ECON1203 Concept 1.docx - ECON1203: Business Analytics &...

ECON1203: Business Analytics & Statistics
Key Concept: Descriptive Statistics (Week 1)
Chapter 2: Data Visualisation
Learning Objectives (Lecture 1.1):
 The nature and potential of data
 Descriptive statistics
 Frequency distributions & histograms
 Shapes of distributions
 Describing bivariate relations
 Measures of central tendency or location
 Measures of dispersion or spread
 Measures of association
 Introduction to linear regression
Introduction To Statistics (Terminology):
Types of Data: Concepts & Jargon
 Variable: A characteristic of a population or of a sample from a population
 We observe values or observations of a variable
 A data set contains observations on variables
Variables may be…
Quantitative or Qualitative (Does It Have Numerical Units):
Qualitative/Categorical Quantitative/Numerical
Non-numerical data that are labels or identifiers
 This can be further categorised into:
Nominal:
Order doesn’t matter
 E.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) or
measurement (continuous)
 I.e. The data has a numerical value
Discrete or Continuous (Can It Take Any Value In An Interval):
Discrete Continuous
A variable that assumes a finite number of isolated values.
 Range of Specified Number: Complete
 Values: Obtained by counting (e.g. football scores)
 Classification: Non-Overlapping
 Assumptions: Distinct and separate values

Represented By: Isolated points (i.e. x = 3)
E.g. 1,2,3,4,5,6 – the number of red cars on a street (can’t
have 2.5 cars)
A variable which assumes infinite number of different values.
 Range of Specified Number: Incomplete
 Values: Obtained by measuring (e.g. time – 3mins, 23secs,
47ms… or weight)
 Classification: Overlapping
 Assumptions: Any value between the two values
 Represented By: Connected points (i.e. 1 < x < 2)
E.g. Weight, time
Continuous data can be discrete if it is given/sorted into categories
(e.g. weight between 50kg-55kg).

Types of Observations
Cross-Sectional Time-Series Data
Data that are collected at a fixed point in time.