A regression analysis relating a company’s sales, their advertising expenditure, price (per unit), and time (taken per unit production) resulted in the following output. Regression StatisticsMultiple R 0.9895R Square 0.9791Adjusted R Square 0.9762Standard Error 232.29Observations 25ANOVA df SS MS F Significance FRegression 3 53184931.86 17728310.62 328.56 0.0000Residual 21 1133108.30 53957.54Total 24 54318040.16 Coefficients Standard Error t Stat P-valueIntercept 927.23 1229.86 0.75 0.4593Advertising (x1) 1.02 3.09 0.33 0.7450Price (x2) 15.61 5.62 2.78 0.0112Time (x3) 170.53 28.18 6.05 0.0000a. Using α = .05, determine whether or not the regression model is significant. Fully explain how you arrived at your conclusion (give numerical reasoning) and what your answer indicates. b. At α = .05, determine which variables are significant and which are not. Explain how you arrived at your conclusion (give numerical reasoning).c. Fully explain the meaning of R Squared, which is given in the above regression results. Be very specific and give numerical explanation.