Top new questions this week:
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When you filter on only viewing results with p<alpha, some of those "statistically significant" results go in the wrong direction. This is called a Type S error ("S" for sign). ...
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I found some data on the number of self-employed people per one thousand inhabitants in Europe here. However, in the year 2020, for instance, some statistical units have over one thousand self-...
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I am trying to compare the lodging resistance scores of different wheat cultivars in an agronomic trial. Lodging is the phenomenon in which wheat plant can bend and lean closer to the ground as a ...
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I’m trying to informally derive the chi-squared test statistic using a combination of basic geometry and algebra. I’m successfully able to obtain a system of equations that contain Karl Pearson’s chi-...
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When using a Poisson GLM, its dispersion parameter can be estimated as "residual deviance/degrees of freedom".
But when analyzing Poisson data with a Poisson model (i.e., not overdispersed), ...
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I've begun working with the Gumbel distribution and started with the example in the package documentation at https://cran.r-project.org/web/packages/fitdistrplus/fitdistrplus.pdf. For the Gumbel ...
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I'm comparing two groups of people on a binary variable. The number of successes is really small for the two groups (which wasn't expected, due to a lack of previous quantitative research on the ...
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Greatest hits from previous weeks:
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In linear regression, we often get multiple R and R squared. What are the differences between them?
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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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They seem so similar. Are they the same thing but just referred to as different names?
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Am I looking for a better behaved distribution for the independent variable in question, or to reduce the effect of outliers, or something else?
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I am working with a small dataset (21 observations) and have the following normal QQ plot in R:
Seeing that the plot does not support normality, what could I infer about the underlying distribution? ...
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Principal component analysis (PCA) is usually explained via an eigen-decomposition of the covariance matrix. However, it can also be performed via singular value decomposition (SVD) of the data matrix ...
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What's the similarities and differences between these 3 methods:
Bagging,
Boosting,
Stacking?
Which is the best one? And why?
Can you give me an example for each?
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Can you answer these questions?
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I want to do a mixed linear model, with two hierarchical random effects. I want to determine wether if these random effects are worth including in the model.
I have tried so far doing :
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I'm interested in the following inference / filtering problem in a hidden Markov model setting. Suppose we have a simple random walk $x_t\in\mathbb{Z}$ and observations are "images" ...
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Suppose that $f(y|\boldsymbol{\theta})$ is a distribution belonging to the exponential family (e.g., a normal distribution). I'm wondering if there is a name for the following distribution, which is ...
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