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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 13 Dec 2007 08:07:19 -0700

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2007/Dec/13/t1197558067furm88zsdcwwkm4.htm/, Retrieved Sun, 05 May 2024 14:44:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14361, Retrieved Sun, 05 May 2024 14:44:52 +0000

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User-defined keywords

multiple regression

Estimated Impact

173

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-       [Multiple Regression] [paper] [2007-12-13 15:07:19] [c17f948852536966abd959cf76a782cb] [Current]

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103.1	98.6	98.1	98.6	0
100.6	98	101.1	98	0
103.1	106.8	111.1	106.8	0
95.5	96.6	93.3	96.7	0
90.5	100.1	100	100.2	0
90.9	107.7	108	107.7	0
88.8	91.5	70.4	92	0
90.7	97.8	75.4	98.4	0
94.3	107.4	105.5	107.4	0
104.6	117.5	112.3	117.7	0
111.1	105.6	102.5	105.7	0
110.8	97.4	93.5	97.5	0
107.2	99.5	86.7	99.9	1
99	98	95.2	98.2	1
99	104.3	103.8	104.5	1
91	100.6	97	100.8	1
96.2	101.1	95.5	101.5	1
96.9	103.9	101	103.9	1
96.2	96.9	67.5	99.6	1
100.1	95.5	64	98.4	1
99	108.4	106.7	112.7	1
115.4	117	100.6	118.4	1
106.9	103.8	101.2	108.1	1
107.1	100.8	93.1	105.4	1
99.3	110.6	84.2	114.6	1
99.2	104	85.8	106.9	1
108.3	112.6	91.8	115.9	1
105.6	107.3	92.4	109.8	1
99.5	98.9	80.3	101.8	1
107.4	109.8	79.7	114.2	1
93.1	104.9	62.5	110.8	1
88.1	102.2	57.1	108.4	1
110.7	123.9	100.8	127.5	1
113.1	124.9	100.7	128.6	1
99.6	112.7	86.2	116.6	1
93.6	121.9	83.2	127.4	1
98.6	100.6	71.7	105	1
99.6	104.3	77.5	108.3	1
114.3	120.4	89.8	125	1
107.8	107.5	80.3	111.6	1
101.2	102.9	78.7	106.5	1
112.5	125.6	93.8	130.3	1
100.5	107.5	57.6	115	1
93.9	108.8	60.6	116.1	1
116.2	128.4	91	134	1
112	121.1	85.3	126.5	1
106.4	119.5	77.4	125.8	1
95.7	128.7	77.3	136.4	1
96	108.7	68.3	114.9	1
95.8	105.5	69.9	110.9	1
103	119.8	81.7	125.5	1
102.2	111.3	75.1	116.8	1
98.4	110.6	69.9	116.8	1
111.4	120.1	84	125.5	1
86.6	97.5	54.3	104.2	1
91.3	107.7	60	115.1	1
107.9	127.3	89.9	132.8	1
101.8	117.2	77	123.3	1
104.4	119.8	85.3	124.8	1
93.4	116.2	77.6	122	1
100.1	111	69.2	117.4	1
98.5	112.4	75.5	117.9	1
112.9	130.6	85.7	137.4	1
101.4	109.1	72.2	114.6	1
107.1	118.8	79.9	124.7	1
110.8	123.9	85.3	129.6	1
90.3	101.6	52.2	109.4	1
95.5	112.8	61.2	120.9	1
111.4	128	82.4	134.9	1
113	129.6	85.4	136.3	1
107.5	125.8	78.2	133.2	1
95.9	119.5	70.2	127.2	1
106.3	115.7	70.2	122.7	1
105.2	113.6	69.3	120.5	1
117.2	129.7	77.5	137.8	1
106.9	112	66.1	119.1	1
108.2	116.8	69	124.3	1
110	126.3	75.3	134.3	1
96.1	112.9	58.2	121.7	1
100.6	115.9	59.7	125	1

Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14361&T=0

[TABLE]
[ROW][C]Summary of compuational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]4 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=14361&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14361&T=0

As an alternative you can also use a QR Code:

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

Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
intermediair-goederen[t] = + 38.7304827602253 + 0.621420888367847`totale-consumptie`[t] + 0.169178306430478`Duurzame-consumptiegoederen`[t] -0.195767517620194`Niet-duurzame-consumptiegoederen`[t] + 3.47440801073859`invoering-Euro`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
intermediair-goederen[t] =  +  38.7304827602253 +  0.621420888367847`totale-consumptie`[t] +  0.169178306430478`Duurzame-consumptiegoederen`[t] -0.195767517620194`Niet-duurzame-consumptiegoederen`[t] +  3.47440801073859`invoering-Euro`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14361&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]intermediair-goederen[t] =  +  38.7304827602253 +  0.621420888367847`totale-consumptie`[t] +  0.169178306430478`Duurzame-consumptiegoederen`[t] -0.195767517620194`Niet-duurzame-consumptiegoederen`[t] +  3.47440801073859`invoering-Euro`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14361&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14361&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
intermediair-goederen[t] = + 38.7304827602253 + 0.621420888367847`totale-consumptie`[t] + 0.169178306430478`Duurzame-consumptiegoederen`[t] -0.195767517620194`Niet-duurzame-consumptiegoederen`[t] + 3.47440801073859`invoering-Euro`[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	38.7304827602253	7.913233	4.8944	6e-06	3e-06
`totale-consumptie`	0.621420888367847	0.825965	0.7524	0.454191	0.227095
`Duurzame-consumptiegoederen`	0.169178306430478	0.102983	1.6428	0.104615	0.052307
`Niet-duurzame-consumptiegoederen`	-0.195767517620194	0.717218	-0.273	0.785639	0.392819
`invoering-Euro`	3.47440801073859	2.325924	1.4938	0.13943	0.069715

\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) & 38.7304827602253 & 7.913233 & 4.8944 & 6e-06 & 3e-06 \tabularnewline
`totale-consumptie` & 0.621420888367847 & 0.825965 & 0.7524 & 0.454191 & 0.227095 \tabularnewline
`Duurzame-consumptiegoederen` & 0.169178306430478 & 0.102983 & 1.6428 & 0.104615 & 0.052307 \tabularnewline
`Niet-duurzame-consumptiegoederen` & -0.195767517620194 & 0.717218 & -0.273 & 0.785639 & 0.392819 \tabularnewline
`invoering-Euro` & 3.47440801073859 & 2.325924 & 1.4938 & 0.13943 & 0.069715 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14361&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]38.7304827602253[/C][C]7.913233[/C][C]4.8944[/C][C]6e-06[/C][C]3e-06[/C][/ROW]
[ROW][C]`totale-consumptie`[/C][C]0.621420888367847[/C][C]0.825965[/C][C]0.7524[/C][C]0.454191[/C][C]0.227095[/C][/ROW]
[ROW][C]`Duurzame-consumptiegoederen`[/C][C]0.169178306430478[/C][C]0.102983[/C][C]1.6428[/C][C]0.104615[/C][C]0.052307[/C][/ROW]
[ROW][C]`Niet-duurzame-consumptiegoederen`[/C][C]-0.195767517620194[/C][C]0.717218[/C][C]-0.273[/C][C]0.785639[/C][C]0.392819[/C][/ROW]
[ROW][C]`invoering-Euro`[/C][C]3.47440801073859[/C][C]2.325924[/C][C]1.4938[/C][C]0.13943[/C][C]0.069715[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14361&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14361&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)	38.7304827602253	7.913233	4.8944	6e-06	3e-06
`totale-consumptie`	0.621420888367847	0.825965	0.7524	0.454191	0.227095
`Duurzame-consumptiegoederen`	0.169178306430478	0.102983	1.6428	0.104615	0.052307
`Niet-duurzame-consumptiegoederen`	-0.195767517620194	0.717218	-0.273	0.785639	0.392819
`invoering-Euro`	3.47440801073859	2.325924	1.4938	0.13943	0.069715

Multiple Linear Regression - Regression Statistics
Multiple R	0.690610406124864
R-squared	0.476942733047949
Adjusted R-squared	0.449046345477173
F-TEST (value)	17.0969352873341
F-TEST (DF numerator)	4
F-TEST (DF denominator)	75
p-value	5.26891974494959e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.59261586000023
Sum Squared Residuals	2345.80141181446

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.690610406124864 \tabularnewline
R-squared & 0.476942733047949 \tabularnewline
Adjusted R-squared & 0.449046345477173 \tabularnewline
F-TEST (value) & 17.0969352873341 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 75 \tabularnewline
p-value & 5.26891974494959e-10 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.59261586000023 \tabularnewline
Sum Squared Residuals & 2345.80141181446 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14361&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.690610406124864[/C][/ROW]
[ROW][C]R-squared[/C][C]0.476942733047949[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.449046345477173[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]17.0969352873341[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]75[/C][/ROW]
[ROW][C]p-value[/C][C]5.26891974494959e-10[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.59261586000023[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2345.80141181446[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14361&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14361&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	0.690610406124864
R-squared	0.476942733047949
Adjusted R-squared	0.449046345477173
F-TEST (value)	17.0969352873341
F-TEST (DF numerator)	4
F-TEST (DF denominator)	75
p-value	5.26891974494959e-10
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.59261586000023
Sum Squared Residuals	2345.80141181446

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	103.1	97.2962969767737	5.80370302322631
2	100.6	97.5484398736166	3.05156012638336
3	103.1	102.985972600501	0.11402739949926
4	95.5	95.6133576126502	-0.113357612650164
5	90.5	98.2366390633512	-7.73663906335116
6	90.9	102.844607884239	-11.9446078842392
7	88.8	89.4900351975311	-0.690035197531108
8	90.7	92.9979662136317	-2.29796621363169
9	94.3	102.293966106939	-7.99396610693868
10	104.6	107.704324131693	-3.10432413169319
11	111.1	101.000678368539	10.0993216314606
12	110.8	95.9877159705344	14.8122840294656
13	107.2	99.1468533208297	8.05314667917027
14	99	99.9855423728914	-0.98554237289136
15	99	104.122092043904	-5.12209204390369
16	91	101.396762088410	-10.3967620884101
17	96.2	101.316667810614	-5.11666781061418
18	96.9	103.517284941123	-6.61728494112332
19	96.2	94.3416657828942	1.85833421710579
20	100.1	93.1144734878168	6.98552651218321
21	99	105.555241130375	-6.55524113037466
22	115.4	108.751598250677	6.64840174932289
23	106.9	102.666754939568	4.23324506043218
24	107.1	99.960720289952	7.13927971004807
25	99.3	102.743896906620	-3.44389690661979
26	99.2	100.420614219356	-1.22061421935625
27	108.3	105.017996039321	3.28200396067914
28	105.6	103.020154172313	2.57984582768725
29	99.5	97.3193013431756	2.1806986568244
30	107.4	101.563764824036	5.83623517596357
31	93.1	96.2745451603384	-3.17454516033843
32	88.1	94.152987949309	-6.05298794930912
33	110.7	111.291753631358	-0.591753631357593
34	113.1	111.680912419700	1.41908758029981
35	99.6	103.995702349813	-4.39570234981285
36	93.6	107.090950413207	-13.4909504132075
37	98.6	96.2943273617142	2.30567263828579
38	99.6	98.9287860178254	0.671213982174624
39	114.3	107.745237945385	6.55476205461464
40	107.8	100.744999310461	7.05500068953882
41	101.2	98.6141922735433	2.58580772645669
42	112.5	110.615771947233	1.88422805276697
43	100.5	96.2390421945807	4.26095780541934
44	93.9	97.339079999368	-3.43907999936808
45	116.2	111.157711361463	5.04228863853704
46	112	107.125278911875	4.8747210881246
47	106.4	104.931534132020	1.4684658679798
48	95.7	108.556552787587	-12.8565527875873
49	96	98.8145318911902	-2.81453189119022
50	95.8	97.8797404091826	-2.07974040918265
51	103	105.904157371468	-2.90415737146767
52	102.2	101.208680401196	0.991319598804501
53	98.4	99.8939585858995	-1.49395858589952
54	111.4	106.479693742768	4.92030625723188
55	86.6	91.5808340899797	-4.98083408997972
56	91.3	96.7497775559254	-5.44977755592537
57	107.9	110.522973268329	-2.62297326832902
58	101.8	103.924013560252	-2.12401356025245
59	104.4	106.650236536952	-2.25023653695152
60	93.4	103.658597428649	-10.2585974286491
61	100.1	99.9066416161732	0.193358383826783
62	98.5	101.74457043159	-3.24457043159012
63	112.9	110.962582731882	1.93741726811799
64	101.4	99.7816258969023	1.61837410309773
65	107.1	105.134829545621	1.96517045437888
66	110.8	108.258378094683	2.54162190531722
67	90.3	92.7553941971589	-2.45539419715887
68	95.5	98.9865864521208	-3.48658645212083
69	111.4	109.278018804956	2.12198119504448
70	113	110.505752620967	2.49424737903276
71	107.5	107.533148743493	-0.0331487434925879
72	95.9	103.439375801052	-7.53937580105248
73	106.3	101.958930254546	4.34106974545445
74	105.2	100.93237445195	4.26762554804995
75	117.2	108.937734812573	8.26226518742705
76	106.9	99.6708049746522	7.22919502534776
77	108.2	102.126251235841	6.07374876415871
78	110	107.137897829646	2.86210217035410
79	96.1	98.38457960757	-2.28457960757003
80	100.6	99.8565769241726	0.743423075827347

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 103.1 & 97.2962969767737 & 5.80370302322631 \tabularnewline
2 & 100.6 & 97.5484398736166 & 3.05156012638336 \tabularnewline
3 & 103.1 & 102.985972600501 & 0.11402739949926 \tabularnewline
4 & 95.5 & 95.6133576126502 & -0.113357612650164 \tabularnewline
5 & 90.5 & 98.2366390633512 & -7.73663906335116 \tabularnewline
6 & 90.9 & 102.844607884239 & -11.9446078842392 \tabularnewline
7 & 88.8 & 89.4900351975311 & -0.690035197531108 \tabularnewline
8 & 90.7 & 92.9979662136317 & -2.29796621363169 \tabularnewline
9 & 94.3 & 102.293966106939 & -7.99396610693868 \tabularnewline
10 & 104.6 & 107.704324131693 & -3.10432413169319 \tabularnewline
11 & 111.1 & 101.000678368539 & 10.0993216314606 \tabularnewline
12 & 110.8 & 95.9877159705344 & 14.8122840294656 \tabularnewline
13 & 107.2 & 99.1468533208297 & 8.05314667917027 \tabularnewline
14 & 99 & 99.9855423728914 & -0.98554237289136 \tabularnewline
15 & 99 & 104.122092043904 & -5.12209204390369 \tabularnewline
16 & 91 & 101.396762088410 & -10.3967620884101 \tabularnewline
17 & 96.2 & 101.316667810614 & -5.11666781061418 \tabularnewline
18 & 96.9 & 103.517284941123 & -6.61728494112332 \tabularnewline
19 & 96.2 & 94.3416657828942 & 1.85833421710579 \tabularnewline
20 & 100.1 & 93.1144734878168 & 6.98552651218321 \tabularnewline
21 & 99 & 105.555241130375 & -6.55524113037466 \tabularnewline
22 & 115.4 & 108.751598250677 & 6.64840174932289 \tabularnewline
23 & 106.9 & 102.666754939568 & 4.23324506043218 \tabularnewline
24 & 107.1 & 99.960720289952 & 7.13927971004807 \tabularnewline
25 & 99.3 & 102.743896906620 & -3.44389690661979 \tabularnewline
26 & 99.2 & 100.420614219356 & -1.22061421935625 \tabularnewline
27 & 108.3 & 105.017996039321 & 3.28200396067914 \tabularnewline
28 & 105.6 & 103.020154172313 & 2.57984582768725 \tabularnewline
29 & 99.5 & 97.3193013431756 & 2.1806986568244 \tabularnewline
30 & 107.4 & 101.563764824036 & 5.83623517596357 \tabularnewline
31 & 93.1 & 96.2745451603384 & -3.17454516033843 \tabularnewline
32 & 88.1 & 94.152987949309 & -6.05298794930912 \tabularnewline
33 & 110.7 & 111.291753631358 & -0.591753631357593 \tabularnewline
34 & 113.1 & 111.680912419700 & 1.41908758029981 \tabularnewline
35 & 99.6 & 103.995702349813 & -4.39570234981285 \tabularnewline
36 & 93.6 & 107.090950413207 & -13.4909504132075 \tabularnewline
37 & 98.6 & 96.2943273617142 & 2.30567263828579 \tabularnewline
38 & 99.6 & 98.9287860178254 & 0.671213982174624 \tabularnewline
39 & 114.3 & 107.745237945385 & 6.55476205461464 \tabularnewline
40 & 107.8 & 100.744999310461 & 7.05500068953882 \tabularnewline
41 & 101.2 & 98.6141922735433 & 2.58580772645669 \tabularnewline
42 & 112.5 & 110.615771947233 & 1.88422805276697 \tabularnewline
43 & 100.5 & 96.2390421945807 & 4.26095780541934 \tabularnewline
44 & 93.9 & 97.339079999368 & -3.43907999936808 \tabularnewline
45 & 116.2 & 111.157711361463 & 5.04228863853704 \tabularnewline
46 & 112 & 107.125278911875 & 4.8747210881246 \tabularnewline
47 & 106.4 & 104.931534132020 & 1.4684658679798 \tabularnewline
48 & 95.7 & 108.556552787587 & -12.8565527875873 \tabularnewline
49 & 96 & 98.8145318911902 & -2.81453189119022 \tabularnewline
50 & 95.8 & 97.8797404091826 & -2.07974040918265 \tabularnewline
51 & 103 & 105.904157371468 & -2.90415737146767 \tabularnewline
52 & 102.2 & 101.208680401196 & 0.991319598804501 \tabularnewline
53 & 98.4 & 99.8939585858995 & -1.49395858589952 \tabularnewline
54 & 111.4 & 106.479693742768 & 4.92030625723188 \tabularnewline
55 & 86.6 & 91.5808340899797 & -4.98083408997972 \tabularnewline
56 & 91.3 & 96.7497775559254 & -5.44977755592537 \tabularnewline
57 & 107.9 & 110.522973268329 & -2.62297326832902 \tabularnewline
58 & 101.8 & 103.924013560252 & -2.12401356025245 \tabularnewline
59 & 104.4 & 106.650236536952 & -2.25023653695152 \tabularnewline
60 & 93.4 & 103.658597428649 & -10.2585974286491 \tabularnewline
61 & 100.1 & 99.9066416161732 & 0.193358383826783 \tabularnewline
62 & 98.5 & 101.74457043159 & -3.24457043159012 \tabularnewline
63 & 112.9 & 110.962582731882 & 1.93741726811799 \tabularnewline
64 & 101.4 & 99.7816258969023 & 1.61837410309773 \tabularnewline
65 & 107.1 & 105.134829545621 & 1.96517045437888 \tabularnewline
66 & 110.8 & 108.258378094683 & 2.54162190531722 \tabularnewline
67 & 90.3 & 92.7553941971589 & -2.45539419715887 \tabularnewline
68 & 95.5 & 98.9865864521208 & -3.48658645212083 \tabularnewline
69 & 111.4 & 109.278018804956 & 2.12198119504448 \tabularnewline
70 & 113 & 110.505752620967 & 2.49424737903276 \tabularnewline
71 & 107.5 & 107.533148743493 & -0.0331487434925879 \tabularnewline
72 & 95.9 & 103.439375801052 & -7.53937580105248 \tabularnewline
73 & 106.3 & 101.958930254546 & 4.34106974545445 \tabularnewline
74 & 105.2 & 100.93237445195 & 4.26762554804995 \tabularnewline
75 & 117.2 & 108.937734812573 & 8.26226518742705 \tabularnewline
76 & 106.9 & 99.6708049746522 & 7.22919502534776 \tabularnewline
77 & 108.2 & 102.126251235841 & 6.07374876415871 \tabularnewline
78 & 110 & 107.137897829646 & 2.86210217035410 \tabularnewline
79 & 96.1 & 98.38457960757 & -2.28457960757003 \tabularnewline
80 & 100.6 & 99.8565769241726 & 0.743423075827347 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14361&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]103.1[/C][C]97.2962969767737[/C][C]5.80370302322631[/C][/ROW]
[ROW][C]2[/C][C]100.6[/C][C]97.5484398736166[/C][C]3.05156012638336[/C][/ROW]
[ROW][C]3[/C][C]103.1[/C][C]102.985972600501[/C][C]0.11402739949926[/C][/ROW]
[ROW][C]4[/C][C]95.5[/C][C]95.6133576126502[/C][C]-0.113357612650164[/C][/ROW]
[ROW][C]5[/C][C]90.5[/C][C]98.2366390633512[/C][C]-7.73663906335116[/C][/ROW]
[ROW][C]6[/C][C]90.9[/C][C]102.844607884239[/C][C]-11.9446078842392[/C][/ROW]
[ROW][C]7[/C][C]88.8[/C][C]89.4900351975311[/C][C]-0.690035197531108[/C][/ROW]
[ROW][C]8[/C][C]90.7[/C][C]92.9979662136317[/C][C]-2.29796621363169[/C][/ROW]
[ROW][C]9[/C][C]94.3[/C][C]102.293966106939[/C][C]-7.99396610693868[/C][/ROW]
[ROW][C]10[/C][C]104.6[/C][C]107.704324131693[/C][C]-3.10432413169319[/C][/ROW]
[ROW][C]11[/C][C]111.1[/C][C]101.000678368539[/C][C]10.0993216314606[/C][/ROW]
[ROW][C]12[/C][C]110.8[/C][C]95.9877159705344[/C][C]14.8122840294656[/C][/ROW]
[ROW][C]13[/C][C]107.2[/C][C]99.1468533208297[/C][C]8.05314667917027[/C][/ROW]
[ROW][C]14[/C][C]99[/C][C]99.9855423728914[/C][C]-0.98554237289136[/C][/ROW]
[ROW][C]15[/C][C]99[/C][C]104.122092043904[/C][C]-5.12209204390369[/C][/ROW]
[ROW][C]16[/C][C]91[/C][C]101.396762088410[/C][C]-10.3967620884101[/C][/ROW]
[ROW][C]17[/C][C]96.2[/C][C]101.316667810614[/C][C]-5.11666781061418[/C][/ROW]
[ROW][C]18[/C][C]96.9[/C][C]103.517284941123[/C][C]-6.61728494112332[/C][/ROW]
[ROW][C]19[/C][C]96.2[/C][C]94.3416657828942[/C][C]1.85833421710579[/C][/ROW]
[ROW][C]20[/C][C]100.1[/C][C]93.1144734878168[/C][C]6.98552651218321[/C][/ROW]
[ROW][C]21[/C][C]99[/C][C]105.555241130375[/C][C]-6.55524113037466[/C][/ROW]
[ROW][C]22[/C][C]115.4[/C][C]108.751598250677[/C][C]6.64840174932289[/C][/ROW]
[ROW][C]23[/C][C]106.9[/C][C]102.666754939568[/C][C]4.23324506043218[/C][/ROW]
[ROW][C]24[/C][C]107.1[/C][C]99.960720289952[/C][C]7.13927971004807[/C][/ROW]
[ROW][C]25[/C][C]99.3[/C][C]102.743896906620[/C][C]-3.44389690661979[/C][/ROW]
[ROW][C]26[/C][C]99.2[/C][C]100.420614219356[/C][C]-1.22061421935625[/C][/ROW]
[ROW][C]27[/C][C]108.3[/C][C]105.017996039321[/C][C]3.28200396067914[/C][/ROW]
[ROW][C]28[/C][C]105.6[/C][C]103.020154172313[/C][C]2.57984582768725[/C][/ROW]
[ROW][C]29[/C][C]99.5[/C][C]97.3193013431756[/C][C]2.1806986568244[/C][/ROW]
[ROW][C]30[/C][C]107.4[/C][C]101.563764824036[/C][C]5.83623517596357[/C][/ROW]
[ROW][C]31[/C][C]93.1[/C][C]96.2745451603384[/C][C]-3.17454516033843[/C][/ROW]
[ROW][C]32[/C][C]88.1[/C][C]94.152987949309[/C][C]-6.05298794930912[/C][/ROW]
[ROW][C]33[/C][C]110.7[/C][C]111.291753631358[/C][C]-0.591753631357593[/C][/ROW]
[ROW][C]34[/C][C]113.1[/C][C]111.680912419700[/C][C]1.41908758029981[/C][/ROW]
[ROW][C]35[/C][C]99.6[/C][C]103.995702349813[/C][C]-4.39570234981285[/C][/ROW]
[ROW][C]36[/C][C]93.6[/C][C]107.090950413207[/C][C]-13.4909504132075[/C][/ROW]
[ROW][C]37[/C][C]98.6[/C][C]96.2943273617142[/C][C]2.30567263828579[/C][/ROW]
[ROW][C]38[/C][C]99.6[/C][C]98.9287860178254[/C][C]0.671213982174624[/C][/ROW]
[ROW][C]39[/C][C]114.3[/C][C]107.745237945385[/C][C]6.55476205461464[/C][/ROW]
[ROW][C]40[/C][C]107.8[/C][C]100.744999310461[/C][C]7.05500068953882[/C][/ROW]
[ROW][C]41[/C][C]101.2[/C][C]98.6141922735433[/C][C]2.58580772645669[/C][/ROW]
[ROW][C]42[/C][C]112.5[/C][C]110.615771947233[/C][C]1.88422805276697[/C][/ROW]
[ROW][C]43[/C][C]100.5[/C][C]96.2390421945807[/C][C]4.26095780541934[/C][/ROW]
[ROW][C]44[/C][C]93.9[/C][C]97.339079999368[/C][C]-3.43907999936808[/C][/ROW]
[ROW][C]45[/C][C]116.2[/C][C]111.157711361463[/C][C]5.04228863853704[/C][/ROW]
[ROW][C]46[/C][C]112[/C][C]107.125278911875[/C][C]4.8747210881246[/C][/ROW]
[ROW][C]47[/C][C]106.4[/C][C]104.931534132020[/C][C]1.4684658679798[/C][/ROW]
[ROW][C]48[/C][C]95.7[/C][C]108.556552787587[/C][C]-12.8565527875873[/C][/ROW]
[ROW][C]49[/C][C]96[/C][C]98.8145318911902[/C][C]-2.81453189119022[/C][/ROW]
[ROW][C]50[/C][C]95.8[/C][C]97.8797404091826[/C][C]-2.07974040918265[/C][/ROW]
[ROW][C]51[/C][C]103[/C][C]105.904157371468[/C][C]-2.90415737146767[/C][/ROW]
[ROW][C]52[/C][C]102.2[/C][C]101.208680401196[/C][C]0.991319598804501[/C][/ROW]
[ROW][C]53[/C][C]98.4[/C][C]99.8939585858995[/C][C]-1.49395858589952[/C][/ROW]
[ROW][C]54[/C][C]111.4[/C][C]106.479693742768[/C][C]4.92030625723188[/C][/ROW]
[ROW][C]55[/C][C]86.6[/C][C]91.5808340899797[/C][C]-4.98083408997972[/C][/ROW]
[ROW][C]56[/C][C]91.3[/C][C]96.7497775559254[/C][C]-5.44977755592537[/C][/ROW]
[ROW][C]57[/C][C]107.9[/C][C]110.522973268329[/C][C]-2.62297326832902[/C][/ROW]
[ROW][C]58[/C][C]101.8[/C][C]103.924013560252[/C][C]-2.12401356025245[/C][/ROW]
[ROW][C]59[/C][C]104.4[/C][C]106.650236536952[/C][C]-2.25023653695152[/C][/ROW]
[ROW][C]60[/C][C]93.4[/C][C]103.658597428649[/C][C]-10.2585974286491[/C][/ROW]
[ROW][C]61[/C][C]100.1[/C][C]99.9066416161732[/C][C]0.193358383826783[/C][/ROW]
[ROW][C]62[/C][C]98.5[/C][C]101.74457043159[/C][C]-3.24457043159012[/C][/ROW]
[ROW][C]63[/C][C]112.9[/C][C]110.962582731882[/C][C]1.93741726811799[/C][/ROW]
[ROW][C]64[/C][C]101.4[/C][C]99.7816258969023[/C][C]1.61837410309773[/C][/ROW]
[ROW][C]65[/C][C]107.1[/C][C]105.134829545621[/C][C]1.96517045437888[/C][/ROW]
[ROW][C]66[/C][C]110.8[/C][C]108.258378094683[/C][C]2.54162190531722[/C][/ROW]
[ROW][C]67[/C][C]90.3[/C][C]92.7553941971589[/C][C]-2.45539419715887[/C][/ROW]
[ROW][C]68[/C][C]95.5[/C][C]98.9865864521208[/C][C]-3.48658645212083[/C][/ROW]
[ROW][C]69[/C][C]111.4[/C][C]109.278018804956[/C][C]2.12198119504448[/C][/ROW]
[ROW][C]70[/C][C]113[/C][C]110.505752620967[/C][C]2.49424737903276[/C][/ROW]
[ROW][C]71[/C][C]107.5[/C][C]107.533148743493[/C][C]-0.0331487434925879[/C][/ROW]
[ROW][C]72[/C][C]95.9[/C][C]103.439375801052[/C][C]-7.53937580105248[/C][/ROW]
[ROW][C]73[/C][C]106.3[/C][C]101.958930254546[/C][C]4.34106974545445[/C][/ROW]
[ROW][C]74[/C][C]105.2[/C][C]100.93237445195[/C][C]4.26762554804995[/C][/ROW]
[ROW][C]75[/C][C]117.2[/C][C]108.937734812573[/C][C]8.26226518742705[/C][/ROW]
[ROW][C]76[/C][C]106.9[/C][C]99.6708049746522[/C][C]7.22919502534776[/C][/ROW]
[ROW][C]77[/C][C]108.2[/C][C]102.126251235841[/C][C]6.07374876415871[/C][/ROW]
[ROW][C]78[/C][C]110[/C][C]107.137897829646[/C][C]2.86210217035410[/C][/ROW]
[ROW][C]79[/C][C]96.1[/C][C]98.38457960757[/C][C]-2.28457960757003[/C][/ROW]
[ROW][C]80[/C][C]100.6[/C][C]99.8565769241726[/C][C]0.743423075827347[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14361&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14361&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	103.1	97.2962969767737	5.80370302322631
2	100.6	97.5484398736166	3.05156012638336
3	103.1	102.985972600501	0.11402739949926
4	95.5	95.6133576126502	-0.113357612650164
5	90.5	98.2366390633512	-7.73663906335116
6	90.9	102.844607884239	-11.9446078842392
7	88.8	89.4900351975311	-0.690035197531108
8	90.7	92.9979662136317	-2.29796621363169
9	94.3	102.293966106939	-7.99396610693868
10	104.6	107.704324131693	-3.10432413169319
11	111.1	101.000678368539	10.0993216314606
12	110.8	95.9877159705344	14.8122840294656
13	107.2	99.1468533208297	8.05314667917027
14	99	99.9855423728914	-0.98554237289136
15	99	104.122092043904	-5.12209204390369
16	91	101.396762088410	-10.3967620884101
17	96.2	101.316667810614	-5.11666781061418
18	96.9	103.517284941123	-6.61728494112332
19	96.2	94.3416657828942	1.85833421710579
20	100.1	93.1144734878168	6.98552651218321
21	99	105.555241130375	-6.55524113037466
22	115.4	108.751598250677	6.64840174932289
23	106.9	102.666754939568	4.23324506043218
24	107.1	99.960720289952	7.13927971004807
25	99.3	102.743896906620	-3.44389690661979
26	99.2	100.420614219356	-1.22061421935625
27	108.3	105.017996039321	3.28200396067914
28	105.6	103.020154172313	2.57984582768725
29	99.5	97.3193013431756	2.1806986568244
30	107.4	101.563764824036	5.83623517596357
31	93.1	96.2745451603384	-3.17454516033843
32	88.1	94.152987949309	-6.05298794930912
33	110.7	111.291753631358	-0.591753631357593
34	113.1	111.680912419700	1.41908758029981
35	99.6	103.995702349813	-4.39570234981285
36	93.6	107.090950413207	-13.4909504132075
37	98.6	96.2943273617142	2.30567263828579
38	99.6	98.9287860178254	0.671213982174624
39	114.3	107.745237945385	6.55476205461464
40	107.8	100.744999310461	7.05500068953882
41	101.2	98.6141922735433	2.58580772645669
42	112.5	110.615771947233	1.88422805276697
43	100.5	96.2390421945807	4.26095780541934
44	93.9	97.339079999368	-3.43907999936808
45	116.2	111.157711361463	5.04228863853704
46	112	107.125278911875	4.8747210881246
47	106.4	104.931534132020	1.4684658679798
48	95.7	108.556552787587	-12.8565527875873
49	96	98.8145318911902	-2.81453189119022
50	95.8	97.8797404091826	-2.07974040918265
51	103	105.904157371468	-2.90415737146767
52	102.2	101.208680401196	0.991319598804501
53	98.4	99.8939585858995	-1.49395858589952
54	111.4	106.479693742768	4.92030625723188
55	86.6	91.5808340899797	-4.98083408997972
56	91.3	96.7497775559254	-5.44977755592537
57	107.9	110.522973268329	-2.62297326832902
58	101.8	103.924013560252	-2.12401356025245
59	104.4	106.650236536952	-2.25023653695152
60	93.4	103.658597428649	-10.2585974286491
61	100.1	99.9066416161732	0.193358383826783
62	98.5	101.74457043159	-3.24457043159012
63	112.9	110.962582731882	1.93741726811799
64	101.4	99.7816258969023	1.61837410309773
65	107.1	105.134829545621	1.96517045437888
66	110.8	108.258378094683	2.54162190531722
67	90.3	92.7553941971589	-2.45539419715887
68	95.5	98.9865864521208	-3.48658645212083
69	111.4	109.278018804956	2.12198119504448
70	113	110.505752620967	2.49424737903276
71	107.5	107.533148743493	-0.0331487434925879
72	95.9	103.439375801052	-7.53937580105248
73	106.3	101.958930254546	4.34106974545445
74	105.2	100.93237445195	4.26762554804995
75	117.2	108.937734812573	8.26226518742705
76	106.9	99.6708049746522	7.22919502534776
77	108.2	102.126251235841	6.07374876415871
78	110	107.137897829646	2.86210217035410
79	96.1	98.38457960757	-2.28457960757003
80	100.6	99.8565769241726	0.743423075827347

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Figure 3

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Figure 4

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Figure 5

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Figure 7

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Figure 8

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Figure 9

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Parameters (Session):

par1 = FALSE ; par2 = 1 ; par3 = 0 ; par4 = 1 ; par5 = 12 ; par6 = 3 ; par7 = 0 ; par8 = 2 ; par9 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

R code (references can be found in the software module):

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code