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# Statistics Project Example

Summary
There are factors that were presumed to affect the price of housing. The factors include the cost of construction, inflation rates, interest rates and real property gains tax and population…

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Download file to see previous pages The main procedure utilized in this study was regression analysis. It was utilized to explain the total variation of the dependent variable, the price of housing. The dependent variable was accompanied by 5 variables, which were tested against the dependent variable to determine how much of the total variation is explained. The analyses also discussed the comparison of the different regression models, and determine which model is the most effective. In regards to the regression analysis results, it is clearly evident that model 4 and 5 are the strongest model and model 1 being the weakest. Model 2 and 3 does not apply because of the reasons given.
The first regression analysis step was to input all the collected data from the surveys into a spreadsheet. This process allows efficient running of regression models. After all the data was entered, there was formulation of the initial regression model. The analysis was done as in the next section.
In summary, basing on the multiple regression above it is observed that the cost of construction, inflation rates, interest rates and real property gains tax influence negatively the price of housing. On the other hand population influences positively the price of housing. The analysis of this model can be further analyzed to ascertain the strength of the influence as seen in the next section.
This first regression analysis shows a relatively weak model. The coefficient of Determination(R squared) shows that only 4% of the total variation is explained by the cost of construction factor. The standard error is 22.39, determined by the low R squared. In regards to the hypothesis testing, by considering an alpha of 0.05, the results indicate that this age variable is to be rejected. The independent variable of cost of construction demonstrates that the higher the cost of construction is, the less the price of housing and this is in line with several studies done. Thus the performance is less by -0.46633. ...Download file to see next pagesRead More
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