4) My understanding on t - test is when we have one regressor (i.e. one independent variable) , we carry out t- test to check if the variable is significant or not (i.e X (Indep. vatiable) has a direct relationship with Y variable or not) by carrying out the hypotehsis test. As we are working on the sample , need to know what would be the ideal sample size to be considered to make inferences about the actual population parameter.

5) As x(independent variable) should be normally distributed to make ineferences about the population , my intention was to use box and whisker plot to remove outliers

6) When we have multiple regressors understand that we need to use F- statitics to check if the var's are significant or not, but in this case how should the samples be selected

- Shall we take samples of x1, x3, x3,....variables in one go

- shall we take samples of x1 first and then check if it is significant or not

Not sure what should be the approach

7) Also see when plotting the residual points, the residual points are very closer to x or y axis and we see that the model is best fit, but i dont see any normality in the residual plot

8) What would be the ideal sample size to be choosen when we have single regression variable ?

What would be the ideal sample size to be choosen when we have multiple regression variable ?

9) Also read somewhere that Y (dependent variable) will follow normality after carrying out predictions on regressors on N samples considered

i mean ,

y1=c+b1x1+b2x2+E (1st sample)

y2=c+b1x1+b2x2+E (2nd sample)

...

Yn=c+b1x1+b2x2+E (nth sample)