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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Fri, 27 Nov 2015 09:56:37 +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/27/t1448618218kxh2oyg93afxnpq.htm/, Retrieved Wed, 15 May 2024 20:25:42 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284281, Retrieved Wed, 15 May 2024 20:25:42 +0000

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

-       [Multiple Regression] [multiple regression] [2015-11-27 09:56:37] [60e7016130b28a0c8bd4011f80276a66] [Current]

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80.8 6.5 2.3
83.7 6.8 1.9
94.2 6.8 0.6
86.2 6.5 0.6
89 6.2 -0.4
94.7 6.2 -1.1
81.9 6.6 -1.7
80.2 6.7 -0.8
96.5 6.5 -1.2
95.6 6.4 -1
91.9 6.5 -0.1
89.9 6.8 0.3
86.5 7.1 0.6
94.6 7.2 0.7
107.1 7.1 1.7
98.3 7 1.8
94.6 6.9 2.3
111.1 6.9 2.5
91.7 7.4 2.6
91.3 7.3 2.3
110.7 7 2.9
106.4 6.8 3
105.1 6.5 2.9
102.6 6.4 3.1
97.5 6.3 3.2
103.7 6 3.4
124.5 5.9 3.5
103.8 5.7 3.4
111.8 5.7 3.4
108.4 5.7 3.7
91.7 6.2 3.8
100.9 6.4 3.6
114.6 6.2 3.6
106.6 6.2 3.6
103.5 6.1 3.9
101.3 6.1 3.5
97.6 6.2 3.7
100.7 6.1 3.7
118.2 6.1 3.4
98.6 6.2 3.2
101.5 6.2 2.8
109.8 6.2 2.3
96.8 6.4 2.3
97.2 6.4 2.9
107 6.4 2.8
111.3 6.7 2.8
104.6 6.9 2.3
98.7 7.1 2.2
97 7.3 1.5
95.5 7.2 1.2
107.7 7.1 1.1
106.9 6.9 1
105.5 6.8 1.2
110 6.7 1.6
103.4 7.2 1.5
92.8 7.2 1
109 7.1 0.9
115.1 7.1 0.6
105.4 7 0.8
102.3 7.1 1
100.4 7.3 1.1
103.3 7.2 1
111.3 7.1 0.9
109.9 7 0.6
106.7 6.9 0.4
114.3 7 0.3
101.5 7.5 0.3
92.5 7.6 0
119 7.5 -0.1
117 7.3 0.1
105.3 7.3 -0.1
105.5 7.4 -0.4
100.4 7.7 -0.7
98.6 7.8 -0.4
118.5 7.7 -0.4
110.1 7.5 0.3
102.8 7.3 0.6
116.5 7.3 0.6
100.5 7.6 0.5
96.8 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	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 5 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&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]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
industrie[t] = + 73.2358 + 3.75789werkloosheid[t] + 2.08705inflatie[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
industrie[t] =  +  73.2358 +  3.75789werkloosheid[t] +  2.08705inflatie[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=284281&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]industrie[t] =  +  73.2358 +  3.75789werkloosheid[t] +  2.08705inflatie[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=284281&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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
industrie[t] = + 73.2358 + 3.75789werkloosheid[t] + 2.08705inflatie[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)	+73.24	17.21	+4.2560e+00	5.799e-05	2.9e-05
werkloosheid	+3.758	2.407	+1.5610e+00	0.1226	0.06128
inflatie	+2.087	0.861	+2.4240e+00	0.0177	0.008851

\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) & +73.24 &  17.21 & +4.2560e+00 &  5.799e-05 &  2.9e-05 \tabularnewline
werkloosheid & +3.758 &  2.407 & +1.5610e+00 &  0.1226 &  0.06128 \tabularnewline
inflatie & +2.087 &  0.861 & +2.4240e+00 &  0.0177 &  0.008851 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&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]+73.24[/C][C] 17.21[/C][C]+4.2560e+00[/C][C] 5.799e-05[/C][C] 2.9e-05[/C][/ROW]
[ROW][C]werkloosheid[/C][C]+3.758[/C][C] 2.407[/C][C]+1.5610e+00[/C][C] 0.1226[/C][C] 0.06128[/C][/ROW]
[ROW][C]inflatie[/C][C]+2.087[/C][C] 0.861[/C][C]+2.4240e+00[/C][C] 0.0177[/C][C] 0.008851[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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)	+73.24	17.21	+4.2560e+00	5.799e-05	2.9e-05
werkloosheid	+3.758	2.407	+1.5610e+00	0.1226	0.06128
inflatie	+2.087	0.861	+2.4240e+00	0.0177	0.008851

Multiple Linear Regression - Regression Statistics
Multiple R	0.2667
R-squared	0.07111
Adjusted R-squared	0.04699
F-TEST (value)	2.948
F-TEST (DF numerator)	2
F-TEST (DF denominator)	77
p-value	0.05842
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.106
Sum Squared Residuals	6385

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2667 \tabularnewline
R-squared &  0.07111 \tabularnewline
Adjusted R-squared &  0.04699 \tabularnewline
F-TEST (value) &  2.948 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 77 \tabularnewline
p-value &  0.05842 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  9.106 \tabularnewline
Sum Squared Residuals &  6385 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2667[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.07111[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.04699[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.948[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]77[/C][/ROW]
[ROW][C]p-value[/C][C] 0.05842[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 9.106[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6385[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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	0.2667
R-squared	0.07111
Adjusted R-squared	0.04699
F-TEST (value)	2.948
F-TEST (DF numerator)	2
F-TEST (DF denominator)	77
p-value	0.05842
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	9.106
Sum Squared Residuals	6385

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	80.8	102.5	-21.66
2	83.7	102.8	-19.05
3	94.2	100	-5.842
4	86.2	98.91	-12.71
5	89	95.7	-6.7
6	94.7	94.24	0.461
7	81.9	94.49	-12.59
8	80.2	96.74	-16.54
9	96.5	95.16	1.342
10	95.6	95.2	0.4007
11	91.9	97.45	-5.553
12	89.9	99.42	-9.516
13	86.5	101.2	-14.67
14	94.6	101.8	-7.154
15	107.1	103.5	3.635
16	98.3	103.3	-4.998
17	94.6	104	-9.365
18	111.1	104.4	6.717
19	91.7	106.5	-14.77
20	91.3	105.5	-14.17
21	110.7	105.6	5.107
22	106.4	105.1	1.349
23	105.1	103.7	1.385
24	102.6	103.8	-1.156
25	97.5	103.6	-6.089
26	103.7	102.9	0.8209
27	124.5	102.7	21.79
28	103.8	101.8	2.048
29	111.8	101.8	10.05
30	108.4	102.4	6.022
31	91.7	104.5	-12.77
32	100.9	104.8	-3.9
33	114.6	104	10.55
34	106.6	104	2.552
35	103.5	104.3	-0.7984
36	101.3	103.5	-2.164
37	97.6	104.3	-6.657
38	100.7	103.9	-3.181
39	118.2	103.3	14.95
40	98.6	103.2	-4.613
41	101.5	102.4	-0.8785
42	109.8	101.3	8.465
43	96.8	102.1	-5.287
44	97.2	103.3	-6.139
45	107	103.1	3.87
46	111.3	104.3	7.043
47	104.6	104	0.6345
48	98.7	104.5	-5.808
49	97	103.8	-6.799
50	95.5	102.8	-7.297
51	107.7	102.2	5.487
52	106.9	101.3	5.648
53	105.5	101.3	4.206
54	110	101.8	8.247
55	103.4	103.4	-0.0232
56	92.8	102.4	-9.58
57	109	101.8	7.205
58	115.1	101.2	13.93
59	105.4	101.2	4.189
60	102.3	102	0.2961
61	100.4	103	-2.564
62	103.3	102.4	0.9203
63	111.3	101.8	9.505
64	109.9	100.8	9.107
65	106.7	100	6.7
66	114.3	100.2	14.13
67	101.5	102	-0.5461
68	92.5	101.8	-9.296
69	119	101.2	17.79
70	117	100.9	16.12
71	105.3	100.5	4.84
72	105.5	100.2	5.291
73	100.4	100.7	-0.3106
74	98.6	101.7	-3.113
75	118.5	101.3	17.16
76	110.1	102	8.054
77	102.8	101.9	0.8794
78	116.5	101.9	14.58
79	100.5	102.8	-2.339
80	96.8	103.7	-6.874

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  80.8 &  102.5 & -21.66 \tabularnewline
2 &  83.7 &  102.8 & -19.05 \tabularnewline
3 &  94.2 &  100 & -5.842 \tabularnewline
4 &  86.2 &  98.91 & -12.71 \tabularnewline
5 &  89 &  95.7 & -6.7 \tabularnewline
6 &  94.7 &  94.24 &  0.461 \tabularnewline
7 &  81.9 &  94.49 & -12.59 \tabularnewline
8 &  80.2 &  96.74 & -16.54 \tabularnewline
9 &  96.5 &  95.16 &  1.342 \tabularnewline
10 &  95.6 &  95.2 &  0.4007 \tabularnewline
11 &  91.9 &  97.45 & -5.553 \tabularnewline
12 &  89.9 &  99.42 & -9.516 \tabularnewline
13 &  86.5 &  101.2 & -14.67 \tabularnewline
14 &  94.6 &  101.8 & -7.154 \tabularnewline
15 &  107.1 &  103.5 &  3.635 \tabularnewline
16 &  98.3 &  103.3 & -4.998 \tabularnewline
17 &  94.6 &  104 & -9.365 \tabularnewline
18 &  111.1 &  104.4 &  6.717 \tabularnewline
19 &  91.7 &  106.5 & -14.77 \tabularnewline
20 &  91.3 &  105.5 & -14.17 \tabularnewline
21 &  110.7 &  105.6 &  5.107 \tabularnewline
22 &  106.4 &  105.1 &  1.349 \tabularnewline
23 &  105.1 &  103.7 &  1.385 \tabularnewline
24 &  102.6 &  103.8 & -1.156 \tabularnewline
25 &  97.5 &  103.6 & -6.089 \tabularnewline
26 &  103.7 &  102.9 &  0.8209 \tabularnewline
27 &  124.5 &  102.7 &  21.79 \tabularnewline
28 &  103.8 &  101.8 &  2.048 \tabularnewline
29 &  111.8 &  101.8 &  10.05 \tabularnewline
30 &  108.4 &  102.4 &  6.022 \tabularnewline
31 &  91.7 &  104.5 & -12.77 \tabularnewline
32 &  100.9 &  104.8 & -3.9 \tabularnewline
33 &  114.6 &  104 &  10.55 \tabularnewline
34 &  106.6 &  104 &  2.552 \tabularnewline
35 &  103.5 &  104.3 & -0.7984 \tabularnewline
36 &  101.3 &  103.5 & -2.164 \tabularnewline
37 &  97.6 &  104.3 & -6.657 \tabularnewline
38 &  100.7 &  103.9 & -3.181 \tabularnewline
39 &  118.2 &  103.3 &  14.95 \tabularnewline
40 &  98.6 &  103.2 & -4.613 \tabularnewline
41 &  101.5 &  102.4 & -0.8785 \tabularnewline
42 &  109.8 &  101.3 &  8.465 \tabularnewline
43 &  96.8 &  102.1 & -5.287 \tabularnewline
44 &  97.2 &  103.3 & -6.139 \tabularnewline
45 &  107 &  103.1 &  3.87 \tabularnewline
46 &  111.3 &  104.3 &  7.043 \tabularnewline
47 &  104.6 &  104 &  0.6345 \tabularnewline
48 &  98.7 &  104.5 & -5.808 \tabularnewline
49 &  97 &  103.8 & -6.799 \tabularnewline
50 &  95.5 &  102.8 & -7.297 \tabularnewline
51 &  107.7 &  102.2 &  5.487 \tabularnewline
52 &  106.9 &  101.3 &  5.648 \tabularnewline
53 &  105.5 &  101.3 &  4.206 \tabularnewline
54 &  110 &  101.8 &  8.247 \tabularnewline
55 &  103.4 &  103.4 & -0.0232 \tabularnewline
56 &  92.8 &  102.4 & -9.58 \tabularnewline
57 &  109 &  101.8 &  7.205 \tabularnewline
58 &  115.1 &  101.2 &  13.93 \tabularnewline
59 &  105.4 &  101.2 &  4.189 \tabularnewline
60 &  102.3 &  102 &  0.2961 \tabularnewline
61 &  100.4 &  103 & -2.564 \tabularnewline
62 &  103.3 &  102.4 &  0.9203 \tabularnewline
63 &  111.3 &  101.8 &  9.505 \tabularnewline
64 &  109.9 &  100.8 &  9.107 \tabularnewline
65 &  106.7 &  100 &  6.7 \tabularnewline
66 &  114.3 &  100.2 &  14.13 \tabularnewline
67 &  101.5 &  102 & -0.5461 \tabularnewline
68 &  92.5 &  101.8 & -9.296 \tabularnewline
69 &  119 &  101.2 &  17.79 \tabularnewline
70 &  117 &  100.9 &  16.12 \tabularnewline
71 &  105.3 &  100.5 &  4.84 \tabularnewline
72 &  105.5 &  100.2 &  5.291 \tabularnewline
73 &  100.4 &  100.7 & -0.3106 \tabularnewline
74 &  98.6 &  101.7 & -3.113 \tabularnewline
75 &  118.5 &  101.3 &  17.16 \tabularnewline
76 &  110.1 &  102 &  8.054 \tabularnewline
77 &  102.8 &  101.9 &  0.8794 \tabularnewline
78 &  116.5 &  101.9 &  14.58 \tabularnewline
79 &  100.5 &  102.8 & -2.339 \tabularnewline
80 &  96.8 &  103.7 & -6.874 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&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] 80.8[/C][C] 102.5[/C][C]-21.66[/C][/ROW]
[ROW][C]2[/C][C] 83.7[/C][C] 102.8[/C][C]-19.05[/C][/ROW]
[ROW][C]3[/C][C] 94.2[/C][C] 100[/C][C]-5.842[/C][/ROW]
[ROW][C]4[/C][C] 86.2[/C][C] 98.91[/C][C]-12.71[/C][/ROW]
[ROW][C]5[/C][C] 89[/C][C] 95.7[/C][C]-6.7[/C][/ROW]
[ROW][C]6[/C][C] 94.7[/C][C] 94.24[/C][C] 0.461[/C][/ROW]
[ROW][C]7[/C][C] 81.9[/C][C] 94.49[/C][C]-12.59[/C][/ROW]
[ROW][C]8[/C][C] 80.2[/C][C] 96.74[/C][C]-16.54[/C][/ROW]
[ROW][C]9[/C][C] 96.5[/C][C] 95.16[/C][C] 1.342[/C][/ROW]
[ROW][C]10[/C][C] 95.6[/C][C] 95.2[/C][C] 0.4007[/C][/ROW]
[ROW][C]11[/C][C] 91.9[/C][C] 97.45[/C][C]-5.553[/C][/ROW]
[ROW][C]12[/C][C] 89.9[/C][C] 99.42[/C][C]-9.516[/C][/ROW]
[ROW][C]13[/C][C] 86.5[/C][C] 101.2[/C][C]-14.67[/C][/ROW]
[ROW][C]14[/C][C] 94.6[/C][C] 101.8[/C][C]-7.154[/C][/ROW]
[ROW][C]15[/C][C] 107.1[/C][C] 103.5[/C][C] 3.635[/C][/ROW]
[ROW][C]16[/C][C] 98.3[/C][C] 103.3[/C][C]-4.998[/C][/ROW]
[ROW][C]17[/C][C] 94.6[/C][C] 104[/C][C]-9.365[/C][/ROW]
[ROW][C]18[/C][C] 111.1[/C][C] 104.4[/C][C] 6.717[/C][/ROW]
[ROW][C]19[/C][C] 91.7[/C][C] 106.5[/C][C]-14.77[/C][/ROW]
[ROW][C]20[/C][C] 91.3[/C][C] 105.5[/C][C]-14.17[/C][/ROW]
[ROW][C]21[/C][C] 110.7[/C][C] 105.6[/C][C] 5.107[/C][/ROW]
[ROW][C]22[/C][C] 106.4[/C][C] 105.1[/C][C] 1.349[/C][/ROW]
[ROW][C]23[/C][C] 105.1[/C][C] 103.7[/C][C] 1.385[/C][/ROW]
[ROW][C]24[/C][C] 102.6[/C][C] 103.8[/C][C]-1.156[/C][/ROW]
[ROW][C]25[/C][C] 97.5[/C][C] 103.6[/C][C]-6.089[/C][/ROW]
[ROW][C]26[/C][C] 103.7[/C][C] 102.9[/C][C] 0.8209[/C][/ROW]
[ROW][C]27[/C][C] 124.5[/C][C] 102.7[/C][C] 21.79[/C][/ROW]
[ROW][C]28[/C][C] 103.8[/C][C] 101.8[/C][C] 2.048[/C][/ROW]
[ROW][C]29[/C][C] 111.8[/C][C] 101.8[/C][C] 10.05[/C][/ROW]
[ROW][C]30[/C][C] 108.4[/C][C] 102.4[/C][C] 6.022[/C][/ROW]
[ROW][C]31[/C][C] 91.7[/C][C] 104.5[/C][C]-12.77[/C][/ROW]
[ROW][C]32[/C][C] 100.9[/C][C] 104.8[/C][C]-3.9[/C][/ROW]
[ROW][C]33[/C][C] 114.6[/C][C] 104[/C][C] 10.55[/C][/ROW]
[ROW][C]34[/C][C] 106.6[/C][C] 104[/C][C] 2.552[/C][/ROW]
[ROW][C]35[/C][C] 103.5[/C][C] 104.3[/C][C]-0.7984[/C][/ROW]
[ROW][C]36[/C][C] 101.3[/C][C] 103.5[/C][C]-2.164[/C][/ROW]
[ROW][C]37[/C][C] 97.6[/C][C] 104.3[/C][C]-6.657[/C][/ROW]
[ROW][C]38[/C][C] 100.7[/C][C] 103.9[/C][C]-3.181[/C][/ROW]
[ROW][C]39[/C][C] 118.2[/C][C] 103.3[/C][C] 14.95[/C][/ROW]
[ROW][C]40[/C][C] 98.6[/C][C] 103.2[/C][C]-4.613[/C][/ROW]
[ROW][C]41[/C][C] 101.5[/C][C] 102.4[/C][C]-0.8785[/C][/ROW]
[ROW][C]42[/C][C] 109.8[/C][C] 101.3[/C][C] 8.465[/C][/ROW]
[ROW][C]43[/C][C] 96.8[/C][C] 102.1[/C][C]-5.287[/C][/ROW]
[ROW][C]44[/C][C] 97.2[/C][C] 103.3[/C][C]-6.139[/C][/ROW]
[ROW][C]45[/C][C] 107[/C][C] 103.1[/C][C] 3.87[/C][/ROW]
[ROW][C]46[/C][C] 111.3[/C][C] 104.3[/C][C] 7.043[/C][/ROW]
[ROW][C]47[/C][C] 104.6[/C][C] 104[/C][C] 0.6345[/C][/ROW]
[ROW][C]48[/C][C] 98.7[/C][C] 104.5[/C][C]-5.808[/C][/ROW]
[ROW][C]49[/C][C] 97[/C][C] 103.8[/C][C]-6.799[/C][/ROW]
[ROW][C]50[/C][C] 95.5[/C][C] 102.8[/C][C]-7.297[/C][/ROW]
[ROW][C]51[/C][C] 107.7[/C][C] 102.2[/C][C] 5.487[/C][/ROW]
[ROW][C]52[/C][C] 106.9[/C][C] 101.3[/C][C] 5.648[/C][/ROW]
[ROW][C]53[/C][C] 105.5[/C][C] 101.3[/C][C] 4.206[/C][/ROW]
[ROW][C]54[/C][C] 110[/C][C] 101.8[/C][C] 8.247[/C][/ROW]
[ROW][C]55[/C][C] 103.4[/C][C] 103.4[/C][C]-0.0232[/C][/ROW]
[ROW][C]56[/C][C] 92.8[/C][C] 102.4[/C][C]-9.58[/C][/ROW]
[ROW][C]57[/C][C] 109[/C][C] 101.8[/C][C] 7.205[/C][/ROW]
[ROW][C]58[/C][C] 115.1[/C][C] 101.2[/C][C] 13.93[/C][/ROW]
[ROW][C]59[/C][C] 105.4[/C][C] 101.2[/C][C] 4.189[/C][/ROW]
[ROW][C]60[/C][C] 102.3[/C][C] 102[/C][C] 0.2961[/C][/ROW]
[ROW][C]61[/C][C] 100.4[/C][C] 103[/C][C]-2.564[/C][/ROW]
[ROW][C]62[/C][C] 103.3[/C][C] 102.4[/C][C] 0.9203[/C][/ROW]
[ROW][C]63[/C][C] 111.3[/C][C] 101.8[/C][C] 9.505[/C][/ROW]
[ROW][C]64[/C][C] 109.9[/C][C] 100.8[/C][C] 9.107[/C][/ROW]
[ROW][C]65[/C][C] 106.7[/C][C] 100[/C][C] 6.7[/C][/ROW]
[ROW][C]66[/C][C] 114.3[/C][C] 100.2[/C][C] 14.13[/C][/ROW]
[ROW][C]67[/C][C] 101.5[/C][C] 102[/C][C]-0.5461[/C][/ROW]
[ROW][C]68[/C][C] 92.5[/C][C] 101.8[/C][C]-9.296[/C][/ROW]
[ROW][C]69[/C][C] 119[/C][C] 101.2[/C][C] 17.79[/C][/ROW]
[ROW][C]70[/C][C] 117[/C][C] 100.9[/C][C] 16.12[/C][/ROW]
[ROW][C]71[/C][C] 105.3[/C][C] 100.5[/C][C] 4.84[/C][/ROW]
[ROW][C]72[/C][C] 105.5[/C][C] 100.2[/C][C] 5.291[/C][/ROW]
[ROW][C]73[/C][C] 100.4[/C][C] 100.7[/C][C]-0.3106[/C][/ROW]
[ROW][C]74[/C][C] 98.6[/C][C] 101.7[/C][C]-3.113[/C][/ROW]
[ROW][C]75[/C][C] 118.5[/C][C] 101.3[/C][C] 17.16[/C][/ROW]
[ROW][C]76[/C][C] 110.1[/C][C] 102[/C][C] 8.054[/C][/ROW]
[ROW][C]77[/C][C] 102.8[/C][C] 101.9[/C][C] 0.8794[/C][/ROW]
[ROW][C]78[/C][C] 116.5[/C][C] 101.9[/C][C] 14.58[/C][/ROW]
[ROW][C]79[/C][C] 100.5[/C][C] 102.8[/C][C]-2.339[/C][/ROW]
[ROW][C]80[/C][C] 96.8[/C][C] 103.7[/C][C]-6.874[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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	80.8	102.5	-21.66
2	83.7	102.8	-19.05
3	94.2	100	-5.842
4	86.2	98.91	-12.71
5	89	95.7	-6.7
6	94.7	94.24	0.461
7	81.9	94.49	-12.59
8	80.2	96.74	-16.54
9	96.5	95.16	1.342
10	95.6	95.2	0.4007
11	91.9	97.45	-5.553
12	89.9	99.42	-9.516
13	86.5	101.2	-14.67
14	94.6	101.8	-7.154
15	107.1	103.5	3.635
16	98.3	103.3	-4.998
17	94.6	104	-9.365
18	111.1	104.4	6.717
19	91.7	106.5	-14.77
20	91.3	105.5	-14.17
21	110.7	105.6	5.107
22	106.4	105.1	1.349
23	105.1	103.7	1.385
24	102.6	103.8	-1.156
25	97.5	103.6	-6.089
26	103.7	102.9	0.8209
27	124.5	102.7	21.79
28	103.8	101.8	2.048
29	111.8	101.8	10.05
30	108.4	102.4	6.022
31	91.7	104.5	-12.77
32	100.9	104.8	-3.9
33	114.6	104	10.55
34	106.6	104	2.552
35	103.5	104.3	-0.7984
36	101.3	103.5	-2.164
37	97.6	104.3	-6.657
38	100.7	103.9	-3.181
39	118.2	103.3	14.95
40	98.6	103.2	-4.613
41	101.5	102.4	-0.8785
42	109.8	101.3	8.465
43	96.8	102.1	-5.287
44	97.2	103.3	-6.139
45	107	103.1	3.87
46	111.3	104.3	7.043
47	104.6	104	0.6345
48	98.7	104.5	-5.808
49	97	103.8	-6.799
50	95.5	102.8	-7.297
51	107.7	102.2	5.487
52	106.9	101.3	5.648
53	105.5	101.3	4.206
54	110	101.8	8.247
55	103.4	103.4	-0.0232
56	92.8	102.4	-9.58
57	109	101.8	7.205
58	115.1	101.2	13.93
59	105.4	101.2	4.189
60	102.3	102	0.2961
61	100.4	103	-2.564
62	103.3	102.4	0.9203
63	111.3	101.8	9.505
64	109.9	100.8	9.107
65	106.7	100	6.7
66	114.3	100.2	14.13
67	101.5	102	-0.5461
68	92.5	101.8	-9.296
69	119	101.2	17.79
70	117	100.9	16.12
71	105.3	100.5	4.84
72	105.5	100.2	5.291
73	100.4	100.7	-0.3106
74	98.6	101.7	-3.113
75	118.5	101.3	17.16
76	110.1	102	8.054
77	102.8	101.9	0.8794
78	116.5	101.9	14.58
79	100.5	102.8	-2.339
80	96.8	103.7	-6.874

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.02376	0.04751	0.9762
7	0.3224	0.6448	0.6776
8	0.3245	0.6489	0.6755
9	0.3589	0.7179	0.6411
10	0.3246	0.6492	0.6754
11	0.2892	0.5784	0.7108
12	0.2825	0.5649	0.7175
13	0.2762	0.5523	0.7238
14	0.3331	0.6663	0.6669
15	0.7388	0.5224	0.2612
16	0.7195	0.5611	0.2805
17	0.6694	0.6612	0.3306
18	0.8756	0.2488	0.1244
19	0.852	0.296	0.148
20	0.8256	0.3487	0.1744
21	0.9161	0.1678	0.08388
22	0.918	0.1641	0.08203
23	0.8972	0.2056	0.1028
24	0.8612	0.2776	0.1388
25	0.8392	0.3216	0.1608
26	0.796	0.408	0.204
27	0.9513	0.09745	0.04873
28	0.9433	0.1135	0.05674
29	0.9239	0.1523	0.07613
30	0.8971	0.2057	0.1029
31	0.9459	0.1083	0.05413
32	0.927	0.1459	0.07296
33	0.9488	0.1023	0.05115
34	0.9335	0.1331	0.06654
35	0.9136	0.1727	0.08636
36	0.8909	0.2182	0.1091
37	0.88	0.2399	0.12
38	0.8533	0.2935	0.1467
39	0.9316	0.1369	0.06843
40	0.9184	0.1632	0.08158
41	0.8981	0.2039	0.1019
42	0.8845	0.2311	0.1155
43	0.8999	0.2001	0.1001
44	0.904	0.1919	0.09597
45	0.879	0.2419	0.121
46	0.9102	0.1795	0.08977
47	0.8971	0.2058	0.1029
48	0.8653	0.2693	0.1347
49	0.8274	0.3451	0.1726
50	0.8147	0.3706	0.1853
51	0.8201	0.3598	0.1799
52	0.8094	0.3812	0.1906
53	0.7901	0.4199	0.2099
54	0.7729	0.4542	0.2271
55	0.7312	0.5375	0.2688
56	0.782	0.436	0.218
57	0.7679	0.4642	0.2321
58	0.8186	0.3628	0.1814
59	0.7855	0.429	0.2145
60	0.7437	0.5126	0.2563
61	0.6848	0.6303	0.3152
62	0.6236	0.7527	0.3764
63	0.5893	0.8214	0.4107
64	0.537	0.926	0.463
65	0.548	0.904	0.452
66	0.5148	0.9704	0.4852
67	0.4356	0.8712	0.5644
68	0.5229	0.9543	0.4771
69	0.6428	0.7144	0.3572
70	0.6433	0.7134	0.3567
71	0.5646	0.8707	0.4354
72	0.5211	0.9577	0.4789
73	0.5814	0.8372	0.4186
74	0.7335	0.533	0.2665

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.02376 &  0.04751 &  0.9762 \tabularnewline
7 &  0.3224 &  0.6448 &  0.6776 \tabularnewline
8 &  0.3245 &  0.6489 &  0.6755 \tabularnewline
9 &  0.3589 &  0.7179 &  0.6411 \tabularnewline
10 &  0.3246 &  0.6492 &  0.6754 \tabularnewline
11 &  0.2892 &  0.5784 &  0.7108 \tabularnewline
12 &  0.2825 &  0.5649 &  0.7175 \tabularnewline
13 &  0.2762 &  0.5523 &  0.7238 \tabularnewline
14 &  0.3331 &  0.6663 &  0.6669 \tabularnewline
15 &  0.7388 &  0.5224 &  0.2612 \tabularnewline
16 &  0.7195 &  0.5611 &  0.2805 \tabularnewline
17 &  0.6694 &  0.6612 &  0.3306 \tabularnewline
18 &  0.8756 &  0.2488 &  0.1244 \tabularnewline
19 &  0.852 &  0.296 &  0.148 \tabularnewline
20 &  0.8256 &  0.3487 &  0.1744 \tabularnewline
21 &  0.9161 &  0.1678 &  0.08388 \tabularnewline
22 &  0.918 &  0.1641 &  0.08203 \tabularnewline
23 &  0.8972 &  0.2056 &  0.1028 \tabularnewline
24 &  0.8612 &  0.2776 &  0.1388 \tabularnewline
25 &  0.8392 &  0.3216 &  0.1608 \tabularnewline
26 &  0.796 &  0.408 &  0.204 \tabularnewline
27 &  0.9513 &  0.09745 &  0.04873 \tabularnewline
28 &  0.9433 &  0.1135 &  0.05674 \tabularnewline
29 &  0.9239 &  0.1523 &  0.07613 \tabularnewline
30 &  0.8971 &  0.2057 &  0.1029 \tabularnewline
31 &  0.9459 &  0.1083 &  0.05413 \tabularnewline
32 &  0.927 &  0.1459 &  0.07296 \tabularnewline
33 &  0.9488 &  0.1023 &  0.05115 \tabularnewline
34 &  0.9335 &  0.1331 &  0.06654 \tabularnewline
35 &  0.9136 &  0.1727 &  0.08636 \tabularnewline
36 &  0.8909 &  0.2182 &  0.1091 \tabularnewline
37 &  0.88 &  0.2399 &  0.12 \tabularnewline
38 &  0.8533 &  0.2935 &  0.1467 \tabularnewline
39 &  0.9316 &  0.1369 &  0.06843 \tabularnewline
40 &  0.9184 &  0.1632 &  0.08158 \tabularnewline
41 &  0.8981 &  0.2039 &  0.1019 \tabularnewline
42 &  0.8845 &  0.2311 &  0.1155 \tabularnewline
43 &  0.8999 &  0.2001 &  0.1001 \tabularnewline
44 &  0.904 &  0.1919 &  0.09597 \tabularnewline
45 &  0.879 &  0.2419 &  0.121 \tabularnewline
46 &  0.9102 &  0.1795 &  0.08977 \tabularnewline
47 &  0.8971 &  0.2058 &  0.1029 \tabularnewline
48 &  0.8653 &  0.2693 &  0.1347 \tabularnewline
49 &  0.8274 &  0.3451 &  0.1726 \tabularnewline
50 &  0.8147 &  0.3706 &  0.1853 \tabularnewline
51 &  0.8201 &  0.3598 &  0.1799 \tabularnewline
52 &  0.8094 &  0.3812 &  0.1906 \tabularnewline
53 &  0.7901 &  0.4199 &  0.2099 \tabularnewline
54 &  0.7729 &  0.4542 &  0.2271 \tabularnewline
55 &  0.7312 &  0.5375 &  0.2688 \tabularnewline
56 &  0.782 &  0.436 &  0.218 \tabularnewline
57 &  0.7679 &  0.4642 &  0.2321 \tabularnewline
58 &  0.8186 &  0.3628 &  0.1814 \tabularnewline
59 &  0.7855 &  0.429 &  0.2145 \tabularnewline
60 &  0.7437 &  0.5126 &  0.2563 \tabularnewline
61 &  0.6848 &  0.6303 &  0.3152 \tabularnewline
62 &  0.6236 &  0.7527 &  0.3764 \tabularnewline
63 &  0.5893 &  0.8214 &  0.4107 \tabularnewline
64 &  0.537 &  0.926 &  0.463 \tabularnewline
65 &  0.548 &  0.904 &  0.452 \tabularnewline
66 &  0.5148 &  0.9704 &  0.4852 \tabularnewline
67 &  0.4356 &  0.8712 &  0.5644 \tabularnewline
68 &  0.5229 &  0.9543 &  0.4771 \tabularnewline
69 &  0.6428 &  0.7144 &  0.3572 \tabularnewline
70 &  0.6433 &  0.7134 &  0.3567 \tabularnewline
71 &  0.5646 &  0.8707 &  0.4354 \tabularnewline
72 &  0.5211 &  0.9577 &  0.4789 \tabularnewline
73 &  0.5814 &  0.8372 &  0.4186 \tabularnewline
74 &  0.7335 &  0.533 &  0.2665 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]6[/C][C] 0.02376[/C][C] 0.04751[/C][C] 0.9762[/C][/ROW]
[ROW][C]7[/C][C] 0.3224[/C][C] 0.6448[/C][C] 0.6776[/C][/ROW]
[ROW][C]8[/C][C] 0.3245[/C][C] 0.6489[/C][C] 0.6755[/C][/ROW]
[ROW][C]9[/C][C] 0.3589[/C][C] 0.7179[/C][C] 0.6411[/C][/ROW]
[ROW][C]10[/C][C] 0.3246[/C][C] 0.6492[/C][C] 0.6754[/C][/ROW]
[ROW][C]11[/C][C] 0.2892[/C][C] 0.5784[/C][C] 0.7108[/C][/ROW]
[ROW][C]12[/C][C] 0.2825[/C][C] 0.5649[/C][C] 0.7175[/C][/ROW]
[ROW][C]13[/C][C] 0.2762[/C][C] 0.5523[/C][C] 0.7238[/C][/ROW]
[ROW][C]14[/C][C] 0.3331[/C][C] 0.6663[/C][C] 0.6669[/C][/ROW]
[ROW][C]15[/C][C] 0.7388[/C][C] 0.5224[/C][C] 0.2612[/C][/ROW]
[ROW][C]16[/C][C] 0.7195[/C][C] 0.5611[/C][C] 0.2805[/C][/ROW]
[ROW][C]17[/C][C] 0.6694[/C][C] 0.6612[/C][C] 0.3306[/C][/ROW]
[ROW][C]18[/C][C] 0.8756[/C][C] 0.2488[/C][C] 0.1244[/C][/ROW]
[ROW][C]19[/C][C] 0.852[/C][C] 0.296[/C][C] 0.148[/C][/ROW]
[ROW][C]20[/C][C] 0.8256[/C][C] 0.3487[/C][C] 0.1744[/C][/ROW]
[ROW][C]21[/C][C] 0.9161[/C][C] 0.1678[/C][C] 0.08388[/C][/ROW]
[ROW][C]22[/C][C] 0.918[/C][C] 0.1641[/C][C] 0.08203[/C][/ROW]
[ROW][C]23[/C][C] 0.8972[/C][C] 0.2056[/C][C] 0.1028[/C][/ROW]
[ROW][C]24[/C][C] 0.8612[/C][C] 0.2776[/C][C] 0.1388[/C][/ROW]
[ROW][C]25[/C][C] 0.8392[/C][C] 0.3216[/C][C] 0.1608[/C][/ROW]
[ROW][C]26[/C][C] 0.796[/C][C] 0.408[/C][C] 0.204[/C][/ROW]
[ROW][C]27[/C][C] 0.9513[/C][C] 0.09745[/C][C] 0.04873[/C][/ROW]
[ROW][C]28[/C][C] 0.9433[/C][C] 0.1135[/C][C] 0.05674[/C][/ROW]
[ROW][C]29[/C][C] 0.9239[/C][C] 0.1523[/C][C] 0.07613[/C][/ROW]
[ROW][C]30[/C][C] 0.8971[/C][C] 0.2057[/C][C] 0.1029[/C][/ROW]
[ROW][C]31[/C][C] 0.9459[/C][C] 0.1083[/C][C] 0.05413[/C][/ROW]
[ROW][C]32[/C][C] 0.927[/C][C] 0.1459[/C][C] 0.07296[/C][/ROW]
[ROW][C]33[/C][C] 0.9488[/C][C] 0.1023[/C][C] 0.05115[/C][/ROW]
[ROW][C]34[/C][C] 0.9335[/C][C] 0.1331[/C][C] 0.06654[/C][/ROW]
[ROW][C]35[/C][C] 0.9136[/C][C] 0.1727[/C][C] 0.08636[/C][/ROW]
[ROW][C]36[/C][C] 0.8909[/C][C] 0.2182[/C][C] 0.1091[/C][/ROW]
[ROW][C]37[/C][C] 0.88[/C][C] 0.2399[/C][C] 0.12[/C][/ROW]
[ROW][C]38[/C][C] 0.8533[/C][C] 0.2935[/C][C] 0.1467[/C][/ROW]
[ROW][C]39[/C][C] 0.9316[/C][C] 0.1369[/C][C] 0.06843[/C][/ROW]
[ROW][C]40[/C][C] 0.9184[/C][C] 0.1632[/C][C] 0.08158[/C][/ROW]
[ROW][C]41[/C][C] 0.8981[/C][C] 0.2039[/C][C] 0.1019[/C][/ROW]
[ROW][C]42[/C][C] 0.8845[/C][C] 0.2311[/C][C] 0.1155[/C][/ROW]
[ROW][C]43[/C][C] 0.8999[/C][C] 0.2001[/C][C] 0.1001[/C][/ROW]
[ROW][C]44[/C][C] 0.904[/C][C] 0.1919[/C][C] 0.09597[/C][/ROW]
[ROW][C]45[/C][C] 0.879[/C][C] 0.2419[/C][C] 0.121[/C][/ROW]
[ROW][C]46[/C][C] 0.9102[/C][C] 0.1795[/C][C] 0.08977[/C][/ROW]
[ROW][C]47[/C][C] 0.8971[/C][C] 0.2058[/C][C] 0.1029[/C][/ROW]
[ROW][C]48[/C][C] 0.8653[/C][C] 0.2693[/C][C] 0.1347[/C][/ROW]
[ROW][C]49[/C][C] 0.8274[/C][C] 0.3451[/C][C] 0.1726[/C][/ROW]
[ROW][C]50[/C][C] 0.8147[/C][C] 0.3706[/C][C] 0.1853[/C][/ROW]
[ROW][C]51[/C][C] 0.8201[/C][C] 0.3598[/C][C] 0.1799[/C][/ROW]
[ROW][C]52[/C][C] 0.8094[/C][C] 0.3812[/C][C] 0.1906[/C][/ROW]
[ROW][C]53[/C][C] 0.7901[/C][C] 0.4199[/C][C] 0.2099[/C][/ROW]
[ROW][C]54[/C][C] 0.7729[/C][C] 0.4542[/C][C] 0.2271[/C][/ROW]
[ROW][C]55[/C][C] 0.7312[/C][C] 0.5375[/C][C] 0.2688[/C][/ROW]
[ROW][C]56[/C][C] 0.782[/C][C] 0.436[/C][C] 0.218[/C][/ROW]
[ROW][C]57[/C][C] 0.7679[/C][C] 0.4642[/C][C] 0.2321[/C][/ROW]
[ROW][C]58[/C][C] 0.8186[/C][C] 0.3628[/C][C] 0.1814[/C][/ROW]
[ROW][C]59[/C][C] 0.7855[/C][C] 0.429[/C][C] 0.2145[/C][/ROW]
[ROW][C]60[/C][C] 0.7437[/C][C] 0.5126[/C][C] 0.2563[/C][/ROW]
[ROW][C]61[/C][C] 0.6848[/C][C] 0.6303[/C][C] 0.3152[/C][/ROW]
[ROW][C]62[/C][C] 0.6236[/C][C] 0.7527[/C][C] 0.3764[/C][/ROW]
[ROW][C]63[/C][C] 0.5893[/C][C] 0.8214[/C][C] 0.4107[/C][/ROW]
[ROW][C]64[/C][C] 0.537[/C][C] 0.926[/C][C] 0.463[/C][/ROW]
[ROW][C]65[/C][C] 0.548[/C][C] 0.904[/C][C] 0.452[/C][/ROW]
[ROW][C]66[/C][C] 0.5148[/C][C] 0.9704[/C][C] 0.4852[/C][/ROW]
[ROW][C]67[/C][C] 0.4356[/C][C] 0.8712[/C][C] 0.5644[/C][/ROW]
[ROW][C]68[/C][C] 0.5229[/C][C] 0.9543[/C][C] 0.4771[/C][/ROW]
[ROW][C]69[/C][C] 0.6428[/C][C] 0.7144[/C][C] 0.3572[/C][/ROW]
[ROW][C]70[/C][C] 0.6433[/C][C] 0.7134[/C][C] 0.3567[/C][/ROW]
[ROW][C]71[/C][C] 0.5646[/C][C] 0.8707[/C][C] 0.4354[/C][/ROW]
[ROW][C]72[/C][C] 0.5211[/C][C] 0.9577[/C][C] 0.4789[/C][/ROW]
[ROW][C]73[/C][C] 0.5814[/C][C] 0.8372[/C][C] 0.4186[/C][/ROW]
[ROW][C]74[/C][C] 0.7335[/C][C] 0.533[/C][C] 0.2665[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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
6	0.02376	0.04751	0.9762
7	0.3224	0.6448	0.6776
8	0.3245	0.6489	0.6755
9	0.3589	0.7179	0.6411
10	0.3246	0.6492	0.6754
11	0.2892	0.5784	0.7108
12	0.2825	0.5649	0.7175
13	0.2762	0.5523	0.7238
14	0.3331	0.6663	0.6669
15	0.7388	0.5224	0.2612
16	0.7195	0.5611	0.2805
17	0.6694	0.6612	0.3306
18	0.8756	0.2488	0.1244
19	0.852	0.296	0.148
20	0.8256	0.3487	0.1744
21	0.9161	0.1678	0.08388
22	0.918	0.1641	0.08203
23	0.8972	0.2056	0.1028
24	0.8612	0.2776	0.1388
25	0.8392	0.3216	0.1608
26	0.796	0.408	0.204
27	0.9513	0.09745	0.04873
28	0.9433	0.1135	0.05674
29	0.9239	0.1523	0.07613
30	0.8971	0.2057	0.1029
31	0.9459	0.1083	0.05413
32	0.927	0.1459	0.07296
33	0.9488	0.1023	0.05115
34	0.9335	0.1331	0.06654
35	0.9136	0.1727	0.08636
36	0.8909	0.2182	0.1091
37	0.88	0.2399	0.12
38	0.8533	0.2935	0.1467
39	0.9316	0.1369	0.06843
40	0.9184	0.1632	0.08158
41	0.8981	0.2039	0.1019
42	0.8845	0.2311	0.1155
43	0.8999	0.2001	0.1001
44	0.904	0.1919	0.09597
45	0.879	0.2419	0.121
46	0.9102	0.1795	0.08977
47	0.8971	0.2058	0.1029
48	0.8653	0.2693	0.1347
49	0.8274	0.3451	0.1726
50	0.8147	0.3706	0.1853
51	0.8201	0.3598	0.1799
52	0.8094	0.3812	0.1906
53	0.7901	0.4199	0.2099
54	0.7729	0.4542	0.2271
55	0.7312	0.5375	0.2688
56	0.782	0.436	0.218
57	0.7679	0.4642	0.2321
58	0.8186	0.3628	0.1814
59	0.7855	0.429	0.2145
60	0.7437	0.5126	0.2563
61	0.6848	0.6303	0.3152
62	0.6236	0.7527	0.3764
63	0.5893	0.8214	0.4107
64	0.537	0.926	0.463
65	0.548	0.904	0.452
66	0.5148	0.9704	0.4852
67	0.4356	0.8712	0.5644
68	0.5229	0.9543	0.4771
69	0.6428	0.7144	0.3572
70	0.6433	0.7134	0.3567
71	0.5646	0.8707	0.4354
72	0.5211	0.9577	0.4789
73	0.5814	0.8372	0.4186
74	0.7335	0.533	0.2665

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	1	0.0144928	OK
10% type I error level	2	0.0289855	OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 &  0 & OK \tabularnewline
5% type I error level & 1 & 0.0144928 & OK \tabularnewline
10% type I error level & 2 & 0.0289855 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=284281&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]1[/C][C]0.0144928[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]2[/C][C]0.0289855[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=284281&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284281&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	1	0.0144928	OK
10% type I error level	2	0.0289855	OK

Figure 1

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

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