Sources & Representation of Data Lecture 02 K Pradhan

Session Objectives By the end of this session learners should be able to: Identify the various types of data Understand Variables & Data Types Identify representations for data types. Identify the sources of data. Comment on how a business can collect data.

LO & ACs LO1 Be able to use a variety of sources for the collection of data, both primary and secondary AC 1.1; 1.2; & 1.3 1.1 create a plan for the collection of primary and secondary data for a given business problem 1.2 present the survey methodology and sampling frame used 1.3 design a questionnaire for a given business problem

Identifying Data Data is simply a scientific term for facts, figures, and measurements. Data is raw which has not been processed at all, but are still just a list of numbers. It can exist in any form, usable or not. Examples The number of tourists who visit Hong Kong each year The sales turnovers of all restaurants in Salisbury The number of people (with black hair) who pass their driving test each year Information is sometimes referred to as processed data. The terms 'information' and 'data' are often used interchangeably.

Descriptive and Inferential Statistics Two branches of statistics: Descriptive statistics Graphical and numerical procedures to summarize and process data Inferential statistics Using data to make predictions, forecasts, and estimates to assist decision making Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Descriptive Statistics Collect data e.g., Survey Present data e.g., Tables and graphs Summarize data e.g., Sample mean = Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Inferential Statistics Estimation e.g., Estimate the population mean weight using the sample mean weight Hypothesis testing e.g., Test the claim that the population mean weight is 140 pounds Inference is the process of drawing conclusions or making decisions about a population based on sample results Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Classification of Variables Data Categorical Numerical Discrete Continuous Examples: Marital Status Are you registered to vote? Eye Color (Defined categories or groups) Examples: Number of Children Defects per hour (Counted items) Examples: Weight Voltage (Measured characteristics) Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Measurement Levels Ratio Data Interval Data Ordinal Data Nominal Data Differences between measurements, true zero exists Ratio Data Quantitative Data Differences between measurements but no true zero Interval Data Ordered Categories (rankings, order, or scaling) Ordinal Data Qualitative Data Categories (no ordering or direction) Nominal Data Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Graphical Presentation of Data Data in raw form are usually not easy to use for decision making Some type of organization is needed Table Graph The type of graph to use depends on the variable being summarized Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Graphical Presentation of Data (continued) Techniques reviewed in this chapter: Categorical Variables Numerical Variables Frequency distribution Cross table Bar chart Pie chart Pareto diagram Line chart Frequency distribution Histogram and ogive Stem-and-leaf display Scatter plot Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Tables and Graphs for Categorical Variables Categorical Data Tabulating Data Graphing Data Frequency Distribution Table Bar Chart Pie Chart Pareto Diagram Copyright © 2013 Pearson Education, Inc. Publishing as Prentice Hall

Activity 1 From the list of surveys below, state whether each is collecting data on attributes, discrete or continuous variables. A survey of statistics textbooks, to determine how many diagrams they contain. A survey of cans in a shop, to determine whether or not each has a price sticker. A survey of athletes to find out how long they take to run a mile. A survey of the heights of students at LSBM college

Primary and Secondary Data Data whether an attribute or variable, quantitative or qualitative, can either be primary or secondary. Primary data are collected especially for the purpose of whatever survey is being conducted. Secondary data are those which have already been collected elsewhere, for some other purpose, but which can be used or adapted for the survey being conducted. “Second Hand Data” (Morris, 2000)

Data Sources Internal Sources External Sources Internal data are collected from the organisation itself. It relates to the activities or transactions performed within the organisation e.g. sales, financial, employee, stocks etc. External data are collected from outside the organisation and relate to the environment in which the organisation operates. PEST and competitors

Types of Data Collection Three Main types of data collection- Census, Sample survey, and Administrative by-product data.

Census & Sample Survey Census Sample Survey Data collection about everyone or everything in a group or population. For example, the ages of all students in a class. High degree of accuracy but Costly and time consuming Only a part of the total population is approached for data. For example ages of students in the first row of a class. Cost less than census and data can be collected at greater speed. There may be inaccuracies, depending on the sample size.

Administrative by-product Collected as a by-product of an organisation’s routine operations. Examples include data on births, deaths, marriages, divorces, airport arrivals, motor vehicle registrations. High degree of accuracy as data is collected on everyone with the service. Data is on-going so trends can be observed.

Few Terminologies Data are the raw materials for data processing. Information is data that has been processed. Quantitative data are data that can be measured. A 'variable' is something which can be measured. Qualitative data cannot be measured, but have attributes (an attribute is something an object either has or does not have) Data may be primary (collected specifically for the purpose of a survey) or secondary (collected for some other purpose). Discrete data/variables can only take on a countable number of values. Continuous data/ variables can take on any value. Data are often collected from a sample rather than from a population. If the whole population is examined, the survey is called a census

Data Warehouse Data warehouse –consists of a database, containing data from various operational systems and reporting and query tools. Examples of a database include a sales order processing system, credit card transactions, demographic data and purchase data from checkout customer scanners at supermarkets. In simple terms a data warehouse is a large database.

Data Mining Data mining is the analysis of large pools of data to unearth unsuspected or unknown relationships, patterns and associations that can be used to guide decision-making and predict future behaviour.

Recap Can You Identify the various types of data? Do You Understand Variables & Data Types? Can You Identify representations for data types? Can You Identify the sources of data? Can You Comment on how a business can collect data?