In an experimental physics activity, I registered 5 values corresponding to 5 tests. The second column corresponds to the experimental value. The third column corresponds to the estimated value (the value obtained by linear regression). The fourth column corresponds to residuals. I plotted a linear regression and calculated the residuals and plotted the residuals as you can see in the graphs.
However, to prove that my linear regression model is adequate for the data, the residuals have to satisfy 4 conditions:
Follow a normal distribution
Have a zero average
Have constant variance (homoscedasticity)
I was able to prove that the mean was zero (or very close to zero). To prove the independence of residuals, I did the Durbin-Watson test.
The problem is that the Durbin-Watson table gives the values of dL and dU, from n = 6. And I, in my experience, only have 5 experimental values, that is, n = 5. Can I approximate, using the values of dL and dU for n = 6, in the case of n = 5?
If you can not use this test, how do I prove that the residuals are independent?
I also have another question. To prove that the residues follow a normal distribution, I have to do the normal Q-Q test and the normal P-P test. In the case of the normal Q-Q test gave me this chart:
If a linear regression occurs, we conclude that the residuals have a normal distribution, which is what I want to prove.
This is the first time I make a graph of this type (the graph was made in Matlab) and it is not working for me. I would be glad if you clarified what was wrong, or show me the right chart and teach me how to do it.
Everything I've done so far has been self-taught work and I feel like I'm at a standstill. Without your precious help I can not advance.