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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 computationMon, 10 Dec 2012 07:50:31 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/10/t1355144069ds4r1fvy35tlojk.htm/, Retrieved Fri, 29 Mar 2024 00:23:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=198121, Retrieved Fri, 29 Mar 2024 00:23:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact100
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Werkeloosheid ver...] [2012-11-15 17:53:40] [8ab8078357d7493428921287469fd527]
- R PD    [Multiple Regression] [] [2012-12-10 12:50:31] [eace0511beeaae09dbb51bfebd62c02b] [Current]
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Dataseries X:
277	5	82	98
232	4	84	100
256	3	85	103
242	4	87	100
302	4	91	100
282	4	94	101
288	5	96	100
321	6	97	100
316	5	99	100
396	5	100	102
362	4	102	103
392	3	104	106
414	2	105	108
417	2	107	105
476	2	108	110
488	1	109	110
489	0	110	110
467	0	110	113
460	1	109	111
482	0	109	111
510	1	109	111
493	0	110	111
476	0	110	107
448	1	110	110
410	2	110	104
466	2	107	105
417	3	108	104
387	3	109	106
370	1	109	105
344	2	110	104
396	3	109	104
349	2	110	104
326	4	110	103
303	4	110	104
300	3	110	98
329	3	110	100
304	3	110	103
286	3	109	100
281	5	110	100
377	5	110	101
344	4	112	100
369	3	112	100
390	2	112	100
406	-1	111	102
426	-4	112	103
467	-5	112	106
437	-4	113	108
410	-2	113	105
390	2	113	110
418	2	112	110
398	2	112	110
422	2	111	113
439	3	112	111
419	1	112	111
484	1	113	111
491	-1	113	111




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 8 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ fisher.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&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]8 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ fisher.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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 time8 seconds
R Server'Sir Ronald Aylmer Fisher' @ fisher.wessa.net







Multiple Linear Regression - Estimated Regression Equation
werkeloosheid[t] = -821.80901969682 -7.73236337144463bbp[t] + 2.19244864752551cpi[t] + 9.42168384625692prijsbouw[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkeloosheid[t] =  -821.80901969682 -7.73236337144463bbp[t] +  2.19244864752551cpi[t] +  9.42168384625692prijsbouw[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkeloosheid[t] =  -821.80901969682 -7.73236337144463bbp[t] +  2.19244864752551cpi[t] +  9.42168384625692prijsbouw[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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
werkeloosheid[t] = -821.80901969682 -7.73236337144463bbp[t] + 2.19244864752551cpi[t] + 9.42168384625692prijsbouw[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-821.80901969682148.260263-5.5431e-061e-06
bbp-7.732363371444632.71561-2.84740.0062970.003149
cpi2.192448647525510.7760032.82530.0066850.003342
prijsbouw9.421683846256921.3917256.769800

\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) & -821.80901969682 & 148.260263 & -5.543 & 1e-06 & 1e-06 \tabularnewline
bbp & -7.73236337144463 & 2.71561 & -2.8474 & 0.006297 & 0.003149 \tabularnewline
cpi & 2.19244864752551 & 0.776003 & 2.8253 & 0.006685 & 0.003342 \tabularnewline
prijsbouw & 9.42168384625692 & 1.391725 & 6.7698 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&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]-821.80901969682[/C][C]148.260263[/C][C]-5.543[/C][C]1e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]bbp[/C][C]-7.73236337144463[/C][C]2.71561[/C][C]-2.8474[/C][C]0.006297[/C][C]0.003149[/C][/ROW]
[ROW][C]cpi[/C][C]2.19244864752551[/C][C]0.776003[/C][C]2.8253[/C][C]0.006685[/C][C]0.003342[/C][/ROW]
[ROW][C]prijsbouw[/C][C]9.42168384625692[/C][C]1.391725[/C][C]6.7698[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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)-821.80901969682148.260263-5.5431e-061e-06
bbp-7.732363371444632.71561-2.84740.0062970.003149
cpi2.192448647525510.7760032.82530.0066850.003342
prijsbouw9.421683846256921.3917256.769800







Multiple Linear Regression - Regression Statistics
Multiple R0.871472642347093
R-squared0.759464566359425
Adjusted R-squared0.74558752211093
F-TEST (value)54.7281216921829
F-TEST (DF numerator)3
F-TEST (DF denominator)52
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37.4142080865911
Sum Squared Residuals72790.7942708304

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.871472642347093 \tabularnewline
R-squared & 0.759464566359425 \tabularnewline
Adjusted R-squared & 0.74558752211093 \tabularnewline
F-TEST (value) & 54.7281216921829 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 52 \tabularnewline
p-value & 4.44089209850063e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 37.4142080865911 \tabularnewline
Sum Squared Residuals & 72790.7942708304 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.871472642347093[/C][/ROW]
[ROW][C]R-squared[/C][C]0.759464566359425[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.74558752211093[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]54.7281216921829[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]52[/C][/ROW]
[ROW][C]p-value[/C][C]4.44089209850063e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]37.4142080865911[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]72790.7942708304[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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 R0.871472642347093
R-squared0.759464566359425
Adjusted R-squared0.74558752211093
F-TEST (value)54.7281216921829
F-TEST (DF numerator)3
F-TEST (DF denominator)52
p-value4.44089209850063e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation37.4142080865911
Sum Squared Residuals72790.7942708304







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1277242.63496947622634.3650305237736
2232273.595597835235-41.5955978352355
3256311.785461392976-55.7854613929764
4242280.172943777812-38.172943777812
5302288.94273836791413.057261632086
6282304.941768156748-22.9417681567475
7288292.172618234097-4.17261823409696
8321286.63270351017834.3672964898222
9316298.74996417667317.2500358233265
10396319.78578051671376.2142194832872
11362341.32472502946520.6752749705346
12392381.70703723473210.2929627652682
13414410.4752169462163.52478305378426
14417386.59506270249630.404937297504
15476435.89593058130640.1040694186939
16488445.82074260027642.1792573997238
17489455.74555461924633.2544453807536
18467484.010606158017-17.0106061580171
19460455.2424264465334.75757355346684
20482462.97478981797819.0252101820222
21510455.24242644653354.7575735534669
22493465.16723846550327.8327615344967
23476427.48050308047648.5194969195244
24448448.013191247802-0.0131912478017405
25410383.75072479881626.2492752011844
26466386.59506270249679.404937297504
27417371.6334641323245.36653586768
28387392.669280472359-5.66928047235932
29370398.712323368992-28.7123233689917
30344383.750724798816-39.7507247988156
31396373.82591277984622.1740872201545
32349383.750724798816-34.7507247988156
33326358.864314209669-32.8643142096695
34303368.285998055926-65.2859980559264
35300319.488258349829-19.4882583498295
36329338.331626042343-9.33162604234332
37304366.596677581114-62.5966775811141
38286336.139177394818-50.1391773948178
39281322.866899299454-41.8668992994541
40377332.28858314571144.711416854289
41344334.984159965959.01584003405028
42369342.71652333739426.2834766626057
43390350.44888670883939.551113291161
44406390.29689586816115.7031041318388
45426425.1081184762770.891881523722553
46467461.1055333864935.89446661350717
47437474.408986355088-37.4089863550876
48410430.679208073428-20.6792080734276
49390446.858173818934-56.8581738189336
50418444.665725171408-26.6657251714081
51398444.665725171408-46.6657251714081
52422470.738328062653-48.7383280626534
53439446.35504564622-7.35504564622043
54419461.81977238911-42.8197723891097
55484464.01222103663519.9877789633648
56491479.47694777952411.5230522204756

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 277 & 242.634969476226 & 34.3650305237736 \tabularnewline
2 & 232 & 273.595597835235 & -41.5955978352355 \tabularnewline
3 & 256 & 311.785461392976 & -55.7854613929764 \tabularnewline
4 & 242 & 280.172943777812 & -38.172943777812 \tabularnewline
5 & 302 & 288.942738367914 & 13.057261632086 \tabularnewline
6 & 282 & 304.941768156748 & -22.9417681567475 \tabularnewline
7 & 288 & 292.172618234097 & -4.17261823409696 \tabularnewline
8 & 321 & 286.632703510178 & 34.3672964898222 \tabularnewline
9 & 316 & 298.749964176673 & 17.2500358233265 \tabularnewline
10 & 396 & 319.785780516713 & 76.2142194832872 \tabularnewline
11 & 362 & 341.324725029465 & 20.6752749705346 \tabularnewline
12 & 392 & 381.707037234732 & 10.2929627652682 \tabularnewline
13 & 414 & 410.475216946216 & 3.52478305378426 \tabularnewline
14 & 417 & 386.595062702496 & 30.404937297504 \tabularnewline
15 & 476 & 435.895930581306 & 40.1040694186939 \tabularnewline
16 & 488 & 445.820742600276 & 42.1792573997238 \tabularnewline
17 & 489 & 455.745554619246 & 33.2544453807536 \tabularnewline
18 & 467 & 484.010606158017 & -17.0106061580171 \tabularnewline
19 & 460 & 455.242426446533 & 4.75757355346684 \tabularnewline
20 & 482 & 462.974789817978 & 19.0252101820222 \tabularnewline
21 & 510 & 455.242426446533 & 54.7575735534669 \tabularnewline
22 & 493 & 465.167238465503 & 27.8327615344967 \tabularnewline
23 & 476 & 427.480503080476 & 48.5194969195244 \tabularnewline
24 & 448 & 448.013191247802 & -0.0131912478017405 \tabularnewline
25 & 410 & 383.750724798816 & 26.2492752011844 \tabularnewline
26 & 466 & 386.595062702496 & 79.404937297504 \tabularnewline
27 & 417 & 371.63346413232 & 45.36653586768 \tabularnewline
28 & 387 & 392.669280472359 & -5.66928047235932 \tabularnewline
29 & 370 & 398.712323368992 & -28.7123233689917 \tabularnewline
30 & 344 & 383.750724798816 & -39.7507247988156 \tabularnewline
31 & 396 & 373.825912779846 & 22.1740872201545 \tabularnewline
32 & 349 & 383.750724798816 & -34.7507247988156 \tabularnewline
33 & 326 & 358.864314209669 & -32.8643142096695 \tabularnewline
34 & 303 & 368.285998055926 & -65.2859980559264 \tabularnewline
35 & 300 & 319.488258349829 & -19.4882583498295 \tabularnewline
36 & 329 & 338.331626042343 & -9.33162604234332 \tabularnewline
37 & 304 & 366.596677581114 & -62.5966775811141 \tabularnewline
38 & 286 & 336.139177394818 & -50.1391773948178 \tabularnewline
39 & 281 & 322.866899299454 & -41.8668992994541 \tabularnewline
40 & 377 & 332.288583145711 & 44.711416854289 \tabularnewline
41 & 344 & 334.98415996595 & 9.01584003405028 \tabularnewline
42 & 369 & 342.716523337394 & 26.2834766626057 \tabularnewline
43 & 390 & 350.448886708839 & 39.551113291161 \tabularnewline
44 & 406 & 390.296895868161 & 15.7031041318388 \tabularnewline
45 & 426 & 425.108118476277 & 0.891881523722553 \tabularnewline
46 & 467 & 461.105533386493 & 5.89446661350717 \tabularnewline
47 & 437 & 474.408986355088 & -37.4089863550876 \tabularnewline
48 & 410 & 430.679208073428 & -20.6792080734276 \tabularnewline
49 & 390 & 446.858173818934 & -56.8581738189336 \tabularnewline
50 & 418 & 444.665725171408 & -26.6657251714081 \tabularnewline
51 & 398 & 444.665725171408 & -46.6657251714081 \tabularnewline
52 & 422 & 470.738328062653 & -48.7383280626534 \tabularnewline
53 & 439 & 446.35504564622 & -7.35504564622043 \tabularnewline
54 & 419 & 461.81977238911 & -42.8197723891097 \tabularnewline
55 & 484 & 464.012221036635 & 19.9877789633648 \tabularnewline
56 & 491 & 479.476947779524 & 11.5230522204756 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&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]277[/C][C]242.634969476226[/C][C]34.3650305237736[/C][/ROW]
[ROW][C]2[/C][C]232[/C][C]273.595597835235[/C][C]-41.5955978352355[/C][/ROW]
[ROW][C]3[/C][C]256[/C][C]311.785461392976[/C][C]-55.7854613929764[/C][/ROW]
[ROW][C]4[/C][C]242[/C][C]280.172943777812[/C][C]-38.172943777812[/C][/ROW]
[ROW][C]5[/C][C]302[/C][C]288.942738367914[/C][C]13.057261632086[/C][/ROW]
[ROW][C]6[/C][C]282[/C][C]304.941768156748[/C][C]-22.9417681567475[/C][/ROW]
[ROW][C]7[/C][C]288[/C][C]292.172618234097[/C][C]-4.17261823409696[/C][/ROW]
[ROW][C]8[/C][C]321[/C][C]286.632703510178[/C][C]34.3672964898222[/C][/ROW]
[ROW][C]9[/C][C]316[/C][C]298.749964176673[/C][C]17.2500358233265[/C][/ROW]
[ROW][C]10[/C][C]396[/C][C]319.785780516713[/C][C]76.2142194832872[/C][/ROW]
[ROW][C]11[/C][C]362[/C][C]341.324725029465[/C][C]20.6752749705346[/C][/ROW]
[ROW][C]12[/C][C]392[/C][C]381.707037234732[/C][C]10.2929627652682[/C][/ROW]
[ROW][C]13[/C][C]414[/C][C]410.475216946216[/C][C]3.52478305378426[/C][/ROW]
[ROW][C]14[/C][C]417[/C][C]386.595062702496[/C][C]30.404937297504[/C][/ROW]
[ROW][C]15[/C][C]476[/C][C]435.895930581306[/C][C]40.1040694186939[/C][/ROW]
[ROW][C]16[/C][C]488[/C][C]445.820742600276[/C][C]42.1792573997238[/C][/ROW]
[ROW][C]17[/C][C]489[/C][C]455.745554619246[/C][C]33.2544453807536[/C][/ROW]
[ROW][C]18[/C][C]467[/C][C]484.010606158017[/C][C]-17.0106061580171[/C][/ROW]
[ROW][C]19[/C][C]460[/C][C]455.242426446533[/C][C]4.75757355346684[/C][/ROW]
[ROW][C]20[/C][C]482[/C][C]462.974789817978[/C][C]19.0252101820222[/C][/ROW]
[ROW][C]21[/C][C]510[/C][C]455.242426446533[/C][C]54.7575735534669[/C][/ROW]
[ROW][C]22[/C][C]493[/C][C]465.167238465503[/C][C]27.8327615344967[/C][/ROW]
[ROW][C]23[/C][C]476[/C][C]427.480503080476[/C][C]48.5194969195244[/C][/ROW]
[ROW][C]24[/C][C]448[/C][C]448.013191247802[/C][C]-0.0131912478017405[/C][/ROW]
[ROW][C]25[/C][C]410[/C][C]383.750724798816[/C][C]26.2492752011844[/C][/ROW]
[ROW][C]26[/C][C]466[/C][C]386.595062702496[/C][C]79.404937297504[/C][/ROW]
[ROW][C]27[/C][C]417[/C][C]371.63346413232[/C][C]45.36653586768[/C][/ROW]
[ROW][C]28[/C][C]387[/C][C]392.669280472359[/C][C]-5.66928047235932[/C][/ROW]
[ROW][C]29[/C][C]370[/C][C]398.712323368992[/C][C]-28.7123233689917[/C][/ROW]
[ROW][C]30[/C][C]344[/C][C]383.750724798816[/C][C]-39.7507247988156[/C][/ROW]
[ROW][C]31[/C][C]396[/C][C]373.825912779846[/C][C]22.1740872201545[/C][/ROW]
[ROW][C]32[/C][C]349[/C][C]383.750724798816[/C][C]-34.7507247988156[/C][/ROW]
[ROW][C]33[/C][C]326[/C][C]358.864314209669[/C][C]-32.8643142096695[/C][/ROW]
[ROW][C]34[/C][C]303[/C][C]368.285998055926[/C][C]-65.2859980559264[/C][/ROW]
[ROW][C]35[/C][C]300[/C][C]319.488258349829[/C][C]-19.4882583498295[/C][/ROW]
[ROW][C]36[/C][C]329[/C][C]338.331626042343[/C][C]-9.33162604234332[/C][/ROW]
[ROW][C]37[/C][C]304[/C][C]366.596677581114[/C][C]-62.5966775811141[/C][/ROW]
[ROW][C]38[/C][C]286[/C][C]336.139177394818[/C][C]-50.1391773948178[/C][/ROW]
[ROW][C]39[/C][C]281[/C][C]322.866899299454[/C][C]-41.8668992994541[/C][/ROW]
[ROW][C]40[/C][C]377[/C][C]332.288583145711[/C][C]44.711416854289[/C][/ROW]
[ROW][C]41[/C][C]344[/C][C]334.98415996595[/C][C]9.01584003405028[/C][/ROW]
[ROW][C]42[/C][C]369[/C][C]342.716523337394[/C][C]26.2834766626057[/C][/ROW]
[ROW][C]43[/C][C]390[/C][C]350.448886708839[/C][C]39.551113291161[/C][/ROW]
[ROW][C]44[/C][C]406[/C][C]390.296895868161[/C][C]15.7031041318388[/C][/ROW]
[ROW][C]45[/C][C]426[/C][C]425.108118476277[/C][C]0.891881523722553[/C][/ROW]
[ROW][C]46[/C][C]467[/C][C]461.105533386493[/C][C]5.89446661350717[/C][/ROW]
[ROW][C]47[/C][C]437[/C][C]474.408986355088[/C][C]-37.4089863550876[/C][/ROW]
[ROW][C]48[/C][C]410[/C][C]430.679208073428[/C][C]-20.6792080734276[/C][/ROW]
[ROW][C]49[/C][C]390[/C][C]446.858173818934[/C][C]-56.8581738189336[/C][/ROW]
[ROW][C]50[/C][C]418[/C][C]444.665725171408[/C][C]-26.6657251714081[/C][/ROW]
[ROW][C]51[/C][C]398[/C][C]444.665725171408[/C][C]-46.6657251714081[/C][/ROW]
[ROW][C]52[/C][C]422[/C][C]470.738328062653[/C][C]-48.7383280626534[/C][/ROW]
[ROW][C]53[/C][C]439[/C][C]446.35504564622[/C][C]-7.35504564622043[/C][/ROW]
[ROW][C]54[/C][C]419[/C][C]461.81977238911[/C][C]-42.8197723891097[/C][/ROW]
[ROW][C]55[/C][C]484[/C][C]464.012221036635[/C][C]19.9877789633648[/C][/ROW]
[ROW][C]56[/C][C]491[/C][C]479.476947779524[/C][C]11.5230522204756[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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
1277242.63496947622634.3650305237736
2232273.595597835235-41.5955978352355
3256311.785461392976-55.7854613929764
4242280.172943777812-38.172943777812
5302288.94273836791413.057261632086
6282304.941768156748-22.9417681567475
7288292.172618234097-4.17261823409696
8321286.63270351017834.3672964898222
9316298.74996417667317.2500358233265
10396319.78578051671376.2142194832872
11362341.32472502946520.6752749705346
12392381.70703723473210.2929627652682
13414410.4752169462163.52478305378426
14417386.59506270249630.404937297504
15476435.89593058130640.1040694186939
16488445.82074260027642.1792573997238
17489455.74555461924633.2544453807536
18467484.010606158017-17.0106061580171
19460455.2424264465334.75757355346684
20482462.97478981797819.0252101820222
21510455.24242644653354.7575735534669
22493465.16723846550327.8327615344967
23476427.48050308047648.5194969195244
24448448.013191247802-0.0131912478017405
25410383.75072479881626.2492752011844
26466386.59506270249679.404937297504
27417371.6334641323245.36653586768
28387392.669280472359-5.66928047235932
29370398.712323368992-28.7123233689917
30344383.750724798816-39.7507247988156
31396373.82591277984622.1740872201545
32349383.750724798816-34.7507247988156
33326358.864314209669-32.8643142096695
34303368.285998055926-65.2859980559264
35300319.488258349829-19.4882583498295
36329338.331626042343-9.33162604234332
37304366.596677581114-62.5966775811141
38286336.139177394818-50.1391773948178
39281322.866899299454-41.8668992994541
40377332.28858314571144.711416854289
41344334.984159965959.01584003405028
42369342.71652333739426.2834766626057
43390350.44888670883939.551113291161
44406390.29689586816115.7031041318388
45426425.1081184762770.891881523722553
46467461.1055333864935.89446661350717
47437474.408986355088-37.4089863550876
48410430.679208073428-20.6792080734276
49390446.858173818934-56.8581738189336
50418444.665725171408-26.6657251714081
51398444.665725171408-46.6657251714081
52422470.738328062653-48.7383280626534
53439446.35504564622-7.35504564622043
54419461.81977238911-42.8197723891097
55484464.01222103663519.9877789633648
56491479.47694777952411.5230522204756







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
70.4018755230197510.8037510460395010.598124476980249
80.2565036613919340.5130073227838670.743496338608066
90.1537326968151490.3074653936302980.846267303184851
100.4613461176013190.9226922352026390.538653882398681
110.3680327565122520.7360655130245030.631967243487748
120.2870083538939150.5740167077878290.712991646106085
130.2342527627552080.4685055255104160.765747237244792
140.2348990802203150.4697981604406290.765100919779685
150.1963651508555830.3927303017111650.803634849144417
160.1839571601258990.3679143202517970.816042839874101
170.15431529460390.30863058920780.8456847053961
180.1450942864469980.2901885728939960.854905713553002
190.101889096979360.203778193958720.89811090302064
200.07066360043186780.1413272008637360.929336399568132
210.08902485479745670.1780497095949130.910975145202543
220.06745928385435090.1349185677087020.932540716145649
230.06729905872565950.1345981174513190.93270094127434
240.05730402530830680.1146080506166140.942695974691693
250.04877387729018110.09754775458036220.951226122709819
260.1638549901288150.3277099802576310.836145009871184
270.2597859809036720.5195719618073440.740214019096328
280.3486551459522980.6973102919045970.651344854047702
290.4425269176216830.8850538352433660.557473082378317
300.5554925113417490.8890149773165030.444507488658251
310.7003763449117560.5992473101764880.299623655088244
320.7095106069155310.5809787861689380.290489393084469
330.7408633479709110.5182733040581780.259136652029089
340.8532706993277640.2934586013444730.146729300672236
350.8060621297005090.3878757405989820.193937870299491
360.7398981859773340.5202036280453320.260101814022666
370.8061330541271570.3877338917456860.193866945872843
380.8277701603880330.3444596792239340.172229839611967
390.9262847555966020.1474304888067960.073715244403398
400.920905480832230.158189038335540.0790945191677699
410.8869739473874960.2260521052250080.113026052612504
420.8395321073021010.3209357853957980.160467892697899
430.8220683586796770.3558632826406460.177931641320323
440.8004816253180240.3990367493639520.199518374681976
450.7391006842768960.5217986314462080.260899315723104
460.7533854505314360.4932290989371290.246614549468564
470.7301923823672720.5396152352654570.269807617632728
480.6069998710114590.7860002579770830.393000128988541
490.9618646737215860.07627065255682750.0381353262784137

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 & 0.401875523019751 & 0.803751046039501 & 0.598124476980249 \tabularnewline
8 & 0.256503661391934 & 0.513007322783867 & 0.743496338608066 \tabularnewline
9 & 0.153732696815149 & 0.307465393630298 & 0.846267303184851 \tabularnewline
10 & 0.461346117601319 & 0.922692235202639 & 0.538653882398681 \tabularnewline
11 & 0.368032756512252 & 0.736065513024503 & 0.631967243487748 \tabularnewline
12 & 0.287008353893915 & 0.574016707787829 & 0.712991646106085 \tabularnewline
13 & 0.234252762755208 & 0.468505525510416 & 0.765747237244792 \tabularnewline
14 & 0.234899080220315 & 0.469798160440629 & 0.765100919779685 \tabularnewline
15 & 0.196365150855583 & 0.392730301711165 & 0.803634849144417 \tabularnewline
16 & 0.183957160125899 & 0.367914320251797 & 0.816042839874101 \tabularnewline
17 & 0.1543152946039 & 0.3086305892078 & 0.8456847053961 \tabularnewline
18 & 0.145094286446998 & 0.290188572893996 & 0.854905713553002 \tabularnewline
19 & 0.10188909697936 & 0.20377819395872 & 0.89811090302064 \tabularnewline
20 & 0.0706636004318678 & 0.141327200863736 & 0.929336399568132 \tabularnewline
21 & 0.0890248547974567 & 0.178049709594913 & 0.910975145202543 \tabularnewline
22 & 0.0674592838543509 & 0.134918567708702 & 0.932540716145649 \tabularnewline
23 & 0.0672990587256595 & 0.134598117451319 & 0.93270094127434 \tabularnewline
24 & 0.0573040253083068 & 0.114608050616614 & 0.942695974691693 \tabularnewline
25 & 0.0487738772901811 & 0.0975477545803622 & 0.951226122709819 \tabularnewline
26 & 0.163854990128815 & 0.327709980257631 & 0.836145009871184 \tabularnewline
27 & 0.259785980903672 & 0.519571961807344 & 0.740214019096328 \tabularnewline
28 & 0.348655145952298 & 0.697310291904597 & 0.651344854047702 \tabularnewline
29 & 0.442526917621683 & 0.885053835243366 & 0.557473082378317 \tabularnewline
30 & 0.555492511341749 & 0.889014977316503 & 0.444507488658251 \tabularnewline
31 & 0.700376344911756 & 0.599247310176488 & 0.299623655088244 \tabularnewline
32 & 0.709510606915531 & 0.580978786168938 & 0.290489393084469 \tabularnewline
33 & 0.740863347970911 & 0.518273304058178 & 0.259136652029089 \tabularnewline
34 & 0.853270699327764 & 0.293458601344473 & 0.146729300672236 \tabularnewline
35 & 0.806062129700509 & 0.387875740598982 & 0.193937870299491 \tabularnewline
36 & 0.739898185977334 & 0.520203628045332 & 0.260101814022666 \tabularnewline
37 & 0.806133054127157 & 0.387733891745686 & 0.193866945872843 \tabularnewline
38 & 0.827770160388033 & 0.344459679223934 & 0.172229839611967 \tabularnewline
39 & 0.926284755596602 & 0.147430488806796 & 0.073715244403398 \tabularnewline
40 & 0.92090548083223 & 0.15818903833554 & 0.0790945191677699 \tabularnewline
41 & 0.886973947387496 & 0.226052105225008 & 0.113026052612504 \tabularnewline
42 & 0.839532107302101 & 0.320935785395798 & 0.160467892697899 \tabularnewline
43 & 0.822068358679677 & 0.355863282640646 & 0.177931641320323 \tabularnewline
44 & 0.800481625318024 & 0.399036749363952 & 0.199518374681976 \tabularnewline
45 & 0.739100684276896 & 0.521798631446208 & 0.260899315723104 \tabularnewline
46 & 0.753385450531436 & 0.493229098937129 & 0.246614549468564 \tabularnewline
47 & 0.730192382367272 & 0.539615235265457 & 0.269807617632728 \tabularnewline
48 & 0.606999871011459 & 0.786000257977083 & 0.393000128988541 \tabularnewline
49 & 0.961864673721586 & 0.0762706525568275 & 0.0381353262784137 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&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]7[/C][C]0.401875523019751[/C][C]0.803751046039501[/C][C]0.598124476980249[/C][/ROW]
[ROW][C]8[/C][C]0.256503661391934[/C][C]0.513007322783867[/C][C]0.743496338608066[/C][/ROW]
[ROW][C]9[/C][C]0.153732696815149[/C][C]0.307465393630298[/C][C]0.846267303184851[/C][/ROW]
[ROW][C]10[/C][C]0.461346117601319[/C][C]0.922692235202639[/C][C]0.538653882398681[/C][/ROW]
[ROW][C]11[/C][C]0.368032756512252[/C][C]0.736065513024503[/C][C]0.631967243487748[/C][/ROW]
[ROW][C]12[/C][C]0.287008353893915[/C][C]0.574016707787829[/C][C]0.712991646106085[/C][/ROW]
[ROW][C]13[/C][C]0.234252762755208[/C][C]0.468505525510416[/C][C]0.765747237244792[/C][/ROW]
[ROW][C]14[/C][C]0.234899080220315[/C][C]0.469798160440629[/C][C]0.765100919779685[/C][/ROW]
[ROW][C]15[/C][C]0.196365150855583[/C][C]0.392730301711165[/C][C]0.803634849144417[/C][/ROW]
[ROW][C]16[/C][C]0.183957160125899[/C][C]0.367914320251797[/C][C]0.816042839874101[/C][/ROW]
[ROW][C]17[/C][C]0.1543152946039[/C][C]0.3086305892078[/C][C]0.8456847053961[/C][/ROW]
[ROW][C]18[/C][C]0.145094286446998[/C][C]0.290188572893996[/C][C]0.854905713553002[/C][/ROW]
[ROW][C]19[/C][C]0.10188909697936[/C][C]0.20377819395872[/C][C]0.89811090302064[/C][/ROW]
[ROW][C]20[/C][C]0.0706636004318678[/C][C]0.141327200863736[/C][C]0.929336399568132[/C][/ROW]
[ROW][C]21[/C][C]0.0890248547974567[/C][C]0.178049709594913[/C][C]0.910975145202543[/C][/ROW]
[ROW][C]22[/C][C]0.0674592838543509[/C][C]0.134918567708702[/C][C]0.932540716145649[/C][/ROW]
[ROW][C]23[/C][C]0.0672990587256595[/C][C]0.134598117451319[/C][C]0.93270094127434[/C][/ROW]
[ROW][C]24[/C][C]0.0573040253083068[/C][C]0.114608050616614[/C][C]0.942695974691693[/C][/ROW]
[ROW][C]25[/C][C]0.0487738772901811[/C][C]0.0975477545803622[/C][C]0.951226122709819[/C][/ROW]
[ROW][C]26[/C][C]0.163854990128815[/C][C]0.327709980257631[/C][C]0.836145009871184[/C][/ROW]
[ROW][C]27[/C][C]0.259785980903672[/C][C]0.519571961807344[/C][C]0.740214019096328[/C][/ROW]
[ROW][C]28[/C][C]0.348655145952298[/C][C]0.697310291904597[/C][C]0.651344854047702[/C][/ROW]
[ROW][C]29[/C][C]0.442526917621683[/C][C]0.885053835243366[/C][C]0.557473082378317[/C][/ROW]
[ROW][C]30[/C][C]0.555492511341749[/C][C]0.889014977316503[/C][C]0.444507488658251[/C][/ROW]
[ROW][C]31[/C][C]0.700376344911756[/C][C]0.599247310176488[/C][C]0.299623655088244[/C][/ROW]
[ROW][C]32[/C][C]0.709510606915531[/C][C]0.580978786168938[/C][C]0.290489393084469[/C][/ROW]
[ROW][C]33[/C][C]0.740863347970911[/C][C]0.518273304058178[/C][C]0.259136652029089[/C][/ROW]
[ROW][C]34[/C][C]0.853270699327764[/C][C]0.293458601344473[/C][C]0.146729300672236[/C][/ROW]
[ROW][C]35[/C][C]0.806062129700509[/C][C]0.387875740598982[/C][C]0.193937870299491[/C][/ROW]
[ROW][C]36[/C][C]0.739898185977334[/C][C]0.520203628045332[/C][C]0.260101814022666[/C][/ROW]
[ROW][C]37[/C][C]0.806133054127157[/C][C]0.387733891745686[/C][C]0.193866945872843[/C][/ROW]
[ROW][C]38[/C][C]0.827770160388033[/C][C]0.344459679223934[/C][C]0.172229839611967[/C][/ROW]
[ROW][C]39[/C][C]0.926284755596602[/C][C]0.147430488806796[/C][C]0.073715244403398[/C][/ROW]
[ROW][C]40[/C][C]0.92090548083223[/C][C]0.15818903833554[/C][C]0.0790945191677699[/C][/ROW]
[ROW][C]41[/C][C]0.886973947387496[/C][C]0.226052105225008[/C][C]0.113026052612504[/C][/ROW]
[ROW][C]42[/C][C]0.839532107302101[/C][C]0.320935785395798[/C][C]0.160467892697899[/C][/ROW]
[ROW][C]43[/C][C]0.822068358679677[/C][C]0.355863282640646[/C][C]0.177931641320323[/C][/ROW]
[ROW][C]44[/C][C]0.800481625318024[/C][C]0.399036749363952[/C][C]0.199518374681976[/C][/ROW]
[ROW][C]45[/C][C]0.739100684276896[/C][C]0.521798631446208[/C][C]0.260899315723104[/C][/ROW]
[ROW][C]46[/C][C]0.753385450531436[/C][C]0.493229098937129[/C][C]0.246614549468564[/C][/ROW]
[ROW][C]47[/C][C]0.730192382367272[/C][C]0.539615235265457[/C][C]0.269807617632728[/C][/ROW]
[ROW][C]48[/C][C]0.606999871011459[/C][C]0.786000257977083[/C][C]0.393000128988541[/C][/ROW]
[ROW][C]49[/C][C]0.961864673721586[/C][C]0.0762706525568275[/C][C]0.0381353262784137[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&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
70.4018755230197510.8037510460395010.598124476980249
80.2565036613919340.5130073227838670.743496338608066
90.1537326968151490.3074653936302980.846267303184851
100.4613461176013190.9226922352026390.538653882398681
110.3680327565122520.7360655130245030.631967243487748
120.2870083538939150.5740167077878290.712991646106085
130.2342527627552080.4685055255104160.765747237244792
140.2348990802203150.4697981604406290.765100919779685
150.1963651508555830.3927303017111650.803634849144417
160.1839571601258990.3679143202517970.816042839874101
170.15431529460390.30863058920780.8456847053961
180.1450942864469980.2901885728939960.854905713553002
190.101889096979360.203778193958720.89811090302064
200.07066360043186780.1413272008637360.929336399568132
210.08902485479745670.1780497095949130.910975145202543
220.06745928385435090.1349185677087020.932540716145649
230.06729905872565950.1345981174513190.93270094127434
240.05730402530830680.1146080506166140.942695974691693
250.04877387729018110.09754775458036220.951226122709819
260.1638549901288150.3277099802576310.836145009871184
270.2597859809036720.5195719618073440.740214019096328
280.3486551459522980.6973102919045970.651344854047702
290.4425269176216830.8850538352433660.557473082378317
300.5554925113417490.8890149773165030.444507488658251
310.7003763449117560.5992473101764880.299623655088244
320.7095106069155310.5809787861689380.290489393084469
330.7408633479709110.5182733040581780.259136652029089
340.8532706993277640.2934586013444730.146729300672236
350.8060621297005090.3878757405989820.193937870299491
360.7398981859773340.5202036280453320.260101814022666
370.8061330541271570.3877338917456860.193866945872843
380.8277701603880330.3444596792239340.172229839611967
390.9262847555966020.1474304888067960.073715244403398
400.920905480832230.158189038335540.0790945191677699
410.8869739473874960.2260521052250080.113026052612504
420.8395321073021010.3209357853957980.160467892697899
430.8220683586796770.3558632826406460.177931641320323
440.8004816253180240.3990367493639520.199518374681976
450.7391006842768960.5217986314462080.260899315723104
460.7533854505314360.4932290989371290.246614549468564
470.7301923823672720.5396152352654570.269807617632728
480.6069998710114590.7860002579770830.393000128988541
490.9618646737215860.07627065255682750.0381353262784137







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0465116279069767OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 2 & 0.0465116279069767 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=198121&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0465116279069767[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=198121&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=198121&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level20.0465116279069767OK



Parameters (Session):
par1 = 1 ; par2 = Include Quarterly Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$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')
}