A real estate agent wants to use a multiple regression model to predict the selling price...
A real estate agent uses a simple regression model to estimate the value of a home based on square size in which Y is the value of the home in dollars and X is the size in total square feet. The regression ˆ equation is Y(hat) = 253000 + 438 X . Interpret the slope using the units of the problem. Estimate the value of a home with 1347 square feet. Will the correlation coefficient be positive or negative in...
11. (35 pts) An agent for a real estate company wanted to predict the monthly rent for one- bedroom apartments, based on the size of the apartment (see summary output below). Using the results, identify the coefficient of determination, r2, and interpret its meaning. SUMMARY OUTPUT Regression Statistics Multiple R 0.354314 R Square 0.125539 Adjusted R Square 0.106529 Standard Error 186.0407 Observations 48 ANOVA df Regression Residual Total 1 46 47 Significance SS MS F F 228565.2 228565.2 6.603807 0.013481439...
I need help understanding how to interpret a linear regression using a Hedonic Model. I have a just of what it is but I am not conveying it correctly. Here is the data I had to do a regression: B is Beta by the way where PH = price of the house ($) B1BEDS = bedrooms (number) B2BATHS = bathrooms (number) B3SQFT = area of the house (feet squared) B4LOT = area of the lot (feet squared) B5DISTANCE = distance...
1. One Price Realty Company wants to develop a model to estimate the value of houses in its inventory The office manager has decided to develop a multiple regression model to help explain the variation in house values. (25 points) The office manager has chosen the following variables to develop the model: X1 square feet X2- age in years x3- dummy variable for house style (1 if ranch, 0 if not) X4-2d dummy variable for house style (I if split...
A real estate analyst has developed a multiple regression line, y = 60 + 0.068 x1 – 2.5 x2, to predict the market price of a home (in $1,000s), using two independent variables, x1 = the total number of square feet of living space, and x2 = the age of the house in years. With this regression model, what is the predicted price of a 10-year old home with 2,500 square feet of living space? Dependent / Response Variable Independent...
5. 1 Data were collected for a random sample of 220 home sales from a U.S. community in 2003 Let Price denote the selling price (in $1000), BDR the number of bedrooms, Bath the number of bathrooms, Hsize the size of the house (in sq. ft.), Lsize the lot size (in sq. ft.), Age the age of the house (in years), and Poor a binary variable that is equal to 1 if the condition of the house is reported as...
Consider the following multiple regression Price - 118.1 +0.562BDR+248Bath +0.192Hsize +0.004L size 0.108Age - 48 Poor, R2071, SER-40.2 (224) (2.08) (8.32) (0.011) (0.00045) (0.356) (105) The numbers in parentheses below each estimated coefficient are the estimated standard errors. A detailed description of the variables used in the data set is available here Suppose you wanted to test the hypothesis that BOR equals zero. That is, HBOR-O vs M, BORHO Report the t-statistic for this test. The I-statistic is a (Round...
A home appraisal company would like to develop a regression model that would predict the selling price of a house based on the age of the house in years (Age), the living area of the house in square feet (Living Area) and the number of bedrooms (Bedrooms). The given Excel output shows the partially completed regression output from a random sample of homes that have recently sold. How many homes were included in the sample? EEB Click the icon to...
A dean of a business school has fit a regression model to predict college GPA based on a student's SAT score (SAT_Score), the percentile at which the student graduated high school (HS_Percentile) (for instance, graduating 4th in a class of 500 implies that 496 other students are at or below that student, so the percentile is 496/500 x 100 = 99), and the total college hours the student has accumulated (Total_Hours). The regression results are shown below SUMMARY OUTPUT Regression...
please help thank you! Selling Information For Real Estate Value Price SqFt Brick (1 if brick, if othewise) $241,255 3,392 0 $184,518 2,038 1 $176,488 1,906 0 $240,068 3,329 0 $169,760 1,828 0 $185,335 2,081 0 $172,735 1, 9260 $224,281 3,4250 $172,589 1,676 1 $214,635 2,735 1 $199,666 2,373 1 $208,348 2,662 1 $218,360 2, 8341 $230,160 3, 2540 $164,812 1,431 0 $191,560 1,839 1 $203,255 2, 4561 $173,325 1,530 $168,073 1.381 1 $179,620 1,4571 TABLE 4 Industrial CEO Salary...