# Tag Info

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I think Peter and user11852 gave great answers. I would also add (by negation) that if a model was really good, it would probably be useless because of overfitting (hence, not generalizable).

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It depends on what you want to use this for. I can easily imagine situations in capacity planning where you would be most interested in extreme occurrences, as these peak events are what strains capacity most. If that is the case, then your tail behaviour would be a problem. I can also imagine other situations where the system is somewhat flexible so that ...

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For me the actual insight lies in the following aspect: A model doesn't have to be correct to be useful. Unfortunately in many sciences it is often forgotten that models don't necessarily need to be exact representations of reality to allow new discoveries and predictions! So don't waste your time building a complicated model that needs accurate ...

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One simple way to fit a gamma distribution to the data is the method of moments: the gamma distribution with parameters $(\alpha, \beta)$ has mean $\frac\alpha\beta$ and variance $\frac\alpha{\beta^2}$. You can use sample estimates of the mean and variance and some algebra to solve for the parameters of the model. Naturally, more advanced methods exist. But ...

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You can check them in separate models, just like main effect terms. The only guideline is that the main effects that are involved in the interaction should be in the model (with rare exceptions). However, if you are concerned about possibly inflating errors, running multiple models with interactions has the same issues as running multiple models without ...

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The error terms (e0, e1, e2) are independent. They could have the same mean and variance (e.g., mean=0, sd=1, "white"), but are still independently generated. The noise reaching the microphones could be the result of a variety of unknown processes --- each microphone could be at a different point in the room, some could be of higher quality than others, ...

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The trouble with using the ACF is that there can be other reasons for significant spikes, not just seasonality. So it is indicative but cannot be conclusive. If the data had a small seasonal period (such as 4 for quarterly data or 12 for monthly data) then a simple approach is to use the ets function in the forecast package for R. If there is a seasonal ...

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I would approach this in terms of a response trajectory whereby you can explicilty examine 1) any change in the mean level pre-to-post and 2) any change in the slope/curve before and after the stimulus. I have used multilevel models to do this in the past. The level 1 model is set up as an interupted time series and the variation in the pre-to-post change is ...

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I am not sure if this will help, but I believe the X12 program provides some general tests for different types of seasonality automatically. You can find it here: http://www.census.gov/srd/www/x12a/

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@Eva, I definitely think R has the functionality to help you out here. The decision about which tool depends a bit on the kind of seasonality you're trying to detect -- can you tell us a bit more about the data you're modeling? I would start with something simple -- for example, let's assume you have a single observation each day for 1000 days, and are ...

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I think its meaning is best analyzed by looking at it in two parts: "All models are wrong" that is, every model is wrong because it is a simplification of reality. Some models, especially in the "hard" sciences, are only a little wrong. They ignore things like friction or the gravitational effect of tiny bodies. Other models are a lot wrong - they ignore ...

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My acid interpretation is: Believing that a mathematical model describes exactly all the factors, and their interactions, governing a phenomenon of interest would be too simplistic and arrogant. We do not even know if the logic we use is enough to understand our universe. However, some mathematical models represent a good enough approximation (in terms of ...

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You might think of it this way. the maximum complexity (i.e., entropy) of an object obeys some form of the Bekenstein bound: $$I \le \frac{2\pi RE}{\hbar c\ln 2}$$ where $E$ is the total rest energy including mass, and $R$ is the radius of a sphere that encloses the object. That's a big number, in most cases: The Bekenstein bound for an average ...

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It means useful insights can be provided from models which are not a perfect representation of the phenomena they model. A statistical model is a description of a system using mathematical concepts. As such in many cases you add a certain layer of abstraction to facilitate your inferential procedure (eg. normality of measurement errors, compound symmetry in ...

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Modeling Change in BMI vs BMI at follow up will likely be determined by the interpretation you plan to use - assuming that you are using linear regression. One can see that they can be formulated equivalently by noting E[Y2-Y1] = E[Y2] - E[Y1]. To demonstrate equivalent formulations, letting XBeta be the linear predictor of covariates and parameters, ...

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Well there is a package which implements entropy-based methods and it is called .... entropy. More information: http://cran.r-project.org/web/packages/entropy/

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