diff --git a/README.md b/README.md
index 1c337cf..782c149 100644
--- a/README.md
+++ b/README.md
@@ -1,45 +1,3 @@
-### Assignment
+# Mean-Variance-Standard Deviation Calculator
 
-Create a function named `calculate()` in `mean_var_std.py` that uses Numpy to output the mean, variance, standard deviation, max, min, and sum of the rows, columns, and elements in a 3 x 3 matrix. 
-
-The input of the function should be a list containing 9 digits. The function should convert the list into a 3 x 3 Numpy array, and then return a dictionary containing the mean, variance, standard deviation, max, min, and sum along both axes and for the flattened matrix. 
-
-The returned dictionary should follow this format:
-```py
-{
-  'mean': [axis1, axis2, flattened],
-  'variance': [axis1, axis2, flattened],
-  'standard deviation': [axis1, axis2, flattened],
-  'max': [axis1, axis2, flattened],
-  'min': [axis1, axis2, flattened],
-  'sum': [axis1, axis2, flattened]
-}
-```
-
-If a list containing less than 9 elements is passed into the function, it should raise a `ValueError` exception with the message: "List must contain nine numbers." The values in the returned dictionary should be lists and not Numpy arrays.
-
-For example, `calculate([0,1,2,3,4,5,6,7,8])` should return:
-```py
-{
-  'mean': [[3.0, 4.0, 5.0], [1.0, 4.0, 7.0], 4.0], 
-  'variance': [[6.0, 6.0, 6.0], [0.6666666666666666, 0.6666666666666666, 0.6666666666666666], 6.666666666666667], 
-  'standard deviation': [[2.449489742783178, 2.449489742783178, 2.449489742783178], [0.816496580927726, 0.816496580927726, 0.816496580927726], 2.581988897471611],
-  'max': [[6, 7, 8], [2, 5, 8], 8],
-  'min': [[0, 1, 2], [0, 3, 6], 0],
-  'sum': [[9, 12, 15], [3, 12, 21], 36]
-}
-```
-
-The unit tests for this project are in `test_module.py`.
-
-### Development
-
-For development, you can use `main.py` to test your `calculate()` function. Click the "run" button and `main.py` will run.
-
-### Testing 
-
-We imported the tests from `test_module.py` to `main.py` for your convenience. The tests will run automatically whenever you hit the "run" button.
-
-### Submitting
-
-Copy your project's URL and submit it to freeCodeCamp.
+This is the boilerplate for the Mean-Variance-Standard Deviation Calculator project. Instructions for building your project can be found at https://www.freecodecamp.org/learn/data-analysis-with-python/data-analysis-with-python-projects/mean-variance-standard-deviation-calculator