1. Decomposition of Sum of Squares
The total sum of squares (SS) in the response variable is
The total SS can be decompose into two main sources; error SS and regression SS…
The error SS is
The regression SS is
It is the amount of variation in Y’s that is explained by the linear relationship of Y with X.
STA302/ week 5

2. Claims First, SSTO = SSR +SSE, that is Proof:….
Alternative decomposition is
Proof: Exercises.
STA302/ week 5

3. Analysis of Variance Table
The decomposition of SS discussed above is usually summarized in analysis of variance table (ANOVA) as follow:
Note that the MSE is s2 our estimate of σ2.
STA302/ week 5

4. Coefficient of Determination
The coefficient of determination is
It must satisfy 0 ≤ R2 ≤ 1.
R2 gives the percentage of variation in Y’s that is explained by the
regression line.
STA302/ week 5

5. Claim
R2 = r2, that is the coefficient of determination is the correlation coefficient square.
Proof:…
STA302/ week 5

6. Important Comments about R2
It is a useful measure but…
There is no absolute rule about how big it should be.
It is not resistant to outliers.
It is not meaningful for models with no intercepts.
It is not useful for comparing models unless same Y and one set of
predictors is a subset of the other.
STA302/ week 5

7. ANOVE F Test The ANOVA table gives us another test of H0: β1 = 0.
The test statistics is
Derivations …
STA302/ week 5

8. Prediction of Mean Response
Very often, we would want to use the estimated regression line to
make prediction about the mean of the response for a particular X
value (assumed to be fixed).
We know that the least square line is an estimate of
Now, we can pick a point, X = x* (in the range in the regression
line) then, is an estimate of
Claim:
Proof:
This is the variance of the estimate of E(Y | X=x*).
STA302/ week 5

9. Confidence Interval for E(Y | X = x*)
For a given x, x* , a 100(1-α)% CI for the mean value of Y is
where
STA302/ week 5

10. Example Consider the smoking and cancer data.
Suppose we wish to predict the mean mortality index when the
smoking index is 101, that is, when x* = 101….
STA302/ week 5

11. Prediction of New Observation
Suppose we want to predict a particular value of Y* when X = x*.
The predicted value of a new point measured when X = x* is
Note, the above predicted value is the same as the estimate of
E(Y | X = x*).
The predicted value has two sources of variability. One is due
to the regression line being estimated by b0+b1X. The second one is
due to ε* i.e., points don’t fall exactly on line.
To calculated the variance in error of prediction we look at the
difference
STA302/ week 5

12. Prediction Interval for New Observation
100(1-α)% prediction interval for when X = x* is
This is not a confidence interval; CI’s are for parameters and we are estimating a value of a random variable.
Prediction interval is wider than CI for E(Y | X = x*).
STA302/ week 5

13. Dummy Variable Regression
Dummy or indicator variable takes two values: 0 or 1.
It indicates which category an observation is in.
Example…
Interpretation of regression coefficient in a dummy variable regression…
STA302/ week 5