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Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationFri, 04 Dec 2015 12:12:12 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/04/t1449231156a7qz2ohm5k3xq8g.htm/, Retrieved Thu, 31 Oct 2024 23:37:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285095, Retrieved Thu, 31 Oct 2024 23:37:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact94
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
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Dataseries X:
1554	53,361	0
1994	56,628	0
1961	62,073	0
1716	62,073	0
1425	71,1295	0
1664	76,86575	0
1524	79,16025	0
1342	81,45475	0
1449	78,969	0
1622	83,755	0
1530	82,5585	0
1385	76,576	0
1117	81,609	0
1253	79,136	0
1088	86,555	0
1167	90,2645	0
1344	78,315	0
1745	82,23075	0
1559	62,652	0
1395	69,17825	0
1521	72,252	0
1890	62,886	0
1531	65,562	0
1635	58,872	0
1269	70,21425	1
1612	72,96775	1
1343	82,605	1
1634	81,22825	1
1571	84,5175	1
1881	80,22	1
1528	75,9225	1
1960	64,4625	1
1676	69,56	1
2166	68,08	1
1663	63,64	1
2067	74	1
1801	80,548	1
2347	96,038	1
1938	89,842	1
1980	103,783	1
2097	91,04325	1
2579	97,43225	1
2191	115,002	1
2449	103,82125	1
2208	101,30575	1
2353	104,62725	1
2151	106,288	1
2307	116,2525	1
1826	130,72	1
2414	123,84	1
2029	129	1
2091	120,4	1
1988	139,593	1
2484	132,246	1
2321	137,75625	1
2614	143,2665	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&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]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Multiple Linear Regression - Estimated Regression Equation
V1[t] = + 1079.3 + 5.99031V2[t] + 347.796V3[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
V1[t] =  +  1079.3 +  5.99031V2[t] +  347.796V3[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]V1[t] =  +  1079.3 +  5.99031V2[t] +  347.796V3[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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
V1[t] = + 1079.3 + 5.99031V2[t] + 347.796V3[t] + e[t]







Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+1079 158.3+6.8190e+00 8.815e-09 4.407e-09
V2+5.99 2.008+2.9830e+00 0.004304 0.002152
V3+347.8 93.49+3.7200e+00 0.0004824 0.0002412

\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) & +1079 &  158.3 & +6.8190e+00 &  8.815e-09 &  4.407e-09 \tabularnewline
V2 & +5.99 &  2.008 & +2.9830e+00 &  0.004304 &  0.002152 \tabularnewline
V3 & +347.8 &  93.49 & +3.7200e+00 &  0.0004824 &  0.0002412 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&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]+1079[/C][C] 158.3[/C][C]+6.8190e+00[/C][C] 8.815e-09[/C][C] 4.407e-09[/C][/ROW]
[ROW][C]V2[/C][C]+5.99[/C][C] 2.008[/C][C]+2.9830e+00[/C][C] 0.004304[/C][C] 0.002152[/C][/ROW]
[ROW][C]V3[/C][C]+347.8[/C][C] 93.49[/C][C]+3.7200e+00[/C][C] 0.0004824[/C][C] 0.0002412[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)+1079 158.3+6.8190e+00 8.815e-09 4.407e-09
V2+5.99 2.008+2.9830e+00 0.004304 0.002152
V3+347.8 93.49+3.7200e+00 0.0004824 0.0002412







Multiple Linear Regression - Regression Statistics
Multiple R 0.695
R-squared 0.483
Adjusted R-squared 0.4635
F-TEST (value) 24.76
F-TEST (DF numerator)2
F-TEST (DF denominator)53
p-value 2.558e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 290.4
Sum Squared Residuals 4.469e+06

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.695 \tabularnewline
R-squared &  0.483 \tabularnewline
Adjusted R-squared &  0.4635 \tabularnewline
F-TEST (value) &  24.76 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 53 \tabularnewline
p-value &  2.558e-08 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  290.4 \tabularnewline
Sum Squared Residuals &  4.469e+06 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.695[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.483[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4635[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 24.76[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]53[/C][/ROW]
[ROW][C]p-value[/C][C] 2.558e-08[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 290.4[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 4.469e+06[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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 0.695
R-squared 0.483
Adjusted R-squared 0.4635
F-TEST (value) 24.76
F-TEST (DF numerator)2
F-TEST (DF denominator)53
p-value 2.558e-08
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation 290.4
Sum Squared Residuals 4.469e+06







Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
1 1554 1399 155
2 1994 1419 575.5
3 1961 1451 509.9
4 1716 1451 264.9
5 1425 1505-80.39
6 1664 1540 124.2
7 1524 1554-29.5
8 1342 1567-225.2
9 1449 1552-103.4
10 1622 1581 40.98
11 1530 1574-43.86
12 1385 1538-153
13 1117 1568-451.2
14 1253 1553-300.4
15 1088 1598-509.8
16 1167 1620-453
17 1344 1548-204.4
18 1745 1572 173.1
19 1559 1455 104.4
20 1395 1494-98.7
21 1521 1512 8.884
22 1890 1456 434
23 1531 1472 58.96
24 1635 1432 203
25 1269 1848-578.7
26 1612 1864-252.2
27 1343 1922-578.9
28 1634 1914-279.7
29 1571 1933-362.4
30 1881 1908-26.64
31 1528 1882-353.9
32 1960 1813 146.8
33 1676 1844-167.8
34 2166 1835 331.1
35 1663 1808-145.3
36 2067 1870 196.6
37 1801 1910-108.6
38 2347 2002 344.6
39 1938 1965-27.28
40 1980 2049-68.79
41 2097 1972 124.5
42 2579 2011 568.3
43 2191 2116 75
44 2449 2049 400
45 2208 2034 174
46 2353 2054 299.2
47 2151 2064 87.2
48 2307 2123 183.5
49 1826 2210-384.2
50 2414 2169 245.1
51 2029 2200-170.8
52 2091 2148-57.33
53 1988 2263-275.3
54 2484 2219 264.7
55 2321 2252 68.7
56 2614 2285 328.7

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  1554 &  1399 &  155 \tabularnewline
2 &  1994 &  1419 &  575.5 \tabularnewline
3 &  1961 &  1451 &  509.9 \tabularnewline
4 &  1716 &  1451 &  264.9 \tabularnewline
5 &  1425 &  1505 & -80.39 \tabularnewline
6 &  1664 &  1540 &  124.2 \tabularnewline
7 &  1524 &  1554 & -29.5 \tabularnewline
8 &  1342 &  1567 & -225.2 \tabularnewline
9 &  1449 &  1552 & -103.4 \tabularnewline
10 &  1622 &  1581 &  40.98 \tabularnewline
11 &  1530 &  1574 & -43.86 \tabularnewline
12 &  1385 &  1538 & -153 \tabularnewline
13 &  1117 &  1568 & -451.2 \tabularnewline
14 &  1253 &  1553 & -300.4 \tabularnewline
15 &  1088 &  1598 & -509.8 \tabularnewline
16 &  1167 &  1620 & -453 \tabularnewline
17 &  1344 &  1548 & -204.4 \tabularnewline
18 &  1745 &  1572 &  173.1 \tabularnewline
19 &  1559 &  1455 &  104.4 \tabularnewline
20 &  1395 &  1494 & -98.7 \tabularnewline
21 &  1521 &  1512 &  8.884 \tabularnewline
22 &  1890 &  1456 &  434 \tabularnewline
23 &  1531 &  1472 &  58.96 \tabularnewline
24 &  1635 &  1432 &  203 \tabularnewline
25 &  1269 &  1848 & -578.7 \tabularnewline
26 &  1612 &  1864 & -252.2 \tabularnewline
27 &  1343 &  1922 & -578.9 \tabularnewline
28 &  1634 &  1914 & -279.7 \tabularnewline
29 &  1571 &  1933 & -362.4 \tabularnewline
30 &  1881 &  1908 & -26.64 \tabularnewline
31 &  1528 &  1882 & -353.9 \tabularnewline
32 &  1960 &  1813 &  146.8 \tabularnewline
33 &  1676 &  1844 & -167.8 \tabularnewline
34 &  2166 &  1835 &  331.1 \tabularnewline
35 &  1663 &  1808 & -145.3 \tabularnewline
36 &  2067 &  1870 &  196.6 \tabularnewline
37 &  1801 &  1910 & -108.6 \tabularnewline
38 &  2347 &  2002 &  344.6 \tabularnewline
39 &  1938 &  1965 & -27.28 \tabularnewline
40 &  1980 &  2049 & -68.79 \tabularnewline
41 &  2097 &  1972 &  124.5 \tabularnewline
42 &  2579 &  2011 &  568.3 \tabularnewline
43 &  2191 &  2116 &  75 \tabularnewline
44 &  2449 &  2049 &  400 \tabularnewline
45 &  2208 &  2034 &  174 \tabularnewline
46 &  2353 &  2054 &  299.2 \tabularnewline
47 &  2151 &  2064 &  87.2 \tabularnewline
48 &  2307 &  2123 &  183.5 \tabularnewline
49 &  1826 &  2210 & -384.2 \tabularnewline
50 &  2414 &  2169 &  245.1 \tabularnewline
51 &  2029 &  2200 & -170.8 \tabularnewline
52 &  2091 &  2148 & -57.33 \tabularnewline
53 &  1988 &  2263 & -275.3 \tabularnewline
54 &  2484 &  2219 &  264.7 \tabularnewline
55 &  2321 &  2252 &  68.7 \tabularnewline
56 &  2614 &  2285 &  328.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&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] 1554[/C][C] 1399[/C][C] 155[/C][/ROW]
[ROW][C]2[/C][C] 1994[/C][C] 1419[/C][C] 575.5[/C][/ROW]
[ROW][C]3[/C][C] 1961[/C][C] 1451[/C][C] 509.9[/C][/ROW]
[ROW][C]4[/C][C] 1716[/C][C] 1451[/C][C] 264.9[/C][/ROW]
[ROW][C]5[/C][C] 1425[/C][C] 1505[/C][C]-80.39[/C][/ROW]
[ROW][C]6[/C][C] 1664[/C][C] 1540[/C][C] 124.2[/C][/ROW]
[ROW][C]7[/C][C] 1524[/C][C] 1554[/C][C]-29.5[/C][/ROW]
[ROW][C]8[/C][C] 1342[/C][C] 1567[/C][C]-225.2[/C][/ROW]
[ROW][C]9[/C][C] 1449[/C][C] 1552[/C][C]-103.4[/C][/ROW]
[ROW][C]10[/C][C] 1622[/C][C] 1581[/C][C] 40.98[/C][/ROW]
[ROW][C]11[/C][C] 1530[/C][C] 1574[/C][C]-43.86[/C][/ROW]
[ROW][C]12[/C][C] 1385[/C][C] 1538[/C][C]-153[/C][/ROW]
[ROW][C]13[/C][C] 1117[/C][C] 1568[/C][C]-451.2[/C][/ROW]
[ROW][C]14[/C][C] 1253[/C][C] 1553[/C][C]-300.4[/C][/ROW]
[ROW][C]15[/C][C] 1088[/C][C] 1598[/C][C]-509.8[/C][/ROW]
[ROW][C]16[/C][C] 1167[/C][C] 1620[/C][C]-453[/C][/ROW]
[ROW][C]17[/C][C] 1344[/C][C] 1548[/C][C]-204.4[/C][/ROW]
[ROW][C]18[/C][C] 1745[/C][C] 1572[/C][C] 173.1[/C][/ROW]
[ROW][C]19[/C][C] 1559[/C][C] 1455[/C][C] 104.4[/C][/ROW]
[ROW][C]20[/C][C] 1395[/C][C] 1494[/C][C]-98.7[/C][/ROW]
[ROW][C]21[/C][C] 1521[/C][C] 1512[/C][C] 8.884[/C][/ROW]
[ROW][C]22[/C][C] 1890[/C][C] 1456[/C][C] 434[/C][/ROW]
[ROW][C]23[/C][C] 1531[/C][C] 1472[/C][C] 58.96[/C][/ROW]
[ROW][C]24[/C][C] 1635[/C][C] 1432[/C][C] 203[/C][/ROW]
[ROW][C]25[/C][C] 1269[/C][C] 1848[/C][C]-578.7[/C][/ROW]
[ROW][C]26[/C][C] 1612[/C][C] 1864[/C][C]-252.2[/C][/ROW]
[ROW][C]27[/C][C] 1343[/C][C] 1922[/C][C]-578.9[/C][/ROW]
[ROW][C]28[/C][C] 1634[/C][C] 1914[/C][C]-279.7[/C][/ROW]
[ROW][C]29[/C][C] 1571[/C][C] 1933[/C][C]-362.4[/C][/ROW]
[ROW][C]30[/C][C] 1881[/C][C] 1908[/C][C]-26.64[/C][/ROW]
[ROW][C]31[/C][C] 1528[/C][C] 1882[/C][C]-353.9[/C][/ROW]
[ROW][C]32[/C][C] 1960[/C][C] 1813[/C][C] 146.8[/C][/ROW]
[ROW][C]33[/C][C] 1676[/C][C] 1844[/C][C]-167.8[/C][/ROW]
[ROW][C]34[/C][C] 2166[/C][C] 1835[/C][C] 331.1[/C][/ROW]
[ROW][C]35[/C][C] 1663[/C][C] 1808[/C][C]-145.3[/C][/ROW]
[ROW][C]36[/C][C] 2067[/C][C] 1870[/C][C] 196.6[/C][/ROW]
[ROW][C]37[/C][C] 1801[/C][C] 1910[/C][C]-108.6[/C][/ROW]
[ROW][C]38[/C][C] 2347[/C][C] 2002[/C][C] 344.6[/C][/ROW]
[ROW][C]39[/C][C] 1938[/C][C] 1965[/C][C]-27.28[/C][/ROW]
[ROW][C]40[/C][C] 1980[/C][C] 2049[/C][C]-68.79[/C][/ROW]
[ROW][C]41[/C][C] 2097[/C][C] 1972[/C][C] 124.5[/C][/ROW]
[ROW][C]42[/C][C] 2579[/C][C] 2011[/C][C] 568.3[/C][/ROW]
[ROW][C]43[/C][C] 2191[/C][C] 2116[/C][C] 75[/C][/ROW]
[ROW][C]44[/C][C] 2449[/C][C] 2049[/C][C] 400[/C][/ROW]
[ROW][C]45[/C][C] 2208[/C][C] 2034[/C][C] 174[/C][/ROW]
[ROW][C]46[/C][C] 2353[/C][C] 2054[/C][C] 299.2[/C][/ROW]
[ROW][C]47[/C][C] 2151[/C][C] 2064[/C][C] 87.2[/C][/ROW]
[ROW][C]48[/C][C] 2307[/C][C] 2123[/C][C] 183.5[/C][/ROW]
[ROW][C]49[/C][C] 1826[/C][C] 2210[/C][C]-384.2[/C][/ROW]
[ROW][C]50[/C][C] 2414[/C][C] 2169[/C][C] 245.1[/C][/ROW]
[ROW][C]51[/C][C] 2029[/C][C] 2200[/C][C]-170.8[/C][/ROW]
[ROW][C]52[/C][C] 2091[/C][C] 2148[/C][C]-57.33[/C][/ROW]
[ROW][C]53[/C][C] 1988[/C][C] 2263[/C][C]-275.3[/C][/ROW]
[ROW][C]54[/C][C] 2484[/C][C] 2219[/C][C] 264.7[/C][/ROW]
[ROW][C]55[/C][C] 2321[/C][C] 2252[/C][C] 68.7[/C][/ROW]
[ROW][C]56[/C][C] 2614[/C][C] 2285[/C][C] 328.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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 IndexActualsInterpolationForecastResidualsPrediction Error
1 1554 1399 155
2 1994 1419 575.5
3 1961 1451 509.9
4 1716 1451 264.9
5 1425 1505-80.39
6 1664 1540 124.2
7 1524 1554-29.5
8 1342 1567-225.2
9 1449 1552-103.4
10 1622 1581 40.98
11 1530 1574-43.86
12 1385 1538-153
13 1117 1568-451.2
14 1253 1553-300.4
15 1088 1598-509.8
16 1167 1620-453
17 1344 1548-204.4
18 1745 1572 173.1
19 1559 1455 104.4
20 1395 1494-98.7
21 1521 1512 8.884
22 1890 1456 434
23 1531 1472 58.96
24 1635 1432 203
25 1269 1848-578.7
26 1612 1864-252.2
27 1343 1922-578.9
28 1634 1914-279.7
29 1571 1933-362.4
30 1881 1908-26.64
31 1528 1882-353.9
32 1960 1813 146.8
33 1676 1844-167.8
34 2166 1835 331.1
35 1663 1808-145.3
36 2067 1870 196.6
37 1801 1910-108.6
38 2347 2002 344.6
39 1938 1965-27.28
40 1980 2049-68.79
41 2097 1972 124.5
42 2579 2011 568.3
43 2191 2116 75
44 2449 2049 400
45 2208 2034 174
46 2353 2054 299.2
47 2151 2064 87.2
48 2307 2123 183.5
49 1826 2210-384.2
50 2414 2169 245.1
51 2029 2200-170.8
52 2091 2148-57.33
53 1988 2263-275.3
54 2484 2219 264.7
55 2321 2252 68.7
56 2614 2285 328.7







Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.5775 0.845 0.4225
7 0.408 0.8161 0.592
8 0.3105 0.6209 0.6895
9 0.1983 0.3967 0.8017
10 0.1536 0.3072 0.8464
11 0.09129 0.1826 0.9087
12 0.06779 0.1356 0.9322
13 0.118 0.2359 0.882
14 0.1052 0.2103 0.8948
15 0.1233 0.2465 0.8767
16 0.1158 0.2316 0.8842
17 0.09158 0.1832 0.9084
18 0.1386 0.2771 0.8614
19 0.1046 0.2091 0.8954
20 0.09308 0.1862 0.9069
21 0.06631 0.1326 0.9337
22 0.06188 0.1238 0.9381
23 0.044 0.08799 0.956
24 0.02941 0.05882 0.9706
25 0.03328 0.06657 0.9667
26 0.03753 0.07507 0.9625
27 0.06065 0.1213 0.9393
28 0.07818 0.1564 0.9218
29 0.1056 0.2113 0.8944
30 0.1381 0.2762 0.8619
31 0.18 0.3599 0.82
32 0.1523 0.3046 0.8477
33 0.1513 0.3026 0.8487
34 0.2045 0.4091 0.7955
35 0.2321 0.4642 0.7679
36 0.2444 0.4888 0.7556
37 0.2909 0.5818 0.7091
38 0.5683 0.8634 0.4317
39 0.586 0.828 0.414
40 0.6247 0.7507 0.3753
41 0.6253 0.7494 0.3747
42 0.7724 0.4552 0.2276
43 0.7123 0.5754 0.2877
44 0.7164 0.5673 0.2836
45 0.6244 0.7512 0.3756
46 0.5694 0.8611 0.4306
47 0.4476 0.8952 0.5524
48 0.3662 0.7324 0.6338
49 0.4626 0.9251 0.5374
50 0.4097 0.8193 0.5903

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.5775 &  0.845 &  0.4225 \tabularnewline
7 &  0.408 &  0.8161 &  0.592 \tabularnewline
8 &  0.3105 &  0.6209 &  0.6895 \tabularnewline
9 &  0.1983 &  0.3967 &  0.8017 \tabularnewline
10 &  0.1536 &  0.3072 &  0.8464 \tabularnewline
11 &  0.09129 &  0.1826 &  0.9087 \tabularnewline
12 &  0.06779 &  0.1356 &  0.9322 \tabularnewline
13 &  0.118 &  0.2359 &  0.882 \tabularnewline
14 &  0.1052 &  0.2103 &  0.8948 \tabularnewline
15 &  0.1233 &  0.2465 &  0.8767 \tabularnewline
16 &  0.1158 &  0.2316 &  0.8842 \tabularnewline
17 &  0.09158 &  0.1832 &  0.9084 \tabularnewline
18 &  0.1386 &  0.2771 &  0.8614 \tabularnewline
19 &  0.1046 &  0.2091 &  0.8954 \tabularnewline
20 &  0.09308 &  0.1862 &  0.9069 \tabularnewline
21 &  0.06631 &  0.1326 &  0.9337 \tabularnewline
22 &  0.06188 &  0.1238 &  0.9381 \tabularnewline
23 &  0.044 &  0.08799 &  0.956 \tabularnewline
24 &  0.02941 &  0.05882 &  0.9706 \tabularnewline
25 &  0.03328 &  0.06657 &  0.9667 \tabularnewline
26 &  0.03753 &  0.07507 &  0.9625 \tabularnewline
27 &  0.06065 &  0.1213 &  0.9393 \tabularnewline
28 &  0.07818 &  0.1564 &  0.9218 \tabularnewline
29 &  0.1056 &  0.2113 &  0.8944 \tabularnewline
30 &  0.1381 &  0.2762 &  0.8619 \tabularnewline
31 &  0.18 &  0.3599 &  0.82 \tabularnewline
32 &  0.1523 &  0.3046 &  0.8477 \tabularnewline
33 &  0.1513 &  0.3026 &  0.8487 \tabularnewline
34 &  0.2045 &  0.4091 &  0.7955 \tabularnewline
35 &  0.2321 &  0.4642 &  0.7679 \tabularnewline
36 &  0.2444 &  0.4888 &  0.7556 \tabularnewline
37 &  0.2909 &  0.5818 &  0.7091 \tabularnewline
38 &  0.5683 &  0.8634 &  0.4317 \tabularnewline
39 &  0.586 &  0.828 &  0.414 \tabularnewline
40 &  0.6247 &  0.7507 &  0.3753 \tabularnewline
41 &  0.6253 &  0.7494 &  0.3747 \tabularnewline
42 &  0.7724 &  0.4552 &  0.2276 \tabularnewline
43 &  0.7123 &  0.5754 &  0.2877 \tabularnewline
44 &  0.7164 &  0.5673 &  0.2836 \tabularnewline
45 &  0.6244 &  0.7512 &  0.3756 \tabularnewline
46 &  0.5694 &  0.8611 &  0.4306 \tabularnewline
47 &  0.4476 &  0.8952 &  0.5524 \tabularnewline
48 &  0.3662 &  0.7324 &  0.6338 \tabularnewline
49 &  0.4626 &  0.9251 &  0.5374 \tabularnewline
50 &  0.4097 &  0.8193 &  0.5903 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&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]6[/C][C] 0.5775[/C][C] 0.845[/C][C] 0.4225[/C][/ROW]
[ROW][C]7[/C][C] 0.408[/C][C] 0.8161[/C][C] 0.592[/C][/ROW]
[ROW][C]8[/C][C] 0.3105[/C][C] 0.6209[/C][C] 0.6895[/C][/ROW]
[ROW][C]9[/C][C] 0.1983[/C][C] 0.3967[/C][C] 0.8017[/C][/ROW]
[ROW][C]10[/C][C] 0.1536[/C][C] 0.3072[/C][C] 0.8464[/C][/ROW]
[ROW][C]11[/C][C] 0.09129[/C][C] 0.1826[/C][C] 0.9087[/C][/ROW]
[ROW][C]12[/C][C] 0.06779[/C][C] 0.1356[/C][C] 0.9322[/C][/ROW]
[ROW][C]13[/C][C] 0.118[/C][C] 0.2359[/C][C] 0.882[/C][/ROW]
[ROW][C]14[/C][C] 0.1052[/C][C] 0.2103[/C][C] 0.8948[/C][/ROW]
[ROW][C]15[/C][C] 0.1233[/C][C] 0.2465[/C][C] 0.8767[/C][/ROW]
[ROW][C]16[/C][C] 0.1158[/C][C] 0.2316[/C][C] 0.8842[/C][/ROW]
[ROW][C]17[/C][C] 0.09158[/C][C] 0.1832[/C][C] 0.9084[/C][/ROW]
[ROW][C]18[/C][C] 0.1386[/C][C] 0.2771[/C][C] 0.8614[/C][/ROW]
[ROW][C]19[/C][C] 0.1046[/C][C] 0.2091[/C][C] 0.8954[/C][/ROW]
[ROW][C]20[/C][C] 0.09308[/C][C] 0.1862[/C][C] 0.9069[/C][/ROW]
[ROW][C]21[/C][C] 0.06631[/C][C] 0.1326[/C][C] 0.9337[/C][/ROW]
[ROW][C]22[/C][C] 0.06188[/C][C] 0.1238[/C][C] 0.9381[/C][/ROW]
[ROW][C]23[/C][C] 0.044[/C][C] 0.08799[/C][C] 0.956[/C][/ROW]
[ROW][C]24[/C][C] 0.02941[/C][C] 0.05882[/C][C] 0.9706[/C][/ROW]
[ROW][C]25[/C][C] 0.03328[/C][C] 0.06657[/C][C] 0.9667[/C][/ROW]
[ROW][C]26[/C][C] 0.03753[/C][C] 0.07507[/C][C] 0.9625[/C][/ROW]
[ROW][C]27[/C][C] 0.06065[/C][C] 0.1213[/C][C] 0.9393[/C][/ROW]
[ROW][C]28[/C][C] 0.07818[/C][C] 0.1564[/C][C] 0.9218[/C][/ROW]
[ROW][C]29[/C][C] 0.1056[/C][C] 0.2113[/C][C] 0.8944[/C][/ROW]
[ROW][C]30[/C][C] 0.1381[/C][C] 0.2762[/C][C] 0.8619[/C][/ROW]
[ROW][C]31[/C][C] 0.18[/C][C] 0.3599[/C][C] 0.82[/C][/ROW]
[ROW][C]32[/C][C] 0.1523[/C][C] 0.3046[/C][C] 0.8477[/C][/ROW]
[ROW][C]33[/C][C] 0.1513[/C][C] 0.3026[/C][C] 0.8487[/C][/ROW]
[ROW][C]34[/C][C] 0.2045[/C][C] 0.4091[/C][C] 0.7955[/C][/ROW]
[ROW][C]35[/C][C] 0.2321[/C][C] 0.4642[/C][C] 0.7679[/C][/ROW]
[ROW][C]36[/C][C] 0.2444[/C][C] 0.4888[/C][C] 0.7556[/C][/ROW]
[ROW][C]37[/C][C] 0.2909[/C][C] 0.5818[/C][C] 0.7091[/C][/ROW]
[ROW][C]38[/C][C] 0.5683[/C][C] 0.8634[/C][C] 0.4317[/C][/ROW]
[ROW][C]39[/C][C] 0.586[/C][C] 0.828[/C][C] 0.414[/C][/ROW]
[ROW][C]40[/C][C] 0.6247[/C][C] 0.7507[/C][C] 0.3753[/C][/ROW]
[ROW][C]41[/C][C] 0.6253[/C][C] 0.7494[/C][C] 0.3747[/C][/ROW]
[ROW][C]42[/C][C] 0.7724[/C][C] 0.4552[/C][C] 0.2276[/C][/ROW]
[ROW][C]43[/C][C] 0.7123[/C][C] 0.5754[/C][C] 0.2877[/C][/ROW]
[ROW][C]44[/C][C] 0.7164[/C][C] 0.5673[/C][C] 0.2836[/C][/ROW]
[ROW][C]45[/C][C] 0.6244[/C][C] 0.7512[/C][C] 0.3756[/C][/ROW]
[ROW][C]46[/C][C] 0.5694[/C][C] 0.8611[/C][C] 0.4306[/C][/ROW]
[ROW][C]47[/C][C] 0.4476[/C][C] 0.8952[/C][C] 0.5524[/C][/ROW]
[ROW][C]48[/C][C] 0.3662[/C][C] 0.7324[/C][C] 0.6338[/C][/ROW]
[ROW][C]49[/C][C] 0.4626[/C][C] 0.9251[/C][C] 0.5374[/C][/ROW]
[ROW][C]50[/C][C] 0.4097[/C][C] 0.8193[/C][C] 0.5903[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
6 0.5775 0.845 0.4225
7 0.408 0.8161 0.592
8 0.3105 0.6209 0.6895
9 0.1983 0.3967 0.8017
10 0.1536 0.3072 0.8464
11 0.09129 0.1826 0.9087
12 0.06779 0.1356 0.9322
13 0.118 0.2359 0.882
14 0.1052 0.2103 0.8948
15 0.1233 0.2465 0.8767
16 0.1158 0.2316 0.8842
17 0.09158 0.1832 0.9084
18 0.1386 0.2771 0.8614
19 0.1046 0.2091 0.8954
20 0.09308 0.1862 0.9069
21 0.06631 0.1326 0.9337
22 0.06188 0.1238 0.9381
23 0.044 0.08799 0.956
24 0.02941 0.05882 0.9706
25 0.03328 0.06657 0.9667
26 0.03753 0.07507 0.9625
27 0.06065 0.1213 0.9393
28 0.07818 0.1564 0.9218
29 0.1056 0.2113 0.8944
30 0.1381 0.2762 0.8619
31 0.18 0.3599 0.82
32 0.1523 0.3046 0.8477
33 0.1513 0.3026 0.8487
34 0.2045 0.4091 0.7955
35 0.2321 0.4642 0.7679
36 0.2444 0.4888 0.7556
37 0.2909 0.5818 0.7091
38 0.5683 0.8634 0.4317
39 0.586 0.828 0.414
40 0.6247 0.7507 0.3753
41 0.6253 0.7494 0.3747
42 0.7724 0.4552 0.2276
43 0.7123 0.5754 0.2877
44 0.7164 0.5673 0.2836
45 0.6244 0.7512 0.3756
46 0.5694 0.8611 0.4306
47 0.4476 0.8952 0.5524
48 0.3662 0.7324 0.6338
49 0.4626 0.9251 0.5374
50 0.4097 0.8193 0.5903







Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level40.0888889OK

\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 & 4 & 0.0888889 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285095&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]4[/C][C]0.0888889[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285095&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285095&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 testsOK/NOK
1% type I error level0 0OK
5% type I error level00OK
10% type I error level40.0888889OK



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;
Parameters (R input):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; 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'){
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(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 (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+par4,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1+par4,])
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')
}
}