FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationMon, 19 Nov 2007 12:55:31 -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/2007/Nov/19/t11955016940btxuudvixwgjsz.htm/, Retrieved Fri, 03 May 2024 14:49:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5776, Retrieved Fri, 03 May 2024 14:49:34 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact485

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Multiple Regression] [] [2007-11-19 19:55:31] [1a83104d28786df2e24859e2e02dc234] [Current]
-    D    [Multiple Regression] [Q3 ] [2008-11-16 22:02:02] [1e1d8320a8a1170c475bf6e4ce119de6]
F    D      [Multiple Regression] [] [2008-11-27 20:26:42] [74be16979710d4c4e7c6647856088456]
F    D    [Multiple Regression] [Regressiemodel we...] [2008-11-19 13:54:19] [819b576fab25b35cfda70f80599828ec]
-           [Multiple Regression] [Paper Hoofdstuk 5...] [2008-12-12 11:48:14] [6fea0e9a9b3b29a63badf2c274e82506]
-           [Multiple Regression] [Paper Hoofdstuk 5...] [2008-12-12 11:48:14] [6fea0e9a9b3b29a63badf2c274e82506]
F    D    [Multiple Regression] [Regressiemodel we...] [2008-11-19 14:09:36] [819b576fab25b35cfda70f80599828ec]
-   P       [Multiple Regression] [Paper Hoofdstuk 5...] [2008-12-12 11:53:38] [6fea0e9a9b3b29a63badf2c274e82506]
-   PD        [Multiple Regression] [Paper Hoofdstuk 5...] [2008-12-12 12:04:19] [819b576fab25b35cfda70f80599828ec]
-    D        [Multiple Regression] [Paper Hoofdstuk 5...] [2008-12-12 12:09:09] [819b576fab25b35cfda70f80599828ec]
-   PD    [Multiple Regression] [Seatbeld law & tu...] [2008-11-20 16:56:33] [cf9c64468d04c2c4dd548cc66b4e3677]
F R  D    [Multiple Regression] [marlies.polfliet_...] [2008-11-22 10:33:23] [fdc296cbeb5d8064cb0dbd634c3fdc55]
F   PD      [Multiple Regression] [marlies.polfliet_...] [2008-11-22 11:23:15] [fdc296cbeb5d8064cb0dbd634c3fdc55]
F    D      [Multiple Regression] [tinneke_debock.wo...] [2008-11-27 08:44:22] [f9c5a49917ff87aeb076072f2749ef70]
F   P         [Multiple Regression] [tinneke_debock.wo...] [2008-11-27 08:57:07] [f9c5a49917ff87aeb076072f2749ef70]
-    D    [Multiple Regression] [Seatbelt Q3 1] [2008-11-22 15:03:17] [2bd2ad6af3eef3a703e9ec23e39bd695]
- R PD    [Multiple Regression] [Case Q3] [2008-11-22 17:58:30] [de72ca3f4fcfd0997c84e1ac92aea119]
F    D      [Multiple Regression] [Case Q3] [2008-11-22 18:09:11] [de72ca3f4fcfd0997c84e1ac92aea119]
-    D    [Multiple Regression] [Q3] [2008-11-23 12:16:32] [2b46c8b774ad566be9a33a8da3812a44]
-    D    [Multiple Regression] [seatbelt_3] [2008-11-23 14:35:32] [922d8ae7bd2fd460a62d9020ccd4931a]
F    D      [Multiple Regression] [seatbelt3CG] [2008-11-23 14:55:53] [922d8ae7bd2fd460a62d9020ccd4931a]
-    D    [Multiple Regression] [Q3: Eigen tijdree...] [2008-11-23 15:23:20] [1ce0d16c8f4225c977b42c8fa93bc163]
F    D    [Multiple Regression] [investeringsgoede...] [2008-11-23 15:44:49] [a4602103a5e123497aa555277d0e627b]
F           [Multiple Regression] [Q3:Multiple linea...] [2008-11-23 20:06:21] [12d343c4448a5f9e527bb31caeac580b]
F             [Multiple Regression] [Investeringen zon...] [2008-11-27 20:13:40] [7a664918911e34206ce9d0436dd7c1c8]
F    D    [Multiple Regression] [Q3] [2008-11-23 17:55:15] [cb714085b233acee8e8acd879ea442b6]
-   PD      [Multiple Regression] [] [2008-11-29 15:21:54] [4c8dfb519edec2da3492d7e6be9a5685]
-             [Multiple Regression] [] [2008-11-29 16:33:12] [888addc516c3b812dd7be4bd54caa358]
-   PD      [Multiple Regression] [] [2008-11-29 15:24:41] [4c8dfb519edec2da3492d7e6be9a5685]
-             [Multiple Regression] [] [2008-11-29 16:38:14] [888addc516c3b812dd7be4bd54caa358]
-             [Multiple Regression] [] [2008-11-30 22:07:08] [cb714085b233acee8e8acd879ea442b6]
F    D    [Multiple Regression] [Q3 invloed rookve...] [2008-11-23 18:38:26] [ed2ba3b6182103c15c0ab511ae4e6284]
F   P       [Multiple Regression] [Q3 invloed rookve...] [2008-11-23 19:01:30] [ed2ba3b6182103c15c0ab511ae4e6284]
F             [Multiple Regression] [q3] [2008-11-24 19:53:03] [4ad596f10399a71ad29b7d76e6ab90ac]
-             [Multiple Regression] [] [2008-12-01 09:11:10] [888addc516c3b812dd7be4bd54caa358]
F           [Multiple Regression] [q3] [2008-11-24 19:50:44] [4ad596f10399a71ad29b7d76e6ab90ac]
- R  D    [Multiple Regression] [] [2008-11-23 18:48:48] [d9be4962be2d3234142c279ef29acbcf]
- R PD    [Multiple Regression] [] [2008-11-23 19:02:03] [d9be4962be2d3234142c279ef29acbcf]
-    D    [Multiple Regression] [9/11 op prijs diesel] [2008-11-23 20:03:30] [8b0d202c3a0c4ea223fd8b8e731dacd8]
-   PD      [Multiple Regression] [9/11 en prijs die...] [2008-11-23 20:07:48] [8b0d202c3a0c4ea223fd8b8e731dacd8]
-    D    [Multiple Regression] [Q3 Berekening zon...] [2008-11-24 10:27:47] [491a70d26f8c977398d8a0c1c87d3dd4]
-    D    [Multiple Regression] [Q3] [2008-11-24 15:21:51] [43d870b30ac8a7afeb5de9ee11dcfc1a]
- R  D    [Multiple Regression] [Q2] [2008-11-24 15:26:41] [7458e879e85b911182071700fff19fbd]
F    D    [Multiple Regression] [Q3] [2008-11-24 16:39:24] [4396f984ebeab43316cd6baa88a4fd40]
F    D    [Multiple Regression] [The Seatbelt Law ...] [2008-11-24 16:41:42] [33f4701c7363e8b81858dafbf0350eed]
-   PD    [Multiple Regression] [Q3 Seatbelt law z...] [2008-11-24 16:42:23] [7d3039e6253bb5fb3b26df1537d500b4]
-   PD      [Multiple Regression] [q3 Seatbelt law m...] [2008-11-24 16:49:56] [7d3039e6253bb5fb3b26df1537d500b4]
-             [Multiple Regression] [Q3 seatbelt trend...] [2008-11-24 19:34:38] [c993f605b206b366f754f7f8c1fcc291]
F    D      [Multiple Regression] [Q3 seatbelt no tr...] [2008-11-24 19:28:46] [c993f605b206b366f754f7f8c1fcc291]

[Truncated]
Feedback Forum
2008-11-29 15:34:42 [Thomas Plasschaert] [reply] 
Zeer goede uitleg, maar slechts een beperkte uitleg/interpretatie van de grafieken, zie voorbeeld op leeromgeving.
2008-11-29 16:04:21 [c97d2ae59c98cf77a04815c1edffab5a] [reply] 
de student heeft de gegevens verkeerd verwerkt. er moesten seasonal dummies en een lineaire trend in verwerkt zijn. hier de juiste link:
http://www.freestatistics.org/blog/index.php?v=date/2008/Nov/29/t1227973493za3lcy8yojrcbon.htm
Omdat ik niet kan zeggen of we een 2-tailed of 1-tailed p-value moeten gebruiken, maak ik hier gebruik van de 2-tailed p-value. normaal gezien zou elke maand de gegevens van de metaalindustrie 5,5 hoger liggen dan de referentiemaand, vanaf dat de dummyvariabele 1 bedraagt.
we gaan na of dit aan toeval is te wijten door naar de 2-tailed P-value van x te kijken. we zien dat deze 0,066 bedraagt wat hoger is dan 0,05. als we uitgaan van een type-I error van 5% en een Ho(dummyvariabele heeft geen invloed) en Ha (dummyvariabele heeft wel een invloed) kunnen we stellen dat er geen significant verschil is tov de Ho, en het dus door toeval komt dat de dummy een invloed had op deze tijdsreeks. bij een one-tailed p-value(0,03) zou dit niet het geval zijn.
de actuals & interpolation vormen een stijgende lijn, De residu's verlopen eerst in dalende lijn en daarnar in stijgende lijn. dit wil zeggen dat er eerst meer negatieve voorspellingsfouten waren en daarna positieve. hier kunnen we ook uit afleiden dat positieve voorspellinsfouten op elkaar volgen en dit geldt ook voor de negatieve. hieruit kunnen we besluiten dat er een positieve autocorrelatie moet bestaan tussen de residu's, waardoor 1 van de assumpties niet is voldaan. dit kan je ook zien in de residual lag plot en in de autocorrelatie van de residu's, waar er streepje buiten het betrouwbaarheidsinterval liggen en dit dus niet aan toeval te wijten was.
als we het gemiddelde van de residu's bestuderen, kunnen we stellen dat dit waarschijnlijk niet gelijk gaat zijn aan 0 en de tweede assumptie van lineaire regressie ook niet voldaan is.
2008-12-01 12:41:42 [Li Tang Hu] [reply] 
de student heeft een verkeerde analyse gedaan, vergeten de lineaire trend en dummy-variabele mee te vervatten. je moet dus in het begin aanduiden dat die 2 mogen. dan bekom je volgende URL
2008-12-01 12:42:42 [Li Tang Hu] [reply] 
http://www.freestatistics.org/blog/index.php?v=date/2008/Dec/01/t12281352619fp952y60t16ia8.htm

hoort nog bij mijn comment over q3
de analyse gebeurt dan op dezelfde manier

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
106,7	0
110,2	0
125,9	0
100,1	0
106,4	0
114,8	0
81,3	0
87	0
104,2	0
108	0
105	0
94,5	0
92	0
95,9	0
108,8	0
103,4	0
102,1	0
110,1	0
83,2	0
82,7	0
106,8	0
113,7	0
102,5	0
96,6	0
92,1	0
95,6	0
102,3	0
98,6	0
98,2	0
104,5	0
84	0
73,8	0
103,9	0
106	0
97,2	0
102,6	0
89	0
93,8	0
116,7	1
106,8	1
98,5	1
118,7	1
90	1
91,9	1
113,3	1
113,1	1
104,1	1
108,7	1
96,7	1
101	1
116,9	1
105,8	1
99	1
129,4	1
83	1
88,9	1
115,9	1
104,2	1
113,4	1
112,2	1
100,8	1
107,3	1
126,6	1
102,9	1
117,9	1
128,8	1
87,5	1
93,8	1
122,7	1
126,2	1
124,6	1
116,7	1
115,2	1
111,1	1
129,9	1
113,3	1
118,5	1
133,5	1
102,1	1
102,4	1

Tables (Output of Computation)





Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5776&T=0

[TABLE]
[ROW][C]Summary of compuational 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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5776&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5776&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Multiple Linear Regression - Estimated Regression Equation
y[t] = + 99.5657894736842 + 10.1961152882206x[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  99.5657894736842 +  10.1961152882206x[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5776&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  99.5657894736842 +  10.1961152882206x[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5776&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5776&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
y[t] = + 99.5657894736842 + 10.1961152882206x[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99.56578947368421.90059752.386600
x10.19611528822062.6230733.88710.0002120.000106

\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) & 99.5657894736842 & 1.900597 & 52.3866 & 0 & 0 \tabularnewline
x & 10.1961152882206 & 2.623073 & 3.8871 & 0.000212 & 0.000106 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5776&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]99.5657894736842[/C][C]1.900597[/C][C]52.3866[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]10.1961152882206[/C][C]2.623073[/C][C]3.8871[/C][C]0.000212[/C][C]0.000106[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5776&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5776&T=2

As an alternative you can also use a QR Code:  
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)99.56578947368421.90059752.386600
x10.19611528822062.6230733.88710.0002120.000106






Multiple Linear Regression - Regression Statistics
Multiple R0.402835258883971
R-squared0.162276245800116
Adjusted R-squared0.151536197669348
F-TEST (value)15.1094523808727
F-TEST (DF numerator)1
F-TEST (DF denominator)78
p-value0.000211700505996615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.7160664254836
Sum Squared Residuals10706.7645739348

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.402835258883971 \tabularnewline
R-squared & 0.162276245800116 \tabularnewline
Adjusted R-squared & 0.151536197669348 \tabularnewline
F-TEST (value) & 15.1094523808727 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value & 0.000211700505996615 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 11.7160664254836 \tabularnewline
Sum Squared Residuals & 10706.7645739348 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5776&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.402835258883971[/C][/ROW]
[ROW][C]R-squared[/C][C]0.162276245800116[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.151536197669348[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.1094523808727[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C]0.000211700505996615[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]11.7160664254836[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]10706.7645739348[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5776&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5776&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.402835258883971
R-squared0.162276245800116
Adjusted R-squared0.151536197669348
F-TEST (value)15.1094523808727
F-TEST (DF numerator)1
F-TEST (DF denominator)78
p-value0.000211700505996615
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation11.7160664254836
Sum Squared Residuals10706.7645739348






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106.799.56578947368447.13421052631564
2110.299.565789473684210.6342105263158
3125.999.565789473684226.3342105263158
4100.199.56578947368420.534210526315787
5106.499.56578947368426.8342105263158
6114.899.565789473684215.2342105263158
781.399.5657894736842-18.2657894736842
88799.5657894736842-12.5657894736842
9104.299.56578947368424.6342105263158
1010899.56578947368428.4342105263158
1110599.56578947368425.43421052631579
1294.599.5657894736842-5.06578947368421
139299.5657894736842-7.5657894736842
1495.999.5657894736842-3.6657894736842
15108.899.56578947368429.2342105263158
16103.499.56578947368423.8342105263158
17102.199.56578947368422.53421052631579
18110.199.565789473684210.5342105263158
1983.299.5657894736842-16.3657894736842
2082.799.5657894736842-16.8657894736842
21106.899.56578947368427.23421052631579
22113.799.565789473684214.1342105263158
23102.599.56578947368422.93421052631579
2496.699.5657894736842-2.96578947368421
2592.199.5657894736842-7.46578947368421
2695.699.5657894736842-3.96578947368421
27102.399.56578947368422.73421052631579
2898.699.5657894736842-0.965789473684213
2998.299.5657894736842-1.36578947368420
30104.599.56578947368424.93421052631579
318499.5657894736842-15.5657894736842
3273.899.5657894736842-25.7657894736842
33103.999.56578947368424.3342105263158
3410699.56578947368426.43421052631579
3597.299.5657894736842-2.36578947368420
36102.699.56578947368423.03421052631579
378999.5657894736842-10.5657894736842
3893.899.5657894736842-5.76578947368421
39116.7109.7619047619056.93809523809524
40106.8109.761904761905-2.96190476190477
4198.5109.761904761905-11.2619047619048
42118.7109.7619047619058.93809523809524
4390109.761904761905-19.7619047619048
4491.9109.761904761905-17.8619047619048
45113.3109.7619047619053.53809523809523
46113.1109.7619047619053.33809523809523
47104.1109.761904761905-5.66190476190477
48108.7109.761904761905-1.06190476190476
4996.7109.761904761905-13.0619047619048
50101109.761904761905-8.76190476190476
51116.9109.7619047619057.13809523809524
52105.8109.761904761905-3.96190476190477
5399109.761904761905-10.7619047619048
54129.4109.76190476190519.6380952380952
5583109.761904761905-26.7619047619048
5688.9109.761904761905-20.8619047619048
57115.9109.7619047619056.13809523809524
58104.2109.761904761905-5.56190476190476
59113.4109.7619047619053.63809523809524
60112.2109.7619047619052.43809523809524
61100.8109.761904761905-8.96190476190477
62107.3109.761904761905-2.46190476190477
63126.6109.76190476190516.8380952380952
64102.9109.761904761905-6.86190476190476
65117.9109.7619047619058.13809523809524
66128.8109.76190476190519.0380952380953
6787.5109.761904761905-22.2619047619048
6893.8109.761904761905-15.9619047619048
69122.7109.76190476190512.9380952380952
70126.2109.76190476190516.4380952380952
71124.6109.76190476190514.8380952380952
72116.7109.7619047619056.93809523809524
73115.2109.7619047619055.43809523809524
74111.1109.7619047619051.33809523809523
75129.9109.76190476190520.1380952380952
76113.3109.7619047619053.53809523809523
77118.5109.7619047619058.73809523809524
78133.5109.76190476190523.7380952380952
79102.1109.761904761905-7.66190476190477
80102.4109.761904761905-7.36190476190476

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 106.7 & 99.5657894736844 & 7.13421052631564 \tabularnewline
2 & 110.2 & 99.5657894736842 & 10.6342105263158 \tabularnewline
3 & 125.9 & 99.5657894736842 & 26.3342105263158 \tabularnewline
4 & 100.1 & 99.5657894736842 & 0.534210526315787 \tabularnewline
5 & 106.4 & 99.5657894736842 & 6.8342105263158 \tabularnewline
6 & 114.8 & 99.5657894736842 & 15.2342105263158 \tabularnewline
7 & 81.3 & 99.5657894736842 & -18.2657894736842 \tabularnewline
8 & 87 & 99.5657894736842 & -12.5657894736842 \tabularnewline
9 & 104.2 & 99.5657894736842 & 4.6342105263158 \tabularnewline
10 & 108 & 99.5657894736842 & 8.4342105263158 \tabularnewline
11 & 105 & 99.5657894736842 & 5.43421052631579 \tabularnewline
12 & 94.5 & 99.5657894736842 & -5.06578947368421 \tabularnewline
13 & 92 & 99.5657894736842 & -7.5657894736842 \tabularnewline
14 & 95.9 & 99.5657894736842 & -3.6657894736842 \tabularnewline
15 & 108.8 & 99.5657894736842 & 9.2342105263158 \tabularnewline
16 & 103.4 & 99.5657894736842 & 3.8342105263158 \tabularnewline
17 & 102.1 & 99.5657894736842 & 2.53421052631579 \tabularnewline
18 & 110.1 & 99.5657894736842 & 10.5342105263158 \tabularnewline
19 & 83.2 & 99.5657894736842 & -16.3657894736842 \tabularnewline
20 & 82.7 & 99.5657894736842 & -16.8657894736842 \tabularnewline
21 & 106.8 & 99.5657894736842 & 7.23421052631579 \tabularnewline
22 & 113.7 & 99.5657894736842 & 14.1342105263158 \tabularnewline
23 & 102.5 & 99.5657894736842 & 2.93421052631579 \tabularnewline
24 & 96.6 & 99.5657894736842 & -2.96578947368421 \tabularnewline
25 & 92.1 & 99.5657894736842 & -7.46578947368421 \tabularnewline
26 & 95.6 & 99.5657894736842 & -3.96578947368421 \tabularnewline
27 & 102.3 & 99.5657894736842 & 2.73421052631579 \tabularnewline
28 & 98.6 & 99.5657894736842 & -0.965789473684213 \tabularnewline
29 & 98.2 & 99.5657894736842 & -1.36578947368420 \tabularnewline
30 & 104.5 & 99.5657894736842 & 4.93421052631579 \tabularnewline
31 & 84 & 99.5657894736842 & -15.5657894736842 \tabularnewline
32 & 73.8 & 99.5657894736842 & -25.7657894736842 \tabularnewline
33 & 103.9 & 99.5657894736842 & 4.3342105263158 \tabularnewline
34 & 106 & 99.5657894736842 & 6.43421052631579 \tabularnewline
35 & 97.2 & 99.5657894736842 & -2.36578947368420 \tabularnewline
36 & 102.6 & 99.5657894736842 & 3.03421052631579 \tabularnewline
37 & 89 & 99.5657894736842 & -10.5657894736842 \tabularnewline
38 & 93.8 & 99.5657894736842 & -5.76578947368421 \tabularnewline
39 & 116.7 & 109.761904761905 & 6.93809523809524 \tabularnewline
40 & 106.8 & 109.761904761905 & -2.96190476190477 \tabularnewline
41 & 98.5 & 109.761904761905 & -11.2619047619048 \tabularnewline
42 & 118.7 & 109.761904761905 & 8.93809523809524 \tabularnewline
43 & 90 & 109.761904761905 & -19.7619047619048 \tabularnewline
44 & 91.9 & 109.761904761905 & -17.8619047619048 \tabularnewline
45 & 113.3 & 109.761904761905 & 3.53809523809523 \tabularnewline
46 & 113.1 & 109.761904761905 & 3.33809523809523 \tabularnewline
47 & 104.1 & 109.761904761905 & -5.66190476190477 \tabularnewline
48 & 108.7 & 109.761904761905 & -1.06190476190476 \tabularnewline
49 & 96.7 & 109.761904761905 & -13.0619047619048 \tabularnewline
50 & 101 & 109.761904761905 & -8.76190476190476 \tabularnewline
51 & 116.9 & 109.761904761905 & 7.13809523809524 \tabularnewline
52 & 105.8 & 109.761904761905 & -3.96190476190477 \tabularnewline
53 & 99 & 109.761904761905 & -10.7619047619048 \tabularnewline
54 & 129.4 & 109.761904761905 & 19.6380952380952 \tabularnewline
55 & 83 & 109.761904761905 & -26.7619047619048 \tabularnewline
56 & 88.9 & 109.761904761905 & -20.8619047619048 \tabularnewline
57 & 115.9 & 109.761904761905 & 6.13809523809524 \tabularnewline
58 & 104.2 & 109.761904761905 & -5.56190476190476 \tabularnewline
59 & 113.4 & 109.761904761905 & 3.63809523809524 \tabularnewline
60 & 112.2 & 109.761904761905 & 2.43809523809524 \tabularnewline
61 & 100.8 & 109.761904761905 & -8.96190476190477 \tabularnewline
62 & 107.3 & 109.761904761905 & -2.46190476190477 \tabularnewline
63 & 126.6 & 109.761904761905 & 16.8380952380952 \tabularnewline
64 & 102.9 & 109.761904761905 & -6.86190476190476 \tabularnewline
65 & 117.9 & 109.761904761905 & 8.13809523809524 \tabularnewline
66 & 128.8 & 109.761904761905 & 19.0380952380953 \tabularnewline
67 & 87.5 & 109.761904761905 & -22.2619047619048 \tabularnewline
68 & 93.8 & 109.761904761905 & -15.9619047619048 \tabularnewline
69 & 122.7 & 109.761904761905 & 12.9380952380952 \tabularnewline
70 & 126.2 & 109.761904761905 & 16.4380952380952 \tabularnewline
71 & 124.6 & 109.761904761905 & 14.8380952380952 \tabularnewline
72 & 116.7 & 109.761904761905 & 6.93809523809524 \tabularnewline
73 & 115.2 & 109.761904761905 & 5.43809523809524 \tabularnewline
74 & 111.1 & 109.761904761905 & 1.33809523809523 \tabularnewline
75 & 129.9 & 109.761904761905 & 20.1380952380952 \tabularnewline
76 & 113.3 & 109.761904761905 & 3.53809523809523 \tabularnewline
77 & 118.5 & 109.761904761905 & 8.73809523809524 \tabularnewline
78 & 133.5 & 109.761904761905 & 23.7380952380952 \tabularnewline
79 & 102.1 & 109.761904761905 & -7.66190476190477 \tabularnewline
80 & 102.4 & 109.761904761905 & -7.36190476190476 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5776&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]106.7[/C][C]99.5657894736844[/C][C]7.13421052631564[/C][/ROW]
[ROW][C]2[/C][C]110.2[/C][C]99.5657894736842[/C][C]10.6342105263158[/C][/ROW]
[ROW][C]3[/C][C]125.9[/C][C]99.5657894736842[/C][C]26.3342105263158[/C][/ROW]
[ROW][C]4[/C][C]100.1[/C][C]99.5657894736842[/C][C]0.534210526315787[/C][/ROW]
[ROW][C]5[/C][C]106.4[/C][C]99.5657894736842[/C][C]6.8342105263158[/C][/ROW]
[ROW][C]6[/C][C]114.8[/C][C]99.5657894736842[/C][C]15.2342105263158[/C][/ROW]
[ROW][C]7[/C][C]81.3[/C][C]99.5657894736842[/C][C]-18.2657894736842[/C][/ROW]
[ROW][C]8[/C][C]87[/C][C]99.5657894736842[/C][C]-12.5657894736842[/C][/ROW]
[ROW][C]9[/C][C]104.2[/C][C]99.5657894736842[/C][C]4.6342105263158[/C][/ROW]
[ROW][C]10[/C][C]108[/C][C]99.5657894736842[/C][C]8.4342105263158[/C][/ROW]
[ROW][C]11[/C][C]105[/C][C]99.5657894736842[/C][C]5.43421052631579[/C][/ROW]
[ROW][C]12[/C][C]94.5[/C][C]99.5657894736842[/C][C]-5.06578947368421[/C][/ROW]
[ROW][C]13[/C][C]92[/C][C]99.5657894736842[/C][C]-7.5657894736842[/C][/ROW]
[ROW][C]14[/C][C]95.9[/C][C]99.5657894736842[/C][C]-3.6657894736842[/C][/ROW]
[ROW][C]15[/C][C]108.8[/C][C]99.5657894736842[/C][C]9.2342105263158[/C][/ROW]
[ROW][C]16[/C][C]103.4[/C][C]99.5657894736842[/C][C]3.8342105263158[/C][/ROW]
[ROW][C]17[/C][C]102.1[/C][C]99.5657894736842[/C][C]2.53421052631579[/C][/ROW]
[ROW][C]18[/C][C]110.1[/C][C]99.5657894736842[/C][C]10.5342105263158[/C][/ROW]
[ROW][C]19[/C][C]83.2[/C][C]99.5657894736842[/C][C]-16.3657894736842[/C][/ROW]
[ROW][C]20[/C][C]82.7[/C][C]99.5657894736842[/C][C]-16.8657894736842[/C][/ROW]
[ROW][C]21[/C][C]106.8[/C][C]99.5657894736842[/C][C]7.23421052631579[/C][/ROW]
[ROW][C]22[/C][C]113.7[/C][C]99.5657894736842[/C][C]14.1342105263158[/C][/ROW]
[ROW][C]23[/C][C]102.5[/C][C]99.5657894736842[/C][C]2.93421052631579[/C][/ROW]
[ROW][C]24[/C][C]96.6[/C][C]99.5657894736842[/C][C]-2.96578947368421[/C][/ROW]
[ROW][C]25[/C][C]92.1[/C][C]99.5657894736842[/C][C]-7.46578947368421[/C][/ROW]
[ROW][C]26[/C][C]95.6[/C][C]99.5657894736842[/C][C]-3.96578947368421[/C][/ROW]
[ROW][C]27[/C][C]102.3[/C][C]99.5657894736842[/C][C]2.73421052631579[/C][/ROW]
[ROW][C]28[/C][C]98.6[/C][C]99.5657894736842[/C][C]-0.965789473684213[/C][/ROW]
[ROW][C]29[/C][C]98.2[/C][C]99.5657894736842[/C][C]-1.36578947368420[/C][/ROW]
[ROW][C]30[/C][C]104.5[/C][C]99.5657894736842[/C][C]4.93421052631579[/C][/ROW]
[ROW][C]31[/C][C]84[/C][C]99.5657894736842[/C][C]-15.5657894736842[/C][/ROW]
[ROW][C]32[/C][C]73.8[/C][C]99.5657894736842[/C][C]-25.7657894736842[/C][/ROW]
[ROW][C]33[/C][C]103.9[/C][C]99.5657894736842[/C][C]4.3342105263158[/C][/ROW]
[ROW][C]34[/C][C]106[/C][C]99.5657894736842[/C][C]6.43421052631579[/C][/ROW]
[ROW][C]35[/C][C]97.2[/C][C]99.5657894736842[/C][C]-2.36578947368420[/C][/ROW]
[ROW][C]36[/C][C]102.6[/C][C]99.5657894736842[/C][C]3.03421052631579[/C][/ROW]
[ROW][C]37[/C][C]89[/C][C]99.5657894736842[/C][C]-10.5657894736842[/C][/ROW]
[ROW][C]38[/C][C]93.8[/C][C]99.5657894736842[/C][C]-5.76578947368421[/C][/ROW]
[ROW][C]39[/C][C]116.7[/C][C]109.761904761905[/C][C]6.93809523809524[/C][/ROW]
[ROW][C]40[/C][C]106.8[/C][C]109.761904761905[/C][C]-2.96190476190477[/C][/ROW]
[ROW][C]41[/C][C]98.5[/C][C]109.761904761905[/C][C]-11.2619047619048[/C][/ROW]
[ROW][C]42[/C][C]118.7[/C][C]109.761904761905[/C][C]8.93809523809524[/C][/ROW]
[ROW][C]43[/C][C]90[/C][C]109.761904761905[/C][C]-19.7619047619048[/C][/ROW]
[ROW][C]44[/C][C]91.9[/C][C]109.761904761905[/C][C]-17.8619047619048[/C][/ROW]
[ROW][C]45[/C][C]113.3[/C][C]109.761904761905[/C][C]3.53809523809523[/C][/ROW]
[ROW][C]46[/C][C]113.1[/C][C]109.761904761905[/C][C]3.33809523809523[/C][/ROW]
[ROW][C]47[/C][C]104.1[/C][C]109.761904761905[/C][C]-5.66190476190477[/C][/ROW]
[ROW][C]48[/C][C]108.7[/C][C]109.761904761905[/C][C]-1.06190476190476[/C][/ROW]
[ROW][C]49[/C][C]96.7[/C][C]109.761904761905[/C][C]-13.0619047619048[/C][/ROW]
[ROW][C]50[/C][C]101[/C][C]109.761904761905[/C][C]-8.76190476190476[/C][/ROW]
[ROW][C]51[/C][C]116.9[/C][C]109.761904761905[/C][C]7.13809523809524[/C][/ROW]
[ROW][C]52[/C][C]105.8[/C][C]109.761904761905[/C][C]-3.96190476190477[/C][/ROW]
[ROW][C]53[/C][C]99[/C][C]109.761904761905[/C][C]-10.7619047619048[/C][/ROW]
[ROW][C]54[/C][C]129.4[/C][C]109.761904761905[/C][C]19.6380952380952[/C][/ROW]
[ROW][C]55[/C][C]83[/C][C]109.761904761905[/C][C]-26.7619047619048[/C][/ROW]
[ROW][C]56[/C][C]88.9[/C][C]109.761904761905[/C][C]-20.8619047619048[/C][/ROW]
[ROW][C]57[/C][C]115.9[/C][C]109.761904761905[/C][C]6.13809523809524[/C][/ROW]
[ROW][C]58[/C][C]104.2[/C][C]109.761904761905[/C][C]-5.56190476190476[/C][/ROW]
[ROW][C]59[/C][C]113.4[/C][C]109.761904761905[/C][C]3.63809523809524[/C][/ROW]
[ROW][C]60[/C][C]112.2[/C][C]109.761904761905[/C][C]2.43809523809524[/C][/ROW]
[ROW][C]61[/C][C]100.8[/C][C]109.761904761905[/C][C]-8.96190476190477[/C][/ROW]
[ROW][C]62[/C][C]107.3[/C][C]109.761904761905[/C][C]-2.46190476190477[/C][/ROW]
[ROW][C]63[/C][C]126.6[/C][C]109.761904761905[/C][C]16.8380952380952[/C][/ROW]
[ROW][C]64[/C][C]102.9[/C][C]109.761904761905[/C][C]-6.86190476190476[/C][/ROW]
[ROW][C]65[/C][C]117.9[/C][C]109.761904761905[/C][C]8.13809523809524[/C][/ROW]
[ROW][C]66[/C][C]128.8[/C][C]109.761904761905[/C][C]19.0380952380953[/C][/ROW]
[ROW][C]67[/C][C]87.5[/C][C]109.761904761905[/C][C]-22.2619047619048[/C][/ROW]
[ROW][C]68[/C][C]93.8[/C][C]109.761904761905[/C][C]-15.9619047619048[/C][/ROW]
[ROW][C]69[/C][C]122.7[/C][C]109.761904761905[/C][C]12.9380952380952[/C][/ROW]
[ROW][C]70[/C][C]126.2[/C][C]109.761904761905[/C][C]16.4380952380952[/C][/ROW]
[ROW][C]71[/C][C]124.6[/C][C]109.761904761905[/C][C]14.8380952380952[/C][/ROW]
[ROW][C]72[/C][C]116.7[/C][C]109.761904761905[/C][C]6.93809523809524[/C][/ROW]
[ROW][C]73[/C][C]115.2[/C][C]109.761904761905[/C][C]5.43809523809524[/C][/ROW]
[ROW][C]74[/C][C]111.1[/C][C]109.761904761905[/C][C]1.33809523809523[/C][/ROW]
[ROW][C]75[/C][C]129.9[/C][C]109.761904761905[/C][C]20.1380952380952[/C][/ROW]
[ROW][C]76[/C][C]113.3[/C][C]109.761904761905[/C][C]3.53809523809523[/C][/ROW]
[ROW][C]77[/C][C]118.5[/C][C]109.761904761905[/C][C]8.73809523809524[/C][/ROW]
[ROW][C]78[/C][C]133.5[/C][C]109.761904761905[/C][C]23.7380952380952[/C][/ROW]
[ROW][C]79[/C][C]102.1[/C][C]109.761904761905[/C][C]-7.66190476190477[/C][/ROW]
[ROW][C]80[/C][C]102.4[/C][C]109.761904761905[/C][C]-7.36190476190476[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5776&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5776&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1106.799.56578947368447.13421052631564
2110.299.565789473684210.6342105263158
3125.999.565789473684226.3342105263158
4100.199.56578947368420.534210526315787
5106.499.56578947368426.8342105263158
6114.899.565789473684215.2342105263158
781.399.5657894736842-18.2657894736842
88799.5657894736842-12.5657894736842
9104.299.56578947368424.6342105263158
1010899.56578947368428.4342105263158
1110599.56578947368425.43421052631579
1294.599.5657894736842-5.06578947368421
139299.5657894736842-7.5657894736842
1495.999.5657894736842-3.6657894736842
15108.899.56578947368429.2342105263158
16103.499.56578947368423.8342105263158
17102.199.56578947368422.53421052631579
18110.199.565789473684210.5342105263158
1983.299.5657894736842-16.3657894736842
2082.799.5657894736842-16.8657894736842
21106.899.56578947368427.23421052631579
22113.799.565789473684214.1342105263158
23102.599.56578947368422.93421052631579
2496.699.5657894736842-2.96578947368421
2592.199.5657894736842-7.46578947368421
2695.699.5657894736842-3.96578947368421
27102.399.56578947368422.73421052631579
2898.699.5657894736842-0.965789473684213
2998.299.5657894736842-1.36578947368420
30104.599.56578947368424.93421052631579
318499.5657894736842-15.5657894736842
3273.899.5657894736842-25.7657894736842
33103.999.56578947368424.3342105263158
3410699.56578947368426.43421052631579
3597.299.5657894736842-2.36578947368420
36102.699.56578947368423.03421052631579
378999.5657894736842-10.5657894736842
3893.899.5657894736842-5.76578947368421
39116.7109.7619047619056.93809523809524
40106.8109.761904761905-2.96190476190477
4198.5109.761904761905-11.2619047619048
42118.7109.7619047619058.93809523809524
4390109.761904761905-19.7619047619048
4491.9109.761904761905-17.8619047619048
45113.3109.7619047619053.53809523809523
46113.1109.7619047619053.33809523809523
47104.1109.761904761905-5.66190476190477
48108.7109.761904761905-1.06190476190476
4996.7109.761904761905-13.0619047619048
50101109.761904761905-8.76190476190476
51116.9109.7619047619057.13809523809524
52105.8109.761904761905-3.96190476190477
5399109.761904761905-10.7619047619048
54129.4109.76190476190519.6380952380952
5583109.761904761905-26.7619047619048
5688.9109.761904761905-20.8619047619048
57115.9109.7619047619056.13809523809524
58104.2109.761904761905-5.56190476190476
59113.4109.7619047619053.63809523809524
60112.2109.7619047619052.43809523809524
61100.8109.761904761905-8.96190476190477
62107.3109.761904761905-2.46190476190477
63126.6109.76190476190516.8380952380952
64102.9109.761904761905-6.86190476190476
65117.9109.7619047619058.13809523809524
66128.8109.76190476190519.0380952380953
6787.5109.761904761905-22.2619047619048
6893.8109.761904761905-15.9619047619048
69122.7109.76190476190512.9380952380952
70126.2109.76190476190516.4380952380952
71124.6109.76190476190514.8380952380952
72116.7109.7619047619056.93809523809524
73115.2109.7619047619055.43809523809524
74111.1109.7619047619051.33809523809523
75129.9109.76190476190520.1380952380952
76113.3109.7619047619053.53809523809523
77118.5109.7619047619058.73809523809524
78133.5109.76190476190523.7380952380952
79102.1109.761904761905-7.66190476190477
80102.4109.761904761905-7.36190476190476


Figures (Output of Computation)

Figure 1
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 2
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 3
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 4
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 5
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 6
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 7
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 8
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Figure 9
PNG link  
QR Code
Postscript link  
QR Code
PDF link  
QR Code


Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
R code (references can be found in the software module):
library(lattice)
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))
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$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$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()
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')