Top new questions this week:
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If we have $k$ categories of a categorical variable, why do we need $k-1$ dummy variables? For example, if there are 8 categories, why don’t we code them as …
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How did the hat matrix get its name $\hat{\mathbf{H}} = \mathbf{X} \left( \mathbf{X}^\textsf{T} \mathbf{X} \right)^{-1} \mathbf{X}^\textsf{T}$ I am interested in the etymology of the term. Who gave it …
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As a non-statistician, I am interested in understanding whether model evaluation should be conducted with the measured values on the x-axis and modelled (or predicted) values on the y-axis, or vice …
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Sometimes it’s quite confusing when it comes to determining how to answer a probability question. Confusions always arise as of whether I should multiply/add or make conditional the probabilities. For …
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I am currently reading ‘Dive into Deep Learning’ and right now I am trying to improve my intuition for the Kullback–Leibler divergence. I get the basic idea, why this metric is not symmetric, however, …
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I got the following question in one interview: suppose we have an N-sided die and given the probability of landing on each side, how to assign values from 1 to N, to make the expected value close to $(…
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I have a following picture and the assumption that I can estimate the effect of Treatment on Growth by accounting for dT. However, I’m not sure if Unobserved confounder is actually a confounder – it …
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Greatest hits from previous weeks:
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For a prediction interval in linear regression you still use $\hat{E}[Y|x] = \hat{\beta_0}+\hat{\beta}_{1}x$ to generate the interval. You also use this to generate a confidence interval of $E[Y|x_0]$….
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The waiting times for poisson distribution is an exponential distribution with parameter lambda. But I don’t understand it. Poisson models the number of arrivals per unit of time for example. How is …
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I’m sure many people will respond with links to ‘let me google that for you’, so I want to say that I’ve tried to figure this out so please forgive my lack of understanding here, but I cannot figure …
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Searched high and low and have not been able to find out what AUC, as in related to prediction, stands for or means.
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Disclaimer: I’m not a statistician but a software engineer. Most of my knowledge in statistics comes from self-education, thus I still have many gaps in understanding concepts that may seem trivial …
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In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect and mixed effect models?
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What is the difference between a population and a sample? What common variables and statistics are used for each one, and how do those relate to each other?
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Can you answer these questions?
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So I‘m basically more or less trying to prove what is stated in the answer by syeh_106 here: Is differential entropy always less than infinity? if the variance is finite, then the differential …
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I am wondering if it is appropriate to use a standard scaler (center and divide by std) to scale categorical and real number variables in a data set for the purpose of comparing the resulting linear …
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As neural networks are a set of linear operators, using categorical variables with an arbitrary assignment of order where there is no justification (e.g., Doctor:1, Teacher:2) will teach the model to …
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