Merge branch 'feature/0133-ppcs' of https://github.com/stan-dev/docs into feature/0133-ppcs

Author: Bob Carpenter

src/stan-users-guide/decision-analysis.Rmd

@@ -41,7 +41,7 @@ another unit of money decreases the more money one has).
 
 This section outlines a very simple decision analysis for a commuter
 deciding among modes of transportation to get to work: walk, bike
-share, public transportion, or cab.  Suppose the commuter has been
+share, public transportation, or cab.  Suppose the commuter has been
 taking various modes of transportation for the previous year and the
 transportation conditions and costs have not changed during that
 time.  Over the year, such a commuter might accumulate two hundred
@@ -54,7 +54,7 @@ A decision consists of the choice of commute mode and the outcome is a
 time and cost.  More formally,
 
 * the set of decisions is $D = 1:4$, corresponding to the commute
-  types walking, bicycling, public transportion, and cab,
+  types walking, bicycling, public transportation, and cab,
   respectively, and
 
 * the set of outcomes $X = \mathbb{R} \times \mathbb{R}_+$ contains
@@ -193,7 +193,7 @@ posterior predictive distribution of utility.
 
 For simplicity in this initial formulation, all four commute options
 have their costs estimated, even though cost is fixed for three of the
-options.  To deal with the fact that some costs ae fixed, the costs
+options.  To deal with the fact that some costs are fixed, the costs
 would have to be hardcoded or read in as data, `nu` and
 `tau` would be declared as univariate, and the RNG for cost would only
 be employed when `k == 4`.

src/stan-users-guide/posterior-prediction.Rmd

@@ -375,7 +375,7 @@ $\tilde{x}$ may be generated whenever needed.  In Stan's interfaces,
 this is done by writing a second Stan program that inputs the original
 program's parameters and the new predictors.  For example, for the
 linear regression case, the program to take posterior draws declares
-the data and parmeters, and defines the model.
+the data and parameters, and defines the model.
 ```
 data {
   int<lower = 0> N;

src/stan-users-guide/poststratification.Rmd

@@ -208,7 +208,7 @@ To poststratify the results, the population size for each combination of
 predictors must still be known.  Then the population estimate is
 constructed as
 $$
-\sum_{i = 1}^5 \sum_{j = 1}^2 \sum{k = 1}^5 \sum{m = 1}^50
+\sum_{i = 1}^5 \sum_{j = 1}^2 \sum{k = 1}^5 \sum{m = 1}^{50}
 \textrm{logit}^{-1}(\alpha + \beta_i + \gamma_j + \delta_k + \eta_m)
 \cdot \textrm{pop}_{i, j, k, m},
 $$