regressionstatistics
I have the following sample :
$$
\begin{array}{c|lr}
X&80&100&120&140&160&180&200&220&240&260\\
\hline
Y & 70 &65&90&95&110&115&120&140&155&150 \\
\end{array}
$$
Now i have to calculate
$\mathbb V(\hat\beta_0)=\sigma^2[\frac{1}{n}+\frac{\bar X^2}{\sum(X_i-\bar X)^2}]$
here,
X is independent and Y response variable.
$\beta_0$ intercept parameter
$\sigma^2$ population variance
But i have my sample data.
For revenue it is a bit difficult. There is a similar question to yours here https://stats.stackexchange.com/questions/72242/bootstrapping-do-i-need-to-remove-outliers-first
If you have large enough samples the CLT lets you just use the mean, $\mu$ and the standard error on the mean, $\sigma_\mu = \sigma / \sqrt{N}$. You can then calculate your 95% CL through the comparison of the two Gaussians formed by $\mu$ and $\sigma_\mu$ for each scenario in your split test.
This is a very stripped down answer and there are a lot of special cases to be considered. However, this is a fast way to get your answer in excel.
The way you have the data, linear regression is not going to work correctly... Here's a quick plot of the data with a regression line. 
In fact, the regression line has a negative slope, quite the opposite of what you wanted. This effect is called Simpson's Paradox.
If I understand correctly, an angle of -170 corresponds to an angle of 190. So just transform your data onto the range 0 to 360 and things will work out for you.
This has a slope of 0.904 as desired.
Best Answer
You can't calculate the population variance $\sigma^2$, but you can estimate it with $$\widehat{\sigma^2} = \frac{\Sigma (Y_i - \hat Y_i)^2}{n-2}.$$
Here, $\hat Y_i= \beta_0 + \beta_1 X_i$ denotes the predicted value of $Y$ given $X_i$.