Question

What do the following terms in a regression table mean and tell us?

1. df

2. SS

3. MS

4. F

5. Significance F

6. Residual

7. ANOVA

8. Multiple R,

9. Adjusted R square

10. Standard error

11. Lower 95% (and 95.0)

12. Upper 95%

Please explain in simple words and provide examples where possible.

Answer 1

1)Df :- here df means degree of freedom (df) . It is independent variables in our recession model example total number of observation on data baset sustracted by the number of variables being estimated.

2) ss:- it's means sum of square (ss) it is tools used to identify dispersion of data and how well data fit in model regression.

3) ms :- meas squared error ( ms) . Mean of sum of square or sum of square divided by degree of freedom both regression and residual

4) f :- it is ratio of mean regression where sum of square devided by means of error sum of square. It's value change from zero to Atbitrarily large number.

5) significance f:- the significance f indicate overall ur linear regression model provides best fit to the data than a model that contain no independent variables . F- test is very flexible.

6) residual:- the difference between observed value of dependent value of variables and the predicted value is called residual.

7) anova:- analysis of variance consist calculation that provides information about level of variability within regression model and form basis test of significance.

8) multiple r:- it told how the strong linear relationship is

Example 1 means perfect positive relationship and 0 means no relationship.