Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_multipleregression.wasp

Title produced by software

Multiple Regression

Date of computation

Tue, 16 Dec 2014 15:30:00 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/16/t14187438270hoatbm0pg1z7cc.htm/, Retrieved Thu, 16 May 2024 22:15:20 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=269724, Retrieved Thu, 16 May 2024 22:15:20 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

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

F     [Histogram] [Frequency Plot (C...] [2010-09-25 09:09:15] [b98453cac15ba1066b407e146608df68]
- RM D  [Histogram] [WS1 - Task 1 - Fr...] [2014-09-26 11:11:25] [3cc57788b191749bdc089f5fad42e0f8]
- RMPD      [Multiple Regression] [] [2014-12-16 15:30:00] [afff6d6b458ee8576605d3685f592a9b] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

7.5 26 50 4 25 21 1.00 0.50 0.67 0.67 0.00 0.50 149 149 0 1.8 2.1 1.5
6.0 57 62 4 16 22 0.89 0.50 0.83 0.33 0.50 1.00 139 139 139 2.1 2.0 2.1
6.5 37 54 5 25 22 0.89 0.40 1.00 0.67 0.00 1.00 148 148 0 2.2 2.0 2.1
1.0 67 71 4 29 18 0.89 0.50 0.83 0.00 0.00 0.00 158 158 158 2.3 2.1 1.9
1.0 43 54 4 25 23 0.89 0.70 0.67 0.00 1.00 1.00 128 128 128 2.1 2.0 1.6
5.5 52 65 9 23 12 0.78 0.30 0.00 0.00 0.50 0.50 224 224 224 2.7 2.3 2.1
8.5 52 73 8 22 20 0.89 0.40 0.83 0.67 0.50 0.00 159 159 0 2.1 2.1 2.1
6.5 43 52 11 23 22 1.00 0.40 0.50 0.67 1.00 1.00 105 105 105 2.4 2.1 2.2
4.5 84 84 4 35 21 0.89 0.70 0.83 0.00 0.50 0.00 159 159 159 2.9 2.2 1.5
2.0 67 42 4 29 19 0.78 0.60 0.33 0.67 0.50 0.50 167 167 167 2.2 2.1 1.9
5.0 49 66 6 27 22 1.00 0.60 0.50 1.00 0.00 0.50 165 165 165 2.1 2.1 2.2
0.5 70 65 4 26 15 0.78 0.20 0.67 0.00 0.50 0.50 159 159 159 2.2 2.1 1.6
5.0 52 78 8 22 20 0.89 0.40 1.00 0.00 0.50 0.50 119 119 119 2.2 2.0 1.5
5.0 58 73 4 31 19 0.89 0.40 0.50 0.67 0.00 1.00 176 176 0 2.7 2.3 1.9
2.5 68 75 4 17 18 0.89 0.50 0.67 0.33 0.00 0.00 54 54 0 1.9 1.8 0.1
5.0 62 72 11 28 15 0.89 0.30 0.17 0.67 0.00 0.50 91 0 0 2.0 2.0 2.2
5.5 43 66 4 30 20 0.89 0.40 0.83 0.33 0.50 0.50 163 163 163 2.5 2.2 1.8
3.5 56 70 4 26 21 0.67 0.70 0.67 0.33 0.50 1.00 124 124 0 2.2 2.0 1.6
3.0 56 61 6 21 21 1.00 0.50 0.67 0.33 0.00 1.00 137 0 137 2.3 2.1 2.2
4.0 74 81 6 26 15 0.78 0.20 0.67 0.00 0.00 1.00 121 121 0 1.9 2.0 2.1
0.5 65 71 4 34 16 0.78 0.30 0.50 0.67 0.00 0.50 153 153 153 2.1 1.8 1.9
6.5 63 69 8 29 23 0.89 0.60 1.00 0.33 0.00 1.00 148 148 148 3.5 2.2 1.6
4.5 58 71 5 28 21 0.78 0.60 0.83 0.33 0.00 1.00 221 221 0 2.1 2.2 1.9
7.5 57 72 4 22 18 0.89 0.20 0.83 0.33 0.00 1.00 188 188 188 2.3 1.7 2.2
5.5 63 68 9 22 25 0.89 0.70 1.00 0.67 1.00 0.00 149 149 149 2.3 2.1 1.8
4.0 53 70 4 25 9 0.33 0.20 0.67 0.00 0.00 0.00 244 244 244 2.2 2.3 2.4
7.5 57 68 7 21 30 1.00 1.00 1.00 0.33 1.00 1.00 148 0 148 3.5 2.7 2.4
7.0 51 61 10 28 20 0.89 0.40 0.83 0.67 0.00 0.50 92 0 0 1.9 1.9 2.5
4.0 64 67 4 27 23 0.89 0.40 1.00 1.00 0.00 1.00 150 150 150 1.9 2.0 1.9
5.5 53 76 4 30 16 0.67 0.20 0.83 0.67 0.00 0.50 153 153 0 1.9 2.0 2.1
2.5 29 70 7 23 16 0.56 0.40 0.67 0.33 0.00 1.00 94 94 0 1.9 1.9 1.9
5.5 54 60 12 27 19 0.89 0.40 0.67 0.00 0.50 1.00 156 156 0 2.1 2.0 2.1
3.5 58 72 7 27 25 0.89 0.70 1.00 0.67 0.50 0.50 132 132 132 2.0 2.0 1.5
2.5 43 69 5 31 18 1.00 0.20 0.67 0.67 0.00 0.50 161 161 161 3.2 2.1 1.9
4.5 51 71 8 28 23 0.78 0.60 1.00 1.00 0.00 0.50 105 105 105 2.3 2.0 2.1
4.5 53 62 5 16 21 0.78 0.30 1.00 1.00 0.50 0.50 97 97 97 2.5 1.8 1.5
4.5 54 70 4 13 10 0.33 0.30 0.50 0.33 0.00 0.00 151 151 0 1.8 2.0 2.1
6.0 56 64 9 30 14 0.78 0.20 0.67 0.00 0.50 0.00 131 0 131 2.4 2.2 2.1
2.5 61 58 7 25 22 0.89 0.50 0.83 0.67 0.50 0.50 166 166 166 2.8 2.2 1.8
5.0 47 76 4 16 26 0.89 0.70 1.00 0.67 0.50 1.00 157 157 0 2.3 2.1 2.4
0.0 39 52 4 26 23 0.78 0.60 1.00 0.67 0.50 0.50 111 111 111 2.0 1.8 2.1
5.0 48 59 4 22 23 0.89 0.40 1.00 0.67 0.50 1.00 145 145 145 2.5 1.9 1.9
6.5 50 68 4 22 24 0.89 0.60 1.00 0.33 0.50 1.00 162 162 162 2.3 2.1 2.1
5.0 35 76 4 28 24 1.00 0.40 1.00 1.00 0.00 1.00 163 163 163 1.8 2.0 1.9
6.0 30 65 7 27 18 0.67 0.30 0.83 0.67 0.00 1.00 59 0 59 1.9 1.9 2.4
4.5 68 67 4 14 23 1.00 0.50 0.83 0.67 0.50 0.50 187 187 0 2.6 2.2 2.1
5.5 49 59 7 28 15 0.89 0.20 0.50 0.00 0.00 1.00 109 109 109 2.0 2.0 2.2
1.0 61 69 4 27 19 0.89 0.30 0.83 0.00 0.50 1.00 90 0 90 2.6 2.0 2.2
7.5 67 76 4 16 16 0.89 0.50 0.17 0.00 0.00 1.00 105 105 0 1.6 1.7 1.8
6.0 47 63 4 23 25 0.78 0.70 0.83 1.00 0.50 1.00 83 0 83 2.2 2.0 2.1
5.0 56 75 4 25 23 0.89 0.40 1.00 0.67 1.00 0.50 116 0 116 2.1 2.2 2.4
1.0 50 63 8 20 17 0.78 0.30 1.00 0.00 0.00 0.50 42 0 42 1.8 1.7 2.2
5.0 43 60 4 28 19 0.78 0.20 0.67 0.67 1.00 1.00 148 148 148 1.8 2.0 2.1
6.5 67 73 4 32 21 1.00 0.50 1.00 0.00 0.00 0.50 155 0 155 1.9 2.2 1.5
7.0 62 63 4 33 18 0.78 0.40 1.00 0.00 0.50 0.00 125 125 125 2.4 2.0 1.9
4.5 57 70 4 26 27 1.00 0.60 1.00 0.67 1.00 1.00 116 116 116 1.9 1.9 1.8
0.0 41 75 7 21 21 0.78 0.40 0.83 1.00 0.00 1.00 128 0 0 2.0 2.0 1.8
8.5 54 66 12 27 13 0.67 0.40 0.33 0.00 0.00 0.50 138 138 138 2.1 2.0 1.6
3.5 45 63 4 20 8 0.33 0.20 0.33 0.33 0.00 0.00 49 0 0 1.7 1.6 1.2
7.5 48 63 4 28 29 1.00 0.90 1.00 0.67 0.50 1.00 96 0 96 1.9 2.1 1.8
3.5 61 64 4 26 28 1.00 0.80 1.00 0.67 1.00 0.50 164 164 164 2.1 2.1 1.5
6.0 56 70 5 21 23 0.78 0.80 0.83 0.00 0.50 1.00 162 162 0 2.4 2.0 2.1
1.5 41 75 15 16 21 0.67 0.30 1.00 1.00 0.50 1.00 99 99 0 1.8 1.9 2.4
9.0 43 61 5 28 19 1.00 0.20 0.83 0.67 0.00 0.50 202 202 202 2.3 2.2 2.4
3.5 53 60 10 19 19 0.89 0.40 0.67 0.00 0.50 1.00 186 186 0 2.1 2.1 1.5
3.5 44 62 9 24 20 0.89 0.20 0.83 1.00 0.00 1.00 66 0 66 2.0 1.8 1.8
4.0 66 73 8 26 18 0.78 0.20 0.67 0.67 0.50 1.00 183 183 0 2.8 2.3 2.1
6.5 58 61 4 24 19 1.00 0.10 0.83 0.67 0.00 1.00 214 214 214 2.0 2.3 2.2
7.5 46 66 5 23 17 0.56 0.40 0.67 1.00 0.50 0.00 188 188 188 2.7 2.2 2.1
6.0 37 64 4 27 19 0.67 0.50 1.00 0.00 0.50 0.50 104 0 0 2.1 2.1 1.9
5.0 51 59 9 29 25 0.89 0.80 0.83 0.33 0.50 1.00 177 177 0 2.9 2.2 2.1
5.5 51 64 4 26 19 0.89 0.40 0.67 0.67 0.00 0.50 126 126 0 2.0 1.9 1.9
3.5 56 60 10 19 22 0.89 0.60 0.83 0.33 0.50 0.50 76 0 0 1.8 1.8 1.6
7.5 66 56 4 19 23 0.89 0.50 0.83 0.67 0.50 1.00 99 0 99 2.6 2.1 2.4
6.5 37 78 4 12 14 0.78 0.30 0.67 0.00 0.00 0.00 139 139 0 2.1 2.0 1.9
NA 59 53 6 23 28 0.89 0.80 1.00 1.00 0.50 1.00 78 78 78 2.3 1.7 1.9
6.5 42 67 7 25 16 1.00 0.40 0.33 0.00 0.50 0.00 162 162 0 2.3 2.1 2.1
6.5 38 59 5 24 24 1.00 0.60 0.83 0.67 0.50 0.50 108 0 108 2.2 2.1 1.8
7.0 66 66 4 26 20 0.89 0.40 1.00 0.33 0.00 0.50 159 159 0 2.0 2.1 2.1
3.5 34 68 4 23 12 0.44 0.30 0.83 0.00 0.00 0.00 74 0 0 2.2 1.8 2.4
1.5 53 71 4 28 24 0.78 0.80 0.83 0.00 1.00 1.00 110 110 110 2.1 2.0 2.1
4.0 49 66 4 25 22 0.89 0.60 0.50 0.33 1.00 1.00 96 0 0 2.1 2.1 2.2
7.5 55 73 4 23 12 0.67 0.30 0.50 0.00 0.00 0.00 116 0 0 1.9 1.9 2.1
4.5 49 72 4 28 22 0.78 0.50 0.83 0.67 0.50 1.00 87 0 0 2.0 2.1 2.2
0.0 59 71 6 23 20 0.78 0.40 1.00 0.33 0.00 1.00 97 0 97 1.7 1.0 1.6
3.5 40 59 10 25 10 0.33 0.30 0.33 0.67 0.00 0.00 127 0 0 2.2 2.2 2.4
5.5 58 64 7 27 23 0.89 0.70 1.00 0.33 0.00 0.50 106 0 106 2.2 2.1 2.1
5.0 60 66 4 28 17 0.89 0.20 0.67 0.33 0.50 0.50 80 0 80 2.3 1.9 1.9
4.5 63 78 4 17 22 0.89 0.40 0.83 1.00 0.00 1.00 74 0 0 2.4 2.0 2.4
2.5 56 68 7 23 24 0.89 0.60 1.00 0.67 0.50 0.50 91 0 0 2.1 1.9 2.1
7.5 54 73 4 24 18 0.56 0.60 0.83 0.00 0.00 1.00 133 0 0 1.9 2.0 1.8
7.0 52 62 8 18 21 0.67 0.60 0.83 0.67 0.50 0.50 74 0 74 1.7 1.8 2.1
0.0 34 65 11 21 20 0.67 0.40 1.00 0.33 0.50 1.00 114 0 114 1.8 2.0 1.8
4.5 69 68 6 34 20 0.78 0.60 0.83 0.00 0.00 1.00 140 0 140 1.5 2.0 1.9
3.0 32 65 14 24 22 0.78 0.50 1.00 0.33 0.50 1.00 95 0 0 1.9 2.0 1.9
1.5 48 60 5 24 19 0.78 0.50 0.83 0.00 0.00 1.00 98 0 98 1.9 1.8 2.4
3.5 67 71 4 28 20 0.89 0.60 0.67 0.00 0.00 1.00 121 0 0 1.7 2.0 1.8
2.5 58 65 8 27 26 1.00 0.80 0.83 0.33 0.50 1.00 126 0 126 1.9 1.1 1.8
5.5 57 68 9 24 23 0.89 0.50 0.83 0.67 1.00 0.50 98 0 98 1.9 1.8 2.1
8.0 42 64 4 19 24 0.89 0.60 0.83 0.67 0.50 1.00 95 0 95 1.8 1.8 2.1
1.0 64 74 4 19 21 0.78 0.40 0.83 0.67 0.50 1.00 110 0 110 2.4 2.0 2.4
5.0 58 69 5 27 21 1.00 0.30 0.67 0.67 0.50 1.00 70 0 70 1.8 1.9 1.9
4.5 66 76 4 28 19 0.78 0.30 0.83 1.00 0.00 0.50 102 0 0 1.9 2.1 1.8
3.0 26 68 5 22 8 0.67 0.20 0.00 0.00 0.00 0.00 86 0 86 1.8 1.6 1.8
3.0 61 72 4 32 17 0.78 0.40 0.83 0.00 0.00 0.50 130 0 130 2.1 2.2 2.2
8.0 52 67 4 20 20 0.89 0.50 1.00 0.00 0.00 0.50 96 0 96 1.9 1.9 2.4
2.5 51 63 7 26 11 0.67 0.30 0.17 0.00 0.50 0.00 102 0 0 2.2 2.0 1.8
7.0 55 59 10 19 8 0.22 0.40 0.17 0.00 0.50 0.00 100 0 0 2.0 2.1 2.4
0.0 50 73 4 24 15 0.44 0.50 0.50 1.00 0.00 0.00 94 0 0 1.7 1.3 1.8
1.0 60 66 5 21 18 0.89 0.30 0.50 0.67 0.00 1.00 52 0 0 1.7 1.8 1.9
3.5 56 62 4 21 18 0.67 0.50 1.00 0.00 0.00 0.50 98 0 0 1.8 1.9 2.4
5.5 63 69 4 27 19 0.89 0.40 0.67 0.67 0.00 0.50 118 0 0 1.9 2.1 2.1
5.5 61 66 4 18 19 0.67 0.40 0.83 0.67 0.00 1.00 99 0 99 1.8 1.8 1.9
0.5 52 51 6 25 23 0.78 0.60 1.00 0.00 1.00 1.00 0 48 48 1 0.75 2.1
7.5 16 56 4 27 22 0.78 0.30 1.00 0.67 1.00 1.00 0 50 50 1 1.5 2.7
9 46 67 8 28 21 0.78 0.40 1.00 0.33 1.00 0.50 0 150 150 4 3 2.1
9.5 56 69 5 28 25 1.00 0.30 1.00 1.00 1.00 1.00 0 154 154 4 2.25 2.1
8.5 52 57 4 19 30 0.78 1.00 1.00 1.00 1.00 1.00 0 0 0 3 3 2.1
7 55 56 17 27 17 0.67 0.40 1.00 0.00 0.00 0.50 0 0 68 2 1.5 2.1
8 50 55 4 26 27 0.89 0.80 0.83 1.00 0.50 1.00 0 194 194 4 3 2.1
10 59 63 4 27 23 0.89 0.30 1.00 0.67 1.00 1.00 0 158 0 4 3 2.1
7 60 67 8 26 23 1.00 0.50 0.83 0.67 0.00 1.00 0 159 159 4 3 2.1
8.5 52 65 4 28 18 0.78 0.40 1.00 0.00 0.00 0.50 0 67 0 2 0.75 2.1
9 44 47 7 20 18 0.67 0.30 0.83 0.67 0.00 1.00 0 147 0 4 3 2.4
9.5 67 76 4 32 23 0.89 0.50 0.83 1.00 0.00 1.00 0 39 39 1 2.25 1.95
4 52 64 4 25 19 0.67 0.30 1.00 0.67 0.00 1.00 0 100 100 3 1.5 2.1
6 55 68 5 33 15 0.67 0.30 0.67 0.00 0.00 1.00 0 111 111 3 1.5 2.1
8 37 64 7 25 20 1.00 0.40 0.83 0.00 0.00 1.00 0 138 138 4 2.25 1.95
5.5 54 65 4 35 16 0.67 0.30 1.00 0.00 0.00 0.50 0 101 101 3 3 2.1
9.5 72 71 4 28 24 1.00 0.60 1.00 0.33 0.50 0.50 0 0 131 4 3 2.4
7.5 51 63 7 30 25 0.89 0.60 0.83 0.67 1.00 1.00 0 101 101 3 1.5 2.1
7 48 60 11 28 25 0.89 0.40 1.00 1.00 1.00 1.00 0 114 114 3 2.25 2.25
7.5 60 68 7 27 19 1.00 0.40 1.00 0.00 0.00 0.00 0 165 0 4 2.25 2.4
8 50 72 4 21 19 0.67 0.40 1.00 0.67 0.00 0.50 0 114 114 3 1.5 2.25
7 63 70 4 25 16 0.44 0.30 0.67 0.67 0.50 1.00 0 111 111 3 2.25 2.55
7 33 61 4 31 19 0.89 0.20 1.00 0.33 1.00 0.00 0 75 75 2 1.5 1.95
6 67 61 4 28 19 0.56 0.50 0.83 0.67 0.00 1.00 0 82 82 2 2.25 2.4
10 46 62 4 29 23 0.78 0.40 1.00 0.67 1.00 1.00 0 121 121 3 2.25 2.1
2.5 54 71 4 35 21 1.00 0.40 1.00 0.67 0.00 0.00 0 32 32 1 3 2.1
9 59 71 6 25 22 1.00 0.40 0.83 0.67 0.00 1.00 0 150 0 4 3 2.4
8 61 51 8 29 19 0.89 0.30 0.67 0.67 0.50 0.50 0 117 117 3 3 2.1
6 33 56 23 12 20 0.67 0.40 0.83 0.67 1.00 0.50 0 0 71 2 1.5 2.1
8.5 47 70 4 30 20 0.89 0.20 1.00 0.33 0.50 1.00 0 165 165 4 3 2.25
6 69 73 8 27 3 0.33 0.00 0.00 0.00 0.00 0.00 0 154 154 4 3 2.25
9 52 76 6 28 23 0.89 0.40 1.00 0.67 0.50 1.00 0 126 126 4 2.25 2.4
8 55 68 4 28 23 0.78 0.60 1.00 0.00 1.00 1.00 0 149 0 4 2.25 2.1
9 41 48 7 25 20 1.00 0.40 0.67 0.67 0.00 0.50 0 145 0 4 2.25 2.4
5.5 73 52 4 28 15 0.44 0.40 1.00 0.00 0.00 0.50 0 120 120 3 3 2.1
7 52 60 4 28 16 0.67 0.40 0.83 0.00 0.50 0.00 0 109 0 3 2.25 2.1
5.5 50 59 4 28 7 0.33 0.20 0.17 0.00 0.50 0.00 0 132 0 4 3 2.25
9 51 57 10 26 24 0.89 0.40 0.83 1.00 1.00 1.00 0 172 172 4 3 2.25
2 60 79 6 22 17 0.89 0.30 0.83 0.00 0.00 0.50 0 169 0 4 1.5 2.4
8.5 56 60 5 24 24 1.00 0.60 0.83 0.67 1.00 0.00 0 114 114 3 2.25 2.25
9 56 60 5 28 24 0.89 0.60 0.83 1.00 0.00 1.00 0 156 156 4 3 2.25
8.5 29 59 4 27 19 0.89 0.40 0.83 0.00 0.00 1.00 0 172 0 4 2.25 2.1
9 66 62 4 27 25 1.00 0.50 1.00 0.67 1.00 0.50 0 0 68 2 1.5 2.1
7.5 66 59 5 26 20 0.89 0.40 0.83 0.00 0.50 1.00 0 0 89 2 2.25 2.1
10 73 61 5 29 28 1.00 0.60 1.00 1.00 1.00 1.00 0 167 167 4 2.25 2.7
9 55 71 5 27 23 0.78 0.60 0.83 0.67 0.50 1.00 0 113 0 3 1.5 2.1
7.5 64 57 5 29 27 0.78 0.90 1.00 0.67 0.50 1.00 0 0 0 3 2.25 2.1
6 40 66 4 28 18 0.67 0.40 0.83 0.67 0.50 0.00 0 0 0 2 1.5 2.25
10.5 46 63 6 28 28 0.89 0.80 1.00 1.00 0.50 1.00 0 0 0 3 2.25 2.7
8.5 58 69 4 27 21 0.67 0.50 0.83 1.00 0.00 1.00 0 0 87 2 3 2.4
8 43 58 4 24 19 0.78 0.40 0.83 1.00 0.00 0.00 0 173 0 4 3 2.1
10 61 59 4 29 23 0.89 0.40 1.00 0.67 1.00 0.50 0 2 2 1 3 2.1
10.5 51 48 9 17 27 0.89 0.70 1.00 1.00 1.00 0.50 0 0 0 4 3 2.4
6.5 50 66 18 27 22 0.78 0.40 1.00 0.33 1.00 1.00 0 0 49 1 1.5 1.95
9.5 52 73 6 23 28 1.00 0.80 1.00 0.67 0.50 1.00 0 0 0 4 2.25 2.7
8.5 54 67 5 27 25 1.00 0.40 1.00 1.00 1.00 0.50 0 0 96 3 1.5 2.1
7.5 66 61 4 22 21 1.00 0.30 1.00 0.67 0.00 0.50 0 0 0 3 2.25 2.25
5 61 68 11 27 22 0.67 0.50 1.00 0.67 0.50 1.00 0 0 0 2 2.25 2.1
8 80 75 4 35 28 0.89 0.80 1.00 0.67 1.00 1.00 0 0 100 3 2.25 2.7
10 51 62 10 22 20 1.00 0.40 0.83 0.33 0.00 0.50 0 0 0 3 3 2.1
7 56 69 6 20 29 1.00 1.00 1.00 1.00 0.50 0.00 0 0 141 4 1.5 2.1
7.5 56 58 8 26 25 0.89 0.50 1.00 0.67 1.00 1.00 0 165 165 4 2.25 1.65
7.5 56 60 8 26 25 0.89 0.50 1.00 0.67 1.00 1.00 0 165 165 4 2.25 1.65
9.5 53 74 6 26 20 0.89 0.30 1.00 0.33 0.00 1.00 0 0 110 3 3 2.1
6 47 55 8 29 20 0.89 0.30 0.83 0.33 0.50 1.00 0 118 118 3 2.25 2.1
10 25 62 4 18 16 0.89 0.30 0.50 0.00 0.00 1.00 0 158 0 4 3 2.1
7 47 63 4 28 20 1.00 0.40 0.67 0.33 0.50 0.50 0 0 146 4 2.25 2.1
3 46 69 9 15 20 0.67 0.50 1.00 0.33 0.00 1.00 0 49 0 1 1.5 2.1
6 50 58 9 19 23 1.00 0.50 0.67 0.67 0.50 1.00 0 0 0 2 3 2.4
7 39 58 5 13 18 0.89 0.40 1.00 0.00 0.00 0.00 0 0 0 3 1.5 2.4
10 51 68 4 26 25 0.89 0.70 1.00 1.00 0.50 0.00 0 155 155 4 3 2.1
7 58 72 4 26 18 0.89 0.50 0.50 0.33 0.00 0.50 0 0 0 3 3 2.25
3.5 35 62 15 21 19 0.89 0.40 0.67 0.33 1.00 0.00 0 0 147 4 3 2.4
8 58 62 10 27 25 1.00 0.70 0.67 1.00 0.00 1.00 0 0 0 3 3 2.1
10 60 65 9 24 25 1.00 0.70 0.67 1.00 0.00 1.00 0 0 0 3 2.25 2.1
5.5 62 69 7 22 25 1.00 0.70 0.67 1.00 0.00 1.00 0 0 0 3 2.25 2.4
6 63 66 9 22 24 0.89 0.70 0.67 1.00 0.00 1.00 0 0 0 3 0.75 2.4
6.5 53 72 6 24 19 0.89 0.70 0.67 0.00 0.00 0.00 0 0 61 1 3 2.1
6.5 46 62 4 22 26 0.89 0.70 1.00 0.67 0.50 1.00 0 0 60 1 0.75 2.1
8.5 67 75 7 27 10 0.33 0.10 0.67 0.33 0.50 0.00 0 0 109 3 1.5 2.4
4 59 58 4 26 17 0.67 0.20 0.67 0.67 0.50 1.00 0 0 68 2 1.5 2.1
9.5 64 66 7 25 13 0.56 0.30 0.33 0.33 0.00 1.00 0 0 0 3 3 2.7
8 38 55 4 20 17 0.44 0.60 0.83 0.33 0.00 0.50 0 0 0 2 1.5 2.1
8.5 50 47 15 28 30 1.00 0.80 1.00 1.00 1.00 1.00 0 0 73 2 2.25 2.1
5.5 48 72 4 22 25 0.89 0.80 1.00 0.33 0.50 0.50 0 151 0 4 3 2.25
7 48 62 9 27 4 0.33 0.00 0.17 0.00 0.00 0.00 0 0 0 2 3 2.1
9 47 64 4 20 16 0.67 0.30 0.67 0.33 0.00 1.00 0 0 0 2 1.5 2.4
8 66 64 4 26 21 0.67 0.60 0.83 0.33 0.50 1.00 0 0 0 3 3 2.25
10 47 19 28 30 23 1.00 0.50 0.83 0.67 0.00 1.00 0 220 220 4 3 2.25
8 63 50 4 24 22 0.78 0.70 1.00 0.33 0.00 0.50 0 0 65 2 1.5 2.1
6 58 68 4 23 17 0.67 0.30 0.83 0.00 0.50 1.00 0 141 0 4 1.5 2.1
8 44 70 4 26 20 1.00 0.30 1.00 0.67 0.00 0.00 0 0 0 3 2.25 2.4
5 51 79 5 24 20 0.78 0.40 1.00 0.67 0.00 0.50 0 122 122 4 1.5 2.25
9 43 69 4 27 22 0.89 0.40 0.83 1.00 0.00 1.00 0 0 0 2 1.5 2.1
4.5 55 71 4 31 16 0.89 0.10 0.83 0.00 0.00 1.00 0 44 44 1 2.25 2.1
8.5 38 48 12 21 23 0.89 0.50 1.00 0.67 0.00 1.00 0 0 52 1 1.5 1.65
9.5 45 73 4 10 0 0.00 0.00 0.00 0.00 0.00 0.00 0 0 0 4 3 2.7
8.5 50 74 6 15 18 0.67 0.40 1.00 0.33 0.50 0.00 0 0 101 3 3 2.1
7.5 54 66 6 29 25 1.00 0.60 0.83 0.67 1.00 0.50 0 0 42 1 0.75 1.95
7.5 57 71 5 29 23 1.00 0.40 1.00 0.33 0.50 1.00 0 152 152 4 1.5 2.25
5 60 74 4 24 12 0.67 0.10 0.33 0.00 0.50 1.00 0 107 0 3 1.5 2.4
7 55 78 4 20 18 0.89 0.30 0.83 0.00 0.00 1.00 0 0 0 2 2.25 1.95
8 56 75 4 25 24 0.89 0.70 0.83 0.67 0.00 1.00 0 154 0 4 2.25 2.1
5.5 49 53 10 25 11 0.56 0.30 0.17 0.00 0.00 1.00 0 103 103 3 1.5 2.4
8.5 37 60 7 25 18 0.67 0.50 0.83 0.33 0.50 0.00 0 0 96 3 2.25 2.1
9.5 59 70 4 31 23 1.00 0.30 0.83 0.67 1.00 1.00 0 175 175 4 2.25 2.4
7 46 69 7 25 24 1.00 0.60 0.67 0.67 0.50 1.00 0 0 57 1 0.75 2.4
8 51 65 4 26 29 1.00 0.90 1.00 1.00 0.00 1.00 0 0 0 3 2.25 2.4
8.5 58 78 4 26 18 0.67 0.40 0.83 0.00 0.50 1.00 0 143 0 4 3 2.25
3.5 64 78 12 33 15 0.44 0.30 1.00 0.00 0.50 0.50 0 0 0 1 0.75 2.4
6.5 53 59 5 27 29 0.89 0.90 1.00 0.67 1.00 1.00 0 110 110 3 0.75 2.1
6.5 48 72 8 21 16 0.44 0.50 1.00 0.00 0.50 0.00 0 131 131 4 3 2.1
10.5 51 70 6 28 19 0.56 0.30 1.00 1.00 0.50 0.50 0 167 0 4 3 1.8
8.5 47 63 17 19 22 0.89 0.60 0.83 0.67 0.00 0.50 0 0 0 1 3 2.7
8 59 63 4 31 16 0.67 0.20 1.00 0.33 0.00 0.50 0 137 0 4 3 2.1
10 62 71 5 27 23 0.89 0.40 0.83 1.00 0.50 1.00 0 0 86 2 1.5 2.1
10 62 74 4 33 23 1.00 0.50 0.83 0.67 0.50 0.50 0 121 121 3 3 2.4
9.5 51 67 5 27 19 0.78 0.40 0.83 0.67 0.00 0.50 0 149 0 4 3 2.55
9 64 66 5 23 4 0.44 0.00 0.00 0.00 0.00 0.00 0 168 0 4 3 2.55
10 52 62 6 23 20 0.89 0.20 1.00 0.33 0.50 1.00 0 140 0 4 3 2.1
7.5 67 80 4 30 24 0.89 0.50 1.00 0.67 0.50 1.00 0 0 88 2 1.5 2.1
4.5 50 73 4 29 20 0.89 0.30 1.00 0.67 0.00 0.50 0 168 168 4 2.25 2.1
4.5 54 67 4 35 4 0.44 0.00 0.00 0.00 0.00 0.00 0 94 94 2 0.75 2.25
0.5 58 61 6 27 24 1.00 0.50 0.83 1.00 0.00 1.00 0 51 51 1 0.75 2.25
6.5 56 73 8 16 22 0.89 0.60 0.83 0.33 0.00 1.00 0 0 0 1 2.25 2.1
4.5 63 74 10 23 16 0.67 0.30 0.83 0.00 0.50 0.50 0 145 145 4 3 2.1
5.5 31 32 4 26 3 0.33 0.00 0.00 0.00 0.00 0.00 0 66 66 2 2.25 1.95
5 65 69 5 33 15 0.78 0.30 0.67 0.00 0.50 0.00 0 0 85 2 3 2.4
6 71 69 4 30 24 0.89 0.50 1.00 0.67 0.50 1.00 0 109 0 3 2.25 2.1
4 50 84 4 30 17 0.78 0.40 0.67 0.00 0.00 1.00 0 0 0 2 3 2.4
8 57 64 4 28 20 0.78 0.50 0.83 0.67 0.00 0.50 0 0 102 3 1.5 2.4
10.5 47 58 16 17 27 0.89 0.70 1.00 1.00 1.00 0.50 0 0 0 4 3 2.4
6.5 47 59 7 24 26 0.78 0.80 1.00 0.67 0.50 1.00 0 0 86 2 0.75 1.95
8 57 78 4 23 23 0.78 0.60 1.00 0.33 0.50 1.00 0 0 114 3 1.5 2.1
8.5 43 57 4 31 17 0.67 0.40 0.83 0.33 0.00 0.50 0 164 0 4 3 2.1
5.5 41 60 14 26 20 0.89 0.50 0.83 0.33 0.50 0.00 0 119 119 3 3 2.55
7 63 68 5 25 22 0.89 0.50 1.00 0.00 0.50 1.00 0 126 0 4 3 2.1
5 63 68 5 26 19 0.78 0.30 1.00 0.33 0.00 1.00 0 132 132 4 2.25 2.1
3.5 56 73 5 28 24 1.00 0.60 1.00 0.00 0.50 1.00 0 142 142 4 2.25 2.1
5 51 69 5 26 19 1.00 0.30 0.67 0.67 0.00 0.50 0 83 0 2 3 1.95
9 50 67 7 28 23 0.78 0.60 0.83 1.00 0.50 0.50 0 0 94 2 1.5 2.25
8.5 22 60 19 27 15 0.78 0.30 0.33 0.33 0.00 1.00 0 0 0 2 2.25 2.4
5 41 65 16 21 27 0.89 0.70 1.00 0.67 1.00 1.00 0 166 166 4 2.25 1.95
9.5 59 66 4 25 26 0.89 0.70 1.00 1.00 0.00 1.00 0 0 0 3 2.25 2.1
3 56 74 4 30 22 0.67 0.60 0.67 1.00 0.50 1.00 0 0 64 2 0.75 2.1
1.5 66 81 7 28 22 1.00 0.50 1.00 0.33 0.50 0.00 0 93 0 2 2.25 1.95
6 53 72 9 19 18 0.67 0.50 0.83 0.33 0.00 0.50 0 0 0 3 1.5 2.1
0.5 42 55 5 27 15 0.56 0.40 0.67 0.00 0.00 1.00 0 0 105 3 2.25 2.1
6.5 52 49 14 27 22 0.78 0.40 1.00 0.33 1.00 1.00 0 0 49 1 1.5 1.95
7.5 54 74 4 19 27 1.00 0.70 1.00 1.00 0.00 1.00 0 0 0 2 0.75 2.1
4.5 44 53 16 28 10 0.67 0.20 0.17 0.00 0.50 0.00 0 0 95 2 1.5 1.95
8 62 64 10 28 20 0.78 0.50 0.83 0.67 0.00 0.50 0 0 102 3 1.5 2.4
9 53 65 5 28 17 0.56 0.40 0.83 0.67 0.50 0.00 0 0 0 3 2.25 2.4
7.5 50 57 6 23 23 1.00 0.20 1.00 0.67 1.00 1.00 0 0 63 2 1.5 2.4
8.5 36 51 4 26 19 0.89 0.50 0.67 0.67 0.00 0.00 0 0 0 2 1.5 1.95
7 76 80 4 28 13 0.44 0.40 0.50 0.00 0.00 1.00 0 0 0 3 3 2.7
9.5 66 67 4 27 27 1.00 0.70 0.67 1.00 1.00 1.00 0 0 117 3 2.25 2.1
6.5 62 70 5 30 23 0.89 0.60 0.83 0.67 1.00 0.00 0 0 57 1 1.5 1.95
9.5 59 74 4 20 16 0.78 0.40 0.83 0.00 0.00 0.00 0 0 0 3 0.75 2.1
6 47 75 4 27 25 0.89 0.50 1.00 0.67 1.00 1.00 0 0 73 2 2.25 1.95
8 55 70 5 25 2 0.11 0.00 0.17 0.00 0.00 0.00 0 0 0 2 3 2.1
9.5 58 69 4 28 26 0.89 0.70 1.00 0.67 0.50 1.00 0 0 0 3 3 2.25
8 60 65 4 26 20 0.89 0.40 0.67 0.67 0.00 1.00 0 0 105 3 1.5 2.7
8 44 55 5 28 23 1.00 0.50 0.67 1.00 0.00 1.00 0 117 0 3 1.5 2.1
9 57 71 8 21 22 0.89 0.60 0.83 0.67 0.00 0.50 0 0 0 3 2.25 2.4
5 45 65 15 11 24 1.00 0.80 0.50 0.67 0.50 0.50 0 0 31 1 0.75 1.35

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 16 seconds \tabularnewline
R Server & 'Gertrude Mary Cox' @ cox.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&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]16 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gertrude Mary Cox' @ cox.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=0

As an alternative you can also use a QR Code:

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

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	16 seconds
R Server	'Gertrude Mary Cox' @ cox.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 2.05524 -0.00130074AMS.I[t] -0.0309119AMS.E[t] -0.0452411AMS.A[t] -0.0118652SOFTSTATTOT[t] + 7.19852NUMERACYTOT[t] -63.11Calculation[t] -71.728Algebraic_Reasoning[t] -43.202Graphical_Interpretation[t] -20.6218Proportionality_and_Ratio[t] -14.1414Probability_and_Sampling[t] -14.7438Estimation[t] -0.0113762lfm_year[t] + 0.00107596lfm_course[t] -0.00294038lfm_gender[t] + 0.455491PR[t] + 0.701299PE[t] + 1.49009PA[t] + e[t]

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Ex[t] =  +  2.05524 -0.00130074AMS.I[t] -0.0309119AMS.E[t] -0.0452411AMS.A[t] -0.0118652SOFTSTATTOT[t] +  7.19852NUMERACYTOT[t] -63.11Calculation[t] -71.728Algebraic_Reasoning[t] -43.202Graphical_Interpretation[t] -20.6218Proportionality_and_Ratio[t] -14.1414Probability_and_Sampling[t] -14.7438Estimation[t] -0.0113762lfm_year[t] +  0.00107596lfm_course[t] -0.00294038lfm_gender[t] +  0.455491PR[t] +  0.701299PE[t] +  1.49009PA[t]  + e[t] \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Ex[t] =  +  2.05524 -0.00130074AMS.I[t] -0.0309119AMS.E[t] -0.0452411AMS.A[t] -0.0118652SOFTSTATTOT[t] +  7.19852NUMERACYTOT[t] -63.11Calculation[t] -71.728Algebraic_Reasoning[t] -43.202Graphical_Interpretation[t] -20.6218Proportionality_and_Ratio[t] -14.1414Probability_and_Sampling[t] -14.7438Estimation[t] -0.0113762lfm_year[t] +  0.00107596lfm_course[t] -0.00294038lfm_gender[t] +  0.455491PR[t] +  0.701299PE[t] +  1.49009PA[t]  + e[t][/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=1

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Estimated Regression Equation
Ex[t] = + 2.05524 -0.00130074AMS.I[t] -0.0309119AMS.E[t] -0.0452411AMS.A[t] -0.0118652SOFTSTATTOT[t] + 7.19852NUMERACYTOT[t] -63.11Calculation[t] -71.728Algebraic_Reasoning[t] -43.202Graphical_Interpretation[t] -20.6218Proportionality_and_Ratio[t] -14.1414Probability_and_Sampling[t] -14.7438Estimation[t] -0.0113762lfm_year[t] + 0.00107596lfm_course[t] -0.00294038lfm_gender[t] + 0.455491PR[t] + 0.701299PE[t] + 1.49009PA[t] + e[t]

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	2.05524	1.85323	1.109	0.268451	0.134226
AMS.I	-0.00130074	0.0136835	-0.09506	0.924341	0.462171
AMS.E	-0.0309119	0.0175678	-1.76	0.0796556	0.0398278
AMS.A	-0.0452411	0.0394959	-1.145	0.253071	0.126535
SOFTSTATTOT	-0.0118652	0.0306713	-0.3869	0.699182	0.349591
NUMERACYTOT	7.19852	5.9417	1.212	0.226794	0.113397
Calculation	-63.11	53.4271	-1.181	0.238589	0.119294
Algebraic_Reasoning	-71.728	59.4952	-1.206	0.229064	0.114532
Graphical_Interpretation	-43.202	35.6752	-1.211	0.227002	0.113501
Proportionality_and_Ratio	-20.6218	17.8032	-1.158	0.247797	0.123899
Probability_and_Sampling	-14.1414	11.8527	-1.193	0.233921	0.11696
Estimation	-14.7438	11.8377	-1.245	0.214071	0.107035
lfm_year	-0.0113762	0.00256704	-4.432	1.37986e-05	6.89931e-06
lfm_course	0.00107596	0.00237024	0.4539	0.650245	0.325123
lfm_gender	-0.00294038	0.00224956	-1.307	0.192338	0.0961689
PR	0.455491	0.213863	2.13	0.034125	0.0170625
PE	0.701299	0.247652	2.832	0.00499095	0.00249548
PA	1.49009	0.485615	3.068	0.00237918	0.00118959

\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) & 2.05524 & 1.85323 & 1.109 & 0.268451 & 0.134226 \tabularnewline
AMS.I & -0.00130074 & 0.0136835 & -0.09506 & 0.924341 & 0.462171 \tabularnewline
AMS.E & -0.0309119 & 0.0175678 & -1.76 & 0.0796556 & 0.0398278 \tabularnewline
AMS.A & -0.0452411 & 0.0394959 & -1.145 & 0.253071 & 0.126535 \tabularnewline
SOFTSTATTOT & -0.0118652 & 0.0306713 & -0.3869 & 0.699182 & 0.349591 \tabularnewline
NUMERACYTOT & 7.19852 & 5.9417 & 1.212 & 0.226794 & 0.113397 \tabularnewline
Calculation & -63.11 & 53.4271 & -1.181 & 0.238589 & 0.119294 \tabularnewline
Algebraic_Reasoning & -71.728 & 59.4952 & -1.206 & 0.229064 & 0.114532 \tabularnewline
Graphical_Interpretation & -43.202 & 35.6752 & -1.211 & 0.227002 & 0.113501 \tabularnewline
Proportionality_and_Ratio & -20.6218 & 17.8032 & -1.158 & 0.247797 & 0.123899 \tabularnewline
Probability_and_Sampling & -14.1414 & 11.8527 & -1.193 & 0.233921 & 0.11696 \tabularnewline
Estimation & -14.7438 & 11.8377 & -1.245 & 0.214071 & 0.107035 \tabularnewline
lfm_year & -0.0113762 & 0.00256704 & -4.432 & 1.37986e-05 & 6.89931e-06 \tabularnewline
lfm_course & 0.00107596 & 0.00237024 & 0.4539 & 0.650245 & 0.325123 \tabularnewline
lfm_gender & -0.00294038 & 0.00224956 & -1.307 & 0.192338 & 0.0961689 \tabularnewline
PR & 0.455491 & 0.213863 & 2.13 & 0.034125 & 0.0170625 \tabularnewline
PE & 0.701299 & 0.247652 & 2.832 & 0.00499095 & 0.00249548 \tabularnewline
PA & 1.49009 & 0.485615 & 3.068 & 0.00237918 & 0.00118959 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&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]2.05524[/C][C]1.85323[/C][C]1.109[/C][C]0.268451[/C][C]0.134226[/C][/ROW]
[ROW][C]AMS.I[/C][C]-0.00130074[/C][C]0.0136835[/C][C]-0.09506[/C][C]0.924341[/C][C]0.462171[/C][/ROW]
[ROW][C]AMS.E[/C][C]-0.0309119[/C][C]0.0175678[/C][C]-1.76[/C][C]0.0796556[/C][C]0.0398278[/C][/ROW]
[ROW][C]AMS.A[/C][C]-0.0452411[/C][C]0.0394959[/C][C]-1.145[/C][C]0.253071[/C][C]0.126535[/C][/ROW]
[ROW][C]SOFTSTATTOT[/C][C]-0.0118652[/C][C]0.0306713[/C][C]-0.3869[/C][C]0.699182[/C][C]0.349591[/C][/ROW]
[ROW][C]NUMERACYTOT[/C][C]7.19852[/C][C]5.9417[/C][C]1.212[/C][C]0.226794[/C][C]0.113397[/C][/ROW]
[ROW][C]Calculation[/C][C]-63.11[/C][C]53.4271[/C][C]-1.181[/C][C]0.238589[/C][C]0.119294[/C][/ROW]
[ROW][C]Algebraic_Reasoning[/C][C]-71.728[/C][C]59.4952[/C][C]-1.206[/C][C]0.229064[/C][C]0.114532[/C][/ROW]
[ROW][C]Graphical_Interpretation[/C][C]-43.202[/C][C]35.6752[/C][C]-1.211[/C][C]0.227002[/C][C]0.113501[/C][/ROW]
[ROW][C]Proportionality_and_Ratio[/C][C]-20.6218[/C][C]17.8032[/C][C]-1.158[/C][C]0.247797[/C][C]0.123899[/C][/ROW]
[ROW][C]Probability_and_Sampling[/C][C]-14.1414[/C][C]11.8527[/C][C]-1.193[/C][C]0.233921[/C][C]0.11696[/C][/ROW]
[ROW][C]Estimation[/C][C]-14.7438[/C][C]11.8377[/C][C]-1.245[/C][C]0.214071[/C][C]0.107035[/C][/ROW]
[ROW][C]lfm_year[/C][C]-0.0113762[/C][C]0.00256704[/C][C]-4.432[/C][C]1.37986e-05[/C][C]6.89931e-06[/C][/ROW]
[ROW][C]lfm_course[/C][C]0.00107596[/C][C]0.00237024[/C][C]0.4539[/C][C]0.650245[/C][C]0.325123[/C][/ROW]
[ROW][C]lfm_gender[/C][C]-0.00294038[/C][C]0.00224956[/C][C]-1.307[/C][C]0.192338[/C][C]0.0961689[/C][/ROW]
[ROW][C]PR[/C][C]0.455491[/C][C]0.213863[/C][C]2.13[/C][C]0.034125[/C][C]0.0170625[/C][/ROW]
[ROW][C]PE[/C][C]0.701299[/C][C]0.247652[/C][C]2.832[/C][C]0.00499095[/C][C]0.00249548[/C][/ROW]
[ROW][C]PA[/C][C]1.49009[/C][C]0.485615[/C][C]3.068[/C][C]0.00237918[/C][C]0.00118959[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=2

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	2.05524	1.85323	1.109	0.268451	0.134226
AMS.I	-0.00130074	0.0136835	-0.09506	0.924341	0.462171
AMS.E	-0.0309119	0.0175678	-1.76	0.0796556	0.0398278
AMS.A	-0.0452411	0.0394959	-1.145	0.253071	0.126535
SOFTSTATTOT	-0.0118652	0.0306713	-0.3869	0.699182	0.349591
NUMERACYTOT	7.19852	5.9417	1.212	0.226794	0.113397
Calculation	-63.11	53.4271	-1.181	0.238589	0.119294
Algebraic_Reasoning	-71.728	59.4952	-1.206	0.229064	0.114532
Graphical_Interpretation	-43.202	35.6752	-1.211	0.227002	0.113501
Proportionality_and_Ratio	-20.6218	17.8032	-1.158	0.247797	0.123899
Probability_and_Sampling	-14.1414	11.8527	-1.193	0.233921	0.11696
Estimation	-14.7438	11.8377	-1.245	0.214071	0.107035
lfm_year	-0.0113762	0.00256704	-4.432	1.37986e-05	6.89931e-06
lfm_course	0.00107596	0.00237024	0.4539	0.650245	0.325123
lfm_gender	-0.00294038	0.00224956	-1.307	0.192338	0.0961689
PR	0.455491	0.213863	2.13	0.034125	0.0170625
PE	0.701299	0.247652	2.832	0.00499095	0.00249548
PA	1.49009	0.485615	3.068	0.00237918	0.00118959

Multiple Linear Regression - Regression Statistics
Multiple R	0.61415
R-squared	0.37718
Adjusted R-squared	0.336458
F-TEST (value)	9.26214
F-TEST (DF numerator)	17
F-TEST (DF denominator)	260
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.06385
Sum Squared Residuals	1107.46

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R & 0.61415 \tabularnewline
R-squared & 0.37718 \tabularnewline
Adjusted R-squared & 0.336458 \tabularnewline
F-TEST (value) & 9.26214 \tabularnewline
F-TEST (DF numerator) & 17 \tabularnewline
F-TEST (DF denominator) & 260 \tabularnewline
p-value & 0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation & 2.06385 \tabularnewline
Sum Squared Residuals & 1107.46 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C]0.61415[/C][/ROW]
[ROW][C]R-squared[/C][C]0.37718[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C]0.336458[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C]9.26214[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]17[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]260[/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]2.06385[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C]1107.46[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=3

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Regression Statistics
Multiple R	0.61415
R-squared	0.37718
Adjusted R-squared	0.336458
F-TEST (value)	9.26214
F-TEST (DF numerator)	17
F-TEST (DF denominator)	260
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	2.06385
Sum Squared Residuals	1107.46

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	5.05246	2.44754
2	6	5.19996	0.800045
3	6.5	5.57053	0.929466
4	1	4.19171	-3.19171
5	1	4.25902	-3.25902
6	5.5	3.49991	2.00009
7	8.5	5.39528	3.10472
8	6.5	6.33879	0.161208
9	4.5	3.61	0.890002
10	2	5.2331	-3.2331
11	5	5.50837	-0.50837
12	0.5	3.23772	-2.73772
13	5	3.48452	1.51548
14	5	4.78709	0.212915
15	2.5	2.77734	-0.277338
16	5	5.23438	-0.234376
17	5.5	4.63372	0.86628
18	3.5	3.95142	-0.451418
19	3	5.11405	-2.11405
20	4	3.74341	0.25659
21	0.5	3.85852	-3.35852
22	6.5	4.30635	2.19365
23	4.5	3.78061	0.719388
24	7.5	3.99556	3.50444
25	5.5	4.85845	0.641553
26	4	2.97495	1.02505
27	7.5	6.48373	1.01627
28	7	6.26042	0.739577
29	4	4.65209	-0.652089
30	5.5	4.42232	1.07768
31	2.5	3.97422	-1.47422
32	5.5	4.30157	1.19843
33	3.5	4.04252	-0.542515
34	2.5	4.88614	-2.38614
35	4.5	5.39697	-0.896968
36	4.5	5.16406	-0.664059
37	4.5	4.51667	-0.0166715
38	6	4.32262	1.67738
39	2.5	5.09098	-2.59098
40	5	5.70452	-0.704516
41	0	5.58275	-5.58275
42	5	4.98339	0.0166071
43	6.5	4.689	1.811
44	5	4.43849	0.56151
45	6	5.65264	0.347362
46	4.5	5.95406	-1.45406
47	5.5	4.78692	0.713077
48	1	5.33177	-4.33177
49	7.5	3.82598	3.67402
50	6	6.1722	-0.172197
51	5	5.77644	-0.776435
52	1	5.18935	-4.18935
53	5	4.57696	0.423042
54	6.5	3.19637	3.30363
55	7	4.51029	2.48971
56	4.5	4.98159	-0.481589
57	0	4.68605	-4.68605
58	8.5	2.97501	5.52499
59	3.5	4.11254	-0.612545
60	7.5	5.43706	2.06294
61	3.5	4.53547	-1.03547
62	6	4.58525	1.41475
63	1.5	5.25258	-3.75258
64	9	5.3176	3.6824
65	3.5	3.35535	0.144652
66	3.5	5.5339	-2.0339
67	4	4.60066	-0.600658
68	6.5	4.66826	1.83174
69	7.5	4.57311	2.92689
70	6	4.58443	1.41557
71	5	5.51917	-0.519172
72	5.5	5.07694	0.423062
73	3.5	5.10905	-1.60905
74	7.5	6.69325	0.806746
75	6.5	4.12104	2.37896
76	NA	NA	1.155
77	6.5	5.78281	0.717192
78	6.5	4.54724	1.95276
79	7	9.19179	-2.19179
80	3.5	6.72379	-3.22379
81	1.5	3.46058	-1.96058
82	4	0.75994	3.24006
83	7.5	8.71386	-1.21386
84	4.5	7.51299	-3.01299
85	0	2.17407	-2.17407
86	3.5	3.26033	0.239672
87	5.5	5.6581	-0.158099
88	5	7.19487	-2.19487
89	4.5	7.7725	-3.2725
90	2.5	-1.70656	4.20656
91	7.5	5.8337	1.6663
92	7	10.7014	-3.70141
93	0	-1.29226	1.29226
94	4.5	6.06075	-1.56075
95	3	6.5088	-3.5088
96	1.5	1.81939	-0.319394
97	3.5	4.9953	-1.4953
98	2.5	2.38777	0.112228
99	5.5	3.03836	2.46164
100	8	12.5386	-4.53858
101	1	1.2459	-0.2459
102	5	5.6433	-0.643304
103	4.5	5.284	-0.783999
104	3	4.3344	-1.3344
105	3	0.262672	2.73733
106	8	9.80942	-1.80942
107	2.5	0.402147	2.09785
108	7	11.6894	-4.68945
109	0	4.46369	-4.46369
110	1	2.60128	-1.60128
111	3.5	3.24307	0.256929
112	5.5	4.41625	1.08375
113	5.5	10.0805	-4.58049
114	0.5	-0.0409008	0.540901
115	7.5	6.23713	1.26287
116	9	7.6588	1.3412
117	9.5	9.71179	-0.211792
118	8.5	6.38147	2.11853
119	7	8.00991	-1.00991
120	8	6.7308	1.2692
121	10	11.1683	-1.16832
122	7	3.68593	3.31407
123	8.5	8.5047	-0.00470051
124	9	5.73546	3.26454
125	9.5	11.6003	-2.1003
126	4	3.09123	0.908774
127	6	5.03155	0.96845
128	8	9.09114	-1.09114
129	5.5	4.53859	0.961414
130	9.5	8.92713	0.572868
131	7.5	8.28786	-0.787862
132	7	7.66057	-0.660571
133	7.5	5.79959	1.70041
134	8	8.18223	-0.182228
135	7	6.41663	0.583372
136	7	7.62983	-0.629835
137	6	3.14713	2.85287
138	10	14.6722	-4.67218
139	2.5	2.52746	-0.0274555
140	9	8.95597	0.0440272
141	8	7.78977	0.210227
142	6	5.39962	0.600377
143	8.5	9.28737	-0.787369
144	6	4.64972	1.35028
145	9	8.2742	0.725797
146	8	8.07243	-0.0724283
147	9	10.6599	-1.65988
148	5.5	5.58374	-0.0837356
149	7	9.37657	-2.37657
150	5.5	5.46481	0.0351864
151	9	14.0937	-5.09372
152	2	2.00349	-0.00348552
153	8.5	8.39364	0.106361
154	9	8.21795	0.782052
155	8.5	6.40099	2.09901
156	9	7.90655	1.09345
157	7.5	6.81911	0.680892
158	10	7.72633	2.27367
159	9	8.9594	0.0405955
160	7.5	8.11911	-0.619107
161	6	4.28207	1.71793
162	10.5	9.47892	1.02108
163	8.5	9.85744	-1.35744
164	8	5.48702	2.51298
165	10	9.54398	0.456018
166	10.5	8.64255	1.85745
167	6.5	5.84305	0.656948
168	9.5	8.45758	1.04242
169	8.5	9.08902	-0.589016
170	7.5	8.56074	-1.06074
171	5	4.81479	0.185206
172	8	6.09305	1.90695
173	10	10.954	-0.954029
174	7	6.59747	0.402532
175	7.5	7.03564	0.464356
176	7.5	4.92821	2.57179
177	9.5	10.7484	-1.24839
178	6	4.082	1.918
179	10	10.6095	-0.609458
180	7	9.0385	-2.0385
181	3	5.17147	-2.17147
182	6	6.33817	-0.338174
183	7	5.85469	1.14531
184	10	10.8512	-0.851234
185	7	11.7562	-4.75625
186	3.5	3.72267	-0.222667
187	8	5.68219	2.31781
188	10	12.6172	-2.61718
189	5.5	6.30976	-0.80976
190	6	5.6628	0.337198
191	6.5	5.52218	0.977818
192	6.5	3.85174	2.64826
193	8.5	10.2575	-1.75754
194	4	2.22728	1.77272
195	9.5	8.01449	1.48551
196	8	7.18437	0.815634
197	8.5	11.7293	-3.22925
198	5.5	4.61628	0.88372
199	7	4.0166	2.9834
200	9	8.69584	0.304163
201	8	6.8265	1.1735
202	10	8.41603	1.58397
203	8	8.47942	-0.479416
204	6	6.18886	-0.188863
205	8	9.69807	-1.69807
206	5	2.7424	2.2576
207	9	10.055	-1.05498
208	4.5	1.31495	3.18505
209	8.5	6.38964	2.11036
210	9.5	8.07658	1.42342
211	8.5	6.72175	1.77825
212	7.5	7.10249	0.397507
213	7.5	8.68841	-1.18841
214	5	3.83457	1.16543
215	7	6.85024	0.14976
216	8	8.14237	-0.142371
217	5.5	4.02263	1.47737
218	8.5	7.29398	1.20602
219	9.5	8.18065	1.31935
220	7	7.53524	-0.535236
221	8	6.93804	1.06196
222	8.5	9.23059	-0.730592
223	3.5	3.56286	-0.062858
224	6.5	7.23819	-0.738187
225	6.5	3.65473	2.84527
226	10.5	9.75146	0.748536
227	8.5	8.35274	0.147263
228	8	4.4856	3.5144
229	10	8.41085	1.58915
230	10	9.56599	0.434014
231	9.5	8.86478	0.635223
232	9	7.36774	1.63226
233	10	8.19122	1.80878
234	7.5	10.3182	-2.81816
235	4.5	4.95778	-0.457781
236	4.5	10.3088	-5.80882
237	0.5	0.0928663	0.407134
238	6.5	8.81928	-2.31928
239	4.5	5.5771	-1.0771
240	5.5	7.27885	-1.77885
241	5	6.38354	-1.38354
242	6	8.21656	-2.21656
243	4	3.01883	0.981173
244	8	6.92338	1.07662
245	10.5	9.37899	1.12101
246	6.5	4.31032	2.18968
247	8	7.78534	0.214662
248	8.5	11.4587	-2.95866
249	5.5	6.35824	-0.858238
250	7	8.89634	-1.89634
251	5	8.53305	-3.53305
252	3.5	5.84091	-2.34091
253	5	2.6909	2.3091
254	9	6.98513	2.01487
255	8.5	10.5946	-2.09461
256	5	3.25088	1.74912
257	9.5	11.7014	-2.20142
258	3	8.12696	-5.12696
259	1.5	1.56666	-0.0666566
260	6	11.2747	-5.27466
261	0.5	-0.653581	1.15358
262	6.5	5.33027	1.16973
263	7.5	7.83382	-0.333816
264	4.5	3.24088	1.25912
265	8	6.53642	1.46358
266	9	8.74451	0.255487
267	7.5	5.78922	1.71078
268	8.5	8.74027	-0.240274
269	7	5.21484	1.78516
270	9.5	9.0199	0.480099
271	6.5	2.69614	3.80386
272	9.5	9.8818	-0.381798
273	6	3.55172	2.44828
274	8	6.60784	1.39216
275	9.5	8.71707	0.782934
276	8	7.69409	0.305909
277	8	6.81258	1.18742
278	9	8.49228	0.507717
279	5	NA	NA

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Actuals, Interpolation, and Residuals \tabularnewline
Time or Index & Actuals & InterpolationForecast & ResidualsPrediction Error \tabularnewline
1 & 7.5 & 5.05246 & 2.44754 \tabularnewline
2 & 6 & 5.19996 & 0.800045 \tabularnewline
3 & 6.5 & 5.57053 & 0.929466 \tabularnewline
4 & 1 & 4.19171 & -3.19171 \tabularnewline
5 & 1 & 4.25902 & -3.25902 \tabularnewline
6 & 5.5 & 3.49991 & 2.00009 \tabularnewline
7 & 8.5 & 5.39528 & 3.10472 \tabularnewline
8 & 6.5 & 6.33879 & 0.161208 \tabularnewline
9 & 4.5 & 3.61 & 0.890002 \tabularnewline
10 & 2 & 5.2331 & -3.2331 \tabularnewline
11 & 5 & 5.50837 & -0.50837 \tabularnewline
12 & 0.5 & 3.23772 & -2.73772 \tabularnewline
13 & 5 & 3.48452 & 1.51548 \tabularnewline
14 & 5 & 4.78709 & 0.212915 \tabularnewline
15 & 2.5 & 2.77734 & -0.277338 \tabularnewline
16 & 5 & 5.23438 & -0.234376 \tabularnewline
17 & 5.5 & 4.63372 & 0.86628 \tabularnewline
18 & 3.5 & 3.95142 & -0.451418 \tabularnewline
19 & 3 & 5.11405 & -2.11405 \tabularnewline
20 & 4 & 3.74341 & 0.25659 \tabularnewline
21 & 0.5 & 3.85852 & -3.35852 \tabularnewline
22 & 6.5 & 4.30635 & 2.19365 \tabularnewline
23 & 4.5 & 3.78061 & 0.719388 \tabularnewline
24 & 7.5 & 3.99556 & 3.50444 \tabularnewline
25 & 5.5 & 4.85845 & 0.641553 \tabularnewline
26 & 4 & 2.97495 & 1.02505 \tabularnewline
27 & 7.5 & 6.48373 & 1.01627 \tabularnewline
28 & 7 & 6.26042 & 0.739577 \tabularnewline
29 & 4 & 4.65209 & -0.652089 \tabularnewline
30 & 5.5 & 4.42232 & 1.07768 \tabularnewline
31 & 2.5 & 3.97422 & -1.47422 \tabularnewline
32 & 5.5 & 4.30157 & 1.19843 \tabularnewline
33 & 3.5 & 4.04252 & -0.542515 \tabularnewline
34 & 2.5 & 4.88614 & -2.38614 \tabularnewline
35 & 4.5 & 5.39697 & -0.896968 \tabularnewline
36 & 4.5 & 5.16406 & -0.664059 \tabularnewline
37 & 4.5 & 4.51667 & -0.0166715 \tabularnewline
38 & 6 & 4.32262 & 1.67738 \tabularnewline
39 & 2.5 & 5.09098 & -2.59098 \tabularnewline
40 & 5 & 5.70452 & -0.704516 \tabularnewline
41 & 0 & 5.58275 & -5.58275 \tabularnewline
42 & 5 & 4.98339 & 0.0166071 \tabularnewline
43 & 6.5 & 4.689 & 1.811 \tabularnewline
44 & 5 & 4.43849 & 0.56151 \tabularnewline
45 & 6 & 5.65264 & 0.347362 \tabularnewline
46 & 4.5 & 5.95406 & -1.45406 \tabularnewline
47 & 5.5 & 4.78692 & 0.713077 \tabularnewline
48 & 1 & 5.33177 & -4.33177 \tabularnewline
49 & 7.5 & 3.82598 & 3.67402 \tabularnewline
50 & 6 & 6.1722 & -0.172197 \tabularnewline
51 & 5 & 5.77644 & -0.776435 \tabularnewline
52 & 1 & 5.18935 & -4.18935 \tabularnewline
53 & 5 & 4.57696 & 0.423042 \tabularnewline
54 & 6.5 & 3.19637 & 3.30363 \tabularnewline
55 & 7 & 4.51029 & 2.48971 \tabularnewline
56 & 4.5 & 4.98159 & -0.481589 \tabularnewline
57 & 0 & 4.68605 & -4.68605 \tabularnewline
58 & 8.5 & 2.97501 & 5.52499 \tabularnewline
59 & 3.5 & 4.11254 & -0.612545 \tabularnewline
60 & 7.5 & 5.43706 & 2.06294 \tabularnewline
61 & 3.5 & 4.53547 & -1.03547 \tabularnewline
62 & 6 & 4.58525 & 1.41475 \tabularnewline
63 & 1.5 & 5.25258 & -3.75258 \tabularnewline
64 & 9 & 5.3176 & 3.6824 \tabularnewline
65 & 3.5 & 3.35535 & 0.144652 \tabularnewline
66 & 3.5 & 5.5339 & -2.0339 \tabularnewline
67 & 4 & 4.60066 & -0.600658 \tabularnewline
68 & 6.5 & 4.66826 & 1.83174 \tabularnewline
69 & 7.5 & 4.57311 & 2.92689 \tabularnewline
70 & 6 & 4.58443 & 1.41557 \tabularnewline
71 & 5 & 5.51917 & -0.519172 \tabularnewline
72 & 5.5 & 5.07694 & 0.423062 \tabularnewline
73 & 3.5 & 5.10905 & -1.60905 \tabularnewline
74 & 7.5 & 6.69325 & 0.806746 \tabularnewline
75 & 6.5 & 4.12104 & 2.37896 \tabularnewline
76 & NA & NA & 1.155 \tabularnewline
77 & 6.5 & 5.78281 & 0.717192 \tabularnewline
78 & 6.5 & 4.54724 & 1.95276 \tabularnewline
79 & 7 & 9.19179 & -2.19179 \tabularnewline
80 & 3.5 & 6.72379 & -3.22379 \tabularnewline
81 & 1.5 & 3.46058 & -1.96058 \tabularnewline
82 & 4 & 0.75994 & 3.24006 \tabularnewline
83 & 7.5 & 8.71386 & -1.21386 \tabularnewline
84 & 4.5 & 7.51299 & -3.01299 \tabularnewline
85 & 0 & 2.17407 & -2.17407 \tabularnewline
86 & 3.5 & 3.26033 & 0.239672 \tabularnewline
87 & 5.5 & 5.6581 & -0.158099 \tabularnewline
88 & 5 & 7.19487 & -2.19487 \tabularnewline
89 & 4.5 & 7.7725 & -3.2725 \tabularnewline
90 & 2.5 & -1.70656 & 4.20656 \tabularnewline
91 & 7.5 & 5.8337 & 1.6663 \tabularnewline
92 & 7 & 10.7014 & -3.70141 \tabularnewline
93 & 0 & -1.29226 & 1.29226 \tabularnewline
94 & 4.5 & 6.06075 & -1.56075 \tabularnewline
95 & 3 & 6.5088 & -3.5088 \tabularnewline
96 & 1.5 & 1.81939 & -0.319394 \tabularnewline
97 & 3.5 & 4.9953 & -1.4953 \tabularnewline
98 & 2.5 & 2.38777 & 0.112228 \tabularnewline
99 & 5.5 & 3.03836 & 2.46164 \tabularnewline
100 & 8 & 12.5386 & -4.53858 \tabularnewline
101 & 1 & 1.2459 & -0.2459 \tabularnewline
102 & 5 & 5.6433 & -0.643304 \tabularnewline
103 & 4.5 & 5.284 & -0.783999 \tabularnewline
104 & 3 & 4.3344 & -1.3344 \tabularnewline
105 & 3 & 0.262672 & 2.73733 \tabularnewline
106 & 8 & 9.80942 & -1.80942 \tabularnewline
107 & 2.5 & 0.402147 & 2.09785 \tabularnewline
108 & 7 & 11.6894 & -4.68945 \tabularnewline
109 & 0 & 4.46369 & -4.46369 \tabularnewline
110 & 1 & 2.60128 & -1.60128 \tabularnewline
111 & 3.5 & 3.24307 & 0.256929 \tabularnewline
112 & 5.5 & 4.41625 & 1.08375 \tabularnewline
113 & 5.5 & 10.0805 & -4.58049 \tabularnewline
114 & 0.5 & -0.0409008 & 0.540901 \tabularnewline
115 & 7.5 & 6.23713 & 1.26287 \tabularnewline
116 & 9 & 7.6588 & 1.3412 \tabularnewline
117 & 9.5 & 9.71179 & -0.211792 \tabularnewline
118 & 8.5 & 6.38147 & 2.11853 \tabularnewline
119 & 7 & 8.00991 & -1.00991 \tabularnewline
120 & 8 & 6.7308 & 1.2692 \tabularnewline
121 & 10 & 11.1683 & -1.16832 \tabularnewline
122 & 7 & 3.68593 & 3.31407 \tabularnewline
123 & 8.5 & 8.5047 & -0.00470051 \tabularnewline
124 & 9 & 5.73546 & 3.26454 \tabularnewline
125 & 9.5 & 11.6003 & -2.1003 \tabularnewline
126 & 4 & 3.09123 & 0.908774 \tabularnewline
127 & 6 & 5.03155 & 0.96845 \tabularnewline
128 & 8 & 9.09114 & -1.09114 \tabularnewline
129 & 5.5 & 4.53859 & 0.961414 \tabularnewline
130 & 9.5 & 8.92713 & 0.572868 \tabularnewline
131 & 7.5 & 8.28786 & -0.787862 \tabularnewline
132 & 7 & 7.66057 & -0.660571 \tabularnewline
133 & 7.5 & 5.79959 & 1.70041 \tabularnewline
134 & 8 & 8.18223 & -0.182228 \tabularnewline
135 & 7 & 6.41663 & 0.583372 \tabularnewline
136 & 7 & 7.62983 & -0.629835 \tabularnewline
137 & 6 & 3.14713 & 2.85287 \tabularnewline
138 & 10 & 14.6722 & -4.67218 \tabularnewline
139 & 2.5 & 2.52746 & -0.0274555 \tabularnewline
140 & 9 & 8.95597 & 0.0440272 \tabularnewline
141 & 8 & 7.78977 & 0.210227 \tabularnewline
142 & 6 & 5.39962 & 0.600377 \tabularnewline
143 & 8.5 & 9.28737 & -0.787369 \tabularnewline
144 & 6 & 4.64972 & 1.35028 \tabularnewline
145 & 9 & 8.2742 & 0.725797 \tabularnewline
146 & 8 & 8.07243 & -0.0724283 \tabularnewline
147 & 9 & 10.6599 & -1.65988 \tabularnewline
148 & 5.5 & 5.58374 & -0.0837356 \tabularnewline
149 & 7 & 9.37657 & -2.37657 \tabularnewline
150 & 5.5 & 5.46481 & 0.0351864 \tabularnewline
151 & 9 & 14.0937 & -5.09372 \tabularnewline
152 & 2 & 2.00349 & -0.00348552 \tabularnewline
153 & 8.5 & 8.39364 & 0.106361 \tabularnewline
154 & 9 & 8.21795 & 0.782052 \tabularnewline
155 & 8.5 & 6.40099 & 2.09901 \tabularnewline
156 & 9 & 7.90655 & 1.09345 \tabularnewline
157 & 7.5 & 6.81911 & 0.680892 \tabularnewline
158 & 10 & 7.72633 & 2.27367 \tabularnewline
159 & 9 & 8.9594 & 0.0405955 \tabularnewline
160 & 7.5 & 8.11911 & -0.619107 \tabularnewline
161 & 6 & 4.28207 & 1.71793 \tabularnewline
162 & 10.5 & 9.47892 & 1.02108 \tabularnewline
163 & 8.5 & 9.85744 & -1.35744 \tabularnewline
164 & 8 & 5.48702 & 2.51298 \tabularnewline
165 & 10 & 9.54398 & 0.456018 \tabularnewline
166 & 10.5 & 8.64255 & 1.85745 \tabularnewline
167 & 6.5 & 5.84305 & 0.656948 \tabularnewline
168 & 9.5 & 8.45758 & 1.04242 \tabularnewline
169 & 8.5 & 9.08902 & -0.589016 \tabularnewline
170 & 7.5 & 8.56074 & -1.06074 \tabularnewline
171 & 5 & 4.81479 & 0.185206 \tabularnewline
172 & 8 & 6.09305 & 1.90695 \tabularnewline
173 & 10 & 10.954 & -0.954029 \tabularnewline
174 & 7 & 6.59747 & 0.402532 \tabularnewline
175 & 7.5 & 7.03564 & 0.464356 \tabularnewline
176 & 7.5 & 4.92821 & 2.57179 \tabularnewline
177 & 9.5 & 10.7484 & -1.24839 \tabularnewline
178 & 6 & 4.082 & 1.918 \tabularnewline
179 & 10 & 10.6095 & -0.609458 \tabularnewline
180 & 7 & 9.0385 & -2.0385 \tabularnewline
181 & 3 & 5.17147 & -2.17147 \tabularnewline
182 & 6 & 6.33817 & -0.338174 \tabularnewline
183 & 7 & 5.85469 & 1.14531 \tabularnewline
184 & 10 & 10.8512 & -0.851234 \tabularnewline
185 & 7 & 11.7562 & -4.75625 \tabularnewline
186 & 3.5 & 3.72267 & -0.222667 \tabularnewline
187 & 8 & 5.68219 & 2.31781 \tabularnewline
188 & 10 & 12.6172 & -2.61718 \tabularnewline
189 & 5.5 & 6.30976 & -0.80976 \tabularnewline
190 & 6 & 5.6628 & 0.337198 \tabularnewline
191 & 6.5 & 5.52218 & 0.977818 \tabularnewline
192 & 6.5 & 3.85174 & 2.64826 \tabularnewline
193 & 8.5 & 10.2575 & -1.75754 \tabularnewline
194 & 4 & 2.22728 & 1.77272 \tabularnewline
195 & 9.5 & 8.01449 & 1.48551 \tabularnewline
196 & 8 & 7.18437 & 0.815634 \tabularnewline
197 & 8.5 & 11.7293 & -3.22925 \tabularnewline
198 & 5.5 & 4.61628 & 0.88372 \tabularnewline
199 & 7 & 4.0166 & 2.9834 \tabularnewline
200 & 9 & 8.69584 & 0.304163 \tabularnewline
201 & 8 & 6.8265 & 1.1735 \tabularnewline
202 & 10 & 8.41603 & 1.58397 \tabularnewline
203 & 8 & 8.47942 & -0.479416 \tabularnewline
204 & 6 & 6.18886 & -0.188863 \tabularnewline
205 & 8 & 9.69807 & -1.69807 \tabularnewline
206 & 5 & 2.7424 & 2.2576 \tabularnewline
207 & 9 & 10.055 & -1.05498 \tabularnewline
208 & 4.5 & 1.31495 & 3.18505 \tabularnewline
209 & 8.5 & 6.38964 & 2.11036 \tabularnewline
210 & 9.5 & 8.07658 & 1.42342 \tabularnewline
211 & 8.5 & 6.72175 & 1.77825 \tabularnewline
212 & 7.5 & 7.10249 & 0.397507 \tabularnewline
213 & 7.5 & 8.68841 & -1.18841 \tabularnewline
214 & 5 & 3.83457 & 1.16543 \tabularnewline
215 & 7 & 6.85024 & 0.14976 \tabularnewline
216 & 8 & 8.14237 & -0.142371 \tabularnewline
217 & 5.5 & 4.02263 & 1.47737 \tabularnewline
218 & 8.5 & 7.29398 & 1.20602 \tabularnewline
219 & 9.5 & 8.18065 & 1.31935 \tabularnewline
220 & 7 & 7.53524 & -0.535236 \tabularnewline
221 & 8 & 6.93804 & 1.06196 \tabularnewline
222 & 8.5 & 9.23059 & -0.730592 \tabularnewline
223 & 3.5 & 3.56286 & -0.062858 \tabularnewline
224 & 6.5 & 7.23819 & -0.738187 \tabularnewline
225 & 6.5 & 3.65473 & 2.84527 \tabularnewline
226 & 10.5 & 9.75146 & 0.748536 \tabularnewline
227 & 8.5 & 8.35274 & 0.147263 \tabularnewline
228 & 8 & 4.4856 & 3.5144 \tabularnewline
229 & 10 & 8.41085 & 1.58915 \tabularnewline
230 & 10 & 9.56599 & 0.434014 \tabularnewline
231 & 9.5 & 8.86478 & 0.635223 \tabularnewline
232 & 9 & 7.36774 & 1.63226 \tabularnewline
233 & 10 & 8.19122 & 1.80878 \tabularnewline
234 & 7.5 & 10.3182 & -2.81816 \tabularnewline
235 & 4.5 & 4.95778 & -0.457781 \tabularnewline
236 & 4.5 & 10.3088 & -5.80882 \tabularnewline
237 & 0.5 & 0.0928663 & 0.407134 \tabularnewline
238 & 6.5 & 8.81928 & -2.31928 \tabularnewline
239 & 4.5 & 5.5771 & -1.0771 \tabularnewline
240 & 5.5 & 7.27885 & -1.77885 \tabularnewline
241 & 5 & 6.38354 & -1.38354 \tabularnewline
242 & 6 & 8.21656 & -2.21656 \tabularnewline
243 & 4 & 3.01883 & 0.981173 \tabularnewline
244 & 8 & 6.92338 & 1.07662 \tabularnewline
245 & 10.5 & 9.37899 & 1.12101 \tabularnewline
246 & 6.5 & 4.31032 & 2.18968 \tabularnewline
247 & 8 & 7.78534 & 0.214662 \tabularnewline
248 & 8.5 & 11.4587 & -2.95866 \tabularnewline
249 & 5.5 & 6.35824 & -0.858238 \tabularnewline
250 & 7 & 8.89634 & -1.89634 \tabularnewline
251 & 5 & 8.53305 & -3.53305 \tabularnewline
252 & 3.5 & 5.84091 & -2.34091 \tabularnewline
253 & 5 & 2.6909 & 2.3091 \tabularnewline
254 & 9 & 6.98513 & 2.01487 \tabularnewline
255 & 8.5 & 10.5946 & -2.09461 \tabularnewline
256 & 5 & 3.25088 & 1.74912 \tabularnewline
257 & 9.5 & 11.7014 & -2.20142 \tabularnewline
258 & 3 & 8.12696 & -5.12696 \tabularnewline
259 & 1.5 & 1.56666 & -0.0666566 \tabularnewline
260 & 6 & 11.2747 & -5.27466 \tabularnewline
261 & 0.5 & -0.653581 & 1.15358 \tabularnewline
262 & 6.5 & 5.33027 & 1.16973 \tabularnewline
263 & 7.5 & 7.83382 & -0.333816 \tabularnewline
264 & 4.5 & 3.24088 & 1.25912 \tabularnewline
265 & 8 & 6.53642 & 1.46358 \tabularnewline
266 & 9 & 8.74451 & 0.255487 \tabularnewline
267 & 7.5 & 5.78922 & 1.71078 \tabularnewline
268 & 8.5 & 8.74027 & -0.240274 \tabularnewline
269 & 7 & 5.21484 & 1.78516 \tabularnewline
270 & 9.5 & 9.0199 & 0.480099 \tabularnewline
271 & 6.5 & 2.69614 & 3.80386 \tabularnewline
272 & 9.5 & 9.8818 & -0.381798 \tabularnewline
273 & 6 & 3.55172 & 2.44828 \tabularnewline
274 & 8 & 6.60784 & 1.39216 \tabularnewline
275 & 9.5 & 8.71707 & 0.782934 \tabularnewline
276 & 8 & 7.69409 & 0.305909 \tabularnewline
277 & 8 & 6.81258 & 1.18742 \tabularnewline
278 & 9 & 8.49228 & 0.507717 \tabularnewline
279 & 5 & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&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]7.5[/C][C]5.05246[/C][C]2.44754[/C][/ROW]
[ROW][C]2[/C][C]6[/C][C]5.19996[/C][C]0.800045[/C][/ROW]
[ROW][C]3[/C][C]6.5[/C][C]5.57053[/C][C]0.929466[/C][/ROW]
[ROW][C]4[/C][C]1[/C][C]4.19171[/C][C]-3.19171[/C][/ROW]
[ROW][C]5[/C][C]1[/C][C]4.25902[/C][C]-3.25902[/C][/ROW]
[ROW][C]6[/C][C]5.5[/C][C]3.49991[/C][C]2.00009[/C][/ROW]
[ROW][C]7[/C][C]8.5[/C][C]5.39528[/C][C]3.10472[/C][/ROW]
[ROW][C]8[/C][C]6.5[/C][C]6.33879[/C][C]0.161208[/C][/ROW]
[ROW][C]9[/C][C]4.5[/C][C]3.61[/C][C]0.890002[/C][/ROW]
[ROW][C]10[/C][C]2[/C][C]5.2331[/C][C]-3.2331[/C][/ROW]
[ROW][C]11[/C][C]5[/C][C]5.50837[/C][C]-0.50837[/C][/ROW]
[ROW][C]12[/C][C]0.5[/C][C]3.23772[/C][C]-2.73772[/C][/ROW]
[ROW][C]13[/C][C]5[/C][C]3.48452[/C][C]1.51548[/C][/ROW]
[ROW][C]14[/C][C]5[/C][C]4.78709[/C][C]0.212915[/C][/ROW]
[ROW][C]15[/C][C]2.5[/C][C]2.77734[/C][C]-0.277338[/C][/ROW]
[ROW][C]16[/C][C]5[/C][C]5.23438[/C][C]-0.234376[/C][/ROW]
[ROW][C]17[/C][C]5.5[/C][C]4.63372[/C][C]0.86628[/C][/ROW]
[ROW][C]18[/C][C]3.5[/C][C]3.95142[/C][C]-0.451418[/C][/ROW]
[ROW][C]19[/C][C]3[/C][C]5.11405[/C][C]-2.11405[/C][/ROW]
[ROW][C]20[/C][C]4[/C][C]3.74341[/C][C]0.25659[/C][/ROW]
[ROW][C]21[/C][C]0.5[/C][C]3.85852[/C][C]-3.35852[/C][/ROW]
[ROW][C]22[/C][C]6.5[/C][C]4.30635[/C][C]2.19365[/C][/ROW]
[ROW][C]23[/C][C]4.5[/C][C]3.78061[/C][C]0.719388[/C][/ROW]
[ROW][C]24[/C][C]7.5[/C][C]3.99556[/C][C]3.50444[/C][/ROW]
[ROW][C]25[/C][C]5.5[/C][C]4.85845[/C][C]0.641553[/C][/ROW]
[ROW][C]26[/C][C]4[/C][C]2.97495[/C][C]1.02505[/C][/ROW]
[ROW][C]27[/C][C]7.5[/C][C]6.48373[/C][C]1.01627[/C][/ROW]
[ROW][C]28[/C][C]7[/C][C]6.26042[/C][C]0.739577[/C][/ROW]
[ROW][C]29[/C][C]4[/C][C]4.65209[/C][C]-0.652089[/C][/ROW]
[ROW][C]30[/C][C]5.5[/C][C]4.42232[/C][C]1.07768[/C][/ROW]
[ROW][C]31[/C][C]2.5[/C][C]3.97422[/C][C]-1.47422[/C][/ROW]
[ROW][C]32[/C][C]5.5[/C][C]4.30157[/C][C]1.19843[/C][/ROW]
[ROW][C]33[/C][C]3.5[/C][C]4.04252[/C][C]-0.542515[/C][/ROW]
[ROW][C]34[/C][C]2.5[/C][C]4.88614[/C][C]-2.38614[/C][/ROW]
[ROW][C]35[/C][C]4.5[/C][C]5.39697[/C][C]-0.896968[/C][/ROW]
[ROW][C]36[/C][C]4.5[/C][C]5.16406[/C][C]-0.664059[/C][/ROW]
[ROW][C]37[/C][C]4.5[/C][C]4.51667[/C][C]-0.0166715[/C][/ROW]
[ROW][C]38[/C][C]6[/C][C]4.32262[/C][C]1.67738[/C][/ROW]
[ROW][C]39[/C][C]2.5[/C][C]5.09098[/C][C]-2.59098[/C][/ROW]
[ROW][C]40[/C][C]5[/C][C]5.70452[/C][C]-0.704516[/C][/ROW]
[ROW][C]41[/C][C]0[/C][C]5.58275[/C][C]-5.58275[/C][/ROW]
[ROW][C]42[/C][C]5[/C][C]4.98339[/C][C]0.0166071[/C][/ROW]
[ROW][C]43[/C][C]6.5[/C][C]4.689[/C][C]1.811[/C][/ROW]
[ROW][C]44[/C][C]5[/C][C]4.43849[/C][C]0.56151[/C][/ROW]
[ROW][C]45[/C][C]6[/C][C]5.65264[/C][C]0.347362[/C][/ROW]
[ROW][C]46[/C][C]4.5[/C][C]5.95406[/C][C]-1.45406[/C][/ROW]
[ROW][C]47[/C][C]5.5[/C][C]4.78692[/C][C]0.713077[/C][/ROW]
[ROW][C]48[/C][C]1[/C][C]5.33177[/C][C]-4.33177[/C][/ROW]
[ROW][C]49[/C][C]7.5[/C][C]3.82598[/C][C]3.67402[/C][/ROW]
[ROW][C]50[/C][C]6[/C][C]6.1722[/C][C]-0.172197[/C][/ROW]
[ROW][C]51[/C][C]5[/C][C]5.77644[/C][C]-0.776435[/C][/ROW]
[ROW][C]52[/C][C]1[/C][C]5.18935[/C][C]-4.18935[/C][/ROW]
[ROW][C]53[/C][C]5[/C][C]4.57696[/C][C]0.423042[/C][/ROW]
[ROW][C]54[/C][C]6.5[/C][C]3.19637[/C][C]3.30363[/C][/ROW]
[ROW][C]55[/C][C]7[/C][C]4.51029[/C][C]2.48971[/C][/ROW]
[ROW][C]56[/C][C]4.5[/C][C]4.98159[/C][C]-0.481589[/C][/ROW]
[ROW][C]57[/C][C]0[/C][C]4.68605[/C][C]-4.68605[/C][/ROW]
[ROW][C]58[/C][C]8.5[/C][C]2.97501[/C][C]5.52499[/C][/ROW]
[ROW][C]59[/C][C]3.5[/C][C]4.11254[/C][C]-0.612545[/C][/ROW]
[ROW][C]60[/C][C]7.5[/C][C]5.43706[/C][C]2.06294[/C][/ROW]
[ROW][C]61[/C][C]3.5[/C][C]4.53547[/C][C]-1.03547[/C][/ROW]
[ROW][C]62[/C][C]6[/C][C]4.58525[/C][C]1.41475[/C][/ROW]
[ROW][C]63[/C][C]1.5[/C][C]5.25258[/C][C]-3.75258[/C][/ROW]
[ROW][C]64[/C][C]9[/C][C]5.3176[/C][C]3.6824[/C][/ROW]
[ROW][C]65[/C][C]3.5[/C][C]3.35535[/C][C]0.144652[/C][/ROW]
[ROW][C]66[/C][C]3.5[/C][C]5.5339[/C][C]-2.0339[/C][/ROW]
[ROW][C]67[/C][C]4[/C][C]4.60066[/C][C]-0.600658[/C][/ROW]
[ROW][C]68[/C][C]6.5[/C][C]4.66826[/C][C]1.83174[/C][/ROW]
[ROW][C]69[/C][C]7.5[/C][C]4.57311[/C][C]2.92689[/C][/ROW]
[ROW][C]70[/C][C]6[/C][C]4.58443[/C][C]1.41557[/C][/ROW]
[ROW][C]71[/C][C]5[/C][C]5.51917[/C][C]-0.519172[/C][/ROW]
[ROW][C]72[/C][C]5.5[/C][C]5.07694[/C][C]0.423062[/C][/ROW]
[ROW][C]73[/C][C]3.5[/C][C]5.10905[/C][C]-1.60905[/C][/ROW]
[ROW][C]74[/C][C]7.5[/C][C]6.69325[/C][C]0.806746[/C][/ROW]
[ROW][C]75[/C][C]6.5[/C][C]4.12104[/C][C]2.37896[/C][/ROW]
[ROW][C]76[/C][C]NA[/C][C]NA[/C][C]1.155[/C][/ROW]
[ROW][C]77[/C][C]6.5[/C][C]5.78281[/C][C]0.717192[/C][/ROW]
[ROW][C]78[/C][C]6.5[/C][C]4.54724[/C][C]1.95276[/C][/ROW]
[ROW][C]79[/C][C]7[/C][C]9.19179[/C][C]-2.19179[/C][/ROW]
[ROW][C]80[/C][C]3.5[/C][C]6.72379[/C][C]-3.22379[/C][/ROW]
[ROW][C]81[/C][C]1.5[/C][C]3.46058[/C][C]-1.96058[/C][/ROW]
[ROW][C]82[/C][C]4[/C][C]0.75994[/C][C]3.24006[/C][/ROW]
[ROW][C]83[/C][C]7.5[/C][C]8.71386[/C][C]-1.21386[/C][/ROW]
[ROW][C]84[/C][C]4.5[/C][C]7.51299[/C][C]-3.01299[/C][/ROW]
[ROW][C]85[/C][C]0[/C][C]2.17407[/C][C]-2.17407[/C][/ROW]
[ROW][C]86[/C][C]3.5[/C][C]3.26033[/C][C]0.239672[/C][/ROW]
[ROW][C]87[/C][C]5.5[/C][C]5.6581[/C][C]-0.158099[/C][/ROW]
[ROW][C]88[/C][C]5[/C][C]7.19487[/C][C]-2.19487[/C][/ROW]
[ROW][C]89[/C][C]4.5[/C][C]7.7725[/C][C]-3.2725[/C][/ROW]
[ROW][C]90[/C][C]2.5[/C][C]-1.70656[/C][C]4.20656[/C][/ROW]
[ROW][C]91[/C][C]7.5[/C][C]5.8337[/C][C]1.6663[/C][/ROW]
[ROW][C]92[/C][C]7[/C][C]10.7014[/C][C]-3.70141[/C][/ROW]
[ROW][C]93[/C][C]0[/C][C]-1.29226[/C][C]1.29226[/C][/ROW]
[ROW][C]94[/C][C]4.5[/C][C]6.06075[/C][C]-1.56075[/C][/ROW]
[ROW][C]95[/C][C]3[/C][C]6.5088[/C][C]-3.5088[/C][/ROW]
[ROW][C]96[/C][C]1.5[/C][C]1.81939[/C][C]-0.319394[/C][/ROW]
[ROW][C]97[/C][C]3.5[/C][C]4.9953[/C][C]-1.4953[/C][/ROW]
[ROW][C]98[/C][C]2.5[/C][C]2.38777[/C][C]0.112228[/C][/ROW]
[ROW][C]99[/C][C]5.5[/C][C]3.03836[/C][C]2.46164[/C][/ROW]
[ROW][C]100[/C][C]8[/C][C]12.5386[/C][C]-4.53858[/C][/ROW]
[ROW][C]101[/C][C]1[/C][C]1.2459[/C][C]-0.2459[/C][/ROW]
[ROW][C]102[/C][C]5[/C][C]5.6433[/C][C]-0.643304[/C][/ROW]
[ROW][C]103[/C][C]4.5[/C][C]5.284[/C][C]-0.783999[/C][/ROW]
[ROW][C]104[/C][C]3[/C][C]4.3344[/C][C]-1.3344[/C][/ROW]
[ROW][C]105[/C][C]3[/C][C]0.262672[/C][C]2.73733[/C][/ROW]
[ROW][C]106[/C][C]8[/C][C]9.80942[/C][C]-1.80942[/C][/ROW]
[ROW][C]107[/C][C]2.5[/C][C]0.402147[/C][C]2.09785[/C][/ROW]
[ROW][C]108[/C][C]7[/C][C]11.6894[/C][C]-4.68945[/C][/ROW]
[ROW][C]109[/C][C]0[/C][C]4.46369[/C][C]-4.46369[/C][/ROW]
[ROW][C]110[/C][C]1[/C][C]2.60128[/C][C]-1.60128[/C][/ROW]
[ROW][C]111[/C][C]3.5[/C][C]3.24307[/C][C]0.256929[/C][/ROW]
[ROW][C]112[/C][C]5.5[/C][C]4.41625[/C][C]1.08375[/C][/ROW]
[ROW][C]113[/C][C]5.5[/C][C]10.0805[/C][C]-4.58049[/C][/ROW]
[ROW][C]114[/C][C]0.5[/C][C]-0.0409008[/C][C]0.540901[/C][/ROW]
[ROW][C]115[/C][C]7.5[/C][C]6.23713[/C][C]1.26287[/C][/ROW]
[ROW][C]116[/C][C]9[/C][C]7.6588[/C][C]1.3412[/C][/ROW]
[ROW][C]117[/C][C]9.5[/C][C]9.71179[/C][C]-0.211792[/C][/ROW]
[ROW][C]118[/C][C]8.5[/C][C]6.38147[/C][C]2.11853[/C][/ROW]
[ROW][C]119[/C][C]7[/C][C]8.00991[/C][C]-1.00991[/C][/ROW]
[ROW][C]120[/C][C]8[/C][C]6.7308[/C][C]1.2692[/C][/ROW]
[ROW][C]121[/C][C]10[/C][C]11.1683[/C][C]-1.16832[/C][/ROW]
[ROW][C]122[/C][C]7[/C][C]3.68593[/C][C]3.31407[/C][/ROW]
[ROW][C]123[/C][C]8.5[/C][C]8.5047[/C][C]-0.00470051[/C][/ROW]
[ROW][C]124[/C][C]9[/C][C]5.73546[/C][C]3.26454[/C][/ROW]
[ROW][C]125[/C][C]9.5[/C][C]11.6003[/C][C]-2.1003[/C][/ROW]
[ROW][C]126[/C][C]4[/C][C]3.09123[/C][C]0.908774[/C][/ROW]
[ROW][C]127[/C][C]6[/C][C]5.03155[/C][C]0.96845[/C][/ROW]
[ROW][C]128[/C][C]8[/C][C]9.09114[/C][C]-1.09114[/C][/ROW]
[ROW][C]129[/C][C]5.5[/C][C]4.53859[/C][C]0.961414[/C][/ROW]
[ROW][C]130[/C][C]9.5[/C][C]8.92713[/C][C]0.572868[/C][/ROW]
[ROW][C]131[/C][C]7.5[/C][C]8.28786[/C][C]-0.787862[/C][/ROW]
[ROW][C]132[/C][C]7[/C][C]7.66057[/C][C]-0.660571[/C][/ROW]
[ROW][C]133[/C][C]7.5[/C][C]5.79959[/C][C]1.70041[/C][/ROW]
[ROW][C]134[/C][C]8[/C][C]8.18223[/C][C]-0.182228[/C][/ROW]
[ROW][C]135[/C][C]7[/C][C]6.41663[/C][C]0.583372[/C][/ROW]
[ROW][C]136[/C][C]7[/C][C]7.62983[/C][C]-0.629835[/C][/ROW]
[ROW][C]137[/C][C]6[/C][C]3.14713[/C][C]2.85287[/C][/ROW]
[ROW][C]138[/C][C]10[/C][C]14.6722[/C][C]-4.67218[/C][/ROW]
[ROW][C]139[/C][C]2.5[/C][C]2.52746[/C][C]-0.0274555[/C][/ROW]
[ROW][C]140[/C][C]9[/C][C]8.95597[/C][C]0.0440272[/C][/ROW]
[ROW][C]141[/C][C]8[/C][C]7.78977[/C][C]0.210227[/C][/ROW]
[ROW][C]142[/C][C]6[/C][C]5.39962[/C][C]0.600377[/C][/ROW]
[ROW][C]143[/C][C]8.5[/C][C]9.28737[/C][C]-0.787369[/C][/ROW]
[ROW][C]144[/C][C]6[/C][C]4.64972[/C][C]1.35028[/C][/ROW]
[ROW][C]145[/C][C]9[/C][C]8.2742[/C][C]0.725797[/C][/ROW]
[ROW][C]146[/C][C]8[/C][C]8.07243[/C][C]-0.0724283[/C][/ROW]
[ROW][C]147[/C][C]9[/C][C]10.6599[/C][C]-1.65988[/C][/ROW]
[ROW][C]148[/C][C]5.5[/C][C]5.58374[/C][C]-0.0837356[/C][/ROW]
[ROW][C]149[/C][C]7[/C][C]9.37657[/C][C]-2.37657[/C][/ROW]
[ROW][C]150[/C][C]5.5[/C][C]5.46481[/C][C]0.0351864[/C][/ROW]
[ROW][C]151[/C][C]9[/C][C]14.0937[/C][C]-5.09372[/C][/ROW]
[ROW][C]152[/C][C]2[/C][C]2.00349[/C][C]-0.00348552[/C][/ROW]
[ROW][C]153[/C][C]8.5[/C][C]8.39364[/C][C]0.106361[/C][/ROW]
[ROW][C]154[/C][C]9[/C][C]8.21795[/C][C]0.782052[/C][/ROW]
[ROW][C]155[/C][C]8.5[/C][C]6.40099[/C][C]2.09901[/C][/ROW]
[ROW][C]156[/C][C]9[/C][C]7.90655[/C][C]1.09345[/C][/ROW]
[ROW][C]157[/C][C]7.5[/C][C]6.81911[/C][C]0.680892[/C][/ROW]
[ROW][C]158[/C][C]10[/C][C]7.72633[/C][C]2.27367[/C][/ROW]
[ROW][C]159[/C][C]9[/C][C]8.9594[/C][C]0.0405955[/C][/ROW]
[ROW][C]160[/C][C]7.5[/C][C]8.11911[/C][C]-0.619107[/C][/ROW]
[ROW][C]161[/C][C]6[/C][C]4.28207[/C][C]1.71793[/C][/ROW]
[ROW][C]162[/C][C]10.5[/C][C]9.47892[/C][C]1.02108[/C][/ROW]
[ROW][C]163[/C][C]8.5[/C][C]9.85744[/C][C]-1.35744[/C][/ROW]
[ROW][C]164[/C][C]8[/C][C]5.48702[/C][C]2.51298[/C][/ROW]
[ROW][C]165[/C][C]10[/C][C]9.54398[/C][C]0.456018[/C][/ROW]
[ROW][C]166[/C][C]10.5[/C][C]8.64255[/C][C]1.85745[/C][/ROW]
[ROW][C]167[/C][C]6.5[/C][C]5.84305[/C][C]0.656948[/C][/ROW]
[ROW][C]168[/C][C]9.5[/C][C]8.45758[/C][C]1.04242[/C][/ROW]
[ROW][C]169[/C][C]8.5[/C][C]9.08902[/C][C]-0.589016[/C][/ROW]
[ROW][C]170[/C][C]7.5[/C][C]8.56074[/C][C]-1.06074[/C][/ROW]
[ROW][C]171[/C][C]5[/C][C]4.81479[/C][C]0.185206[/C][/ROW]
[ROW][C]172[/C][C]8[/C][C]6.09305[/C][C]1.90695[/C][/ROW]
[ROW][C]173[/C][C]10[/C][C]10.954[/C][C]-0.954029[/C][/ROW]
[ROW][C]174[/C][C]7[/C][C]6.59747[/C][C]0.402532[/C][/ROW]
[ROW][C]175[/C][C]7.5[/C][C]7.03564[/C][C]0.464356[/C][/ROW]
[ROW][C]176[/C][C]7.5[/C][C]4.92821[/C][C]2.57179[/C][/ROW]
[ROW][C]177[/C][C]9.5[/C][C]10.7484[/C][C]-1.24839[/C][/ROW]
[ROW][C]178[/C][C]6[/C][C]4.082[/C][C]1.918[/C][/ROW]
[ROW][C]179[/C][C]10[/C][C]10.6095[/C][C]-0.609458[/C][/ROW]
[ROW][C]180[/C][C]7[/C][C]9.0385[/C][C]-2.0385[/C][/ROW]
[ROW][C]181[/C][C]3[/C][C]5.17147[/C][C]-2.17147[/C][/ROW]
[ROW][C]182[/C][C]6[/C][C]6.33817[/C][C]-0.338174[/C][/ROW]
[ROW][C]183[/C][C]7[/C][C]5.85469[/C][C]1.14531[/C][/ROW]
[ROW][C]184[/C][C]10[/C][C]10.8512[/C][C]-0.851234[/C][/ROW]
[ROW][C]185[/C][C]7[/C][C]11.7562[/C][C]-4.75625[/C][/ROW]
[ROW][C]186[/C][C]3.5[/C][C]3.72267[/C][C]-0.222667[/C][/ROW]
[ROW][C]187[/C][C]8[/C][C]5.68219[/C][C]2.31781[/C][/ROW]
[ROW][C]188[/C][C]10[/C][C]12.6172[/C][C]-2.61718[/C][/ROW]
[ROW][C]189[/C][C]5.5[/C][C]6.30976[/C][C]-0.80976[/C][/ROW]
[ROW][C]190[/C][C]6[/C][C]5.6628[/C][C]0.337198[/C][/ROW]
[ROW][C]191[/C][C]6.5[/C][C]5.52218[/C][C]0.977818[/C][/ROW]
[ROW][C]192[/C][C]6.5[/C][C]3.85174[/C][C]2.64826[/C][/ROW]
[ROW][C]193[/C][C]8.5[/C][C]10.2575[/C][C]-1.75754[/C][/ROW]
[ROW][C]194[/C][C]4[/C][C]2.22728[/C][C]1.77272[/C][/ROW]
[ROW][C]195[/C][C]9.5[/C][C]8.01449[/C][C]1.48551[/C][/ROW]
[ROW][C]196[/C][C]8[/C][C]7.18437[/C][C]0.815634[/C][/ROW]
[ROW][C]197[/C][C]8.5[/C][C]11.7293[/C][C]-3.22925[/C][/ROW]
[ROW][C]198[/C][C]5.5[/C][C]4.61628[/C][C]0.88372[/C][/ROW]
[ROW][C]199[/C][C]7[/C][C]4.0166[/C][C]2.9834[/C][/ROW]
[ROW][C]200[/C][C]9[/C][C]8.69584[/C][C]0.304163[/C][/ROW]
[ROW][C]201[/C][C]8[/C][C]6.8265[/C][C]1.1735[/C][/ROW]
[ROW][C]202[/C][C]10[/C][C]8.41603[/C][C]1.58397[/C][/ROW]
[ROW][C]203[/C][C]8[/C][C]8.47942[/C][C]-0.479416[/C][/ROW]
[ROW][C]204[/C][C]6[/C][C]6.18886[/C][C]-0.188863[/C][/ROW]
[ROW][C]205[/C][C]8[/C][C]9.69807[/C][C]-1.69807[/C][/ROW]
[ROW][C]206[/C][C]5[/C][C]2.7424[/C][C]2.2576[/C][/ROW]
[ROW][C]207[/C][C]9[/C][C]10.055[/C][C]-1.05498[/C][/ROW]
[ROW][C]208[/C][C]4.5[/C][C]1.31495[/C][C]3.18505[/C][/ROW]
[ROW][C]209[/C][C]8.5[/C][C]6.38964[/C][C]2.11036[/C][/ROW]
[ROW][C]210[/C][C]9.5[/C][C]8.07658[/C][C]1.42342[/C][/ROW]
[ROW][C]211[/C][C]8.5[/C][C]6.72175[/C][C]1.77825[/C][/ROW]
[ROW][C]212[/C][C]7.5[/C][C]7.10249[/C][C]0.397507[/C][/ROW]
[ROW][C]213[/C][C]7.5[/C][C]8.68841[/C][C]-1.18841[/C][/ROW]
[ROW][C]214[/C][C]5[/C][C]3.83457[/C][C]1.16543[/C][/ROW]
[ROW][C]215[/C][C]7[/C][C]6.85024[/C][C]0.14976[/C][/ROW]
[ROW][C]216[/C][C]8[/C][C]8.14237[/C][C]-0.142371[/C][/ROW]
[ROW][C]217[/C][C]5.5[/C][C]4.02263[/C][C]1.47737[/C][/ROW]
[ROW][C]218[/C][C]8.5[/C][C]7.29398[/C][C]1.20602[/C][/ROW]
[ROW][C]219[/C][C]9.5[/C][C]8.18065[/C][C]1.31935[/C][/ROW]
[ROW][C]220[/C][C]7[/C][C]7.53524[/C][C]-0.535236[/C][/ROW]
[ROW][C]221[/C][C]8[/C][C]6.93804[/C][C]1.06196[/C][/ROW]
[ROW][C]222[/C][C]8.5[/C][C]9.23059[/C][C]-0.730592[/C][/ROW]
[ROW][C]223[/C][C]3.5[/C][C]3.56286[/C][C]-0.062858[/C][/ROW]
[ROW][C]224[/C][C]6.5[/C][C]7.23819[/C][C]-0.738187[/C][/ROW]
[ROW][C]225[/C][C]6.5[/C][C]3.65473[/C][C]2.84527[/C][/ROW]
[ROW][C]226[/C][C]10.5[/C][C]9.75146[/C][C]0.748536[/C][/ROW]
[ROW][C]227[/C][C]8.5[/C][C]8.35274[/C][C]0.147263[/C][/ROW]
[ROW][C]228[/C][C]8[/C][C]4.4856[/C][C]3.5144[/C][/ROW]
[ROW][C]229[/C][C]10[/C][C]8.41085[/C][C]1.58915[/C][/ROW]
[ROW][C]230[/C][C]10[/C][C]9.56599[/C][C]0.434014[/C][/ROW]
[ROW][C]231[/C][C]9.5[/C][C]8.86478[/C][C]0.635223[/C][/ROW]
[ROW][C]232[/C][C]9[/C][C]7.36774[/C][C]1.63226[/C][/ROW]
[ROW][C]233[/C][C]10[/C][C]8.19122[/C][C]1.80878[/C][/ROW]
[ROW][C]234[/C][C]7.5[/C][C]10.3182[/C][C]-2.81816[/C][/ROW]
[ROW][C]235[/C][C]4.5[/C][C]4.95778[/C][C]-0.457781[/C][/ROW]
[ROW][C]236[/C][C]4.5[/C][C]10.3088[/C][C]-5.80882[/C][/ROW]
[ROW][C]237[/C][C]0.5[/C][C]0.0928663[/C][C]0.407134[/C][/ROW]
[ROW][C]238[/C][C]6.5[/C][C]8.81928[/C][C]-2.31928[/C][/ROW]
[ROW][C]239[/C][C]4.5[/C][C]5.5771[/C][C]-1.0771[/C][/ROW]
[ROW][C]240[/C][C]5.5[/C][C]7.27885[/C][C]-1.77885[/C][/ROW]
[ROW][C]241[/C][C]5[/C][C]6.38354[/C][C]-1.38354[/C][/ROW]
[ROW][C]242[/C][C]6[/C][C]8.21656[/C][C]-2.21656[/C][/ROW]
[ROW][C]243[/C][C]4[/C][C]3.01883[/C][C]0.981173[/C][/ROW]
[ROW][C]244[/C][C]8[/C][C]6.92338[/C][C]1.07662[/C][/ROW]
[ROW][C]245[/C][C]10.5[/C][C]9.37899[/C][C]1.12101[/C][/ROW]
[ROW][C]246[/C][C]6.5[/C][C]4.31032[/C][C]2.18968[/C][/ROW]
[ROW][C]247[/C][C]8[/C][C]7.78534[/C][C]0.214662[/C][/ROW]
[ROW][C]248[/C][C]8.5[/C][C]11.4587[/C][C]-2.95866[/C][/ROW]
[ROW][C]249[/C][C]5.5[/C][C]6.35824[/C][C]-0.858238[/C][/ROW]
[ROW][C]250[/C][C]7[/C][C]8.89634[/C][C]-1.89634[/C][/ROW]
[ROW][C]251[/C][C]5[/C][C]8.53305[/C][C]-3.53305[/C][/ROW]
[ROW][C]252[/C][C]3.5[/C][C]5.84091[/C][C]-2.34091[/C][/ROW]
[ROW][C]253[/C][C]5[/C][C]2.6909[/C][C]2.3091[/C][/ROW]
[ROW][C]254[/C][C]9[/C][C]6.98513[/C][C]2.01487[/C][/ROW]
[ROW][C]255[/C][C]8.5[/C][C]10.5946[/C][C]-2.09461[/C][/ROW]
[ROW][C]256[/C][C]5[/C][C]3.25088[/C][C]1.74912[/C][/ROW]
[ROW][C]257[/C][C]9.5[/C][C]11.7014[/C][C]-2.20142[/C][/ROW]
[ROW][C]258[/C][C]3[/C][C]8.12696[/C][C]-5.12696[/C][/ROW]
[ROW][C]259[/C][C]1.5[/C][C]1.56666[/C][C]-0.0666566[/C][/ROW]
[ROW][C]260[/C][C]6[/C][C]11.2747[/C][C]-5.27466[/C][/ROW]
[ROW][C]261[/C][C]0.5[/C][C]-0.653581[/C][C]1.15358[/C][/ROW]
[ROW][C]262[/C][C]6.5[/C][C]5.33027[/C][C]1.16973[/C][/ROW]
[ROW][C]263[/C][C]7.5[/C][C]7.83382[/C][C]-0.333816[/C][/ROW]
[ROW][C]264[/C][C]4.5[/C][C]3.24088[/C][C]1.25912[/C][/ROW]
[ROW][C]265[/C][C]8[/C][C]6.53642[/C][C]1.46358[/C][/ROW]
[ROW][C]266[/C][C]9[/C][C]8.74451[/C][C]0.255487[/C][/ROW]
[ROW][C]267[/C][C]7.5[/C][C]5.78922[/C][C]1.71078[/C][/ROW]
[ROW][C]268[/C][C]8.5[/C][C]8.74027[/C][C]-0.240274[/C][/ROW]
[ROW][C]269[/C][C]7[/C][C]5.21484[/C][C]1.78516[/C][/ROW]
[ROW][C]270[/C][C]9.5[/C][C]9.0199[/C][C]0.480099[/C][/ROW]
[ROW][C]271[/C][C]6.5[/C][C]2.69614[/C][C]3.80386[/C][/ROW]
[ROW][C]272[/C][C]9.5[/C][C]9.8818[/C][C]-0.381798[/C][/ROW]
[ROW][C]273[/C][C]6[/C][C]3.55172[/C][C]2.44828[/C][/ROW]
[ROW][C]274[/C][C]8[/C][C]6.60784[/C][C]1.39216[/C][/ROW]
[ROW][C]275[/C][C]9.5[/C][C]8.71707[/C][C]0.782934[/C][/ROW]
[ROW][C]276[/C][C]8[/C][C]7.69409[/C][C]0.305909[/C][/ROW]
[ROW][C]277[/C][C]8[/C][C]6.81258[/C][C]1.18742[/C][/ROW]
[ROW][C]278[/C][C]9[/C][C]8.49228[/C][C]0.507717[/C][/ROW]
[ROW][C]279[/C][C]5[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=4

As an alternative you can also use a QR Code:

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

Multiple Linear Regression - Actuals, Interpolation, and Residuals
Time or Index	Actuals	InterpolationForecast	ResidualsPrediction Error
1	7.5	5.05246	2.44754
2	6	5.19996	0.800045
3	6.5	5.57053	0.929466
4	1	4.19171	-3.19171
5	1	4.25902	-3.25902
6	5.5	3.49991	2.00009
7	8.5	5.39528	3.10472
8	6.5	6.33879	0.161208
9	4.5	3.61	0.890002
10	2	5.2331	-3.2331
11	5	5.50837	-0.50837
12	0.5	3.23772	-2.73772
13	5	3.48452	1.51548
14	5	4.78709	0.212915
15	2.5	2.77734	-0.277338
16	5	5.23438	-0.234376
17	5.5	4.63372	0.86628
18	3.5	3.95142	-0.451418
19	3	5.11405	-2.11405
20	4	3.74341	0.25659
21	0.5	3.85852	-3.35852
22	6.5	4.30635	2.19365
23	4.5	3.78061	0.719388
24	7.5	3.99556	3.50444
25	5.5	4.85845	0.641553
26	4	2.97495	1.02505
27	7.5	6.48373	1.01627
28	7	6.26042	0.739577
29	4	4.65209	-0.652089
30	5.5	4.42232	1.07768
31	2.5	3.97422	-1.47422
32	5.5	4.30157	1.19843
33	3.5	4.04252	-0.542515
34	2.5	4.88614	-2.38614
35	4.5	5.39697	-0.896968
36	4.5	5.16406	-0.664059
37	4.5	4.51667	-0.0166715
38	6	4.32262	1.67738
39	2.5	5.09098	-2.59098
40	5	5.70452	-0.704516
41	0	5.58275	-5.58275
42	5	4.98339	0.0166071
43	6.5	4.689	1.811
44	5	4.43849	0.56151
45	6	5.65264	0.347362
46	4.5	5.95406	-1.45406
47	5.5	4.78692	0.713077
48	1	5.33177	-4.33177
49	7.5	3.82598	3.67402
50	6	6.1722	-0.172197
51	5	5.77644	-0.776435
52	1	5.18935	-4.18935
53	5	4.57696	0.423042
54	6.5	3.19637	3.30363
55	7	4.51029	2.48971
56	4.5	4.98159	-0.481589
57	0	4.68605	-4.68605
58	8.5	2.97501	5.52499
59	3.5	4.11254	-0.612545
60	7.5	5.43706	2.06294
61	3.5	4.53547	-1.03547
62	6	4.58525	1.41475
63	1.5	5.25258	-3.75258
64	9	5.3176	3.6824
65	3.5	3.35535	0.144652
66	3.5	5.5339	-2.0339
67	4	4.60066	-0.600658
68	6.5	4.66826	1.83174
69	7.5	4.57311	2.92689
70	6	4.58443	1.41557
71	5	5.51917	-0.519172
72	5.5	5.07694	0.423062
73	3.5	5.10905	-1.60905
74	7.5	6.69325	0.806746
75	6.5	4.12104	2.37896
76	NA	NA	1.155
77	6.5	5.78281	0.717192
78	6.5	4.54724	1.95276
79	7	9.19179	-2.19179
80	3.5	6.72379	-3.22379
81	1.5	3.46058	-1.96058
82	4	0.75994	3.24006
83	7.5	8.71386	-1.21386
84	4.5	7.51299	-3.01299
85	0	2.17407	-2.17407
86	3.5	3.26033	0.239672
87	5.5	5.6581	-0.158099
88	5	7.19487	-2.19487
89	4.5	7.7725	-3.2725
90	2.5	-1.70656	4.20656
91	7.5	5.8337	1.6663
92	7	10.7014	-3.70141
93	0	-1.29226	1.29226
94	4.5	6.06075	-1.56075
95	3	6.5088	-3.5088
96	1.5	1.81939	-0.319394
97	3.5	4.9953	-1.4953
98	2.5	2.38777	0.112228
99	5.5	3.03836	2.46164
100	8	12.5386	-4.53858
101	1	1.2459	-0.2459
102	5	5.6433	-0.643304
103	4.5	5.284	-0.783999
104	3	4.3344	-1.3344
105	3	0.262672	2.73733
106	8	9.80942	-1.80942
107	2.5	0.402147	2.09785
108	7	11.6894	-4.68945
109	0	4.46369	-4.46369
110	1	2.60128	-1.60128
111	3.5	3.24307	0.256929
112	5.5	4.41625	1.08375
113	5.5	10.0805	-4.58049
114	0.5	-0.0409008	0.540901
115	7.5	6.23713	1.26287
116	9	7.6588	1.3412
117	9.5	9.71179	-0.211792
118	8.5	6.38147	2.11853
119	7	8.00991	-1.00991
120	8	6.7308	1.2692
121	10	11.1683	-1.16832
122	7	3.68593	3.31407
123	8.5	8.5047	-0.00470051
124	9	5.73546	3.26454
125	9.5	11.6003	-2.1003
126	4	3.09123	0.908774
127	6	5.03155	0.96845
128	8	9.09114	-1.09114
129	5.5	4.53859	0.961414
130	9.5	8.92713	0.572868
131	7.5	8.28786	-0.787862
132	7	7.66057	-0.660571
133	7.5	5.79959	1.70041
134	8	8.18223	-0.182228
135	7	6.41663	0.583372
136	7	7.62983	-0.629835
137	6	3.14713	2.85287
138	10	14.6722	-4.67218
139	2.5	2.52746	-0.0274555
140	9	8.95597	0.0440272
141	8	7.78977	0.210227
142	6	5.39962	0.600377
143	8.5	9.28737	-0.787369
144	6	4.64972	1.35028
145	9	8.2742	0.725797
146	8	8.07243	-0.0724283
147	9	10.6599	-1.65988
148	5.5	5.58374	-0.0837356
149	7	9.37657	-2.37657
150	5.5	5.46481	0.0351864
151	9	14.0937	-5.09372
152	2	2.00349	-0.00348552
153	8.5	8.39364	0.106361
154	9	8.21795	0.782052
155	8.5	6.40099	2.09901
156	9	7.90655	1.09345
157	7.5	6.81911	0.680892
158	10	7.72633	2.27367
159	9	8.9594	0.0405955
160	7.5	8.11911	-0.619107
161	6	4.28207	1.71793
162	10.5	9.47892	1.02108
163	8.5	9.85744	-1.35744
164	8	5.48702	2.51298
165	10	9.54398	0.456018
166	10.5	8.64255	1.85745
167	6.5	5.84305	0.656948
168	9.5	8.45758	1.04242
169	8.5	9.08902	-0.589016
170	7.5	8.56074	-1.06074
171	5	4.81479	0.185206
172	8	6.09305	1.90695
173	10	10.954	-0.954029
174	7	6.59747	0.402532
175	7.5	7.03564	0.464356
176	7.5	4.92821	2.57179
177	9.5	10.7484	-1.24839
178	6	4.082	1.918
179	10	10.6095	-0.609458
180	7	9.0385	-2.0385
181	3	5.17147	-2.17147
182	6	6.33817	-0.338174
183	7	5.85469	1.14531
184	10	10.8512	-0.851234
185	7	11.7562	-4.75625
186	3.5	3.72267	-0.222667
187	8	5.68219	2.31781
188	10	12.6172	-2.61718
189	5.5	6.30976	-0.80976
190	6	5.6628	0.337198
191	6.5	5.52218	0.977818
192	6.5	3.85174	2.64826
193	8.5	10.2575	-1.75754
194	4	2.22728	1.77272
195	9.5	8.01449	1.48551
196	8	7.18437	0.815634
197	8.5	11.7293	-3.22925
198	5.5	4.61628	0.88372
199	7	4.0166	2.9834
200	9	8.69584	0.304163
201	8	6.8265	1.1735
202	10	8.41603	1.58397
203	8	8.47942	-0.479416
204	6	6.18886	-0.188863
205	8	9.69807	-1.69807
206	5	2.7424	2.2576
207	9	10.055	-1.05498
208	4.5	1.31495	3.18505
209	8.5	6.38964	2.11036
210	9.5	8.07658	1.42342
211	8.5	6.72175	1.77825
212	7.5	7.10249	0.397507
213	7.5	8.68841	-1.18841
214	5	3.83457	1.16543
215	7	6.85024	0.14976
216	8	8.14237	-0.142371
217	5.5	4.02263	1.47737
218	8.5	7.29398	1.20602
219	9.5	8.18065	1.31935
220	7	7.53524	-0.535236
221	8	6.93804	1.06196
222	8.5	9.23059	-0.730592
223	3.5	3.56286	-0.062858
224	6.5	7.23819	-0.738187
225	6.5	3.65473	2.84527
226	10.5	9.75146	0.748536
227	8.5	8.35274	0.147263
228	8	4.4856	3.5144
229	10	8.41085	1.58915
230	10	9.56599	0.434014
231	9.5	8.86478	0.635223
232	9	7.36774	1.63226
233	10	8.19122	1.80878
234	7.5	10.3182	-2.81816
235	4.5	4.95778	-0.457781
236	4.5	10.3088	-5.80882
237	0.5	0.0928663	0.407134
238	6.5	8.81928	-2.31928
239	4.5	5.5771	-1.0771
240	5.5	7.27885	-1.77885
241	5	6.38354	-1.38354
242	6	8.21656	-2.21656
243	4	3.01883	0.981173
244	8	6.92338	1.07662
245	10.5	9.37899	1.12101
246	6.5	4.31032	2.18968
247	8	7.78534	0.214662
248	8.5	11.4587	-2.95866
249	5.5	6.35824	-0.858238
250	7	8.89634	-1.89634
251	5	8.53305	-3.53305
252	3.5	5.84091	-2.34091
253	5	2.6909	2.3091
254	9	6.98513	2.01487
255	8.5	10.5946	-2.09461
256	5	3.25088	1.74912
257	9.5	11.7014	-2.20142
258	3	8.12696	-5.12696
259	1.5	1.56666	-0.0666566
260	6	11.2747	-5.27466
261	0.5	-0.653581	1.15358
262	6.5	5.33027	1.16973
263	7.5	7.83382	-0.333816
264	4.5	3.24088	1.25912
265	8	6.53642	1.46358
266	9	8.74451	0.255487
267	7.5	5.78922	1.71078
268	8.5	8.74027	-0.240274
269	7	5.21484	1.78516
270	9.5	9.0199	0.480099
271	6.5	2.69614	3.80386
272	9.5	9.8818	-0.381798
273	6	3.55172	2.44828
274	8	6.60784	1.39216
275	9.5	8.71707	0.782934
276	8	7.69409	0.305909
277	8	6.81258	1.18742
278	9	8.49228	0.507717
279	5	NA	NA

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.598125	0.803749	0.401875
22	0.448339	0.896677	0.551661
23	0.487271	0.974543	0.512729
24	0.484063	0.968127	0.515937
25	0.371169	0.742338	0.628831
26	0.284144	0.568288	0.715856
27	0.202558	0.405116	0.797442
28	0.146159	0.292317	0.853841
29	0.118322	0.236644	0.881678
30	0.0776463	0.155293	0.922354
31	0.0747745	0.149549	0.925226
32	0.0482896	0.0965791	0.95171
33	0.0305149	0.0610299	0.969485
34	0.0642329	0.128466	0.935767
35	0.0428276	0.0856552	0.957172
36	0.0335551	0.0671103	0.966445
37	0.0438371	0.0876743	0.956163
38	0.0541577	0.108315	0.945842
39	0.0857067	0.171413	0.914293
40	0.069823	0.139646	0.930177
41	0.0850696	0.170139	0.91493
42	0.0687594	0.137519	0.931241
43	0.0715973	0.143195	0.928403
44	0.06075	0.1215	0.93925
45	0.0531751	0.10635	0.946825
46	0.0575319	0.115064	0.942468
47	0.046838	0.0936759	0.953162
48	0.100107	0.200213	0.899893
49	0.245423	0.490845	0.754577
50	0.234876	0.469753	0.765124
51	0.199248	0.398495	0.800752
52	0.239917	0.479835	0.760083
53	0.220789	0.441578	0.779211
54	0.222628	0.445257	0.777372
55	0.483288	0.966577	0.516712
56	0.434587	0.869175	0.565413
57	0.74737	0.505259	0.25263
58	0.841067	0.317866	0.158933
59	0.811757	0.376485	0.188243
60	0.831261	0.337479	0.168739
61	0.808012	0.383975	0.191988
62	0.783766	0.432469	0.216234
63	0.863544	0.272911	0.136456
64	0.8846	0.2308	0.1154
65	0.870905	0.258189	0.129095
66	0.858226	0.283549	0.141774
67	0.83327	0.33346	0.16673
68	0.816095	0.36781	0.183905
69	0.891683	0.216634	0.108317
70	0.881402	0.237196	0.118598
71	0.862936	0.274128	0.137064
72	0.844338	0.311325	0.155662
73	0.824868	0.350265	0.175132
74	0.844469	0.311063	0.155531
75	0.8434	0.313201	0.1566
76	0.842427	0.315145	0.157573
77	0.820092	0.359815	0.179908
78	0.837709	0.324582	0.162291
79	0.830437	0.339126	0.169563
80	0.837082	0.325835	0.162918
81	0.817748	0.364503	0.182252
82	0.860718	0.278564	0.139282
83	0.838704	0.322591	0.161296
84	0.829173	0.341654	0.170827
85	0.821524	0.356952	0.178476
86	0.796779	0.406443	0.203221
87	0.772886	0.454227	0.227114
88	0.755101	0.489798	0.244899
89	0.764136	0.471729	0.235864
90	0.850868	0.298264	0.149132
91	0.87263	0.254741	0.12737
92	0.915331	0.169339	0.0846694
93	0.911142	0.177716	0.0888579
94	0.900002	0.199996	0.0999981
95	0.920326	0.159348	0.0796738
96	0.911361	0.177277	0.0886387
97	0.898374	0.203251	0.101626
98	0.889923	0.220154	0.110077
99	0.914215	0.171571	0.0857854
100	0.943582	0.112835	0.0564177
101	0.933509	0.132983	0.0664914
102	0.921122	0.157756	0.0788778
103	0.912425	0.17515	0.087575
104	0.906915	0.18617	0.0930849
105	0.926771	0.146457	0.0732286
106	0.918796	0.162408	0.081204
107	0.940928	0.118145	0.0590724
108	0.961712	0.076575	0.0382875
109	0.981187	0.0376253	0.0188126
110	0.978737	0.0425268	0.0212634
111	0.973577	0.052845	0.0264225
112	0.971815	0.0563704	0.0281852
113	0.979207	0.0415853	0.0207927
114	0.981307	0.0373861	0.018693
115	0.97984	0.0403206	0.0201603
116	0.983344	0.0333122	0.0166561
117	0.980327	0.0393464	0.0196732
118	0.98434	0.031319	0.0156595
119	0.982642	0.0347151	0.0173576
120	0.979713	0.0405738	0.0202869
121	0.979401	0.0411986	0.0205993
122	0.992459	0.0150817	0.00754084
123	0.990417	0.0191651	0.00958254
124	0.993014	0.0139724	0.00698619
125	0.992835	0.0143306	0.00716532
126	0.992419	0.0151623	0.00758114
127	0.99082	0.018359	0.0091795
128	0.991282	0.0174366	0.00871828
129	0.989329	0.0213413	0.0106706
130	0.988278	0.0234443	0.0117222
131	0.986309	0.0273826	0.0136913
132	0.986132	0.0277367	0.0138684
133	0.987144	0.0257126	0.0128563
134	0.984557	0.0308852	0.0154426
135	0.981129	0.0377414	0.0188707
136	0.976733	0.0465339	0.023267
137	0.981618	0.0367634	0.0183817
138	0.994359	0.0112825	0.00564124
139	0.992773	0.0144546	0.00722728
140	0.990886	0.0182284	0.00911421
141	0.988935	0.0221292	0.0110646
142	0.986456	0.0270874	0.0135437
143	0.98387	0.0322603	0.0161302
144	0.982342	0.0353161	0.0176581
145	0.979763	0.0404741	0.0202371
146	0.975402	0.0491961	0.024598
147	0.973926	0.0521488	0.0260744
148	0.968582	0.0628353	0.0314176
149	0.97117	0.0576606	0.0288303
150	0.965062	0.0698758	0.0349379
151	0.985512	0.0289761	0.0144881
152	0.981987	0.0360265	0.0180132
153	0.977495	0.0450106	0.0225053
154	0.974012	0.0519769	0.0259885
155	0.976553	0.0468932	0.0234466
156	0.972934	0.0541313	0.0270656
157	0.969418	0.0611636	0.0305818
158	0.973207	0.0535852	0.0267926
159	0.967695	0.064611	0.0323055
160	0.962247	0.0755064	0.0377532
161	0.959798	0.0804036	0.0402018
162	0.952646	0.0947083	0.0473541
163	0.948671	0.102658	0.051329
164	0.952168	0.0956636	0.0478318
165	0.945457	0.109086	0.0545428
166	0.942561	0.114879	0.0574393
167	0.932146	0.135708	0.067854
168	0.923756	0.152487	0.0762435
169	0.916201	0.167597	0.0837987
170	0.909265	0.181469	0.0907347
171	0.895542	0.208916	0.104458
172	0.885028	0.229944	0.114972
173	0.874225	0.25155	0.125775
174	0.853972	0.292056	0.146028
175	0.832104	0.335793	0.167896
176	0.835453	0.329094	0.164547
177	0.819189	0.361622	0.180811
178	0.83771	0.324579	0.16229
179	0.815198	0.369605	0.184802
180	0.814725	0.37055	0.185275
181	0.826277	0.347446	0.173723
182	0.800931	0.398139	0.199069
183	0.790946	0.418108	0.209054
184	0.766809	0.466382	0.233191
185	0.904543	0.190915	0.0954573
186	0.891293	0.217414	0.108707
187	0.888522	0.222957	0.111478
188	0.91891	0.162181	0.0810903
189	0.919242	0.161516	0.0807579
190	0.928732	0.142536	0.0712682
191	0.920974	0.158051	0.0790257
192	0.919618	0.160764	0.080382
193	0.933358	0.133285	0.0666423
194	0.923901	0.152197	0.0760986
195	0.914324	0.171351	0.0856757
196	0.900904	0.198191	0.0990956
197	0.907372	0.185255	0.0926277
198	0.889951	0.220097	0.110049
199	0.910424	0.179153	0.0895764
200	0.895999	0.208002	0.104001
201	0.897062	0.205877	0.102938
202	0.898877	0.202247	0.101123
203	0.879909	0.240183	0.120091
204	0.869942	0.260115	0.130058
205	0.85684	0.28632	0.14316
206	0.840236	0.319528	0.159764
207	0.81642	0.367161	0.18358
208	0.874956	0.250088	0.125044
209	0.874558	0.250884	0.125442
210	0.852002	0.295996	0.147998
211	0.836694	0.326612	0.163306
212	0.819731	0.360539	0.180269
213	0.824569	0.350862	0.175431
214	0.801614	0.396772	0.198386
215	0.76825	0.463499	0.23175
216	0.762077	0.475846	0.237923
217	0.727918	0.544164	0.272082
218	0.707495	0.58501	0.292505
219	0.708136	0.583727	0.291864
220	0.690138	0.619723	0.309862
221	0.679872	0.640256	0.320128
222	0.642474	0.715052	0.357526
223	0.63599	0.72802	0.36401
224	0.590321	0.819357	0.409679
225	0.603925	0.792149	0.396075
226	0.634586	0.730828	0.365414
227	0.588686	0.822629	0.411314
228	0.587478	0.825043	0.412522
229	0.700268	0.599464	0.299732
230	0.693206	0.613588	0.306794
231	0.660305	0.67939	0.339695
232	0.644528	0.710945	0.355472
233	0.644741	0.710517	0.355259
234	0.603119	0.793762	0.396881
235	0.545872	0.908256	0.454128
236	0.812793	0.374414	0.187207
237	0.789156	0.421688	0.210844
238	0.758069	0.483861	0.241931
239	0.822585	0.354831	0.177415
240	0.79653	0.40694	0.20347
241	0.748127	0.503746	0.251873
242	0.763405	0.47319	0.236595
243	0.712938	0.574124	0.287062
244	0.732098	0.535805	0.267902
245	0.712337	0.575325	0.287663
246	0.770447	0.459106	0.229553
247	0.702182	0.595637	0.297818
248	0.665356	0.669287	0.334644
249	0.645499	0.709002	0.354501
250	0.560835	0.87833	0.439165
251	0.494314	0.988628	0.505686
252	0.414674	0.829347	0.585326
253	0.318807	0.637613	0.681193
254	0.235178	0.470356	0.764822
255	0.298103	0.596205	0.701897
256	0.267693	0.535386	0.732307
257	0.309276	0.618552	0.690724
258	0.475271	0.950541	0.524729

\begin{tabular}{lllllllll}
\hline
Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
p-values & Alternative Hypothesis \tabularnewline
breakpoint index & greater & 2-sided & less \tabularnewline
21 & 0.598125 & 0.803749 & 0.401875 \tabularnewline
22 & 0.448339 & 0.896677 & 0.551661 \tabularnewline
23 & 0.487271 & 0.974543 & 0.512729 \tabularnewline
24 & 0.484063 & 0.968127 & 0.515937 \tabularnewline
25 & 0.371169 & 0.742338 & 0.628831 \tabularnewline
26 & 0.284144 & 0.568288 & 0.715856 \tabularnewline
27 & 0.202558 & 0.405116 & 0.797442 \tabularnewline
28 & 0.146159 & 0.292317 & 0.853841 \tabularnewline
29 & 0.118322 & 0.236644 & 0.881678 \tabularnewline
30 & 0.0776463 & 0.155293 & 0.922354 \tabularnewline
31 & 0.0747745 & 0.149549 & 0.925226 \tabularnewline
32 & 0.0482896 & 0.0965791 & 0.95171 \tabularnewline
33 & 0.0305149 & 0.0610299 & 0.969485 \tabularnewline
34 & 0.0642329 & 0.128466 & 0.935767 \tabularnewline
35 & 0.0428276 & 0.0856552 & 0.957172 \tabularnewline
36 & 0.0335551 & 0.0671103 & 0.966445 \tabularnewline
37 & 0.0438371 & 0.0876743 & 0.956163 \tabularnewline
38 & 0.0541577 & 0.108315 & 0.945842 \tabularnewline
39 & 0.0857067 & 0.171413 & 0.914293 \tabularnewline
40 & 0.069823 & 0.139646 & 0.930177 \tabularnewline
41 & 0.0850696 & 0.170139 & 0.91493 \tabularnewline
42 & 0.0687594 & 0.137519 & 0.931241 \tabularnewline
43 & 0.0715973 & 0.143195 & 0.928403 \tabularnewline
44 & 0.06075 & 0.1215 & 0.93925 \tabularnewline
45 & 0.0531751 & 0.10635 & 0.946825 \tabularnewline
46 & 0.0575319 & 0.115064 & 0.942468 \tabularnewline
47 & 0.046838 & 0.0936759 & 0.953162 \tabularnewline
48 & 0.100107 & 0.200213 & 0.899893 \tabularnewline
49 & 0.245423 & 0.490845 & 0.754577 \tabularnewline
50 & 0.234876 & 0.469753 & 0.765124 \tabularnewline
51 & 0.199248 & 0.398495 & 0.800752 \tabularnewline
52 & 0.239917 & 0.479835 & 0.760083 \tabularnewline
53 & 0.220789 & 0.441578 & 0.779211 \tabularnewline
54 & 0.222628 & 0.445257 & 0.777372 \tabularnewline
55 & 0.483288 & 0.966577 & 0.516712 \tabularnewline
56 & 0.434587 & 0.869175 & 0.565413 \tabularnewline
57 & 0.74737 & 0.505259 & 0.25263 \tabularnewline
58 & 0.841067 & 0.317866 & 0.158933 \tabularnewline
59 & 0.811757 & 0.376485 & 0.188243 \tabularnewline
60 & 0.831261 & 0.337479 & 0.168739 \tabularnewline
61 & 0.808012 & 0.383975 & 0.191988 \tabularnewline
62 & 0.783766 & 0.432469 & 0.216234 \tabularnewline
63 & 0.863544 & 0.272911 & 0.136456 \tabularnewline
64 & 0.8846 & 0.2308 & 0.1154 \tabularnewline
65 & 0.870905 & 0.258189 & 0.129095 \tabularnewline
66 & 0.858226 & 0.283549 & 0.141774 \tabularnewline
67 & 0.83327 & 0.33346 & 0.16673 \tabularnewline
68 & 0.816095 & 0.36781 & 0.183905 \tabularnewline
69 & 0.891683 & 0.216634 & 0.108317 \tabularnewline
70 & 0.881402 & 0.237196 & 0.118598 \tabularnewline
71 & 0.862936 & 0.274128 & 0.137064 \tabularnewline
72 & 0.844338 & 0.311325 & 0.155662 \tabularnewline
73 & 0.824868 & 0.350265 & 0.175132 \tabularnewline
74 & 0.844469 & 0.311063 & 0.155531 \tabularnewline
75 & 0.8434 & 0.313201 & 0.1566 \tabularnewline
76 & 0.842427 & 0.315145 & 0.157573 \tabularnewline
77 & 0.820092 & 0.359815 & 0.179908 \tabularnewline
78 & 0.837709 & 0.324582 & 0.162291 \tabularnewline
79 & 0.830437 & 0.339126 & 0.169563 \tabularnewline
80 & 0.837082 & 0.325835 & 0.162918 \tabularnewline
81 & 0.817748 & 0.364503 & 0.182252 \tabularnewline
82 & 0.860718 & 0.278564 & 0.139282 \tabularnewline
83 & 0.838704 & 0.322591 & 0.161296 \tabularnewline
84 & 0.829173 & 0.341654 & 0.170827 \tabularnewline
85 & 0.821524 & 0.356952 & 0.178476 \tabularnewline
86 & 0.796779 & 0.406443 & 0.203221 \tabularnewline
87 & 0.772886 & 0.454227 & 0.227114 \tabularnewline
88 & 0.755101 & 0.489798 & 0.244899 \tabularnewline
89 & 0.764136 & 0.471729 & 0.235864 \tabularnewline
90 & 0.850868 & 0.298264 & 0.149132 \tabularnewline
91 & 0.87263 & 0.254741 & 0.12737 \tabularnewline
92 & 0.915331 & 0.169339 & 0.0846694 \tabularnewline
93 & 0.911142 & 0.177716 & 0.0888579 \tabularnewline
94 & 0.900002 & 0.199996 & 0.0999981 \tabularnewline
95 & 0.920326 & 0.159348 & 0.0796738 \tabularnewline
96 & 0.911361 & 0.177277 & 0.0886387 \tabularnewline
97 & 0.898374 & 0.203251 & 0.101626 \tabularnewline
98 & 0.889923 & 0.220154 & 0.110077 \tabularnewline
99 & 0.914215 & 0.171571 & 0.0857854 \tabularnewline
100 & 0.943582 & 0.112835 & 0.0564177 \tabularnewline
101 & 0.933509 & 0.132983 & 0.0664914 \tabularnewline
102 & 0.921122 & 0.157756 & 0.0788778 \tabularnewline
103 & 0.912425 & 0.17515 & 0.087575 \tabularnewline
104 & 0.906915 & 0.18617 & 0.0930849 \tabularnewline
105 & 0.926771 & 0.146457 & 0.0732286 \tabularnewline
106 & 0.918796 & 0.162408 & 0.081204 \tabularnewline
107 & 0.940928 & 0.118145 & 0.0590724 \tabularnewline
108 & 0.961712 & 0.076575 & 0.0382875 \tabularnewline
109 & 0.981187 & 0.0376253 & 0.0188126 \tabularnewline
110 & 0.978737 & 0.0425268 & 0.0212634 \tabularnewline
111 & 0.973577 & 0.052845 & 0.0264225 \tabularnewline
112 & 0.971815 & 0.0563704 & 0.0281852 \tabularnewline
113 & 0.979207 & 0.0415853 & 0.0207927 \tabularnewline
114 & 0.981307 & 0.0373861 & 0.018693 \tabularnewline
115 & 0.97984 & 0.0403206 & 0.0201603 \tabularnewline
116 & 0.983344 & 0.0333122 & 0.0166561 \tabularnewline
117 & 0.980327 & 0.0393464 & 0.0196732 \tabularnewline
118 & 0.98434 & 0.031319 & 0.0156595 \tabularnewline
119 & 0.982642 & 0.0347151 & 0.0173576 \tabularnewline
120 & 0.979713 & 0.0405738 & 0.0202869 \tabularnewline
121 & 0.979401 & 0.0411986 & 0.0205993 \tabularnewline
122 & 0.992459 & 0.0150817 & 0.00754084 \tabularnewline
123 & 0.990417 & 0.0191651 & 0.00958254 \tabularnewline
124 & 0.993014 & 0.0139724 & 0.00698619 \tabularnewline
125 & 0.992835 & 0.0143306 & 0.00716532 \tabularnewline
126 & 0.992419 & 0.0151623 & 0.00758114 \tabularnewline
127 & 0.99082 & 0.018359 & 0.0091795 \tabularnewline
128 & 0.991282 & 0.0174366 & 0.00871828 \tabularnewline
129 & 0.989329 & 0.0213413 & 0.0106706 \tabularnewline
130 & 0.988278 & 0.0234443 & 0.0117222 \tabularnewline
131 & 0.986309 & 0.0273826 & 0.0136913 \tabularnewline
132 & 0.986132 & 0.0277367 & 0.0138684 \tabularnewline
133 & 0.987144 & 0.0257126 & 0.0128563 \tabularnewline
134 & 0.984557 & 0.0308852 & 0.0154426 \tabularnewline
135 & 0.981129 & 0.0377414 & 0.0188707 \tabularnewline
136 & 0.976733 & 0.0465339 & 0.023267 \tabularnewline
137 & 0.981618 & 0.0367634 & 0.0183817 \tabularnewline
138 & 0.994359 & 0.0112825 & 0.00564124 \tabularnewline
139 & 0.992773 & 0.0144546 & 0.00722728 \tabularnewline
140 & 0.990886 & 0.0182284 & 0.00911421 \tabularnewline
141 & 0.988935 & 0.0221292 & 0.0110646 \tabularnewline
142 & 0.986456 & 0.0270874 & 0.0135437 \tabularnewline
143 & 0.98387 & 0.0322603 & 0.0161302 \tabularnewline
144 & 0.982342 & 0.0353161 & 0.0176581 \tabularnewline
145 & 0.979763 & 0.0404741 & 0.0202371 \tabularnewline
146 & 0.975402 & 0.0491961 & 0.024598 \tabularnewline
147 & 0.973926 & 0.0521488 & 0.0260744 \tabularnewline
148 & 0.968582 & 0.0628353 & 0.0314176 \tabularnewline
149 & 0.97117 & 0.0576606 & 0.0288303 \tabularnewline
150 & 0.965062 & 0.0698758 & 0.0349379 \tabularnewline
151 & 0.985512 & 0.0289761 & 0.0144881 \tabularnewline
152 & 0.981987 & 0.0360265 & 0.0180132 \tabularnewline
153 & 0.977495 & 0.0450106 & 0.0225053 \tabularnewline
154 & 0.974012 & 0.0519769 & 0.0259885 \tabularnewline
155 & 0.976553 & 0.0468932 & 0.0234466 \tabularnewline
156 & 0.972934 & 0.0541313 & 0.0270656 \tabularnewline
157 & 0.969418 & 0.0611636 & 0.0305818 \tabularnewline
158 & 0.973207 & 0.0535852 & 0.0267926 \tabularnewline
159 & 0.967695 & 0.064611 & 0.0323055 \tabularnewline
160 & 0.962247 & 0.0755064 & 0.0377532 \tabularnewline
161 & 0.959798 & 0.0804036 & 0.0402018 \tabularnewline
162 & 0.952646 & 0.0947083 & 0.0473541 \tabularnewline
163 & 0.948671 & 0.102658 & 0.051329 \tabularnewline
164 & 0.952168 & 0.0956636 & 0.0478318 \tabularnewline
165 & 0.945457 & 0.109086 & 0.0545428 \tabularnewline
166 & 0.942561 & 0.114879 & 0.0574393 \tabularnewline
167 & 0.932146 & 0.135708 & 0.067854 \tabularnewline
168 & 0.923756 & 0.152487 & 0.0762435 \tabularnewline
169 & 0.916201 & 0.167597 & 0.0837987 \tabularnewline
170 & 0.909265 & 0.181469 & 0.0907347 \tabularnewline
171 & 0.895542 & 0.208916 & 0.104458 \tabularnewline
172 & 0.885028 & 0.229944 & 0.114972 \tabularnewline
173 & 0.874225 & 0.25155 & 0.125775 \tabularnewline
174 & 0.853972 & 0.292056 & 0.146028 \tabularnewline
175 & 0.832104 & 0.335793 & 0.167896 \tabularnewline
176 & 0.835453 & 0.329094 & 0.164547 \tabularnewline
177 & 0.819189 & 0.361622 & 0.180811 \tabularnewline
178 & 0.83771 & 0.324579 & 0.16229 \tabularnewline
179 & 0.815198 & 0.369605 & 0.184802 \tabularnewline
180 & 0.814725 & 0.37055 & 0.185275 \tabularnewline
181 & 0.826277 & 0.347446 & 0.173723 \tabularnewline
182 & 0.800931 & 0.398139 & 0.199069 \tabularnewline
183 & 0.790946 & 0.418108 & 0.209054 \tabularnewline
184 & 0.766809 & 0.466382 & 0.233191 \tabularnewline
185 & 0.904543 & 0.190915 & 0.0954573 \tabularnewline
186 & 0.891293 & 0.217414 & 0.108707 \tabularnewline
187 & 0.888522 & 0.222957 & 0.111478 \tabularnewline
188 & 0.91891 & 0.162181 & 0.0810903 \tabularnewline
189 & 0.919242 & 0.161516 & 0.0807579 \tabularnewline
190 & 0.928732 & 0.142536 & 0.0712682 \tabularnewline
191 & 0.920974 & 0.158051 & 0.0790257 \tabularnewline
192 & 0.919618 & 0.160764 & 0.080382 \tabularnewline
193 & 0.933358 & 0.133285 & 0.0666423 \tabularnewline
194 & 0.923901 & 0.152197 & 0.0760986 \tabularnewline
195 & 0.914324 & 0.171351 & 0.0856757 \tabularnewline
196 & 0.900904 & 0.198191 & 0.0990956 \tabularnewline
197 & 0.907372 & 0.185255 & 0.0926277 \tabularnewline
198 & 0.889951 & 0.220097 & 0.110049 \tabularnewline
199 & 0.910424 & 0.179153 & 0.0895764 \tabularnewline
200 & 0.895999 & 0.208002 & 0.104001 \tabularnewline
201 & 0.897062 & 0.205877 & 0.102938 \tabularnewline
202 & 0.898877 & 0.202247 & 0.101123 \tabularnewline
203 & 0.879909 & 0.240183 & 0.120091 \tabularnewline
204 & 0.869942 & 0.260115 & 0.130058 \tabularnewline
205 & 0.85684 & 0.28632 & 0.14316 \tabularnewline
206 & 0.840236 & 0.319528 & 0.159764 \tabularnewline
207 & 0.81642 & 0.367161 & 0.18358 \tabularnewline
208 & 0.874956 & 0.250088 & 0.125044 \tabularnewline
209 & 0.874558 & 0.250884 & 0.125442 \tabularnewline
210 & 0.852002 & 0.295996 & 0.147998 \tabularnewline
211 & 0.836694 & 0.326612 & 0.163306 \tabularnewline
212 & 0.819731 & 0.360539 & 0.180269 \tabularnewline
213 & 0.824569 & 0.350862 & 0.175431 \tabularnewline
214 & 0.801614 & 0.396772 & 0.198386 \tabularnewline
215 & 0.76825 & 0.463499 & 0.23175 \tabularnewline
216 & 0.762077 & 0.475846 & 0.237923 \tabularnewline
217 & 0.727918 & 0.544164 & 0.272082 \tabularnewline
218 & 0.707495 & 0.58501 & 0.292505 \tabularnewline
219 & 0.708136 & 0.583727 & 0.291864 \tabularnewline
220 & 0.690138 & 0.619723 & 0.309862 \tabularnewline
221 & 0.679872 & 0.640256 & 0.320128 \tabularnewline
222 & 0.642474 & 0.715052 & 0.357526 \tabularnewline
223 & 0.63599 & 0.72802 & 0.36401 \tabularnewline
224 & 0.590321 & 0.819357 & 0.409679 \tabularnewline
225 & 0.603925 & 0.792149 & 0.396075 \tabularnewline
226 & 0.634586 & 0.730828 & 0.365414 \tabularnewline
227 & 0.588686 & 0.822629 & 0.411314 \tabularnewline
228 & 0.587478 & 0.825043 & 0.412522 \tabularnewline
229 & 0.700268 & 0.599464 & 0.299732 \tabularnewline
230 & 0.693206 & 0.613588 & 0.306794 \tabularnewline
231 & 0.660305 & 0.67939 & 0.339695 \tabularnewline
232 & 0.644528 & 0.710945 & 0.355472 \tabularnewline
233 & 0.644741 & 0.710517 & 0.355259 \tabularnewline
234 & 0.603119 & 0.793762 & 0.396881 \tabularnewline
235 & 0.545872 & 0.908256 & 0.454128 \tabularnewline
236 & 0.812793 & 0.374414 & 0.187207 \tabularnewline
237 & 0.789156 & 0.421688 & 0.210844 \tabularnewline
238 & 0.758069 & 0.483861 & 0.241931 \tabularnewline
239 & 0.822585 & 0.354831 & 0.177415 \tabularnewline
240 & 0.79653 & 0.40694 & 0.20347 \tabularnewline
241 & 0.748127 & 0.503746 & 0.251873 \tabularnewline
242 & 0.763405 & 0.47319 & 0.236595 \tabularnewline
243 & 0.712938 & 0.574124 & 0.287062 \tabularnewline
244 & 0.732098 & 0.535805 & 0.267902 \tabularnewline
245 & 0.712337 & 0.575325 & 0.287663 \tabularnewline
246 & 0.770447 & 0.459106 & 0.229553 \tabularnewline
247 & 0.702182 & 0.595637 & 0.297818 \tabularnewline
248 & 0.665356 & 0.669287 & 0.334644 \tabularnewline
249 & 0.645499 & 0.709002 & 0.354501 \tabularnewline
250 & 0.560835 & 0.87833 & 0.439165 \tabularnewline
251 & 0.494314 & 0.988628 & 0.505686 \tabularnewline
252 & 0.414674 & 0.829347 & 0.585326 \tabularnewline
253 & 0.318807 & 0.637613 & 0.681193 \tabularnewline
254 & 0.235178 & 0.470356 & 0.764822 \tabularnewline
255 & 0.298103 & 0.596205 & 0.701897 \tabularnewline
256 & 0.267693 & 0.535386 & 0.732307 \tabularnewline
257 & 0.309276 & 0.618552 & 0.690724 \tabularnewline
258 & 0.475271 & 0.950541 & 0.524729 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&T=5

[TABLE]
[ROW][C]Goldfeld-Quandt test for Heteroskedasticity[/C][/ROW]
[ROW][C]p-values[/C][C]Alternative Hypothesis[/C][/ROW]
[ROW][C]breakpoint index[/C][C]greater[/C][C]2-sided[/C][C]less[/C][/ROW]
[ROW][C]21[/C][C]0.598125[/C][C]0.803749[/C][C]0.401875[/C][/ROW]
[ROW][C]22[/C][C]0.448339[/C][C]0.896677[/C][C]0.551661[/C][/ROW]
[ROW][C]23[/C][C]0.487271[/C][C]0.974543[/C][C]0.512729[/C][/ROW]
[ROW][C]24[/C][C]0.484063[/C][C]0.968127[/C][C]0.515937[/C][/ROW]
[ROW][C]25[/C][C]0.371169[/C][C]0.742338[/C][C]0.628831[/C][/ROW]
[ROW][C]26[/C][C]0.284144[/C][C]0.568288[/C][C]0.715856[/C][/ROW]
[ROW][C]27[/C][C]0.202558[/C][C]0.405116[/C][C]0.797442[/C][/ROW]
[ROW][C]28[/C][C]0.146159[/C][C]0.292317[/C][C]0.853841[/C][/ROW]
[ROW][C]29[/C][C]0.118322[/C][C]0.236644[/C][C]0.881678[/C][/ROW]
[ROW][C]30[/C][C]0.0776463[/C][C]0.155293[/C][C]0.922354[/C][/ROW]
[ROW][C]31[/C][C]0.0747745[/C][C]0.149549[/C][C]0.925226[/C][/ROW]
[ROW][C]32[/C][C]0.0482896[/C][C]0.0965791[/C][C]0.95171[/C][/ROW]
[ROW][C]33[/C][C]0.0305149[/C][C]0.0610299[/C][C]0.969485[/C][/ROW]
[ROW][C]34[/C][C]0.0642329[/C][C]0.128466[/C][C]0.935767[/C][/ROW]
[ROW][C]35[/C][C]0.0428276[/C][C]0.0856552[/C][C]0.957172[/C][/ROW]
[ROW][C]36[/C][C]0.0335551[/C][C]0.0671103[/C][C]0.966445[/C][/ROW]
[ROW][C]37[/C][C]0.0438371[/C][C]0.0876743[/C][C]0.956163[/C][/ROW]
[ROW][C]38[/C][C]0.0541577[/C][C]0.108315[/C][C]0.945842[/C][/ROW]
[ROW][C]39[/C][C]0.0857067[/C][C]0.171413[/C][C]0.914293[/C][/ROW]
[ROW][C]40[/C][C]0.069823[/C][C]0.139646[/C][C]0.930177[/C][/ROW]
[ROW][C]41[/C][C]0.0850696[/C][C]0.170139[/C][C]0.91493[/C][/ROW]
[ROW][C]42[/C][C]0.0687594[/C][C]0.137519[/C][C]0.931241[/C][/ROW]
[ROW][C]43[/C][C]0.0715973[/C][C]0.143195[/C][C]0.928403[/C][/ROW]
[ROW][C]44[/C][C]0.06075[/C][C]0.1215[/C][C]0.93925[/C][/ROW]
[ROW][C]45[/C][C]0.0531751[/C][C]0.10635[/C][C]0.946825[/C][/ROW]
[ROW][C]46[/C][C]0.0575319[/C][C]0.115064[/C][C]0.942468[/C][/ROW]
[ROW][C]47[/C][C]0.046838[/C][C]0.0936759[/C][C]0.953162[/C][/ROW]
[ROW][C]48[/C][C]0.100107[/C][C]0.200213[/C][C]0.899893[/C][/ROW]
[ROW][C]49[/C][C]0.245423[/C][C]0.490845[/C][C]0.754577[/C][/ROW]
[ROW][C]50[/C][C]0.234876[/C][C]0.469753[/C][C]0.765124[/C][/ROW]
[ROW][C]51[/C][C]0.199248[/C][C]0.398495[/C][C]0.800752[/C][/ROW]
[ROW][C]52[/C][C]0.239917[/C][C]0.479835[/C][C]0.760083[/C][/ROW]
[ROW][C]53[/C][C]0.220789[/C][C]0.441578[/C][C]0.779211[/C][/ROW]
[ROW][C]54[/C][C]0.222628[/C][C]0.445257[/C][C]0.777372[/C][/ROW]
[ROW][C]55[/C][C]0.483288[/C][C]0.966577[/C][C]0.516712[/C][/ROW]
[ROW][C]56[/C][C]0.434587[/C][C]0.869175[/C][C]0.565413[/C][/ROW]
[ROW][C]57[/C][C]0.74737[/C][C]0.505259[/C][C]0.25263[/C][/ROW]
[ROW][C]58[/C][C]0.841067[/C][C]0.317866[/C][C]0.158933[/C][/ROW]
[ROW][C]59[/C][C]0.811757[/C][C]0.376485[/C][C]0.188243[/C][/ROW]
[ROW][C]60[/C][C]0.831261[/C][C]0.337479[/C][C]0.168739[/C][/ROW]
[ROW][C]61[/C][C]0.808012[/C][C]0.383975[/C][C]0.191988[/C][/ROW]
[ROW][C]62[/C][C]0.783766[/C][C]0.432469[/C][C]0.216234[/C][/ROW]
[ROW][C]63[/C][C]0.863544[/C][C]0.272911[/C][C]0.136456[/C][/ROW]
[ROW][C]64[/C][C]0.8846[/C][C]0.2308[/C][C]0.1154[/C][/ROW]
[ROW][C]65[/C][C]0.870905[/C][C]0.258189[/C][C]0.129095[/C][/ROW]
[ROW][C]66[/C][C]0.858226[/C][C]0.283549[/C][C]0.141774[/C][/ROW]
[ROW][C]67[/C][C]0.83327[/C][C]0.33346[/C][C]0.16673[/C][/ROW]
[ROW][C]68[/C][C]0.816095[/C][C]0.36781[/C][C]0.183905[/C][/ROW]
[ROW][C]69[/C][C]0.891683[/C][C]0.216634[/C][C]0.108317[/C][/ROW]
[ROW][C]70[/C][C]0.881402[/C][C]0.237196[/C][C]0.118598[/C][/ROW]
[ROW][C]71[/C][C]0.862936[/C][C]0.274128[/C][C]0.137064[/C][/ROW]
[ROW][C]72[/C][C]0.844338[/C][C]0.311325[/C][C]0.155662[/C][/ROW]
[ROW][C]73[/C][C]0.824868[/C][C]0.350265[/C][C]0.175132[/C][/ROW]
[ROW][C]74[/C][C]0.844469[/C][C]0.311063[/C][C]0.155531[/C][/ROW]
[ROW][C]75[/C][C]0.8434[/C][C]0.313201[/C][C]0.1566[/C][/ROW]
[ROW][C]76[/C][C]0.842427[/C][C]0.315145[/C][C]0.157573[/C][/ROW]
[ROW][C]77[/C][C]0.820092[/C][C]0.359815[/C][C]0.179908[/C][/ROW]
[ROW][C]78[/C][C]0.837709[/C][C]0.324582[/C][C]0.162291[/C][/ROW]
[ROW][C]79[/C][C]0.830437[/C][C]0.339126[/C][C]0.169563[/C][/ROW]
[ROW][C]80[/C][C]0.837082[/C][C]0.325835[/C][C]0.162918[/C][/ROW]
[ROW][C]81[/C][C]0.817748[/C][C]0.364503[/C][C]0.182252[/C][/ROW]
[ROW][C]82[/C][C]0.860718[/C][C]0.278564[/C][C]0.139282[/C][/ROW]
[ROW][C]83[/C][C]0.838704[/C][C]0.322591[/C][C]0.161296[/C][/ROW]
[ROW][C]84[/C][C]0.829173[/C][C]0.341654[/C][C]0.170827[/C][/ROW]
[ROW][C]85[/C][C]0.821524[/C][C]0.356952[/C][C]0.178476[/C][/ROW]
[ROW][C]86[/C][C]0.796779[/C][C]0.406443[/C][C]0.203221[/C][/ROW]
[ROW][C]87[/C][C]0.772886[/C][C]0.454227[/C][C]0.227114[/C][/ROW]
[ROW][C]88[/C][C]0.755101[/C][C]0.489798[/C][C]0.244899[/C][/ROW]
[ROW][C]89[/C][C]0.764136[/C][C]0.471729[/C][C]0.235864[/C][/ROW]
[ROW][C]90[/C][C]0.850868[/C][C]0.298264[/C][C]0.149132[/C][/ROW]
[ROW][C]91[/C][C]0.87263[/C][C]0.254741[/C][C]0.12737[/C][/ROW]
[ROW][C]92[/C][C]0.915331[/C][C]0.169339[/C][C]0.0846694[/C][/ROW]
[ROW][C]93[/C][C]0.911142[/C][C]0.177716[/C][C]0.0888579[/C][/ROW]
[ROW][C]94[/C][C]0.900002[/C][C]0.199996[/C][C]0.0999981[/C][/ROW]
[ROW][C]95[/C][C]0.920326[/C][C]0.159348[/C][C]0.0796738[/C][/ROW]
[ROW][C]96[/C][C]0.911361[/C][C]0.177277[/C][C]0.0886387[/C][/ROW]
[ROW][C]97[/C][C]0.898374[/C][C]0.203251[/C][C]0.101626[/C][/ROW]
[ROW][C]98[/C][C]0.889923[/C][C]0.220154[/C][C]0.110077[/C][/ROW]
[ROW][C]99[/C][C]0.914215[/C][C]0.171571[/C][C]0.0857854[/C][/ROW]
[ROW][C]100[/C][C]0.943582[/C][C]0.112835[/C][C]0.0564177[/C][/ROW]
[ROW][C]101[/C][C]0.933509[/C][C]0.132983[/C][C]0.0664914[/C][/ROW]
[ROW][C]102[/C][C]0.921122[/C][C]0.157756[/C][C]0.0788778[/C][/ROW]
[ROW][C]103[/C][C]0.912425[/C][C]0.17515[/C][C]0.087575[/C][/ROW]
[ROW][C]104[/C][C]0.906915[/C][C]0.18617[/C][C]0.0930849[/C][/ROW]
[ROW][C]105[/C][C]0.926771[/C][C]0.146457[/C][C]0.0732286[/C][/ROW]
[ROW][C]106[/C][C]0.918796[/C][C]0.162408[/C][C]0.081204[/C][/ROW]
[ROW][C]107[/C][C]0.940928[/C][C]0.118145[/C][C]0.0590724[/C][/ROW]
[ROW][C]108[/C][C]0.961712[/C][C]0.076575[/C][C]0.0382875[/C][/ROW]
[ROW][C]109[/C][C]0.981187[/C][C]0.0376253[/C][C]0.0188126[/C][/ROW]
[ROW][C]110[/C][C]0.978737[/C][C]0.0425268[/C][C]0.0212634[/C][/ROW]
[ROW][C]111[/C][C]0.973577[/C][C]0.052845[/C][C]0.0264225[/C][/ROW]
[ROW][C]112[/C][C]0.971815[/C][C]0.0563704[/C][C]0.0281852[/C][/ROW]
[ROW][C]113[/C][C]0.979207[/C][C]0.0415853[/C][C]0.0207927[/C][/ROW]
[ROW][C]114[/C][C]0.981307[/C][C]0.0373861[/C][C]0.018693[/C][/ROW]
[ROW][C]115[/C][C]0.97984[/C][C]0.0403206[/C][C]0.0201603[/C][/ROW]
[ROW][C]116[/C][C]0.983344[/C][C]0.0333122[/C][C]0.0166561[/C][/ROW]
[ROW][C]117[/C][C]0.980327[/C][C]0.0393464[/C][C]0.0196732[/C][/ROW]
[ROW][C]118[/C][C]0.98434[/C][C]0.031319[/C][C]0.0156595[/C][/ROW]
[ROW][C]119[/C][C]0.982642[/C][C]0.0347151[/C][C]0.0173576[/C][/ROW]
[ROW][C]120[/C][C]0.979713[/C][C]0.0405738[/C][C]0.0202869[/C][/ROW]
[ROW][C]121[/C][C]0.979401[/C][C]0.0411986[/C][C]0.0205993[/C][/ROW]
[ROW][C]122[/C][C]0.992459[/C][C]0.0150817[/C][C]0.00754084[/C][/ROW]
[ROW][C]123[/C][C]0.990417[/C][C]0.0191651[/C][C]0.00958254[/C][/ROW]
[ROW][C]124[/C][C]0.993014[/C][C]0.0139724[/C][C]0.00698619[/C][/ROW]
[ROW][C]125[/C][C]0.992835[/C][C]0.0143306[/C][C]0.00716532[/C][/ROW]
[ROW][C]126[/C][C]0.992419[/C][C]0.0151623[/C][C]0.00758114[/C][/ROW]
[ROW][C]127[/C][C]0.99082[/C][C]0.018359[/C][C]0.0091795[/C][/ROW]
[ROW][C]128[/C][C]0.991282[/C][C]0.0174366[/C][C]0.00871828[/C][/ROW]
[ROW][C]129[/C][C]0.989329[/C][C]0.0213413[/C][C]0.0106706[/C][/ROW]
[ROW][C]130[/C][C]0.988278[/C][C]0.0234443[/C][C]0.0117222[/C][/ROW]
[ROW][C]131[/C][C]0.986309[/C][C]0.0273826[/C][C]0.0136913[/C][/ROW]
[ROW][C]132[/C][C]0.986132[/C][C]0.0277367[/C][C]0.0138684[/C][/ROW]
[ROW][C]133[/C][C]0.987144[/C][C]0.0257126[/C][C]0.0128563[/C][/ROW]
[ROW][C]134[/C][C]0.984557[/C][C]0.0308852[/C][C]0.0154426[/C][/ROW]
[ROW][C]135[/C][C]0.981129[/C][C]0.0377414[/C][C]0.0188707[/C][/ROW]
[ROW][C]136[/C][C]0.976733[/C][C]0.0465339[/C][C]0.023267[/C][/ROW]
[ROW][C]137[/C][C]0.981618[/C][C]0.0367634[/C][C]0.0183817[/C][/ROW]
[ROW][C]138[/C][C]0.994359[/C][C]0.0112825[/C][C]0.00564124[/C][/ROW]
[ROW][C]139[/C][C]0.992773[/C][C]0.0144546[/C][C]0.00722728[/C][/ROW]
[ROW][C]140[/C][C]0.990886[/C][C]0.0182284[/C][C]0.00911421[/C][/ROW]
[ROW][C]141[/C][C]0.988935[/C][C]0.0221292[/C][C]0.0110646[/C][/ROW]
[ROW][C]142[/C][C]0.986456[/C][C]0.0270874[/C][C]0.0135437[/C][/ROW]
[ROW][C]143[/C][C]0.98387[/C][C]0.0322603[/C][C]0.0161302[/C][/ROW]
[ROW][C]144[/C][C]0.982342[/C][C]0.0353161[/C][C]0.0176581[/C][/ROW]
[ROW][C]145[/C][C]0.979763[/C][C]0.0404741[/C][C]0.0202371[/C][/ROW]
[ROW][C]146[/C][C]0.975402[/C][C]0.0491961[/C][C]0.024598[/C][/ROW]
[ROW][C]147[/C][C]0.973926[/C][C]0.0521488[/C][C]0.0260744[/C][/ROW]
[ROW][C]148[/C][C]0.968582[/C][C]0.0628353[/C][C]0.0314176[/C][/ROW]
[ROW][C]149[/C][C]0.97117[/C][C]0.0576606[/C][C]0.0288303[/C][/ROW]
[ROW][C]150[/C][C]0.965062[/C][C]0.0698758[/C][C]0.0349379[/C][/ROW]
[ROW][C]151[/C][C]0.985512[/C][C]0.0289761[/C][C]0.0144881[/C][/ROW]
[ROW][C]152[/C][C]0.981987[/C][C]0.0360265[/C][C]0.0180132[/C][/ROW]
[ROW][C]153[/C][C]0.977495[/C][C]0.0450106[/C][C]0.0225053[/C][/ROW]
[ROW][C]154[/C][C]0.974012[/C][C]0.0519769[/C][C]0.0259885[/C][/ROW]
[ROW][C]155[/C][C]0.976553[/C][C]0.0468932[/C][C]0.0234466[/C][/ROW]
[ROW][C]156[/C][C]0.972934[/C][C]0.0541313[/C][C]0.0270656[/C][/ROW]
[ROW][C]157[/C][C]0.969418[/C][C]0.0611636[/C][C]0.0305818[/C][/ROW]
[ROW][C]158[/C][C]0.973207[/C][C]0.0535852[/C][C]0.0267926[/C][/ROW]
[ROW][C]159[/C][C]0.967695[/C][C]0.064611[/C][C]0.0323055[/C][/ROW]
[ROW][C]160[/C][C]0.962247[/C][C]0.0755064[/C][C]0.0377532[/C][/ROW]
[ROW][C]161[/C][C]0.959798[/C][C]0.0804036[/C][C]0.0402018[/C][/ROW]
[ROW][C]162[/C][C]0.952646[/C][C]0.0947083[/C][C]0.0473541[/C][/ROW]
[ROW][C]163[/C][C]0.948671[/C][C]0.102658[/C][C]0.051329[/C][/ROW]
[ROW][C]164[/C][C]0.952168[/C][C]0.0956636[/C][C]0.0478318[/C][/ROW]
[ROW][C]165[/C][C]0.945457[/C][C]0.109086[/C][C]0.0545428[/C][/ROW]
[ROW][C]166[/C][C]0.942561[/C][C]0.114879[/C][C]0.0574393[/C][/ROW]
[ROW][C]167[/C][C]0.932146[/C][C]0.135708[/C][C]0.067854[/C][/ROW]
[ROW][C]168[/C][C]0.923756[/C][C]0.152487[/C][C]0.0762435[/C][/ROW]
[ROW][C]169[/C][C]0.916201[/C][C]0.167597[/C][C]0.0837987[/C][/ROW]
[ROW][C]170[/C][C]0.909265[/C][C]0.181469[/C][C]0.0907347[/C][/ROW]
[ROW][C]171[/C][C]0.895542[/C][C]0.208916[/C][C]0.104458[/C][/ROW]
[ROW][C]172[/C][C]0.885028[/C][C]0.229944[/C][C]0.114972[/C][/ROW]
[ROW][C]173[/C][C]0.874225[/C][C]0.25155[/C][C]0.125775[/C][/ROW]
[ROW][C]174[/C][C]0.853972[/C][C]0.292056[/C][C]0.146028[/C][/ROW]
[ROW][C]175[/C][C]0.832104[/C][C]0.335793[/C][C]0.167896[/C][/ROW]
[ROW][C]176[/C][C]0.835453[/C][C]0.329094[/C][C]0.164547[/C][/ROW]
[ROW][C]177[/C][C]0.819189[/C][C]0.361622[/C][C]0.180811[/C][/ROW]
[ROW][C]178[/C][C]0.83771[/C][C]0.324579[/C][C]0.16229[/C][/ROW]
[ROW][C]179[/C][C]0.815198[/C][C]0.369605[/C][C]0.184802[/C][/ROW]
[ROW][C]180[/C][C]0.814725[/C][C]0.37055[/C][C]0.185275[/C][/ROW]
[ROW][C]181[/C][C]0.826277[/C][C]0.347446[/C][C]0.173723[/C][/ROW]
[ROW][C]182[/C][C]0.800931[/C][C]0.398139[/C][C]0.199069[/C][/ROW]
[ROW][C]183[/C][C]0.790946[/C][C]0.418108[/C][C]0.209054[/C][/ROW]
[ROW][C]184[/C][C]0.766809[/C][C]0.466382[/C][C]0.233191[/C][/ROW]
[ROW][C]185[/C][C]0.904543[/C][C]0.190915[/C][C]0.0954573[/C][/ROW]
[ROW][C]186[/C][C]0.891293[/C][C]0.217414[/C][C]0.108707[/C][/ROW]
[ROW][C]187[/C][C]0.888522[/C][C]0.222957[/C][C]0.111478[/C][/ROW]
[ROW][C]188[/C][C]0.91891[/C][C]0.162181[/C][C]0.0810903[/C][/ROW]
[ROW][C]189[/C][C]0.919242[/C][C]0.161516[/C][C]0.0807579[/C][/ROW]
[ROW][C]190[/C][C]0.928732[/C][C]0.142536[/C][C]0.0712682[/C][/ROW]
[ROW][C]191[/C][C]0.920974[/C][C]0.158051[/C][C]0.0790257[/C][/ROW]
[ROW][C]192[/C][C]0.919618[/C][C]0.160764[/C][C]0.080382[/C][/ROW]
[ROW][C]193[/C][C]0.933358[/C][C]0.133285[/C][C]0.0666423[/C][/ROW]
[ROW][C]194[/C][C]0.923901[/C][C]0.152197[/C][C]0.0760986[/C][/ROW]
[ROW][C]195[/C][C]0.914324[/C][C]0.171351[/C][C]0.0856757[/C][/ROW]
[ROW][C]196[/C][C]0.900904[/C][C]0.198191[/C][C]0.0990956[/C][/ROW]
[ROW][C]197[/C][C]0.907372[/C][C]0.185255[/C][C]0.0926277[/C][/ROW]
[ROW][C]198[/C][C]0.889951[/C][C]0.220097[/C][C]0.110049[/C][/ROW]
[ROW][C]199[/C][C]0.910424[/C][C]0.179153[/C][C]0.0895764[/C][/ROW]
[ROW][C]200[/C][C]0.895999[/C][C]0.208002[/C][C]0.104001[/C][/ROW]
[ROW][C]201[/C][C]0.897062[/C][C]0.205877[/C][C]0.102938[/C][/ROW]
[ROW][C]202[/C][C]0.898877[/C][C]0.202247[/C][C]0.101123[/C][/ROW]
[ROW][C]203[/C][C]0.879909[/C][C]0.240183[/C][C]0.120091[/C][/ROW]
[ROW][C]204[/C][C]0.869942[/C][C]0.260115[/C][C]0.130058[/C][/ROW]
[ROW][C]205[/C][C]0.85684[/C][C]0.28632[/C][C]0.14316[/C][/ROW]
[ROW][C]206[/C][C]0.840236[/C][C]0.319528[/C][C]0.159764[/C][/ROW]
[ROW][C]207[/C][C]0.81642[/C][C]0.367161[/C][C]0.18358[/C][/ROW]
[ROW][C]208[/C][C]0.874956[/C][C]0.250088[/C][C]0.125044[/C][/ROW]
[ROW][C]209[/C][C]0.874558[/C][C]0.250884[/C][C]0.125442[/C][/ROW]
[ROW][C]210[/C][C]0.852002[/C][C]0.295996[/C][C]0.147998[/C][/ROW]
[ROW][C]211[/C][C]0.836694[/C][C]0.326612[/C][C]0.163306[/C][/ROW]
[ROW][C]212[/C][C]0.819731[/C][C]0.360539[/C][C]0.180269[/C][/ROW]
[ROW][C]213[/C][C]0.824569[/C][C]0.350862[/C][C]0.175431[/C][/ROW]
[ROW][C]214[/C][C]0.801614[/C][C]0.396772[/C][C]0.198386[/C][/ROW]
[ROW][C]215[/C][C]0.76825[/C][C]0.463499[/C][C]0.23175[/C][/ROW]
[ROW][C]216[/C][C]0.762077[/C][C]0.475846[/C][C]0.237923[/C][/ROW]
[ROW][C]217[/C][C]0.727918[/C][C]0.544164[/C][C]0.272082[/C][/ROW]
[ROW][C]218[/C][C]0.707495[/C][C]0.58501[/C][C]0.292505[/C][/ROW]
[ROW][C]219[/C][C]0.708136[/C][C]0.583727[/C][C]0.291864[/C][/ROW]
[ROW][C]220[/C][C]0.690138[/C][C]0.619723[/C][C]0.309862[/C][/ROW]
[ROW][C]221[/C][C]0.679872[/C][C]0.640256[/C][C]0.320128[/C][/ROW]
[ROW][C]222[/C][C]0.642474[/C][C]0.715052[/C][C]0.357526[/C][/ROW]
[ROW][C]223[/C][C]0.63599[/C][C]0.72802[/C][C]0.36401[/C][/ROW]
[ROW][C]224[/C][C]0.590321[/C][C]0.819357[/C][C]0.409679[/C][/ROW]
[ROW][C]225[/C][C]0.603925[/C][C]0.792149[/C][C]0.396075[/C][/ROW]
[ROW][C]226[/C][C]0.634586[/C][C]0.730828[/C][C]0.365414[/C][/ROW]
[ROW][C]227[/C][C]0.588686[/C][C]0.822629[/C][C]0.411314[/C][/ROW]
[ROW][C]228[/C][C]0.587478[/C][C]0.825043[/C][C]0.412522[/C][/ROW]
[ROW][C]229[/C][C]0.700268[/C][C]0.599464[/C][C]0.299732[/C][/ROW]
[ROW][C]230[/C][C]0.693206[/C][C]0.613588[/C][C]0.306794[/C][/ROW]
[ROW][C]231[/C][C]0.660305[/C][C]0.67939[/C][C]0.339695[/C][/ROW]
[ROW][C]232[/C][C]0.644528[/C][C]0.710945[/C][C]0.355472[/C][/ROW]
[ROW][C]233[/C][C]0.644741[/C][C]0.710517[/C][C]0.355259[/C][/ROW]
[ROW][C]234[/C][C]0.603119[/C][C]0.793762[/C][C]0.396881[/C][/ROW]
[ROW][C]235[/C][C]0.545872[/C][C]0.908256[/C][C]0.454128[/C][/ROW]
[ROW][C]236[/C][C]0.812793[/C][C]0.374414[/C][C]0.187207[/C][/ROW]
[ROW][C]237[/C][C]0.789156[/C][C]0.421688[/C][C]0.210844[/C][/ROW]
[ROW][C]238[/C][C]0.758069[/C][C]0.483861[/C][C]0.241931[/C][/ROW]
[ROW][C]239[/C][C]0.822585[/C][C]0.354831[/C][C]0.177415[/C][/ROW]
[ROW][C]240[/C][C]0.79653[/C][C]0.40694[/C][C]0.20347[/C][/ROW]
[ROW][C]241[/C][C]0.748127[/C][C]0.503746[/C][C]0.251873[/C][/ROW]
[ROW][C]242[/C][C]0.763405[/C][C]0.47319[/C][C]0.236595[/C][/ROW]
[ROW][C]243[/C][C]0.712938[/C][C]0.574124[/C][C]0.287062[/C][/ROW]
[ROW][C]244[/C][C]0.732098[/C][C]0.535805[/C][C]0.267902[/C][/ROW]
[ROW][C]245[/C][C]0.712337[/C][C]0.575325[/C][C]0.287663[/C][/ROW]
[ROW][C]246[/C][C]0.770447[/C][C]0.459106[/C][C]0.229553[/C][/ROW]
[ROW][C]247[/C][C]0.702182[/C][C]0.595637[/C][C]0.297818[/C][/ROW]
[ROW][C]248[/C][C]0.665356[/C][C]0.669287[/C][C]0.334644[/C][/ROW]
[ROW][C]249[/C][C]0.645499[/C][C]0.709002[/C][C]0.354501[/C][/ROW]
[ROW][C]250[/C][C]0.560835[/C][C]0.87833[/C][C]0.439165[/C][/ROW]
[ROW][C]251[/C][C]0.494314[/C][C]0.988628[/C][C]0.505686[/C][/ROW]
[ROW][C]252[/C][C]0.414674[/C][C]0.829347[/C][C]0.585326[/C][/ROW]
[ROW][C]253[/C][C]0.318807[/C][C]0.637613[/C][C]0.681193[/C][/ROW]
[ROW][C]254[/C][C]0.235178[/C][C]0.470356[/C][C]0.764822[/C][/ROW]
[ROW][C]255[/C][C]0.298103[/C][C]0.596205[/C][C]0.701897[/C][/ROW]
[ROW][C]256[/C][C]0.267693[/C][C]0.535386[/C][C]0.732307[/C][/ROW]
[ROW][C]257[/C][C]0.309276[/C][C]0.618552[/C][C]0.690724[/C][/ROW]
[ROW][C]258[/C][C]0.475271[/C][C]0.950541[/C][C]0.524729[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=5

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=5

As an alternative you can also use a QR Code:

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

Goldfeld-Quandt test for Heteroskedasticity
p-values	Alternative Hypothesis
breakpoint index	greater	2-sided	less
21	0.598125	0.803749	0.401875
22	0.448339	0.896677	0.551661
23	0.487271	0.974543	0.512729
24	0.484063	0.968127	0.515937
25	0.371169	0.742338	0.628831
26	0.284144	0.568288	0.715856
27	0.202558	0.405116	0.797442
28	0.146159	0.292317	0.853841
29	0.118322	0.236644	0.881678
30	0.0776463	0.155293	0.922354
31	0.0747745	0.149549	0.925226
32	0.0482896	0.0965791	0.95171
33	0.0305149	0.0610299	0.969485
34	0.0642329	0.128466	0.935767
35	0.0428276	0.0856552	0.957172
36	0.0335551	0.0671103	0.966445
37	0.0438371	0.0876743	0.956163
38	0.0541577	0.108315	0.945842
39	0.0857067	0.171413	0.914293
40	0.069823	0.139646	0.930177
41	0.0850696	0.170139	0.91493
42	0.0687594	0.137519	0.931241
43	0.0715973	0.143195	0.928403
44	0.06075	0.1215	0.93925
45	0.0531751	0.10635	0.946825
46	0.0575319	0.115064	0.942468
47	0.046838	0.0936759	0.953162
48	0.100107	0.200213	0.899893
49	0.245423	0.490845	0.754577
50	0.234876	0.469753	0.765124
51	0.199248	0.398495	0.800752
52	0.239917	0.479835	0.760083
53	0.220789	0.441578	0.779211
54	0.222628	0.445257	0.777372
55	0.483288	0.966577	0.516712
56	0.434587	0.869175	0.565413
57	0.74737	0.505259	0.25263
58	0.841067	0.317866	0.158933
59	0.811757	0.376485	0.188243
60	0.831261	0.337479	0.168739
61	0.808012	0.383975	0.191988
62	0.783766	0.432469	0.216234
63	0.863544	0.272911	0.136456
64	0.8846	0.2308	0.1154
65	0.870905	0.258189	0.129095
66	0.858226	0.283549	0.141774
67	0.83327	0.33346	0.16673
68	0.816095	0.36781	0.183905
69	0.891683	0.216634	0.108317
70	0.881402	0.237196	0.118598
71	0.862936	0.274128	0.137064
72	0.844338	0.311325	0.155662
73	0.824868	0.350265	0.175132
74	0.844469	0.311063	0.155531
75	0.8434	0.313201	0.1566
76	0.842427	0.315145	0.157573
77	0.820092	0.359815	0.179908
78	0.837709	0.324582	0.162291
79	0.830437	0.339126	0.169563
80	0.837082	0.325835	0.162918
81	0.817748	0.364503	0.182252
82	0.860718	0.278564	0.139282
83	0.838704	0.322591	0.161296
84	0.829173	0.341654	0.170827
85	0.821524	0.356952	0.178476
86	0.796779	0.406443	0.203221
87	0.772886	0.454227	0.227114
88	0.755101	0.489798	0.244899
89	0.764136	0.471729	0.235864
90	0.850868	0.298264	0.149132
91	0.87263	0.254741	0.12737
92	0.915331	0.169339	0.0846694
93	0.911142	0.177716	0.0888579
94	0.900002	0.199996	0.0999981
95	0.920326	0.159348	0.0796738
96	0.911361	0.177277	0.0886387
97	0.898374	0.203251	0.101626
98	0.889923	0.220154	0.110077
99	0.914215	0.171571	0.0857854
100	0.943582	0.112835	0.0564177
101	0.933509	0.132983	0.0664914
102	0.921122	0.157756	0.0788778
103	0.912425	0.17515	0.087575
104	0.906915	0.18617	0.0930849
105	0.926771	0.146457	0.0732286
106	0.918796	0.162408	0.081204
107	0.940928	0.118145	0.0590724
108	0.961712	0.076575	0.0382875
109	0.981187	0.0376253	0.0188126
110	0.978737	0.0425268	0.0212634
111	0.973577	0.052845	0.0264225
112	0.971815	0.0563704	0.0281852
113	0.979207	0.0415853	0.0207927
114	0.981307	0.0373861	0.018693
115	0.97984	0.0403206	0.0201603
116	0.983344	0.0333122	0.0166561
117	0.980327	0.0393464	0.0196732
118	0.98434	0.031319	0.0156595
119	0.982642	0.0347151	0.0173576
120	0.979713	0.0405738	0.0202869
121	0.979401	0.0411986	0.0205993
122	0.992459	0.0150817	0.00754084
123	0.990417	0.0191651	0.00958254
124	0.993014	0.0139724	0.00698619
125	0.992835	0.0143306	0.00716532
126	0.992419	0.0151623	0.00758114
127	0.99082	0.018359	0.0091795
128	0.991282	0.0174366	0.00871828
129	0.989329	0.0213413	0.0106706
130	0.988278	0.0234443	0.0117222
131	0.986309	0.0273826	0.0136913
132	0.986132	0.0277367	0.0138684
133	0.987144	0.0257126	0.0128563
134	0.984557	0.0308852	0.0154426
135	0.981129	0.0377414	0.0188707
136	0.976733	0.0465339	0.023267
137	0.981618	0.0367634	0.0183817
138	0.994359	0.0112825	0.00564124
139	0.992773	0.0144546	0.00722728
140	0.990886	0.0182284	0.00911421
141	0.988935	0.0221292	0.0110646
142	0.986456	0.0270874	0.0135437
143	0.98387	0.0322603	0.0161302
144	0.982342	0.0353161	0.0176581
145	0.979763	0.0404741	0.0202371
146	0.975402	0.0491961	0.024598
147	0.973926	0.0521488	0.0260744
148	0.968582	0.0628353	0.0314176
149	0.97117	0.0576606	0.0288303
150	0.965062	0.0698758	0.0349379
151	0.985512	0.0289761	0.0144881
152	0.981987	0.0360265	0.0180132
153	0.977495	0.0450106	0.0225053
154	0.974012	0.0519769	0.0259885
155	0.976553	0.0468932	0.0234466
156	0.972934	0.0541313	0.0270656
157	0.969418	0.0611636	0.0305818
158	0.973207	0.0535852	0.0267926
159	0.967695	0.064611	0.0323055
160	0.962247	0.0755064	0.0377532
161	0.959798	0.0804036	0.0402018
162	0.952646	0.0947083	0.0473541
163	0.948671	0.102658	0.051329
164	0.952168	0.0956636	0.0478318
165	0.945457	0.109086	0.0545428
166	0.942561	0.114879	0.0574393
167	0.932146	0.135708	0.067854
168	0.923756	0.152487	0.0762435
169	0.916201	0.167597	0.0837987
170	0.909265	0.181469	0.0907347
171	0.895542	0.208916	0.104458
172	0.885028	0.229944	0.114972
173	0.874225	0.25155	0.125775
174	0.853972	0.292056	0.146028
175	0.832104	0.335793	0.167896
176	0.835453	0.329094	0.164547
177	0.819189	0.361622	0.180811
178	0.83771	0.324579	0.16229
179	0.815198	0.369605	0.184802
180	0.814725	0.37055	0.185275
181	0.826277	0.347446	0.173723
182	0.800931	0.398139	0.199069
183	0.790946	0.418108	0.209054
184	0.766809	0.466382	0.233191
185	0.904543	0.190915	0.0954573
186	0.891293	0.217414	0.108707
187	0.888522	0.222957	0.111478
188	0.91891	0.162181	0.0810903
189	0.919242	0.161516	0.0807579
190	0.928732	0.142536	0.0712682
191	0.920974	0.158051	0.0790257
192	0.919618	0.160764	0.080382
193	0.933358	0.133285	0.0666423
194	0.923901	0.152197	0.0760986
195	0.914324	0.171351	0.0856757
196	0.900904	0.198191	0.0990956
197	0.907372	0.185255	0.0926277
198	0.889951	0.220097	0.110049
199	0.910424	0.179153	0.0895764
200	0.895999	0.208002	0.104001
201	0.897062	0.205877	0.102938
202	0.898877	0.202247	0.101123
203	0.879909	0.240183	0.120091
204	0.869942	0.260115	0.130058
205	0.85684	0.28632	0.14316
206	0.840236	0.319528	0.159764
207	0.81642	0.367161	0.18358
208	0.874956	0.250088	0.125044
209	0.874558	0.250884	0.125442
210	0.852002	0.295996	0.147998
211	0.836694	0.326612	0.163306
212	0.819731	0.360539	0.180269
213	0.824569	0.350862	0.175431
214	0.801614	0.396772	0.198386
215	0.76825	0.463499	0.23175
216	0.762077	0.475846	0.237923
217	0.727918	0.544164	0.272082
218	0.707495	0.58501	0.292505
219	0.708136	0.583727	0.291864
220	0.690138	0.619723	0.309862
221	0.679872	0.640256	0.320128
222	0.642474	0.715052	0.357526
223	0.63599	0.72802	0.36401
224	0.590321	0.819357	0.409679
225	0.603925	0.792149	0.396075
226	0.634586	0.730828	0.365414
227	0.588686	0.822629	0.411314
228	0.587478	0.825043	0.412522
229	0.700268	0.599464	0.299732
230	0.693206	0.613588	0.306794
231	0.660305	0.67939	0.339695
232	0.644528	0.710945	0.355472
233	0.644741	0.710517	0.355259
234	0.603119	0.793762	0.396881
235	0.545872	0.908256	0.454128
236	0.812793	0.374414	0.187207
237	0.789156	0.421688	0.210844
238	0.758069	0.483861	0.241931
239	0.822585	0.354831	0.177415
240	0.79653	0.40694	0.20347
241	0.748127	0.503746	0.251873
242	0.763405	0.47319	0.236595
243	0.712938	0.574124	0.287062
244	0.732098	0.535805	0.267902
245	0.712337	0.575325	0.287663
246	0.770447	0.459106	0.229553
247	0.702182	0.595637	0.297818
248	0.665356	0.669287	0.334644
249	0.645499	0.709002	0.354501
250	0.560835	0.87833	0.439165
251	0.494314	0.988628	0.505686
252	0.414674	0.829347	0.585326
253	0.318807	0.637613	0.681193
254	0.235178	0.470356	0.764822
255	0.298103	0.596205	0.701897
256	0.267693	0.535386	0.732307
257	0.309276	0.618552	0.690724
258	0.475271	0.950541	0.524729

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	40	0.168067	NOK
10% type I error level	62	0.260504	NOK

\begin{tabular}{lllllllll}
\hline
Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity \tabularnewline
Description & # significant tests & % significant tests & OK/NOK \tabularnewline
1% type I error level & 0 & 0 & OK \tabularnewline
5% type I error level & 40 & 0.168067 & NOK \tabularnewline
10% type I error level & 62 & 0.260504 & NOK \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=269724&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]40[/C][C]0.168067[/C][C]NOK[/C][/ROW]
[ROW][C]10% type I error level[/C][C]62[/C][C]0.260504[/C][C]NOK[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=269724&T=6

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=269724&T=6

As an alternative you can also use a QR Code:

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

Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity
Description	# significant tests	% significant tests	OK/NOK
1% type I error level	0	0	OK
5% type I error level	40	0.168067	NOK
10% type I error level	62	0.260504	NOK

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Figure 3

PNG link

Postscript link

PDF link

Figure 4

PNG link

Postscript link

PDF link

Figure 5

PNG link

Postscript link

PDF link

Figure 6

PNG link

Postscript link

PDF link

Figure 7

PNG link

Postscript link

PDF link

Figure 8

PNG link

Postscript link

PDF link

Figure 9

PNG link

Postscript link

PDF link

Figure 10

PNG link

Postscript link

PDF link

Parameters (Session):

par1 = 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')
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code