BSB123 Lecture Notes - Lecture 1: Frequency Distribution, Line Graph, Long-Distance Running
Data Analysis – Organising and Presenting Data
- Data can be obtained from several sources
oPublished resources
oExperiments
oInterviews
oSurveys
oObservations etc.
- Descriptive statistics
oDescribe a set of data
oMeans to an end
oHighlights features of a data set
- Inferential statistics
oMakes decisions based on a sample of a population
- Types of Data
oQualitative (Categorical)
Cannot take on naturally occurring numerical values (can only be named or
categorised)
Nominal
Unique categories
No order
Example: means of commuting
Ordinal
Unique categories
Order
Example: rating in a restaurant
oQuantitative (Numerical)
Measured on a naturally occurring numerical scale
Continuous
Many infinite values between two numbers
Data can take on any real number
Tend to be measures of time, weight, volume, temperature etc.
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Document Summary
Data can be obtained from several sources: published resources, experiments, interviews, surveys, observations etc. Descriptive statistics: describe a set of data, means to an end, highlights features of a data set. Inferential statistics: makes decisions based on a sample of a population. Cannot take on naturally occurring numerical values (can only be named or categorised) Example: rating in a restaurant: quantitative (numerical) Measured on a naturally occurring numerical scale. Data can take on any real number. Tend to be measures of time, weight, volume, temperature etc. Example: finish time for a long distance runner in a race. Qualitative vs. quantitative: quantitative can have meaningful calculations applied/qualitative can"t. Data of element x in a specific year. Example: survey of shoppers on bank charges on a particular day. Data of element x over multiple years. Example: businesses recording sales over a number of years: ways to organise qualitative data.
