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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, 14 Dec 2008 06:42:08 -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/14/t12292623478dxyxzdyt0ksl9p.htm/, Retrieved Thu, 31 Oct 2024 22:49:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=33371, Retrieved Thu, 31 Oct 2024 22:49:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact226
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] [Case seatbelt law...] [2008-11-24 09:36:19] [b82ef11dce0545f3fd4676ec3ebed828]
-    D      [Multiple Regression] [Paper: Multiple R...] [2008-12-14 13:42:08] [8da7502cfecb272886bc60b3f290b8b8] [Current]
-   PD        [Multiple Regression] [Paper: Multiple R...] [2008-12-16 18:26:09] [9e54d1454d464f1bf9ee4a54d5d56945]
-               [Multiple Regression] [] [2008-12-22 22:26:41] [187876c4ad94aebda16017e4a72ae602]
-             [Multiple Regression] [] [2008-12-22 22:28:47] [187876c4ad94aebda16017e4a72ae602]
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Dataseries X:
17,3	0
15,4	0
16,9	0
20,8	0
16,4	0
11,3	0
17,5	0
16,6	0
17,5	0
19,5	0
18,8	0
20,2	0
19,2	0
14,4	0
24,5	0
25,7	0
27,1	0
21	0
18,6	0
20	0
21,8	0
20,4	0
18	1
21,5	1
19,1	1
19,7	1
26	1
26,3	1
24,6	1
22,4	1
32	1
24	1
30	1
24,1	1
26,3	1
29,8	1
21,9	1
22,8	1
29,2	1
27,5	1
27,4	1
31	1
26,1	1
22,2	1
34	1
26,9	1
31,9	1
34,2	1
31,2	1
28,5	1
37,1	1
36	1
34,8	1
32,1	1
37,2	1
36,3	1
39,5	1
37,1	1
35,6	1
36,2	1
35,9	1
32,5	1
39,2	1
39,4	1
42,8	1
34,5	1
43,7	1
46,3	1
40,8	1
48,4	1
43,2	1
48,1	1
42,8	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 5 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33371&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33371&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33371&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 computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 15.5292371134021 -3.9141443298969x[t] -3.05177712322042M1[t] -5.48349631811487M2[t] + 0.654617574864998M3[t] + 0.659398134511537M4[t] -0.235821305841926M5[t] -4.16437407952872M6[t] -0.826260186548845M7[t] -2.90481296023564M8[t] -0.333365733922435M9[t] -1.99525184094256M10[t] -2.23811389297987M11[t] + 0.461886107020128t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  15.5292371134021 -3.9141443298969x[t] -3.05177712322042M1[t] -5.48349631811487M2[t] +  0.654617574864998M3[t] +  0.659398134511537M4[t] -0.235821305841926M5[t] -4.16437407952872M6[t] -0.826260186548845M7[t] -2.90481296023564M8[t] -0.333365733922435M9[t] -1.99525184094256M10[t] -2.23811389297987M11[t] +  0.461886107020128t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33371&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  15.5292371134021 -3.9141443298969x[t] -3.05177712322042M1[t] -5.48349631811487M2[t] +  0.654617574864998M3[t] +  0.659398134511537M4[t] -0.235821305841926M5[t] -4.16437407952872M6[t] -0.826260186548845M7[t] -2.90481296023564M8[t] -0.333365733922435M9[t] -1.99525184094256M10[t] -2.23811389297987M11[t] +  0.461886107020128t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33371&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33371&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] = + 15.5292371134021 -3.9141443298969x[t] -3.05177712322042M1[t] -5.48349631811487M2[t] + 0.654617574864998M3[t] + 0.659398134511537M4[t] -0.235821305841926M5[t] -4.16437407952872M6[t] -0.826260186548845M7[t] -2.90481296023564M8[t] -0.333365733922435M9[t] -1.99525184094256M10[t] -2.23811389297987M11[t] + 0.461886107020128t + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.52923711340211.39193711.156600
x-3.91414432989691.253735-3.1220.0027810.001391
M1-3.051777123220421.63822-1.86290.0674620.033731
M2-5.483496318114871.705414-3.21530.0021150.001057
M30.6546175748649981.7039210.38420.7022240.351112
M40.6593981345115371.7028660.38720.699980.34999
M5-0.2358213058419261.702249-0.13850.8902890.445145
M6-4.164374079528721.702071-2.44670.0174180.008709
M7-0.8262601865488451.702332-0.48540.6292120.314606
M8-2.904812960235641.703032-1.70570.0933290.046665
M9-0.3333657339224351.70417-0.19560.8455820.422791
M10-1.995251840942561.705745-1.16970.2468160.123408
M11-2.238113892979871.69761-1.31840.1924680.096234
t0.4618861070201280.02733616.896400

\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) & 15.5292371134021 & 1.391937 & 11.1566 & 0 & 0 \tabularnewline
x & -3.9141443298969 & 1.253735 & -3.122 & 0.002781 & 0.001391 \tabularnewline
M1 & -3.05177712322042 & 1.63822 & -1.8629 & 0.067462 & 0.033731 \tabularnewline
M2 & -5.48349631811487 & 1.705414 & -3.2153 & 0.002115 & 0.001057 \tabularnewline
M3 & 0.654617574864998 & 1.703921 & 0.3842 & 0.702224 & 0.351112 \tabularnewline
M4 & 0.659398134511537 & 1.702866 & 0.3872 & 0.69998 & 0.34999 \tabularnewline
M5 & -0.235821305841926 & 1.702249 & -0.1385 & 0.890289 & 0.445145 \tabularnewline
M6 & -4.16437407952872 & 1.702071 & -2.4467 & 0.017418 & 0.008709 \tabularnewline
M7 & -0.826260186548845 & 1.702332 & -0.4854 & 0.629212 & 0.314606 \tabularnewline
M8 & -2.90481296023564 & 1.703032 & -1.7057 & 0.093329 & 0.046665 \tabularnewline
M9 & -0.333365733922435 & 1.70417 & -0.1956 & 0.845582 & 0.422791 \tabularnewline
M10 & -1.99525184094256 & 1.705745 & -1.1697 & 0.246816 & 0.123408 \tabularnewline
M11 & -2.23811389297987 & 1.69761 & -1.3184 & 0.192468 & 0.096234 \tabularnewline
t & 0.461886107020128 & 0.027336 & 16.8964 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33371&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]15.5292371134021[/C][C]1.391937[/C][C]11.1566[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]-3.9141443298969[/C][C]1.253735[/C][C]-3.122[/C][C]0.002781[/C][C]0.001391[/C][/ROW]
[ROW][C]M1[/C][C]-3.05177712322042[/C][C]1.63822[/C][C]-1.8629[/C][C]0.067462[/C][C]0.033731[/C][/ROW]
[ROW][C]M2[/C][C]-5.48349631811487[/C][C]1.705414[/C][C]-3.2153[/C][C]0.002115[/C][C]0.001057[/C][/ROW]
[ROW][C]M3[/C][C]0.654617574864998[/C][C]1.703921[/C][C]0.3842[/C][C]0.702224[/C][C]0.351112[/C][/ROW]
[ROW][C]M4[/C][C]0.659398134511537[/C][C]1.702866[/C][C]0.3872[/C][C]0.69998[/C][C]0.34999[/C][/ROW]
[ROW][C]M5[/C][C]-0.235821305841926[/C][C]1.702249[/C][C]-0.1385[/C][C]0.890289[/C][C]0.445145[/C][/ROW]
[ROW][C]M6[/C][C]-4.16437407952872[/C][C]1.702071[/C][C]-2.4467[/C][C]0.017418[/C][C]0.008709[/C][/ROW]
[ROW][C]M7[/C][C]-0.826260186548845[/C][C]1.702332[/C][C]-0.4854[/C][C]0.629212[/C][C]0.314606[/C][/ROW]
[ROW][C]M8[/C][C]-2.90481296023564[/C][C]1.703032[/C][C]-1.7057[/C][C]0.093329[/C][C]0.046665[/C][/ROW]
[ROW][C]M9[/C][C]-0.333365733922435[/C][C]1.70417[/C][C]-0.1956[/C][C]0.845582[/C][C]0.422791[/C][/ROW]
[ROW][C]M10[/C][C]-1.99525184094256[/C][C]1.705745[/C][C]-1.1697[/C][C]0.246816[/C][C]0.123408[/C][/ROW]
[ROW][C]M11[/C][C]-2.23811389297987[/C][C]1.69761[/C][C]-1.3184[/C][C]0.192468[/C][C]0.096234[/C][/ROW]
[ROW][C]t[/C][C]0.461886107020128[/C][C]0.027336[/C][C]16.8964[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33371&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33371&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)15.52923711340211.39193711.156600
x-3.91414432989691.253735-3.1220.0027810.001391
M1-3.051777123220421.63822-1.86290.0674620.033731
M2-5.483496318114871.705414-3.21530.0021150.001057
M30.6546175748649981.7039210.38420.7022240.351112
M40.6593981345115371.7028660.38720.699980.34999
M5-0.2358213058419261.702249-0.13850.8902890.445145
M6-4.164374079528721.702071-2.44670.0174180.008709
M7-0.8262601865488451.702332-0.48540.6292120.314606
M8-2.904812960235641.703032-1.70570.0933290.046665
M9-0.3333657339224351.70417-0.19560.8455820.422791
M10-1.995251840942561.705745-1.16970.2468160.123408
M11-2.238113892979871.69761-1.31840.1924680.096234
t0.4618861070201280.02733616.896400







Multiple Linear Regression - Regression Statistics
Multiple R0.956071854573462
R-squared0.914073391107539
Adjusted R-squared0.895140409487166
F-TEST (value)48.2794210355095
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.93996566100965
Sum Squared Residuals509.96048718704

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.956071854573462 \tabularnewline
R-squared & 0.914073391107539 \tabularnewline
Adjusted R-squared & 0.895140409487166 \tabularnewline
F-TEST (value) & 48.2794210355095 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 59 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.93996566100965 \tabularnewline
Sum Squared Residuals & 509.96048718704 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33371&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.956071854573462[/C][/ROW]
[ROW][C]R-squared[/C][C]0.914073391107539[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.895140409487166[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]48.2794210355095[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]59[/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]2.93996566100965[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]509.96048718704[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33371&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33371&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 R0.956071854573462
R-squared0.914073391107539
Adjusted R-squared0.895140409487166
F-TEST (value)48.2794210355095
F-TEST (DF numerator)13
F-TEST (DF denominator)59
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.93996566100965
Sum Squared Residuals509.96048718704







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
117.312.93934609720184.36065390279825
215.410.96951300932744.43048699067256
316.917.5695130093274-0.669513009327444
420.818.03617967599412.76382032400589
516.417.6028463426608-1.20284634266078
611.314.1361796759941-2.83617967599411
717.517.9361796759941-0.436179675994112
816.616.31951300932740.280486990672557
917.519.3528463426608-1.85284634266078
1019.518.15284634266081.34715365733922
1118.818.37187039764360.428129602356406
1220.221.0718703976436-0.871870397643594
1319.218.48197938144330.718020618556698
1414.416.5121462935690-2.11214629356898
1524.523.11214629356901.38785370643103
1625.723.57881296023562.12118703976436
1727.123.14547962690233.95452037309769
182119.67881296023561.32118703976436
1918.623.4788129602356-4.87881296023564
202021.8621462935690-1.86214629356897
2121.824.8954796269023-3.09547962690231
2220.423.6954796269023-3.29547962690231
231820.0003593519882-2.00035935198822
2421.522.7003593519882-1.20035935198822
2519.120.1104683357879-1.01046833578792
2619.718.14063524791361.55936475208640
272624.74063524791361.2593647520864
2826.325.20730191458031.09269808541973
2924.624.7739685812469-0.173968581246931
3022.421.30730191458031.09269808541973
313225.10730191458036.89269808541973
322423.49063524791360.509364752086404
333026.52396858124693.47603141875307
3424.125.3239685812469-1.22396858124693
3526.325.54299263622980.757007363770248
3629.828.24299263622981.55700736377025
3721.925.6531016200295-3.75310162002946
3822.823.6832685321551-0.883268532155128
3929.230.2832685321551-1.08326853215513
4027.530.7499351988218-3.24993519882180
4127.430.3166018654885-2.91660186548846
423126.84993519882184.1500648011782
4326.130.6499351988218-4.5499351988218
4422.229.0332685321551-6.83326853215513
453432.06660186548851.93339813451154
4626.930.8666018654885-3.96660186548846
4731.931.08562592047130.814374079528716
4834.233.78562592047130.414374079528724
4931.231.1957349042710.00426509572901176
5028.529.2259018163967-0.72590181639666
5137.135.82590181639671.27409818360334
523636.2925684830633-0.292568483063328
5334.835.85923514973-1.05923514973000
5432.132.3925684830633-0.292568483063327
5537.236.19256848306331.00743151693667
5636.334.57590181639671.72409818360334
5739.537.609235149731.89076485027000
5837.136.409235149730.690764850270007
5935.636.6282592047128-1.02825920471281
6036.239.3282592047128-3.12825920471281
6135.936.7383681885125-0.838368188512521
6232.534.7685351006382-2.26853510063819
6339.241.3685351006382-2.16853510063819
6439.441.8352017673049-2.43520176730486
6542.841.40186843397151.39813156602847
6634.537.9352017673049-3.43520176730486
6743.741.73520176730491.96479823269514
6846.340.11853510063826.1814648993618
6940.843.1518684339715-2.35186843397153
7048.441.95186843397156.44813156602847
7143.242.17089248895431.02910751104566
7248.144.87089248895433.22910751104566
7342.842.28100147275400.518998527245946

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 17.3 & 12.9393460972018 & 4.36065390279825 \tabularnewline
2 & 15.4 & 10.9695130093274 & 4.43048699067256 \tabularnewline
3 & 16.9 & 17.5695130093274 & -0.669513009327444 \tabularnewline
4 & 20.8 & 18.0361796759941 & 2.76382032400589 \tabularnewline
5 & 16.4 & 17.6028463426608 & -1.20284634266078 \tabularnewline
6 & 11.3 & 14.1361796759941 & -2.83617967599411 \tabularnewline
7 & 17.5 & 17.9361796759941 & -0.436179675994112 \tabularnewline
8 & 16.6 & 16.3195130093274 & 0.280486990672557 \tabularnewline
9 & 17.5 & 19.3528463426608 & -1.85284634266078 \tabularnewline
10 & 19.5 & 18.1528463426608 & 1.34715365733922 \tabularnewline
11 & 18.8 & 18.3718703976436 & 0.428129602356406 \tabularnewline
12 & 20.2 & 21.0718703976436 & -0.871870397643594 \tabularnewline
13 & 19.2 & 18.4819793814433 & 0.718020618556698 \tabularnewline
14 & 14.4 & 16.5121462935690 & -2.11214629356898 \tabularnewline
15 & 24.5 & 23.1121462935690 & 1.38785370643103 \tabularnewline
16 & 25.7 & 23.5788129602356 & 2.12118703976436 \tabularnewline
17 & 27.1 & 23.1454796269023 & 3.95452037309769 \tabularnewline
18 & 21 & 19.6788129602356 & 1.32118703976436 \tabularnewline
19 & 18.6 & 23.4788129602356 & -4.87881296023564 \tabularnewline
20 & 20 & 21.8621462935690 & -1.86214629356897 \tabularnewline
21 & 21.8 & 24.8954796269023 & -3.09547962690231 \tabularnewline
22 & 20.4 & 23.6954796269023 & -3.29547962690231 \tabularnewline
23 & 18 & 20.0003593519882 & -2.00035935198822 \tabularnewline
24 & 21.5 & 22.7003593519882 & -1.20035935198822 \tabularnewline
25 & 19.1 & 20.1104683357879 & -1.01046833578792 \tabularnewline
26 & 19.7 & 18.1406352479136 & 1.55936475208640 \tabularnewline
27 & 26 & 24.7406352479136 & 1.2593647520864 \tabularnewline
28 & 26.3 & 25.2073019145803 & 1.09269808541973 \tabularnewline
29 & 24.6 & 24.7739685812469 & -0.173968581246931 \tabularnewline
30 & 22.4 & 21.3073019145803 & 1.09269808541973 \tabularnewline
31 & 32 & 25.1073019145803 & 6.89269808541973 \tabularnewline
32 & 24 & 23.4906352479136 & 0.509364752086404 \tabularnewline
33 & 30 & 26.5239685812469 & 3.47603141875307 \tabularnewline
34 & 24.1 & 25.3239685812469 & -1.22396858124693 \tabularnewline
35 & 26.3 & 25.5429926362298 & 0.757007363770248 \tabularnewline
36 & 29.8 & 28.2429926362298 & 1.55700736377025 \tabularnewline
37 & 21.9 & 25.6531016200295 & -3.75310162002946 \tabularnewline
38 & 22.8 & 23.6832685321551 & -0.883268532155128 \tabularnewline
39 & 29.2 & 30.2832685321551 & -1.08326853215513 \tabularnewline
40 & 27.5 & 30.7499351988218 & -3.24993519882180 \tabularnewline
41 & 27.4 & 30.3166018654885 & -2.91660186548846 \tabularnewline
42 & 31 & 26.8499351988218 & 4.1500648011782 \tabularnewline
43 & 26.1 & 30.6499351988218 & -4.5499351988218 \tabularnewline
44 & 22.2 & 29.0332685321551 & -6.83326853215513 \tabularnewline
45 & 34 & 32.0666018654885 & 1.93339813451154 \tabularnewline
46 & 26.9 & 30.8666018654885 & -3.96660186548846 \tabularnewline
47 & 31.9 & 31.0856259204713 & 0.814374079528716 \tabularnewline
48 & 34.2 & 33.7856259204713 & 0.414374079528724 \tabularnewline
49 & 31.2 & 31.195734904271 & 0.00426509572901176 \tabularnewline
50 & 28.5 & 29.2259018163967 & -0.72590181639666 \tabularnewline
51 & 37.1 & 35.8259018163967 & 1.27409818360334 \tabularnewline
52 & 36 & 36.2925684830633 & -0.292568483063328 \tabularnewline
53 & 34.8 & 35.85923514973 & -1.05923514973000 \tabularnewline
54 & 32.1 & 32.3925684830633 & -0.292568483063327 \tabularnewline
55 & 37.2 & 36.1925684830633 & 1.00743151693667 \tabularnewline
56 & 36.3 & 34.5759018163967 & 1.72409818360334 \tabularnewline
57 & 39.5 & 37.60923514973 & 1.89076485027000 \tabularnewline
58 & 37.1 & 36.40923514973 & 0.690764850270007 \tabularnewline
59 & 35.6 & 36.6282592047128 & -1.02825920471281 \tabularnewline
60 & 36.2 & 39.3282592047128 & -3.12825920471281 \tabularnewline
61 & 35.9 & 36.7383681885125 & -0.838368188512521 \tabularnewline
62 & 32.5 & 34.7685351006382 & -2.26853510063819 \tabularnewline
63 & 39.2 & 41.3685351006382 & -2.16853510063819 \tabularnewline
64 & 39.4 & 41.8352017673049 & -2.43520176730486 \tabularnewline
65 & 42.8 & 41.4018684339715 & 1.39813156602847 \tabularnewline
66 & 34.5 & 37.9352017673049 & -3.43520176730486 \tabularnewline
67 & 43.7 & 41.7352017673049 & 1.96479823269514 \tabularnewline
68 & 46.3 & 40.1185351006382 & 6.1814648993618 \tabularnewline
69 & 40.8 & 43.1518684339715 & -2.35186843397153 \tabularnewline
70 & 48.4 & 41.9518684339715 & 6.44813156602847 \tabularnewline
71 & 43.2 & 42.1708924889543 & 1.02910751104566 \tabularnewline
72 & 48.1 & 44.8708924889543 & 3.22910751104566 \tabularnewline
73 & 42.8 & 42.2810014727540 & 0.518998527245946 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=33371&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]17.3[/C][C]12.9393460972018[/C][C]4.36065390279825[/C][/ROW]
[ROW][C]2[/C][C]15.4[/C][C]10.9695130093274[/C][C]4.43048699067256[/C][/ROW]
[ROW][C]3[/C][C]16.9[/C][C]17.5695130093274[/C][C]-0.669513009327444[/C][/ROW]
[ROW][C]4[/C][C]20.8[/C][C]18.0361796759941[/C][C]2.76382032400589[/C][/ROW]
[ROW][C]5[/C][C]16.4[/C][C]17.6028463426608[/C][C]-1.20284634266078[/C][/ROW]
[ROW][C]6[/C][C]11.3[/C][C]14.1361796759941[/C][C]-2.83617967599411[/C][/ROW]
[ROW][C]7[/C][C]17.5[/C][C]17.9361796759941[/C][C]-0.436179675994112[/C][/ROW]
[ROW][C]8[/C][C]16.6[/C][C]16.3195130093274[/C][C]0.280486990672557[/C][/ROW]
[ROW][C]9[/C][C]17.5[/C][C]19.3528463426608[/C][C]-1.85284634266078[/C][/ROW]
[ROW][C]10[/C][C]19.5[/C][C]18.1528463426608[/C][C]1.34715365733922[/C][/ROW]
[ROW][C]11[/C][C]18.8[/C][C]18.3718703976436[/C][C]0.428129602356406[/C][/ROW]
[ROW][C]12[/C][C]20.2[/C][C]21.0718703976436[/C][C]-0.871870397643594[/C][/ROW]
[ROW][C]13[/C][C]19.2[/C][C]18.4819793814433[/C][C]0.718020618556698[/C][/ROW]
[ROW][C]14[/C][C]14.4[/C][C]16.5121462935690[/C][C]-2.11214629356898[/C][/ROW]
[ROW][C]15[/C][C]24.5[/C][C]23.1121462935690[/C][C]1.38785370643103[/C][/ROW]
[ROW][C]16[/C][C]25.7[/C][C]23.5788129602356[/C][C]2.12118703976436[/C][/ROW]
[ROW][C]17[/C][C]27.1[/C][C]23.1454796269023[/C][C]3.95452037309769[/C][/ROW]
[ROW][C]18[/C][C]21[/C][C]19.6788129602356[/C][C]1.32118703976436[/C][/ROW]
[ROW][C]19[/C][C]18.6[/C][C]23.4788129602356[/C][C]-4.87881296023564[/C][/ROW]
[ROW][C]20[/C][C]20[/C][C]21.8621462935690[/C][C]-1.86214629356897[/C][/ROW]
[ROW][C]21[/C][C]21.8[/C][C]24.8954796269023[/C][C]-3.09547962690231[/C][/ROW]
[ROW][C]22[/C][C]20.4[/C][C]23.6954796269023[/C][C]-3.29547962690231[/C][/ROW]
[ROW][C]23[/C][C]18[/C][C]20.0003593519882[/C][C]-2.00035935198822[/C][/ROW]
[ROW][C]24[/C][C]21.5[/C][C]22.7003593519882[/C][C]-1.20035935198822[/C][/ROW]
[ROW][C]25[/C][C]19.1[/C][C]20.1104683357879[/C][C]-1.01046833578792[/C][/ROW]
[ROW][C]26[/C][C]19.7[/C][C]18.1406352479136[/C][C]1.55936475208640[/C][/ROW]
[ROW][C]27[/C][C]26[/C][C]24.7406352479136[/C][C]1.2593647520864[/C][/ROW]
[ROW][C]28[/C][C]26.3[/C][C]25.2073019145803[/C][C]1.09269808541973[/C][/ROW]
[ROW][C]29[/C][C]24.6[/C][C]24.7739685812469[/C][C]-0.173968581246931[/C][/ROW]
[ROW][C]30[/C][C]22.4[/C][C]21.3073019145803[/C][C]1.09269808541973[/C][/ROW]
[ROW][C]31[/C][C]32[/C][C]25.1073019145803[/C][C]6.89269808541973[/C][/ROW]
[ROW][C]32[/C][C]24[/C][C]23.4906352479136[/C][C]0.509364752086404[/C][/ROW]
[ROW][C]33[/C][C]30[/C][C]26.5239685812469[/C][C]3.47603141875307[/C][/ROW]
[ROW][C]34[/C][C]24.1[/C][C]25.3239685812469[/C][C]-1.22396858124693[/C][/ROW]
[ROW][C]35[/C][C]26.3[/C][C]25.5429926362298[/C][C]0.757007363770248[/C][/ROW]
[ROW][C]36[/C][C]29.8[/C][C]28.2429926362298[/C][C]1.55700736377025[/C][/ROW]
[ROW][C]37[/C][C]21.9[/C][C]25.6531016200295[/C][C]-3.75310162002946[/C][/ROW]
[ROW][C]38[/C][C]22.8[/C][C]23.6832685321551[/C][C]-0.883268532155128[/C][/ROW]
[ROW][C]39[/C][C]29.2[/C][C]30.2832685321551[/C][C]-1.08326853215513[/C][/ROW]
[ROW][C]40[/C][C]27.5[/C][C]30.7499351988218[/C][C]-3.24993519882180[/C][/ROW]
[ROW][C]41[/C][C]27.4[/C][C]30.3166018654885[/C][C]-2.91660186548846[/C][/ROW]
[ROW][C]42[/C][C]31[/C][C]26.8499351988218[/C][C]4.1500648011782[/C][/ROW]
[ROW][C]43[/C][C]26.1[/C][C]30.6499351988218[/C][C]-4.5499351988218[/C][/ROW]
[ROW][C]44[/C][C]22.2[/C][C]29.0332685321551[/C][C]-6.83326853215513[/C][/ROW]
[ROW][C]45[/C][C]34[/C][C]32.0666018654885[/C][C]1.93339813451154[/C][/ROW]
[ROW][C]46[/C][C]26.9[/C][C]30.8666018654885[/C][C]-3.96660186548846[/C][/ROW]
[ROW][C]47[/C][C]31.9[/C][C]31.0856259204713[/C][C]0.814374079528716[/C][/ROW]
[ROW][C]48[/C][C]34.2[/C][C]33.7856259204713[/C][C]0.414374079528724[/C][/ROW]
[ROW][C]49[/C][C]31.2[/C][C]31.195734904271[/C][C]0.00426509572901176[/C][/ROW]
[ROW][C]50[/C][C]28.5[/C][C]29.2259018163967[/C][C]-0.72590181639666[/C][/ROW]
[ROW][C]51[/C][C]37.1[/C][C]35.8259018163967[/C][C]1.27409818360334[/C][/ROW]
[ROW][C]52[/C][C]36[/C][C]36.2925684830633[/C][C]-0.292568483063328[/C][/ROW]
[ROW][C]53[/C][C]34.8[/C][C]35.85923514973[/C][C]-1.05923514973000[/C][/ROW]
[ROW][C]54[/C][C]32.1[/C][C]32.3925684830633[/C][C]-0.292568483063327[/C][/ROW]
[ROW][C]55[/C][C]37.2[/C][C]36.1925684830633[/C][C]1.00743151693667[/C][/ROW]
[ROW][C]56[/C][C]36.3[/C][C]34.5759018163967[/C][C]1.72409818360334[/C][/ROW]
[ROW][C]57[/C][C]39.5[/C][C]37.60923514973[/C][C]1.89076485027000[/C][/ROW]
[ROW][C]58[/C][C]37.1[/C][C]36.40923514973[/C][C]0.690764850270007[/C][/ROW]
[ROW][C]59[/C][C]35.6[/C][C]36.6282592047128[/C][C]-1.02825920471281[/C][/ROW]
[ROW][C]60[/C][C]36.2[/C][C]39.3282592047128[/C][C]-3.12825920471281[/C][/ROW]
[ROW][C]61[/C][C]35.9[/C][C]36.7383681885125[/C][C]-0.838368188512521[/C][/ROW]
[ROW][C]62[/C][C]32.5[/C][C]34.7685351006382[/C][C]-2.26853510063819[/C][/ROW]
[ROW][C]63[/C][C]39.2[/C][C]41.3685351006382[/C][C]-2.16853510063819[/C][/ROW]
[ROW][C]64[/C][C]39.4[/C][C]41.8352017673049[/C][C]-2.43520176730486[/C][/ROW]
[ROW][C]65[/C][C]42.8[/C][C]41.4018684339715[/C][C]1.39813156602847[/C][/ROW]
[ROW][C]66[/C][C]34.5[/C][C]37.9352017673049[/C][C]-3.43520176730486[/C][/ROW]
[ROW][C]67[/C][C]43.7[/C][C]41.7352017673049[/C][C]1.96479823269514[/C][/ROW]
[ROW][C]68[/C][C]46.3[/C][C]40.1185351006382[/C][C]6.1814648993618[/C][/ROW]
[ROW][C]69[/C][C]40.8[/C][C]43.1518684339715[/C][C]-2.35186843397153[/C][/ROW]
[ROW][C]70[/C][C]48.4[/C][C]41.9518684339715[/C][C]6.44813156602847[/C][/ROW]
[ROW][C]71[/C][C]43.2[/C][C]42.1708924889543[/C][C]1.02910751104566[/C][/ROW]
[ROW][C]72[/C][C]48.1[/C][C]44.8708924889543[/C][C]3.22910751104566[/C][/ROW]
[ROW][C]73[/C][C]42.8[/C][C]42.2810014727540[/C][C]0.518998527245946[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=33371&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=33371&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 IndexActualsInterpolationForecastResidualsPrediction Error
117.312.93934609720184.36065390279825
215.410.96951300932744.43048699067256
316.917.5695130093274-0.669513009327444
420.818.03617967599412.76382032400589
516.417.6028463426608-1.20284634266078
611.314.1361796759941-2.83617967599411
717.517.9361796759941-0.436179675994112
816.616.31951300932740.280486990672557
917.519.3528463426608-1.85284634266078
1019.518.15284634266081.34715365733922
1118.818.37187039764360.428129602356406
1220.221.0718703976436-0.871870397643594
1319.218.48197938144330.718020618556698
1414.416.5121462935690-2.11214629356898
1524.523.11214629356901.38785370643103
1625.723.57881296023562.12118703976436
1727.123.14547962690233.95452037309769
182119.67881296023561.32118703976436
1918.623.4788129602356-4.87881296023564
202021.8621462935690-1.86214629356897
2121.824.8954796269023-3.09547962690231
2220.423.6954796269023-3.29547962690231
231820.0003593519882-2.00035935198822
2421.522.7003593519882-1.20035935198822
2519.120.1104683357879-1.01046833578792
2619.718.14063524791361.55936475208640
272624.74063524791361.2593647520864
2826.325.20730191458031.09269808541973
2924.624.7739685812469-0.173968581246931
3022.421.30730191458031.09269808541973
313225.10730191458036.89269808541973
322423.49063524791360.509364752086404
333026.52396858124693.47603141875307
3424.125.3239685812469-1.22396858124693
3526.325.54299263622980.757007363770248
3629.828.24299263622981.55700736377025
3721.925.6531016200295-3.75310162002946
3822.823.6832685321551-0.883268532155128
3929.230.2832685321551-1.08326853215513
4027.530.7499351988218-3.24993519882180
4127.430.3166018654885-2.91660186548846
423126.84993519882184.1500648011782
4326.130.6499351988218-4.5499351988218
4422.229.0332685321551-6.83326853215513
453432.06660186548851.93339813451154
4626.930.8666018654885-3.96660186548846
4731.931.08562592047130.814374079528716
4834.233.78562592047130.414374079528724
4931.231.1957349042710.00426509572901176
5028.529.2259018163967-0.72590181639666
5137.135.82590181639671.27409818360334
523636.2925684830633-0.292568483063328
5334.835.85923514973-1.05923514973000
5432.132.3925684830633-0.292568483063327
5537.236.19256848306331.00743151693667
5636.334.57590181639671.72409818360334
5739.537.609235149731.89076485027000
5837.136.409235149730.690764850270007
5935.636.6282592047128-1.02825920471281
6036.239.3282592047128-3.12825920471281
6135.936.7383681885125-0.838368188512521
6232.534.7685351006382-2.26853510063819
6339.241.3685351006382-2.16853510063819
6439.441.8352017673049-2.43520176730486
6542.841.40186843397151.39813156602847
6634.537.9352017673049-3.43520176730486
6743.741.73520176730491.96479823269514
6846.340.11853510063826.1814648993618
6940.843.1518684339715-2.35186843397153
7048.441.95186843397156.44813156602847
7143.242.17089248895431.02910751104566
7248.144.87089248895433.22910751104566
7342.842.28100147275400.518998527245946



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