Bstat1 - Lessons in Business Statistics Prepared By P.K....

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Unformatted text preview: Lessons in Business Statistics Prepared By P.K. Viswanathan Chapter 1: An Overview of Statistics Introduction Managers make sound decisions when they use all relevant information in an effective and meaningful manner. The principal purpose of statistics is to provide decision-makers with a set of techniques for collecting, analyzing, and interpreting data into actionable recommendations. Statistical methods are widely used to aid decision-makers in all functional areas of management. This chapter provides the basic ideas and concepts at a general level. 1) Why Should I Study Statistics? Situation 1 A company has to decide whether to introduce a new product into the market or not. The company will introduce the product into the market if 30% of the target audience in the relevant population will accept the product so that the risk of product failure is minimized. Obviously consumer acceptance is paramount in making this decision. To know about the consumer acceptance in a reasonable manner, the company has done a "test marketing" exercise. In the test market, 30% of the sample target audience (based on a sample of 150 consumers) indicate their acceptance of the product. Does the sample result at 95% confidence level suggest that 30% of the target audience in the population (entire market) will accept the product? 1) Why Should I Study Statistics? Continues Situation 2 A bank which has been steadily losing customers in the light of intense competition wants to investigate the reasons for the loss of customers on account of perceived service quality in critical dimensions like response time, reliability, courtesy of the service staff, and credibility. The bank would like to conduct a comprehensive survey to measure the perceived service quality from the customers' angle on these dimensions with that of competition. This would help the bank develop and implement effective strategies to woo its present customers back as well as to attract new customers. 1) Why Should I Study Statistics? Continues Can you make the right decision in situation 1 and situation 2 with minimum risk without the help of statistics? The answer is clearly a "No". Information based decision making using statistical analysis is absolutely essential in the present environment characterized by intense competition, onslaught of new products and services, globalization, and revolution of information technology. 2 ) What is Statistics? By "Statistics" we mean methods specially adapted to the collection, classification, analysis, and interpretation of data for making effective decisions in all functional areas of management. AMEX Gained by Statistical Analysis American Express Company (AMEX), the pioneer in personal charge cards during the eighties used to systematically collect customer feedback data from the marketplace on a continuous basis. AMEX is well known for its caring attitude towards customers. The Analysis and Interpretation of the customer data revealed that the customers wanted the new card to be processed within three weeks where as AMEX was taking around 5 weeks. AMEX decided to issue new cards within two weeks. Similarly another analysis revealed that the customers wanted the stolen/lost cards to be replaced within two days where as AMEX was taking two or more weeks to issue replacement cards. AMEX decided to replace the lost cards within two days. As a result of these two decisions, AMEX could generate $1.4million additional profit per year. 3) Typical Application Areas Quality Management Materials Management SQC Techniques Inventory Control Process Capability Incoming Quality Assessment Finance Marketing Financial Ratio Analysis Marketing Research Cash Forecasting Demand Projections 4) Types of Statistics Descriptive Statistics is concerned with Data Summarization, Graphs/Charts, and Tables Inferential Statistics is a method used to talk about a Population Parameter from a Sample. It involves Point Estimation, Interval Estimation, and Hypothesis Testing Descriptive Statistics Example 50 45 40 35 30 25 20 15 10 5 0 Machine 1 Machine 2 Machine 3 Machine 4 1st Qtr 3rd Qtr The Quality Control Department of a large manufacturing company would like to compute summary measures such as the mean production per shift for a particular item. The department would also like to get a comparative picture of performance of the mean production across the four machines in the plant by tabulation. Further the company might like to graph the comparative performance of the four machines by a bar chart depicting the mean production per shift. Inferential Statistics -Example Suppose you, as a marketing manager would like to identify a niche market for your product. You know from your experience that an accurate assessment of the income of a typical family is crucial. The average income of this type of families in the population is estimated by you to be Rs. 320000 based on figures obtained from a sample. In this example, average income based on sample is a Point Estimate of the population. The average income that falls with in a statistically formed interval of 320000 plus or minus 40000 is called an Interval Estimate. Any statement such as the average income in the population is more than Rs. 300000 per year is a Hypothesis. As a manager, the interval estimate may be much more important to you than the rest! Caution: Inferential Statistics assumes that the sampling methodology is random (i.e. based on probability sampling)! 5) Some Key Terms Used in Statistics Population is the collection of all possible observations of a specified characteristic of interest. An example is all the students in the Quantitative Methods course in an MBA program. Sample is a subset of the population. Suppose you want to select a team of 20 students from 200 students in an MBA program for participating in a management quiz. The total students 200 is the population. 20 students selected for the quiz is the sample. Parameter is the population characteristic of interest. For example, you are interested in the average income of a particular class of people. The average income of this entire class of people is called a parameter. Statistic is based on a sample to make inferences about the population parameter. If you look at the previous example, the average income in the population can be estimated by the average income based on the sample. This sample average is called a statistic. 6) Types of Data Qualitative Data are nonnumeric in nature and can't be measured. Examples are gender, religion, and place of birth. Quantitative Data are numerical in nature and can be measured. Examples are balance in your savings bank account, and number of members in your family. Quantitative data can be classified into discrete type or continuous type. Discrete type can take only certain values, and there are discontinuities between values, such as the number of rooms in a hotel, which cannot be in fraction. Continuous type can take any value within a specific interval, such as the production quantity of a particular type of paper (measured in kilograms). 7) Types of Data Measurements-Picture Nominal Ordinal Interval Ratio Information Content Increases 7) Types of Data Measurements Nominal Data: The weakest data measurement. Numbers are used to label an item or characteristic. Categorization is the main purpose of this measurement. Examples: A business school may designate subject specialization by numbers, i.e., MBA in Finance =1, MBA in Systems = 2. Various brands of toothpaste; savings bank account numbers are other examples of nominal data. Note that nominal data cannot be manipulated in a numerical fashion. Ordinal or Rank Data: Numbers are used to rank. An example is Customer Preference for your brand. An average preference is rated at 3, a strong preference at 5. Simple arithmetic operations are not possible for ordinal data. Ordinal data can also be verbalized on a continuum like excellent, good, fair and poor. In ordinal data, distance between objects cannot be measured. 7) Types of Data Measurements Continues Interval Data: If you have data with ordinal properties and can also measure the distance between objects, you have an interval measurement. Interval data are superior to ordinal data because, with them, decision makers can measure the distances between objects. For example, frozen-food distributors are concerned with temperature, which is an interval measurement. Interval data have arbitrary zero point. Basic arithmetic operations are possible with interval data Ratio Data: It is the highest level of measurement and allows you to perform all basic arithmetic operations, including division and multiplication. Data measured on a ratio scale have a fixed zero point. Examples include business data, such as cost, revenue, market share and profit. 8) Data Sources Primary Data are collected by the organization itself for a particular purpose. The benefits of primary data are that they fit the needs exactly, are up to date, and reliable. Secondary Data are collected by other organizations or for other purposes. Any data, which are not collected by the organization for the specified purpose, are secondary data. These may be published by other organizations, available from research studies, published by the government, and so on. Secondary data have the advantages of being much cheaper and faster to collect. They also have the benefit of using sources, which are not generally available. Companies will, for example, respond to a survey by the Government of India, or Confederation of Indian Industry, but they would not answer questions from another company. 9) A Step-By- Step Approach to Statistical Investigation ...
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This note was uploaded on 02/24/2012 for the course BUSINESS 281 taught by Professor Gray during the Spring '12 term at Florida State College.

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