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R Software Module

rwasp_multipleregression.wasp

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Multiple Regression

Date of computation

Wed, 25 Jan 2017 10:46:00 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Jan/25/t14853376273v0c17sz50hohvn.htm/, Retrieved Tue, 14 May 2024 17:57:31 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306347, Retrieved Tue, 14 May 2024 17:57:31 +0000

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-       [Multiple Regression] [multiple regression] [2017-01-25 09:46:00] [cdc1701924ae7eaac4e060a8f804232e] [Current]

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22 14 4 2 4 3 5 4
24 19 5 3 3 4 5 4
21 17 4 4 5 4 5 4
21 17 3 4 3 3 4 4
24 15 4 4 5 4 5 4
20 20 3 4 4 4 5 5
22 15 3 4 4 3 3 4
20 19 3 4 5 4 4 4
19 15 4 5 4 4 5 5
23 15 4 5 5 4 5 5
21 19 4 4 2 4 5 4
19 NA 4 4 5 3 5 4
19 20 4 4 4 3 4 5
21 18 3 3 5 4 4 5
21 15 4 4 5 4 2 5
22 14 3 4 5 4 4 5
22 20 3 4 5 4 4 5
19 NA NA NA 5 NA 5 5
21 16 5 5 4 3 4 4
21 16 4 4 4 4 5 4
21 16 3 4 5 3 4 5
20 10 4 4 4 4 5 5
22 19 4 4 5 4 4 5
22 19 4 4 5 4 4 4
24 16 4 4 5 4 4 5
21 15 3 4 4 4 4 4
19 18 3 4 4 3 5 5
19 17 4 4 4 4 4 4
23 19 2 4 5 4 5 5
21 17 5 4 4 4 4 4
21 NA 4 3 5 4 4 4
19 19 4 5 5 4 5 5
21 20 5 4 5 4 4 5
19 5 4 3 5 4 NA 5
21 19 2 3 5 4 5 4
21 16 4 5 2 4 4 4
23 15 3 4 5 4 4 4
19 16 4 3 5 3 4 5
19 18 4 3 3 4 4 4
19 16 4 4 5 4 4 4
18 15 5 4 4 4 4 4
22 17 4 5 5 4 5 5
18 NA 3 3 4 4 4 4
22 20 5 5 5 3 5 5
18 19 5 4 5 3 4 4
22 7 4 4 4 3 4 5
22 13 4 4 4 4 4 4
19 16 3 5 5 3 3 4
22 16 4 4 4 4 5 4
25 NA 2 3 4 2 NA 4
19 18 4 5 5 4 4 4
19 18 5 5 2 4 5 4
19 16 5 5 5 4 4 4
19 17 4 3 5 4 5 5
21 19 4 3 4 3 4 5
21 16 4 4 5 4 4 4
20 19 3 4 4 3 3 4
19 13 3 4 4 4 4 3
19 16 4 4 4 3 5 4
22 13 4 4 4 4 5 4
26 12 5 5 3 4 5 5
19 17 2 4 4 4 5 5
21 17 4 4 4 4 5 5
21 17 3 4 4 4 2 4
20 16 4 4 5 4 5 5
23 16 4 2 4 4 4 4
22 14 4 4 4 3 5 3
22 16 4 4 4 3 5 4
22 13 5 4 5 3 3 5
21 16 3 4 4 3 5 5
21 14 3 4 4 3 4 5
22 20 4 5 5 5 5 4
23 12 4 4 3 4 NA 4
18 13 4 4 4 4 4 4
24 18 4 4 4 5 5 4
22 14 3 4 3 4 4 4
21 19 4 4 4 4 5 4
21 18 3 4 5 3 5 5
21 14 3 3 5 4 4 5
23 18 4 3 5 4 4 4
21 19 4 4 5 4 4 5
23 15 3 3 3 4 4 4
21 14 4 4 4 4 5 4
19 17 4 4 3 4 5 5
21 19 4 4 4 4 5 5
21 13 5 4 4 4 4 4
21 19 5 4 3 5 4 5
23 18 4 4 5 4 5 5
23 20 3 4 5 4 4 5
20 15 3 NA 4 4 4 4
20 15 4 2 3 3 4 4
19 15 4 4 5 4 4 3
23 20 4 4 5 4 4 5
22 15 4 4 4 4 5 4
19 19 4 5 4 4 5 3
23 18 3 4 4 3 5 5
22 18 4 4 5 4 4 5
22 15 5 4 3 4 4 5
21 20 5 4 5 5 4 5
21 17 4 5 4 4 5 5
21 12 3 4 5 4 4 5
21 18 5 3 4 4 5 5
22 19 4 4 5 4 4 5
25 20 5 4 4 4 4 5
21 NA 3 4 4 3 NA 4
23 17 5 4 4 5 5 5
19 15 4 4 5 3 NA 5
22 16 4 4 3 3 4 3
20 18 4 4 5 4 4 4
21 18 4 4 5 4 4 4
25 14 3 4 5 4 5 3
21 15 4 4 4 4 4 4
19 12 4 4 4 3 4 5
23 17 3 3 4 3 5 5
22 14 4 4 4 3 4 4
21 18 3 4 5 4 4 4
24 17 4 4 5 4 3 4
21 17 5 4 5 1 5 5
19 20 5 4 5 4 5 5
18 16 4 4 4 4 4 3
19 14 4 4 5 3 4 4
20 15 3 4 4 3 4 5
19 18 4 4 4 4 4 4
22 20 4 4 4 4 5 4
21 17 4 5 3 4 4 4
22 17 3 4 4 4 4 4
24 17 4 4 4 3 4 4
28 17 4 4 4 4 4 5
19 15 3 4 3 3 4 4
18 17 4 4 4 3 4 3
23 18 3 2 4 2 4 4
19 17 4 4 4 3 5 4
23 20 5 4 4 3 5 4
19 15 2 4 4 3 3 5
22 16 3 3 4 4 4 4
21 15 4 4 4 3 4 4
19 18 5 5 4 4 5 4
22 11 NA NA 2 NA NA NA
21 15 4 5 5 4 4 4
23 18 5 5 5 5 5 4
22 20 4 5 5 4 5 5
19 19 4 4 4 3 4 5
19 14 3 4 5 4 5 4
21 16 4 4 5 4 4 4
22 15 4 4 2 4 4 4
21 17 4 4 3 4 5 5
20 18 4 4 4 4 5 5
23 20 5 4 5 3 5 4
22 17 4 3 5 4 4 4
23 18 4 4 5 4 4 4
22 15 3 3 2 3 4 4
21 16 4 5 5 4 4 3
20 11 4 4 4 3 4 4
18 15 4 4 4 4 4 5
18 18 3 4 5 3 5 5
20 17 4 4 5 4 4 5
19 16 5 4 5 4 5 4
21 12 4 4 5 4 3 4
24 19 2 3 5 4 4 4
19 18 4 4 4 4 4 5
20 15 4 3 4 3 5 5
19 17 4 4 4 4 4 3
23 19 4 5 5 5 4 4
22 18 5 4 3 4 4 4
21 19 5 4 4 3 4 4
24 16 3 3 1 4 5 5
21 16 4 4 4 4 4 5
21 16 4 4 4 4 5 4
22 14 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	10 seconds
R Server	Big Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time10 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&T=0

[TABLE]
[ROW]

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

Raw Input[/C]

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

Raw Output[/C]

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

Computing time[/C]

10 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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	10 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Bevr_Leeftijd[t] = + 19.253 + 0.0162631ITHSUM[t] + 0.0160102SKEOU1[t] -0.472561SKEOU2[t] -0.0357214SKEOU3[t] + 0.563467SKEOU4[t] + 0.109965SKEOU5[t] + 0.218169SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Bevr_Leeftijd[t] =  +  19.253 +  0.0162631ITHSUM[t] +  0.0160102SKEOU1[t] -0.472561SKEOU2[t] -0.0357214SKEOU3[t] +  0.563467SKEOU4[t] +  0.109965SKEOU5[t] +  0.218169SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Bevr_Leeftijd[t] =  +  19.253 +  0.0162631ITHSUM[t] +  0.0160102SKEOU1[t] -0.472561SKEOU2[t] -0.0357214SKEOU3[t] +  0.563467SKEOU4[t] +  0.109965SKEOU5[t] +  0.218169SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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
Bevr_Leeftijd[t] = + 19.253 + 0.0162631ITHSUM[t] + 0.0160102SKEOU1[t] -0.472561SKEOU2[t] -0.0357214SKEOU3[t] + 0.563467SKEOU4[t] + 0.109965SKEOU5[t] + 0.218169SKEOU6[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+19.25	1.956	+9.8420e+00	6.092e-18	3.046e-18
ITHSUM	+0.01626	0.06339	+2.5660e-01	0.7979	0.3989
SKEOU1	+0.01601	0.1992	+8.0370e-02	0.9361	0.468
SKEOU2	-0.4726	0.2461	-1.9200e+00	0.05672	0.02836
SKEOU3	-0.03572	0.1808	-1.9760e-01	0.8436	0.4218
SKEOU4	+0.5635	0.2449	+2.3010e+00	0.02277	0.01139
SKEOU5	+0.11	0.2313	+4.7540e-01	0.6352	0.3176
SKEOU6	+0.2182	0.238	+9.1680e-01	0.3607	0.1804

\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) & +19.25 &  1.956 & +9.8420e+00 &  6.092e-18 &  3.046e-18 \tabularnewline
ITHSUM & +0.01626 &  0.06339 & +2.5660e-01 &  0.7979 &  0.3989 \tabularnewline
SKEOU1 & +0.01601 &  0.1992 & +8.0370e-02 &  0.9361 &  0.468 \tabularnewline
SKEOU2 & -0.4726 &  0.2461 & -1.9200e+00 &  0.05672 &  0.02836 \tabularnewline
SKEOU3 & -0.03572 &  0.1808 & -1.9760e-01 &  0.8436 &  0.4218 \tabularnewline
SKEOU4 & +0.5635 &  0.2449 & +2.3010e+00 &  0.02277 &  0.01139 \tabularnewline
SKEOU5 & +0.11 &  0.2313 & +4.7540e-01 &  0.6352 &  0.3176 \tabularnewline
SKEOU6 & +0.2182 &  0.238 & +9.1680e-01 &  0.3607 &  0.1804 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&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]+19.25[/C][C] 1.956[/C][C]+9.8420e+00[/C][C] 6.092e-18[/C][C] 3.046e-18[/C][/ROW]
[ROW][C]ITHSUM[/C][C]+0.01626[/C][C] 0.06339[/C][C]+2.5660e-01[/C][C] 0.7979[/C][C] 0.3989[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.01601[/C][C] 0.1992[/C][C]+8.0370e-02[/C][C] 0.9361[/C][C] 0.468[/C][/ROW]
[ROW][C]SKEOU2[/C][C]-0.4726[/C][C] 0.2461[/C][C]-1.9200e+00[/C][C] 0.05672[/C][C] 0.02836[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.03572[/C][C] 0.1808[/C][C]-1.9760e-01[/C][C] 0.8436[/C][C] 0.4218[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.5635[/C][C] 0.2449[/C][C]+2.3010e+00[/C][C] 0.02277[/C][C] 0.01139[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.11[/C][C] 0.2313[/C][C]+4.7540e-01[/C][C] 0.6352[/C][C] 0.3176[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.2182[/C][C] 0.238[/C][C]+9.1680e-01[/C][C] 0.3607[/C][C] 0.1804[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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)	+19.25	1.956	+9.8420e+00	6.092e-18	3.046e-18
ITHSUM	+0.01626	0.06339	+2.5660e-01	0.7979	0.3989
SKEOU1	+0.01601	0.1992	+8.0370e-02	0.9361	0.468
SKEOU2	-0.4726	0.2461	-1.9200e+00	0.05672	0.02836
SKEOU3	-0.03572	0.1808	-1.9760e-01	0.8436	0.4218
SKEOU4	+0.5635	0.2449	+2.3010e+00	0.02277	0.01139
SKEOU5	+0.11	0.2313	+4.7540e-01	0.6352	0.3176
SKEOU6	+0.2182	0.238	+9.1680e-01	0.3607	0.1804

Multiple Linear Regression - Regression Statistics
Multiple R	0.2437
R-squared	0.05939
Adjusted R-squared	0.01549
F-TEST (value)	1.353
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.2295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.741
Sum Squared Residuals	454.9

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.2437 \tabularnewline
R-squared &  0.05939 \tabularnewline
Adjusted R-squared &  0.01549 \tabularnewline
F-TEST (value) &  1.353 \tabularnewline
F-TEST (DF numerator) & 7 \tabularnewline
F-TEST (DF denominator) & 150 \tabularnewline
p-value &  0.2295 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.741 \tabularnewline
Sum Squared Residuals &  454.9 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.2437[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.05939[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.01549[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 1.353[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]7[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]150[/C][/ROW]
[ROW][C]p-value[/C][C] 0.2295[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.741[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 454.9[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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	0.2437
R-squared	0.05939
Adjusted R-squared	0.01549
F-TEST (value)	1.353
F-TEST (DF numerator)	7
F-TEST (DF denominator)	150
p-value	0.2295
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.741
Sum Squared Residuals	454.9

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	22	21.57	0.4304
2	24	21.79	2.206
3	21	21.2	-0.201
4	21	20.58	0.417
5	24	21.17	2.831
6	20	21.49	-1.488
7	22	20.4	1.595
8	20	21.11	-1.108
9	19	20.95	-1.95
10	23	20.91	2.086
11	21	21.34	-0.3407
12	19	20.83	-1.83
13	21	21.78	-0.7821
14	21	21.06	-0.05678
15	22	21.24	0.7556
16	22	21.34	0.658
17	21	20.09	0.9095
18	21	21.22	-0.2205
19	21	20.71	0.2865
20	20	21.34	-1.341
21	22	21.34	0.6582
22	22	21.12	0.8764
23	24	21.29	2.707
24	21	21.08	-0.07825
25	19	20.89	-1.892
26	19	21.13	-2.127
27	23	21.42	1.58
28	21	21.14	-0.1428
29	19	20.98	-1.979
30	21	21.37	-0.374
31	21	21.67	-0.6741
32	21	20.71	0.2906
33	23	21.04	1.957
34	19	21.2	-2.202
35	19	21.65	-2.651
36	19	21.07	-2.075
37	18	21.11	-3.11
38	22	20.95	1.053
39	22	20.45	1.552
40	18	20.58	-2.576
41	22	20.62	1.381
42	22	21.06	0.9383
43	19	19.91	-0.9128
44	22	21.22	0.7795
45	19	20.63	-1.635
46	19	20.87	-1.868
47	19	20.62	-1.618
48	19	21.89	-2.892
49	21	21.29	-0.2866
50	21	21.07	-0.0748
51	20	20.47	-0.4699
52	19	20.83	-1.828
53	19	20.66	-1.657
54	22	21.17	0.8283
55	26	20.95	5.047
56	19	21.42	-2.423
57	21	21.45	-0.4549
58	21	20.89	0.1092
59	20	21.4	-1.403
60	23	22.06	0.9444
61	22	20.41	1.594
62	22	20.66	1.343
63	22	20.59	1.413
64	21	20.86	0.1408
65	21	20.72	0.2833
66	22	21.34	0.6593
67	18	21.06	-3.062
68	24	21.82	2.184
69	22	21.1	0.9023
70	21	21.27	-0.2693
71	21	20.86	0.144
72	21	21.72	-0.717
73	23	21.58	1.42
74	21	21.34	-0.3418
75	23	21.59	1.413
76	21	21.19	-0.188
77	19	21.49	-2.491
78	21	21.49	-0.4874
79	21	21.08	-0.07775
80	21	21.99	-0.9927
81	23	21.44	1.565
82	23	21.34	1.658
83	20	21.51	-1.512
84	19	20.84	-1.84
85	23	21.36	1.642
86	22	21.2	0.7958
87	19	20.58	-1.579
88	23	20.89	2.108
89	22	21.33	0.6745
90	22	21.36	0.6358
91	21	21.94	-0.9375
92	21	20.98	0.01764
93	21	21.21	-0.2119
94	21	21.96	-0.9598
95	22	21.34	0.6582
96	25	21.41	3.59
97	23	22.03	0.9656
98	22	20.36	1.635
99	20	21.11	-1.107
100	21	21.11	-0.1073
101	25	20.92	4.082
102	21	21.09	-0.09426
103	19	20.7	-1.7
104	23	21.35	1.652
105	22	20.51	1.485
106	21	21.09	-0.09132
107	24	20.98	3.019
108	21	19.74	1.255
109	19	21.48	-2.484
110	18	20.89	-2.892
111	19	20.48	-1.479
112	20	20.73	-0.733
113	19	21.14	-2.143
114	22	21.29	0.7145
115	21	20.69	0.3101
116	22	21.11	0.8892
117	24	20.56	3.437
118	28	21.34	6.655
119	19	20.55	-1.551
120	18	20.35	-2.345
121	23	20.95	2.055
122	19	20.67	-1.673
123	23	20.74	2.262
124	19	20.61	-1.607
125	22	21.57	0.4329
126	21	20.53	0.4692
127	19	20.8	-1.796
128	21	20.59	0.414
129	23	21.32	1.676
130	22	21	1.005
131	19	20.81	-1.814
132	19	21.14	-2.136
133	21	21.07	-0.0748
134	22	21.17	0.8343
135	21	21.49	-0.4906
136	20	21.47	-1.471
137	23	20.7	2.298
138	22	21.56	0.4364
139	23	21.11	1.893
140	22	21.06	0.9412
141	21	20.38	0.6159
142	20	20.47	-0.4657
143	18	21.31	-3.312
144	18	20.86	-2.856
145	20	21.31	-1.309
146	19	21.2	-2.201
147	21	20.9	0.1002
148	24	21.56	2.436
149	19	21.36	-2.361
150	20	21.33	-1.331
151	19	20.91	-1.909
152	23	21.21	1.786
153	22	21.19	0.8052
154	21	20.61	0.3881
155	24	22	1.998
156	21	21.33	-0.3287
157	21	21.22	-0.2205
158	22	22.19	-0.192

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  22 &  21.57 &  0.4304 \tabularnewline
2 &  24 &  21.79 &  2.206 \tabularnewline
3 &  21 &  21.2 & -0.201 \tabularnewline
4 &  21 &  20.58 &  0.417 \tabularnewline
5 &  24 &  21.17 &  2.831 \tabularnewline
6 &  20 &  21.49 & -1.488 \tabularnewline
7 &  22 &  20.4 &  1.595 \tabularnewline
8 &  20 &  21.11 & -1.108 \tabularnewline
9 &  19 &  20.95 & -1.95 \tabularnewline
10 &  23 &  20.91 &  2.086 \tabularnewline
11 &  21 &  21.34 & -0.3407 \tabularnewline
12 &  19 &  20.83 & -1.83 \tabularnewline
13 &  21 &  21.78 & -0.7821 \tabularnewline
14 &  21 &  21.06 & -0.05678 \tabularnewline
15 &  22 &  21.24 &  0.7556 \tabularnewline
16 &  22 &  21.34 &  0.658 \tabularnewline
17 &  21 &  20.09 &  0.9095 \tabularnewline
18 &  21 &  21.22 & -0.2205 \tabularnewline
19 &  21 &  20.71 &  0.2865 \tabularnewline
20 &  20 &  21.34 & -1.341 \tabularnewline
21 &  22 &  21.34 &  0.6582 \tabularnewline
22 &  22 &  21.12 &  0.8764 \tabularnewline
23 &  24 &  21.29 &  2.707 \tabularnewline
24 &  21 &  21.08 & -0.07825 \tabularnewline
25 &  19 &  20.89 & -1.892 \tabularnewline
26 &  19 &  21.13 & -2.127 \tabularnewline
27 &  23 &  21.42 &  1.58 \tabularnewline
28 &  21 &  21.14 & -0.1428 \tabularnewline
29 &  19 &  20.98 & -1.979 \tabularnewline
30 &  21 &  21.37 & -0.374 \tabularnewline
31 &  21 &  21.67 & -0.6741 \tabularnewline
32 &  21 &  20.71 &  0.2906 \tabularnewline
33 &  23 &  21.04 &  1.957 \tabularnewline
34 &  19 &  21.2 & -2.202 \tabularnewline
35 &  19 &  21.65 & -2.651 \tabularnewline
36 &  19 &  21.07 & -2.075 \tabularnewline
37 &  18 &  21.11 & -3.11 \tabularnewline
38 &  22 &  20.95 &  1.053 \tabularnewline
39 &  22 &  20.45 &  1.552 \tabularnewline
40 &  18 &  20.58 & -2.576 \tabularnewline
41 &  22 &  20.62 &  1.381 \tabularnewline
42 &  22 &  21.06 &  0.9383 \tabularnewline
43 &  19 &  19.91 & -0.9128 \tabularnewline
44 &  22 &  21.22 &  0.7795 \tabularnewline
45 &  19 &  20.63 & -1.635 \tabularnewline
46 &  19 &  20.87 & -1.868 \tabularnewline
47 &  19 &  20.62 & -1.618 \tabularnewline
48 &  19 &  21.89 & -2.892 \tabularnewline
49 &  21 &  21.29 & -0.2866 \tabularnewline
50 &  21 &  21.07 & -0.0748 \tabularnewline
51 &  20 &  20.47 & -0.4699 \tabularnewline
52 &  19 &  20.83 & -1.828 \tabularnewline
53 &  19 &  20.66 & -1.657 \tabularnewline
54 &  22 &  21.17 &  0.8283 \tabularnewline
55 &  26 &  20.95 &  5.047 \tabularnewline
56 &  19 &  21.42 & -2.423 \tabularnewline
57 &  21 &  21.45 & -0.4549 \tabularnewline
58 &  21 &  20.89 &  0.1092 \tabularnewline
59 &  20 &  21.4 & -1.403 \tabularnewline
60 &  23 &  22.06 &  0.9444 \tabularnewline
61 &  22 &  20.41 &  1.594 \tabularnewline
62 &  22 &  20.66 &  1.343 \tabularnewline
63 &  22 &  20.59 &  1.413 \tabularnewline
64 &  21 &  20.86 &  0.1408 \tabularnewline
65 &  21 &  20.72 &  0.2833 \tabularnewline
66 &  22 &  21.34 &  0.6593 \tabularnewline
67 &  18 &  21.06 & -3.062 \tabularnewline
68 &  24 &  21.82 &  2.184 \tabularnewline
69 &  22 &  21.1 &  0.9023 \tabularnewline
70 &  21 &  21.27 & -0.2693 \tabularnewline
71 &  21 &  20.86 &  0.144 \tabularnewline
72 &  21 &  21.72 & -0.717 \tabularnewline
73 &  23 &  21.58 &  1.42 \tabularnewline
74 &  21 &  21.34 & -0.3418 \tabularnewline
75 &  23 &  21.59 &  1.413 \tabularnewline
76 &  21 &  21.19 & -0.188 \tabularnewline
77 &  19 &  21.49 & -2.491 \tabularnewline
78 &  21 &  21.49 & -0.4874 \tabularnewline
79 &  21 &  21.08 & -0.07775 \tabularnewline
80 &  21 &  21.99 & -0.9927 \tabularnewline
81 &  23 &  21.44 &  1.565 \tabularnewline
82 &  23 &  21.34 &  1.658 \tabularnewline
83 &  20 &  21.51 & -1.512 \tabularnewline
84 &  19 &  20.84 & -1.84 \tabularnewline
85 &  23 &  21.36 &  1.642 \tabularnewline
86 &  22 &  21.2 &  0.7958 \tabularnewline
87 &  19 &  20.58 & -1.579 \tabularnewline
88 &  23 &  20.89 &  2.108 \tabularnewline
89 &  22 &  21.33 &  0.6745 \tabularnewline
90 &  22 &  21.36 &  0.6358 \tabularnewline
91 &  21 &  21.94 & -0.9375 \tabularnewline
92 &  21 &  20.98 &  0.01764 \tabularnewline
93 &  21 &  21.21 & -0.2119 \tabularnewline
94 &  21 &  21.96 & -0.9598 \tabularnewline
95 &  22 &  21.34 &  0.6582 \tabularnewline
96 &  25 &  21.41 &  3.59 \tabularnewline
97 &  23 &  22.03 &  0.9656 \tabularnewline
98 &  22 &  20.36 &  1.635 \tabularnewline
99 &  20 &  21.11 & -1.107 \tabularnewline
100 &  21 &  21.11 & -0.1073 \tabularnewline
101 &  25 &  20.92 &  4.082 \tabularnewline
102 &  21 &  21.09 & -0.09426 \tabularnewline
103 &  19 &  20.7 & -1.7 \tabularnewline
104 &  23 &  21.35 &  1.652 \tabularnewline
105 &  22 &  20.51 &  1.485 \tabularnewline
106 &  21 &  21.09 & -0.09132 \tabularnewline
107 &  24 &  20.98 &  3.019 \tabularnewline
108 &  21 &  19.74 &  1.255 \tabularnewline
109 &  19 &  21.48 & -2.484 \tabularnewline
110 &  18 &  20.89 & -2.892 \tabularnewline
111 &  19 &  20.48 & -1.479 \tabularnewline
112 &  20 &  20.73 & -0.733 \tabularnewline
113 &  19 &  21.14 & -2.143 \tabularnewline
114 &  22 &  21.29 &  0.7145 \tabularnewline
115 &  21 &  20.69 &  0.3101 \tabularnewline
116 &  22 &  21.11 &  0.8892 \tabularnewline
117 &  24 &  20.56 &  3.437 \tabularnewline
118 &  28 &  21.34 &  6.655 \tabularnewline
119 &  19 &  20.55 & -1.551 \tabularnewline
120 &  18 &  20.35 & -2.345 \tabularnewline
121 &  23 &  20.95 &  2.055 \tabularnewline
122 &  19 &  20.67 & -1.673 \tabularnewline
123 &  23 &  20.74 &  2.262 \tabularnewline
124 &  19 &  20.61 & -1.607 \tabularnewline
125 &  22 &  21.57 &  0.4329 \tabularnewline
126 &  21 &  20.53 &  0.4692 \tabularnewline
127 &  19 &  20.8 & -1.796 \tabularnewline
128 &  21 &  20.59 &  0.414 \tabularnewline
129 &  23 &  21.32 &  1.676 \tabularnewline
130 &  22 &  21 &  1.005 \tabularnewline
131 &  19 &  20.81 & -1.814 \tabularnewline
132 &  19 &  21.14 & -2.136 \tabularnewline
133 &  21 &  21.07 & -0.0748 \tabularnewline
134 &  22 &  21.17 &  0.8343 \tabularnewline
135 &  21 &  21.49 & -0.4906 \tabularnewline
136 &  20 &  21.47 & -1.471 \tabularnewline
137 &  23 &  20.7 &  2.298 \tabularnewline
138 &  22 &  21.56 &  0.4364 \tabularnewline
139 &  23 &  21.11 &  1.893 \tabularnewline
140 &  22 &  21.06 &  0.9412 \tabularnewline
141 &  21 &  20.38 &  0.6159 \tabularnewline
142 &  20 &  20.47 & -0.4657 \tabularnewline
143 &  18 &  21.31 & -3.312 \tabularnewline
144 &  18 &  20.86 & -2.856 \tabularnewline
145 &  20 &  21.31 & -1.309 \tabularnewline
146 &  19 &  21.2 & -2.201 \tabularnewline
147 &  21 &  20.9 &  0.1002 \tabularnewline
148 &  24 &  21.56 &  2.436 \tabularnewline
149 &  19 &  21.36 & -2.361 \tabularnewline
150 &  20 &  21.33 & -1.331 \tabularnewline
151 &  19 &  20.91 & -1.909 \tabularnewline
152 &  23 &  21.21 &  1.786 \tabularnewline
153 &  22 &  21.19 &  0.8052 \tabularnewline
154 &  21 &  20.61 &  0.3881 \tabularnewline
155 &  24 &  22 &  1.998 \tabularnewline
156 &  21 &  21.33 & -0.3287 \tabularnewline
157 &  21 &  21.22 & -0.2205 \tabularnewline
158 &  22 &  22.19 & -0.192 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&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] 22[/C][C] 21.57[/C][C] 0.4304[/C][/ROW]
[ROW][C]2[/C][C] 24[/C][C] 21.79[/C][C] 2.206[/C][/ROW]
[ROW][C]3[/C][C] 21[/C][C] 21.2[/C][C]-0.201[/C][/ROW]
[ROW][C]4[/C][C] 21[/C][C] 20.58[/C][C] 0.417[/C][/ROW]
[ROW][C]5[/C][C] 24[/C][C] 21.17[/C][C] 2.831[/C][/ROW]
[ROW][C]6[/C][C] 20[/C][C] 21.49[/C][C]-1.488[/C][/ROW]
[ROW][C]7[/C][C] 22[/C][C] 20.4[/C][C] 1.595[/C][/ROW]
[ROW][C]8[/C][C] 20[/C][C] 21.11[/C][C]-1.108[/C][/ROW]
[ROW][C]9[/C][C] 19[/C][C] 20.95[/C][C]-1.95[/C][/ROW]
[ROW][C]10[/C][C] 23[/C][C] 20.91[/C][C] 2.086[/C][/ROW]
[ROW][C]11[/C][C] 21[/C][C] 21.34[/C][C]-0.3407[/C][/ROW]
[ROW][C]12[/C][C] 19[/C][C] 20.83[/C][C]-1.83[/C][/ROW]
[ROW][C]13[/C][C] 21[/C][C] 21.78[/C][C]-0.7821[/C][/ROW]
[ROW][C]14[/C][C] 21[/C][C] 21.06[/C][C]-0.05678[/C][/ROW]
[ROW][C]15[/C][C] 22[/C][C] 21.24[/C][C] 0.7556[/C][/ROW]
[ROW][C]16[/C][C] 22[/C][C] 21.34[/C][C] 0.658[/C][/ROW]
[ROW][C]17[/C][C] 21[/C][C] 20.09[/C][C] 0.9095[/C][/ROW]
[ROW][C]18[/C][C] 21[/C][C] 21.22[/C][C]-0.2205[/C][/ROW]
[ROW][C]19[/C][C] 21[/C][C] 20.71[/C][C] 0.2865[/C][/ROW]
[ROW][C]20[/C][C] 20[/C][C] 21.34[/C][C]-1.341[/C][/ROW]
[ROW][C]21[/C][C] 22[/C][C] 21.34[/C][C] 0.6582[/C][/ROW]
[ROW][C]22[/C][C] 22[/C][C] 21.12[/C][C] 0.8764[/C][/ROW]
[ROW][C]23[/C][C] 24[/C][C] 21.29[/C][C] 2.707[/C][/ROW]
[ROW][C]24[/C][C] 21[/C][C] 21.08[/C][C]-0.07825[/C][/ROW]
[ROW][C]25[/C][C] 19[/C][C] 20.89[/C][C]-1.892[/C][/ROW]
[ROW][C]26[/C][C] 19[/C][C] 21.13[/C][C]-2.127[/C][/ROW]
[ROW][C]27[/C][C] 23[/C][C] 21.42[/C][C] 1.58[/C][/ROW]
[ROW][C]28[/C][C] 21[/C][C] 21.14[/C][C]-0.1428[/C][/ROW]
[ROW][C]29[/C][C] 19[/C][C] 20.98[/C][C]-1.979[/C][/ROW]
[ROW][C]30[/C][C] 21[/C][C] 21.37[/C][C]-0.374[/C][/ROW]
[ROW][C]31[/C][C] 21[/C][C] 21.67[/C][C]-0.6741[/C][/ROW]
[ROW][C]32[/C][C] 21[/C][C] 20.71[/C][C] 0.2906[/C][/ROW]
[ROW][C]33[/C][C] 23[/C][C] 21.04[/C][C] 1.957[/C][/ROW]
[ROW][C]34[/C][C] 19[/C][C] 21.2[/C][C]-2.202[/C][/ROW]
[ROW][C]35[/C][C] 19[/C][C] 21.65[/C][C]-2.651[/C][/ROW]
[ROW][C]36[/C][C] 19[/C][C] 21.07[/C][C]-2.075[/C][/ROW]
[ROW][C]37[/C][C] 18[/C][C] 21.11[/C][C]-3.11[/C][/ROW]
[ROW][C]38[/C][C] 22[/C][C] 20.95[/C][C] 1.053[/C][/ROW]
[ROW][C]39[/C][C] 22[/C][C] 20.45[/C][C] 1.552[/C][/ROW]
[ROW][C]40[/C][C] 18[/C][C] 20.58[/C][C]-2.576[/C][/ROW]
[ROW][C]41[/C][C] 22[/C][C] 20.62[/C][C] 1.381[/C][/ROW]
[ROW][C]42[/C][C] 22[/C][C] 21.06[/C][C] 0.9383[/C][/ROW]
[ROW][C]43[/C][C] 19[/C][C] 19.91[/C][C]-0.9128[/C][/ROW]
[ROW][C]44[/C][C] 22[/C][C] 21.22[/C][C] 0.7795[/C][/ROW]
[ROW][C]45[/C][C] 19[/C][C] 20.63[/C][C]-1.635[/C][/ROW]
[ROW][C]46[/C][C] 19[/C][C] 20.87[/C][C]-1.868[/C][/ROW]
[ROW][C]47[/C][C] 19[/C][C] 20.62[/C][C]-1.618[/C][/ROW]
[ROW][C]48[/C][C] 19[/C][C] 21.89[/C][C]-2.892[/C][/ROW]
[ROW][C]49[/C][C] 21[/C][C] 21.29[/C][C]-0.2866[/C][/ROW]
[ROW][C]50[/C][C] 21[/C][C] 21.07[/C][C]-0.0748[/C][/ROW]
[ROW][C]51[/C][C] 20[/C][C] 20.47[/C][C]-0.4699[/C][/ROW]
[ROW][C]52[/C][C] 19[/C][C] 20.83[/C][C]-1.828[/C][/ROW]
[ROW][C]53[/C][C] 19[/C][C] 20.66[/C][C]-1.657[/C][/ROW]
[ROW][C]54[/C][C] 22[/C][C] 21.17[/C][C] 0.8283[/C][/ROW]
[ROW][C]55[/C][C] 26[/C][C] 20.95[/C][C] 5.047[/C][/ROW]
[ROW][C]56[/C][C] 19[/C][C] 21.42[/C][C]-2.423[/C][/ROW]
[ROW][C]57[/C][C] 21[/C][C] 21.45[/C][C]-0.4549[/C][/ROW]
[ROW][C]58[/C][C] 21[/C][C] 20.89[/C][C] 0.1092[/C][/ROW]
[ROW][C]59[/C][C] 20[/C][C] 21.4[/C][C]-1.403[/C][/ROW]
[ROW][C]60[/C][C] 23[/C][C] 22.06[/C][C] 0.9444[/C][/ROW]
[ROW][C]61[/C][C] 22[/C][C] 20.41[/C][C] 1.594[/C][/ROW]
[ROW][C]62[/C][C] 22[/C][C] 20.66[/C][C] 1.343[/C][/ROW]
[ROW][C]63[/C][C] 22[/C][C] 20.59[/C][C] 1.413[/C][/ROW]
[ROW][C]64[/C][C] 21[/C][C] 20.86[/C][C] 0.1408[/C][/ROW]
[ROW][C]65[/C][C] 21[/C][C] 20.72[/C][C] 0.2833[/C][/ROW]
[ROW][C]66[/C][C] 22[/C][C] 21.34[/C][C] 0.6593[/C][/ROW]
[ROW][C]67[/C][C] 18[/C][C] 21.06[/C][C]-3.062[/C][/ROW]
[ROW][C]68[/C][C] 24[/C][C] 21.82[/C][C] 2.184[/C][/ROW]
[ROW][C]69[/C][C] 22[/C][C] 21.1[/C][C] 0.9023[/C][/ROW]
[ROW][C]70[/C][C] 21[/C][C] 21.27[/C][C]-0.2693[/C][/ROW]
[ROW][C]71[/C][C] 21[/C][C] 20.86[/C][C] 0.144[/C][/ROW]
[ROW][C]72[/C][C] 21[/C][C] 21.72[/C][C]-0.717[/C][/ROW]
[ROW][C]73[/C][C] 23[/C][C] 21.58[/C][C] 1.42[/C][/ROW]
[ROW][C]74[/C][C] 21[/C][C] 21.34[/C][C]-0.3418[/C][/ROW]
[ROW][C]75[/C][C] 23[/C][C] 21.59[/C][C] 1.413[/C][/ROW]
[ROW][C]76[/C][C] 21[/C][C] 21.19[/C][C]-0.188[/C][/ROW]
[ROW][C]77[/C][C] 19[/C][C] 21.49[/C][C]-2.491[/C][/ROW]
[ROW][C]78[/C][C] 21[/C][C] 21.49[/C][C]-0.4874[/C][/ROW]
[ROW][C]79[/C][C] 21[/C][C] 21.08[/C][C]-0.07775[/C][/ROW]
[ROW][C]80[/C][C] 21[/C][C] 21.99[/C][C]-0.9927[/C][/ROW]
[ROW][C]81[/C][C] 23[/C][C] 21.44[/C][C] 1.565[/C][/ROW]
[ROW][C]82[/C][C] 23[/C][C] 21.34[/C][C] 1.658[/C][/ROW]
[ROW][C]83[/C][C] 20[/C][C] 21.51[/C][C]-1.512[/C][/ROW]
[ROW][C]84[/C][C] 19[/C][C] 20.84[/C][C]-1.84[/C][/ROW]
[ROW][C]85[/C][C] 23[/C][C] 21.36[/C][C] 1.642[/C][/ROW]
[ROW][C]86[/C][C] 22[/C][C] 21.2[/C][C] 0.7958[/C][/ROW]
[ROW][C]87[/C][C] 19[/C][C] 20.58[/C][C]-1.579[/C][/ROW]
[ROW][C]88[/C][C] 23[/C][C] 20.89[/C][C] 2.108[/C][/ROW]
[ROW][C]89[/C][C] 22[/C][C] 21.33[/C][C] 0.6745[/C][/ROW]
[ROW][C]90[/C][C] 22[/C][C] 21.36[/C][C] 0.6358[/C][/ROW]
[ROW][C]91[/C][C] 21[/C][C] 21.94[/C][C]-0.9375[/C][/ROW]
[ROW][C]92[/C][C] 21[/C][C] 20.98[/C][C] 0.01764[/C][/ROW]
[ROW][C]93[/C][C] 21[/C][C] 21.21[/C][C]-0.2119[/C][/ROW]
[ROW][C]94[/C][C] 21[/C][C] 21.96[/C][C]-0.9598[/C][/ROW]
[ROW][C]95[/C][C] 22[/C][C] 21.34[/C][C] 0.6582[/C][/ROW]
[ROW][C]96[/C][C] 25[/C][C] 21.41[/C][C] 3.59[/C][/ROW]
[ROW][C]97[/C][C] 23[/C][C] 22.03[/C][C] 0.9656[/C][/ROW]
[ROW][C]98[/C][C] 22[/C][C] 20.36[/C][C] 1.635[/C][/ROW]
[ROW][C]99[/C][C] 20[/C][C] 21.11[/C][C]-1.107[/C][/ROW]
[ROW][C]100[/C][C] 21[/C][C] 21.11[/C][C]-0.1073[/C][/ROW]
[ROW][C]101[/C][C] 25[/C][C] 20.92[/C][C] 4.082[/C][/ROW]
[ROW][C]102[/C][C] 21[/C][C] 21.09[/C][C]-0.09426[/C][/ROW]
[ROW][C]103[/C][C] 19[/C][C] 20.7[/C][C]-1.7[/C][/ROW]
[ROW][C]104[/C][C] 23[/C][C] 21.35[/C][C] 1.652[/C][/ROW]
[ROW][C]105[/C][C] 22[/C][C] 20.51[/C][C] 1.485[/C][/ROW]
[ROW][C]106[/C][C] 21[/C][C] 21.09[/C][C]-0.09132[/C][/ROW]
[ROW][C]107[/C][C] 24[/C][C] 20.98[/C][C] 3.019[/C][/ROW]
[ROW][C]108[/C][C] 21[/C][C] 19.74[/C][C] 1.255[/C][/ROW]
[ROW][C]109[/C][C] 19[/C][C] 21.48[/C][C]-2.484[/C][/ROW]
[ROW][C]110[/C][C] 18[/C][C] 20.89[/C][C]-2.892[/C][/ROW]
[ROW][C]111[/C][C] 19[/C][C] 20.48[/C][C]-1.479[/C][/ROW]
[ROW][C]112[/C][C] 20[/C][C] 20.73[/C][C]-0.733[/C][/ROW]
[ROW][C]113[/C][C] 19[/C][C] 21.14[/C][C]-2.143[/C][/ROW]
[ROW][C]114[/C][C] 22[/C][C] 21.29[/C][C] 0.7145[/C][/ROW]
[ROW][C]115[/C][C] 21[/C][C] 20.69[/C][C] 0.3101[/C][/ROW]
[ROW][C]116[/C][C] 22[/C][C] 21.11[/C][C] 0.8892[/C][/ROW]
[ROW][C]117[/C][C] 24[/C][C] 20.56[/C][C] 3.437[/C][/ROW]
[ROW][C]118[/C][C] 28[/C][C] 21.34[/C][C] 6.655[/C][/ROW]
[ROW][C]119[/C][C] 19[/C][C] 20.55[/C][C]-1.551[/C][/ROW]
[ROW][C]120[/C][C] 18[/C][C] 20.35[/C][C]-2.345[/C][/ROW]
[ROW][C]121[/C][C] 23[/C][C] 20.95[/C][C] 2.055[/C][/ROW]
[ROW][C]122[/C][C] 19[/C][C] 20.67[/C][C]-1.673[/C][/ROW]
[ROW][C]123[/C][C] 23[/C][C] 20.74[/C][C] 2.262[/C][/ROW]
[ROW][C]124[/C][C] 19[/C][C] 20.61[/C][C]-1.607[/C][/ROW]
[ROW][C]125[/C][C] 22[/C][C] 21.57[/C][C] 0.4329[/C][/ROW]
[ROW][C]126[/C][C] 21[/C][C] 20.53[/C][C] 0.4692[/C][/ROW]
[ROW][C]127[/C][C] 19[/C][C] 20.8[/C][C]-1.796[/C][/ROW]
[ROW][C]128[/C][C] 21[/C][C] 20.59[/C][C] 0.414[/C][/ROW]
[ROW][C]129[/C][C] 23[/C][C] 21.32[/C][C] 1.676[/C][/ROW]
[ROW][C]130[/C][C] 22[/C][C] 21[/C][C] 1.005[/C][/ROW]
[ROW][C]131[/C][C] 19[/C][C] 20.81[/C][C]-1.814[/C][/ROW]
[ROW][C]132[/C][C] 19[/C][C] 21.14[/C][C]-2.136[/C][/ROW]
[ROW][C]133[/C][C] 21[/C][C] 21.07[/C][C]-0.0748[/C][/ROW]
[ROW][C]134[/C][C] 22[/C][C] 21.17[/C][C] 0.8343[/C][/ROW]
[ROW][C]135[/C][C] 21[/C][C] 21.49[/C][C]-0.4906[/C][/ROW]
[ROW][C]136[/C][C] 20[/C][C] 21.47[/C][C]-1.471[/C][/ROW]
[ROW][C]137[/C][C] 23[/C][C] 20.7[/C][C] 2.298[/C][/ROW]
[ROW][C]138[/C][C] 22[/C][C] 21.56[/C][C] 0.4364[/C][/ROW]
[ROW][C]139[/C][C] 23[/C][C] 21.11[/C][C] 1.893[/C][/ROW]
[ROW][C]140[/C][C] 22[/C][C] 21.06[/C][C] 0.9412[/C][/ROW]
[ROW][C]141[/C][C] 21[/C][C] 20.38[/C][C] 0.6159[/C][/ROW]
[ROW][C]142[/C][C] 20[/C][C] 20.47[/C][C]-0.4657[/C][/ROW]
[ROW][C]143[/C][C] 18[/C][C] 21.31[/C][C]-3.312[/C][/ROW]
[ROW][C]144[/C][C] 18[/C][C] 20.86[/C][C]-2.856[/C][/ROW]
[ROW][C]145[/C][C] 20[/C][C] 21.31[/C][C]-1.309[/C][/ROW]
[ROW][C]146[/C][C] 19[/C][C] 21.2[/C][C]-2.201[/C][/ROW]
[ROW][C]147[/C][C] 21[/C][C] 20.9[/C][C] 0.1002[/C][/ROW]
[ROW][C]148[/C][C] 24[/C][C] 21.56[/C][C] 2.436[/C][/ROW]
[ROW][C]149[/C][C] 19[/C][C] 21.36[/C][C]-2.361[/C][/ROW]
[ROW][C]150[/C][C] 20[/C][C] 21.33[/C][C]-1.331[/C][/ROW]
[ROW][C]151[/C][C] 19[/C][C] 20.91[/C][C]-1.909[/C][/ROW]
[ROW][C]152[/C][C] 23[/C][C] 21.21[/C][C] 1.786[/C][/ROW]
[ROW][C]153[/C][C] 22[/C][C] 21.19[/C][C] 0.8052[/C][/ROW]
[ROW][C]154[/C][C] 21[/C][C] 20.61[/C][C] 0.3881[/C][/ROW]
[ROW][C]155[/C][C] 24[/C][C] 22[/C][C] 1.998[/C][/ROW]
[ROW][C]156[/C][C] 21[/C][C] 21.33[/C][C]-0.3287[/C][/ROW]
[ROW][C]157[/C][C] 21[/C][C] 21.22[/C][C]-0.2205[/C][/ROW]
[ROW][C]158[/C][C] 22[/C][C] 22.19[/C][C]-0.192[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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	22	21.57	0.4304
2	24	21.79	2.206
3	21	21.2	-0.201
4	21	20.58	0.417
5	24	21.17	2.831
6	20	21.49	-1.488
7	22	20.4	1.595
8	20	21.11	-1.108
9	19	20.95	-1.95
10	23	20.91	2.086
11	21	21.34	-0.3407
12	19	20.83	-1.83
13	21	21.78	-0.7821
14	21	21.06	-0.05678
15	22	21.24	0.7556
16	22	21.34	0.658
17	21	20.09	0.9095
18	21	21.22	-0.2205
19	21	20.71	0.2865
20	20	21.34	-1.341
21	22	21.34	0.6582
22	22	21.12	0.8764
23	24	21.29	2.707
24	21	21.08	-0.07825
25	19	20.89	-1.892
26	19	21.13	-2.127
27	23	21.42	1.58
28	21	21.14	-0.1428
29	19	20.98	-1.979
30	21	21.37	-0.374
31	21	21.67	-0.6741
32	21	20.71	0.2906
33	23	21.04	1.957
34	19	21.2	-2.202
35	19	21.65	-2.651
36	19	21.07	-2.075
37	18	21.11	-3.11
38	22	20.95	1.053
39	22	20.45	1.552
40	18	20.58	-2.576
41	22	20.62	1.381
42	22	21.06	0.9383
43	19	19.91	-0.9128
44	22	21.22	0.7795
45	19	20.63	-1.635
46	19	20.87	-1.868
47	19	20.62	-1.618
48	19	21.89	-2.892
49	21	21.29	-0.2866
50	21	21.07	-0.0748
51	20	20.47	-0.4699
52	19	20.83	-1.828
53	19	20.66	-1.657
54	22	21.17	0.8283
55	26	20.95	5.047
56	19	21.42	-2.423
57	21	21.45	-0.4549
58	21	20.89	0.1092
59	20	21.4	-1.403
60	23	22.06	0.9444
61	22	20.41	1.594
62	22	20.66	1.343
63	22	20.59	1.413
64	21	20.86	0.1408
65	21	20.72	0.2833
66	22	21.34	0.6593
67	18	21.06	-3.062
68	24	21.82	2.184
69	22	21.1	0.9023
70	21	21.27	-0.2693
71	21	20.86	0.144
72	21	21.72	-0.717
73	23	21.58	1.42
74	21	21.34	-0.3418
75	23	21.59	1.413
76	21	21.19	-0.188
77	19	21.49	-2.491
78	21	21.49	-0.4874
79	21	21.08	-0.07775
80	21	21.99	-0.9927
81	23	21.44	1.565
82	23	21.34	1.658
83	20	21.51	-1.512
84	19	20.84	-1.84
85	23	21.36	1.642
86	22	21.2	0.7958
87	19	20.58	-1.579
88	23	20.89	2.108
89	22	21.33	0.6745
90	22	21.36	0.6358
91	21	21.94	-0.9375
92	21	20.98	0.01764
93	21	21.21	-0.2119
94	21	21.96	-0.9598
95	22	21.34	0.6582
96	25	21.41	3.59
97	23	22.03	0.9656
98	22	20.36	1.635
99	20	21.11	-1.107
100	21	21.11	-0.1073
101	25	20.92	4.082
102	21	21.09	-0.09426
103	19	20.7	-1.7
104	23	21.35	1.652
105	22	20.51	1.485
106	21	21.09	-0.09132
107	24	20.98	3.019
108	21	19.74	1.255
109	19	21.48	-2.484
110	18	20.89	-2.892
111	19	20.48	-1.479
112	20	20.73	-0.733
113	19	21.14	-2.143
114	22	21.29	0.7145
115	21	20.69	0.3101
116	22	21.11	0.8892
117	24	20.56	3.437
118	28	21.34	6.655
119	19	20.55	-1.551
120	18	20.35	-2.345
121	23	20.95	2.055
122	19	20.67	-1.673
123	23	20.74	2.262
124	19	20.61	-1.607
125	22	21.57	0.4329
126	21	20.53	0.4692
127	19	20.8	-1.796
128	21	20.59	0.414
129	23	21.32	1.676
130	22	21	1.005
131	19	20.81	-1.814
132	19	21.14	-2.136
133	21	21.07	-0.0748
134	22	21.17	0.8343
135	21	21.49	-0.4906
136	20	21.47	-1.471
137	23	20.7	2.298
138	22	21.56	0.4364
139	23	21.11	1.893
140	22	21.06	0.9412
141	21	20.38	0.6159
142	20	20.47	-0.4657
143	18	21.31	-3.312
144	18	20.86	-2.856
145	20	21.31	-1.309
146	19	21.2	-2.201
147	21	20.9	0.1002
148	24	21.56	2.436
149	19	21.36	-2.361
150	20	21.33	-1.331
151	19	20.91	-1.909
152	23	21.21	1.786
153	22	21.19	0.8052
154	21	20.61	0.3881
155	24	22	1.998
156	21	21.33	-0.3287
157	21	21.22	-0.2205
158	22	22.19	-0.192

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
11	0.769	0.462	0.231
12	0.7067	0.5867	0.2933
13	0.5757	0.8486	0.4243
14	0.4949	0.9898	0.5051
15	0.3971	0.7943	0.6029
16	0.424	0.8479	0.576
17	0.3251	0.6503	0.6749
18	0.2826	0.5651	0.7174
19	0.215	0.4301	0.785
20	0.2146	0.4293	0.7854
21	0.1642	0.3284	0.8358
22	0.1178	0.2356	0.8822
23	0.179	0.3581	0.8209
24	0.1323	0.2646	0.8677
25	0.1052	0.2105	0.8948
26	0.1863	0.3727	0.8136
27	0.2224	0.4447	0.7776
28	0.1806	0.3611	0.8194
29	0.1952	0.3903	0.8048
30	0.1527	0.3053	0.8473
31	0.1338	0.2675	0.8662
32	0.1087	0.2175	0.8913
33	0.09542	0.1908	0.9046
34	0.1131	0.2261	0.8869
35	0.1493	0.2986	0.8507
36	0.2184	0.4368	0.7816
37	0.3372	0.6744	0.6628
38	0.3012	0.6023	0.6988
39	0.287	0.5741	0.713
40	0.3524	0.7047	0.6476
41	0.3234	0.6468	0.6766
42	0.2836	0.5672	0.7164
43	0.2611	0.5223	0.7389
44	0.224	0.4481	0.776
45	0.2232	0.4465	0.7768
46	0.2123	0.4245	0.7877
47	0.2047	0.4094	0.7953
48	0.2671	0.5341	0.7329
49	0.2315	0.463	0.7685
50	0.1929	0.3858	0.8071
51	0.1609	0.3218	0.8391
52	0.1705	0.3409	0.8295
53	0.1627	0.3253	0.8373
54	0.1403	0.2805	0.8597
55	0.4243	0.8485	0.5757
56	0.4829	0.9658	0.5171
57	0.4356	0.8713	0.5644
58	0.3956	0.7912	0.6044
59	0.3776	0.7553	0.6224
60	0.3596	0.7192	0.6404
61	0.3627	0.7254	0.6373
62	0.3489	0.6978	0.6511
63	0.334	0.668	0.666
64	0.2908	0.5816	0.7092
65	0.252	0.5041	0.748
66	0.2275	0.4549	0.7725
67	0.3135	0.6271	0.6865
68	0.3495	0.699	0.6505
69	0.3178	0.6356	0.6822
70	0.2782	0.5565	0.7218
71	0.2405	0.481	0.7595
72	0.2099	0.4199	0.7901
73	0.2044	0.4087	0.7956
74	0.1747	0.3495	0.8253
75	0.1652	0.3305	0.8348
76	0.1393	0.2786	0.8607
77	0.1634	0.3268	0.8366
78	0.1383	0.2767	0.8617
79	0.1161	0.2323	0.8839
80	0.1015	0.2031	0.8985
81	0.09895	0.1979	0.9011
82	0.0974	0.1948	0.9026
83	0.09185	0.1837	0.9081
84	0.09372	0.1874	0.9063
85	0.09081	0.1816	0.9092
86	0.07765	0.1553	0.9223
87	0.07464	0.1493	0.9254
88	0.08321	0.1664	0.9168
89	0.0685	0.137	0.9315
90	0.05639	0.1128	0.9436
91	0.04894	0.09788	0.9511
92	0.03858	0.07717	0.9614
93	0.03156	0.06312	0.9684
94	0.02677	0.05355	0.9732
95	0.02086	0.04171	0.9791
96	0.04421	0.08843	0.9558
97	0.03781	0.07562	0.9622
98	0.03611	0.07221	0.9639
99	0.03142	0.06283	0.9686
100	0.02407	0.04814	0.9759
101	0.09047	0.1809	0.9095
102	0.07197	0.1439	0.928
103	0.06516	0.1303	0.9348
104	0.06517	0.1303	0.9348
105	0.06544	0.1309	0.9346
106	0.05122	0.1024	0.9488
107	0.06898	0.138	0.931
108	0.07519	0.1504	0.9248
109	0.1009	0.2017	0.8991
110	0.1545	0.309	0.8455
111	0.1348	0.2696	0.8652
112	0.1113	0.2227	0.8887
113	0.1507	0.3015	0.8493
114	0.1248	0.2496	0.8752
115	0.09967	0.1993	0.9003
116	0.0806	0.1612	0.9194
117	0.1536	0.3073	0.8464
118	0.8323	0.3353	0.1677
119	0.8091	0.3817	0.1909
120	0.9011	0.1978	0.09889
121	0.8922	0.2156	0.1078
122	0.8887	0.2226	0.1113
123	0.8938	0.2124	0.1062
124	0.8719	0.2562	0.1281
125	0.8356	0.3288	0.1644
126	0.8073	0.3854	0.1927
127	0.8228	0.3545	0.1772
128	0.7946	0.4108	0.2054
129	0.7925	0.415	0.2075
130	0.8236	0.3529	0.1764
131	0.8138	0.3723	0.1862
132	0.783	0.434	0.217
133	0.7241	0.5519	0.2759
134	0.659	0.682	0.341
135	0.5895	0.821	0.4105
136	0.5209	0.9583	0.4792
137	0.6544	0.6912	0.3456
138	0.5722	0.8556	0.4278
139	0.6069	0.7863	0.3931
140	0.5264	0.9472	0.4736
141	0.4421	0.8842	0.5579
142	0.4065	0.813	0.5935
143	0.4972	0.9943	0.5028
144	0.4569	0.9138	0.5431
145	0.3554	0.7109	0.6446
146	0.2423	0.4845	0.7577
147	0.3173	0.6346	0.6827

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
11 &  0.769 &  0.462 &  0.231 \tabularnewline
12 &  0.7067 &  0.5867 &  0.2933 \tabularnewline
13 &  0.5757 &  0.8486 &  0.4243 \tabularnewline
14 &  0.4949 &  0.9898 &  0.5051 \tabularnewline
15 &  0.3971 &  0.7943 &  0.6029 \tabularnewline
16 &  0.424 &  0.8479 &  0.576 \tabularnewline
17 &  0.3251 &  0.6503 &  0.6749 \tabularnewline
18 &  0.2826 &  0.5651 &  0.7174 \tabularnewline
19 &  0.215 &  0.4301 &  0.785 \tabularnewline
20 &  0.2146 &  0.4293 &  0.7854 \tabularnewline
21 &  0.1642 &  0.3284 &  0.8358 \tabularnewline
22 &  0.1178 &  0.2356 &  0.8822 \tabularnewline
23 &  0.179 &  0.3581 &  0.8209 \tabularnewline
24 &  0.1323 &  0.2646 &  0.8677 \tabularnewline
25 &  0.1052 &  0.2105 &  0.8948 \tabularnewline
26 &  0.1863 &  0.3727 &  0.8136 \tabularnewline
27 &  0.2224 &  0.4447 &  0.7776 \tabularnewline
28 &  0.1806 &  0.3611 &  0.8194 \tabularnewline
29 &  0.1952 &  0.3903 &  0.8048 \tabularnewline
30 &  0.1527 &  0.3053 &  0.8473 \tabularnewline
31 &  0.1338 &  0.2675 &  0.8662 \tabularnewline
32 &  0.1087 &  0.2175 &  0.8913 \tabularnewline
33 &  0.09542 &  0.1908 &  0.9046 \tabularnewline
34 &  0.1131 &  0.2261 &  0.8869 \tabularnewline
35 &  0.1493 &  0.2986 &  0.8507 \tabularnewline
36 &  0.2184 &  0.4368 &  0.7816 \tabularnewline
37 &  0.3372 &  0.6744 &  0.6628 \tabularnewline
38 &  0.3012 &  0.6023 &  0.6988 \tabularnewline
39 &  0.287 &  0.5741 &  0.713 \tabularnewline
40 &  0.3524 &  0.7047 &  0.6476 \tabularnewline
41 &  0.3234 &  0.6468 &  0.6766 \tabularnewline
42 &  0.2836 &  0.5672 &  0.7164 \tabularnewline
43 &  0.2611 &  0.5223 &  0.7389 \tabularnewline
44 &  0.224 &  0.4481 &  0.776 \tabularnewline
45 &  0.2232 &  0.4465 &  0.7768 \tabularnewline
46 &  0.2123 &  0.4245 &  0.7877 \tabularnewline
47 &  0.2047 &  0.4094 &  0.7953 \tabularnewline
48 &  0.2671 &  0.5341 &  0.7329 \tabularnewline
49 &  0.2315 &  0.463 &  0.7685 \tabularnewline
50 &  0.1929 &  0.3858 &  0.8071 \tabularnewline
51 &  0.1609 &  0.3218 &  0.8391 \tabularnewline
52 &  0.1705 &  0.3409 &  0.8295 \tabularnewline
53 &  0.1627 &  0.3253 &  0.8373 \tabularnewline
54 &  0.1403 &  0.2805 &  0.8597 \tabularnewline
55 &  0.4243 &  0.8485 &  0.5757 \tabularnewline
56 &  0.4829 &  0.9658 &  0.5171 \tabularnewline
57 &  0.4356 &  0.8713 &  0.5644 \tabularnewline
58 &  0.3956 &  0.7912 &  0.6044 \tabularnewline
59 &  0.3776 &  0.7553 &  0.6224 \tabularnewline
60 &  0.3596 &  0.7192 &  0.6404 \tabularnewline
61 &  0.3627 &  0.7254 &  0.6373 \tabularnewline
62 &  0.3489 &  0.6978 &  0.6511 \tabularnewline
63 &  0.334 &  0.668 &  0.666 \tabularnewline
64 &  0.2908 &  0.5816 &  0.7092 \tabularnewline
65 &  0.252 &  0.5041 &  0.748 \tabularnewline
66 &  0.2275 &  0.4549 &  0.7725 \tabularnewline
67 &  0.3135 &  0.6271 &  0.6865 \tabularnewline
68 &  0.3495 &  0.699 &  0.6505 \tabularnewline
69 &  0.3178 &  0.6356 &  0.6822 \tabularnewline
70 &  0.2782 &  0.5565 &  0.7218 \tabularnewline
71 &  0.2405 &  0.481 &  0.7595 \tabularnewline
72 &  0.2099 &  0.4199 &  0.7901 \tabularnewline
73 &  0.2044 &  0.4087 &  0.7956 \tabularnewline
74 &  0.1747 &  0.3495 &  0.8253 \tabularnewline
75 &  0.1652 &  0.3305 &  0.8348 \tabularnewline
76 &  0.1393 &  0.2786 &  0.8607 \tabularnewline
77 &  0.1634 &  0.3268 &  0.8366 \tabularnewline
78 &  0.1383 &  0.2767 &  0.8617 \tabularnewline
79 &  0.1161 &  0.2323 &  0.8839 \tabularnewline
80 &  0.1015 &  0.2031 &  0.8985 \tabularnewline
81 &  0.09895 &  0.1979 &  0.9011 \tabularnewline
82 &  0.0974 &  0.1948 &  0.9026 \tabularnewline
83 &  0.09185 &  0.1837 &  0.9081 \tabularnewline
84 &  0.09372 &  0.1874 &  0.9063 \tabularnewline
85 &  0.09081 &  0.1816 &  0.9092 \tabularnewline
86 &  0.07765 &  0.1553 &  0.9223 \tabularnewline
87 &  0.07464 &  0.1493 &  0.9254 \tabularnewline
88 &  0.08321 &  0.1664 &  0.9168 \tabularnewline
89 &  0.0685 &  0.137 &  0.9315 \tabularnewline
90 &  0.05639 &  0.1128 &  0.9436 \tabularnewline
91 &  0.04894 &  0.09788 &  0.9511 \tabularnewline
92 &  0.03858 &  0.07717 &  0.9614 \tabularnewline
93 &  0.03156 &  0.06312 &  0.9684 \tabularnewline
94 &  0.02677 &  0.05355 &  0.9732 \tabularnewline
95 &  0.02086 &  0.04171 &  0.9791 \tabularnewline
96 &  0.04421 &  0.08843 &  0.9558 \tabularnewline
97 &  0.03781 &  0.07562 &  0.9622 \tabularnewline
98 &  0.03611 &  0.07221 &  0.9639 \tabularnewline
99 &  0.03142 &  0.06283 &  0.9686 \tabularnewline
100 &  0.02407 &  0.04814 &  0.9759 \tabularnewline
101 &  0.09047 &  0.1809 &  0.9095 \tabularnewline
102 &  0.07197 &  0.1439 &  0.928 \tabularnewline
103 &  0.06516 &  0.1303 &  0.9348 \tabularnewline
104 &  0.06517 &  0.1303 &  0.9348 \tabularnewline
105 &  0.06544 &  0.1309 &  0.9346 \tabularnewline
106 &  0.05122 &  0.1024 &  0.9488 \tabularnewline
107 &  0.06898 &  0.138 &  0.931 \tabularnewline
108 &  0.07519 &  0.1504 &  0.9248 \tabularnewline
109 &  0.1009 &  0.2017 &  0.8991 \tabularnewline
110 &  0.1545 &  0.309 &  0.8455 \tabularnewline
111 &  0.1348 &  0.2696 &  0.8652 \tabularnewline
112 &  0.1113 &  0.2227 &  0.8887 \tabularnewline
113 &  0.1507 &  0.3015 &  0.8493 \tabularnewline
114 &  0.1248 &  0.2496 &  0.8752 \tabularnewline
115 &  0.09967 &  0.1993 &  0.9003 \tabularnewline
116 &  0.0806 &  0.1612 &  0.9194 \tabularnewline
117 &  0.1536 &  0.3073 &  0.8464 \tabularnewline
118 &  0.8323 &  0.3353 &  0.1677 \tabularnewline
119 &  0.8091 &  0.3817 &  0.1909 \tabularnewline
120 &  0.9011 &  0.1978 &  0.09889 \tabularnewline
121 &  0.8922 &  0.2156 &  0.1078 \tabularnewline
122 &  0.8887 &  0.2226 &  0.1113 \tabularnewline
123 &  0.8938 &  0.2124 &  0.1062 \tabularnewline
124 &  0.8719 &  0.2562 &  0.1281 \tabularnewline
125 &  0.8356 &  0.3288 &  0.1644 \tabularnewline
126 &  0.8073 &  0.3854 &  0.1927 \tabularnewline
127 &  0.8228 &  0.3545 &  0.1772 \tabularnewline
128 &  0.7946 &  0.4108 &  0.2054 \tabularnewline
129 &  0.7925 &  0.415 &  0.2075 \tabularnewline
130 &  0.8236 &  0.3529 &  0.1764 \tabularnewline
131 &  0.8138 &  0.3723 &  0.1862 \tabularnewline
132 &  0.783 &  0.434 &  0.217 \tabularnewline
133 &  0.7241 &  0.5519 &  0.2759 \tabularnewline
134 &  0.659 &  0.682 &  0.341 \tabularnewline
135 &  0.5895 &  0.821 &  0.4105 \tabularnewline
136 &  0.5209 &  0.9583 &  0.4792 \tabularnewline
137 &  0.6544 &  0.6912 &  0.3456 \tabularnewline
138 &  0.5722 &  0.8556 &  0.4278 \tabularnewline
139 &  0.6069 &  0.7863 &  0.3931 \tabularnewline
140 &  0.5264 &  0.9472 &  0.4736 \tabularnewline
141 &  0.4421 &  0.8842 &  0.5579 \tabularnewline
142 &  0.4065 &  0.813 &  0.5935 \tabularnewline
143 &  0.4972 &  0.9943 &  0.5028 \tabularnewline
144 &  0.4569 &  0.9138 &  0.5431 \tabularnewline
145 &  0.3554 &  0.7109 &  0.6446 \tabularnewline
146 &  0.2423 &  0.4845 &  0.7577 \tabularnewline
147 &  0.3173 &  0.6346 &  0.6827 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&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]11[/C][C] 0.769[/C][C] 0.462[/C][C] 0.231[/C][/ROW]
[ROW][C]12[/C][C] 0.7067[/C][C] 0.5867[/C][C] 0.2933[/C][/ROW]
[ROW][C]13[/C][C] 0.5757[/C][C] 0.8486[/C][C] 0.4243[/C][/ROW]
[ROW][C]14[/C][C] 0.4949[/C][C] 0.9898[/C][C] 0.5051[/C][/ROW]
[ROW][C]15[/C][C] 0.3971[/C][C] 0.7943[/C][C] 0.6029[/C][/ROW]
[ROW][C]16[/C][C] 0.424[/C][C] 0.8479[/C][C] 0.576[/C][/ROW]
[ROW][C]17[/C][C] 0.3251[/C][C] 0.6503[/C][C] 0.6749[/C][/ROW]
[ROW][C]18[/C][C] 0.2826[/C][C] 0.5651[/C][C] 0.7174[/C][/ROW]
[ROW][C]19[/C][C] 0.215[/C][C] 0.4301[/C][C] 0.785[/C][/ROW]
[ROW][C]20[/C][C] 0.2146[/C][C] 0.4293[/C][C] 0.7854[/C][/ROW]
[ROW][C]21[/C][C] 0.1642[/C][C] 0.3284[/C][C] 0.8358[/C][/ROW]
[ROW][C]22[/C][C] 0.1178[/C][C] 0.2356[/C][C] 0.8822[/C][/ROW]
[ROW][C]23[/C][C] 0.179[/C][C] 0.3581[/C][C] 0.8209[/C][/ROW]
[ROW][C]24[/C][C] 0.1323[/C][C] 0.2646[/C][C] 0.8677[/C][/ROW]
[ROW][C]25[/C][C] 0.1052[/C][C] 0.2105[/C][C] 0.8948[/C][/ROW]
[ROW][C]26[/C][C] 0.1863[/C][C] 0.3727[/C][C] 0.8136[/C][/ROW]
[ROW][C]27[/C][C] 0.2224[/C][C] 0.4447[/C][C] 0.7776[/C][/ROW]
[ROW][C]28[/C][C] 0.1806[/C][C] 0.3611[/C][C] 0.8194[/C][/ROW]
[ROW][C]29[/C][C] 0.1952[/C][C] 0.3903[/C][C] 0.8048[/C][/ROW]
[ROW][C]30[/C][C] 0.1527[/C][C] 0.3053[/C][C] 0.8473[/C][/ROW]
[ROW][C]31[/C][C] 0.1338[/C][C] 0.2675[/C][C] 0.8662[/C][/ROW]
[ROW][C]32[/C][C] 0.1087[/C][C] 0.2175[/C][C] 0.8913[/C][/ROW]
[ROW][C]33[/C][C] 0.09542[/C][C] 0.1908[/C][C] 0.9046[/C][/ROW]
[ROW][C]34[/C][C] 0.1131[/C][C] 0.2261[/C][C] 0.8869[/C][/ROW]
[ROW][C]35[/C][C] 0.1493[/C][C] 0.2986[/C][C] 0.8507[/C][/ROW]
[ROW][C]36[/C][C] 0.2184[/C][C] 0.4368[/C][C] 0.7816[/C][/ROW]
[ROW][C]37[/C][C] 0.3372[/C][C] 0.6744[/C][C] 0.6628[/C][/ROW]
[ROW][C]38[/C][C] 0.3012[/C][C] 0.6023[/C][C] 0.6988[/C][/ROW]
[ROW][C]39[/C][C] 0.287[/C][C] 0.5741[/C][C] 0.713[/C][/ROW]
[ROW][C]40[/C][C] 0.3524[/C][C] 0.7047[/C][C] 0.6476[/C][/ROW]
[ROW][C]41[/C][C] 0.3234[/C][C] 0.6468[/C][C] 0.6766[/C][/ROW]
[ROW][C]42[/C][C] 0.2836[/C][C] 0.5672[/C][C] 0.7164[/C][/ROW]
[ROW][C]43[/C][C] 0.2611[/C][C] 0.5223[/C][C] 0.7389[/C][/ROW]
[ROW][C]44[/C][C] 0.224[/C][C] 0.4481[/C][C] 0.776[/C][/ROW]
[ROW][C]45[/C][C] 0.2232[/C][C] 0.4465[/C][C] 0.7768[/C][/ROW]
[ROW][C]46[/C][C] 0.2123[/C][C] 0.4245[/C][C] 0.7877[/C][/ROW]
[ROW][C]47[/C][C] 0.2047[/C][C] 0.4094[/C][C] 0.7953[/C][/ROW]
[ROW][C]48[/C][C] 0.2671[/C][C] 0.5341[/C][C] 0.7329[/C][/ROW]
[ROW][C]49[/C][C] 0.2315[/C][C] 0.463[/C][C] 0.7685[/C][/ROW]
[ROW][C]50[/C][C] 0.1929[/C][C] 0.3858[/C][C] 0.8071[/C][/ROW]
[ROW][C]51[/C][C] 0.1609[/C][C] 0.3218[/C][C] 0.8391[/C][/ROW]
[ROW][C]52[/C][C] 0.1705[/C][C] 0.3409[/C][C] 0.8295[/C][/ROW]
[ROW][C]53[/C][C] 0.1627[/C][C] 0.3253[/C][C] 0.8373[/C][/ROW]
[ROW][C]54[/C][C] 0.1403[/C][C] 0.2805[/C][C] 0.8597[/C][/ROW]
[ROW][C]55[/C][C] 0.4243[/C][C] 0.8485[/C][C] 0.5757[/C][/ROW]
[ROW][C]56[/C][C] 0.4829[/C][C] 0.9658[/C][C] 0.5171[/C][/ROW]
[ROW][C]57[/C][C] 0.4356[/C][C] 0.8713[/C][C] 0.5644[/C][/ROW]
[ROW][C]58[/C][C] 0.3956[/C][C] 0.7912[/C][C] 0.6044[/C][/ROW]
[ROW][C]59[/C][C] 0.3776[/C][C] 0.7553[/C][C] 0.6224[/C][/ROW]
[ROW][C]60[/C][C] 0.3596[/C][C] 0.7192[/C][C] 0.6404[/C][/ROW]
[ROW][C]61[/C][C] 0.3627[/C][C] 0.7254[/C][C] 0.6373[/C][/ROW]
[ROW][C]62[/C][C] 0.3489[/C][C] 0.6978[/C][C] 0.6511[/C][/ROW]
[ROW][C]63[/C][C] 0.334[/C][C] 0.668[/C][C] 0.666[/C][/ROW]
[ROW][C]64[/C][C] 0.2908[/C][C] 0.5816[/C][C] 0.7092[/C][/ROW]
[ROW][C]65[/C][C] 0.252[/C][C] 0.5041[/C][C] 0.748[/C][/ROW]
[ROW][C]66[/C][C] 0.2275[/C][C] 0.4549[/C][C] 0.7725[/C][/ROW]
[ROW][C]67[/C][C] 0.3135[/C][C] 0.6271[/C][C] 0.6865[/C][/ROW]
[ROW][C]68[/C][C] 0.3495[/C][C] 0.699[/C][C] 0.6505[/C][/ROW]
[ROW][C]69[/C][C] 0.3178[/C][C] 0.6356[/C][C] 0.6822[/C][/ROW]
[ROW][C]70[/C][C] 0.2782[/C][C] 0.5565[/C][C] 0.7218[/C][/ROW]
[ROW][C]71[/C][C] 0.2405[/C][C] 0.481[/C][C] 0.7595[/C][/ROW]
[ROW][C]72[/C][C] 0.2099[/C][C] 0.4199[/C][C] 0.7901[/C][/ROW]
[ROW][C]73[/C][C] 0.2044[/C][C] 0.4087[/C][C] 0.7956[/C][/ROW]
[ROW][C]74[/C][C] 0.1747[/C][C] 0.3495[/C][C] 0.8253[/C][/ROW]
[ROW][C]75[/C][C] 0.1652[/C][C] 0.3305[/C][C] 0.8348[/C][/ROW]
[ROW][C]76[/C][C] 0.1393[/C][C] 0.2786[/C][C] 0.8607[/C][/ROW]
[ROW][C]77[/C][C] 0.1634[/C][C] 0.3268[/C][C] 0.8366[/C][/ROW]
[ROW][C]78[/C][C] 0.1383[/C][C] 0.2767[/C][C] 0.8617[/C][/ROW]
[ROW][C]79[/C][C] 0.1161[/C][C] 0.2323[/C][C] 0.8839[/C][/ROW]
[ROW][C]80[/C][C] 0.1015[/C][C] 0.2031[/C][C] 0.8985[/C][/ROW]
[ROW][C]81[/C][C] 0.09895[/C][C] 0.1979[/C][C] 0.9011[/C][/ROW]
[ROW][C]82[/C][C] 0.0974[/C][C] 0.1948[/C][C] 0.9026[/C][/ROW]
[ROW][C]83[/C][C] 0.09185[/C][C] 0.1837[/C][C] 0.9081[/C][/ROW]
[ROW][C]84[/C][C] 0.09372[/C][C] 0.1874[/C][C] 0.9063[/C][/ROW]
[ROW][C]85[/C][C] 0.09081[/C][C] 0.1816[/C][C] 0.9092[/C][/ROW]
[ROW][C]86[/C][C] 0.07765[/C][C] 0.1553[/C][C] 0.9223[/C][/ROW]
[ROW][C]87[/C][C] 0.07464[/C][C] 0.1493[/C][C] 0.9254[/C][/ROW]
[ROW][C]88[/C][C] 0.08321[/C][C] 0.1664[/C][C] 0.9168[/C][/ROW]
[ROW][C]89[/C][C] 0.0685[/C][C] 0.137[/C][C] 0.9315[/C][/ROW]
[ROW][C]90[/C][C] 0.05639[/C][C] 0.1128[/C][C] 0.9436[/C][/ROW]
[ROW][C]91[/C][C] 0.04894[/C][C] 0.09788[/C][C] 0.9511[/C][/ROW]
[ROW][C]92[/C][C] 0.03858[/C][C] 0.07717[/C][C] 0.9614[/C][/ROW]
[ROW][C]93[/C][C] 0.03156[/C][C] 0.06312[/C][C] 0.9684[/C][/ROW]
[ROW][C]94[/C][C] 0.02677[/C][C] 0.05355[/C][C] 0.9732[/C][/ROW]
[ROW][C]95[/C][C] 0.02086[/C][C] 0.04171[/C][C] 0.9791[/C][/ROW]
[ROW][C]96[/C][C] 0.04421[/C][C] 0.08843[/C][C] 0.9558[/C][/ROW]
[ROW][C]97[/C][C] 0.03781[/C][C] 0.07562[/C][C] 0.9622[/C][/ROW]
[ROW][C]98[/C][C] 0.03611[/C][C] 0.07221[/C][C] 0.9639[/C][/ROW]
[ROW][C]99[/C][C] 0.03142[/C][C] 0.06283[/C][C] 0.9686[/C][/ROW]
[ROW][C]100[/C][C] 0.02407[/C][C] 0.04814[/C][C] 0.9759[/C][/ROW]
[ROW][C]101[/C][C] 0.09047[/C][C] 0.1809[/C][C] 0.9095[/C][/ROW]
[ROW][C]102[/C][C] 0.07197[/C][C] 0.1439[/C][C] 0.928[/C][/ROW]
[ROW][C]103[/C][C] 0.06516[/C][C] 0.1303[/C][C] 0.9348[/C][/ROW]
[ROW][C]104[/C][C] 0.06517[/C][C] 0.1303[/C][C] 0.9348[/C][/ROW]
[ROW][C]105[/C][C] 0.06544[/C][C] 0.1309[/C][C] 0.9346[/C][/ROW]
[ROW][C]106[/C][C] 0.05122[/C][C] 0.1024[/C][C] 0.9488[/C][/ROW]
[ROW][C]107[/C][C] 0.06898[/C][C] 0.138[/C][C] 0.931[/C][/ROW]
[ROW][C]108[/C][C] 0.07519[/C][C] 0.1504[/C][C] 0.9248[/C][/ROW]
[ROW][C]109[/C][C] 0.1009[/C][C] 0.2017[/C][C] 0.8991[/C][/ROW]
[ROW][C]110[/C][C] 0.1545[/C][C] 0.309[/C][C] 0.8455[/C][/ROW]
[ROW][C]111[/C][C] 0.1348[/C][C] 0.2696[/C][C] 0.8652[/C][/ROW]
[ROW][C]112[/C][C] 0.1113[/C][C] 0.2227[/C][C] 0.8887[/C][/ROW]
[ROW][C]113[/C][C] 0.1507[/C][C] 0.3015[/C][C] 0.8493[/C][/ROW]
[ROW][C]114[/C][C] 0.1248[/C][C] 0.2496[/C][C] 0.8752[/C][/ROW]
[ROW][C]115[/C][C] 0.09967[/C][C] 0.1993[/C][C] 0.9003[/C][/ROW]
[ROW][C]116[/C][C] 0.0806[/C][C] 0.1612[/C][C] 0.9194[/C][/ROW]
[ROW][C]117[/C][C] 0.1536[/C][C] 0.3073[/C][C] 0.8464[/C][/ROW]
[ROW][C]118[/C][C] 0.8323[/C][C] 0.3353[/C][C] 0.1677[/C][/ROW]
[ROW][C]119[/C][C] 0.8091[/C][C] 0.3817[/C][C] 0.1909[/C][/ROW]
[ROW][C]120[/C][C] 0.9011[/C][C] 0.1978[/C][C] 0.09889[/C][/ROW]
[ROW][C]121[/C][C] 0.8922[/C][C] 0.2156[/C][C] 0.1078[/C][/ROW]
[ROW][C]122[/C][C] 0.8887[/C][C] 0.2226[/C][C] 0.1113[/C][/ROW]
[ROW][C]123[/C][C] 0.8938[/C][C] 0.2124[/C][C] 0.1062[/C][/ROW]
[ROW][C]124[/C][C] 0.8719[/C][C] 0.2562[/C][C] 0.1281[/C][/ROW]
[ROW][C]125[/C][C] 0.8356[/C][C] 0.3288[/C][C] 0.1644[/C][/ROW]
[ROW][C]126[/C][C] 0.8073[/C][C] 0.3854[/C][C] 0.1927[/C][/ROW]
[ROW][C]127[/C][C] 0.8228[/C][C] 0.3545[/C][C] 0.1772[/C][/ROW]
[ROW][C]128[/C][C] 0.7946[/C][C] 0.4108[/C][C] 0.2054[/C][/ROW]
[ROW][C]129[/C][C] 0.7925[/C][C] 0.415[/C][C] 0.2075[/C][/ROW]
[ROW][C]130[/C][C] 0.8236[/C][C] 0.3529[/C][C] 0.1764[/C][/ROW]
[ROW][C]131[/C][C] 0.8138[/C][C] 0.3723[/C][C] 0.1862[/C][/ROW]
[ROW][C]132[/C][C] 0.783[/C][C] 0.434[/C][C] 0.217[/C][/ROW]
[ROW][C]133[/C][C] 0.7241[/C][C] 0.5519[/C][C] 0.2759[/C][/ROW]
[ROW][C]134[/C][C] 0.659[/C][C] 0.682[/C][C] 0.341[/C][/ROW]
[ROW][C]135[/C][C] 0.5895[/C][C] 0.821[/C][C] 0.4105[/C][/ROW]
[ROW][C]136[/C][C] 0.5209[/C][C] 0.9583[/C][C] 0.4792[/C][/ROW]
[ROW][C]137[/C][C] 0.6544[/C][C] 0.6912[/C][C] 0.3456[/C][/ROW]
[ROW][C]138[/C][C] 0.5722[/C][C] 0.8556[/C][C] 0.4278[/C][/ROW]
[ROW][C]139[/C][C] 0.6069[/C][C] 0.7863[/C][C] 0.3931[/C][/ROW]
[ROW][C]140[/C][C] 0.5264[/C][C] 0.9472[/C][C] 0.4736[/C][/ROW]
[ROW][C]141[/C][C] 0.4421[/C][C] 0.8842[/C][C] 0.5579[/C][/ROW]
[ROW][C]142[/C][C] 0.4065[/C][C] 0.813[/C][C] 0.5935[/C][/ROW]
[ROW][C]143[/C][C] 0.4972[/C][C] 0.9943[/C][C] 0.5028[/C][/ROW]
[ROW][C]144[/C][C] 0.4569[/C][C] 0.9138[/C][C] 0.5431[/C][/ROW]
[ROW][C]145[/C][C] 0.3554[/C][C] 0.7109[/C][C] 0.6446[/C][/ROW]
[ROW][C]146[/C][C] 0.2423[/C][C] 0.4845[/C][C] 0.7577[/C][/ROW]
[ROW][C]147[/C][C] 0.3173[/C][C] 0.6346[/C][C] 0.6827[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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
11	0.769	0.462	0.231
12	0.7067	0.5867	0.2933
13	0.5757	0.8486	0.4243
14	0.4949	0.9898	0.5051
15	0.3971	0.7943	0.6029
16	0.424	0.8479	0.576
17	0.3251	0.6503	0.6749
18	0.2826	0.5651	0.7174
19	0.215	0.4301	0.785
20	0.2146	0.4293	0.7854
21	0.1642	0.3284	0.8358
22	0.1178	0.2356	0.8822
23	0.179	0.3581	0.8209
24	0.1323	0.2646	0.8677
25	0.1052	0.2105	0.8948
26	0.1863	0.3727	0.8136
27	0.2224	0.4447	0.7776
28	0.1806	0.3611	0.8194
29	0.1952	0.3903	0.8048
30	0.1527	0.3053	0.8473
31	0.1338	0.2675	0.8662
32	0.1087	0.2175	0.8913
33	0.09542	0.1908	0.9046
34	0.1131	0.2261	0.8869
35	0.1493	0.2986	0.8507
36	0.2184	0.4368	0.7816
37	0.3372	0.6744	0.6628
38	0.3012	0.6023	0.6988
39	0.287	0.5741	0.713
40	0.3524	0.7047	0.6476
41	0.3234	0.6468	0.6766
42	0.2836	0.5672	0.7164
43	0.2611	0.5223	0.7389
44	0.224	0.4481	0.776
45	0.2232	0.4465	0.7768
46	0.2123	0.4245	0.7877
47	0.2047	0.4094	0.7953
48	0.2671	0.5341	0.7329
49	0.2315	0.463	0.7685
50	0.1929	0.3858	0.8071
51	0.1609	0.3218	0.8391
52	0.1705	0.3409	0.8295
53	0.1627	0.3253	0.8373
54	0.1403	0.2805	0.8597
55	0.4243	0.8485	0.5757
56	0.4829	0.9658	0.5171
57	0.4356	0.8713	0.5644
58	0.3956	0.7912	0.6044
59	0.3776	0.7553	0.6224
60	0.3596	0.7192	0.6404
61	0.3627	0.7254	0.6373
62	0.3489	0.6978	0.6511
63	0.334	0.668	0.666
64	0.2908	0.5816	0.7092
65	0.252	0.5041	0.748
66	0.2275	0.4549	0.7725
67	0.3135	0.6271	0.6865
68	0.3495	0.699	0.6505
69	0.3178	0.6356	0.6822
70	0.2782	0.5565	0.7218
71	0.2405	0.481	0.7595
72	0.2099	0.4199	0.7901
73	0.2044	0.4087	0.7956
74	0.1747	0.3495	0.8253
75	0.1652	0.3305	0.8348
76	0.1393	0.2786	0.8607
77	0.1634	0.3268	0.8366
78	0.1383	0.2767	0.8617
79	0.1161	0.2323	0.8839
80	0.1015	0.2031	0.8985
81	0.09895	0.1979	0.9011
82	0.0974	0.1948	0.9026
83	0.09185	0.1837	0.9081
84	0.09372	0.1874	0.9063
85	0.09081	0.1816	0.9092
86	0.07765	0.1553	0.9223
87	0.07464	0.1493	0.9254
88	0.08321	0.1664	0.9168
89	0.0685	0.137	0.9315
90	0.05639	0.1128	0.9436
91	0.04894	0.09788	0.9511
92	0.03858	0.07717	0.9614
93	0.03156	0.06312	0.9684
94	0.02677	0.05355	0.9732
95	0.02086	0.04171	0.9791
96	0.04421	0.08843	0.9558
97	0.03781	0.07562	0.9622
98	0.03611	0.07221	0.9639
99	0.03142	0.06283	0.9686
100	0.02407	0.04814	0.9759
101	0.09047	0.1809	0.9095
102	0.07197	0.1439	0.928
103	0.06516	0.1303	0.9348
104	0.06517	0.1303	0.9348
105	0.06544	0.1309	0.9346
106	0.05122	0.1024	0.9488
107	0.06898	0.138	0.931
108	0.07519	0.1504	0.9248
109	0.1009	0.2017	0.8991
110	0.1545	0.309	0.8455
111	0.1348	0.2696	0.8652
112	0.1113	0.2227	0.8887
113	0.1507	0.3015	0.8493
114	0.1248	0.2496	0.8752
115	0.09967	0.1993	0.9003
116	0.0806	0.1612	0.9194
117	0.1536	0.3073	0.8464
118	0.8323	0.3353	0.1677
119	0.8091	0.3817	0.1909
120	0.9011	0.1978	0.09889
121	0.8922	0.2156	0.1078
122	0.8887	0.2226	0.1113
123	0.8938	0.2124	0.1062
124	0.8719	0.2562	0.1281
125	0.8356	0.3288	0.1644
126	0.8073	0.3854	0.1927
127	0.8228	0.3545	0.1772
128	0.7946	0.4108	0.2054
129	0.7925	0.415	0.2075
130	0.8236	0.3529	0.1764
131	0.8138	0.3723	0.1862
132	0.783	0.434	0.217
133	0.7241	0.5519	0.2759
134	0.659	0.682	0.341
135	0.5895	0.821	0.4105
136	0.5209	0.9583	0.4792
137	0.6544	0.6912	0.3456
138	0.5722	0.8556	0.4278
139	0.6069	0.7863	0.3931
140	0.5264	0.9472	0.4736
141	0.4421	0.8842	0.5579
142	0.4065	0.813	0.5935
143	0.4972	0.9943	0.5028
144	0.4569	0.9138	0.5431
145	0.3554	0.7109	0.6446
146	0.2423	0.4845	0.7577
147	0.3173	0.6346	0.6827

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	2	0.0145985	OK
10% type I error level	10	0.0729927	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 & 2 & 0.0145985 & OK \tabularnewline
10% type I error level & 10 & 0.0729927 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&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]2[/C][C]0.0145985[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]10[/C][C]0.0729927[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&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	2	0.0145985	OK
10% type I error level	10	0.0729927	OK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.24701, df1 = 2, df2 = 148, p-value = 0.7815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.63699, df1 = 14, df2 = 136, p-value = 0.8302
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.85647, df1 = 2, df2 = 148, p-value = 0.4268

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.24701, df1 = 2, df2 = 148, p-value = 0.7815
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63699, df1 = 14, df2 = 136, p-value = 0.8302
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.85647, df1 = 2, df2 = 148, p-value = 0.4268
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.24701, df1 = 2, df2 = 148, p-value = 0.7815
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.63699, df1 = 14, df2 = 136, p-value = 0.8302
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.85647, df1 = 2, df2 = 148, p-value = 0.4268
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.24701, df1 = 2, df2 = 148, p-value = 0.7815
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.63699, df1 = 14, df2 = 136, p-value = 0.8302
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.85647, df1 = 2, df2 = 148, p-value = 0.4268

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.091640 1.102041 1.135190 1.081087 1.065424 1.068211 1.048667

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
  ITHSUM   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.091640 1.102041 1.135190 1.081087 1.065424 1.068211 1.048667 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306347&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
  ITHSUM   SKEOU1   SKEOU2   SKEOU3   SKEOU4   SKEOU5   SKEOU6 
1.091640 1.102041 1.135190 1.081087 1.065424 1.068211 1.048667 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306347&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306347&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 1.091640 1.102041 1.135190 1.081087 1.065424 1.068211 1.048667

Figure 1

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Figure 9

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Figure 10

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Parameters (Session):

Parameters (R input):

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

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

par5 <- ''
par4 <- ''
par3 <- 'No Linear Trend'
par2 <- 'Do not include Seasonal Dummies'
par1 <- '1'
library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
print(x)
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
sresid <- studres(mylm)
hist(sresid, freq=FALSE, main='Distribution of Studentized Residuals')
xfit<-seq(min(sresid),max(sresid),length=40)
yfit<-dnorm(xfit)
lines(xfit, yfit)
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqPlot(mylm, main='QQ Plot')
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
print(z)
plot(z,main=paste('Residual Lag plot, lowess, and regression line'), ylab='values of Residuals', xlab='lagged values of Residuals')
lines(lowess(z))
abline(lm(z))
grid()
dev.off()
bitmap(file='test6.png')
acf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Autocorrelation Function')
grid()
dev.off()
bitmap(file='test7.png')
pacf(mysum$resid, lag.max=length(mysum$resid)/2, main='Residual Partial Autocorrelation Function')
grid()
dev.off()
bitmap(file='test8.png')
opar <- par(mfrow = c(2,2), oma = c(0, 0, 1.1, 0))
plot(mylm, las = 1, sub='Residual Diagnostics')
par(opar)
dev.off()
if (n > n25) {
bitmap(file='test9.png')
plot(kp3:nmkm3,gqarr[,2], main='Goldfeld-Quandt test',ylab='2-sided p-value',xlab='breakpoint')
grid()
dev.off()
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Estimated Regression Equation', 1, TRUE)
a<-table.row.end(a)
myeq <- colnames(x)[1]
myeq <- paste(myeq, '[t] = ', sep='')
for (i in 1:k){
if (mysum$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Multiple Linear Regression - Ordinary Least Squares', 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
myr <- as.numeric(mysum$resid)
myr
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of fitted values',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_fitted <- resettest(mylm,power=2:3,type='fitted')
a<-table.element(a,paste('',RC.texteval('reset_test_fitted'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of regressors',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_regressors <- resettest(mylm,power=2:3,type='regressor')
a<-table.element(a,paste('',RC.texteval('reset_test_regressors'),'
',sep=''))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Ramsey RESET F-Test for powers (2 and 3) of principal components',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
reset_test_principal_components <- resettest(mylm,power=2:3,type='princomp')
a<-table.element(a,paste('',RC.texteval('reset_test_principal_components'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable8.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Variance Inflation Factors (Multicollinearity)',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
vif <- vif(mylm)
a<-table.element(a,paste('',RC.texteval('vif'),'
',sep=''))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable9.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code