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Model Building

What Is Specification Error in Statistical Models?

by Karen Grace-Martin  Leave a Comment

When we think about model assumptions, we tend to focus on assumptions like independence, normality, and constant variance. The other big assumption, which is harder to see or test, is that there is no specification error. The assumption of linearity is part of this, but it’s actually a bigger assumption.

What is this assumption of no specification error? [Read more…] about What Is Specification Error in Statistical Models?

Tagged With: curvilinear effect, interaction, Model Building, predictors, specification error, statistical model, transformation

Related Posts

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  • Differences in Model Building Between Explanatory and Predictive Models
  • Overfitting in Regression Models
  • What It Really Means to Remove an Interaction From a Model

Overfitting in Regression Models

by Karen Grace-Martin  1 Comment

The practice of choosing predictors for a regression model, called model building, is an area of real craft.Stage 2

There are many possible strategies and approaches and they all work well in some situations. Every one of them requires making a lot of decisions along the way. As you make decisions, one danger to look out for is overfitting—creating a model that is too complex for the the data. [Read more…] about Overfitting in Regression Models

Tagged With: linear regression, Linear Regression Model, Model Building, overfitting, regression model

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  • What is Multicollinearity? A Visual Description
  • Differences in Model Building Between Explanatory and Predictive Models
  • Member Training: The Link Between ANOVA and Regression

What It Really Means to Remove an Interaction From a Model

by Karen Grace-Martin  3 Comments

When you’re model building, a key decision is which interaction terms to include. And which interactions to remove.Stage 2

As a general rule, the default in regression is to leave them out. Add interactions only with a solid reason. It would seem like data fishing to simply add in all possible interactions.

And yet, that’s a common practice in most ANOVA models: put in all possible interactions and only take them out if there’s a solid reason. Even many software procedures default to creating interactions among categorical predictors.

[Read more…] about What It Really Means to Remove an Interaction From a Model

Tagged With: categorical predictor, interaction, Model Building

Related Posts

  • Simplifying a Categorical Predictor in Regression Models
  • Differences in Model Building Between Explanatory and Predictive Models
  • Should I Specify a Model Predictor as Categorical or Continuous?
  • The Impact of Removing the Constant from a Regression Model: The Categorical Case

Simplifying a Categorical Predictor in Regression Models

by Jeff Meyer  Leave a Comment

One of the many decisions you have to make when model building is which form each predictor variable should take. One specific version of thisStage 2 decision is whether to combine categories of a categorical predictor.

The greater the number of parameter estimates in a model the greater the number of observations that are needed to keep power constant. The parameter estimates in a linear [Read more…] about Simplifying a Categorical Predictor in Regression Models

Tagged With: categorical predictor, interpreting regression coefficients, Model Building, pairwise, R-squared

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  • What It Really Means to Remove an Interaction From a Model
  • Differences in Model Building Between Explanatory and Predictive Models
  • The Difference Between R-squared and Adjusted R-squared
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Descriptives Before Model Building

by Jeff Meyer  Leave a Comment

Stage 2One approach to model building is to use all predictors that make theoretical sense in the first model. For example, a first model for determining birth weight could include mother’s age, education, marital status, race, weight gain during pregnancy and gestation period.

The main effects of this model show that a mother’s education level and marital status are insignificant.
[Read more…] about Descriptives Before Model Building

Tagged With: Model Building, predictive models, significant

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  • Overfitting in Regression Models
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Member Training: Model Building Approaches

by TAF Support 

There is a bit of art and experience to model building. You need to build a model to answer your research question but how do you build a statistical model when there are no instructions in the box? 

Should you start with all your predictors or look at each one separately? Do you always take out non-significant variables and do you always leave in significant ones?

[Read more…] about Member Training: Model Building Approaches

Tagged With: centering, interaction, lasso, Missing Data, Model Building, Model Fit, Multicollinearity, overfitting, Research Question, sample size, specification error, statistical model, Stepwise

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  • What Is Specification Error in Statistical Models?
  • Member Training: The LASSO Regression Model
  • Steps to Take When Your Regression (or Other Statistical) Results Just Look…Wrong
  • Member Training: Centering

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