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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 29 Nov 2015 13:04:18 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Nov/29/t14488022684jg7izxof38c9ro.htm/, Retrieved Wed, 15 May 2024 00:52:41 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284426, Retrieved Wed, 15 May 2024 00:52:41 +0000

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Estimated Impact

120

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

-       [Multiple Regression] [multiple regression] [2015-11-29 13:04:18] [60e7016130b28a0c8bd4011f80276a66] [Current]

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Dataseries X:

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6.5 2.3
6.8 1.9
6.8 0.6
6.5 0.6
6.2 -0.4
6.2 -1.1
6.6 -1.7
6.7 -0.8
6.5 -1.2
6.4 -1
6.5 -0.1
6.8 0.3
7.1 0.6
7.2 0.7
7.1 1.7
7 1.8
6.9 2.3
6.9 2.5
7.4 2.6
7.3 2.3
7 2.9
6.8 3
6.5 2.9
6.4 3.1
6.3 3.2
6 3.4
5.9 3.5
5.7 3.4
5.7 3.4
5.7 3.7
6.2 3.8
6.4 3.6
6.2 3.6
6.2 3.6
6.1 3.9
6.1 3.5
6.2 3.7
6.1 3.7
6.1 3.4
6.2 3.2
6.2 2.8
6.2 2.3
6.4 2.3
6.4 2.9
6.4 2.8
6.7 2.8
6.9 2.3
7.1 2.2
7.3 1.5
7.2 1.2
7.1 1.1
6.9 1
6.8 1.2
6.7 1.6
7.2 1.5
7.2 1
7.1 0.9
7.1 0.6
7 0.8
7.1 1
7.3 1.1
7.2 1
7.1 0.9
7 0.6
6.9 0.4
7 0.3
7.5 0.3
7.6 0
7.5 -0.1
7.3 0.1
7.3 -0.1
7.4 -0.4
7.7 -0.7
7.8 -0.4
7.7 -0.4
7.5 0.3
7.3 0.6
7.3 0.6
7.6 0.5
7.6 0.9

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	5 seconds
R Server	'George Udny Yule' @ yule.wessa.net

Multiple Linear Regression - Estimated Regression Equation
werkloosheid[t] = + 7.12264 -0.213735inflatie[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
werkloosheid[t] =  +  7.12264 -0.213735inflatie[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]werkloosheid[t] =  +  7.12264 -0.213735inflatie[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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
werkloosheid[t] = + 7.12264 -0.213735inflatie[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+7.123	0.06882	+1.0350e+02	2.871e-85	1.435e-85
inflatie	-0.2137	0.03248	-6.5800e+00	4.9e-09	2.45e-09

\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) & +7.123 &  0.06882 & +1.0350e+02 &  2.871e-85 &  1.435e-85 \tabularnewline
inflatie & -0.2137 &  0.03248 & -6.5800e+00 &  4.9e-09 &  2.45e-09 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&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]+7.123[/C][C] 0.06882[/C][C]+1.0350e+02[/C][C] 2.871e-85[/C][C] 1.435e-85[/C][/ROW]
[ROW][C]inflatie[/C][C]-0.2137[/C][C] 0.03248[/C][C]-6.5800e+00[/C][C] 4.9e-09[/C][C] 2.45e-09[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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)	+7.123	0.06882	+1.0350e+02	2.871e-85	1.435e-85
inflatie	-0.2137	0.03248	-6.5800e+00	4.9e-09	2.45e-09

Multiple Linear Regression - Regression Statistics
Multiple R	0.5975
R-squared	0.357
Adjusted R-squared	0.3487
F-TEST (value)	43.3
F-TEST (DF numerator)	1
F-TEST (DF denominator)	78
p-value	4.9e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4284
Sum Squared Residuals	14.31

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5975 \tabularnewline
R-squared &  0.357 \tabularnewline
Adjusted R-squared &  0.3487 \tabularnewline
F-TEST (value) &  43.3 \tabularnewline
F-TEST (DF numerator) & 1 \tabularnewline
F-TEST (DF denominator) & 78 \tabularnewline
p-value &  4.9e-09 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  0.4284 \tabularnewline
Sum Squared Residuals &  14.31 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5975[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.357[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.3487[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 43.3[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]1[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]78[/C][/ROW]
[ROW][C]p-value[/C][C] 4.9e-09[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 0.4284[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 14.31[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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	0.5975
R-squared	0.357
Adjusted R-squared	0.3487
F-TEST (value)	43.3
F-TEST (DF numerator)	1
F-TEST (DF denominator)	78
p-value	4.9e-09
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	0.4284
Sum Squared Residuals	14.31

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6.5	6.631	-0.1311
2	6.8	6.717	0.08345
3	6.8	6.994	-0.1944
4	6.5	6.994	-0.4944
5	6.2	7.208	-1.008
6	6.2	7.358	-1.158
7	6.6	7.486	-0.886
8	6.7	7.294	-0.5936
9	6.5	7.379	-0.8791
10	6.4	7.336	-0.9364
11	6.5	7.144	-0.644
12	6.8	7.059	-0.2585
13	7.1	6.994	0.1056
14	7.2	6.973	0.227
15	7.1	6.759	0.3407
16	7	6.738	0.2621
17	6.9	6.631	0.2689
18	6.9	6.588	0.3117
19	7.4	6.567	0.8331
20	7.3	6.631	0.6689
21	7	6.503	0.4972
22	6.8	6.481	0.3186
23	6.5	6.503	-0.002813
24	6.4	6.46	-0.06007
25	6.3	6.439	-0.1387
26	6	6.396	-0.3959
27	5.9	6.375	-0.4746
28	5.7	6.396	-0.6959
29	5.7	6.396	-0.6959
30	5.7	6.332	-0.6318
31	6.2	6.31	-0.1105
32	6.4	6.353	0.0468
33	6.2	6.353	-0.1532
34	6.2	6.353	-0.1532
35	6.1	6.289	-0.1891
36	6.1	6.375	-0.2746
37	6.2	6.332	-0.1318
38	6.1	6.332	-0.2318
39	6.1	6.396	-0.2959
40	6.2	6.439	-0.2387
41	6.2	6.524	-0.3242
42	6.2	6.631	-0.4311
43	6.4	6.631	-0.2311
44	6.4	6.503	-0.1028
45	6.4	6.524	-0.1242
46	6.7	6.524	0.1758
47	6.9	6.631	0.2689
48	7.1	6.652	0.4476
49	7.3	6.802	0.498
50	7.2	6.866	0.3338
51	7.1	6.888	0.2125
52	6.9	6.909	-0.008909
53	6.8	6.866	-0.06616
54	6.7	6.781	-0.08067
55	7.2	6.802	0.398
56	7.2	6.909	0.2911
57	7.1	6.93	0.1697
58	7.1	6.994	0.1056
59	7	6.952	0.04834
60	7.1	6.909	0.1911
61	7.3	6.888	0.4125
62	7.2	6.909	0.2911
63	7.1	6.93	0.1697
64	7	6.994	0.005597
65	6.9	7.037	-0.1371
66	7	7.059	-0.05852
67	7.5	7.059	0.4415
68	7.6	7.123	0.4774
69	7.5	7.144	0.356
70	7.3	7.101	0.1987
71	7.3	7.144	0.156
72	7.4	7.208	0.1919
73	7.7	7.272	0.4277
74	7.8	7.208	0.5919
75	7.7	7.208	0.4919
76	7.5	7.059	0.4415
77	7.3	6.994	0.3056
78	7.3	6.994	0.3056
79	7.6	7.016	0.5842
80	7.6	6.93	0.6697

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6.5 &  6.631 & -0.1311 \tabularnewline
2 &  6.8 &  6.717 &  0.08345 \tabularnewline
3 &  6.8 &  6.994 & -0.1944 \tabularnewline
4 &  6.5 &  6.994 & -0.4944 \tabularnewline
5 &  6.2 &  7.208 & -1.008 \tabularnewline
6 &  6.2 &  7.358 & -1.158 \tabularnewline
7 &  6.6 &  7.486 & -0.886 \tabularnewline
8 &  6.7 &  7.294 & -0.5936 \tabularnewline
9 &  6.5 &  7.379 & -0.8791 \tabularnewline
10 &  6.4 &  7.336 & -0.9364 \tabularnewline
11 &  6.5 &  7.144 & -0.644 \tabularnewline
12 &  6.8 &  7.059 & -0.2585 \tabularnewline
13 &  7.1 &  6.994 &  0.1056 \tabularnewline
14 &  7.2 &  6.973 &  0.227 \tabularnewline
15 &  7.1 &  6.759 &  0.3407 \tabularnewline
16 &  7 &  6.738 &  0.2621 \tabularnewline
17 &  6.9 &  6.631 &  0.2689 \tabularnewline
18 &  6.9 &  6.588 &  0.3117 \tabularnewline
19 &  7.4 &  6.567 &  0.8331 \tabularnewline
20 &  7.3 &  6.631 &  0.6689 \tabularnewline
21 &  7 &  6.503 &  0.4972 \tabularnewline
22 &  6.8 &  6.481 &  0.3186 \tabularnewline
23 &  6.5 &  6.503 & -0.002813 \tabularnewline
24 &  6.4 &  6.46 & -0.06007 \tabularnewline
25 &  6.3 &  6.439 & -0.1387 \tabularnewline
26 &  6 &  6.396 & -0.3959 \tabularnewline
27 &  5.9 &  6.375 & -0.4746 \tabularnewline
28 &  5.7 &  6.396 & -0.6959 \tabularnewline
29 &  5.7 &  6.396 & -0.6959 \tabularnewline
30 &  5.7 &  6.332 & -0.6318 \tabularnewline
31 &  6.2 &  6.31 & -0.1105 \tabularnewline
32 &  6.4 &  6.353 &  0.0468 \tabularnewline
33 &  6.2 &  6.353 & -0.1532 \tabularnewline
34 &  6.2 &  6.353 & -0.1532 \tabularnewline
35 &  6.1 &  6.289 & -0.1891 \tabularnewline
36 &  6.1 &  6.375 & -0.2746 \tabularnewline
37 &  6.2 &  6.332 & -0.1318 \tabularnewline
38 &  6.1 &  6.332 & -0.2318 \tabularnewline
39 &  6.1 &  6.396 & -0.2959 \tabularnewline
40 &  6.2 &  6.439 & -0.2387 \tabularnewline
41 &  6.2 &  6.524 & -0.3242 \tabularnewline
42 &  6.2 &  6.631 & -0.4311 \tabularnewline
43 &  6.4 &  6.631 & -0.2311 \tabularnewline
44 &  6.4 &  6.503 & -0.1028 \tabularnewline
45 &  6.4 &  6.524 & -0.1242 \tabularnewline
46 &  6.7 &  6.524 &  0.1758 \tabularnewline
47 &  6.9 &  6.631 &  0.2689 \tabularnewline
48 &  7.1 &  6.652 &  0.4476 \tabularnewline
49 &  7.3 &  6.802 &  0.498 \tabularnewline
50 &  7.2 &  6.866 &  0.3338 \tabularnewline
51 &  7.1 &  6.888 &  0.2125 \tabularnewline
52 &  6.9 &  6.909 & -0.008909 \tabularnewline
53 &  6.8 &  6.866 & -0.06616 \tabularnewline
54 &  6.7 &  6.781 & -0.08067 \tabularnewline
55 &  7.2 &  6.802 &  0.398 \tabularnewline
56 &  7.2 &  6.909 &  0.2911 \tabularnewline
57 &  7.1 &  6.93 &  0.1697 \tabularnewline
58 &  7.1 &  6.994 &  0.1056 \tabularnewline
59 &  7 &  6.952 &  0.04834 \tabularnewline
60 &  7.1 &  6.909 &  0.1911 \tabularnewline
61 &  7.3 &  6.888 &  0.4125 \tabularnewline
62 &  7.2 &  6.909 &  0.2911 \tabularnewline
63 &  7.1 &  6.93 &  0.1697 \tabularnewline
64 &  7 &  6.994 &  0.005597 \tabularnewline
65 &  6.9 &  7.037 & -0.1371 \tabularnewline
66 &  7 &  7.059 & -0.05852 \tabularnewline
67 &  7.5 &  7.059 &  0.4415 \tabularnewline
68 &  7.6 &  7.123 &  0.4774 \tabularnewline
69 &  7.5 &  7.144 &  0.356 \tabularnewline
70 &  7.3 &  7.101 &  0.1987 \tabularnewline
71 &  7.3 &  7.144 &  0.156 \tabularnewline
72 &  7.4 &  7.208 &  0.1919 \tabularnewline
73 &  7.7 &  7.272 &  0.4277 \tabularnewline
74 &  7.8 &  7.208 &  0.5919 \tabularnewline
75 &  7.7 &  7.208 &  0.4919 \tabularnewline
76 &  7.5 &  7.059 &  0.4415 \tabularnewline
77 &  7.3 &  6.994 &  0.3056 \tabularnewline
78 &  7.3 &  6.994 &  0.3056 \tabularnewline
79 &  7.6 &  7.016 &  0.5842 \tabularnewline
80 &  7.6 &  6.93 &  0.6697 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&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] 6.5[/C][C] 6.631[/C][C]-0.1311[/C][/ROW]
[ROW][C]2[/C][C] 6.8[/C][C] 6.717[/C][C] 0.08345[/C][/ROW]
[ROW][C]3[/C][C] 6.8[/C][C] 6.994[/C][C]-0.1944[/C][/ROW]
[ROW][C]4[/C][C] 6.5[/C][C] 6.994[/C][C]-0.4944[/C][/ROW]
[ROW][C]5[/C][C] 6.2[/C][C] 7.208[/C][C]-1.008[/C][/ROW]
[ROW][C]6[/C][C] 6.2[/C][C] 7.358[/C][C]-1.158[/C][/ROW]
[ROW][C]7[/C][C] 6.6[/C][C] 7.486[/C][C]-0.886[/C][/ROW]
[ROW][C]8[/C][C] 6.7[/C][C] 7.294[/C][C]-0.5936[/C][/ROW]
[ROW][C]9[/C][C] 6.5[/C][C] 7.379[/C][C]-0.8791[/C][/ROW]
[ROW][C]10[/C][C] 6.4[/C][C] 7.336[/C][C]-0.9364[/C][/ROW]
[ROW][C]11[/C][C] 6.5[/C][C] 7.144[/C][C]-0.644[/C][/ROW]
[ROW][C]12[/C][C] 6.8[/C][C] 7.059[/C][C]-0.2585[/C][/ROW]
[ROW][C]13[/C][C] 7.1[/C][C] 6.994[/C][C] 0.1056[/C][/ROW]
[ROW][C]14[/C][C] 7.2[/C][C] 6.973[/C][C] 0.227[/C][/ROW]
[ROW][C]15[/C][C] 7.1[/C][C] 6.759[/C][C] 0.3407[/C][/ROW]
[ROW][C]16[/C][C] 7[/C][C] 6.738[/C][C] 0.2621[/C][/ROW]
[ROW][C]17[/C][C] 6.9[/C][C] 6.631[/C][C] 0.2689[/C][/ROW]
[ROW][C]18[/C][C] 6.9[/C][C] 6.588[/C][C] 0.3117[/C][/ROW]
[ROW][C]19[/C][C] 7.4[/C][C] 6.567[/C][C] 0.8331[/C][/ROW]
[ROW][C]20[/C][C] 7.3[/C][C] 6.631[/C][C] 0.6689[/C][/ROW]
[ROW][C]21[/C][C] 7[/C][C] 6.503[/C][C] 0.4972[/C][/ROW]
[ROW][C]22[/C][C] 6.8[/C][C] 6.481[/C][C] 0.3186[/C][/ROW]
[ROW][C]23[/C][C] 6.5[/C][C] 6.503[/C][C]-0.002813[/C][/ROW]
[ROW][C]24[/C][C] 6.4[/C][C] 6.46[/C][C]-0.06007[/C][/ROW]
[ROW][C]25[/C][C] 6.3[/C][C] 6.439[/C][C]-0.1387[/C][/ROW]
[ROW][C]26[/C][C] 6[/C][C] 6.396[/C][C]-0.3959[/C][/ROW]
[ROW][C]27[/C][C] 5.9[/C][C] 6.375[/C][C]-0.4746[/C][/ROW]
[ROW][C]28[/C][C] 5.7[/C][C] 6.396[/C][C]-0.6959[/C][/ROW]
[ROW][C]29[/C][C] 5.7[/C][C] 6.396[/C][C]-0.6959[/C][/ROW]
[ROW][C]30[/C][C] 5.7[/C][C] 6.332[/C][C]-0.6318[/C][/ROW]
[ROW][C]31[/C][C] 6.2[/C][C] 6.31[/C][C]-0.1105[/C][/ROW]
[ROW][C]32[/C][C] 6.4[/C][C] 6.353[/C][C] 0.0468[/C][/ROW]
[ROW][C]33[/C][C] 6.2[/C][C] 6.353[/C][C]-0.1532[/C][/ROW]
[ROW][C]34[/C][C] 6.2[/C][C] 6.353[/C][C]-0.1532[/C][/ROW]
[ROW][C]35[/C][C] 6.1[/C][C] 6.289[/C][C]-0.1891[/C][/ROW]
[ROW][C]36[/C][C] 6.1[/C][C] 6.375[/C][C]-0.2746[/C][/ROW]
[ROW][C]37[/C][C] 6.2[/C][C] 6.332[/C][C]-0.1318[/C][/ROW]
[ROW][C]38[/C][C] 6.1[/C][C] 6.332[/C][C]-0.2318[/C][/ROW]
[ROW][C]39[/C][C] 6.1[/C][C] 6.396[/C][C]-0.2959[/C][/ROW]
[ROW][C]40[/C][C] 6.2[/C][C] 6.439[/C][C]-0.2387[/C][/ROW]
[ROW][C]41[/C][C] 6.2[/C][C] 6.524[/C][C]-0.3242[/C][/ROW]
[ROW][C]42[/C][C] 6.2[/C][C] 6.631[/C][C]-0.4311[/C][/ROW]
[ROW][C]43[/C][C] 6.4[/C][C] 6.631[/C][C]-0.2311[/C][/ROW]
[ROW][C]44[/C][C] 6.4[/C][C] 6.503[/C][C]-0.1028[/C][/ROW]
[ROW][C]45[/C][C] 6.4[/C][C] 6.524[/C][C]-0.1242[/C][/ROW]
[ROW][C]46[/C][C] 6.7[/C][C] 6.524[/C][C] 0.1758[/C][/ROW]
[ROW][C]47[/C][C] 6.9[/C][C] 6.631[/C][C] 0.2689[/C][/ROW]
[ROW][C]48[/C][C] 7.1[/C][C] 6.652[/C][C] 0.4476[/C][/ROW]
[ROW][C]49[/C][C] 7.3[/C][C] 6.802[/C][C] 0.498[/C][/ROW]
[ROW][C]50[/C][C] 7.2[/C][C] 6.866[/C][C] 0.3338[/C][/ROW]
[ROW][C]51[/C][C] 7.1[/C][C] 6.888[/C][C] 0.2125[/C][/ROW]
[ROW][C]52[/C][C] 6.9[/C][C] 6.909[/C][C]-0.008909[/C][/ROW]
[ROW][C]53[/C][C] 6.8[/C][C] 6.866[/C][C]-0.06616[/C][/ROW]
[ROW][C]54[/C][C] 6.7[/C][C] 6.781[/C][C]-0.08067[/C][/ROW]
[ROW][C]55[/C][C] 7.2[/C][C] 6.802[/C][C] 0.398[/C][/ROW]
[ROW][C]56[/C][C] 7.2[/C][C] 6.909[/C][C] 0.2911[/C][/ROW]
[ROW][C]57[/C][C] 7.1[/C][C] 6.93[/C][C] 0.1697[/C][/ROW]
[ROW][C]58[/C][C] 7.1[/C][C] 6.994[/C][C] 0.1056[/C][/ROW]
[ROW][C]59[/C][C] 7[/C][C] 6.952[/C][C] 0.04834[/C][/ROW]
[ROW][C]60[/C][C] 7.1[/C][C] 6.909[/C][C] 0.1911[/C][/ROW]
[ROW][C]61[/C][C] 7.3[/C][C] 6.888[/C][C] 0.4125[/C][/ROW]
[ROW][C]62[/C][C] 7.2[/C][C] 6.909[/C][C] 0.2911[/C][/ROW]
[ROW][C]63[/C][C] 7.1[/C][C] 6.93[/C][C] 0.1697[/C][/ROW]
[ROW][C]64[/C][C] 7[/C][C] 6.994[/C][C] 0.005597[/C][/ROW]
[ROW][C]65[/C][C] 6.9[/C][C] 7.037[/C][C]-0.1371[/C][/ROW]
[ROW][C]66[/C][C] 7[/C][C] 7.059[/C][C]-0.05852[/C][/ROW]
[ROW][C]67[/C][C] 7.5[/C][C] 7.059[/C][C] 0.4415[/C][/ROW]
[ROW][C]68[/C][C] 7.6[/C][C] 7.123[/C][C] 0.4774[/C][/ROW]
[ROW][C]69[/C][C] 7.5[/C][C] 7.144[/C][C] 0.356[/C][/ROW]
[ROW][C]70[/C][C] 7.3[/C][C] 7.101[/C][C] 0.1987[/C][/ROW]
[ROW][C]71[/C][C] 7.3[/C][C] 7.144[/C][C] 0.156[/C][/ROW]
[ROW][C]72[/C][C] 7.4[/C][C] 7.208[/C][C] 0.1919[/C][/ROW]
[ROW][C]73[/C][C] 7.7[/C][C] 7.272[/C][C] 0.4277[/C][/ROW]
[ROW][C]74[/C][C] 7.8[/C][C] 7.208[/C][C] 0.5919[/C][/ROW]
[ROW][C]75[/C][C] 7.7[/C][C] 7.208[/C][C] 0.4919[/C][/ROW]
[ROW][C]76[/C][C] 7.5[/C][C] 7.059[/C][C] 0.4415[/C][/ROW]
[ROW][C]77[/C][C] 7.3[/C][C] 6.994[/C][C] 0.3056[/C][/ROW]
[ROW][C]78[/C][C] 7.3[/C][C] 6.994[/C][C] 0.3056[/C][/ROW]
[ROW][C]79[/C][C] 7.6[/C][C] 7.016[/C][C] 0.5842[/C][/ROW]
[ROW][C]80[/C][C] 7.6[/C][C] 6.93[/C][C] 0.6697[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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	6.5	6.631	-0.1311
2	6.8	6.717	0.08345
3	6.8	6.994	-0.1944
4	6.5	6.994	-0.4944
5	6.2	7.208	-1.008
6	6.2	7.358	-1.158
7	6.6	7.486	-0.886
8	6.7	7.294	-0.5936
9	6.5	7.379	-0.8791
10	6.4	7.336	-0.9364
11	6.5	7.144	-0.644
12	6.8	7.059	-0.2585
13	7.1	6.994	0.1056
14	7.2	6.973	0.227
15	7.1	6.759	0.3407
16	7	6.738	0.2621
17	6.9	6.631	0.2689
18	6.9	6.588	0.3117
19	7.4	6.567	0.8331
20	7.3	6.631	0.6689
21	7	6.503	0.4972
22	6.8	6.481	0.3186
23	6.5	6.503	-0.002813
24	6.4	6.46	-0.06007
25	6.3	6.439	-0.1387
26	6	6.396	-0.3959
27	5.9	6.375	-0.4746
28	5.7	6.396	-0.6959
29	5.7	6.396	-0.6959
30	5.7	6.332	-0.6318
31	6.2	6.31	-0.1105
32	6.4	6.353	0.0468
33	6.2	6.353	-0.1532
34	6.2	6.353	-0.1532
35	6.1	6.289	-0.1891
36	6.1	6.375	-0.2746
37	6.2	6.332	-0.1318
38	6.1	6.332	-0.2318
39	6.1	6.396	-0.2959
40	6.2	6.439	-0.2387
41	6.2	6.524	-0.3242
42	6.2	6.631	-0.4311
43	6.4	6.631	-0.2311
44	6.4	6.503	-0.1028
45	6.4	6.524	-0.1242
46	6.7	6.524	0.1758
47	6.9	6.631	0.2689
48	7.1	6.652	0.4476
49	7.3	6.802	0.498
50	7.2	6.866	0.3338
51	7.1	6.888	0.2125
52	6.9	6.909	-0.008909
53	6.8	6.866	-0.06616
54	6.7	6.781	-0.08067
55	7.2	6.802	0.398
56	7.2	6.909	0.2911
57	7.1	6.93	0.1697
58	7.1	6.994	0.1056
59	7	6.952	0.04834
60	7.1	6.909	0.1911
61	7.3	6.888	0.4125
62	7.2	6.909	0.2911
63	7.1	6.93	0.1697
64	7	6.994	0.005597
65	6.9	7.037	-0.1371
66	7	7.059	-0.05852
67	7.5	7.059	0.4415
68	7.6	7.123	0.4774
69	7.5	7.144	0.356
70	7.3	7.101	0.1987
71	7.3	7.144	0.156
72	7.4	7.208	0.1919
73	7.7	7.272	0.4277
74	7.8	7.208	0.5919
75	7.7	7.208	0.4919
76	7.5	7.059	0.4415
77	7.3	6.994	0.3056
78	7.3	6.994	0.3056
79	7.6	7.016	0.5842
80	7.6	6.93	0.6697

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
5	0.2085	0.4169	0.7915
6	0.1238	0.2477	0.8762
7	0.1596	0.3193	0.8404
8	0.1453	0.2906	0.8547
9	0.1208	0.2416	0.8792
10	0.1349	0.2698	0.8651
11	0.1295	0.2591	0.8705
12	0.1529	0.3058	0.8471
13	0.3429	0.6858	0.6571
14	0.5465	0.9069	0.4535
15	0.5354	0.9291	0.4646
16	0.4672	0.9344	0.5328
17	0.3968	0.7936	0.6032
18	0.3408	0.6816	0.6592
19	0.5646	0.8708	0.4354
20	0.6765	0.647	0.3235
21	0.7261	0.5478	0.2739
22	0.7886	0.4229	0.2114
23	0.8786	0.2427	0.1214
24	0.9399	0.1203	0.06014
25	0.9719	0.05613	0.02807
26	0.9948	0.01032	0.005161
27	0.999	0.001938	0.0009692
28	0.9999	0.0001147	5.734e-05
29	1	7.888e-06	3.944e-06
30	1	1.097e-06	5.486e-07
31	1	2.099e-06	1.05e-06
32	1	3.359e-06	1.68e-06
33	1	6.561e-06	3.28e-06
34	1	1.275e-05	6.376e-06
35	1	2.274e-05	1.137e-05
36	1	3.792e-05	1.896e-05
37	1	7.122e-05	3.561e-05
38	0.9999	0.0001268	6.339e-05
39	0.9999	0.0001972	9.861e-05
40	0.9998	0.0003286	0.0001643
41	0.9998	0.0003592	0.0001796
42	0.9999	0.000135	6.752e-05
43	0.9999	0.0001208	6.038e-05
44	0.9999	0.0001825	9.125e-05
45	0.9999	0.0002119	0.0001059
46	0.9998	0.000367	0.0001835
47	0.9997	0.0005243	0.0002622
48	0.9998	0.0003503	0.0001752
49	0.9999	0.0001582	7.908e-05
50	0.9999	0.00018	9e-05
51	0.9999	0.000292	0.000146
52	0.9998	0.0004035	0.0002018
53	0.9998	0.0004743	0.0002371
54	0.9997	0.0005223	0.0002612
55	0.9997	0.0005973	0.0002987
56	0.9995	0.0009572	0.0004786
57	0.9992	0.001662	0.0008309
58	0.9988	0.002482	0.001241
59	0.9984	0.003266	0.001633
60	0.9972	0.005564	0.002782
61	0.9962	0.007623	0.003811
62	0.9935	0.01293	0.006466
63	0.9892	0.02166	0.01083
64	0.9888	0.02236	0.01118
65	0.9967	0.006675	0.003338
66	0.9994	0.001188	0.0005938
67	0.9987	0.002609	0.001305
68	0.9974	0.005117	0.002558
69	0.9942	0.01161	0.005805
70	0.9919	0.01611	0.008055
71	0.9923	0.01536	0.007678
72	0.9935	0.01309	0.006547
73	0.9828	0.03449	0.01724
74	0.9617	0.07662	0.03831
75	0.9169	0.1662	0.0831

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
5 &  0.2085 &  0.4169 &  0.7915 \tabularnewline
6 &  0.1238 &  0.2477 &  0.8762 \tabularnewline
7 &  0.1596 &  0.3193 &  0.8404 \tabularnewline
8 &  0.1453 &  0.2906 &  0.8547 \tabularnewline
9 &  0.1208 &  0.2416 &  0.8792 \tabularnewline
10 &  0.1349 &  0.2698 &  0.8651 \tabularnewline
11 &  0.1295 &  0.2591 &  0.8705 \tabularnewline
12 &  0.1529 &  0.3058 &  0.8471 \tabularnewline
13 &  0.3429 &  0.6858 &  0.6571 \tabularnewline
14 &  0.5465 &  0.9069 &  0.4535 \tabularnewline
15 &  0.5354 &  0.9291 &  0.4646 \tabularnewline
16 &  0.4672 &  0.9344 &  0.5328 \tabularnewline
17 &  0.3968 &  0.7936 &  0.6032 \tabularnewline
18 &  0.3408 &  0.6816 &  0.6592 \tabularnewline
19 &  0.5646 &  0.8708 &  0.4354 \tabularnewline
20 &  0.6765 &  0.647 &  0.3235 \tabularnewline
21 &  0.7261 &  0.5478 &  0.2739 \tabularnewline
22 &  0.7886 &  0.4229 &  0.2114 \tabularnewline
23 &  0.8786 &  0.2427 &  0.1214 \tabularnewline
24 &  0.9399 &  0.1203 &  0.06014 \tabularnewline
25 &  0.9719 &  0.05613 &  0.02807 \tabularnewline
26 &  0.9948 &  0.01032 &  0.005161 \tabularnewline
27 &  0.999 &  0.001938 &  0.0009692 \tabularnewline
28 &  0.9999 &  0.0001147 &  5.734e-05 \tabularnewline
29 &  1 &  7.888e-06 &  3.944e-06 \tabularnewline
30 &  1 &  1.097e-06 &  5.486e-07 \tabularnewline
31 &  1 &  2.099e-06 &  1.05e-06 \tabularnewline
32 &  1 &  3.359e-06 &  1.68e-06 \tabularnewline
33 &  1 &  6.561e-06 &  3.28e-06 \tabularnewline
34 &  1 &  1.275e-05 &  6.376e-06 \tabularnewline
35 &  1 &  2.274e-05 &  1.137e-05 \tabularnewline
36 &  1 &  3.792e-05 &  1.896e-05 \tabularnewline
37 &  1 &  7.122e-05 &  3.561e-05 \tabularnewline
38 &  0.9999 &  0.0001268 &  6.339e-05 \tabularnewline
39 &  0.9999 &  0.0001972 &  9.861e-05 \tabularnewline
40 &  0.9998 &  0.0003286 &  0.0001643 \tabularnewline
41 &  0.9998 &  0.0003592 &  0.0001796 \tabularnewline
42 &  0.9999 &  0.000135 &  6.752e-05 \tabularnewline
43 &  0.9999 &  0.0001208 &  6.038e-05 \tabularnewline
44 &  0.9999 &  0.0001825 &  9.125e-05 \tabularnewline
45 &  0.9999 &  0.0002119 &  0.0001059 \tabularnewline
46 &  0.9998 &  0.000367 &  0.0001835 \tabularnewline
47 &  0.9997 &  0.0005243 &  0.0002622 \tabularnewline
48 &  0.9998 &  0.0003503 &  0.0001752 \tabularnewline
49 &  0.9999 &  0.0001582 &  7.908e-05 \tabularnewline
50 &  0.9999 &  0.00018 &  9e-05 \tabularnewline
51 &  0.9999 &  0.000292 &  0.000146 \tabularnewline
52 &  0.9998 &  0.0004035 &  0.0002018 \tabularnewline
53 &  0.9998 &  0.0004743 &  0.0002371 \tabularnewline
54 &  0.9997 &  0.0005223 &  0.0002612 \tabularnewline
55 &  0.9997 &  0.0005973 &  0.0002987 \tabularnewline
56 &  0.9995 &  0.0009572 &  0.0004786 \tabularnewline
57 &  0.9992 &  0.001662 &  0.0008309 \tabularnewline
58 &  0.9988 &  0.002482 &  0.001241 \tabularnewline
59 &  0.9984 &  0.003266 &  0.001633 \tabularnewline
60 &  0.9972 &  0.005564 &  0.002782 \tabularnewline
61 &  0.9962 &  0.007623 &  0.003811 \tabularnewline
62 &  0.9935 &  0.01293 &  0.006466 \tabularnewline
63 &  0.9892 &  0.02166 &  0.01083 \tabularnewline
64 &  0.9888 &  0.02236 &  0.01118 \tabularnewline
65 &  0.9967 &  0.006675 &  0.003338 \tabularnewline
66 &  0.9994 &  0.001188 &  0.0005938 \tabularnewline
67 &  0.9987 &  0.002609 &  0.001305 \tabularnewline
68 &  0.9974 &  0.005117 &  0.002558 \tabularnewline
69 &  0.9942 &  0.01161 &  0.005805 \tabularnewline
70 &  0.9919 &  0.01611 &  0.008055 \tabularnewline
71 &  0.9923 &  0.01536 &  0.007678 \tabularnewline
72 &  0.9935 &  0.01309 &  0.006547 \tabularnewline
73 &  0.9828 &  0.03449 &  0.01724 \tabularnewline
74 &  0.9617 &  0.07662 &  0.03831 \tabularnewline
75 &  0.9169 &  0.1662 &  0.0831 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&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] 0.2085[/C][C] 0.4169[/C][C] 0.7915[/C][/ROW]
[ROW][C]6[/C][C] 0.1238[/C][C] 0.2477[/C][C] 0.8762[/C][/ROW]
[ROW][C]7[/C][C] 0.1596[/C][C] 0.3193[/C][C] 0.8404[/C][/ROW]
[ROW][C]8[/C][C] 0.1453[/C][C] 0.2906[/C][C] 0.8547[/C][/ROW]
[ROW][C]9[/C][C] 0.1208[/C][C] 0.2416[/C][C] 0.8792[/C][/ROW]
[ROW][C]10[/C][C] 0.1349[/C][C] 0.2698[/C][C] 0.8651[/C][/ROW]
[ROW][C]11[/C][C] 0.1295[/C][C] 0.2591[/C][C] 0.8705[/C][/ROW]
[ROW][C]12[/C][C] 0.1529[/C][C] 0.3058[/C][C] 0.8471[/C][/ROW]
[ROW][C]13[/C][C] 0.3429[/C][C] 0.6858[/C][C] 0.6571[/C][/ROW]
[ROW][C]14[/C][C] 0.5465[/C][C] 0.9069[/C][C] 0.4535[/C][/ROW]
[ROW][C]15[/C][C] 0.5354[/C][C] 0.9291[/C][C] 0.4646[/C][/ROW]
[ROW][C]16[/C][C] 0.4672[/C][C] 0.9344[/C][C] 0.5328[/C][/ROW]
[ROW][C]17[/C][C] 0.3968[/C][C] 0.7936[/C][C] 0.6032[/C][/ROW]
[ROW][C]18[/C][C] 0.3408[/C][C] 0.6816[/C][C] 0.6592[/C][/ROW]
[ROW][C]19[/C][C] 0.5646[/C][C] 0.8708[/C][C] 0.4354[/C][/ROW]
[ROW][C]20[/C][C] 0.6765[/C][C] 0.647[/C][C] 0.3235[/C][/ROW]
[ROW][C]21[/C][C] 0.7261[/C][C] 0.5478[/C][C] 0.2739[/C][/ROW]
[ROW][C]22[/C][C] 0.7886[/C][C] 0.4229[/C][C] 0.2114[/C][/ROW]
[ROW][C]23[/C][C] 0.8786[/C][C] 0.2427[/C][C] 0.1214[/C][/ROW]
[ROW][C]24[/C][C] 0.9399[/C][C] 0.1203[/C][C] 0.06014[/C][/ROW]
[ROW][C]25[/C][C] 0.9719[/C][C] 0.05613[/C][C] 0.02807[/C][/ROW]
[ROW][C]26[/C][C] 0.9948[/C][C] 0.01032[/C][C] 0.005161[/C][/ROW]
[ROW][C]27[/C][C] 0.999[/C][C] 0.001938[/C][C] 0.0009692[/C][/ROW]
[ROW][C]28[/C][C] 0.9999[/C][C] 0.0001147[/C][C] 5.734e-05[/C][/ROW]
[ROW][C]29[/C][C] 1[/C][C] 7.888e-06[/C][C] 3.944e-06[/C][/ROW]
[ROW][C]30[/C][C] 1[/C][C] 1.097e-06[/C][C] 5.486e-07[/C][/ROW]
[ROW][C]31[/C][C] 1[/C][C] 2.099e-06[/C][C] 1.05e-06[/C][/ROW]
[ROW][C]32[/C][C] 1[/C][C] 3.359e-06[/C][C] 1.68e-06[/C][/ROW]
[ROW][C]33[/C][C] 1[/C][C] 6.561e-06[/C][C] 3.28e-06[/C][/ROW]
[ROW][C]34[/C][C] 1[/C][C] 1.275e-05[/C][C] 6.376e-06[/C][/ROW]
[ROW][C]35[/C][C] 1[/C][C] 2.274e-05[/C][C] 1.137e-05[/C][/ROW]
[ROW][C]36[/C][C] 1[/C][C] 3.792e-05[/C][C] 1.896e-05[/C][/ROW]
[ROW][C]37[/C][C] 1[/C][C] 7.122e-05[/C][C] 3.561e-05[/C][/ROW]
[ROW][C]38[/C][C] 0.9999[/C][C] 0.0001268[/C][C] 6.339e-05[/C][/ROW]
[ROW][C]39[/C][C] 0.9999[/C][C] 0.0001972[/C][C] 9.861e-05[/C][/ROW]
[ROW][C]40[/C][C] 0.9998[/C][C] 0.0003286[/C][C] 0.0001643[/C][/ROW]
[ROW][C]41[/C][C] 0.9998[/C][C] 0.0003592[/C][C] 0.0001796[/C][/ROW]
[ROW][C]42[/C][C] 0.9999[/C][C] 0.000135[/C][C] 6.752e-05[/C][/ROW]
[ROW][C]43[/C][C] 0.9999[/C][C] 0.0001208[/C][C] 6.038e-05[/C][/ROW]
[ROW][C]44[/C][C] 0.9999[/C][C] 0.0001825[/C][C] 9.125e-05[/C][/ROW]
[ROW][C]45[/C][C] 0.9999[/C][C] 0.0002119[/C][C] 0.0001059[/C][/ROW]
[ROW][C]46[/C][C] 0.9998[/C][C] 0.000367[/C][C] 0.0001835[/C][/ROW]
[ROW][C]47[/C][C] 0.9997[/C][C] 0.0005243[/C][C] 0.0002622[/C][/ROW]
[ROW][C]48[/C][C] 0.9998[/C][C] 0.0003503[/C][C] 0.0001752[/C][/ROW]
[ROW][C]49[/C][C] 0.9999[/C][C] 0.0001582[/C][C] 7.908e-05[/C][/ROW]
[ROW][C]50[/C][C] 0.9999[/C][C] 0.00018[/C][C] 9e-05[/C][/ROW]
[ROW][C]51[/C][C] 0.9999[/C][C] 0.000292[/C][C] 0.000146[/C][/ROW]
[ROW][C]52[/C][C] 0.9998[/C][C] 0.0004035[/C][C] 0.0002018[/C][/ROW]
[ROW][C]53[/C][C] 0.9998[/C][C] 0.0004743[/C][C] 0.0002371[/C][/ROW]
[ROW][C]54[/C][C] 0.9997[/C][C] 0.0005223[/C][C] 0.0002612[/C][/ROW]
[ROW][C]55[/C][C] 0.9997[/C][C] 0.0005973[/C][C] 0.0002987[/C][/ROW]
[ROW][C]56[/C][C] 0.9995[/C][C] 0.0009572[/C][C] 0.0004786[/C][/ROW]
[ROW][C]57[/C][C] 0.9992[/C][C] 0.001662[/C][C] 0.0008309[/C][/ROW]
[ROW][C]58[/C][C] 0.9988[/C][C] 0.002482[/C][C] 0.001241[/C][/ROW]
[ROW][C]59[/C][C] 0.9984[/C][C] 0.003266[/C][C] 0.001633[/C][/ROW]
[ROW][C]60[/C][C] 0.9972[/C][C] 0.005564[/C][C] 0.002782[/C][/ROW]
[ROW][C]61[/C][C] 0.9962[/C][C] 0.007623[/C][C] 0.003811[/C][/ROW]
[ROW][C]62[/C][C] 0.9935[/C][C] 0.01293[/C][C] 0.006466[/C][/ROW]
[ROW][C]63[/C][C] 0.9892[/C][C] 0.02166[/C][C] 0.01083[/C][/ROW]
[ROW][C]64[/C][C] 0.9888[/C][C] 0.02236[/C][C] 0.01118[/C][/ROW]
[ROW][C]65[/C][C] 0.9967[/C][C] 0.006675[/C][C] 0.003338[/C][/ROW]
[ROW][C]66[/C][C] 0.9994[/C][C] 0.001188[/C][C] 0.0005938[/C][/ROW]
[ROW][C]67[/C][C] 0.9987[/C][C] 0.002609[/C][C] 0.001305[/C][/ROW]
[ROW][C]68[/C][C] 0.9974[/C][C] 0.005117[/C][C] 0.002558[/C][/ROW]
[ROW][C]69[/C][C] 0.9942[/C][C] 0.01161[/C][C] 0.005805[/C][/ROW]
[ROW][C]70[/C][C] 0.9919[/C][C] 0.01611[/C][C] 0.008055[/C][/ROW]
[ROW][C]71[/C][C] 0.9923[/C][C] 0.01536[/C][C] 0.007678[/C][/ROW]
[ROW][C]72[/C][C] 0.9935[/C][C] 0.01309[/C][C] 0.006547[/C][/ROW]
[ROW][C]73[/C][C] 0.9828[/C][C] 0.03449[/C][C] 0.01724[/C][/ROW]
[ROW][C]74[/C][C] 0.9617[/C][C] 0.07662[/C][C] 0.03831[/C][/ROW]
[ROW][C]75[/C][C] 0.9169[/C][C] 0.1662[/C][C] 0.0831[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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	0.2085	0.4169	0.7915
6	0.1238	0.2477	0.8762
7	0.1596	0.3193	0.8404
8	0.1453	0.2906	0.8547
9	0.1208	0.2416	0.8792
10	0.1349	0.2698	0.8651
11	0.1295	0.2591	0.8705
12	0.1529	0.3058	0.8471
13	0.3429	0.6858	0.6571
14	0.5465	0.9069	0.4535
15	0.5354	0.9291	0.4646
16	0.4672	0.9344	0.5328
17	0.3968	0.7936	0.6032
18	0.3408	0.6816	0.6592
19	0.5646	0.8708	0.4354
20	0.6765	0.647	0.3235
21	0.7261	0.5478	0.2739
22	0.7886	0.4229	0.2114
23	0.8786	0.2427	0.1214
24	0.9399	0.1203	0.06014
25	0.9719	0.05613	0.02807
26	0.9948	0.01032	0.005161
27	0.999	0.001938	0.0009692
28	0.9999	0.0001147	5.734e-05
29	1	7.888e-06	3.944e-06
30	1	1.097e-06	5.486e-07
31	1	2.099e-06	1.05e-06
32	1	3.359e-06	1.68e-06
33	1	6.561e-06	3.28e-06
34	1	1.275e-05	6.376e-06
35	1	2.274e-05	1.137e-05
36	1	3.792e-05	1.896e-05
37	1	7.122e-05	3.561e-05
38	0.9999	0.0001268	6.339e-05
39	0.9999	0.0001972	9.861e-05
40	0.9998	0.0003286	0.0001643
41	0.9998	0.0003592	0.0001796
42	0.9999	0.000135	6.752e-05
43	0.9999	0.0001208	6.038e-05
44	0.9999	0.0001825	9.125e-05
45	0.9999	0.0002119	0.0001059
46	0.9998	0.000367	0.0001835
47	0.9997	0.0005243	0.0002622
48	0.9998	0.0003503	0.0001752
49	0.9999	0.0001582	7.908e-05
50	0.9999	0.00018	9e-05
51	0.9999	0.000292	0.000146
52	0.9998	0.0004035	0.0002018
53	0.9998	0.0004743	0.0002371
54	0.9997	0.0005223	0.0002612
55	0.9997	0.0005973	0.0002987
56	0.9995	0.0009572	0.0004786
57	0.9992	0.001662	0.0008309
58	0.9988	0.002482	0.001241
59	0.9984	0.003266	0.001633
60	0.9972	0.005564	0.002782
61	0.9962	0.007623	0.003811
62	0.9935	0.01293	0.006466
63	0.9892	0.02166	0.01083
64	0.9888	0.02236	0.01118
65	0.9967	0.006675	0.003338
66	0.9994	0.001188	0.0005938
67	0.9987	0.002609	0.001305
68	0.9974	0.005117	0.002558
69	0.9942	0.01161	0.005805
70	0.9919	0.01611	0.008055
71	0.9923	0.01536	0.007678
72	0.9935	0.01309	0.006547
73	0.9828	0.03449	0.01724
74	0.9617	0.07662	0.03831
75	0.9169	0.1662	0.0831

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	39	0.5493	NOK
5% type I error level	48	0.676056	NOK
10% type I error level	50	0.704225	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 & 39 &  0.5493 & NOK \tabularnewline
5% type I error level & 48 & 0.676056 & NOK \tabularnewline
10% type I error level & 50 & 0.704225 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284426&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]39[/C][C] 0.5493[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]48[/C][C]0.676056[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]50[/C][C]0.704225[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284426&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284426&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	39	0.5493	NOK
5% type I error level	48	0.676056	NOK
10% type I error level	50	0.704225	NOK

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):

par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

Parameters (R input):

par1 = ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
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.'
}
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 (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
z
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code