Solution :
From given output ,
df = 14
n = df + 1 = 14 + 1 = 15
15 homes were included in the sample .
A)
A home appraisal company would like to develop a regression model that would predict the selling...
A home appraisal company would like to develop a regression model that would predict the sling price of a house based on the age of the house in years oltre living area of the house in fost (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 According to the analysis, what effect on an additional year in the age of the...
Heat Power is a utility company that would like to predict the monthly heating bill for a household in a particular region during the month of January. A random sample of 18 households in the region were selected and their January heating bill recorded. The data is shown in the table below along with the square footage of the house (SF), the age of the heating system in years (Age), and the type of heating system (Type: heat pump =...
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...
SUMMARY OUTPUT Confidence Interval Estimate and Prediction Interval Data ression Statistics Confidence Level 95% Multiple R R Square Adjusted R Square Standard Error Observations 0.9035 iven vaue iven value Sa ED1 given value ED2 given value 400 1.7353 ANOVA Predicted Y (YHat) 11.37451 sS Significance F 4.0112E-07 MS For Average Predicted Y (YHat) Regression Residual Total Interval Half Width Confidence Interval Lower Limit Confidence Interval U 1.867459 9.507054 13.24197 60.23 327.84 24 r Limit We were unable to transcribe this...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
A hospital would like to develop a regression model to predict the total hospital bill for a patient based on his or her length of stay, number of days in the hospitais intensive care una (CU), and age of the patient Data for these variables can be found in the accompanying table Complete parts (a) through (e) below. Click the icon to view the data table a) Using technology, construct a regression model using all three independent variables, where y...
Heat Power would now like to determine the best subset regression model for the heating bill data using only the independent variables that do not exhibit multicollinearity issues. Based on the best subset output below, what should it be their best subset choice? Model X се 10.07 161.78 61.36 10.15 5.71 50.94 K+1 R-Square Adj. R-Square Std. Error 2 0.87 0.86 26.37 2 0.04 -0.02 74.01 2 0.59 0.56 48.08 3 0.88 0.87 26.15 3 0.91 0.90 23.09 3 0.66...
Heat Power is a utility company that would like to predict the monthly heating bill for a household in a particular region during the month of January. A random sample of 18 households in the region were selected and their January heating bill recorded. The data is shown in the table below along with the square footage of the house (SF), the age of the heating system in years (Age), and the type of heating system (Type: heat pump =...
a. $48,626
b. $97,252
c. $28,545
d. none of the above
From the regression example discussed in class and based on the information below, what is the impact on the price of a house if you add two bathrooms? SUMMARY OUTPUT Regression Statistics Multiple R 0.92 R Square 0.85 Aqusted R Square 0.84 Standard Error 32685.63 Observations ANOVA MS F 43.60 O 46,578,674,391.09 1,068,350,694.88 Significance F 0.00 Regression Residual Total SS 5 232,893,371,965.43 37 39,528,975,710.43 42 272.422,347 665.86 Stat Intercept...
Develop an estimated simple linear regression model that can be used to predict the alumni giving rate, given the graduation rate. Below is the data sets and the regression, I just need to know what it means so that I am able to write about it. SUMMARY OUTPUT Regression Statistics Multiple R 0.749592336 R Square 0.561888671 Adjusted R Square 0.552152864 Standard Error 5.752079289 Observations 47 ANOVA df SS MS F Significance F Regression 1 1909.537 1909.537 57.71362 1.34E-09 re Residual...