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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 01:51:35 -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/t1258620989z144dgiv07t546s.htm/, Retrieved Fri, 29 Mar 2024 07:46:17 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=57659, Retrieved Fri, 29 Mar 2024 07:46:17 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153
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]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-   PD      [Multiple Regression] [model 5] [2009-11-19 08:51:35] [c60887983b0820a525cba943a935572d] [Current]
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Dataseries X:
135	0	139	149
130	0	135	139
127	0	130	135
122	0	127	130
117	0	122	127
112	0	117	122
113	0	112	117
149	0	113	112
157	0	149	113
157	0	157	149
147	0	157	157
137	0	147	157
132	0	137	147
125	0	132	137
123	0	125	132
117	0	123	125
114	0	117	123
111	0	114	117
112	0	111	114
144	0	112	111
150	0	144	112
149	0	150	144
134	0	149	150
123	0	134	149
116	0	123	134
117	0	116	123
111	0	117	116
105	0	111	117
102	0	105	111
95	0	102	105
93	0	95	102
124	0	93	95
130	0	124	93
124	0	130	124
115	0	124	130
106	0	115	124
105	0	106	115
105	0	105	106
101	0	105	105
95	0	101	105
93	0	95	101
84	0	93	95
87	0	84	93
116	0	87	84
120	0	116	87
117	1	120	116
109	1	117	120
105	1	109	117
107	1	105	109
109	1	107	105
109	1	109	107
108	1	109	109
107	1	108	109
99	1	107	108
103	1	99	107
131	1	103	99
137	1	131	103
135	1	137	131




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=57659&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=57659&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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
WLH[t] = + 12.3068296466899 + 4.33014916145824X[t] + 1.01868507239736`Y(t-1)`[t] -0.148970908506937`Y(t-2)`[t] + 4.26344451199578M1[t] + 4.33777525395675M2[t] + 2.85371517838113M3[t] + 0.970842279890648M4[t] + 2.74283742150709M5[t] -1.39068521698368M6[t] + 6.24100022216994M7[t] + 35.1906468259991M8[t] + 9.74545135874112M9[t] + 5.14442295711167M10[t] -1.95298550853488M11[t] -0.129219519629923t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WLH[t] =  +  12.3068296466899 +  4.33014916145824X[t] +  1.01868507239736`Y(t-1)`[t] -0.148970908506937`Y(t-2)`[t] +  4.26344451199578M1[t] +  4.33777525395675M2[t] +  2.85371517838113M3[t] +  0.970842279890648M4[t] +  2.74283742150709M5[t] -1.39068521698368M6[t] +  6.24100022216994M7[t] +  35.1906468259991M8[t] +  9.74545135874112M9[t] +  5.14442295711167M10[t] -1.95298550853488M11[t] -0.129219519629923t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WLH[t] =  +  12.3068296466899 +  4.33014916145824X[t] +  1.01868507239736`Y(t-1)`[t] -0.148970908506937`Y(t-2)`[t] +  4.26344451199578M1[t] +  4.33777525395675M2[t] +  2.85371517838113M3[t] +  0.970842279890648M4[t] +  2.74283742150709M5[t] -1.39068521698368M6[t] +  6.24100022216994M7[t] +  35.1906468259991M8[t] +  9.74545135874112M9[t] +  5.14442295711167M10[t] -1.95298550853488M11[t] -0.129219519629923t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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
WLH[t] = + 12.3068296466899 + 4.33014916145824X[t] + 1.01868507239736`Y(t-1)`[t] -0.148970908506937`Y(t-2)`[t] + 4.26344451199578M1[t] + 4.33777525395675M2[t] + 2.85371517838113M3[t] + 0.970842279890648M4[t] + 2.74283742150709M5[t] -1.39068521698368M6[t] + 6.24100022216994M7[t] + 35.1906468259991M8[t] + 9.74545135874112M9[t] + 5.14442295711167M10[t] -1.95298550853488M11[t] -0.129219519629923t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)12.30682964668998.193041.50210.1405510.070276
X4.330149161458241.3407063.22980.0024080.001204
`Y(t-1)`1.018685072397360.1444557.051900
`Y(t-2)`-0.1489709085069370.14104-1.05620.2968990.148449
M14.263444511995781.6813372.53570.015030.007515
M24.337775253956752.0286132.13830.0383550.019177
M32.853715178381132.1444121.33080.1904450.095223
M40.9708422798906482.087890.4650.6443420.322171
M52.742837421507092.0520611.33660.1885410.09427
M6-1.390685216983682.245373-0.61940.5390270.269514
M76.241000222169942.1849152.85640.0066350.003317
M835.19064682599912.70429113.012900
M99.745451358741126.0799081.60290.1164530.058227
M105.144422957111672.8146231.82770.0746970.037349
M11-1.952985508534882.059561-0.94830.3484250.174213
t-0.1292195196299230.051414-2.51330.0158840.007942

\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) & 12.3068296466899 & 8.19304 & 1.5021 & 0.140551 & 0.070276 \tabularnewline
X & 4.33014916145824 & 1.340706 & 3.2298 & 0.002408 & 0.001204 \tabularnewline
`Y(t-1)` & 1.01868507239736 & 0.144455 & 7.0519 & 0 & 0 \tabularnewline
`Y(t-2)` & -0.148970908506937 & 0.14104 & -1.0562 & 0.296899 & 0.148449 \tabularnewline
M1 & 4.26344451199578 & 1.681337 & 2.5357 & 0.01503 & 0.007515 \tabularnewline
M2 & 4.33777525395675 & 2.028613 & 2.1383 & 0.038355 & 0.019177 \tabularnewline
M3 & 2.85371517838113 & 2.144412 & 1.3308 & 0.190445 & 0.095223 \tabularnewline
M4 & 0.970842279890648 & 2.08789 & 0.465 & 0.644342 & 0.322171 \tabularnewline
M5 & 2.74283742150709 & 2.052061 & 1.3366 & 0.188541 & 0.09427 \tabularnewline
M6 & -1.39068521698368 & 2.245373 & -0.6194 & 0.539027 & 0.269514 \tabularnewline
M7 & 6.24100022216994 & 2.184915 & 2.8564 & 0.006635 & 0.003317 \tabularnewline
M8 & 35.1906468259991 & 2.704291 & 13.0129 & 0 & 0 \tabularnewline
M9 & 9.74545135874112 & 6.079908 & 1.6029 & 0.116453 & 0.058227 \tabularnewline
M10 & 5.14442295711167 & 2.814623 & 1.8277 & 0.074697 & 0.037349 \tabularnewline
M11 & -1.95298550853488 & 2.059561 & -0.9483 & 0.348425 & 0.174213 \tabularnewline
t & -0.129219519629923 & 0.051414 & -2.5133 & 0.015884 & 0.007942 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&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]12.3068296466899[/C][C]8.19304[/C][C]1.5021[/C][C]0.140551[/C][C]0.070276[/C][/ROW]
[ROW][C]X[/C][C]4.33014916145824[/C][C]1.340706[/C][C]3.2298[/C][C]0.002408[/C][C]0.001204[/C][/ROW]
[ROW][C]`Y(t-1)`[/C][C]1.01868507239736[/C][C]0.144455[/C][C]7.0519[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Y(t-2)`[/C][C]-0.148970908506937[/C][C]0.14104[/C][C]-1.0562[/C][C]0.296899[/C][C]0.148449[/C][/ROW]
[ROW][C]M1[/C][C]4.26344451199578[/C][C]1.681337[/C][C]2.5357[/C][C]0.01503[/C][C]0.007515[/C][/ROW]
[ROW][C]M2[/C][C]4.33777525395675[/C][C]2.028613[/C][C]2.1383[/C][C]0.038355[/C][C]0.019177[/C][/ROW]
[ROW][C]M3[/C][C]2.85371517838113[/C][C]2.144412[/C][C]1.3308[/C][C]0.190445[/C][C]0.095223[/C][/ROW]
[ROW][C]M4[/C][C]0.970842279890648[/C][C]2.08789[/C][C]0.465[/C][C]0.644342[/C][C]0.322171[/C][/ROW]
[ROW][C]M5[/C][C]2.74283742150709[/C][C]2.052061[/C][C]1.3366[/C][C]0.188541[/C][C]0.09427[/C][/ROW]
[ROW][C]M6[/C][C]-1.39068521698368[/C][C]2.245373[/C][C]-0.6194[/C][C]0.539027[/C][C]0.269514[/C][/ROW]
[ROW][C]M7[/C][C]6.24100022216994[/C][C]2.184915[/C][C]2.8564[/C][C]0.006635[/C][C]0.003317[/C][/ROW]
[ROW][C]M8[/C][C]35.1906468259991[/C][C]2.704291[/C][C]13.0129[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M9[/C][C]9.74545135874112[/C][C]6.079908[/C][C]1.6029[/C][C]0.116453[/C][C]0.058227[/C][/ROW]
[ROW][C]M10[/C][C]5.14442295711167[/C][C]2.814623[/C][C]1.8277[/C][C]0.074697[/C][C]0.037349[/C][/ROW]
[ROW][C]M11[/C][C]-1.95298550853488[/C][C]2.059561[/C][C]-0.9483[/C][C]0.348425[/C][C]0.174213[/C][/ROW]
[ROW][C]t[/C][C]-0.129219519629923[/C][C]0.051414[/C][C]-2.5133[/C][C]0.015884[/C][C]0.007942[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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)12.30682964668998.193041.50210.1405510.070276
X4.330149161458241.3407063.22980.0024080.001204
`Y(t-1)`1.018685072397360.1444557.051900
`Y(t-2)`-0.1489709085069370.14104-1.05620.2968990.148449
M14.263444511995781.6813372.53570.015030.007515
M24.337775253956752.0286132.13830.0383550.019177
M32.853715178381132.1444121.33080.1904450.095223
M40.9708422798906482.087890.4650.6443420.322171
M52.742837421507092.0520611.33660.1885410.09427
M6-1.390685216983682.245373-0.61940.5390270.269514
M76.241000222169942.1849152.85640.0066350.003317
M835.19064682599912.70429113.012900
M99.745451358741126.0799081.60290.1164530.058227
M105.144422957111672.8146231.82770.0746970.037349
M11-1.952985508534882.059561-0.94830.3484250.174213
t-0.1292195196299230.051414-2.51330.0158840.007942







Multiple Linear Regression - Regression Statistics
Multiple R0.993002779799179
R-squared0.986054520688897
Adjusted R-squared0.981073992363503
F-TEST (value)197.98191201149
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.38810920845841
Sum Squared Residuals239.528754844003

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.993002779799179 \tabularnewline
R-squared & 0.986054520688897 \tabularnewline
Adjusted R-squared & 0.981073992363503 \tabularnewline
F-TEST (value) & 197.98191201149 \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 & 2.38810920845841 \tabularnewline
Sum Squared Residuals & 239.528754844003 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.993002779799179[/C][/ROW]
[ROW][C]R-squared[/C][C]0.986054520688897[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.981073992363503[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]197.98191201149[/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]2.38810920845841[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]239.528754844003[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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 R0.993002779799179
R-squared0.986054520688897
Adjusted R-squared0.981073992363503
F-TEST (value)197.98191201149
F-TEST (DF numerator)15
F-TEST (DF denominator)42
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.38810920845841
Sum Squared Residuals239.528754844003







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1135135.841614334755-0.841614334755449
2130133.201694352566-3.20169435256636
3127127.090873029402-0.0908730294017102
4122122.767579936624-0.767579936623912
5117119.763842922144-2.76384292214445
6112111.1525299445720.847470055428355
7113114.306425044643-1.30642504464321
8149144.8903917437754.10960825622543
9157155.8396684546851.16033154531534
10157153.8959484063543.10405159364556
11147145.4775531530221.52244684697752
12137137.114468417954-0.114468417953803
13132132.551551771415-0.551551771415443
14125128.892946716829-3.89294671682906
15123120.8937261573772.10627384262333
16117117.88705995401-0.887059954010097
17114113.7156669586260.284333041373675
18111107.2906950343553.70930496564482
19112112.184018462208-0.184018462207592
20144142.4700433443251.52995665567495
21150149.3445797656460.655420234354274
22149145.9593732065493.04062679345147
23134136.820234697833-2.82023469783308
24123123.512695509285-0.512695509284553
25116118.675948332883-2.6759483328835
26117113.1289440420093.87105595799067
27111113.577145878750-2.57714587874970
28105105.303972117738-0.303972117738187
29102101.7284627563820.271537243617835
309595.303490832111-0.303490832111012
319396.122073970374-3.12207397037399
32124123.9479272693270.0520727306728794
33130130.250691343771-0.250691343771236
34124127.014455693181-3.01445569318098
35115112.7818918224792.21810817752128
36106106.331317610849-0.331317610849049
37105102.6381151282012.36188487179890
38105102.9052794546972.09472054530279
39101101.440970767999-0.440970767998598
409595.3541380602887-0.354138060288751
419391.48068688191881.51931311808115
428486.074400030045-2.07440003004506
438784.70664211500642.29335788499362
44116117.923862592960-1.92386259296019
45120121.444401980075-1.44440198007489
46117120.798887163162-3.79888716316202
47109109.920320326666-0.92032032666572
48105104.0415184619130.958481538087397
49107105.2927704327451.70722956725549
50109107.8711354338981.12886456610197
51109107.9972841664731.00271583352667
52108105.6872499313392.31275006866095
53107106.3113404809280.688659519071789
5499101.178884158917-2.1788841589171
55103100.6808404077692.31915959223117
56131134.767775049613-3.76777504961307
57137137.120658455823-0.120658455823482
58135134.3313355307540.668664469245966

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 135 & 135.841614334755 & -0.841614334755449 \tabularnewline
2 & 130 & 133.201694352566 & -3.20169435256636 \tabularnewline
3 & 127 & 127.090873029402 & -0.0908730294017102 \tabularnewline
4 & 122 & 122.767579936624 & -0.767579936623912 \tabularnewline
5 & 117 & 119.763842922144 & -2.76384292214445 \tabularnewline
6 & 112 & 111.152529944572 & 0.847470055428355 \tabularnewline
7 & 113 & 114.306425044643 & -1.30642504464321 \tabularnewline
8 & 149 & 144.890391743775 & 4.10960825622543 \tabularnewline
9 & 157 & 155.839668454685 & 1.16033154531534 \tabularnewline
10 & 157 & 153.895948406354 & 3.10405159364556 \tabularnewline
11 & 147 & 145.477553153022 & 1.52244684697752 \tabularnewline
12 & 137 & 137.114468417954 & -0.114468417953803 \tabularnewline
13 & 132 & 132.551551771415 & -0.551551771415443 \tabularnewline
14 & 125 & 128.892946716829 & -3.89294671682906 \tabularnewline
15 & 123 & 120.893726157377 & 2.10627384262333 \tabularnewline
16 & 117 & 117.88705995401 & -0.887059954010097 \tabularnewline
17 & 114 & 113.715666958626 & 0.284333041373675 \tabularnewline
18 & 111 & 107.290695034355 & 3.70930496564482 \tabularnewline
19 & 112 & 112.184018462208 & -0.184018462207592 \tabularnewline
20 & 144 & 142.470043344325 & 1.52995665567495 \tabularnewline
21 & 150 & 149.344579765646 & 0.655420234354274 \tabularnewline
22 & 149 & 145.959373206549 & 3.04062679345147 \tabularnewline
23 & 134 & 136.820234697833 & -2.82023469783308 \tabularnewline
24 & 123 & 123.512695509285 & -0.512695509284553 \tabularnewline
25 & 116 & 118.675948332883 & -2.6759483328835 \tabularnewline
26 & 117 & 113.128944042009 & 3.87105595799067 \tabularnewline
27 & 111 & 113.577145878750 & -2.57714587874970 \tabularnewline
28 & 105 & 105.303972117738 & -0.303972117738187 \tabularnewline
29 & 102 & 101.728462756382 & 0.271537243617835 \tabularnewline
30 & 95 & 95.303490832111 & -0.303490832111012 \tabularnewline
31 & 93 & 96.122073970374 & -3.12207397037399 \tabularnewline
32 & 124 & 123.947927269327 & 0.0520727306728794 \tabularnewline
33 & 130 & 130.250691343771 & -0.250691343771236 \tabularnewline
34 & 124 & 127.014455693181 & -3.01445569318098 \tabularnewline
35 & 115 & 112.781891822479 & 2.21810817752128 \tabularnewline
36 & 106 & 106.331317610849 & -0.331317610849049 \tabularnewline
37 & 105 & 102.638115128201 & 2.36188487179890 \tabularnewline
38 & 105 & 102.905279454697 & 2.09472054530279 \tabularnewline
39 & 101 & 101.440970767999 & -0.440970767998598 \tabularnewline
40 & 95 & 95.3541380602887 & -0.354138060288751 \tabularnewline
41 & 93 & 91.4806868819188 & 1.51931311808115 \tabularnewline
42 & 84 & 86.074400030045 & -2.07440003004506 \tabularnewline
43 & 87 & 84.7066421150064 & 2.29335788499362 \tabularnewline
44 & 116 & 117.923862592960 & -1.92386259296019 \tabularnewline
45 & 120 & 121.444401980075 & -1.44440198007489 \tabularnewline
46 & 117 & 120.798887163162 & -3.79888716316202 \tabularnewline
47 & 109 & 109.920320326666 & -0.92032032666572 \tabularnewline
48 & 105 & 104.041518461913 & 0.958481538087397 \tabularnewline
49 & 107 & 105.292770432745 & 1.70722956725549 \tabularnewline
50 & 109 & 107.871135433898 & 1.12886456610197 \tabularnewline
51 & 109 & 107.997284166473 & 1.00271583352667 \tabularnewline
52 & 108 & 105.687249931339 & 2.31275006866095 \tabularnewline
53 & 107 & 106.311340480928 & 0.688659519071789 \tabularnewline
54 & 99 & 101.178884158917 & -2.1788841589171 \tabularnewline
55 & 103 & 100.680840407769 & 2.31915959223117 \tabularnewline
56 & 131 & 134.767775049613 & -3.76777504961307 \tabularnewline
57 & 137 & 137.120658455823 & -0.120658455823482 \tabularnewline
58 & 135 & 134.331335530754 & 0.668664469245966 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&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]135[/C][C]135.841614334755[/C][C]-0.841614334755449[/C][/ROW]
[ROW][C]2[/C][C]130[/C][C]133.201694352566[/C][C]-3.20169435256636[/C][/ROW]
[ROW][C]3[/C][C]127[/C][C]127.090873029402[/C][C]-0.0908730294017102[/C][/ROW]
[ROW][C]4[/C][C]122[/C][C]122.767579936624[/C][C]-0.767579936623912[/C][/ROW]
[ROW][C]5[/C][C]117[/C][C]119.763842922144[/C][C]-2.76384292214445[/C][/ROW]
[ROW][C]6[/C][C]112[/C][C]111.152529944572[/C][C]0.847470055428355[/C][/ROW]
[ROW][C]7[/C][C]113[/C][C]114.306425044643[/C][C]-1.30642504464321[/C][/ROW]
[ROW][C]8[/C][C]149[/C][C]144.890391743775[/C][C]4.10960825622543[/C][/ROW]
[ROW][C]9[/C][C]157[/C][C]155.839668454685[/C][C]1.16033154531534[/C][/ROW]
[ROW][C]10[/C][C]157[/C][C]153.895948406354[/C][C]3.10405159364556[/C][/ROW]
[ROW][C]11[/C][C]147[/C][C]145.477553153022[/C][C]1.52244684697752[/C][/ROW]
[ROW][C]12[/C][C]137[/C][C]137.114468417954[/C][C]-0.114468417953803[/C][/ROW]
[ROW][C]13[/C][C]132[/C][C]132.551551771415[/C][C]-0.551551771415443[/C][/ROW]
[ROW][C]14[/C][C]125[/C][C]128.892946716829[/C][C]-3.89294671682906[/C][/ROW]
[ROW][C]15[/C][C]123[/C][C]120.893726157377[/C][C]2.10627384262333[/C][/ROW]
[ROW][C]16[/C][C]117[/C][C]117.88705995401[/C][C]-0.887059954010097[/C][/ROW]
[ROW][C]17[/C][C]114[/C][C]113.715666958626[/C][C]0.284333041373675[/C][/ROW]
[ROW][C]18[/C][C]111[/C][C]107.290695034355[/C][C]3.70930496564482[/C][/ROW]
[ROW][C]19[/C][C]112[/C][C]112.184018462208[/C][C]-0.184018462207592[/C][/ROW]
[ROW][C]20[/C][C]144[/C][C]142.470043344325[/C][C]1.52995665567495[/C][/ROW]
[ROW][C]21[/C][C]150[/C][C]149.344579765646[/C][C]0.655420234354274[/C][/ROW]
[ROW][C]22[/C][C]149[/C][C]145.959373206549[/C][C]3.04062679345147[/C][/ROW]
[ROW][C]23[/C][C]134[/C][C]136.820234697833[/C][C]-2.82023469783308[/C][/ROW]
[ROW][C]24[/C][C]123[/C][C]123.512695509285[/C][C]-0.512695509284553[/C][/ROW]
[ROW][C]25[/C][C]116[/C][C]118.675948332883[/C][C]-2.6759483328835[/C][/ROW]
[ROW][C]26[/C][C]117[/C][C]113.128944042009[/C][C]3.87105595799067[/C][/ROW]
[ROW][C]27[/C][C]111[/C][C]113.577145878750[/C][C]-2.57714587874970[/C][/ROW]
[ROW][C]28[/C][C]105[/C][C]105.303972117738[/C][C]-0.303972117738187[/C][/ROW]
[ROW][C]29[/C][C]102[/C][C]101.728462756382[/C][C]0.271537243617835[/C][/ROW]
[ROW][C]30[/C][C]95[/C][C]95.303490832111[/C][C]-0.303490832111012[/C][/ROW]
[ROW][C]31[/C][C]93[/C][C]96.122073970374[/C][C]-3.12207397037399[/C][/ROW]
[ROW][C]32[/C][C]124[/C][C]123.947927269327[/C][C]0.0520727306728794[/C][/ROW]
[ROW][C]33[/C][C]130[/C][C]130.250691343771[/C][C]-0.250691343771236[/C][/ROW]
[ROW][C]34[/C][C]124[/C][C]127.014455693181[/C][C]-3.01445569318098[/C][/ROW]
[ROW][C]35[/C][C]115[/C][C]112.781891822479[/C][C]2.21810817752128[/C][/ROW]
[ROW][C]36[/C][C]106[/C][C]106.331317610849[/C][C]-0.331317610849049[/C][/ROW]
[ROW][C]37[/C][C]105[/C][C]102.638115128201[/C][C]2.36188487179890[/C][/ROW]
[ROW][C]38[/C][C]105[/C][C]102.905279454697[/C][C]2.09472054530279[/C][/ROW]
[ROW][C]39[/C][C]101[/C][C]101.440970767999[/C][C]-0.440970767998598[/C][/ROW]
[ROW][C]40[/C][C]95[/C][C]95.3541380602887[/C][C]-0.354138060288751[/C][/ROW]
[ROW][C]41[/C][C]93[/C][C]91.4806868819188[/C][C]1.51931311808115[/C][/ROW]
[ROW][C]42[/C][C]84[/C][C]86.074400030045[/C][C]-2.07440003004506[/C][/ROW]
[ROW][C]43[/C][C]87[/C][C]84.7066421150064[/C][C]2.29335788499362[/C][/ROW]
[ROW][C]44[/C][C]116[/C][C]117.923862592960[/C][C]-1.92386259296019[/C][/ROW]
[ROW][C]45[/C][C]120[/C][C]121.444401980075[/C][C]-1.44440198007489[/C][/ROW]
[ROW][C]46[/C][C]117[/C][C]120.798887163162[/C][C]-3.79888716316202[/C][/ROW]
[ROW][C]47[/C][C]109[/C][C]109.920320326666[/C][C]-0.92032032666572[/C][/ROW]
[ROW][C]48[/C][C]105[/C][C]104.041518461913[/C][C]0.958481538087397[/C][/ROW]
[ROW][C]49[/C][C]107[/C][C]105.292770432745[/C][C]1.70722956725549[/C][/ROW]
[ROW][C]50[/C][C]109[/C][C]107.871135433898[/C][C]1.12886456610197[/C][/ROW]
[ROW][C]51[/C][C]109[/C][C]107.997284166473[/C][C]1.00271583352667[/C][/ROW]
[ROW][C]52[/C][C]108[/C][C]105.687249931339[/C][C]2.31275006866095[/C][/ROW]
[ROW][C]53[/C][C]107[/C][C]106.311340480928[/C][C]0.688659519071789[/C][/ROW]
[ROW][C]54[/C][C]99[/C][C]101.178884158917[/C][C]-2.1788841589171[/C][/ROW]
[ROW][C]55[/C][C]103[/C][C]100.680840407769[/C][C]2.31915959223117[/C][/ROW]
[ROW][C]56[/C][C]131[/C][C]134.767775049613[/C][C]-3.76777504961307[/C][/ROW]
[ROW][C]57[/C][C]137[/C][C]137.120658455823[/C][C]-0.120658455823482[/C][/ROW]
[ROW][C]58[/C][C]135[/C][C]134.331335530754[/C][C]0.668664469245966[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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
1135135.841614334755-0.841614334755449
2130133.201694352566-3.20169435256636
3127127.090873029402-0.0908730294017102
4122122.767579936624-0.767579936623912
5117119.763842922144-2.76384292214445
6112111.1525299445720.847470055428355
7113114.306425044643-1.30642504464321
8149144.8903917437754.10960825622543
9157155.8396684546851.16033154531534
10157153.8959484063543.10405159364556
11147145.4775531530221.52244684697752
12137137.114468417954-0.114468417953803
13132132.551551771415-0.551551771415443
14125128.892946716829-3.89294671682906
15123120.8937261573772.10627384262333
16117117.88705995401-0.887059954010097
17114113.7156669586260.284333041373675
18111107.2906950343553.70930496564482
19112112.184018462208-0.184018462207592
20144142.4700433443251.52995665567495
21150149.3445797656460.655420234354274
22149145.9593732065493.04062679345147
23134136.820234697833-2.82023469783308
24123123.512695509285-0.512695509284553
25116118.675948332883-2.6759483328835
26117113.1289440420093.87105595799067
27111113.577145878750-2.57714587874970
28105105.303972117738-0.303972117738187
29102101.7284627563820.271537243617835
309595.303490832111-0.303490832111012
319396.122073970374-3.12207397037399
32124123.9479272693270.0520727306728794
33130130.250691343771-0.250691343771236
34124127.014455693181-3.01445569318098
35115112.7818918224792.21810817752128
36106106.331317610849-0.331317610849049
37105102.6381151282012.36188487179890
38105102.9052794546972.09472054530279
39101101.440970767999-0.440970767998598
409595.3541380602887-0.354138060288751
419391.48068688191881.51931311808115
428486.074400030045-2.07440003004506
438784.70664211500642.29335788499362
44116117.923862592960-1.92386259296019
45120121.444401980075-1.44440198007489
46117120.798887163162-3.79888716316202
47109109.920320326666-0.92032032666572
48105104.0415184619130.958481538087397
49107105.2927704327451.70722956725549
50109107.8711354338981.12886456610197
51109107.9972841664731.00271583352667
52108105.6872499313392.31275006866095
53107106.3113404809280.688659519071789
5499101.178884158917-2.1788841589171
55103100.6808404077692.31915959223117
56131134.767775049613-3.76777504961307
57137137.120658455823-0.120658455823482
58135134.3313355307540.668664469245966







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
190.1928301440167460.3856602880334930.807169855983254
200.1405758072832620.2811516145665240.859424192716738
210.07171626802002370.1434325360400470.928283731979976
220.2088512348810680.4177024697621360.791148765118932
230.562890318441680.8742193631166390.437109681558319
240.448769864913390.897539729826780.55123013508661
250.5222636317125890.9554727365748220.477736368287411
260.886980994536390.2260380109272190.113019005463609
270.893233021010320.2135339579793600.106766978989680
280.8514544368645610.2970911262708770.148545563135439
290.784678279373040.430643441253920.21532172062696
300.7862748313098620.4274503373802760.213725168690138
310.906478906318790.1870421873624190.0935210936812095
320.945540947134920.1089181057301610.0544590528650805
330.9615349003006350.07693019939872930.0384650996993647
340.9477385481920840.1045229036158310.0522614518079157
350.9806717188778770.03865656224424670.0193282811221234
360.9588851980744120.08222960385117560.0411148019255878
370.9420681801253940.1158636397492130.0579318198746063
380.9638639955200630.07227200895987480.0361360044799374
390.9887217757141460.02255644857170790.0112782242858540

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 & 0.192830144016746 & 0.385660288033493 & 0.807169855983254 \tabularnewline
20 & 0.140575807283262 & 0.281151614566524 & 0.859424192716738 \tabularnewline
21 & 0.0717162680200237 & 0.143432536040047 & 0.928283731979976 \tabularnewline
22 & 0.208851234881068 & 0.417702469762136 & 0.791148765118932 \tabularnewline
23 & 0.56289031844168 & 0.874219363116639 & 0.437109681558319 \tabularnewline
24 & 0.44876986491339 & 0.89753972982678 & 0.55123013508661 \tabularnewline
25 & 0.522263631712589 & 0.955472736574822 & 0.477736368287411 \tabularnewline
26 & 0.88698099453639 & 0.226038010927219 & 0.113019005463609 \tabularnewline
27 & 0.89323302101032 & 0.213533957979360 & 0.106766978989680 \tabularnewline
28 & 0.851454436864561 & 0.297091126270877 & 0.148545563135439 \tabularnewline
29 & 0.78467827937304 & 0.43064344125392 & 0.21532172062696 \tabularnewline
30 & 0.786274831309862 & 0.427450337380276 & 0.213725168690138 \tabularnewline
31 & 0.90647890631879 & 0.187042187362419 & 0.0935210936812095 \tabularnewline
32 & 0.94554094713492 & 0.108918105730161 & 0.0544590528650805 \tabularnewline
33 & 0.961534900300635 & 0.0769301993987293 & 0.0384650996993647 \tabularnewline
34 & 0.947738548192084 & 0.104522903615831 & 0.0522614518079157 \tabularnewline
35 & 0.980671718877877 & 0.0386565622442467 & 0.0193282811221234 \tabularnewline
36 & 0.958885198074412 & 0.0822296038511756 & 0.0411148019255878 \tabularnewline
37 & 0.942068180125394 & 0.115863639749213 & 0.0579318198746063 \tabularnewline
38 & 0.963863995520063 & 0.0722720089598748 & 0.0361360044799374 \tabularnewline
39 & 0.988721775714146 & 0.0225564485717079 & 0.0112782242858540 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&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]0.192830144016746[/C][C]0.385660288033493[/C][C]0.807169855983254[/C][/ROW]
[ROW][C]20[/C][C]0.140575807283262[/C][C]0.281151614566524[/C][C]0.859424192716738[/C][/ROW]
[ROW][C]21[/C][C]0.0717162680200237[/C][C]0.143432536040047[/C][C]0.928283731979976[/C][/ROW]
[ROW][C]22[/C][C]0.208851234881068[/C][C]0.417702469762136[/C][C]0.791148765118932[/C][/ROW]
[ROW][C]23[/C][C]0.56289031844168[/C][C]0.874219363116639[/C][C]0.437109681558319[/C][/ROW]
[ROW][C]24[/C][C]0.44876986491339[/C][C]0.89753972982678[/C][C]0.55123013508661[/C][/ROW]
[ROW][C]25[/C][C]0.522263631712589[/C][C]0.955472736574822[/C][C]0.477736368287411[/C][/ROW]
[ROW][C]26[/C][C]0.88698099453639[/C][C]0.226038010927219[/C][C]0.113019005463609[/C][/ROW]
[ROW][C]27[/C][C]0.89323302101032[/C][C]0.213533957979360[/C][C]0.106766978989680[/C][/ROW]
[ROW][C]28[/C][C]0.851454436864561[/C][C]0.297091126270877[/C][C]0.148545563135439[/C][/ROW]
[ROW][C]29[/C][C]0.78467827937304[/C][C]0.43064344125392[/C][C]0.21532172062696[/C][/ROW]
[ROW][C]30[/C][C]0.786274831309862[/C][C]0.427450337380276[/C][C]0.213725168690138[/C][/ROW]
[ROW][C]31[/C][C]0.90647890631879[/C][C]0.187042187362419[/C][C]0.0935210936812095[/C][/ROW]
[ROW][C]32[/C][C]0.94554094713492[/C][C]0.108918105730161[/C][C]0.0544590528650805[/C][/ROW]
[ROW][C]33[/C][C]0.961534900300635[/C][C]0.0769301993987293[/C][C]0.0384650996993647[/C][/ROW]
[ROW][C]34[/C][C]0.947738548192084[/C][C]0.104522903615831[/C][C]0.0522614518079157[/C][/ROW]
[ROW][C]35[/C][C]0.980671718877877[/C][C]0.0386565622442467[/C][C]0.0193282811221234[/C][/ROW]
[ROW][C]36[/C][C]0.958885198074412[/C][C]0.0822296038511756[/C][C]0.0411148019255878[/C][/ROW]
[ROW][C]37[/C][C]0.942068180125394[/C][C]0.115863639749213[/C][C]0.0579318198746063[/C][/ROW]
[ROW][C]38[/C][C]0.963863995520063[/C][C]0.0722720089598748[/C][C]0.0361360044799374[/C][/ROW]
[ROW][C]39[/C][C]0.988721775714146[/C][C]0.0225564485717079[/C][C]0.0112782242858540[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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
190.1928301440167460.3856602880334930.807169855983254
200.1405758072832620.2811516145665240.859424192716738
210.07171626802002370.1434325360400470.928283731979976
220.2088512348810680.4177024697621360.791148765118932
230.562890318441680.8742193631166390.437109681558319
240.448769864913390.897539729826780.55123013508661
250.5222636317125890.9554727365748220.477736368287411
260.886980994536390.2260380109272190.113019005463609
270.893233021010320.2135339579793600.106766978989680
280.8514544368645610.2970911262708770.148545563135439
290.784678279373040.430643441253920.21532172062696
300.7862748313098620.4274503373802760.213725168690138
310.906478906318790.1870421873624190.0935210936812095
320.945540947134920.1089181057301610.0544590528650805
330.9615349003006350.07693019939872930.0384650996993647
340.9477385481920840.1045229036158310.0522614518079157
350.9806717188778770.03865656224424670.0193282811221234
360.9588851980744120.08222960385117560.0411148019255878
370.9420681801253940.1158636397492130.0579318198746063
380.9638639955200630.07227200895987480.0361360044799374
390.9887217757141460.02255644857170790.0112782242858540







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level20.0952380952380952NOK
10% type I error level50.238095238095238NOK

\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 & 2 & 0.0952380952380952 & NOK \tabularnewline
10% type I error level & 5 & 0.238095238095238 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=57659&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]2[/C][C]0.0952380952380952[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]5[/C][C]0.238095238095238[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=57659&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=57659&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 level20.0952380952380952NOK
10% type I error level50.238095238095238NOK



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')
}