Statistical Modeling Quotes

A potentially useful property of forecasts based on cointegration is that when extended some way ahead, the forecasts of the two series will form a constant ratio, as is expected by some asymptotic economic theory.

The analysis of variance is not a mathematical theorem, but rather a convenient method of arranging the arithmetic.

Math is a language that you use to describe statistics, but really it's about collecting information and putting it in an order that makes sense.

There will always be plenty of things to compute in the detailed affairs of millions of people doing complicated things.

I don’t believe in statistics. I believe in calculus.

Before the curse of statistics fell upon mankind we lived a happy, innocent life, full of merriment and go and informed by fairly good judgment.

It came as a surprise to find that a professional society and journal (Econometrica) were flourishing, and I entered this area of study with great enthusiasm.

We spend our time bandying statistics about.

Investors should be skeptical of history-based models. Constructed by a nerdy-sounding priesthood using esoteric terms such as beta, gamma, sigma and the like, these models tend to look impressive. Too often, though, investors forget to examine the assumptions behind the models. Beware of geeks bearing formulas.

We want to get 80%-85% of predictions right, not 100%. Or else we calibrated our estimates in the wrong way.

I have never been a stats person.

Statistics is the first of the inexact sciences.

My figures come and go, suggested by fortune or misfortune. I try to fix them divested of their apparent accidental quality.

If your experiment needs statistics, you ought to have done a better experiment.

I think that a failure of statistical thinking is the major intellectual shortcoming of our universities, journalism and intellectual culture.

For 'Power of 10,' you can look at the methodology at CBS.com, it's a company called Rasmussen Reports. We poll thousands and thousands of people for each question, a real cross section of the United States.

The ability to foretell what is going to happen tomorrow, next week, next month, and next year – and to have the ability afterwards to explain why it didn’t happen.

When I look at the next set of technologies that we have to build in Salesforce, it's all data-science-based technology. We don't need more cloud. We don't need more mobile. We don't need more social. We need more data science.

The best thing about being a statistician is that you get to play in everyone's backyard.

You can build a statistical model and that's all well and good, but if you're dealing with a new type of financial instrument, for example, or a new type of situation - then the choices you're making are pretty arbitrary in a lot of respects.

In modern times sound policy-making must often come to grips with numbers.

With four parameters I can fit an elephant, and with five I can make him wiggle his trunk.

Looking at scientific inquiry, next paradigm will be based on very large datasets. Scientists are in the lead in handling very large datasets - Hubble telescope or Large Hadron Collider are massive datasets.

[Statistics are] the only tools by which an opening can be cut through the formidable thicket of difficulties that bars the path of those who pursue the Science of Man.

This is what analysts have calculated. It's a number that's out there and we're not arguing with it.