Regression Statistics 0.994215326 0.988464115 0.985580144 3.96139482 Multiple R R Square Adjusted R Square Standard Error Observations ANOVA Regression Residual Total Intercept X Variable 1 df 6 SS MS F Significance F 1 5378.562738 5378.563 342.7441 5.00969E-05 4 62.77059569 15.69265 5 5441.33333 Coefficients Standard Errort Stat P-value Lower 95% 91.99493029 4.936028654 18.63744 4.88E-05 78.2903177 1.279087452 0.069090009 18.51335 5.01E-05 1.087262835 Upper 95% 105.699542 1.4709120

Calculate two lines of regression and calculate a linear regression equation to model the data given below:  years  (1980,1985,1990,1995,2000) Enrolment ( 21,25,29,39,47)

Yummy Lunch Restaurant needs to decide the most profitable location for their business expansion. Marketing manager plans to use a multiple regression model to achieve their target. His model considers yearly revenue as the dependent variable. He found that number of people within 2KM (People), Mean household income(income), no of competitors and price as explanatory variables of company yearly revenue. The following is the descriptive statistics and regression output from Excel.       Revenue People Income Competitors Price             Mean 343965.68 5970.26 41522.96 2.8 5.68 Standard Error 5307.89863 139.0845281 582.1376385 0.142857 0.051030203 Median 345166.5 6032 41339.5 3 5.75 Mode #N/A 5917 #N/A 3 6 Standard Deviation 37532.51115 983.47613 4116.334718 1.010153 0.360838027 Sample Variance 1408689393 967225.2984 16944211.51 1.020408 0.130204082 Sum 17198284 298513 2076148…

This was not the correct quadratic model for the data