \tabularnewline
93 & 51 & 53.1844 & -2.18439 \tabularnewline
94 & 43 & 53.5068 & -10.5068 \tabularnewline
95 & 55 & 52.5397 & 2.46035 \tabularnewline
96 & 38 & 53.6679 & -15.6679 \tabularnewline
97 & 45 & 49.9607 & -4.9607 \tabularnewline
98 & 50 & 52.862 & -2.86202 \tabularnewline
99 & 54 & 53.9903 & 0.00968766 \tabularnewline
100 & 57 & 53.6679 & 3.33206 \tabularnewline
101 & 60 & 51.8949 & 8.10509 \tabularnewline
102 & 55 & 52.862 & 2.13798 \tabularnewline
103 & 56 & 53.8291 & 2.17087 \tabularnewline
104 & 49 & 51.7337 & -2.73373 \tabularnewline
105 & 37 & 52.862 & -15.862 \tabularnewline
106 & 59 & 53.6679 & 5.33206 \tabularnewline
107 & 46 & 53.8291 & -7.82913 \tabularnewline
108 & 51 & 54.6351 & -3.63505 \tabularnewline
109 & 58 & 52.862 & 5.13798 \tabularnewline
110 & 64 & 52.3785 & 11.6215 \tabularnewline
111 & 53 & 54.6351 & -1.63505 \tabularnewline
112 & 48 & 52.5397 & -4.53965 \tabularnewline
113 & 51 & 53.0232 & -2.02321 \tabularnewline
114 & 47 & 53.5068 & -6.50676 \tabularnewline
115 & 59 & 52.5397 & 6.46035 \tabularnewline
116 & 62 & 53.6679 & 8.33206 \tabularnewline
117 & 62 & 53.6679 & 8.33206 \tabularnewline
118 & 51 & 53.0232 & -2.02321 \tabularnewline
119 & 64 & 50.6054 & 13.3946 \tabularnewline
120 & 52 & 53.1844 & -1.18439 \tabularnewline
121 & 67 & 53.8291 & 13.1709 \tabularnewline
122 & 50 & 53.1844 & -3.18439 \tabularnewline
123 & 54 & 50.6054 & 3.39456 \tabularnewline
124 & 58 & 53.8291 & 4.17087 \tabularnewline
125 & 56 & 53.5068 & 2.49324 \tabularnewline
126 & 63 & 52.5397 & 10.4603 \tabularnewline
127 & 31 & 50.4443 & -19.4443 \tabularnewline
128 & 65 & 52.3785 & 12.6215 \tabularnewline
129 & 71 & 53.8291 & 17.1709 \tabularnewline
130 & 50 & 52.7008 & -2.70084 \tabularnewline
131 & 57 & 53.1844 & 3.81561 \tabularnewline
132 & 47 & 54.3127 & -7.31268 \tabularnewline
133 & 47 & 54.1515 & -7.1515 \tabularnewline
134 & 57 & 53.6679 & 3.33206 \tabularnewline
135 & 43 & 52.7008 & -9.70084 \tabularnewline
136 & 41 & 53.1844 & -12.1844 \tabularnewline
137 & 63 & 53.5068 & 9.49324 \tabularnewline
138 & 63 & 53.0232 & 9.97679 \tabularnewline
139 & 56 & 53.8291 & 2.17087 \tabularnewline
140 & 51 & 53.0232 & -2.02321 \tabularnewline
141 & 50 & 53.6679 & -3.66794 \tabularnewline
142 & 22 & 52.3785 & -30.3785 \tabularnewline
143 & 41 & 54.3127 & -13.3127 \tabularnewline
144 & 59 & 54.1515 & 4.8485 \tabularnewline
145 & 56 & 53.5068 & 2.49324 \tabularnewline
146 & 66 & 53.5068 & 12.4932 \tabularnewline
147 & 53 & 52.862 & 0.137979 \tabularnewline
148 & 42 & 52.3785 & -10.3785 \tabularnewline
149 & 52 & 53.5068 & -1.50676 \tabularnewline
150 & 54 & 54.3127 & -0.312681 \tabularnewline
151 & 44 & 51.5725 & -7.57254 \tabularnewline
152 & 62 & 53.1844 & 8.81561 \tabularnewline
153 & 53 & 52.7008 & 0.299164 \tabularnewline
154 & 50 & 53.6679 & -3.66794 \tabularnewline
155 & 36 & 53.0232 & -17.0232 \tabularnewline
156 & 76 & 52.0561 & 23.9439 \tabularnewline
157 & 66 & 54.3127 & 11.6873 \tabularnewline
158 & 62 & 53.6679 & 8.33206 \tabularnewline
159 & 59 & 52.5397 & 6.46035 \tabularnewline
160 & 47 & 53.9903 & -6.99031 \tabularnewline
161 & 55 & 50.2831 & 4.71693 \tabularnewline
162 & 58 & 54.1515 & 3.8485 \tabularnewline
163 & 60 & 53.1844 & 6.81561 \tabularnewline
164 & 44 & 53.6679 & -9.66794 \tabularnewline
165 & 57 & 53.5068 & 3.49324 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&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]52[/C][C]53.6679[/C][C]-1.66794[/C][/ROW]
[ROW][C]2[/C][C]16[/C][C]53.5068[/C][C]-37.5068[/C][/ROW]
[ROW][C]3[/C][C]46[/C][C]53.3456[/C][C]-7.34557[/C][/ROW]
[ROW][C]4[/C][C]56[/C][C]53.9903[/C][C]2.00969[/C][/ROW]
[ROW][C]5[/C][C]52[/C][C]54.7962[/C][C]-2.79623[/C][/ROW]
[ROW][C]6[/C][C]55[/C][C]52.7008[/C][C]2.29916[/C][/ROW]
[ROW][C]7[/C][C]50[/C][C]54.3127[/C][C]-4.31268[/C][/ROW]
[ROW][C]8[/C][C]59[/C][C]53.6679[/C][C]5.33206[/C][/ROW]
[ROW][C]9[/C][C]60[/C][C]53.6679[/C][C]6.33206[/C][/ROW]
[ROW][C]10[/C][C]52[/C][C]52.862[/C][C]-0.862021[/C][/ROW]
[ROW][C]11[/C][C]44[/C][C]52.862[/C][C]-8.86202[/C][/ROW]
[ROW][C]12[/C][C]67[/C][C]53.6679[/C][C]13.3321[/C][/ROW]
[ROW][C]13[/C][C]52[/C][C]53.0232[/C][C]-1.02321[/C][/ROW]
[ROW][C]14[/C][C]55[/C][C]52.3785[/C][C]2.62153[/C][/ROW]
[ROW][C]15[/C][C]37[/C][C]53.1844[/C][C]-16.1844[/C][/ROW]
[ROW][C]16[/C][C]54[/C][C]52.5397[/C][C]1.46035[/C][/ROW]
[ROW][C]17[/C][C]72[/C][C]53.8291[/C][C]18.1709[/C][/ROW]
[ROW][C]18[/C][C]51[/C][C]53.9903[/C][C]-2.99031[/C][/ROW]
[ROW][C]19[/C][C]48[/C][C]53.9903[/C][C]-5.99031[/C][/ROW]
[ROW][C]20[/C][C]60[/C][C]53.0232[/C][C]6.97679[/C][/ROW]
[ROW][C]21[/C][C]50[/C][C]53.0232[/C][C]-3.02321[/C][/ROW]
[ROW][C]22[/C][C]63[/C][C]52.5397[/C][C]10.4603[/C][/ROW]
[ROW][C]23[/C][C]33[/C][C]53.0232[/C][C]-20.0232[/C][/ROW]
[ROW][C]24[/C][C]67[/C][C]53.0232[/C][C]13.9768[/C][/ROW]
[ROW][C]25[/C][C]46[/C][C]53.6679[/C][C]-7.66794[/C][/ROW]
[ROW][C]26[/C][C]54[/C][C]53.3456[/C][C]0.654426[/C][/ROW]
[ROW][C]27[/C][C]59[/C][C]53.5068[/C][C]5.49324[/C][/ROW]
[ROW][C]28[/C][C]61[/C][C]53.0232[/C][C]7.97679[/C][/ROW]
[ROW][C]29[/C][C]33[/C][C]53.1844[/C][C]-20.1844[/C][/ROW]
[ROW][C]30[/C][C]47[/C][C]53.1844[/C][C]-6.18439[/C][/ROW]
[ROW][C]31[/C][C]69[/C][C]50.4443[/C][C]18.5557[/C][/ROW]
[ROW][C]32[/C][C]52[/C][C]53.6679[/C][C]-1.66794[/C][/ROW]
[ROW][C]33[/C][C]55[/C][C]53.6679[/C][C]1.33206[/C][/ROW]
[ROW][C]34[/C][C]41[/C][C]53.1844[/C][C]-12.1844[/C][/ROW]
[ROW][C]35[/C][C]73[/C][C]52.3785[/C][C]20.6215[/C][/ROW]
[ROW][C]36[/C][C]52[/C][C]52.5397[/C][C]-0.539652[/C][/ROW]
[ROW][C]37[/C][C]50[/C][C]51.089[/C][C]-1.08899[/C][/ROW]
[ROW][C]38[/C][C]51[/C][C]53.8291[/C][C]-2.82913[/C][/ROW]
[ROW][C]39[/C][C]60[/C][C]52.7008[/C][C]7.29916[/C][/ROW]
[ROW][C]40[/C][C]56[/C][C]53.8291[/C][C]2.17087[/C][/ROW]
[ROW][C]41[/C][C]56[/C][C]53.8291[/C][C]2.17087[/C][/ROW]
[ROW][C]42[/C][C]29[/C][C]53.0232[/C][C]-24.0232[/C][/ROW]
[ROW][C]43[/C][C]66[/C][C]53.9903[/C][C]12.0097[/C][/ROW]
[ROW][C]44[/C][C]66[/C][C]53.1844[/C][C]12.8156[/C][/ROW]
[ROW][C]45[/C][C]73[/C][C]54.4739[/C][C]18.5261[/C][/ROW]
[ROW][C]46[/C][C]55[/C][C]53.6679[/C][C]1.33206[/C][/ROW]
[ROW][C]47[/C][C]64[/C][C]54.3127[/C][C]9.68732[/C][/ROW]
[ROW][C]48[/C][C]40[/C][C]52.862[/C][C]-12.862[/C][/ROW]
[ROW][C]49[/C][C]46[/C][C]54.4739[/C][C]-8.47387[/C][/ROW]
[ROW][C]50[/C][C]58[/C][C]53.3456[/C][C]4.65443[/C][/ROW]
[ROW][C]51[/C][C]43[/C][C]53.0232[/C][C]-10.0232[/C][/ROW]
[ROW][C]52[/C][C]61[/C][C]53.6679[/C][C]7.33206[/C][/ROW]
[ROW][C]53[/C][C]51[/C][C]54.3127[/C][C]-3.31268[/C][/ROW]
[ROW][C]54[/C][C]50[/C][C]53.5068[/C][C]-3.50676[/C][/ROW]
[ROW][C]55[/C][C]52[/C][C]54.4739[/C][C]-2.47387[/C][/ROW]
[ROW][C]56[/C][C]54[/C][C]53.9903[/C][C]0.00968766[/C][/ROW]
[ROW][C]57[/C][C]66[/C][C]53.3456[/C][C]12.6544[/C][/ROW]
[ROW][C]58[/C][C]61[/C][C]53.5068[/C][C]7.49324[/C][/ROW]
[ROW][C]59[/C][C]80[/C][C]54.4739[/C][C]25.5261[/C][/ROW]
[ROW][C]60[/C][C]51[/C][C]53.1844[/C][C]-2.18439[/C][/ROW]
[ROW][C]61[/C][C]56[/C][C]54.6351[/C][C]1.36495[/C][/ROW]
[ROW][C]62[/C][C]56[/C][C]53.9903[/C][C]2.00969[/C][/ROW]
[ROW][C]63[/C][C]56[/C][C]53.9903[/C][C]2.00969[/C][/ROW]
[ROW][C]64[/C][C]53[/C][C]53.1844[/C][C]-0.18439[/C][/ROW]
[ROW][C]65[/C][C]47[/C][C]53.1844[/C][C]-6.18439[/C][/ROW]
[ROW][C]66[/C][C]25[/C][C]52.5397[/C][C]-27.5397[/C][/ROW]
[ROW][C]67[/C][C]47[/C][C]53.1844[/C][C]-6.18439[/C][/ROW]
[ROW][C]68[/C][C]46[/C][C]53.1844[/C][C]-7.18439[/C][/ROW]
[ROW][C]69[/C][C]50[/C][C]53.6679[/C][C]-3.66794[/C][/ROW]
[ROW][C]70[/C][C]39[/C][C]52.862[/C][C]-13.862[/C][/ROW]
[ROW][C]71[/C][C]51[/C][C]53.9903[/C][C]-2.99031[/C][/ROW]
[ROW][C]72[/C][C]58[/C][C]52.862[/C][C]5.13798[/C][/ROW]
[ROW][C]73[/C][C]35[/C][C]53.0232[/C][C]-18.0232[/C][/ROW]
[ROW][C]74[/C][C]58[/C][C]53.9903[/C][C]4.00969[/C][/ROW]
[ROW][C]75[/C][C]60[/C][C]53.9903[/C][C]6.00969[/C][/ROW]
[ROW][C]76[/C][C]62[/C][C]53.9903[/C][C]8.00969[/C][/ROW]
[ROW][C]77[/C][C]63[/C][C]53.8291[/C][C]9.17087[/C][/ROW]
[ROW][C]78[/C][C]53[/C][C]53.0232[/C][C]-0.0232054[/C][/ROW]
[ROW][C]79[/C][C]46[/C][C]54.1515[/C][C]-8.1515[/C][/ROW]
[ROW][C]80[/C][C]67[/C][C]51.5725[/C][C]15.4275[/C][/ROW]
[ROW][C]81[/C][C]59[/C][C]52.7008[/C][C]6.29916[/C][/ROW]
[ROW][C]82[/C][C]64[/C][C]52.0561[/C][C]11.9439[/C][/ROW]
[ROW][C]83[/C][C]38[/C][C]52.7008[/C][C]-14.7008[/C][/ROW]
[ROW][C]84[/C][C]50[/C][C]54.7962[/C][C]-4.79623[/C][/ROW]
[ROW][C]85[/C][C]48[/C][C]53.9903[/C][C]-5.99031[/C][/ROW]
[ROW][C]86[/C][C]48[/C][C]50.6054[/C][C]-2.60544[/C][/ROW]
[ROW][C]87[/C][C]47[/C][C]52.5397[/C][C]-5.53965[/C][/ROW]
[ROW][C]88[/C][C]66[/C][C]53.3456[/C][C]12.6544[/C][/ROW]
[ROW][C]89[/C][C]47[/C][C]53.6679[/C][C]-6.66794[/C][/ROW]
[ROW][C]90[/C][C]63[/C][C]53.5068[/C][C]9.49324[/C][/ROW]
[ROW][C]91[/C][C]58[/C][C]52.7008[/C][C]5.29916[/C][/ROW]
[ROW][C]92[/C][C]44[/C][C]53.1844[/C][C]-9.18439[/C][/ROW]
[ROW][C]93[/C][C]51[/C][C]53.1844[/C][C]-2.18439[/C][/ROW]
[ROW][C]94[/C][C]43[/C][C]53.5068[/C][C]-10.5068[/C][/ROW]
[ROW][C]95[/C][C]55[/C][C]52.5397[/C][C]2.46035[/C][/ROW]
[ROW][C]96[/C][C]38[/C][C]53.6679[/C][C]-15.6679[/C][/ROW]
[ROW][C]97[/C][C]45[/C][C]49.9607[/C][C]-4.9607[/C][/ROW]
[ROW][C]98[/C][C]50[/C][C]52.862[/C][C]-2.86202[/C][/ROW]
[ROW][C]99[/C][C]54[/C][C]53.9903[/C][C]0.00968766[/C][/ROW]
[ROW][C]100[/C][C]57[/C][C]53.6679[/C][C]3.33206[/C][/ROW]
[ROW][C]101[/C][C]60[/C][C]51.8949[/C][C]8.10509[/C][/ROW]
[ROW][C]102[/C][C]55[/C][C]52.862[/C][C]2.13798[/C][/ROW]
[ROW][C]103[/C][C]56[/C][C]53.8291[/C][C]2.17087[/C][/ROW]
[ROW][C]104[/C][C]49[/C][C]51.7337[/C][C]-2.73373[/C][/ROW]
[ROW][C]105[/C][C]37[/C][C]52.862[/C][C]-15.862[/C][/ROW]
[ROW][C]106[/C][C]59[/C][C]53.6679[/C][C]5.33206[/C][/ROW]
[ROW][C]107[/C][C]46[/C][C]53.8291[/C][C]-7.82913[/C][/ROW]
[ROW][C]108[/C][C]51[/C][C]54.6351[/C][C]-3.63505[/C][/ROW]
[ROW][C]109[/C][C]58[/C][C]52.862[/C][C]5.13798[/C][/ROW]
[ROW][C]110[/C][C]64[/C][C]52.3785[/C][C]11.6215[/C][/ROW]
[ROW][C]111[/C][C]53[/C][C]54.6351[/C][C]-1.63505[/C][/ROW]
[ROW][C]112[/C][C]48[/C][C]52.5397[/C][C]-4.53965[/C][/ROW]
[ROW][C]113[/C][C]51[/C][C]53.0232[/C][C]-2.02321[/C][/ROW]
[ROW][C]114[/C][C]47[/C][C]53.5068[/C][C]-6.50676[/C][/ROW]
[ROW][C]115[/C][C]59[/C][C]52.5397[/C][C]6.46035[/C][/ROW]
[ROW][C]116[/C][C]62[/C][C]53.6679[/C][C]8.33206[/C][/ROW]
[ROW][C]117[/C][C]62[/C][C]53.6679[/C][C]8.33206[/C][/ROW]
[ROW][C]118[/C][C]51[/C][C]53.0232[/C][C]-2.02321[/C][/ROW]
[ROW][C]119[/C][C]64[/C][C]50.6054[/C][C]13.3946[/C][/ROW]
[ROW][C]120[/C][C]52[/C][C]53.1844[/C][C]-1.18439[/C][/ROW]
[ROW][C]121[/C][C]67[/C][C]53.8291[/C][C]13.1709[/C][/ROW]
[ROW][C]122[/C][C]50[/C][C]53.1844[/C][C]-3.18439[/C][/ROW]
[ROW][C]123[/C][C]54[/C][C]50.6054[/C][C]3.39456[/C][/ROW]
[ROW][C]124[/C][C]58[/C][C]53.8291[/C][C]4.17087[/C][/ROW]
[ROW][C]125[/C][C]56[/C][C]53.5068[/C][C]2.49324[/C][/ROW]
[ROW][C]126[/C][C]63[/C][C]52.5397[/C][C]10.4603[/C][/ROW]
[ROW][C]127[/C][C]31[/C][C]50.4443[/C][C]-19.4443[/C][/ROW]
[ROW][C]128[/C][C]65[/C][C]52.3785[/C][C]12.6215[/C][/ROW]
[ROW][C]129[/C][C]71[/C][C]53.8291[/C][C]17.1709[/C][/ROW]
[ROW][C]130[/C][C]50[/C][C]52.7008[/C][C]-2.70084[/C][/ROW]
[ROW][C]131[/C][C]57[/C][C]53.1844[/C][C]3.81561[/C][/ROW]
[ROW][C]132[/C][C]47[/C][C]54.3127[/C][C]-7.31268[/C][/ROW]
[ROW][C]133[/C][C]47[/C][C]54.1515[/C][C]-7.1515[/C][/ROW]
[ROW][C]134[/C][C]57[/C][C]53.6679[/C][C]3.33206[/C][/ROW]
[ROW][C]135[/C][C]43[/C][C]52.7008[/C][C]-9.70084[/C][/ROW]
[ROW][C]136[/C][C]41[/C][C]53.1844[/C][C]-12.1844[/C][/ROW]
[ROW][C]137[/C][C]63[/C][C]53.5068[/C][C]9.49324[/C][/ROW]
[ROW][C]138[/C][C]63[/C][C]53.0232[/C][C]9.97679[/C][/ROW]
[ROW][C]139[/C][C]56[/C][C]53.8291[/C][C]2.17087[/C][/ROW]
[ROW][C]140[/C][C]51[/C][C]53.0232[/C][C]-2.02321[/C][/ROW]
[ROW][C]141[/C][C]50[/C][C]53.6679[/C][C]-3.66794[/C][/ROW]
[ROW][C]142[/C][C]22[/C][C]52.3785[/C][C]-30.3785[/C][/ROW]
[ROW][C]143[/C][C]41[/C][C]54.3127[/C][C]-13.3127[/C][/ROW]
[ROW][C]144[/C][C]59[/C][C]54.1515[/C][C]4.8485[/C][/ROW]
[ROW][C]145[/C][C]56[/C][C]53.5068[/C][C]2.49324[/C][/ROW]
[ROW][C]146[/C][C]66[/C][C]53.5068[/C][C]12.4932[/C][/ROW]
[ROW][C]147[/C][C]53[/C][C]52.862[/C][C]0.137979[/C][/ROW]
[ROW][C]148[/C][C]42[/C][C]52.3785[/C][C]-10.3785[/C][/ROW]
[ROW][C]149[/C][C]52[/C][C]53.5068[/C][C]-1.50676[/C][/ROW]
[ROW][C]150[/C][C]54[/C][C]54.3127[/C][C]-0.312681[/C][/ROW]
[ROW][C]151[/C][C]44[/C][C]51.5725[/C][C]-7.57254[/C][/ROW]
[ROW][C]152[/C][C]62[/C][C]53.1844[/C][C]8.81561[/C][/ROW]
[ROW][C]153[/C][C]53[/C][C]52.7008[/C][C]0.299164[/C][/ROW]
[ROW][C]154[/C][C]50[/C][C]53.6679[/C][C]-3.66794[/C][/ROW]
[ROW][C]155[/C][C]36[/C][C]53.0232[/C][C]-17.0232[/C][/ROW]
[ROW][C]156[/C][C]76[/C][C]52.0561[/C][C]23.9439[/C][/ROW]
[ROW][C]157[/C][C]66[/C][C]54.3127[/C][C]11.6873[/C][/ROW]
[ROW][C]158[/C][C]62[/C][C]53.6679[/C][C]8.33206[/C][/ROW]
[ROW][C]159[/C][C]59[/C][C]52.5397[/C][C]6.46035[/C][/ROW]
[ROW][C]160[/C][C]47[/C][C]53.9903[/C][C]-6.99031[/C][/ROW]
[ROW][C]161[/C][C]55[/C][C]50.2831[/C][C]4.71693[/C][/ROW]
[ROW][C]162[/C][C]58[/C][C]54.1515[/C][C]3.8485[/C][/ROW]
[ROW][C]163[/C][C]60[/C][C]53.1844[/C][C]6.81561[/C][/ROW]
[ROW][C]164[/C][C]44[/C][C]53.6679[/C][C]-9.66794[/C][/ROW]
[ROW][C]165[/C][C]57[/C][C]53.5068[/C][C]3.49324[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=4

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
15253.6679-1.66794
21653.5068-37.5068
34653.3456-7.34557
45653.99032.00969
55254.7962-2.79623
65552.70082.29916
75054.3127-4.31268
85953.66795.33206
96053.66796.33206
105252.862-0.862021
114452.862-8.86202
126753.667913.3321
135253.0232-1.02321
145552.37852.62153
153753.1844-16.1844
165452.53971.46035
177253.829118.1709
185153.9903-2.99031
194853.9903-5.99031
206053.02326.97679
215053.0232-3.02321
226352.539710.4603
233353.0232-20.0232
246753.023213.9768
254653.6679-7.66794
265453.34560.654426
275953.50685.49324
286153.02327.97679
293353.1844-20.1844
304753.1844-6.18439
316950.444318.5557
325253.6679-1.66794
335553.66791.33206
344153.1844-12.1844
357352.378520.6215
365252.5397-0.539652
375051.089-1.08899
385153.8291-2.82913
396052.70087.29916
405653.82912.17087
415653.82912.17087
422953.0232-24.0232
436653.990312.0097
446653.184412.8156
457354.473918.5261
465553.66791.33206
476454.31279.68732
484052.862-12.862
494654.4739-8.47387
505853.34564.65443
514353.0232-10.0232
526153.66797.33206
535154.3127-3.31268
545053.5068-3.50676
555254.4739-2.47387
565453.99030.00968766
576653.345612.6544
586153.50687.49324
598054.473925.5261
605153.1844-2.18439
615654.63511.36495
625653.99032.00969
635653.99032.00969
645353.1844-0.18439
654753.1844-6.18439
662552.5397-27.5397
674753.1844-6.18439
684653.1844-7.18439
695053.6679-3.66794
703952.862-13.862
715153.9903-2.99031
725852.8625.13798
733553.0232-18.0232
745853.99034.00969
756053.99036.00969
766253.99038.00969
776353.82919.17087
785353.0232-0.0232054
794654.1515-8.1515
806751.572515.4275
815952.70086.29916
826452.056111.9439
833852.7008-14.7008
845054.7962-4.79623
854853.9903-5.99031
864850.6054-2.60544
874752.5397-5.53965
886653.345612.6544
894753.6679-6.66794
906353.50689.49324
915852.70085.29916
924453.1844-9.18439
935153.1844-2.18439
944353.5068-10.5068
955552.53972.46035
963853.6679-15.6679
974549.9607-4.9607
985052.862-2.86202
995453.99030.00968766
1005753.66793.33206
1016051.89498.10509
1025552.8622.13798
1035653.82912.17087
1044951.7337-2.73373
1053752.862-15.862
1065953.66795.33206
1074653.8291-7.82913
1085154.6351-3.63505
1095852.8625.13798
1106452.378511.6215
1115354.6351-1.63505
1124852.5397-4.53965
1135153.0232-2.02321
1144753.5068-6.50676
1155952.53976.46035
1166253.66798.33206
1176253.66798.33206
1185153.0232-2.02321
1196450.605413.3946
1205253.1844-1.18439
1216753.829113.1709
1225053.1844-3.18439
1235450.60543.39456
1245853.82914.17087
1255653.50682.49324
1266352.539710.4603
1273150.4443-19.4443
1286552.378512.6215
1297153.829117.1709
1305052.7008-2.70084
1315753.18443.81561
1324754.3127-7.31268
1334754.1515-7.1515
1345753.66793.33206
1354352.7008-9.70084
1364153.1844-12.1844
1376353.50689.49324
1386353.02329.97679
1395653.82912.17087
1405153.0232-2.02321
1415053.6679-3.66794
1422252.3785-30.3785
1434154.3127-13.3127
1445954.15154.8485
1455653.50682.49324
1466653.506812.4932
1475352.8620.137979
1484252.3785-10.3785
1495253.5068-1.50676
1505454.3127-0.312681
1514451.5725-7.57254
1526253.18448.81561
1535352.70080.299164
1545053.6679-3.66794
1553653.0232-17.0232
1567652.056123.9439
1576654.312711.6873
1586253.66798.33206
1595952.53976.46035
1604753.9903-6.99031
1615550.28314.71693
1625854.15153.8485
1636053.18446.81561
1644453.6679-9.66794
1655753.50683.49324







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.9683990.06320240.0316012
60.9794020.04119650.0205983
70.9595350.08093020.0404651
80.9559670.08806520.0440326
90.9514560.09708740.0485437
100.9243740.1512510.0756256
110.8926750.2146510.107325
120.9310920.1378170.0689085
130.8994940.2010110.100506
140.8685380.2629250.131462
150.8902320.2195350.109768
160.8565460.2869080.143454
170.9318160.1363680.068184
180.9057870.1884270.0942135
190.8794970.2410060.120503
200.8661840.2676320.133816
210.8271050.345790.172895
220.8316160.3367670.168384
230.9040480.1919040.0959518
240.9265820.1468370.0734185
250.9115580.1768850.0884424
260.8853010.2293970.114699
270.8661610.2676790.133839
280.8537380.2925250.146262
290.9183150.163370.0816849
300.9004650.1990690.0995346
310.9199640.1600710.0800356
320.897370.205260.10263
330.8731410.2537190.126859
340.8799420.2401170.120058
350.9298420.1403150.0701577
360.9112930.1774130.0887066
370.9001840.1996320.0998159
380.8759150.2481690.124085
390.8591930.2816150.140807
400.8336520.3326970.166348
410.8050550.3898890.194945
420.9219050.156190.0780952
430.9354170.1291670.0645834
440.9437010.1125980.0562991
450.9726770.05464670.0273233
460.9642570.07148560.0357428
470.964160.07168070.0358403
480.9694370.06112610.0305631
490.9655430.06891440.0344572
500.9576040.08479210.0423961
510.957070.08586050.0429302
520.9517970.09640690.0482034
530.9397950.1204090.0602046
540.9261220.1477570.0738784
550.9090030.1819930.0909966
560.8884310.2231370.111569
570.8994210.2011580.100579
580.8895640.2208710.110436
590.9666720.06665570.0333278
600.9577530.08449370.0422468
610.9465770.1068460.0534229
620.9334730.1330540.0665268
630.9180460.1639090.0819544
640.8994180.2011640.100582
650.8865020.2269960.113498
660.972520.05496050.0274802
670.9676110.06477860.0323893
680.9631720.07365520.0368276
690.9545350.09093060.0454653
700.9626710.07465850.0373292
710.9535430.09291330.0464567
720.9450680.1098630.0549316
730.9669690.06606280.0330314
740.9592920.08141610.040708
750.9524060.09518890.0475944
760.9479520.1040950.0520475
770.9457580.1084850.0542424
780.9323010.1353970.0676986
790.9274180.1451630.0725816
800.9471560.1056880.0528438
810.939430.1211390.0605696
820.9447460.1105080.0552541
830.957810.08438030.0421901
840.9494940.1010120.0505059
850.9416950.1166090.0583047
860.9284050.1431910.0715954
870.9172020.1655960.0827981
880.9267470.1465050.0732527
890.9178420.1643160.0821578
900.9158940.1682120.084106
910.9025330.1949340.0974671
920.8996880.2006240.100312
930.8793880.2412240.120612
940.8825220.2349570.117478
950.8599030.2801930.140097
960.8958440.2083120.104156
970.8792390.2415220.120761
980.8572040.2855910.142796
990.8297120.3405750.170288
1000.8018350.3963310.198165
1010.7897220.4205560.210278
1020.7560470.4879060.243953
1030.7195130.5609740.280487
1040.6823330.6353330.317667
1050.7464910.5070180.253509
1060.7171660.5656680.282834
1070.7046660.5906680.295334
1080.6707390.6585220.329261
1090.6364990.7270020.363501
1100.6485850.702830.351415
1110.6062690.7874620.393731
1120.570310.8593790.42969
1130.52550.9490.4745
1140.5018950.9962110.498105
1150.470620.9412410.52938
1160.4496840.8993670.550316
1170.4293170.8586340.570683
1180.3840010.7680020.615999
1190.4301870.8603740.569813
1200.3825220.7650440.617478
1210.4068410.8136830.593159
1220.3634640.7269290.636536
1230.3297830.6595660.670217
1240.2896020.5792030.710398
1250.2485920.4971830.751408
1260.2552180.5104360.744782
1270.3487670.6975350.651233
1280.3750540.7501090.624946
1290.4739840.9479680.526016
1300.4222510.8445030.577749
1310.37750.7549990.6225
1320.3499150.6998310.650085
1330.3238640.6477270.676136
1340.2789470.5578950.721053
1350.2703490.5406990.729651
1360.2906950.5813890.709305
1370.2794980.5589950.720502
1380.2756940.5513870.724306
1390.2289540.4579080.771046
1400.1858140.3716290.814186
1410.1508690.3017380.849131
1420.6094550.7810890.390545
1430.6774530.6450940.322547
1440.6201080.7597840.379892
1450.5511210.8977580.448879
1460.5727620.8544760.427238
1470.4987880.9975750.501212
1480.536450.92710.46355
1490.4637440.9274880.536256
1500.3849220.7698450.615078
1510.4154330.8308650.584567
1520.3641610.7283220.635839
1530.2898030.5796060.710197
1540.2324090.4648170.767591
1550.5274860.9450280.472514
1560.8055640.3888710.194436
1570.8411010.3177970.158899
1580.8264850.3470310.173515
1590.7429210.5141580.257079
1600.6733970.6532050.326603

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 & 0.968399 & 0.0632024 & 0.0316012 \tabularnewline
6 & 0.979402 & 0.0411965 & 0.0205983 \tabularnewline
7 & 0.959535 & 0.0809302 & 0.0404651 \tabularnewline
8 & 0.955967 & 0.0880652 & 0.0440326 \tabularnewline
9 & 0.951456 & 0.0970874 & 0.0485437 \tabularnewline
10 & 0.924374 & 0.151251 & 0.0756256 \tabularnewline
11 & 0.892675 & 0.214651 & 0.107325 \tabularnewline
12 & 0.931092 & 0.137817 & 0.0689085 \tabularnewline
13 & 0.899494 & 0.201011 & 0.100506 \tabularnewline
14 & 0.868538 & 0.262925 & 0.131462 \tabularnewline
15 & 0.890232 & 0.219535 & 0.109768 \tabularnewline
16 & 0.856546 & 0.286908 & 0.143454 \tabularnewline
17 & 0.931816 & 0.136368 & 0.068184 \tabularnewline
18 & 0.905787 & 0.188427 & 0.0942135 \tabularnewline
19 & 0.879497 & 0.241006 & 0.120503 \tabularnewline
20 & 0.866184 & 0.267632 & 0.133816 \tabularnewline
21 & 0.827105 & 0.34579 & 0.172895 \tabularnewline
22 & 0.831616 & 0.336767 & 0.168384 \tabularnewline
23 & 0.904048 & 0.191904 & 0.0959518 \tabularnewline
24 & 0.926582 & 0.146837 & 0.0734185 \tabularnewline
25 & 0.911558 & 0.176885 & 0.0884424 \tabularnewline
26 & 0.885301 & 0.229397 & 0.114699 \tabularnewline
27 & 0.866161 & 0.267679 & 0.133839 \tabularnewline
28 & 0.853738 & 0.292525 & 0.146262 \tabularnewline
29 & 0.918315 & 0.16337 & 0.0816849 \tabularnewline
30 & 0.900465 & 0.199069 & 0.0995346 \tabularnewline
31 & 0.919964 & 0.160071 & 0.0800356 \tabularnewline
32 & 0.89737 & 0.20526 & 0.10263 \tabularnewline
33 & 0.873141 & 0.253719 & 0.126859 \tabularnewline
34 & 0.879942 & 0.240117 & 0.120058 \tabularnewline
35 & 0.929842 & 0.140315 & 0.0701577 \tabularnewline
36 & 0.911293 & 0.177413 & 0.0887066 \tabularnewline
37 & 0.900184 & 0.199632 & 0.0998159 \tabularnewline
38 & 0.875915 & 0.248169 & 0.124085 \tabularnewline
39 & 0.859193 & 0.281615 & 0.140807 \tabularnewline
40 & 0.833652 & 0.332697 & 0.166348 \tabularnewline
41 & 0.805055 & 0.389889 & 0.194945 \tabularnewline
42 & 0.921905 & 0.15619 & 0.0780952 \tabularnewline
43 & 0.935417 & 0.129167 & 0.0645834 \tabularnewline
44 & 0.943701 & 0.112598 & 0.0562991 \tabularnewline
45 & 0.972677 & 0.0546467 & 0.0273233 \tabularnewline
46 & 0.964257 & 0.0714856 & 0.0357428 \tabularnewline
47 & 0.96416 & 0.0716807 & 0.0358403 \tabularnewline
48 & 0.969437 & 0.0611261 & 0.0305631 \tabularnewline
49 & 0.965543 & 0.0689144 & 0.0344572 \tabularnewline
50 & 0.957604 & 0.0847921 & 0.0423961 \tabularnewline
51 & 0.95707 & 0.0858605 & 0.0429302 \tabularnewline
52 & 0.951797 & 0.0964069 & 0.0482034 \tabularnewline
53 & 0.939795 & 0.120409 & 0.0602046 \tabularnewline
54 & 0.926122 & 0.147757 & 0.0738784 \tabularnewline
55 & 0.909003 & 0.181993 & 0.0909966 \tabularnewline
56 & 0.888431 & 0.223137 & 0.111569 \tabularnewline
57 & 0.899421 & 0.201158 & 0.100579 \tabularnewline
58 & 0.889564 & 0.220871 & 0.110436 \tabularnewline
59 & 0.966672 & 0.0666557 & 0.0333278 \tabularnewline
60 & 0.957753 & 0.0844937 & 0.0422468 \tabularnewline
61 & 0.946577 & 0.106846 & 0.0534229 \tabularnewline
62 & 0.933473 & 0.133054 & 0.0665268 \tabularnewline
63 & 0.918046 & 0.163909 & 0.0819544 \tabularnewline
64 & 0.899418 & 0.201164 & 0.100582 \tabularnewline
65 & 0.886502 & 0.226996 & 0.113498 \tabularnewline
66 & 0.97252 & 0.0549605 & 0.0274802 \tabularnewline
67 & 0.967611 & 0.0647786 & 0.0323893 \tabularnewline
68 & 0.963172 & 0.0736552 & 0.0368276 \tabularnewline
69 & 0.954535 & 0.0909306 & 0.0454653 \tabularnewline
70 & 0.962671 & 0.0746585 & 0.0373292 \tabularnewline
71 & 0.953543 & 0.0929133 & 0.0464567 \tabularnewline
72 & 0.945068 & 0.109863 & 0.0549316 \tabularnewline
73 & 0.966969 & 0.0660628 & 0.0330314 \tabularnewline
74 & 0.959292 & 0.0814161 & 0.040708 \tabularnewline
75 & 0.952406 & 0.0951889 & 0.0475944 \tabularnewline
76 & 0.947952 & 0.104095 & 0.0520475 \tabularnewline
77 & 0.945758 & 0.108485 & 0.0542424 \tabularnewline
78 & 0.932301 & 0.135397 & 0.0676986 \tabularnewline
79 & 0.927418 & 0.145163 & 0.0725816 \tabularnewline
80 & 0.947156 & 0.105688 & 0.0528438 \tabularnewline
81 & 0.93943 & 0.121139 & 0.0605696 \tabularnewline
82 & 0.944746 & 0.110508 & 0.0552541 \tabularnewline
83 & 0.95781 & 0.0843803 & 0.0421901 \tabularnewline
84 & 0.949494 & 0.101012 & 0.0505059 \tabularnewline
85 & 0.941695 & 0.116609 & 0.0583047 \tabularnewline
86 & 0.928405 & 0.143191 & 0.0715954 \tabularnewline
87 & 0.917202 & 0.165596 & 0.0827981 \tabularnewline
88 & 0.926747 & 0.146505 & 0.0732527 \tabularnewline
89 & 0.917842 & 0.164316 & 0.0821578 \tabularnewline
90 & 0.915894 & 0.168212 & 0.084106 \tabularnewline
91 & 0.902533 & 0.194934 & 0.0974671 \tabularnewline
92 & 0.899688 & 0.200624 & 0.100312 \tabularnewline
93 & 0.879388 & 0.241224 & 0.120612 \tabularnewline
94 & 0.882522 & 0.234957 & 0.117478 \tabularnewline
95 & 0.859903 & 0.280193 & 0.140097 \tabularnewline
96 & 0.895844 & 0.208312 & 0.104156 \tabularnewline
97 & 0.879239 & 0.241522 & 0.120761 \tabularnewline
98 & 0.857204 & 0.285591 & 0.142796 \tabularnewline
99 & 0.829712 & 0.340575 & 0.170288 \tabularnewline
100 & 0.801835 & 0.396331 & 0.198165 \tabularnewline
101 & 0.789722 & 0.420556 & 0.210278 \tabularnewline
102 & 0.756047 & 0.487906 & 0.243953 \tabularnewline
103 & 0.719513 & 0.560974 & 0.280487 \tabularnewline
104 & 0.682333 & 0.635333 & 0.317667 \tabularnewline
105 & 0.746491 & 0.507018 & 0.253509 \tabularnewline
106 & 0.717166 & 0.565668 & 0.282834 \tabularnewline
107 & 0.704666 & 0.590668 & 0.295334 \tabularnewline
108 & 0.670739 & 0.658522 & 0.329261 \tabularnewline
109 & 0.636499 & 0.727002 & 0.363501 \tabularnewline
110 & 0.648585 & 0.70283 & 0.351415 \tabularnewline
111 & 0.606269 & 0.787462 & 0.393731 \tabularnewline
112 & 0.57031 & 0.859379 & 0.42969 \tabularnewline
113 & 0.5255 & 0.949 & 0.4745 \tabularnewline
114 & 0.501895 & 0.996211 & 0.498105 \tabularnewline
115 & 0.47062 & 0.941241 & 0.52938 \tabularnewline
116 & 0.449684 & 0.899367 & 0.550316 \tabularnewline
117 & 0.429317 & 0.858634 & 0.570683 \tabularnewline
118 & 0.384001 & 0.768002 & 0.615999 \tabularnewline
119 & 0.430187 & 0.860374 & 0.569813 \tabularnewline
120 & 0.382522 & 0.765044 & 0.617478 \tabularnewline
121 & 0.406841 & 0.813683 & 0.593159 \tabularnewline
122 & 0.363464 & 0.726929 & 0.636536 \tabularnewline
123 & 0.329783 & 0.659566 & 0.670217 \tabularnewline
124 & 0.289602 & 0.579203 & 0.710398 \tabularnewline
125 & 0.248592 & 0.497183 & 0.751408 \tabularnewline
126 & 0.255218 & 0.510436 & 0.744782 \tabularnewline
127 & 0.348767 & 0.697535 & 0.651233 \tabularnewline
128 & 0.375054 & 0.750109 & 0.624946 \tabularnewline
129 & 0.473984 & 0.947968 & 0.526016 \tabularnewline
130 & 0.422251 & 0.844503 & 0.577749 \tabularnewline
131 & 0.3775 & 0.754999 & 0.6225 \tabularnewline
132 & 0.349915 & 0.699831 & 0.650085 \tabularnewline
133 & 0.323864 & 0.647727 & 0.676136 \tabularnewline
134 & 0.278947 & 0.557895 & 0.721053 \tabularnewline
135 & 0.270349 & 0.540699 & 0.729651 \tabularnewline
136 & 0.290695 & 0.581389 & 0.709305 \tabularnewline
137 & 0.279498 & 0.558995 & 0.720502 \tabularnewline
138 & 0.275694 & 0.551387 & 0.724306 \tabularnewline
139 & 0.228954 & 0.457908 & 0.771046 \tabularnewline
140 & 0.185814 & 0.371629 & 0.814186 \tabularnewline
141 & 0.150869 & 0.301738 & 0.849131 \tabularnewline
142 & 0.609455 & 0.781089 & 0.390545 \tabularnewline
143 & 0.677453 & 0.645094 & 0.322547 \tabularnewline
144 & 0.620108 & 0.759784 & 0.379892 \tabularnewline
145 & 0.551121 & 0.897758 & 0.448879 \tabularnewline
146 & 0.572762 & 0.854476 & 0.427238 \tabularnewline
147 & 0.498788 & 0.997575 & 0.501212 \tabularnewline
148 & 0.53645 & 0.9271 & 0.46355 \tabularnewline
149 & 0.463744 & 0.927488 & 0.536256 \tabularnewline
150 & 0.384922 & 0.769845 & 0.615078 \tabularnewline
151 & 0.415433 & 0.830865 & 0.584567 \tabularnewline
152 & 0.364161 & 0.728322 & 0.635839 \tabularnewline
153 & 0.289803 & 0.579606 & 0.710197 \tabularnewline
154 & 0.232409 & 0.464817 & 0.767591 \tabularnewline
155 & 0.527486 & 0.945028 & 0.472514 \tabularnewline
156 & 0.805564 & 0.388871 & 0.194436 \tabularnewline
157 & 0.841101 & 0.317797 & 0.158899 \tabularnewline
158 & 0.826485 & 0.347031 & 0.173515 \tabularnewline
159 & 0.742921 & 0.514158 & 0.257079 \tabularnewline
160 & 0.673397 & 0.653205 & 0.326603 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&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]5[/C][C]0.968399[/C][C]0.0632024[/C][C]0.0316012[/C][/ROW]
[ROW][C]6[/C][C]0.979402[/C][C]0.0411965[/C][C]0.0205983[/C][/ROW]
[ROW][C]7[/C][C]0.959535[/C][C]0.0809302[/C][C]0.0404651[/C][/ROW]
[ROW][C]8[/C][C]0.955967[/C][C]0.0880652[/C][C]0.0440326[/C][/ROW]
[ROW][C]9[/C][C]0.951456[/C][C]0.0970874[/C][C]0.0485437[/C][/ROW]
[ROW][C]10[/C][C]0.924374[/C][C]0.151251[/C][C]0.0756256[/C][/ROW]
[ROW][C]11[/C][C]0.892675[/C][C]0.214651[/C][C]0.107325[/C][/ROW]
[ROW][C]12[/C][C]0.931092[/C][C]0.137817[/C][C]0.0689085[/C][/ROW]
[ROW][C]13[/C][C]0.899494[/C][C]0.201011[/C][C]0.100506[/C][/ROW]
[ROW][C]14[/C][C]0.868538[/C][C]0.262925[/C][C]0.131462[/C][/ROW]
[ROW][C]15[/C][C]0.890232[/C][C]0.219535[/C][C]0.109768[/C][/ROW]
[ROW][C]16[/C][C]0.856546[/C][C]0.286908[/C][C]0.143454[/C][/ROW]
[ROW][C]17[/C][C]0.931816[/C][C]0.136368[/C][C]0.068184[/C][/ROW]
[ROW][C]18[/C][C]0.905787[/C][C]0.188427[/C][C]0.0942135[/C][/ROW]
[ROW][C]19[/C][C]0.879497[/C][C]0.241006[/C][C]0.120503[/C][/ROW]
[ROW][C]20[/C][C]0.866184[/C][C]0.267632[/C][C]0.133816[/C][/ROW]
[ROW][C]21[/C][C]0.827105[/C][C]0.34579[/C][C]0.172895[/C][/ROW]
[ROW][C]22[/C][C]0.831616[/C][C]0.336767[/C][C]0.168384[/C][/ROW]
[ROW][C]23[/C][C]0.904048[/C][C]0.191904[/C][C]0.0959518[/C][/ROW]
[ROW][C]24[/C][C]0.926582[/C][C]0.146837[/C][C]0.0734185[/C][/ROW]
[ROW][C]25[/C][C]0.911558[/C][C]0.176885[/C][C]0.0884424[/C][/ROW]
[ROW][C]26[/C][C]0.885301[/C][C]0.229397[/C][C]0.114699[/C][/ROW]
[ROW][C]27[/C][C]0.866161[/C][C]0.267679[/C][C]0.133839[/C][/ROW]
[ROW][C]28[/C][C]0.853738[/C][C]0.292525[/C][C]0.146262[/C][/ROW]
[ROW][C]29[/C][C]0.918315[/C][C]0.16337[/C][C]0.0816849[/C][/ROW]
[ROW][C]30[/C][C]0.900465[/C][C]0.199069[/C][C]0.0995346[/C][/ROW]
[ROW][C]31[/C][C]0.919964[/C][C]0.160071[/C][C]0.0800356[/C][/ROW]
[ROW][C]32[/C][C]0.89737[/C][C]0.20526[/C][C]0.10263[/C][/ROW]
[ROW][C]33[/C][C]0.873141[/C][C]0.253719[/C][C]0.126859[/C][/ROW]
[ROW][C]34[/C][C]0.879942[/C][C]0.240117[/C][C]0.120058[/C][/ROW]
[ROW][C]35[/C][C]0.929842[/C][C]0.140315[/C][C]0.0701577[/C][/ROW]
[ROW][C]36[/C][C]0.911293[/C][C]0.177413[/C][C]0.0887066[/C][/ROW]
[ROW][C]37[/C][C]0.900184[/C][C]0.199632[/C][C]0.0998159[/C][/ROW]
[ROW][C]38[/C][C]0.875915[/C][C]0.248169[/C][C]0.124085[/C][/ROW]
[ROW][C]39[/C][C]0.859193[/C][C]0.281615[/C][C]0.140807[/C][/ROW]
[ROW][C]40[/C][C]0.833652[/C][C]0.332697[/C][C]0.166348[/C][/ROW]
[ROW][C]41[/C][C]0.805055[/C][C]0.389889[/C][C]0.194945[/C][/ROW]
[ROW][C]42[/C][C]0.921905[/C][C]0.15619[/C][C]0.0780952[/C][/ROW]
[ROW][C]43[/C][C]0.935417[/C][C]0.129167[/C][C]0.0645834[/C][/ROW]
[ROW][C]44[/C][C]0.943701[/C][C]0.112598[/C][C]0.0562991[/C][/ROW]
[ROW][C]45[/C][C]0.972677[/C][C]0.0546467[/C][C]0.0273233[/C][/ROW]
[ROW][C]46[/C][C]0.964257[/C][C]0.0714856[/C][C]0.0357428[/C][/ROW]
[ROW][C]47[/C][C]0.96416[/C][C]0.0716807[/C][C]0.0358403[/C][/ROW]
[ROW][C]48[/C][C]0.969437[/C][C]0.0611261[/C][C]0.0305631[/C][/ROW]
[ROW][C]49[/C][C]0.965543[/C][C]0.0689144[/C][C]0.0344572[/C][/ROW]
[ROW][C]50[/C][C]0.957604[/C][C]0.0847921[/C][C]0.0423961[/C][/ROW]
[ROW][C]51[/C][C]0.95707[/C][C]0.0858605[/C][C]0.0429302[/C][/ROW]
[ROW][C]52[/C][C]0.951797[/C][C]0.0964069[/C][C]0.0482034[/C][/ROW]
[ROW][C]53[/C][C]0.939795[/C][C]0.120409[/C][C]0.0602046[/C][/ROW]
[ROW][C]54[/C][C]0.926122[/C][C]0.147757[/C][C]0.0738784[/C][/ROW]
[ROW][C]55[/C][C]0.909003[/C][C]0.181993[/C][C]0.0909966[/C][/ROW]
[ROW][C]56[/C][C]0.888431[/C][C]0.223137[/C][C]0.111569[/C][/ROW]
[ROW][C]57[/C][C]0.899421[/C][C]0.201158[/C][C]0.100579[/C][/ROW]
[ROW][C]58[/C][C]0.889564[/C][C]0.220871[/C][C]0.110436[/C][/ROW]
[ROW][C]59[/C][C]0.966672[/C][C]0.0666557[/C][C]0.0333278[/C][/ROW]
[ROW][C]60[/C][C]0.957753[/C][C]0.0844937[/C][C]0.0422468[/C][/ROW]
[ROW][C]61[/C][C]0.946577[/C][C]0.106846[/C][C]0.0534229[/C][/ROW]
[ROW][C]62[/C][C]0.933473[/C][C]0.133054[/C][C]0.0665268[/C][/ROW]
[ROW][C]63[/C][C]0.918046[/C][C]0.163909[/C][C]0.0819544[/C][/ROW]
[ROW][C]64[/C][C]0.899418[/C][C]0.201164[/C][C]0.100582[/C][/ROW]
[ROW][C]65[/C][C]0.886502[/C][C]0.226996[/C][C]0.113498[/C][/ROW]
[ROW][C]66[/C][C]0.97252[/C][C]0.0549605[/C][C]0.0274802[/C][/ROW]
[ROW][C]67[/C][C]0.967611[/C][C]0.0647786[/C][C]0.0323893[/C][/ROW]
[ROW][C]68[/C][C]0.963172[/C][C]0.0736552[/C][C]0.0368276[/C][/ROW]
[ROW][C]69[/C][C]0.954535[/C][C]0.0909306[/C][C]0.0454653[/C][/ROW]
[ROW][C]70[/C][C]0.962671[/C][C]0.0746585[/C][C]0.0373292[/C][/ROW]
[ROW][C]71[/C][C]0.953543[/C][C]0.0929133[/C][C]0.0464567[/C][/ROW]
[ROW][C]72[/C][C]0.945068[/C][C]0.109863[/C][C]0.0549316[/C][/ROW]
[ROW][C]73[/C][C]0.966969[/C][C]0.0660628[/C][C]0.0330314[/C][/ROW]
[ROW][C]74[/C][C]0.959292[/C][C]0.0814161[/C][C]0.040708[/C][/ROW]
[ROW][C]75[/C][C]0.952406[/C][C]0.0951889[/C][C]0.0475944[/C][/ROW]
[ROW][C]76[/C][C]0.947952[/C][C]0.104095[/C][C]0.0520475[/C][/ROW]
[ROW][C]77[/C][C]0.945758[/C][C]0.108485[/C][C]0.0542424[/C][/ROW]
[ROW][C]78[/C][C]0.932301[/C][C]0.135397[/C][C]0.0676986[/C][/ROW]
[ROW][C]79[/C][C]0.927418[/C][C]0.145163[/C][C]0.0725816[/C][/ROW]
[ROW][C]80[/C][C]0.947156[/C][C]0.105688[/C][C]0.0528438[/C][/ROW]
[ROW][C]81[/C][C]0.93943[/C][C]0.121139[/C][C]0.0605696[/C][/ROW]
[ROW][C]82[/C][C]0.944746[/C][C]0.110508[/C][C]0.0552541[/C][/ROW]
[ROW][C]83[/C][C]0.95781[/C][C]0.0843803[/C][C]0.0421901[/C][/ROW]
[ROW][C]84[/C][C]0.949494[/C][C]0.101012[/C][C]0.0505059[/C][/ROW]
[ROW][C]85[/C][C]0.941695[/C][C]0.116609[/C][C]0.0583047[/C][/ROW]
[ROW][C]86[/C][C]0.928405[/C][C]0.143191[/C][C]0.0715954[/C][/ROW]
[ROW][C]87[/C][C]0.917202[/C][C]0.165596[/C][C]0.0827981[/C][/ROW]
[ROW][C]88[/C][C]0.926747[/C][C]0.146505[/C][C]0.0732527[/C][/ROW]
[ROW][C]89[/C][C]0.917842[/C][C]0.164316[/C][C]0.0821578[/C][/ROW]
[ROW][C]90[/C][C]0.915894[/C][C]0.168212[/C][C]0.084106[/C][/ROW]
[ROW][C]91[/C][C]0.902533[/C][C]0.194934[/C][C]0.0974671[/C][/ROW]
[ROW][C]92[/C][C]0.899688[/C][C]0.200624[/C][C]0.100312[/C][/ROW]
[ROW][C]93[/C][C]0.879388[/C][C]0.241224[/C][C]0.120612[/C][/ROW]
[ROW][C]94[/C][C]0.882522[/C][C]0.234957[/C][C]0.117478[/C][/ROW]
[ROW][C]95[/C][C]0.859903[/C][C]0.280193[/C][C]0.140097[/C][/ROW]
[ROW][C]96[/C][C]0.895844[/C][C]0.208312[/C][C]0.104156[/C][/ROW]
[ROW][C]97[/C][C]0.879239[/C][C]0.241522[/C][C]0.120761[/C][/ROW]
[ROW][C]98[/C][C]0.857204[/C][C]0.285591[/C][C]0.142796[/C][/ROW]
[ROW][C]99[/C][C]0.829712[/C][C]0.340575[/C][C]0.170288[/C][/ROW]
[ROW][C]100[/C][C]0.801835[/C][C]0.396331[/C][C]0.198165[/C][/ROW]
[ROW][C]101[/C][C]0.789722[/C][C]0.420556[/C][C]0.210278[/C][/ROW]
[ROW][C]102[/C][C]0.756047[/C][C]0.487906[/C][C]0.243953[/C][/ROW]
[ROW][C]103[/C][C]0.719513[/C][C]0.560974[/C][C]0.280487[/C][/ROW]
[ROW][C]104[/C][C]0.682333[/C][C]0.635333[/C][C]0.317667[/C][/ROW]
[ROW][C]105[/C][C]0.746491[/C][C]0.507018[/C][C]0.253509[/C][/ROW]
[ROW][C]106[/C][C]0.717166[/C][C]0.565668[/C][C]0.282834[/C][/ROW]
[ROW][C]107[/C][C]0.704666[/C][C]0.590668[/C][C]0.295334[/C][/ROW]
[ROW][C]108[/C][C]0.670739[/C][C]0.658522[/C][C]0.329261[/C][/ROW]
[ROW][C]109[/C][C]0.636499[/C][C]0.727002[/C][C]0.363501[/C][/ROW]
[ROW][C]110[/C][C]0.648585[/C][C]0.70283[/C][C]0.351415[/C][/ROW]
[ROW][C]111[/C][C]0.606269[/C][C]0.787462[/C][C]0.393731[/C][/ROW]
[ROW][C]112[/C][C]0.57031[/C][C]0.859379[/C][C]0.42969[/C][/ROW]
[ROW][C]113[/C][C]0.5255[/C][C]0.949[/C][C]0.4745[/C][/ROW]
[ROW][C]114[/C][C]0.501895[/C][C]0.996211[/C][C]0.498105[/C][/ROW]
[ROW][C]115[/C][C]0.47062[/C][C]0.941241[/C][C]0.52938[/C][/ROW]
[ROW][C]116[/C][C]0.449684[/C][C]0.899367[/C][C]0.550316[/C][/ROW]
[ROW][C]117[/C][C]0.429317[/C][C]0.858634[/C][C]0.570683[/C][/ROW]
[ROW][C]118[/C][C]0.384001[/C][C]0.768002[/C][C]0.615999[/C][/ROW]
[ROW][C]119[/C][C]0.430187[/C][C]0.860374[/C][C]0.569813[/C][/ROW]
[ROW][C]120[/C][C]0.382522[/C][C]0.765044[/C][C]0.617478[/C][/ROW]
[ROW][C]121[/C][C]0.406841[/C][C]0.813683[/C][C]0.593159[/C][/ROW]
[ROW][C]122[/C][C]0.363464[/C][C]0.726929[/C][C]0.636536[/C][/ROW]
[ROW][C]123[/C][C]0.329783[/C][C]0.659566[/C][C]0.670217[/C][/ROW]
[ROW][C]124[/C][C]0.289602[/C][C]0.579203[/C][C]0.710398[/C][/ROW]
[ROW][C]125[/C][C]0.248592[/C][C]0.497183[/C][C]0.751408[/C][/ROW]
[ROW][C]126[/C][C]0.255218[/C][C]0.510436[/C][C]0.744782[/C][/ROW]
[ROW][C]127[/C][C]0.348767[/C][C]0.697535[/C][C]0.651233[/C][/ROW]
[ROW][C]128[/C][C]0.375054[/C][C]0.750109[/C][C]0.624946[/C][/ROW]
[ROW][C]129[/C][C]0.473984[/C][C]0.947968[/C][C]0.526016[/C][/ROW]
[ROW][C]130[/C][C]0.422251[/C][C]0.844503[/C][C]0.577749[/C][/ROW]
[ROW][C]131[/C][C]0.3775[/C][C]0.754999[/C][C]0.6225[/C][/ROW]
[ROW][C]132[/C][C]0.349915[/C][C]0.699831[/C][C]0.650085[/C][/ROW]
[ROW][C]133[/C][C]0.323864[/C][C]0.647727[/C][C]0.676136[/C][/ROW]
[ROW][C]134[/C][C]0.278947[/C][C]0.557895[/C][C]0.721053[/C][/ROW]
[ROW][C]135[/C][C]0.270349[/C][C]0.540699[/C][C]0.729651[/C][/ROW]
[ROW][C]136[/C][C]0.290695[/C][C]0.581389[/C][C]0.709305[/C][/ROW]
[ROW][C]137[/C][C]0.279498[/C][C]0.558995[/C][C]0.720502[/C][/ROW]
[ROW][C]138[/C][C]0.275694[/C][C]0.551387[/C][C]0.724306[/C][/ROW]
[ROW][C]139[/C][C]0.228954[/C][C]0.457908[/C][C]0.771046[/C][/ROW]
[ROW][C]140[/C][C]0.185814[/C][C]0.371629[/C][C]0.814186[/C][/ROW]
[ROW][C]141[/C][C]0.150869[/C][C]0.301738[/C][C]0.849131[/C][/ROW]
[ROW][C]142[/C][C]0.609455[/C][C]0.781089[/C][C]0.390545[/C][/ROW]
[ROW][C]143[/C][C]0.677453[/C][C]0.645094[/C][C]0.322547[/C][/ROW]
[ROW][C]144[/C][C]0.620108[/C][C]0.759784[/C][C]0.379892[/C][/ROW]
[ROW][C]145[/C][C]0.551121[/C][C]0.897758[/C][C]0.448879[/C][/ROW]
[ROW][C]146[/C][C]0.572762[/C][C]0.854476[/C][C]0.427238[/C][/ROW]
[ROW][C]147[/C][C]0.498788[/C][C]0.997575[/C][C]0.501212[/C][/ROW]
[ROW][C]148[/C][C]0.53645[/C][C]0.9271[/C][C]0.46355[/C][/ROW]
[ROW][C]149[/C][C]0.463744[/C][C]0.927488[/C][C]0.536256[/C][/ROW]
[ROW][C]150[/C][C]0.384922[/C][C]0.769845[/C][C]0.615078[/C][/ROW]
[ROW][C]151[/C][C]0.415433[/C][C]0.830865[/C][C]0.584567[/C][/ROW]
[ROW][C]152[/C][C]0.364161[/C][C]0.728322[/C][C]0.635839[/C][/ROW]
[ROW][C]153[/C][C]0.289803[/C][C]0.579606[/C][C]0.710197[/C][/ROW]
[ROW][C]154[/C][C]0.232409[/C][C]0.464817[/C][C]0.767591[/C][/ROW]
[ROW][C]155[/C][C]0.527486[/C][C]0.945028[/C][C]0.472514[/C][/ROW]
[ROW][C]156[/C][C]0.805564[/C][C]0.388871[/C][C]0.194436[/C][/ROW]
[ROW][C]157[/C][C]0.841101[/C][C]0.317797[/C][C]0.158899[/C][/ROW]
[ROW][C]158[/C][C]0.826485[/C][C]0.347031[/C][C]0.173515[/C][/ROW]
[ROW][C]159[/C][C]0.742921[/C][C]0.514158[/C][C]0.257079[/C][/ROW]
[ROW][C]160[/C][C]0.673397[/C][C]0.653205[/C][C]0.326603[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
50.9683990.06320240.0316012
60.9794020.04119650.0205983
70.9595350.08093020.0404651
80.9559670.08806520.0440326
90.9514560.09708740.0485437
100.9243740.1512510.0756256
110.8926750.2146510.107325
120.9310920.1378170.0689085
130.8994940.2010110.100506
140.8685380.2629250.131462
150.8902320.2195350.109768
160.8565460.2869080.143454
170.9318160.1363680.068184
180.9057870.1884270.0942135
190.8794970.2410060.120503
200.8661840.2676320.133816
210.8271050.345790.172895
220.8316160.3367670.168384
230.9040480.1919040.0959518
240.9265820.1468370.0734185
250.9115580.1768850.0884424
260.8853010.2293970.114699
270.8661610.2676790.133839
280.8537380.2925250.146262
290.9183150.163370.0816849
300.9004650.1990690.0995346
310.9199640.1600710.0800356
320.897370.205260.10263
330.8731410.2537190.126859
340.8799420.2401170.120058
350.9298420.1403150.0701577
360.9112930.1774130.0887066
370.9001840.1996320.0998159
380.8759150.2481690.124085
390.8591930.2816150.140807
400.8336520.3326970.166348
410.8050550.3898890.194945
420.9219050.156190.0780952
430.9354170.1291670.0645834
440.9437010.1125980.0562991
450.9726770.05464670.0273233
460.9642570.07148560.0357428
470.964160.07168070.0358403
480.9694370.06112610.0305631
490.9655430.06891440.0344572
500.9576040.08479210.0423961
510.957070.08586050.0429302
520.9517970.09640690.0482034
530.9397950.1204090.0602046
540.9261220.1477570.0738784
550.9090030.1819930.0909966
560.8884310.2231370.111569
570.8994210.2011580.100579
580.8895640.2208710.110436
590.9666720.06665570.0333278
600.9577530.08449370.0422468
610.9465770.1068460.0534229
620.9334730.1330540.0665268
630.9180460.1639090.0819544
640.8994180.2011640.100582
650.8865020.2269960.113498
660.972520.05496050.0274802
670.9676110.06477860.0323893
680.9631720.07365520.0368276
690.9545350.09093060.0454653
700.9626710.07465850.0373292
710.9535430.09291330.0464567
720.9450680.1098630.0549316
730.9669690.06606280.0330314
740.9592920.08141610.040708
750.9524060.09518890.0475944
760.9479520.1040950.0520475
770.9457580.1084850.0542424
780.9323010.1353970.0676986
790.9274180.1451630.0725816
800.9471560.1056880.0528438
810.939430.1211390.0605696
820.9447460.1105080.0552541
830.957810.08438030.0421901
840.9494940.1010120.0505059
850.9416950.1166090.0583047
860.9284050.1431910.0715954
870.9172020.1655960.0827981
880.9267470.1465050.0732527
890.9178420.1643160.0821578
900.9158940.1682120.084106
910.9025330.1949340.0974671
920.8996880.2006240.100312
930.8793880.2412240.120612
940.8825220.2349570.117478
950.8599030.2801930.140097
960.8958440.2083120.104156
970.8792390.2415220.120761
980.8572040.2855910.142796
990.8297120.3405750.170288
1000.8018350.3963310.198165
1010.7897220.4205560.210278
1020.7560470.4879060.243953
1030.7195130.5609740.280487
1040.6823330.6353330.317667
1050.7464910.5070180.253509
1060.7171660.5656680.282834
1070.7046660.5906680.295334
1080.6707390.6585220.329261
1090.6364990.7270020.363501
1100.6485850.702830.351415
1110.6062690.7874620.393731
1120.570310.8593790.42969
1130.52550.9490.4745
1140.5018950.9962110.498105
1150.470620.9412410.52938
1160.4496840.8993670.550316
1170.4293170.8586340.570683
1180.3840010.7680020.615999
1190.4301870.8603740.569813
1200.3825220.7650440.617478
1210.4068410.8136830.593159
1220.3634640.7269290.636536
1230.3297830.6595660.670217
1240.2896020.5792030.710398
1250.2485920.4971830.751408
1260.2552180.5104360.744782
1270.3487670.6975350.651233
1280.3750540.7501090.624946
1290.4739840.9479680.526016
1300.4222510.8445030.577749
1310.37750.7549990.6225
1320.3499150.6998310.650085
1330.3238640.6477270.676136
1340.2789470.5578950.721053
1350.2703490.5406990.729651
1360.2906950.5813890.709305
1370.2794980.5589950.720502
1380.2756940.5513870.724306
1390.2289540.4579080.771046
1400.1858140.3716290.814186
1410.1508690.3017380.849131
1420.6094550.7810890.390545
1430.6774530.6450940.322547
1440.6201080.7597840.379892
1450.5511210.8977580.448879
1460.5727620.8544760.427238
1470.4987880.9975750.501212
1480.536450.92710.46355
1490.4637440.9274880.536256
1500.3849220.7698450.615078
1510.4154330.8308650.584567
1520.3641610.7283220.635839
1530.2898030.5796060.710197
1540.2324090.4648170.767591
1550.5274860.9450280.472514
1560.8055640.3888710.194436
1570.8411010.3177970.158899
1580.8264850.3470310.173515
1590.7429210.5141580.257079
1600.6733970.6532050.326603







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00641026OK
10% type I error level250.160256NOK

\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 & 1 & 0.00641026 & OK \tabularnewline
10% type I error level & 25 & 0.160256 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269150&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]1[/C][C]0.00641026[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]25[/C][C]0.160256[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269150&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269150&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level10.00641026OK
10% type I error level250.160256NOK



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')
}