TAILIEUCHUNG - Quantitative Methods for Business chapter 4

C H A P T E R 4 Filling up – fuelling quantitative analysis Chapter objectives This chapter will help you to: ■ ■ ■ ■ ■ understand key statistical terms distinguish between primary and secondary data recognize different types of data arrange data using basic tabulation and frequency distributions use the technology: arrange data in EXCEL, MINITAB and SPSS. | CHAPTER 4 Filling up fuelling quantitative analysis Chapter objectives This chapter will help you to understand key statistical terms distinguish between primary and secondary data recognize different types of data arrange data using basic tabulation and frequency distributions use the technology arrange data in EXCEL MINITAB and SPSS. In previous chapters we have concentrated on techniques or models involving single values that are known with certainty. Examples of these are break-even analysis and linear programming which we looked at in Chapter 2 and the Economic Order Quantity model featured in Chapter 3. In break-even analysis the revenue per unit the fixed cost and the variable cost per unit are in each case a specified single value. In linear programming we assume that both profit per unit and resource usage are constant amounts. In the Economic Order Quantity model the order cost and the stock-holding cost per unit are each known single values. Because these types of models involve values that are fixed or predetermined they are called deterministic models. Deterministic models can be useful means of understanding and resolving business problems. Their reliance on known single value inputs makes them relatively easy to use but is their key shortcoming. Companies simply cannot rely on a figure such as the amount of Chapter 4 Filling up - fuelling quantitative analysis 109 raw material used per unit of production being a single constant value. In practice such an amount may not be known with certainty because it is subject to chance variation. Because of this company managers may well need to study the variation and incorporate it within the models they use to guide them. Models that use input values that are uncertain rather than certain values that are subject to chance variation rather than known are called probabilistic models after the field of probability which involves the measurement and analysis of chance. We shall be dealing with probability in .

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