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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 19 Nov 2007 02:50:12 -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/19/t1195465490v9xxcyhhwzvq5is.htm/, Retrieved Fri, 03 May 2024 11:06:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=5644, Retrieved Fri, 03 May 2024 11:06:04 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

234

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

-       [Multiple Regression] [regr anal] [2007-11-19 09:50:12] [079615521100262cd8b5675a0217a3b1] [Current]

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Dataseries X:

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Histogram

Boxplots

106.54	107.89
106.44	107.26
106.57	107.76
106.12	107.32
106.13	107.15
106.26	108.04
105.78	106.52
105.77	106.62
105.2	106.47
105.15	105.46
105.01	106.13
104.75	105.15
104.96	105.39
105.26	104.57
105.13	104.29
104.77	104.09
104.79	104.51
104.4	103.39
103.89	102.71
103.93	102.62
103.48	101.94
103.45	101.65
103.47	101.86
103.5	101.27
103.69	101.21
103.57	102.15
103.47	102.07
102.85	102.8
102.54	103.39
102.39	102.71
102.16	102.65
101.51	101.12
100.83	100.29
100.55	99.79
100.88	100.11
101	99.76
100.51	99.96
100.44	99.98
100.32	100.49
99.98	100.75
100.03	100.84
99.64	100.44
99.11	99.57
98.97	99.22
98.6	99.08
98.31	98.04
98.37	98.73
98.19	98.72
98.51	100.07
98.23	99.02
97.96	98.94
97.77	99
97.49	98.54
97.76	98.42
98.01	97.9
97.73	97.46
97.06	97
96.63	95.97
96.58	96.55
96.66	96.51
96.77	96.76
96.5	96.05
96.53	96.47
96.22	96.38
96.49	97.27
96.34	96.67
96.31	96.59
96.06	96.06
95.9	96.92
95.33	94.96
95.53	95.59
95.42	95.68
95.57	95.35
95.3	95.41
95.31	95.32
95.38	95.8
95.22	95.46
94.62	94.16
93.81	92.49
93.6	91.58
93.2	91.5
93.29	90.83
93.54	91.28
93.23	90.57
93.46	90.93
92.82	90.9
92.85	91.49
92.67	91.38
92.32	90.91
92.06	90.72
91.88	89.53
91.53	89.47
91.19	89.28

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=5644&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=5644&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5644&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
y[t] = -9.34493940380609 + 1.09136281362709x[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
y[t] =  -9.34493940380609 +  1.09136281362709x[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5644&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]y[t] =  -9.34493940380609 +  1.09136281362709x[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5644&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5644&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
y[t] = -9.34493940380609 + 1.09136281362709x[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)	-9.34493940380609	2.376769	-3.9318	0.000164	8.2e-05
x	1.09136281362709	0.023936	45.595	0	0

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & -9.34493940380609 & 2.376769 & -3.9318 & 0.000164 & 8.2e-05 \tabularnewline
x & 1.09136281362709 & 0.023936 & 45.595 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5644&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]-9.34493940380609[/C][C]2.376769[/C][C]-3.9318[/C][C]0.000164[/C][C]8.2e-05[/C][/ROW]
[ROW][C]x[/C][C]1.09136281362709[/C][C]0.023936[/C][C]45.595[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5644&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5644&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)	-9.34493940380609	2.376769	-3.9318	0.000164	8.2e-05
x	1.09136281362709	0.023936	45.595	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.978806779791498
R-squared	0.958062712165803
Adjusted R-squared	0.957601862848944
F-TEST (value)	2078.90665585664
F-TEST (DF numerator)	1
F-TEST (DF denominator)	91
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.02853187503467
Sum Squared Residuals	96.2668814345723

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.978806779791498 \tabularnewline
R-squared & 0.958062712165803 \tabularnewline
Adjusted R-squared & 0.957601862848944 \tabularnewline
F-TEST (value) & 2078.90665585664 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 91 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 1.02853187503467 \tabularnewline
Sum Squared Residuals & 96.2668814345723 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5644&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.978806779791498[/C][/ROW]
[ROW][C]R-squared[/C][C]0.958062712165803[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.957601862848944[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]2078.90665585664[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]91[/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]1.02853187503467[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]96.2668814345723[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5644&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5644&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	0.978806779791498
R-squared	0.958062712165803
Adjusted R-squared	0.957601862848944
F-TEST (value)	2078.90665585664
F-TEST (DF numerator)	1
F-TEST (DF denominator)	91
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.02853187503467
Sum Squared Residuals	96.2668814345723

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	107.89	106.928854760024	0.96114523997586
2	107.26	106.819718478661	0.440281521338944
3	107.76	106.961595644433	0.79840435556744
4	107.32	106.470482378300	0.849517621699604
5	107.15	106.481396006437	0.668603993563355
6	108.04	106.623273172208	1.41672682779182
7	106.52	106.099419021667	0.420580978332819
8	106.62	106.088505393531	0.531494606469105
9	106.47	105.466428589763	1.00357141023653
10	105.46	105.411860449082	0.0481395509178773
11	106.13	105.259069655174	0.870930344825672
12	105.15	104.975315323631	0.174684676368731
13	105.39	105.204501514493	0.185498485507044
14	104.57	105.531910358581	-0.961910358581102
15	104.29	105.390033192810	-1.10003319280956
16	104.09	104.997142579904	-0.90714257990381
17	104.51	105.018969836176	-0.50896983617636
18	103.39	104.593338338862	-1.2033383388618
19	102.71	104.036743303912	-1.32674330391199
20	102.62	104.080397816457	-1.46039781645707
21	101.94	103.589284550325	-1.64928455032488
22	101.65	103.556543665916	-1.90654366591606
23	101.86	103.578370922189	-1.71837092218860
24	101.27	103.611111806597	-2.34111180659742
25	101.21	103.818470741187	-2.60847074118657
26	102.15	103.687507203551	-1.5375072035513
27	102.07	103.578370922189	-1.50837092218861
28	102.8	102.901725977740	-0.101725977739807
29	103.39	102.563403505515	0.82659649448458
30	102.71	102.399699083471	0.310300916528643
31	102.65	102.148685636337	0.501314363662889
32	101.12	101.439299807480	-0.319299807479515
33	100.29	100.697173094213	-0.407173094213087
34	99.79	100.391591506398	-0.601591506397501
35	100.11	100.751741234894	-0.641741234894445
36	99.76	100.882704772530	-1.12270477252969
37	99.96	100.347936993852	-0.387936993852439
38	99.98	100.271541596899	-0.291541596898525
39	100.49	100.140578059263	0.349421940736722
40	100.75	99.76951470263	0.980485297369925
41	100.84	99.8240828433114	1.01591715668858
42	100.44	99.3984513459969	1.04154865400314
43	99.57	98.8200290547745	0.749970945225488
44	99.22	98.6672382608667	0.552761739133287
45	99.08	98.2634340198247	0.816565980175313
46	98.04	97.9469388038728	0.0930611961271679
47	98.73	98.0124205726905	0.717579427309538
48	98.72	97.8159752662376	0.904024733762416
49	100.07	98.1652113665983	1.90478863340173
50	99.02	97.8596297787827	1.16037022121732
51	98.94	97.5649618191033	1.37503818089665
52	99	97.3576028845142	1.64239711548580
53	98.54	97.0520212966986	1.48797870330139
54	98.42	97.346689256378	1.07331074362206
55	97.9	97.6195299597847	0.28047004021529
56	97.46	97.3139483719691	0.146051628030864
57	97	96.582735286839	0.41726471316102
58	95.97	96.1134492769793	-0.143449276979326
59	96.55	96.058881136298	0.491118863702023
60	96.51	96.1461901613881	0.363809838611866
61	96.76	96.2662400708871	0.493759929112887
62	96.05	95.9715721112078	0.0784278887921881
63	96.47	96.0043129956166	0.465687004383376
64	96.38	95.6659905233922	0.714009476607772
65	97.27	95.9606584830715	1.30934151692846
66	96.67	95.7969540610275	0.873045938972523
67	96.59	95.7642131766187	0.825786823381338
68	96.06	95.4913724732119	0.568627526788109
69	96.92	95.3167544230316	1.60324557696844
70	94.96	94.694677619264	0.265322380735878
71	95.59	94.9129501819895	0.677049818010468
72	95.68	94.7929002724906	0.88709972750945
73	95.35	94.9566046945346	0.393395305465384
74	95.41	94.6619367348553	0.748063265144695
75	95.32	94.6728503629916	0.647149637008415
76	95.8	94.7492457599455	1.05075424005453
77	95.46	94.5746277097651	0.885372290234858
78	94.16	93.919810021589	0.240189978411106
79	92.49	93.035806142551	-0.545806142550952
80	91.58	92.8066199516893	-1.22661995168925
81	91.5	92.3700748262384	-0.870074826238425
82	90.83	92.4682974794649	-1.63829747946487
83	91.28	92.7411381828716	-1.46113818287164
84	90.57	92.4028157106472	-1.83281571064725
85	90.93	92.6538291577815	-1.72382915778145
86	90.9	91.9553569570601	-1.05535695706012
87	91.49	91.988097841469	-0.49809784146894
88	91.38	91.7916525350161	-0.411652535016073
89	90.91	91.4096755502466	-0.499675550246581
90	90.72	91.1259212187036	-0.405921218703547
91	89.53	90.9294759122507	-1.39947591225066
92	89.47	90.5474989274812	-1.07749892748119
93	89.28	90.176435570848	-0.896435570847973

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 107.89 & 106.928854760024 & 0.96114523997586 \tabularnewline
2 & 107.26 & 106.819718478661 & 0.440281521338944 \tabularnewline
3 & 107.76 & 106.961595644433 & 0.79840435556744 \tabularnewline
4 & 107.32 & 106.470482378300 & 0.849517621699604 \tabularnewline
5 & 107.15 & 106.481396006437 & 0.668603993563355 \tabularnewline
6 & 108.04 & 106.623273172208 & 1.41672682779182 \tabularnewline
7 & 106.52 & 106.099419021667 & 0.420580978332819 \tabularnewline
8 & 106.62 & 106.088505393531 & 0.531494606469105 \tabularnewline
9 & 106.47 & 105.466428589763 & 1.00357141023653 \tabularnewline
10 & 105.46 & 105.411860449082 & 0.0481395509178773 \tabularnewline
11 & 106.13 & 105.259069655174 & 0.870930344825672 \tabularnewline
12 & 105.15 & 104.975315323631 & 0.174684676368731 \tabularnewline
13 & 105.39 & 105.204501514493 & 0.185498485507044 \tabularnewline
14 & 104.57 & 105.531910358581 & -0.961910358581102 \tabularnewline
15 & 104.29 & 105.390033192810 & -1.10003319280956 \tabularnewline
16 & 104.09 & 104.997142579904 & -0.90714257990381 \tabularnewline
17 & 104.51 & 105.018969836176 & -0.50896983617636 \tabularnewline
18 & 103.39 & 104.593338338862 & -1.2033383388618 \tabularnewline
19 & 102.71 & 104.036743303912 & -1.32674330391199 \tabularnewline
20 & 102.62 & 104.080397816457 & -1.46039781645707 \tabularnewline
21 & 101.94 & 103.589284550325 & -1.64928455032488 \tabularnewline
22 & 101.65 & 103.556543665916 & -1.90654366591606 \tabularnewline
23 & 101.86 & 103.578370922189 & -1.71837092218860 \tabularnewline
24 & 101.27 & 103.611111806597 & -2.34111180659742 \tabularnewline
25 & 101.21 & 103.818470741187 & -2.60847074118657 \tabularnewline
26 & 102.15 & 103.687507203551 & -1.5375072035513 \tabularnewline
27 & 102.07 & 103.578370922189 & -1.50837092218861 \tabularnewline
28 & 102.8 & 102.901725977740 & -0.101725977739807 \tabularnewline
29 & 103.39 & 102.563403505515 & 0.82659649448458 \tabularnewline
30 & 102.71 & 102.399699083471 & 0.310300916528643 \tabularnewline
31 & 102.65 & 102.148685636337 & 0.501314363662889 \tabularnewline
32 & 101.12 & 101.439299807480 & -0.319299807479515 \tabularnewline
33 & 100.29 & 100.697173094213 & -0.407173094213087 \tabularnewline
34 & 99.79 & 100.391591506398 & -0.601591506397501 \tabularnewline
35 & 100.11 & 100.751741234894 & -0.641741234894445 \tabularnewline
36 & 99.76 & 100.882704772530 & -1.12270477252969 \tabularnewline
37 & 99.96 & 100.347936993852 & -0.387936993852439 \tabularnewline
38 & 99.98 & 100.271541596899 & -0.291541596898525 \tabularnewline
39 & 100.49 & 100.140578059263 & 0.349421940736722 \tabularnewline
40 & 100.75 & 99.76951470263 & 0.980485297369925 \tabularnewline
41 & 100.84 & 99.8240828433114 & 1.01591715668858 \tabularnewline
42 & 100.44 & 99.3984513459969 & 1.04154865400314 \tabularnewline
43 & 99.57 & 98.8200290547745 & 0.749970945225488 \tabularnewline
44 & 99.22 & 98.6672382608667 & 0.552761739133287 \tabularnewline
45 & 99.08 & 98.2634340198247 & 0.816565980175313 \tabularnewline
46 & 98.04 & 97.9469388038728 & 0.0930611961271679 \tabularnewline
47 & 98.73 & 98.0124205726905 & 0.717579427309538 \tabularnewline
48 & 98.72 & 97.8159752662376 & 0.904024733762416 \tabularnewline
49 & 100.07 & 98.1652113665983 & 1.90478863340173 \tabularnewline
50 & 99.02 & 97.8596297787827 & 1.16037022121732 \tabularnewline
51 & 98.94 & 97.5649618191033 & 1.37503818089665 \tabularnewline
52 & 99 & 97.3576028845142 & 1.64239711548580 \tabularnewline
53 & 98.54 & 97.0520212966986 & 1.48797870330139 \tabularnewline
54 & 98.42 & 97.346689256378 & 1.07331074362206 \tabularnewline
55 & 97.9 & 97.6195299597847 & 0.28047004021529 \tabularnewline
56 & 97.46 & 97.3139483719691 & 0.146051628030864 \tabularnewline
57 & 97 & 96.582735286839 & 0.41726471316102 \tabularnewline
58 & 95.97 & 96.1134492769793 & -0.143449276979326 \tabularnewline
59 & 96.55 & 96.058881136298 & 0.491118863702023 \tabularnewline
60 & 96.51 & 96.1461901613881 & 0.363809838611866 \tabularnewline
61 & 96.76 & 96.2662400708871 & 0.493759929112887 \tabularnewline
62 & 96.05 & 95.9715721112078 & 0.0784278887921881 \tabularnewline
63 & 96.47 & 96.0043129956166 & 0.465687004383376 \tabularnewline
64 & 96.38 & 95.6659905233922 & 0.714009476607772 \tabularnewline
65 & 97.27 & 95.9606584830715 & 1.30934151692846 \tabularnewline
66 & 96.67 & 95.7969540610275 & 0.873045938972523 \tabularnewline
67 & 96.59 & 95.7642131766187 & 0.825786823381338 \tabularnewline
68 & 96.06 & 95.4913724732119 & 0.568627526788109 \tabularnewline
69 & 96.92 & 95.3167544230316 & 1.60324557696844 \tabularnewline
70 & 94.96 & 94.694677619264 & 0.265322380735878 \tabularnewline
71 & 95.59 & 94.9129501819895 & 0.677049818010468 \tabularnewline
72 & 95.68 & 94.7929002724906 & 0.88709972750945 \tabularnewline
73 & 95.35 & 94.9566046945346 & 0.393395305465384 \tabularnewline
74 & 95.41 & 94.6619367348553 & 0.748063265144695 \tabularnewline
75 & 95.32 & 94.6728503629916 & 0.647149637008415 \tabularnewline
76 & 95.8 & 94.7492457599455 & 1.05075424005453 \tabularnewline
77 & 95.46 & 94.5746277097651 & 0.885372290234858 \tabularnewline
78 & 94.16 & 93.919810021589 & 0.240189978411106 \tabularnewline
79 & 92.49 & 93.035806142551 & -0.545806142550952 \tabularnewline
80 & 91.58 & 92.8066199516893 & -1.22661995168925 \tabularnewline
81 & 91.5 & 92.3700748262384 & -0.870074826238425 \tabularnewline
82 & 90.83 & 92.4682974794649 & -1.63829747946487 \tabularnewline
83 & 91.28 & 92.7411381828716 & -1.46113818287164 \tabularnewline
84 & 90.57 & 92.4028157106472 & -1.83281571064725 \tabularnewline
85 & 90.93 & 92.6538291577815 & -1.72382915778145 \tabularnewline
86 & 90.9 & 91.9553569570601 & -1.05535695706012 \tabularnewline
87 & 91.49 & 91.988097841469 & -0.49809784146894 \tabularnewline
88 & 91.38 & 91.7916525350161 & -0.411652535016073 \tabularnewline
89 & 90.91 & 91.4096755502466 & -0.499675550246581 \tabularnewline
90 & 90.72 & 91.1259212187036 & -0.405921218703547 \tabularnewline
91 & 89.53 & 90.9294759122507 & -1.39947591225066 \tabularnewline
92 & 89.47 & 90.5474989274812 & -1.07749892748119 \tabularnewline
93 & 89.28 & 90.176435570848 & -0.896435570847973 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=5644&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]107.89[/C][C]106.928854760024[/C][C]0.96114523997586[/C][/ROW]
[ROW][C]2[/C][C]107.26[/C][C]106.819718478661[/C][C]0.440281521338944[/C][/ROW]
[ROW][C]3[/C][C]107.76[/C][C]106.961595644433[/C][C]0.79840435556744[/C][/ROW]
[ROW][C]4[/C][C]107.32[/C][C]106.470482378300[/C][C]0.849517621699604[/C][/ROW]
[ROW][C]5[/C][C]107.15[/C][C]106.481396006437[/C][C]0.668603993563355[/C][/ROW]
[ROW][C]6[/C][C]108.04[/C][C]106.623273172208[/C][C]1.41672682779182[/C][/ROW]
[ROW][C]7[/C][C]106.52[/C][C]106.099419021667[/C][C]0.420580978332819[/C][/ROW]
[ROW][C]8[/C][C]106.62[/C][C]106.088505393531[/C][C]0.531494606469105[/C][/ROW]
[ROW][C]9[/C][C]106.47[/C][C]105.466428589763[/C][C]1.00357141023653[/C][/ROW]
[ROW][C]10[/C][C]105.46[/C][C]105.411860449082[/C][C]0.0481395509178773[/C][/ROW]
[ROW][C]11[/C][C]106.13[/C][C]105.259069655174[/C][C]0.870930344825672[/C][/ROW]
[ROW][C]12[/C][C]105.15[/C][C]104.975315323631[/C][C]0.174684676368731[/C][/ROW]
[ROW][C]13[/C][C]105.39[/C][C]105.204501514493[/C][C]0.185498485507044[/C][/ROW]
[ROW][C]14[/C][C]104.57[/C][C]105.531910358581[/C][C]-0.961910358581102[/C][/ROW]
[ROW][C]15[/C][C]104.29[/C][C]105.390033192810[/C][C]-1.10003319280956[/C][/ROW]
[ROW][C]16[/C][C]104.09[/C][C]104.997142579904[/C][C]-0.90714257990381[/C][/ROW]
[ROW][C]17[/C][C]104.51[/C][C]105.018969836176[/C][C]-0.50896983617636[/C][/ROW]
[ROW][C]18[/C][C]103.39[/C][C]104.593338338862[/C][C]-1.2033383388618[/C][/ROW]
[ROW][C]19[/C][C]102.71[/C][C]104.036743303912[/C][C]-1.32674330391199[/C][/ROW]
[ROW][C]20[/C][C]102.62[/C][C]104.080397816457[/C][C]-1.46039781645707[/C][/ROW]
[ROW][C]21[/C][C]101.94[/C][C]103.589284550325[/C][C]-1.64928455032488[/C][/ROW]
[ROW][C]22[/C][C]101.65[/C][C]103.556543665916[/C][C]-1.90654366591606[/C][/ROW]
[ROW][C]23[/C][C]101.86[/C][C]103.578370922189[/C][C]-1.71837092218860[/C][/ROW]
[ROW][C]24[/C][C]101.27[/C][C]103.611111806597[/C][C]-2.34111180659742[/C][/ROW]
[ROW][C]25[/C][C]101.21[/C][C]103.818470741187[/C][C]-2.60847074118657[/C][/ROW]
[ROW][C]26[/C][C]102.15[/C][C]103.687507203551[/C][C]-1.5375072035513[/C][/ROW]
[ROW][C]27[/C][C]102.07[/C][C]103.578370922189[/C][C]-1.50837092218861[/C][/ROW]
[ROW][C]28[/C][C]102.8[/C][C]102.901725977740[/C][C]-0.101725977739807[/C][/ROW]
[ROW][C]29[/C][C]103.39[/C][C]102.563403505515[/C][C]0.82659649448458[/C][/ROW]
[ROW][C]30[/C][C]102.71[/C][C]102.399699083471[/C][C]0.310300916528643[/C][/ROW]
[ROW][C]31[/C][C]102.65[/C][C]102.148685636337[/C][C]0.501314363662889[/C][/ROW]
[ROW][C]32[/C][C]101.12[/C][C]101.439299807480[/C][C]-0.319299807479515[/C][/ROW]
[ROW][C]33[/C][C]100.29[/C][C]100.697173094213[/C][C]-0.407173094213087[/C][/ROW]
[ROW][C]34[/C][C]99.79[/C][C]100.391591506398[/C][C]-0.601591506397501[/C][/ROW]
[ROW][C]35[/C][C]100.11[/C][C]100.751741234894[/C][C]-0.641741234894445[/C][/ROW]
[ROW][C]36[/C][C]99.76[/C][C]100.882704772530[/C][C]-1.12270477252969[/C][/ROW]
[ROW][C]37[/C][C]99.96[/C][C]100.347936993852[/C][C]-0.387936993852439[/C][/ROW]
[ROW][C]38[/C][C]99.98[/C][C]100.271541596899[/C][C]-0.291541596898525[/C][/ROW]
[ROW][C]39[/C][C]100.49[/C][C]100.140578059263[/C][C]0.349421940736722[/C][/ROW]
[ROW][C]40[/C][C]100.75[/C][C]99.76951470263[/C][C]0.980485297369925[/C][/ROW]
[ROW][C]41[/C][C]100.84[/C][C]99.8240828433114[/C][C]1.01591715668858[/C][/ROW]
[ROW][C]42[/C][C]100.44[/C][C]99.3984513459969[/C][C]1.04154865400314[/C][/ROW]
[ROW][C]43[/C][C]99.57[/C][C]98.8200290547745[/C][C]0.749970945225488[/C][/ROW]
[ROW][C]44[/C][C]99.22[/C][C]98.6672382608667[/C][C]0.552761739133287[/C][/ROW]
[ROW][C]45[/C][C]99.08[/C][C]98.2634340198247[/C][C]0.816565980175313[/C][/ROW]
[ROW][C]46[/C][C]98.04[/C][C]97.9469388038728[/C][C]0.0930611961271679[/C][/ROW]
[ROW][C]47[/C][C]98.73[/C][C]98.0124205726905[/C][C]0.717579427309538[/C][/ROW]
[ROW][C]48[/C][C]98.72[/C][C]97.8159752662376[/C][C]0.904024733762416[/C][/ROW]
[ROW][C]49[/C][C]100.07[/C][C]98.1652113665983[/C][C]1.90478863340173[/C][/ROW]
[ROW][C]50[/C][C]99.02[/C][C]97.8596297787827[/C][C]1.16037022121732[/C][/ROW]
[ROW][C]51[/C][C]98.94[/C][C]97.5649618191033[/C][C]1.37503818089665[/C][/ROW]
[ROW][C]52[/C][C]99[/C][C]97.3576028845142[/C][C]1.64239711548580[/C][/ROW]
[ROW][C]53[/C][C]98.54[/C][C]97.0520212966986[/C][C]1.48797870330139[/C][/ROW]
[ROW][C]54[/C][C]98.42[/C][C]97.346689256378[/C][C]1.07331074362206[/C][/ROW]
[ROW][C]55[/C][C]97.9[/C][C]97.6195299597847[/C][C]0.28047004021529[/C][/ROW]
[ROW][C]56[/C][C]97.46[/C][C]97.3139483719691[/C][C]0.146051628030864[/C][/ROW]
[ROW][C]57[/C][C]97[/C][C]96.582735286839[/C][C]0.41726471316102[/C][/ROW]
[ROW][C]58[/C][C]95.97[/C][C]96.1134492769793[/C][C]-0.143449276979326[/C][/ROW]
[ROW][C]59[/C][C]96.55[/C][C]96.058881136298[/C][C]0.491118863702023[/C][/ROW]
[ROW][C]60[/C][C]96.51[/C][C]96.1461901613881[/C][C]0.363809838611866[/C][/ROW]
[ROW][C]61[/C][C]96.76[/C][C]96.2662400708871[/C][C]0.493759929112887[/C][/ROW]
[ROW][C]62[/C][C]96.05[/C][C]95.9715721112078[/C][C]0.0784278887921881[/C][/ROW]
[ROW][C]63[/C][C]96.47[/C][C]96.0043129956166[/C][C]0.465687004383376[/C][/ROW]
[ROW][C]64[/C][C]96.38[/C][C]95.6659905233922[/C][C]0.714009476607772[/C][/ROW]
[ROW][C]65[/C][C]97.27[/C][C]95.9606584830715[/C][C]1.30934151692846[/C][/ROW]
[ROW][C]66[/C][C]96.67[/C][C]95.7969540610275[/C][C]0.873045938972523[/C][/ROW]
[ROW][C]67[/C][C]96.59[/C][C]95.7642131766187[/C][C]0.825786823381338[/C][/ROW]
[ROW][C]68[/C][C]96.06[/C][C]95.4913724732119[/C][C]0.568627526788109[/C][/ROW]
[ROW][C]69[/C][C]96.92[/C][C]95.3167544230316[/C][C]1.60324557696844[/C][/ROW]
[ROW][C]70[/C][C]94.96[/C][C]94.694677619264[/C][C]0.265322380735878[/C][/ROW]
[ROW][C]71[/C][C]95.59[/C][C]94.9129501819895[/C][C]0.677049818010468[/C][/ROW]
[ROW][C]72[/C][C]95.68[/C][C]94.7929002724906[/C][C]0.88709972750945[/C][/ROW]
[ROW][C]73[/C][C]95.35[/C][C]94.9566046945346[/C][C]0.393395305465384[/C][/ROW]
[ROW][C]74[/C][C]95.41[/C][C]94.6619367348553[/C][C]0.748063265144695[/C][/ROW]
[ROW][C]75[/C][C]95.32[/C][C]94.6728503629916[/C][C]0.647149637008415[/C][/ROW]
[ROW][C]76[/C][C]95.8[/C][C]94.7492457599455[/C][C]1.05075424005453[/C][/ROW]
[ROW][C]77[/C][C]95.46[/C][C]94.5746277097651[/C][C]0.885372290234858[/C][/ROW]
[ROW][C]78[/C][C]94.16[/C][C]93.919810021589[/C][C]0.240189978411106[/C][/ROW]
[ROW][C]79[/C][C]92.49[/C][C]93.035806142551[/C][C]-0.545806142550952[/C][/ROW]
[ROW][C]80[/C][C]91.58[/C][C]92.8066199516893[/C][C]-1.22661995168925[/C][/ROW]
[ROW][C]81[/C][C]91.5[/C][C]92.3700748262384[/C][C]-0.870074826238425[/C][/ROW]
[ROW][C]82[/C][C]90.83[/C][C]92.4682974794649[/C][C]-1.63829747946487[/C][/ROW]
[ROW][C]83[/C][C]91.28[/C][C]92.7411381828716[/C][C]-1.46113818287164[/C][/ROW]
[ROW][C]84[/C][C]90.57[/C][C]92.4028157106472[/C][C]-1.83281571064725[/C][/ROW]
[ROW][C]85[/C][C]90.93[/C][C]92.6538291577815[/C][C]-1.72382915778145[/C][/ROW]
[ROW][C]86[/C][C]90.9[/C][C]91.9553569570601[/C][C]-1.05535695706012[/C][/ROW]
[ROW][C]87[/C][C]91.49[/C][C]91.988097841469[/C][C]-0.49809784146894[/C][/ROW]
[ROW][C]88[/C][C]91.38[/C][C]91.7916525350161[/C][C]-0.411652535016073[/C][/ROW]
[ROW][C]89[/C][C]90.91[/C][C]91.4096755502466[/C][C]-0.499675550246581[/C][/ROW]
[ROW][C]90[/C][C]90.72[/C][C]91.1259212187036[/C][C]-0.405921218703547[/C][/ROW]
[ROW][C]91[/C][C]89.53[/C][C]90.9294759122507[/C][C]-1.39947591225066[/C][/ROW]
[ROW][C]92[/C][C]89.47[/C][C]90.5474989274812[/C][C]-1.07749892748119[/C][/ROW]
[ROW][C]93[/C][C]89.28[/C][C]90.176435570848[/C][C]-0.896435570847973[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=5644&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=5644&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	107.89	106.928854760024	0.96114523997586
2	107.26	106.819718478661	0.440281521338944
3	107.76	106.961595644433	0.79840435556744
4	107.32	106.470482378300	0.849517621699604
5	107.15	106.481396006437	0.668603993563355
6	108.04	106.623273172208	1.41672682779182
7	106.52	106.099419021667	0.420580978332819
8	106.62	106.088505393531	0.531494606469105
9	106.47	105.466428589763	1.00357141023653
10	105.46	105.411860449082	0.0481395509178773
11	106.13	105.259069655174	0.870930344825672
12	105.15	104.975315323631	0.174684676368731
13	105.39	105.204501514493	0.185498485507044
14	104.57	105.531910358581	-0.961910358581102
15	104.29	105.390033192810	-1.10003319280956
16	104.09	104.997142579904	-0.90714257990381
17	104.51	105.018969836176	-0.50896983617636
18	103.39	104.593338338862	-1.2033383388618
19	102.71	104.036743303912	-1.32674330391199
20	102.62	104.080397816457	-1.46039781645707
21	101.94	103.589284550325	-1.64928455032488
22	101.65	103.556543665916	-1.90654366591606
23	101.86	103.578370922189	-1.71837092218860
24	101.27	103.611111806597	-2.34111180659742
25	101.21	103.818470741187	-2.60847074118657
26	102.15	103.687507203551	-1.5375072035513
27	102.07	103.578370922189	-1.50837092218861
28	102.8	102.901725977740	-0.101725977739807
29	103.39	102.563403505515	0.82659649448458
30	102.71	102.399699083471	0.310300916528643
31	102.65	102.148685636337	0.501314363662889
32	101.12	101.439299807480	-0.319299807479515
33	100.29	100.697173094213	-0.407173094213087
34	99.79	100.391591506398	-0.601591506397501
35	100.11	100.751741234894	-0.641741234894445
36	99.76	100.882704772530	-1.12270477252969
37	99.96	100.347936993852	-0.387936993852439
38	99.98	100.271541596899	-0.291541596898525
39	100.49	100.140578059263	0.349421940736722
40	100.75	99.76951470263	0.980485297369925
41	100.84	99.8240828433114	1.01591715668858
42	100.44	99.3984513459969	1.04154865400314
43	99.57	98.8200290547745	0.749970945225488
44	99.22	98.6672382608667	0.552761739133287
45	99.08	98.2634340198247	0.816565980175313
46	98.04	97.9469388038728	0.0930611961271679
47	98.73	98.0124205726905	0.717579427309538
48	98.72	97.8159752662376	0.904024733762416
49	100.07	98.1652113665983	1.90478863340173
50	99.02	97.8596297787827	1.16037022121732
51	98.94	97.5649618191033	1.37503818089665
52	99	97.3576028845142	1.64239711548580
53	98.54	97.0520212966986	1.48797870330139
54	98.42	97.346689256378	1.07331074362206
55	97.9	97.6195299597847	0.28047004021529
56	97.46	97.3139483719691	0.146051628030864
57	97	96.582735286839	0.41726471316102
58	95.97	96.1134492769793	-0.143449276979326
59	96.55	96.058881136298	0.491118863702023
60	96.51	96.1461901613881	0.363809838611866
61	96.76	96.2662400708871	0.493759929112887
62	96.05	95.9715721112078	0.0784278887921881
63	96.47	96.0043129956166	0.465687004383376
64	96.38	95.6659905233922	0.714009476607772
65	97.27	95.9606584830715	1.30934151692846
66	96.67	95.7969540610275	0.873045938972523
67	96.59	95.7642131766187	0.825786823381338
68	96.06	95.4913724732119	0.568627526788109
69	96.92	95.3167544230316	1.60324557696844
70	94.96	94.694677619264	0.265322380735878
71	95.59	94.9129501819895	0.677049818010468
72	95.68	94.7929002724906	0.88709972750945
73	95.35	94.9566046945346	0.393395305465384
74	95.41	94.6619367348553	0.748063265144695
75	95.32	94.6728503629916	0.647149637008415
76	95.8	94.7492457599455	1.05075424005453
77	95.46	94.5746277097651	0.885372290234858
78	94.16	93.919810021589	0.240189978411106
79	92.49	93.035806142551	-0.545806142550952
80	91.58	92.8066199516893	-1.22661995168925
81	91.5	92.3700748262384	-0.870074826238425
82	90.83	92.4682974794649	-1.63829747946487
83	91.28	92.7411381828716	-1.46113818287164
84	90.57	92.4028157106472	-1.83281571064725
85	90.93	92.6538291577815	-1.72382915778145
86	90.9	91.9553569570601	-1.05535695706012
87	91.49	91.988097841469	-0.49809784146894
88	91.38	91.7916525350161	-0.411652535016073
89	90.91	91.4096755502466	-0.499675550246581
90	90.72	91.1259212187036	-0.405921218703547
91	89.53	90.9294759122507	-1.39947591225066
92	89.47	90.5474989274812	-1.07749892748119
93	89.28	90.176435570848	-0.896435570847973

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 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = 2 ; 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