Uncertainty consistency? 
*

*The problem statement, all variables and given/known data


I am given a set of x and y values x: (1,2,3,etc.) y: (1.2,2.2,3.1,etc.) with a given uncertainty and am asked
a) find the best fit 
b) at what prob can you rule out a 5% higher slope
c) is the stated uncertainty consistent with the data?
I can find the best fit relatively easily by minimizing chi-sqd and setting the derivatives to 0. I am confident in my result as it matches with the graph given by excel. 
For part 2 I tried putting a higher value for the given slope into the chi-sqd equation and checking a chart but that didn't give me a reasonable answer (not completely sure about the degree of freedom)
I am not sure how to approach the third part. I have computed the errors in the fitting coefficients but that doesn't seem to play into it. 
Thanks,
 A: Without more context is difficult to be sure.  There are many models that could be fit, and many criteria used to fit those models.
Part (b) suggests that the model is linear ("slope") which suggests that the criterion is Least Squares.
Part (c) reinforces the hint that Least Squares is the criterion, because the MS Residual estimates the uncertainty from the data.  This could be used for a $\chi^2$ test of the uncertainty (with $n-2$ df).
A: Probably, in the 5% slope it is asking what is the probability to have a slope that is 5% higher than the estimated one.
For example, let us assume that you have a slope of 1.00, then what is the probability to have a a slope that is 1.05 or above?
You can estimate using the Standard Error for the slope .When you do a regression the software should provide you the Standard Error for the slope.
For example, let us assume  Standard Error to be 0.003,  you could reframe the question in :
What is the probability for a Normal Distribution with mean 1.00 and standard deviation 0.003 to have a slope of 1.05 or higher.
If this probability is very low  then you can say that 1.05 is not consistent with the data
