00:01
Once again, welcome to a new problem.
00:04
When you think about regression, when you think about regression, we have a typical regression equation, and your typical regression equation is the same as y hat equals to beta not plus beta 1 x, and this represents a simple linear regression.
00:32
Represents a simple linear regression in the sense that beta not is the slope.
00:40
And this slope is providing, beta not is not the slope, but better not is the intercept.
00:51
Beta not is the intercept.
00:54
And as you can imagine, it represents the value, the value of the predicted variable, the predicted variable y, when x is 0.
01:17
And then beta 1 represents the slope that shows their relationship, the relationship between the independent y variable, and the dependent or the independent x variable and the independent x variable and the dependent y variable.
02:01
We're looking at a new problem and in this particular problem that we're looking at, we're given a requirement to estimate the the intercept which is beta not and the slope which is beta 1.
02:21
So we want to give an estimate of these two coefficients...