PLEASE ANSWER ALL QUESTIONS FOR THUMBS UP The data set is big, therefore I have
ID: 3266525 • Letter: P
Question
PLEASE ANSWER ALL QUESTIONS FOR THUMBS UP
The data set is big, therefore I have uploaded it on OneDrive. Please find the data set in following link with name "hprice1"
https://1drv.ms/x/s!AnCfB5fz9u5ZgfZ0wIUtyb6ahfRIKg
What is the R-squared and explain what it means in this scenario.
Write out a model to test the relationship between the price of a home the square footage of the home.
Estimate your model and include the OLS output below.
Interpret Bo?
Explain how your model says that a home with no square footage sells for a positive price, how can this be? (hint: it has nothing to do with the value of the land)
Interpret B1?
Does adding more square footage to the home increase the price of the home? Explain.
What is the R-squared and explain what it means in this scenario.
Explanation / Answer
here price is dependent variable
Write out a model to test the relationship between the price of a home the square footage of the home.
price^ = b0 + b1 * sqrft
Estimate your model and include the OLS output below.
Interpret Bo?
B0 = 11204.14482 , it means when there is no sqr foot ,then price is 114204.14482
Explain how your model says that a home with no square footage sells for a positive price, how can this be? (hint: it has nothing to do with the value of the land)
since no house can be without aby square footage , bo in this model does not mean anything
Interpret B1?
b1 - if we change sqrfoot by 1 unit , change in price = b1 = 140.2109
Does adding more square footage to the home increase the price of the home? Explain.
yes , as b1 is positive , adding more square footage to the home increases th price of the home
What is the R-squared and explain what it means in this scenario.
R^2 = 0.620
it means 62% of variation in price is explained by this model
SUMMARY OUTPUT Regression Statistics Multiple R 0.787906548 R Square 0.620796728 Adjusted R Square 0.616387387 Standard Error 63617.08058 Observations 88 ANOVA df SS MS F Significance F Regression 1 5.69801E+11 5.69801E+11 140.7912919 8.42341E-20 Residual 86 3.48053E+11 4047132941 Total 87 9.17855E+11 Coefficients Standard Error t Stat P-value Lower 95% Intercept 11204.14482 24742.60761 0.452827972 0.651812965 -37982.53121 sqrft 140.2109774 11.81664313 11.86555064 8.42341E-20 116.7202683Related Questions
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