Pretty much all of the common statistical models we use, with the exception of OLS Linear Models, use Maximum Likelihood estimation.
This includes favorites like:
That’s a lot of models.
If you’ve ever learned any of these, you’ve heard that some of the statistics that compare model fit in competing models require (more…)
We’ve talked a lot around here about the reasons to use syntax — not only menus — in your statistical analyses.
Regardless of which software you use, the syntax file is pretty much always a text file. This is true for R, SPSS, SAS, Stata — just about all of them.
This is important because it means you can use an unlikely tool to help you code: Microsoft Word.
I know what you’re thinking. Word? Really?
Yep, it’s true. Essentially it’s because Word has much better Search-and-Replace options than your stat software’s editor.
Here are a couple features of Word’s search-and-replace that I use to help me code faster:
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There are many rules of thumb in statistical analysis that make decision making and understanding results much easier.
Have you ever stopped to wonder where these rules came from, let alone if there is any scientific basis for them? Is there logic behind these rules, or is it propagation of urban legends?
In this webinar, we’ll explore and question the origins, justifications, and some of the most common rules of thumb in statistical analysis, like:
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The normal distribution is so ubiquitous in statistics that those of us who use a lot of statistics tend to forget it’s not always so common in actual data.
And since the normal distribution is continuous, many people describe all numerical variables as continuous. I get it: I’m guilty of using those terms interchangeably, too, but they’re not exactly the same.
Numerical variables can be either continuous or discrete.
The difference? Continuous variables can take any number within a range. Discrete variables can only be whole numbers.
So 3.04873658 is a possible value of a continuous variable, but not discrete.
Count variables, as the name implies, are frequencies of some event or state. Number of arrests, fish (more…)
Outliers are one of those realities of data analysis that no one can avoid.
Those pesky extreme values cause biased parameter estimates, non-normality in otherwise beautifully normal variables, and inflated variances.
Everyone agrees that outliers cause trouble with parametric analyses. But not everyone agrees that they’re always a problem, or what to do about them even if they are.
Sometimes a nonparametric or robust alternative is available — and sometimes not.
There are a number of approaches in statistical analysis for dealing with outliers and the problems they create. It’s common for committee members or Reviewer #2 to have very strong opinions that there is one and only one good approach.
Two approaches that I’ve commonly seen are: 1) delete outliers from the sample, or 2) winsorize them (i.e., replace the outlier value with one that is less extreme).
The problem with both of these “solutions” is that they also cause problems — biased parameter estimates and underweighted or eliminated valid values. (more…)
A common situation with count outcome variables is there are a lot of zero values. The Poisson distribution used for modeling count variables takes into account that zeros are often the most
common value, but sometimes there are even more zeros than the Poisson distribution can account for.
This can happen in continuous variables as well–most of the distribution follows a beautiful normal distribution, except for the big stack of zeros.
This webinar will explore two ways of modeling zero-inflated data: the Zero Inflated model and the Hurdle model. Both assume there are two different processes: one that affects the probability of a zero and one that affects the actual values, and both allow different sets of predictors for each process.
We’ll explore these models as well as some related models, like Zero-One Inflated Beta models for proportion data.
Note: This training is an exclusive benefit to members of the Statistically Speaking Membership Program and part of the Stat’s Amore Trainings Series. Each Stat’s Amore Training is approximately 90 minutes long.
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