# DADM1.3 - Data Analysis for Decision Making Part 3...

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Data Analysis for Decision Making Part 3: Probability 1

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Why Probability What will be sales volume next year? What will be the profit? How many customers will churn? Which product will sale the best? Which worst? Which advertising campaign will provide be return? Which stock will perform? How do I make up my portfolio so that my income next year is maximized? 2
Probability: Basic Concepts Probability refers to chance or likelihood of a particular event taking place. An event is an outcome of an experiment. An experiment is a process that is performed to understand and observe possible outcomes. Examples include tossing a coin, drawing a card from a well-shuffled pack of cards, running several ad campaigns etc Set of all outcomes of an experiment is called the sample space. 3

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Example In a manufacturing unit three parts from the assembly are selected. You are observing whether they are defective or non- defective. Determine The sample space. The event of getting at least two defective parts. 4
Example 5 Let S = Sample Space. D = Defective G = Non-Defective E denote the event of getting at least two defective parts. This implies that E will contain two defectives or three defectives. E ={GDD, DGD, DDG, DDD}. It is easy to see that E is a part of S and commonly called as a subset of S. Hence an event is always a subset of the sample space.

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Formal Definition Probability of an event A is defined as the ratio of two numbers m and n. In symbols P (A) = m= number of ways that are favorable to the occurrence of A n= the total number of outcomes of the experiment (all possible outcomes) 0 ≤ P (A) ≤ 1 P (A) is a pure number 6
Probability is Relative Frequency What is the chance that our company will achieve a sale of more than 15000 tons of specialty paper? What is the chance that there will be no stock out problem this year?

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