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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_multipleregression.wasp
Title produced by softwareMultiple Regression
Date of computationTue, 21 Dec 2010 16:54:00 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Dec/21/t1292950312yqorm89ispoi2e4.htm/, Retrieved Sun, 19 May 2024 19:48:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=113750, Retrieved Sun, 19 May 2024 19:48:35 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Multiple Regression] [] [2010-12-05 18:56:24] [b98453cac15ba1066b407e146608df68]
-   PD  [Multiple Regression] [] [2010-12-21 15:51:03] [1908ef7bb1a3d37a854f5aaad1a1c348]
-    D      [Multiple Regression] [] [2010-12-21 16:54:00] [23ca1b0f6f6de1e008a90be3f55e3db8] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1,3067	8,7000	113,0000	2579,3900	19,6000	18,9000	3,0000	-2,0000	16,0000
1,2894	8,9000	95,4000	2504,5800	16,0000	16,6000	3,0000	-4,0000	17,0000
1,2770	8,9000	86,2000	2462,3200	17,7000	17,2000	7,0000	-4,0000	23,0000
1,2208	8,1000	111,7000	2467,3800	19,8000	19,2000	4,0000	-7,0000	24,0000
1,2565	8,0000	97,5000	2446,6600	17,0000	17,1000	-4,0000	-9,0000	27,0000
1,3406	8,3000	99,7000	2656,3200	17,4000	17,7000	-6,0000	-13,0000	31,0000
1,3569	8,5000	111,5000	2626,1500	18,9000	18,7000	8,0000	-8,0000	40,0000
1,3686	8,7000	91,8000	2482,6000	15,7000	15,9000	2,0000	-13,0000	47,0000
1,4272	8,6000	86,3000	2539,9100	15,2000	16,0000	-1,0000	-15,0000	43,0000
1,4614	8,3000	88,7000	2502,6600	15,8000	16,8000	-2,0000	-15,0000	60,0000
1,4914	7,9000	95,1000	2466,9200	16,0000	16,0000	0,0000	-15,0000	64,0000
1,4816	7,9000	105,1000	2513,1700	16,1000	16,8000	10,0000	-10,0000	65,0000
1,4562	8,1000	104,5000	2443,2700	16,2000	16,3000	3,0000	-12,0000	65,0000
1,4268	8,3000	89,1000	2293,4100	12,5000	13,6000	6,0000	-11,0000	55,0000
1,4088	8,1000	82,6000	2070,8300	14,8000	14,3000	7,0000	-11,0000	57,0000
1,4016	7,4000	102,7000	2029,6000	15,4000	15,5000	-4,0000	-17,0000	57,0000
1,3650	7,3000	91,8000	2052,0200	13,6000	13,9000	-5,0000	-18,0000	57,0000
1,3190	7,7000	94,1000	1864,4400	14,2000	14,3000	-7,0000	-19,0000	65,0000
1,3050	8,0000	103,1000	1670,0700	15,0000	15,8000	-10,0000	-22,0000	69,0000
1,2785	8,0000	93,2000	1810,9900	14,1000	14,5000	-21,0000	-24,0000	70,0000
1,3239	7,7000	91,0000	1905,4100	13,7000	15,1000	-22,0000	-24,0000	71,0000
1,3449	6,9000	94,3000	1862,8300	14,4000	15,8000	-16,0000	-20,0000	71,0000
1,2732	6,6000	99,4000	2014,4500	15,6000	17,2000	-25,0000	-25,0000	73,0000
1,3322	6,9000	115,7000	2197,8200	19,7000	20,4000	-22,0000	-22,0000	68,0000
1,4369	7,5000	116,8000	2962,3400	20,4000	21,3000	-22,0000	-17,0000	65,0000
1,4975	7,9000	99,8000	3047,0300	16,1000	18,2000	-19,0000	-9,0000	57,0000
1,5770	7,7000	96,0000	3032,6000	20,1000	20,2000	-21,0000	-11,0000	41,0000
1,5553	6,5000	115,9000	3504,3700	20,6000	21,1000	-31,0000	-13,0000	21,0000
1,5557	6,1000	109,1000	3801,0600	19,3000	19,7000	-28,0000	-11,0000	21,0000
1,5750	6,4000	117,3000	3857,6200	20,5000	21,5000	-23,0000	-9,0000	17,0000
1,5527	6,8000	109,8000	3674,4000	19,2000	20,2000	-17,0000	-7,0000	9,0000
1,4748	7,1000	112,8000	3720,9800	19,0000	19,0000	-12,0000	-3,0000	11,0000
1,4718	7,3000	110,7000	3844,4900	18,7000	20,2000	-14,0000	-3,0000	6,0000
1,4570	7,2000	100,0000	4116,6800	16,5000	18,0000	-18,0000	-6,0000	-2,0000
1,4684	7,0000	113,3000	4105,1800	19,0000	19,5000	-16,0000	-4,0000	0,0000
1,4227	7,0000	122,4000	4435,2300	20,5000	20,3000	-22,0000	-8,0000	5,0000
1,3896	7,0000	112,5000	4296,4900	18,4000	18,0000	-9,0000	-1,0000	3,0000
1,3622	7,3000	104,2000	4202,5200	16,2000	16,4000	-10,0000	-2,0000	7,0000
1,3716	7,5000	92,5000	4562,8400	18,1000	17,8000	-10,0000	-2,0000	4,0000
1,3419	7,2000	117,2000	4621,4000	19,3000	18,5000	0,0000	-1,0000	8,0000
1,3511	7,7000	109,3000	4696,9600	18,3000	18,2000	3,0000	1,0000	9,0000
1,3516	8,0000	106,1000	4591,2700	17,2000	16,7000	2,0000	2,0000	14,0000
1,3242	7,9000	118,8000	4356,9800	19,6000	19,1000	4,0000	2,0000	12,0000
1,3074	8,0000	105,3000	4502,6400	17,2000	16,8000	-3,0000	-1,0000	12,0000
1,2999	8,0000	106,0000	4443,9100	17,4000	17,5000	0,0000	1,0000	7,0000
1,3213	7,9000	102,0000	4290,8900	16,0000	16,2000	-1,0000	-1,0000	15,0000
1,2881	7,9000	112,9000	4199,7500	18,5000	17,9000	-7,0000	-8,0000	14,0000
1,2611	8,0000	116,5000	4138,5200	18,4000	17,7000	2,0000	1,0000	19,0000
1,2727	8,1000	114,8000	3970,1000	18,2000	17,2000	3,0000	2,0000	39,0000
1,2811	8,1000	100,5000	3862,2700	14,9000	15,7000	-3,0000	-2,0000	12,0000
1,2684	8,2000	85,4000	3701,6100	16,3000	15,2000	-5,0000	-2,0000	11,0000
1,2650	8,0000	114,6000	3570,1200	18,3000	17,7000	0,0000	-2,0000	17,0000
1,2770	8,3000	109,9000	3801,0600	18,0000	17,4000	-3,0000	-2,0000	16,0000
1,2271	8,5000	100,7000	3895,5100	15,9000	15,9000	-7,0000	-6,0000	25,0000
1,2020	8,6000	115,5000	3917,9600	19,6000	19,7000	-7,0000	-4,0000	24,0000
1,1938	8,7000	100,7000	3813,0600	16,6000	16,7000	-7,0000	-5,0000	28,0000
1,2103	8,7000	99,0000	3667,0300	16,2000	16,9000	-4,0000	-2,0000	25,0000
1,1856	8,5000	102,3000	3494,1700	16,6000	18,0000	-3,0000	-1,0000	31,0000
1,1786	8,4000	108,8000	3363,9900	17,5000	17,6000	-6,0000	-5,0000	24,0000
1,2015	8,5000	105,9000	3295,3200	16,2000	15,2000	-10,0000	-9,0000	24,0000
1,2256	8,7000	113,2000	3277,0100	17,5000	16,5000	-10,0000	-8,0000	33,0000
1,2292	8,7000	95,7000	3257,1600	13,8000	14,7000	-23,0000	-14,0000	37,0000
1,2037	8,6000	80,9000	3161,6900	14,9000	14,1000	-13,0000	-10,0000	35,0000
1,2165	7,9000	113,9000	3097,3100	17,2000	16,9000	-18,0000	-11,0000	37,0000
1,2694	8,1000	98,1000	3061,2600	15,6000	15,2000	-16,0000	-11,0000	38,0000
1,2938	8,2000	102,8000	3119,3100	16,2000	15,4000	-15,0000	-11,0000	42,0000
1,3201	8,5000	104,7000	3106,2200	17,4000	16,8000	-5,0000	-5,0000	43,0000
1,3014	8,6000	95,9000	3080,5800	15,1000	14,8000	2,0000	-2,0000	44,0000
1,3119	8,5000	94,6000	2981,8500	14,5000	14,1000	-2,0000	-3,0000	32,0000
1,3408	8,3000	101,6000	2921,4400	15,1000	15,0000	-4,0000	-6,0000	32,0000
1,2991	8,2000	103,9000	2849,2700	15,5000	14,8000	-4,0000	-6,0000	37,0000
1,2490	8,7000	110,3000	2756,7600	15,9000	15,0000	-6,0000	-7,0000	38,0000
1,2218	9,3000	114,1000	2645,6400	15,9000	15,1000	-7,0000	-6,0000	39,0000
1,2176	9,3000	96,8000	2497,8400	12,3000	12,8000	0,0000	-2,0000	38,0000
1,2266	8,8000	87,4000	2448,0500	14,4000	13,0000	1,0000	-2,0000	39,0000
1,2138	7,4000	111,4000	2454,6200	16,0000	15,7000	-3,0000	-4,0000	30,0000
1,2007	7,2000	97,4000	2407,6000	13,9000	12,8000	6,0000	0,0000	28,0000
1,1985	7,5000	102,9000	2472,8100	14,7000	13,9000	-2,0000	-6,0000	31,0000
1,2262	8,3000	112,7000	2408,6400	16,2000	15,4000	2,0000	-4,0000	28,0000
1,2646	8,8000	97,0000	2440,2500	13,8000	13,2000	5,0000	-3,0000	38,0000
1,2613	8,9000	95,1000	2350,4400	13,2000	12,7000	7,0000	-1,0000	37,0000
1,2286	8,6000	96,9000	2196,7200	13,5000	13,5000	4,0000	-3,0000	34,0000
1,1702	8,4000	98,6000	2174,5600	13,5000	12,8000	0,0000	-6,0000	32,0000
1,1692	8,4000	111,7000	2120,8800	15,0000	13,9000	0,0000	-6,0000	33,0000
1,1222	8,4000	109,8000	2093,4800	14,5000	13,3000	-13,0000	-15,0000	39,0000
1,1139	8,4000	89,9000	2061,4100	10,5000	10,7000	-2,0000	-5,0000	42,0000
1,1372	8,3000	87,4000	1969,6000	13,7000	12,3000	-10,0000	-11,0000	57,0000
1,1663	7,6000	104,5000	1959,6700	13,9000	12,9000	-12,0000	-13,0000	36,0000
1,1582	7,6000	98,1000	1910,4300	13,4000	12,5000	-9,0000	-10,0000	42,0000
1,0848	7,9000	102,7000	1833,4200	14,0000	13,0000	-4,0000	-9,0000	49,0000
1,0807	8,0000	105,4000	1635,2500	14,3000	13,9000	-11,0000	-11,0000	44,0000
1,0773	8,2000	97,0000	1765,9000	13,3000	13,1000	-28,0000	-18,0000	44,0000
1,0622	8,3000	97,4000	1946,8100	13,2000	13,1000	-19,0000	-13,0000	43,0000
1,0183	8,2000	92,0000	1995,3700	12,6000	13,0000	-16,0000	-9,0000	50,0000
1,0014	8,1000	101,7000	2042,0000	13,7000	12,8000	-8,0000	-8,0000	45,0000
0,9811	8,0000	112,6000	1940,4900	15,6000	14,2000	-1,0000	-4,0000	40,0000
0,9808	7,8000	106,9000	2065,8100	14,4000	13,0000	-2,0000	-3,0000	38,0000
0,9778	7,6000	92,1000	2214,9500	11,0000	11,2000	-4,0000	-3,0000	29,0000
0,9922	7,5000	86,0000	2304,9800	13,7000	12,1000	-5,0000	-3,0000	27,0000
0,9554	6,8000	104,7000	2555,2800	13,8000	12,9000	0,0000	-1,0000	27,0000
0,9170	6,9000	102,0000	2799,4300	14,3000	13,2000	5,0000	0,0000	27,0000
0,8858	7,1000	103,1000	2811,7000	14,0000	13,2000	5,0000	1,0000	32,0000
0,8758	7,3000	106,0000	2735,7000	14,6000	13,5000	2,0000	0,0000	24,0000
0,8700	7,4000	96,1000	2745,8800	13,1000	12,4000	6,0000	2,0000	22,0000
0,8833	7,6000	96,2000	2720,2500	13,2000	12,4000	3,0000	1,0000	22,0000
0,8924	7,6000	90,7000	2638,5300	11,6000	11,6000	1,0000	-1,0000	23,0000
0,8883	7,5000	102,3000	2659,8100	13,3000	12,6000	-9,0000	-8,0000	23,0000
0,9059	7,5000	109,4000	2641,6500	14,4000	13,1000	-26,0000	-18,0000	28,0000
0,9111	6,8000	101,0000	2604,4200	13,3000	12,3000	-25,0000	-14,0000	36,0000
0,9005	6,4000	94,7000	2892,6300	11,3000	11,4000	-13,0000	-4,0000	60,0000
0,8607	6,2000	81,0000	2915,0300	13,2000	11,8000	-6,0000	0,0000	43,0000
0,8532	6,0000	106,2000	2845,2600	14,1000	13,4000	-1,0000	4,0000	23,0000
0,8742	6,3000	101,9000	2794,8300	14,0000	13,6000	1,0000	4,0000	15,0000
0,8920	6,3000	96,4000	2848,9600	12,9000	12,9000	1,0000	3,0000	7,0000
0,9095	6,1000	110,4000	2833,1800	15,2000	14,5000	-2,0000	3,0000	6,0000
0,9217	6,1000	100,5000	2995,5500	13,6000	13,3000	2,0000	7,0000	8,0000
0,9383	6,3000	98,8000	2987,1000	13,7000	13,5000	3,0000	8,0000	5,0000

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time9 seconds
R Server'George Udny Yule' @ 72.249.76.132






Multiple Linear Regression - Estimated Regression Equation
WER[t] = + 6.82292982030508 + 1.56450104187246WSK[t] -0.00595415777078123INP[t] + 0.000104365867968344BE2[t] + 0.198628141969308Uit[t] -0.248400955218052INV[t] + 0.0354669084592271`CE-AES`[t] -0.0377754001710171`CE-CV`[t] + 0.00229192324071131`CE-WER`[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
WER[t] =  +  6.82292982030508 +  1.56450104187246WSK[t] -0.00595415777078123INP[t] +  0.000104365867968344BE2[t] +  0.198628141969308Uit[t] -0.248400955218052INV[t] +  0.0354669084592271`CE-AES`[t] -0.0377754001710171`CE-CV`[t] +  0.00229192324071131`CE-WER`[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]WER[t] =  +  6.82292982030508 +  1.56450104187246WSK[t] -0.00595415777078123INP[t] +  0.000104365867968344BE2[t] +  0.198628141969308Uit[t] -0.248400955218052INV[t] +  0.0354669084592271`CE-AES`[t] -0.0377754001710171`CE-CV`[t] +  0.00229192324071131`CE-WER`[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Estimated Regression Equation
WER[t] = + 6.82292982030508 + 1.56450104187246WSK[t] -0.00595415777078123INP[t] + 0.000104365867968344BE2[t] + 0.198628141969308Uit[t] -0.248400955218052INV[t] + 0.0354669084592271`CE-AES`[t] -0.0377754001710171`CE-CV`[t] + 0.00229192324071131`CE-WER`[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.822929820305080.9484977.193400
WSK1.564501041872460.6435332.43110.0166990.008349
INP-0.005954157770781230.010102-0.58940.5568180.278409
BE20.0001043658679683440.0001560.67090.5037050.251852
Uit0.1986281419693080.1087281.82680.0704870.035243
INV-0.2484009552180520.104877-2.36850.0196370.009819
`CE-AES`0.03546690845922710.0110083.22190.0016840.000842
`CE-CV`-0.03777540017101710.023311-1.62050.1080460.054023
`CE-WER`0.002291923240711310.006670.34360.73180.3659

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Ordinary Least Squares \tabularnewline
Variable & Parameter & S.D. & T-STATH0: parameter = 0 & 2-tail p-value & 1-tail p-value \tabularnewline
(Intercept) & 6.82292982030508 & 0.948497 & 7.1934 & 0 & 0 \tabularnewline
WSK & 1.56450104187246 & 0.643533 & 2.4311 & 0.016699 & 0.008349 \tabularnewline
INP & -0.00595415777078123 & 0.010102 & -0.5894 & 0.556818 & 0.278409 \tabularnewline
BE2 & 0.000104365867968344 & 0.000156 & 0.6709 & 0.503705 & 0.251852 \tabularnewline
Uit & 0.198628141969308 & 0.108728 & 1.8268 & 0.070487 & 0.035243 \tabularnewline
INV & -0.248400955218052 & 0.104877 & -2.3685 & 0.019637 & 0.009819 \tabularnewline
`CE-AES` & 0.0354669084592271 & 0.011008 & 3.2219 & 0.001684 & 0.000842 \tabularnewline
`CE-CV` & -0.0377754001710171 & 0.023311 & -1.6205 & 0.108046 & 0.054023 \tabularnewline
`CE-WER` & 0.00229192324071131 & 0.00667 & 0.3436 & 0.7318 & 0.3659 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&T=2

[TABLE]
[ROW][C]Multiple Linear Regression - Ordinary Least Squares[/C][/ROW]
[ROW][C]Variable[/C][C]Parameter[/C][C]S.D.[/C][C]T-STATH0: parameter = 0[/C][C]2-tail p-value[/C][C]1-tail p-value[/C][/ROW]
[ROW][C](Intercept)[/C][C]6.82292982030508[/C][C]0.948497[/C][C]7.1934[/C][C]0[/C][C]0[/C][/ROW]
[ROW][C]WSK[/C][C]1.56450104187246[/C][C]0.643533[/C][C]2.4311[/C][C]0.016699[/C][C]0.008349[/C][/ROW]
[ROW][C]INP[/C][C]-0.00595415777078123[/C][C]0.010102[/C][C]-0.5894[/C][C]0.556818[/C][C]0.278409[/C][/ROW]
[ROW][C]BE2[/C][C]0.000104365867968344[/C][C]0.000156[/C][C]0.6709[/C][C]0.503705[/C][C]0.251852[/C][/ROW]
[ROW][C]Uit[/C][C]0.198628141969308[/C][C]0.108728[/C][C]1.8268[/C][C]0.070487[/C][C]0.035243[/C][/ROW]
[ROW][C]INV[/C][C]-0.248400955218052[/C][C]0.104877[/C][C]-2.3685[/C][C]0.019637[/C][C]0.009819[/C][/ROW]
[ROW][C]`CE-AES`[/C][C]0.0354669084592271[/C][C]0.011008[/C][C]3.2219[/C][C]0.001684[/C][C]0.000842[/C][/ROW]
[ROW][C]`CE-CV`[/C][C]-0.0377754001710171[/C][C]0.023311[/C][C]-1.6205[/C][C]0.108046[/C][C]0.054023[/C][/ROW]
[ROW][C]`CE-WER`[/C][C]0.00229192324071131[/C][C]0.00667[/C][C]0.3436[/C][C]0.7318[/C][C]0.3659[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=2

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)6.822929820305080.9484977.193400
WSK1.564501041872460.6435332.43110.0166990.008349
INP-0.005954157770781230.010102-0.58940.5568180.278409
BE20.0001043658679683440.0001560.67090.5037050.251852
Uit0.1986281419693080.1087281.82680.0704870.035243
INV-0.2484009552180520.104877-2.36850.0196370.009819
`CE-AES`0.03546690845922710.0110083.22190.0016840.000842
`CE-CV`-0.03777540017101710.023311-1.62050.1080460.054023
`CE-WER`0.002291923240711310.006670.34360.73180.3659






Multiple Linear Regression - Regression Statistics
Multiple R0.517622740435239
R-squared0.267933301415687
Adjusted R-squared0.213706138557589
F-TEST (value)4.94094264375992
F-TEST (DF numerator)8
F-TEST (DF denominator)108
p-value3.17793046415993e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.676848102638881
Sum Squared Residuals49.4773222369521

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.517622740435239 \tabularnewline
R-squared & 0.267933301415687 \tabularnewline
Adjusted R-squared & 0.213706138557589 \tabularnewline
F-TEST (value) & 4.94094264375992 \tabularnewline
F-TEST (DF numerator) & 8 \tabularnewline
F-TEST (DF denominator) & 108 \tabularnewline
p-value & 3.17793046415993e-05 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 0.676848102638881 \tabularnewline
Sum Squared Residuals & 49.4773222369521 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.517622740435239[/C][/ROW]
[ROW][C]R-squared[/C][C]0.267933301415687[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.213706138557589[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]4.94094264375992[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]8[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]108[/C][/ROW]
[ROW][C]p-value[/C][C]3.17793046415993e-05[/C][/ROW]
[ROW][C]Multiple Linear Regression - Residual Statistics[/C][/ROW]
[ROW][C]Residual Standard Deviation[/C][C]0.676848102638881[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]49.4773222369521[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Regression Statistics
Multiple R0.517622740435239
R-squared0.267933301415687
Adjusted R-squared0.213706138557589
F-TEST (value)4.94094264375992
F-TEST (DF numerator)8
F-TEST (DF denominator)108
p-value3.17793046415993e-05
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation0.676848102638881
Sum Squared Residuals49.4773222369521






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.77.880599606348720.819400393651278
28.97.884622914002151.01537708599786
38.98.259837292491940.640162707508063
48.17.950143988151230.149856011848771
587.852557765741070.147442234258935
68.38.012660664530410.287339335469587
78.58.342582536057520.157417463942484
88.78.515234019078420.184765980921577
98.68.481471071259760.118528928740241
108.38.4407513073001-0.140751307300108
117.98.7643975951517-0.864397595151697
127.98.68357688562746-0.783576885627456
138.18.61146161258878-0.511461612588779
148.38.62298358925588-0.322983589255877
158.18.93330965393728-0.833309653937279
167.48.45567581740129-1.05567581740129
177.38.50787464624604-1.20787464624604
187.78.40762955820622-0.707629558206224
1988.11424777877255-0.114247778772555
2087.97830454575010.0216954542498931
217.77.81061945026334-0.110619450263336
226.97.84624023363816-0.94624023363816
236.67.48437436146764-0.884374361467642
246.97.4998719545212-0.599871954521206
257.57.45664150254160.0433584974584010
267.97.363313781932380.536686218067618
277.77.77446828382875-0.0744682838287536
286.57.22206401933694-0.722064019336944
296.17.41453707974335-1.31453707974335
306.47.2856588895853-0.885658889585307
316.87.45992470701426-0.659924707014256
327.17.6142212706313-0.514221270631305
337.37.194858705202630.105141294797368
347.27.32644429343027-0.126444293430272
3577.38782488462863-0.387824884628626
3677.34557152093568-0.34557152093568
3777.68451423901589-0.684514239015891
387.37.65319496004413-0.353194960044127
397.57.7979263880165-0.297926388016500
407.28.00103915445745-0.80103915445745
417.77.979390288288-0.279390288288002
4288.08052319932402-0.0805231993240162
437.97.884481406521530.0155185934784696
4487.913453348825530.0865466511744678
4587.776657541719480.223342458280524
467.97.9212455508171-0.0212455508170908
477.97.9185150495724-0.018515049572402
4887.868948799015460.131051200984542
498.18.007646602145880.0923533978541212
508.17.688225882234620.411774117765384
518.28.070551217200730.12944878279927
5287.844987416406280.155012583593718
538.37.822087419336260.477912580663738
548.57.793998035772720.706001964227281
558.67.382108310225141.21789168977486
568.77.642714490019571.05728550998043
578.77.520477585032531.17952241496746
588.57.261798258498041.23820174150196
598.47.505341499483740.894658500516256
608.57.898448501534890.601551498465111
618.77.808923937646830.89107606235317
628.77.403630118032121.29636988196788
638.68.008408233780850.591591766219152
647.97.45157432237640.448425677623603
658.17.80235206760850.297647932391502
668.27.932731085703120.267268914296877
678.57.982099453992950.517900546007047
688.68.179755197887050.420244802112949
698.58.116727292700840.383272707299162
708.38.051965935411450.248034064588551
718.28.106090658436190.0939093415638127
728.77.978852232297010.721147767702986
739.37.806284388269131.49371561173087
749.37.838430859243881.46156914075612
758.88.308481813891810.49151818610819
767.47.70642040321052-0.306420403210521
777.28.23113676620541-1.03113676620541
787.58.03720706033422-0.537207060334223
798.38.000277679192530.299722320807472
808.88.318452919655330.48154708034467
818.98.313344553142550.586655446857452
828.68.058566747520390.541433252479611
838.48.095520459677890.304479540322113
848.48.037347217501060.362652782498944
858.47.914655776674850.485344223325151
868.47.887398821033280.512601178966725
878.38.144619767313540.155380232686461
887.67.93446594731442-0.334465947314417
897.67.96163349868095-0.361633498680953
907.97.96195179335233-0.0619517933523347
9187.57062933686270.429370663137305
928.27.290543338840350.90945666115965
938.37.391588977014230.90841102298577
948.27.23713363750560.962866362494395
958.17.660476218453840.439523781546162
9687.668561623108220.331438376891778
977.87.697011323449730.102988676550268
987.67.476229391492470.123770608507532
997.57.81715899667062-0.317158996670620
1006.87.59729117674517-0.797291176745172
1016.97.7431244159272-0.843124415927207
1027.17.60313875251465-0.503138752514646
1037.37.51999076607887-0.219990766078876
1047.47.60795709130201-0.207957091302014
1057.67.576732131176080.0232678688239221
1067.67.503012823353880.0969871766461182
1077.57.428776647346130.071223352653871
1087.57.292708714012310.207291285987688
1096.87.2299040051492-0.429904005149202
1106.47.21007040820369-0.810070408203688
1116.27.56795017473225-1.36795017473225
11267.1606083107077-1.16060831070770
1136.37.19685796603196-0.896857966031963
1146.37.23793300348144-0.93793300348144
1156.17.13101721908886-1.03101721908886
1166.17.20162217845749-1.10162217845749
1176.37.19783143409797-0.897831434097968

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 8.7 & 7.88059960634872 & 0.819400393651278 \tabularnewline
2 & 8.9 & 7.88462291400215 & 1.01537708599786 \tabularnewline
3 & 8.9 & 8.25983729249194 & 0.640162707508063 \tabularnewline
4 & 8.1 & 7.95014398815123 & 0.149856011848771 \tabularnewline
5 & 8 & 7.85255776574107 & 0.147442234258935 \tabularnewline
6 & 8.3 & 8.01266066453041 & 0.287339335469587 \tabularnewline
7 & 8.5 & 8.34258253605752 & 0.157417463942484 \tabularnewline
8 & 8.7 & 8.51523401907842 & 0.184765980921577 \tabularnewline
9 & 8.6 & 8.48147107125976 & 0.118528928740241 \tabularnewline
10 & 8.3 & 8.4407513073001 & -0.140751307300108 \tabularnewline
11 & 7.9 & 8.7643975951517 & -0.864397595151697 \tabularnewline
12 & 7.9 & 8.68357688562746 & -0.783576885627456 \tabularnewline
13 & 8.1 & 8.61146161258878 & -0.511461612588779 \tabularnewline
14 & 8.3 & 8.62298358925588 & -0.322983589255877 \tabularnewline
15 & 8.1 & 8.93330965393728 & -0.833309653937279 \tabularnewline
16 & 7.4 & 8.45567581740129 & -1.05567581740129 \tabularnewline
17 & 7.3 & 8.50787464624604 & -1.20787464624604 \tabularnewline
18 & 7.7 & 8.40762955820622 & -0.707629558206224 \tabularnewline
19 & 8 & 8.11424777877255 & -0.114247778772555 \tabularnewline
20 & 8 & 7.9783045457501 & 0.0216954542498931 \tabularnewline
21 & 7.7 & 7.81061945026334 & -0.110619450263336 \tabularnewline
22 & 6.9 & 7.84624023363816 & -0.94624023363816 \tabularnewline
23 & 6.6 & 7.48437436146764 & -0.884374361467642 \tabularnewline
24 & 6.9 & 7.4998719545212 & -0.599871954521206 \tabularnewline
25 & 7.5 & 7.4566415025416 & 0.0433584974584010 \tabularnewline
26 & 7.9 & 7.36331378193238 & 0.536686218067618 \tabularnewline
27 & 7.7 & 7.77446828382875 & -0.0744682838287536 \tabularnewline
28 & 6.5 & 7.22206401933694 & -0.722064019336944 \tabularnewline
29 & 6.1 & 7.41453707974335 & -1.31453707974335 \tabularnewline
30 & 6.4 & 7.2856588895853 & -0.885658889585307 \tabularnewline
31 & 6.8 & 7.45992470701426 & -0.659924707014256 \tabularnewline
32 & 7.1 & 7.6142212706313 & -0.514221270631305 \tabularnewline
33 & 7.3 & 7.19485870520263 & 0.105141294797368 \tabularnewline
34 & 7.2 & 7.32644429343027 & -0.126444293430272 \tabularnewline
35 & 7 & 7.38782488462863 & -0.387824884628626 \tabularnewline
36 & 7 & 7.34557152093568 & -0.34557152093568 \tabularnewline
37 & 7 & 7.68451423901589 & -0.684514239015891 \tabularnewline
38 & 7.3 & 7.65319496004413 & -0.353194960044127 \tabularnewline
39 & 7.5 & 7.7979263880165 & -0.297926388016500 \tabularnewline
40 & 7.2 & 8.00103915445745 & -0.80103915445745 \tabularnewline
41 & 7.7 & 7.979390288288 & -0.279390288288002 \tabularnewline
42 & 8 & 8.08052319932402 & -0.0805231993240162 \tabularnewline
43 & 7.9 & 7.88448140652153 & 0.0155185934784696 \tabularnewline
44 & 8 & 7.91345334882553 & 0.0865466511744678 \tabularnewline
45 & 8 & 7.77665754171948 & 0.223342458280524 \tabularnewline
46 & 7.9 & 7.9212455508171 & -0.0212455508170908 \tabularnewline
47 & 7.9 & 7.9185150495724 & -0.018515049572402 \tabularnewline
48 & 8 & 7.86894879901546 & 0.131051200984542 \tabularnewline
49 & 8.1 & 8.00764660214588 & 0.0923533978541212 \tabularnewline
50 & 8.1 & 7.68822588223462 & 0.411774117765384 \tabularnewline
51 & 8.2 & 8.07055121720073 & 0.12944878279927 \tabularnewline
52 & 8 & 7.84498741640628 & 0.155012583593718 \tabularnewline
53 & 8.3 & 7.82208741933626 & 0.477912580663738 \tabularnewline
54 & 8.5 & 7.79399803577272 & 0.706001964227281 \tabularnewline
55 & 8.6 & 7.38210831022514 & 1.21789168977486 \tabularnewline
56 & 8.7 & 7.64271449001957 & 1.05728550998043 \tabularnewline
57 & 8.7 & 7.52047758503253 & 1.17952241496746 \tabularnewline
58 & 8.5 & 7.26179825849804 & 1.23820174150196 \tabularnewline
59 & 8.4 & 7.50534149948374 & 0.894658500516256 \tabularnewline
60 & 8.5 & 7.89844850153489 & 0.601551498465111 \tabularnewline
61 & 8.7 & 7.80892393764683 & 0.89107606235317 \tabularnewline
62 & 8.7 & 7.40363011803212 & 1.29636988196788 \tabularnewline
63 & 8.6 & 8.00840823378085 & 0.591591766219152 \tabularnewline
64 & 7.9 & 7.4515743223764 & 0.448425677623603 \tabularnewline
65 & 8.1 & 7.8023520676085 & 0.297647932391502 \tabularnewline
66 & 8.2 & 7.93273108570312 & 0.267268914296877 \tabularnewline
67 & 8.5 & 7.98209945399295 & 0.517900546007047 \tabularnewline
68 & 8.6 & 8.17975519788705 & 0.420244802112949 \tabularnewline
69 & 8.5 & 8.11672729270084 & 0.383272707299162 \tabularnewline
70 & 8.3 & 8.05196593541145 & 0.248034064588551 \tabularnewline
71 & 8.2 & 8.10609065843619 & 0.0939093415638127 \tabularnewline
72 & 8.7 & 7.97885223229701 & 0.721147767702986 \tabularnewline
73 & 9.3 & 7.80628438826913 & 1.49371561173087 \tabularnewline
74 & 9.3 & 7.83843085924388 & 1.46156914075612 \tabularnewline
75 & 8.8 & 8.30848181389181 & 0.49151818610819 \tabularnewline
76 & 7.4 & 7.70642040321052 & -0.306420403210521 \tabularnewline
77 & 7.2 & 8.23113676620541 & -1.03113676620541 \tabularnewline
78 & 7.5 & 8.03720706033422 & -0.537207060334223 \tabularnewline
79 & 8.3 & 8.00027767919253 & 0.299722320807472 \tabularnewline
80 & 8.8 & 8.31845291965533 & 0.48154708034467 \tabularnewline
81 & 8.9 & 8.31334455314255 & 0.586655446857452 \tabularnewline
82 & 8.6 & 8.05856674752039 & 0.541433252479611 \tabularnewline
83 & 8.4 & 8.09552045967789 & 0.304479540322113 \tabularnewline
84 & 8.4 & 8.03734721750106 & 0.362652782498944 \tabularnewline
85 & 8.4 & 7.91465577667485 & 0.485344223325151 \tabularnewline
86 & 8.4 & 7.88739882103328 & 0.512601178966725 \tabularnewline
87 & 8.3 & 8.14461976731354 & 0.155380232686461 \tabularnewline
88 & 7.6 & 7.93446594731442 & -0.334465947314417 \tabularnewline
89 & 7.6 & 7.96163349868095 & -0.361633498680953 \tabularnewline
90 & 7.9 & 7.96195179335233 & -0.0619517933523347 \tabularnewline
91 & 8 & 7.5706293368627 & 0.429370663137305 \tabularnewline
92 & 8.2 & 7.29054333884035 & 0.90945666115965 \tabularnewline
93 & 8.3 & 7.39158897701423 & 0.90841102298577 \tabularnewline
94 & 8.2 & 7.2371336375056 & 0.962866362494395 \tabularnewline
95 & 8.1 & 7.66047621845384 & 0.439523781546162 \tabularnewline
96 & 8 & 7.66856162310822 & 0.331438376891778 \tabularnewline
97 & 7.8 & 7.69701132344973 & 0.102988676550268 \tabularnewline
98 & 7.6 & 7.47622939149247 & 0.123770608507532 \tabularnewline
99 & 7.5 & 7.81715899667062 & -0.317158996670620 \tabularnewline
100 & 6.8 & 7.59729117674517 & -0.797291176745172 \tabularnewline
101 & 6.9 & 7.7431244159272 & -0.843124415927207 \tabularnewline
102 & 7.1 & 7.60313875251465 & -0.503138752514646 \tabularnewline
103 & 7.3 & 7.51999076607887 & -0.219990766078876 \tabularnewline
104 & 7.4 & 7.60795709130201 & -0.207957091302014 \tabularnewline
105 & 7.6 & 7.57673213117608 & 0.0232678688239221 \tabularnewline
106 & 7.6 & 7.50301282335388 & 0.0969871766461182 \tabularnewline
107 & 7.5 & 7.42877664734613 & 0.071223352653871 \tabularnewline
108 & 7.5 & 7.29270871401231 & 0.207291285987688 \tabularnewline
109 & 6.8 & 7.2299040051492 & -0.429904005149202 \tabularnewline
110 & 6.4 & 7.21007040820369 & -0.810070408203688 \tabularnewline
111 & 6.2 & 7.56795017473225 & -1.36795017473225 \tabularnewline
112 & 6 & 7.1606083107077 & -1.16060831070770 \tabularnewline
113 & 6.3 & 7.19685796603196 & -0.896857966031963 \tabularnewline
114 & 6.3 & 7.23793300348144 & -0.93793300348144 \tabularnewline
115 & 6.1 & 7.13101721908886 & -1.03101721908886 \tabularnewline
116 & 6.1 & 7.20162217845749 & -1.10162217845749 \tabularnewline
117 & 6.3 & 7.19783143409797 & -0.897831434097968 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&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]8.7[/C][C]7.88059960634872[/C][C]0.819400393651278[/C][/ROW]
[ROW][C]2[/C][C]8.9[/C][C]7.88462291400215[/C][C]1.01537708599786[/C][/ROW]
[ROW][C]3[/C][C]8.9[/C][C]8.25983729249194[/C][C]0.640162707508063[/C][/ROW]
[ROW][C]4[/C][C]8.1[/C][C]7.95014398815123[/C][C]0.149856011848771[/C][/ROW]
[ROW][C]5[/C][C]8[/C][C]7.85255776574107[/C][C]0.147442234258935[/C][/ROW]
[ROW][C]6[/C][C]8.3[/C][C]8.01266066453041[/C][C]0.287339335469587[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]8.34258253605752[/C][C]0.157417463942484[/C][/ROW]
[ROW][C]8[/C][C]8.7[/C][C]8.51523401907842[/C][C]0.184765980921577[/C][/ROW]
[ROW][C]9[/C][C]8.6[/C][C]8.48147107125976[/C][C]0.118528928740241[/C][/ROW]
[ROW][C]10[/C][C]8.3[/C][C]8.4407513073001[/C][C]-0.140751307300108[/C][/ROW]
[ROW][C]11[/C][C]7.9[/C][C]8.7643975951517[/C][C]-0.864397595151697[/C][/ROW]
[ROW][C]12[/C][C]7.9[/C][C]8.68357688562746[/C][C]-0.783576885627456[/C][/ROW]
[ROW][C]13[/C][C]8.1[/C][C]8.61146161258878[/C][C]-0.511461612588779[/C][/ROW]
[ROW][C]14[/C][C]8.3[/C][C]8.62298358925588[/C][C]-0.322983589255877[/C][/ROW]
[ROW][C]15[/C][C]8.1[/C][C]8.93330965393728[/C][C]-0.833309653937279[/C][/ROW]
[ROW][C]16[/C][C]7.4[/C][C]8.45567581740129[/C][C]-1.05567581740129[/C][/ROW]
[ROW][C]17[/C][C]7.3[/C][C]8.50787464624604[/C][C]-1.20787464624604[/C][/ROW]
[ROW][C]18[/C][C]7.7[/C][C]8.40762955820622[/C][C]-0.707629558206224[/C][/ROW]
[ROW][C]19[/C][C]8[/C][C]8.11424777877255[/C][C]-0.114247778772555[/C][/ROW]
[ROW][C]20[/C][C]8[/C][C]7.9783045457501[/C][C]0.0216954542498931[/C][/ROW]
[ROW][C]21[/C][C]7.7[/C][C]7.81061945026334[/C][C]-0.110619450263336[/C][/ROW]
[ROW][C]22[/C][C]6.9[/C][C]7.84624023363816[/C][C]-0.94624023363816[/C][/ROW]
[ROW][C]23[/C][C]6.6[/C][C]7.48437436146764[/C][C]-0.884374361467642[/C][/ROW]
[ROW][C]24[/C][C]6.9[/C][C]7.4998719545212[/C][C]-0.599871954521206[/C][/ROW]
[ROW][C]25[/C][C]7.5[/C][C]7.4566415025416[/C][C]0.0433584974584010[/C][/ROW]
[ROW][C]26[/C][C]7.9[/C][C]7.36331378193238[/C][C]0.536686218067618[/C][/ROW]
[ROW][C]27[/C][C]7.7[/C][C]7.77446828382875[/C][C]-0.0744682838287536[/C][/ROW]
[ROW][C]28[/C][C]6.5[/C][C]7.22206401933694[/C][C]-0.722064019336944[/C][/ROW]
[ROW][C]29[/C][C]6.1[/C][C]7.41453707974335[/C][C]-1.31453707974335[/C][/ROW]
[ROW][C]30[/C][C]6.4[/C][C]7.2856588895853[/C][C]-0.885658889585307[/C][/ROW]
[ROW][C]31[/C][C]6.8[/C][C]7.45992470701426[/C][C]-0.659924707014256[/C][/ROW]
[ROW][C]32[/C][C]7.1[/C][C]7.6142212706313[/C][C]-0.514221270631305[/C][/ROW]
[ROW][C]33[/C][C]7.3[/C][C]7.19485870520263[/C][C]0.105141294797368[/C][/ROW]
[ROW][C]34[/C][C]7.2[/C][C]7.32644429343027[/C][C]-0.126444293430272[/C][/ROW]
[ROW][C]35[/C][C]7[/C][C]7.38782488462863[/C][C]-0.387824884628626[/C][/ROW]
[ROW][C]36[/C][C]7[/C][C]7.34557152093568[/C][C]-0.34557152093568[/C][/ROW]
[ROW][C]37[/C][C]7[/C][C]7.68451423901589[/C][C]-0.684514239015891[/C][/ROW]
[ROW][C]38[/C][C]7.3[/C][C]7.65319496004413[/C][C]-0.353194960044127[/C][/ROW]
[ROW][C]39[/C][C]7.5[/C][C]7.7979263880165[/C][C]-0.297926388016500[/C][/ROW]
[ROW][C]40[/C][C]7.2[/C][C]8.00103915445745[/C][C]-0.80103915445745[/C][/ROW]
[ROW][C]41[/C][C]7.7[/C][C]7.979390288288[/C][C]-0.279390288288002[/C][/ROW]
[ROW][C]42[/C][C]8[/C][C]8.08052319932402[/C][C]-0.0805231993240162[/C][/ROW]
[ROW][C]43[/C][C]7.9[/C][C]7.88448140652153[/C][C]0.0155185934784696[/C][/ROW]
[ROW][C]44[/C][C]8[/C][C]7.91345334882553[/C][C]0.0865466511744678[/C][/ROW]
[ROW][C]45[/C][C]8[/C][C]7.77665754171948[/C][C]0.223342458280524[/C][/ROW]
[ROW][C]46[/C][C]7.9[/C][C]7.9212455508171[/C][C]-0.0212455508170908[/C][/ROW]
[ROW][C]47[/C][C]7.9[/C][C]7.9185150495724[/C][C]-0.018515049572402[/C][/ROW]
[ROW][C]48[/C][C]8[/C][C]7.86894879901546[/C][C]0.131051200984542[/C][/ROW]
[ROW][C]49[/C][C]8.1[/C][C]8.00764660214588[/C][C]0.0923533978541212[/C][/ROW]
[ROW][C]50[/C][C]8.1[/C][C]7.68822588223462[/C][C]0.411774117765384[/C][/ROW]
[ROW][C]51[/C][C]8.2[/C][C]8.07055121720073[/C][C]0.12944878279927[/C][/ROW]
[ROW][C]52[/C][C]8[/C][C]7.84498741640628[/C][C]0.155012583593718[/C][/ROW]
[ROW][C]53[/C][C]8.3[/C][C]7.82208741933626[/C][C]0.477912580663738[/C][/ROW]
[ROW][C]54[/C][C]8.5[/C][C]7.79399803577272[/C][C]0.706001964227281[/C][/ROW]
[ROW][C]55[/C][C]8.6[/C][C]7.38210831022514[/C][C]1.21789168977486[/C][/ROW]
[ROW][C]56[/C][C]8.7[/C][C]7.64271449001957[/C][C]1.05728550998043[/C][/ROW]
[ROW][C]57[/C][C]8.7[/C][C]7.52047758503253[/C][C]1.17952241496746[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]7.26179825849804[/C][C]1.23820174150196[/C][/ROW]
[ROW][C]59[/C][C]8.4[/C][C]7.50534149948374[/C][C]0.894658500516256[/C][/ROW]
[ROW][C]60[/C][C]8.5[/C][C]7.89844850153489[/C][C]0.601551498465111[/C][/ROW]
[ROW][C]61[/C][C]8.7[/C][C]7.80892393764683[/C][C]0.89107606235317[/C][/ROW]
[ROW][C]62[/C][C]8.7[/C][C]7.40363011803212[/C][C]1.29636988196788[/C][/ROW]
[ROW][C]63[/C][C]8.6[/C][C]8.00840823378085[/C][C]0.591591766219152[/C][/ROW]
[ROW][C]64[/C][C]7.9[/C][C]7.4515743223764[/C][C]0.448425677623603[/C][/ROW]
[ROW][C]65[/C][C]8.1[/C][C]7.8023520676085[/C][C]0.297647932391502[/C][/ROW]
[ROW][C]66[/C][C]8.2[/C][C]7.93273108570312[/C][C]0.267268914296877[/C][/ROW]
[ROW][C]67[/C][C]8.5[/C][C]7.98209945399295[/C][C]0.517900546007047[/C][/ROW]
[ROW][C]68[/C][C]8.6[/C][C]8.17975519788705[/C][C]0.420244802112949[/C][/ROW]
[ROW][C]69[/C][C]8.5[/C][C]8.11672729270084[/C][C]0.383272707299162[/C][/ROW]
[ROW][C]70[/C][C]8.3[/C][C]8.05196593541145[/C][C]0.248034064588551[/C][/ROW]
[ROW][C]71[/C][C]8.2[/C][C]8.10609065843619[/C][C]0.0939093415638127[/C][/ROW]
[ROW][C]72[/C][C]8.7[/C][C]7.97885223229701[/C][C]0.721147767702986[/C][/ROW]
[ROW][C]73[/C][C]9.3[/C][C]7.80628438826913[/C][C]1.49371561173087[/C][/ROW]
[ROW][C]74[/C][C]9.3[/C][C]7.83843085924388[/C][C]1.46156914075612[/C][/ROW]
[ROW][C]75[/C][C]8.8[/C][C]8.30848181389181[/C][C]0.49151818610819[/C][/ROW]
[ROW][C]76[/C][C]7.4[/C][C]7.70642040321052[/C][C]-0.306420403210521[/C][/ROW]
[ROW][C]77[/C][C]7.2[/C][C]8.23113676620541[/C][C]-1.03113676620541[/C][/ROW]
[ROW][C]78[/C][C]7.5[/C][C]8.03720706033422[/C][C]-0.537207060334223[/C][/ROW]
[ROW][C]79[/C][C]8.3[/C][C]8.00027767919253[/C][C]0.299722320807472[/C][/ROW]
[ROW][C]80[/C][C]8.8[/C][C]8.31845291965533[/C][C]0.48154708034467[/C][/ROW]
[ROW][C]81[/C][C]8.9[/C][C]8.31334455314255[/C][C]0.586655446857452[/C][/ROW]
[ROW][C]82[/C][C]8.6[/C][C]8.05856674752039[/C][C]0.541433252479611[/C][/ROW]
[ROW][C]83[/C][C]8.4[/C][C]8.09552045967789[/C][C]0.304479540322113[/C][/ROW]
[ROW][C]84[/C][C]8.4[/C][C]8.03734721750106[/C][C]0.362652782498944[/C][/ROW]
[ROW][C]85[/C][C]8.4[/C][C]7.91465577667485[/C][C]0.485344223325151[/C][/ROW]
[ROW][C]86[/C][C]8.4[/C][C]7.88739882103328[/C][C]0.512601178966725[/C][/ROW]
[ROW][C]87[/C][C]8.3[/C][C]8.14461976731354[/C][C]0.155380232686461[/C][/ROW]
[ROW][C]88[/C][C]7.6[/C][C]7.93446594731442[/C][C]-0.334465947314417[/C][/ROW]
[ROW][C]89[/C][C]7.6[/C][C]7.96163349868095[/C][C]-0.361633498680953[/C][/ROW]
[ROW][C]90[/C][C]7.9[/C][C]7.96195179335233[/C][C]-0.0619517933523347[/C][/ROW]
[ROW][C]91[/C][C]8[/C][C]7.5706293368627[/C][C]0.429370663137305[/C][/ROW]
[ROW][C]92[/C][C]8.2[/C][C]7.29054333884035[/C][C]0.90945666115965[/C][/ROW]
[ROW][C]93[/C][C]8.3[/C][C]7.39158897701423[/C][C]0.90841102298577[/C][/ROW]
[ROW][C]94[/C][C]8.2[/C][C]7.2371336375056[/C][C]0.962866362494395[/C][/ROW]
[ROW][C]95[/C][C]8.1[/C][C]7.66047621845384[/C][C]0.439523781546162[/C][/ROW]
[ROW][C]96[/C][C]8[/C][C]7.66856162310822[/C][C]0.331438376891778[/C][/ROW]
[ROW][C]97[/C][C]7.8[/C][C]7.69701132344973[/C][C]0.102988676550268[/C][/ROW]
[ROW][C]98[/C][C]7.6[/C][C]7.47622939149247[/C][C]0.123770608507532[/C][/ROW]
[ROW][C]99[/C][C]7.5[/C][C]7.81715899667062[/C][C]-0.317158996670620[/C][/ROW]
[ROW][C]100[/C][C]6.8[/C][C]7.59729117674517[/C][C]-0.797291176745172[/C][/ROW]
[ROW][C]101[/C][C]6.9[/C][C]7.7431244159272[/C][C]-0.843124415927207[/C][/ROW]
[ROW][C]102[/C][C]7.1[/C][C]7.60313875251465[/C][C]-0.503138752514646[/C][/ROW]
[ROW][C]103[/C][C]7.3[/C][C]7.51999076607887[/C][C]-0.219990766078876[/C][/ROW]
[ROW][C]104[/C][C]7.4[/C][C]7.60795709130201[/C][C]-0.207957091302014[/C][/ROW]
[ROW][C]105[/C][C]7.6[/C][C]7.57673213117608[/C][C]0.0232678688239221[/C][/ROW]
[ROW][C]106[/C][C]7.6[/C][C]7.50301282335388[/C][C]0.0969871766461182[/C][/ROW]
[ROW][C]107[/C][C]7.5[/C][C]7.42877664734613[/C][C]0.071223352653871[/C][/ROW]
[ROW][C]108[/C][C]7.5[/C][C]7.29270871401231[/C][C]0.207291285987688[/C][/ROW]
[ROW][C]109[/C][C]6.8[/C][C]7.2299040051492[/C][C]-0.429904005149202[/C][/ROW]
[ROW][C]110[/C][C]6.4[/C][C]7.21007040820369[/C][C]-0.810070408203688[/C][/ROW]
[ROW][C]111[/C][C]6.2[/C][C]7.56795017473225[/C][C]-1.36795017473225[/C][/ROW]
[ROW][C]112[/C][C]6[/C][C]7.1606083107077[/C][C]-1.16060831070770[/C][/ROW]
[ROW][C]113[/C][C]6.3[/C][C]7.19685796603196[/C][C]-0.896857966031963[/C][/ROW]
[ROW][C]114[/C][C]6.3[/C][C]7.23793300348144[/C][C]-0.93793300348144[/C][/ROW]
[ROW][C]115[/C][C]6.1[/C][C]7.13101721908886[/C][C]-1.03101721908886[/C][/ROW]
[ROW][C]116[/C][C]6.1[/C][C]7.20162217845749[/C][C]-1.10162217845749[/C][/ROW]
[ROW][C]117[/C][C]6.3[/C][C]7.19783143409797[/C][C]-0.897831434097968[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
18.77.880599606348720.819400393651278
28.97.884622914002151.01537708599786
38.98.259837292491940.640162707508063
48.17.950143988151230.149856011848771
587.852557765741070.147442234258935
68.38.012660664530410.287339335469587
78.58.342582536057520.157417463942484
88.78.515234019078420.184765980921577
98.68.481471071259760.118528928740241
108.38.4407513073001-0.140751307300108
117.98.7643975951517-0.864397595151697
127.98.68357688562746-0.783576885627456
138.18.61146161258878-0.511461612588779
148.38.62298358925588-0.322983589255877
158.18.93330965393728-0.833309653937279
167.48.45567581740129-1.05567581740129
177.38.50787464624604-1.20787464624604
187.78.40762955820622-0.707629558206224
1988.11424777877255-0.114247778772555
2087.97830454575010.0216954542498931
217.77.81061945026334-0.110619450263336
226.97.84624023363816-0.94624023363816
236.67.48437436146764-0.884374361467642
246.97.4998719545212-0.599871954521206
257.57.45664150254160.0433584974584010
267.97.363313781932380.536686218067618
277.77.77446828382875-0.0744682838287536
286.57.22206401933694-0.722064019336944
296.17.41453707974335-1.31453707974335
306.47.2856588895853-0.885658889585307
316.87.45992470701426-0.659924707014256
327.17.6142212706313-0.514221270631305
337.37.194858705202630.105141294797368
347.27.32644429343027-0.126444293430272
3577.38782488462863-0.387824884628626
3677.34557152093568-0.34557152093568
3777.68451423901589-0.684514239015891
387.37.65319496004413-0.353194960044127
397.57.7979263880165-0.297926388016500
407.28.00103915445745-0.80103915445745
417.77.979390288288-0.279390288288002
4288.08052319932402-0.0805231993240162
437.97.884481406521530.0155185934784696
4487.913453348825530.0865466511744678
4587.776657541719480.223342458280524
467.97.9212455508171-0.0212455508170908
477.97.9185150495724-0.018515049572402
4887.868948799015460.131051200984542
498.18.007646602145880.0923533978541212
508.17.688225882234620.411774117765384
518.28.070551217200730.12944878279927
5287.844987416406280.155012583593718
538.37.822087419336260.477912580663738
548.57.793998035772720.706001964227281
558.67.382108310225141.21789168977486
568.77.642714490019571.05728550998043
578.77.520477585032531.17952241496746
588.57.261798258498041.23820174150196
598.47.505341499483740.894658500516256
608.57.898448501534890.601551498465111
618.77.808923937646830.89107606235317
628.77.403630118032121.29636988196788
638.68.008408233780850.591591766219152
647.97.45157432237640.448425677623603
658.17.80235206760850.297647932391502
668.27.932731085703120.267268914296877
678.57.982099453992950.517900546007047
688.68.179755197887050.420244802112949
698.58.116727292700840.383272707299162
708.38.051965935411450.248034064588551
718.28.106090658436190.0939093415638127
728.77.978852232297010.721147767702986
739.37.806284388269131.49371561173087
749.37.838430859243881.46156914075612
758.88.308481813891810.49151818610819
767.47.70642040321052-0.306420403210521
777.28.23113676620541-1.03113676620541
787.58.03720706033422-0.537207060334223
798.38.000277679192530.299722320807472
808.88.318452919655330.48154708034467
818.98.313344553142550.586655446857452
828.68.058566747520390.541433252479611
838.48.095520459677890.304479540322113
848.48.037347217501060.362652782498944
858.47.914655776674850.485344223325151
868.47.887398821033280.512601178966725
878.38.144619767313540.155380232686461
887.67.93446594731442-0.334465947314417
897.67.96163349868095-0.361633498680953
907.97.96195179335233-0.0619517933523347
9187.57062933686270.429370663137305
928.27.290543338840350.90945666115965
938.37.391588977014230.90841102298577
948.27.23713363750560.962866362494395
958.17.660476218453840.439523781546162
9687.668561623108220.331438376891778
977.87.697011323449730.102988676550268
987.67.476229391492470.123770608507532
997.57.81715899667062-0.317158996670620
1006.87.59729117674517-0.797291176745172
1016.97.7431244159272-0.843124415927207
1027.17.60313875251465-0.503138752514646
1037.37.51999076607887-0.219990766078876
1047.47.60795709130201-0.207957091302014
1057.67.576732131176080.0232678688239221
1067.67.503012823353880.0969871766461182
1077.57.428776647346130.071223352653871
1087.57.292708714012310.207291285987688
1096.87.2299040051492-0.429904005149202
1106.47.21007040820369-0.810070408203688
1116.27.56795017473225-1.36795017473225
11267.1606083107077-1.16060831070770
1136.37.19685796603196-0.896857966031963
1146.37.23793300348144-0.93793300348144
1156.17.13101721908886-1.03101721908886
1166.17.20162217845749-1.10162217845749
1176.37.19783143409797-0.897831434097968






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.1083524268536030.2167048537072060.891647573146397
130.06221589870640550.1244317974128110.937784101293595
140.02334638164046350.04669276328092690.976653618359537
150.008277837431871120.01655567486374220.991722162568129
160.003625098379102220.007250196758204430.996374901620898
170.001874788632895060.003749577265790120.998125211367105
180.008274509873317660.01654901974663530.991725490126682
190.01766530135456460.03533060270912920.982334698645435
200.01526924496648010.03053848993296020.98473075503352
210.01138688683456130.02277377366912260.988613113165439
220.05810385736770660.1162077147354130.941896142632293
230.1677930390656730.3355860781313470.832206960934327
240.2194287964995790.4388575929991590.780571203500421
250.2472858231784480.4945716463568950.752714176821552
260.2012143834623470.4024287669246930.798785616537653
270.1633300815760060.3266601631520120.836669918423994
280.2584252757338870.5168505514677750.741574724266113
290.4555881968094120.9111763936188230.544411803190588
300.54542701313160.90914597373680.4545729868684
310.6054423147247120.7891153705505750.394557685275288
320.5537463590656590.8925072818686830.446253640934341
330.511800945851020.976398108297960.48819905414898
340.4764986187961250.952997237592250.523501381203875
350.4346426351471450.869285270294290.565357364852855
360.4214332955654660.8428665911309320.578566704434534
370.4148816479561220.8297632959122450.585118352043878
380.3610260573805410.7220521147610820.638973942619459
390.3370806267852690.6741612535705370.662919373214731
400.3453520670416880.6907041340833750.654647932958312
410.323025538210920.646051076421840.67697446178908
420.2908972551776970.5817945103553940.709102744822303
430.2462597334866810.4925194669733630.753740266513319
440.2250343004257730.4500686008515470.774965699574227
450.1829752355459740.3659504710919480.817024764454026
460.1554086374575080.3108172749150150.844591362542492
470.1993203088372360.3986406176744710.800679691162764
480.1617307851466110.3234615702932220.838269214853389
490.1287705492655270.2575410985310540.871229450734473
500.1080474291427910.2160948582855810.89195257085721
510.08704424129280270.1740884825856050.912955758707197
520.07014099783542220.1402819956708440.929859002164578
530.06742977796625750.1348595559325150.932570222033743
540.07539888889006550.1507977777801310.924601111109934
550.07587265928076920.1517453185615380.92412734071923
560.07338168357216370.1467633671443270.926618316427836
570.06072201860988840.1214440372197770.939277981390112
580.050376411765350.10075282353070.94962358823465
590.03866173617331850.0773234723466370.961338263826681
600.05973625919140770.1194725183828150.940263740808592
610.1170620654621820.2341241309243640.882937934537818
620.1874925654390270.3749851308780540.812507434560973
630.1631032034161530.3262064068323060.836896796583847
640.1317909301280720.2635818602561440.868209069871928
650.1067225098225760.2134450196451510.893277490177424
660.09085557943079680.1817111588615940.909144420569203
670.07303934059037580.1460786811807520.926960659409624
680.0552514266666180.1105028533332360.944748573333382
690.04230305787556350.0846061157511270.957696942124436
700.03626474044129020.07252948088258030.96373525955871
710.02922387536057110.05844775072114220.970776124639429
720.0280036023490330.0560072046980660.971996397650967
730.1984871169088090.3969742338176190.80151288309119
740.4279310560438180.8558621120876360.572068943956182
750.6206008188433750.7587983623132510.379399181156625
760.7445307751225020.5109384497549960.255469224877498
770.9259591636708560.1480816726582880.0740408363291442
780.9575659510813070.08486809783738630.0424340489186932
790.9411087766934590.1177824466130830.0588912233065413
800.9338604169183550.1322791661632900.0661395830816448
810.9627763469145230.07444730617095440.0372236530854772
820.9563229706422160.08735405871556780.0436770293577839
830.947718369489440.1045632610211180.0522816305105588
840.9691832658579020.06163346828419510.0308167341420976
850.9745535077270580.0508929845458840.025446492272942
860.980448658197190.03910268360562220.0195513418028111
870.9819047080408030.03619058391839350.0180952919591967
880.9735555612458550.05288887750829020.0264444387541451
890.9683000974963960.06339980500720860.0316999025036043
900.9795013763034280.04099724739314420.0204986236965721
910.9938460354609670.01230792907806640.00615396453903322
920.9911345232125150.01773095357496990.00886547678748494
930.9840800751472930.03183984970541420.0159199248527071
940.9817817462794720.03643650744105550.0182182537205277
950.97334154357810.05331691284379830.0266584564218991
960.9674554358637470.06508912827250630.0325445641362532
970.9637819637851790.07243607242964260.0362180362148213
980.9486006674905430.1027986650189150.0513993325094575
990.9578841353669840.08423172926603250.0421158646330163
1000.970513592501540.05897281499691810.0294864074984590
1010.9974097233616140.005180553276771720.00259027663838586
1020.9983680687036830.00326386259263460.0016319312963173
1030.995038309527030.009923380945942180.00496169047297109
1040.9893467208600750.02130655827985060.0106532791399253
1050.9600082354025950.0799835291948110.0399917645974055

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
12 & 0.108352426853603 & 0.216704853707206 & 0.891647573146397 \tabularnewline
13 & 0.0622158987064055 & 0.124431797412811 & 0.937784101293595 \tabularnewline
14 & 0.0233463816404635 & 0.0466927632809269 & 0.976653618359537 \tabularnewline
15 & 0.00827783743187112 & 0.0165556748637422 & 0.991722162568129 \tabularnewline
16 & 0.00362509837910222 & 0.00725019675820443 & 0.996374901620898 \tabularnewline
17 & 0.00187478863289506 & 0.00374957726579012 & 0.998125211367105 \tabularnewline
18 & 0.00827450987331766 & 0.0165490197466353 & 0.991725490126682 \tabularnewline
19 & 0.0176653013545646 & 0.0353306027091292 & 0.982334698645435 \tabularnewline
20 & 0.0152692449664801 & 0.0305384899329602 & 0.98473075503352 \tabularnewline
21 & 0.0113868868345613 & 0.0227737736691226 & 0.988613113165439 \tabularnewline
22 & 0.0581038573677066 & 0.116207714735413 & 0.941896142632293 \tabularnewline
23 & 0.167793039065673 & 0.335586078131347 & 0.832206960934327 \tabularnewline
24 & 0.219428796499579 & 0.438857592999159 & 0.780571203500421 \tabularnewline
25 & 0.247285823178448 & 0.494571646356895 & 0.752714176821552 \tabularnewline
26 & 0.201214383462347 & 0.402428766924693 & 0.798785616537653 \tabularnewline
27 & 0.163330081576006 & 0.326660163152012 & 0.836669918423994 \tabularnewline
28 & 0.258425275733887 & 0.516850551467775 & 0.741574724266113 \tabularnewline
29 & 0.455588196809412 & 0.911176393618823 & 0.544411803190588 \tabularnewline
30 & 0.5454270131316 & 0.9091459737368 & 0.4545729868684 \tabularnewline
31 & 0.605442314724712 & 0.789115370550575 & 0.394557685275288 \tabularnewline
32 & 0.553746359065659 & 0.892507281868683 & 0.446253640934341 \tabularnewline
33 & 0.51180094585102 & 0.97639810829796 & 0.48819905414898 \tabularnewline
34 & 0.476498618796125 & 0.95299723759225 & 0.523501381203875 \tabularnewline
35 & 0.434642635147145 & 0.86928527029429 & 0.565357364852855 \tabularnewline
36 & 0.421433295565466 & 0.842866591130932 & 0.578566704434534 \tabularnewline
37 & 0.414881647956122 & 0.829763295912245 & 0.585118352043878 \tabularnewline
38 & 0.361026057380541 & 0.722052114761082 & 0.638973942619459 \tabularnewline
39 & 0.337080626785269 & 0.674161253570537 & 0.662919373214731 \tabularnewline
40 & 0.345352067041688 & 0.690704134083375 & 0.654647932958312 \tabularnewline
41 & 0.32302553821092 & 0.64605107642184 & 0.67697446178908 \tabularnewline
42 & 0.290897255177697 & 0.581794510355394 & 0.709102744822303 \tabularnewline
43 & 0.246259733486681 & 0.492519466973363 & 0.753740266513319 \tabularnewline
44 & 0.225034300425773 & 0.450068600851547 & 0.774965699574227 \tabularnewline
45 & 0.182975235545974 & 0.365950471091948 & 0.817024764454026 \tabularnewline
46 & 0.155408637457508 & 0.310817274915015 & 0.844591362542492 \tabularnewline
47 & 0.199320308837236 & 0.398640617674471 & 0.800679691162764 \tabularnewline
48 & 0.161730785146611 & 0.323461570293222 & 0.838269214853389 \tabularnewline
49 & 0.128770549265527 & 0.257541098531054 & 0.871229450734473 \tabularnewline
50 & 0.108047429142791 & 0.216094858285581 & 0.89195257085721 \tabularnewline
51 & 0.0870442412928027 & 0.174088482585605 & 0.912955758707197 \tabularnewline
52 & 0.0701409978354222 & 0.140281995670844 & 0.929859002164578 \tabularnewline
53 & 0.0674297779662575 & 0.134859555932515 & 0.932570222033743 \tabularnewline
54 & 0.0753988888900655 & 0.150797777780131 & 0.924601111109934 \tabularnewline
55 & 0.0758726592807692 & 0.151745318561538 & 0.92412734071923 \tabularnewline
56 & 0.0733816835721637 & 0.146763367144327 & 0.926618316427836 \tabularnewline
57 & 0.0607220186098884 & 0.121444037219777 & 0.939277981390112 \tabularnewline
58 & 0.05037641176535 & 0.1007528235307 & 0.94962358823465 \tabularnewline
59 & 0.0386617361733185 & 0.077323472346637 & 0.961338263826681 \tabularnewline
60 & 0.0597362591914077 & 0.119472518382815 & 0.940263740808592 \tabularnewline
61 & 0.117062065462182 & 0.234124130924364 & 0.882937934537818 \tabularnewline
62 & 0.187492565439027 & 0.374985130878054 & 0.812507434560973 \tabularnewline
63 & 0.163103203416153 & 0.326206406832306 & 0.836896796583847 \tabularnewline
64 & 0.131790930128072 & 0.263581860256144 & 0.868209069871928 \tabularnewline
65 & 0.106722509822576 & 0.213445019645151 & 0.893277490177424 \tabularnewline
66 & 0.0908555794307968 & 0.181711158861594 & 0.909144420569203 \tabularnewline
67 & 0.0730393405903758 & 0.146078681180752 & 0.926960659409624 \tabularnewline
68 & 0.055251426666618 & 0.110502853333236 & 0.944748573333382 \tabularnewline
69 & 0.0423030578755635 & 0.084606115751127 & 0.957696942124436 \tabularnewline
70 & 0.0362647404412902 & 0.0725294808825803 & 0.96373525955871 \tabularnewline
71 & 0.0292238753605711 & 0.0584477507211422 & 0.970776124639429 \tabularnewline
72 & 0.028003602349033 & 0.056007204698066 & 0.971996397650967 \tabularnewline
73 & 0.198487116908809 & 0.396974233817619 & 0.80151288309119 \tabularnewline
74 & 0.427931056043818 & 0.855862112087636 & 0.572068943956182 \tabularnewline
75 & 0.620600818843375 & 0.758798362313251 & 0.379399181156625 \tabularnewline
76 & 0.744530775122502 & 0.510938449754996 & 0.255469224877498 \tabularnewline
77 & 0.925959163670856 & 0.148081672658288 & 0.0740408363291442 \tabularnewline
78 & 0.957565951081307 & 0.0848680978373863 & 0.0424340489186932 \tabularnewline
79 & 0.941108776693459 & 0.117782446613083 & 0.0588912233065413 \tabularnewline
80 & 0.933860416918355 & 0.132279166163290 & 0.0661395830816448 \tabularnewline
81 & 0.962776346914523 & 0.0744473061709544 & 0.0372236530854772 \tabularnewline
82 & 0.956322970642216 & 0.0873540587155678 & 0.0436770293577839 \tabularnewline
83 & 0.94771836948944 & 0.104563261021118 & 0.0522816305105588 \tabularnewline
84 & 0.969183265857902 & 0.0616334682841951 & 0.0308167341420976 \tabularnewline
85 & 0.974553507727058 & 0.050892984545884 & 0.025446492272942 \tabularnewline
86 & 0.98044865819719 & 0.0391026836056222 & 0.0195513418028111 \tabularnewline
87 & 0.981904708040803 & 0.0361905839183935 & 0.0180952919591967 \tabularnewline
88 & 0.973555561245855 & 0.0528888775082902 & 0.0264444387541451 \tabularnewline
89 & 0.968300097496396 & 0.0633998050072086 & 0.0316999025036043 \tabularnewline
90 & 0.979501376303428 & 0.0409972473931442 & 0.0204986236965721 \tabularnewline
91 & 0.993846035460967 & 0.0123079290780664 & 0.00615396453903322 \tabularnewline
92 & 0.991134523212515 & 0.0177309535749699 & 0.00886547678748494 \tabularnewline
93 & 0.984080075147293 & 0.0318398497054142 & 0.0159199248527071 \tabularnewline
94 & 0.981781746279472 & 0.0364365074410555 & 0.0182182537205277 \tabularnewline
95 & 0.9733415435781 & 0.0533169128437983 & 0.0266584564218991 \tabularnewline
96 & 0.967455435863747 & 0.0650891282725063 & 0.0325445641362532 \tabularnewline
97 & 0.963781963785179 & 0.0724360724296426 & 0.0362180362148213 \tabularnewline
98 & 0.948600667490543 & 0.102798665018915 & 0.0513993325094575 \tabularnewline
99 & 0.957884135366984 & 0.0842317292660325 & 0.0421158646330163 \tabularnewline
100 & 0.97051359250154 & 0.0589728149969181 & 0.0294864074984590 \tabularnewline
101 & 0.997409723361614 & 0.00518055327677172 & 0.00259027663838586 \tabularnewline
102 & 0.998368068703683 & 0.0032638625926346 & 0.0016319312963173 \tabularnewline
103 & 0.99503830952703 & 0.00992338094594218 & 0.00496169047297109 \tabularnewline
104 & 0.989346720860075 & 0.0213065582798506 & 0.0106532791399253 \tabularnewline
105 & 0.960008235402595 & 0.079983529194811 & 0.0399917645974055 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]12[/C][C]0.108352426853603[/C][C]0.216704853707206[/C][C]0.891647573146397[/C][/ROW]
[ROW][C]13[/C][C]0.0622158987064055[/C][C]0.124431797412811[/C][C]0.937784101293595[/C][/ROW]
[ROW][C]14[/C][C]0.0233463816404635[/C][C]0.0466927632809269[/C][C]0.976653618359537[/C][/ROW]
[ROW][C]15[/C][C]0.00827783743187112[/C][C]0.0165556748637422[/C][C]0.991722162568129[/C][/ROW]
[ROW][C]16[/C][C]0.00362509837910222[/C][C]0.00725019675820443[/C][C]0.996374901620898[/C][/ROW]
[ROW][C]17[/C][C]0.00187478863289506[/C][C]0.00374957726579012[/C][C]0.998125211367105[/C][/ROW]
[ROW][C]18[/C][C]0.00827450987331766[/C][C]0.0165490197466353[/C][C]0.991725490126682[/C][/ROW]
[ROW][C]19[/C][C]0.0176653013545646[/C][C]0.0353306027091292[/C][C]0.982334698645435[/C][/ROW]
[ROW][C]20[/C][C]0.0152692449664801[/C][C]0.0305384899329602[/C][C]0.98473075503352[/C][/ROW]
[ROW][C]21[/C][C]0.0113868868345613[/C][C]0.0227737736691226[/C][C]0.988613113165439[/C][/ROW]
[ROW][C]22[/C][C]0.0581038573677066[/C][C]0.116207714735413[/C][C]0.941896142632293[/C][/ROW]
[ROW][C]23[/C][C]0.167793039065673[/C][C]0.335586078131347[/C][C]0.832206960934327[/C][/ROW]
[ROW][C]24[/C][C]0.219428796499579[/C][C]0.438857592999159[/C][C]0.780571203500421[/C][/ROW]
[ROW][C]25[/C][C]0.247285823178448[/C][C]0.494571646356895[/C][C]0.752714176821552[/C][/ROW]
[ROW][C]26[/C][C]0.201214383462347[/C][C]0.402428766924693[/C][C]0.798785616537653[/C][/ROW]
[ROW][C]27[/C][C]0.163330081576006[/C][C]0.326660163152012[/C][C]0.836669918423994[/C][/ROW]
[ROW][C]28[/C][C]0.258425275733887[/C][C]0.516850551467775[/C][C]0.741574724266113[/C][/ROW]
[ROW][C]29[/C][C]0.455588196809412[/C][C]0.911176393618823[/C][C]0.544411803190588[/C][/ROW]
[ROW][C]30[/C][C]0.5454270131316[/C][C]0.9091459737368[/C][C]0.4545729868684[/C][/ROW]
[ROW][C]31[/C][C]0.605442314724712[/C][C]0.789115370550575[/C][C]0.394557685275288[/C][/ROW]
[ROW][C]32[/C][C]0.553746359065659[/C][C]0.892507281868683[/C][C]0.446253640934341[/C][/ROW]
[ROW][C]33[/C][C]0.51180094585102[/C][C]0.97639810829796[/C][C]0.48819905414898[/C][/ROW]
[ROW][C]34[/C][C]0.476498618796125[/C][C]0.95299723759225[/C][C]0.523501381203875[/C][/ROW]
[ROW][C]35[/C][C]0.434642635147145[/C][C]0.86928527029429[/C][C]0.565357364852855[/C][/ROW]
[ROW][C]36[/C][C]0.421433295565466[/C][C]0.842866591130932[/C][C]0.578566704434534[/C][/ROW]
[ROW][C]37[/C][C]0.414881647956122[/C][C]0.829763295912245[/C][C]0.585118352043878[/C][/ROW]
[ROW][C]38[/C][C]0.361026057380541[/C][C]0.722052114761082[/C][C]0.638973942619459[/C][/ROW]
[ROW][C]39[/C][C]0.337080626785269[/C][C]0.674161253570537[/C][C]0.662919373214731[/C][/ROW]
[ROW][C]40[/C][C]0.345352067041688[/C][C]0.690704134083375[/C][C]0.654647932958312[/C][/ROW]
[ROW][C]41[/C][C]0.32302553821092[/C][C]0.64605107642184[/C][C]0.67697446178908[/C][/ROW]
[ROW][C]42[/C][C]0.290897255177697[/C][C]0.581794510355394[/C][C]0.709102744822303[/C][/ROW]
[ROW][C]43[/C][C]0.246259733486681[/C][C]0.492519466973363[/C][C]0.753740266513319[/C][/ROW]
[ROW][C]44[/C][C]0.225034300425773[/C][C]0.450068600851547[/C][C]0.774965699574227[/C][/ROW]
[ROW][C]45[/C][C]0.182975235545974[/C][C]0.365950471091948[/C][C]0.817024764454026[/C][/ROW]
[ROW][C]46[/C][C]0.155408637457508[/C][C]0.310817274915015[/C][C]0.844591362542492[/C][/ROW]
[ROW][C]47[/C][C]0.199320308837236[/C][C]0.398640617674471[/C][C]0.800679691162764[/C][/ROW]
[ROW][C]48[/C][C]0.161730785146611[/C][C]0.323461570293222[/C][C]0.838269214853389[/C][/ROW]
[ROW][C]49[/C][C]0.128770549265527[/C][C]0.257541098531054[/C][C]0.871229450734473[/C][/ROW]
[ROW][C]50[/C][C]0.108047429142791[/C][C]0.216094858285581[/C][C]0.89195257085721[/C][/ROW]
[ROW][C]51[/C][C]0.0870442412928027[/C][C]0.174088482585605[/C][C]0.912955758707197[/C][/ROW]
[ROW][C]52[/C][C]0.0701409978354222[/C][C]0.140281995670844[/C][C]0.929859002164578[/C][/ROW]
[ROW][C]53[/C][C]0.0674297779662575[/C][C]0.134859555932515[/C][C]0.932570222033743[/C][/ROW]
[ROW][C]54[/C][C]0.0753988888900655[/C][C]0.150797777780131[/C][C]0.924601111109934[/C][/ROW]
[ROW][C]55[/C][C]0.0758726592807692[/C][C]0.151745318561538[/C][C]0.92412734071923[/C][/ROW]
[ROW][C]56[/C][C]0.0733816835721637[/C][C]0.146763367144327[/C][C]0.926618316427836[/C][/ROW]
[ROW][C]57[/C][C]0.0607220186098884[/C][C]0.121444037219777[/C][C]0.939277981390112[/C][/ROW]
[ROW][C]58[/C][C]0.05037641176535[/C][C]0.1007528235307[/C][C]0.94962358823465[/C][/ROW]
[ROW][C]59[/C][C]0.0386617361733185[/C][C]0.077323472346637[/C][C]0.961338263826681[/C][/ROW]
[ROW][C]60[/C][C]0.0597362591914077[/C][C]0.119472518382815[/C][C]0.940263740808592[/C][/ROW]
[ROW][C]61[/C][C]0.117062065462182[/C][C]0.234124130924364[/C][C]0.882937934537818[/C][/ROW]
[ROW][C]62[/C][C]0.187492565439027[/C][C]0.374985130878054[/C][C]0.812507434560973[/C][/ROW]
[ROW][C]63[/C][C]0.163103203416153[/C][C]0.326206406832306[/C][C]0.836896796583847[/C][/ROW]
[ROW][C]64[/C][C]0.131790930128072[/C][C]0.263581860256144[/C][C]0.868209069871928[/C][/ROW]
[ROW][C]65[/C][C]0.106722509822576[/C][C]0.213445019645151[/C][C]0.893277490177424[/C][/ROW]
[ROW][C]66[/C][C]0.0908555794307968[/C][C]0.181711158861594[/C][C]0.909144420569203[/C][/ROW]
[ROW][C]67[/C][C]0.0730393405903758[/C][C]0.146078681180752[/C][C]0.926960659409624[/C][/ROW]
[ROW][C]68[/C][C]0.055251426666618[/C][C]0.110502853333236[/C][C]0.944748573333382[/C][/ROW]
[ROW][C]69[/C][C]0.0423030578755635[/C][C]0.084606115751127[/C][C]0.957696942124436[/C][/ROW]
[ROW][C]70[/C][C]0.0362647404412902[/C][C]0.0725294808825803[/C][C]0.96373525955871[/C][/ROW]
[ROW][C]71[/C][C]0.0292238753605711[/C][C]0.0584477507211422[/C][C]0.970776124639429[/C][/ROW]
[ROW][C]72[/C][C]0.028003602349033[/C][C]0.056007204698066[/C][C]0.971996397650967[/C][/ROW]
[ROW][C]73[/C][C]0.198487116908809[/C][C]0.396974233817619[/C][C]0.80151288309119[/C][/ROW]
[ROW][C]74[/C][C]0.427931056043818[/C][C]0.855862112087636[/C][C]0.572068943956182[/C][/ROW]
[ROW][C]75[/C][C]0.620600818843375[/C][C]0.758798362313251[/C][C]0.379399181156625[/C][/ROW]
[ROW][C]76[/C][C]0.744530775122502[/C][C]0.510938449754996[/C][C]0.255469224877498[/C][/ROW]
[ROW][C]77[/C][C]0.925959163670856[/C][C]0.148081672658288[/C][C]0.0740408363291442[/C][/ROW]
[ROW][C]78[/C][C]0.957565951081307[/C][C]0.0848680978373863[/C][C]0.0424340489186932[/C][/ROW]
[ROW][C]79[/C][C]0.941108776693459[/C][C]0.117782446613083[/C][C]0.0588912233065413[/C][/ROW]
[ROW][C]80[/C][C]0.933860416918355[/C][C]0.132279166163290[/C][C]0.0661395830816448[/C][/ROW]
[ROW][C]81[/C][C]0.962776346914523[/C][C]0.0744473061709544[/C][C]0.0372236530854772[/C][/ROW]
[ROW][C]82[/C][C]0.956322970642216[/C][C]0.0873540587155678[/C][C]0.0436770293577839[/C][/ROW]
[ROW][C]83[/C][C]0.94771836948944[/C][C]0.104563261021118[/C][C]0.0522816305105588[/C][/ROW]
[ROW][C]84[/C][C]0.969183265857902[/C][C]0.0616334682841951[/C][C]0.0308167341420976[/C][/ROW]
[ROW][C]85[/C][C]0.974553507727058[/C][C]0.050892984545884[/C][C]0.025446492272942[/C][/ROW]
[ROW][C]86[/C][C]0.98044865819719[/C][C]0.0391026836056222[/C][C]0.0195513418028111[/C][/ROW]
[ROW][C]87[/C][C]0.981904708040803[/C][C]0.0361905839183935[/C][C]0.0180952919591967[/C][/ROW]
[ROW][C]88[/C][C]0.973555561245855[/C][C]0.0528888775082902[/C][C]0.0264444387541451[/C][/ROW]
[ROW][C]89[/C][C]0.968300097496396[/C][C]0.0633998050072086[/C][C]0.0316999025036043[/C][/ROW]
[ROW][C]90[/C][C]0.979501376303428[/C][C]0.0409972473931442[/C][C]0.0204986236965721[/C][/ROW]
[ROW][C]91[/C][C]0.993846035460967[/C][C]0.0123079290780664[/C][C]0.00615396453903322[/C][/ROW]
[ROW][C]92[/C][C]0.991134523212515[/C][C]0.0177309535749699[/C][C]0.00886547678748494[/C][/ROW]
[ROW][C]93[/C][C]0.984080075147293[/C][C]0.0318398497054142[/C][C]0.0159199248527071[/C][/ROW]
[ROW][C]94[/C][C]0.981781746279472[/C][C]0.0364365074410555[/C][C]0.0182182537205277[/C][/ROW]
[ROW][C]95[/C][C]0.9733415435781[/C][C]0.0533169128437983[/C][C]0.0266584564218991[/C][/ROW]
[ROW][C]96[/C][C]0.967455435863747[/C][C]0.0650891282725063[/C][C]0.0325445641362532[/C][/ROW]
[ROW][C]97[/C][C]0.963781963785179[/C][C]0.0724360724296426[/C][C]0.0362180362148213[/C][/ROW]
[ROW][C]98[/C][C]0.948600667490543[/C][C]0.102798665018915[/C][C]0.0513993325094575[/C][/ROW]
[ROW][C]99[/C][C]0.957884135366984[/C][C]0.0842317292660325[/C][C]0.0421158646330163[/C][/ROW]
[ROW][C]100[/C][C]0.97051359250154[/C][C]0.0589728149969181[/C][C]0.0294864074984590[/C][/ROW]
[ROW][C]101[/C][C]0.997409723361614[/C][C]0.00518055327677172[/C][C]0.00259027663838586[/C][/ROW]
[ROW][C]102[/C][C]0.998368068703683[/C][C]0.0032638625926346[/C][C]0.0016319312963173[/C][/ROW]
[ROW][C]103[/C][C]0.99503830952703[/C][C]0.00992338094594218[/C][C]0.00496169047297109[/C][/ROW]
[ROW][C]104[/C][C]0.989346720860075[/C][C]0.0213065582798506[/C][C]0.0106532791399253[/C][/ROW]
[ROW][C]105[/C][C]0.960008235402595[/C][C]0.079983529194811[/C][C]0.0399917645974055[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=5

As an alternative you can also use a QR Code:  
QR Code


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

Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
120.1083524268536030.2167048537072060.891647573146397
130.06221589870640550.1244317974128110.937784101293595
140.02334638164046350.04669276328092690.976653618359537
150.008277837431871120.01655567486374220.991722162568129
160.003625098379102220.007250196758204430.996374901620898
170.001874788632895060.003749577265790120.998125211367105
180.008274509873317660.01654901974663530.991725490126682
190.01766530135456460.03533060270912920.982334698645435
200.01526924496648010.03053848993296020.98473075503352
210.01138688683456130.02277377366912260.988613113165439
220.05810385736770660.1162077147354130.941896142632293
230.1677930390656730.3355860781313470.832206960934327
240.2194287964995790.4388575929991590.780571203500421
250.2472858231784480.4945716463568950.752714176821552
260.2012143834623470.4024287669246930.798785616537653
270.1633300815760060.3266601631520120.836669918423994
280.2584252757338870.5168505514677750.741574724266113
290.4555881968094120.9111763936188230.544411803190588
300.54542701313160.90914597373680.4545729868684
310.6054423147247120.7891153705505750.394557685275288
320.5537463590656590.8925072818686830.446253640934341
330.511800945851020.976398108297960.48819905414898
340.4764986187961250.952997237592250.523501381203875
350.4346426351471450.869285270294290.565357364852855
360.4214332955654660.8428665911309320.578566704434534
370.4148816479561220.8297632959122450.585118352043878
380.3610260573805410.7220521147610820.638973942619459
390.3370806267852690.6741612535705370.662919373214731
400.3453520670416880.6907041340833750.654647932958312
410.323025538210920.646051076421840.67697446178908
420.2908972551776970.5817945103553940.709102744822303
430.2462597334866810.4925194669733630.753740266513319
440.2250343004257730.4500686008515470.774965699574227
450.1829752355459740.3659504710919480.817024764454026
460.1554086374575080.3108172749150150.844591362542492
470.1993203088372360.3986406176744710.800679691162764
480.1617307851466110.3234615702932220.838269214853389
490.1287705492655270.2575410985310540.871229450734473
500.1080474291427910.2160948582855810.89195257085721
510.08704424129280270.1740884825856050.912955758707197
520.07014099783542220.1402819956708440.929859002164578
530.06742977796625750.1348595559325150.932570222033743
540.07539888889006550.1507977777801310.924601111109934
550.07587265928076920.1517453185615380.92412734071923
560.07338168357216370.1467633671443270.926618316427836
570.06072201860988840.1214440372197770.939277981390112
580.050376411765350.10075282353070.94962358823465
590.03866173617331850.0773234723466370.961338263826681
600.05973625919140770.1194725183828150.940263740808592
610.1170620654621820.2341241309243640.882937934537818
620.1874925654390270.3749851308780540.812507434560973
630.1631032034161530.3262064068323060.836896796583847
640.1317909301280720.2635818602561440.868209069871928
650.1067225098225760.2134450196451510.893277490177424
660.09085557943079680.1817111588615940.909144420569203
670.07303934059037580.1460786811807520.926960659409624
680.0552514266666180.1105028533332360.944748573333382
690.04230305787556350.0846061157511270.957696942124436
700.03626474044129020.07252948088258030.96373525955871
710.02922387536057110.05844775072114220.970776124639429
720.0280036023490330.0560072046980660.971996397650967
730.1984871169088090.3969742338176190.80151288309119
740.4279310560438180.8558621120876360.572068943956182
750.6206008188433750.7587983623132510.379399181156625
760.7445307751225020.5109384497549960.255469224877498
770.9259591636708560.1480816726582880.0740408363291442
780.9575659510813070.08486809783738630.0424340489186932
790.9411087766934590.1177824466130830.0588912233065413
800.9338604169183550.1322791661632900.0661395830816448
810.9627763469145230.07444730617095440.0372236530854772
820.9563229706422160.08735405871556780.0436770293577839
830.947718369489440.1045632610211180.0522816305105588
840.9691832658579020.06163346828419510.0308167341420976
850.9745535077270580.0508929845458840.025446492272942
860.980448658197190.03910268360562220.0195513418028111
870.9819047080408030.03619058391839350.0180952919591967
880.9735555612458550.05288887750829020.0264444387541451
890.9683000974963960.06339980500720860.0316999025036043
900.9795013763034280.04099724739314420.0204986236965721
910.9938460354609670.01230792907806640.00615396453903322
920.9911345232125150.01773095357496990.00886547678748494
930.9840800751472930.03183984970541420.0159199248527071
940.9817817462794720.03643650744105550.0182182537205277
950.97334154357810.05331691284379830.0266584564218991
960.9674554358637470.06508912827250630.0325445641362532
970.9637819637851790.07243607242964260.0362180362148213
980.9486006674905430.1027986650189150.0513993325094575
990.9578841353669840.08423172926603250.0421158646330163
1000.970513592501540.05897281499691810.0294864074984590
1010.9974097233616140.005180553276771720.00259027663838586
1020.9983680687036830.00326386259263460.0016319312963173
1030.995038309527030.009923380945942180.00496169047297109
1040.9893467208600750.02130655827985060.0106532791399253
1050.9600082354025950.0799835291948110.0399917645974055






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level50.0531914893617021NOK
5% type I error level190.202127659574468NOK
10% type I error level370.393617021276596NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 5 & 0.0531914893617021 & NOK \tabularnewline
5% type I error level & 19 & 0.202127659574468 & NOK \tabularnewline
10% type I error level & 37 & 0.393617021276596 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=113750&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]5[/C][C]0.0531914893617021[/C][C]NOK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]19[/C][C]0.202127659574468[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]37[/C][C]0.393617021276596[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=113750&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=113750&T=6

As an alternative you can also use a QR Code:  
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 testsOK/NOK
1% type I error level50.0531914893617021NOK
5% type I error level190.202127659574468NOK
10% type I error level370.393617021276596NOK


Figures (Output of Computation)

Figure 1
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Figure 2
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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
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Input Parameters & R Code

Parameters (Session):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 2 ; par2 = Do not include Seasonal Dummies ; par3 = No 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')
}