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regression models

Eight Ways to Detect Multicollinearity

by Karen Grace-Martin 4 Comments

Multicollinearity can affect any regression model with more than one predictor. It occurs when two or more predictor variables overlap so much in what they measure that their effects are indistinguishable.

When the model tries to estimate their unique effects, it goes wonky (yes, that’s a technical term).

So for example, you may be interested in understanding the separate effects of altitude and temperature on the growth of a certain species of mountain tree.

[Read more…] about Eight Ways to Detect Multicollinearity

Tagged With: Bivariate Statistics, Correlated Predictors, linear regression, logistic regression, Multicollinearity, p-value, predictor variable, regression models

Related Posts

  • A Visual Description of Multicollinearity
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Is Multicollinearity the Bogeyman?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

Parametric or Semi-Parametric Models in Survival Analysis?

by guest Leave a Comment

It was Casey Stengel who offered the sage advice, “If you come to a fork in the road, take it.”

When you need to fit a regression model to survival data, you have to take a fork in the road. One road asks you to make a distributional assumption about your data and the other does not. [Read more…] about Parametric or Semi-Parametric Models in Survival Analysis?

Tagged With: cox, distributions, exponential, gamma, hazard function, lognormal, parametric models, regression models, semi-parametric, survival data, Weibull

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  • What Is a Hazard Function in Survival Analysis?
  • Six Types of Survival Analysis and Challenges in Learning Them
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  • What is Survival Analysis and When Can It Be Used?

Why ANOVA is Really a Linear Regression, Despite the Difference in Notation

by Karen Grace-Martin 2 Comments

When I was in graduate school, stat professors would say “ANOVA is just a special case of linear regression.”  But they never explained why.

And I couldn’t figure it out.

The model notation is different.

The output looks different.

The vocabulary is different.

The focus of what we’re testing is completely different. How can they be the same model?

[Read more…] about Why ANOVA is Really a Linear Regression, Despite the Difference in Notation

Tagged With: ANOVA, linear regression, notation, regression models

Related Posts

  • 7 Practical Guidelines for Accurate Statistical Model Building
  • The Steps for Running any Statistical Model
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  • Why ANOVA and Linear Regression are the Same Analysis

Can We Use PCA for Reducing Both Predictors and Response Variables?

by Karen Grace-Martin 5 Comments

I recently gave a free webinar on Principal Component Analysis. We had almost 300 researchers attend and didn’t get through all the questions. This is part of a series of answers to those questions.

If you missed it, you can get the webinar recording here.

Question: Can we use PCA for reducing both predictors and response variables?

In fact, there were a few related but separate questions about using and interpreting the resulting component scores, so I’ll answer them together here.

How could you use the component scores?

A lot of times PCAs are used for further analysis — say, regression. How can we interpret the results of regression?

Let’s say I would like to interpret my regression results in terms of original data, but they are hiding under PCAs. What is the best interpretation that we can do in this case?

Answer:

So yes, the point of PCA is to reduce variables — create an index score variable that is an optimally weighted combination of a group of correlated variables.

And yes, you can use this index variable as either a predictor or response variable.

It is often used as a solution for multicollinearity among predictor variables in a regression model. Rather than include multiple correlated predictors, none of which is significant, if you can combine them using PCA, then use that.

It’s also used as a solution to avoid inflated familywise Type I error caused by running the same analysis on multiple correlated outcome variables. Combine the correlated outcomes using PCA, then use that as the single outcome variable. (This is, incidentally, what MANOVA does).

In both cases, you can no longer interpret the individual variables.

You may want to, but you can’t. [Read more…] about Can We Use PCA for Reducing Both Predictors and Response Variables?

Tagged With: Component Score, index variable, MANOVA, Multicollinearity, principal component analysis, regression models, Type I error

Related Posts

  • How To Calculate an Index Score from a Factor Analysis
  • How to Reduce the Number of Variables to Analyze
  • Eight Ways to Detect Multicollinearity
  • Four Common Misconceptions in Exploratory Factor Analysis

Member Training: The LASSO Regression Model

by guest Leave a Comment

The LASSO model (Least Absolute Shrinkage and Selection Operator) is a recent development that allows you to find a good fitting model in the regression context. It avoids many of the problems of overfitting that plague other model-building approaches.

In this month’s Statistically Speaking webinar, guest instructor Steve Simon, PhD, will explain what overfitting is — and why it’s a problem.

Then he’ll illustrate the geometry of the LASSO model in comparison to other regression approaches, ridge regression and stepwise variable selection.

Finally, he’ll show you how LASSO regression works with a real data set.


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.

[Read more…] about Member Training: The LASSO Regression Model

Tagged With: lasso, Model Building, overfitting, regression models

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Member Training: Multicollinearity

by Karen Grace-Martin Leave a Comment

Multicollinearity isn’t an assumption of regression models; it’s a data issue.

And while it can be seriously problematic, more often it’s just a nuisance.

In this webinar, we’ll discuss:

  • What multicollinearity is and isn’t
  • What it does to your model and estimates
  • How to detect it
  • What to do about it, depending on how serious it is

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.

[Read more…] about Member Training: Multicollinearity

Tagged With: Multicollinearity, regression models

Related Posts

  • Member Training: Missing Data
  • A Visual Description of Multicollinearity
  • Member Training: Elements of Experimental Design
  • Eight Ways to Detect Multicollinearity

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