diff --git a/notebooks/seasonality,_holiday_effects,_and_regressors.ipynb b/notebooks/seasonality,_holiday_effects,_and_regressors.ipynb
index 2ef32e4..27ab63a 100644
--- a/notebooks/seasonality,_holiday_effects,_and_regressors.ipynb
+++ b/notebooks/seasonality,_holiday_effects,_and_regressors.ipynb
@@ -144,7 +144,7 @@
    "source": [
     "Above we have included the superbowl days as both playoff games and superbowl games. This means that the superbowl effect will be an additional additive bonus on top of the playoff effect.\n",
     "\n",
-    "Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the Quickstart:"
+    "Once the table is created, holiday effects are included in the forecast by passing them in with the `holidays` argument. Here we do it with the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html):"
    ]
   },
   {
@@ -580,7 +580,7 @@
    "source": [
     "### Fourier Order for Seasonalities\n",
     "\n",
-    "Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the Quickstart. The default Fourier order for yearly seasonality is 10, which produces this fit:"
+    "Seasonalities are estimated using a partial Fourier sum. See [the paper](https://peerj.com/preprints/3190/) for complete details, and [this figure on Wikipedia](https://en.wikipedia.org/wiki/Fourier_series#/media/File:Fourier_Series.svg) for an illustration of how a partial Fourier sum can approximate an aribtrary periodic signal. The number of terms in the partial sum (the order) is a parameter that determines how quickly the seasonality can change. To illustrate this, consider the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default Fourier order for yearly seasonality is 10, which produces this fit:"
    ]
   },
   {
@@ -704,7 +704,7 @@
     "\n",
     "The inputs to this function are a name, the period of the seasonality in days, and the Fourier order for the seasonality. For reference, by default Prophet uses a Fourier order of 3 for weekly seasonality and 10 for yearly seasonality. An optional input to `add_seasonality` is the prior scale for that seasonal component - this is discussed below.\n",
     "\n",
-    "As an example, here we fit the Peyton Manning data from the Quickstart, but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:"
+    "As an example, here we fit the Peyton Manning data from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html), but replace the weekly seasonality with monthly seasonality. The monthly seasonality then will appear in the components plot:"
    ]
   },
   {
@@ -769,7 +769,7 @@
     "### Seasonalities that depend on other factors\n",
     "In some instances the seasonality may depend on other factors, such as a weekly seasonal pattern that is different during the summer than it is during the rest of the year, or a daily seasonal pattern that is different on weekends vs. on weekdays. These types of seasonalities can be modeled using conditional seasonalities.\n",
     "\n",
-    "Consider the Peyton Manning example from the Quickstart. The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.\n",
+    "Consider the Peyton Manning example from the [Quickstart](https://facebook.github.io/prophet/docs/quick_start.html). The default weekly seasonality assumes that the pattern of weekly seasonality is the same throughout the year, but we'd expect the pattern of weekly seasonality to be different during the on-season (when there are games every Sunday) and the off-season. We can use conditional seasonalities to construct separate on-season and off-season weekly seasonalities.\n",
     "\n",
     "First we add a boolean column to the dataframe that indicates whether each date is during the on-season or the off-season:"
    ]