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Example 10.7: Fisher vs. Pearson

October 29, 2012
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Example 10.7: Fisher vs. Pearson

In the early days of the discipline of statistics, R.A. Fisher argued with great vehemence against Egon Pearson (and Jerzy Neyman) over the foundational notions supporting statistical inference. The personal invective recorded is somewhat amusing an...

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Example 10.6: Should Poisson regression ever be used? Negative binomial vs. Poisson regression

October 15, 2012
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Example 10.6: Should Poisson regression ever be used? Negative binomial vs. Poisson regression

In practice, we often find that count data is not well modeled by Poisson regression, though Poisson models are often presented as the natural approach for such data. In contrast, the negative binomial regression model is much more flexible and is therefore likely to fit better, if the data are not Poisson. In example 8.30 we compared...

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Example 10.5: Convert a character-valued categorical variable to numeric

October 8, 2012
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Example 10.5: Convert a character-valued categorical variable to numeric

In some settings it may be necessary to recode a categorical variable with character values into a variable with numeric values. For example, the matching macro we discussed in example 7.35 will only match on numeric variables. One way to conve...

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Example 10.4: Multiple comparisons and confidence limits

October 1, 2012
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Example 10.4: Multiple comparisons and confidence limits

A colleague is a devotee of confidence intervals. To him, the CI have the magical property that they are immune to the multiple comparison problem-- in other words, he feels its OK to look at a bunch of 95% CI and focus on the ones that appear to exclude the null. This though he knows well the...

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Example 10.3: Enhanced scatterplot with marginal histograms

September 24, 2012
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Example 10.3: Enhanced scatterplot with marginal histograms

Back in example 8.41 we showed how to make a graphic combining a scatterplot with histograms of each variable. A commenter suggested we change the R graphic to allow post-hoc plotting of, for example, lowess lines. In addition, there are further refinements to be made. In this R-only entry, we'll make the figure more flexible and a bit...

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Example 10.2: Custom graphic layouts

September 17, 2012
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Example 10.2: Custom graphic layouts

In example 10.1 we introduced data from a CPAP machine. In brief, it's hard to tell exactly what's being recorded in the data set, but it seems to be related to the pattern of breathing. Measurements are taken five times a second, leading to on the o...

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Example 10.1: Read a file byte by byte

September 10, 2012
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Example 10.1: Read a file byte by byte

More and more makers of electronic devices use standard storage media to record data. Sometimes this is central to the device's function, as in a camera, so that the data must be easy to recover. Other times, it's effectively incidental, and the device maker may not provide easy access to the stored data. For example, I...

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Third year wrap-up

July 23, 2012
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Third year wrap-up

July marks the end of three years of blogging for us. By our count, we've posted 121 examples across the first three years. We aim to be helpful and interesting.As always, it's hard to get a sense of our readership. At the time we wrote this, Feedbur...

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Citing R or SAS

July 2, 2012
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Citing R or SAS

One of us recently read a colleague's first draft of a paper, in which she had written: "All analyses were done in R 2.14.0." We assume we're preaching to the converted here, when we say that the enormous amount of work that goes into R needs to be re...

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Example 9.36: Levene’s test for equal variances

June 25, 2012
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Example 9.36: Levene’s test for equal variances

The assumption of equal variances among the groups in analysis of variance is an expression of the assumption of homoscedasticity for linear models more generally. For ANOVA, this assumption can be tested via Levene's test. The test is a function of the residuals and means within each group, though various modifications are used, including the Brown-Forsythe test....

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