A ski resort asked a random sample of guests to rate their satisfaction on various attributes of their visit on a scale of 1–5 with 1 = very unsatisfied and 5 = very satisfied. The estimated regression model was Y = overall satisfaction score, X1 = lift line wait, X2 = amount of ski trail grooming, X3 = safety patrol visibility, and X4 = friendliness of guest services. Predictor Coefficient Intercept 2.9833 LiftWait 0.1458 AmountGroomed 0.2562 SkiPatrolVisibility 0.0428 FriendlinessHosts −0.1298 (a) Write the fitted regression equation. (Round your answers to 4 decimal places. Negative values should be indicated by a minus sign.) yˆy^ = + * LiftWait + * AmountGroomed + * SkiPatrolVisibility + * FriendlinessHosts (b) Interpret each coefficient. Overall satisfaction increases with an increase in satisfaction for each individual predictor except for friendliness of hosts. (d) Make a prediction for Overall Satisfaction when a guest’s satisfaction in all four areas is rated a 3. (Round your answer to 4 decimal places.) Overall satisfaction score
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A ski resort asked a random sample of guests to rate their satisfaction on various attributes of their visit on a scale of 1–5 with 1 = very unsatisfied and 5 = very satisfied. The estimated regression model was Y = overall satisfaction score, X1 = lift line wait, X2 = amount of ski trail grooming, X3 = safety patrol visibility, and X4 = friendliness of guest services.
| Predictor | Coefficient | |
| Intercept | 2.9833 | |
| LiftWait | 0.1458 | |
| AmountGroomed | 0.2562 | |
| SkiPatrolVisibility | 0.0428 | |
| FriendlinessHosts | −0.1298 | |
(a) Write the fitted regression equation. (Round your answers to 4 decimal places. Negative values should be indicated by a minus sign.)
yˆy^ = + * LiftWait + * AmountGroomed + * SkiPatrolVisibility + * FriendlinessHosts
(b) Interpret each coefficient.
Overall satisfaction increases with an increase in satisfaction for each individual predictor except for friendliness of hosts.
(d) Make a prediction for Overall Satisfaction when a guest’s satisfaction in all four areas is rated a 3. (Round your answer to 4 decimal places.)
Overall satisfaction score
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