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Simple Linear Regression

https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s

In general, there are 3 main stages in Linear regression:

1. Using Least-squares to fit a line to the data

2. Calculating $R^{2}$

3. Calculating a $p-value$ for $R^{2}$

1. Using Least-squares to fit a line to the data

Takinf from https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s

• First, draw a line through the data.

• Second, calculate the Residual sum of squares: Measure the distance from the line to each data point (residual), square each distance, and then add them up.

The distance from a line to a data point is called a residual

• Then, we rotate the line a little bit and calculate the RSS. We do this many times.
• ...

• Then, the line that represents the linear regression is the one corresponding to the rotation that has the least RSS. The regression equation:

$y=a+bx$

The equation is composed of 2 parameters:

• Slope: $b$

The slope is the amount of change in units of $y$ for each unitchange in $x$.

• The $y-axis$ intercept: $a$

2. Calculating $R^{2}$

Takinf from https://www.youtube.com/watch?v=nk2CQITm_eo&t=267s

In the above example, they are using different terminology to the one that we saw in Section Data_Science#The_coefficient_of_determination_R.5E2

It is very important to note how the result of $R^{2}$. In our example There is a 60% reduction in variance when we take the mouse weight into account or Mouse weight "explains" 60% of the variation in mouse size.