diff --git a/24_Day_Statistics/24_statistics.md b/24_Day_Statistics/24_statistics.md
index 4978e3c..97c3d57 100644
--- a/24_Day_Statistics/24_statistics.md
+++ b/24_Day_Statistics/24_statistics.md
@@ -172,12 +172,15 @@ The shape method provide the shape of the array as a tuple. The first is the row
     nums = np.array([1, 2, 3, 4, 5])
     print(nums)
     print('shape of nums: ', nums.shape)
+    numpy_two_dimensional_list = np.array([[0,1,2],[3,4,5],[6,7,8]])
     print(numpy_two_dimensional_list)
     print('shape of numpy_two_dimensional_list: ', numpy_two_dimensional_list.shape)
     three_by_four_array = np.array([[0, 1, 2, 3],
         [4,5,6,7],
-        [8,9,10, 11]])
-    print(three_by_four_array.shape)
+        [8,9,10,11]]
+    print(three_by_four_array)
+    print('shape of three_by_four_array: ', three_by_four_array.shape)
+
 ```
 
 ```sh
@@ -930,7 +933,7 @@ Numpy is equipped with the robust statistical function as listed below
   - Max np.max()
   - Mean np.mean()
   - Median np.median()
-  - Varience
+  - Variance
   - Percentile
   - Standard deviation np.std()
 
@@ -1218,7 +1221,7 @@ plt.show()
 
 ![png](../test_files/test_143_0.png)
 
-# Summery
+# Summary
 
 To summarize, the main differences with python lists are: