# Completely lost on Reggie's Linear Regression

Good day everyone,

I have made it to the Reggie’s Linear Regression project in Learn Python 3. I have been doing well and thought I knew the information they were giving me, but I just cannot do this project without looking at the solution. I literally started the entire course over. Reviewed everything and came back and it still doesn’t make sense to me. It’s frustrating, because I thought I was doing so well. I have even been doing some side projects with Tech with Tim and others.

That being said, my question is: Has anyone else had this problem? What am I missing? Do any of you have any suggestion why I would I be having so much trouble here? I just don’t get it :(. I am not going to give up, but I need to understand something that I am not and I don’t know how to get to where I can.

Finally, should I just move on and keep going and come back to this later? Or is that a bad idea?

I found this. It seems I am not the only one who thinks this was too early ha ha. I am going to skip and move on. I am sorry I used search I promise. I searched outside of the forum and found it like that. Posting this in case anyone else has the same issue.

Yea, you can come back and do it after doing some other stuff in the course.

Some of the concepts are a little unexpected for people who haven’t seen that kind of math stuff in the past.

mathematical modeling
( in this case, using a linear function as a model )
y = mx + b    equation of a line
f(x) = mx + b
where m is slope, and b is the y-intercept
x is for the input [which is ball size in cm]
and for the stuff above, y or f(x) is the predicted output (for that value of x) [meaning the predicted bounce height in m for a ball of a given size]

And error is the difference between the actual y values (bounce heights) and what’s predicted for y using the linear function. [We can find the predicted y-value (bounce height) for each data point (meaning each pair of ball size and bounce height recorded as (x,y) ].

another detail

This kind of error is called a residual in Statistics.

I think some pictures or diagrams in the project would have been helpful.