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 20:05:09 +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/t1513796825ywmixefjkac95fz.htm/, Retrieved Tue, 14 May 2024 12:39:14 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310564, Retrieved Tue, 14 May 2024 12:39:14 +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

114

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

-       [Multiple Regression] [MR ] [2017-12-20 19:05:09] [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	11 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 time11 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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]

11 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310564&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	11 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
X64[t] = + 85.724 -0.024775X58[t] + 0.118682X14[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
X64[t] =  +  85.724 -0.024775X58[t] +  0.118682X14[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]X64[t] =  +  85.724 -0.024775X58[t] +  0.118682X14[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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
X64[t] = + 85.724 -0.024775X58[t] + 0.118682X14[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)	+85.72	3.872	+2.2140e+01	1.139e-48	5.694e-49
X58	-0.02477	0.08503	-2.9140e-01	0.7712	0.3856
X14	+0.1187	0.1139	+1.0420e+00	0.299	0.1495

\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) & +85.72 &  3.872 & +2.2140e+01 &  1.139e-48 &  5.694e-49 \tabularnewline
X58 & -0.02477 &  0.08503 & -2.9140e-01 &  0.7712 &  0.3856 \tabularnewline
X14 & +0.1187 &  0.1139 & +1.0420e+00 &  0.299 &  0.1495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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]+85.72[/C][C] 3.872[/C][C]+2.2140e+01[/C][C] 1.139e-48[/C][C] 5.694e-49[/C][/ROW]
[ROW][C]X58[/C][C]-0.02477[/C][C] 0.08503[/C][C]-2.9140e-01[/C][C] 0.7712[/C][C] 0.3856[/C][/ROW]
[ROW][C]X14[/C][C]+0.1187[/C][C] 0.1139[/C][C]+1.0420e+00[/C][C] 0.299[/C][C] 0.1495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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)	+85.72	3.872	+2.2140e+01	1.139e-48	5.694e-49
X58	-0.02477	0.08503	-2.9140e-01	0.7712	0.3856
X14	+0.1187	0.1139	+1.0420e+00	0.299	0.1495

Multiple Linear Regression - Regression Statistics
Multiple R	0.1867
R-squared	0.03486
Adjusted R-squared	0.02173
F-TEST (value)	2.655
F-TEST (DF numerator)	2
F-TEST (DF denominator)	147
p-value	0.07367
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.617
Sum Squared Residuals	6436

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.1867 \tabularnewline
R-squared &  0.03486 \tabularnewline
Adjusted R-squared &  0.02173 \tabularnewline
F-TEST (value) &  2.655 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 147 \tabularnewline
p-value &  0.07367 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  6.617 \tabularnewline
Sum Squared Residuals &  6436 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.1867[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.03486[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.02173[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 2.655[/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] 0.07367[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 6.617[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 6436[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	0.1867
R-squared	0.03486
Adjusted R-squared	0.02173
F-TEST (value)	2.655
F-TEST (DF numerator)	2
F-TEST (DF denominator)	147
p-value	0.07367
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	6.617
Sum Squared Residuals	6436

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=310564&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=310564&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	97.7	91.34	6.361
2	88.9	91.83	-2.927
3	96.5	92.62	3.883
4	89.5	91.8	-2.303
5	85.4	92.56	-7.164
6	84.3	92.35	-8.049
7	83.7	91.31	-7.606
8	86.2	91.82	-5.618
9	90.7	92.82	-2.117
10	95.7	92.82	2.88
11	95.6	92.75	2.853
12	97	91.86	5.142
13	97.2	92.4	4.799
14	86.6	92.44	-5.841
15	88.4	92.98	-4.579
16	81.4	92.31	-10.91
17	86.9	92.79	-5.889
18	84.9	93.04	-8.137
19	83.7	91.76	-8.063
20	86.8	92.07	-5.268
21	88.3	92.62	-4.323
22	92.5	93.13	-0.6304
23	94.7	92.57	2.13
24	94.5	91.76	2.736
25	98.7	92.43	6.273
26	88.6	92.33	-3.732
27	95.2	92.92	2.276
28	91.3	92.83	-1.531
29	91.7	92.69	-0.988
30	89.3	92.81	-3.511
31	88.7	92.3	-3.595
32	91.2	92.05	-0.8508
33	88.6	93.03	-4.432
34	94.6	93.38	1.219
35	96	92.67	3.33
36	94.3	91.99	2.305
37	102	92.83	9.17
38	93.4	92.46	0.9443
39	96.7	93.18	3.517
40	93.7	93.03	0.6657
41	91.6	92.51	-0.9087
42	89.6	93.01	-3.411
43	92.9	92.52	0.3845
44	94.1	92.03	2.071
45	92	93.53	-1.53
46	97.5	93.7	3.796
47	92.7	92.64	0.05688
48	100.7	92.99	7.706
49	105.9	92.56	13.34
50	95.3	92.9	2.404
51	99.8	94.13	5.671
52	91.3	93.48	-2.185
53	90.8	92.95	-2.154
54	87.1	94.22	-7.119
55	91.4	93.04	-1.642
56	86.1	92.96	-6.856
57	87.1	94.31	-7.214
58	92.6	94.13	-1.533
59	96.6	93.88	2.725
60	105.3	93.98	11.32
61	102.4	93.39	9.005
62	98.2	93.21	4.99
63	98.6	94.19	4.406
64	92.6	93.95	-1.355
65	87.9	93.5	-5.603
66	84.1	94.26	-10.16
67	86.7	92.68	-5.977
68	84.4	93.08	-8.678
69	86	94.58	-8.576
70	90.4	94.1	-3.702
71	92.9	94.19	-1.286
72	105.8	93.62	12.18
73	106	93.81	12.19
74	99.1	93.81	5.292
75	99.9	95.12	4.775
76	88.1	93.86	-5.757
77	87.8	94.47	-6.671
78	87.1	94.91	-7.812
79	85.9	93.27	-7.367
80	86.5	93.72	-7.222
81	84.1	94.86	-10.76
82	92.1	95.14	-3.04
83	93.3	94.83	-1.53
84	98.9	93.98	4.923
85	103	94.41	8.594
86	98.4	94.16	4.235
87	100.7	95.28	5.416
88	92.3	94.29	-1.992
89	89	94.75	-5.748
90	88.9	95.32	-6.419
91	85.5	94.27	-8.771
92	90.1	94.23	-4.131
93	87	94.78	-7.776
94	97.1	95.63	1.467
95	101.5	94.99	6.506
96	103	94.13	8.874
97	106.1	95.11	10.99
98	96.1	95.12	0.9783
99	94.2	95.35	-1.15
100	89.1	95.74	-6.643
101	85.2	95.08	-9.883
102	86.5	95.66	-9.163
103	88	95.05	-7.048
104	88.4	94.09	-5.685
105	87.9	96.05	-8.154
106	95.7	95.85	-0.1502
107	94.8	94.25	0.5457
108	105.2	94.39	10.81
109	108.7	93.32	15.38
110	96.1	93.5	2.598
111	98.3	94.49	3.809
112	88.6	93.79	-5.194
113	90.8	93.55	-2.747
114	88.1	94.46	-6.358
115	91.9	93.73	-1.832
116	98.5	93.32	5.175
117	98.6	94.82	3.777
118	100.3	94.76	5.539
119	98.7	94.25	4.455
120	110.7	94.07	16.63
121	115.4	94.02	21.38
122	105.4	94.65	10.75
123	108	95.84	12.16
124	94.5	94.82	-0.3168
125	96.5	94.64	1.855
126	91	96.14	-5.144
127	94.1	94.7	-0.5979
128	96.4	94.44	1.957
129	93.1	95.84	-2.74
130	97.5	95.62	1.88
131	102.5	95.63	6.868
132	105.7	95.05	10.65
133	109.1	94.9	14.2
134	97.2	95.29	1.909
135	100.3	97.27	3.027
136	91.3	95.41	-4.112
137	94.3	95.97	-1.668
138	89.5	95.7	-6.197
139	89.3	94.52	-5.215
140	93.4	95.45	-2.046
141	91.9	96.26	-4.361
142	92.9	95.49	-2.593
143	93.7	95.15	-1.451
144	100.1	94.7	5.397
145	105.5	94.92	10.58
146	110.5	95.15	15.35
147	89.5	96.66	-7.164
148	90.4	94.9	-4.5
149	89.9	95.15	-5.252
150	84.6	95.75	-11.15

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  97.7 &  91.34 &  6.361 \tabularnewline
2 &  88.9 &  91.83 & -2.927 \tabularnewline
3 &  96.5 &  92.62 &  3.883 \tabularnewline
4 &  89.5 &  91.8 & -2.303 \tabularnewline
5 &  85.4 &  92.56 & -7.164 \tabularnewline
6 &  84.3 &  92.35 & -8.049 \tabularnewline
7 &  83.7 &  91.31 & -7.606 \tabularnewline
8 &  86.2 &  91.82 & -5.618 \tabularnewline
9 &  90.7 &  92.82 & -2.117 \tabularnewline
10 &  95.7 &  92.82 &  2.88 \tabularnewline
11 &  95.6 &  92.75 &  2.853 \tabularnewline
12 &  97 &  91.86 &  5.142 \tabularnewline
13 &  97.2 &  92.4 &  4.799 \tabularnewline
14 &  86.6 &  92.44 & -5.841 \tabularnewline
15 &  88.4 &  92.98 & -4.579 \tabularnewline
16 &  81.4 &  92.31 & -10.91 \tabularnewline
17 &  86.9 &  92.79 & -5.889 \tabularnewline
18 &  84.9 &  93.04 & -8.137 \tabularnewline
19 &  83.7 &  91.76 & -8.063 \tabularnewline
20 &  86.8 &  92.07 & -5.268 \tabularnewline
21 &  88.3 &  92.62 & -4.323 \tabularnewline
22 &  92.5 &  93.13 & -0.6304 \tabularnewline
23 &  94.7 &  92.57 &  2.13 \tabularnewline
24 &  94.5 &  91.76 &  2.736 \tabularnewline
25 &  98.7 &  92.43 &  6.273 \tabularnewline
26 &  88.6 &  92.33 & -3.732 \tabularnewline
27 &  95.2 &  92.92 &  2.276 \tabularnewline
28 &  91.3 &  92.83 & -1.531 \tabularnewline
29 &  91.7 &  92.69 & -0.988 \tabularnewline
30 &  89.3 &  92.81 & -3.511 \tabularnewline
31 &  88.7 &  92.3 & -3.595 \tabularnewline
32 &  91.2 &  92.05 & -0.8508 \tabularnewline
33 &  88.6 &  93.03 & -4.432 \tabularnewline
34 &  94.6 &  93.38 &  1.219 \tabularnewline
35 &  96 &  92.67 &  3.33 \tabularnewline
36 &  94.3 &  91.99 &  2.305 \tabularnewline
37 &  102 &  92.83 &  9.17 \tabularnewline
38 &  93.4 &  92.46 &  0.9443 \tabularnewline
39 &  96.7 &  93.18 &  3.517 \tabularnewline
40 &  93.7 &  93.03 &  0.6657 \tabularnewline
41 &  91.6 &  92.51 & -0.9087 \tabularnewline
42 &  89.6 &  93.01 & -3.411 \tabularnewline
43 &  92.9 &  92.52 &  0.3845 \tabularnewline
44 &  94.1 &  92.03 &  2.071 \tabularnewline
45 &  92 &  93.53 & -1.53 \tabularnewline
46 &  97.5 &  93.7 &  3.796 \tabularnewline
47 &  92.7 &  92.64 &  0.05688 \tabularnewline
48 &  100.7 &  92.99 &  7.706 \tabularnewline
49 &  105.9 &  92.56 &  13.34 \tabularnewline
50 &  95.3 &  92.9 &  2.404 \tabularnewline
51 &  99.8 &  94.13 &  5.671 \tabularnewline
52 &  91.3 &  93.48 & -2.185 \tabularnewline
53 &  90.8 &  92.95 & -2.154 \tabularnewline
54 &  87.1 &  94.22 & -7.119 \tabularnewline
55 &  91.4 &  93.04 & -1.642 \tabularnewline
56 &  86.1 &  92.96 & -6.856 \tabularnewline
57 &  87.1 &  94.31 & -7.214 \tabularnewline
58 &  92.6 &  94.13 & -1.533 \tabularnewline
59 &  96.6 &  93.88 &  2.725 \tabularnewline
60 &  105.3 &  93.98 &  11.32 \tabularnewline
61 &  102.4 &  93.39 &  9.005 \tabularnewline
62 &  98.2 &  93.21 &  4.99 \tabularnewline
63 &  98.6 &  94.19 &  4.406 \tabularnewline
64 &  92.6 &  93.95 & -1.355 \tabularnewline
65 &  87.9 &  93.5 & -5.603 \tabularnewline
66 &  84.1 &  94.26 & -10.16 \tabularnewline
67 &  86.7 &  92.68 & -5.977 \tabularnewline
68 &  84.4 &  93.08 & -8.678 \tabularnewline
69 &  86 &  94.58 & -8.576 \tabularnewline
70 &  90.4 &  94.1 & -3.702 \tabularnewline
71 &  92.9 &  94.19 & -1.286 \tabularnewline
72 &  105.8 &  93.62 &  12.18 \tabularnewline
73 &  106 &  93.81 &  12.19 \tabularnewline
74 &  99.1 &  93.81 &  5.292 \tabularnewline
75 &  99.9 &  95.12 &  4.775 \tabularnewline
76 &  88.1 &  93.86 & -5.757 \tabularnewline
77 &  87.8 &  94.47 & -6.671 \tabularnewline
78 &  87.1 &  94.91 & -7.812 \tabularnewline
79 &  85.9 &  93.27 & -7.367 \tabularnewline
80 &  86.5 &  93.72 & -7.222 \tabularnewline
81 &  84.1 &  94.86 & -10.76 \tabularnewline
82 &  92.1 &  95.14 & -3.04 \tabularnewline
83 &  93.3 &  94.83 & -1.53 \tabularnewline
84 &  98.9 &  93.98 &  4.923 \tabularnewline
85 &  103 &  94.41 &  8.594 \tabularnewline
86 &  98.4 &  94.16 &  4.235 \tabularnewline
87 &  100.7 &  95.28 &  5.416 \tabularnewline
88 &  92.3 &  94.29 & -1.992 \tabularnewline
89 &  89 &  94.75 & -5.748 \tabularnewline
90 &  88.9 &  95.32 & -6.419 \tabularnewline
91 &  85.5 &  94.27 & -8.771 \tabularnewline
92 &  90.1 &  94.23 & -4.131 \tabularnewline
93 &  87 &  94.78 & -7.776 \tabularnewline
94 &  97.1 &  95.63 &  1.467 \tabularnewline
95 &  101.5 &  94.99 &  6.506 \tabularnewline
96 &  103 &  94.13 &  8.874 \tabularnewline
97 &  106.1 &  95.11 &  10.99 \tabularnewline
98 &  96.1 &  95.12 &  0.9783 \tabularnewline
99 &  94.2 &  95.35 & -1.15 \tabularnewline
100 &  89.1 &  95.74 & -6.643 \tabularnewline
101 &  85.2 &  95.08 & -9.883 \tabularnewline
102 &  86.5 &  95.66 & -9.163 \tabularnewline
103 &  88 &  95.05 & -7.048 \tabularnewline
104 &  88.4 &  94.09 & -5.685 \tabularnewline
105 &  87.9 &  96.05 & -8.154 \tabularnewline
106 &  95.7 &  95.85 & -0.1502 \tabularnewline
107 &  94.8 &  94.25 &  0.5457 \tabularnewline
108 &  105.2 &  94.39 &  10.81 \tabularnewline
109 &  108.7 &  93.32 &  15.38 \tabularnewline
110 &  96.1 &  93.5 &  2.598 \tabularnewline
111 &  98.3 &  94.49 &  3.809 \tabularnewline
112 &  88.6 &  93.79 & -5.194 \tabularnewline
113 &  90.8 &  93.55 & -2.747 \tabularnewline
114 &  88.1 &  94.46 & -6.358 \tabularnewline
115 &  91.9 &  93.73 & -1.832 \tabularnewline
116 &  98.5 &  93.32 &  5.175 \tabularnewline
117 &  98.6 &  94.82 &  3.777 \tabularnewline
118 &  100.3 &  94.76 &  5.539 \tabularnewline
119 &  98.7 &  94.25 &  4.455 \tabularnewline
120 &  110.7 &  94.07 &  16.63 \tabularnewline
121 &  115.4 &  94.02 &  21.38 \tabularnewline
122 &  105.4 &  94.65 &  10.75 \tabularnewline
123 &  108 &  95.84 &  12.16 \tabularnewline
124 &  94.5 &  94.82 & -0.3168 \tabularnewline
125 &  96.5 &  94.64 &  1.855 \tabularnewline
126 &  91 &  96.14 & -5.144 \tabularnewline
127 &  94.1 &  94.7 & -0.5979 \tabularnewline
128 &  96.4 &  94.44 &  1.957 \tabularnewline
129 &  93.1 &  95.84 & -2.74 \tabularnewline
130 &  97.5 &  95.62 &  1.88 \tabularnewline
131 &  102.5 &  95.63 &  6.868 \tabularnewline
132 &  105.7 &  95.05 &  10.65 \tabularnewline
133 &  109.1 &  94.9 &  14.2 \tabularnewline
134 &  97.2 &  95.29 &  1.909 \tabularnewline
135 &  100.3 &  97.27 &  3.027 \tabularnewline
136 &  91.3 &  95.41 & -4.112 \tabularnewline
137 &  94.3 &  95.97 & -1.668 \tabularnewline
138 &  89.5 &  95.7 & -6.197 \tabularnewline
139 &  89.3 &  94.52 & -5.215 \tabularnewline
140 &  93.4 &  95.45 & -2.046 \tabularnewline
141 &  91.9 &  96.26 & -4.361 \tabularnewline
142 &  92.9 &  95.49 & -2.593 \tabularnewline
143 &  93.7 &  95.15 & -1.451 \tabularnewline
144 &  100.1 &  94.7 &  5.397 \tabularnewline
145 &  105.5 &  94.92 &  10.58 \tabularnewline
146 &  110.5 &  95.15 &  15.35 \tabularnewline
147 &  89.5 &  96.66 & -7.164 \tabularnewline
148 &  90.4 &  94.9 & -4.5 \tabularnewline
149 &  89.9 &  95.15 & -5.252 \tabularnewline
150 &  84.6 &  95.75 & -11.15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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] 97.7[/C][C] 91.34[/C][C] 6.361[/C][/ROW]
[ROW][C]2[/C][C] 88.9[/C][C] 91.83[/C][C]-2.927[/C][/ROW]
[ROW][C]3[/C][C] 96.5[/C][C] 92.62[/C][C] 3.883[/C][/ROW]
[ROW][C]4[/C][C] 89.5[/C][C] 91.8[/C][C]-2.303[/C][/ROW]
[ROW][C]5[/C][C] 85.4[/C][C] 92.56[/C][C]-7.164[/C][/ROW]
[ROW][C]6[/C][C] 84.3[/C][C] 92.35[/C][C]-8.049[/C][/ROW]
[ROW][C]7[/C][C] 83.7[/C][C] 91.31[/C][C]-7.606[/C][/ROW]
[ROW][C]8[/C][C] 86.2[/C][C] 91.82[/C][C]-5.618[/C][/ROW]
[ROW][C]9[/C][C] 90.7[/C][C] 92.82[/C][C]-2.117[/C][/ROW]
[ROW][C]10[/C][C] 95.7[/C][C] 92.82[/C][C] 2.88[/C][/ROW]
[ROW][C]11[/C][C] 95.6[/C][C] 92.75[/C][C] 2.853[/C][/ROW]
[ROW][C]12[/C][C] 97[/C][C] 91.86[/C][C] 5.142[/C][/ROW]
[ROW][C]13[/C][C] 97.2[/C][C] 92.4[/C][C] 4.799[/C][/ROW]
[ROW][C]14[/C][C] 86.6[/C][C] 92.44[/C][C]-5.841[/C][/ROW]
[ROW][C]15[/C][C] 88.4[/C][C] 92.98[/C][C]-4.579[/C][/ROW]
[ROW][C]16[/C][C] 81.4[/C][C] 92.31[/C][C]-10.91[/C][/ROW]
[ROW][C]17[/C][C] 86.9[/C][C] 92.79[/C][C]-5.889[/C][/ROW]
[ROW][C]18[/C][C] 84.9[/C][C] 93.04[/C][C]-8.137[/C][/ROW]
[ROW][C]19[/C][C] 83.7[/C][C] 91.76[/C][C]-8.063[/C][/ROW]
[ROW][C]20[/C][C] 86.8[/C][C] 92.07[/C][C]-5.268[/C][/ROW]
[ROW][C]21[/C][C] 88.3[/C][C] 92.62[/C][C]-4.323[/C][/ROW]
[ROW][C]22[/C][C] 92.5[/C][C] 93.13[/C][C]-0.6304[/C][/ROW]
[ROW][C]23[/C][C] 94.7[/C][C] 92.57[/C][C] 2.13[/C][/ROW]
[ROW][C]24[/C][C] 94.5[/C][C] 91.76[/C][C] 2.736[/C][/ROW]
[ROW][C]25[/C][C] 98.7[/C][C] 92.43[/C][C] 6.273[/C][/ROW]
[ROW][C]26[/C][C] 88.6[/C][C] 92.33[/C][C]-3.732[/C][/ROW]
[ROW][C]27[/C][C] 95.2[/C][C] 92.92[/C][C] 2.276[/C][/ROW]
[ROW][C]28[/C][C] 91.3[/C][C] 92.83[/C][C]-1.531[/C][/ROW]
[ROW][C]29[/C][C] 91.7[/C][C] 92.69[/C][C]-0.988[/C][/ROW]
[ROW][C]30[/C][C] 89.3[/C][C] 92.81[/C][C]-3.511[/C][/ROW]
[ROW][C]31[/C][C] 88.7[/C][C] 92.3[/C][C]-3.595[/C][/ROW]
[ROW][C]32[/C][C] 91.2[/C][C] 92.05[/C][C]-0.8508[/C][/ROW]
[ROW][C]33[/C][C] 88.6[/C][C] 93.03[/C][C]-4.432[/C][/ROW]
[ROW][C]34[/C][C] 94.6[/C][C] 93.38[/C][C] 1.219[/C][/ROW]
[ROW][C]35[/C][C] 96[/C][C] 92.67[/C][C] 3.33[/C][/ROW]
[ROW][C]36[/C][C] 94.3[/C][C] 91.99[/C][C] 2.305[/C][/ROW]
[ROW][C]37[/C][C] 102[/C][C] 92.83[/C][C] 9.17[/C][/ROW]
[ROW][C]38[/C][C] 93.4[/C][C] 92.46[/C][C] 0.9443[/C][/ROW]
[ROW][C]39[/C][C] 96.7[/C][C] 93.18[/C][C] 3.517[/C][/ROW]
[ROW][C]40[/C][C] 93.7[/C][C] 93.03[/C][C] 0.6657[/C][/ROW]
[ROW][C]41[/C][C] 91.6[/C][C] 92.51[/C][C]-0.9087[/C][/ROW]
[ROW][C]42[/C][C] 89.6[/C][C] 93.01[/C][C]-3.411[/C][/ROW]
[ROW][C]43[/C][C] 92.9[/C][C] 92.52[/C][C] 0.3845[/C][/ROW]
[ROW][C]44[/C][C] 94.1[/C][C] 92.03[/C][C] 2.071[/C][/ROW]
[ROW][C]45[/C][C] 92[/C][C] 93.53[/C][C]-1.53[/C][/ROW]
[ROW][C]46[/C][C] 97.5[/C][C] 93.7[/C][C] 3.796[/C][/ROW]
[ROW][C]47[/C][C] 92.7[/C][C] 92.64[/C][C] 0.05688[/C][/ROW]
[ROW][C]48[/C][C] 100.7[/C][C] 92.99[/C][C] 7.706[/C][/ROW]
[ROW][C]49[/C][C] 105.9[/C][C] 92.56[/C][C] 13.34[/C][/ROW]
[ROW][C]50[/C][C] 95.3[/C][C] 92.9[/C][C] 2.404[/C][/ROW]
[ROW][C]51[/C][C] 99.8[/C][C] 94.13[/C][C] 5.671[/C][/ROW]
[ROW][C]52[/C][C] 91.3[/C][C] 93.48[/C][C]-2.185[/C][/ROW]
[ROW][C]53[/C][C] 90.8[/C][C] 92.95[/C][C]-2.154[/C][/ROW]
[ROW][C]54[/C][C] 87.1[/C][C] 94.22[/C][C]-7.119[/C][/ROW]
[ROW][C]55[/C][C] 91.4[/C][C] 93.04[/C][C]-1.642[/C][/ROW]
[ROW][C]56[/C][C] 86.1[/C][C] 92.96[/C][C]-6.856[/C][/ROW]
[ROW][C]57[/C][C] 87.1[/C][C] 94.31[/C][C]-7.214[/C][/ROW]
[ROW][C]58[/C][C] 92.6[/C][C] 94.13[/C][C]-1.533[/C][/ROW]
[ROW][C]59[/C][C] 96.6[/C][C] 93.88[/C][C] 2.725[/C][/ROW]
[ROW][C]60[/C][C] 105.3[/C][C] 93.98[/C][C] 11.32[/C][/ROW]
[ROW][C]61[/C][C] 102.4[/C][C] 93.39[/C][C] 9.005[/C][/ROW]
[ROW][C]62[/C][C] 98.2[/C][C] 93.21[/C][C] 4.99[/C][/ROW]
[ROW][C]63[/C][C] 98.6[/C][C] 94.19[/C][C] 4.406[/C][/ROW]
[ROW][C]64[/C][C] 92.6[/C][C] 93.95[/C][C]-1.355[/C][/ROW]
[ROW][C]65[/C][C] 87.9[/C][C] 93.5[/C][C]-5.603[/C][/ROW]
[ROW][C]66[/C][C] 84.1[/C][C] 94.26[/C][C]-10.16[/C][/ROW]
[ROW][C]67[/C][C] 86.7[/C][C] 92.68[/C][C]-5.977[/C][/ROW]
[ROW][C]68[/C][C] 84.4[/C][C] 93.08[/C][C]-8.678[/C][/ROW]
[ROW][C]69[/C][C] 86[/C][C] 94.58[/C][C]-8.576[/C][/ROW]
[ROW][C]70[/C][C] 90.4[/C][C] 94.1[/C][C]-3.702[/C][/ROW]
[ROW][C]71[/C][C] 92.9[/C][C] 94.19[/C][C]-1.286[/C][/ROW]
[ROW][C]72[/C][C] 105.8[/C][C] 93.62[/C][C] 12.18[/C][/ROW]
[ROW][C]73[/C][C] 106[/C][C] 93.81[/C][C] 12.19[/C][/ROW]
[ROW][C]74[/C][C] 99.1[/C][C] 93.81[/C][C] 5.292[/C][/ROW]
[ROW][C]75[/C][C] 99.9[/C][C] 95.12[/C][C] 4.775[/C][/ROW]
[ROW][C]76[/C][C] 88.1[/C][C] 93.86[/C][C]-5.757[/C][/ROW]
[ROW][C]77[/C][C] 87.8[/C][C] 94.47[/C][C]-6.671[/C][/ROW]
[ROW][C]78[/C][C] 87.1[/C][C] 94.91[/C][C]-7.812[/C][/ROW]
[ROW][C]79[/C][C] 85.9[/C][C] 93.27[/C][C]-7.367[/C][/ROW]
[ROW][C]80[/C][C] 86.5[/C][C] 93.72[/C][C]-7.222[/C][/ROW]
[ROW][C]81[/C][C] 84.1[/C][C] 94.86[/C][C]-10.76[/C][/ROW]
[ROW][C]82[/C][C] 92.1[/C][C] 95.14[/C][C]-3.04[/C][/ROW]
[ROW][C]83[/C][C] 93.3[/C][C] 94.83[/C][C]-1.53[/C][/ROW]
[ROW][C]84[/C][C] 98.9[/C][C] 93.98[/C][C] 4.923[/C][/ROW]
[ROW][C]85[/C][C] 103[/C][C] 94.41[/C][C] 8.594[/C][/ROW]
[ROW][C]86[/C][C] 98.4[/C][C] 94.16[/C][C] 4.235[/C][/ROW]
[ROW][C]87[/C][C] 100.7[/C][C] 95.28[/C][C] 5.416[/C][/ROW]
[ROW][C]88[/C][C] 92.3[/C][C] 94.29[/C][C]-1.992[/C][/ROW]
[ROW][C]89[/C][C] 89[/C][C] 94.75[/C][C]-5.748[/C][/ROW]
[ROW][C]90[/C][C] 88.9[/C][C] 95.32[/C][C]-6.419[/C][/ROW]
[ROW][C]91[/C][C] 85.5[/C][C] 94.27[/C][C]-8.771[/C][/ROW]
[ROW][C]92[/C][C] 90.1[/C][C] 94.23[/C][C]-4.131[/C][/ROW]
[ROW][C]93[/C][C] 87[/C][C] 94.78[/C][C]-7.776[/C][/ROW]
[ROW][C]94[/C][C] 97.1[/C][C] 95.63[/C][C] 1.467[/C][/ROW]
[ROW][C]95[/C][C] 101.5[/C][C] 94.99[/C][C] 6.506[/C][/ROW]
[ROW][C]96[/C][C] 103[/C][C] 94.13[/C][C] 8.874[/C][/ROW]
[ROW][C]97[/C][C] 106.1[/C][C] 95.11[/C][C] 10.99[/C][/ROW]
[ROW][C]98[/C][C] 96.1[/C][C] 95.12[/C][C] 0.9783[/C][/ROW]
[ROW][C]99[/C][C] 94.2[/C][C] 95.35[/C][C]-1.15[/C][/ROW]
[ROW][C]100[/C][C] 89.1[/C][C] 95.74[/C][C]-6.643[/C][/ROW]
[ROW][C]101[/C][C] 85.2[/C][C] 95.08[/C][C]-9.883[/C][/ROW]
[ROW][C]102[/C][C] 86.5[/C][C] 95.66[/C][C]-9.163[/C][/ROW]
[ROW][C]103[/C][C] 88[/C][C] 95.05[/C][C]-7.048[/C][/ROW]
[ROW][C]104[/C][C] 88.4[/C][C] 94.09[/C][C]-5.685[/C][/ROW]
[ROW][C]105[/C][C] 87.9[/C][C] 96.05[/C][C]-8.154[/C][/ROW]
[ROW][C]106[/C][C] 95.7[/C][C] 95.85[/C][C]-0.1502[/C][/ROW]
[ROW][C]107[/C][C] 94.8[/C][C] 94.25[/C][C] 0.5457[/C][/ROW]
[ROW][C]108[/C][C] 105.2[/C][C] 94.39[/C][C] 10.81[/C][/ROW]
[ROW][C]109[/C][C] 108.7[/C][C] 93.32[/C][C] 15.38[/C][/ROW]
[ROW][C]110[/C][C] 96.1[/C][C] 93.5[/C][C] 2.598[/C][/ROW]
[ROW][C]111[/C][C] 98.3[/C][C] 94.49[/C][C] 3.809[/C][/ROW]
[ROW][C]112[/C][C] 88.6[/C][C] 93.79[/C][C]-5.194[/C][/ROW]
[ROW][C]113[/C][C] 90.8[/C][C] 93.55[/C][C]-2.747[/C][/ROW]
[ROW][C]114[/C][C] 88.1[/C][C] 94.46[/C][C]-6.358[/C][/ROW]
[ROW][C]115[/C][C] 91.9[/C][C] 93.73[/C][C]-1.832[/C][/ROW]
[ROW][C]116[/C][C] 98.5[/C][C] 93.32[/C][C] 5.175[/C][/ROW]
[ROW][C]117[/C][C] 98.6[/C][C] 94.82[/C][C] 3.777[/C][/ROW]
[ROW][C]118[/C][C] 100.3[/C][C] 94.76[/C][C] 5.539[/C][/ROW]
[ROW][C]119[/C][C] 98.7[/C][C] 94.25[/C][C] 4.455[/C][/ROW]
[ROW][C]120[/C][C] 110.7[/C][C] 94.07[/C][C] 16.63[/C][/ROW]
[ROW][C]121[/C][C] 115.4[/C][C] 94.02[/C][C] 21.38[/C][/ROW]
[ROW][C]122[/C][C] 105.4[/C][C] 94.65[/C][C] 10.75[/C][/ROW]
[ROW][C]123[/C][C] 108[/C][C] 95.84[/C][C] 12.16[/C][/ROW]
[ROW][C]124[/C][C] 94.5[/C][C] 94.82[/C][C]-0.3168[/C][/ROW]
[ROW][C]125[/C][C] 96.5[/C][C] 94.64[/C][C] 1.855[/C][/ROW]
[ROW][C]126[/C][C] 91[/C][C] 96.14[/C][C]-5.144[/C][/ROW]
[ROW][C]127[/C][C] 94.1[/C][C] 94.7[/C][C]-0.5979[/C][/ROW]
[ROW][C]128[/C][C] 96.4[/C][C] 94.44[/C][C] 1.957[/C][/ROW]
[ROW][C]129[/C][C] 93.1[/C][C] 95.84[/C][C]-2.74[/C][/ROW]
[ROW][C]130[/C][C] 97.5[/C][C] 95.62[/C][C] 1.88[/C][/ROW]
[ROW][C]131[/C][C] 102.5[/C][C] 95.63[/C][C] 6.868[/C][/ROW]
[ROW][C]132[/C][C] 105.7[/C][C] 95.05[/C][C] 10.65[/C][/ROW]
[ROW][C]133[/C][C] 109.1[/C][C] 94.9[/C][C] 14.2[/C][/ROW]
[ROW][C]134[/C][C] 97.2[/C][C] 95.29[/C][C] 1.909[/C][/ROW]
[ROW][C]135[/C][C] 100.3[/C][C] 97.27[/C][C] 3.027[/C][/ROW]
[ROW][C]136[/C][C] 91.3[/C][C] 95.41[/C][C]-4.112[/C][/ROW]
[ROW][C]137[/C][C] 94.3[/C][C] 95.97[/C][C]-1.668[/C][/ROW]
[ROW][C]138[/C][C] 89.5[/C][C] 95.7[/C][C]-6.197[/C][/ROW]
[ROW][C]139[/C][C] 89.3[/C][C] 94.52[/C][C]-5.215[/C][/ROW]
[ROW][C]140[/C][C] 93.4[/C][C] 95.45[/C][C]-2.046[/C][/ROW]
[ROW][C]141[/C][C] 91.9[/C][C] 96.26[/C][C]-4.361[/C][/ROW]
[ROW][C]142[/C][C] 92.9[/C][C] 95.49[/C][C]-2.593[/C][/ROW]
[ROW][C]143[/C][C] 93.7[/C][C] 95.15[/C][C]-1.451[/C][/ROW]
[ROW][C]144[/C][C] 100.1[/C][C] 94.7[/C][C] 5.397[/C][/ROW]
[ROW][C]145[/C][C] 105.5[/C][C] 94.92[/C][C] 10.58[/C][/ROW]
[ROW][C]146[/C][C] 110.5[/C][C] 95.15[/C][C] 15.35[/C][/ROW]
[ROW][C]147[/C][C] 89.5[/C][C] 96.66[/C][C]-7.164[/C][/ROW]
[ROW][C]148[/C][C] 90.4[/C][C] 94.9[/C][C]-4.5[/C][/ROW]
[ROW][C]149[/C][C] 89.9[/C][C] 95.15[/C][C]-5.252[/C][/ROW]
[ROW][C]150[/C][C] 84.6[/C][C] 95.75[/C][C]-11.15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	97.7	91.34	6.361
2	88.9	91.83	-2.927
3	96.5	92.62	3.883
4	89.5	91.8	-2.303
5	85.4	92.56	-7.164
6	84.3	92.35	-8.049
7	83.7	91.31	-7.606
8	86.2	91.82	-5.618
9	90.7	92.82	-2.117
10	95.7	92.82	2.88
11	95.6	92.75	2.853
12	97	91.86	5.142
13	97.2	92.4	4.799
14	86.6	92.44	-5.841
15	88.4	92.98	-4.579
16	81.4	92.31	-10.91
17	86.9	92.79	-5.889
18	84.9	93.04	-8.137
19	83.7	91.76	-8.063
20	86.8	92.07	-5.268
21	88.3	92.62	-4.323
22	92.5	93.13	-0.6304
23	94.7	92.57	2.13
24	94.5	91.76	2.736
25	98.7	92.43	6.273
26	88.6	92.33	-3.732
27	95.2	92.92	2.276
28	91.3	92.83	-1.531
29	91.7	92.69	-0.988
30	89.3	92.81	-3.511
31	88.7	92.3	-3.595
32	91.2	92.05	-0.8508
33	88.6	93.03	-4.432
34	94.6	93.38	1.219
35	96	92.67	3.33
36	94.3	91.99	2.305
37	102	92.83	9.17
38	93.4	92.46	0.9443
39	96.7	93.18	3.517
40	93.7	93.03	0.6657
41	91.6	92.51	-0.9087
42	89.6	93.01	-3.411
43	92.9	92.52	0.3845
44	94.1	92.03	2.071
45	92	93.53	-1.53
46	97.5	93.7	3.796
47	92.7	92.64	0.05688
48	100.7	92.99	7.706
49	105.9	92.56	13.34
50	95.3	92.9	2.404
51	99.8	94.13	5.671
52	91.3	93.48	-2.185
53	90.8	92.95	-2.154
54	87.1	94.22	-7.119
55	91.4	93.04	-1.642
56	86.1	92.96	-6.856
57	87.1	94.31	-7.214
58	92.6	94.13	-1.533
59	96.6	93.88	2.725
60	105.3	93.98	11.32
61	102.4	93.39	9.005
62	98.2	93.21	4.99
63	98.6	94.19	4.406
64	92.6	93.95	-1.355
65	87.9	93.5	-5.603
66	84.1	94.26	-10.16
67	86.7	92.68	-5.977
68	84.4	93.08	-8.678
69	86	94.58	-8.576
70	90.4	94.1	-3.702
71	92.9	94.19	-1.286
72	105.8	93.62	12.18
73	106	93.81	12.19
74	99.1	93.81	5.292
75	99.9	95.12	4.775
76	88.1	93.86	-5.757
77	87.8	94.47	-6.671
78	87.1	94.91	-7.812
79	85.9	93.27	-7.367
80	86.5	93.72	-7.222
81	84.1	94.86	-10.76
82	92.1	95.14	-3.04
83	93.3	94.83	-1.53
84	98.9	93.98	4.923
85	103	94.41	8.594
86	98.4	94.16	4.235
87	100.7	95.28	5.416
88	92.3	94.29	-1.992
89	89	94.75	-5.748
90	88.9	95.32	-6.419
91	85.5	94.27	-8.771
92	90.1	94.23	-4.131
93	87	94.78	-7.776
94	97.1	95.63	1.467
95	101.5	94.99	6.506
96	103	94.13	8.874
97	106.1	95.11	10.99
98	96.1	95.12	0.9783
99	94.2	95.35	-1.15
100	89.1	95.74	-6.643
101	85.2	95.08	-9.883
102	86.5	95.66	-9.163
103	88	95.05	-7.048
104	88.4	94.09	-5.685
105	87.9	96.05	-8.154
106	95.7	95.85	-0.1502
107	94.8	94.25	0.5457
108	105.2	94.39	10.81
109	108.7	93.32	15.38
110	96.1	93.5	2.598
111	98.3	94.49	3.809
112	88.6	93.79	-5.194
113	90.8	93.55	-2.747
114	88.1	94.46	-6.358
115	91.9	93.73	-1.832
116	98.5	93.32	5.175
117	98.6	94.82	3.777
118	100.3	94.76	5.539
119	98.7	94.25	4.455
120	110.7	94.07	16.63
121	115.4	94.02	21.38
122	105.4	94.65	10.75
123	108	95.84	12.16
124	94.5	94.82	-0.3168
125	96.5	94.64	1.855
126	91	96.14	-5.144
127	94.1	94.7	-0.5979
128	96.4	94.44	1.957
129	93.1	95.84	-2.74
130	97.5	95.62	1.88
131	102.5	95.63	6.868
132	105.7	95.05	10.65
133	109.1	94.9	14.2
134	97.2	95.29	1.909
135	100.3	97.27	3.027
136	91.3	95.41	-4.112
137	94.3	95.97	-1.668
138	89.5	95.7	-6.197
139	89.3	94.52	-5.215
140	93.4	95.45	-2.046
141	91.9	96.26	-4.361
142	92.9	95.49	-2.593
143	93.7	95.15	-1.451
144	100.1	94.7	5.397
145	105.5	94.92	10.58
146	110.5	95.15	15.35
147	89.5	96.66	-7.164
148	90.4	94.9	-4.5
149	89.9	95.15	-5.252
150	84.6	95.75	-11.15

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.4614	0.9228	0.5386
7	0.5993	0.8014	0.4007
8	0.5164	0.9671	0.4835
9	0.3858	0.7715	0.6142
10	0.3051	0.6102	0.6949
11	0.2608	0.5217	0.7392
12	0.2935	0.5869	0.7065
13	0.2433	0.4865	0.7567
14	0.2388	0.4775	0.7612
15	0.2177	0.4353	0.7823
16	0.3144	0.6289	0.6856
17	0.2729	0.5457	0.7271
18	0.263	0.526	0.737
19	0.265	0.5299	0.735
20	0.2209	0.4418	0.7791
21	0.1744	0.3489	0.8256
22	0.1391	0.2782	0.8609
23	0.1238	0.2477	0.8762
24	0.1101	0.2202	0.8899
25	0.1392	0.2785	0.8608
26	0.1105	0.221	0.8895
27	0.09371	0.1874	0.9063
28	0.06945	0.1389	0.9305
29	0.05065	0.1013	0.9494
30	0.03796	0.07593	0.962
31	0.02888	0.05776	0.9711
32	0.02026	0.04052	0.9797
33	0.01557	0.03113	0.9844
34	0.01165	0.0233	0.9884
35	0.01022	0.02044	0.9898
36	0.008538	0.01708	0.9915
37	0.01608	0.03216	0.9839
38	0.01158	0.02317	0.9884
39	0.008811	0.01762	0.9912
40	0.0059	0.0118	0.9941
41	0.003983	0.007966	0.996
42	0.003	0.006001	0.997
43	0.001943	0.003886	0.9981
44	0.001256	0.002512	0.9987
45	0.0008549	0.00171	0.9991
46	0.0005766	0.001153	0.9994
47	0.0003616	0.0007232	0.9996
48	0.0006335	0.001267	0.9994
49	0.002867	0.005733	0.9971
50	0.001921	0.003842	0.9981
51	0.001457	0.002914	0.9985
52	0.001141	0.002282	0.9989
53	0.0008417	0.001683	0.9992
54	0.001234	0.002468	0.9988
55	0.0009499	0.0019	0.999
56	0.001332	0.002664	0.9987
57	0.001636	0.003272	0.9984
58	0.001102	0.002203	0.9989
59	0.0007761	0.001552	0.9992
60	0.00232	0.004639	0.9977
61	0.003222	0.006445	0.9968
62	0.002694	0.005387	0.9973
63	0.002101	0.004202	0.9979
64	0.001505	0.00301	0.9985
65	0.001592	0.003185	0.9984
66	0.002847	0.005694	0.9972
67	0.003345	0.00669	0.9967
68	0.005869	0.01174	0.9941
69	0.007988	0.01598	0.992
70	0.006603	0.01321	0.9934
71	0.004823	0.009646	0.9952
72	0.01158	0.02315	0.9884
73	0.02319	0.04638	0.9768
74	0.02018	0.04037	0.9798
75	0.01723	0.03447	0.9828
76	0.01748	0.03497	0.9825
77	0.01894	0.03788	0.9811
78	0.02183	0.04367	0.9782
79	0.02824	0.05648	0.9718
80	0.03528	0.07056	0.9647
81	0.05611	0.1122	0.9439
82	0.04616	0.09233	0.9538
83	0.03696	0.07393	0.963
84	0.03188	0.06375	0.9681
85	0.03688	0.07376	0.9631
86	0.03072	0.06143	0.9693
87	0.02804	0.05609	0.972
88	0.02301	0.04602	0.977
89	0.02304	0.04608	0.977
90	0.02254	0.04509	0.9775
91	0.03403	0.06806	0.966
92	0.03303	0.06606	0.967
93	0.03783	0.07567	0.9622
94	0.03069	0.06138	0.9693
95	0.03073	0.06147	0.9693
96	0.03403	0.06805	0.966
97	0.05175	0.1035	0.9482
98	0.0401	0.0802	0.9599
99	0.03084	0.06168	0.9692
100	0.02943	0.05886	0.9706
101	0.04314	0.08628	0.9569
102	0.05049	0.101	0.9495
103	0.05743	0.1149	0.9426
104	0.06916	0.1383	0.9308
105	0.07156	0.1431	0.9284
106	0.05609	0.1122	0.9439
107	0.04502	0.09003	0.955
108	0.059	0.118	0.941
109	0.104	0.208	0.896
110	0.08476	0.1695	0.9152
111	0.06892	0.1378	0.9311
112	0.0822	0.1644	0.9178
113	0.07941	0.1588	0.9206
114	0.1023	0.2046	0.8977
115	0.1246	0.2492	0.8754
116	0.1194	0.2388	0.8806
117	0.09799	0.196	0.902
118	0.08153	0.1631	0.9185
119	0.06692	0.1338	0.9331
120	0.1132	0.2264	0.8868
121	0.3179	0.6359	0.6821
122	0.3478	0.6956	0.6522
123	0.506	0.9881	0.494
124	0.4481	0.8963	0.5519
125	0.3859	0.7719	0.6141
126	0.3424	0.6848	0.6576
127	0.2972	0.5943	0.7028
128	0.2469	0.4937	0.7531
129	0.2011	0.4023	0.7989
130	0.1574	0.3149	0.8426
131	0.1526	0.3052	0.8474
132	0.1988	0.3976	0.8012
133	0.3616	0.7232	0.6384
134	0.2982	0.5964	0.7018
135	0.3881	0.7761	0.6119
136	0.3173	0.6347	0.6827
137	0.252	0.5039	0.748
138	0.1994	0.3988	0.8006
139	0.2515	0.5029	0.7485
140	0.2009	0.4018	0.7991
141	0.136	0.2719	0.864
142	0.08607	0.1721	0.9139
143	0.04916	0.09833	0.9508
144	0.1154	0.2309	0.8846

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.4614 &  0.9228 &  0.5386 \tabularnewline
7 &  0.5993 &  0.8014 &  0.4007 \tabularnewline
8 &  0.5164 &  0.9671 &  0.4835 \tabularnewline
9 &  0.3858 &  0.7715 &  0.6142 \tabularnewline
10 &  0.3051 &  0.6102 &  0.6949 \tabularnewline
11 &  0.2608 &  0.5217 &  0.7392 \tabularnewline
12 &  0.2935 &  0.5869 &  0.7065 \tabularnewline
13 &  0.2433 &  0.4865 &  0.7567 \tabularnewline
14 &  0.2388 &  0.4775 &  0.7612 \tabularnewline
15 &  0.2177 &  0.4353 &  0.7823 \tabularnewline
16 &  0.3144 &  0.6289 &  0.6856 \tabularnewline
17 &  0.2729 &  0.5457 &  0.7271 \tabularnewline
18 &  0.263 &  0.526 &  0.737 \tabularnewline
19 &  0.265 &  0.5299 &  0.735 \tabularnewline
20 &  0.2209 &  0.4418 &  0.7791 \tabularnewline
21 &  0.1744 &  0.3489 &  0.8256 \tabularnewline
22 &  0.1391 &  0.2782 &  0.8609 \tabularnewline
23 &  0.1238 &  0.2477 &  0.8762 \tabularnewline
24 &  0.1101 &  0.2202 &  0.8899 \tabularnewline
25 &  0.1392 &  0.2785 &  0.8608 \tabularnewline
26 &  0.1105 &  0.221 &  0.8895 \tabularnewline
27 &  0.09371 &  0.1874 &  0.9063 \tabularnewline
28 &  0.06945 &  0.1389 &  0.9305 \tabularnewline
29 &  0.05065 &  0.1013 &  0.9494 \tabularnewline
30 &  0.03796 &  0.07593 &  0.962 \tabularnewline
31 &  0.02888 &  0.05776 &  0.9711 \tabularnewline
32 &  0.02026 &  0.04052 &  0.9797 \tabularnewline
33 &  0.01557 &  0.03113 &  0.9844 \tabularnewline
34 &  0.01165 &  0.0233 &  0.9884 \tabularnewline
35 &  0.01022 &  0.02044 &  0.9898 \tabularnewline
36 &  0.008538 &  0.01708 &  0.9915 \tabularnewline
37 &  0.01608 &  0.03216 &  0.9839 \tabularnewline
38 &  0.01158 &  0.02317 &  0.9884 \tabularnewline
39 &  0.008811 &  0.01762 &  0.9912 \tabularnewline
40 &  0.0059 &  0.0118 &  0.9941 \tabularnewline
41 &  0.003983 &  0.007966 &  0.996 \tabularnewline
42 &  0.003 &  0.006001 &  0.997 \tabularnewline
43 &  0.001943 &  0.003886 &  0.9981 \tabularnewline
44 &  0.001256 &  0.002512 &  0.9987 \tabularnewline
45 &  0.0008549 &  0.00171 &  0.9991 \tabularnewline
46 &  0.0005766 &  0.001153 &  0.9994 \tabularnewline
47 &  0.0003616 &  0.0007232 &  0.9996 \tabularnewline
48 &  0.0006335 &  0.001267 &  0.9994 \tabularnewline
49 &  0.002867 &  0.005733 &  0.9971 \tabularnewline
50 &  0.001921 &  0.003842 &  0.9981 \tabularnewline
51 &  0.001457 &  0.002914 &  0.9985 \tabularnewline
52 &  0.001141 &  0.002282 &  0.9989 \tabularnewline
53 &  0.0008417 &  0.001683 &  0.9992 \tabularnewline
54 &  0.001234 &  0.002468 &  0.9988 \tabularnewline
55 &  0.0009499 &  0.0019 &  0.999 \tabularnewline
56 &  0.001332 &  0.002664 &  0.9987 \tabularnewline
57 &  0.001636 &  0.003272 &  0.9984 \tabularnewline
58 &  0.001102 &  0.002203 &  0.9989 \tabularnewline
59 &  0.0007761 &  0.001552 &  0.9992 \tabularnewline
60 &  0.00232 &  0.004639 &  0.9977 \tabularnewline
61 &  0.003222 &  0.006445 &  0.9968 \tabularnewline
62 &  0.002694 &  0.005387 &  0.9973 \tabularnewline
63 &  0.002101 &  0.004202 &  0.9979 \tabularnewline
64 &  0.001505 &  0.00301 &  0.9985 \tabularnewline
65 &  0.001592 &  0.003185 &  0.9984 \tabularnewline
66 &  0.002847 &  0.005694 &  0.9972 \tabularnewline
67 &  0.003345 &  0.00669 &  0.9967 \tabularnewline
68 &  0.005869 &  0.01174 &  0.9941 \tabularnewline
69 &  0.007988 &  0.01598 &  0.992 \tabularnewline
70 &  0.006603 &  0.01321 &  0.9934 \tabularnewline
71 &  0.004823 &  0.009646 &  0.9952 \tabularnewline
72 &  0.01158 &  0.02315 &  0.9884 \tabularnewline
73 &  0.02319 &  0.04638 &  0.9768 \tabularnewline
74 &  0.02018 &  0.04037 &  0.9798 \tabularnewline
75 &  0.01723 &  0.03447 &  0.9828 \tabularnewline
76 &  0.01748 &  0.03497 &  0.9825 \tabularnewline
77 &  0.01894 &  0.03788 &  0.9811 \tabularnewline
78 &  0.02183 &  0.04367 &  0.9782 \tabularnewline
79 &  0.02824 &  0.05648 &  0.9718 \tabularnewline
80 &  0.03528 &  0.07056 &  0.9647 \tabularnewline
81 &  0.05611 &  0.1122 &  0.9439 \tabularnewline
82 &  0.04616 &  0.09233 &  0.9538 \tabularnewline
83 &  0.03696 &  0.07393 &  0.963 \tabularnewline
84 &  0.03188 &  0.06375 &  0.9681 \tabularnewline
85 &  0.03688 &  0.07376 &  0.9631 \tabularnewline
86 &  0.03072 &  0.06143 &  0.9693 \tabularnewline
87 &  0.02804 &  0.05609 &  0.972 \tabularnewline
88 &  0.02301 &  0.04602 &  0.977 \tabularnewline
89 &  0.02304 &  0.04608 &  0.977 \tabularnewline
90 &  0.02254 &  0.04509 &  0.9775 \tabularnewline
91 &  0.03403 &  0.06806 &  0.966 \tabularnewline
92 &  0.03303 &  0.06606 &  0.967 \tabularnewline
93 &  0.03783 &  0.07567 &  0.9622 \tabularnewline
94 &  0.03069 &  0.06138 &  0.9693 \tabularnewline
95 &  0.03073 &  0.06147 &  0.9693 \tabularnewline
96 &  0.03403 &  0.06805 &  0.966 \tabularnewline
97 &  0.05175 &  0.1035 &  0.9482 \tabularnewline
98 &  0.0401 &  0.0802 &  0.9599 \tabularnewline
99 &  0.03084 &  0.06168 &  0.9692 \tabularnewline
100 &  0.02943 &  0.05886 &  0.9706 \tabularnewline
101 &  0.04314 &  0.08628 &  0.9569 \tabularnewline
102 &  0.05049 &  0.101 &  0.9495 \tabularnewline
103 &  0.05743 &  0.1149 &  0.9426 \tabularnewline
104 &  0.06916 &  0.1383 &  0.9308 \tabularnewline
105 &  0.07156 &  0.1431 &  0.9284 \tabularnewline
106 &  0.05609 &  0.1122 &  0.9439 \tabularnewline
107 &  0.04502 &  0.09003 &  0.955 \tabularnewline
108 &  0.059 &  0.118 &  0.941 \tabularnewline
109 &  0.104 &  0.208 &  0.896 \tabularnewline
110 &  0.08476 &  0.1695 &  0.9152 \tabularnewline
111 &  0.06892 &  0.1378 &  0.9311 \tabularnewline
112 &  0.0822 &  0.1644 &  0.9178 \tabularnewline
113 &  0.07941 &  0.1588 &  0.9206 \tabularnewline
114 &  0.1023 &  0.2046 &  0.8977 \tabularnewline
115 &  0.1246 &  0.2492 &  0.8754 \tabularnewline
116 &  0.1194 &  0.2388 &  0.8806 \tabularnewline
117 &  0.09799 &  0.196 &  0.902 \tabularnewline
118 &  0.08153 &  0.1631 &  0.9185 \tabularnewline
119 &  0.06692 &  0.1338 &  0.9331 \tabularnewline
120 &  0.1132 &  0.2264 &  0.8868 \tabularnewline
121 &  0.3179 &  0.6359 &  0.6821 \tabularnewline
122 &  0.3478 &  0.6956 &  0.6522 \tabularnewline
123 &  0.506 &  0.9881 &  0.494 \tabularnewline
124 &  0.4481 &  0.8963 &  0.5519 \tabularnewline
125 &  0.3859 &  0.7719 &  0.6141 \tabularnewline
126 &  0.3424 &  0.6848 &  0.6576 \tabularnewline
127 &  0.2972 &  0.5943 &  0.7028 \tabularnewline
128 &  0.2469 &  0.4937 &  0.7531 \tabularnewline
129 &  0.2011 &  0.4023 &  0.7989 \tabularnewline
130 &  0.1574 &  0.3149 &  0.8426 \tabularnewline
131 &  0.1526 &  0.3052 &  0.8474 \tabularnewline
132 &  0.1988 &  0.3976 &  0.8012 \tabularnewline
133 &  0.3616 &  0.7232 &  0.6384 \tabularnewline
134 &  0.2982 &  0.5964 &  0.7018 \tabularnewline
135 &  0.3881 &  0.7761 &  0.6119 \tabularnewline
136 &  0.3173 &  0.6347 &  0.6827 \tabularnewline
137 &  0.252 &  0.5039 &  0.748 \tabularnewline
138 &  0.1994 &  0.3988 &  0.8006 \tabularnewline
139 &  0.2515 &  0.5029 &  0.7485 \tabularnewline
140 &  0.2009 &  0.4018 &  0.7991 \tabularnewline
141 &  0.136 &  0.2719 &  0.864 \tabularnewline
142 &  0.08607 &  0.1721 &  0.9139 \tabularnewline
143 &  0.04916 &  0.09833 &  0.9508 \tabularnewline
144 &  0.1154 &  0.2309 &  0.8846 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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] 0.4614[/C][C] 0.9228[/C][C] 0.5386[/C][/ROW]
[ROW][C]7[/C][C] 0.5993[/C][C] 0.8014[/C][C] 0.4007[/C][/ROW]
[ROW][C]8[/C][C] 0.5164[/C][C] 0.9671[/C][C] 0.4835[/C][/ROW]
[ROW][C]9[/C][C] 0.3858[/C][C] 0.7715[/C][C] 0.6142[/C][/ROW]
[ROW][C]10[/C][C] 0.3051[/C][C] 0.6102[/C][C] 0.6949[/C][/ROW]
[ROW][C]11[/C][C] 0.2608[/C][C] 0.5217[/C][C] 0.7392[/C][/ROW]
[ROW][C]12[/C][C] 0.2935[/C][C] 0.5869[/C][C] 0.7065[/C][/ROW]
[ROW][C]13[/C][C] 0.2433[/C][C] 0.4865[/C][C] 0.7567[/C][/ROW]
[ROW][C]14[/C][C] 0.2388[/C][C] 0.4775[/C][C] 0.7612[/C][/ROW]
[ROW][C]15[/C][C] 0.2177[/C][C] 0.4353[/C][C] 0.7823[/C][/ROW]
[ROW][C]16[/C][C] 0.3144[/C][C] 0.6289[/C][C] 0.6856[/C][/ROW]
[ROW][C]17[/C][C] 0.2729[/C][C] 0.5457[/C][C] 0.7271[/C][/ROW]
[ROW][C]18[/C][C] 0.263[/C][C] 0.526[/C][C] 0.737[/C][/ROW]
[ROW][C]19[/C][C] 0.265[/C][C] 0.5299[/C][C] 0.735[/C][/ROW]
[ROW][C]20[/C][C] 0.2209[/C][C] 0.4418[/C][C] 0.7791[/C][/ROW]
[ROW][C]21[/C][C] 0.1744[/C][C] 0.3489[/C][C] 0.8256[/C][/ROW]
[ROW][C]22[/C][C] 0.1391[/C][C] 0.2782[/C][C] 0.8609[/C][/ROW]
[ROW][C]23[/C][C] 0.1238[/C][C] 0.2477[/C][C] 0.8762[/C][/ROW]
[ROW][C]24[/C][C] 0.1101[/C][C] 0.2202[/C][C] 0.8899[/C][/ROW]
[ROW][C]25[/C][C] 0.1392[/C][C] 0.2785[/C][C] 0.8608[/C][/ROW]
[ROW][C]26[/C][C] 0.1105[/C][C] 0.221[/C][C] 0.8895[/C][/ROW]
[ROW][C]27[/C][C] 0.09371[/C][C] 0.1874[/C][C] 0.9063[/C][/ROW]
[ROW][C]28[/C][C] 0.06945[/C][C] 0.1389[/C][C] 0.9305[/C][/ROW]
[ROW][C]29[/C][C] 0.05065[/C][C] 0.1013[/C][C] 0.9494[/C][/ROW]
[ROW][C]30[/C][C] 0.03796[/C][C] 0.07593[/C][C] 0.962[/C][/ROW]
[ROW][C]31[/C][C] 0.02888[/C][C] 0.05776[/C][C] 0.9711[/C][/ROW]
[ROW][C]32[/C][C] 0.02026[/C][C] 0.04052[/C][C] 0.9797[/C][/ROW]
[ROW][C]33[/C][C] 0.01557[/C][C] 0.03113[/C][C] 0.9844[/C][/ROW]
[ROW][C]34[/C][C] 0.01165[/C][C] 0.0233[/C][C] 0.9884[/C][/ROW]
[ROW][C]35[/C][C] 0.01022[/C][C] 0.02044[/C][C] 0.9898[/C][/ROW]
[ROW][C]36[/C][C] 0.008538[/C][C] 0.01708[/C][C] 0.9915[/C][/ROW]
[ROW][C]37[/C][C] 0.01608[/C][C] 0.03216[/C][C] 0.9839[/C][/ROW]
[ROW][C]38[/C][C] 0.01158[/C][C] 0.02317[/C][C] 0.9884[/C][/ROW]
[ROW][C]39[/C][C] 0.008811[/C][C] 0.01762[/C][C] 0.9912[/C][/ROW]
[ROW][C]40[/C][C] 0.0059[/C][C] 0.0118[/C][C] 0.9941[/C][/ROW]
[ROW][C]41[/C][C] 0.003983[/C][C] 0.007966[/C][C] 0.996[/C][/ROW]
[ROW][C]42[/C][C] 0.003[/C][C] 0.006001[/C][C] 0.997[/C][/ROW]
[ROW][C]43[/C][C] 0.001943[/C][C] 0.003886[/C][C] 0.9981[/C][/ROW]
[ROW][C]44[/C][C] 0.001256[/C][C] 0.002512[/C][C] 0.9987[/C][/ROW]
[ROW][C]45[/C][C] 0.0008549[/C][C] 0.00171[/C][C] 0.9991[/C][/ROW]
[ROW][C]46[/C][C] 0.0005766[/C][C] 0.001153[/C][C] 0.9994[/C][/ROW]
[ROW][C]47[/C][C] 0.0003616[/C][C] 0.0007232[/C][C] 0.9996[/C][/ROW]
[ROW][C]48[/C][C] 0.0006335[/C][C] 0.001267[/C][C] 0.9994[/C][/ROW]
[ROW][C]49[/C][C] 0.002867[/C][C] 0.005733[/C][C] 0.9971[/C][/ROW]
[ROW][C]50[/C][C] 0.001921[/C][C] 0.003842[/C][C] 0.9981[/C][/ROW]
[ROW][C]51[/C][C] 0.001457[/C][C] 0.002914[/C][C] 0.9985[/C][/ROW]
[ROW][C]52[/C][C] 0.001141[/C][C] 0.002282[/C][C] 0.9989[/C][/ROW]
[ROW][C]53[/C][C] 0.0008417[/C][C] 0.001683[/C][C] 0.9992[/C][/ROW]
[ROW][C]54[/C][C] 0.001234[/C][C] 0.002468[/C][C] 0.9988[/C][/ROW]
[ROW][C]55[/C][C] 0.0009499[/C][C] 0.0019[/C][C] 0.999[/C][/ROW]
[ROW][C]56[/C][C] 0.001332[/C][C] 0.002664[/C][C] 0.9987[/C][/ROW]
[ROW][C]57[/C][C] 0.001636[/C][C] 0.003272[/C][C] 0.9984[/C][/ROW]
[ROW][C]58[/C][C] 0.001102[/C][C] 0.002203[/C][C] 0.9989[/C][/ROW]
[ROW][C]59[/C][C] 0.0007761[/C][C] 0.001552[/C][C] 0.9992[/C][/ROW]
[ROW][C]60[/C][C] 0.00232[/C][C] 0.004639[/C][C] 0.9977[/C][/ROW]
[ROW][C]61[/C][C] 0.003222[/C][C] 0.006445[/C][C] 0.9968[/C][/ROW]
[ROW][C]62[/C][C] 0.002694[/C][C] 0.005387[/C][C] 0.9973[/C][/ROW]
[ROW][C]63[/C][C] 0.002101[/C][C] 0.004202[/C][C] 0.9979[/C][/ROW]
[ROW][C]64[/C][C] 0.001505[/C][C] 0.00301[/C][C] 0.9985[/C][/ROW]
[ROW][C]65[/C][C] 0.001592[/C][C] 0.003185[/C][C] 0.9984[/C][/ROW]
[ROW][C]66[/C][C] 0.002847[/C][C] 0.005694[/C][C] 0.9972[/C][/ROW]
[ROW][C]67[/C][C] 0.003345[/C][C] 0.00669[/C][C] 0.9967[/C][/ROW]
[ROW][C]68[/C][C] 0.005869[/C][C] 0.01174[/C][C] 0.9941[/C][/ROW]
[ROW][C]69[/C][C] 0.007988[/C][C] 0.01598[/C][C] 0.992[/C][/ROW]
[ROW][C]70[/C][C] 0.006603[/C][C] 0.01321[/C][C] 0.9934[/C][/ROW]
[ROW][C]71[/C][C] 0.004823[/C][C] 0.009646[/C][C] 0.9952[/C][/ROW]
[ROW][C]72[/C][C] 0.01158[/C][C] 0.02315[/C][C] 0.9884[/C][/ROW]
[ROW][C]73[/C][C] 0.02319[/C][C] 0.04638[/C][C] 0.9768[/C][/ROW]
[ROW][C]74[/C][C] 0.02018[/C][C] 0.04037[/C][C] 0.9798[/C][/ROW]
[ROW][C]75[/C][C] 0.01723[/C][C] 0.03447[/C][C] 0.9828[/C][/ROW]
[ROW][C]76[/C][C] 0.01748[/C][C] 0.03497[/C][C] 0.9825[/C][/ROW]
[ROW][C]77[/C][C] 0.01894[/C][C] 0.03788[/C][C] 0.9811[/C][/ROW]
[ROW][C]78[/C][C] 0.02183[/C][C] 0.04367[/C][C] 0.9782[/C][/ROW]
[ROW][C]79[/C][C] 0.02824[/C][C] 0.05648[/C][C] 0.9718[/C][/ROW]
[ROW][C]80[/C][C] 0.03528[/C][C] 0.07056[/C][C] 0.9647[/C][/ROW]
[ROW][C]81[/C][C] 0.05611[/C][C] 0.1122[/C][C] 0.9439[/C][/ROW]
[ROW][C]82[/C][C] 0.04616[/C][C] 0.09233[/C][C] 0.9538[/C][/ROW]
[ROW][C]83[/C][C] 0.03696[/C][C] 0.07393[/C][C] 0.963[/C][/ROW]
[ROW][C]84[/C][C] 0.03188[/C][C] 0.06375[/C][C] 0.9681[/C][/ROW]
[ROW][C]85[/C][C] 0.03688[/C][C] 0.07376[/C][C] 0.9631[/C][/ROW]
[ROW][C]86[/C][C] 0.03072[/C][C] 0.06143[/C][C] 0.9693[/C][/ROW]
[ROW][C]87[/C][C] 0.02804[/C][C] 0.05609[/C][C] 0.972[/C][/ROW]
[ROW][C]88[/C][C] 0.02301[/C][C] 0.04602[/C][C] 0.977[/C][/ROW]
[ROW][C]89[/C][C] 0.02304[/C][C] 0.04608[/C][C] 0.977[/C][/ROW]
[ROW][C]90[/C][C] 0.02254[/C][C] 0.04509[/C][C] 0.9775[/C][/ROW]
[ROW][C]91[/C][C] 0.03403[/C][C] 0.06806[/C][C] 0.966[/C][/ROW]
[ROW][C]92[/C][C] 0.03303[/C][C] 0.06606[/C][C] 0.967[/C][/ROW]
[ROW][C]93[/C][C] 0.03783[/C][C] 0.07567[/C][C] 0.9622[/C][/ROW]
[ROW][C]94[/C][C] 0.03069[/C][C] 0.06138[/C][C] 0.9693[/C][/ROW]
[ROW][C]95[/C][C] 0.03073[/C][C] 0.06147[/C][C] 0.9693[/C][/ROW]
[ROW][C]96[/C][C] 0.03403[/C][C] 0.06805[/C][C] 0.966[/C][/ROW]
[ROW][C]97[/C][C] 0.05175[/C][C] 0.1035[/C][C] 0.9482[/C][/ROW]
[ROW][C]98[/C][C] 0.0401[/C][C] 0.0802[/C][C] 0.9599[/C][/ROW]
[ROW][C]99[/C][C] 0.03084[/C][C] 0.06168[/C][C] 0.9692[/C][/ROW]
[ROW][C]100[/C][C] 0.02943[/C][C] 0.05886[/C][C] 0.9706[/C][/ROW]
[ROW][C]101[/C][C] 0.04314[/C][C] 0.08628[/C][C] 0.9569[/C][/ROW]
[ROW][C]102[/C][C] 0.05049[/C][C] 0.101[/C][C] 0.9495[/C][/ROW]
[ROW][C]103[/C][C] 0.05743[/C][C] 0.1149[/C][C] 0.9426[/C][/ROW]
[ROW][C]104[/C][C] 0.06916[/C][C] 0.1383[/C][C] 0.9308[/C][/ROW]
[ROW][C]105[/C][C] 0.07156[/C][C] 0.1431[/C][C] 0.9284[/C][/ROW]
[ROW][C]106[/C][C] 0.05609[/C][C] 0.1122[/C][C] 0.9439[/C][/ROW]
[ROW][C]107[/C][C] 0.04502[/C][C] 0.09003[/C][C] 0.955[/C][/ROW]
[ROW][C]108[/C][C] 0.059[/C][C] 0.118[/C][C] 0.941[/C][/ROW]
[ROW][C]109[/C][C] 0.104[/C][C] 0.208[/C][C] 0.896[/C][/ROW]
[ROW][C]110[/C][C] 0.08476[/C][C] 0.1695[/C][C] 0.9152[/C][/ROW]
[ROW][C]111[/C][C] 0.06892[/C][C] 0.1378[/C][C] 0.9311[/C][/ROW]
[ROW][C]112[/C][C] 0.0822[/C][C] 0.1644[/C][C] 0.9178[/C][/ROW]
[ROW][C]113[/C][C] 0.07941[/C][C] 0.1588[/C][C] 0.9206[/C][/ROW]
[ROW][C]114[/C][C] 0.1023[/C][C] 0.2046[/C][C] 0.8977[/C][/ROW]
[ROW][C]115[/C][C] 0.1246[/C][C] 0.2492[/C][C] 0.8754[/C][/ROW]
[ROW][C]116[/C][C] 0.1194[/C][C] 0.2388[/C][C] 0.8806[/C][/ROW]
[ROW][C]117[/C][C] 0.09799[/C][C] 0.196[/C][C] 0.902[/C][/ROW]
[ROW][C]118[/C][C] 0.08153[/C][C] 0.1631[/C][C] 0.9185[/C][/ROW]
[ROW][C]119[/C][C] 0.06692[/C][C] 0.1338[/C][C] 0.9331[/C][/ROW]
[ROW][C]120[/C][C] 0.1132[/C][C] 0.2264[/C][C] 0.8868[/C][/ROW]
[ROW][C]121[/C][C] 0.3179[/C][C] 0.6359[/C][C] 0.6821[/C][/ROW]
[ROW][C]122[/C][C] 0.3478[/C][C] 0.6956[/C][C] 0.6522[/C][/ROW]
[ROW][C]123[/C][C] 0.506[/C][C] 0.9881[/C][C] 0.494[/C][/ROW]
[ROW][C]124[/C][C] 0.4481[/C][C] 0.8963[/C][C] 0.5519[/C][/ROW]
[ROW][C]125[/C][C] 0.3859[/C][C] 0.7719[/C][C] 0.6141[/C][/ROW]
[ROW][C]126[/C][C] 0.3424[/C][C] 0.6848[/C][C] 0.6576[/C][/ROW]
[ROW][C]127[/C][C] 0.2972[/C][C] 0.5943[/C][C] 0.7028[/C][/ROW]
[ROW][C]128[/C][C] 0.2469[/C][C] 0.4937[/C][C] 0.7531[/C][/ROW]
[ROW][C]129[/C][C] 0.2011[/C][C] 0.4023[/C][C] 0.7989[/C][/ROW]
[ROW][C]130[/C][C] 0.1574[/C][C] 0.3149[/C][C] 0.8426[/C][/ROW]
[ROW][C]131[/C][C] 0.1526[/C][C] 0.3052[/C][C] 0.8474[/C][/ROW]
[ROW][C]132[/C][C] 0.1988[/C][C] 0.3976[/C][C] 0.8012[/C][/ROW]
[ROW][C]133[/C][C] 0.3616[/C][C] 0.7232[/C][C] 0.6384[/C][/ROW]
[ROW][C]134[/C][C] 0.2982[/C][C] 0.5964[/C][C] 0.7018[/C][/ROW]
[ROW][C]135[/C][C] 0.3881[/C][C] 0.7761[/C][C] 0.6119[/C][/ROW]
[ROW][C]136[/C][C] 0.3173[/C][C] 0.6347[/C][C] 0.6827[/C][/ROW]
[ROW][C]137[/C][C] 0.252[/C][C] 0.5039[/C][C] 0.748[/C][/ROW]
[ROW][C]138[/C][C] 0.1994[/C][C] 0.3988[/C][C] 0.8006[/C][/ROW]
[ROW][C]139[/C][C] 0.2515[/C][C] 0.5029[/C][C] 0.7485[/C][/ROW]
[ROW][C]140[/C][C] 0.2009[/C][C] 0.4018[/C][C] 0.7991[/C][/ROW]
[ROW][C]141[/C][C] 0.136[/C][C] 0.2719[/C][C] 0.864[/C][/ROW]
[ROW][C]142[/C][C] 0.08607[/C][C] 0.1721[/C][C] 0.9139[/C][/ROW]
[ROW][C]143[/C][C] 0.04916[/C][C] 0.09833[/C][C] 0.9508[/C][/ROW]
[ROW][C]144[/C][C] 0.1154[/C][C] 0.2309[/C][C] 0.8846[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	0.4614	0.9228	0.5386
7	0.5993	0.8014	0.4007
8	0.5164	0.9671	0.4835
9	0.3858	0.7715	0.6142
10	0.3051	0.6102	0.6949
11	0.2608	0.5217	0.7392
12	0.2935	0.5869	0.7065
13	0.2433	0.4865	0.7567
14	0.2388	0.4775	0.7612
15	0.2177	0.4353	0.7823
16	0.3144	0.6289	0.6856
17	0.2729	0.5457	0.7271
18	0.263	0.526	0.737
19	0.265	0.5299	0.735
20	0.2209	0.4418	0.7791
21	0.1744	0.3489	0.8256
22	0.1391	0.2782	0.8609
23	0.1238	0.2477	0.8762
24	0.1101	0.2202	0.8899
25	0.1392	0.2785	0.8608
26	0.1105	0.221	0.8895
27	0.09371	0.1874	0.9063
28	0.06945	0.1389	0.9305
29	0.05065	0.1013	0.9494
30	0.03796	0.07593	0.962
31	0.02888	0.05776	0.9711
32	0.02026	0.04052	0.9797
33	0.01557	0.03113	0.9844
34	0.01165	0.0233	0.9884
35	0.01022	0.02044	0.9898
36	0.008538	0.01708	0.9915
37	0.01608	0.03216	0.9839
38	0.01158	0.02317	0.9884
39	0.008811	0.01762	0.9912
40	0.0059	0.0118	0.9941
41	0.003983	0.007966	0.996
42	0.003	0.006001	0.997
43	0.001943	0.003886	0.9981
44	0.001256	0.002512	0.9987
45	0.0008549	0.00171	0.9991
46	0.0005766	0.001153	0.9994
47	0.0003616	0.0007232	0.9996
48	0.0006335	0.001267	0.9994
49	0.002867	0.005733	0.9971
50	0.001921	0.003842	0.9981
51	0.001457	0.002914	0.9985
52	0.001141	0.002282	0.9989
53	0.0008417	0.001683	0.9992
54	0.001234	0.002468	0.9988
55	0.0009499	0.0019	0.999
56	0.001332	0.002664	0.9987
57	0.001636	0.003272	0.9984
58	0.001102	0.002203	0.9989
59	0.0007761	0.001552	0.9992
60	0.00232	0.004639	0.9977
61	0.003222	0.006445	0.9968
62	0.002694	0.005387	0.9973
63	0.002101	0.004202	0.9979
64	0.001505	0.00301	0.9985
65	0.001592	0.003185	0.9984
66	0.002847	0.005694	0.9972
67	0.003345	0.00669	0.9967
68	0.005869	0.01174	0.9941
69	0.007988	0.01598	0.992
70	0.006603	0.01321	0.9934
71	0.004823	0.009646	0.9952
72	0.01158	0.02315	0.9884
73	0.02319	0.04638	0.9768
74	0.02018	0.04037	0.9798
75	0.01723	0.03447	0.9828
76	0.01748	0.03497	0.9825
77	0.01894	0.03788	0.9811
78	0.02183	0.04367	0.9782
79	0.02824	0.05648	0.9718
80	0.03528	0.07056	0.9647
81	0.05611	0.1122	0.9439
82	0.04616	0.09233	0.9538
83	0.03696	0.07393	0.963
84	0.03188	0.06375	0.9681
85	0.03688	0.07376	0.9631
86	0.03072	0.06143	0.9693
87	0.02804	0.05609	0.972
88	0.02301	0.04602	0.977
89	0.02304	0.04608	0.977
90	0.02254	0.04509	0.9775
91	0.03403	0.06806	0.966
92	0.03303	0.06606	0.967
93	0.03783	0.07567	0.9622
94	0.03069	0.06138	0.9693
95	0.03073	0.06147	0.9693
96	0.03403	0.06805	0.966
97	0.05175	0.1035	0.9482
98	0.0401	0.0802	0.9599
99	0.03084	0.06168	0.9692
100	0.02943	0.05886	0.9706
101	0.04314	0.08628	0.9569
102	0.05049	0.101	0.9495
103	0.05743	0.1149	0.9426
104	0.06916	0.1383	0.9308
105	0.07156	0.1431	0.9284
106	0.05609	0.1122	0.9439
107	0.04502	0.09003	0.955
108	0.059	0.118	0.941
109	0.104	0.208	0.896
110	0.08476	0.1695	0.9152
111	0.06892	0.1378	0.9311
112	0.0822	0.1644	0.9178
113	0.07941	0.1588	0.9206
114	0.1023	0.2046	0.8977
115	0.1246	0.2492	0.8754
116	0.1194	0.2388	0.8806
117	0.09799	0.196	0.902
118	0.08153	0.1631	0.9185
119	0.06692	0.1338	0.9331
120	0.1132	0.2264	0.8868
121	0.3179	0.6359	0.6821
122	0.3478	0.6956	0.6522
123	0.506	0.9881	0.494
124	0.4481	0.8963	0.5519
125	0.3859	0.7719	0.6141
126	0.3424	0.6848	0.6576
127	0.2972	0.5943	0.7028
128	0.2469	0.4937	0.7531
129	0.2011	0.4023	0.7989
130	0.1574	0.3149	0.8426
131	0.1526	0.3052	0.8474
132	0.1988	0.3976	0.8012
133	0.3616	0.7232	0.6384
134	0.2982	0.5964	0.7018
135	0.3881	0.7761	0.6119
136	0.3173	0.6347	0.6827
137	0.252	0.5039	0.748
138	0.1994	0.3988	0.8006
139	0.2515	0.5029	0.7485
140	0.2009	0.4018	0.7991
141	0.136	0.2719	0.864
142	0.08607	0.1721	0.9139
143	0.04916	0.09833	0.9508
144	0.1154	0.2309	0.8846

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	28	0.2014	NOK
5% type I error level	50	0.359712	NOK
10% type I error level	72	0.517986	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 & 28 &  0.2014 & NOK \tabularnewline
5% type I error level & 50 & 0.359712 & NOK \tabularnewline
10% type I error level & 72 & 0.517986 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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]28[/C][C] 0.2014[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]50[/C][C]0.359712[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]72[/C][C]0.517986[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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	28	0.2014	NOK
5% type I error level	50	0.359712	NOK
10% type I error level	72	0.517986	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.9348, df1 = 2, df2 = 145, p-value = 0.1482
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5706, df1 = 4, df2 = 143, p-value = 0.1853
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.0613, df1 = 2, df2 = 145, p-value = 0.131

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 1.9348, df1 = 2, df2 = 145, p-value = 0.1482
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.5706, df1 = 4, df2 = 143, p-value = 0.1853
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 2.0613, df1 = 2, df2 = 145, p-value = 0.131
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&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 = 1.9348, df1 = 2, df2 = 145, p-value = 0.1482
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.5706, df1 = 4, df2 = 143, p-value = 0.1853
[/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 = 2.0613, df1 = 2, df2 = 145, p-value = 0.131
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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 = 1.9348, df1 = 2, df2 = 145, p-value = 0.1482
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.5706, df1 = 4, df2 = 143, p-value = 0.1853
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 2.0613, df1 = 2, df2 = 145, p-value = 0.131

Variance Inflation Factors (Multicollinearity)
> vif X58 X14 8.861003 8.861003

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     X58      X14 
8.861003 8.861003 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310564&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     X58      X14 
8.861003 8.861003 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310564&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310564&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 X58 X14 8.861003 8.861003

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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par6 = 12 ;

Parameters (R input):

par1 = 1 ; 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