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*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 06 Mar 2012 10:35:32 -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/06/t133104823638971tjg7jsv8l9.htm/, Retrieved Wed, 01 May 2024 17:46:47 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=163540, Retrieved Wed, 01 May 2024 17:46:47 +0000

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IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

180

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] [74be16979710d4c4e7c6647856088456]
-    D    [Multiple Regression] [Reduced model ] [2012-03-06 15:35:32] [d41d8cd98f00b204e9800998ecf8427e] [Current]
-    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] 

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Dataseries X:

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1217.00	1210.00	31.00	48.00	961.00	2304.00
1202.00	1209.00	34.40	38.00	1183.36	1444.00
1180.00	1207.00	35.60	37.00	1267.36	1369.00
1167.00	1206.00	32.80	48.00	1075.84	2304.00
1186.00	1204.00	23.30	81.00	542.89	6561.00
1168.00	1201.00	20.00	58.00	400.00	3364.00
1142.00	1199.00	16.70	93.00	278.89	8649.00
1147.00	1198.00	17.80	86.00	316.84	7396.00
1183.00	1196.00	21.20	68.00	449.44	4624.00
1149.00	1195.00	23.90	68.00	571.21	4624.00
1197.00	1193.00	28.80	68.00	829.44	4624.00
1210.00	1191.00	25.60	59.00	655.36	3481.00
1206.00	1190.00	29.40	43.00	864.36	1849.00
1196.00	1188.00	22.80	59.00	519.84	3481.00
1190.00	1187.00	16.10	31.00	259.21	961.00
1175.00	1185.00	16.10	49.00	259.21	2401.00
1186.00	1183.00	20.00	52.00	400.00	2704.00
1172.00	1182.00	20.60	75.00	424.36	5625.00
1152.00	1185.00	18.30	90.00	334.89	8100.00
1154.00	1179.00	21.60	86.00	466.56	7396.00
1168.00	1177.00	22.80	87.00	519.84	7569.00
1180.00	1175.00	22.80	47.00	519.84	2209.00
1169.00	1174.00	17.20	70.00	295.84	4900.00
1166.00	1170.00	22.20	61.00	492.84	3721.00
1177.00	1169.00	20.60	48.00	424.36	2304.00
1168.00	1167.00	18.30	67.00	334.89	4489.00
1160.00	1166.00	16.70	74.00	278.89	5476.00
1147.00	1164.00	22.80	55.00	519.84	3025.00
1161.00	1162.00	13.90	47.00	193.21	2209.00
1143.00	1161.00	10.00	65.00	100.00	4225.00
1161.00	1159.00	16.10	28.00	259.21	784.00
1161.00	1158.00	20.60	30.00	424.36	900.00
1168.00	1156.00	19.40	67.00	376.36	4489.00
1172.00	1155.00	25.60	32.00	655.36	1024.00

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	'Gertrude Mary Cox' @ cox.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 & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
15thbird[t] = + 548.77411711376 + 0.457919900566662Sunset[t] + 5.5127280172675Temp[t] + 1.09183657659764humidity[t] -0.102083596676507`Temp^2`[t] -0.013112777176766`Hum^2`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
15thbird[t] =  +  548.77411711376 +  0.457919900566662Sunset[t] +  5.5127280172675Temp[t] +  1.09183657659764humidity[t] -0.102083596676507`Temp^2`[t] -0.013112777176766`Hum^2`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]15thbird[t] =  +  548.77411711376 +  0.457919900566662Sunset[t] +  5.5127280172675Temp[t] +  1.09183657659764humidity[t] -0.102083596676507`Temp^2`[t] -0.013112777176766`Hum^2`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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
15thbird[t] = + 548.77411711376 + 0.457919900566662Sunset[t] + 5.5127280172675Temp[t] + 1.09183657659764humidity[t] -0.102083596676507`Temp^2`[t] -0.013112777176766`Hum^2`[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)	548.77411711376	240.427948	2.2825	0.030258	0.015129
Sunset	0.457919900566662	0.204089	2.2437	0.032941	0.016471
Temp	5.5127280172675	2.802577	1.967	0.059161	0.029581
humidity	1.09183657659764	0.87557	1.247	0.222728	0.111364
`Temp^2`	-0.102083596676507	0.060835	-1.678	0.104469	0.052234
`Hum^2`	-0.013112777176766	0.007202	-1.8208	0.079337	0.039669

\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) & 548.77411711376 & 240.427948 & 2.2825 & 0.030258 & 0.015129 \tabularnewline
Sunset & 0.457919900566662 & 0.204089 & 2.2437 & 0.032941 & 0.016471 \tabularnewline
Temp & 5.5127280172675 & 2.802577 & 1.967 & 0.059161 & 0.029581 \tabularnewline
humidity & 1.09183657659764 & 0.87557 & 1.247 & 0.222728 & 0.111364 \tabularnewline
`Temp^2` & -0.102083596676507 & 0.060835 & -1.678 & 0.104469 & 0.052234 \tabularnewline
`Hum^2` & -0.013112777176766 & 0.007202 & -1.8208 & 0.079337 & 0.039669 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&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]548.77411711376[/C][C]240.427948[/C][C]2.2825[/C][C]0.030258[/C][C]0.015129[/C][/ROW]
[ROW][C]Sunset[/C][C]0.457919900566662[/C][C]0.204089[/C][C]2.2437[/C][C]0.032941[/C][C]0.016471[/C][/ROW]
[ROW][C]Temp[/C][C]5.5127280172675[/C][C]2.802577[/C][C]1.967[/C][C]0.059161[/C][C]0.029581[/C][/ROW]
[ROW][C]humidity[/C][C]1.09183657659764[/C][C]0.87557[/C][C]1.247[/C][C]0.222728[/C][C]0.111364[/C][/ROW]
[ROW][C]`Temp^2`[/C][C]-0.102083596676507[/C][C]0.060835[/C][C]-1.678[/C][C]0.104469[/C][C]0.052234[/C][/ROW]
[ROW][C]`Hum^2`[/C][C]-0.013112777176766[/C][C]0.007202[/C][C]-1.8208[/C][C]0.079337[/C][C]0.039669[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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)	548.77411711376	240.427948	2.2825	0.030258	0.015129
Sunset	0.457919900566662	0.204089	2.2437	0.032941	0.016471
Temp	5.5127280172675	2.802577	1.967	0.059161	0.029581
humidity	1.09183657659764	0.87557	1.247	0.222728	0.111364
`Temp^2`	-0.102083596676507	0.060835	-1.678	0.104469	0.052234
`Hum^2`	-0.013112777176766	0.007202	-1.8208	0.079337	0.039669

Multiple Linear Regression - Regression Statistics
Multiple R	0.737883439021702
R-squared	0.544471969582493
Adjusted R-squared	0.46312767843651
F-TEST (value)	6.69342570833109
F-TEST (DF numerator)	5
F-TEST (DF denominator)	28
p-value	0.00032362923653384
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.3059540940118
Sum Squared Residuals	5730.48903111925

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.737883439021702 \tabularnewline
R-squared & 0.544471969582493 \tabularnewline
Adjusted R-squared & 0.46312767843651 \tabularnewline
F-TEST (value) & 6.69342570833109 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 28 \tabularnewline
p-value & 0.00032362923653384 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 14.3059540940118 \tabularnewline
Sum Squared Residuals & 5730.48903111925 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.737883439021702[/C][/ROW]
[ROW][C]R-squared[/C][C]0.544471969582493[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.46312767843651[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]6.69342570833109[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]28[/C][/ROW]
[ROW][C]p-value[/C][C]0.00032362923653384[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]14.3059540940118[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]5730.48903111925[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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	0.737883439021702
R-squared	0.544471969582493
Adjusted R-squared	0.46312767843651
F-TEST (value)	6.69342570833109
F-TEST (DF numerator)	5
F-TEST (DF denominator)	28
p-value	0.00032362923653384
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	14.3059540940118
Sum Squared Residuals	5730.48903111925

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1217	1197.84574599001	19.1542540099918
2	1202	1193.79041539721	8.2095846027948
3	1180	1190.80644880763	-10.8064488076261
4	1167	1194.21369657649	-27.2136965764929
5	1186	1175.54190804629	10.458091953708
6	1168	1187.37217838909	-19.3721783890906
7	1142	1149.54093332618	-7.54093332617526
8	1147	1160.06039551703	-13.0603955170338
9	1183	1181.04710601054	1.95289398945705
10	1149	1183.0428321893	-34.0428321893003
11	1197	1182.778312503	14.2216874969967
12	1210	1187.15383067973	22.8461693202749
13	1206	1190.2394726663	15.7605273336952
14	1196	1184.17880155128	11.8211984487237
15	1190	1175.86442607753	14.1355739224681
16	1175	1175.71924552061	-0.719245520613063
17	1186	1181.23303365597	4.76696634402964
18	1172	1168.40581327914	3.59418672086332
19	1152	1150.15714307224	1.84285692776279
20	1154	1157.02432777748	-3.02432777747701
21	1168	1156.10807369116	11.8919263088425
22	1180	1181.80325649358	-1.80325649358442
23	1169	1163.16654323093	5.83345676907427
24	1166	1174.42147027175	-8.42147027175305
25	1177	1176.52080000767	0.479199992326284
26	1168	1164.15258198559	3.84741801440634
27	1160	1155.291523634	4.70847636600119
28	1147	1174.80080402389	-27.8008040238912
29	1161	1160.13058361498	0.869416385015417
30	1143	1140.90593608369	2.09406391631086
31	1161	1162.08812069216	-1.08812069216002
32	1161	1170.24096187886	-9.24096187886238
33	1168	1160.94605714418	7.05394285582015
34	1172	1173.4072202145	-1.40722021450305

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 1217 & 1197.84574599001 & 19.1542540099918 \tabularnewline
2 & 1202 & 1193.79041539721 & 8.2095846027948 \tabularnewline
3 & 1180 & 1190.80644880763 & -10.8064488076261 \tabularnewline
4 & 1167 & 1194.21369657649 & -27.2136965764929 \tabularnewline
5 & 1186 & 1175.54190804629 & 10.458091953708 \tabularnewline
6 & 1168 & 1187.37217838909 & -19.3721783890906 \tabularnewline
7 & 1142 & 1149.54093332618 & -7.54093332617526 \tabularnewline
8 & 1147 & 1160.06039551703 & -13.0603955170338 \tabularnewline
9 & 1183 & 1181.04710601054 & 1.95289398945705 \tabularnewline
10 & 1149 & 1183.0428321893 & -34.0428321893003 \tabularnewline
11 & 1197 & 1182.778312503 & 14.2216874969967 \tabularnewline
12 & 1210 & 1187.15383067973 & 22.8461693202749 \tabularnewline
13 & 1206 & 1190.2394726663 & 15.7605273336952 \tabularnewline
14 & 1196 & 1184.17880155128 & 11.8211984487237 \tabularnewline
15 & 1190 & 1175.86442607753 & 14.1355739224681 \tabularnewline
16 & 1175 & 1175.71924552061 & -0.719245520613063 \tabularnewline
17 & 1186 & 1181.23303365597 & 4.76696634402964 \tabularnewline
18 & 1172 & 1168.40581327914 & 3.59418672086332 \tabularnewline
19 & 1152 & 1150.15714307224 & 1.84285692776279 \tabularnewline
20 & 1154 & 1157.02432777748 & -3.02432777747701 \tabularnewline
21 & 1168 & 1156.10807369116 & 11.8919263088425 \tabularnewline
22 & 1180 & 1181.80325649358 & -1.80325649358442 \tabularnewline
23 & 1169 & 1163.16654323093 & 5.83345676907427 \tabularnewline
24 & 1166 & 1174.42147027175 & -8.42147027175305 \tabularnewline
25 & 1177 & 1176.52080000767 & 0.479199992326284 \tabularnewline
26 & 1168 & 1164.15258198559 & 3.84741801440634 \tabularnewline
27 & 1160 & 1155.291523634 & 4.70847636600119 \tabularnewline
28 & 1147 & 1174.80080402389 & -27.8008040238912 \tabularnewline
29 & 1161 & 1160.13058361498 & 0.869416385015417 \tabularnewline
30 & 1143 & 1140.90593608369 & 2.09406391631086 \tabularnewline
31 & 1161 & 1162.08812069216 & -1.08812069216002 \tabularnewline
32 & 1161 & 1170.24096187886 & -9.24096187886238 \tabularnewline
33 & 1168 & 1160.94605714418 & 7.05394285582015 \tabularnewline
34 & 1172 & 1173.4072202145 & -1.40722021450305 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&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]1197.84574599001[/C][C]19.1542540099918[/C][/ROW]
[ROW][C]2[/C][C]1202[/C][C]1193.79041539721[/C][C]8.2095846027948[/C][/ROW]
[ROW][C]3[/C][C]1180[/C][C]1190.80644880763[/C][C]-10.8064488076261[/C][/ROW]
[ROW][C]4[/C][C]1167[/C][C]1194.21369657649[/C][C]-27.2136965764929[/C][/ROW]
[ROW][C]5[/C][C]1186[/C][C]1175.54190804629[/C][C]10.458091953708[/C][/ROW]
[ROW][C]6[/C][C]1168[/C][C]1187.37217838909[/C][C]-19.3721783890906[/C][/ROW]
[ROW][C]7[/C][C]1142[/C][C]1149.54093332618[/C][C]-7.54093332617526[/C][/ROW]
[ROW][C]8[/C][C]1147[/C][C]1160.06039551703[/C][C]-13.0603955170338[/C][/ROW]
[ROW][C]9[/C][C]1183[/C][C]1181.04710601054[/C][C]1.95289398945705[/C][/ROW]
[ROW][C]10[/C][C]1149[/C][C]1183.0428321893[/C][C]-34.0428321893003[/C][/ROW]
[ROW][C]11[/C][C]1197[/C][C]1182.778312503[/C][C]14.2216874969967[/C][/ROW]
[ROW][C]12[/C][C]1210[/C][C]1187.15383067973[/C][C]22.8461693202749[/C][/ROW]
[ROW][C]13[/C][C]1206[/C][C]1190.2394726663[/C][C]15.7605273336952[/C][/ROW]
[ROW][C]14[/C][C]1196[/C][C]1184.17880155128[/C][C]11.8211984487237[/C][/ROW]
[ROW][C]15[/C][C]1190[/C][C]1175.86442607753[/C][C]14.1355739224681[/C][/ROW]
[ROW][C]16[/C][C]1175[/C][C]1175.71924552061[/C][C]-0.719245520613063[/C][/ROW]
[ROW][C]17[/C][C]1186[/C][C]1181.23303365597[/C][C]4.76696634402964[/C][/ROW]
[ROW][C]18[/C][C]1172[/C][C]1168.40581327914[/C][C]3.59418672086332[/C][/ROW]
[ROW][C]19[/C][C]1152[/C][C]1150.15714307224[/C][C]1.84285692776279[/C][/ROW]
[ROW][C]20[/C][C]1154[/C][C]1157.02432777748[/C][C]-3.02432777747701[/C][/ROW]
[ROW][C]21[/C][C]1168[/C][C]1156.10807369116[/C][C]11.8919263088425[/C][/ROW]
[ROW][C]22[/C][C]1180[/C][C]1181.80325649358[/C][C]-1.80325649358442[/C][/ROW]
[ROW][C]23[/C][C]1169[/C][C]1163.16654323093[/C][C]5.83345676907427[/C][/ROW]
[ROW][C]24[/C][C]1166[/C][C]1174.42147027175[/C][C]-8.42147027175305[/C][/ROW]
[ROW][C]25[/C][C]1177[/C][C]1176.52080000767[/C][C]0.479199992326284[/C][/ROW]
[ROW][C]26[/C][C]1168[/C][C]1164.15258198559[/C][C]3.84741801440634[/C][/ROW]
[ROW][C]27[/C][C]1160[/C][C]1155.291523634[/C][C]4.70847636600119[/C][/ROW]
[ROW][C]28[/C][C]1147[/C][C]1174.80080402389[/C][C]-27.8008040238912[/C][/ROW]
[ROW][C]29[/C][C]1161[/C][C]1160.13058361498[/C][C]0.869416385015417[/C][/ROW]
[ROW][C]30[/C][C]1143[/C][C]1140.90593608369[/C][C]2.09406391631086[/C][/ROW]
[ROW][C]31[/C][C]1161[/C][C]1162.08812069216[/C][C]-1.08812069216002[/C][/ROW]
[ROW][C]32[/C][C]1161[/C][C]1170.24096187886[/C][C]-9.24096187886238[/C][/ROW]
[ROW][C]33[/C][C]1168[/C][C]1160.94605714418[/C][C]7.05394285582015[/C][/ROW]
[ROW][C]34[/C][C]1172[/C][C]1173.4072202145[/C][C]-1.40722021450305[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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	1197.84574599001	19.1542540099918
2	1202	1193.79041539721	8.2095846027948
3	1180	1190.80644880763	-10.8064488076261
4	1167	1194.21369657649	-27.2136965764929
5	1186	1175.54190804629	10.458091953708
6	1168	1187.37217838909	-19.3721783890906
7	1142	1149.54093332618	-7.54093332617526
8	1147	1160.06039551703	-13.0603955170338
9	1183	1181.04710601054	1.95289398945705
10	1149	1183.0428321893	-34.0428321893003
11	1197	1182.778312503	14.2216874969967
12	1210	1187.15383067973	22.8461693202749
13	1206	1190.2394726663	15.7605273336952
14	1196	1184.17880155128	11.8211984487237
15	1190	1175.86442607753	14.1355739224681
16	1175	1175.71924552061	-0.719245520613063
17	1186	1181.23303365597	4.76696634402964
18	1172	1168.40581327914	3.59418672086332
19	1152	1150.15714307224	1.84285692776279
20	1154	1157.02432777748	-3.02432777747701
21	1168	1156.10807369116	11.8919263088425
22	1180	1181.80325649358	-1.80325649358442
23	1169	1163.16654323093	5.83345676907427
24	1166	1174.42147027175	-8.42147027175305
25	1177	1176.52080000767	0.479199992326284
26	1168	1164.15258198559	3.84741801440634
27	1160	1155.291523634	4.70847636600119
28	1147	1174.80080402389	-27.8008040238912
29	1161	1160.13058361498	0.869416385015417
30	1143	1140.90593608369	2.09406391631086
31	1161	1162.08812069216	-1.08812069216002
32	1161	1170.24096187886	-9.24096187886238
33	1168	1160.94605714418	7.05394285582015
34	1172	1173.4072202145	-1.40722021450305

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.528964221757403	0.942071556485195	0.471035778242597
10	0.974589641767614	0.0508207164647723	0.0254103582323862
11	0.995929164317119	0.00814167136576186	0.00407083568288093
12	0.996067764943444	0.00786447011311206	0.00393223505655603
13	0.994683714983558	0.0106325700328832	0.00531628501644162
14	0.99334556766119	0.0133088646776198	0.0066544323388099
15	0.989233688063174	0.0215326238736529	0.0107663119368264
16	0.985190672154118	0.0296186556917635	0.0148093278458818
17	0.974252028333812	0.0514959433323753	0.0257479716661877
18	0.950811168766909	0.0983776624661815	0.0491888312330908
19	0.920455698218556	0.159088603562888	0.0795443017814438
20	0.923610747070469	0.152778505859063	0.0763892529295315
21	0.860763200700414	0.278473598599173	0.139236799299586
22	0.831113030035072	0.337773939929855	0.168886969964928
23	0.746755032473676	0.506489935052647	0.253244967526324
24	0.621823174558418	0.756353650883164	0.378176825441582
25	0.637208068541588	0.725583862916824	0.362791931458412

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 & 0.528964221757403 & 0.942071556485195 & 0.471035778242597 \tabularnewline
10 & 0.974589641767614 & 0.0508207164647723 & 0.0254103582323862 \tabularnewline
11 & 0.995929164317119 & 0.00814167136576186 & 0.00407083568288093 \tabularnewline
12 & 0.996067764943444 & 0.00786447011311206 & 0.00393223505655603 \tabularnewline
13 & 0.994683714983558 & 0.0106325700328832 & 0.00531628501644162 \tabularnewline
14 & 0.99334556766119 & 0.0133088646776198 & 0.0066544323388099 \tabularnewline
15 & 0.989233688063174 & 0.0215326238736529 & 0.0107663119368264 \tabularnewline
16 & 0.985190672154118 & 0.0296186556917635 & 0.0148093278458818 \tabularnewline
17 & 0.974252028333812 & 0.0514959433323753 & 0.0257479716661877 \tabularnewline
18 & 0.950811168766909 & 0.0983776624661815 & 0.0491888312330908 \tabularnewline
19 & 0.920455698218556 & 0.159088603562888 & 0.0795443017814438 \tabularnewline
20 & 0.923610747070469 & 0.152778505859063 & 0.0763892529295315 \tabularnewline
21 & 0.860763200700414 & 0.278473598599173 & 0.139236799299586 \tabularnewline
22 & 0.831113030035072 & 0.337773939929855 & 0.168886969964928 \tabularnewline
23 & 0.746755032473676 & 0.506489935052647 & 0.253244967526324 \tabularnewline
24 & 0.621823174558418 & 0.756353650883164 & 0.378176825441582 \tabularnewline
25 & 0.637208068541588 & 0.725583862916824 & 0.362791931458412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&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]9[/C][C]0.528964221757403[/C][C]0.942071556485195[/C][C]0.471035778242597[/C][/ROW]
[ROW][C]10[/C][C]0.974589641767614[/C][C]0.0508207164647723[/C][C]0.0254103582323862[/C][/ROW]
[ROW][C]11[/C][C]0.995929164317119[/C][C]0.00814167136576186[/C][C]0.00407083568288093[/C][/ROW]
[ROW][C]12[/C][C]0.996067764943444[/C][C]0.00786447011311206[/C][C]0.00393223505655603[/C][/ROW]
[ROW][C]13[/C][C]0.994683714983558[/C][C]0.0106325700328832[/C][C]0.00531628501644162[/C][/ROW]
[ROW][C]14[/C][C]0.99334556766119[/C][C]0.0133088646776198[/C][C]0.0066544323388099[/C][/ROW]
[ROW][C]15[/C][C]0.989233688063174[/C][C]0.0215326238736529[/C][C]0.0107663119368264[/C][/ROW]
[ROW][C]16[/C][C]0.985190672154118[/C][C]0.0296186556917635[/C][C]0.0148093278458818[/C][/ROW]
[ROW][C]17[/C][C]0.974252028333812[/C][C]0.0514959433323753[/C][C]0.0257479716661877[/C][/ROW]
[ROW][C]18[/C][C]0.950811168766909[/C][C]0.0983776624661815[/C][C]0.0491888312330908[/C][/ROW]
[ROW][C]19[/C][C]0.920455698218556[/C][C]0.159088603562888[/C][C]0.0795443017814438[/C][/ROW]
[ROW][C]20[/C][C]0.923610747070469[/C][C]0.152778505859063[/C][C]0.0763892529295315[/C][/ROW]
[ROW][C]21[/C][C]0.860763200700414[/C][C]0.278473598599173[/C][C]0.139236799299586[/C][/ROW]
[ROW][C]22[/C][C]0.831113030035072[/C][C]0.337773939929855[/C][C]0.168886969964928[/C][/ROW]
[ROW][C]23[/C][C]0.746755032473676[/C][C]0.506489935052647[/C][C]0.253244967526324[/C][/ROW]
[ROW][C]24[/C][C]0.621823174558418[/C][C]0.756353650883164[/C][C]0.378176825441582[/C][/ROW]
[ROW][C]25[/C][C]0.637208068541588[/C][C]0.725583862916824[/C][C]0.362791931458412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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
9	0.528964221757403	0.942071556485195	0.471035778242597
10	0.974589641767614	0.0508207164647723	0.0254103582323862
11	0.995929164317119	0.00814167136576186	0.00407083568288093
12	0.996067764943444	0.00786447011311206	0.00393223505655603
13	0.994683714983558	0.0106325700328832	0.00531628501644162
14	0.99334556766119	0.0133088646776198	0.0066544323388099
15	0.989233688063174	0.0215326238736529	0.0107663119368264
16	0.985190672154118	0.0296186556917635	0.0148093278458818
17	0.974252028333812	0.0514959433323753	0.0257479716661877
18	0.950811168766909	0.0983776624661815	0.0491888312330908
19	0.920455698218556	0.159088603562888	0.0795443017814438
20	0.923610747070469	0.152778505859063	0.0763892529295315
21	0.860763200700414	0.278473598599173	0.139236799299586
22	0.831113030035072	0.337773939929855	0.168886969964928
23	0.746755032473676	0.506489935052647	0.253244967526324
24	0.621823174558418	0.756353650883164	0.378176825441582
25	0.637208068541588	0.725583862916824	0.362791931458412

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	2	0.117647058823529	NOK
5% type I error level	6	0.352941176470588	NOK
10% type I error level	9	0.529411764705882	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 & 2 & 0.117647058823529 & NOK \tabularnewline
5% type I error level & 6 & 0.352941176470588 & NOK \tabularnewline
10% type I error level & 9 & 0.529411764705882 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=163540&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]2[/C][C]0.117647058823529[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]6[/C][C]0.352941176470588[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]9[/C][C]0.529411764705882[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=163540&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=163540&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	2	0.117647058823529	NOK
5% type I error level	6	0.352941176470588	NOK
10% type I error level	9	0.529411764705882	NOK

Figure 1

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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