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Here is how you can interpret for matrix-based visualization (refer to https://cran.r-project.org/web/packages/arulesViz/vignettes/arulesViz.pdf): Formally, the visualized matrix is constructed as follows. We start with the set of association rules $R = \{<a_1, c_1, m_1>, \ldots <a_i, c_i, m_i>, \ldots <a_n, c_n, m_n>\}$ where $a_i$ is the ...


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Ed Tufte's spare redesign of the boxplot permits a large "small multiple" graphic to be displayed. Another point Tufte makes is that by ordering small multiples according to another factor, one often gets "free" information out of the graphic. Ordering the plots by median or box height is usually insightful, because relationships among ...


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In my opinion the 2nd plot is pretty good. I might just add colour = so that each individual has their own colour, but the two main things that jump out about that plot are: there is considerably variation between individuals there is, by comparison, much less variation within individuals there is considerable heterogeneity. In particular, three ...


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What problem are you trying to solve? A correlation between the variance of two regions doesn't make sense if you exclude the temporal dimension. At each time step the variance has a probability distribution and thus you have an infinite number of distributions for which you are observing a finite number of samples. You need to compare these stochastic ...


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This is essentially the binomial distribution, with $p=0.20$ and $n=|\text{set}|,$ the number of elements in the set.


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With only three years for each occupation, I would start out with visualization. Make a lineplot with 250 lines, years along the x-axis. Plotting ratios directly, that will confound levels with slope, but if there are groups of occupations with rather different slopes, that should stand<out in the plot. You could augment the plot with line coloring ...


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Other options for representing aspects of a distribution are the boxplot and violin plot. Both of these typically represent the median and upper/lower quartile values, showing the range in which the middle 50% of your values fall, and the rest of the plot can give you a sense of the upper/lower range. The violin plot is similar to smoothed histogram or ...


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If age and its square are the only predictors, then you should have a coefficient of log-odds on age, call it $b_1$, and one on age squared, call it $b_2$. Hence those are just two terms like those of any quadratic $$b_1 x + b_2 x^2$$ so that a maximum with $x$ will be observed whenever its derivative $b_1 + 2 b_2 x$ is $0$, so long as $b_2 <0$. So the ...


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So, you know that the true values of this unknown value must be greater than zero? This seems like the perfect time to incorporate a prior. Let's call the random variable $X$. Since we know $X$ cannot be less than zero, you are correct in saying that reporting a value of $X$ as, for example, $X=0.23 \pm 0.44$, does not make much sense. I think in this case, ...


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