The provided dataset "Franchises Dataset" contains data collected from different 100 franchises. The data contains the net profit (million $) for each franchise, the counter sales (million $), the drive-through sales (million $), the number of customers visiting the business daily, and the type of franchise.

1. Interpret the meaning of the Intercept and the slop of the linear regression model. Interpret the accuracy measure of the model. ( How we can explain )

2. What is the forecast of the net profit, if the counter sales are 500,000 $, drive-through

sales are 700,000$, and the franchise is a pizza store using both models (linear regression

and Random Forest). Comment on the forecasted value.

SUMMARY OUTPUT Regression Statistics Multiple R 0.933403909 R Square 0.871242858 Adjusted R Square 0.865821504 Standard Error 0.216144343 Observations 100 ANOVA of SS MS F ignificance F Regression 4 30.03165418 7.507914 160.705787 2.19E-41 Residual 95 4.438245815 0.046718 Total 99 34.4699 Coefficients Standard Error t Stat P-value Lower 95% Upper 95% ower 95.0%Upper 95.0% ntercept 0.303758941 0.078712826 3.859078 0.000207415 0.147494 0.460024 0.147494 0.460024 Counter Sales 0.088002558 0.012374323 7.111707 2.13067E-10 0.063436 0.112569 0.063436 0.112569 Drive-through Sales 0.112466318 0.010936665 10.28342 4.10254E-17 0.090754 0.134178 0.090754 0.134178 Pizza Store Vs. Referenc -0.513770937 0.055819733 -9.20411 8.30622E-15 -0.62459 -0.40295 -0.62459 -0.40295 Burger store Vs. Referer 0.465363148 0.051369268 9.059174 1.69442E-14 0.363382 0.567344 0.363382 0.567344 Equation=0.3075+0.088*counter sales+0.1124*drive through sales-0.513*Pizza vs ref+0.4653 *Burger vs ref