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rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 13 Dec 2007 08:31:38 -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/13/t1197559024ebj5pg49vd6rqk0.htm/, Retrieved Sun, 05 May 2024 13:37:27 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14362, Retrieved Sun, 05 May 2024 13:37:27 +0000

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User-defined keywords

multiple regression

Estimated Impact

189

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-       [Multiple Regression] [paper] [2007-12-13 15:31:38] [c4d22cfe215e32305e2a296619965f6c] [Current]

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103,1	98,6	98,1	98,6
100,6	98	101,1	98
103,1	106,8	111,1	106,8
95,5	96,6	93,3	96,7
90,5	100,1	100	100,2
90,9	107,7	108	107,7
88,8	91,5	70,4	92
90,7	97,8	75,4	98,4
94,3	107,4	105,5	107,4
104,6	117,5	112,3	117,7
111,1	105,6	102,5	105,7
110,8	97,4	93,5	97,5
107,2	99,5	86,7	99,9
99	98	95,2	98,2
99	104,3	103,8	104,5
91	100,6	97	100,8
96,2	101,1	95,5	101,5
96,9	103,9	101	103,9
96,2	96,9	67,5	99,6
100,1	95,5	64	98,4
99	108,4	106,7	112,7
115,4	117	100,6	118,4
106,9	103,8	101,2	108,1
107,1	100,8	93,1	105,4
99,3	110,6	84,2	114,6
99,2	104	85,8	106,9
108,3	112,6	91,8	115,9
105,6	107,3	92,4	109,8
99,5	98,9	80,3	101,8
107,4	109,8	79,7	114,2
93,1	104,9	62,5	110,8
88,1	102,2	57,1	108,4
110,7	123,9	100,8	127,5
113,1	124,9	100,7	128,6
99,6	112,7	86,2	116,6
93,6	121,9	83,2	127,4
98,6	100,6	71,7	105
99,6	104,3	77,5	108,3
114,3	120,4	89,8	125
107,8	107,5	80,3	111,6
101,2	102,9	78,7	106,5
112,5	125,6	93,8	130,3
100,5	107,5	57,6	115
93,9	108,8	60,6	116,1
116,2	128,4	91	134
112	121,1	85,3	126,5
106,4	119,5	77,4	125,8
95,7	128,7	77,3	136,4
96	108,7	68,3	114,9
95,8	105,5	69,9	110,9
103	119,8	81,7	125,5
102,2	111,3	75,1	116,8
98,4	110,6	69,9	116,8
111,4	120,1	84	125,5
86,6	97,5	54,3	104,2
91,3	107,7	60	115,1
107,9	127,3	89,9	132,8
101,8	117,2	77	123,3
104,4	119,8	85,3	124,8
93,4	116,2	77,6	122
100,1	111	69,2	117,4
98,5	112,4	75,5	117,9
112,9	130,6	85,7	137,4
101,4	109,1	72,2	114,6
107,1	118,8	79,9	124,7
110,8	123,9	85,3	129,6
90,3	101,6	52,2	109,4
95,5	112,8	61,2	120,9
111,4	128	82,4	134,9
113	129,6	85,4	136,3
107,5	125,8	78,2	133,2
95,9	119,5	70,2	127,2
106,3	115,7	70,2	122,7
105,2	113,6	69,3	120,5
117,2	129,7	77,5	137,8
106,9	112	66,1	119,1
108,2	116,8	69	124,3
110	126,3	75,3	134,3
96,1	112,9	58,2	121,7
100,6	115,9	59,7	125

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=14362&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=14362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14362&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
intermediair-goederen[t] = + 40.2461048789932 + 0.241619248015509`totale-consumptie`[t] + 0.181476139543005`Duurzame-consumptiegoederen`[t] + 0.17331501498115`Niet-duurzame-consumptiegoederen`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
intermediair-goederen[t] =  +  40.2461048789932 +  0.241619248015509`totale-consumptie`[t] +  0.181476139543005`Duurzame-consumptiegoederen`[t] +  0.17331501498115`Niet-duurzame-consumptiegoederen`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14362&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]intermediair-goederen[t] =  +  40.2461048789932 +  0.241619248015509`totale-consumptie`[t] +  0.181476139543005`Duurzame-consumptiegoederen`[t] +  0.17331501498115`Niet-duurzame-consumptiegoederen`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14362&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
intermediair-goederen[t] = + 40.2461048789932 + 0.241619248015509`totale-consumptie`[t] + 0.181476139543005`Duurzame-consumptiegoederen`[t] + 0.17331501498115`Niet-duurzame-consumptiegoederen`[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)	40.2461048789932	7.911237	5.0872	3e-06	1e-06
`totale-consumptie`	0.241619248015509	0.792198	0.305	0.761201	0.3806
`Duurzame-consumptiegoederen`	0.181476139543005	0.103482	1.7537	0.083513	0.041756
`Niet-duurzame-consumptiegoederen`	0.17331501498115	0.678748	0.2553	0.799146	0.399573

\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) & 40.2461048789932 & 7.911237 & 5.0872 & 3e-06 & 1e-06 \tabularnewline
`totale-consumptie` & 0.241619248015509 & 0.792198 & 0.305 & 0.761201 & 0.3806 \tabularnewline
`Duurzame-consumptiegoederen` & 0.181476139543005 & 0.103482 & 1.7537 & 0.083513 & 0.041756 \tabularnewline
`Niet-duurzame-consumptiegoederen` & 0.17331501498115 & 0.678748 & 0.2553 & 0.799146 & 0.399573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14362&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]40.2461048789932[/C][C]7.911237[/C][C]5.0872[/C][C]3e-06[/C][C]1e-06[/C][/ROW]
[ROW][C]`totale-consumptie`[/C][C]0.241619248015509[/C][C]0.792198[/C][C]0.305[/C][C]0.761201[/C][C]0.3806[/C][/ROW]
[ROW][C]`Duurzame-consumptiegoederen`[/C][C]0.181476139543005[/C][C]0.103482[/C][C]1.7537[/C][C]0.083513[/C][C]0.041756[/C][/ROW]
[ROW][C]`Niet-duurzame-consumptiegoederen`[/C][C]0.17331501498115[/C][C]0.678748[/C][C]0.2553[/C][C]0.799146[/C][C]0.399573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14362&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14362&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)	40.2461048789932	7.911237	5.0872	3e-06	1e-06
`totale-consumptie`	0.241619248015509	0.792198	0.305	0.761201	0.3806
`Duurzame-consumptiegoederen`	0.181476139543005	0.103482	1.7537	0.083513	0.041756
`Niet-duurzame-consumptiegoederen`	0.17331501498115	0.678748	0.2553	0.799146	0.399573

Multiple Linear Regression - Regression Statistics
Multiple R	0.679250303354806
R-squared	0.461380974607596
Adjusted R-squared	0.440119697289475
F-TEST (value)	21.7005294509919
F-TEST (DF numerator)	3
F-TEST (DF denominator)	76
p-value	2.97640134760968e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.63774009051499
Sum Squared Residuals	2415.59261294320

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.679250303354806 \tabularnewline
R-squared & 0.461380974607596 \tabularnewline
Adjusted R-squared & 0.440119697289475 \tabularnewline
F-TEST (value) & 21.7005294509919 \tabularnewline
F-TEST (DF numerator) & 3 \tabularnewline
F-TEST (DF denominator) & 76 \tabularnewline
p-value & 2.97640134760968e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.63774009051499 \tabularnewline
Sum Squared Residuals & 2415.59261294320 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14362&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.679250303354806[/C][/ROW]
[ROW][C]R-squared[/C][C]0.461380974607596[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.440119697289475[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]21.7005294509919[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]3[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]76[/C][/ROW]
[ROW][C]p-value[/C][C]2.97640134760968e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.63774009051499[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2415.59261294320[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14362&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14362&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	0.679250303354806
R-squared	0.461380974607596
Adjusted R-squared	0.440119697289475
F-TEST (value)	21.7005294509919
F-TEST (DF numerator)	3
F-TEST (DF denominator)	76
p-value	2.97640134760968e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.63774009051499
Sum Squared Residuals	2415.59261294320

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.1	98.9614324996327	4.1385675003673
2	100.6	99.2569003604635	1.34309963953647
3	103.1	104.723083270264	-1.62308327026415
4	95.5	97.2778100053309	-1.77781000533088
5	90.5	99.9459700607573	-9.4459700607573
6	90.9	104.533948074378	-13.6339480743778
7	88.8	91.0751676745056	-2.27516767450558
8	90.7	94.6139657305977	-3.91396573059766
9	94.3	103.955777446621	-9.65577744662134
10	104.6	109.415314254776	-4.81531425477625
11	111.1	102.681798856096	8.41820114390355
12	110.8	97.6460526436368	13.1539473563632
13	107.2	97.3353713515317	9.8646286484683
14	99	98.220854140156	0.779145859843976
15	99	102.395634797105	-3.39563479710481
16	91	99.6263402751247	-8.62634027512474
17	96.2	99.5962562003048	-3.39625620030479
18	96.9	101.686864898190	-4.7868648981895
19	96.2	93.1708249229713	3.02917507702866
20	100.1	91.9894134693717	8.11058653062825
21	99	105.333737641489	-6.33373764148854
22	115.4	107.292554308602	8.10744569139786
23	106.9	102.426921264217	4.47307873578262
24	107.1	99.7641562494234	7.33584375057658
25	99.3	102.111385375869	-2.81138537586924
26	99.2	99.4725345468808	-0.272534546880835
27	108.3	104.199152051903	4.10084794809741
28	105.6	101.970234129761	3.62976587023881
29	99.5	96.3582510381113	3.14174896188865
30	107.4	101.032121343521	6.36787865647915
31	93.1	96.1375263771693	-3.03752637716928
32	88.1	94.0892272180404	-5.98922721804042
33	110.7	110.573188984146	0.126811015853781
34	113.1	110.987307134687	2.1126928653133
35	99.6	103.328368105750	-3.72836810575013
36	93.6	106.878638930660	-13.2786389306602
37	98.6	95.7629170076076	2.83708299239244
38	99.6	98.2814093840522	1.31859061594783
39	114.3	107.297996543666	7.00200345633398
40	107.8	100.13466371786	7.66533628214
41	101.2	97.848946777316	3.35105322268402
42	112.5	110.198890770919	2.30110922908123
43	100.5	96.6044264011697	3.89557359883030
44	93.9	97.6536063586981	-3.75360635869814
45	116.2	111.008557030072	5.19144296992796
46	112	106.910459911805	5.08954008819493
47	106.4	104.968887102104	1.43111289789628
48	95.7	109.010775728692	-13.3107757286923
49	96	98.8188326904004	-2.81883269040035
50	95.8	97.642752860095	-1.84275286009493
51	103	105.769725772049	-2.76972577204895
52	102.2	101.010379012597	1.18962098740272
53	98.4	99.8975696133628	-1.49756961336280
54	111.4	106.259606667403	5.1403933325975
55	86.6	91.7175604987263	-5.11756049872629
56	91.3	97.1056244871741	-5.80562448717414
57	107.9	110.335174085780	-2.43517408578029
58	101.8	103.907284838398	-2.10728483839798
59	104.4	106.301719363917	-1.90171936391695
60	93.4	103.549241754633	-10.1492417546328
61	100.1	99.9711730238776	0.128826976122394
62	98.5	101.539397157711	-3.03939715771082
63	112.9	111.167566887064	1.73243311293586
64	101.4	99.57124282933	1.82875717067008
65	107.1	105.062797460871	2.03720253912888
66	110.8	108.12427035269	2.67572964730993
67	90.3	93.2283376004515	-2.92833760045154
68	95.5	99.5608811063955	-4.0608811063955
69	111.4	109.507198044279	1.89280195572097
70	113	110.680858280706	2.31914171929353
71	107.5	107.918800387096	-0.418800387096338
72	95.9	103.904899918368	-8.00489991836769
73	106.3	102.206829208494	4.09317079150641
74	105.2	101.154807229114	4.04519277088622
75	117.2	109.53133122559	7.66866877440999
76	106.9	99.9448517647778	6.95514823522226
77	108.2	102.532143037829	5.66785696217112
78	110	107.703975722909	2.29602427709135
79	96.1	99.179266624553	-3.07926662455297
80	100.6	100.748278127352	-0.148278127351796

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.1 & 98.9614324996327 & 4.1385675003673 \tabularnewline
2 & 100.6 & 99.2569003604635 & 1.34309963953647 \tabularnewline
3 & 103.1 & 104.723083270264 & -1.62308327026415 \tabularnewline
4 & 95.5 & 97.2778100053309 & -1.77781000533088 \tabularnewline
5 & 90.5 & 99.9459700607573 & -9.4459700607573 \tabularnewline
6 & 90.9 & 104.533948074378 & -13.6339480743778 \tabularnewline
7 & 88.8 & 91.0751676745056 & -2.27516767450558 \tabularnewline
8 & 90.7 & 94.6139657305977 & -3.91396573059766 \tabularnewline
9 & 94.3 & 103.955777446621 & -9.65577744662134 \tabularnewline
10 & 104.6 & 109.415314254776 & -4.81531425477625 \tabularnewline
11 & 111.1 & 102.681798856096 & 8.41820114390355 \tabularnewline
12 & 110.8 & 97.6460526436368 & 13.1539473563632 \tabularnewline
13 & 107.2 & 97.3353713515317 & 9.8646286484683 \tabularnewline
14 & 99 & 98.220854140156 & 0.779145859843976 \tabularnewline
15 & 99 & 102.395634797105 & -3.39563479710481 \tabularnewline
16 & 91 & 99.6263402751247 & -8.62634027512474 \tabularnewline
17 & 96.2 & 99.5962562003048 & -3.39625620030479 \tabularnewline
18 & 96.9 & 101.686864898190 & -4.7868648981895 \tabularnewline
19 & 96.2 & 93.1708249229713 & 3.02917507702866 \tabularnewline
20 & 100.1 & 91.9894134693717 & 8.11058653062825 \tabularnewline
21 & 99 & 105.333737641489 & -6.33373764148854 \tabularnewline
22 & 115.4 & 107.292554308602 & 8.10744569139786 \tabularnewline
23 & 106.9 & 102.426921264217 & 4.47307873578262 \tabularnewline
24 & 107.1 & 99.7641562494234 & 7.33584375057658 \tabularnewline
25 & 99.3 & 102.111385375869 & -2.81138537586924 \tabularnewline
26 & 99.2 & 99.4725345468808 & -0.272534546880835 \tabularnewline
27 & 108.3 & 104.199152051903 & 4.10084794809741 \tabularnewline
28 & 105.6 & 101.970234129761 & 3.62976587023881 \tabularnewline
29 & 99.5 & 96.3582510381113 & 3.14174896188865 \tabularnewline
30 & 107.4 & 101.032121343521 & 6.36787865647915 \tabularnewline
31 & 93.1 & 96.1375263771693 & -3.03752637716928 \tabularnewline
32 & 88.1 & 94.0892272180404 & -5.98922721804042 \tabularnewline
33 & 110.7 & 110.573188984146 & 0.126811015853781 \tabularnewline
34 & 113.1 & 110.987307134687 & 2.1126928653133 \tabularnewline
35 & 99.6 & 103.328368105750 & -3.72836810575013 \tabularnewline
36 & 93.6 & 106.878638930660 & -13.2786389306602 \tabularnewline
37 & 98.6 & 95.7629170076076 & 2.83708299239244 \tabularnewline
38 & 99.6 & 98.2814093840522 & 1.31859061594783 \tabularnewline
39 & 114.3 & 107.297996543666 & 7.00200345633398 \tabularnewline
40 & 107.8 & 100.13466371786 & 7.66533628214 \tabularnewline
41 & 101.2 & 97.848946777316 & 3.35105322268402 \tabularnewline
42 & 112.5 & 110.198890770919 & 2.30110922908123 \tabularnewline
43 & 100.5 & 96.6044264011697 & 3.89557359883030 \tabularnewline
44 & 93.9 & 97.6536063586981 & -3.75360635869814 \tabularnewline
45 & 116.2 & 111.008557030072 & 5.19144296992796 \tabularnewline
46 & 112 & 106.910459911805 & 5.08954008819493 \tabularnewline
47 & 106.4 & 104.968887102104 & 1.43111289789628 \tabularnewline
48 & 95.7 & 109.010775728692 & -13.3107757286923 \tabularnewline
49 & 96 & 98.8188326904004 & -2.81883269040035 \tabularnewline
50 & 95.8 & 97.642752860095 & -1.84275286009493 \tabularnewline
51 & 103 & 105.769725772049 & -2.76972577204895 \tabularnewline
52 & 102.2 & 101.010379012597 & 1.18962098740272 \tabularnewline
53 & 98.4 & 99.8975696133628 & -1.49756961336280 \tabularnewline
54 & 111.4 & 106.259606667403 & 5.1403933325975 \tabularnewline
55 & 86.6 & 91.7175604987263 & -5.11756049872629 \tabularnewline
56 & 91.3 & 97.1056244871741 & -5.80562448717414 \tabularnewline
57 & 107.9 & 110.335174085780 & -2.43517408578029 \tabularnewline
58 & 101.8 & 103.907284838398 & -2.10728483839798 \tabularnewline
59 & 104.4 & 106.301719363917 & -1.90171936391695 \tabularnewline
60 & 93.4 & 103.549241754633 & -10.1492417546328 \tabularnewline
61 & 100.1 & 99.9711730238776 & 0.128826976122394 \tabularnewline
62 & 98.5 & 101.539397157711 & -3.03939715771082 \tabularnewline
63 & 112.9 & 111.167566887064 & 1.73243311293586 \tabularnewline
64 & 101.4 & 99.57124282933 & 1.82875717067008 \tabularnewline
65 & 107.1 & 105.062797460871 & 2.03720253912888 \tabularnewline
66 & 110.8 & 108.12427035269 & 2.67572964730993 \tabularnewline
67 & 90.3 & 93.2283376004515 & -2.92833760045154 \tabularnewline
68 & 95.5 & 99.5608811063955 & -4.0608811063955 \tabularnewline
69 & 111.4 & 109.507198044279 & 1.89280195572097 \tabularnewline
70 & 113 & 110.680858280706 & 2.31914171929353 \tabularnewline
71 & 107.5 & 107.918800387096 & -0.418800387096338 \tabularnewline
72 & 95.9 & 103.904899918368 & -8.00489991836769 \tabularnewline
73 & 106.3 & 102.206829208494 & 4.09317079150641 \tabularnewline
74 & 105.2 & 101.154807229114 & 4.04519277088622 \tabularnewline
75 & 117.2 & 109.53133122559 & 7.66866877440999 \tabularnewline
76 & 106.9 & 99.9448517647778 & 6.95514823522226 \tabularnewline
77 & 108.2 & 102.532143037829 & 5.66785696217112 \tabularnewline
78 & 110 & 107.703975722909 & 2.29602427709135 \tabularnewline
79 & 96.1 & 99.179266624553 & -3.07926662455297 \tabularnewline
80 & 100.6 & 100.748278127352 & -0.148278127351796 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14362&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]103.1[/C][C]98.9614324996327[/C][C]4.1385675003673[/C][/ROW]
[ROW][C]2[/C][C]100.6[/C][C]99.2569003604635[/C][C]1.34309963953647[/C][/ROW]
[ROW][C]3[/C][C]103.1[/C][C]104.723083270264[/C][C]-1.62308327026415[/C][/ROW]
[ROW][C]4[/C][C]95.5[/C][C]97.2778100053309[/C][C]-1.77781000533088[/C][/ROW]
[ROW][C]5[/C][C]90.5[/C][C]99.9459700607573[/C][C]-9.4459700607573[/C][/ROW]
[ROW][C]6[/C][C]90.9[/C][C]104.533948074378[/C][C]-13.6339480743778[/C][/ROW]
[ROW][C]7[/C][C]88.8[/C][C]91.0751676745056[/C][C]-2.27516767450558[/C][/ROW]
[ROW][C]8[/C][C]90.7[/C][C]94.6139657305977[/C][C]-3.91396573059766[/C][/ROW]
[ROW][C]9[/C][C]94.3[/C][C]103.955777446621[/C][C]-9.65577744662134[/C][/ROW]
[ROW][C]10[/C][C]104.6[/C][C]109.415314254776[/C][C]-4.81531425477625[/C][/ROW]
[ROW][C]11[/C][C]111.1[/C][C]102.681798856096[/C][C]8.41820114390355[/C][/ROW]
[ROW][C]12[/C][C]110.8[/C][C]97.6460526436368[/C][C]13.1539473563632[/C][/ROW]
[ROW][C]13[/C][C]107.2[/C][C]97.3353713515317[/C][C]9.8646286484683[/C][/ROW]
[ROW][C]14[/C][C]99[/C][C]98.220854140156[/C][C]0.779145859843976[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]102.395634797105[/C][C]-3.39563479710481[/C][/ROW]
[ROW][C]16[/C][C]91[/C][C]99.6263402751247[/C][C]-8.62634027512474[/C][/ROW]
[ROW][C]17[/C][C]96.2[/C][C]99.5962562003048[/C][C]-3.39625620030479[/C][/ROW]
[ROW][C]18[/C][C]96.9[/C][C]101.686864898190[/C][C]-4.7868648981895[/C][/ROW]
[ROW][C]19[/C][C]96.2[/C][C]93.1708249229713[/C][C]3.02917507702866[/C][/ROW]
[ROW][C]20[/C][C]100.1[/C][C]91.9894134693717[/C][C]8.11058653062825[/C][/ROW]
[ROW][C]21[/C][C]99[/C][C]105.333737641489[/C][C]-6.33373764148854[/C][/ROW]
[ROW][C]22[/C][C]115.4[/C][C]107.292554308602[/C][C]8.10744569139786[/C][/ROW]
[ROW][C]23[/C][C]106.9[/C][C]102.426921264217[/C][C]4.47307873578262[/C][/ROW]
[ROW][C]24[/C][C]107.1[/C][C]99.7641562494234[/C][C]7.33584375057658[/C][/ROW]
[ROW][C]25[/C][C]99.3[/C][C]102.111385375869[/C][C]-2.81138537586924[/C][/ROW]
[ROW][C]26[/C][C]99.2[/C][C]99.4725345468808[/C][C]-0.272534546880835[/C][/ROW]
[ROW][C]27[/C][C]108.3[/C][C]104.199152051903[/C][C]4.10084794809741[/C][/ROW]
[ROW][C]28[/C][C]105.6[/C][C]101.970234129761[/C][C]3.62976587023881[/C][/ROW]
[ROW][C]29[/C][C]99.5[/C][C]96.3582510381113[/C][C]3.14174896188865[/C][/ROW]
[ROW][C]30[/C][C]107.4[/C][C]101.032121343521[/C][C]6.36787865647915[/C][/ROW]
[ROW][C]31[/C][C]93.1[/C][C]96.1375263771693[/C][C]-3.03752637716928[/C][/ROW]
[ROW][C]32[/C][C]88.1[/C][C]94.0892272180404[/C][C]-5.98922721804042[/C][/ROW]
[ROW][C]33[/C][C]110.7[/C][C]110.573188984146[/C][C]0.126811015853781[/C][/ROW]
[ROW][C]34[/C][C]113.1[/C][C]110.987307134687[/C][C]2.1126928653133[/C][/ROW]
[ROW][C]35[/C][C]99.6[/C][C]103.328368105750[/C][C]-3.72836810575013[/C][/ROW]
[ROW][C]36[/C][C]93.6[/C][C]106.878638930660[/C][C]-13.2786389306602[/C][/ROW]
[ROW][C]37[/C][C]98.6[/C][C]95.7629170076076[/C][C]2.83708299239244[/C][/ROW]
[ROW][C]38[/C][C]99.6[/C][C]98.2814093840522[/C][C]1.31859061594783[/C][/ROW]
[ROW][C]39[/C][C]114.3[/C][C]107.297996543666[/C][C]7.00200345633398[/C][/ROW]
[ROW][C]40[/C][C]107.8[/C][C]100.13466371786[/C][C]7.66533628214[/C][/ROW]
[ROW][C]41[/C][C]101.2[/C][C]97.848946777316[/C][C]3.35105322268402[/C][/ROW]
[ROW][C]42[/C][C]112.5[/C][C]110.198890770919[/C][C]2.30110922908123[/C][/ROW]
[ROW][C]43[/C][C]100.5[/C][C]96.6044264011697[/C][C]3.89557359883030[/C][/ROW]
[ROW][C]44[/C][C]93.9[/C][C]97.6536063586981[/C][C]-3.75360635869814[/C][/ROW]
[ROW][C]45[/C][C]116.2[/C][C]111.008557030072[/C][C]5.19144296992796[/C][/ROW]
[ROW][C]46[/C][C]112[/C][C]106.910459911805[/C][C]5.08954008819493[/C][/ROW]
[ROW][C]47[/C][C]106.4[/C][C]104.968887102104[/C][C]1.43111289789628[/C][/ROW]
[ROW][C]48[/C][C]95.7[/C][C]109.010775728692[/C][C]-13.3107757286923[/C][/ROW]
[ROW][C]49[/C][C]96[/C][C]98.8188326904004[/C][C]-2.81883269040035[/C][/ROW]
[ROW][C]50[/C][C]95.8[/C][C]97.642752860095[/C][C]-1.84275286009493[/C][/ROW]
[ROW][C]51[/C][C]103[/C][C]105.769725772049[/C][C]-2.76972577204895[/C][/ROW]
[ROW][C]52[/C][C]102.2[/C][C]101.010379012597[/C][C]1.18962098740272[/C][/ROW]
[ROW][C]53[/C][C]98.4[/C][C]99.8975696133628[/C][C]-1.49756961336280[/C][/ROW]
[ROW][C]54[/C][C]111.4[/C][C]106.259606667403[/C][C]5.1403933325975[/C][/ROW]
[ROW][C]55[/C][C]86.6[/C][C]91.7175604987263[/C][C]-5.11756049872629[/C][/ROW]
[ROW][C]56[/C][C]91.3[/C][C]97.1056244871741[/C][C]-5.80562448717414[/C][/ROW]
[ROW][C]57[/C][C]107.9[/C][C]110.335174085780[/C][C]-2.43517408578029[/C][/ROW]
[ROW][C]58[/C][C]101.8[/C][C]103.907284838398[/C][C]-2.10728483839798[/C][/ROW]
[ROW][C]59[/C][C]104.4[/C][C]106.301719363917[/C][C]-1.90171936391695[/C][/ROW]
[ROW][C]60[/C][C]93.4[/C][C]103.549241754633[/C][C]-10.1492417546328[/C][/ROW]
[ROW][C]61[/C][C]100.1[/C][C]99.9711730238776[/C][C]0.128826976122394[/C][/ROW]
[ROW][C]62[/C][C]98.5[/C][C]101.539397157711[/C][C]-3.03939715771082[/C][/ROW]
[ROW][C]63[/C][C]112.9[/C][C]111.167566887064[/C][C]1.73243311293586[/C][/ROW]
[ROW][C]64[/C][C]101.4[/C][C]99.57124282933[/C][C]1.82875717067008[/C][/ROW]
[ROW][C]65[/C][C]107.1[/C][C]105.062797460871[/C][C]2.03720253912888[/C][/ROW]
[ROW][C]66[/C][C]110.8[/C][C]108.12427035269[/C][C]2.67572964730993[/C][/ROW]
[ROW][C]67[/C][C]90.3[/C][C]93.2283376004515[/C][C]-2.92833760045154[/C][/ROW]
[ROW][C]68[/C][C]95.5[/C][C]99.5608811063955[/C][C]-4.0608811063955[/C][/ROW]
[ROW][C]69[/C][C]111.4[/C][C]109.507198044279[/C][C]1.89280195572097[/C][/ROW]
[ROW][C]70[/C][C]113[/C][C]110.680858280706[/C][C]2.31914171929353[/C][/ROW]
[ROW][C]71[/C][C]107.5[/C][C]107.918800387096[/C][C]-0.418800387096338[/C][/ROW]
[ROW][C]72[/C][C]95.9[/C][C]103.904899918368[/C][C]-8.00489991836769[/C][/ROW]
[ROW][C]73[/C][C]106.3[/C][C]102.206829208494[/C][C]4.09317079150641[/C][/ROW]
[ROW][C]74[/C][C]105.2[/C][C]101.154807229114[/C][C]4.04519277088622[/C][/ROW]
[ROW][C]75[/C][C]117.2[/C][C]109.53133122559[/C][C]7.66866877440999[/C][/ROW]
[ROW][C]76[/C][C]106.9[/C][C]99.9448517647778[/C][C]6.95514823522226[/C][/ROW]
[ROW][C]77[/C][C]108.2[/C][C]102.532143037829[/C][C]5.66785696217112[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]107.703975722909[/C][C]2.29602427709135[/C][/ROW]
[ROW][C]79[/C][C]96.1[/C][C]99.179266624553[/C][C]-3.07926662455297[/C][/ROW]
[ROW][C]80[/C][C]100.6[/C][C]100.748278127352[/C][C]-0.148278127351796[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14362&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14362&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	103.1	98.9614324996327	4.1385675003673
2	100.6	99.2569003604635	1.34309963953647
3	103.1	104.723083270264	-1.62308327026415
4	95.5	97.2778100053309	-1.77781000533088
5	90.5	99.9459700607573	-9.4459700607573
6	90.9	104.533948074378	-13.6339480743778
7	88.8	91.0751676745056	-2.27516767450558
8	90.7	94.6139657305977	-3.91396573059766
9	94.3	103.955777446621	-9.65577744662134
10	104.6	109.415314254776	-4.81531425477625
11	111.1	102.681798856096	8.41820114390355
12	110.8	97.6460526436368	13.1539473563632
13	107.2	97.3353713515317	9.8646286484683
14	99	98.220854140156	0.779145859843976
15	99	102.395634797105	-3.39563479710481
16	91	99.6263402751247	-8.62634027512474
17	96.2	99.5962562003048	-3.39625620030479
18	96.9	101.686864898190	-4.7868648981895
19	96.2	93.1708249229713	3.02917507702866
20	100.1	91.9894134693717	8.11058653062825
21	99	105.333737641489	-6.33373764148854
22	115.4	107.292554308602	8.10744569139786
23	106.9	102.426921264217	4.47307873578262
24	107.1	99.7641562494234	7.33584375057658
25	99.3	102.111385375869	-2.81138537586924
26	99.2	99.4725345468808	-0.272534546880835
27	108.3	104.199152051903	4.10084794809741
28	105.6	101.970234129761	3.62976587023881
29	99.5	96.3582510381113	3.14174896188865
30	107.4	101.032121343521	6.36787865647915
31	93.1	96.1375263771693	-3.03752637716928
32	88.1	94.0892272180404	-5.98922721804042
33	110.7	110.573188984146	0.126811015853781
34	113.1	110.987307134687	2.1126928653133
35	99.6	103.328368105750	-3.72836810575013
36	93.6	106.878638930660	-13.2786389306602
37	98.6	95.7629170076076	2.83708299239244
38	99.6	98.2814093840522	1.31859061594783
39	114.3	107.297996543666	7.00200345633398
40	107.8	100.13466371786	7.66533628214
41	101.2	97.848946777316	3.35105322268402
42	112.5	110.198890770919	2.30110922908123
43	100.5	96.6044264011697	3.89557359883030
44	93.9	97.6536063586981	-3.75360635869814
45	116.2	111.008557030072	5.19144296992796
46	112	106.910459911805	5.08954008819493
47	106.4	104.968887102104	1.43111289789628
48	95.7	109.010775728692	-13.3107757286923
49	96	98.8188326904004	-2.81883269040035
50	95.8	97.642752860095	-1.84275286009493
51	103	105.769725772049	-2.76972577204895
52	102.2	101.010379012597	1.18962098740272
53	98.4	99.8975696133628	-1.49756961336280
54	111.4	106.259606667403	5.1403933325975
55	86.6	91.7175604987263	-5.11756049872629
56	91.3	97.1056244871741	-5.80562448717414
57	107.9	110.335174085780	-2.43517408578029
58	101.8	103.907284838398	-2.10728483839798
59	104.4	106.301719363917	-1.90171936391695
60	93.4	103.549241754633	-10.1492417546328
61	100.1	99.9711730238776	0.128826976122394
62	98.5	101.539397157711	-3.03939715771082
63	112.9	111.167566887064	1.73243311293586
64	101.4	99.57124282933	1.82875717067008
65	107.1	105.062797460871	2.03720253912888
66	110.8	108.12427035269	2.67572964730993
67	90.3	93.2283376004515	-2.92833760045154
68	95.5	99.5608811063955	-4.0608811063955
69	111.4	109.507198044279	1.89280195572097
70	113	110.680858280706	2.31914171929353
71	107.5	107.918800387096	-0.418800387096338
72	95.9	103.904899918368	-8.00489991836769
73	106.3	102.206829208494	4.09317079150641
74	105.2	101.154807229114	4.04519277088622
75	117.2	109.53133122559	7.66866877440999
76	106.9	99.9448517647778	6.95514823522226
77	108.2	102.532143037829	5.66785696217112
78	110	107.703975722909	2.29602427709135
79	96.1	99.179266624553	-3.07926662455297
80	100.6	100.748278127352	-0.148278127351796

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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 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 0 ;

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