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The value of R square would not decrease when more variables are added to the model. As a result, there is always a temptation to add more variables in the model, because of which problem can arise therefore in that case Adjusted R2 is used as a measure.
R Squared and Adjusted R Squared. Regression analysis evaluates the effects of one or more independent variables on a single dependent variable. Regression arrives at an equation to predict performance based on each of the inputs. The R Squared and Adjusted R Squared values give the goodness of fit. Anything over 0.80 (80%) is considered a good fit. R Squared can be biased. So, Adjusted R Squared tweaks the R Squared based on degrees of freedom. Analysis: If R Squared is greater than 0.80, as it is in this case, there is a good fit to the data. Some statistics references recommend using the Adjusted R Squared value.In this example, R Squared of 0.980 means that 98% of the variation can be explained the independent variables.