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2 votes

Transformations for zero inflated non-negative continuous response variable in R

Your idea to work with logs here is heading in the right direction, but there's a better way to do it. It's generally best to work as close as possible to the original data when modeling. Your ...
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1 vote

Summarising and Visualising three attributes in R

A Wilkinson dot plot is a handy graphics for visualizing counts. It's a histogram of stacked dots. Here is a Wilkinson dot plot of the sample data, split by deprivation. It's easy to notice for ...
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5 votes

Summarising and Visualising three attributes in R

Here is a possibility: plot hospital stay against age, with five different loess fit lines, one per deprivation level. In the plot below, I used your example data (actually, I replicated it five times,...
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4 votes

log(x * constant) transformation

As noticed in the comments, $$ \log(x \times c) = \log x + \log c $$ by the properties of the logarithms. So what you are doing is you shift the data by a constant. It doesn't change anything about ...
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4 votes

log(x * constant) transformation

[Update] Now that we know the question is about visualization and not analysis, the mathematical properties of the logarithm seem less relevant than how people perceive log-scaled data. The goal of ...
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2 votes

Is it logical to look for a correlation between average and percentage?

The biggest risk of looking at statistics of the aggregates is ecological fallacy (example here). Group-level aggregates do not necessarily are suitable for inferring individual-level characteristics. ...
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1 vote
Accepted

dataset in log & lin-lin regression function vs. dataset not in log & log-log regression function: Why different results in R?

That should not be happening. Perhaps your "already transformed" data takes a different type of logarithm. To take a toy example when I try ...
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0 votes
Accepted

Distribution of an RBF-transformed normal variable

Expanding on whuber's answer (+1), I'd like to cover a more general case, as stated in the question. We have a standard normal random variable $X \sim N(0, 1)$. We transform this variable by passing ...
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2 votes

Distribution of an RBF-transformed normal variable

To get to the essence of this question, let's generalize it a little. Suppose $X$ is a random variable supported on a set (of real numbers) $\mathcal A$ where it has a density proportional to $$f_X(x)...
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1 vote

F-test and violated assumptions

Model assumptions are mathematical constructs and are never precisely fulfilled in reality. The relevant question is not whether assumptions are fulfilled or not (as they never are), but rather ...
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