landroni
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Taken from xkcd.com: Cell Phones

Take a look at these slides on "Regression diagnostics" by John Fox (available from here, complete with references), which briefly discuss the issue of transforming nonlinearity. It covers Tukey's "...

To complement @gung's and @xan's answers, here's an example of mosaic and association plots using vcd in R. > tab period activity morning noon afternoon evening feed 28 4 ...

According to Wooldridge 2009 (p. 192), the log(1 + x) transformation may retain the usual interpretation of log(x): In cases where a variable $y$ is nonnegative but can take on the value 0, \$log(1+...

Your question is not very clear, and the link to the data is no longer working... For the time fixed effects, your call should look like this: fixed <- plm(Price ~ Income + Housing_units + ...

Brambor, Clark and Golder (2006) (which comes with an internet appendix) have a very clear take on how to understand interaction models and how to avoid the common pitfalls, including why you should (...

You can replicate the UCLA FAQ on proportions (with a percentage as a dependent variable) as follows: require(foreign);require(lmtest);require(sandwich) meals <- read.dta("http://www.ats.ucla.edu/...

While I'm no expert in repeated measures ANOVA, I have some familiarity with the Anova() function in car. Type I or sequential Anova estimates a sequence of models in an effectively arbitrary order, ...

In addition to @gung's answer, I discovered that mosaic plots are very useful in assessing the relative relationships with categorical data. Once we establish (actually, we assume here) departure from ...

In R the effects package can easily help with interpreting such coefficients by producing the appropriate graphs. From CRAN: effects: Effect Displays for Linear, Generalized Linear, and Other Models ...

If you don't have a time index, you don't need one: plm will add a fictitious one by itself, and it won't be used unless you ask for it. So this call should work: > x <- plm(price ~ carat, ...