Linear regression implementation in R (for university course).
| X (key) | Y (value) |
|---|---|
| 1 | 2 |
| 2 | 2 |
| 3 | 5 |
| 4 | 4 |
| 5 | 5 |
| 6 | 6 |
| 7 | 6 |
We'll first enter the given data in R and create a scatter plot for it. Then we'll craft for our plot the "line of best fit", or the "least squares regression line".
After that, we'll define the "Pearson's correlation coefficient", commonly called "the correlation coefficient".
The correlation coefficient 0.9053 satisfies the condition -1 <= Rxy <= 1, and indicates a quite strong degree of linear dependence between the given variables.
Finally, we'll predict the value for key = 8.
- Fork it
- Create your feature branch: git checkout -b my-new-feature
- Commit your changes: git commit -am 'Add some feature'
- Push to the branch: git push origin my-new-feature
- Submit a pull request
This project is licensed under the MIT License - see the LICENSE.md file for details