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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 computationFri, 20 Nov 2009 07:06:51 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t1258726193a5pn1ys93gmm5rz.htm/, Retrieved Fri, 29 Mar 2024 04:45:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58180, Retrieved Fri, 29 Mar 2024 04:45:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact154
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 14:03:14] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 14:06:51] [27b6e36591879260e4dc6bb7e89a38fd] [Current]
-    D        [Multiple Regression] [] [2009-12-15 16:06:53] [e149fd9094b67af26551857fa83a9d9d]
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Dataseries X:
613	0
611	0
594	0
595	0
591	0
589	0
584	0
573	0
567	0
569	0
621	0
629	0
628	0
612	0
595	0
597	0
593	0
590	0
580	0
574	0
573	0
573	0
620	0
626	0
620	0
588	0
566	0
557	0
561	0
549	0
532	0
526	0
511	0
499	0
555	0
565	0
542	0
527	0
510	0
514	0
517	0
508	0
493	0
490	0
469	0
478	0
528	0
534	0
518	1
506	1
502	1
516	1
528	1
533	1
536	1
537	1
524	1
536	1
587	1
597	1
581	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
WklBe[t] = + 595.594736842105 -26.9736842105263X[t] -2.93684210526333M1[t] -21.4M2[t] -36.8M3[t] -34.4M4[t] -32.1999999999999M5[t] -36.3999999999999M6[t] -45.2M7[t] -50.2M8[t] -61.4M9[t] -59.2M10[t] -7.99999999999997M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WklBe[t] =  +  595.594736842105 -26.9736842105263X[t] -2.93684210526333M1[t] -21.4M2[t] -36.8M3[t] -34.4M4[t] -32.1999999999999M5[t] -36.3999999999999M6[t] -45.2M7[t] -50.2M8[t] -61.4M9[t] -59.2M10[t] -7.99999999999997M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WklBe[t] =  +  595.594736842105 -26.9736842105263X[t] -2.93684210526333M1[t] -21.4M2[t] -36.8M3[t] -34.4M4[t] -32.1999999999999M5[t] -36.3999999999999M6[t] -45.2M7[t] -50.2M8[t] -61.4M9[t] -59.2M10[t] -7.99999999999997M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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
WklBe[t] = + 595.594736842105 -26.9736842105263X[t] -2.93684210526333M1[t] -21.4M2[t] -36.8M3[t] -34.4M4[t] -32.1999999999999M5[t] -36.3999999999999M6[t] -45.2M7[t] -50.2M8[t] -61.4M9[t] -59.2M10[t] -7.99999999999997M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)595.59473684210517.90812633.258400
X-26.973684210526312.457042-2.16530.0353620.017681
M1-2.9368421052633324.069292-0.1220.9033960.451698
M2-21.425.079628-0.85330.3977410.19887
M3-36.825.079628-1.46730.1488090.074405
M4-34.425.079628-1.37160.1765560.088278
M5-32.199999999999925.079628-1.28390.2053360.102668
M6-36.399999999999925.079628-1.45140.153180.07659
M7-45.225.079628-1.80230.0777860.038893
M8-50.225.079628-2.00160.0509950.025497
M9-61.425.079628-2.44820.0180630.009031
M10-59.225.079628-2.36050.0223620.011181
M11-7.9999999999999725.079628-0.3190.7511230.375561

\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) & 595.594736842105 & 17.908126 & 33.2584 & 0 & 0 \tabularnewline
X & -26.9736842105263 & 12.457042 & -2.1653 & 0.035362 & 0.017681 \tabularnewline
M1 & -2.93684210526333 & 24.069292 & -0.122 & 0.903396 & 0.451698 \tabularnewline
M2 & -21.4 & 25.079628 & -0.8533 & 0.397741 & 0.19887 \tabularnewline
M3 & -36.8 & 25.079628 & -1.4673 & 0.148809 & 0.074405 \tabularnewline
M4 & -34.4 & 25.079628 & -1.3716 & 0.176556 & 0.088278 \tabularnewline
M5 & -32.1999999999999 & 25.079628 & -1.2839 & 0.205336 & 0.102668 \tabularnewline
M6 & -36.3999999999999 & 25.079628 & -1.4514 & 0.15318 & 0.07659 \tabularnewline
M7 & -45.2 & 25.079628 & -1.8023 & 0.077786 & 0.038893 \tabularnewline
M8 & -50.2 & 25.079628 & -2.0016 & 0.050995 & 0.025497 \tabularnewline
M9 & -61.4 & 25.079628 & -2.4482 & 0.018063 & 0.009031 \tabularnewline
M10 & -59.2 & 25.079628 & -2.3605 & 0.022362 & 0.011181 \tabularnewline
M11 & -7.99999999999997 & 25.079628 & -0.319 & 0.751123 & 0.375561 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&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]595.594736842105[/C][C]17.908126[/C][C]33.2584[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]-26.9736842105263[/C][C]12.457042[/C][C]-2.1653[/C][C]0.035362[/C][C]0.017681[/C][/ROW]
[ROW][C]M1[/C][C]-2.93684210526333[/C][C]24.069292[/C][C]-0.122[/C][C]0.903396[/C][C]0.451698[/C][/ROW]
[ROW][C]M2[/C][C]-21.4[/C][C]25.079628[/C][C]-0.8533[/C][C]0.397741[/C][C]0.19887[/C][/ROW]
[ROW][C]M3[/C][C]-36.8[/C][C]25.079628[/C][C]-1.4673[/C][C]0.148809[/C][C]0.074405[/C][/ROW]
[ROW][C]M4[/C][C]-34.4[/C][C]25.079628[/C][C]-1.3716[/C][C]0.176556[/C][C]0.088278[/C][/ROW]
[ROW][C]M5[/C][C]-32.1999999999999[/C][C]25.079628[/C][C]-1.2839[/C][C]0.205336[/C][C]0.102668[/C][/ROW]
[ROW][C]M6[/C][C]-36.3999999999999[/C][C]25.079628[/C][C]-1.4514[/C][C]0.15318[/C][C]0.07659[/C][/ROW]
[ROW][C]M7[/C][C]-45.2[/C][C]25.079628[/C][C]-1.8023[/C][C]0.077786[/C][C]0.038893[/C][/ROW]
[ROW][C]M8[/C][C]-50.2[/C][C]25.079628[/C][C]-2.0016[/C][C]0.050995[/C][C]0.025497[/C][/ROW]
[ROW][C]M9[/C][C]-61.4[/C][C]25.079628[/C][C]-2.4482[/C][C]0.018063[/C][C]0.009031[/C][/ROW]
[ROW][C]M10[/C][C]-59.2[/C][C]25.079628[/C][C]-2.3605[/C][C]0.022362[/C][C]0.011181[/C][/ROW]
[ROW][C]M11[/C][C]-7.99999999999997[/C][C]25.079628[/C][C]-0.319[/C][C]0.751123[/C][C]0.375561[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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)595.59473684210517.90812633.258400
X-26.973684210526312.457042-2.16530.0353620.017681
M1-2.9368421052633324.069292-0.1220.9033960.451698
M2-21.425.079628-0.85330.3977410.19887
M3-36.825.079628-1.46730.1488090.074405
M4-34.425.079628-1.37160.1765560.088278
M5-32.199999999999925.079628-1.28390.2053360.102668
M6-36.399999999999925.079628-1.45140.153180.07659
M7-45.225.079628-1.80230.0777860.038893
M8-50.225.079628-2.00160.0509950.025497
M9-61.425.079628-2.44820.0180630.009031
M10-59.225.079628-2.36050.0223620.011181
M11-7.9999999999999725.079628-0.3190.7511230.375561







Multiple Linear Regression - Regression Statistics
Multiple R0.537167659901329
R-squared0.288549094843869
Adjusted R-squared0.110686368554837
F-TEST (value)1.62231345973505
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.116971788217152
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.6543730028053
Sum Squared Residuals75478.5263157894

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.537167659901329 \tabularnewline
R-squared & 0.288549094843869 \tabularnewline
Adjusted R-squared & 0.110686368554837 \tabularnewline
F-TEST (value) & 1.62231345973505 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.116971788217152 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 39.6543730028053 \tabularnewline
Sum Squared Residuals & 75478.5263157894 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.537167659901329[/C][/ROW]
[ROW][C]R-squared[/C][C]0.288549094843869[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.110686368554837[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.62231345973505[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.116971788217152[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]39.6543730028053[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]75478.5263157894[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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 R0.537167659901329
R-squared0.288549094843869
Adjusted R-squared0.110686368554837
F-TEST (value)1.62231345973505
F-TEST (DF numerator)12
F-TEST (DF denominator)48
p-value0.116971788217152
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation39.6543730028053
Sum Squared Residuals75478.5263157894







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1613592.65789473684320.3421052631569
2611574.19473684210536.8052631578947
3594558.79473684210535.2052631578947
4595561.19473684210533.8052631578948
5591563.39473684210527.6052631578948
6589559.19473684210529.8052631578948
7584550.39473684210533.6052631578947
8573545.39473684210527.6052631578947
9567534.19473684210532.8052631578947
10569536.39473684210532.6052631578948
11621587.59473684210533.4052631578948
12629595.59473684210533.4052631578948
13628592.65789473684235.3421052631581
14612574.19473684210537.8052631578947
15595558.79473684210536.2052631578947
16597561.19473684210535.8052631578947
17593563.39473684210529.6052631578947
18590559.19473684210530.8052631578947
19580550.39473684210529.6052631578947
20574545.39473684210528.6052631578947
21573534.19473684210538.8052631578948
22573536.39473684210536.6052631578947
23620587.59473684210532.4052631578947
24626595.59473684210530.4052631578948
25620592.65789473684227.3421052631581
26588574.19473684210513.8052631578947
27566558.7947368421057.20526315789473
28557561.194736842105-4.19473684210529
29561563.394736842105-2.39473684210529
30549559.194736842105-10.1947368421053
31532550.394736842105-18.3947368421052
32526545.394736842105-19.3947368421052
33511534.194736842105-23.1947368421053
34499536.394736842105-37.3947368421053
35555587.594736842105-32.5947368421053
36565595.594736842105-30.5947368421052
37542592.657894736842-50.6578947368419
38527574.194736842105-47.1947368421053
39510558.794736842105-48.7947368421053
40514561.194736842105-47.1947368421053
41517563.394736842105-46.3947368421053
42508559.194736842105-51.1947368421053
43493550.394736842105-57.3947368421052
44490545.394736842105-55.3947368421052
45469534.194736842105-65.1947368421052
46478536.394736842105-58.3947368421052
47528587.594736842105-59.5947368421052
48534595.594736842105-61.5947368421052
49518565.684210526316-47.6842105263156
50506547.221052631579-41.221052631579
51502531.821052631579-29.821052631579
52516534.221052631579-18.221052631579
53528536.421052631579-8.421052631579
54533532.2210526315790.778947368421
55536523.42105263157912.5789473684210
56537518.42105263157918.5789473684210
57524507.22105263157916.7789473684210
58536509.42105263157926.578947368421
59587560.62105263157926.378947368421
60597568.62105263157928.3789473684211
61581565.68421052631615.3157894736844

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 613 & 592.657894736843 & 20.3421052631569 \tabularnewline
2 & 611 & 574.194736842105 & 36.8052631578947 \tabularnewline
3 & 594 & 558.794736842105 & 35.2052631578947 \tabularnewline
4 & 595 & 561.194736842105 & 33.8052631578948 \tabularnewline
5 & 591 & 563.394736842105 & 27.6052631578948 \tabularnewline
6 & 589 & 559.194736842105 & 29.8052631578948 \tabularnewline
7 & 584 & 550.394736842105 & 33.6052631578947 \tabularnewline
8 & 573 & 545.394736842105 & 27.6052631578947 \tabularnewline
9 & 567 & 534.194736842105 & 32.8052631578947 \tabularnewline
10 & 569 & 536.394736842105 & 32.6052631578948 \tabularnewline
11 & 621 & 587.594736842105 & 33.4052631578948 \tabularnewline
12 & 629 & 595.594736842105 & 33.4052631578948 \tabularnewline
13 & 628 & 592.657894736842 & 35.3421052631581 \tabularnewline
14 & 612 & 574.194736842105 & 37.8052631578947 \tabularnewline
15 & 595 & 558.794736842105 & 36.2052631578947 \tabularnewline
16 & 597 & 561.194736842105 & 35.8052631578947 \tabularnewline
17 & 593 & 563.394736842105 & 29.6052631578947 \tabularnewline
18 & 590 & 559.194736842105 & 30.8052631578947 \tabularnewline
19 & 580 & 550.394736842105 & 29.6052631578947 \tabularnewline
20 & 574 & 545.394736842105 & 28.6052631578947 \tabularnewline
21 & 573 & 534.194736842105 & 38.8052631578948 \tabularnewline
22 & 573 & 536.394736842105 & 36.6052631578947 \tabularnewline
23 & 620 & 587.594736842105 & 32.4052631578947 \tabularnewline
24 & 626 & 595.594736842105 & 30.4052631578948 \tabularnewline
25 & 620 & 592.657894736842 & 27.3421052631581 \tabularnewline
26 & 588 & 574.194736842105 & 13.8052631578947 \tabularnewline
27 & 566 & 558.794736842105 & 7.20526315789473 \tabularnewline
28 & 557 & 561.194736842105 & -4.19473684210529 \tabularnewline
29 & 561 & 563.394736842105 & -2.39473684210529 \tabularnewline
30 & 549 & 559.194736842105 & -10.1947368421053 \tabularnewline
31 & 532 & 550.394736842105 & -18.3947368421052 \tabularnewline
32 & 526 & 545.394736842105 & -19.3947368421052 \tabularnewline
33 & 511 & 534.194736842105 & -23.1947368421053 \tabularnewline
34 & 499 & 536.394736842105 & -37.3947368421053 \tabularnewline
35 & 555 & 587.594736842105 & -32.5947368421053 \tabularnewline
36 & 565 & 595.594736842105 & -30.5947368421052 \tabularnewline
37 & 542 & 592.657894736842 & -50.6578947368419 \tabularnewline
38 & 527 & 574.194736842105 & -47.1947368421053 \tabularnewline
39 & 510 & 558.794736842105 & -48.7947368421053 \tabularnewline
40 & 514 & 561.194736842105 & -47.1947368421053 \tabularnewline
41 & 517 & 563.394736842105 & -46.3947368421053 \tabularnewline
42 & 508 & 559.194736842105 & -51.1947368421053 \tabularnewline
43 & 493 & 550.394736842105 & -57.3947368421052 \tabularnewline
44 & 490 & 545.394736842105 & -55.3947368421052 \tabularnewline
45 & 469 & 534.194736842105 & -65.1947368421052 \tabularnewline
46 & 478 & 536.394736842105 & -58.3947368421052 \tabularnewline
47 & 528 & 587.594736842105 & -59.5947368421052 \tabularnewline
48 & 534 & 595.594736842105 & -61.5947368421052 \tabularnewline
49 & 518 & 565.684210526316 & -47.6842105263156 \tabularnewline
50 & 506 & 547.221052631579 & -41.221052631579 \tabularnewline
51 & 502 & 531.821052631579 & -29.821052631579 \tabularnewline
52 & 516 & 534.221052631579 & -18.221052631579 \tabularnewline
53 & 528 & 536.421052631579 & -8.421052631579 \tabularnewline
54 & 533 & 532.221052631579 & 0.778947368421 \tabularnewline
55 & 536 & 523.421052631579 & 12.5789473684210 \tabularnewline
56 & 537 & 518.421052631579 & 18.5789473684210 \tabularnewline
57 & 524 & 507.221052631579 & 16.7789473684210 \tabularnewline
58 & 536 & 509.421052631579 & 26.578947368421 \tabularnewline
59 & 587 & 560.621052631579 & 26.378947368421 \tabularnewline
60 & 597 & 568.621052631579 & 28.3789473684211 \tabularnewline
61 & 581 & 565.684210526316 & 15.3157894736844 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&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]613[/C][C]592.657894736843[/C][C]20.3421052631569[/C][/ROW]
[ROW][C]2[/C][C]611[/C][C]574.194736842105[/C][C]36.8052631578947[/C][/ROW]
[ROW][C]3[/C][C]594[/C][C]558.794736842105[/C][C]35.2052631578947[/C][/ROW]
[ROW][C]4[/C][C]595[/C][C]561.194736842105[/C][C]33.8052631578948[/C][/ROW]
[ROW][C]5[/C][C]591[/C][C]563.394736842105[/C][C]27.6052631578948[/C][/ROW]
[ROW][C]6[/C][C]589[/C][C]559.194736842105[/C][C]29.8052631578948[/C][/ROW]
[ROW][C]7[/C][C]584[/C][C]550.394736842105[/C][C]33.6052631578947[/C][/ROW]
[ROW][C]8[/C][C]573[/C][C]545.394736842105[/C][C]27.6052631578947[/C][/ROW]
[ROW][C]9[/C][C]567[/C][C]534.194736842105[/C][C]32.8052631578947[/C][/ROW]
[ROW][C]10[/C][C]569[/C][C]536.394736842105[/C][C]32.6052631578948[/C][/ROW]
[ROW][C]11[/C][C]621[/C][C]587.594736842105[/C][C]33.4052631578948[/C][/ROW]
[ROW][C]12[/C][C]629[/C][C]595.594736842105[/C][C]33.4052631578948[/C][/ROW]
[ROW][C]13[/C][C]628[/C][C]592.657894736842[/C][C]35.3421052631581[/C][/ROW]
[ROW][C]14[/C][C]612[/C][C]574.194736842105[/C][C]37.8052631578947[/C][/ROW]
[ROW][C]15[/C][C]595[/C][C]558.794736842105[/C][C]36.2052631578947[/C][/ROW]
[ROW][C]16[/C][C]597[/C][C]561.194736842105[/C][C]35.8052631578947[/C][/ROW]
[ROW][C]17[/C][C]593[/C][C]563.394736842105[/C][C]29.6052631578947[/C][/ROW]
[ROW][C]18[/C][C]590[/C][C]559.194736842105[/C][C]30.8052631578947[/C][/ROW]
[ROW][C]19[/C][C]580[/C][C]550.394736842105[/C][C]29.6052631578947[/C][/ROW]
[ROW][C]20[/C][C]574[/C][C]545.394736842105[/C][C]28.6052631578947[/C][/ROW]
[ROW][C]21[/C][C]573[/C][C]534.194736842105[/C][C]38.8052631578948[/C][/ROW]
[ROW][C]22[/C][C]573[/C][C]536.394736842105[/C][C]36.6052631578947[/C][/ROW]
[ROW][C]23[/C][C]620[/C][C]587.594736842105[/C][C]32.4052631578947[/C][/ROW]
[ROW][C]24[/C][C]626[/C][C]595.594736842105[/C][C]30.4052631578948[/C][/ROW]
[ROW][C]25[/C][C]620[/C][C]592.657894736842[/C][C]27.3421052631581[/C][/ROW]
[ROW][C]26[/C][C]588[/C][C]574.194736842105[/C][C]13.8052631578947[/C][/ROW]
[ROW][C]27[/C][C]566[/C][C]558.794736842105[/C][C]7.20526315789473[/C][/ROW]
[ROW][C]28[/C][C]557[/C][C]561.194736842105[/C][C]-4.19473684210529[/C][/ROW]
[ROW][C]29[/C][C]561[/C][C]563.394736842105[/C][C]-2.39473684210529[/C][/ROW]
[ROW][C]30[/C][C]549[/C][C]559.194736842105[/C][C]-10.1947368421053[/C][/ROW]
[ROW][C]31[/C][C]532[/C][C]550.394736842105[/C][C]-18.3947368421052[/C][/ROW]
[ROW][C]32[/C][C]526[/C][C]545.394736842105[/C][C]-19.3947368421052[/C][/ROW]
[ROW][C]33[/C][C]511[/C][C]534.194736842105[/C][C]-23.1947368421053[/C][/ROW]
[ROW][C]34[/C][C]499[/C][C]536.394736842105[/C][C]-37.3947368421053[/C][/ROW]
[ROW][C]35[/C][C]555[/C][C]587.594736842105[/C][C]-32.5947368421053[/C][/ROW]
[ROW][C]36[/C][C]565[/C][C]595.594736842105[/C][C]-30.5947368421052[/C][/ROW]
[ROW][C]37[/C][C]542[/C][C]592.657894736842[/C][C]-50.6578947368419[/C][/ROW]
[ROW][C]38[/C][C]527[/C][C]574.194736842105[/C][C]-47.1947368421053[/C][/ROW]
[ROW][C]39[/C][C]510[/C][C]558.794736842105[/C][C]-48.7947368421053[/C][/ROW]
[ROW][C]40[/C][C]514[/C][C]561.194736842105[/C][C]-47.1947368421053[/C][/ROW]
[ROW][C]41[/C][C]517[/C][C]563.394736842105[/C][C]-46.3947368421053[/C][/ROW]
[ROW][C]42[/C][C]508[/C][C]559.194736842105[/C][C]-51.1947368421053[/C][/ROW]
[ROW][C]43[/C][C]493[/C][C]550.394736842105[/C][C]-57.3947368421052[/C][/ROW]
[ROW][C]44[/C][C]490[/C][C]545.394736842105[/C][C]-55.3947368421052[/C][/ROW]
[ROW][C]45[/C][C]469[/C][C]534.194736842105[/C][C]-65.1947368421052[/C][/ROW]
[ROW][C]46[/C][C]478[/C][C]536.394736842105[/C][C]-58.3947368421052[/C][/ROW]
[ROW][C]47[/C][C]528[/C][C]587.594736842105[/C][C]-59.5947368421052[/C][/ROW]
[ROW][C]48[/C][C]534[/C][C]595.594736842105[/C][C]-61.5947368421052[/C][/ROW]
[ROW][C]49[/C][C]518[/C][C]565.684210526316[/C][C]-47.6842105263156[/C][/ROW]
[ROW][C]50[/C][C]506[/C][C]547.221052631579[/C][C]-41.221052631579[/C][/ROW]
[ROW][C]51[/C][C]502[/C][C]531.821052631579[/C][C]-29.821052631579[/C][/ROW]
[ROW][C]52[/C][C]516[/C][C]534.221052631579[/C][C]-18.221052631579[/C][/ROW]
[ROW][C]53[/C][C]528[/C][C]536.421052631579[/C][C]-8.421052631579[/C][/ROW]
[ROW][C]54[/C][C]533[/C][C]532.221052631579[/C][C]0.778947368421[/C][/ROW]
[ROW][C]55[/C][C]536[/C][C]523.421052631579[/C][C]12.5789473684210[/C][/ROW]
[ROW][C]56[/C][C]537[/C][C]518.421052631579[/C][C]18.5789473684210[/C][/ROW]
[ROW][C]57[/C][C]524[/C][C]507.221052631579[/C][C]16.7789473684210[/C][/ROW]
[ROW][C]58[/C][C]536[/C][C]509.421052631579[/C][C]26.578947368421[/C][/ROW]
[ROW][C]59[/C][C]587[/C][C]560.621052631579[/C][C]26.378947368421[/C][/ROW]
[ROW][C]60[/C][C]597[/C][C]568.621052631579[/C][C]28.3789473684211[/C][/ROW]
[ROW][C]61[/C][C]581[/C][C]565.684210526316[/C][C]15.3157894736844[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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
1613592.65789473684320.3421052631569
2611574.19473684210536.8052631578947
3594558.79473684210535.2052631578947
4595561.19473684210533.8052631578948
5591563.39473684210527.6052631578948
6589559.19473684210529.8052631578948
7584550.39473684210533.6052631578947
8573545.39473684210527.6052631578947
9567534.19473684210532.8052631578947
10569536.39473684210532.6052631578948
11621587.59473684210533.4052631578948
12629595.59473684210533.4052631578948
13628592.65789473684235.3421052631581
14612574.19473684210537.8052631578947
15595558.79473684210536.2052631578947
16597561.19473684210535.8052631578947
17593563.39473684210529.6052631578947
18590559.19473684210530.8052631578947
19580550.39473684210529.6052631578947
20574545.39473684210528.6052631578947
21573534.19473684210538.8052631578948
22573536.39473684210536.6052631578947
23620587.59473684210532.4052631578947
24626595.59473684210530.4052631578948
25620592.65789473684227.3421052631581
26588574.19473684210513.8052631578947
27566558.7947368421057.20526315789473
28557561.194736842105-4.19473684210529
29561563.394736842105-2.39473684210529
30549559.194736842105-10.1947368421053
31532550.394736842105-18.3947368421052
32526545.394736842105-19.3947368421052
33511534.194736842105-23.1947368421053
34499536.394736842105-37.3947368421053
35555587.594736842105-32.5947368421053
36565595.594736842105-30.5947368421052
37542592.657894736842-50.6578947368419
38527574.194736842105-47.1947368421053
39510558.794736842105-48.7947368421053
40514561.194736842105-47.1947368421053
41517563.394736842105-46.3947368421053
42508559.194736842105-51.1947368421053
43493550.394736842105-57.3947368421052
44490545.394736842105-55.3947368421052
45469534.194736842105-65.1947368421052
46478536.394736842105-58.3947368421052
47528587.594736842105-59.5947368421052
48534595.594736842105-61.5947368421052
49518565.684210526316-47.6842105263156
50506547.221052631579-41.221052631579
51502531.821052631579-29.821052631579
52516534.221052631579-18.221052631579
53528536.421052631579-8.421052631579
54533532.2210526315790.778947368421
55536523.42105263157912.5789473684210
56537518.42105263157918.5789473684210
57524507.22105263157916.7789473684210
58536509.42105263157926.578947368421
59587560.62105263157926.378947368421
60597568.62105263157928.3789473684211
61581565.68421052631615.3157894736844







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.005796367046776840.01159273409355370.994203632953223
170.0008448655984879980.001689731196976000.999155134401512
180.0001158555359943660.0002317110719887320.999884144464006
191.85471221270918e-053.70942442541836e-050.999981452877873
202.47079153541971e-064.94158307083941e-060.999997529208465
216.48299488131684e-071.29659897626337e-060.999999351700512
221.43604406882979e-072.87208813765959e-070.999999856395593
232.67486062750629e-085.34972125501259e-080.999999973251394
246.14066803656605e-091.22813360731321e-080.999999993859332
252.26669095644399e-094.53338191288799e-090.99999999773331
261.90142111547462e-063.80284223094923e-060.999998098578885
270.0001254155631886280.0002508311263772560.999874584436811
280.004366502053140350.00873300410628070.99563349794686
290.01686518745420790.03373037490841580.983134812545792
300.06644272328618580.1328854465723720.933557276713814
310.1895690486886510.3791380973773010.81043095131135
320.3023486419523310.6046972839046620.697651358047669
330.486571724434940.973143448869880.51342827556506
340.6402811478958940.7194377042082130.359718852104106
350.6999019500107150.600196099978570.300098049989285
360.7224876021638750.5550247956722510.277512397836125
370.7938849989857510.4122300020284980.206115001014249
380.898456753504250.2030864929915010.101543246495751
390.9409543829526360.1180912340947270.0590456170473637
400.9577525965387540.08449480692249270.0422474034612464
410.96376222159320.07247555681359930.0362377784067996
420.9564564972608270.08708700547834610.0435435027391731
430.9255025530712870.1489948938574270.0744974469287134
440.864721901182340.270556197635320.13527809881766
450.7583393197687840.4833213604624330.241660680231216

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.00579636704677684 & 0.0115927340935537 & 0.994203632953223 \tabularnewline
17 & 0.000844865598487998 & 0.00168973119697600 & 0.999155134401512 \tabularnewline
18 & 0.000115855535994366 & 0.000231711071988732 & 0.999884144464006 \tabularnewline
19 & 1.85471221270918e-05 & 3.70942442541836e-05 & 0.999981452877873 \tabularnewline
20 & 2.47079153541971e-06 & 4.94158307083941e-06 & 0.999997529208465 \tabularnewline
21 & 6.48299488131684e-07 & 1.29659897626337e-06 & 0.999999351700512 \tabularnewline
22 & 1.43604406882979e-07 & 2.87208813765959e-07 & 0.999999856395593 \tabularnewline
23 & 2.67486062750629e-08 & 5.34972125501259e-08 & 0.999999973251394 \tabularnewline
24 & 6.14066803656605e-09 & 1.22813360731321e-08 & 0.999999993859332 \tabularnewline
25 & 2.26669095644399e-09 & 4.53338191288799e-09 & 0.99999999773331 \tabularnewline
26 & 1.90142111547462e-06 & 3.80284223094923e-06 & 0.999998098578885 \tabularnewline
27 & 0.000125415563188628 & 0.000250831126377256 & 0.999874584436811 \tabularnewline
28 & 0.00436650205314035 & 0.0087330041062807 & 0.99563349794686 \tabularnewline
29 & 0.0168651874542079 & 0.0337303749084158 & 0.983134812545792 \tabularnewline
30 & 0.0664427232861858 & 0.132885446572372 & 0.933557276713814 \tabularnewline
31 & 0.189569048688651 & 0.379138097377301 & 0.81043095131135 \tabularnewline
32 & 0.302348641952331 & 0.604697283904662 & 0.697651358047669 \tabularnewline
33 & 0.48657172443494 & 0.97314344886988 & 0.51342827556506 \tabularnewline
34 & 0.640281147895894 & 0.719437704208213 & 0.359718852104106 \tabularnewline
35 & 0.699901950010715 & 0.60019609997857 & 0.300098049989285 \tabularnewline
36 & 0.722487602163875 & 0.555024795672251 & 0.277512397836125 \tabularnewline
37 & 0.793884998985751 & 0.412230002028498 & 0.206115001014249 \tabularnewline
38 & 0.89845675350425 & 0.203086492991501 & 0.101543246495751 \tabularnewline
39 & 0.940954382952636 & 0.118091234094727 & 0.0590456170473637 \tabularnewline
40 & 0.957752596538754 & 0.0844948069224927 & 0.0422474034612464 \tabularnewline
41 & 0.9637622215932 & 0.0724755568135993 & 0.0362377784067996 \tabularnewline
42 & 0.956456497260827 & 0.0870870054783461 & 0.0435435027391731 \tabularnewline
43 & 0.925502553071287 & 0.148994893857427 & 0.0744974469287134 \tabularnewline
44 & 0.86472190118234 & 0.27055619763532 & 0.13527809881766 \tabularnewline
45 & 0.758339319768784 & 0.483321360462433 & 0.241660680231216 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&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]16[/C][C]0.00579636704677684[/C][C]0.0115927340935537[/C][C]0.994203632953223[/C][/ROW]
[ROW][C]17[/C][C]0.000844865598487998[/C][C]0.00168973119697600[/C][C]0.999155134401512[/C][/ROW]
[ROW][C]18[/C][C]0.000115855535994366[/C][C]0.000231711071988732[/C][C]0.999884144464006[/C][/ROW]
[ROW][C]19[/C][C]1.85471221270918e-05[/C][C]3.70942442541836e-05[/C][C]0.999981452877873[/C][/ROW]
[ROW][C]20[/C][C]2.47079153541971e-06[/C][C]4.94158307083941e-06[/C][C]0.999997529208465[/C][/ROW]
[ROW][C]21[/C][C]6.48299488131684e-07[/C][C]1.29659897626337e-06[/C][C]0.999999351700512[/C][/ROW]
[ROW][C]22[/C][C]1.43604406882979e-07[/C][C]2.87208813765959e-07[/C][C]0.999999856395593[/C][/ROW]
[ROW][C]23[/C][C]2.67486062750629e-08[/C][C]5.34972125501259e-08[/C][C]0.999999973251394[/C][/ROW]
[ROW][C]24[/C][C]6.14066803656605e-09[/C][C]1.22813360731321e-08[/C][C]0.999999993859332[/C][/ROW]
[ROW][C]25[/C][C]2.26669095644399e-09[/C][C]4.53338191288799e-09[/C][C]0.99999999773331[/C][/ROW]
[ROW][C]26[/C][C]1.90142111547462e-06[/C][C]3.80284223094923e-06[/C][C]0.999998098578885[/C][/ROW]
[ROW][C]27[/C][C]0.000125415563188628[/C][C]0.000250831126377256[/C][C]0.999874584436811[/C][/ROW]
[ROW][C]28[/C][C]0.00436650205314035[/C][C]0.0087330041062807[/C][C]0.99563349794686[/C][/ROW]
[ROW][C]29[/C][C]0.0168651874542079[/C][C]0.0337303749084158[/C][C]0.983134812545792[/C][/ROW]
[ROW][C]30[/C][C]0.0664427232861858[/C][C]0.132885446572372[/C][C]0.933557276713814[/C][/ROW]
[ROW][C]31[/C][C]0.189569048688651[/C][C]0.379138097377301[/C][C]0.81043095131135[/C][/ROW]
[ROW][C]32[/C][C]0.302348641952331[/C][C]0.604697283904662[/C][C]0.697651358047669[/C][/ROW]
[ROW][C]33[/C][C]0.48657172443494[/C][C]0.97314344886988[/C][C]0.51342827556506[/C][/ROW]
[ROW][C]34[/C][C]0.640281147895894[/C][C]0.719437704208213[/C][C]0.359718852104106[/C][/ROW]
[ROW][C]35[/C][C]0.699901950010715[/C][C]0.60019609997857[/C][C]0.300098049989285[/C][/ROW]
[ROW][C]36[/C][C]0.722487602163875[/C][C]0.555024795672251[/C][C]0.277512397836125[/C][/ROW]
[ROW][C]37[/C][C]0.793884998985751[/C][C]0.412230002028498[/C][C]0.206115001014249[/C][/ROW]
[ROW][C]38[/C][C]0.89845675350425[/C][C]0.203086492991501[/C][C]0.101543246495751[/C][/ROW]
[ROW][C]39[/C][C]0.940954382952636[/C][C]0.118091234094727[/C][C]0.0590456170473637[/C][/ROW]
[ROW][C]40[/C][C]0.957752596538754[/C][C]0.0844948069224927[/C][C]0.0422474034612464[/C][/ROW]
[ROW][C]41[/C][C]0.9637622215932[/C][C]0.0724755568135993[/C][C]0.0362377784067996[/C][/ROW]
[ROW][C]42[/C][C]0.956456497260827[/C][C]0.0870870054783461[/C][C]0.0435435027391731[/C][/ROW]
[ROW][C]43[/C][C]0.925502553071287[/C][C]0.148994893857427[/C][C]0.0744974469287134[/C][/ROW]
[ROW][C]44[/C][C]0.86472190118234[/C][C]0.27055619763532[/C][C]0.13527809881766[/C][/ROW]
[ROW][C]45[/C][C]0.758339319768784[/C][C]0.483321360462433[/C][C]0.241660680231216[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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
160.005796367046776840.01159273409355370.994203632953223
170.0008448655984879980.001689731196976000.999155134401512
180.0001158555359943660.0002317110719887320.999884144464006
191.85471221270918e-053.70942442541836e-050.999981452877873
202.47079153541971e-064.94158307083941e-060.999997529208465
216.48299488131684e-071.29659897626337e-060.999999351700512
221.43604406882979e-072.87208813765959e-070.999999856395593
232.67486062750629e-085.34972125501259e-080.999999973251394
246.14066803656605e-091.22813360731321e-080.999999993859332
252.26669095644399e-094.53338191288799e-090.99999999773331
261.90142111547462e-063.80284223094923e-060.999998098578885
270.0001254155631886280.0002508311263772560.999874584436811
280.004366502053140350.00873300410628070.99563349794686
290.01686518745420790.03373037490841580.983134812545792
300.06644272328618580.1328854465723720.933557276713814
310.1895690486886510.3791380973773010.81043095131135
320.3023486419523310.6046972839046620.697651358047669
330.486571724434940.973143448869880.51342827556506
340.6402811478958940.7194377042082130.359718852104106
350.6999019500107150.600196099978570.300098049989285
360.7224876021638750.5550247956722510.277512397836125
370.7938849989857510.4122300020284980.206115001014249
380.898456753504250.2030864929915010.101543246495751
390.9409543829526360.1180912340947270.0590456170473637
400.9577525965387540.08449480692249270.0422474034612464
410.96376222159320.07247555681359930.0362377784067996
420.9564564972608270.08708700547834610.0435435027391731
430.9255025530712870.1489948938574270.0744974469287134
440.864721901182340.270556197635320.13527809881766
450.7583393197687840.4833213604624330.241660680231216







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level120.4NOK
5% type I error level140.466666666666667NOK
10% type I error level170.566666666666667NOK

\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 & 12 & 0.4 & NOK \tabularnewline
5% type I error level & 14 & 0.466666666666667 & NOK \tabularnewline
10% type I error level & 17 & 0.566666666666667 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58180&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]12[/C][C]0.4[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]14[/C][C]0.466666666666667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.566666666666667[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58180&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58180&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 level120.4NOK
5% type I error level140.466666666666667NOK
10% type I error level170.566666666666667NOK



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