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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 11:32:49 -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/t1258743027bn7ygp9do00jsfx.htm/, Retrieved Thu, 28 Mar 2024 08:26:02 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58415, Retrieved Thu, 28 Mar 2024 08:26:02 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact138
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 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [] [2009-11-20 18:32:49] [aa8eb70c35ea8a87edcd21d6427e653e] [Current]
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Dataseries X:
594	139
595	135
591	130
589	127
584	122
573	117
567	112
569	113
621	149
629	157
628	157
612	147
595	137
597	132
593	125
590	123
580	117
574	114
573	111
573	112
620	144
626	150
620	149
588	134
566	123
557	116
561	117
549	111
532	105
526	102
511	95
499	93
555	124
565	130
542	124
527	115
510	106
514	105
517	105
508	101
493	95
490	93
469	84
478	87
528	116
534	120
518	117
506	109
502	105
516	107
528	109
533	109
536	108
537	107
524	99
536	103
587	131
597	137
581	135
564	124




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=58415&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=58415&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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] = + 158.174107787313 + 3.15903440532521X[t] + 7.17135863608437M1[t] + 18.9423684069826M2[t] + 26.7225368914909M3[t] + 31.8935466623894M4[t] + 38.1508183628733M5[t] + 41.8900212527067M6[t] + 50.8017480017109M7[t] + 48.4730063891785M8[t] + 1.00503949795491M9[t] -10.0552603790734M10[t] -14.9796712513701M11[t] + 0.106093445077124t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  158.174107787313 +  3.15903440532521X[t] +  7.17135863608437M1[t] +  18.9423684069826M2[t] +  26.7225368914909M3[t] +  31.8935466623894M4[t] +  38.1508183628733M5[t] +  41.8900212527067M6[t] +  50.8017480017109M7[t] +  48.4730063891785M8[t] +  1.00503949795491M9[t] -10.0552603790734M10[t] -14.9796712513701M11[t] +  0.106093445077124t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  158.174107787313 +  3.15903440532521X[t] +  7.17135863608437M1[t] +  18.9423684069826M2[t] +  26.7225368914909M3[t] +  31.8935466623894M4[t] +  38.1508183628733M5[t] +  41.8900212527067M6[t] +  50.8017480017109M7[t] +  48.4730063891785M8[t] +  1.00503949795491M9[t] -10.0552603790734M10[t] -14.9796712513701M11[t] +  0.106093445077124t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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] = + 158.174107787313 + 3.15903440532521X[t] + 7.17135863608437M1[t] + 18.9423684069826M2[t] + 26.7225368914909M3[t] + 31.8935466623894M4[t] + 38.1508183628733M5[t] + 41.8900212527067M6[t] + 50.8017480017109M7[t] + 48.4730063891785M8[t] + 1.00503949795491M9[t] -10.0552603790734M10[t] -14.9796712513701M11[t] + 0.106093445077124t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)158.17410778731320.368037.765800
X3.159034405325210.1374222.988200
M17.171358636084375.2280041.37170.1768040.088402
M218.94236840698265.3172143.56250.0008680.000434
M326.72253689149095.3679044.97829e-065e-06
M431.89354666238945.4878565.81171e-060
M538.15081836287335.7414416.644800
M641.89002125270675.8937957.107500
M750.80174800171096.349088.001400
M848.47300638917856.1871577.834500
M91.005039497954915.0693410.19830.8437160.421858
M10-10.05526037907345.274256-1.90650.0628460.031423
M11-14.97967125137015.199309-2.88110.0060020.003001
t0.1060934450771240.0951911.11450.2708420.135421

\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) & 158.174107787313 & 20.36803 & 7.7658 & 0 & 0 \tabularnewline
X & 3.15903440532521 & 0.13742 & 22.9882 & 0 & 0 \tabularnewline
M1 & 7.17135863608437 & 5.228004 & 1.3717 & 0.176804 & 0.088402 \tabularnewline
M2 & 18.9423684069826 & 5.317214 & 3.5625 & 0.000868 & 0.000434 \tabularnewline
M3 & 26.7225368914909 & 5.367904 & 4.9782 & 9e-06 & 5e-06 \tabularnewline
M4 & 31.8935466623894 & 5.487856 & 5.8117 & 1e-06 & 0 \tabularnewline
M5 & 38.1508183628733 & 5.741441 & 6.6448 & 0 & 0 \tabularnewline
M6 & 41.8900212527067 & 5.893795 & 7.1075 & 0 & 0 \tabularnewline
M7 & 50.8017480017109 & 6.34908 & 8.0014 & 0 & 0 \tabularnewline
M8 & 48.4730063891785 & 6.187157 & 7.8345 & 0 & 0 \tabularnewline
M9 & 1.00503949795491 & 5.069341 & 0.1983 & 0.843716 & 0.421858 \tabularnewline
M10 & -10.0552603790734 & 5.274256 & -1.9065 & 0.062846 & 0.031423 \tabularnewline
M11 & -14.9796712513701 & 5.199309 & -2.8811 & 0.006002 & 0.003001 \tabularnewline
t & 0.106093445077124 & 0.095191 & 1.1145 & 0.270842 & 0.135421 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&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]158.174107787313[/C][C]20.36803[/C][C]7.7658[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]3.15903440532521[/C][C]0.13742[/C][C]22.9882[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]7.17135863608437[/C][C]5.228004[/C][C]1.3717[/C][C]0.176804[/C][C]0.088402[/C][/ROW]
[ROW][C]M2[/C][C]18.9423684069826[/C][C]5.317214[/C][C]3.5625[/C][C]0.000868[/C][C]0.000434[/C][/ROW]
[ROW][C]M3[/C][C]26.7225368914909[/C][C]5.367904[/C][C]4.9782[/C][C]9e-06[/C][C]5e-06[/C][/ROW]
[ROW][C]M4[/C][C]31.8935466623894[/C][C]5.487856[/C][C]5.8117[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]38.1508183628733[/C][C]5.741441[/C][C]6.6448[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M6[/C][C]41.8900212527067[/C][C]5.893795[/C][C]7.1075[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M7[/C][C]50.8017480017109[/C][C]6.34908[/C][C]8.0014[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M8[/C][C]48.4730063891785[/C][C]6.187157[/C][C]7.8345[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]1.00503949795491[/C][C]5.069341[/C][C]0.1983[/C][C]0.843716[/C][C]0.421858[/C][/ROW]
[ROW][C]M10[/C][C]-10.0552603790734[/C][C]5.274256[/C][C]-1.9065[/C][C]0.062846[/C][C]0.031423[/C][/ROW]
[ROW][C]M11[/C][C]-14.9796712513701[/C][C]5.199309[/C][C]-2.8811[/C][C]0.006002[/C][C]0.003001[/C][/ROW]
[ROW][C]t[/C][C]0.106093445077124[/C][C]0.095191[/C][C]1.1145[/C][C]0.270842[/C][C]0.135421[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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)158.17410778731320.368037.765800
X3.159034405325210.1374222.988200
M17.171358636084375.2280041.37170.1768040.088402
M218.94236840698265.3172143.56250.0008680.000434
M326.72253689149095.3679044.97829e-065e-06
M431.89354666238945.4878565.81171e-060
M538.15081836287335.7414416.644800
M641.89002125270675.8937957.107500
M750.80174800171096.349088.001400
M848.47300638917856.1871577.834500
M91.005039497954915.0693410.19830.8437160.421858
M10-10.05526037907345.274256-1.90650.0628460.031423
M11-14.97967125137015.199309-2.88110.0060020.003001
t0.1060934450771240.0951911.11450.2708420.135421







Multiple Linear Regression - Regression Statistics
Multiple R0.985469174259445
R-squared0.971149493415593
Adjusted R-squared0.96299608938087
F-TEST (value)119.109698143213
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.92410001262523
Sum Squared Residuals2888.40260646401

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.985469174259445 \tabularnewline
R-squared & 0.971149493415593 \tabularnewline
Adjusted R-squared & 0.96299608938087 \tabularnewline
F-TEST (value) & 119.109698143213 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.92410001262523 \tabularnewline
Sum Squared Residuals & 2888.40260646401 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.985469174259445[/C][/ROW]
[ROW][C]R-squared[/C][C]0.971149493415593[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.96299608938087[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]119.109698143213[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/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]7.92410001262523[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2888.40260646401[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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 R0.985469174259445
R-squared0.971149493415593
Adjusted R-squared0.96299608938087
F-TEST (value)119.109698143213
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation7.92410001262523
Sum Squared Residuals2888.40260646401







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1594604.557342208677-10.5573422086767
2595603.798307803352-8.79830780335244
3591595.889397706312-4.88939770631167
4589591.689397706312-2.68939770631171
5584582.2575908252471.74240917475338
6573570.3077151335312.69228486646883
7567563.5303633009863.46963669901351
8569564.4667495388564.53325046114361
9621630.830114684417-9.83011468441741
10629645.148183495068-16.1481834950678
11628640.329866067848-12.3298660678483
12612623.825286711043-11.8252867110434
13595599.512394738953-4.51239473895285
14597595.5943259283021.40567407169782
15593581.36734702061111.6326529793889
16590580.3263814259369.67361857406367
17580567.73554013954612.2644598604539
18574562.10373325848111.8962667415190
19573561.64445023658711.3555497634133
20573562.58083647445710.4191635255433
21620616.3080639987173.69193600128316
22626624.3080639987171.69193600128316
23620616.3307121661723.66928783382787
24588584.0309607827413.96903921725878
25566556.5590344053259.44096559467457
26557546.32289678402410.6771032159756
27561557.3681931189353.63180688106504
28549543.6910899029595.30891009704066
29532531.1002486165690.899751383430905
30526525.4684417355040.531558264495951
31511512.373021092309-1.37302109230892
32499503.832304114203-4.83230411420321
33555554.4004972331380.599502766861832
34565562.4004972331382.59950276686183
35542538.6279733739673.37202662603257
36527525.2824284224881.71757157751225
37510504.1285708557225.87142914427761
38514512.8466396663731.15336033362744
39517520.732901595958-3.73290159595796
40508513.373867190633-5.37386719063274
41493500.783025904242-7.7830259042425
42490498.310253428503-8.31025342850267
43469478.896763974657-9.89676397465712
44478486.151219023177-8.15121902317745
45528530.401343331462-2.40134333146199
46534532.0832745208121.91672547918841
47518517.7878538776160.212146122383543
48506507.601343331462-1.60134333146200
49502502.242657791323-0.242657791322671
50516520.437829817948-4.43782981794846
51528534.642160558184-6.64216055818427
52533539.91926377416-6.91926377415988
53536543.123594514396-7.12359451439568
54537543.809856443981-6.80985644398105
55524527.555401395461-3.55540139546071
56536537.968890849306-1.96889084930627
57587579.0599807522667.9400192477344
58597587.0599807522669.9400192477344
59581575.9235945143965.07640548560431
60564556.2599807522667.7400192477344

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 594 & 604.557342208677 & -10.5573422086767 \tabularnewline
2 & 595 & 603.798307803352 & -8.79830780335244 \tabularnewline
3 & 591 & 595.889397706312 & -4.88939770631167 \tabularnewline
4 & 589 & 591.689397706312 & -2.68939770631171 \tabularnewline
5 & 584 & 582.257590825247 & 1.74240917475338 \tabularnewline
6 & 573 & 570.307715133531 & 2.69228486646883 \tabularnewline
7 & 567 & 563.530363300986 & 3.46963669901351 \tabularnewline
8 & 569 & 564.466749538856 & 4.53325046114361 \tabularnewline
9 & 621 & 630.830114684417 & -9.83011468441741 \tabularnewline
10 & 629 & 645.148183495068 & -16.1481834950678 \tabularnewline
11 & 628 & 640.329866067848 & -12.3298660678483 \tabularnewline
12 & 612 & 623.825286711043 & -11.8252867110434 \tabularnewline
13 & 595 & 599.512394738953 & -4.51239473895285 \tabularnewline
14 & 597 & 595.594325928302 & 1.40567407169782 \tabularnewline
15 & 593 & 581.367347020611 & 11.6326529793889 \tabularnewline
16 & 590 & 580.326381425936 & 9.67361857406367 \tabularnewline
17 & 580 & 567.735540139546 & 12.2644598604539 \tabularnewline
18 & 574 & 562.103733258481 & 11.8962667415190 \tabularnewline
19 & 573 & 561.644450236587 & 11.3555497634133 \tabularnewline
20 & 573 & 562.580836474457 & 10.4191635255433 \tabularnewline
21 & 620 & 616.308063998717 & 3.69193600128316 \tabularnewline
22 & 626 & 624.308063998717 & 1.69193600128316 \tabularnewline
23 & 620 & 616.330712166172 & 3.66928783382787 \tabularnewline
24 & 588 & 584.030960782741 & 3.96903921725878 \tabularnewline
25 & 566 & 556.559034405325 & 9.44096559467457 \tabularnewline
26 & 557 & 546.322896784024 & 10.6771032159756 \tabularnewline
27 & 561 & 557.368193118935 & 3.63180688106504 \tabularnewline
28 & 549 & 543.691089902959 & 5.30891009704066 \tabularnewline
29 & 532 & 531.100248616569 & 0.899751383430905 \tabularnewline
30 & 526 & 525.468441735504 & 0.531558264495951 \tabularnewline
31 & 511 & 512.373021092309 & -1.37302109230892 \tabularnewline
32 & 499 & 503.832304114203 & -4.83230411420321 \tabularnewline
33 & 555 & 554.400497233138 & 0.599502766861832 \tabularnewline
34 & 565 & 562.400497233138 & 2.59950276686183 \tabularnewline
35 & 542 & 538.627973373967 & 3.37202662603257 \tabularnewline
36 & 527 & 525.282428422488 & 1.71757157751225 \tabularnewline
37 & 510 & 504.128570855722 & 5.87142914427761 \tabularnewline
38 & 514 & 512.846639666373 & 1.15336033362744 \tabularnewline
39 & 517 & 520.732901595958 & -3.73290159595796 \tabularnewline
40 & 508 & 513.373867190633 & -5.37386719063274 \tabularnewline
41 & 493 & 500.783025904242 & -7.7830259042425 \tabularnewline
42 & 490 & 498.310253428503 & -8.31025342850267 \tabularnewline
43 & 469 & 478.896763974657 & -9.89676397465712 \tabularnewline
44 & 478 & 486.151219023177 & -8.15121902317745 \tabularnewline
45 & 528 & 530.401343331462 & -2.40134333146199 \tabularnewline
46 & 534 & 532.083274520812 & 1.91672547918841 \tabularnewline
47 & 518 & 517.787853877616 & 0.212146122383543 \tabularnewline
48 & 506 & 507.601343331462 & -1.60134333146200 \tabularnewline
49 & 502 & 502.242657791323 & -0.242657791322671 \tabularnewline
50 & 516 & 520.437829817948 & -4.43782981794846 \tabularnewline
51 & 528 & 534.642160558184 & -6.64216055818427 \tabularnewline
52 & 533 & 539.91926377416 & -6.91926377415988 \tabularnewline
53 & 536 & 543.123594514396 & -7.12359451439568 \tabularnewline
54 & 537 & 543.809856443981 & -6.80985644398105 \tabularnewline
55 & 524 & 527.555401395461 & -3.55540139546071 \tabularnewline
56 & 536 & 537.968890849306 & -1.96889084930627 \tabularnewline
57 & 587 & 579.059980752266 & 7.9400192477344 \tabularnewline
58 & 597 & 587.059980752266 & 9.9400192477344 \tabularnewline
59 & 581 & 575.923594514396 & 5.07640548560431 \tabularnewline
60 & 564 & 556.259980752266 & 7.7400192477344 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&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]594[/C][C]604.557342208677[/C][C]-10.5573422086767[/C][/ROW]
[ROW][C]2[/C][C]595[/C][C]603.798307803352[/C][C]-8.79830780335244[/C][/ROW]
[ROW][C]3[/C][C]591[/C][C]595.889397706312[/C][C]-4.88939770631167[/C][/ROW]
[ROW][C]4[/C][C]589[/C][C]591.689397706312[/C][C]-2.68939770631171[/C][/ROW]
[ROW][C]5[/C][C]584[/C][C]582.257590825247[/C][C]1.74240917475338[/C][/ROW]
[ROW][C]6[/C][C]573[/C][C]570.307715133531[/C][C]2.69228486646883[/C][/ROW]
[ROW][C]7[/C][C]567[/C][C]563.530363300986[/C][C]3.46963669901351[/C][/ROW]
[ROW][C]8[/C][C]569[/C][C]564.466749538856[/C][C]4.53325046114361[/C][/ROW]
[ROW][C]9[/C][C]621[/C][C]630.830114684417[/C][C]-9.83011468441741[/C][/ROW]
[ROW][C]10[/C][C]629[/C][C]645.148183495068[/C][C]-16.1481834950678[/C][/ROW]
[ROW][C]11[/C][C]628[/C][C]640.329866067848[/C][C]-12.3298660678483[/C][/ROW]
[ROW][C]12[/C][C]612[/C][C]623.825286711043[/C][C]-11.8252867110434[/C][/ROW]
[ROW][C]13[/C][C]595[/C][C]599.512394738953[/C][C]-4.51239473895285[/C][/ROW]
[ROW][C]14[/C][C]597[/C][C]595.594325928302[/C][C]1.40567407169782[/C][/ROW]
[ROW][C]15[/C][C]593[/C][C]581.367347020611[/C][C]11.6326529793889[/C][/ROW]
[ROW][C]16[/C][C]590[/C][C]580.326381425936[/C][C]9.67361857406367[/C][/ROW]
[ROW][C]17[/C][C]580[/C][C]567.735540139546[/C][C]12.2644598604539[/C][/ROW]
[ROW][C]18[/C][C]574[/C][C]562.103733258481[/C][C]11.8962667415190[/C][/ROW]
[ROW][C]19[/C][C]573[/C][C]561.644450236587[/C][C]11.3555497634133[/C][/ROW]
[ROW][C]20[/C][C]573[/C][C]562.580836474457[/C][C]10.4191635255433[/C][/ROW]
[ROW][C]21[/C][C]620[/C][C]616.308063998717[/C][C]3.69193600128316[/C][/ROW]
[ROW][C]22[/C][C]626[/C][C]624.308063998717[/C][C]1.69193600128316[/C][/ROW]
[ROW][C]23[/C][C]620[/C][C]616.330712166172[/C][C]3.66928783382787[/C][/ROW]
[ROW][C]24[/C][C]588[/C][C]584.030960782741[/C][C]3.96903921725878[/C][/ROW]
[ROW][C]25[/C][C]566[/C][C]556.559034405325[/C][C]9.44096559467457[/C][/ROW]
[ROW][C]26[/C][C]557[/C][C]546.322896784024[/C][C]10.6771032159756[/C][/ROW]
[ROW][C]27[/C][C]561[/C][C]557.368193118935[/C][C]3.63180688106504[/C][/ROW]
[ROW][C]28[/C][C]549[/C][C]543.691089902959[/C][C]5.30891009704066[/C][/ROW]
[ROW][C]29[/C][C]532[/C][C]531.100248616569[/C][C]0.899751383430905[/C][/ROW]
[ROW][C]30[/C][C]526[/C][C]525.468441735504[/C][C]0.531558264495951[/C][/ROW]
[ROW][C]31[/C][C]511[/C][C]512.373021092309[/C][C]-1.37302109230892[/C][/ROW]
[ROW][C]32[/C][C]499[/C][C]503.832304114203[/C][C]-4.83230411420321[/C][/ROW]
[ROW][C]33[/C][C]555[/C][C]554.400497233138[/C][C]0.599502766861832[/C][/ROW]
[ROW][C]34[/C][C]565[/C][C]562.400497233138[/C][C]2.59950276686183[/C][/ROW]
[ROW][C]35[/C][C]542[/C][C]538.627973373967[/C][C]3.37202662603257[/C][/ROW]
[ROW][C]36[/C][C]527[/C][C]525.282428422488[/C][C]1.71757157751225[/C][/ROW]
[ROW][C]37[/C][C]510[/C][C]504.128570855722[/C][C]5.87142914427761[/C][/ROW]
[ROW][C]38[/C][C]514[/C][C]512.846639666373[/C][C]1.15336033362744[/C][/ROW]
[ROW][C]39[/C][C]517[/C][C]520.732901595958[/C][C]-3.73290159595796[/C][/ROW]
[ROW][C]40[/C][C]508[/C][C]513.373867190633[/C][C]-5.37386719063274[/C][/ROW]
[ROW][C]41[/C][C]493[/C][C]500.783025904242[/C][C]-7.7830259042425[/C][/ROW]
[ROW][C]42[/C][C]490[/C][C]498.310253428503[/C][C]-8.31025342850267[/C][/ROW]
[ROW][C]43[/C][C]469[/C][C]478.896763974657[/C][C]-9.89676397465712[/C][/ROW]
[ROW][C]44[/C][C]478[/C][C]486.151219023177[/C][C]-8.15121902317745[/C][/ROW]
[ROW][C]45[/C][C]528[/C][C]530.401343331462[/C][C]-2.40134333146199[/C][/ROW]
[ROW][C]46[/C][C]534[/C][C]532.083274520812[/C][C]1.91672547918841[/C][/ROW]
[ROW][C]47[/C][C]518[/C][C]517.787853877616[/C][C]0.212146122383543[/C][/ROW]
[ROW][C]48[/C][C]506[/C][C]507.601343331462[/C][C]-1.60134333146200[/C][/ROW]
[ROW][C]49[/C][C]502[/C][C]502.242657791323[/C][C]-0.242657791322671[/C][/ROW]
[ROW][C]50[/C][C]516[/C][C]520.437829817948[/C][C]-4.43782981794846[/C][/ROW]
[ROW][C]51[/C][C]528[/C][C]534.642160558184[/C][C]-6.64216055818427[/C][/ROW]
[ROW][C]52[/C][C]533[/C][C]539.91926377416[/C][C]-6.91926377415988[/C][/ROW]
[ROW][C]53[/C][C]536[/C][C]543.123594514396[/C][C]-7.12359451439568[/C][/ROW]
[ROW][C]54[/C][C]537[/C][C]543.809856443981[/C][C]-6.80985644398105[/C][/ROW]
[ROW][C]55[/C][C]524[/C][C]527.555401395461[/C][C]-3.55540139546071[/C][/ROW]
[ROW][C]56[/C][C]536[/C][C]537.968890849306[/C][C]-1.96889084930627[/C][/ROW]
[ROW][C]57[/C][C]587[/C][C]579.059980752266[/C][C]7.9400192477344[/C][/ROW]
[ROW][C]58[/C][C]597[/C][C]587.059980752266[/C][C]9.9400192477344[/C][/ROW]
[ROW][C]59[/C][C]581[/C][C]575.923594514396[/C][C]5.07640548560431[/C][/ROW]
[ROW][C]60[/C][C]564[/C][C]556.259980752266[/C][C]7.7400192477344[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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
1594604.557342208677-10.5573422086767
2595603.798307803352-8.79830780335244
3591595.889397706312-4.88939770631167
4589591.689397706312-2.68939770631171
5584582.2575908252471.74240917475338
6573570.3077151335312.69228486646883
7567563.5303633009863.46963669901351
8569564.4667495388564.53325046114361
9621630.830114684417-9.83011468441741
10629645.148183495068-16.1481834950678
11628640.329866067848-12.3298660678483
12612623.825286711043-11.8252867110434
13595599.512394738953-4.51239473895285
14597595.5943259283021.40567407169782
15593581.36734702061111.6326529793889
16590580.3263814259369.67361857406367
17580567.73554013954612.2644598604539
18574562.10373325848111.8962667415190
19573561.64445023658711.3555497634133
20573562.58083647445710.4191635255433
21620616.3080639987173.69193600128316
22626624.3080639987171.69193600128316
23620616.3307121661723.66928783382787
24588584.0309607827413.96903921725878
25566556.5590344053259.44096559467457
26557546.32289678402410.6771032159756
27561557.3681931189353.63180688106504
28549543.6910899029595.30891009704066
29532531.1002486165690.899751383430905
30526525.4684417355040.531558264495951
31511512.373021092309-1.37302109230892
32499503.832304114203-4.83230411420321
33555554.4004972331380.599502766861832
34565562.4004972331382.59950276686183
35542538.6279733739673.37202662603257
36527525.2824284224881.71757157751225
37510504.1285708557225.87142914427761
38514512.8466396663731.15336033362744
39517520.732901595958-3.73290159595796
40508513.373867190633-5.37386719063274
41493500.783025904242-7.7830259042425
42490498.310253428503-8.31025342850267
43469478.896763974657-9.89676397465712
44478486.151219023177-8.15121902317745
45528530.401343331462-2.40134333146199
46534532.0832745208121.91672547918841
47518517.7878538776160.212146122383543
48506507.601343331462-1.60134333146200
49502502.242657791323-0.242657791322671
50516520.437829817948-4.43782981794846
51528534.642160558184-6.64216055818427
52533539.91926377416-6.91926377415988
53536543.123594514396-7.12359451439568
54537543.809856443981-6.80985644398105
55524527.555401395461-3.55540139546071
56536537.968890849306-1.96889084930627
57587579.0599807522667.9400192477344
58597587.0599807522669.9400192477344
59581575.9235945143965.07640548560431
60564556.2599807522667.7400192477344







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.06984105058175220.1396821011635040.930158949418248
180.02657732238062660.05315464476125320.973422677619373
190.01653516756983080.03307033513966160.983464832430169
200.007645044672410150.01529008934482030.99235495532759
210.002541748291975770.005083496583951550.997458251708024
220.001605374192408220.003210748384816430.998394625807592
230.002986092515688920.005972185031377840.99701390748431
240.07195220194743920.1439044038948780.92804779805256
250.2463098321641530.4926196643283060.753690167835847
260.421737149410430.843474298820860.57826285058957
270.9416886270463380.1166227459073230.0583113729536615
280.987159708168470.02568058366306110.0128402918315306
290.998595587432320.002808825135361340.00140441256768067
300.99973094334180.0005381133163988720.000269056658199436
310.9998105057501710.0003789884996572030.000189494249828602
320.9997264507067520.0005470985864956090.000273549293247805
330.9995411999081330.0009176001837340920.000458800091867046
340.9998201725540440.0003596548919116930.000179827445955846
350.9996243177438060.0007513645123880640.000375682256194032
360.9999334572500530.0001330854998936506.65427499468252e-05
370.9997705232961140.0004589534077716950.000229476703885848
380.99924286589880.001514268202399660.000757134101199828
390.9983656956750770.003268608649846440.00163430432492322
400.9959189389598850.00816212208022940.0040810610401147
410.9946419829201890.01071603415962230.00535801707981113
420.998841693927750.002316612144500800.00115830607225040
430.9976144610563280.004771077887344140.00238553894367207

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.0698410505817522 & 0.139682101163504 & 0.930158949418248 \tabularnewline
18 & 0.0265773223806266 & 0.0531546447612532 & 0.973422677619373 \tabularnewline
19 & 0.0165351675698308 & 0.0330703351396616 & 0.983464832430169 \tabularnewline
20 & 0.00764504467241015 & 0.0152900893448203 & 0.99235495532759 \tabularnewline
21 & 0.00254174829197577 & 0.00508349658395155 & 0.997458251708024 \tabularnewline
22 & 0.00160537419240822 & 0.00321074838481643 & 0.998394625807592 \tabularnewline
23 & 0.00298609251568892 & 0.00597218503137784 & 0.99701390748431 \tabularnewline
24 & 0.0719522019474392 & 0.143904403894878 & 0.92804779805256 \tabularnewline
25 & 0.246309832164153 & 0.492619664328306 & 0.753690167835847 \tabularnewline
26 & 0.42173714941043 & 0.84347429882086 & 0.57826285058957 \tabularnewline
27 & 0.941688627046338 & 0.116622745907323 & 0.0583113729536615 \tabularnewline
28 & 0.98715970816847 & 0.0256805836630611 & 0.0128402918315306 \tabularnewline
29 & 0.99859558743232 & 0.00280882513536134 & 0.00140441256768067 \tabularnewline
30 & 0.9997309433418 & 0.000538113316398872 & 0.000269056658199436 \tabularnewline
31 & 0.999810505750171 & 0.000378988499657203 & 0.000189494249828602 \tabularnewline
32 & 0.999726450706752 & 0.000547098586495609 & 0.000273549293247805 \tabularnewline
33 & 0.999541199908133 & 0.000917600183734092 & 0.000458800091867046 \tabularnewline
34 & 0.999820172554044 & 0.000359654891911693 & 0.000179827445955846 \tabularnewline
35 & 0.999624317743806 & 0.000751364512388064 & 0.000375682256194032 \tabularnewline
36 & 0.999933457250053 & 0.000133085499893650 & 6.65427499468252e-05 \tabularnewline
37 & 0.999770523296114 & 0.000458953407771695 & 0.000229476703885848 \tabularnewline
38 & 0.9992428658988 & 0.00151426820239966 & 0.000757134101199828 \tabularnewline
39 & 0.998365695675077 & 0.00326860864984644 & 0.00163430432492322 \tabularnewline
40 & 0.995918938959885 & 0.0081621220802294 & 0.0040810610401147 \tabularnewline
41 & 0.994641982920189 & 0.0107160341596223 & 0.00535801707981113 \tabularnewline
42 & 0.99884169392775 & 0.00231661214450080 & 0.00115830607225040 \tabularnewline
43 & 0.997614461056328 & 0.00477107788734414 & 0.00238553894367207 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&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]17[/C][C]0.0698410505817522[/C][C]0.139682101163504[/C][C]0.930158949418248[/C][/ROW]
[ROW][C]18[/C][C]0.0265773223806266[/C][C]0.0531546447612532[/C][C]0.973422677619373[/C][/ROW]
[ROW][C]19[/C][C]0.0165351675698308[/C][C]0.0330703351396616[/C][C]0.983464832430169[/C][/ROW]
[ROW][C]20[/C][C]0.00764504467241015[/C][C]0.0152900893448203[/C][C]0.99235495532759[/C][/ROW]
[ROW][C]21[/C][C]0.00254174829197577[/C][C]0.00508349658395155[/C][C]0.997458251708024[/C][/ROW]
[ROW][C]22[/C][C]0.00160537419240822[/C][C]0.00321074838481643[/C][C]0.998394625807592[/C][/ROW]
[ROW][C]23[/C][C]0.00298609251568892[/C][C]0.00597218503137784[/C][C]0.99701390748431[/C][/ROW]
[ROW][C]24[/C][C]0.0719522019474392[/C][C]0.143904403894878[/C][C]0.92804779805256[/C][/ROW]
[ROW][C]25[/C][C]0.246309832164153[/C][C]0.492619664328306[/C][C]0.753690167835847[/C][/ROW]
[ROW][C]26[/C][C]0.42173714941043[/C][C]0.84347429882086[/C][C]0.57826285058957[/C][/ROW]
[ROW][C]27[/C][C]0.941688627046338[/C][C]0.116622745907323[/C][C]0.0583113729536615[/C][/ROW]
[ROW][C]28[/C][C]0.98715970816847[/C][C]0.0256805836630611[/C][C]0.0128402918315306[/C][/ROW]
[ROW][C]29[/C][C]0.99859558743232[/C][C]0.00280882513536134[/C][C]0.00140441256768067[/C][/ROW]
[ROW][C]30[/C][C]0.9997309433418[/C][C]0.000538113316398872[/C][C]0.000269056658199436[/C][/ROW]
[ROW][C]31[/C][C]0.999810505750171[/C][C]0.000378988499657203[/C][C]0.000189494249828602[/C][/ROW]
[ROW][C]32[/C][C]0.999726450706752[/C][C]0.000547098586495609[/C][C]0.000273549293247805[/C][/ROW]
[ROW][C]33[/C][C]0.999541199908133[/C][C]0.000917600183734092[/C][C]0.000458800091867046[/C][/ROW]
[ROW][C]34[/C][C]0.999820172554044[/C][C]0.000359654891911693[/C][C]0.000179827445955846[/C][/ROW]
[ROW][C]35[/C][C]0.999624317743806[/C][C]0.000751364512388064[/C][C]0.000375682256194032[/C][/ROW]
[ROW][C]36[/C][C]0.999933457250053[/C][C]0.000133085499893650[/C][C]6.65427499468252e-05[/C][/ROW]
[ROW][C]37[/C][C]0.999770523296114[/C][C]0.000458953407771695[/C][C]0.000229476703885848[/C][/ROW]
[ROW][C]38[/C][C]0.9992428658988[/C][C]0.00151426820239966[/C][C]0.000757134101199828[/C][/ROW]
[ROW][C]39[/C][C]0.998365695675077[/C][C]0.00326860864984644[/C][C]0.00163430432492322[/C][/ROW]
[ROW][C]40[/C][C]0.995918938959885[/C][C]0.0081621220802294[/C][C]0.0040810610401147[/C][/ROW]
[ROW][C]41[/C][C]0.994641982920189[/C][C]0.0107160341596223[/C][C]0.00535801707981113[/C][/ROW]
[ROW][C]42[/C][C]0.99884169392775[/C][C]0.00231661214450080[/C][C]0.00115830607225040[/C][/ROW]
[ROW][C]43[/C][C]0.997614461056328[/C][C]0.00477107788734414[/C][C]0.00238553894367207[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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
170.06984105058175220.1396821011635040.930158949418248
180.02657732238062660.05315464476125320.973422677619373
190.01653516756983080.03307033513966160.983464832430169
200.007645044672410150.01529008934482030.99235495532759
210.002541748291975770.005083496583951550.997458251708024
220.001605374192408220.003210748384816430.998394625807592
230.002986092515688920.005972185031377840.99701390748431
240.07195220194743920.1439044038948780.92804779805256
250.2463098321641530.4926196643283060.753690167835847
260.421737149410430.843474298820860.57826285058957
270.9416886270463380.1166227459073230.0583113729536615
280.987159708168470.02568058366306110.0128402918315306
290.998595587432320.002808825135361340.00140441256768067
300.99973094334180.0005381133163988720.000269056658199436
310.9998105057501710.0003789884996572030.000189494249828602
320.9997264507067520.0005470985864956090.000273549293247805
330.9995411999081330.0009176001837340920.000458800091867046
340.9998201725540440.0003596548919116930.000179827445955846
350.9996243177438060.0007513645123880640.000375682256194032
360.9999334572500530.0001330854998936506.65427499468252e-05
370.9997705232961140.0004589534077716950.000229476703885848
380.99924286589880.001514268202399660.000757134101199828
390.9983656956750770.003268608649846440.00163430432492322
400.9959189389598850.00816212208022940.0040810610401147
410.9946419829201890.01071603415962230.00535801707981113
420.998841693927750.002316612144500800.00115830607225040
430.9976144610563280.004771077887344140.00238553894367207







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level170.62962962962963NOK
5% type I error level210.777777777777778NOK
10% type I error level220.814814814814815NOK

\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 & 17 & 0.62962962962963 & NOK \tabularnewline
5% type I error level & 21 & 0.777777777777778 & NOK \tabularnewline
10% type I error level & 22 & 0.814814814814815 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58415&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]17[/C][C]0.62962962962963[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]21[/C][C]0.777777777777778[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]22[/C][C]0.814814814814815[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58415&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58415&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 level170.62962962962963NOK
5% type I error level210.777777777777778NOK
10% type I error level220.814814814814815NOK



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')
}