Using data on the appraised values of homes, a real estate agent computes the least squares...
Using data on the appraised values of homes, a real estate agent computes the least squares regression line for predicting a home's value in the year 2010 (y) from its value in 2000 (x). The Equation of the regression line is y=$22,000 + 1.6x
1. By how much does a home's 2010 value change for each $1 increase in the home's 2000 value?
2. If we were to use the regression line to predict the home's value in the year 2025, would the prediction be trustworthy? Why or Why not?
Homework Answers
1)
1.6$ (slope is 1.6)
2)
No, because we did regression of home's value in the year 2010 , mean in year 2025 can be different
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Using data on the appraised values of homes, a real estate agent
computes the least squares...
3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of th...
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need answer asap please shows? 3. Using data on the appraised value of homes, a real...
need answer asap please
shows? 3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of the least-squares regression line is: where y represents a home's value in 2002 and x is the value in 1992. $44,000+1.8x y Question: Explain y, $22,000, 1.6 and x separately? Suppose Julia owns a home that was worth $100,000 in 1992....
A county real estate appraiser wants to develop a statistical model to predict the appraised value...
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 linear regression model E(y)= Bo +P1x, where y equals the appraised value of the house in thousands of dollars) and X equals the number...
A county real estate appraiser wants to develop a statistical model to predict the appraised value...
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, y=β0+β1x, where y=appraised value of the house (in $thousands) and x=number of rooms. Using data collected for a sample of...
A county real estate appraiser wants to develop a statistical model to predict the appraised value...
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 linear regression model: E(y) = β0 + β1x, where y = appraised value of the house (in thousands of dollars) and x = number...
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3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of the least-squares regression line is: y $44,000 + 1.8x where y represents a home's value in 2002 and x is the value in 1992 Question: Explain y, $22,000, 1.6 and x separately? Suppose Julia owns a home that was worth S100,000 in 1992. What would be...
need answer asap please
shows? 3. Using data on the appraised value of homes, a real estate agent computes the least- squares regression line for predicting a home's value in 2002 from its value in 1992. The equation of the least-squares regression line is: where y represents a home's value in 2002 and x is the value in 1992. $44,000+1.8x y Question: Explain y, $22,000, 1.6 and x separately? Suppose Julia owns a home that was worth $100,000 in 1992....
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 linear regression model E(y)= Bo +P1x, where y equals the appraised value of the house in thousands of dollars) and X equals the number...
how to find the b0 through excel
An agent for a residential real estate company in a suburb located outside a major city has the business objective of developing more accurate estimates of the monthly rental cost for apartments. Toward the goal, the agent would like to use the size of an apartment, as defined by square footage to predict the monthly rental cost. The agent selects a sample of a one-bedroom apartments and the data are shown. Complete parls...
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freedom associated with the t-distribution required to construct a
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