change $i$ to i in Rmd template

Author: topepo

man/rmd/linear_reg_glmer.md

@@ -48,7 +48,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/linear_reg_lmer.md

@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/linear_reg_stan_glmer.md

@@ -53,7 +53,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/logistic_reg_glmer.md

@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/logistic_reg_stan_glmer.md

@@ -52,7 +52,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/poisson_reg_glmer.md

@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/poisson_reg_stan_glmer.md

@@ -52,7 +52,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/rmd/template-no-pooling.Rmd

@@ -6,7 +6,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: