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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationSun, 21 Dec 2008 06:40:29 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/21/t1229866877kdrh0gc0v2lfdsb.htm/, Retrieved Sun, 09 Nov 2025 02:40:27 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35579, Retrieved Sun, 09 Nov 2025 02:40:27 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords7
Estimated Impact492

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
F    D  [Multiple Regression] [Q1 T6] [2008-11-18 17:59:48] [fe7291e888d31b8c4db0b24d6c0f75c6]
-    D    [Multiple Regression] [6] [2008-12-21 13:33:45] [fe7291e888d31b8c4db0b24d6c0f75c6]
-   PD        [Multiple Regression] [7] [2008-12-21 13:40:29] [783db4b4a0f63b73ca8b14666b7f4329] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
99984	0
99981	0
99972	0
99989	0
99996	0
99991	0
99988	0
99990	0
99998	0
99987	0
100000	0
100000	0
100004	0
100007	0
100005	0
100002	0
99998	0
100006	0
99997	0
100001	0
100000	0
99993	0
99994	0
99996	0
99996	0
99998	0
100002	0
99995	0
99985	0
99984	0
99982	0
99987	0
99977	0
99990	0
99990	0
99994	0
99997	0
99996	0
99993	0
99993	0
99993	0
99997	0
100000	0
99995	0
99997	0
100003	0
100002	0
99993	0
99999	1
100000	1
99997	1
100004	1
100002	1
100003	1
100000	1
99990	1
99990	1
99991	1
99978	1
99984	1
99982	1
99986	1
99988	1
99983	1
99977	1
99972	1
99969	1
99979	1
99981	1
99978	1
99978	1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational 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=35579&T=0

[TABLE]
[ROW][C]Summary of computational 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=35579&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35579&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Multiple Linear Regression - Estimated Regression Equation
Economie[t] = + 99994.125 -6.69021739130032Kredietcrisis[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Economie[t] =  +  99994.125 -6.69021739130032Kredietcrisis[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35579&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Economie[t] =  +  99994.125 -6.69021739130032Kredietcrisis[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35579&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35579&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Estimated Regression Equation
Economie[t] = + 99994.125 -6.69021739130032Kredietcrisis[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99994.1251.24272180463.882200
Kredietcrisis-6.690217391300322.183427-3.06410.0031140.001557

\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) & 99994.125 & 1.242721 & 80463.8822 & 0 & 0 \tabularnewline
Kredietcrisis & -6.69021739130032 & 2.183427 & -3.0641 & 0.003114 & 0.001557 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35579&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]99994.125[/C][C]1.242721[/C][C]80463.8822[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Kredietcrisis[/C][C]-6.69021739130032[/C][C]2.183427[/C][C]-3.0641[/C][C]0.003114[/C][C]0.001557[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35579&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35579&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)99994.1251.24272180463.882200
Kredietcrisis-6.690217391300322.183427-3.06410.0031140.001557






Multiple Linear Regression - Regression Statistics
Multiple R0.346078716489396
R-squared0.119770478006947
Adjusted R-squared0.107013528412845
F-TEST (value)9.38864554754686
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value0.00311367226414694
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.60982096290564
Sum Squared Residuals5114.90217391696

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.346078716489396 \tabularnewline
R-squared & 0.119770478006947 \tabularnewline
Adjusted R-squared & 0.107013528412845 \tabularnewline
F-TEST (value) & 9.38864554754686 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 69 \tabularnewline
p-value & 0.00311367226414694 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 8.60982096290564 \tabularnewline
Sum Squared Residuals & 5114.90217391696 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35579&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.346078716489396[/C][/ROW]
[ROW][C]R-squared[/C][C]0.119770478006947[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.107013528412845[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.38864554754686[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]69[/C][/ROW]
[ROW][C]p-value[/C][C]0.00311367226414694[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]8.60982096290564[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5114.90217391696[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35579&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35579&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.346078716489396
R-squared0.119770478006947
Adjusted R-squared0.107013528412845
F-TEST (value)9.38864554754686
F-TEST (DF numerator)1
F-TEST (DF denominator)69
p-value0.00311367226414694
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation8.60982096290564
Sum Squared Residuals5114.90217391696






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19998499994.1250000002-10.1250000001896
29998199994.125-13.1249999999960
39997299994.125-22.1249999999960
49998999994.125-5.12499999999597
59999699994.1251.87500000000403
69999199994.125-3.12499999999597
79998899994.125-6.12499999999597
89999099994.125-4.12499999999597
99999899994.1253.87500000000403
109998799994.125-7.12499999999597
111e+0599994.1255.87500000000403
121e+0599994.1255.87500000000403
1310000499994.1259.87500000000403
1410000799994.12512.8750000000040
1510000599994.12510.8750000000040
1610000299994.1257.87500000000403
179999899994.1253.87500000000403
1810000699994.12511.8750000000040
199999799994.1252.87500000000403
2010000199994.1256.87500000000403
211e+0599994.1255.87500000000403
229999399994.125-1.12499999999597
239999499994.125-0.124999999995967
249999699994.1251.87500000000403
259999699994.1251.87500000000403
269999899994.1253.87500000000403
2710000299994.1257.87500000000403
289999599994.1250.875000000004033
299998599994.125-9.12499999999597
309998499994.125-10.1249999999960
319998299994.125-12.1249999999960
329998799994.125-7.12499999999597
339997799994.125-17.1249999999960
349999099994.125-4.12499999999597
359999099994.125-4.12499999999597
369999499994.125-0.124999999995967
379999799994.1252.87500000000403
389999699994.1251.87500000000403
399999399994.125-1.12499999999597
409999399994.125-1.12499999999597
419999399994.125-1.12499999999597
429999799994.1252.87500000000403
431e+0599994.1255.87500000000403
449999599994.1250.875000000004033
459999799994.1252.87500000000403
4610000399994.1258.87500000000403
4710000299994.1257.87500000000403
489999399994.125-1.12499999999597
499999999987.434782608711.5652173913043
501e+0599987.434782608712.5652173913043
519999799987.43478260879.56521739130435
5210000499987.434782608716.5652173913044
5310000299987.434782608714.5652173913043
5410000399987.434782608715.5652173913043
551e+0599987.434782608712.5652173913043
569999099987.43478260872.56521739130435
579999099987.43478260872.56521739130435
589999199987.43478260873.56521739130435
599997899987.4347826087-9.43478260869565
609998499987.4347826087-3.43478260869565
619998299987.4347826087-5.43478260869565
629998699987.4347826087-1.43478260869565
639998899987.43478260870.565217391304348
649998399987.4347826087-4.43478260869565
659997799987.4347826087-10.4347826086957
669997299987.4347826087-15.4347826086957
679996999987.4347826087-18.4347826086956
689997999987.4347826087-8.43478260869565
699998199987.4347826087-6.43478260869565
709997899987.4347826087-9.43478260869565
719997899987.4347826087-9.43478260869565

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 99984 & 99994.1250000002 & -10.1250000001896 \tabularnewline
2 & 99981 & 99994.125 & -13.1249999999960 \tabularnewline
3 & 99972 & 99994.125 & -22.1249999999960 \tabularnewline
4 & 99989 & 99994.125 & -5.12499999999597 \tabularnewline
5 & 99996 & 99994.125 & 1.87500000000403 \tabularnewline
6 & 99991 & 99994.125 & -3.12499999999597 \tabularnewline
7 & 99988 & 99994.125 & -6.12499999999597 \tabularnewline
8 & 99990 & 99994.125 & -4.12499999999597 \tabularnewline
9 & 99998 & 99994.125 & 3.87500000000403 \tabularnewline
10 & 99987 & 99994.125 & -7.12499999999597 \tabularnewline
11 & 1e+05 & 99994.125 & 5.87500000000403 \tabularnewline
12 & 1e+05 & 99994.125 & 5.87500000000403 \tabularnewline
13 & 100004 & 99994.125 & 9.87500000000403 \tabularnewline
14 & 100007 & 99994.125 & 12.8750000000040 \tabularnewline
15 & 100005 & 99994.125 & 10.8750000000040 \tabularnewline
16 & 100002 & 99994.125 & 7.87500000000403 \tabularnewline
17 & 99998 & 99994.125 & 3.87500000000403 \tabularnewline
18 & 100006 & 99994.125 & 11.8750000000040 \tabularnewline
19 & 99997 & 99994.125 & 2.87500000000403 \tabularnewline
20 & 100001 & 99994.125 & 6.87500000000403 \tabularnewline
21 & 1e+05 & 99994.125 & 5.87500000000403 \tabularnewline
22 & 99993 & 99994.125 & -1.12499999999597 \tabularnewline
23 & 99994 & 99994.125 & -0.124999999995967 \tabularnewline
24 & 99996 & 99994.125 & 1.87500000000403 \tabularnewline
25 & 99996 & 99994.125 & 1.87500000000403 \tabularnewline
26 & 99998 & 99994.125 & 3.87500000000403 \tabularnewline
27 & 100002 & 99994.125 & 7.87500000000403 \tabularnewline
28 & 99995 & 99994.125 & 0.875000000004033 \tabularnewline
29 & 99985 & 99994.125 & -9.12499999999597 \tabularnewline
30 & 99984 & 99994.125 & -10.1249999999960 \tabularnewline
31 & 99982 & 99994.125 & -12.1249999999960 \tabularnewline
32 & 99987 & 99994.125 & -7.12499999999597 \tabularnewline
33 & 99977 & 99994.125 & -17.1249999999960 \tabularnewline
34 & 99990 & 99994.125 & -4.12499999999597 \tabularnewline
35 & 99990 & 99994.125 & -4.12499999999597 \tabularnewline
36 & 99994 & 99994.125 & -0.124999999995967 \tabularnewline
37 & 99997 & 99994.125 & 2.87500000000403 \tabularnewline
38 & 99996 & 99994.125 & 1.87500000000403 \tabularnewline
39 & 99993 & 99994.125 & -1.12499999999597 \tabularnewline
40 & 99993 & 99994.125 & -1.12499999999597 \tabularnewline
41 & 99993 & 99994.125 & -1.12499999999597 \tabularnewline
42 & 99997 & 99994.125 & 2.87500000000403 \tabularnewline
43 & 1e+05 & 99994.125 & 5.87500000000403 \tabularnewline
44 & 99995 & 99994.125 & 0.875000000004033 \tabularnewline
45 & 99997 & 99994.125 & 2.87500000000403 \tabularnewline
46 & 100003 & 99994.125 & 8.87500000000403 \tabularnewline
47 & 100002 & 99994.125 & 7.87500000000403 \tabularnewline
48 & 99993 & 99994.125 & -1.12499999999597 \tabularnewline
49 & 99999 & 99987.4347826087 & 11.5652173913043 \tabularnewline
50 & 1e+05 & 99987.4347826087 & 12.5652173913043 \tabularnewline
51 & 99997 & 99987.4347826087 & 9.56521739130435 \tabularnewline
52 & 100004 & 99987.4347826087 & 16.5652173913044 \tabularnewline
53 & 100002 & 99987.4347826087 & 14.5652173913043 \tabularnewline
54 & 100003 & 99987.4347826087 & 15.5652173913043 \tabularnewline
55 & 1e+05 & 99987.4347826087 & 12.5652173913043 \tabularnewline
56 & 99990 & 99987.4347826087 & 2.56521739130435 \tabularnewline
57 & 99990 & 99987.4347826087 & 2.56521739130435 \tabularnewline
58 & 99991 & 99987.4347826087 & 3.56521739130435 \tabularnewline
59 & 99978 & 99987.4347826087 & -9.43478260869565 \tabularnewline
60 & 99984 & 99987.4347826087 & -3.43478260869565 \tabularnewline
61 & 99982 & 99987.4347826087 & -5.43478260869565 \tabularnewline
62 & 99986 & 99987.4347826087 & -1.43478260869565 \tabularnewline
63 & 99988 & 99987.4347826087 & 0.565217391304348 \tabularnewline
64 & 99983 & 99987.4347826087 & -4.43478260869565 \tabularnewline
65 & 99977 & 99987.4347826087 & -10.4347826086957 \tabularnewline
66 & 99972 & 99987.4347826087 & -15.4347826086957 \tabularnewline
67 & 99969 & 99987.4347826087 & -18.4347826086956 \tabularnewline
68 & 99979 & 99987.4347826087 & -8.43478260869565 \tabularnewline
69 & 99981 & 99987.4347826087 & -6.43478260869565 \tabularnewline
70 & 99978 & 99987.4347826087 & -9.43478260869565 \tabularnewline
71 & 99978 & 99987.4347826087 & -9.43478260869565 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35579&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]99984[/C][C]99994.1250000002[/C][C]-10.1250000001896[/C][/ROW]
[ROW][C]2[/C][C]99981[/C][C]99994.125[/C][C]-13.1249999999960[/C][/ROW]
[ROW][C]3[/C][C]99972[/C][C]99994.125[/C][C]-22.1249999999960[/C][/ROW]
[ROW][C]4[/C][C]99989[/C][C]99994.125[/C][C]-5.12499999999597[/C][/ROW]
[ROW][C]5[/C][C]99996[/C][C]99994.125[/C][C]1.87500000000403[/C][/ROW]
[ROW][C]6[/C][C]99991[/C][C]99994.125[/C][C]-3.12499999999597[/C][/ROW]
[ROW][C]7[/C][C]99988[/C][C]99994.125[/C][C]-6.12499999999597[/C][/ROW]
[ROW][C]8[/C][C]99990[/C][C]99994.125[/C][C]-4.12499999999597[/C][/ROW]
[ROW][C]9[/C][C]99998[/C][C]99994.125[/C][C]3.87500000000403[/C][/ROW]
[ROW][C]10[/C][C]99987[/C][C]99994.125[/C][C]-7.12499999999597[/C][/ROW]
[ROW][C]11[/C][C]1e+05[/C][C]99994.125[/C][C]5.87500000000403[/C][/ROW]
[ROW][C]12[/C][C]1e+05[/C][C]99994.125[/C][C]5.87500000000403[/C][/ROW]
[ROW][C]13[/C][C]100004[/C][C]99994.125[/C][C]9.87500000000403[/C][/ROW]
[ROW][C]14[/C][C]100007[/C][C]99994.125[/C][C]12.8750000000040[/C][/ROW]
[ROW][C]15[/C][C]100005[/C][C]99994.125[/C][C]10.8750000000040[/C][/ROW]
[ROW][C]16[/C][C]100002[/C][C]99994.125[/C][C]7.87500000000403[/C][/ROW]
[ROW][C]17[/C][C]99998[/C][C]99994.125[/C][C]3.87500000000403[/C][/ROW]
[ROW][C]18[/C][C]100006[/C][C]99994.125[/C][C]11.8750000000040[/C][/ROW]
[ROW][C]19[/C][C]99997[/C][C]99994.125[/C][C]2.87500000000403[/C][/ROW]
[ROW][C]20[/C][C]100001[/C][C]99994.125[/C][C]6.87500000000403[/C][/ROW]
[ROW][C]21[/C][C]1e+05[/C][C]99994.125[/C][C]5.87500000000403[/C][/ROW]
[ROW][C]22[/C][C]99993[/C][C]99994.125[/C][C]-1.12499999999597[/C][/ROW]
[ROW][C]23[/C][C]99994[/C][C]99994.125[/C][C]-0.124999999995967[/C][/ROW]
[ROW][C]24[/C][C]99996[/C][C]99994.125[/C][C]1.87500000000403[/C][/ROW]
[ROW][C]25[/C][C]99996[/C][C]99994.125[/C][C]1.87500000000403[/C][/ROW]
[ROW][C]26[/C][C]99998[/C][C]99994.125[/C][C]3.87500000000403[/C][/ROW]
[ROW][C]27[/C][C]100002[/C][C]99994.125[/C][C]7.87500000000403[/C][/ROW]
[ROW][C]28[/C][C]99995[/C][C]99994.125[/C][C]0.875000000004033[/C][/ROW]
[ROW][C]29[/C][C]99985[/C][C]99994.125[/C][C]-9.12499999999597[/C][/ROW]
[ROW][C]30[/C][C]99984[/C][C]99994.125[/C][C]-10.1249999999960[/C][/ROW]
[ROW][C]31[/C][C]99982[/C][C]99994.125[/C][C]-12.1249999999960[/C][/ROW]
[ROW][C]32[/C][C]99987[/C][C]99994.125[/C][C]-7.12499999999597[/C][/ROW]
[ROW][C]33[/C][C]99977[/C][C]99994.125[/C][C]-17.1249999999960[/C][/ROW]
[ROW][C]34[/C][C]99990[/C][C]99994.125[/C][C]-4.12499999999597[/C][/ROW]
[ROW][C]35[/C][C]99990[/C][C]99994.125[/C][C]-4.12499999999597[/C][/ROW]
[ROW][C]36[/C][C]99994[/C][C]99994.125[/C][C]-0.124999999995967[/C][/ROW]
[ROW][C]37[/C][C]99997[/C][C]99994.125[/C][C]2.87500000000403[/C][/ROW]
[ROW][C]38[/C][C]99996[/C][C]99994.125[/C][C]1.87500000000403[/C][/ROW]
[ROW][C]39[/C][C]99993[/C][C]99994.125[/C][C]-1.12499999999597[/C][/ROW]
[ROW][C]40[/C][C]99993[/C][C]99994.125[/C][C]-1.12499999999597[/C][/ROW]
[ROW][C]41[/C][C]99993[/C][C]99994.125[/C][C]-1.12499999999597[/C][/ROW]
[ROW][C]42[/C][C]99997[/C][C]99994.125[/C][C]2.87500000000403[/C][/ROW]
[ROW][C]43[/C][C]1e+05[/C][C]99994.125[/C][C]5.87500000000403[/C][/ROW]
[ROW][C]44[/C][C]99995[/C][C]99994.125[/C][C]0.875000000004033[/C][/ROW]
[ROW][C]45[/C][C]99997[/C][C]99994.125[/C][C]2.87500000000403[/C][/ROW]
[ROW][C]46[/C][C]100003[/C][C]99994.125[/C][C]8.87500000000403[/C][/ROW]
[ROW][C]47[/C][C]100002[/C][C]99994.125[/C][C]7.87500000000403[/C][/ROW]
[ROW][C]48[/C][C]99993[/C][C]99994.125[/C][C]-1.12499999999597[/C][/ROW]
[ROW][C]49[/C][C]99999[/C][C]99987.4347826087[/C][C]11.5652173913043[/C][/ROW]
[ROW][C]50[/C][C]1e+05[/C][C]99987.4347826087[/C][C]12.5652173913043[/C][/ROW]
[ROW][C]51[/C][C]99997[/C][C]99987.4347826087[/C][C]9.56521739130435[/C][/ROW]
[ROW][C]52[/C][C]100004[/C][C]99987.4347826087[/C][C]16.5652173913044[/C][/ROW]
[ROW][C]53[/C][C]100002[/C][C]99987.4347826087[/C][C]14.5652173913043[/C][/ROW]
[ROW][C]54[/C][C]100003[/C][C]99987.4347826087[/C][C]15.5652173913043[/C][/ROW]
[ROW][C]55[/C][C]1e+05[/C][C]99987.4347826087[/C][C]12.5652173913043[/C][/ROW]
[ROW][C]56[/C][C]99990[/C][C]99987.4347826087[/C][C]2.56521739130435[/C][/ROW]
[ROW][C]57[/C][C]99990[/C][C]99987.4347826087[/C][C]2.56521739130435[/C][/ROW]
[ROW][C]58[/C][C]99991[/C][C]99987.4347826087[/C][C]3.56521739130435[/C][/ROW]
[ROW][C]59[/C][C]99978[/C][C]99987.4347826087[/C][C]-9.43478260869565[/C][/ROW]
[ROW][C]60[/C][C]99984[/C][C]99987.4347826087[/C][C]-3.43478260869565[/C][/ROW]
[ROW][C]61[/C][C]99982[/C][C]99987.4347826087[/C][C]-5.43478260869565[/C][/ROW]
[ROW][C]62[/C][C]99986[/C][C]99987.4347826087[/C][C]-1.43478260869565[/C][/ROW]
[ROW][C]63[/C][C]99988[/C][C]99987.4347826087[/C][C]0.565217391304348[/C][/ROW]
[ROW][C]64[/C][C]99983[/C][C]99987.4347826087[/C][C]-4.43478260869565[/C][/ROW]
[ROW][C]65[/C][C]99977[/C][C]99987.4347826087[/C][C]-10.4347826086957[/C][/ROW]
[ROW][C]66[/C][C]99972[/C][C]99987.4347826087[/C][C]-15.4347826086957[/C][/ROW]
[ROW][C]67[/C][C]99969[/C][C]99987.4347826087[/C][C]-18.4347826086956[/C][/ROW]
[ROW][C]68[/C][C]99979[/C][C]99987.4347826087[/C][C]-8.43478260869565[/C][/ROW]
[ROW][C]69[/C][C]99981[/C][C]99987.4347826087[/C][C]-6.43478260869565[/C][/ROW]
[ROW][C]70[/C][C]99978[/C][C]99987.4347826087[/C][C]-9.43478260869565[/C][/ROW]
[ROW][C]71[/C][C]99978[/C][C]99987.4347826087[/C][C]-9.43478260869565[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35579&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35579&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
19998499994.1250000002-10.1250000001896
29998199994.125-13.1249999999960
39997299994.125-22.1249999999960
49998999994.125-5.12499999999597
59999699994.1251.87500000000403
69999199994.125-3.12499999999597
79998899994.125-6.12499999999597
89999099994.125-4.12499999999597
99999899994.1253.87500000000403
109998799994.125-7.12499999999597
111e+0599994.1255.87500000000403
121e+0599994.1255.87500000000403
1310000499994.1259.87500000000403
1410000799994.12512.8750000000040
1510000599994.12510.8750000000040
1610000299994.1257.87500000000403
179999899994.1253.87500000000403
1810000699994.12511.8750000000040
199999799994.1252.87500000000403
2010000199994.1256.87500000000403
211e+0599994.1255.87500000000403
229999399994.125-1.12499999999597
239999499994.125-0.124999999995967
249999699994.1251.87500000000403
259999699994.1251.87500000000403
269999899994.1253.87500000000403
2710000299994.1257.87500000000403
289999599994.1250.875000000004033
299998599994.125-9.12499999999597
309998499994.125-10.1249999999960
319998299994.125-12.1249999999960
329998799994.125-7.12499999999597
339997799994.125-17.1249999999960
349999099994.125-4.12499999999597
359999099994.125-4.12499999999597
369999499994.125-0.124999999995967
379999799994.1252.87500000000403
389999699994.1251.87500000000403
399999399994.125-1.12499999999597
409999399994.125-1.12499999999597
419999399994.125-1.12499999999597
429999799994.1252.87500000000403
431e+0599994.1255.87500000000403
449999599994.1250.875000000004033
459999799994.1252.87500000000403
4610000399994.1258.87500000000403
4710000299994.1257.87500000000403
489999399994.125-1.12499999999597
499999999987.434782608711.5652173913043
501e+0599987.434782608712.5652173913043
519999799987.43478260879.56521739130435
5210000499987.434782608716.5652173913044
5310000299987.434782608714.5652173913043
5410000399987.434782608715.5652173913043
551e+0599987.434782608712.5652173913043
569999099987.43478260872.56521739130435
579999099987.43478260872.56521739130435
589999199987.43478260873.56521739130435
599997899987.4347826087-9.43478260869565
609998499987.4347826087-3.43478260869565
619998299987.4347826087-5.43478260869565
629998699987.4347826087-1.43478260869565
639998899987.43478260870.565217391304348
649998399987.4347826087-4.43478260869565
659997799987.4347826087-10.4347826086957
669997299987.4347826087-15.4347826086957
679996999987.4347826087-18.4347826086956
689997999987.4347826087-8.43478260869565
699998199987.4347826087-6.43478260869565
709997899987.4347826087-9.43478260869565
719997899987.4347826087-9.43478260869565


Figures (Output of Computation)

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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Input Parameters & R Code

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')