April 2017
Beginner to intermediate
420 pages
9h 58m
English
To do this part of the process, we will repeat the steps and code from Chapter 2, Linear Regression - The Blocking and Tackling of Machine Learning. If you haven't done so, please look at Chapter 2, Linear Regression - The Blocking and Tackling of Machine Learning for some insight on how to interpret the following output.
We will use the following lm() function to create our linear model with all the factors as inputs and then summarize the results:
> nhl.lm <- lm(ppg ~ ., data = pca.scores) > summary(nhl.lm) Call: lm(formula = ppg ~ ., data = pca.scores) Residuals: Min 1Q Median 3Q Max -0.163274 -0.048189 0.003718 0.038723 0.165905 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.111333 0.015752 70.551 ...Read now
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