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

Sun, 10 Dec 2017 18:16:23 +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/10/t1512927547cdrcw95je0ir1qu.htm/, Retrieved Wed, 15 May 2024 08:22:53 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308940, Retrieved Wed, 15 May 2024 08:22:53 +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

LBE

Estimated Impact

104

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

-       [Multiple Regression] [Regressie dataset 1] [2017-12-10 17:16:23] [3c189a0c4f7caff37e2cfca896353419] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

10 10 10 10 21 36
9 8 8 15 22 32
12 8 6 14 17 33
14 9 10 14 21 39
6 5 8 8 19 34
13 10 10 19 23 39
12 8 7 17 21 36
13 9 10 18 22 33
6 8 6 10 11 30
12 7 7 15 20 39
10 10 9 16 18 37
9 10 6 12 16 37
12 9 7 13 18 35
7 4 6 10 13 32
10 4 4 14 17 36
11 8 6 15 20 36
15 9 8 20 20 41
10 10 9 9 15 36
12 8 8 12 18 37
10 5 6 13 15 29
12 10 6 16 19 39
11 8 10 12 19 37
11 7 8 14 19 32
12 8 8 15 20 36
15 8 7 19 20 43
12 9 4 16 16 30
11 8 9 16 18 33
9 6 8 14 17 28
11 8 10 14 18 30
11 8 8 14 13 28
9 5 6 13 20 39
15 9 7 18 21 34
12 8 8 15 17 34
9 8 5 15 19 29
12 8 10 15 20 32
12 6 2 13 15 33
9 6 6 14 15 27
9 9 7 15 19 35
11 8 5 14 18 38
12 9 8 19 22 40
12 10 7 16 20 34
12 8 7 16 18 34
12 8 10 12 14 26
6 7 7 10 15 39
11 7 6 11 17 34
12 10 10 13 16 39
9 8 6 14 17 26
11 7 5 11 15 30
9 10 8 11 17 34
10 7 8 16 18 34
10 7 5 9 16 29
9 9 8 16 18 41
12 9 10 19 22 43
11 8 7 13 16 31
9 6 7 15 16 33
9 8 7 14 20 34
12 9 7 15 18 30
6 2 2 11 16 23
10 6 4 14 16 29
12 8 6 15 20 35
11 8 7 17 21 40
14 7 9 16 18 27
8 8 9 13 15 30
9 6 4 15 18 27
10 10 9 14 18 29
10 10 9 15 20 33
10 10 8 14 18 32
11 8 7 12 16 33
10 8 9 12 19 36
12 7 7 15 20 34
14 10 6 17 22 45
10 5 7 13 18 30
8 3 2 5 8 22
8 2 3 7 13 24
7 3 4 10 13 25
11 4 5 15 18 26
6 2 2 9 12 27
9 6 6 9 16 27
12 8 8 15 21 35
12 8 5 14 20 36
12 5 4 11 18 32
9 10 10 18 22 35
15 9 10 20 23 35
15 8 10 20 23 36
13 9 9 16 21 37
9 8 5 15 16 33
12 5 5 14 14 25
9 7 7 13 18 35
15 9 10 18 22 37
11 8 9 14 20 36
11 4 8 12 18 35
6 7 8 9 12 29
14 8 8 19 17 35
11 7 8 13 15 31
8 7 8 12 18 30
10 9 7 14 18 37
10 6 6 6 15 36
9 7 8 14 16 35
8 4 2 11 15 32
9 6 5 11 16 34
10 10 4 14 19 37
11 9 9 12 19 36
14 10 10 19 23 39
12 8 6 13 20 37
9 4 4 14 18 31
13 8 10 17 21 40
8 5 6 12 19 38
12 8 7 16 18 35
14 9 7 15 19 38
9 8 8 15 17 32
10 4 6 15 21 41
12 8 5 16 19 28
12 10 6 15 24 40
9 6 7 12 12 25
9 7 6 13 15 28
12 10 9 14 18 37
15 9 9 17 19 37
12 8 7 14 22 40
11 3 6 14 19 26
8 8 7 14 16 30
11 7 7 15 19 32
11 7 8 11 18 31
10 8 7 11 18 28
12 8 8 16 19 34
9 7 7 12 21 39
11 7 4 12 19 33
15 9 10 19 22 43
14 9 8 18 23 37
6 9 8 16 17 31
9 4 2 16 18 31
9 6 6 13 19 34
8 6 4 11 15 32
7 6 4 10 14 27
10 8 9 14 18 34
6 3 2 14 17 28
9 8 6 14 19 32
9 8 7 16 16 39
7 6 4 10 14 28
11 10 10 16 20 39
9 2 3 7 16 32
12 9 7 16 18 36
9 6 4 15 16 31
10 6 8 17 21 39
11 5 4 11 16 23
7 4 5 11 14 25
12 7 6 10 16 32
8 5 5 13 19 32
13 8 9 14 19 36
11 6 6 13 19 39
11 9 8 13 18 31
12 6 4 12 16 32
11 4 4 10 14 28
12 7 8 15 19 34
3 2 4 6 11 28
10 8 10 15 18 38
13 9 8 15 18 35
10 6 5 11 16 32
6 5 3 14 20 26
11 7 7 14 18 32
12 8 6 16 20 28
9 4 5 12 16 31
10 9 5 15 18 33
15 9 9 20 19 38
9 9 2 12 19 38
6 7 7 9 15 36
9 5 7 13 17 31
15 7 5 15 21 36
15 9 9 19 24 43
9 8 4 11 16 37
11 6 5 11 13 28
9 9 9 17 21 35
11 8 7 15 16 34
10 7 6 14 17 40
9 7 8 15 17 31
6 7 7 11 18 41
12 8 6 12 18 35
13 10 8 15 23 38
12 6 6 16 20 37
12 6 7 16 20 31

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 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 time9 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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]

9 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308940&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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	9 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
Perceived_Usefulness[t] = + 1.20422 + 0.17019Intention_to_Use[t] + 0.0538825Relative_Advantage[t] + 0.315153Perceived_Ease_of_Use[t] + 0.148972Information_Quality[t] + 0.0200463System_Quality[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Perceived_Usefulness[t] =  +  1.20422 +  0.17019Intention_to_Use[t] +  0.0538825Relative_Advantage[t] +  0.315153Perceived_Ease_of_Use[t] +  0.148972Information_Quality[t] +  0.0200463System_Quality[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Perceived_Usefulness[t] =  +  1.20422 +  0.17019Intention_to_Use[t] +  0.0538825Relative_Advantage[t] +  0.315153Perceived_Ease_of_Use[t] +  0.148972Information_Quality[t] +  0.0200463System_Quality[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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
Perceived_Usefulness[t] = + 1.20422 + 0.17019Intention_to_Use[t] + 0.0538825Relative_Advantage[t] + 0.315153Perceived_Ease_of_Use[t] + 0.148972Information_Quality[t] + 0.0200463System_Quality[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)	+1.204	0.998	+1.2070e+00	0.2292	0.1146
Intention_to_Use	+0.1702	0.09354	+1.8190e+00	0.07058	0.03529
Relative_Advantage	+0.05388	0.08177	+6.5900e-01	0.5108	0.2554
Perceived_Ease_of_Use	+0.3151	0.06528	+4.8280e+00	3.018e-06	1.509e-06
Information_Quality	+0.149	0.07483	+1.9910e+00	0.04809	0.02404
System_Quality	+0.02005	0.03766	+5.3230e-01	0.5952	0.2976

\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) & +1.204 &  0.998 & +1.2070e+00 &  0.2292 &  0.1146 \tabularnewline
Intention_to_Use & +0.1702 &  0.09354 & +1.8190e+00 &  0.07058 &  0.03529 \tabularnewline
Relative_Advantage & +0.05388 &  0.08177 & +6.5900e-01 &  0.5108 &  0.2554 \tabularnewline
Perceived_Ease_of_Use & +0.3151 &  0.06528 & +4.8280e+00 &  3.018e-06 &  1.509e-06 \tabularnewline
Information_Quality & +0.149 &  0.07483 & +1.9910e+00 &  0.04809 &  0.02404 \tabularnewline
System_Quality & +0.02005 &  0.03766 & +5.3230e-01 &  0.5952 &  0.2976 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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]+1.204[/C][C] 0.998[/C][C]+1.2070e+00[/C][C] 0.2292[/C][C] 0.1146[/C][/ROW]
[ROW][C]Intention_to_Use[/C][C]+0.1702[/C][C] 0.09354[/C][C]+1.8190e+00[/C][C] 0.07058[/C][C] 0.03529[/C][/ROW]
[ROW][C]Relative_Advantage[/C][C]+0.05388[/C][C] 0.08177[/C][C]+6.5900e-01[/C][C] 0.5108[/C][C] 0.2554[/C][/ROW]
[ROW][C]Perceived_Ease_of_Use[/C][C]+0.3151[/C][C] 0.06528[/C][C]+4.8280e+00[/C][C] 3.018e-06[/C][C] 1.509e-06[/C][/ROW]
[ROW][C]Information_Quality[/C][C]+0.149[/C][C] 0.07483[/C][C]+1.9910e+00[/C][C] 0.04809[/C][C] 0.02404[/C][/ROW]
[ROW][C]System_Quality[/C][C]+0.02005[/C][C] 0.03766[/C][C]+5.3230e-01[/C][C] 0.5952[/C][C] 0.2976[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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)	+1.204	0.998	+1.2070e+00	0.2292	0.1146
Intention_to_Use	+0.1702	0.09354	+1.8190e+00	0.07058	0.03529
Relative_Advantage	+0.05388	0.08177	+6.5900e-01	0.5108	0.2554
Perceived_Ease_of_Use	+0.3151	0.06528	+4.8280e+00	3.018e-06	1.509e-06
Information_Quality	+0.149	0.07483	+1.9910e+00	0.04809	0.02404
System_Quality	+0.02005	0.03766	+5.3230e-01	0.5952	0.2976

Multiple Linear Regression - Regression Statistics
Multiple R	0.6825
R-squared	0.4658
Adjusted R-squared	0.4504
F-TEST (value)	30.17
F-TEST (DF numerator)	5
F-TEST (DF denominator)	173
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.695
Sum Squared Residuals	497.2

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.6825 \tabularnewline
R-squared &  0.4658 \tabularnewline
Adjusted R-squared &  0.4504 \tabularnewline
F-TEST (value) &  30.17 \tabularnewline
F-TEST (DF numerator) & 5 \tabularnewline
F-TEST (DF denominator) & 173 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.695 \tabularnewline
Sum Squared Residuals &  497.2 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.6825[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.4658[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.4504[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 30.17[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]5[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]173[/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] 1.695[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 497.2[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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	0.6825
R-squared	0.4658
Adjusted R-squared	0.4504
F-TEST (value)	30.17
F-TEST (DF numerator)	5
F-TEST (DF denominator)	173
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.695
Sum Squared Residuals	497.2

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=308940&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=308940&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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	10	10.45	-0.4465
2	9	11.64	-2.643
3	12	10.5	1.505
4	14	11.6	2.403
5	6	8.519	-2.519
6	13	13.64	-0.641
7	12	12.15	-0.1506
8	13	12.89	0.1136
9	6	8.281	-2.281
10	12	11.26	0.7387
11	10	11.86	-1.857
12	9	10.14	-1.137
13	12	10.59	1.407
14	7	7.938	-0.9379
15	10	9.767	0.2332
16	11	11.32	-0.3174
17	15	13.27	1.729
18	10	9.184	0.8163
19	12	10.2	1.798
20	10	9.291	0.7086
21	12	11.88	0.1159
22	11	10.46	0.5414
23	11	10.71	0.2893
24	12	11.43	0.5748
25	15	12.77	2.228
26	12	10.98	1.021
27	11	11.44	-0.4361
28	9	10.16	-1.162
29	11	10.8	0.2004
30	11	9.907	1.093
31	9	10.24	-1.237
32	15	12.6	2.404
33	12	10.94	1.062
34	9	10.97	-1.974
35	12	11.45	0.5472
36	12	9.326	2.674
37	9	9.737	-0.7366
38	9	11.37	-2.372
39	11	10.69	0.3095
40	12	13.23	-1.234
41	12	11.99	0.01325
42	12	11.35	0.6516
43	12	9.493	2.507
44	6	8.941	-2.941
45	11	9.4	1.6
46	12	10.71	1.293
47	9	10.35	-1.355
48	11	8.968	2.032
49	9	10.02	-1.018
50	10	11.23	-1.232
51	10	8.466	1.534
52	9	11.71	-2.713
53	12	13.4	-1.402
54	11	10.04	0.9551
55	9	10.37	-1.375
56	9	11.02	-2.016
57	12	11.12	0.8767
58	6	7.964	-1.964
59	10	9.818	0.1821
60	12	11.3	0.7026
61	11	12.23	-1.231
62	14	11.15	2.854
63	8	9.984	-1.984
64	9	10.39	-1.391
65	10	11.07	-1.066
66	10	11.76	-1.759
67	10	11.07	-1.072
68	11	9.77	1.23
69	10	10.38	-0.3846
70	12	11.16	0.839
71	14	12.77	1.234
72	10	9.812	0.1878
73	8	5.031	2.969
74	8	6.33	1.67
75	7	7.52	-0.5196
76	11	10.08	0.9156
77	6	6.818	-0.8177
78	9	8.31	0.6902
79	12	11.55	0.4459
80	12	10.95	1.052
81	12	9.06	2.94
82	9	13.1	-4.097
83	15	13.71	1.294
84	15	13.56	1.444
85	13	12.13	0.8666
86	9	10.61	-1.608
87	12	9.323	2.677
88	9	10.25	-1.253
89	15	12.97	2.033
90	11	11.16	-0.1639
91	11	9.481	1.519
92	6	8.032	-2.032
93	14	12.22	1.781
94	11	9.78	1.22
95	8	9.891	-1.891
96	10	10.95	-0.9485
97	10	7.396	2.604
98	9	10.32	-1.324
99	8	8.335	-0.3355
100	9	9.027	-0.02658
101	10	11.11	-1.106
102	11	10.55	0.4452
103	14	13.64	0.359
104	12	10.71	1.293
105	9	9.816	-0.8156
106	13	12.39	0.6076
107	8	9.753	-1.753
108	12	11.37	0.6315
109	14	11.43	2.567
110	9	10.9	-1.898
111	10	10.89	-0.8859
112	12	11.27	0.7306
113	12	12.33	-0.3339
114	9	8.673	0.3268
115	9	9.612	-0.6117
116	12	11.23	0.7736
117	15	12.15	2.849
118	12	11.43	0.5657
119	11	9.802	1.198
120	8	10.34	-2.34
121	11	10.97	0.02804
122	11	9.596	1.404
123	10	9.652	0.3476
124	12	11.55	0.4487
125	9	10.46	-1.465
126	11	9.885	1.115
127	15	13.4	1.598
128	14	13.01	0.9922
129	6	11.36	-5.363
130	9	10.34	-1.338
131	9	10.16	-1.158
132	8	8.784	-0.7836
133	7	8.219	-1.219
134	10	10.83	-0.8259
135	6	9.329	-3.329
136	9	10.77	-1.773
137	9	11.15	-2.151
138	7	8.239	-1.239
139	11	12.25	-1.249
140	9	6.937	2.063
141	12	11.56	0.4413
142	9	10.17	-1.173
143	10	11.92	-1.924
144	11	8.582	2.418
145	7	8.208	-1.208
146	12	8.895	3.105
147	8	9.894	-1.894
148	13	11.02	1.985
149	11	10.26	0.7421
150	11	10.57	0.4331
151	12	9.248	2.752
152	11	7.899	3.101
153	12	11.07	0.9341
154	3	5.851	-2.851
155	10	11.28	-1.275
156	13	11.28	1.723
157	10	8.986	1.014
158	6	10.13	-4.13
159	11	10.51	0.4922
160	12	11.47	0.5278
161	9	8.941	0.05879
162	10	11.08	-1.076
163	15	13.12	1.884
164	9	10.22	-1.218
165	6	8.565	-2.565
166	9	9.683	-0.6833
167	15	11.24	3.758
168	15	13.65	1.354
169	9	9.373	-0.3732
170	11	8.459	2.541
171	9	12.41	-3.409
172	11	10.74	0.2647
173	10	10.47	-0.4654
174	9	10.71	-1.708
175	6	9.743	-3.743
176	12	10.05	1.946
177	13	12.25	0.7474
178	12	11.31	0.6878
179	12	11.25	0.7541

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  10 &  10.45 & -0.4465 \tabularnewline
2 &  9 &  11.64 & -2.643 \tabularnewline
3 &  12 &  10.5 &  1.505 \tabularnewline
4 &  14 &  11.6 &  2.403 \tabularnewline
5 &  6 &  8.519 & -2.519 \tabularnewline
6 &  13 &  13.64 & -0.641 \tabularnewline
7 &  12 &  12.15 & -0.1506 \tabularnewline
8 &  13 &  12.89 &  0.1136 \tabularnewline
9 &  6 &  8.281 & -2.281 \tabularnewline
10 &  12 &  11.26 &  0.7387 \tabularnewline
11 &  10 &  11.86 & -1.857 \tabularnewline
12 &  9 &  10.14 & -1.137 \tabularnewline
13 &  12 &  10.59 &  1.407 \tabularnewline
14 &  7 &  7.938 & -0.9379 \tabularnewline
15 &  10 &  9.767 &  0.2332 \tabularnewline
16 &  11 &  11.32 & -0.3174 \tabularnewline
17 &  15 &  13.27 &  1.729 \tabularnewline
18 &  10 &  9.184 &  0.8163 \tabularnewline
19 &  12 &  10.2 &  1.798 \tabularnewline
20 &  10 &  9.291 &  0.7086 \tabularnewline
21 &  12 &  11.88 &  0.1159 \tabularnewline
22 &  11 &  10.46 &  0.5414 \tabularnewline
23 &  11 &  10.71 &  0.2893 \tabularnewline
24 &  12 &  11.43 &  0.5748 \tabularnewline
25 &  15 &  12.77 &  2.228 \tabularnewline
26 &  12 &  10.98 &  1.021 \tabularnewline
27 &  11 &  11.44 & -0.4361 \tabularnewline
28 &  9 &  10.16 & -1.162 \tabularnewline
29 &  11 &  10.8 &  0.2004 \tabularnewline
30 &  11 &  9.907 &  1.093 \tabularnewline
31 &  9 &  10.24 & -1.237 \tabularnewline
32 &  15 &  12.6 &  2.404 \tabularnewline
33 &  12 &  10.94 &  1.062 \tabularnewline
34 &  9 &  10.97 & -1.974 \tabularnewline
35 &  12 &  11.45 &  0.5472 \tabularnewline
36 &  12 &  9.326 &  2.674 \tabularnewline
37 &  9 &  9.737 & -0.7366 \tabularnewline
38 &  9 &  11.37 & -2.372 \tabularnewline
39 &  11 &  10.69 &  0.3095 \tabularnewline
40 &  12 &  13.23 & -1.234 \tabularnewline
41 &  12 &  11.99 &  0.01325 \tabularnewline
42 &  12 &  11.35 &  0.6516 \tabularnewline
43 &  12 &  9.493 &  2.507 \tabularnewline
44 &  6 &  8.941 & -2.941 \tabularnewline
45 &  11 &  9.4 &  1.6 \tabularnewline
46 &  12 &  10.71 &  1.293 \tabularnewline
47 &  9 &  10.35 & -1.355 \tabularnewline
48 &  11 &  8.968 &  2.032 \tabularnewline
49 &  9 &  10.02 & -1.018 \tabularnewline
50 &  10 &  11.23 & -1.232 \tabularnewline
51 &  10 &  8.466 &  1.534 \tabularnewline
52 &  9 &  11.71 & -2.713 \tabularnewline
53 &  12 &  13.4 & -1.402 \tabularnewline
54 &  11 &  10.04 &  0.9551 \tabularnewline
55 &  9 &  10.37 & -1.375 \tabularnewline
56 &  9 &  11.02 & -2.016 \tabularnewline
57 &  12 &  11.12 &  0.8767 \tabularnewline
58 &  6 &  7.964 & -1.964 \tabularnewline
59 &  10 &  9.818 &  0.1821 \tabularnewline
60 &  12 &  11.3 &  0.7026 \tabularnewline
61 &  11 &  12.23 & -1.231 \tabularnewline
62 &  14 &  11.15 &  2.854 \tabularnewline
63 &  8 &  9.984 & -1.984 \tabularnewline
64 &  9 &  10.39 & -1.391 \tabularnewline
65 &  10 &  11.07 & -1.066 \tabularnewline
66 &  10 &  11.76 & -1.759 \tabularnewline
67 &  10 &  11.07 & -1.072 \tabularnewline
68 &  11 &  9.77 &  1.23 \tabularnewline
69 &  10 &  10.38 & -0.3846 \tabularnewline
70 &  12 &  11.16 &  0.839 \tabularnewline
71 &  14 &  12.77 &  1.234 \tabularnewline
72 &  10 &  9.812 &  0.1878 \tabularnewline
73 &  8 &  5.031 &  2.969 \tabularnewline
74 &  8 &  6.33 &  1.67 \tabularnewline
75 &  7 &  7.52 & -0.5196 \tabularnewline
76 &  11 &  10.08 &  0.9156 \tabularnewline
77 &  6 &  6.818 & -0.8177 \tabularnewline
78 &  9 &  8.31 &  0.6902 \tabularnewline
79 &  12 &  11.55 &  0.4459 \tabularnewline
80 &  12 &  10.95 &  1.052 \tabularnewline
81 &  12 &  9.06 &  2.94 \tabularnewline
82 &  9 &  13.1 & -4.097 \tabularnewline
83 &  15 &  13.71 &  1.294 \tabularnewline
84 &  15 &  13.56 &  1.444 \tabularnewline
85 &  13 &  12.13 &  0.8666 \tabularnewline
86 &  9 &  10.61 & -1.608 \tabularnewline
87 &  12 &  9.323 &  2.677 \tabularnewline
88 &  9 &  10.25 & -1.253 \tabularnewline
89 &  15 &  12.97 &  2.033 \tabularnewline
90 &  11 &  11.16 & -0.1639 \tabularnewline
91 &  11 &  9.481 &  1.519 \tabularnewline
92 &  6 &  8.032 & -2.032 \tabularnewline
93 &  14 &  12.22 &  1.781 \tabularnewline
94 &  11 &  9.78 &  1.22 \tabularnewline
95 &  8 &  9.891 & -1.891 \tabularnewline
96 &  10 &  10.95 & -0.9485 \tabularnewline
97 &  10 &  7.396 &  2.604 \tabularnewline
98 &  9 &  10.32 & -1.324 \tabularnewline
99 &  8 &  8.335 & -0.3355 \tabularnewline
100 &  9 &  9.027 & -0.02658 \tabularnewline
101 &  10 &  11.11 & -1.106 \tabularnewline
102 &  11 &  10.55 &  0.4452 \tabularnewline
103 &  14 &  13.64 &  0.359 \tabularnewline
104 &  12 &  10.71 &  1.293 \tabularnewline
105 &  9 &  9.816 & -0.8156 \tabularnewline
106 &  13 &  12.39 &  0.6076 \tabularnewline
107 &  8 &  9.753 & -1.753 \tabularnewline
108 &  12 &  11.37 &  0.6315 \tabularnewline
109 &  14 &  11.43 &  2.567 \tabularnewline
110 &  9 &  10.9 & -1.898 \tabularnewline
111 &  10 &  10.89 & -0.8859 \tabularnewline
112 &  12 &  11.27 &  0.7306 \tabularnewline
113 &  12 &  12.33 & -0.3339 \tabularnewline
114 &  9 &  8.673 &  0.3268 \tabularnewline
115 &  9 &  9.612 & -0.6117 \tabularnewline
116 &  12 &  11.23 &  0.7736 \tabularnewline
117 &  15 &  12.15 &  2.849 \tabularnewline
118 &  12 &  11.43 &  0.5657 \tabularnewline
119 &  11 &  9.802 &  1.198 \tabularnewline
120 &  8 &  10.34 & -2.34 \tabularnewline
121 &  11 &  10.97 &  0.02804 \tabularnewline
122 &  11 &  9.596 &  1.404 \tabularnewline
123 &  10 &  9.652 &  0.3476 \tabularnewline
124 &  12 &  11.55 &  0.4487 \tabularnewline
125 &  9 &  10.46 & -1.465 \tabularnewline
126 &  11 &  9.885 &  1.115 \tabularnewline
127 &  15 &  13.4 &  1.598 \tabularnewline
128 &  14 &  13.01 &  0.9922 \tabularnewline
129 &  6 &  11.36 & -5.363 \tabularnewline
130 &  9 &  10.34 & -1.338 \tabularnewline
131 &  9 &  10.16 & -1.158 \tabularnewline
132 &  8 &  8.784 & -0.7836 \tabularnewline
133 &  7 &  8.219 & -1.219 \tabularnewline
134 &  10 &  10.83 & -0.8259 \tabularnewline
135 &  6 &  9.329 & -3.329 \tabularnewline
136 &  9 &  10.77 & -1.773 \tabularnewline
137 &  9 &  11.15 & -2.151 \tabularnewline
138 &  7 &  8.239 & -1.239 \tabularnewline
139 &  11 &  12.25 & -1.249 \tabularnewline
140 &  9 &  6.937 &  2.063 \tabularnewline
141 &  12 &  11.56 &  0.4413 \tabularnewline
142 &  9 &  10.17 & -1.173 \tabularnewline
143 &  10 &  11.92 & -1.924 \tabularnewline
144 &  11 &  8.582 &  2.418 \tabularnewline
145 &  7 &  8.208 & -1.208 \tabularnewline
146 &  12 &  8.895 &  3.105 \tabularnewline
147 &  8 &  9.894 & -1.894 \tabularnewline
148 &  13 &  11.02 &  1.985 \tabularnewline
149 &  11 &  10.26 &  0.7421 \tabularnewline
150 &  11 &  10.57 &  0.4331 \tabularnewline
151 &  12 &  9.248 &  2.752 \tabularnewline
152 &  11 &  7.899 &  3.101 \tabularnewline
153 &  12 &  11.07 &  0.9341 \tabularnewline
154 &  3 &  5.851 & -2.851 \tabularnewline
155 &  10 &  11.28 & -1.275 \tabularnewline
156 &  13 &  11.28 &  1.723 \tabularnewline
157 &  10 &  8.986 &  1.014 \tabularnewline
158 &  6 &  10.13 & -4.13 \tabularnewline
159 &  11 &  10.51 &  0.4922 \tabularnewline
160 &  12 &  11.47 &  0.5278 \tabularnewline
161 &  9 &  8.941 &  0.05879 \tabularnewline
162 &  10 &  11.08 & -1.076 \tabularnewline
163 &  15 &  13.12 &  1.884 \tabularnewline
164 &  9 &  10.22 & -1.218 \tabularnewline
165 &  6 &  8.565 & -2.565 \tabularnewline
166 &  9 &  9.683 & -0.6833 \tabularnewline
167 &  15 &  11.24 &  3.758 \tabularnewline
168 &  15 &  13.65 &  1.354 \tabularnewline
169 &  9 &  9.373 & -0.3732 \tabularnewline
170 &  11 &  8.459 &  2.541 \tabularnewline
171 &  9 &  12.41 & -3.409 \tabularnewline
172 &  11 &  10.74 &  0.2647 \tabularnewline
173 &  10 &  10.47 & -0.4654 \tabularnewline
174 &  9 &  10.71 & -1.708 \tabularnewline
175 &  6 &  9.743 & -3.743 \tabularnewline
176 &  12 &  10.05 &  1.946 \tabularnewline
177 &  13 &  12.25 &  0.7474 \tabularnewline
178 &  12 &  11.31 &  0.6878 \tabularnewline
179 &  12 &  11.25 &  0.7541 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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] 10[/C][C] 10.45[/C][C]-0.4465[/C][/ROW]
[ROW][C]2[/C][C] 9[/C][C] 11.64[/C][C]-2.643[/C][/ROW]
[ROW][C]3[/C][C] 12[/C][C] 10.5[/C][C] 1.505[/C][/ROW]
[ROW][C]4[/C][C] 14[/C][C] 11.6[/C][C] 2.403[/C][/ROW]
[ROW][C]5[/C][C] 6[/C][C] 8.519[/C][C]-2.519[/C][/ROW]
[ROW][C]6[/C][C] 13[/C][C] 13.64[/C][C]-0.641[/C][/ROW]
[ROW][C]7[/C][C] 12[/C][C] 12.15[/C][C]-0.1506[/C][/ROW]
[ROW][C]8[/C][C] 13[/C][C] 12.89[/C][C] 0.1136[/C][/ROW]
[ROW][C]9[/C][C] 6[/C][C] 8.281[/C][C]-2.281[/C][/ROW]
[ROW][C]10[/C][C] 12[/C][C] 11.26[/C][C] 0.7387[/C][/ROW]
[ROW][C]11[/C][C] 10[/C][C] 11.86[/C][C]-1.857[/C][/ROW]
[ROW][C]12[/C][C] 9[/C][C] 10.14[/C][C]-1.137[/C][/ROW]
[ROW][C]13[/C][C] 12[/C][C] 10.59[/C][C] 1.407[/C][/ROW]
[ROW][C]14[/C][C] 7[/C][C] 7.938[/C][C]-0.9379[/C][/ROW]
[ROW][C]15[/C][C] 10[/C][C] 9.767[/C][C] 0.2332[/C][/ROW]
[ROW][C]16[/C][C] 11[/C][C] 11.32[/C][C]-0.3174[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 13.27[/C][C] 1.729[/C][/ROW]
[ROW][C]18[/C][C] 10[/C][C] 9.184[/C][C] 0.8163[/C][/ROW]
[ROW][C]19[/C][C] 12[/C][C] 10.2[/C][C] 1.798[/C][/ROW]
[ROW][C]20[/C][C] 10[/C][C] 9.291[/C][C] 0.7086[/C][/ROW]
[ROW][C]21[/C][C] 12[/C][C] 11.88[/C][C] 0.1159[/C][/ROW]
[ROW][C]22[/C][C] 11[/C][C] 10.46[/C][C] 0.5414[/C][/ROW]
[ROW][C]23[/C][C] 11[/C][C] 10.71[/C][C] 0.2893[/C][/ROW]
[ROW][C]24[/C][C] 12[/C][C] 11.43[/C][C] 0.5748[/C][/ROW]
[ROW][C]25[/C][C] 15[/C][C] 12.77[/C][C] 2.228[/C][/ROW]
[ROW][C]26[/C][C] 12[/C][C] 10.98[/C][C] 1.021[/C][/ROW]
[ROW][C]27[/C][C] 11[/C][C] 11.44[/C][C]-0.4361[/C][/ROW]
[ROW][C]28[/C][C] 9[/C][C] 10.16[/C][C]-1.162[/C][/ROW]
[ROW][C]29[/C][C] 11[/C][C] 10.8[/C][C] 0.2004[/C][/ROW]
[ROW][C]30[/C][C] 11[/C][C] 9.907[/C][C] 1.093[/C][/ROW]
[ROW][C]31[/C][C] 9[/C][C] 10.24[/C][C]-1.237[/C][/ROW]
[ROW][C]32[/C][C] 15[/C][C] 12.6[/C][C] 2.404[/C][/ROW]
[ROW][C]33[/C][C] 12[/C][C] 10.94[/C][C] 1.062[/C][/ROW]
[ROW][C]34[/C][C] 9[/C][C] 10.97[/C][C]-1.974[/C][/ROW]
[ROW][C]35[/C][C] 12[/C][C] 11.45[/C][C] 0.5472[/C][/ROW]
[ROW][C]36[/C][C] 12[/C][C] 9.326[/C][C] 2.674[/C][/ROW]
[ROW][C]37[/C][C] 9[/C][C] 9.737[/C][C]-0.7366[/C][/ROW]
[ROW][C]38[/C][C] 9[/C][C] 11.37[/C][C]-2.372[/C][/ROW]
[ROW][C]39[/C][C] 11[/C][C] 10.69[/C][C] 0.3095[/C][/ROW]
[ROW][C]40[/C][C] 12[/C][C] 13.23[/C][C]-1.234[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 11.99[/C][C] 0.01325[/C][/ROW]
[ROW][C]42[/C][C] 12[/C][C] 11.35[/C][C] 0.6516[/C][/ROW]
[ROW][C]43[/C][C] 12[/C][C] 9.493[/C][C] 2.507[/C][/ROW]
[ROW][C]44[/C][C] 6[/C][C] 8.941[/C][C]-2.941[/C][/ROW]
[ROW][C]45[/C][C] 11[/C][C] 9.4[/C][C] 1.6[/C][/ROW]
[ROW][C]46[/C][C] 12[/C][C] 10.71[/C][C] 1.293[/C][/ROW]
[ROW][C]47[/C][C] 9[/C][C] 10.35[/C][C]-1.355[/C][/ROW]
[ROW][C]48[/C][C] 11[/C][C] 8.968[/C][C] 2.032[/C][/ROW]
[ROW][C]49[/C][C] 9[/C][C] 10.02[/C][C]-1.018[/C][/ROW]
[ROW][C]50[/C][C] 10[/C][C] 11.23[/C][C]-1.232[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 8.466[/C][C] 1.534[/C][/ROW]
[ROW][C]52[/C][C] 9[/C][C] 11.71[/C][C]-2.713[/C][/ROW]
[ROW][C]53[/C][C] 12[/C][C] 13.4[/C][C]-1.402[/C][/ROW]
[ROW][C]54[/C][C] 11[/C][C] 10.04[/C][C] 0.9551[/C][/ROW]
[ROW][C]55[/C][C] 9[/C][C] 10.37[/C][C]-1.375[/C][/ROW]
[ROW][C]56[/C][C] 9[/C][C] 11.02[/C][C]-2.016[/C][/ROW]
[ROW][C]57[/C][C] 12[/C][C] 11.12[/C][C] 0.8767[/C][/ROW]
[ROW][C]58[/C][C] 6[/C][C] 7.964[/C][C]-1.964[/C][/ROW]
[ROW][C]59[/C][C] 10[/C][C] 9.818[/C][C] 0.1821[/C][/ROW]
[ROW][C]60[/C][C] 12[/C][C] 11.3[/C][C] 0.7026[/C][/ROW]
[ROW][C]61[/C][C] 11[/C][C] 12.23[/C][C]-1.231[/C][/ROW]
[ROW][C]62[/C][C] 14[/C][C] 11.15[/C][C] 2.854[/C][/ROW]
[ROW][C]63[/C][C] 8[/C][C] 9.984[/C][C]-1.984[/C][/ROW]
[ROW][C]64[/C][C] 9[/C][C] 10.39[/C][C]-1.391[/C][/ROW]
[ROW][C]65[/C][C] 10[/C][C] 11.07[/C][C]-1.066[/C][/ROW]
[ROW][C]66[/C][C] 10[/C][C] 11.76[/C][C]-1.759[/C][/ROW]
[ROW][C]67[/C][C] 10[/C][C] 11.07[/C][C]-1.072[/C][/ROW]
[ROW][C]68[/C][C] 11[/C][C] 9.77[/C][C] 1.23[/C][/ROW]
[ROW][C]69[/C][C] 10[/C][C] 10.38[/C][C]-0.3846[/C][/ROW]
[ROW][C]70[/C][C] 12[/C][C] 11.16[/C][C] 0.839[/C][/ROW]
[ROW][C]71[/C][C] 14[/C][C] 12.77[/C][C] 1.234[/C][/ROW]
[ROW][C]72[/C][C] 10[/C][C] 9.812[/C][C] 0.1878[/C][/ROW]
[ROW][C]73[/C][C] 8[/C][C] 5.031[/C][C] 2.969[/C][/ROW]
[ROW][C]74[/C][C] 8[/C][C] 6.33[/C][C] 1.67[/C][/ROW]
[ROW][C]75[/C][C] 7[/C][C] 7.52[/C][C]-0.5196[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 10.08[/C][C] 0.9156[/C][/ROW]
[ROW][C]77[/C][C] 6[/C][C] 6.818[/C][C]-0.8177[/C][/ROW]
[ROW][C]78[/C][C] 9[/C][C] 8.31[/C][C] 0.6902[/C][/ROW]
[ROW][C]79[/C][C] 12[/C][C] 11.55[/C][C] 0.4459[/C][/ROW]
[ROW][C]80[/C][C] 12[/C][C] 10.95[/C][C] 1.052[/C][/ROW]
[ROW][C]81[/C][C] 12[/C][C] 9.06[/C][C] 2.94[/C][/ROW]
[ROW][C]82[/C][C] 9[/C][C] 13.1[/C][C]-4.097[/C][/ROW]
[ROW][C]83[/C][C] 15[/C][C] 13.71[/C][C] 1.294[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 13.56[/C][C] 1.444[/C][/ROW]
[ROW][C]85[/C][C] 13[/C][C] 12.13[/C][C] 0.8666[/C][/ROW]
[ROW][C]86[/C][C] 9[/C][C] 10.61[/C][C]-1.608[/C][/ROW]
[ROW][C]87[/C][C] 12[/C][C] 9.323[/C][C] 2.677[/C][/ROW]
[ROW][C]88[/C][C] 9[/C][C] 10.25[/C][C]-1.253[/C][/ROW]
[ROW][C]89[/C][C] 15[/C][C] 12.97[/C][C] 2.033[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 11.16[/C][C]-0.1639[/C][/ROW]
[ROW][C]91[/C][C] 11[/C][C] 9.481[/C][C] 1.519[/C][/ROW]
[ROW][C]92[/C][C] 6[/C][C] 8.032[/C][C]-2.032[/C][/ROW]
[ROW][C]93[/C][C] 14[/C][C] 12.22[/C][C] 1.781[/C][/ROW]
[ROW][C]94[/C][C] 11[/C][C] 9.78[/C][C] 1.22[/C][/ROW]
[ROW][C]95[/C][C] 8[/C][C] 9.891[/C][C]-1.891[/C][/ROW]
[ROW][C]96[/C][C] 10[/C][C] 10.95[/C][C]-0.9485[/C][/ROW]
[ROW][C]97[/C][C] 10[/C][C] 7.396[/C][C] 2.604[/C][/ROW]
[ROW][C]98[/C][C] 9[/C][C] 10.32[/C][C]-1.324[/C][/ROW]
[ROW][C]99[/C][C] 8[/C][C] 8.335[/C][C]-0.3355[/C][/ROW]
[ROW][C]100[/C][C] 9[/C][C] 9.027[/C][C]-0.02658[/C][/ROW]
[ROW][C]101[/C][C] 10[/C][C] 11.11[/C][C]-1.106[/C][/ROW]
[ROW][C]102[/C][C] 11[/C][C] 10.55[/C][C] 0.4452[/C][/ROW]
[ROW][C]103[/C][C] 14[/C][C] 13.64[/C][C] 0.359[/C][/ROW]
[ROW][C]104[/C][C] 12[/C][C] 10.71[/C][C] 1.293[/C][/ROW]
[ROW][C]105[/C][C] 9[/C][C] 9.816[/C][C]-0.8156[/C][/ROW]
[ROW][C]106[/C][C] 13[/C][C] 12.39[/C][C] 0.6076[/C][/ROW]
[ROW][C]107[/C][C] 8[/C][C] 9.753[/C][C]-1.753[/C][/ROW]
[ROW][C]108[/C][C] 12[/C][C] 11.37[/C][C] 0.6315[/C][/ROW]
[ROW][C]109[/C][C] 14[/C][C] 11.43[/C][C] 2.567[/C][/ROW]
[ROW][C]110[/C][C] 9[/C][C] 10.9[/C][C]-1.898[/C][/ROW]
[ROW][C]111[/C][C] 10[/C][C] 10.89[/C][C]-0.8859[/C][/ROW]
[ROW][C]112[/C][C] 12[/C][C] 11.27[/C][C] 0.7306[/C][/ROW]
[ROW][C]113[/C][C] 12[/C][C] 12.33[/C][C]-0.3339[/C][/ROW]
[ROW][C]114[/C][C] 9[/C][C] 8.673[/C][C] 0.3268[/C][/ROW]
[ROW][C]115[/C][C] 9[/C][C] 9.612[/C][C]-0.6117[/C][/ROW]
[ROW][C]116[/C][C] 12[/C][C] 11.23[/C][C] 0.7736[/C][/ROW]
[ROW][C]117[/C][C] 15[/C][C] 12.15[/C][C] 2.849[/C][/ROW]
[ROW][C]118[/C][C] 12[/C][C] 11.43[/C][C] 0.5657[/C][/ROW]
[ROW][C]119[/C][C] 11[/C][C] 9.802[/C][C] 1.198[/C][/ROW]
[ROW][C]120[/C][C] 8[/C][C] 10.34[/C][C]-2.34[/C][/ROW]
[ROW][C]121[/C][C] 11[/C][C] 10.97[/C][C] 0.02804[/C][/ROW]
[ROW][C]122[/C][C] 11[/C][C] 9.596[/C][C] 1.404[/C][/ROW]
[ROW][C]123[/C][C] 10[/C][C] 9.652[/C][C] 0.3476[/C][/ROW]
[ROW][C]124[/C][C] 12[/C][C] 11.55[/C][C] 0.4487[/C][/ROW]
[ROW][C]125[/C][C] 9[/C][C] 10.46[/C][C]-1.465[/C][/ROW]
[ROW][C]126[/C][C] 11[/C][C] 9.885[/C][C] 1.115[/C][/ROW]
[ROW][C]127[/C][C] 15[/C][C] 13.4[/C][C] 1.598[/C][/ROW]
[ROW][C]128[/C][C] 14[/C][C] 13.01[/C][C] 0.9922[/C][/ROW]
[ROW][C]129[/C][C] 6[/C][C] 11.36[/C][C]-5.363[/C][/ROW]
[ROW][C]130[/C][C] 9[/C][C] 10.34[/C][C]-1.338[/C][/ROW]
[ROW][C]131[/C][C] 9[/C][C] 10.16[/C][C]-1.158[/C][/ROW]
[ROW][C]132[/C][C] 8[/C][C] 8.784[/C][C]-0.7836[/C][/ROW]
[ROW][C]133[/C][C] 7[/C][C] 8.219[/C][C]-1.219[/C][/ROW]
[ROW][C]134[/C][C] 10[/C][C] 10.83[/C][C]-0.8259[/C][/ROW]
[ROW][C]135[/C][C] 6[/C][C] 9.329[/C][C]-3.329[/C][/ROW]
[ROW][C]136[/C][C] 9[/C][C] 10.77[/C][C]-1.773[/C][/ROW]
[ROW][C]137[/C][C] 9[/C][C] 11.15[/C][C]-2.151[/C][/ROW]
[ROW][C]138[/C][C] 7[/C][C] 8.239[/C][C]-1.239[/C][/ROW]
[ROW][C]139[/C][C] 11[/C][C] 12.25[/C][C]-1.249[/C][/ROW]
[ROW][C]140[/C][C] 9[/C][C] 6.937[/C][C] 2.063[/C][/ROW]
[ROW][C]141[/C][C] 12[/C][C] 11.56[/C][C] 0.4413[/C][/ROW]
[ROW][C]142[/C][C] 9[/C][C] 10.17[/C][C]-1.173[/C][/ROW]
[ROW][C]143[/C][C] 10[/C][C] 11.92[/C][C]-1.924[/C][/ROW]
[ROW][C]144[/C][C] 11[/C][C] 8.582[/C][C] 2.418[/C][/ROW]
[ROW][C]145[/C][C] 7[/C][C] 8.208[/C][C]-1.208[/C][/ROW]
[ROW][C]146[/C][C] 12[/C][C] 8.895[/C][C] 3.105[/C][/ROW]
[ROW][C]147[/C][C] 8[/C][C] 9.894[/C][C]-1.894[/C][/ROW]
[ROW][C]148[/C][C] 13[/C][C] 11.02[/C][C] 1.985[/C][/ROW]
[ROW][C]149[/C][C] 11[/C][C] 10.26[/C][C] 0.7421[/C][/ROW]
[ROW][C]150[/C][C] 11[/C][C] 10.57[/C][C] 0.4331[/C][/ROW]
[ROW][C]151[/C][C] 12[/C][C] 9.248[/C][C] 2.752[/C][/ROW]
[ROW][C]152[/C][C] 11[/C][C] 7.899[/C][C] 3.101[/C][/ROW]
[ROW][C]153[/C][C] 12[/C][C] 11.07[/C][C] 0.9341[/C][/ROW]
[ROW][C]154[/C][C] 3[/C][C] 5.851[/C][C]-2.851[/C][/ROW]
[ROW][C]155[/C][C] 10[/C][C] 11.28[/C][C]-1.275[/C][/ROW]
[ROW][C]156[/C][C] 13[/C][C] 11.28[/C][C] 1.723[/C][/ROW]
[ROW][C]157[/C][C] 10[/C][C] 8.986[/C][C] 1.014[/C][/ROW]
[ROW][C]158[/C][C] 6[/C][C] 10.13[/C][C]-4.13[/C][/ROW]
[ROW][C]159[/C][C] 11[/C][C] 10.51[/C][C] 0.4922[/C][/ROW]
[ROW][C]160[/C][C] 12[/C][C] 11.47[/C][C] 0.5278[/C][/ROW]
[ROW][C]161[/C][C] 9[/C][C] 8.941[/C][C] 0.05879[/C][/ROW]
[ROW][C]162[/C][C] 10[/C][C] 11.08[/C][C]-1.076[/C][/ROW]
[ROW][C]163[/C][C] 15[/C][C] 13.12[/C][C] 1.884[/C][/ROW]
[ROW][C]164[/C][C] 9[/C][C] 10.22[/C][C]-1.218[/C][/ROW]
[ROW][C]165[/C][C] 6[/C][C] 8.565[/C][C]-2.565[/C][/ROW]
[ROW][C]166[/C][C] 9[/C][C] 9.683[/C][C]-0.6833[/C][/ROW]
[ROW][C]167[/C][C] 15[/C][C] 11.24[/C][C] 3.758[/C][/ROW]
[ROW][C]168[/C][C] 15[/C][C] 13.65[/C][C] 1.354[/C][/ROW]
[ROW][C]169[/C][C] 9[/C][C] 9.373[/C][C]-0.3732[/C][/ROW]
[ROW][C]170[/C][C] 11[/C][C] 8.459[/C][C] 2.541[/C][/ROW]
[ROW][C]171[/C][C] 9[/C][C] 12.41[/C][C]-3.409[/C][/ROW]
[ROW][C]172[/C][C] 11[/C][C] 10.74[/C][C] 0.2647[/C][/ROW]
[ROW][C]173[/C][C] 10[/C][C] 10.47[/C][C]-0.4654[/C][/ROW]
[ROW][C]174[/C][C] 9[/C][C] 10.71[/C][C]-1.708[/C][/ROW]
[ROW][C]175[/C][C] 6[/C][C] 9.743[/C][C]-3.743[/C][/ROW]
[ROW][C]176[/C][C] 12[/C][C] 10.05[/C][C] 1.946[/C][/ROW]
[ROW][C]177[/C][C] 13[/C][C] 12.25[/C][C] 0.7474[/C][/ROW]
[ROW][C]178[/C][C] 12[/C][C] 11.31[/C][C] 0.6878[/C][/ROW]
[ROW][C]179[/C][C] 12[/C][C] 11.25[/C][C] 0.7541[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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	10	10.45	-0.4465
2	9	11.64	-2.643
3	12	10.5	1.505
4	14	11.6	2.403
5	6	8.519	-2.519
6	13	13.64	-0.641
7	12	12.15	-0.1506
8	13	12.89	0.1136
9	6	8.281	-2.281
10	12	11.26	0.7387
11	10	11.86	-1.857
12	9	10.14	-1.137
13	12	10.59	1.407
14	7	7.938	-0.9379
15	10	9.767	0.2332
16	11	11.32	-0.3174
17	15	13.27	1.729
18	10	9.184	0.8163
19	12	10.2	1.798
20	10	9.291	0.7086
21	12	11.88	0.1159
22	11	10.46	0.5414
23	11	10.71	0.2893
24	12	11.43	0.5748
25	15	12.77	2.228
26	12	10.98	1.021
27	11	11.44	-0.4361
28	9	10.16	-1.162
29	11	10.8	0.2004
30	11	9.907	1.093
31	9	10.24	-1.237
32	15	12.6	2.404
33	12	10.94	1.062
34	9	10.97	-1.974
35	12	11.45	0.5472
36	12	9.326	2.674
37	9	9.737	-0.7366
38	9	11.37	-2.372
39	11	10.69	0.3095
40	12	13.23	-1.234
41	12	11.99	0.01325
42	12	11.35	0.6516
43	12	9.493	2.507
44	6	8.941	-2.941
45	11	9.4	1.6
46	12	10.71	1.293
47	9	10.35	-1.355
48	11	8.968	2.032
49	9	10.02	-1.018
50	10	11.23	-1.232
51	10	8.466	1.534
52	9	11.71	-2.713
53	12	13.4	-1.402
54	11	10.04	0.9551
55	9	10.37	-1.375
56	9	11.02	-2.016
57	12	11.12	0.8767
58	6	7.964	-1.964
59	10	9.818	0.1821
60	12	11.3	0.7026
61	11	12.23	-1.231
62	14	11.15	2.854
63	8	9.984	-1.984
64	9	10.39	-1.391
65	10	11.07	-1.066
66	10	11.76	-1.759
67	10	11.07	-1.072
68	11	9.77	1.23
69	10	10.38	-0.3846
70	12	11.16	0.839
71	14	12.77	1.234
72	10	9.812	0.1878
73	8	5.031	2.969
74	8	6.33	1.67
75	7	7.52	-0.5196
76	11	10.08	0.9156
77	6	6.818	-0.8177
78	9	8.31	0.6902
79	12	11.55	0.4459
80	12	10.95	1.052
81	12	9.06	2.94
82	9	13.1	-4.097
83	15	13.71	1.294
84	15	13.56	1.444
85	13	12.13	0.8666
86	9	10.61	-1.608
87	12	9.323	2.677
88	9	10.25	-1.253
89	15	12.97	2.033
90	11	11.16	-0.1639
91	11	9.481	1.519
92	6	8.032	-2.032
93	14	12.22	1.781
94	11	9.78	1.22
95	8	9.891	-1.891
96	10	10.95	-0.9485
97	10	7.396	2.604
98	9	10.32	-1.324
99	8	8.335	-0.3355
100	9	9.027	-0.02658
101	10	11.11	-1.106
102	11	10.55	0.4452
103	14	13.64	0.359
104	12	10.71	1.293
105	9	9.816	-0.8156
106	13	12.39	0.6076
107	8	9.753	-1.753
108	12	11.37	0.6315
109	14	11.43	2.567
110	9	10.9	-1.898
111	10	10.89	-0.8859
112	12	11.27	0.7306
113	12	12.33	-0.3339
114	9	8.673	0.3268
115	9	9.612	-0.6117
116	12	11.23	0.7736
117	15	12.15	2.849
118	12	11.43	0.5657
119	11	9.802	1.198
120	8	10.34	-2.34
121	11	10.97	0.02804
122	11	9.596	1.404
123	10	9.652	0.3476
124	12	11.55	0.4487
125	9	10.46	-1.465
126	11	9.885	1.115
127	15	13.4	1.598
128	14	13.01	0.9922
129	6	11.36	-5.363
130	9	10.34	-1.338
131	9	10.16	-1.158
132	8	8.784	-0.7836
133	7	8.219	-1.219
134	10	10.83	-0.8259
135	6	9.329	-3.329
136	9	10.77	-1.773
137	9	11.15	-2.151
138	7	8.239	-1.239
139	11	12.25	-1.249
140	9	6.937	2.063
141	12	11.56	0.4413
142	9	10.17	-1.173
143	10	11.92	-1.924
144	11	8.582	2.418
145	7	8.208	-1.208
146	12	8.895	3.105
147	8	9.894	-1.894
148	13	11.02	1.985
149	11	10.26	0.7421
150	11	10.57	0.4331
151	12	9.248	2.752
152	11	7.899	3.101
153	12	11.07	0.9341
154	3	5.851	-2.851
155	10	11.28	-1.275
156	13	11.28	1.723
157	10	8.986	1.014
158	6	10.13	-4.13
159	11	10.51	0.4922
160	12	11.47	0.5278
161	9	8.941	0.05879
162	10	11.08	-1.076
163	15	13.12	1.884
164	9	10.22	-1.218
165	6	8.565	-2.565
166	9	9.683	-0.6833
167	15	11.24	3.758
168	15	13.65	1.354
169	9	9.373	-0.3732
170	11	8.459	2.541
171	9	12.41	-3.409
172	11	10.74	0.2647
173	10	10.47	-0.4654
174	9	10.71	-1.708
175	6	9.743	-3.743
176	12	10.05	1.946
177	13	12.25	0.7474
178	12	11.31	0.6878
179	12	11.25	0.7541

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
9	0.8201	0.3598	0.1799
10	0.7015	0.5971	0.2985
11	0.763	0.4741	0.237
12	0.7069	0.5862	0.2931
13	0.717	0.566	0.283
14	0.6296	0.7408	0.3704
15	0.5299	0.9402	0.4701
16	0.4366	0.8731	0.5634
17	0.3542	0.7084	0.6458
18	0.317	0.634	0.683
19	0.3179	0.6358	0.6821
20	0.3598	0.7195	0.6402
21	0.2907	0.5815	0.7093
22	0.228	0.456	0.772
23	0.1894	0.3788	0.8106
24	0.1461	0.2922	0.8539
25	0.1159	0.2318	0.8841
26	0.122	0.244	0.878
27	0.09174	0.1835	0.9083
28	0.06759	0.1352	0.9324
29	0.05377	0.1075	0.9462
30	0.04824	0.09647	0.9518
31	0.04278	0.08557	0.9572
32	0.06051	0.121	0.9395
33	0.04679	0.09358	0.9532
34	0.04358	0.08717	0.9564
35	0.03271	0.06541	0.9673
36	0.0671	0.1342	0.9329
37	0.05117	0.1023	0.9488
38	0.0824	0.1648	0.9176
39	0.06293	0.1259	0.9371
40	0.07462	0.1492	0.9254
41	0.05656	0.1131	0.9434
42	0.04324	0.08648	0.9568
43	0.06826	0.1365	0.9317
44	0.129	0.2579	0.871
45	0.1435	0.287	0.8565
46	0.1226	0.2452	0.8774
47	0.109	0.2179	0.891
48	0.134	0.2681	0.866
49	0.1155	0.2311	0.8845
50	0.1125	0.2249	0.8875
51	0.1237	0.2474	0.8763
52	0.1931	0.3863	0.8069
53	0.1826	0.3653	0.8174
54	0.1592	0.3183	0.8408
55	0.1515	0.303	0.8485
56	0.1609	0.3219	0.8391
57	0.1384	0.2768	0.8616
58	0.1372	0.2743	0.8628
59	0.1128	0.2256	0.8872
60	0.09611	0.1922	0.9039
61	0.08544	0.1709	0.9146
62	0.1241	0.2483	0.8759
63	0.1416	0.2833	0.8584
64	0.1324	0.2648	0.8676
65	0.1209	0.2418	0.8791
66	0.1233	0.2465	0.8767
67	0.1105	0.2209	0.8895
68	0.1013	0.2025	0.8987
69	0.08319	0.1664	0.9168
70	0.07282	0.1456	0.9272
71	0.06635	0.1327	0.9336
72	0.054	0.108	0.946
73	0.08305	0.1661	0.9169
74	0.08148	0.163	0.9185
75	0.06856	0.1371	0.9314
76	0.05883	0.1177	0.9412
77	0.05133	0.1027	0.9487
78	0.04237	0.08475	0.9576
79	0.03433	0.06866	0.9657
80	0.02964	0.05929	0.9704
81	0.0483	0.0966	0.9517
82	0.1264	0.2527	0.8736
83	0.1218	0.2436	0.8782
84	0.1183	0.2365	0.8817
85	0.1033	0.2066	0.8967
86	0.103	0.206	0.897
87	0.1347	0.2694	0.8653
88	0.1245	0.249	0.8755
89	0.1343	0.2686	0.8657
90	0.1126	0.2251	0.8874
91	0.1068	0.2136	0.8932
92	0.1162	0.2324	0.8838
93	0.1207	0.2414	0.8793
94	0.1105	0.221	0.8895
95	0.1182	0.2365	0.8818
96	0.1038	0.2075	0.8962
97	0.1288	0.2575	0.8712
98	0.1199	0.2398	0.8801
99	0.1024	0.2047	0.8976
100	0.08438	0.1688	0.9156
101	0.07437	0.1487	0.9256
102	0.06109	0.1222	0.9389
103	0.04937	0.09874	0.9506
104	0.04413	0.08826	0.9559
105	0.03699	0.07399	0.963
106	0.02961	0.05922	0.9704
107	0.03004	0.06009	0.97
108	0.02426	0.04851	0.9757
109	0.03325	0.06651	0.9667
110	0.03469	0.06939	0.9653
111	0.02873	0.05747	0.9713
112	0.02337	0.04675	0.9766
113	0.01836	0.03671	0.9816
114	0.01447	0.02894	0.9855
115	0.01115	0.0223	0.9889
116	0.008712	0.01742	0.9913
117	0.01504	0.03008	0.985
118	0.01145	0.02289	0.9886
119	0.009756	0.01951	0.9902
120	0.01159	0.02318	0.9884
121	0.008539	0.01708	0.9915
122	0.007401	0.0148	0.9926
123	0.005385	0.01077	0.9946
124	0.00397	0.00794	0.996
125	0.00389	0.00778	0.9961
126	0.003052	0.006104	0.9969
127	0.002944	0.005888	0.9971
128	0.002292	0.004584	0.9977
129	0.02702	0.05405	0.973
130	0.02252	0.04504	0.9775
131	0.01873	0.03745	0.9813
132	0.01446	0.02891	0.9855
133	0.01239	0.02478	0.9876
134	0.00973	0.01946	0.9903
135	0.0183	0.03661	0.9817
136	0.01913	0.03826	0.9809
137	0.02073	0.04145	0.9793
138	0.01875	0.03751	0.9812
139	0.01595	0.03189	0.9841
140	0.02241	0.04483	0.9776
141	0.01645	0.0329	0.9836
142	0.01556	0.03112	0.9844
143	0.01455	0.02911	0.9854
144	0.01816	0.03632	0.9818
145	0.0149	0.0298	0.9851
146	0.03306	0.06613	0.9669
147	0.03128	0.06255	0.9687
148	0.03826	0.07652	0.9617
149	0.03069	0.06138	0.9693
150	0.02389	0.04777	0.9761
151	0.03205	0.0641	0.9679
152	0.07709	0.1542	0.9229
153	0.06429	0.1286	0.9357
154	0.05865	0.1173	0.9414
155	0.04759	0.09517	0.9524
156	0.04365	0.0873	0.9563
157	0.04	0.07999	0.96
158	0.2389	0.4779	0.7611
159	0.19	0.3799	0.81
160	0.1489	0.2979	0.8511
161	0.1116	0.2231	0.8884
162	0.1239	0.2478	0.8761
163	0.11	0.2199	0.89
164	0.4156	0.8312	0.5844
165	0.3531	0.7062	0.6469
166	0.265	0.5301	0.735
167	0.2042	0.4083	0.7958
168	0.3808	0.7617	0.6192
169	0.9434	0.1131	0.05657
170	0.916	0.1681	0.08404

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
9 &  0.8201 &  0.3598 &  0.1799 \tabularnewline
10 &  0.7015 &  0.5971 &  0.2985 \tabularnewline
11 &  0.763 &  0.4741 &  0.237 \tabularnewline
12 &  0.7069 &  0.5862 &  0.2931 \tabularnewline
13 &  0.717 &  0.566 &  0.283 \tabularnewline
14 &  0.6296 &  0.7408 &  0.3704 \tabularnewline
15 &  0.5299 &  0.9402 &  0.4701 \tabularnewline
16 &  0.4366 &  0.8731 &  0.5634 \tabularnewline
17 &  0.3542 &  0.7084 &  0.6458 \tabularnewline
18 &  0.317 &  0.634 &  0.683 \tabularnewline
19 &  0.3179 &  0.6358 &  0.6821 \tabularnewline
20 &  0.3598 &  0.7195 &  0.6402 \tabularnewline
21 &  0.2907 &  0.5815 &  0.7093 \tabularnewline
22 &  0.228 &  0.456 &  0.772 \tabularnewline
23 &  0.1894 &  0.3788 &  0.8106 \tabularnewline
24 &  0.1461 &  0.2922 &  0.8539 \tabularnewline
25 &  0.1159 &  0.2318 &  0.8841 \tabularnewline
26 &  0.122 &  0.244 &  0.878 \tabularnewline
27 &  0.09174 &  0.1835 &  0.9083 \tabularnewline
28 &  0.06759 &  0.1352 &  0.9324 \tabularnewline
29 &  0.05377 &  0.1075 &  0.9462 \tabularnewline
30 &  0.04824 &  0.09647 &  0.9518 \tabularnewline
31 &  0.04278 &  0.08557 &  0.9572 \tabularnewline
32 &  0.06051 &  0.121 &  0.9395 \tabularnewline
33 &  0.04679 &  0.09358 &  0.9532 \tabularnewline
34 &  0.04358 &  0.08717 &  0.9564 \tabularnewline
35 &  0.03271 &  0.06541 &  0.9673 \tabularnewline
36 &  0.0671 &  0.1342 &  0.9329 \tabularnewline
37 &  0.05117 &  0.1023 &  0.9488 \tabularnewline
38 &  0.0824 &  0.1648 &  0.9176 \tabularnewline
39 &  0.06293 &  0.1259 &  0.9371 \tabularnewline
40 &  0.07462 &  0.1492 &  0.9254 \tabularnewline
41 &  0.05656 &  0.1131 &  0.9434 \tabularnewline
42 &  0.04324 &  0.08648 &  0.9568 \tabularnewline
43 &  0.06826 &  0.1365 &  0.9317 \tabularnewline
44 &  0.129 &  0.2579 &  0.871 \tabularnewline
45 &  0.1435 &  0.287 &  0.8565 \tabularnewline
46 &  0.1226 &  0.2452 &  0.8774 \tabularnewline
47 &  0.109 &  0.2179 &  0.891 \tabularnewline
48 &  0.134 &  0.2681 &  0.866 \tabularnewline
49 &  0.1155 &  0.2311 &  0.8845 \tabularnewline
50 &  0.1125 &  0.2249 &  0.8875 \tabularnewline
51 &  0.1237 &  0.2474 &  0.8763 \tabularnewline
52 &  0.1931 &  0.3863 &  0.8069 \tabularnewline
53 &  0.1826 &  0.3653 &  0.8174 \tabularnewline
54 &  0.1592 &  0.3183 &  0.8408 \tabularnewline
55 &  0.1515 &  0.303 &  0.8485 \tabularnewline
56 &  0.1609 &  0.3219 &  0.8391 \tabularnewline
57 &  0.1384 &  0.2768 &  0.8616 \tabularnewline
58 &  0.1372 &  0.2743 &  0.8628 \tabularnewline
59 &  0.1128 &  0.2256 &  0.8872 \tabularnewline
60 &  0.09611 &  0.1922 &  0.9039 \tabularnewline
61 &  0.08544 &  0.1709 &  0.9146 \tabularnewline
62 &  0.1241 &  0.2483 &  0.8759 \tabularnewline
63 &  0.1416 &  0.2833 &  0.8584 \tabularnewline
64 &  0.1324 &  0.2648 &  0.8676 \tabularnewline
65 &  0.1209 &  0.2418 &  0.8791 \tabularnewline
66 &  0.1233 &  0.2465 &  0.8767 \tabularnewline
67 &  0.1105 &  0.2209 &  0.8895 \tabularnewline
68 &  0.1013 &  0.2025 &  0.8987 \tabularnewline
69 &  0.08319 &  0.1664 &  0.9168 \tabularnewline
70 &  0.07282 &  0.1456 &  0.9272 \tabularnewline
71 &  0.06635 &  0.1327 &  0.9336 \tabularnewline
72 &  0.054 &  0.108 &  0.946 \tabularnewline
73 &  0.08305 &  0.1661 &  0.9169 \tabularnewline
74 &  0.08148 &  0.163 &  0.9185 \tabularnewline
75 &  0.06856 &  0.1371 &  0.9314 \tabularnewline
76 &  0.05883 &  0.1177 &  0.9412 \tabularnewline
77 &  0.05133 &  0.1027 &  0.9487 \tabularnewline
78 &  0.04237 &  0.08475 &  0.9576 \tabularnewline
79 &  0.03433 &  0.06866 &  0.9657 \tabularnewline
80 &  0.02964 &  0.05929 &  0.9704 \tabularnewline
81 &  0.0483 &  0.0966 &  0.9517 \tabularnewline
82 &  0.1264 &  0.2527 &  0.8736 \tabularnewline
83 &  0.1218 &  0.2436 &  0.8782 \tabularnewline
84 &  0.1183 &  0.2365 &  0.8817 \tabularnewline
85 &  0.1033 &  0.2066 &  0.8967 \tabularnewline
86 &  0.103 &  0.206 &  0.897 \tabularnewline
87 &  0.1347 &  0.2694 &  0.8653 \tabularnewline
88 &  0.1245 &  0.249 &  0.8755 \tabularnewline
89 &  0.1343 &  0.2686 &  0.8657 \tabularnewline
90 &  0.1126 &  0.2251 &  0.8874 \tabularnewline
91 &  0.1068 &  0.2136 &  0.8932 \tabularnewline
92 &  0.1162 &  0.2324 &  0.8838 \tabularnewline
93 &  0.1207 &  0.2414 &  0.8793 \tabularnewline
94 &  0.1105 &  0.221 &  0.8895 \tabularnewline
95 &  0.1182 &  0.2365 &  0.8818 \tabularnewline
96 &  0.1038 &  0.2075 &  0.8962 \tabularnewline
97 &  0.1288 &  0.2575 &  0.8712 \tabularnewline
98 &  0.1199 &  0.2398 &  0.8801 \tabularnewline
99 &  0.1024 &  0.2047 &  0.8976 \tabularnewline
100 &  0.08438 &  0.1688 &  0.9156 \tabularnewline
101 &  0.07437 &  0.1487 &  0.9256 \tabularnewline
102 &  0.06109 &  0.1222 &  0.9389 \tabularnewline
103 &  0.04937 &  0.09874 &  0.9506 \tabularnewline
104 &  0.04413 &  0.08826 &  0.9559 \tabularnewline
105 &  0.03699 &  0.07399 &  0.963 \tabularnewline
106 &  0.02961 &  0.05922 &  0.9704 \tabularnewline
107 &  0.03004 &  0.06009 &  0.97 \tabularnewline
108 &  0.02426 &  0.04851 &  0.9757 \tabularnewline
109 &  0.03325 &  0.06651 &  0.9667 \tabularnewline
110 &  0.03469 &  0.06939 &  0.9653 \tabularnewline
111 &  0.02873 &  0.05747 &  0.9713 \tabularnewline
112 &  0.02337 &  0.04675 &  0.9766 \tabularnewline
113 &  0.01836 &  0.03671 &  0.9816 \tabularnewline
114 &  0.01447 &  0.02894 &  0.9855 \tabularnewline
115 &  0.01115 &  0.0223 &  0.9889 \tabularnewline
116 &  0.008712 &  0.01742 &  0.9913 \tabularnewline
117 &  0.01504 &  0.03008 &  0.985 \tabularnewline
118 &  0.01145 &  0.02289 &  0.9886 \tabularnewline
119 &  0.009756 &  0.01951 &  0.9902 \tabularnewline
120 &  0.01159 &  0.02318 &  0.9884 \tabularnewline
121 &  0.008539 &  0.01708 &  0.9915 \tabularnewline
122 &  0.007401 &  0.0148 &  0.9926 \tabularnewline
123 &  0.005385 &  0.01077 &  0.9946 \tabularnewline
124 &  0.00397 &  0.00794 &  0.996 \tabularnewline
125 &  0.00389 &  0.00778 &  0.9961 \tabularnewline
126 &  0.003052 &  0.006104 &  0.9969 \tabularnewline
127 &  0.002944 &  0.005888 &  0.9971 \tabularnewline
128 &  0.002292 &  0.004584 &  0.9977 \tabularnewline
129 &  0.02702 &  0.05405 &  0.973 \tabularnewline
130 &  0.02252 &  0.04504 &  0.9775 \tabularnewline
131 &  0.01873 &  0.03745 &  0.9813 \tabularnewline
132 &  0.01446 &  0.02891 &  0.9855 \tabularnewline
133 &  0.01239 &  0.02478 &  0.9876 \tabularnewline
134 &  0.00973 &  0.01946 &  0.9903 \tabularnewline
135 &  0.0183 &  0.03661 &  0.9817 \tabularnewline
136 &  0.01913 &  0.03826 &  0.9809 \tabularnewline
137 &  0.02073 &  0.04145 &  0.9793 \tabularnewline
138 &  0.01875 &  0.03751 &  0.9812 \tabularnewline
139 &  0.01595 &  0.03189 &  0.9841 \tabularnewline
140 &  0.02241 &  0.04483 &  0.9776 \tabularnewline
141 &  0.01645 &  0.0329 &  0.9836 \tabularnewline
142 &  0.01556 &  0.03112 &  0.9844 \tabularnewline
143 &  0.01455 &  0.02911 &  0.9854 \tabularnewline
144 &  0.01816 &  0.03632 &  0.9818 \tabularnewline
145 &  0.0149 &  0.0298 &  0.9851 \tabularnewline
146 &  0.03306 &  0.06613 &  0.9669 \tabularnewline
147 &  0.03128 &  0.06255 &  0.9687 \tabularnewline
148 &  0.03826 &  0.07652 &  0.9617 \tabularnewline
149 &  0.03069 &  0.06138 &  0.9693 \tabularnewline
150 &  0.02389 &  0.04777 &  0.9761 \tabularnewline
151 &  0.03205 &  0.0641 &  0.9679 \tabularnewline
152 &  0.07709 &  0.1542 &  0.9229 \tabularnewline
153 &  0.06429 &  0.1286 &  0.9357 \tabularnewline
154 &  0.05865 &  0.1173 &  0.9414 \tabularnewline
155 &  0.04759 &  0.09517 &  0.9524 \tabularnewline
156 &  0.04365 &  0.0873 &  0.9563 \tabularnewline
157 &  0.04 &  0.07999 &  0.96 \tabularnewline
158 &  0.2389 &  0.4779 &  0.7611 \tabularnewline
159 &  0.19 &  0.3799 &  0.81 \tabularnewline
160 &  0.1489 &  0.2979 &  0.8511 \tabularnewline
161 &  0.1116 &  0.2231 &  0.8884 \tabularnewline
162 &  0.1239 &  0.2478 &  0.8761 \tabularnewline
163 &  0.11 &  0.2199 &  0.89 \tabularnewline
164 &  0.4156 &  0.8312 &  0.5844 \tabularnewline
165 &  0.3531 &  0.7062 &  0.6469 \tabularnewline
166 &  0.265 &  0.5301 &  0.735 \tabularnewline
167 &  0.2042 &  0.4083 &  0.7958 \tabularnewline
168 &  0.3808 &  0.7617 &  0.6192 \tabularnewline
169 &  0.9434 &  0.1131 &  0.05657 \tabularnewline
170 &  0.916 &  0.1681 &  0.08404 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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]9[/C][C] 0.8201[/C][C] 0.3598[/C][C] 0.1799[/C][/ROW]
[ROW][C]10[/C][C] 0.7015[/C][C] 0.5971[/C][C] 0.2985[/C][/ROW]
[ROW][C]11[/C][C] 0.763[/C][C] 0.4741[/C][C] 0.237[/C][/ROW]
[ROW][C]12[/C][C] 0.7069[/C][C] 0.5862[/C][C] 0.2931[/C][/ROW]
[ROW][C]13[/C][C] 0.717[/C][C] 0.566[/C][C] 0.283[/C][/ROW]
[ROW][C]14[/C][C] 0.6296[/C][C] 0.7408[/C][C] 0.3704[/C][/ROW]
[ROW][C]15[/C][C] 0.5299[/C][C] 0.9402[/C][C] 0.4701[/C][/ROW]
[ROW][C]16[/C][C] 0.4366[/C][C] 0.8731[/C][C] 0.5634[/C][/ROW]
[ROW][C]17[/C][C] 0.3542[/C][C] 0.7084[/C][C] 0.6458[/C][/ROW]
[ROW][C]18[/C][C] 0.317[/C][C] 0.634[/C][C] 0.683[/C][/ROW]
[ROW][C]19[/C][C] 0.3179[/C][C] 0.6358[/C][C] 0.6821[/C][/ROW]
[ROW][C]20[/C][C] 0.3598[/C][C] 0.7195[/C][C] 0.6402[/C][/ROW]
[ROW][C]21[/C][C] 0.2907[/C][C] 0.5815[/C][C] 0.7093[/C][/ROW]
[ROW][C]22[/C][C] 0.228[/C][C] 0.456[/C][C] 0.772[/C][/ROW]
[ROW][C]23[/C][C] 0.1894[/C][C] 0.3788[/C][C] 0.8106[/C][/ROW]
[ROW][C]24[/C][C] 0.1461[/C][C] 0.2922[/C][C] 0.8539[/C][/ROW]
[ROW][C]25[/C][C] 0.1159[/C][C] 0.2318[/C][C] 0.8841[/C][/ROW]
[ROW][C]26[/C][C] 0.122[/C][C] 0.244[/C][C] 0.878[/C][/ROW]
[ROW][C]27[/C][C] 0.09174[/C][C] 0.1835[/C][C] 0.9083[/C][/ROW]
[ROW][C]28[/C][C] 0.06759[/C][C] 0.1352[/C][C] 0.9324[/C][/ROW]
[ROW][C]29[/C][C] 0.05377[/C][C] 0.1075[/C][C] 0.9462[/C][/ROW]
[ROW][C]30[/C][C] 0.04824[/C][C] 0.09647[/C][C] 0.9518[/C][/ROW]
[ROW][C]31[/C][C] 0.04278[/C][C] 0.08557[/C][C] 0.9572[/C][/ROW]
[ROW][C]32[/C][C] 0.06051[/C][C] 0.121[/C][C] 0.9395[/C][/ROW]
[ROW][C]33[/C][C] 0.04679[/C][C] 0.09358[/C][C] 0.9532[/C][/ROW]
[ROW][C]34[/C][C] 0.04358[/C][C] 0.08717[/C][C] 0.9564[/C][/ROW]
[ROW][C]35[/C][C] 0.03271[/C][C] 0.06541[/C][C] 0.9673[/C][/ROW]
[ROW][C]36[/C][C] 0.0671[/C][C] 0.1342[/C][C] 0.9329[/C][/ROW]
[ROW][C]37[/C][C] 0.05117[/C][C] 0.1023[/C][C] 0.9488[/C][/ROW]
[ROW][C]38[/C][C] 0.0824[/C][C] 0.1648[/C][C] 0.9176[/C][/ROW]
[ROW][C]39[/C][C] 0.06293[/C][C] 0.1259[/C][C] 0.9371[/C][/ROW]
[ROW][C]40[/C][C] 0.07462[/C][C] 0.1492[/C][C] 0.9254[/C][/ROW]
[ROW][C]41[/C][C] 0.05656[/C][C] 0.1131[/C][C] 0.9434[/C][/ROW]
[ROW][C]42[/C][C] 0.04324[/C][C] 0.08648[/C][C] 0.9568[/C][/ROW]
[ROW][C]43[/C][C] 0.06826[/C][C] 0.1365[/C][C] 0.9317[/C][/ROW]
[ROW][C]44[/C][C] 0.129[/C][C] 0.2579[/C][C] 0.871[/C][/ROW]
[ROW][C]45[/C][C] 0.1435[/C][C] 0.287[/C][C] 0.8565[/C][/ROW]
[ROW][C]46[/C][C] 0.1226[/C][C] 0.2452[/C][C] 0.8774[/C][/ROW]
[ROW][C]47[/C][C] 0.109[/C][C] 0.2179[/C][C] 0.891[/C][/ROW]
[ROW][C]48[/C][C] 0.134[/C][C] 0.2681[/C][C] 0.866[/C][/ROW]
[ROW][C]49[/C][C] 0.1155[/C][C] 0.2311[/C][C] 0.8845[/C][/ROW]
[ROW][C]50[/C][C] 0.1125[/C][C] 0.2249[/C][C] 0.8875[/C][/ROW]
[ROW][C]51[/C][C] 0.1237[/C][C] 0.2474[/C][C] 0.8763[/C][/ROW]
[ROW][C]52[/C][C] 0.1931[/C][C] 0.3863[/C][C] 0.8069[/C][/ROW]
[ROW][C]53[/C][C] 0.1826[/C][C] 0.3653[/C][C] 0.8174[/C][/ROW]
[ROW][C]54[/C][C] 0.1592[/C][C] 0.3183[/C][C] 0.8408[/C][/ROW]
[ROW][C]55[/C][C] 0.1515[/C][C] 0.303[/C][C] 0.8485[/C][/ROW]
[ROW][C]56[/C][C] 0.1609[/C][C] 0.3219[/C][C] 0.8391[/C][/ROW]
[ROW][C]57[/C][C] 0.1384[/C][C] 0.2768[/C][C] 0.8616[/C][/ROW]
[ROW][C]58[/C][C] 0.1372[/C][C] 0.2743[/C][C] 0.8628[/C][/ROW]
[ROW][C]59[/C][C] 0.1128[/C][C] 0.2256[/C][C] 0.8872[/C][/ROW]
[ROW][C]60[/C][C] 0.09611[/C][C] 0.1922[/C][C] 0.9039[/C][/ROW]
[ROW][C]61[/C][C] 0.08544[/C][C] 0.1709[/C][C] 0.9146[/C][/ROW]
[ROW][C]62[/C][C] 0.1241[/C][C] 0.2483[/C][C] 0.8759[/C][/ROW]
[ROW][C]63[/C][C] 0.1416[/C][C] 0.2833[/C][C] 0.8584[/C][/ROW]
[ROW][C]64[/C][C] 0.1324[/C][C] 0.2648[/C][C] 0.8676[/C][/ROW]
[ROW][C]65[/C][C] 0.1209[/C][C] 0.2418[/C][C] 0.8791[/C][/ROW]
[ROW][C]66[/C][C] 0.1233[/C][C] 0.2465[/C][C] 0.8767[/C][/ROW]
[ROW][C]67[/C][C] 0.1105[/C][C] 0.2209[/C][C] 0.8895[/C][/ROW]
[ROW][C]68[/C][C] 0.1013[/C][C] 0.2025[/C][C] 0.8987[/C][/ROW]
[ROW][C]69[/C][C] 0.08319[/C][C] 0.1664[/C][C] 0.9168[/C][/ROW]
[ROW][C]70[/C][C] 0.07282[/C][C] 0.1456[/C][C] 0.9272[/C][/ROW]
[ROW][C]71[/C][C] 0.06635[/C][C] 0.1327[/C][C] 0.9336[/C][/ROW]
[ROW][C]72[/C][C] 0.054[/C][C] 0.108[/C][C] 0.946[/C][/ROW]
[ROW][C]73[/C][C] 0.08305[/C][C] 0.1661[/C][C] 0.9169[/C][/ROW]
[ROW][C]74[/C][C] 0.08148[/C][C] 0.163[/C][C] 0.9185[/C][/ROW]
[ROW][C]75[/C][C] 0.06856[/C][C] 0.1371[/C][C] 0.9314[/C][/ROW]
[ROW][C]76[/C][C] 0.05883[/C][C] 0.1177[/C][C] 0.9412[/C][/ROW]
[ROW][C]77[/C][C] 0.05133[/C][C] 0.1027[/C][C] 0.9487[/C][/ROW]
[ROW][C]78[/C][C] 0.04237[/C][C] 0.08475[/C][C] 0.9576[/C][/ROW]
[ROW][C]79[/C][C] 0.03433[/C][C] 0.06866[/C][C] 0.9657[/C][/ROW]
[ROW][C]80[/C][C] 0.02964[/C][C] 0.05929[/C][C] 0.9704[/C][/ROW]
[ROW][C]81[/C][C] 0.0483[/C][C] 0.0966[/C][C] 0.9517[/C][/ROW]
[ROW][C]82[/C][C] 0.1264[/C][C] 0.2527[/C][C] 0.8736[/C][/ROW]
[ROW][C]83[/C][C] 0.1218[/C][C] 0.2436[/C][C] 0.8782[/C][/ROW]
[ROW][C]84[/C][C] 0.1183[/C][C] 0.2365[/C][C] 0.8817[/C][/ROW]
[ROW][C]85[/C][C] 0.1033[/C][C] 0.2066[/C][C] 0.8967[/C][/ROW]
[ROW][C]86[/C][C] 0.103[/C][C] 0.206[/C][C] 0.897[/C][/ROW]
[ROW][C]87[/C][C] 0.1347[/C][C] 0.2694[/C][C] 0.8653[/C][/ROW]
[ROW][C]88[/C][C] 0.1245[/C][C] 0.249[/C][C] 0.8755[/C][/ROW]
[ROW][C]89[/C][C] 0.1343[/C][C] 0.2686[/C][C] 0.8657[/C][/ROW]
[ROW][C]90[/C][C] 0.1126[/C][C] 0.2251[/C][C] 0.8874[/C][/ROW]
[ROW][C]91[/C][C] 0.1068[/C][C] 0.2136[/C][C] 0.8932[/C][/ROW]
[ROW][C]92[/C][C] 0.1162[/C][C] 0.2324[/C][C] 0.8838[/C][/ROW]
[ROW][C]93[/C][C] 0.1207[/C][C] 0.2414[/C][C] 0.8793[/C][/ROW]
[ROW][C]94[/C][C] 0.1105[/C][C] 0.221[/C][C] 0.8895[/C][/ROW]
[ROW][C]95[/C][C] 0.1182[/C][C] 0.2365[/C][C] 0.8818[/C][/ROW]
[ROW][C]96[/C][C] 0.1038[/C][C] 0.2075[/C][C] 0.8962[/C][/ROW]
[ROW][C]97[/C][C] 0.1288[/C][C] 0.2575[/C][C] 0.8712[/C][/ROW]
[ROW][C]98[/C][C] 0.1199[/C][C] 0.2398[/C][C] 0.8801[/C][/ROW]
[ROW][C]99[/C][C] 0.1024[/C][C] 0.2047[/C][C] 0.8976[/C][/ROW]
[ROW][C]100[/C][C] 0.08438[/C][C] 0.1688[/C][C] 0.9156[/C][/ROW]
[ROW][C]101[/C][C] 0.07437[/C][C] 0.1487[/C][C] 0.9256[/C][/ROW]
[ROW][C]102[/C][C] 0.06109[/C][C] 0.1222[/C][C] 0.9389[/C][/ROW]
[ROW][C]103[/C][C] 0.04937[/C][C] 0.09874[/C][C] 0.9506[/C][/ROW]
[ROW][C]104[/C][C] 0.04413[/C][C] 0.08826[/C][C] 0.9559[/C][/ROW]
[ROW][C]105[/C][C] 0.03699[/C][C] 0.07399[/C][C] 0.963[/C][/ROW]
[ROW][C]106[/C][C] 0.02961[/C][C] 0.05922[/C][C] 0.9704[/C][/ROW]
[ROW][C]107[/C][C] 0.03004[/C][C] 0.06009[/C][C] 0.97[/C][/ROW]
[ROW][C]108[/C][C] 0.02426[/C][C] 0.04851[/C][C] 0.9757[/C][/ROW]
[ROW][C]109[/C][C] 0.03325[/C][C] 0.06651[/C][C] 0.9667[/C][/ROW]
[ROW][C]110[/C][C] 0.03469[/C][C] 0.06939[/C][C] 0.9653[/C][/ROW]
[ROW][C]111[/C][C] 0.02873[/C][C] 0.05747[/C][C] 0.9713[/C][/ROW]
[ROW][C]112[/C][C] 0.02337[/C][C] 0.04675[/C][C] 0.9766[/C][/ROW]
[ROW][C]113[/C][C] 0.01836[/C][C] 0.03671[/C][C] 0.9816[/C][/ROW]
[ROW][C]114[/C][C] 0.01447[/C][C] 0.02894[/C][C] 0.9855[/C][/ROW]
[ROW][C]115[/C][C] 0.01115[/C][C] 0.0223[/C][C] 0.9889[/C][/ROW]
[ROW][C]116[/C][C] 0.008712[/C][C] 0.01742[/C][C] 0.9913[/C][/ROW]
[ROW][C]117[/C][C] 0.01504[/C][C] 0.03008[/C][C] 0.985[/C][/ROW]
[ROW][C]118[/C][C] 0.01145[/C][C] 0.02289[/C][C] 0.9886[/C][/ROW]
[ROW][C]119[/C][C] 0.009756[/C][C] 0.01951[/C][C] 0.9902[/C][/ROW]
[ROW][C]120[/C][C] 0.01159[/C][C] 0.02318[/C][C] 0.9884[/C][/ROW]
[ROW][C]121[/C][C] 0.008539[/C][C] 0.01708[/C][C] 0.9915[/C][/ROW]
[ROW][C]122[/C][C] 0.007401[/C][C] 0.0148[/C][C] 0.9926[/C][/ROW]
[ROW][C]123[/C][C] 0.005385[/C][C] 0.01077[/C][C] 0.9946[/C][/ROW]
[ROW][C]124[/C][C] 0.00397[/C][C] 0.00794[/C][C] 0.996[/C][/ROW]
[ROW][C]125[/C][C] 0.00389[/C][C] 0.00778[/C][C] 0.9961[/C][/ROW]
[ROW][C]126[/C][C] 0.003052[/C][C] 0.006104[/C][C] 0.9969[/C][/ROW]
[ROW][C]127[/C][C] 0.002944[/C][C] 0.005888[/C][C] 0.9971[/C][/ROW]
[ROW][C]128[/C][C] 0.002292[/C][C] 0.004584[/C][C] 0.9977[/C][/ROW]
[ROW][C]129[/C][C] 0.02702[/C][C] 0.05405[/C][C] 0.973[/C][/ROW]
[ROW][C]130[/C][C] 0.02252[/C][C] 0.04504[/C][C] 0.9775[/C][/ROW]
[ROW][C]131[/C][C] 0.01873[/C][C] 0.03745[/C][C] 0.9813[/C][/ROW]
[ROW][C]132[/C][C] 0.01446[/C][C] 0.02891[/C][C] 0.9855[/C][/ROW]
[ROW][C]133[/C][C] 0.01239[/C][C] 0.02478[/C][C] 0.9876[/C][/ROW]
[ROW][C]134[/C][C] 0.00973[/C][C] 0.01946[/C][C] 0.9903[/C][/ROW]
[ROW][C]135[/C][C] 0.0183[/C][C] 0.03661[/C][C] 0.9817[/C][/ROW]
[ROW][C]136[/C][C] 0.01913[/C][C] 0.03826[/C][C] 0.9809[/C][/ROW]
[ROW][C]137[/C][C] 0.02073[/C][C] 0.04145[/C][C] 0.9793[/C][/ROW]
[ROW][C]138[/C][C] 0.01875[/C][C] 0.03751[/C][C] 0.9812[/C][/ROW]
[ROW][C]139[/C][C] 0.01595[/C][C] 0.03189[/C][C] 0.9841[/C][/ROW]
[ROW][C]140[/C][C] 0.02241[/C][C] 0.04483[/C][C] 0.9776[/C][/ROW]
[ROW][C]141[/C][C] 0.01645[/C][C] 0.0329[/C][C] 0.9836[/C][/ROW]
[ROW][C]142[/C][C] 0.01556[/C][C] 0.03112[/C][C] 0.9844[/C][/ROW]
[ROW][C]143[/C][C] 0.01455[/C][C] 0.02911[/C][C] 0.9854[/C][/ROW]
[ROW][C]144[/C][C] 0.01816[/C][C] 0.03632[/C][C] 0.9818[/C][/ROW]
[ROW][C]145[/C][C] 0.0149[/C][C] 0.0298[/C][C] 0.9851[/C][/ROW]
[ROW][C]146[/C][C] 0.03306[/C][C] 0.06613[/C][C] 0.9669[/C][/ROW]
[ROW][C]147[/C][C] 0.03128[/C][C] 0.06255[/C][C] 0.9687[/C][/ROW]
[ROW][C]148[/C][C] 0.03826[/C][C] 0.07652[/C][C] 0.9617[/C][/ROW]
[ROW][C]149[/C][C] 0.03069[/C][C] 0.06138[/C][C] 0.9693[/C][/ROW]
[ROW][C]150[/C][C] 0.02389[/C][C] 0.04777[/C][C] 0.9761[/C][/ROW]
[ROW][C]151[/C][C] 0.03205[/C][C] 0.0641[/C][C] 0.9679[/C][/ROW]
[ROW][C]152[/C][C] 0.07709[/C][C] 0.1542[/C][C] 0.9229[/C][/ROW]
[ROW][C]153[/C][C] 0.06429[/C][C] 0.1286[/C][C] 0.9357[/C][/ROW]
[ROW][C]154[/C][C] 0.05865[/C][C] 0.1173[/C][C] 0.9414[/C][/ROW]
[ROW][C]155[/C][C] 0.04759[/C][C] 0.09517[/C][C] 0.9524[/C][/ROW]
[ROW][C]156[/C][C] 0.04365[/C][C] 0.0873[/C][C] 0.9563[/C][/ROW]
[ROW][C]157[/C][C] 0.04[/C][C] 0.07999[/C][C] 0.96[/C][/ROW]
[ROW][C]158[/C][C] 0.2389[/C][C] 0.4779[/C][C] 0.7611[/C][/ROW]
[ROW][C]159[/C][C] 0.19[/C][C] 0.3799[/C][C] 0.81[/C][/ROW]
[ROW][C]160[/C][C] 0.1489[/C][C] 0.2979[/C][C] 0.8511[/C][/ROW]
[ROW][C]161[/C][C] 0.1116[/C][C] 0.2231[/C][C] 0.8884[/C][/ROW]
[ROW][C]162[/C][C] 0.1239[/C][C] 0.2478[/C][C] 0.8761[/C][/ROW]
[ROW][C]163[/C][C] 0.11[/C][C] 0.2199[/C][C] 0.89[/C][/ROW]
[ROW][C]164[/C][C] 0.4156[/C][C] 0.8312[/C][C] 0.5844[/C][/ROW]
[ROW][C]165[/C][C] 0.3531[/C][C] 0.7062[/C][C] 0.6469[/C][/ROW]
[ROW][C]166[/C][C] 0.265[/C][C] 0.5301[/C][C] 0.735[/C][/ROW]
[ROW][C]167[/C][C] 0.2042[/C][C] 0.4083[/C][C] 0.7958[/C][/ROW]
[ROW][C]168[/C][C] 0.3808[/C][C] 0.7617[/C][C] 0.6192[/C][/ROW]
[ROW][C]169[/C][C] 0.9434[/C][C] 0.1131[/C][C] 0.05657[/C][/ROW]
[ROW][C]170[/C][C] 0.916[/C][C] 0.1681[/C][C] 0.08404[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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
9	0.8201	0.3598	0.1799
10	0.7015	0.5971	0.2985
11	0.763	0.4741	0.237
12	0.7069	0.5862	0.2931
13	0.717	0.566	0.283
14	0.6296	0.7408	0.3704
15	0.5299	0.9402	0.4701
16	0.4366	0.8731	0.5634
17	0.3542	0.7084	0.6458
18	0.317	0.634	0.683
19	0.3179	0.6358	0.6821
20	0.3598	0.7195	0.6402
21	0.2907	0.5815	0.7093
22	0.228	0.456	0.772
23	0.1894	0.3788	0.8106
24	0.1461	0.2922	0.8539
25	0.1159	0.2318	0.8841
26	0.122	0.244	0.878
27	0.09174	0.1835	0.9083
28	0.06759	0.1352	0.9324
29	0.05377	0.1075	0.9462
30	0.04824	0.09647	0.9518
31	0.04278	0.08557	0.9572
32	0.06051	0.121	0.9395
33	0.04679	0.09358	0.9532
34	0.04358	0.08717	0.9564
35	0.03271	0.06541	0.9673
36	0.0671	0.1342	0.9329
37	0.05117	0.1023	0.9488
38	0.0824	0.1648	0.9176
39	0.06293	0.1259	0.9371
40	0.07462	0.1492	0.9254
41	0.05656	0.1131	0.9434
42	0.04324	0.08648	0.9568
43	0.06826	0.1365	0.9317
44	0.129	0.2579	0.871
45	0.1435	0.287	0.8565
46	0.1226	0.2452	0.8774
47	0.109	0.2179	0.891
48	0.134	0.2681	0.866
49	0.1155	0.2311	0.8845
50	0.1125	0.2249	0.8875
51	0.1237	0.2474	0.8763
52	0.1931	0.3863	0.8069
53	0.1826	0.3653	0.8174
54	0.1592	0.3183	0.8408
55	0.1515	0.303	0.8485
56	0.1609	0.3219	0.8391
57	0.1384	0.2768	0.8616
58	0.1372	0.2743	0.8628
59	0.1128	0.2256	0.8872
60	0.09611	0.1922	0.9039
61	0.08544	0.1709	0.9146
62	0.1241	0.2483	0.8759
63	0.1416	0.2833	0.8584
64	0.1324	0.2648	0.8676
65	0.1209	0.2418	0.8791
66	0.1233	0.2465	0.8767
67	0.1105	0.2209	0.8895
68	0.1013	0.2025	0.8987
69	0.08319	0.1664	0.9168
70	0.07282	0.1456	0.9272
71	0.06635	0.1327	0.9336
72	0.054	0.108	0.946
73	0.08305	0.1661	0.9169
74	0.08148	0.163	0.9185
75	0.06856	0.1371	0.9314
76	0.05883	0.1177	0.9412
77	0.05133	0.1027	0.9487
78	0.04237	0.08475	0.9576
79	0.03433	0.06866	0.9657
80	0.02964	0.05929	0.9704
81	0.0483	0.0966	0.9517
82	0.1264	0.2527	0.8736
83	0.1218	0.2436	0.8782
84	0.1183	0.2365	0.8817
85	0.1033	0.2066	0.8967
86	0.103	0.206	0.897
87	0.1347	0.2694	0.8653
88	0.1245	0.249	0.8755
89	0.1343	0.2686	0.8657
90	0.1126	0.2251	0.8874
91	0.1068	0.2136	0.8932
92	0.1162	0.2324	0.8838
93	0.1207	0.2414	0.8793
94	0.1105	0.221	0.8895
95	0.1182	0.2365	0.8818
96	0.1038	0.2075	0.8962
97	0.1288	0.2575	0.8712
98	0.1199	0.2398	0.8801
99	0.1024	0.2047	0.8976
100	0.08438	0.1688	0.9156
101	0.07437	0.1487	0.9256
102	0.06109	0.1222	0.9389
103	0.04937	0.09874	0.9506
104	0.04413	0.08826	0.9559
105	0.03699	0.07399	0.963
106	0.02961	0.05922	0.9704
107	0.03004	0.06009	0.97
108	0.02426	0.04851	0.9757
109	0.03325	0.06651	0.9667
110	0.03469	0.06939	0.9653
111	0.02873	0.05747	0.9713
112	0.02337	0.04675	0.9766
113	0.01836	0.03671	0.9816
114	0.01447	0.02894	0.9855
115	0.01115	0.0223	0.9889
116	0.008712	0.01742	0.9913
117	0.01504	0.03008	0.985
118	0.01145	0.02289	0.9886
119	0.009756	0.01951	0.9902
120	0.01159	0.02318	0.9884
121	0.008539	0.01708	0.9915
122	0.007401	0.0148	0.9926
123	0.005385	0.01077	0.9946
124	0.00397	0.00794	0.996
125	0.00389	0.00778	0.9961
126	0.003052	0.006104	0.9969
127	0.002944	0.005888	0.9971
128	0.002292	0.004584	0.9977
129	0.02702	0.05405	0.973
130	0.02252	0.04504	0.9775
131	0.01873	0.03745	0.9813
132	0.01446	0.02891	0.9855
133	0.01239	0.02478	0.9876
134	0.00973	0.01946	0.9903
135	0.0183	0.03661	0.9817
136	0.01913	0.03826	0.9809
137	0.02073	0.04145	0.9793
138	0.01875	0.03751	0.9812
139	0.01595	0.03189	0.9841
140	0.02241	0.04483	0.9776
141	0.01645	0.0329	0.9836
142	0.01556	0.03112	0.9844
143	0.01455	0.02911	0.9854
144	0.01816	0.03632	0.9818
145	0.0149	0.0298	0.9851
146	0.03306	0.06613	0.9669
147	0.03128	0.06255	0.9687
148	0.03826	0.07652	0.9617
149	0.03069	0.06138	0.9693
150	0.02389	0.04777	0.9761
151	0.03205	0.0641	0.9679
152	0.07709	0.1542	0.9229
153	0.06429	0.1286	0.9357
154	0.05865	0.1173	0.9414
155	0.04759	0.09517	0.9524
156	0.04365	0.0873	0.9563
157	0.04	0.07999	0.96
158	0.2389	0.4779	0.7611
159	0.19	0.3799	0.81
160	0.1489	0.2979	0.8511
161	0.1116	0.2231	0.8884
162	0.1239	0.2478	0.8761
163	0.11	0.2199	0.89
164	0.4156	0.8312	0.5844
165	0.3531	0.7062	0.6469
166	0.265	0.5301	0.735
167	0.2042	0.4083	0.7958
168	0.3808	0.7617	0.6192
169	0.9434	0.1131	0.05657
170	0.916	0.1681	0.08404

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	5	0.03086	NOK
5% type I error level	35	0.216049	NOK
10% type I error level	62	0.382716	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 & 5 &  0.03086 & NOK \tabularnewline
5% type I error level & 35 & 0.216049 & NOK \tabularnewline
10% type I error level & 62 & 0.382716 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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]5[/C][C] 0.03086[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]35[/C][C]0.216049[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]62[/C][C]0.382716[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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	5	0.03086	NOK
5% type I error level	35	0.216049	NOK
10% type I error level	62	0.382716	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 1.6198, df1 = 2, df2 = 171, p-value = 0.201
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4426, df1 = 10, df2 = 163, p-value = 0.1659
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6403, df1 = 2, df2 = 171, p-value = 0.197

\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 = 1.6198, df1 = 2, df2 = 171, p-value = 0.201
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 1.4426, df1 = 10, df2 = 163, p-value = 0.1659
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.6403, df1 = 2, df2 = 171, p-value = 0.197
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&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 = 1.6198, df1 = 2, df2 = 171, p-value = 0.201
[/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 = 1.4426, df1 = 10, df2 = 163, p-value = 0.1659
[/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 = 1.6403, df1 = 2, df2 = 171, p-value = 0.197
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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 = 1.6198, df1 = 2, df2 = 171, p-value = 0.201
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 1.4426, df1 = 10, df2 = 163, p-value = 0.1659
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.6403, df1 = 2, df2 = 171, p-value = 0.197

Variance Inflation Factors (Multicollinearity)
> vif Intention_to_Use Relative_Advantage Perceived_Ease_of_Use 2.088804 1.794113 2.168089 Information_Quality System_Quality 2.614023 1.855533

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     Intention_to_Use    Relative_Advantage Perceived_Ease_of_Use 
             2.088804              1.794113              2.168089 
  Information_Quality        System_Quality 
             2.614023              1.855533 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308940&T=9

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     Intention_to_Use    Relative_Advantage Perceived_Ease_of_Use 
             2.088804              1.794113              2.168089 
  Information_Quality        System_Quality 
             2.614023              1.855533 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308940&T=9

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308940&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 Intention_to_Use Relative_Advantage Perceived_Ease_of_Use 2.088804 1.794113 2.168089 Information_Quality System_Quality 2.614023 1.855533

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 = 1 ; par3 = 1 ; par4 = 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