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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 13 Dec 2007 13:05:34 -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/t1197575459fv8382v7h6qbzo6.htm/, Retrieved Sun, 05 May 2024 12:37:07 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=3704, Retrieved Sun, 05 May 2024 12:37:07 +0000

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No (this computation is public)

User-defined keywords

zonder iets

Estimated Impact

181

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

-       [Multiple Regression] [paper] [2007-12-13 20:05:34] [8ce1ad2ac57e06e10fb37a1292ae8cb6] [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=3704&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=3704&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=3704&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
Totaal[t] = + 0.985561382674693 + 0.00512745466226638Intermediaire[t] + 0.0791606277930283Duurzame[t] + 0.90284901497009`Niet-Duurzaam`[t] + 0.432723397663192M1[t] + 0.516710710020281M2[t] + 0.490458302459475M3[t] + 0.669615430179371M4[t] + 0.61141179663758M5[t] + 0.753034886197928M6[t] + 0.209506335240211M7[t] + 0.237812110676649M8[t] + 0.227756213647851M9[t] + 0.872187181607412M10[t] + 0.158817514259807M11[t] -0.0396749980712203t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Totaal[t] =  +  0.985561382674693 +  0.00512745466226638Intermediaire[t] +  0.0791606277930283Duurzame[t] +  0.90284901497009`Niet-Duurzaam`[t] +  0.432723397663192M1[t] +  0.516710710020281M2[t] +  0.490458302459475M3[t] +  0.669615430179371M4[t] +  0.61141179663758M5[t] +  0.753034886197928M6[t] +  0.209506335240211M7[t] +  0.237812110676649M8[t] +  0.227756213647851M9[t] +  0.872187181607412M10[t] +  0.158817514259807M11[t] -0.0396749980712203t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=3704&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Totaal[t] =  +  0.985561382674693 +  0.00512745466226638Intermediaire[t] +  0.0791606277930283Duurzame[t] +  0.90284901497009`Niet-Duurzaam`[t] +  0.432723397663192M1[t] +  0.516710710020281M2[t] +  0.490458302459475M3[t] +  0.669615430179371M4[t] +  0.61141179663758M5[t] +  0.753034886197928M6[t] +  0.209506335240211M7[t] +  0.237812110676649M8[t] +  0.227756213647851M9[t] +  0.872187181607412M10[t] +  0.158817514259807M11[t] -0.0396749980712203t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=3704&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=3704&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
Totaal[t] = + 0.985561382674693 + 0.00512745466226638Intermediaire[t] + 0.0791606277930283Duurzame[t] + 0.90284901497009`Niet-Duurzaam`[t] + 0.432723397663192M1[t] + 0.516710710020281M2[t] + 0.490458302459475M3[t] + 0.669615430179371M4[t] + 0.61141179663758M5[t] + 0.753034886197928M6[t] + 0.209506335240211M7[t] + 0.237812110676649M8[t] + 0.227756213647851M9[t] + 0.872187181607412M10[t] + 0.158817514259807M11[t] -0.0396749980712203t + 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)	0.985561382674693	2.772767	0.3554	0.723426	0.361713
Intermediaire	0.00512745466226638	0.017206	0.298	0.766664	0.383332
Duurzame	0.0791606277930283	0.018007	4.396	4.2e-05	2.1e-05
`Niet-Duurzaam`	0.90284901497009	0.019418	46.4959	0	0
M1	0.432723397663192	0.468245	0.9241	0.358885	0.179443
M2	0.516710710020281	0.468198	1.1036	0.273892	0.136946
M3	0.490458302459475	0.477218	1.0277	0.307938	0.153969
M4	0.669615430179371	0.467555	1.4322	0.156963	0.078481
M5	0.61141179663758	0.460817	1.3268	0.18929	0.094645
M6	0.753034886197928	0.460018	1.637	0.106545	0.053273
M7	0.209506335240211	0.612222	0.3422	0.733317	0.366658
M8	0.237812110676649	0.565533	0.4205	0.675521	0.337761
M9	0.227756213647851	0.518448	0.4393	0.661921	0.330961
M10	0.872187181607412	0.517443	1.6856	0.096748	0.048374
M11	0.158817514259807	0.471645	0.3367	0.737421	0.368711
t	-0.0396749980712203	0.011194	-3.5444	0.000741	0.000371

\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) & 0.985561382674693 & 2.772767 & 0.3554 & 0.723426 & 0.361713 \tabularnewline
Intermediaire & 0.00512745466226638 & 0.017206 & 0.298 & 0.766664 & 0.383332 \tabularnewline
Duurzame & 0.0791606277930283 & 0.018007 & 4.396 & 4.2e-05 & 2.1e-05 \tabularnewline
`Niet-Duurzaam` & 0.90284901497009 & 0.019418 & 46.4959 & 0 & 0 \tabularnewline
M1 & 0.432723397663192 & 0.468245 & 0.9241 & 0.358885 & 0.179443 \tabularnewline
M2 & 0.516710710020281 & 0.468198 & 1.1036 & 0.273892 & 0.136946 \tabularnewline
M3 & 0.490458302459475 & 0.477218 & 1.0277 & 0.307938 & 0.153969 \tabularnewline
M4 & 0.669615430179371 & 0.467555 & 1.4322 & 0.156963 & 0.078481 \tabularnewline
M5 & 0.61141179663758 & 0.460817 & 1.3268 & 0.18929 & 0.094645 \tabularnewline
M6 & 0.753034886197928 & 0.460018 & 1.637 & 0.106545 & 0.053273 \tabularnewline
M7 & 0.209506335240211 & 0.612222 & 0.3422 & 0.733317 & 0.366658 \tabularnewline
M8 & 0.237812110676649 & 0.565533 & 0.4205 & 0.675521 & 0.337761 \tabularnewline
M9 & 0.227756213647851 & 0.518448 & 0.4393 & 0.661921 & 0.330961 \tabularnewline
M10 & 0.872187181607412 & 0.517443 & 1.6856 & 0.096748 & 0.048374 \tabularnewline
M11 & 0.158817514259807 & 0.471645 & 0.3367 & 0.737421 & 0.368711 \tabularnewline
t & -0.0396749980712203 & 0.011194 & -3.5444 & 0.000741 & 0.000371 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=3704&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]0.985561382674693[/C][C]2.772767[/C][C]0.3554[/C][C]0.723426[/C][C]0.361713[/C][/ROW]
[ROW][C]Intermediaire[/C][C]0.00512745466226638[/C][C]0.017206[/C][C]0.298[/C][C]0.766664[/C][C]0.383332[/C][/ROW]
[ROW][C]Duurzame[/C][C]0.0791606277930283[/C][C]0.018007[/C][C]4.396[/C][C]4.2e-05[/C][C]2.1e-05[/C][/ROW]
[ROW][C]`Niet-Duurzaam`[/C][C]0.90284901497009[/C][C]0.019418[/C][C]46.4959[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]M1[/C][C]0.432723397663192[/C][C]0.468245[/C][C]0.9241[/C][C]0.358885[/C][C]0.179443[/C][/ROW]
[ROW][C]M2[/C][C]0.516710710020281[/C][C]0.468198[/C][C]1.1036[/C][C]0.273892[/C][C]0.136946[/C][/ROW]
[ROW][C]M3[/C][C]0.490458302459475[/C][C]0.477218[/C][C]1.0277[/C][C]0.307938[/C][C]0.153969[/C][/ROW]
[ROW][C]M4[/C][C]0.669615430179371[/C][C]0.467555[/C][C]1.4322[/C][C]0.156963[/C][C]0.078481[/C][/ROW]
[ROW][C]M5[/C][C]0.61141179663758[/C][C]0.460817[/C][C]1.3268[/C][C]0.18929[/C][C]0.094645[/C][/ROW]
[ROW][C]M6[/C][C]0.753034886197928[/C][C]0.460018[/C][C]1.637[/C][C]0.106545[/C][C]0.053273[/C][/ROW]
[ROW][C]M7[/C][C]0.209506335240211[/C][C]0.612222[/C][C]0.3422[/C][C]0.733317[/C][C]0.366658[/C][/ROW]
[ROW][C]M8[/C][C]0.237812110676649[/C][C]0.565533[/C][C]0.4205[/C][C]0.675521[/C][C]0.337761[/C][/ROW]
[ROW][C]M9[/C][C]0.227756213647851[/C][C]0.518448[/C][C]0.4393[/C][C]0.661921[/C][C]0.330961[/C][/ROW]
[ROW][C]M10[/C][C]0.872187181607412[/C][C]0.517443[/C][C]1.6856[/C][C]0.096748[/C][C]0.048374[/C][/ROW]
[ROW][C]M11[/C][C]0.158817514259807[/C][C]0.471645[/C][C]0.3367[/C][C]0.737421[/C][C]0.368711[/C][/ROW]
[ROW][C]t[/C][C]-0.0396749980712203[/C][C]0.011194[/C][C]-3.5444[/C][C]0.000741[/C][C]0.000371[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=3704&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=3704&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)	0.985561382674693	2.772767	0.3554	0.723426	0.361713
Intermediaire	0.00512745466226638	0.017206	0.298	0.766664	0.383332
Duurzame	0.0791606277930283	0.018007	4.396	4.2e-05	2.1e-05
`Niet-Duurzaam`	0.90284901497009	0.019418	46.4959	0	0
M1	0.432723397663192	0.468245	0.9241	0.358885	0.179443
M2	0.516710710020281	0.468198	1.1036	0.273892	0.136946
M3	0.490458302459475	0.477218	1.0277	0.307938	0.153969
M4	0.669615430179371	0.467555	1.4322	0.156963	0.078481
M5	0.61141179663758	0.460817	1.3268	0.18929	0.094645
M6	0.753034886197928	0.460018	1.637	0.106545	0.053273
M7	0.209506335240211	0.612222	0.3422	0.733317	0.366658
M8	0.237812110676649	0.565533	0.4205	0.675521	0.337761
M9	0.227756213647851	0.518448	0.4393	0.661921	0.330961
M10	0.872187181607412	0.517443	1.6856	0.096748	0.048374
M11	0.158817514259807	0.471645	0.3367	0.737421	0.368711
t	-0.0396749980712203	0.011194	-3.5444	0.000741	0.000371

Multiple Linear Regression - Regression Statistics
Multiple R	0.997583351309772
R-squared	0.995172542810435
Adjusted R-squared	0.994041107531631
F-TEST (value)	879.566477600183
F-TEST (DF numerator)	15
F-TEST (DF denominator)	64
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.776644682526761
Sum Squared Residuals	38.603325625414

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.997583351309772 \tabularnewline
R-squared & 0.995172542810435 \tabularnewline
Adjusted R-squared & 0.994041107531631 \tabularnewline
F-TEST (value) & 879.566477600183 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 64 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.776644682526761 \tabularnewline
Sum Squared Residuals & 38.603325625414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=3704&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.997583351309772[/C][/ROW]
[ROW][C]R-squared[/C][C]0.995172542810435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.994041107531631[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]879.566477600183[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]64[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.776644682526761[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]38.603325625414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=3704&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=3704&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	0.997583351309772
R-squared	0.995172542810435
Adjusted R-squared	0.994041107531631
F-TEST (value)	879.566477600183
F-TEST (DF numerator)	15
F-TEST (DF denominator)	64
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.776644682526761
Sum Squared Residuals	38.603325625414

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	98.6	98.693820820494	-0.0938208204939932
2	98	98.4210869725206	-0.421086972520612
3	106.8	107.104655813211	-0.304655813211284
4	96.6	96.677335061513	-0.0773350615129729
5	100.1	100.244166915197	-0.144166915197194
6	107.7	107.752818623171	-0.0528186231711168
7	91.5	90.0076782793031	1.49232172069685
8	97.8	96.1800880553004	1.61991194469960
9	107.4	106.657192028285	0.742807971714512
10	117.5	117.152397904380	0.347602095620312
11	105.6	104.822719362253	0.777280637747145
12	97.4	96.5068810406312	0.893118959368851
13	99.5	98.5100159703746	0.989984029625405
14	98	97.6503051672215	0.349694832778541
15	104.3	103.953107954921	0.346892045078962
16	100.6	100.172736822890	0.427263177110341
17	101.1	100.61477432431	0.485225675690042
18	103.9	103.322532722853	0.577467277147464
19	96.9	96.2016081601222	0.69839183987783
20	95.5	94.8497549954305	0.650245004569463
21	108.4	111.085283621037	-2.6852836210366
22	117	116.437489403178	0.56251059682184
23	103.8	106.389012895614	-2.58901289561396
24	100.8	103.112652448673	-2.31265244867262
25	110.6	111.067388052266	-0.46738805226578
26	104	104.285907210285	-0.285907210284583
27	112.6	112.867244543568	-0.267244543568163
28	107.3	107.532999930987	-0.232999930986981
29	98.9	99.2232081098778	-0.323208109877778
30	109.8	110.513494502152	-0.713494502152117
31	104.9	105.425718902514	-0.525718902514363
32	102.2	102.794407380558	-0.59440738055769
33	123.9	123.564292581309	0.335707418691071
34	124.9	125.166572296075	-0.266572296074500
35	112.7	112.362289710075	0.337710289924902
36	121.9	121.646319948068	0.253680051931641
37	100.6	100.930840466022	-0.330840466021845
38	104.3	104.418813625571	-0.118813625570833
39	120.4	120.479514075329	-0.0795140753288577
40	107.5	107.735464985040	-0.235464985039839
41	102.9	102.872558171840	0.0274418281604338
42	125.6	125.715578536975	-0.115578536975179
43	107.5	108.391640876849	-0.891640876849039
44	108.8	109.577046253289	-0.777046253289476
45	128.4	128.209138050031	0.190861949969342
46	121.1	121.569775519642	-0.469775519641559
47	119.5	119.53065383807	-0.0306538380700469
48	128.7	128.839581056756	-0.139581056756431
49	108.7	109.110468220753	-0.410468220752892
50	105.5	105.669015988695	-0.169015988694799
51	119.8	119.755697283152	0.0443027168478744
52	111.3	111.513830875397	-0.213830875397224
53	110.6	110.984832651544	-0.384832651543857
54	120.1	120.124388935764	-0.0243889357639288
55	97.5	97.832269846795	-0.332269846794936
56	107.7	108.137269502667	-0.437269502667028
57	127.3	126.519984690943	0.780015309057217
58	117.2	117.495225446645	-0.295225446645365
59	119.8	118.766818896486	1.03318110351429
60	116.2	115.374410306947	0.82558969305282
61	111	110.983757910452	0.016242089547506
62	112.4	111.970002759860	0.429997240140147
63	130.6	130.39090489677	0.209095103229893
64	109.1	108.817795281279	0.28220471872121
65	118.8	118.477455026445	0.322544973555076
66	123.9	123.449802263620	0.45019773637979
67	101.6	101.903719011670	-0.303719011669791
68	112.8	113.014221875572	-0.21422187557207
69	128	127.364109028396	0.635890971604458
70	129.6	129.478539430081	0.121460569919273
71	125.8	125.328505297502	0.471494702497667
72	119.5	119.020155198924	0.479844801075742
73	115.7	115.403708559638	0.296291440361599
74	113.6	113.384868275848	0.21513172415214
75	129.7	129.648875433048	0.0511245669515757
76	112	111.949837042895	0.0501629571054628
77	116.8	116.783004800787	0.0169951992132756
78	126.3	126.421384415465	-0.121384415464915
79	112.9	113.037364922747	-0.137364922746547
80	115.9	116.147211937183	-0.247211937182799

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 98.6 & 98.693820820494 & -0.0938208204939932 \tabularnewline
2 & 98 & 98.4210869725206 & -0.421086972520612 \tabularnewline
3 & 106.8 & 107.104655813211 & -0.304655813211284 \tabularnewline
4 & 96.6 & 96.677335061513 & -0.0773350615129729 \tabularnewline
5 & 100.1 & 100.244166915197 & -0.144166915197194 \tabularnewline
6 & 107.7 & 107.752818623171 & -0.0528186231711168 \tabularnewline
7 & 91.5 & 90.0076782793031 & 1.49232172069685 \tabularnewline
8 & 97.8 & 96.1800880553004 & 1.61991194469960 \tabularnewline
9 & 107.4 & 106.657192028285 & 0.742807971714512 \tabularnewline
10 & 117.5 & 117.152397904380 & 0.347602095620312 \tabularnewline
11 & 105.6 & 104.822719362253 & 0.777280637747145 \tabularnewline
12 & 97.4 & 96.5068810406312 & 0.893118959368851 \tabularnewline
13 & 99.5 & 98.5100159703746 & 0.989984029625405 \tabularnewline
14 & 98 & 97.6503051672215 & 0.349694832778541 \tabularnewline
15 & 104.3 & 103.953107954921 & 0.346892045078962 \tabularnewline
16 & 100.6 & 100.172736822890 & 0.427263177110341 \tabularnewline
17 & 101.1 & 100.61477432431 & 0.485225675690042 \tabularnewline
18 & 103.9 & 103.322532722853 & 0.577467277147464 \tabularnewline
19 & 96.9 & 96.2016081601222 & 0.69839183987783 \tabularnewline
20 & 95.5 & 94.8497549954305 & 0.650245004569463 \tabularnewline
21 & 108.4 & 111.085283621037 & -2.6852836210366 \tabularnewline
22 & 117 & 116.437489403178 & 0.56251059682184 \tabularnewline
23 & 103.8 & 106.389012895614 & -2.58901289561396 \tabularnewline
24 & 100.8 & 103.112652448673 & -2.31265244867262 \tabularnewline
25 & 110.6 & 111.067388052266 & -0.46738805226578 \tabularnewline
26 & 104 & 104.285907210285 & -0.285907210284583 \tabularnewline
27 & 112.6 & 112.867244543568 & -0.267244543568163 \tabularnewline
28 & 107.3 & 107.532999930987 & -0.232999930986981 \tabularnewline
29 & 98.9 & 99.2232081098778 & -0.323208109877778 \tabularnewline
30 & 109.8 & 110.513494502152 & -0.713494502152117 \tabularnewline
31 & 104.9 & 105.425718902514 & -0.525718902514363 \tabularnewline
32 & 102.2 & 102.794407380558 & -0.59440738055769 \tabularnewline
33 & 123.9 & 123.564292581309 & 0.335707418691071 \tabularnewline
34 & 124.9 & 125.166572296075 & -0.266572296074500 \tabularnewline
35 & 112.7 & 112.362289710075 & 0.337710289924902 \tabularnewline
36 & 121.9 & 121.646319948068 & 0.253680051931641 \tabularnewline
37 & 100.6 & 100.930840466022 & -0.330840466021845 \tabularnewline
38 & 104.3 & 104.418813625571 & -0.118813625570833 \tabularnewline
39 & 120.4 & 120.479514075329 & -0.0795140753288577 \tabularnewline
40 & 107.5 & 107.735464985040 & -0.235464985039839 \tabularnewline
41 & 102.9 & 102.872558171840 & 0.0274418281604338 \tabularnewline
42 & 125.6 & 125.715578536975 & -0.115578536975179 \tabularnewline
43 & 107.5 & 108.391640876849 & -0.891640876849039 \tabularnewline
44 & 108.8 & 109.577046253289 & -0.777046253289476 \tabularnewline
45 & 128.4 & 128.209138050031 & 0.190861949969342 \tabularnewline
46 & 121.1 & 121.569775519642 & -0.469775519641559 \tabularnewline
47 & 119.5 & 119.53065383807 & -0.0306538380700469 \tabularnewline
48 & 128.7 & 128.839581056756 & -0.139581056756431 \tabularnewline
49 & 108.7 & 109.110468220753 & -0.410468220752892 \tabularnewline
50 & 105.5 & 105.669015988695 & -0.169015988694799 \tabularnewline
51 & 119.8 & 119.755697283152 & 0.0443027168478744 \tabularnewline
52 & 111.3 & 111.513830875397 & -0.213830875397224 \tabularnewline
53 & 110.6 & 110.984832651544 & -0.384832651543857 \tabularnewline
54 & 120.1 & 120.124388935764 & -0.0243889357639288 \tabularnewline
55 & 97.5 & 97.832269846795 & -0.332269846794936 \tabularnewline
56 & 107.7 & 108.137269502667 & -0.437269502667028 \tabularnewline
57 & 127.3 & 126.519984690943 & 0.780015309057217 \tabularnewline
58 & 117.2 & 117.495225446645 & -0.295225446645365 \tabularnewline
59 & 119.8 & 118.766818896486 & 1.03318110351429 \tabularnewline
60 & 116.2 & 115.374410306947 & 0.82558969305282 \tabularnewline
61 & 111 & 110.983757910452 & 0.016242089547506 \tabularnewline
62 & 112.4 & 111.970002759860 & 0.429997240140147 \tabularnewline
63 & 130.6 & 130.39090489677 & 0.209095103229893 \tabularnewline
64 & 109.1 & 108.817795281279 & 0.28220471872121 \tabularnewline
65 & 118.8 & 118.477455026445 & 0.322544973555076 \tabularnewline
66 & 123.9 & 123.449802263620 & 0.45019773637979 \tabularnewline
67 & 101.6 & 101.903719011670 & -0.303719011669791 \tabularnewline
68 & 112.8 & 113.014221875572 & -0.21422187557207 \tabularnewline
69 & 128 & 127.364109028396 & 0.635890971604458 \tabularnewline
70 & 129.6 & 129.478539430081 & 0.121460569919273 \tabularnewline
71 & 125.8 & 125.328505297502 & 0.471494702497667 \tabularnewline
72 & 119.5 & 119.020155198924 & 0.479844801075742 \tabularnewline
73 & 115.7 & 115.403708559638 & 0.296291440361599 \tabularnewline
74 & 113.6 & 113.384868275848 & 0.21513172415214 \tabularnewline
75 & 129.7 & 129.648875433048 & 0.0511245669515757 \tabularnewline
76 & 112 & 111.949837042895 & 0.0501629571054628 \tabularnewline
77 & 116.8 & 116.783004800787 & 0.0169951992132756 \tabularnewline
78 & 126.3 & 126.421384415465 & -0.121384415464915 \tabularnewline
79 & 112.9 & 113.037364922747 & -0.137364922746547 \tabularnewline
80 & 115.9 & 116.147211937183 & -0.247211937182799 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=3704&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]98.6[/C][C]98.693820820494[/C][C]-0.0938208204939932[/C][/ROW]
[ROW][C]2[/C][C]98[/C][C]98.4210869725206[/C][C]-0.421086972520612[/C][/ROW]
[ROW][C]3[/C][C]106.8[/C][C]107.104655813211[/C][C]-0.304655813211284[/C][/ROW]
[ROW][C]4[/C][C]96.6[/C][C]96.677335061513[/C][C]-0.0773350615129729[/C][/ROW]
[ROW][C]5[/C][C]100.1[/C][C]100.244166915197[/C][C]-0.144166915197194[/C][/ROW]
[ROW][C]6[/C][C]107.7[/C][C]107.752818623171[/C][C]-0.0528186231711168[/C][/ROW]
[ROW][C]7[/C][C]91.5[/C][C]90.0076782793031[/C][C]1.49232172069685[/C][/ROW]
[ROW][C]8[/C][C]97.8[/C][C]96.1800880553004[/C][C]1.61991194469960[/C][/ROW]
[ROW][C]9[/C][C]107.4[/C][C]106.657192028285[/C][C]0.742807971714512[/C][/ROW]
[ROW][C]10[/C][C]117.5[/C][C]117.152397904380[/C][C]0.347602095620312[/C][/ROW]
[ROW][C]11[/C][C]105.6[/C][C]104.822719362253[/C][C]0.777280637747145[/C][/ROW]
[ROW][C]12[/C][C]97.4[/C][C]96.5068810406312[/C][C]0.893118959368851[/C][/ROW]
[ROW][C]13[/C][C]99.5[/C][C]98.5100159703746[/C][C]0.989984029625405[/C][/ROW]
[ROW][C]14[/C][C]98[/C][C]97.6503051672215[/C][C]0.349694832778541[/C][/ROW]
[ROW][C]15[/C][C]104.3[/C][C]103.953107954921[/C][C]0.346892045078962[/C][/ROW]
[ROW][C]16[/C][C]100.6[/C][C]100.172736822890[/C][C]0.427263177110341[/C][/ROW]
[ROW][C]17[/C][C]101.1[/C][C]100.61477432431[/C][C]0.485225675690042[/C][/ROW]
[ROW][C]18[/C][C]103.9[/C][C]103.322532722853[/C][C]0.577467277147464[/C][/ROW]
[ROW][C]19[/C][C]96.9[/C][C]96.2016081601222[/C][C]0.69839183987783[/C][/ROW]
[ROW][C]20[/C][C]95.5[/C][C]94.8497549954305[/C][C]0.650245004569463[/C][/ROW]
[ROW][C]21[/C][C]108.4[/C][C]111.085283621037[/C][C]-2.6852836210366[/C][/ROW]
[ROW][C]22[/C][C]117[/C][C]116.437489403178[/C][C]0.56251059682184[/C][/ROW]
[ROW][C]23[/C][C]103.8[/C][C]106.389012895614[/C][C]-2.58901289561396[/C][/ROW]
[ROW][C]24[/C][C]100.8[/C][C]103.112652448673[/C][C]-2.31265244867262[/C][/ROW]
[ROW][C]25[/C][C]110.6[/C][C]111.067388052266[/C][C]-0.46738805226578[/C][/ROW]
[ROW][C]26[/C][C]104[/C][C]104.285907210285[/C][C]-0.285907210284583[/C][/ROW]
[ROW][C]27[/C][C]112.6[/C][C]112.867244543568[/C][C]-0.267244543568163[/C][/ROW]
[ROW][C]28[/C][C]107.3[/C][C]107.532999930987[/C][C]-0.232999930986981[/C][/ROW]
[ROW][C]29[/C][C]98.9[/C][C]99.2232081098778[/C][C]-0.323208109877778[/C][/ROW]
[ROW][C]30[/C][C]109.8[/C][C]110.513494502152[/C][C]-0.713494502152117[/C][/ROW]
[ROW][C]31[/C][C]104.9[/C][C]105.425718902514[/C][C]-0.525718902514363[/C][/ROW]
[ROW][C]32[/C][C]102.2[/C][C]102.794407380558[/C][C]-0.59440738055769[/C][/ROW]
[ROW][C]33[/C][C]123.9[/C][C]123.564292581309[/C][C]0.335707418691071[/C][/ROW]
[ROW][C]34[/C][C]124.9[/C][C]125.166572296075[/C][C]-0.266572296074500[/C][/ROW]
[ROW][C]35[/C][C]112.7[/C][C]112.362289710075[/C][C]0.337710289924902[/C][/ROW]
[ROW][C]36[/C][C]121.9[/C][C]121.646319948068[/C][C]0.253680051931641[/C][/ROW]
[ROW][C]37[/C][C]100.6[/C][C]100.930840466022[/C][C]-0.330840466021845[/C][/ROW]
[ROW][C]38[/C][C]104.3[/C][C]104.418813625571[/C][C]-0.118813625570833[/C][/ROW]
[ROW][C]39[/C][C]120.4[/C][C]120.479514075329[/C][C]-0.0795140753288577[/C][/ROW]
[ROW][C]40[/C][C]107.5[/C][C]107.735464985040[/C][C]-0.235464985039839[/C][/ROW]
[ROW][C]41[/C][C]102.9[/C][C]102.872558171840[/C][C]0.0274418281604338[/C][/ROW]
[ROW][C]42[/C][C]125.6[/C][C]125.715578536975[/C][C]-0.115578536975179[/C][/ROW]
[ROW][C]43[/C][C]107.5[/C][C]108.391640876849[/C][C]-0.891640876849039[/C][/ROW]
[ROW][C]44[/C][C]108.8[/C][C]109.577046253289[/C][C]-0.777046253289476[/C][/ROW]
[ROW][C]45[/C][C]128.4[/C][C]128.209138050031[/C][C]0.190861949969342[/C][/ROW]
[ROW][C]46[/C][C]121.1[/C][C]121.569775519642[/C][C]-0.469775519641559[/C][/ROW]
[ROW][C]47[/C][C]119.5[/C][C]119.53065383807[/C][C]-0.0306538380700469[/C][/ROW]
[ROW][C]48[/C][C]128.7[/C][C]128.839581056756[/C][C]-0.139581056756431[/C][/ROW]
[ROW][C]49[/C][C]108.7[/C][C]109.110468220753[/C][C]-0.410468220752892[/C][/ROW]
[ROW][C]50[/C][C]105.5[/C][C]105.669015988695[/C][C]-0.169015988694799[/C][/ROW]
[ROW][C]51[/C][C]119.8[/C][C]119.755697283152[/C][C]0.0443027168478744[/C][/ROW]
[ROW][C]52[/C][C]111.3[/C][C]111.513830875397[/C][C]-0.213830875397224[/C][/ROW]
[ROW][C]53[/C][C]110.6[/C][C]110.984832651544[/C][C]-0.384832651543857[/C][/ROW]
[ROW][C]54[/C][C]120.1[/C][C]120.124388935764[/C][C]-0.0243889357639288[/C][/ROW]
[ROW][C]55[/C][C]97.5[/C][C]97.832269846795[/C][C]-0.332269846794936[/C][/ROW]
[ROW][C]56[/C][C]107.7[/C][C]108.137269502667[/C][C]-0.437269502667028[/C][/ROW]
[ROW][C]57[/C][C]127.3[/C][C]126.519984690943[/C][C]0.780015309057217[/C][/ROW]
[ROW][C]58[/C][C]117.2[/C][C]117.495225446645[/C][C]-0.295225446645365[/C][/ROW]
[ROW][C]59[/C][C]119.8[/C][C]118.766818896486[/C][C]1.03318110351429[/C][/ROW]
[ROW][C]60[/C][C]116.2[/C][C]115.374410306947[/C][C]0.82558969305282[/C][/ROW]
[ROW][C]61[/C][C]111[/C][C]110.983757910452[/C][C]0.016242089547506[/C][/ROW]
[ROW][C]62[/C][C]112.4[/C][C]111.970002759860[/C][C]0.429997240140147[/C][/ROW]
[ROW][C]63[/C][C]130.6[/C][C]130.39090489677[/C][C]0.209095103229893[/C][/ROW]
[ROW][C]64[/C][C]109.1[/C][C]108.817795281279[/C][C]0.28220471872121[/C][/ROW]
[ROW][C]65[/C][C]118.8[/C][C]118.477455026445[/C][C]0.322544973555076[/C][/ROW]
[ROW][C]66[/C][C]123.9[/C][C]123.449802263620[/C][C]0.45019773637979[/C][/ROW]
[ROW][C]67[/C][C]101.6[/C][C]101.903719011670[/C][C]-0.303719011669791[/C][/ROW]
[ROW][C]68[/C][C]112.8[/C][C]113.014221875572[/C][C]-0.21422187557207[/C][/ROW]
[ROW][C]69[/C][C]128[/C][C]127.364109028396[/C][C]0.635890971604458[/C][/ROW]
[ROW][C]70[/C][C]129.6[/C][C]129.478539430081[/C][C]0.121460569919273[/C][/ROW]
[ROW][C]71[/C][C]125.8[/C][C]125.328505297502[/C][C]0.471494702497667[/C][/ROW]
[ROW][C]72[/C][C]119.5[/C][C]119.020155198924[/C][C]0.479844801075742[/C][/ROW]
[ROW][C]73[/C][C]115.7[/C][C]115.403708559638[/C][C]0.296291440361599[/C][/ROW]
[ROW][C]74[/C][C]113.6[/C][C]113.384868275848[/C][C]0.21513172415214[/C][/ROW]
[ROW][C]75[/C][C]129.7[/C][C]129.648875433048[/C][C]0.0511245669515757[/C][/ROW]
[ROW][C]76[/C][C]112[/C][C]111.949837042895[/C][C]0.0501629571054628[/C][/ROW]
[ROW][C]77[/C][C]116.8[/C][C]116.783004800787[/C][C]0.0169951992132756[/C][/ROW]
[ROW][C]78[/C][C]126.3[/C][C]126.421384415465[/C][C]-0.121384415464915[/C][/ROW]
[ROW][C]79[/C][C]112.9[/C][C]113.037364922747[/C][C]-0.137364922746547[/C][/ROW]
[ROW][C]80[/C][C]115.9[/C][C]116.147211937183[/C][C]-0.247211937182799[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=3704&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=3704&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	98.6	98.693820820494	-0.0938208204939932
2	98	98.4210869725206	-0.421086972520612
3	106.8	107.104655813211	-0.304655813211284
4	96.6	96.677335061513	-0.0773350615129729
5	100.1	100.244166915197	-0.144166915197194
6	107.7	107.752818623171	-0.0528186231711168
7	91.5	90.0076782793031	1.49232172069685
8	97.8	96.1800880553004	1.61991194469960
9	107.4	106.657192028285	0.742807971714512
10	117.5	117.152397904380	0.347602095620312
11	105.6	104.822719362253	0.777280637747145
12	97.4	96.5068810406312	0.893118959368851
13	99.5	98.5100159703746	0.989984029625405
14	98	97.6503051672215	0.349694832778541
15	104.3	103.953107954921	0.346892045078962
16	100.6	100.172736822890	0.427263177110341
17	101.1	100.61477432431	0.485225675690042
18	103.9	103.322532722853	0.577467277147464
19	96.9	96.2016081601222	0.69839183987783
20	95.5	94.8497549954305	0.650245004569463
21	108.4	111.085283621037	-2.6852836210366
22	117	116.437489403178	0.56251059682184
23	103.8	106.389012895614	-2.58901289561396
24	100.8	103.112652448673	-2.31265244867262
25	110.6	111.067388052266	-0.46738805226578
26	104	104.285907210285	-0.285907210284583
27	112.6	112.867244543568	-0.267244543568163
28	107.3	107.532999930987	-0.232999930986981
29	98.9	99.2232081098778	-0.323208109877778
30	109.8	110.513494502152	-0.713494502152117
31	104.9	105.425718902514	-0.525718902514363
32	102.2	102.794407380558	-0.59440738055769
33	123.9	123.564292581309	0.335707418691071
34	124.9	125.166572296075	-0.266572296074500
35	112.7	112.362289710075	0.337710289924902
36	121.9	121.646319948068	0.253680051931641
37	100.6	100.930840466022	-0.330840466021845
38	104.3	104.418813625571	-0.118813625570833
39	120.4	120.479514075329	-0.0795140753288577
40	107.5	107.735464985040	-0.235464985039839
41	102.9	102.872558171840	0.0274418281604338
42	125.6	125.715578536975	-0.115578536975179
43	107.5	108.391640876849	-0.891640876849039
44	108.8	109.577046253289	-0.777046253289476
45	128.4	128.209138050031	0.190861949969342
46	121.1	121.569775519642	-0.469775519641559
47	119.5	119.53065383807	-0.0306538380700469
48	128.7	128.839581056756	-0.139581056756431
49	108.7	109.110468220753	-0.410468220752892
50	105.5	105.669015988695	-0.169015988694799
51	119.8	119.755697283152	0.0443027168478744
52	111.3	111.513830875397	-0.213830875397224
53	110.6	110.984832651544	-0.384832651543857
54	120.1	120.124388935764	-0.0243889357639288
55	97.5	97.832269846795	-0.332269846794936
56	107.7	108.137269502667	-0.437269502667028
57	127.3	126.519984690943	0.780015309057217
58	117.2	117.495225446645	-0.295225446645365
59	119.8	118.766818896486	1.03318110351429
60	116.2	115.374410306947	0.82558969305282
61	111	110.983757910452	0.016242089547506
62	112.4	111.970002759860	0.429997240140147
63	130.6	130.39090489677	0.209095103229893
64	109.1	108.817795281279	0.28220471872121
65	118.8	118.477455026445	0.322544973555076
66	123.9	123.449802263620	0.45019773637979
67	101.6	101.903719011670	-0.303719011669791
68	112.8	113.014221875572	-0.21422187557207
69	128	127.364109028396	0.635890971604458
70	129.6	129.478539430081	0.121460569919273
71	125.8	125.328505297502	0.471494702497667
72	119.5	119.020155198924	0.479844801075742
73	115.7	115.403708559638	0.296291440361599
74	113.6	113.384868275848	0.21513172415214
75	129.7	129.648875433048	0.0511245669515757
76	112	111.949837042895	0.0501629571054628
77	116.8	116.783004800787	0.0169951992132756
78	126.3	126.421384415465	-0.121384415464915
79	112.9	113.037364922747	-0.137364922746547
80	115.9	116.147211937183	-0.247211937182799

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 = 2 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Include Monthly Dummies ; par3 = 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