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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 computationSun, 14 Dec 2014 08:44:35 +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/2014/Dec/14/t1418546697vlhvydwl594sehr.htm/, Retrieved Thu, 16 May 2024 23:35:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=267334, Retrieved Thu, 16 May 2024 23:35:11 +0000
QR Codes:

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] [] [2014-12-14 08:44:35] [f235c2d73cdbd6a2c0ce149cb9653e7d] [Current]
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Dataset

Dataseries X:
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Histogram
Boxplots 
4.35 0 1 22 48 23 12 41 7 6 6 0 2 2
12.7 0 1 22 50 16 45 146 7 3 6 2 2 2
18.1 0 1 22 150 33 37 182 7 4 6 1 2 1
17.85 0 1 20 154 32 37 192 9 3 6 3 2 2
16.6 1 0 19 109 37 108 263 7 10 6 3 2 2
12.6 1 1 20 68 14 10 35 6 4 6 0 0 1
17.1 0 1 22 194 52 68 439 8 8 5 3 1 2
19.1 0 0 21 158 75 72 214 8 3 6 2 2 2
16.1 0 1 21 159 72 143 341 9 5 5 2 0 2
13.35 0 0 21 67 15 9 58 7 4 6 0 0 1
18.4 0 0 21 147 29 55 292 6 3 5 2 0 2
14.7 0 1 21 39 13 17 85 8 5 5 3 0 2
10.6 0 1 21 100 40 37 200 6 3 6 2 0 2
12.6 0 1 21 111 19 27 158 6 3 4 0 0 2
16.2 0 1 22 138 24 37 199 9 4 5 0 0 2
13.6 0 1 24 101 121 58 297 6 3 6 0 0 1
18.9 1 1 21 131 93 66 227 9 6 6 1 1 1
14.1 0 1 22 101 36 21 108 8 6 5 2 2 2
14.5 0 1 20 114 23 19 86 8 4 6 3 2 2
16.15 0 0 21 165 85 78 302 9 4 6 0 0 0
14.75 0 1 24 114 41 35 148 6 4 6 2 0 1
14.8 0 1 25 111 46 48 178 4 3 4 2 1 2
12.45 0 1 22 75 18 27 120 8 2 6 1 2 0
12.65 0 1 21 82 35 43 207 5 5 5 2 0 2
17.35 0 1 21 121 17 30 157 7 4 6 2 2 2
8.6 0 1 22 32 4 25 128 9 4 6 2 0 0
18.4 0 0 23 150 28 69 296 9 4 5 2 0 2
16.1 0 1 24 117 44 72 323 8 3 4 2 1 1
11.6 1 1 20 71 10 23 79 6 4 5 2 2 1
17.75 0 1 22 165 38 13 70 8 2 6 1 1 2
15.25 0 1 25 154 57 61 146 3 0 0 0 0 0
17.65 0 1 22 126 23 43 246 8 4 6 2 1 2
15.6 0 0 22 138 26 22 145 7 3 4 0 0 0
16.35 0 0 21 149 36 51 196 7 6 6 0 2 2
17.65 0 0 21 145 22 67 199 9 4 4 2 0 1
13.6 0 1 21 120 40 36 127 4 4 6 0 0 1
11.7 0 0 22 138 18 21 91 7 2 4 0 0 0
14.35 0 0 22 109 31 44 153 6 4 5 0 1 0
14.75 0 0 22 132 11 45 299 3 2 1 0 1 0
18.25 0 1 21 172 38 34 228 8 4 5 3 2 2
9.9 0 0 22 169 24 36 190 8 3 5 0 0 1
16 0 1 23 114 37 72 180 9 6 5 2 2 0
18.25 0 1 21 156 37 39 212 8 6 5 3 0 2
16.85 0 0 21 172 22 43 269 8 4 5 0 0 2
14.6 1 1 21 68 15 25 130 9 5 6 2 2 1
13.85 1 1 19 89 2 56 179 8 4 5 0 1 2
18.95 0 1 21 167 43 80 243 9 6 6 3 2 2
15.6 0 0 21 113 31 40 190 7 6 5 2 1 2
14.85 1 0 19 115 29 73 299 7 9 6 2 1 2
11.75 1 0 18 78 45 34 121 6 4 5 2 1 0
18.45 1 0 19 118 25 72 137 8 8 6 3 1 2
15.9 1 1 21 87 4 42 305 6 5 5 3 0 2
17.1 0 0 22 173 31 61 157 7 4 5 3 0 0
16.1 0 1 22 2 -4 23 96 8 4 6 2 2 1
19.9 1 0 19 162 66 74 183 8 7 6 3 2 1
10.95 1 1 20 49 61 16 52 7 4 6 1 2 2
18.45 1 0 19 122 32 66 238 9 8 6 2 1 2
15.1 1 1 21 96 31 9 40 9 4 6 3 2 1
15 1 0 19 100 39 41 226 9 3 6 2 0 1
11.35 1 0 20 82 19 57 190 6 5 6 2 1 2
15.95 1 1 21 100 31 48 214 8 8 6 2 2 2
18.1 1 0 19 115 36 51 145 9 4 5 1 0 1
14.6 1 1 21 141 42 53 119 9 10 6 3 1 0
15.4 0 1 21 165 21 29 222 8 5 6 2 2 2
15.4 0 1 21 165 21 29 222 8 5 6 2 2 2
17.6 1 1 19 110 25 55 159 8 3 6 1 0 2
13.35 0 1 25 118 32 54 165 8 3 5 1 1 2
19.1 0 0 21 158 26 43 249 8 3 3 0 0 2
15.35 1 1 20 146 28 51 125 9 4 4 1 1 1
7.6 0 0 25 49 32 20 122 6 5 6 1 0 2
13.4 1 0 19 90 41 79 186 9 5 4 2 1 2
13.9 1 0 20 121 29 39 148 8 4 6 0 0 0
19.1 0 1 22 155 33 61 274 8 7 6 3 1 0
15.25 1 0 19 104 17 55 172 8 5 3 1 0 1
12.9 1 1 20 147 13 30 84 8 4 4 1 2 0
16.1 1 0 19 110 32 55 168 9 7 4 3 0 2
17.35 1 0 19 108 30 22 102 9 7 4 3 0 2
13.15 1 0 18 113 34 37 106 9 7 4 3 0 2
12.15 1 0 19 115 59 2 2 8 7 4 3 0 2
12.6 1 1 21 61 13 38 139 8 7 4 0 0 0
10.35 1 1 19 60 23 27 95 8 7 6 2 1 2
15.4 1 1 20 109 10 56 130 3 1 4 1 1 0
9.6 1 1 20 68 5 25 72 6 2 4 2 1 2
18.2 1 0 19 111 31 39 141 5 3 2 1 0 2
13.6 1 0 19 77 19 33 113 4 6 5 1 0 1
14.85 1 1 22 73 32 43 206 9 8 6 3 2 2
14.75 0 0 21 151 30 57 268 8 8 6 1 1 1
14.1 1 0 19 89 25 43 175 3 0 1 0 0 0
14.9 1 0 19 78 48 23 77 6 3 4 1 0 2
16.25 1 0 19 110 35 44 125 6 6 5 1 1 2
19.25 0 1 23 220 67 54 255 9 5 5 2 0 2
13.6 1 1 19 65 15 28 111 7 7 6 1 0 1
13.6 0 0 20 141 22 36 132 6 3 5 0 1 2
15.65 1 0 19 117 18 39 211 9 3 6 2 0 0
12.75 0 1 22 122 33 16 92 7 4 6 2 0 1
14.6 1 0 19 63 46 23 76 8 4 5 3 0 2
9.85 0 1 25 44 24 40 171 8 1 5 0 0 2
12.65 1 1 19 52 14 24 83 8 5 6 2 0 2
11.9 1 1 20 62 23 29 119 7 3 4 1 0 1
19.2 1 0 19 131 12 78 266 0 0 0 0 0 0
16.6 1 1 19 101 38 57 186 6 4 6 1 1 0
11.2 1 1 20 42 12 37 50 9 6 5 2 2 1
15.25 0 1 20 152 28 27 117 9 4 6 1 1 2
11.9 0 0 21 107 41 61 219 6 1 2 0 1 2
13.2 1 0 19 77 12 27 246 8 3 5 0 0 2
16.35 0 0 21 154 31 69 279 8 7 5 2 0 2
12.4 0 1 23 103 33 34 148 5 3 1 0 0 2
15.85 1 1 19 96 34 44 137 6 5 5 1 1 0
14.35 0 0 21 154 41 21 130 6 3 4 1 0 0
18.15 0 1 22 175 21 34 181 9 3 5 2 2 2
11.15 1 1 20 57 20 39 98 9 6 4 2 1 2
15.65 1 0 18 112 44 51 226 9 9 6 3 0 2
17.75 0 0 21 143 52 34 234 6 4 5 0 1 2
7.65 1 0 20 49 7 31 138 4 3 6 0 1 1
12.35 0 1 21 110 29 13 85 8 9 6 2 2 2
15.6 0 1 21 131 11 12 66 4 5 6 0 1 0
19.3 0 0 21 167 26 51 236 5 3 6 3 1 1
15.2 1 0 19 56 24 24 106 8 6 5 2 0 1
17.1 0 0 21 137 7 19 135 6 2 6 1 0 1
15.6 1 1 19 86 60 30 122 8 4 5 3 1 2
18.4 0 1 21 121 13 81 218 9 5 5 2 1 1
19.05 0 0 21 149 20 42 199 7 4 5 2 0 1
18.55 0 0 22 168 52 22 112 4 0 0 0 0 0
19.1 0 0 21 140 28 85 278 8 2 6 1 1 2
13.1 1 1 22 88 25 27 94 8 5 6 2 1 2
12.85 0 1 22 168 39 25 113 8 3 6 2 0 1
9.5 0 1 22 94 9 22 84 4 0 0 0 0 0
4.5 0 1 22 51 19 19 86 9 5 5 3 0 2
11.85 1 0 21 48 13 14 62 8 6 5 1 0 2
13.6 0 1 22 145 60 45 222 6 3 5 0 1 1
11.7 0 1 23 66 19 45 167 3 0 0 0 0 0
12.4 1 1 19 85 34 28 82 7 3 4 0 1 0
13.35 0 0 22 109 14 51 207 8 5 6 2 1 2
11.4 1 0 21 63 17 41 184 7 4 4 0 0 2
14.9 1 1 19 102 45 31 83 7 5 5 2 0 1
19.9 1 0 19 162 66 74 183 8 7 6 3 2 1
17.75 0 1 20 128 24 24 85 8 4 5 3 1 2
11.2 1 1 20 86 48 19 89 7 8 6 2 1 2
14.6 1 1 18 114 29 51 225 7 6 6 1 1 2
17.6 0 0 21 164 -2 73 237 6 4 5 1 0 1
14.05 0 1 21 119 51 24 102 8 5 5 1 1 0
16.1 0 0 20 126 2 61 221 8 5 6 0 1 2
13.35 0 1 20 132 24 23 128 7 3 6 1 0 2
11.85 0 1 21 142 40 14 91 9 6 6 0 1 2
11.95 0 0 21 83 20 54 198 9 3 4 2 0 1
14.75 1 1 19 94 19 51 204 7 6 5 3 1 1
15.15 1 0 19 81 16 62 158 7 3 2 1 0 2
13.2 0 1 21 166 20 36 138 8 7 6 2 2 2
16.85 1 0 19 110 40 59 226 8 7 6 3 0 2
7.85 1 1 19 64 27 24 44 6 6 4 3 1 2
7.7 0 0 24 93 25 26 196 9 5 6 1 1 0
12.6 1 0 19 104 49 54 83 6 5 5 1 0 1
7.85 1 1 19 105 39 39 79 5 4 4 0 0 2
10.95 1 1 20 49 61 16 52 7 4 6 1 2 2
12.35 1 0 19 88 19 36 105 9 7 6 3 0 2
9.95 1 1 19 95 67 31 116 6 2 1 0 1 0
14.9 1 1 19 102 45 31 83 7 5 5 2 0 1
16.65 1 0 19 99 30 42 196 5 4 5 2 1 0
13.4 1 1 19 63 8 39 153 9 2 6 2 2 2
13.95 1 0 19 76 19 25 157 8 5 4 2 0 0
15.7 1 0 20 109 52 31 75 4 4 3 0 0 2
16.85 1 1 20 117 22 38 106 9 7 4 3 2 2
10.95 1 1 19 57 17 31 58 8 6 5 2 2 0
15.35 1 0 21 120 33 17 75 7 4 5 0 0 0
12.2 1 1 19 73 34 22 74 8 5 6 2 2 2
15.1 1 0 19 91 22 55 185 1 0 1 0 0 0
17.75 1 0 19 108 30 62 265 8 7 6 2 1 2
15.2 1 1 21 105 25 51 131 8 4 4 2 0 2
14.6 0 0 22 117 38 30 139 9 5 4 3 0 2
16.65 1 0 19 119 26 49 196 8 6 5 2 0 1
8.1 1 1 19 31 13 16 78 9 8 3 2 1 1

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ yule.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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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 time7 seconds
R Server'George Udny Yule' @ yule.wessa.net






Multiple Linear Regression - Estimated Regression Equation
TOT[t] = + 10.754 + 0.906974programma[t] -0.515656gender[t] -0.167144age[t] + 0.0457459LFM[t] -0.00534471PRH[t] + 0.0188627CH[t] + 0.00669572Blogs[t] -0.0304009Calculation[t] -0.263557Algebraic_Reasoning[t] + 0.140456Graphical_Interpretation[t] + 0.651259Proportionality_and_Ratio[t] + 0.211395Probability_and_Sampling[t] -0.0531906Estimation[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
TOT[t] =  +  10.754 +  0.906974programma[t] -0.515656gender[t] -0.167144age[t] +  0.0457459LFM[t] -0.00534471PRH[t] +  0.0188627CH[t] +  0.00669572Blogs[t] -0.0304009Calculation[t] -0.263557Algebraic_Reasoning[t] +  0.140456Graphical_Interpretation[t] +  0.651259Proportionality_and_Ratio[t] +  0.211395Probability_and_Sampling[t] -0.0531906Estimation[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]TOT[t] =  +  10.754 +  0.906974programma[t] -0.515656gender[t] -0.167144age[t] +  0.0457459LFM[t] -0.00534471PRH[t] +  0.0188627CH[t] +  0.00669572Blogs[t] -0.0304009Calculation[t] -0.263557Algebraic_Reasoning[t] +  0.140456Graphical_Interpretation[t] +  0.651259Proportionality_and_Ratio[t] +  0.211395Probability_and_Sampling[t] -0.0531906Estimation[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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
TOT[t] = + 10.754 + 0.906974programma[t] -0.515656gender[t] -0.167144age[t] + 0.0457459LFM[t] -0.00534471PRH[t] + 0.0188627CH[t] + 0.00669572Blogs[t] -0.0304009Calculation[t] -0.263557Algebraic_Reasoning[t] + 0.140456Graphical_Interpretation[t] + 0.651259Proportionality_and_Ratio[t] + 0.211395Probability_and_Sampling[t] -0.0531906Estimation[t] + e[t]






Multiple Linear Regression - Ordinary Least Squares
VariableParameterS.D.T-STATH0: parameter = 02-tail p-value1-tail p-value
(Intercept)10.7543.853882.790.005916770.00295838
programma0.9069740.5675441.5980.1120380.0560192
gender-0.5156560.378795-1.3610.1753670.0876837
age-0.1671440.166989-1.0010.3184010.159201
LFM0.04574590.00571348.0072.46755e-131.23378e-13
PRH-0.005344710.00990804-0.53940.5903530.295176
CH0.01886270.01247221.5120.132450.066225
Blogs0.006695720.003655911.8310.06892450.0344623
Calculation-0.03040090.122859-0.24740.8048870.402443
Algebraic_Reasoning-0.2635570.110951-2.3750.01873530.00936766
Graphical_Interpretation0.1404560.1469280.9560.3405670.170283
Proportionality_and_Ratio0.6512590.1876673.470.0006714420.000335721
Probability_and_Sampling0.2113950.2365390.89370.372850.186425
Estimation-0.05319060.21935-0.24250.8087160.404358

\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) & 10.754 & 3.85388 & 2.79 & 0.00591677 & 0.00295838 \tabularnewline
programma & 0.906974 & 0.567544 & 1.598 & 0.112038 & 0.0560192 \tabularnewline
gender & -0.515656 & 0.378795 & -1.361 & 0.175367 & 0.0876837 \tabularnewline
age & -0.167144 & 0.166989 & -1.001 & 0.318401 & 0.159201 \tabularnewline
LFM & 0.0457459 & 0.0057134 & 8.007 & 2.46755e-13 & 1.23378e-13 \tabularnewline
PRH & -0.00534471 & 0.00990804 & -0.5394 & 0.590353 & 0.295176 \tabularnewline
CH & 0.0188627 & 0.0124722 & 1.512 & 0.13245 & 0.066225 \tabularnewline
Blogs & 0.00669572 & 0.00365591 & 1.831 & 0.0689245 & 0.0344623 \tabularnewline
Calculation & -0.0304009 & 0.122859 & -0.2474 & 0.804887 & 0.402443 \tabularnewline
Algebraic_Reasoning & -0.263557 & 0.110951 & -2.375 & 0.0187353 & 0.00936766 \tabularnewline
Graphical_Interpretation & 0.140456 & 0.146928 & 0.956 & 0.340567 & 0.170283 \tabularnewline
Proportionality_and_Ratio & 0.651259 & 0.187667 & 3.47 & 0.000671442 & 0.000335721 \tabularnewline
Probability_and_Sampling & 0.211395 & 0.236539 & 0.8937 & 0.37285 & 0.186425 \tabularnewline
Estimation & -0.0531906 & 0.21935 & -0.2425 & 0.808716 & 0.404358 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&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]10.754[/C][C]3.85388[/C][C]2.79[/C][C]0.00591677[/C][C]0.00295838[/C][/ROW]
[ROW][C]programma[/C][C]0.906974[/C][C]0.567544[/C][C]1.598[/C][C]0.112038[/C][C]0.0560192[/C][/ROW]
[ROW][C]gender[/C][C]-0.515656[/C][C]0.378795[/C][C]-1.361[/C][C]0.175367[/C][C]0.0876837[/C][/ROW]
[ROW][C]age[/C][C]-0.167144[/C][C]0.166989[/C][C]-1.001[/C][C]0.318401[/C][C]0.159201[/C][/ROW]
[ROW][C]LFM[/C][C]0.0457459[/C][C]0.0057134[/C][C]8.007[/C][C]2.46755e-13[/C][C]1.23378e-13[/C][/ROW]
[ROW][C]PRH[/C][C]-0.00534471[/C][C]0.00990804[/C][C]-0.5394[/C][C]0.590353[/C][C]0.295176[/C][/ROW]
[ROW][C]CH[/C][C]0.0188627[/C][C]0.0124722[/C][C]1.512[/C][C]0.13245[/C][C]0.066225[/C][/ROW]
[ROW][C]Blogs[/C][C]0.00669572[/C][C]0.00365591[/C][C]1.831[/C][C]0.0689245[/C][C]0.0344623[/C][/ROW]
[ROW][C]Calculation[/C][C]-0.0304009[/C][C]0.122859[/C][C]-0.2474[/C][C]0.804887[/C][C]0.402443[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-0.263557[/C][C]0.110951[/C][C]-2.375[/C][C]0.0187353[/C][C]0.00936766[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]0.140456[/C][C]0.146928[/C][C]0.956[/C][C]0.340567[/C][C]0.170283[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]0.651259[/C][C]0.187667[/C][C]3.47[/C][C]0.000671442[/C][C]0.000335721[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]0.211395[/C][C]0.236539[/C][C]0.8937[/C][C]0.37285[/C][C]0.186425[/C][/ROW]
[ROW][C]Estimation[/C][C]-0.0531906[/C][C]0.21935[/C][C]-0.2425[/C][C]0.808716[/C][C]0.404358[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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)10.7543.853882.790.005916770.00295838
programma0.9069740.5675441.5980.1120380.0560192
gender-0.5156560.378795-1.3610.1753670.0876837
age-0.1671440.166989-1.0010.3184010.159201
LFM0.04574590.00571348.0072.46755e-131.23378e-13
PRH-0.005344710.00990804-0.53940.5903530.295176
CH0.01886270.01247221.5120.132450.066225
Blogs0.006695720.003655911.8310.06892450.0344623
Calculation-0.03040090.122859-0.24740.8048870.402443
Algebraic_Reasoning-0.2635570.110951-2.3750.01873530.00936766
Graphical_Interpretation0.1404560.1469280.9560.3405670.170283
Proportionality_and_Ratio0.6512590.1876673.470.0006714420.000335721
Probability_and_Sampling0.2113950.2365390.89370.372850.186425
Estimation-0.05319060.21935-0.24250.8087160.404358






Multiple Linear Regression - Regression Statistics
Multiple R0.749307
R-squared0.561461
Adjusted R-squared0.525149
F-TEST (value)15.4621
F-TEST (DF numerator)13
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.08698
Sum Squared Residuals683.811

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.749307 \tabularnewline
R-squared & 0.561461 \tabularnewline
Adjusted R-squared & 0.525149 \tabularnewline
F-TEST (value) & 15.4621 \tabularnewline
F-TEST (DF numerator) & 13 \tabularnewline
F-TEST (DF denominator) & 157 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.08698 \tabularnewline
Sum Squared Residuals & 683.811 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.749307[/C][/ROW]
[ROW][C]R-squared[/C][C]0.561461[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.525149[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]15.4621[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]13[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]157[/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]2.08698[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]683.811[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&T=3

As an alternative you can also use a QR Code:  
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The GUIDs for individual cells are displayed in the table below:

Multiple Linear Regression - Regression Statistics
Multiple R0.749307
R-squared0.561461
Adjusted R-squared0.525149
F-TEST (value)15.4621
F-TEST (DF numerator)13
F-TEST (DF denominator)157
p-value0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation2.08698
Sum Squared Residuals683.811






Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or IndexActualsInterpolationForecastResidualsPrediction Error
14.358.49988-4.14988
212.712.04750.652505
318.115.75972.34025
417.8517.80140.0485955
516.617.3364-0.736436
612.610.81421.78582
717.119.7894-2.68941
819.118.28970.810283
916.118.9047-2.80468
1013.3510.30943.04063
1118.417.73150.668475
1214.710.32144.37863
1310.614.1919-3.59194
1412.612.7541-0.154107
1516.214.04422.15577
1613.613.09960.500397
1718.915.6933.20698
1814.112.6051.49503
1914.514.7372-0.23724
2016.1517.346-1.196
2114.7513.72931.02066
2214.814.0460.754032
2312.4512.35510.0948852
2412.6512.9181-0.268114
2517.3514.98442.36559
268.610.1497-1.54966
2718.417.47590.924081
2816.115.85340.246553
2911.613.0975-1.49748
3017.7515.44872.30128
3115.2514.83690.413082
3217.6515.61332.03673
3315.614.1951.40503
3416.3515.5070.842979
3517.6516.83910.810929
3613.613.14710.452875
3711.714.1209-2.42085
3814.3513.42890.921148
3914.7515.6408-0.890833
4018.2518.23650.0135354
419.916.246-6.34603
421614.54721.45275
4318.2516.54721.70285
4416.8516.9054-0.0553552
4514.612.93131.66873
4613.8513.79490.0550613
4718.9518.53210.41793
4815.614.58991.01011
4914.8516.6354-1.78542
5011.7514.4111-2.66108
5118.4516.57491.87511
5215.915.47770.422321
5317.118.416-1.31602
5416.18.968057.13195
5519.919.24240.657553
5610.9510.91130.0387317
5718.4516.60191.84811
5815.114.1370.962955
591516.1657-1.16573
6011.3515.0651-3.7151
6115.9514.49241.45755
6218.115.4592.64104
6314.615.7562-1.15619
6415.417.0983-1.69826
6515.417.0983-1.69826
6617.615.32382.27623
6713.3513.8347-0.48474
6819.116.09233.00774
6915.3516.1741-0.824061
707.69.72743-2.12743
7113.415.4967-2.09671
7213.914.9702-1.07022
7319.117.38041.71957
7415.2514.79950.450533
7512.915.9243-3.02432
7616.115.79930.300742
7717.3514.65412.69593
7813.1515.3383-2.18829
7912.1513.8029-1.65287
8012.610.47752.12253
8110.3511.8989-1.54891
8215.415.7317-0.331725
839.613.0999-3.49989
8418.214.96023.23984
8513.612.88260.71743
8614.8513.55781.2922
8714.7516.1614-1.41139
8814.114.4551-0.355063
8914.912.87992.02013
9016.2514.69191.55809
9119.2519.1330.116987
9213.611.51732.08267
9313.615.1408-1.54078
9415.6516.9707-1.32068
9512.7513.7086-0.958599
9614.613.31631.28371
979.859.93285-0.0828548
9812.6511.75980.890169
9911.912.0159-0.115937
10019.217.66611.53388
10116.615.17611.42389
10211.211.2117-0.0117408
10315.2515.263-0.0130151
10411.914.4766-2.57656
10513.213.662-0.461954
10616.3517.1031-0.75306
10712.411.65310.746861
10815.8513.99141.85856
10914.3515.5762-1.22624
11018.1517.56460.585384
11111.1511.8092-0.65925
11215.6516.0605-0.410457
11317.7515.28652.46354
1147.6512.1196-4.46961
11512.3512.26610.083872
11615.612.94522.65479
11719.319.29880.00123083
11815.212.20822.9918
11917.115.46731.63269
12015.614.42941.1706
12118.415.75322.64679
12219.0516.76242.28756
12318.5515.52753.02252
12419.117.79211.30787
12513.113.1881-0.0880962
12612.8516.3244-3.47437
1279.511.669-2.16896
1284.510.6851-6.18513
12911.8510.37911.47095
13013.615.0963-1.49629
13111.711.18750.512529
13212.412.5232-0.123168
13313.3515.0256-1.67555
13411.412.1358-0.735829
13514.913.95660.943384
13619.919.24240.657553
13717.7515.10812.6419
13811.212.3633-1.16332
13914.615.4701-0.870116
14017.617.7845-0.184529
14114.0513.03911.01093
14216.115.18150.918491
14313.3514.4806-1.13064
14411.8512.9766-1.1266
14511.9514.0252-2.07516
14614.7515.5161-0.766144
14715.1514.15480.995178
14813.216.1918-2.99183
14916.8516.53160.318385
1507.8512.3572-4.50716
1517.712.78-5.08004
15212.614.3554-1.75537
1537.8513.0782-5.22823
15410.9510.91130.0387317
15512.3514.363-2.01302
1569.9512.9611-3.01108
15714.913.95660.943384
15816.6515.96830.681745
15913.414.2298-0.829806
16013.9513.68650.263518
16115.713.25252.44749
16216.8515.17711.67288
16310.9512.0623-1.11233
16415.3513.72211.62787
16512.212.9384-0.738406
16615.114.91670.1833
16717.7516.37141.37857
16815.214.60460.595384
16914.614.54040.0596238
17016.6516.15370.496311
1718.19.64228-1.54228

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 4.35 & 8.49988 & -4.14988 \tabularnewline
2 & 12.7 & 12.0475 & 0.652505 \tabularnewline
3 & 18.1 & 15.7597 & 2.34025 \tabularnewline
4 & 17.85 & 17.8014 & 0.0485955 \tabularnewline
5 & 16.6 & 17.3364 & -0.736436 \tabularnewline
6 & 12.6 & 10.8142 & 1.78582 \tabularnewline
7 & 17.1 & 19.7894 & -2.68941 \tabularnewline
8 & 19.1 & 18.2897 & 0.810283 \tabularnewline
9 & 16.1 & 18.9047 & -2.80468 \tabularnewline
10 & 13.35 & 10.3094 & 3.04063 \tabularnewline
11 & 18.4 & 17.7315 & 0.668475 \tabularnewline
12 & 14.7 & 10.3214 & 4.37863 \tabularnewline
13 & 10.6 & 14.1919 & -3.59194 \tabularnewline
14 & 12.6 & 12.7541 & -0.154107 \tabularnewline
15 & 16.2 & 14.0442 & 2.15577 \tabularnewline
16 & 13.6 & 13.0996 & 0.500397 \tabularnewline
17 & 18.9 & 15.693 & 3.20698 \tabularnewline
18 & 14.1 & 12.605 & 1.49503 \tabularnewline
19 & 14.5 & 14.7372 & -0.23724 \tabularnewline
20 & 16.15 & 17.346 & -1.196 \tabularnewline
21 & 14.75 & 13.7293 & 1.02066 \tabularnewline
22 & 14.8 & 14.046 & 0.754032 \tabularnewline
23 & 12.45 & 12.3551 & 0.0948852 \tabularnewline
24 & 12.65 & 12.9181 & -0.268114 \tabularnewline
25 & 17.35 & 14.9844 & 2.36559 \tabularnewline
26 & 8.6 & 10.1497 & -1.54966 \tabularnewline
27 & 18.4 & 17.4759 & 0.924081 \tabularnewline
28 & 16.1 & 15.8534 & 0.246553 \tabularnewline
29 & 11.6 & 13.0975 & -1.49748 \tabularnewline
30 & 17.75 & 15.4487 & 2.30128 \tabularnewline
31 & 15.25 & 14.8369 & 0.413082 \tabularnewline
32 & 17.65 & 15.6133 & 2.03673 \tabularnewline
33 & 15.6 & 14.195 & 1.40503 \tabularnewline
34 & 16.35 & 15.507 & 0.842979 \tabularnewline
35 & 17.65 & 16.8391 & 0.810929 \tabularnewline
36 & 13.6 & 13.1471 & 0.452875 \tabularnewline
37 & 11.7 & 14.1209 & -2.42085 \tabularnewline
38 & 14.35 & 13.4289 & 0.921148 \tabularnewline
39 & 14.75 & 15.6408 & -0.890833 \tabularnewline
40 & 18.25 & 18.2365 & 0.0135354 \tabularnewline
41 & 9.9 & 16.246 & -6.34603 \tabularnewline
42 & 16 & 14.5472 & 1.45275 \tabularnewline
43 & 18.25 & 16.5472 & 1.70285 \tabularnewline
44 & 16.85 & 16.9054 & -0.0553552 \tabularnewline
45 & 14.6 & 12.9313 & 1.66873 \tabularnewline
46 & 13.85 & 13.7949 & 0.0550613 \tabularnewline
47 & 18.95 & 18.5321 & 0.41793 \tabularnewline
48 & 15.6 & 14.5899 & 1.01011 \tabularnewline
49 & 14.85 & 16.6354 & -1.78542 \tabularnewline
50 & 11.75 & 14.4111 & -2.66108 \tabularnewline
51 & 18.45 & 16.5749 & 1.87511 \tabularnewline
52 & 15.9 & 15.4777 & 0.422321 \tabularnewline
53 & 17.1 & 18.416 & -1.31602 \tabularnewline
54 & 16.1 & 8.96805 & 7.13195 \tabularnewline
55 & 19.9 & 19.2424 & 0.657553 \tabularnewline
56 & 10.95 & 10.9113 & 0.0387317 \tabularnewline
57 & 18.45 & 16.6019 & 1.84811 \tabularnewline
58 & 15.1 & 14.137 & 0.962955 \tabularnewline
59 & 15 & 16.1657 & -1.16573 \tabularnewline
60 & 11.35 & 15.0651 & -3.7151 \tabularnewline
61 & 15.95 & 14.4924 & 1.45755 \tabularnewline
62 & 18.1 & 15.459 & 2.64104 \tabularnewline
63 & 14.6 & 15.7562 & -1.15619 \tabularnewline
64 & 15.4 & 17.0983 & -1.69826 \tabularnewline
65 & 15.4 & 17.0983 & -1.69826 \tabularnewline
66 & 17.6 & 15.3238 & 2.27623 \tabularnewline
67 & 13.35 & 13.8347 & -0.48474 \tabularnewline
68 & 19.1 & 16.0923 & 3.00774 \tabularnewline
69 & 15.35 & 16.1741 & -0.824061 \tabularnewline
70 & 7.6 & 9.72743 & -2.12743 \tabularnewline
71 & 13.4 & 15.4967 & -2.09671 \tabularnewline
72 & 13.9 & 14.9702 & -1.07022 \tabularnewline
73 & 19.1 & 17.3804 & 1.71957 \tabularnewline
74 & 15.25 & 14.7995 & 0.450533 \tabularnewline
75 & 12.9 & 15.9243 & -3.02432 \tabularnewline
76 & 16.1 & 15.7993 & 0.300742 \tabularnewline
77 & 17.35 & 14.6541 & 2.69593 \tabularnewline
78 & 13.15 & 15.3383 & -2.18829 \tabularnewline
79 & 12.15 & 13.8029 & -1.65287 \tabularnewline
80 & 12.6 & 10.4775 & 2.12253 \tabularnewline
81 & 10.35 & 11.8989 & -1.54891 \tabularnewline
82 & 15.4 & 15.7317 & -0.331725 \tabularnewline
83 & 9.6 & 13.0999 & -3.49989 \tabularnewline
84 & 18.2 & 14.9602 & 3.23984 \tabularnewline
85 & 13.6 & 12.8826 & 0.71743 \tabularnewline
86 & 14.85 & 13.5578 & 1.2922 \tabularnewline
87 & 14.75 & 16.1614 & -1.41139 \tabularnewline
88 & 14.1 & 14.4551 & -0.355063 \tabularnewline
89 & 14.9 & 12.8799 & 2.02013 \tabularnewline
90 & 16.25 & 14.6919 & 1.55809 \tabularnewline
91 & 19.25 & 19.133 & 0.116987 \tabularnewline
92 & 13.6 & 11.5173 & 2.08267 \tabularnewline
93 & 13.6 & 15.1408 & -1.54078 \tabularnewline
94 & 15.65 & 16.9707 & -1.32068 \tabularnewline
95 & 12.75 & 13.7086 & -0.958599 \tabularnewline
96 & 14.6 & 13.3163 & 1.28371 \tabularnewline
97 & 9.85 & 9.93285 & -0.0828548 \tabularnewline
98 & 12.65 & 11.7598 & 0.890169 \tabularnewline
99 & 11.9 & 12.0159 & -0.115937 \tabularnewline
100 & 19.2 & 17.6661 & 1.53388 \tabularnewline
101 & 16.6 & 15.1761 & 1.42389 \tabularnewline
102 & 11.2 & 11.2117 & -0.0117408 \tabularnewline
103 & 15.25 & 15.263 & -0.0130151 \tabularnewline
104 & 11.9 & 14.4766 & -2.57656 \tabularnewline
105 & 13.2 & 13.662 & -0.461954 \tabularnewline
106 & 16.35 & 17.1031 & -0.75306 \tabularnewline
107 & 12.4 & 11.6531 & 0.746861 \tabularnewline
108 & 15.85 & 13.9914 & 1.85856 \tabularnewline
109 & 14.35 & 15.5762 & -1.22624 \tabularnewline
110 & 18.15 & 17.5646 & 0.585384 \tabularnewline
111 & 11.15 & 11.8092 & -0.65925 \tabularnewline
112 & 15.65 & 16.0605 & -0.410457 \tabularnewline
113 & 17.75 & 15.2865 & 2.46354 \tabularnewline
114 & 7.65 & 12.1196 & -4.46961 \tabularnewline
115 & 12.35 & 12.2661 & 0.083872 \tabularnewline
116 & 15.6 & 12.9452 & 2.65479 \tabularnewline
117 & 19.3 & 19.2988 & 0.00123083 \tabularnewline
118 & 15.2 & 12.2082 & 2.9918 \tabularnewline
119 & 17.1 & 15.4673 & 1.63269 \tabularnewline
120 & 15.6 & 14.4294 & 1.1706 \tabularnewline
121 & 18.4 & 15.7532 & 2.64679 \tabularnewline
122 & 19.05 & 16.7624 & 2.28756 \tabularnewline
123 & 18.55 & 15.5275 & 3.02252 \tabularnewline
124 & 19.1 & 17.7921 & 1.30787 \tabularnewline
125 & 13.1 & 13.1881 & -0.0880962 \tabularnewline
126 & 12.85 & 16.3244 & -3.47437 \tabularnewline
127 & 9.5 & 11.669 & -2.16896 \tabularnewline
128 & 4.5 & 10.6851 & -6.18513 \tabularnewline
129 & 11.85 & 10.3791 & 1.47095 \tabularnewline
130 & 13.6 & 15.0963 & -1.49629 \tabularnewline
131 & 11.7 & 11.1875 & 0.512529 \tabularnewline
132 & 12.4 & 12.5232 & -0.123168 \tabularnewline
133 & 13.35 & 15.0256 & -1.67555 \tabularnewline
134 & 11.4 & 12.1358 & -0.735829 \tabularnewline
135 & 14.9 & 13.9566 & 0.943384 \tabularnewline
136 & 19.9 & 19.2424 & 0.657553 \tabularnewline
137 & 17.75 & 15.1081 & 2.6419 \tabularnewline
138 & 11.2 & 12.3633 & -1.16332 \tabularnewline
139 & 14.6 & 15.4701 & -0.870116 \tabularnewline
140 & 17.6 & 17.7845 & -0.184529 \tabularnewline
141 & 14.05 & 13.0391 & 1.01093 \tabularnewline
142 & 16.1 & 15.1815 & 0.918491 \tabularnewline
143 & 13.35 & 14.4806 & -1.13064 \tabularnewline
144 & 11.85 & 12.9766 & -1.1266 \tabularnewline
145 & 11.95 & 14.0252 & -2.07516 \tabularnewline
146 & 14.75 & 15.5161 & -0.766144 \tabularnewline
147 & 15.15 & 14.1548 & 0.995178 \tabularnewline
148 & 13.2 & 16.1918 & -2.99183 \tabularnewline
149 & 16.85 & 16.5316 & 0.318385 \tabularnewline
150 & 7.85 & 12.3572 & -4.50716 \tabularnewline
151 & 7.7 & 12.78 & -5.08004 \tabularnewline
152 & 12.6 & 14.3554 & -1.75537 \tabularnewline
153 & 7.85 & 13.0782 & -5.22823 \tabularnewline
154 & 10.95 & 10.9113 & 0.0387317 \tabularnewline
155 & 12.35 & 14.363 & -2.01302 \tabularnewline
156 & 9.95 & 12.9611 & -3.01108 \tabularnewline
157 & 14.9 & 13.9566 & 0.943384 \tabularnewline
158 & 16.65 & 15.9683 & 0.681745 \tabularnewline
159 & 13.4 & 14.2298 & -0.829806 \tabularnewline
160 & 13.95 & 13.6865 & 0.263518 \tabularnewline
161 & 15.7 & 13.2525 & 2.44749 \tabularnewline
162 & 16.85 & 15.1771 & 1.67288 \tabularnewline
163 & 10.95 & 12.0623 & -1.11233 \tabularnewline
164 & 15.35 & 13.7221 & 1.62787 \tabularnewline
165 & 12.2 & 12.9384 & -0.738406 \tabularnewline
166 & 15.1 & 14.9167 & 0.1833 \tabularnewline
167 & 17.75 & 16.3714 & 1.37857 \tabularnewline
168 & 15.2 & 14.6046 & 0.595384 \tabularnewline
169 & 14.6 & 14.5404 & 0.0596238 \tabularnewline
170 & 16.65 & 16.1537 & 0.496311 \tabularnewline
171 & 8.1 & 9.64228 & -1.54228 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&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]4.35[/C][C]8.49988[/C][C]-4.14988[/C][/ROW]
[ROW][C]2[/C][C]12.7[/C][C]12.0475[/C][C]0.652505[/C][/ROW]
[ROW][C]3[/C][C]18.1[/C][C]15.7597[/C][C]2.34025[/C][/ROW]
[ROW][C]4[/C][C]17.85[/C][C]17.8014[/C][C]0.0485955[/C][/ROW]
[ROW][C]5[/C][C]16.6[/C][C]17.3364[/C][C]-0.736436[/C][/ROW]
[ROW][C]6[/C][C]12.6[/C][C]10.8142[/C][C]1.78582[/C][/ROW]
[ROW][C]7[/C][C]17.1[/C][C]19.7894[/C][C]-2.68941[/C][/ROW]
[ROW][C]8[/C][C]19.1[/C][C]18.2897[/C][C]0.810283[/C][/ROW]
[ROW][C]9[/C][C]16.1[/C][C]18.9047[/C][C]-2.80468[/C][/ROW]
[ROW][C]10[/C][C]13.35[/C][C]10.3094[/C][C]3.04063[/C][/ROW]
[ROW][C]11[/C][C]18.4[/C][C]17.7315[/C][C]0.668475[/C][/ROW]
[ROW][C]12[/C][C]14.7[/C][C]10.3214[/C][C]4.37863[/C][/ROW]
[ROW][C]13[/C][C]10.6[/C][C]14.1919[/C][C]-3.59194[/C][/ROW]
[ROW][C]14[/C][C]12.6[/C][C]12.7541[/C][C]-0.154107[/C][/ROW]
[ROW][C]15[/C][C]16.2[/C][C]14.0442[/C][C]2.15577[/C][/ROW]
[ROW][C]16[/C][C]13.6[/C][C]13.0996[/C][C]0.500397[/C][/ROW]
[ROW][C]17[/C][C]18.9[/C][C]15.693[/C][C]3.20698[/C][/ROW]
[ROW][C]18[/C][C]14.1[/C][C]12.605[/C][C]1.49503[/C][/ROW]
[ROW][C]19[/C][C]14.5[/C][C]14.7372[/C][C]-0.23724[/C][/ROW]
[ROW][C]20[/C][C]16.15[/C][C]17.346[/C][C]-1.196[/C][/ROW]
[ROW][C]21[/C][C]14.75[/C][C]13.7293[/C][C]1.02066[/C][/ROW]
[ROW][C]22[/C][C]14.8[/C][C]14.046[/C][C]0.754032[/C][/ROW]
[ROW][C]23[/C][C]12.45[/C][C]12.3551[/C][C]0.0948852[/C][/ROW]
[ROW][C]24[/C][C]12.65[/C][C]12.9181[/C][C]-0.268114[/C][/ROW]
[ROW][C]25[/C][C]17.35[/C][C]14.9844[/C][C]2.36559[/C][/ROW]
[ROW][C]26[/C][C]8.6[/C][C]10.1497[/C][C]-1.54966[/C][/ROW]
[ROW][C]27[/C][C]18.4[/C][C]17.4759[/C][C]0.924081[/C][/ROW]
[ROW][C]28[/C][C]16.1[/C][C]15.8534[/C][C]0.246553[/C][/ROW]
[ROW][C]29[/C][C]11.6[/C][C]13.0975[/C][C]-1.49748[/C][/ROW]
[ROW][C]30[/C][C]17.75[/C][C]15.4487[/C][C]2.30128[/C][/ROW]
[ROW][C]31[/C][C]15.25[/C][C]14.8369[/C][C]0.413082[/C][/ROW]
[ROW][C]32[/C][C]17.65[/C][C]15.6133[/C][C]2.03673[/C][/ROW]
[ROW][C]33[/C][C]15.6[/C][C]14.195[/C][C]1.40503[/C][/ROW]
[ROW][C]34[/C][C]16.35[/C][C]15.507[/C][C]0.842979[/C][/ROW]
[ROW][C]35[/C][C]17.65[/C][C]16.8391[/C][C]0.810929[/C][/ROW]
[ROW][C]36[/C][C]13.6[/C][C]13.1471[/C][C]0.452875[/C][/ROW]
[ROW][C]37[/C][C]11.7[/C][C]14.1209[/C][C]-2.42085[/C][/ROW]
[ROW][C]38[/C][C]14.35[/C][C]13.4289[/C][C]0.921148[/C][/ROW]
[ROW][C]39[/C][C]14.75[/C][C]15.6408[/C][C]-0.890833[/C][/ROW]
[ROW][C]40[/C][C]18.25[/C][C]18.2365[/C][C]0.0135354[/C][/ROW]
[ROW][C]41[/C][C]9.9[/C][C]16.246[/C][C]-6.34603[/C][/ROW]
[ROW][C]42[/C][C]16[/C][C]14.5472[/C][C]1.45275[/C][/ROW]
[ROW][C]43[/C][C]18.25[/C][C]16.5472[/C][C]1.70285[/C][/ROW]
[ROW][C]44[/C][C]16.85[/C][C]16.9054[/C][C]-0.0553552[/C][/ROW]
[ROW][C]45[/C][C]14.6[/C][C]12.9313[/C][C]1.66873[/C][/ROW]
[ROW][C]46[/C][C]13.85[/C][C]13.7949[/C][C]0.0550613[/C][/ROW]
[ROW][C]47[/C][C]18.95[/C][C]18.5321[/C][C]0.41793[/C][/ROW]
[ROW][C]48[/C][C]15.6[/C][C]14.5899[/C][C]1.01011[/C][/ROW]
[ROW][C]49[/C][C]14.85[/C][C]16.6354[/C][C]-1.78542[/C][/ROW]
[ROW][C]50[/C][C]11.75[/C][C]14.4111[/C][C]-2.66108[/C][/ROW]
[ROW][C]51[/C][C]18.45[/C][C]16.5749[/C][C]1.87511[/C][/ROW]
[ROW][C]52[/C][C]15.9[/C][C]15.4777[/C][C]0.422321[/C][/ROW]
[ROW][C]53[/C][C]17.1[/C][C]18.416[/C][C]-1.31602[/C][/ROW]
[ROW][C]54[/C][C]16.1[/C][C]8.96805[/C][C]7.13195[/C][/ROW]
[ROW][C]55[/C][C]19.9[/C][C]19.2424[/C][C]0.657553[/C][/ROW]
[ROW][C]56[/C][C]10.95[/C][C]10.9113[/C][C]0.0387317[/C][/ROW]
[ROW][C]57[/C][C]18.45[/C][C]16.6019[/C][C]1.84811[/C][/ROW]
[ROW][C]58[/C][C]15.1[/C][C]14.137[/C][C]0.962955[/C][/ROW]
[ROW][C]59[/C][C]15[/C][C]16.1657[/C][C]-1.16573[/C][/ROW]
[ROW][C]60[/C][C]11.35[/C][C]15.0651[/C][C]-3.7151[/C][/ROW]
[ROW][C]61[/C][C]15.95[/C][C]14.4924[/C][C]1.45755[/C][/ROW]
[ROW][C]62[/C][C]18.1[/C][C]15.459[/C][C]2.64104[/C][/ROW]
[ROW][C]63[/C][C]14.6[/C][C]15.7562[/C][C]-1.15619[/C][/ROW]
[ROW][C]64[/C][C]15.4[/C][C]17.0983[/C][C]-1.69826[/C][/ROW]
[ROW][C]65[/C][C]15.4[/C][C]17.0983[/C][C]-1.69826[/C][/ROW]
[ROW][C]66[/C][C]17.6[/C][C]15.3238[/C][C]2.27623[/C][/ROW]
[ROW][C]67[/C][C]13.35[/C][C]13.8347[/C][C]-0.48474[/C][/ROW]
[ROW][C]68[/C][C]19.1[/C][C]16.0923[/C][C]3.00774[/C][/ROW]
[ROW][C]69[/C][C]15.35[/C][C]16.1741[/C][C]-0.824061[/C][/ROW]
[ROW][C]70[/C][C]7.6[/C][C]9.72743[/C][C]-2.12743[/C][/ROW]
[ROW][C]71[/C][C]13.4[/C][C]15.4967[/C][C]-2.09671[/C][/ROW]
[ROW][C]72[/C][C]13.9[/C][C]14.9702[/C][C]-1.07022[/C][/ROW]
[ROW][C]73[/C][C]19.1[/C][C]17.3804[/C][C]1.71957[/C][/ROW]
[ROW][C]74[/C][C]15.25[/C][C]14.7995[/C][C]0.450533[/C][/ROW]
[ROW][C]75[/C][C]12.9[/C][C]15.9243[/C][C]-3.02432[/C][/ROW]
[ROW][C]76[/C][C]16.1[/C][C]15.7993[/C][C]0.300742[/C][/ROW]
[ROW][C]77[/C][C]17.35[/C][C]14.6541[/C][C]2.69593[/C][/ROW]
[ROW][C]78[/C][C]13.15[/C][C]15.3383[/C][C]-2.18829[/C][/ROW]
[ROW][C]79[/C][C]12.15[/C][C]13.8029[/C][C]-1.65287[/C][/ROW]
[ROW][C]80[/C][C]12.6[/C][C]10.4775[/C][C]2.12253[/C][/ROW]
[ROW][C]81[/C][C]10.35[/C][C]11.8989[/C][C]-1.54891[/C][/ROW]
[ROW][C]82[/C][C]15.4[/C][C]15.7317[/C][C]-0.331725[/C][/ROW]
[ROW][C]83[/C][C]9.6[/C][C]13.0999[/C][C]-3.49989[/C][/ROW]
[ROW][C]84[/C][C]18.2[/C][C]14.9602[/C][C]3.23984[/C][/ROW]
[ROW][C]85[/C][C]13.6[/C][C]12.8826[/C][C]0.71743[/C][/ROW]
[ROW][C]86[/C][C]14.85[/C][C]13.5578[/C][C]1.2922[/C][/ROW]
[ROW][C]87[/C][C]14.75[/C][C]16.1614[/C][C]-1.41139[/C][/ROW]
[ROW][C]88[/C][C]14.1[/C][C]14.4551[/C][C]-0.355063[/C][/ROW]
[ROW][C]89[/C][C]14.9[/C][C]12.8799[/C][C]2.02013[/C][/ROW]
[ROW][C]90[/C][C]16.25[/C][C]14.6919[/C][C]1.55809[/C][/ROW]
[ROW][C]91[/C][C]19.25[/C][C]19.133[/C][C]0.116987[/C][/ROW]
[ROW][C]92[/C][C]13.6[/C][C]11.5173[/C][C]2.08267[/C][/ROW]
[ROW][C]93[/C][C]13.6[/C][C]15.1408[/C][C]-1.54078[/C][/ROW]
[ROW][C]94[/C][C]15.65[/C][C]16.9707[/C][C]-1.32068[/C][/ROW]
[ROW][C]95[/C][C]12.75[/C][C]13.7086[/C][C]-0.958599[/C][/ROW]
[ROW][C]96[/C][C]14.6[/C][C]13.3163[/C][C]1.28371[/C][/ROW]
[ROW][C]97[/C][C]9.85[/C][C]9.93285[/C][C]-0.0828548[/C][/ROW]
[ROW][C]98[/C][C]12.65[/C][C]11.7598[/C][C]0.890169[/C][/ROW]
[ROW][C]99[/C][C]11.9[/C][C]12.0159[/C][C]-0.115937[/C][/ROW]
[ROW][C]100[/C][C]19.2[/C][C]17.6661[/C][C]1.53388[/C][/ROW]
[ROW][C]101[/C][C]16.6[/C][C]15.1761[/C][C]1.42389[/C][/ROW]
[ROW][C]102[/C][C]11.2[/C][C]11.2117[/C][C]-0.0117408[/C][/ROW]
[ROW][C]103[/C][C]15.25[/C][C]15.263[/C][C]-0.0130151[/C][/ROW]
[ROW][C]104[/C][C]11.9[/C][C]14.4766[/C][C]-2.57656[/C][/ROW]
[ROW][C]105[/C][C]13.2[/C][C]13.662[/C][C]-0.461954[/C][/ROW]
[ROW][C]106[/C][C]16.35[/C][C]17.1031[/C][C]-0.75306[/C][/ROW]
[ROW][C]107[/C][C]12.4[/C][C]11.6531[/C][C]0.746861[/C][/ROW]
[ROW][C]108[/C][C]15.85[/C][C]13.9914[/C][C]1.85856[/C][/ROW]
[ROW][C]109[/C][C]14.35[/C][C]15.5762[/C][C]-1.22624[/C][/ROW]
[ROW][C]110[/C][C]18.15[/C][C]17.5646[/C][C]0.585384[/C][/ROW]
[ROW][C]111[/C][C]11.15[/C][C]11.8092[/C][C]-0.65925[/C][/ROW]
[ROW][C]112[/C][C]15.65[/C][C]16.0605[/C][C]-0.410457[/C][/ROW]
[ROW][C]113[/C][C]17.75[/C][C]15.2865[/C][C]2.46354[/C][/ROW]
[ROW][C]114[/C][C]7.65[/C][C]12.1196[/C][C]-4.46961[/C][/ROW]
[ROW][C]115[/C][C]12.35[/C][C]12.2661[/C][C]0.083872[/C][/ROW]
[ROW][C]116[/C][C]15.6[/C][C]12.9452[/C][C]2.65479[/C][/ROW]
[ROW][C]117[/C][C]19.3[/C][C]19.2988[/C][C]0.00123083[/C][/ROW]
[ROW][C]118[/C][C]15.2[/C][C]12.2082[/C][C]2.9918[/C][/ROW]
[ROW][C]119[/C][C]17.1[/C][C]15.4673[/C][C]1.63269[/C][/ROW]
[ROW][C]120[/C][C]15.6[/C][C]14.4294[/C][C]1.1706[/C][/ROW]
[ROW][C]121[/C][C]18.4[/C][C]15.7532[/C][C]2.64679[/C][/ROW]
[ROW][C]122[/C][C]19.05[/C][C]16.7624[/C][C]2.28756[/C][/ROW]
[ROW][C]123[/C][C]18.55[/C][C]15.5275[/C][C]3.02252[/C][/ROW]
[ROW][C]124[/C][C]19.1[/C][C]17.7921[/C][C]1.30787[/C][/ROW]
[ROW][C]125[/C][C]13.1[/C][C]13.1881[/C][C]-0.0880962[/C][/ROW]
[ROW][C]126[/C][C]12.85[/C][C]16.3244[/C][C]-3.47437[/C][/ROW]
[ROW][C]127[/C][C]9.5[/C][C]11.669[/C][C]-2.16896[/C][/ROW]
[ROW][C]128[/C][C]4.5[/C][C]10.6851[/C][C]-6.18513[/C][/ROW]
[ROW][C]129[/C][C]11.85[/C][C]10.3791[/C][C]1.47095[/C][/ROW]
[ROW][C]130[/C][C]13.6[/C][C]15.0963[/C][C]-1.49629[/C][/ROW]
[ROW][C]131[/C][C]11.7[/C][C]11.1875[/C][C]0.512529[/C][/ROW]
[ROW][C]132[/C][C]12.4[/C][C]12.5232[/C][C]-0.123168[/C][/ROW]
[ROW][C]133[/C][C]13.35[/C][C]15.0256[/C][C]-1.67555[/C][/ROW]
[ROW][C]134[/C][C]11.4[/C][C]12.1358[/C][C]-0.735829[/C][/ROW]
[ROW][C]135[/C][C]14.9[/C][C]13.9566[/C][C]0.943384[/C][/ROW]
[ROW][C]136[/C][C]19.9[/C][C]19.2424[/C][C]0.657553[/C][/ROW]
[ROW][C]137[/C][C]17.75[/C][C]15.1081[/C][C]2.6419[/C][/ROW]
[ROW][C]138[/C][C]11.2[/C][C]12.3633[/C][C]-1.16332[/C][/ROW]
[ROW][C]139[/C][C]14.6[/C][C]15.4701[/C][C]-0.870116[/C][/ROW]
[ROW][C]140[/C][C]17.6[/C][C]17.7845[/C][C]-0.184529[/C][/ROW]
[ROW][C]141[/C][C]14.05[/C][C]13.0391[/C][C]1.01093[/C][/ROW]
[ROW][C]142[/C][C]16.1[/C][C]15.1815[/C][C]0.918491[/C][/ROW]
[ROW][C]143[/C][C]13.35[/C][C]14.4806[/C][C]-1.13064[/C][/ROW]
[ROW][C]144[/C][C]11.85[/C][C]12.9766[/C][C]-1.1266[/C][/ROW]
[ROW][C]145[/C][C]11.95[/C][C]14.0252[/C][C]-2.07516[/C][/ROW]
[ROW][C]146[/C][C]14.75[/C][C]15.5161[/C][C]-0.766144[/C][/ROW]
[ROW][C]147[/C][C]15.15[/C][C]14.1548[/C][C]0.995178[/C][/ROW]
[ROW][C]148[/C][C]13.2[/C][C]16.1918[/C][C]-2.99183[/C][/ROW]
[ROW][C]149[/C][C]16.85[/C][C]16.5316[/C][C]0.318385[/C][/ROW]
[ROW][C]150[/C][C]7.85[/C][C]12.3572[/C][C]-4.50716[/C][/ROW]
[ROW][C]151[/C][C]7.7[/C][C]12.78[/C][C]-5.08004[/C][/ROW]
[ROW][C]152[/C][C]12.6[/C][C]14.3554[/C][C]-1.75537[/C][/ROW]
[ROW][C]153[/C][C]7.85[/C][C]13.0782[/C][C]-5.22823[/C][/ROW]
[ROW][C]154[/C][C]10.95[/C][C]10.9113[/C][C]0.0387317[/C][/ROW]
[ROW][C]155[/C][C]12.35[/C][C]14.363[/C][C]-2.01302[/C][/ROW]
[ROW][C]156[/C][C]9.95[/C][C]12.9611[/C][C]-3.01108[/C][/ROW]
[ROW][C]157[/C][C]14.9[/C][C]13.9566[/C][C]0.943384[/C][/ROW]
[ROW][C]158[/C][C]16.65[/C][C]15.9683[/C][C]0.681745[/C][/ROW]
[ROW][C]159[/C][C]13.4[/C][C]14.2298[/C][C]-0.829806[/C][/ROW]
[ROW][C]160[/C][C]13.95[/C][C]13.6865[/C][C]0.263518[/C][/ROW]
[ROW][C]161[/C][C]15.7[/C][C]13.2525[/C][C]2.44749[/C][/ROW]
[ROW][C]162[/C][C]16.85[/C][C]15.1771[/C][C]1.67288[/C][/ROW]
[ROW][C]163[/C][C]10.95[/C][C]12.0623[/C][C]-1.11233[/C][/ROW]
[ROW][C]164[/C][C]15.35[/C][C]13.7221[/C][C]1.62787[/C][/ROW]
[ROW][C]165[/C][C]12.2[/C][C]12.9384[/C][C]-0.738406[/C][/ROW]
[ROW][C]166[/C][C]15.1[/C][C]14.9167[/C][C]0.1833[/C][/ROW]
[ROW][C]167[/C][C]17.75[/C][C]16.3714[/C][C]1.37857[/C][/ROW]
[ROW][C]168[/C][C]15.2[/C][C]14.6046[/C][C]0.595384[/C][/ROW]
[ROW][C]169[/C][C]14.6[/C][C]14.5404[/C][C]0.0596238[/C][/ROW]
[ROW][C]170[/C][C]16.65[/C][C]16.1537[/C][C]0.496311[/C][/ROW]
[ROW][C]171[/C][C]8.1[/C][C]9.64228[/C][C]-1.54228[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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
14.358.49988-4.14988
212.712.04750.652505
318.115.75972.34025
417.8517.80140.0485955
516.617.3364-0.736436
612.610.81421.78582
717.119.7894-2.68941
819.118.28970.810283
916.118.9047-2.80468
1013.3510.30943.04063
1118.417.73150.668475
1214.710.32144.37863
1310.614.1919-3.59194
1412.612.7541-0.154107
1516.214.04422.15577
1613.613.09960.500397
1718.915.6933.20698
1814.112.6051.49503
1914.514.7372-0.23724
2016.1517.346-1.196
2114.7513.72931.02066
2214.814.0460.754032
2312.4512.35510.0948852
2412.6512.9181-0.268114
2517.3514.98442.36559
268.610.1497-1.54966
2718.417.47590.924081
2816.115.85340.246553
2911.613.0975-1.49748
3017.7515.44872.30128
3115.2514.83690.413082
3217.6515.61332.03673
3315.614.1951.40503
3416.3515.5070.842979
3517.6516.83910.810929
3613.613.14710.452875
3711.714.1209-2.42085
3814.3513.42890.921148
3914.7515.6408-0.890833
4018.2518.23650.0135354
419.916.246-6.34603
421614.54721.45275
4318.2516.54721.70285
4416.8516.9054-0.0553552
4514.612.93131.66873
4613.8513.79490.0550613
4718.9518.53210.41793
4815.614.58991.01011
4914.8516.6354-1.78542
5011.7514.4111-2.66108
5118.4516.57491.87511
5215.915.47770.422321
5317.118.416-1.31602
5416.18.968057.13195
5519.919.24240.657553
5610.9510.91130.0387317
5718.4516.60191.84811
5815.114.1370.962955
591516.1657-1.16573
6011.3515.0651-3.7151
6115.9514.49241.45755
6218.115.4592.64104
6314.615.7562-1.15619
6415.417.0983-1.69826
6515.417.0983-1.69826
6617.615.32382.27623
6713.3513.8347-0.48474
6819.116.09233.00774
6915.3516.1741-0.824061
707.69.72743-2.12743
7113.415.4967-2.09671
7213.914.9702-1.07022
7319.117.38041.71957
7415.2514.79950.450533
7512.915.9243-3.02432
7616.115.79930.300742
7717.3514.65412.69593
7813.1515.3383-2.18829
7912.1513.8029-1.65287
8012.610.47752.12253
8110.3511.8989-1.54891
8215.415.7317-0.331725
839.613.0999-3.49989
8418.214.96023.23984
8513.612.88260.71743
8614.8513.55781.2922
8714.7516.1614-1.41139
8814.114.4551-0.355063
8914.912.87992.02013
9016.2514.69191.55809
9119.2519.1330.116987
9213.611.51732.08267
9313.615.1408-1.54078
9415.6516.9707-1.32068
9512.7513.7086-0.958599
9614.613.31631.28371
979.859.93285-0.0828548
9812.6511.75980.890169
9911.912.0159-0.115937
10019.217.66611.53388
10116.615.17611.42389
10211.211.2117-0.0117408
10315.2515.263-0.0130151
10411.914.4766-2.57656
10513.213.662-0.461954
10616.3517.1031-0.75306
10712.411.65310.746861
10815.8513.99141.85856
10914.3515.5762-1.22624
11018.1517.56460.585384
11111.1511.8092-0.65925
11215.6516.0605-0.410457
11317.7515.28652.46354
1147.6512.1196-4.46961
11512.3512.26610.083872
11615.612.94522.65479
11719.319.29880.00123083
11815.212.20822.9918
11917.115.46731.63269
12015.614.42941.1706
12118.415.75322.64679
12219.0516.76242.28756
12318.5515.52753.02252
12419.117.79211.30787
12513.113.1881-0.0880962
12612.8516.3244-3.47437
1279.511.669-2.16896
1284.510.6851-6.18513
12911.8510.37911.47095
13013.615.0963-1.49629
13111.711.18750.512529
13212.412.5232-0.123168
13313.3515.0256-1.67555
13411.412.1358-0.735829
13514.913.95660.943384
13619.919.24240.657553
13717.7515.10812.6419
13811.212.3633-1.16332
13914.615.4701-0.870116
14017.617.7845-0.184529
14114.0513.03911.01093
14216.115.18150.918491
14313.3514.4806-1.13064
14411.8512.9766-1.1266
14511.9514.0252-2.07516
14614.7515.5161-0.766144
14715.1514.15480.995178
14813.216.1918-2.99183
14916.8516.53160.318385
1507.8512.3572-4.50716
1517.712.78-5.08004
15212.614.3554-1.75537
1537.8513.0782-5.22823
15410.9510.91130.0387317
15512.3514.363-2.01302
1569.9512.9611-3.01108
15714.913.95660.943384
15816.6515.96830.681745
15913.414.2298-0.829806
16013.9513.68650.263518
16115.713.25252.44749
16216.8515.17711.67288
16310.9512.0623-1.11233
16415.3513.72211.62787
16512.212.9384-0.738406
16615.114.91670.1833
16717.7516.37141.37857
16815.214.60460.595384
16914.614.54040.0596238
17016.6516.15370.496311
1718.19.64228-1.54228






Goldfeld-Quandt test for Heteroskedasticity
p-valuesAlternative Hypothesis
breakpoint indexgreater2-sidedless
170.4200890.8401780.579911
180.2960280.5920560.703972
190.1792150.358430.820785
200.3286020.6572050.671398
210.4955130.9910270.504487
220.5387450.922510.461255
230.5875810.8248370.412419
240.5854530.8290940.414547
250.7026170.5947650.297383
260.7451320.5097370.254868
270.6987910.6024180.301209
280.6283150.743370.371685
290.7157610.5684780.284239
300.671410.657180.32859
310.6060590.7878820.393941
320.5891390.8217210.410861
330.5261610.9476780.473839
340.4710460.9420930.528954
350.4058860.8117720.594114
360.3742260.7484520.625774
370.5116680.9766640.488332
380.4757370.9514730.524263
390.4235390.8470770.576461
400.3657040.7314080.634296
410.8498590.3002830.150141
420.8322740.3354520.167726
430.8068580.3862850.193142
440.7671060.4657880.232894
450.7314420.5371170.268558
460.6842840.6314320.315716
470.6349690.7300610.365031
480.5869570.8260860.413043
490.5649130.8701740.435087
500.5841260.8317480.415874
510.5539870.8920260.446013
520.5018820.9962350.498118
530.4666750.9333510.533325
540.8648560.2702880.135144
550.8388520.3222960.161148
560.8244650.3510710.175535
570.808190.3836210.19181
580.7938910.4122190.206109
590.766610.466780.23339
600.8325580.3348840.167442
610.8083840.3832310.191616
620.8236560.3526870.176344
630.8144410.3711190.185559
640.7994510.4010990.200549
650.7822750.435450.217725
660.7873920.4252160.212608
670.7766190.4467610.223381
680.790130.4197390.20987
690.7679190.4641610.232081
700.7861060.4277870.213894
710.8096620.3806760.190338
720.7836920.4326170.216308
730.7758880.4482240.224112
740.7384490.5231020.261551
750.7873080.4253850.212692
760.7516620.4966750.248338
770.7496090.5007820.250391
780.7790370.4419270.220963
790.7785460.4429090.221454
800.7686180.4627640.231382
810.7575750.4848490.242425
820.7236940.5526110.276306
830.7837310.4325380.216269
840.8284410.3431190.171559
850.8046150.3907690.195385
860.7897460.4205090.210254
870.7677320.4645350.232268
880.7338810.5322380.266119
890.7277380.5445240.272262
900.7079270.5841470.292073
910.6669550.666090.333045
920.6726930.6546150.327307
930.6528210.6943580.347179
940.6350820.7298360.364918
950.5956060.8087880.404394
960.5712230.8575550.428777
970.571140.8577210.42886
980.553610.892780.44639
990.5134440.9731120.486556
1000.494230.988460.50577
1010.4777920.9555840.522208
1020.441880.883760.55812
1030.3945310.7890620.605469
1040.427910.855820.57209
1050.3832650.766530.616735
1060.3466360.6932720.653364
1070.3206530.6413060.679347
1080.3211380.6422770.678862
1090.3108750.6217490.689125
1100.2707770.5415540.729223
1110.2413830.4827660.758617
1120.2090330.4180660.790967
1130.214070.428140.78593
1140.3128960.6257920.687104
1150.2817180.5634370.718282
1160.3603660.7207320.639634
1170.3184620.6369230.681538
1180.3901240.7802470.609876
1190.3739660.7479320.626034
1200.3485690.6971380.651431
1210.4719940.9439880.528006
1220.4803060.9606110.519694
1230.4614440.9228880.538556
1240.4390190.8780380.560981
1250.3955310.7910620.604469
1260.4567640.9135280.543236
1270.4490120.8980240.550988
1280.6430020.7139970.356998
1290.6505070.6989860.349493
1300.6029120.7941760.397088
1310.663530.6729410.33647
1320.6078940.7842120.392106
1330.5520680.8958630.447932
1340.4934220.9868440.506578
1350.4592750.918550.540725
1360.4166040.8332080.583396
1370.4723930.9447860.527607
1380.4164280.8328570.583572
1390.3538550.707710.646145
1400.2917810.5835620.708219
1410.4266160.8532310.573384
1420.4330420.8660830.566958
1430.4012590.8025170.598741
1440.4386270.8772530.561373
1450.4122670.8245350.587733
1460.337130.6742610.66287
1470.273950.54790.72605
1480.2285160.4570330.771484
1490.1650340.3300680.834966
1500.4438430.8876870.556157
1510.6150060.7699890.384994
1520.4902190.9804380.509781
1530.8389860.3220280.161014
1540.7179660.5640680.282034

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
17 & 0.420089 & 0.840178 & 0.579911 \tabularnewline
18 & 0.296028 & 0.592056 & 0.703972 \tabularnewline
19 & 0.179215 & 0.35843 & 0.820785 \tabularnewline
20 & 0.328602 & 0.657205 & 0.671398 \tabularnewline
21 & 0.495513 & 0.991027 & 0.504487 \tabularnewline
22 & 0.538745 & 0.92251 & 0.461255 \tabularnewline
23 & 0.587581 & 0.824837 & 0.412419 \tabularnewline
24 & 0.585453 & 0.829094 & 0.414547 \tabularnewline
25 & 0.702617 & 0.594765 & 0.297383 \tabularnewline
26 & 0.745132 & 0.509737 & 0.254868 \tabularnewline
27 & 0.698791 & 0.602418 & 0.301209 \tabularnewline
28 & 0.628315 & 0.74337 & 0.371685 \tabularnewline
29 & 0.715761 & 0.568478 & 0.284239 \tabularnewline
30 & 0.67141 & 0.65718 & 0.32859 \tabularnewline
31 & 0.606059 & 0.787882 & 0.393941 \tabularnewline
32 & 0.589139 & 0.821721 & 0.410861 \tabularnewline
33 & 0.526161 & 0.947678 & 0.473839 \tabularnewline
34 & 0.471046 & 0.942093 & 0.528954 \tabularnewline
35 & 0.405886 & 0.811772 & 0.594114 \tabularnewline
36 & 0.374226 & 0.748452 & 0.625774 \tabularnewline
37 & 0.511668 & 0.976664 & 0.488332 \tabularnewline
38 & 0.475737 & 0.951473 & 0.524263 \tabularnewline
39 & 0.423539 & 0.847077 & 0.576461 \tabularnewline
40 & 0.365704 & 0.731408 & 0.634296 \tabularnewline
41 & 0.849859 & 0.300283 & 0.150141 \tabularnewline
42 & 0.832274 & 0.335452 & 0.167726 \tabularnewline
43 & 0.806858 & 0.386285 & 0.193142 \tabularnewline
44 & 0.767106 & 0.465788 & 0.232894 \tabularnewline
45 & 0.731442 & 0.537117 & 0.268558 \tabularnewline
46 & 0.684284 & 0.631432 & 0.315716 \tabularnewline
47 & 0.634969 & 0.730061 & 0.365031 \tabularnewline
48 & 0.586957 & 0.826086 & 0.413043 \tabularnewline
49 & 0.564913 & 0.870174 & 0.435087 \tabularnewline
50 & 0.584126 & 0.831748 & 0.415874 \tabularnewline
51 & 0.553987 & 0.892026 & 0.446013 \tabularnewline
52 & 0.501882 & 0.996235 & 0.498118 \tabularnewline
53 & 0.466675 & 0.933351 & 0.533325 \tabularnewline
54 & 0.864856 & 0.270288 & 0.135144 \tabularnewline
55 & 0.838852 & 0.322296 & 0.161148 \tabularnewline
56 & 0.824465 & 0.351071 & 0.175535 \tabularnewline
57 & 0.80819 & 0.383621 & 0.19181 \tabularnewline
58 & 0.793891 & 0.412219 & 0.206109 \tabularnewline
59 & 0.76661 & 0.46678 & 0.23339 \tabularnewline
60 & 0.832558 & 0.334884 & 0.167442 \tabularnewline
61 & 0.808384 & 0.383231 & 0.191616 \tabularnewline
62 & 0.823656 & 0.352687 & 0.176344 \tabularnewline
63 & 0.814441 & 0.371119 & 0.185559 \tabularnewline
64 & 0.799451 & 0.401099 & 0.200549 \tabularnewline
65 & 0.782275 & 0.43545 & 0.217725 \tabularnewline
66 & 0.787392 & 0.425216 & 0.212608 \tabularnewline
67 & 0.776619 & 0.446761 & 0.223381 \tabularnewline
68 & 0.79013 & 0.419739 & 0.20987 \tabularnewline
69 & 0.767919 & 0.464161 & 0.232081 \tabularnewline
70 & 0.786106 & 0.427787 & 0.213894 \tabularnewline
71 & 0.809662 & 0.380676 & 0.190338 \tabularnewline
72 & 0.783692 & 0.432617 & 0.216308 \tabularnewline
73 & 0.775888 & 0.448224 & 0.224112 \tabularnewline
74 & 0.738449 & 0.523102 & 0.261551 \tabularnewline
75 & 0.787308 & 0.425385 & 0.212692 \tabularnewline
76 & 0.751662 & 0.496675 & 0.248338 \tabularnewline
77 & 0.749609 & 0.500782 & 0.250391 \tabularnewline
78 & 0.779037 & 0.441927 & 0.220963 \tabularnewline
79 & 0.778546 & 0.442909 & 0.221454 \tabularnewline
80 & 0.768618 & 0.462764 & 0.231382 \tabularnewline
81 & 0.757575 & 0.484849 & 0.242425 \tabularnewline
82 & 0.723694 & 0.552611 & 0.276306 \tabularnewline
83 & 0.783731 & 0.432538 & 0.216269 \tabularnewline
84 & 0.828441 & 0.343119 & 0.171559 \tabularnewline
85 & 0.804615 & 0.390769 & 0.195385 \tabularnewline
86 & 0.789746 & 0.420509 & 0.210254 \tabularnewline
87 & 0.767732 & 0.464535 & 0.232268 \tabularnewline
88 & 0.733881 & 0.532238 & 0.266119 \tabularnewline
89 & 0.727738 & 0.544524 & 0.272262 \tabularnewline
90 & 0.707927 & 0.584147 & 0.292073 \tabularnewline
91 & 0.666955 & 0.66609 & 0.333045 \tabularnewline
92 & 0.672693 & 0.654615 & 0.327307 \tabularnewline
93 & 0.652821 & 0.694358 & 0.347179 \tabularnewline
94 & 0.635082 & 0.729836 & 0.364918 \tabularnewline
95 & 0.595606 & 0.808788 & 0.404394 \tabularnewline
96 & 0.571223 & 0.857555 & 0.428777 \tabularnewline
97 & 0.57114 & 0.857721 & 0.42886 \tabularnewline
98 & 0.55361 & 0.89278 & 0.44639 \tabularnewline
99 & 0.513444 & 0.973112 & 0.486556 \tabularnewline
100 & 0.49423 & 0.98846 & 0.50577 \tabularnewline
101 & 0.477792 & 0.955584 & 0.522208 \tabularnewline
102 & 0.44188 & 0.88376 & 0.55812 \tabularnewline
103 & 0.394531 & 0.789062 & 0.605469 \tabularnewline
104 & 0.42791 & 0.85582 & 0.57209 \tabularnewline
105 & 0.383265 & 0.76653 & 0.616735 \tabularnewline
106 & 0.346636 & 0.693272 & 0.653364 \tabularnewline
107 & 0.320653 & 0.641306 & 0.679347 \tabularnewline
108 & 0.321138 & 0.642277 & 0.678862 \tabularnewline
109 & 0.310875 & 0.621749 & 0.689125 \tabularnewline
110 & 0.270777 & 0.541554 & 0.729223 \tabularnewline
111 & 0.241383 & 0.482766 & 0.758617 \tabularnewline
112 & 0.209033 & 0.418066 & 0.790967 \tabularnewline
113 & 0.21407 & 0.42814 & 0.78593 \tabularnewline
114 & 0.312896 & 0.625792 & 0.687104 \tabularnewline
115 & 0.281718 & 0.563437 & 0.718282 \tabularnewline
116 & 0.360366 & 0.720732 & 0.639634 \tabularnewline
117 & 0.318462 & 0.636923 & 0.681538 \tabularnewline
118 & 0.390124 & 0.780247 & 0.609876 \tabularnewline
119 & 0.373966 & 0.747932 & 0.626034 \tabularnewline
120 & 0.348569 & 0.697138 & 0.651431 \tabularnewline
121 & 0.471994 & 0.943988 & 0.528006 \tabularnewline
122 & 0.480306 & 0.960611 & 0.519694 \tabularnewline
123 & 0.461444 & 0.922888 & 0.538556 \tabularnewline
124 & 0.439019 & 0.878038 & 0.560981 \tabularnewline
125 & 0.395531 & 0.791062 & 0.604469 \tabularnewline
126 & 0.456764 & 0.913528 & 0.543236 \tabularnewline
127 & 0.449012 & 0.898024 & 0.550988 \tabularnewline
128 & 0.643002 & 0.713997 & 0.356998 \tabularnewline
129 & 0.650507 & 0.698986 & 0.349493 \tabularnewline
130 & 0.602912 & 0.794176 & 0.397088 \tabularnewline
131 & 0.66353 & 0.672941 & 0.33647 \tabularnewline
132 & 0.607894 & 0.784212 & 0.392106 \tabularnewline
133 & 0.552068 & 0.895863 & 0.447932 \tabularnewline
134 & 0.493422 & 0.986844 & 0.506578 \tabularnewline
135 & 0.459275 & 0.91855 & 0.540725 \tabularnewline
136 & 0.416604 & 0.833208 & 0.583396 \tabularnewline
137 & 0.472393 & 0.944786 & 0.527607 \tabularnewline
138 & 0.416428 & 0.832857 & 0.583572 \tabularnewline
139 & 0.353855 & 0.70771 & 0.646145 \tabularnewline
140 & 0.291781 & 0.583562 & 0.708219 \tabularnewline
141 & 0.426616 & 0.853231 & 0.573384 \tabularnewline
142 & 0.433042 & 0.866083 & 0.566958 \tabularnewline
143 & 0.401259 & 0.802517 & 0.598741 \tabularnewline
144 & 0.438627 & 0.877253 & 0.561373 \tabularnewline
145 & 0.412267 & 0.824535 & 0.587733 \tabularnewline
146 & 0.33713 & 0.674261 & 0.66287 \tabularnewline
147 & 0.27395 & 0.5479 & 0.72605 \tabularnewline
148 & 0.228516 & 0.457033 & 0.771484 \tabularnewline
149 & 0.165034 & 0.330068 & 0.834966 \tabularnewline
150 & 0.443843 & 0.887687 & 0.556157 \tabularnewline
151 & 0.615006 & 0.769989 & 0.384994 \tabularnewline
152 & 0.490219 & 0.980438 & 0.509781 \tabularnewline
153 & 0.838986 & 0.322028 & 0.161014 \tabularnewline
154 & 0.717966 & 0.564068 & 0.282034 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&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]17[/C][C]0.420089[/C][C]0.840178[/C][C]0.579911[/C][/ROW]
[ROW][C]18[/C][C]0.296028[/C][C]0.592056[/C][C]0.703972[/C][/ROW]
[ROW][C]19[/C][C]0.179215[/C][C]0.35843[/C][C]0.820785[/C][/ROW]
[ROW][C]20[/C][C]0.328602[/C][C]0.657205[/C][C]0.671398[/C][/ROW]
[ROW][C]21[/C][C]0.495513[/C][C]0.991027[/C][C]0.504487[/C][/ROW]
[ROW][C]22[/C][C]0.538745[/C][C]0.92251[/C][C]0.461255[/C][/ROW]
[ROW][C]23[/C][C]0.587581[/C][C]0.824837[/C][C]0.412419[/C][/ROW]
[ROW][C]24[/C][C]0.585453[/C][C]0.829094[/C][C]0.414547[/C][/ROW]
[ROW][C]25[/C][C]0.702617[/C][C]0.594765[/C][C]0.297383[/C][/ROW]
[ROW][C]26[/C][C]0.745132[/C][C]0.509737[/C][C]0.254868[/C][/ROW]
[ROW][C]27[/C][C]0.698791[/C][C]0.602418[/C][C]0.301209[/C][/ROW]
[ROW][C]28[/C][C]0.628315[/C][C]0.74337[/C][C]0.371685[/C][/ROW]
[ROW][C]29[/C][C]0.715761[/C][C]0.568478[/C][C]0.284239[/C][/ROW]
[ROW][C]30[/C][C]0.67141[/C][C]0.65718[/C][C]0.32859[/C][/ROW]
[ROW][C]31[/C][C]0.606059[/C][C]0.787882[/C][C]0.393941[/C][/ROW]
[ROW][C]32[/C][C]0.589139[/C][C]0.821721[/C][C]0.410861[/C][/ROW]
[ROW][C]33[/C][C]0.526161[/C][C]0.947678[/C][C]0.473839[/C][/ROW]
[ROW][C]34[/C][C]0.471046[/C][C]0.942093[/C][C]0.528954[/C][/ROW]
[ROW][C]35[/C][C]0.405886[/C][C]0.811772[/C][C]0.594114[/C][/ROW]
[ROW][C]36[/C][C]0.374226[/C][C]0.748452[/C][C]0.625774[/C][/ROW]
[ROW][C]37[/C][C]0.511668[/C][C]0.976664[/C][C]0.488332[/C][/ROW]
[ROW][C]38[/C][C]0.475737[/C][C]0.951473[/C][C]0.524263[/C][/ROW]
[ROW][C]39[/C][C]0.423539[/C][C]0.847077[/C][C]0.576461[/C][/ROW]
[ROW][C]40[/C][C]0.365704[/C][C]0.731408[/C][C]0.634296[/C][/ROW]
[ROW][C]41[/C][C]0.849859[/C][C]0.300283[/C][C]0.150141[/C][/ROW]
[ROW][C]42[/C][C]0.832274[/C][C]0.335452[/C][C]0.167726[/C][/ROW]
[ROW][C]43[/C][C]0.806858[/C][C]0.386285[/C][C]0.193142[/C][/ROW]
[ROW][C]44[/C][C]0.767106[/C][C]0.465788[/C][C]0.232894[/C][/ROW]
[ROW][C]45[/C][C]0.731442[/C][C]0.537117[/C][C]0.268558[/C][/ROW]
[ROW][C]46[/C][C]0.684284[/C][C]0.631432[/C][C]0.315716[/C][/ROW]
[ROW][C]47[/C][C]0.634969[/C][C]0.730061[/C][C]0.365031[/C][/ROW]
[ROW][C]48[/C][C]0.586957[/C][C]0.826086[/C][C]0.413043[/C][/ROW]
[ROW][C]49[/C][C]0.564913[/C][C]0.870174[/C][C]0.435087[/C][/ROW]
[ROW][C]50[/C][C]0.584126[/C][C]0.831748[/C][C]0.415874[/C][/ROW]
[ROW][C]51[/C][C]0.553987[/C][C]0.892026[/C][C]0.446013[/C][/ROW]
[ROW][C]52[/C][C]0.501882[/C][C]0.996235[/C][C]0.498118[/C][/ROW]
[ROW][C]53[/C][C]0.466675[/C][C]0.933351[/C][C]0.533325[/C][/ROW]
[ROW][C]54[/C][C]0.864856[/C][C]0.270288[/C][C]0.135144[/C][/ROW]
[ROW][C]55[/C][C]0.838852[/C][C]0.322296[/C][C]0.161148[/C][/ROW]
[ROW][C]56[/C][C]0.824465[/C][C]0.351071[/C][C]0.175535[/C][/ROW]
[ROW][C]57[/C][C]0.80819[/C][C]0.383621[/C][C]0.19181[/C][/ROW]
[ROW][C]58[/C][C]0.793891[/C][C]0.412219[/C][C]0.206109[/C][/ROW]
[ROW][C]59[/C][C]0.76661[/C][C]0.46678[/C][C]0.23339[/C][/ROW]
[ROW][C]60[/C][C]0.832558[/C][C]0.334884[/C][C]0.167442[/C][/ROW]
[ROW][C]61[/C][C]0.808384[/C][C]0.383231[/C][C]0.191616[/C][/ROW]
[ROW][C]62[/C][C]0.823656[/C][C]0.352687[/C][C]0.176344[/C][/ROW]
[ROW][C]63[/C][C]0.814441[/C][C]0.371119[/C][C]0.185559[/C][/ROW]
[ROW][C]64[/C][C]0.799451[/C][C]0.401099[/C][C]0.200549[/C][/ROW]
[ROW][C]65[/C][C]0.782275[/C][C]0.43545[/C][C]0.217725[/C][/ROW]
[ROW][C]66[/C][C]0.787392[/C][C]0.425216[/C][C]0.212608[/C][/ROW]
[ROW][C]67[/C][C]0.776619[/C][C]0.446761[/C][C]0.223381[/C][/ROW]
[ROW][C]68[/C][C]0.79013[/C][C]0.419739[/C][C]0.20987[/C][/ROW]
[ROW][C]69[/C][C]0.767919[/C][C]0.464161[/C][C]0.232081[/C][/ROW]
[ROW][C]70[/C][C]0.786106[/C][C]0.427787[/C][C]0.213894[/C][/ROW]
[ROW][C]71[/C][C]0.809662[/C][C]0.380676[/C][C]0.190338[/C][/ROW]
[ROW][C]72[/C][C]0.783692[/C][C]0.432617[/C][C]0.216308[/C][/ROW]
[ROW][C]73[/C][C]0.775888[/C][C]0.448224[/C][C]0.224112[/C][/ROW]
[ROW][C]74[/C][C]0.738449[/C][C]0.523102[/C][C]0.261551[/C][/ROW]
[ROW][C]75[/C][C]0.787308[/C][C]0.425385[/C][C]0.212692[/C][/ROW]
[ROW][C]76[/C][C]0.751662[/C][C]0.496675[/C][C]0.248338[/C][/ROW]
[ROW][C]77[/C][C]0.749609[/C][C]0.500782[/C][C]0.250391[/C][/ROW]
[ROW][C]78[/C][C]0.779037[/C][C]0.441927[/C][C]0.220963[/C][/ROW]
[ROW][C]79[/C][C]0.778546[/C][C]0.442909[/C][C]0.221454[/C][/ROW]
[ROW][C]80[/C][C]0.768618[/C][C]0.462764[/C][C]0.231382[/C][/ROW]
[ROW][C]81[/C][C]0.757575[/C][C]0.484849[/C][C]0.242425[/C][/ROW]
[ROW][C]82[/C][C]0.723694[/C][C]0.552611[/C][C]0.276306[/C][/ROW]
[ROW][C]83[/C][C]0.783731[/C][C]0.432538[/C][C]0.216269[/C][/ROW]
[ROW][C]84[/C][C]0.828441[/C][C]0.343119[/C][C]0.171559[/C][/ROW]
[ROW][C]85[/C][C]0.804615[/C][C]0.390769[/C][C]0.195385[/C][/ROW]
[ROW][C]86[/C][C]0.789746[/C][C]0.420509[/C][C]0.210254[/C][/ROW]
[ROW][C]87[/C][C]0.767732[/C][C]0.464535[/C][C]0.232268[/C][/ROW]
[ROW][C]88[/C][C]0.733881[/C][C]0.532238[/C][C]0.266119[/C][/ROW]
[ROW][C]89[/C][C]0.727738[/C][C]0.544524[/C][C]0.272262[/C][/ROW]
[ROW][C]90[/C][C]0.707927[/C][C]0.584147[/C][C]0.292073[/C][/ROW]
[ROW][C]91[/C][C]0.666955[/C][C]0.66609[/C][C]0.333045[/C][/ROW]
[ROW][C]92[/C][C]0.672693[/C][C]0.654615[/C][C]0.327307[/C][/ROW]
[ROW][C]93[/C][C]0.652821[/C][C]0.694358[/C][C]0.347179[/C][/ROW]
[ROW][C]94[/C][C]0.635082[/C][C]0.729836[/C][C]0.364918[/C][/ROW]
[ROW][C]95[/C][C]0.595606[/C][C]0.808788[/C][C]0.404394[/C][/ROW]
[ROW][C]96[/C][C]0.571223[/C][C]0.857555[/C][C]0.428777[/C][/ROW]
[ROW][C]97[/C][C]0.57114[/C][C]0.857721[/C][C]0.42886[/C][/ROW]
[ROW][C]98[/C][C]0.55361[/C][C]0.89278[/C][C]0.44639[/C][/ROW]
[ROW][C]99[/C][C]0.513444[/C][C]0.973112[/C][C]0.486556[/C][/ROW]
[ROW][C]100[/C][C]0.49423[/C][C]0.98846[/C][C]0.50577[/C][/ROW]
[ROW][C]101[/C][C]0.477792[/C][C]0.955584[/C][C]0.522208[/C][/ROW]
[ROW][C]102[/C][C]0.44188[/C][C]0.88376[/C][C]0.55812[/C][/ROW]
[ROW][C]103[/C][C]0.394531[/C][C]0.789062[/C][C]0.605469[/C][/ROW]
[ROW][C]104[/C][C]0.42791[/C][C]0.85582[/C][C]0.57209[/C][/ROW]
[ROW][C]105[/C][C]0.383265[/C][C]0.76653[/C][C]0.616735[/C][/ROW]
[ROW][C]106[/C][C]0.346636[/C][C]0.693272[/C][C]0.653364[/C][/ROW]
[ROW][C]107[/C][C]0.320653[/C][C]0.641306[/C][C]0.679347[/C][/ROW]
[ROW][C]108[/C][C]0.321138[/C][C]0.642277[/C][C]0.678862[/C][/ROW]
[ROW][C]109[/C][C]0.310875[/C][C]0.621749[/C][C]0.689125[/C][/ROW]
[ROW][C]110[/C][C]0.270777[/C][C]0.541554[/C][C]0.729223[/C][/ROW]
[ROW][C]111[/C][C]0.241383[/C][C]0.482766[/C][C]0.758617[/C][/ROW]
[ROW][C]112[/C][C]0.209033[/C][C]0.418066[/C][C]0.790967[/C][/ROW]
[ROW][C]113[/C][C]0.21407[/C][C]0.42814[/C][C]0.78593[/C][/ROW]
[ROW][C]114[/C][C]0.312896[/C][C]0.625792[/C][C]0.687104[/C][/ROW]
[ROW][C]115[/C][C]0.281718[/C][C]0.563437[/C][C]0.718282[/C][/ROW]
[ROW][C]116[/C][C]0.360366[/C][C]0.720732[/C][C]0.639634[/C][/ROW]
[ROW][C]117[/C][C]0.318462[/C][C]0.636923[/C][C]0.681538[/C][/ROW]
[ROW][C]118[/C][C]0.390124[/C][C]0.780247[/C][C]0.609876[/C][/ROW]
[ROW][C]119[/C][C]0.373966[/C][C]0.747932[/C][C]0.626034[/C][/ROW]
[ROW][C]120[/C][C]0.348569[/C][C]0.697138[/C][C]0.651431[/C][/ROW]
[ROW][C]121[/C][C]0.471994[/C][C]0.943988[/C][C]0.528006[/C][/ROW]
[ROW][C]122[/C][C]0.480306[/C][C]0.960611[/C][C]0.519694[/C][/ROW]
[ROW][C]123[/C][C]0.461444[/C][C]0.922888[/C][C]0.538556[/C][/ROW]
[ROW][C]124[/C][C]0.439019[/C][C]0.878038[/C][C]0.560981[/C][/ROW]
[ROW][C]125[/C][C]0.395531[/C][C]0.791062[/C][C]0.604469[/C][/ROW]
[ROW][C]126[/C][C]0.456764[/C][C]0.913528[/C][C]0.543236[/C][/ROW]
[ROW][C]127[/C][C]0.449012[/C][C]0.898024[/C][C]0.550988[/C][/ROW]
[ROW][C]128[/C][C]0.643002[/C][C]0.713997[/C][C]0.356998[/C][/ROW]
[ROW][C]129[/C][C]0.650507[/C][C]0.698986[/C][C]0.349493[/C][/ROW]
[ROW][C]130[/C][C]0.602912[/C][C]0.794176[/C][C]0.397088[/C][/ROW]
[ROW][C]131[/C][C]0.66353[/C][C]0.672941[/C][C]0.33647[/C][/ROW]
[ROW][C]132[/C][C]0.607894[/C][C]0.784212[/C][C]0.392106[/C][/ROW]
[ROW][C]133[/C][C]0.552068[/C][C]0.895863[/C][C]0.447932[/C][/ROW]
[ROW][C]134[/C][C]0.493422[/C][C]0.986844[/C][C]0.506578[/C][/ROW]
[ROW][C]135[/C][C]0.459275[/C][C]0.91855[/C][C]0.540725[/C][/ROW]
[ROW][C]136[/C][C]0.416604[/C][C]0.833208[/C][C]0.583396[/C][/ROW]
[ROW][C]137[/C][C]0.472393[/C][C]0.944786[/C][C]0.527607[/C][/ROW]
[ROW][C]138[/C][C]0.416428[/C][C]0.832857[/C][C]0.583572[/C][/ROW]
[ROW][C]139[/C][C]0.353855[/C][C]0.70771[/C][C]0.646145[/C][/ROW]
[ROW][C]140[/C][C]0.291781[/C][C]0.583562[/C][C]0.708219[/C][/ROW]
[ROW][C]141[/C][C]0.426616[/C][C]0.853231[/C][C]0.573384[/C][/ROW]
[ROW][C]142[/C][C]0.433042[/C][C]0.866083[/C][C]0.566958[/C][/ROW]
[ROW][C]143[/C][C]0.401259[/C][C]0.802517[/C][C]0.598741[/C][/ROW]
[ROW][C]144[/C][C]0.438627[/C][C]0.877253[/C][C]0.561373[/C][/ROW]
[ROW][C]145[/C][C]0.412267[/C][C]0.824535[/C][C]0.587733[/C][/ROW]
[ROW][C]146[/C][C]0.33713[/C][C]0.674261[/C][C]0.66287[/C][/ROW]
[ROW][C]147[/C][C]0.27395[/C][C]0.5479[/C][C]0.72605[/C][/ROW]
[ROW][C]148[/C][C]0.228516[/C][C]0.457033[/C][C]0.771484[/C][/ROW]
[ROW][C]149[/C][C]0.165034[/C][C]0.330068[/C][C]0.834966[/C][/ROW]
[ROW][C]150[/C][C]0.443843[/C][C]0.887687[/C][C]0.556157[/C][/ROW]
[ROW][C]151[/C][C]0.615006[/C][C]0.769989[/C][C]0.384994[/C][/ROW]
[ROW][C]152[/C][C]0.490219[/C][C]0.980438[/C][C]0.509781[/C][/ROW]
[ROW][C]153[/C][C]0.838986[/C][C]0.322028[/C][C]0.161014[/C][/ROW]
[ROW][C]154[/C][C]0.717966[/C][C]0.564068[/C][C]0.282034[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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
170.4200890.8401780.579911
180.2960280.5920560.703972
190.1792150.358430.820785
200.3286020.6572050.671398
210.4955130.9910270.504487
220.5387450.922510.461255
230.5875810.8248370.412419
240.5854530.8290940.414547
250.7026170.5947650.297383
260.7451320.5097370.254868
270.6987910.6024180.301209
280.6283150.743370.371685
290.7157610.5684780.284239
300.671410.657180.32859
310.6060590.7878820.393941
320.5891390.8217210.410861
330.5261610.9476780.473839
340.4710460.9420930.528954
350.4058860.8117720.594114
360.3742260.7484520.625774
370.5116680.9766640.488332
380.4757370.9514730.524263
390.4235390.8470770.576461
400.3657040.7314080.634296
410.8498590.3002830.150141
420.8322740.3354520.167726
430.8068580.3862850.193142
440.7671060.4657880.232894
450.7314420.5371170.268558
460.6842840.6314320.315716
470.6349690.7300610.365031
480.5869570.8260860.413043
490.5649130.8701740.435087
500.5841260.8317480.415874
510.5539870.8920260.446013
520.5018820.9962350.498118
530.4666750.9333510.533325
540.8648560.2702880.135144
550.8388520.3222960.161148
560.8244650.3510710.175535
570.808190.3836210.19181
580.7938910.4122190.206109
590.766610.466780.23339
600.8325580.3348840.167442
610.8083840.3832310.191616
620.8236560.3526870.176344
630.8144410.3711190.185559
640.7994510.4010990.200549
650.7822750.435450.217725
660.7873920.4252160.212608
670.7766190.4467610.223381
680.790130.4197390.20987
690.7679190.4641610.232081
700.7861060.4277870.213894
710.8096620.3806760.190338
720.7836920.4326170.216308
730.7758880.4482240.224112
740.7384490.5231020.261551
750.7873080.4253850.212692
760.7516620.4966750.248338
770.7496090.5007820.250391
780.7790370.4419270.220963
790.7785460.4429090.221454
800.7686180.4627640.231382
810.7575750.4848490.242425
820.7236940.5526110.276306
830.7837310.4325380.216269
840.8284410.3431190.171559
850.8046150.3907690.195385
860.7897460.4205090.210254
870.7677320.4645350.232268
880.7338810.5322380.266119
890.7277380.5445240.272262
900.7079270.5841470.292073
910.6669550.666090.333045
920.6726930.6546150.327307
930.6528210.6943580.347179
940.6350820.7298360.364918
950.5956060.8087880.404394
960.5712230.8575550.428777
970.571140.8577210.42886
980.553610.892780.44639
990.5134440.9731120.486556
1000.494230.988460.50577
1010.4777920.9555840.522208
1020.441880.883760.55812
1030.3945310.7890620.605469
1040.427910.855820.57209
1050.3832650.766530.616735
1060.3466360.6932720.653364
1070.3206530.6413060.679347
1080.3211380.6422770.678862
1090.3108750.6217490.689125
1100.2707770.5415540.729223
1110.2413830.4827660.758617
1120.2090330.4180660.790967
1130.214070.428140.78593
1140.3128960.6257920.687104
1150.2817180.5634370.718282
1160.3603660.7207320.639634
1170.3184620.6369230.681538
1180.3901240.7802470.609876
1190.3739660.7479320.626034
1200.3485690.6971380.651431
1210.4719940.9439880.528006
1220.4803060.9606110.519694
1230.4614440.9228880.538556
1240.4390190.8780380.560981
1250.3955310.7910620.604469
1260.4567640.9135280.543236
1270.4490120.8980240.550988
1280.6430020.7139970.356998
1290.6505070.6989860.349493
1300.6029120.7941760.397088
1310.663530.6729410.33647
1320.6078940.7842120.392106
1330.5520680.8958630.447932
1340.4934220.9868440.506578
1350.4592750.918550.540725
1360.4166040.8332080.583396
1370.4723930.9447860.527607
1380.4164280.8328570.583572
1390.3538550.707710.646145
1400.2917810.5835620.708219
1410.4266160.8532310.573384
1420.4330420.8660830.566958
1430.4012590.8025170.598741
1440.4386270.8772530.561373
1450.4122670.8245350.587733
1460.337130.6742610.66287
1470.273950.54790.72605
1480.2285160.4570330.771484
1490.1650340.3300680.834966
1500.4438430.8876870.556157
1510.6150060.7699890.384994
1520.4902190.9804380.509781
1530.8389860.3220280.161014
1540.7179660.5640680.282034






Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description# significant tests% significant testsOK/NOK
1% type I error level00OK
5% type I error level00OK
10% type I error level00OK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 0 & 0 & OK \tabularnewline
10% type I error level & 0 & 0 & OK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=267334&T=6

[TABLE]
[ROW][C]Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]Description[/C][C]# significant tests[/C][C]% significant tests[/C][C]OK/NOK[/C][/ROW]
[ROW][C]1% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]5% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]0[/C][C]0[/C][C]OK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=267334&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=267334&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 level00OK
5% type I error level00OK
10% type I error level00OK


Figures (Output of Computation)

Figure 1
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Figure 2
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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 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ;
Parameters (R input):
par1 = 1 ; 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$coefficients[i,1] > 0) myeq <- paste(myeq, '+', '')
myeq <- paste(myeq, signif(mysum$coefficients[i,1],6), sep=' ')
if (rownames(mysum$coefficients)[i] != '(Intercept)') {
myeq <- paste(myeq, rownames(mysum$coefficients)[i], sep='')
if (rownames(mysum$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,signif(mysum$coefficients[i,1],6))
a<-table.element(a, signif(mysum$coefficients[i,2],6))
a<-table.element(a, signif(mysum$coefficients[i,3],4))
a<-table.element(a, signif(mysum$coefficients[i,4],6))
a<-table.element(a, signif(mysum$coefficients[i,4]/2,6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a, signif(sqrt(mysum$r.squared),6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a, signif(mysum$r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a, signif(mysum$adj.r.squared,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[1],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a, signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6))
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, signif(mysum$sigma,6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a, signif(sum(myerror*myerror),6))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,signif(x[i],6))
a<-table.element(a,signif(x[i]-mysum$resid[i],6))
a<-table.element(a,signif(mysum$resid[i],6))
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,signif(gqarr[mypoint-kp3+1,1],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,2],6))
a<-table.element(a,signif(gqarr[mypoint-kp3+1,3],6))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,signif(numsignificant1/numgqtests,6))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}