Average Cost of New Homes In United States of America Finally after many days of research I narrowed it down to one topic, the Average Cost of new homes in America. Through out this project you will find out if the research supports or rejects its findings by using the quantitative methods taught to me over the course of this semester. What are some methods behind the cost of new homes in united states rising? First thing that comes to mind is politics. Political impacts that move costs, Is it the crime rate in the surrounding environments that have an affect on the price, or is it the cost of materials that are required to develop a new house. When formulating ways to see change take place, these are factors I used, politics, environment,
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Sample size is 300. The Prime Rate, High and Low, was collected from a year-to-year basis from forecast-chart.com; GDP was collected quarterly from January 1975 through December 1999. When looking for different independent variables, multiple regressions perfect. Utilizing the normal logarithmic function of the initial information normalized all factors. Turned out to help produce a more accurate answer when using the regression. The exact data for this formula is: Avg. New Home Cost in USA=27363.2499785281+LnAyNHC(-1.19590994147098) + LnPR[high](0.231754421588769) + LnPR[low](0.15333069426377) + LnGDP(1.91818152955761) + LnPPI(1.51846414419262)+ LnTime(30.43345674552164)+LnTImeSq(-7.38133402628136E-06)+E. The chosen variables proved to have high impact on the cost of modern homes. Multiple of R= 99.99%, R square of 99.99% and Adj. R Square of 99% all meaning the numbers are appropriate for the model. Multiple R describes the strength of correlation coefficient is strong in linear relationship. To find out how close the regression line is, you’d use R Squared. The Adjusted R Square is for variables used, increasing it means that the added variable is influencing y. P values are important because it shows that its greater then 0.05 meaning failure to reject the
Sample size is 300. The Prime Rate, High and Low, was collected from a year-to-year basis from forecast-chart.com; GDP was collected quarterly from January 1975 through December 1999. When looking for different independent variables, multiple regressions perfect. Utilizing the normal logarithmic function of the initial information normalized all factors. Turned out to help produce a more accurate answer when using the regression. The exact data for this formula is: Avg. New Home Cost in USA=27363.2499785281+LnAyNHC(-1.19590994147098) + LnPR[high](0.231754421588769) + LnPR[low](0.15333069426377) + LnGDP(1.91818152955761) + LnPPI(1.51846414419262)+ LnTime(30.43345674552164)+LnTImeSq(-7.38133402628136E-06)+E. The chosen variables proved to have high impact on the cost of modern homes. Multiple of R= 99.99%, R square of 99.99% and Adj. R Square of 99% all meaning the numbers are appropriate for the model. Multiple R describes the strength of correlation coefficient is strong in linear relationship. To find out how close the regression line is, you’d use R Squared. The Adjusted R Square is for variables used, increasing it means that the added variable is influencing y. P values are important because it shows that its greater then 0.05 meaning failure to reject the