Managers are quick to claim that quantitative analysts talk to them in a jargon

Question

Managers are quick to claim that quantitative analysts talk to them in a jargon that does not sound like English. List FIVE terms from Chapter 1 of the text book (Quantitative Analysis for Management, Render et al.) that might not be understood by a Manager. Then EXPLAIN in non-technical terms what each of Five terms means with suitable example where needed. 1) Sensititvity analysis with example 2) Algorithm with example 3) Break even point with example 4) Probabilistic model with example 5) Deterministtic model with example

Explanation / Answer

1

Sensitivity analysis is the analysis of inherent variance in the input to any of the equation, calculation or tools and how much impact it will make into the maths calculations. For example, in equity valuation, ROA (Return on Assets) ratios assumes that a valid, calculations of a company's assets can be arrived at and that it is reasonable to analyze profit, or returns, in relation to assets as a means of evaluating a company for investment purpose.

2.

An Algorithm is a finite set of instructions for a process, most often used in solving a problem or performing tasks. For example, identifying the even and odd serios from 5 to 10. So algorithm will decide by dividing each number by 2 and if return number is integer then it is even other wise it is considered odd. Thus algorithm works on a set of instructions.

3.

Breakeven point is a point where any organization is neither making loss nor making any profits but only able to meet its operational cost. For example, if any organization has $1000 expenses and it is earning $1000 then it is neither making profit nor loss and thus it is called breakeven point for the organization.

4.

Probabilistic model is the way of predicting the future where it considers the randomness which play a role in predicting future. It takes into consideration that randomness factors everytime. For example, We take life insurance to secure incase of any mishappening. Though we know we will die, we dont know when which is randomness.

5.

Deterministic models does not take randomness into consideration and always predicts the correct future analysis or finding out any results. For example, conversion from celcius to kelvin is deterministic as there is defined formula and results are accurate for it and always give you exact answers.

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