data is denoted by (X 1
​
,Y 1
​
),(X 2
​
,Y 2
​
),…,(X n
​
,Y n
​
). Set formulas down for each of the following entities. (a) Least Squares Estimator of β 1
​
= β
^
​
1
​
(b) Least Squares Estimator of β 0
​
= β
^
​
0
​
(c) Residual Sum of Squares = RSS d) S xx
​
(e) S XY
​
(f) S YY
​
(g) R 2
(h) Sample Correlation Coefficient r (i) Estimator of σ 2
= σ
^
2
(i) Variance of β
^
​
1
​
(k) Standard Error of β
^
​
1
​
(1) Distribution of β
^
​
1
​
/SE( β
^
​
1
​
) under the null hypothesis β 1
​
=0. (m) AlC 1. The simple linear regression model is at the forefront of clinical diagnostics. The protagonists are two numeric variables Y (Response) and X (predictor). The model is epitomized by: Y∣X∼Normal(β 0
​
+β 1
∗
​
X,σ 2
). It has three parameters. In order to estimate the parameters of the model, data is collected on (X,Y) for a random sample of individuals. Symbolically, the