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 number
of rooms in the house. Consequently, the appraiser decided to fit the simple linear
regression model :
E(y) = B0 + B1x,
where y = appraised value of the house (in thousands of dollars) and x = number of
rooms. using data collected for a sample of n = 86 houses in East Meadow, the
following results were obtained:
y^ = 86.80 + 19.72x
Give a practical interpretation of the estimate of the y-intercept of the least squares
line.
Give this one a try later!
, there is no practical interpretation, since a house with 0 rooms is
nonsensical.
interpret the coefficient for the tuition variable shown on the printout, multiply
coefficient
Give this one a try later!
multiply by 1000
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 number
of rooms in the house. Consequently, the appraiser decided to fit the simple linear
regression model :
E(y) = B0 + B1x,
where y = appraised value of the house (in thousands of dollars) and x = number of
rooms. using data collected for a sample of n = 86 houses in East Meadow, the
following results were obtained:
y^ = 86.80 + 19.72x
give a practical interpretation of the estimate of the slope of the least squares line.
Give this one a try later!
for each additional room in the house, we estimate the appraised value to
increase $19,720
an academic advisor wants to predict the typical starting salary of a graduate at a top
business school using the GMAT score of the school as a predictor variable. a simple
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 number
of rooms in the house. Consequently, the appraiser decided to fit the simple linear
regression model :
E(y) = B0 + B1x,
where y = appraised value of the house (in thousands of dollars) and x = number of
rooms. using data collected for a sample of n = 86 houses in East Meadow, the
following results were obtained:
y^ = 86.80 + 19.72x
Give a practical interpretation of the estimate of the y-intercept of the least squares
line.
Give this one a try later!
, there is no practical interpretation, since a house with 0 rooms is
nonsensical.
interpret the coefficient for the tuition variable shown on the printout, multiply
coefficient
Give this one a try later!
multiply by 1000
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 number
of rooms in the house. Consequently, the appraiser decided to fit the simple linear
regression model :
E(y) = B0 + B1x,
where y = appraised value of the house (in thousands of dollars) and x = number of
rooms. using data collected for a sample of n = 86 houses in East Meadow, the
following results were obtained:
y^ = 86.80 + 19.72x
give a practical interpretation of the estimate of the slope of the least squares line.
Give this one a try later!
for each additional room in the house, we estimate the appraised value to
increase $19,720
an academic advisor wants to predict the typical starting salary of a graduate at a top
business school using the GMAT score of the school as a predictor variable. a simple
