I’m completely making this up for an example, so these results look far too clean but stay on target. I’m getting to my point. Anyway, the blue dots signify the number of results at each degree of coffee consumption and the red series is the median observation. We see that although each additional sit down elsewhere does improve concentrate, the marginal impact of that 3rd glass is limited pretty.

So I’m sure those who do any amount of econometric work look at this chart and see a nice quadratic. Where C is the real number of cups and Err is a random error term. So, let’s say you divine that equation from your data. Even if your equation perfectly explains what happens to the average person who ingests 0-3 cups of coffee, the applications of the formula are limited still.

• 7 years back from Minnesota
• The customers take the products directly from the seller instead of the Bank
• Biomethane injection into the gas grid, and
• Travel to inspect your investment property

For example, you are told by it nothing at all about what happens if you drink 4 mugs of coffee. Or if you take more than 1 hour to drink your coffee. Each day for a calendar year Or the effect if you drink 4 mugs. Before I turn us back again to the bond market, let me explain that this is freshman statistics stuff.

CFA Level 1 stuff. Anyone who doesn’t understand this should be banned from using Excel’s regression function. Now let’s look at the sub-prime home loan market. Remember that CDOs work by buying higher-risk securities, then distributing the idiosyncratic (i.e., solitary security) risk out among many possessions. So CDO managers are pleased to own higher credit risk securities, as long as they believe they can effectively spread the chance away. Ergo, a CDO manager looking to create an ABS deal would be looking for higher risk/higher yielding residential mortgage deals. Managers began to find that the higher-yielding items pools released by underwriters seen as having weaker credit requirements.

Maybe these private pools had an increased percentage of 100% (or higher) LTV and/or mentioned income loans. But so far as the CDO manager was worried, that wasn’t a problem, because that risk could be disseminated in an exceedingly large stock portfolio of bonds. How did s/he understand how much in high LTV or low-doc loans was wise? Well, CDOs presume some degree of defaults will take place always, and usually they can perform quite nicely at default levels a good bit greater than the assumed level. But what they can not withstand is a big amount of defaults occurring over a short period of time. More on that in a moment.

Anyway, if you would like to avoid a big quantity of defaults all at one time, you will need credits with a low correlation. (Fortunately!?!) correlation was right up the alley of the quant wizards working CDO portfolios. Armed with reams of historical data, they determined the delinquency relationship of high LTV, low doc, low FICO, etc.