The manager of YorkU Sports is investigating why certain sport stores in York Region are performing better than others. She believes that 3 factors are related to any store's overall success (i.e. total sales revenue); the number of competing stores within a 10 km radius of each other, the population in the surrounding area within 50 km of any store and the amount any store spends on Google Adwords. She hires some smart 2320 student from York to investigate this and provide some insight. A random sample of 30 stores is selected. For each store, the following information was gathered: y = total sales last year (in $000's) *1 = the number of competitors within a 10 km radius X2 = the population within the area (in 000's) X3 = the amount spent on Google Adwords (in $000's) The sample data produced the following results: SUMMARY OUTPUT Regression Statistics Multiple R 0.7622 R Square 0.5810 Adj R Square 0.5326 Standard Error 9.1987 Observations 30 ANOVA df SS Regression 3 3,050.0000 Residual 26 2,200.0000 Total 29 5,250.0000 Regression output Coefficients Std Error t Stat Intercept 14.0000 7.0000 2.0000 Competitors -1.0000 0.7000 -1.4286 Population 30.0000 5.2000 5.7692 Adwords 0.2000 0.0800 2.5000 Is there enough evidence to conclude at the 5% significance level that x, and y are linearly related (show your work)? State the population and sample regression line. C. Test the validity of the model (show your work) at a 5% significance level. If additional relevant variables are added, what will most likely happen to R