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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 20 Nov 2009 05:47:15 -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/2009/Nov/20/t1258721291o04ktk9d364b91z.htm/, Retrieved Fri, 19 Apr 2024 18:45:02 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=58092, Retrieved Fri, 19 Apr 2024 18:45:02 +0000

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User-defined keywords

Workshop 7 Model 5

Estimated Impact

204

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

-     [Multiple Regression] [Q1 The Seatbeltlaw] [2007-11-14 19:27:43] [8cd6641b921d30ebe00b648d1481bba0]
- RMPD  [Multiple Regression] [Seatbelt] [2009-11-12 13:54:52] [b98453cac15ba1066b407e146608df68]
-    D    [Multiple Regression] [WS7 No seasonal D...] [2009-11-18 15:26:28] [445b292c553470d9fed8bc2796fd3a00]
- R PD        [Multiple Regression] [shw-ws7] [2009-11-20 12:47:15] [5b5bced41faf164488f2c271c918b21f] [Current]
-   P           [Multiple Regression] [Multiple_Regressi...] [2009-12-29 14:59:39] [2663058f2a5dda519058ac6b2228468f] 

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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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&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]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=58092&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&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	3 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Multiple Linear Regression - Estimated Regression Equation
Y[t] = + 632.244505651929 + 6.14920105647311X[t] + 70.0919714626187M1[t] -21.2602057945611M2[t] + 119.638415891786M3[t] -509.343601576689M4[t] -182.313541791994M5[t] -130.214920105647M6[t] -247.918507903497M7[t] -199.249726428445M8[t] -400.681743896919M9[t] + 547.587836521659M10[t] -2.53068226740704M11[t] -10.9671835879982t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Y[t] =  +  632.244505651929 +  6.14920105647311X[t] +  70.0919714626187M1[t] -21.2602057945611M2[t] +  119.638415891786M3[t] -509.343601576689M4[t] -182.313541791994M5[t] -130.214920105647M6[t] -247.918507903497M7[t] -199.249726428445M8[t] -400.681743896919M9[t] +  547.587836521659M10[t] -2.53068226740704M11[t] -10.9671835879982t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Y[t] =  +  632.244505651929 +  6.14920105647311X[t] +  70.0919714626187M1[t] -21.2602057945611M2[t] +  119.638415891786M3[t] -509.343601576689M4[t] -182.313541791994M5[t] -130.214920105647M6[t] -247.918507903497M7[t] -199.249726428445M8[t] -400.681743896919M9[t] +  547.587836521659M10[t] -2.53068226740704M11[t] -10.9671835879982t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&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] = + 632.244505651929 + 6.14920105647311X[t] + 70.0919714626187M1[t] -21.2602057945611M2[t] + 119.638415891786M3[t] -509.343601576689M4[t] -182.313541791994M5[t] -130.214920105647M6[t] -247.918507903497M7[t] -199.249726428445M8[t] -400.681743896919M9[t] + 547.587836521659M10[t] -2.53068226740704M11[t] -10.9671835879982t + 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)	632.244505651929	1461.420121	0.4326	0.667612	0.333806
X	6.14920105647311	4.334653	1.4186	0.163757	0.081878
M1	70.0919714626187	111.340253	0.6295	0.532582	0.266291
M2	-21.2602057945611	111.468448	-0.1907	0.849703	0.424852
M3	119.638415891786	111.286723	1.075	0.288796	0.144398
M4	-509.343601576689	111.407597	-4.5719	4.6e-05	2.3e-05
M5	-182.313541791994	110.911423	-1.6438	0.108061	0.05403
M6	-130.214920105647	110.898206	-1.1742	0.247265	0.123632
M7	-247.918507903497	117.494905	-2.11	0.041158	0.020579
M8	-199.249726428445	117.252797	-1.6993	0.097025	0.048513
M9	-400.681743896919	117.329468	-3.415	0.001475	0.000738
M10	547.587836521659	116.957697	4.6819	3.2e-05	1.6e-05
M11	-2.53068226740704	116.90892	-0.0216	0.982837	0.491419
t	-10.9671835879982	6.5271	-1.6803	0.100703	0.050351

\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) & 632.244505651929 & 1461.420121 & 0.4326 & 0.667612 & 0.333806 \tabularnewline
X & 6.14920105647311 & 4.334653 & 1.4186 & 0.163757 & 0.081878 \tabularnewline
M1 & 70.0919714626187 & 111.340253 & 0.6295 & 0.532582 & 0.266291 \tabularnewline
M2 & -21.2602057945611 & 111.468448 & -0.1907 & 0.849703 & 0.424852 \tabularnewline
M3 & 119.638415891786 & 111.286723 & 1.075 & 0.288796 & 0.144398 \tabularnewline
M4 & -509.343601576689 & 111.407597 & -4.5719 & 4.6e-05 & 2.3e-05 \tabularnewline
M5 & -182.313541791994 & 110.911423 & -1.6438 & 0.108061 & 0.05403 \tabularnewline
M6 & -130.214920105647 & 110.898206 & -1.1742 & 0.247265 & 0.123632 \tabularnewline
M7 & -247.918507903497 & 117.494905 & -2.11 & 0.041158 & 0.020579 \tabularnewline
M8 & -199.249726428445 & 117.252797 & -1.6993 & 0.097025 & 0.048513 \tabularnewline
M9 & -400.681743896919 & 117.329468 & -3.415 & 0.001475 & 0.000738 \tabularnewline
M10 & 547.587836521659 & 116.957697 & 4.6819 & 3.2e-05 & 1.6e-05 \tabularnewline
M11 & -2.53068226740704 & 116.90892 & -0.0216 & 0.982837 & 0.491419 \tabularnewline
t & -10.9671835879982 & 6.5271 & -1.6803 & 0.100703 & 0.050351 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&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]632.244505651929[/C][C]1461.420121[/C][C]0.4326[/C][C]0.667612[/C][C]0.333806[/C][/ROW]
[ROW][C]X[/C][C]6.14920105647311[/C][C]4.334653[/C][C]1.4186[/C][C]0.163757[/C][C]0.081878[/C][/ROW]
[ROW][C]M1[/C][C]70.0919714626187[/C][C]111.340253[/C][C]0.6295[/C][C]0.532582[/C][C]0.266291[/C][/ROW]
[ROW][C]M2[/C][C]-21.2602057945611[/C][C]111.468448[/C][C]-0.1907[/C][C]0.849703[/C][C]0.424852[/C][/ROW]
[ROW][C]M3[/C][C]119.638415891786[/C][C]111.286723[/C][C]1.075[/C][C]0.288796[/C][C]0.144398[/C][/ROW]
[ROW][C]M4[/C][C]-509.343601576689[/C][C]111.407597[/C][C]-4.5719[/C][C]4.6e-05[/C][C]2.3e-05[/C][/ROW]
[ROW][C]M5[/C][C]-182.313541791994[/C][C]110.911423[/C][C]-1.6438[/C][C]0.108061[/C][C]0.05403[/C][/ROW]
[ROW][C]M6[/C][C]-130.214920105647[/C][C]110.898206[/C][C]-1.1742[/C][C]0.247265[/C][C]0.123632[/C][/ROW]
[ROW][C]M7[/C][C]-247.918507903497[/C][C]117.494905[/C][C]-2.11[/C][C]0.041158[/C][C]0.020579[/C][/ROW]
[ROW][C]M8[/C][C]-199.249726428445[/C][C]117.252797[/C][C]-1.6993[/C][C]0.097025[/C][C]0.048513[/C][/ROW]
[ROW][C]M9[/C][C]-400.681743896919[/C][C]117.329468[/C][C]-3.415[/C][C]0.001475[/C][C]0.000738[/C][/ROW]
[ROW][C]M10[/C][C]547.587836521659[/C][C]116.957697[/C][C]4.6819[/C][C]3.2e-05[/C][C]1.6e-05[/C][/ROW]
[ROW][C]M11[/C][C]-2.53068226740704[/C][C]116.90892[/C][C]-0.0216[/C][C]0.982837[/C][C]0.491419[/C][/ROW]
[ROW][C]t[/C][C]-10.9671835879982[/C][C]6.5271[/C][C]-1.6803[/C][C]0.100703[/C][C]0.050351[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&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)	632.244505651929	1461.420121	0.4326	0.667612	0.333806
X	6.14920105647311	4.334653	1.4186	0.163757	0.081878
M1	70.0919714626187	111.340253	0.6295	0.532582	0.266291
M2	-21.2602057945611	111.468448	-0.1907	0.849703	0.424852
M3	119.638415891786	111.286723	1.075	0.288796	0.144398
M4	-509.343601576689	111.407597	-4.5719	4.6e-05	2.3e-05
M5	-182.313541791994	110.911423	-1.6438	0.108061	0.05403
M6	-130.214920105647	110.898206	-1.1742	0.247265	0.123632
M7	-247.918507903497	117.494905	-2.11	0.041158	0.020579
M8	-199.249726428445	117.252797	-1.6993	0.097025	0.048513
M9	-400.681743896919	117.329468	-3.415	0.001475	0.000738
M10	547.587836521659	116.957697	4.6819	3.2e-05	1.6e-05
M11	-2.53068226740704	116.90892	-0.0216	0.982837	0.491419
t	-10.9671835879982	6.5271	-1.6803	0.100703	0.050351

Multiple Linear Regression - Regression Statistics
Multiple R	0.878170238609815
R-squared	0.77118296798002
Adjusted R-squared	0.696817432573527
F-TEST (value)	10.3701662842151
F-TEST (DF numerator)	13
F-TEST (DF denominator)	40
p-value	4.5114849633876e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	165.316021337892
Sum Squared Residuals	1093175.47643962

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.878170238609815 \tabularnewline
R-squared & 0.77118296798002 \tabularnewline
Adjusted R-squared & 0.696817432573527 \tabularnewline
F-TEST (value) & 10.3701662842151 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 40 \tabularnewline
p-value & 4.5114849633876e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 165.316021337892 \tabularnewline
Sum Squared Residuals & 1093175.47643962 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.878170238609815[/C][/ROW]
[ROW][C]R-squared[/C][C]0.77118296798002[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.696817432573527[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]10.3701662842151[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]40[/C][/ROW]
[ROW][C]p-value[/C][C]4.5114849633876e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]165.316021337892[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1093175.47643962[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&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	0.878170238609815
R-squared	0.77118296798002
Adjusted R-squared	0.696817432573527
F-TEST (value)	10.3701662842151
F-TEST (DF numerator)	13
F-TEST (DF denominator)	40
p-value	4.5114849633876e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	165.316021337892
Sum Squared Residuals	1093175.47643962

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	2354	2720.60564216268	-366.60564216268
2	2697	2624.43548237397	72.5645176260287
3	2651	2760.51612152879	-109.516121528793
4	2067	2132.86532258527	-65.8653225852655
5	2641	2448.92819878196	192.071801218038
6	2539	2490.05963688031	48.9403631196892
7	2294	2392.13487077683	-98.1348707768288
8	2712	2466.73167500272	245.268324997279
9	2314	2260.48167500272	53.5183249972787
10	3092	3234.67927817214	-142.679278172141
11	2677	2692.04117896450	-15.0411789644958
12	2813	2702.05228081332	110.947719186676
13	2668	2779.62467185736	-111.624671857363
14	2939	2689.60371312513	249.396286874868
15	2617	2825.68435227995	-208.684352279953
16	2231	2191.88435227995	39.1156477200466
17	2481	2514.09642953312	-33.0964295331232
18	2421	2579.82467185736	-158.824671857364
19	2408	2457.30310152799	-49.3031015279895
20	2560	2495.00469941504	64.9953005849567
21	2100	2288.75469941504	-188.754699415043
22	3315	3232.2062973021	82.793702697903
23	2801	2671.12059492503	129.879405074968
24	2403	2668.83329466091	-265.833294660914
25	3024	2734.10728359201	289.892716407992
26	2507	2631.78792274683	-124.78792274683
27	2980	2767.86856190165	212.131438098349
28	2211	2127.91936084518	83.0806391548213
29	2471	2450.13143809835	20.8685619016516
30	2594	2491.26287619670	102.737123803303
31	2452	2368.74130586732	83.2586941326776
32	2232	2412.59210481085	-180.592104810849
33	2373	2206.34210481085	166.657895189151
34	3127	3168.24130586732	-41.2413058673222
35	2802	2637.90160877262	164.098391227377
36	2641	2635.61430850851	5.38569149149479
37	2787	2700.8882974396	86.1117025604014
38	2619	2604.71813765089	14.2818623491061
39	2806	2740.79877680572	65.2012231942845
40	2193	2106.99877680572	86.0012231942845
41	2323	2423.06165300241	-100.061653002412
42	2529	2470.34229215723	58.6577078427661
43	2412	2347.82072182786	64.1792781721407
44	2262	2391.67152077139	-129.671520771386
45	2154	2185.42152077139	-31.4215207713861
46	3230	3128.87311865844	101.126881341560
47	2295	2573.93661733785	-278.936617337848
48	2715	2565.50011601726	149.499883982743
49	2733	2630.77410494835	102.225895051649
50	2317	2528.45474410317	-211.454744103173
51	2730	2689.13218748389	40.8678125161131
52	1913	2055.33218748389	-142.332187483887
53	2390	2469.78228058415	-79.7822805841535
54	2484	2535.51052290839	-51.5105229083943

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 2354 & 2720.60564216268 & -366.60564216268 \tabularnewline
2 & 2697 & 2624.43548237397 & 72.5645176260287 \tabularnewline
3 & 2651 & 2760.51612152879 & -109.516121528793 \tabularnewline
4 & 2067 & 2132.86532258527 & -65.8653225852655 \tabularnewline
5 & 2641 & 2448.92819878196 & 192.071801218038 \tabularnewline
6 & 2539 & 2490.05963688031 & 48.9403631196892 \tabularnewline
7 & 2294 & 2392.13487077683 & -98.1348707768288 \tabularnewline
8 & 2712 & 2466.73167500272 & 245.268324997279 \tabularnewline
9 & 2314 & 2260.48167500272 & 53.5183249972787 \tabularnewline
10 & 3092 & 3234.67927817214 & -142.679278172141 \tabularnewline
11 & 2677 & 2692.04117896450 & -15.0411789644958 \tabularnewline
12 & 2813 & 2702.05228081332 & 110.947719186676 \tabularnewline
13 & 2668 & 2779.62467185736 & -111.624671857363 \tabularnewline
14 & 2939 & 2689.60371312513 & 249.396286874868 \tabularnewline
15 & 2617 & 2825.68435227995 & -208.684352279953 \tabularnewline
16 & 2231 & 2191.88435227995 & 39.1156477200466 \tabularnewline
17 & 2481 & 2514.09642953312 & -33.0964295331232 \tabularnewline
18 & 2421 & 2579.82467185736 & -158.824671857364 \tabularnewline
19 & 2408 & 2457.30310152799 & -49.3031015279895 \tabularnewline
20 & 2560 & 2495.00469941504 & 64.9953005849567 \tabularnewline
21 & 2100 & 2288.75469941504 & -188.754699415043 \tabularnewline
22 & 3315 & 3232.2062973021 & 82.793702697903 \tabularnewline
23 & 2801 & 2671.12059492503 & 129.879405074968 \tabularnewline
24 & 2403 & 2668.83329466091 & -265.833294660914 \tabularnewline
25 & 3024 & 2734.10728359201 & 289.892716407992 \tabularnewline
26 & 2507 & 2631.78792274683 & -124.78792274683 \tabularnewline
27 & 2980 & 2767.86856190165 & 212.131438098349 \tabularnewline
28 & 2211 & 2127.91936084518 & 83.0806391548213 \tabularnewline
29 & 2471 & 2450.13143809835 & 20.8685619016516 \tabularnewline
30 & 2594 & 2491.26287619670 & 102.737123803303 \tabularnewline
31 & 2452 & 2368.74130586732 & 83.2586941326776 \tabularnewline
32 & 2232 & 2412.59210481085 & -180.592104810849 \tabularnewline
33 & 2373 & 2206.34210481085 & 166.657895189151 \tabularnewline
34 & 3127 & 3168.24130586732 & -41.2413058673222 \tabularnewline
35 & 2802 & 2637.90160877262 & 164.098391227377 \tabularnewline
36 & 2641 & 2635.61430850851 & 5.38569149149479 \tabularnewline
37 & 2787 & 2700.8882974396 & 86.1117025604014 \tabularnewline
38 & 2619 & 2604.71813765089 & 14.2818623491061 \tabularnewline
39 & 2806 & 2740.79877680572 & 65.2012231942845 \tabularnewline
40 & 2193 & 2106.99877680572 & 86.0012231942845 \tabularnewline
41 & 2323 & 2423.06165300241 & -100.061653002412 \tabularnewline
42 & 2529 & 2470.34229215723 & 58.6577078427661 \tabularnewline
43 & 2412 & 2347.82072182786 & 64.1792781721407 \tabularnewline
44 & 2262 & 2391.67152077139 & -129.671520771386 \tabularnewline
45 & 2154 & 2185.42152077139 & -31.4215207713861 \tabularnewline
46 & 3230 & 3128.87311865844 & 101.126881341560 \tabularnewline
47 & 2295 & 2573.93661733785 & -278.936617337848 \tabularnewline
48 & 2715 & 2565.50011601726 & 149.499883982743 \tabularnewline
49 & 2733 & 2630.77410494835 & 102.225895051649 \tabularnewline
50 & 2317 & 2528.45474410317 & -211.454744103173 \tabularnewline
51 & 2730 & 2689.13218748389 & 40.8678125161131 \tabularnewline
52 & 1913 & 2055.33218748389 & -142.332187483887 \tabularnewline
53 & 2390 & 2469.78228058415 & -79.7822805841535 \tabularnewline
54 & 2484 & 2535.51052290839 & -51.5105229083943 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&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]2354[/C][C]2720.60564216268[/C][C]-366.60564216268[/C][/ROW]
[ROW][C]2[/C][C]2697[/C][C]2624.43548237397[/C][C]72.5645176260287[/C][/ROW]
[ROW][C]3[/C][C]2651[/C][C]2760.51612152879[/C][C]-109.516121528793[/C][/ROW]
[ROW][C]4[/C][C]2067[/C][C]2132.86532258527[/C][C]-65.8653225852655[/C][/ROW]
[ROW][C]5[/C][C]2641[/C][C]2448.92819878196[/C][C]192.071801218038[/C][/ROW]
[ROW][C]6[/C][C]2539[/C][C]2490.05963688031[/C][C]48.9403631196892[/C][/ROW]
[ROW][C]7[/C][C]2294[/C][C]2392.13487077683[/C][C]-98.1348707768288[/C][/ROW]
[ROW][C]8[/C][C]2712[/C][C]2466.73167500272[/C][C]245.268324997279[/C][/ROW]
[ROW][C]9[/C][C]2314[/C][C]2260.48167500272[/C][C]53.5183249972787[/C][/ROW]
[ROW][C]10[/C][C]3092[/C][C]3234.67927817214[/C][C]-142.679278172141[/C][/ROW]
[ROW][C]11[/C][C]2677[/C][C]2692.04117896450[/C][C]-15.0411789644958[/C][/ROW]
[ROW][C]12[/C][C]2813[/C][C]2702.05228081332[/C][C]110.947719186676[/C][/ROW]
[ROW][C]13[/C][C]2668[/C][C]2779.62467185736[/C][C]-111.624671857363[/C][/ROW]
[ROW][C]14[/C][C]2939[/C][C]2689.60371312513[/C][C]249.396286874868[/C][/ROW]
[ROW][C]15[/C][C]2617[/C][C]2825.68435227995[/C][C]-208.684352279953[/C][/ROW]
[ROW][C]16[/C][C]2231[/C][C]2191.88435227995[/C][C]39.1156477200466[/C][/ROW]
[ROW][C]17[/C][C]2481[/C][C]2514.09642953312[/C][C]-33.0964295331232[/C][/ROW]
[ROW][C]18[/C][C]2421[/C][C]2579.82467185736[/C][C]-158.824671857364[/C][/ROW]
[ROW][C]19[/C][C]2408[/C][C]2457.30310152799[/C][C]-49.3031015279895[/C][/ROW]
[ROW][C]20[/C][C]2560[/C][C]2495.00469941504[/C][C]64.9953005849567[/C][/ROW]
[ROW][C]21[/C][C]2100[/C][C]2288.75469941504[/C][C]-188.754699415043[/C][/ROW]
[ROW][C]22[/C][C]3315[/C][C]3232.2062973021[/C][C]82.793702697903[/C][/ROW]
[ROW][C]23[/C][C]2801[/C][C]2671.12059492503[/C][C]129.879405074968[/C][/ROW]
[ROW][C]24[/C][C]2403[/C][C]2668.83329466091[/C][C]-265.833294660914[/C][/ROW]
[ROW][C]25[/C][C]3024[/C][C]2734.10728359201[/C][C]289.892716407992[/C][/ROW]
[ROW][C]26[/C][C]2507[/C][C]2631.78792274683[/C][C]-124.78792274683[/C][/ROW]
[ROW][C]27[/C][C]2980[/C][C]2767.86856190165[/C][C]212.131438098349[/C][/ROW]
[ROW][C]28[/C][C]2211[/C][C]2127.91936084518[/C][C]83.0806391548213[/C][/ROW]
[ROW][C]29[/C][C]2471[/C][C]2450.13143809835[/C][C]20.8685619016516[/C][/ROW]
[ROW][C]30[/C][C]2594[/C][C]2491.26287619670[/C][C]102.737123803303[/C][/ROW]
[ROW][C]31[/C][C]2452[/C][C]2368.74130586732[/C][C]83.2586941326776[/C][/ROW]
[ROW][C]32[/C][C]2232[/C][C]2412.59210481085[/C][C]-180.592104810849[/C][/ROW]
[ROW][C]33[/C][C]2373[/C][C]2206.34210481085[/C][C]166.657895189151[/C][/ROW]
[ROW][C]34[/C][C]3127[/C][C]3168.24130586732[/C][C]-41.2413058673222[/C][/ROW]
[ROW][C]35[/C][C]2802[/C][C]2637.90160877262[/C][C]164.098391227377[/C][/ROW]
[ROW][C]36[/C][C]2641[/C][C]2635.61430850851[/C][C]5.38569149149479[/C][/ROW]
[ROW][C]37[/C][C]2787[/C][C]2700.8882974396[/C][C]86.1117025604014[/C][/ROW]
[ROW][C]38[/C][C]2619[/C][C]2604.71813765089[/C][C]14.2818623491061[/C][/ROW]
[ROW][C]39[/C][C]2806[/C][C]2740.79877680572[/C][C]65.2012231942845[/C][/ROW]
[ROW][C]40[/C][C]2193[/C][C]2106.99877680572[/C][C]86.0012231942845[/C][/ROW]
[ROW][C]41[/C][C]2323[/C][C]2423.06165300241[/C][C]-100.061653002412[/C][/ROW]
[ROW][C]42[/C][C]2529[/C][C]2470.34229215723[/C][C]58.6577078427661[/C][/ROW]
[ROW][C]43[/C][C]2412[/C][C]2347.82072182786[/C][C]64.1792781721407[/C][/ROW]
[ROW][C]44[/C][C]2262[/C][C]2391.67152077139[/C][C]-129.671520771386[/C][/ROW]
[ROW][C]45[/C][C]2154[/C][C]2185.42152077139[/C][C]-31.4215207713861[/C][/ROW]
[ROW][C]46[/C][C]3230[/C][C]3128.87311865844[/C][C]101.126881341560[/C][/ROW]
[ROW][C]47[/C][C]2295[/C][C]2573.93661733785[/C][C]-278.936617337848[/C][/ROW]
[ROW][C]48[/C][C]2715[/C][C]2565.50011601726[/C][C]149.499883982743[/C][/ROW]
[ROW][C]49[/C][C]2733[/C][C]2630.77410494835[/C][C]102.225895051649[/C][/ROW]
[ROW][C]50[/C][C]2317[/C][C]2528.45474410317[/C][C]-211.454744103173[/C][/ROW]
[ROW][C]51[/C][C]2730[/C][C]2689.13218748389[/C][C]40.8678125161131[/C][/ROW]
[ROW][C]52[/C][C]1913[/C][C]2055.33218748389[/C][C]-142.332187483887[/C][/ROW]
[ROW][C]53[/C][C]2390[/C][C]2469.78228058415[/C][C]-79.7822805841535[/C][/ROW]
[ROW][C]54[/C][C]2484[/C][C]2535.51052290839[/C][C]-51.5105229083943[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&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	2354	2720.60564216268	-366.60564216268
2	2697	2624.43548237397	72.5645176260287
3	2651	2760.51612152879	-109.516121528793
4	2067	2132.86532258527	-65.8653225852655
5	2641	2448.92819878196	192.071801218038
6	2539	2490.05963688031	48.9403631196892
7	2294	2392.13487077683	-98.1348707768288
8	2712	2466.73167500272	245.268324997279
9	2314	2260.48167500272	53.5183249972787
10	3092	3234.67927817214	-142.679278172141
11	2677	2692.04117896450	-15.0411789644958
12	2813	2702.05228081332	110.947719186676
13	2668	2779.62467185736	-111.624671857363
14	2939	2689.60371312513	249.396286874868
15	2617	2825.68435227995	-208.684352279953
16	2231	2191.88435227995	39.1156477200466
17	2481	2514.09642953312	-33.0964295331232
18	2421	2579.82467185736	-158.824671857364
19	2408	2457.30310152799	-49.3031015279895
20	2560	2495.00469941504	64.9953005849567
21	2100	2288.75469941504	-188.754699415043
22	3315	3232.2062973021	82.793702697903
23	2801	2671.12059492503	129.879405074968
24	2403	2668.83329466091	-265.833294660914
25	3024	2734.10728359201	289.892716407992
26	2507	2631.78792274683	-124.78792274683
27	2980	2767.86856190165	212.131438098349
28	2211	2127.91936084518	83.0806391548213
29	2471	2450.13143809835	20.8685619016516
30	2594	2491.26287619670	102.737123803303
31	2452	2368.74130586732	83.2586941326776
32	2232	2412.59210481085	-180.592104810849
33	2373	2206.34210481085	166.657895189151
34	3127	3168.24130586732	-41.2413058673222
35	2802	2637.90160877262	164.098391227377
36	2641	2635.61430850851	5.38569149149479
37	2787	2700.8882974396	86.1117025604014
38	2619	2604.71813765089	14.2818623491061
39	2806	2740.79877680572	65.2012231942845
40	2193	2106.99877680572	86.0012231942845
41	2323	2423.06165300241	-100.061653002412
42	2529	2470.34229215723	58.6577078427661
43	2412	2347.82072182786	64.1792781721407
44	2262	2391.67152077139	-129.671520771386
45	2154	2185.42152077139	-31.4215207713861
46	3230	3128.87311865844	101.126881341560
47	2295	2573.93661733785	-278.936617337848
48	2715	2565.50011601726	149.499883982743
49	2733	2630.77410494835	102.225895051649
50	2317	2528.45474410317	-211.454744103173
51	2730	2689.13218748389	40.8678125161131
52	1913	2055.33218748389	-142.332187483887
53	2390	2469.78228058415	-79.7822805841535
54	2484	2535.51052290839	-51.5105229083943

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.48726721964324	0.97453443928648	0.51273278035676
18	0.48768556490862	0.97537112981724	0.51231443509138
19	0.353518613416789	0.707037226833578	0.646481386583211
20	0.583948903700651	0.832102192598699	0.416051096299349
21	0.644559093926964	0.710881812146073	0.355440906073036
22	0.651422663209063	0.697154673581874	0.348577336790937
23	0.555547157176050	0.8889056856479	0.44445284282395
24	0.923206128458256	0.153587743083489	0.0767938715417443
25	0.976351896100162	0.047296207799675	0.0236481038998375
26	0.986280074570088	0.0274398508598237	0.0137199254299119
27	0.982293924305201	0.0354121513895978	0.0177060756947989
28	0.964762598469404	0.0704748030611929	0.0352374015305964
29	0.940382174853949	0.119235650292102	0.0596178251460512
30	0.896374988059373	0.207250023881253	0.103625011940627
31	0.832777637189325	0.334444725621350	0.167222362810675
32	0.843905980774298	0.312188038451404	0.156094019225702
33	0.782949886125718	0.434100227748563	0.217050113874282
34	0.766826999891848	0.466346000216304	0.233173000108152
35	0.889162939520421	0.221674120959157	0.110837060479579
36	0.918890215088324	0.162219569823351	0.0811097849116756
37	0.903347445104934	0.193305109790133	0.0966525548950663

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.48726721964324 & 0.97453443928648 & 0.51273278035676 \tabularnewline
18 & 0.48768556490862 & 0.97537112981724 & 0.51231443509138 \tabularnewline
19 & 0.353518613416789 & 0.707037226833578 & 0.646481386583211 \tabularnewline
20 & 0.583948903700651 & 0.832102192598699 & 0.416051096299349 \tabularnewline
21 & 0.644559093926964 & 0.710881812146073 & 0.355440906073036 \tabularnewline
22 & 0.651422663209063 & 0.697154673581874 & 0.348577336790937 \tabularnewline
23 & 0.555547157176050 & 0.8889056856479 & 0.44445284282395 \tabularnewline
24 & 0.923206128458256 & 0.153587743083489 & 0.0767938715417443 \tabularnewline
25 & 0.976351896100162 & 0.047296207799675 & 0.0236481038998375 \tabularnewline
26 & 0.986280074570088 & 0.0274398508598237 & 0.0137199254299119 \tabularnewline
27 & 0.982293924305201 & 0.0354121513895978 & 0.0177060756947989 \tabularnewline
28 & 0.964762598469404 & 0.0704748030611929 & 0.0352374015305964 \tabularnewline
29 & 0.940382174853949 & 0.119235650292102 & 0.0596178251460512 \tabularnewline
30 & 0.896374988059373 & 0.207250023881253 & 0.103625011940627 \tabularnewline
31 & 0.832777637189325 & 0.334444725621350 & 0.167222362810675 \tabularnewline
32 & 0.843905980774298 & 0.312188038451404 & 0.156094019225702 \tabularnewline
33 & 0.782949886125718 & 0.434100227748563 & 0.217050113874282 \tabularnewline
34 & 0.766826999891848 & 0.466346000216304 & 0.233173000108152 \tabularnewline
35 & 0.889162939520421 & 0.221674120959157 & 0.110837060479579 \tabularnewline
36 & 0.918890215088324 & 0.162219569823351 & 0.0811097849116756 \tabularnewline
37 & 0.903347445104934 & 0.193305109790133 & 0.0966525548950663 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]17[/C][C]0.48726721964324[/C][C]0.97453443928648[/C][C]0.51273278035676[/C][/ROW]
[ROW][C]18[/C][C]0.48768556490862[/C][C]0.97537112981724[/C][C]0.51231443509138[/C][/ROW]
[ROW][C]19[/C][C]0.353518613416789[/C][C]0.707037226833578[/C][C]0.646481386583211[/C][/ROW]
[ROW][C]20[/C][C]0.583948903700651[/C][C]0.832102192598699[/C][C]0.416051096299349[/C][/ROW]
[ROW][C]21[/C][C]0.644559093926964[/C][C]0.710881812146073[/C][C]0.355440906073036[/C][/ROW]
[ROW][C]22[/C][C]0.651422663209063[/C][C]0.697154673581874[/C][C]0.348577336790937[/C][/ROW]
[ROW][C]23[/C][C]0.555547157176050[/C][C]0.8889056856479[/C][C]0.44445284282395[/C][/ROW]
[ROW][C]24[/C][C]0.923206128458256[/C][C]0.153587743083489[/C][C]0.0767938715417443[/C][/ROW]
[ROW][C]25[/C][C]0.976351896100162[/C][C]0.047296207799675[/C][C]0.0236481038998375[/C][/ROW]
[ROW][C]26[/C][C]0.986280074570088[/C][C]0.0274398508598237[/C][C]0.0137199254299119[/C][/ROW]
[ROW][C]27[/C][C]0.982293924305201[/C][C]0.0354121513895978[/C][C]0.0177060756947989[/C][/ROW]
[ROW][C]28[/C][C]0.964762598469404[/C][C]0.0704748030611929[/C][C]0.0352374015305964[/C][/ROW]
[ROW][C]29[/C][C]0.940382174853949[/C][C]0.119235650292102[/C][C]0.0596178251460512[/C][/ROW]
[ROW][C]30[/C][C]0.896374988059373[/C][C]0.207250023881253[/C][C]0.103625011940627[/C][/ROW]
[ROW][C]31[/C][C]0.832777637189325[/C][C]0.334444725621350[/C][C]0.167222362810675[/C][/ROW]
[ROW][C]32[/C][C]0.843905980774298[/C][C]0.312188038451404[/C][C]0.156094019225702[/C][/ROW]
[ROW][C]33[/C][C]0.782949886125718[/C][C]0.434100227748563[/C][C]0.217050113874282[/C][/ROW]
[ROW][C]34[/C][C]0.766826999891848[/C][C]0.466346000216304[/C][C]0.233173000108152[/C][/ROW]
[ROW][C]35[/C][C]0.889162939520421[/C][C]0.221674120959157[/C][C]0.110837060479579[/C][/ROW]
[ROW][C]36[/C][C]0.918890215088324[/C][C]0.162219569823351[/C][C]0.0811097849116756[/C][/ROW]
[ROW][C]37[/C][C]0.903347445104934[/C][C]0.193305109790133[/C][C]0.0966525548950663[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&T=5

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
17	0.48726721964324	0.97453443928648	0.51273278035676
18	0.48768556490862	0.97537112981724	0.51231443509138
19	0.353518613416789	0.707037226833578	0.646481386583211
20	0.583948903700651	0.832102192598699	0.416051096299349
21	0.644559093926964	0.710881812146073	0.355440906073036
22	0.651422663209063	0.697154673581874	0.348577336790937
23	0.555547157176050	0.8889056856479	0.44445284282395
24	0.923206128458256	0.153587743083489	0.0767938715417443
25	0.976351896100162	0.047296207799675	0.0236481038998375
26	0.986280074570088	0.0274398508598237	0.0137199254299119
27	0.982293924305201	0.0354121513895978	0.0177060756947989
28	0.964762598469404	0.0704748030611929	0.0352374015305964
29	0.940382174853949	0.119235650292102	0.0596178251460512
30	0.896374988059373	0.207250023881253	0.103625011940627
31	0.832777637189325	0.334444725621350	0.167222362810675
32	0.843905980774298	0.312188038451404	0.156094019225702
33	0.782949886125718	0.434100227748563	0.217050113874282
34	0.766826999891848	0.466346000216304	0.233173000108152
35	0.889162939520421	0.221674120959157	0.110837060479579
36	0.918890215088324	0.162219569823351	0.0811097849116756
37	0.903347445104934	0.193305109790133	0.0966525548950663

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.142857142857143	NOK
10% type I error level	4	0.190476190476190	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 3 & 0.142857142857143 & NOK \tabularnewline
10% type I error level & 4 & 0.190476190476190 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=58092&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]3[/C][C]0.142857142857143[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]4[/C][C]0.190476190476190[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=58092&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=58092&T=6

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	3	0.142857142857143	NOK
10% type I error level	4	0.190476190476190	NOK

Figure 1

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Figure 8

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Figure 9

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Figure 10

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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)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$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')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$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()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
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')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code