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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 05 Mar 2012 22:20:16 -0500

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/2012/Mar/05/t1331004472g7ii4cspls9cd75.htm/, Retrieved Thu, 02 May 2024 23:26:04 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=163508, Retrieved Thu, 02 May 2024 23:26:04 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

171

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

-       [Multiple Regression] [15th bird enterin...] [2012-03-06 03:20:16] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    D    [Multiple Regression] [Fixed full model ...] [2012-03-06 15:22:26] [74be16979710d4c4e7c6647856088456] 
-    D    [Multiple Regression] [Model without dew...] [2012-03-06 15:32:03] [74be16979710d4c4e7c6647856088456] 
-    D    [Multiple Regression] [Reduced model ] [2012-03-06 15:35:32] [74be16979710d4c4e7c6647856088456] 
-    D      [Multiple Regression] [Chimney swift ent...] [2012-03-07 21:49:25] [74be16979710d4c4e7c6647856088456] 
-    D        [Multiple Regression] [Chimney swift ent...] [2012-03-08 21:24:40] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [TimeIn vs Sunset ...] [2012-03-09 17:37:07] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [TimeIn vs Temp Rain] [2012-03-09 17:38:55] [74be16979710d4c4e7c6647856088456] 
-    D        [Multiple Regression] [Poster regression...] [2012-04-02 17:00:19] [74be16979710d4c4e7c6647856088456] 
-    D          [Multiple Regression] [Including SeasonD...] [2012-04-09 18:04:16] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Chimney Swift Roo...] [2012-05-08 00:52:59] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Chimney Swift Roo...] [2012-05-08 01:12:12] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Fixed 5-7-2012] [2012-05-08 04:26:12] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Full Model] [2012-06-06 13:44:04] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Final model] [2012-06-06 13:46:11] [74be16979710d4c4e7c6647856088456] 
- R  D        [Multiple Regression] [Final Chimney Swi...] [2012-06-08 15:27:31] [74be16979710d4c4e7c6647856088456] 
-    D      [Multiple Regression] [Chimney swift ent...] [2012-03-07 21:54:59] [74be16979710d4c4e7c6647856088456] 
-    D    [Multiple Regression] [Full model with p...] [2012-03-06 15:49:05] [74be16979710d4c4e7c6647856088456] 
-    D    [Multiple Regression] [without visibility] [2012-03-06 15:52:33] [74be16979710d4c4e7c6647856088456] 
-    D    [Multiple Regression] [without TempxHum] [2012-03-06 15:54:55] [74be16979710d4c4e7c6647856088456] 

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Dataseries X:

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Histogram

Boxplots

1217.00 1210.00 31.00 19.00 48.00
1202.00 1209.00 34.40 18.30 38.00
1180.00 1207.00 35.60 18.90 37.00
1167.00 1206.00 32.80 20.60 48.00
1186.00 1204.00 23.30 20.00 81.00
1168.00 1201.00 20.00 11.76 58.00
1142.00 1199.00 16.70 15.60 93.00
1147.00 1198.00 17.80 15.60 86.00
1183.00 1196.00 21.20 15.80 68.00
1149.00 1195.00 23.90 17.80 68.00
1197.00 1193.00 28.80 16.70 68.00
1210.00 1191.00 25.60 17.20 59.00
1206.00 1190.00 29.40 15.60 43.00
1196.00 1188.00 22.80 14.40 59.00
1190.00 1187.00 16.10 -0.60 31.00
1175.00 1185.00 16.10 5.60 49.00
1186.00 1183.00 20.00 10.08 52.00
1172.00 1182.00 20.60 16.10 75.00
1152.00 1185.00 18.30 16.70 90.00
1154.00 1179.00 21.60 18.30 86.00
1168.00 1177.00 22.80 20.60 87.00
1180.00 1175.00 22.80 11.10 47.00
1169.00 1174.00 17.20 11.70 70.00
1166.00 1170.00 22.20 14.40 61.00
1177.00 1169.00 20.60 9.40 48.00
1168.00 1167.00 18.30 12.20 67.00
1160.00 1166.00 16.70 12.20 74.00
1147.00 1164.00 22.80 13.30 55.00
1161.00 1162.00 13.90 2.80 47.00
1143.00 1161.00 10.00 3.90 65.00
1159.00 1159.00 0.30 0.30 0.30
1158.00 1158.00 0.30 0.30 0.30
1156.00 1156.00 0.30 0.30 0.30
1155.00 1155.00 0.30 0.30 0.30

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

\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 & 4 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&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]4 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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	4 seconds
R Server	'Herman Ole Andreas Wold' @ wold.wessa.net

Multiple Linear Regression - Estimated Regression Equation
timein[t] = + 850.97661095942 + 0.264456987979047sunset[t] + 1.76566057630534temp[t] -0.880457889815843dewpoint[t] -0.282616075335568humidity[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
timein[t] =  +  850.97661095942 +  0.264456987979047sunset[t] +  1.76566057630534temp[t] -0.880457889815843dewpoint[t] -0.282616075335568humidity[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]timein[t] =  +  850.97661095942 +  0.264456987979047sunset[t] +  1.76566057630534temp[t] -0.880457889815843dewpoint[t] -0.282616075335568humidity[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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
timein[t] = + 850.97661095942 + 0.264456987979047sunset[t] + 1.76566057630534temp[t] -0.880457889815843dewpoint[t] -0.282616075335568humidity[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)	850.97661095942	286.949297	2.9656	0.005991	0.002995
sunset	0.264456987979047	0.248133	1.0658	0.295312	0.147656
temp	1.76566057630534	0.606706	2.9102	0.006871	0.003435
dewpoint	-0.880457889815843	0.948247	-0.9285	0.360807	0.180404
humidity	-0.282616075335568	0.148785	-1.8995	0.067487	0.033744

\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) & 850.97661095942 & 286.949297 & 2.9656 & 0.005991 & 0.002995 \tabularnewline
sunset & 0.264456987979047 & 0.248133 & 1.0658 & 0.295312 & 0.147656 \tabularnewline
temp & 1.76566057630534 & 0.606706 & 2.9102 & 0.006871 & 0.003435 \tabularnewline
dewpoint & -0.880457889815843 & 0.948247 & -0.9285 & 0.360807 & 0.180404 \tabularnewline
humidity & -0.282616075335568 & 0.148785 & -1.8995 & 0.067487 & 0.033744 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&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]850.97661095942[/C][C]286.949297[/C][C]2.9656[/C][C]0.005991[/C][C]0.002995[/C][/ROW]
[ROW][C]sunset[/C][C]0.264456987979047[/C][C]0.248133[/C][C]1.0658[/C][C]0.295312[/C][C]0.147656[/C][/ROW]
[ROW][C]temp[/C][C]1.76566057630534[/C][C]0.606706[/C][C]2.9102[/C][C]0.006871[/C][C]0.003435[/C][/ROW]
[ROW][C]dewpoint[/C][C]-0.880457889815843[/C][C]0.948247[/C][C]-0.9285[/C][C]0.360807[/C][C]0.180404[/C][/ROW]
[ROW][C]humidity[/C][C]-0.282616075335568[/C][C]0.148785[/C][C]-1.8995[/C][C]0.067487[/C][C]0.033744[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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)	850.97661095942	286.949297	2.9656	0.005991	0.002995
sunset	0.264456987979047	0.248133	1.0658	0.295312	0.147656
temp	1.76566057630534	0.606706	2.9102	0.006871	0.003435
dewpoint	-0.880457889815843	0.948247	-0.9285	0.360807	0.180404
humidity	-0.282616075335568	0.148785	-1.8995	0.067487	0.033744

Multiple Linear Regression - Regression Statistics
Multiple R	0.714906749147207
R-squared	0.511091659976227
Adjusted R-squared	0.4436560268695
F-TEST (value)	7.57895546360996
F-TEST (DF numerator)	4
F-TEST (DF denominator)	29
p-value	0.000262202464874628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.9526204447375
Sum Squared Residuals	6483.84488676706

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.714906749147207 \tabularnewline
R-squared & 0.511091659976227 \tabularnewline
Adjusted R-squared & 0.4436560268695 \tabularnewline
F-TEST (value) & 7.57895546360996 \tabularnewline
F-TEST (DF numerator) & 4 \tabularnewline
F-TEST (DF denominator) & 29 \tabularnewline
p-value & 0.000262202464874628 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.9526204447375 \tabularnewline
Sum Squared Residuals & 6483.84488676706 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.714906749147207[/C][/ROW]
[ROW][C]R-squared[/C][C]0.511091659976227[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.4436560268695[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]7.57895546360996[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]4[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]29[/C][/ROW]
[ROW][C]p-value[/C][C]0.000262202464874628[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.9526204447375[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]6483.84488676706[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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	0.714906749147207
R-squared	0.511091659976227
Adjusted R-squared	0.4436560268695
F-TEST (value)	7.57895546360996
F-TEST (DF numerator)	4
F-TEST (DF denominator)	29
p-value	0.000262202464874628
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.9526204447375
Sum Squared Residuals	6483.84488676706

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1217	1195.41077275692	21.5892272430767
2	1202	1204.59204300461	-2.59204300460964
3	1180	1205.93626306166	-25.936263061664
4	1167	1196.12240121865	-29.1224012186518
5	1186	1170.02165601561	15.9783439843912
6	1168	1177.15674789466	-9.15674789466464
7	1142	1157.52863308326	-15.5286330832612
8	1147	1161.18471525657	-14.184715256567
9	1183	1171.57004501812	11.4299549818759
10	1149	1174.31195580654	-25.3119558065378
11	1197	1183.40328233327	13.5967176667267
12	1210	1179.32757024625	30.6724297537497
13	1206	1191.70321327731	14.296786722694
14	1196	1176.05563176014	19.9443682398574
15	1190	1185.08136736755	4.91863263244868
16	1175	1174.00652511869	0.993474881305227
17	1186	1175.57138781795	10.4286121820542
18	1172	1164.56580094634	7.43419905365946
19	1152	1156.53063672085	-4.53063672085237
20	1154	1160.49230637242	-6.49230637242264
21	1168	1159.77451586612	8.22548413388106
22	1180	1178.91459485683	1.08540514316592
23	1169	1161.73399417494	7.26600582506244
24	1166	1169.67077748007	-3.6707774800654
25	1177	1174.65756199844	2.34243800156059
26	1168	1162.23264117412	5.76735882588112
27	1160	1157.1648147367	2.83518526329768
28	1147	1171.80763202879	-24.8076320287852
29	1161	1167.07007536946	-6.07007536946044
30	1143	1153.86394909905	-10.8639490990529
31	1159	1157.66303601048	1.3369639895187
32	1158	1157.3985790225	0.601420977497743
33	1156	1156.86966504654	-0.869665046544163
34	1155	1156.60520805857	-1.60520805856512

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1217 & 1195.41077275692 & 21.5892272430767 \tabularnewline
2 & 1202 & 1204.59204300461 & -2.59204300460964 \tabularnewline
3 & 1180 & 1205.93626306166 & -25.936263061664 \tabularnewline
4 & 1167 & 1196.12240121865 & -29.1224012186518 \tabularnewline
5 & 1186 & 1170.02165601561 & 15.9783439843912 \tabularnewline
6 & 1168 & 1177.15674789466 & -9.15674789466464 \tabularnewline
7 & 1142 & 1157.52863308326 & -15.5286330832612 \tabularnewline
8 & 1147 & 1161.18471525657 & -14.184715256567 \tabularnewline
9 & 1183 & 1171.57004501812 & 11.4299549818759 \tabularnewline
10 & 1149 & 1174.31195580654 & -25.3119558065378 \tabularnewline
11 & 1197 & 1183.40328233327 & 13.5967176667267 \tabularnewline
12 & 1210 & 1179.32757024625 & 30.6724297537497 \tabularnewline
13 & 1206 & 1191.70321327731 & 14.296786722694 \tabularnewline
14 & 1196 & 1176.05563176014 & 19.9443682398574 \tabularnewline
15 & 1190 & 1185.08136736755 & 4.91863263244868 \tabularnewline
16 & 1175 & 1174.00652511869 & 0.993474881305227 \tabularnewline
17 & 1186 & 1175.57138781795 & 10.4286121820542 \tabularnewline
18 & 1172 & 1164.56580094634 & 7.43419905365946 \tabularnewline
19 & 1152 & 1156.53063672085 & -4.53063672085237 \tabularnewline
20 & 1154 & 1160.49230637242 & -6.49230637242264 \tabularnewline
21 & 1168 & 1159.77451586612 & 8.22548413388106 \tabularnewline
22 & 1180 & 1178.91459485683 & 1.08540514316592 \tabularnewline
23 & 1169 & 1161.73399417494 & 7.26600582506244 \tabularnewline
24 & 1166 & 1169.67077748007 & -3.6707774800654 \tabularnewline
25 & 1177 & 1174.65756199844 & 2.34243800156059 \tabularnewline
26 & 1168 & 1162.23264117412 & 5.76735882588112 \tabularnewline
27 & 1160 & 1157.1648147367 & 2.83518526329768 \tabularnewline
28 & 1147 & 1171.80763202879 & -24.8076320287852 \tabularnewline
29 & 1161 & 1167.07007536946 & -6.07007536946044 \tabularnewline
30 & 1143 & 1153.86394909905 & -10.8639490990529 \tabularnewline
31 & 1159 & 1157.66303601048 & 1.3369639895187 \tabularnewline
32 & 1158 & 1157.3985790225 & 0.601420977497743 \tabularnewline
33 & 1156 & 1156.86966504654 & -0.869665046544163 \tabularnewline
34 & 1155 & 1156.60520805857 & -1.60520805856512 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&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]1217[/C][C]1195.41077275692[/C][C]21.5892272430767[/C][/ROW]
[ROW][C]2[/C][C]1202[/C][C]1204.59204300461[/C][C]-2.59204300460964[/C][/ROW]
[ROW][C]3[/C][C]1180[/C][C]1205.93626306166[/C][C]-25.936263061664[/C][/ROW]
[ROW][C]4[/C][C]1167[/C][C]1196.12240121865[/C][C]-29.1224012186518[/C][/ROW]
[ROW][C]5[/C][C]1186[/C][C]1170.02165601561[/C][C]15.9783439843912[/C][/ROW]
[ROW][C]6[/C][C]1168[/C][C]1177.15674789466[/C][C]-9.15674789466464[/C][/ROW]
[ROW][C]7[/C][C]1142[/C][C]1157.52863308326[/C][C]-15.5286330832612[/C][/ROW]
[ROW][C]8[/C][C]1147[/C][C]1161.18471525657[/C][C]-14.184715256567[/C][/ROW]
[ROW][C]9[/C][C]1183[/C][C]1171.57004501812[/C][C]11.4299549818759[/C][/ROW]
[ROW][C]10[/C][C]1149[/C][C]1174.31195580654[/C][C]-25.3119558065378[/C][/ROW]
[ROW][C]11[/C][C]1197[/C][C]1183.40328233327[/C][C]13.5967176667267[/C][/ROW]
[ROW][C]12[/C][C]1210[/C][C]1179.32757024625[/C][C]30.6724297537497[/C][/ROW]
[ROW][C]13[/C][C]1206[/C][C]1191.70321327731[/C][C]14.296786722694[/C][/ROW]
[ROW][C]14[/C][C]1196[/C][C]1176.05563176014[/C][C]19.9443682398574[/C][/ROW]
[ROW][C]15[/C][C]1190[/C][C]1185.08136736755[/C][C]4.91863263244868[/C][/ROW]
[ROW][C]16[/C][C]1175[/C][C]1174.00652511869[/C][C]0.993474881305227[/C][/ROW]
[ROW][C]17[/C][C]1186[/C][C]1175.57138781795[/C][C]10.4286121820542[/C][/ROW]
[ROW][C]18[/C][C]1172[/C][C]1164.56580094634[/C][C]7.43419905365946[/C][/ROW]
[ROW][C]19[/C][C]1152[/C][C]1156.53063672085[/C][C]-4.53063672085237[/C][/ROW]
[ROW][C]20[/C][C]1154[/C][C]1160.49230637242[/C][C]-6.49230637242264[/C][/ROW]
[ROW][C]21[/C][C]1168[/C][C]1159.77451586612[/C][C]8.22548413388106[/C][/ROW]
[ROW][C]22[/C][C]1180[/C][C]1178.91459485683[/C][C]1.08540514316592[/C][/ROW]
[ROW][C]23[/C][C]1169[/C][C]1161.73399417494[/C][C]7.26600582506244[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1169.67077748007[/C][C]-3.6707774800654[/C][/ROW]
[ROW][C]25[/C][C]1177[/C][C]1174.65756199844[/C][C]2.34243800156059[/C][/ROW]
[ROW][C]26[/C][C]1168[/C][C]1162.23264117412[/C][C]5.76735882588112[/C][/ROW]
[ROW][C]27[/C][C]1160[/C][C]1157.1648147367[/C][C]2.83518526329768[/C][/ROW]
[ROW][C]28[/C][C]1147[/C][C]1171.80763202879[/C][C]-24.8076320287852[/C][/ROW]
[ROW][C]29[/C][C]1161[/C][C]1167.07007536946[/C][C]-6.07007536946044[/C][/ROW]
[ROW][C]30[/C][C]1143[/C][C]1153.86394909905[/C][C]-10.8639490990529[/C][/ROW]
[ROW][C]31[/C][C]1159[/C][C]1157.66303601048[/C][C]1.3369639895187[/C][/ROW]
[ROW][C]32[/C][C]1158[/C][C]1157.3985790225[/C][C]0.601420977497743[/C][/ROW]
[ROW][C]33[/C][C]1156[/C][C]1156.86966504654[/C][C]-0.869665046544163[/C][/ROW]
[ROW][C]34[/C][C]1155[/C][C]1156.60520805857[/C][C]-1.60520805856512[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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	1217	1195.41077275692	21.5892272430767
2	1202	1204.59204300461	-2.59204300460964
3	1180	1205.93626306166	-25.936263061664
4	1167	1196.12240121865	-29.1224012186518
5	1186	1170.02165601561	15.9783439843912
6	1168	1177.15674789466	-9.15674789466464
7	1142	1157.52863308326	-15.5286330832612
8	1147	1161.18471525657	-14.184715256567
9	1183	1171.57004501812	11.4299549818759
10	1149	1174.31195580654	-25.3119558065378
11	1197	1183.40328233327	13.5967176667267
12	1210	1179.32757024625	30.6724297537497
13	1206	1191.70321327731	14.296786722694
14	1196	1176.05563176014	19.9443682398574
15	1190	1185.08136736755	4.91863263244868
16	1175	1174.00652511869	0.993474881305227
17	1186	1175.57138781795	10.4286121820542
18	1172	1164.56580094634	7.43419905365946
19	1152	1156.53063672085	-4.53063672085237
20	1154	1160.49230637242	-6.49230637242264
21	1168	1159.77451586612	8.22548413388106
22	1180	1178.91459485683	1.08540514316592
23	1169	1161.73399417494	7.26600582506244
24	1166	1169.67077748007	-3.6707774800654
25	1177	1174.65756199844	2.34243800156059
26	1168	1162.23264117412	5.76735882588112
27	1160	1157.1648147367	2.83518526329768
28	1147	1171.80763202879	-24.8076320287852
29	1161	1167.07007536946	-6.07007536946044
30	1143	1153.86394909905	-10.8639490990529
31	1159	1157.66303601048	1.3369639895187
32	1158	1157.3985790225	0.601420977497743
33	1156	1156.86966504654	-0.869665046544163
34	1155	1156.60520805857	-1.60520805856512

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
8	0.387155023831265	0.77431004766253	0.612844976168735
9	0.921913237268814	0.156173525462373	0.0780867627311864
10	0.990745005754705	0.0185099884905896	0.00925499424529481
11	0.999602536445008	0.000794927109984864	0.000397463554992432
12	0.999941358104765	0.000117283790470674	5.86418952353371e-05
13	0.999812815178229	0.000374369643541339	0.00018718482177067
14	0.999753381877477	0.000493236245046033	0.000246618122523016
15	0.999257724330591	0.00148455133881849	0.000742275669409247
16	0.99856227788414	0.00287544423172091	0.00143772211586045
17	0.996502247168791	0.00699550566241863	0.00349775283120932
18	0.992444066777374	0.015111866445253	0.00755593322262649
19	0.994677433043594	0.0106451339128117	0.00532256695640585
20	0.998808363297283	0.00238327340543478	0.00119163670271739
21	0.99648366084852	0.00703267830295924	0.00351633915147962
22	0.991626377024241	0.0167472459515191	0.00837362297575954
23	0.99031794704819	0.0193641059036208	0.00968205295181041
24	0.981528219403648	0.0369435611927048	0.0184717805963524
25	0.946666612208732	0.106666775582536	0.0533333877912682
26	0.902339880419498	0.195320239161003	0.0976601195805015

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
8 & 0.387155023831265 & 0.77431004766253 & 0.612844976168735 \tabularnewline
9 & 0.921913237268814 & 0.156173525462373 & 0.0780867627311864 \tabularnewline
10 & 0.990745005754705 & 0.0185099884905896 & 0.00925499424529481 \tabularnewline
11 & 0.999602536445008 & 0.000794927109984864 & 0.000397463554992432 \tabularnewline
12 & 0.999941358104765 & 0.000117283790470674 & 5.86418952353371e-05 \tabularnewline
13 & 0.999812815178229 & 0.000374369643541339 & 0.00018718482177067 \tabularnewline
14 & 0.999753381877477 & 0.000493236245046033 & 0.000246618122523016 \tabularnewline
15 & 0.999257724330591 & 0.00148455133881849 & 0.000742275669409247 \tabularnewline
16 & 0.99856227788414 & 0.00287544423172091 & 0.00143772211586045 \tabularnewline
17 & 0.996502247168791 & 0.00699550566241863 & 0.00349775283120932 \tabularnewline
18 & 0.992444066777374 & 0.015111866445253 & 0.00755593322262649 \tabularnewline
19 & 0.994677433043594 & 0.0106451339128117 & 0.00532256695640585 \tabularnewline
20 & 0.998808363297283 & 0.00238327340543478 & 0.00119163670271739 \tabularnewline
21 & 0.99648366084852 & 0.00703267830295924 & 0.00351633915147962 \tabularnewline
22 & 0.991626377024241 & 0.0167472459515191 & 0.00837362297575954 \tabularnewline
23 & 0.99031794704819 & 0.0193641059036208 & 0.00968205295181041 \tabularnewline
24 & 0.981528219403648 & 0.0369435611927048 & 0.0184717805963524 \tabularnewline
25 & 0.946666612208732 & 0.106666775582536 & 0.0533333877912682 \tabularnewline
26 & 0.902339880419498 & 0.195320239161003 & 0.0976601195805015 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&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]8[/C][C]0.387155023831265[/C][C]0.77431004766253[/C][C]0.612844976168735[/C][/ROW]
[ROW][C]9[/C][C]0.921913237268814[/C][C]0.156173525462373[/C][C]0.0780867627311864[/C][/ROW]
[ROW][C]10[/C][C]0.990745005754705[/C][C]0.0185099884905896[/C][C]0.00925499424529481[/C][/ROW]
[ROW][C]11[/C][C]0.999602536445008[/C][C]0.000794927109984864[/C][C]0.000397463554992432[/C][/ROW]
[ROW][C]12[/C][C]0.999941358104765[/C][C]0.000117283790470674[/C][C]5.86418952353371e-05[/C][/ROW]
[ROW][C]13[/C][C]0.999812815178229[/C][C]0.000374369643541339[/C][C]0.00018718482177067[/C][/ROW]
[ROW][C]14[/C][C]0.999753381877477[/C][C]0.000493236245046033[/C][C]0.000246618122523016[/C][/ROW]
[ROW][C]15[/C][C]0.999257724330591[/C][C]0.00148455133881849[/C][C]0.000742275669409247[/C][/ROW]
[ROW][C]16[/C][C]0.99856227788414[/C][C]0.00287544423172091[/C][C]0.00143772211586045[/C][/ROW]
[ROW][C]17[/C][C]0.996502247168791[/C][C]0.00699550566241863[/C][C]0.00349775283120932[/C][/ROW]
[ROW][C]18[/C][C]0.992444066777374[/C][C]0.015111866445253[/C][C]0.00755593322262649[/C][/ROW]
[ROW][C]19[/C][C]0.994677433043594[/C][C]0.0106451339128117[/C][C]0.00532256695640585[/C][/ROW]
[ROW][C]20[/C][C]0.998808363297283[/C][C]0.00238327340543478[/C][C]0.00119163670271739[/C][/ROW]
[ROW][C]21[/C][C]0.99648366084852[/C][C]0.00703267830295924[/C][C]0.00351633915147962[/C][/ROW]
[ROW][C]22[/C][C]0.991626377024241[/C][C]0.0167472459515191[/C][C]0.00837362297575954[/C][/ROW]
[ROW][C]23[/C][C]0.99031794704819[/C][C]0.0193641059036208[/C][C]0.00968205295181041[/C][/ROW]
[ROW][C]24[/C][C]0.981528219403648[/C][C]0.0369435611927048[/C][C]0.0184717805963524[/C][/ROW]
[ROW][C]25[/C][C]0.946666612208732[/C][C]0.106666775582536[/C][C]0.0533333877912682[/C][/ROW]
[ROW][C]26[/C][C]0.902339880419498[/C][C]0.195320239161003[/C][C]0.0976601195805015[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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
8	0.387155023831265	0.77431004766253	0.612844976168735
9	0.921913237268814	0.156173525462373	0.0780867627311864
10	0.990745005754705	0.0185099884905896	0.00925499424529481
11	0.999602536445008	0.000794927109984864	0.000397463554992432
12	0.999941358104765	0.000117283790470674	5.86418952353371e-05
13	0.999812815178229	0.000374369643541339	0.00018718482177067
14	0.999753381877477	0.000493236245046033	0.000246618122523016
15	0.999257724330591	0.00148455133881849	0.000742275669409247
16	0.99856227788414	0.00287544423172091	0.00143772211586045
17	0.996502247168791	0.00699550566241863	0.00349775283120932
18	0.992444066777374	0.015111866445253	0.00755593322262649
19	0.994677433043594	0.0106451339128117	0.00532256695640585
20	0.998808363297283	0.00238327340543478	0.00119163670271739
21	0.99648366084852	0.00703267830295924	0.00351633915147962
22	0.991626377024241	0.0167472459515191	0.00837362297575954
23	0.99031794704819	0.0193641059036208	0.00968205295181041
24	0.981528219403648	0.0369435611927048	0.0184717805963524
25	0.946666612208732	0.106666775582536	0.0533333877912682
26	0.902339880419498	0.195320239161003	0.0976601195805015

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	9	0.473684210526316	NOK
5% type I error level	15	0.789473684210526	NOK
10% type I error level	15	0.789473684210526	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 & 9 & 0.473684210526316 & NOK \tabularnewline
5% type I error level & 15 & 0.789473684210526 & NOK \tabularnewline
10% type I error level & 15 & 0.789473684210526 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163508&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]9[/C][C]0.473684210526316[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]15[/C][C]0.789473684210526[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]15[/C][C]0.789473684210526[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163508&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163508&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	9	0.473684210526316	NOK
5% type I error level	15	0.789473684210526	NOK
10% type I error level	15	0.789473684210526	NOK

Figure 1

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Figure 3

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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