A county real estate appraiser wants to develop a statistical model to predict the appraised value of houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the total number of rooms in the house. Consequently the appraiser decided to fit the simple linear regression model, ^y=β0+β1x, where y= the appraised value of the house (in thousands of dollars) and x= the number of rooms. Using data collected for a sample of n = 74 houses in East Meadow, the following results were obtained:¯y=74.80+24.93xRange of the x-values: 5 - 11Range of the y-values: 160 - 300Give a practical interpretation of the estimate of the slope and the y-intercept of the least-squares line.

D. For each additional room in the house, the appraised value is estimated to increase $74,800

Step-by-step explanation:

The statistical model is used to predict the appraised value of houses in East Meadows. The value of a house is determined by using variables. The regression model helps to analyse the value of a house. It is predictive modelling technique which determines a relationship between dependent and independent variable.

answer: the price of one pound cabbage = $ 0.8

the point (10,8) represents that the cost of 10 pounds cabbage is $ 8

step-by-step explanation:

since, here is a proportional relation between the number of pounds of cabbage (x) and the price in dollars (y).

therefore, we can write,

⇒ y = k x

where k is the constant of proportionality.

now, point (5,4) is in the line.

thus, it will satisfies the above equation of line.

⇒ 4 = 5 k

⇒ k = 0.8

thus, the equation of the line is,

y = 0.8 x

a) for x = 1,

y = 0.8

therefore, the cost of one pound of cabbage is 0.8 dollars.

b) in the below graph (10,8) represents that the cost of 10 pounds of cabbage is 8 dollars.

step-by-step explanation:

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