Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Wed, 25 Jan 2017 10:58:35 +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/Jan/25/t1485338331nwzrbbrex5g37vy.htm/, Retrieved Mon, 13 May 2024 21:57:44 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=306484, Retrieved Mon, 13 May 2024 21:57:44 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

-       [Multiple Regression] [Multiple Regression] [2017-01-25 09:58:35] [e0044ff8caf2d68149dcdb0ba8e86f31] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

13 14 22 4 2 4 3 5 4
16 19 24 5 3 3 4 5 4
17 17 26 4 4 5 4 5 4
NA 17 21 3 4 3 3 4 4
NA 15 26 4 4 5 4 5 4
16 20 25 3 4 4 4 5 5
NA 15 21 3 4 4 3 3 4
NA 19 24 3 4 5 4 4 4
NA 15 27 4 5 4 4 5 5
17 15 28 4 5 5 4 5 5
17 19 23 4 4 2 4 5 4
15 NA 25 4 4 5 3 5 4
16 20 24 4 4 4 3 4 5
14 18 24 3 3 5 4 4 5
16 15 24 4 4 5 4 2 5
17 14 25 3 4 5 4 4 5
NA 20 25 3 4 5 4 4 5
NA NA NA NA NA 5 NA 5 5
NA 16 25 5 5 4 3 4 4
NA 16 25 4 4 4 4 5 4
16 16 24 3 4 5 3 4 5
NA 10 26 4 4 4 4 5 5
16 19 26 4 4 5 4 4 5
NA 19 25 4 4 5 4 4 4
NA 16 26 4 4 5 4 4 5
NA 15 23 3 4 4 4 4 4
16 18 24 3 4 4 3 5 5
15 17 24 4 4 4 4 4 4
16 19 25 2 4 5 4 5 5
16 17 25 5 4 4 4 4 4
13 NA 24 4 3 5 4 4 4
15 19 28 4 5 5 4 5 5
17 20 27 5 4 5 4 4 5
NA 5 NA 4 3 5 4 NA 5
13 19 23 2 3 5 4 5 4
17 16 23 4 5 2 4 4 4
NA 15 24 3 4 5 4 4 4
14 16 24 4 3 5 3 4 5
14 18 22 4 3 3 4 4 4
18 16 25 4 4 5 4 4 4
NA 15 25 5 4 4 4 4 4
17 17 28 4 5 5 4 5 5
13 NA 22 3 3 4 4 4 4
16 20 28 5 5 5 3 5 5
15 19 25 5 4 5 3 4 4
15 7 24 4 4 4 3 4 5
NA 13 24 4 4 4 4 4 4
15 16 23 3 5 5 3 3 4
13 16 25 4 4 4 4 5 4
NA NA NA 2 3 4 2 NA 4
17 18 26 4 5 5 4 4 4
NA 18 25 5 5 2 4 5 4
NA 16 27 5 5 5 4 4 4
11 17 26 4 3 5 4 5 5
14 19 23 4 3 4 3 4 5
13 16 25 4 4 5 4 4 4
NA 19 21 3 4 4 3 3 4
17 13 22 3 4 4 4 4 3
16 16 24 4 4 4 3 5 4
NA 13 25 4 4 4 4 5 4
17 12 27 5 5 3 4 5 5
16 17 24 2 4 4 4 5 5
16 17 26 4 4 4 4 5 5
16 17 21 3 4 4 4 2 4
15 16 27 4 4 5 4 5 5
12 16 22 4 2 4 4 4 4
17 14 23 4 4 4 3 5 3
14 16 24 4 4 4 3 5 4
14 13 25 5 4 5 3 3 5
16 16 24 3 4 4 3 5 5
NA 14 23 3 4 4 3 4 5
NA 20 28 4 5 5 5 5 4
NA 12 NA 4 4 3 4 NA 4
NA 13 24 4 4 4 4 4 4
NA 18 26 4 4 4 5 5 4
15 14 22 3 4 3 4 4 4
16 19 25 4 4 4 4 5 4
14 18 25 3 4 5 3 5 5
15 14 24 3 3 5 4 4 5
17 18 24 4 3 5 4 4 4
NA 19 26 4 4 5 4 4 5
10 15 21 3 3 3 4 4 4
NA 14 25 4 4 4 4 5 4
17 17 25 4 4 3 4 5 5
NA 19 26 4 4 4 4 5 5
20 13 25 5 4 4 4 4 4
17 19 26 5 4 3 5 4 5
18 18 27 4 4 5 4 5 5
NA 20 25 3 4 5 4 4 5
17 15 NA 3 NA 4 4 4 4
14 15 20 4 2 3 3 4 4
NA 15 24 4 4 5 4 4 3
17 20 26 4 4 5 4 4 5
NA 15 25 4 4 4 4 5 4
17 19 25 4 5 4 4 5 3
NA 18 24 3 4 4 3 5 5
16 18 26 4 4 5 4 4 5
18 15 25 5 4 3 4 4 5
18 20 28 5 4 5 5 4 5
16 17 27 4 5 4 4 5 5
NA 12 25 3 4 5 4 4 5
NA 18 26 5 3 4 4 5 5
15 19 26 4 4 5 4 4 5
13 20 26 5 4 4 4 4 5
NA NA NA 3 4 4 3 NA 4
NA 17 28 5 4 4 5 5 5
NA 15 NA 4 4 5 3 NA 5
NA 16 21 4 4 3 3 4 3
NA 18 25 4 4 5 4 4 4
16 18 25 4 4 5 4 4 4
NA 14 24 3 4 5 4 5 3
NA 15 24 4 4 4 4 4 4
NA 12 24 4 4 4 3 4 5
12 17 23 3 3 4 3 5 5
NA 14 23 4 4 4 3 4 4
16 18 24 3 4 5 4 4 4
16 17 24 4 4 5 4 3 4
NA 17 25 5 4 5 1 5 5
16 20 28 5 4 5 4 5 5
14 16 23 4 4 4 4 4 3
15 14 24 4 4 5 3 4 4
14 15 23 3 4 4 3 4 5
NA 18 24 4 4 4 4 4 4
15 20 25 4 4 4 4 5 4
NA 17 24 4 5 3 4 4 4
15 17 23 3 4 4 4 4 4
16 17 23 4 4 4 3 4 4
NA 17 25 4 4 4 4 4 5
NA 15 21 3 4 3 3 4 4
NA 17 22 4 4 4 3 4 3
11 18 19 3 2 4 2 4 4
NA 17 24 4 4 4 3 5 4
18 20 25 5 4 4 3 5 4
NA 15 21 2 4 4 3 3 5
11 16 22 3 3 4 4 4 4
NA 15 23 4 4 4 3 4 4
18 18 27 5 5 4 4 5 4
NA 11 NA NA NA 2 0 0
15 15 26 4 5 5 4 4 4
19 18 29 5 5 5 5 5 4
17 20 28 4 5 5 4 5 5
NA 19 24 4 4 4 3 4 5
14 14 25 3 4 5 4 5 4
NA 16 25 4 4 5 4 4 4
13 15 22 4 4 2 4 4 4
17 17 25 4 4 3 4 5 5
14 18 26 4 4 4 4 5 5
19 20 26 5 4 5 3 5 4
14 17 24 4 3 5 4 4 4
NA 18 25 4 4 5 4 4 4
NA 15 19 3 3 2 3 4 4
16 16 25 4 5 5 4 4 3
16 11 23 4 4 4 3 4 4
15 15 25 4 4 4 4 4 5
12 18 25 3 4 5 3 5 5
NA 17 26 4 4 5 4 4 5
17 16 27 5 4 5 4 5 4
NA 12 24 4 4 5 4 3 4
NA 19 22 2 3 5 4 4 4
18 18 25 4 4 4 4 4 5
15 15 24 4 3 4 3 5 5
18 17 23 4 4 4 4 4 3
15 19 27 4 5 5 5 4 4
NA 18 24 5 4 3 4 4 4
NA 19 24 5 4 4 3 4 4
NA 16 21 3 3 1 4 5 5
16 16 25 4 4 4 4 4 5
NA 16 25 4 4 4 4 5 4
16 14 23 2 3 4 5 5 4

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	8 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 time8 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&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]

8 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=306484&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&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	8 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
TVDC[t] = + 6.17227 -0.0483258ITHSUM[t] + 0.00895799SKEOUSUM[t] + 0.448601SKEOU1[t] + 0.993215SKEOU2[t] -0.00739145SKEOU3[t] + 0.770855SKEOU4[t] + 0.257587SKEOU5[t] + 0.0731786SKEOU6[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TVDC[t] =  +  6.17227 -0.0483258ITHSUM[t] +  0.00895799SKEOUSUM[t] +  0.448601SKEOU1[t] +  0.993215SKEOU2[t] -0.00739145SKEOU3[t] +  0.770855SKEOU4[t] +  0.257587SKEOU5[t] +  0.0731786SKEOU6[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TVDC[t] =  +  6.17227 -0.0483258ITHSUM[t] +  0.00895799SKEOUSUM[t] +  0.448601SKEOU1[t] +  0.993215SKEOU2[t] -0.00739145SKEOU3[t] +  0.770855SKEOU4[t] +  0.257587SKEOU5[t] +  0.0731786SKEOU6[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&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
TVDC[t] = + 6.17227 -0.0483258ITHSUM[t] + 0.00895799SKEOUSUM[t] + 0.448601SKEOU1[t] + 0.993215SKEOU2[t] -0.00739145SKEOU3[t] + 0.770855SKEOU4[t] + 0.257587SKEOU5[t] + 0.0731786SKEOU6[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)	+6.172	2.353	+2.6230e+00	0.01023	0.005117
ITHSUM	-0.04833	0.08114	-5.9560e-01	0.5529	0.2765
SKEOUSUM	+0.008958	0.07809	+1.1470e-01	0.9089	0.4545
SKEOU1	+0.4486	0.2587	+1.7340e+00	0.08629	0.04314
SKEOU2	+0.9932	0.2291	+4.3360e+00	3.779e-05	1.889e-05
SKEOU3	-0.007391	0.2631	-2.8090e-02	0.9777	0.4888
SKEOU4	+0.7709	0.3321	+2.3210e+00	0.02253	0.01126
SKEOU5	+0.2576	0.2781	+9.2640e-01	0.3567	0.1784
SKEOU6	+0.07318	0.1355	+5.4020e-01	0.5904	0.2952

\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) & +6.172 &  2.353 & +2.6230e+00 &  0.01023 &  0.005117 \tabularnewline
ITHSUM & -0.04833 &  0.08114 & -5.9560e-01 &  0.5529 &  0.2765 \tabularnewline
SKEOUSUM & +0.008958 &  0.07809 & +1.1470e-01 &  0.9089 &  0.4545 \tabularnewline
SKEOU1 & +0.4486 &  0.2587 & +1.7340e+00 &  0.08629 &  0.04314 \tabularnewline
SKEOU2 & +0.9932 &  0.2291 & +4.3360e+00 &  3.779e-05 &  1.889e-05 \tabularnewline
SKEOU3 & -0.007391 &  0.2631 & -2.8090e-02 &  0.9777 &  0.4888 \tabularnewline
SKEOU4 & +0.7709 &  0.3321 & +2.3210e+00 &  0.02253 &  0.01126 \tabularnewline
SKEOU5 & +0.2576 &  0.2781 & +9.2640e-01 &  0.3567 &  0.1784 \tabularnewline
SKEOU6 & +0.07318 &  0.1355 & +5.4020e-01 &  0.5904 &  0.2952 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&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]+6.172[/C][C] 2.353[/C][C]+2.6230e+00[/C][C] 0.01023[/C][C] 0.005117[/C][/ROW]
[ROW][C]ITHSUM[/C][C]-0.04833[/C][C] 0.08114[/C][C]-5.9560e-01[/C][C] 0.5529[/C][C] 0.2765[/C][/ROW]
[ROW][C]SKEOUSUM[/C][C]+0.008958[/C][C] 0.07809[/C][C]+1.1470e-01[/C][C] 0.9089[/C][C] 0.4545[/C][/ROW]
[ROW][C]SKEOU1[/C][C]+0.4486[/C][C] 0.2587[/C][C]+1.7340e+00[/C][C] 0.08629[/C][C] 0.04314[/C][/ROW]
[ROW][C]SKEOU2[/C][C]+0.9932[/C][C] 0.2291[/C][C]+4.3360e+00[/C][C] 3.779e-05[/C][C] 1.889e-05[/C][/ROW]
[ROW][C]SKEOU3[/C][C]-0.007391[/C][C] 0.2631[/C][C]-2.8090e-02[/C][C] 0.9777[/C][C] 0.4888[/C][/ROW]
[ROW][C]SKEOU4[/C][C]+0.7709[/C][C] 0.3321[/C][C]+2.3210e+00[/C][C] 0.02253[/C][C] 0.01126[/C][/ROW]
[ROW][C]SKEOU5[/C][C]+0.2576[/C][C] 0.2781[/C][C]+9.2640e-01[/C][C] 0.3567[/C][C] 0.1784[/C][/ROW]
[ROW][C]SKEOU6[/C][C]+0.07318[/C][C] 0.1355[/C][C]+5.4020e-01[/C][C] 0.5904[/C][C] 0.2952[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&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)	+6.172	2.353	+2.6230e+00	0.01023	0.005117
ITHSUM	-0.04833	0.08114	-5.9560e-01	0.5529	0.2765
SKEOUSUM	+0.008958	0.07809	+1.1470e-01	0.9089	0.4545
SKEOU1	+0.4486	0.2587	+1.7340e+00	0.08629	0.04314
SKEOU2	+0.9932	0.2291	+4.3360e+00	3.779e-05	1.889e-05
SKEOU3	-0.007391	0.2631	-2.8090e-02	0.9777	0.4888
SKEOU4	+0.7709	0.3321	+2.3210e+00	0.02253	0.01126
SKEOU5	+0.2576	0.2781	+9.2640e-01	0.3567	0.1784
SKEOU6	+0.07318	0.1355	+5.4020e-01	0.5904	0.2952

Multiple Linear Regression - Regression Statistics
Multiple R	0.584
R-squared	0.341
Adjusted R-squared	0.2824
F-TEST (value)	5.822
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	5.514e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.635
Sum Squared Residuals	240.6

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.584 \tabularnewline
R-squared &  0.341 \tabularnewline
Adjusted R-squared &  0.2824 \tabularnewline
F-TEST (value) &  5.822 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 90 \tabularnewline
p-value &  5.514e-06 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1.635 \tabularnewline
Sum Squared Residuals &  240.6 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.584[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.341[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2824[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 5.822[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]90[/C][/ROW]
[ROW][C]p-value[/C][C] 5.514e-06[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1.635[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 240.6[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&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	0.584
R-squared	0.341
Adjusted R-squared	0.2824
F-TEST (value)	5.822
F-TEST (DF numerator)	8
F-TEST (DF denominator)	90
p-value	5.514e-06
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1.635
Sum Squared Residuals	240.6

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.34	-0.3373
2	16	15.33	0.6664
3	17	15.98	1.022
4	16	15.46	0.544
5	17	17.16	-0.159
6	17	15.88	1.123
7	16	14.87	1.133
8	14	14.29	-0.2855
9	16	15.36	0.6428
10	17	15.48	1.519
11	16	14.6	1.395
12	16	15.7	0.303
13	16	14.77	1.227
14	15	15.71	-0.7099
15	16	15.05	0.9516
16	16	16.17	-0.1675
17	15	16.97	-1.966
18	17	16.11	0.8938
19	13	13.96	-0.9641
20	17	16.76	0.2427
21	14	14.06	-0.05994
22	14	14.66	-0.6578
23	18	15.76	2.24
24	17	17.06	-0.06232
25	16	16.6	-0.5951
26	15	15.29	-0.2926
27	15	15.5	-0.4955
28	15	15.26	-0.258
29	13	16.02	-3.025
30	17	16.67	0.3347
31	11	15.06	-4.058
32	14	13.91	0.0866
33	13	15.76	-2.76
34	17	15.36	1.636
35	16	15.24	0.755
36	17	17.76	-0.7584
37	16	15.14	0.8565
38	16	16.06	-0.05858
39	16	14.72	1.281
40	15	16.11	-1.108
41	12	13.75	-1.754
42	17	15.26	1.741
43	14	15.24	-1.245
44	14	15.4	-1.398
45	16	14.87	1.13
46	15	15.4	-0.3958
47	16	15.88	0.1202
48	14	14.77	-0.7744
49	15	14.48	0.5212
50	17	14.66	2.339
51	10	14.35	-4.345
52	17	16.06	0.943
53	20	16.36	3.639
54	17	16.93	0.06881
55	18	16.01	1.988
56	14	13.02	0.9792
57	17	15.65	1.351
58	17	16.8	0.2002
59	16	15.75	0.2547
60	18	16.34	1.655
61	18	16.89	1.114
62	16	17.06	-1.061
63	15	15.7	-0.6969
64	13	16.1	-3.105
65	16	15.66	0.3369
66	12	13.82	-1.819
67	16	15.21	0.7944
68	16	15.44	0.5551
69	16	16.37	-0.3727
70	14	15.68	-1.676
71	15	15.08	-0.07663
72	14	14.65	-0.6513
73	15	15.83	-0.8315
74	15	15.25	-0.2523
75	16	14.93	1.07
76	11	11.64	-0.64
77	18	15.51	2.491
78	11	14.3	-3.298
79	18	17.39	0.6121
80	15	17.64	-2.635
81	18	18.12	-0.1164
82	19	16.19	2.811
83	16	16.8	-0.796
84	15	14.25	0.746
85	17	15.91	1.089
86	18	17.22	0.7781
87	20	17.61	2.392
88	15	13.88	1.124
89	16	17.21	-1.206
90	11	16.05	-5.053
91	15	15.99	-0.9871
92	17	17.3	-0.2979
93	19	16.84	2.16
94	18	16.21	1.793
95	15	16.8	-1.804
96	17	15.79	1.212
97	16	13.81	2.193
98	16	16.79	-0.7931
99	14	16.2	-2.196

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  13 &  13.34 & -0.3373 \tabularnewline
2 &  16 &  15.33 &  0.6664 \tabularnewline
3 &  17 &  15.98 &  1.022 \tabularnewline
4 &  16 &  15.46 &  0.544 \tabularnewline
5 &  17 &  17.16 & -0.159 \tabularnewline
6 &  17 &  15.88 &  1.123 \tabularnewline
7 &  16 &  14.87 &  1.133 \tabularnewline
8 &  14 &  14.29 & -0.2855 \tabularnewline
9 &  16 &  15.36 &  0.6428 \tabularnewline
10 &  17 &  15.48 &  1.519 \tabularnewline
11 &  16 &  14.6 &  1.395 \tabularnewline
12 &  16 &  15.7 &  0.303 \tabularnewline
13 &  16 &  14.77 &  1.227 \tabularnewline
14 &  15 &  15.71 & -0.7099 \tabularnewline
15 &  16 &  15.05 &  0.9516 \tabularnewline
16 &  16 &  16.17 & -0.1675 \tabularnewline
17 &  15 &  16.97 & -1.966 \tabularnewline
18 &  17 &  16.11 &  0.8938 \tabularnewline
19 &  13 &  13.96 & -0.9641 \tabularnewline
20 &  17 &  16.76 &  0.2427 \tabularnewline
21 &  14 &  14.06 & -0.05994 \tabularnewline
22 &  14 &  14.66 & -0.6578 \tabularnewline
23 &  18 &  15.76 &  2.24 \tabularnewline
24 &  17 &  17.06 & -0.06232 \tabularnewline
25 &  16 &  16.6 & -0.5951 \tabularnewline
26 &  15 &  15.29 & -0.2926 \tabularnewline
27 &  15 &  15.5 & -0.4955 \tabularnewline
28 &  15 &  15.26 & -0.258 \tabularnewline
29 &  13 &  16.02 & -3.025 \tabularnewline
30 &  17 &  16.67 &  0.3347 \tabularnewline
31 &  11 &  15.06 & -4.058 \tabularnewline
32 &  14 &  13.91 &  0.0866 \tabularnewline
33 &  13 &  15.76 & -2.76 \tabularnewline
34 &  17 &  15.36 &  1.636 \tabularnewline
35 &  16 &  15.24 &  0.755 \tabularnewline
36 &  17 &  17.76 & -0.7584 \tabularnewline
37 &  16 &  15.14 &  0.8565 \tabularnewline
38 &  16 &  16.06 & -0.05858 \tabularnewline
39 &  16 &  14.72 &  1.281 \tabularnewline
40 &  15 &  16.11 & -1.108 \tabularnewline
41 &  12 &  13.75 & -1.754 \tabularnewline
42 &  17 &  15.26 &  1.741 \tabularnewline
43 &  14 &  15.24 & -1.245 \tabularnewline
44 &  14 &  15.4 & -1.398 \tabularnewline
45 &  16 &  14.87 &  1.13 \tabularnewline
46 &  15 &  15.4 & -0.3958 \tabularnewline
47 &  16 &  15.88 &  0.1202 \tabularnewline
48 &  14 &  14.77 & -0.7744 \tabularnewline
49 &  15 &  14.48 &  0.5212 \tabularnewline
50 &  17 &  14.66 &  2.339 \tabularnewline
51 &  10 &  14.35 & -4.345 \tabularnewline
52 &  17 &  16.06 &  0.943 \tabularnewline
53 &  20 &  16.36 &  3.639 \tabularnewline
54 &  17 &  16.93 &  0.06881 \tabularnewline
55 &  18 &  16.01 &  1.988 \tabularnewline
56 &  14 &  13.02 &  0.9792 \tabularnewline
57 &  17 &  15.65 &  1.351 \tabularnewline
58 &  17 &  16.8 &  0.2002 \tabularnewline
59 &  16 &  15.75 &  0.2547 \tabularnewline
60 &  18 &  16.34 &  1.655 \tabularnewline
61 &  18 &  16.89 &  1.114 \tabularnewline
62 &  16 &  17.06 & -1.061 \tabularnewline
63 &  15 &  15.7 & -0.6969 \tabularnewline
64 &  13 &  16.1 & -3.105 \tabularnewline
65 &  16 &  15.66 &  0.3369 \tabularnewline
66 &  12 &  13.82 & -1.819 \tabularnewline
67 &  16 &  15.21 &  0.7944 \tabularnewline
68 &  16 &  15.44 &  0.5551 \tabularnewline
69 &  16 &  16.37 & -0.3727 \tabularnewline
70 &  14 &  15.68 & -1.676 \tabularnewline
71 &  15 &  15.08 & -0.07663 \tabularnewline
72 &  14 &  14.65 & -0.6513 \tabularnewline
73 &  15 &  15.83 & -0.8315 \tabularnewline
74 &  15 &  15.25 & -0.2523 \tabularnewline
75 &  16 &  14.93 &  1.07 \tabularnewline
76 &  11 &  11.64 & -0.64 \tabularnewline
77 &  18 &  15.51 &  2.491 \tabularnewline
78 &  11 &  14.3 & -3.298 \tabularnewline
79 &  18 &  17.39 &  0.6121 \tabularnewline
80 &  15 &  17.64 & -2.635 \tabularnewline
81 &  18 &  18.12 & -0.1164 \tabularnewline
82 &  19 &  16.19 &  2.811 \tabularnewline
83 &  16 &  16.8 & -0.796 \tabularnewline
84 &  15 &  14.25 &  0.746 \tabularnewline
85 &  17 &  15.91 &  1.089 \tabularnewline
86 &  18 &  17.22 &  0.7781 \tabularnewline
87 &  20 &  17.61 &  2.392 \tabularnewline
88 &  15 &  13.88 &  1.124 \tabularnewline
89 &  16 &  17.21 & -1.206 \tabularnewline
90 &  11 &  16.05 & -5.053 \tabularnewline
91 &  15 &  15.99 & -0.9871 \tabularnewline
92 &  17 &  17.3 & -0.2979 \tabularnewline
93 &  19 &  16.84 &  2.16 \tabularnewline
94 &  18 &  16.21 &  1.793 \tabularnewline
95 &  15 &  16.8 & -1.804 \tabularnewline
96 &  17 &  15.79 &  1.212 \tabularnewline
97 &  16 &  13.81 &  2.193 \tabularnewline
98 &  16 &  16.79 & -0.7931 \tabularnewline
99 &  14 &  16.2 & -2.196 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=4

[TABLE]
[ROW][C]Multiple Linear Regression - Actuals, Interpolation, and Residuals[/C][/ROW]
[ROW][C]Time or Index[/C][C]Actuals[/C][C]InterpolationForecast[/C][C]ResidualsPrediction Error[/C][/ROW]
[ROW][C]1[/C][C] 13[/C][C] 13.34[/C][C]-0.3373[/C][/ROW]
[ROW][C]2[/C][C] 16[/C][C] 15.33[/C][C] 0.6664[/C][/ROW]
[ROW][C]3[/C][C] 17[/C][C] 15.98[/C][C] 1.022[/C][/ROW]
[ROW][C]4[/C][C] 16[/C][C] 15.46[/C][C] 0.544[/C][/ROW]
[ROW][C]5[/C][C] 17[/C][C] 17.16[/C][C]-0.159[/C][/ROW]
[ROW][C]6[/C][C] 17[/C][C] 15.88[/C][C] 1.123[/C][/ROW]
[ROW][C]7[/C][C] 16[/C][C] 14.87[/C][C] 1.133[/C][/ROW]
[ROW][C]8[/C][C] 14[/C][C] 14.29[/C][C]-0.2855[/C][/ROW]
[ROW][C]9[/C][C] 16[/C][C] 15.36[/C][C] 0.6428[/C][/ROW]
[ROW][C]10[/C][C] 17[/C][C] 15.48[/C][C] 1.519[/C][/ROW]
[ROW][C]11[/C][C] 16[/C][C] 14.6[/C][C] 1.395[/C][/ROW]
[ROW][C]12[/C][C] 16[/C][C] 15.7[/C][C] 0.303[/C][/ROW]
[ROW][C]13[/C][C] 16[/C][C] 14.77[/C][C] 1.227[/C][/ROW]
[ROW][C]14[/C][C] 15[/C][C] 15.71[/C][C]-0.7099[/C][/ROW]
[ROW][C]15[/C][C] 16[/C][C] 15.05[/C][C] 0.9516[/C][/ROW]
[ROW][C]16[/C][C] 16[/C][C] 16.17[/C][C]-0.1675[/C][/ROW]
[ROW][C]17[/C][C] 15[/C][C] 16.97[/C][C]-1.966[/C][/ROW]
[ROW][C]18[/C][C] 17[/C][C] 16.11[/C][C] 0.8938[/C][/ROW]
[ROW][C]19[/C][C] 13[/C][C] 13.96[/C][C]-0.9641[/C][/ROW]
[ROW][C]20[/C][C] 17[/C][C] 16.76[/C][C] 0.2427[/C][/ROW]
[ROW][C]21[/C][C] 14[/C][C] 14.06[/C][C]-0.05994[/C][/ROW]
[ROW][C]22[/C][C] 14[/C][C] 14.66[/C][C]-0.6578[/C][/ROW]
[ROW][C]23[/C][C] 18[/C][C] 15.76[/C][C] 2.24[/C][/ROW]
[ROW][C]24[/C][C] 17[/C][C] 17.06[/C][C]-0.06232[/C][/ROW]
[ROW][C]25[/C][C] 16[/C][C] 16.6[/C][C]-0.5951[/C][/ROW]
[ROW][C]26[/C][C] 15[/C][C] 15.29[/C][C]-0.2926[/C][/ROW]
[ROW][C]27[/C][C] 15[/C][C] 15.5[/C][C]-0.4955[/C][/ROW]
[ROW][C]28[/C][C] 15[/C][C] 15.26[/C][C]-0.258[/C][/ROW]
[ROW][C]29[/C][C] 13[/C][C] 16.02[/C][C]-3.025[/C][/ROW]
[ROW][C]30[/C][C] 17[/C][C] 16.67[/C][C] 0.3347[/C][/ROW]
[ROW][C]31[/C][C] 11[/C][C] 15.06[/C][C]-4.058[/C][/ROW]
[ROW][C]32[/C][C] 14[/C][C] 13.91[/C][C] 0.0866[/C][/ROW]
[ROW][C]33[/C][C] 13[/C][C] 15.76[/C][C]-2.76[/C][/ROW]
[ROW][C]34[/C][C] 17[/C][C] 15.36[/C][C] 1.636[/C][/ROW]
[ROW][C]35[/C][C] 16[/C][C] 15.24[/C][C] 0.755[/C][/ROW]
[ROW][C]36[/C][C] 17[/C][C] 17.76[/C][C]-0.7584[/C][/ROW]
[ROW][C]37[/C][C] 16[/C][C] 15.14[/C][C] 0.8565[/C][/ROW]
[ROW][C]38[/C][C] 16[/C][C] 16.06[/C][C]-0.05858[/C][/ROW]
[ROW][C]39[/C][C] 16[/C][C] 14.72[/C][C] 1.281[/C][/ROW]
[ROW][C]40[/C][C] 15[/C][C] 16.11[/C][C]-1.108[/C][/ROW]
[ROW][C]41[/C][C] 12[/C][C] 13.75[/C][C]-1.754[/C][/ROW]
[ROW][C]42[/C][C] 17[/C][C] 15.26[/C][C] 1.741[/C][/ROW]
[ROW][C]43[/C][C] 14[/C][C] 15.24[/C][C]-1.245[/C][/ROW]
[ROW][C]44[/C][C] 14[/C][C] 15.4[/C][C]-1.398[/C][/ROW]
[ROW][C]45[/C][C] 16[/C][C] 14.87[/C][C] 1.13[/C][/ROW]
[ROW][C]46[/C][C] 15[/C][C] 15.4[/C][C]-0.3958[/C][/ROW]
[ROW][C]47[/C][C] 16[/C][C] 15.88[/C][C] 0.1202[/C][/ROW]
[ROW][C]48[/C][C] 14[/C][C] 14.77[/C][C]-0.7744[/C][/ROW]
[ROW][C]49[/C][C] 15[/C][C] 14.48[/C][C] 0.5212[/C][/ROW]
[ROW][C]50[/C][C] 17[/C][C] 14.66[/C][C] 2.339[/C][/ROW]
[ROW][C]51[/C][C] 10[/C][C] 14.35[/C][C]-4.345[/C][/ROW]
[ROW][C]52[/C][C] 17[/C][C] 16.06[/C][C] 0.943[/C][/ROW]
[ROW][C]53[/C][C] 20[/C][C] 16.36[/C][C] 3.639[/C][/ROW]
[ROW][C]54[/C][C] 17[/C][C] 16.93[/C][C] 0.06881[/C][/ROW]
[ROW][C]55[/C][C] 18[/C][C] 16.01[/C][C] 1.988[/C][/ROW]
[ROW][C]56[/C][C] 14[/C][C] 13.02[/C][C] 0.9792[/C][/ROW]
[ROW][C]57[/C][C] 17[/C][C] 15.65[/C][C] 1.351[/C][/ROW]
[ROW][C]58[/C][C] 17[/C][C] 16.8[/C][C] 0.2002[/C][/ROW]
[ROW][C]59[/C][C] 16[/C][C] 15.75[/C][C] 0.2547[/C][/ROW]
[ROW][C]60[/C][C] 18[/C][C] 16.34[/C][C] 1.655[/C][/ROW]
[ROW][C]61[/C][C] 18[/C][C] 16.89[/C][C] 1.114[/C][/ROW]
[ROW][C]62[/C][C] 16[/C][C] 17.06[/C][C]-1.061[/C][/ROW]
[ROW][C]63[/C][C] 15[/C][C] 15.7[/C][C]-0.6969[/C][/ROW]
[ROW][C]64[/C][C] 13[/C][C] 16.1[/C][C]-3.105[/C][/ROW]
[ROW][C]65[/C][C] 16[/C][C] 15.66[/C][C] 0.3369[/C][/ROW]
[ROW][C]66[/C][C] 12[/C][C] 13.82[/C][C]-1.819[/C][/ROW]
[ROW][C]67[/C][C] 16[/C][C] 15.21[/C][C] 0.7944[/C][/ROW]
[ROW][C]68[/C][C] 16[/C][C] 15.44[/C][C] 0.5551[/C][/ROW]
[ROW][C]69[/C][C] 16[/C][C] 16.37[/C][C]-0.3727[/C][/ROW]
[ROW][C]70[/C][C] 14[/C][C] 15.68[/C][C]-1.676[/C][/ROW]
[ROW][C]71[/C][C] 15[/C][C] 15.08[/C][C]-0.07663[/C][/ROW]
[ROW][C]72[/C][C] 14[/C][C] 14.65[/C][C]-0.6513[/C][/ROW]
[ROW][C]73[/C][C] 15[/C][C] 15.83[/C][C]-0.8315[/C][/ROW]
[ROW][C]74[/C][C] 15[/C][C] 15.25[/C][C]-0.2523[/C][/ROW]
[ROW][C]75[/C][C] 16[/C][C] 14.93[/C][C] 1.07[/C][/ROW]
[ROW][C]76[/C][C] 11[/C][C] 11.64[/C][C]-0.64[/C][/ROW]
[ROW][C]77[/C][C] 18[/C][C] 15.51[/C][C] 2.491[/C][/ROW]
[ROW][C]78[/C][C] 11[/C][C] 14.3[/C][C]-3.298[/C][/ROW]
[ROW][C]79[/C][C] 18[/C][C] 17.39[/C][C] 0.6121[/C][/ROW]
[ROW][C]80[/C][C] 15[/C][C] 17.64[/C][C]-2.635[/C][/ROW]
[ROW][C]81[/C][C] 18[/C][C] 18.12[/C][C]-0.1164[/C][/ROW]
[ROW][C]82[/C][C] 19[/C][C] 16.19[/C][C] 2.811[/C][/ROW]
[ROW][C]83[/C][C] 16[/C][C] 16.8[/C][C]-0.796[/C][/ROW]
[ROW][C]84[/C][C] 15[/C][C] 14.25[/C][C] 0.746[/C][/ROW]
[ROW][C]85[/C][C] 17[/C][C] 15.91[/C][C] 1.089[/C][/ROW]
[ROW][C]86[/C][C] 18[/C][C] 17.22[/C][C] 0.7781[/C][/ROW]
[ROW][C]87[/C][C] 20[/C][C] 17.61[/C][C] 2.392[/C][/ROW]
[ROW][C]88[/C][C] 15[/C][C] 13.88[/C][C] 1.124[/C][/ROW]
[ROW][C]89[/C][C] 16[/C][C] 17.21[/C][C]-1.206[/C][/ROW]
[ROW][C]90[/C][C] 11[/C][C] 16.05[/C][C]-5.053[/C][/ROW]
[ROW][C]91[/C][C] 15[/C][C] 15.99[/C][C]-0.9871[/C][/ROW]
[ROW][C]92[/C][C] 17[/C][C] 17.3[/C][C]-0.2979[/C][/ROW]
[ROW][C]93[/C][C] 19[/C][C] 16.84[/C][C] 2.16[/C][/ROW]
[ROW][C]94[/C][C] 18[/C][C] 16.21[/C][C] 1.793[/C][/ROW]
[ROW][C]95[/C][C] 15[/C][C] 16.8[/C][C]-1.804[/C][/ROW]
[ROW][C]96[/C][C] 17[/C][C] 15.79[/C][C] 1.212[/C][/ROW]
[ROW][C]97[/C][C] 16[/C][C] 13.81[/C][C] 2.193[/C][/ROW]
[ROW][C]98[/C][C] 16[/C][C] 16.79[/C][C]-0.7931[/C][/ROW]
[ROW][C]99[/C][C] 14[/C][C] 16.2[/C][C]-2.196[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	13	13.34	-0.3373
2	16	15.33	0.6664
3	17	15.98	1.022
4	16	15.46	0.544
5	17	17.16	-0.159
6	17	15.88	1.123
7	16	14.87	1.133
8	14	14.29	-0.2855
9	16	15.36	0.6428
10	17	15.48	1.519
11	16	14.6	1.395
12	16	15.7	0.303
13	16	14.77	1.227
14	15	15.71	-0.7099
15	16	15.05	0.9516
16	16	16.17	-0.1675
17	15	16.97	-1.966
18	17	16.11	0.8938
19	13	13.96	-0.9641
20	17	16.76	0.2427
21	14	14.06	-0.05994
22	14	14.66	-0.6578
23	18	15.76	2.24
24	17	17.06	-0.06232
25	16	16.6	-0.5951
26	15	15.29	-0.2926
27	15	15.5	-0.4955
28	15	15.26	-0.258
29	13	16.02	-3.025
30	17	16.67	0.3347
31	11	15.06	-4.058
32	14	13.91	0.0866
33	13	15.76	-2.76
34	17	15.36	1.636
35	16	15.24	0.755
36	17	17.76	-0.7584
37	16	15.14	0.8565
38	16	16.06	-0.05858
39	16	14.72	1.281
40	15	16.11	-1.108
41	12	13.75	-1.754
42	17	15.26	1.741
43	14	15.24	-1.245
44	14	15.4	-1.398
45	16	14.87	1.13
46	15	15.4	-0.3958
47	16	15.88	0.1202
48	14	14.77	-0.7744
49	15	14.48	0.5212
50	17	14.66	2.339
51	10	14.35	-4.345
52	17	16.06	0.943
53	20	16.36	3.639
54	17	16.93	0.06881
55	18	16.01	1.988
56	14	13.02	0.9792
57	17	15.65	1.351
58	17	16.8	0.2002
59	16	15.75	0.2547
60	18	16.34	1.655
61	18	16.89	1.114
62	16	17.06	-1.061
63	15	15.7	-0.6969
64	13	16.1	-3.105
65	16	15.66	0.3369
66	12	13.82	-1.819
67	16	15.21	0.7944
68	16	15.44	0.5551
69	16	16.37	-0.3727
70	14	15.68	-1.676
71	15	15.08	-0.07663
72	14	14.65	-0.6513
73	15	15.83	-0.8315
74	15	15.25	-0.2523
75	16	14.93	1.07
76	11	11.64	-0.64
77	18	15.51	2.491
78	11	14.3	-3.298
79	18	17.39	0.6121
80	15	17.64	-2.635
81	18	18.12	-0.1164
82	19	16.19	2.811
83	16	16.8	-0.796
84	15	14.25	0.746
85	17	15.91	1.089
86	18	17.22	0.7781
87	20	17.61	2.392
88	15	13.88	1.124
89	16	17.21	-1.206
90	11	16.05	-5.053
91	15	15.99	-0.9871
92	17	17.3	-0.2979
93	19	16.84	2.16
94	18	16.21	1.793
95	15	16.8	-1.804
96	17	15.79	1.212
97	16	13.81	2.193
98	16	16.79	-0.7931
99	14	16.2	-2.196

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.1252	0.2503	0.8748
13	0.04931	0.09862	0.9507
14	0.09975	0.1995	0.9003
15	0.04873	0.09745	0.9513
16	0.02306	0.04613	0.9769
17	0.06493	0.1299	0.9351
18	0.0611	0.1222	0.9389
19	0.043	0.086	0.957
20	0.02695	0.0539	0.973
21	0.01782	0.03563	0.9822
22	0.01301	0.02603	0.987
23	0.033	0.066	0.967
24	0.01979	0.03958	0.9802
25	0.01389	0.02778	0.9861
26	0.008034	0.01607	0.992
27	0.006083	0.01217	0.9939
28	0.003915	0.007831	0.9961
29	0.01928	0.03856	0.9807
30	0.01245	0.02489	0.9876
31	0.09931	0.1986	0.9007
32	0.07223	0.1445	0.9278
33	0.1169	0.2337	0.8831
34	0.1211	0.2422	0.8789
35	0.09545	0.1909	0.9046
36	0.07283	0.1457	0.9272
37	0.05563	0.1113	0.9444
38	0.03938	0.07876	0.9606
39	0.03746	0.07491	0.9625
40	0.03095	0.0619	0.969
41	0.02948	0.05896	0.9705
42	0.02765	0.0553	0.9723
43	0.02874	0.05749	0.9713
44	0.02999	0.05999	0.97
45	0.02326	0.04653	0.9767
46	0.01831	0.03663	0.9817
47	0.01227	0.02453	0.9877
48	0.009608	0.01922	0.9904
49	0.007195	0.01439	0.9928
50	0.01458	0.02917	0.9854
51	0.1111	0.2223	0.8889
52	0.09623	0.1925	0.9038
53	0.2097	0.4193	0.7903
54	0.168	0.336	0.832
55	0.1821	0.3643	0.8179
56	0.1566	0.3131	0.8434
57	0.1507	0.3015	0.8493
58	0.1181	0.2363	0.8819
59	0.09117	0.1823	0.9088
60	0.09019	0.1804	0.9098
61	0.08113	0.1623	0.9189
62	0.06587	0.1317	0.9341
63	0.04952	0.09903	0.9505
64	0.08723	0.1745	0.9128
65	0.06526	0.1305	0.9347
66	0.06737	0.1347	0.9326
67	0.05607	0.1121	0.9439
68	0.05176	0.1035	0.9482
69	0.03691	0.07381	0.9631
70	0.034	0.06801	0.966
71	0.02272	0.04545	0.9773
72	0.01517	0.03034	0.9848
73	0.0107	0.02139	0.9893
74	0.006897	0.01379	0.9931
75	0.005326	0.01065	0.9947
76	0.003113	0.006226	0.9969
77	0.004879	0.009758	0.9951
78	0.0118	0.02361	0.9882
79	0.006759	0.01352	0.9932
80	0.005542	0.01108	0.9945
81	0.003278	0.006555	0.9967
82	0.01244	0.02487	0.9876
83	0.007067	0.01413	0.9929
84	0.004818	0.009636	0.9952
85	0.003618	0.007237	0.9964
86	0.001572	0.003144	0.9984
87	0.2314	0.4627	0.7686

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 &  0.1252 &  0.2503 &  0.8748 \tabularnewline
13 &  0.04931 &  0.09862 &  0.9507 \tabularnewline
14 &  0.09975 &  0.1995 &  0.9003 \tabularnewline
15 &  0.04873 &  0.09745 &  0.9513 \tabularnewline
16 &  0.02306 &  0.04613 &  0.9769 \tabularnewline
17 &  0.06493 &  0.1299 &  0.9351 \tabularnewline
18 &  0.0611 &  0.1222 &  0.9389 \tabularnewline
19 &  0.043 &  0.086 &  0.957 \tabularnewline
20 &  0.02695 &  0.0539 &  0.973 \tabularnewline
21 &  0.01782 &  0.03563 &  0.9822 \tabularnewline
22 &  0.01301 &  0.02603 &  0.987 \tabularnewline
23 &  0.033 &  0.066 &  0.967 \tabularnewline
24 &  0.01979 &  0.03958 &  0.9802 \tabularnewline
25 &  0.01389 &  0.02778 &  0.9861 \tabularnewline
26 &  0.008034 &  0.01607 &  0.992 \tabularnewline
27 &  0.006083 &  0.01217 &  0.9939 \tabularnewline
28 &  0.003915 &  0.007831 &  0.9961 \tabularnewline
29 &  0.01928 &  0.03856 &  0.9807 \tabularnewline
30 &  0.01245 &  0.02489 &  0.9876 \tabularnewline
31 &  0.09931 &  0.1986 &  0.9007 \tabularnewline
32 &  0.07223 &  0.1445 &  0.9278 \tabularnewline
33 &  0.1169 &  0.2337 &  0.8831 \tabularnewline
34 &  0.1211 &  0.2422 &  0.8789 \tabularnewline
35 &  0.09545 &  0.1909 &  0.9046 \tabularnewline
36 &  0.07283 &  0.1457 &  0.9272 \tabularnewline
37 &  0.05563 &  0.1113 &  0.9444 \tabularnewline
38 &  0.03938 &  0.07876 &  0.9606 \tabularnewline
39 &  0.03746 &  0.07491 &  0.9625 \tabularnewline
40 &  0.03095 &  0.0619 &  0.969 \tabularnewline
41 &  0.02948 &  0.05896 &  0.9705 \tabularnewline
42 &  0.02765 &  0.0553 &  0.9723 \tabularnewline
43 &  0.02874 &  0.05749 &  0.9713 \tabularnewline
44 &  0.02999 &  0.05999 &  0.97 \tabularnewline
45 &  0.02326 &  0.04653 &  0.9767 \tabularnewline
46 &  0.01831 &  0.03663 &  0.9817 \tabularnewline
47 &  0.01227 &  0.02453 &  0.9877 \tabularnewline
48 &  0.009608 &  0.01922 &  0.9904 \tabularnewline
49 &  0.007195 &  0.01439 &  0.9928 \tabularnewline
50 &  0.01458 &  0.02917 &  0.9854 \tabularnewline
51 &  0.1111 &  0.2223 &  0.8889 \tabularnewline
52 &  0.09623 &  0.1925 &  0.9038 \tabularnewline
53 &  0.2097 &  0.4193 &  0.7903 \tabularnewline
54 &  0.168 &  0.336 &  0.832 \tabularnewline
55 &  0.1821 &  0.3643 &  0.8179 \tabularnewline
56 &  0.1566 &  0.3131 &  0.8434 \tabularnewline
57 &  0.1507 &  0.3015 &  0.8493 \tabularnewline
58 &  0.1181 &  0.2363 &  0.8819 \tabularnewline
59 &  0.09117 &  0.1823 &  0.9088 \tabularnewline
60 &  0.09019 &  0.1804 &  0.9098 \tabularnewline
61 &  0.08113 &  0.1623 &  0.9189 \tabularnewline
62 &  0.06587 &  0.1317 &  0.9341 \tabularnewline
63 &  0.04952 &  0.09903 &  0.9505 \tabularnewline
64 &  0.08723 &  0.1745 &  0.9128 \tabularnewline
65 &  0.06526 &  0.1305 &  0.9347 \tabularnewline
66 &  0.06737 &  0.1347 &  0.9326 \tabularnewline
67 &  0.05607 &  0.1121 &  0.9439 \tabularnewline
68 &  0.05176 &  0.1035 &  0.9482 \tabularnewline
69 &  0.03691 &  0.07381 &  0.9631 \tabularnewline
70 &  0.034 &  0.06801 &  0.966 \tabularnewline
71 &  0.02272 &  0.04545 &  0.9773 \tabularnewline
72 &  0.01517 &  0.03034 &  0.9848 \tabularnewline
73 &  0.0107 &  0.02139 &  0.9893 \tabularnewline
74 &  0.006897 &  0.01379 &  0.9931 \tabularnewline
75 &  0.005326 &  0.01065 &  0.9947 \tabularnewline
76 &  0.003113 &  0.006226 &  0.9969 \tabularnewline
77 &  0.004879 &  0.009758 &  0.9951 \tabularnewline
78 &  0.0118 &  0.02361 &  0.9882 \tabularnewline
79 &  0.006759 &  0.01352 &  0.9932 \tabularnewline
80 &  0.005542 &  0.01108 &  0.9945 \tabularnewline
81 &  0.003278 &  0.006555 &  0.9967 \tabularnewline
82 &  0.01244 &  0.02487 &  0.9876 \tabularnewline
83 &  0.007067 &  0.01413 &  0.9929 \tabularnewline
84 &  0.004818 &  0.009636 &  0.9952 \tabularnewline
85 &  0.003618 &  0.007237 &  0.9964 \tabularnewline
86 &  0.001572 &  0.003144 &  0.9984 \tabularnewline
87 &  0.2314 &  0.4627 &  0.7686 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C] 0.1252[/C][C] 0.2503[/C][C] 0.8748[/C][/ROW]
[ROW][C]13[/C][C] 0.04931[/C][C] 0.09862[/C][C] 0.9507[/C][/ROW]
[ROW][C]14[/C][C] 0.09975[/C][C] 0.1995[/C][C] 0.9003[/C][/ROW]
[ROW][C]15[/C][C] 0.04873[/C][C] 0.09745[/C][C] 0.9513[/C][/ROW]
[ROW][C]16[/C][C] 0.02306[/C][C] 0.04613[/C][C] 0.9769[/C][/ROW]
[ROW][C]17[/C][C] 0.06493[/C][C] 0.1299[/C][C] 0.9351[/C][/ROW]
[ROW][C]18[/C][C] 0.0611[/C][C] 0.1222[/C][C] 0.9389[/C][/ROW]
[ROW][C]19[/C][C] 0.043[/C][C] 0.086[/C][C] 0.957[/C][/ROW]
[ROW][C]20[/C][C] 0.02695[/C][C] 0.0539[/C][C] 0.973[/C][/ROW]
[ROW][C]21[/C][C] 0.01782[/C][C] 0.03563[/C][C] 0.9822[/C][/ROW]
[ROW][C]22[/C][C] 0.01301[/C][C] 0.02603[/C][C] 0.987[/C][/ROW]
[ROW][C]23[/C][C] 0.033[/C][C] 0.066[/C][C] 0.967[/C][/ROW]
[ROW][C]24[/C][C] 0.01979[/C][C] 0.03958[/C][C] 0.9802[/C][/ROW]
[ROW][C]25[/C][C] 0.01389[/C][C] 0.02778[/C][C] 0.9861[/C][/ROW]
[ROW][C]26[/C][C] 0.008034[/C][C] 0.01607[/C][C] 0.992[/C][/ROW]
[ROW][C]27[/C][C] 0.006083[/C][C] 0.01217[/C][C] 0.9939[/C][/ROW]
[ROW][C]28[/C][C] 0.003915[/C][C] 0.007831[/C][C] 0.9961[/C][/ROW]
[ROW][C]29[/C][C] 0.01928[/C][C] 0.03856[/C][C] 0.9807[/C][/ROW]
[ROW][C]30[/C][C] 0.01245[/C][C] 0.02489[/C][C] 0.9876[/C][/ROW]
[ROW][C]31[/C][C] 0.09931[/C][C] 0.1986[/C][C] 0.9007[/C][/ROW]
[ROW][C]32[/C][C] 0.07223[/C][C] 0.1445[/C][C] 0.9278[/C][/ROW]
[ROW][C]33[/C][C] 0.1169[/C][C] 0.2337[/C][C] 0.8831[/C][/ROW]
[ROW][C]34[/C][C] 0.1211[/C][C] 0.2422[/C][C] 0.8789[/C][/ROW]
[ROW][C]35[/C][C] 0.09545[/C][C] 0.1909[/C][C] 0.9046[/C][/ROW]
[ROW][C]36[/C][C] 0.07283[/C][C] 0.1457[/C][C] 0.9272[/C][/ROW]
[ROW][C]37[/C][C] 0.05563[/C][C] 0.1113[/C][C] 0.9444[/C][/ROW]
[ROW][C]38[/C][C] 0.03938[/C][C] 0.07876[/C][C] 0.9606[/C][/ROW]
[ROW][C]39[/C][C] 0.03746[/C][C] 0.07491[/C][C] 0.9625[/C][/ROW]
[ROW][C]40[/C][C] 0.03095[/C][C] 0.0619[/C][C] 0.969[/C][/ROW]
[ROW][C]41[/C][C] 0.02948[/C][C] 0.05896[/C][C] 0.9705[/C][/ROW]
[ROW][C]42[/C][C] 0.02765[/C][C] 0.0553[/C][C] 0.9723[/C][/ROW]
[ROW][C]43[/C][C] 0.02874[/C][C] 0.05749[/C][C] 0.9713[/C][/ROW]
[ROW][C]44[/C][C] 0.02999[/C][C] 0.05999[/C][C] 0.97[/C][/ROW]
[ROW][C]45[/C][C] 0.02326[/C][C] 0.04653[/C][C] 0.9767[/C][/ROW]
[ROW][C]46[/C][C] 0.01831[/C][C] 0.03663[/C][C] 0.9817[/C][/ROW]
[ROW][C]47[/C][C] 0.01227[/C][C] 0.02453[/C][C] 0.9877[/C][/ROW]
[ROW][C]48[/C][C] 0.009608[/C][C] 0.01922[/C][C] 0.9904[/C][/ROW]
[ROW][C]49[/C][C] 0.007195[/C][C] 0.01439[/C][C] 0.9928[/C][/ROW]
[ROW][C]50[/C][C] 0.01458[/C][C] 0.02917[/C][C] 0.9854[/C][/ROW]
[ROW][C]51[/C][C] 0.1111[/C][C] 0.2223[/C][C] 0.8889[/C][/ROW]
[ROW][C]52[/C][C] 0.09623[/C][C] 0.1925[/C][C] 0.9038[/C][/ROW]
[ROW][C]53[/C][C] 0.2097[/C][C] 0.4193[/C][C] 0.7903[/C][/ROW]
[ROW][C]54[/C][C] 0.168[/C][C] 0.336[/C][C] 0.832[/C][/ROW]
[ROW][C]55[/C][C] 0.1821[/C][C] 0.3643[/C][C] 0.8179[/C][/ROW]
[ROW][C]56[/C][C] 0.1566[/C][C] 0.3131[/C][C] 0.8434[/C][/ROW]
[ROW][C]57[/C][C] 0.1507[/C][C] 0.3015[/C][C] 0.8493[/C][/ROW]
[ROW][C]58[/C][C] 0.1181[/C][C] 0.2363[/C][C] 0.8819[/C][/ROW]
[ROW][C]59[/C][C] 0.09117[/C][C] 0.1823[/C][C] 0.9088[/C][/ROW]
[ROW][C]60[/C][C] 0.09019[/C][C] 0.1804[/C][C] 0.9098[/C][/ROW]
[ROW][C]61[/C][C] 0.08113[/C][C] 0.1623[/C][C] 0.9189[/C][/ROW]
[ROW][C]62[/C][C] 0.06587[/C][C] 0.1317[/C][C] 0.9341[/C][/ROW]
[ROW][C]63[/C][C] 0.04952[/C][C] 0.09903[/C][C] 0.9505[/C][/ROW]
[ROW][C]64[/C][C] 0.08723[/C][C] 0.1745[/C][C] 0.9128[/C][/ROW]
[ROW][C]65[/C][C] 0.06526[/C][C] 0.1305[/C][C] 0.9347[/C][/ROW]
[ROW][C]66[/C][C] 0.06737[/C][C] 0.1347[/C][C] 0.9326[/C][/ROW]
[ROW][C]67[/C][C] 0.05607[/C][C] 0.1121[/C][C] 0.9439[/C][/ROW]
[ROW][C]68[/C][C] 0.05176[/C][C] 0.1035[/C][C] 0.9482[/C][/ROW]
[ROW][C]69[/C][C] 0.03691[/C][C] 0.07381[/C][C] 0.9631[/C][/ROW]
[ROW][C]70[/C][C] 0.034[/C][C] 0.06801[/C][C] 0.966[/C][/ROW]
[ROW][C]71[/C][C] 0.02272[/C][C] 0.04545[/C][C] 0.9773[/C][/ROW]
[ROW][C]72[/C][C] 0.01517[/C][C] 0.03034[/C][C] 0.9848[/C][/ROW]
[ROW][C]73[/C][C] 0.0107[/C][C] 0.02139[/C][C] 0.9893[/C][/ROW]
[ROW][C]74[/C][C] 0.006897[/C][C] 0.01379[/C][C] 0.9931[/C][/ROW]
[ROW][C]75[/C][C] 0.005326[/C][C] 0.01065[/C][C] 0.9947[/C][/ROW]
[ROW][C]76[/C][C] 0.003113[/C][C] 0.006226[/C][C] 0.9969[/C][/ROW]
[ROW][C]77[/C][C] 0.004879[/C][C] 0.009758[/C][C] 0.9951[/C][/ROW]
[ROW][C]78[/C][C] 0.0118[/C][C] 0.02361[/C][C] 0.9882[/C][/ROW]
[ROW][C]79[/C][C] 0.006759[/C][C] 0.01352[/C][C] 0.9932[/C][/ROW]
[ROW][C]80[/C][C] 0.005542[/C][C] 0.01108[/C][C] 0.9945[/C][/ROW]
[ROW][C]81[/C][C] 0.003278[/C][C] 0.006555[/C][C] 0.9967[/C][/ROW]
[ROW][C]82[/C][C] 0.01244[/C][C] 0.02487[/C][C] 0.9876[/C][/ROW]
[ROW][C]83[/C][C] 0.007067[/C][C] 0.01413[/C][C] 0.9929[/C][/ROW]
[ROW][C]84[/C][C] 0.004818[/C][C] 0.009636[/C][C] 0.9952[/C][/ROW]
[ROW][C]85[/C][C] 0.003618[/C][C] 0.007237[/C][C] 0.9964[/C][/ROW]
[ROW][C]86[/C][C] 0.001572[/C][C] 0.003144[/C][C] 0.9984[/C][/ROW]
[ROW][C]87[/C][C] 0.2314[/C][C] 0.4627[/C][C] 0.7686[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
12	0.1252	0.2503	0.8748
13	0.04931	0.09862	0.9507
14	0.09975	0.1995	0.9003
15	0.04873	0.09745	0.9513
16	0.02306	0.04613	0.9769
17	0.06493	0.1299	0.9351
18	0.0611	0.1222	0.9389
19	0.043	0.086	0.957
20	0.02695	0.0539	0.973
21	0.01782	0.03563	0.9822
22	0.01301	0.02603	0.987
23	0.033	0.066	0.967
24	0.01979	0.03958	0.9802
25	0.01389	0.02778	0.9861
26	0.008034	0.01607	0.992
27	0.006083	0.01217	0.9939
28	0.003915	0.007831	0.9961
29	0.01928	0.03856	0.9807
30	0.01245	0.02489	0.9876
31	0.09931	0.1986	0.9007
32	0.07223	0.1445	0.9278
33	0.1169	0.2337	0.8831
34	0.1211	0.2422	0.8789
35	0.09545	0.1909	0.9046
36	0.07283	0.1457	0.9272
37	0.05563	0.1113	0.9444
38	0.03938	0.07876	0.9606
39	0.03746	0.07491	0.9625
40	0.03095	0.0619	0.969
41	0.02948	0.05896	0.9705
42	0.02765	0.0553	0.9723
43	0.02874	0.05749	0.9713
44	0.02999	0.05999	0.97
45	0.02326	0.04653	0.9767
46	0.01831	0.03663	0.9817
47	0.01227	0.02453	0.9877
48	0.009608	0.01922	0.9904
49	0.007195	0.01439	0.9928
50	0.01458	0.02917	0.9854
51	0.1111	0.2223	0.8889
52	0.09623	0.1925	0.9038
53	0.2097	0.4193	0.7903
54	0.168	0.336	0.832
55	0.1821	0.3643	0.8179
56	0.1566	0.3131	0.8434
57	0.1507	0.3015	0.8493
58	0.1181	0.2363	0.8819
59	0.09117	0.1823	0.9088
60	0.09019	0.1804	0.9098
61	0.08113	0.1623	0.9189
62	0.06587	0.1317	0.9341
63	0.04952	0.09903	0.9505
64	0.08723	0.1745	0.9128
65	0.06526	0.1305	0.9347
66	0.06737	0.1347	0.9326
67	0.05607	0.1121	0.9439
68	0.05176	0.1035	0.9482
69	0.03691	0.07381	0.9631
70	0.034	0.06801	0.966
71	0.02272	0.04545	0.9773
72	0.01517	0.03034	0.9848
73	0.0107	0.02139	0.9893
74	0.006897	0.01379	0.9931
75	0.005326	0.01065	0.9947
76	0.003113	0.006226	0.9969
77	0.004879	0.009758	0.9951
78	0.0118	0.02361	0.9882
79	0.006759	0.01352	0.9932
80	0.005542	0.01108	0.9945
81	0.003278	0.006555	0.9967
82	0.01244	0.02487	0.9876
83	0.007067	0.01413	0.9929
84	0.004818	0.009636	0.9952
85	0.003618	0.007237	0.9964
86	0.001572	0.003144	0.9984
87	0.2314	0.4627	0.7686

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.09211	NOK
5% type I error level	32	0.421053	NOK
10% type I error level	47	0.618421	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 & 7 &  0.09211 & NOK \tabularnewline
5% type I error level & 32 & 0.421053 & NOK \tabularnewline
10% type I error level & 47 & 0.618421 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]7[/C][C] 0.09211[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]32[/C][C]0.421053[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]47[/C][C]0.618421[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	7	0.09211	NOK
5% type I error level	32	0.421053	NOK
10% type I error level	47	0.618421	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.37423, df1 = 2, df2 = 88, p-value = 0.6889
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.62918, df1 = 16, df2 = 74, p-value = 0.8502
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.059958, df1 = 2, df2 = 88, p-value = 0.9418

\begin{tabular}{lllllllll}
\hline
Ramsey RESET F-Test for powers (2 and 3) of fitted values \tabularnewline
> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.37423, df1 = 2, df2 = 88, p-value = 0.6889
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.62918, df1 = 16, df2 = 74, p-value = 0.8502
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.059958, df1 = 2, df2 = 88, p-value = 0.9418
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=7

[TABLE]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of fitted values[/C][/ROW]
[ROW][C]> reset_test_fitted
	RESET test
data:  mylm
RESET = 0.37423, df1 = 2, df2 = 88, p-value = 0.6889
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 0.62918, df1 = 16, df2 = 74, p-value = 0.8502
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of principal components[/C][/ROW]
[ROW][C]> reset_test_principal_components
	RESET test
data:  mylm
RESET = 0.059958, df1 = 2, df2 = 88, p-value = 0.9418
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&T=7

As an alternative you can also use a QR Code:

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

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 0.37423, df1 = 2, df2 = 88, p-value = 0.6889
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 0.62918, df1 = 16, df2 = 74, p-value = 0.8502
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 0.059958, df1 = 2, df2 = 88, p-value = 0.9418

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOUSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 3.318447 16.306299 1.314824 1.236183 1.433578 1.403048 1.262393 14.594267

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
   ITHSUM  SKEOUSUM    SKEOU1    SKEOU2    SKEOU3    SKEOU4    SKEOU5    SKEOU6 
 3.318447 16.306299  1.314824  1.236183  1.433578  1.403048  1.262393 14.594267 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=306484&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
   ITHSUM  SKEOUSUM    SKEOU1    SKEOU2    SKEOU3    SKEOU4    SKEOU5    SKEOU6 
 3.318447 16.306299  1.314824  1.236183  1.433578  1.403048  1.262393 14.594267 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=306484&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=306484&T=8

As an alternative you can also use a QR Code:

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

Variance Inflation Factors (Multicollinearity)
> vif ITHSUM SKEOUSUM SKEOU1 SKEOU2 SKEOU3 SKEOU4 SKEOU5 SKEOU6 3.318447 16.306299 1.314824 1.236183 1.433578 1.403048 1.262393 14.594267

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 = 12 ;

Parameters (R input):

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

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

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

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code