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A real estate agency collected the data shown below, where
y = sales price of a house (in thousands of dollars)
x1 = home size (in hundreds of square feet)
x2 = rating (an overall rating for the house expressed on a scale from 1 (worst) to 10 (best).
Sales Price (y) Home Size (x1) Rating (x2)
180.0 23 5
98.1 11 2
173.1 20 9
136.5 17 3
141.0 15 8
165.9 21 4
193.5 24 7
127.8 13 6
163.5 19 7
172.5 25 2
The agency developed the following regression model:
y = βo + β1 x1 + β2 x2+ €
a) Show why this may be a reasonable model for the relationship between the sales price and home size?
b) What factors are represented in the error term in this model? Give a specific example of these factors.
Question: (a) (i) Define the term multicollinearity. (ii) Explain why it is important to guard against multicollinearity. (b) (i) Sometimes we encounter missing values
Other Measures of Dispersion In this section, we look at relatively less used measures of dispersion like fractiles, deciles, percentiles, quartiles, interquartile range and f
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