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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 21 Dec 2008 08:09:11 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/21/t1229874525uvwjpi3ibzqq9hk.htm/, Retrieved Mon, 29 Apr 2024 09:17:30 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=35639, Retrieved Mon, 29 Apr 2024 09:17:30 +0000

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IsPrivate?

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User-defined keywords

Estimated Impact

259

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] [The Seatbelt Law ...] [2008-11-15 12:00:57] [93834488277b53a4510bfd06084ae13b]
-   PD    [Multiple Regression] [] [2008-11-15 18:50:27] [93834488277b53a4510bfd06084ae13b]
-    D      [Multiple Regression] [Paper - Multiple ...] [2008-12-21 14:45:31] [85841a4a203c2f9589565c024425a91b]
-   PD          [Multiple Regression] [Paper - Multiple ...] [2008-12-21 15:09:11] [07b7cf1321bc38017c2c7efcf91ca696] [Current]
- RMPD            [ARIMA Forecasting] [Paper - Arima for...] [2008-12-21 19:50:13] [85841a4a203c2f9589565c024425a91b] 
-   PD              [ARIMA Forecasting] [arima forecast gas] [2008-12-22 17:09:25] [44a98561a4b3e6ab8cd5a857b48b0914] 
- RMPD            [ARIMA Forecasting] [Paper - Arima for...] [2008-12-21 20:01:58] [85841a4a203c2f9589565c024425a91b] 
- R PD            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:36:38] [85841a4a203c2f9589565c024425a91b] 
- R  D            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:38:40] [85841a4a203c2f9589565c024425a91b] 
- R PD            [Multiple Regression] [Paper - Multiple ...] [2008-12-22 11:39:51] [85841a4a203c2f9589565c024425a91b] 
-   PD              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:06:36] [85841a4a203c2f9589565c024425a91b] 
-    D              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:08:28] [85841a4a203c2f9589565c024425a91b] 
-   PD              [Multiple Regression] [Paper - Multiple ...] [2008-12-22 12:11:46] [85841a4a203c2f9589565c024425a91b] 

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Dataseries X:

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Histogram

Boxplots

20.7246301	0
21.44580352	0
22.09413114	0
21.53321848	0
23.3470789	0
23.5656163	0
26.42117166	0
25.21193138	0
26.43574082	0
29.33500366	0
29.40056488	0
33.05013946	0
28.38072368	0
26.0059506	0
29.31314992	0
30.36212944	0
35.74543406	0
36.15337054	0
34.20838768	0
37.90895432	0
38.70297354	0
42.11944156	0
42.16314904	0
39.79566054	0
37.36261082	0
38.3533137	0
42.60022384	0
41.24529196	0
42.15586446	0
46.94183352	0
47.42990038	0
47.0583868	0
50.18347162	0
50.12519498	0
43.22669772	0
40.04333626	0
40.37114236	0
42.2141411	0
36.99838182	0
39.74466848	0
42.68035422	0
46.2935059	0
46.97097184	0
48.72655562	0
52.36884562	1
50.05234918	1
54.03701444	1
57.78128856	1
64.71620872	1
63.4122689	1
64.3592643	1
66.02743312	1
72.13919574	1
76.60464328	1
86.97060062	1
93.48301514	1
95.58825876	1
81.88596378	1
70.5511573	1
50.38015528	1
36.24807008	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35639&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35639&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35639&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 Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Multiple Linear Regression - Estimated Regression Equation
Olie[t] = + 17.6500718165444 + 16.2406478298225Dumivariabele[t] + 0.333236249115063M1[t] + 2.89758274094412M2[t] + 3.12699001784616M3[t] + 3.27918074674819M4[t] + 6.15289056365023M5[t] + 8.29377163255228M6[t] + 10.2248567974543M7[t] + 11.7450916503563M8[t] + 10.1177241412939M9[t] + 7.60812933819593M10[t] + 4.22292801909796M11[t] + 0.557327363097962t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Olie[t] =  +  17.6500718165444 +  16.2406478298225Dumivariabele[t] +  0.333236249115063M1[t] +  2.89758274094412M2[t] +  3.12699001784616M3[t] +  3.27918074674819M4[t] +  6.15289056365023M5[t] +  8.29377163255228M6[t] +  10.2248567974543M7[t] +  11.7450916503563M8[t] +  10.1177241412939M9[t] +  7.60812933819593M10[t] +  4.22292801909796M11[t] +  0.557327363097962t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35639&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Olie[t] =  +  17.6500718165444 +  16.2406478298225Dumivariabele[t] +  0.333236249115063M1[t] +  2.89758274094412M2[t] +  3.12699001784616M3[t] +  3.27918074674819M4[t] +  6.15289056365023M5[t] +  8.29377163255228M6[t] +  10.2248567974543M7[t] +  11.7450916503563M8[t] +  10.1177241412939M9[t] +  7.60812933819593M10[t] +  4.22292801909796M11[t] +  0.557327363097962t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35639&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35639&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
Olie[t] = + 17.6500718165444 + 16.2406478298225Dumivariabele[t] + 0.333236249115063M1[t] + 2.89758274094412M2[t] + 3.12699001784616M3[t] + 3.27918074674819M4[t] + 6.15289056365023M5[t] + 8.29377163255228M6[t] + 10.2248567974543M7[t] + 11.7450916503563M8[t] + 10.1177241412939M9[t] + 7.60812933819593M10[t] + 4.22292801909796M11[t] + 0.557327363097962t + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	17.6500718165444	4.197969	4.2044	0.000116	5.8e-05
Dumivariabele	16.2406478298225	3.432567	4.7313	2.1e-05	1e-05
M1	0.333236249115063	4.820103	0.0691	0.945176	0.472588
M2	2.89758274094412	5.033216	0.5757	0.56757	0.283785
M3	3.12699001784616	5.028022	0.6219	0.537005	0.268502
M4	3.27918074674819	5.024257	0.6527	0.517149	0.258574
M5	6.15289056365023	5.021925	1.2252	0.226603	0.113301
M6	8.29377163255228	5.021029	1.6518	0.10524	0.05262
M7	10.2248567974543	5.021568	2.0362	0.047389	0.023694
M8	11.7450916503563	5.023542	2.338	0.023694	0.011847
M9	10.1177241412939	5.005302	2.0214	0.048954	0.024477
M10	7.60812933819593	5.0017	1.5211	0.134932	0.067466
M11	4.22292801909796	4.999538	0.8447	0.402578	0.201289
t	0.557327363097962	0.084906	6.5641	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 17.6500718165444 & 4.197969 & 4.2044 & 0.000116 & 5.8e-05 \tabularnewline
Dumivariabele & 16.2406478298225 & 3.432567 & 4.7313 & 2.1e-05 & 1e-05 \tabularnewline
M1 & 0.333236249115063 & 4.820103 & 0.0691 & 0.945176 & 0.472588 \tabularnewline
M2 & 2.89758274094412 & 5.033216 & 0.5757 & 0.56757 & 0.283785 \tabularnewline
M3 & 3.12699001784616 & 5.028022 & 0.6219 & 0.537005 & 0.268502 \tabularnewline
M4 & 3.27918074674819 & 5.024257 & 0.6527 & 0.517149 & 0.258574 \tabularnewline
M5 & 6.15289056365023 & 5.021925 & 1.2252 & 0.226603 & 0.113301 \tabularnewline
M6 & 8.29377163255228 & 5.021029 & 1.6518 & 0.10524 & 0.05262 \tabularnewline
M7 & 10.2248567974543 & 5.021568 & 2.0362 & 0.047389 & 0.023694 \tabularnewline
M8 & 11.7450916503563 & 5.023542 & 2.338 & 0.023694 & 0.011847 \tabularnewline
M9 & 10.1177241412939 & 5.005302 & 2.0214 & 0.048954 & 0.024477 \tabularnewline
M10 & 7.60812933819593 & 5.0017 & 1.5211 & 0.134932 & 0.067466 \tabularnewline
M11 & 4.22292801909796 & 4.999538 & 0.8447 & 0.402578 & 0.201289 \tabularnewline
t & 0.557327363097962 & 0.084906 & 6.5641 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35639&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]17.6500718165444[/C][C]4.197969[/C][C]4.2044[/C][C]0.000116[/C][C]5.8e-05[/C][/ROW]
[ROW][C]Dumivariabele[/C][C]16.2406478298225[/C][C]3.432567[/C][C]4.7313[/C][C]2.1e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]M1[/C][C]0.333236249115063[/C][C]4.820103[/C][C]0.0691[/C][C]0.945176[/C][C]0.472588[/C][/ROW]
[ROW][C]M2[/C][C]2.89758274094412[/C][C]5.033216[/C][C]0.5757[/C][C]0.56757[/C][C]0.283785[/C][/ROW]
[ROW][C]M3[/C][C]3.12699001784616[/C][C]5.028022[/C][C]0.6219[/C][C]0.537005[/C][C]0.268502[/C][/ROW]
[ROW][C]M4[/C][C]3.27918074674819[/C][C]5.024257[/C][C]0.6527[/C][C]0.517149[/C][C]0.258574[/C][/ROW]
[ROW][C]M5[/C][C]6.15289056365023[/C][C]5.021925[/C][C]1.2252[/C][C]0.226603[/C][C]0.113301[/C][/ROW]
[ROW][C]M6[/C][C]8.29377163255228[/C][C]5.021029[/C][C]1.6518[/C][C]0.10524[/C][C]0.05262[/C][/ROW]
[ROW][C]M7[/C][C]10.2248567974543[/C][C]5.021568[/C][C]2.0362[/C][C]0.047389[/C][C]0.023694[/C][/ROW]
[ROW][C]M8[/C][C]11.7450916503563[/C][C]5.023542[/C][C]2.338[/C][C]0.023694[/C][C]0.011847[/C][/ROW]
[ROW][C]M9[/C][C]10.1177241412939[/C][C]5.005302[/C][C]2.0214[/C][C]0.048954[/C][C]0.024477[/C][/ROW]
[ROW][C]M10[/C][C]7.60812933819593[/C][C]5.0017[/C][C]1.5211[/C][C]0.134932[/C][C]0.067466[/C][/ROW]
[ROW][C]M11[/C][C]4.22292801909796[/C][C]4.999538[/C][C]0.8447[/C][C]0.402578[/C][C]0.201289[/C][/ROW]
[ROW][C]t[/C][C]0.557327363097962[/C][C]0.084906[/C][C]6.5641[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35639&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35639&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	17.6500718165444	4.197969	4.2044	0.000116	5.8e-05
Dumivariabele	16.2406478298225	3.432567	4.7313	2.1e-05	1e-05
M1	0.333236249115063	4.820103	0.0691	0.945176	0.472588
M2	2.89758274094412	5.033216	0.5757	0.56757	0.283785
M3	3.12699001784616	5.028022	0.6219	0.537005	0.268502
M4	3.27918074674819	5.024257	0.6527	0.517149	0.258574
M5	6.15289056365023	5.021925	1.2252	0.226603	0.113301
M6	8.29377163255228	5.021029	1.6518	0.10524	0.05262
M7	10.2248567974543	5.021568	2.0362	0.047389	0.023694
M8	11.7450916503563	5.023542	2.338	0.023694	0.011847
M9	10.1177241412939	5.005302	2.0214	0.048954	0.024477
M10	7.60812933819593	5.0017	1.5211	0.134932	0.067466
M11	4.22292801909796	4.999538	0.8447	0.402578	0.201289
t	0.557327363097962	0.084906	6.5641	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.920083557992498
R-squared	0.846553753688134
Adjusted R-squared	0.804111174921023
F-TEST (value)	19.9458604608662
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	7.54951656745106e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.90382360321959
Sum Squared Residuals	2936.11009488812

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.920083557992498 \tabularnewline
R-squared & 0.846553753688134 \tabularnewline
Adjusted R-squared & 0.804111174921023 \tabularnewline
F-TEST (value) & 19.9458604608662 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 47 \tabularnewline
p-value & 7.54951656745106e-15 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 7.90382360321959 \tabularnewline
Sum Squared Residuals & 2936.11009488812 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35639&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.920083557992498[/C][/ROW]
[ROW][C]R-squared[/C][C]0.846553753688134[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.804111174921023[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]19.9458604608662[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]47[/C][/ROW]
[ROW][C]p-value[/C][C]7.54951656745106e-15[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]7.90382360321959[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2936.11009488812[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35639&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35639&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.920083557992498
R-squared	0.846553753688134
Adjusted R-squared	0.804111174921023
F-TEST (value)	19.9458604608662
F-TEST (DF numerator)	13
F-TEST (DF denominator)	47
p-value	7.54951656745106e-15
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	7.90382360321959
Sum Squared Residuals	2936.11009488812

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	20.7246301	18.5406354287574	2.18399467124262
2	21.44580352	21.6623092836844	-0.216505763684425
3	22.09413114	22.4490439236844	-0.354912783684422
4	21.53321848	23.1585620156844	-1.62534353568442
5	23.3470789	26.5895991956844	-3.24252029568442
6	23.5656163	29.2878076276844	-5.72219132768441
7	26.42117166	31.7762201556844	-5.35504849568441
8	25.21193138	33.8537823716844	-8.64185099168443
9	26.43574082	32.7837422257199	-6.34800140571993
10	29.33500366	30.8314747857199	-1.49647112571992
11	29.40056488	28.0036008297199	1.39696405028008
12	33.05013946	24.3380001737199	8.71213928628008
13	28.38072368	25.2285637859329	3.15215989406706
14	26.0059506	28.3502376408600	-2.34428704085995
15	29.31314992	29.1369722808600	0.17617763914004
16	30.36212944	29.8464903728600	0.51563906714004
17	35.74543406	33.2775275528600	2.46790650714004
18	36.15337054	35.9757359848600	0.177634555140037
19	34.20838768	38.4641485128600	-4.25576083285996
20	37.90895432	40.54171072886	-2.63275640885996
21	38.70297354	39.4716705828955	-0.768697042895461
22	42.11944156	37.5194031428955	4.60003841710453
23	42.16314904	34.6915291868955	7.47161985310454
24	39.79566054	31.0259285308955	8.76973200910454
25	37.36261082	31.9164921431085	5.44611867689152
26	38.3533137	35.0381659980355	3.3151477019645
27	42.60022384	35.8249006380355	6.7753232019645
28	41.24529196	36.5344187300355	4.7108732299645
29	42.15586446	39.9654559100355	2.19040854996450
30	46.94183352	42.6636643420355	4.2781691779645
31	47.42990038	45.1520768700355	2.2778235099645
32	47.0583868	47.2296390860355	-0.171252286035499
33	50.18347162	46.159598940071	4.023872679929
34	50.12519498	44.207331500071	5.917863479929
35	43.22669772	41.379457544071	1.84724017592899
36	40.04333626	37.713856888071	2.32947937192899
37	40.37114236	38.604420500284	1.76672185971597
38	42.2141411	41.7260943552110	0.488046744788953
39	36.99838182	42.5128289952110	-5.51444717521105
40	39.74466848	43.2223470872110	-3.47767860721104
41	42.68035422	46.653384267211	-3.97303004721105
42	46.2935059	49.351592699211	-3.05808679921105
43	46.97097184	51.840005227211	-4.86903338721105
44	48.72655562	53.917567443211	-5.19101182321105
45	52.36884562	69.088175127069	-16.7193295070690
46	50.05234918	67.135907687069	-17.0835585070690
47	54.03701444	64.308033731069	-10.2710192910690
48	57.78128856	60.642433075069	-2.86114451506903
49	64.71620872	61.532996687282	3.18321203271794
50	63.4122689	64.6546705422091	-1.24240164220907
51	64.3592643	65.4414051822091	-1.08214088220907
52	66.02743312	66.1509232742091	-0.123490154209080
53	72.13919574	69.5819604542091	2.55723528579093
54	76.60464328	72.2801688862091	4.32447439379093
55	86.97060062	74.7685814142091	12.2020192057909
56	93.48301514	76.846143630209	16.6368715097909
57	95.58825876	75.7761034842446	19.8121552757554
58	81.88596378	73.8238360442446	8.06212773575542
59	70.5511573	70.9959620882446	-0.444804788244578
60	50.38015528	67.3303614322446	-16.9502061522446
61	36.24807008	51.9802772146351	-15.7322071346351

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 20.7246301 & 18.5406354287574 & 2.18399467124262 \tabularnewline
2 & 21.44580352 & 21.6623092836844 & -0.216505763684425 \tabularnewline
3 & 22.09413114 & 22.4490439236844 & -0.354912783684422 \tabularnewline
4 & 21.53321848 & 23.1585620156844 & -1.62534353568442 \tabularnewline
5 & 23.3470789 & 26.5895991956844 & -3.24252029568442 \tabularnewline
6 & 23.5656163 & 29.2878076276844 & -5.72219132768441 \tabularnewline
7 & 26.42117166 & 31.7762201556844 & -5.35504849568441 \tabularnewline
8 & 25.21193138 & 33.8537823716844 & -8.64185099168443 \tabularnewline
9 & 26.43574082 & 32.7837422257199 & -6.34800140571993 \tabularnewline
10 & 29.33500366 & 30.8314747857199 & -1.49647112571992 \tabularnewline
11 & 29.40056488 & 28.0036008297199 & 1.39696405028008 \tabularnewline
12 & 33.05013946 & 24.3380001737199 & 8.71213928628008 \tabularnewline
13 & 28.38072368 & 25.2285637859329 & 3.15215989406706 \tabularnewline
14 & 26.0059506 & 28.3502376408600 & -2.34428704085995 \tabularnewline
15 & 29.31314992 & 29.1369722808600 & 0.17617763914004 \tabularnewline
16 & 30.36212944 & 29.8464903728600 & 0.51563906714004 \tabularnewline
17 & 35.74543406 & 33.2775275528600 & 2.46790650714004 \tabularnewline
18 & 36.15337054 & 35.9757359848600 & 0.177634555140037 \tabularnewline
19 & 34.20838768 & 38.4641485128600 & -4.25576083285996 \tabularnewline
20 & 37.90895432 & 40.54171072886 & -2.63275640885996 \tabularnewline
21 & 38.70297354 & 39.4716705828955 & -0.768697042895461 \tabularnewline
22 & 42.11944156 & 37.5194031428955 & 4.60003841710453 \tabularnewline
23 & 42.16314904 & 34.6915291868955 & 7.47161985310454 \tabularnewline
24 & 39.79566054 & 31.0259285308955 & 8.76973200910454 \tabularnewline
25 & 37.36261082 & 31.9164921431085 & 5.44611867689152 \tabularnewline
26 & 38.3533137 & 35.0381659980355 & 3.3151477019645 \tabularnewline
27 & 42.60022384 & 35.8249006380355 & 6.7753232019645 \tabularnewline
28 & 41.24529196 & 36.5344187300355 & 4.7108732299645 \tabularnewline
29 & 42.15586446 & 39.9654559100355 & 2.19040854996450 \tabularnewline
30 & 46.94183352 & 42.6636643420355 & 4.2781691779645 \tabularnewline
31 & 47.42990038 & 45.1520768700355 & 2.2778235099645 \tabularnewline
32 & 47.0583868 & 47.2296390860355 & -0.171252286035499 \tabularnewline
33 & 50.18347162 & 46.159598940071 & 4.023872679929 \tabularnewline
34 & 50.12519498 & 44.207331500071 & 5.917863479929 \tabularnewline
35 & 43.22669772 & 41.379457544071 & 1.84724017592899 \tabularnewline
36 & 40.04333626 & 37.713856888071 & 2.32947937192899 \tabularnewline
37 & 40.37114236 & 38.604420500284 & 1.76672185971597 \tabularnewline
38 & 42.2141411 & 41.7260943552110 & 0.488046744788953 \tabularnewline
39 & 36.99838182 & 42.5128289952110 & -5.51444717521105 \tabularnewline
40 & 39.74466848 & 43.2223470872110 & -3.47767860721104 \tabularnewline
41 & 42.68035422 & 46.653384267211 & -3.97303004721105 \tabularnewline
42 & 46.2935059 & 49.351592699211 & -3.05808679921105 \tabularnewline
43 & 46.97097184 & 51.840005227211 & -4.86903338721105 \tabularnewline
44 & 48.72655562 & 53.917567443211 & -5.19101182321105 \tabularnewline
45 & 52.36884562 & 69.088175127069 & -16.7193295070690 \tabularnewline
46 & 50.05234918 & 67.135907687069 & -17.0835585070690 \tabularnewline
47 & 54.03701444 & 64.308033731069 & -10.2710192910690 \tabularnewline
48 & 57.78128856 & 60.642433075069 & -2.86114451506903 \tabularnewline
49 & 64.71620872 & 61.532996687282 & 3.18321203271794 \tabularnewline
50 & 63.4122689 & 64.6546705422091 & -1.24240164220907 \tabularnewline
51 & 64.3592643 & 65.4414051822091 & -1.08214088220907 \tabularnewline
52 & 66.02743312 & 66.1509232742091 & -0.123490154209080 \tabularnewline
53 & 72.13919574 & 69.5819604542091 & 2.55723528579093 \tabularnewline
54 & 76.60464328 & 72.2801688862091 & 4.32447439379093 \tabularnewline
55 & 86.97060062 & 74.7685814142091 & 12.2020192057909 \tabularnewline
56 & 93.48301514 & 76.846143630209 & 16.6368715097909 \tabularnewline
57 & 95.58825876 & 75.7761034842446 & 19.8121552757554 \tabularnewline
58 & 81.88596378 & 73.8238360442446 & 8.06212773575542 \tabularnewline
59 & 70.5511573 & 70.9959620882446 & -0.444804788244578 \tabularnewline
60 & 50.38015528 & 67.3303614322446 & -16.9502061522446 \tabularnewline
61 & 36.24807008 & 51.9802772146351 & -15.7322071346351 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=35639&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]20.7246301[/C][C]18.5406354287574[/C][C]2.18399467124262[/C][/ROW]
[ROW][C]2[/C][C]21.44580352[/C][C]21.6623092836844[/C][C]-0.216505763684425[/C][/ROW]
[ROW][C]3[/C][C]22.09413114[/C][C]22.4490439236844[/C][C]-0.354912783684422[/C][/ROW]
[ROW][C]4[/C][C]21.53321848[/C][C]23.1585620156844[/C][C]-1.62534353568442[/C][/ROW]
[ROW][C]5[/C][C]23.3470789[/C][C]26.5895991956844[/C][C]-3.24252029568442[/C][/ROW]
[ROW][C]6[/C][C]23.5656163[/C][C]29.2878076276844[/C][C]-5.72219132768441[/C][/ROW]
[ROW][C]7[/C][C]26.42117166[/C][C]31.7762201556844[/C][C]-5.35504849568441[/C][/ROW]
[ROW][C]8[/C][C]25.21193138[/C][C]33.8537823716844[/C][C]-8.64185099168443[/C][/ROW]
[ROW][C]9[/C][C]26.43574082[/C][C]32.7837422257199[/C][C]-6.34800140571993[/C][/ROW]
[ROW][C]10[/C][C]29.33500366[/C][C]30.8314747857199[/C][C]-1.49647112571992[/C][/ROW]
[ROW][C]11[/C][C]29.40056488[/C][C]28.0036008297199[/C][C]1.39696405028008[/C][/ROW]
[ROW][C]12[/C][C]33.05013946[/C][C]24.3380001737199[/C][C]8.71213928628008[/C][/ROW]
[ROW][C]13[/C][C]28.38072368[/C][C]25.2285637859329[/C][C]3.15215989406706[/C][/ROW]
[ROW][C]14[/C][C]26.0059506[/C][C]28.3502376408600[/C][C]-2.34428704085995[/C][/ROW]
[ROW][C]15[/C][C]29.31314992[/C][C]29.1369722808600[/C][C]0.17617763914004[/C][/ROW]
[ROW][C]16[/C][C]30.36212944[/C][C]29.8464903728600[/C][C]0.51563906714004[/C][/ROW]
[ROW][C]17[/C][C]35.74543406[/C][C]33.2775275528600[/C][C]2.46790650714004[/C][/ROW]
[ROW][C]18[/C][C]36.15337054[/C][C]35.9757359848600[/C][C]0.177634555140037[/C][/ROW]
[ROW][C]19[/C][C]34.20838768[/C][C]38.4641485128600[/C][C]-4.25576083285996[/C][/ROW]
[ROW][C]20[/C][C]37.90895432[/C][C]40.54171072886[/C][C]-2.63275640885996[/C][/ROW]
[ROW][C]21[/C][C]38.70297354[/C][C]39.4716705828955[/C][C]-0.768697042895461[/C][/ROW]
[ROW][C]22[/C][C]42.11944156[/C][C]37.5194031428955[/C][C]4.60003841710453[/C][/ROW]
[ROW][C]23[/C][C]42.16314904[/C][C]34.6915291868955[/C][C]7.47161985310454[/C][/ROW]
[ROW][C]24[/C][C]39.79566054[/C][C]31.0259285308955[/C][C]8.76973200910454[/C][/ROW]
[ROW][C]25[/C][C]37.36261082[/C][C]31.9164921431085[/C][C]5.44611867689152[/C][/ROW]
[ROW][C]26[/C][C]38.3533137[/C][C]35.0381659980355[/C][C]3.3151477019645[/C][/ROW]
[ROW][C]27[/C][C]42.60022384[/C][C]35.8249006380355[/C][C]6.7753232019645[/C][/ROW]
[ROW][C]28[/C][C]41.24529196[/C][C]36.5344187300355[/C][C]4.7108732299645[/C][/ROW]
[ROW][C]29[/C][C]42.15586446[/C][C]39.9654559100355[/C][C]2.19040854996450[/C][/ROW]
[ROW][C]30[/C][C]46.94183352[/C][C]42.6636643420355[/C][C]4.2781691779645[/C][/ROW]
[ROW][C]31[/C][C]47.42990038[/C][C]45.1520768700355[/C][C]2.2778235099645[/C][/ROW]
[ROW][C]32[/C][C]47.0583868[/C][C]47.2296390860355[/C][C]-0.171252286035499[/C][/ROW]
[ROW][C]33[/C][C]50.18347162[/C][C]46.159598940071[/C][C]4.023872679929[/C][/ROW]
[ROW][C]34[/C][C]50.12519498[/C][C]44.207331500071[/C][C]5.917863479929[/C][/ROW]
[ROW][C]35[/C][C]43.22669772[/C][C]41.379457544071[/C][C]1.84724017592899[/C][/ROW]
[ROW][C]36[/C][C]40.04333626[/C][C]37.713856888071[/C][C]2.32947937192899[/C][/ROW]
[ROW][C]37[/C][C]40.37114236[/C][C]38.604420500284[/C][C]1.76672185971597[/C][/ROW]
[ROW][C]38[/C][C]42.2141411[/C][C]41.7260943552110[/C][C]0.488046744788953[/C][/ROW]
[ROW][C]39[/C][C]36.99838182[/C][C]42.5128289952110[/C][C]-5.51444717521105[/C][/ROW]
[ROW][C]40[/C][C]39.74466848[/C][C]43.2223470872110[/C][C]-3.47767860721104[/C][/ROW]
[ROW][C]41[/C][C]42.68035422[/C][C]46.653384267211[/C][C]-3.97303004721105[/C][/ROW]
[ROW][C]42[/C][C]46.2935059[/C][C]49.351592699211[/C][C]-3.05808679921105[/C][/ROW]
[ROW][C]43[/C][C]46.97097184[/C][C]51.840005227211[/C][C]-4.86903338721105[/C][/ROW]
[ROW][C]44[/C][C]48.72655562[/C][C]53.917567443211[/C][C]-5.19101182321105[/C][/ROW]
[ROW][C]45[/C][C]52.36884562[/C][C]69.088175127069[/C][C]-16.7193295070690[/C][/ROW]
[ROW][C]46[/C][C]50.05234918[/C][C]67.135907687069[/C][C]-17.0835585070690[/C][/ROW]
[ROW][C]47[/C][C]54.03701444[/C][C]64.308033731069[/C][C]-10.2710192910690[/C][/ROW]
[ROW][C]48[/C][C]57.78128856[/C][C]60.642433075069[/C][C]-2.86114451506903[/C][/ROW]
[ROW][C]49[/C][C]64.71620872[/C][C]61.532996687282[/C][C]3.18321203271794[/C][/ROW]
[ROW][C]50[/C][C]63.4122689[/C][C]64.6546705422091[/C][C]-1.24240164220907[/C][/ROW]
[ROW][C]51[/C][C]64.3592643[/C][C]65.4414051822091[/C][C]-1.08214088220907[/C][/ROW]
[ROW][C]52[/C][C]66.02743312[/C][C]66.1509232742091[/C][C]-0.123490154209080[/C][/ROW]
[ROW][C]53[/C][C]72.13919574[/C][C]69.5819604542091[/C][C]2.55723528579093[/C][/ROW]
[ROW][C]54[/C][C]76.60464328[/C][C]72.2801688862091[/C][C]4.32447439379093[/C][/ROW]
[ROW][C]55[/C][C]86.97060062[/C][C]74.7685814142091[/C][C]12.2020192057909[/C][/ROW]
[ROW][C]56[/C][C]93.48301514[/C][C]76.846143630209[/C][C]16.6368715097909[/C][/ROW]
[ROW][C]57[/C][C]95.58825876[/C][C]75.7761034842446[/C][C]19.8121552757554[/C][/ROW]
[ROW][C]58[/C][C]81.88596378[/C][C]73.8238360442446[/C][C]8.06212773575542[/C][/ROW]
[ROW][C]59[/C][C]70.5511573[/C][C]70.9959620882446[/C][C]-0.444804788244578[/C][/ROW]
[ROW][C]60[/C][C]50.38015528[/C][C]67.3303614322446[/C][C]-16.9502061522446[/C][/ROW]
[ROW][C]61[/C][C]36.24807008[/C][C]51.9802772146351[/C][C]-15.7322071346351[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=35639&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=35639&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	20.7246301	18.5406354287574	2.18399467124262
2	21.44580352	21.6623092836844	-0.216505763684425
3	22.09413114	22.4490439236844	-0.354912783684422
4	21.53321848	23.1585620156844	-1.62534353568442
5	23.3470789	26.5895991956844	-3.24252029568442
6	23.5656163	29.2878076276844	-5.72219132768441
7	26.42117166	31.7762201556844	-5.35504849568441
8	25.21193138	33.8537823716844	-8.64185099168443
9	26.43574082	32.7837422257199	-6.34800140571993
10	29.33500366	30.8314747857199	-1.49647112571992
11	29.40056488	28.0036008297199	1.39696405028008
12	33.05013946	24.3380001737199	8.71213928628008
13	28.38072368	25.2285637859329	3.15215989406706
14	26.0059506	28.3502376408600	-2.34428704085995
15	29.31314992	29.1369722808600	0.17617763914004
16	30.36212944	29.8464903728600	0.51563906714004
17	35.74543406	33.2775275528600	2.46790650714004
18	36.15337054	35.9757359848600	0.177634555140037
19	34.20838768	38.4641485128600	-4.25576083285996
20	37.90895432	40.54171072886	-2.63275640885996
21	38.70297354	39.4716705828955	-0.768697042895461
22	42.11944156	37.5194031428955	4.60003841710453
23	42.16314904	34.6915291868955	7.47161985310454
24	39.79566054	31.0259285308955	8.76973200910454
25	37.36261082	31.9164921431085	5.44611867689152
26	38.3533137	35.0381659980355	3.3151477019645
27	42.60022384	35.8249006380355	6.7753232019645
28	41.24529196	36.5344187300355	4.7108732299645
29	42.15586446	39.9654559100355	2.19040854996450
30	46.94183352	42.6636643420355	4.2781691779645
31	47.42990038	45.1520768700355	2.2778235099645
32	47.0583868	47.2296390860355	-0.171252286035499
33	50.18347162	46.159598940071	4.023872679929
34	50.12519498	44.207331500071	5.917863479929
35	43.22669772	41.379457544071	1.84724017592899
36	40.04333626	37.713856888071	2.32947937192899
37	40.37114236	38.604420500284	1.76672185971597
38	42.2141411	41.7260943552110	0.488046744788953
39	36.99838182	42.5128289952110	-5.51444717521105
40	39.74466848	43.2223470872110	-3.47767860721104
41	42.68035422	46.653384267211	-3.97303004721105
42	46.2935059	49.351592699211	-3.05808679921105
43	46.97097184	51.840005227211	-4.86903338721105
44	48.72655562	53.917567443211	-5.19101182321105
45	52.36884562	69.088175127069	-16.7193295070690
46	50.05234918	67.135907687069	-17.0835585070690
47	54.03701444	64.308033731069	-10.2710192910690
48	57.78128856	60.642433075069	-2.86114451506903
49	64.71620872	61.532996687282	3.18321203271794
50	63.4122689	64.6546705422091	-1.24240164220907
51	64.3592643	65.4414051822091	-1.08214088220907
52	66.02743312	66.1509232742091	-0.123490154209080
53	72.13919574	69.5819604542091	2.55723528579093
54	76.60464328	72.2801688862091	4.32447439379093
55	86.97060062	74.7685814142091	12.2020192057909
56	93.48301514	76.846143630209	16.6368715097909
57	95.58825876	75.7761034842446	19.8121552757554
58	81.88596378	73.8238360442446	8.06212773575542
59	70.5511573	70.9959620882446	-0.444804788244578
60	50.38015528	67.3303614322446	-16.9502061522446
61	36.24807008	51.9802772146351	-15.7322071346351

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Figure 3

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Figure 4

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Figure 9

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = No 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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code