There is a lot of interest in measuring change in all sorts of areas:
Am I losing weight?
Is Bush losing support?
Is our children learning? (hehe).
At first, this seems quite simple. Weigh yourself Monday. Weigh yourself next Monday. Did your weight go down or up?
But it’s not so simple, and for a couple of reasons. Continue reading 'Measuring change'»
Today, I’ll look at how to make and evaluate a good statistical argument. I’m going to base this on the absolutely wonderful book: Statistics as Principled Argument by Robert Abelson.
It’s an easy read, and I urge those interested in this stuff to go buy a copy.
The book makes the point of the title: Statistics should be presented as part of a principled argument. You are trying to make a case, and your argument will be better if it meets certain criteria; but which criteria are the right ones? Continue reading 'Book Review: Statistics as Principled Argument by Robert Abelson'»
Introduction to multiple linear regression
In an earlier article, we looked at simple linear regression, which involves one independent variable (IV) and one dependent variable (DV).
When there are more than one IVs, the method is quite similar, but instead of a scatterplot in two dimensions, we have to imagine a space with as many dimensions as there are variables, and then minimize the distances in that space. Fortunately, the computer takes care of all this, and gives us output. The only difference that need concern us is that now if there are p IVs, the equation looks like . That is, each of the IVs has an associated parameter.
How multiple linear regression controls for the effects of other variables
One interesting feature of multiple linear regression is that the effect of each IV is “controlled” for the other IVs. That is, the parameter for variable accounts for the effect of on, assuming that , and so on stay the same. If, for example, we were interested in people’s weights as effects. Continue reading 'What is multiple linear regression?'»
Regression refers to a collection of techniques for modeling one variable (the dependent variable or DV), as a function of some other variables (the independent variables or IVs). Different regression techniques should be applied for different types of DVs. If the DV is a dichotomy (like living vs. dead), then the most common method is logistic regression. If the DV has multiple categories (e.g. Republican, Democrat, Independent) then the usual method is either multinomial or ordinal logistic regression. If the DV is a count (such as number of times something happens) then there are Poisson regression and negative binomial regression. If the DV is a time to an event (such as time to death) then there are a range of techniques known as survival analysis. There are other varieties too. But the most common type of DV is one that is continuous, or nearly so, such as weight, IQ, income, and so on. Continue reading 'Peter Flom’s statistics 101: What is simple linear regression?'»