The Condé Nast Traveler Gold List provides ratings for the top 20 small cruise ships. The data shown are the scores each ship received based on the results from Condé Nast Traveler's annual Reader's Choice Survey. Each score represents the percentage of respondents who rated a ship as excellent or very good on several criteria, including Shore Excursions and Food/Dining. An overall score was also reported and used to rank the ships.

- The highest-ranked ship, the Seabourn Odyssey, has an overall score of 91.4, with the highest component being 97.9 for Food/Dining.

| Ship | Overall | Shore Excursions | Food/Dining |
|-------------------------------|---------|------------------|-------------|
| Seabourn Odyssey | 94.4 | 90.8 | 97.9 |
| Seabourn Pride | 92.8 | 84.1 | 96.7 |
| National Geographic Endeavor | 92.7 | 100.0 | 88.6 |
| Seabourn Sojourn | 91.5 | 94.8 | 96.9 |
| Paul Gauguin | 90.6 | 80.0 | 91.3 |
| Seabourn Legend | 90.1 | 82.2 | 98.8 |
| Seabourn Saint | 90.4 | 86.5 | 91.8 |
| Silver Explorer | 90.1 | | |
| Silver Spirit | 89.3 | 56.1 | 90.6 |
| Seven Seas Navigator | 89.0 | 31.0 | 90.5 |
| Silver Whisperer | 89.1 | 82.0 | |
| National Geographic Explorer | 89.0 | 93.1 | 89.6 |
| Silver Cloud | 88.9 | 78.1 | 91.2 |
| Celebrity Xpedition | 87.2 | 91.8 | 73.6 |
| Silver Shadow | 87.1 | 74.9 | 89.7 |
| Silver Wind | 86.4 | 78.1 | 91.5 |
| SeaDream | 86.2 | 77.5 | 91.0 |
| Wind Star | 86.0 | 76.3 | 91.3 |
| Wind Surf | 85.0 | 72.2 | 89.2 |
| Wind Spirit | 85.3 | 77.6 | 91.8 |

a. Determine an estimated regression equation that can be used to predict the overall score given the score for Shore Excursions (to 3 decimals).

b. Consider the addition of the independent variable Food/Dining. Develop the estimated regression equation that can be used to predict the overall score given the scores for Shore Excursions and Food/Dining (to 3 decimals).

Predict the overall score for a cruise ship with a Shore Excursions score of 80 and a Food/Dining score of 90 (to 2 decimals).

A multiple linear regression model would allow us to predict overall scores based on 'Shore Excursions' and 'Food/Dining' scores. The form of equation would be Y = a + b*X1 + c*X2, where b and c are coefficients obtained from the regression analysis. Actual computation, however, requires the full dataset and appropriate statistical software.

In order to predict the overall score using independent variables 'Shore Excursions' and 'Food/Dining', we need to build a multiple linear regression model.

This data analysis technique is used to explain the relationship between one dependent variable and two or more independent variables.

In this case, we will use 'Shore Excursions' and 'Food/Dining' as independent variables and 'Overall Score' as the dependent variable.

The general form of the model is Y = a + bX1 + cX2 where, Y represents the dependent variable, a is the y-intercept, b and c are the coefficients of the independent variables X1 and X2 respectively.

The coefficients b and c estimate the change in overall score for each unit increase in the Shore Excursions and Food/Dining scores, given that the other variable is held constant.

However, without the actual dataset or the statistical software, it's impossible to perform the regression analysis and establish the estimated regression equation.

Therefore, Unable to predict the overall score given the scores for 'Shore Excursions' and 'Food/Dining' specifically for a Shore Excursions score of 80 and Food/Dining score of 90.

Learn more about Multiple Linear Regression here:

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