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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationThu, 19 Nov 2009 11:04:50 -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/19/t12586539634vi57aaih17sfhz.htm/, Retrieved Thu, 28 Mar 2024 13:24:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57860, Retrieved Thu, 28 Mar 2024 13:24:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact130
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] [] [2009-11-19 18:04:50] [faa1ded5041cd5a0e2be04844f08502a] [Current]
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Dataseries X:
29	27	24
26	28	29
26	25	26
21	19	26
23	19	21
22	19	23
21	20	22
16	16	21
19	22	16
16	21	19
25	25	16
27	29	25
23	28	27
22	25	23
23	26	22
20	24	23
24	28	20
23	28	24
20	28	23
21	28	20
22	32	21
17	31	22
21	22	17
19	29	21
23	31	19
22	29	23
15	32	22
23	32	15
21	31	23
18	29	21
18	28	18
18	28	18
18	29	18
10	22	18
13	26	10
10	24	13
9	27	10
9	27	9
6	23	9
11	21	6
9	19	11
10	17	9
9	19	10
16	21	9
10	13	16
7	8	10
7	5	7
14	10	7
11	6	14
10	6	11
6	8	10
8	11	6
13	12	8
12	13	13
15	19	12
16	19	15
16	18	16




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=57860&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=57860&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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
s[t] = + 8.69448494597229 + 0.128118770999304consv[t] + 0.538300231532793`y(t-1)`[t] -0.344447299199426M1[t] -1.44883997873163M2[t] -3.2764835967175M3[t] -0.196764365358310M4[t] + 0.499140152070973M5[t] -1.07683655950044M6[t] -1.04275401159163M7[t] + 0.124585939396124M8[t] -0.656529404254919M9[t] -5.28497294610116M10[t] + 1.50084427465382M11[t] -0.100772349974912t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
s[t] =  +  8.69448494597229 +  0.128118770999304consv[t] +  0.538300231532793`y(t-1)`[t] -0.344447299199426M1[t] -1.44883997873163M2[t] -3.2764835967175M3[t] -0.196764365358310M4[t] +  0.499140152070973M5[t] -1.07683655950044M6[t] -1.04275401159163M7[t] +  0.124585939396124M8[t] -0.656529404254919M9[t] -5.28497294610116M10[t] +  1.50084427465382M11[t] -0.100772349974912t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]s[t] =  +  8.69448494597229 +  0.128118770999304consv[t] +  0.538300231532793`y(t-1)`[t] -0.344447299199426M1[t] -1.44883997873163M2[t] -3.2764835967175M3[t] -0.196764365358310M4[t] +  0.499140152070973M5[t] -1.07683655950044M6[t] -1.04275401159163M7[t] +  0.124585939396124M8[t] -0.656529404254919M9[t] -5.28497294610116M10[t] +  1.50084427465382M11[t] -0.100772349974912t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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
s[t] = + 8.69448494597229 + 0.128118770999304consv[t] + 0.538300231532793`y(t-1)`[t] -0.344447299199426M1[t] -1.44883997873163M2[t] -3.2764835967175M3[t] -0.196764365358310M4[t] + 0.499140152070973M5[t] -1.07683655950044M6[t] -1.04275401159163M7[t] + 0.124585939396124M8[t] -0.656529404254919M9[t] -5.28497294610116M10[t] + 1.50084427465382M11[t] -0.100772349974912t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)8.694484945972293.8466232.26030.0290480.014524
consv0.1281187709993040.0705981.81480.0767050.038353
`y(t-1)`0.5383002315327930.129774.14810.000168e-05
M1-0.3444472991994262.096805-0.16430.8703050.435152
M2-1.448839978731632.101621-0.68940.4943680.247184
M3-3.27648359671752.093383-1.56520.1250490.062525
M4-0.1967643653583102.105631-0.09340.9259930.462996
M50.4991401520709732.0923870.23860.8126140.406307
M6-1.076836559500442.106001-0.51130.6118060.305903
M7-1.042754011591632.092644-0.49830.6208760.310438
M80.1245859393961242.0926690.05950.9528090.476404
M9-0.6565294042549192.103696-0.31210.7565210.37826
M10-5.284972946101162.214199-2.38690.0215730.010787
M111.500844274653822.2705960.6610.5122280.256114
t-0.1007723499749120.046164-2.18290.0346810.01734

\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) & 8.69448494597229 & 3.846623 & 2.2603 & 0.029048 & 0.014524 \tabularnewline
consv & 0.128118770999304 & 0.070598 & 1.8148 & 0.076705 & 0.038353 \tabularnewline
`y(t-1)` & 0.538300231532793 & 0.12977 & 4.1481 & 0.00016 & 8e-05 \tabularnewline
M1 & -0.344447299199426 & 2.096805 & -0.1643 & 0.870305 & 0.435152 \tabularnewline
M2 & -1.44883997873163 & 2.101621 & -0.6894 & 0.494368 & 0.247184 \tabularnewline
M3 & -3.2764835967175 & 2.093383 & -1.5652 & 0.125049 & 0.062525 \tabularnewline
M4 & -0.196764365358310 & 2.105631 & -0.0934 & 0.925993 & 0.462996 \tabularnewline
M5 & 0.499140152070973 & 2.092387 & 0.2386 & 0.812614 & 0.406307 \tabularnewline
M6 & -1.07683655950044 & 2.106001 & -0.5113 & 0.611806 & 0.305903 \tabularnewline
M7 & -1.04275401159163 & 2.092644 & -0.4983 & 0.620876 & 0.310438 \tabularnewline
M8 & 0.124585939396124 & 2.092669 & 0.0595 & 0.952809 & 0.476404 \tabularnewline
M9 & -0.656529404254919 & 2.103696 & -0.3121 & 0.756521 & 0.37826 \tabularnewline
M10 & -5.28497294610116 & 2.214199 & -2.3869 & 0.021573 & 0.010787 \tabularnewline
M11 & 1.50084427465382 & 2.270596 & 0.661 & 0.512228 & 0.256114 \tabularnewline
t & -0.100772349974912 & 0.046164 & -2.1829 & 0.034681 & 0.01734 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&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]8.69448494597229[/C][C]3.846623[/C][C]2.2603[/C][C]0.029048[/C][C]0.014524[/C][/ROW]
[ROW][C]consv[/C][C]0.128118770999304[/C][C]0.070598[/C][C]1.8148[/C][C]0.076705[/C][C]0.038353[/C][/ROW]
[ROW][C]`y(t-1)`[/C][C]0.538300231532793[/C][C]0.12977[/C][C]4.1481[/C][C]0.00016[/C][C]8e-05[/C][/ROW]
[ROW][C]M1[/C][C]-0.344447299199426[/C][C]2.096805[/C][C]-0.1643[/C][C]0.870305[/C][C]0.435152[/C][/ROW]
[ROW][C]M2[/C][C]-1.44883997873163[/C][C]2.101621[/C][C]-0.6894[/C][C]0.494368[/C][C]0.247184[/C][/ROW]
[ROW][C]M3[/C][C]-3.2764835967175[/C][C]2.093383[/C][C]-1.5652[/C][C]0.125049[/C][C]0.062525[/C][/ROW]
[ROW][C]M4[/C][C]-0.196764365358310[/C][C]2.105631[/C][C]-0.0934[/C][C]0.925993[/C][C]0.462996[/C][/ROW]
[ROW][C]M5[/C][C]0.499140152070973[/C][C]2.092387[/C][C]0.2386[/C][C]0.812614[/C][C]0.406307[/C][/ROW]
[ROW][C]M6[/C][C]-1.07683655950044[/C][C]2.106001[/C][C]-0.5113[/C][C]0.611806[/C][C]0.305903[/C][/ROW]
[ROW][C]M7[/C][C]-1.04275401159163[/C][C]2.092644[/C][C]-0.4983[/C][C]0.620876[/C][C]0.310438[/C][/ROW]
[ROW][C]M8[/C][C]0.124585939396124[/C][C]2.092669[/C][C]0.0595[/C][C]0.952809[/C][C]0.476404[/C][/ROW]
[ROW][C]M9[/C][C]-0.656529404254919[/C][C]2.103696[/C][C]-0.3121[/C][C]0.756521[/C][C]0.37826[/C][/ROW]
[ROW][C]M10[/C][C]-5.28497294610116[/C][C]2.214199[/C][C]-2.3869[/C][C]0.021573[/C][C]0.010787[/C][/ROW]
[ROW][C]M11[/C][C]1.50084427465382[/C][C]2.270596[/C][C]0.661[/C][C]0.512228[/C][C]0.256114[/C][/ROW]
[ROW][C]t[/C][C]-0.100772349974912[/C][C]0.046164[/C][C]-2.1829[/C][C]0.034681[/C][C]0.01734[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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)8.694484945972293.8466232.26030.0290480.014524
consv0.1281187709993040.0705981.81480.0767050.038353
`y(t-1)`0.5383002315327930.129774.14810.000168e-05
M1-0.3444472991994262.096805-0.16430.8703050.435152
M2-1.448839978731632.101621-0.68940.4943680.247184
M3-3.27648359671752.093383-1.56520.1250490.062525
M4-0.1967643653583102.105631-0.09340.9259930.462996
M50.4991401520709732.0923870.23860.8126140.406307
M6-1.076836559500442.106001-0.51130.6118060.305903
M7-1.042754011591632.092644-0.49830.6208760.310438
M80.1245859393961242.0926690.05950.9528090.476404
M9-0.6565294042549192.103696-0.31210.7565210.37826
M10-5.284972946101162.214199-2.38690.0215730.010787
M111.500844274653822.2705960.6610.5122280.256114
t-0.1007723499749120.046164-2.18290.0346810.01734







Multiple Linear Regression - Regression Statistics
Multiple R0.89863671548725
R-squared0.807547946421712
Adjusted R-squared0.743397261895616
F-TEST (value)12.5882981980217
F-TEST (DF numerator)14
F-TEST (DF denominator)42
p-value8.20095102938012e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11547870816889
Sum Squared Residuals407.660718404254

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.89863671548725 \tabularnewline
R-squared & 0.807547946421712 \tabularnewline
Adjusted R-squared & 0.743397261895616 \tabularnewline
F-TEST (value) & 12.5882981980217 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 42 \tabularnewline
p-value & 8.20095102938012e-11 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 3.11547870816889 \tabularnewline
Sum Squared Residuals & 407.660718404254 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.89863671548725[/C][/ROW]
[ROW][C]R-squared[/C][C]0.807547946421712[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.743397261895616[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]12.5882981980217[/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]8.20095102938012e-11[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]3.11547870816889[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]407.660718404254[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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 R0.89863671548725
R-squared0.807547946421712
Adjusted R-squared0.743397261895616
F-TEST (value)12.5882981980217
F-TEST (DF numerator)14
F-TEST (DF denominator)42
p-value8.20095102938012e-11
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation3.11547870816889
Sum Squared Residuals407.660718404254







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
12924.62767767056624.37232232943383
22626.2421325697223-0.242132569722334
32622.31445959416523.68554040583475
42124.5246938495537-3.52469384955372
52322.42832485934410.571675140655879
62221.82817626086340.171823739136617
72121.3513049982638-0.351304998263802
81621.3670972837466-5.36709728374664
91918.56242105845250.437578941547465
101615.31998709023050.680012909769545
112520.90260635040944.09739364959064
122724.6581668935732.34183310642702
132325.1614289364649-2.16142893646492
142221.41870666782870.581293332171277
152319.08010923933443.91989076066555
162022.3411188102529-2.34111881025292
172421.83382536710612.16617463289388
182322.31027723169100.689722768309033
192021.7052871980921-1.70528719809208
202121.1569541045065-0.156954104506540
212221.32584172641060.674158273589407
221717.0068072951229-0.00680729512292821
232119.84728206924531.15271793075470
241921.2956977677429-2.29569776774287
252320.03011519750152.96988480249845
262220.7219135521271.27808644787300
271518.6395536656313-3.63955366563133
282317.85039892628615.14960107371394
292122.6238141750035-1.62381417500347
301819.6142271083929-1.61422710839295
311817.80451784072920.195482159270827
321818.871085441742-0.87108544174201
331818.1173165191154-0.117316519115359
341012.4912692302991-2.49126923029908
351315.3823873328140-2.38238733281402
361015.1394338607851-5.13943386078506
37913.4636698300103-4.46366983001026
38911.7202045689703-2.72020456897034
3969.27931351701234-3.27931351701234
401110.38712216179960.61287783820036
41913.4175179449194-4.41751794491936
421010.4079308783088-0.407930878308846
43911.1357788497742-2.13577884977415
441611.92028376125284.0797162387472
451013.7815475203620-3.78154752036197
4675.181936384347541.81806361565246
4779.86772424753132-2.86772424753132
48148.90670147789915.09329852210089
491111.7171083654571-0.7171083654571
50108.89704264135161.10295735864839
5166.68656398385663-0.686563983856633
5287.896666252107660.103333747892342
53139.696517653626913.30348234637309
541210.83938852074391.16061147925614
551511.00311111314083.99688888685921
561613.6845794087522.31542059124799
571613.21287317565952.78712682434046

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 29 & 24.6276776705662 & 4.37232232943383 \tabularnewline
2 & 26 & 26.2421325697223 & -0.242132569722334 \tabularnewline
3 & 26 & 22.3144595941652 & 3.68554040583475 \tabularnewline
4 & 21 & 24.5246938495537 & -3.52469384955372 \tabularnewline
5 & 23 & 22.4283248593441 & 0.571675140655879 \tabularnewline
6 & 22 & 21.8281762608634 & 0.171823739136617 \tabularnewline
7 & 21 & 21.3513049982638 & -0.351304998263802 \tabularnewline
8 & 16 & 21.3670972837466 & -5.36709728374664 \tabularnewline
9 & 19 & 18.5624210584525 & 0.437578941547465 \tabularnewline
10 & 16 & 15.3199870902305 & 0.680012909769545 \tabularnewline
11 & 25 & 20.9026063504094 & 4.09739364959064 \tabularnewline
12 & 27 & 24.658166893573 & 2.34183310642702 \tabularnewline
13 & 23 & 25.1614289364649 & -2.16142893646492 \tabularnewline
14 & 22 & 21.4187066678287 & 0.581293332171277 \tabularnewline
15 & 23 & 19.0801092393344 & 3.91989076066555 \tabularnewline
16 & 20 & 22.3411188102529 & -2.34111881025292 \tabularnewline
17 & 24 & 21.8338253671061 & 2.16617463289388 \tabularnewline
18 & 23 & 22.3102772316910 & 0.689722768309033 \tabularnewline
19 & 20 & 21.7052871980921 & -1.70528719809208 \tabularnewline
20 & 21 & 21.1569541045065 & -0.156954104506540 \tabularnewline
21 & 22 & 21.3258417264106 & 0.674158273589407 \tabularnewline
22 & 17 & 17.0068072951229 & -0.00680729512292821 \tabularnewline
23 & 21 & 19.8472820692453 & 1.15271793075470 \tabularnewline
24 & 19 & 21.2956977677429 & -2.29569776774287 \tabularnewline
25 & 23 & 20.0301151975015 & 2.96988480249845 \tabularnewline
26 & 22 & 20.721913552127 & 1.27808644787300 \tabularnewline
27 & 15 & 18.6395536656313 & -3.63955366563133 \tabularnewline
28 & 23 & 17.8503989262861 & 5.14960107371394 \tabularnewline
29 & 21 & 22.6238141750035 & -1.62381417500347 \tabularnewline
30 & 18 & 19.6142271083929 & -1.61422710839295 \tabularnewline
31 & 18 & 17.8045178407292 & 0.195482159270827 \tabularnewline
32 & 18 & 18.871085441742 & -0.87108544174201 \tabularnewline
33 & 18 & 18.1173165191154 & -0.117316519115359 \tabularnewline
34 & 10 & 12.4912692302991 & -2.49126923029908 \tabularnewline
35 & 13 & 15.3823873328140 & -2.38238733281402 \tabularnewline
36 & 10 & 15.1394338607851 & -5.13943386078506 \tabularnewline
37 & 9 & 13.4636698300103 & -4.46366983001026 \tabularnewline
38 & 9 & 11.7202045689703 & -2.72020456897034 \tabularnewline
39 & 6 & 9.27931351701234 & -3.27931351701234 \tabularnewline
40 & 11 & 10.3871221617996 & 0.61287783820036 \tabularnewline
41 & 9 & 13.4175179449194 & -4.41751794491936 \tabularnewline
42 & 10 & 10.4079308783088 & -0.407930878308846 \tabularnewline
43 & 9 & 11.1357788497742 & -2.13577884977415 \tabularnewline
44 & 16 & 11.9202837612528 & 4.0797162387472 \tabularnewline
45 & 10 & 13.7815475203620 & -3.78154752036197 \tabularnewline
46 & 7 & 5.18193638434754 & 1.81806361565246 \tabularnewline
47 & 7 & 9.86772424753132 & -2.86772424753132 \tabularnewline
48 & 14 & 8.9067014778991 & 5.09329852210089 \tabularnewline
49 & 11 & 11.7171083654571 & -0.7171083654571 \tabularnewline
50 & 10 & 8.8970426413516 & 1.10295735864839 \tabularnewline
51 & 6 & 6.68656398385663 & -0.686563983856633 \tabularnewline
52 & 8 & 7.89666625210766 & 0.103333747892342 \tabularnewline
53 & 13 & 9.69651765362691 & 3.30348234637309 \tabularnewline
54 & 12 & 10.8393885207439 & 1.16061147925614 \tabularnewline
55 & 15 & 11.0031111131408 & 3.99688888685921 \tabularnewline
56 & 16 & 13.684579408752 & 2.31542059124799 \tabularnewline
57 & 16 & 13.2128731756595 & 2.78712682434046 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&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]29[/C][C]24.6276776705662[/C][C]4.37232232943383[/C][/ROW]
[ROW][C]2[/C][C]26[/C][C]26.2421325697223[/C][C]-0.242132569722334[/C][/ROW]
[ROW][C]3[/C][C]26[/C][C]22.3144595941652[/C][C]3.68554040583475[/C][/ROW]
[ROW][C]4[/C][C]21[/C][C]24.5246938495537[/C][C]-3.52469384955372[/C][/ROW]
[ROW][C]5[/C][C]23[/C][C]22.4283248593441[/C][C]0.571675140655879[/C][/ROW]
[ROW][C]6[/C][C]22[/C][C]21.8281762608634[/C][C]0.171823739136617[/C][/ROW]
[ROW][C]7[/C][C]21[/C][C]21.3513049982638[/C][C]-0.351304998263802[/C][/ROW]
[ROW][C]8[/C][C]16[/C][C]21.3670972837466[/C][C]-5.36709728374664[/C][/ROW]
[ROW][C]9[/C][C]19[/C][C]18.5624210584525[/C][C]0.437578941547465[/C][/ROW]
[ROW][C]10[/C][C]16[/C][C]15.3199870902305[/C][C]0.680012909769545[/C][/ROW]
[ROW][C]11[/C][C]25[/C][C]20.9026063504094[/C][C]4.09739364959064[/C][/ROW]
[ROW][C]12[/C][C]27[/C][C]24.658166893573[/C][C]2.34183310642702[/C][/ROW]
[ROW][C]13[/C][C]23[/C][C]25.1614289364649[/C][C]-2.16142893646492[/C][/ROW]
[ROW][C]14[/C][C]22[/C][C]21.4187066678287[/C][C]0.581293332171277[/C][/ROW]
[ROW][C]15[/C][C]23[/C][C]19.0801092393344[/C][C]3.91989076066555[/C][/ROW]
[ROW][C]16[/C][C]20[/C][C]22.3411188102529[/C][C]-2.34111881025292[/C][/ROW]
[ROW][C]17[/C][C]24[/C][C]21.8338253671061[/C][C]2.16617463289388[/C][/ROW]
[ROW][C]18[/C][C]23[/C][C]22.3102772316910[/C][C]0.689722768309033[/C][/ROW]
[ROW][C]19[/C][C]20[/C][C]21.7052871980921[/C][C]-1.70528719809208[/C][/ROW]
[ROW][C]20[/C][C]21[/C][C]21.1569541045065[/C][C]-0.156954104506540[/C][/ROW]
[ROW][C]21[/C][C]22[/C][C]21.3258417264106[/C][C]0.674158273589407[/C][/ROW]
[ROW][C]22[/C][C]17[/C][C]17.0068072951229[/C][C]-0.00680729512292821[/C][/ROW]
[ROW][C]23[/C][C]21[/C][C]19.8472820692453[/C][C]1.15271793075470[/C][/ROW]
[ROW][C]24[/C][C]19[/C][C]21.2956977677429[/C][C]-2.29569776774287[/C][/ROW]
[ROW][C]25[/C][C]23[/C][C]20.0301151975015[/C][C]2.96988480249845[/C][/ROW]
[ROW][C]26[/C][C]22[/C][C]20.721913552127[/C][C]1.27808644787300[/C][/ROW]
[ROW][C]27[/C][C]15[/C][C]18.6395536656313[/C][C]-3.63955366563133[/C][/ROW]
[ROW][C]28[/C][C]23[/C][C]17.8503989262861[/C][C]5.14960107371394[/C][/ROW]
[ROW][C]29[/C][C]21[/C][C]22.6238141750035[/C][C]-1.62381417500347[/C][/ROW]
[ROW][C]30[/C][C]18[/C][C]19.6142271083929[/C][C]-1.61422710839295[/C][/ROW]
[ROW][C]31[/C][C]18[/C][C]17.8045178407292[/C][C]0.195482159270827[/C][/ROW]
[ROW][C]32[/C][C]18[/C][C]18.871085441742[/C][C]-0.87108544174201[/C][/ROW]
[ROW][C]33[/C][C]18[/C][C]18.1173165191154[/C][C]-0.117316519115359[/C][/ROW]
[ROW][C]34[/C][C]10[/C][C]12.4912692302991[/C][C]-2.49126923029908[/C][/ROW]
[ROW][C]35[/C][C]13[/C][C]15.3823873328140[/C][C]-2.38238733281402[/C][/ROW]
[ROW][C]36[/C][C]10[/C][C]15.1394338607851[/C][C]-5.13943386078506[/C][/ROW]
[ROW][C]37[/C][C]9[/C][C]13.4636698300103[/C][C]-4.46366983001026[/C][/ROW]
[ROW][C]38[/C][C]9[/C][C]11.7202045689703[/C][C]-2.72020456897034[/C][/ROW]
[ROW][C]39[/C][C]6[/C][C]9.27931351701234[/C][C]-3.27931351701234[/C][/ROW]
[ROW][C]40[/C][C]11[/C][C]10.3871221617996[/C][C]0.61287783820036[/C][/ROW]
[ROW][C]41[/C][C]9[/C][C]13.4175179449194[/C][C]-4.41751794491936[/C][/ROW]
[ROW][C]42[/C][C]10[/C][C]10.4079308783088[/C][C]-0.407930878308846[/C][/ROW]
[ROW][C]43[/C][C]9[/C][C]11.1357788497742[/C][C]-2.13577884977415[/C][/ROW]
[ROW][C]44[/C][C]16[/C][C]11.9202837612528[/C][C]4.0797162387472[/C][/ROW]
[ROW][C]45[/C][C]10[/C][C]13.7815475203620[/C][C]-3.78154752036197[/C][/ROW]
[ROW][C]46[/C][C]7[/C][C]5.18193638434754[/C][C]1.81806361565246[/C][/ROW]
[ROW][C]47[/C][C]7[/C][C]9.86772424753132[/C][C]-2.86772424753132[/C][/ROW]
[ROW][C]48[/C][C]14[/C][C]8.9067014778991[/C][C]5.09329852210089[/C][/ROW]
[ROW][C]49[/C][C]11[/C][C]11.7171083654571[/C][C]-0.7171083654571[/C][/ROW]
[ROW][C]50[/C][C]10[/C][C]8.8970426413516[/C][C]1.10295735864839[/C][/ROW]
[ROW][C]51[/C][C]6[/C][C]6.68656398385663[/C][C]-0.686563983856633[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.89666625210766[/C][C]0.103333747892342[/C][/ROW]
[ROW][C]53[/C][C]13[/C][C]9.69651765362691[/C][C]3.30348234637309[/C][/ROW]
[ROW][C]54[/C][C]12[/C][C]10.8393885207439[/C][C]1.16061147925614[/C][/ROW]
[ROW][C]55[/C][C]15[/C][C]11.0031111131408[/C][C]3.99688888685921[/C][/ROW]
[ROW][C]56[/C][C]16[/C][C]13.684579408752[/C][C]2.31542059124799[/C][/ROW]
[ROW][C]57[/C][C]16[/C][C]13.2128731756595[/C][C]2.78712682434046[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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
12924.62767767056624.37232232943383
22626.2421325697223-0.242132569722334
32622.31445959416523.68554040583475
42124.5246938495537-3.52469384955372
52322.42832485934410.571675140655879
62221.82817626086340.171823739136617
72121.3513049982638-0.351304998263802
81621.3670972837466-5.36709728374664
91918.56242105845250.437578941547465
101615.31998709023050.680012909769545
112520.90260635040944.09739364959064
122724.6581668935732.34183310642702
132325.1614289364649-2.16142893646492
142221.41870666782870.581293332171277
152319.08010923933443.91989076066555
162022.3411188102529-2.34111881025292
172421.83382536710612.16617463289388
182322.31027723169100.689722768309033
192021.7052871980921-1.70528719809208
202121.1569541045065-0.156954104506540
212221.32584172641060.674158273589407
221717.0068072951229-0.00680729512292821
232119.84728206924531.15271793075470
241921.2956977677429-2.29569776774287
252320.03011519750152.96988480249845
262220.7219135521271.27808644787300
271518.6395536656313-3.63955366563133
282317.85039892628615.14960107371394
292122.6238141750035-1.62381417500347
301819.6142271083929-1.61422710839295
311817.80451784072920.195482159270827
321818.871085441742-0.87108544174201
331818.1173165191154-0.117316519115359
341012.4912692302991-2.49126923029908
351315.3823873328140-2.38238733281402
361015.1394338607851-5.13943386078506
37913.4636698300103-4.46366983001026
38911.7202045689703-2.72020456897034
3969.27931351701234-3.27931351701234
401110.38712216179960.61287783820036
41913.4175179449194-4.41751794491936
421010.4079308783088-0.407930878308846
43911.1357788497742-2.13577884977415
441611.92028376125284.0797162387472
451013.7815475203620-3.78154752036197
4675.181936384347541.81806361565246
4779.86772424753132-2.86772424753132
48148.90670147789915.09329852210089
491111.7171083654571-0.7171083654571
50108.89704264135161.10295735864839
5166.68656398385663-0.686563983856633
5287.896666252107660.103333747892342
53139.696517653626913.30348234637309
541210.83938852074391.16061147925614
551511.00311111314083.99688888685921
561613.6845794087522.31542059124799
571613.21287317565952.78712682434046







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
180.003976256828745860.007952513657491730.996023743171254
190.000772986032717840.001545972065435680.999227013967282
200.001022337280377470.002044674560754940.998977662719623
210.004873946740123630.009747893480247260.995126053259876
220.001420373611723390.002840747223446770.998579626388277
230.002311556666731670.004623113333463340.997688443333268
240.009461558504166270.01892311700833250.990538441495834
250.00903440656468540.01806881312937080.990965593435315
260.009698684421735470.01939736884347090.990301315578265
270.1190790094426390.2381580188852780.880920990557361
280.1875771164612070.3751542329224140.812422883538793
290.1399695074431430.2799390148862860.860030492556857
300.1055057870653260.2110115741306520.894494212934674
310.1145975412331910.2291950824663810.88540245876681
320.09687144173017060.1937428834603410.903128558269829
330.2334736073588090.4669472147176190.76652639264119
340.2211354589385590.4422709178771170.778864541061441
350.6229800041496390.7540399917007230.377019995850361
360.5520717653225720.8958564693548560.447928234677428
370.6181657838936660.7636684322126680.381834216106334
380.6847900929215030.6304198141569940.315209907078497
390.6458629282430610.7082741435138780.354137071756939

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
18 & 0.00397625682874586 & 0.00795251365749173 & 0.996023743171254 \tabularnewline
19 & 0.00077298603271784 & 0.00154597206543568 & 0.999227013967282 \tabularnewline
20 & 0.00102233728037747 & 0.00204467456075494 & 0.998977662719623 \tabularnewline
21 & 0.00487394674012363 & 0.00974789348024726 & 0.995126053259876 \tabularnewline
22 & 0.00142037361172339 & 0.00284074722344677 & 0.998579626388277 \tabularnewline
23 & 0.00231155666673167 & 0.00462311333346334 & 0.997688443333268 \tabularnewline
24 & 0.00946155850416627 & 0.0189231170083325 & 0.990538441495834 \tabularnewline
25 & 0.0090344065646854 & 0.0180688131293708 & 0.990965593435315 \tabularnewline
26 & 0.00969868442173547 & 0.0193973688434709 & 0.990301315578265 \tabularnewline
27 & 0.119079009442639 & 0.238158018885278 & 0.880920990557361 \tabularnewline
28 & 0.187577116461207 & 0.375154232922414 & 0.812422883538793 \tabularnewline
29 & 0.139969507443143 & 0.279939014886286 & 0.860030492556857 \tabularnewline
30 & 0.105505787065326 & 0.211011574130652 & 0.894494212934674 \tabularnewline
31 & 0.114597541233191 & 0.229195082466381 & 0.88540245876681 \tabularnewline
32 & 0.0968714417301706 & 0.193742883460341 & 0.903128558269829 \tabularnewline
33 & 0.233473607358809 & 0.466947214717619 & 0.76652639264119 \tabularnewline
34 & 0.221135458938559 & 0.442270917877117 & 0.778864541061441 \tabularnewline
35 & 0.622980004149639 & 0.754039991700723 & 0.377019995850361 \tabularnewline
36 & 0.552071765322572 & 0.895856469354856 & 0.447928234677428 \tabularnewline
37 & 0.618165783893666 & 0.763668432212668 & 0.381834216106334 \tabularnewline
38 & 0.684790092921503 & 0.630419814156994 & 0.315209907078497 \tabularnewline
39 & 0.645862928243061 & 0.708274143513878 & 0.354137071756939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&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]0.00397625682874586[/C][C]0.00795251365749173[/C][C]0.996023743171254[/C][/ROW]
[ROW][C]19[/C][C]0.00077298603271784[/C][C]0.00154597206543568[/C][C]0.999227013967282[/C][/ROW]
[ROW][C]20[/C][C]0.00102233728037747[/C][C]0.00204467456075494[/C][C]0.998977662719623[/C][/ROW]
[ROW][C]21[/C][C]0.00487394674012363[/C][C]0.00974789348024726[/C][C]0.995126053259876[/C][/ROW]
[ROW][C]22[/C][C]0.00142037361172339[/C][C]0.00284074722344677[/C][C]0.998579626388277[/C][/ROW]
[ROW][C]23[/C][C]0.00231155666673167[/C][C]0.00462311333346334[/C][C]0.997688443333268[/C][/ROW]
[ROW][C]24[/C][C]0.00946155850416627[/C][C]0.0189231170083325[/C][C]0.990538441495834[/C][/ROW]
[ROW][C]25[/C][C]0.0090344065646854[/C][C]0.0180688131293708[/C][C]0.990965593435315[/C][/ROW]
[ROW][C]26[/C][C]0.00969868442173547[/C][C]0.0193973688434709[/C][C]0.990301315578265[/C][/ROW]
[ROW][C]27[/C][C]0.119079009442639[/C][C]0.238158018885278[/C][C]0.880920990557361[/C][/ROW]
[ROW][C]28[/C][C]0.187577116461207[/C][C]0.375154232922414[/C][C]0.812422883538793[/C][/ROW]
[ROW][C]29[/C][C]0.139969507443143[/C][C]0.279939014886286[/C][C]0.860030492556857[/C][/ROW]
[ROW][C]30[/C][C]0.105505787065326[/C][C]0.211011574130652[/C][C]0.894494212934674[/C][/ROW]
[ROW][C]31[/C][C]0.114597541233191[/C][C]0.229195082466381[/C][C]0.88540245876681[/C][/ROW]
[ROW][C]32[/C][C]0.0968714417301706[/C][C]0.193742883460341[/C][C]0.903128558269829[/C][/ROW]
[ROW][C]33[/C][C]0.233473607358809[/C][C]0.466947214717619[/C][C]0.76652639264119[/C][/ROW]
[ROW][C]34[/C][C]0.221135458938559[/C][C]0.442270917877117[/C][C]0.778864541061441[/C][/ROW]
[ROW][C]35[/C][C]0.622980004149639[/C][C]0.754039991700723[/C][C]0.377019995850361[/C][/ROW]
[ROW][C]36[/C][C]0.552071765322572[/C][C]0.895856469354856[/C][C]0.447928234677428[/C][/ROW]
[ROW][C]37[/C][C]0.618165783893666[/C][C]0.763668432212668[/C][C]0.381834216106334[/C][/ROW]
[ROW][C]38[/C][C]0.684790092921503[/C][C]0.630419814156994[/C][C]0.315209907078497[/C][/ROW]
[ROW][C]39[/C][C]0.645862928243061[/C][C]0.708274143513878[/C][C]0.354137071756939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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
180.003976256828745860.007952513657491730.996023743171254
190.000772986032717840.001545972065435680.999227013967282
200.001022337280377470.002044674560754940.998977662719623
210.004873946740123630.009747893480247260.995126053259876
220.001420373611723390.002840747223446770.998579626388277
230.002311556666731670.004623113333463340.997688443333268
240.009461558504166270.01892311700833250.990538441495834
250.00903440656468540.01806881312937080.990965593435315
260.009698684421735470.01939736884347090.990301315578265
270.1190790094426390.2381580188852780.880920990557361
280.1875771164612070.3751542329224140.812422883538793
290.1399695074431430.2799390148862860.860030492556857
300.1055057870653260.2110115741306520.894494212934674
310.1145975412331910.2291950824663810.88540245876681
320.09687144173017060.1937428834603410.903128558269829
330.2334736073588090.4669472147176190.76652639264119
340.2211354589385590.4422709178771170.778864541061441
350.6229800041496390.7540399917007230.377019995850361
360.5520717653225720.8958564693548560.447928234677428
370.6181657838936660.7636684322126680.381834216106334
380.6847900929215030.6304198141569940.315209907078497
390.6458629282430610.7082741435138780.354137071756939







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level60.272727272727273NOK
5% type I error level90.409090909090909NOK
10% type I error level90.409090909090909NOK

\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 & 6 & 0.272727272727273 & NOK \tabularnewline
5% type I error level & 9 & 0.409090909090909 & NOK \tabularnewline
10% type I error level & 9 & 0.409090909090909 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57860&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]6[/C][C]0.272727272727273[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]9[/C][C]0.409090909090909[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.409090909090909[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57860&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57860&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 level60.272727272727273NOK
5% type I error level90.409090909090909NOK
10% type I error level90.409090909090909NOK



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')
}