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dependent variable

Member Training: Confusing Statistical Terms

by guest

Learning statistics is difficult enough; throw in some especially confusing terminology and it can feel impossible! There are many ways that statistical language can be confusing.

Some terms mean one thing in the English language, but have another (usually more specific) meaning in statistics.  [Read more…] about Member Training: Confusing Statistical Terms

Tagged With: ancova, association, confounding variable, confusing statistical terms, Correlation, Covariate, dependent variable, Error, factor, General Linear Model, generalized linear models, independent variable, learning statistics, levels, listwise deletion, multivariate, odds, pairwise deletion, random error, selection bias, significant

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How to Reduce the Number of Variables to Analyze

by Christos Giannoulis 1 Comment

by Christos Giannoulis

Many data sets contain well over a thousand variables. Such complexity, the speed of contemporary desktop computers, and the ease of use of statistical analysis packages can encourage ill-directed analysis.

It is easy to generate a vast array of poor ‘results’ by throwing everything into your software and waiting to see what turns up. [Read more…] about How to Reduce the Number of Variables to Analyze

Tagged With: common factor analysis, dependent variable, Factor Analysis, independent variable

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When to Use Logistic Regression for Percentages and Counts

by Karen Grace-Martin 1 Comment

One important yet difficult skill in statistics is choosing a type model for different data situations. One key consideration is the dependent variable.

For linear models, the dependent variable doesn’t have to be normally distributed, but it does have to be continuous, unbounded, and measured on an interval or ratio scale.

Percentages don’t fit these criteria. Yes, they’re continuous and ratio scale. The issue is the [Read more…] about When to Use Logistic Regression for Percentages and Counts

Tagged With: binomial, Count data, count model, dependent variable, events, logistic regression, Negative Binomial Regression, percentage data, Poisson Regression, trials

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Segmented Regression for Non-Constant Relationships

by Jeff Meyer 2 Comments

by Jeff Meyer

We often have a continuous predictor in a model that we believe has non-constant relationship with the dependent variable along the predictor’s range. But how can we be certain? What is the best way to measure this?

Sometimes including a quadratic term will capture the change in the slope as we move from the bottom of the range to the top of the range. But a quadratic term only works in two situations:

  1. The rate of change increases and then at some point decreases, or:
  2. The opposite happens – the rate of change decreases and at some point increases.

We could also create a categorical variable. Each category within the categorical variable would represent a specific range within the continuous variable. [Read more…] about Segmented Regression for Non-Constant Relationships

Tagged With: continuous predictor, dependent variable, non-constant relationship, piecewise regression, segmented regression, slopes

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When Dependent Variables Are Not Fit for Linear Models, Now What?

by Karen Grace-Martin 28 Comments

When your dependent variable is not continuous, unbounded, and measured on an interval or ratio scale, your model will not meet the assumptions of linear models.

Today I’m going to go into more detail about 6 common types of dependent variables that are not continuous, unbounded, and measured on an interval or ratio scale and the tests that work instead.

Side note: the usual advice is to use nonparametric tests when normality [Read more…] about When Dependent Variables Are Not Fit for Linear Models, Now What?

Tagged With: binary variable, categorical variable, Censored, dependent variable, Discrete Counts, Multinomial, ordinal variable, Poisson Regression, Proportion, Proportional Odds Model, regression models, Truncated, Zero Inflated

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6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

by Karen Grace-Martin 11 Comments

The assumptions of normality and constant variance in a linear model (both OLS regression and ANOVA) are quite robust to departures.  That means that even if the assumptions aren’t met perfectly, the resulting p-values will still be reasonable estimates.

But you need to check the assumptions anyway, because some departures are so far that the p-value become inaccurate.  And in many cases there are remedial measures you can take to turn non-normal residuals into normal ones.

But sometimes you can’t.

Sometimes it’s because the dependent variable just isn’t appropriate for a linear model.  The [Read more…] about 6 Types of Dependent Variables that will Never Meet the Linear Model Normality Assumption

Tagged With: Assumptions, categorical outcome, categorical variable, Censored, Constant Variance, dependent variable, Discrete Counts, normality, ordinal variable, Proportion, Truncated, Zero Inflated

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  • Member Training: Types of Regression Models and When to Use Them

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