Asked by Mariya&Eliza George on Jul 04, 2024
Verified
Ten Ford Escort classified ads were selected.The age and prices of several used Ford Escorts are given in the table. Age (years) Price 1$10,0002$85002$80003$60003$59004$58004$50005$30006$20006$1900\begin{array} { c l } \text { Age (years) } & \text { Price } \\1 & \$ 10,000 \\2 & \$ 8500 \\2 & \$ 8000 \\3 & \$ 6000 \\3 & \$ 5900 \\4 & \$ 5800 \\4 & \$ 5000 \\5 & \$ 3000 \\6 & \$ 2000 \\6 & \$ 1900\end{array} Age (years) 1223344566 Price $10,000$8500$8000$6000$5900$5800$5000$3000$2000$1900
A) price ^\hat{\text { price }} price ^ = 11291 - 1578 age
B) price ^\hat{\text { price }} price ^ = 7200 - 692 age
C) price ^\hat{\text { price }} price ^ = 7.05 -0.000616 age
D) price ^\hat{\text { price }} price ^ = -1580 + 11300 age
E) price ^\hat{\text { price }} price ^ = 10000 - 1600 age
Classified Ads
Short advertisements placed in newspapers or online platforms, sorted into categories.
Age
The length of time that a person or object has existed since its beginning or birth.
Price
The amount of money required to purchase a good or service.
- Comprehend the fundamental principles of linear regression, including the prediction of values through the application of a regression equation.
- Understand the meaning of the slope and the intercept within a regression line based on the data presented.
Verified Answer
ZK
Zybrea KnightJul 06, 2024
Final Answer :
A
Explanation :
Option A is the correct choice because it represents a linear regression equation that likely fits the given data, where the intercept (11291) represents the estimated price when the age is 0, and the slope (-1578) indicates the decrease in price for each additional year of age. This choice is reasonable given the pattern of decreasing prices with increasing age in the provided data.
Learning Objectives
- Comprehend the fundamental principles of linear regression, including the prediction of values through the application of a regression equation.
- Understand the meaning of the slope and the intercept within a regression line based on the data presented.
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