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of Irreproducible Research!

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 14 Dec 2020 01:41:32 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2020/Dec/14/t1607906665rvgynfdf6lvuspo.htm/, Retrieved Fri, 29 Mar 2024 13:49:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319320, Retrieved Fri, 29 Mar 2024 13:49:50 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsNessa frequência, a fase foi mais importante que a absorção.
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Multiple Regression] [1440] [2020-12-14 00:41:32] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
30 0.7560230719931639 0.7390225531785027 0.017000518814661095
30 0.8160249031037324 0.9300283822138127 -0.11400347911008027
30 0.811024750511185 0.9260282601397748 -0.11000335703604236
30 0.6980213019196143 0.8260252082888272 -0.12700387585070344
30 0.6930211493270669 0.8320253913998841 -0.13900424207281717
30 0.6930211493270669 0.8260252082888272 -0.13300405896176032
30 0.6780206915494248 0.8350254829554125 -0.15600476088747825
30 0.6840208746604817 0.8200250251777704 0.017000518814661095
30 0.6740205694753869 0.8060245979186377 -0.13200402844325085
30 0.6800207525864437 0.8290252998443557 -0.14900454725791193
30 0.6820208136234627 0.8010244453260903 -0.11900363170262765
30 0.6770206610309153 0.7970243232520524 -0.11900363170262765
30 0.6900210577715385 0.7880240485854672 -0.09800299081392864
30 0.6740205694753869 0.7770237128818629 -0.10300314340647604
30 0.8460258186590167 0.853026032288583 0.017000518814661095
30 0.6850209051789911 0.8040245368816188 -0.11900363170262765
30 0.6810207831049532 0.7890240791039766 -0.10700326548051393
30 0.6770206610309153 0.7980243537705619 -0.12000366222113712
30 0.6780206915494248 0.794024231696524 -0.11600354014709922
30 0.7010213934751427 0.8450257881405072 -0.14400439466536455
30 0.7040214850306711 0.8420256965849787 -0.13900424207281717
30 0.6900210577715385 0.8260252082888272 -0.13500411999877926
30 0.6910210882900479 0.8100247199926756 -0.12000366222113712
30 0.689021027253029 0.8080246589556567 -0.11800360118411818
30 0.6880209967345196 0.8210250556962798 -0.13300405896176032
30 0.7000213629566333 0.8120247810296946 -0.11100338755455184
30 0.6920211188085574 0.8190249946592609 -0.12600384533219397
30 0.689021027253029 0.8160249031037324 -0.12700387585070344
30 0.6970212714011048 0.8180249641407514 -0.12100369273964659
30 0.68702096621601 0.8120247810296946 -0.1250038148136845
30 0.6900210577715385 0.8120247810296946 -0.12300375377666554
30 0.6900210577715385 0.8330254219183935 -0.10600323496200445
30 0.6690204168828395 0.7960242927335429 -0.12600384533219397
30 0.8850270088808863 0.8860270393993958 0.007000213629566332
30 0.7070215765861995 0.8660264290292062 0.04800146488845485
30 0.6650202948088015 0.8030245063631093 -0.1380042115543077
30 0.666020325327311 0.8210250556962798 -0.15500473036896878
30 0.6800207525864437 0.8320253913998841 -0.15200463881344034
30 0.66802038636433 0.7000213629566333 -0.032000976592303235
30 0.6820208136234627 0.8070246284371472 -0.12600384533219397
30 0.6820208136234627 0.8140248420667135 -0.13100399792474135
30 0.6800207525864437 0.7850239570299387 -0.10400317392498551
30 0.6790207220679343 0.792024170659505 -0.11300344859157078
30 0.6780206915494248 0.7890240791039766 -0.11100338755455184
1 0.8720266121402631 0.7910241401409955 0.08800268562883388
1 0.6690204168828395 0.7530229804376354 -0.08400256355479599
1 0.6760206305124058 0.7980243537705619 -0.12100369273964659
1 0.6800207525864437 0.794024231696524 -0.11400347911008027
1 0.6650202948088015 0.7110216986602373 -0.0460014038514359
1 0.7250221259193701 0.7580231330301828 -0.09100277718436231
1 0.6600201422162542 0.7580231330301828 -0.09800299081392864
1 0.6520198980681784 0.7110216986602373 -0.05900180059205909
1 0.6620202032532732 0.7390225531785027 -0.07800238044373912
1 0.6750205999938963 0.7610232245857113 -0.08500259407330546
1 0.6570200506607258 0.7510229194006165 -0.09400286873989075
1 0.6560200201422163 0.7790237739188818 -0.12200372325815607
1 0.6590201116977447 0.6490198065126499 0.01000030518509476
1 0.6730205389568774 0.7570231025116734 -0.08400256355479599
1 0.6780206915494248 0.7790237739188818 -0.10200311288796655
1 0.6790207220679343 0.773023590807825 -0.09300283822138126
1 0.6840208746604817 0.771023529770806 -0.08700265511032441
1 0.6760206305124058 0.7760236823633534 -0.09900302133243813
1 0.6750205999938963 0.7450227362895596 -0.06900210577715385
1 0.6750205999938963 0.748022827845088 -0.07300222785119174
1 0.6740205694753869 0.7550230414746544 -0.08000244148075808
1 0.68702096621601 0.7550230414746544 -0.06900210577715385
1 0.6830208441419722 0.773023590807825 -0.08800268562883388
1 0.6830208441419722 0.7850239570299387 -0.10100308236945708
1 0.6850209051789911 0.7750236518448439 -0.09100277718436231
1 0.6830208441419722 0.7760236823633534 -0.09400286873989075
1 0.6820208136234627 0.7960242927335429 -0.11400347911008027
1 0.6840208746604817 0.769023468733787 -0.08500259407330546
1 0.6860209356975006 0.7810238349559008 -0.09400286873989075
1 0.6720205084383679 0.6640202642902922 0.008000244148075809
1 0.68702096621601 0.7930242011780145 -0.10600323496200445
1 0.6970212714011048 0.8010244453260903 -0.1080032959990234
1 0.6830208441419722 0.7930242011780145 -0.11000335703604236
1 0.689021027253029 0.7880240485854672 -0.09900302133243813
1 0.689021027253029 0.7890240791039766 -0.10100308236945708




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time2 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]2 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319320&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R ServerBig Analytics Cloud Computing Center







Multiple Linear Regression - Estimated Regression Equation
a[t] = -115.487 -69.3828b[t] + 230.365c[t] + 20.2521d[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  -115.487 -69.3828b[t] +  230.365c[t] +  20.2521d[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  -115.487 -69.3828b[t] +  230.365c[t] +  20.2521d[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&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
a[t] = -115.487 -69.3828b[t] + 230.365c[t] + 20.2521d[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-115.5 26.01-4.4400e+00 3.044e-05 1.522e-05
b-69.38 56.22-1.2340e+00 0.221 0.1105
c+230.4 50.6+4.5530e+00 2.007e-05 1.003e-05
d+20.25 45.9+4.4120e-01 0.6603 0.3302

\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) & -115.5 &  26.01 & -4.4400e+00 &  3.044e-05 &  1.522e-05 \tabularnewline
b & -69.38 &  56.22 & -1.2340e+00 &  0.221 &  0.1105 \tabularnewline
c & +230.4 &  50.6 & +4.5530e+00 &  2.007e-05 &  1.003e-05 \tabularnewline
d & +20.25 &  45.9 & +4.4120e-01 &  0.6603 &  0.3302 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&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]-115.5[/C][C] 26.01[/C][C]-4.4400e+00[/C][C] 3.044e-05[/C][C] 1.522e-05[/C][/ROW]
[ROW][C]b[/C][C]-69.38[/C][C] 56.22[/C][C]-1.2340e+00[/C][C] 0.221[/C][C] 0.1105[/C][/ROW]
[ROW][C]c[/C][C]+230.4[/C][C] 50.6[/C][C]+4.5530e+00[/C][C] 2.007e-05[/C][C] 1.003e-05[/C][/ROW]
[ROW][C]d[/C][C]+20.25[/C][C] 45.9[/C][C]+4.4120e-01[/C][C] 0.6603[/C][C] 0.3302[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&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)-115.5 26.01-4.4400e+00 3.044e-05 1.522e-05
b-69.38 56.22-1.2340e+00 0.221 0.1105
c+230.4 50.6+4.5530e+00 2.007e-05 1.003e-05
d+20.25 45.9+4.4120e-01 0.6603 0.3302







Multiple Linear Regression - Regression Statistics
Multiple R 0.6082
R-squared 0.3699
Adjusted R-squared 0.3447
F-TEST (value) 14.68
F-TEST (DF numerator)3
F-TEST (DF denominator)75
p-value 1.304e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 11.74
Sum Squared Residuals 1.033e+04

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6082 \tabularnewline
R-squared &  0.3699 \tabularnewline
Adjusted R-squared &  0.3447 \tabularnewline
F-TEST (value) &  14.68 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value &  1.304e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  11.74 \tabularnewline
Sum Squared Residuals &  1.033e+04 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6082[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.3699[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3447[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 14.68[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/C][/ROW]
[ROW][C]p-value[/C][C] 1.304e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 11.74[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.033e+04[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&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 R 0.6082
R-squared 0.3699
Adjusted R-squared 0.3447
F-TEST (value) 14.68
F-TEST (DF numerator)3
F-TEST (DF denominator)75
p-value 1.304e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 11.74
Sum Squared Residuals 1.033e+04







Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=4

As an alternative you can also use a QR Code:  

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

Menu of Residual Diagnostics
DescriptionLink
HistogramCompute
Central TendencyCompute
QQ PlotCompute
Kernel Density PlotCompute
Skewness/Kurtosis TestCompute
Skewness-Kurtosis PlotCompute
Harrell-Davis PlotCompute
Bootstrap Plot -- Central TendencyCompute
Blocked Bootstrap Plot -- Central TendencyCompute
(Partial) Autocorrelation PlotCompute
Spectral AnalysisCompute
Tukey lambda PPCC PlotCompute
Box-Cox Normality PlotCompute
Summary StatisticsCompute







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 30 2.647 27.35
2 30 39.83-9.832
3 30 39.34-9.338
4 30 23.8 6.203
5 30 25.28 4.717
6 30 24.02 5.978
7 30 26.67 3.329
8 30 26.3 3.697
9 30 20.75 9.246
10 30 25.29 4.708
11 30 19.31 10.69
12 30 18.74 11.26
13 30 16.19 13.81
14 30 14.66 15.34
15 30 22.66 7.335
16 30 19.79 10.21
17 30 16.86 13.14
18 30 18.95 11.05
19 30 18.04 11.96
20 30 27.62 2.378
21 30 26.82 3.176
22 30 24.19 5.81
23 30 20.74 9.261
24 30 20.46 9.543
25 30 23.22 6.782
26 30 20.76 9.243
27 30 22.62 7.379
28 30 22.12 7.882
29 30 22.14 7.855
30 30 21.38 8.624
31 30 21.21 8.792
32 30 26.39 3.61
33 30 18.92 11.08
34 30 27.36 2.642
35 30 35.93-5.932
36 30 20.57 9.435
37 30 24.3 5.702
38 30 25.92 4.078
39 30-1.224 31.22
40 30 20.55 9.45
41 30 22.06 7.938
42 30 16.07 13.93
43 30 17.57 12.43
44 30 16.98 13.02
45 1 8.015-7.015
46 1 9.863-8.863
47 1 18.99-17.99
48 1 17.94-16.94
49 1 1.234-0.2345
50 1 6.987-5.987
51 1 11.36-10.36
52 1 1.873-0.8732
53 1 7.245-6.245
54 1 11.27-10.27
55 1 10.03-9.032
56 1 15.98-14.98
57 1-11.5 12.5
58 1 10.51-9.507
59 1 14.86-13.86
60 1 13.59-12.59
61 1 12.91-11.91
62 1 14.37-13.37
63 1 7.907-6.907
64 1 8.518-7.518
65 1 10.06-9.058
66 1 9.379-8.379
67 1 13.42-12.42
68 1 15.92-14.92
69 1 13.68-12.68
70 1 13.99-12.99
71 1 18.26-17.26
72 1 12.49-11.49
73 1 14.93-13.93
74 1-8.985 9.985
75 1 17.38-16.38
76 1 18.49-17.49
77 1 17.58-16.58
78 1 16.23-15.23
79 1 16.42-15.42

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  30 &  2.647 &  27.35 \tabularnewline
2 &  30 &  39.83 & -9.832 \tabularnewline
3 &  30 &  39.34 & -9.338 \tabularnewline
4 &  30 &  23.8 &  6.203 \tabularnewline
5 &  30 &  25.28 &  4.717 \tabularnewline
6 &  30 &  24.02 &  5.978 \tabularnewline
7 &  30 &  26.67 &  3.329 \tabularnewline
8 &  30 &  26.3 &  3.697 \tabularnewline
9 &  30 &  20.75 &  9.246 \tabularnewline
10 &  30 &  25.29 &  4.708 \tabularnewline
11 &  30 &  19.31 &  10.69 \tabularnewline
12 &  30 &  18.74 &  11.26 \tabularnewline
13 &  30 &  16.19 &  13.81 \tabularnewline
14 &  30 &  14.66 &  15.34 \tabularnewline
15 &  30 &  22.66 &  7.335 \tabularnewline
16 &  30 &  19.79 &  10.21 \tabularnewline
17 &  30 &  16.86 &  13.14 \tabularnewline
18 &  30 &  18.95 &  11.05 \tabularnewline
19 &  30 &  18.04 &  11.96 \tabularnewline
20 &  30 &  27.62 &  2.378 \tabularnewline
21 &  30 &  26.82 &  3.176 \tabularnewline
22 &  30 &  24.19 &  5.81 \tabularnewline
23 &  30 &  20.74 &  9.261 \tabularnewline
24 &  30 &  20.46 &  9.543 \tabularnewline
25 &  30 &  23.22 &  6.782 \tabularnewline
26 &  30 &  20.76 &  9.243 \tabularnewline
27 &  30 &  22.62 &  7.379 \tabularnewline
28 &  30 &  22.12 &  7.882 \tabularnewline
29 &  30 &  22.14 &  7.855 \tabularnewline
30 &  30 &  21.38 &  8.624 \tabularnewline
31 &  30 &  21.21 &  8.792 \tabularnewline
32 &  30 &  26.39 &  3.61 \tabularnewline
33 &  30 &  18.92 &  11.08 \tabularnewline
34 &  30 &  27.36 &  2.642 \tabularnewline
35 &  30 &  35.93 & -5.932 \tabularnewline
36 &  30 &  20.57 &  9.435 \tabularnewline
37 &  30 &  24.3 &  5.702 \tabularnewline
38 &  30 &  25.92 &  4.078 \tabularnewline
39 &  30 & -1.224 &  31.22 \tabularnewline
40 &  30 &  20.55 &  9.45 \tabularnewline
41 &  30 &  22.06 &  7.938 \tabularnewline
42 &  30 &  16.07 &  13.93 \tabularnewline
43 &  30 &  17.57 &  12.43 \tabularnewline
44 &  30 &  16.98 &  13.02 \tabularnewline
45 &  1 &  8.015 & -7.015 \tabularnewline
46 &  1 &  9.863 & -8.863 \tabularnewline
47 &  1 &  18.99 & -17.99 \tabularnewline
48 &  1 &  17.94 & -16.94 \tabularnewline
49 &  1 &  1.234 & -0.2345 \tabularnewline
50 &  1 &  6.987 & -5.987 \tabularnewline
51 &  1 &  11.36 & -10.36 \tabularnewline
52 &  1 &  1.873 & -0.8732 \tabularnewline
53 &  1 &  7.245 & -6.245 \tabularnewline
54 &  1 &  11.27 & -10.27 \tabularnewline
55 &  1 &  10.03 & -9.032 \tabularnewline
56 &  1 &  15.98 & -14.98 \tabularnewline
57 &  1 & -11.5 &  12.5 \tabularnewline
58 &  1 &  10.51 & -9.507 \tabularnewline
59 &  1 &  14.86 & -13.86 \tabularnewline
60 &  1 &  13.59 & -12.59 \tabularnewline
61 &  1 &  12.91 & -11.91 \tabularnewline
62 &  1 &  14.37 & -13.37 \tabularnewline
63 &  1 &  7.907 & -6.907 \tabularnewline
64 &  1 &  8.518 & -7.518 \tabularnewline
65 &  1 &  10.06 & -9.058 \tabularnewline
66 &  1 &  9.379 & -8.379 \tabularnewline
67 &  1 &  13.42 & -12.42 \tabularnewline
68 &  1 &  15.92 & -14.92 \tabularnewline
69 &  1 &  13.68 & -12.68 \tabularnewline
70 &  1 &  13.99 & -12.99 \tabularnewline
71 &  1 &  18.26 & -17.26 \tabularnewline
72 &  1 &  12.49 & -11.49 \tabularnewline
73 &  1 &  14.93 & -13.93 \tabularnewline
74 &  1 & -8.985 &  9.985 \tabularnewline
75 &  1 &  17.38 & -16.38 \tabularnewline
76 &  1 &  18.49 & -17.49 \tabularnewline
77 &  1 &  17.58 & -16.58 \tabularnewline
78 &  1 &  16.23 & -15.23 \tabularnewline
79 &  1 &  16.42 & -15.42 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=5

[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] 30[/C][C] 2.647[/C][C] 27.35[/C][/ROW]
[ROW][C]2[/C][C] 30[/C][C] 39.83[/C][C]-9.832[/C][/ROW]
[ROW][C]3[/C][C] 30[/C][C] 39.34[/C][C]-9.338[/C][/ROW]
[ROW][C]4[/C][C] 30[/C][C] 23.8[/C][C] 6.203[/C][/ROW]
[ROW][C]5[/C][C] 30[/C][C] 25.28[/C][C] 4.717[/C][/ROW]
[ROW][C]6[/C][C] 30[/C][C] 24.02[/C][C] 5.978[/C][/ROW]
[ROW][C]7[/C][C] 30[/C][C] 26.67[/C][C] 3.329[/C][/ROW]
[ROW][C]8[/C][C] 30[/C][C] 26.3[/C][C] 3.697[/C][/ROW]
[ROW][C]9[/C][C] 30[/C][C] 20.75[/C][C] 9.246[/C][/ROW]
[ROW][C]10[/C][C] 30[/C][C] 25.29[/C][C] 4.708[/C][/ROW]
[ROW][C]11[/C][C] 30[/C][C] 19.31[/C][C] 10.69[/C][/ROW]
[ROW][C]12[/C][C] 30[/C][C] 18.74[/C][C] 11.26[/C][/ROW]
[ROW][C]13[/C][C] 30[/C][C] 16.19[/C][C] 13.81[/C][/ROW]
[ROW][C]14[/C][C] 30[/C][C] 14.66[/C][C] 15.34[/C][/ROW]
[ROW][C]15[/C][C] 30[/C][C] 22.66[/C][C] 7.335[/C][/ROW]
[ROW][C]16[/C][C] 30[/C][C] 19.79[/C][C] 10.21[/C][/ROW]
[ROW][C]17[/C][C] 30[/C][C] 16.86[/C][C] 13.14[/C][/ROW]
[ROW][C]18[/C][C] 30[/C][C] 18.95[/C][C] 11.05[/C][/ROW]
[ROW][C]19[/C][C] 30[/C][C] 18.04[/C][C] 11.96[/C][/ROW]
[ROW][C]20[/C][C] 30[/C][C] 27.62[/C][C] 2.378[/C][/ROW]
[ROW][C]21[/C][C] 30[/C][C] 26.82[/C][C] 3.176[/C][/ROW]
[ROW][C]22[/C][C] 30[/C][C] 24.19[/C][C] 5.81[/C][/ROW]
[ROW][C]23[/C][C] 30[/C][C] 20.74[/C][C] 9.261[/C][/ROW]
[ROW][C]24[/C][C] 30[/C][C] 20.46[/C][C] 9.543[/C][/ROW]
[ROW][C]25[/C][C] 30[/C][C] 23.22[/C][C] 6.782[/C][/ROW]
[ROW][C]26[/C][C] 30[/C][C] 20.76[/C][C] 9.243[/C][/ROW]
[ROW][C]27[/C][C] 30[/C][C] 22.62[/C][C] 7.379[/C][/ROW]
[ROW][C]28[/C][C] 30[/C][C] 22.12[/C][C] 7.882[/C][/ROW]
[ROW][C]29[/C][C] 30[/C][C] 22.14[/C][C] 7.855[/C][/ROW]
[ROW][C]30[/C][C] 30[/C][C] 21.38[/C][C] 8.624[/C][/ROW]
[ROW][C]31[/C][C] 30[/C][C] 21.21[/C][C] 8.792[/C][/ROW]
[ROW][C]32[/C][C] 30[/C][C] 26.39[/C][C] 3.61[/C][/ROW]
[ROW][C]33[/C][C] 30[/C][C] 18.92[/C][C] 11.08[/C][/ROW]
[ROW][C]34[/C][C] 30[/C][C] 27.36[/C][C] 2.642[/C][/ROW]
[ROW][C]35[/C][C] 30[/C][C] 35.93[/C][C]-5.932[/C][/ROW]
[ROW][C]36[/C][C] 30[/C][C] 20.57[/C][C] 9.435[/C][/ROW]
[ROW][C]37[/C][C] 30[/C][C] 24.3[/C][C] 5.702[/C][/ROW]
[ROW][C]38[/C][C] 30[/C][C] 25.92[/C][C] 4.078[/C][/ROW]
[ROW][C]39[/C][C] 30[/C][C]-1.224[/C][C] 31.22[/C][/ROW]
[ROW][C]40[/C][C] 30[/C][C] 20.55[/C][C] 9.45[/C][/ROW]
[ROW][C]41[/C][C] 30[/C][C] 22.06[/C][C] 7.938[/C][/ROW]
[ROW][C]42[/C][C] 30[/C][C] 16.07[/C][C] 13.93[/C][/ROW]
[ROW][C]43[/C][C] 30[/C][C] 17.57[/C][C] 12.43[/C][/ROW]
[ROW][C]44[/C][C] 30[/C][C] 16.98[/C][C] 13.02[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 8.015[/C][C]-7.015[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 9.863[/C][C]-8.863[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 18.99[/C][C]-17.99[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 17.94[/C][C]-16.94[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 1.234[/C][C]-0.2345[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 6.987[/C][C]-5.987[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 11.36[/C][C]-10.36[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 1.873[/C][C]-0.8732[/C][/ROW]
[ROW][C]53[/C][C] 1[/C][C] 7.245[/C][C]-6.245[/C][/ROW]
[ROW][C]54[/C][C] 1[/C][C] 11.27[/C][C]-10.27[/C][/ROW]
[ROW][C]55[/C][C] 1[/C][C] 10.03[/C][C]-9.032[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 15.98[/C][C]-14.98[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C]-11.5[/C][C] 12.5[/C][/ROW]
[ROW][C]58[/C][C] 1[/C][C] 10.51[/C][C]-9.507[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 14.86[/C][C]-13.86[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 13.59[/C][C]-12.59[/C][/ROW]
[ROW][C]61[/C][C] 1[/C][C] 12.91[/C][C]-11.91[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 14.37[/C][C]-13.37[/C][/ROW]
[ROW][C]63[/C][C] 1[/C][C] 7.907[/C][C]-6.907[/C][/ROW]
[ROW][C]64[/C][C] 1[/C][C] 8.518[/C][C]-7.518[/C][/ROW]
[ROW][C]65[/C][C] 1[/C][C] 10.06[/C][C]-9.058[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 9.379[/C][C]-8.379[/C][/ROW]
[ROW][C]67[/C][C] 1[/C][C] 13.42[/C][C]-12.42[/C][/ROW]
[ROW][C]68[/C][C] 1[/C][C] 15.92[/C][C]-14.92[/C][/ROW]
[ROW][C]69[/C][C] 1[/C][C] 13.68[/C][C]-12.68[/C][/ROW]
[ROW][C]70[/C][C] 1[/C][C] 13.99[/C][C]-12.99[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 18.26[/C][C]-17.26[/C][/ROW]
[ROW][C]72[/C][C] 1[/C][C] 12.49[/C][C]-11.49[/C][/ROW]
[ROW][C]73[/C][C] 1[/C][C] 14.93[/C][C]-13.93[/C][/ROW]
[ROW][C]74[/C][C] 1[/C][C]-8.985[/C][C] 9.985[/C][/ROW]
[ROW][C]75[/C][C] 1[/C][C] 17.38[/C][C]-16.38[/C][/ROW]
[ROW][C]76[/C][C] 1[/C][C] 18.49[/C][C]-17.49[/C][/ROW]
[ROW][C]77[/C][C] 1[/C][C] 17.58[/C][C]-16.58[/C][/ROW]
[ROW][C]78[/C][C] 1[/C][C] 16.23[/C][C]-15.23[/C][/ROW]
[ROW][C]79[/C][C] 1[/C][C] 16.42[/C][C]-15.42[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=5

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
1 30 2.647 27.35
2 30 39.83-9.832
3 30 39.34-9.338
4 30 23.8 6.203
5 30 25.28 4.717
6 30 24.02 5.978
7 30 26.67 3.329
8 30 26.3 3.697
9 30 20.75 9.246
10 30 25.29 4.708
11 30 19.31 10.69
12 30 18.74 11.26
13 30 16.19 13.81
14 30 14.66 15.34
15 30 22.66 7.335
16 30 19.79 10.21
17 30 16.86 13.14
18 30 18.95 11.05
19 30 18.04 11.96
20 30 27.62 2.378
21 30 26.82 3.176
22 30 24.19 5.81
23 30 20.74 9.261
24 30 20.46 9.543
25 30 23.22 6.782
26 30 20.76 9.243
27 30 22.62 7.379
28 30 22.12 7.882
29 30 22.14 7.855
30 30 21.38 8.624
31 30 21.21 8.792
32 30 26.39 3.61
33 30 18.92 11.08
34 30 27.36 2.642
35 30 35.93-5.932
36 30 20.57 9.435
37 30 24.3 5.702
38 30 25.92 4.078
39 30-1.224 31.22
40 30 20.55 9.45
41 30 22.06 7.938
42 30 16.07 13.93
43 30 17.57 12.43
44 30 16.98 13.02
45 1 8.015-7.015
46 1 9.863-8.863
47 1 18.99-17.99
48 1 17.94-16.94
49 1 1.234-0.2345
50 1 6.987-5.987
51 1 11.36-10.36
52 1 1.873-0.8732
53 1 7.245-6.245
54 1 11.27-10.27
55 1 10.03-9.032
56 1 15.98-14.98
57 1-11.5 12.5
58 1 10.51-9.507
59 1 14.86-13.86
60 1 13.59-12.59
61 1 12.91-11.91
62 1 14.37-13.37
63 1 7.907-6.907
64 1 8.518-7.518
65 1 10.06-9.058
66 1 9.379-8.379
67 1 13.42-12.42
68 1 15.92-14.92
69 1 13.68-12.68
70 1 13.99-12.99
71 1 18.26-17.26
72 1 12.49-11.49
73 1 14.93-13.93
74 1-8.985 9.985
75 1 17.38-16.38
76 1 18.49-17.49
77 1 17.58-16.58
78 1 16.23-15.23
79 1 16.42-15.42







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
7 3.162e-49 6.323e-49 1
8 0 0 1
9 0 0 1
10 1.748e-95 3.496e-95 1
11 3.38e-109 6.76e-109 1
12 4.8e-128 9.599e-128 1
13 5.335e-140 1.067e-139 1
14 6.683e-158 1.337e-157 1
15 5.957e-166 1.191e-165 1
16 0 0 1
17 0 0 1
18 2.016e-217 4.032e-217 1
19 1.195e-225 2.39e-225 1
20 3.097e-248 6.195e-248 1
21 1.408e-258 2.817e-258 1
22 9.747e-274 1.949e-273 1
23 0 0 1
24 5.789e-296 1.158e-295 1
25 0 0 1
26 0 0 1
27 0 0 1
28 0 0 1
29 0 0 1
30 0 0 1
31 0 0 1
32 0 0 1
33 0 0 1
34 0 0 1
35 0 0 1
36 0 0 1
37 0 0 1
38 0 0 1
39 0 0 1
40 0 0 1
41 0 0 1
42 0 0 1
43 0 0 1
44 1 7.677e-56 3.839e-56
45 1 0 0
46 1 0 0
47 1 0 0
48 1 0 0
49 1 0 0
50 1 0 0
51 1 0 0
52 1 0 0
53 1 0 0
54 1 0 0
55 1 0 0
56 1 2.777e-306 1.389e-306
57 1 3.683e-288 1.841e-288
58 1 1.076e-272 5.379e-273
59 1 5.732e-256 2.866e-256
60 1 3.721e-247 1.861e-247
61 1 3.542e-227 1.771e-227
62 1 8.617e-215 4.308e-215
63 1 0 0
64 1 1.335e-182 6.674e-183
65 1 0 0
66 1 0 0
67 1 4.145e-130 2.073e-130
68 1 6.904e-116 3.452e-116
69 1 0 0
70 1 0 0
71 1 1.834e-66 9.17e-67
72 1 0 0

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
7 &  3.162e-49 &  6.323e-49 &  1 \tabularnewline
8 &  0 &  0 &  1 \tabularnewline
9 &  0 &  0 &  1 \tabularnewline
10 &  1.748e-95 &  3.496e-95 &  1 \tabularnewline
11 &  3.38e-109 &  6.76e-109 &  1 \tabularnewline
12 &  4.8e-128 &  9.599e-128 &  1 \tabularnewline
13 &  5.335e-140 &  1.067e-139 &  1 \tabularnewline
14 &  6.683e-158 &  1.337e-157 &  1 \tabularnewline
15 &  5.957e-166 &  1.191e-165 &  1 \tabularnewline
16 &  0 &  0 &  1 \tabularnewline
17 &  0 &  0 &  1 \tabularnewline
18 &  2.016e-217 &  4.032e-217 &  1 \tabularnewline
19 &  1.195e-225 &  2.39e-225 &  1 \tabularnewline
20 &  3.097e-248 &  6.195e-248 &  1 \tabularnewline
21 &  1.408e-258 &  2.817e-258 &  1 \tabularnewline
22 &  9.747e-274 &  1.949e-273 &  1 \tabularnewline
23 &  0 &  0 &  1 \tabularnewline
24 &  5.789e-296 &  1.158e-295 &  1 \tabularnewline
25 &  0 &  0 &  1 \tabularnewline
26 &  0 &  0 &  1 \tabularnewline
27 &  0 &  0 &  1 \tabularnewline
28 &  0 &  0 &  1 \tabularnewline
29 &  0 &  0 &  1 \tabularnewline
30 &  0 &  0 &  1 \tabularnewline
31 &  0 &  0 &  1 \tabularnewline
32 &  0 &  0 &  1 \tabularnewline
33 &  0 &  0 &  1 \tabularnewline
34 &  0 &  0 &  1 \tabularnewline
35 &  0 &  0 &  1 \tabularnewline
36 &  0 &  0 &  1 \tabularnewline
37 &  0 &  0 &  1 \tabularnewline
38 &  0 &  0 &  1 \tabularnewline
39 &  0 &  0 &  1 \tabularnewline
40 &  0 &  0 &  1 \tabularnewline
41 &  0 &  0 &  1 \tabularnewline
42 &  0 &  0 &  1 \tabularnewline
43 &  0 &  0 &  1 \tabularnewline
44 &  1 &  7.677e-56 &  3.839e-56 \tabularnewline
45 &  1 &  0 &  0 \tabularnewline
46 &  1 &  0 &  0 \tabularnewline
47 &  1 &  0 &  0 \tabularnewline
48 &  1 &  0 &  0 \tabularnewline
49 &  1 &  0 &  0 \tabularnewline
50 &  1 &  0 &  0 \tabularnewline
51 &  1 &  0 &  0 \tabularnewline
52 &  1 &  0 &  0 \tabularnewline
53 &  1 &  0 &  0 \tabularnewline
54 &  1 &  0 &  0 \tabularnewline
55 &  1 &  0 &  0 \tabularnewline
56 &  1 &  2.777e-306 &  1.389e-306 \tabularnewline
57 &  1 &  3.683e-288 &  1.841e-288 \tabularnewline
58 &  1 &  1.076e-272 &  5.379e-273 \tabularnewline
59 &  1 &  5.732e-256 &  2.866e-256 \tabularnewline
60 &  1 &  3.721e-247 &  1.861e-247 \tabularnewline
61 &  1 &  3.542e-227 &  1.771e-227 \tabularnewline
62 &  1 &  8.617e-215 &  4.308e-215 \tabularnewline
63 &  1 &  0 &  0 \tabularnewline
64 &  1 &  1.335e-182 &  6.674e-183 \tabularnewline
65 &  1 &  0 &  0 \tabularnewline
66 &  1 &  0 &  0 \tabularnewline
67 &  1 &  4.145e-130 &  2.073e-130 \tabularnewline
68 &  1 &  6.904e-116 &  3.452e-116 \tabularnewline
69 &  1 &  0 &  0 \tabularnewline
70 &  1 &  0 &  0 \tabularnewline
71 &  1 &  1.834e-66 &  9.17e-67 \tabularnewline
72 &  1 &  0 &  0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=6

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]7[/C][C] 3.162e-49[/C][C] 6.323e-49[/C][C] 1[/C][/ROW]
[ROW][C]8[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]9[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]10[/C][C] 1.748e-95[/C][C] 3.496e-95[/C][C] 1[/C][/ROW]
[ROW][C]11[/C][C] 3.38e-109[/C][C] 6.76e-109[/C][C] 1[/C][/ROW]
[ROW][C]12[/C][C] 4.8e-128[/C][C] 9.599e-128[/C][C] 1[/C][/ROW]
[ROW][C]13[/C][C] 5.335e-140[/C][C] 1.067e-139[/C][C] 1[/C][/ROW]
[ROW][C]14[/C][C] 6.683e-158[/C][C] 1.337e-157[/C][C] 1[/C][/ROW]
[ROW][C]15[/C][C] 5.957e-166[/C][C] 1.191e-165[/C][C] 1[/C][/ROW]
[ROW][C]16[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]17[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]18[/C][C] 2.016e-217[/C][C] 4.032e-217[/C][C] 1[/C][/ROW]
[ROW][C]19[/C][C] 1.195e-225[/C][C] 2.39e-225[/C][C] 1[/C][/ROW]
[ROW][C]20[/C][C] 3.097e-248[/C][C] 6.195e-248[/C][C] 1[/C][/ROW]
[ROW][C]21[/C][C] 1.408e-258[/C][C] 2.817e-258[/C][C] 1[/C][/ROW]
[ROW][C]22[/C][C] 9.747e-274[/C][C] 1.949e-273[/C][C] 1[/C][/ROW]
[ROW][C]23[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]24[/C][C] 5.789e-296[/C][C] 1.158e-295[/C][C] 1[/C][/ROW]
[ROW][C]25[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]26[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]27[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]28[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]29[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]30[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]31[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]32[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]33[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]34[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]35[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]36[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]37[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]38[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]39[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]40[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]41[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]42[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]43[/C][C] 0[/C][C] 0[/C][C] 1[/C][/ROW]
[ROW][C]44[/C][C] 1[/C][C] 7.677e-56[/C][C] 3.839e-56[/C][/ROW]
[ROW][C]45[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]46[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]47[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]48[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]49[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]50[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]51[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]52[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]53[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]54[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]55[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]56[/C][C] 1[/C][C] 2.777e-306[/C][C] 1.389e-306[/C][/ROW]
[ROW][C]57[/C][C] 1[/C][C] 3.683e-288[/C][C] 1.841e-288[/C][/ROW]
[ROW][C]58[/C][C] 1[/C][C] 1.076e-272[/C][C] 5.379e-273[/C][/ROW]
[ROW][C]59[/C][C] 1[/C][C] 5.732e-256[/C][C] 2.866e-256[/C][/ROW]
[ROW][C]60[/C][C] 1[/C][C] 3.721e-247[/C][C] 1.861e-247[/C][/ROW]
[ROW][C]61[/C][C] 1[/C][C] 3.542e-227[/C][C] 1.771e-227[/C][/ROW]
[ROW][C]62[/C][C] 1[/C][C] 8.617e-215[/C][C] 4.308e-215[/C][/ROW]
[ROW][C]63[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]64[/C][C] 1[/C][C] 1.335e-182[/C][C] 6.674e-183[/C][/ROW]
[ROW][C]65[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]66[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]67[/C][C] 1[/C][C] 4.145e-130[/C][C] 2.073e-130[/C][/ROW]
[ROW][C]68[/C][C] 1[/C][C] 6.904e-116[/C][C] 3.452e-116[/C][/ROW]
[ROW][C]69[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]70[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[ROW][C]71[/C][C] 1[/C][C] 1.834e-66[/C][C] 9.17e-67[/C][/ROW]
[ROW][C]72[/C][C] 1[/C][C] 0[/C][C] 0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=6

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
7 3.162e-49 6.323e-49 1
8 0 0 1
9 0 0 1
10 1.748e-95 3.496e-95 1
11 3.38e-109 6.76e-109 1
12 4.8e-128 9.599e-128 1
13 5.335e-140 1.067e-139 1
14 6.683e-158 1.337e-157 1
15 5.957e-166 1.191e-165 1
16 0 0 1
17 0 0 1
18 2.016e-217 4.032e-217 1
19 1.195e-225 2.39e-225 1
20 3.097e-248 6.195e-248 1
21 1.408e-258 2.817e-258 1
22 9.747e-274 1.949e-273 1
23 0 0 1
24 5.789e-296 1.158e-295 1
25 0 0 1
26 0 0 1
27 0 0 1
28 0 0 1
29 0 0 1
30 0 0 1
31 0 0 1
32 0 0 1
33 0 0 1
34 0 0 1
35 0 0 1
36 0 0 1
37 0 0 1
38 0 0 1
39 0 0 1
40 0 0 1
41 0 0 1
42 0 0 1
43 0 0 1
44 1 7.677e-56 3.839e-56
45 1 0 0
46 1 0 0
47 1 0 0
48 1 0 0
49 1 0 0
50 1 0 0
51 1 0 0
52 1 0 0
53 1 0 0
54 1 0 0
55 1 0 0
56 1 2.777e-306 1.389e-306
57 1 3.683e-288 1.841e-288
58 1 1.076e-272 5.379e-273
59 1 5.732e-256 2.866e-256
60 1 3.721e-247 1.861e-247
61 1 3.542e-227 1.771e-227
62 1 8.617e-215 4.308e-215
63 1 0 0
64 1 1.335e-182 6.674e-183
65 1 0 0
66 1 0 0
67 1 4.145e-130 2.073e-130
68 1 6.904e-116 3.452e-116
69 1 0 0
70 1 0 0
71 1 1.834e-66 9.17e-67
72 1 0 0







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level66 1NOK
5% type I error level661NOK
10% type I error level661NOK

\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 & 66 &  1 & NOK \tabularnewline
5% type I error level & 66 & 1 & NOK \tabularnewline
10% type I error level & 66 & 1 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319320&T=7

[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]66[/C][C] 1[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]66[/C][C]1[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]66[/C][C]1[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=319320&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=7

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 level66 1NOK
5% type I error level661NOK
10% type I error level661NOK







Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.8206, df1 = 2, df2 = 73, p-value = 0.0008368
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 4.1706, df1 = 6, df2 = 69, p-value = 0.001237
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10171, df1 = 2, df2 = 73, p-value = 0.9034

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.8206, df1 = 2, df2 = 73, p-value = 0.0008368
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 4.1706, df1 = 6, df2 = 69, p-value = 0.001237
\tabularnewline Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10171, df1 = 2, df2 = 73, p-value = 0.9034
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319320&T=8

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.8206, df1 = 2, df2 = 73, p-value = 0.0008368
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW] [ROW][C]
> reset_test_regressors
	RESET test
data:  mylm
RESET = 4.1706, df1 = 6, df2 = 69, p-value = 0.001237
[/C][/ROW] [ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW] [ROW][C]
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10171, df1 = 2, df2 = 73, p-value = 0.9034
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319320&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=8

As an alternative you can also use a QR Code:  

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted
	RESET test
data:  mylm
RESET = 7.8206, df1 = 2, df2 = 73, p-value = 0.0008368
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors
	RESET test
data:  mylm
RESET = 4.1706, df1 = 6, df2 = 69, p-value = 0.001237
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.10171, df1 = 2, df2 = 73, p-value = 0.9034







Variance Inflation Factors (Multicollinearity)
> vif
       b        c        d 
3.352905 3.002299 2.736238 

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
       b        c        d 
3.352905 3.002299 2.736238 
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=319320&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]
> vif
       b        c        d 
3.352905 3.002299 2.736238 
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319320&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319320&T=9

As an alternative you can also use a QR Code:  

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

Variance Inflation Factors (Multicollinearity)
> vif
       b        c        d 
3.352905 3.002299 2.736238 



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
R code (references can be found in the software module):
par6 <- '12'
par5 <- '0'
par4 <- '0'
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$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.row.start(a)
a<-table.element(a, mywarning)
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,'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$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
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,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('
',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('
',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('
',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('
',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')