The Condé Nast Traveler Gold List provides ratings for the top 20 small cruise ships. The following data shown are the scores each ship received based upon the results from Condé Nast Traveler's annual Readers' 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 94.3, the highest component of which is 97.6 for Food/Dining Overall 94.3 92.9 92.8 Food/Dining 97.6 96.6 88.7 96.9 91.3 90.7 90.4 90.4 90.1 91.2 99.0 92.0 Shore Excursions 90.7 84.2 99.9 94.9 87.8 82.3 86.3 92.5 86.0 83.3 81.9 93.2 78.4 91.8 74.9 78.1 89.0 Ship Seabourn Odyssey Seabourn Pride National Geographic Endeavor Seabourn Sojourn Paul Gauguin Seabourn Legend Seabourn Spirit Silver Explorer Silver Spirit Seven Seas Navigator Silver Whisperer National Geographic Explorer Silver Cloud Celebrity Xpedition Silver Shadow Silver Wind SeaDream II Wind Star Wind Surf Wind Spirit 89.6 89.1 89.4 89.1 90.7 90.3 88.5 89.7 88.6 87.2 91.3 23,7 89.6 91.6 87.4 86.5 91.0 86.1 B6.3 91.6 76.4 72.4 89.3 85.9 35.0 77.5 92.1 Wind Spirit 85.0 92.1 a. Determine an estimated regression equation that can be used to predict the overall score given the score for Shore Excursions (to 3 decimals). Overall + Shore Excursions 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). Overall Shore Excursions + Food/Dining c. 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) Hint(s) Check My Work (1 remaining) 0 Icon Key

This problem involves linear regression, which is used to create an equation predicting the overall score of a ship based on its Shore Excursions and/or Food/Dining scores. Without the exact data, it's impossible to provide the precise regression equations or make an accurate prediction. Typically, software or a statistical calculator is used to generate these equations.

Explanation:

This problem involves linear regression, which is a statistical method used to create a linear equation from observed data. The equation helps us predict future data points based on current inputs.

In this case, we want to predict the overall score (dependent variable) of a ship given the ship's scores for Shore Excursions and/or Food/Dining (independent variables).

Part A

We're requested to create a regression equation for predicting the overall score based on the Shore Excursions score. However, without exact data to generate the equation, it's impossible to provide an accurate answer. Typically, you'd use software or a statistical calculator to come up with coefficients that minimize the difference (or error) between observed and predicted values. The equation might look similar to this: Overall = a + (b × Shore Excursions).

Part B

Similarly, without specific data, we can't give the exact equation. But it would incorporate both Shore Excursions and Food/Dining scores, and might look like this: Overall = a + (b × Shore Excursions) + (c × Food/Dining).

Part C

To predict the overall score given specific scores for Shore Excursions and Food/Dining, you'd plug the given scores into the equation you derived in Part B. Without the exact equation, though, we can't make a precise prediction.

Learn more about Linear Regression here:

https://brainly.com/question/32505018

#SPJ11