From 814ac613da531f7b84c0a8ae594c37288c6ee084 Mon Sep 17 00:00:00 2001 From: Matthew Sedaghatfar Date: Fri, 12 Aug 2016 13:43:43 -0400 Subject: [PATCH 1/2] Typo on 4.6.7 Exercises #2 (#239) Typo on 4.6.7 Exercises #2 appraoch to approach --- transform.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/transform.Rmd b/transform.Rmd index 488d5ae..afbed38 100644 --- a/transform.Rmd +++ b/transform.Rmd @@ -810,7 +810,7 @@ daily %>% Which is more important: arrival delay or departure delay? -1. Come up with another appraoch that will give you the same output as +1. Come up with another approach that will give you the same output as `not_cancelled %>% count(dest)` and `not_cancelled %>% count(tailnum, wt = distance)` (without using `count()`). From 4e87d50a932832e8437a2713036a7aee2dd958aa Mon Sep 17 00:00:00 2001 From: S'busiso Mkhondwane Date: Fri, 12 Aug 2016 19:43:58 +0200 Subject: [PATCH 2/2] Update model-basics.Rmd (#228) --- model-basics.Rmd | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/model-basics.Rmd b/model-basics.Rmd index 3ff445c..dc74286 100644 --- a/model-basics.Rmd +++ b/model-basics.Rmd @@ -239,7 +239,7 @@ These are exactly the same values we got with `optim()`! Behind the scenes `lm() For simple models, like the one above, you can figure out what pattern the model captures by carefully studying the model family and the fitted coefficients. And if you ever take a statistics course on modelling, you're likely to spend a lot of time doing just that. Here, however, we're going to take a different tack. We're going to focus on understanding a model by looking at its predictions. This has a big advantage: every type of predictive model makes predictions (otherwise what use would it be?) so we can use the same set of techniques to understand any type of predictive model. -It's also useful to see what the model doesn't capture, the so called residuals which are left after subtracting the predictions from the data. Residuals are a powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain. +It's also useful to see what the model doesn't capture, the so called residuals which are left after subtracting the predictions from the data. Residuals are powerful because they allow us to use models to remove striking patterns so we can study the subtler trends that remain. ### Predictions