Reference no: EM132542009

For this discussion, begin by reviewing the technique of quantitative analysis in your textbook. Then, keeping this technique in mind, read the following quotes:

- "Data do not give up their secrets easily. They must be tortured to confess."-Jeff Hopper, Bell Labs

- "Statistics is a body of methods for learning from experience."-Lincoln Moses

- "The time may not be very remote when it will be understood that for complete initiation as an efficient citizen of one of the new great complex worldwide states that are now developing, it is as necessary to be able to compute, to think in averages and maxima and minima, as it is now to be able to read and write."-H.G. Wells

- "The coming century is surely the century of data."-David Donoho (2000)

- "Another mistaken notion connected with the law of large numbers is the idea that an event is more or less likely to occur because it has or has not happened recently. The idea that the odds of an event with a fixed probability increase or decrease depending on recent occurrences of the event is called the gambler's fallacy. For example, if Kerrich landed, say, 44 heads in the first 100 tosses, the coin would not develop a bias towards the tails in order to catch up! That's what is at the root of such ideas as ‘her luck has run out' and ‘he is due.' That does not happen. For what it's worth, a good streak doesn't jinx you, and a bad one, unfortunately, does not mean better luck is in store."-Leonard Mlodinow, The Drunkard's Walk: How Randomness Rules Our Lives

- "A certain elementary training in statistical method is becoming as necessary for everyone living in this world of today as reading and writing."-H.G. Wells, World Brain

- "The non-scientist in the street probably has a clearer notion of physics, chemistry and biology than of statistics, regarding statisticians as numerical philatelists, mere collector of numbers."-Stephen Senn, Dicing with Death: Chance, Risk and Health

Based on the above quotes, along with this week's assigned readings and Instructor Guidance, discuss why quantitative analysis is important for describing data sets and presenting distribution information.