256 search results for "anova"

Two-way analysis of variance: two-way ANOVA in R

August 7, 2009
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The one-way analysis of variance is a useful technique to verify if the means of more groups are equals. But this analysis may not be very useful for more complex problems. For example, it may be necessary to take into account two factors of variability to determine if the averages between the groups depend on the group classification...

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Analysis of variance: ANOVA, for multiple comparisons

July 30, 2009
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Analysis of variance: ANOVA, for multiple comparisonsThe ANOVA model can be used to compare the mean of several groups with each other, using a parametric method (assuming that the groups follow a Gaussian distribution).Proceed with the following example:The manager of a supermarket chain wants to see if the consumption in kilowatts of 4 stores between them are equal. He...

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Analysis of variance: ANOVA, for multiple comparisons

July 30, 2009
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Analysis of variance: ANOVA, for multiple comparisonsThe ANOVA model can be used to compare the mean of several groups with each other, using a parametric method (assuming that the groups follow a Gaussian distribution).Proceed with the following example:The manager of a supermarket chain wants to see if the consumption in kilowatts of 4 stores between them are equal. He...

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Repeated Measures ANOVA using R

March 9, 2009
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Repeated Measures ANOVA using R

While so-called “between-subjects” ANOVA is absolutely straightforward in R, performing repeated measures (within-subjects) ANOVA is not so obvious. I have come across at least three different ways of performing repeated measures ANOVA in R. Which method you use depends on … Continue reading →

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Analysis of Variance (ANOVA) using R

February 5, 2009
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Analysis of Variance (ANOVA) using R

I found some useful websites showing examples of how to use R for various sorts of ANOVA (between, within, mixed designs, etc): Using R for Psychological Research Quick-R for SAS/SPSS/Stata users enjoy

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Simpson’s Paradox Is Back

April 21, 2014
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Simpson’s Paradox Is Back

The latest issue of the American Statistician has a set of thought-provoking point/counterpoint papers on Simpson’s Paradox, with a tie-in to the controversial issue of causality. (I will not address the causality issue here.) Since I have long had my own thoughts about Simpson’s, I’ll postpone the topic I had planned to post this week,

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Interpreting interaction coefficient in R (Part1 lm)

April 8, 2014
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Interpreting interaction coefficient in R (Part1 lm)

Interaction are the funny interesting part of ecology, the most fun during data analysis is when you try to understand and to derive explanations from the estimated coefficients of your model. However you do need to know what is behind these estimate, there is a mathematical foundation between them that you need to be aware

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Scraping organism metadata for Treebase repositories from GOLD using Python and R

Scraping organism metadata for Treebase repositories from GOLD using Python and RI recently wanted to get hold of habitat/phenotype/sequencing metadata for the individual organisms of an archived Treebase project.)The GOLD database holds more than 18000 full genomes. For many of these it provides pretty good metadata (GOLDcards) which are indirectly linked to...

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R User Group Activity for Q1 2014

March 27, 2014
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R User Group Activity for Q1 2014

by Joseph Rickert Worldwide R user group activity for the first Quarter of 2014 appears to be way up compared to previous years as the following plot shows. The plot was built by counting the meetings on Revolution Analytics R Community Calendar. R users continue to value the live, in person events and face-to-face meetings with their peers. Moreover,...

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Seasonal Unit Roots

March 26, 2014
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Seasonal Unit Roots

As discussed in the MAT8181 course, there are – at least – two kinds of non-stationary time series: those with a trend, and those with a unit-root (they will be called integrated). Unit root tests cannot be used to assess whether a time series is stationary, or not. They can only detect integrated time series. And the same holds...

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