# Tag Info

9

Thanks for a very good question! I will try to give my intuition behind it. In order to understand this, remember the "ingredients" of random forest classifier (there are some modifications, but this is the general pipeline): At each step of building individual tree we find the best split of data While building a tree we use not the whole dataset, but ...

3

390 is a very decent sample size. Since you haven't posted any details of the results, all we can do is list some possible reasons: 1) Your model is wrong - the relationship you tested is too weak to detect 2) Your model is wrong - you have left out variables that are important 3) Your model is wrong - you have violated the assumptions in some way that ...

3

Heart rates vary in a cyclic pattern that is driven by the respiratory rate. Inspiration causes decreased filling of the left atrium and the heart rate increase to maintain cardiac output. You need to detrend the respiratory influence. Because the instantaneous heart rate is just the inverse of the RR interval, you do not need to wait for 15 or 20 seconds ...

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You should not be using Cronbach's alpha in your case. I don't think you should be using any measure of internal reliability because your variables are not intended to form a scale. And, if your professor really said that the first thing you do, in all cases, is Cronbach's alpha, then I would drop the class. That is like a cooking class where the teacher ...

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You can set any significance level you like before you start looking at the data. The higher you choose to set it, the greater the rate of Type I errors you're prepared to accept (that is, the more frequently you say you'd be prepared to claim a real effect in the presence of nothing but noise) If you choose a high significance level, even before the fact, ...

3

Things like the predictions, residuals, full-reduced model tests, etc. will not be affected by the change that you propose, but what does change is the interpretation and tests on the individual terms. Most regression routines will provide an automatic test of whether a term is 0 or not. This is meaningful when a term represents the difference between two ...

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I don't think continuous scale is absolutely essential for regression of the usual kind. There are plenty of examples in which I might use regression on a response (what you call DV) that is a counted variable. But in essence I think you are right. The key word here is ordinal or ordered: your response variable (DV in your notation) is on an ordinal or ...

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Let me use an example: Say you want to quantify the (causal) effect of education on income. You take education years and income data and regress one against the other. Did you recover what you wanted? Probably not! This is because the income is also caused by things other than education, but which are correlated to education. Let's call them "skill": We ...

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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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Well, simple linear regression usually refers to a model with only a single predictor, so the relationship would be linear (if you transform a variable then the relationship is still linear between the transformed variables). Most curved relationships are modeled using polynomials or splines which moves away from simple linear regression into multiple ...

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@GregSnow is right that this change doesn't really matter. Let me add a few details to extend that. What you are talking about is sometimes called cell means coding, whereas the default coding scheme is called reference cell coding. Note that there are many possible valid coding schemes. If you have a categorical variable with only two levels, then the ...

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For some time series, like equity prices exchange rates and GDP growth, log returns are approximately invariant, meaning that it is stable over time. One way to see this would be to plot the histogram of the first and second half of the available data, which should be roughly similar, and check a scatter plot of the returns versus their lags, which should ...

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(A somewhat handwavy answer which hopefully gives some sense of the circumstances under which the answer could be a qualified yes) Let's start with linear regression; The standard errors come from the variance-covariance matrix, $\text{Var}(\hat \beta) = \sigma^2 (X'X)^{-1}$. In the large and small samples, the population quantity $\sigma^2$ is assumed ...

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The Standard error is an estimate of the variance of the strength of the effect, or the strength of the relationship between each causal variable and the predicted variable. If it's high, then the effect size will have to be stronger for us to be able to be sure that it's a real effect, and not just an artefact of randomness. The t-statistic is an estimate ...

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It sounds like you need a decent basic statistics text that covers at least basic location tests, simple regression and multiple regression. Std. Error,t value and Pr. i) Std. Error is the standard deviation of the sampling distribution of the estimate of the coefficient under the standard regression assumptions. Such standard deviations are called ...

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You calculate the full transformed confidence interval and then transform it back. Let's say the transformed confidence interval is 5 ± 3, or CI95% = [2, 8]. You would take the 2, and 8 values and transform them back. You do NOT transform the 3 (the width of the confidence interval). The result in this example CI95% = [0.25, 0.016]. Be careful of ...

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