Data stored in a database is often also useful for statistical purposes. If you own a web-shop you want to be able to create a report about turnover. You can get statistical information by using GROUP BY, eg.

SELECT DATE_FORMAT(invoice.date, '%M') AS `month`, COUNT(*) AS `invoice_count`, SUM(`invoice`.`amount`) AS `turnover`
FROM `invoice`
WHERE `date` BETWEEN '2008-01-01' AND '2008-12-31'
GROUP BY MONTH(`invoice`.`date`)
month     invoice_count  turnover
January   84             9532.26
February  141            20857.61
March     91             10922.71
April     112            15044.48
May       101            9676.60
June      137            12860.88
July      281            34291.20
August    191            26377.66
September 103            16324.78
October   99             12873.23

If you are selling a wide variety of products, you might like to see the turnover for each product category. You could do this with a simple GROUP BY as:

SELECT DATE_FORMAT(`invoice`.`date`, '%M') AS `month`, `category`.`description` AS `category`, COUNT(*) AS `product_count`, SUM(`invoice_product`.`amount`) AS `turnover`
FROM `invoice` INNER JOIN `invoice_product` ON `invoice`.`id` = `invoice_product`.`invoice_id` LEFT JOIN `product` ON `invoice_product`.`product_id` = `product`.`id` LEFT JOIN `category` ON `product`.`category_id` = `category`.`id`
WHERE `date` BETWEEN '2008-01-01' AND '2008-12-31' 
GROUP BY MONTH(`invoice`.`date`), `category`.`id`
month     category   product_count  turnover
January   Hardware   62             4821.31
January   Software   51             4419.41
January   Cables     12             291.54
February  Hardware   71             8408.93
February  Software   101            11726.36
February  Cables     17             312.32
February  Other      2              410.00
March     Hardware   21             2371.58
March     Software   81             8238.81
March     Cables     13             312.32
...

This would give you each category in a different row, ordered by month. Though this contains all the information the format is far from nice. Instead you would like to have 1 row per month with each category as a column as the information about the invoices as well.

SELECT DATE_FORMAT(`invoice`.`date`, '%M') AS `month`, COUNT(DISTINCT `invoice`.`id`) AS `product_count`, COUNT(*) AS `invoice_count`, SUM(`invoice_product`.`amount`) AS `turnover`,
  SUM(`product`.`category_id`=1) AS `hardware_count`, SUM(IF(`product`.`category_id`=1, `invoice_product`.`amount`, 0)) AS `hardware_turnover`,
  SUM(`product`.`category_id`=2) AS `software_count`, SUM(IF(`product`.`category_id`=2, `invoice_product`.`amount`, 0)) AS `software_turnover`,
  SUM(`product`.`category_id`=3) AS `cables_count`, SUM(IF(`product`.`category_id`=3, `invoice_product`.`amount`, 0)) AS `cables_turnover`,
  SUM(`product`.`category_id`=4) AS `other_count`, SUM(IF(`product`.`category_id`=4, `invoice_product`.`amount`, 0)) AS `other_turnover`
FROM `invoice` INNER JOIN `invoice_product` ON `invoice`.`id` = `invoice_product`.`invoice_id` LEFT JOIN `product` ON `invoice_product`.`product_id` = `product`.`id` LEFT JOIN `category` ON `product`.`category_id` = `category`.`id`
WHERE `date` BETWEEN '2008-01-01' AND '2008-12-31'
GROUP BY MONTH(`invoice`.`date`), `category`.`id`
month     invoice_count  turnover    hardware_count  hardware_turnover  software_count  software_turnover  cables_count  cables_turnover  other_count  other_turnover
January   84             9532.26     62              4821.31            51              4419.41            12            291.54           0            0
February  141            20857.61    71              8408.93            101             11726.36           17            312.32           2            410.00
March     91             10922.71    21              2371.58            81              8238.81            13            312.32           0            0
...

The big downside of this method is that you need to modify the query if a category is added. This can be solved though by dynamically creating the query in a PHP script.

If you want to do advanced statistical you should have a look at OLAP cubes. Pentaho is an open-source reporting app which supports MySQL. http://mondrian.pentaho.org/

All data is fictional. The SQL queries are untested.