From 0557e58ca0bbbfdbceb277c47ae43b403cd31b34 Mon Sep 17 00:00:00 2001 From: Garrett Date: Fri, 1 Apr 2016 12:46:14 -0400 Subject: [PATCH] Adds two missing dollar signs to model.Rmd --- model.Rmd | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/model.Rmd b/model.Rmd index b2e79d4..1c2cba7 100644 --- a/model.Rmd +++ b/model.Rmd @@ -1,3 +1,6 @@ +--- +output: pdf_document +--- # Model A model is a function that summarizes how the values of one variable vary in relation to the values of other variables. Models play a large role in hypothesis testing and prediction, but for the moment you should think of models just like you think of statistics. A statistic summarizes a *distribution* in a way that is easy to understand; and a model summarizes *covariation* in a way that is easy to understand. In other words, a model is just another way to describe data. @@ -127,7 +130,7 @@ ggplot(data = heights, mapping = aes(x = height, y = income)) + `lm()` treats the variable(s) on the right-hand side of the formula as _explanatory variables_ that partially determine the value of the variable on the left-hand side of the formula, which is known as the _response variable_. In other words, it acts as if the _response variable_ is determined by a function of the _explanatory variables_. It then spots the linear function that best fits the data. -Linear models are straightforward to interpret. Incomes have a baseline mean of $`r coef(h)[1]`. Each one inch increase of height above zero is associated with an increase of $`r coef(h)[2]` in income. +Linear models are straightforward to interpret. Incomes have a baseline mean of $`r coef(h)[1]`$. Each one inch increase of height above zero is associated with an increase of $`r coef(h)[2]`$ in income. ```{r} summary(h)