Week 3 - Statistical Learning Course.
Just started Linear Regression chapter, in which the basics of how linear regression works were explained.
Just started Linear Regression chapter, in which the basics of how linear regression works were explained.
- Learned about regression parameters.
- Residual sum of squares - RSS, which is the sum of the squared differences between the real value ( target ) and the predicted value.
- The idea behind this is to calculate/estimate the best parameters that minimized the difference of RSS so that we know the predicted values are closest to the real value.
- On the other hand, we want to calculate how far we are from the real values of the parameters, and this can be calculated through the standard error for each parameter using Confidence Intervals.
- Confidence intervals are a calculated range of values in which you say: Out of all 100% values on the range, there is a 95% chance of your truth parameter being on this interval.