# Download PDQ statistics by Geoffrey R. Norman, David L. Streiner PDF

April 5, 2017 | | By admin |

By Geoffrey R. Norman, David L. Streiner

PDQ facts, 3rd version bargains knowledgeable assessment of all significant statistical tools, giving the reader a radical figuring out of facts and the way they're utilized in examine articles. The e-book covers the foremost different types variable and descriptive facts, parametric records, non-parametric statistics, and multivariate records. the reasons are transparent, succinct, and loaded with sensible examples. This 3rd variation contains new chapters on hierarchical and logistic regression, course research, and structural equation modeling. PDQ data, 3rd variation additionally is helping the reader determine these statistical analyses with uncomplicated flaws in layout or learn. The booklet s beautiful layout and funny writing sort make the subject material obtainable and fascinating.

Best mathematicsematical statistics books

Lectures on Probability Theory and Statistics

Facing the topic of chance concept and facts, this article comprises assurance of: inverse difficulties; isoperimetry and gaussian research; and perturbation tools of the idea of Gibbsian fields.

Anthology of statistics in sports

This undertaking, together produced through educational institutions, contains reprints of previously-published articles in 4 records journals (Journal of the yankee Statistical organization, the yankee Statistician, likelihood, and complaints of the facts in activities part of the yankee Statistical Association), prepared into separate sections for 4 really well-studied activities (football, baseball, basketball, hockey, and a one for less-studies activities similar to football, tennis, and tune, between others).

Extra resources for PDQ statistics

Sample text

A cautionary note: the next bit involves some high school algebra around exponents and logs. If you don't remember this arcane stuff, either go read about it or skip the next paragraph. Likely, you'll be no worse off at the end. So here we go. What's happening is this: we began with something that had all the b coefficients in a linear equation inside an exponential; now we're working backwards to that original equation. Suppose the only variable that was predictive was drug use (DRUG), which has only two values, 1 (present) or 0 (absent).

For arcane reasons, that's a no-no here. Instead, we must use an alternative and computationally intensive method called maximum likelihood estimation (MLE). Why you use this is of interest only to real statisticians, so present company (you and we) are exempt from having to undergo explanation. Suffice it to say that at the end, we have a set of parameters, the bs, with associated significance tests, just like we did with multiple regression. If you have the misfortune of actually doing the analysis, you might, to your amazement, find that the printout is not too weird.

Let's think for a moment about a lifetime of booze consumption. We are not passing judgment in doing so; unlike the prohibitionists, we prefer to point out that moderate drinking, particularly of red wine at an average of two glasses per day, apparently confers a health benefit, to the tune of a 10% reduction in overall mortality-far better, we might point out, than cholesterollowering drugs (although apparently ethanol is one, and certainly one of the most pleasant, at that). A sinall hooker is that those concerned with alcohol consumption regard the same two glasses a day as borderline alcoholism in North America (although in Europe, it's probably five glasses per day, and in Russia, we imagine that anything less than five bottles of vodka a day is viewed as abstaining).