Odd results in model prediction using pymc.sample_posterior_predictive

Questions v5
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2

P.S. This is the code just as a reference

#### modeling part using the MutableData so that I could predict new y values given new x values
x = x_values
y = mag
with pm.Model() as model_final:
    x_final = pm.MutableData("x_final", x)
    y_mag = pm.MutableData("y_mag", y)
    err_odr = pm.HalfNormal('err_odr', 5.)
    err_param = pm.HalfNormal('err_param', 1.)
    x_lat = pm.Normal('x_mu', 0, 5., shape=x_final.shape[0])
    x_obs = pm.Normal('x_obs', mu=x_lat, sigma=err_odr, observed=x_final, shape=x_final.shape[0])
    offset = pm.Normal('Intercept', 0, err_param)
    slope = pm.Normal('Slope', 0, err_param)
    y_pred = pm.Deterministic('y_pred', offset + slope * x_lat)
    y_obs = pm.Normal('y', mu=y_pred, sigma=err_odr, observed=y_mag)
    
    trace_final = pm.sample(4000, tune=2500, chains=8, cores=8, init='jitter+adapt_diag', random_seed= 42)


### prediction part
with model_final:
    pm.set_data({"x_final" : [-2,-1,1.5,2.5,4,5,7]})
    pred = pm.sample_posterior_predictive(trace_final)

### This is how I look at the std and mean values for each x_final value prediction
pred.posterior_predictive['y'].mean(('chain', 'draw'))

pred.posterior_predictive['y'].std(('chain', 'draw'))