ECON1203: Business Analytics & StatisticsKey Concept: Descriptive Statistics (Week 1)Chapter 2: Data VisualisationLearning Objectives (Lecture 1.1):The nature and potential of dataDescriptive statisticsFrequency distributions & histogramsShapes of distributionsDescribing bivariate relationsMeasures of central tendency or locationMeasures of dispersion or spreadMeasures of associationIntroduction to linear regressionIntroduction To Statistics (Terminology):Types of Data: Concepts & JargonVariable: A characteristic of a population or of a sample from a populationWe observe values orobservationsof a variableA data setcontains observations on variablesVariables may be…Quantitative or Qualitative (Does It Have Numerical Units):Qualitative/CategoricalQuantitative/NumericalNon-numerical data that are labels or identifiersThis can be further categorised into:Nominal:Order doesn’t matterE.g. Occupation (teacher, plumber, engineer)Ordinal:Order does matter (natural order)E.g. Grade scores (A = highest, F = lowest)Data that takes numerical values from either counting (discrete) ormeasurement (continuous)I.e. The data has a numerical valueDiscrete or Continuous (Can It Take Any Value In An Interval):DiscreteContinuousA variable that assumes a finite number of isolated values.Range of Specified Number:CompleteValues:Obtained by counting (e.g. football scores)Classification:Non-OverlappingAssumptions:Distinct and separate valuesRepresented By:Isolated points (i.e. x = 3)E.g. 1,2,3,4,5,6 – the number of red cars on a street (can’thave 2.5 cars)A variable which assumes infinite number of different values.Range of Specified Number:IncompleteValues:Obtained by measuring (e.g. time – 3mins, 23secs,47ms… or weight)Classification:OverlappingAssumptions:Any value between the two valuesRepresented By:Connected points (i.e. 1 < x < 2)E.g. Weight, timeContinuous data can be discrete if it is given/sorted into categories(e.g. weight between 50kg-55kg).