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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Thu, 11 Dec 2008 07:24:38 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Dec/11/t1229005540sqkdwled31z4gc3.htm/, Retrieved Sun, 19 May 2024 05:39:54 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=32259, Retrieved Sun, 19 May 2024 05:39:54 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

199

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

F     [Multiple Regression] [] [2007-11-19 20:22:41] [3a1956effdcb54c39e5044435310d6c8]
-    D  [Multiple Regression] [seatbelt_3.2.] [2008-11-23 14:44:53] [922d8ae7bd2fd460a62d9020ccd4931a]
F   PD    [Multiple Regression] [seatbelt3CG2] [2008-11-23 15:00:12] [922d8ae7bd2fd460a62d9020ccd4931a]
-   PD      [Multiple Regression] [dummy] [2008-12-07 12:19:24] [922d8ae7bd2fd460a62d9020ccd4931a]
-    D          [Multiple Regression] [dummy3] [2008-12-11 14:24:38] [89a49ebb3ece8e9a225c7f9f53a14c57] [Current]
- RMPD            [Standard Deviation-Mean Plot] [lambda] [2008-12-11 16:25:56] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RM D              [Variance Reduction Matrix] [denD] [2008-12-11 16:30:20] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RMP                 [(Partial) Autocorrelation Function] [autocorrelation] [2008-12-11 16:35:54] [922d8ae7bd2fd460a62d9020ccd4931a] 
-   P                   [(Partial) Autocorrelation Function] [autocorrelation2] [2008-12-11 16:40:41] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RMP                     [Spectral Analysis] [spectrum] [2008-12-11 16:45:17] [922d8ae7bd2fd460a62d9020ccd4931a] 
-   P                       [Spectral Analysis] [spectrum2] [2008-12-11 16:48:27] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RMP                         [(Partial) Autocorrelation Function] [autocorrelation] [2008-12-11 17:56:59] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RMP                           [ARIMA Backward Selection] [ARMAproces] [2008-12-11 18:10:55] [922d8ae7bd2fd460a62d9020ccd4931a] 
- RMP                             [ARIMA Forecasting] [ARIMAforecasting] [2008-12-11 18:25:54] [922d8ae7bd2fd460a62d9020ccd4931a] 

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Dataseries X:

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Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 6 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32259&T=0

[TABLE]
[ROW][C]Summary of computational 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]6 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32259&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32259&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 computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	6 seconds
R Server	'Herman Ole Andreas Wold' @ 193.190.124.10:1001

Multiple Linear Regression - Estimated Regression Equation
BEL-20[t] = + 2091.23875 -772.189431818182`Wel(1)_geen(0)_financi�le_crisis`[t] + 202.302645202020M1[t] + 172.389659090909M2[t] + 105.977784090909M3[t] + 185.075909090909M4[t] + 147.012922979798M5[t] + 58.5399368686865M6[t] -3.84304924242442M7[t] -14.4649242424245M8[t] -58.4534659090913M9[t] -118.578674242424M10[t] -142.831660353536M11[t] + 18.3729861111111t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
BEL-20[t] =  +  2091.23875 -772.189431818182`Wel(1)_geen(0)_financi�le_crisis`[t] +  202.302645202020M1[t] +  172.389659090909M2[t] +  105.977784090909M3[t] +  185.075909090909M4[t] +  147.012922979798M5[t] +  58.5399368686865M6[t] -3.84304924242442M7[t] -14.4649242424245M8[t] -58.4534659090913M9[t] -118.578674242424M10[t] -142.831660353536M11[t] +  18.3729861111111t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32259&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]BEL-20[t] =  +  2091.23875 -772.189431818182`Wel(1)_geen(0)_financi�le_crisis`[t] +  202.302645202020M1[t] +  172.389659090909M2[t] +  105.977784090909M3[t] +  185.075909090909M4[t] +  147.012922979798M5[t] +  58.5399368686865M6[t] -3.84304924242442M7[t] -14.4649242424245M8[t] -58.4534659090913M9[t] -118.578674242424M10[t] -142.831660353536M11[t] +  18.3729861111111t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32259&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32259&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
BEL-20[t] = + 2091.23875 -772.189431818182`Wel(1)_geen(0)_financi�le_crisis`[t] + 202.302645202020M1[t] + 172.389659090909M2[t] + 105.977784090909M3[t] + 185.075909090909M4[t] + 147.012922979798M5[t] + 58.5399368686865M6[t] -3.84304924242442M7[t] -14.4649242424245M8[t] -58.4534659090913M9[t] -118.578674242424M10[t] -142.831660353536M11[t] + 18.3729861111111t + 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)	2091.23875	255.317836	8.1907	0	0
`Wel(1)_geen(0)_financi�le_crisis`	-772.189431818182	237.103251	-3.2568	0.001573	0.000787
M1	202.302645202020	307.142885	0.6587	0.511742	0.255871
M2	172.389659090909	306.956936	0.5616	0.575734	0.287867
M3	105.977784090909	306.788598	0.3454	0.730542	0.365271
M4	185.075909090909	306.637902	0.6036	0.547602	0.273801
M5	147.012922979798	306.504873	0.4796	0.632607	0.316304
M6	58.5399368686865	306.389535	0.1911	0.848892	0.424446
M7	-3.84304924242442	306.291907	-0.0125	0.990016	0.495008
M8	-14.4649242424245	306.212007	-0.0472	0.962425	0.481212
M9	-58.4534659090913	306.149848	-0.1909	0.848996	0.424498
M10	-118.578674242424	306.105441	-0.3874	0.699361	0.349681
M11	-142.831660353536	306.078793	-0.4666	0.641842	0.320921
t	18.3729861111111	2.331889	7.879	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) & 2091.23875 & 255.317836 & 8.1907 & 0 & 0 \tabularnewline
`Wel(1)_geen(0)_financi�le_crisis` & -772.189431818182 & 237.103251 & -3.2568 & 0.001573 & 0.000787 \tabularnewline
M1 & 202.302645202020 & 307.142885 & 0.6587 & 0.511742 & 0.255871 \tabularnewline
M2 & 172.389659090909 & 306.956936 & 0.5616 & 0.575734 & 0.287867 \tabularnewline
M3 & 105.977784090909 & 306.788598 & 0.3454 & 0.730542 & 0.365271 \tabularnewline
M4 & 185.075909090909 & 306.637902 & 0.6036 & 0.547602 & 0.273801 \tabularnewline
M5 & 147.012922979798 & 306.504873 & 0.4796 & 0.632607 & 0.316304 \tabularnewline
M6 & 58.5399368686865 & 306.389535 & 0.1911 & 0.848892 & 0.424446 \tabularnewline
M7 & -3.84304924242442 & 306.291907 & -0.0125 & 0.990016 & 0.495008 \tabularnewline
M8 & -14.4649242424245 & 306.212007 & -0.0472 & 0.962425 & 0.481212 \tabularnewline
M9 & -58.4534659090913 & 306.149848 & -0.1909 & 0.848996 & 0.424498 \tabularnewline
M10 & -118.578674242424 & 306.105441 & -0.3874 & 0.699361 & 0.349681 \tabularnewline
M11 & -142.831660353536 & 306.078793 & -0.4666 & 0.641842 & 0.320921 \tabularnewline
t & 18.3729861111111 & 2.331889 & 7.879 & 0 & 0 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32259&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]2091.23875[/C][C]255.317836[/C][C]8.1907[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]`Wel(1)_geen(0)_financi�le_crisis`[/C][C]-772.189431818182[/C][C]237.103251[/C][C]-3.2568[/C][C]0.001573[/C][C]0.000787[/C][/ROW]
[ROW][C]M1[/C][C]202.302645202020[/C][C]307.142885[/C][C]0.6587[/C][C]0.511742[/C][C]0.255871[/C][/ROW]
[ROW][C]M2[/C][C]172.389659090909[/C][C]306.956936[/C][C]0.5616[/C][C]0.575734[/C][C]0.287867[/C][/ROW]
[ROW][C]M3[/C][C]105.977784090909[/C][C]306.788598[/C][C]0.3454[/C][C]0.730542[/C][C]0.365271[/C][/ROW]
[ROW][C]M4[/C][C]185.075909090909[/C][C]306.637902[/C][C]0.6036[/C][C]0.547602[/C][C]0.273801[/C][/ROW]
[ROW][C]M5[/C][C]147.012922979798[/C][C]306.504873[/C][C]0.4796[/C][C]0.632607[/C][C]0.316304[/C][/ROW]
[ROW][C]M6[/C][C]58.5399368686865[/C][C]306.389535[/C][C]0.1911[/C][C]0.848892[/C][C]0.424446[/C][/ROW]
[ROW][C]M7[/C][C]-3.84304924242442[/C][C]306.291907[/C][C]-0.0125[/C][C]0.990016[/C][C]0.495008[/C][/ROW]
[ROW][C]M8[/C][C]-14.4649242424245[/C][C]306.212007[/C][C]-0.0472[/C][C]0.962425[/C][C]0.481212[/C][/ROW]
[ROW][C]M9[/C][C]-58.4534659090913[/C][C]306.149848[/C][C]-0.1909[/C][C]0.848996[/C][C]0.424498[/C][/ROW]
[ROW][C]M10[/C][C]-118.578674242424[/C][C]306.105441[/C][C]-0.3874[/C][C]0.699361[/C][C]0.349681[/C][/ROW]
[ROW][C]M11[/C][C]-142.831660353536[/C][C]306.078793[/C][C]-0.4666[/C][C]0.641842[/C][C]0.320921[/C][/ROW]
[ROW][C]t[/C][C]18.3729861111111[/C][C]2.331889[/C][C]7.879[/C][C]0[/C][C]0[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32259&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32259&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)	2091.23875	255.317836	8.1907	0	0
`Wel(1)_geen(0)_financi�le_crisis`	-772.189431818182	237.103251	-3.2568	0.001573	0.000787
M1	202.302645202020	307.142885	0.6587	0.511742	0.255871
M2	172.389659090909	306.956936	0.5616	0.575734	0.287867
M3	105.977784090909	306.788598	0.3454	0.730542	0.365271
M4	185.075909090909	306.637902	0.6036	0.547602	0.273801
M5	147.012922979798	306.504873	0.4796	0.632607	0.316304
M6	58.5399368686865	306.389535	0.1911	0.848892	0.424446
M7	-3.84304924242442	306.291907	-0.0125	0.990016	0.495008
M8	-14.4649242424245	306.212007	-0.0472	0.962425	0.481212
M9	-58.4534659090913	306.149848	-0.1909	0.848996	0.424498
M10	-118.578674242424	306.105441	-0.3874	0.699361	0.349681
M11	-142.831660353536	306.078793	-0.4666	0.641842	0.320921
t	18.3729861111111	2.331889	7.879	0	0

Multiple Linear Regression - Regression Statistics
Multiple R	0.639511886232537
R-squared	0.408975452632698
Adjusted R-squared	0.326359118054473
F-TEST (value)	4.95029771921751
F-TEST (DF numerator)	13
F-TEST (DF denominator)	93
p-value	1.55710322635727e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	628.209214384781
Sum Squared Residuals	36702153.9845288

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.639511886232537 \tabularnewline
R-squared & 0.408975452632698 \tabularnewline
Adjusted R-squared & 0.326359118054473 \tabularnewline
F-TEST (value) & 4.95029771921751 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 93 \tabularnewline
p-value & 1.55710322635727e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 628.209214384781 \tabularnewline
Sum Squared Residuals & 36702153.9845288 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32259&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.639511886232537[/C][/ROW]
[ROW][C]R-squared[/C][C]0.408975452632698[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.326359118054473[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.95029771921751[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]93[/C][/ROW]
[ROW][C]p-value[/C][C]1.55710322635727e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]628.209214384781[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]36702153.9845288[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32259&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32259&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	0.639511886232537
R-squared	0.408975452632698
Adjusted R-squared	0.326359118054473
F-TEST (value)	4.95029771921751
F-TEST (DF numerator)	13
F-TEST (DF denominator)	93
p-value	1.55710322635727e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	628.209214384781
Sum Squared Residuals	36702153.9845288

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	3030.29	2311.91438131313	718.375618686866
2	2803.47	2300.37438131313	503.095618686868
3	2767.63	2252.33549242424	515.294507575758
4	2882.6	2349.80660353535	532.793396464646
5	2863.36	2330.11660353535	533.243396464647
6	2897.06	2260.01660353535	637.043396464647
7	3012.61	2216.00660353535	796.603396464647
8	3142.95	2223.75771464646	919.192285353535
9	3032.93	2198.14215909091	834.787840909091
10	3045.78	2156.38993686869	889.390063131313
11	3110.52	2150.50993686869	960.010063131314
12	3013.24	2311.71458333333	701.525416666666
13	2987.1	2532.39021464646	454.709785353536
14	2995.55	2520.85021464646	474.699785353536
15	2833.18	2472.81132575758	360.368674242424
16	2848.96	2570.28243686869	278.677563131313
17	2794.83	2550.59243686869	244.237563131313
18	2845.26	2480.49243686869	364.767563131313
19	2915.02	2436.48243686869	478.537563131313
20	2892.63	2444.2335479798	448.396452020202
21	2604.42	2418.61799242424	185.802007575758
22	2641.65	2376.86577020202	264.784229797980
23	2659.81	2370.98577020202	288.824229797980
24	2638.53	2532.19041666667	106.339583333333
25	2720.25	2752.8660479798	-32.6160479797978
26	2745.88	2741.3260479798	4.55395202020212
27	2735.7	2693.28715909091	42.4128409090909
28	2811.7	2790.75827020202	20.9417297979795
29	2799.43	2771.06827020202	28.3617297979798
30	2555.28	2700.96827020202	-145.68827020202
31	2304.98	2656.95827020202	-351.97827020202
32	2214.95	2664.70938131313	-449.759381313131
33	2065.81	2639.09382575758	-573.283825757575
34	1940.49	2597.34160353535	-656.851603535353
35	2042	2591.46160353535	-549.461603535353
36	1995.37	2752.66625	-757.29625
37	1946.81	2973.34188131313	-1026.53188131313
38	1765.9	2961.80188131313	-1195.90188131313
39	1635.25	2913.76299242424	-1278.51299242424
40	1833.42	3011.23410353535	-1177.81410353535
41	1910.43	2991.54410353535	-1081.11410353535
42	1959.67	2921.44410353535	-961.774103535353
43	1969.6	2877.43410353535	-907.834103535353
44	2061.41	2885.18521464646	-823.775214646465
45	2093.48	2859.56965909091	-766.089659090909
46	2120.88	2817.81743686869	-696.937436868687
47	2174.56	2811.93743686869	-637.377436868687
48	2196.72	2973.14208333333	-776.422083333333
49	2350.44	3193.81771464646	-843.377714646464
50	2440.25	3182.27771464646	-742.027714646465
51	2408.64	3134.23882575758	-725.598825757576
52	2472.81	3231.70993686869	-758.899936868687
53	2407.6	3212.01993686869	-804.419936868687
54	2454.62	3141.91993686869	-687.299936868687
55	2448.05	3097.90993686869	-649.859936868687
56	2497.84	3105.6610479798	-607.821047979798
57	2645.64	3080.04549242424	-434.405492424243
58	2756.76	3038.29327020202	-281.53327020202
59	2849.27	3032.41327020202	-183.143270202020
60	2921.44	3193.61791666667	-272.177916666667
61	2981.85	3414.2935479798	-432.443547979798
62	3080.58	3402.7535479798	-322.173547979798
63	3106.22	3354.71465909091	-248.494659090910
64	3119.31	3452.18577020202	-332.875770202020
65	3061.26	3432.49577020202	-371.23577020202
66	3097.31	3362.39577020202	-265.08577020202
67	3161.69	3318.38577020202	-156.695770202020
68	3257.16	3326.13688131313	-68.9768813131319
69	3277.01	3300.52132575758	-23.5113257575756
70	3295.32	3258.76910353535	36.5508964646464
71	3363.99	3252.88910353535	111.100896464646
72	3494.17	3414.09375	80.0762499999997
73	3667.03	3634.76938131313	32.2606186868692
74	3813.06	3623.22938131313	189.830618686869
75	3917.96	3575.19049242424	342.769507575758
76	3895.51	3672.66160353535	222.848396464646
77	3801.06	3652.97160353535	148.088396464646
78	3570.12	3582.87160353535	-12.7516035353539
79	3701.61	3538.86160353535	162.748396464646
80	3862.27	3546.61271464646	315.657285353535
81	3970.1	3520.99715909091	449.102840909091
82	4138.52	3479.24493686869	659.275063131313
83	4199.75	3473.36493686869	726.385063131313
84	4290.89	3634.56958333333	656.320416666667
85	4443.91	3855.24521464646	588.664785353536
86	4502.64	3843.70521464646	658.934785353536
87	4356.98	3795.66632575758	561.313674242424
88	4591.27	3893.13743686869	698.132563131314
89	4696.96	3873.44743686869	823.512563131313
90	4621.4	3803.34743686869	818.052563131313
91	4562.84	3759.33743686869	803.502563131313
92	4202.52	3767.0885479798	435.431452020202
93	4296.49	3741.47299242424	555.017007575757
94	4435.23	3699.72077020202	735.509229797979
95	4105.18	3693.84077020202	411.33922979798
96	4116.68	3855.04541666667	261.634583333333
97	3844.49	3303.53161616162	540.958383838384
98	3720.98	3291.99161616162	428.988383838384
99	3674.4	3243.95272727273	430.447272727273
100	3857.62	3341.42383838384	516.196161616161
101	3801.06	3321.73383838384	479.326161616162
102	3504.37	3251.63383838384	252.736161616161
103	3032.6	3207.62383838384	-175.023838383839
104	3047.03	3215.37494949495	-168.344949494949
105	2962.34	3189.75939393939	-227.419393939394
106	2197.82	3148.00717171717	-950.187171717171
107	2014.45	3142.12717171717	-1127.67717171717

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 3030.29 & 2311.91438131313 & 718.375618686866 \tabularnewline
2 & 2803.47 & 2300.37438131313 & 503.095618686868 \tabularnewline
3 & 2767.63 & 2252.33549242424 & 515.294507575758 \tabularnewline
4 & 2882.6 & 2349.80660353535 & 532.793396464646 \tabularnewline
5 & 2863.36 & 2330.11660353535 & 533.243396464647 \tabularnewline
6 & 2897.06 & 2260.01660353535 & 637.043396464647 \tabularnewline
7 & 3012.61 & 2216.00660353535 & 796.603396464647 \tabularnewline
8 & 3142.95 & 2223.75771464646 & 919.192285353535 \tabularnewline
9 & 3032.93 & 2198.14215909091 & 834.787840909091 \tabularnewline
10 & 3045.78 & 2156.38993686869 & 889.390063131313 \tabularnewline
11 & 3110.52 & 2150.50993686869 & 960.010063131314 \tabularnewline
12 & 3013.24 & 2311.71458333333 & 701.525416666666 \tabularnewline
13 & 2987.1 & 2532.39021464646 & 454.709785353536 \tabularnewline
14 & 2995.55 & 2520.85021464646 & 474.699785353536 \tabularnewline
15 & 2833.18 & 2472.81132575758 & 360.368674242424 \tabularnewline
16 & 2848.96 & 2570.28243686869 & 278.677563131313 \tabularnewline
17 & 2794.83 & 2550.59243686869 & 244.237563131313 \tabularnewline
18 & 2845.26 & 2480.49243686869 & 364.767563131313 \tabularnewline
19 & 2915.02 & 2436.48243686869 & 478.537563131313 \tabularnewline
20 & 2892.63 & 2444.2335479798 & 448.396452020202 \tabularnewline
21 & 2604.42 & 2418.61799242424 & 185.802007575758 \tabularnewline
22 & 2641.65 & 2376.86577020202 & 264.784229797980 \tabularnewline
23 & 2659.81 & 2370.98577020202 & 288.824229797980 \tabularnewline
24 & 2638.53 & 2532.19041666667 & 106.339583333333 \tabularnewline
25 & 2720.25 & 2752.8660479798 & -32.6160479797978 \tabularnewline
26 & 2745.88 & 2741.3260479798 & 4.55395202020212 \tabularnewline
27 & 2735.7 & 2693.28715909091 & 42.4128409090909 \tabularnewline
28 & 2811.7 & 2790.75827020202 & 20.9417297979795 \tabularnewline
29 & 2799.43 & 2771.06827020202 & 28.3617297979798 \tabularnewline
30 & 2555.28 & 2700.96827020202 & -145.68827020202 \tabularnewline
31 & 2304.98 & 2656.95827020202 & -351.97827020202 \tabularnewline
32 & 2214.95 & 2664.70938131313 & -449.759381313131 \tabularnewline
33 & 2065.81 & 2639.09382575758 & -573.283825757575 \tabularnewline
34 & 1940.49 & 2597.34160353535 & -656.851603535353 \tabularnewline
35 & 2042 & 2591.46160353535 & -549.461603535353 \tabularnewline
36 & 1995.37 & 2752.66625 & -757.29625 \tabularnewline
37 & 1946.81 & 2973.34188131313 & -1026.53188131313 \tabularnewline
38 & 1765.9 & 2961.80188131313 & -1195.90188131313 \tabularnewline
39 & 1635.25 & 2913.76299242424 & -1278.51299242424 \tabularnewline
40 & 1833.42 & 3011.23410353535 & -1177.81410353535 \tabularnewline
41 & 1910.43 & 2991.54410353535 & -1081.11410353535 \tabularnewline
42 & 1959.67 & 2921.44410353535 & -961.774103535353 \tabularnewline
43 & 1969.6 & 2877.43410353535 & -907.834103535353 \tabularnewline
44 & 2061.41 & 2885.18521464646 & -823.775214646465 \tabularnewline
45 & 2093.48 & 2859.56965909091 & -766.089659090909 \tabularnewline
46 & 2120.88 & 2817.81743686869 & -696.937436868687 \tabularnewline
47 & 2174.56 & 2811.93743686869 & -637.377436868687 \tabularnewline
48 & 2196.72 & 2973.14208333333 & -776.422083333333 \tabularnewline
49 & 2350.44 & 3193.81771464646 & -843.377714646464 \tabularnewline
50 & 2440.25 & 3182.27771464646 & -742.027714646465 \tabularnewline
51 & 2408.64 & 3134.23882575758 & -725.598825757576 \tabularnewline
52 & 2472.81 & 3231.70993686869 & -758.899936868687 \tabularnewline
53 & 2407.6 & 3212.01993686869 & -804.419936868687 \tabularnewline
54 & 2454.62 & 3141.91993686869 & -687.299936868687 \tabularnewline
55 & 2448.05 & 3097.90993686869 & -649.859936868687 \tabularnewline
56 & 2497.84 & 3105.6610479798 & -607.821047979798 \tabularnewline
57 & 2645.64 & 3080.04549242424 & -434.405492424243 \tabularnewline
58 & 2756.76 & 3038.29327020202 & -281.53327020202 \tabularnewline
59 & 2849.27 & 3032.41327020202 & -183.143270202020 \tabularnewline
60 & 2921.44 & 3193.61791666667 & -272.177916666667 \tabularnewline
61 & 2981.85 & 3414.2935479798 & -432.443547979798 \tabularnewline
62 & 3080.58 & 3402.7535479798 & -322.173547979798 \tabularnewline
63 & 3106.22 & 3354.71465909091 & -248.494659090910 \tabularnewline
64 & 3119.31 & 3452.18577020202 & -332.875770202020 \tabularnewline
65 & 3061.26 & 3432.49577020202 & -371.23577020202 \tabularnewline
66 & 3097.31 & 3362.39577020202 & -265.08577020202 \tabularnewline
67 & 3161.69 & 3318.38577020202 & -156.695770202020 \tabularnewline
68 & 3257.16 & 3326.13688131313 & -68.9768813131319 \tabularnewline
69 & 3277.01 & 3300.52132575758 & -23.5113257575756 \tabularnewline
70 & 3295.32 & 3258.76910353535 & 36.5508964646464 \tabularnewline
71 & 3363.99 & 3252.88910353535 & 111.100896464646 \tabularnewline
72 & 3494.17 & 3414.09375 & 80.0762499999997 \tabularnewline
73 & 3667.03 & 3634.76938131313 & 32.2606186868692 \tabularnewline
74 & 3813.06 & 3623.22938131313 & 189.830618686869 \tabularnewline
75 & 3917.96 & 3575.19049242424 & 342.769507575758 \tabularnewline
76 & 3895.51 & 3672.66160353535 & 222.848396464646 \tabularnewline
77 & 3801.06 & 3652.97160353535 & 148.088396464646 \tabularnewline
78 & 3570.12 & 3582.87160353535 & -12.7516035353539 \tabularnewline
79 & 3701.61 & 3538.86160353535 & 162.748396464646 \tabularnewline
80 & 3862.27 & 3546.61271464646 & 315.657285353535 \tabularnewline
81 & 3970.1 & 3520.99715909091 & 449.102840909091 \tabularnewline
82 & 4138.52 & 3479.24493686869 & 659.275063131313 \tabularnewline
83 & 4199.75 & 3473.36493686869 & 726.385063131313 \tabularnewline
84 & 4290.89 & 3634.56958333333 & 656.320416666667 \tabularnewline
85 & 4443.91 & 3855.24521464646 & 588.664785353536 \tabularnewline
86 & 4502.64 & 3843.70521464646 & 658.934785353536 \tabularnewline
87 & 4356.98 & 3795.66632575758 & 561.313674242424 \tabularnewline
88 & 4591.27 & 3893.13743686869 & 698.132563131314 \tabularnewline
89 & 4696.96 & 3873.44743686869 & 823.512563131313 \tabularnewline
90 & 4621.4 & 3803.34743686869 & 818.052563131313 \tabularnewline
91 & 4562.84 & 3759.33743686869 & 803.502563131313 \tabularnewline
92 & 4202.52 & 3767.0885479798 & 435.431452020202 \tabularnewline
93 & 4296.49 & 3741.47299242424 & 555.017007575757 \tabularnewline
94 & 4435.23 & 3699.72077020202 & 735.509229797979 \tabularnewline
95 & 4105.18 & 3693.84077020202 & 411.33922979798 \tabularnewline
96 & 4116.68 & 3855.04541666667 & 261.634583333333 \tabularnewline
97 & 3844.49 & 3303.53161616162 & 540.958383838384 \tabularnewline
98 & 3720.98 & 3291.99161616162 & 428.988383838384 \tabularnewline
99 & 3674.4 & 3243.95272727273 & 430.447272727273 \tabularnewline
100 & 3857.62 & 3341.42383838384 & 516.196161616161 \tabularnewline
101 & 3801.06 & 3321.73383838384 & 479.326161616162 \tabularnewline
102 & 3504.37 & 3251.63383838384 & 252.736161616161 \tabularnewline
103 & 3032.6 & 3207.62383838384 & -175.023838383839 \tabularnewline
104 & 3047.03 & 3215.37494949495 & -168.344949494949 \tabularnewline
105 & 2962.34 & 3189.75939393939 & -227.419393939394 \tabularnewline
106 & 2197.82 & 3148.00717171717 & -950.187171717171 \tabularnewline
107 & 2014.45 & 3142.12717171717 & -1127.67717171717 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=32259&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]3030.29[/C][C]2311.91438131313[/C][C]718.375618686866[/C][/ROW]
[ROW][C]2[/C][C]2803.47[/C][C]2300.37438131313[/C][C]503.095618686868[/C][/ROW]
[ROW][C]3[/C][C]2767.63[/C][C]2252.33549242424[/C][C]515.294507575758[/C][/ROW]
[ROW][C]4[/C][C]2882.6[/C][C]2349.80660353535[/C][C]532.793396464646[/C][/ROW]
[ROW][C]5[/C][C]2863.36[/C][C]2330.11660353535[/C][C]533.243396464647[/C][/ROW]
[ROW][C]6[/C][C]2897.06[/C][C]2260.01660353535[/C][C]637.043396464647[/C][/ROW]
[ROW][C]7[/C][C]3012.61[/C][C]2216.00660353535[/C][C]796.603396464647[/C][/ROW]
[ROW][C]8[/C][C]3142.95[/C][C]2223.75771464646[/C][C]919.192285353535[/C][/ROW]
[ROW][C]9[/C][C]3032.93[/C][C]2198.14215909091[/C][C]834.787840909091[/C][/ROW]
[ROW][C]10[/C][C]3045.78[/C][C]2156.38993686869[/C][C]889.390063131313[/C][/ROW]
[ROW][C]11[/C][C]3110.52[/C][C]2150.50993686869[/C][C]960.010063131314[/C][/ROW]
[ROW][C]12[/C][C]3013.24[/C][C]2311.71458333333[/C][C]701.525416666666[/C][/ROW]
[ROW][C]13[/C][C]2987.1[/C][C]2532.39021464646[/C][C]454.709785353536[/C][/ROW]
[ROW][C]14[/C][C]2995.55[/C][C]2520.85021464646[/C][C]474.699785353536[/C][/ROW]
[ROW][C]15[/C][C]2833.18[/C][C]2472.81132575758[/C][C]360.368674242424[/C][/ROW]
[ROW][C]16[/C][C]2848.96[/C][C]2570.28243686869[/C][C]278.677563131313[/C][/ROW]
[ROW][C]17[/C][C]2794.83[/C][C]2550.59243686869[/C][C]244.237563131313[/C][/ROW]
[ROW][C]18[/C][C]2845.26[/C][C]2480.49243686869[/C][C]364.767563131313[/C][/ROW]
[ROW][C]19[/C][C]2915.02[/C][C]2436.48243686869[/C][C]478.537563131313[/C][/ROW]
[ROW][C]20[/C][C]2892.63[/C][C]2444.2335479798[/C][C]448.396452020202[/C][/ROW]
[ROW][C]21[/C][C]2604.42[/C][C]2418.61799242424[/C][C]185.802007575758[/C][/ROW]
[ROW][C]22[/C][C]2641.65[/C][C]2376.86577020202[/C][C]264.784229797980[/C][/ROW]
[ROW][C]23[/C][C]2659.81[/C][C]2370.98577020202[/C][C]288.824229797980[/C][/ROW]
[ROW][C]24[/C][C]2638.53[/C][C]2532.19041666667[/C][C]106.339583333333[/C][/ROW]
[ROW][C]25[/C][C]2720.25[/C][C]2752.8660479798[/C][C]-32.6160479797978[/C][/ROW]
[ROW][C]26[/C][C]2745.88[/C][C]2741.3260479798[/C][C]4.55395202020212[/C][/ROW]
[ROW][C]27[/C][C]2735.7[/C][C]2693.28715909091[/C][C]42.4128409090909[/C][/ROW]
[ROW][C]28[/C][C]2811.7[/C][C]2790.75827020202[/C][C]20.9417297979795[/C][/ROW]
[ROW][C]29[/C][C]2799.43[/C][C]2771.06827020202[/C][C]28.3617297979798[/C][/ROW]
[ROW][C]30[/C][C]2555.28[/C][C]2700.96827020202[/C][C]-145.68827020202[/C][/ROW]
[ROW][C]31[/C][C]2304.98[/C][C]2656.95827020202[/C][C]-351.97827020202[/C][/ROW]
[ROW][C]32[/C][C]2214.95[/C][C]2664.70938131313[/C][C]-449.759381313131[/C][/ROW]
[ROW][C]33[/C][C]2065.81[/C][C]2639.09382575758[/C][C]-573.283825757575[/C][/ROW]
[ROW][C]34[/C][C]1940.49[/C][C]2597.34160353535[/C][C]-656.851603535353[/C][/ROW]
[ROW][C]35[/C][C]2042[/C][C]2591.46160353535[/C][C]-549.461603535353[/C][/ROW]
[ROW][C]36[/C][C]1995.37[/C][C]2752.66625[/C][C]-757.29625[/C][/ROW]
[ROW][C]37[/C][C]1946.81[/C][C]2973.34188131313[/C][C]-1026.53188131313[/C][/ROW]
[ROW][C]38[/C][C]1765.9[/C][C]2961.80188131313[/C][C]-1195.90188131313[/C][/ROW]
[ROW][C]39[/C][C]1635.25[/C][C]2913.76299242424[/C][C]-1278.51299242424[/C][/ROW]
[ROW][C]40[/C][C]1833.42[/C][C]3011.23410353535[/C][C]-1177.81410353535[/C][/ROW]
[ROW][C]41[/C][C]1910.43[/C][C]2991.54410353535[/C][C]-1081.11410353535[/C][/ROW]
[ROW][C]42[/C][C]1959.67[/C][C]2921.44410353535[/C][C]-961.774103535353[/C][/ROW]
[ROW][C]43[/C][C]1969.6[/C][C]2877.43410353535[/C][C]-907.834103535353[/C][/ROW]
[ROW][C]44[/C][C]2061.41[/C][C]2885.18521464646[/C][C]-823.775214646465[/C][/ROW]
[ROW][C]45[/C][C]2093.48[/C][C]2859.56965909091[/C][C]-766.089659090909[/C][/ROW]
[ROW][C]46[/C][C]2120.88[/C][C]2817.81743686869[/C][C]-696.937436868687[/C][/ROW]
[ROW][C]47[/C][C]2174.56[/C][C]2811.93743686869[/C][C]-637.377436868687[/C][/ROW]
[ROW][C]48[/C][C]2196.72[/C][C]2973.14208333333[/C][C]-776.422083333333[/C][/ROW]
[ROW][C]49[/C][C]2350.44[/C][C]3193.81771464646[/C][C]-843.377714646464[/C][/ROW]
[ROW][C]50[/C][C]2440.25[/C][C]3182.27771464646[/C][C]-742.027714646465[/C][/ROW]
[ROW][C]51[/C][C]2408.64[/C][C]3134.23882575758[/C][C]-725.598825757576[/C][/ROW]
[ROW][C]52[/C][C]2472.81[/C][C]3231.70993686869[/C][C]-758.899936868687[/C][/ROW]
[ROW][C]53[/C][C]2407.6[/C][C]3212.01993686869[/C][C]-804.419936868687[/C][/ROW]
[ROW][C]54[/C][C]2454.62[/C][C]3141.91993686869[/C][C]-687.299936868687[/C][/ROW]
[ROW][C]55[/C][C]2448.05[/C][C]3097.90993686869[/C][C]-649.859936868687[/C][/ROW]
[ROW][C]56[/C][C]2497.84[/C][C]3105.6610479798[/C][C]-607.821047979798[/C][/ROW]
[ROW][C]57[/C][C]2645.64[/C][C]3080.04549242424[/C][C]-434.405492424243[/C][/ROW]
[ROW][C]58[/C][C]2756.76[/C][C]3038.29327020202[/C][C]-281.53327020202[/C][/ROW]
[ROW][C]59[/C][C]2849.27[/C][C]3032.41327020202[/C][C]-183.143270202020[/C][/ROW]
[ROW][C]60[/C][C]2921.44[/C][C]3193.61791666667[/C][C]-272.177916666667[/C][/ROW]
[ROW][C]61[/C][C]2981.85[/C][C]3414.2935479798[/C][C]-432.443547979798[/C][/ROW]
[ROW][C]62[/C][C]3080.58[/C][C]3402.7535479798[/C][C]-322.173547979798[/C][/ROW]
[ROW][C]63[/C][C]3106.22[/C][C]3354.71465909091[/C][C]-248.494659090910[/C][/ROW]
[ROW][C]64[/C][C]3119.31[/C][C]3452.18577020202[/C][C]-332.875770202020[/C][/ROW]
[ROW][C]65[/C][C]3061.26[/C][C]3432.49577020202[/C][C]-371.23577020202[/C][/ROW]
[ROW][C]66[/C][C]3097.31[/C][C]3362.39577020202[/C][C]-265.08577020202[/C][/ROW]
[ROW][C]67[/C][C]3161.69[/C][C]3318.38577020202[/C][C]-156.695770202020[/C][/ROW]
[ROW][C]68[/C][C]3257.16[/C][C]3326.13688131313[/C][C]-68.9768813131319[/C][/ROW]
[ROW][C]69[/C][C]3277.01[/C][C]3300.52132575758[/C][C]-23.5113257575756[/C][/ROW]
[ROW][C]70[/C][C]3295.32[/C][C]3258.76910353535[/C][C]36.5508964646464[/C][/ROW]
[ROW][C]71[/C][C]3363.99[/C][C]3252.88910353535[/C][C]111.100896464646[/C][/ROW]
[ROW][C]72[/C][C]3494.17[/C][C]3414.09375[/C][C]80.0762499999997[/C][/ROW]
[ROW][C]73[/C][C]3667.03[/C][C]3634.76938131313[/C][C]32.2606186868692[/C][/ROW]
[ROW][C]74[/C][C]3813.06[/C][C]3623.22938131313[/C][C]189.830618686869[/C][/ROW]
[ROW][C]75[/C][C]3917.96[/C][C]3575.19049242424[/C][C]342.769507575758[/C][/ROW]
[ROW][C]76[/C][C]3895.51[/C][C]3672.66160353535[/C][C]222.848396464646[/C][/ROW]
[ROW][C]77[/C][C]3801.06[/C][C]3652.97160353535[/C][C]148.088396464646[/C][/ROW]
[ROW][C]78[/C][C]3570.12[/C][C]3582.87160353535[/C][C]-12.7516035353539[/C][/ROW]
[ROW][C]79[/C][C]3701.61[/C][C]3538.86160353535[/C][C]162.748396464646[/C][/ROW]
[ROW][C]80[/C][C]3862.27[/C][C]3546.61271464646[/C][C]315.657285353535[/C][/ROW]
[ROW][C]81[/C][C]3970.1[/C][C]3520.99715909091[/C][C]449.102840909091[/C][/ROW]
[ROW][C]82[/C][C]4138.52[/C][C]3479.24493686869[/C][C]659.275063131313[/C][/ROW]
[ROW][C]83[/C][C]4199.75[/C][C]3473.36493686869[/C][C]726.385063131313[/C][/ROW]
[ROW][C]84[/C][C]4290.89[/C][C]3634.56958333333[/C][C]656.320416666667[/C][/ROW]
[ROW][C]85[/C][C]4443.91[/C][C]3855.24521464646[/C][C]588.664785353536[/C][/ROW]
[ROW][C]86[/C][C]4502.64[/C][C]3843.70521464646[/C][C]658.934785353536[/C][/ROW]
[ROW][C]87[/C][C]4356.98[/C][C]3795.66632575758[/C][C]561.313674242424[/C][/ROW]
[ROW][C]88[/C][C]4591.27[/C][C]3893.13743686869[/C][C]698.132563131314[/C][/ROW]
[ROW][C]89[/C][C]4696.96[/C][C]3873.44743686869[/C][C]823.512563131313[/C][/ROW]
[ROW][C]90[/C][C]4621.4[/C][C]3803.34743686869[/C][C]818.052563131313[/C][/ROW]
[ROW][C]91[/C][C]4562.84[/C][C]3759.33743686869[/C][C]803.502563131313[/C][/ROW]
[ROW][C]92[/C][C]4202.52[/C][C]3767.0885479798[/C][C]435.431452020202[/C][/ROW]
[ROW][C]93[/C][C]4296.49[/C][C]3741.47299242424[/C][C]555.017007575757[/C][/ROW]
[ROW][C]94[/C][C]4435.23[/C][C]3699.72077020202[/C][C]735.509229797979[/C][/ROW]
[ROW][C]95[/C][C]4105.18[/C][C]3693.84077020202[/C][C]411.33922979798[/C][/ROW]
[ROW][C]96[/C][C]4116.68[/C][C]3855.04541666667[/C][C]261.634583333333[/C][/ROW]
[ROW][C]97[/C][C]3844.49[/C][C]3303.53161616162[/C][C]540.958383838384[/C][/ROW]
[ROW][C]98[/C][C]3720.98[/C][C]3291.99161616162[/C][C]428.988383838384[/C][/ROW]
[ROW][C]99[/C][C]3674.4[/C][C]3243.95272727273[/C][C]430.447272727273[/C][/ROW]
[ROW][C]100[/C][C]3857.62[/C][C]3341.42383838384[/C][C]516.196161616161[/C][/ROW]
[ROW][C]101[/C][C]3801.06[/C][C]3321.73383838384[/C][C]479.326161616162[/C][/ROW]
[ROW][C]102[/C][C]3504.37[/C][C]3251.63383838384[/C][C]252.736161616161[/C][/ROW]
[ROW][C]103[/C][C]3032.6[/C][C]3207.62383838384[/C][C]-175.023838383839[/C][/ROW]
[ROW][C]104[/C][C]3047.03[/C][C]3215.37494949495[/C][C]-168.344949494949[/C][/ROW]
[ROW][C]105[/C][C]2962.34[/C][C]3189.75939393939[/C][C]-227.419393939394[/C][/ROW]
[ROW][C]106[/C][C]2197.82[/C][C]3148.00717171717[/C][C]-950.187171717171[/C][/ROW]
[ROW][C]107[/C][C]2014.45[/C][C]3142.12717171717[/C][C]-1127.67717171717[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=32259&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=32259&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	3030.29	2311.91438131313	718.375618686866
2	2803.47	2300.37438131313	503.095618686868
3	2767.63	2252.33549242424	515.294507575758
4	2882.6	2349.80660353535	532.793396464646
5	2863.36	2330.11660353535	533.243396464647
6	2897.06	2260.01660353535	637.043396464647
7	3012.61	2216.00660353535	796.603396464647
8	3142.95	2223.75771464646	919.192285353535
9	3032.93	2198.14215909091	834.787840909091
10	3045.78	2156.38993686869	889.390063131313
11	3110.52	2150.50993686869	960.010063131314
12	3013.24	2311.71458333333	701.525416666666
13	2987.1	2532.39021464646	454.709785353536
14	2995.55	2520.85021464646	474.699785353536
15	2833.18	2472.81132575758	360.368674242424
16	2848.96	2570.28243686869	278.677563131313
17	2794.83	2550.59243686869	244.237563131313
18	2845.26	2480.49243686869	364.767563131313
19	2915.02	2436.48243686869	478.537563131313
20	2892.63	2444.2335479798	448.396452020202
21	2604.42	2418.61799242424	185.802007575758
22	2641.65	2376.86577020202	264.784229797980
23	2659.81	2370.98577020202	288.824229797980
24	2638.53	2532.19041666667	106.339583333333
25	2720.25	2752.8660479798	-32.6160479797978
26	2745.88	2741.3260479798	4.55395202020212
27	2735.7	2693.28715909091	42.4128409090909
28	2811.7	2790.75827020202	20.9417297979795
29	2799.43	2771.06827020202	28.3617297979798
30	2555.28	2700.96827020202	-145.68827020202
31	2304.98	2656.95827020202	-351.97827020202
32	2214.95	2664.70938131313	-449.759381313131
33	2065.81	2639.09382575758	-573.283825757575
34	1940.49	2597.34160353535	-656.851603535353
35	2042	2591.46160353535	-549.461603535353
36	1995.37	2752.66625	-757.29625
37	1946.81	2973.34188131313	-1026.53188131313
38	1765.9	2961.80188131313	-1195.90188131313
39	1635.25	2913.76299242424	-1278.51299242424
40	1833.42	3011.23410353535	-1177.81410353535
41	1910.43	2991.54410353535	-1081.11410353535
42	1959.67	2921.44410353535	-961.774103535353
43	1969.6	2877.43410353535	-907.834103535353
44	2061.41	2885.18521464646	-823.775214646465
45	2093.48	2859.56965909091	-766.089659090909
46	2120.88	2817.81743686869	-696.937436868687
47	2174.56	2811.93743686869	-637.377436868687
48	2196.72	2973.14208333333	-776.422083333333
49	2350.44	3193.81771464646	-843.377714646464
50	2440.25	3182.27771464646	-742.027714646465
51	2408.64	3134.23882575758	-725.598825757576
52	2472.81	3231.70993686869	-758.899936868687
53	2407.6	3212.01993686869	-804.419936868687
54	2454.62	3141.91993686869	-687.299936868687
55	2448.05	3097.90993686869	-649.859936868687
56	2497.84	3105.6610479798	-607.821047979798
57	2645.64	3080.04549242424	-434.405492424243
58	2756.76	3038.29327020202	-281.53327020202
59	2849.27	3032.41327020202	-183.143270202020
60	2921.44	3193.61791666667	-272.177916666667
61	2981.85	3414.2935479798	-432.443547979798
62	3080.58	3402.7535479798	-322.173547979798
63	3106.22	3354.71465909091	-248.494659090910
64	3119.31	3452.18577020202	-332.875770202020
65	3061.26	3432.49577020202	-371.23577020202
66	3097.31	3362.39577020202	-265.08577020202
67	3161.69	3318.38577020202	-156.695770202020
68	3257.16	3326.13688131313	-68.9768813131319
69	3277.01	3300.52132575758	-23.5113257575756
70	3295.32	3258.76910353535	36.5508964646464
71	3363.99	3252.88910353535	111.100896464646
72	3494.17	3414.09375	80.0762499999997
73	3667.03	3634.76938131313	32.2606186868692
74	3813.06	3623.22938131313	189.830618686869
75	3917.96	3575.19049242424	342.769507575758
76	3895.51	3672.66160353535	222.848396464646
77	3801.06	3652.97160353535	148.088396464646
78	3570.12	3582.87160353535	-12.7516035353539
79	3701.61	3538.86160353535	162.748396464646
80	3862.27	3546.61271464646	315.657285353535
81	3970.1	3520.99715909091	449.102840909091
82	4138.52	3479.24493686869	659.275063131313
83	4199.75	3473.36493686869	726.385063131313
84	4290.89	3634.56958333333	656.320416666667
85	4443.91	3855.24521464646	588.664785353536
86	4502.64	3843.70521464646	658.934785353536
87	4356.98	3795.66632575758	561.313674242424
88	4591.27	3893.13743686869	698.132563131314
89	4696.96	3873.44743686869	823.512563131313
90	4621.4	3803.34743686869	818.052563131313
91	4562.84	3759.33743686869	803.502563131313
92	4202.52	3767.0885479798	435.431452020202
93	4296.49	3741.47299242424	555.017007575757
94	4435.23	3699.72077020202	735.509229797979
95	4105.18	3693.84077020202	411.33922979798
96	4116.68	3855.04541666667	261.634583333333
97	3844.49	3303.53161616162	540.958383838384
98	3720.98	3291.99161616162	428.988383838384
99	3674.4	3243.95272727273	430.447272727273
100	3857.62	3341.42383838384	516.196161616161
101	3801.06	3321.73383838384	479.326161616162
102	3504.37	3251.63383838384	252.736161616161
103	3032.6	3207.62383838384	-175.023838383839
104	3047.03	3215.37494949495	-168.344949494949
105	2962.34	3189.75939393939	-227.419393939394
106	2197.82	3148.00717171717	-950.187171717171
107	2014.45	3142.12717171717	-1127.67717171717

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 = Include Monthly Dummies ; par3 = Linear Trend ;

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