Quantitative Methods for Business Studies Nishantha Palihawadana BScHon

Quantitative Methods for Business Studies Nishantha Palihawadana BSc(Hon), MBA, Mecon, FCPM

Intended Learning Outcomes of the Module: � Knowledge � 1. Understand mathematical statistical concepts and their applications to business scenarios � 2. Articulate how and why quantitative rationales are relevant to business management purposes � 3. Interpret value proposition based on quantitative perspectives in a context of competition � 4. Debate the quantitative rationales of business management in relation to complexities being developed � Competencies � 1. . Use quantitative data in decision-making and strategy formulation scenarios � 2. Evaluate businesses and organizational situations using relevant quantitative data � Skills � 1. Lead any management related discussion based on relevant quantitative data and related quantitative analyses � 2. Prepare suitable basic management reports using above data.

Module content : 1. Importance of quantitative data in business management purposes – 5% 2. Linear and non-linear models and their uses in business purposes – 20% 3. Basic statistics, regression methods and their uses in business purposes with an emphasis on data presentation methods -20% 4. Business use of price indices and investment appraisal methods – 10% 5. Business use of probability and decision analyses – 25% 6. Business use of sampling – 10% 7. Use of QM in management reports – 10%

Importance of quantitative data in business management purposes

What is quantitative data Data Quantitative Data Qualitative Data

�Data are the numerical results of any scientific measurement. �Quantitative data are measures of values or counts and are expressed as numbers. data about numeric variables �Qualitative data is typically descriptive data and as such is harder to analyze than quantitative data.

What is 'Quantitative Analysis' �Quantitative analysis refers to: Economic Business Financial analysis that aims to understand or predict behaviour through the use of mathematical measurements and calculations, statistical modelling and research.

How does the QT helps in Business Decision making process �In order to work effectively in a modern business organisation, whether the organisation is a private commercial company, a government agency, a state industry or whatever, managers must be able to use quantitative techniques in a confident and reliable manner. �Accountants make decisions based on the information relating to the financial state of organization. �Economists make decision based on the information relating to the economic framework in which the organization operates.

�Marketing staff make decisions based on customer response to product and design. �Personnel managers make decisions based on the information relating to the levels of employment in the organization

Applications of QT data analysis �Business use of break-even analysis and Linear programming �Identifying market equilibrium point �Identifying maximum or minimum points (Cost and profit functions) �Applications of displaying quantitative data �Business use of summary measures �Relationships between economic variables

Applications of QT data analysis �Forecasting Economic variables using models �Applications of price indices �Evaluating viability of Investments �Evaluating Risk and uncertainty of business decision �Estimating unknown parameters �Testing population proportions

What is Statistics �Statistics is a mathematical science pertaining to the 1. Collection of data 2. Analysis of the data 3. presentation of data 4. Interpretation or explanation

Statistics Descriptive statistics Inferential statistics

Descriptive statistics : The study of how data can be summarized efficiently to describe the important aspects of large data sets. Further this deals with method of collecting and describing set of data. Inferential statistics: This is concerned with making forecasts, estimates, or judgments about the larger group from the smaller group actually observed.

Data and Information �Data is raw, unorganized facts that need to be processed. Data can be something simple and seemingly random and useless until it is organized.

Data and Information �When data is processed, organized, structured or presented in a given context so as to make it useful, it is called Information �Eg: Data: Each student's test score is one piece of data �Information: The class' average score or the school's average score is the information that can be concluded from the given data

Data Primary Data Secondary Data

Primary Data: Primary data are collected specifically for the analysis desired. Methods of collecting primary data include personal investigation, teams of investigators and through questionnaires etc.

Secondary Data : Secondary data are data that were originally collected as primary data for one purpose, or for general use, but are now being used for another purpose. Eg. The Central bank periodically collects economic data. Such data are primary for Central bank, however if we use such data then it is called as secondary data.

Variables Discrete Continuous

• Population • Sample investigation

Methods of Collecting Data �Census. A census is a study that obtains data from every member of a population. In most studies, a census is not practical, because of the cost and/or time required. �Sample survey. A sample survey is a study that obtains data from a subset of a population, in order to estimate population attributes. �Experiment. An experiment is a controlled study in which the researcher attempts to understand cause-and-effect relationships. �Observational study. Like experiments, observational studies attempt to gather data from directly observing a particular event.

Interviews �Face -to -face interviews �Telephone interviews �Computer Assisted Personal Interviewing (CAPI)

Face -to -face interviews �Face -to -face interviews have a distinct advantage of enabling the enumerator to establish relationship with potential participants and therefore gain their cooperation. These interviews yield highest response rates in a survey. They also allow the researcher to clarify ambiguous answers and when appropriate, seek follow-up information. �Disadvantages include impractical when large samples are involved time consuming and expensive. Further some people deliberately provide wrong answers or sometimes they may forget the information.

Telephone interviews �Telephone interviews are less time consuming and less expensive and the enumerator has ready access to anyone on the planet who has a telephone. Disadvantages are that the response rate is not as high as the face-to- face interview but considerably higher than the mailed questionnaire.

Computer Assisted Personal Interviewing (CAPI) �This is a form of personal interviewing, but instead of completing a questionnaire, the interviewer brings along a laptop or hand-held computer to enter the information directly into the database. This method saves time involved in processing the data, as well as saving the interviewer from carrying around hundreds of questionnaires. However, this type of data collection method can be expensive to set up and requires that interviewers have computer and typing skills.