Zagats publishes restaurant ratings for various locations in the United States. The file Restaurants (posted in Blackboard) contains the Zagat rating for food, dcor, service, and the cost per person for a sample of 100 restaurants located in the center of New York City and in an outlying area of New York City. Develop a regression model to predict the cost per person, based on a variable that represents the sum of ratings for food dcor, and service.
Construct a Scatter Plot
Assuming a linear relationship, use the least-squares method to compute the regression coefficients b0 and b1.
Interpret the meaning of the Y intercept, b0 and the slope, b1 in this problem.
Predict the mean cost per person for a restaurant with a summated rating of 35.
What should you tell the owner of a group of restaurants in this geographical area about the relationship between the summated rating and the cost of a meal?
. Construct a Scatter Plot
To visualize the relationship between the summated rating (food, decor, and service) and the cost per person, I would create a scatter plot. The summated rating would be plotted on the x-axis, and the cost per person would be plotted on the y-axis. This plot will help in understanding if a linear relationship exists between the two variables.
2. Compute Regression Coefficients
Assuming a linear relationship, I would use the least-squares method to calculate the regression coefficients b0 (y-intercept) and b1 (slope). The least-squares method minimizes the sum of the squared differences between the observed and predicted values of the cost per person.
. Construct a Scatter Plot
To visualize the relationship between the summated rating (food, decor, and service) and the cost per person, I would create a scatter plot. The summated rating would be plotted on the x-axis, and the cost per person would be plotted on the y-axis. This plot will help in understanding if a linear relationship exists between the two variables.
2. Compute Regression Coefficients
Assuming a linear relationship, I would use the least-squares method to calculate the regression coefficients b0 (y-intercept) and b1 (slope). The least-squares method minimizes the sum of the squared differences between the observed and predicted values of the cost per person.