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The sale price will go down by $0.771. The SE (β9) is 1.697, which is not high. Therefore the figure is considered credible. * Distance from Elevator and View of Ocean (x2x3) Since interaction is present, when the distance from the elevator is one unit further away and the unit possesses ocean view, the sale price will go higher by $1.317. Although the unit is far from the elevator, the resident is still satisfied and willing to pay higher price for the ocean view. The SE (β10) is 0.731, which is not high. Therefore the figure is considered reliable. * Distance from elevator and End Unit (x2x4) Since interaction is present, when the distance from the elevator is one unit further away and the unit is an end unit, the sale price will go down by $9.371, which is the highest figure that poses the highest threatening effect to the response variable. Other than that, it shows that with both elevator distance and end unit, it will have impact on the sale price, although end unit alone does not have any impact. The SE (β11) is 3.418, which is not high. Therefore the figure is considered reliable to a certain
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It means that 55.9% of the variations of the sale price, a response variable, are explained by the predictor variables. With this result we can infer that there is a rather weak relationship between predictor variables and response variable. Further, the value of Ra2 = 0.529 which provides a somewhat similar interpretation, with regards to the measurement of the model adequacy. <Note: the sample contains substantially more data points than the number of β parameters in the model. >
Despite their utility, R2 and Ra2 are only sample statistics. Therefore, it is dangerous to judge the global usefulness of the model based solely on these values. A better method is to conduct a test of hypothesis involving all the β parameters (except β0) in a model. From the ANOVA section under the SPSS output, SSR is 133482.6, SSE is 105410.2, MSR is 10267.888 and MSE is 540.565. With this, the F-ratio is 18.995 with a p-value of approximately 0 and SSyy is 238892.8. With the F-test results obtained from the ANOVA table, we conduct the Global F-test with, H0: β1= β2 = β3 = β4 = β5 = β6 = β7 = β8 = β9 = β10 = β11 = β12 = β13 = β14 = β15 =
It means that 55.9% of the variations of the sale price, a response variable, are explained by the predictor variables. With this result we can infer that there is a rather weak relationship between predictor variables and response variable. Further, the value of Ra2 = 0.529 which provides a somewhat similar interpretation, with regards to the measurement of the model adequacy. <Note: the sample contains substantially more data points than the number of β parameters in the model. >
Despite their utility, R2 and Ra2 are only sample statistics. Therefore, it is dangerous to judge the global usefulness of the model based solely on these values. A better method is to conduct a test of hypothesis involving all the β parameters (except β0) in a model. From the ANOVA section under the SPSS output, SSR is 133482.6, SSE is 105410.2, MSR is 10267.888 and MSE is 540.565. With this, the F-ratio is 18.995 with a p-value of approximately 0 and SSyy is 238892.8. With the F-test results obtained from the ANOVA table, we conduct the Global F-test with, H0: β1= β2 = β3 = β4 = β5 = β6 = β7 = β8 = β9 = β10 = β11 = β12 = β13 = β14 = β15 =