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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 16 Nov 2007 09:33:57 -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/Nov/16/t1195230571jh907c36pq2mpa6.htm/, Retrieved Mon, 06 May 2024 05:10:06 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5500, Retrieved Mon, 06 May 2024 05:10:06 +0000

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Estimated Impact

257

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-       [Multiple Regression] [Paper 20] [2007-11-16 16:33:57] [640491d00f3c9cca22cbf779aa38ac16] [Current]

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100,70	99,20	100,80	99,20	101,30	99,80
97,90	95,80	100,80	98,10	97,60	103,50
96,50	93,80	100,80	96,10	96,40	103,10
96,60	94,40	90,10	95,50	97,00	105,60
96,60	94,80	95,70	95,70	96,40	105,70
95,50	96,90	88,60	95,90	94,70	106,60
91,80	93,10	93,30	96,20	89,30	107,00
89,30	92,20	93,30	95,70	85,90	105,20
87,00	90,80	93,30	93,40	83,30	105,70
85,90	92,40	93,30	93,40	81,50	105,00
88,00	91,20	93,30	91,90	85,00	105,10
87,90	89,80	93,30	92,80	84,80	105,90
89,20	85,80	88,60	93,20	87,50	105,30
90,90	88,20	97,40	93,80	89,00	104,90
91,60	89,90	97,40	93,80	90,00	103,20
90,20	91,80	102,90	85,10	89,60	103,40
89,10	93,40	102,90	86,10	87,40	104,40
87,50	93,80	102,90	86,50	84,80	104,50
86,30	95,00	105,10	90,00	81,90	105,90
86,00	97,30	105,10	89,10	81,10	110,60
84,40	95,30	105,10	88,40	79,10	112,40
86,10	97,90	105,10	91,40	80,50	111,80
91,00	97,00	105,10	88,00	88,50	111,00
92,70	97,00	105,10	87,80	90,90	111,00
88,00	65,20	96,90	87,40	84,90	109,10
84,30	64,10	96,90	86,20	80,00	107,80
82,20	65,60	96,90	87,80	76,50	107,20
80,80	66,50	96,90	84,60	75,40	108,40
79,40	65,60	96,90	85,00	73,50	107,50
80,20	66,10	96,90	85,70	74,30	106,40
82,20	65,90	96,50	83,90	77,70	106,20
82,20	65,80	96,50	83,60	77,90	104,90
81,20	64,10	96,50	82,60	76,70	106,20
82,10	63,50	96,00	84,90	77,20	107,60
88,10	64,40	96,00	84,20	86,00	107,00
88,50	66,00	96,00	83,80	86,90	104,50
92,10	67,70	96,00	84,20	92,00	105,10
98,60	70,40	96,00	84,40	101,70	104,70
100,90	74,10	96,00	86,00	104,50	103,70
100,60	75,50	105,80	89,70	101,70	104,90
101,10	80,80	105,80	93,90	100,60	105,90
102,10	83,90	105,80	98,40	100,30	106,10
103,60	83,60	105,80	98,30	102,50	106,10
102,80	88,70	105,80	99,30	101,00	106,80
108,30	87,00	105,80	100,50	108,60	106,40
104,00	86,90	105,80	96,90	103,40	107,80
106,10	88,60	105,80	97,50	106,40	107,60
106,30	88,60	105,80	97,50	106,60	107,60
109,00	86,90	123,60	98,90	108,90	108,40
111,00	86,10	142,20	99,30	110,50	109,50
113,70	86,60	142,20	100,60	114,00	109,20
112,70	83,90	141,20	99,90	112,80	109,10
110,30	84,20	141,20	98,80	109,60	110,00
114,50	83,20	141,20	98,60	116,00	109,00
119,30	78,40	124,70	98,20	124,60	109,00
121,80	77,60	124,70	96,30	129,00	111,90
125,40	77,10	124,70	103,40	131,50	109,30
129,70	76,80	122,70	102,70	138,60	112,10
129,40	76,80	122,70	102,70	138,10	112,10
134,50	76,80	122,70	102,60	146,30	112,50
141,20	76,80	123,30	101,60	157,60	113,60
141,40	78,70	123,30	100,90	158,40	112,90
152,20	78,70	123,30	101,10	176,30	114,00
167,70	78,70	127,20	105,60	199,90	116,10
173,30	78,70	127,20	104,70	210,40	116,50
168,70	78,70	127,20	103,80	202,60	117,10
172,60	83,70	140,00	105,40	207,10	117,10
169,80	83,70	140,00	105,60	202,00	117,10
172,00	87,90	140,00	109,20	203,40	116,50
179,40	87,90	140,00	109,50	216,30	116,50
174,60	84,50	140,00	110,10	207,30	116,30
172,50	84,50	140,00	110,00	203,50	116,50
172,60	84,40	139,00	110,30	204,40	119,20
176,30	87,50	139,00	109,30	203,70	118,60
178,90	89,20	139,00	110,20	205,70	117,50
179,60	88,90	147,00	113,00	208,00	117,10
179,90	88,80	147,00	113,60	209,30	117,60
180,30	89,50	147,00	111,20	208,70	118,30
180,90	90,40	147,10	111,30	206,50	118,60
177,70	87,90	147,10	115,00	204,50	116,70

Summary of compuational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5500&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]3 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=5500&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5500&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
TM[t] = -7.79895678054507 -0.0335405225086837EGKS[t] + 0.0994588527017785BUIZEN[t] + 0.338478881529632NEGKS[t] + 0.624837353466008NF[t] + 0.0535379228849357`GM `[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TM[t] =  -7.79895678054507 -0.0335405225086837EGKS[t] +  0.0994588527017785BUIZEN[t] +  0.338478881529632NEGKS[t] +  0.624837353466008NF[t] +  0.0535379228849357`GM
`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5500&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TM[t] =  -7.79895678054507 -0.0335405225086837EGKS[t] +  0.0994588527017785BUIZEN[t] +  0.338478881529632NEGKS[t] +  0.624837353466008NF[t] +  0.0535379228849357`GM
`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5500&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5500&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
TM[t] = -7.79895678054507 -0.0335405225086837EGKS[t] + 0.0994588527017785BUIZEN[t] + 0.338478881529632NEGKS[t] + 0.624837353466008NF[t] + 0.0535379228849357`GM `[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)	-7.79895678054507	7.161466	-1.089	0.27968	0.13984
EGKS	-0.0335405225086837	0.016356	-2.0506	0.043843	0.021922
BUIZEN	0.0994588527017785	0.015323	6.4907	0	0
NEGKS	0.338478881529632	0.043035	7.8652	0	0
NF	0.624837353466008	0.009053	69.0174	0	0
`GM `	0.0535379228849357	0.064902	0.8249	0.412076	0.206038

\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) & -7.79895678054507 & 7.161466 & -1.089 & 0.27968 & 0.13984 \tabularnewline
EGKS & -0.0335405225086837 & 0.016356 & -2.0506 & 0.043843 & 0.021922 \tabularnewline
BUIZEN & 0.0994588527017785 & 0.015323 & 6.4907 & 0 & 0 \tabularnewline
NEGKS & 0.338478881529632 & 0.043035 & 7.8652 & 0 & 0 \tabularnewline
NF & 0.624837353466008 & 0.009053 & 69.0174 & 0 & 0 \tabularnewline
`GM
` & 0.0535379228849357 & 0.064902 & 0.8249 & 0.412076 & 0.206038 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5500&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]-7.79895678054507[/C][C]7.161466[/C][C]-1.089[/C][C]0.27968[/C][C]0.13984[/C][/ROW]
[ROW][C]EGKS[/C][C]-0.0335405225086837[/C][C]0.016356[/C][C]-2.0506[/C][C]0.043843[/C][C]0.021922[/C][/ROW]
[ROW][C]BUIZEN[/C][C]0.0994588527017785[/C][C]0.015323[/C][C]6.4907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NEGKS[/C][C]0.338478881529632[/C][C]0.043035[/C][C]7.8652[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NF[/C][C]0.624837353466008[/C][C]0.009053[/C][C]69.0174[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`GM
`[/C][C]0.0535379228849357[/C][C]0.064902[/C][C]0.8249[/C][C]0.412076[/C][C]0.206038[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5500&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5500&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)	-7.79895678054507	7.161466	-1.089	0.27968	0.13984
EGKS	-0.0335405225086837	0.016356	-2.0506	0.043843	0.021922
BUIZEN	0.0994588527017785	0.015323	6.4907	0	0
NEGKS	0.338478881529632	0.043035	7.8652	0	0
NF	0.624837353466008	0.009053	69.0174	0	0
`GM `	0.0535379228849357	0.064902	0.8249	0.412076	0.206038

Multiple Linear Regression - Regression Statistics
Multiple R	0.99943890991353
R-squared	0.998878134649145
Adjusted R-squared	0.99880233293625
F-TEST (value)	13177.5140230020
F-TEST (DF numerator)	5
F-TEST (DF denominator)	74
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.19634092346888
Sum Squared Residuals	105.911138782311

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.99943890991353 \tabularnewline
R-squared & 0.998878134649145 \tabularnewline
Adjusted R-squared & 0.99880233293625 \tabularnewline
F-TEST (value) & 13177.5140230020 \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 & 1.19634092346888 \tabularnewline
Sum Squared Residuals & 105.911138782311 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5500&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.99943890991353[/C][/ROW]
[ROW][C]R-squared[/C][C]0.998878134649145[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.99880233293625[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]13177.5140230020[/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]1.19634092346888[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]105.911138782311[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5500&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5500&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	0.99943890991353
R-squared	0.998878134649145
Adjusted R-squared	0.99880233293625
F-TEST (value)	13177.5140230020
F-TEST (DF numerator)	5
F-TEST (DF denominator)	74
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.19634092346888
Sum Squared Residuals	105.911138782311

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	100.7	101.115489396696	-0.415489396695537
2	97.9	98.7433925103924	-0.843392510392405
3	96.5	97.3622957990373	-0.862295799037326
4	96.6	96.5836216519973	0.0163783480026958
5	96.6	96.8253221746386	-0.225322174638585
6	95.5	95.1023856291979	0.397614370802138
7	91.8	92.4461333473256	-0.646133347325636
8	89.3	90.0862651138413	-0.786265113841327
9	87	87.7569122602662	-0.756912260266172
10	85.9	86.541063641994	-0.641063641994005
11	88	88.2658784761295	-0.265878476129506
12	87.9	88.5353290686331	-0.63532906863307
13	89.2	89.9923642042086	-0.792364204208559
14	90.9	91.9060330439262	-1.00603304392619
15	91.6	92.382837040223	-0.782837040223049
16	90.2	89.6821401111991	0.517859888800899
17	89.1	88.6458499019746	0.454150098025423
18	87.5	87.1486019188598	0.351398081140185
19	86.3	86.7747636201345	-0.474763620134514
20	86	86.1447477797742	-0.144747779774249
21	84.4	84.8215871619818	-0.421587161981748
22	86.1	86.5924679891695	-0.492467989169524
23	91	90.4276947516467	0.572305248353299
24	92.7	91.8596086236592	0.840391376340807
25	88	88.1244969203915	-0.124496920391489
26	84.3	84.6239145055816	-0.323914505581620
27	82.2	82.896116441404	-0.696116441404008
28	80.8	81.1597219689007	-0.359721968900693
29	79.4	80.0899248895885	-0.689924889588492
30	80.2	80.7510680130043	-0.551068013004276
31	82.2	82.2224700068794	-0.022470006879401
32	82.2	82.1796485656142	0.0203514343858335
33	81.2	81.2179830479405	-0.0179830479405009
34	82.1	82.3542511313849	-0.254251131384905
35	88.1	87.5535754008262	0.546424599173750
36	88.5	87.7930278231076	0.706972176892433
37	92.1	91.0901937438623	1.00980625613773
38	98.6	97.106837268861	1.49316273113894
39	100.9	99.2203102128462	1.67968978715379
40	100.6	99.7151230172282	0.884876982771768
41	101.1	100.325186384429	0.774813615571013
42	102.1	101.567622110073	0.532377889927401
43	103.6	102.918478556297	0.681521443702544
44	102.8	102.186121288853	0.613878711146758
45	108.3	107.376663552141	0.923336447858752
46	104	102.987292484901	1.01270751509888
47	106.1	104.997165401375	1.10283459862483
48	106.3	105.122132872068	1.17786712793164
49	109	108.903346023846	0.0966539761539607
50	111	111.974136135437	-0.97413613543695
51	113.7	114.568257780437	-0.868257780436668
52	112.7	113.567264504990	-0.867264504989893
53	110.3	111.23358017806	-0.933580178059907
54	114.5	115.144846063560	-0.644846063560175
55	119.3	118.902979189218	0.397020810781655
56	121.8	121.191246063936	0.608753936064255
57	125.4	125.034111168215	0.365888831785339
58	129.7	129.194571796179	0.50542820382055
59	129.4	128.882153119446	0.51784688055357
60	134.5	133.993386698869	0.506613301131277
61	141.2	140.834136938299	0.365863061700521
62	141.4	140.995868065216	0.404131934784417
63	152.2	152.307044183736	-0.107044183736498
64	167.7	169.076679856013	-1.37667985601293
65	173.3	175.354256243183	-2.05425624318331
66	168.7	170.208016646503	-1.50801664650274
67	172.6	174.666721649587	-2.06672164958653
68	169.8	171.547746923216	-1.74774692321580
69	172	173.468050243307	-1.46805024330748
70	179.4	181.629995767478	-2.22999576747787
71	174.6	176.312877107154	-1.71287710715413
72	172.5	173.915354860407	-1.41535486040731
73	172.6	174.627699734324	-2.02769973432402
74	176.3	173.715736331860	2.58426366813971
75	178.9	175.154131428731	3.74586857126923
76	179.6	178.323316019198	1.27668398080155
77	179.9	179.368814921315	0.531185078684633
78	180.3	178.195561373828	2.104438626172
79	180.9	176.850587876234	4.0494121237664
80	177.7	176.835414283752	0.864585716248428

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 100.7 & 101.115489396696 & -0.415489396695537 \tabularnewline
2 & 97.9 & 98.7433925103924 & -0.843392510392405 \tabularnewline
3 & 96.5 & 97.3622957990373 & -0.862295799037326 \tabularnewline
4 & 96.6 & 96.5836216519973 & 0.0163783480026958 \tabularnewline
5 & 96.6 & 96.8253221746386 & -0.225322174638585 \tabularnewline
6 & 95.5 & 95.1023856291979 & 0.397614370802138 \tabularnewline
7 & 91.8 & 92.4461333473256 & -0.646133347325636 \tabularnewline
8 & 89.3 & 90.0862651138413 & -0.786265113841327 \tabularnewline
9 & 87 & 87.7569122602662 & -0.756912260266172 \tabularnewline
10 & 85.9 & 86.541063641994 & -0.641063641994005 \tabularnewline
11 & 88 & 88.2658784761295 & -0.265878476129506 \tabularnewline
12 & 87.9 & 88.5353290686331 & -0.63532906863307 \tabularnewline
13 & 89.2 & 89.9923642042086 & -0.792364204208559 \tabularnewline
14 & 90.9 & 91.9060330439262 & -1.00603304392619 \tabularnewline
15 & 91.6 & 92.382837040223 & -0.782837040223049 \tabularnewline
16 & 90.2 & 89.6821401111991 & 0.517859888800899 \tabularnewline
17 & 89.1 & 88.6458499019746 & 0.454150098025423 \tabularnewline
18 & 87.5 & 87.1486019188598 & 0.351398081140185 \tabularnewline
19 & 86.3 & 86.7747636201345 & -0.474763620134514 \tabularnewline
20 & 86 & 86.1447477797742 & -0.144747779774249 \tabularnewline
21 & 84.4 & 84.8215871619818 & -0.421587161981748 \tabularnewline
22 & 86.1 & 86.5924679891695 & -0.492467989169524 \tabularnewline
23 & 91 & 90.4276947516467 & 0.572305248353299 \tabularnewline
24 & 92.7 & 91.8596086236592 & 0.840391376340807 \tabularnewline
25 & 88 & 88.1244969203915 & -0.124496920391489 \tabularnewline
26 & 84.3 & 84.6239145055816 & -0.323914505581620 \tabularnewline
27 & 82.2 & 82.896116441404 & -0.696116441404008 \tabularnewline
28 & 80.8 & 81.1597219689007 & -0.359721968900693 \tabularnewline
29 & 79.4 & 80.0899248895885 & -0.689924889588492 \tabularnewline
30 & 80.2 & 80.7510680130043 & -0.551068013004276 \tabularnewline
31 & 82.2 & 82.2224700068794 & -0.022470006879401 \tabularnewline
32 & 82.2 & 82.1796485656142 & 0.0203514343858335 \tabularnewline
33 & 81.2 & 81.2179830479405 & -0.0179830479405009 \tabularnewline
34 & 82.1 & 82.3542511313849 & -0.254251131384905 \tabularnewline
35 & 88.1 & 87.5535754008262 & 0.546424599173750 \tabularnewline
36 & 88.5 & 87.7930278231076 & 0.706972176892433 \tabularnewline
37 & 92.1 & 91.0901937438623 & 1.00980625613773 \tabularnewline
38 & 98.6 & 97.106837268861 & 1.49316273113894 \tabularnewline
39 & 100.9 & 99.2203102128462 & 1.67968978715379 \tabularnewline
40 & 100.6 & 99.7151230172282 & 0.884876982771768 \tabularnewline
41 & 101.1 & 100.325186384429 & 0.774813615571013 \tabularnewline
42 & 102.1 & 101.567622110073 & 0.532377889927401 \tabularnewline
43 & 103.6 & 102.918478556297 & 0.681521443702544 \tabularnewline
44 & 102.8 & 102.186121288853 & 0.613878711146758 \tabularnewline
45 & 108.3 & 107.376663552141 & 0.923336447858752 \tabularnewline
46 & 104 & 102.987292484901 & 1.01270751509888 \tabularnewline
47 & 106.1 & 104.997165401375 & 1.10283459862483 \tabularnewline
48 & 106.3 & 105.122132872068 & 1.17786712793164 \tabularnewline
49 & 109 & 108.903346023846 & 0.0966539761539607 \tabularnewline
50 & 111 & 111.974136135437 & -0.97413613543695 \tabularnewline
51 & 113.7 & 114.568257780437 & -0.868257780436668 \tabularnewline
52 & 112.7 & 113.567264504990 & -0.867264504989893 \tabularnewline
53 & 110.3 & 111.23358017806 & -0.933580178059907 \tabularnewline
54 & 114.5 & 115.144846063560 & -0.644846063560175 \tabularnewline
55 & 119.3 & 118.902979189218 & 0.397020810781655 \tabularnewline
56 & 121.8 & 121.191246063936 & 0.608753936064255 \tabularnewline
57 & 125.4 & 125.034111168215 & 0.365888831785339 \tabularnewline
58 & 129.7 & 129.194571796179 & 0.50542820382055 \tabularnewline
59 & 129.4 & 128.882153119446 & 0.51784688055357 \tabularnewline
60 & 134.5 & 133.993386698869 & 0.506613301131277 \tabularnewline
61 & 141.2 & 140.834136938299 & 0.365863061700521 \tabularnewline
62 & 141.4 & 140.995868065216 & 0.404131934784417 \tabularnewline
63 & 152.2 & 152.307044183736 & -0.107044183736498 \tabularnewline
64 & 167.7 & 169.076679856013 & -1.37667985601293 \tabularnewline
65 & 173.3 & 175.354256243183 & -2.05425624318331 \tabularnewline
66 & 168.7 & 170.208016646503 & -1.50801664650274 \tabularnewline
67 & 172.6 & 174.666721649587 & -2.06672164958653 \tabularnewline
68 & 169.8 & 171.547746923216 & -1.74774692321580 \tabularnewline
69 & 172 & 173.468050243307 & -1.46805024330748 \tabularnewline
70 & 179.4 & 181.629995767478 & -2.22999576747787 \tabularnewline
71 & 174.6 & 176.312877107154 & -1.71287710715413 \tabularnewline
72 & 172.5 & 173.915354860407 & -1.41535486040731 \tabularnewline
73 & 172.6 & 174.627699734324 & -2.02769973432402 \tabularnewline
74 & 176.3 & 173.715736331860 & 2.58426366813971 \tabularnewline
75 & 178.9 & 175.154131428731 & 3.74586857126923 \tabularnewline
76 & 179.6 & 178.323316019198 & 1.27668398080155 \tabularnewline
77 & 179.9 & 179.368814921315 & 0.531185078684633 \tabularnewline
78 & 180.3 & 178.195561373828 & 2.104438626172 \tabularnewline
79 & 180.9 & 176.850587876234 & 4.0494121237664 \tabularnewline
80 & 177.7 & 176.835414283752 & 0.864585716248428 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5500&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]100.7[/C][C]101.115489396696[/C][C]-0.415489396695537[/C][/ROW]
[ROW][C]2[/C][C]97.9[/C][C]98.7433925103924[/C][C]-0.843392510392405[/C][/ROW]
[ROW][C]3[/C][C]96.5[/C][C]97.3622957990373[/C][C]-0.862295799037326[/C][/ROW]
[ROW][C]4[/C][C]96.6[/C][C]96.5836216519973[/C][C]0.0163783480026958[/C][/ROW]
[ROW][C]5[/C][C]96.6[/C][C]96.8253221746386[/C][C]-0.225322174638585[/C][/ROW]
[ROW][C]6[/C][C]95.5[/C][C]95.1023856291979[/C][C]0.397614370802138[/C][/ROW]
[ROW][C]7[/C][C]91.8[/C][C]92.4461333473256[/C][C]-0.646133347325636[/C][/ROW]
[ROW][C]8[/C][C]89.3[/C][C]90.0862651138413[/C][C]-0.786265113841327[/C][/ROW]
[ROW][C]9[/C][C]87[/C][C]87.7569122602662[/C][C]-0.756912260266172[/C][/ROW]
[ROW][C]10[/C][C]85.9[/C][C]86.541063641994[/C][C]-0.641063641994005[/C][/ROW]
[ROW][C]11[/C][C]88[/C][C]88.2658784761295[/C][C]-0.265878476129506[/C][/ROW]
[ROW][C]12[/C][C]87.9[/C][C]88.5353290686331[/C][C]-0.63532906863307[/C][/ROW]
[ROW][C]13[/C][C]89.2[/C][C]89.9923642042086[/C][C]-0.792364204208559[/C][/ROW]
[ROW][C]14[/C][C]90.9[/C][C]91.9060330439262[/C][C]-1.00603304392619[/C][/ROW]
[ROW][C]15[/C][C]91.6[/C][C]92.382837040223[/C][C]-0.782837040223049[/C][/ROW]
[ROW][C]16[/C][C]90.2[/C][C]89.6821401111991[/C][C]0.517859888800899[/C][/ROW]
[ROW][C]17[/C][C]89.1[/C][C]88.6458499019746[/C][C]0.454150098025423[/C][/ROW]
[ROW][C]18[/C][C]87.5[/C][C]87.1486019188598[/C][C]0.351398081140185[/C][/ROW]
[ROW][C]19[/C][C]86.3[/C][C]86.7747636201345[/C][C]-0.474763620134514[/C][/ROW]
[ROW][C]20[/C][C]86[/C][C]86.1447477797742[/C][C]-0.144747779774249[/C][/ROW]
[ROW][C]21[/C][C]84.4[/C][C]84.8215871619818[/C][C]-0.421587161981748[/C][/ROW]
[ROW][C]22[/C][C]86.1[/C][C]86.5924679891695[/C][C]-0.492467989169524[/C][/ROW]
[ROW][C]23[/C][C]91[/C][C]90.4276947516467[/C][C]0.572305248353299[/C][/ROW]
[ROW][C]24[/C][C]92.7[/C][C]91.8596086236592[/C][C]0.840391376340807[/C][/ROW]
[ROW][C]25[/C][C]88[/C][C]88.1244969203915[/C][C]-0.124496920391489[/C][/ROW]
[ROW][C]26[/C][C]84.3[/C][C]84.6239145055816[/C][C]-0.323914505581620[/C][/ROW]
[ROW][C]27[/C][C]82.2[/C][C]82.896116441404[/C][C]-0.696116441404008[/C][/ROW]
[ROW][C]28[/C][C]80.8[/C][C]81.1597219689007[/C][C]-0.359721968900693[/C][/ROW]
[ROW][C]29[/C][C]79.4[/C][C]80.0899248895885[/C][C]-0.689924889588492[/C][/ROW]
[ROW][C]30[/C][C]80.2[/C][C]80.7510680130043[/C][C]-0.551068013004276[/C][/ROW]
[ROW][C]31[/C][C]82.2[/C][C]82.2224700068794[/C][C]-0.022470006879401[/C][/ROW]
[ROW][C]32[/C][C]82.2[/C][C]82.1796485656142[/C][C]0.0203514343858335[/C][/ROW]
[ROW][C]33[/C][C]81.2[/C][C]81.2179830479405[/C][C]-0.0179830479405009[/C][/ROW]
[ROW][C]34[/C][C]82.1[/C][C]82.3542511313849[/C][C]-0.254251131384905[/C][/ROW]
[ROW][C]35[/C][C]88.1[/C][C]87.5535754008262[/C][C]0.546424599173750[/C][/ROW]
[ROW][C]36[/C][C]88.5[/C][C]87.7930278231076[/C][C]0.706972176892433[/C][/ROW]
[ROW][C]37[/C][C]92.1[/C][C]91.0901937438623[/C][C]1.00980625613773[/C][/ROW]
[ROW][C]38[/C][C]98.6[/C][C]97.106837268861[/C][C]1.49316273113894[/C][/ROW]
[ROW][C]39[/C][C]100.9[/C][C]99.2203102128462[/C][C]1.67968978715379[/C][/ROW]
[ROW][C]40[/C][C]100.6[/C][C]99.7151230172282[/C][C]0.884876982771768[/C][/ROW]
[ROW][C]41[/C][C]101.1[/C][C]100.325186384429[/C][C]0.774813615571013[/C][/ROW]
[ROW][C]42[/C][C]102.1[/C][C]101.567622110073[/C][C]0.532377889927401[/C][/ROW]
[ROW][C]43[/C][C]103.6[/C][C]102.918478556297[/C][C]0.681521443702544[/C][/ROW]
[ROW][C]44[/C][C]102.8[/C][C]102.186121288853[/C][C]0.613878711146758[/C][/ROW]
[ROW][C]45[/C][C]108.3[/C][C]107.376663552141[/C][C]0.923336447858752[/C][/ROW]
[ROW][C]46[/C][C]104[/C][C]102.987292484901[/C][C]1.01270751509888[/C][/ROW]
[ROW][C]47[/C][C]106.1[/C][C]104.997165401375[/C][C]1.10283459862483[/C][/ROW]
[ROW][C]48[/C][C]106.3[/C][C]105.122132872068[/C][C]1.17786712793164[/C][/ROW]
[ROW][C]49[/C][C]109[/C][C]108.903346023846[/C][C]0.0966539761539607[/C][/ROW]
[ROW][C]50[/C][C]111[/C][C]111.974136135437[/C][C]-0.97413613543695[/C][/ROW]
[ROW][C]51[/C][C]113.7[/C][C]114.568257780437[/C][C]-0.868257780436668[/C][/ROW]
[ROW][C]52[/C][C]112.7[/C][C]113.567264504990[/C][C]-0.867264504989893[/C][/ROW]
[ROW][C]53[/C][C]110.3[/C][C]111.23358017806[/C][C]-0.933580178059907[/C][/ROW]
[ROW][C]54[/C][C]114.5[/C][C]115.144846063560[/C][C]-0.644846063560175[/C][/ROW]
[ROW][C]55[/C][C]119.3[/C][C]118.902979189218[/C][C]0.397020810781655[/C][/ROW]
[ROW][C]56[/C][C]121.8[/C][C]121.191246063936[/C][C]0.608753936064255[/C][/ROW]
[ROW][C]57[/C][C]125.4[/C][C]125.034111168215[/C][C]0.365888831785339[/C][/ROW]
[ROW][C]58[/C][C]129.7[/C][C]129.194571796179[/C][C]0.50542820382055[/C][/ROW]
[ROW][C]59[/C][C]129.4[/C][C]128.882153119446[/C][C]0.51784688055357[/C][/ROW]
[ROW][C]60[/C][C]134.5[/C][C]133.993386698869[/C][C]0.506613301131277[/C][/ROW]
[ROW][C]61[/C][C]141.2[/C][C]140.834136938299[/C][C]0.365863061700521[/C][/ROW]
[ROW][C]62[/C][C]141.4[/C][C]140.995868065216[/C][C]0.404131934784417[/C][/ROW]
[ROW][C]63[/C][C]152.2[/C][C]152.307044183736[/C][C]-0.107044183736498[/C][/ROW]
[ROW][C]64[/C][C]167.7[/C][C]169.076679856013[/C][C]-1.37667985601293[/C][/ROW]
[ROW][C]65[/C][C]173.3[/C][C]175.354256243183[/C][C]-2.05425624318331[/C][/ROW]
[ROW][C]66[/C][C]168.7[/C][C]170.208016646503[/C][C]-1.50801664650274[/C][/ROW]
[ROW][C]67[/C][C]172.6[/C][C]174.666721649587[/C][C]-2.06672164958653[/C][/ROW]
[ROW][C]68[/C][C]169.8[/C][C]171.547746923216[/C][C]-1.74774692321580[/C][/ROW]
[ROW][C]69[/C][C]172[/C][C]173.468050243307[/C][C]-1.46805024330748[/C][/ROW]
[ROW][C]70[/C][C]179.4[/C][C]181.629995767478[/C][C]-2.22999576747787[/C][/ROW]
[ROW][C]71[/C][C]174.6[/C][C]176.312877107154[/C][C]-1.71287710715413[/C][/ROW]
[ROW][C]72[/C][C]172.5[/C][C]173.915354860407[/C][C]-1.41535486040731[/C][/ROW]
[ROW][C]73[/C][C]172.6[/C][C]174.627699734324[/C][C]-2.02769973432402[/C][/ROW]
[ROW][C]74[/C][C]176.3[/C][C]173.715736331860[/C][C]2.58426366813971[/C][/ROW]
[ROW][C]75[/C][C]178.9[/C][C]175.154131428731[/C][C]3.74586857126923[/C][/ROW]
[ROW][C]76[/C][C]179.6[/C][C]178.323316019198[/C][C]1.27668398080155[/C][/ROW]
[ROW][C]77[/C][C]179.9[/C][C]179.368814921315[/C][C]0.531185078684633[/C][/ROW]
[ROW][C]78[/C][C]180.3[/C][C]178.195561373828[/C][C]2.104438626172[/C][/ROW]
[ROW][C]79[/C][C]180.9[/C][C]176.850587876234[/C][C]4.0494121237664[/C][/ROW]
[ROW][C]80[/C][C]177.7[/C][C]176.835414283752[/C][C]0.864585716248428[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5500&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5500&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	100.7	101.115489396696	-0.415489396695537
2	97.9	98.7433925103924	-0.843392510392405
3	96.5	97.3622957990373	-0.862295799037326
4	96.6	96.5836216519973	0.0163783480026958
5	96.6	96.8253221746386	-0.225322174638585
6	95.5	95.1023856291979	0.397614370802138
7	91.8	92.4461333473256	-0.646133347325636
8	89.3	90.0862651138413	-0.786265113841327
9	87	87.7569122602662	-0.756912260266172
10	85.9	86.541063641994	-0.641063641994005
11	88	88.2658784761295	-0.265878476129506
12	87.9	88.5353290686331	-0.63532906863307
13	89.2	89.9923642042086	-0.792364204208559
14	90.9	91.9060330439262	-1.00603304392619
15	91.6	92.382837040223	-0.782837040223049
16	90.2	89.6821401111991	0.517859888800899
17	89.1	88.6458499019746	0.454150098025423
18	87.5	87.1486019188598	0.351398081140185
19	86.3	86.7747636201345	-0.474763620134514
20	86	86.1447477797742	-0.144747779774249
21	84.4	84.8215871619818	-0.421587161981748
22	86.1	86.5924679891695	-0.492467989169524
23	91	90.4276947516467	0.572305248353299
24	92.7	91.8596086236592	0.840391376340807
25	88	88.1244969203915	-0.124496920391489
26	84.3	84.6239145055816	-0.323914505581620
27	82.2	82.896116441404	-0.696116441404008
28	80.8	81.1597219689007	-0.359721968900693
29	79.4	80.0899248895885	-0.689924889588492
30	80.2	80.7510680130043	-0.551068013004276
31	82.2	82.2224700068794	-0.022470006879401
32	82.2	82.1796485656142	0.0203514343858335
33	81.2	81.2179830479405	-0.0179830479405009
34	82.1	82.3542511313849	-0.254251131384905
35	88.1	87.5535754008262	0.546424599173750
36	88.5	87.7930278231076	0.706972176892433
37	92.1	91.0901937438623	1.00980625613773
38	98.6	97.106837268861	1.49316273113894
39	100.9	99.2203102128462	1.67968978715379
40	100.6	99.7151230172282	0.884876982771768
41	101.1	100.325186384429	0.774813615571013
42	102.1	101.567622110073	0.532377889927401
43	103.6	102.918478556297	0.681521443702544
44	102.8	102.186121288853	0.613878711146758
45	108.3	107.376663552141	0.923336447858752
46	104	102.987292484901	1.01270751509888
47	106.1	104.997165401375	1.10283459862483
48	106.3	105.122132872068	1.17786712793164
49	109	108.903346023846	0.0966539761539607
50	111	111.974136135437	-0.97413613543695
51	113.7	114.568257780437	-0.868257780436668
52	112.7	113.567264504990	-0.867264504989893
53	110.3	111.23358017806	-0.933580178059907
54	114.5	115.144846063560	-0.644846063560175
55	119.3	118.902979189218	0.397020810781655
56	121.8	121.191246063936	0.608753936064255
57	125.4	125.034111168215	0.365888831785339
58	129.7	129.194571796179	0.50542820382055
59	129.4	128.882153119446	0.51784688055357
60	134.5	133.993386698869	0.506613301131277
61	141.2	140.834136938299	0.365863061700521
62	141.4	140.995868065216	0.404131934784417
63	152.2	152.307044183736	-0.107044183736498
64	167.7	169.076679856013	-1.37667985601293
65	173.3	175.354256243183	-2.05425624318331
66	168.7	170.208016646503	-1.50801664650274
67	172.6	174.666721649587	-2.06672164958653
68	169.8	171.547746923216	-1.74774692321580
69	172	173.468050243307	-1.46805024330748
70	179.4	181.629995767478	-2.22999576747787
71	174.6	176.312877107154	-1.71287710715413
72	172.5	173.915354860407	-1.41535486040731
73	172.6	174.627699734324	-2.02769973432402
74	176.3	173.715736331860	2.58426366813971
75	178.9	175.154131428731	3.74586857126923
76	179.6	178.323316019198	1.27668398080155
77	179.9	179.368814921315	0.531185078684633
78	180.3	178.195561373828	2.104438626172
79	180.9	176.850587876234	4.0494121237664
80	177.7	176.835414283752	0.864585716248428

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code