In statistics, you frequently have to collect and analyse data. The vast majority of the times you do this, you will get a large amount of data that is almost impossible to deal with unless you write it as a list.

To get round this, we use frequency tables.

**What is a frequency table?**

The word frequency just means **how often something happens**, so a frequency table is just a table that groups things together and tells you how often something happens.

**Discrete and continuous data**

In statistics, there are two types of data - discrete data and continuous data. For each type, we have to use a slightly different approach.

**Continuous data**** **is something that could be broken down into smaller measurements (eg. height - whatever units you are using to measure it, there's always a smaller unit.)

**Discrete data** is something that can't be split down (e.g. hair colour, the number of rooms in a house)

**Example 1:**

**I do a survey to record the colour of cars and get the following colours:**

Red, Blue, Blue, Red, Green, Blue, Red, Red, Blue, Green, Red, Black, Blue, Blue, Red, Green, Green, Red, Red, Blue, Green

Put this data into a frequency table.

The first thing we need to do is create a table to put this data in. We need two columns, one for the category (in this case colour) and one for the frequency (how many times we get this colour).

Colour | Frequency |

Red | |

Blue | |

Green | |

Black |

We now need to count how many of each colour we have, and this will give us the following, complete frequency table:

Colour |
Frequency |

Red | 8 |

Blue | 7 |

Green | 5 |

Black | 1 |

**Example 2:**

**I measure the heights of some children and get the following results (all given in centimetres):**

132, 145, 165, 133, 142, 147, 152, 161, 164, 131, 135, 137, 149, 148, 171, 140, 150

Put this data into a grouped frequency table.

This one is slightly different - we can't use exactly the same approach as before because all the numbers are different. This will usually happen if you are measuring something that can be measured on a scale, such as heights or weights.

To deal with this, we use a** ****grouped **frequency table. The difference between this and a frequency table is that instead of having one thing for the category, we have a **range.**

Height (cm) |
Frequency |

130 < h ≤ 140 | |

140 < h ≤ 150 | |

150 < h ≤ 160 | |

160 < h ≤ 170 | |

170 < h ≤ 180 |

Let's take a couple as examples.

The first number is 132, which would clearly go into the 130 < h ≤ 140 box.

The last number is 150 - you have to think about this a bit more. 140 < h ≤ 150 means anything up to and including 150, whereas 150 < h ≤ 160 means anything above 150.

150 would go into the 140 < h ≤ 150 box.

Height (cm) |
Frequency |

130 < h ≤ 140 | 6 |

140 < h ≤ 150 | 6 |

150 < h ≤ 160 | 1 |

160 < h ≤ 170 | 3 |

170 < h ≤ 180 | 1 |

Time for some questions now.