Monday, October 24, 2011

Chapter 5: Quantitative Analysis Outline


Quantitative analysis (QA) is one of the most important tools in a business curriculum and is used principally in finance, accounting, marketing and operations. QA helps individuals to remain objective when solving complicated problems. Important topics in QA include:

o   Decision tree
§  Simplifies decision making based on conditional probability
§  Start a business, etc. look at data or other people’s research to get probability
o   Cash flow analysis
§  How much does an investment cost and how much cash will it generate each year?
§  Too much cash? What to do with it?
·         Invest/Reinvest in company
·         Acquire other businesses (strategically)
·         Pay dividends
o   NPV – Net Present Value
§  A dollar tomorrow is almost always worth less than a dollar today.
·         Depends on the interest rate, inflation rate, opportunity costs
§  To compare different investments, convert them all to today’s dollar.
·         Called the discount rate, and it is different for different risks – very subjective.
o   IRR – Internal Rate of Return
§  Based on NPV
§  IRR of an investment is the interest rate that makes the sum of the net present value of an income stream equal zero in today’s dollars.
·         7% for 7 years will double your money.
o   Normal distribution – Bell Curve
§  Determined by the mean and standard deviation
§  Variance is the distance between sigma and negative sigma, or within one standard deviation.
o   Regression analysis and forecasting
§  Linear regression
·         Uses historical data to extrapolate the relationship between variables
·         Does not hold in the extreme
·         Excel data table – completes a table based on a relationship and givens
o   Business world does not tend us go as far as academics in finding the right curve for the data, usually just a straight line
·         R Square – if too low it doesn’t fit the data too well (70% very high, 30% not bad)
·         T Test – how strong X affects Y (-2<T<2 = data is significant)
·         Both T and R have to be high for the equation to be a good forecasting tool.

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