CQP Foundation Level
Quantitative Skills and Data Analytics cheat sheet
Every chapter's key points, formulas and definitions on one page, for the last week before the exam. Each heading links to the chapter that explains it.
Introduction to analytics
- Descriptive analytics asks what happened, diagnostic why, predictive what is likely to happen and prescriptive what should be done; each type rests on the one below it, and most organisations fail at the base.
- The level of measurement decides which statistics are permissible: no mean of nominal or ordinal codes, and no ratios on an interval scale such as degrees Celsius.
- Counted data are attribute data, analysed with p charts, c charts and proportion tests; measured data are variable data, analysed with mean and range charts, t-tests, ANOVA and capability indices.
- Never delete an outlier without investigating it: it is either an error to correct with the reason recorded, or often the most informative observation in the dataset.
- Communicate the answer first, choose the chart from the question, state the finding in the chart's title and start a bar chart's axis at zero.
Key terms
- Data
- Raw, unprocessed facts and figures: 496, 502, 488. Data has no meaning until it is given context.
- Information
- Data placed in context and processed: 'the mean fill of the last twenty 500 ml bottles was 495.3 ml'.
- Knowledge
- Information combined with experience and interpretation: 'the filler is drifting low, as it did before the last bearing failure'.
- Data analytics
- The systematic examination of data to draw conclusions, using statistical, computational and visual methods.
- Qualitative or categorical data
- Data recording a category: nominal and ordinal. In quality work these are attribute data, counted rather than measured, and analysed with p charts and c charts.
- Quantitative or numerical data
- Data recording a quantity: interval and ratio. In quality work these are variable data, measured on a continuous scale, and analysed with mean and range charts and capability indices.
- Discrete quantitative data
- Counts, which can only take whole values: number of defects per unit, number of complaints per month.
- Continuous quantitative data
- Measurements, which can take any value in a range: fill volume, moisture content, tensile strength.
- Primary data
- Collected by the analyst for the present purpose: a survey, a set of measurements taken from the line.
- Secondary data
- Collected by someone else for another purpose: KNBS statistics, sector reports, the organisation's own historical records.
- Structured, semi-structured and unstructured data
- Structured data sits in defined fields in a database; semi-structured carries tags but no fixed schema; unstructured data is free text, images, audio and video, which is the majority of what an organisation holds and the hardest to analyse.
The rest of the sheet covers
- Statistical data analysis with SPSS or JASP
- Structured query language (SQL)
- Spreadsheets for data analysis
- The data analytics project
- Worked examples
- Exam technique and revision
- Glossary