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