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Should I transform my data before or after removing outliers? (Highly . . . I usually find that removing so-called "outliers" in biochemical data rarely makes sense If you need more normaly-distributed data for some reason (e g , you are using a model that assumes normality, such as LDA or a t-test), then you can transform it using a power transform (including a log transform) The exception is when you have additional, reliable information that some data points are
CodyCross Working From Home Group 1239 Answers CodyCross Working From Home Group 1239 Answers Please find below all the CodyCross Working From Home Group 1239 Answers CodyCross is a famous newly released game which is developed by Fanatee It has many crosswords divided into different worlds and groups Each world has more than 20 groups with 5 puzzles each
How to correlate ordinal and nominal variables in SPSS? Two more columns are just text, e g , location (home, commuting etc ); these are nominal variables Now, I want to correlate these variables with each other in order to find meaningful patterns (In particular, I want to correlate my ordinal variables with my nominal variables, but I don't know how ) How do I do this in SPSS?
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CodyCross Home Sweet Home Answers Please find below all the CodyCross Home Sweet Home Answers CodyCross is a famous newly released game which is developed by Fanatee It has many crosswords divided into different worlds and groups Each world has more than 20 groups with 5 puzzles each Some of the worlds are: Planet Earth, Under The Sea, Inventions, Seasons, Continue reading ‘CodyCross Home Sweet Home Answers’ »
Time series analysis for predicting a binary outcome I'm fairly new to time series analysis I want to analyze two series of variables in a span of time to predict a binary outcome For example i collect data over time at my home of two variables:
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r - When to remove insignificant variables? - Cross Validated What is the correlations among the independent variables? This is less important for pure prediction, but if you want to gain some inferential information it is important that the independent variables be fairly uncorrelated Typically, when you use logistic regression in a business setting, both inferential information about the variables used along with a good prediction are what