diff --git a/jfcal/Plotter.py b/jfcal/Plotter.py
index a4e5273..884b006 100644
--- a/jfcal/Plotter.py
+++ b/jfcal/Plotter.py
@@ -7,6 +7,8 @@ from jfcal.utils import create_histogram_from_data
 
 import matplotlib.pyplot as plt
 
+import numpy as np 
+
 from enum import Enum
 
 class Parameters(Enum): 
@@ -81,7 +83,7 @@ class Plotter:
             The axis with the histogram and fitted function plot.
         """
 
-        function_parameters = self.calibration.fit_results["par"][pixel[0],pixel[1],:]
+        function_parameters = self.calibration.fit_result["par"][pixel[0],pixel[1],:]
 
         variable_name_width = 8
         variable_width = 8
@@ -125,21 +127,21 @@ class Plotter:
         """
         match parameter_name:
             case Parameters.ELASTIC_SCATTERING_INTERCEPT.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 0], parameter_name="Elastic scattering intercept", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 0], parameter_name="Elastic scattering intercept", suppress_outliers=suppress_outliers)
             case Parameters.ELASTIC_SCATTERING_SLOPE.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 1], parameter_name="Elastic scattering slope", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 1], parameter_name="Elastic scattering slope", suppress_outliers=suppress_outliers)
             case Parameters.K_ALPHA_MEAN.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 2], parameter_name="Cu K_alpha mean", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 2], parameter_name="Cu K_alpha mean", suppress_outliers=suppress_outliers)
             case Parameters.SIGMA.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 3], parameter_name="Charge sharing sigma", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 3], parameter_name="Charge sharing sigma", suppress_outliers=suppress_outliers)
             case Parameters.K_ALPHA_AMPLITUDE.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 4], parameter_name="Cu K_alpha amplitude", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 4], parameter_name="Cu K_alpha amplitude", suppress_outliers=suppress_outliers)
             case Parameters.RATIO_AMPLITUDE_CHARGE_SHARING.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 5], parameter_name="Charge sharing amplitude ratio", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 5], parameter_name="Charge sharing amplitude ratio", suppress_outliers=suppress_outliers)
             case Parameters.RATIO_MEAN_K_BETA.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 6], parameter_name="Cu K_beta mean ratio", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 6], parameter_name="Cu K_beta mean ratio", suppress_outliers=suppress_outliers)
             case Parameters.RATIO_AMPLITUDE_K_BETA.value:
-                plot_parameter(self.calibration.fit_results["par"][:, :, 7], parameter_name="Cu K_beta amplitude ratio", suppress_outliers=suppress_outliers)
+                plot_parameter(self.calibration.fit_result["par"][:, :, 7], parameter_name="Cu K_beta amplitude ratio", suppress_outliers=suppress_outliers)
             case _:
                 raise ValueError(f"Unknown parameter name: {parameter_name}. Valid options are: {[param.value for param in Parameters]}")
 
@@ -169,37 +171,37 @@ class Plotter:
 
         match parameter_name:
             case Parameters.ELASTIC_SCATTERING_INTERCEPT.value:
-                data = self.calibration.fit_results["par"][:, :, 0].flatten()
+                data = self.calibration.fit_result["par"][:, :, 0].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering intercept", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering intercept", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.ELASTIC_SCATTERING_SLOPE.value:
-                data = self.calibration.fit_results["par"][:, :, 1].flatten()
+                data = self.calibration.fit_result["par"][:, :, 1].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering slope", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Elastic scattering slope", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.K_ALPHA_MEAN.value:
-                data = self.calibration.fit_results["par"][:, :, 2].flatten()
+                data = self.calibration.fit_result["par"][:, :, 2].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ mean", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ mean", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.SIGMA.value:
-                data = self.calibration.fit_results["par"][:, :, 3].flatten()
+                data = self.calibration.fit_result["par"][:, :, 3].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$\sigma$", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$\sigma$", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.K_ALPHA_AMPLITUDE.value:
-                data = self.calibration.fit_results["par"][:, :, 4].flatten()
+                data = self.calibration.fit_result["par"][:, :, 4].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ amplitude", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"$k_\alpha$ amplitude", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.RATIO_AMPLITUDE_CHARGE_SHARING.value:
-                data = self.calibration.fit_results["par"][:, :, 5].flatten()
+                data = self.calibration.fit_result["par"][:, :, 5].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio amplitude charge sharing", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio amplitude charge sharing", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.RATIO_MEAN_K_BETA.value:
-                data = self.calibration.fit_results["par"][:, :, 6].flatten()
+                data = self.calibration.fit_result["par"][:, :, 6].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ mean", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ mean", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case Parameters.RATIO_AMPLITUDE_K_BETA.value:
-                data = self.calibration.fit_results["par"][:, :, 7].flatten()
+                data = self.calibration.fit_result["par"][:, :, 7].flatten()
                 histogram = create_histogram_from_data(data, bin_range = bin_range, bin_width = bin_width)
-                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ amplitude", axis=axis)
+                ax = plot_histogram(histogram, histogram.axes[0].edges[:], xlabel=r"Ratio $k_\beta$ amplitude", label = f"Mean: {np.mean(data):.3f}\nStd: {np.std(data):.3f}", axis=axis)
             case _:
                 raise ValueError(f"Unknown parameter name: {parameter_name}. Valid options are: {[param.value for param in Parameters]}")