Based on the regression model output, the R-squared value is 0.073, which indicates that only 7.3% of the variation in salary can be explained by the degree of specialization alone. The adjusted R-squared value is slightly lower at 0.058, which suggests that the model may not be a good fit for the data. The standard error of the estimate is quite high at 82949.958, which means that the predicted salaries may have a large margin of error.

Looking at the coefficients, it can be seen that the degree of engineering has the highest coefficient of 336963.38, which means that students with an engineering degree are likely to earn a higher salary compared to other degree disciplines. The ENGPERCENT variable also has a negative coefficient of -5444.13, which suggests that as the percentage of English medium education decreases, the salary earned by the student may also decrease.

However, it is important to note that the coefficients for the other degree disciplines are not statistically significant, as their p-values are greater than 0.05. This means that there is not enough evidence to suggest that students with degrees in arts, commerce, computer applications, or management are likely to earn a significantly different salary compared to other degree disciplines.

Therefore, based on the regression model output, it may not be necessary for the dean to give more importance to certain degree disciplines while admitting students to the MBA program. Instead, the school may need to consider other factors that could influence salary, such as work experience, industry, and location. Additionally, it may be beneficial to improve the model by including more variables and increasing the sample size to obtain more accurate predictions.