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1b NOT PRINTED
Statistics
| Question | Answer |
|---|---|
| Examples of processing data | rounding, grouping and finding the mean |
| Raw data | hasn’t been processed in any way. (unprocessed) |
| Quantitative data | can be measured with a number. Tends to be easier to analyse than qualitative. Either discrete or continuous. (measures quantities) |
| Qualitative data | uses words to describe it. Often subjective, and is often harder to analyse than quantitative. (is descriptive) |
| Categorical scales | give names or numbers to classes of qualitative data so the data can be more easily processed. The numbers are only used for labelling the classes - with no other meaning |
| Ordinal scales | give numbers to the classes, which can be ordered in a meaningful way. Discrete data: countable in whole numbers, or can only take certain individual values. (can be measured exactly) |
| Continuous data | could always be more accurately measured. (can take any value at an interval) |
| Discrete data | countable in whole numbers, or can only take certain individual values. (can be measured exactly) |
| Variable | anything that can be measured that may change |
| Bivariate data | made of two variables. Can be qualitative/quantitative, discrete/continuous, grouped/ungrouped. Variables often called ‘explanatory’ and ‘response’ variable. Consists of data pairs. Both variables describe the same thing, often to investigate links. |
| Multivariate data | made up of more than two variables. Can be qualitative or quantitative, discrete or continuous and grouped or ungrouped. There are different combinations of variables to compare |
| Grouping data | Data can be grouped in well-defined, not overlapping classes. Each piece of data can only go one class. Be careful rounding data before grouping, as it may be put in the wrong class and distort data. |
| Class interval | Range of a class, sometimes shown by a hyphen (-) or inequalities |
| Upper/Lower class boundaries (Class limits) | Values at the top or bottom of the class |
| Class width | Difference between the lower boundary of one group and the lower boundary of the next. Classes can have different widths. Use narrower classes when the data values are close together, and wider classes when they are spread out |
| Grouping data (pros) | Easy to read and process data See patterns and compare classes |
| Grouping data (cons) | Lose accuracy as no longer know exact data values Calculations are only estimates, and diagrams can be misleading |
| Explanatory variable | Variable you are in control of (independent) (x-axis) |
| Response variable | Variable you measure - changes as a result of changing the explanatory variable (dependent) (y-axis) |
| Primary data | data you have collected yourself, e.g. from questionnaires, interviews, experiments, observations etc. It is usually raw data that is collected |
| Primary data (pros) | Data directly relates to hypothesis Complete control over method of collection - so you know how reliable it is |
| Primary data (cons) | Some methods aren’t reliable Cost time and money Difficult to collect your own data |
| Secondary data | Data someone else has collected, e.g. newspapers, magazines, websites, Office for National Statistics (ONS), historical records. Data has often already been processed. It is important to acknowledge your source |
| Secondary data (pros) | Easier to get hold of Quickly and cheaply get data Data sets are often bigger |
| Secondary data (cons) | Wrong format or rounded Hard to find data that matches hypothesis Don't know how accurate it is - may be bias or poorly collected |
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