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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 12:11:22 -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/t1258745130bpezakk0i5yg7pz.htm/, Retrieved Thu, 28 Mar 2024 22:44:20 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58438, Retrieved Thu, 28 Mar 2024 22:44:20 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111
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]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 19:11:22] [aa8eb70c35ea8a87edcd21d6427e653e] [Current]
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Dataseries X:
573	122	589	130	17,9	2849,27
567	117	584	127	17,4	2921,44
569	112	573	122	16,7	2981,85
621	113	567	117	16	3080,58
629	149	569	112	16,6	3106,22
628	157	621	113	19,1	3119,31
612	157	629	149	17,8	3061,26
595	147	628	157	17,2	3097,31
597	137	612	157	18,6	3161,69
593	132	595	147	16,3	3257,16
590	125	597	137	15,1	3277,01
580	123	593	132	19,2	3295,32
574	117	590	125	17,7	3363,99
573	114	580	123	19,1	3494,17
573	111	574	117	18	3667,03
620	112	573	114	17,5	3813,06
626	144	573	111	17,8	3917,96
620	150	620	112	21,1	3895,51
588	149	626	144	17,2	3801,06
566	134	620	150	19,4	3570,12
557	123	588	149	19,8	3701,61
561	116	566	134	17,6	3862,27
549	117	557	123	16,2	3970,1
532	111	561	116	19,5	4138,52
526	105	549	117	19,9	4199,75
511	102	532	111	20	4290,89
499	95	526	105	17,3	4443,91
555	93	511	102	18,9	4502,64
565	124	499	95	18,6	4356,98
542	130	555	93	21,4	4591,27
527	124	565	124	18,6	4696,96
510	115	542	130	19,8	4621,4
514	106	527	124	20,8	4562,84
517	105	510	115	19,6	4202,52
508	105	514	106	17,7	4296,49
493	101	517	105	19,8	4435,23
490	95	508	105	22,2	4105,18
469	93	493	101	20,7	4116,68
478	84	490	95	17,9	3844,49
528	87	469	93	20,9	3720,98
534	116	478	84	21,2	3674,4
518	120	528	87	21,4	3857,62
506	117	534	116	23	3801,06
502	109	518	120	21,3	3504,37
516	105	506	117	23,9	3032,6
528	107	502	109	22,4	3047,03
533	109	516	105	18,3	2962,34
536	109	528	107	22,8	2197,82
537	108	533	109	22,3	2014,45
524	107	536	109	17,8	1862,83
536	99	537	108	16,4	1905,41
587	103	524	107	16	1810,99
597	131	536	99	16,4	1670,07
581	137	587	103	17,7	1864,44
564	135	597	131	16,6	2052,02




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=58438&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=58438&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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
Y[t] = + 63.2419011471966 + 1.16232044459361X[t] + 0.780421649089665Y1[t] -0.792732218814561Y2[t] + 2.1038959924636Y3[t] -0.00841697355310678Y4[t] + 2.76698178211044M1[t] + 1.94119087812141M2[t] + 15.8696883851779M3[t] + 71.1133254322051M4[t] + 35.4385310945394M5[t] -25.7506260674336M6[t] -19.305462552463M7[t] -9.41876002842492M8[t] + 12.5861140469189M9[t] + 26.7813873646183M10[t] + 19.3890177289188M11[t] -0.268119756372126t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  63.2419011471966 +  1.16232044459361X[t] +  0.780421649089665Y1[t] -0.792732218814561Y2[t] +  2.1038959924636Y3[t] -0.00841697355310678Y4[t] +  2.76698178211044M1[t] +  1.94119087812141M2[t] +  15.8696883851779M3[t] +  71.1133254322051M4[t] +  35.4385310945394M5[t] -25.7506260674336M6[t] -19.305462552463M7[t] -9.41876002842492M8[t] +  12.5861140469189M9[t] +  26.7813873646183M10[t] +  19.3890177289188M11[t] -0.268119756372126t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  63.2419011471966 +  1.16232044459361X[t] +  0.780421649089665Y1[t] -0.792732218814561Y2[t] +  2.1038959924636Y3[t] -0.00841697355310678Y4[t] +  2.76698178211044M1[t] +  1.94119087812141M2[t] +  15.8696883851779M3[t] +  71.1133254322051M4[t] +  35.4385310945394M5[t] -25.7506260674336M6[t] -19.305462552463M7[t] -9.41876002842492M8[t] +  12.5861140469189M9[t] +  26.7813873646183M10[t] +  19.3890177289188M11[t] -0.268119756372126t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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
Y[t] = + 63.2419011471966 + 1.16232044459361X[t] + 0.780421649089665Y1[t] -0.792732218814561Y2[t] + 2.1038959924636Y3[t] -0.00841697355310678Y4[t] + 2.76698178211044M1[t] + 1.94119087812141M2[t] + 15.8696883851779M3[t] + 71.1133254322051M4[t] + 35.4385310945394M5[t] -25.7506260674336M6[t] -19.305462552463M7[t] -9.41876002842492M8[t] + 12.5861140469189M9[t] + 26.7813873646183M10[t] + 19.3890177289188M11[t] -0.268119756372126t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)63.241901147196652.5692791.2030.236610.118305
X1.162320444593610.356313.26210.0023810.00119
Y10.7804216490896650.1308985.96211e-060
Y2-0.7927322188145610.3909-2.0280.049810.024905
Y32.10389599246360.8679912.42390.0203620.010181
Y4-0.008416973553106780.002094-4.01890.0002760.000138
M12.766981782110444.6224430.59860.5530890.276544
M21.941190878121414.8673640.39880.692320.34616
M315.86968838517796.4040882.47810.0178980.008949
M471.11332543220515.92928111.993600
M535.438531094539412.0321872.94530.0055510.002776
M6-25.750626067433612.556784-2.05070.0474260.023713
M7-19.3054625524638.113687-2.37940.0226120.011306
M8-9.418760028424928.46238-1.1130.2728810.13644
M912.58611404691898.9351261.40860.1672990.083649
M1026.78138736461836.7586113.96260.0003250.000163
M1119.38901772891885.7871963.35030.0018680.000934
t-0.2681197563721260.15773-1.69990.0975490.048775

\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) & 63.2419011471966 & 52.569279 & 1.203 & 0.23661 & 0.118305 \tabularnewline
X & 1.16232044459361 & 0.35631 & 3.2621 & 0.002381 & 0.00119 \tabularnewline
Y1 & 0.780421649089665 & 0.130898 & 5.9621 & 1e-06 & 0 \tabularnewline
Y2 & -0.792732218814561 & 0.3909 & -2.028 & 0.04981 & 0.024905 \tabularnewline
Y3 & 2.1038959924636 & 0.867991 & 2.4239 & 0.020362 & 0.010181 \tabularnewline
Y4 & -0.00841697355310678 & 0.002094 & -4.0189 & 0.000276 & 0.000138 \tabularnewline
M1 & 2.76698178211044 & 4.622443 & 0.5986 & 0.553089 & 0.276544 \tabularnewline
M2 & 1.94119087812141 & 4.867364 & 0.3988 & 0.69232 & 0.34616 \tabularnewline
M3 & 15.8696883851779 & 6.404088 & 2.4781 & 0.017898 & 0.008949 \tabularnewline
M4 & 71.1133254322051 & 5.929281 & 11.9936 & 0 & 0 \tabularnewline
M5 & 35.4385310945394 & 12.032187 & 2.9453 & 0.005551 & 0.002776 \tabularnewline
M6 & -25.7506260674336 & 12.556784 & -2.0507 & 0.047426 & 0.023713 \tabularnewline
M7 & -19.305462552463 & 8.113687 & -2.3794 & 0.022612 & 0.011306 \tabularnewline
M8 & -9.41876002842492 & 8.46238 & -1.113 & 0.272881 & 0.13644 \tabularnewline
M9 & 12.5861140469189 & 8.935126 & 1.4086 & 0.167299 & 0.083649 \tabularnewline
M10 & 26.7813873646183 & 6.758611 & 3.9626 & 0.000325 & 0.000163 \tabularnewline
M11 & 19.3890177289188 & 5.787196 & 3.3503 & 0.001868 & 0.000934 \tabularnewline
t & -0.268119756372126 & 0.15773 & -1.6999 & 0.097549 & 0.048775 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&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]63.2419011471966[/C][C]52.569279[/C][C]1.203[/C][C]0.23661[/C][C]0.118305[/C][/ROW]
[ROW][C]X[/C][C]1.16232044459361[/C][C]0.35631[/C][C]3.2621[/C][C]0.002381[/C][C]0.00119[/C][/ROW]
[ROW][C]Y1[/C][C]0.780421649089665[/C][C]0.130898[/C][C]5.9621[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Y2[/C][C]-0.792732218814561[/C][C]0.3909[/C][C]-2.028[/C][C]0.04981[/C][C]0.024905[/C][/ROW]
[ROW][C]Y3[/C][C]2.1038959924636[/C][C]0.867991[/C][C]2.4239[/C][C]0.020362[/C][C]0.010181[/C][/ROW]
[ROW][C]Y4[/C][C]-0.00841697355310678[/C][C]0.002094[/C][C]-4.0189[/C][C]0.000276[/C][C]0.000138[/C][/ROW]
[ROW][C]M1[/C][C]2.76698178211044[/C][C]4.622443[/C][C]0.5986[/C][C]0.553089[/C][C]0.276544[/C][/ROW]
[ROW][C]M2[/C][C]1.94119087812141[/C][C]4.867364[/C][C]0.3988[/C][C]0.69232[/C][C]0.34616[/C][/ROW]
[ROW][C]M3[/C][C]15.8696883851779[/C][C]6.404088[/C][C]2.4781[/C][C]0.017898[/C][C]0.008949[/C][/ROW]
[ROW][C]M4[/C][C]71.1133254322051[/C][C]5.929281[/C][C]11.9936[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]35.4385310945394[/C][C]12.032187[/C][C]2.9453[/C][C]0.005551[/C][C]0.002776[/C][/ROW]
[ROW][C]M6[/C][C]-25.7506260674336[/C][C]12.556784[/C][C]-2.0507[/C][C]0.047426[/C][C]0.023713[/C][/ROW]
[ROW][C]M7[/C][C]-19.305462552463[/C][C]8.113687[/C][C]-2.3794[/C][C]0.022612[/C][C]0.011306[/C][/ROW]
[ROW][C]M8[/C][C]-9.41876002842492[/C][C]8.46238[/C][C]-1.113[/C][C]0.272881[/C][C]0.13644[/C][/ROW]
[ROW][C]M9[/C][C]12.5861140469189[/C][C]8.935126[/C][C]1.4086[/C][C]0.167299[/C][C]0.083649[/C][/ROW]
[ROW][C]M10[/C][C]26.7813873646183[/C][C]6.758611[/C][C]3.9626[/C][C]0.000325[/C][C]0.000163[/C][/ROW]
[ROW][C]M11[/C][C]19.3890177289188[/C][C]5.787196[/C][C]3.3503[/C][C]0.001868[/C][C]0.000934[/C][/ROW]
[ROW][C]t[/C][C]-0.268119756372126[/C][C]0.15773[/C][C]-1.6999[/C][C]0.097549[/C][C]0.048775[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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)63.241901147196652.5692791.2030.236610.118305
X1.162320444593610.356313.26210.0023810.00119
Y10.7804216490896650.1308985.96211e-060
Y2-0.7927322188145610.3909-2.0280.049810.024905
Y32.10389599246360.8679912.42390.0203620.010181
Y4-0.008416973553106780.002094-4.01890.0002760.000138
M12.766981782110444.6224430.59860.5530890.276544
M21.941190878121414.8673640.39880.692320.34616
M315.86968838517796.4040882.47810.0178980.008949
M471.11332543220515.92928111.993600
M535.438531094539412.0321872.94530.0055510.002776
M6-25.750626067433612.556784-2.05070.0474260.023713
M7-19.3054625524638.113687-2.37940.0226120.011306
M8-9.418760028424928.46238-1.1130.2728810.13644
M912.58611404691898.9351261.40860.1672990.083649
M1026.78138736461836.7586113.96260.0003250.000163
M1119.38901772891885.7871963.35030.0018680.000934
t-0.2681197563721260.15773-1.69990.0975490.048775







Multiple Linear Regression - Regression Statistics
Multiple R0.991847531674969
R-squared0.983761526089728
Adjusted R-squared0.976300605644468
F-TEST (value)131.855249403537
F-TEST (DF numerator)17
F-TEST (DF denominator)37
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.40981784371308
Sum Squared Residuals1520.17329721456

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.991847531674969 \tabularnewline
R-squared & 0.983761526089728 \tabularnewline
Adjusted R-squared & 0.976300605644468 \tabularnewline
F-TEST (value) & 131.855249403537 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 37 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.40981784371308 \tabularnewline
Sum Squared Residuals & 1520.17329721456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.991847531674969[/C][/ROW]
[ROW][C]R-squared[/C][C]0.983761526089728[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.976300605644468[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]131.855249403537[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]37[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.40981784371308[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1520.17329721456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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 R0.991847531674969
R-squared0.983761526089728
Adjusted R-squared0.976300605644468
F-TEST (value)131.855249403537
F-TEST (DF numerator)17
F-TEST (DF denominator)37
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.40981784371308
Sum Squared Residuals1520.17329721456







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1573577.834528310712-4.8345283107124
2567567.74570286082-0.745702860820217
3569568.9923047755550.00769522444461956
4621622.107538716716-1.10753871671601
5629634.579191413877-5.5791914138769
6628627.3592333834780.640766616522358
7612608.9948309820243.0051690179756
8595598.30216041208-3.30216041208038
9597598.332533531781-1.33253353178128
10593595.46570977598-2.46570977598044
11590586.4653906420933.53460935790709
12580573.7974455486726.20255445132768
13574568.7964079286955.20359207130536
14573559.84651665359113.1534833464093
15573566.3245528487446.6754471512564
16620621.779086947154-1.77908694715427
17626625.1568520085740.843147991426047
18620613.6924008775876.30759912241258
19588590.612011865547-2.61201186554723
20566579.929251812658-13.9292518126580
21557564.434531233533-7.4345312335326
22561558.9663065304882.03369346951169
23549550.311340500483-1.31134050048295
24532537.876362565007-5.87636256500654
25526523.5696970216212.43030297837865
26511509.9213169354591.07868306454139
27499508.550830519589-9.55083051958893
28555554.7454835723230.254516427676753
29565551.6134165733213.3865834266802
30542546.338035154327-4.33803515432753
31527521.9901752392845.00982476071635
32510501.6024444762868.39755552371353
33514508.5251773341925.47482266580849
34517515.6655611054311.33443889456896
35508513.473002888583-5.47300288858287
36493495.550991464417-2.55099146441657
37490491.879508483902-1.87950848390173
38469476.67292188871-7.67292188871048
39478478.687651256193-0.687651256192748
40528529.69800806831-1.69800806830980
41534542.644003104468-8.64400310446758
42518521.357515066629-3.35751506662908
43506509.583190652468-3.58319065246789
44502493.1661432989758.83385670102484
45516512.7077579004953.29224209950538
46528528.9024225881-0.902422588100215
47533529.7502659688413.24973403115873
48536533.7752004219052.22479957809542
49537537.91985825507-0.919858255069904
50524529.81354166142-5.81354166141999
51536532.4446605999193.55533940008066
52587582.6698826954974.33011730450333
53597597.006536899762-0.00653689976171934
54581580.2528155179780.747184482021662
55564565.819791260677-1.81979126067682

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 573 & 577.834528310712 & -4.8345283107124 \tabularnewline
2 & 567 & 567.74570286082 & -0.745702860820217 \tabularnewline
3 & 569 & 568.992304775555 & 0.00769522444461956 \tabularnewline
4 & 621 & 622.107538716716 & -1.10753871671601 \tabularnewline
5 & 629 & 634.579191413877 & -5.5791914138769 \tabularnewline
6 & 628 & 627.359233383478 & 0.640766616522358 \tabularnewline
7 & 612 & 608.994830982024 & 3.0051690179756 \tabularnewline
8 & 595 & 598.30216041208 & -3.30216041208038 \tabularnewline
9 & 597 & 598.332533531781 & -1.33253353178128 \tabularnewline
10 & 593 & 595.46570977598 & -2.46570977598044 \tabularnewline
11 & 590 & 586.465390642093 & 3.53460935790709 \tabularnewline
12 & 580 & 573.797445548672 & 6.20255445132768 \tabularnewline
13 & 574 & 568.796407928695 & 5.20359207130536 \tabularnewline
14 & 573 & 559.846516653591 & 13.1534833464093 \tabularnewline
15 & 573 & 566.324552848744 & 6.6754471512564 \tabularnewline
16 & 620 & 621.779086947154 & -1.77908694715427 \tabularnewline
17 & 626 & 625.156852008574 & 0.843147991426047 \tabularnewline
18 & 620 & 613.692400877587 & 6.30759912241258 \tabularnewline
19 & 588 & 590.612011865547 & -2.61201186554723 \tabularnewline
20 & 566 & 579.929251812658 & -13.9292518126580 \tabularnewline
21 & 557 & 564.434531233533 & -7.4345312335326 \tabularnewline
22 & 561 & 558.966306530488 & 2.03369346951169 \tabularnewline
23 & 549 & 550.311340500483 & -1.31134050048295 \tabularnewline
24 & 532 & 537.876362565007 & -5.87636256500654 \tabularnewline
25 & 526 & 523.569697021621 & 2.43030297837865 \tabularnewline
26 & 511 & 509.921316935459 & 1.07868306454139 \tabularnewline
27 & 499 & 508.550830519589 & -9.55083051958893 \tabularnewline
28 & 555 & 554.745483572323 & 0.254516427676753 \tabularnewline
29 & 565 & 551.61341657332 & 13.3865834266802 \tabularnewline
30 & 542 & 546.338035154327 & -4.33803515432753 \tabularnewline
31 & 527 & 521.990175239284 & 5.00982476071635 \tabularnewline
32 & 510 & 501.602444476286 & 8.39755552371353 \tabularnewline
33 & 514 & 508.525177334192 & 5.47482266580849 \tabularnewline
34 & 517 & 515.665561105431 & 1.33443889456896 \tabularnewline
35 & 508 & 513.473002888583 & -5.47300288858287 \tabularnewline
36 & 493 & 495.550991464417 & -2.55099146441657 \tabularnewline
37 & 490 & 491.879508483902 & -1.87950848390173 \tabularnewline
38 & 469 & 476.67292188871 & -7.67292188871048 \tabularnewline
39 & 478 & 478.687651256193 & -0.687651256192748 \tabularnewline
40 & 528 & 529.69800806831 & -1.69800806830980 \tabularnewline
41 & 534 & 542.644003104468 & -8.64400310446758 \tabularnewline
42 & 518 & 521.357515066629 & -3.35751506662908 \tabularnewline
43 & 506 & 509.583190652468 & -3.58319065246789 \tabularnewline
44 & 502 & 493.166143298975 & 8.83385670102484 \tabularnewline
45 & 516 & 512.707757900495 & 3.29224209950538 \tabularnewline
46 & 528 & 528.9024225881 & -0.902422588100215 \tabularnewline
47 & 533 & 529.750265968841 & 3.24973403115873 \tabularnewline
48 & 536 & 533.775200421905 & 2.22479957809542 \tabularnewline
49 & 537 & 537.91985825507 & -0.919858255069904 \tabularnewline
50 & 524 & 529.81354166142 & -5.81354166141999 \tabularnewline
51 & 536 & 532.444660599919 & 3.55533940008066 \tabularnewline
52 & 587 & 582.669882695497 & 4.33011730450333 \tabularnewline
53 & 597 & 597.006536899762 & -0.00653689976171934 \tabularnewline
54 & 581 & 580.252815517978 & 0.747184482021662 \tabularnewline
55 & 564 & 565.819791260677 & -1.81979126067682 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&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]573[/C][C]577.834528310712[/C][C]-4.8345283107124[/C][/ROW]
[ROW][C]2[/C][C]567[/C][C]567.74570286082[/C][C]-0.745702860820217[/C][/ROW]
[ROW][C]3[/C][C]569[/C][C]568.992304775555[/C][C]0.00769522444461956[/C][/ROW]
[ROW][C]4[/C][C]621[/C][C]622.107538716716[/C][C]-1.10753871671601[/C][/ROW]
[ROW][C]5[/C][C]629[/C][C]634.579191413877[/C][C]-5.5791914138769[/C][/ROW]
[ROW][C]6[/C][C]628[/C][C]627.359233383478[/C][C]0.640766616522358[/C][/ROW]
[ROW][C]7[/C][C]612[/C][C]608.994830982024[/C][C]3.0051690179756[/C][/ROW]
[ROW][C]8[/C][C]595[/C][C]598.30216041208[/C][C]-3.30216041208038[/C][/ROW]
[ROW][C]9[/C][C]597[/C][C]598.332533531781[/C][C]-1.33253353178128[/C][/ROW]
[ROW][C]10[/C][C]593[/C][C]595.46570977598[/C][C]-2.46570977598044[/C][/ROW]
[ROW][C]11[/C][C]590[/C][C]586.465390642093[/C][C]3.53460935790709[/C][/ROW]
[ROW][C]12[/C][C]580[/C][C]573.797445548672[/C][C]6.20255445132768[/C][/ROW]
[ROW][C]13[/C][C]574[/C][C]568.796407928695[/C][C]5.20359207130536[/C][/ROW]
[ROW][C]14[/C][C]573[/C][C]559.846516653591[/C][C]13.1534833464093[/C][/ROW]
[ROW][C]15[/C][C]573[/C][C]566.324552848744[/C][C]6.6754471512564[/C][/ROW]
[ROW][C]16[/C][C]620[/C][C]621.779086947154[/C][C]-1.77908694715427[/C][/ROW]
[ROW][C]17[/C][C]626[/C][C]625.156852008574[/C][C]0.843147991426047[/C][/ROW]
[ROW][C]18[/C][C]620[/C][C]613.692400877587[/C][C]6.30759912241258[/C][/ROW]
[ROW][C]19[/C][C]588[/C][C]590.612011865547[/C][C]-2.61201186554723[/C][/ROW]
[ROW][C]20[/C][C]566[/C][C]579.929251812658[/C][C]-13.9292518126580[/C][/ROW]
[ROW][C]21[/C][C]557[/C][C]564.434531233533[/C][C]-7.4345312335326[/C][/ROW]
[ROW][C]22[/C][C]561[/C][C]558.966306530488[/C][C]2.03369346951169[/C][/ROW]
[ROW][C]23[/C][C]549[/C][C]550.311340500483[/C][C]-1.31134050048295[/C][/ROW]
[ROW][C]24[/C][C]532[/C][C]537.876362565007[/C][C]-5.87636256500654[/C][/ROW]
[ROW][C]25[/C][C]526[/C][C]523.569697021621[/C][C]2.43030297837865[/C][/ROW]
[ROW][C]26[/C][C]511[/C][C]509.921316935459[/C][C]1.07868306454139[/C][/ROW]
[ROW][C]27[/C][C]499[/C][C]508.550830519589[/C][C]-9.55083051958893[/C][/ROW]
[ROW][C]28[/C][C]555[/C][C]554.745483572323[/C][C]0.254516427676753[/C][/ROW]
[ROW][C]29[/C][C]565[/C][C]551.61341657332[/C][C]13.3865834266802[/C][/ROW]
[ROW][C]30[/C][C]542[/C][C]546.338035154327[/C][C]-4.33803515432753[/C][/ROW]
[ROW][C]31[/C][C]527[/C][C]521.990175239284[/C][C]5.00982476071635[/C][/ROW]
[ROW][C]32[/C][C]510[/C][C]501.602444476286[/C][C]8.39755552371353[/C][/ROW]
[ROW][C]33[/C][C]514[/C][C]508.525177334192[/C][C]5.47482266580849[/C][/ROW]
[ROW][C]34[/C][C]517[/C][C]515.665561105431[/C][C]1.33443889456896[/C][/ROW]
[ROW][C]35[/C][C]508[/C][C]513.473002888583[/C][C]-5.47300288858287[/C][/ROW]
[ROW][C]36[/C][C]493[/C][C]495.550991464417[/C][C]-2.55099146441657[/C][/ROW]
[ROW][C]37[/C][C]490[/C][C]491.879508483902[/C][C]-1.87950848390173[/C][/ROW]
[ROW][C]38[/C][C]469[/C][C]476.67292188871[/C][C]-7.67292188871048[/C][/ROW]
[ROW][C]39[/C][C]478[/C][C]478.687651256193[/C][C]-0.687651256192748[/C][/ROW]
[ROW][C]40[/C][C]528[/C][C]529.69800806831[/C][C]-1.69800806830980[/C][/ROW]
[ROW][C]41[/C][C]534[/C][C]542.644003104468[/C][C]-8.64400310446758[/C][/ROW]
[ROW][C]42[/C][C]518[/C][C]521.357515066629[/C][C]-3.35751506662908[/C][/ROW]
[ROW][C]43[/C][C]506[/C][C]509.583190652468[/C][C]-3.58319065246789[/C][/ROW]
[ROW][C]44[/C][C]502[/C][C]493.166143298975[/C][C]8.83385670102484[/C][/ROW]
[ROW][C]45[/C][C]516[/C][C]512.707757900495[/C][C]3.29224209950538[/C][/ROW]
[ROW][C]46[/C][C]528[/C][C]528.9024225881[/C][C]-0.902422588100215[/C][/ROW]
[ROW][C]47[/C][C]533[/C][C]529.750265968841[/C][C]3.24973403115873[/C][/ROW]
[ROW][C]48[/C][C]536[/C][C]533.775200421905[/C][C]2.22479957809542[/C][/ROW]
[ROW][C]49[/C][C]537[/C][C]537.91985825507[/C][C]-0.919858255069904[/C][/ROW]
[ROW][C]50[/C][C]524[/C][C]529.81354166142[/C][C]-5.81354166141999[/C][/ROW]
[ROW][C]51[/C][C]536[/C][C]532.444660599919[/C][C]3.55533940008066[/C][/ROW]
[ROW][C]52[/C][C]587[/C][C]582.669882695497[/C][C]4.33011730450333[/C][/ROW]
[ROW][C]53[/C][C]597[/C][C]597.006536899762[/C][C]-0.00653689976171934[/C][/ROW]
[ROW][C]54[/C][C]581[/C][C]580.252815517978[/C][C]0.747184482021662[/C][/ROW]
[ROW][C]55[/C][C]564[/C][C]565.819791260677[/C][C]-1.81979126067682[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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
1573577.834528310712-4.8345283107124
2567567.74570286082-0.745702860820217
3569568.9923047755550.00769522444461956
4621622.107538716716-1.10753871671601
5629634.579191413877-5.5791914138769
6628627.3592333834780.640766616522358
7612608.9948309820243.0051690179756
8595598.30216041208-3.30216041208038
9597598.332533531781-1.33253353178128
10593595.46570977598-2.46570977598044
11590586.4653906420933.53460935790709
12580573.7974455486726.20255445132768
13574568.7964079286955.20359207130536
14573559.84651665359113.1534833464093
15573566.3245528487446.6754471512564
16620621.779086947154-1.77908694715427
17626625.1568520085740.843147991426047
18620613.6924008775876.30759912241258
19588590.612011865547-2.61201186554723
20566579.929251812658-13.9292518126580
21557564.434531233533-7.4345312335326
22561558.9663065304882.03369346951169
23549550.311340500483-1.31134050048295
24532537.876362565007-5.87636256500654
25526523.5696970216212.43030297837865
26511509.9213169354591.07868306454139
27499508.550830519589-9.55083051958893
28555554.7454835723230.254516427676753
29565551.6134165733213.3865834266802
30542546.338035154327-4.33803515432753
31527521.9901752392845.00982476071635
32510501.6024444762868.39755552371353
33514508.5251773341925.47482266580849
34517515.6655611054311.33443889456896
35508513.473002888583-5.47300288858287
36493495.550991464417-2.55099146441657
37490491.879508483902-1.87950848390173
38469476.67292188871-7.67292188871048
39478478.687651256193-0.687651256192748
40528529.69800806831-1.69800806830980
41534542.644003104468-8.64400310446758
42518521.357515066629-3.35751506662908
43506509.583190652468-3.58319065246789
44502493.1661432989758.83385670102484
45516512.7077579004953.29224209950538
46528528.9024225881-0.902422588100215
47533529.7502659688413.24973403115873
48536533.7752004219052.22479957809542
49537537.91985825507-0.919858255069904
50524529.81354166142-5.81354166141999
51536532.4446605999193.55533940008066
52587582.6698826954974.33011730450333
53597597.006536899762-0.00653689976171934
54581580.2528155179780.747184482021662
55564565.819791260677-1.81979126067682







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
210.1985290940719200.3970581881438410.80147090592808
220.7388147354875180.5223705290249630.261185264512482
230.8370922891143360.3258154217713280.162907710885664
240.8017419356728680.3965161286542640.198258064327132
250.7198026263354850.560394747329030.280197373664515
260.6758135522093650.648372895581270.324186447790635
270.8403542137856540.3192915724286930.159645786214346
280.8659546089312490.2680907821375020.134045391068751
290.9356585638814820.1286828722370350.0643414361185176
300.8843436211157440.2313127577685130.115656378884256
310.9872848344035050.02543033119299040.0127151655964952
320.981660391537870.03667921692426010.0183396084621300
330.999731156840920.0005376863181618810.000268843159080941
340.9980809326626380.003838134674724020.00191906733736201

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.198529094071920 & 0.397058188143841 & 0.80147090592808 \tabularnewline
22 & 0.738814735487518 & 0.522370529024963 & 0.261185264512482 \tabularnewline
23 & 0.837092289114336 & 0.325815421771328 & 0.162907710885664 \tabularnewline
24 & 0.801741935672868 & 0.396516128654264 & 0.198258064327132 \tabularnewline
25 & 0.719802626335485 & 0.56039474732903 & 0.280197373664515 \tabularnewline
26 & 0.675813552209365 & 0.64837289558127 & 0.324186447790635 \tabularnewline
27 & 0.840354213785654 & 0.319291572428693 & 0.159645786214346 \tabularnewline
28 & 0.865954608931249 & 0.268090782137502 & 0.134045391068751 \tabularnewline
29 & 0.935658563881482 & 0.128682872237035 & 0.0643414361185176 \tabularnewline
30 & 0.884343621115744 & 0.231312757768513 & 0.115656378884256 \tabularnewline
31 & 0.987284834403505 & 0.0254303311929904 & 0.0127151655964952 \tabularnewline
32 & 0.98166039153787 & 0.0366792169242601 & 0.0183396084621300 \tabularnewline
33 & 0.99973115684092 & 0.000537686318161881 & 0.000268843159080941 \tabularnewline
34 & 0.998080932662638 & 0.00383813467472402 & 0.00191906733736201 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&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]21[/C][C]0.198529094071920[/C][C]0.397058188143841[/C][C]0.80147090592808[/C][/ROW]
[ROW][C]22[/C][C]0.738814735487518[/C][C]0.522370529024963[/C][C]0.261185264512482[/C][/ROW]
[ROW][C]23[/C][C]0.837092289114336[/C][C]0.325815421771328[/C][C]0.162907710885664[/C][/ROW]
[ROW][C]24[/C][C]0.801741935672868[/C][C]0.396516128654264[/C][C]0.198258064327132[/C][/ROW]
[ROW][C]25[/C][C]0.719802626335485[/C][C]0.56039474732903[/C][C]0.280197373664515[/C][/ROW]
[ROW][C]26[/C][C]0.675813552209365[/C][C]0.64837289558127[/C][C]0.324186447790635[/C][/ROW]
[ROW][C]27[/C][C]0.840354213785654[/C][C]0.319291572428693[/C][C]0.159645786214346[/C][/ROW]
[ROW][C]28[/C][C]0.865954608931249[/C][C]0.268090782137502[/C][C]0.134045391068751[/C][/ROW]
[ROW][C]29[/C][C]0.935658563881482[/C][C]0.128682872237035[/C][C]0.0643414361185176[/C][/ROW]
[ROW][C]30[/C][C]0.884343621115744[/C][C]0.231312757768513[/C][C]0.115656378884256[/C][/ROW]
[ROW][C]31[/C][C]0.987284834403505[/C][C]0.0254303311929904[/C][C]0.0127151655964952[/C][/ROW]
[ROW][C]32[/C][C]0.98166039153787[/C][C]0.0366792169242601[/C][C]0.0183396084621300[/C][/ROW]
[ROW][C]33[/C][C]0.99973115684092[/C][C]0.000537686318161881[/C][C]0.000268843159080941[/C][/ROW]
[ROW][C]34[/C][C]0.998080932662638[/C][C]0.00383813467472402[/C][C]0.00191906733736201[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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
210.1985290940719200.3970581881438410.80147090592808
220.7388147354875180.5223705290249630.261185264512482
230.8370922891143360.3258154217713280.162907710885664
240.8017419356728680.3965161286542640.198258064327132
250.7198026263354850.560394747329030.280197373664515
260.6758135522093650.648372895581270.324186447790635
270.8403542137856540.3192915724286930.159645786214346
280.8659546089312490.2680907821375020.134045391068751
290.9356585638814820.1286828722370350.0643414361185176
300.8843436211157440.2313127577685130.115656378884256
310.9872848344035050.02543033119299040.0127151655964952
320.981660391537870.03667921692426010.0183396084621300
330.999731156840920.0005376863181618810.000268843159080941
340.9980809326626380.003838134674724020.00191906733736201







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level20.142857142857143NOK
5% type I error level40.285714285714286NOK
10% type I error level40.285714285714286NOK

\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 & 2 & 0.142857142857143 & NOK \tabularnewline
5% type I error level & 4 & 0.285714285714286 & NOK \tabularnewline
10% type I error level & 4 & 0.285714285714286 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58438&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]2[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.285714285714286[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58438&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58438&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 level20.142857142857143NOK
5% type I error level40.285714285714286NOK
10% type I error level40.285714285714286NOK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = 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')
}