Answer:
For each hundred-thousand-dollar increase in the listing price, the sales price is predicted to increase by $110,000.
Step-by-step explanation:
The sales price is not predicted to increase by $1.1; it is predicted to increase by $110,000 because S is measured in hundred-thousands of dollars. The sales price will not decrease by $110,000; it is predicted to increase by $110,000.
A linear regression is represented as [tex]^\wedge y = mx + c[/tex].
The true option is: E. For each hundred-thousand-dollar increase in the listing price, the sales price is predicted to increase by $110,000.
Given that:
[tex]^\wedge S = 0.5 + 1.1L[/tex]
From the general linear equation, we have:
[tex]1.1 \to[/tex] The slope
[tex]0.5 \to[/tex] The y-intercept
The slope is the difference between the sales price of one listing and the next.
From the question, we understand that the listed amount is in 100,000's.
This means that there is an increment of [tex]\$1.1 \times 100000[/tex] between the sales price of concurrent listing
So, we have:
[tex]\$1.1 \times 100000 =\$110000[/tex]
From the list of given options (see attachment), we can then conclude that (e) is correct.
Hence, for each hundred-thousand-dollar increase in the listing price, the sales price is predicted to increase by $110,000.
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