Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 20 Dec 2017 20:35:31 +0100

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/20/t15138058937r6w7syyfw301ky.htm/, Retrieved Tue, 14 May 2024 13:13:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310583, Retrieved Tue, 14 May 2024 13:13:14 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

108

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

-       [Multiple Regression] [MR] [2017-12-20 19:35:31] [10ffd28249f7eed11c347be075080a78] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

97.7 53.1 58.4
88.9 64.1 64.8
96.5 75.3 73.8
89.5 66 65
85.4 73.6 73
84.3 73.2 71.1
83.7 53.5 58.2
86.2 60.6 64
90.7 73 75
95.7 72.4 74.9
95.6 75.8 75
97 79.6 68.3
97.2 77.8 72.5
86.6 75.7 72.4
88.4 88.5 79.6
81.4 72.9 70.7
86.9 80.8 76.4
84.9 86.6 79.7
83.7 63.8 64.2
86.8 69.2 67.9
88.3 76.5 74.1
92.5 77.1 78.5
94.7 75.3 73.4
94.5 69.5 65.4
98.7 64.3 69.9
88.6 66.7 69.6
95.2 77.3 76.8
91.3 75.3 75.6
91.7 73.4 74
89.3 78 76
88.7 61 68.1
91.2 58.4 65.5
88.6 73.4 76.9
94.6 82.3 81.7
96 72.2 73.6
94.3 76 68.7
102 64.3 73.3
93.4 70.8 71.5
96.7 74 78.3
93.7 71.4 76.5
91.6 70.1 71.8
89.6 77.6 77.6
92.9 61.2 70
94.1 52.1 64
92 74.4 81.3
97.5 73.1 82.5
92.7 70.9 73.1
100.7 80.7 78.1
105.9 62.9 70.7
95.3 69.3 74.9
99.8 82.3 88
91.3 76.2 81.3
90.8 70.8 75.7
87.1 87.3 89.8
91.4 62 74.6
86.1 66.9 74.9
87.1 84.4 90
92.6 82.6 88.1
96.6 77.7 84.9
105.3 87 87.7
102.4 76 80.5
98.2 76.3 79
98.6 88.8 89.9
92.6 81.2 86.3
87.9 74.5 81.1
84.1 98.1 92.4
86.7 63.3 71.8
84.4 67.7 76.1
86 85.8 92.5
90.4 78.6 87
92.9 87.2 89.5
105.8 106.4 88.7
106 75 83.8
99.1 80.4 84.9
99.9 94.8 99
88.1 77 84.6
87.8 91 92.7
87.1 96.7 97.6
85.9 69.2 78
86.5 69.5 81.9
84.1 93.7 96.5
92.1 98.5 99.9
93.3 93.3 96.2
98.9 100.4 90.5
103 87.4 91.4
98.4 89 89.7
100.7 106.1 102.7
92.3 92.5 91.5
89 96.6 96.2
88.9 113.3 104.5
85.5 87.6 90.3
90.1 89.2 90.3
87 115.6 100.4
97.1 133.2 111.3
101.5 111.1 101.3
103 113.1 94.4
106.1 102 100.4
96.1 109.3 102
94.2 111.1 104.3
89.1 116.8 108.8
85.2 107.5 101.3
86.5 120.5 108.9
88 95.5 98.5
88.4 87.9 88.8
87.9 118.6 111.8
95.7 116.3 109.6
94.8 98.8 92.5
105.2 102.9 94.5
108.7 80.4 80.8
96.1 87 83.7
98.3 97.4 94.2
88.6 87.2 86.2
90.8 110.6 89
88.1 101.1 94.7
91.9 69.1 81.9
98.5 77.4 80.2
98.6 95 96.5
100.3 93.2 95.6
98.7 96.3 91.9
110.7 93.9 89.9
115.4 78.5 86.3
105.4 90 94
108 109.2 108
94.5 94.3 96.3
96.5 93.1 94.6
91 114.5 111.7
94.1 78.5 92
96.4 88.3 91.9
93.1 114.8 109.2
97.5 112.2 106.8
102.5 106.9 105.8
105.7 119.7 103.6
109.1 97.1 97.6
97.2 106.3 102.8
100.3 131.7 124.8
91.3 106.7 103.9
94.3 124 112.2
89.5 117.2 108.5
89.3 87.8 92.4
93.4 91.9 101.1
91.9 125.1 114.9
92.9 115.4 106.4
93.7 117.7 104
100.1 124.3 101.6
105.5 104.8 99.4
110.5 109.6 102.3
89.5 139.5 121.3
90.4 105.3 99.3
89.9 112.4 102.9
84.6 128.9 111.4
86.2 91.6 98.5
83.4 98.7 98.5
82.9 117.8 108.5
81.8 117.4 112.1
87.6 110.5 105.3
94.6 103.1 95.2
99.6 95.8 98.2
96.7 98.2 96.6
99.8 117.2 109.6
83.8 108.5 108
82.4 113.2 106.7
86.8 120.2 111.5
91 102.8 104.5
85.3 89.4 94.3
83.6 119.8 109.6
94 126.9 116.4
100.3 114.4 106.5
107.1 117.4 100.5
100.7 109.4 101.7
95.5 111.1 104.1
92.9 121 112.3
79.2 116.6 111.2
82 119.5 108.2
79.3 121.2 115.1
81.5 101 102.3
76 92.7 93.6
73.1 125.5 120.6
80.4 123.4 118.4
82.1 110.3 106.6
90.5 118.8 105.3
98.1 97.1 101.5
89.5 107.6 100.1
86.5 131 119.5
77 117.9 111.2
74.7 111 103.7
73.4 131.4 117.8
72.5 101.8 101.7
69.3 93.9 97.4
75.2 138.5 120
83.5 131.1 117
90.5 124.9 110.6
92.2 126.6 105.3
110.5 102.7 100.9
101.8 121.6 108.1
107.4 132.8 119.3
95.5 123 113
84.5 116 108.6
81.1 135 123.3
86.2 93.7 101.4
91.5 98.4 103.5
84.7 129.8 119.4
92.2 121.9 113.1
99.2 124.8 112
104.5 126.9 115.8
113 102 105.4
100.4 117.7 110.9
101 144.8 128.5
84.8 113.3 109
86.5 129.3 117.2
91.7 135.7 124.4
94.8 94.3 104.7
95 106 108.6

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=310583&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=310583&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&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
X14[t] = + 19.908 + 0.06182X64[t] + 0.699727X58[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X14[t] =  +  19.908 +  0.06182X64[t] +  0.699727X58[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X14[t] =  +  19.908 +  0.06182X64[t] +  0.699727X58[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&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
X14[t] = + 19.908 + 0.06182X64[t] + 0.699727X58[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)	+19.91	5.581	+3.5670e+00	0.0004881	0.000244
X64	+0.06182	0.05931	+1.0420e+00	0.299	0.1495
X58	+0.6997	0.02091	+3.3470e+01	1.211e-70	6.055e-71

\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) & +19.91 &  5.581 & +3.5670e+00 &  0.0004881 &  0.000244 \tabularnewline
X64 & +0.06182 &  0.05931 & +1.0420e+00 &  0.299 &  0.1495 \tabularnewline
X58 & +0.6997 &  0.02091 & +3.3470e+01 &  1.211e-70 &  6.055e-71 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&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]+19.91[/C][C] 5.581[/C][C]+3.5670e+00[/C][C] 0.0004881[/C][C] 0.000244[/C][/ROW]
[ROW][C]X64[/C][C]+0.06182[/C][C] 0.05931[/C][C]+1.0420e+00[/C][C] 0.299[/C][C] 0.1495[/C][/ROW]
[ROW][C]X58[/C][C]+0.6997[/C][C] 0.02091[/C][C]+3.3470e+01[/C][C] 1.211e-70[/C][C] 6.055e-71[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&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)	+19.91	5.581	+3.5670e+00	0.0004881	0.000244
X64	+0.06182	0.05931	+1.0420e+00	0.299	0.1495
X58	+0.6997	0.02091	+3.3470e+01	1.211e-70	6.055e-71

Multiple Linear Regression - Regression Statistics
Multiple R	0.9423
R-squared	0.888
Adjusted R-squared	0.8864
F-TEST (value)	582.6
F-TEST (DF numerator)	2
F-TEST (DF denominator)	147
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.776
Sum Squared Residuals	3353

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.9423 \tabularnewline
R-squared &  0.888 \tabularnewline
Adjusted R-squared &  0.8864 \tabularnewline
F-TEST (value) &  582.6 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  4.776 \tabularnewline
Sum Squared Residuals &  3353 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.9423[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.888[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.8864[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 582.6[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]147[/C][/ROW]
[ROW][C]p-value[/C][C] 0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 4.776[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 3353[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&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	0.9423
R-squared	0.888
Adjusted R-squared	0.8864
F-TEST (value)	582.6
F-TEST (DF numerator)	2
F-TEST (DF denominator)	147
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	4.776
Sum Squared Residuals	3353

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

\begin{tabular}{lllllllll}
\hline
Menu of Residual Diagnostics \tabularnewline
Description & Link \tabularnewline
Histogram & Compute \tabularnewline
Central Tendency & Compute \tabularnewline
QQ Plot & Compute \tabularnewline
Kernel Density Plot & Compute \tabularnewline
Skewness/Kurtosis Test & Compute \tabularnewline
Skewness-Kurtosis Plot & Compute \tabularnewline
Harrell-Davis Plot & Compute \tabularnewline
Bootstrap Plot -- Central Tendency & Compute \tabularnewline
Blocked Bootstrap Plot -- Central Tendency & Compute \tabularnewline
(Partial) Autocorrelation Plot & Compute \tabularnewline
Spectral Analysis & Compute \tabularnewline
Tukey lambda PPCC Plot & Compute \tabularnewline
Box-Cox Normality Plot & Compute \tabularnewline
Summary Statistics & Compute \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=4

[TABLE]
[ROW][C]Menu of Residual Diagnostics[/C][/ROW]
[ROW][C]Description[/C][C]Link[/C][/ROW]
[ROW][C]Histogram[/C][C]Compute[/C][/ROW]
[ROW][C]Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]QQ Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Kernel Density Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness/Kurtosis Test[/C][C]Compute[/C][/ROW]
[ROW][C]Skewness-Kurtosis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Harrell-Davis Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C]Blocked Bootstrap Plot -- Central Tendency[/C][C]Compute[/C][/ROW]
[ROW][C](Partial) Autocorrelation Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Spectral Analysis[/C][C]Compute[/C][/ROW]
[ROW][C]Tukey lambda PPCC Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Box-Cox Normality Plot[/C][C]Compute[/C][/ROW]
[ROW][C]Summary Statistics[/C][C]Compute[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=4

As an alternative you can also use a QR Code:

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

Menu of Residual Diagnostics
Description	Link
Histogram	Compute
Central Tendency	Compute
QQ Plot	Compute
Kernel Density Plot	Compute
Skewness/Kurtosis Test	Compute
Skewness-Kurtosis Plot	Compute
Harrell-Davis Plot	Compute
Bootstrap Plot -- Central Tendency	Compute
Blocked Bootstrap Plot -- Central Tendency	Compute
(Partial) Autocorrelation Plot	Compute
Spectral Analysis	Compute
Tukey lambda PPCC Plot	Compute
Box-Cox Normality Plot	Compute
Summary Statistics	Compute

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	58.4	63.1	-4.703
2	64.8	70.26	-5.456
3	73.8	78.56	-4.763
4	65	71.62	-6.623
5	73	76.69	-3.687
6	71.1	76.34	-5.239
7	58.2	62.52	-4.318
8	64	67.64	-3.64
9	75	76.6	-1.595
10	74.9	76.48	-1.584
11	75	78.86	-3.857
12	68.3	81.6	-13.3
13	72.5	80.36	-7.856
14	72.4	78.23	-5.831
15	79.6	87.3	-7.699
16	70.7	75.95	-5.25
17	76.4	81.82	-5.418
18	79.7	85.75	-6.053
19	64.2	69.72	-5.525
20	67.9	73.7	-5.795
21	74.1	78.9	-4.796
22	78.5	79.58	-1.075
23	73.4	78.45	-5.052
24	65.4	74.38	-8.981
25	69.9	71	-1.102
26	69.6	72.06	-2.457
27	76.8	79.88	-3.082
28	75.6	78.24	-2.642
29	74	76.94	-2.937
30	76	80.01	-4.007
31	68.1	68.07	0.02521
32	65.5	66.41	-0.9101
33	76.9	76.75	0.1548
34	81.7	83.34	-1.644
35	73.6	76.36	-2.763
36	68.7	78.92	-10.22
37	73.3	71.21	2.094
38	71.5	75.22	-3.723
39	78.3	77.67	0.6342
40	76.5	75.66	0.8389
41	71.8	74.62	-2.822
42	77.6	79.75	-2.146
43	70	68.47	1.526
44	64	62.18	1.819
45	81.3	77.66	3.645
46	82.5	77.09	5.414
47	73.1	75.25	-2.149
48	78.1	82.6	-4.501
49	70.7	70.47	0.2324
50	74.9	74.29	0.6095
51	88	83.67	4.335
52	81.3	78.87	2.429
53	75.7	75.06	0.6381
54	89.8	86.38	3.421
55	74.6	68.94	5.659
56	74.9	72.04	2.858
57	90	84.35	5.651
58	88.1	83.43	4.67
59	84.9	80.25	4.651
60	87.7	87.29	0.4061
61	80.5	79.42	1.082
62	79	79.37	-0.3679
63	89.9	88.14	1.761
64	86.3	82.45	3.85
65	81.1	77.47	3.628
66	92.4	93.75	-1.35
67	71.8	69.56	2.239
68	76.1	72.5	3.603
69	92.5	85.26	7.239
70	87	80.5	6.505
71	89.5	86.67	2.833
72	88.7	100.9	-12.2
73	83.8	78.94	4.86
74	84.9	82.29	2.608
75	99	92.42	6.582
76	84.6	79.23	5.367
77	92.7	89.01	3.689
78	97.6	92.96	4.644
79	78	73.64	4.361
80	81.9	73.89	8.014
81	96.5	90.67	5.828
82	99.9	94.52	5.375
83	96.2	90.96	5.24
84	90.5	96.27	-5.775
85	91.4	87.43	3.968
86	89.7	88.27	1.433
87	102.7	100.4	2.326
88	91.5	90.34	1.161
89	96.2	93	3.196
90	104.5	104.7	-0.1829
91	90.3	86.49	3.81
92	90.3	87.89	2.406
93	100.4	106.2	-5.775
94	111.3	119.1	-7.814
95	101.3	103.9	-2.622
96	94.4	105.4	-11.01
97	100.4	97.84	2.561
98	102	102.3	-0.3291
99	104.3	103.5	0.8289
100	108.8	107.1	1.656
101	101.3	100.4	0.9043
102	108.9	109.6	-0.6726
103	98.5	92.17	6.328
104	88.8	86.88	1.921
105	111.8	108.3	3.47
106	109.6	107.2	2.398
107	92.5	94.9	-2.402
108	94.5	98.41	-3.913
109	80.8	82.89	-2.086
110	83.7	86.73	-3.025
111	94.2	94.14	0.06166
112	86.2	86.4	-0.2015
113	89	102.9	-13.91
114	94.7	96.1	-1.397
115	81.9	73.94	7.96
116	80.2	80.16	0.04384
117	96.5	92.48	4.022
118	95.6	91.32	4.277
119	91.9	93.39	-1.493
120	89.9	92.46	-2.556
121	86.3	81.97	4.329
122	94	89.4	4.601
123	108	103	5.005
124	96.3	91.73	4.566
125	94.6	91.02	3.582
126	111.7	105.7	6.048
127	92	80.65	11.35
128	91.9	87.65	4.247
129	109.2	106	3.208
130	106.8	104.4	2.355
131	105.8	101	4.755
132	103.6	110.2	-6.6
133	97.6	94.6	3.004
134	102.8	100.3	2.502
135	124.8	118.3	6.537
136	103.9	100.2	3.687
137	112.2	112.5	-0.3038
138	108.5	107.4	1.051
139	92.4	86.86	5.535
140	101.1	89.99	11.11
141	114.9	113.1	1.775
142	106.4	106.4	0.0003907
143	104	108.1	-4.058
144	101.6	113.1	-11.47
145	99.4	99.76	-0.3614
146	102.3	103.4	-1.129
147	121.3	123.1	-1.753
148	99.3	99.18	0.1222
149	102.9	104.1	-1.215
150	111.4	115.3	-3.933

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  58.4 &  63.1 & -4.703 \tabularnewline
2 &  64.8 &  70.26 & -5.456 \tabularnewline
3 &  73.8 &  78.56 & -4.763 \tabularnewline
4 &  65 &  71.62 & -6.623 \tabularnewline
5 &  73 &  76.69 & -3.687 \tabularnewline
6 &  71.1 &  76.34 & -5.239 \tabularnewline
7 &  58.2 &  62.52 & -4.318 \tabularnewline
8 &  64 &  67.64 & -3.64 \tabularnewline
9 &  75 &  76.6 & -1.595 \tabularnewline
10 &  74.9 &  76.48 & -1.584 \tabularnewline
11 &  75 &  78.86 & -3.857 \tabularnewline
12 &  68.3 &  81.6 & -13.3 \tabularnewline
13 &  72.5 &  80.36 & -7.856 \tabularnewline
14 &  72.4 &  78.23 & -5.831 \tabularnewline
15 &  79.6 &  87.3 & -7.699 \tabularnewline
16 &  70.7 &  75.95 & -5.25 \tabularnewline
17 &  76.4 &  81.82 & -5.418 \tabularnewline
18 &  79.7 &  85.75 & -6.053 \tabularnewline
19 &  64.2 &  69.72 & -5.525 \tabularnewline
20 &  67.9 &  73.7 & -5.795 \tabularnewline
21 &  74.1 &  78.9 & -4.796 \tabularnewline
22 &  78.5 &  79.58 & -1.075 \tabularnewline
23 &  73.4 &  78.45 & -5.052 \tabularnewline
24 &  65.4 &  74.38 & -8.981 \tabularnewline
25 &  69.9 &  71 & -1.102 \tabularnewline
26 &  69.6 &  72.06 & -2.457 \tabularnewline
27 &  76.8 &  79.88 & -3.082 \tabularnewline
28 &  75.6 &  78.24 & -2.642 \tabularnewline
29 &  74 &  76.94 & -2.937 \tabularnewline
30 &  76 &  80.01 & -4.007 \tabularnewline
31 &  68.1 &  68.07 &  0.02521 \tabularnewline
32 &  65.5 &  66.41 & -0.9101 \tabularnewline
33 &  76.9 &  76.75 &  0.1548 \tabularnewline
34 &  81.7 &  83.34 & -1.644 \tabularnewline
35 &  73.6 &  76.36 & -2.763 \tabularnewline
36 &  68.7 &  78.92 & -10.22 \tabularnewline
37 &  73.3 &  71.21 &  2.094 \tabularnewline
38 &  71.5 &  75.22 & -3.723 \tabularnewline
39 &  78.3 &  77.67 &  0.6342 \tabularnewline
40 &  76.5 &  75.66 &  0.8389 \tabularnewline
41 &  71.8 &  74.62 & -2.822 \tabularnewline
42 &  77.6 &  79.75 & -2.146 \tabularnewline
43 &  70 &  68.47 &  1.526 \tabularnewline
44 &  64 &  62.18 &  1.819 \tabularnewline
45 &  81.3 &  77.66 &  3.645 \tabularnewline
46 &  82.5 &  77.09 &  5.414 \tabularnewline
47 &  73.1 &  75.25 & -2.149 \tabularnewline
48 &  78.1 &  82.6 & -4.501 \tabularnewline
49 &  70.7 &  70.47 &  0.2324 \tabularnewline
50 &  74.9 &  74.29 &  0.6095 \tabularnewline
51 &  88 &  83.67 &  4.335 \tabularnewline
52 &  81.3 &  78.87 &  2.429 \tabularnewline
53 &  75.7 &  75.06 &  0.6381 \tabularnewline
54 &  89.8 &  86.38 &  3.421 \tabularnewline
55 &  74.6 &  68.94 &  5.659 \tabularnewline
56 &  74.9 &  72.04 &  2.858 \tabularnewline
57 &  90 &  84.35 &  5.651 \tabularnewline
58 &  88.1 &  83.43 &  4.67 \tabularnewline
59 &  84.9 &  80.25 &  4.651 \tabularnewline
60 &  87.7 &  87.29 &  0.4061 \tabularnewline
61 &  80.5 &  79.42 &  1.082 \tabularnewline
62 &  79 &  79.37 & -0.3679 \tabularnewline
63 &  89.9 &  88.14 &  1.761 \tabularnewline
64 &  86.3 &  82.45 &  3.85 \tabularnewline
65 &  81.1 &  77.47 &  3.628 \tabularnewline
66 &  92.4 &  93.75 & -1.35 \tabularnewline
67 &  71.8 &  69.56 &  2.239 \tabularnewline
68 &  76.1 &  72.5 &  3.603 \tabularnewline
69 &  92.5 &  85.26 &  7.239 \tabularnewline
70 &  87 &  80.5 &  6.505 \tabularnewline
71 &  89.5 &  86.67 &  2.833 \tabularnewline
72 &  88.7 &  100.9 & -12.2 \tabularnewline
73 &  83.8 &  78.94 &  4.86 \tabularnewline
74 &  84.9 &  82.29 &  2.608 \tabularnewline
75 &  99 &  92.42 &  6.582 \tabularnewline
76 &  84.6 &  79.23 &  5.367 \tabularnewline
77 &  92.7 &  89.01 &  3.689 \tabularnewline
78 &  97.6 &  92.96 &  4.644 \tabularnewline
79 &  78 &  73.64 &  4.361 \tabularnewline
80 &  81.9 &  73.89 &  8.014 \tabularnewline
81 &  96.5 &  90.67 &  5.828 \tabularnewline
82 &  99.9 &  94.52 &  5.375 \tabularnewline
83 &  96.2 &  90.96 &  5.24 \tabularnewline
84 &  90.5 &  96.27 & -5.775 \tabularnewline
85 &  91.4 &  87.43 &  3.968 \tabularnewline
86 &  89.7 &  88.27 &  1.433 \tabularnewline
87 &  102.7 &  100.4 &  2.326 \tabularnewline
88 &  91.5 &  90.34 &  1.161 \tabularnewline
89 &  96.2 &  93 &  3.196 \tabularnewline
90 &  104.5 &  104.7 & -0.1829 \tabularnewline
91 &  90.3 &  86.49 &  3.81 \tabularnewline
92 &  90.3 &  87.89 &  2.406 \tabularnewline
93 &  100.4 &  106.2 & -5.775 \tabularnewline
94 &  111.3 &  119.1 & -7.814 \tabularnewline
95 &  101.3 &  103.9 & -2.622 \tabularnewline
96 &  94.4 &  105.4 & -11.01 \tabularnewline
97 &  100.4 &  97.84 &  2.561 \tabularnewline
98 &  102 &  102.3 & -0.3291 \tabularnewline
99 &  104.3 &  103.5 &  0.8289 \tabularnewline
100 &  108.8 &  107.1 &  1.656 \tabularnewline
101 &  101.3 &  100.4 &  0.9043 \tabularnewline
102 &  108.9 &  109.6 & -0.6726 \tabularnewline
103 &  98.5 &  92.17 &  6.328 \tabularnewline
104 &  88.8 &  86.88 &  1.921 \tabularnewline
105 &  111.8 &  108.3 &  3.47 \tabularnewline
106 &  109.6 &  107.2 &  2.398 \tabularnewline
107 &  92.5 &  94.9 & -2.402 \tabularnewline
108 &  94.5 &  98.41 & -3.913 \tabularnewline
109 &  80.8 &  82.89 & -2.086 \tabularnewline
110 &  83.7 &  86.73 & -3.025 \tabularnewline
111 &  94.2 &  94.14 &  0.06166 \tabularnewline
112 &  86.2 &  86.4 & -0.2015 \tabularnewline
113 &  89 &  102.9 & -13.91 \tabularnewline
114 &  94.7 &  96.1 & -1.397 \tabularnewline
115 &  81.9 &  73.94 &  7.96 \tabularnewline
116 &  80.2 &  80.16 &  0.04384 \tabularnewline
117 &  96.5 &  92.48 &  4.022 \tabularnewline
118 &  95.6 &  91.32 &  4.277 \tabularnewline
119 &  91.9 &  93.39 & -1.493 \tabularnewline
120 &  89.9 &  92.46 & -2.556 \tabularnewline
121 &  86.3 &  81.97 &  4.329 \tabularnewline
122 &  94 &  89.4 &  4.601 \tabularnewline
123 &  108 &  103 &  5.005 \tabularnewline
124 &  96.3 &  91.73 &  4.566 \tabularnewline
125 &  94.6 &  91.02 &  3.582 \tabularnewline
126 &  111.7 &  105.7 &  6.048 \tabularnewline
127 &  92 &  80.65 &  11.35 \tabularnewline
128 &  91.9 &  87.65 &  4.247 \tabularnewline
129 &  109.2 &  106 &  3.208 \tabularnewline
130 &  106.8 &  104.4 &  2.355 \tabularnewline
131 &  105.8 &  101 &  4.755 \tabularnewline
132 &  103.6 &  110.2 & -6.6 \tabularnewline
133 &  97.6 &  94.6 &  3.004 \tabularnewline
134 &  102.8 &  100.3 &  2.502 \tabularnewline
135 &  124.8 &  118.3 &  6.537 \tabularnewline
136 &  103.9 &  100.2 &  3.687 \tabularnewline
137 &  112.2 &  112.5 & -0.3038 \tabularnewline
138 &  108.5 &  107.4 &  1.051 \tabularnewline
139 &  92.4 &  86.86 &  5.535 \tabularnewline
140 &  101.1 &  89.99 &  11.11 \tabularnewline
141 &  114.9 &  113.1 &  1.775 \tabularnewline
142 &  106.4 &  106.4 &  0.0003907 \tabularnewline
143 &  104 &  108.1 & -4.058 \tabularnewline
144 &  101.6 &  113.1 & -11.47 \tabularnewline
145 &  99.4 &  99.76 & -0.3614 \tabularnewline
146 &  102.3 &  103.4 & -1.129 \tabularnewline
147 &  121.3 &  123.1 & -1.753 \tabularnewline
148 &  99.3 &  99.18 &  0.1222 \tabularnewline
149 &  102.9 &  104.1 & -1.215 \tabularnewline
150 &  111.4 &  115.3 & -3.933 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=5

[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] 58.4[/C][C] 63.1[/C][C]-4.703[/C][/ROW]
[ROW][C]2[/C][C] 64.8[/C][C] 70.26[/C][C]-5.456[/C][/ROW]
[ROW][C]3[/C][C] 73.8[/C][C] 78.56[/C][C]-4.763[/C][/ROW]
[ROW][C]4[/C][C] 65[/C][C] 71.62[/C][C]-6.623[/C][/ROW]
[ROW][C]5[/C][C] 73[/C][C] 76.69[/C][C]-3.687[/C][/ROW]
[ROW][C]6[/C][C] 71.1[/C][C] 76.34[/C][C]-5.239[/C][/ROW]
[ROW][C]7[/C][C] 58.2[/C][C] 62.52[/C][C]-4.318[/C][/ROW]
[ROW][C]8[/C][C] 64[/C][C] 67.64[/C][C]-3.64[/C][/ROW]
[ROW][C]9[/C][C] 75[/C][C] 76.6[/C][C]-1.595[/C][/ROW]
[ROW][C]10[/C][C] 74.9[/C][C] 76.48[/C][C]-1.584[/C][/ROW]
[ROW][C]11[/C][C] 75[/C][C] 78.86[/C][C]-3.857[/C][/ROW]
[ROW][C]12[/C][C] 68.3[/C][C] 81.6[/C][C]-13.3[/C][/ROW]
[ROW][C]13[/C][C] 72.5[/C][C] 80.36[/C][C]-7.856[/C][/ROW]
[ROW][C]14[/C][C] 72.4[/C][C] 78.23[/C][C]-5.831[/C][/ROW]
[ROW][C]15[/C][C] 79.6[/C][C] 87.3[/C][C]-7.699[/C][/ROW]
[ROW][C]16[/C][C] 70.7[/C][C] 75.95[/C][C]-5.25[/C][/ROW]
[ROW][C]17[/C][C] 76.4[/C][C] 81.82[/C][C]-5.418[/C][/ROW]
[ROW][C]18[/C][C] 79.7[/C][C] 85.75[/C][C]-6.053[/C][/ROW]
[ROW][C]19[/C][C] 64.2[/C][C] 69.72[/C][C]-5.525[/C][/ROW]
[ROW][C]20[/C][C] 67.9[/C][C] 73.7[/C][C]-5.795[/C][/ROW]
[ROW][C]21[/C][C] 74.1[/C][C] 78.9[/C][C]-4.796[/C][/ROW]
[ROW][C]22[/C][C] 78.5[/C][C] 79.58[/C][C]-1.075[/C][/ROW]
[ROW][C]23[/C][C] 73.4[/C][C] 78.45[/C][C]-5.052[/C][/ROW]
[ROW][C]24[/C][C] 65.4[/C][C] 74.38[/C][C]-8.981[/C][/ROW]
[ROW][C]25[/C][C] 69.9[/C][C] 71[/C][C]-1.102[/C][/ROW]
[ROW][C]26[/C][C] 69.6[/C][C] 72.06[/C][C]-2.457[/C][/ROW]
[ROW][C]27[/C][C] 76.8[/C][C] 79.88[/C][C]-3.082[/C][/ROW]
[ROW][C]28[/C][C] 75.6[/C][C] 78.24[/C][C]-2.642[/C][/ROW]
[ROW][C]29[/C][C] 74[/C][C] 76.94[/C][C]-2.937[/C][/ROW]
[ROW][C]30[/C][C] 76[/C][C] 80.01[/C][C]-4.007[/C][/ROW]
[ROW][C]31[/C][C] 68.1[/C][C] 68.07[/C][C] 0.02521[/C][/ROW]
[ROW][C]32[/C][C] 65.5[/C][C] 66.41[/C][C]-0.9101[/C][/ROW]
[ROW][C]33[/C][C] 76.9[/C][C] 76.75[/C][C] 0.1548[/C][/ROW]
[ROW][C]34[/C][C] 81.7[/C][C] 83.34[/C][C]-1.644[/C][/ROW]
[ROW][C]35[/C][C] 73.6[/C][C] 76.36[/C][C]-2.763[/C][/ROW]
[ROW][C]36[/C][C] 68.7[/C][C] 78.92[/C][C]-10.22[/C][/ROW]
[ROW][C]37[/C][C] 73.3[/C][C] 71.21[/C][C] 2.094[/C][/ROW]
[ROW][C]38[/C][C] 71.5[/C][C] 75.22[/C][C]-3.723[/C][/ROW]
[ROW][C]39[/C][C] 78.3[/C][C] 77.67[/C][C] 0.6342[/C][/ROW]
[ROW][C]40[/C][C] 76.5[/C][C] 75.66[/C][C] 0.8389[/C][/ROW]
[ROW][C]41[/C][C] 71.8[/C][C] 74.62[/C][C]-2.822[/C][/ROW]
[ROW][C]42[/C][C] 77.6[/C][C] 79.75[/C][C]-2.146[/C][/ROW]
[ROW][C]43[/C][C] 70[/C][C] 68.47[/C][C] 1.526[/C][/ROW]
[ROW][C]44[/C][C] 64[/C][C] 62.18[/C][C] 1.819[/C][/ROW]
[ROW][C]45[/C][C] 81.3[/C][C] 77.66[/C][C] 3.645[/C][/ROW]
[ROW][C]46[/C][C] 82.5[/C][C] 77.09[/C][C] 5.414[/C][/ROW]
[ROW][C]47[/C][C] 73.1[/C][C] 75.25[/C][C]-2.149[/C][/ROW]
[ROW][C]48[/C][C] 78.1[/C][C] 82.6[/C][C]-4.501[/C][/ROW]
[ROW][C]49[/C][C] 70.7[/C][C] 70.47[/C][C] 0.2324[/C][/ROW]
[ROW][C]50[/C][C] 74.9[/C][C] 74.29[/C][C] 0.6095[/C][/ROW]
[ROW][C]51[/C][C] 88[/C][C] 83.67[/C][C] 4.335[/C][/ROW]
[ROW][C]52[/C][C] 81.3[/C][C] 78.87[/C][C] 2.429[/C][/ROW]
[ROW][C]53[/C][C] 75.7[/C][C] 75.06[/C][C] 0.6381[/C][/ROW]
[ROW][C]54[/C][C] 89.8[/C][C] 86.38[/C][C] 3.421[/C][/ROW]
[ROW][C]55[/C][C] 74.6[/C][C] 68.94[/C][C] 5.659[/C][/ROW]
[ROW][C]56[/C][C] 74.9[/C][C] 72.04[/C][C] 2.858[/C][/ROW]
[ROW][C]57[/C][C] 90[/C][C] 84.35[/C][C] 5.651[/C][/ROW]
[ROW][C]58[/C][C] 88.1[/C][C] 83.43[/C][C] 4.67[/C][/ROW]
[ROW][C]59[/C][C] 84.9[/C][C] 80.25[/C][C] 4.651[/C][/ROW]
[ROW][C]60[/C][C] 87.7[/C][C] 87.29[/C][C] 0.4061[/C][/ROW]
[ROW][C]61[/C][C] 80.5[/C][C] 79.42[/C][C] 1.082[/C][/ROW]
[ROW][C]62[/C][C] 79[/C][C] 79.37[/C][C]-0.3679[/C][/ROW]
[ROW][C]63[/C][C] 89.9[/C][C] 88.14[/C][C] 1.761[/C][/ROW]
[ROW][C]64[/C][C] 86.3[/C][C] 82.45[/C][C] 3.85[/C][/ROW]
[ROW][C]65[/C][C] 81.1[/C][C] 77.47[/C][C] 3.628[/C][/ROW]
[ROW][C]66[/C][C] 92.4[/C][C] 93.75[/C][C]-1.35[/C][/ROW]
[ROW][C]67[/C][C] 71.8[/C][C] 69.56[/C][C] 2.239[/C][/ROW]
[ROW][C]68[/C][C] 76.1[/C][C] 72.5[/C][C] 3.603[/C][/ROW]
[ROW][C]69[/C][C] 92.5[/C][C] 85.26[/C][C] 7.239[/C][/ROW]
[ROW][C]70[/C][C] 87[/C][C] 80.5[/C][C] 6.505[/C][/ROW]
[ROW][C]71[/C][C] 89.5[/C][C] 86.67[/C][C] 2.833[/C][/ROW]
[ROW][C]72[/C][C] 88.7[/C][C] 100.9[/C][C]-12.2[/C][/ROW]
[ROW][C]73[/C][C] 83.8[/C][C] 78.94[/C][C] 4.86[/C][/ROW]
[ROW][C]74[/C][C] 84.9[/C][C] 82.29[/C][C] 2.608[/C][/ROW]
[ROW][C]75[/C][C] 99[/C][C] 92.42[/C][C] 6.582[/C][/ROW]
[ROW][C]76[/C][C] 84.6[/C][C] 79.23[/C][C] 5.367[/C][/ROW]
[ROW][C]77[/C][C] 92.7[/C][C] 89.01[/C][C] 3.689[/C][/ROW]
[ROW][C]78[/C][C] 97.6[/C][C] 92.96[/C][C] 4.644[/C][/ROW]
[ROW][C]79[/C][C] 78[/C][C] 73.64[/C][C] 4.361[/C][/ROW]
[ROW][C]80[/C][C] 81.9[/C][C] 73.89[/C][C] 8.014[/C][/ROW]
[ROW][C]81[/C][C] 96.5[/C][C] 90.67[/C][C] 5.828[/C][/ROW]
[ROW][C]82[/C][C] 99.9[/C][C] 94.52[/C][C] 5.375[/C][/ROW]
[ROW][C]83[/C][C] 96.2[/C][C] 90.96[/C][C] 5.24[/C][/ROW]
[ROW][C]84[/C][C] 90.5[/C][C] 96.27[/C][C]-5.775[/C][/ROW]
[ROW][C]85[/C][C] 91.4[/C][C] 87.43[/C][C] 3.968[/C][/ROW]
[ROW][C]86[/C][C] 89.7[/C][C] 88.27[/C][C] 1.433[/C][/ROW]
[ROW][C]87[/C][C] 102.7[/C][C] 100.4[/C][C] 2.326[/C][/ROW]
[ROW][C]88[/C][C] 91.5[/C][C] 90.34[/C][C] 1.161[/C][/ROW]
[ROW][C]89[/C][C] 96.2[/C][C] 93[/C][C] 3.196[/C][/ROW]
[ROW][C]90[/C][C] 104.5[/C][C] 104.7[/C][C]-0.1829[/C][/ROW]
[ROW][C]91[/C][C] 90.3[/C][C] 86.49[/C][C] 3.81[/C][/ROW]
[ROW][C]92[/C][C] 90.3[/C][C] 87.89[/C][C] 2.406[/C][/ROW]
[ROW][C]93[/C][C] 100.4[/C][C] 106.2[/C][C]-5.775[/C][/ROW]
[ROW][C]94[/C][C] 111.3[/C][C] 119.1[/C][C]-7.814[/C][/ROW]
[ROW][C]95[/C][C] 101.3[/C][C] 103.9[/C][C]-2.622[/C][/ROW]
[ROW][C]96[/C][C] 94.4[/C][C] 105.4[/C][C]-11.01[/C][/ROW]
[ROW][C]97[/C][C] 100.4[/C][C] 97.84[/C][C] 2.561[/C][/ROW]
[ROW][C]98[/C][C] 102[/C][C] 102.3[/C][C]-0.3291[/C][/ROW]
[ROW][C]99[/C][C] 104.3[/C][C] 103.5[/C][C] 0.8289[/C][/ROW]
[ROW][C]100[/C][C] 108.8[/C][C] 107.1[/C][C] 1.656[/C][/ROW]
[ROW][C]101[/C][C] 101.3[/C][C] 100.4[/C][C] 0.9043[/C][/ROW]
[ROW][C]102[/C][C] 108.9[/C][C] 109.6[/C][C]-0.6726[/C][/ROW]
[ROW][C]103[/C][C] 98.5[/C][C] 92.17[/C][C] 6.328[/C][/ROW]
[ROW][C]104[/C][C] 88.8[/C][C] 86.88[/C][C] 1.921[/C][/ROW]
[ROW][C]105[/C][C] 111.8[/C][C] 108.3[/C][C] 3.47[/C][/ROW]
[ROW][C]106[/C][C] 109.6[/C][C] 107.2[/C][C] 2.398[/C][/ROW]
[ROW][C]107[/C][C] 92.5[/C][C] 94.9[/C][C]-2.402[/C][/ROW]
[ROW][C]108[/C][C] 94.5[/C][C] 98.41[/C][C]-3.913[/C][/ROW]
[ROW][C]109[/C][C] 80.8[/C][C] 82.89[/C][C]-2.086[/C][/ROW]
[ROW][C]110[/C][C] 83.7[/C][C] 86.73[/C][C]-3.025[/C][/ROW]
[ROW][C]111[/C][C] 94.2[/C][C] 94.14[/C][C] 0.06166[/C][/ROW]
[ROW][C]112[/C][C] 86.2[/C][C] 86.4[/C][C]-0.2015[/C][/ROW]
[ROW][C]113[/C][C] 89[/C][C] 102.9[/C][C]-13.91[/C][/ROW]
[ROW][C]114[/C][C] 94.7[/C][C] 96.1[/C][C]-1.397[/C][/ROW]
[ROW][C]115[/C][C] 81.9[/C][C] 73.94[/C][C] 7.96[/C][/ROW]
[ROW][C]116[/C][C] 80.2[/C][C] 80.16[/C][C] 0.04384[/C][/ROW]
[ROW][C]117[/C][C] 96.5[/C][C] 92.48[/C][C] 4.022[/C][/ROW]
[ROW][C]118[/C][C] 95.6[/C][C] 91.32[/C][C] 4.277[/C][/ROW]
[ROW][C]119[/C][C] 91.9[/C][C] 93.39[/C][C]-1.493[/C][/ROW]
[ROW][C]120[/C][C] 89.9[/C][C] 92.46[/C][C]-2.556[/C][/ROW]
[ROW][C]121[/C][C] 86.3[/C][C] 81.97[/C][C] 4.329[/C][/ROW]
[ROW][C]122[/C][C] 94[/C][C] 89.4[/C][C] 4.601[/C][/ROW]
[ROW][C]123[/C][C] 108[/C][C] 103[/C][C] 5.005[/C][/ROW]
[ROW][C]124[/C][C] 96.3[/C][C] 91.73[/C][C] 4.566[/C][/ROW]
[ROW][C]125[/C][C] 94.6[/C][C] 91.02[/C][C] 3.582[/C][/ROW]
[ROW][C]126[/C][C] 111.7[/C][C] 105.7[/C][C] 6.048[/C][/ROW]
[ROW][C]127[/C][C] 92[/C][C] 80.65[/C][C] 11.35[/C][/ROW]
[ROW][C]128[/C][C] 91.9[/C][C] 87.65[/C][C] 4.247[/C][/ROW]
[ROW][C]129[/C][C] 109.2[/C][C] 106[/C][C] 3.208[/C][/ROW]
[ROW][C]130[/C][C] 106.8[/C][C] 104.4[/C][C] 2.355[/C][/ROW]
[ROW][C]131[/C][C] 105.8[/C][C] 101[/C][C] 4.755[/C][/ROW]
[ROW][C]132[/C][C] 103.6[/C][C] 110.2[/C][C]-6.6[/C][/ROW]
[ROW][C]133[/C][C] 97.6[/C][C] 94.6[/C][C] 3.004[/C][/ROW]
[ROW][C]134[/C][C] 102.8[/C][C] 100.3[/C][C] 2.502[/C][/ROW]
[ROW][C]135[/C][C] 124.8[/C][C] 118.3[/C][C] 6.537[/C][/ROW]
[ROW][C]136[/C][C] 103.9[/C][C] 100.2[/C][C] 3.687[/C][/ROW]
[ROW][C]137[/C][C] 112.2[/C][C] 112.5[/C][C]-0.3038[/C][/ROW]
[ROW][C]138[/C][C] 108.5[/C][C] 107.4[/C][C] 1.051[/C][/ROW]
[ROW][C]139[/C][C] 92.4[/C][C] 86.86[/C][C] 5.535[/C][/ROW]
[ROW][C]140[/C][C] 101.1[/C][C] 89.99[/C][C] 11.11[/C][/ROW]
[ROW][C]141[/C][C] 114.9[/C][C] 113.1[/C][C] 1.775[/C][/ROW]
[ROW][C]142[/C][C] 106.4[/C][C] 106.4[/C][C] 0.0003907[/C][/ROW]
[ROW][C]143[/C][C] 104[/C][C] 108.1[/C][C]-4.058[/C][/ROW]
[ROW][C]144[/C][C] 101.6[/C][C] 113.1[/C][C]-11.47[/C][/ROW]
[ROW][C]145[/C][C] 99.4[/C][C] 99.76[/C][C]-0.3614[/C][/ROW]
[ROW][C]146[/C][C] 102.3[/C][C] 103.4[/C][C]-1.129[/C][/ROW]
[ROW][C]147[/C][C] 121.3[/C][C] 123.1[/C][C]-1.753[/C][/ROW]
[ROW][C]148[/C][C] 99.3[/C][C] 99.18[/C][C] 0.1222[/C][/ROW]
[ROW][C]149[/C][C] 102.9[/C][C] 104.1[/C][C]-1.215[/C][/ROW]
[ROW][C]150[/C][C] 111.4[/C][C] 115.3[/C][C]-3.933[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=5

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	58.4	63.1	-4.703
2	64.8	70.26	-5.456
3	73.8	78.56	-4.763
4	65	71.62	-6.623
5	73	76.69	-3.687
6	71.1	76.34	-5.239
7	58.2	62.52	-4.318
8	64	67.64	-3.64
9	75	76.6	-1.595
10	74.9	76.48	-1.584
11	75	78.86	-3.857
12	68.3	81.6	-13.3
13	72.5	80.36	-7.856
14	72.4	78.23	-5.831
15	79.6	87.3	-7.699
16	70.7	75.95	-5.25
17	76.4	81.82	-5.418
18	79.7	85.75	-6.053
19	64.2	69.72	-5.525
20	67.9	73.7	-5.795
21	74.1	78.9	-4.796
22	78.5	79.58	-1.075
23	73.4	78.45	-5.052
24	65.4	74.38	-8.981
25	69.9	71	-1.102
26	69.6	72.06	-2.457
27	76.8	79.88	-3.082
28	75.6	78.24	-2.642
29	74	76.94	-2.937
30	76	80.01	-4.007
31	68.1	68.07	0.02521
32	65.5	66.41	-0.9101
33	76.9	76.75	0.1548
34	81.7	83.34	-1.644
35	73.6	76.36	-2.763
36	68.7	78.92	-10.22
37	73.3	71.21	2.094
38	71.5	75.22	-3.723
39	78.3	77.67	0.6342
40	76.5	75.66	0.8389
41	71.8	74.62	-2.822
42	77.6	79.75	-2.146
43	70	68.47	1.526
44	64	62.18	1.819
45	81.3	77.66	3.645
46	82.5	77.09	5.414
47	73.1	75.25	-2.149
48	78.1	82.6	-4.501
49	70.7	70.47	0.2324
50	74.9	74.29	0.6095
51	88	83.67	4.335
52	81.3	78.87	2.429
53	75.7	75.06	0.6381
54	89.8	86.38	3.421
55	74.6	68.94	5.659
56	74.9	72.04	2.858
57	90	84.35	5.651
58	88.1	83.43	4.67
59	84.9	80.25	4.651
60	87.7	87.29	0.4061
61	80.5	79.42	1.082
62	79	79.37	-0.3679
63	89.9	88.14	1.761
64	86.3	82.45	3.85
65	81.1	77.47	3.628
66	92.4	93.75	-1.35
67	71.8	69.56	2.239
68	76.1	72.5	3.603
69	92.5	85.26	7.239
70	87	80.5	6.505
71	89.5	86.67	2.833
72	88.7	100.9	-12.2
73	83.8	78.94	4.86
74	84.9	82.29	2.608
75	99	92.42	6.582
76	84.6	79.23	5.367
77	92.7	89.01	3.689
78	97.6	92.96	4.644
79	78	73.64	4.361
80	81.9	73.89	8.014
81	96.5	90.67	5.828
82	99.9	94.52	5.375
83	96.2	90.96	5.24
84	90.5	96.27	-5.775
85	91.4	87.43	3.968
86	89.7	88.27	1.433
87	102.7	100.4	2.326
88	91.5	90.34	1.161
89	96.2	93	3.196
90	104.5	104.7	-0.1829
91	90.3	86.49	3.81
92	90.3	87.89	2.406
93	100.4	106.2	-5.775
94	111.3	119.1	-7.814
95	101.3	103.9	-2.622
96	94.4	105.4	-11.01
97	100.4	97.84	2.561
98	102	102.3	-0.3291
99	104.3	103.5	0.8289
100	108.8	107.1	1.656
101	101.3	100.4	0.9043
102	108.9	109.6	-0.6726
103	98.5	92.17	6.328
104	88.8	86.88	1.921
105	111.8	108.3	3.47
106	109.6	107.2	2.398
107	92.5	94.9	-2.402
108	94.5	98.41	-3.913
109	80.8	82.89	-2.086
110	83.7	86.73	-3.025
111	94.2	94.14	0.06166
112	86.2	86.4	-0.2015
113	89	102.9	-13.91
114	94.7	96.1	-1.397
115	81.9	73.94	7.96
116	80.2	80.16	0.04384
117	96.5	92.48	4.022
118	95.6	91.32	4.277
119	91.9	93.39	-1.493
120	89.9	92.46	-2.556
121	86.3	81.97	4.329
122	94	89.4	4.601
123	108	103	5.005
124	96.3	91.73	4.566
125	94.6	91.02	3.582
126	111.7	105.7	6.048
127	92	80.65	11.35
128	91.9	87.65	4.247
129	109.2	106	3.208
130	106.8	104.4	2.355
131	105.8	101	4.755
132	103.6	110.2	-6.6
133	97.6	94.6	3.004
134	102.8	100.3	2.502
135	124.8	118.3	6.537
136	103.9	100.2	3.687
137	112.2	112.5	-0.3038
138	108.5	107.4	1.051
139	92.4	86.86	5.535
140	101.1	89.99	11.11
141	114.9	113.1	1.775
142	106.4	106.4	0.0003907
143	104	108.1	-4.058
144	101.6	113.1	-11.47
145	99.4	99.76	-0.3614
146	102.3	103.4	-1.129
147	121.3	123.1	-1.753
148	99.3	99.18	0.1222
149	102.9	104.1	-1.215
150	111.4	115.3	-3.933

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.02203	0.04405	0.978
7	0.006143	0.01229	0.9939
8	0.002116	0.004232	0.9979
9	0.005309	0.01062	0.9947
10	0.004151	0.008303	0.9958
11	0.001409	0.002818	0.9986
12	0.1118	0.2236	0.8882
13	0.08047	0.1609	0.9195
14	0.05208	0.1042	0.9479
15	0.03507	0.07015	0.9649
16	0.02176	0.04352	0.9782
17	0.01331	0.02663	0.9867
18	0.008072	0.01614	0.9919
19	0.005476	0.01095	0.9945
20	0.003494	0.006987	0.9965
21	0.002154	0.004307	0.9978
22	0.004377	0.008755	0.9956
23	0.002763	0.005527	0.9972
24	0.004431	0.008862	0.9956
25	0.005555	0.01111	0.9944
26	0.004453	0.008907	0.9955
27	0.003741	0.007481	0.9963
28	0.003244	0.006487	0.9968
29	0.002572	0.005143	0.9974
30	0.001916	0.003833	0.9981
31	0.002364	0.004727	0.9976
32	0.001984	0.003969	0.998
33	0.003278	0.006557	0.9967
34	0.003661	0.007321	0.9963
35	0.002823	0.005647	0.9972
36	0.01105	0.0221	0.9889
37	0.01697	0.03394	0.983
38	0.01479	0.02958	0.9852
39	0.01854	0.03707	0.9815
40	0.02271	0.04542	0.9773
41	0.02048	0.04097	0.9795
42	0.02021	0.04042	0.9798
43	0.02231	0.04462	0.9777
44	0.02057	0.04115	0.9794
45	0.04791	0.09582	0.9521
46	0.1034	0.2068	0.8966
47	0.0982	0.1964	0.9018
48	0.09845	0.1969	0.9016
49	0.08296	0.1659	0.917
50	0.07985	0.1597	0.9201
51	0.1256	0.2512	0.8744
52	0.1543	0.3086	0.8457
53	0.1584	0.3168	0.8416
54	0.2444	0.4888	0.7556
55	0.3154	0.6307	0.6846
56	0.3503	0.7006	0.6497
57	0.4838	0.9675	0.5162
58	0.536	0.9279	0.464
59	0.5598	0.8805	0.4402
60	0.515	0.97	0.485
61	0.4778	0.9555	0.5222
62	0.4527	0.9054	0.5473
63	0.4209	0.8418	0.5791
64	0.4288	0.8577	0.5712
65	0.4463	0.8925	0.5537
66	0.4231	0.8461	0.5769
67	0.445	0.89	0.555
68	0.4756	0.9512	0.5244
69	0.56	0.8799	0.44
70	0.6002	0.7996	0.3998
71	0.5725	0.855	0.4275
72	0.8177	0.3646	0.1823
73	0.8152	0.3696	0.1848
74	0.7959	0.4082	0.2041
75	0.8305	0.339	0.1695
76	0.8327	0.3346	0.1673
77	0.8185	0.363	0.1815
78	0.8099	0.3802	0.1901
79	0.8063	0.3874	0.1937
80	0.8295	0.341	0.1705
81	0.8247	0.3505	0.1753
82	0.8198	0.3604	0.1802
83	0.8096	0.3807	0.1903
84	0.844	0.3121	0.156
85	0.8268	0.3464	0.1732
86	0.7972	0.4057	0.2028
87	0.7687	0.4625	0.2313
88	0.7339	0.5322	0.2661
89	0.6978	0.6045	0.3022
90	0.6572	0.6856	0.3428
91	0.6217	0.7566	0.3783
92	0.5798	0.8404	0.4202
93	0.6284	0.7432	0.3716
94	0.6692	0.6615	0.3308
95	0.6331	0.7338	0.3669
96	0.7983	0.4033	0.2017
97	0.7751	0.4498	0.2249
98	0.7367	0.5265	0.2633
99	0.6956	0.6087	0.3044
100	0.6559	0.6881	0.3441
101	0.6096	0.7807	0.3904
102	0.5607	0.8785	0.4393
103	0.5611	0.8778	0.4389
104	0.5175	0.965	0.4825
105	0.4938	0.9875	0.5062
106	0.4601	0.9202	0.5399
107	0.4404	0.8809	0.5596
108	0.4292	0.8585	0.5708
109	0.4351	0.8702	0.5649
110	0.4694	0.9388	0.5306
111	0.4258	0.8516	0.5742
112	0.4146	0.8292	0.5854
113	0.8882	0.2236	0.1118
114	0.8929	0.2142	0.1071
115	0.8834	0.2332	0.1166
116	0.9121	0.1757	0.08786
117	0.8913	0.2174	0.1087
118	0.8674	0.2652	0.1326
119	0.8762	0.2475	0.1238
120	0.8869	0.2262	0.1131
121	0.8654	0.2691	0.1346
122	0.8353	0.3293	0.1647
123	0.8458	0.3084	0.1542
124	0.8105	0.3789	0.1895
125	0.7722	0.4555	0.2278
126	0.7844	0.4313	0.2156
127	0.7938	0.4123	0.2062
128	0.7493	0.5014	0.2507
129	0.7074	0.5852	0.2926
130	0.6503	0.6993	0.3497
131	0.6317	0.7367	0.3683
132	0.6413	0.7174	0.3587
133	0.5675	0.865	0.4325
134	0.4889	0.9777	0.5111
135	0.8501	0.2998	0.1499
136	0.7964	0.4071	0.2036
137	0.7481	0.5038	0.2519
138	0.6676	0.6648	0.3324
139	0.6041	0.7918	0.3959
140	0.7257	0.5485	0.2743
141	0.7544	0.4912	0.2456
142	0.6615	0.6769	0.3385
143	0.5332	0.9336	0.4668
144	0.9764	0.04728	0.02364

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.02203 &  0.04405 &  0.978 \tabularnewline
7 &  0.006143 &  0.01229 &  0.9939 \tabularnewline
8 &  0.002116 &  0.004232 &  0.9979 \tabularnewline
9 &  0.005309 &  0.01062 &  0.9947 \tabularnewline
10 &  0.004151 &  0.008303 &  0.9958 \tabularnewline
11 &  0.001409 &  0.002818 &  0.9986 \tabularnewline
12 &  0.1118 &  0.2236 &  0.8882 \tabularnewline
13 &  0.08047 &  0.1609 &  0.9195 \tabularnewline
14 &  0.05208 &  0.1042 &  0.9479 \tabularnewline
15 &  0.03507 &  0.07015 &  0.9649 \tabularnewline
16 &  0.02176 &  0.04352 &  0.9782 \tabularnewline
17 &  0.01331 &  0.02663 &  0.9867 \tabularnewline
18 &  0.008072 &  0.01614 &  0.9919 \tabularnewline
19 &  0.005476 &  0.01095 &  0.9945 \tabularnewline
20 &  0.003494 &  0.006987 &  0.9965 \tabularnewline
21 &  0.002154 &  0.004307 &  0.9978 \tabularnewline
22 &  0.004377 &  0.008755 &  0.9956 \tabularnewline
23 &  0.002763 &  0.005527 &  0.9972 \tabularnewline
24 &  0.004431 &  0.008862 &  0.9956 \tabularnewline
25 &  0.005555 &  0.01111 &  0.9944 \tabularnewline
26 &  0.004453 &  0.008907 &  0.9955 \tabularnewline
27 &  0.003741 &  0.007481 &  0.9963 \tabularnewline
28 &  0.003244 &  0.006487 &  0.9968 \tabularnewline
29 &  0.002572 &  0.005143 &  0.9974 \tabularnewline
30 &  0.001916 &  0.003833 &  0.9981 \tabularnewline
31 &  0.002364 &  0.004727 &  0.9976 \tabularnewline
32 &  0.001984 &  0.003969 &  0.998 \tabularnewline
33 &  0.003278 &  0.006557 &  0.9967 \tabularnewline
34 &  0.003661 &  0.007321 &  0.9963 \tabularnewline
35 &  0.002823 &  0.005647 &  0.9972 \tabularnewline
36 &  0.01105 &  0.0221 &  0.9889 \tabularnewline
37 &  0.01697 &  0.03394 &  0.983 \tabularnewline
38 &  0.01479 &  0.02958 &  0.9852 \tabularnewline
39 &  0.01854 &  0.03707 &  0.9815 \tabularnewline
40 &  0.02271 &  0.04542 &  0.9773 \tabularnewline
41 &  0.02048 &  0.04097 &  0.9795 \tabularnewline
42 &  0.02021 &  0.04042 &  0.9798 \tabularnewline
43 &  0.02231 &  0.04462 &  0.9777 \tabularnewline
44 &  0.02057 &  0.04115 &  0.9794 \tabularnewline
45 &  0.04791 &  0.09582 &  0.9521 \tabularnewline
46 &  0.1034 &  0.2068 &  0.8966 \tabularnewline
47 &  0.0982 &  0.1964 &  0.9018 \tabularnewline
48 &  0.09845 &  0.1969 &  0.9016 \tabularnewline
49 &  0.08296 &  0.1659 &  0.917 \tabularnewline
50 &  0.07985 &  0.1597 &  0.9201 \tabularnewline
51 &  0.1256 &  0.2512 &  0.8744 \tabularnewline
52 &  0.1543 &  0.3086 &  0.8457 \tabularnewline
53 &  0.1584 &  0.3168 &  0.8416 \tabularnewline
54 &  0.2444 &  0.4888 &  0.7556 \tabularnewline
55 &  0.3154 &  0.6307 &  0.6846 \tabularnewline
56 &  0.3503 &  0.7006 &  0.6497 \tabularnewline
57 &  0.4838 &  0.9675 &  0.5162 \tabularnewline
58 &  0.536 &  0.9279 &  0.464 \tabularnewline
59 &  0.5598 &  0.8805 &  0.4402 \tabularnewline
60 &  0.515 &  0.97 &  0.485 \tabularnewline
61 &  0.4778 &  0.9555 &  0.5222 \tabularnewline
62 &  0.4527 &  0.9054 &  0.5473 \tabularnewline
63 &  0.4209 &  0.8418 &  0.5791 \tabularnewline
64 &  0.4288 &  0.8577 &  0.5712 \tabularnewline
65 &  0.4463 &  0.8925 &  0.5537 \tabularnewline
66 &  0.4231 &  0.8461 &  0.5769 \tabularnewline
67 &  0.445 &  0.89 &  0.555 \tabularnewline
68 &  0.4756 &  0.9512 &  0.5244 \tabularnewline
69 &  0.56 &  0.8799 &  0.44 \tabularnewline
70 &  0.6002 &  0.7996 &  0.3998 \tabularnewline
71 &  0.5725 &  0.855 &  0.4275 \tabularnewline
72 &  0.8177 &  0.3646 &  0.1823 \tabularnewline
73 &  0.8152 &  0.3696 &  0.1848 \tabularnewline
74 &  0.7959 &  0.4082 &  0.2041 \tabularnewline
75 &  0.8305 &  0.339 &  0.1695 \tabularnewline
76 &  0.8327 &  0.3346 &  0.1673 \tabularnewline
77 &  0.8185 &  0.363 &  0.1815 \tabularnewline
78 &  0.8099 &  0.3802 &  0.1901 \tabularnewline
79 &  0.8063 &  0.3874 &  0.1937 \tabularnewline
80 &  0.8295 &  0.341 &  0.1705 \tabularnewline
81 &  0.8247 &  0.3505 &  0.1753 \tabularnewline
82 &  0.8198 &  0.3604 &  0.1802 \tabularnewline
83 &  0.8096 &  0.3807 &  0.1903 \tabularnewline
84 &  0.844 &  0.3121 &  0.156 \tabularnewline
85 &  0.8268 &  0.3464 &  0.1732 \tabularnewline
86 &  0.7972 &  0.4057 &  0.2028 \tabularnewline
87 &  0.7687 &  0.4625 &  0.2313 \tabularnewline
88 &  0.7339 &  0.5322 &  0.2661 \tabularnewline
89 &  0.6978 &  0.6045 &  0.3022 \tabularnewline
90 &  0.6572 &  0.6856 &  0.3428 \tabularnewline
91 &  0.6217 &  0.7566 &  0.3783 \tabularnewline
92 &  0.5798 &  0.8404 &  0.4202 \tabularnewline
93 &  0.6284 &  0.7432 &  0.3716 \tabularnewline
94 &  0.6692 &  0.6615 &  0.3308 \tabularnewline
95 &  0.6331 &  0.7338 &  0.3669 \tabularnewline
96 &  0.7983 &  0.4033 &  0.2017 \tabularnewline
97 &  0.7751 &  0.4498 &  0.2249 \tabularnewline
98 &  0.7367 &  0.5265 &  0.2633 \tabularnewline
99 &  0.6956 &  0.6087 &  0.3044 \tabularnewline
100 &  0.6559 &  0.6881 &  0.3441 \tabularnewline
101 &  0.6096 &  0.7807 &  0.3904 \tabularnewline
102 &  0.5607 &  0.8785 &  0.4393 \tabularnewline
103 &  0.5611 &  0.8778 &  0.4389 \tabularnewline
104 &  0.5175 &  0.965 &  0.4825 \tabularnewline
105 &  0.4938 &  0.9875 &  0.5062 \tabularnewline
106 &  0.4601 &  0.9202 &  0.5399 \tabularnewline
107 &  0.4404 &  0.8809 &  0.5596 \tabularnewline
108 &  0.4292 &  0.8585 &  0.5708 \tabularnewline
109 &  0.4351 &  0.8702 &  0.5649 \tabularnewline
110 &  0.4694 &  0.9388 &  0.5306 \tabularnewline
111 &  0.4258 &  0.8516 &  0.5742 \tabularnewline
112 &  0.4146 &  0.8292 &  0.5854 \tabularnewline
113 &  0.8882 &  0.2236 &  0.1118 \tabularnewline
114 &  0.8929 &  0.2142 &  0.1071 \tabularnewline
115 &  0.8834 &  0.2332 &  0.1166 \tabularnewline
116 &  0.9121 &  0.1757 &  0.08786 \tabularnewline
117 &  0.8913 &  0.2174 &  0.1087 \tabularnewline
118 &  0.8674 &  0.2652 &  0.1326 \tabularnewline
119 &  0.8762 &  0.2475 &  0.1238 \tabularnewline
120 &  0.8869 &  0.2262 &  0.1131 \tabularnewline
121 &  0.8654 &  0.2691 &  0.1346 \tabularnewline
122 &  0.8353 &  0.3293 &  0.1647 \tabularnewline
123 &  0.8458 &  0.3084 &  0.1542 \tabularnewline
124 &  0.8105 &  0.3789 &  0.1895 \tabularnewline
125 &  0.7722 &  0.4555 &  0.2278 \tabularnewline
126 &  0.7844 &  0.4313 &  0.2156 \tabularnewline
127 &  0.7938 &  0.4123 &  0.2062 \tabularnewline
128 &  0.7493 &  0.5014 &  0.2507 \tabularnewline
129 &  0.7074 &  0.5852 &  0.2926 \tabularnewline
130 &  0.6503 &  0.6993 &  0.3497 \tabularnewline
131 &  0.6317 &  0.7367 &  0.3683 \tabularnewline
132 &  0.6413 &  0.7174 &  0.3587 \tabularnewline
133 &  0.5675 &  0.865 &  0.4325 \tabularnewline
134 &  0.4889 &  0.9777 &  0.5111 \tabularnewline
135 &  0.8501 &  0.2998 &  0.1499 \tabularnewline
136 &  0.7964 &  0.4071 &  0.2036 \tabularnewline
137 &  0.7481 &  0.5038 &  0.2519 \tabularnewline
138 &  0.6676 &  0.6648 &  0.3324 \tabularnewline
139 &  0.6041 &  0.7918 &  0.3959 \tabularnewline
140 &  0.7257 &  0.5485 &  0.2743 \tabularnewline
141 &  0.7544 &  0.4912 &  0.2456 \tabularnewline
142 &  0.6615 &  0.6769 &  0.3385 \tabularnewline
143 &  0.5332 &  0.9336 &  0.4668 \tabularnewline
144 &  0.9764 &  0.04728 &  0.02364 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=6

[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] 0.02203[/C][C] 0.04405[/C][C] 0.978[/C][/ROW]
[ROW][C]7[/C][C] 0.006143[/C][C] 0.01229[/C][C] 0.9939[/C][/ROW]
[ROW][C]8[/C][C] 0.002116[/C][C] 0.004232[/C][C] 0.9979[/C][/ROW]
[ROW][C]9[/C][C] 0.005309[/C][C] 0.01062[/C][C] 0.9947[/C][/ROW]
[ROW][C]10[/C][C] 0.004151[/C][C] 0.008303[/C][C] 0.9958[/C][/ROW]
[ROW][C]11[/C][C] 0.001409[/C][C] 0.002818[/C][C] 0.9986[/C][/ROW]
[ROW][C]12[/C][C] 0.1118[/C][C] 0.2236[/C][C] 0.8882[/C][/ROW]
[ROW][C]13[/C][C] 0.08047[/C][C] 0.1609[/C][C] 0.9195[/C][/ROW]
[ROW][C]14[/C][C] 0.05208[/C][C] 0.1042[/C][C] 0.9479[/C][/ROW]
[ROW][C]15[/C][C] 0.03507[/C][C] 0.07015[/C][C] 0.9649[/C][/ROW]
[ROW][C]16[/C][C] 0.02176[/C][C] 0.04352[/C][C] 0.9782[/C][/ROW]
[ROW][C]17[/C][C] 0.01331[/C][C] 0.02663[/C][C] 0.9867[/C][/ROW]
[ROW][C]18[/C][C] 0.008072[/C][C] 0.01614[/C][C] 0.9919[/C][/ROW]
[ROW][C]19[/C][C] 0.005476[/C][C] 0.01095[/C][C] 0.9945[/C][/ROW]
[ROW][C]20[/C][C] 0.003494[/C][C] 0.006987[/C][C] 0.9965[/C][/ROW]
[ROW][C]21[/C][C] 0.002154[/C][C] 0.004307[/C][C] 0.9978[/C][/ROW]
[ROW][C]22[/C][C] 0.004377[/C][C] 0.008755[/C][C] 0.9956[/C][/ROW]
[ROW][C]23[/C][C] 0.002763[/C][C] 0.005527[/C][C] 0.9972[/C][/ROW]
[ROW][C]24[/C][C] 0.004431[/C][C] 0.008862[/C][C] 0.9956[/C][/ROW]
[ROW][C]25[/C][C] 0.005555[/C][C] 0.01111[/C][C] 0.9944[/C][/ROW]
[ROW][C]26[/C][C] 0.004453[/C][C] 0.008907[/C][C] 0.9955[/C][/ROW]
[ROW][C]27[/C][C] 0.003741[/C][C] 0.007481[/C][C] 0.9963[/C][/ROW]
[ROW][C]28[/C][C] 0.003244[/C][C] 0.006487[/C][C] 0.9968[/C][/ROW]
[ROW][C]29[/C][C] 0.002572[/C][C] 0.005143[/C][C] 0.9974[/C][/ROW]
[ROW][C]30[/C][C] 0.001916[/C][C] 0.003833[/C][C] 0.9981[/C][/ROW]
[ROW][C]31[/C][C] 0.002364[/C][C] 0.004727[/C][C] 0.9976[/C][/ROW]
[ROW][C]32[/C][C] 0.001984[/C][C] 0.003969[/C][C] 0.998[/C][/ROW]
[ROW][C]33[/C][C] 0.003278[/C][C] 0.006557[/C][C] 0.9967[/C][/ROW]
[ROW][C]34[/C][C] 0.003661[/C][C] 0.007321[/C][C] 0.9963[/C][/ROW]
[ROW][C]35[/C][C] 0.002823[/C][C] 0.005647[/C][C] 0.9972[/C][/ROW]
[ROW][C]36[/C][C] 0.01105[/C][C] 0.0221[/C][C] 0.9889[/C][/ROW]
[ROW][C]37[/C][C] 0.01697[/C][C] 0.03394[/C][C] 0.983[/C][/ROW]
[ROW][C]38[/C][C] 0.01479[/C][C] 0.02958[/C][C] 0.9852[/C][/ROW]
[ROW][C]39[/C][C] 0.01854[/C][C] 0.03707[/C][C] 0.9815[/C][/ROW]
[ROW][C]40[/C][C] 0.02271[/C][C] 0.04542[/C][C] 0.9773[/C][/ROW]
[ROW][C]41[/C][C] 0.02048[/C][C] 0.04097[/C][C] 0.9795[/C][/ROW]
[ROW][C]42[/C][C] 0.02021[/C][C] 0.04042[/C][C] 0.9798[/C][/ROW]
[ROW][C]43[/C][C] 0.02231[/C][C] 0.04462[/C][C] 0.9777[/C][/ROW]
[ROW][C]44[/C][C] 0.02057[/C][C] 0.04115[/C][C] 0.9794[/C][/ROW]
[ROW][C]45[/C][C] 0.04791[/C][C] 0.09582[/C][C] 0.9521[/C][/ROW]
[ROW][C]46[/C][C] 0.1034[/C][C] 0.2068[/C][C] 0.8966[/C][/ROW]
[ROW][C]47[/C][C] 0.0982[/C][C] 0.1964[/C][C] 0.9018[/C][/ROW]
[ROW][C]48[/C][C] 0.09845[/C][C] 0.1969[/C][C] 0.9016[/C][/ROW]
[ROW][C]49[/C][C] 0.08296[/C][C] 0.1659[/C][C] 0.917[/C][/ROW]
[ROW][C]50[/C][C] 0.07985[/C][C] 0.1597[/C][C] 0.9201[/C][/ROW]
[ROW][C]51[/C][C] 0.1256[/C][C] 0.2512[/C][C] 0.8744[/C][/ROW]
[ROW][C]52[/C][C] 0.1543[/C][C] 0.3086[/C][C] 0.8457[/C][/ROW]
[ROW][C]53[/C][C] 0.1584[/C][C] 0.3168[/C][C] 0.8416[/C][/ROW]
[ROW][C]54[/C][C] 0.2444[/C][C] 0.4888[/C][C] 0.7556[/C][/ROW]
[ROW][C]55[/C][C] 0.3154[/C][C] 0.6307[/C][C] 0.6846[/C][/ROW]
[ROW][C]56[/C][C] 0.3503[/C][C] 0.7006[/C][C] 0.6497[/C][/ROW]
[ROW][C]57[/C][C] 0.4838[/C][C] 0.9675[/C][C] 0.5162[/C][/ROW]
[ROW][C]58[/C][C] 0.536[/C][C] 0.9279[/C][C] 0.464[/C][/ROW]
[ROW][C]59[/C][C] 0.5598[/C][C] 0.8805[/C][C] 0.4402[/C][/ROW]
[ROW][C]60[/C][C] 0.515[/C][C] 0.97[/C][C] 0.485[/C][/ROW]
[ROW][C]61[/C][C] 0.4778[/C][C] 0.9555[/C][C] 0.5222[/C][/ROW]
[ROW][C]62[/C][C] 0.4527[/C][C] 0.9054[/C][C] 0.5473[/C][/ROW]
[ROW][C]63[/C][C] 0.4209[/C][C] 0.8418[/C][C] 0.5791[/C][/ROW]
[ROW][C]64[/C][C] 0.4288[/C][C] 0.8577[/C][C] 0.5712[/C][/ROW]
[ROW][C]65[/C][C] 0.4463[/C][C] 0.8925[/C][C] 0.5537[/C][/ROW]
[ROW][C]66[/C][C] 0.4231[/C][C] 0.8461[/C][C] 0.5769[/C][/ROW]
[ROW][C]67[/C][C] 0.445[/C][C] 0.89[/C][C] 0.555[/C][/ROW]
[ROW][C]68[/C][C] 0.4756[/C][C] 0.9512[/C][C] 0.5244[/C][/ROW]
[ROW][C]69[/C][C] 0.56[/C][C] 0.8799[/C][C] 0.44[/C][/ROW]
[ROW][C]70[/C][C] 0.6002[/C][C] 0.7996[/C][C] 0.3998[/C][/ROW]
[ROW][C]71[/C][C] 0.5725[/C][C] 0.855[/C][C] 0.4275[/C][/ROW]
[ROW][C]72[/C][C] 0.8177[/C][C] 0.3646[/C][C] 0.1823[/C][/ROW]
[ROW][C]73[/C][C] 0.8152[/C][C] 0.3696[/C][C] 0.1848[/C][/ROW]
[ROW][C]74[/C][C] 0.7959[/C][C] 0.4082[/C][C] 0.2041[/C][/ROW]
[ROW][C]75[/C][C] 0.8305[/C][C] 0.339[/C][C] 0.1695[/C][/ROW]
[ROW][C]76[/C][C] 0.8327[/C][C] 0.3346[/C][C] 0.1673[/C][/ROW]
[ROW][C]77[/C][C] 0.8185[/C][C] 0.363[/C][C] 0.1815[/C][/ROW]
[ROW][C]78[/C][C] 0.8099[/C][C] 0.3802[/C][C] 0.1901[/C][/ROW]
[ROW][C]79[/C][C] 0.8063[/C][C] 0.3874[/C][C] 0.1937[/C][/ROW]
[ROW][C]80[/C][C] 0.8295[/C][C] 0.341[/C][C] 0.1705[/C][/ROW]
[ROW][C]81[/C][C] 0.8247[/C][C] 0.3505[/C][C] 0.1753[/C][/ROW]
[ROW][C]82[/C][C] 0.8198[/C][C] 0.3604[/C][C] 0.1802[/C][/ROW]
[ROW][C]83[/C][C] 0.8096[/C][C] 0.3807[/C][C] 0.1903[/C][/ROW]
[ROW][C]84[/C][C] 0.844[/C][C] 0.3121[/C][C] 0.156[/C][/ROW]
[ROW][C]85[/C][C] 0.8268[/C][C] 0.3464[/C][C] 0.1732[/C][/ROW]
[ROW][C]86[/C][C] 0.7972[/C][C] 0.4057[/C][C] 0.2028[/C][/ROW]
[ROW][C]87[/C][C] 0.7687[/C][C] 0.4625[/C][C] 0.2313[/C][/ROW]
[ROW][C]88[/C][C] 0.7339[/C][C] 0.5322[/C][C] 0.2661[/C][/ROW]
[ROW][C]89[/C][C] 0.6978[/C][C] 0.6045[/C][C] 0.3022[/C][/ROW]
[ROW][C]90[/C][C] 0.6572[/C][C] 0.6856[/C][C] 0.3428[/C][/ROW]
[ROW][C]91[/C][C] 0.6217[/C][C] 0.7566[/C][C] 0.3783[/C][/ROW]
[ROW][C]92[/C][C] 0.5798[/C][C] 0.8404[/C][C] 0.4202[/C][/ROW]
[ROW][C]93[/C][C] 0.6284[/C][C] 0.7432[/C][C] 0.3716[/C][/ROW]
[ROW][C]94[/C][C] 0.6692[/C][C] 0.6615[/C][C] 0.3308[/C][/ROW]
[ROW][C]95[/C][C] 0.6331[/C][C] 0.7338[/C][C] 0.3669[/C][/ROW]
[ROW][C]96[/C][C] 0.7983[/C][C] 0.4033[/C][C] 0.2017[/C][/ROW]
[ROW][C]97[/C][C] 0.7751[/C][C] 0.4498[/C][C] 0.2249[/C][/ROW]
[ROW][C]98[/C][C] 0.7367[/C][C] 0.5265[/C][C] 0.2633[/C][/ROW]
[ROW][C]99[/C][C] 0.6956[/C][C] 0.6087[/C][C] 0.3044[/C][/ROW]
[ROW][C]100[/C][C] 0.6559[/C][C] 0.6881[/C][C] 0.3441[/C][/ROW]
[ROW][C]101[/C][C] 0.6096[/C][C] 0.7807[/C][C] 0.3904[/C][/ROW]
[ROW][C]102[/C][C] 0.5607[/C][C] 0.8785[/C][C] 0.4393[/C][/ROW]
[ROW][C]103[/C][C] 0.5611[/C][C] 0.8778[/C][C] 0.4389[/C][/ROW]
[ROW][C]104[/C][C] 0.5175[/C][C] 0.965[/C][C] 0.4825[/C][/ROW]
[ROW][C]105[/C][C] 0.4938[/C][C] 0.9875[/C][C] 0.5062[/C][/ROW]
[ROW][C]106[/C][C] 0.4601[/C][C] 0.9202[/C][C] 0.5399[/C][/ROW]
[ROW][C]107[/C][C] 0.4404[/C][C] 0.8809[/C][C] 0.5596[/C][/ROW]
[ROW][C]108[/C][C] 0.4292[/C][C] 0.8585[/C][C] 0.5708[/C][/ROW]
[ROW][C]109[/C][C] 0.4351[/C][C] 0.8702[/C][C] 0.5649[/C][/ROW]
[ROW][C]110[/C][C] 0.4694[/C][C] 0.9388[/C][C] 0.5306[/C][/ROW]
[ROW][C]111[/C][C] 0.4258[/C][C] 0.8516[/C][C] 0.5742[/C][/ROW]
[ROW][C]112[/C][C] 0.4146[/C][C] 0.8292[/C][C] 0.5854[/C][/ROW]
[ROW][C]113[/C][C] 0.8882[/C][C] 0.2236[/C][C] 0.1118[/C][/ROW]
[ROW][C]114[/C][C] 0.8929[/C][C] 0.2142[/C][C] 0.1071[/C][/ROW]
[ROW][C]115[/C][C] 0.8834[/C][C] 0.2332[/C][C] 0.1166[/C][/ROW]
[ROW][C]116[/C][C] 0.9121[/C][C] 0.1757[/C][C] 0.08786[/C][/ROW]
[ROW][C]117[/C][C] 0.8913[/C][C] 0.2174[/C][C] 0.1087[/C][/ROW]
[ROW][C]118[/C][C] 0.8674[/C][C] 0.2652[/C][C] 0.1326[/C][/ROW]
[ROW][C]119[/C][C] 0.8762[/C][C] 0.2475[/C][C] 0.1238[/C][/ROW]
[ROW][C]120[/C][C] 0.8869[/C][C] 0.2262[/C][C] 0.1131[/C][/ROW]
[ROW][C]121[/C][C] 0.8654[/C][C] 0.2691[/C][C] 0.1346[/C][/ROW]
[ROW][C]122[/C][C] 0.8353[/C][C] 0.3293[/C][C] 0.1647[/C][/ROW]
[ROW][C]123[/C][C] 0.8458[/C][C] 0.3084[/C][C] 0.1542[/C][/ROW]
[ROW][C]124[/C][C] 0.8105[/C][C] 0.3789[/C][C] 0.1895[/C][/ROW]
[ROW][C]125[/C][C] 0.7722[/C][C] 0.4555[/C][C] 0.2278[/C][/ROW]
[ROW][C]126[/C][C] 0.7844[/C][C] 0.4313[/C][C] 0.2156[/C][/ROW]
[ROW][C]127[/C][C] 0.7938[/C][C] 0.4123[/C][C] 0.2062[/C][/ROW]
[ROW][C]128[/C][C] 0.7493[/C][C] 0.5014[/C][C] 0.2507[/C][/ROW]
[ROW][C]129[/C][C] 0.7074[/C][C] 0.5852[/C][C] 0.2926[/C][/ROW]
[ROW][C]130[/C][C] 0.6503[/C][C] 0.6993[/C][C] 0.3497[/C][/ROW]
[ROW][C]131[/C][C] 0.6317[/C][C] 0.7367[/C][C] 0.3683[/C][/ROW]
[ROW][C]132[/C][C] 0.6413[/C][C] 0.7174[/C][C] 0.3587[/C][/ROW]
[ROW][C]133[/C][C] 0.5675[/C][C] 0.865[/C][C] 0.4325[/C][/ROW]
[ROW][C]134[/C][C] 0.4889[/C][C] 0.9777[/C][C] 0.5111[/C][/ROW]
[ROW][C]135[/C][C] 0.8501[/C][C] 0.2998[/C][C] 0.1499[/C][/ROW]
[ROW][C]136[/C][C] 0.7964[/C][C] 0.4071[/C][C] 0.2036[/C][/ROW]
[ROW][C]137[/C][C] 0.7481[/C][C] 0.5038[/C][C] 0.2519[/C][/ROW]
[ROW][C]138[/C][C] 0.6676[/C][C] 0.6648[/C][C] 0.3324[/C][/ROW]
[ROW][C]139[/C][C] 0.6041[/C][C] 0.7918[/C][C] 0.3959[/C][/ROW]
[ROW][C]140[/C][C] 0.7257[/C][C] 0.5485[/C][C] 0.2743[/C][/ROW]
[ROW][C]141[/C][C] 0.7544[/C][C] 0.4912[/C][C] 0.2456[/C][/ROW]
[ROW][C]142[/C][C] 0.6615[/C][C] 0.6769[/C][C] 0.3385[/C][/ROW]
[ROW][C]143[/C][C] 0.5332[/C][C] 0.9336[/C][C] 0.4668[/C][/ROW]
[ROW][C]144[/C][C] 0.9764[/C][C] 0.04728[/C][C] 0.02364[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=6

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	0.02203	0.04405	0.978
7	0.006143	0.01229	0.9939
8	0.002116	0.004232	0.9979
9	0.005309	0.01062	0.9947
10	0.004151	0.008303	0.9958
11	0.001409	0.002818	0.9986
12	0.1118	0.2236	0.8882
13	0.08047	0.1609	0.9195
14	0.05208	0.1042	0.9479
15	0.03507	0.07015	0.9649
16	0.02176	0.04352	0.9782
17	0.01331	0.02663	0.9867
18	0.008072	0.01614	0.9919
19	0.005476	0.01095	0.9945
20	0.003494	0.006987	0.9965
21	0.002154	0.004307	0.9978
22	0.004377	0.008755	0.9956
23	0.002763	0.005527	0.9972
24	0.004431	0.008862	0.9956
25	0.005555	0.01111	0.9944
26	0.004453	0.008907	0.9955
27	0.003741	0.007481	0.9963
28	0.003244	0.006487	0.9968
29	0.002572	0.005143	0.9974
30	0.001916	0.003833	0.9981
31	0.002364	0.004727	0.9976
32	0.001984	0.003969	0.998
33	0.003278	0.006557	0.9967
34	0.003661	0.007321	0.9963
35	0.002823	0.005647	0.9972
36	0.01105	0.0221	0.9889
37	0.01697	0.03394	0.983
38	0.01479	0.02958	0.9852
39	0.01854	0.03707	0.9815
40	0.02271	0.04542	0.9773
41	0.02048	0.04097	0.9795
42	0.02021	0.04042	0.9798
43	0.02231	0.04462	0.9777
44	0.02057	0.04115	0.9794
45	0.04791	0.09582	0.9521
46	0.1034	0.2068	0.8966
47	0.0982	0.1964	0.9018
48	0.09845	0.1969	0.9016
49	0.08296	0.1659	0.917
50	0.07985	0.1597	0.9201
51	0.1256	0.2512	0.8744
52	0.1543	0.3086	0.8457
53	0.1584	0.3168	0.8416
54	0.2444	0.4888	0.7556
55	0.3154	0.6307	0.6846
56	0.3503	0.7006	0.6497
57	0.4838	0.9675	0.5162
58	0.536	0.9279	0.464
59	0.5598	0.8805	0.4402
60	0.515	0.97	0.485
61	0.4778	0.9555	0.5222
62	0.4527	0.9054	0.5473
63	0.4209	0.8418	0.5791
64	0.4288	0.8577	0.5712
65	0.4463	0.8925	0.5537
66	0.4231	0.8461	0.5769
67	0.445	0.89	0.555
68	0.4756	0.9512	0.5244
69	0.56	0.8799	0.44
70	0.6002	0.7996	0.3998
71	0.5725	0.855	0.4275
72	0.8177	0.3646	0.1823
73	0.8152	0.3696	0.1848
74	0.7959	0.4082	0.2041
75	0.8305	0.339	0.1695
76	0.8327	0.3346	0.1673
77	0.8185	0.363	0.1815
78	0.8099	0.3802	0.1901
79	0.8063	0.3874	0.1937
80	0.8295	0.341	0.1705
81	0.8247	0.3505	0.1753
82	0.8198	0.3604	0.1802
83	0.8096	0.3807	0.1903
84	0.844	0.3121	0.156
85	0.8268	0.3464	0.1732
86	0.7972	0.4057	0.2028
87	0.7687	0.4625	0.2313
88	0.7339	0.5322	0.2661
89	0.6978	0.6045	0.3022
90	0.6572	0.6856	0.3428
91	0.6217	0.7566	0.3783
92	0.5798	0.8404	0.4202
93	0.6284	0.7432	0.3716
94	0.6692	0.6615	0.3308
95	0.6331	0.7338	0.3669
96	0.7983	0.4033	0.2017
97	0.7751	0.4498	0.2249
98	0.7367	0.5265	0.2633
99	0.6956	0.6087	0.3044
100	0.6559	0.6881	0.3441
101	0.6096	0.7807	0.3904
102	0.5607	0.8785	0.4393
103	0.5611	0.8778	0.4389
104	0.5175	0.965	0.4825
105	0.4938	0.9875	0.5062
106	0.4601	0.9202	0.5399
107	0.4404	0.8809	0.5596
108	0.4292	0.8585	0.5708
109	0.4351	0.8702	0.5649
110	0.4694	0.9388	0.5306
111	0.4258	0.8516	0.5742
112	0.4146	0.8292	0.5854
113	0.8882	0.2236	0.1118
114	0.8929	0.2142	0.1071
115	0.8834	0.2332	0.1166
116	0.9121	0.1757	0.08786
117	0.8913	0.2174	0.1087
118	0.8674	0.2652	0.1326
119	0.8762	0.2475	0.1238
120	0.8869	0.2262	0.1131
121	0.8654	0.2691	0.1346
122	0.8353	0.3293	0.1647
123	0.8458	0.3084	0.1542
124	0.8105	0.3789	0.1895
125	0.7722	0.4555	0.2278
126	0.7844	0.4313	0.2156
127	0.7938	0.4123	0.2062
128	0.7493	0.5014	0.2507
129	0.7074	0.5852	0.2926
130	0.6503	0.6993	0.3497
131	0.6317	0.7367	0.3683
132	0.6413	0.7174	0.3587
133	0.5675	0.865	0.4325
134	0.4889	0.9777	0.5111
135	0.8501	0.2998	0.1499
136	0.7964	0.4071	0.2036
137	0.7481	0.5038	0.2519
138	0.6676	0.6648	0.3324
139	0.6041	0.7918	0.3959
140	0.7257	0.5485	0.2743
141	0.7544	0.4912	0.2456
142	0.6615	0.6769	0.3385
143	0.5332	0.9336	0.4668
144	0.9764	0.04728	0.02364

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	18	0.1295	NOK
5% type I error level	36	0.258993	NOK
10% type I error level	38	0.273381	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 18 &  0.1295 & NOK \tabularnewline
5% type I error level & 36 & 0.258993 & NOK \tabularnewline
10% type I error level & 38 & 0.273381 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=7

[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]18[/C][C] 0.1295[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]36[/C][C]0.258993[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]38[/C][C]0.273381[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=7

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	18	0.1295	NOK
5% type I error level	36	0.258993	NOK
10% type I error level	38	0.273381	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 3.6894, df1 = 2, df2 = 145, p-value = 0.02736
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4957, df1 = 4, df2 = 143, p-value = 0.04548
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.638, df1 = 2, df2 = 145, p-value = 0.02874

\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 = 3.6894, df1 = 2, df2 = 145, p-value = 0.02736
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.4957, df1 = 4, df2 = 143, p-value = 0.04548
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 3.638, df1 = 2, df2 = 145, p-value = 0.02874
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=8

[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 = 3.6894, df1 = 2, df2 = 145, p-value = 0.02736
[/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 = 2.4957, df1 = 4, df2 = 143, p-value = 0.04548
[/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 = 3.638, df1 = 2, df2 = 145, p-value = 0.02874
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=8

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 = 3.6894, df1 = 2, df2 = 145, p-value = 0.02736
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4957, df1 = 4, df2 = 143, p-value = 0.04548
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 3.638, df1 = 2, df2 = 145, p-value = 0.02874

Variance Inflation Factors (Multicollinearity)
> vif X64 X58 1.028521 1.028521

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     X64      X58 
1.028521 1.028521 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310583&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     X64      X58 
1.028521 1.028521 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310583&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310583&T=9

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 X64 X58 1.028521 1.028521

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 3 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ; par6 = 12 ;

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

library(lattice)
library(lmtest)
library(car)
library(MASS)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par6 <- as.numeric(par6)
if(is.na(par6)) {
par6 <- 12
mywarning = 'Warning: you did not specify the seasonality. The seasonal period was set to s = 12.'
}
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 (!is.numeric(par4)) par4 <- 0
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
if (!is.numeric(par5)) par5 <- 0
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)'){
(n <- n - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s)'){
(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 - par6)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-Bs)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+par6,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*par6,par5), dimnames=list(1:(n-par5*par6), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*par6)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*par6-j*par6,par1]
}
}
x <- cbind(x[(par5*par6+1):n,], x2)
n <- n - par5*par6
}
if (par2 == 'Include Seasonal Dummies'){
x2 <- array(0, dim=c(n,par6-1), dimnames=list(1:n, paste('M', seq(1:(par6-1)), sep ='')))
for (i in 1:(par6-1)){
x2[seq(i,n,par6),i] <- 1
}
x <- cbind(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[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$resid)
myr
a <-table.start()
a <- table.row.start(a)
a <- table.element(a,'Menu of Residual Diagnostics',2,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Description',1,TRUE)
a <- table.element(a,'Link',1,TRUE)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Histogram',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_histogram.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_centraltendency.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'QQ Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_fitdistrnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Kernel Density Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_density.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness/Kurtosis Test',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Skewness-Kurtosis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_skewness_kurtosis_plot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Harrell-Davis Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_harrell_davis.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Blocked Bootstrap Plot -- Central Tendency',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_bootstrapplot.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'(Partial) Autocorrelation Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_autocorrelation.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Spectral Analysis',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_spectrum.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Tukey lambda PPCC Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_tukeylambda.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <-table.element(a,'Box-Cox Normality Plot',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_boxcoxnorm.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a <- table.row.start(a)
a <- table.element(a,'Summary Statistics',1,header=TRUE)
a <- table.element(a,hyperlink( paste('https://supernova.wessa.net/rwasp_summary1.wasp?convertgetintopost=1&data=',paste(as.character(mysum$resid),sep='',collapse=' '),sep='') ,'Compute','Click here to examine the Residuals.'),1)
a <- table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable7.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$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')
}
}
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