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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 08:01:29 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/20/t12587294079cucmk81rlh4eh5.htm/, Retrieved Thu, 28 Mar 2024 16:42:57 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58244, Retrieved Thu, 28 Mar 2024 16:42:57 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RM D  [Multiple Regression] [Seatbelt] [2009-11-12 14:10:54] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [] [2009-11-20 15:01:29] [429631dabc57c2ce83a6344a979b9063] [Current]
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Dataseries X:
107.1	32.5	115.6
100.6	33.5	111.9
99.2	31.5	107
108.4	31.2	107.1
103	27	100.6
99.8	26.7	99.2
115	26.5	108.4
90.8	26	103
95.9	27.2	99.8
114.4	30.5	115
108.2	33.7	90.8
112.6	34.2	95.9
109.1	36.7	114.4
105	36.2	108.2
105	38.5	112.6
118.5	40	109.1
103.7	42.5	105
112.5	43.5	105
116.6	43.3	118.5
96.6	45.5	103.7
101.9	44.3	112.5
116.5	43	116.6
119.3	43.5	96.6
115.4	41.5	101.9
108.5	42.5	116.5
111.5	41.3	119.3
108.8	39.5	115.4
121.8	38.5	108.5
109.6	41	111.5
112.2	44.5	108.8
119.6	46	121.8
104.1	44	109.6
105.3	41.5	112.2
115	41.3	119.6
124.1	38	104.1
116.8	38	105.3
107.5	36.2	115
115.6	38.7	124.1
116.2	38.7	116.8
116.3	39.2	107.5
119	35.7	115.6
111.9	36.5	116.2
118.6	36.7	116.3
106.9	34.7	119
103.2	35	111.9
118.6	28.2	118.6
118.7	23.7	106.9
102.8	15	103.2
100.6	8.7	118.6
94.9	11	118.7
94.5	7.5	102.8
102.9	5.7	100.6
95.3	9.3	94.9
92.5	10.2	94.5
102.7	15.7	102.9
91.5	18.1	95.3
89.5	20.8	92.5




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&T=0

As an alternative you can also use a QR Code:  

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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
Ipzb[t] = + 51.0021787849945 + 0.358521246109701Cvn[t] + 0.469136445952469Y3[t] -11.5251626923923M1[t] -13.1131829422958M2[t] -11.0020240353219M3[t] -0.0947099776184548M4[t] -7.1883374189211M5[t] -7.64246158228107M6[t] -3.61421218000356M7[t] -16.6986202389138M8[t] -15.4519614926944M9[t] -4.3902634927507M10[t] + 5.74431082396985M11[t] + 0.0569955207934599t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ipzb[t] =  +  51.0021787849945 +  0.358521246109701Cvn[t] +  0.469136445952469Y3[t] -11.5251626923923M1[t] -13.1131829422958M2[t] -11.0020240353219M3[t] -0.0947099776184548M4[t] -7.1883374189211M5[t] -7.64246158228107M6[t] -3.61421218000356M7[t] -16.6986202389138M8[t] -15.4519614926944M9[t] -4.3902634927507M10[t] +  5.74431082396985M11[t] +  0.0569955207934599t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ipzb[t] =  +  51.0021787849945 +  0.358521246109701Cvn[t] +  0.469136445952469Y3[t] -11.5251626923923M1[t] -13.1131829422958M2[t] -11.0020240353219M3[t] -0.0947099776184548M4[t] -7.1883374189211M5[t] -7.64246158228107M6[t] -3.61421218000356M7[t] -16.6986202389138M8[t] -15.4519614926944M9[t] -4.3902634927507M10[t] +  5.74431082396985M11[t] +  0.0569955207934599t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Ipzb[t] = + 51.0021787849945 + 0.358521246109701Cvn[t] + 0.469136445952469Y3[t] -11.5251626923923M1[t] -13.1131829422958M2[t] -11.0020240353219M3[t] -0.0947099776184548M4[t] -7.1883374189211M5[t] -7.64246158228107M6[t] -3.61421218000356M7[t] -16.6986202389138M8[t] -15.4519614926944M9[t] -4.3902634927507M10[t] + 5.74431082396985M11[t] + 0.0569955207934599t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)51.00217878499458.6369485.90511e-060
Cvn0.3585212461097010.0605975.91651e-060
Y30.4691364459524690.0978554.79422.1e-051e-05
M1-11.52516269239232.71509-4.24490.0001185.9e-05
M2-13.11318294229582.708507-4.84151.8e-059e-06
M3-11.00202403532192.456632-4.47855.7e-052.8e-05
M4-0.09470997761845482.313072-0.04090.9675330.483767
M5-7.18833741892112.284485-3.14660.0030340.001517
M6-7.642461582281072.260646-3.38070.0015730.000786
M7-3.614212180003562.49769-1.4470.1553140.077657
M8-16.69862023891382.270694-7.35400
M9-15.45196149269442.264783-6.822700
M10-4.39026349275072.771358-1.58420.120660.06033
M115.744310823969852.3797512.41380.0202240.010112
t0.05699552079345990.0345231.65090.1062140.053107

\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) & 51.0021787849945 & 8.636948 & 5.9051 & 1e-06 & 0 \tabularnewline
Cvn & 0.358521246109701 & 0.060597 & 5.9165 & 1e-06 & 0 \tabularnewline
Y3 & 0.469136445952469 & 0.097855 & 4.7942 & 2.1e-05 & 1e-05 \tabularnewline
M1 & -11.5251626923923 & 2.71509 & -4.2449 & 0.000118 & 5.9e-05 \tabularnewline
M2 & -13.1131829422958 & 2.708507 & -4.8415 & 1.8e-05 & 9e-06 \tabularnewline
M3 & -11.0020240353219 & 2.456632 & -4.4785 & 5.7e-05 & 2.8e-05 \tabularnewline
M4 & -0.0947099776184548 & 2.313072 & -0.0409 & 0.967533 & 0.483767 \tabularnewline
M5 & -7.1883374189211 & 2.284485 & -3.1466 & 0.003034 & 0.001517 \tabularnewline
M6 & -7.64246158228107 & 2.260646 & -3.3807 & 0.001573 & 0.000786 \tabularnewline
M7 & -3.61421218000356 & 2.49769 & -1.447 & 0.155314 & 0.077657 \tabularnewline
M8 & -16.6986202389138 & 2.270694 & -7.354 & 0 & 0 \tabularnewline
M9 & -15.4519614926944 & 2.264783 & -6.8227 & 0 & 0 \tabularnewline
M10 & -4.3902634927507 & 2.771358 & -1.5842 & 0.12066 & 0.06033 \tabularnewline
M11 & 5.74431082396985 & 2.379751 & 2.4138 & 0.020224 & 0.010112 \tabularnewline
t & 0.0569955207934599 & 0.034523 & 1.6509 & 0.106214 & 0.053107 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&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]51.0021787849945[/C][C]8.636948[/C][C]5.9051[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Cvn[/C][C]0.358521246109701[/C][C]0.060597[/C][C]5.9165[/C][C]1e-06[/C][C]0[/C][/ROW]
[ROW][C]Y3[/C][C]0.469136445952469[/C][C]0.097855[/C][C]4.7942[/C][C]2.1e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M1[/C][C]-11.5251626923923[/C][C]2.71509[/C][C]-4.2449[/C][C]0.000118[/C][C]5.9e-05[/C][/ROW]
[ROW][C]M2[/C][C]-13.1131829422958[/C][C]2.708507[/C][C]-4.8415[/C][C]1.8e-05[/C][C]9e-06[/C][/ROW]
[ROW][C]M3[/C][C]-11.0020240353219[/C][C]2.456632[/C][C]-4.4785[/C][C]5.7e-05[/C][C]2.8e-05[/C][/ROW]
[ROW][C]M4[/C][C]-0.0947099776184548[/C][C]2.313072[/C][C]-0.0409[/C][C]0.967533[/C][C]0.483767[/C][/ROW]
[ROW][C]M5[/C][C]-7.1883374189211[/C][C]2.284485[/C][C]-3.1466[/C][C]0.003034[/C][C]0.001517[/C][/ROW]
[ROW][C]M6[/C][C]-7.64246158228107[/C][C]2.260646[/C][C]-3.3807[/C][C]0.001573[/C][C]0.000786[/C][/ROW]
[ROW][C]M7[/C][C]-3.61421218000356[/C][C]2.49769[/C][C]-1.447[/C][C]0.155314[/C][C]0.077657[/C][/ROW]
[ROW][C]M8[/C][C]-16.6986202389138[/C][C]2.270694[/C][C]-7.354[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]-15.4519614926944[/C][C]2.264783[/C][C]-6.8227[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M10[/C][C]-4.3902634927507[/C][C]2.771358[/C][C]-1.5842[/C][C]0.12066[/C][C]0.06033[/C][/ROW]
[ROW][C]M11[/C][C]5.74431082396985[/C][C]2.379751[/C][C]2.4138[/C][C]0.020224[/C][C]0.010112[/C][/ROW]
[ROW][C]t[/C][C]0.0569955207934599[/C][C]0.034523[/C][C]1.6509[/C][C]0.106214[/C][C]0.053107[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&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)51.00217878499458.6369485.90511e-060
Cvn0.3585212461097010.0605975.91651e-060
Y30.4691364459524690.0978554.79422.1e-051e-05
M1-11.52516269239232.71509-4.24490.0001185.9e-05
M2-13.11318294229582.708507-4.84151.8e-059e-06
M3-11.00202403532192.456632-4.47855.7e-052.8e-05
M4-0.09470997761845482.313072-0.04090.9675330.483767
M5-7.18833741892112.284485-3.14660.0030340.001517
M6-7.642461582281072.260646-3.38070.0015730.000786
M7-3.614212180003562.49769-1.4470.1553140.077657
M8-16.69862023891382.270694-7.35400
M9-15.45196149269442.264783-6.822700
M10-4.39026349275072.771358-1.58420.120660.06033
M115.744310823969852.3797512.41380.0202240.010112
t0.05699552079345990.0345231.65090.1062140.053107







Multiple Linear Regression - Regression Statistics
Multiple R0.945402534098933
R-squared0.893785951480684
Adjusted R-squared0.858381268640912
F-TEST (value)25.2448512397531
F-TEST (DF numerator)14
F-TEST (DF denominator)42
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.33903353153817
Sum Squared Residuals468.264086838924

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.945402534098933 \tabularnewline
R-squared & 0.893785951480684 \tabularnewline
Adjusted R-squared & 0.858381268640912 \tabularnewline
F-TEST (value) & 25.2448512397531 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 5.55111512312578e-16 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.33903353153817 \tabularnewline
Sum Squared Residuals & 468.264086838924 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.945402534098933[/C][/ROW]
[ROW][C]R-squared[/C][C]0.893785951480684[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.858381268640912[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.2448512397531[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]5.55111512312578e-16[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.33903353153817[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]468.264086838924[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&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 R0.945402534098933
R-squared0.893785951480684
Adjusted R-squared0.858381268640912
F-TEST (value)25.2448512397531
F-TEST (DF numerator)14
F-TEST (DF denominator)42
p-value5.55111512312578e-16
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.33903353153817
Sum Squared Residuals468.264086838924







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1107.1105.4181252640661.68187473593373
2100.6102.509816931042-1.90981693104196
399.2101.662160281423-2.46216028142278
4108.4112.565827130682-4.16582713068201
5103100.9740190778212.02598092217896
699.899.8125430370881-0.0125430370881479
7115108.14213901376.85786098630009
890.892.402129044385-1.60212904438496
995.992.63477217968163.26522782031844
10114.4112.0674597910582.33254020894176
11108.2112.053195624074-3.85319562407355
12112.6108.9377368183103.66226318169039
13109.1107.0448970121062.05510298789429
14105102.4259656950362.57403430496448
15105107.482919351046-2.48291935104603
16118.5117.3430332378741.15696676212616
17103.7109.279245004234-5.57924500423379
18112.5109.2406376077773.25936239222302
19116.6119.587520301984-2.98752030198435
2096.6100.405635105212-3.80563510521238
21101.9105.407464601275-3.50746460127532
22116.5117.983539930475-1.48353993047498
23119.3118.9716414719940.328358528005538
24115.4115.0537068401470.346293159853243
25108.5110.793453025564-2.29345302556367
26111.5110.1457848497891.35421515021108
27108.8109.838968895344-1.03896889534417
28121.8117.2077157506594.59228424934067
29109.6112.474796283282-2.87479628328181
30112.2112.0658235980280.134176401972424
31119.6122.787624187645-3.18762418764521
32104.1103.3197045166890.780295483311092
33105.3104.9468104279040.353189572096055
34115119.465409399467-4.46540939946741
35124.1121.2022442125562.89775578744385
36116.8116.0778926445230.722107355477284
37107.5108.515010755665-1.01501075566537
38115.6112.1494307999973.45056920000292
39116.2110.8928891723115.30711082768863
40116.3117.673490426505-1.37349042650517
41119113.1820393568275.81796064317297
42111.9113.353209578720-1.45320957871975
43118.6117.5570723956081.04292760439208
44106.9105.0792857693431.82071423065660
45103.2103.1596276439270.0403723560733419
46118.6114.9835908789993.61640912100062
47118.7118.0729186913760.62708130862416
48102.8107.530663697021-4.73066369702092
49100.6101.028513942599-0.428513942598980
5094.9100.369001724137-5.46900172413651
5194.593.82306229987560.676937700124361
52102.9103.109933454280-0.209933454279638
5395.394.68990027783630.610099722163675
5492.594.4277861783875-1.92778617838755
55102.7104.425644101063-1.72564410106262
5691.588.69324556437042.80675443562964
5789.589.6513251472125-0.151325147212517

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 107.1 & 105.418125264066 & 1.68187473593373 \tabularnewline
2 & 100.6 & 102.509816931042 & -1.90981693104196 \tabularnewline
3 & 99.2 & 101.662160281423 & -2.46216028142278 \tabularnewline
4 & 108.4 & 112.565827130682 & -4.16582713068201 \tabularnewline
5 & 103 & 100.974019077821 & 2.02598092217896 \tabularnewline
6 & 99.8 & 99.8125430370881 & -0.0125430370881479 \tabularnewline
7 & 115 & 108.1421390137 & 6.85786098630009 \tabularnewline
8 & 90.8 & 92.402129044385 & -1.60212904438496 \tabularnewline
9 & 95.9 & 92.6347721796816 & 3.26522782031844 \tabularnewline
10 & 114.4 & 112.067459791058 & 2.33254020894176 \tabularnewline
11 & 108.2 & 112.053195624074 & -3.85319562407355 \tabularnewline
12 & 112.6 & 108.937736818310 & 3.66226318169039 \tabularnewline
13 & 109.1 & 107.044897012106 & 2.05510298789429 \tabularnewline
14 & 105 & 102.425965695036 & 2.57403430496448 \tabularnewline
15 & 105 & 107.482919351046 & -2.48291935104603 \tabularnewline
16 & 118.5 & 117.343033237874 & 1.15696676212616 \tabularnewline
17 & 103.7 & 109.279245004234 & -5.57924500423379 \tabularnewline
18 & 112.5 & 109.240637607777 & 3.25936239222302 \tabularnewline
19 & 116.6 & 119.587520301984 & -2.98752030198435 \tabularnewline
20 & 96.6 & 100.405635105212 & -3.80563510521238 \tabularnewline
21 & 101.9 & 105.407464601275 & -3.50746460127532 \tabularnewline
22 & 116.5 & 117.983539930475 & -1.48353993047498 \tabularnewline
23 & 119.3 & 118.971641471994 & 0.328358528005538 \tabularnewline
24 & 115.4 & 115.053706840147 & 0.346293159853243 \tabularnewline
25 & 108.5 & 110.793453025564 & -2.29345302556367 \tabularnewline
26 & 111.5 & 110.145784849789 & 1.35421515021108 \tabularnewline
27 & 108.8 & 109.838968895344 & -1.03896889534417 \tabularnewline
28 & 121.8 & 117.207715750659 & 4.59228424934067 \tabularnewline
29 & 109.6 & 112.474796283282 & -2.87479628328181 \tabularnewline
30 & 112.2 & 112.065823598028 & 0.134176401972424 \tabularnewline
31 & 119.6 & 122.787624187645 & -3.18762418764521 \tabularnewline
32 & 104.1 & 103.319704516689 & 0.780295483311092 \tabularnewline
33 & 105.3 & 104.946810427904 & 0.353189572096055 \tabularnewline
34 & 115 & 119.465409399467 & -4.46540939946741 \tabularnewline
35 & 124.1 & 121.202244212556 & 2.89775578744385 \tabularnewline
36 & 116.8 & 116.077892644523 & 0.722107355477284 \tabularnewline
37 & 107.5 & 108.515010755665 & -1.01501075566537 \tabularnewline
38 & 115.6 & 112.149430799997 & 3.45056920000292 \tabularnewline
39 & 116.2 & 110.892889172311 & 5.30711082768863 \tabularnewline
40 & 116.3 & 117.673490426505 & -1.37349042650517 \tabularnewline
41 & 119 & 113.182039356827 & 5.81796064317297 \tabularnewline
42 & 111.9 & 113.353209578720 & -1.45320957871975 \tabularnewline
43 & 118.6 & 117.557072395608 & 1.04292760439208 \tabularnewline
44 & 106.9 & 105.079285769343 & 1.82071423065660 \tabularnewline
45 & 103.2 & 103.159627643927 & 0.0403723560733419 \tabularnewline
46 & 118.6 & 114.983590878999 & 3.61640912100062 \tabularnewline
47 & 118.7 & 118.072918691376 & 0.62708130862416 \tabularnewline
48 & 102.8 & 107.530663697021 & -4.73066369702092 \tabularnewline
49 & 100.6 & 101.028513942599 & -0.428513942598980 \tabularnewline
50 & 94.9 & 100.369001724137 & -5.46900172413651 \tabularnewline
51 & 94.5 & 93.8230622998756 & 0.676937700124361 \tabularnewline
52 & 102.9 & 103.109933454280 & -0.209933454279638 \tabularnewline
53 & 95.3 & 94.6899002778363 & 0.610099722163675 \tabularnewline
54 & 92.5 & 94.4277861783875 & -1.92778617838755 \tabularnewline
55 & 102.7 & 104.425644101063 & -1.72564410106262 \tabularnewline
56 & 91.5 & 88.6932455643704 & 2.80675443562964 \tabularnewline
57 & 89.5 & 89.6513251472125 & -0.151325147212517 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&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]107.1[/C][C]105.418125264066[/C][C]1.68187473593373[/C][/ROW]
[ROW][C]2[/C][C]100.6[/C][C]102.509816931042[/C][C]-1.90981693104196[/C][/ROW]
[ROW][C]3[/C][C]99.2[/C][C]101.662160281423[/C][C]-2.46216028142278[/C][/ROW]
[ROW][C]4[/C][C]108.4[/C][C]112.565827130682[/C][C]-4.16582713068201[/C][/ROW]
[ROW][C]5[/C][C]103[/C][C]100.974019077821[/C][C]2.02598092217896[/C][/ROW]
[ROW][C]6[/C][C]99.8[/C][C]99.8125430370881[/C][C]-0.0125430370881479[/C][/ROW]
[ROW][C]7[/C][C]115[/C][C]108.1421390137[/C][C]6.85786098630009[/C][/ROW]
[ROW][C]8[/C][C]90.8[/C][C]92.402129044385[/C][C]-1.60212904438496[/C][/ROW]
[ROW][C]9[/C][C]95.9[/C][C]92.6347721796816[/C][C]3.26522782031844[/C][/ROW]
[ROW][C]10[/C][C]114.4[/C][C]112.067459791058[/C][C]2.33254020894176[/C][/ROW]
[ROW][C]11[/C][C]108.2[/C][C]112.053195624074[/C][C]-3.85319562407355[/C][/ROW]
[ROW][C]12[/C][C]112.6[/C][C]108.937736818310[/C][C]3.66226318169039[/C][/ROW]
[ROW][C]13[/C][C]109.1[/C][C]107.044897012106[/C][C]2.05510298789429[/C][/ROW]
[ROW][C]14[/C][C]105[/C][C]102.425965695036[/C][C]2.57403430496448[/C][/ROW]
[ROW][C]15[/C][C]105[/C][C]107.482919351046[/C][C]-2.48291935104603[/C][/ROW]
[ROW][C]16[/C][C]118.5[/C][C]117.343033237874[/C][C]1.15696676212616[/C][/ROW]
[ROW][C]17[/C][C]103.7[/C][C]109.279245004234[/C][C]-5.57924500423379[/C][/ROW]
[ROW][C]18[/C][C]112.5[/C][C]109.240637607777[/C][C]3.25936239222302[/C][/ROW]
[ROW][C]19[/C][C]116.6[/C][C]119.587520301984[/C][C]-2.98752030198435[/C][/ROW]
[ROW][C]20[/C][C]96.6[/C][C]100.405635105212[/C][C]-3.80563510521238[/C][/ROW]
[ROW][C]21[/C][C]101.9[/C][C]105.407464601275[/C][C]-3.50746460127532[/C][/ROW]
[ROW][C]22[/C][C]116.5[/C][C]117.983539930475[/C][C]-1.48353993047498[/C][/ROW]
[ROW][C]23[/C][C]119.3[/C][C]118.971641471994[/C][C]0.328358528005538[/C][/ROW]
[ROW][C]24[/C][C]115.4[/C][C]115.053706840147[/C][C]0.346293159853243[/C][/ROW]
[ROW][C]25[/C][C]108.5[/C][C]110.793453025564[/C][C]-2.29345302556367[/C][/ROW]
[ROW][C]26[/C][C]111.5[/C][C]110.145784849789[/C][C]1.35421515021108[/C][/ROW]
[ROW][C]27[/C][C]108.8[/C][C]109.838968895344[/C][C]-1.03896889534417[/C][/ROW]
[ROW][C]28[/C][C]121.8[/C][C]117.207715750659[/C][C]4.59228424934067[/C][/ROW]
[ROW][C]29[/C][C]109.6[/C][C]112.474796283282[/C][C]-2.87479628328181[/C][/ROW]
[ROW][C]30[/C][C]112.2[/C][C]112.065823598028[/C][C]0.134176401972424[/C][/ROW]
[ROW][C]31[/C][C]119.6[/C][C]122.787624187645[/C][C]-3.18762418764521[/C][/ROW]
[ROW][C]32[/C][C]104.1[/C][C]103.319704516689[/C][C]0.780295483311092[/C][/ROW]
[ROW][C]33[/C][C]105.3[/C][C]104.946810427904[/C][C]0.353189572096055[/C][/ROW]
[ROW][C]34[/C][C]115[/C][C]119.465409399467[/C][C]-4.46540939946741[/C][/ROW]
[ROW][C]35[/C][C]124.1[/C][C]121.202244212556[/C][C]2.89775578744385[/C][/ROW]
[ROW][C]36[/C][C]116.8[/C][C]116.077892644523[/C][C]0.722107355477284[/C][/ROW]
[ROW][C]37[/C][C]107.5[/C][C]108.515010755665[/C][C]-1.01501075566537[/C][/ROW]
[ROW][C]38[/C][C]115.6[/C][C]112.149430799997[/C][C]3.45056920000292[/C][/ROW]
[ROW][C]39[/C][C]116.2[/C][C]110.892889172311[/C][C]5.30711082768863[/C][/ROW]
[ROW][C]40[/C][C]116.3[/C][C]117.673490426505[/C][C]-1.37349042650517[/C][/ROW]
[ROW][C]41[/C][C]119[/C][C]113.182039356827[/C][C]5.81796064317297[/C][/ROW]
[ROW][C]42[/C][C]111.9[/C][C]113.353209578720[/C][C]-1.45320957871975[/C][/ROW]
[ROW][C]43[/C][C]118.6[/C][C]117.557072395608[/C][C]1.04292760439208[/C][/ROW]
[ROW][C]44[/C][C]106.9[/C][C]105.079285769343[/C][C]1.82071423065660[/C][/ROW]
[ROW][C]45[/C][C]103.2[/C][C]103.159627643927[/C][C]0.0403723560733419[/C][/ROW]
[ROW][C]46[/C][C]118.6[/C][C]114.983590878999[/C][C]3.61640912100062[/C][/ROW]
[ROW][C]47[/C][C]118.7[/C][C]118.072918691376[/C][C]0.62708130862416[/C][/ROW]
[ROW][C]48[/C][C]102.8[/C][C]107.530663697021[/C][C]-4.73066369702092[/C][/ROW]
[ROW][C]49[/C][C]100.6[/C][C]101.028513942599[/C][C]-0.428513942598980[/C][/ROW]
[ROW][C]50[/C][C]94.9[/C][C]100.369001724137[/C][C]-5.46900172413651[/C][/ROW]
[ROW][C]51[/C][C]94.5[/C][C]93.8230622998756[/C][C]0.676937700124361[/C][/ROW]
[ROW][C]52[/C][C]102.9[/C][C]103.109933454280[/C][C]-0.209933454279638[/C][/ROW]
[ROW][C]53[/C][C]95.3[/C][C]94.6899002778363[/C][C]0.610099722163675[/C][/ROW]
[ROW][C]54[/C][C]92.5[/C][C]94.4277861783875[/C][C]-1.92778617838755[/C][/ROW]
[ROW][C]55[/C][C]102.7[/C][C]104.425644101063[/C][C]-1.72564410106262[/C][/ROW]
[ROW][C]56[/C][C]91.5[/C][C]88.6932455643704[/C][C]2.80675443562964[/C][/ROW]
[ROW][C]57[/C][C]89.5[/C][C]89.6513251472125[/C][C]-0.151325147212517[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&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
1107.1105.4181252640661.68187473593373
2100.6102.509816931042-1.90981693104196
399.2101.662160281423-2.46216028142278
4108.4112.565827130682-4.16582713068201
5103100.9740190778212.02598092217896
699.899.8125430370881-0.0125430370881479
7115108.14213901376.85786098630009
890.892.402129044385-1.60212904438496
995.992.63477217968163.26522782031844
10114.4112.0674597910582.33254020894176
11108.2112.053195624074-3.85319562407355
12112.6108.9377368183103.66226318169039
13109.1107.0448970121062.05510298789429
14105102.4259656950362.57403430496448
15105107.482919351046-2.48291935104603
16118.5117.3430332378741.15696676212616
17103.7109.279245004234-5.57924500423379
18112.5109.2406376077773.25936239222302
19116.6119.587520301984-2.98752030198435
2096.6100.405635105212-3.80563510521238
21101.9105.407464601275-3.50746460127532
22116.5117.983539930475-1.48353993047498
23119.3118.9716414719940.328358528005538
24115.4115.0537068401470.346293159853243
25108.5110.793453025564-2.29345302556367
26111.5110.1457848497891.35421515021108
27108.8109.838968895344-1.03896889534417
28121.8117.2077157506594.59228424934067
29109.6112.474796283282-2.87479628328181
30112.2112.0658235980280.134176401972424
31119.6122.787624187645-3.18762418764521
32104.1103.3197045166890.780295483311092
33105.3104.9468104279040.353189572096055
34115119.465409399467-4.46540939946741
35124.1121.2022442125562.89775578744385
36116.8116.0778926445230.722107355477284
37107.5108.515010755665-1.01501075566537
38115.6112.1494307999973.45056920000292
39116.2110.8928891723115.30711082768863
40116.3117.673490426505-1.37349042650517
41119113.1820393568275.81796064317297
42111.9113.353209578720-1.45320957871975
43118.6117.5570723956081.04292760439208
44106.9105.0792857693431.82071423065660
45103.2103.1596276439270.0403723560733419
46118.6114.9835908789993.61640912100062
47118.7118.0729186913760.62708130862416
48102.8107.530663697021-4.73066369702092
49100.6101.028513942599-0.428513942598980
5094.9100.369001724137-5.46900172413651
5194.593.82306229987560.676937700124361
52102.9103.109933454280-0.209933454279638
5395.394.68990027783630.610099722163675
5492.594.4277861783875-1.92778617838755
55102.7104.425644101063-1.72564410106262
5691.588.69324556437042.80675443562964
5789.589.6513251472125-0.151325147212517







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.8738479639189470.2523040721621050.126152036081052
190.8744280913736190.2511438172527620.125571908626381
200.8040843047500840.3918313904998320.195915695249916
210.6986596131082890.6026807737834230.301340386891711
220.6278729812345830.7442540375308350.372127018765417
230.6396549826275190.7206900347449630.360345017372481
240.5857424073746480.8285151852507030.414257592625352
250.6243790618516580.7512418762966830.375620938148342
260.5720575313294740.8558849373410510.427942468670525
270.4885695328418290.9771390656836590.511430467158171
280.5901488678159140.8197022643681720.409851132184086
290.5941703524281880.8116592951436240.405829647571812
300.51288990533160.97422018933680.4871100946684
310.4705283979107710.9410567958215420.529471602089229
320.4088326673957180.8176653347914370.591167332604282
330.3304283058082910.6608566116165820.669571694191709
340.6603174410554270.6793651178891470.339682558944573
350.5679478311692960.8641043376614070.432052168830704
360.5253236981110240.9493526037779510.474676301888976
370.5698879327284650.860224134543070.430112067271535
380.7321494091883310.5357011816233380.267850590811669
390.6834625836398970.6330748327202060.316537416360103

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.873847963918947 & 0.252304072162105 & 0.126152036081052 \tabularnewline
19 & 0.874428091373619 & 0.251143817252762 & 0.125571908626381 \tabularnewline
20 & 0.804084304750084 & 0.391831390499832 & 0.195915695249916 \tabularnewline
21 & 0.698659613108289 & 0.602680773783423 & 0.301340386891711 \tabularnewline
22 & 0.627872981234583 & 0.744254037530835 & 0.372127018765417 \tabularnewline
23 & 0.639654982627519 & 0.720690034744963 & 0.360345017372481 \tabularnewline
24 & 0.585742407374648 & 0.828515185250703 & 0.414257592625352 \tabularnewline
25 & 0.624379061851658 & 0.751241876296683 & 0.375620938148342 \tabularnewline
26 & 0.572057531329474 & 0.855884937341051 & 0.427942468670525 \tabularnewline
27 & 0.488569532841829 & 0.977139065683659 & 0.511430467158171 \tabularnewline
28 & 0.590148867815914 & 0.819702264368172 & 0.409851132184086 \tabularnewline
29 & 0.594170352428188 & 0.811659295143624 & 0.405829647571812 \tabularnewline
30 & 0.5128899053316 & 0.9742201893368 & 0.4871100946684 \tabularnewline
31 & 0.470528397910771 & 0.941056795821542 & 0.529471602089229 \tabularnewline
32 & 0.408832667395718 & 0.817665334791437 & 0.591167332604282 \tabularnewline
33 & 0.330428305808291 & 0.660856611616582 & 0.669571694191709 \tabularnewline
34 & 0.660317441055427 & 0.679365117889147 & 0.339682558944573 \tabularnewline
35 & 0.567947831169296 & 0.864104337661407 & 0.432052168830704 \tabularnewline
36 & 0.525323698111024 & 0.949352603777951 & 0.474676301888976 \tabularnewline
37 & 0.569887932728465 & 0.86022413454307 & 0.430112067271535 \tabularnewline
38 & 0.732149409188331 & 0.535701181623338 & 0.267850590811669 \tabularnewline
39 & 0.683462583639897 & 0.633074832720206 & 0.316537416360103 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58244&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]18[/C][C]0.873847963918947[/C][C]0.252304072162105[/C][C]0.126152036081052[/C][/ROW]
[ROW][C]19[/C][C]0.874428091373619[/C][C]0.251143817252762[/C][C]0.125571908626381[/C][/ROW]
[ROW][C]20[/C][C]0.804084304750084[/C][C]0.391831390499832[/C][C]0.195915695249916[/C][/ROW]
[ROW][C]21[/C][C]0.698659613108289[/C][C]0.602680773783423[/C][C]0.301340386891711[/C][/ROW]
[ROW][C]22[/C][C]0.627872981234583[/C][C]0.744254037530835[/C][C]0.372127018765417[/C][/ROW]
[ROW][C]23[/C][C]0.639654982627519[/C][C]0.720690034744963[/C][C]0.360345017372481[/C][/ROW]
[ROW][C]24[/C][C]0.585742407374648[/C][C]0.828515185250703[/C][C]0.414257592625352[/C][/ROW]
[ROW][C]25[/C][C]0.624379061851658[/C][C]0.751241876296683[/C][C]0.375620938148342[/C][/ROW]
[ROW][C]26[/C][C]0.572057531329474[/C][C]0.855884937341051[/C][C]0.427942468670525[/C][/ROW]
[ROW][C]27[/C][C]0.488569532841829[/C][C]0.977139065683659[/C][C]0.511430467158171[/C][/ROW]
[ROW][C]28[/C][C]0.590148867815914[/C][C]0.819702264368172[/C][C]0.409851132184086[/C][/ROW]
[ROW][C]29[/C][C]0.594170352428188[/C][C]0.811659295143624[/C][C]0.405829647571812[/C][/ROW]
[ROW][C]30[/C][C]0.5128899053316[/C][C]0.9742201893368[/C][C]0.4871100946684[/C][/ROW]
[ROW][C]31[/C][C]0.470528397910771[/C][C]0.941056795821542[/C][C]0.529471602089229[/C][/ROW]
[ROW][C]32[/C][C]0.408832667395718[/C][C]0.817665334791437[/C][C]0.591167332604282[/C][/ROW]
[ROW][C]33[/C][C]0.330428305808291[/C][C]0.660856611616582[/C][C]0.669571694191709[/C][/ROW]
[ROW][C]34[/C][C]0.660317441055427[/C][C]0.679365117889147[/C][C]0.339682558944573[/C][/ROW]
[ROW][C]35[/C][C]0.567947831169296[/C][C]0.864104337661407[/C][C]0.432052168830704[/C][/ROW]
[ROW][C]36[/C][C]0.525323698111024[/C][C]0.949352603777951[/C][C]0.474676301888976[/C][/ROW]
[ROW][C]37[/C][C]0.569887932728465[/C][C]0.86022413454307[/C][C]0.430112067271535[/C][/ROW]
[ROW][C]38[/C][C]0.732149409188331[/C][C]0.535701181623338[/C][C]0.267850590811669[/C][/ROW]
[ROW][C]39[/C][C]0.683462583639897[/C][C]0.633074832720206[/C][C]0.316537416360103[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58244&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&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
180.8738479639189470.2523040721621050.126152036081052
190.8744280913736190.2511438172527620.125571908626381
200.8040843047500840.3918313904998320.195915695249916
210.6986596131082890.6026807737834230.301340386891711
220.6278729812345830.7442540375308350.372127018765417
230.6396549826275190.7206900347449630.360345017372481
240.5857424073746480.8285151852507030.414257592625352
250.6243790618516580.7512418762966830.375620938148342
260.5720575313294740.8558849373410510.427942468670525
270.4885695328418290.9771390656836590.511430467158171
280.5901488678159140.8197022643681720.409851132184086
290.5941703524281880.8116592951436240.405829647571812
300.51288990533160.97422018933680.4871100946684
310.4705283979107710.9410567958215420.529471602089229
320.4088326673957180.8176653347914370.591167332604282
330.3304283058082910.6608566116165820.669571694191709
340.6603174410554270.6793651178891470.339682558944573
350.5679478311692960.8641043376614070.432052168830704
360.5253236981110240.9493526037779510.474676301888976
370.5698879327284650.860224134543070.430112067271535
380.7321494091883310.5357011816233380.267850590811669
390.6834625836398970.6330748327202060.316537416360103







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=58244&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=58244&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58244&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):
Parameters (R input):
par1 = 1 ; par2 = Include Monthly 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')
}