Regression with Python & R.
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Multinomial Logistic Regression.md
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Multinomial logistic regression.png
README.md
_config.yml
multinomial+logistic+regression+.ipynb

README.md

Linear Regression

Linear regression implementation in R (for university course).

The initial data
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".

plot

After that, we'll define the "Pearson's correlation coefficient", commonly called "the correlation coefficient".

correlation

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.

LinearRegressionwithR

Contributing

  1. Fork it
  2. Create your feature branch: git checkout -b my-new-feature
  3. Commit your changes: git commit -am 'Add some feature'
  4. Push to the branch: git push origin my-new-feature
  5. Submit a pull request

Authors

Shimanto

License

This project is licensed under the MIT License - see the LICENSE.md file for details