Match List I with List II.

List I Regression variations		List II Measurement
A.	Total sum of square	I.
B.	Sum of square due to regression	II.
C.	Sum of square due to curve	III.
D.	Standard error of estimate	IV.

Choose the correct answer answer from the options given below:

Regression variations

Measurement

Total sum of square

• It is the sum of squared differences between the observed dependent variables and the overall mean.
• ">∑(yi−y¯i)2∑(yi−y¯i)2∑(yi−y¯i)2∑(yi−y¯i)2 $\rm \sum( y_i-\bar y_i)^2$

Sum of square due to regression

• It measures the level of variance in the error term, or residuals, of a regression model   
•      

Sum of square due to curve

• It is the sum of the differences between the predicted value and the mean of the dependent variable
•  

Standard error of estimate

• It represents the average distance that the observed values fall from the regression line
• .