Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 04:09:40 -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/t12587154657tzecmno3qiaysj.htm/, Retrieved Fri, 29 Mar 2024 00:11:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58033, Retrieved Fri, 29 Mar 2024 00:11:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131
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 14:14:11] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [WS 7: Multiple Re...] [2009-11-20 11:09:40] [ac86848d66148c9c4c9404e0c9a511eb] [Current]
Feedback Forum

Post a new message
Dataseries X:
113.6	123.06	83.4	79.8
112.9	123.39	113.6	83.4
104	120.28	112.9	113.6
109.9	115.33	104	112.9
99	110.4	109.9	104
106.3	114.49	99	109.9
128.9	132.03	106.3	99
111.1	123.16	128.9	106.3
102.9	118.82	111.1	128.9
130	128.32	102.9	111.1
87	112.24	130	102.9
87.5	104.53	87	130
117.6	132.57	87.5	87
103.4	122.52	117.6	87.5
110.8	131.8	103.4	117.6
112.6	124.55	110.8	103.4
102.5	120.96	112.6	110.8
112.4	122.6	102.5	112.6
135.6	145.52	112.4	102.5
105.1	118.57	135.6	112.4
127.7	134.25	105.1	135.6
137	136.7	127.7	105.1
91	121.37	137	127.7
90.5	111.63	91	137
122.4	134.42	90.5	91
123.3	137.65	122.4	90.5
124.3	137.86	123.3	122.4
120	119.77	124.3	123.3
118.1	130.69	120	124.3
119	128.28	118.1	120
142.7	147.45	119	118.1
123.6	128.42	142.7	119
129.6	136.9	123.6	142.7
151.6	143.95	129.6	123.6
110.4	135.64	151.6	129.6
99.2	122.48	110.4	151.6
130.5	136.83	99.2	110.4
136.2	153.04	130.5	99.2
129.7	142.71	136.2	130.5
128	123.46	129.7	136.2
121.6	144.37	128	129.7
135.8	146.15	121.6	128
143.8	147.61	135.8	121.6
147.5	158.51	143.8	135.8
136.2	147.4	147.5	143.8
156.6	165.05	136.2	147.5
123.3	154.64	156.6	136.2
104.5	126.2	123.3	156.6
139.8	157.36	104.5	123.3
136.5	154.15	139.8	104.5
112.1	123.21	136.5	139.8
118.5	113.07	112.1	136.5
94.4	110.45	118.5	112.1
102.3	113.57	94.4	118.5
111.4	122.44	102.3	94.4
99.2	114.93	111.4	102.3
87.8	111.85	99.2	111.4
115.8	126.04	87.8	99.2




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=58033&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=58033&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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
Y[t] = -37.2940689141077 + 0.73903374269513X[t] + 0.15933358934725Y1[t] + 0.234922754766544Y2[t] + 25.8124488053722M1[t] + 18.8216956913314M2[t] + 10.7186918552078M3[t] + 22.8169458116256M4[t] + 10.4075301409941M5[t] + 18.6651780109597M6[t] + 26.9840623171739M7[t] + 14.8732723526244M8[t] + 12.0424536035340M9[t] + 29.6388908477154M10[t] -5.40627929365437M11[t] -0.111511471449605t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  -37.2940689141077 +  0.73903374269513X[t] +  0.15933358934725Y1[t] +  0.234922754766544Y2[t] +  25.8124488053722M1[t] +  18.8216956913314M2[t] +  10.7186918552078M3[t] +  22.8169458116256M4[t] +  10.4075301409941M5[t] +  18.6651780109597M6[t] +  26.9840623171739M7[t] +  14.8732723526244M8[t] +  12.0424536035340M9[t] +  29.6388908477154M10[t] -5.40627929365437M11[t] -0.111511471449605t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58033&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  -37.2940689141077 +  0.73903374269513X[t] +  0.15933358934725Y1[t] +  0.234922754766544Y2[t] +  25.8124488053722M1[t] +  18.8216956913314M2[t] +  10.7186918552078M3[t] +  22.8169458116256M4[t] +  10.4075301409941M5[t] +  18.6651780109597M6[t] +  26.9840623171739M7[t] +  14.8732723526244M8[t] +  12.0424536035340M9[t] +  29.6388908477154M10[t] -5.40627929365437M11[t] -0.111511471449605t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58033&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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
Y[t] = -37.2940689141077 + 0.73903374269513X[t] + 0.15933358934725Y1[t] + 0.234922754766544Y2[t] + 25.8124488053722M1[t] + 18.8216956913314M2[t] + 10.7186918552078M3[t] + 22.8169458116256M4[t] + 10.4075301409941M5[t] + 18.6651780109597M6[t] + 26.9840623171739M7[t] + 14.8732723526244M8[t] + 12.0424536035340M9[t] + 29.6388908477154M10[t] -5.40627929365437M11[t] -0.111511471449605t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)-37.29406891410777.498728-4.97341.2e-056e-06
X0.739033742695130.0751029.840400
Y10.159333589347250.0751922.1190.0400450.020022
Y20.2349227547665440.0883722.65830.0110620.005531
M125.81244880537225.4390574.74582.4e-051.2e-05
M218.82169569133146.6319152.8380.0069620.003481
M310.71869185520784.0424112.65160.0112540.005627
M422.81694581162563.7286296.119400
M510.40753014099414.2597192.44320.0188410.00942
M618.66517801095973.8899514.79832e-051e-05
M726.98406231717395.3303715.06239e-064e-06
M814.87327235262445.0692712.9340.0054020.002701
M912.04245360353403.4505273.490.0011490.000574
M1029.63889084771544.7125456.289400
M11-5.406279293654375.118222-1.05630.2968780.148439
t-0.1115114714496050.038-2.93450.0053950.002697

\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) & -37.2940689141077 & 7.498728 & -4.9734 & 1.2e-05 & 6e-06 \tabularnewline
X & 0.73903374269513 & 0.075102 & 9.8404 & 0 & 0 \tabularnewline
Y1 & 0.15933358934725 & 0.075192 & 2.119 & 0.040045 & 0.020022 \tabularnewline
Y2 & 0.234922754766544 & 0.088372 & 2.6583 & 0.011062 & 0.005531 \tabularnewline
M1 & 25.8124488053722 & 5.439057 & 4.7458 & 2.4e-05 & 1.2e-05 \tabularnewline
M2 & 18.8216956913314 & 6.631915 & 2.838 & 0.006962 & 0.003481 \tabularnewline
M3 & 10.7186918552078 & 4.042411 & 2.6516 & 0.011254 & 0.005627 \tabularnewline
M4 & 22.8169458116256 & 3.728629 & 6.1194 & 0 & 0 \tabularnewline
M5 & 10.4075301409941 & 4.259719 & 2.4432 & 0.018841 & 0.00942 \tabularnewline
M6 & 18.6651780109597 & 3.889951 & 4.7983 & 2e-05 & 1e-05 \tabularnewline
M7 & 26.9840623171739 & 5.330371 & 5.0623 & 9e-06 & 4e-06 \tabularnewline
M8 & 14.8732723526244 & 5.069271 & 2.934 & 0.005402 & 0.002701 \tabularnewline
M9 & 12.0424536035340 & 3.450527 & 3.49 & 0.001149 & 0.000574 \tabularnewline
M10 & 29.6388908477154 & 4.712545 & 6.2894 & 0 & 0 \tabularnewline
M11 & -5.40627929365437 & 5.118222 & -1.0563 & 0.296878 & 0.148439 \tabularnewline
t & -0.111511471449605 & 0.038 & -2.9345 & 0.005395 & 0.002697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58033&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]-37.2940689141077[/C][C]7.498728[/C][C]-4.9734[/C][C]1.2e-05[/C][C]6e-06[/C][/ROW]
[ROW][C]X[/C][C]0.73903374269513[/C][C]0.075102[/C][C]9.8404[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Y1[/C][C]0.15933358934725[/C][C]0.075192[/C][C]2.119[/C][C]0.040045[/C][C]0.020022[/C][/ROW]
[ROW][C]Y2[/C][C]0.234922754766544[/C][C]0.088372[/C][C]2.6583[/C][C]0.011062[/C][C]0.005531[/C][/ROW]
[ROW][C]M1[/C][C]25.8124488053722[/C][C]5.439057[/C][C]4.7458[/C][C]2.4e-05[/C][C]1.2e-05[/C][/ROW]
[ROW][C]M2[/C][C]18.8216956913314[/C][C]6.631915[/C][C]2.838[/C][C]0.006962[/C][C]0.003481[/C][/ROW]
[ROW][C]M3[/C][C]10.7186918552078[/C][C]4.042411[/C][C]2.6516[/C][C]0.011254[/C][C]0.005627[/C][/ROW]
[ROW][C]M4[/C][C]22.8169458116256[/C][C]3.728629[/C][C]6.1194[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M5[/C][C]10.4075301409941[/C][C]4.259719[/C][C]2.4432[/C][C]0.018841[/C][C]0.00942[/C][/ROW]
[ROW][C]M6[/C][C]18.6651780109597[/C][C]3.889951[/C][C]4.7983[/C][C]2e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M7[/C][C]26.9840623171739[/C][C]5.330371[/C][C]5.0623[/C][C]9e-06[/C][C]4e-06[/C][/ROW]
[ROW][C]M8[/C][C]14.8732723526244[/C][C]5.069271[/C][C]2.934[/C][C]0.005402[/C][C]0.002701[/C][/ROW]
[ROW][C]M9[/C][C]12.0424536035340[/C][C]3.450527[/C][C]3.49[/C][C]0.001149[/C][C]0.000574[/C][/ROW]
[ROW][C]M10[/C][C]29.6388908477154[/C][C]4.712545[/C][C]6.2894[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M11[/C][C]-5.40627929365437[/C][C]5.118222[/C][C]-1.0563[/C][C]0.296878[/C][C]0.148439[/C][/ROW]
[ROW][C]t[/C][C]-0.111511471449605[/C][C]0.038[/C][C]-2.9345[/C][C]0.005395[/C][C]0.002697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58033&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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)-37.29406891410777.498728-4.97341.2e-056e-06
X0.739033742695130.0751029.840400
Y10.159333589347250.0751922.1190.0400450.020022
Y20.2349227547665440.0883722.65830.0110620.005531
M125.81244880537225.4390574.74582.4e-051.2e-05
M218.82169569133146.6319152.8380.0069620.003481
M310.71869185520784.0424112.65160.0112540.005627
M422.81694581162563.7286296.119400
M510.40753014099414.2597192.44320.0188410.00942
M618.66517801095973.8899514.79832e-051e-05
M726.98406231717395.3303715.06239e-064e-06
M814.87327235262445.0692712.9340.0054020.002701
M912.04245360353403.4505273.490.0011490.000574
M1029.63889084771544.7125456.289400
M11-5.406279293654375.118222-1.05630.2968780.148439
t-0.1115114714496050.038-2.93450.0053950.002697







Multiple Linear Regression - Regression Statistics
Multiple R0.97835470633545
R-squared0.957177931408722
Adjusted R-squared0.941884335483265
F-TEST (value)62.5868458977316
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.06605318956999
Sum Squared Residuals694.377118697318

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.97835470633545 \tabularnewline
R-squared & 0.957177931408722 \tabularnewline
Adjusted R-squared & 0.941884335483265 \tabularnewline
F-TEST (value) & 62.5868458977316 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.06605318956999 \tabularnewline
Sum Squared Residuals & 694.377118697318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58033&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.97835470633545[/C][/ROW]
[ROW][C]R-squared[/C][C]0.957177931408722[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.941884335483265[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]62.5868458977316[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]42[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.06605318956999[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]694.377118697318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58033&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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 R0.97835470633545
R-squared0.957177931408722
Adjusted R-squared0.941884335483265
F-TEST (value)62.5868458977316
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation4.06605318956999
Sum Squared Residuals694.377118697318







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1113.6111.3876179778082.21238202219182
2112.9110.1868308428542.71316915714603
3104106.657054276905-2.65705427690547
4109.9113.403064862006-3.50306486200567
59996.08795702816412.91204297183586
6106.3106.906049563541-0.606049563540803
7128.9126.6785514204582.22144857954247
8111.1113.216895915796-2.11689591579626
9102.9109.540275619302-6.6402756193022
10130128.5578614801461.44213851985418
118783.90909096701363.09090903298642
1287.583.02097094528054.4790290547195
13117.6119.422402764087-1.82240276408679
14103.4109.806251481246-6.40625148124578
15110.8113.258607255625-2.45860725562543
16112.6117.730520549539-5.13052054953865
17102.5104.581691117279-2.08169111727950
18112.4112.753434559988-0.353434559988104
19135.6137.104143488721-1.50414348872073
20105.1110.987157232133-5.88715723213283
21127.7120.2234095325457.47659046745484
22137135.9547630737481.04523692625173
239196.259750824066-5.25975082406591
2490.589.21176650177551.28823349822453
25122.4120.8691693177851.53083068221457
26123.3121.1192638439942.1807361560057
27124.3120.6973817298523.60261827014765
28120119.6857678781030.314232121897236
29118.1114.7848775268263.31512247317414
30119119.837040940191-0.83704094019072
31142.7141.9087376187770.791262381223005
32123.6119.6102606061093.98973939389073
33129.6125.4593342550594.14066574494139
34151.6144.6234248338346.97657516616645
35110.4108.2402483134562.15975168654358
3699.2102.413088805551-3.21308880555058
37130.5127.2558066500783.24419334992250
38136.2134.4892855268591.71071447314124
39129.7126.9018353407172.79816465928299
40128124.9655696502163.03443034978389
41121.6126.099973060017-4.49997306001733
42135.8134.1424858656051.65751413439484
43143.8144.187879302930-0.387879302929657
44147.5144.6316174947702.86838250522960
45136.2135.9475387116050.252461288395305
46156.6165.545154675918-8.94515467591782
47123.3123.2909098954640.00909010453591619
48104.5107.054173747393-2.55417374739345
49139.8144.965003290242-5.16500329024210
50136.5136.698368305047-0.198368305047200
51112.1113.385121396900-1.28512139689974
52118.5113.2150770601375.28492293986319
5394.494.04550126771320.354498732286814
54102.3102.1609890706750.139010929324792
55111.4112.520688169115-1.12068816911509
5699.298.05406875119121.14593124880876
5787.893.0294418814893-5.22944188148933
58115.8116.318795936355-0.518795936354544

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 113.6 & 111.387617977808 & 2.21238202219182 \tabularnewline
2 & 112.9 & 110.186830842854 & 2.71316915714603 \tabularnewline
3 & 104 & 106.657054276905 & -2.65705427690547 \tabularnewline
4 & 109.9 & 113.403064862006 & -3.50306486200567 \tabularnewline
5 & 99 & 96.0879570281641 & 2.91204297183586 \tabularnewline
6 & 106.3 & 106.906049563541 & -0.606049563540803 \tabularnewline
7 & 128.9 & 126.678551420458 & 2.22144857954247 \tabularnewline
8 & 111.1 & 113.216895915796 & -2.11689591579626 \tabularnewline
9 & 102.9 & 109.540275619302 & -6.6402756193022 \tabularnewline
10 & 130 & 128.557861480146 & 1.44213851985418 \tabularnewline
11 & 87 & 83.9090909670136 & 3.09090903298642 \tabularnewline
12 & 87.5 & 83.0209709452805 & 4.4790290547195 \tabularnewline
13 & 117.6 & 119.422402764087 & -1.82240276408679 \tabularnewline
14 & 103.4 & 109.806251481246 & -6.40625148124578 \tabularnewline
15 & 110.8 & 113.258607255625 & -2.45860725562543 \tabularnewline
16 & 112.6 & 117.730520549539 & -5.13052054953865 \tabularnewline
17 & 102.5 & 104.581691117279 & -2.08169111727950 \tabularnewline
18 & 112.4 & 112.753434559988 & -0.353434559988104 \tabularnewline
19 & 135.6 & 137.104143488721 & -1.50414348872073 \tabularnewline
20 & 105.1 & 110.987157232133 & -5.88715723213283 \tabularnewline
21 & 127.7 & 120.223409532545 & 7.47659046745484 \tabularnewline
22 & 137 & 135.954763073748 & 1.04523692625173 \tabularnewline
23 & 91 & 96.259750824066 & -5.25975082406591 \tabularnewline
24 & 90.5 & 89.2117665017755 & 1.28823349822453 \tabularnewline
25 & 122.4 & 120.869169317785 & 1.53083068221457 \tabularnewline
26 & 123.3 & 121.119263843994 & 2.1807361560057 \tabularnewline
27 & 124.3 & 120.697381729852 & 3.60261827014765 \tabularnewline
28 & 120 & 119.685767878103 & 0.314232121897236 \tabularnewline
29 & 118.1 & 114.784877526826 & 3.31512247317414 \tabularnewline
30 & 119 & 119.837040940191 & -0.83704094019072 \tabularnewline
31 & 142.7 & 141.908737618777 & 0.791262381223005 \tabularnewline
32 & 123.6 & 119.610260606109 & 3.98973939389073 \tabularnewline
33 & 129.6 & 125.459334255059 & 4.14066574494139 \tabularnewline
34 & 151.6 & 144.623424833834 & 6.97657516616645 \tabularnewline
35 & 110.4 & 108.240248313456 & 2.15975168654358 \tabularnewline
36 & 99.2 & 102.413088805551 & -3.21308880555058 \tabularnewline
37 & 130.5 & 127.255806650078 & 3.24419334992250 \tabularnewline
38 & 136.2 & 134.489285526859 & 1.71071447314124 \tabularnewline
39 & 129.7 & 126.901835340717 & 2.79816465928299 \tabularnewline
40 & 128 & 124.965569650216 & 3.03443034978389 \tabularnewline
41 & 121.6 & 126.099973060017 & -4.49997306001733 \tabularnewline
42 & 135.8 & 134.142485865605 & 1.65751413439484 \tabularnewline
43 & 143.8 & 144.187879302930 & -0.387879302929657 \tabularnewline
44 & 147.5 & 144.631617494770 & 2.86838250522960 \tabularnewline
45 & 136.2 & 135.947538711605 & 0.252461288395305 \tabularnewline
46 & 156.6 & 165.545154675918 & -8.94515467591782 \tabularnewline
47 & 123.3 & 123.290909895464 & 0.00909010453591619 \tabularnewline
48 & 104.5 & 107.054173747393 & -2.55417374739345 \tabularnewline
49 & 139.8 & 144.965003290242 & -5.16500329024210 \tabularnewline
50 & 136.5 & 136.698368305047 & -0.198368305047200 \tabularnewline
51 & 112.1 & 113.385121396900 & -1.28512139689974 \tabularnewline
52 & 118.5 & 113.215077060137 & 5.28492293986319 \tabularnewline
53 & 94.4 & 94.0455012677132 & 0.354498732286814 \tabularnewline
54 & 102.3 & 102.160989070675 & 0.139010929324792 \tabularnewline
55 & 111.4 & 112.520688169115 & -1.12068816911509 \tabularnewline
56 & 99.2 & 98.0540687511912 & 1.14593124880876 \tabularnewline
57 & 87.8 & 93.0294418814893 & -5.22944188148933 \tabularnewline
58 & 115.8 & 116.318795936355 & -0.518795936354544 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58033&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]113.6[/C][C]111.387617977808[/C][C]2.21238202219182[/C][/ROW]
[ROW][C]2[/C][C]112.9[/C][C]110.186830842854[/C][C]2.71316915714603[/C][/ROW]
[ROW][C]3[/C][C]104[/C][C]106.657054276905[/C][C]-2.65705427690547[/C][/ROW]
[ROW][C]4[/C][C]109.9[/C][C]113.403064862006[/C][C]-3.50306486200567[/C][/ROW]
[ROW][C]5[/C][C]99[/C][C]96.0879570281641[/C][C]2.91204297183586[/C][/ROW]
[ROW][C]6[/C][C]106.3[/C][C]106.906049563541[/C][C]-0.606049563540803[/C][/ROW]
[ROW][C]7[/C][C]128.9[/C][C]126.678551420458[/C][C]2.22144857954247[/C][/ROW]
[ROW][C]8[/C][C]111.1[/C][C]113.216895915796[/C][C]-2.11689591579626[/C][/ROW]
[ROW][C]9[/C][C]102.9[/C][C]109.540275619302[/C][C]-6.6402756193022[/C][/ROW]
[ROW][C]10[/C][C]130[/C][C]128.557861480146[/C][C]1.44213851985418[/C][/ROW]
[ROW][C]11[/C][C]87[/C][C]83.9090909670136[/C][C]3.09090903298642[/C][/ROW]
[ROW][C]12[/C][C]87.5[/C][C]83.0209709452805[/C][C]4.4790290547195[/C][/ROW]
[ROW][C]13[/C][C]117.6[/C][C]119.422402764087[/C][C]-1.82240276408679[/C][/ROW]
[ROW][C]14[/C][C]103.4[/C][C]109.806251481246[/C][C]-6.40625148124578[/C][/ROW]
[ROW][C]15[/C][C]110.8[/C][C]113.258607255625[/C][C]-2.45860725562543[/C][/ROW]
[ROW][C]16[/C][C]112.6[/C][C]117.730520549539[/C][C]-5.13052054953865[/C][/ROW]
[ROW][C]17[/C][C]102.5[/C][C]104.581691117279[/C][C]-2.08169111727950[/C][/ROW]
[ROW][C]18[/C][C]112.4[/C][C]112.753434559988[/C][C]-0.353434559988104[/C][/ROW]
[ROW][C]19[/C][C]135.6[/C][C]137.104143488721[/C][C]-1.50414348872073[/C][/ROW]
[ROW][C]20[/C][C]105.1[/C][C]110.987157232133[/C][C]-5.88715723213283[/C][/ROW]
[ROW][C]21[/C][C]127.7[/C][C]120.223409532545[/C][C]7.47659046745484[/C][/ROW]
[ROW][C]22[/C][C]137[/C][C]135.954763073748[/C][C]1.04523692625173[/C][/ROW]
[ROW][C]23[/C][C]91[/C][C]96.259750824066[/C][C]-5.25975082406591[/C][/ROW]
[ROW][C]24[/C][C]90.5[/C][C]89.2117665017755[/C][C]1.28823349822453[/C][/ROW]
[ROW][C]25[/C][C]122.4[/C][C]120.869169317785[/C][C]1.53083068221457[/C][/ROW]
[ROW][C]26[/C][C]123.3[/C][C]121.119263843994[/C][C]2.1807361560057[/C][/ROW]
[ROW][C]27[/C][C]124.3[/C][C]120.697381729852[/C][C]3.60261827014765[/C][/ROW]
[ROW][C]28[/C][C]120[/C][C]119.685767878103[/C][C]0.314232121897236[/C][/ROW]
[ROW][C]29[/C][C]118.1[/C][C]114.784877526826[/C][C]3.31512247317414[/C][/ROW]
[ROW][C]30[/C][C]119[/C][C]119.837040940191[/C][C]-0.83704094019072[/C][/ROW]
[ROW][C]31[/C][C]142.7[/C][C]141.908737618777[/C][C]0.791262381223005[/C][/ROW]
[ROW][C]32[/C][C]123.6[/C][C]119.610260606109[/C][C]3.98973939389073[/C][/ROW]
[ROW][C]33[/C][C]129.6[/C][C]125.459334255059[/C][C]4.14066574494139[/C][/ROW]
[ROW][C]34[/C][C]151.6[/C][C]144.623424833834[/C][C]6.97657516616645[/C][/ROW]
[ROW][C]35[/C][C]110.4[/C][C]108.240248313456[/C][C]2.15975168654358[/C][/ROW]
[ROW][C]36[/C][C]99.2[/C][C]102.413088805551[/C][C]-3.21308880555058[/C][/ROW]
[ROW][C]37[/C][C]130.5[/C][C]127.255806650078[/C][C]3.24419334992250[/C][/ROW]
[ROW][C]38[/C][C]136.2[/C][C]134.489285526859[/C][C]1.71071447314124[/C][/ROW]
[ROW][C]39[/C][C]129.7[/C][C]126.901835340717[/C][C]2.79816465928299[/C][/ROW]
[ROW][C]40[/C][C]128[/C][C]124.965569650216[/C][C]3.03443034978389[/C][/ROW]
[ROW][C]41[/C][C]121.6[/C][C]126.099973060017[/C][C]-4.49997306001733[/C][/ROW]
[ROW][C]42[/C][C]135.8[/C][C]134.142485865605[/C][C]1.65751413439484[/C][/ROW]
[ROW][C]43[/C][C]143.8[/C][C]144.187879302930[/C][C]-0.387879302929657[/C][/ROW]
[ROW][C]44[/C][C]147.5[/C][C]144.631617494770[/C][C]2.86838250522960[/C][/ROW]
[ROW][C]45[/C][C]136.2[/C][C]135.947538711605[/C][C]0.252461288395305[/C][/ROW]
[ROW][C]46[/C][C]156.6[/C][C]165.545154675918[/C][C]-8.94515467591782[/C][/ROW]
[ROW][C]47[/C][C]123.3[/C][C]123.290909895464[/C][C]0.00909010453591619[/C][/ROW]
[ROW][C]48[/C][C]104.5[/C][C]107.054173747393[/C][C]-2.55417374739345[/C][/ROW]
[ROW][C]49[/C][C]139.8[/C][C]144.965003290242[/C][C]-5.16500329024210[/C][/ROW]
[ROW][C]50[/C][C]136.5[/C][C]136.698368305047[/C][C]-0.198368305047200[/C][/ROW]
[ROW][C]51[/C][C]112.1[/C][C]113.385121396900[/C][C]-1.28512139689974[/C][/ROW]
[ROW][C]52[/C][C]118.5[/C][C]113.215077060137[/C][C]5.28492293986319[/C][/ROW]
[ROW][C]53[/C][C]94.4[/C][C]94.0455012677132[/C][C]0.354498732286814[/C][/ROW]
[ROW][C]54[/C][C]102.3[/C][C]102.160989070675[/C][C]0.139010929324792[/C][/ROW]
[ROW][C]55[/C][C]111.4[/C][C]112.520688169115[/C][C]-1.12068816911509[/C][/ROW]
[ROW][C]56[/C][C]99.2[/C][C]98.0540687511912[/C][C]1.14593124880876[/C][/ROW]
[ROW][C]57[/C][C]87.8[/C][C]93.0294418814893[/C][C]-5.22944188148933[/C][/ROW]
[ROW][C]58[/C][C]115.8[/C][C]116.318795936355[/C][C]-0.518795936354544[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58033&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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
1113.6111.3876179778082.21238202219182
2112.9110.1868308428542.71316915714603
3104106.657054276905-2.65705427690547
4109.9113.403064862006-3.50306486200567
59996.08795702816412.91204297183586
6106.3106.906049563541-0.606049563540803
7128.9126.6785514204582.22144857954247
8111.1113.216895915796-2.11689591579626
9102.9109.540275619302-6.6402756193022
10130128.5578614801461.44213851985418
118783.90909096701363.09090903298642
1287.583.02097094528054.4790290547195
13117.6119.422402764087-1.82240276408679
14103.4109.806251481246-6.40625148124578
15110.8113.258607255625-2.45860725562543
16112.6117.730520549539-5.13052054953865
17102.5104.581691117279-2.08169111727950
18112.4112.753434559988-0.353434559988104
19135.6137.104143488721-1.50414348872073
20105.1110.987157232133-5.88715723213283
21127.7120.2234095325457.47659046745484
22137135.9547630737481.04523692625173
239196.259750824066-5.25975082406591
2490.589.21176650177551.28823349822453
25122.4120.8691693177851.53083068221457
26123.3121.1192638439942.1807361560057
27124.3120.6973817298523.60261827014765
28120119.6857678781030.314232121897236
29118.1114.7848775268263.31512247317414
30119119.837040940191-0.83704094019072
31142.7141.9087376187770.791262381223005
32123.6119.6102606061093.98973939389073
33129.6125.4593342550594.14066574494139
34151.6144.6234248338346.97657516616645
35110.4108.2402483134562.15975168654358
3699.2102.413088805551-3.21308880555058
37130.5127.2558066500783.24419334992250
38136.2134.4892855268591.71071447314124
39129.7126.9018353407172.79816465928299
40128124.9655696502163.03443034978389
41121.6126.099973060017-4.49997306001733
42135.8134.1424858656051.65751413439484
43143.8144.187879302930-0.387879302929657
44147.5144.6316174947702.86838250522960
45136.2135.9475387116050.252461288395305
46156.6165.545154675918-8.94515467591782
47123.3123.2909098954640.00909010453591619
48104.5107.054173747393-2.55417374739345
49139.8144.965003290242-5.16500329024210
50136.5136.698368305047-0.198368305047200
51112.1113.385121396900-1.28512139689974
52118.5113.2150770601375.28492293986319
5394.494.04550126771320.354498732286814
54102.3102.1609890706750.139010929324792
55111.4112.520688169115-1.12068816911509
5699.298.05406875119121.14593124880876
5787.893.0294418814893-5.22944188148933
58115.8116.318795936355-0.518795936354544







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.1195946199447990.2391892398895980.880405380055201
200.1730436417027310.3460872834054610.82695635829727
210.7164517433445770.5670965133108460.283548256655423
220.7139729015127120.5720541969745760.286027098487288
230.7532405162702240.4935189674595510.246759483729776
240.6464945009815020.7070109980369970.353505499018498
250.6426185439352890.7147629121294220.357381456064711
260.5970153039846370.8059693920307260.402984696015363
270.66968919847020.66062160305960.3303108015298
280.8462185856599940.3075628286800130.153781414340006
290.7860719178420320.4278561643159350.213928082157968
300.8135223711078440.3729552577843120.186477628892156
310.7273207977871650.545358404425670.272679202212835
320.7934810370019640.4130379259960730.206518962998036
330.8256172330661760.3487655338676480.174382766933824
340.8380069281672710.3239861436654580.161993071832729
350.7465447983726330.5069104032547350.253455201627367
360.6911527981546310.6176944036907380.308847201845369
370.651102394331120.697795211337760.34889760566888
380.6469606542166480.7060786915667040.353039345783352
390.5384009542967480.9231980914065040.461599045703252

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.119594619944799 & 0.239189239889598 & 0.880405380055201 \tabularnewline
20 & 0.173043641702731 & 0.346087283405461 & 0.82695635829727 \tabularnewline
21 & 0.716451743344577 & 0.567096513310846 & 0.283548256655423 \tabularnewline
22 & 0.713972901512712 & 0.572054196974576 & 0.286027098487288 \tabularnewline
23 & 0.753240516270224 & 0.493518967459551 & 0.246759483729776 \tabularnewline
24 & 0.646494500981502 & 0.707010998036997 & 0.353505499018498 \tabularnewline
25 & 0.642618543935289 & 0.714762912129422 & 0.357381456064711 \tabularnewline
26 & 0.597015303984637 & 0.805969392030726 & 0.402984696015363 \tabularnewline
27 & 0.6696891984702 & 0.6606216030596 & 0.3303108015298 \tabularnewline
28 & 0.846218585659994 & 0.307562828680013 & 0.153781414340006 \tabularnewline
29 & 0.786071917842032 & 0.427856164315935 & 0.213928082157968 \tabularnewline
30 & 0.813522371107844 & 0.372955257784312 & 0.186477628892156 \tabularnewline
31 & 0.727320797787165 & 0.54535840442567 & 0.272679202212835 \tabularnewline
32 & 0.793481037001964 & 0.413037925996073 & 0.206518962998036 \tabularnewline
33 & 0.825617233066176 & 0.348765533867648 & 0.174382766933824 \tabularnewline
34 & 0.838006928167271 & 0.323986143665458 & 0.161993071832729 \tabularnewline
35 & 0.746544798372633 & 0.506910403254735 & 0.253455201627367 \tabularnewline
36 & 0.691152798154631 & 0.617694403690738 & 0.308847201845369 \tabularnewline
37 & 0.65110239433112 & 0.69779521133776 & 0.34889760566888 \tabularnewline
38 & 0.646960654216648 & 0.706078691566704 & 0.353039345783352 \tabularnewline
39 & 0.538400954296748 & 0.923198091406504 & 0.461599045703252 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58033&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]19[/C][C]0.119594619944799[/C][C]0.239189239889598[/C][C]0.880405380055201[/C][/ROW]
[ROW][C]20[/C][C]0.173043641702731[/C][C]0.346087283405461[/C][C]0.82695635829727[/C][/ROW]
[ROW][C]21[/C][C]0.716451743344577[/C][C]0.567096513310846[/C][C]0.283548256655423[/C][/ROW]
[ROW][C]22[/C][C]0.713972901512712[/C][C]0.572054196974576[/C][C]0.286027098487288[/C][/ROW]
[ROW][C]23[/C][C]0.753240516270224[/C][C]0.493518967459551[/C][C]0.246759483729776[/C][/ROW]
[ROW][C]24[/C][C]0.646494500981502[/C][C]0.707010998036997[/C][C]0.353505499018498[/C][/ROW]
[ROW][C]25[/C][C]0.642618543935289[/C][C]0.714762912129422[/C][C]0.357381456064711[/C][/ROW]
[ROW][C]26[/C][C]0.597015303984637[/C][C]0.805969392030726[/C][C]0.402984696015363[/C][/ROW]
[ROW][C]27[/C][C]0.6696891984702[/C][C]0.6606216030596[/C][C]0.3303108015298[/C][/ROW]
[ROW][C]28[/C][C]0.846218585659994[/C][C]0.307562828680013[/C][C]0.153781414340006[/C][/ROW]
[ROW][C]29[/C][C]0.786071917842032[/C][C]0.427856164315935[/C][C]0.213928082157968[/C][/ROW]
[ROW][C]30[/C][C]0.813522371107844[/C][C]0.372955257784312[/C][C]0.186477628892156[/C][/ROW]
[ROW][C]31[/C][C]0.727320797787165[/C][C]0.54535840442567[/C][C]0.272679202212835[/C][/ROW]
[ROW][C]32[/C][C]0.793481037001964[/C][C]0.413037925996073[/C][C]0.206518962998036[/C][/ROW]
[ROW][C]33[/C][C]0.825617233066176[/C][C]0.348765533867648[/C][C]0.174382766933824[/C][/ROW]
[ROW][C]34[/C][C]0.838006928167271[/C][C]0.323986143665458[/C][C]0.161993071832729[/C][/ROW]
[ROW][C]35[/C][C]0.746544798372633[/C][C]0.506910403254735[/C][C]0.253455201627367[/C][/ROW]
[ROW][C]36[/C][C]0.691152798154631[/C][C]0.617694403690738[/C][C]0.308847201845369[/C][/ROW]
[ROW][C]37[/C][C]0.65110239433112[/C][C]0.69779521133776[/C][C]0.34889760566888[/C][/ROW]
[ROW][C]38[/C][C]0.646960654216648[/C][C]0.706078691566704[/C][C]0.353039345783352[/C][/ROW]
[ROW][C]39[/C][C]0.538400954296748[/C][C]0.923198091406504[/C][C]0.461599045703252[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58033&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&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
190.1195946199447990.2391892398895980.880405380055201
200.1730436417027310.3460872834054610.82695635829727
210.7164517433445770.5670965133108460.283548256655423
220.7139729015127120.5720541969745760.286027098487288
230.7532405162702240.4935189674595510.246759483729776
240.6464945009815020.7070109980369970.353505499018498
250.6426185439352890.7147629121294220.357381456064711
260.5970153039846370.8059693920307260.402984696015363
270.66968919847020.66062160305960.3303108015298
280.8462185856599940.3075628286800130.153781414340006
290.7860719178420320.4278561643159350.213928082157968
300.8135223711078440.3729552577843120.186477628892156
310.7273207977871650.545358404425670.272679202212835
320.7934810370019640.4130379259960730.206518962998036
330.8256172330661760.3487655338676480.174382766933824
340.8380069281672710.3239861436654580.161993071832729
350.7465447983726330.5069104032547350.253455201627367
360.6911527981546310.6176944036907380.308847201845369
370.651102394331120.697795211337760.34889760566888
380.6469606542166480.7060786915667040.353039345783352
390.5384009542967480.9231980914065040.461599045703252







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=58033&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=58033&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58033&T=6

As an alternative you can also use a QR Code:  

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK



Parameters (Session):
par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;
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')
}