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R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Sun, 04 Dec 2016 20:00:13 +0100

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Dec/04/t1480878052mheydrtdk0tjj8e.htm/, Retrieved Fri, 17 May 2024 19:57:19 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=297694, Retrieved Fri, 17 May 2024 19:57:19 +0000

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No (this computation is public)

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Estimated Impact

115

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

-       [Multiple Regression] [voorbeeldje] [2016-12-04 19:00:13] [f0fcaf0884a2ab8e55345d70fdb8db2d] [Current]

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Dataseries X:

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Histogram

Boxplots

6063	5200	3410
7355	4500	3160
7234	6100	3010
10281	5200	3025
2841	6500	3015
2924	6750	3390
2015	5200	3500
2079	7750	3205
4320	8550	3470
5366	7800	4135
3315	7500	4715
2053	6450	4775
2575	7300	5105
2416	5850	5295
3392	6450	4980
2130	7300	3870
2805	6600	4235
2669	6250	5020
3651	8950	5175
2698	7850	4830
3355	5700	4925
6396	6500	4635
5254	7600	4395
4595	6350	4370
2732	8100	4805
2971	7000	4780

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	7 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 time7 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&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]

7 seconds[/C][/ROW] [ROW]

R Server[/C]

Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=297694&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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	7 seconds
R Server	Big Analytics Cloud Computing Center

Multiple Linear Regression - Estimated Regression Equation
1[t] = + 11294.1 -0.488487`2`[t] -0.956965`3`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
1[t] =  +  11294.1 -0.488487`2`[t] -0.956965`3`[t]  + e[t] \tabularnewline
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]1[t] =  +  11294.1 -0.488487`2`[t] -0.956965`3`[t]  + e[t][/C][/ROW]
[ROW][C][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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
1[t] = + 11294.1 -0.488487`2`[t] -0.956965`3`[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)	+1.129e+04	2624	+4.3040e+00	0.0002641	0.0001321
`2`	-0.4885	0.3526	-1.3850e+00	0.1792	0.08961
`3`	-0.957	0.496	-1.9290e+00	0.06612	0.03306

\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) & +1.129e+04 &  2624 & +4.3040e+00 &  0.0002641 &  0.0001321 \tabularnewline
`2` & -0.4885 &  0.3526 & -1.3850e+00 &  0.1792 &  0.08961 \tabularnewline
`3` & -0.957 &  0.496 & -1.9290e+00 &  0.06612 &  0.03306 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&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]+1.129e+04[/C][C] 2624[/C][C]+4.3040e+00[/C][C] 0.0002641[/C][C] 0.0001321[/C][/ROW]
[ROW][C]`2`[/C][C]-0.4885[/C][C] 0.3526[/C][C]-1.3850e+00[/C][C] 0.1792[/C][C] 0.08961[/C][/ROW]
[ROW][C]`3`[/C][C]-0.957[/C][C] 0.496[/C][C]-1.9290e+00[/C][C] 0.06612[/C][C] 0.03306[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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)	+1.129e+04	2624	+4.3040e+00	0.0002641	0.0001321
`2`	-0.4885	0.3526	-1.3850e+00	0.1792	0.08961
`3`	-0.957	0.496	-1.9290e+00	0.06612	0.03306

Multiple Linear Regression - Regression Statistics
Multiple R	0.5143
R-squared	0.2645
Adjusted R-squared	0.2006
F-TEST (value)	4.136
F-TEST (DF numerator)	2
F-TEST (DF denominator)	23
p-value	0.02922
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1841
Sum Squared Residuals	7.792e+07

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.5143 \tabularnewline
R-squared &  0.2645 \tabularnewline
Adjusted R-squared &  0.2006 \tabularnewline
F-TEST (value) &  4.136 \tabularnewline
F-TEST (DF numerator) & 2 \tabularnewline
F-TEST (DF denominator) & 23 \tabularnewline
p-value &  0.02922 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  1841 \tabularnewline
Sum Squared Residuals &  7.792e+07 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.5143[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.2645[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.2006[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 4.136[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]2[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]23[/C][/ROW]
[ROW][C]p-value[/C][C] 0.02922[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C] 1841[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 7.792e+07[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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	0.5143
R-squared	0.2645
Adjusted R-squared	0.2006
F-TEST (value)	4.136
F-TEST (DF numerator)	2
F-TEST (DF denominator)	23
p-value	0.02922
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	1841
Sum Squared Residuals	7.792e+07

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	6063	5491	572.3
2	7355	6072	1283
3	7234	5434	1800
4	1.028e+04	5859	4422
5	2841	5234	-2393
6	2924	4753	-1829
7	2015	5405	-3390
8	2079	4441	-2362
9	4320	3797	523.2
10	5366	3527	1839
11	3315	3118	196.7
12	2053	3574	-1521
13	2575	2843	-267.8
14	2416	3369	-953.3
15	3392	3378	14.35
16	2130	4025	-1895
17	2805	4017	-1212
18	2669	3437	-768.1
19	3651	1970	1681
20	2698	2837	-139.3
21	3355	3797	-441.7
22	6396	3683	2713
23	5254	3376	1878
24	4595	4010	584.8
25	2732	2739	-7.118
26	2971	3300	-329.4

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 &  6063 &  5491 &  572.3 \tabularnewline
2 &  7355 &  6072 &  1283 \tabularnewline
3 &  7234 &  5434 &  1800 \tabularnewline
4 &  1.028e+04 &  5859 &  4422 \tabularnewline
5 &  2841 &  5234 & -2393 \tabularnewline
6 &  2924 &  4753 & -1829 \tabularnewline
7 &  2015 &  5405 & -3390 \tabularnewline
8 &  2079 &  4441 & -2362 \tabularnewline
9 &  4320 &  3797 &  523.2 \tabularnewline
10 &  5366 &  3527 &  1839 \tabularnewline
11 &  3315 &  3118 &  196.7 \tabularnewline
12 &  2053 &  3574 & -1521 \tabularnewline
13 &  2575 &  2843 & -267.8 \tabularnewline
14 &  2416 &  3369 & -953.3 \tabularnewline
15 &  3392 &  3378 &  14.35 \tabularnewline
16 &  2130 &  4025 & -1895 \tabularnewline
17 &  2805 &  4017 & -1212 \tabularnewline
18 &  2669 &  3437 & -768.1 \tabularnewline
19 &  3651 &  1970 &  1681 \tabularnewline
20 &  2698 &  2837 & -139.3 \tabularnewline
21 &  3355 &  3797 & -441.7 \tabularnewline
22 &  6396 &  3683 &  2713 \tabularnewline
23 &  5254 &  3376 &  1878 \tabularnewline
24 &  4595 &  4010 &  584.8 \tabularnewline
25 &  2732 &  2739 & -7.118 \tabularnewline
26 &  2971 &  3300 & -329.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&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] 6063[/C][C] 5491[/C][C] 572.3[/C][/ROW]
[ROW][C]2[/C][C] 7355[/C][C] 6072[/C][C] 1283[/C][/ROW]
[ROW][C]3[/C][C] 7234[/C][C] 5434[/C][C] 1800[/C][/ROW]
[ROW][C]4[/C][C] 1.028e+04[/C][C] 5859[/C][C] 4422[/C][/ROW]
[ROW][C]5[/C][C] 2841[/C][C] 5234[/C][C]-2393[/C][/ROW]
[ROW][C]6[/C][C] 2924[/C][C] 4753[/C][C]-1829[/C][/ROW]
[ROW][C]7[/C][C] 2015[/C][C] 5405[/C][C]-3390[/C][/ROW]
[ROW][C]8[/C][C] 2079[/C][C] 4441[/C][C]-2362[/C][/ROW]
[ROW][C]9[/C][C] 4320[/C][C] 3797[/C][C] 523.2[/C][/ROW]
[ROW][C]10[/C][C] 5366[/C][C] 3527[/C][C] 1839[/C][/ROW]
[ROW][C]11[/C][C] 3315[/C][C] 3118[/C][C] 196.7[/C][/ROW]
[ROW][C]12[/C][C] 2053[/C][C] 3574[/C][C]-1521[/C][/ROW]
[ROW][C]13[/C][C] 2575[/C][C] 2843[/C][C]-267.8[/C][/ROW]
[ROW][C]14[/C][C] 2416[/C][C] 3369[/C][C]-953.3[/C][/ROW]
[ROW][C]15[/C][C] 3392[/C][C] 3378[/C][C] 14.35[/C][/ROW]
[ROW][C]16[/C][C] 2130[/C][C] 4025[/C][C]-1895[/C][/ROW]
[ROW][C]17[/C][C] 2805[/C][C] 4017[/C][C]-1212[/C][/ROW]
[ROW][C]18[/C][C] 2669[/C][C] 3437[/C][C]-768.1[/C][/ROW]
[ROW][C]19[/C][C] 3651[/C][C] 1970[/C][C] 1681[/C][/ROW]
[ROW][C]20[/C][C] 2698[/C][C] 2837[/C][C]-139.3[/C][/ROW]
[ROW][C]21[/C][C] 3355[/C][C] 3797[/C][C]-441.7[/C][/ROW]
[ROW][C]22[/C][C] 6396[/C][C] 3683[/C][C] 2713[/C][/ROW]
[ROW][C]23[/C][C] 5254[/C][C] 3376[/C][C] 1878[/C][/ROW]
[ROW][C]24[/C][C] 4595[/C][C] 4010[/C][C] 584.8[/C][/ROW]
[ROW][C]25[/C][C] 2732[/C][C] 2739[/C][C]-7.118[/C][/ROW]
[ROW][C]26[/C][C] 2971[/C][C] 3300[/C][C]-329.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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	6063	5491	572.3
2	7355	6072	1283
3	7234	5434	1800
4	1.028e+04	5859	4422
5	2841	5234	-2393
6	2924	4753	-1829
7	2015	5405	-3390
8	2079	4441	-2362
9	4320	3797	523.2
10	5366	3527	1839
11	3315	3118	196.7
12	2053	3574	-1521
13	2575	2843	-267.8
14	2416	3369	-953.3
15	3392	3378	14.35
16	2130	4025	-1895
17	2805	4017	-1212
18	2669	3437	-768.1
19	3651	1970	1681
20	2698	2837	-139.3
21	3355	3797	-441.7
22	6396	3683	2713
23	5254	3376	1878
24	4595	4010	584.8
25	2732	2739	-7.118
26	2971	3300	-329.4

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
6	0.932	0.136	0.068
7	0.9519	0.09622	0.04811
8	0.9419	0.1161	0.05805
9	0.985	0.0301	0.01505
10	0.9928	0.0143	0.007151
11	0.9828	0.03447	0.01724
12	0.9759	0.04828	0.02414
13	0.9532	0.09355	0.04677
14	0.9252	0.1496	0.07482
15	0.8659	0.2682	0.1341
16	0.8747	0.2505	0.1253
17	0.8995	0.201	0.1005
18	0.8186	0.3628	0.1814
19	0.8467	0.3065	0.1533
20	0.7022	0.5955	0.2978

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
6 &  0.932 &  0.136 &  0.068 \tabularnewline
7 &  0.9519 &  0.09622 &  0.04811 \tabularnewline
8 &  0.9419 &  0.1161 &  0.05805 \tabularnewline
9 &  0.985 &  0.0301 &  0.01505 \tabularnewline
10 &  0.9928 &  0.0143 &  0.007151 \tabularnewline
11 &  0.9828 &  0.03447 &  0.01724 \tabularnewline
12 &  0.9759 &  0.04828 &  0.02414 \tabularnewline
13 &  0.9532 &  0.09355 &  0.04677 \tabularnewline
14 &  0.9252 &  0.1496 &  0.07482 \tabularnewline
15 &  0.8659 &  0.2682 &  0.1341 \tabularnewline
16 &  0.8747 &  0.2505 &  0.1253 \tabularnewline
17 &  0.8995 &  0.201 &  0.1005 \tabularnewline
18 &  0.8186 &  0.3628 &  0.1814 \tabularnewline
19 &  0.8467 &  0.3065 &  0.1533 \tabularnewline
20 &  0.7022 &  0.5955 &  0.2978 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&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]6[/C][C] 0.932[/C][C] 0.136[/C][C] 0.068[/C][/ROW]
[ROW][C]7[/C][C] 0.9519[/C][C] 0.09622[/C][C] 0.04811[/C][/ROW]
[ROW][C]8[/C][C] 0.9419[/C][C] 0.1161[/C][C] 0.05805[/C][/ROW]
[ROW][C]9[/C][C] 0.985[/C][C] 0.0301[/C][C] 0.01505[/C][/ROW]
[ROW][C]10[/C][C] 0.9928[/C][C] 0.0143[/C][C] 0.007151[/C][/ROW]
[ROW][C]11[/C][C] 0.9828[/C][C] 0.03447[/C][C] 0.01724[/C][/ROW]
[ROW][C]12[/C][C] 0.9759[/C][C] 0.04828[/C][C] 0.02414[/C][/ROW]
[ROW][C]13[/C][C] 0.9532[/C][C] 0.09355[/C][C] 0.04677[/C][/ROW]
[ROW][C]14[/C][C] 0.9252[/C][C] 0.1496[/C][C] 0.07482[/C][/ROW]
[ROW][C]15[/C][C] 0.8659[/C][C] 0.2682[/C][C] 0.1341[/C][/ROW]
[ROW][C]16[/C][C] 0.8747[/C][C] 0.2505[/C][C] 0.1253[/C][/ROW]
[ROW][C]17[/C][C] 0.8995[/C][C] 0.201[/C][C] 0.1005[/C][/ROW]
[ROW][C]18[/C][C] 0.8186[/C][C] 0.3628[/C][C] 0.1814[/C][/ROW]
[ROW][C]19[/C][C] 0.8467[/C][C] 0.3065[/C][C] 0.1533[/C][/ROW]
[ROW][C]20[/C][C] 0.7022[/C][C] 0.5955[/C][C] 0.2978[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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
6	0.932	0.136	0.068
7	0.9519	0.09622	0.04811
8	0.9419	0.1161	0.05805
9	0.985	0.0301	0.01505
10	0.9928	0.0143	0.007151
11	0.9828	0.03447	0.01724
12	0.9759	0.04828	0.02414
13	0.9532	0.09355	0.04677
14	0.9252	0.1496	0.07482
15	0.8659	0.2682	0.1341
16	0.8747	0.2505	0.1253
17	0.8995	0.201	0.1005
18	0.8186	0.3628	0.1814
19	0.8467	0.3065	0.1533
20	0.7022	0.5955	0.2978

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	4	0.266667	NOK
10% type I error level	6	0.4	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 & 0 &  0 & OK \tabularnewline
5% type I error level & 4 & 0.266667 & NOK \tabularnewline
10% type I error level & 6 & 0.4 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C] 0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]4[/C][C]0.266667[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]6[/C][C]0.4[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	4	0.266667	NOK
10% type I error level	6	0.4	NOK

Ramsey RESET F-Test for powers (2 and 3) of fitted values
> reset_test_fitted RESET test data: mylm RESET = 4.178, df1 = 2, df2 = 21, p-value = 0.02968
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4086, df1 = 4, df2 = 19, p-value = 0.08523
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9011, df1 = 2, df2 = 21, p-value = 0.1742

\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 = 4.178, df1 = 2, df2 = 21, p-value = 0.02968
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of regressors \tabularnewline
> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.4086, df1 = 4, df2 = 19, p-value = 0.08523
 \tabularnewline
Ramsey RESET F-Test for powers (2 and 3) of principal components \tabularnewline
> reset_test_principal_components
	RESET test
data:  mylm
RESET = 1.9011, df1 = 2, df2 = 21, p-value = 0.1742
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&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 = 4.178, df1 = 2, df2 = 21, p-value = 0.02968
[/C][/ROW]
[ROW][C]Ramsey RESET F-Test for powers (2 and 3) of regressors[/C][/ROW]
[ROW][C]> reset_test_regressors
	RESET test
data:  mylm
RESET = 2.4086, df1 = 4, df2 = 19, p-value = 0.08523
[/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 = 1.9011, df1 = 2, df2 = 21, p-value = 0.1742
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=7

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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 = 4.178, df1 = 2, df2 = 21, p-value = 0.02968
Ramsey RESET F-Test for powers (2 and 3) of regressors
> reset_test_regressors RESET test data: mylm RESET = 2.4086, df1 = 4, df2 = 19, p-value = 0.08523
Ramsey RESET F-Test for powers (2 and 3) of principal components
> reset_test_principal_components RESET test data: mylm RESET = 1.9011, df1 = 2, df2 = 21, p-value = 0.1742

Variance Inflation Factors (Multicollinearity)
> vif `2` `3` 1.119526 1.119526

\begin{tabular}{lllllllll}
\hline
Variance Inflation Factors (Multicollinearity) \tabularnewline
> vif
     `2`      `3` 
1.119526 1.119526 
 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=297694&T=8

[TABLE]
[ROW][C]Variance Inflation Factors (Multicollinearity)[/C][/ROW]
[ROW][C]> vif
     `2`      `3` 
1.119526 1.119526 
[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=297694&T=8

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=297694&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 `2` `3` 1.119526 1.119526

Figure 1

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Postscript link

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Figure 2

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Figure 3

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Figure 4

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Figure 5

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Figure 6

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Figure 7

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Figure 8

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Figure 9

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Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ;

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 <- ''
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