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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 05 Dec 2007 15:01:56 -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/05/t1196891347n9ja58b9854epid.htm/, Retrieved Fri, 03 May 2024 00:49:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=2531, Retrieved Fri, 03 May 2024 00:49:04 +0000

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Estimated Impact

225

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Multiple Regression] [paper] [2007-12-05 22:01:56] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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106.7	97.3	0	104.8	93.5
110.2	101	0	105.6	94.7
125.9	113.2	0	118.3	112.9
100.1	101	0	89.9	99.2
106.4	105.7	0	90.2	105.6
114.8	113.9	0	107	113
81.3	86.4	0	64.5	83.1
87	96.5	0	92.6	81.1
104.2	103.3	0	95.8	96.9
108	114.9	0	94.3	104.3
105	105.8	0	91.2	97.7
94.5	94.2	0	86.3	102.6
92	98.4	0	77.6	89.9
95.9	99.4	0	82.5	96
108.8	108.8	0	97.7	112.7
103.4	112.6	0	83.3	107.1
102.1	104.4	0	84.2	106.2
110.1	112.2	0	92.8	121
83.2	81.1	0	77.4	101.2
82.7	97.1	0	72.5	83.2
106.8	112.6	0	88.8	105.1
113.7	113.8	0	93.4	113.3
102.5	107.8	0	92.6	99.1
96.6	103.2	0	90.7	100.3
92.1	103.3	0	81.6	93.5
95.6	101.2	0	84.1	98.8
102.3	107.7	0	88.1	106.2
98.6	110.4	0	85.3	98.3
98.2	101.9	0	82.9	102.1
104.5	115.9	0	84.8	117.1
84	89.9	0	71.2	101.5
73.8	88.6	0	68.9	80.5
103.9	117.2	0	94.3	105.9
106	123.9	0	97.6	109.5
97.2	100	0	85.6	97.2
102.6	103.6	0	91.9	114.5
89	94.1	0	75.8	93.5
93.8	98.7	0	79.8	100.9
116.7	119.5	0	99	121.1
106.8	112.7	0	88.5	116.5
98.5	104.4	0	86.7	109.3
118.7	124.7	0	97.9	118.1
90	89.1	0	94.3	108.3
91.9	97	0	72.9	105.4
113.3	121.6	0	91.8	116.2
113.1	118.8	0	93.2	111.2
104.1	114	0	86.5	105.8
108.7	111.5	0	98.9	122.7
96.7	97.2	0	77.2	99.5
101	102.5	0	79.4	107.9
116.9	113.4	0	90.4	124.6
105.8	109.8	0	81.4	115
99	104.9	0	85.8	110.3
129.4	126.1	0	103.6	132.7
83	80	0	73.6	99.7
88.9	96.8	0	75.7	96.5
115.9	117.2	1	99.2	118.7
104.2	112.3	1	88.7	112.9
113.4	117.3	1	94.6	130.5
112.2	111.1	1	98.7	137.9
100.8	102.2	1	84.2	115
107.3	104.3	1	87.7	116.8
126.6	122.9	1	103.3	140.9
102.9	107.6	1	88.2	120.7
117.9	121.3	1	93.4	134.2
128.8	131.5	1	106.3	147.3
87.5	89	1	73.1	112.4
93.8	104.4	1	78.6	107.1
122.7	128.9	1	101.6	128.4
126.2	135.9	1	101.4	137.7
124.6	133.3	1	98.5	135
116.7	121.3	1	99	151
115.2	120.5	1	89.5	137.4
111.1	120.4	1	83.5	132.4
129.9	137.9	1	97.4	161.3
113.3	126.1	1	87.8	139.8
118.5	133.2	1	90.4	146
133.5	146.6	1	97.1	154.6
102.1	103.4	1	79.4	142.1
102.4	117.2	1	85	120.5

Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2531&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]5 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=2531&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2531&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	5 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Prod[t] = + 19.1968864455273 + 1.1337268515953Tot[t] -0.264635094351931Conjun[t] -0.264628251011800Mach[t] -0.0782681301598515`Elek `[t] + 0.0924904460551557t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Prod[t] =  +  19.1968864455273 +  1.1337268515953Tot[t] -0.264635094351931Conjun[t] -0.264628251011800Mach[t] -0.0782681301598515`Elek
`[t] +  0.0924904460551557t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2531&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Prod[t] =  +  19.1968864455273 +  1.1337268515953Tot[t] -0.264635094351931Conjun[t] -0.264628251011800Mach[t] -0.0782681301598515`Elek
`[t] +  0.0924904460551557t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2531&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2531&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
Prod[t] = + 19.1968864455273 + 1.1337268515953Tot[t] -0.264635094351931Conjun[t] -0.264628251011800Mach[t] -0.0782681301598515`Elek `[t] + 0.0924904460551557t + 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)	19.1968864455273	6.173543	3.1095	0.00266	0.00133
Tot	1.1337268515953	0.135972	8.3379	0	0
Conjun	-0.264635094351931	2.274984	-0.1163	0.907711	0.453855
Mach	-0.264628251011800	0.12479	-2.1206	0.037307	0.018654
`Elek `	-0.0782681301598515	0.095623	-0.8185	0.415698	0.207849
t	0.0924904460551557	0.05528	1.6731	0.098528	0.049264

\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) & 19.1968864455273 & 6.173543 & 3.1095 & 0.00266 & 0.00133 \tabularnewline
Tot & 1.1337268515953 & 0.135972 & 8.3379 & 0 & 0 \tabularnewline
Conjun & -0.264635094351931 & 2.274984 & -0.1163 & 0.907711 & 0.453855 \tabularnewline
Mach & -0.264628251011800 & 0.12479 & -2.1206 & 0.037307 & 0.018654 \tabularnewline
`Elek
` & -0.0782681301598515 & 0.095623 & -0.8185 & 0.415698 & 0.207849 \tabularnewline
t & 0.0924904460551557 & 0.05528 & 1.6731 & 0.098528 & 0.049264 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2531&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]19.1968864455273[/C][C]6.173543[/C][C]3.1095[/C][C]0.00266[/C][C]0.00133[/C][/ROW]
[ROW][C]Tot[/C][C]1.1337268515953[/C][C]0.135972[/C][C]8.3379[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]Conjun[/C][C]-0.264635094351931[/C][C]2.274984[/C][C]-0.1163[/C][C]0.907711[/C][C]0.453855[/C][/ROW]
[ROW][C]Mach[/C][C]-0.264628251011800[/C][C]0.12479[/C][C]-2.1206[/C][C]0.037307[/C][C]0.018654[/C][/ROW]
[ROW][C]`Elek
`[/C][C]-0.0782681301598515[/C][C]0.095623[/C][C]-0.8185[/C][C]0.415698[/C][C]0.207849[/C][/ROW]
[ROW][C]t[/C][C]0.0924904460551557[/C][C]0.05528[/C][C]1.6731[/C][C]0.098528[/C][C]0.049264[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2531&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2531&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)	19.1968864455273	6.173543	3.1095	0.00266	0.00133
Tot	1.1337268515953	0.135972	8.3379	0	0
Conjun	-0.264635094351931	2.274984	-0.1163	0.907711	0.453855
Mach	-0.264628251011800	0.12479	-2.1206	0.037307	0.018654
`Elek `	-0.0782681301598515	0.095623	-0.8185	0.415698	0.207849
t	0.0924904460551557	0.05528	1.6731	0.098528	0.049264

Multiple Linear Regression - Regression Statistics
Multiple R	0.922878188965104
R-squared	0.85170415166751
Adjusted R-squared	0.841684161915316
F-TEST (value)	85.0005012710633
F-TEST (DF numerator)	5
F-TEST (DF denominator)	74
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.25380915697527
Sum Squared Residuals	2042.58578868587

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.922878188965104 \tabularnewline
R-squared & 0.85170415166751 \tabularnewline
Adjusted R-squared & 0.841684161915316 \tabularnewline
F-TEST (value) & 85.0005012710633 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 74 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 5.25380915697527 \tabularnewline
Sum Squared Residuals & 2042.58578868587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2531&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.922878188965104[/C][/ROW]
[ROW][C]R-squared[/C][C]0.85170415166751[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.841684161915316[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]85.0005012710633[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]74[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]5.25380915697527[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]2042.58578868587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2531&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2531&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	0.922878188965104
R-squared	0.85170415166751
Adjusted R-squared	0.841684161915316
F-TEST (value)	85.0005012710633
F-TEST (DF numerator)	5
F-TEST (DF denominator)	74
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	5.25380915697527
Sum Squared Residuals	2042.58578868587

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	97.3	105.206921080818	-7.9069210808179
2	101	108.961831150456	-7.96183115045556
3	113.2	122.068574409798	-8.86857440979768
4	101	101.498627796619	-0.498627796619182
5	105.7	108.153292899398	-2.45329289939816
6	113.9	112.744150118673	1.15584988132733
7	86.4	88.4437087960664	-2.04370879606636
8	96.5	87.7189247031028	8.78107529689716
9	103.3	105.228070136834	-1.92807013683375
10	114.9	109.446480832286	5.45351916771417
11	105.8	107.474707960747	-1.67470796074669
12	94.2	96.5762310572258	-2.37623105722575
13	98.4	97.1306754111254	1.26932458887457
14	99.4	99.8705865544694	-0.470586554469342
15	108.8	109.258726197055	-0.458726197054978
16	112.6	107.478039987961	5.12196001203938
17	104.4	105.928961418175	-1.52896141817511
18	112.2	111.657095391925	0.542904608074616
19	81.1	86.8773175728138	-5.77731757281377
20	97.1	89.1084493659064	7.99155063409357
21	112.6	110.496244393415	2.10375560658479
22	113.8	116.552361493513	-2.75236149351288
23	107.8	105.27022125078	2.52977874922
24	103.2	99.0825951931535	4.11740480684653
25	103.3	97.0136551763242	6.28634482367584
26	101.2	99.9977978855862	1.20220211441385
27	107.7	106.048561070100	1.65143892990029
28	110.4	103.305539496348	7.09446050365188
29	101.9	103.282228109586	-1.38222810958605
30	115.9	108.840382091371	7.0596179086286
31	89.9	90.511399123977	-0.611399123977073
32	88.6	81.2921513944442	7.3078486055558
33	117.2	106.800251991758	10.3997480082421
34	123.9	108.118530329249	15.7814696707512
35	100	102.372461494373	-2.3724614943731
36	103.6	105.565880305903	-1.96588030590310
37	94.1	96.143831144909	-2.04383114490905
38	98.7	100.040513311392	-1.34051331139153
39	119.5	119.433470010324	0.0665299896765028
40	112.7	111.440694659944	1.25930534005560
41	104.4	103.163113626731	1.23688637326927
42	124.7	122.504290518272	2.19570948172791
43	89.1	91.7785097027512	-2.67850970275117
44	97	99.9151033159535	-2.9151033159535
45	121.6	118.422578636299	3.17742136370136
46	118.8	118.309184811417	0.490815188582529
47	114	110.393790777757	3.60620922224281
48	111.5	111.097303028903	0.402696971097087
49	97.2	105.143324922479	-7.9433249224791
50	102.5	108.871206384825	-6.37120638482532
51	113.4	122.771965236446	-9.37196523644642
52	109.8	113.413115938435	-3.61311593843453
53	104.9	104.999759700941	-0.0997597009410196
54	126.1	133.093957451903	-6.99395745190259
55	80	91.103217809565	-11.1032178095649
56	96.8	97.579435369419	-0.779435369419105
57	117.2	120.061599325869	-2.86159932586941
58	112.3	110.122037398811	2.17796260118940
59	117.3	117.705989107760	-0.405989107759516
60	111.1	114.773847339569	-3.67384733956903
61	102.2	107.571301497769	-5.37130149776945
62	104.3	113.965934966365	-9.66593496636503
63	122.9	129.924890995573	-7.02489099557296
64	107.6	108.724957878327	-1.12495787832671
65	121.3	123.390664435892	-2.09066443589201
66	131.5	131.401760621190	0.0982393788103453
67	89	96.1885477725295	-7.1885477725295
68	104.4	102.382883092917	2.01711690708265
69	128.9	127.486518604400	1.41348139559956
70	135.9	130.872085070755	5.02791492924512
71	133.3	130.129358433623	3.17064156637664
72	121.3	119.880802544012	1.41919745598785
73	120.5	121.851117667460	-1.35111766746044
74	120.4	119.274438178845	1.12556182115509
75	137.9	134.740711784208	3.15928821579202
76	126.1	120.236532501931	5.86346749806875
77	133.2	125.051106716660	8.14889328333979
78	146.6	139.703384735491	6.89661526450892
79	103.4	109.859123711361	-6.4591237113608
80	117.2	110.500405618681	6.69959438131872

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 97.3 & 105.206921080818 & -7.9069210808179 \tabularnewline
2 & 101 & 108.961831150456 & -7.96183115045556 \tabularnewline
3 & 113.2 & 122.068574409798 & -8.86857440979768 \tabularnewline
4 & 101 & 101.498627796619 & -0.498627796619182 \tabularnewline
5 & 105.7 & 108.153292899398 & -2.45329289939816 \tabularnewline
6 & 113.9 & 112.744150118673 & 1.15584988132733 \tabularnewline
7 & 86.4 & 88.4437087960664 & -2.04370879606636 \tabularnewline
8 & 96.5 & 87.7189247031028 & 8.78107529689716 \tabularnewline
9 & 103.3 & 105.228070136834 & -1.92807013683375 \tabularnewline
10 & 114.9 & 109.446480832286 & 5.45351916771417 \tabularnewline
11 & 105.8 & 107.474707960747 & -1.67470796074669 \tabularnewline
12 & 94.2 & 96.5762310572258 & -2.37623105722575 \tabularnewline
13 & 98.4 & 97.1306754111254 & 1.26932458887457 \tabularnewline
14 & 99.4 & 99.8705865544694 & -0.470586554469342 \tabularnewline
15 & 108.8 & 109.258726197055 & -0.458726197054978 \tabularnewline
16 & 112.6 & 107.478039987961 & 5.12196001203938 \tabularnewline
17 & 104.4 & 105.928961418175 & -1.52896141817511 \tabularnewline
18 & 112.2 & 111.657095391925 & 0.542904608074616 \tabularnewline
19 & 81.1 & 86.8773175728138 & -5.77731757281377 \tabularnewline
20 & 97.1 & 89.1084493659064 & 7.99155063409357 \tabularnewline
21 & 112.6 & 110.496244393415 & 2.10375560658479 \tabularnewline
22 & 113.8 & 116.552361493513 & -2.75236149351288 \tabularnewline
23 & 107.8 & 105.27022125078 & 2.52977874922 \tabularnewline
24 & 103.2 & 99.0825951931535 & 4.11740480684653 \tabularnewline
25 & 103.3 & 97.0136551763242 & 6.28634482367584 \tabularnewline
26 & 101.2 & 99.9977978855862 & 1.20220211441385 \tabularnewline
27 & 107.7 & 106.048561070100 & 1.65143892990029 \tabularnewline
28 & 110.4 & 103.305539496348 & 7.09446050365188 \tabularnewline
29 & 101.9 & 103.282228109586 & -1.38222810958605 \tabularnewline
30 & 115.9 & 108.840382091371 & 7.0596179086286 \tabularnewline
31 & 89.9 & 90.511399123977 & -0.611399123977073 \tabularnewline
32 & 88.6 & 81.2921513944442 & 7.3078486055558 \tabularnewline
33 & 117.2 & 106.800251991758 & 10.3997480082421 \tabularnewline
34 & 123.9 & 108.118530329249 & 15.7814696707512 \tabularnewline
35 & 100 & 102.372461494373 & -2.3724614943731 \tabularnewline
36 & 103.6 & 105.565880305903 & -1.96588030590310 \tabularnewline
37 & 94.1 & 96.143831144909 & -2.04383114490905 \tabularnewline
38 & 98.7 & 100.040513311392 & -1.34051331139153 \tabularnewline
39 & 119.5 & 119.433470010324 & 0.0665299896765028 \tabularnewline
40 & 112.7 & 111.440694659944 & 1.25930534005560 \tabularnewline
41 & 104.4 & 103.163113626731 & 1.23688637326927 \tabularnewline
42 & 124.7 & 122.504290518272 & 2.19570948172791 \tabularnewline
43 & 89.1 & 91.7785097027512 & -2.67850970275117 \tabularnewline
44 & 97 & 99.9151033159535 & -2.9151033159535 \tabularnewline
45 & 121.6 & 118.422578636299 & 3.17742136370136 \tabularnewline
46 & 118.8 & 118.309184811417 & 0.490815188582529 \tabularnewline
47 & 114 & 110.393790777757 & 3.60620922224281 \tabularnewline
48 & 111.5 & 111.097303028903 & 0.402696971097087 \tabularnewline
49 & 97.2 & 105.143324922479 & -7.9433249224791 \tabularnewline
50 & 102.5 & 108.871206384825 & -6.37120638482532 \tabularnewline
51 & 113.4 & 122.771965236446 & -9.37196523644642 \tabularnewline
52 & 109.8 & 113.413115938435 & -3.61311593843453 \tabularnewline
53 & 104.9 & 104.999759700941 & -0.0997597009410196 \tabularnewline
54 & 126.1 & 133.093957451903 & -6.99395745190259 \tabularnewline
55 & 80 & 91.103217809565 & -11.1032178095649 \tabularnewline
56 & 96.8 & 97.579435369419 & -0.779435369419105 \tabularnewline
57 & 117.2 & 120.061599325869 & -2.86159932586941 \tabularnewline
58 & 112.3 & 110.122037398811 & 2.17796260118940 \tabularnewline
59 & 117.3 & 117.705989107760 & -0.405989107759516 \tabularnewline
60 & 111.1 & 114.773847339569 & -3.67384733956903 \tabularnewline
61 & 102.2 & 107.571301497769 & -5.37130149776945 \tabularnewline
62 & 104.3 & 113.965934966365 & -9.66593496636503 \tabularnewline
63 & 122.9 & 129.924890995573 & -7.02489099557296 \tabularnewline
64 & 107.6 & 108.724957878327 & -1.12495787832671 \tabularnewline
65 & 121.3 & 123.390664435892 & -2.09066443589201 \tabularnewline
66 & 131.5 & 131.401760621190 & 0.0982393788103453 \tabularnewline
67 & 89 & 96.1885477725295 & -7.1885477725295 \tabularnewline
68 & 104.4 & 102.382883092917 & 2.01711690708265 \tabularnewline
69 & 128.9 & 127.486518604400 & 1.41348139559956 \tabularnewline
70 & 135.9 & 130.872085070755 & 5.02791492924512 \tabularnewline
71 & 133.3 & 130.129358433623 & 3.17064156637664 \tabularnewline
72 & 121.3 & 119.880802544012 & 1.41919745598785 \tabularnewline
73 & 120.5 & 121.851117667460 & -1.35111766746044 \tabularnewline
74 & 120.4 & 119.274438178845 & 1.12556182115509 \tabularnewline
75 & 137.9 & 134.740711784208 & 3.15928821579202 \tabularnewline
76 & 126.1 & 120.236532501931 & 5.86346749806875 \tabularnewline
77 & 133.2 & 125.051106716660 & 8.14889328333979 \tabularnewline
78 & 146.6 & 139.703384735491 & 6.89661526450892 \tabularnewline
79 & 103.4 & 109.859123711361 & -6.4591237113608 \tabularnewline
80 & 117.2 & 110.500405618681 & 6.69959438131872 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=2531&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]97.3[/C][C]105.206921080818[/C][C]-7.9069210808179[/C][/ROW]
[ROW][C]2[/C][C]101[/C][C]108.961831150456[/C][C]-7.96183115045556[/C][/ROW]
[ROW][C]3[/C][C]113.2[/C][C]122.068574409798[/C][C]-8.86857440979768[/C][/ROW]
[ROW][C]4[/C][C]101[/C][C]101.498627796619[/C][C]-0.498627796619182[/C][/ROW]
[ROW][C]5[/C][C]105.7[/C][C]108.153292899398[/C][C]-2.45329289939816[/C][/ROW]
[ROW][C]6[/C][C]113.9[/C][C]112.744150118673[/C][C]1.15584988132733[/C][/ROW]
[ROW][C]7[/C][C]86.4[/C][C]88.4437087960664[/C][C]-2.04370879606636[/C][/ROW]
[ROW][C]8[/C][C]96.5[/C][C]87.7189247031028[/C][C]8.78107529689716[/C][/ROW]
[ROW][C]9[/C][C]103.3[/C][C]105.228070136834[/C][C]-1.92807013683375[/C][/ROW]
[ROW][C]10[/C][C]114.9[/C][C]109.446480832286[/C][C]5.45351916771417[/C][/ROW]
[ROW][C]11[/C][C]105.8[/C][C]107.474707960747[/C][C]-1.67470796074669[/C][/ROW]
[ROW][C]12[/C][C]94.2[/C][C]96.5762310572258[/C][C]-2.37623105722575[/C][/ROW]
[ROW][C]13[/C][C]98.4[/C][C]97.1306754111254[/C][C]1.26932458887457[/C][/ROW]
[ROW][C]14[/C][C]99.4[/C][C]99.8705865544694[/C][C]-0.470586554469342[/C][/ROW]
[ROW][C]15[/C][C]108.8[/C][C]109.258726197055[/C][C]-0.458726197054978[/C][/ROW]
[ROW][C]16[/C][C]112.6[/C][C]107.478039987961[/C][C]5.12196001203938[/C][/ROW]
[ROW][C]17[/C][C]104.4[/C][C]105.928961418175[/C][C]-1.52896141817511[/C][/ROW]
[ROW][C]18[/C][C]112.2[/C][C]111.657095391925[/C][C]0.542904608074616[/C][/ROW]
[ROW][C]19[/C][C]81.1[/C][C]86.8773175728138[/C][C]-5.77731757281377[/C][/ROW]
[ROW][C]20[/C][C]97.1[/C][C]89.1084493659064[/C][C]7.99155063409357[/C][/ROW]
[ROW][C]21[/C][C]112.6[/C][C]110.496244393415[/C][C]2.10375560658479[/C][/ROW]
[ROW][C]22[/C][C]113.8[/C][C]116.552361493513[/C][C]-2.75236149351288[/C][/ROW]
[ROW][C]23[/C][C]107.8[/C][C]105.27022125078[/C][C]2.52977874922[/C][/ROW]
[ROW][C]24[/C][C]103.2[/C][C]99.0825951931535[/C][C]4.11740480684653[/C][/ROW]
[ROW][C]25[/C][C]103.3[/C][C]97.0136551763242[/C][C]6.28634482367584[/C][/ROW]
[ROW][C]26[/C][C]101.2[/C][C]99.9977978855862[/C][C]1.20220211441385[/C][/ROW]
[ROW][C]27[/C][C]107.7[/C][C]106.048561070100[/C][C]1.65143892990029[/C][/ROW]
[ROW][C]28[/C][C]110.4[/C][C]103.305539496348[/C][C]7.09446050365188[/C][/ROW]
[ROW][C]29[/C][C]101.9[/C][C]103.282228109586[/C][C]-1.38222810958605[/C][/ROW]
[ROW][C]30[/C][C]115.9[/C][C]108.840382091371[/C][C]7.0596179086286[/C][/ROW]
[ROW][C]31[/C][C]89.9[/C][C]90.511399123977[/C][C]-0.611399123977073[/C][/ROW]
[ROW][C]32[/C][C]88.6[/C][C]81.2921513944442[/C][C]7.3078486055558[/C][/ROW]
[ROW][C]33[/C][C]117.2[/C][C]106.800251991758[/C][C]10.3997480082421[/C][/ROW]
[ROW][C]34[/C][C]123.9[/C][C]108.118530329249[/C][C]15.7814696707512[/C][/ROW]
[ROW][C]35[/C][C]100[/C][C]102.372461494373[/C][C]-2.3724614943731[/C][/ROW]
[ROW][C]36[/C][C]103.6[/C][C]105.565880305903[/C][C]-1.96588030590310[/C][/ROW]
[ROW][C]37[/C][C]94.1[/C][C]96.143831144909[/C][C]-2.04383114490905[/C][/ROW]
[ROW][C]38[/C][C]98.7[/C][C]100.040513311392[/C][C]-1.34051331139153[/C][/ROW]
[ROW][C]39[/C][C]119.5[/C][C]119.433470010324[/C][C]0.0665299896765028[/C][/ROW]
[ROW][C]40[/C][C]112.7[/C][C]111.440694659944[/C][C]1.25930534005560[/C][/ROW]
[ROW][C]41[/C][C]104.4[/C][C]103.163113626731[/C][C]1.23688637326927[/C][/ROW]
[ROW][C]42[/C][C]124.7[/C][C]122.504290518272[/C][C]2.19570948172791[/C][/ROW]
[ROW][C]43[/C][C]89.1[/C][C]91.7785097027512[/C][C]-2.67850970275117[/C][/ROW]
[ROW][C]44[/C][C]97[/C][C]99.9151033159535[/C][C]-2.9151033159535[/C][/ROW]
[ROW][C]45[/C][C]121.6[/C][C]118.422578636299[/C][C]3.17742136370136[/C][/ROW]
[ROW][C]46[/C][C]118.8[/C][C]118.309184811417[/C][C]0.490815188582529[/C][/ROW]
[ROW][C]47[/C][C]114[/C][C]110.393790777757[/C][C]3.60620922224281[/C][/ROW]
[ROW][C]48[/C][C]111.5[/C][C]111.097303028903[/C][C]0.402696971097087[/C][/ROW]
[ROW][C]49[/C][C]97.2[/C][C]105.143324922479[/C][C]-7.9433249224791[/C][/ROW]
[ROW][C]50[/C][C]102.5[/C][C]108.871206384825[/C][C]-6.37120638482532[/C][/ROW]
[ROW][C]51[/C][C]113.4[/C][C]122.771965236446[/C][C]-9.37196523644642[/C][/ROW]
[ROW][C]52[/C][C]109.8[/C][C]113.413115938435[/C][C]-3.61311593843453[/C][/ROW]
[ROW][C]53[/C][C]104.9[/C][C]104.999759700941[/C][C]-0.0997597009410196[/C][/ROW]
[ROW][C]54[/C][C]126.1[/C][C]133.093957451903[/C][C]-6.99395745190259[/C][/ROW]
[ROW][C]55[/C][C]80[/C][C]91.103217809565[/C][C]-11.1032178095649[/C][/ROW]
[ROW][C]56[/C][C]96.8[/C][C]97.579435369419[/C][C]-0.779435369419105[/C][/ROW]
[ROW][C]57[/C][C]117.2[/C][C]120.061599325869[/C][C]-2.86159932586941[/C][/ROW]
[ROW][C]58[/C][C]112.3[/C][C]110.122037398811[/C][C]2.17796260118940[/C][/ROW]
[ROW][C]59[/C][C]117.3[/C][C]117.705989107760[/C][C]-0.405989107759516[/C][/ROW]
[ROW][C]60[/C][C]111.1[/C][C]114.773847339569[/C][C]-3.67384733956903[/C][/ROW]
[ROW][C]61[/C][C]102.2[/C][C]107.571301497769[/C][C]-5.37130149776945[/C][/ROW]
[ROW][C]62[/C][C]104.3[/C][C]113.965934966365[/C][C]-9.66593496636503[/C][/ROW]
[ROW][C]63[/C][C]122.9[/C][C]129.924890995573[/C][C]-7.02489099557296[/C][/ROW]
[ROW][C]64[/C][C]107.6[/C][C]108.724957878327[/C][C]-1.12495787832671[/C][/ROW]
[ROW][C]65[/C][C]121.3[/C][C]123.390664435892[/C][C]-2.09066443589201[/C][/ROW]
[ROW][C]66[/C][C]131.5[/C][C]131.401760621190[/C][C]0.0982393788103453[/C][/ROW]
[ROW][C]67[/C][C]89[/C][C]96.1885477725295[/C][C]-7.1885477725295[/C][/ROW]
[ROW][C]68[/C][C]104.4[/C][C]102.382883092917[/C][C]2.01711690708265[/C][/ROW]
[ROW][C]69[/C][C]128.9[/C][C]127.486518604400[/C][C]1.41348139559956[/C][/ROW]
[ROW][C]70[/C][C]135.9[/C][C]130.872085070755[/C][C]5.02791492924512[/C][/ROW]
[ROW][C]71[/C][C]133.3[/C][C]130.129358433623[/C][C]3.17064156637664[/C][/ROW]
[ROW][C]72[/C][C]121.3[/C][C]119.880802544012[/C][C]1.41919745598785[/C][/ROW]
[ROW][C]73[/C][C]120.5[/C][C]121.851117667460[/C][C]-1.35111766746044[/C][/ROW]
[ROW][C]74[/C][C]120.4[/C][C]119.274438178845[/C][C]1.12556182115509[/C][/ROW]
[ROW][C]75[/C][C]137.9[/C][C]134.740711784208[/C][C]3.15928821579202[/C][/ROW]
[ROW][C]76[/C][C]126.1[/C][C]120.236532501931[/C][C]5.86346749806875[/C][/ROW]
[ROW][C]77[/C][C]133.2[/C][C]125.051106716660[/C][C]8.14889328333979[/C][/ROW]
[ROW][C]78[/C][C]146.6[/C][C]139.703384735491[/C][C]6.89661526450892[/C][/ROW]
[ROW][C]79[/C][C]103.4[/C][C]109.859123711361[/C][C]-6.4591237113608[/C][/ROW]
[ROW][C]80[/C][C]117.2[/C][C]110.500405618681[/C][C]6.69959438131872[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=2531&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=2531&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	97.3	105.206921080818	-7.9069210808179
2	101	108.961831150456	-7.96183115045556
3	113.2	122.068574409798	-8.86857440979768
4	101	101.498627796619	-0.498627796619182
5	105.7	108.153292899398	-2.45329289939816
6	113.9	112.744150118673	1.15584988132733
7	86.4	88.4437087960664	-2.04370879606636
8	96.5	87.7189247031028	8.78107529689716
9	103.3	105.228070136834	-1.92807013683375
10	114.9	109.446480832286	5.45351916771417
11	105.8	107.474707960747	-1.67470796074669
12	94.2	96.5762310572258	-2.37623105722575
13	98.4	97.1306754111254	1.26932458887457
14	99.4	99.8705865544694	-0.470586554469342
15	108.8	109.258726197055	-0.458726197054978
16	112.6	107.478039987961	5.12196001203938
17	104.4	105.928961418175	-1.52896141817511
18	112.2	111.657095391925	0.542904608074616
19	81.1	86.8773175728138	-5.77731757281377
20	97.1	89.1084493659064	7.99155063409357
21	112.6	110.496244393415	2.10375560658479
22	113.8	116.552361493513	-2.75236149351288
23	107.8	105.27022125078	2.52977874922
24	103.2	99.0825951931535	4.11740480684653
25	103.3	97.0136551763242	6.28634482367584
26	101.2	99.9977978855862	1.20220211441385
27	107.7	106.048561070100	1.65143892990029
28	110.4	103.305539496348	7.09446050365188
29	101.9	103.282228109586	-1.38222810958605
30	115.9	108.840382091371	7.0596179086286
31	89.9	90.511399123977	-0.611399123977073
32	88.6	81.2921513944442	7.3078486055558
33	117.2	106.800251991758	10.3997480082421
34	123.9	108.118530329249	15.7814696707512
35	100	102.372461494373	-2.3724614943731
36	103.6	105.565880305903	-1.96588030590310
37	94.1	96.143831144909	-2.04383114490905
38	98.7	100.040513311392	-1.34051331139153
39	119.5	119.433470010324	0.0665299896765028
40	112.7	111.440694659944	1.25930534005560
41	104.4	103.163113626731	1.23688637326927
42	124.7	122.504290518272	2.19570948172791
43	89.1	91.7785097027512	-2.67850970275117
44	97	99.9151033159535	-2.9151033159535
45	121.6	118.422578636299	3.17742136370136
46	118.8	118.309184811417	0.490815188582529
47	114	110.393790777757	3.60620922224281
48	111.5	111.097303028903	0.402696971097087
49	97.2	105.143324922479	-7.9433249224791
50	102.5	108.871206384825	-6.37120638482532
51	113.4	122.771965236446	-9.37196523644642
52	109.8	113.413115938435	-3.61311593843453
53	104.9	104.999759700941	-0.0997597009410196
54	126.1	133.093957451903	-6.99395745190259
55	80	91.103217809565	-11.1032178095649
56	96.8	97.579435369419	-0.779435369419105
57	117.2	120.061599325869	-2.86159932586941
58	112.3	110.122037398811	2.17796260118940
59	117.3	117.705989107760	-0.405989107759516
60	111.1	114.773847339569	-3.67384733956903
61	102.2	107.571301497769	-5.37130149776945
62	104.3	113.965934966365	-9.66593496636503
63	122.9	129.924890995573	-7.02489099557296
64	107.6	108.724957878327	-1.12495787832671
65	121.3	123.390664435892	-2.09066443589201
66	131.5	131.401760621190	0.0982393788103453
67	89	96.1885477725295	-7.1885477725295
68	104.4	102.382883092917	2.01711690708265
69	128.9	127.486518604400	1.41348139559956
70	135.9	130.872085070755	5.02791492924512
71	133.3	130.129358433623	3.17064156637664
72	121.3	119.880802544012	1.41919745598785
73	120.5	121.851117667460	-1.35111766746044
74	120.4	119.274438178845	1.12556182115509
75	137.9	134.740711784208	3.15928821579202
76	126.1	120.236532501931	5.86346749806875
77	133.2	125.051106716660	8.14889328333979
78	146.6	139.703384735491	6.89661526450892
79	103.4	109.859123711361	-6.4591237113608
80	117.2	110.500405618681	6.69959438131872

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Figure 3

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Figure 9

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Parameters (Session):

par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 2 ; par2 = Do not include Seasonal 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