Multiple Linear Regression Output (please answer all 7 questions below) A restaurant owner wanted to know if her patron satisfaction levels (measured on a scale of 1-7, with 7 meaning very highly satisfied) were affected by the quality of the food, the speed of the service, the menu variety or the cleanliness of the bathrooms (all measured on a scale of 1-7, with 7 meaning the patron strongly agreed that the restaurant performed well in that area and 1 meaning the patron strongly disagreed that the restaurant performed well in that area). Model Summary Model R R Square Adjusted R Square .410(a) .168 .160 a Predictors: (Constant), food quality, service speed, menu variety, bathroom cleanliness Std. Error of the Estimate 1.5964 Standardized Coefficients t Sig. Tol. VIF Beta .001 Coefficients(a) Unstandardized Model Coefficients B Std. Error 1 (Constant) 3.332 1.364 Food quality .097 055 Service Speed .017 045 Menu Variety .067 035 Bathroom .065 Cleanliness a Dependent Variable: satisfaction .343 .005 243 3.908 2.753 0.753 3.753 .033 .541 .007 .767 543 .627 .694 1.265 2.435 2.129 1.765 .057 .127 1.753 .048 1. How much variance in the dependent variable is explained by the independent variable? Please provide a percentage. (1 point) 2. What is one specific null hypothesis for this scenario? (2 points) 3. Do you reject the null hypothesis listed in #2 above? (1 point) 4. Which variable has the strongest impact on the dependent variable? (2 points) 5. Do you see a multicollinearity problem in the output? Why or why not? (1 point) 6. In your own words, interpret the overall results. (4 points) 7. If you had to predict satisfaction based on this regression output, what would the formula look like? (1 point; Please put placeholders in for the different variables (e. g., food quality) since you are not given a specific values to use for your independent variables. Please make sure to write as much of the equation as is possible given the data provided.)