Stats: Simple Linear Regression

[Ted123]

Homework Statement

[PLAIN]http://img822.imageshack.us/img822/4421/statsii.jpg

The Attempt at a Solution

Done parts (a) and (b). How do I do parts (c) and (d)?

Is the simple linear regression model just Y_i=\beta_0+\beta_1 X_i + \varepsilon_i where \varepsilon_i \stackrel {\text{i.i.d.}}{\sim} N(0,\sigma^2)

Does X_i respresent the age of the bus and Y_i the maintenance cost?

 

[lanedance]

a simple linear regression model would be
M = b.A + c + e
where e is a normally distributed error term centred on zero

 

[lanedance]

check this out
http://www.itl.nist.gov/div898/handbook/pmd/section4/pmd431.htm

 

[Ted123]

a simple linear regression model would be
M = b.A + c + e
where e is a normally distributed error term centred on zero

In part (c) the MLEs of \beta_0 and \beta_1 are the same as the least squares estimates of the intercept and the slope (from part (b)).

I've calculated the values of a and b in the least squares regression line using the definitions. Is there any way of getting these from the R output?

The correlation coefficient in part (a) is 0.9340776458684332 (can get this from R output - square root of the 'mutiple R-squared' value) and this implies there is a strong positive linear correlation.

What can I say about whether X and Y are related in part (d)?