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Protected: Comparing Vectors Exercises
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Calculating Marginal Effects Exercises
A common experience for those in the social sciences migrating to R from SPSS or STATA is that some procedures that happened at the click of a button will now require more work or are too obscured by the unfamiliar language to see how to accomplish. One such procedure that I’ve experienced is when calculating […]
Calculating Marginal Effect Solutions
Below are the solutions to these exercises on marginal effects. #################### # # # Exercise 1 # # # #################### data(mtcars) str(mtcars) ## ‘data.frame’: 32 obs. of 11 variables: ## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 … ## $ cyl : num 6 6 4 6 8 […]
Protected: Bonus: Dummify Your Data Solutions
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Protected: Bonus: Dummify your Data Exercises
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Bayesian A/B Testing Made Easy
A/B Testing is a familiar task for many working in business analytics. Essentially, A/B Testing is a simple form of hypothesis testing with one control group and one treatment group. Classical frequentist methodology instructs the analyst to estimate the expected effect of the treatment, calculate the required sample size, and perform a test to determine […]
Sending Emails from R Solutions
Below are the solutions to these exercises on Sending Emails from R. #################### # # # Exercise 1 # # # #################### library(mailR) # Note that I am using environment variables. send.mail(from = Sys.getenv(“sender”) , to = Sys.getenv(“recipient”) , body = “Hello World. This is my email!” , html = TRUE , inline = FALSE […]
Sending Emails from R Exercises
When monitoring a data source, model, or other automated process, it’s convienent to have method for easily delivering performance metrics and notifying you whenever something is amiss. One option is to use a dashboard; however, this requires active time and effort to grab numbers and catch errors. An alternative approach is to send an email […]
