Asked by Kaitlin Ptacek on Jun 04, 2024
Verified
A random sample of 150 yachts sold in Canada last year was taken.A regression to predict the price (in thousands of dollars) from length (in metres) has an R2=18.3%\mathrm { R } ^ { 2 } = 18.3 \%R2=18.3% What is correlation between length and price?
A) 0.428
B) 0.033
C) 0.667
D) 0.904
E) 0.183
Random Sample
A selection from a larger population that is made in such a way that every individual has an equal chance of being included.
Yachts
Luxury vessels used for private cruising, racing, or other recreational sailing activities.
Correlation
A statistical measure that expresses the extent to which two variables change together.
- Decode the meaning of the correlation coefficient (\(r\)) and understand its connection to \(R^2\).
Verified Answer
MB
mellissa brownJun 05, 2024
Final Answer :
A
Explanation :
The correlation coefficient (r) measures the strength and direction of the linear relationship between two variables. Its value ranges from -1 to +1, where -1 indicates a strong negative relationship, +1 indicates a strong positive relationship, and 0 means no linear relationship.
In this case, the regression analysis is used to predict the price of a yacht based on its length, so it makes sense to calculate the correlation between length and price.
The correlation coefficient is given by r = sqrt(R-squared), where R-squared is the coefficient of determination from the regression analysis. In this case, R-squared is equal to 0.183, so r = sqrt(0.183) = 0.428.
Therefore, the answer is A) 0.428.
In this case, the regression analysis is used to predict the price of a yacht based on its length, so it makes sense to calculate the correlation between length and price.
The correlation coefficient is given by r = sqrt(R-squared), where R-squared is the coefficient of determination from the regression analysis. In this case, R-squared is equal to 0.183, so r = sqrt(0.183) = 0.428.
Therefore, the answer is A) 0.428.
Learning Objectives
- Decode the meaning of the correlation coefficient (\(r\)) and understand its connection to \(R^2\).
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