A real estate analyst believes that the three main factors that influence an apartment's rent in a college town are the number of bedrooms, the number ofbathrooms, and the apartment's square footage. For 40 apartments, she collects data on the rent (y, in $), the number of bedrooms (x1), the number ofbathrooms (x2), and its square footage (x3). She estimates the following model: Rent = Bo + B1 Bedroom + B2 Bath + B3Sqft + ɛ. Thefollowing table shows a portion of the regression results.dfMSSignificance FRegression35,694,7171,898,23950.884.99E-13Residual361,343,17637,310Total397,037,893CoefficientsStandard Errort-statp-valueIntercept30084.03.570.0010Bedroom22660.33.750.0006Bath8955.91.590.1195Sqft0.20.092.220.0276When testing whether the explanatory variables jointly influence the response variable, the null hypothesis isMultiple ChoiceHọ: Bo = B1 = B2 = B3 = 0Họ: B1 = B2 = B3 = 0 Multiple ChoiceHọ: Bo = B1 = B2 = B3 = 0Ho: B1 = B2 = B3 = 0Ho: Bo + B1 + B2 + B3 = 0Họ : B1 + B2 + B3 = 0

From the given information, The regression line is, y = β0+β1+β2+β3+ε Null hypothesis: The null…

A real estate analyst believes that the three main factors that influence an apartment's rent in a college town are the number of bedrooms, the number of bathrooms, and the apartment's square footage. For 40 apartments, she collects data on the rent (y, in $), the number of bedrooms (x1), the number of bathrooms (x2), and its square footage (x3). She estimates the following model: Rent = ßo + B1 Bedroom + B2 Bath + B3Sqft + ɛ. The following table shows a portion of the regression results. df Significance F MS F Regression 3 5,694,717 1,898,239 50.88 4.99E–13 Residual 36 1,343,176 37,310 Total 39 7,037,893 Coefficients Standard Error t-stat p-value Intercept 300 84.0 3.57 0.0010 Bedroom 226 60.3 3.75 0.0006 Bath 89 55.9 1.59 0.1195 Sqft 0.2 0.09 2.22 0.0276 When testing whether the explanatory variables jointly influence the response variable, the null hypothesis is Multiple Choice Ho: Bo = B1 = B2 = B3 = 0 Họ: B1 = B2 = B3 = 0