An analyst for a shopping district would like to determine the rent that should be charged for retail spaces depending on how close they are to parking. That is, they would like to predict the rent to charge, using distance from parking. The analyst takes a random sample of retail spaces in similar shopping districts and measures the monthly rent ($) and distance from parking (in yards) for each retail space. 1) What are the dependent and independent variables in this analysis? y= x= Here is the scatterplot of the data: Monthyl Rent ($) Rent and Distance from Parking 18000 17000 16000 15000 14000 . 13000 12000 . 11000 10000 0 25 50 75 100 125 150 175 200 225 250 275 300 Distance from Parking (yards) 2. Do you anticipate that the regression line will have a positive or a negative slope? Why? Here is the Coefficients Table from the regression output (See the Regression Output Equations handout - the one-page roadmap-for reference. This is the third table down): Intercept Distance Coefficients 15003.10 -11.42 Standard Error 249.3464 t Stat P-value 60.17 1.4E-52 1.5239 -7.49 5.28E-10 Lower 95% Upper 95% 14503.59 -14.47 15502.6 -8.37 3. What is the sample intercept, bo? What is the sample slope, b? 4. Write down the Estimated Regression Equation (ERE). 5. What is the predicted rent for retail spaces that are 50 yards from parking? What is the predicted rent for retail spaces that are 175 yards from parking? Interpret both of these predicted values in words.