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normal distribution

How the Population Distribution Influences the Confidence Interval

by Jeff Meyer  Leave a Comment

Spoiler alert, real data are seldom normally distributed. How does the population distribution influence the estimate of the population mean and its confidence interval?

To figure this out, we randomly draw 100 observations 100 times from three distinct populations and plot the mean and corresponding 95% confidence interval of each sample.
[Read more…] about How the Population Distribution Influences the Confidence Interval

Tagged With: confidence interval, Estimated marginal Means, normal distribution, population, right skewed, sample, sample size, shape of distribution, standard deviation, Uniform distribution

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Count vs. Continuous Variables: Differences Under the Hood

by Jeff Meyer  Leave a Comment

by Jeff Meyer, MBA, MPA

One of the most important concepts in data analysis is that the analysis needs to be appropriate for the scale of measurement of the variable. The focus of these decisions about scale tends to focus on levels of measurement: nominal, ordinal, interval, ratio.

These levels of measurement tell you about the amount of information in the variable. But there are other ways of distinguishing the scales that are also important and often overlooked.

[Read more…] about Count vs. Continuous Variables: Differences Under the Hood

Tagged With: normal distribution, pmf, Poisson distribution, probability mass function

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Member Training: Logistic Regression for Count and Proportion Data

by Karen Grace-Martin  Leave a Comment

Most of us know that binary logistic regression is appropriate when the outcome variable has two possible outcomes: success and failure.

There are two more situations that are also appropriate for binary logistic regression, but they don’t always look like they should be.

[Read more…] about Member Training: Logistic Regression for Count and Proportion Data

Tagged With: Bernoulli, binomial, Discrete Counts, logistic regression, normal distribution, outcome variable, poisson

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Differences Between the Normal and Poisson Distributions

by Karen Grace-Martin  4 Comments

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 [Read more…] about Differences Between the Normal and Poisson Distributions

Tagged With: continuous variable, discrete, negative binomial, normal distribution, normality, numeric variable, Poisson Regression

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When Can Count Data be Considered Continuous?

by Karen Grace-Martin  32 Comments

Last month I did a webinar on Poisson and negative binomial models for count data. With a few hundred participants, we ran out of time to get through all the questions, so I’m answering some of them here on the blog.

This set of questions are all related to when it’s appropriate to treat count data as continuous and run the more familiar and simpler linear model.

Q: Do you have any guidelines or rules of thumb as far as how many discrete values an outcome variable can take on before it makes more sense to just treat it as continuous?

The issue usually isn’t a matter of how many values there are.  [Read more...] about When Can Count Data be Considered Continuous?

Tagged With: continuous variable, Count data, count model, linear regression, normal distribution, Poisson distribution

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • The Problem with Linear Regression for Count Data
  • Count Models: Understanding the Log Link Function
  • Count vs. Continuous Variables: Differences Under the Hood

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