MKT 300 Lecture Notes - Lecture 22: Exploratory Data Analysis, Variance, Statistical Inference
Michael Le
ARC
MKT 300
Principles of Marketing
Basic statistical concepts
● Population – A collection of objects (often called units or subjects) of interest – i.e. all
small businesses, all workers currently employed by BHP Billiton
● Census – Collection of data on a whole population
● Sample – A subset of the units in a population – can be expected to be a representation of
the population.
● Parameter – A descriptive measure of the population – e.g. population mean, population
standard deviation, and population variance.
● Statistic – A descriptive measure of a sample – e.g. sample mean, sample standard
deviation, and sample variance. Sample mean is used to measure population mean.
o The basis for inferential statistics, is the ability to make decisions about
parameters without having to complete a census of the population.
● Absolute value – Use for comparing exact values – e.g. countries have the same number
of team members at Olympics
● Relative value – Used for comparing 2 data sets that are unequal in size – e.g. number of
gold members to measure success.
Two steps in analysing data from a sample:
1. Exploratory data analysis – numerical, tabular and graphical summaries (frequency
tables, means, standard deviations) of data are produced to summarise and highlight
the key aspects or any special features of data.
2. Statistical inference – uses sample data to reach conclusions about the population
from which the sample was drawn.
a. A conclusion that patterns observed in the data (sample) are present in the
wider population from which the data was collected.
b. An inference based on a probability model linking the data to the population.
Types of data
● Data can be broadly classified as qualitative (categorical) or quantitative (numerical).
● Categorical data can be either nominal or ordinal
● Numerical data can be either discrete or continuous
1. Numerical data
● Numbers represent some quantity
● Discrete – we can list the possible values
● Continuous – we can only give a range of possible values for the data
● Discrete data often arises from counting processes, while continuous data arise from
measurements.
2. Categorical data
● Simply an identifier or label and has no numerical meaning
● Data often not numbers – the employment of a person etc., grade in a test (A, B, C)
Document Summary
Population a collection of objects (often called units or subjects) of interest i. e. all small businesses, all workers currently employed by bhp billiton. Census collection of data on a whole population. Sample a subset of the units in a population can be expected to be a representation of the population. Parameter a descriptive measure of the population e. g. population mean, population standard deviation, and population variance. Statistic a descriptive measure of a sample e. g. sample mean, sample standard deviation, and sample variance. Sample mean is used to measure population mean: the basis for inferential statistics, is the ability to make decisions about parameters without having to complete a census of the population. Absolute value use for comparing exact values e. g. countries have the same number of team members at olympics. Relative value used for comparing 2 data sets that are unequal in size e. g. number of gold members to measure success.
