Answer:
Step-by-step explanation:
Hello!
The objective is to determine if there is a linear association between the price of a mountain bike (Y) and it's the weight (X).
a.
Looking at the Scatterplot the data is dispersed in all quadrants of the graph, at first glance there seems to be some kind of functional relationship between the price and weight of mountain bike bicycles. If you are not too strict maybe there could be a slight negative relationship between them.
b.
The linear regression model is E(Yi)= α + βXi
To find the regression model for this particular set of variables you have to estimate the intercept and the slope.
a= Y[bar] - bX[bar] = 1961.40 $
b=
= -42.99 $/LB
^Yi= 1961.40 - 42.99Xi
$ 1961.40 is the estimated value of the average price if the weight of the mountain bike is zero. (It has no contextual sense)
-42.99 $/LB is the modification of the estimated average price every time the weight of the mountain bike.
c.
In this item, you need yo obtain a value of price for a given value of weight. To do so all you have to do is replace the value on the estimated regression model:
^Y/X=3232= 1961.40 - 42.99*32= $585.72
I hope it helps!
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Answer:
(x, y) = (0, -26)
Step-by-step explanation:
The y-intercept is the y-value that corresponds to x=0. Here, we have a table that has x-values that differ by 12 and 24. We want to find the y-value that corresponds to an x-value 24 less than the lowest one on the table:
24 -24 = 0
We notice that when x-values differ by 24, the y-values differ by 10 -(-8) = 18. So, the y-value we want is 18 less than the lowest one in the table:
y = -8 -18 = -26
The (x, y) coordinates of the y-intercept are (0, -26).