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Author

*Unverified author*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Fri, 21 Dec 2012 03:01:57 -0500

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/2012/Dec/21/t1356076946l4rx47qlr60oymw.htm/, Retrieved Thu, 18 Apr 2024 15:31:48 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=203267, Retrieved Thu, 18 Apr 2024 15:31:48 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

150

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

-     [Bivariate Data Series] [Bivariate dataset] [2008-01-05 23:51:08] [74be16979710d4c4e7c6647856088456]
F RMPD  [Univariate Explorative Data Analysis] [Colombia Coffee] [2008-01-07 14:21:11] [74be16979710d4c4e7c6647856088456]
- RMPD    [Univariate Explorative Data Analysis] [Workshop 6, Tutor...] [2010-11-07 12:24:29] [8ffb4cfa64b4677df0d2c448735a40bb]
- R P       [Univariate Explorative Data Analysis] [WS6 2. Technique 2] [2010-11-11 18:06:41] [afe9379cca749d06b3d6872e02cc47ed]
- RMPD        [Multiple Regression] [Apple Inc - Multi...] [2010-12-11 10:33:09] [afe9379cca749d06b3d6872e02cc47ed]
-    D          [Multiple Regression] [WS10 Multiple Reg...] [2010-12-13 13:48:19] [afe9379cca749d06b3d6872e02cc47ed]
- R PD            [Multiple Regression] [] [2012-12-20 16:07:49] [d1865ed705b6ad9ba3d459a02c528b22]
-    D              [Multiple Regression] [] [2012-12-20 16:20:46] [d1865ed705b6ad9ba3d459a02c528b22]
-   PD                  [Multiple Regression] [] [2012-12-21 08:01:57] [d41d8cd98f00b204e9800998ecf8427e] [Current]
- R PD                    [Multiple Regression] [] [2012-12-21 08:23:32] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R  D                      [Multiple Regression] [] [2012-12-21 08:31:34] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:35:48] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:42:03] [d1865ed705b6ad9ba3d459a02c528b22] 
- R PD                      [Multiple Regression] [] [2012-12-21 08:45:23] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:21:24] [d1865ed705b6ad9ba3d459a02c528b22] 
- RMPD                      [Testing Mean with unknown Variance - Critical Value] [] [2012-12-21 09:34:12] [d1865ed705b6ad9ba3d459a02c528b22] 

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Dataseries X:

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Histogram

Boxplots

27.72	91.51	2747.48	0.016	62.7	0.16
26.90	91.09	2760.01	0.016	62.7	0.17
25.86	93.00	2778.11	0.016	62.7	0.17
26.81	93.08	2844.72	0.016	62.7	0.16
26.31	94.13	2831.02	0.016	62.7	0.16
27.10	96.26	2858.42	0.016	62.7	0.17
27.00	94.29	2809.73	0.016	62.7	0.17
27.40	94.46	2843.07	0.016	62.7	0.16
27.27	95.53	2818.61	0.016	62.7	0.17
28.29	98.29	2836.33	0.016	62.7	0.17
30.01	102.01	2872.80	0.016	62.7	0.18
31.41	105.16	2895.33	0.016	62.7	0.17
31.91	105.34	2929.76	0.016	62.7	0.17
31.60	105.27	2930.45	0.016	62.7	0.16
31.84	102.19	2859.09	0.016	62.7	0.17
33.05	106.85	2892.42	0.016	62.7	0.17
32.06	103.05	2836.16	0.016	62.7	0.17
33.10	106.42	2854.06	0.016	62.7	0.16
32.23	105.17	2875.32	0.016	62.7	0.15
31.36	102.74	2849.49	0.016	62.7	0.15
31.09	106.27	2935.05	0.016	62.7	0.09
30.77	107.63	2951.23	0.0141	65.4	0.18
31.20	108.54	2976.08	0.0141	65.4	0.17
31.47	108.24	2976.12	0.0141	65.4	0.17
31.73	108.86	2937.33	0.0141	65.4	0.17
32.17	102.98	2931.77	0.0141	65.4	0.17
31.47	99.53	2902.33	0.0141	65.4	0.17
30.97	101.08	2887.98	0.0141	65.4	0.17
30.81	104.64	2866.19	0.0141	65.4	0.18
30.72	105.59	2908.47	0.0141	65.4	0.19
28.24	103.21	2896.94	0.0141	65.4	0.18
28.09	103.84	2910.04	0.0141	65.4	0.17
29.11	104.61	2942.60	0.0141	65.4	0.16
29.00	108.65	2965.90	0.0141	65.4	0.13
28.76	106.26	2925.30	0.0141	65.4	0.13
28.75	104.20	2890.15	0.0141	65.4	0.14
28.45	102.99	2862.99	0.0141	65.4	0.15
29.34	102.19	2854.24	0.0141	65.4	0.15
26.84	100.82	2893.25	0.0141	65.4	0.14
23.70	103.42	2958.09	0.0141	65.4	0.14
23.15	104.18	2945.84	0.0141	65.4	0.14
21.71	102.65	2939.52	0.0141	65.4	0.13
20.88	95.64	2920.21	0.0169	61.3	0.14
20.04	93.51	2909.77	0.0169	61.3	0.14
21.09	108.51	2967.90	0.0169	61.3	0.14
21.92	111.55	2989.91	0.0169	61.3	0.14
20.72	106.70	3015.86	0.0169	61.3	0.13
20.72	104.93	3011.25	0.0169	61.3	0.13
21.01	105.23	3018.64	0.0169	61.3	0.13
21.80	104.92	3020.86	0.0169	61.3	0.13
21.60	104.60	3022.52	0.0169	61.3	0.13
20.38	101.76	3016.98	0.0169	61.3	0.13
21.20	102.23	3030.93	0.0169	61.3	0.13
19.87	103.99	3062.39	0.0169	61.3	0.13
19.05	101.36	3076.59	0.0169	61.3	0.13
20.01	102.92	3076.21	0.0169	61.3	0.13
19.15	105.25	3067.26	0.0169	61.3	0.13
19.43	105.71	3073.67	0.0169	61.3	0.13
19.44	105.42	3053.40	0.0169	61.3	0.13
19.40	105.11	3069.79	0.0169	61.3	0.13
19.15	104.67	3073.19	0.0169	61.3	0.13
19.34	107.51	3077.14	0.0169	61.3	0.13
19.10	109.00	3081.19	0.0169	61.3	0.13
19.08	107.37	3048.71	0.0169	61.3	0.14
18.05	107.30	3066.96	0.0169	61.3	0.13
17.72	107.37	3075.06	0.0199	70.3	0.14
18.58	113.28	3069.27	0.0199	70.3	0.16
18.96	119.10	3135.81	0.0199	70.3	0.16
18.98	119.04	3136.42	0.0199	70.3	0.15
18.81	117.80	3104.02	0.0199	70.3	0.15
19.43	117.90	3104.53	0.0199	70.3	0.15
20.93	119.55	3114.31	0.0199	70.3	0.15
20.71	119.47	3155.83	0.0199	70.3	0.15
22.00	123.23	3183.95	0.0199	70.3	0.16
21.52	121.40	3178.67	0.0199	70.3	0.16
21.87	121.43	3177.80	0.0199	70.3	0.16
23.29	122.51	3182.62	0.0199	70.3	0.15
22.59	122.78	3175.96	0.0199	70.3	0.16
22.86	122.84	3179.96	0.0199	70.3	0.15
20.79	122.70	3160.78	0.0199	70.3	0.16
20.28	119.89	3117.73	0.0199	70.3	0.15
20.62	118.00	3093.70	0.0199	70.3	0.16
20.32	119.61	3136.60	0.0199	70.3	0.14
21.66	120.40	3116.23	0.0199	70.3	0.09
21.99	117.94	3113.53	0.0216	73.1	0.15
22.27	118.77	3120.04	0.0216	73.1	0.16
21.83	121.68	3135.23	0.0216	73.1	0.16
21.94	121.98	3149.46	0.0216	73.1	0.15
20.91	118.83	3136.19	0.0216	73.1	0.15
20.40	117.97	3112.35	0.0216	73.1	0.15
20.22	113.07	3065.02	0.0216	73.1	0.16
19.64	111.98	3051.78	0.0216	73.1	0.16
19.75	113.77	3049.41	0.0216	73.1	0.16
19.51	110.41	3044.11	0.0216	73.1	0.16
19.52	110.85	3064.18	0.0216	73.1	0.16
19.48	111.18	3101.17	0.0216	73.1	0.16
19.88	109.42	3104.12	0.0216	73.1	0.15
18.97	108.87	3072.87	0.0216	73.1	0.15
19.00	106.72	3005.62	0.0216	73.1	0.16
19.32	107.28	3016.96	0.0216	73.1	0.15
19.50	104.13	2990.46	0.0216	73.1	0.15
23.22	107.55	2981.70	0.0216	73.1	0.17
22.56	105.72	2986.12	0.0216	73.1	0.16
21.94	104.55	2987.95	0.0216	73.1	0.16
21.11	106.93	2977.23	0.0216	73.1	0.18
21.21	106.85	3020.06	0.0176	73.1	0.17
21.18	106.78	2982.13	0.0176	73.1	0.16
21.25	107.29	2999.66	0.0176	73.1	0.17
21.17	104.14	3011.93	0.0176	73.1	0.16
20.47	101.21	2937.29	0.0176	73.1	0.16
19.99	96.35	2895.58	0.0176	73.1	0.16
19.21	95.62	2904.87	0.0176	73.1	0.16
20.07	99.00	2904.26	0.0176	73.1	0.16
19.86	99.26	2883.89	0.0176	73.1	0.16
22.36	98.77	2846.81	0.0176	73.1	0.16
22.17	100.65	2836.94	0.0176	73.1	0.16
23.56	103.13	2853.13	0.0176	73.1	0.16
22.92	105.53	2916.07	0.0176	73.1	0.16
23.10	106.76	2916.68	0.0176	73.1	0.16
24.32	107.59	2926.55	0.0176	73.1	0.16
23.99	107.62	2966.85	0.0176	73.1	0.16
25.94	108.82	2976.78	0.0176	73.1	0.16
26.15	107.59	2967.79	0.0176	73.1	0.16
26.36	107.85	2991.78	0.0176	73.1	0.16
27.32	107.11	3012.03	0.0176	73.1	0.16
28.00	108.14	3010.24	0.0176	73.1	0.16

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 9 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]9 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	9 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
FACEBOOK[t] = + 82.282919242259 + 0.431769223184462LINKEDIN[t] -0.0345547988011659NASDAQ[t] -761.520442009693INFLATION[t] + 0.134198894804541CONS.CONF[t] + 38.4397831371381FED.FUNDS.RATE[t] + 0.414752562361286M1[t] + 0.585660782608443M2[t] + 0.0982950525574734M3[t] + 0.330412872399994M4[t] + 0.158974668941033M5[t] + 0.0694178296759307M6[t] -0.18145426471401M7[t] -0.465781129463628M8[t] -0.876691418040543M9[t] -1.31408475815492M10[t] -0.831274393257356M11[t] -0.0451337480006446t + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
FACEBOOK[t] =  +  82.282919242259 +  0.431769223184462LINKEDIN[t] -0.0345547988011659NASDAQ[t] -761.520442009693INFLATION[t] +  0.134198894804541CONS.CONF[t] +  38.4397831371381FED.FUNDS.RATE[t] +  0.414752562361286M1[t] +  0.585660782608443M2[t] +  0.0982950525574734M3[t] +  0.330412872399994M4[t] +  0.158974668941033M5[t] +  0.0694178296759307M6[t] -0.18145426471401M7[t] -0.465781129463628M8[t] -0.876691418040543M9[t] -1.31408475815492M10[t] -0.831274393257356M11[t] -0.0451337480006446t  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]FACEBOOK[t] =  +  82.282919242259 +  0.431769223184462LINKEDIN[t] -0.0345547988011659NASDAQ[t] -761.520442009693INFLATION[t] +  0.134198894804541CONS.CONF[t] +  38.4397831371381FED.FUNDS.RATE[t] +  0.414752562361286M1[t] +  0.585660782608443M2[t] +  0.0982950525574734M3[t] +  0.330412872399994M4[t] +  0.158974668941033M5[t] +  0.0694178296759307M6[t] -0.18145426471401M7[t] -0.465781129463628M8[t] -0.876691418040543M9[t] -1.31408475815492M10[t] -0.831274393257356M11[t] -0.0451337480006446t  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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
FACEBOOK[t] = + 82.282919242259 + 0.431769223184462LINKEDIN[t] -0.0345547988011659NASDAQ[t] -761.520442009693INFLATION[t] + 0.134198894804541CONS.CONF[t] + 38.4397831371381FED.FUNDS.RATE[t] + 0.414752562361286M1[t] + 0.585660782608443M2[t] + 0.0982950525574734M3[t] + 0.330412872399994M4[t] + 0.158974668941033M5[t] + 0.0694178296759307M6[t] -0.18145426471401M7[t] -0.465781129463628M8[t] -0.876691418040543M9[t] -1.31408475815492M10[t] -0.831274393257356M11[t] -0.0451337480006446t + 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)	82.282919242259	13.365071	6.1566	0	0
LINKEDIN	0.431769223184462	0.063152	6.837	0	0
NASDAQ	-0.0345547988011659	0.005153	-6.7062	0	0
INFLATION	-761.520442009693	141.233007	-5.3919	0	0
CONS.CONF	0.134198894804541	0.117043	1.1466	0.254089	0.127044
FED.FUNDS.RATE	38.4397831371381	16.062802	2.3931	0.018433	0.009216
M1	0.414752562361286	0.944202	0.4393	0.661349	0.330674
M2	0.585660782608443	0.945797	0.6192	0.537072	0.268536
M3	0.0982950525574734	0.949788	0.1035	0.917765	0.458882
M4	0.330412872399994	0.949188	0.3481	0.728442	0.364221
M5	0.158974668941033	0.945237	0.1682	0.866752	0.433376
M6	0.0694178296759307	0.947654	0.0733	0.941741	0.47087
M7	-0.18145426471401	0.982704	-0.1846	0.853851	0.426926
M8	-0.465781129463628	0.979446	-0.4756	0.635351	0.317676
M9	-0.876691418040543	0.968226	-0.9055	0.367237	0.183618
M10	-1.31408475815492	0.964027	-1.3631	0.17568	0.08784
M11	-0.831274393257356	0.959037	-0.8668	0.387984	0.193992
t	-0.0451337480006446	0.013134	-3.4365	0.000838	0.000419

\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) & 82.282919242259 & 13.365071 & 6.1566 & 0 & 0 \tabularnewline
LINKEDIN & 0.431769223184462 & 0.063152 & 6.837 & 0 & 0 \tabularnewline
NASDAQ & -0.0345547988011659 & 0.005153 & -6.7062 & 0 & 0 \tabularnewline
INFLATION & -761.520442009693 & 141.233007 & -5.3919 & 0 & 0 \tabularnewline
CONS.CONF & 0.134198894804541 & 0.117043 & 1.1466 & 0.254089 & 0.127044 \tabularnewline
FED.FUNDS.RATE & 38.4397831371381 & 16.062802 & 2.3931 & 0.018433 & 0.009216 \tabularnewline
M1 & 0.414752562361286 & 0.944202 & 0.4393 & 0.661349 & 0.330674 \tabularnewline
M2 & 0.585660782608443 & 0.945797 & 0.6192 & 0.537072 & 0.268536 \tabularnewline
M3 & 0.0982950525574734 & 0.949788 & 0.1035 & 0.917765 & 0.458882 \tabularnewline
M4 & 0.330412872399994 & 0.949188 & 0.3481 & 0.728442 & 0.364221 \tabularnewline
M5 & 0.158974668941033 & 0.945237 & 0.1682 & 0.866752 & 0.433376 \tabularnewline
M6 & 0.0694178296759307 & 0.947654 & 0.0733 & 0.941741 & 0.47087 \tabularnewline
M7 & -0.18145426471401 & 0.982704 & -0.1846 & 0.853851 & 0.426926 \tabularnewline
M8 & -0.465781129463628 & 0.979446 & -0.4756 & 0.635351 & 0.317676 \tabularnewline
M9 & -0.876691418040543 & 0.968226 & -0.9055 & 0.367237 & 0.183618 \tabularnewline
M10 & -1.31408475815492 & 0.964027 & -1.3631 & 0.17568 & 0.08784 \tabularnewline
M11 & -0.831274393257356 & 0.959037 & -0.8668 & 0.387984 & 0.193992 \tabularnewline
t & -0.0451337480006446 & 0.013134 & -3.4365 & 0.000838 & 0.000419 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&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]82.282919242259[/C][C]13.365071[/C][C]6.1566[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]LINKEDIN[/C][C]0.431769223184462[/C][C]0.063152[/C][C]6.837[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]NASDAQ[/C][C]-0.0345547988011659[/C][C]0.005153[/C][C]-6.7062[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]INFLATION[/C][C]-761.520442009693[/C][C]141.233007[/C][C]-5.3919[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]CONS.CONF[/C][C]0.134198894804541[/C][C]0.117043[/C][C]1.1466[/C][C]0.254089[/C][C]0.127044[/C][/ROW]
[ROW][C]FED.FUNDS.RATE[/C][C]38.4397831371381[/C][C]16.062802[/C][C]2.3931[/C][C]0.018433[/C][C]0.009216[/C][/ROW]
[ROW][C]M1[/C][C]0.414752562361286[/C][C]0.944202[/C][C]0.4393[/C][C]0.661349[/C][C]0.330674[/C][/ROW]
[ROW][C]M2[/C][C]0.585660782608443[/C][C]0.945797[/C][C]0.6192[/C][C]0.537072[/C][C]0.268536[/C][/ROW]
[ROW][C]M3[/C][C]0.0982950525574734[/C][C]0.949788[/C][C]0.1035[/C][C]0.917765[/C][C]0.458882[/C][/ROW]
[ROW][C]M4[/C][C]0.330412872399994[/C][C]0.949188[/C][C]0.3481[/C][C]0.728442[/C][C]0.364221[/C][/ROW]
[ROW][C]M5[/C][C]0.158974668941033[/C][C]0.945237[/C][C]0.1682[/C][C]0.866752[/C][C]0.433376[/C][/ROW]
[ROW][C]M6[/C][C]0.0694178296759307[/C][C]0.947654[/C][C]0.0733[/C][C]0.941741[/C][C]0.47087[/C][/ROW]
[ROW][C]M7[/C][C]-0.18145426471401[/C][C]0.982704[/C][C]-0.1846[/C][C]0.853851[/C][C]0.426926[/C][/ROW]
[ROW][C]M8[/C][C]-0.465781129463628[/C][C]0.979446[/C][C]-0.4756[/C][C]0.635351[/C][C]0.317676[/C][/ROW]
[ROW][C]M9[/C][C]-0.876691418040543[/C][C]0.968226[/C][C]-0.9055[/C][C]0.367237[/C][C]0.183618[/C][/ROW]
[ROW][C]M10[/C][C]-1.31408475815492[/C][C]0.964027[/C][C]-1.3631[/C][C]0.17568[/C][C]0.08784[/C][/ROW]
[ROW][C]M11[/C][C]-0.831274393257356[/C][C]0.959037[/C][C]-0.8668[/C][C]0.387984[/C][C]0.193992[/C][/ROW]
[ROW][C]t[/C][C]-0.0451337480006446[/C][C]0.013134[/C][C]-3.4365[/C][C]0.000838[/C][C]0.000419[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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)	82.282919242259	13.365071	6.1566	0	0
LINKEDIN	0.431769223184462	0.063152	6.837	0	0
NASDAQ	-0.0345547988011659	0.005153	-6.7062	0	0
INFLATION	-761.520442009693	141.233007	-5.3919	0	0
CONS.CONF	0.134198894804541	0.117043	1.1466	0.254089	0.127044
FED.FUNDS.RATE	38.4397831371381	16.062802	2.3931	0.018433	0.009216
M1	0.414752562361286	0.944202	0.4393	0.661349	0.330674
M2	0.585660782608443	0.945797	0.6192	0.537072	0.268536
M3	0.0982950525574734	0.949788	0.1035	0.917765	0.458882
M4	0.330412872399994	0.949188	0.3481	0.728442	0.364221
M5	0.158974668941033	0.945237	0.1682	0.866752	0.433376
M6	0.0694178296759307	0.947654	0.0733	0.941741	0.47087
M7	-0.18145426471401	0.982704	-0.1846	0.853851	0.426926
M8	-0.465781129463628	0.979446	-0.4756	0.635351	0.317676
M9	-0.876691418040543	0.968226	-0.9055	0.367237	0.183618
M10	-1.31408475815492	0.964027	-1.3631	0.17568	0.08784
M11	-0.831274393257356	0.959037	-0.8668	0.387984	0.193992
t	-0.0451337480006446	0.013134	-3.4365	0.000838	0.000419

Multiple Linear Regression - Regression Statistics
Multiple R	0.895946244190784
R-squared	0.802719672479571
Adjusted R-squared	0.771666287592096
F-TEST (value)	25.8496674481158
F-TEST (DF numerator)	17
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.14072292871527
Sum Squared Residuals	494.931023012948

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.895946244190784 \tabularnewline
R-squared & 0.802719672479571 \tabularnewline
Adjusted R-squared & 0.771666287592096 \tabularnewline
F-TEST (value) & 25.8496674481158 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.14072292871527 \tabularnewline
Sum Squared Residuals & 494.931023012948 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.895946244190784[/C][/ROW]
[ROW][C]R-squared[/C][C]0.802719672479571[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.771666287592096[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]25.8496674481158[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]0[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]2.14072292871527[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]494.931023012948[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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	0.895946244190784
R-squared	0.802719672479571
Adjusted R-squared	0.771666287592096
F-TEST (value)	25.8496674481158
F-TEST (DF numerator)	17
F-TEST (DF denominator)	108
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.14072292871527
Sum Squared Residuals	494.931023012948

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	27.72	29.6054299940344	-1.88542999403444
2	26.9	29.5012875949361	-2.60128759493606
3	25.86	29.1680254748657	-3.30802547486566
4	26.81	26.7034581050453	0.10654189495474
5	26.31	27.4136445815053	-1.10364458150531
6	27.1	27.6362187838419	-0.536218783841898
7	27	28.1721007254067	-1.17210072540669
8	27.4	26.3795860571955	1.02041394280447
9	27.27	27.6151432994732	-0.345143299473246
10	28.29	27.7119882325907	0.578011767409308
11	30.01	28.8800306788267	1.12996932117334
12	31.41	29.8633269287528	1.54667307124722
13	31.91	29.1209424805625	2.78905751943752
14	31.6	28.8082524646419	2.7917475353581
15	31.84	29.7961320530047	2.04386794699529
16	33.05	30.8434492608433	2.20655073915667
17	32.06	30.9302072418364	1.12979275816364
18	33.1	31.24765020679	1.85234979320999
19	32.23	29.2928999815347	2.93710001846533
20	31.36	28.8067906094803	2.55320939051971
21	31.09	24.6119963570878	6.47800364291218
22	30.77	29.4263851060339	1.34361489396608
23	31.2	29.0138871344484	2.18611286555165
24	31.47	29.6691148207977	1.80088517920232
25	31.73	31.6468111990299	0.0831888009700907
26	32.17	29.4259073202863	2.74409267971373
27	31.47	28.4210972989546	3.04890270104542
28	30.97	29.7731850295291	1.1968149704709
29	30.81	32.231058409855	-1.42105840985497
30	30.72	31.4299695226726	-0.709969522672558
31	28.24	30.120371927909	-1.880371927909
32	28.09	29.2258602300983	-1.1358602300983
33	29.11	27.5927764150354	1.51722358496457
34	29	26.8962766824043	2.10372331759567
35	28.76	27.7049496872177	1.05505031278229
36	28.75	29.2006447419468	-0.4506447419468
37	28.45	30.3707289630653	-1.9207289630653
38	29.34	30.4534425462744	-1.11344254627444
39	26.84	27.5970386998552	-0.757038699855242
40	23.7	26.6660895977091	-2.96608959770912
41	23.15	27.200958541184	-4.05095854118399
42	21.71	26.239649539498	-4.52964953949801
43	20.88	21.2861197324805	-0.406119732480474
44	20.04	20.3977427738315	-0.357742773831483
45	21.09	24.4095666307091	-3.31956663070908
46	21.92	24.4790668594612	-2.55906685946116
47	20.72	21.5415678836518	-0.821567883651799
48	20.72	21.7227746263454	-1.00277462634539
49	21.01	21.9665642445208	-0.956564244520756
50	21.8	21.8817786042415	-0.0817786042414891
51	21.6	21.1537520087609	0.446247991239088
52	20.38	20.3059450721174	0.0740549278826204
53	21.2	19.8102652122782	1.38973478772179
54	19.87	19.3483944875324	0.521605512467568
55	19.05	17.4261574451901	1.62384255480985
56	20.01	17.7833876441521	2.22661235584791
57	19.15	18.6426313468648	0.507368653135238
58	19.43	18.1372218410991	1.29277815890088
59	19.44	19.1501111549722	0.28988884502782
60	19.4	19.2360501886906	0.163949811309395
61	19.15	19.2982042289261	-0.148204228926117
62	19.34	20.5137118397519	-1.1737118397519
63	19.1	20.4846015691004	-1.38460156910041
64	19.08	21.4745395035849	-2.39453950358486
65	18.05	20.2127207970097	-2.16272079700968
66	17.72	19.1359867436606	-1.41598674366059
67	18.58	22.3606049580917	-3.78060495809168
68	18.96	22.2447649120454	-3.28476491204541
69	18.98	21.3573384634367	-2.37733846343669
70	18.81	21.4589930197307	-2.64899301973071
71	19.43	21.9222236115575	-2.49222361155748
72	20.93	23.0828375427932	-2.15283754279315
73	20.71	21.9831995730746	-1.27319957307463
74	22	23.1451432135773	-1.14514321357732
75	21.52	22.0049553947683	-0.48495539476829
76	21.87	22.2349552182627	-0.364955218262709
77	23.29	21.9337420662493	1.35625793375067
78	22.59	22.5301619606305	0.0598380393694708
79	22.86	21.737445245055	1.12255475494503
80	20.79	22.3946958134366	-1.60469581343662
81	20.28	21.8285665167295	-1.54856651672953
82	20.62	21.7447452433593	-1.12474524335929
83	20.32	20.6263737782704	-0.306373778270407
84	21.66	20.5355042045657	1.12449579543431
85	21.99	21.3238278289202	0.666172171079786
86	22.27	21.9674168475856	0.302583152414372
87	21.83	22.1664784152111	-0.336478415211093
88	21.94	21.6068806356963	0.33311936430367
89	20.91	20.4887778112971	0.421222188702862
90	20.4	20.8065520955126	-0.406552095512556
91	20.22	20.4147535181487	-0.194753518148665
92	19.64	20.0721699882548	-0.43216998825477
93	19.75	20.4708877343362	-0.720887734336171
94	19.51	18.7207564899675	0.789243510032472
95	19.52	18.6548967531262	0.865103246873781
96	19.48	18.3053392343787	1.17466076562133
97	19.88	17.4287097280998	2.45129027190016
98	18.97	18.3968485901313	0.573151409868662
99	19	19.6442533329829	-0.644253332982917
100	19.32	19.2967789200315	0.0232210799685173
101	19.5	18.6358360837717	0.864163916228283
102	23.22	21.0492919400378	2.17070805996218
103	22.56	19.4260183771471	3.13398162285287
104	21.94	18.5281524914649	3.41184750853509
105	21.11	20.2389423119576	0.871057688042369
106	21.21	20.9035755900013	0.306424409998696
107	21.18	22.2372940484321	-1.05729404843215
108	21.25	23.0222892058999	-1.77228920589989
109	21.17	21.2234497545678	-0.0534497545677839
110	20.47	22.6633105854028	-2.19331058540284
111	19.99	21.4736933406714	-1.48369334067137
112	19.21	21.0244717987258	-1.81447179872576
113	20.07	22.2883582488983	-2.21835824889834
114	19.86	22.9698089112403	-3.10980891124032
115	22.36	23.7435280890366	-1.38352808903657
116	22.17	24.5668494800406	-2.39684948004061
117	23.56	24.6221509243696	-1.06215092436963
118	22.92	23.0009909353519	-0.0809909353519383
119	23.1	23.948665269497	-0.848665269497045
120	24.32	24.7521185058293	-0.43211850582934
121	23.99	23.7421320051985	0.24786799480146
122	25.94	24.0429003931708	1.89709960682919
123	26.15	23.2899724118248	2.86002758817519
124	26.36	22.7602468584547	3.59975314154534
125	27.32	21.5244310061149	5.79556899388506
126	28	21.8963158085833	6.10368419141671

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 27.72 & 29.6054299940344 & -1.88542999403444 \tabularnewline
2 & 26.9 & 29.5012875949361 & -2.60128759493606 \tabularnewline
3 & 25.86 & 29.1680254748657 & -3.30802547486566 \tabularnewline
4 & 26.81 & 26.7034581050453 & 0.10654189495474 \tabularnewline
5 & 26.31 & 27.4136445815053 & -1.10364458150531 \tabularnewline
6 & 27.1 & 27.6362187838419 & -0.536218783841898 \tabularnewline
7 & 27 & 28.1721007254067 & -1.17210072540669 \tabularnewline
8 & 27.4 & 26.3795860571955 & 1.02041394280447 \tabularnewline
9 & 27.27 & 27.6151432994732 & -0.345143299473246 \tabularnewline
10 & 28.29 & 27.7119882325907 & 0.578011767409308 \tabularnewline
11 & 30.01 & 28.8800306788267 & 1.12996932117334 \tabularnewline
12 & 31.41 & 29.8633269287528 & 1.54667307124722 \tabularnewline
13 & 31.91 & 29.1209424805625 & 2.78905751943752 \tabularnewline
14 & 31.6 & 28.8082524646419 & 2.7917475353581 \tabularnewline
15 & 31.84 & 29.7961320530047 & 2.04386794699529 \tabularnewline
16 & 33.05 & 30.8434492608433 & 2.20655073915667 \tabularnewline
17 & 32.06 & 30.9302072418364 & 1.12979275816364 \tabularnewline
18 & 33.1 & 31.24765020679 & 1.85234979320999 \tabularnewline
19 & 32.23 & 29.2928999815347 & 2.93710001846533 \tabularnewline
20 & 31.36 & 28.8067906094803 & 2.55320939051971 \tabularnewline
21 & 31.09 & 24.6119963570878 & 6.47800364291218 \tabularnewline
22 & 30.77 & 29.4263851060339 & 1.34361489396608 \tabularnewline
23 & 31.2 & 29.0138871344484 & 2.18611286555165 \tabularnewline
24 & 31.47 & 29.6691148207977 & 1.80088517920232 \tabularnewline
25 & 31.73 & 31.6468111990299 & 0.0831888009700907 \tabularnewline
26 & 32.17 & 29.4259073202863 & 2.74409267971373 \tabularnewline
27 & 31.47 & 28.4210972989546 & 3.04890270104542 \tabularnewline
28 & 30.97 & 29.7731850295291 & 1.1968149704709 \tabularnewline
29 & 30.81 & 32.231058409855 & -1.42105840985497 \tabularnewline
30 & 30.72 & 31.4299695226726 & -0.709969522672558 \tabularnewline
31 & 28.24 & 30.120371927909 & -1.880371927909 \tabularnewline
32 & 28.09 & 29.2258602300983 & -1.1358602300983 \tabularnewline
33 & 29.11 & 27.5927764150354 & 1.51722358496457 \tabularnewline
34 & 29 & 26.8962766824043 & 2.10372331759567 \tabularnewline
35 & 28.76 & 27.7049496872177 & 1.05505031278229 \tabularnewline
36 & 28.75 & 29.2006447419468 & -0.4506447419468 \tabularnewline
37 & 28.45 & 30.3707289630653 & -1.9207289630653 \tabularnewline
38 & 29.34 & 30.4534425462744 & -1.11344254627444 \tabularnewline
39 & 26.84 & 27.5970386998552 & -0.757038699855242 \tabularnewline
40 & 23.7 & 26.6660895977091 & -2.96608959770912 \tabularnewline
41 & 23.15 & 27.200958541184 & -4.05095854118399 \tabularnewline
42 & 21.71 & 26.239649539498 & -4.52964953949801 \tabularnewline
43 & 20.88 & 21.2861197324805 & -0.406119732480474 \tabularnewline
44 & 20.04 & 20.3977427738315 & -0.357742773831483 \tabularnewline
45 & 21.09 & 24.4095666307091 & -3.31956663070908 \tabularnewline
46 & 21.92 & 24.4790668594612 & -2.55906685946116 \tabularnewline
47 & 20.72 & 21.5415678836518 & -0.821567883651799 \tabularnewline
48 & 20.72 & 21.7227746263454 & -1.00277462634539 \tabularnewline
49 & 21.01 & 21.9665642445208 & -0.956564244520756 \tabularnewline
50 & 21.8 & 21.8817786042415 & -0.0817786042414891 \tabularnewline
51 & 21.6 & 21.1537520087609 & 0.446247991239088 \tabularnewline
52 & 20.38 & 20.3059450721174 & 0.0740549278826204 \tabularnewline
53 & 21.2 & 19.8102652122782 & 1.38973478772179 \tabularnewline
54 & 19.87 & 19.3483944875324 & 0.521605512467568 \tabularnewline
55 & 19.05 & 17.4261574451901 & 1.62384255480985 \tabularnewline
56 & 20.01 & 17.7833876441521 & 2.22661235584791 \tabularnewline
57 & 19.15 & 18.6426313468648 & 0.507368653135238 \tabularnewline
58 & 19.43 & 18.1372218410991 & 1.29277815890088 \tabularnewline
59 & 19.44 & 19.1501111549722 & 0.28988884502782 \tabularnewline
60 & 19.4 & 19.2360501886906 & 0.163949811309395 \tabularnewline
61 & 19.15 & 19.2982042289261 & -0.148204228926117 \tabularnewline
62 & 19.34 & 20.5137118397519 & -1.1737118397519 \tabularnewline
63 & 19.1 & 20.4846015691004 & -1.38460156910041 \tabularnewline
64 & 19.08 & 21.4745395035849 & -2.39453950358486 \tabularnewline
65 & 18.05 & 20.2127207970097 & -2.16272079700968 \tabularnewline
66 & 17.72 & 19.1359867436606 & -1.41598674366059 \tabularnewline
67 & 18.58 & 22.3606049580917 & -3.78060495809168 \tabularnewline
68 & 18.96 & 22.2447649120454 & -3.28476491204541 \tabularnewline
69 & 18.98 & 21.3573384634367 & -2.37733846343669 \tabularnewline
70 & 18.81 & 21.4589930197307 & -2.64899301973071 \tabularnewline
71 & 19.43 & 21.9222236115575 & -2.49222361155748 \tabularnewline
72 & 20.93 & 23.0828375427932 & -2.15283754279315 \tabularnewline
73 & 20.71 & 21.9831995730746 & -1.27319957307463 \tabularnewline
74 & 22 & 23.1451432135773 & -1.14514321357732 \tabularnewline
75 & 21.52 & 22.0049553947683 & -0.48495539476829 \tabularnewline
76 & 21.87 & 22.2349552182627 & -0.364955218262709 \tabularnewline
77 & 23.29 & 21.9337420662493 & 1.35625793375067 \tabularnewline
78 & 22.59 & 22.5301619606305 & 0.0598380393694708 \tabularnewline
79 & 22.86 & 21.737445245055 & 1.12255475494503 \tabularnewline
80 & 20.79 & 22.3946958134366 & -1.60469581343662 \tabularnewline
81 & 20.28 & 21.8285665167295 & -1.54856651672953 \tabularnewline
82 & 20.62 & 21.7447452433593 & -1.12474524335929 \tabularnewline
83 & 20.32 & 20.6263737782704 & -0.306373778270407 \tabularnewline
84 & 21.66 & 20.5355042045657 & 1.12449579543431 \tabularnewline
85 & 21.99 & 21.3238278289202 & 0.666172171079786 \tabularnewline
86 & 22.27 & 21.9674168475856 & 0.302583152414372 \tabularnewline
87 & 21.83 & 22.1664784152111 & -0.336478415211093 \tabularnewline
88 & 21.94 & 21.6068806356963 & 0.33311936430367 \tabularnewline
89 & 20.91 & 20.4887778112971 & 0.421222188702862 \tabularnewline
90 & 20.4 & 20.8065520955126 & -0.406552095512556 \tabularnewline
91 & 20.22 & 20.4147535181487 & -0.194753518148665 \tabularnewline
92 & 19.64 & 20.0721699882548 & -0.43216998825477 \tabularnewline
93 & 19.75 & 20.4708877343362 & -0.720887734336171 \tabularnewline
94 & 19.51 & 18.7207564899675 & 0.789243510032472 \tabularnewline
95 & 19.52 & 18.6548967531262 & 0.865103246873781 \tabularnewline
96 & 19.48 & 18.3053392343787 & 1.17466076562133 \tabularnewline
97 & 19.88 & 17.4287097280998 & 2.45129027190016 \tabularnewline
98 & 18.97 & 18.3968485901313 & 0.573151409868662 \tabularnewline
99 & 19 & 19.6442533329829 & -0.644253332982917 \tabularnewline
100 & 19.32 & 19.2967789200315 & 0.0232210799685173 \tabularnewline
101 & 19.5 & 18.6358360837717 & 0.864163916228283 \tabularnewline
102 & 23.22 & 21.0492919400378 & 2.17070805996218 \tabularnewline
103 & 22.56 & 19.4260183771471 & 3.13398162285287 \tabularnewline
104 & 21.94 & 18.5281524914649 & 3.41184750853509 \tabularnewline
105 & 21.11 & 20.2389423119576 & 0.871057688042369 \tabularnewline
106 & 21.21 & 20.9035755900013 & 0.306424409998696 \tabularnewline
107 & 21.18 & 22.2372940484321 & -1.05729404843215 \tabularnewline
108 & 21.25 & 23.0222892058999 & -1.77228920589989 \tabularnewline
109 & 21.17 & 21.2234497545678 & -0.0534497545677839 \tabularnewline
110 & 20.47 & 22.6633105854028 & -2.19331058540284 \tabularnewline
111 & 19.99 & 21.4736933406714 & -1.48369334067137 \tabularnewline
112 & 19.21 & 21.0244717987258 & -1.81447179872576 \tabularnewline
113 & 20.07 & 22.2883582488983 & -2.21835824889834 \tabularnewline
114 & 19.86 & 22.9698089112403 & -3.10980891124032 \tabularnewline
115 & 22.36 & 23.7435280890366 & -1.38352808903657 \tabularnewline
116 & 22.17 & 24.5668494800406 & -2.39684948004061 \tabularnewline
117 & 23.56 & 24.6221509243696 & -1.06215092436963 \tabularnewline
118 & 22.92 & 23.0009909353519 & -0.0809909353519383 \tabularnewline
119 & 23.1 & 23.948665269497 & -0.848665269497045 \tabularnewline
120 & 24.32 & 24.7521185058293 & -0.43211850582934 \tabularnewline
121 & 23.99 & 23.7421320051985 & 0.24786799480146 \tabularnewline
122 & 25.94 & 24.0429003931708 & 1.89709960682919 \tabularnewline
123 & 26.15 & 23.2899724118248 & 2.86002758817519 \tabularnewline
124 & 26.36 & 22.7602468584547 & 3.59975314154534 \tabularnewline
125 & 27.32 & 21.5244310061149 & 5.79556899388506 \tabularnewline
126 & 28 & 21.8963158085833 & 6.10368419141671 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&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]27.72[/C][C]29.6054299940344[/C][C]-1.88542999403444[/C][/ROW]
[ROW][C]2[/C][C]26.9[/C][C]29.5012875949361[/C][C]-2.60128759493606[/C][/ROW]
[ROW][C]3[/C][C]25.86[/C][C]29.1680254748657[/C][C]-3.30802547486566[/C][/ROW]
[ROW][C]4[/C][C]26.81[/C][C]26.7034581050453[/C][C]0.10654189495474[/C][/ROW]
[ROW][C]5[/C][C]26.31[/C][C]27.4136445815053[/C][C]-1.10364458150531[/C][/ROW]
[ROW][C]6[/C][C]27.1[/C][C]27.6362187838419[/C][C]-0.536218783841898[/C][/ROW]
[ROW][C]7[/C][C]27[/C][C]28.1721007254067[/C][C]-1.17210072540669[/C][/ROW]
[ROW][C]8[/C][C]27.4[/C][C]26.3795860571955[/C][C]1.02041394280447[/C][/ROW]
[ROW][C]9[/C][C]27.27[/C][C]27.6151432994732[/C][C]-0.345143299473246[/C][/ROW]
[ROW][C]10[/C][C]28.29[/C][C]27.7119882325907[/C][C]0.578011767409308[/C][/ROW]
[ROW][C]11[/C][C]30.01[/C][C]28.8800306788267[/C][C]1.12996932117334[/C][/ROW]
[ROW][C]12[/C][C]31.41[/C][C]29.8633269287528[/C][C]1.54667307124722[/C][/ROW]
[ROW][C]13[/C][C]31.91[/C][C]29.1209424805625[/C][C]2.78905751943752[/C][/ROW]
[ROW][C]14[/C][C]31.6[/C][C]28.8082524646419[/C][C]2.7917475353581[/C][/ROW]
[ROW][C]15[/C][C]31.84[/C][C]29.7961320530047[/C][C]2.04386794699529[/C][/ROW]
[ROW][C]16[/C][C]33.05[/C][C]30.8434492608433[/C][C]2.20655073915667[/C][/ROW]
[ROW][C]17[/C][C]32.06[/C][C]30.9302072418364[/C][C]1.12979275816364[/C][/ROW]
[ROW][C]18[/C][C]33.1[/C][C]31.24765020679[/C][C]1.85234979320999[/C][/ROW]
[ROW][C]19[/C][C]32.23[/C][C]29.2928999815347[/C][C]2.93710001846533[/C][/ROW]
[ROW][C]20[/C][C]31.36[/C][C]28.8067906094803[/C][C]2.55320939051971[/C][/ROW]
[ROW][C]21[/C][C]31.09[/C][C]24.6119963570878[/C][C]6.47800364291218[/C][/ROW]
[ROW][C]22[/C][C]30.77[/C][C]29.4263851060339[/C][C]1.34361489396608[/C][/ROW]
[ROW][C]23[/C][C]31.2[/C][C]29.0138871344484[/C][C]2.18611286555165[/C][/ROW]
[ROW][C]24[/C][C]31.47[/C][C]29.6691148207977[/C][C]1.80088517920232[/C][/ROW]
[ROW][C]25[/C][C]31.73[/C][C]31.6468111990299[/C][C]0.0831888009700907[/C][/ROW]
[ROW][C]26[/C][C]32.17[/C][C]29.4259073202863[/C][C]2.74409267971373[/C][/ROW]
[ROW][C]27[/C][C]31.47[/C][C]28.4210972989546[/C][C]3.04890270104542[/C][/ROW]
[ROW][C]28[/C][C]30.97[/C][C]29.7731850295291[/C][C]1.1968149704709[/C][/ROW]
[ROW][C]29[/C][C]30.81[/C][C]32.231058409855[/C][C]-1.42105840985497[/C][/ROW]
[ROW][C]30[/C][C]30.72[/C][C]31.4299695226726[/C][C]-0.709969522672558[/C][/ROW]
[ROW][C]31[/C][C]28.24[/C][C]30.120371927909[/C][C]-1.880371927909[/C][/ROW]
[ROW][C]32[/C][C]28.09[/C][C]29.2258602300983[/C][C]-1.1358602300983[/C][/ROW]
[ROW][C]33[/C][C]29.11[/C][C]27.5927764150354[/C][C]1.51722358496457[/C][/ROW]
[ROW][C]34[/C][C]29[/C][C]26.8962766824043[/C][C]2.10372331759567[/C][/ROW]
[ROW][C]35[/C][C]28.76[/C][C]27.7049496872177[/C][C]1.05505031278229[/C][/ROW]
[ROW][C]36[/C][C]28.75[/C][C]29.2006447419468[/C][C]-0.4506447419468[/C][/ROW]
[ROW][C]37[/C][C]28.45[/C][C]30.3707289630653[/C][C]-1.9207289630653[/C][/ROW]
[ROW][C]38[/C][C]29.34[/C][C]30.4534425462744[/C][C]-1.11344254627444[/C][/ROW]
[ROW][C]39[/C][C]26.84[/C][C]27.5970386998552[/C][C]-0.757038699855242[/C][/ROW]
[ROW][C]40[/C][C]23.7[/C][C]26.6660895977091[/C][C]-2.96608959770912[/C][/ROW]
[ROW][C]41[/C][C]23.15[/C][C]27.200958541184[/C][C]-4.05095854118399[/C][/ROW]
[ROW][C]42[/C][C]21.71[/C][C]26.239649539498[/C][C]-4.52964953949801[/C][/ROW]
[ROW][C]43[/C][C]20.88[/C][C]21.2861197324805[/C][C]-0.406119732480474[/C][/ROW]
[ROW][C]44[/C][C]20.04[/C][C]20.3977427738315[/C][C]-0.357742773831483[/C][/ROW]
[ROW][C]45[/C][C]21.09[/C][C]24.4095666307091[/C][C]-3.31956663070908[/C][/ROW]
[ROW][C]46[/C][C]21.92[/C][C]24.4790668594612[/C][C]-2.55906685946116[/C][/ROW]
[ROW][C]47[/C][C]20.72[/C][C]21.5415678836518[/C][C]-0.821567883651799[/C][/ROW]
[ROW][C]48[/C][C]20.72[/C][C]21.7227746263454[/C][C]-1.00277462634539[/C][/ROW]
[ROW][C]49[/C][C]21.01[/C][C]21.9665642445208[/C][C]-0.956564244520756[/C][/ROW]
[ROW][C]50[/C][C]21.8[/C][C]21.8817786042415[/C][C]-0.0817786042414891[/C][/ROW]
[ROW][C]51[/C][C]21.6[/C][C]21.1537520087609[/C][C]0.446247991239088[/C][/ROW]
[ROW][C]52[/C][C]20.38[/C][C]20.3059450721174[/C][C]0.0740549278826204[/C][/ROW]
[ROW][C]53[/C][C]21.2[/C][C]19.8102652122782[/C][C]1.38973478772179[/C][/ROW]
[ROW][C]54[/C][C]19.87[/C][C]19.3483944875324[/C][C]0.521605512467568[/C][/ROW]
[ROW][C]55[/C][C]19.05[/C][C]17.4261574451901[/C][C]1.62384255480985[/C][/ROW]
[ROW][C]56[/C][C]20.01[/C][C]17.7833876441521[/C][C]2.22661235584791[/C][/ROW]
[ROW][C]57[/C][C]19.15[/C][C]18.6426313468648[/C][C]0.507368653135238[/C][/ROW]
[ROW][C]58[/C][C]19.43[/C][C]18.1372218410991[/C][C]1.29277815890088[/C][/ROW]
[ROW][C]59[/C][C]19.44[/C][C]19.1501111549722[/C][C]0.28988884502782[/C][/ROW]
[ROW][C]60[/C][C]19.4[/C][C]19.2360501886906[/C][C]0.163949811309395[/C][/ROW]
[ROW][C]61[/C][C]19.15[/C][C]19.2982042289261[/C][C]-0.148204228926117[/C][/ROW]
[ROW][C]62[/C][C]19.34[/C][C]20.5137118397519[/C][C]-1.1737118397519[/C][/ROW]
[ROW][C]63[/C][C]19.1[/C][C]20.4846015691004[/C][C]-1.38460156910041[/C][/ROW]
[ROW][C]64[/C][C]19.08[/C][C]21.4745395035849[/C][C]-2.39453950358486[/C][/ROW]
[ROW][C]65[/C][C]18.05[/C][C]20.2127207970097[/C][C]-2.16272079700968[/C][/ROW]
[ROW][C]66[/C][C]17.72[/C][C]19.1359867436606[/C][C]-1.41598674366059[/C][/ROW]
[ROW][C]67[/C][C]18.58[/C][C]22.3606049580917[/C][C]-3.78060495809168[/C][/ROW]
[ROW][C]68[/C][C]18.96[/C][C]22.2447649120454[/C][C]-3.28476491204541[/C][/ROW]
[ROW][C]69[/C][C]18.98[/C][C]21.3573384634367[/C][C]-2.37733846343669[/C][/ROW]
[ROW][C]70[/C][C]18.81[/C][C]21.4589930197307[/C][C]-2.64899301973071[/C][/ROW]
[ROW][C]71[/C][C]19.43[/C][C]21.9222236115575[/C][C]-2.49222361155748[/C][/ROW]
[ROW][C]72[/C][C]20.93[/C][C]23.0828375427932[/C][C]-2.15283754279315[/C][/ROW]
[ROW][C]73[/C][C]20.71[/C][C]21.9831995730746[/C][C]-1.27319957307463[/C][/ROW]
[ROW][C]74[/C][C]22[/C][C]23.1451432135773[/C][C]-1.14514321357732[/C][/ROW]
[ROW][C]75[/C][C]21.52[/C][C]22.0049553947683[/C][C]-0.48495539476829[/C][/ROW]
[ROW][C]76[/C][C]21.87[/C][C]22.2349552182627[/C][C]-0.364955218262709[/C][/ROW]
[ROW][C]77[/C][C]23.29[/C][C]21.9337420662493[/C][C]1.35625793375067[/C][/ROW]
[ROW][C]78[/C][C]22.59[/C][C]22.5301619606305[/C][C]0.0598380393694708[/C][/ROW]
[ROW][C]79[/C][C]22.86[/C][C]21.737445245055[/C][C]1.12255475494503[/C][/ROW]
[ROW][C]80[/C][C]20.79[/C][C]22.3946958134366[/C][C]-1.60469581343662[/C][/ROW]
[ROW][C]81[/C][C]20.28[/C][C]21.8285665167295[/C][C]-1.54856651672953[/C][/ROW]
[ROW][C]82[/C][C]20.62[/C][C]21.7447452433593[/C][C]-1.12474524335929[/C][/ROW]
[ROW][C]83[/C][C]20.32[/C][C]20.6263737782704[/C][C]-0.306373778270407[/C][/ROW]
[ROW][C]84[/C][C]21.66[/C][C]20.5355042045657[/C][C]1.12449579543431[/C][/ROW]
[ROW][C]85[/C][C]21.99[/C][C]21.3238278289202[/C][C]0.666172171079786[/C][/ROW]
[ROW][C]86[/C][C]22.27[/C][C]21.9674168475856[/C][C]0.302583152414372[/C][/ROW]
[ROW][C]87[/C][C]21.83[/C][C]22.1664784152111[/C][C]-0.336478415211093[/C][/ROW]
[ROW][C]88[/C][C]21.94[/C][C]21.6068806356963[/C][C]0.33311936430367[/C][/ROW]
[ROW][C]89[/C][C]20.91[/C][C]20.4887778112971[/C][C]0.421222188702862[/C][/ROW]
[ROW][C]90[/C][C]20.4[/C][C]20.8065520955126[/C][C]-0.406552095512556[/C][/ROW]
[ROW][C]91[/C][C]20.22[/C][C]20.4147535181487[/C][C]-0.194753518148665[/C][/ROW]
[ROW][C]92[/C][C]19.64[/C][C]20.0721699882548[/C][C]-0.43216998825477[/C][/ROW]
[ROW][C]93[/C][C]19.75[/C][C]20.4708877343362[/C][C]-0.720887734336171[/C][/ROW]
[ROW][C]94[/C][C]19.51[/C][C]18.7207564899675[/C][C]0.789243510032472[/C][/ROW]
[ROW][C]95[/C][C]19.52[/C][C]18.6548967531262[/C][C]0.865103246873781[/C][/ROW]
[ROW][C]96[/C][C]19.48[/C][C]18.3053392343787[/C][C]1.17466076562133[/C][/ROW]
[ROW][C]97[/C][C]19.88[/C][C]17.4287097280998[/C][C]2.45129027190016[/C][/ROW]
[ROW][C]98[/C][C]18.97[/C][C]18.3968485901313[/C][C]0.573151409868662[/C][/ROW]
[ROW][C]99[/C][C]19[/C][C]19.6442533329829[/C][C]-0.644253332982917[/C][/ROW]
[ROW][C]100[/C][C]19.32[/C][C]19.2967789200315[/C][C]0.0232210799685173[/C][/ROW]
[ROW][C]101[/C][C]19.5[/C][C]18.6358360837717[/C][C]0.864163916228283[/C][/ROW]
[ROW][C]102[/C][C]23.22[/C][C]21.0492919400378[/C][C]2.17070805996218[/C][/ROW]
[ROW][C]103[/C][C]22.56[/C][C]19.4260183771471[/C][C]3.13398162285287[/C][/ROW]
[ROW][C]104[/C][C]21.94[/C][C]18.5281524914649[/C][C]3.41184750853509[/C][/ROW]
[ROW][C]105[/C][C]21.11[/C][C]20.2389423119576[/C][C]0.871057688042369[/C][/ROW]
[ROW][C]106[/C][C]21.21[/C][C]20.9035755900013[/C][C]0.306424409998696[/C][/ROW]
[ROW][C]107[/C][C]21.18[/C][C]22.2372940484321[/C][C]-1.05729404843215[/C][/ROW]
[ROW][C]108[/C][C]21.25[/C][C]23.0222892058999[/C][C]-1.77228920589989[/C][/ROW]
[ROW][C]109[/C][C]21.17[/C][C]21.2234497545678[/C][C]-0.0534497545677839[/C][/ROW]
[ROW][C]110[/C][C]20.47[/C][C]22.6633105854028[/C][C]-2.19331058540284[/C][/ROW]
[ROW][C]111[/C][C]19.99[/C][C]21.4736933406714[/C][C]-1.48369334067137[/C][/ROW]
[ROW][C]112[/C][C]19.21[/C][C]21.0244717987258[/C][C]-1.81447179872576[/C][/ROW]
[ROW][C]113[/C][C]20.07[/C][C]22.2883582488983[/C][C]-2.21835824889834[/C][/ROW]
[ROW][C]114[/C][C]19.86[/C][C]22.9698089112403[/C][C]-3.10980891124032[/C][/ROW]
[ROW][C]115[/C][C]22.36[/C][C]23.7435280890366[/C][C]-1.38352808903657[/C][/ROW]
[ROW][C]116[/C][C]22.17[/C][C]24.5668494800406[/C][C]-2.39684948004061[/C][/ROW]
[ROW][C]117[/C][C]23.56[/C][C]24.6221509243696[/C][C]-1.06215092436963[/C][/ROW]
[ROW][C]118[/C][C]22.92[/C][C]23.0009909353519[/C][C]-0.0809909353519383[/C][/ROW]
[ROW][C]119[/C][C]23.1[/C][C]23.948665269497[/C][C]-0.848665269497045[/C][/ROW]
[ROW][C]120[/C][C]24.32[/C][C]24.7521185058293[/C][C]-0.43211850582934[/C][/ROW]
[ROW][C]121[/C][C]23.99[/C][C]23.7421320051985[/C][C]0.24786799480146[/C][/ROW]
[ROW][C]122[/C][C]25.94[/C][C]24.0429003931708[/C][C]1.89709960682919[/C][/ROW]
[ROW][C]123[/C][C]26.15[/C][C]23.2899724118248[/C][C]2.86002758817519[/C][/ROW]
[ROW][C]124[/C][C]26.36[/C][C]22.7602468584547[/C][C]3.59975314154534[/C][/ROW]
[ROW][C]125[/C][C]27.32[/C][C]21.5244310061149[/C][C]5.79556899388506[/C][/ROW]
[ROW][C]126[/C][C]28[/C][C]21.8963158085833[/C][C]6.10368419141671[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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	27.72	29.6054299940344	-1.88542999403444
2	26.9	29.5012875949361	-2.60128759493606
3	25.86	29.1680254748657	-3.30802547486566
4	26.81	26.7034581050453	0.10654189495474
5	26.31	27.4136445815053	-1.10364458150531
6	27.1	27.6362187838419	-0.536218783841898
7	27	28.1721007254067	-1.17210072540669
8	27.4	26.3795860571955	1.02041394280447
9	27.27	27.6151432994732	-0.345143299473246
10	28.29	27.7119882325907	0.578011767409308
11	30.01	28.8800306788267	1.12996932117334
12	31.41	29.8633269287528	1.54667307124722
13	31.91	29.1209424805625	2.78905751943752
14	31.6	28.8082524646419	2.7917475353581
15	31.84	29.7961320530047	2.04386794699529
16	33.05	30.8434492608433	2.20655073915667
17	32.06	30.9302072418364	1.12979275816364
18	33.1	31.24765020679	1.85234979320999
19	32.23	29.2928999815347	2.93710001846533
20	31.36	28.8067906094803	2.55320939051971
21	31.09	24.6119963570878	6.47800364291218
22	30.77	29.4263851060339	1.34361489396608
23	31.2	29.0138871344484	2.18611286555165
24	31.47	29.6691148207977	1.80088517920232
25	31.73	31.6468111990299	0.0831888009700907
26	32.17	29.4259073202863	2.74409267971373
27	31.47	28.4210972989546	3.04890270104542
28	30.97	29.7731850295291	1.1968149704709
29	30.81	32.231058409855	-1.42105840985497
30	30.72	31.4299695226726	-0.709969522672558
31	28.24	30.120371927909	-1.880371927909
32	28.09	29.2258602300983	-1.1358602300983
33	29.11	27.5927764150354	1.51722358496457
34	29	26.8962766824043	2.10372331759567
35	28.76	27.7049496872177	1.05505031278229
36	28.75	29.2006447419468	-0.4506447419468
37	28.45	30.3707289630653	-1.9207289630653
38	29.34	30.4534425462744	-1.11344254627444
39	26.84	27.5970386998552	-0.757038699855242
40	23.7	26.6660895977091	-2.96608959770912
41	23.15	27.200958541184	-4.05095854118399
42	21.71	26.239649539498	-4.52964953949801
43	20.88	21.2861197324805	-0.406119732480474
44	20.04	20.3977427738315	-0.357742773831483
45	21.09	24.4095666307091	-3.31956663070908
46	21.92	24.4790668594612	-2.55906685946116
47	20.72	21.5415678836518	-0.821567883651799
48	20.72	21.7227746263454	-1.00277462634539
49	21.01	21.9665642445208	-0.956564244520756
50	21.8	21.8817786042415	-0.0817786042414891
51	21.6	21.1537520087609	0.446247991239088
52	20.38	20.3059450721174	0.0740549278826204
53	21.2	19.8102652122782	1.38973478772179
54	19.87	19.3483944875324	0.521605512467568
55	19.05	17.4261574451901	1.62384255480985
56	20.01	17.7833876441521	2.22661235584791
57	19.15	18.6426313468648	0.507368653135238
58	19.43	18.1372218410991	1.29277815890088
59	19.44	19.1501111549722	0.28988884502782
60	19.4	19.2360501886906	0.163949811309395
61	19.15	19.2982042289261	-0.148204228926117
62	19.34	20.5137118397519	-1.1737118397519
63	19.1	20.4846015691004	-1.38460156910041
64	19.08	21.4745395035849	-2.39453950358486
65	18.05	20.2127207970097	-2.16272079700968
66	17.72	19.1359867436606	-1.41598674366059
67	18.58	22.3606049580917	-3.78060495809168
68	18.96	22.2447649120454	-3.28476491204541
69	18.98	21.3573384634367	-2.37733846343669
70	18.81	21.4589930197307	-2.64899301973071
71	19.43	21.9222236115575	-2.49222361155748
72	20.93	23.0828375427932	-2.15283754279315
73	20.71	21.9831995730746	-1.27319957307463
74	22	23.1451432135773	-1.14514321357732
75	21.52	22.0049553947683	-0.48495539476829
76	21.87	22.2349552182627	-0.364955218262709
77	23.29	21.9337420662493	1.35625793375067
78	22.59	22.5301619606305	0.0598380393694708
79	22.86	21.737445245055	1.12255475494503
80	20.79	22.3946958134366	-1.60469581343662
81	20.28	21.8285665167295	-1.54856651672953
82	20.62	21.7447452433593	-1.12474524335929
83	20.32	20.6263737782704	-0.306373778270407
84	21.66	20.5355042045657	1.12449579543431
85	21.99	21.3238278289202	0.666172171079786
86	22.27	21.9674168475856	0.302583152414372
87	21.83	22.1664784152111	-0.336478415211093
88	21.94	21.6068806356963	0.33311936430367
89	20.91	20.4887778112971	0.421222188702862
90	20.4	20.8065520955126	-0.406552095512556
91	20.22	20.4147535181487	-0.194753518148665
92	19.64	20.0721699882548	-0.43216998825477
93	19.75	20.4708877343362	-0.720887734336171
94	19.51	18.7207564899675	0.789243510032472
95	19.52	18.6548967531262	0.865103246873781
96	19.48	18.3053392343787	1.17466076562133
97	19.88	17.4287097280998	2.45129027190016
98	18.97	18.3968485901313	0.573151409868662
99	19	19.6442533329829	-0.644253332982917
100	19.32	19.2967789200315	0.0232210799685173
101	19.5	18.6358360837717	0.864163916228283
102	23.22	21.0492919400378	2.17070805996218
103	22.56	19.4260183771471	3.13398162285287
104	21.94	18.5281524914649	3.41184750853509
105	21.11	20.2389423119576	0.871057688042369
106	21.21	20.9035755900013	0.306424409998696
107	21.18	22.2372940484321	-1.05729404843215
108	21.25	23.0222892058999	-1.77228920589989
109	21.17	21.2234497545678	-0.0534497545677839
110	20.47	22.6633105854028	-2.19331058540284
111	19.99	21.4736933406714	-1.48369334067137
112	19.21	21.0244717987258	-1.81447179872576
113	20.07	22.2883582488983	-2.21835824889834
114	19.86	22.9698089112403	-3.10980891124032
115	22.36	23.7435280890366	-1.38352808903657
116	22.17	24.5668494800406	-2.39684948004061
117	23.56	24.6221509243696	-1.06215092436963
118	22.92	23.0009909353519	-0.0809909353519383
119	23.1	23.948665269497	-0.848665269497045
120	24.32	24.7521185058293	-0.43211850582934
121	23.99	23.7421320051985	0.24786799480146
122	25.94	24.0429003931708	1.89709960682919
123	26.15	23.2899724118248	2.86002758817519
124	26.36	22.7602468584547	3.59975314154534
125	27.32	21.5244310061149	5.79556899388506
126	28	21.8963158085833	6.10368419141671

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.073255582024188	0.146511164048376	0.926744417975812
22	0.0230756800430053	0.0461513600860107	0.976924319956995
23	0.00775657702204722	0.0155131540440944	0.992243422977953
24	0.00309793533689452	0.00619587067378903	0.996902064663105
25	0.00576602133451926	0.0115320426690385	0.994233978665481
26	0.00341494943751145	0.00682989887502291	0.996585050562489
27	0.00173597382839936	0.00347194765679872	0.998264026171601
28	0.00175668347837859	0.00351336695675718	0.998243316521621
29	0.00144487032353552	0.00288974064707103	0.998555129676465
30	0.0017609167830241	0.0035218335660482	0.998239083216976
31	0.00962534285532597	0.0192506857106519	0.990374657144674
32	0.0236158184427291	0.0472316368854582	0.976384181557271
33	0.0209969279840446	0.0419938559680891	0.979003072015955
34	0.0660896160238632	0.132179232047726	0.933910383976137
35	0.093360496243757	0.186720992487514	0.906639503756243
36	0.0951416710396094	0.190283342079219	0.904858328960391
37	0.096125829772661	0.192251659545322	0.903874170227339
38	0.114279824938499	0.228559649876999	0.885720175061501
39	0.19879259642808	0.39758519285616	0.80120740357192
40	0.573168853489954	0.853662293020092	0.426831146510046
41	0.719275769367494	0.561448461265013	0.280724230632506
42	0.767871831043673	0.464256337912654	0.232128168956327
43	0.750572295292903	0.498855409414195	0.249427704707097
44	0.762248253101767	0.475503493796465	0.237751746898233
45	0.979973176710543	0.0400536465789131	0.0200268232894565
46	0.985093394030139	0.0298132119397228	0.0149066059698614
47	0.983350983181942	0.0332980336361157	0.0166490168180579
48	0.981386014270593	0.0372279714588131	0.0186139857294066
49	0.978636251687851	0.0427274966242979	0.021363748312149
50	0.983572201245251	0.0328555975094983	0.0164277987547491
51	0.990258522540515	0.0194829549189707	0.00974147745948534
52	0.995167106105916	0.00966578778816845	0.00483289389408423
53	0.999437007544333	0.00112598491133487	0.000562992455667434
54	0.999325622250822	0.00134875549835506	0.000674377749177532
55	0.998943743738871	0.00211251252225738	0.00105625626112869
56	0.999230248081879	0.00153950383624205	0.000769751918121025
57	0.999071685811364	0.00185662837727297	0.000928314188636484
58	0.999116945129854	0.0017661097402915	0.000883054870145749
59	0.999313118176673	0.00137376364665339	0.000686881823326697
60	0.999385208286591	0.0012295834268169	0.000614791713408449
61	0.999280289073828	0.00143942185234487	0.000719710926172437
62	0.999293392168541	0.00141321566291845	0.000706607831459224
63	0.999307870079247	0.00138425984150574	0.000692129920752872
64	0.999008516628168	0.00198296674366316	0.000991483371831582
65	0.999314159428757	0.00137168114248606	0.00068584057124303
66	0.999734567387187	0.000530865225626814	0.000265432612813407
67	0.999705298697299	0.000589402605401134	0.000294701302700567
68	0.999711397165477	0.000577205669046321	0.00028860283452316
69	0.999563683727977	0.000872632544045589	0.000436316272022794
70	0.999307653269067	0.00138469346186634	0.000692346730933169
71	0.999194687722783	0.00161062455443309	0.000805312277216545
72	0.999305172766788	0.00138965446642304	0.000694827233211522
73	0.999015297223637	0.00196940555272523	0.000984702776362613
74	0.99844108694511	0.00311782610978051	0.00155891305489025
75	0.997686564643516	0.00462687071296739	0.0023134353564837
76	0.997342507072223	0.00531498585555361	0.00265749292777681
77	0.998812102389753	0.00237579522049329	0.00118789761024664
78	0.998606019085317	0.0027879618293661	0.00139398091468305
79	0.997904289361281	0.00419142127743782	0.00209571063871891
80	0.997719865937211	0.004560268125579	0.0022801340627895
81	0.996930391207441	0.00613921758511885	0.00306960879255943
82	0.996554249027424	0.00689150194515257	0.00344575097257629
83	0.997364886726953	0.0052702265460939	0.00263511327304695
84	0.996103237490751	0.00779352501849761	0.0038967625092488
85	0.999575248393586	0.000849503212827826	0.000424751606413913
86	0.999919392710974	0.000161214578051655	8.06072890258276e-05
87	0.999861056202434	0.000277887595132382	0.000138943797566191
88	0.999874496027495	0.000251007945010163	0.000125503972505081
89	0.999838120499107	0.000323759001785753	0.000161879500892877
90	0.999685897078791	0.000628205842417618	0.000314102921208809
91	0.999538101156675	0.00092379768665101	0.000461898843325505
92	0.999303460346548	0.0013930793069038	0.0006965396534519
93	0.998947958276931	0.0021040834461383	0.00105204172306915
94	0.998365920366391	0.00326815926721836	0.00163407963360918
95	0.996899738282219	0.00620052343556296	0.00310026171778148
96	0.994380453889087	0.0112390922218257	0.00561954611091285
97	0.990953718119514	0.0180925637609721	0.00904628188048605
98	0.987533839002122	0.0249323219957563	0.0124661609978781
99	0.984810048133878	0.0303799037322449	0.0151899518661224
100	0.982943324142959	0.0341133517140827	0.0170566758570414
101	0.978758031686192	0.0424839366276165	0.0212419683138082
102	0.999993301416548	1.33971669031156e-05	6.69858345155778e-06
103	0.999971093923603	5.78121527940155e-05	2.89060763970078e-05
104	0.999838624602619	0.000322750794761292	0.000161375397380646
105	0.998183083004196	0.0036338339916088	0.0018169169958044

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.073255582024188 & 0.146511164048376 & 0.926744417975812 \tabularnewline
22 & 0.0230756800430053 & 0.0461513600860107 & 0.976924319956995 \tabularnewline
23 & 0.00775657702204722 & 0.0155131540440944 & 0.992243422977953 \tabularnewline
24 & 0.00309793533689452 & 0.00619587067378903 & 0.996902064663105 \tabularnewline
25 & 0.00576602133451926 & 0.0115320426690385 & 0.994233978665481 \tabularnewline
26 & 0.00341494943751145 & 0.00682989887502291 & 0.996585050562489 \tabularnewline
27 & 0.00173597382839936 & 0.00347194765679872 & 0.998264026171601 \tabularnewline
28 & 0.00175668347837859 & 0.00351336695675718 & 0.998243316521621 \tabularnewline
29 & 0.00144487032353552 & 0.00288974064707103 & 0.998555129676465 \tabularnewline
30 & 0.0017609167830241 & 0.0035218335660482 & 0.998239083216976 \tabularnewline
31 & 0.00962534285532597 & 0.0192506857106519 & 0.990374657144674 \tabularnewline
32 & 0.0236158184427291 & 0.0472316368854582 & 0.976384181557271 \tabularnewline
33 & 0.0209969279840446 & 0.0419938559680891 & 0.979003072015955 \tabularnewline
34 & 0.0660896160238632 & 0.132179232047726 & 0.933910383976137 \tabularnewline
35 & 0.093360496243757 & 0.186720992487514 & 0.906639503756243 \tabularnewline
36 & 0.0951416710396094 & 0.190283342079219 & 0.904858328960391 \tabularnewline
37 & 0.096125829772661 & 0.192251659545322 & 0.903874170227339 \tabularnewline
38 & 0.114279824938499 & 0.228559649876999 & 0.885720175061501 \tabularnewline
39 & 0.19879259642808 & 0.39758519285616 & 0.80120740357192 \tabularnewline
40 & 0.573168853489954 & 0.853662293020092 & 0.426831146510046 \tabularnewline
41 & 0.719275769367494 & 0.561448461265013 & 0.280724230632506 \tabularnewline
42 & 0.767871831043673 & 0.464256337912654 & 0.232128168956327 \tabularnewline
43 & 0.750572295292903 & 0.498855409414195 & 0.249427704707097 \tabularnewline
44 & 0.762248253101767 & 0.475503493796465 & 0.237751746898233 \tabularnewline
45 & 0.979973176710543 & 0.0400536465789131 & 0.0200268232894565 \tabularnewline
46 & 0.985093394030139 & 0.0298132119397228 & 0.0149066059698614 \tabularnewline
47 & 0.983350983181942 & 0.0332980336361157 & 0.0166490168180579 \tabularnewline
48 & 0.981386014270593 & 0.0372279714588131 & 0.0186139857294066 \tabularnewline
49 & 0.978636251687851 & 0.0427274966242979 & 0.021363748312149 \tabularnewline
50 & 0.983572201245251 & 0.0328555975094983 & 0.0164277987547491 \tabularnewline
51 & 0.990258522540515 & 0.0194829549189707 & 0.00974147745948534 \tabularnewline
52 & 0.995167106105916 & 0.00966578778816845 & 0.00483289389408423 \tabularnewline
53 & 0.999437007544333 & 0.00112598491133487 & 0.000562992455667434 \tabularnewline
54 & 0.999325622250822 & 0.00134875549835506 & 0.000674377749177532 \tabularnewline
55 & 0.998943743738871 & 0.00211251252225738 & 0.00105625626112869 \tabularnewline
56 & 0.999230248081879 & 0.00153950383624205 & 0.000769751918121025 \tabularnewline
57 & 0.999071685811364 & 0.00185662837727297 & 0.000928314188636484 \tabularnewline
58 & 0.999116945129854 & 0.0017661097402915 & 0.000883054870145749 \tabularnewline
59 & 0.999313118176673 & 0.00137376364665339 & 0.000686881823326697 \tabularnewline
60 & 0.999385208286591 & 0.0012295834268169 & 0.000614791713408449 \tabularnewline
61 & 0.999280289073828 & 0.00143942185234487 & 0.000719710926172437 \tabularnewline
62 & 0.999293392168541 & 0.00141321566291845 & 0.000706607831459224 \tabularnewline
63 & 0.999307870079247 & 0.00138425984150574 & 0.000692129920752872 \tabularnewline
64 & 0.999008516628168 & 0.00198296674366316 & 0.000991483371831582 \tabularnewline
65 & 0.999314159428757 & 0.00137168114248606 & 0.00068584057124303 \tabularnewline
66 & 0.999734567387187 & 0.000530865225626814 & 0.000265432612813407 \tabularnewline
67 & 0.999705298697299 & 0.000589402605401134 & 0.000294701302700567 \tabularnewline
68 & 0.999711397165477 & 0.000577205669046321 & 0.00028860283452316 \tabularnewline
69 & 0.999563683727977 & 0.000872632544045589 & 0.000436316272022794 \tabularnewline
70 & 0.999307653269067 & 0.00138469346186634 & 0.000692346730933169 \tabularnewline
71 & 0.999194687722783 & 0.00161062455443309 & 0.000805312277216545 \tabularnewline
72 & 0.999305172766788 & 0.00138965446642304 & 0.000694827233211522 \tabularnewline
73 & 0.999015297223637 & 0.00196940555272523 & 0.000984702776362613 \tabularnewline
74 & 0.99844108694511 & 0.00311782610978051 & 0.00155891305489025 \tabularnewline
75 & 0.997686564643516 & 0.00462687071296739 & 0.0023134353564837 \tabularnewline
76 & 0.997342507072223 & 0.00531498585555361 & 0.00265749292777681 \tabularnewline
77 & 0.998812102389753 & 0.00237579522049329 & 0.00118789761024664 \tabularnewline
78 & 0.998606019085317 & 0.0027879618293661 & 0.00139398091468305 \tabularnewline
79 & 0.997904289361281 & 0.00419142127743782 & 0.00209571063871891 \tabularnewline
80 & 0.997719865937211 & 0.004560268125579 & 0.0022801340627895 \tabularnewline
81 & 0.996930391207441 & 0.00613921758511885 & 0.00306960879255943 \tabularnewline
82 & 0.996554249027424 & 0.00689150194515257 & 0.00344575097257629 \tabularnewline
83 & 0.997364886726953 & 0.0052702265460939 & 0.00263511327304695 \tabularnewline
84 & 0.996103237490751 & 0.00779352501849761 & 0.0038967625092488 \tabularnewline
85 & 0.999575248393586 & 0.000849503212827826 & 0.000424751606413913 \tabularnewline
86 & 0.999919392710974 & 0.000161214578051655 & 8.06072890258276e-05 \tabularnewline
87 & 0.999861056202434 & 0.000277887595132382 & 0.000138943797566191 \tabularnewline
88 & 0.999874496027495 & 0.000251007945010163 & 0.000125503972505081 \tabularnewline
89 & 0.999838120499107 & 0.000323759001785753 & 0.000161879500892877 \tabularnewline
90 & 0.999685897078791 & 0.000628205842417618 & 0.000314102921208809 \tabularnewline
91 & 0.999538101156675 & 0.00092379768665101 & 0.000461898843325505 \tabularnewline
92 & 0.999303460346548 & 0.0013930793069038 & 0.0006965396534519 \tabularnewline
93 & 0.998947958276931 & 0.0021040834461383 & 0.00105204172306915 \tabularnewline
94 & 0.998365920366391 & 0.00326815926721836 & 0.00163407963360918 \tabularnewline
95 & 0.996899738282219 & 0.00620052343556296 & 0.00310026171778148 \tabularnewline
96 & 0.994380453889087 & 0.0112390922218257 & 0.00561954611091285 \tabularnewline
97 & 0.990953718119514 & 0.0180925637609721 & 0.00904628188048605 \tabularnewline
98 & 0.987533839002122 & 0.0249323219957563 & 0.0124661609978781 \tabularnewline
99 & 0.984810048133878 & 0.0303799037322449 & 0.0151899518661224 \tabularnewline
100 & 0.982943324142959 & 0.0341133517140827 & 0.0170566758570414 \tabularnewline
101 & 0.978758031686192 & 0.0424839366276165 & 0.0212419683138082 \tabularnewline
102 & 0.999993301416548 & 1.33971669031156e-05 & 6.69858345155778e-06 \tabularnewline
103 & 0.999971093923603 & 5.78121527940155e-05 & 2.89060763970078e-05 \tabularnewline
104 & 0.999838624602619 & 0.000322750794761292 & 0.000161375397380646 \tabularnewline
105 & 0.998183083004196 & 0.0036338339916088 & 0.0018169169958044 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&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]21[/C][C]0.073255582024188[/C][C]0.146511164048376[/C][C]0.926744417975812[/C][/ROW]
[ROW][C]22[/C][C]0.0230756800430053[/C][C]0.0461513600860107[/C][C]0.976924319956995[/C][/ROW]
[ROW][C]23[/C][C]0.00775657702204722[/C][C]0.0155131540440944[/C][C]0.992243422977953[/C][/ROW]
[ROW][C]24[/C][C]0.00309793533689452[/C][C]0.00619587067378903[/C][C]0.996902064663105[/C][/ROW]
[ROW][C]25[/C][C]0.00576602133451926[/C][C]0.0115320426690385[/C][C]0.994233978665481[/C][/ROW]
[ROW][C]26[/C][C]0.00341494943751145[/C][C]0.00682989887502291[/C][C]0.996585050562489[/C][/ROW]
[ROW][C]27[/C][C]0.00173597382839936[/C][C]0.00347194765679872[/C][C]0.998264026171601[/C][/ROW]
[ROW][C]28[/C][C]0.00175668347837859[/C][C]0.00351336695675718[/C][C]0.998243316521621[/C][/ROW]
[ROW][C]29[/C][C]0.00144487032353552[/C][C]0.00288974064707103[/C][C]0.998555129676465[/C][/ROW]
[ROW][C]30[/C][C]0.0017609167830241[/C][C]0.0035218335660482[/C][C]0.998239083216976[/C][/ROW]
[ROW][C]31[/C][C]0.00962534285532597[/C][C]0.0192506857106519[/C][C]0.990374657144674[/C][/ROW]
[ROW][C]32[/C][C]0.0236158184427291[/C][C]0.0472316368854582[/C][C]0.976384181557271[/C][/ROW]
[ROW][C]33[/C][C]0.0209969279840446[/C][C]0.0419938559680891[/C][C]0.979003072015955[/C][/ROW]
[ROW][C]34[/C][C]0.0660896160238632[/C][C]0.132179232047726[/C][C]0.933910383976137[/C][/ROW]
[ROW][C]35[/C][C]0.093360496243757[/C][C]0.186720992487514[/C][C]0.906639503756243[/C][/ROW]
[ROW][C]36[/C][C]0.0951416710396094[/C][C]0.190283342079219[/C][C]0.904858328960391[/C][/ROW]
[ROW][C]37[/C][C]0.096125829772661[/C][C]0.192251659545322[/C][C]0.903874170227339[/C][/ROW]
[ROW][C]38[/C][C]0.114279824938499[/C][C]0.228559649876999[/C][C]0.885720175061501[/C][/ROW]
[ROW][C]39[/C][C]0.19879259642808[/C][C]0.39758519285616[/C][C]0.80120740357192[/C][/ROW]
[ROW][C]40[/C][C]0.573168853489954[/C][C]0.853662293020092[/C][C]0.426831146510046[/C][/ROW]
[ROW][C]41[/C][C]0.719275769367494[/C][C]0.561448461265013[/C][C]0.280724230632506[/C][/ROW]
[ROW][C]42[/C][C]0.767871831043673[/C][C]0.464256337912654[/C][C]0.232128168956327[/C][/ROW]
[ROW][C]43[/C][C]0.750572295292903[/C][C]0.498855409414195[/C][C]0.249427704707097[/C][/ROW]
[ROW][C]44[/C][C]0.762248253101767[/C][C]0.475503493796465[/C][C]0.237751746898233[/C][/ROW]
[ROW][C]45[/C][C]0.979973176710543[/C][C]0.0400536465789131[/C][C]0.0200268232894565[/C][/ROW]
[ROW][C]46[/C][C]0.985093394030139[/C][C]0.0298132119397228[/C][C]0.0149066059698614[/C][/ROW]
[ROW][C]47[/C][C]0.983350983181942[/C][C]0.0332980336361157[/C][C]0.0166490168180579[/C][/ROW]
[ROW][C]48[/C][C]0.981386014270593[/C][C]0.0372279714588131[/C][C]0.0186139857294066[/C][/ROW]
[ROW][C]49[/C][C]0.978636251687851[/C][C]0.0427274966242979[/C][C]0.021363748312149[/C][/ROW]
[ROW][C]50[/C][C]0.983572201245251[/C][C]0.0328555975094983[/C][C]0.0164277987547491[/C][/ROW]
[ROW][C]51[/C][C]0.990258522540515[/C][C]0.0194829549189707[/C][C]0.00974147745948534[/C][/ROW]
[ROW][C]52[/C][C]0.995167106105916[/C][C]0.00966578778816845[/C][C]0.00483289389408423[/C][/ROW]
[ROW][C]53[/C][C]0.999437007544333[/C][C]0.00112598491133487[/C][C]0.000562992455667434[/C][/ROW]
[ROW][C]54[/C][C]0.999325622250822[/C][C]0.00134875549835506[/C][C]0.000674377749177532[/C][/ROW]
[ROW][C]55[/C][C]0.998943743738871[/C][C]0.00211251252225738[/C][C]0.00105625626112869[/C][/ROW]
[ROW][C]56[/C][C]0.999230248081879[/C][C]0.00153950383624205[/C][C]0.000769751918121025[/C][/ROW]
[ROW][C]57[/C][C]0.999071685811364[/C][C]0.00185662837727297[/C][C]0.000928314188636484[/C][/ROW]
[ROW][C]58[/C][C]0.999116945129854[/C][C]0.0017661097402915[/C][C]0.000883054870145749[/C][/ROW]
[ROW][C]59[/C][C]0.999313118176673[/C][C]0.00137376364665339[/C][C]0.000686881823326697[/C][/ROW]
[ROW][C]60[/C][C]0.999385208286591[/C][C]0.0012295834268169[/C][C]0.000614791713408449[/C][/ROW]
[ROW][C]61[/C][C]0.999280289073828[/C][C]0.00143942185234487[/C][C]0.000719710926172437[/C][/ROW]
[ROW][C]62[/C][C]0.999293392168541[/C][C]0.00141321566291845[/C][C]0.000706607831459224[/C][/ROW]
[ROW][C]63[/C][C]0.999307870079247[/C][C]0.00138425984150574[/C][C]0.000692129920752872[/C][/ROW]
[ROW][C]64[/C][C]0.999008516628168[/C][C]0.00198296674366316[/C][C]0.000991483371831582[/C][/ROW]
[ROW][C]65[/C][C]0.999314159428757[/C][C]0.00137168114248606[/C][C]0.00068584057124303[/C][/ROW]
[ROW][C]66[/C][C]0.999734567387187[/C][C]0.000530865225626814[/C][C]0.000265432612813407[/C][/ROW]
[ROW][C]67[/C][C]0.999705298697299[/C][C]0.000589402605401134[/C][C]0.000294701302700567[/C][/ROW]
[ROW][C]68[/C][C]0.999711397165477[/C][C]0.000577205669046321[/C][C]0.00028860283452316[/C][/ROW]
[ROW][C]69[/C][C]0.999563683727977[/C][C]0.000872632544045589[/C][C]0.000436316272022794[/C][/ROW]
[ROW][C]70[/C][C]0.999307653269067[/C][C]0.00138469346186634[/C][C]0.000692346730933169[/C][/ROW]
[ROW][C]71[/C][C]0.999194687722783[/C][C]0.00161062455443309[/C][C]0.000805312277216545[/C][/ROW]
[ROW][C]72[/C][C]0.999305172766788[/C][C]0.00138965446642304[/C][C]0.000694827233211522[/C][/ROW]
[ROW][C]73[/C][C]0.999015297223637[/C][C]0.00196940555272523[/C][C]0.000984702776362613[/C][/ROW]
[ROW][C]74[/C][C]0.99844108694511[/C][C]0.00311782610978051[/C][C]0.00155891305489025[/C][/ROW]
[ROW][C]75[/C][C]0.997686564643516[/C][C]0.00462687071296739[/C][C]0.0023134353564837[/C][/ROW]
[ROW][C]76[/C][C]0.997342507072223[/C][C]0.00531498585555361[/C][C]0.00265749292777681[/C][/ROW]
[ROW][C]77[/C][C]0.998812102389753[/C][C]0.00237579522049329[/C][C]0.00118789761024664[/C][/ROW]
[ROW][C]78[/C][C]0.998606019085317[/C][C]0.0027879618293661[/C][C]0.00139398091468305[/C][/ROW]
[ROW][C]79[/C][C]0.997904289361281[/C][C]0.00419142127743782[/C][C]0.00209571063871891[/C][/ROW]
[ROW][C]80[/C][C]0.997719865937211[/C][C]0.004560268125579[/C][C]0.0022801340627895[/C][/ROW]
[ROW][C]81[/C][C]0.996930391207441[/C][C]0.00613921758511885[/C][C]0.00306960879255943[/C][/ROW]
[ROW][C]82[/C][C]0.996554249027424[/C][C]0.00689150194515257[/C][C]0.00344575097257629[/C][/ROW]
[ROW][C]83[/C][C]0.997364886726953[/C][C]0.0052702265460939[/C][C]0.00263511327304695[/C][/ROW]
[ROW][C]84[/C][C]0.996103237490751[/C][C]0.00779352501849761[/C][C]0.0038967625092488[/C][/ROW]
[ROW][C]85[/C][C]0.999575248393586[/C][C]0.000849503212827826[/C][C]0.000424751606413913[/C][/ROW]
[ROW][C]86[/C][C]0.999919392710974[/C][C]0.000161214578051655[/C][C]8.06072890258276e-05[/C][/ROW]
[ROW][C]87[/C][C]0.999861056202434[/C][C]0.000277887595132382[/C][C]0.000138943797566191[/C][/ROW]
[ROW][C]88[/C][C]0.999874496027495[/C][C]0.000251007945010163[/C][C]0.000125503972505081[/C][/ROW]
[ROW][C]89[/C][C]0.999838120499107[/C][C]0.000323759001785753[/C][C]0.000161879500892877[/C][/ROW]
[ROW][C]90[/C][C]0.999685897078791[/C][C]0.000628205842417618[/C][C]0.000314102921208809[/C][/ROW]
[ROW][C]91[/C][C]0.999538101156675[/C][C]0.00092379768665101[/C][C]0.000461898843325505[/C][/ROW]
[ROW][C]92[/C][C]0.999303460346548[/C][C]0.0013930793069038[/C][C]0.0006965396534519[/C][/ROW]
[ROW][C]93[/C][C]0.998947958276931[/C][C]0.0021040834461383[/C][C]0.00105204172306915[/C][/ROW]
[ROW][C]94[/C][C]0.998365920366391[/C][C]0.00326815926721836[/C][C]0.00163407963360918[/C][/ROW]
[ROW][C]95[/C][C]0.996899738282219[/C][C]0.00620052343556296[/C][C]0.00310026171778148[/C][/ROW]
[ROW][C]96[/C][C]0.994380453889087[/C][C]0.0112390922218257[/C][C]0.00561954611091285[/C][/ROW]
[ROW][C]97[/C][C]0.990953718119514[/C][C]0.0180925637609721[/C][C]0.00904628188048605[/C][/ROW]
[ROW][C]98[/C][C]0.987533839002122[/C][C]0.0249323219957563[/C][C]0.0124661609978781[/C][/ROW]
[ROW][C]99[/C][C]0.984810048133878[/C][C]0.0303799037322449[/C][C]0.0151899518661224[/C][/ROW]
[ROW][C]100[/C][C]0.982943324142959[/C][C]0.0341133517140827[/C][C]0.0170566758570414[/C][/ROW]
[ROW][C]101[/C][C]0.978758031686192[/C][C]0.0424839366276165[/C][C]0.0212419683138082[/C][/ROW]
[ROW][C]102[/C][C]0.999993301416548[/C][C]1.33971669031156e-05[/C][C]6.69858345155778e-06[/C][/ROW]
[ROW][C]103[/C][C]0.999971093923603[/C][C]5.78121527940155e-05[/C][C]2.89060763970078e-05[/C][/ROW]
[ROW][C]104[/C][C]0.999838624602619[/C][C]0.000322750794761292[/C][C]0.000161375397380646[/C][/ROW]
[ROW][C]105[/C][C]0.998183083004196[/C][C]0.0036338339916088[/C][C]0.0018169169958044[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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
21	0.073255582024188	0.146511164048376	0.926744417975812
22	0.0230756800430053	0.0461513600860107	0.976924319956995
23	0.00775657702204722	0.0155131540440944	0.992243422977953
24	0.00309793533689452	0.00619587067378903	0.996902064663105
25	0.00576602133451926	0.0115320426690385	0.994233978665481
26	0.00341494943751145	0.00682989887502291	0.996585050562489
27	0.00173597382839936	0.00347194765679872	0.998264026171601
28	0.00175668347837859	0.00351336695675718	0.998243316521621
29	0.00144487032353552	0.00288974064707103	0.998555129676465
30	0.0017609167830241	0.0035218335660482	0.998239083216976
31	0.00962534285532597	0.0192506857106519	0.990374657144674
32	0.0236158184427291	0.0472316368854582	0.976384181557271
33	0.0209969279840446	0.0419938559680891	0.979003072015955
34	0.0660896160238632	0.132179232047726	0.933910383976137
35	0.093360496243757	0.186720992487514	0.906639503756243
36	0.0951416710396094	0.190283342079219	0.904858328960391
37	0.096125829772661	0.192251659545322	0.903874170227339
38	0.114279824938499	0.228559649876999	0.885720175061501
39	0.19879259642808	0.39758519285616	0.80120740357192
40	0.573168853489954	0.853662293020092	0.426831146510046
41	0.719275769367494	0.561448461265013	0.280724230632506
42	0.767871831043673	0.464256337912654	0.232128168956327
43	0.750572295292903	0.498855409414195	0.249427704707097
44	0.762248253101767	0.475503493796465	0.237751746898233
45	0.979973176710543	0.0400536465789131	0.0200268232894565
46	0.985093394030139	0.0298132119397228	0.0149066059698614
47	0.983350983181942	0.0332980336361157	0.0166490168180579
48	0.981386014270593	0.0372279714588131	0.0186139857294066
49	0.978636251687851	0.0427274966242979	0.021363748312149
50	0.983572201245251	0.0328555975094983	0.0164277987547491
51	0.990258522540515	0.0194829549189707	0.00974147745948534
52	0.995167106105916	0.00966578778816845	0.00483289389408423
53	0.999437007544333	0.00112598491133487	0.000562992455667434
54	0.999325622250822	0.00134875549835506	0.000674377749177532
55	0.998943743738871	0.00211251252225738	0.00105625626112869
56	0.999230248081879	0.00153950383624205	0.000769751918121025
57	0.999071685811364	0.00185662837727297	0.000928314188636484
58	0.999116945129854	0.0017661097402915	0.000883054870145749
59	0.999313118176673	0.00137376364665339	0.000686881823326697
60	0.999385208286591	0.0012295834268169	0.000614791713408449
61	0.999280289073828	0.00143942185234487	0.000719710926172437
62	0.999293392168541	0.00141321566291845	0.000706607831459224
63	0.999307870079247	0.00138425984150574	0.000692129920752872
64	0.999008516628168	0.00198296674366316	0.000991483371831582
65	0.999314159428757	0.00137168114248606	0.00068584057124303
66	0.999734567387187	0.000530865225626814	0.000265432612813407
67	0.999705298697299	0.000589402605401134	0.000294701302700567
68	0.999711397165477	0.000577205669046321	0.00028860283452316
69	0.999563683727977	0.000872632544045589	0.000436316272022794
70	0.999307653269067	0.00138469346186634	0.000692346730933169
71	0.999194687722783	0.00161062455443309	0.000805312277216545
72	0.999305172766788	0.00138965446642304	0.000694827233211522
73	0.999015297223637	0.00196940555272523	0.000984702776362613
74	0.99844108694511	0.00311782610978051	0.00155891305489025
75	0.997686564643516	0.00462687071296739	0.0023134353564837
76	0.997342507072223	0.00531498585555361	0.00265749292777681
77	0.998812102389753	0.00237579522049329	0.00118789761024664
78	0.998606019085317	0.0027879618293661	0.00139398091468305
79	0.997904289361281	0.00419142127743782	0.00209571063871891
80	0.997719865937211	0.004560268125579	0.0022801340627895
81	0.996930391207441	0.00613921758511885	0.00306960879255943
82	0.996554249027424	0.00689150194515257	0.00344575097257629
83	0.997364886726953	0.0052702265460939	0.00263511327304695
84	0.996103237490751	0.00779352501849761	0.0038967625092488
85	0.999575248393586	0.000849503212827826	0.000424751606413913
86	0.999919392710974	0.000161214578051655	8.06072890258276e-05
87	0.999861056202434	0.000277887595132382	0.000138943797566191
88	0.999874496027495	0.000251007945010163	0.000125503972505081
89	0.999838120499107	0.000323759001785753	0.000161879500892877
90	0.999685897078791	0.000628205842417618	0.000314102921208809
91	0.999538101156675	0.00092379768665101	0.000461898843325505
92	0.999303460346548	0.0013930793069038	0.0006965396534519
93	0.998947958276931	0.0021040834461383	0.00105204172306915
94	0.998365920366391	0.00326815926721836	0.00163407963360918
95	0.996899738282219	0.00620052343556296	0.00310026171778148
96	0.994380453889087	0.0112390922218257	0.00561954611091285
97	0.990953718119514	0.0180925637609721	0.00904628188048605
98	0.987533839002122	0.0249323219957563	0.0124661609978781
99	0.984810048133878	0.0303799037322449	0.0151899518661224
100	0.982943324142959	0.0341133517140827	0.0170566758570414
101	0.978758031686192	0.0424839366276165	0.0212419683138082
102	0.999993301416548	1.33971669031156e-05	6.69858345155778e-06
103	0.999971093923603	5.78121527940155e-05	2.89060763970078e-05
104	0.999838624602619	0.000322750794761292	0.000161375397380646
105	0.998183083004196	0.0036338339916088	0.0018169169958044

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	54	0.635294117647059	NOK
5% type I error level	73	0.858823529411765	NOK
10% type I error level	73	0.858823529411765	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 & 54 & 0.635294117647059 & NOK \tabularnewline
5% type I error level & 73 & 0.858823529411765 & NOK \tabularnewline
10% type I error level & 73 & 0.858823529411765 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=203267&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]54[/C][C]0.635294117647059[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]73[/C][C]0.858823529411765[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]73[/C][C]0.858823529411765[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=203267&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=203267&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	54	0.635294117647059	NOK
5% type I error level	73	0.858823529411765	NOK
10% type I error level	73	0.858823529411765	NOK

Figure 1

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Figure 8

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Figure 9

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Figure 10

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Parameters (Session):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

Parameters (R input):

par1 = 1 ; par2 = Include Monthly Dummies ; par3 = Linear Trend ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
par1 <- as.numeric(par1)
x <- 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'){
x2 <- array(0, dim=c(n-1,k), dimnames=list(1:(n-1), paste('(1-B)',colnames(x),sep='')))
for (i in 1:n-1) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
k <- length(x[1,])
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
k <- length(x[1,])
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$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$resid, main='Residual Histogram', xlab='values of Residuals')
grid()
dev.off()
bitmap(file='test3.png')
densityplot(~mysum$resid,col='black',main='Residual Density Plot', xlab='values of Residuals')
dev.off()
bitmap(file='test4.png')
qqnorm(mysum$resid, main='Residual Normal Q-Q Plot')
qqline(mysum$resid)
grid()
dev.off()
(myerror <- as.ts(mysum$resid))
bitmap(file='test5.png')
dum <- cbind(lag(myerror,k=1),myerror)
dum
dum1 <- dum[2:length(myerror),]
dum1
z <- as.data.frame(dum1)
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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, mysum$coefficients[i,1], sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,mysum$coefficients[i,1])
a<-table.element(a, round(mysum$coefficients[i,2],6))
a<-table.element(a, round(mysum$coefficients[i,3],4))
a<-table.element(a, round(mysum$coefficients[i,4],6))
a<-table.element(a, round(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, sqrt(mysum$r.squared))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, mysum$r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, mysum$adj.r.squared)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, mysum$fstatistic[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[2])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, mysum$fstatistic[3])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, 1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]))
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, mysum$sigma)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, sum(myerror*myerror))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,x[i])
a<-table.element(a,x[i]-mysum$resid[i])
a<-table.element(a,mysum$resid[i])
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,gqarr[mypoint-kp3+1,1])
a<-table.element(a,gqarr[mypoint-kp3+1,2])
a<-table.element(a,gqarr[mypoint-kp3+1,3])
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,numsignificant1)
a<-table.element(a,numsignificant1/numgqtests)
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,numsignificant5)
a<-table.element(a,numsignificant5/numgqtests)
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,numsignificant10)
a<-table.element(a,numsignificant10/numgqtests)
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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code