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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 07 Dec 2007 04:03:33 -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/07/t1197024689216oxf1tv50llon.htm/, Retrieved Sun, 28 Apr 2024 19:06:18 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=2743, Retrieved Sun, 28 Apr 2024 19:06:18 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

olieprijs vs war

Estimated Impact

228

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

-       [Multiple Regression] [paper] [2007-12-07 11:03:33] [e24e91da8d334fb8882bf413603fde71] [Current]

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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=2743&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=2743&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2743&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
prijs/olie[t] = + 128.0575 + 64.8625`war? `[t] -32.1675000000001M1[t] -29.1075000000001M2[t] -24.5675000000001M3[t] -20.7875000000000M4[t] -26.4000000000001M5[t] -21.0800000000000M6[t] -19.8800000000000M7[t] -13.5600000000000M8[t] -3.26000000000002M9[t] + 1.19999999999995M10[t] -3.62000000000005M11[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
prijs/olie[t] =  +  128.0575 +  64.8625`war?
`[t] -32.1675000000001M1[t] -29.1075000000001M2[t] -24.5675000000001M3[t] -20.7875000000000M4[t] -26.4000000000001M5[t] -21.0800000000000M6[t] -19.8800000000000M7[t] -13.5600000000000M8[t] -3.26000000000002M9[t] +  1.19999999999995M10[t] -3.62000000000005M11[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2743&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]prijs/olie[t] =  +  128.0575 +  64.8625`war?
`[t] -32.1675000000001M1[t] -29.1075000000001M2[t] -24.5675000000001M3[t] -20.7875000000000M4[t] -26.4000000000001M5[t] -21.0800000000000M6[t] -19.8800000000000M7[t] -13.5600000000000M8[t] -3.26000000000002M9[t] +  1.19999999999995M10[t] -3.62000000000005M11[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2743&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2743&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
prijs/olie[t] = + 128.0575 + 64.8625`war? `[t] -32.1675000000001M1[t] -29.1075000000001M2[t] -24.5675000000001M3[t] -20.7875000000000M4[t] -26.4000000000001M5[t] -21.0800000000000M6[t] -19.8800000000000M7[t] -13.5600000000000M8[t] -3.26000000000002M9[t] + 1.19999999999995M10[t] -3.62000000000005M11[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)	128.0575	42.517811	3.0119	0.004171	0.002085
`war? `	64.8625	33.200833	1.9536	0.05671	0.028355
M1	-32.1675000000001	38.144853	-0.8433	0.403333	0.201666
M2	-29.1075000000001	38.144853	-0.7631	0.44923	0.224615
M3	-24.5675000000001	38.144853	-0.6441	0.522669	0.261334
M4	-20.7875000000000	38.144853	-0.545	0.588356	0.294178
M5	-26.4000000000001	37.562454	-0.7028	0.485629	0.242814
M6	-21.0800000000000	37.562454	-0.5612	0.577328	0.288664
M7	-19.8800000000000	37.562454	-0.5293	0.599122	0.299561
M8	-13.5600000000000	37.562454	-0.361	0.719718	0.359859
M9	-3.26000000000002	37.562454	-0.0868	0.931208	0.465604
M10	1.19999999999995	37.562454	0.0319	0.97465	0.487325
M11	-3.62000000000005	37.562454	-0.0964	0.923634	0.461817

\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) & 128.0575 & 42.517811 & 3.0119 & 0.004171 & 0.002085 \tabularnewline
`war?
` & 64.8625 & 33.200833 & 1.9536 & 0.05671 & 0.028355 \tabularnewline
M1 & -32.1675000000001 & 38.144853 & -0.8433 & 0.403333 & 0.201666 \tabularnewline
M2 & -29.1075000000001 & 38.144853 & -0.7631 & 0.44923 & 0.224615 \tabularnewline
M3 & -24.5675000000001 & 38.144853 & -0.6441 & 0.522669 & 0.261334 \tabularnewline
M4 & -20.7875000000000 & 38.144853 & -0.545 & 0.588356 & 0.294178 \tabularnewline
M5 & -26.4000000000001 & 37.562454 & -0.7028 & 0.485629 & 0.242814 \tabularnewline
M6 & -21.0800000000000 & 37.562454 & -0.5612 & 0.577328 & 0.288664 \tabularnewline
M7 & -19.8800000000000 & 37.562454 & -0.5293 & 0.599122 & 0.299561 \tabularnewline
M8 & -13.5600000000000 & 37.562454 & -0.361 & 0.719718 & 0.359859 \tabularnewline
M9 & -3.26000000000002 & 37.562454 & -0.0868 & 0.931208 & 0.465604 \tabularnewline
M10 & 1.19999999999995 & 37.562454 & 0.0319 & 0.97465 & 0.487325 \tabularnewline
M11 & -3.62000000000005 & 37.562454 & -0.0964 & 0.923634 & 0.461817 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2743&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]128.0575[/C][C]42.517811[/C][C]3.0119[/C][C]0.004171[/C][C]0.002085[/C][/ROW]
[ROW][C]`war?
`[/C][C]64.8625[/C][C]33.200833[/C][C]1.9536[/C][C]0.05671[/C][C]0.028355[/C][/ROW]
[ROW][C]M1[/C][C]-32.1675000000001[/C][C]38.144853[/C][C]-0.8433[/C][C]0.403333[/C][C]0.201666[/C][/ROW]
[ROW][C]M2[/C][C]-29.1075000000001[/C][C]38.144853[/C][C]-0.7631[/C][C]0.44923[/C][C]0.224615[/C][/ROW]
[ROW][C]M3[/C][C]-24.5675000000001[/C][C]38.144853[/C][C]-0.6441[/C][C]0.522669[/C][C]0.261334[/C][/ROW]
[ROW][C]M4[/C][C]-20.7875000000000[/C][C]38.144853[/C][C]-0.545[/C][C]0.588356[/C][C]0.294178[/C][/ROW]
[ROW][C]M5[/C][C]-26.4000000000001[/C][C]37.562454[/C][C]-0.7028[/C][C]0.485629[/C][C]0.242814[/C][/ROW]
[ROW][C]M6[/C][C]-21.0800000000000[/C][C]37.562454[/C][C]-0.5612[/C][C]0.577328[/C][C]0.288664[/C][/ROW]
[ROW][C]M7[/C][C]-19.8800000000000[/C][C]37.562454[/C][C]-0.5293[/C][C]0.599122[/C][C]0.299561[/C][/ROW]
[ROW][C]M8[/C][C]-13.5600000000000[/C][C]37.562454[/C][C]-0.361[/C][C]0.719718[/C][C]0.359859[/C][/ROW]
[ROW][C]M9[/C][C]-3.26000000000002[/C][C]37.562454[/C][C]-0.0868[/C][C]0.931208[/C][C]0.465604[/C][/ROW]
[ROW][C]M10[/C][C]1.19999999999995[/C][C]37.562454[/C][C]0.0319[/C][C]0.97465[/C][C]0.487325[/C][/ROW]
[ROW][C]M11[/C][C]-3.62000000000005[/C][C]37.562454[/C][C]-0.0964[/C][C]0.923634[/C][C]0.461817[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2743&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2743&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)	128.0575	42.517811	3.0119	0.004171	0.002085
`war? `	64.8625	33.200833	1.9536	0.05671	0.028355
M1	-32.1675000000001	38.144853	-0.8433	0.403333	0.201666
M2	-29.1075000000001	38.144853	-0.7631	0.44923	0.224615
M3	-24.5675000000001	38.144853	-0.6441	0.522669	0.261334
M4	-20.7875000000000	38.144853	-0.545	0.588356	0.294178
M5	-26.4000000000001	37.562454	-0.7028	0.485629	0.242814
M6	-21.0800000000000	37.562454	-0.5612	0.577328	0.288664
M7	-19.8800000000000	37.562454	-0.5293	0.599122	0.299561
M8	-13.5600000000000	37.562454	-0.361	0.719718	0.359859
M9	-3.26000000000002	37.562454	-0.0868	0.931208	0.465604
M10	1.19999999999995	37.562454	0.0319	0.97465	0.487325
M11	-3.62000000000005	37.562454	-0.0964	0.923634	0.461817

Multiple Linear Regression - Regression Statistics
Multiple R	0.385526675943825
R-squared	0.148630817864295
Adjusted R-squared	-0.068740037149076
F-TEST (value)	0.683766081957732
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.75815768298169
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	59.3914549874973
Sum Squared Residuals	165785.2115

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.385526675943825 \tabularnewline
R-squared & 0.148630817864295 \tabularnewline
Adjusted R-squared & -0.068740037149076 \tabularnewline
F-TEST (value) & 0.683766081957732 \tabularnewline
F-TEST (DF numerator) & 12 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 0.75815768298169 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 59.3914549874973 \tabularnewline
Sum Squared Residuals & 165785.2115 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2743&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.385526675943825[/C][/ROW]
[ROW][C]R-squared[/C][C]0.148630817864295[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]-0.068740037149076[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]0.683766081957732[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]12[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]0.75815768298169[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]59.3914549874973[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]165785.2115[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2743&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2743&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	0.385526675943825
R-squared	0.148630817864295
Adjusted R-squared	-0.068740037149076
F-TEST (value)	0.683766081957732
F-TEST (DF numerator)	12
F-TEST (DF denominator)	47
p-value	0.75815768298169
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	59.3914549874973
Sum Squared Residuals	165785.2115

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	87	95.8900000000002	-8.8900000000002
2	96.3	98.95	-2.64999999999993
3	107.1	103.49	3.60999999999998
4	115.2	107.270000000000	7.93000000000012
5	106.1	166.52	-60.42
6	89.5	171.84	-82.34
7	91.3	173.04	-81.74
8	97.6	179.36	-81.76
9	100.7	189.66	-88.96
10	104.6	194.12	-89.52
11	94.7	189.3	-94.6
12	101.8	192.92	-91.12
13	102.5	160.7525	-58.2525
14	105.3	163.8125	-58.5125
15	110.3	168.3525	-58.0525
16	109.8	172.1325	-62.3325
17	117.3	166.52	-49.22
18	118.8	171.84	-53.04
19	131.3	173.04	-41.74
20	125.9	179.36	-53.46
21	133.1	189.66	-56.56
22	147	194.12	-47.12
23	145.8	189.3	-43.5
24	164.4	192.92	-28.52
25	149.8	160.7525	-10.9524999999999
26	137.7	163.8125	-26.1125
27	151.7	168.3525	-16.6525000000000
28	156.8	172.1325	-15.3325
29	180	166.52	13.4800000000000
30	180.4	171.84	8.56
31	170.4	173.04	-2.63999999999998
32	191.6	179.36	12.24
33	199.5	189.66	9.84
34	218.2	194.12	24.08
35	217.5	189.3	28.2000000000000
36	205	192.92	12.0800000000000
37	194	160.7525	33.2475000000001
38	199.3	163.8125	35.4875
39	219.3	168.3525	50.9475
40	211.1	172.1325	38.9675
41	215.2	166.52	48.68
42	240.2	171.84	68.36
43	242.2	173.04	69.16
44	240.7	179.36	61.34
45	255.4	189.66	65.74
46	253	194.12	58.88
47	218.2	189.3	28.9
48	203.7	192.92	10.7799999999999
49	205.6	160.7525	44.8475000000001
50	215.6	163.8125	51.7875
51	188.5	168.3525	20.1475000000000
52	202.9	172.1325	30.7675
53	214	166.52	47.48
54	230.3	171.84	58.46
55	230	173.04	56.96
56	241	179.36	61.64
57	259.6	189.66	69.94
58	247.8	194.12	53.68
59	270.3	189.3	81
60	289.7	192.92	96.78

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 87 & 95.8900000000002 & -8.8900000000002 \tabularnewline
2 & 96.3 & 98.95 & -2.64999999999993 \tabularnewline
3 & 107.1 & 103.49 & 3.60999999999998 \tabularnewline
4 & 115.2 & 107.270000000000 & 7.93000000000012 \tabularnewline
5 & 106.1 & 166.52 & -60.42 \tabularnewline
6 & 89.5 & 171.84 & -82.34 \tabularnewline
7 & 91.3 & 173.04 & -81.74 \tabularnewline
8 & 97.6 & 179.36 & -81.76 \tabularnewline
9 & 100.7 & 189.66 & -88.96 \tabularnewline
10 & 104.6 & 194.12 & -89.52 \tabularnewline
11 & 94.7 & 189.3 & -94.6 \tabularnewline
12 & 101.8 & 192.92 & -91.12 \tabularnewline
13 & 102.5 & 160.7525 & -58.2525 \tabularnewline
14 & 105.3 & 163.8125 & -58.5125 \tabularnewline
15 & 110.3 & 168.3525 & -58.0525 \tabularnewline
16 & 109.8 & 172.1325 & -62.3325 \tabularnewline
17 & 117.3 & 166.52 & -49.22 \tabularnewline
18 & 118.8 & 171.84 & -53.04 \tabularnewline
19 & 131.3 & 173.04 & -41.74 \tabularnewline
20 & 125.9 & 179.36 & -53.46 \tabularnewline
21 & 133.1 & 189.66 & -56.56 \tabularnewline
22 & 147 & 194.12 & -47.12 \tabularnewline
23 & 145.8 & 189.3 & -43.5 \tabularnewline
24 & 164.4 & 192.92 & -28.52 \tabularnewline
25 & 149.8 & 160.7525 & -10.9524999999999 \tabularnewline
26 & 137.7 & 163.8125 & -26.1125 \tabularnewline
27 & 151.7 & 168.3525 & -16.6525000000000 \tabularnewline
28 & 156.8 & 172.1325 & -15.3325 \tabularnewline
29 & 180 & 166.52 & 13.4800000000000 \tabularnewline
30 & 180.4 & 171.84 & 8.56 \tabularnewline
31 & 170.4 & 173.04 & -2.63999999999998 \tabularnewline
32 & 191.6 & 179.36 & 12.24 \tabularnewline
33 & 199.5 & 189.66 & 9.84 \tabularnewline
34 & 218.2 & 194.12 & 24.08 \tabularnewline
35 & 217.5 & 189.3 & 28.2000000000000 \tabularnewline
36 & 205 & 192.92 & 12.0800000000000 \tabularnewline
37 & 194 & 160.7525 & 33.2475000000001 \tabularnewline
38 & 199.3 & 163.8125 & 35.4875 \tabularnewline
39 & 219.3 & 168.3525 & 50.9475 \tabularnewline
40 & 211.1 & 172.1325 & 38.9675 \tabularnewline
41 & 215.2 & 166.52 & 48.68 \tabularnewline
42 & 240.2 & 171.84 & 68.36 \tabularnewline
43 & 242.2 & 173.04 & 69.16 \tabularnewline
44 & 240.7 & 179.36 & 61.34 \tabularnewline
45 & 255.4 & 189.66 & 65.74 \tabularnewline
46 & 253 & 194.12 & 58.88 \tabularnewline
47 & 218.2 & 189.3 & 28.9 \tabularnewline
48 & 203.7 & 192.92 & 10.7799999999999 \tabularnewline
49 & 205.6 & 160.7525 & 44.8475000000001 \tabularnewline
50 & 215.6 & 163.8125 & 51.7875 \tabularnewline
51 & 188.5 & 168.3525 & 20.1475000000000 \tabularnewline
52 & 202.9 & 172.1325 & 30.7675 \tabularnewline
53 & 214 & 166.52 & 47.48 \tabularnewline
54 & 230.3 & 171.84 & 58.46 \tabularnewline
55 & 230 & 173.04 & 56.96 \tabularnewline
56 & 241 & 179.36 & 61.64 \tabularnewline
57 & 259.6 & 189.66 & 69.94 \tabularnewline
58 & 247.8 & 194.12 & 53.68 \tabularnewline
59 & 270.3 & 189.3 & 81 \tabularnewline
60 & 289.7 & 192.92 & 96.78 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2743&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]87[/C][C]95.8900000000002[/C][C]-8.8900000000002[/C][/ROW]
[ROW][C]2[/C][C]96.3[/C][C]98.95[/C][C]-2.64999999999993[/C][/ROW]
[ROW][C]3[/C][C]107.1[/C][C]103.49[/C][C]3.60999999999998[/C][/ROW]
[ROW][C]4[/C][C]115.2[/C][C]107.270000000000[/C][C]7.93000000000012[/C][/ROW]
[ROW][C]5[/C][C]106.1[/C][C]166.52[/C][C]-60.42[/C][/ROW]
[ROW][C]6[/C][C]89.5[/C][C]171.84[/C][C]-82.34[/C][/ROW]
[ROW][C]7[/C][C]91.3[/C][C]173.04[/C][C]-81.74[/C][/ROW]
[ROW][C]8[/C][C]97.6[/C][C]179.36[/C][C]-81.76[/C][/ROW]
[ROW][C]9[/C][C]100.7[/C][C]189.66[/C][C]-88.96[/C][/ROW]
[ROW][C]10[/C][C]104.6[/C][C]194.12[/C][C]-89.52[/C][/ROW]
[ROW][C]11[/C][C]94.7[/C][C]189.3[/C][C]-94.6[/C][/ROW]
[ROW][C]12[/C][C]101.8[/C][C]192.92[/C][C]-91.12[/C][/ROW]
[ROW][C]13[/C][C]102.5[/C][C]160.7525[/C][C]-58.2525[/C][/ROW]
[ROW][C]14[/C][C]105.3[/C][C]163.8125[/C][C]-58.5125[/C][/ROW]
[ROW][C]15[/C][C]110.3[/C][C]168.3525[/C][C]-58.0525[/C][/ROW]
[ROW][C]16[/C][C]109.8[/C][C]172.1325[/C][C]-62.3325[/C][/ROW]
[ROW][C]17[/C][C]117.3[/C][C]166.52[/C][C]-49.22[/C][/ROW]
[ROW][C]18[/C][C]118.8[/C][C]171.84[/C][C]-53.04[/C][/ROW]
[ROW][C]19[/C][C]131.3[/C][C]173.04[/C][C]-41.74[/C][/ROW]
[ROW][C]20[/C][C]125.9[/C][C]179.36[/C][C]-53.46[/C][/ROW]
[ROW][C]21[/C][C]133.1[/C][C]189.66[/C][C]-56.56[/C][/ROW]
[ROW][C]22[/C][C]147[/C][C]194.12[/C][C]-47.12[/C][/ROW]
[ROW][C]23[/C][C]145.8[/C][C]189.3[/C][C]-43.5[/C][/ROW]
[ROW][C]24[/C][C]164.4[/C][C]192.92[/C][C]-28.52[/C][/ROW]
[ROW][C]25[/C][C]149.8[/C][C]160.7525[/C][C]-10.9524999999999[/C][/ROW]
[ROW][C]26[/C][C]137.7[/C][C]163.8125[/C][C]-26.1125[/C][/ROW]
[ROW][C]27[/C][C]151.7[/C][C]168.3525[/C][C]-16.6525000000000[/C][/ROW]
[ROW][C]28[/C][C]156.8[/C][C]172.1325[/C][C]-15.3325[/C][/ROW]
[ROW][C]29[/C][C]180[/C][C]166.52[/C][C]13.4800000000000[/C][/ROW]
[ROW][C]30[/C][C]180.4[/C][C]171.84[/C][C]8.56[/C][/ROW]
[ROW][C]31[/C][C]170.4[/C][C]173.04[/C][C]-2.63999999999998[/C][/ROW]
[ROW][C]32[/C][C]191.6[/C][C]179.36[/C][C]12.24[/C][/ROW]
[ROW][C]33[/C][C]199.5[/C][C]189.66[/C][C]9.84[/C][/ROW]
[ROW][C]34[/C][C]218.2[/C][C]194.12[/C][C]24.08[/C][/ROW]
[ROW][C]35[/C][C]217.5[/C][C]189.3[/C][C]28.2000000000000[/C][/ROW]
[ROW][C]36[/C][C]205[/C][C]192.92[/C][C]12.0800000000000[/C][/ROW]
[ROW][C]37[/C][C]194[/C][C]160.7525[/C][C]33.2475000000001[/C][/ROW]
[ROW][C]38[/C][C]199.3[/C][C]163.8125[/C][C]35.4875[/C][/ROW]
[ROW][C]39[/C][C]219.3[/C][C]168.3525[/C][C]50.9475[/C][/ROW]
[ROW][C]40[/C][C]211.1[/C][C]172.1325[/C][C]38.9675[/C][/ROW]
[ROW][C]41[/C][C]215.2[/C][C]166.52[/C][C]48.68[/C][/ROW]
[ROW][C]42[/C][C]240.2[/C][C]171.84[/C][C]68.36[/C][/ROW]
[ROW][C]43[/C][C]242.2[/C][C]173.04[/C][C]69.16[/C][/ROW]
[ROW][C]44[/C][C]240.7[/C][C]179.36[/C][C]61.34[/C][/ROW]
[ROW][C]45[/C][C]255.4[/C][C]189.66[/C][C]65.74[/C][/ROW]
[ROW][C]46[/C][C]253[/C][C]194.12[/C][C]58.88[/C][/ROW]
[ROW][C]47[/C][C]218.2[/C][C]189.3[/C][C]28.9[/C][/ROW]
[ROW][C]48[/C][C]203.7[/C][C]192.92[/C][C]10.7799999999999[/C][/ROW]
[ROW][C]49[/C][C]205.6[/C][C]160.7525[/C][C]44.8475000000001[/C][/ROW]
[ROW][C]50[/C][C]215.6[/C][C]163.8125[/C][C]51.7875[/C][/ROW]
[ROW][C]51[/C][C]188.5[/C][C]168.3525[/C][C]20.1475000000000[/C][/ROW]
[ROW][C]52[/C][C]202.9[/C][C]172.1325[/C][C]30.7675[/C][/ROW]
[ROW][C]53[/C][C]214[/C][C]166.52[/C][C]47.48[/C][/ROW]
[ROW][C]54[/C][C]230.3[/C][C]171.84[/C][C]58.46[/C][/ROW]
[ROW][C]55[/C][C]230[/C][C]173.04[/C][C]56.96[/C][/ROW]
[ROW][C]56[/C][C]241[/C][C]179.36[/C][C]61.64[/C][/ROW]
[ROW][C]57[/C][C]259.6[/C][C]189.66[/C][C]69.94[/C][/ROW]
[ROW][C]58[/C][C]247.8[/C][C]194.12[/C][C]53.68[/C][/ROW]
[ROW][C]59[/C][C]270.3[/C][C]189.3[/C][C]81[/C][/ROW]
[ROW][C]60[/C][C]289.7[/C][C]192.92[/C][C]96.78[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2743&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2743&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	87	95.8900000000002	-8.8900000000002
2	96.3	98.95	-2.64999999999993
3	107.1	103.49	3.60999999999998
4	115.2	107.270000000000	7.93000000000012
5	106.1	166.52	-60.42
6	89.5	171.84	-82.34
7	91.3	173.04	-81.74
8	97.6	179.36	-81.76
9	100.7	189.66	-88.96
10	104.6	194.12	-89.52
11	94.7	189.3	-94.6
12	101.8	192.92	-91.12
13	102.5	160.7525	-58.2525
14	105.3	163.8125	-58.5125
15	110.3	168.3525	-58.0525
16	109.8	172.1325	-62.3325
17	117.3	166.52	-49.22
18	118.8	171.84	-53.04
19	131.3	173.04	-41.74
20	125.9	179.36	-53.46
21	133.1	189.66	-56.56
22	147	194.12	-47.12
23	145.8	189.3	-43.5
24	164.4	192.92	-28.52
25	149.8	160.7525	-10.9524999999999
26	137.7	163.8125	-26.1125
27	151.7	168.3525	-16.6525000000000
28	156.8	172.1325	-15.3325
29	180	166.52	13.4800000000000
30	180.4	171.84	8.56
31	170.4	173.04	-2.63999999999998
32	191.6	179.36	12.24
33	199.5	189.66	9.84
34	218.2	194.12	24.08
35	217.5	189.3	28.2000000000000
36	205	192.92	12.0800000000000
37	194	160.7525	33.2475000000001
38	199.3	163.8125	35.4875
39	219.3	168.3525	50.9475
40	211.1	172.1325	38.9675
41	215.2	166.52	48.68
42	240.2	171.84	68.36
43	242.2	173.04	69.16
44	240.7	179.36	61.34
45	255.4	189.66	65.74
46	253	194.12	58.88
47	218.2	189.3	28.9
48	203.7	192.92	10.7799999999999
49	205.6	160.7525	44.8475000000001
50	215.6	163.8125	51.7875
51	188.5	168.3525	20.1475000000000
52	202.9	172.1325	30.7675
53	214	166.52	47.48
54	230.3	171.84	58.46
55	230	173.04	56.96
56	241	179.36	61.64
57	259.6	189.66	69.94
58	247.8	194.12	53.68
59	270.3	189.3	81
60	289.7	192.92	96.78

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