Question

The sport of bicycling is extremely competitive. One area that all participants can easily control is the weight of their equipment.

Bicycling World is a new online retailer and is curious about the relationship between the weight of the individual bicycle frame and the price. They believe that this should be useful to help better set future prices for new bicycle frames as they are released by the various manufacturers.

Please create a simple regression model, using the data file attached, predicting Price base upon the weight of the bicycle frame (α = 0.05).

Model	Weight	Price
Fierro 7B	17.9	2200
HX 5000	16.2	6350
Durbin Ultralight	15.0	8470
Schmidt	16.0	6300
WSilton Advanced	17.3	4100
bicyclette vélo	13.2	8700
Supremo Team	16.3	6100
XTC Racer	17.2	2680
D’Onofrio Pro	17.7	3500
Americana #6	14.2	8100

1)What is the Sum of Squares Regression (Please round to 2 decimal places)

2)

What is the correlation between "price" and "weight"?

Question options:

50.703

10

-.9293

86.3%

3) What is the Sum of Squares Residual (Please round to 2 decimal places)

4) How much of the variation in "price" can be explained by the predictor "weight"?

Answer 1

The statistical software output for this problem is :

1)

Sum of Squares Regression = 45017877

2)

correlation between "price" and "weight" = -0.9293

3)

Sum of Squares Residual = 887865.32

4)

86.37%