I want to calculate tha Average Slope of 4 Slopes, but Im not too sure if this will require me to calculate the Average error when I do calculate the average of the 4 slopes.
Im obviously calculating the Average as (slope $i := s_i$): $$\frac{s_1+s_2+s_3+s_4}{4}$$
But will the average Slope result if $m=2.6$ as an example, in this have same effect on $Y$ when $X$ is decreased or increased?, obviously based on the equation: $y=mx+b$
My main and ultimate goal is to determine the relationship of $Y$ and $X$ from the equation.
An example of what I am looking for, the Average of the 4 Slopes is 2.989 for example, and I had X was the value of Experience at a workplace and Y was the Salary, what would the Average result of +2.989 for the relationship of work experience and Salary for example?
If this was a normal calculation of y=mx+b then I wouldve said that for each unit increase in the input variable x (Experience), the output y (Salary) increases by 2.989 units, BUT in this case its different, as I have the average of 4 calculated slopes.
It's very hard to figure out what you are doing. Here's my guess. You have $5$ $x$-values, $x_1\lt x_2\lt\cdots\lt x_5$, and corresponding $y$-values, $y_1,\dots,y_5$, and you have calculated the slopes $$s_i={y_{i+1}-y_i\over x_{i+1}-x_i}$$ for $i=1,2,3,4$ and now you want to know what the number $$S={s_1+\cdots+s_4\over4}$$ tells you about the relation between $y$ and $x$.
If this is your question, then I would say $S$ tells you very little of interest about the relation between $y$ and $x$. You are better off learning about "linear least squares fit" which will give you the best fit of the form $y=mx+b$ to your data points.