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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

Fri, 11 Dec 2015 11:36:05 +0000

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Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2015/Dec/11/t14498351661ybex4ii2og2men.htm/, Retrieved Thu, 16 May 2024 08:13:51 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=285904, Retrieved Thu, 16 May 2024 08:13:51 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Correct, alle variabelen

Estimated Impact

131

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

-     [Multiple Regression] [Paper: Regression...] [2015-12-11 10:03:51] [9bd698ecfffbb3f270ebcac6c258c074]
- R PD    [Multiple Regression] [Paper statistiek:...] [2015-12-11 11:36:05] [2b85ca57f8232cceb816903a348a0bf5] [Current]

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Dataseries X:

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2441 84609 3.22 11.59 1 0 0 0
3406 84609 3.22 44.39 1 0 0 0
4029 84609 3.22 38.84 0 1 0 0
1924 84609 3.22 3.65 0 1 0 0
2319 84609 3.22 18.63 0 1 0 0
2156 84609 3.22 29.97 0 1 0 0
2117 84609 3.22 9.06 0 1 0 0
2189 84609 3.22 3.56 0 1 0 0
2625 84609 3.22 5.69 0 0 1 0
1959 84609 3.22 11.96 0 0 1 0
3096 84609 3.22 4.78 0 0 1 0
1997 84609 3.22 6.92 0 0 1 0
1813 84609 3.22 10.41 0 0 0 1
2648 84609 3.22 5.23 0 0 0 1
2648 84609 3.22 10.59 0 0 0 1
5782 84609 3.22 64.87 0 0 0 1
2218 84609 3.22 12.67 0 0 0 1
25289 374609 49.13 5.68 1 0 0 0
25389 374609 49.13 8.3 1 0 0 0
25196 374609 49.13 12.79 0 1 0 0
24153 374609 49.13 11.9 0 1 0 0
26079 374609 49.13 3.19 0 1 0 0
26540 374609 49.13 5.46 0 1 0 0
25099 374609 49.13 19.11 0 0 1 0
27402 374609 49.13 67.99 0 0 1 0
26206 374609 49.13 12.62 0 0 1 0
26526 374609 49.13 4.18 0 0 1 0
27154 374609 49.13 25.56 0 0 1 0
24464 374609 49.13 10.57 0 0 1 0
24967 374609 49.13 6.05 0 0 0 1
24713 374609 49.13 35.71 0 0 0 1
26733 374609 49.13 5.67 0 0 0 1
24578 374609 49.13 4.97 0 0 0 1
26092 374609 49.13 10.44 0 0 0 1
24486 374609 44.39 3.65 0 1 0 0
24630 374609 44.39 10.41 0 1 0 0
26233 374609 44.39 9.06 0 1 0 0
26492 374609 44.39 6.92 0 1 0 0
24973 374609 44.39 3.56 0 1 0 0
27437 374609 44.39 64.87 0 1 0 0
23829 374609 44.39 12.67 0 1 0 0
27158 374609 44.39 38.84 0 0 1 0
25670 374609 44.39 4.78 0 0 1 0
23530 374609 44.39 3.22 0 0 1 0
24474 374609 44.39 29.97 0 0 1 0
26668 374609 44.39 5.23 0 0 1 0
26060 374609 44.39 18.63 0 0 0 1
24856 374609 44.39 10.59 0 0 0 1
24067 374609 44.39 5.69 0 0 0 1
25545 374609 44.39 11.96 0 0 0 1
24213 374609 44.39 11.59 0 0 0 1
23703 374609 42.53 12.09 1 0 0 0
23566 374609 42.53 3.97 1 0 0 0
22876 374609 42.53 23.48 0 1 0 0
22744 374609 42.53 8.98 0 1 0 0
27615 374609 42.53 63.34 0 1 0 0
24421 374609 42.53 19.97 0 1 0 0
24728 374609 42.53 7.36 0 1 0 0
25732 374609 42.53 11.5 0 1 0 0
24204 374609 42.53 10.07 0 1 0 0
23869 374609 42.53 5.56 0 0 1 0
24120 374609 42.53 9.03 0 0 1 0
22474 374609 42.53 11.54 0 0 1 0
26406 374609 42.53 48.32 0 0 1 0
22440 374609 42.53 5.28 0 0 0 1
20387 374609 42.53 3.97 0 0 0 0
21609 374609 42.53 19.97 0 0 0 0
24905 374609 42.53 63.34 0 0 0 0
21584 374609 42.53 7.36 0 0 0 0
20920 374609 42.53 8.98 0 0 0 0
5042 98607 11.9 19.11 1 0 0 0
4353 98607 11.9 5.68 1 0 0 0
7996 98607 11.9 67.99 0 1 0 0
3998 98607 11.9 12.79 0 1 0 0
4697 98607 11.9 35.71 0 1 0 0
7837 98607 11.9 25.56 0 1 0 0
3512 98607 11.9 6.05 0 0 1 0
3503 98607 11.9 4.97 0 0 1 0
3572 98607 11.9 8.3 0 0 1 0
3918 98607 11.9 10.44 0 0 1 0
4767 98607 11.9 4.18 0 0 1 0
5833 98607 11.9 49.13 0 0 0 1
4154 98607 11.9 10.57 0 0 0 1
3894 98607 11.9 3.19 0 0 0 1
4133 98607 11.9 5.46 0 0 0 1
4273 98607 11.9 5.67 0 0 0 1
5574 98607 11.9 12.62 0 0 0 1
5029 98607 11.59 4.78 1 0 0 0
5279 98607 11.59 18.63 0 1 0 0
4876 98607 11.59 29.97 0 1 0 0
3850 98607 11.59 5.23 0 1 0 0
4109 98607 11.59 9.06 0 1 0 0
4137 98607 11.59 10.59 0 1 0 0
3725 98607 11.59 11.96 0 1 0 0
5675 98607 11.59 44.39 0 0 1 0
3405 98607 11.59 3.22 0 0 1 0
3568 98607 11.59 3.65 0 0 1 0
3408 98607 11.59 10.41 0 0 1 0
7203 98607 11.59 64.87 0 0 1 0
5392 98607 11.59 3.56 0 0 0 1
4053 98607 11.59 5.69 0 0 0 1
7863 98607 11.59 38.84 0 0 0 1
3716 98607 11.59 12.67 0 0 0 1
4027 98607 11.59 6.92 0 0 0 1
3608 98607 10.52 5.28 1 0 0 0
3333 98607 10.52 9.03 1 0 0 0
3014 98607 10.52 11.54 0 1 0 0
5014 98607 10.52 63.34 0 1 0 0
4328 98607 10.52 42.53 0 1 0 0
2956 98607 10.52 7.36 0 1 0 0
6535 98607 10.52 48.32 0 1 0 0
3153 98607 10.52 10.07 0 1 0 0
3081 98607 10.52 23.48 0 1 0 0
2996 98607 10.52 12.09 0 0 1 0
3150 134152 6.05 10.44 1 0 0 0
3673 134152 6.05 19.11 1 0 0 0
2870 134152 6.05 4.97 0 1 0 0
3230 134152 6.05 8.3 0 1 0 0
3821 134152 6.05 49.13 0 1 0 0
3178 134152 6.05 10.57 0 1 0 0
2988 134152 6.05 3.19 0 1 0 0
2347 134152 6.05 35.71 0 0 1 0
2891 134152 6.05 5.67 0 0 1 0
4775 134152 6.05 25.56 0 0 1 0
4758 134152 6.05 67.99 0 0 1 0
2962 134152 6.05 5.68 0 0 1 0
2687 134152 6.05 12.79 0 0 0 1
2825 134152 6.05 12.62 0 0 0 1
4201 134152 6.05 4.18 0 0 0 1
2545 134152 6.05 11.9 0 0 0 1
2626 134152 6.05 5.46 0 0 0 1
3556 131700 3.19 12.62 1 0 0 0
6069 131700 3.19 67.99 1 0 0 0
2795 131700 3.19 8.3 0 1 0 0
2763 131700 3.19 5.68 0 1 0 0
3024 131700 3.19 12.79 0 1 0 0
2622 131700 3.19 11.9 0 1 0 0
3800 131700 3.19 35.71 0 0 1 0
5217 131700 3.19 25.56 0 0 1 0
3163 131700 3.19 4.97 0 0 1 0
3765 131700 3.19 5.67 0 0 1 0
2991 131700 3.19 10.44 0 0 1 0
4856 131700 3.19 4.18 0 0 0 1
5752 131700 3.19 49.13 0 0 0 1
3351 131700 3.19 10.57 0 0 0 1
3392 131700 3.19 6.05 0 0 0 1
3145 131700 3.19 5.46 0 0 0 1
3820 131700 3.19 19.11 0 0 0 1
4790 131700 3.65 38.84 1 0 0 0
2729 131700 3.65 11.59 1 0 0 0
3025 131700 3.65 6.92 0 1 0 0
2428 131700 3.65 29.97 0 1 0 0
2981 131700 3.65 10.41 0 1 0 0
3051 131700 3.65 3.56 0 1 0 0
6330 131700 3.65 64.87 0 1 0 0
3006 131700 3.65 11.96 0 0 1 0
3301 131700 3.65 12.67 0 0 1 0
5265 131700 3.65 44.39 0 0 1 0
3975 131700 3.65 4.78 0 0 1 0
2643 131700 3.65 3.22 0 0 1 0
3130 131700 3.65 9.06 0 0 0 1
3832 131700 3.65 18.63 0 0 0 1
3819 131700 3.65 5.23 0 0 0 1
3037 131700 3.65 5.69 0 0 0 1
4272 131700 3.65 10.59 0 0 0 1
10589 526903 12.62 49.13 1 0 0 0
8945 526903 12.62 35.71 1 0 0 0
7764 526903 12.62 10.57 0 1 0 0
8704 526903 12.62 19.11 0 1 0 0
7546 526903 12.62 6.05 0 1 0 0
7694 526903 12.62 4.97 0 1 0 0
10499 526903 12.62 67.99 0 1 0 0
7614 526903 12.62 5.68 0 0 1 0
8248 526903 12.62 11.9 0 0 1 0
8158 526903 12.62 3.19 0 0 1 0
8174 526903 12.62 12.79 0 0 1 0
8097 526903 12.62 5.46 0 0 1 0
9154 526903 12.62 5.67 0 0 0 1
10287 526903 12.62 25.56 0 0 0 1
7972 526903 12.62 10.44 0 0 0 1
7518 526903 12.62 8.3 0 0 0 1
9492 526903 12.62 4.18 0 0 0 1
8317 526903 12.67 3.65 1 0 0 0
8158 526903 12.67 5.23 0 1 0 0
9174 526903 12.67 18.63 0 1 0 0
8262 526903 12.67 10.59 0 1 0 0
10533 526903 12.67 64.87 0 1 0 0
10434 526903 12.67 38.84 0 1 0 0
8047 526903 12.67 11.96 0 1 0 0
7831 526903 12.67 11.59 0 0 1 0
8062 526903 12.67 3.22 0 0 1 0
8834 526903 12.67 29.97 0 0 1 0
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8753 526903 12.67 9.06 0 0 1 0
7663 526903 12.67 3.56 0 0 1 0
8290 526903 12.67 5.69 0 0 0 1
8435 526903 12.67 6.92 0 0 0 1
10802 526903 12.67 44.39 0 0 0 1
9391 526903 12.67 4.78 0 0 0 1
10280 526903 11.5 48.32 1 0 0 0
8461 526903 11.5 5.28 1 0 0 0
9152 526903 11.5 5.56 1 0 0 0
8380 526903 11.5 12.09 0 1 0 0
8171 526903 11.5 3.97 0 1 0 0
8386 526903 11.5 11.54 0 1 0 0
8212 526903 11.5 10.52 0 1 0 0
9103 526903 11.5 7.36 0 1 0 0
8461 526903 11.5 5.28 0 1 0 0
8443 526903 11.5 10.07 0 0 1 0
9253 526903 11.5 23.48 0 0 1 0
8220 526903 11.5 9.03 0 0 1 0
10435 526903 11.5 63.34 0 0 1 0
8627 526903 11.5 19.97 0 0 1 0
8196 526903 11.5 8.98 0 0 0 1
9431 526903 11.5 42.53 0 0 0 1
7917 526903 11.5 23.48 0 0 0 0
8186 526903 11.5 48.32 0 0 0 0
4350 526903 11.5 10.07 0 0 0 0
9341 462944 18.63 10.59 1 0 0 0
9545 462944 19.11 10.07 0 0 0 1
10624 462944 19.11 35.71 1 0 0 0
10665 462944 19.11 5.46 1 0 0 0
11698 462944 19.11 25.56 0 1 0 0
9516 462944 19.11 4.97 0 1 0 0
8815 462944 19.11 10.44 0 1 0 0
8389 462944 19.11 5.68 0 1 0 0
10475 462944 19.11 4.18 0 1 0 0
10170 462944 19.11 12.79 0 0 1 0
9192 462944 19.11 3.19 0 0 1 0
9198 462944 19.11 11.9 0 0 1 0
8764 462944 19.11 6.05 0 0 1 0
9996 462944 19.11 5.67 0 0 1 0
9219 462944 19.11 12.62 0 0 0 1
10801 462944 19.11 67.99 0 0 0 1
8631 462944 19.11 8.3 0 0 0 1
11110 462944 19.11 49.13 0 0 0 1
8101 462944 19.11 10.57 0 0 0 1
9696 462944 18.63 5.69 1 0 0 0
10542 462944 18.63 4.78 0 1 0 0
10069 462944 18.63 11.96 0 1 0 0
11789 462944 18.63 44.39 0 1 0 0
9416 462944 18.63 3.65 0 1 0 0
9543 462944 18.63 29.97 0 1 0 0
8919 462944 18.63 5.23 0 1 0 0
8958 462944 18.63 3.56 0 0 1 0
8933 462944 18.63 11.59 0 0 1 0
11251 462944 18.63 64.87 0 0 1 0
9589 462944 18.63 12.67 0 0 1 0
8870 462944 18.63 3.22 0 0 0 1
9108 462944 18.63 6.92 0 0 0 1
9544 462944 18.63 10.41 0 0 0 1
9611 462944 18.63 9.06 0 0 0 1
11798 462944 18.63 38.84 0 0 0 1
11269 462944 19.97 5.56 1 0 0 0
10411 462944 19.97 12.09 1 0 0 0
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10330 462944 19.97 11.5 0 1 0 0
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3597 208435 3.56 10.59 0 0 1 0
3647 208435 3.56 11.96 0 0 0 1
8287 208435 3.56 44.39 0 0 0 1
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4046 208435 3.56 3.22 0 0 0 1
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6041 208435 3.97 63.34 0 0 0 0
11379 283481 4.18 67.99 1 0 0 0
10702 283481 4.18 49.13 1 0 0 0
7455 283481 4.18 11.9 0 1 0 0
8425 283481 4.18 3.19 0 1 0 0
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7238 283481 4.18 4.97 0 0 1 0
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7776 283481 4.18 10.57 0 0 1 0
7870 283481 4.18 12.79 0 0 1 0
8122 283481 4.18 5.46 0 0 0 1
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10187 283481 4.18 25.56 0 0 0 1
8315 283481 4.18 19.11 0 0 0 1
8424 283481 4.18 10.44 0 0 0 1
7731 283481 4.18 8.3 0 0 0 1
8079 283481 4.78 6.92 1 0 0 0
7926 283481 4.78 3.22 0 1 0 0
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8397 283481 4.78 3.65 0 1 0 0
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8015 283481 4.78 3.56 0 0 0 1
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8504 283481 4.78 11.96 0 0 0 1
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2305 249511 5.69 3.56 0 0 1 0
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17622 479221 35.71 10.57 0 1 0 0
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18330 479221 35.71 19.11 0 0 1 0
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17287 479221 35.71 11.9 0 0 0 1
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15312 479221 29.97 5.69 0 0 1 0
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17699 479221 29.97 10.41 0 0 0 1
16100 479221 29.97 18.63 0 0 0 1
16252 479221 29.97 3.56 0 0 0 1
17874 479221 29.97 64.87 0 0 0 1
14058 479221 23.48 10.52 1 0 0 0
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14531 479221 23.48 11.5 1 0 0 0
14102 479221 23.48 10.07 0 1 0 0
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14903 479221 23.48 9.03 0 1 0 0
16411 479221 23.48 63.34 0 1 0 0
14687 479221 23.48 11.54 0 0 1 0
14363 479221 23.48 8.98 0 0 1 0
16062 479221 23.48 7.36 0 0 1 0
15361 479221 23.48 42.53 0 0 1 0
16134 479221 23.48 48.32 0 0 1 0
14256 479221 23.48 3.97 0 0 0 1
15863 479221 23.48 48.32 0 0 0 0
14196 479221 23.48 10.07 0 0 0 0
14120 479221 23.48 11.5 0 0 0 0
14825 479221 23.48 11.54 0 0 0 0
16946 479221 23.48 7.36 0 0 0 0
23867 1466998 67.99 12.79 1 0 0 0
24107 1466998 67.99 6.05 1 0 0 0
24041 1466998 67.99 5.46 0 1 0 0
24415 1466998 67.99 10.57 0 1 0 0
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24022 1466998 67.99 10.44 0 1 0 0
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23869 1466998 67.99 5.68 0 0 1 0
24495 1466998 67.99 4.18 0 0 1 0
23818 1466998 67.99 3.19 0 0 1 0
24081 1466998 67.99 11.9 0 0 1 0
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23622 1466998 67.99 5.67 0 0 0 1
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23942 1466998 67.99 8.3 0 0 0 1
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23085 1466998 64.87 18.63 1 0 0 0
22612 1466998 64.87 3.56 1 0 0 0
22960 1466998 64.87 10.59 0 1 0 0
22921 1466998 64.87 11.59 0 1 0 0
23510 1466998 64.87 4.78 0 1 0 0
22729 1466998 64.87 11.96 0 1 0 0
23047 1466998 64.87 10.41 0 1 0 0
22850 1466998 64.87 3.22 0 0 1 0
23426 1466998 64.87 29.97 0 0 1 0
22812 1466998 64.87 9.06 0 0 1 0
22446 1466998 64.87 5.69 0 0 1 0
23567 1466998 64.87 38.84 0 0 1 0
23185 1466998 64.87 6.92 0 0 0 1
22777 1466998 64.87 12.67 0 0 0 1
23508 1466998 64.87 44.39 0 0 0 1
23193 1466998 64.87 3.65 0 0 0 1
23006 1466998 64.87 5.23 0 0 0 1
22332 1466998 63.34 9.03 1 0 0 0
22347 1466998 63.34 11.54 1 0 0 0
23061 1466998 63.34 48.32 1 0 0 0
22887 1466998 63.34 19.97 0 1 0 0
22890 1466998 63.34 11.5 0 1 0 0
22701 1466998 63.34 10.07 0 1 0 0
22467 1466998 63.34 12.09 0 1 0 0
22357 1466998 63.34 5.28 0 1 0 0
22443 1466998 63.34 3.97 0 1 0 0
22824 1466998 63.34 8.98 0 0 1 0
22906 1466998 63.34 42.53 0 0 1 0
23059 1466998 63.34 7.36 0 0 1 0
23055 1466998 63.34 5.56 0 0 1 0
22564 1466998 63.34 23.48 0 0 0 1
18570 1466998 63.34 8.98 0 0 0 0
20329 1466998 63.34 42.53 0 0 0 0
19279 1466998 63.34 3.97 0 0 0 0
19541 1466998 63.34 19.97 0 0 0 0
19517 1466998 63.34 7.36 0 0 0 0
6519 466184 10.57 4.97 1 0 0 0
7169 466184 10.57 4.18 1 0 0 0
8107 466184 10.57 5.68 1 0 0 0
10668 466184 10.57 49.13 0 1 0 0
6650 466184 10.57 11.9 0 1 0 0
5726 466184 10.57 3.19 0 1 0 0
5224 466184 10.57 5.46 0 0 1 0
6297 466184 10.57 19.11 0 0 1 0
5011 466184 10.57 10.44 0 0 1 0
5075 466184 10.57 8.3 0 0 1 0
5434 466184 10.57 12.62 0 0 1 0
11758 466184 10.57 67.99 0 0 0 1
4531 466184 10.57 12.79 0 0 0 1
5373 466184 10.57 6.05 0 0 0 1
6343 466184 10.57 35.71 0 0 0 1
20051 466184 10.57 25.56 0 0 0 1
5482 466184 10.57 5.67 0 0 0 1
5066 466184 10.59 5.23 1 0 0 0
5040 466184 10.59 9.06 0 1 0 0
5100 466184 10.59 3.56 0 1 0 0
4679 466184 10.59 11.96 0 1 0 0
10940 466184 10.59 44.39 0 1 0 0
6035 466184 10.59 3.22 0 1 0 0
5364 466184 10.59 4.78 0 0 1 0
4424 466184 10.59 3.65 0 0 1 0
4486 466184 10.59 10.41 0 0 1 0
4962 466184 10.59 18.63 0 0 1 0
10445 466184 10.59 64.87 0 0 1 0
5973 466184 10.59 5.69 0 0 1 0
5415 466184 10.59 12.67 0 0 0 1
17792 466184 10.59 38.84 0 0 0 1
5581 466184 10.59 11.59 0 0 0 1
4997 466184 10.59 6.92 0 0 0 1
6893 466184 10.59 29.97 0 0 0 1
10181 466184 10.07 42.53 1 0 0 0
7007 466184 10.07 11.5 1 0 0 0
6621 466184 10.07 9.03 0 1 0 0
7309 466184 10.07 5.56 0 1 0 0
6114 466184 10.07 3.97 0 1 0 0
5521 466184 10.07 11.54 0 1 0 0
5263 466184 10.07 19.97 0 1 0 0
5400 466184 10.07 7.36 0 0 1 0
6141 466184 10.07 5.28 0 0 1 0
5736 466184 10.07 23.48 0 0 1 0
16104 466184 10.07 48.32 0 0 1 0
10810 466184 10.07 63.34 0 0 0 1
5057 466184 10.07 8.98 0 0 0 1
5732 466184 10.07 12.09 0 0 0 1
4000 466184 10.07 11.5 0 0 0 0
4000 466184 10.07 23.48 0 0 0 0
4200 466184 10.07 48.32 0 0 0 0
3551 143920 12.79 8.3 1 0 0 0
4025 143920 12.79 12.62 1 0 0 0
5591 143920 12.79 4.18 0 1 0 0
3868 143920 12.79 6.05 0 1 0 0
3566 143920 12.79 10.57 0 1 0 0
4525 143920 12.79 35.71 0 1 0 0
3752 143920 12.79 5.67 0 0 1 0
3182 143920 12.79 10.44 0 0 1 0
6152 143920 12.79 67.99 0 0 1 0
3548 143920 12.79 5.68 0 0 1 0
6876 143920 12.79 49.13 0 0 1 0
3199 143920 12.79 11.9 0 0 0 1
3386 143920 12.79 3.19 0 0 0 1
3411 143920 12.79 5.46 0 0 0 1
6892 143920 12.79 25.56 0 0 0 1
4920 143920 12.79 19.11 0 0 0 1
3193 143920 12.79 4.97 0 0 0 1
3054 143920 11.96 3.22 1 0 0 0
3262 143920 11.96 10.41 0 1 0 0
3509 143920 11.96 9.06 0 1 0 0
3471 143920 11.96 3.56 0 1 0 0
3101 143920 11.96 5.69 0 1 0 0
5956 143920 11.96 38.84 0 1 0 0
6232 143920 11.96 44.39 0 0 1 0
5456 143920 11.96 4.78 0 0 1 0
3186 143920 11.96 6.92 0 0 1 0
3751 143920 11.96 29.97 0 0 1 0
2973 143920 11.96 5.23 0 0 1 0
5548 143920 11.96 18.63 0 0 0 1
3219 143920 11.96 10.59 0 0 0 1
6595 143920 11.96 64.87 0 0 0 1
4886 143920 11.96 12.67 0 0 0 1
3082 143920 11.96 11.59 0 0 0 1
3516 143920 11.96 3.65 0 0 0 1
3807 143920 12.09 8.98 1 0 0 0
3607 143920 12.09 7.36 1 0 0 0
3163 143920 12.09 10.52 1 0 0 0
4981 143920 12.09 48.32 0 1 0 0
3276 143920 12.09 10.07 0 1 0 0
3278 143920 12.09 9.03 0 1 0 0
3850 143920 12.09 23.48 0 1 0 0
3439 143920 12.09 3.97 0 1 0 0
5545 143920 12.09 42.53 0 0 1 0
4749 143920 12.09 19.97 0 0 1 0
3656 143920 12.09 11.5 0 0 1 0
3520 143920 12.09 5.28 0 0 1 0
4392 143920 12.09 5.56 0 0 1 0
3057 143920 12.09 11.54 0 0 1 0
6542 143920 12.09 63.34 0 0 0 1
2785 143920 12.09 9.03 0 0 0 0
3057 143920 12.09 11.54 0 0 0 0
4379 143920 12.09 5.56 0 0 0 0
23934 787601 25.56 6.05 1 0 0 0
23625 787601 25.56 5.67 1 0 0 0
26185 787601 25.56 49.13 0 1 0 0
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24586 787601 25.56 12.62 0 1 0 0
25439 787601 25.56 4.18 0 1 0 0
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24023 787601 25.56 5.46 0 0 1 0
23901 787601 25.56 4.97 0 0 1 0
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24560 787601 25.56 8.3 0 0 0 1
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25466 787601 25.56 67.99 0 0 0 1
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23761 787601 38.84 10.41 1 0 0 0
23868 787601 38.84 3.56 0 1 0 0
26118 787601 38.84 64.87 0 1 0 0
25120 787601 38.84 5.69 0 1 0 0
26119 787601 38.84 10.59 0 1 0 0
25440 787601 38.84 11.59 0 1 0 0
24206 787601 38.84 6.92 0 0 1 0
25312 787601 38.84 18.63 0 0 1 0
24499 787601 38.84 3.65 0 0 1 0
24330 787601 38.84 5.23 0 0 1 0
24217 787601 38.84 3.22 0 0 1 0
25047 787601 38.84 9.06 0 0 0 1
25817 787601 38.84 29.97 0 0 0 1
25466 787601 38.84 12.67 0 0 0 1
25410 787601 38.84 11.96 0 0 0 1
26246 787601 38.84 4.78 0 0 0 1
25718 787601 38.84 44.39 0 0 0 1
26543 787601 48.32 7.36 1 0 0 0
26723 787601 48.32 10.07 1 0 0 0
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24743 787601 48.32 9.03 0 1 0 0
25520 787601 48.32 23.48 0 1 0 0
25298 787601 48.32 8.98 0 1 0 0
26382 787601 48.32 42.53 0 1 0 0
25719 787601 48.32 11.5 0 0 1 0
24547 787601 48.32 5.28 0 0 1 0
25640 787601 48.32 12.09 0 0 1 0
26103 787601 48.32 63.34 0 0 1 0
24911 787601 48.32 3.97 0 0 1 0
25199 787601 48.32 11.54 0 0 1 0
25308 787601 48.32 19.97 0 0 0 1
17000 787601 48.32 10.07 0 0 0 0
12000 787601 48.32 11.5 0 0 0 0
20000 787601 48.32 23.48 0 0 0 0
3915 146788 8.3 11.9 1 0 0 0
3229 146788 8.3 10.57 1 0 0 0
6671 146788 8.3 35.71 0 1 0 0
5937 146788 8.3 25.56 0 1 0 0
3639 146788 8.3 5.46 0 1 0 0
4274 146788 8.3 19.11 0 1 0 0
3781 146788 8.3 12.62 0 0 1 0
5612 146788 8.3 67.99 0 0 1 0
4498 146788 8.3 4.18 0 0 1 0
3520 146788 8.3 12.79 0 0 1 0
6323 146788 8.3 49.13 0 0 1 0
3622 146788 8.3 3.19 0 0 1 0
4085 146788 8.3 6.05 0 0 0 1
3978 146788 8.3 5.67 0 0 0 1
3788 146788 8.3 4.97 0 0 0 1
3973 146788 8.3 10.44 0 0 0 1
3268 146788 8.3 5.68 0 0 0 1
6852 146788 7.36 10.07 1 0 0 0
6237 146788 7.36 5.28 1 0 0 0
9194 146788 7.36 63.34 0 1 0 0
9177 146788 7.36 23.48 0 1 0 0
6170 146788 7.36 8.98 0 1 0 0
6295 146788 7.36 19.97 0 1 0 0
5878 146788 7.36 9.03 0 1 0 0
7172 146788 7.36 48.32 0 1 0 0
5741 146788 7.36 3.97 0 0 1 0
7093 146788 7.36 5.56 0 0 1 0
5774 146788 7.36 12.09 0 0 1 0
5690 146788 7.36 11.54 0 0 1 0
7717 146788 7.36 42.53 0 0 1 0
6511 146788 7.36 11.5 0 0 0 1
6989 146788 7.36 42.53 0 0 0 0
9006 146788 7.36 63.34 0 0 0 0
6052 146788 7.36 3.97 0 0 0 0
5094 146788 7.36 8.98 0 0 0 0
6198 146788 7.36 19.97 0 0 0 0
8845 146788 7.36 23.48 0 0 0 0
6219 127152 5.67 5.46 1 0 0 0
5984 127152 5.67 3.19 1 0 0 0
7303 127152 5.67 19.11 0 1 0 0
6887 127152 5.67 12.62 0 1 0 0
8083 127152 5.67 8.3 0 1 0 0
18978 127152 5.67 67.99 0 1 0 0
25222 127152 5.67 49.13 0 1 0 0
6093 127152 5.67 11.9 0 0 1 0
7206 127152 5.67 10.57 0 0 1 0
8070 127152 5.67 35.71 0 0 1 0
16129 127152 5.67 25.56 0 0 1 0
7646 127152 5.67 4.97 0 0 1 0
5415 127152 5.67 10.44 0 0 0 1
10480 127152 5.67 4.18 0 0 0 1
5998 127152 5.67 5.68 0 0 0 1
6289 127152 5.67 12.79 0 0 0 1
6146 127152 5.67 6.05 0 0 0 1
12308 127152 9.06 64.87 1 0 0 0
7128 127152 9.06 5.69 1 0 0 0
7653 127152 9.06 12.67 0 1 0 0
10130 127152 9.06 38.84 0 1 0 0
8741 127152 9.06 3.65 0 1 0 0
7719 127152 9.06 6.92 0 1 0 0
8167 127152 9.06 4.78 0 1 0 0
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8091 127152 9.06 5.23 0 0 1 0
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7219 127152 9.06 10.59 0 0 1 0
7373 127152 9.06 11.96 0 0 1 0
20659 127152 9.06 44.39 0 0 0 1
7158 127152 9.06 11.59 0 0 0 1
7503 127152 9.06 3.22 0 0 0 1
7654 127152 9.06 29.97 0 0 0 1
7747 127152 9.06 10.41 0 0 0 1
8631 127152 9.03 19.97 1 0 0 0
6867 127152 9.03 8.98 1 0 0 0
17989 127152 9.03 42.53 1 0 0 0
6798 127152 9.03 11.5 0 1 0 0
7485 127152 9.03 5.56 0 1 0 0
7085 127152 9.03 3.97 0 1 0 0
7235 127152 9.03 11.54 0 1 0 0
8534 127152 9.03 63.34 0 0 1 0
7785 127152 9.03 48.32 0 0 1 0
7017 127152 9.03 5.28 0 0 1 0
5951 127152 9.03 10.07 0 0 1 0
6709 127152 9.03 23.48 0 0 1 0
6999 127152 9.03 12.09 0 0 0 1
6175 127152 9.03 7.36 0 0 0 1
5927 127152 9.03 11.54 0 0 0 0
6703 127152 9.03 5.56 0 0 0 0
6118 127152 9.03 12.09 0 0 0 0
4565 183693 4.97 3.19 1 0 0 0
6178 183693 4.97 25.56 1 0 0 0
5389 183693 4.97 5.67 0 1 0 0
5079 183693 4.97 5.46 0 1 0 0
7382 183693 4.97 67.99 0 1 0 0
4305 183693 4.97 8.3 0 1 0 0
4216 183693 4.97 5.68 0 1 0 0
7849 183693 4.97 49.13 0 0 1 0
4248 183693 4.97 10.57 0 0 1 0
4238 183693 4.97 12.79 0 0 1 0
4746 183693 4.97 35.71 0 0 1 0
4227 183693 4.97 6.05 0 0 1 0
4946 183693 4.97 19.11 0 0 0 1
4234 183693 4.97 10.44 0 0 0 1
4379 183693 4.97 12.62 0 0 0 1
5464 183693 4.97 4.18 0 0 0 1
4240 183693 4.97 11.9 0 0 0 1
5465 183693 5.23 3.56 1 0 0 0
5634 183693 5.23 38.84 1 0 0 0
3984 183693 5.23 11.96 0 1 0 0
6957 183693 5.23 44.39 0 1 0 0
4492 183693 5.23 6.92 0 1 0 0
3863 183693 5.23 3.22 0 1 0 0
3845 183693 5.23 3.65 0 1 0 0
3768 183693 5.23 10.41 0 0 1 0
6071 183693 5.23 64.87 0 0 1 0
3794 183693 5.23 10.59 0 0 1 0
4078 183693 5.23 12.67 0 0 1 0
3927 183693 5.23 5.69 0 0 1 0
3931 183693 5.23 11.59 0 0 0 1
5368 183693 5.23 4.78 0 0 0 1
5142 183693 5.23 29.97 0 0 0 1
5165 183693 5.23 18.63 0 0 0 1
4432 183693 5.23 9.06 0 0 0 1
5082 183693 5.28 11.5 1 0 0 0
6087 183693 5.28 48.32 1 0 0 0
4434 183693 5.28 10.07 1 0 0 0
4360 183693 5.28 12.09 0 1 0 0
5634 183693 5.28 9.03 0 1 0 0
7836 183693 5.28 42.53 0 1 0 0
5394 183693 5.28 8.98 0 1 0 0
4327 183693 5.28 10.52 0 0 1 0
4142 183693 5.28 7.36 0 0 1 0
5251 183693 5.28 19.97 0 0 1 0
4951 183693 5.28 5.56 0 0 1 0
4565 183693 5.28 3.97 0 0 1 0
4463 183693 5.28 23.48 0 0 1 0
5922 183693 5.28 63.34 0 0 1 0
4581 183693 5.28 11.54 0 0 0 1
7566 147239 5.46 25.56 1 0 0 0
5500 147239 5.46 10.44 1 0 0 0
5745 147239 5.46 12.62 0 1 0 0
6924 147239 5.46 4.18 0 1 0 0
5354 147239 5.46 5.68 0 1 0 0
5563 147239 5.46 12.79 0 1 0 0
5369 147239 5.46 11.9 0 1 0 0
5658 147239 5.46 3.19 0 0 1 0
5215 147239 5.46 6.05 0 0 1 0
5824 147239 5.46 5.67 0 0 1 0
6667 147239 5.46 19.11 0 0 1 0
7795 147239 5.46 67.99 0 0 1 0
5490 147239 5.46 4.97 0 0 0 1
5232 147239 5.46 8.3 0 0 0 1
7739 147239 5.46 49.13 0 0 0 1
5404 147239 5.46 10.57 0 0 0 1
6045 147239 5.46 35.71 0 0 0 1
6012 147239 6.92 11.96 1 0 0 0
6287 147239 6.92 29.97 0 1 0 0
5185 147239 6.92 3.22 0 1 0 0
8080 147239 6.92 64.87 0 1 0 0
7229 147239 6.92 18.63 0 1 0 0
5602 147239 6.92 12.67 0 1 0 0
5329 147239 6.92 10.59 0 1 0 0
5401 147239 6.92 11.59 0 0 1 0
8283 147239 6.92 44.39 0 0 1 0
6359 147239 6.92 4.78 0 0 1 0
5457 147239 6.92 3.65 0 0 1 0
5654 147239 6.92 10.41 0 0 1 0
6391 147239 6.92 5.23 0 0 0 1
5765 147239 6.92 9.06 0 0 0 1
6707 147239 6.92 3.56 0 0 0 1
8214 147239 6.92 38.84 0 0 0 1
5621 147239 6.92 5.69 0 0 0 1
6387 147239 8.98 23.48 1 0 0 0
8299 147239 8.98 63.34 1 0 0 0
6526 147239 8.98 3.97 0 1 0 0
5514 147239 8.98 10.52 0 1 0 0
6659 147239 8.98 19.97 0 1 0 0
6023 147239 8.98 11.5 0 1 0 0
5701 147239 8.98 10.07 0 1 0 0
6628 147239 8.98 5.56 0 1 0 0
5845 147239 8.98 12.09 0 1 0 0
5778 147239 8.98 9.03 0 0 1 0
5668 147239 8.98 11.54 0 0 1 0
5982 147239 8.98 7.36 0 0 1 0
8294 147239 8.98 42.53 0 0 1 0
5970 147239 8.98 5.28 0 0 1 0
7440 147239 8.98 48.32 0 0 0 1
5385 147239 8.98 7.36 0 0 0 0
6226 147239 8.98 19.97 0 0 0 0
6905 147239 8.98 42.53 0 0 0 0
7566 147239 8.98 63.34 0 0 0 0
6033 147239 8.98 3.97 0 0 0 0
3338 162295 10.44 10.57 1 0 0 0
2778 162295 10.44 11.9 1 0 0 0
2876 162295 10.44 3.19 0 1 0 0
3059 162295 10.44 5.67 0 1 0 0
2827 162295 10.44 4.97 0 1 0 0
3819 162295 10.44 12.62 0 1 0 0
3319 162295 10.44 8.3 0 1 0 0
5529 162295 10.44 4.18 0 0 1 0
2791 162295 10.44 6.05 0 0 1 0
6521 162295 10.44 49.13 0 0 1 0
2959 162295 10.44 5.46 0 0 1 0
4378 162295 10.44 35.71 0 0 1 0
6042 162295 10.44 25.56 0 0 0 1
3715 162295 10.44 19.11 0 0 0 1
6219 162295 10.44 67.99 0 0 0 1
2890 162295 10.44 5.68 0 0 0 1
3134 162295 10.44 12.79 0 0 0 1
3544 162295 10.41 10.59 1 0 0 0
3915 162295 10.41 12.67 1 0 0 0
3139 162295 10.41 11.59 0 1 0 0
2989 162295 10.41 6.92 0 1 0 0
2856 162295 10.41 3.22 0 1 0 0
5619 162295 10.41 29.97 0 1 0 0
3955 162295 10.41 18.63 0 1 0 0
3027 162295 10.41 5.69 0 1 0 0
3760 162295 10.41 9.06 0 0 1 0
6323 162295 10.41 38.84 0 0 1 0
3362 162295 10.41 11.96 0 0 1 0
6263 162295 10.41 44.39 0 0 1 0
5720 162295 10.41 4.78 0 0 0 1
3035 162295 10.41 3.65 0 0 0 1
6509 162295 10.41 64.87 0 0 0 1
3123 162295 10.41 5.23 0 0 0 1
3332 162295 10.41 3.56 0 0 0 1
3298 162295 11.54 3.97 1 0 0 0
4579 162295 11.54 23.48 1 0 0 0
2963 162295 11.54 8.98 1 0 0 0
5861 162295 11.54 42.53 0 1 0 0
4549 162295 11.54 7.36 0 1 0 0
6211 162295 11.54 48.32 0 1 0 0
2942 162295 11.54 5.28 0 1 0 0
3181 162295 11.54 12.09 0 1 0 0
5019 162295 11.54 5.56 0 1 0 0
6590 162295 11.54 63.34 0 0 1 0
4528 162295 11.54 19.97 0 0 1 0
3744 162295 11.54 11.5 0 0 1 0
3096 162295 11.54 10.07 0 0 1 0
2893 162295 11.54 9.03 0 0 0 1
3946 162295 11.54 5.56 0 0 0 0
2838 162295 11.54 12.09 0 0 0 0
2804 162295 11.54 9.03 0 0 0 0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	18 seconds
R Server	'Gwilym Jenkins' @ jenkins.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 & 18 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285904&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]18 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285904&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285904&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	18 seconds
R Server	'Gwilym Jenkins' @ jenkins.wessa.net

Multiple Linear Regression - Estimated Regression Equation
Toeschouwers[t] = + 1274.2 -0.00147327inwonersaantal_thuis[t] + 410.217reputatiecoef_thuisploeg[t] + 53.957reputatiecoef_bezoeker[t] + 1258.01`juli-aug`[t] + 1369.17`sep-nov`[t] + 1254.4`dec-feb`[t] + 1368.66`ma-eind`[t] -194.635Q1[t] -93.5148Q2[t] -6.09519Q3[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Estimated Regression Equation \tabularnewline
Toeschouwers[t] =  +  1274.2 -0.00147327inwonersaantal_thuis[t] +  410.217reputatiecoef_thuisploeg[t] +  53.957reputatiecoef_bezoeker[t] +  1258.01`juli-aug`[t] +  1369.17`sep-nov`[t] +  1254.4`dec-feb`[t] +  1368.66`ma-eind`[t] -194.635Q1[t] -93.5148Q2[t] -6.09519Q3[t]  + e[t] \tabularnewline
Warning: you did not specify the column number of the endogenous series! The first column was selected by default. \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285904&T=1

[TABLE]
[ROW][C]Multiple Linear Regression - Estimated Regression Equation[/C][/ROW]
[ROW][C]Toeschouwers[t] =  +  1274.2 -0.00147327inwonersaantal_thuis[t] +  410.217reputatiecoef_thuisploeg[t] +  53.957reputatiecoef_bezoeker[t] +  1258.01`juli-aug`[t] +  1369.17`sep-nov`[t] +  1254.4`dec-feb`[t] +  1368.66`ma-eind`[t] -194.635Q1[t] -93.5148Q2[t] -6.09519Q3[t]  + e[t][/C][/ROW]
[ROW][C]Warning: you did not specify the column number of the endogenous series! The first column was selected by default.[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285904&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285904&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
Toeschouwers[t] = + 1274.2 -0.00147327inwonersaantal_thuis[t] + 410.217reputatiecoef_thuisploeg[t] + 53.957reputatiecoef_bezoeker[t] + 1258.01`juli-aug`[t] + 1369.17`sep-nov`[t] + 1254.4`dec-feb`[t] + 1368.66`ma-eind`[t] -194.635Q1[t] -93.5148Q2[t] -6.09519Q3[t] + e[t]
Warning: you did not specify the column number of the endogenous series! The first column was selected by default.

Multiple Linear Regression - Ordinary Least Squares
Variable	Parameter	S.D.	T-STATH0: parameter = 0	2-tail p-value	1-tail p-value
(Intercept)	+1274	557	+2.2880e+00	0.02239	0.0112
inwonersaantal_thuis	-0.001473	0.000661	-2.2290e+00	0.02607	0.01304
reputatiecoef_thuisploeg	+410.2	12.89	+3.1810e+01	8.762e-149	4.381e-149
reputatiecoef_bezoeker	+53.96	6.969	+7.7420e+00	2.657e-14	1.328e-14
`juli-aug`	+1258	589.8	+2.1330e+00	0.03322	0.01661
`sep-nov`	+1369	520.4	+2.6310e+00	0.008664	0.004332
`dec-feb`	+1254	522.4	+2.4010e+00	0.01655	0.008277
`ma-eind`	+1369	533.8	+2.5640e+00	0.01051	0.005254
Q1	-194.6	336.8	-5.7790e-01	0.5634	0.2817
Q2	-93.52	337.1	-2.7740e-01	0.7815	0.3908
Q3	-6.095	337.3	-1.8070e-02	0.9856	0.4928

\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) & +1274 &  557 & +2.2880e+00 &  0.02239 &  0.0112 \tabularnewline
inwonersaantal_thuis & -0.001473 &  0.000661 & -2.2290e+00 &  0.02607 &  0.01304 \tabularnewline
reputatiecoef_thuisploeg & +410.2 &  12.89 & +3.1810e+01 &  8.762e-149 &  4.381e-149 \tabularnewline
reputatiecoef_bezoeker & +53.96 &  6.969 & +7.7420e+00 &  2.657e-14 &  1.328e-14 \tabularnewline
`juli-aug` & +1258 &  589.8 & +2.1330e+00 &  0.03322 &  0.01661 \tabularnewline
`sep-nov` & +1369 &  520.4 & +2.6310e+00 &  0.008664 &  0.004332 \tabularnewline
`dec-feb` & +1254 &  522.4 & +2.4010e+00 &  0.01655 &  0.008277 \tabularnewline
`ma-eind` & +1369 &  533.8 & +2.5640e+00 &  0.01051 &  0.005254 \tabularnewline
Q1 & -194.6 &  336.8 & -5.7790e-01 &  0.5634 &  0.2817 \tabularnewline
Q2 & -93.52 &  337.1 & -2.7740e-01 &  0.7815 &  0.3908 \tabularnewline
Q3 & -6.095 &  337.3 & -1.8070e-02 &  0.9856 &  0.4928 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285904&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]+1274[/C][C] 557[/C][C]+2.2880e+00[/C][C] 0.02239[/C][C] 0.0112[/C][/ROW]
[ROW][C]inwonersaantal_thuis[/C][C]-0.001473[/C][C] 0.000661[/C][C]-2.2290e+00[/C][C] 0.02607[/C][C] 0.01304[/C][/ROW]
[ROW][C]reputatiecoef_thuisploeg[/C][C]+410.2[/C][C] 12.89[/C][C]+3.1810e+01[/C][C] 8.762e-149[/C][C] 4.381e-149[/C][/ROW]
[ROW][C]reputatiecoef_bezoeker[/C][C]+53.96[/C][C] 6.969[/C][C]+7.7420e+00[/C][C] 2.657e-14[/C][C] 1.328e-14[/C][/ROW]
[ROW][C]`juli-aug`[/C][C]+1258[/C][C] 589.8[/C][C]+2.1330e+00[/C][C] 0.03322[/C][C] 0.01661[/C][/ROW]
[ROW][C]`sep-nov`[/C][C]+1369[/C][C] 520.4[/C][C]+2.6310e+00[/C][C] 0.008664[/C][C] 0.004332[/C][/ROW]
[ROW][C]`dec-feb`[/C][C]+1254[/C][C] 522.4[/C][C]+2.4010e+00[/C][C] 0.01655[/C][C] 0.008277[/C][/ROW]
[ROW][C]`ma-eind`[/C][C]+1369[/C][C] 533.8[/C][C]+2.5640e+00[/C][C] 0.01051[/C][C] 0.005254[/C][/ROW]
[ROW][C]Q1[/C][C]-194.6[/C][C] 336.8[/C][C]-5.7790e-01[/C][C] 0.5634[/C][C] 0.2817[/C][/ROW]
[ROW][C]Q2[/C][C]-93.52[/C][C] 337.1[/C][C]-2.7740e-01[/C][C] 0.7815[/C][C] 0.3908[/C][/ROW]
[ROW][C]Q3[/C][C]-6.095[/C][C] 337.3[/C][C]-1.8070e-02[/C][C] 0.9856[/C][C] 0.4928[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285904&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285904&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)	+1274	557	+2.2880e+00	0.02239	0.0112
inwonersaantal_thuis	-0.001473	0.000661	-2.2290e+00	0.02607	0.01304
reputatiecoef_thuisploeg	+410.2	12.89	+3.1810e+01	8.762e-149	4.381e-149
reputatiecoef_bezoeker	+53.96	6.969	+7.7420e+00	2.657e-14	1.328e-14
`juli-aug`	+1258	589.8	+2.1330e+00	0.03322	0.01661
`sep-nov`	+1369	520.4	+2.6310e+00	0.008664	0.004332
`dec-feb`	+1254	522.4	+2.4010e+00	0.01655	0.008277
`ma-eind`	+1369	533.8	+2.5640e+00	0.01051	0.005254
Q1	-194.6	336.8	-5.7790e-01	0.5634	0.2817
Q2	-93.52	337.1	-2.7740e-01	0.7815	0.3908
Q3	-6.095	337.3	-1.8070e-02	0.9856	0.4928

Multiple Linear Regression - Regression Statistics
Multiple R	0.8808
R-squared	0.7758
Adjusted R-squared	0.7733
F-TEST (value)	306.9
F-TEST (DF numerator)	10
F-TEST (DF denominator)	887
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3562
Sum Squared Residuals	1.125e+10

\begin{tabular}{lllllllll}
\hline
Multiple Linear Regression - Regression Statistics \tabularnewline
Multiple R &  0.8808 \tabularnewline
R-squared &  0.7758 \tabularnewline
Adjusted R-squared &  0.7733 \tabularnewline
F-TEST (value) &  306.9 \tabularnewline
F-TEST (DF numerator) & 10 \tabularnewline
F-TEST (DF denominator) & 887 \tabularnewline
p-value &  0 \tabularnewline
Multiple Linear Regression - Residual Statistics \tabularnewline
Residual Standard Deviation &  3562 \tabularnewline
Sum Squared Residuals &  1.125e+10 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=285904&T=3

[TABLE]
[ROW][C]Multiple Linear Regression - Regression Statistics[/C][/ROW]
[ROW][C]Multiple R[/C][C] 0.8808[/C][/ROW]
[ROW][C]R-squared[/C][C] 0.7758[/C][/ROW]
[ROW][C]Adjusted R-squared[/C][C] 0.7733[/C][/ROW]
[ROW][C]F-TEST (value)[/C][C] 306.9[/C][/ROW]
[ROW][C]F-TEST (DF numerator)[/C][C]10[/C][/ROW]
[ROW][C]F-TEST (DF denominator)[/C][C]887[/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] 3562[/C][/ROW]
[ROW][C]Sum Squared Residuals[/C][C] 1.125e+10[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=285904&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=285904&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	0.8808
R-squared	0.7758
Adjusted R-squared	0.7733
F-TEST (value)	306.9
F-TEST (DF numerator)	10
F-TEST (DF denominator)	887
p-value	0
Multiple Linear Regression - Residual Statistics
Residual Standard Deviation	3562
Sum Squared Residuals	1.125e+10

Figure 1

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Parameters (Session):

Parameters (R input):

par1 = ; par2 = Include Quarterly Dummies ; par3 = No Linear Trend ; par4 = ; par5 = ;

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

library(lattice)
library(lmtest)
n25 <- 25 #minimum number of obs. for Goldfeld-Quandt test
mywarning <- ''
par1 <- as.numeric(par1)
if(is.na(par1)) {
par1 <- 1
mywarning = 'Warning: you did not specify the column number of the endogenous series! The first column was selected by default.'
}
if (par4=='') par4 <- 0
par4 <- as.numeric(par4)
if (par5=='') par5 <- 0
par5 <- as.numeric(par5)
x <- na.omit(t(y))
k <- length(x[1,])
n <- length(x[,1])
x1 <- cbind(x[,par1], x[,1:k!=par1])
mycolnames <- c(colnames(x)[par1], colnames(x)[1:k!=par1])
colnames(x1) <- mycolnames #colnames(x)[par1]
x <- x1
if (par3 == 'First Differences'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'Seasonal Differences (s=12)'){
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if (par3 == 'First and Seasonal Differences (s=12)'){
(n <- n -1)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+1,j] - x[i,j]
}
}
x <- x2
(n <- n - 12)
x2 <- array(0, dim=c(n,k), dimnames=list(1:n, paste('(1-B12)',colnames(x),sep='')))
for (i in 1:n) {
for (j in 1:k) {
x2[i,j] <- x[i+12,j] - x[i,j]
}
}
x <- x2
}
if(par4 > 0) {
x2 <- array(0, dim=c(n-par4,par4), dimnames=list(1:(n-par4), paste(colnames(x)[par1],'(t-',1:par4,')',sep='')))
for (i in 1:(n-par4)) {
for (j in 1:par4) {
x2[i,j] <- x[i+par4-j,par1]
}
}
x <- cbind(x[(par4+1):n,], x2)
n <- n - par4
}
if(par5 > 0) {
x2 <- array(0, dim=c(n-par5*12,par5), dimnames=list(1:(n-par5*12), paste(colnames(x)[par1],'(t-',1:par5,'s)',sep='')))
for (i in 1:(n-par5*12)) {
for (j in 1:par5) {
x2[i,j] <- x[i+par5*12-j*12,par1]
}
}
x <- cbind(x[(par5*12+1):n,], x2)
n <- n - par5*12
}
if (par2 == 'Include Monthly Dummies'){
x2 <- array(0, dim=c(n,11), dimnames=list(1:n, paste('M', seq(1:11), sep ='')))
for (i in 1:11){
x2[seq(i,n,12),i] <- 1
}
x <- cbind(x, x2)
}
if (par2 == 'Include Quarterly Dummies'){
x2 <- array(0, dim=c(n,3), dimnames=list(1:n, paste('Q', seq(1:3), sep ='')))
for (i in 1:3){
x2[seq(i,n,4),i] <- 1
}
x <- cbind(x, x2)
}
(k <- length(x[n,]))
if (par3 == 'Linear Trend'){
x <- cbind(x, c(1:n))
colnames(x)[k+1] <- 't'
}
x
(k <- length(x[n,]))
head(x)
df <- as.data.frame(x)
(mylm <- lm(df))
(mysum <- summary(mylm))
if (n > n25) {
kp3 <- k + 3
nmkm3 <- n - k - 3
gqarr <- array(NA, dim=c(nmkm3-kp3+1,3))
numgqtests <- 0
numsignificant1 <- 0
numsignificant5 <- 0
numsignificant10 <- 0
for (mypoint in kp3:nmkm3) {
j <- 0
numgqtests <- numgqtests + 1
for (myalt in c('greater', 'two.sided', 'less')) {
j <- j + 1
gqarr[mypoint-kp3+1,j] <- gqtest(mylm, point=mypoint, alternative=myalt)$p.value
}
if (gqarr[mypoint-kp3+1,2] < 0.01) numsignificant1 <- numsignificant1 + 1
if (gqarr[mypoint-kp3+1,2] < 0.05) numsignificant5 <- numsignificant5 + 1
if (gqarr[mypoint-kp3+1,2] < 0.10) numsignificant10 <- numsignificant10 + 1
}
gqarr
}
bitmap(file='test0.png')
plot(x[,1], type='l', main='Actuals and Interpolation', ylab='value of Actuals and Interpolation (dots)', xlab='time or index')
points(x[,1]-mysum$resid)
grid()
dev.off()
bitmap(file='test1.png')
plot(mysum$resid, type='b', pch=19, main='Residuals', ylab='value of Residuals', xlab='time or index')
grid()
dev.off()
bitmap(file='test2.png')
hist(mysum$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$coefficients)[i] != 't') myeq <- paste(myeq, '[t]', sep='')
}
}
myeq <- paste(myeq, ' + e[t]')
a<-table.row.start(a)
a<-table.element(a, myeq)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, mywarning)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,hyperlink('ols1.htm','Multiple Linear Regression - Ordinary Least Squares',''), 6, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Variable',header=TRUE)
a<-table.element(a,'Parameter',header=TRUE)
a<-table.element(a,'S.D.',header=TRUE)
a<-table.element(a,'T-STAT
H0: parameter = 0',header=TRUE)
a<-table.element(a,'2-tail p-value',header=TRUE)
a<-table.element(a,'1-tail p-value',header=TRUE)
a<-table.row.end(a)
for (i in 1:k){
a<-table.row.start(a)
a<-table.element(a,rownames(mysum$coefficients)[i],header=TRUE)
a<-table.element(a,formatC(signif(mysum$coefficients[i,1],5),format='g',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,2],5),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,3],4),format='e',flag='+'))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4],4),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$coefficients[i,4]/2,4),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Regression Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple R',1,TRUE)
a<-table.element(a,formatC(signif(sqrt(mysum$r.squared),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Adjusted R-squared',1,TRUE)
a<-table.element(a,formatC(signif(mysum$adj.r.squared,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (value)',1,TRUE)
a<-table.element(a,formatC(signif(mysum$fstatistic[1],6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF numerator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[2],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'F-TEST (DF denominator)',1,TRUE)
a<-table.element(a, signif(mysum$fstatistic[3],6))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'p-value',1,TRUE)
a<-table.element(a,formatC(signif(1-pf(mysum$fstatistic[1],mysum$fstatistic[2],mysum$fstatistic[3]),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Residual Statistics', 2, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residual Standard Deviation',1,TRUE)
a<-table.element(a,formatC(signif(mysum$sigma,6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Sum Squared Residuals',1,TRUE)
a<-table.element(a,formatC(signif(sum(myerror*myerror),6),format='g',flag=' '))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')
if(n < 200) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a, 'Multiple Linear Regression - Actuals, Interpolation, and Residuals', 4, TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Time or Index', 1, TRUE)
a<-table.element(a, 'Actuals', 1, TRUE)
a<-table.element(a, 'Interpolation
Forecast', 1, TRUE)
a<-table.element(a, 'Residuals
Prediction Error', 1, TRUE)
a<-table.row.end(a)
for (i in 1:n) {
a<-table.row.start(a)
a<-table.element(a,i, 1, TRUE)
a<-table.element(a,formatC(signif(x[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(x[i]-mysum$resid[i],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(mysum$resid[i],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable4.tab')
if (n > n25) {
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'p-values',header=TRUE)
a<-table.element(a,'Alternative Hypothesis',3,header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'breakpoint index',header=TRUE)
a<-table.element(a,'greater',header=TRUE)
a<-table.element(a,'2-sided',header=TRUE)
a<-table.element(a,'less',header=TRUE)
a<-table.row.end(a)
for (mypoint in kp3:nmkm3) {
a<-table.row.start(a)
a<-table.element(a,mypoint,header=TRUE)
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,1],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,2],6),format='g',flag=' '))
a<-table.element(a,formatC(signif(gqarr[mypoint-kp3+1,3],6),format='g',flag=' '))
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable5.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Meta Analysis of Goldfeld-Quandt test for Heteroskedasticity',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Description',header=TRUE)
a<-table.element(a,'# significant tests',header=TRUE)
a<-table.element(a,'% significant tests',header=TRUE)
a<-table.element(a,'OK/NOK',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'1% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant1,6))
a<-table.element(a,formatC(signif(numsignificant1/numgqtests,6),format='g',flag=' '))
if (numsignificant1/numgqtests < 0.01) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'5% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant5,6))
a<-table.element(a,signif(numsignificant5/numgqtests,6))
if (numsignificant5/numgqtests < 0.05) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'10% type I error level',header=TRUE)
a<-table.element(a,signif(numsignificant10,6))
a<-table.element(a,signif(numsignificant10/numgqtests,6))
if (numsignificant10/numgqtests < 0.1) dum <- 'OK' else dum <- 'NOK'
a<-table.element(a,dum)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable6.tab')
}
}

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code