# Posts Tagged ‘ ANOVA ’

## The fallacy of placing confidence in confidence intervals (version 2)

April 21, 2015
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I, with my coathors, have submitted a new draft of our paper "The fallacy of placing confidence in confidence intervals". This paper is substantially modified from its previous incarnation. Here is the main argument:"[C]onfidence intervals may not be u...

## Multiple Comparisons with BayesFactor, Part 2 – order restrictions

January 18, 2015
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In my previous post, I described how to do multiple comparisons using the BayesFactor package. Part 1 concentrated on testing equality constraints among effects: for instance, that the the effects of two factor levels are equal, while leaving the third free to be different. In this second part, I will describe how to test order restrictions on factor level effects. This post will be a little more involved than the…

## Multiple Comparisons with BayesFactor, Part 1

January 17, 2015
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One of the most frequently-asked questions about the BayesFactor package is how to do multiple comparisons; that is, given that some effect exists across factor levels or means, how can we test whether two specific effects are unequal. In the next two posts, I'll explain how this can be done in two cases: in Part 1, I'll cover tests for equality, and in Part 2 I'll cover tests for specific…

## One-way ANOVA with fixed and random effects from a Bayesian perspective

December 22, 2014
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This blog post is derived from a computer practical session that I ran as part of my new course on Statistics for Big Data, previously discussed. This course covered a lot of material very quickly. In particular, I deferred introducing notions of hierarchical modelling until the Bayesian part of the course, where I feel it … Continue reading One-way ANOVA with fixed and random effects from a Bayesian perspective

## Applied Statistics Lesson of the Day – Additive Models vs. Interaction Models in 2-Factor Experimental Designs

$Applied Statistics Lesson of the Day – Additive Models vs. Interaction Models in 2-Factor Experimental Designs$

In a recent “Machine Learning Lesson of the Day“, I discussed the difference between a supervised learning model in machine learning and a regression model in statistics.  In that lesson, I mentioned that a statistical regression model usually consists of a systematic component and a random component.  Today’s lesson strictly concerns the systematic component. An […]

## Example 2014.3: Allow different variances by group

February 27, 2014
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One common violation of the assumptions needed for linear regression is heterscedasticity by group membership. Both SAS and R can easily accommodate this setting. Our data today comes from a real example of vitamin D supplementation of milk. Four sup...

## Applied Statistics Lesson of the Day – The Completely Randomized Design with 1 Factor

The simplest experimental design is the completely randomized design with 1 factor.  In this design, each experimental unit is randomly assigned to each factor level.  This design is most useful for a homogeneous population (one that does not have major differences between any sub-populations).  It is appealing because of its simplicity and flexibility – it can […]

## Some Common Approaches for Analyzing Likert Scales and Other Categorical Data

July 2, 2013
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$Some Common Approaches for Analyzing Likert Scales and Other Categorical Data$

Analyzing Likert scale responses really comes down to what you want to accomplish (e.g. Are you trying to provide a formal report with probabilities or are you trying to simply understand the data better). Sometimes a couple of graphs are sufficient and a formalize statistical test isn’t even necessary. However, with how easy it is […]

June 13, 2013
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## Neuroscience, statistical power and how to increase it

April 21, 2013
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There has been quite a bit of buzz recently about the Button et al. Nature Reviews Neuroscience paper on statistical power. Several similar reviews have been published in psychology and other disciplines and come to broadly the same conclusion - that m...