Free Statistics

of Irreproducible Research!

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationWed, 19 Nov 2008 11:53:58 -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/Nov/19/t1227120898ddz90vdb060w41j.htm/, Retrieved Thu, 31 Oct 2024 23:39:16 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=25050, Retrieved Thu, 31 Oct 2024 23:39:16 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact266
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] [2008-11-13 18:17:22] [1e1d8320a8a1170c475bf6e4ce119de6]
-   PD      [Multiple Regression] [Q3 geen seasonal ...] [2008-11-19 18:53:58] [fdd69703d301fae09456f660b2f52997] [Current]
-   P         [Multiple Regression] [Q3 seasonal dummi...] [2008-11-19 19:19:18] [1e1d8320a8a1170c475bf6e4ce119de6]
Feedback Forum

Post a new message
Dataseries X:
1332.7	0
1343.8	0
1421.6	0
1329.8	0
1306.8	0
1412.8	0
1358.1	0
1163.9	0
1467.9	0
1433.7	0
1362.2	0
1299	0
1291.5	0
1452.7	0
1555.4	0
1402.5	0
1242.9	0
1514.6	0
1308.6	0
1239.3	0
1519.9	0
1659.4	0
1597.6	0
1340.6	0
1427.2	0
1438.1	0
1616.2	0
1392.8	0
1318.7	0
1420.9	0
1221	0
1310	0
1466.7	0
1299.3	0
1640	0
1506.3	0
1530.2	0
1661.9	0
1880.3	1
1230.8	0
1406.5	0
1523.5	0
1323.2	0
1319.2	0
1500.7	0
1483	0
1497	0
1219.8	0
1472.9	0
1423.9	0
1629.6	0
1353.4	0
1366.8	0
1527.1	0
1487.6	0
1478.6	0
1536.7	0
1682.1	0
1576.5	0
1280.5	0




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25050&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]2 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=25050&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25050&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Multiple Linear Regression - Estimated Regression Equation
y[t] = + 1422.47457627119 + 457.825423728813x[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  +  1422.47457627119 +  457.825423728813x[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25050&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  +  1422.47457627119 +  457.825423728813x[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25050&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25050&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] = + 1422.47457627119 + 457.825423728813x[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)1422.4745762711916.37867786.849200
x457.825423728813126.8686873.60870.0006420.000321

\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) & 1422.47457627119 & 16.378677 & 86.8492 & 0 & 0 \tabularnewline
x & 457.825423728813 & 126.868687 & 3.6087 & 0.000642 & 0.000321 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25050&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]1422.47457627119[/C][C]16.378677[/C][C]86.8492[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]x[/C][C]457.825423728813[/C][C]126.868687[/C][C]3.6087[/C][C]0.000642[/C][C]0.000321[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25050&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25050&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)1422.4745762711916.37867786.849200
x457.825423728813126.8686873.60870.0006420.000321







Multiple Linear Regression - Regression Statistics
Multiple R0.428201120612388
R-squared0.183356199693705
Adjusted R-squared0.169276134171182
F-TEST (value)13.0223967637374
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.000642403807251335
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation125.807005442272
Sum Squared Residuals917989.351864407

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.428201120612388 \tabularnewline
R-squared & 0.183356199693705 \tabularnewline
Adjusted R-squared & 0.169276134171182 \tabularnewline
F-TEST (value) & 13.0223967637374 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 58 \tabularnewline
p-value & 0.000642403807251335 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 125.807005442272 \tabularnewline
Sum Squared Residuals & 917989.351864407 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25050&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.428201120612388[/C][/ROW]
[ROW][C]R-squared[/C][C]0.183356199693705[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.169276134171182[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13.0223967637374[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]58[/C][/ROW]
[ROW][C]p-value[/C][C]0.000642403807251335[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]125.807005442272[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]917989.351864407[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25050&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25050&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 R0.428201120612388
R-squared0.183356199693705
Adjusted R-squared0.169276134171182
F-TEST (value)13.0223967637374
F-TEST (DF numerator)1
F-TEST (DF denominator)58
p-value0.000642403807251335
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation125.807005442272
Sum Squared Residuals917989.351864407







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
11332.71422.47457627119-89.7745762711878
21343.81422.47457627119-78.6745762711863
31421.61422.47457627119-0.874576271186511
41329.81422.47457627119-92.6745762711865
51306.81422.47457627119-115.674576271186
61412.81422.47457627119-9.67457627118647
71358.11422.47457627119-64.3745762711865
81163.91422.47457627119-258.574576271186
91467.91422.4745762711945.4254237288137
101433.71422.4745762711911.2254237288136
111362.21422.47457627119-60.2745762711864
1212991422.47457627119-123.474576271186
131291.51422.47457627119-130.974576271186
141452.71422.4745762711930.2254237288136
151555.41422.47457627119132.925423728814
161402.51422.47457627119-19.9745762711864
171242.91422.47457627119-179.574576271186
181514.61422.4745762711992.1254237288135
191308.61422.47457627119-113.874576271187
201239.31422.47457627119-183.174576271186
211519.91422.4745762711997.4254237288137
221659.41422.47457627119236.925423728814
231597.61422.47457627119175.125423728814
241340.61422.47457627119-81.8745762711865
251427.21422.474576271194.72542372881363
261438.11422.4745762711915.6254237288135
271616.21422.47457627119193.725423728814
281392.81422.47457627119-29.6745762711865
291318.71422.47457627119-103.774576271186
301420.91422.47457627119-1.57457627118633
3112211422.47457627119-201.474576271186
3213101422.47457627119-112.474576271186
331466.71422.4745762711944.2254237288136
341299.31422.47457627119-123.174576271186
3516401422.47457627119217.525423728814
361506.31422.4745762711983.8254237288135
371530.21422.47457627119107.725423728814
381661.91422.47457627119239.425423728814
391880.31880.3-1.77635683940025e-15
401230.81422.47457627119-191.674576271186
411406.51422.47457627119-15.9745762711864
421523.51422.47457627119101.025423728814
431323.21422.47457627119-99.2745762711864
441319.21422.47457627119-103.274576271186
451500.71422.4745762711978.2254237288136
4614831422.4745762711960.5254237288136
4714971422.4745762711974.5254237288136
481219.81422.47457627119-202.674576271186
491472.91422.4745762711950.4254237288137
501423.91422.474576271191.42542372881367
511629.61422.47457627119207.125423728814
521353.41422.47457627119-69.0745762711863
531366.81422.47457627119-55.6745762711865
541527.11422.47457627119104.625423728813
551487.61422.4745762711965.1254237288135
561478.61422.4745762711956.1254237288135
571536.71422.47457627119114.225423728814
581682.11422.47457627119259.625423728813
591576.51422.47457627119154.025423728814
601280.51422.47457627119-141.974576271186

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1332.7 & 1422.47457627119 & -89.7745762711878 \tabularnewline
2 & 1343.8 & 1422.47457627119 & -78.6745762711863 \tabularnewline
3 & 1421.6 & 1422.47457627119 & -0.874576271186511 \tabularnewline
4 & 1329.8 & 1422.47457627119 & -92.6745762711865 \tabularnewline
5 & 1306.8 & 1422.47457627119 & -115.674576271186 \tabularnewline
6 & 1412.8 & 1422.47457627119 & -9.67457627118647 \tabularnewline
7 & 1358.1 & 1422.47457627119 & -64.3745762711865 \tabularnewline
8 & 1163.9 & 1422.47457627119 & -258.574576271186 \tabularnewline
9 & 1467.9 & 1422.47457627119 & 45.4254237288137 \tabularnewline
10 & 1433.7 & 1422.47457627119 & 11.2254237288136 \tabularnewline
11 & 1362.2 & 1422.47457627119 & -60.2745762711864 \tabularnewline
12 & 1299 & 1422.47457627119 & -123.474576271186 \tabularnewline
13 & 1291.5 & 1422.47457627119 & -130.974576271186 \tabularnewline
14 & 1452.7 & 1422.47457627119 & 30.2254237288136 \tabularnewline
15 & 1555.4 & 1422.47457627119 & 132.925423728814 \tabularnewline
16 & 1402.5 & 1422.47457627119 & -19.9745762711864 \tabularnewline
17 & 1242.9 & 1422.47457627119 & -179.574576271186 \tabularnewline
18 & 1514.6 & 1422.47457627119 & 92.1254237288135 \tabularnewline
19 & 1308.6 & 1422.47457627119 & -113.874576271187 \tabularnewline
20 & 1239.3 & 1422.47457627119 & -183.174576271186 \tabularnewline
21 & 1519.9 & 1422.47457627119 & 97.4254237288137 \tabularnewline
22 & 1659.4 & 1422.47457627119 & 236.925423728814 \tabularnewline
23 & 1597.6 & 1422.47457627119 & 175.125423728814 \tabularnewline
24 & 1340.6 & 1422.47457627119 & -81.8745762711865 \tabularnewline
25 & 1427.2 & 1422.47457627119 & 4.72542372881363 \tabularnewline
26 & 1438.1 & 1422.47457627119 & 15.6254237288135 \tabularnewline
27 & 1616.2 & 1422.47457627119 & 193.725423728814 \tabularnewline
28 & 1392.8 & 1422.47457627119 & -29.6745762711865 \tabularnewline
29 & 1318.7 & 1422.47457627119 & -103.774576271186 \tabularnewline
30 & 1420.9 & 1422.47457627119 & -1.57457627118633 \tabularnewline
31 & 1221 & 1422.47457627119 & -201.474576271186 \tabularnewline
32 & 1310 & 1422.47457627119 & -112.474576271186 \tabularnewline
33 & 1466.7 & 1422.47457627119 & 44.2254237288136 \tabularnewline
34 & 1299.3 & 1422.47457627119 & -123.174576271186 \tabularnewline
35 & 1640 & 1422.47457627119 & 217.525423728814 \tabularnewline
36 & 1506.3 & 1422.47457627119 & 83.8254237288135 \tabularnewline
37 & 1530.2 & 1422.47457627119 & 107.725423728814 \tabularnewline
38 & 1661.9 & 1422.47457627119 & 239.425423728814 \tabularnewline
39 & 1880.3 & 1880.3 & -1.77635683940025e-15 \tabularnewline
40 & 1230.8 & 1422.47457627119 & -191.674576271186 \tabularnewline
41 & 1406.5 & 1422.47457627119 & -15.9745762711864 \tabularnewline
42 & 1523.5 & 1422.47457627119 & 101.025423728814 \tabularnewline
43 & 1323.2 & 1422.47457627119 & -99.2745762711864 \tabularnewline
44 & 1319.2 & 1422.47457627119 & -103.274576271186 \tabularnewline
45 & 1500.7 & 1422.47457627119 & 78.2254237288136 \tabularnewline
46 & 1483 & 1422.47457627119 & 60.5254237288136 \tabularnewline
47 & 1497 & 1422.47457627119 & 74.5254237288136 \tabularnewline
48 & 1219.8 & 1422.47457627119 & -202.674576271186 \tabularnewline
49 & 1472.9 & 1422.47457627119 & 50.4254237288137 \tabularnewline
50 & 1423.9 & 1422.47457627119 & 1.42542372881367 \tabularnewline
51 & 1629.6 & 1422.47457627119 & 207.125423728814 \tabularnewline
52 & 1353.4 & 1422.47457627119 & -69.0745762711863 \tabularnewline
53 & 1366.8 & 1422.47457627119 & -55.6745762711865 \tabularnewline
54 & 1527.1 & 1422.47457627119 & 104.625423728813 \tabularnewline
55 & 1487.6 & 1422.47457627119 & 65.1254237288135 \tabularnewline
56 & 1478.6 & 1422.47457627119 & 56.1254237288135 \tabularnewline
57 & 1536.7 & 1422.47457627119 & 114.225423728814 \tabularnewline
58 & 1682.1 & 1422.47457627119 & 259.625423728813 \tabularnewline
59 & 1576.5 & 1422.47457627119 & 154.025423728814 \tabularnewline
60 & 1280.5 & 1422.47457627119 & -141.974576271186 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=25050&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]1332.7[/C][C]1422.47457627119[/C][C]-89.7745762711878[/C][/ROW]
[ROW][C]2[/C][C]1343.8[/C][C]1422.47457627119[/C][C]-78.6745762711863[/C][/ROW]
[ROW][C]3[/C][C]1421.6[/C][C]1422.47457627119[/C][C]-0.874576271186511[/C][/ROW]
[ROW][C]4[/C][C]1329.8[/C][C]1422.47457627119[/C][C]-92.6745762711865[/C][/ROW]
[ROW][C]5[/C][C]1306.8[/C][C]1422.47457627119[/C][C]-115.674576271186[/C][/ROW]
[ROW][C]6[/C][C]1412.8[/C][C]1422.47457627119[/C][C]-9.67457627118647[/C][/ROW]
[ROW][C]7[/C][C]1358.1[/C][C]1422.47457627119[/C][C]-64.3745762711865[/C][/ROW]
[ROW][C]8[/C][C]1163.9[/C][C]1422.47457627119[/C][C]-258.574576271186[/C][/ROW]
[ROW][C]9[/C][C]1467.9[/C][C]1422.47457627119[/C][C]45.4254237288137[/C][/ROW]
[ROW][C]10[/C][C]1433.7[/C][C]1422.47457627119[/C][C]11.2254237288136[/C][/ROW]
[ROW][C]11[/C][C]1362.2[/C][C]1422.47457627119[/C][C]-60.2745762711864[/C][/ROW]
[ROW][C]12[/C][C]1299[/C][C]1422.47457627119[/C][C]-123.474576271186[/C][/ROW]
[ROW][C]13[/C][C]1291.5[/C][C]1422.47457627119[/C][C]-130.974576271186[/C][/ROW]
[ROW][C]14[/C][C]1452.7[/C][C]1422.47457627119[/C][C]30.2254237288136[/C][/ROW]
[ROW][C]15[/C][C]1555.4[/C][C]1422.47457627119[/C][C]132.925423728814[/C][/ROW]
[ROW][C]16[/C][C]1402.5[/C][C]1422.47457627119[/C][C]-19.9745762711864[/C][/ROW]
[ROW][C]17[/C][C]1242.9[/C][C]1422.47457627119[/C][C]-179.574576271186[/C][/ROW]
[ROW][C]18[/C][C]1514.6[/C][C]1422.47457627119[/C][C]92.1254237288135[/C][/ROW]
[ROW][C]19[/C][C]1308.6[/C][C]1422.47457627119[/C][C]-113.874576271187[/C][/ROW]
[ROW][C]20[/C][C]1239.3[/C][C]1422.47457627119[/C][C]-183.174576271186[/C][/ROW]
[ROW][C]21[/C][C]1519.9[/C][C]1422.47457627119[/C][C]97.4254237288137[/C][/ROW]
[ROW][C]22[/C][C]1659.4[/C][C]1422.47457627119[/C][C]236.925423728814[/C][/ROW]
[ROW][C]23[/C][C]1597.6[/C][C]1422.47457627119[/C][C]175.125423728814[/C][/ROW]
[ROW][C]24[/C][C]1340.6[/C][C]1422.47457627119[/C][C]-81.8745762711865[/C][/ROW]
[ROW][C]25[/C][C]1427.2[/C][C]1422.47457627119[/C][C]4.72542372881363[/C][/ROW]
[ROW][C]26[/C][C]1438.1[/C][C]1422.47457627119[/C][C]15.6254237288135[/C][/ROW]
[ROW][C]27[/C][C]1616.2[/C][C]1422.47457627119[/C][C]193.725423728814[/C][/ROW]
[ROW][C]28[/C][C]1392.8[/C][C]1422.47457627119[/C][C]-29.6745762711865[/C][/ROW]
[ROW][C]29[/C][C]1318.7[/C][C]1422.47457627119[/C][C]-103.774576271186[/C][/ROW]
[ROW][C]30[/C][C]1420.9[/C][C]1422.47457627119[/C][C]-1.57457627118633[/C][/ROW]
[ROW][C]31[/C][C]1221[/C][C]1422.47457627119[/C][C]-201.474576271186[/C][/ROW]
[ROW][C]32[/C][C]1310[/C][C]1422.47457627119[/C][C]-112.474576271186[/C][/ROW]
[ROW][C]33[/C][C]1466.7[/C][C]1422.47457627119[/C][C]44.2254237288136[/C][/ROW]
[ROW][C]34[/C][C]1299.3[/C][C]1422.47457627119[/C][C]-123.174576271186[/C][/ROW]
[ROW][C]35[/C][C]1640[/C][C]1422.47457627119[/C][C]217.525423728814[/C][/ROW]
[ROW][C]36[/C][C]1506.3[/C][C]1422.47457627119[/C][C]83.8254237288135[/C][/ROW]
[ROW][C]37[/C][C]1530.2[/C][C]1422.47457627119[/C][C]107.725423728814[/C][/ROW]
[ROW][C]38[/C][C]1661.9[/C][C]1422.47457627119[/C][C]239.425423728814[/C][/ROW]
[ROW][C]39[/C][C]1880.3[/C][C]1880.3[/C][C]-1.77635683940025e-15[/C][/ROW]
[ROW][C]40[/C][C]1230.8[/C][C]1422.47457627119[/C][C]-191.674576271186[/C][/ROW]
[ROW][C]41[/C][C]1406.5[/C][C]1422.47457627119[/C][C]-15.9745762711864[/C][/ROW]
[ROW][C]42[/C][C]1523.5[/C][C]1422.47457627119[/C][C]101.025423728814[/C][/ROW]
[ROW][C]43[/C][C]1323.2[/C][C]1422.47457627119[/C][C]-99.2745762711864[/C][/ROW]
[ROW][C]44[/C][C]1319.2[/C][C]1422.47457627119[/C][C]-103.274576271186[/C][/ROW]
[ROW][C]45[/C][C]1500.7[/C][C]1422.47457627119[/C][C]78.2254237288136[/C][/ROW]
[ROW][C]46[/C][C]1483[/C][C]1422.47457627119[/C][C]60.5254237288136[/C][/ROW]
[ROW][C]47[/C][C]1497[/C][C]1422.47457627119[/C][C]74.5254237288136[/C][/ROW]
[ROW][C]48[/C][C]1219.8[/C][C]1422.47457627119[/C][C]-202.674576271186[/C][/ROW]
[ROW][C]49[/C][C]1472.9[/C][C]1422.47457627119[/C][C]50.4254237288137[/C][/ROW]
[ROW][C]50[/C][C]1423.9[/C][C]1422.47457627119[/C][C]1.42542372881367[/C][/ROW]
[ROW][C]51[/C][C]1629.6[/C][C]1422.47457627119[/C][C]207.125423728814[/C][/ROW]
[ROW][C]52[/C][C]1353.4[/C][C]1422.47457627119[/C][C]-69.0745762711863[/C][/ROW]
[ROW][C]53[/C][C]1366.8[/C][C]1422.47457627119[/C][C]-55.6745762711865[/C][/ROW]
[ROW][C]54[/C][C]1527.1[/C][C]1422.47457627119[/C][C]104.625423728813[/C][/ROW]
[ROW][C]55[/C][C]1487.6[/C][C]1422.47457627119[/C][C]65.1254237288135[/C][/ROW]
[ROW][C]56[/C][C]1478.6[/C][C]1422.47457627119[/C][C]56.1254237288135[/C][/ROW]
[ROW][C]57[/C][C]1536.7[/C][C]1422.47457627119[/C][C]114.225423728814[/C][/ROW]
[ROW][C]58[/C][C]1682.1[/C][C]1422.47457627119[/C][C]259.625423728813[/C][/ROW]
[ROW][C]59[/C][C]1576.5[/C][C]1422.47457627119[/C][C]154.025423728814[/C][/ROW]
[ROW][C]60[/C][C]1280.5[/C][C]1422.47457627119[/C][C]-141.974576271186[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=25050&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=25050&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
11332.71422.47457627119-89.7745762711878
21343.81422.47457627119-78.6745762711863
31421.61422.47457627119-0.874576271186511
41329.81422.47457627119-92.6745762711865
51306.81422.47457627119-115.674576271186
61412.81422.47457627119-9.67457627118647
71358.11422.47457627119-64.3745762711865
81163.91422.47457627119-258.574576271186
91467.91422.4745762711945.4254237288137
101433.71422.4745762711911.2254237288136
111362.21422.47457627119-60.2745762711864
1212991422.47457627119-123.474576271186
131291.51422.47457627119-130.974576271186
141452.71422.4745762711930.2254237288136
151555.41422.47457627119132.925423728814
161402.51422.47457627119-19.9745762711864
171242.91422.47457627119-179.574576271186
181514.61422.4745762711992.1254237288135
191308.61422.47457627119-113.874576271187
201239.31422.47457627119-183.174576271186
211519.91422.4745762711997.4254237288137
221659.41422.47457627119236.925423728814
231597.61422.47457627119175.125423728814
241340.61422.47457627119-81.8745762711865
251427.21422.474576271194.72542372881363
261438.11422.4745762711915.6254237288135
271616.21422.47457627119193.725423728814
281392.81422.47457627119-29.6745762711865
291318.71422.47457627119-103.774576271186
301420.91422.47457627119-1.57457627118633
3112211422.47457627119-201.474576271186
3213101422.47457627119-112.474576271186
331466.71422.4745762711944.2254237288136
341299.31422.47457627119-123.174576271186
3516401422.47457627119217.525423728814
361506.31422.4745762711983.8254237288135
371530.21422.47457627119107.725423728814
381661.91422.47457627119239.425423728814
391880.31880.3-1.77635683940025e-15
401230.81422.47457627119-191.674576271186
411406.51422.47457627119-15.9745762711864
421523.51422.47457627119101.025423728814
431323.21422.47457627119-99.2745762711864
441319.21422.47457627119-103.274576271186
451500.71422.4745762711978.2254237288136
4614831422.4745762711960.5254237288136
4714971422.4745762711974.5254237288136
481219.81422.47457627119-202.674576271186
491472.91422.4745762711950.4254237288137
501423.91422.474576271191.42542372881367
511629.61422.47457627119207.125423728814
521353.41422.47457627119-69.0745762711863
531366.81422.47457627119-55.6745762711865
541527.11422.47457627119104.625423728813
551487.61422.4745762711965.1254237288135
561478.61422.4745762711956.1254237288135
571536.71422.47457627119114.225423728814
581682.11422.47457627119259.625423728813
591576.51422.47457627119154.025423728814
601280.51422.47457627119-141.974576271186



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')