Mega Code Archive
Calculating Linear Regressions or Correlation Coefficients
<![CDATA[
mysql>
mysql> CREATE TABLE testscore
-> (
-> subject INT UNSIGNED NOT NULL AUTO_INCREMENT,
-> age INT UNSIGNED NOT NULL,
-> sex ENUM('M','F') NOT NULL,
-> score INT,
-> PRIMARY KEY (subject)
-> );
Query OK, 0 rows affected (0.01 sec)
mysql>
mysql> INSERT INTO testscore (age,sex,score)
-> VALUES
-> (5,'M',5),
-> (5,'M',4),
-> (5,'F',6),
-> (5,'F',7),
-> (6,'M',8),
-> (6,'M',9),
-> (6,'F',4),
-> (6,'F',6),
-> (7,'M',8),
-> (7,'M',6),
-> (7,'F',9),
-> (7,'F',7),
-> (8,'M',9),
-> (8,'M',6),
-> (8,'F',7),
-> (8,'F',10),
-> (9,'M',9),
-> (9,'M',7),
-> (9,'F',10),
-> (9,'F',9)
-> ;
Query OK, 20 rows affected (0.00 sec)
Records: 20 Duplicates: 0 Warnings: 0
mysql>
mysql> SELECT subject, age, sex, score FROM testscore ORDER BY subject;
+---------+-----+-----+-------+
| subject | age | sex | score |
+---------+-----+-----+-------+
| 1 | 5 | M | 5 |
| 2 | 5 | M | 4 |
| 3 | 5 | F | 6 |
| 4 | 5 | F | 7 |
| 5 | 6 | M | 8 |
| 6 | 6 | M | 9 |
| 7 | 6 | F | 4 |
| 8 | 6 | F | 6 |
| 9 | 7 | M | 8 |
| 10 | 7 | M | 6 |
| 11 | 7 | F | 9 |
| 12 | 7 | F | 7 |
| 13 | 8 | M | 9 |
| 14 | 8 | M | 6 |
| 15 | 8 | F | 7 |
| 16 | 8 | F | 10 |
| 17 | 9 | M | 9 |
| 18 | 9 | M | 7 |
| 19 | 9 | F | 10 |
| 20 | 9 | F | 9 |
+---------+-----+-----+-------+
20 rows in set (0.00 sec)
mysql>
mysql> SELECT
-> @n := COUNT(score) AS N,
-> @meanX := AVG(age) AS "X mean",
-> @sumX := SUM(age) AS "X sum",
-> @sumXX := SUM(age*age) "X sum of squares",
-> @meanY := AVG(score) AS "Y mean",
-> @sumY := SUM(score) AS "Y sum",
-> @sumYY := SUM(score*score) "Y sum of square",
-> @sumXY := SUM(age*score) AS "X*Y sum"
-> FROM testscore\G
*************************** 1. row ***************************
N: 20
X mean: 7.000000000
X sum: 140
X sum of squares: 1020
Y mean: 7.300000000
Y sum: 146
Y sum of square: 1130
X*Y sum: 1053
1 row in set (0.00 sec)
mysql>
mysql>
mysql> drop table testscore;
Query OK, 0 rows affected (0.00 sec)
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