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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 computationThu, 24 Nov 2011 11:46:00 -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/2011/Nov/24/t1322153229197wpc5obknipbh.htm/, Retrieved Fri, 29 Mar 2024 07:33:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147082, Retrieved Fri, 29 Mar 2024 07:33:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact87
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Decreasing Compet...] [2010-11-17 09:04:39] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 12:52:07] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D    [Multiple Regression] [WS7 comp 3] [2010-11-23 09:24:05] [dc30d19c3bc2be07fe595ad36c2cf923]
-           [Multiple Regression] [] [2010-12-02 15:15:07] [2e1e44f0ae3cb9513dc28781dfdb387b]
- R  D          [Multiple Regression] [WS 7 ] [2011-11-24 16:46:00] [cca7c4e9d798594cd7da8d83786e39be] [Current]
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Dataseries X:
8	78	284	9,100000381	109
9,300000191	68	433	8,699999809	144
7,5	70	739	7,199999809	113
8,899999619	96	1792	8,899999619	97
10,19999981	74	477	8,300000191	206
8,300000191	111	362	10,89999962	124
8,800000191	77	671	10	152
8,800000191	168	636	9,100000381	162
10,69999981	82	329	8,699999809	150
11,69999981	89	634	7,599999905	134
8,5	149	631	10,80000019	292
8,300000191	60	257	9,5	108
8,199999809	96	284	8,800000191	111
7,900000095	83	603	9,5	182
10,30000019	130	686	8,699999809	129
7,400000095	145	345	11,19999981	158
9,600000381	112	1357	9,699999809	186
9,300000191	131	544	9,600000381	177
10,60000038	80	205	9,100000381	127
9,699999809	130	1264	9,199999809	179
11,60000038	140	688	8,300000191	80
8,100000381	154	354	8,399999619	103
9,800000191	118	1632	9,399999619	101
7,400000095	94	348	9,800000191	117
9,399999619	119	370	10,39999962	88
11,19999981	153	648	9,899999619	78
9,100000381	116	366	9,199999809	102
10,5	97	540	10,30000019	95
11,89999962	176	680	8,899999619	80
8,399999619	75	345	9,600000381	92
5	134	525	10,30000019	126
9,800000191	161	870	10,39999962	108
9,800000191	111	669	9,699999809	77
10,80000019	114	452	9,600000381	60
10,10000038	142	430	10,69999981	71
10,89999962	238	822	10,30000019	86
9,199999809	78	190	10,69999981	93
8,300000191	196	867	9,600000381	106
7,300000191	125	969	10,5	162
9,399999619	82	499	7,699999809	95
9,399999619	125	925	10,19999981	91
9,800000191	129	353	9,899999619	52
3,599999905	84	288	8,399999619	110
8,399999619	183	718	10,39999962	69
10,80000019	119	540	9,199999809	57
10,10000038	180	668	13	106
9	82	347	8,800000191	40
10	71	345	9,199999809	50
11,30000019	118	463	7,800000191	35
11,30000019	121	728	8,199999809	86
12,80000019	68	383	7,400000095	57
10	112	316	10,39999962	57
6,699999809	109	388	8,899999619	94




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.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 & 3 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gertrude Mary Cox' @ cox.wessa.net







Multiple Linear Regression - Estimated Regression Equation
doctor[t] = -77.1180198794039 + 3.12904649550141death[t] + 0.0325065912039845hospital[t] + 16.2481033907153income[t] -0.0758510688606824population[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
doctor[t] =  -77.1180198794039 +  3.12904649550141death[t] +  0.0325065912039845hospital[t] +  16.2481033907153income[t] -0.0758510688606824population[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]doctor[t] =  -77.1180198794039 +  3.12904649550141death[t] +  0.0325065912039845hospital[t] +  16.2481033907153income[t] -0.0758510688606824population[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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
doctor[t] = -77.1180198794039 + 3.12904649550141death[t] + 0.0325065912039845hospital[t] + 16.2481033907153income[t] -0.0758510688606824population[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-77.118019879403954.135332-1.42450.1607590.08038
death3.129046495501412.9351781.06610.2917340.145867
hospital0.03250659120398450.0141992.28940.0264980.013249
income16.24810339071534.3302073.75230.0004730.000236
population-0.07585106886068240.103823-0.73060.4685870.234294

\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) & -77.1180198794039 & 54.135332 & -1.4245 & 0.160759 & 0.08038 \tabularnewline
death & 3.12904649550141 & 2.935178 & 1.0661 & 0.291734 & 0.145867 \tabularnewline
hospital & 0.0325065912039845 & 0.014199 & 2.2894 & 0.026498 & 0.013249 \tabularnewline
income & 16.2481033907153 & 4.330207 & 3.7523 & 0.000473 & 0.000236 \tabularnewline
population & -0.0758510688606824 & 0.103823 & -0.7306 & 0.468587 & 0.234294 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&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]-77.1180198794039[/C][C]54.135332[/C][C]-1.4245[/C][C]0.160759[/C][C]0.08038[/C][/ROW]
[ROW][C]death[/C][C]3.12904649550141[/C][C]2.935178[/C][C]1.0661[/C][C]0.291734[/C][C]0.145867[/C][/ROW]
[ROW][C]hospital[/C][C]0.0325065912039845[/C][C]0.014199[/C][C]2.2894[/C][C]0.026498[/C][C]0.013249[/C][/ROW]
[ROW][C]income[/C][C]16.2481033907153[/C][C]4.330207[/C][C]3.7523[/C][C]0.000473[/C][C]0.000236[/C][/ROW]
[ROW][C]population[/C][C]-0.0758510688606824[/C][C]0.103823[/C][C]-0.7306[/C][C]0.468587[/C][C]0.234294[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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)-77.118019879403954.135332-1.42450.1607590.08038
death3.129046495501412.9351781.06610.2917340.145867
hospital0.03250659120398450.0141992.28940.0264980.013249
income16.24810339071534.3302073.75230.0004730.000236
population-0.07585106886068240.103823-0.73060.4685870.234294







Multiple Linear Regression - Regression Statistics
Multiple R0.549532412033932
R-squared0.301985871875831
Adjusted R-squared0.243818027865484
F-TEST (value)5.19162910391025
F-TEST (DF numerator)4
F-TEST (DF denominator)48
p-value0.00147595090210906
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32.945707638151
Sum Squared Residuals52100.1432853689

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.549532412033932 \tabularnewline
R-squared & 0.301985871875831 \tabularnewline
Adjusted R-squared & 0.243818027865484 \tabularnewline
F-TEST (value) & 5.19162910391025 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 48 \tabularnewline
p-value & 0.00147595090210906 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 32.945707638151 \tabularnewline
Sum Squared Residuals & 52100.1432853689 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.549532412033932[/C][/ROW]
[ROW][C]R-squared[/C][C]0.301985871875831[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.243818027865484[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]5.19162910391025[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]48[/C][/ROW]
[ROW][C]p-value[/C][C]0.00147595090210906[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]32.945707638151[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]52100.1432853689[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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 R0.549532412033932
R-squared0.301985871875831
Adjusted R-squared0.243818027865484
F-TEST (value)5.19162910391025
F-TEST (DF numerator)4
F-TEST (DF denominator)48
p-value0.00147595090210906
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation32.945707638151
Sum Squared Residuals52100.1432853689







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
17896.7362045267615-18.7362045267615
26896.4934095976297-28.4934095976298
37078.7873702651068-8.78737026510675
496146.232864483285-50.232864483285
57489.5378388455167-15.5378388455167
6111128.319240892541-17.3192408925413
777123.18118401686-46.1811840168596
8168106.66165577499761.3383442250031
98297.0382816007865-15.0382816007865
108993.4225433453052-4.42254334530517
11149123.32154198161825.6784580183817
1260103.372327345172-43.3723273451716
139692.33587698613833.66412301386169
1483107.755009907471-24.7550099074708
15130108.9843896975221.0156103024796
16145127.24598445882517.7540155411751
17112140.530572911847-28.5305729118473
18131112.22185829442818.7781417055716
1980100.938386659496-20.9383866594958
20130130.227268576279-0.227268576279427
21140110.33463114326229.6653688567381
2215488.405993411365965.5940065886341
23118151.668600946328-33.6686009463283
2494107.706059499251-13.7060594992507
25119126.627829761037-7.627829761037
26153133.93140538229619.0685946177038
27116104.99945586988811.0005441301123
2897133.440144043255-36.4401440432553
29176120.76214272471955.2378572752809
3075109.384243862228-34.3842438622277
31134113.39140631525720.6085936847434
32161142.61572437383118.3842756261692
33111127.059613373902-16.0596133739018
34114122.799396700485-8.79939670048477
35142136.9324624363175.0675375636827
36238144.54127979168893.4587202083121
3778124.646013399789-46.6460133997892
38196124.97786664692271.0221333530775
39125135.540119459146-10.5401194591457
408286.4203464602864-4.42034646028639
41125141.191817081663-16.1918170816629
42129121.9334248659637.06657513403676
438471.648890190695712.3511098093043
44183136.25224731287546.7477526871248
45119119.388279282816-0.388279282816178
46180179.3848846185270.615115381473365
4782102.272455915147-20.2724559151468
4871111.077213689144-40.077213689144
4911897.371179982569120.6288200174309
50121108.61625728924112.3837427107592
516891.2962559984634-23.2962559984634
52112129.101286060171-17.1012860601705
5310993.937261943888615.0627380561114

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 78 & 96.7362045267615 & -18.7362045267615 \tabularnewline
2 & 68 & 96.4934095976297 & -28.4934095976298 \tabularnewline
3 & 70 & 78.7873702651068 & -8.78737026510675 \tabularnewline
4 & 96 & 146.232864483285 & -50.232864483285 \tabularnewline
5 & 74 & 89.5378388455167 & -15.5378388455167 \tabularnewline
6 & 111 & 128.319240892541 & -17.3192408925413 \tabularnewline
7 & 77 & 123.18118401686 & -46.1811840168596 \tabularnewline
8 & 168 & 106.661655774997 & 61.3383442250031 \tabularnewline
9 & 82 & 97.0382816007865 & -15.0382816007865 \tabularnewline
10 & 89 & 93.4225433453052 & -4.42254334530517 \tabularnewline
11 & 149 & 123.321541981618 & 25.6784580183817 \tabularnewline
12 & 60 & 103.372327345172 & -43.3723273451716 \tabularnewline
13 & 96 & 92.3358769861383 & 3.66412301386169 \tabularnewline
14 & 83 & 107.755009907471 & -24.7550099074708 \tabularnewline
15 & 130 & 108.98438969752 & 21.0156103024796 \tabularnewline
16 & 145 & 127.245984458825 & 17.7540155411751 \tabularnewline
17 & 112 & 140.530572911847 & -28.5305729118473 \tabularnewline
18 & 131 & 112.221858294428 & 18.7781417055716 \tabularnewline
19 & 80 & 100.938386659496 & -20.9383866594958 \tabularnewline
20 & 130 & 130.227268576279 & -0.227268576279427 \tabularnewline
21 & 140 & 110.334631143262 & 29.6653688567381 \tabularnewline
22 & 154 & 88.4059934113659 & 65.5940065886341 \tabularnewline
23 & 118 & 151.668600946328 & -33.6686009463283 \tabularnewline
24 & 94 & 107.706059499251 & -13.7060594992507 \tabularnewline
25 & 119 & 126.627829761037 & -7.627829761037 \tabularnewline
26 & 153 & 133.931405382296 & 19.0685946177038 \tabularnewline
27 & 116 & 104.999455869888 & 11.0005441301123 \tabularnewline
28 & 97 & 133.440144043255 & -36.4401440432553 \tabularnewline
29 & 176 & 120.762142724719 & 55.2378572752809 \tabularnewline
30 & 75 & 109.384243862228 & -34.3842438622277 \tabularnewline
31 & 134 & 113.391406315257 & 20.6085936847434 \tabularnewline
32 & 161 & 142.615724373831 & 18.3842756261692 \tabularnewline
33 & 111 & 127.059613373902 & -16.0596133739018 \tabularnewline
34 & 114 & 122.799396700485 & -8.79939670048477 \tabularnewline
35 & 142 & 136.932462436317 & 5.0675375636827 \tabularnewline
36 & 238 & 144.541279791688 & 93.4587202083121 \tabularnewline
37 & 78 & 124.646013399789 & -46.6460133997892 \tabularnewline
38 & 196 & 124.977866646922 & 71.0221333530775 \tabularnewline
39 & 125 & 135.540119459146 & -10.5401194591457 \tabularnewline
40 & 82 & 86.4203464602864 & -4.42034646028639 \tabularnewline
41 & 125 & 141.191817081663 & -16.1918170816629 \tabularnewline
42 & 129 & 121.933424865963 & 7.06657513403676 \tabularnewline
43 & 84 & 71.6488901906957 & 12.3511098093043 \tabularnewline
44 & 183 & 136.252247312875 & 46.7477526871248 \tabularnewline
45 & 119 & 119.388279282816 & -0.388279282816178 \tabularnewline
46 & 180 & 179.384884618527 & 0.615115381473365 \tabularnewline
47 & 82 & 102.272455915147 & -20.2724559151468 \tabularnewline
48 & 71 & 111.077213689144 & -40.077213689144 \tabularnewline
49 & 118 & 97.3711799825691 & 20.6288200174309 \tabularnewline
50 & 121 & 108.616257289241 & 12.3837427107592 \tabularnewline
51 & 68 & 91.2962559984634 & -23.2962559984634 \tabularnewline
52 & 112 & 129.101286060171 & -17.1012860601705 \tabularnewline
53 & 109 & 93.9372619438886 & 15.0627380561114 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&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]78[/C][C]96.7362045267615[/C][C]-18.7362045267615[/C][/ROW]
[ROW][C]2[/C][C]68[/C][C]96.4934095976297[/C][C]-28.4934095976298[/C][/ROW]
[ROW][C]3[/C][C]70[/C][C]78.7873702651068[/C][C]-8.78737026510675[/C][/ROW]
[ROW][C]4[/C][C]96[/C][C]146.232864483285[/C][C]-50.232864483285[/C][/ROW]
[ROW][C]5[/C][C]74[/C][C]89.5378388455167[/C][C]-15.5378388455167[/C][/ROW]
[ROW][C]6[/C][C]111[/C][C]128.319240892541[/C][C]-17.3192408925413[/C][/ROW]
[ROW][C]7[/C][C]77[/C][C]123.18118401686[/C][C]-46.1811840168596[/C][/ROW]
[ROW][C]8[/C][C]168[/C][C]106.661655774997[/C][C]61.3383442250031[/C][/ROW]
[ROW][C]9[/C][C]82[/C][C]97.0382816007865[/C][C]-15.0382816007865[/C][/ROW]
[ROW][C]10[/C][C]89[/C][C]93.4225433453052[/C][C]-4.42254334530517[/C][/ROW]
[ROW][C]11[/C][C]149[/C][C]123.321541981618[/C][C]25.6784580183817[/C][/ROW]
[ROW][C]12[/C][C]60[/C][C]103.372327345172[/C][C]-43.3723273451716[/C][/ROW]
[ROW][C]13[/C][C]96[/C][C]92.3358769861383[/C][C]3.66412301386169[/C][/ROW]
[ROW][C]14[/C][C]83[/C][C]107.755009907471[/C][C]-24.7550099074708[/C][/ROW]
[ROW][C]15[/C][C]130[/C][C]108.98438969752[/C][C]21.0156103024796[/C][/ROW]
[ROW][C]16[/C][C]145[/C][C]127.245984458825[/C][C]17.7540155411751[/C][/ROW]
[ROW][C]17[/C][C]112[/C][C]140.530572911847[/C][C]-28.5305729118473[/C][/ROW]
[ROW][C]18[/C][C]131[/C][C]112.221858294428[/C][C]18.7781417055716[/C][/ROW]
[ROW][C]19[/C][C]80[/C][C]100.938386659496[/C][C]-20.9383866594958[/C][/ROW]
[ROW][C]20[/C][C]130[/C][C]130.227268576279[/C][C]-0.227268576279427[/C][/ROW]
[ROW][C]21[/C][C]140[/C][C]110.334631143262[/C][C]29.6653688567381[/C][/ROW]
[ROW][C]22[/C][C]154[/C][C]88.4059934113659[/C][C]65.5940065886341[/C][/ROW]
[ROW][C]23[/C][C]118[/C][C]151.668600946328[/C][C]-33.6686009463283[/C][/ROW]
[ROW][C]24[/C][C]94[/C][C]107.706059499251[/C][C]-13.7060594992507[/C][/ROW]
[ROW][C]25[/C][C]119[/C][C]126.627829761037[/C][C]-7.627829761037[/C][/ROW]
[ROW][C]26[/C][C]153[/C][C]133.931405382296[/C][C]19.0685946177038[/C][/ROW]
[ROW][C]27[/C][C]116[/C][C]104.999455869888[/C][C]11.0005441301123[/C][/ROW]
[ROW][C]28[/C][C]97[/C][C]133.440144043255[/C][C]-36.4401440432553[/C][/ROW]
[ROW][C]29[/C][C]176[/C][C]120.762142724719[/C][C]55.2378572752809[/C][/ROW]
[ROW][C]30[/C][C]75[/C][C]109.384243862228[/C][C]-34.3842438622277[/C][/ROW]
[ROW][C]31[/C][C]134[/C][C]113.391406315257[/C][C]20.6085936847434[/C][/ROW]
[ROW][C]32[/C][C]161[/C][C]142.615724373831[/C][C]18.3842756261692[/C][/ROW]
[ROW][C]33[/C][C]111[/C][C]127.059613373902[/C][C]-16.0596133739018[/C][/ROW]
[ROW][C]34[/C][C]114[/C][C]122.799396700485[/C][C]-8.79939670048477[/C][/ROW]
[ROW][C]35[/C][C]142[/C][C]136.932462436317[/C][C]5.0675375636827[/C][/ROW]
[ROW][C]36[/C][C]238[/C][C]144.541279791688[/C][C]93.4587202083121[/C][/ROW]
[ROW][C]37[/C][C]78[/C][C]124.646013399789[/C][C]-46.6460133997892[/C][/ROW]
[ROW][C]38[/C][C]196[/C][C]124.977866646922[/C][C]71.0221333530775[/C][/ROW]
[ROW][C]39[/C][C]125[/C][C]135.540119459146[/C][C]-10.5401194591457[/C][/ROW]
[ROW][C]40[/C][C]82[/C][C]86.4203464602864[/C][C]-4.42034646028639[/C][/ROW]
[ROW][C]41[/C][C]125[/C][C]141.191817081663[/C][C]-16.1918170816629[/C][/ROW]
[ROW][C]42[/C][C]129[/C][C]121.933424865963[/C][C]7.06657513403676[/C][/ROW]
[ROW][C]43[/C][C]84[/C][C]71.6488901906957[/C][C]12.3511098093043[/C][/ROW]
[ROW][C]44[/C][C]183[/C][C]136.252247312875[/C][C]46.7477526871248[/C][/ROW]
[ROW][C]45[/C][C]119[/C][C]119.388279282816[/C][C]-0.388279282816178[/C][/ROW]
[ROW][C]46[/C][C]180[/C][C]179.384884618527[/C][C]0.615115381473365[/C][/ROW]
[ROW][C]47[/C][C]82[/C][C]102.272455915147[/C][C]-20.2724559151468[/C][/ROW]
[ROW][C]48[/C][C]71[/C][C]111.077213689144[/C][C]-40.077213689144[/C][/ROW]
[ROW][C]49[/C][C]118[/C][C]97.3711799825691[/C][C]20.6288200174309[/C][/ROW]
[ROW][C]50[/C][C]121[/C][C]108.616257289241[/C][C]12.3837427107592[/C][/ROW]
[ROW][C]51[/C][C]68[/C][C]91.2962559984634[/C][C]-23.2962559984634[/C][/ROW]
[ROW][C]52[/C][C]112[/C][C]129.101286060171[/C][C]-17.1012860601705[/C][/ROW]
[ROW][C]53[/C][C]109[/C][C]93.9372619438886[/C][C]15.0627380561114[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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
17896.7362045267615-18.7362045267615
26896.4934095976297-28.4934095976298
37078.7873702651068-8.78737026510675
496146.232864483285-50.232864483285
57489.5378388455167-15.5378388455167
6111128.319240892541-17.3192408925413
777123.18118401686-46.1811840168596
8168106.66165577499761.3383442250031
98297.0382816007865-15.0382816007865
108993.4225433453052-4.42254334530517
11149123.32154198161825.6784580183817
1260103.372327345172-43.3723273451716
139692.33587698613833.66412301386169
1483107.755009907471-24.7550099074708
15130108.9843896975221.0156103024796
16145127.24598445882517.7540155411751
17112140.530572911847-28.5305729118473
18131112.22185829442818.7781417055716
1980100.938386659496-20.9383866594958
20130130.227268576279-0.227268576279427
21140110.33463114326229.6653688567381
2215488.405993411365965.5940065886341
23118151.668600946328-33.6686009463283
2494107.706059499251-13.7060594992507
25119126.627829761037-7.627829761037
26153133.93140538229619.0685946177038
27116104.99945586988811.0005441301123
2897133.440144043255-36.4401440432553
29176120.76214272471955.2378572752809
3075109.384243862228-34.3842438622277
31134113.39140631525720.6085936847434
32161142.61572437383118.3842756261692
33111127.059613373902-16.0596133739018
34114122.799396700485-8.79939670048477
35142136.9324624363175.0675375636827
36238144.54127979168893.4587202083121
3778124.646013399789-46.6460133997892
38196124.97786664692271.0221333530775
39125135.540119459146-10.5401194591457
408286.4203464602864-4.42034646028639
41125141.191817081663-16.1918170816629
42129121.9334248659637.06657513403676
438471.648890190695712.3511098093043
44183136.25224731287546.7477526871248
45119119.388279282816-0.388279282816178
46180179.3848846185270.615115381473365
4782102.272455915147-20.2724559151468
4871111.077213689144-40.077213689144
4911897.371179982569120.6288200174309
50121108.61625728924112.3837427107592
516891.2962559984634-23.2962559984634
52112129.101286060171-17.1012860601705
5310993.937261943888615.0627380561114







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
80.8209078355204470.3581843289591060.179092164479553
90.77450597520740.4509880495851990.2254940247926
100.7062328552859180.5875342894281640.293767144714082
110.5986636170663520.8026727658672960.401336382933648
120.5499019425446670.9001961149106660.450098057455333
130.4661375498785110.9322750997570210.533862450121489
140.4178424616381960.8356849232763920.582157538361804
150.4406626974760690.8813253949521380.559337302523931
160.4147101985022050.829420397004410.585289801497795
170.3640316367444080.7280632734888170.635968363255592
180.3126255563265640.6252511126531280.687374443673436
190.2437675076355440.4875350152710880.756232492364456
200.1915897295409270.3831794590818540.808410270459073
210.2451793703124820.4903587406249650.754820629687518
220.5178763658122470.9642472683755060.482123634187753
230.7540983812347510.4918032375304980.245901618765249
240.6831317735961270.6337364528077470.316868226403874
250.6070413958099950.785917208380010.392958604190005
260.5696370262414110.8607259475171780.430362973758589
270.5388788335663220.9222423328673560.461121166433678
280.5218980613699390.9562038772601230.478101938630061
290.6835344852425390.6329310295149230.316465514757461
300.6450741099734130.7098517800531750.354925890026587
310.6200829332409850.7598341335180310.379917066759016
320.5589686676488040.8820626647023920.441031332351196
330.5390692926374240.9218614147251520.460930707362576
340.4496039148259440.8992078296518880.550396085174056
350.3719050458635810.7438100917271620.628094954136419
360.8632970549729630.2734058900540740.136702945027037
370.8332011239877930.3335977520244140.166798876012207
380.9489200937715320.1021598124569360.0510799062284679
390.9291842185975630.1416315628048730.0708157814024366
400.8781645845874020.2436708308251960.121835415412598
410.9713847215267850.057230556946430.028615278473215
420.9671754034618710.06564919307625820.0328245965381291
430.926304254388210.1473914912235810.0736957456117904
440.8697088455274590.2605823089450820.130291154472541
450.7373993408491040.5252013183017930.262600659150896

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.820907835520447 & 0.358184328959106 & 0.179092164479553 \tabularnewline
9 & 0.7745059752074 & 0.450988049585199 & 0.2254940247926 \tabularnewline
10 & 0.706232855285918 & 0.587534289428164 & 0.293767144714082 \tabularnewline
11 & 0.598663617066352 & 0.802672765867296 & 0.401336382933648 \tabularnewline
12 & 0.549901942544667 & 0.900196114910666 & 0.450098057455333 \tabularnewline
13 & 0.466137549878511 & 0.932275099757021 & 0.533862450121489 \tabularnewline
14 & 0.417842461638196 & 0.835684923276392 & 0.582157538361804 \tabularnewline
15 & 0.440662697476069 & 0.881325394952138 & 0.559337302523931 \tabularnewline
16 & 0.414710198502205 & 0.82942039700441 & 0.585289801497795 \tabularnewline
17 & 0.364031636744408 & 0.728063273488817 & 0.635968363255592 \tabularnewline
18 & 0.312625556326564 & 0.625251112653128 & 0.687374443673436 \tabularnewline
19 & 0.243767507635544 & 0.487535015271088 & 0.756232492364456 \tabularnewline
20 & 0.191589729540927 & 0.383179459081854 & 0.808410270459073 \tabularnewline
21 & 0.245179370312482 & 0.490358740624965 & 0.754820629687518 \tabularnewline
22 & 0.517876365812247 & 0.964247268375506 & 0.482123634187753 \tabularnewline
23 & 0.754098381234751 & 0.491803237530498 & 0.245901618765249 \tabularnewline
24 & 0.683131773596127 & 0.633736452807747 & 0.316868226403874 \tabularnewline
25 & 0.607041395809995 & 0.78591720838001 & 0.392958604190005 \tabularnewline
26 & 0.569637026241411 & 0.860725947517178 & 0.430362973758589 \tabularnewline
27 & 0.538878833566322 & 0.922242332867356 & 0.461121166433678 \tabularnewline
28 & 0.521898061369939 & 0.956203877260123 & 0.478101938630061 \tabularnewline
29 & 0.683534485242539 & 0.632931029514923 & 0.316465514757461 \tabularnewline
30 & 0.645074109973413 & 0.709851780053175 & 0.354925890026587 \tabularnewline
31 & 0.620082933240985 & 0.759834133518031 & 0.379917066759016 \tabularnewline
32 & 0.558968667648804 & 0.882062664702392 & 0.441031332351196 \tabularnewline
33 & 0.539069292637424 & 0.921861414725152 & 0.460930707362576 \tabularnewline
34 & 0.449603914825944 & 0.899207829651888 & 0.550396085174056 \tabularnewline
35 & 0.371905045863581 & 0.743810091727162 & 0.628094954136419 \tabularnewline
36 & 0.863297054972963 & 0.273405890054074 & 0.136702945027037 \tabularnewline
37 & 0.833201123987793 & 0.333597752024414 & 0.166798876012207 \tabularnewline
38 & 0.948920093771532 & 0.102159812456936 & 0.0510799062284679 \tabularnewline
39 & 0.929184218597563 & 0.141631562804873 & 0.0708157814024366 \tabularnewline
40 & 0.878164584587402 & 0.243670830825196 & 0.121835415412598 \tabularnewline
41 & 0.971384721526785 & 0.05723055694643 & 0.028615278473215 \tabularnewline
42 & 0.967175403461871 & 0.0656491930762582 & 0.0328245965381291 \tabularnewline
43 & 0.92630425438821 & 0.147391491223581 & 0.0736957456117904 \tabularnewline
44 & 0.869708845527459 & 0.260582308945082 & 0.130291154472541 \tabularnewline
45 & 0.737399340849104 & 0.525201318301793 & 0.262600659150896 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&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]8[/C][C]0.820907835520447[/C][C]0.358184328959106[/C][C]0.179092164479553[/C][/ROW]
[ROW][C]9[/C][C]0.7745059752074[/C][C]0.450988049585199[/C][C]0.2254940247926[/C][/ROW]
[ROW][C]10[/C][C]0.706232855285918[/C][C]0.587534289428164[/C][C]0.293767144714082[/C][/ROW]
[ROW][C]11[/C][C]0.598663617066352[/C][C]0.802672765867296[/C][C]0.401336382933648[/C][/ROW]
[ROW][C]12[/C][C]0.549901942544667[/C][C]0.900196114910666[/C][C]0.450098057455333[/C][/ROW]
[ROW][C]13[/C][C]0.466137549878511[/C][C]0.932275099757021[/C][C]0.533862450121489[/C][/ROW]
[ROW][C]14[/C][C]0.417842461638196[/C][C]0.835684923276392[/C][C]0.582157538361804[/C][/ROW]
[ROW][C]15[/C][C]0.440662697476069[/C][C]0.881325394952138[/C][C]0.559337302523931[/C][/ROW]
[ROW][C]16[/C][C]0.414710198502205[/C][C]0.82942039700441[/C][C]0.585289801497795[/C][/ROW]
[ROW][C]17[/C][C]0.364031636744408[/C][C]0.728063273488817[/C][C]0.635968363255592[/C][/ROW]
[ROW][C]18[/C][C]0.312625556326564[/C][C]0.625251112653128[/C][C]0.687374443673436[/C][/ROW]
[ROW][C]19[/C][C]0.243767507635544[/C][C]0.487535015271088[/C][C]0.756232492364456[/C][/ROW]
[ROW][C]20[/C][C]0.191589729540927[/C][C]0.383179459081854[/C][C]0.808410270459073[/C][/ROW]
[ROW][C]21[/C][C]0.245179370312482[/C][C]0.490358740624965[/C][C]0.754820629687518[/C][/ROW]
[ROW][C]22[/C][C]0.517876365812247[/C][C]0.964247268375506[/C][C]0.482123634187753[/C][/ROW]
[ROW][C]23[/C][C]0.754098381234751[/C][C]0.491803237530498[/C][C]0.245901618765249[/C][/ROW]
[ROW][C]24[/C][C]0.683131773596127[/C][C]0.633736452807747[/C][C]0.316868226403874[/C][/ROW]
[ROW][C]25[/C][C]0.607041395809995[/C][C]0.78591720838001[/C][C]0.392958604190005[/C][/ROW]
[ROW][C]26[/C][C]0.569637026241411[/C][C]0.860725947517178[/C][C]0.430362973758589[/C][/ROW]
[ROW][C]27[/C][C]0.538878833566322[/C][C]0.922242332867356[/C][C]0.461121166433678[/C][/ROW]
[ROW][C]28[/C][C]0.521898061369939[/C][C]0.956203877260123[/C][C]0.478101938630061[/C][/ROW]
[ROW][C]29[/C][C]0.683534485242539[/C][C]0.632931029514923[/C][C]0.316465514757461[/C][/ROW]
[ROW][C]30[/C][C]0.645074109973413[/C][C]0.709851780053175[/C][C]0.354925890026587[/C][/ROW]
[ROW][C]31[/C][C]0.620082933240985[/C][C]0.759834133518031[/C][C]0.379917066759016[/C][/ROW]
[ROW][C]32[/C][C]0.558968667648804[/C][C]0.882062664702392[/C][C]0.441031332351196[/C][/ROW]
[ROW][C]33[/C][C]0.539069292637424[/C][C]0.921861414725152[/C][C]0.460930707362576[/C][/ROW]
[ROW][C]34[/C][C]0.449603914825944[/C][C]0.899207829651888[/C][C]0.550396085174056[/C][/ROW]
[ROW][C]35[/C][C]0.371905045863581[/C][C]0.743810091727162[/C][C]0.628094954136419[/C][/ROW]
[ROW][C]36[/C][C]0.863297054972963[/C][C]0.273405890054074[/C][C]0.136702945027037[/C][/ROW]
[ROW][C]37[/C][C]0.833201123987793[/C][C]0.333597752024414[/C][C]0.166798876012207[/C][/ROW]
[ROW][C]38[/C][C]0.948920093771532[/C][C]0.102159812456936[/C][C]0.0510799062284679[/C][/ROW]
[ROW][C]39[/C][C]0.929184218597563[/C][C]0.141631562804873[/C][C]0.0708157814024366[/C][/ROW]
[ROW][C]40[/C][C]0.878164584587402[/C][C]0.243670830825196[/C][C]0.121835415412598[/C][/ROW]
[ROW][C]41[/C][C]0.971384721526785[/C][C]0.05723055694643[/C][C]0.028615278473215[/C][/ROW]
[ROW][C]42[/C][C]0.967175403461871[/C][C]0.0656491930762582[/C][C]0.0328245965381291[/C][/ROW]
[ROW][C]43[/C][C]0.92630425438821[/C][C]0.147391491223581[/C][C]0.0736957456117904[/C][/ROW]
[ROW][C]44[/C][C]0.869708845527459[/C][C]0.260582308945082[/C][C]0.130291154472541[/C][/ROW]
[ROW][C]45[/C][C]0.737399340849104[/C][C]0.525201318301793[/C][C]0.262600659150896[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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
80.8209078355204470.3581843289591060.179092164479553
90.77450597520740.4509880495851990.2254940247926
100.7062328552859180.5875342894281640.293767144714082
110.5986636170663520.8026727658672960.401336382933648
120.5499019425446670.9001961149106660.450098057455333
130.4661375498785110.9322750997570210.533862450121489
140.4178424616381960.8356849232763920.582157538361804
150.4406626974760690.8813253949521380.559337302523931
160.4147101985022050.829420397004410.585289801497795
170.3640316367444080.7280632734888170.635968363255592
180.3126255563265640.6252511126531280.687374443673436
190.2437675076355440.4875350152710880.756232492364456
200.1915897295409270.3831794590818540.808410270459073
210.2451793703124820.4903587406249650.754820629687518
220.5178763658122470.9642472683755060.482123634187753
230.7540983812347510.4918032375304980.245901618765249
240.6831317735961270.6337364528077470.316868226403874
250.6070413958099950.785917208380010.392958604190005
260.5696370262414110.8607259475171780.430362973758589
270.5388788335663220.9222423328673560.461121166433678
280.5218980613699390.9562038772601230.478101938630061
290.6835344852425390.6329310295149230.316465514757461
300.6450741099734130.7098517800531750.354925890026587
310.6200829332409850.7598341335180310.379917066759016
320.5589686676488040.8820626647023920.441031332351196
330.5390692926374240.9218614147251520.460930707362576
340.4496039148259440.8992078296518880.550396085174056
350.3719050458635810.7438100917271620.628094954136419
360.8632970549729630.2734058900540740.136702945027037
370.8332011239877930.3335977520244140.166798876012207
380.9489200937715320.1021598124569360.0510799062284679
390.9291842185975630.1416315628048730.0708157814024366
400.8781645845874020.2436708308251960.121835415412598
410.9713847215267850.057230556946430.028615278473215
420.9671754034618710.06564919307625820.0328245965381291
430.926304254388210.1473914912235810.0736957456117904
440.8697088455274590.2605823089450820.130291154472541
450.7373993408491040.5252013183017930.262600659150896







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 level20.0526315789473684OK

\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 & 0.0526315789473684 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147082&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]0.0526315789473684[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147082&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147082&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 level20.0526315789473684OK



Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; 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')
}