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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sat, 15 Dec 2007 15:33:52 -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/15/t1197757071xjvl0k6lusgejz2.htm/, Retrieved Fri, 03 May 2024 01:15:57 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14382, Retrieved Fri, 03 May 2024 01:15:57 +0000

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Estimated Impact

183

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

-       [Multiple Regression] [] [2007-12-15 22:33:52] [1b3996ef3154d2cbb97d82693ebca0bb] [Current]

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Dataseries X:

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85,6	92,81	88
89	59,04	88,4
97,5	72,81	95
104	91,81	101,8
99,4	68,07	107,6
103,2	49,16	118,9
103	124,61	126,9
91,2	109,89	106,3
85,9	110,51	109,2
80,7	114,77	104,6
86,7	92,37	100,8
80,7	103,63	92,1
81,5	90,43	86,4
83,4	65,86	96
83,5	83,33	98,5
89,5	94,49	112
85,8	68,98	113,9
77,4	55,46	120
67,5	132,89	126,7
63,7	121,71	112,8
59,4	127,01	116,2
62	134,04	110,6
62,4	106,48	105
58,1	117,55	101,2
58	101,61	99,3
56,3	82,66	101,9
61,4	89,28	106,4
59,8	109,24	118,9
54,3	88,16	121,9
47	59,23	132
50,5	164,21	121,4
48,1	125,13	117
58,8	152,68	122,7
70,4	132,96	113
71,9	112,42	104
73,3	136,43	101,2
83,5	107,32	100,8
90,1	87,61	98,9
101,3	97,86	103
98,3	106,60	117,8
106,7	92,17	126,6
109,9	65,31	127,6
111,1	161,49	115,8
119	162,25	114,8
120,7	175,13	119,2
104,5	147,28	109,9
121,6	144,48	98,9
129,6	122,67	98,6
124,5	102,27	96,6
130,1	88,64	96,7
142,3	89,59	103,5
140	112,20	115,3
143,3	91,98	122,5
113,4	57,85	125,3
113,8	160,49	111,2
120,7	128,33	110,7
112,9	140,69	114,2
115,5	126,61	105,6
121,9	129,27	95,5
119,3	124,27	97,3
111	112,90	95,5
114,2	92,54	96,3
113,5	85,70	100,2
94	116,72	113,4
83,2	92,08	121,4
82,8	58,98	122,1
85,8	154,50	119,3
88,7	145,55	110,8
105,3	146,60	110,1
113,1	143,51	99,7
113,8	113,52	104,8
109,4	104,80	105,4

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=14382&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=14382&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14382&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
Cons[t] = + 256.521579660717 + 0.0429515667088332Inc[t] -1.96337780006562Price[t] -5.76209304124217M1[t] + 1.46122371707541M2[t] + 15.8420581706374M3[t] + 35.8000746525779M4[t] + 45.255922000083M5[t] + 49.6631558322402M6[t] + 36.6500797201941M7[t] + 20.675647460892M8[t] + 27.7870742076745M9[t] + 12.2536481449044M10[t] + 6.39040017517229M11[t] + 0.676917942340819t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Cons[t] =  +  256.521579660717 +  0.0429515667088332Inc[t] -1.96337780006562Price[t] -5.76209304124217M1[t] +  1.46122371707541M2[t] +  15.8420581706374M3[t] +  35.8000746525779M4[t] +  45.255922000083M5[t] +  49.6631558322402M6[t] +  36.6500797201941M7[t] +  20.675647460892M8[t] +  27.7870742076745M9[t] +  12.2536481449044M10[t] +  6.39040017517229M11[t] +  0.676917942340819t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14382&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Cons[t] =  +  256.521579660717 +  0.0429515667088332Inc[t] -1.96337780006562Price[t] -5.76209304124217M1[t] +  1.46122371707541M2[t] +  15.8420581706374M3[t] +  35.8000746525779M4[t] +  45.255922000083M5[t] +  49.6631558322402M6[t] +  36.6500797201941M7[t] +  20.675647460892M8[t] +  27.7870742076745M9[t] +  12.2536481449044M10[t] +  6.39040017517229M11[t] +  0.676917942340819t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14382&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14382&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
Cons[t] = + 256.521579660717 + 0.0429515667088332Inc[t] -1.96337780006562Price[t] -5.76209304124217M1[t] + 1.46122371707541M2[t] + 15.8420581706374M3[t] + 35.8000746525779M4[t] + 45.255922000083M5[t] + 49.6631558322402M6[t] + 36.6500797201941M7[t] + 20.675647460892M8[t] + 27.7870742076745M9[t] + 12.2536481449044M10[t] + 6.39040017517229M11[t] + 0.676917942340819t + 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)	256.521579660717	55.945675	4.5852	2.5e-05	1.3e-05
Inc	0.0429515667088332	0.255417	0.1682	0.867051	0.433525
Price	-1.96337780006562	0.551182	-3.5621	0.000751	0.000376
M1	-5.76209304124217	12.803181	-0.4501	0.65438	0.32719
M2	1.46122371707541	15.074805	0.0969	0.923121	0.46156
M3	15.8420581706374	14.38964	1.1009	0.275552	0.137776
M4	35.8000746525779	15.024785	2.3827	0.020542	0.010271
M5	45.255922000083	19.26961	2.3486	0.022337	0.011169
M6	49.6631558322402	25.339309	1.9599	0.0549	0.02745
M7	36.6500797201941	17.925209	2.0446	0.045521	0.022761
M8	20.675647460892	14.328529	1.443	0.154502	0.077251
M9	27.7870742076745	15.740121	1.7654	0.082859	0.041429
M10	12.2536481449044	13.344154	0.9183	0.362343	0.181171
M11	6.39040017517229	12.274365	0.5206	0.604641	0.302321
t	0.676917942340819	0.156374	4.3288	6.1e-05	3.1e-05

\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) & 256.521579660717 & 55.945675 & 4.5852 & 2.5e-05 & 1.3e-05 \tabularnewline
Inc & 0.0429515667088332 & 0.255417 & 0.1682 & 0.867051 & 0.433525 \tabularnewline
Price & -1.96337780006562 & 0.551182 & -3.5621 & 0.000751 & 0.000376 \tabularnewline
M1 & -5.76209304124217 & 12.803181 & -0.4501 & 0.65438 & 0.32719 \tabularnewline
M2 & 1.46122371707541 & 15.074805 & 0.0969 & 0.923121 & 0.46156 \tabularnewline
M3 & 15.8420581706374 & 14.38964 & 1.1009 & 0.275552 & 0.137776 \tabularnewline
M4 & 35.8000746525779 & 15.024785 & 2.3827 & 0.020542 & 0.010271 \tabularnewline
M5 & 45.255922000083 & 19.26961 & 2.3486 & 0.022337 & 0.011169 \tabularnewline
M6 & 49.6631558322402 & 25.339309 & 1.9599 & 0.0549 & 0.02745 \tabularnewline
M7 & 36.6500797201941 & 17.925209 & 2.0446 & 0.045521 & 0.022761 \tabularnewline
M8 & 20.675647460892 & 14.328529 & 1.443 & 0.154502 & 0.077251 \tabularnewline
M9 & 27.7870742076745 & 15.740121 & 1.7654 & 0.082859 & 0.041429 \tabularnewline
M10 & 12.2536481449044 & 13.344154 & 0.9183 & 0.362343 & 0.181171 \tabularnewline
M11 & 6.39040017517229 & 12.274365 & 0.5206 & 0.604641 & 0.302321 \tabularnewline
t & 0.676917942340819 & 0.156374 & 4.3288 & 6.1e-05 & 3.1e-05 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14382&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]256.521579660717[/C][C]55.945675[/C][C]4.5852[/C][C]2.5e-05[/C][C]1.3e-05[/C][/ROW]
[ROW][C]Inc[/C][C]0.0429515667088332[/C][C]0.255417[/C][C]0.1682[/C][C]0.867051[/C][C]0.433525[/C][/ROW]
[ROW][C]Price[/C][C]-1.96337780006562[/C][C]0.551182[/C][C]-3.5621[/C][C]0.000751[/C][C]0.000376[/C][/ROW]
[ROW][C]M1[/C][C]-5.76209304124217[/C][C]12.803181[/C][C]-0.4501[/C][C]0.65438[/C][C]0.32719[/C][/ROW]
[ROW][C]M2[/C][C]1.46122371707541[/C][C]15.074805[/C][C]0.0969[/C][C]0.923121[/C][C]0.46156[/C][/ROW]
[ROW][C]M3[/C][C]15.8420581706374[/C][C]14.38964[/C][C]1.1009[/C][C]0.275552[/C][C]0.137776[/C][/ROW]
[ROW][C]M4[/C][C]35.8000746525779[/C][C]15.024785[/C][C]2.3827[/C][C]0.020542[/C][C]0.010271[/C][/ROW]
[ROW][C]M5[/C][C]45.255922000083[/C][C]19.26961[/C][C]2.3486[/C][C]0.022337[/C][C]0.011169[/C][/ROW]
[ROW][C]M6[/C][C]49.6631558322402[/C][C]25.339309[/C][C]1.9599[/C][C]0.0549[/C][C]0.02745[/C][/ROW]
[ROW][C]M7[/C][C]36.6500797201941[/C][C]17.925209[/C][C]2.0446[/C][C]0.045521[/C][C]0.022761[/C][/ROW]
[ROW][C]M8[/C][C]20.675647460892[/C][C]14.328529[/C][C]1.443[/C][C]0.154502[/C][C]0.077251[/C][/ROW]
[ROW][C]M9[/C][C]27.7870742076745[/C][C]15.740121[/C][C]1.7654[/C][C]0.082859[/C][C]0.041429[/C][/ROW]
[ROW][C]M10[/C][C]12.2536481449044[/C][C]13.344154[/C][C]0.9183[/C][C]0.362343[/C][C]0.181171[/C][/ROW]
[ROW][C]M11[/C][C]6.39040017517229[/C][C]12.274365[/C][C]0.5206[/C][C]0.604641[/C][C]0.302321[/C][/ROW]
[ROW][C]t[/C][C]0.676917942340819[/C][C]0.156374[/C][C]4.3288[/C][C]6.1e-05[/C][C]3.1e-05[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14382&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14382&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)	256.521579660717	55.945675	4.5852	2.5e-05	1.3e-05
Inc	0.0429515667088332	0.255417	0.1682	0.867051	0.433525
Price	-1.96337780006562	0.551182	-3.5621	0.000751	0.000376
M1	-5.76209304124217	12.803181	-0.4501	0.65438	0.32719
M2	1.46122371707541	15.074805	0.0969	0.923121	0.46156
M3	15.8420581706374	14.38964	1.1009	0.275552	0.137776
M4	35.8000746525779	15.024785	2.3827	0.020542	0.010271
M5	45.255922000083	19.26961	2.3486	0.022337	0.011169
M6	49.6631558322402	25.339309	1.9599	0.0549	0.02745
M7	36.6500797201941	17.925209	2.0446	0.045521	0.022761
M8	20.675647460892	14.328529	1.443	0.154502	0.077251
M9	27.7870742076745	15.740121	1.7654	0.082859	0.041429
M10	12.2536481449044	13.344154	0.9183	0.362343	0.181171
M11	6.39040017517229	12.274365	0.5206	0.604641	0.302321
t	0.676917942340819	0.156374	4.3288	6.1e-05	3.1e-05

Multiple Linear Regression - Regression Statistics
Multiple R	0.637679073411161
R-squared	0.406634600666517
Adjusted R-squared	0.260895730654784
F-TEST (value)	2.79015886862428
F-TEST (DF numerator)	14
F-TEST (DF denominator)	57
p-value	0.00320286516875912
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21.1289643553327
Sum Squared Residuals	25446.6886795483

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.637679073411161 \tabularnewline
R-squared & 0.406634600666517 \tabularnewline
Adjusted R-squared & 0.260895730654784 \tabularnewline
F-TEST (value) & 2.79015886862428 \tabularnewline
F-TEST (DF numerator) & 14 \tabularnewline
F-TEST (DF denominator) & 57 \tabularnewline
p-value & 0.00320286516875912 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 21.1289643553327 \tabularnewline
Sum Squared Residuals & 25446.6886795483 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14382&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.637679073411161[/C][/ROW]
[ROW][C]R-squared[/C][C]0.406634600666517[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.260895730654784[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2.79015886862428[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]14[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]57[/C][/ROW]
[ROW][C]p-value[/C][C]0.00320286516875912[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]21.1289643553327[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]25446.6886795483[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14382&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14382&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	0.637679073411161
R-squared	0.406634600666517
Adjusted R-squared	0.260895730654784
F-TEST (value)	2.79015886862428
F-TEST (DF numerator)	14
F-TEST (DF denominator)	57
p-value	0.00320286516875912
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	21.1289643553327
Sum Squared Residuals	25446.6886795483

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	85.6	82.6454930622864	2.95450693771357
2	89	88.3099022351623	0.69009776483771
3	97.5	91.0008042242127	6.49919577578734
4	104	99.1008493755155	4.89915062448454
5	99.4	96.8263532313132	2.5736467686868
6	103.2	78.9121217386056	24.2878782613944
7	103	54.1096368765568	48.8903631234432
8	91.2	78.6254581789933	12.5745418210067
9	85.9	80.7466372192858	5.1533627807142
10	80.7	75.104640653338	5.595359346662
11	86.7	76.4170311719183	10.2829688280817
12	80.7	88.2685704407991	-7.56857044079914
13	81.5	93.8076881217153	-12.3076881217153
14	83.4	81.8041759477076	1.59582405229238
15	83.5	92.7038477138497	-9.20384771384965
16	89.5	87.3125213217156	2.18747867828439
17	85.8	92.6191743246946	-6.8191743246946
18	77.4	85.1460163368889	-7.74601633688888
19	67.5	62.9809667170088	4.51903328299118
20	63.7	74.494205305155	-10.7942053051550
21	59.4	75.834708777612	-16.434708777612
22	62	72.2750658515133	-10.2750658515133
23	62.4	76.899906325994	-14.4999063259941
24	58.1	79.1227335768788	-21.0227335768788
25	58	77.0833283247633	-19.0833283247633
26	56.3	79.0648485561187	-22.7648485561187
27	61.4	85.5717402233386	-24.1717402233386
28	59.8	82.5217654183079	-22.7217654183079
29	54.3	85.8589782817349	-31.5589782817349
30	47	69.8704254506835	-22.8704254506835
31	50.5	82.855127434767	-32.3551274347671
32	48.1	74.5179282111134	-26.4179282111134
33	58.8	72.298335102691	-13.4983351026910
34	70.4	75.6395867474	-5.23958674740007
35	71.9	87.2414317404	-15.3414317404
36	73.3	88.0566744644314	-14.7566744644314
37	83.5	82.5065303786621	0.993469621337868
38	90.1	93.290607519614	-3.19060751961409
39	101.3	100.738764494013	0.561235505986628
40	98.3	92.6911041703586	5.60889582964137
41	106.7	84.9263537120187	21.7736462879813
42	109.9	86.8934486046518	23.0065513953482
43	111.1	101.856230161776	9.24376983822357
44	119	88.5547368355795	30.4452631644205
45	120.7	88.2574353836239	32.4425646163762
46	104.5	90.4641396709638	14.0358603290362
47	121.6	106.754701057510	14.8452989424903
48	129.6	100.693458494778	28.9065415052217
49	124.5	98.658827035148	25.841172964852
50	130.1	105.777294101558	24.3227058984416
51	142.3	107.524881445388	34.7751185546117
52	140	105.963092752182	34.036907247818
53	143.3	101.091057202703	42.2089427972971
54	113.4	99.2118141652447	14.1881858347553
55	113.8	118.967831783459	-5.16783178345927
56	120.7	103.270683981175	17.4293160188253
57	112.9	104.718087734590	8.18191226541041
58	115.5	106.141870635464	9.35812936453573
59	121.9	120.899907556181	1.00009244381870
60	119.3	111.437587449688	7.86241255031245
61	111	109.398133077425	1.60186692257512
62	114.2	114.853171639839	-0.65317163983893
63	113.5	121.959961899197	-8.45996189919737
64	94	118.010666961920	-24.0106669619204
65	83.2	111.378083247536	-28.1780832475358
66	82.8	113.666173703925	-30.8661737039255
67	85.8	110.930207026432	-25.1302070264317
68	88.7	111.936987487984	-23.2369874879841
69	105.3	121.144795782198	-15.8447957821977
70	113.1	126.574696441321	-13.4746964413206
71	113.8	110.087022147997	3.71297785200329
72	109.4	102.820975573425	6.57902442657519

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 85.6 & 82.6454930622864 & 2.95450693771357 \tabularnewline
2 & 89 & 88.3099022351623 & 0.69009776483771 \tabularnewline
3 & 97.5 & 91.0008042242127 & 6.49919577578734 \tabularnewline
4 & 104 & 99.1008493755155 & 4.89915062448454 \tabularnewline
5 & 99.4 & 96.8263532313132 & 2.5736467686868 \tabularnewline
6 & 103.2 & 78.9121217386056 & 24.2878782613944 \tabularnewline
7 & 103 & 54.1096368765568 & 48.8903631234432 \tabularnewline
8 & 91.2 & 78.6254581789933 & 12.5745418210067 \tabularnewline
9 & 85.9 & 80.7466372192858 & 5.1533627807142 \tabularnewline
10 & 80.7 & 75.104640653338 & 5.595359346662 \tabularnewline
11 & 86.7 & 76.4170311719183 & 10.2829688280817 \tabularnewline
12 & 80.7 & 88.2685704407991 & -7.56857044079914 \tabularnewline
13 & 81.5 & 93.8076881217153 & -12.3076881217153 \tabularnewline
14 & 83.4 & 81.8041759477076 & 1.59582405229238 \tabularnewline
15 & 83.5 & 92.7038477138497 & -9.20384771384965 \tabularnewline
16 & 89.5 & 87.3125213217156 & 2.18747867828439 \tabularnewline
17 & 85.8 & 92.6191743246946 & -6.8191743246946 \tabularnewline
18 & 77.4 & 85.1460163368889 & -7.74601633688888 \tabularnewline
19 & 67.5 & 62.9809667170088 & 4.51903328299118 \tabularnewline
20 & 63.7 & 74.494205305155 & -10.7942053051550 \tabularnewline
21 & 59.4 & 75.834708777612 & -16.434708777612 \tabularnewline
22 & 62 & 72.2750658515133 & -10.2750658515133 \tabularnewline
23 & 62.4 & 76.899906325994 & -14.4999063259941 \tabularnewline
24 & 58.1 & 79.1227335768788 & -21.0227335768788 \tabularnewline
25 & 58 & 77.0833283247633 & -19.0833283247633 \tabularnewline
26 & 56.3 & 79.0648485561187 & -22.7648485561187 \tabularnewline
27 & 61.4 & 85.5717402233386 & -24.1717402233386 \tabularnewline
28 & 59.8 & 82.5217654183079 & -22.7217654183079 \tabularnewline
29 & 54.3 & 85.8589782817349 & -31.5589782817349 \tabularnewline
30 & 47 & 69.8704254506835 & -22.8704254506835 \tabularnewline
31 & 50.5 & 82.855127434767 & -32.3551274347671 \tabularnewline
32 & 48.1 & 74.5179282111134 & -26.4179282111134 \tabularnewline
33 & 58.8 & 72.298335102691 & -13.4983351026910 \tabularnewline
34 & 70.4 & 75.6395867474 & -5.23958674740007 \tabularnewline
35 & 71.9 & 87.2414317404 & -15.3414317404 \tabularnewline
36 & 73.3 & 88.0566744644314 & -14.7566744644314 \tabularnewline
37 & 83.5 & 82.5065303786621 & 0.993469621337868 \tabularnewline
38 & 90.1 & 93.290607519614 & -3.19060751961409 \tabularnewline
39 & 101.3 & 100.738764494013 & 0.561235505986628 \tabularnewline
40 & 98.3 & 92.6911041703586 & 5.60889582964137 \tabularnewline
41 & 106.7 & 84.9263537120187 & 21.7736462879813 \tabularnewline
42 & 109.9 & 86.8934486046518 & 23.0065513953482 \tabularnewline
43 & 111.1 & 101.856230161776 & 9.24376983822357 \tabularnewline
44 & 119 & 88.5547368355795 & 30.4452631644205 \tabularnewline
45 & 120.7 & 88.2574353836239 & 32.4425646163762 \tabularnewline
46 & 104.5 & 90.4641396709638 & 14.0358603290362 \tabularnewline
47 & 121.6 & 106.754701057510 & 14.8452989424903 \tabularnewline
48 & 129.6 & 100.693458494778 & 28.9065415052217 \tabularnewline
49 & 124.5 & 98.658827035148 & 25.841172964852 \tabularnewline
50 & 130.1 & 105.777294101558 & 24.3227058984416 \tabularnewline
51 & 142.3 & 107.524881445388 & 34.7751185546117 \tabularnewline
52 & 140 & 105.963092752182 & 34.036907247818 \tabularnewline
53 & 143.3 & 101.091057202703 & 42.2089427972971 \tabularnewline
54 & 113.4 & 99.2118141652447 & 14.1881858347553 \tabularnewline
55 & 113.8 & 118.967831783459 & -5.16783178345927 \tabularnewline
56 & 120.7 & 103.270683981175 & 17.4293160188253 \tabularnewline
57 & 112.9 & 104.718087734590 & 8.18191226541041 \tabularnewline
58 & 115.5 & 106.141870635464 & 9.35812936453573 \tabularnewline
59 & 121.9 & 120.899907556181 & 1.00009244381870 \tabularnewline
60 & 119.3 & 111.437587449688 & 7.86241255031245 \tabularnewline
61 & 111 & 109.398133077425 & 1.60186692257512 \tabularnewline
62 & 114.2 & 114.853171639839 & -0.65317163983893 \tabularnewline
63 & 113.5 & 121.959961899197 & -8.45996189919737 \tabularnewline
64 & 94 & 118.010666961920 & -24.0106669619204 \tabularnewline
65 & 83.2 & 111.378083247536 & -28.1780832475358 \tabularnewline
66 & 82.8 & 113.666173703925 & -30.8661737039255 \tabularnewline
67 & 85.8 & 110.930207026432 & -25.1302070264317 \tabularnewline
68 & 88.7 & 111.936987487984 & -23.2369874879841 \tabularnewline
69 & 105.3 & 121.144795782198 & -15.8447957821977 \tabularnewline
70 & 113.1 & 126.574696441321 & -13.4746964413206 \tabularnewline
71 & 113.8 & 110.087022147997 & 3.71297785200329 \tabularnewline
72 & 109.4 & 102.820975573425 & 6.57902442657519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14382&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]85.6[/C][C]82.6454930622864[/C][C]2.95450693771357[/C][/ROW]
[ROW][C]2[/C][C]89[/C][C]88.3099022351623[/C][C]0.69009776483771[/C][/ROW]
[ROW][C]3[/C][C]97.5[/C][C]91.0008042242127[/C][C]6.49919577578734[/C][/ROW]
[ROW][C]4[/C][C]104[/C][C]99.1008493755155[/C][C]4.89915062448454[/C][/ROW]
[ROW][C]5[/C][C]99.4[/C][C]96.8263532313132[/C][C]2.5736467686868[/C][/ROW]
[ROW][C]6[/C][C]103.2[/C][C]78.9121217386056[/C][C]24.2878782613944[/C][/ROW]
[ROW][C]7[/C][C]103[/C][C]54.1096368765568[/C][C]48.8903631234432[/C][/ROW]
[ROW][C]8[/C][C]91.2[/C][C]78.6254581789933[/C][C]12.5745418210067[/C][/ROW]
[ROW][C]9[/C][C]85.9[/C][C]80.7466372192858[/C][C]5.1533627807142[/C][/ROW]
[ROW][C]10[/C][C]80.7[/C][C]75.104640653338[/C][C]5.595359346662[/C][/ROW]
[ROW][C]11[/C][C]86.7[/C][C]76.4170311719183[/C][C]10.2829688280817[/C][/ROW]
[ROW][C]12[/C][C]80.7[/C][C]88.2685704407991[/C][C]-7.56857044079914[/C][/ROW]
[ROW][C]13[/C][C]81.5[/C][C]93.8076881217153[/C][C]-12.3076881217153[/C][/ROW]
[ROW][C]14[/C][C]83.4[/C][C]81.8041759477076[/C][C]1.59582405229238[/C][/ROW]
[ROW][C]15[/C][C]83.5[/C][C]92.7038477138497[/C][C]-9.20384771384965[/C][/ROW]
[ROW][C]16[/C][C]89.5[/C][C]87.3125213217156[/C][C]2.18747867828439[/C][/ROW]
[ROW][C]17[/C][C]85.8[/C][C]92.6191743246946[/C][C]-6.8191743246946[/C][/ROW]
[ROW][C]18[/C][C]77.4[/C][C]85.1460163368889[/C][C]-7.74601633688888[/C][/ROW]
[ROW][C]19[/C][C]67.5[/C][C]62.9809667170088[/C][C]4.51903328299118[/C][/ROW]
[ROW][C]20[/C][C]63.7[/C][C]74.494205305155[/C][C]-10.7942053051550[/C][/ROW]
[ROW][C]21[/C][C]59.4[/C][C]75.834708777612[/C][C]-16.434708777612[/C][/ROW]
[ROW][C]22[/C][C]62[/C][C]72.2750658515133[/C][C]-10.2750658515133[/C][/ROW]
[ROW][C]23[/C][C]62.4[/C][C]76.899906325994[/C][C]-14.4999063259941[/C][/ROW]
[ROW][C]24[/C][C]58.1[/C][C]79.1227335768788[/C][C]-21.0227335768788[/C][/ROW]
[ROW][C]25[/C][C]58[/C][C]77.0833283247633[/C][C]-19.0833283247633[/C][/ROW]
[ROW][C]26[/C][C]56.3[/C][C]79.0648485561187[/C][C]-22.7648485561187[/C][/ROW]
[ROW][C]27[/C][C]61.4[/C][C]85.5717402233386[/C][C]-24.1717402233386[/C][/ROW]
[ROW][C]28[/C][C]59.8[/C][C]82.5217654183079[/C][C]-22.7217654183079[/C][/ROW]
[ROW][C]29[/C][C]54.3[/C][C]85.8589782817349[/C][C]-31.5589782817349[/C][/ROW]
[ROW][C]30[/C][C]47[/C][C]69.8704254506835[/C][C]-22.8704254506835[/C][/ROW]
[ROW][C]31[/C][C]50.5[/C][C]82.855127434767[/C][C]-32.3551274347671[/C][/ROW]
[ROW][C]32[/C][C]48.1[/C][C]74.5179282111134[/C][C]-26.4179282111134[/C][/ROW]
[ROW][C]33[/C][C]58.8[/C][C]72.298335102691[/C][C]-13.4983351026910[/C][/ROW]
[ROW][C]34[/C][C]70.4[/C][C]75.6395867474[/C][C]-5.23958674740007[/C][/ROW]
[ROW][C]35[/C][C]71.9[/C][C]87.2414317404[/C][C]-15.3414317404[/C][/ROW]
[ROW][C]36[/C][C]73.3[/C][C]88.0566744644314[/C][C]-14.7566744644314[/C][/ROW]
[ROW][C]37[/C][C]83.5[/C][C]82.5065303786621[/C][C]0.993469621337868[/C][/ROW]
[ROW][C]38[/C][C]90.1[/C][C]93.290607519614[/C][C]-3.19060751961409[/C][/ROW]
[ROW][C]39[/C][C]101.3[/C][C]100.738764494013[/C][C]0.561235505986628[/C][/ROW]
[ROW][C]40[/C][C]98.3[/C][C]92.6911041703586[/C][C]5.60889582964137[/C][/ROW]
[ROW][C]41[/C][C]106.7[/C][C]84.9263537120187[/C][C]21.7736462879813[/C][/ROW]
[ROW][C]42[/C][C]109.9[/C][C]86.8934486046518[/C][C]23.0065513953482[/C][/ROW]
[ROW][C]43[/C][C]111.1[/C][C]101.856230161776[/C][C]9.24376983822357[/C][/ROW]
[ROW][C]44[/C][C]119[/C][C]88.5547368355795[/C][C]30.4452631644205[/C][/ROW]
[ROW][C]45[/C][C]120.7[/C][C]88.2574353836239[/C][C]32.4425646163762[/C][/ROW]
[ROW][C]46[/C][C]104.5[/C][C]90.4641396709638[/C][C]14.0358603290362[/C][/ROW]
[ROW][C]47[/C][C]121.6[/C][C]106.754701057510[/C][C]14.8452989424903[/C][/ROW]
[ROW][C]48[/C][C]129.6[/C][C]100.693458494778[/C][C]28.9065415052217[/C][/ROW]
[ROW][C]49[/C][C]124.5[/C][C]98.658827035148[/C][C]25.841172964852[/C][/ROW]
[ROW][C]50[/C][C]130.1[/C][C]105.777294101558[/C][C]24.3227058984416[/C][/ROW]
[ROW][C]51[/C][C]142.3[/C][C]107.524881445388[/C][C]34.7751185546117[/C][/ROW]
[ROW][C]52[/C][C]140[/C][C]105.963092752182[/C][C]34.036907247818[/C][/ROW]
[ROW][C]53[/C][C]143.3[/C][C]101.091057202703[/C][C]42.2089427972971[/C][/ROW]
[ROW][C]54[/C][C]113.4[/C][C]99.2118141652447[/C][C]14.1881858347553[/C][/ROW]
[ROW][C]55[/C][C]113.8[/C][C]118.967831783459[/C][C]-5.16783178345927[/C][/ROW]
[ROW][C]56[/C][C]120.7[/C][C]103.270683981175[/C][C]17.4293160188253[/C][/ROW]
[ROW][C]57[/C][C]112.9[/C][C]104.718087734590[/C][C]8.18191226541041[/C][/ROW]
[ROW][C]58[/C][C]115.5[/C][C]106.141870635464[/C][C]9.35812936453573[/C][/ROW]
[ROW][C]59[/C][C]121.9[/C][C]120.899907556181[/C][C]1.00009244381870[/C][/ROW]
[ROW][C]60[/C][C]119.3[/C][C]111.437587449688[/C][C]7.86241255031245[/C][/ROW]
[ROW][C]61[/C][C]111[/C][C]109.398133077425[/C][C]1.60186692257512[/C][/ROW]
[ROW][C]62[/C][C]114.2[/C][C]114.853171639839[/C][C]-0.65317163983893[/C][/ROW]
[ROW][C]63[/C][C]113.5[/C][C]121.959961899197[/C][C]-8.45996189919737[/C][/ROW]
[ROW][C]64[/C][C]94[/C][C]118.010666961920[/C][C]-24.0106669619204[/C][/ROW]
[ROW][C]65[/C][C]83.2[/C][C]111.378083247536[/C][C]-28.1780832475358[/C][/ROW]
[ROW][C]66[/C][C]82.8[/C][C]113.666173703925[/C][C]-30.8661737039255[/C][/ROW]
[ROW][C]67[/C][C]85.8[/C][C]110.930207026432[/C][C]-25.1302070264317[/C][/ROW]
[ROW][C]68[/C][C]88.7[/C][C]111.936987487984[/C][C]-23.2369874879841[/C][/ROW]
[ROW][C]69[/C][C]105.3[/C][C]121.144795782198[/C][C]-15.8447957821977[/C][/ROW]
[ROW][C]70[/C][C]113.1[/C][C]126.574696441321[/C][C]-13.4746964413206[/C][/ROW]
[ROW][C]71[/C][C]113.8[/C][C]110.087022147997[/C][C]3.71297785200329[/C][/ROW]
[ROW][C]72[/C][C]109.4[/C][C]102.820975573425[/C][C]6.57902442657519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14382&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14382&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	85.6	82.6454930622864	2.95450693771357
2	89	88.3099022351623	0.69009776483771
3	97.5	91.0008042242127	6.49919577578734
4	104	99.1008493755155	4.89915062448454
5	99.4	96.8263532313132	2.5736467686868
6	103.2	78.9121217386056	24.2878782613944
7	103	54.1096368765568	48.8903631234432
8	91.2	78.6254581789933	12.5745418210067
9	85.9	80.7466372192858	5.1533627807142
10	80.7	75.104640653338	5.595359346662
11	86.7	76.4170311719183	10.2829688280817
12	80.7	88.2685704407991	-7.56857044079914
13	81.5	93.8076881217153	-12.3076881217153
14	83.4	81.8041759477076	1.59582405229238
15	83.5	92.7038477138497	-9.20384771384965
16	89.5	87.3125213217156	2.18747867828439
17	85.8	92.6191743246946	-6.8191743246946
18	77.4	85.1460163368889	-7.74601633688888
19	67.5	62.9809667170088	4.51903328299118
20	63.7	74.494205305155	-10.7942053051550
21	59.4	75.834708777612	-16.434708777612
22	62	72.2750658515133	-10.2750658515133
23	62.4	76.899906325994	-14.4999063259941
24	58.1	79.1227335768788	-21.0227335768788
25	58	77.0833283247633	-19.0833283247633
26	56.3	79.0648485561187	-22.7648485561187
27	61.4	85.5717402233386	-24.1717402233386
28	59.8	82.5217654183079	-22.7217654183079
29	54.3	85.8589782817349	-31.5589782817349
30	47	69.8704254506835	-22.8704254506835
31	50.5	82.855127434767	-32.3551274347671
32	48.1	74.5179282111134	-26.4179282111134
33	58.8	72.298335102691	-13.4983351026910
34	70.4	75.6395867474	-5.23958674740007
35	71.9	87.2414317404	-15.3414317404
36	73.3	88.0566744644314	-14.7566744644314
37	83.5	82.5065303786621	0.993469621337868
38	90.1	93.290607519614	-3.19060751961409
39	101.3	100.738764494013	0.561235505986628
40	98.3	92.6911041703586	5.60889582964137
41	106.7	84.9263537120187	21.7736462879813
42	109.9	86.8934486046518	23.0065513953482
43	111.1	101.856230161776	9.24376983822357
44	119	88.5547368355795	30.4452631644205
45	120.7	88.2574353836239	32.4425646163762
46	104.5	90.4641396709638	14.0358603290362
47	121.6	106.754701057510	14.8452989424903
48	129.6	100.693458494778	28.9065415052217
49	124.5	98.658827035148	25.841172964852
50	130.1	105.777294101558	24.3227058984416
51	142.3	107.524881445388	34.7751185546117
52	140	105.963092752182	34.036907247818
53	143.3	101.091057202703	42.2089427972971
54	113.4	99.2118141652447	14.1881858347553
55	113.8	118.967831783459	-5.16783178345927
56	120.7	103.270683981175	17.4293160188253
57	112.9	104.718087734590	8.18191226541041
58	115.5	106.141870635464	9.35812936453573
59	121.9	120.899907556181	1.00009244381870
60	119.3	111.437587449688	7.86241255031245
61	111	109.398133077425	1.60186692257512
62	114.2	114.853171639839	-0.65317163983893
63	113.5	121.959961899197	-8.45996189919737
64	94	118.010666961920	-24.0106669619204
65	83.2	111.378083247536	-28.1780832475358
66	82.8	113.666173703925	-30.8661737039255
67	85.8	110.930207026432	-25.1302070264317
68	88.7	111.936987487984	-23.2369874879841
69	105.3	121.144795782198	-15.8447957821977
70	113.1	126.574696441321	-13.4746964413206
71	113.8	110.087022147997	3.71297785200329
72	109.4	102.820975573425	6.57902442657519

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Figure 6

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Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Parameters (Session):

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