Regression is a set of statistical techniques for relating a dependent variable to one or more independent variables. Briefly, a dependent variable (sometimes called an outcome variable) is one that you think is related to the independent variables. Although regression can’t prove causation, you usually think that the relationship goes from the independent variable(s) and to the dependent variable. (I will discuss the distinction in another post).
Continue reading 'Peter Flom’s Statistics 101: Which kind of regression model should I choose?'»
So, you’ve got to read a statistics book. Maybe you’re taking a statistics course, or maybe you are working on some research and need to learn something about statistics. And the text isn’t something with a bright colored cover and a title like “Statistics for Dummies who think they don’t like statistics: The cartoon version”. No, it’s a text. And it hasn’t got a lot of fancy sidebars and things. And it’s got formulas. And you don’t like formulas.
What to do?
Continue reading 'Peter Flom’s Statistics 101: How to read a statistics book'»
When learning statistics, you may learn about ANOVA (analysis of variance), ANCOVA (analysis of covariance) and ordinary least squares regression. The way these are taught in many fields leaves many people confused. Indeed, many people do not realize these are all the same model. Continue reading 'Peter Flom’s Statistics 101: ANOVA, ANCOVA, regression: What’s what?'»
Often, when reading a statistics book, you will see some variation on the phrase “independent data“. Many models assume that the data are independent. Sometimes this is abbreviated as part of the acronym iid which means independent and identically distributed.
You may get confused between this and the case of independent and dependent variables. But the two ideas are quite different.
Continue reading 'Peter Flom’s statistics 101: Dependent and independent data'»
Note: This is a brief introduction to observational designs. For more on this type of study, see books by Paul Rosenbaum: Observational Studies and, for a less mathematical approach Design of Observational Studies
In statistics and research design, there are two types of study: Experiments and observational studies. Some people also use the term “quasi-experiment” but I do not like it. In an experiment, the key thing is <em>randomization</em>. We assign subjects (e.g. people) to different conditions (e.g. drug and placebo) randomly. Often, though, such assignment is not possible or not ethical. In social sciences, it is rarely possible. We cannot, for example, randomly assign people to different levels of education. We can only observe relationships between (say) education and political party.
Continue reading 'Some thoughts on observational studies'»
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