Question

1-what is the dummy variable and what is the purpose of included in regression model ?

2- explaining the meaning of adjust r square?

3-if adjust r square computed. would it be higher , equal of lower than value of r square?

Answer 1

1.What is the Dummy variable and what is the purpose included in regression model :-

Sham factors are "intermediary" factors or numeric stand-ins for subjective realities in a relapse display. In relapse examination, the needy factors might be impacted not just by quantitative factors (pay, yield, costs, and so on.), yet in addition by subjective factors (sex, religion, geographic area, and so forth.). A fake autonomous variable (additionally called a sham logical variable) which for some perception has an estimation of 0 will make that variable's coefficient have no job in affecting the needy variable, while when the spurious goes up against an esteem 1 its coefficient demonstrations to change the block.

Relapse examination treats all free (X) factors in the investigation as numerical. Numerical factors

are interim or proportion scale factors whose qualities are specifically equivalent, e.g. '10 is twice as much as 5', or

'3 short 1 levels with 2'. Frequently, nonetheless, you should need to incorporate a trait or ostensible scale variable such

as 'Item Brand' or 'Sort of Defect' in your examination. Let's assume you have three kinds of imperfections, numbered '1', '2'

furthermore, '3'. For this situation, '3 short 1' doesn't mean anything… you can't subtracting imperfection 1 from deformity 3. The

numbers here are utilized to show or distinguish the levels of 'Imperfection Type' and don't have natural significance of

their own. Sham factors are made in this circumstance to 'trap' the relapse calculation into effectively

examining characteristic factors.

2.Explaining the meaning of adjust r square:-

R-squared estimates the extent of the variety in your reliant variable (Y) clarified by your autonomous factors (X) for a straight relapse display. Balanced R-squared modifies the measurement dependent on the quantity of free factors in the model. ... That is the coveted property of an integrity of-fit measurement.

3.-if adjust r square computed. would it be higher , equal of lower than value of r square:-

R-squared or R^2 discloses how much your info factors clarify the variety of your yield/anticipated variable. In this way, if R-square is 0.8, it implies 80% of the variety in the yield variable is clarified by the information factors. Along these lines, in straightforward terms, higher the R squared, the more variety is clarified by your info factors and consequently better is your model.

In any case, the issue with R-squared is that it will either remain the equivalent or increment with expansion of more factors, regardless of whether they don't have any association with the yield factors. This is the place "Balanced R square" comes to help. Balanced R-square punishes you for including factors which don't enhance your current model.

Consequently, on the off chance that you are building Linear relapse on numerous variable, it is constantly recommended that you utilize Adjusted R-squared to pass judgment on integrity of model. In the event that you just have one info variable, R-square and Adjusted R squared would be actually same.

Regularly, the more non-critical factors you include into the model, the hole in R-squared and Adjusted R-squared increments.