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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Sat, 21 Nov 2009 03:59:25 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Nov/21/t12588012139ln8uzti3v87hag.htm/, Retrieved Sun, 28 Apr 2024 19:40:45 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58527, Retrieved Sun, 28 Apr 2024 19:40:45 +0000

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Estimated Impact

187

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [] [2009-11-21 10:59:25] [4c719cde102be108d35939b6cdb81c0f] [Current]

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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=58527&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=58527&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
CPItot[t] = + 121.12 -1.02727272727274CPIlandbouw[t] -1.80909090909093M1[t] -1.41818181818181M2[t] -2.20363636363636M3[t] -1.15272727272726M4[t] -1.22363636363636M5[t] -1.0690909090909M6[t] -0.47818181818181M7[t] -0.543636363636355M8[t] -0.31272727272726M9[t] -1.38M10[t] + 0.476363636363643M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
CPItot[t] =  +  121.12 -1.02727272727274CPIlandbouw[t] -1.80909090909093M1[t] -1.41818181818181M2[t] -2.20363636363636M3[t] -1.15272727272726M4[t] -1.22363636363636M5[t] -1.0690909090909M6[t] -0.47818181818181M7[t] -0.543636363636355M8[t] -0.31272727272726M9[t] -1.38M10[t] +  0.476363636363643M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]CPItot[t] =  +  121.12 -1.02727272727274CPIlandbouw[t] -1.80909090909093M1[t] -1.41818181818181M2[t] -2.20363636363636M3[t] -1.15272727272726M4[t] -1.22363636363636M5[t] -1.0690909090909M6[t] -0.47818181818181M7[t] -0.543636363636355M8[t] -0.31272727272726M9[t] -1.38M10[t] +  0.476363636363643M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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
CPItot[t] = + 121.12 -1.02727272727274CPIlandbouw[t] -1.80909090909093M1[t] -1.41818181818181M2[t] -2.20363636363636M3[t] -1.15272727272726M4[t] -1.22363636363636M5[t] -1.0690909090909M6[t] -0.47818181818181M7[t] -0.543636363636355M8[t] -0.31272727272726M9[t] -1.38M10[t] + 0.476363636363643M11[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	121.12	1.937919	62.5	0	0
CPIlandbouw	-1.02727272727274	1.460761	-0.7032	0.485372	0.242686
M1	-1.80909090909093	2.802226	-0.6456	0.521684	0.260842
M2	-1.41818181818181	2.97938	-0.476	0.63628	0.31814
M3	-2.20363636363636	2.877366	-0.7659	0.447594	0.223797
M4	-1.15272727272726	3.105621	-0.3712	0.712176	0.356088
M5	-1.22363636363636	2.877366	-0.4253	0.672586	0.336293
M6	-1.0690909090909	2.802226	-0.3815	0.704541	0.35227
M7	-0.47818181818181	2.97938	-0.1605	0.873177	0.436589
M8	-0.543636363636355	2.877366	-0.1889	0.850957	0.425478
M9	-0.31272727272726	3.105621	-0.1007	0.920219	0.46011
M10	-1.38	2.740631	-0.5035	0.616941	0.308471
M11	0.476363636363643	2.877366	0.1656	0.869217	0.434608

\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) & 121.12 & 1.937919 & 62.5 & 0 & 0 \tabularnewline
CPIlandbouw & -1.02727272727274 & 1.460761 & -0.7032 & 0.485372 & 0.242686 \tabularnewline
M1 & -1.80909090909093 & 2.802226 & -0.6456 & 0.521684 & 0.260842 \tabularnewline
M2 & -1.41818181818181 & 2.97938 & -0.476 & 0.63628 & 0.31814 \tabularnewline
M3 & -2.20363636363636 & 2.877366 & -0.7659 & 0.447594 & 0.223797 \tabularnewline
M4 & -1.15272727272726 & 3.105621 & -0.3712 & 0.712176 & 0.356088 \tabularnewline
M5 & -1.22363636363636 & 2.877366 & -0.4253 & 0.672586 & 0.336293 \tabularnewline
M6 & -1.0690909090909 & 2.802226 & -0.3815 & 0.704541 & 0.35227 \tabularnewline
M7 & -0.47818181818181 & 2.97938 & -0.1605 & 0.873177 & 0.436589 \tabularnewline
M8 & -0.543636363636355 & 2.877366 & -0.1889 & 0.850957 & 0.425478 \tabularnewline
M9 & -0.31272727272726 & 3.105621 & -0.1007 & 0.920219 & 0.46011 \tabularnewline
M10 & -1.38 & 2.740631 & -0.5035 & 0.616941 & 0.308471 \tabularnewline
M11 & 0.476363636363643 & 2.877366 & 0.1656 & 0.869217 & 0.434608 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&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]121.12[/C][C]1.937919[/C][C]62.5[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CPIlandbouw[/C][C]-1.02727272727274[/C][C]1.460761[/C][C]-0.7032[/C][C]0.485372[/C][C]0.242686[/C][/ROW]
[ROW][C]M1[/C][C]-1.80909090909093[/C][C]2.802226[/C][C]-0.6456[/C][C]0.521684[/C][C]0.260842[/C][/ROW]
[ROW][C]M2[/C][C]-1.41818181818181[/C][C]2.97938[/C][C]-0.476[/C][C]0.63628[/C][C]0.31814[/C][/ROW]
[ROW][C]M3[/C][C]-2.20363636363636[/C][C]2.877366[/C][C]-0.7659[/C][C]0.447594[/C][C]0.223797[/C][/ROW]
[ROW][C]M4[/C][C]-1.15272727272726[/C][C]3.105621[/C][C]-0.3712[/C][C]0.712176[/C][C]0.356088[/C][/ROW]
[ROW][C]M5[/C][C]-1.22363636363636[/C][C]2.877366[/C][C]-0.4253[/C][C]0.672586[/C][C]0.336293[/C][/ROW]
[ROW][C]M6[/C][C]-1.0690909090909[/C][C]2.802226[/C][C]-0.3815[/C][C]0.704541[/C][C]0.35227[/C][/ROW]
[ROW][C]M7[/C][C]-0.47818181818181[/C][C]2.97938[/C][C]-0.1605[/C][C]0.873177[/C][C]0.436589[/C][/ROW]
[ROW][C]M8[/C][C]-0.543636363636355[/C][C]2.877366[/C][C]-0.1889[/C][C]0.850957[/C][C]0.425478[/C][/ROW]
[ROW][C]M9[/C][C]-0.31272727272726[/C][C]3.105621[/C][C]-0.1007[/C][C]0.920219[/C][C]0.46011[/C][/ROW]
[ROW][C]M10[/C][C]-1.38[/C][C]2.740631[/C][C]-0.5035[/C][C]0.616941[/C][C]0.308471[/C][/ROW]
[ROW][C]M11[/C][C]0.476363636363643[/C][C]2.877366[/C][C]0.1656[/C][C]0.869217[/C][C]0.434608[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	121.12	1.937919	62.5	0	0
CPIlandbouw	-1.02727272727274	1.460761	-0.7032	0.485372	0.242686
M1	-1.80909090909093	2.802226	-0.6456	0.521684	0.260842
M2	-1.41818181818181	2.97938	-0.476	0.63628	0.31814
M3	-2.20363636363636	2.877366	-0.7659	0.447594	0.223797
M4	-1.15272727272726	3.105621	-0.3712	0.712176	0.356088
M5	-1.22363636363636	2.877366	-0.4253	0.672586	0.336293
M6	-1.0690909090909	2.802226	-0.3815	0.704541	0.35227
M7	-0.47818181818181	2.97938	-0.1605	0.873177	0.436589
M8	-0.543636363636355	2.877366	-0.1889	0.850957	0.425478
M9	-0.31272727272726	3.105621	-0.1007	0.920219	0.46011
M10	-1.38	2.740631	-0.5035	0.616941	0.308471
M11	0.476363636363643	2.877366	0.1656	0.869217	0.434608

Multiple Linear Regression - Regression Statistics
Multiple R	0.226426138076746
R-squared	0.0512687960043497
Adjusted R-squared	-0.190960447569008
F-TEST (value)	0.211654031726451
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.997170235499864
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.33331835537758
Sum Squared Residuals	882.549454545456

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.226426138076746 \tabularnewline
R-squared & 0.0512687960043497 \tabularnewline
Adjusted R-squared & -0.190960447569008 \tabularnewline
F-TEST (value) & 0.211654031726451 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.997170235499864 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.33331835537758 \tabularnewline
Sum Squared Residuals & 882.549454545456 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.226426138076746[/C][/ROW]
[ROW][C]R-squared[/C][C]0.0512687960043497[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.190960447569008[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.211654031726451[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.997170235499864[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.33331835537758[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]882.549454545456[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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 R	0.226426138076746
R-squared	0.0512687960043497
Adjusted R-squared	-0.190960447569008
F-TEST (value)	0.211654031726451
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.997170235499864
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.33331835537758
Sum Squared Residuals	882.549454545456

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	114	118.283636363636	-4.28363636363644
2	113.8	118.674545454545	-4.87454545454545
3	113.6	117.889090909091	-4.2890909090909
4	113.7	118.94	-5.24
5	114.2	118.869090909091	-4.6690909090909
6	114.8	120.050909090909	-5.25090909090911
7	115.2	119.614545454545	-4.41454545454545
8	115.3	119.549090909091	-4.24909090909091
9	114.9	119.78	-4.88
10	115.1	119.74	-4.64000000000001
11	116	121.596363636364	-5.59636363636364
12	116	121.12	-5.12
13	116	119.310909090909	-3.31090909090908
14	115.9	118.674545454545	-2.77454545454545
15	115.6	117.889090909091	-2.28909090909091
16	116.6	118.94	-2.34000000000001
17	116.9	119.896363636364	-2.99636363636364
18	117.9	119.023636363636	-1.12363636363636
19	117.9	119.614545454545	-1.71454545454545
20	117.7	120.576363636364	-2.87636363636364
21	117.4	119.78	-2.38
22	117.3	119.74	-2.44000000000000
23	119	120.569090909091	-1.56909090909091
24	119.1	121.12	-2.02000000000000
25	119	119.310909090909	-0.310909090909073
26	118.5	119.701818181818	-1.20181818181819
27	117	117.889090909091	-0.889090909090904
28	117.5	118.94	-1.44000000000000
29	118.2	118.869090909091	-0.669090909090905
30	118.2	119.023636363636	-0.82363636363636
31	118.3	120.641818181818	-2.34181818181819
32	118.2	119.549090909091	-1.34909090909090
33	117.9	119.78	-1.88
34	117.8	119.74	-1.94000000000000
35	118.6	121.596363636364	-2.99636363636365
36	118.9	121.12	-2.21999999999999
37	120.8	118.283636363636	2.51636363636366
38	121.8	118.674545454545	3.12545454545455
39	121.3	118.916363636364	2.38363636363636
40	121.9	118.94	2.96000000000000
41	122	118.869090909091	3.13090909090909
42	121.9	120.050909090909	1.84909090909091
43	122	119.614545454545	2.38545454545455
44	122.2	120.576363636364	1.62363636363636
45	123	119.78	3.22000000000000
46	123.1	119.74	3.35999999999999
47	124.9	120.569090909091	4.3309090909091
48	125.4	121.12	4.28000000000001
49	124.7	119.310909090909	5.38909090909093
50	124.4	118.674545454545	5.72545454545455
51	124	118.916363636364	5.08363636363636
52	125	118.94	6.06
53	125.1	119.896363636364	5.20363636363635
54	125.4	120.050909090909	5.34909090909091
55	125.7	119.614545454545	6.08545454545455
56	126.4	119.549090909091	6.8509090909091
57	125.7	119.78	5.92
58	125.4	119.74	5.66000000000001
59	126.4	120.569090909091	5.8309090909091
60	126.2	121.12	5.08000000000001

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 114 & 118.283636363636 & -4.28363636363644 \tabularnewline
2 & 113.8 & 118.674545454545 & -4.87454545454545 \tabularnewline
3 & 113.6 & 117.889090909091 & -4.2890909090909 \tabularnewline
4 & 113.7 & 118.94 & -5.24 \tabularnewline
5 & 114.2 & 118.869090909091 & -4.6690909090909 \tabularnewline
6 & 114.8 & 120.050909090909 & -5.25090909090911 \tabularnewline
7 & 115.2 & 119.614545454545 & -4.41454545454545 \tabularnewline
8 & 115.3 & 119.549090909091 & -4.24909090909091 \tabularnewline
9 & 114.9 & 119.78 & -4.88 \tabularnewline
10 & 115.1 & 119.74 & -4.64000000000001 \tabularnewline
11 & 116 & 121.596363636364 & -5.59636363636364 \tabularnewline
12 & 116 & 121.12 & -5.12 \tabularnewline
13 & 116 & 119.310909090909 & -3.31090909090908 \tabularnewline
14 & 115.9 & 118.674545454545 & -2.77454545454545 \tabularnewline
15 & 115.6 & 117.889090909091 & -2.28909090909091 \tabularnewline
16 & 116.6 & 118.94 & -2.34000000000001 \tabularnewline
17 & 116.9 & 119.896363636364 & -2.99636363636364 \tabularnewline
18 & 117.9 & 119.023636363636 & -1.12363636363636 \tabularnewline
19 & 117.9 & 119.614545454545 & -1.71454545454545 \tabularnewline
20 & 117.7 & 120.576363636364 & -2.87636363636364 \tabularnewline
21 & 117.4 & 119.78 & -2.38 \tabularnewline
22 & 117.3 & 119.74 & -2.44000000000000 \tabularnewline
23 & 119 & 120.569090909091 & -1.56909090909091 \tabularnewline
24 & 119.1 & 121.12 & -2.02000000000000 \tabularnewline
25 & 119 & 119.310909090909 & -0.310909090909073 \tabularnewline
26 & 118.5 & 119.701818181818 & -1.20181818181819 \tabularnewline
27 & 117 & 117.889090909091 & -0.889090909090904 \tabularnewline
28 & 117.5 & 118.94 & -1.44000000000000 \tabularnewline
29 & 118.2 & 118.869090909091 & -0.669090909090905 \tabularnewline
30 & 118.2 & 119.023636363636 & -0.82363636363636 \tabularnewline
31 & 118.3 & 120.641818181818 & -2.34181818181819 \tabularnewline
32 & 118.2 & 119.549090909091 & -1.34909090909090 \tabularnewline
33 & 117.9 & 119.78 & -1.88 \tabularnewline
34 & 117.8 & 119.74 & -1.94000000000000 \tabularnewline
35 & 118.6 & 121.596363636364 & -2.99636363636365 \tabularnewline
36 & 118.9 & 121.12 & -2.21999999999999 \tabularnewline
37 & 120.8 & 118.283636363636 & 2.51636363636366 \tabularnewline
38 & 121.8 & 118.674545454545 & 3.12545454545455 \tabularnewline
39 & 121.3 & 118.916363636364 & 2.38363636363636 \tabularnewline
40 & 121.9 & 118.94 & 2.96000000000000 \tabularnewline
41 & 122 & 118.869090909091 & 3.13090909090909 \tabularnewline
42 & 121.9 & 120.050909090909 & 1.84909090909091 \tabularnewline
43 & 122 & 119.614545454545 & 2.38545454545455 \tabularnewline
44 & 122.2 & 120.576363636364 & 1.62363636363636 \tabularnewline
45 & 123 & 119.78 & 3.22000000000000 \tabularnewline
46 & 123.1 & 119.74 & 3.35999999999999 \tabularnewline
47 & 124.9 & 120.569090909091 & 4.3309090909091 \tabularnewline
48 & 125.4 & 121.12 & 4.28000000000001 \tabularnewline
49 & 124.7 & 119.310909090909 & 5.38909090909093 \tabularnewline
50 & 124.4 & 118.674545454545 & 5.72545454545455 \tabularnewline
51 & 124 & 118.916363636364 & 5.08363636363636 \tabularnewline
52 & 125 & 118.94 & 6.06 \tabularnewline
53 & 125.1 & 119.896363636364 & 5.20363636363635 \tabularnewline
54 & 125.4 & 120.050909090909 & 5.34909090909091 \tabularnewline
55 & 125.7 & 119.614545454545 & 6.08545454545455 \tabularnewline
56 & 126.4 & 119.549090909091 & 6.8509090909091 \tabularnewline
57 & 125.7 & 119.78 & 5.92 \tabularnewline
58 & 125.4 & 119.74 & 5.66000000000001 \tabularnewline
59 & 126.4 & 120.569090909091 & 5.8309090909091 \tabularnewline
60 & 126.2 & 121.12 & 5.08000000000001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&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]114[/C][C]118.283636363636[/C][C]-4.28363636363644[/C][/ROW]
[ROW][C]2[/C][C]113.8[/C][C]118.674545454545[/C][C]-4.87454545454545[/C][/ROW]
[ROW][C]3[/C][C]113.6[/C][C]117.889090909091[/C][C]-4.2890909090909[/C][/ROW]
[ROW][C]4[/C][C]113.7[/C][C]118.94[/C][C]-5.24[/C][/ROW]
[ROW][C]5[/C][C]114.2[/C][C]118.869090909091[/C][C]-4.6690909090909[/C][/ROW]
[ROW][C]6[/C][C]114.8[/C][C]120.050909090909[/C][C]-5.25090909090911[/C][/ROW]
[ROW][C]7[/C][C]115.2[/C][C]119.614545454545[/C][C]-4.41454545454545[/C][/ROW]
[ROW][C]8[/C][C]115.3[/C][C]119.549090909091[/C][C]-4.24909090909091[/C][/ROW]
[ROW][C]9[/C][C]114.9[/C][C]119.78[/C][C]-4.88[/C][/ROW]
[ROW][C]10[/C][C]115.1[/C][C]119.74[/C][C]-4.64000000000001[/C][/ROW]
[ROW][C]11[/C][C]116[/C][C]121.596363636364[/C][C]-5.59636363636364[/C][/ROW]
[ROW][C]12[/C][C]116[/C][C]121.12[/C][C]-5.12[/C][/ROW]
[ROW][C]13[/C][C]116[/C][C]119.310909090909[/C][C]-3.31090909090908[/C][/ROW]
[ROW][C]14[/C][C]115.9[/C][C]118.674545454545[/C][C]-2.77454545454545[/C][/ROW]
[ROW][C]15[/C][C]115.6[/C][C]117.889090909091[/C][C]-2.28909090909091[/C][/ROW]
[ROW][C]16[/C][C]116.6[/C][C]118.94[/C][C]-2.34000000000001[/C][/ROW]
[ROW][C]17[/C][C]116.9[/C][C]119.896363636364[/C][C]-2.99636363636364[/C][/ROW]
[ROW][C]18[/C][C]117.9[/C][C]119.023636363636[/C][C]-1.12363636363636[/C][/ROW]
[ROW][C]19[/C][C]117.9[/C][C]119.614545454545[/C][C]-1.71454545454545[/C][/ROW]
[ROW][C]20[/C][C]117.7[/C][C]120.576363636364[/C][C]-2.87636363636364[/C][/ROW]
[ROW][C]21[/C][C]117.4[/C][C]119.78[/C][C]-2.38[/C][/ROW]
[ROW][C]22[/C][C]117.3[/C][C]119.74[/C][C]-2.44000000000000[/C][/ROW]
[ROW][C]23[/C][C]119[/C][C]120.569090909091[/C][C]-1.56909090909091[/C][/ROW]
[ROW][C]24[/C][C]119.1[/C][C]121.12[/C][C]-2.02000000000000[/C][/ROW]
[ROW][C]25[/C][C]119[/C][C]119.310909090909[/C][C]-0.310909090909073[/C][/ROW]
[ROW][C]26[/C][C]118.5[/C][C]119.701818181818[/C][C]-1.20181818181819[/C][/ROW]
[ROW][C]27[/C][C]117[/C][C]117.889090909091[/C][C]-0.889090909090904[/C][/ROW]
[ROW][C]28[/C][C]117.5[/C][C]118.94[/C][C]-1.44000000000000[/C][/ROW]
[ROW][C]29[/C][C]118.2[/C][C]118.869090909091[/C][C]-0.669090909090905[/C][/ROW]
[ROW][C]30[/C][C]118.2[/C][C]119.023636363636[/C][C]-0.82363636363636[/C][/ROW]
[ROW][C]31[/C][C]118.3[/C][C]120.641818181818[/C][C]-2.34181818181819[/C][/ROW]
[ROW][C]32[/C][C]118.2[/C][C]119.549090909091[/C][C]-1.34909090909090[/C][/ROW]
[ROW][C]33[/C][C]117.9[/C][C]119.78[/C][C]-1.88[/C][/ROW]
[ROW][C]34[/C][C]117.8[/C][C]119.74[/C][C]-1.94000000000000[/C][/ROW]
[ROW][C]35[/C][C]118.6[/C][C]121.596363636364[/C][C]-2.99636363636365[/C][/ROW]
[ROW][C]36[/C][C]118.9[/C][C]121.12[/C][C]-2.21999999999999[/C][/ROW]
[ROW][C]37[/C][C]120.8[/C][C]118.283636363636[/C][C]2.51636363636366[/C][/ROW]
[ROW][C]38[/C][C]121.8[/C][C]118.674545454545[/C][C]3.12545454545455[/C][/ROW]
[ROW][C]39[/C][C]121.3[/C][C]118.916363636364[/C][C]2.38363636363636[/C][/ROW]
[ROW][C]40[/C][C]121.9[/C][C]118.94[/C][C]2.96000000000000[/C][/ROW]
[ROW][C]41[/C][C]122[/C][C]118.869090909091[/C][C]3.13090909090909[/C][/ROW]
[ROW][C]42[/C][C]121.9[/C][C]120.050909090909[/C][C]1.84909090909091[/C][/ROW]
[ROW][C]43[/C][C]122[/C][C]119.614545454545[/C][C]2.38545454545455[/C][/ROW]
[ROW][C]44[/C][C]122.2[/C][C]120.576363636364[/C][C]1.62363636363636[/C][/ROW]
[ROW][C]45[/C][C]123[/C][C]119.78[/C][C]3.22000000000000[/C][/ROW]
[ROW][C]46[/C][C]123.1[/C][C]119.74[/C][C]3.35999999999999[/C][/ROW]
[ROW][C]47[/C][C]124.9[/C][C]120.569090909091[/C][C]4.3309090909091[/C][/ROW]
[ROW][C]48[/C][C]125.4[/C][C]121.12[/C][C]4.28000000000001[/C][/ROW]
[ROW][C]49[/C][C]124.7[/C][C]119.310909090909[/C][C]5.38909090909093[/C][/ROW]
[ROW][C]50[/C][C]124.4[/C][C]118.674545454545[/C][C]5.72545454545455[/C][/ROW]
[ROW][C]51[/C][C]124[/C][C]118.916363636364[/C][C]5.08363636363636[/C][/ROW]
[ROW][C]52[/C][C]125[/C][C]118.94[/C][C]6.06[/C][/ROW]
[ROW][C]53[/C][C]125.1[/C][C]119.896363636364[/C][C]5.20363636363635[/C][/ROW]
[ROW][C]54[/C][C]125.4[/C][C]120.050909090909[/C][C]5.34909090909091[/C][/ROW]
[ROW][C]55[/C][C]125.7[/C][C]119.614545454545[/C][C]6.08545454545455[/C][/ROW]
[ROW][C]56[/C][C]126.4[/C][C]119.549090909091[/C][C]6.8509090909091[/C][/ROW]
[ROW][C]57[/C][C]125.7[/C][C]119.78[/C][C]5.92[/C][/ROW]
[ROW][C]58[/C][C]125.4[/C][C]119.74[/C][C]5.66000000000001[/C][/ROW]
[ROW][C]59[/C][C]126.4[/C][C]120.569090909091[/C][C]5.8309090909091[/C][/ROW]
[ROW][C]60[/C][C]126.2[/C][C]121.12[/C][C]5.08000000000001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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 Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	114	118.283636363636	-4.28363636363644
2	113.8	118.674545454545	-4.87454545454545
3	113.6	117.889090909091	-4.2890909090909
4	113.7	118.94	-5.24
5	114.2	118.869090909091	-4.6690909090909
6	114.8	120.050909090909	-5.25090909090911
7	115.2	119.614545454545	-4.41454545454545
8	115.3	119.549090909091	-4.24909090909091
9	114.9	119.78	-4.88
10	115.1	119.74	-4.64000000000001
11	116	121.596363636364	-5.59636363636364
12	116	121.12	-5.12
13	116	119.310909090909	-3.31090909090908
14	115.9	118.674545454545	-2.77454545454545
15	115.6	117.889090909091	-2.28909090909091
16	116.6	118.94	-2.34000000000001
17	116.9	119.896363636364	-2.99636363636364
18	117.9	119.023636363636	-1.12363636363636
19	117.9	119.614545454545	-1.71454545454545
20	117.7	120.576363636364	-2.87636363636364
21	117.4	119.78	-2.38
22	117.3	119.74	-2.44000000000000
23	119	120.569090909091	-1.56909090909091
24	119.1	121.12	-2.02000000000000
25	119	119.310909090909	-0.310909090909073
26	118.5	119.701818181818	-1.20181818181819
27	117	117.889090909091	-0.889090909090904
28	117.5	118.94	-1.44000000000000
29	118.2	118.869090909091	-0.669090909090905
30	118.2	119.023636363636	-0.82363636363636
31	118.3	120.641818181818	-2.34181818181819
32	118.2	119.549090909091	-1.34909090909090
33	117.9	119.78	-1.88
34	117.8	119.74	-1.94000000000000
35	118.6	121.596363636364	-2.99636363636365
36	118.9	121.12	-2.21999999999999
37	120.8	118.283636363636	2.51636363636366
38	121.8	118.674545454545	3.12545454545455
39	121.3	118.916363636364	2.38363636363636
40	121.9	118.94	2.96000000000000
41	122	118.869090909091	3.13090909090909
42	121.9	120.050909090909	1.84909090909091
43	122	119.614545454545	2.38545454545455
44	122.2	120.576363636364	1.62363636363636
45	123	119.78	3.22000000000000
46	123.1	119.74	3.35999999999999
47	124.9	120.569090909091	4.3309090909091
48	125.4	121.12	4.28000000000001
49	124.7	119.310909090909	5.38909090909093
50	124.4	118.674545454545	5.72545454545455
51	124	118.916363636364	5.08363636363636
52	125	118.94	6.06
53	125.1	119.896363636364	5.20363636363635
54	125.4	120.050909090909	5.34909090909091
55	125.7	119.614545454545	6.08545454545455
56	126.4	119.549090909091	6.8509090909091
57	125.7	119.78	5.92
58	125.4	119.74	5.66000000000001
59	126.4	120.569090909091	5.8309090909091
60	126.2	121.12	5.08000000000001

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.100547587614722	0.201095175229445	0.899452412385278
17	0.0380663931256433	0.0761327862512867	0.961933606874357
18	0.0666291685949199	0.133258337189840	0.93337083140508
19	0.0466789041028267	0.0933578082056533	0.953321095897173
20	0.0261095034003548	0.0522190068007096	0.973890496599645
21	0.0186283265025068	0.0372566530050136	0.981371673497493
22	0.0125622495801014	0.0251244991602028	0.987437750419899
23	0.0131255832336783	0.0262511664673565	0.986874416766322
24	0.0115729077556159	0.0231458155112317	0.988427092244384
25	0.0146041753393795	0.0292083506787591	0.98539582466062
26	0.0133268469391413	0.0266536938782825	0.986673153060859
27	0.0116061167549792	0.0232122335099585	0.98839388324502
28	0.0114959582916885	0.022991916583377	0.988504041708312
29	0.0139351467129258	0.0278702934258517	0.986064853287074
30	0.0164694538862224	0.0329389077724449	0.983530546113778
31	0.0122307307407537	0.0244614614815074	0.987769269259246
32	0.0172843215493617	0.0345686430987234	0.982715678450638
33	0.0252795539956810	0.0505591079913619	0.97472044600432
34	0.0424634759807013	0.0849269519614025	0.957536524019299
35	0.086707417768969	0.173414835537938	0.913292582231031
36	0.259091449689726	0.518182899379453	0.740908550310273
37	0.389740635101265	0.779481270202531	0.610259364898735
38	0.498423578301869	0.996847156603738	0.501576421698131
39	0.559023591391972	0.881952817216055	0.440976408608028
40	0.625881129585552	0.748237740828897	0.374118870414448
41	0.811735810703998	0.376528378592003	0.188264189296002
42	0.846754310817758	0.306491378364484	0.153245689182242
43	0.907807953950998	0.184384092098005	0.0921920460490023
44	0.809440252719386	0.381119494561229	0.190559747280614

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
16 & 0.100547587614722 & 0.201095175229445 & 0.899452412385278 \tabularnewline
17 & 0.0380663931256433 & 0.0761327862512867 & 0.961933606874357 \tabularnewline
18 & 0.0666291685949199 & 0.133258337189840 & 0.93337083140508 \tabularnewline
19 & 0.0466789041028267 & 0.0933578082056533 & 0.953321095897173 \tabularnewline
20 & 0.0261095034003548 & 0.0522190068007096 & 0.973890496599645 \tabularnewline
21 & 0.0186283265025068 & 0.0372566530050136 & 0.981371673497493 \tabularnewline
22 & 0.0125622495801014 & 0.0251244991602028 & 0.987437750419899 \tabularnewline
23 & 0.0131255832336783 & 0.0262511664673565 & 0.986874416766322 \tabularnewline
24 & 0.0115729077556159 & 0.0231458155112317 & 0.988427092244384 \tabularnewline
25 & 0.0146041753393795 & 0.0292083506787591 & 0.98539582466062 \tabularnewline
26 & 0.0133268469391413 & 0.0266536938782825 & 0.986673153060859 \tabularnewline
27 & 0.0116061167549792 & 0.0232122335099585 & 0.98839388324502 \tabularnewline
28 & 0.0114959582916885 & 0.022991916583377 & 0.988504041708312 \tabularnewline
29 & 0.0139351467129258 & 0.0278702934258517 & 0.986064853287074 \tabularnewline
30 & 0.0164694538862224 & 0.0329389077724449 & 0.983530546113778 \tabularnewline
31 & 0.0122307307407537 & 0.0244614614815074 & 0.987769269259246 \tabularnewline
32 & 0.0172843215493617 & 0.0345686430987234 & 0.982715678450638 \tabularnewline
33 & 0.0252795539956810 & 0.0505591079913619 & 0.97472044600432 \tabularnewline
34 & 0.0424634759807013 & 0.0849269519614025 & 0.957536524019299 \tabularnewline
35 & 0.086707417768969 & 0.173414835537938 & 0.913292582231031 \tabularnewline
36 & 0.259091449689726 & 0.518182899379453 & 0.740908550310273 \tabularnewline
37 & 0.389740635101265 & 0.779481270202531 & 0.610259364898735 \tabularnewline
38 & 0.498423578301869 & 0.996847156603738 & 0.501576421698131 \tabularnewline
39 & 0.559023591391972 & 0.881952817216055 & 0.440976408608028 \tabularnewline
40 & 0.625881129585552 & 0.748237740828897 & 0.374118870414448 \tabularnewline
41 & 0.811735810703998 & 0.376528378592003 & 0.188264189296002 \tabularnewline
42 & 0.846754310817758 & 0.306491378364484 & 0.153245689182242 \tabularnewline
43 & 0.907807953950998 & 0.184384092098005 & 0.0921920460490023 \tabularnewline
44 & 0.809440252719386 & 0.381119494561229 & 0.190559747280614 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&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]16[/C][C]0.100547587614722[/C][C]0.201095175229445[/C][C]0.899452412385278[/C][/ROW]
[ROW][C]17[/C][C]0.0380663931256433[/C][C]0.0761327862512867[/C][C]0.961933606874357[/C][/ROW]
[ROW][C]18[/C][C]0.0666291685949199[/C][C]0.133258337189840[/C][C]0.93337083140508[/C][/ROW]
[ROW][C]19[/C][C]0.0466789041028267[/C][C]0.0933578082056533[/C][C]0.953321095897173[/C][/ROW]
[ROW][C]20[/C][C]0.0261095034003548[/C][C]0.0522190068007096[/C][C]0.973890496599645[/C][/ROW]
[ROW][C]21[/C][C]0.0186283265025068[/C][C]0.0372566530050136[/C][C]0.981371673497493[/C][/ROW]
[ROW][C]22[/C][C]0.0125622495801014[/C][C]0.0251244991602028[/C][C]0.987437750419899[/C][/ROW]
[ROW][C]23[/C][C]0.0131255832336783[/C][C]0.0262511664673565[/C][C]0.986874416766322[/C][/ROW]
[ROW][C]24[/C][C]0.0115729077556159[/C][C]0.0231458155112317[/C][C]0.988427092244384[/C][/ROW]
[ROW][C]25[/C][C]0.0146041753393795[/C][C]0.0292083506787591[/C][C]0.98539582466062[/C][/ROW]
[ROW][C]26[/C][C]0.0133268469391413[/C][C]0.0266536938782825[/C][C]0.986673153060859[/C][/ROW]
[ROW][C]27[/C][C]0.0116061167549792[/C][C]0.0232122335099585[/C][C]0.98839388324502[/C][/ROW]
[ROW][C]28[/C][C]0.0114959582916885[/C][C]0.022991916583377[/C][C]0.988504041708312[/C][/ROW]
[ROW][C]29[/C][C]0.0139351467129258[/C][C]0.0278702934258517[/C][C]0.986064853287074[/C][/ROW]
[ROW][C]30[/C][C]0.0164694538862224[/C][C]0.0329389077724449[/C][C]0.983530546113778[/C][/ROW]
[ROW][C]31[/C][C]0.0122307307407537[/C][C]0.0244614614815074[/C][C]0.987769269259246[/C][/ROW]
[ROW][C]32[/C][C]0.0172843215493617[/C][C]0.0345686430987234[/C][C]0.982715678450638[/C][/ROW]
[ROW][C]33[/C][C]0.0252795539956810[/C][C]0.0505591079913619[/C][C]0.97472044600432[/C][/ROW]
[ROW][C]34[/C][C]0.0424634759807013[/C][C]0.0849269519614025[/C][C]0.957536524019299[/C][/ROW]
[ROW][C]35[/C][C]0.086707417768969[/C][C]0.173414835537938[/C][C]0.913292582231031[/C][/ROW]
[ROW][C]36[/C][C]0.259091449689726[/C][C]0.518182899379453[/C][C]0.740908550310273[/C][/ROW]
[ROW][C]37[/C][C]0.389740635101265[/C][C]0.779481270202531[/C][C]0.610259364898735[/C][/ROW]
[ROW][C]38[/C][C]0.498423578301869[/C][C]0.996847156603738[/C][C]0.501576421698131[/C][/ROW]
[ROW][C]39[/C][C]0.559023591391972[/C][C]0.881952817216055[/C][C]0.440976408608028[/C][/ROW]
[ROW][C]40[/C][C]0.625881129585552[/C][C]0.748237740828897[/C][C]0.374118870414448[/C][/ROW]
[ROW][C]41[/C][C]0.811735810703998[/C][C]0.376528378592003[/C][C]0.188264189296002[/C][/ROW]
[ROW][C]42[/C][C]0.846754310817758[/C][C]0.306491378364484[/C][C]0.153245689182242[/C][/ROW]
[ROW][C]43[/C][C]0.907807953950998[/C][C]0.184384092098005[/C][C]0.0921920460490023[/C][/ROW]
[ROW][C]44[/C][C]0.809440252719386[/C][C]0.381119494561229[/C][C]0.190559747280614[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
16	0.100547587614722	0.201095175229445	0.899452412385278
17	0.0380663931256433	0.0761327862512867	0.961933606874357
18	0.0666291685949199	0.133258337189840	0.93337083140508
19	0.0466789041028267	0.0933578082056533	0.953321095897173
20	0.0261095034003548	0.0522190068007096	0.973890496599645
21	0.0186283265025068	0.0372566530050136	0.981371673497493
22	0.0125622495801014	0.0251244991602028	0.987437750419899
23	0.0131255832336783	0.0262511664673565	0.986874416766322
24	0.0115729077556159	0.0231458155112317	0.988427092244384
25	0.0146041753393795	0.0292083506787591	0.98539582466062
26	0.0133268469391413	0.0266536938782825	0.986673153060859
27	0.0116061167549792	0.0232122335099585	0.98839388324502
28	0.0114959582916885	0.022991916583377	0.988504041708312
29	0.0139351467129258	0.0278702934258517	0.986064853287074
30	0.0164694538862224	0.0329389077724449	0.983530546113778
31	0.0122307307407537	0.0244614614815074	0.987769269259246
32	0.0172843215493617	0.0345686430987234	0.982715678450638
33	0.0252795539956810	0.0505591079913619	0.97472044600432
34	0.0424634759807013	0.0849269519614025	0.957536524019299
35	0.086707417768969	0.173414835537938	0.913292582231031
36	0.259091449689726	0.518182899379453	0.740908550310273
37	0.389740635101265	0.779481270202531	0.610259364898735
38	0.498423578301869	0.996847156603738	0.501576421698131
39	0.559023591391972	0.881952817216055	0.440976408608028
40	0.625881129585552	0.748237740828897	0.374118870414448
41	0.811735810703998	0.376528378592003	0.188264189296002
42	0.846754310817758	0.306491378364484	0.153245689182242
43	0.907807953950998	0.184384092098005	0.0921920460490023
44	0.809440252719386	0.381119494561229	0.190559747280614

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	12	0.413793103448276	NOK
10% type I error level	17	0.586206896551724	NOK

\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 & 12 & 0.413793103448276 & NOK \tabularnewline
10% type I error level & 17 & 0.586206896551724 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58527&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]12[/C][C]0.413793103448276[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]17[/C][C]0.586206896551724[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58527&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58527&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 tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	12	0.413793103448276	NOK
10% type I error level	17	0.586206896551724	NOK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code