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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 07 Dec 2016 23:47:40 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/08/t14811518244h0dasiidprdwbg.htm/, Retrieved Fri, 01 Nov 2024 04:29:03 +0100

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Fri, 01 Nov 2024 04:29:03 +0100

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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	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]

Summary of computational transaction[/C][/ROW] [ROW]

Raw Input[/C]

view raw input (R code) [/C][/ROW] [ROW]

Raw Output[/C]

view raw output of R engine [/C][/ROW] [ROW]

Computing time[/C]

4 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [ROW]

R Engine error message[/C][C]

Error in vif.default(mylm) : model contains fewer than 2 terms
Calls: vif -> vif.default
Execution halted

[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	Big Analytics Cloud Computing Center
R Engine error message	Error in vif.default(mylm) : model contains fewer than 2 terms Calls: vif -> vif.default Execution halted

Multiple Linear Regression - Estimated Regression Equation
GW[t] = + 12.4001 -0.154457Imago[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
GW[t] =  +  12.4001 -0.154457Imago[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]GW[t] =  +  12.4001 -0.154457Imago[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
GW[t] = + 12.4001 -0.154457Imago[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)	+12.4	0.535	+2.3180e+01	4.188e-41	2.094e-41
Imago	-0.1545	0.1431	-1.0790e+00	0.2833	0.1417

\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) & +12.4 &  0.535 & +2.3180e+01 &  4.188e-41 &  2.094e-41 \tabularnewline
Imago & -0.1545 &  0.1431 & -1.0790e+00 &  0.2833 &  0.1417 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]+12.4[/C][C] 0.535[/C][C]+2.3180e+01[/C][C] 4.188e-41[/C][C] 2.094e-41[/C][/ROW]
[ROW][C]Imago[/C][C]-0.1545[/C][C] 0.1431[/C][C]-1.0790e+00[/C][C] 0.2833[/C][C] 0.1417[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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)	+12.4	0.535	+2.3180e+01	4.188e-41	2.094e-41
Imago	-0.1545	0.1431	-1.0790e+00	0.2833	0.1417

Multiple Linear Regression - Regression Statistics
Multiple R	0.1095
R-squared	0.01198
Adjusted R-squared	0.00169
F-TEST (value)	1.164
F-TEST (DF numerator)	1
F-TEST (DF denominator)	96
p-value	0.2833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.514
Sum Squared Residuals	220

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1095 \tabularnewline
R-squared &  0.01198 \tabularnewline
Adjusted R-squared &  0.00169 \tabularnewline
F-TEST (value) &  1.164 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 96 \tabularnewline
p-value &  0.2833 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.514 \tabularnewline
Sum Squared Residuals &  220 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1095[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.01198[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.00169[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.164[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]96[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2833[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.514[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 220[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	0.1095
R-squared	0.01198
Adjusted R-squared	0.00169
F-TEST (value)	1.164
F-TEST (DF numerator)	1
F-TEST (DF denominator)	96
p-value	0.2833
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.514
Sum Squared Residuals	220

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	11	12.09	-1.091
2	9	11.78	-2.782
3	12	11.78	0.2177
4	12	11.78	0.2177
5	12	12.25	-0.2457
6	11	11.78	-0.7823
7	12	12.09	-0.09123
8	12	11.78	0.2177
9	15	11.78	3.218
10	13	11.63	1.372
11	12	11.78	0.2177
12	11	12.25	-1.246
13	9	11.78	-2.782
14	11	11.63	-0.6279
15	12	12.25	-0.2457
16	12	11.63	0.3721
17	12	11.94	0.06322
18	14	11.63	2.372
19	12	11.63	0.3721
20	9	12.09	-3.091
21	13	12.25	0.7543
22	13	11.78	1.218
23	12	11.78	0.2177
24	12	11.63	0.3721
25	12	11.78	0.2177
26	12	12.09	-0.09123
27	11	11.94	-0.9368
28	13	12.25	0.7543
29	13	11.94	1.063
30	13	11.78	1.218
31	10	11.78	-1.782
32	13	11.78	1.218
33	5	11.63	-6.628
34	10	11.94	-1.937
35	15	11.78	3.218
36	13	11.78	1.218
37	12	11.78	0.2177
38	13	11.78	1.218
39	13	11.78	1.218
40	11	11.78	-0.7823
41	12	11.78	0.2177
42	12	11.78	0.2177
43	13	11.78	1.218
44	14	11.78	2.218
45	12	11.63	0.3721
46	12	11.78	0.2177
47	10	11.63	-1.628
48	12	11.63	0.3721
49	12	12.09	-0.09123
50	12	11.78	0.2177
51	13	11.94	1.063
52	14	12.09	1.909
53	10	11.63	-1.628
54	12	11.94	0.06322
55	13	12.09	0.9088
56	11	11.78	-0.7823
57	12	11.94	0.06322
58	12	11.78	0.2177
59	13	11.78	1.218
60	12	11.94	0.06322
61	9	11.78	-2.782
62	12	11.94	0.06322
63	14	11.78	2.218
64	11	11.94	-0.9368
65	12	11.78	0.2177
66	12	11.78	0.2177
67	9	11.78	-2.782
68	13	12.09	0.9088
69	10	11.63	-1.628
70	14	11.78	2.218
71	10	11.94	-1.937
72	12	11.78	0.2177
73	11	11.78	-0.7823
74	14	12.09	1.909
75	13	11.78	1.218
76	12	11.78	0.2177
77	10	11.94	-1.937
78	12	11.63	0.3721
79	12	11.63	0.3721
80	15	11.94	3.063
81	12	12.09	-0.09123
82	12	12.09	-0.09123
83	10	11.78	-1.782
84	12	11.78	0.2177
85	12	11.63	0.3721
86	12	11.94	0.06322
87	11	11.78	-0.7823
88	13	11.78	1.218
89	13	11.78	1.218
90	11	11.78	-0.7823
91	10	12.09	-2.091
92	9	11.63	-2.628
93	12	11.78	0.2177
94	13	11.78	1.218
95	10	12.09	-2.091
96	13	11.94	1.063
97	12	11.78	0.2177
98	12	11.94	0.06322

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  11 &  12.09 & -1.091 \tabularnewline
2 &  9 &  11.78 & -2.782 \tabularnewline
3 &  12 &  11.78 &  0.2177 \tabularnewline
4 &  12 &  11.78 &  0.2177 \tabularnewline
5 &  12 &  12.25 & -0.2457 \tabularnewline
6 &  11 &  11.78 & -0.7823 \tabularnewline
7 &  12 &  12.09 & -0.09123 \tabularnewline
8 &  12 &  11.78 &  0.2177 \tabularnewline
9 &  15 &  11.78 &  3.218 \tabularnewline
10 &  13 &  11.63 &  1.372 \tabularnewline
11 &  12 &  11.78 &  0.2177 \tabularnewline
12 &  11 &  12.25 & -1.246 \tabularnewline
13 &  9 &  11.78 & -2.782 \tabularnewline
14 &  11 &  11.63 & -0.6279 \tabularnewline
15 &  12 &  12.25 & -0.2457 \tabularnewline
16 &  12 &  11.63 &  0.3721 \tabularnewline
17 &  12 &  11.94 &  0.06322 \tabularnewline
18 &  14 &  11.63 &  2.372 \tabularnewline
19 &  12 &  11.63 &  0.3721 \tabularnewline
20 &  9 &  12.09 & -3.091 \tabularnewline
21 &  13 &  12.25 &  0.7543 \tabularnewline
22 &  13 &  11.78 &  1.218 \tabularnewline
23 &  12 &  11.78 &  0.2177 \tabularnewline
24 &  12 &  11.63 &  0.3721 \tabularnewline
25 &  12 &  11.78 &  0.2177 \tabularnewline
26 &  12 &  12.09 & -0.09123 \tabularnewline
27 &  11 &  11.94 & -0.9368 \tabularnewline
28 &  13 &  12.25 &  0.7543 \tabularnewline
29 &  13 &  11.94 &  1.063 \tabularnewline
30 &  13 &  11.78 &  1.218 \tabularnewline
31 &  10 &  11.78 & -1.782 \tabularnewline
32 &  13 &  11.78 &  1.218 \tabularnewline
33 &  5 &  11.63 & -6.628 \tabularnewline
34 &  10 &  11.94 & -1.937 \tabularnewline
35 &  15 &  11.78 &  3.218 \tabularnewline
36 &  13 &  11.78 &  1.218 \tabularnewline
37 &  12 &  11.78 &  0.2177 \tabularnewline
38 &  13 &  11.78 &  1.218 \tabularnewline
39 &  13 &  11.78 &  1.218 \tabularnewline
40 &  11 &  11.78 & -0.7823 \tabularnewline
41 &  12 &  11.78 &  0.2177 \tabularnewline
42 &  12 &  11.78 &  0.2177 \tabularnewline
43 &  13 &  11.78 &  1.218 \tabularnewline
44 &  14 &  11.78 &  2.218 \tabularnewline
45 &  12 &  11.63 &  0.3721 \tabularnewline
46 &  12 &  11.78 &  0.2177 \tabularnewline
47 &  10 &  11.63 & -1.628 \tabularnewline
48 &  12 &  11.63 &  0.3721 \tabularnewline
49 &  12 &  12.09 & -0.09123 \tabularnewline
50 &  12 &  11.78 &  0.2177 \tabularnewline
51 &  13 &  11.94 &  1.063 \tabularnewline
52 &  14 &  12.09 &  1.909 \tabularnewline
53 &  10 &  11.63 & -1.628 \tabularnewline
54 &  12 &  11.94 &  0.06322 \tabularnewline
55 &  13 &  12.09 &  0.9088 \tabularnewline
56 &  11 &  11.78 & -0.7823 \tabularnewline
57 &  12 &  11.94 &  0.06322 \tabularnewline
58 &  12 &  11.78 &  0.2177 \tabularnewline
59 &  13 &  11.78 &  1.218 \tabularnewline
60 &  12 &  11.94 &  0.06322 \tabularnewline
61 &  9 &  11.78 & -2.782 \tabularnewline
62 &  12 &  11.94 &  0.06322 \tabularnewline
63 &  14 &  11.78 &  2.218 \tabularnewline
64 &  11 &  11.94 & -0.9368 \tabularnewline
65 &  12 &  11.78 &  0.2177 \tabularnewline
66 &  12 &  11.78 &  0.2177 \tabularnewline
67 &  9 &  11.78 & -2.782 \tabularnewline
68 &  13 &  12.09 &  0.9088 \tabularnewline
69 &  10 &  11.63 & -1.628 \tabularnewline
70 &  14 &  11.78 &  2.218 \tabularnewline
71 &  10 &  11.94 & -1.937 \tabularnewline
72 &  12 &  11.78 &  0.2177 \tabularnewline
73 &  11 &  11.78 & -0.7823 \tabularnewline
74 &  14 &  12.09 &  1.909 \tabularnewline
75 &  13 &  11.78 &  1.218 \tabularnewline
76 &  12 &  11.78 &  0.2177 \tabularnewline
77 &  10 &  11.94 & -1.937 \tabularnewline
78 &  12 &  11.63 &  0.3721 \tabularnewline
79 &  12 &  11.63 &  0.3721 \tabularnewline
80 &  15 &  11.94 &  3.063 \tabularnewline
81 &  12 &  12.09 & -0.09123 \tabularnewline
82 &  12 &  12.09 & -0.09123 \tabularnewline
83 &  10 &  11.78 & -1.782 \tabularnewline
84 &  12 &  11.78 &  0.2177 \tabularnewline
85 &  12 &  11.63 &  0.3721 \tabularnewline
86 &  12 &  11.94 &  0.06322 \tabularnewline
87 &  11 &  11.78 & -0.7823 \tabularnewline
88 &  13 &  11.78 &  1.218 \tabularnewline
89 &  13 &  11.78 &  1.218 \tabularnewline
90 &  11 &  11.78 & -0.7823 \tabularnewline
91 &  10 &  12.09 & -2.091 \tabularnewline
92 &  9 &  11.63 & -2.628 \tabularnewline
93 &  12 &  11.78 &  0.2177 \tabularnewline
94 &  13 &  11.78 &  1.218 \tabularnewline
95 &  10 &  12.09 & -2.091 \tabularnewline
96 &  13 &  11.94 &  1.063 \tabularnewline
97 &  12 &  11.78 &  0.2177 \tabularnewline
98 &  12 &  11.94 &  0.06322 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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] 11[/C][C] 12.09[/C][C]-1.091[/C][/ROW]
[ROW][C]2[/C][C] 9[/C][C] 11.78[/C][C]-2.782[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]4[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]5[/C][C] 12[/C][C] 12.25[/C][C]-0.2457[/C][/ROW]
[ROW][C]6[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 12.09[/C][C]-0.09123[/C][/ROW]
[ROW][C]8[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]9[/C][C] 15[/C][C] 11.78[/C][C] 3.218[/C][/ROW]
[ROW][C]10[/C][C] 13[/C][C] 11.63[/C][C] 1.372[/C][/ROW]
[ROW][C]11[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]12[/C][C] 11[/C][C] 12.25[/C][C]-1.246[/C][/ROW]
[ROW][C]13[/C][C] 9[/C][C] 11.78[/C][C]-2.782[/C][/ROW]
[ROW][C]14[/C][C] 11[/C][C] 11.63[/C][C]-0.6279[/C][/ROW]
[ROW][C]15[/C][C] 12[/C][C] 12.25[/C][C]-0.2457[/C][/ROW]
[ROW][C]16[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]17[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]18[/C][C] 14[/C][C] 11.63[/C][C] 2.372[/C][/ROW]
[ROW][C]19[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]20[/C][C] 9[/C][C] 12.09[/C][C]-3.091[/C][/ROW]
[ROW][C]21[/C][C] 13[/C][C] 12.25[/C][C] 0.7543[/C][/ROW]
[ROW][C]22[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]23[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]25[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 12.09[/C][C]-0.09123[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 11.94[/C][C]-0.9368[/C][/ROW]
[ROW][C]28[/C][C] 13[/C][C] 12.25[/C][C] 0.7543[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 11.94[/C][C] 1.063[/C][/ROW]
[ROW][C]30[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]31[/C][C] 10[/C][C] 11.78[/C][C]-1.782[/C][/ROW]
[ROW][C]32[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]33[/C][C] 5[/C][C] 11.63[/C][C]-6.628[/C][/ROW]
[ROW][C]34[/C][C] 10[/C][C] 11.94[/C][C]-1.937[/C][/ROW]
[ROW][C]35[/C][C] 15[/C][C] 11.78[/C][C] 3.218[/C][/ROW]
[ROW][C]36[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]37[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]38[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]39[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]40[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]42[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]43[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 11.78[/C][C] 2.218[/C][/ROW]
[ROW][C]45[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]47[/C][C] 10[/C][C] 11.63[/C][C]-1.628[/C][/ROW]
[ROW][C]48[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]49[/C][C] 12[/C][C] 12.09[/C][C]-0.09123[/C][/ROW]
[ROW][C]50[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]51[/C][C] 13[/C][C] 11.94[/C][C] 1.063[/C][/ROW]
[ROW][C]52[/C][C] 14[/C][C] 12.09[/C][C] 1.909[/C][/ROW]
[ROW][C]53[/C][C] 10[/C][C] 11.63[/C][C]-1.628[/C][/ROW]
[ROW][C]54[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]55[/C][C] 13[/C][C] 12.09[/C][C] 0.9088[/C][/ROW]
[ROW][C]56[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]58[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]59[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]61[/C][C] 9[/C][C] 11.78[/C][C]-2.782[/C][/ROW]
[ROW][C]62[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]63[/C][C] 14[/C][C] 11.78[/C][C] 2.218[/C][/ROW]
[ROW][C]64[/C][C] 11[/C][C] 11.94[/C][C]-0.9368[/C][/ROW]
[ROW][C]65[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]67[/C][C] 9[/C][C] 11.78[/C][C]-2.782[/C][/ROW]
[ROW][C]68[/C][C] 13[/C][C] 12.09[/C][C] 0.9088[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 11.63[/C][C]-1.628[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 11.78[/C][C] 2.218[/C][/ROW]
[ROW][C]71[/C][C] 10[/C][C] 11.94[/C][C]-1.937[/C][/ROW]
[ROW][C]72[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]73[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]74[/C][C] 14[/C][C] 12.09[/C][C] 1.909[/C][/ROW]
[ROW][C]75[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]76[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]77[/C][C] 10[/C][C] 11.94[/C][C]-1.937[/C][/ROW]
[ROW][C]78[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 11.94[/C][C] 3.063[/C][/ROW]
[ROW][C]81[/C][C] 12[/C][C] 12.09[/C][C]-0.09123[/C][/ROW]
[ROW][C]82[/C][C] 12[/C][C] 12.09[/C][C]-0.09123[/C][/ROW]
[ROW][C]83[/C][C] 10[/C][C] 11.78[/C][C]-1.782[/C][/ROW]
[ROW][C]84[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]85[/C][C] 12[/C][C] 11.63[/C][C] 0.3721[/C][/ROW]
[ROW][C]86[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[ROW][C]87[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]88[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]89[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 11.78[/C][C]-0.7823[/C][/ROW]
[ROW][C]91[/C][C] 10[/C][C] 12.09[/C][C]-2.091[/C][/ROW]
[ROW][C]92[/C][C] 9[/C][C] 11.63[/C][C]-2.628[/C][/ROW]
[ROW][C]93[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]94[/C][C] 13[/C][C] 11.78[/C][C] 1.218[/C][/ROW]
[ROW][C]95[/C][C] 10[/C][C] 12.09[/C][C]-2.091[/C][/ROW]
[ROW][C]96[/C][C] 13[/C][C] 11.94[/C][C] 1.063[/C][/ROW]
[ROW][C]97[/C][C] 12[/C][C] 11.78[/C][C] 0.2177[/C][/ROW]
[ROW][C]98[/C][C] 12[/C][C] 11.94[/C][C] 0.06322[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	11	12.09	-1.091
2	9	11.78	-2.782
3	12	11.78	0.2177
4	12	11.78	0.2177
5	12	12.25	-0.2457
6	11	11.78	-0.7823
7	12	12.09	-0.09123
8	12	11.78	0.2177
9	15	11.78	3.218
10	13	11.63	1.372
11	12	11.78	0.2177
12	11	12.25	-1.246
13	9	11.78	-2.782
14	11	11.63	-0.6279
15	12	12.25	-0.2457
16	12	11.63	0.3721
17	12	11.94	0.06322
18	14	11.63	2.372
19	12	11.63	0.3721
20	9	12.09	-3.091
21	13	12.25	0.7543
22	13	11.78	1.218
23	12	11.78	0.2177
24	12	11.63	0.3721
25	12	11.78	0.2177
26	12	12.09	-0.09123
27	11	11.94	-0.9368
28	13	12.25	0.7543
29	13	11.94	1.063
30	13	11.78	1.218
31	10	11.78	-1.782
32	13	11.78	1.218
33	5	11.63	-6.628
34	10	11.94	-1.937
35	15	11.78	3.218
36	13	11.78	1.218
37	12	11.78	0.2177
38	13	11.78	1.218
39	13	11.78	1.218
40	11	11.78	-0.7823
41	12	11.78	0.2177
42	12	11.78	0.2177
43	13	11.78	1.218
44	14	11.78	2.218
45	12	11.63	0.3721
46	12	11.78	0.2177
47	10	11.63	-1.628
48	12	11.63	0.3721
49	12	12.09	-0.09123
50	12	11.78	0.2177
51	13	11.94	1.063
52	14	12.09	1.909
53	10	11.63	-1.628
54	12	11.94	0.06322
55	13	12.09	0.9088
56	11	11.78	-0.7823
57	12	11.94	0.06322
58	12	11.78	0.2177
59	13	11.78	1.218
60	12	11.94	0.06322
61	9	11.78	-2.782
62	12	11.94	0.06322
63	14	11.78	2.218
64	11	11.94	-0.9368
65	12	11.78	0.2177
66	12	11.78	0.2177
67	9	11.78	-2.782
68	13	12.09	0.9088
69	10	11.63	-1.628
70	14	11.78	2.218
71	10	11.94	-1.937
72	12	11.78	0.2177
73	11	11.78	-0.7823
74	14	12.09	1.909
75	13	11.78	1.218
76	12	11.78	0.2177
77	10	11.94	-1.937
78	12	11.63	0.3721
79	12	11.63	0.3721
80	15	11.94	3.063
81	12	12.09	-0.09123
82	12	12.09	-0.09123
83	10	11.78	-1.782
84	12	11.78	0.2177
85	12	11.63	0.3721
86	12	11.94	0.06322
87	11	11.78	-0.7823
88	13	11.78	1.218
89	13	11.78	1.218
90	11	11.78	-0.7823
91	10	12.09	-2.091
92	9	11.63	-2.628
93	12	11.78	0.2177
94	13	11.78	1.218
95	10	12.09	-2.091
96	13	11.94	1.063
97	12	11.78	0.2177
98	12	11.94	0.06322

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.5589	0.8823	0.4411
6	0.3889	0.7777	0.6111
7	0.2645	0.529	0.7355
8	0.2035	0.4069	0.7965
9	0.6968	0.6063	0.3032
10	0.638	0.7241	0.362
11	0.5367	0.9266	0.4633
12	0.4512	0.9024	0.5488
13	0.6686	0.6629	0.3314
14	0.602	0.796	0.398
15	0.5252	0.9495	0.4748
16	0.4446	0.8893	0.5554
17	0.368	0.736	0.632
18	0.4582	0.9163	0.5418
19	0.3827	0.7654	0.6173
20	0.54	0.9199	0.46
21	0.5454	0.9092	0.4546
22	0.5168	0.9665	0.4832
23	0.446	0.8921	0.554
24	0.3798	0.7596	0.6202
25	0.3158	0.6317	0.6842
26	0.2617	0.5235	0.7383
27	0.2255	0.451	0.7745
28	0.2149	0.4298	0.7851
29	0.1951	0.3903	0.8049
30	0.1768	0.3537	0.8232
31	0.2048	0.4096	0.7952
32	0.1873	0.3746	0.8127
33	0.97	0.05998	0.02999
34	0.9747	0.05059	0.0253
35	0.9933	0.01349	0.006745
36	0.9922	0.01565	0.007823
37	0.9884	0.02315	0.01157
38	0.9867	0.02662	0.01331
39	0.9847	0.03054	0.01527
40	0.9799	0.04011	0.02006
41	0.9718	0.05643	0.02821
42	0.9611	0.07788	0.03894
43	0.9565	0.0871	0.04355
44	0.9701	0.05987	0.02993
45	0.9601	0.07971	0.03986
46	0.9463	0.1075	0.05373
47	0.9473	0.1055	0.05274
48	0.9317	0.1365	0.06826
49	0.911	0.1779	0.08896
50	0.8858	0.2284	0.1142
51	0.8698	0.2604	0.1302
52	0.8836	0.2328	0.1164
53	0.8852	0.2296	0.1148
54	0.8537	0.2926	0.1463
55	0.8302	0.3396	0.1698
56	0.8003	0.3995	0.1997
57	0.7558	0.4884	0.2442
58	0.7076	0.5848	0.2924
59	0.6909	0.6181	0.3091
60	0.6359	0.7282	0.3641
61	0.7561	0.4878	0.2439
62	0.7054	0.5892	0.2946
63	0.7674	0.4652	0.2326
64	0.7356	0.5288	0.2644
65	0.6835	0.633	0.3165
66	0.6272	0.7455	0.3728
67	0.7555	0.4891	0.2445
68	0.7229	0.5542	0.2771
69	0.7379	0.5242	0.2621
70	0.7978	0.4043	0.2022
71	0.8268	0.3464	0.1732
72	0.78	0.44	0.22
73	0.7419	0.5161	0.2581
74	0.7943	0.4114	0.2057
75	0.7778	0.4445	0.2222
76	0.721	0.558	0.279
77	0.7509	0.4982	0.2491
78	0.6888	0.6223	0.3112
79	0.6203	0.7594	0.3797
80	0.8662	0.2675	0.1338
81	0.8213	0.3574	0.1787
82	0.7705	0.4589	0.2295
83	0.7961	0.4078	0.2039
84	0.7277	0.5447	0.2723
85	0.6451	0.7099	0.3549
86	0.5613	0.8774	0.4387
87	0.4833	0.9666	0.5167
88	0.4546	0.9092	0.5454
89	0.4459	0.8917	0.5541
90	0.3411	0.6822	0.6589
91	0.3352	0.6704	0.6648
92	0.8442	0.3116	0.1558
93	0.7555	0.4889	0.2445

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.5589 &  0.8823 &  0.4411 \tabularnewline
6 &  0.3889 &  0.7777 &  0.6111 \tabularnewline
7 &  0.2645 &  0.529 &  0.7355 \tabularnewline
8 &  0.2035 &  0.4069 &  0.7965 \tabularnewline
9 &  0.6968 &  0.6063 &  0.3032 \tabularnewline
10 &  0.638 &  0.7241 &  0.362 \tabularnewline
11 &  0.5367 &  0.9266 &  0.4633 \tabularnewline
12 &  0.4512 &  0.9024 &  0.5488 \tabularnewline
13 &  0.6686 &  0.6629 &  0.3314 \tabularnewline
14 &  0.602 &  0.796 &  0.398 \tabularnewline
15 &  0.5252 &  0.9495 &  0.4748 \tabularnewline
16 &  0.4446 &  0.8893 &  0.5554 \tabularnewline
17 &  0.368 &  0.736 &  0.632 \tabularnewline
18 &  0.4582 &  0.9163 &  0.5418 \tabularnewline
19 &  0.3827 &  0.7654 &  0.6173 \tabularnewline
20 &  0.54 &  0.9199 &  0.46 \tabularnewline
21 &  0.5454 &  0.9092 &  0.4546 \tabularnewline
22 &  0.5168 &  0.9665 &  0.4832 \tabularnewline
23 &  0.446 &  0.8921 &  0.554 \tabularnewline
24 &  0.3798 &  0.7596 &  0.6202 \tabularnewline
25 &  0.3158 &  0.6317 &  0.6842 \tabularnewline
26 &  0.2617 &  0.5235 &  0.7383 \tabularnewline
27 &  0.2255 &  0.451 &  0.7745 \tabularnewline
28 &  0.2149 &  0.4298 &  0.7851 \tabularnewline
29 &  0.1951 &  0.3903 &  0.8049 \tabularnewline
30 &  0.1768 &  0.3537 &  0.8232 \tabularnewline
31 &  0.2048 &  0.4096 &  0.7952 \tabularnewline
32 &  0.1873 &  0.3746 &  0.8127 \tabularnewline
33 &  0.97 &  0.05998 &  0.02999 \tabularnewline
34 &  0.9747 &  0.05059 &  0.0253 \tabularnewline
35 &  0.9933 &  0.01349 &  0.006745 \tabularnewline
36 &  0.9922 &  0.01565 &  0.007823 \tabularnewline
37 &  0.9884 &  0.02315 &  0.01157 \tabularnewline
38 &  0.9867 &  0.02662 &  0.01331 \tabularnewline
39 &  0.9847 &  0.03054 &  0.01527 \tabularnewline
40 &  0.9799 &  0.04011 &  0.02006 \tabularnewline
41 &  0.9718 &  0.05643 &  0.02821 \tabularnewline
42 &  0.9611 &  0.07788 &  0.03894 \tabularnewline
43 &  0.9565 &  0.0871 &  0.04355 \tabularnewline
44 &  0.9701 &  0.05987 &  0.02993 \tabularnewline
45 &  0.9601 &  0.07971 &  0.03986 \tabularnewline
46 &  0.9463 &  0.1075 &  0.05373 \tabularnewline
47 &  0.9473 &  0.1055 &  0.05274 \tabularnewline
48 &  0.9317 &  0.1365 &  0.06826 \tabularnewline
49 &  0.911 &  0.1779 &  0.08896 \tabularnewline
50 &  0.8858 &  0.2284 &  0.1142 \tabularnewline
51 &  0.8698 &  0.2604 &  0.1302 \tabularnewline
52 &  0.8836 &  0.2328 &  0.1164 \tabularnewline
53 &  0.8852 &  0.2296 &  0.1148 \tabularnewline
54 &  0.8537 &  0.2926 &  0.1463 \tabularnewline
55 &  0.8302 &  0.3396 &  0.1698 \tabularnewline
56 &  0.8003 &  0.3995 &  0.1997 \tabularnewline
57 &  0.7558 &  0.4884 &  0.2442 \tabularnewline
58 &  0.7076 &  0.5848 &  0.2924 \tabularnewline
59 &  0.6909 &  0.6181 &  0.3091 \tabularnewline
60 &  0.6359 &  0.7282 &  0.3641 \tabularnewline
61 &  0.7561 &  0.4878 &  0.2439 \tabularnewline
62 &  0.7054 &  0.5892 &  0.2946 \tabularnewline
63 &  0.7674 &  0.4652 &  0.2326 \tabularnewline
64 &  0.7356 &  0.5288 &  0.2644 \tabularnewline
65 &  0.6835 &  0.633 &  0.3165 \tabularnewline
66 &  0.6272 &  0.7455 &  0.3728 \tabularnewline
67 &  0.7555 &  0.4891 &  0.2445 \tabularnewline
68 &  0.7229 &  0.5542 &  0.2771 \tabularnewline
69 &  0.7379 &  0.5242 &  0.2621 \tabularnewline
70 &  0.7978 &  0.4043 &  0.2022 \tabularnewline
71 &  0.8268 &  0.3464 &  0.1732 \tabularnewline
72 &  0.78 &  0.44 &  0.22 \tabularnewline
73 &  0.7419 &  0.5161 &  0.2581 \tabularnewline
74 &  0.7943 &  0.4114 &  0.2057 \tabularnewline
75 &  0.7778 &  0.4445 &  0.2222 \tabularnewline
76 &  0.721 &  0.558 &  0.279 \tabularnewline
77 &  0.7509 &  0.4982 &  0.2491 \tabularnewline
78 &  0.6888 &  0.6223 &  0.3112 \tabularnewline
79 &  0.6203 &  0.7594 &  0.3797 \tabularnewline
80 &  0.8662 &  0.2675 &  0.1338 \tabularnewline
81 &  0.8213 &  0.3574 &  0.1787 \tabularnewline
82 &  0.7705 &  0.4589 &  0.2295 \tabularnewline
83 &  0.7961 &  0.4078 &  0.2039 \tabularnewline
84 &  0.7277 &  0.5447 &  0.2723 \tabularnewline
85 &  0.6451 &  0.7099 &  0.3549 \tabularnewline
86 &  0.5613 &  0.8774 &  0.4387 \tabularnewline
87 &  0.4833 &  0.9666 &  0.5167 \tabularnewline
88 &  0.4546 &  0.9092 &  0.5454 \tabularnewline
89 &  0.4459 &  0.8917 &  0.5541 \tabularnewline
90 &  0.3411 &  0.6822 &  0.6589 \tabularnewline
91 &  0.3352 &  0.6704 &  0.6648 \tabularnewline
92 &  0.8442 &  0.3116 &  0.1558 \tabularnewline
93 &  0.7555 &  0.4889 &  0.2445 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]5[/C][C] 0.5589[/C][C] 0.8823[/C][C] 0.4411[/C][/ROW]
[ROW][C]6[/C][C] 0.3889[/C][C] 0.7777[/C][C] 0.6111[/C][/ROW]
[ROW][C]7[/C][C] 0.2645[/C][C] 0.529[/C][C] 0.7355[/C][/ROW]
[ROW][C]8[/C][C] 0.2035[/C][C] 0.4069[/C][C] 0.7965[/C][/ROW]
[ROW][C]9[/C][C] 0.6968[/C][C] 0.6063[/C][C] 0.3032[/C][/ROW]
[ROW][C]10[/C][C] 0.638[/C][C] 0.7241[/C][C] 0.362[/C][/ROW]
[ROW][C]11[/C][C] 0.5367[/C][C] 0.9266[/C][C] 0.4633[/C][/ROW]
[ROW][C]12[/C][C] 0.4512[/C][C] 0.9024[/C][C] 0.5488[/C][/ROW]
[ROW][C]13[/C][C] 0.6686[/C][C] 0.6629[/C][C] 0.3314[/C][/ROW]
[ROW][C]14[/C][C] 0.602[/C][C] 0.796[/C][C] 0.398[/C][/ROW]
[ROW][C]15[/C][C] 0.5252[/C][C] 0.9495[/C][C] 0.4748[/C][/ROW]
[ROW][C]16[/C][C] 0.4446[/C][C] 0.8893[/C][C] 0.5554[/C][/ROW]
[ROW][C]17[/C][C] 0.368[/C][C] 0.736[/C][C] 0.632[/C][/ROW]
[ROW][C]18[/C][C] 0.4582[/C][C] 0.9163[/C][C] 0.5418[/C][/ROW]
[ROW][C]19[/C][C] 0.3827[/C][C] 0.7654[/C][C] 0.6173[/C][/ROW]
[ROW][C]20[/C][C] 0.54[/C][C] 0.9199[/C][C] 0.46[/C][/ROW]
[ROW][C]21[/C][C] 0.5454[/C][C] 0.9092[/C][C] 0.4546[/C][/ROW]
[ROW][C]22[/C][C] 0.5168[/C][C] 0.9665[/C][C] 0.4832[/C][/ROW]
[ROW][C]23[/C][C] 0.446[/C][C] 0.8921[/C][C] 0.554[/C][/ROW]
[ROW][C]24[/C][C] 0.3798[/C][C] 0.7596[/C][C] 0.6202[/C][/ROW]
[ROW][C]25[/C][C] 0.3158[/C][C] 0.6317[/C][C] 0.6842[/C][/ROW]
[ROW][C]26[/C][C] 0.2617[/C][C] 0.5235[/C][C] 0.7383[/C][/ROW]
[ROW][C]27[/C][C] 0.2255[/C][C] 0.451[/C][C] 0.7745[/C][/ROW]
[ROW][C]28[/C][C] 0.2149[/C][C] 0.4298[/C][C] 0.7851[/C][/ROW]
[ROW][C]29[/C][C] 0.1951[/C][C] 0.3903[/C][C] 0.8049[/C][/ROW]
[ROW][C]30[/C][C] 0.1768[/C][C] 0.3537[/C][C] 0.8232[/C][/ROW]
[ROW][C]31[/C][C] 0.2048[/C][C] 0.4096[/C][C] 0.7952[/C][/ROW]
[ROW][C]32[/C][C] 0.1873[/C][C] 0.3746[/C][C] 0.8127[/C][/ROW]
[ROW][C]33[/C][C] 0.97[/C][C] 0.05998[/C][C] 0.02999[/C][/ROW]
[ROW][C]34[/C][C] 0.9747[/C][C] 0.05059[/C][C] 0.0253[/C][/ROW]
[ROW][C]35[/C][C] 0.9933[/C][C] 0.01349[/C][C] 0.006745[/C][/ROW]
[ROW][C]36[/C][C] 0.9922[/C][C] 0.01565[/C][C] 0.007823[/C][/ROW]
[ROW][C]37[/C][C] 0.9884[/C][C] 0.02315[/C][C] 0.01157[/C][/ROW]
[ROW][C]38[/C][C] 0.9867[/C][C] 0.02662[/C][C] 0.01331[/C][/ROW]
[ROW][C]39[/C][C] 0.9847[/C][C] 0.03054[/C][C] 0.01527[/C][/ROW]
[ROW][C]40[/C][C] 0.9799[/C][C] 0.04011[/C][C] 0.02006[/C][/ROW]
[ROW][C]41[/C][C] 0.9718[/C][C] 0.05643[/C][C] 0.02821[/C][/ROW]
[ROW][C]42[/C][C] 0.9611[/C][C] 0.07788[/C][C] 0.03894[/C][/ROW]
[ROW][C]43[/C][C] 0.9565[/C][C] 0.0871[/C][C] 0.04355[/C][/ROW]
[ROW][C]44[/C][C] 0.9701[/C][C] 0.05987[/C][C] 0.02993[/C][/ROW]
[ROW][C]45[/C][C] 0.9601[/C][C] 0.07971[/C][C] 0.03986[/C][/ROW]
[ROW][C]46[/C][C] 0.9463[/C][C] 0.1075[/C][C] 0.05373[/C][/ROW]
[ROW][C]47[/C][C] 0.9473[/C][C] 0.1055[/C][C] 0.05274[/C][/ROW]
[ROW][C]48[/C][C] 0.9317[/C][C] 0.1365[/C][C] 0.06826[/C][/ROW]
[ROW][C]49[/C][C] 0.911[/C][C] 0.1779[/C][C] 0.08896[/C][/ROW]
[ROW][C]50[/C][C] 0.8858[/C][C] 0.2284[/C][C] 0.1142[/C][/ROW]
[ROW][C]51[/C][C] 0.8698[/C][C] 0.2604[/C][C] 0.1302[/C][/ROW]
[ROW][C]52[/C][C] 0.8836[/C][C] 0.2328[/C][C] 0.1164[/C][/ROW]
[ROW][C]53[/C][C] 0.8852[/C][C] 0.2296[/C][C] 0.1148[/C][/ROW]
[ROW][C]54[/C][C] 0.8537[/C][C] 0.2926[/C][C] 0.1463[/C][/ROW]
[ROW][C]55[/C][C] 0.8302[/C][C] 0.3396[/C][C] 0.1698[/C][/ROW]
[ROW][C]56[/C][C] 0.8003[/C][C] 0.3995[/C][C] 0.1997[/C][/ROW]
[ROW][C]57[/C][C] 0.7558[/C][C] 0.4884[/C][C] 0.2442[/C][/ROW]
[ROW][C]58[/C][C] 0.7076[/C][C] 0.5848[/C][C] 0.2924[/C][/ROW]
[ROW][C]59[/C][C] 0.6909[/C][C] 0.6181[/C][C] 0.3091[/C][/ROW]
[ROW][C]60[/C][C] 0.6359[/C][C] 0.7282[/C][C] 0.3641[/C][/ROW]
[ROW][C]61[/C][C] 0.7561[/C][C] 0.4878[/C][C] 0.2439[/C][/ROW]
[ROW][C]62[/C][C] 0.7054[/C][C] 0.5892[/C][C] 0.2946[/C][/ROW]
[ROW][C]63[/C][C] 0.7674[/C][C] 0.4652[/C][C] 0.2326[/C][/ROW]
[ROW][C]64[/C][C] 0.7356[/C][C] 0.5288[/C][C] 0.2644[/C][/ROW]
[ROW][C]65[/C][C] 0.6835[/C][C] 0.633[/C][C] 0.3165[/C][/ROW]
[ROW][C]66[/C][C] 0.6272[/C][C] 0.7455[/C][C] 0.3728[/C][/ROW]
[ROW][C]67[/C][C] 0.7555[/C][C] 0.4891[/C][C] 0.2445[/C][/ROW]
[ROW][C]68[/C][C] 0.7229[/C][C] 0.5542[/C][C] 0.2771[/C][/ROW]
[ROW][C]69[/C][C] 0.7379[/C][C] 0.5242[/C][C] 0.2621[/C][/ROW]
[ROW][C]70[/C][C] 0.7978[/C][C] 0.4043[/C][C] 0.2022[/C][/ROW]
[ROW][C]71[/C][C] 0.8268[/C][C] 0.3464[/C][C] 0.1732[/C][/ROW]
[ROW][C]72[/C][C] 0.78[/C][C] 0.44[/C][C] 0.22[/C][/ROW]
[ROW][C]73[/C][C] 0.7419[/C][C] 0.5161[/C][C] 0.2581[/C][/ROW]
[ROW][C]74[/C][C] 0.7943[/C][C] 0.4114[/C][C] 0.2057[/C][/ROW]
[ROW][C]75[/C][C] 0.7778[/C][C] 0.4445[/C][C] 0.2222[/C][/ROW]
[ROW][C]76[/C][C] 0.721[/C][C] 0.558[/C][C] 0.279[/C][/ROW]
[ROW][C]77[/C][C] 0.7509[/C][C] 0.4982[/C][C] 0.2491[/C][/ROW]
[ROW][C]78[/C][C] 0.6888[/C][C] 0.6223[/C][C] 0.3112[/C][/ROW]
[ROW][C]79[/C][C] 0.6203[/C][C] 0.7594[/C][C] 0.3797[/C][/ROW]
[ROW][C]80[/C][C] 0.8662[/C][C] 0.2675[/C][C] 0.1338[/C][/ROW]
[ROW][C]81[/C][C] 0.8213[/C][C] 0.3574[/C][C] 0.1787[/C][/ROW]
[ROW][C]82[/C][C] 0.7705[/C][C] 0.4589[/C][C] 0.2295[/C][/ROW]
[ROW][C]83[/C][C] 0.7961[/C][C] 0.4078[/C][C] 0.2039[/C][/ROW]
[ROW][C]84[/C][C] 0.7277[/C][C] 0.5447[/C][C] 0.2723[/C][/ROW]
[ROW][C]85[/C][C] 0.6451[/C][C] 0.7099[/C][C] 0.3549[/C][/ROW]
[ROW][C]86[/C][C] 0.5613[/C][C] 0.8774[/C][C] 0.4387[/C][/ROW]
[ROW][C]87[/C][C] 0.4833[/C][C] 0.9666[/C][C] 0.5167[/C][/ROW]
[ROW][C]88[/C][C] 0.4546[/C][C] 0.9092[/C][C] 0.5454[/C][/ROW]
[ROW][C]89[/C][C] 0.4459[/C][C] 0.8917[/C][C] 0.5541[/C][/ROW]
[ROW][C]90[/C][C] 0.3411[/C][C] 0.6822[/C][C] 0.6589[/C][/ROW]
[ROW][C]91[/C][C] 0.3352[/C][C] 0.6704[/C][C] 0.6648[/C][/ROW]
[ROW][C]92[/C][C] 0.8442[/C][C] 0.3116[/C][C] 0.1558[/C][/ROW]
[ROW][C]93[/C][C] 0.7555[/C][C] 0.4889[/C][C] 0.2445[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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
5	0.5589	0.8823	0.4411
6	0.3889	0.7777	0.6111
7	0.2645	0.529	0.7355
8	0.2035	0.4069	0.7965
9	0.6968	0.6063	0.3032
10	0.638	0.7241	0.362
11	0.5367	0.9266	0.4633
12	0.4512	0.9024	0.5488
13	0.6686	0.6629	0.3314
14	0.602	0.796	0.398
15	0.5252	0.9495	0.4748
16	0.4446	0.8893	0.5554
17	0.368	0.736	0.632
18	0.4582	0.9163	0.5418
19	0.3827	0.7654	0.6173
20	0.54	0.9199	0.46
21	0.5454	0.9092	0.4546
22	0.5168	0.9665	0.4832
23	0.446	0.8921	0.554
24	0.3798	0.7596	0.6202
25	0.3158	0.6317	0.6842
26	0.2617	0.5235	0.7383
27	0.2255	0.451	0.7745
28	0.2149	0.4298	0.7851
29	0.1951	0.3903	0.8049
30	0.1768	0.3537	0.8232
31	0.2048	0.4096	0.7952
32	0.1873	0.3746	0.8127
33	0.97	0.05998	0.02999
34	0.9747	0.05059	0.0253
35	0.9933	0.01349	0.006745
36	0.9922	0.01565	0.007823
37	0.9884	0.02315	0.01157
38	0.9867	0.02662	0.01331
39	0.9847	0.03054	0.01527
40	0.9799	0.04011	0.02006
41	0.9718	0.05643	0.02821
42	0.9611	0.07788	0.03894
43	0.9565	0.0871	0.04355
44	0.9701	0.05987	0.02993
45	0.9601	0.07971	0.03986
46	0.9463	0.1075	0.05373
47	0.9473	0.1055	0.05274
48	0.9317	0.1365	0.06826
49	0.911	0.1779	0.08896
50	0.8858	0.2284	0.1142
51	0.8698	0.2604	0.1302
52	0.8836	0.2328	0.1164
53	0.8852	0.2296	0.1148
54	0.8537	0.2926	0.1463
55	0.8302	0.3396	0.1698
56	0.8003	0.3995	0.1997
57	0.7558	0.4884	0.2442
58	0.7076	0.5848	0.2924
59	0.6909	0.6181	0.3091
60	0.6359	0.7282	0.3641
61	0.7561	0.4878	0.2439
62	0.7054	0.5892	0.2946
63	0.7674	0.4652	0.2326
64	0.7356	0.5288	0.2644
65	0.6835	0.633	0.3165
66	0.6272	0.7455	0.3728
67	0.7555	0.4891	0.2445
68	0.7229	0.5542	0.2771
69	0.7379	0.5242	0.2621
70	0.7978	0.4043	0.2022
71	0.8268	0.3464	0.1732
72	0.78	0.44	0.22
73	0.7419	0.5161	0.2581
74	0.7943	0.4114	0.2057
75	0.7778	0.4445	0.2222
76	0.721	0.558	0.279
77	0.7509	0.4982	0.2491
78	0.6888	0.6223	0.3112
79	0.6203	0.7594	0.3797
80	0.8662	0.2675	0.1338
81	0.8213	0.3574	0.1787
82	0.7705	0.4589	0.2295
83	0.7961	0.4078	0.2039
84	0.7277	0.5447	0.2723
85	0.6451	0.7099	0.3549
86	0.5613	0.8774	0.4387
87	0.4833	0.9666	0.5167
88	0.4546	0.9092	0.5454
89	0.4459	0.8917	0.5541
90	0.3411	0.6822	0.6589
91	0.3352	0.6704	0.6648
92	0.8442	0.3116	0.1558
93	0.7555	0.4889	0.2445

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	6	0.0674157	NOK
10% type I error level	13	0.146067	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 6 & 0.0674157 & NOK \tabularnewline
10% type I error level & 13 & 0.146067 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&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]6[/C][C]0.0674157[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]13[/C][C]0.146067[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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	6	0.0674157	NOK
10% type I error level	13	0.146067	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&T=7

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.7487, df1 = 2, df2 = 94, p-value = 0.1796

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 = 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):

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
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'
}
print(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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$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,'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')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code