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Adds two missing dollar signs to model.Rmd

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Garrett 2016-04-01 12:46:14 -04:00
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model.Rmd
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---
output: pdf_document
---
# Model # 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. 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.
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`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. `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} ```{r}
summary(h) summary(h)