**[edit: escaped code and auto-indented]**

Hello there, I have an output from a stan model and saved it as text. My struggle now is summarising it into statistics that i should include in the results section of my paper.

some guide

here is the code that generated the results

```
data {
int<lower=1> Nobs; // number of observations
int<lower=1> N1; // number of students
int<lower=1> N2; // number of course units
int<lower=1> J; // number of fixed effects
int y[Nobs]; // resits
matrix[Nobs,J] X; //the model matrix
int male[Nobs]; // sex of student
int bbs[Nobs]; // BBS student
int bps[Nobs]; // BPS student
int bqe[Nobs]; // BQE student
int sas[Nobs]; // SAS student
int<lower=1, upper=N1> id[Nobs]; // student
int<lower=1, upper=N2> course[Nobs]; // course unit
}
parameters {
vector[J] delta;
vector<lower=0>[J] tau;
vector<lower=0, upper=1>[N2] gamma; // geometric paratemeter
vector[N2] theta1; // scale parameter
real<lower=0> sigma1; // scale parameter
vector[J] beta[N2]; // fixed effects parameters
real b[N1]; // student random effects
}
transformed parameters {
vector<lower=0>[N2] mu ; // scale parameter
vector<lower=0>[N2] theta ; // scale parameter
vector<lower=0>[Nobs] mu1[N2];
vector<lower=0>[N2] betamu; // mean parameter
vector<lower=0>[N2] betasigm; // variance parameter
for (k in 1:N2){
for (i in 1:Nobs){
mu1[k,i] = inv_logit(X[i]*beta[course[i]] + b[id[i]]);
}
}
for (n in 1:N2){
mu[n] = mean(mu1[n]);
theta[n] = exp(theta1[n]);
betamu[n] =(1-theta[n])/(mu[n]-theta[n]);
betasigm[n] = (mu[n]*(1-mu[n])*(1-theta[n]))/pow((mu[n] -theta[n]),2)*(mu[n] -(2*theta[n]));
}
}
model {
delta ~ normal(0,5); //weakly informative priors on the regression coefficients
tau ~ cauchy(0,2.5); //weakly informative priors
sigma1 ~ gamma(2,0.1); //weakly informative priors
b ~ normal(0.0, sigma1); //student random effects
for(j in 1:N2){
beta[j] ~ normal(delta,tau); //hyperprior for group-level regression coefficients
theta1[j] ~ lognormal(0,1); //hyperprior theta1
gamma[j] ~ beta((mu[j])/theta[j], (1 - (mu[j]))/theta[j]); //prior gamma
}
for (k in 1:Nobs){
target += neg_binomial_lpmf(y[k] | 1, gamma[course[k]]/(1-gamma[course[k]])); // log-likelihood
}
}
generated quantities {
int y_rep[Nobs];
vector[Nobs] log_lik;
for (i in 1:Nobs) {
y_rep[i] = neg_binomial_rng(1, gamma[course[i]]/(1-gamma[course[i]]));
log_lik[i] = neg_binomial_lpmf(y[i] |1, gamma[course[i]]/(1-gamma[course[i]]));
}
}
```