In AMC’s The Walking Dead, former sheriff’s deputy Rick Grimes awakens from a coma and finds himself in what must surely seem at first to be a nightmare. Highways are littered with abandoned vehicles, towns and cities are deserted, and the entire world is overrun with flesh-eating zombies called “walkers.”
In the wake of civilization’s destruction, Rick and his son Carl must now band together with any other humans they can find who might be able to help them survive. And as the series progresses (Season 5 concluded this past March), it becomes evident that the most dangerous threat to Rick and his group might not be walkers at all, but rather fellow survivors such as themselves.
Based on a comic book series of the same name, The Walking Dead is not only wildly popular, (averaging 14.4 million viewers per episode during the fifth and most recent season), but also notoriously unpredictable and stressful to watch.
Read more “What are the Chances of Surviving the Zombie Apocalypse? A Survival Analysis of The Walking Dead”
We’re still a long way from knowing who will be the eventual Democratic and Republican candidates for President in 2016. So you might think it’s a bit premature to make a prediction about who will win the election and go on to become the 45th President of the United States.
And that’s probably true.
However, in my last post I showed that by looking at the amount of alcohol people consume over the course of a year, we can actually do a fairly good job of predicting which states will be won by a Democratic candidate and which will be won by a Republican candidate (insert joke here).
An analysis of the five most recent presidential elections suggests that we can predict the winning presidential candidate in about 79% of states (including the District of Columbia) based on nothing more than the following three variables: Read more “Projected Level of Alcohol Consumption Suggests a Democratic Win in 2016”
Analysis of variance (ANOVA) is a widely used statistical test in the behavioral and social sciences.
In a nutshell, ANOVA is used to evaluate differences between (at least) three group means to determine whether there is a “statistically significant” difference somewhere among them (i.e., a difference that is unlikely due to chance factors).
ANOVA is commonly used in conjunction with an experimental research design, in which a researcher randomly assigns participants to one of several groups and tests to see whether an experimental treatment variable leads to group differences on a given dependent measure.
Read more “Analyzing Analysis of Variance: Violation of Assumptions”
This is the first in a new series of posts exploring assumptions behind various statistical tests and measures, with a focus on understanding what happens when those assumptions are violated.
In this first post, I’ll take a look at the use of Analysis of Covariance (ANCOVA) to statistically “control for” and “remove” the effects of an extraneous, third variable from a general linear model that describes the relationship between a dichotomous predictor variable and a continuous dependent measure.
Specifically, I’ll take a look at the appropriateness of using ANCOVA to help answer the following question:
Are people more likely to relocate to a new town or city if there is a four-year college or university within the same county?
Read more “Do Four-Year Colleges & Universities Increase Migration to a Region?”