query: When removing outlier is right? Removing techniques for outliers in R
positive_passages.docid: 81816
positive_passages.text: Whether to delete cases that are flagged as outliers by statistical software when performing multiple regression?
negative_passages.docid: 58209
negative_passages.text: p values and significance in RLM (MASS package) R
negative_passages.docid: 127861
negative_passages.text: Finding Local Minima / Maxima in Noisy Data
negative_passages.docid: 3665
negative_passages.text: Standard error clustering in R (either manually or in plm)
negative_passages.docid: 39978
negative_passages.text: Interpreting Residual and Null Deviance in GLM R
negative_passages.docid: 145064
negative_passages.text: Techniques to detect overfitting
negative_passages.docid: 121868
negative_passages.text: Logistic regression residual analysis
negative_passages.docid: 75677
negative_passages.text: Dealing with new Factor Levels in a Regression in R
negative_passages.docid: 69637
negative_passages.text: Random Forests and data transformations
negative_passages.docid: 154818
negative_passages.text: Testing two-tailed p-values using Stouffer's approach
negative_passages.docid: 105828
negative_passages.text: Diagnostics and residual analysis for Poisson regression
negative_passages.docid: 127708
negative_passages.text: Rolling analysis with out-of sample
negative_passages.docid: 39105
negative_passages.text: Meta-analysis of means and medians in R?
negative_passages.docid: 40108
negative_passages.text: standardising non normally distributed predictors for regression
negative_passages.docid: 16519
negative_passages.text: Transformed data due to non-normal residuals - how to see if it actually improved the model?
negative_passages.docid: 85590
negative_passages.text: Tools to measure the nonrandomness of database
negative_passages.docid: 27143
negative_passages.text: Detecting significant predictors out of many independent variables