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linear regression

Count Models: Understanding the Log Link Function

by Jeff Meyer 2 Comments

When we run a statistical model, we are in a sense creating a mathematical equation. The simplest regression model looks like this:

Yi = β0 + β1X+ εi

The left side of the equation is the sum of two parts on the right: the fixed component, β0 + β1X, and the random component, εi.

You’ll also sometimes see the equation written [Read more…] about Count Models: Understanding the Log Link Function

Tagged With: count model, generalized linear models, linear regression, link function, log link, log transformation, Negative Binomial Regression, Poisson Regression

Related Posts

  • The Importance of Including an Exposure Variable in Count Models
  • The Difference Between Link Functions and Data Transformations
  • Getting Accurate Predicted Counts When There Are No Zeros in the Data
  • The Problem with Linear Regression for Count Data

Member Training: Preparing to Use (and Interpret) a Linear Regression Model

by TAF Support

You think a linear regression might be an appropriate statistical analysis for your data, but you’re not entirely sure. What should you check before running your model to find out?

[Read more…] about Member Training: Preparing to Use (and Interpret) a Linear Regression Model

Tagged With: Bivariate Statistics, histogram, interpreting regression coefficients, linear regression, Multiple Regression, scatterplot, Univariate statistics

Related Posts

  • The Difference Between R-squared and Adjusted R-squared
  • Member Training: Goodness of Fit Statistics
  • Member Training: Using Transformations to Improve Your Linear Regression Model
  • Member Training: Segmented Regression

Same Statistical Models, Different (and Confusing) Output Terms

by Jeff Meyer Leave a Comment

Learning how to analyze data can be frustrating at times. Why do statistical software companies have to add to our confusion?Stage 2

I do not have a good answer to that question. What I will do is show examples. In upcoming blog posts, I will explain what each output means and how they are used in a model.

We will focus on ANOVA and linear regression models using SPSS and Stata software. As you will see, the biggest differences are not across software, but across procedures in the same software.

[Read more…] about Same Statistical Models, Different (and Confusing) Output Terms

Tagged With: ANOVA, between groups, categorical predictor, linear regression, oneway, residuals, software, SPSS, SPSS output, Stata, Stata output, Statistical Software, within groups

Related Posts

  • Why report estimated marginal means?
  • Statistical Software Access From Home
  • Member Training: What’s the Best Statistical Package for You?
  • Ten Ways Learning a Statistical Software Package is Like Learning a New Language

What is Multicollinearity? A Visual Description

by Karen Grace-Martin 6 Comments

Multicollinearity is one of those terms in statistics that is often defined in one of two ways:

1. Very mathematical terms that make no sense — I mean, what is a linear combination anyway?

2. Completely oversimplified in order to avoid the mathematical terms — it’s a high correlation, right?

So what is it really? In English?

[Read more…] about What is Multicollinearity? A Visual Description

Tagged With: confounding variable, correlations, linear combination, linear regression, logistic regression, Multicollinearity, predictor variable, Regression, regression coefficients, variance, Variance inflation factor

Related Posts

  • Eight Ways to Detect Multicollinearity
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case
  • Centering for Multicollinearity Between Main effects and Quadratic terms
  • The Difference Between R-squared and Adjusted R-squared

Linear Regression for an Outcome Variable with Boundaries

by Karen Grace-Martin 4 Comments

The following statement might surprise you, but it’s true.

To run a linear model, you don’t need an outcome variable Y that’s normally distributed. Instead, you need a dependent variable that is:

  • Continuous
  • Unbounded
  • Measured on an interval or ratio scale

The normality assumption is about the errors in the model, which have the same distribution as Y|X. It’s absolutely possible to have a skewed distribution of Y and a normal distribution of errors because of the effect of X. [Read more…] about Linear Regression for an Outcome Variable with Boundaries

Tagged With: bounded, categorical variable, ceiling effect, floor effect, linear regression, logistic regression, unbounded

Related Posts

  • Understanding Interactions Between Categorical and Continuous Variables in Linear Regression
  • Understanding Interaction Between Dummy Coded Categorical Variables in Linear Regression
  • Interpreting Regression Coefficients
  • Member Training: Goodness of Fit Statistics

Member Training: A Predictive Modeling Primer: Regression and Beyond

by guest contributer

Predicting future outcomes, the next steps in a process, or the best choice(s) from an array of possibilities are all essential needs in many fields. The predictive model is used as a decision making tool in advertising and marketing, meteorology, economics, insurance, health care, engineering, and would probably be useful in your work too! [Read more…] about Member Training: A Predictive Modeling Primer: Regression and Beyond

Tagged With: bagging, boosting, Bootstrap, cross-validation, decision trees, discriminant analysis, K-Nearest Neighbors, lasso, linear regression, logistic regression, predictive models, random forests, Regression, Resampling Techniques, ridge regression, shrinkage methods, subset selection, supervised learning, tree-based methods

Related Posts

  • Member Training: Generalized Linear Models
  • Member Training: Resampling Techniques
  • Member Training: Types of Regression Models and When to Use Them
  • Member Training: Goodness of Fit Statistics

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