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 computationThu, 24 Nov 2011 12:16:22 -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/t1322155053v55givdbew21qdm.htm/, Retrieved Thu, 31 Oct 2024 23:18:21 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=147107, Retrieved Thu, 31 Oct 2024 23:18:21 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact161
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-11-17 09:20:01] [b98453cac15ba1066b407e146608df68]
- R PD  [Multiple Regression] [Workshop 7 - Corr...] [2010-11-19 13:21:15] [8b017ffbf7b0eded54d8efebfb3e4cfa]
-         [Multiple Regression] [workshop 7 - tuto...] [2010-11-19 16:22:39] [956e8df26b41c50d9c6c2ec1b6a122a8]
-    D      [Multiple Regression] [WS7 comp 5] [2010-11-23 09:29:52] [dc30d19c3bc2be07fe595ad36c2cf923]
-             [Multiple Regression] [] [2010-12-02 15:19:40] [2e1e44f0ae3cb9513dc28781dfdb387b]
- R  D            [Multiple Regression] [WS 7 multicolline...] [2011-11-24 17:16:22] [cca7c4e9d798594cd7da8d83786e39be] [Current]
Feedback Forum

Post a new message
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 time4 seconds
R Server'Gwilym Jenkins' @ jenkins.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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
deatch[t] = + 12.5335133176914 + 0.0081609339403342doctor[t] + 0.000547364246523704hospital[t] -0.312252717491008income[t] -0.0115497060273195population[t] -0.0101435573362954t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
deatch[t] =  +  12.5335133176914 +  0.0081609339403342doctor[t] +  0.000547364246523704hospital[t] -0.312252717491008income[t] -0.0115497060273195population[t] -0.0101435573362954t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]deatch[t] =  +  12.5335133176914 +  0.0081609339403342doctor[t] +  0.000547364246523704hospital[t] -0.312252717491008income[t] -0.0115497060273195population[t] -0.0101435573362954t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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
deatch[t] = + 12.5335133176914 + 0.0081609339403342doctor[t] + 0.000547364246523704hospital[t] -0.312252717491008income[t] -0.0115497060273195population[t] -0.0101435573362954t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.53351331769142.1016375.963700
doctor0.00816093394033420.0071471.14190.25930.12965
hospital0.0005473642465237040.0007310.74880.4577020.228851
income-0.3122527174910080.238966-1.30670.1976780.098839
population-0.01154970602731950.006391-1.80710.0771520.038576
t-0.01014355733629540.019802-0.51230.6108690.305434

\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) & 12.5335133176914 & 2.101637 & 5.9637 & 0 & 0 \tabularnewline
doctor & 0.0081609339403342 & 0.007147 & 1.1419 & 0.2593 & 0.12965 \tabularnewline
hospital & 0.000547364246523704 & 0.000731 & 0.7488 & 0.457702 & 0.228851 \tabularnewline
income & -0.312252717491008 & 0.238966 & -1.3067 & 0.197678 & 0.098839 \tabularnewline
population & -0.0115497060273195 & 0.006391 & -1.8071 & 0.077152 & 0.038576 \tabularnewline
t & -0.0101435573362954 & 0.019802 & -0.5123 & 0.610869 & 0.305434 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&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]12.5335133176914[/C][C]2.101637[/C][C]5.9637[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]doctor[/C][C]0.0081609339403342[/C][C]0.007147[/C][C]1.1419[/C][C]0.2593[/C][C]0.12965[/C][/ROW]
[ROW][C]hospital[/C][C]0.000547364246523704[/C][C]0.000731[/C][C]0.7488[/C][C]0.457702[/C][C]0.228851[/C][/ROW]
[ROW][C]income[/C][C]-0.312252717491008[/C][C]0.238966[/C][C]-1.3067[/C][C]0.197678[/C][C]0.098839[/C][/ROW]
[ROW][C]population[/C][C]-0.0115497060273195[/C][C]0.006391[/C][C]-1.8071[/C][C]0.077152[/C][C]0.038576[/C][/ROW]
[ROW][C]t[/C][C]-0.0101435573362954[/C][C]0.019802[/C][C]-0.5123[/C][C]0.610869[/C][C]0.305434[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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)12.53351331769142.1016375.963700
doctor0.00816093394033420.0071471.14190.25930.12965
hospital0.0005473642465237040.0007310.74880.4577020.228851
income-0.3122527174910080.238966-1.30670.1976780.098839
population-0.01154970602731950.006391-1.80710.0771520.038576
t-0.01014355733629540.019802-0.51230.6108690.305434







Multiple Linear Regression - Regression Statistics
Multiple R0.385302816744244
R-squared0.148458260591049
Adjusted R-squared0.0578687138454155
F-TEST (value)1.6388012295492
F-TEST (DF numerator)5
F-TEST (DF denominator)47
p-value0.168303993876313
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.61370889576017
Sum Squared Residuals122.390650812009

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.385302816744244 \tabularnewline
R-squared & 0.148458260591049 \tabularnewline
Adjusted R-squared & 0.0578687138454155 \tabularnewline
F-TEST (value) & 1.6388012295492 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.168303993876313 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.61370889576017 \tabularnewline
Sum Squared Residuals & 122.390650812009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.385302816744244[/C][/ROW]
[ROW][C]R-squared[/C][C]0.148458260591049[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0578687138454155[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.6388012295492[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.168303993876313[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]1.61370889576017[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]122.390650812009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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 R0.385302816744244
R-squared0.148458260591049
Adjusted R-squared0.0578687138454155
F-TEST (value)1.6388012295492
F-TEST (DF numerator)5
F-TEST (DF denominator)47
p-value0.168303993876313
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation1.61370889576017
Sum Squared Residuals122.390650812009







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
189.21495624859957-1.21495624859957
29.3000001918.925422179240760.374578011759243
37.59.9255139123048-2.4255139123048
48.89999961910.3578949250371-1.45789530603707
510.199999818.376860331742981.82313947825703
68.3000001918.7409434489087-0.440943257908699
78.8000001918.580099248097790.219900942902206
88.8000001919.45897319720401-0.658973006204008
910.699999818.842446235248991.85755357475101
1011.699999819.584648566385722.11535124361428
118.57.238456615450171.26154338454983
128.3000001918.82835021031739-0.528350019317385
138.1999998099.31070683401074-1.11070702501074
147.9000000958.33047435954804-0.430474264548038
1510.300000199.611262582949930.688737607050072
167.4000000958.42130855782202-1.02130846282202
179.6000003818.840774105720510.759226275279493
189.3000001918.675853608213210.624146582786789
1910.600000388.797557600459821.80244277954018
209.6999998099.143309670647670.556690138352328
2111.6000003810.32394186988311.27605851011693
228.1000003819.94836339760364-1.84836301660364
239.80000019110.0548044200362-0.254804229036235
247.4000000958.83628619355341-1.43628609855341
259.3999996199.189798020742960.210201598257043
2611.199999819.880916897242571.31908291275743
279.1000003819.35584596485424-0.255845583854243
2810.59.023255875529561.47674412447044
2911.8999996210.34485666718651.5551429528135
308.3999996198.96991814678289-0.569918527782893
3158.92852840876828-3.92852840876828
329.8000001919.504240347598390.295759843401612
339.8000001919.552647609768970.247352581231035
3410.800000199.675778908363011.12422128163699
3510.100000389.411574910688230.688425469311775
3610.8999996210.35110317419190.548896445808116
379.1999998098.483527072067530.716472736932472
388.30000019110.0002709471759-1.70027075617587
397.3000001918.53872136891775-1.23872117791775
409.3999996199.56853442872644-0.16853480972644
419.3999996199.40805522991312-0.0080556109131186
429.8000001919.661577469279640.138422721720366
433.5999999059.04710933525624-5.44710943025624
448.3999996199.92929737584405-1.52929775684405
4510.800000199.812722884746430.987277305253569
4610.100000388.61796293988081.4820374401192
4799.7060858848827-0.706085884882697
48109.364679297720620.635320702279382
4911.3000001910.41308789228650.886912297713508
5011.300000199.858542686990821.44145750300918
5112.800000199.811772525246932.98822766475307
52109.187278652615270.812721347384732
536.6999998099.23310247274562-2.53310266374562

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8 & 9.21495624859957 & -1.21495624859957 \tabularnewline
2 & 9.300000191 & 8.92542217924076 & 0.374578011759243 \tabularnewline
3 & 7.5 & 9.9255139123048 & -2.4255139123048 \tabularnewline
4 & 8.899999619 & 10.3578949250371 & -1.45789530603707 \tabularnewline
5 & 10.19999981 & 8.37686033174298 & 1.82313947825703 \tabularnewline
6 & 8.300000191 & 8.7409434489087 & -0.440943257908699 \tabularnewline
7 & 8.800000191 & 8.58009924809779 & 0.219900942902206 \tabularnewline
8 & 8.800000191 & 9.45897319720401 & -0.658973006204008 \tabularnewline
9 & 10.69999981 & 8.84244623524899 & 1.85755357475101 \tabularnewline
10 & 11.69999981 & 9.58464856638572 & 2.11535124361428 \tabularnewline
11 & 8.5 & 7.23845661545017 & 1.26154338454983 \tabularnewline
12 & 8.300000191 & 8.82835021031739 & -0.528350019317385 \tabularnewline
13 & 8.199999809 & 9.31070683401074 & -1.11070702501074 \tabularnewline
14 & 7.900000095 & 8.33047435954804 & -0.430474264548038 \tabularnewline
15 & 10.30000019 & 9.61126258294993 & 0.688737607050072 \tabularnewline
16 & 7.400000095 & 8.42130855782202 & -1.02130846282202 \tabularnewline
17 & 9.600000381 & 8.84077410572051 & 0.759226275279493 \tabularnewline
18 & 9.300000191 & 8.67585360821321 & 0.624146582786789 \tabularnewline
19 & 10.60000038 & 8.79755760045982 & 1.80244277954018 \tabularnewline
20 & 9.699999809 & 9.14330967064767 & 0.556690138352328 \tabularnewline
21 & 11.60000038 & 10.3239418698831 & 1.27605851011693 \tabularnewline
22 & 8.100000381 & 9.94836339760364 & -1.84836301660364 \tabularnewline
23 & 9.800000191 & 10.0548044200362 & -0.254804229036235 \tabularnewline
24 & 7.400000095 & 8.83628619355341 & -1.43628609855341 \tabularnewline
25 & 9.399999619 & 9.18979802074296 & 0.210201598257043 \tabularnewline
26 & 11.19999981 & 9.88091689724257 & 1.31908291275743 \tabularnewline
27 & 9.100000381 & 9.35584596485424 & -0.255845583854243 \tabularnewline
28 & 10.5 & 9.02325587552956 & 1.47674412447044 \tabularnewline
29 & 11.89999962 & 10.3448566671865 & 1.5551429528135 \tabularnewline
30 & 8.399999619 & 8.96991814678289 & -0.569918527782893 \tabularnewline
31 & 5 & 8.92852840876828 & -3.92852840876828 \tabularnewline
32 & 9.800000191 & 9.50424034759839 & 0.295759843401612 \tabularnewline
33 & 9.800000191 & 9.55264760976897 & 0.247352581231035 \tabularnewline
34 & 10.80000019 & 9.67577890836301 & 1.12422128163699 \tabularnewline
35 & 10.10000038 & 9.41157491068823 & 0.688425469311775 \tabularnewline
36 & 10.89999962 & 10.3511031741919 & 0.548896445808116 \tabularnewline
37 & 9.199999809 & 8.48352707206753 & 0.716472736932472 \tabularnewline
38 & 8.300000191 & 10.0002709471759 & -1.70027075617587 \tabularnewline
39 & 7.300000191 & 8.53872136891775 & -1.23872117791775 \tabularnewline
40 & 9.399999619 & 9.56853442872644 & -0.16853480972644 \tabularnewline
41 & 9.399999619 & 9.40805522991312 & -0.0080556109131186 \tabularnewline
42 & 9.800000191 & 9.66157746927964 & 0.138422721720366 \tabularnewline
43 & 3.599999905 & 9.04710933525624 & -5.44710943025624 \tabularnewline
44 & 8.399999619 & 9.92929737584405 & -1.52929775684405 \tabularnewline
45 & 10.80000019 & 9.81272288474643 & 0.987277305253569 \tabularnewline
46 & 10.10000038 & 8.6179629398808 & 1.4820374401192 \tabularnewline
47 & 9 & 9.7060858848827 & -0.706085884882697 \tabularnewline
48 & 10 & 9.36467929772062 & 0.635320702279382 \tabularnewline
49 & 11.30000019 & 10.4130878922865 & 0.886912297713508 \tabularnewline
50 & 11.30000019 & 9.85854268699082 & 1.44145750300918 \tabularnewline
51 & 12.80000019 & 9.81177252524693 & 2.98822766475307 \tabularnewline
52 & 10 & 9.18727865261527 & 0.812721347384732 \tabularnewline
53 & 6.699999809 & 9.23310247274562 & -2.53310266374562 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&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]8[/C][C]9.21495624859957[/C][C]-1.21495624859957[/C][/ROW]
[ROW][C]2[/C][C]9.300000191[/C][C]8.92542217924076[/C][C]0.374578011759243[/C][/ROW]
[ROW][C]3[/C][C]7.5[/C][C]9.9255139123048[/C][C]-2.4255139123048[/C][/ROW]
[ROW][C]4[/C][C]8.899999619[/C][C]10.3578949250371[/C][C]-1.45789530603707[/C][/ROW]
[ROW][C]5[/C][C]10.19999981[/C][C]8.37686033174298[/C][C]1.82313947825703[/C][/ROW]
[ROW][C]6[/C][C]8.300000191[/C][C]8.7409434489087[/C][C]-0.440943257908699[/C][/ROW]
[ROW][C]7[/C][C]8.800000191[/C][C]8.58009924809779[/C][C]0.219900942902206[/C][/ROW]
[ROW][C]8[/C][C]8.800000191[/C][C]9.45897319720401[/C][C]-0.658973006204008[/C][/ROW]
[ROW][C]9[/C][C]10.69999981[/C][C]8.84244623524899[/C][C]1.85755357475101[/C][/ROW]
[ROW][C]10[/C][C]11.69999981[/C][C]9.58464856638572[/C][C]2.11535124361428[/C][/ROW]
[ROW][C]11[/C][C]8.5[/C][C]7.23845661545017[/C][C]1.26154338454983[/C][/ROW]
[ROW][C]12[/C][C]8.300000191[/C][C]8.82835021031739[/C][C]-0.528350019317385[/C][/ROW]
[ROW][C]13[/C][C]8.199999809[/C][C]9.31070683401074[/C][C]-1.11070702501074[/C][/ROW]
[ROW][C]14[/C][C]7.900000095[/C][C]8.33047435954804[/C][C]-0.430474264548038[/C][/ROW]
[ROW][C]15[/C][C]10.30000019[/C][C]9.61126258294993[/C][C]0.688737607050072[/C][/ROW]
[ROW][C]16[/C][C]7.400000095[/C][C]8.42130855782202[/C][C]-1.02130846282202[/C][/ROW]
[ROW][C]17[/C][C]9.600000381[/C][C]8.84077410572051[/C][C]0.759226275279493[/C][/ROW]
[ROW][C]18[/C][C]9.300000191[/C][C]8.67585360821321[/C][C]0.624146582786789[/C][/ROW]
[ROW][C]19[/C][C]10.60000038[/C][C]8.79755760045982[/C][C]1.80244277954018[/C][/ROW]
[ROW][C]20[/C][C]9.699999809[/C][C]9.14330967064767[/C][C]0.556690138352328[/C][/ROW]
[ROW][C]21[/C][C]11.60000038[/C][C]10.3239418698831[/C][C]1.27605851011693[/C][/ROW]
[ROW][C]22[/C][C]8.100000381[/C][C]9.94836339760364[/C][C]-1.84836301660364[/C][/ROW]
[ROW][C]23[/C][C]9.800000191[/C][C]10.0548044200362[/C][C]-0.254804229036235[/C][/ROW]
[ROW][C]24[/C][C]7.400000095[/C][C]8.83628619355341[/C][C]-1.43628609855341[/C][/ROW]
[ROW][C]25[/C][C]9.399999619[/C][C]9.18979802074296[/C][C]0.210201598257043[/C][/ROW]
[ROW][C]26[/C][C]11.19999981[/C][C]9.88091689724257[/C][C]1.31908291275743[/C][/ROW]
[ROW][C]27[/C][C]9.100000381[/C][C]9.35584596485424[/C][C]-0.255845583854243[/C][/ROW]
[ROW][C]28[/C][C]10.5[/C][C]9.02325587552956[/C][C]1.47674412447044[/C][/ROW]
[ROW][C]29[/C][C]11.89999962[/C][C]10.3448566671865[/C][C]1.5551429528135[/C][/ROW]
[ROW][C]30[/C][C]8.399999619[/C][C]8.96991814678289[/C][C]-0.569918527782893[/C][/ROW]
[ROW][C]31[/C][C]5[/C][C]8.92852840876828[/C][C]-3.92852840876828[/C][/ROW]
[ROW][C]32[/C][C]9.800000191[/C][C]9.50424034759839[/C][C]0.295759843401612[/C][/ROW]
[ROW][C]33[/C][C]9.800000191[/C][C]9.55264760976897[/C][C]0.247352581231035[/C][/ROW]
[ROW][C]34[/C][C]10.80000019[/C][C]9.67577890836301[/C][C]1.12422128163699[/C][/ROW]
[ROW][C]35[/C][C]10.10000038[/C][C]9.41157491068823[/C][C]0.688425469311775[/C][/ROW]
[ROW][C]36[/C][C]10.89999962[/C][C]10.3511031741919[/C][C]0.548896445808116[/C][/ROW]
[ROW][C]37[/C][C]9.199999809[/C][C]8.48352707206753[/C][C]0.716472736932472[/C][/ROW]
[ROW][C]38[/C][C]8.300000191[/C][C]10.0002709471759[/C][C]-1.70027075617587[/C][/ROW]
[ROW][C]39[/C][C]7.300000191[/C][C]8.53872136891775[/C][C]-1.23872117791775[/C][/ROW]
[ROW][C]40[/C][C]9.399999619[/C][C]9.56853442872644[/C][C]-0.16853480972644[/C][/ROW]
[ROW][C]41[/C][C]9.399999619[/C][C]9.40805522991312[/C][C]-0.0080556109131186[/C][/ROW]
[ROW][C]42[/C][C]9.800000191[/C][C]9.66157746927964[/C][C]0.138422721720366[/C][/ROW]
[ROW][C]43[/C][C]3.599999905[/C][C]9.04710933525624[/C][C]-5.44710943025624[/C][/ROW]
[ROW][C]44[/C][C]8.399999619[/C][C]9.92929737584405[/C][C]-1.52929775684405[/C][/ROW]
[ROW][C]45[/C][C]10.80000019[/C][C]9.81272288474643[/C][C]0.987277305253569[/C][/ROW]
[ROW][C]46[/C][C]10.10000038[/C][C]8.6179629398808[/C][C]1.4820374401192[/C][/ROW]
[ROW][C]47[/C][C]9[/C][C]9.7060858848827[/C][C]-0.706085884882697[/C][/ROW]
[ROW][C]48[/C][C]10[/C][C]9.36467929772062[/C][C]0.635320702279382[/C][/ROW]
[ROW][C]49[/C][C]11.30000019[/C][C]10.4130878922865[/C][C]0.886912297713508[/C][/ROW]
[ROW][C]50[/C][C]11.30000019[/C][C]9.85854268699082[/C][C]1.44145750300918[/C][/ROW]
[ROW][C]51[/C][C]12.80000019[/C][C]9.81177252524693[/C][C]2.98822766475307[/C][/ROW]
[ROW][C]52[/C][C]10[/C][C]9.18727865261527[/C][C]0.812721347384732[/C][/ROW]
[ROW][C]53[/C][C]6.699999809[/C][C]9.23310247274562[/C][C]-2.53310266374562[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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
189.21495624859957-1.21495624859957
29.3000001918.925422179240760.374578011759243
37.59.9255139123048-2.4255139123048
48.89999961910.3578949250371-1.45789530603707
510.199999818.376860331742981.82313947825703
68.3000001918.7409434489087-0.440943257908699
78.8000001918.580099248097790.219900942902206
88.8000001919.45897319720401-0.658973006204008
910.699999818.842446235248991.85755357475101
1011.699999819.584648566385722.11535124361428
118.57.238456615450171.26154338454983
128.3000001918.82835021031739-0.528350019317385
138.1999998099.31070683401074-1.11070702501074
147.9000000958.33047435954804-0.430474264548038
1510.300000199.611262582949930.688737607050072
167.4000000958.42130855782202-1.02130846282202
179.6000003818.840774105720510.759226275279493
189.3000001918.675853608213210.624146582786789
1910.600000388.797557600459821.80244277954018
209.6999998099.143309670647670.556690138352328
2111.6000003810.32394186988311.27605851011693
228.1000003819.94836339760364-1.84836301660364
239.80000019110.0548044200362-0.254804229036235
247.4000000958.83628619355341-1.43628609855341
259.3999996199.189798020742960.210201598257043
2611.199999819.880916897242571.31908291275743
279.1000003819.35584596485424-0.255845583854243
2810.59.023255875529561.47674412447044
2911.8999996210.34485666718651.5551429528135
308.3999996198.96991814678289-0.569918527782893
3158.92852840876828-3.92852840876828
329.8000001919.504240347598390.295759843401612
339.8000001919.552647609768970.247352581231035
3410.800000199.675778908363011.12422128163699
3510.100000389.411574910688230.688425469311775
3610.8999996210.35110317419190.548896445808116
379.1999998098.483527072067530.716472736932472
388.30000019110.0002709471759-1.70027075617587
397.3000001918.53872136891775-1.23872117791775
409.3999996199.56853442872644-0.16853480972644
419.3999996199.40805522991312-0.0080556109131186
429.8000001919.661577469279640.138422721720366
433.5999999059.04710933525624-5.44710943025624
448.3999996199.92929737584405-1.52929775684405
4510.800000199.812722884746430.987277305253569
4610.100000388.61796293988081.4820374401192
4799.7060858848827-0.706085884882697
48109.364679297720620.635320702279382
4911.3000001910.41308789228650.886912297713508
5011.300000199.858542686990821.44145750300918
5112.800000199.811772525246932.98822766475307
52109.187278652615270.812721347384732
536.6999998099.23310247274562-2.53310266374562







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
90.2495768439887690.4991536879775380.750423156011231
100.2251861584446380.4503723168892760.774813841555362
110.2296804582846460.4593609165692920.770319541715354
120.3595046157802020.7190092315604030.640495384219798
130.342754312100090.685508624200180.65724568789991
140.3287385557231780.6574771114463560.671261444276822
150.2613617544040550.5227235088081090.738638245595945
160.1945982746173490.3891965492346980.805401725382651
170.13538608256250.2707721651249990.8646139174375
180.1070959492402450.214191898480490.892904050759755
190.1280549885213130.2561099770426250.871945011478688
200.1216007314007850.243201462801570.878399268599215
210.1142643860222270.2285287720444550.885735613977773
220.167026616579910.3340532331598210.83297338342009
230.1758466604525670.3516933209051330.824153339547433
240.1878720071260570.3757440142521150.812127992873943
250.1465928275827570.2931856551655130.853407172417243
260.1588541596346970.3177083192693940.841145840365303
270.1301795898390340.2603591796780680.869820410160966
280.1187409207285510.2374818414571020.881259079271449
290.1442828901501430.2885657803002860.855717109849857
300.1194984655076450.2389969310152910.880501534492355
310.3683245427383130.7366490854766250.631675457261687
320.2908962565460750.5817925130921490.709103743453925
330.2261759281920310.4523518563840620.773824071807969
340.1781446033142940.3562892066285880.821855396685706
350.1334297901715040.2668595803430080.866570209828496
360.1325520450249720.2651040900499430.867447954975028
370.1801909900568140.3603819801136280.819809009943186
380.1567007470111290.3134014940222580.843299252988871
390.1196359565302870.2392719130605750.880364043469713
400.1858445898392520.3716891796785040.814155410160748
410.1689314080885760.3378628161771520.831068591911424
420.2577953359450660.5155906718901320.742204664054934
430.3307468512377220.6614937024754440.669253148762278
440.2728041736985160.5456083473970320.727195826301484

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.249576843988769 & 0.499153687977538 & 0.750423156011231 \tabularnewline
10 & 0.225186158444638 & 0.450372316889276 & 0.774813841555362 \tabularnewline
11 & 0.229680458284646 & 0.459360916569292 & 0.770319541715354 \tabularnewline
12 & 0.359504615780202 & 0.719009231560403 & 0.640495384219798 \tabularnewline
13 & 0.34275431210009 & 0.68550862420018 & 0.65724568789991 \tabularnewline
14 & 0.328738555723178 & 0.657477111446356 & 0.671261444276822 \tabularnewline
15 & 0.261361754404055 & 0.522723508808109 & 0.738638245595945 \tabularnewline
16 & 0.194598274617349 & 0.389196549234698 & 0.805401725382651 \tabularnewline
17 & 0.1353860825625 & 0.270772165124999 & 0.8646139174375 \tabularnewline
18 & 0.107095949240245 & 0.21419189848049 & 0.892904050759755 \tabularnewline
19 & 0.128054988521313 & 0.256109977042625 & 0.871945011478688 \tabularnewline
20 & 0.121600731400785 & 0.24320146280157 & 0.878399268599215 \tabularnewline
21 & 0.114264386022227 & 0.228528772044455 & 0.885735613977773 \tabularnewline
22 & 0.16702661657991 & 0.334053233159821 & 0.83297338342009 \tabularnewline
23 & 0.175846660452567 & 0.351693320905133 & 0.824153339547433 \tabularnewline
24 & 0.187872007126057 & 0.375744014252115 & 0.812127992873943 \tabularnewline
25 & 0.146592827582757 & 0.293185655165513 & 0.853407172417243 \tabularnewline
26 & 0.158854159634697 & 0.317708319269394 & 0.841145840365303 \tabularnewline
27 & 0.130179589839034 & 0.260359179678068 & 0.869820410160966 \tabularnewline
28 & 0.118740920728551 & 0.237481841457102 & 0.881259079271449 \tabularnewline
29 & 0.144282890150143 & 0.288565780300286 & 0.855717109849857 \tabularnewline
30 & 0.119498465507645 & 0.238996931015291 & 0.880501534492355 \tabularnewline
31 & 0.368324542738313 & 0.736649085476625 & 0.631675457261687 \tabularnewline
32 & 0.290896256546075 & 0.581792513092149 & 0.709103743453925 \tabularnewline
33 & 0.226175928192031 & 0.452351856384062 & 0.773824071807969 \tabularnewline
34 & 0.178144603314294 & 0.356289206628588 & 0.821855396685706 \tabularnewline
35 & 0.133429790171504 & 0.266859580343008 & 0.866570209828496 \tabularnewline
36 & 0.132552045024972 & 0.265104090049943 & 0.867447954975028 \tabularnewline
37 & 0.180190990056814 & 0.360381980113628 & 0.819809009943186 \tabularnewline
38 & 0.156700747011129 & 0.313401494022258 & 0.843299252988871 \tabularnewline
39 & 0.119635956530287 & 0.239271913060575 & 0.880364043469713 \tabularnewline
40 & 0.185844589839252 & 0.371689179678504 & 0.814155410160748 \tabularnewline
41 & 0.168931408088576 & 0.337862816177152 & 0.831068591911424 \tabularnewline
42 & 0.257795335945066 & 0.515590671890132 & 0.742204664054934 \tabularnewline
43 & 0.330746851237722 & 0.661493702475444 & 0.669253148762278 \tabularnewline
44 & 0.272804173698516 & 0.545608347397032 & 0.727195826301484 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&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]9[/C][C]0.249576843988769[/C][C]0.499153687977538[/C][C]0.750423156011231[/C][/ROW]
[ROW][C]10[/C][C]0.225186158444638[/C][C]0.450372316889276[/C][C]0.774813841555362[/C][/ROW]
[ROW][C]11[/C][C]0.229680458284646[/C][C]0.459360916569292[/C][C]0.770319541715354[/C][/ROW]
[ROW][C]12[/C][C]0.359504615780202[/C][C]0.719009231560403[/C][C]0.640495384219798[/C][/ROW]
[ROW][C]13[/C][C]0.34275431210009[/C][C]0.68550862420018[/C][C]0.65724568789991[/C][/ROW]
[ROW][C]14[/C][C]0.328738555723178[/C][C]0.657477111446356[/C][C]0.671261444276822[/C][/ROW]
[ROW][C]15[/C][C]0.261361754404055[/C][C]0.522723508808109[/C][C]0.738638245595945[/C][/ROW]
[ROW][C]16[/C][C]0.194598274617349[/C][C]0.389196549234698[/C][C]0.805401725382651[/C][/ROW]
[ROW][C]17[/C][C]0.1353860825625[/C][C]0.270772165124999[/C][C]0.8646139174375[/C][/ROW]
[ROW][C]18[/C][C]0.107095949240245[/C][C]0.21419189848049[/C][C]0.892904050759755[/C][/ROW]
[ROW][C]19[/C][C]0.128054988521313[/C][C]0.256109977042625[/C][C]0.871945011478688[/C][/ROW]
[ROW][C]20[/C][C]0.121600731400785[/C][C]0.24320146280157[/C][C]0.878399268599215[/C][/ROW]
[ROW][C]21[/C][C]0.114264386022227[/C][C]0.228528772044455[/C][C]0.885735613977773[/C][/ROW]
[ROW][C]22[/C][C]0.16702661657991[/C][C]0.334053233159821[/C][C]0.83297338342009[/C][/ROW]
[ROW][C]23[/C][C]0.175846660452567[/C][C]0.351693320905133[/C][C]0.824153339547433[/C][/ROW]
[ROW][C]24[/C][C]0.187872007126057[/C][C]0.375744014252115[/C][C]0.812127992873943[/C][/ROW]
[ROW][C]25[/C][C]0.146592827582757[/C][C]0.293185655165513[/C][C]0.853407172417243[/C][/ROW]
[ROW][C]26[/C][C]0.158854159634697[/C][C]0.317708319269394[/C][C]0.841145840365303[/C][/ROW]
[ROW][C]27[/C][C]0.130179589839034[/C][C]0.260359179678068[/C][C]0.869820410160966[/C][/ROW]
[ROW][C]28[/C][C]0.118740920728551[/C][C]0.237481841457102[/C][C]0.881259079271449[/C][/ROW]
[ROW][C]29[/C][C]0.144282890150143[/C][C]0.288565780300286[/C][C]0.855717109849857[/C][/ROW]
[ROW][C]30[/C][C]0.119498465507645[/C][C]0.238996931015291[/C][C]0.880501534492355[/C][/ROW]
[ROW][C]31[/C][C]0.368324542738313[/C][C]0.736649085476625[/C][C]0.631675457261687[/C][/ROW]
[ROW][C]32[/C][C]0.290896256546075[/C][C]0.581792513092149[/C][C]0.709103743453925[/C][/ROW]
[ROW][C]33[/C][C]0.226175928192031[/C][C]0.452351856384062[/C][C]0.773824071807969[/C][/ROW]
[ROW][C]34[/C][C]0.178144603314294[/C][C]0.356289206628588[/C][C]0.821855396685706[/C][/ROW]
[ROW][C]35[/C][C]0.133429790171504[/C][C]0.266859580343008[/C][C]0.866570209828496[/C][/ROW]
[ROW][C]36[/C][C]0.132552045024972[/C][C]0.265104090049943[/C][C]0.867447954975028[/C][/ROW]
[ROW][C]37[/C][C]0.180190990056814[/C][C]0.360381980113628[/C][C]0.819809009943186[/C][/ROW]
[ROW][C]38[/C][C]0.156700747011129[/C][C]0.313401494022258[/C][C]0.843299252988871[/C][/ROW]
[ROW][C]39[/C][C]0.119635956530287[/C][C]0.239271913060575[/C][C]0.880364043469713[/C][/ROW]
[ROW][C]40[/C][C]0.185844589839252[/C][C]0.371689179678504[/C][C]0.814155410160748[/C][/ROW]
[ROW][C]41[/C][C]0.168931408088576[/C][C]0.337862816177152[/C][C]0.831068591911424[/C][/ROW]
[ROW][C]42[/C][C]0.257795335945066[/C][C]0.515590671890132[/C][C]0.742204664054934[/C][/ROW]
[ROW][C]43[/C][C]0.330746851237722[/C][C]0.661493702475444[/C][C]0.669253148762278[/C][/ROW]
[ROW][C]44[/C][C]0.272804173698516[/C][C]0.545608347397032[/C][C]0.727195826301484[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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
90.2495768439887690.4991536879775380.750423156011231
100.2251861584446380.4503723168892760.774813841555362
110.2296804582846460.4593609165692920.770319541715354
120.3595046157802020.7190092315604030.640495384219798
130.342754312100090.685508624200180.65724568789991
140.3287385557231780.6574771114463560.671261444276822
150.2613617544040550.5227235088081090.738638245595945
160.1945982746173490.3891965492346980.805401725382651
170.13538608256250.2707721651249990.8646139174375
180.1070959492402450.214191898480490.892904050759755
190.1280549885213130.2561099770426250.871945011478688
200.1216007314007850.243201462801570.878399268599215
210.1142643860222270.2285287720444550.885735613977773
220.167026616579910.3340532331598210.83297338342009
230.1758466604525670.3516933209051330.824153339547433
240.1878720071260570.3757440142521150.812127992873943
250.1465928275827570.2931856551655130.853407172417243
260.1588541596346970.3177083192693940.841145840365303
270.1301795898390340.2603591796780680.869820410160966
280.1187409207285510.2374818414571020.881259079271449
290.1442828901501430.2885657803002860.855717109849857
300.1194984655076450.2389969310152910.880501534492355
310.3683245427383130.7366490854766250.631675457261687
320.2908962565460750.5817925130921490.709103743453925
330.2261759281920310.4523518563840620.773824071807969
340.1781446033142940.3562892066285880.821855396685706
350.1334297901715040.2668595803430080.866570209828496
360.1325520450249720.2651040900499430.867447954975028
370.1801909900568140.3603819801136280.819809009943186
380.1567007470111290.3134014940222580.843299252988871
390.1196359565302870.2392719130605750.880364043469713
400.1858445898392520.3716891796785040.814155410160748
410.1689314080885760.3378628161771520.831068591911424
420.2577953359450660.5155906718901320.742204664054934
430.3307468512377220.6614937024754440.669253148762278
440.2728041736985160.5456083473970320.727195826301484







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 level00OK

\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 & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=147107&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=147107&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=147107&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 level00OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;
R code (references can be found in the software module):
library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}