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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 computationSun, 14 Dec 2014 20:53:40 +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/14/t1418590511bojryyt1m6zp6kd.htm/, Retrieved Thu, 31 Oct 2024 23:49:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267870, Retrieved Thu, 31 Oct 2024 23:49:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [] [2014-12-14 20:53:40] [8145b3fe416df466b077d26de89041cd] [Current]
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Dataseries X:
13 50 26
13 62 57
11 54 37
14 71 67
15 54 43
14 65 52
11 73 52
13 52 43
16 84 84
14 42 67
14 66 49
15 65 70
15 78 52
13 73 58
14 75 68
11 72 62
12 66 43
14 70 56
13 61 56
12 81 74
15 71 65
15 69 63
14 71 58
14 72 57
12 68 63
12 70 53
12 68 57
15 61 51
14 67 64
16 76 53
12 70 29
12 60 54
NA 77 51
14 72 58
16 69 43
15 71 51
12 62 53
14 70 54
13 64 56
14 58 61
16 76 47
12 52 39
14 59 48
15 68 50
13 76 35
16 65 30
16 67 68
12 59 49
12 69 61
16 76 67
12 63 47
15 75 56
12 63 50
13 60 43
12 73 67
14 63 62
14 70 57
11 75 41
10 66 54
12 63 45
11 63 48
16 64 61
14 70 56
14 75 41
15 61 43
15 60 53
14 62 44
13 73 66
11 61 58
16 66 46
12 64 37
15 59 51
14 64 51
15 60 56
14 56 66
NA 66 45
13 78 37
6 53 59
12 67 42
12 59 38
14 66 66
14 68 34
15 71 53
11 66 49
13 73 55
14 72 49
16 71 59
13 59 40
14 64 58
16 66 60
11 78 63
13 68 56
13 73 54
15 62 52
12 65 34
13 68 69
12 65 32
14 60 48
14 71 67
16 65 58
15 68 57
14 64 42
13 74 64
14 69 58
15 76 66
14 68 26
12 72 61
7 67 52
12 63 51
15 59 55
12 73 50
13 66 60
11 62 56
14 69 63
13 66 61




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

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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'Sir Maurice George Kendall' @ kendall.wessa.net







Multiple Linear Regression - Estimated Regression Equation
STRESSTOT[t] = + 10.3317 + 0.0314863AMS.E[t] + 0.0182568AMS.I[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
STRESSTOT[t] =  +  10.3317 +  0.0314863AMS.E[t] +  0.0182568AMS.I[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]STRESSTOT[t] =  +  10.3317 +  0.0314863AMS.E[t] +  0.0182568AMS.I[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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
STRESSTOT[t] = + 10.3317 + 0.0314863AMS.E[t] + 0.0182568AMS.I[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.33171.643066.2886.66695e-093.33348e-09
AMS.E0.03148630.02498831.260.210320.10516
AMS.I0.01825680.01604951.1380.2577890.128895

\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) & 10.3317 & 1.64306 & 6.288 & 6.66695e-09 & 3.33348e-09 \tabularnewline
AMS.E & 0.0314863 & 0.0249883 & 1.26 & 0.21032 & 0.10516 \tabularnewline
AMS.I & 0.0182568 & 0.0160495 & 1.138 & 0.257789 & 0.128895 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&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]10.3317[/C][C]1.64306[/C][C]6.288[/C][C]6.66695e-09[/C][C]3.33348e-09[/C][/ROW]
[ROW][C]AMS.E[/C][C]0.0314863[/C][C]0.0249883[/C][C]1.26[/C][C]0.21032[/C][C]0.10516[/C][/ROW]
[ROW][C]AMS.I[/C][C]0.0182568[/C][C]0.0160495[/C][C]1.138[/C][C]0.257789[/C][C]0.128895[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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)10.33171.643066.2886.66695e-093.33348e-09
AMS.E0.03148630.02498831.260.210320.10516
AMS.I0.01825680.01604951.1380.2577890.128895







Multiple Linear Regression - Regression Statistics
Multiple R0.188499
R-squared0.0355318
Adjusted R-squared0.017996
F-TEST (value)2.02624
F-TEST (DF numerator)2
F-TEST (DF denominator)110
p-value0.136721
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.73943
Sum Squared Residuals332.818

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.188499 \tabularnewline
R-squared & 0.0355318 \tabularnewline
Adjusted R-squared & 0.017996 \tabularnewline
F-TEST (value) & 2.02624 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 110 \tabularnewline
p-value & 0.136721 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.73943 \tabularnewline
Sum Squared Residuals & 332.818 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.188499[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0355318[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.017996[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.02624[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]110[/C][/ROW]
[ROW][C]p-value[/C][C]0.136721[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.73943[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]332.818[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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 R0.188499
R-squared0.0355318
Adjusted R-squared0.017996
F-TEST (value)2.02624
F-TEST (DF numerator)2
F-TEST (DF denominator)110
p-value0.136721
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.73943
Sum Squared Residuals332.818







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11312.38070.619343
21313.3245-0.324454
31112.7074-1.70743
41413.79040.209601
51512.8172.18303
61413.32760.672371
71113.5795-2.57952
81312.7540.246004
91614.51011.48991
101412.87731.1227
111413.30430.695655
121513.65631.34375
131513.7371.26305
141313.6891-0.68906
151413.93460.0653993
161113.7306-2.7306
171213.1948-1.1948
181413.55810.441912
191313.2747-0.274711
201214.2331-2.23306
211513.75391.24611
221513.65441.3456
231413.62610.373913
241413.63930.360683
251213.6229-1.62291
261213.5033-1.50332
271213.5134-1.51337
281513.18341.81657
291413.60970.390317
301613.69222.30777
311213.0652-1.06515
321213.2067-1.20671
33NANA0.342426
341411.28932.71074
351614.49831.50171
361516.2514-1.25143
371211.52160.478426
381414.3692-0.36917
391312.27150.728464
401411.58272.41731
411616.681-0.680968
421211.06570.934316
431412.38561.61443
441515.3636-0.363612
45139.925983.07402
461613.68272.31729
471617.0839-1.08394
481213.6179-1.61789
49129.947832.05217
501617.1734-1.17337
511210.71551.28448
521516.2281-1.22814
531212.0059-0.00588606
541314.8534-1.85337
551211.44720.552776
561413.57630.423656
571416.4417-2.44167
581114.3956-3.39563
591011.1369-1.13686
601214.1916-2.19163
61118.460452.53955
621615.55810.441912
631413.44170.558333
641412.03741.96263
651513.18851.81155
661514.08710.912885
671414.8351-0.835114
681315.3112-2.31122
69118.249572.75043
701617.0223-1.02229
711210.12051.87955
721514.27790.722114
731412.24321.75678
741514.29980.700153
751414.4631-0.463099
76NANA-7.07759
771314.208-1.20803
7866.88312-0.883116
791211.61470.38529
801211.09350.906535
811412.53481.4652
821416.3043-2.30434
831515.6343-0.63429
841110.49330.506738
851310.64432.35566
861413.91960.0803706
871615.40570.594317
881310.50522.49483
891416.9378-2.93778
901616.4951-0.495115
911111.616-0.616033
921311.23321.76683
931313.999-0.999006
941515.7325-0.732453
951212.9625-0.962493
961312.09720.90283
971211.79040.209601
981411.43722.56283
991412.51341.48663
1001615.11360.886426
1011515.8301-0.830087
1021413.56310.436885
1031311.92961.07043
1041412.94741.05259
1051516.7123-1.71234
1061420.3906-6.3906
1071213.2464-1.2464
10875.193481.80652
1091213.543-1.54301
1101515.5052-0.50517
1111214.3062-2.3062
1121312.65440.345601
1131111.5234-0.523426
11414NANA
11513NANA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 13 & 12.3807 & 0.619343 \tabularnewline
2 & 13 & 13.3245 & -0.324454 \tabularnewline
3 & 11 & 12.7074 & -1.70743 \tabularnewline
4 & 14 & 13.7904 & 0.209601 \tabularnewline
5 & 15 & 12.817 & 2.18303 \tabularnewline
6 & 14 & 13.3276 & 0.672371 \tabularnewline
7 & 11 & 13.5795 & -2.57952 \tabularnewline
8 & 13 & 12.754 & 0.246004 \tabularnewline
9 & 16 & 14.5101 & 1.48991 \tabularnewline
10 & 14 & 12.8773 & 1.1227 \tabularnewline
11 & 14 & 13.3043 & 0.695655 \tabularnewline
12 & 15 & 13.6563 & 1.34375 \tabularnewline
13 & 15 & 13.737 & 1.26305 \tabularnewline
14 & 13 & 13.6891 & -0.68906 \tabularnewline
15 & 14 & 13.9346 & 0.0653993 \tabularnewline
16 & 11 & 13.7306 & -2.7306 \tabularnewline
17 & 12 & 13.1948 & -1.1948 \tabularnewline
18 & 14 & 13.5581 & 0.441912 \tabularnewline
19 & 13 & 13.2747 & -0.274711 \tabularnewline
20 & 12 & 14.2331 & -2.23306 \tabularnewline
21 & 15 & 13.7539 & 1.24611 \tabularnewline
22 & 15 & 13.6544 & 1.3456 \tabularnewline
23 & 14 & 13.6261 & 0.373913 \tabularnewline
24 & 14 & 13.6393 & 0.360683 \tabularnewline
25 & 12 & 13.6229 & -1.62291 \tabularnewline
26 & 12 & 13.5033 & -1.50332 \tabularnewline
27 & 12 & 13.5134 & -1.51337 \tabularnewline
28 & 15 & 13.1834 & 1.81657 \tabularnewline
29 & 14 & 13.6097 & 0.390317 \tabularnewline
30 & 16 & 13.6922 & 2.30777 \tabularnewline
31 & 12 & 13.0652 & -1.06515 \tabularnewline
32 & 12 & 13.2067 & -1.20671 \tabularnewline
33 & NA & NA & 0.342426 \tabularnewline
34 & 14 & 11.2893 & 2.71074 \tabularnewline
35 & 16 & 14.4983 & 1.50171 \tabularnewline
36 & 15 & 16.2514 & -1.25143 \tabularnewline
37 & 12 & 11.5216 & 0.478426 \tabularnewline
38 & 14 & 14.3692 & -0.36917 \tabularnewline
39 & 13 & 12.2715 & 0.728464 \tabularnewline
40 & 14 & 11.5827 & 2.41731 \tabularnewline
41 & 16 & 16.681 & -0.680968 \tabularnewline
42 & 12 & 11.0657 & 0.934316 \tabularnewline
43 & 14 & 12.3856 & 1.61443 \tabularnewline
44 & 15 & 15.3636 & -0.363612 \tabularnewline
45 & 13 & 9.92598 & 3.07402 \tabularnewline
46 & 16 & 13.6827 & 2.31729 \tabularnewline
47 & 16 & 17.0839 & -1.08394 \tabularnewline
48 & 12 & 13.6179 & -1.61789 \tabularnewline
49 & 12 & 9.94783 & 2.05217 \tabularnewline
50 & 16 & 17.1734 & -1.17337 \tabularnewline
51 & 12 & 10.7155 & 1.28448 \tabularnewline
52 & 15 & 16.2281 & -1.22814 \tabularnewline
53 & 12 & 12.0059 & -0.00588606 \tabularnewline
54 & 13 & 14.8534 & -1.85337 \tabularnewline
55 & 12 & 11.4472 & 0.552776 \tabularnewline
56 & 14 & 13.5763 & 0.423656 \tabularnewline
57 & 14 & 16.4417 & -2.44167 \tabularnewline
58 & 11 & 14.3956 & -3.39563 \tabularnewline
59 & 10 & 11.1369 & -1.13686 \tabularnewline
60 & 12 & 14.1916 & -2.19163 \tabularnewline
61 & 11 & 8.46045 & 2.53955 \tabularnewline
62 & 16 & 15.5581 & 0.441912 \tabularnewline
63 & 14 & 13.4417 & 0.558333 \tabularnewline
64 & 14 & 12.0374 & 1.96263 \tabularnewline
65 & 15 & 13.1885 & 1.81155 \tabularnewline
66 & 15 & 14.0871 & 0.912885 \tabularnewline
67 & 14 & 14.8351 & -0.835114 \tabularnewline
68 & 13 & 15.3112 & -2.31122 \tabularnewline
69 & 11 & 8.24957 & 2.75043 \tabularnewline
70 & 16 & 17.0223 & -1.02229 \tabularnewline
71 & 12 & 10.1205 & 1.87955 \tabularnewline
72 & 15 & 14.2779 & 0.722114 \tabularnewline
73 & 14 & 12.2432 & 1.75678 \tabularnewline
74 & 15 & 14.2998 & 0.700153 \tabularnewline
75 & 14 & 14.4631 & -0.463099 \tabularnewline
76 & NA & NA & -7.07759 \tabularnewline
77 & 13 & 14.208 & -1.20803 \tabularnewline
78 & 6 & 6.88312 & -0.883116 \tabularnewline
79 & 12 & 11.6147 & 0.38529 \tabularnewline
80 & 12 & 11.0935 & 0.906535 \tabularnewline
81 & 14 & 12.5348 & 1.4652 \tabularnewline
82 & 14 & 16.3043 & -2.30434 \tabularnewline
83 & 15 & 15.6343 & -0.63429 \tabularnewline
84 & 11 & 10.4933 & 0.506738 \tabularnewline
85 & 13 & 10.6443 & 2.35566 \tabularnewline
86 & 14 & 13.9196 & 0.0803706 \tabularnewline
87 & 16 & 15.4057 & 0.594317 \tabularnewline
88 & 13 & 10.5052 & 2.49483 \tabularnewline
89 & 14 & 16.9378 & -2.93778 \tabularnewline
90 & 16 & 16.4951 & -0.495115 \tabularnewline
91 & 11 & 11.616 & -0.616033 \tabularnewline
92 & 13 & 11.2332 & 1.76683 \tabularnewline
93 & 13 & 13.999 & -0.999006 \tabularnewline
94 & 15 & 15.7325 & -0.732453 \tabularnewline
95 & 12 & 12.9625 & -0.962493 \tabularnewline
96 & 13 & 12.0972 & 0.90283 \tabularnewline
97 & 12 & 11.7904 & 0.209601 \tabularnewline
98 & 14 & 11.4372 & 2.56283 \tabularnewline
99 & 14 & 12.5134 & 1.48663 \tabularnewline
100 & 16 & 15.1136 & 0.886426 \tabularnewline
101 & 15 & 15.8301 & -0.830087 \tabularnewline
102 & 14 & 13.5631 & 0.436885 \tabularnewline
103 & 13 & 11.9296 & 1.07043 \tabularnewline
104 & 14 & 12.9474 & 1.05259 \tabularnewline
105 & 15 & 16.7123 & -1.71234 \tabularnewline
106 & 14 & 20.3906 & -6.3906 \tabularnewline
107 & 12 & 13.2464 & -1.2464 \tabularnewline
108 & 7 & 5.19348 & 1.80652 \tabularnewline
109 & 12 & 13.543 & -1.54301 \tabularnewline
110 & 15 & 15.5052 & -0.50517 \tabularnewline
111 & 12 & 14.3062 & -2.3062 \tabularnewline
112 & 13 & 12.6544 & 0.345601 \tabularnewline
113 & 11 & 11.5234 & -0.523426 \tabularnewline
114 & 14 & NA & NA \tabularnewline
115 & 13 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&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]12.3807[/C][C]0.619343[/C][/ROW]
[ROW][C]2[/C][C]13[/C][C]13.3245[/C][C]-0.324454[/C][/ROW]
[ROW][C]3[/C][C]11[/C][C]12.7074[/C][C]-1.70743[/C][/ROW]
[ROW][C]4[/C][C]14[/C][C]13.7904[/C][C]0.209601[/C][/ROW]
[ROW][C]5[/C][C]15[/C][C]12.817[/C][C]2.18303[/C][/ROW]
[ROW][C]6[/C][C]14[/C][C]13.3276[/C][C]0.672371[/C][/ROW]
[ROW][C]7[/C][C]11[/C][C]13.5795[/C][C]-2.57952[/C][/ROW]
[ROW][C]8[/C][C]13[/C][C]12.754[/C][C]0.246004[/C][/ROW]
[ROW][C]9[/C][C]16[/C][C]14.5101[/C][C]1.48991[/C][/ROW]
[ROW][C]10[/C][C]14[/C][C]12.8773[/C][C]1.1227[/C][/ROW]
[ROW][C]11[/C][C]14[/C][C]13.3043[/C][C]0.695655[/C][/ROW]
[ROW][C]12[/C][C]15[/C][C]13.6563[/C][C]1.34375[/C][/ROW]
[ROW][C]13[/C][C]15[/C][C]13.737[/C][C]1.26305[/C][/ROW]
[ROW][C]14[/C][C]13[/C][C]13.6891[/C][C]-0.68906[/C][/ROW]
[ROW][C]15[/C][C]14[/C][C]13.9346[/C][C]0.0653993[/C][/ROW]
[ROW][C]16[/C][C]11[/C][C]13.7306[/C][C]-2.7306[/C][/ROW]
[ROW][C]17[/C][C]12[/C][C]13.1948[/C][C]-1.1948[/C][/ROW]
[ROW][C]18[/C][C]14[/C][C]13.5581[/C][C]0.441912[/C][/ROW]
[ROW][C]19[/C][C]13[/C][C]13.2747[/C][C]-0.274711[/C][/ROW]
[ROW][C]20[/C][C]12[/C][C]14.2331[/C][C]-2.23306[/C][/ROW]
[ROW][C]21[/C][C]15[/C][C]13.7539[/C][C]1.24611[/C][/ROW]
[ROW][C]22[/C][C]15[/C][C]13.6544[/C][C]1.3456[/C][/ROW]
[ROW][C]23[/C][C]14[/C][C]13.6261[/C][C]0.373913[/C][/ROW]
[ROW][C]24[/C][C]14[/C][C]13.6393[/C][C]0.360683[/C][/ROW]
[ROW][C]25[/C][C]12[/C][C]13.6229[/C][C]-1.62291[/C][/ROW]
[ROW][C]26[/C][C]12[/C][C]13.5033[/C][C]-1.50332[/C][/ROW]
[ROW][C]27[/C][C]12[/C][C]13.5134[/C][C]-1.51337[/C][/ROW]
[ROW][C]28[/C][C]15[/C][C]13.1834[/C][C]1.81657[/C][/ROW]
[ROW][C]29[/C][C]14[/C][C]13.6097[/C][C]0.390317[/C][/ROW]
[ROW][C]30[/C][C]16[/C][C]13.6922[/C][C]2.30777[/C][/ROW]
[ROW][C]31[/C][C]12[/C][C]13.0652[/C][C]-1.06515[/C][/ROW]
[ROW][C]32[/C][C]12[/C][C]13.2067[/C][C]-1.20671[/C][/ROW]
[ROW][C]33[/C][C]NA[/C][C]NA[/C][C]0.342426[/C][/ROW]
[ROW][C]34[/C][C]14[/C][C]11.2893[/C][C]2.71074[/C][/ROW]
[ROW][C]35[/C][C]16[/C][C]14.4983[/C][C]1.50171[/C][/ROW]
[ROW][C]36[/C][C]15[/C][C]16.2514[/C][C]-1.25143[/C][/ROW]
[ROW][C]37[/C][C]12[/C][C]11.5216[/C][C]0.478426[/C][/ROW]
[ROW][C]38[/C][C]14[/C][C]14.3692[/C][C]-0.36917[/C][/ROW]
[ROW][C]39[/C][C]13[/C][C]12.2715[/C][C]0.728464[/C][/ROW]
[ROW][C]40[/C][C]14[/C][C]11.5827[/C][C]2.41731[/C][/ROW]
[ROW][C]41[/C][C]16[/C][C]16.681[/C][C]-0.680968[/C][/ROW]
[ROW][C]42[/C][C]12[/C][C]11.0657[/C][C]0.934316[/C][/ROW]
[ROW][C]43[/C][C]14[/C][C]12.3856[/C][C]1.61443[/C][/ROW]
[ROW][C]44[/C][C]15[/C][C]15.3636[/C][C]-0.363612[/C][/ROW]
[ROW][C]45[/C][C]13[/C][C]9.92598[/C][C]3.07402[/C][/ROW]
[ROW][C]46[/C][C]16[/C][C]13.6827[/C][C]2.31729[/C][/ROW]
[ROW][C]47[/C][C]16[/C][C]17.0839[/C][C]-1.08394[/C][/ROW]
[ROW][C]48[/C][C]12[/C][C]13.6179[/C][C]-1.61789[/C][/ROW]
[ROW][C]49[/C][C]12[/C][C]9.94783[/C][C]2.05217[/C][/ROW]
[ROW][C]50[/C][C]16[/C][C]17.1734[/C][C]-1.17337[/C][/ROW]
[ROW][C]51[/C][C]12[/C][C]10.7155[/C][C]1.28448[/C][/ROW]
[ROW][C]52[/C][C]15[/C][C]16.2281[/C][C]-1.22814[/C][/ROW]
[ROW][C]53[/C][C]12[/C][C]12.0059[/C][C]-0.00588606[/C][/ROW]
[ROW][C]54[/C][C]13[/C][C]14.8534[/C][C]-1.85337[/C][/ROW]
[ROW][C]55[/C][C]12[/C][C]11.4472[/C][C]0.552776[/C][/ROW]
[ROW][C]56[/C][C]14[/C][C]13.5763[/C][C]0.423656[/C][/ROW]
[ROW][C]57[/C][C]14[/C][C]16.4417[/C][C]-2.44167[/C][/ROW]
[ROW][C]58[/C][C]11[/C][C]14.3956[/C][C]-3.39563[/C][/ROW]
[ROW][C]59[/C][C]10[/C][C]11.1369[/C][C]-1.13686[/C][/ROW]
[ROW][C]60[/C][C]12[/C][C]14.1916[/C][C]-2.19163[/C][/ROW]
[ROW][C]61[/C][C]11[/C][C]8.46045[/C][C]2.53955[/C][/ROW]
[ROW][C]62[/C][C]16[/C][C]15.5581[/C][C]0.441912[/C][/ROW]
[ROW][C]63[/C][C]14[/C][C]13.4417[/C][C]0.558333[/C][/ROW]
[ROW][C]64[/C][C]14[/C][C]12.0374[/C][C]1.96263[/C][/ROW]
[ROW][C]65[/C][C]15[/C][C]13.1885[/C][C]1.81155[/C][/ROW]
[ROW][C]66[/C][C]15[/C][C]14.0871[/C][C]0.912885[/C][/ROW]
[ROW][C]67[/C][C]14[/C][C]14.8351[/C][C]-0.835114[/C][/ROW]
[ROW][C]68[/C][C]13[/C][C]15.3112[/C][C]-2.31122[/C][/ROW]
[ROW][C]69[/C][C]11[/C][C]8.24957[/C][C]2.75043[/C][/ROW]
[ROW][C]70[/C][C]16[/C][C]17.0223[/C][C]-1.02229[/C][/ROW]
[ROW][C]71[/C][C]12[/C][C]10.1205[/C][C]1.87955[/C][/ROW]
[ROW][C]72[/C][C]15[/C][C]14.2779[/C][C]0.722114[/C][/ROW]
[ROW][C]73[/C][C]14[/C][C]12.2432[/C][C]1.75678[/C][/ROW]
[ROW][C]74[/C][C]15[/C][C]14.2998[/C][C]0.700153[/C][/ROW]
[ROW][C]75[/C][C]14[/C][C]14.4631[/C][C]-0.463099[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]-7.07759[/C][/ROW]
[ROW][C]77[/C][C]13[/C][C]14.208[/C][C]-1.20803[/C][/ROW]
[ROW][C]78[/C][C]6[/C][C]6.88312[/C][C]-0.883116[/C][/ROW]
[ROW][C]79[/C][C]12[/C][C]11.6147[/C][C]0.38529[/C][/ROW]
[ROW][C]80[/C][C]12[/C][C]11.0935[/C][C]0.906535[/C][/ROW]
[ROW][C]81[/C][C]14[/C][C]12.5348[/C][C]1.4652[/C][/ROW]
[ROW][C]82[/C][C]14[/C][C]16.3043[/C][C]-2.30434[/C][/ROW]
[ROW][C]83[/C][C]15[/C][C]15.6343[/C][C]-0.63429[/C][/ROW]
[ROW][C]84[/C][C]11[/C][C]10.4933[/C][C]0.506738[/C][/ROW]
[ROW][C]85[/C][C]13[/C][C]10.6443[/C][C]2.35566[/C][/ROW]
[ROW][C]86[/C][C]14[/C][C]13.9196[/C][C]0.0803706[/C][/ROW]
[ROW][C]87[/C][C]16[/C][C]15.4057[/C][C]0.594317[/C][/ROW]
[ROW][C]88[/C][C]13[/C][C]10.5052[/C][C]2.49483[/C][/ROW]
[ROW][C]89[/C][C]14[/C][C]16.9378[/C][C]-2.93778[/C][/ROW]
[ROW][C]90[/C][C]16[/C][C]16.4951[/C][C]-0.495115[/C][/ROW]
[ROW][C]91[/C][C]11[/C][C]11.616[/C][C]-0.616033[/C][/ROW]
[ROW][C]92[/C][C]13[/C][C]11.2332[/C][C]1.76683[/C][/ROW]
[ROW][C]93[/C][C]13[/C][C]13.999[/C][C]-0.999006[/C][/ROW]
[ROW][C]94[/C][C]15[/C][C]15.7325[/C][C]-0.732453[/C][/ROW]
[ROW][C]95[/C][C]12[/C][C]12.9625[/C][C]-0.962493[/C][/ROW]
[ROW][C]96[/C][C]13[/C][C]12.0972[/C][C]0.90283[/C][/ROW]
[ROW][C]97[/C][C]12[/C][C]11.7904[/C][C]0.209601[/C][/ROW]
[ROW][C]98[/C][C]14[/C][C]11.4372[/C][C]2.56283[/C][/ROW]
[ROW][C]99[/C][C]14[/C][C]12.5134[/C][C]1.48663[/C][/ROW]
[ROW][C]100[/C][C]16[/C][C]15.1136[/C][C]0.886426[/C][/ROW]
[ROW][C]101[/C][C]15[/C][C]15.8301[/C][C]-0.830087[/C][/ROW]
[ROW][C]102[/C][C]14[/C][C]13.5631[/C][C]0.436885[/C][/ROW]
[ROW][C]103[/C][C]13[/C][C]11.9296[/C][C]1.07043[/C][/ROW]
[ROW][C]104[/C][C]14[/C][C]12.9474[/C][C]1.05259[/C][/ROW]
[ROW][C]105[/C][C]15[/C][C]16.7123[/C][C]-1.71234[/C][/ROW]
[ROW][C]106[/C][C]14[/C][C]20.3906[/C][C]-6.3906[/C][/ROW]
[ROW][C]107[/C][C]12[/C][C]13.2464[/C][C]-1.2464[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]5.19348[/C][C]1.80652[/C][/ROW]
[ROW][C]109[/C][C]12[/C][C]13.543[/C][C]-1.54301[/C][/ROW]
[ROW][C]110[/C][C]15[/C][C]15.5052[/C][C]-0.50517[/C][/ROW]
[ROW][C]111[/C][C]12[/C][C]14.3062[/C][C]-2.3062[/C][/ROW]
[ROW][C]112[/C][C]13[/C][C]12.6544[/C][C]0.345601[/C][/ROW]
[ROW][C]113[/C][C]11[/C][C]11.5234[/C][C]-0.523426[/C][/ROW]
[ROW][C]114[/C][C]14[/C][C]NA[/C][C]NA[/C][/ROW]
[ROW][C]115[/C][C]13[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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
11312.38070.619343
21313.3245-0.324454
31112.7074-1.70743
41413.79040.209601
51512.8172.18303
61413.32760.672371
71113.5795-2.57952
81312.7540.246004
91614.51011.48991
101412.87731.1227
111413.30430.695655
121513.65631.34375
131513.7371.26305
141313.6891-0.68906
151413.93460.0653993
161113.7306-2.7306
171213.1948-1.1948
181413.55810.441912
191313.2747-0.274711
201214.2331-2.23306
211513.75391.24611
221513.65441.3456
231413.62610.373913
241413.63930.360683
251213.6229-1.62291
261213.5033-1.50332
271213.5134-1.51337
281513.18341.81657
291413.60970.390317
301613.69222.30777
311213.0652-1.06515
321213.2067-1.20671
33NANA0.342426
341411.28932.71074
351614.49831.50171
361516.2514-1.25143
371211.52160.478426
381414.3692-0.36917
391312.27150.728464
401411.58272.41731
411616.681-0.680968
421211.06570.934316
431412.38561.61443
441515.3636-0.363612
45139.925983.07402
461613.68272.31729
471617.0839-1.08394
481213.6179-1.61789
49129.947832.05217
501617.1734-1.17337
511210.71551.28448
521516.2281-1.22814
531212.0059-0.00588606
541314.8534-1.85337
551211.44720.552776
561413.57630.423656
571416.4417-2.44167
581114.3956-3.39563
591011.1369-1.13686
601214.1916-2.19163
61118.460452.53955
621615.55810.441912
631413.44170.558333
641412.03741.96263
651513.18851.81155
661514.08710.912885
671414.8351-0.835114
681315.3112-2.31122
69118.249572.75043
701617.0223-1.02229
711210.12051.87955
721514.27790.722114
731412.24321.75678
741514.29980.700153
751414.4631-0.463099
76NANA-7.07759
771314.208-1.20803
7866.88312-0.883116
791211.61470.38529
801211.09350.906535
811412.53481.4652
821416.3043-2.30434
831515.6343-0.63429
841110.49330.506738
851310.64432.35566
861413.91960.0803706
871615.40570.594317
881310.50522.49483
891416.9378-2.93778
901616.4951-0.495115
911111.616-0.616033
921311.23321.76683
931313.999-0.999006
941515.7325-0.732453
951212.9625-0.962493
961312.09720.90283
971211.79040.209601
981411.43722.56283
991412.51341.48663
1001615.11360.886426
1011515.8301-0.830087
1021413.56310.436885
1031311.92961.07043
1041412.94741.05259
1051516.7123-1.71234
1061420.3906-6.3906
1071213.2464-1.2464
10875.193481.80652
1091213.543-1.54301
1101515.5052-0.50517
1111214.3062-2.3062
1121312.65440.345601
1131111.5234-0.523426
11414NANA
11513NANA







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
60.5313530.9372950.468647
70.4063550.8127090.593645
80.307630.6152590.69237
90.3050310.6100620.694969
100.3113180.6226350.688682
110.2410370.4820750.758963
120.1732020.3464040.826798
130.1687850.337570.831215
140.1294970.2589940.870503
150.08652410.1730480.913476
160.2054860.4109730.794514
170.1629910.3259830.837009
180.1204970.2409940.879503
190.08669060.1733810.913309
200.113850.2277010.88615
210.1006060.2012110.899394
220.08873950.1774790.911261
230.06407040.1281410.93593
240.04562630.09125250.954374
250.05048780.1009760.949512
260.04414760.08829520.955852
270.04089830.08179660.959102
280.04516490.09032990.954835
290.03134640.06269280.968654
300.06068940.1213790.939311
310.04553780.09107560.954462
320.04131960.08263920.95868
330.02969050.0593810.970309
340.06124670.1224930.938753
350.05837660.1167530.941623
360.05290920.1058180.947091
370.03922160.07844320.960778
380.02866980.05733960.97133
390.02097860.04195720.979021
400.03155580.06311160.968444
410.02427550.0485510.975725
420.01866650.0373330.981333
430.01764350.03528690.982357
440.01252720.02505430.987473
450.02626070.05252140.973739
460.03401190.06802380.965988
470.02950150.05900290.970499
480.02996520.05993040.970035
490.03367430.06734850.966326
500.0295930.05918610.970407
510.02525190.05050380.974748
520.02228110.04456230.977719
530.0158690.0317380.984131
540.01739990.03479970.9826
550.01265650.0253130.987344
560.00895060.01790120.991049
570.01367060.02734110.986329
580.03565280.07130550.964347
590.0299460.05989190.970054
600.03582180.07164350.964178
610.04938280.09876560.950617
620.03759050.07518110.962409
630.0285520.05710390.971448
640.03068850.0613770.969311
650.03142060.06284110.968579
660.02503790.05007580.974962
670.0194080.0388160.980592
680.02415420.04830830.975846
690.03798630.07597260.962014
700.03068130.06136270.969319
710.03288310.06576610.967117
720.02557470.05114950.974425
730.02706990.05413980.97293
740.02238460.04476930.977615
750.01677810.03355630.983222
760.4732450.9464910.526755
770.435850.87170.56415
780.3991690.7983390.600831
790.343670.6873390.65633
800.3142170.6284350.685783
810.3142910.6285820.685709
820.3472890.6945780.652711
830.2942490.5884980.705751
840.2593940.5187890.740606
850.3302560.6605120.669744
860.2758760.5517520.724124
870.2262570.4525150.773743
880.2754280.5508560.724572
890.3002370.6004740.699763
900.2447360.4894710.755264
910.1948480.3896960.805152
920.1897290.3794580.810271
930.1510020.3020040.848998
940.1153710.2307430.884629
950.08750120.1750020.912499
960.06577950.1315590.934221
970.04580490.09160980.954195
980.07346670.1469330.926533
990.07550780.1510160.924492
1000.06173150.1234630.938268
1010.03942120.07884240.960579
1020.02795150.05590290.972049
1030.03718330.07436650.962817
1040.1000750.2001490.899925
1050.06224570.1244910.937754
1060.601830.7963390.39817
1070.4460270.8920540.553973
1080.9911340.01773120.00886559
1090.8948960.2102070.105104

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 & 0.531353 & 0.937295 & 0.468647 \tabularnewline
7 & 0.406355 & 0.812709 & 0.593645 \tabularnewline
8 & 0.30763 & 0.615259 & 0.69237 \tabularnewline
9 & 0.305031 & 0.610062 & 0.694969 \tabularnewline
10 & 0.311318 & 0.622635 & 0.688682 \tabularnewline
11 & 0.241037 & 0.482075 & 0.758963 \tabularnewline
12 & 0.173202 & 0.346404 & 0.826798 \tabularnewline
13 & 0.168785 & 0.33757 & 0.831215 \tabularnewline
14 & 0.129497 & 0.258994 & 0.870503 \tabularnewline
15 & 0.0865241 & 0.173048 & 0.913476 \tabularnewline
16 & 0.205486 & 0.410973 & 0.794514 \tabularnewline
17 & 0.162991 & 0.325983 & 0.837009 \tabularnewline
18 & 0.120497 & 0.240994 & 0.879503 \tabularnewline
19 & 0.0866906 & 0.173381 & 0.913309 \tabularnewline
20 & 0.11385 & 0.227701 & 0.88615 \tabularnewline
21 & 0.100606 & 0.201211 & 0.899394 \tabularnewline
22 & 0.0887395 & 0.177479 & 0.911261 \tabularnewline
23 & 0.0640704 & 0.128141 & 0.93593 \tabularnewline
24 & 0.0456263 & 0.0912525 & 0.954374 \tabularnewline
25 & 0.0504878 & 0.100976 & 0.949512 \tabularnewline
26 & 0.0441476 & 0.0882952 & 0.955852 \tabularnewline
27 & 0.0408983 & 0.0817966 & 0.959102 \tabularnewline
28 & 0.0451649 & 0.0903299 & 0.954835 \tabularnewline
29 & 0.0313464 & 0.0626928 & 0.968654 \tabularnewline
30 & 0.0606894 & 0.121379 & 0.939311 \tabularnewline
31 & 0.0455378 & 0.0910756 & 0.954462 \tabularnewline
32 & 0.0413196 & 0.0826392 & 0.95868 \tabularnewline
33 & 0.0296905 & 0.059381 & 0.970309 \tabularnewline
34 & 0.0612467 & 0.122493 & 0.938753 \tabularnewline
35 & 0.0583766 & 0.116753 & 0.941623 \tabularnewline
36 & 0.0529092 & 0.105818 & 0.947091 \tabularnewline
37 & 0.0392216 & 0.0784432 & 0.960778 \tabularnewline
38 & 0.0286698 & 0.0573396 & 0.97133 \tabularnewline
39 & 0.0209786 & 0.0419572 & 0.979021 \tabularnewline
40 & 0.0315558 & 0.0631116 & 0.968444 \tabularnewline
41 & 0.0242755 & 0.048551 & 0.975725 \tabularnewline
42 & 0.0186665 & 0.037333 & 0.981333 \tabularnewline
43 & 0.0176435 & 0.0352869 & 0.982357 \tabularnewline
44 & 0.0125272 & 0.0250543 & 0.987473 \tabularnewline
45 & 0.0262607 & 0.0525214 & 0.973739 \tabularnewline
46 & 0.0340119 & 0.0680238 & 0.965988 \tabularnewline
47 & 0.0295015 & 0.0590029 & 0.970499 \tabularnewline
48 & 0.0299652 & 0.0599304 & 0.970035 \tabularnewline
49 & 0.0336743 & 0.0673485 & 0.966326 \tabularnewline
50 & 0.029593 & 0.0591861 & 0.970407 \tabularnewline
51 & 0.0252519 & 0.0505038 & 0.974748 \tabularnewline
52 & 0.0222811 & 0.0445623 & 0.977719 \tabularnewline
53 & 0.015869 & 0.031738 & 0.984131 \tabularnewline
54 & 0.0173999 & 0.0347997 & 0.9826 \tabularnewline
55 & 0.0126565 & 0.025313 & 0.987344 \tabularnewline
56 & 0.0089506 & 0.0179012 & 0.991049 \tabularnewline
57 & 0.0136706 & 0.0273411 & 0.986329 \tabularnewline
58 & 0.0356528 & 0.0713055 & 0.964347 \tabularnewline
59 & 0.029946 & 0.0598919 & 0.970054 \tabularnewline
60 & 0.0358218 & 0.0716435 & 0.964178 \tabularnewline
61 & 0.0493828 & 0.0987656 & 0.950617 \tabularnewline
62 & 0.0375905 & 0.0751811 & 0.962409 \tabularnewline
63 & 0.028552 & 0.0571039 & 0.971448 \tabularnewline
64 & 0.0306885 & 0.061377 & 0.969311 \tabularnewline
65 & 0.0314206 & 0.0628411 & 0.968579 \tabularnewline
66 & 0.0250379 & 0.0500758 & 0.974962 \tabularnewline
67 & 0.019408 & 0.038816 & 0.980592 \tabularnewline
68 & 0.0241542 & 0.0483083 & 0.975846 \tabularnewline
69 & 0.0379863 & 0.0759726 & 0.962014 \tabularnewline
70 & 0.0306813 & 0.0613627 & 0.969319 \tabularnewline
71 & 0.0328831 & 0.0657661 & 0.967117 \tabularnewline
72 & 0.0255747 & 0.0511495 & 0.974425 \tabularnewline
73 & 0.0270699 & 0.0541398 & 0.97293 \tabularnewline
74 & 0.0223846 & 0.0447693 & 0.977615 \tabularnewline
75 & 0.0167781 & 0.0335563 & 0.983222 \tabularnewline
76 & 0.473245 & 0.946491 & 0.526755 \tabularnewline
77 & 0.43585 & 0.8717 & 0.56415 \tabularnewline
78 & 0.399169 & 0.798339 & 0.600831 \tabularnewline
79 & 0.34367 & 0.687339 & 0.65633 \tabularnewline
80 & 0.314217 & 0.628435 & 0.685783 \tabularnewline
81 & 0.314291 & 0.628582 & 0.685709 \tabularnewline
82 & 0.347289 & 0.694578 & 0.652711 \tabularnewline
83 & 0.294249 & 0.588498 & 0.705751 \tabularnewline
84 & 0.259394 & 0.518789 & 0.740606 \tabularnewline
85 & 0.330256 & 0.660512 & 0.669744 \tabularnewline
86 & 0.275876 & 0.551752 & 0.724124 \tabularnewline
87 & 0.226257 & 0.452515 & 0.773743 \tabularnewline
88 & 0.275428 & 0.550856 & 0.724572 \tabularnewline
89 & 0.300237 & 0.600474 & 0.699763 \tabularnewline
90 & 0.244736 & 0.489471 & 0.755264 \tabularnewline
91 & 0.194848 & 0.389696 & 0.805152 \tabularnewline
92 & 0.189729 & 0.379458 & 0.810271 \tabularnewline
93 & 0.151002 & 0.302004 & 0.848998 \tabularnewline
94 & 0.115371 & 0.230743 & 0.884629 \tabularnewline
95 & 0.0875012 & 0.175002 & 0.912499 \tabularnewline
96 & 0.0657795 & 0.131559 & 0.934221 \tabularnewline
97 & 0.0458049 & 0.0916098 & 0.954195 \tabularnewline
98 & 0.0734667 & 0.146933 & 0.926533 \tabularnewline
99 & 0.0755078 & 0.151016 & 0.924492 \tabularnewline
100 & 0.0617315 & 0.123463 & 0.938268 \tabularnewline
101 & 0.0394212 & 0.0788424 & 0.960579 \tabularnewline
102 & 0.0279515 & 0.0559029 & 0.972049 \tabularnewline
103 & 0.0371833 & 0.0743665 & 0.962817 \tabularnewline
104 & 0.100075 & 0.200149 & 0.899925 \tabularnewline
105 & 0.0622457 & 0.124491 & 0.937754 \tabularnewline
106 & 0.60183 & 0.796339 & 0.39817 \tabularnewline
107 & 0.446027 & 0.892054 & 0.553973 \tabularnewline
108 & 0.991134 & 0.0177312 & 0.00886559 \tabularnewline
109 & 0.894896 & 0.210207 & 0.105104 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&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]6[/C][C]0.531353[/C][C]0.937295[/C][C]0.468647[/C][/ROW]
[ROW][C]7[/C][C]0.406355[/C][C]0.812709[/C][C]0.593645[/C][/ROW]
[ROW][C]8[/C][C]0.30763[/C][C]0.615259[/C][C]0.69237[/C][/ROW]
[ROW][C]9[/C][C]0.305031[/C][C]0.610062[/C][C]0.694969[/C][/ROW]
[ROW][C]10[/C][C]0.311318[/C][C]0.622635[/C][C]0.688682[/C][/ROW]
[ROW][C]11[/C][C]0.241037[/C][C]0.482075[/C][C]0.758963[/C][/ROW]
[ROW][C]12[/C][C]0.173202[/C][C]0.346404[/C][C]0.826798[/C][/ROW]
[ROW][C]13[/C][C]0.168785[/C][C]0.33757[/C][C]0.831215[/C][/ROW]
[ROW][C]14[/C][C]0.129497[/C][C]0.258994[/C][C]0.870503[/C][/ROW]
[ROW][C]15[/C][C]0.0865241[/C][C]0.173048[/C][C]0.913476[/C][/ROW]
[ROW][C]16[/C][C]0.205486[/C][C]0.410973[/C][C]0.794514[/C][/ROW]
[ROW][C]17[/C][C]0.162991[/C][C]0.325983[/C][C]0.837009[/C][/ROW]
[ROW][C]18[/C][C]0.120497[/C][C]0.240994[/C][C]0.879503[/C][/ROW]
[ROW][C]19[/C][C]0.0866906[/C][C]0.173381[/C][C]0.913309[/C][/ROW]
[ROW][C]20[/C][C]0.11385[/C][C]0.227701[/C][C]0.88615[/C][/ROW]
[ROW][C]21[/C][C]0.100606[/C][C]0.201211[/C][C]0.899394[/C][/ROW]
[ROW][C]22[/C][C]0.0887395[/C][C]0.177479[/C][C]0.911261[/C][/ROW]
[ROW][C]23[/C][C]0.0640704[/C][C]0.128141[/C][C]0.93593[/C][/ROW]
[ROW][C]24[/C][C]0.0456263[/C][C]0.0912525[/C][C]0.954374[/C][/ROW]
[ROW][C]25[/C][C]0.0504878[/C][C]0.100976[/C][C]0.949512[/C][/ROW]
[ROW][C]26[/C][C]0.0441476[/C][C]0.0882952[/C][C]0.955852[/C][/ROW]
[ROW][C]27[/C][C]0.0408983[/C][C]0.0817966[/C][C]0.959102[/C][/ROW]
[ROW][C]28[/C][C]0.0451649[/C][C]0.0903299[/C][C]0.954835[/C][/ROW]
[ROW][C]29[/C][C]0.0313464[/C][C]0.0626928[/C][C]0.968654[/C][/ROW]
[ROW][C]30[/C][C]0.0606894[/C][C]0.121379[/C][C]0.939311[/C][/ROW]
[ROW][C]31[/C][C]0.0455378[/C][C]0.0910756[/C][C]0.954462[/C][/ROW]
[ROW][C]32[/C][C]0.0413196[/C][C]0.0826392[/C][C]0.95868[/C][/ROW]
[ROW][C]33[/C][C]0.0296905[/C][C]0.059381[/C][C]0.970309[/C][/ROW]
[ROW][C]34[/C][C]0.0612467[/C][C]0.122493[/C][C]0.938753[/C][/ROW]
[ROW][C]35[/C][C]0.0583766[/C][C]0.116753[/C][C]0.941623[/C][/ROW]
[ROW][C]36[/C][C]0.0529092[/C][C]0.105818[/C][C]0.947091[/C][/ROW]
[ROW][C]37[/C][C]0.0392216[/C][C]0.0784432[/C][C]0.960778[/C][/ROW]
[ROW][C]38[/C][C]0.0286698[/C][C]0.0573396[/C][C]0.97133[/C][/ROW]
[ROW][C]39[/C][C]0.0209786[/C][C]0.0419572[/C][C]0.979021[/C][/ROW]
[ROW][C]40[/C][C]0.0315558[/C][C]0.0631116[/C][C]0.968444[/C][/ROW]
[ROW][C]41[/C][C]0.0242755[/C][C]0.048551[/C][C]0.975725[/C][/ROW]
[ROW][C]42[/C][C]0.0186665[/C][C]0.037333[/C][C]0.981333[/C][/ROW]
[ROW][C]43[/C][C]0.0176435[/C][C]0.0352869[/C][C]0.982357[/C][/ROW]
[ROW][C]44[/C][C]0.0125272[/C][C]0.0250543[/C][C]0.987473[/C][/ROW]
[ROW][C]45[/C][C]0.0262607[/C][C]0.0525214[/C][C]0.973739[/C][/ROW]
[ROW][C]46[/C][C]0.0340119[/C][C]0.0680238[/C][C]0.965988[/C][/ROW]
[ROW][C]47[/C][C]0.0295015[/C][C]0.0590029[/C][C]0.970499[/C][/ROW]
[ROW][C]48[/C][C]0.0299652[/C][C]0.0599304[/C][C]0.970035[/C][/ROW]
[ROW][C]49[/C][C]0.0336743[/C][C]0.0673485[/C][C]0.966326[/C][/ROW]
[ROW][C]50[/C][C]0.029593[/C][C]0.0591861[/C][C]0.970407[/C][/ROW]
[ROW][C]51[/C][C]0.0252519[/C][C]0.0505038[/C][C]0.974748[/C][/ROW]
[ROW][C]52[/C][C]0.0222811[/C][C]0.0445623[/C][C]0.977719[/C][/ROW]
[ROW][C]53[/C][C]0.015869[/C][C]0.031738[/C][C]0.984131[/C][/ROW]
[ROW][C]54[/C][C]0.0173999[/C][C]0.0347997[/C][C]0.9826[/C][/ROW]
[ROW][C]55[/C][C]0.0126565[/C][C]0.025313[/C][C]0.987344[/C][/ROW]
[ROW][C]56[/C][C]0.0089506[/C][C]0.0179012[/C][C]0.991049[/C][/ROW]
[ROW][C]57[/C][C]0.0136706[/C][C]0.0273411[/C][C]0.986329[/C][/ROW]
[ROW][C]58[/C][C]0.0356528[/C][C]0.0713055[/C][C]0.964347[/C][/ROW]
[ROW][C]59[/C][C]0.029946[/C][C]0.0598919[/C][C]0.970054[/C][/ROW]
[ROW][C]60[/C][C]0.0358218[/C][C]0.0716435[/C][C]0.964178[/C][/ROW]
[ROW][C]61[/C][C]0.0493828[/C][C]0.0987656[/C][C]0.950617[/C][/ROW]
[ROW][C]62[/C][C]0.0375905[/C][C]0.0751811[/C][C]0.962409[/C][/ROW]
[ROW][C]63[/C][C]0.028552[/C][C]0.0571039[/C][C]0.971448[/C][/ROW]
[ROW][C]64[/C][C]0.0306885[/C][C]0.061377[/C][C]0.969311[/C][/ROW]
[ROW][C]65[/C][C]0.0314206[/C][C]0.0628411[/C][C]0.968579[/C][/ROW]
[ROW][C]66[/C][C]0.0250379[/C][C]0.0500758[/C][C]0.974962[/C][/ROW]
[ROW][C]67[/C][C]0.019408[/C][C]0.038816[/C][C]0.980592[/C][/ROW]
[ROW][C]68[/C][C]0.0241542[/C][C]0.0483083[/C][C]0.975846[/C][/ROW]
[ROW][C]69[/C][C]0.0379863[/C][C]0.0759726[/C][C]0.962014[/C][/ROW]
[ROW][C]70[/C][C]0.0306813[/C][C]0.0613627[/C][C]0.969319[/C][/ROW]
[ROW][C]71[/C][C]0.0328831[/C][C]0.0657661[/C][C]0.967117[/C][/ROW]
[ROW][C]72[/C][C]0.0255747[/C][C]0.0511495[/C][C]0.974425[/C][/ROW]
[ROW][C]73[/C][C]0.0270699[/C][C]0.0541398[/C][C]0.97293[/C][/ROW]
[ROW][C]74[/C][C]0.0223846[/C][C]0.0447693[/C][C]0.977615[/C][/ROW]
[ROW][C]75[/C][C]0.0167781[/C][C]0.0335563[/C][C]0.983222[/C][/ROW]
[ROW][C]76[/C][C]0.473245[/C][C]0.946491[/C][C]0.526755[/C][/ROW]
[ROW][C]77[/C][C]0.43585[/C][C]0.8717[/C][C]0.56415[/C][/ROW]
[ROW][C]78[/C][C]0.399169[/C][C]0.798339[/C][C]0.600831[/C][/ROW]
[ROW][C]79[/C][C]0.34367[/C][C]0.687339[/C][C]0.65633[/C][/ROW]
[ROW][C]80[/C][C]0.314217[/C][C]0.628435[/C][C]0.685783[/C][/ROW]
[ROW][C]81[/C][C]0.314291[/C][C]0.628582[/C][C]0.685709[/C][/ROW]
[ROW][C]82[/C][C]0.347289[/C][C]0.694578[/C][C]0.652711[/C][/ROW]
[ROW][C]83[/C][C]0.294249[/C][C]0.588498[/C][C]0.705751[/C][/ROW]
[ROW][C]84[/C][C]0.259394[/C][C]0.518789[/C][C]0.740606[/C][/ROW]
[ROW][C]85[/C][C]0.330256[/C][C]0.660512[/C][C]0.669744[/C][/ROW]
[ROW][C]86[/C][C]0.275876[/C][C]0.551752[/C][C]0.724124[/C][/ROW]
[ROW][C]87[/C][C]0.226257[/C][C]0.452515[/C][C]0.773743[/C][/ROW]
[ROW][C]88[/C][C]0.275428[/C][C]0.550856[/C][C]0.724572[/C][/ROW]
[ROW][C]89[/C][C]0.300237[/C][C]0.600474[/C][C]0.699763[/C][/ROW]
[ROW][C]90[/C][C]0.244736[/C][C]0.489471[/C][C]0.755264[/C][/ROW]
[ROW][C]91[/C][C]0.194848[/C][C]0.389696[/C][C]0.805152[/C][/ROW]
[ROW][C]92[/C][C]0.189729[/C][C]0.379458[/C][C]0.810271[/C][/ROW]
[ROW][C]93[/C][C]0.151002[/C][C]0.302004[/C][C]0.848998[/C][/ROW]
[ROW][C]94[/C][C]0.115371[/C][C]0.230743[/C][C]0.884629[/C][/ROW]
[ROW][C]95[/C][C]0.0875012[/C][C]0.175002[/C][C]0.912499[/C][/ROW]
[ROW][C]96[/C][C]0.0657795[/C][C]0.131559[/C][C]0.934221[/C][/ROW]
[ROW][C]97[/C][C]0.0458049[/C][C]0.0916098[/C][C]0.954195[/C][/ROW]
[ROW][C]98[/C][C]0.0734667[/C][C]0.146933[/C][C]0.926533[/C][/ROW]
[ROW][C]99[/C][C]0.0755078[/C][C]0.151016[/C][C]0.924492[/C][/ROW]
[ROW][C]100[/C][C]0.0617315[/C][C]0.123463[/C][C]0.938268[/C][/ROW]
[ROW][C]101[/C][C]0.0394212[/C][C]0.0788424[/C][C]0.960579[/C][/ROW]
[ROW][C]102[/C][C]0.0279515[/C][C]0.0559029[/C][C]0.972049[/C][/ROW]
[ROW][C]103[/C][C]0.0371833[/C][C]0.0743665[/C][C]0.962817[/C][/ROW]
[ROW][C]104[/C][C]0.100075[/C][C]0.200149[/C][C]0.899925[/C][/ROW]
[ROW][C]105[/C][C]0.0622457[/C][C]0.124491[/C][C]0.937754[/C][/ROW]
[ROW][C]106[/C][C]0.60183[/C][C]0.796339[/C][C]0.39817[/C][/ROW]
[ROW][C]107[/C][C]0.446027[/C][C]0.892054[/C][C]0.553973[/C][/ROW]
[ROW][C]108[/C][C]0.991134[/C][C]0.0177312[/C][C]0.00886559[/C][/ROW]
[ROW][C]109[/C][C]0.894896[/C][C]0.210207[/C][C]0.105104[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267870&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
60.5313530.9372950.468647
70.4063550.8127090.593645
80.307630.6152590.69237
90.3050310.6100620.694969
100.3113180.6226350.688682
110.2410370.4820750.758963
120.1732020.3464040.826798
130.1687850.337570.831215
140.1294970.2589940.870503
150.08652410.1730480.913476
160.2054860.4109730.794514
170.1629910.3259830.837009
180.1204970.2409940.879503
190.08669060.1733810.913309
200.113850.2277010.88615
210.1006060.2012110.899394
220.08873950.1774790.911261
230.06407040.1281410.93593
240.04562630.09125250.954374
250.05048780.1009760.949512
260.04414760.08829520.955852
270.04089830.08179660.959102
280.04516490.09032990.954835
290.03134640.06269280.968654
300.06068940.1213790.939311
310.04553780.09107560.954462
320.04131960.08263920.95868
330.02969050.0593810.970309
340.06124670.1224930.938753
350.05837660.1167530.941623
360.05290920.1058180.947091
370.03922160.07844320.960778
380.02866980.05733960.97133
390.02097860.04195720.979021
400.03155580.06311160.968444
410.02427550.0485510.975725
420.01866650.0373330.981333
430.01764350.03528690.982357
440.01252720.02505430.987473
450.02626070.05252140.973739
460.03401190.06802380.965988
470.02950150.05900290.970499
480.02996520.05993040.970035
490.03367430.06734850.966326
500.0295930.05918610.970407
510.02525190.05050380.974748
520.02228110.04456230.977719
530.0158690.0317380.984131
540.01739990.03479970.9826
550.01265650.0253130.987344
560.00895060.01790120.991049
570.01367060.02734110.986329
580.03565280.07130550.964347
590.0299460.05989190.970054
600.03582180.07164350.964178
610.04938280.09876560.950617
620.03759050.07518110.962409
630.0285520.05710390.971448
640.03068850.0613770.969311
650.03142060.06284110.968579
660.02503790.05007580.974962
670.0194080.0388160.980592
680.02415420.04830830.975846
690.03798630.07597260.962014
700.03068130.06136270.969319
710.03288310.06576610.967117
720.02557470.05114950.974425
730.02706990.05413980.97293
740.02238460.04476930.977615
750.01677810.03355630.983222
760.4732450.9464910.526755
770.435850.87170.56415
780.3991690.7983390.600831
790.343670.6873390.65633
800.3142170.6284350.685783
810.3142910.6285820.685709
820.3472890.6945780.652711
830.2942490.5884980.705751
840.2593940.5187890.740606
850.3302560.6605120.669744
860.2758760.5517520.724124
870.2262570.4525150.773743
880.2754280.5508560.724572
890.3002370.6004740.699763
900.2447360.4894710.755264
910.1948480.3896960.805152
920.1897290.3794580.810271
930.1510020.3020040.848998
940.1153710.2307430.884629
950.08750120.1750020.912499
960.06577950.1315590.934221
970.04580490.09160980.954195
980.07346670.1469330.926533
990.07550780.1510160.924492
1000.06173150.1234630.938268
1010.03942120.07884240.960579
1020.02795150.05590290.972049
1030.03718330.07436650.962817
1040.1000750.2001490.899925
1050.06224570.1244910.937754
1060.601830.7963390.39817
1070.4460270.8920540.553973
1080.9911340.01773120.00886559
1090.8948960.2102070.105104







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level160.153846NOK
10% type I error level520.5NOK

\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 & 16 & 0.153846 & NOK \tabularnewline
10% type I error level & 52 & 0.5 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267870&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]16[/C][C]0.153846[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]52[/C][C]0.5[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267870&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 level160.153846NOK
10% type I error level520.5NOK



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')
}