This project develops a linear regression model to predict average annual sales prices for new homes in the United States. The data, sourced from the U.S. Census Bureau, covers the period from 2000 to 2016, with additional data available from 1963. The analysis includes a scatter plot illustrating the upward trend in housing prices over the years, reflecting population growth and land value increases. A linear regression equation is formulated, with the slope indicating the average annual increase in housing costs. The coefficient of determination (R2) and the coefficient of correlation (R) are calculated to assess the model's accuracy. Predictions for the average cost of a house in 2018 are made using the regression equation, and the model's fitness is tested using historical data from 1963. The project concludes with a comparison of two linear models, highlighting the moderately strong relationship between years and housing prices, and acknowledges the influence of various factors, such as inflation and population growth, on actual housing costs. The project also compares the predicted values with the actual values to assess the model's accuracy. The student used the Desmos graphing calculator to create the scatterplot and find the regression line and correlation coefficient.
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