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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 17 Dec 2007 13:07:59 -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/17/t1197921073vemeek5jsv2lizw.htm/, Retrieved Fri, 03 May 2024 19:22:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4423, Retrieved Fri, 03 May 2024 19:22:51 +0000

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Estimated Impact

177

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

-       [Multiple Regression] [] [2007-12-17 20:07:59] [ba3202e2798d2e4685d19d988e9c69df] [Current]

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85,6	92,81	88	75,6
89	59,04	88,4	48,7
97,5	72,81	95	111,7
104	91,81	101,8	119,5
99,4	68,07	107,6	103,4
103,2	49,16	118,9	96,3
103	124,61	126,9	96,6
91,2	109,89	106,3	110,4
85,9	110,51	109,2	104,4
80,7	114,77	104,6	110,7
86,7	92,37	100,8	93,6
80,7	103,63	92,1	114,8
81,5	90,43	86,4	74,9
83,4	65,86	96	69,8
83,5	83,33	98,5	104,2
89,5	94,49	112	109,3
85,8	68,98	113,9	92,7
77,4	55,46	120	91,7
67,5	132,89	126,7	84,4
63,7	121,71	112,8	94,5
59,4	127,01	116,2	103,6
62	134,04	110,6	105,9
62,4	106,48	105	108
58,1	117,55	101,2	119,9
58	101,61	99,3	84,5
56,3	82,66	101,9	76,7
61,4	89,28	106,4	120,5
59,8	109,24	118,9	119,6
54,3	88,16	121,9	102,3
47	59,23	132	101,7
50,5	164,21	121,4	86,9
48,1	125,13	117	93,6
58,8	152,68	122,7	113,6
70,4	132,96	113	106,7
71,9	112,42	104	96,1
73,3	136,43	101,2	124,6
83,5	107,32	100,8	72,5
90,1	87,61	98,9	89
101,3	97,86	103	115,3
98,3	106,60	117,8	119,1
106,7	92,17	126,6	104,4
109,9	65,31	127,6	104,9
111,1	161,49	115,8	76,9
119	162,25	114,8	95,3
120,7	175,13	119,2	114,9
104,5	147,28	109,9	98,9
121,6	144,48	98,9	102,9
129,6	122,67	98,6	90
124,5	102,27	96,6	81
130,1	88,64	96,7	66,2
142,3	89,59	103,5	86,7
140	112,20	115,3	86,7
143,3	91,98	122,5	103,4
113,4	57,85	125,3	89,5
113,8	160,49	111,2	113,7
120,7	128,33	110,7	120,2
112,9	140,69	114,2	137,8
115,5	126,61	105,6	99,1
121,9	129,27	95,5	110,4
119,3	124,27	97,3	112,3
111	112,90	95,5	82,2
114,2	92,54	96,3	72,2
113,5	85,70	100,2	106,7
94	116,72	113,4	106,4
83,2	92,08	121,4	110
82,8	58,98	122,1	87,5
85,8	154,50	119,3	79,7
88,7	145,55	110,8	83,3
105,3	146,60	110,1	91,8
113,1	143,51	99,7	89,2
113,8	113,52	104,8	90,6
109,4	104,80	105,4	92,6

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=4423&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=4423&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4423&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
olie[t] = + 228.284544908443 + 0.733864986672665wagens[t] -1.77035047399589electrici[t] -0.405227125252465vervoesm[t] -12.9153073390046M1[t] + 6.44696189065542M2[t] + 30.7564967394615M3[t] + 37.1491621565794M4[t] + 58.0929382664058M5[t] + 77.0358378597182M6[t] -0.351737687677874M7[t] + 2.04439297403099M8[t] + 6.98015692645306M9[t] -3.92591598571114M10[t] + 2.98112666239964M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
olie[t] =  +  228.284544908443 +  0.733864986672665wagens[t] -1.77035047399589electrici[t] -0.405227125252465vervoesm[t] -12.9153073390046M1[t] +  6.44696189065542M2[t] +  30.7564967394615M3[t] +  37.1491621565794M4[t] +  58.0929382664058M5[t] +  77.0358378597182M6[t] -0.351737687677874M7[t] +  2.04439297403099M8[t] +  6.98015692645306M9[t] -3.92591598571114M10[t] +  2.98112666239964M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4423&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]olie[t] =  +  228.284544908443 +  0.733864986672665wagens[t] -1.77035047399589electrici[t] -0.405227125252465vervoesm[t] -12.9153073390046M1[t] +  6.44696189065542M2[t] +  30.7564967394615M3[t] +  37.1491621565794M4[t] +  58.0929382664058M5[t] +  77.0358378597182M6[t] -0.351737687677874M7[t] +  2.04439297403099M8[t] +  6.98015692645306M9[t] -3.92591598571114M10[t] +  2.98112666239964M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4423&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4423&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
olie[t] = + 228.284544908443 + 0.733864986672665wagens[t] -1.77035047399589electrici[t] -0.405227125252465vervoesm[t] -12.9153073390046M1[t] + 6.44696189065542M2[t] + 30.7564967394615M3[t] + 37.1491621565794M4[t] + 58.0929382664058M5[t] + 77.0358378597182M6[t] -0.351737687677874M7[t] + 2.04439297403099M8[t] + 6.98015692645306M9[t] -3.92591598571114M10[t] + 2.98112666239964M11[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)	228.284544908443	65.886436	3.4648	0.001015	0.000507
wagens	0.733864986672665	0.230682	3.1813	0.002373	0.001187
electrici	-1.77035047399589	0.623493	-2.8394	0.006254	0.003127
vervoesm	-0.405227125252465	0.27606	-1.4679	0.14763	0.073815
M1	-12.9153073390046	16.514008	-0.7821	0.437403	0.218702
M2	6.44696189065542	19.0784	0.3379	0.736665	0.368333
M3	30.7564967394615	15.808927	1.9455	0.056651	0.028325
M4	37.1491621565794	16.997423	2.1856	0.03297	0.016485
M5	58.0929382664058	21.502083	2.7017	0.00907	0.004535
M6	77.0358378597182	27.609449	2.7902	0.007152	0.003576
M7	-0.351737687677874	20.066747	-0.0175	0.986076	0.493038
M8	2.04439297403099	16.138309	0.1267	0.89964	0.44982
M9	6.98015692645306	17.007198	0.4104	0.683034	0.341517
M10	-3.92591598571114	14.953701	-0.2625	0.793853	0.396927
M11	2.98112666239964	14.106203	0.2113	0.83338	0.41669

\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) & 228.284544908443 & 65.886436 & 3.4648 & 0.001015 & 0.000507 \tabularnewline
wagens & 0.733864986672665 & 0.230682 & 3.1813 & 0.002373 & 0.001187 \tabularnewline
electrici & -1.77035047399589 & 0.623493 & -2.8394 & 0.006254 & 0.003127 \tabularnewline
vervoesm & -0.405227125252465 & 0.27606 & -1.4679 & 0.14763 & 0.073815 \tabularnewline
M1 & -12.9153073390046 & 16.514008 & -0.7821 & 0.437403 & 0.218702 \tabularnewline
M2 & 6.44696189065542 & 19.0784 & 0.3379 & 0.736665 & 0.368333 \tabularnewline
M3 & 30.7564967394615 & 15.808927 & 1.9455 & 0.056651 & 0.028325 \tabularnewline
M4 & 37.1491621565794 & 16.997423 & 2.1856 & 0.03297 & 0.016485 \tabularnewline
M5 & 58.0929382664058 & 21.502083 & 2.7017 & 0.00907 & 0.004535 \tabularnewline
M6 & 77.0358378597182 & 27.609449 & 2.7902 & 0.007152 & 0.003576 \tabularnewline
M7 & -0.351737687677874 & 20.066747 & -0.0175 & 0.986076 & 0.493038 \tabularnewline
M8 & 2.04439297403099 & 16.138309 & 0.1267 & 0.89964 & 0.44982 \tabularnewline
M9 & 6.98015692645306 & 17.007198 & 0.4104 & 0.683034 & 0.341517 \tabularnewline
M10 & -3.92591598571114 & 14.953701 & -0.2625 & 0.793853 & 0.396927 \tabularnewline
M11 & 2.98112666239964 & 14.106203 & 0.2113 & 0.83338 & 0.41669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4423&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]228.284544908443[/C][C]65.886436[/C][C]3.4648[/C][C]0.001015[/C][C]0.000507[/C][/ROW]
[ROW][C]wagens[/C][C]0.733864986672665[/C][C]0.230682[/C][C]3.1813[/C][C]0.002373[/C][C]0.001187[/C][/ROW]
[ROW][C]electrici[/C][C]-1.77035047399589[/C][C]0.623493[/C][C]-2.8394[/C][C]0.006254[/C][C]0.003127[/C][/ROW]
[ROW][C]vervoesm[/C][C]-0.405227125252465[/C][C]0.27606[/C][C]-1.4679[/C][C]0.14763[/C][C]0.073815[/C][/ROW]
[ROW][C]M1[/C][C]-12.9153073390046[/C][C]16.514008[/C][C]-0.7821[/C][C]0.437403[/C][C]0.218702[/C][/ROW]
[ROW][C]M2[/C][C]6.44696189065542[/C][C]19.0784[/C][C]0.3379[/C][C]0.736665[/C][C]0.368333[/C][/ROW]
[ROW][C]M3[/C][C]30.7564967394615[/C][C]15.808927[/C][C]1.9455[/C][C]0.056651[/C][C]0.028325[/C][/ROW]
[ROW][C]M4[/C][C]37.1491621565794[/C][C]16.997423[/C][C]2.1856[/C][C]0.03297[/C][C]0.016485[/C][/ROW]
[ROW][C]M5[/C][C]58.0929382664058[/C][C]21.502083[/C][C]2.7017[/C][C]0.00907[/C][C]0.004535[/C][/ROW]
[ROW][C]M6[/C][C]77.0358378597182[/C][C]27.609449[/C][C]2.7902[/C][C]0.007152[/C][C]0.003576[/C][/ROW]
[ROW][C]M7[/C][C]-0.351737687677874[/C][C]20.066747[/C][C]-0.0175[/C][C]0.986076[/C][C]0.493038[/C][/ROW]
[ROW][C]M8[/C][C]2.04439297403099[/C][C]16.138309[/C][C]0.1267[/C][C]0.89964[/C][C]0.44982[/C][/ROW]
[ROW][C]M9[/C][C]6.98015692645306[/C][C]17.007198[/C][C]0.4104[/C][C]0.683034[/C][C]0.341517[/C][/ROW]
[ROW][C]M10[/C][C]-3.92591598571114[/C][C]14.953701[/C][C]-0.2625[/C][C]0.793853[/C][C]0.396927[/C][/ROW]
[ROW][C]M11[/C][C]2.98112666239964[/C][C]14.106203[/C][C]0.2113[/C][C]0.83338[/C][C]0.41669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4423&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4423&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)	228.284544908443	65.886436	3.4648	0.001015	0.000507
wagens	0.733864986672665	0.230682	3.1813	0.002373	0.001187
electrici	-1.77035047399589	0.623493	-2.8394	0.006254	0.003127
vervoesm	-0.405227125252465	0.27606	-1.4679	0.14763	0.073815
M1	-12.9153073390046	16.514008	-0.7821	0.437403	0.218702
M2	6.44696189065542	19.0784	0.3379	0.736665	0.368333
M3	30.7564967394615	15.808927	1.9455	0.056651	0.028325
M4	37.1491621565794	16.997423	2.1856	0.03297	0.016485
M5	58.0929382664058	21.502083	2.7017	0.00907	0.004535
M6	77.0358378597182	27.609449	2.7902	0.007152	0.003576
M7	-0.351737687677874	20.066747	-0.0175	0.986076	0.493038
M8	2.04439297403099	16.138309	0.1267	0.89964	0.44982
M9	6.98015692645306	17.007198	0.4104	0.683034	0.341517
M10	-3.92591598571114	14.953701	-0.2625	0.793853	0.396927
M11	2.98112666239964	14.106203	0.2113	0.83338	0.41669

Multiple Linear Regression - Regression Statistics
Multiple R	0.49018821485491
R-squared	0.240284485982644
Adjusted R-squared	0.0536876930660999
F-TEST (value)	1.28772034195739
F-TEST (DF numerator)	14
F-TEST (DF denominator)	57
p-value	0.243593442519630
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	23.9079643232651
Sum Squared Residuals	32580.6732107033

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.49018821485491 \tabularnewline
R-squared & 0.240284485982644 \tabularnewline
Adjusted R-squared & 0.0536876930660999 \tabularnewline
F-TEST (value) & 1.28772034195739 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.243593442519630 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 23.9079643232651 \tabularnewline
Sum Squared Residuals & 32580.6732107033 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4423&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.49018821485491[/C][/ROW]
[ROW][C]R-squared[/C][C]0.240284485982644[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.0536876930660999[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]1.28772034195739[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.243593442519630[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]23.9079643232651[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]32580.6732107033[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4423&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4423&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	0.49018821485491
R-squared	0.240284485982644
Adjusted R-squared	0.0536876930660999
F-TEST (value)	1.28772034195739
F-TEST (DF numerator)	14
F-TEST (DF denominator)	57
p-value	0.243593442519630
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	23.9079643232651
Sum Squared Residuals	32580.6732107033

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	85.6	97.053234601803	-11.4532346018030
2	89	101.825352711221	-12.8253527112210
3	97.5	99.0265864072316	-1.52658640723161
4	104	104.163531770989	-0.163531770988733
5	99.4	103.941477064595	-4.54147706459462
6	103.2	91.8791419930658	11.3208580069342
7	103	55.5773077605794	47.4226922394206
8	91.2	78.048031254298	13.1519687457019
9	85.9	80.7361378753838	5.16386212461621
10	80.7	78.5470110977358	2.15298890226422
11	86.7	82.6721936873804	4.0278063126196
12	80.7	94.765620843327	-14.065620843327
13	81.5	98.4228556795931	-16.9228556795931
14	83.4	84.8253559751328	-1.42535597513277
15	83.5	103.589822847436	-20.0898228474358
16	89.5	92.2060317780885	-2.70603177808851
17	85.8	97.792016456494	-11.9920164564940
18	77.4	96.4191506638695	-19.0191506638695
19	67.5	66.9515508731083	0.548449126891701
20	63.7	81.6581486073098	-17.9581486073098
21	59.4	80.7766385377136	-21.3766385377136
22	62	84.0115767481545	-22.0115767481545
23	62.4	79.7562860549135	-17.3562860549135
24	58.1	86.8041738056603	-28.7041738056603
25	58	79.8997647136228	-21.8997647136228
26	56.3	83.9131527904158	-27.6131527904158
27	61.4	87.3653486319555	-25.9653486319555
28	59.8	86.6412826708383	-26.8412826708383
29	54.3	93.814562706485	-39.5145627064849
30	47	73.8893447231501	-26.8893447231501
31	50.5	98.3059919547432	-47.8059919547432
32	48.1	77.0971992836748	-28.9971992836748
33	58.8	84.0554034121029	-25.2554034121029
34	70.4	78.6459797247559	-8.2459797247559
35	71.9	90.7079973402493	-18.8079973402493
36	73.3	98.7549772653536	-25.4549772653536
37	83.5	86.2973335795595	-2.79733357955948
38	90.1	87.8725422558278	2.22745774417217
39	101.3	101.788282880506	-0.488282880505797
40	98.3	86.8538781900441	11.4461218099558
41	106.7	87.5857371122315	19.1142628877686
42	109.9	84.844059126894	25.0559408731060
43	111.1	110.276113097895	0.823886902104746
44	119	107.544152518826	11.4558474811741
45	120.7	106.200103759062	14.4998962409384
46	104.5	97.803784380265	6.69621561973505
47	121.6	120.508951778637	1.09104822136278
48	129.6	107.280764814862	22.3192351851376
49	124.5	86.5823568229993	37.9176431770007
50	130.1	101.762372690648	28.3376273093522
51	142.3	106.423539985945	35.8764600140546
52	140	108.518757158581	31.4812428414193
53	143.3	95.1099668333993	48.1900331666007
54	113.4	89.6817301453944	23.7182698546056
55	113.8	102.773502082313	11.0264979176870
56	120.7	79.8197336954859	40.8802663045141
57	112.9	80.497844819753	32.4021551802469
58	115.5	90.1662567188728	25.3337432811272
59	121.9	112.326853503539	9.57314649646145
60	119.3	101.719839516603	17.5801604833967
61	111	95.8444546024223	15.1555453975777
62	114.2	102.901223576755	11.2987764232452
63	113.5	101.306419246926	12.1935807530741
64	94	107.216518431460	-13.2165184314597
65	83.2	94.4562398267957	-11.2562398267957
66	82.8	96.9865733476263	-14.1865733476263
67	85.8	97.8155342313608	-12.0155342313608
68	88.7	107.232734640406	-18.5327346404055
69	105.3	110.733871595985	-5.43387159598508
70	113.1	117.025391330216	-3.92539133021603
71	113.8	92.327717635281	21.4722823647189
72	109.4	81.0746237541934	28.3253762458067

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 85.6 & 97.053234601803 & -11.4532346018030 \tabularnewline
2 & 89 & 101.825352711221 & -12.8253527112210 \tabularnewline
3 & 97.5 & 99.0265864072316 & -1.52658640723161 \tabularnewline
4 & 104 & 104.163531770989 & -0.163531770988733 \tabularnewline
5 & 99.4 & 103.941477064595 & -4.54147706459462 \tabularnewline
6 & 103.2 & 91.8791419930658 & 11.3208580069342 \tabularnewline
7 & 103 & 55.5773077605794 & 47.4226922394206 \tabularnewline
8 & 91.2 & 78.048031254298 & 13.1519687457019 \tabularnewline
9 & 85.9 & 80.7361378753838 & 5.16386212461621 \tabularnewline
10 & 80.7 & 78.5470110977358 & 2.15298890226422 \tabularnewline
11 & 86.7 & 82.6721936873804 & 4.0278063126196 \tabularnewline
12 & 80.7 & 94.765620843327 & -14.065620843327 \tabularnewline
13 & 81.5 & 98.4228556795931 & -16.9228556795931 \tabularnewline
14 & 83.4 & 84.8253559751328 & -1.42535597513277 \tabularnewline
15 & 83.5 & 103.589822847436 & -20.0898228474358 \tabularnewline
16 & 89.5 & 92.2060317780885 & -2.70603177808851 \tabularnewline
17 & 85.8 & 97.792016456494 & -11.9920164564940 \tabularnewline
18 & 77.4 & 96.4191506638695 & -19.0191506638695 \tabularnewline
19 & 67.5 & 66.9515508731083 & 0.548449126891701 \tabularnewline
20 & 63.7 & 81.6581486073098 & -17.9581486073098 \tabularnewline
21 & 59.4 & 80.7766385377136 & -21.3766385377136 \tabularnewline
22 & 62 & 84.0115767481545 & -22.0115767481545 \tabularnewline
23 & 62.4 & 79.7562860549135 & -17.3562860549135 \tabularnewline
24 & 58.1 & 86.8041738056603 & -28.7041738056603 \tabularnewline
25 & 58 & 79.8997647136228 & -21.8997647136228 \tabularnewline
26 & 56.3 & 83.9131527904158 & -27.6131527904158 \tabularnewline
27 & 61.4 & 87.3653486319555 & -25.9653486319555 \tabularnewline
28 & 59.8 & 86.6412826708383 & -26.8412826708383 \tabularnewline
29 & 54.3 & 93.814562706485 & -39.5145627064849 \tabularnewline
30 & 47 & 73.8893447231501 & -26.8893447231501 \tabularnewline
31 & 50.5 & 98.3059919547432 & -47.8059919547432 \tabularnewline
32 & 48.1 & 77.0971992836748 & -28.9971992836748 \tabularnewline
33 & 58.8 & 84.0554034121029 & -25.2554034121029 \tabularnewline
34 & 70.4 & 78.6459797247559 & -8.2459797247559 \tabularnewline
35 & 71.9 & 90.7079973402493 & -18.8079973402493 \tabularnewline
36 & 73.3 & 98.7549772653536 & -25.4549772653536 \tabularnewline
37 & 83.5 & 86.2973335795595 & -2.79733357955948 \tabularnewline
38 & 90.1 & 87.8725422558278 & 2.22745774417217 \tabularnewline
39 & 101.3 & 101.788282880506 & -0.488282880505797 \tabularnewline
40 & 98.3 & 86.8538781900441 & 11.4461218099558 \tabularnewline
41 & 106.7 & 87.5857371122315 & 19.1142628877686 \tabularnewline
42 & 109.9 & 84.844059126894 & 25.0559408731060 \tabularnewline
43 & 111.1 & 110.276113097895 & 0.823886902104746 \tabularnewline
44 & 119 & 107.544152518826 & 11.4558474811741 \tabularnewline
45 & 120.7 & 106.200103759062 & 14.4998962409384 \tabularnewline
46 & 104.5 & 97.803784380265 & 6.69621561973505 \tabularnewline
47 & 121.6 & 120.508951778637 & 1.09104822136278 \tabularnewline
48 & 129.6 & 107.280764814862 & 22.3192351851376 \tabularnewline
49 & 124.5 & 86.5823568229993 & 37.9176431770007 \tabularnewline
50 & 130.1 & 101.762372690648 & 28.3376273093522 \tabularnewline
51 & 142.3 & 106.423539985945 & 35.8764600140546 \tabularnewline
52 & 140 & 108.518757158581 & 31.4812428414193 \tabularnewline
53 & 143.3 & 95.1099668333993 & 48.1900331666007 \tabularnewline
54 & 113.4 & 89.6817301453944 & 23.7182698546056 \tabularnewline
55 & 113.8 & 102.773502082313 & 11.0264979176870 \tabularnewline
56 & 120.7 & 79.8197336954859 & 40.8802663045141 \tabularnewline
57 & 112.9 & 80.497844819753 & 32.4021551802469 \tabularnewline
58 & 115.5 & 90.1662567188728 & 25.3337432811272 \tabularnewline
59 & 121.9 & 112.326853503539 & 9.57314649646145 \tabularnewline
60 & 119.3 & 101.719839516603 & 17.5801604833967 \tabularnewline
61 & 111 & 95.8444546024223 & 15.1555453975777 \tabularnewline
62 & 114.2 & 102.901223576755 & 11.2987764232452 \tabularnewline
63 & 113.5 & 101.306419246926 & 12.1935807530741 \tabularnewline
64 & 94 & 107.216518431460 & -13.2165184314597 \tabularnewline
65 & 83.2 & 94.4562398267957 & -11.2562398267957 \tabularnewline
66 & 82.8 & 96.9865733476263 & -14.1865733476263 \tabularnewline
67 & 85.8 & 97.8155342313608 & -12.0155342313608 \tabularnewline
68 & 88.7 & 107.232734640406 & -18.5327346404055 \tabularnewline
69 & 105.3 & 110.733871595985 & -5.43387159598508 \tabularnewline
70 & 113.1 & 117.025391330216 & -3.92539133021603 \tabularnewline
71 & 113.8 & 92.327717635281 & 21.4722823647189 \tabularnewline
72 & 109.4 & 81.0746237541934 & 28.3253762458067 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4423&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]85.6[/C][C]97.053234601803[/C][C]-11.4532346018030[/C][/ROW]
[ROW][C]2[/C][C]89[/C][C]101.825352711221[/C][C]-12.8253527112210[/C][/ROW]
[ROW][C]3[/C][C]97.5[/C][C]99.0265864072316[/C][C]-1.52658640723161[/C][/ROW]
[ROW][C]4[/C][C]104[/C][C]104.163531770989[/C][C]-0.163531770988733[/C][/ROW]
[ROW][C]5[/C][C]99.4[/C][C]103.941477064595[/C][C]-4.54147706459462[/C][/ROW]
[ROW][C]6[/C][C]103.2[/C][C]91.8791419930658[/C][C]11.3208580069342[/C][/ROW]
[ROW][C]7[/C][C]103[/C][C]55.5773077605794[/C][C]47.4226922394206[/C][/ROW]
[ROW][C]8[/C][C]91.2[/C][C]78.048031254298[/C][C]13.1519687457019[/C][/ROW]
[ROW][C]9[/C][C]85.9[/C][C]80.7361378753838[/C][C]5.16386212461621[/C][/ROW]
[ROW][C]10[/C][C]80.7[/C][C]78.5470110977358[/C][C]2.15298890226422[/C][/ROW]
[ROW][C]11[/C][C]86.7[/C][C]82.6721936873804[/C][C]4.0278063126196[/C][/ROW]
[ROW][C]12[/C][C]80.7[/C][C]94.765620843327[/C][C]-14.065620843327[/C][/ROW]
[ROW][C]13[/C][C]81.5[/C][C]98.4228556795931[/C][C]-16.9228556795931[/C][/ROW]
[ROW][C]14[/C][C]83.4[/C][C]84.8253559751328[/C][C]-1.42535597513277[/C][/ROW]
[ROW][C]15[/C][C]83.5[/C][C]103.589822847436[/C][C]-20.0898228474358[/C][/ROW]
[ROW][C]16[/C][C]89.5[/C][C]92.2060317780885[/C][C]-2.70603177808851[/C][/ROW]
[ROW][C]17[/C][C]85.8[/C][C]97.792016456494[/C][C]-11.9920164564940[/C][/ROW]
[ROW][C]18[/C][C]77.4[/C][C]96.4191506638695[/C][C]-19.0191506638695[/C][/ROW]
[ROW][C]19[/C][C]67.5[/C][C]66.9515508731083[/C][C]0.548449126891701[/C][/ROW]
[ROW][C]20[/C][C]63.7[/C][C]81.6581486073098[/C][C]-17.9581486073098[/C][/ROW]
[ROW][C]21[/C][C]59.4[/C][C]80.7766385377136[/C][C]-21.3766385377136[/C][/ROW]
[ROW][C]22[/C][C]62[/C][C]84.0115767481545[/C][C]-22.0115767481545[/C][/ROW]
[ROW][C]23[/C][C]62.4[/C][C]79.7562860549135[/C][C]-17.3562860549135[/C][/ROW]
[ROW][C]24[/C][C]58.1[/C][C]86.8041738056603[/C][C]-28.7041738056603[/C][/ROW]
[ROW][C]25[/C][C]58[/C][C]79.8997647136228[/C][C]-21.8997647136228[/C][/ROW]
[ROW][C]26[/C][C]56.3[/C][C]83.9131527904158[/C][C]-27.6131527904158[/C][/ROW]
[ROW][C]27[/C][C]61.4[/C][C]87.3653486319555[/C][C]-25.9653486319555[/C][/ROW]
[ROW][C]28[/C][C]59.8[/C][C]86.6412826708383[/C][C]-26.8412826708383[/C][/ROW]
[ROW][C]29[/C][C]54.3[/C][C]93.814562706485[/C][C]-39.5145627064849[/C][/ROW]
[ROW][C]30[/C][C]47[/C][C]73.8893447231501[/C][C]-26.8893447231501[/C][/ROW]
[ROW][C]31[/C][C]50.5[/C][C]98.3059919547432[/C][C]-47.8059919547432[/C][/ROW]
[ROW][C]32[/C][C]48.1[/C][C]77.0971992836748[/C][C]-28.9971992836748[/C][/ROW]
[ROW][C]33[/C][C]58.8[/C][C]84.0554034121029[/C][C]-25.2554034121029[/C][/ROW]
[ROW][C]34[/C][C]70.4[/C][C]78.6459797247559[/C][C]-8.2459797247559[/C][/ROW]
[ROW][C]35[/C][C]71.9[/C][C]90.7079973402493[/C][C]-18.8079973402493[/C][/ROW]
[ROW][C]36[/C][C]73.3[/C][C]98.7549772653536[/C][C]-25.4549772653536[/C][/ROW]
[ROW][C]37[/C][C]83.5[/C][C]86.2973335795595[/C][C]-2.79733357955948[/C][/ROW]
[ROW][C]38[/C][C]90.1[/C][C]87.8725422558278[/C][C]2.22745774417217[/C][/ROW]
[ROW][C]39[/C][C]101.3[/C][C]101.788282880506[/C][C]-0.488282880505797[/C][/ROW]
[ROW][C]40[/C][C]98.3[/C][C]86.8538781900441[/C][C]11.4461218099558[/C][/ROW]
[ROW][C]41[/C][C]106.7[/C][C]87.5857371122315[/C][C]19.1142628877686[/C][/ROW]
[ROW][C]42[/C][C]109.9[/C][C]84.844059126894[/C][C]25.0559408731060[/C][/ROW]
[ROW][C]43[/C][C]111.1[/C][C]110.276113097895[/C][C]0.823886902104746[/C][/ROW]
[ROW][C]44[/C][C]119[/C][C]107.544152518826[/C][C]11.4558474811741[/C][/ROW]
[ROW][C]45[/C][C]120.7[/C][C]106.200103759062[/C][C]14.4998962409384[/C][/ROW]
[ROW][C]46[/C][C]104.5[/C][C]97.803784380265[/C][C]6.69621561973505[/C][/ROW]
[ROW][C]47[/C][C]121.6[/C][C]120.508951778637[/C][C]1.09104822136278[/C][/ROW]
[ROW][C]48[/C][C]129.6[/C][C]107.280764814862[/C][C]22.3192351851376[/C][/ROW]
[ROW][C]49[/C][C]124.5[/C][C]86.5823568229993[/C][C]37.9176431770007[/C][/ROW]
[ROW][C]50[/C][C]130.1[/C][C]101.762372690648[/C][C]28.3376273093522[/C][/ROW]
[ROW][C]51[/C][C]142.3[/C][C]106.423539985945[/C][C]35.8764600140546[/C][/ROW]
[ROW][C]52[/C][C]140[/C][C]108.518757158581[/C][C]31.4812428414193[/C][/ROW]
[ROW][C]53[/C][C]143.3[/C][C]95.1099668333993[/C][C]48.1900331666007[/C][/ROW]
[ROW][C]54[/C][C]113.4[/C][C]89.6817301453944[/C][C]23.7182698546056[/C][/ROW]
[ROW][C]55[/C][C]113.8[/C][C]102.773502082313[/C][C]11.0264979176870[/C][/ROW]
[ROW][C]56[/C][C]120.7[/C][C]79.8197336954859[/C][C]40.8802663045141[/C][/ROW]
[ROW][C]57[/C][C]112.9[/C][C]80.497844819753[/C][C]32.4021551802469[/C][/ROW]
[ROW][C]58[/C][C]115.5[/C][C]90.1662567188728[/C][C]25.3337432811272[/C][/ROW]
[ROW][C]59[/C][C]121.9[/C][C]112.326853503539[/C][C]9.57314649646145[/C][/ROW]
[ROW][C]60[/C][C]119.3[/C][C]101.719839516603[/C][C]17.5801604833967[/C][/ROW]
[ROW][C]61[/C][C]111[/C][C]95.8444546024223[/C][C]15.1555453975777[/C][/ROW]
[ROW][C]62[/C][C]114.2[/C][C]102.901223576755[/C][C]11.2987764232452[/C][/ROW]
[ROW][C]63[/C][C]113.5[/C][C]101.306419246926[/C][C]12.1935807530741[/C][/ROW]
[ROW][C]64[/C][C]94[/C][C]107.216518431460[/C][C]-13.2165184314597[/C][/ROW]
[ROW][C]65[/C][C]83.2[/C][C]94.4562398267957[/C][C]-11.2562398267957[/C][/ROW]
[ROW][C]66[/C][C]82.8[/C][C]96.9865733476263[/C][C]-14.1865733476263[/C][/ROW]
[ROW][C]67[/C][C]85.8[/C][C]97.8155342313608[/C][C]-12.0155342313608[/C][/ROW]
[ROW][C]68[/C][C]88.7[/C][C]107.232734640406[/C][C]-18.5327346404055[/C][/ROW]
[ROW][C]69[/C][C]105.3[/C][C]110.733871595985[/C][C]-5.43387159598508[/C][/ROW]
[ROW][C]70[/C][C]113.1[/C][C]117.025391330216[/C][C]-3.92539133021603[/C][/ROW]
[ROW][C]71[/C][C]113.8[/C][C]92.327717635281[/C][C]21.4722823647189[/C][/ROW]
[ROW][C]72[/C][C]109.4[/C][C]81.0746237541934[/C][C]28.3253762458067[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4423&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4423&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	85.6	97.053234601803	-11.4532346018030
2	89	101.825352711221	-12.8253527112210
3	97.5	99.0265864072316	-1.52658640723161
4	104	104.163531770989	-0.163531770988733
5	99.4	103.941477064595	-4.54147706459462
6	103.2	91.8791419930658	11.3208580069342
7	103	55.5773077605794	47.4226922394206
8	91.2	78.048031254298	13.1519687457019
9	85.9	80.7361378753838	5.16386212461621
10	80.7	78.5470110977358	2.15298890226422
11	86.7	82.6721936873804	4.0278063126196
12	80.7	94.765620843327	-14.065620843327
13	81.5	98.4228556795931	-16.9228556795931
14	83.4	84.8253559751328	-1.42535597513277
15	83.5	103.589822847436	-20.0898228474358
16	89.5	92.2060317780885	-2.70603177808851
17	85.8	97.792016456494	-11.9920164564940
18	77.4	96.4191506638695	-19.0191506638695
19	67.5	66.9515508731083	0.548449126891701
20	63.7	81.6581486073098	-17.9581486073098
21	59.4	80.7766385377136	-21.3766385377136
22	62	84.0115767481545	-22.0115767481545
23	62.4	79.7562860549135	-17.3562860549135
24	58.1	86.8041738056603	-28.7041738056603
25	58	79.8997647136228	-21.8997647136228
26	56.3	83.9131527904158	-27.6131527904158
27	61.4	87.3653486319555	-25.9653486319555
28	59.8	86.6412826708383	-26.8412826708383
29	54.3	93.814562706485	-39.5145627064849
30	47	73.8893447231501	-26.8893447231501
31	50.5	98.3059919547432	-47.8059919547432
32	48.1	77.0971992836748	-28.9971992836748
33	58.8	84.0554034121029	-25.2554034121029
34	70.4	78.6459797247559	-8.2459797247559
35	71.9	90.7079973402493	-18.8079973402493
36	73.3	98.7549772653536	-25.4549772653536
37	83.5	86.2973335795595	-2.79733357955948
38	90.1	87.8725422558278	2.22745774417217
39	101.3	101.788282880506	-0.488282880505797
40	98.3	86.8538781900441	11.4461218099558
41	106.7	87.5857371122315	19.1142628877686
42	109.9	84.844059126894	25.0559408731060
43	111.1	110.276113097895	0.823886902104746
44	119	107.544152518826	11.4558474811741
45	120.7	106.200103759062	14.4998962409384
46	104.5	97.803784380265	6.69621561973505
47	121.6	120.508951778637	1.09104822136278
48	129.6	107.280764814862	22.3192351851376
49	124.5	86.5823568229993	37.9176431770007
50	130.1	101.762372690648	28.3376273093522
51	142.3	106.423539985945	35.8764600140546
52	140	108.518757158581	31.4812428414193
53	143.3	95.1099668333993	48.1900331666007
54	113.4	89.6817301453944	23.7182698546056
55	113.8	102.773502082313	11.0264979176870
56	120.7	79.8197336954859	40.8802663045141
57	112.9	80.497844819753	32.4021551802469
58	115.5	90.1662567188728	25.3337432811272
59	121.9	112.326853503539	9.57314649646145
60	119.3	101.719839516603	17.5801604833967
61	111	95.8444546024223	15.1555453975777
62	114.2	102.901223576755	11.2987764232452
63	113.5	101.306419246926	12.1935807530741
64	94	107.216518431460	-13.2165184314597
65	83.2	94.4562398267957	-11.2562398267957
66	82.8	96.9865733476263	-14.1865733476263
67	85.8	97.8155342313608	-12.0155342313608
68	88.7	107.232734640406	-18.5327346404055
69	105.3	110.733871595985	-5.43387159598508
70	113.1	117.025391330216	-3.92539133021603
71	113.8	92.327717635281	21.4722823647189
72	109.4	81.0746237541934	28.3253762458067

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 = 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)
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