Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 07:39:26 -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/t1258728094jcdkexzgu30n154.htm/, Retrieved Fri, 29 Mar 2024 11:00:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58222, Retrieved Fri, 29 Mar 2024 11:00:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact147
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-20 14:39:26] [429631dabc57c2ce83a6344a979b9063] [Current]
Feedback Forum

Post a new message
Dataseries X:
115.6	37.2
111.9	37.2
107	34.7
107.1	32.5
100.6	33.5
99.2	31.5
108.4	31.2
103	27
99.8	26.7
115	26.5
90.8	26
95.9	27.2
114.4	30.5
108.2	33.7
112.6	34.2
109.1	36.7
105	36.2
105	38.5
118.5	40
103.7	42.5
112.5	43.5
116.6	43.3
96.6	45.5
101.9	44.3
116.5	43
119.3	43.5
115.4	41.5
108.5	42.5
111.5	41.3
108.8	39.5
121.8	38.5
109.6	41
112.2	44.5
119.6	46
104.1	44
105.3	41.5
115	41.3
124.1	38
116.8	38
107.5	36.2
115.6	38.7
116.2	38.7
116.3	39.2
119	35.7
111.9	36.5
118.6	36.7
106.9	34.7
103.2	35
118.6	28.2
118.7	23.7
102.8	15
100.6	8.7
94.9	11
94.5	7.5
102.9	5.7
95.3	9.3
92.5	10.2
102.7	15.7
91.5	18.1
89.5	20.8




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&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]4 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=58222&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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 time4 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Ipzb[t] = + 83.9746433207604 + 0.449803219171789Cvn[t] + 15.8344486602883M1[t] + 16.6232873000092M2[t] + 12.2457874767055M3[t] + 8.49751985477916M4[t] + 7.08868121505829M5[t] + 6.75848443423008M6[t] + 15.6974411424479M7[t] + 8.15647656299695M8[t] + 7.28570876437425M9[t] + 15.3939763863006M10[t] -1.13501967808283M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ipzb[t] =  +  83.9746433207604 +  0.449803219171789Cvn[t] +  15.8344486602883M1[t] +  16.6232873000092M2[t] +  12.2457874767055M3[t] +  8.49751985477916M4[t] +  7.08868121505829M5[t] +  6.75848443423008M6[t] +  15.6974411424479M7[t] +  8.15647656299695M8[t] +  7.28570876437425M9[t] +  15.3939763863006M10[t] -1.13501967808283M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ipzb[t] =  +  83.9746433207604 +  0.449803219171789Cvn[t] +  15.8344486602883M1[t] +  16.6232873000092M2[t] +  12.2457874767055M3[t] +  8.49751985477916M4[t] +  7.08868121505829M5[t] +  6.75848443423008M6[t] +  15.6974411424479M7[t] +  8.15647656299695M8[t] +  7.28570876437425M9[t] +  15.3939763863006M10[t] -1.13501967808283M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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
Ipzb[t] = + 83.9746433207604 + 0.449803219171789Cvn[t] + 15.8344486602883M1[t] + 16.6232873000092M2[t] + 12.2457874767055M3[t] + 8.49751985477916M4[t] + 7.08868121505829M5[t] + 6.75848443423008M6[t] + 15.6974411424479M7[t] + 8.15647656299695M8[t] + 7.28570876437425M9[t] + 15.3939763863006M10[t] -1.13501967808283M11[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)83.97464332076042.78663530.134800
Cvn0.4498032191717890.0557558.067500
M115.83444866028832.9087465.44372e-061e-06
M216.62328730000922.9071075.71821e-060
M312.24578747670552.9065914.21310.0001135.7e-05
M48.497519854779162.909152.9210.0053460.002673
M57.088681215058292.9073712.43820.0185960.009298
M66.758484434230082.9096362.32280.024570.012285
M715.69744114244792.9102785.39382e-061e-06
M88.156476562996952.9097492.80320.0073310.003665
M97.285708764374252.9071392.50610.0157250.007862
M1015.39397638630062.9059755.29743e-062e-06
M11-1.135019678082832.905973-0.39060.6978710.348936

\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) & 83.9746433207604 & 2.786635 & 30.1348 & 0 & 0 \tabularnewline
Cvn & 0.449803219171789 & 0.055755 & 8.0675 & 0 & 0 \tabularnewline
M1 & 15.8344486602883 & 2.908746 & 5.4437 & 2e-06 & 1e-06 \tabularnewline
M2 & 16.6232873000092 & 2.907107 & 5.7182 & 1e-06 & 0 \tabularnewline
M3 & 12.2457874767055 & 2.906591 & 4.2131 & 0.000113 & 5.7e-05 \tabularnewline
M4 & 8.49751985477916 & 2.90915 & 2.921 & 0.005346 & 0.002673 \tabularnewline
M5 & 7.08868121505829 & 2.907371 & 2.4382 & 0.018596 & 0.009298 \tabularnewline
M6 & 6.75848443423008 & 2.909636 & 2.3228 & 0.02457 & 0.012285 \tabularnewline
M7 & 15.6974411424479 & 2.910278 & 5.3938 & 2e-06 & 1e-06 \tabularnewline
M8 & 8.15647656299695 & 2.909749 & 2.8032 & 0.007331 & 0.003665 \tabularnewline
M9 & 7.28570876437425 & 2.907139 & 2.5061 & 0.015725 & 0.007862 \tabularnewline
M10 & 15.3939763863006 & 2.905975 & 5.2974 & 3e-06 & 2e-06 \tabularnewline
M11 & -1.13501967808283 & 2.905973 & -0.3906 & 0.697871 & 0.348936 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&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]83.9746433207604[/C][C]2.786635[/C][C]30.1348[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Cvn[/C][C]0.449803219171789[/C][C]0.055755[/C][C]8.0675[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]15.8344486602883[/C][C]2.908746[/C][C]5.4437[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M2[/C][C]16.6232873000092[/C][C]2.907107[/C][C]5.7182[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]M3[/C][C]12.2457874767055[/C][C]2.906591[/C][C]4.2131[/C][C]0.000113[/C][C]5.7e-05[/C][/ROW]
[ROW][C]M4[/C][C]8.49751985477916[/C][C]2.90915[/C][C]2.921[/C][C]0.005346[/C][C]0.002673[/C][/ROW]
[ROW][C]M5[/C][C]7.08868121505829[/C][C]2.907371[/C][C]2.4382[/C][C]0.018596[/C][C]0.009298[/C][/ROW]
[ROW][C]M6[/C][C]6.75848443423008[/C][C]2.909636[/C][C]2.3228[/C][C]0.02457[/C][C]0.012285[/C][/ROW]
[ROW][C]M7[/C][C]15.6974411424479[/C][C]2.910278[/C][C]5.3938[/C][C]2e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]M8[/C][C]8.15647656299695[/C][C]2.909749[/C][C]2.8032[/C][C]0.007331[/C][C]0.003665[/C][/ROW]
[ROW][C]M9[/C][C]7.28570876437425[/C][C]2.907139[/C][C]2.5061[/C][C]0.015725[/C][C]0.007862[/C][/ROW]
[ROW][C]M10[/C][C]15.3939763863006[/C][C]2.905975[/C][C]5.2974[/C][C]3e-06[/C][C]2e-06[/C][/ROW]
[ROW][C]M11[/C][C]-1.13501967808283[/C][C]2.905973[/C][C]-0.3906[/C][C]0.697871[/C][C]0.348936[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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)83.97464332076042.78663530.134800
Cvn0.4498032191717890.0557558.067500
M115.83444866028832.9087465.44372e-061e-06
M216.62328730000922.9071075.71821e-060
M312.24578747670552.9065914.21310.0001135.7e-05
M48.497519854779162.909152.9210.0053460.002673
M57.088681215058292.9073712.43820.0185960.009298
M66.758484434230082.9096362.32280.024570.012285
M715.69744114244792.9102785.39382e-061e-06
M88.156476562996952.9097492.80320.0073310.003665
M97.285708764374252.9071392.50610.0157250.007862
M1015.39397638630062.9059755.29743e-062e-06
M11-1.135019678082832.905973-0.39060.6978710.348936







Multiple Linear Regression - Regression Statistics
Multiple R0.882397287601617
R-squared0.77862497316669
Adjusted R-squared0.722103689719887
F-TEST (value)13.7757836638569
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.33876243424425e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.59473754725227
Sum Squared Residuals992.245817022102

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.882397287601617 \tabularnewline
R-squared & 0.77862497316669 \tabularnewline
Adjusted R-squared & 0.722103689719887 \tabularnewline
F-TEST (value) & 13.7757836638569 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 1.33876243424425e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.59473754725227 \tabularnewline
Sum Squared Residuals & 992.245817022102 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.882397287601617[/C][/ROW]
[ROW][C]R-squared[/C][C]0.77862497316669[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.722103689719887[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.7757836638569[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]1.33876243424425e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.59473754725227[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]992.245817022102[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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 R0.882397287601617
R-squared0.77862497316669
Adjusted R-squared0.722103689719887
F-TEST (value)13.7757836638569
F-TEST (DF numerator)12
F-TEST (DF denominator)47
p-value1.33876243424425e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.59473754725227
Sum Squared Residuals992.245817022102







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1115.6116.541771734239-0.941771734239318
2111.9117.330610373960-5.43061037396014
3107111.828602502727-4.82860250272701
4107.1107.0907677986230.00923220137728752
5100.6106.131732378074-5.53173237807365
699.2104.901929158902-5.70192915890184
7108.4113.705944901368-5.3059449013681
8103104.275806801396-1.27580680139567
999.8103.270098037021-3.47009803702143
10115111.2884050151133.71159498488658
1190.894.534507341144-3.7345073411441
1295.996.209290882233-0.309290882233061
13114.4113.5280901657880.871909834211727
14108.2115.756299106859-7.55629910685888
15112.6111.6037008931410.996299106858876
16109.1108.9799413191440.120058680855773
17105107.346201069837-2.34620106983746
18105108.050551693104-3.05055169310437
19118.5117.6642132300800.835786769920157
20103.7111.247756698558-7.54775669855839
21112.5110.8267921191071.67320788089253
22116.6118.845099097199-2.24509909719948
2396.6103.305670114994-6.70567011499398
24101.9103.900925930071-2.00092593007065
25116.5119.150630405436-2.65063040543564
26119.3120.164370654742-0.864370654742411
27115.4114.8872643930950.512735606904831
28108.5111.588799990341-3.08879999034060
29111.5109.6401974876141.85980251238642
30108.8108.5003549122760.299645087723842
31121.8116.9895084013224.81049159867784
32109.6110.573051869801-0.973051869800714
33112.2111.2765953382790.923404661720744
34119.6120.059567788963-0.459567788963312
35104.1102.6309652862361.4690347137637
36105.3102.6414769163902.65852308361035
37115118.385964932844-3.3859649328436
38124.1117.6904529492986.40954705070242
39116.8113.3129531259943.48704687400608
40107.5108.755039709558-1.25503970955833
41115.6108.4707091177677.12929088223307
42116.2108.1405123369398.05948766306128
43116.3117.304370654742-1.00437065474242
44119108.18909480819010.8109051918098
45111.9107.6781695849054.22183041509506
46118.6115.8763978506662.72360214933432
47106.998.44779534793878.45220465206135
48103.299.7177559917733.48224400822698
49118.6112.4935427616936.10645723830683
50118.7111.2582669151417.44173308485901
51102.8102.967479085043-0.167479085042776
52100.696.38545118233414.21454881766586
5394.996.0111599467084-1.11115994670838
5494.594.1066518987790.393348101221087
55102.9102.2359628124870.664037187512518
5695.396.314289822055-1.01428982205500
5792.595.848344920687-3.3483449206869
58102.7106.430530248058-3.73053024805811
5991.590.9810619096870.518938090313036
6089.593.3305502795336-3.83055027953362

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 115.6 & 116.541771734239 & -0.941771734239318 \tabularnewline
2 & 111.9 & 117.330610373960 & -5.43061037396014 \tabularnewline
3 & 107 & 111.828602502727 & -4.82860250272701 \tabularnewline
4 & 107.1 & 107.090767798623 & 0.00923220137728752 \tabularnewline
5 & 100.6 & 106.131732378074 & -5.53173237807365 \tabularnewline
6 & 99.2 & 104.901929158902 & -5.70192915890184 \tabularnewline
7 & 108.4 & 113.705944901368 & -5.3059449013681 \tabularnewline
8 & 103 & 104.275806801396 & -1.27580680139567 \tabularnewline
9 & 99.8 & 103.270098037021 & -3.47009803702143 \tabularnewline
10 & 115 & 111.288405015113 & 3.71159498488658 \tabularnewline
11 & 90.8 & 94.534507341144 & -3.7345073411441 \tabularnewline
12 & 95.9 & 96.209290882233 & -0.309290882233061 \tabularnewline
13 & 114.4 & 113.528090165788 & 0.871909834211727 \tabularnewline
14 & 108.2 & 115.756299106859 & -7.55629910685888 \tabularnewline
15 & 112.6 & 111.603700893141 & 0.996299106858876 \tabularnewline
16 & 109.1 & 108.979941319144 & 0.120058680855773 \tabularnewline
17 & 105 & 107.346201069837 & -2.34620106983746 \tabularnewline
18 & 105 & 108.050551693104 & -3.05055169310437 \tabularnewline
19 & 118.5 & 117.664213230080 & 0.835786769920157 \tabularnewline
20 & 103.7 & 111.247756698558 & -7.54775669855839 \tabularnewline
21 & 112.5 & 110.826792119107 & 1.67320788089253 \tabularnewline
22 & 116.6 & 118.845099097199 & -2.24509909719948 \tabularnewline
23 & 96.6 & 103.305670114994 & -6.70567011499398 \tabularnewline
24 & 101.9 & 103.900925930071 & -2.00092593007065 \tabularnewline
25 & 116.5 & 119.150630405436 & -2.65063040543564 \tabularnewline
26 & 119.3 & 120.164370654742 & -0.864370654742411 \tabularnewline
27 & 115.4 & 114.887264393095 & 0.512735606904831 \tabularnewline
28 & 108.5 & 111.588799990341 & -3.08879999034060 \tabularnewline
29 & 111.5 & 109.640197487614 & 1.85980251238642 \tabularnewline
30 & 108.8 & 108.500354912276 & 0.299645087723842 \tabularnewline
31 & 121.8 & 116.989508401322 & 4.81049159867784 \tabularnewline
32 & 109.6 & 110.573051869801 & -0.973051869800714 \tabularnewline
33 & 112.2 & 111.276595338279 & 0.923404661720744 \tabularnewline
34 & 119.6 & 120.059567788963 & -0.459567788963312 \tabularnewline
35 & 104.1 & 102.630965286236 & 1.4690347137637 \tabularnewline
36 & 105.3 & 102.641476916390 & 2.65852308361035 \tabularnewline
37 & 115 & 118.385964932844 & -3.3859649328436 \tabularnewline
38 & 124.1 & 117.690452949298 & 6.40954705070242 \tabularnewline
39 & 116.8 & 113.312953125994 & 3.48704687400608 \tabularnewline
40 & 107.5 & 108.755039709558 & -1.25503970955833 \tabularnewline
41 & 115.6 & 108.470709117767 & 7.12929088223307 \tabularnewline
42 & 116.2 & 108.140512336939 & 8.05948766306128 \tabularnewline
43 & 116.3 & 117.304370654742 & -1.00437065474242 \tabularnewline
44 & 119 & 108.189094808190 & 10.8109051918098 \tabularnewline
45 & 111.9 & 107.678169584905 & 4.22183041509506 \tabularnewline
46 & 118.6 & 115.876397850666 & 2.72360214933432 \tabularnewline
47 & 106.9 & 98.4477953479387 & 8.45220465206135 \tabularnewline
48 & 103.2 & 99.717755991773 & 3.48224400822698 \tabularnewline
49 & 118.6 & 112.493542761693 & 6.10645723830683 \tabularnewline
50 & 118.7 & 111.258266915141 & 7.44173308485901 \tabularnewline
51 & 102.8 & 102.967479085043 & -0.167479085042776 \tabularnewline
52 & 100.6 & 96.3854511823341 & 4.21454881766586 \tabularnewline
53 & 94.9 & 96.0111599467084 & -1.11115994670838 \tabularnewline
54 & 94.5 & 94.106651898779 & 0.393348101221087 \tabularnewline
55 & 102.9 & 102.235962812487 & 0.664037187512518 \tabularnewline
56 & 95.3 & 96.314289822055 & -1.01428982205500 \tabularnewline
57 & 92.5 & 95.848344920687 & -3.3483449206869 \tabularnewline
58 & 102.7 & 106.430530248058 & -3.73053024805811 \tabularnewline
59 & 91.5 & 90.981061909687 & 0.518938090313036 \tabularnewline
60 & 89.5 & 93.3305502795336 & -3.83055027953362 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&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]115.6[/C][C]116.541771734239[/C][C]-0.941771734239318[/C][/ROW]
[ROW][C]2[/C][C]111.9[/C][C]117.330610373960[/C][C]-5.43061037396014[/C][/ROW]
[ROW][C]3[/C][C]107[/C][C]111.828602502727[/C][C]-4.82860250272701[/C][/ROW]
[ROW][C]4[/C][C]107.1[/C][C]107.090767798623[/C][C]0.00923220137728752[/C][/ROW]
[ROW][C]5[/C][C]100.6[/C][C]106.131732378074[/C][C]-5.53173237807365[/C][/ROW]
[ROW][C]6[/C][C]99.2[/C][C]104.901929158902[/C][C]-5.70192915890184[/C][/ROW]
[ROW][C]7[/C][C]108.4[/C][C]113.705944901368[/C][C]-5.3059449013681[/C][/ROW]
[ROW][C]8[/C][C]103[/C][C]104.275806801396[/C][C]-1.27580680139567[/C][/ROW]
[ROW][C]9[/C][C]99.8[/C][C]103.270098037021[/C][C]-3.47009803702143[/C][/ROW]
[ROW][C]10[/C][C]115[/C][C]111.288405015113[/C][C]3.71159498488658[/C][/ROW]
[ROW][C]11[/C][C]90.8[/C][C]94.534507341144[/C][C]-3.7345073411441[/C][/ROW]
[ROW][C]12[/C][C]95.9[/C][C]96.209290882233[/C][C]-0.309290882233061[/C][/ROW]
[ROW][C]13[/C][C]114.4[/C][C]113.528090165788[/C][C]0.871909834211727[/C][/ROW]
[ROW][C]14[/C][C]108.2[/C][C]115.756299106859[/C][C]-7.55629910685888[/C][/ROW]
[ROW][C]15[/C][C]112.6[/C][C]111.603700893141[/C][C]0.996299106858876[/C][/ROW]
[ROW][C]16[/C][C]109.1[/C][C]108.979941319144[/C][C]0.120058680855773[/C][/ROW]
[ROW][C]17[/C][C]105[/C][C]107.346201069837[/C][C]-2.34620106983746[/C][/ROW]
[ROW][C]18[/C][C]105[/C][C]108.050551693104[/C][C]-3.05055169310437[/C][/ROW]
[ROW][C]19[/C][C]118.5[/C][C]117.664213230080[/C][C]0.835786769920157[/C][/ROW]
[ROW][C]20[/C][C]103.7[/C][C]111.247756698558[/C][C]-7.54775669855839[/C][/ROW]
[ROW][C]21[/C][C]112.5[/C][C]110.826792119107[/C][C]1.67320788089253[/C][/ROW]
[ROW][C]22[/C][C]116.6[/C][C]118.845099097199[/C][C]-2.24509909719948[/C][/ROW]
[ROW][C]23[/C][C]96.6[/C][C]103.305670114994[/C][C]-6.70567011499398[/C][/ROW]
[ROW][C]24[/C][C]101.9[/C][C]103.900925930071[/C][C]-2.00092593007065[/C][/ROW]
[ROW][C]25[/C][C]116.5[/C][C]119.150630405436[/C][C]-2.65063040543564[/C][/ROW]
[ROW][C]26[/C][C]119.3[/C][C]120.164370654742[/C][C]-0.864370654742411[/C][/ROW]
[ROW][C]27[/C][C]115.4[/C][C]114.887264393095[/C][C]0.512735606904831[/C][/ROW]
[ROW][C]28[/C][C]108.5[/C][C]111.588799990341[/C][C]-3.08879999034060[/C][/ROW]
[ROW][C]29[/C][C]111.5[/C][C]109.640197487614[/C][C]1.85980251238642[/C][/ROW]
[ROW][C]30[/C][C]108.8[/C][C]108.500354912276[/C][C]0.299645087723842[/C][/ROW]
[ROW][C]31[/C][C]121.8[/C][C]116.989508401322[/C][C]4.81049159867784[/C][/ROW]
[ROW][C]32[/C][C]109.6[/C][C]110.573051869801[/C][C]-0.973051869800714[/C][/ROW]
[ROW][C]33[/C][C]112.2[/C][C]111.276595338279[/C][C]0.923404661720744[/C][/ROW]
[ROW][C]34[/C][C]119.6[/C][C]120.059567788963[/C][C]-0.459567788963312[/C][/ROW]
[ROW][C]35[/C][C]104.1[/C][C]102.630965286236[/C][C]1.4690347137637[/C][/ROW]
[ROW][C]36[/C][C]105.3[/C][C]102.641476916390[/C][C]2.65852308361035[/C][/ROW]
[ROW][C]37[/C][C]115[/C][C]118.385964932844[/C][C]-3.3859649328436[/C][/ROW]
[ROW][C]38[/C][C]124.1[/C][C]117.690452949298[/C][C]6.40954705070242[/C][/ROW]
[ROW][C]39[/C][C]116.8[/C][C]113.312953125994[/C][C]3.48704687400608[/C][/ROW]
[ROW][C]40[/C][C]107.5[/C][C]108.755039709558[/C][C]-1.25503970955833[/C][/ROW]
[ROW][C]41[/C][C]115.6[/C][C]108.470709117767[/C][C]7.12929088223307[/C][/ROW]
[ROW][C]42[/C][C]116.2[/C][C]108.140512336939[/C][C]8.05948766306128[/C][/ROW]
[ROW][C]43[/C][C]116.3[/C][C]117.304370654742[/C][C]-1.00437065474242[/C][/ROW]
[ROW][C]44[/C][C]119[/C][C]108.189094808190[/C][C]10.8109051918098[/C][/ROW]
[ROW][C]45[/C][C]111.9[/C][C]107.678169584905[/C][C]4.22183041509506[/C][/ROW]
[ROW][C]46[/C][C]118.6[/C][C]115.876397850666[/C][C]2.72360214933432[/C][/ROW]
[ROW][C]47[/C][C]106.9[/C][C]98.4477953479387[/C][C]8.45220465206135[/C][/ROW]
[ROW][C]48[/C][C]103.2[/C][C]99.717755991773[/C][C]3.48224400822698[/C][/ROW]
[ROW][C]49[/C][C]118.6[/C][C]112.493542761693[/C][C]6.10645723830683[/C][/ROW]
[ROW][C]50[/C][C]118.7[/C][C]111.258266915141[/C][C]7.44173308485901[/C][/ROW]
[ROW][C]51[/C][C]102.8[/C][C]102.967479085043[/C][C]-0.167479085042776[/C][/ROW]
[ROW][C]52[/C][C]100.6[/C][C]96.3854511823341[/C][C]4.21454881766586[/C][/ROW]
[ROW][C]53[/C][C]94.9[/C][C]96.0111599467084[/C][C]-1.11115994670838[/C][/ROW]
[ROW][C]54[/C][C]94.5[/C][C]94.106651898779[/C][C]0.393348101221087[/C][/ROW]
[ROW][C]55[/C][C]102.9[/C][C]102.235962812487[/C][C]0.664037187512518[/C][/ROW]
[ROW][C]56[/C][C]95.3[/C][C]96.314289822055[/C][C]-1.01428982205500[/C][/ROW]
[ROW][C]57[/C][C]92.5[/C][C]95.848344920687[/C][C]-3.3483449206869[/C][/ROW]
[ROW][C]58[/C][C]102.7[/C][C]106.430530248058[/C][C]-3.73053024805811[/C][/ROW]
[ROW][C]59[/C][C]91.5[/C][C]90.981061909687[/C][C]0.518938090313036[/C][/ROW]
[ROW][C]60[/C][C]89.5[/C][C]93.3305502795336[/C][C]-3.83055027953362[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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
1115.6116.541771734239-0.941771734239318
2111.9117.330610373960-5.43061037396014
3107111.828602502727-4.82860250272701
4107.1107.0907677986230.00923220137728752
5100.6106.131732378074-5.53173237807365
699.2104.901929158902-5.70192915890184
7108.4113.705944901368-5.3059449013681
8103104.275806801396-1.27580680139567
999.8103.270098037021-3.47009803702143
10115111.2884050151133.71159498488658
1190.894.534507341144-3.7345073411441
1295.996.209290882233-0.309290882233061
13114.4113.5280901657880.871909834211727
14108.2115.756299106859-7.55629910685888
15112.6111.6037008931410.996299106858876
16109.1108.9799413191440.120058680855773
17105107.346201069837-2.34620106983746
18105108.050551693104-3.05055169310437
19118.5117.6642132300800.835786769920157
20103.7111.247756698558-7.54775669855839
21112.5110.8267921191071.67320788089253
22116.6118.845099097199-2.24509909719948
2396.6103.305670114994-6.70567011499398
24101.9103.900925930071-2.00092593007065
25116.5119.150630405436-2.65063040543564
26119.3120.164370654742-0.864370654742411
27115.4114.8872643930950.512735606904831
28108.5111.588799990341-3.08879999034060
29111.5109.6401974876141.85980251238642
30108.8108.5003549122760.299645087723842
31121.8116.9895084013224.81049159867784
32109.6110.573051869801-0.973051869800714
33112.2111.2765953382790.923404661720744
34119.6120.059567788963-0.459567788963312
35104.1102.6309652862361.4690347137637
36105.3102.6414769163902.65852308361035
37115118.385964932844-3.3859649328436
38124.1117.6904529492986.40954705070242
39116.8113.3129531259943.48704687400608
40107.5108.755039709558-1.25503970955833
41115.6108.4707091177677.12929088223307
42116.2108.1405123369398.05948766306128
43116.3117.304370654742-1.00437065474242
44119108.18909480819010.8109051918098
45111.9107.6781695849054.22183041509506
46118.6115.8763978506662.72360214933432
47106.998.44779534793878.45220465206135
48103.299.7177559917733.48224400822698
49118.6112.4935427616936.10645723830683
50118.7111.2582669151417.44173308485901
51102.8102.967479085043-0.167479085042776
52100.696.38545118233414.21454881766586
5394.996.0111599467084-1.11115994670838
5494.594.1066518987790.393348101221087
55102.9102.2359628124870.664037187512518
5695.396.314289822055-1.01428982205500
5792.595.848344920687-3.3483449206869
58102.7106.430530248058-3.73053024805811
5991.590.9810619096870.518938090313036
6089.593.3305502795336-3.83055027953362







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1928355383616760.3856710767233520.807164461638324
170.1288770474460120.2577540948920230.871122952553988
180.07260524161386620.1452104832277320.927394758386134
190.05408050108274150.1081610021654830.945919498917259
200.1742503690442080.3485007380884150.825749630955792
210.1489777351006870.2979554702013740.851022264899313
220.1607384616320940.3214769232641890.839261538367906
230.1700190956692230.3400381913384460.829980904330777
240.1148276600407530.2296553200815050.885172339959247
250.08353942554881940.1670788510976390.91646057445118
260.1549994507826010.3099989015652010.8450005492174
270.1176434656218230.2352869312436470.882356534378177
280.1102703962513080.2205407925026150.889729603748692
290.1286212447022810.2572424894045620.871378755297719
300.1416305971445010.2832611942890020.858369402855499
310.1761353333353170.3522706666706330.823864666664683
320.2143415701343700.4286831402687390.78565842986563
330.1593878490891730.3187756981783450.840612150910827
340.1156879076025010.2313758152050020.884312092397499
350.1523623830113970.3047247660227930.847637616988603
360.1121242029139790.2242484058279580.887875797086021
370.2378953222776850.475790644555370.762104677722315
380.3654415908973660.7308831817947320.634558409102634
390.2914977842890260.5829955685780510.708502215710974
400.5661139159304080.8677721681391840.433886084069592
410.5370093236039770.9259813527920470.462990676396023
420.5013382476895610.9973235046208780.498661752310439
430.9711245204004160.05775095919916910.0288754795995845
440.9572632911439310.08547341771213710.0427367088560685

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.192835538361676 & 0.385671076723352 & 0.807164461638324 \tabularnewline
17 & 0.128877047446012 & 0.257754094892023 & 0.871122952553988 \tabularnewline
18 & 0.0726052416138662 & 0.145210483227732 & 0.927394758386134 \tabularnewline
19 & 0.0540805010827415 & 0.108161002165483 & 0.945919498917259 \tabularnewline
20 & 0.174250369044208 & 0.348500738088415 & 0.825749630955792 \tabularnewline
21 & 0.148977735100687 & 0.297955470201374 & 0.851022264899313 \tabularnewline
22 & 0.160738461632094 & 0.321476923264189 & 0.839261538367906 \tabularnewline
23 & 0.170019095669223 & 0.340038191338446 & 0.829980904330777 \tabularnewline
24 & 0.114827660040753 & 0.229655320081505 & 0.885172339959247 \tabularnewline
25 & 0.0835394255488194 & 0.167078851097639 & 0.91646057445118 \tabularnewline
26 & 0.154999450782601 & 0.309998901565201 & 0.8450005492174 \tabularnewline
27 & 0.117643465621823 & 0.235286931243647 & 0.882356534378177 \tabularnewline
28 & 0.110270396251308 & 0.220540792502615 & 0.889729603748692 \tabularnewline
29 & 0.128621244702281 & 0.257242489404562 & 0.871378755297719 \tabularnewline
30 & 0.141630597144501 & 0.283261194289002 & 0.858369402855499 \tabularnewline
31 & 0.176135333335317 & 0.352270666670633 & 0.823864666664683 \tabularnewline
32 & 0.214341570134370 & 0.428683140268739 & 0.78565842986563 \tabularnewline
33 & 0.159387849089173 & 0.318775698178345 & 0.840612150910827 \tabularnewline
34 & 0.115687907602501 & 0.231375815205002 & 0.884312092397499 \tabularnewline
35 & 0.152362383011397 & 0.304724766022793 & 0.847637616988603 \tabularnewline
36 & 0.112124202913979 & 0.224248405827958 & 0.887875797086021 \tabularnewline
37 & 0.237895322277685 & 0.47579064455537 & 0.762104677722315 \tabularnewline
38 & 0.365441590897366 & 0.730883181794732 & 0.634558409102634 \tabularnewline
39 & 0.291497784289026 & 0.582995568578051 & 0.708502215710974 \tabularnewline
40 & 0.566113915930408 & 0.867772168139184 & 0.433886084069592 \tabularnewline
41 & 0.537009323603977 & 0.925981352792047 & 0.462990676396023 \tabularnewline
42 & 0.501338247689561 & 0.997323504620878 & 0.498661752310439 \tabularnewline
43 & 0.971124520400416 & 0.0577509591991691 & 0.0288754795995845 \tabularnewline
44 & 0.957263291143931 & 0.0854734177121371 & 0.0427367088560685 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]16[/C][C]0.192835538361676[/C][C]0.385671076723352[/C][C]0.807164461638324[/C][/ROW]
[ROW][C]17[/C][C]0.128877047446012[/C][C]0.257754094892023[/C][C]0.871122952553988[/C][/ROW]
[ROW][C]18[/C][C]0.0726052416138662[/C][C]0.145210483227732[/C][C]0.927394758386134[/C][/ROW]
[ROW][C]19[/C][C]0.0540805010827415[/C][C]0.108161002165483[/C][C]0.945919498917259[/C][/ROW]
[ROW][C]20[/C][C]0.174250369044208[/C][C]0.348500738088415[/C][C]0.825749630955792[/C][/ROW]
[ROW][C]21[/C][C]0.148977735100687[/C][C]0.297955470201374[/C][C]0.851022264899313[/C][/ROW]
[ROW][C]22[/C][C]0.160738461632094[/C][C]0.321476923264189[/C][C]0.839261538367906[/C][/ROW]
[ROW][C]23[/C][C]0.170019095669223[/C][C]0.340038191338446[/C][C]0.829980904330777[/C][/ROW]
[ROW][C]24[/C][C]0.114827660040753[/C][C]0.229655320081505[/C][C]0.885172339959247[/C][/ROW]
[ROW][C]25[/C][C]0.0835394255488194[/C][C]0.167078851097639[/C][C]0.91646057445118[/C][/ROW]
[ROW][C]26[/C][C]0.154999450782601[/C][C]0.309998901565201[/C][C]0.8450005492174[/C][/ROW]
[ROW][C]27[/C][C]0.117643465621823[/C][C]0.235286931243647[/C][C]0.882356534378177[/C][/ROW]
[ROW][C]28[/C][C]0.110270396251308[/C][C]0.220540792502615[/C][C]0.889729603748692[/C][/ROW]
[ROW][C]29[/C][C]0.128621244702281[/C][C]0.257242489404562[/C][C]0.871378755297719[/C][/ROW]
[ROW][C]30[/C][C]0.141630597144501[/C][C]0.283261194289002[/C][C]0.858369402855499[/C][/ROW]
[ROW][C]31[/C][C]0.176135333335317[/C][C]0.352270666670633[/C][C]0.823864666664683[/C][/ROW]
[ROW][C]32[/C][C]0.214341570134370[/C][C]0.428683140268739[/C][C]0.78565842986563[/C][/ROW]
[ROW][C]33[/C][C]0.159387849089173[/C][C]0.318775698178345[/C][C]0.840612150910827[/C][/ROW]
[ROW][C]34[/C][C]0.115687907602501[/C][C]0.231375815205002[/C][C]0.884312092397499[/C][/ROW]
[ROW][C]35[/C][C]0.152362383011397[/C][C]0.304724766022793[/C][C]0.847637616988603[/C][/ROW]
[ROW][C]36[/C][C]0.112124202913979[/C][C]0.224248405827958[/C][C]0.887875797086021[/C][/ROW]
[ROW][C]37[/C][C]0.237895322277685[/C][C]0.47579064455537[/C][C]0.762104677722315[/C][/ROW]
[ROW][C]38[/C][C]0.365441590897366[/C][C]0.730883181794732[/C][C]0.634558409102634[/C][/ROW]
[ROW][C]39[/C][C]0.291497784289026[/C][C]0.582995568578051[/C][C]0.708502215710974[/C][/ROW]
[ROW][C]40[/C][C]0.566113915930408[/C][C]0.867772168139184[/C][C]0.433886084069592[/C][/ROW]
[ROW][C]41[/C][C]0.537009323603977[/C][C]0.925981352792047[/C][C]0.462990676396023[/C][/ROW]
[ROW][C]42[/C][C]0.501338247689561[/C][C]0.997323504620878[/C][C]0.498661752310439[/C][/ROW]
[ROW][C]43[/C][C]0.971124520400416[/C][C]0.0577509591991691[/C][C]0.0288754795995845[/C][/ROW]
[ROW][C]44[/C][C]0.957263291143931[/C][C]0.0854734177121371[/C][C]0.0427367088560685[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&T=5

As an alternative you can also use a QR Code:  

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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
160.1928355383616760.3856710767233520.807164461638324
170.1288770474460120.2577540948920230.871122952553988
180.07260524161386620.1452104832277320.927394758386134
190.05408050108274150.1081610021654830.945919498917259
200.1742503690442080.3485007380884150.825749630955792
210.1489777351006870.2979554702013740.851022264899313
220.1607384616320940.3214769232641890.839261538367906
230.1700190956692230.3400381913384460.829980904330777
240.1148276600407530.2296553200815050.885172339959247
250.08353942554881940.1670788510976390.91646057445118
260.1549994507826010.3099989015652010.8450005492174
270.1176434656218230.2352869312436470.882356534378177
280.1102703962513080.2205407925026150.889729603748692
290.1286212447022810.2572424894045620.871378755297719
300.1416305971445010.2832611942890020.858369402855499
310.1761353333353170.3522706666706330.823864666664683
320.2143415701343700.4286831402687390.78565842986563
330.1593878490891730.3187756981783450.840612150910827
340.1156879076025010.2313758152050020.884312092397499
350.1523623830113970.3047247660227930.847637616988603
360.1121242029139790.2242484058279580.887875797086021
370.2378953222776850.475790644555370.762104677722315
380.3654415908973660.7308831817947320.634558409102634
390.2914977842890260.5829955685780510.708502215710974
400.5661139159304080.8677721681391840.433886084069592
410.5370093236039770.9259813527920470.462990676396023
420.5013382476895610.9973235046208780.498661752310439
430.9711245204004160.05775095919916910.0288754795995845
440.9572632911439310.08547341771213710.0427367088560685







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 level20.0689655172413793OK

\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 & 0.0689655172413793 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58222&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]0.0689655172413793[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58222&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58222&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 level20.0689655172413793OK



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