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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Thu, 18 Jun 2009 07:25:40 -0600

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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/Jun/18/t124533192979i1mpdhb5wgqg9.htm/, Retrieved Fri, 10 May 2024 10:13:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=42282, Retrieved Fri, 10 May 2024 10:13:53 +0000

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241

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-       [Multiple Regression] [Enrolment Linear] [2009-06-18 13:25:40] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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101.553223	49.35172089	6.840672934	314.3897134	7.95126257	26	47.49	9.529999733
77.35	0.45	0.78	463.05	12.85	8	33.2	20
43.81	0.61	4.94	142.88	30	4	20	16
102.03	7.03	5.84	286.44	5.92	9	69.3	16.2
86.03	0.36	0.84	567	8.2	39	67	8.1
72.87	0.05	0.16	372.25	8	16	48.57	2.7
65.89	0.73	0.5	200.71	8.1	3	25.65	0.8
69.28	1.07	12.26	767.43	16.91	10	48.73	9.7
32.49	98	60	60.5	56	11	51.4	45
76.64	0.69	1.58	253.28	14.1	49	0.7	35.4
79.78	0.51	9.04	315.59	5.4	5	57.9	29.6
61.32	5.94	5.64	168.97	10.01	11	41.1	16.5
93.89	33.44	120.92	372.26	14.52	13	60.1	47.46
96.87	1.58	24.93	313.43	22.16	46	73.61	12.1
96.81	75.33	187.57	845.24	5.27	93	98.7	91.1
108.89	18.27	12.31	450.23	3.9	14	67.9	44.5
113.99	0.9	60.61	499.9	24.07	32	82.22	18.3
99.07	31.03	12.24	178.64	11.36	9	70.68	11.6
136.86	2.46	6.81	135.72	11.5	56	64.13	19
60.91	5.05	5.61	209.13	16.2	36	21	12.1
89.14	9.8	7.79	441.64	11	11	51.21	11.3
101.38	13.97	229.09	661.38	7.6	72	95	86.1
98.94	7.14	40.3	1014.13	40	26	97.77	3.5
104.32	17.54	10.7	450	2.1	53	89.89	11.44
116.68	46.73	24.1	196.87	10.34	17	48	30.8
100.62	2.84	14.87	1846.99	6.7	32	76.1	11.06
32.76	0.5	0.09	140.19	21	2	22.3	25
69.18	82.41	150.53	713.04	2.6	30	43	56
95.81	0.78	0.67	168.89	7.43	24	64.9	8.3
66.95	4.33	2.97	237	16	9	38.2	29.3
48.95	11.39	7.3	633.43	6	34	60.93	36.3
68.73	1.6	8.49	258.26	2.2	35	68.3	4.2
103.63	0.27	0.32	338.13	12.22	5	53.16	31.6
125.42	0.13	9.29	215.64	10.1	32	66.81	23
99.12	88.73	379.89	1415.42	9.4	93	99.17	87.3
105.75	36.86	262.49	276.53	1.8	43	91	25
74.26	29.96	41.04	340.91	9.6	24	50.2	15.53
80.11	2.65	11.41	205.7	7.34	47	68.5	22

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ 72.249.76.132

\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 & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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	5 seconds
R Server	'George Udny Yule' @ 72.249.76.132

Multiple Linear Regression - Estimated Regression Equation
Enrol[t] = + 58.7578561581236 -0.0536291396091595Irr[t] -0.0378072341617618Mach[t] -0.00677597264230691Val[t] -0.570464946780196Exp[t] + 0.128149474300660San[t] + 0.633378330948962Lit[t] -0.0426406763956305Road[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Enrol[t] =  +  58.7578561581236 -0.0536291396091595Irr[t] -0.0378072341617618Mach[t] -0.00677597264230691Val[t] -0.570464946780196Exp[t] +  0.128149474300660San[t] +  0.633378330948962Lit[t] -0.0426406763956305Road[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Enrol[t] =  +  58.7578561581236 -0.0536291396091595Irr[t] -0.0378072341617618Mach[t] -0.00677597264230691Val[t] -0.570464946780196Exp[t] +  0.128149474300660San[t] +  0.633378330948962Lit[t] -0.0426406763956305Road[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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
Enrol[t] = + 58.7578561581236 -0.0536291396091595Irr[t] -0.0378072341617618Mach[t] -0.00677597264230691Val[t] -0.570464946780196Exp[t] + 0.128149474300660San[t] + 0.633378330948962Lit[t] -0.0426406763956305Road[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)	58.7578561581236	11.65004	5.0436	2.1e-05	1e-05
Irr	-0.0536291396091595	0.170869	-0.3139	0.755799	0.3779
Mach	-0.0378072341617618	0.072728	-0.5198	0.606986	0.303493
Val	-0.00677597264230691	0.01062	-0.638	0.5283	0.26415
Exp	-0.570464946780196	0.326293	-1.7483	0.090637	0.045318
San	0.128149474300660	0.209944	0.6104	0.546195	0.273097
Lit	0.633378330948962	0.184593	3.4312	0.001772	0.000886
Road	-0.0426406763956305	0.238806	-0.1786	0.859485	0.429743

\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) & 58.7578561581236 & 11.65004 & 5.0436 & 2.1e-05 & 1e-05 \tabularnewline
Irr & -0.0536291396091595 & 0.170869 & -0.3139 & 0.755799 & 0.3779 \tabularnewline
Mach & -0.0378072341617618 & 0.072728 & -0.5198 & 0.606986 & 0.303493 \tabularnewline
Val & -0.00677597264230691 & 0.01062 & -0.638 & 0.5283 & 0.26415 \tabularnewline
Exp & -0.570464946780196 & 0.326293 & -1.7483 & 0.090637 & 0.045318 \tabularnewline
San & 0.128149474300660 & 0.209944 & 0.6104 & 0.546195 & 0.273097 \tabularnewline
Lit & 0.633378330948962 & 0.184593 & 3.4312 & 0.001772 & 0.000886 \tabularnewline
Road & -0.0426406763956305 & 0.238806 & -0.1786 & 0.859485 & 0.429743 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&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]58.7578561581236[/C][C]11.65004[/C][C]5.0436[/C][C]2.1e-05[/C][C]1e-05[/C][/ROW]
[ROW][C]Irr[/C][C]-0.0536291396091595[/C][C]0.170869[/C][C]-0.3139[/C][C]0.755799[/C][C]0.3779[/C][/ROW]
[ROW][C]Mach[/C][C]-0.0378072341617618[/C][C]0.072728[/C][C]-0.5198[/C][C]0.606986[/C][C]0.303493[/C][/ROW]
[ROW][C]Val[/C][C]-0.00677597264230691[/C][C]0.01062[/C][C]-0.638[/C][C]0.5283[/C][C]0.26415[/C][/ROW]
[ROW][C]Exp[/C][C]-0.570464946780196[/C][C]0.326293[/C][C]-1.7483[/C][C]0.090637[/C][C]0.045318[/C][/ROW]
[ROW][C]San[/C][C]0.128149474300660[/C][C]0.209944[/C][C]0.6104[/C][C]0.546195[/C][C]0.273097[/C][/ROW]
[ROW][C]Lit[/C][C]0.633378330948962[/C][C]0.184593[/C][C]3.4312[/C][C]0.001772[/C][C]0.000886[/C][/ROW]
[ROW][C]Road[/C][C]-0.0426406763956305[/C][C]0.238806[/C][C]-0.1786[/C][C]0.859485[/C][C]0.429743[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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)	58.7578561581236	11.65004	5.0436	2.1e-05	1e-05
Irr	-0.0536291396091595	0.170869	-0.3139	0.755799	0.3779
Mach	-0.0378072341617618	0.072728	-0.5198	0.606986	0.303493
Val	-0.00677597264230691	0.01062	-0.638	0.5283	0.26415
Exp	-0.570464946780196	0.326293	-1.7483	0.090637	0.045318
San	0.128149474300660	0.209944	0.6104	0.546195	0.273097
Lit	0.633378330948962	0.184593	3.4312	0.001772	0.000886
Road	-0.0426406763956305	0.238806	-0.1786	0.859485	0.429743

Multiple Linear Regression - Regression Statistics
Multiple R	0.673547174765554
R-squared	0.45366579663466
Adjusted R-squared	0.326187815849414
F-TEST (value)	3.55877771078695
F-TEST (DF numerator)	7
F-TEST (DF denominator)	30
p-value	0.00666327090111318
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	19.8100601062949
Sum Squared Residuals	11773.1544424505

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.673547174765554 \tabularnewline
R-squared & 0.45366579663466 \tabularnewline
Adjusted R-squared & 0.326187815849414 \tabularnewline
F-TEST (value) & 3.55877771078695 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 30 \tabularnewline
p-value & 0.00666327090111318 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 19.8100601062949 \tabularnewline
Sum Squared Residuals & 11773.1544424505 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.673547174765554[/C][/ROW]
[ROW][C]R-squared[/C][C]0.45366579663466[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.326187815849414[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]3.55877771078695[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]30[/C][/ROW]
[ROW][C]p-value[/C][C]0.00666327090111318[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]19.8100601062949[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]11773.1544424505[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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	0.673547174765554
R-squared	0.45366579663466
Adjusted R-squared	0.326187815849414
F-TEST (value)	3.55877771078695
F-TEST (DF numerator)	7
F-TEST (DF denominator)	30
p-value	0.00666327090111318
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	19.8100601062949
Sum Squared Residuals	11773.1544424505

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	101.553223	82.1909838631884	19.3622391368116
2	77.35	69.4366875585058	7.91331244149423
3	43.81	52.954188965516	-9.14418896551604
4	102.03	97.1976716164252	4.83232838357485
5	86.03	97.2757907318844	-11.2457907318844
6	72.87	84.3614974500704	-11.4914974500704
7	65.89	69.3155218017974	-3.42552180179738
8	69.28	75.1227156020918	-5.84271560209179
9	32.49	50.9242430524503	-18.4342430524503
10	76.64	54.1145516493648	22.5254483506352
11	79.78	89.2209766934394	-9.44097669343944
12	61.32	78.1086985123142	-16.7886985123142
13	93.89	79.295526725884	14.5944732741160
14	96.87	94.9671940185574	1.90280598144265
15	96.81	109.440573528911	-12.6305735289109
16	108.89	94.9400564817826	13.9499435182174
17	113.99	94.6965186472472	19.2934813527528
18	99.07	94.365935715656	4.70406428434405
19	136.86	97.8732293881342	38.9867706118658
20	60.91	65.0147129632899	-4.10471296328989
21	89.14	82.033226165436	7.10677383456389
22	101.38	106.256712774102	-4.87671277410237
23	98.94	92.2690409389571	6.67095906104285
24	104.32	116.403990535543	-12.0839905355428
25	116.68	79.3753869527669	37.3046130472331
26	100.62	93.5353552575079	7.0846447424921
27	32.76	59.1125702690078	-26.3525702690078
28	69.18	71.0242819917336	-1.84428199173359
29	95.81	97.1356694559238	-1.32566945592375
30	66.95	71.77903552601	-4.82903552600995
31	48.95	91.5571006948781	-42.6071006948781
32	68.73	102.911961302673	-34.1819613026725
33	103.63	82.4327307669483	21.1972692330517
34	125.42	96.6128423727664	28.8071576272336
35	99.12	95.6910338586147	3.42896614138525
36	105.75	106.038307159421	-0.288307159420816
37	74.26	82.0220278152853	-7.76202781528534
38	80.11	101.074674195916	-20.9646741959155

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 101.553223 & 82.1909838631884 & 19.3622391368116 \tabularnewline
2 & 77.35 & 69.4366875585058 & 7.91331244149423 \tabularnewline
3 & 43.81 & 52.954188965516 & -9.14418896551604 \tabularnewline
4 & 102.03 & 97.1976716164252 & 4.83232838357485 \tabularnewline
5 & 86.03 & 97.2757907318844 & -11.2457907318844 \tabularnewline
6 & 72.87 & 84.3614974500704 & -11.4914974500704 \tabularnewline
7 & 65.89 & 69.3155218017974 & -3.42552180179738 \tabularnewline
8 & 69.28 & 75.1227156020918 & -5.84271560209179 \tabularnewline
9 & 32.49 & 50.9242430524503 & -18.4342430524503 \tabularnewline
10 & 76.64 & 54.1145516493648 & 22.5254483506352 \tabularnewline
11 & 79.78 & 89.2209766934394 & -9.44097669343944 \tabularnewline
12 & 61.32 & 78.1086985123142 & -16.7886985123142 \tabularnewline
13 & 93.89 & 79.295526725884 & 14.5944732741160 \tabularnewline
14 & 96.87 & 94.9671940185574 & 1.90280598144265 \tabularnewline
15 & 96.81 & 109.440573528911 & -12.6305735289109 \tabularnewline
16 & 108.89 & 94.9400564817826 & 13.9499435182174 \tabularnewline
17 & 113.99 & 94.6965186472472 & 19.2934813527528 \tabularnewline
18 & 99.07 & 94.365935715656 & 4.70406428434405 \tabularnewline
19 & 136.86 & 97.8732293881342 & 38.9867706118658 \tabularnewline
20 & 60.91 & 65.0147129632899 & -4.10471296328989 \tabularnewline
21 & 89.14 & 82.033226165436 & 7.10677383456389 \tabularnewline
22 & 101.38 & 106.256712774102 & -4.87671277410237 \tabularnewline
23 & 98.94 & 92.2690409389571 & 6.67095906104285 \tabularnewline
24 & 104.32 & 116.403990535543 & -12.0839905355428 \tabularnewline
25 & 116.68 & 79.3753869527669 & 37.3046130472331 \tabularnewline
26 & 100.62 & 93.5353552575079 & 7.0846447424921 \tabularnewline
27 & 32.76 & 59.1125702690078 & -26.3525702690078 \tabularnewline
28 & 69.18 & 71.0242819917336 & -1.84428199173359 \tabularnewline
29 & 95.81 & 97.1356694559238 & -1.32566945592375 \tabularnewline
30 & 66.95 & 71.77903552601 & -4.82903552600995 \tabularnewline
31 & 48.95 & 91.5571006948781 & -42.6071006948781 \tabularnewline
32 & 68.73 & 102.911961302673 & -34.1819613026725 \tabularnewline
33 & 103.63 & 82.4327307669483 & 21.1972692330517 \tabularnewline
34 & 125.42 & 96.6128423727664 & 28.8071576272336 \tabularnewline
35 & 99.12 & 95.6910338586147 & 3.42896614138525 \tabularnewline
36 & 105.75 & 106.038307159421 & -0.288307159420816 \tabularnewline
37 & 74.26 & 82.0220278152853 & -7.76202781528534 \tabularnewline
38 & 80.11 & 101.074674195916 & -20.9646741959155 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&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]101.553223[/C][C]82.1909838631884[/C][C]19.3622391368116[/C][/ROW]
[ROW][C]2[/C][C]77.35[/C][C]69.4366875585058[/C][C]7.91331244149423[/C][/ROW]
[ROW][C]3[/C][C]43.81[/C][C]52.954188965516[/C][C]-9.14418896551604[/C][/ROW]
[ROW][C]4[/C][C]102.03[/C][C]97.1976716164252[/C][C]4.83232838357485[/C][/ROW]
[ROW][C]5[/C][C]86.03[/C][C]97.2757907318844[/C][C]-11.2457907318844[/C][/ROW]
[ROW][C]6[/C][C]72.87[/C][C]84.3614974500704[/C][C]-11.4914974500704[/C][/ROW]
[ROW][C]7[/C][C]65.89[/C][C]69.3155218017974[/C][C]-3.42552180179738[/C][/ROW]
[ROW][C]8[/C][C]69.28[/C][C]75.1227156020918[/C][C]-5.84271560209179[/C][/ROW]
[ROW][C]9[/C][C]32.49[/C][C]50.9242430524503[/C][C]-18.4342430524503[/C][/ROW]
[ROW][C]10[/C][C]76.64[/C][C]54.1145516493648[/C][C]22.5254483506352[/C][/ROW]
[ROW][C]11[/C][C]79.78[/C][C]89.2209766934394[/C][C]-9.44097669343944[/C][/ROW]
[ROW][C]12[/C][C]61.32[/C][C]78.1086985123142[/C][C]-16.7886985123142[/C][/ROW]
[ROW][C]13[/C][C]93.89[/C][C]79.295526725884[/C][C]14.5944732741160[/C][/ROW]
[ROW][C]14[/C][C]96.87[/C][C]94.9671940185574[/C][C]1.90280598144265[/C][/ROW]
[ROW][C]15[/C][C]96.81[/C][C]109.440573528911[/C][C]-12.6305735289109[/C][/ROW]
[ROW][C]16[/C][C]108.89[/C][C]94.9400564817826[/C][C]13.9499435182174[/C][/ROW]
[ROW][C]17[/C][C]113.99[/C][C]94.6965186472472[/C][C]19.2934813527528[/C][/ROW]
[ROW][C]18[/C][C]99.07[/C][C]94.365935715656[/C][C]4.70406428434405[/C][/ROW]
[ROW][C]19[/C][C]136.86[/C][C]97.8732293881342[/C][C]38.9867706118658[/C][/ROW]
[ROW][C]20[/C][C]60.91[/C][C]65.0147129632899[/C][C]-4.10471296328989[/C][/ROW]
[ROW][C]21[/C][C]89.14[/C][C]82.033226165436[/C][C]7.10677383456389[/C][/ROW]
[ROW][C]22[/C][C]101.38[/C][C]106.256712774102[/C][C]-4.87671277410237[/C][/ROW]
[ROW][C]23[/C][C]98.94[/C][C]92.2690409389571[/C][C]6.67095906104285[/C][/ROW]
[ROW][C]24[/C][C]104.32[/C][C]116.403990535543[/C][C]-12.0839905355428[/C][/ROW]
[ROW][C]25[/C][C]116.68[/C][C]79.3753869527669[/C][C]37.3046130472331[/C][/ROW]
[ROW][C]26[/C][C]100.62[/C][C]93.5353552575079[/C][C]7.0846447424921[/C][/ROW]
[ROW][C]27[/C][C]32.76[/C][C]59.1125702690078[/C][C]-26.3525702690078[/C][/ROW]
[ROW][C]28[/C][C]69.18[/C][C]71.0242819917336[/C][C]-1.84428199173359[/C][/ROW]
[ROW][C]29[/C][C]95.81[/C][C]97.1356694559238[/C][C]-1.32566945592375[/C][/ROW]
[ROW][C]30[/C][C]66.95[/C][C]71.77903552601[/C][C]-4.82903552600995[/C][/ROW]
[ROW][C]31[/C][C]48.95[/C][C]91.5571006948781[/C][C]-42.6071006948781[/C][/ROW]
[ROW][C]32[/C][C]68.73[/C][C]102.911961302673[/C][C]-34.1819613026725[/C][/ROW]
[ROW][C]33[/C][C]103.63[/C][C]82.4327307669483[/C][C]21.1972692330517[/C][/ROW]
[ROW][C]34[/C][C]125.42[/C][C]96.6128423727664[/C][C]28.8071576272336[/C][/ROW]
[ROW][C]35[/C][C]99.12[/C][C]95.6910338586147[/C][C]3.42896614138525[/C][/ROW]
[ROW][C]36[/C][C]105.75[/C][C]106.038307159421[/C][C]-0.288307159420816[/C][/ROW]
[ROW][C]37[/C][C]74.26[/C][C]82.0220278152853[/C][C]-7.76202781528534[/C][/ROW]
[ROW][C]38[/C][C]80.11[/C][C]101.074674195916[/C][C]-20.9646741959155[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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	101.553223	82.1909838631884	19.3622391368116
2	77.35	69.4366875585058	7.91331244149423
3	43.81	52.954188965516	-9.14418896551604
4	102.03	97.1976716164252	4.83232838357485
5	86.03	97.2757907318844	-11.2457907318844
6	72.87	84.3614974500704	-11.4914974500704
7	65.89	69.3155218017974	-3.42552180179738
8	69.28	75.1227156020918	-5.84271560209179
9	32.49	50.9242430524503	-18.4342430524503
10	76.64	54.1145516493648	22.5254483506352
11	79.78	89.2209766934394	-9.44097669343944
12	61.32	78.1086985123142	-16.7886985123142
13	93.89	79.295526725884	14.5944732741160
14	96.87	94.9671940185574	1.90280598144265
15	96.81	109.440573528911	-12.6305735289109
16	108.89	94.9400564817826	13.9499435182174
17	113.99	94.6965186472472	19.2934813527528
18	99.07	94.365935715656	4.70406428434405
19	136.86	97.8732293881342	38.9867706118658
20	60.91	65.0147129632899	-4.10471296328989
21	89.14	82.033226165436	7.10677383456389
22	101.38	106.256712774102	-4.87671277410237
23	98.94	92.2690409389571	6.67095906104285
24	104.32	116.403990535543	-12.0839905355428
25	116.68	79.3753869527669	37.3046130472331
26	100.62	93.5353552575079	7.0846447424921
27	32.76	59.1125702690078	-26.3525702690078
28	69.18	71.0242819917336	-1.84428199173359
29	95.81	97.1356694559238	-1.32566945592375
30	66.95	71.77903552601	-4.82903552600995
31	48.95	91.5571006948781	-42.6071006948781
32	68.73	102.911961302673	-34.1819613026725
33	103.63	82.4327307669483	21.1972692330517
34	125.42	96.6128423727664	28.8071576272336
35	99.12	95.6910338586147	3.42896614138525
36	105.75	106.038307159421	-0.288307159420816
37	74.26	82.0220278152853	-7.76202781528534
38	80.11	101.074674195916	-20.9646741959155

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.102544542692506	0.205089085385012	0.897455457307494
12	0.0727917281111342	0.145583456222268	0.927208271888866
13	0.0704695942600996	0.140939188520199	0.9295304057399
14	0.0646381259484203	0.129276251896841	0.93536187405158
15	0.143561901477979	0.287123802955958	0.856438098522021
16	0.111362004601392	0.222724009202783	0.888637995398608
17	0.148450487678625	0.296900975357249	0.851549512321375
18	0.089833347583056	0.179666695166112	0.910166652416944
19	0.208888732167372	0.417777464334745	0.791111267832628
20	0.260226596097727	0.520453192195453	0.739773403902273
21	0.184046408882169	0.368092817764338	0.815953591117831
22	0.118135959072772	0.236271918145545	0.881864040927228
23	0.363299060994133	0.726598121988265	0.636700939005867
24	0.4997694444462	0.9995388888924	0.5002305555538
25	0.534248267837791	0.931503464324418	0.465751732162209
26	0.797667470988761	0.404665058022478	0.202332529011239
27	0.683738261118427	0.632523477763146	0.316261738881573

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 & 0.102544542692506 & 0.205089085385012 & 0.897455457307494 \tabularnewline
12 & 0.0727917281111342 & 0.145583456222268 & 0.927208271888866 \tabularnewline
13 & 0.0704695942600996 & 0.140939188520199 & 0.9295304057399 \tabularnewline
14 & 0.0646381259484203 & 0.129276251896841 & 0.93536187405158 \tabularnewline
15 & 0.143561901477979 & 0.287123802955958 & 0.856438098522021 \tabularnewline
16 & 0.111362004601392 & 0.222724009202783 & 0.888637995398608 \tabularnewline
17 & 0.148450487678625 & 0.296900975357249 & 0.851549512321375 \tabularnewline
18 & 0.089833347583056 & 0.179666695166112 & 0.910166652416944 \tabularnewline
19 & 0.208888732167372 & 0.417777464334745 & 0.791111267832628 \tabularnewline
20 & 0.260226596097727 & 0.520453192195453 & 0.739773403902273 \tabularnewline
21 & 0.184046408882169 & 0.368092817764338 & 0.815953591117831 \tabularnewline
22 & 0.118135959072772 & 0.236271918145545 & 0.881864040927228 \tabularnewline
23 & 0.363299060994133 & 0.726598121988265 & 0.636700939005867 \tabularnewline
24 & 0.4997694444462 & 0.9995388888924 & 0.5002305555538 \tabularnewline
25 & 0.534248267837791 & 0.931503464324418 & 0.465751732162209 \tabularnewline
26 & 0.797667470988761 & 0.404665058022478 & 0.202332529011239 \tabularnewline
27 & 0.683738261118427 & 0.632523477763146 & 0.316261738881573 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&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]11[/C][C]0.102544542692506[/C][C]0.205089085385012[/C][C]0.897455457307494[/C][/ROW]
[ROW][C]12[/C][C]0.0727917281111342[/C][C]0.145583456222268[/C][C]0.927208271888866[/C][/ROW]
[ROW][C]13[/C][C]0.0704695942600996[/C][C]0.140939188520199[/C][C]0.9295304057399[/C][/ROW]
[ROW][C]14[/C][C]0.0646381259484203[/C][C]0.129276251896841[/C][C]0.93536187405158[/C][/ROW]
[ROW][C]15[/C][C]0.143561901477979[/C][C]0.287123802955958[/C][C]0.856438098522021[/C][/ROW]
[ROW][C]16[/C][C]0.111362004601392[/C][C]0.222724009202783[/C][C]0.888637995398608[/C][/ROW]
[ROW][C]17[/C][C]0.148450487678625[/C][C]0.296900975357249[/C][C]0.851549512321375[/C][/ROW]
[ROW][C]18[/C][C]0.089833347583056[/C][C]0.179666695166112[/C][C]0.910166652416944[/C][/ROW]
[ROW][C]19[/C][C]0.208888732167372[/C][C]0.417777464334745[/C][C]0.791111267832628[/C][/ROW]
[ROW][C]20[/C][C]0.260226596097727[/C][C]0.520453192195453[/C][C]0.739773403902273[/C][/ROW]
[ROW][C]21[/C][C]0.184046408882169[/C][C]0.368092817764338[/C][C]0.815953591117831[/C][/ROW]
[ROW][C]22[/C][C]0.118135959072772[/C][C]0.236271918145545[/C][C]0.881864040927228[/C][/ROW]
[ROW][C]23[/C][C]0.363299060994133[/C][C]0.726598121988265[/C][C]0.636700939005867[/C][/ROW]
[ROW][C]24[/C][C]0.4997694444462[/C][C]0.9995388888924[/C][C]0.5002305555538[/C][/ROW]
[ROW][C]25[/C][C]0.534248267837791[/C][C]0.931503464324418[/C][C]0.465751732162209[/C][/ROW]
[ROW][C]26[/C][C]0.797667470988761[/C][C]0.404665058022478[/C][C]0.202332529011239[/C][/ROW]
[ROW][C]27[/C][C]0.683738261118427[/C][C]0.632523477763146[/C][C]0.316261738881573[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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
11	0.102544542692506	0.205089085385012	0.897455457307494
12	0.0727917281111342	0.145583456222268	0.927208271888866
13	0.0704695942600996	0.140939188520199	0.9295304057399
14	0.0646381259484203	0.129276251896841	0.93536187405158
15	0.143561901477979	0.287123802955958	0.856438098522021
16	0.111362004601392	0.222724009202783	0.888637995398608
17	0.148450487678625	0.296900975357249	0.851549512321375
18	0.089833347583056	0.179666695166112	0.910166652416944
19	0.208888732167372	0.417777464334745	0.791111267832628
20	0.260226596097727	0.520453192195453	0.739773403902273
21	0.184046408882169	0.368092817764338	0.815953591117831
22	0.118135959072772	0.236271918145545	0.881864040927228
23	0.363299060994133	0.726598121988265	0.636700939005867
24	0.4997694444462	0.9995388888924	0.5002305555538
25	0.534248267837791	0.931503464324418	0.465751732162209
26	0.797667470988761	0.404665058022478	0.202332529011239
27	0.683738261118427	0.632523477763146	0.316261738881573

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	0	0	OK
10% type I error level	0	0	OK

\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 & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=42282&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]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=42282&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=42282&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	0	0	OK
10% type I error level	0	0	OK

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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Figure 10

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Parameters (Session):

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

Parameters (R input):

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

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

library(lattice)
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