business statistics Table 3: Stata Output for Sales Price of the Housereg_ss salesprice sqft bedroons lotsizedfNumber of obs-676.690.00050.24170.20561.let05Source|MSE3,63) -Model 2.4910e+11Residual | 7.8158e+113 8.3035e+10631.2406e+10Prob > PR-squaredAdj R-squared -Root MSETotal 1.0307e+1266 1.561 6e+10aalesprice |Coef.Std. Err.(953 Conf. Interval]14906.48aqfti00 |bedrocma |lotsize I9893.5242508.5613,94-2.46D.0004880.564-36018.6814665.170.017-65324.68-6712.6752.5125111.1245312.230.029-2653148דסד59ד.4cons |290558.175398.793.850.000139885.6441230.5Seguent ial Sum of Squares for Regressionsalesprice |Coef.Seq SsaflProb > Faqft100 |bedrocma -34701.036187.9931.18e+111639.4741340.00316.96a+10635.6050860.0210lotsize2.5125116.19a+10634.9893260.0291reg_ss salesprice bedrooms sqtt lotsizedfNumber of obs -676.690.00050.24170.20561.let05Source |MSE( 3,Prob > P63)Model | 2.4910e+113 8.3035e+10631.2406e+10Residual I 7.8158e+11R-squaredAdj R-squared -Root MSETotal 1.0307e+1266 1.5616e+10aalesprice |Coef.Std. Err.(953 Conf. Interval]bedrooms | -36018.68sqtti00 |lotsize I14665.17-2.460.017-65324.68-6712.6759893.5242508.5613.94D.0004880.56414906.482.5125111.1245312.230.029.26531484.759707cons290558.175398.793.850.000139885.6441230.5Seguential Sum of Squares for Regressionsalesprice |Coef.Segdf1df2Prob > Ebedrocma-668. 89454.02a+07163-00324190.9548aqfti00lotsize |9739.0831.87a+116315.075980.00032.5125116.19a+101634.9893260.0291Required:i.Write the estimated regression equation for the full model with all three (3)variables. Conduct the global test, state the test statistic, the p-value, andevaluate whether or not to reject the null hypothesis using a = 0.05i.Give the numerical values (from the output) for two different test statisticsfor testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluateyour answer in words [1-2 sentences], in the context of the real estatesituation, what this hypothesis is testing.İi.Assess the result if you use bedrooms as the only predictor to fit thesimple linear regression model. Conclude your answer based on theinformation provided in the output. Table 3 gives Stata output for the following situation. The data consist of the 68houses that have Quality = 1. Y = Sales Price of the house ("salesprice" on theoutput), and the three predictor variables are:X1 = Square feet divided by 100 = "sqft100" on the outputX2 = Number of bedrooms = "bedrooms" on the outputX3 = Lot size in square feet = "lotsize" on the outputNotice in the output that the model was fit twice, with the variables in two differentorders, but the designation of X1, X2 and X3 are as defined above. In otherwords, we define X1 = Square feet divided by 100, and so on, no matter whatorder they appear in the Stata command.

Name: business statistics Table 3: Stata Output for Sales Price of the House
Uploaded: 2024-03-20T07:06:41.000Z
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Description: business statistics Table 3: Stata Output for Sales Price of the Housereg_ss salesprice sqft bedroons lotsizedfNumber of obs-676.690.00050.24170.20561.let05Source|MSE3,63) -Model 2.4910e+11Residual | 7.8158e+113 8.3035e+10631.2406e+10Prob > PR-square

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Required: i. Write the estimated regression equation for the full model with all three (3) variables. Conduct the global test, state the test statistic, the p-value, and evaluate whether or not to reject the null hypothesis using a = 0.05 ii. Give the numerical values (from the output) for two different test statistics for testing Ho: B3 = 0 (given that X1 and X2 are in the model). Evaluate your answer in words [1-2 sentences], in the context of the real estate situation, what this hypothesis is testing. ii. Assess the result if you use bedrooms as the only predictor to fit the simple linear regression model. Conclude your answer based on the information provided in the output.