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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Mon, 14 Dec 2015 11:36:05 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/14/t1450093181cwf3n8u8r4831kj.htm/, Retrieved Thu, 16 May 2024 12:22:39 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=286266, Retrieved Thu, 16 May 2024 12:22:39 +0000

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Estimated Impact

122

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

-       [Multiple Regression] [Multiple Regression] [2015-12-14 11:36:05] [0699448209a825438cb2d76a05e8a0a6] [Current]

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1554	53.361	0	0
1994	56.628	0	0
1961	62.073	0	0
1716	62.073	0	0
1425	71.1295	0	0
1664	76.86575	0	0
1524	79.16025	0	0
1342	81.45475	0	0
1449	78.969	0	0
1622	83.755	0	0
1530	82.5585	0	0
1385	76.576	0	0
1117	81.609	0	0
1253	79.136	0	0
1088	86.555	0	1
1167	90.2645	0	1
1344	78.315	0	0
1745	82.23075	0	0
1559	62.652	0	0
1395	69.17825	0	0
1521	72.252	0	0
1890	62.886	0	0
1531	65.562	0	0
1635	58.872	0	0
1269	70.21425	1	0
1612	72.96775	1	1
1343	82.605	1	0
1634	81.22825	1	0
1571	84.5175	1	0
1881	80.22	1	0
1528	75.9225	1	0
1960	64.4625	1	0
1676	69.56	1	0
2166	68.08	1	0
1663	63.64	1	0
2067	74	1	0
1801	80.548	1	1
2347	96.038	1	1
1938	89.842	1	1
1980	103.783	1	1
2097	91.04325	1	1
2579	97.43225	1	1
2191	115.002	1	1
2449	103.82125	1	1
2208	101.30575	1	1
2353	104.62725	1	1
2151	106.288	1	1
2307	116.2525	1	1
1826	130.72	1	1
2414	123.84	1	1
2029	129	1	1
2091	120.4	1	1
1988	139.593	1	1
2484	132.246	1	1
2321	137.75625	1	1
2614	143.2665	1	1

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	'Gwilym Jenkins' @ jenkins.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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
a[t] = + 196.316 -0.253606b[t] + 166.219c[t] + 54.6572d[t] + 0.579904`a(t-1)`[t] + 0.172311`a(t-2)`[t] -0.0568802`a(t-3)`[t] + 0.23277`a(t-4)`[t] -0.300536`a(t-5)`[t] + 0.126768`a(t-6)`[t] -0.230364`a(t-7)`[t] + 0.235512`a(t-8)`[t] -0.127812`a(t-9)`[t] -0.0615059`a(t-10)`[t] + 0.0386636`a(t-11)`[t] + 0.242696`a(t-12)`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
a[t] =  +  196.316 -0.253606b[t] +  166.219c[t] +  54.6572d[t] +  0.579904`a(t-1)`[t] +  0.172311`a(t-2)`[t] -0.0568802`a(t-3)`[t] +  0.23277`a(t-4)`[t] -0.300536`a(t-5)`[t] +  0.126768`a(t-6)`[t] -0.230364`a(t-7)`[t] +  0.235512`a(t-8)`[t] -0.127812`a(t-9)`[t] -0.0615059`a(t-10)`[t] +  0.0386636`a(t-11)`[t] +  0.242696`a(t-12)`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]a[t] =  +  196.316 -0.253606b[t] +  166.219c[t] +  54.6572d[t] +  0.579904`a(t-1)`[t] +  0.172311`a(t-2)`[t] -0.0568802`a(t-3)`[t] +  0.23277`a(t-4)`[t] -0.300536`a(t-5)`[t] +  0.126768`a(t-6)`[t] -0.230364`a(t-7)`[t] +  0.235512`a(t-8)`[t] -0.127812`a(t-9)`[t] -0.0615059`a(t-10)`[t] +  0.0386636`a(t-11)`[t] +  0.242696`a(t-12)`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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
a[t] = + 196.316 -0.253606b[t] + 166.219c[t] + 54.6572d[t] + 0.579904`a(t-1)`[t] + 0.172311`a(t-2)`[t] -0.0568802`a(t-3)`[t] + 0.23277`a(t-4)`[t] -0.300536`a(t-5)`[t] + 0.126768`a(t-6)`[t] -0.230364`a(t-7)`[t] + 0.235512`a(t-8)`[t] -0.127812`a(t-9)`[t] -0.0615059`a(t-10)`[t] + 0.0386636`a(t-11)`[t] + 0.242696`a(t-12)`[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)	+196.3	277.8	+7.0660e-01	0.4857	0.2428
b	-0.2536	4.947	-5.1270e-02	0.9595	0.4797
c	+166.2	126.3	+1.3160e+00	0.199	0.0995
d	+54.66	123.7	+4.4190e-01	0.662	0.331
`a(t-1)`	+0.5799	0.1876	+3.0910e+00	0.004477	0.002238
`a(t-2)`	+0.1723	0.218	+7.9060e-01	0.4358	0.2179
`a(t-3)`	-0.05688	0.2249	-2.5290e-01	0.8022	0.4011
`a(t-4)`	+0.2328	0.2212	+1.0520e+00	0.3017	0.1508
`a(t-5)`	-0.3005	0.2215	-1.3570e+00	0.1857	0.09285
`a(t-6)`	+0.1268	0.2445	+5.1860e-01	0.6081	0.3041
`a(t-7)`	-0.2304	0.2356	-9.7770e-01	0.3366	0.1683
`a(t-8)`	+0.2355	0.2532	+9.3030e-01	0.3602	0.1801
`a(t-9)`	-0.1278	0.2358	-5.4200e-01	0.5921	0.296
`a(t-10)`	-0.06151	0.2265	-2.7160e-01	0.7879	0.394
`a(t-11)`	+0.03866	0.2456	+1.5740e-01	0.876	0.438
`a(t-12)`	+0.2427	0.2069	+1.1730e+00	0.2507	0.1253

\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) & +196.3 &  277.8 & +7.0660e-01 &  0.4857 &  0.2428 \tabularnewline
b & -0.2536 &  4.947 & -5.1270e-02 &  0.9595 &  0.4797 \tabularnewline
c & +166.2 &  126.3 & +1.3160e+00 &  0.199 &  0.0995 \tabularnewline
d & +54.66 &  123.7 & +4.4190e-01 &  0.662 &  0.331 \tabularnewline
`a(t-1)` & +0.5799 &  0.1876 & +3.0910e+00 &  0.004477 &  0.002238 \tabularnewline
`a(t-2)` & +0.1723 &  0.218 & +7.9060e-01 &  0.4358 &  0.2179 \tabularnewline
`a(t-3)` & -0.05688 &  0.2249 & -2.5290e-01 &  0.8022 &  0.4011 \tabularnewline
`a(t-4)` & +0.2328 &  0.2212 & +1.0520e+00 &  0.3017 &  0.1508 \tabularnewline
`a(t-5)` & -0.3005 &  0.2215 & -1.3570e+00 &  0.1857 &  0.09285 \tabularnewline
`a(t-6)` & +0.1268 &  0.2445 & +5.1860e-01 &  0.6081 &  0.3041 \tabularnewline
`a(t-7)` & -0.2304 &  0.2356 & -9.7770e-01 &  0.3366 &  0.1683 \tabularnewline
`a(t-8)` & +0.2355 &  0.2532 & +9.3030e-01 &  0.3602 &  0.1801 \tabularnewline
`a(t-9)` & -0.1278 &  0.2358 & -5.4200e-01 &  0.5921 &  0.296 \tabularnewline
`a(t-10)` & -0.06151 &  0.2265 & -2.7160e-01 &  0.7879 &  0.394 \tabularnewline
`a(t-11)` & +0.03866 &  0.2456 & +1.5740e-01 &  0.876 &  0.438 \tabularnewline
`a(t-12)` & +0.2427 &  0.2069 & +1.1730e+00 &  0.2507 &  0.1253 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&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]+196.3[/C][C] 277.8[/C][C]+7.0660e-01[/C][C] 0.4857[/C][C] 0.2428[/C][/ROW]
[ROW][C]b[/C][C]-0.2536[/C][C] 4.947[/C][C]-5.1270e-02[/C][C] 0.9595[/C][C] 0.4797[/C][/ROW]
[ROW][C]c[/C][C]+166.2[/C][C] 126.3[/C][C]+1.3160e+00[/C][C] 0.199[/C][C] 0.0995[/C][/ROW]
[ROW][C]d[/C][C]+54.66[/C][C] 123.7[/C][C]+4.4190e-01[/C][C] 0.662[/C][C] 0.331[/C][/ROW]
[ROW][C]`a(t-1)`[/C][C]+0.5799[/C][C] 0.1876[/C][C]+3.0910e+00[/C][C] 0.004477[/C][C] 0.002238[/C][/ROW]
[ROW][C]`a(t-2)`[/C][C]+0.1723[/C][C] 0.218[/C][C]+7.9060e-01[/C][C] 0.4358[/C][C] 0.2179[/C][/ROW]
[ROW][C]`a(t-3)`[/C][C]-0.05688[/C][C] 0.2249[/C][C]-2.5290e-01[/C][C] 0.8022[/C][C] 0.4011[/C][/ROW]
[ROW][C]`a(t-4)`[/C][C]+0.2328[/C][C] 0.2212[/C][C]+1.0520e+00[/C][C] 0.3017[/C][C] 0.1508[/C][/ROW]
[ROW][C]`a(t-5)`[/C][C]-0.3005[/C][C] 0.2215[/C][C]-1.3570e+00[/C][C] 0.1857[/C][C] 0.09285[/C][/ROW]
[ROW][C]`a(t-6)`[/C][C]+0.1268[/C][C] 0.2445[/C][C]+5.1860e-01[/C][C] 0.6081[/C][C] 0.3041[/C][/ROW]
[ROW][C]`a(t-7)`[/C][C]-0.2304[/C][C] 0.2356[/C][C]-9.7770e-01[/C][C] 0.3366[/C][C] 0.1683[/C][/ROW]
[ROW][C]`a(t-8)`[/C][C]+0.2355[/C][C] 0.2532[/C][C]+9.3030e-01[/C][C] 0.3602[/C][C] 0.1801[/C][/ROW]
[ROW][C]`a(t-9)`[/C][C]-0.1278[/C][C] 0.2358[/C][C]-5.4200e-01[/C][C] 0.5921[/C][C] 0.296[/C][/ROW]
[ROW][C]`a(t-10)`[/C][C]-0.06151[/C][C] 0.2265[/C][C]-2.7160e-01[/C][C] 0.7879[/C][C] 0.394[/C][/ROW]
[ROW][C]`a(t-11)`[/C][C]+0.03866[/C][C] 0.2456[/C][C]+1.5740e-01[/C][C] 0.876[/C][C] 0.438[/C][/ROW]
[ROW][C]`a(t-12)`[/C][C]+0.2427[/C][C] 0.2069[/C][C]+1.1730e+00[/C][C] 0.2507[/C][C] 0.1253[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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)	+196.3	277.8	+7.0660e-01	0.4857	0.2428
b	-0.2536	4.947	-5.1270e-02	0.9595	0.4797
c	+166.2	126.3	+1.3160e+00	0.199	0.0995
d	+54.66	123.7	+4.4190e-01	0.662	0.331
`a(t-1)`	+0.5799	0.1876	+3.0910e+00	0.004477	0.002238
`a(t-2)`	+0.1723	0.218	+7.9060e-01	0.4358	0.2179
`a(t-3)`	-0.05688	0.2249	-2.5290e-01	0.8022	0.4011
`a(t-4)`	+0.2328	0.2212	+1.0520e+00	0.3017	0.1508
`a(t-5)`	-0.3005	0.2215	-1.3570e+00	0.1857	0.09285
`a(t-6)`	+0.1268	0.2445	+5.1860e-01	0.6081	0.3041
`a(t-7)`	-0.2304	0.2356	-9.7770e-01	0.3366	0.1683
`a(t-8)`	+0.2355	0.2532	+9.3030e-01	0.3602	0.1801
`a(t-9)`	-0.1278	0.2358	-5.4200e-01	0.5921	0.296
`a(t-10)`	-0.06151	0.2265	-2.7160e-01	0.7879	0.394
`a(t-11)`	+0.03866	0.2456	+1.5740e-01	0.876	0.438
`a(t-12)`	+0.2427	0.2069	+1.1730e+00	0.2507	0.1253

Multiple Linear Regression - Regression Statistics
Multiple R	0.9184
R-squared	0.8435
Adjusted R-squared	0.7596
F-TEST (value)	10.06
F-TEST (DF numerator)	15
F-TEST (DF denominator)	28
p-value	1.292e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	205
Sum Squared Residuals	1.176e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9184 \tabularnewline
R-squared &  0.8435 \tabularnewline
Adjusted R-squared &  0.7596 \tabularnewline
F-TEST (value) &  10.06 \tabularnewline
F-TEST (DF numerator) & 15 \tabularnewline
F-TEST (DF denominator) & 28 \tabularnewline
p-value &  1.292e-07 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  205 \tabularnewline
Sum Squared Residuals &  1.176e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9184[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.8435[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7596[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 10.06[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]15[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]28[/C][/ROW]
[ROW][C]p-value[/C][C] 1.292e-07[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 205[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.176e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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	0.9184
R-squared	0.8435
Adjusted R-squared	0.7596
F-TEST (value)	10.06
F-TEST (DF numerator)	15
F-TEST (DF denominator)	28
p-value	1.292e-07
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	205
Sum Squared Residuals	1.176e+06

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	1117	1344	-226.9
2	1253	1401	-147.7
3	1088	1413	-325.4
4	1167	1236	-69.26
5	1344	1120	224.3
6	1745	1450	294.8
7	1559	1542	16.55
8	1395	1566	-170.9
9	1521	1374	146.9
10	1890	1634	256.4
11	1531	1651	-119.7
12	1635	1514	120.8
13	1269	1600	-330.6
14	1612	1643	-31.33
15	1343	1433	-89.51
16	1634	1493	140.9
17	1571	1469	102.2
18	1881	1956	-74.51
19	1528	1682	-154
20	1960	1826	133.7
21	1676	1749	-73.26
22	2166	2066	99.75
23	1663	1860	-196.9
24	2067	2020	47.5
25	1801	1769	31.91
26	2347	2181	166.4
27	1938	1857	80.77
28	1980	2294	-314.5
29	2097	1757	340.2
30	2579	2414	164.6
31	2191	2089	102.4
32	2449	2429	19.85
33	2208	2189	19.21
34	2353	2528	-175.5
35	2151	2070	81.26
36	2307	2316	-8.93
37	1826	2070	-243.7
38	2414	2272	142.2
39	2029	2081	-52.36
40	2091	2255	-164.3
41	1988	1950	38.1
42	2484	2423	60.58
43	2321	2190	131.3
44	2614	2607	6.518

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1117 &  1344 & -226.9 \tabularnewline
2 &  1253 &  1401 & -147.7 \tabularnewline
3 &  1088 &  1413 & -325.4 \tabularnewline
4 &  1167 &  1236 & -69.26 \tabularnewline
5 &  1344 &  1120 &  224.3 \tabularnewline
6 &  1745 &  1450 &  294.8 \tabularnewline
7 &  1559 &  1542 &  16.55 \tabularnewline
8 &  1395 &  1566 & -170.9 \tabularnewline
9 &  1521 &  1374 &  146.9 \tabularnewline
10 &  1890 &  1634 &  256.4 \tabularnewline
11 &  1531 &  1651 & -119.7 \tabularnewline
12 &  1635 &  1514 &  120.8 \tabularnewline
13 &  1269 &  1600 & -330.6 \tabularnewline
14 &  1612 &  1643 & -31.33 \tabularnewline
15 &  1343 &  1433 & -89.51 \tabularnewline
16 &  1634 &  1493 &  140.9 \tabularnewline
17 &  1571 &  1469 &  102.2 \tabularnewline
18 &  1881 &  1956 & -74.51 \tabularnewline
19 &  1528 &  1682 & -154 \tabularnewline
20 &  1960 &  1826 &  133.7 \tabularnewline
21 &  1676 &  1749 & -73.26 \tabularnewline
22 &  2166 &  2066 &  99.75 \tabularnewline
23 &  1663 &  1860 & -196.9 \tabularnewline
24 &  2067 &  2020 &  47.5 \tabularnewline
25 &  1801 &  1769 &  31.91 \tabularnewline
26 &  2347 &  2181 &  166.4 \tabularnewline
27 &  1938 &  1857 &  80.77 \tabularnewline
28 &  1980 &  2294 & -314.5 \tabularnewline
29 &  2097 &  1757 &  340.2 \tabularnewline
30 &  2579 &  2414 &  164.6 \tabularnewline
31 &  2191 &  2089 &  102.4 \tabularnewline
32 &  2449 &  2429 &  19.85 \tabularnewline
33 &  2208 &  2189 &  19.21 \tabularnewline
34 &  2353 &  2528 & -175.5 \tabularnewline
35 &  2151 &  2070 &  81.26 \tabularnewline
36 &  2307 &  2316 & -8.93 \tabularnewline
37 &  1826 &  2070 & -243.7 \tabularnewline
38 &  2414 &  2272 &  142.2 \tabularnewline
39 &  2029 &  2081 & -52.36 \tabularnewline
40 &  2091 &  2255 & -164.3 \tabularnewline
41 &  1988 &  1950 &  38.1 \tabularnewline
42 &  2484 &  2423 &  60.58 \tabularnewline
43 &  2321 &  2190 &  131.3 \tabularnewline
44 &  2614 &  2607 &  6.518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&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] 1117[/C][C] 1344[/C][C]-226.9[/C][/ROW]
[ROW][C]2[/C][C] 1253[/C][C] 1401[/C][C]-147.7[/C][/ROW]
[ROW][C]3[/C][C] 1088[/C][C] 1413[/C][C]-325.4[/C][/ROW]
[ROW][C]4[/C][C] 1167[/C][C] 1236[/C][C]-69.26[/C][/ROW]
[ROW][C]5[/C][C] 1344[/C][C] 1120[/C][C] 224.3[/C][/ROW]
[ROW][C]6[/C][C] 1745[/C][C] 1450[/C][C] 294.8[/C][/ROW]
[ROW][C]7[/C][C] 1559[/C][C] 1542[/C][C] 16.55[/C][/ROW]
[ROW][C]8[/C][C] 1395[/C][C] 1566[/C][C]-170.9[/C][/ROW]
[ROW][C]9[/C][C] 1521[/C][C] 1374[/C][C] 146.9[/C][/ROW]
[ROW][C]10[/C][C] 1890[/C][C] 1634[/C][C] 256.4[/C][/ROW]
[ROW][C]11[/C][C] 1531[/C][C] 1651[/C][C]-119.7[/C][/ROW]
[ROW][C]12[/C][C] 1635[/C][C] 1514[/C][C] 120.8[/C][/ROW]
[ROW][C]13[/C][C] 1269[/C][C] 1600[/C][C]-330.6[/C][/ROW]
[ROW][C]14[/C][C] 1612[/C][C] 1643[/C][C]-31.33[/C][/ROW]
[ROW][C]15[/C][C] 1343[/C][C] 1433[/C][C]-89.51[/C][/ROW]
[ROW][C]16[/C][C] 1634[/C][C] 1493[/C][C] 140.9[/C][/ROW]
[ROW][C]17[/C][C] 1571[/C][C] 1469[/C][C] 102.2[/C][/ROW]
[ROW][C]18[/C][C] 1881[/C][C] 1956[/C][C]-74.51[/C][/ROW]
[ROW][C]19[/C][C] 1528[/C][C] 1682[/C][C]-154[/C][/ROW]
[ROW][C]20[/C][C] 1960[/C][C] 1826[/C][C] 133.7[/C][/ROW]
[ROW][C]21[/C][C] 1676[/C][C] 1749[/C][C]-73.26[/C][/ROW]
[ROW][C]22[/C][C] 2166[/C][C] 2066[/C][C] 99.75[/C][/ROW]
[ROW][C]23[/C][C] 1663[/C][C] 1860[/C][C]-196.9[/C][/ROW]
[ROW][C]24[/C][C] 2067[/C][C] 2020[/C][C] 47.5[/C][/ROW]
[ROW][C]25[/C][C] 1801[/C][C] 1769[/C][C] 31.91[/C][/ROW]
[ROW][C]26[/C][C] 2347[/C][C] 2181[/C][C] 166.4[/C][/ROW]
[ROW][C]27[/C][C] 1938[/C][C] 1857[/C][C] 80.77[/C][/ROW]
[ROW][C]28[/C][C] 1980[/C][C] 2294[/C][C]-314.5[/C][/ROW]
[ROW][C]29[/C][C] 2097[/C][C] 1757[/C][C] 340.2[/C][/ROW]
[ROW][C]30[/C][C] 2579[/C][C] 2414[/C][C] 164.6[/C][/ROW]
[ROW][C]31[/C][C] 2191[/C][C] 2089[/C][C] 102.4[/C][/ROW]
[ROW][C]32[/C][C] 2449[/C][C] 2429[/C][C] 19.85[/C][/ROW]
[ROW][C]33[/C][C] 2208[/C][C] 2189[/C][C] 19.21[/C][/ROW]
[ROW][C]34[/C][C] 2353[/C][C] 2528[/C][C]-175.5[/C][/ROW]
[ROW][C]35[/C][C] 2151[/C][C] 2070[/C][C] 81.26[/C][/ROW]
[ROW][C]36[/C][C] 2307[/C][C] 2316[/C][C]-8.93[/C][/ROW]
[ROW][C]37[/C][C] 1826[/C][C] 2070[/C][C]-243.7[/C][/ROW]
[ROW][C]38[/C][C] 2414[/C][C] 2272[/C][C] 142.2[/C][/ROW]
[ROW][C]39[/C][C] 2029[/C][C] 2081[/C][C]-52.36[/C][/ROW]
[ROW][C]40[/C][C] 2091[/C][C] 2255[/C][C]-164.3[/C][/ROW]
[ROW][C]41[/C][C] 1988[/C][C] 1950[/C][C] 38.1[/C][/ROW]
[ROW][C]42[/C][C] 2484[/C][C] 2423[/C][C] 60.58[/C][/ROW]
[ROW][C]43[/C][C] 2321[/C][C] 2190[/C][C] 131.3[/C][/ROW]
[ROW][C]44[/C][C] 2614[/C][C] 2607[/C][C] 6.518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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	1117	1344	-226.9
2	1253	1401	-147.7
3	1088	1413	-325.4
4	1167	1236	-69.26
5	1344	1120	224.3
6	1745	1450	294.8
7	1559	1542	16.55
8	1395	1566	-170.9
9	1521	1374	146.9
10	1890	1634	256.4
11	1531	1651	-119.7
12	1635	1514	120.8
13	1269	1600	-330.6
14	1612	1643	-31.33
15	1343	1433	-89.51
16	1634	1493	140.9
17	1571	1469	102.2
18	1881	1956	-74.51
19	1528	1682	-154
20	1960	1826	133.7
21	1676	1749	-73.26
22	2166	2066	99.75
23	1663	1860	-196.9
24	2067	2020	47.5
25	1801	1769	31.91
26	2347	2181	166.4
27	1938	1857	80.77
28	1980	2294	-314.5
29	2097	1757	340.2
30	2579	2414	164.6
31	2191	2089	102.4
32	2449	2429	19.85
33	2208	2189	19.21
34	2353	2528	-175.5
35	2151	2070	81.26
36	2307	2316	-8.93
37	1826	2070	-243.7
38	2414	2272	142.2
39	2029	2081	-52.36
40	2091	2255	-164.3
41	1988	1950	38.1
42	2484	2423	60.58
43	2321	2190	131.3
44	2614	2607	6.518

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
19	0.873	0.2541	0.127
20	0.8099	0.3802	0.1901
21	0.6997	0.6007	0.3003
22	0.5397	0.9205	0.4603
23	0.6761	0.6478	0.3239
24	0.544	0.9119	0.456
25	0.5624	0.8752	0.4376

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
19 &  0.873 &  0.2541 &  0.127 \tabularnewline
20 &  0.8099 &  0.3802 &  0.1901 \tabularnewline
21 &  0.6997 &  0.6007 &  0.3003 \tabularnewline
22 &  0.5397 &  0.9205 &  0.4603 \tabularnewline
23 &  0.6761 &  0.6478 &  0.3239 \tabularnewline
24 &  0.544 &  0.9119 &  0.456 \tabularnewline
25 &  0.5624 &  0.8752 &  0.4376 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=286266&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]19[/C][C] 0.873[/C][C] 0.2541[/C][C] 0.127[/C][/ROW]
[ROW][C]20[/C][C] 0.8099[/C][C] 0.3802[/C][C] 0.1901[/C][/ROW]
[ROW][C]21[/C][C] 0.6997[/C][C] 0.6007[/C][C] 0.3003[/C][/ROW]
[ROW][C]22[/C][C] 0.5397[/C][C] 0.9205[/C][C] 0.4603[/C][/ROW]
[ROW][C]23[/C][C] 0.6761[/C][C] 0.6478[/C][C] 0.3239[/C][/ROW]
[ROW][C]24[/C][C] 0.544[/C][C] 0.9119[/C][C] 0.456[/C][/ROW]
[ROW][C]25[/C][C] 0.5624[/C][C] 0.8752[/C][C] 0.4376[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=286266&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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
19	0.873	0.2541	0.127
20	0.8099	0.3802	0.1901
21	0.6997	0.6007	0.3003
22	0.5397	0.9205	0.4603
23	0.6761	0.6478	0.3239
24	0.544	0.9119	0.456
25	0.5624	0.8752	0.4376

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=286266&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=286266&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=286266&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 ; par4 = 12 ; par5 = 0 ;

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 12 ; par5 = 0 ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(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'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
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[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
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,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
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,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
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,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
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,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
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,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
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