diff --git a/posts/2023-10-25/index.qmd b/posts/2023-10-25/index.qmd
index 707ed2c5..60b31920 100644
--- a/posts/2023-10-25/index.qmd
+++ b/posts/2023-10-25/index.qmd
@@ -64,7 +64,7 @@ This code will generate a simple Bland-Altman plot, and here's what each part do
 
 - `plot()`: Creates the scatter plot with means on the X-axis and differences on the Y-axis.
 - `abline(h = mean(diff_values), col = "red", lty = 2)`: Adds a red dashed line at the mean difference.
-- `abline(h = upper, col = "green", lty = 2)`: Adds blue dashed lines representing the 95% limits of agreement.
+- `abline(h = upper, col = "blue", lty = 2)`: Adds blue dashed lines representing the 95% limits of agreement.
 
 # Step 4: Interpretation
 
@@ -79,4 +79,4 @@ If most data points fall within the blue lines, it indicates good agreement betw
 
 I encourage you to try this out with your own data. Replace the example data with your measurements and see what insights your Bland-Altman plot reveals.
 
-In conclusion, creating a Bland-Altman plot in R is a valuable technique to visualize agreement or bias between two measurement methods. It's an essential tool for quality control and validation in various fields. I hope this step-by-step guide helps you get started. Happy plotting!
\ No newline at end of file
+In conclusion, creating a Bland-Altman plot in R is a valuable technique to visualize agreement or bias between two measurement methods. It's an essential tool for quality control and validation in various fields. I hope this step-by-step guide helps you get started. Happy plotting!