Data Analysis Flashcards
Data definition
Numbers, letters, symbols, raw facts, events and transactions
Recorded but not yet processed into a form suitable for management use
Information
Data which has been processed
So it is meaningful to the person receiving it
Information ‘formula’
Data + Meaning
Uses of information
- Planning
- Decision making
- Controlling
When preparing for a budgeting exercise, management accountants must identify what?
Appropriate sources of information
Types of data
- Quantitative
- Qualitative
- Discrete
- Continuous
Quantitative data
Numerical data
Measurements or quantities
Can be analysed using statistical methods (risks management)
Qualitative data
Cannot be expressed as numbers/values
Harder to analyse
E.g. nationality, hair colour
Discrete data
Non-continuous data
Can only take certain values e.g. integers
Discrete data is counted
Continuous data
No gaps
Can take on any value
(within a range)
E.g. time/distance
Continuous data is measured
Types of sources of data
Internal
External
Internal data sources
E.g.
Accounting records
HR records
Payroll records
Machine logs
Computer systems
Procurement data system
Timesheets
Communication with staff
Two types of external information
Formally gathered
Informally gathered
Formally gathered data examples
Marketing research
E.g. new trends, customer tastes, competitor products
R&D
Tax and accounting specialists
E.g. new legislation/standards
Legal specialists info
E.g. changes in health and safety at work
Informally gathered data
Data gathered on an ongoing basis
E.g. newspapers, internet, meetings with external colleagues
Qualities of good information
- Accurate
No typos. roundings, categorised, assumptions - Complete
All information provided for the purpose - Cost beneficial
Benefit > cost of producing info - User-targeted
Understandable and useful to recipient - Relevant
For purpose intended - Authoritative
Genuine, highest quality for purpose, source should be knows and reliable - Timely
Produced in advance when needed - Easy to use
Clear, concise, constructive, communicated appropriately
Data analysis steps
- Identify information needs
- Collect the data
- Analyse the data
- Present the information
- Use the information
Three types of data analysis
- Inferential statistics
- Exploratory data analysis
- Confirmatory data analysis
Inferential statistics
Uses random sample of data from pop
To describe and make inferences about it
Exploratory data analysis
Pattern identified in a set of data
May be:
Regression analysis
Correlation analysis
Confirmatory data analysis
Confirms(/disconfirms) hypothesis
Using statistical methods
E.g. price increase of 3% will reduce demand by 5%
When is sampling appropriate
When possible to select units from the population
Three reasons sampling is necessary
- Whole population may not be known
- Testing whole population costly (time+money)
- Items may be destroyed in testing
2 rules of sampling
- Must be a certain size
- Must be representative
