# Evaluation Of A Student 's Name

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Regression Analysis Student’s Name Student ID Professor’s Name Course Title Date of Submission Abstract This research attempts to know whether the factors, restaurant types and the average price determines customer satisfaction in the full service restaurant chains .The variable types indicate whether the restaurant is an Italian restaurant or a seafood /steakhouse while the price indicates the average amount paid per person for dinner and drinks, minus tip. The customer diner overall satisfaction is reflected as score with higher value indicating greater overall satisfaction. The regression model is used to determine whether the overall satisfaction is related to average meal price and the type of restaurant.…show more content…
The analysis done at 5% significance lever show that overall customer satisfaction was significantly related to average meal price and the type of restaurant. The regression equation show that an increase in meal expenditure means the more the customer is satisfied. The multiple regression equation indicates that Italian customer is more satisfied compared to seafood restaurant. Data analysis The regression problem involves the dependent variable customers’ satisfaction, a quantitative independent variable average meal price and a qualitative variable type of restaurants with two levers (Italian and seafood/steakhouse). These variables are defined as follows; y=score indicating customers’ satisfaction X1= average meal price The summary of the output of the data of the relation between the overall customer satisfaction and the average meal price is represented in the table below. Regression Statistic Multiple R 0.587 R squared 0.344 Adjusted R squared 0.310 Standard error 3.026 observations 21 The analysis of variance of regression showing the overall customer satisfaction and its relation with the price gives the following outcome. Source of variation df SS MS F Significance F Regression residual 1 19 91.290 173.948 91.29 9.155 9.971 0.005 Totals 20 265.238 The estimated multiple