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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 20 Nov 2009 05:14:35 -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/20/t1258719337tmchde966qpml4h.htm/, Retrieved Thu, 28 Mar 2024 14:58:09 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58070, Retrieved Thu, 28 Mar 2024 14:58:09 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsbhschhwsw7l3
Estimated Impact142
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 14:06:21] [b98453cac15ba1066b407e146608df68]
-    D      [Multiple Regression] [Ws7] [2009-11-20 12:14:35] [682632737e024f9e62885141c5f654cd] [Current]
-    D        [Multiple Regression] [Revieuw] [2009-11-24 20:28:44] [214e6e00abbde49700521a7ef1d30da2]
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Dataseries X:
126.51	0
131.02	0
136.51	0
138.04	0
132.92	0
129.61	0
122.96	0
124.04	0
121.29	0
124.56	0
118.53	0
113.14	0
114.15	0
122.17	0
129.23	0
131.19	0
129.12	0
128.28	0
126.83	0
138.13	0
140.52	0
146.83	0
135.14	0
131.84	0
125.7	0
128.98	0
133.25	0
136.76	0
133.24	0
128.54	0
121.08	0
120.23	0
119.08	0
125.75	0
126.89	0
126.6	0
121.89	0
123.44	0
126.46	0
129.49	0
127.78	0
125.29	0
119.02	0
119.96	0
122.86	0
131.89	0
132.73	0
135.01	0
136.71	1
142.73	1
144.43	1
144.93	1
138.75	1
130.22	1
122.19	1
128.4	1
140.43	1
153.5	1
149.33	1
142.97	1




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=58070&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=58070&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&T=0

As an alternative you can also use a QR Code:  

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

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







Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 128.755416666667 + 12.9404166666667X[t] -5.35740277777782M1[t] -0.641638888888884M2[t] + 3.70612499999999M3[t] + 5.85188888888888M4[t] + 2.17165277777778M5[t] -1.76258333333333M6[t] -7.69481944444444M7[t] -3.91905555555555M8[t] -1.19529166666666M9[t] + 6.51447222222222M10[t] + 2.57223611111111M11[t] -0.0397638888888885t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  128.755416666667 +  12.9404166666667X[t] -5.35740277777782M1[t] -0.641638888888884M2[t] +  3.70612499999999M3[t] +  5.85188888888888M4[t] +  2.17165277777778M5[t] -1.76258333333333M6[t] -7.69481944444444M7[t] -3.91905555555555M8[t] -1.19529166666666M9[t] +  6.51447222222222M10[t] +  2.57223611111111M11[t] -0.0397638888888885t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  128.755416666667 +  12.9404166666667X[t] -5.35740277777782M1[t] -0.641638888888884M2[t] +  3.70612499999999M3[t] +  5.85188888888888M4[t] +  2.17165277777778M5[t] -1.76258333333333M6[t] -7.69481944444444M7[t] -3.91905555555555M8[t] -1.19529166666666M9[t] +  6.51447222222222M10[t] +  2.57223611111111M11[t] -0.0397638888888885t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&T=1

As an alternative you can also use a QR Code:  

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

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 128.755416666667 + 12.9404166666667X[t] -5.35740277777782M1[t] -0.641638888888884M2[t] + 3.70612499999999M3[t] + 5.85188888888888M4[t] + 2.17165277777778M5[t] -1.76258333333333M6[t] -7.69481944444444M7[t] -3.91905555555555M8[t] -1.19529166666666M9[t] + 6.51447222222222M10[t] + 2.57223611111111M11[t] -0.0397638888888885t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)128.7554166666673.69111334.882500
X12.94041666666673.0340764.2659.8e-054.9e-05
M1-5.357402777777824.277103-1.25260.216690.108345
M2-0.6416388888888844.264529-0.15050.881060.44053
M33.706124999999994.2531210.87140.3880670.194033
M45.851888888888884.2428881.37920.1744950.087248
M52.171652777777784.2338380.51290.6104560.305228
M6-1.762583333333334.225979-0.41710.6785580.339279
M7-7.694819444444444.219318-1.82370.0746960.037348
M8-3.919055555555554.21386-0.930.3572060.178603
M9-1.195291666666664.20961-0.28390.7777280.388864
M106.514472222222224.2065711.54860.1283210.064161
M112.572236111111114.2047470.61170.5437180.271859
t-0.03976388888888850.071514-0.5560.5808850.290442

\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) & 128.755416666667 & 3.691113 & 34.8825 & 0 & 0 \tabularnewline
X & 12.9404166666667 & 3.034076 & 4.265 & 9.8e-05 & 4.9e-05 \tabularnewline
M1 & -5.35740277777782 & 4.277103 & -1.2526 & 0.21669 & 0.108345 \tabularnewline
M2 & -0.641638888888884 & 4.264529 & -0.1505 & 0.88106 & 0.44053 \tabularnewline
M3 & 3.70612499999999 & 4.253121 & 0.8714 & 0.388067 & 0.194033 \tabularnewline
M4 & 5.85188888888888 & 4.242888 & 1.3792 & 0.174495 & 0.087248 \tabularnewline
M5 & 2.17165277777778 & 4.233838 & 0.5129 & 0.610456 & 0.305228 \tabularnewline
M6 & -1.76258333333333 & 4.225979 & -0.4171 & 0.678558 & 0.339279 \tabularnewline
M7 & -7.69481944444444 & 4.219318 & -1.8237 & 0.074696 & 0.037348 \tabularnewline
M8 & -3.91905555555555 & 4.21386 & -0.93 & 0.357206 & 0.178603 \tabularnewline
M9 & -1.19529166666666 & 4.20961 & -0.2839 & 0.777728 & 0.388864 \tabularnewline
M10 & 6.51447222222222 & 4.206571 & 1.5486 & 0.128321 & 0.064161 \tabularnewline
M11 & 2.57223611111111 & 4.204747 & 0.6117 & 0.543718 & 0.271859 \tabularnewline
t & -0.0397638888888885 & 0.071514 & -0.556 & 0.580885 & 0.290442 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&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]128.755416666667[/C][C]3.691113[/C][C]34.8825[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]X[/C][C]12.9404166666667[/C][C]3.034076[/C][C]4.265[/C][C]9.8e-05[/C][C]4.9e-05[/C][/ROW]
[ROW][C]M1[/C][C]-5.35740277777782[/C][C]4.277103[/C][C]-1.2526[/C][C]0.21669[/C][C]0.108345[/C][/ROW]
[ROW][C]M2[/C][C]-0.641638888888884[/C][C]4.264529[/C][C]-0.1505[/C][C]0.88106[/C][C]0.44053[/C][/ROW]
[ROW][C]M3[/C][C]3.70612499999999[/C][C]4.253121[/C][C]0.8714[/C][C]0.388067[/C][C]0.194033[/C][/ROW]
[ROW][C]M4[/C][C]5.85188888888888[/C][C]4.242888[/C][C]1.3792[/C][C]0.174495[/C][C]0.087248[/C][/ROW]
[ROW][C]M5[/C][C]2.17165277777778[/C][C]4.233838[/C][C]0.5129[/C][C]0.610456[/C][C]0.305228[/C][/ROW]
[ROW][C]M6[/C][C]-1.76258333333333[/C][C]4.225979[/C][C]-0.4171[/C][C]0.678558[/C][C]0.339279[/C][/ROW]
[ROW][C]M7[/C][C]-7.69481944444444[/C][C]4.219318[/C][C]-1.8237[/C][C]0.074696[/C][C]0.037348[/C][/ROW]
[ROW][C]M8[/C][C]-3.91905555555555[/C][C]4.21386[/C][C]-0.93[/C][C]0.357206[/C][C]0.178603[/C][/ROW]
[ROW][C]M9[/C][C]-1.19529166666666[/C][C]4.20961[/C][C]-0.2839[/C][C]0.777728[/C][C]0.388864[/C][/ROW]
[ROW][C]M10[/C][C]6.51447222222222[/C][C]4.206571[/C][C]1.5486[/C][C]0.128321[/C][C]0.064161[/C][/ROW]
[ROW][C]M11[/C][C]2.57223611111111[/C][C]4.204747[/C][C]0.6117[/C][C]0.543718[/C][C]0.271859[/C][/ROW]
[ROW][C]t[/C][C]-0.0397638888888885[/C][C]0.071514[/C][C]-0.556[/C][C]0.580885[/C][C]0.290442[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&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)128.7554166666673.69111334.882500
X12.94041666666673.0340764.2659.8e-054.9e-05
M1-5.357402777777824.277103-1.25260.216690.108345
M2-0.6416388888888844.264529-0.15050.881060.44053
M33.706124999999994.2531210.87140.3880670.194033
M45.851888888888884.2428881.37920.1744950.087248
M52.171652777777784.2338380.51290.6104560.305228
M6-1.762583333333334.225979-0.41710.6785580.339279
M7-7.694819444444444.219318-1.82370.0746960.037348
M8-3.919055555555554.21386-0.930.3572060.178603
M9-1.195291666666664.20961-0.28390.7777280.388864
M106.514472222222224.2065711.54860.1283210.064161
M112.572236111111114.2047470.61170.5437180.271859
t-0.03976388888888850.071514-0.5560.5808850.290442







Multiple Linear Regression - Regression Statistics
Multiple R0.734406514839355
R-squared0.539352929038487
Adjusted R-squared0.40917006115806
F-TEST (value)4.14304076888121
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0.000163786037073121
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.64732770487073
Sum Squared Residuals2032.60041833333

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.734406514839355 \tabularnewline
R-squared & 0.539352929038487 \tabularnewline
Adjusted R-squared & 0.40917006115806 \tabularnewline
F-TEST (value) & 4.14304076888121 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 46 \tabularnewline
p-value & 0.000163786037073121 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 6.64732770487073 \tabularnewline
Sum Squared Residuals & 2032.60041833333 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.734406514839355[/C][/ROW]
[ROW][C]R-squared[/C][C]0.539352929038487[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.40917006115806[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.14304076888121[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]46[/C][/ROW]
[ROW][C]p-value[/C][C]0.000163786037073121[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]6.64732770487073[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2032.60041833333[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&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 R0.734406514839355
R-squared0.539352929038487
Adjusted R-squared0.40917006115806
F-TEST (value)4.14304076888121
F-TEST (DF numerator)13
F-TEST (DF denominator)46
p-value0.000163786037073121
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation6.64732770487073
Sum Squared Residuals2032.60041833333







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1126.51123.3582500000003.1517499999998
2131.02128.034252.98575
3136.51132.342254.16775000000001
4138.04134.448253.59174999999999
5132.92130.728252.19175000000001
6129.61126.754252.85575000000003
7122.96120.782252.17775000000002
8124.04124.51825-0.478249999999984
9121.29127.20225-5.91224999999999
10124.56134.87225-10.3122500000000
11118.53130.89025-12.3602500000000
12113.14128.27825-15.13825
13114.15122.881083333333-8.73108333333327
14122.17127.557083333333-5.38708333333332
15129.23131.865083333333-2.63508333333333
16131.19133.971083333333-2.78108333333333
17129.12130.251083333333-1.13108333333333
18128.28126.2770833333332.00291666666667
19126.83120.3050833333336.52491666666667
20138.13124.04108333333314.0889166666667
21140.52126.72508333333313.7949166666667
22146.83134.39508333333312.4349166666667
23135.14130.4130833333334.72691666666666
24131.84127.8010833333334.03891666666667
25125.7122.4039166666673.29608333333338
26128.98127.0799166666671.90008333333333
27133.25131.3879166666671.86208333333334
28136.76133.4939166666673.26608333333333
29133.24129.7739166666673.46608333333334
30128.54125.7999166666672.74008333333332
31121.08119.8279166666671.25208333333333
32120.23123.563916666667-3.33391666666666
33119.08126.247916666667-7.16791666666667
34125.75133.917916666667-8.16791666666666
35126.89129.935916666667-3.04591666666666
36126.6127.323916666667-0.723916666666674
37121.89121.92675-0.0367499999999588
38123.44126.60275-3.16275
39126.46130.91075-4.45075000000001
40129.49133.01675-3.52674999999999
41127.78129.29675-1.51675000000001
42125.29125.32275-0.0327500000000041
43119.02119.35075-0.330750000000012
44119.96123.08675-3.12675000000001
45122.86125.77075-2.91075000000001
46131.89133.44075-1.55075000000002
47132.73129.458753.27124999999998
48135.01126.846758.16324999999999
49136.71134.392.32000000000005
50142.73139.0663.66399999999999
51144.43143.3741.05600000000000
52144.93145.48-0.549999999999996
53138.75141.76-3.01000000000001
54130.22137.786-7.56600000000001
55122.19131.814-9.62400000000001
56128.4135.55-7.15
57140.43138.2342.196
58153.5145.9047.596
59149.33141.9227.408
60142.97139.313.65999999999999

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 126.51 & 123.358250000000 & 3.1517499999998 \tabularnewline
2 & 131.02 & 128.03425 & 2.98575 \tabularnewline
3 & 136.51 & 132.34225 & 4.16775000000001 \tabularnewline
4 & 138.04 & 134.44825 & 3.59174999999999 \tabularnewline
5 & 132.92 & 130.72825 & 2.19175000000001 \tabularnewline
6 & 129.61 & 126.75425 & 2.85575000000003 \tabularnewline
7 & 122.96 & 120.78225 & 2.17775000000002 \tabularnewline
8 & 124.04 & 124.51825 & -0.478249999999984 \tabularnewline
9 & 121.29 & 127.20225 & -5.91224999999999 \tabularnewline
10 & 124.56 & 134.87225 & -10.3122500000000 \tabularnewline
11 & 118.53 & 130.89025 & -12.3602500000000 \tabularnewline
12 & 113.14 & 128.27825 & -15.13825 \tabularnewline
13 & 114.15 & 122.881083333333 & -8.73108333333327 \tabularnewline
14 & 122.17 & 127.557083333333 & -5.38708333333332 \tabularnewline
15 & 129.23 & 131.865083333333 & -2.63508333333333 \tabularnewline
16 & 131.19 & 133.971083333333 & -2.78108333333333 \tabularnewline
17 & 129.12 & 130.251083333333 & -1.13108333333333 \tabularnewline
18 & 128.28 & 126.277083333333 & 2.00291666666667 \tabularnewline
19 & 126.83 & 120.305083333333 & 6.52491666666667 \tabularnewline
20 & 138.13 & 124.041083333333 & 14.0889166666667 \tabularnewline
21 & 140.52 & 126.725083333333 & 13.7949166666667 \tabularnewline
22 & 146.83 & 134.395083333333 & 12.4349166666667 \tabularnewline
23 & 135.14 & 130.413083333333 & 4.72691666666666 \tabularnewline
24 & 131.84 & 127.801083333333 & 4.03891666666667 \tabularnewline
25 & 125.7 & 122.403916666667 & 3.29608333333338 \tabularnewline
26 & 128.98 & 127.079916666667 & 1.90008333333333 \tabularnewline
27 & 133.25 & 131.387916666667 & 1.86208333333334 \tabularnewline
28 & 136.76 & 133.493916666667 & 3.26608333333333 \tabularnewline
29 & 133.24 & 129.773916666667 & 3.46608333333334 \tabularnewline
30 & 128.54 & 125.799916666667 & 2.74008333333332 \tabularnewline
31 & 121.08 & 119.827916666667 & 1.25208333333333 \tabularnewline
32 & 120.23 & 123.563916666667 & -3.33391666666666 \tabularnewline
33 & 119.08 & 126.247916666667 & -7.16791666666667 \tabularnewline
34 & 125.75 & 133.917916666667 & -8.16791666666666 \tabularnewline
35 & 126.89 & 129.935916666667 & -3.04591666666666 \tabularnewline
36 & 126.6 & 127.323916666667 & -0.723916666666674 \tabularnewline
37 & 121.89 & 121.92675 & -0.0367499999999588 \tabularnewline
38 & 123.44 & 126.60275 & -3.16275 \tabularnewline
39 & 126.46 & 130.91075 & -4.45075000000001 \tabularnewline
40 & 129.49 & 133.01675 & -3.52674999999999 \tabularnewline
41 & 127.78 & 129.29675 & -1.51675000000001 \tabularnewline
42 & 125.29 & 125.32275 & -0.0327500000000041 \tabularnewline
43 & 119.02 & 119.35075 & -0.330750000000012 \tabularnewline
44 & 119.96 & 123.08675 & -3.12675000000001 \tabularnewline
45 & 122.86 & 125.77075 & -2.91075000000001 \tabularnewline
46 & 131.89 & 133.44075 & -1.55075000000002 \tabularnewline
47 & 132.73 & 129.45875 & 3.27124999999998 \tabularnewline
48 & 135.01 & 126.84675 & 8.16324999999999 \tabularnewline
49 & 136.71 & 134.39 & 2.32000000000005 \tabularnewline
50 & 142.73 & 139.066 & 3.66399999999999 \tabularnewline
51 & 144.43 & 143.374 & 1.05600000000000 \tabularnewline
52 & 144.93 & 145.48 & -0.549999999999996 \tabularnewline
53 & 138.75 & 141.76 & -3.01000000000001 \tabularnewline
54 & 130.22 & 137.786 & -7.56600000000001 \tabularnewline
55 & 122.19 & 131.814 & -9.62400000000001 \tabularnewline
56 & 128.4 & 135.55 & -7.15 \tabularnewline
57 & 140.43 & 138.234 & 2.196 \tabularnewline
58 & 153.5 & 145.904 & 7.596 \tabularnewline
59 & 149.33 & 141.922 & 7.408 \tabularnewline
60 & 142.97 & 139.31 & 3.65999999999999 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&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]126.51[/C][C]123.358250000000[/C][C]3.1517499999998[/C][/ROW]
[ROW][C]2[/C][C]131.02[/C][C]128.03425[/C][C]2.98575[/C][/ROW]
[ROW][C]3[/C][C]136.51[/C][C]132.34225[/C][C]4.16775000000001[/C][/ROW]
[ROW][C]4[/C][C]138.04[/C][C]134.44825[/C][C]3.59174999999999[/C][/ROW]
[ROW][C]5[/C][C]132.92[/C][C]130.72825[/C][C]2.19175000000001[/C][/ROW]
[ROW][C]6[/C][C]129.61[/C][C]126.75425[/C][C]2.85575000000003[/C][/ROW]
[ROW][C]7[/C][C]122.96[/C][C]120.78225[/C][C]2.17775000000002[/C][/ROW]
[ROW][C]8[/C][C]124.04[/C][C]124.51825[/C][C]-0.478249999999984[/C][/ROW]
[ROW][C]9[/C][C]121.29[/C][C]127.20225[/C][C]-5.91224999999999[/C][/ROW]
[ROW][C]10[/C][C]124.56[/C][C]134.87225[/C][C]-10.3122500000000[/C][/ROW]
[ROW][C]11[/C][C]118.53[/C][C]130.89025[/C][C]-12.3602500000000[/C][/ROW]
[ROW][C]12[/C][C]113.14[/C][C]128.27825[/C][C]-15.13825[/C][/ROW]
[ROW][C]13[/C][C]114.15[/C][C]122.881083333333[/C][C]-8.73108333333327[/C][/ROW]
[ROW][C]14[/C][C]122.17[/C][C]127.557083333333[/C][C]-5.38708333333332[/C][/ROW]
[ROW][C]15[/C][C]129.23[/C][C]131.865083333333[/C][C]-2.63508333333333[/C][/ROW]
[ROW][C]16[/C][C]131.19[/C][C]133.971083333333[/C][C]-2.78108333333333[/C][/ROW]
[ROW][C]17[/C][C]129.12[/C][C]130.251083333333[/C][C]-1.13108333333333[/C][/ROW]
[ROW][C]18[/C][C]128.28[/C][C]126.277083333333[/C][C]2.00291666666667[/C][/ROW]
[ROW][C]19[/C][C]126.83[/C][C]120.305083333333[/C][C]6.52491666666667[/C][/ROW]
[ROW][C]20[/C][C]138.13[/C][C]124.041083333333[/C][C]14.0889166666667[/C][/ROW]
[ROW][C]21[/C][C]140.52[/C][C]126.725083333333[/C][C]13.7949166666667[/C][/ROW]
[ROW][C]22[/C][C]146.83[/C][C]134.395083333333[/C][C]12.4349166666667[/C][/ROW]
[ROW][C]23[/C][C]135.14[/C][C]130.413083333333[/C][C]4.72691666666666[/C][/ROW]
[ROW][C]24[/C][C]131.84[/C][C]127.801083333333[/C][C]4.03891666666667[/C][/ROW]
[ROW][C]25[/C][C]125.7[/C][C]122.403916666667[/C][C]3.29608333333338[/C][/ROW]
[ROW][C]26[/C][C]128.98[/C][C]127.079916666667[/C][C]1.90008333333333[/C][/ROW]
[ROW][C]27[/C][C]133.25[/C][C]131.387916666667[/C][C]1.86208333333334[/C][/ROW]
[ROW][C]28[/C][C]136.76[/C][C]133.493916666667[/C][C]3.26608333333333[/C][/ROW]
[ROW][C]29[/C][C]133.24[/C][C]129.773916666667[/C][C]3.46608333333334[/C][/ROW]
[ROW][C]30[/C][C]128.54[/C][C]125.799916666667[/C][C]2.74008333333332[/C][/ROW]
[ROW][C]31[/C][C]121.08[/C][C]119.827916666667[/C][C]1.25208333333333[/C][/ROW]
[ROW][C]32[/C][C]120.23[/C][C]123.563916666667[/C][C]-3.33391666666666[/C][/ROW]
[ROW][C]33[/C][C]119.08[/C][C]126.247916666667[/C][C]-7.16791666666667[/C][/ROW]
[ROW][C]34[/C][C]125.75[/C][C]133.917916666667[/C][C]-8.16791666666666[/C][/ROW]
[ROW][C]35[/C][C]126.89[/C][C]129.935916666667[/C][C]-3.04591666666666[/C][/ROW]
[ROW][C]36[/C][C]126.6[/C][C]127.323916666667[/C][C]-0.723916666666674[/C][/ROW]
[ROW][C]37[/C][C]121.89[/C][C]121.92675[/C][C]-0.0367499999999588[/C][/ROW]
[ROW][C]38[/C][C]123.44[/C][C]126.60275[/C][C]-3.16275[/C][/ROW]
[ROW][C]39[/C][C]126.46[/C][C]130.91075[/C][C]-4.45075000000001[/C][/ROW]
[ROW][C]40[/C][C]129.49[/C][C]133.01675[/C][C]-3.52674999999999[/C][/ROW]
[ROW][C]41[/C][C]127.78[/C][C]129.29675[/C][C]-1.51675000000001[/C][/ROW]
[ROW][C]42[/C][C]125.29[/C][C]125.32275[/C][C]-0.0327500000000041[/C][/ROW]
[ROW][C]43[/C][C]119.02[/C][C]119.35075[/C][C]-0.330750000000012[/C][/ROW]
[ROW][C]44[/C][C]119.96[/C][C]123.08675[/C][C]-3.12675000000001[/C][/ROW]
[ROW][C]45[/C][C]122.86[/C][C]125.77075[/C][C]-2.91075000000001[/C][/ROW]
[ROW][C]46[/C][C]131.89[/C][C]133.44075[/C][C]-1.55075000000002[/C][/ROW]
[ROW][C]47[/C][C]132.73[/C][C]129.45875[/C][C]3.27124999999998[/C][/ROW]
[ROW][C]48[/C][C]135.01[/C][C]126.84675[/C][C]8.16324999999999[/C][/ROW]
[ROW][C]49[/C][C]136.71[/C][C]134.39[/C][C]2.32000000000005[/C][/ROW]
[ROW][C]50[/C][C]142.73[/C][C]139.066[/C][C]3.66399999999999[/C][/ROW]
[ROW][C]51[/C][C]144.43[/C][C]143.374[/C][C]1.05600000000000[/C][/ROW]
[ROW][C]52[/C][C]144.93[/C][C]145.48[/C][C]-0.549999999999996[/C][/ROW]
[ROW][C]53[/C][C]138.75[/C][C]141.76[/C][C]-3.01000000000001[/C][/ROW]
[ROW][C]54[/C][C]130.22[/C][C]137.786[/C][C]-7.56600000000001[/C][/ROW]
[ROW][C]55[/C][C]122.19[/C][C]131.814[/C][C]-9.62400000000001[/C][/ROW]
[ROW][C]56[/C][C]128.4[/C][C]135.55[/C][C]-7.15[/C][/ROW]
[ROW][C]57[/C][C]140.43[/C][C]138.234[/C][C]2.196[/C][/ROW]
[ROW][C]58[/C][C]153.5[/C][C]145.904[/C][C]7.596[/C][/ROW]
[ROW][C]59[/C][C]149.33[/C][C]141.922[/C][C]7.408[/C][/ROW]
[ROW][C]60[/C][C]142.97[/C][C]139.31[/C][C]3.65999999999999[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&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
1126.51123.3582500000003.1517499999998
2131.02128.034252.98575
3136.51132.342254.16775000000001
4138.04134.448253.59174999999999
5132.92130.728252.19175000000001
6129.61126.754252.85575000000003
7122.96120.782252.17775000000002
8124.04124.51825-0.478249999999984
9121.29127.20225-5.91224999999999
10124.56134.87225-10.3122500000000
11118.53130.89025-12.3602500000000
12113.14128.27825-15.13825
13114.15122.881083333333-8.73108333333327
14122.17127.557083333333-5.38708333333332
15129.23131.865083333333-2.63508333333333
16131.19133.971083333333-2.78108333333333
17129.12130.251083333333-1.13108333333333
18128.28126.2770833333332.00291666666667
19126.83120.3050833333336.52491666666667
20138.13124.04108333333314.0889166666667
21140.52126.72508333333313.7949166666667
22146.83134.39508333333312.4349166666667
23135.14130.4130833333334.72691666666666
24131.84127.8010833333334.03891666666667
25125.7122.4039166666673.29608333333338
26128.98127.0799166666671.90008333333333
27133.25131.3879166666671.86208333333334
28136.76133.4939166666673.26608333333333
29133.24129.7739166666673.46608333333334
30128.54125.7999166666672.74008333333332
31121.08119.8279166666671.25208333333333
32120.23123.563916666667-3.33391666666666
33119.08126.247916666667-7.16791666666667
34125.75133.917916666667-8.16791666666666
35126.89129.935916666667-3.04591666666666
36126.6127.323916666667-0.723916666666674
37121.89121.92675-0.0367499999999588
38123.44126.60275-3.16275
39126.46130.91075-4.45075000000001
40129.49133.01675-3.52674999999999
41127.78129.29675-1.51675000000001
42125.29125.32275-0.0327500000000041
43119.02119.35075-0.330750000000012
44119.96123.08675-3.12675000000001
45122.86125.77075-2.91075000000001
46131.89133.44075-1.55075000000002
47132.73129.458753.27124999999998
48135.01126.846758.16324999999999
49136.71134.392.32000000000005
50142.73139.0663.66399999999999
51144.43143.3741.05600000000000
52144.93145.48-0.549999999999996
53138.75141.76-3.01000000000001
54130.22137.786-7.56600000000001
55122.19131.814-9.62400000000001
56128.4135.55-7.15
57140.43138.2342.196
58153.5145.9047.596
59149.33141.9227.408
60142.97139.313.65999999999999







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.1169482663712620.2338965327425240.883051733628738
180.1259616924984770.2519233849969540.874038307501523
190.2562803763984460.5125607527968910.743719623601554
200.780925919878310.4381481602433790.219074080121689
210.9720913376438140.05581732471237180.0279086623561859
220.9969663423134640.006067315373071730.00303365768653586
230.9969022877560440.006195424487912480.00309771224395624
240.99675610211740.006487795765198670.00324389788259934
250.9933324252687040.01333514946259160.00666757473129582
260.9875845419743070.0248309160513850.0124154580256925
270.98059650923320.03880698153360110.0194034907668006
280.9734588048431950.05308239031361040.0265411951568052
290.9688041267513670.06239174649726520.0311958732486326
300.9747862527180180.05042749456396380.0252137472819819
310.9887385629569030.02252287408619350.0112614370430967
320.9954599348985560.00908013020288820.0045400651014441
330.9939811821853920.01203763562921580.00601881781460791
340.9910160890621220.01796782187575670.00898391093787835
350.9813353637683360.0373292724633280.018664636231664
360.9636170407206550.07276591855868910.0363829592793445
370.932938276000660.1341234479986800.0670617239993402
380.913249128487440.1735017430251190.0867508715125594
390.8844992055138130.2310015889723740.115500794486187
400.822477876966110.3550442460677810.177522123033891
410.7108150887443680.5783698225112630.289184911255632
420.6444833973209510.7110332053580980.355516602679049
430.6855097078130740.6289805843738520.314490292186926

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.116948266371262 & 0.233896532742524 & 0.883051733628738 \tabularnewline
18 & 0.125961692498477 & 0.251923384996954 & 0.874038307501523 \tabularnewline
19 & 0.256280376398446 & 0.512560752796891 & 0.743719623601554 \tabularnewline
20 & 0.78092591987831 & 0.438148160243379 & 0.219074080121689 \tabularnewline
21 & 0.972091337643814 & 0.0558173247123718 & 0.0279086623561859 \tabularnewline
22 & 0.996966342313464 & 0.00606731537307173 & 0.00303365768653586 \tabularnewline
23 & 0.996902287756044 & 0.00619542448791248 & 0.00309771224395624 \tabularnewline
24 & 0.9967561021174 & 0.00648779576519867 & 0.00324389788259934 \tabularnewline
25 & 0.993332425268704 & 0.0133351494625916 & 0.00666757473129582 \tabularnewline
26 & 0.987584541974307 & 0.024830916051385 & 0.0124154580256925 \tabularnewline
27 & 0.9805965092332 & 0.0388069815336011 & 0.0194034907668006 \tabularnewline
28 & 0.973458804843195 & 0.0530823903136104 & 0.0265411951568052 \tabularnewline
29 & 0.968804126751367 & 0.0623917464972652 & 0.0311958732486326 \tabularnewline
30 & 0.974786252718018 & 0.0504274945639638 & 0.0252137472819819 \tabularnewline
31 & 0.988738562956903 & 0.0225228740861935 & 0.0112614370430967 \tabularnewline
32 & 0.995459934898556 & 0.0090801302028882 & 0.0045400651014441 \tabularnewline
33 & 0.993981182185392 & 0.0120376356292158 & 0.00601881781460791 \tabularnewline
34 & 0.991016089062122 & 0.0179678218757567 & 0.00898391093787835 \tabularnewline
35 & 0.981335363768336 & 0.037329272463328 & 0.018664636231664 \tabularnewline
36 & 0.963617040720655 & 0.0727659185586891 & 0.0363829592793445 \tabularnewline
37 & 0.93293827600066 & 0.134123447998680 & 0.0670617239993402 \tabularnewline
38 & 0.91324912848744 & 0.173501743025119 & 0.0867508715125594 \tabularnewline
39 & 0.884499205513813 & 0.231001588972374 & 0.115500794486187 \tabularnewline
40 & 0.82247787696611 & 0.355044246067781 & 0.177522123033891 \tabularnewline
41 & 0.710815088744368 & 0.578369822511263 & 0.289184911255632 \tabularnewline
42 & 0.644483397320951 & 0.711033205358098 & 0.355516602679049 \tabularnewline
43 & 0.685509707813074 & 0.628980584373852 & 0.314490292186926 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&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]17[/C][C]0.116948266371262[/C][C]0.233896532742524[/C][C]0.883051733628738[/C][/ROW]
[ROW][C]18[/C][C]0.125961692498477[/C][C]0.251923384996954[/C][C]0.874038307501523[/C][/ROW]
[ROW][C]19[/C][C]0.256280376398446[/C][C]0.512560752796891[/C][C]0.743719623601554[/C][/ROW]
[ROW][C]20[/C][C]0.78092591987831[/C][C]0.438148160243379[/C][C]0.219074080121689[/C][/ROW]
[ROW][C]21[/C][C]0.972091337643814[/C][C]0.0558173247123718[/C][C]0.0279086623561859[/C][/ROW]
[ROW][C]22[/C][C]0.996966342313464[/C][C]0.00606731537307173[/C][C]0.00303365768653586[/C][/ROW]
[ROW][C]23[/C][C]0.996902287756044[/C][C]0.00619542448791248[/C][C]0.00309771224395624[/C][/ROW]
[ROW][C]24[/C][C]0.9967561021174[/C][C]0.00648779576519867[/C][C]0.00324389788259934[/C][/ROW]
[ROW][C]25[/C][C]0.993332425268704[/C][C]0.0133351494625916[/C][C]0.00666757473129582[/C][/ROW]
[ROW][C]26[/C][C]0.987584541974307[/C][C]0.024830916051385[/C][C]0.0124154580256925[/C][/ROW]
[ROW][C]27[/C][C]0.9805965092332[/C][C]0.0388069815336011[/C][C]0.0194034907668006[/C][/ROW]
[ROW][C]28[/C][C]0.973458804843195[/C][C]0.0530823903136104[/C][C]0.0265411951568052[/C][/ROW]
[ROW][C]29[/C][C]0.968804126751367[/C][C]0.0623917464972652[/C][C]0.0311958732486326[/C][/ROW]
[ROW][C]30[/C][C]0.974786252718018[/C][C]0.0504274945639638[/C][C]0.0252137472819819[/C][/ROW]
[ROW][C]31[/C][C]0.988738562956903[/C][C]0.0225228740861935[/C][C]0.0112614370430967[/C][/ROW]
[ROW][C]32[/C][C]0.995459934898556[/C][C]0.0090801302028882[/C][C]0.0045400651014441[/C][/ROW]
[ROW][C]33[/C][C]0.993981182185392[/C][C]0.0120376356292158[/C][C]0.00601881781460791[/C][/ROW]
[ROW][C]34[/C][C]0.991016089062122[/C][C]0.0179678218757567[/C][C]0.00898391093787835[/C][/ROW]
[ROW][C]35[/C][C]0.981335363768336[/C][C]0.037329272463328[/C][C]0.018664636231664[/C][/ROW]
[ROW][C]36[/C][C]0.963617040720655[/C][C]0.0727659185586891[/C][C]0.0363829592793445[/C][/ROW]
[ROW][C]37[/C][C]0.93293827600066[/C][C]0.134123447998680[/C][C]0.0670617239993402[/C][/ROW]
[ROW][C]38[/C][C]0.91324912848744[/C][C]0.173501743025119[/C][C]0.0867508715125594[/C][/ROW]
[ROW][C]39[/C][C]0.884499205513813[/C][C]0.231001588972374[/C][C]0.115500794486187[/C][/ROW]
[ROW][C]40[/C][C]0.82247787696611[/C][C]0.355044246067781[/C][C]0.177522123033891[/C][/ROW]
[ROW][C]41[/C][C]0.710815088744368[/C][C]0.578369822511263[/C][C]0.289184911255632[/C][/ROW]
[ROW][C]42[/C][C]0.644483397320951[/C][C]0.711033205358098[/C][C]0.355516602679049[/C][/ROW]
[ROW][C]43[/C][C]0.685509707813074[/C][C]0.628980584373852[/C][C]0.314490292186926[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&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
170.1169482663712620.2338965327425240.883051733628738
180.1259616924984770.2519233849969540.874038307501523
190.2562803763984460.5125607527968910.743719623601554
200.780925919878310.4381481602433790.219074080121689
210.9720913376438140.05581732471237180.0279086623561859
220.9969663423134640.006067315373071730.00303365768653586
230.9969022877560440.006195424487912480.00309771224395624
240.99675610211740.006487795765198670.00324389788259934
250.9933324252687040.01333514946259160.00666757473129582
260.9875845419743070.0248309160513850.0124154580256925
270.98059650923320.03880698153360110.0194034907668006
280.9734588048431950.05308239031361040.0265411951568052
290.9688041267513670.06239174649726520.0311958732486326
300.9747862527180180.05042749456396380.0252137472819819
310.9887385629569030.02252287408619350.0112614370430967
320.9954599348985560.00908013020288820.0045400651014441
330.9939811821853920.01203763562921580.00601881781460791
340.9910160890621220.01796782187575670.00898391093787835
350.9813353637683360.0373292724633280.018664636231664
360.9636170407206550.07276591855868910.0363829592793445
370.932938276000660.1341234479986800.0670617239993402
380.913249128487440.1735017430251190.0867508715125594
390.8844992055138130.2310015889723740.115500794486187
400.822477876966110.3550442460677810.177522123033891
410.7108150887443680.5783698225112630.289184911255632
420.6444833973209510.7110332053580980.355516602679049
430.6855097078130740.6289805843738520.314490292186926







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level40.148148148148148NOK
5% type I error level110.407407407407407NOK
10% type I error level160.592592592592593NOK

\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 & 4 & 0.148148148148148 & NOK \tabularnewline
5% type I error level & 11 & 0.407407407407407 & NOK \tabularnewline
10% type I error level & 16 & 0.592592592592593 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58070&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]4[/C][C]0.148148148148148[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]11[/C][C]0.407407407407407[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]16[/C][C]0.592592592592593[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58070&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58070&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 level40.148148148148148NOK
5% type I error level110.407407407407407NOK
10% type I error level160.592592592592593NOK



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')
}