diff --git a/scripts/device_accuracy.py b/scripts/device_accuracy.py
index de78926..eb8a972 100644
--- a/scripts/device_accuracy.py
+++ b/scripts/device_accuracy.py
@@ -7,6 +7,7 @@ from datetime import datetime
 import math
 import matplotlib.pyplot as plt
 import io
+from scipy.stats import linregress
 
 curdir = os.getcwd()
 path_delim = '/'
@@ -186,6 +187,10 @@ if not df.empty:
     wks1.write(0, 0, 'Solution')
     wks1.write(0, 1, 'Slope')
     wks1.write(0, 2, 'Intercept')
+    wks1.write(0, 3, 'R^2')
+    # Create a new DataFrame to store linear fit results
+    df_linearfit_results = pd.DataFrame(columns=['Solution', 'Slope', 'Intercept', 'R^2'])
+
     row = 1 
 
     fig, ax = plt.subplots()
@@ -200,20 +205,27 @@ if not df.empty:
             
             # Fit a linear regression model
             coefficients = np.polyfit(concentration_means.index, concentration_means.values, 1)
+
+            # Use linregress to get additional statistics including R-squared
+            slope, intercept, r_value, p_value, std_err = linregress(concentration_means.index, concentration_means.values)
             
-            print(f'Coefficients for {solution}: Slope={coefficients[0]}, Intercept={coefficients[1]}')
+            print(f'For {solution}: Slope={slope:.4f}, Intercept={intercept:.4f}, R^2={r_value**2:.4f}')
 
             ax.plot(concentration_means.index, concentration_means.values, marker='o', linestyle='-', label=f'Mean Absorbance for {solution}')
 
             ax.plot(concentration_means.index, np.polyval(coefficients, concentration_means.index), linestyle='--', label=f'Linear Fit for {solution}')
             
-            annotation_text = f'Slope: {coefficients[0]:.4f}\nIntercept: {coefficients[1]:.4f}'
+            annotation_text = f'Slope: {slope:.4f}\nIntercept: {intercept:.4f}\nR^2: {r_value**2:.4f}'
             ax.text(concentration_means.index[-1] + 5, np.polyval(coefficients, concentration_means.index[-1]), annotation_text, fontsize=10, verticalalignment='center')
 
             wks1.write(row, 0, solution)
-            wks1.write(row, 1, str(coefficients[0]))
-            wks1.write(row, 2, str(coefficients[1]))
+            wks1.write(row, 1, str(slope))
+            wks1.write(row, 2, str(intercept))
+            wks1.write(row, 3, str(r_value**2))
             
+            # Add the linear fit results to the new DataFrame
+            df_linearfit_results = df_linearfit_results.append({'Solution': solution, 'Slope': slope, 'Intercept': intercept, 'R^2': r_value**2}, ignore_index=True)
+
             row += 1
 
     ax.set_title('Mean Absorbance for Each Solution')
@@ -224,7 +236,11 @@ if not df.empty:
 
     imgdata=io.BytesIO()
     fig.savefig(imgdata, format='png')
-    wks1.insert_image(2,2, '', {'image_data': imgdata})
+    wks1.insert_image(6,0, '', {'image_data': imgdata})
+
+    # Write linear fit results to Excel
+    df_linearfit_results.to_excel(writer, sheet_name="linearfit_results")
+
 
     ### debug each row
     # # Merge df and df_device_precicion_results on deviceId