diff --git a/content/mooreslaw-tutorial.md b/content/mooreslaw-tutorial.md
index 638853c4..5f2c7e7e 100644
--- a/content/mooreslaw-tutorial.md
+++ b/content/mooreslaw-tutorial.md
@@ -13,9 +13,8 @@ kernelspec:
 ---
 
 # Determining Moore's Law with real data in NumPy
-![Scatter plot of MOS transistor count per microprocessor every two years as a demonstration of Moore's Law.](_static/01-mooreslaw-tutorial-intro.png)
 
-_The number of transistors reported per a given chip plotted on a log scale in the y axis with the date of introduction on the linear scale x-axis. The blue data points are from a [transistor count table](https://en.wikipedia.org/wiki/Transistor_count#Microprocessors). The red line is an ordinary least squares prediction and the orange line is Moore's law._
+![](#img:mooreslaw-mainfig)
 
 ## What you'll do
 
@@ -287,6 +286,8 @@ The style sheet replicates
 [`plt.style.use`](https://matplotlib.org/api/style_api.html#matplotlib.style.use).
 
 ```{code-cell}
+:label: img:mooreslaw-mainfig
+
 transistor_count_predicted = np.exp(B) * np.exp(A * year)
 transistor_Moores_law = Moores_law(year)
 plt.style.use("fivethirtyeight")
@@ -305,8 +306,6 @@ plt.legend(loc="center left", bbox_to_anchor=(1, 0.5))
 plt.ylabel("# of transistors\nper microprocessor")
 ```
 
-_A scatter plot of MOS transistor count per microprocessor every two years with a red line for the ordinary least squares prediction and an orange line for Moore's law._
-
 The linear regression captures the increase in the number of transistors
 per semiconductors each year.  In 2015, semiconductor manufacturers
 claimed they could not keep up with Moore's law anymore. Your analysis