diff --git a/docs/source/tutorials/basic_renewal_model.qmd b/docs/source/tutorials/basic_renewal_model.qmd
index cd0eb647..b5fff380 100644
--- a/docs/source/tutorials/basic_renewal_model.qmd
+++ b/docs/source/tutorials/basic_renewal_model.qmd
@@ -254,34 +254,28 @@ Now, let's investigate the output, particularly the posterior distribution of th
 # | fig-cap: Rt posterior distribution
 import arviz as az
 
-az.style.use("arviz-doc")
-
+# Create arviz inference data object
 idata = az.from_numpyro(
     posterior=model1.mcmc,
 )
-idata = az.extract(idata, num_samples=100)
 
-# Extract Rt signal
+# Extract Rt signal samples across chains
+rt = az.extract(idata.posterior["Rt"], num_samples=100)["Rt"].values
 
-rt = (
-    az.extract(idata.posterior["Rt"], num_samples=100)
-    .to_dataarray()
-    .squeeze()
-    .T
-)
+
+# Plot Rt signal
 fig, ax = plt.subplots(1, 1, figsize=(8, 6))
-print(rt)
 ax.plot(
-    np.arange(100),
+    np.arange(rt.shape[0]),
     rt,
-    color="gray",
-    alpha=0.25,
+    color="skyblue",
+    alpha=0.10,
 )
-ax.plot([], [], color="gray", alpha=0.25, label="Rt Posterior Samples")
+ax.plot([], [], color="skyblue", alpha=0.05, label="Rt Posterior Samples")
 ax.plot(
-    np.arange(100),
-    rt.mean(dim="Rt_dim_0"),
-    color="red",
+    np.arange(rt.shape[0]),
+    rt.mean(axis=1),
+    color="black",
     linewidth=2.0,
     linestyle="--",
     label="Sample Mean",
diff --git a/docs/source/tutorials/day_of_the_week.qmd b/docs/source/tutorials/day_of_the_week.qmd
index d0602b06..23c866d0 100644
--- a/docs/source/tutorials/day_of_the_week.qmd
+++ b/docs/source/tutorials/day_of_the_week.qmd
@@ -218,17 +218,19 @@ idata = az.from_numpyro(
     posterior_predictive=ppc_samples,
 )
 
-# Convert hospital admissions from discrete to continuous
-
-
 # Use a time series plot (plot_ts) from arviz for plotting
 axes = az.plot_ts(
     idata,
     y="negbinom_rv",
     y_hat="negbinom_rv",
-    num_samples=150,
-    y_kwargs={"color": "red", "linewidth": 2.5},
-    y_hat_plot_kwargs={"color": "gray"},
+    num_samples=200,
+    y_kwargs={
+        "color": "blue",
+        "linewidth": 1.0,
+        "marker": "o",
+        "linestyle": "solid",
+    },
+    y_hat_plot_kwargs={"color": "skyblue", "alpha": 0.05},
     y_mean_plot_kwargs={"color": "black", "linestyle": "--", "linewidth": 2.5},
     backend_kwargs={"figsize": (8, 6)},
     textsize=15.0,
@@ -361,9 +363,14 @@ axes = az.plot_ts(
     idata,
     y="negbinom_rv",
     y_hat="negbinom_rv",
-    num_samples=150,
-    y_kwargs={"color": "red", "linewidth": 2.5},
-    y_hat_plot_kwargs={"color": "gray"},
+    num_samples=200,
+    y_kwargs={
+        "color": "blue",
+        "linewidth": 1.0,
+        "marker": "o",
+        "linestyle": "solid",
+    },
+    y_hat_plot_kwargs={"color": "skyblue", "alpha": 0.05},
     y_mean_plot_kwargs={"color": "black", "linestyle": "--", "linewidth": 2.5},
     backend_kwargs={"figsize": (8, 6)},
     textsize=15.0,
@@ -396,9 +403,14 @@ axes = az.plot_ts(
     idata,
     y="negbinom_rv",
     y_hat="negbinom_rv",
-    num_samples=150,
-    y_kwargs={"color": "red", "linewidth": 2.5},
-    y_hat_plot_kwargs={"color": "gray"},
+    num_samples=200,
+    y_kwargs={
+        "color": "blue",
+        "linewidth": 1.0,
+        "marker": "o",
+        "linestyle": "solid",
+    },
+    y_hat_plot_kwargs={"color": "skyblue", "alpha": 0.05},
     y_mean_plot_kwargs={"color": "black", "linestyle": "--", "linewidth": 2.5},
     backend_kwargs={"figsize": (8, 6)},
     textsize=15.0,
diff --git a/docs/source/tutorials/hospital_admissions_model.qmd b/docs/source/tutorials/hospital_admissions_model.qmd
index 488f317e..0acebc6b 100644
--- a/docs/source/tutorials/hospital_admissions_model.qmd
+++ b/docs/source/tutorials/hospital_admissions_model.qmd
@@ -319,8 +319,13 @@ axes = az.plot_ts(
     y="negbinom_rv",
     y_hat="negbinom_rv",
     num_samples=200,
-    y_kwargs={"color": "red", "linewidth": 2.5},
-    y_hat_plot_kwargs={"color": "gray"},
+    y_kwargs={
+        "color": "blue",
+        "linewidth": 1.0,
+        "marker": "o",
+        "linestyle": "solid",
+    },
+    y_hat_plot_kwargs={"color": "skyblue", "alpha": 0.05},
     y_mean_plot_kwargs={"color": "black", "linestyle": "--", "linewidth": 2.5},
     backend_kwargs={"figsize": (8, 6)},
     textsize=15.0,
@@ -341,7 +346,6 @@ To explore further, We can use [ArviZ](https://www.arviz.org/) to visualize the
 
 ```{python}
 # | label: convert-inferenceData
-import arviz as az
 
 idata = az.from_numpyro(hosp_model.mcmc)
 ```
@@ -443,7 +447,6 @@ We can use the `Model`'s `posterior_predictive` and `prior_predictive` methods t
 
 ```{python}
 # | label: demonstrate-use-of-predictive-methods
-import arviz as az
 
 idata = az.from_numpyro(
     hosp_model.mcmc,