![]() ![]() That, of course, was the motivation behind the creation of theįunction I didn’t like the choices made by R’s core team with Is shown that perhaps not everyone would be interested in, some may be Like any output, this one is somewhat opinionated - some information R² = 0.55 # Standard errors: OLS # - # Est. deleted) # Dependent Variable: metascore # Type: OLS linear regression # MODEL FIT: # F(6,824) = 169.37, p = 0.00 # R² = 0.55 # Adj. Library ( jtools ) # Load jtools data ( movies ) # Telling R we want to use this data fit <- lm ( metascore ~ imdb_rating + log ( us_gross ) + genre5, data = movies ) summ ( fit ) # MODEL INFO: # Observations: 831 (10 missing obs. With no user-specified arguments except a fitted model, the output of The genre ( genre5) with “Action” as the reference IMDB ( imdb_rating), and a categorical variable reflecting Revenue in the United States ( us_gross), the fan rating at Predicting the Metacritic metascore, which ranges from 0 toġ00 (where higher numbers reflect more positive reviews) using the gross Information about over 800 movies across several decades. Multiple occasions, I thought it would be best to pack things into aįor example purposes, we’ll create a model using the After creating output tables “by hand” on Summary() like robust standard errors, scaled coefficients,Īnd VIFs since the functions for estimating these don’t append them to a Wanted to give them information that is not included in the ![]() The output generally was not clear to them. ![]() When sharing analyses with colleagues unfamiliar with R, I found that ![]()
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