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Conditional operator in regression
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3 votes

| means 'group by' and I'm not sure but I don't think it is used in lm at all. In a mixed effects model, it is used to define the random effect. That is, a different random effect is fitted for each ...

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Recalibration by regressing on intercept only with offset
2 votes

If you read: Validation and updating of predictive logistic regression models: a study on sample size and shrinkage (pdf) The same author explains in a little bit of detail. The recalibration ...

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linear decision boundaries of using the Euclidean distance?
1 votes

K-means clustering works by creating a Voronoi diagram that has linear decision boundaries between clusters, as you know. This means that you can draw lines/planes/hyperplanes to classify your data. ...

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How to check if a value in a time series is significantly different from other values?
1 votes

I would like to know if that value is significantly greater than the other values From your question, I am interpreting that the order of your values is not important to you. As such, you have a ...

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Random forest - OOB error rate when some observations appear in all tree
1 votes

The simple answer is, if you do not have any bootstrap datasets that do not include an observation, then that observation cannot be used to calculate the OOB error rate because you don't have any out ...

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normal vs negative binomial regression results
1 votes

Ordinary Least Squares regression ("normal" linear regression) makes certain assumptions about the data. Here, the most salient assumptions are: Observed values can take any real number The errors ...

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Error in knnImputation in r: Not sufficient complete cases for computing neighbors
1 votes

I feel like this question would better belong on StackOverflow but I'll give it a go anyway: paraphrasing the code for knnImputation (https://gist.github.com/tengpeng/fb6809717361319d8bde), you get ...

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Statistical test in Python to decide if data transformation has to be applied on time series
1 votes

So you can use a test for normality as you are currently doing where the skewness should ideally be between -0.8 and 0.8 but you should probably also test for kurtosis if you are going this way. ...

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Prediction Interval, linear regression - why is a future response a random variable but other responses are not random variables?
1 votes

I will assume that $x_i$ are a set of observations with known values of $Y_i$ to which you are fitting a model. Once the model has been fitted, you are left with known values of $\beta_0$ and $\beta_1$...

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Distance measures accounting for correlated variables in data
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1 votes

I would start by reading the question, How would PCA help with a k-means clustering analysis?, which suggests doing the sort of PCA in your question.

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Null hypothesis of an ANOVA when comparing regression models?
1 votes

Assuming your models are nested, which I think is safe given that you have referred to them as full and reduced models, we'll consider the first model. As stated in the helptext for anova.lmlist, "It ...

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Multiple linear regression, standardization and cross validation
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0 votes

When you are fitting a model, it is good practice to perform some kind of model validation. In your case, you are trying holdout validation and cross validation. Note, however, that these methods are ...

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