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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 20 Dec 2007 07:12:19 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2007/Dec/20/t1198158901sjtd6sel8v70kop.htm/, Retrieved Mon, 29 Apr 2024 14:56:16 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4718, Retrieved Mon, 29 Apr 2024 14:56:16 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

223

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

-       [Multiple Regression] [brood en bakmeel] [2007-12-20 14:12:19] [7eb5b05bf0841f2a6d4b99da83be8d69] [Current]

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Dataseries X:

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1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,5
1,43	0,51
1,43	0,51
1,43	0,5
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,51
1,43	0,52
1,43	0,52
1,44	0,52
1,48	0,53
1,48	0,53
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,52
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,54
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,53
1,48	0,53
1,57	0,54
1,58	0,55
1,58	0,55
1,58	0,55
1,58	0,55
1,59	0,55
1,6	0,55
1,6	0,55
1,61	0,55
1,61	0,56
1,61	0,56
1,62	0,56
1,63	0,56
1,63	0,56
1,64	0,55
1,64	0,56
1,64	0,55
1,64	0,55
1,64	0,56
1,65	0,55
1,65	0,55
1,65	0,55
1,65	0,55

Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4718&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]4 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=4718&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4718&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	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
y[t] = -0.591894562376485 + 3.95161598776056x[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  -0.591894562376485 +  3.95161598776056x[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4718&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  -0.591894562376485 +  3.95161598776056x[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4718&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4718&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
y[t] = -0.591894562376485 + 3.95161598776056x[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	-0.591894562376485	0.126746	-4.6699	1.4e-05	7e-06
x	3.95161598776056	0.23808	16.5978	0	0

\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) & -0.591894562376485 & 0.126746 & -4.6699 & 1.4e-05 & 7e-06 \tabularnewline
x & 3.95161598776056 & 0.23808 & 16.5978 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4718&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]-0.591894562376485[/C][C]0.126746[/C][C]-4.6699[/C][C]1.4e-05[/C][C]7e-06[/C][/ROW]
[ROW][C]x[/C][C]3.95161598776056[/C][C]0.23808[/C][C]16.5978[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4718&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4718&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)	-0.591894562376485	0.126746	-4.6699	1.4e-05	7e-06
x	3.95161598776056	0.23808	16.5978	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.892965712216058
R-squared	0.797387763193532
Adjusted R-squared	0.794493302667725
F-TEST (value)	275.487523869858
F-TEST (DF numerator)	1
F-TEST (DF denominator)	70
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0351420802494691
Sum Squared Residuals	0.0864476062982087

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.892965712216058 \tabularnewline
R-squared & 0.797387763193532 \tabularnewline
Adjusted R-squared & 0.794493302667725 \tabularnewline
F-TEST (value) & 275.487523869858 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 70 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.0351420802494691 \tabularnewline
Sum Squared Residuals & 0.0864476062982087 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4718&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.892965712216058[/C][/ROW]
[ROW][C]R-squared[/C][C]0.797387763193532[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.794493302667725[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]275.487523869858[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]70[/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]0.0351420802494691[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.0864476062982087[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4718&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4718&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	0.892965712216058
R-squared	0.797387763193532
Adjusted R-squared	0.794493302667725
F-TEST (value)	275.487523869858
F-TEST (DF numerator)	1
F-TEST (DF denominator)	70
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.0351420802494691
Sum Squared Residuals	0.0864476062982087

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.43	1.42342959138139	0.00657040861860759
2	1.43	1.4234295913814	0.006570408618601
3	1.43	1.42342959138140	0.00657040861860114
4	1.43	1.42342959138140	0.00657040861860114
5	1.43	1.42342959138140	0.00657040861860114
6	1.43	1.42342959138140	0.00657040861860114
7	1.43	1.42342959138140	0.00657040861860114
8	1.43	1.42342959138140	0.00657040861860114
9	1.43	1.38391343150379	0.0460865684962067
10	1.43	1.42342959138140	0.00657040861860114
11	1.43	1.42342959138140	0.00657040861860114
12	1.43	1.38391343150379	0.0460865684962067
13	1.43	1.42342959138140	0.00657040861860114
14	1.43	1.42342959138140	0.00657040861860114
15	1.43	1.42342959138140	0.00657040861860114
16	1.43	1.42342959138140	0.00657040861860114
17	1.43	1.46294575125900	-0.0329457512590045
18	1.43	1.46294575125900	-0.0329457512590045
19	1.44	1.46294575125900	-0.0229457512590045
20	1.48	1.50246191113661	-0.0224619111366100
21	1.48	1.50246191113661	-0.0224619111366100
22	1.48	1.46294575125900	0.0170542487409956
23	1.48	1.46294575125900	0.0170542487409956
24	1.48	1.46294575125900	0.0170542487409956
25	1.48	1.46294575125900	0.0170542487409956
26	1.48	1.46294575125900	0.0170542487409956
27	1.48	1.46294575125900	0.0170542487409956
28	1.48	1.46294575125900	0.0170542487409956
29	1.48	1.46294575125900	0.0170542487409956
30	1.48	1.46294575125900	0.0170542487409956
31	1.48	1.46294575125900	0.0170542487409956
32	1.48	1.50246191113661	-0.0224619111366100
33	1.48	1.50246191113661	-0.0224619111366100
34	1.48	1.50246191113661	-0.0224619111366100
35	1.48	1.54197807101422	-0.0619780710142156
36	1.48	1.54197807101422	-0.0619780710142156
37	1.48	1.54197807101422	-0.0619780710142156
38	1.48	1.54197807101422	-0.0619780710142156
39	1.48	1.54197807101422	-0.0619780710142156
40	1.48	1.54197807101422	-0.0619780710142156
41	1.48	1.54197807101422	-0.0619780710142156
42	1.48	1.54197807101422	-0.0619780710142156
43	1.48	1.54197807101422	-0.0619780710142156
44	1.48	1.54197807101422	-0.0619780710142156
45	1.48	1.50246191113661	-0.0224619111366100
46	1.48	1.50246191113661	-0.0224619111366100
47	1.48	1.50246191113661	-0.0224619111366100
48	1.48	1.50246191113661	-0.0224619111366100
49	1.48	1.50246191113661	-0.0224619111366100
50	1.57	1.54197807101422	0.0280219289857845
51	1.58	1.58149423089182	-0.00149423089182114
52	1.58	1.58149423089182	-0.00149423089182114
53	1.58	1.58149423089182	-0.00149423089182114
54	1.58	1.58149423089182	-0.00149423089182114
55	1.59	1.58149423089182	0.00850576910817887
56	1.6	1.58149423089182	0.0185057691081789
57	1.6	1.58149423089182	0.0185057691081789
58	1.61	1.58149423089182	0.0285057691081789
59	1.61	1.62101039076943	-0.0110103907694267
60	1.61	1.62101039076943	-0.0110103907694267
61	1.62	1.62101039076943	-0.00101039076942671
62	1.63	1.62101039076943	0.00898960923057308
63	1.63	1.62101039076943	0.00898960923057308
64	1.64	1.58149423089182	0.0585057691081787
65	1.64	1.62101039076943	0.0189896092305731
66	1.64	1.58149423089182	0.0585057691081787
67	1.64	1.58149423089182	0.0585057691081787
68	1.64	1.62101039076943	0.0189896092305731
69	1.65	1.58149423089182	0.0685057691081787
70	1.65	1.58149423089182	0.0685057691081787
71	1.65	1.58149423089182	0.0685057691081787
72	1.65	1.58149423089182	0.0685057691081787

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.42342959138139 & 0.00657040861860759 \tabularnewline
2 & 1.43 & 1.4234295913814 & 0.006570408618601 \tabularnewline
3 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
4 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
5 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
6 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
7 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
8 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
9 & 1.43 & 1.38391343150379 & 0.0460865684962067 \tabularnewline
10 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
11 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
12 & 1.43 & 1.38391343150379 & 0.0460865684962067 \tabularnewline
13 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
14 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
15 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
16 & 1.43 & 1.42342959138140 & 0.00657040861860114 \tabularnewline
17 & 1.43 & 1.46294575125900 & -0.0329457512590045 \tabularnewline
18 & 1.43 & 1.46294575125900 & -0.0329457512590045 \tabularnewline
19 & 1.44 & 1.46294575125900 & -0.0229457512590045 \tabularnewline
20 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
21 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
22 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
23 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
24 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
25 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
26 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
27 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
28 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
29 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
30 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
31 & 1.48 & 1.46294575125900 & 0.0170542487409956 \tabularnewline
32 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
33 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
34 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
35 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
36 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
37 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
38 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
39 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
40 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
41 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
42 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
43 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
44 & 1.48 & 1.54197807101422 & -0.0619780710142156 \tabularnewline
45 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
46 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
47 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
48 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
49 & 1.48 & 1.50246191113661 & -0.0224619111366100 \tabularnewline
50 & 1.57 & 1.54197807101422 & 0.0280219289857845 \tabularnewline
51 & 1.58 & 1.58149423089182 & -0.00149423089182114 \tabularnewline
52 & 1.58 & 1.58149423089182 & -0.00149423089182114 \tabularnewline
53 & 1.58 & 1.58149423089182 & -0.00149423089182114 \tabularnewline
54 & 1.58 & 1.58149423089182 & -0.00149423089182114 \tabularnewline
55 & 1.59 & 1.58149423089182 & 0.00850576910817887 \tabularnewline
56 & 1.6 & 1.58149423089182 & 0.0185057691081789 \tabularnewline
57 & 1.6 & 1.58149423089182 & 0.0185057691081789 \tabularnewline
58 & 1.61 & 1.58149423089182 & 0.0285057691081789 \tabularnewline
59 & 1.61 & 1.62101039076943 & -0.0110103907694267 \tabularnewline
60 & 1.61 & 1.62101039076943 & -0.0110103907694267 \tabularnewline
61 & 1.62 & 1.62101039076943 & -0.00101039076942671 \tabularnewline
62 & 1.63 & 1.62101039076943 & 0.00898960923057308 \tabularnewline
63 & 1.63 & 1.62101039076943 & 0.00898960923057308 \tabularnewline
64 & 1.64 & 1.58149423089182 & 0.0585057691081787 \tabularnewline
65 & 1.64 & 1.62101039076943 & 0.0189896092305731 \tabularnewline
66 & 1.64 & 1.58149423089182 & 0.0585057691081787 \tabularnewline
67 & 1.64 & 1.58149423089182 & 0.0585057691081787 \tabularnewline
68 & 1.64 & 1.62101039076943 & 0.0189896092305731 \tabularnewline
69 & 1.65 & 1.58149423089182 & 0.0685057691081787 \tabularnewline
70 & 1.65 & 1.58149423089182 & 0.0685057691081787 \tabularnewline
71 & 1.65 & 1.58149423089182 & 0.0685057691081787 \tabularnewline
72 & 1.65 & 1.58149423089182 & 0.0685057691081787 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4718&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]1.43[/C][C]1.42342959138139[/C][C]0.00657040861860759[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.4234295913814[/C][C]0.006570408618601[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]7[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]8[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]9[/C][C]1.43[/C][C]1.38391343150379[/C][C]0.0460865684962067[/C][/ROW]
[ROW][C]10[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]11[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]12[/C][C]1.43[/C][C]1.38391343150379[/C][C]0.0460865684962067[/C][/ROW]
[ROW][C]13[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]14[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]15[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]16[/C][C]1.43[/C][C]1.42342959138140[/C][C]0.00657040861860114[/C][/ROW]
[ROW][C]17[/C][C]1.43[/C][C]1.46294575125900[/C][C]-0.0329457512590045[/C][/ROW]
[ROW][C]18[/C][C]1.43[/C][C]1.46294575125900[/C][C]-0.0329457512590045[/C][/ROW]
[ROW][C]19[/C][C]1.44[/C][C]1.46294575125900[/C][C]-0.0229457512590045[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.46294575125900[/C][C]0.0170542487409956[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]38[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]39[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]40[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]41[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]42[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]43[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]44[/C][C]1.48[/C][C]1.54197807101422[/C][C]-0.0619780710142156[/C][/ROW]
[ROW][C]45[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]46[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]47[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]48[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]49[/C][C]1.48[/C][C]1.50246191113661[/C][C]-0.0224619111366100[/C][/ROW]
[ROW][C]50[/C][C]1.57[/C][C]1.54197807101422[/C][C]0.0280219289857845[/C][/ROW]
[ROW][C]51[/C][C]1.58[/C][C]1.58149423089182[/C][C]-0.00149423089182114[/C][/ROW]
[ROW][C]52[/C][C]1.58[/C][C]1.58149423089182[/C][C]-0.00149423089182114[/C][/ROW]
[ROW][C]53[/C][C]1.58[/C][C]1.58149423089182[/C][C]-0.00149423089182114[/C][/ROW]
[ROW][C]54[/C][C]1.58[/C][C]1.58149423089182[/C][C]-0.00149423089182114[/C][/ROW]
[ROW][C]55[/C][C]1.59[/C][C]1.58149423089182[/C][C]0.00850576910817887[/C][/ROW]
[ROW][C]56[/C][C]1.6[/C][C]1.58149423089182[/C][C]0.0185057691081789[/C][/ROW]
[ROW][C]57[/C][C]1.6[/C][C]1.58149423089182[/C][C]0.0185057691081789[/C][/ROW]
[ROW][C]58[/C][C]1.61[/C][C]1.58149423089182[/C][C]0.0285057691081789[/C][/ROW]
[ROW][C]59[/C][C]1.61[/C][C]1.62101039076943[/C][C]-0.0110103907694267[/C][/ROW]
[ROW][C]60[/C][C]1.61[/C][C]1.62101039076943[/C][C]-0.0110103907694267[/C][/ROW]
[ROW][C]61[/C][C]1.62[/C][C]1.62101039076943[/C][C]-0.00101039076942671[/C][/ROW]
[ROW][C]62[/C][C]1.63[/C][C]1.62101039076943[/C][C]0.00898960923057308[/C][/ROW]
[ROW][C]63[/C][C]1.63[/C][C]1.62101039076943[/C][C]0.00898960923057308[/C][/ROW]
[ROW][C]64[/C][C]1.64[/C][C]1.58149423089182[/C][C]0.0585057691081787[/C][/ROW]
[ROW][C]65[/C][C]1.64[/C][C]1.62101039076943[/C][C]0.0189896092305731[/C][/ROW]
[ROW][C]66[/C][C]1.64[/C][C]1.58149423089182[/C][C]0.0585057691081787[/C][/ROW]
[ROW][C]67[/C][C]1.64[/C][C]1.58149423089182[/C][C]0.0585057691081787[/C][/ROW]
[ROW][C]68[/C][C]1.64[/C][C]1.62101039076943[/C][C]0.0189896092305731[/C][/ROW]
[ROW][C]69[/C][C]1.65[/C][C]1.58149423089182[/C][C]0.0685057691081787[/C][/ROW]
[ROW][C]70[/C][C]1.65[/C][C]1.58149423089182[/C][C]0.0685057691081787[/C][/ROW]
[ROW][C]71[/C][C]1.65[/C][C]1.58149423089182[/C][C]0.0685057691081787[/C][/ROW]
[ROW][C]72[/C][C]1.65[/C][C]1.58149423089182[/C][C]0.0685057691081787[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4718&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4718&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	1.43	1.42342959138139	0.00657040861860759
2	1.43	1.4234295913814	0.006570408618601
3	1.43	1.42342959138140	0.00657040861860114
4	1.43	1.42342959138140	0.00657040861860114
5	1.43	1.42342959138140	0.00657040861860114
6	1.43	1.42342959138140	0.00657040861860114
7	1.43	1.42342959138140	0.00657040861860114
8	1.43	1.42342959138140	0.00657040861860114
9	1.43	1.38391343150379	0.0460865684962067
10	1.43	1.42342959138140	0.00657040861860114
11	1.43	1.42342959138140	0.00657040861860114
12	1.43	1.38391343150379	0.0460865684962067
13	1.43	1.42342959138140	0.00657040861860114
14	1.43	1.42342959138140	0.00657040861860114
15	1.43	1.42342959138140	0.00657040861860114
16	1.43	1.42342959138140	0.00657040861860114
17	1.43	1.46294575125900	-0.0329457512590045
18	1.43	1.46294575125900	-0.0329457512590045
19	1.44	1.46294575125900	-0.0229457512590045
20	1.48	1.50246191113661	-0.0224619111366100
21	1.48	1.50246191113661	-0.0224619111366100
22	1.48	1.46294575125900	0.0170542487409956
23	1.48	1.46294575125900	0.0170542487409956
24	1.48	1.46294575125900	0.0170542487409956
25	1.48	1.46294575125900	0.0170542487409956
26	1.48	1.46294575125900	0.0170542487409956
27	1.48	1.46294575125900	0.0170542487409956
28	1.48	1.46294575125900	0.0170542487409956
29	1.48	1.46294575125900	0.0170542487409956
30	1.48	1.46294575125900	0.0170542487409956
31	1.48	1.46294575125900	0.0170542487409956
32	1.48	1.50246191113661	-0.0224619111366100
33	1.48	1.50246191113661	-0.0224619111366100
34	1.48	1.50246191113661	-0.0224619111366100
35	1.48	1.54197807101422	-0.0619780710142156
36	1.48	1.54197807101422	-0.0619780710142156
37	1.48	1.54197807101422	-0.0619780710142156
38	1.48	1.54197807101422	-0.0619780710142156
39	1.48	1.54197807101422	-0.0619780710142156
40	1.48	1.54197807101422	-0.0619780710142156
41	1.48	1.54197807101422	-0.0619780710142156
42	1.48	1.54197807101422	-0.0619780710142156
43	1.48	1.54197807101422	-0.0619780710142156
44	1.48	1.54197807101422	-0.0619780710142156
45	1.48	1.50246191113661	-0.0224619111366100
46	1.48	1.50246191113661	-0.0224619111366100
47	1.48	1.50246191113661	-0.0224619111366100
48	1.48	1.50246191113661	-0.0224619111366100
49	1.48	1.50246191113661	-0.0224619111366100
50	1.57	1.54197807101422	0.0280219289857845
51	1.58	1.58149423089182	-0.00149423089182114
52	1.58	1.58149423089182	-0.00149423089182114
53	1.58	1.58149423089182	-0.00149423089182114
54	1.58	1.58149423089182	-0.00149423089182114
55	1.59	1.58149423089182	0.00850576910817887
56	1.6	1.58149423089182	0.0185057691081789
57	1.6	1.58149423089182	0.0185057691081789
58	1.61	1.58149423089182	0.0285057691081789
59	1.61	1.62101039076943	-0.0110103907694267
60	1.61	1.62101039076943	-0.0110103907694267
61	1.62	1.62101039076943	-0.00101039076942671
62	1.63	1.62101039076943	0.00898960923057308
63	1.63	1.62101039076943	0.00898960923057308
64	1.64	1.58149423089182	0.0585057691081787
65	1.64	1.62101039076943	0.0189896092305731
66	1.64	1.58149423089182	0.0585057691081787
67	1.64	1.58149423089182	0.0585057691081787
68	1.64	1.62101039076943	0.0189896092305731
69	1.65	1.58149423089182	0.0685057691081787
70	1.65	1.58149423089182	0.0685057691081787
71	1.65	1.58149423089182	0.0685057691081787
72	1.65	1.58149423089182	0.0685057691081787

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Parameters (Session):

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

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code