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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 18 Dec 2007 12:55:54 -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/2007/Dec/18/t1198006808qru0muo8o9fg0z1.htm/, Retrieved Sat, 04 May 2024 14:42:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4588, Retrieved Sat, 04 May 2024 14:42:57 +0000

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No (this computation is public)

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PMRM

Estimated Impact

172

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

-       [Multiple Regression] [Paper - Multiple ...] [2007-12-18 19:55:54] [e51d7ab0e549b3dc96ac85a81d9bd259] [Current]

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0,65	-0,36	-15,57	-0,39
-1,53	3,69	-7	4,81
-3,36	0,68	-2,08	-5,87
10,51	7,63	6,06	16,07
-1,02	-1,52	3,1	-8,58
-2,68	5,71	-7,28	5,08
-9,75	13,19	11,93	0,09
3,64	-5,5	-15,18	3,83
9,5	1,77	-4,97	5,82
-0,33	1,97	-3,06	37,12
-0,99	-5,61	5,18	-0,46
3,16	-7,32	-2,3	-3,63
4,27	-1,09	-1,86	-3,88
-2,97	-0,1	-8,75	4,62
-7,49	-9,4	-8,02	-12,97
0,33	-6,39	4,8	3,2
-2,57	-2,03	3,9	-5,75
4,07	0,9	-11,31	-0,57
5,31	7,57	4,26	-7,16
-8,09	-2,19	-6,89	-18,05
5,29	3,59	4,13	15,63
7,38	-1,69	4,25	-18,68
-6,12	-1,54	-5,92	-0,7
-3,38	13,36	-2,62	2,59
-8,61	-3,66	-5,12	2,57
2,58	0,61	-6,19	0,36
10,02	9,25	1,58	32,56
7,08	11,03	6,93	8,53
-2,75	-3,5	1,3	1,42
3,42	-6,56	0,7	3,53
-1,6	5,55	18,15	0,9
0,65	16,5	-13,63	-1,1
2,86	-0,09	-8,97	13,32
-3,52	-10,19	-3,48	-33,59
5,65	-1,56	0,13	-0,85
4,31	3,62	0,16	42,09
-4,39	-3,46	-1,28	-6,25
-5,85	-0,84	-8,46	-11,08
-5,47	-1,75	-2,92	-29,29
-2,3	-5,59	0,15	-11,17
-0,14	-4,31	3,87	13,92
8,08	8,29	7,71	13,54
-7,43	-14,07	-4,12	-16,49
0,02	-4,08	-2,74	-9,38
-2,47	3,96	3,19	-2,84
-2,11	-2,54	-6,22	-2,88
7,87	24,36	1,25	6,18
4,66	11,73	4,24	3,71
3,6	3,82	0,15	3,18
-3,64	-2,98	-2,06	-4,18
7,26	7,46	2,14	13,6
-7,62	-6,39	-1,68	4,82
13,83	11,7	5,03	10,05
1,28	2,36	2,25	9,69
-0,32	-7,48	-6,58	-13,63
-2,9	-2,54	-2,85	2,81
4,92	-2,31	5,04	2,39
11,99	10,86	4,44	9,12
10,06	-2,11	-0,68	14,21
-2,22	3,41	-2,51	3,49
3,97	11,2	2,36	5,51
0,56	-1,21	-6,32	-6,15
3,34	5,82	-3,82	2,72
-2,86	-2,61	2,17	-11,12
4,38	-1,54	4,14	-4,7
1,43	-5,42	-6,96	1,82
-0,49	11,6	5,28	-10,44
-1,23	-9,07	4,28	6,04

Summary of compuational 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 compuational 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=4588&T=0

[TABLE]
[ROW][C]Summary of compuational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]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=4588&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4588&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 compuational 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
Producten[t] = + 0.612086579111762 + 0.196270967804096Machines[t] + 0.101082982803162Electronics[t] + 0.151155540401199Medisch[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Producten[t] =  +  0.612086579111762 +  0.196270967804096Machines[t] +  0.101082982803162Electronics[t] +  0.151155540401199Medisch[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4588&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Producten[t] =  +  0.612086579111762 +  0.196270967804096Machines[t] +  0.101082982803162Electronics[t] +  0.151155540401199Medisch[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4588&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4588&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
Producten[t] = + 0.612086579111762 + 0.196270967804096Machines[t] + 0.101082982803162Electronics[t] + 0.151155540401199Medisch[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)	0.612086579111762	0.571641	1.0708	0.288302	0.144151
Machines	0.196270967804096	0.087031	2.2552	0.02755	0.013775
Electronics	0.101082982803162	0.094776	1.0665	0.290185	0.145092
Medisch	0.151155540401199	0.048211	3.1353	0.002592	0.001296

\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) & 0.612086579111762 & 0.571641 & 1.0708 & 0.288302 & 0.144151 \tabularnewline
Machines & 0.196270967804096 & 0.087031 & 2.2552 & 0.02755 & 0.013775 \tabularnewline
Electronics & 0.101082982803162 & 0.094776 & 1.0665 & 0.290185 & 0.145092 \tabularnewline
Medisch & 0.151155540401199 & 0.048211 & 3.1353 & 0.002592 & 0.001296 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4588&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]0.612086579111762[/C][C]0.571641[/C][C]1.0708[/C][C]0.288302[/C][C]0.144151[/C][/ROW]
[ROW][C]Machines[/C][C]0.196270967804096[/C][C]0.087031[/C][C]2.2552[/C][C]0.02755[/C][C]0.013775[/C][/ROW]
[ROW][C]Electronics[/C][C]0.101082982803162[/C][C]0.094776[/C][C]1.0665[/C][C]0.290185[/C][C]0.145092[/C][/ROW]
[ROW][C]Medisch[/C][C]0.151155540401199[/C][C]0.048211[/C][C]3.1353[/C][C]0.002592[/C][C]0.001296[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4588&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4588&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)	0.612086579111762	0.571641	1.0708	0.288302	0.144151
Machines	0.196270967804096	0.087031	2.2552	0.02755	0.013775
Electronics	0.101082982803162	0.094776	1.0665	0.290185	0.145092
Medisch	0.151155540401199	0.048211	3.1353	0.002592	0.001296

Multiple Linear Regression - Regression Statistics
Multiple R	0.553315703864187
R-squared	0.30615826814272
Adjusted R-squared	0.27363443696191
F-TEST (value)	9.41335190312273
F-TEST (DF numerator)	3
F-TEST (DF denominator)	64
p-value	3.07323357684464e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.56267090271832
Sum Squared Residuals	1332.34980905679

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.553315703864187 \tabularnewline
R-squared & 0.30615826814272 \tabularnewline
Adjusted R-squared & 0.27363443696191 \tabularnewline
F-TEST (value) & 9.41335190312273 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 3.07323357684464e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 4.56267090271832 \tabularnewline
Sum Squared Residuals & 1332.34980905679 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4588&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.553315703864187[/C][/ROW]
[ROW][C]R-squared[/C][C]0.30615826814272[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.27363443696191[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.41335190312273[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C]3.07323357684464e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]4.56267090271832[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1332.34980905679[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4588&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4588&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	0.553315703864187
R-squared	0.30615826814272
Adjusted R-squared	0.27363443696191
F-TEST (value)	9.41335190312273
F-TEST (DF numerator)	3
F-TEST (DF denominator)	64
p-value	3.07323357684464e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.56267090271832
Sum Squared Residuals	1332.34980905679

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	0.65	-1.09138367229942	1.74138367229942
2	-1.53	1.35580372001652	-2.88580372001652
3	-3.36	-0.351984789167067	-3.00801521083293
4	10.51	5.15126647349144	5.35873352650856
5	-1.02	-0.66980258190295	-0.350197418097050
6	-2.68	1.76477983570422	-4.44477983570422
7	-9.75	4.42042462792562	-14.1704246279256
8	3.64	-1.42291770302617	5.06291770302617
9	9.5	1.33682901272828	8.16317098727172
10	-0.33	6.30032011800066	-6.63032011800066
11	-0.99	-0.0349152479333883	-0.955084752066612
12	3.16	-1.60580237731785	4.76580237731785
13	4.27	-0.376346620565236	4.64634662056524
14	-2.97	0.406321979457223	-3.37632197945722
15	-7.49	-4.00403339933165	-3.48596660066835
16	0.33	0.326811141582604	0.00318885841739619
17	-2.57	-0.261264209905115	-2.30873579009489
18	4.07	-0.440676743397	4.510676743397
19	5.31	1.44619764285765	3.86380235714235
20	-8.09	-3.24256609613464	-4.84743390386536
21	5.29	4.09673316897627	1.19326683102373
22	7.38	-2.11359417425812	9.49359417425812
23	-6.12	-0.394390847782105	-5.7256091522179
24	-3.38	3.36092214366931	-6.7409221436693
25	-8.61	-0.235340296172335	-8.37465970382766
26	2.58	0.160524200465118	2.41947579953488
27	10.02	7.50892853959169	2.51107146040831
28	7.08	4.76681718443908	2.31318281556092
29	-2.75	0.271186936811239	-3.02118693681124
30	3.42	-0.0711138241046605	3.49111382410466
31	-1.6	3.67208657466297	-5.27208657466297
32	0.65	2.30652539783093	-1.65652539783093
33	2.86	1.701099634409	1.158900365591
34	-3.52	-6.81699796504326	3.29699796504326
35	5.65	0.190562447760764	5.45943755223924
36	4.31	7.70089745529757	-3.39089745529757
37	-4.39	-1.14111931498595	-3.24888068501405
38	-5.85	-2.08274645600372	-3.76725354399628
39	-5.47	-4.45389570268176	-1.01610429731824
40	-2.3	-2.15831306977405	-0.141686930225946
41	-0.14	2.26143497370904	-2.40143497370904
42	8.08	5.06516871665233	3.01483128334767
43	-7.43	-5.05846268825667	-2.37153731174333
44	0.02	-1.88350531137286	1.90350531137286
45	-2.47	1.28249259201867	-3.75249259201867
46	-2.11	-0.950505788501764	-1.15949421149824
47	7.87	6.4537423230029	1.41625767699710
48	4.66	3.90372393342766	0.756276066572336
49	3.6	1.85767874201970	1.74232125798030
50	-3.64	-0.81286200839597	-2.82713799160403
51	7.26	4.34830093158539	2.91169906841461
52	-7.62	-0.0833346115319431	-7.53666538846806
53	13.83	4.93601748695164	8.89398251304836
54	1.28	2.76741996092416	-1.48741996092416
55	-0.32	-3.58139630257603	3.26139630257603
56	-2.9	0.250218888427715	-3.15021888842772
57	4.92	1.02942061837110	3.8905793816289
58	11.99	4.57093626156922	7.41906373843078
59	10.06	2.27713863784001	7.78286136215999
60	-2.22	1.55518512848798	-3.77518512848798
61	3.97	3.88174428554371	0.0882557144562938
62	0.56	-1.19385231671455	1.75385231671455
63	3.34	1.77938968731478	1.56061031268522
64	-2.86	-1.3616801834354	-1.4983198165646
65	4.38	0.0178817976129109	4.36211820238709
66	1.43	-0.880136543166265	2.31013654316626
67	-0.49	1.84448411305145	-2.33448411305145
68	-1.23	0.177523531549388	-1.40752353154939

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 0.65 & -1.09138367229942 & 1.74138367229942 \tabularnewline
2 & -1.53 & 1.35580372001652 & -2.88580372001652 \tabularnewline
3 & -3.36 & -0.351984789167067 & -3.00801521083293 \tabularnewline
4 & 10.51 & 5.15126647349144 & 5.35873352650856 \tabularnewline
5 & -1.02 & -0.66980258190295 & -0.350197418097050 \tabularnewline
6 & -2.68 & 1.76477983570422 & -4.44477983570422 \tabularnewline
7 & -9.75 & 4.42042462792562 & -14.1704246279256 \tabularnewline
8 & 3.64 & -1.42291770302617 & 5.06291770302617 \tabularnewline
9 & 9.5 & 1.33682901272828 & 8.16317098727172 \tabularnewline
10 & -0.33 & 6.30032011800066 & -6.63032011800066 \tabularnewline
11 & -0.99 & -0.0349152479333883 & -0.955084752066612 \tabularnewline
12 & 3.16 & -1.60580237731785 & 4.76580237731785 \tabularnewline
13 & 4.27 & -0.376346620565236 & 4.64634662056524 \tabularnewline
14 & -2.97 & 0.406321979457223 & -3.37632197945722 \tabularnewline
15 & -7.49 & -4.00403339933165 & -3.48596660066835 \tabularnewline
16 & 0.33 & 0.326811141582604 & 0.00318885841739619 \tabularnewline
17 & -2.57 & -0.261264209905115 & -2.30873579009489 \tabularnewline
18 & 4.07 & -0.440676743397 & 4.510676743397 \tabularnewline
19 & 5.31 & 1.44619764285765 & 3.86380235714235 \tabularnewline
20 & -8.09 & -3.24256609613464 & -4.84743390386536 \tabularnewline
21 & 5.29 & 4.09673316897627 & 1.19326683102373 \tabularnewline
22 & 7.38 & -2.11359417425812 & 9.49359417425812 \tabularnewline
23 & -6.12 & -0.394390847782105 & -5.7256091522179 \tabularnewline
24 & -3.38 & 3.36092214366931 & -6.7409221436693 \tabularnewline
25 & -8.61 & -0.235340296172335 & -8.37465970382766 \tabularnewline
26 & 2.58 & 0.160524200465118 & 2.41947579953488 \tabularnewline
27 & 10.02 & 7.50892853959169 & 2.51107146040831 \tabularnewline
28 & 7.08 & 4.76681718443908 & 2.31318281556092 \tabularnewline
29 & -2.75 & 0.271186936811239 & -3.02118693681124 \tabularnewline
30 & 3.42 & -0.0711138241046605 & 3.49111382410466 \tabularnewline
31 & -1.6 & 3.67208657466297 & -5.27208657466297 \tabularnewline
32 & 0.65 & 2.30652539783093 & -1.65652539783093 \tabularnewline
33 & 2.86 & 1.701099634409 & 1.158900365591 \tabularnewline
34 & -3.52 & -6.81699796504326 & 3.29699796504326 \tabularnewline
35 & 5.65 & 0.190562447760764 & 5.45943755223924 \tabularnewline
36 & 4.31 & 7.70089745529757 & -3.39089745529757 \tabularnewline
37 & -4.39 & -1.14111931498595 & -3.24888068501405 \tabularnewline
38 & -5.85 & -2.08274645600372 & -3.76725354399628 \tabularnewline
39 & -5.47 & -4.45389570268176 & -1.01610429731824 \tabularnewline
40 & -2.3 & -2.15831306977405 & -0.141686930225946 \tabularnewline
41 & -0.14 & 2.26143497370904 & -2.40143497370904 \tabularnewline
42 & 8.08 & 5.06516871665233 & 3.01483128334767 \tabularnewline
43 & -7.43 & -5.05846268825667 & -2.37153731174333 \tabularnewline
44 & 0.02 & -1.88350531137286 & 1.90350531137286 \tabularnewline
45 & -2.47 & 1.28249259201867 & -3.75249259201867 \tabularnewline
46 & -2.11 & -0.950505788501764 & -1.15949421149824 \tabularnewline
47 & 7.87 & 6.4537423230029 & 1.41625767699710 \tabularnewline
48 & 4.66 & 3.90372393342766 & 0.756276066572336 \tabularnewline
49 & 3.6 & 1.85767874201970 & 1.74232125798030 \tabularnewline
50 & -3.64 & -0.81286200839597 & -2.82713799160403 \tabularnewline
51 & 7.26 & 4.34830093158539 & 2.91169906841461 \tabularnewline
52 & -7.62 & -0.0833346115319431 & -7.53666538846806 \tabularnewline
53 & 13.83 & 4.93601748695164 & 8.89398251304836 \tabularnewline
54 & 1.28 & 2.76741996092416 & -1.48741996092416 \tabularnewline
55 & -0.32 & -3.58139630257603 & 3.26139630257603 \tabularnewline
56 & -2.9 & 0.250218888427715 & -3.15021888842772 \tabularnewline
57 & 4.92 & 1.02942061837110 & 3.8905793816289 \tabularnewline
58 & 11.99 & 4.57093626156922 & 7.41906373843078 \tabularnewline
59 & 10.06 & 2.27713863784001 & 7.78286136215999 \tabularnewline
60 & -2.22 & 1.55518512848798 & -3.77518512848798 \tabularnewline
61 & 3.97 & 3.88174428554371 & 0.0882557144562938 \tabularnewline
62 & 0.56 & -1.19385231671455 & 1.75385231671455 \tabularnewline
63 & 3.34 & 1.77938968731478 & 1.56061031268522 \tabularnewline
64 & -2.86 & -1.3616801834354 & -1.4983198165646 \tabularnewline
65 & 4.38 & 0.0178817976129109 & 4.36211820238709 \tabularnewline
66 & 1.43 & -0.880136543166265 & 2.31013654316626 \tabularnewline
67 & -0.49 & 1.84448411305145 & -2.33448411305145 \tabularnewline
68 & -1.23 & 0.177523531549388 & -1.40752353154939 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4588&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]0.65[/C][C]-1.09138367229942[/C][C]1.74138367229942[/C][/ROW]
[ROW][C]2[/C][C]-1.53[/C][C]1.35580372001652[/C][C]-2.88580372001652[/C][/ROW]
[ROW][C]3[/C][C]-3.36[/C][C]-0.351984789167067[/C][C]-3.00801521083293[/C][/ROW]
[ROW][C]4[/C][C]10.51[/C][C]5.15126647349144[/C][C]5.35873352650856[/C][/ROW]
[ROW][C]5[/C][C]-1.02[/C][C]-0.66980258190295[/C][C]-0.350197418097050[/C][/ROW]
[ROW][C]6[/C][C]-2.68[/C][C]1.76477983570422[/C][C]-4.44477983570422[/C][/ROW]
[ROW][C]7[/C][C]-9.75[/C][C]4.42042462792562[/C][C]-14.1704246279256[/C][/ROW]
[ROW][C]8[/C][C]3.64[/C][C]-1.42291770302617[/C][C]5.06291770302617[/C][/ROW]
[ROW][C]9[/C][C]9.5[/C][C]1.33682901272828[/C][C]8.16317098727172[/C][/ROW]
[ROW][C]10[/C][C]-0.33[/C][C]6.30032011800066[/C][C]-6.63032011800066[/C][/ROW]
[ROW][C]11[/C][C]-0.99[/C][C]-0.0349152479333883[/C][C]-0.955084752066612[/C][/ROW]
[ROW][C]12[/C][C]3.16[/C][C]-1.60580237731785[/C][C]4.76580237731785[/C][/ROW]
[ROW][C]13[/C][C]4.27[/C][C]-0.376346620565236[/C][C]4.64634662056524[/C][/ROW]
[ROW][C]14[/C][C]-2.97[/C][C]0.406321979457223[/C][C]-3.37632197945722[/C][/ROW]
[ROW][C]15[/C][C]-7.49[/C][C]-4.00403339933165[/C][C]-3.48596660066835[/C][/ROW]
[ROW][C]16[/C][C]0.33[/C][C]0.326811141582604[/C][C]0.00318885841739619[/C][/ROW]
[ROW][C]17[/C][C]-2.57[/C][C]-0.261264209905115[/C][C]-2.30873579009489[/C][/ROW]
[ROW][C]18[/C][C]4.07[/C][C]-0.440676743397[/C][C]4.510676743397[/C][/ROW]
[ROW][C]19[/C][C]5.31[/C][C]1.44619764285765[/C][C]3.86380235714235[/C][/ROW]
[ROW][C]20[/C][C]-8.09[/C][C]-3.24256609613464[/C][C]-4.84743390386536[/C][/ROW]
[ROW][C]21[/C][C]5.29[/C][C]4.09673316897627[/C][C]1.19326683102373[/C][/ROW]
[ROW][C]22[/C][C]7.38[/C][C]-2.11359417425812[/C][C]9.49359417425812[/C][/ROW]
[ROW][C]23[/C][C]-6.12[/C][C]-0.394390847782105[/C][C]-5.7256091522179[/C][/ROW]
[ROW][C]24[/C][C]-3.38[/C][C]3.36092214366931[/C][C]-6.7409221436693[/C][/ROW]
[ROW][C]25[/C][C]-8.61[/C][C]-0.235340296172335[/C][C]-8.37465970382766[/C][/ROW]
[ROW][C]26[/C][C]2.58[/C][C]0.160524200465118[/C][C]2.41947579953488[/C][/ROW]
[ROW][C]27[/C][C]10.02[/C][C]7.50892853959169[/C][C]2.51107146040831[/C][/ROW]
[ROW][C]28[/C][C]7.08[/C][C]4.76681718443908[/C][C]2.31318281556092[/C][/ROW]
[ROW][C]29[/C][C]-2.75[/C][C]0.271186936811239[/C][C]-3.02118693681124[/C][/ROW]
[ROW][C]30[/C][C]3.42[/C][C]-0.0711138241046605[/C][C]3.49111382410466[/C][/ROW]
[ROW][C]31[/C][C]-1.6[/C][C]3.67208657466297[/C][C]-5.27208657466297[/C][/ROW]
[ROW][C]32[/C][C]0.65[/C][C]2.30652539783093[/C][C]-1.65652539783093[/C][/ROW]
[ROW][C]33[/C][C]2.86[/C][C]1.701099634409[/C][C]1.158900365591[/C][/ROW]
[ROW][C]34[/C][C]-3.52[/C][C]-6.81699796504326[/C][C]3.29699796504326[/C][/ROW]
[ROW][C]35[/C][C]5.65[/C][C]0.190562447760764[/C][C]5.45943755223924[/C][/ROW]
[ROW][C]36[/C][C]4.31[/C][C]7.70089745529757[/C][C]-3.39089745529757[/C][/ROW]
[ROW][C]37[/C][C]-4.39[/C][C]-1.14111931498595[/C][C]-3.24888068501405[/C][/ROW]
[ROW][C]38[/C][C]-5.85[/C][C]-2.08274645600372[/C][C]-3.76725354399628[/C][/ROW]
[ROW][C]39[/C][C]-5.47[/C][C]-4.45389570268176[/C][C]-1.01610429731824[/C][/ROW]
[ROW][C]40[/C][C]-2.3[/C][C]-2.15831306977405[/C][C]-0.141686930225946[/C][/ROW]
[ROW][C]41[/C][C]-0.14[/C][C]2.26143497370904[/C][C]-2.40143497370904[/C][/ROW]
[ROW][C]42[/C][C]8.08[/C][C]5.06516871665233[/C][C]3.01483128334767[/C][/ROW]
[ROW][C]43[/C][C]-7.43[/C][C]-5.05846268825667[/C][C]-2.37153731174333[/C][/ROW]
[ROW][C]44[/C][C]0.02[/C][C]-1.88350531137286[/C][C]1.90350531137286[/C][/ROW]
[ROW][C]45[/C][C]-2.47[/C][C]1.28249259201867[/C][C]-3.75249259201867[/C][/ROW]
[ROW][C]46[/C][C]-2.11[/C][C]-0.950505788501764[/C][C]-1.15949421149824[/C][/ROW]
[ROW][C]47[/C][C]7.87[/C][C]6.4537423230029[/C][C]1.41625767699710[/C][/ROW]
[ROW][C]48[/C][C]4.66[/C][C]3.90372393342766[/C][C]0.756276066572336[/C][/ROW]
[ROW][C]49[/C][C]3.6[/C][C]1.85767874201970[/C][C]1.74232125798030[/C][/ROW]
[ROW][C]50[/C][C]-3.64[/C][C]-0.81286200839597[/C][C]-2.82713799160403[/C][/ROW]
[ROW][C]51[/C][C]7.26[/C][C]4.34830093158539[/C][C]2.91169906841461[/C][/ROW]
[ROW][C]52[/C][C]-7.62[/C][C]-0.0833346115319431[/C][C]-7.53666538846806[/C][/ROW]
[ROW][C]53[/C][C]13.83[/C][C]4.93601748695164[/C][C]8.89398251304836[/C][/ROW]
[ROW][C]54[/C][C]1.28[/C][C]2.76741996092416[/C][C]-1.48741996092416[/C][/ROW]
[ROW][C]55[/C][C]-0.32[/C][C]-3.58139630257603[/C][C]3.26139630257603[/C][/ROW]
[ROW][C]56[/C][C]-2.9[/C][C]0.250218888427715[/C][C]-3.15021888842772[/C][/ROW]
[ROW][C]57[/C][C]4.92[/C][C]1.02942061837110[/C][C]3.8905793816289[/C][/ROW]
[ROW][C]58[/C][C]11.99[/C][C]4.57093626156922[/C][C]7.41906373843078[/C][/ROW]
[ROW][C]59[/C][C]10.06[/C][C]2.27713863784001[/C][C]7.78286136215999[/C][/ROW]
[ROW][C]60[/C][C]-2.22[/C][C]1.55518512848798[/C][C]-3.77518512848798[/C][/ROW]
[ROW][C]61[/C][C]3.97[/C][C]3.88174428554371[/C][C]0.0882557144562938[/C][/ROW]
[ROW][C]62[/C][C]0.56[/C][C]-1.19385231671455[/C][C]1.75385231671455[/C][/ROW]
[ROW][C]63[/C][C]3.34[/C][C]1.77938968731478[/C][C]1.56061031268522[/C][/ROW]
[ROW][C]64[/C][C]-2.86[/C][C]-1.3616801834354[/C][C]-1.4983198165646[/C][/ROW]
[ROW][C]65[/C][C]4.38[/C][C]0.0178817976129109[/C][C]4.36211820238709[/C][/ROW]
[ROW][C]66[/C][C]1.43[/C][C]-0.880136543166265[/C][C]2.31013654316626[/C][/ROW]
[ROW][C]67[/C][C]-0.49[/C][C]1.84448411305145[/C][C]-2.33448411305145[/C][/ROW]
[ROW][C]68[/C][C]-1.23[/C][C]0.177523531549388[/C][C]-1.40752353154939[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4588&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4588&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	0.65	-1.09138367229942	1.74138367229942
2	-1.53	1.35580372001652	-2.88580372001652
3	-3.36	-0.351984789167067	-3.00801521083293
4	10.51	5.15126647349144	5.35873352650856
5	-1.02	-0.66980258190295	-0.350197418097050
6	-2.68	1.76477983570422	-4.44477983570422
7	-9.75	4.42042462792562	-14.1704246279256
8	3.64	-1.42291770302617	5.06291770302617
9	9.5	1.33682901272828	8.16317098727172
10	-0.33	6.30032011800066	-6.63032011800066
11	-0.99	-0.0349152479333883	-0.955084752066612
12	3.16	-1.60580237731785	4.76580237731785
13	4.27	-0.376346620565236	4.64634662056524
14	-2.97	0.406321979457223	-3.37632197945722
15	-7.49	-4.00403339933165	-3.48596660066835
16	0.33	0.326811141582604	0.00318885841739619
17	-2.57	-0.261264209905115	-2.30873579009489
18	4.07	-0.440676743397	4.510676743397
19	5.31	1.44619764285765	3.86380235714235
20	-8.09	-3.24256609613464	-4.84743390386536
21	5.29	4.09673316897627	1.19326683102373
22	7.38	-2.11359417425812	9.49359417425812
23	-6.12	-0.394390847782105	-5.7256091522179
24	-3.38	3.36092214366931	-6.7409221436693
25	-8.61	-0.235340296172335	-8.37465970382766
26	2.58	0.160524200465118	2.41947579953488
27	10.02	7.50892853959169	2.51107146040831
28	7.08	4.76681718443908	2.31318281556092
29	-2.75	0.271186936811239	-3.02118693681124
30	3.42	-0.0711138241046605	3.49111382410466
31	-1.6	3.67208657466297	-5.27208657466297
32	0.65	2.30652539783093	-1.65652539783093
33	2.86	1.701099634409	1.158900365591
34	-3.52	-6.81699796504326	3.29699796504326
35	5.65	0.190562447760764	5.45943755223924
36	4.31	7.70089745529757	-3.39089745529757
37	-4.39	-1.14111931498595	-3.24888068501405
38	-5.85	-2.08274645600372	-3.76725354399628
39	-5.47	-4.45389570268176	-1.01610429731824
40	-2.3	-2.15831306977405	-0.141686930225946
41	-0.14	2.26143497370904	-2.40143497370904
42	8.08	5.06516871665233	3.01483128334767
43	-7.43	-5.05846268825667	-2.37153731174333
44	0.02	-1.88350531137286	1.90350531137286
45	-2.47	1.28249259201867	-3.75249259201867
46	-2.11	-0.950505788501764	-1.15949421149824
47	7.87	6.4537423230029	1.41625767699710
48	4.66	3.90372393342766	0.756276066572336
49	3.6	1.85767874201970	1.74232125798030
50	-3.64	-0.81286200839597	-2.82713799160403
51	7.26	4.34830093158539	2.91169906841461
52	-7.62	-0.0833346115319431	-7.53666538846806
53	13.83	4.93601748695164	8.89398251304836
54	1.28	2.76741996092416	-1.48741996092416
55	-0.32	-3.58139630257603	3.26139630257603
56	-2.9	0.250218888427715	-3.15021888842772
57	4.92	1.02942061837110	3.8905793816289
58	11.99	4.57093626156922	7.41906373843078
59	10.06	2.27713863784001	7.78286136215999
60	-2.22	1.55518512848798	-3.77518512848798
61	3.97	3.88174428554371	0.0882557144562938
62	0.56	-1.19385231671455	1.75385231671455
63	3.34	1.77938968731478	1.56061031268522
64	-2.86	-1.3616801834354	-1.4983198165646
65	4.38	0.0178817976129109	4.36211820238709
66	1.43	-0.880136543166265	2.31013654316626
67	-0.49	1.84448411305145	-2.33448411305145
68	-1.23	0.177523531549388	-1.40752353154939

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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Parameters (Session):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

library(lattice)
par1 <- as.numeric(par1)
x <- t(y)
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code