Quantitative Business Analysis
General information on Statistics
Analytics: discovery and communication of meaningful patterns in data. It involves statistics, computer
programming and operations research.
Statistics: branch of maths that makes numbers (data) informative.
Descriptive statistics are the methods that help collect, summarise, present and analyse data.
Inferential statistics are methods that use the data collected from a small group to draw conclusions
about a larger group.
4 important uses of statistics
o Visualise and summarize data (e.g. tables, charts. Descriptive method)
o Reach conclusions about a large group based on data collected from small group (Inferential).
o Make reliable predictions based on statistical models (Inferential)
o Improve business processes.
Statistical Process
1. Define: set clearly defined goals for the investigation and formulate the research
question/hypothesis.
2. Collect: decide what data is appropriate and how to collect them. Collect the data.
3. Organise: display, describe and summarise the data. E.g. tables. Check for any usual features.
4. Extract information: Choose and apply appropriate statistical methods to extract useful information.
5. Conclusion: Interpret information, draw conclusions and communicate results to others.
Or DCOVA for Define, collect, organise, visualise and analyse.
Sources, sampling and variables overview
After defining goal and variables, data must be collected. Data can be a primary or secondary source.
Primary source: the analyst is the data collector.
o Organisations and individuals that collect and publish data use their data as a primary source
(e.g. ABS) and let others use their data as a secondary source.
o E.g. data from a political survey, experiment and directly observed data.
Secondary source: data for analysis has been collected by someone else.
o Company database, internet information, textbook, data made on stock markets.
Population: all the items or individuals (elements) which you want to reach conclusions.
Sample: portion of a population selected for analysis. The process of selecting the sample is sampling.
o If sample is to be informative of total population, sampling must be done carefully, impartially
and objectively.
o Random sampling: selecting individuals in a totally random fashion to prevent bias. Can be used
to draw conclusions about larger population.
Characteristics of an individual/element is a variable and this affects results. Variables can be:
o Categorical (qualitative): variables can be placed into categories like yes/no or true/false.
Nominal scale: classifies data into categories without ranking. E.g. type of email
provider.
Ordinal scale: classifies values into distinct categories with ranking. E.g. rating service.
o Numerical (quantitative): variables have values that represent actual quantities.
Discrete: values that arise from counting process (integer values). E.g. No. of TV
channels.
Continuous: arise from a measuring process and can be assigned any value within a
given interval. Can have decimal point values E.g. room temperature/height of child.
Numerical variables are measured on an interval or ratio scale.
