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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 19 Dec 2007 11:54:32 -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/19/t1198089438184u6rh73aj1tzn.htm/, Retrieved Mon, 06 May 2024 16:55:05 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=4698, Retrieved Mon, 06 May 2024 16:55:05 +0000

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No (this computation is public)

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Estimated Impact

189

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

-       [Multiple Regression] [broodprijs dummy1...] [2007-12-19 18:54:32] [5a8e7c1f041681f87e3014e302618e0c] [Current]

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1,43	0	0	0	0
1,43	0	0	0	0
1,43	0	0	0	0
1,43	0	0	0	0
1,43	0	0	0	0
1,43	0	0	0	0
1,44	0	0	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,48	0	1	0	0
1,57	0	1	1	0
1,58	0	1	1	0
1,58	0	1	1	0
1,58	0	1	1	0
1,58	0	1	1	0
1,59	1	1	1	43
1,6	1	1	1	44
1,6	1	1	1	45
1,61	1	1	1	46
1,61	1	1	1	47
1,61	1	1	1	48
1,62	1	1	1	49
1,63	1	1	1	50
1,63	1	1	1	51
1,64	1	1	1	52
1,64	1	1	1	53
1,64	1	1	1	54
1,64	1	1	1	55
1,64	1	1	1	56
1,65	1	1	1	57
1,65	1	1	1	58
1,65	1	1	1	59
1,65	1	1	1	60

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=4698&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=4698&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4698&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
broodprijs[t] = + 1.43142857142857 -0.135175782593738dummy1[t] + 0.0485714285714295`dummy2+`[t] + 0.098`dummy3+`[t] + 0.00359133126934983trend[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
broodprijs[t] =  +  1.43142857142857 -0.135175782593738dummy1[t] +  0.0485714285714295`dummy2+`[t] +  0.098`dummy3+`[t] +  0.00359133126934983trend[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4698&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]broodprijs[t] =  +  1.43142857142857 -0.135175782593738dummy1[t] +  0.0485714285714295`dummy2+`[t] +  0.098`dummy3+`[t] +  0.00359133126934983trend[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4698&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4698&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
broodprijs[t] = + 1.43142857142857 -0.135175782593738dummy1[t] + 0.0485714285714295`dummy2+`[t] + 0.098`dummy3+`[t] + 0.00359133126934983trend[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)	1.43142857142857	0.001277	1120.8608	0	0
dummy1	-0.135175782593738	0.008088	-16.7134	0	0
`dummy2+`	0.0485714285714295	0.001418	34.247	0	0
`dummy3+`	0.098	0.001632	60.0441	0	0
trend	0.00359133126934983	0.000154	23.3957	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) & 1.43142857142857 & 0.001277 & 1120.8608 & 0 & 0 \tabularnewline
dummy1 & -0.135175782593738 & 0.008088 & -16.7134 & 0 & 0 \tabularnewline
`dummy2+` & 0.0485714285714295 & 0.001418 & 34.247 & 0 & 0 \tabularnewline
`dummy3+` & 0.098 & 0.001632 & 60.0441 & 0 & 0 \tabularnewline
trend & 0.00359133126934983 & 0.000154 & 23.3957 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4698&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]1.43142857142857[/C][C]0.001277[/C][C]1120.8608[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]dummy1[/C][C]-0.135175782593738[/C][C]0.008088[/C][C]-16.7134[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`dummy2+`[/C][C]0.0485714285714295[/C][C]0.001418[/C][C]34.247[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`dummy3+`[/C][C]0.098[/C][C]0.001632[/C][C]60.0441[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]trend[/C][C]0.00359133126934983[/C][C]0.000154[/C][C]23.3957[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4698&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4698&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)	1.43142857142857	0.001277	1120.8608	0	0
dummy1	-0.135175782593738	0.008088	-16.7134	0	0
`dummy2+`	0.0485714285714295	0.001418	34.247	0	0
`dummy3+`	0.098	0.001632	60.0441	0	0
trend	0.00359133126934983	0.000154	23.3957	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.999056460646552
R-squared	0.998113811559615
Adjusted R-squared	0.997976634218496
F-TEST (value)	7276.08366963869
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00337883516280303
Sum Squared Residuals	0.000627908988156681

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.999056460646552 \tabularnewline
R-squared & 0.998113811559615 \tabularnewline
Adjusted R-squared & 0.997976634218496 \tabularnewline
F-TEST (value) & 7276.08366963869 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 55 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.00337883516280303 \tabularnewline
Sum Squared Residuals & 0.000627908988156681 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4698&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.999056460646552[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998113811559615[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.997976634218496[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7276.08366963869[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]55[/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]0.00337883516280303[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]0.000627908988156681[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4698&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4698&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	0.999056460646552
R-squared	0.998113811559615
Adjusted R-squared	0.997976634218496
F-TEST (value)	7276.08366963869
F-TEST (DF numerator)	4
F-TEST (DF denominator)	55
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.00337883516280303
Sum Squared Residuals	0.000627908988156681

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1.43	1.43142857142858	-0.00142857142857822
2	1.43	1.43142857142857	-0.00142857142857024
3	1.43	1.43142857142857	-0.00142857142857009
4	1.43	1.43142857142857	-0.00142857142857029
5	1.43	1.43142857142857	-0.00142857142857042
6	1.43	1.43142857142857	-0.00142857142857038
7	1.44	1.43142857142857	0.00857142857142963
8	1.48	1.48	3.25260651745651e-19
9	1.48	1.48	3.25260651745651e-19
10	1.48	1.48	3.25260651745651e-19
11	1.48	1.48	3.25260651745651e-19
12	1.48	1.48	3.25260651745651e-19
13	1.48	1.48	3.25260651745651e-19
14	1.48	1.48	3.25260651745651e-19
15	1.48	1.48	3.25260651745651e-19
16	1.48	1.48	3.25260651745651e-19
17	1.48	1.48	3.25260651745651e-19
18	1.48	1.48	3.25260651745651e-19
19	1.48	1.48	3.25260651745651e-19
20	1.48	1.48	3.25260651745651e-19
21	1.48	1.48	3.25260651745651e-19
22	1.48	1.48	3.25260651745651e-19
23	1.48	1.48	3.25260651745651e-19
24	1.48	1.48	3.25260651745651e-19
25	1.48	1.48	3.25260651745651e-19
26	1.48	1.48	3.25260651745651e-19
27	1.48	1.48	3.25260651745651e-19
28	1.48	1.48	3.25260651745651e-19
29	1.48	1.48	3.25260651745651e-19
30	1.48	1.48	3.25260651745651e-19
31	1.48	1.48	3.25260651745651e-19
32	1.48	1.48	3.25260651745651e-19
33	1.48	1.48	3.25260651745651e-19
34	1.48	1.48	3.25260651745651e-19
35	1.48	1.48	3.25260651745651e-19
36	1.48	1.48	3.25260651745651e-19
37	1.48	1.48	3.25260651745651e-19
38	1.57	1.578	-0.008
39	1.58	1.578	0.00200000000000000
40	1.58	1.578	0.00200000000000000
41	1.58	1.578	0.00200000000000000
42	1.58	1.578	0.00200000000000000
43	1.59	1.59725146198830	-0.00725146198830411
44	1.6	1.60084279325765	-0.000842793257653937
45	1.6	1.60443412452700	-0.00443412452700377
46	1.61	1.60802545579635	0.00197454420364641
47	1.61	1.61161678706570	-0.00161678706570342
48	1.61	1.61520811833505	-0.00520811833505325
49	1.62	1.61879944960440	0.00120055039559693
50	1.63	1.62239078087375	0.00760921912624688
51	1.63	1.62598211214310	0.00401788785689705
52	1.64	1.62957344341245	0.0104265565875472
53	1.64	1.63316477468180	0.0068352253181974
54	1.64	1.63675610595115	0.00324389404884757
55	1.64	1.64034743722050	-0.000347437220502263
56	1.64	1.64393876848985	-0.00393876848985209
57	1.65	1.6475300997592	0.00246990024079809
58	1.65	1.65112143102855	-0.00112143102855174
59	1.65	1.65471276229790	-0.00471276229790157
60	1.65	1.65830409356725	-0.0083040935672514

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1.43 & 1.43142857142858 & -0.00142857142857822 \tabularnewline
2 & 1.43 & 1.43142857142857 & -0.00142857142857024 \tabularnewline
3 & 1.43 & 1.43142857142857 & -0.00142857142857009 \tabularnewline
4 & 1.43 & 1.43142857142857 & -0.00142857142857029 \tabularnewline
5 & 1.43 & 1.43142857142857 & -0.00142857142857042 \tabularnewline
6 & 1.43 & 1.43142857142857 & -0.00142857142857038 \tabularnewline
7 & 1.44 & 1.43142857142857 & 0.00857142857142963 \tabularnewline
8 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
9 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
10 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
11 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
12 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
13 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
14 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
15 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
16 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
17 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
18 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
19 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
20 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
21 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
22 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
23 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
24 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
25 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
26 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
27 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
28 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
29 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
30 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
31 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
32 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
33 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
34 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
35 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
36 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
37 & 1.48 & 1.48 & 3.25260651745651e-19 \tabularnewline
38 & 1.57 & 1.578 & -0.008 \tabularnewline
39 & 1.58 & 1.578 & 0.00200000000000000 \tabularnewline
40 & 1.58 & 1.578 & 0.00200000000000000 \tabularnewline
41 & 1.58 & 1.578 & 0.00200000000000000 \tabularnewline
42 & 1.58 & 1.578 & 0.00200000000000000 \tabularnewline
43 & 1.59 & 1.59725146198830 & -0.00725146198830411 \tabularnewline
44 & 1.6 & 1.60084279325765 & -0.000842793257653937 \tabularnewline
45 & 1.6 & 1.60443412452700 & -0.00443412452700377 \tabularnewline
46 & 1.61 & 1.60802545579635 & 0.00197454420364641 \tabularnewline
47 & 1.61 & 1.61161678706570 & -0.00161678706570342 \tabularnewline
48 & 1.61 & 1.61520811833505 & -0.00520811833505325 \tabularnewline
49 & 1.62 & 1.61879944960440 & 0.00120055039559693 \tabularnewline
50 & 1.63 & 1.62239078087375 & 0.00760921912624688 \tabularnewline
51 & 1.63 & 1.62598211214310 & 0.00401788785689705 \tabularnewline
52 & 1.64 & 1.62957344341245 & 0.0104265565875472 \tabularnewline
53 & 1.64 & 1.63316477468180 & 0.0068352253181974 \tabularnewline
54 & 1.64 & 1.63675610595115 & 0.00324389404884757 \tabularnewline
55 & 1.64 & 1.64034743722050 & -0.000347437220502263 \tabularnewline
56 & 1.64 & 1.64393876848985 & -0.00393876848985209 \tabularnewline
57 & 1.65 & 1.6475300997592 & 0.00246990024079809 \tabularnewline
58 & 1.65 & 1.65112143102855 & -0.00112143102855174 \tabularnewline
59 & 1.65 & 1.65471276229790 & -0.00471276229790157 \tabularnewline
60 & 1.65 & 1.65830409356725 & -0.0083040935672514 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=4698&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]1.43142857142858[/C][C]-0.00142857142857822[/C][/ROW]
[ROW][C]2[/C][C]1.43[/C][C]1.43142857142857[/C][C]-0.00142857142857024[/C][/ROW]
[ROW][C]3[/C][C]1.43[/C][C]1.43142857142857[/C][C]-0.00142857142857009[/C][/ROW]
[ROW][C]4[/C][C]1.43[/C][C]1.43142857142857[/C][C]-0.00142857142857029[/C][/ROW]
[ROW][C]5[/C][C]1.43[/C][C]1.43142857142857[/C][C]-0.00142857142857042[/C][/ROW]
[ROW][C]6[/C][C]1.43[/C][C]1.43142857142857[/C][C]-0.00142857142857038[/C][/ROW]
[ROW][C]7[/C][C]1.44[/C][C]1.43142857142857[/C][C]0.00857142857142963[/C][/ROW]
[ROW][C]8[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]9[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]10[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]11[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]12[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]13[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]14[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]15[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]16[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]17[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]18[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]19[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]20[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]21[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]22[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]23[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]24[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]25[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]26[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]27[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]28[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]29[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]30[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]31[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]32[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]33[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]34[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]35[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]36[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]37[/C][C]1.48[/C][C]1.48[/C][C]3.25260651745651e-19[/C][/ROW]
[ROW][C]38[/C][C]1.57[/C][C]1.578[/C][C]-0.008[/C][/ROW]
[ROW][C]39[/C][C]1.58[/C][C]1.578[/C][C]0.00200000000000000[/C][/ROW]
[ROW][C]40[/C][C]1.58[/C][C]1.578[/C][C]0.00200000000000000[/C][/ROW]
[ROW][C]41[/C][C]1.58[/C][C]1.578[/C][C]0.00200000000000000[/C][/ROW]
[ROW][C]42[/C][C]1.58[/C][C]1.578[/C][C]0.00200000000000000[/C][/ROW]
[ROW][C]43[/C][C]1.59[/C][C]1.59725146198830[/C][C]-0.00725146198830411[/C][/ROW]
[ROW][C]44[/C][C]1.6[/C][C]1.60084279325765[/C][C]-0.000842793257653937[/C][/ROW]
[ROW][C]45[/C][C]1.6[/C][C]1.60443412452700[/C][C]-0.00443412452700377[/C][/ROW]
[ROW][C]46[/C][C]1.61[/C][C]1.60802545579635[/C][C]0.00197454420364641[/C][/ROW]
[ROW][C]47[/C][C]1.61[/C][C]1.61161678706570[/C][C]-0.00161678706570342[/C][/ROW]
[ROW][C]48[/C][C]1.61[/C][C]1.61520811833505[/C][C]-0.00520811833505325[/C][/ROW]
[ROW][C]49[/C][C]1.62[/C][C]1.61879944960440[/C][C]0.00120055039559693[/C][/ROW]
[ROW][C]50[/C][C]1.63[/C][C]1.62239078087375[/C][C]0.00760921912624688[/C][/ROW]
[ROW][C]51[/C][C]1.63[/C][C]1.62598211214310[/C][C]0.00401788785689705[/C][/ROW]
[ROW][C]52[/C][C]1.64[/C][C]1.62957344341245[/C][C]0.0104265565875472[/C][/ROW]
[ROW][C]53[/C][C]1.64[/C][C]1.63316477468180[/C][C]0.0068352253181974[/C][/ROW]
[ROW][C]54[/C][C]1.64[/C][C]1.63675610595115[/C][C]0.00324389404884757[/C][/ROW]
[ROW][C]55[/C][C]1.64[/C][C]1.64034743722050[/C][C]-0.000347437220502263[/C][/ROW]
[ROW][C]56[/C][C]1.64[/C][C]1.64393876848985[/C][C]-0.00393876848985209[/C][/ROW]
[ROW][C]57[/C][C]1.65[/C][C]1.6475300997592[/C][C]0.00246990024079809[/C][/ROW]
[ROW][C]58[/C][C]1.65[/C][C]1.65112143102855[/C][C]-0.00112143102855174[/C][/ROW]
[ROW][C]59[/C][C]1.65[/C][C]1.65471276229790[/C][C]-0.00471276229790157[/C][/ROW]
[ROW][C]60[/C][C]1.65[/C][C]1.65830409356725[/C][C]-0.0083040935672514[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=4698&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=4698&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	1.43142857142858	-0.00142857142857822
2	1.43	1.43142857142857	-0.00142857142857024
3	1.43	1.43142857142857	-0.00142857142857009
4	1.43	1.43142857142857	-0.00142857142857029
5	1.43	1.43142857142857	-0.00142857142857042
6	1.43	1.43142857142857	-0.00142857142857038
7	1.44	1.43142857142857	0.00857142857142963
8	1.48	1.48	3.25260651745651e-19
9	1.48	1.48	3.25260651745651e-19
10	1.48	1.48	3.25260651745651e-19
11	1.48	1.48	3.25260651745651e-19
12	1.48	1.48	3.25260651745651e-19
13	1.48	1.48	3.25260651745651e-19
14	1.48	1.48	3.25260651745651e-19
15	1.48	1.48	3.25260651745651e-19
16	1.48	1.48	3.25260651745651e-19
17	1.48	1.48	3.25260651745651e-19
18	1.48	1.48	3.25260651745651e-19
19	1.48	1.48	3.25260651745651e-19
20	1.48	1.48	3.25260651745651e-19
21	1.48	1.48	3.25260651745651e-19
22	1.48	1.48	3.25260651745651e-19
23	1.48	1.48	3.25260651745651e-19
24	1.48	1.48	3.25260651745651e-19
25	1.48	1.48	3.25260651745651e-19
26	1.48	1.48	3.25260651745651e-19
27	1.48	1.48	3.25260651745651e-19
28	1.48	1.48	3.25260651745651e-19
29	1.48	1.48	3.25260651745651e-19
30	1.48	1.48	3.25260651745651e-19
31	1.48	1.48	3.25260651745651e-19
32	1.48	1.48	3.25260651745651e-19
33	1.48	1.48	3.25260651745651e-19
34	1.48	1.48	3.25260651745651e-19
35	1.48	1.48	3.25260651745651e-19
36	1.48	1.48	3.25260651745651e-19
37	1.48	1.48	3.25260651745651e-19
38	1.57	1.578	-0.008
39	1.58	1.578	0.00200000000000000
40	1.58	1.578	0.00200000000000000
41	1.58	1.578	0.00200000000000000
42	1.58	1.578	0.00200000000000000
43	1.59	1.59725146198830	-0.00725146198830411
44	1.6	1.60084279325765	-0.000842793257653937
45	1.6	1.60443412452700	-0.00443412452700377
46	1.61	1.60802545579635	0.00197454420364641
47	1.61	1.61161678706570	-0.00161678706570342
48	1.61	1.61520811833505	-0.00520811833505325
49	1.62	1.61879944960440	0.00120055039559693
50	1.63	1.62239078087375	0.00760921912624688
51	1.63	1.62598211214310	0.00401788785689705
52	1.64	1.62957344341245	0.0104265565875472
53	1.64	1.63316477468180	0.0068352253181974
54	1.64	1.63675610595115	0.00324389404884757
55	1.64	1.64034743722050	-0.000347437220502263
56	1.64	1.64393876848985	-0.00393876848985209
57	1.65	1.6475300997592	0.00246990024079809
58	1.65	1.65112143102855	-0.00112143102855174
59	1.65	1.65471276229790	-0.00471276229790157
60	1.65	1.65830409356725	-0.0083040935672514

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