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+# Pearson Regression
+
+**Tier:** 1-Beginner
+
+There are few, if any, applications that don't require some form of 
+cross-disciplinary knowledge in order to implement useful functionality for
+a user. In the case of an app for the medical profession it might be domain
+expertise in biology or pharmacology. A paint manufacturer or a crop science
+business might rely on apps with an intimate knowledge of chemistry. And, a 
+payroll application will certainly incorporate HR and accounting concepts.
+
+Regardless of the industry segement an app is developed for one cross domain
+expertise in common with them all is mathematics. As an application developer
+you don't have to be a mathemetician, but it's useful to have an understanding
+of how to apply mathematical concepts to the problems you are trying to solve.
+
+The objective of this app is to apply the Pearson Correlation Coefficient
+against two sets of data to provide the user with the degree to which they
+may or may not be related. For example, given a set of temperatures and another
+set of car prices this would let the user test whether or not they are related
+(spoiler alert: they are unrelated!).
+
+### Constraints
+
+- The Developer must program all calculations without relying on a package.
+
+## User Stories
+
+-   [ ] User can see an input panel with two input fields allowing entry of `x` 
+and `y` coordinates, and 'Add' and 'Calculate' buttons.
+-   [ ] User can enter numbers into these boxes where `x` and `y` are observations
+from the two data sets.
+-   [ ] User can click the 'Add' button to add the `x` and `y` to a tabular
+output area listing the pairs of observations.
+-   [ ] User can see and error message if either of the two input fields are
+empty or do not contain valid real numbers.
+-   [ ] User can see the 'Calculate' button is disabled until errors have been
+corrected.
+-   [ ] User can click the 'Calculate' button to perform the regression analysis
+and to display its results.
+-   [ ] User can see results of the calculation which include:
+    - Arithmetic means for both the `x` and `y` observations
+    - Standard deviations for both the `x` and `y` observations
+    - Pearson correlation coefficient with one of the following interpretations:
+      - No correlation
+      - Neutral
+      - Some correlation
+
+## Bonus features
+
+-   [ ] User can see a scatter plot of the observations
+-   [ ] User can upload observations from a file on the local machine.
+-   [ ] User can see a regression line overlaying the scatter plot
+
+## Useful links and resources
+
+- [Pearson Correlation Coefficient (Wikipedia)](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient)
+- [Linear Regression](https://en.wikipedia.org/wiki/Linear_regression)
+- [Pearson's Correlation Coefficient](http://www.code-in-javascript.com/pearsons-correlation-coefficient-in-javascript/)
+
+## Example projects
+
+[Correlation](https://memory.psych.mun.ca/tech/js/correlation.shtml)
diff --git a/README.md b/README.md
index 1adc9cac..9f74fab5 100644
--- a/README.md
+++ b/README.md
@@ -56,6 +56,7 @@ required to complete them.
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 | [Notes App](./Projects/Notes-App.md)                                   | Create an online note pad                                 | 1-Beginner |
+| [🌟Pearson Regression](./Projects/Pearson-Regression-App.md)             | Calculate the correlation coefficent for two sets of data | 1-Beginner |
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