4-3 ?Project One Submission
The Correct Answer and Explanation is :
The “4-3 Project One Submission” is an assignment from the Applied Statistics course (MAT240) at Southern New Hampshire University. The project involves developing a median housing price prediction model for D. M. Pan National Real Estate Company, focusing on predicting housing prices based on square footage. The assignment requires students to perform linear regression analysis, interpret statistical data, and present findings in a comprehensive report.
Correct Answer:
The correct answer to the assignment involves creating a linear regression model that predicts median housing prices based on square footage. This includes:
- Data Collection: Selecting a random sample of 50 counties and identifying response (median listing price) and predictor (square footage) variables.
- Data Analysis: Creating histograms for both variables, calculating summary statistics (mean, median, standard deviation), and interpreting the graphs and statistics to understand the data’s distribution and characteristics.
- Regression Model Development: Creating a scatterplot with a line of best fit, calculating the correlation coefficient (r), and developing the regression equation.
- Interpretation: Interpreting the slope and intercept of the regression equation, assessing the strength of the model using R-squared, and using the regression equation to make predictions.
- Conclusion: Summarizing findings in clear and concise language, discussing any unexpected results, and suggesting areas for further research.
300-Word Explanation:
In this project, the objective is to develop a predictive model for median housing prices based on square footage. Linear regression is appropriate when there is a linear relationship between a dependent variable (response) and an independent variable (predictor). In this case, the response variable is the median listing price, and the predictor variable is the square footage of the house.
The first step is data collection, where a random sample of 50 counties is selected. For each county, data on median listing prices and square footage are gathered. This data is then analyzed by creating histograms to visualize the distribution of each variable and calculating summary statistics to understand the central tendency and variability.
Next, a scatterplot is created to visualize the relationship between square footage and median listing price. A line of best fit is added to the scatterplot to represent the linear relationship. The correlation coefficient (r) is calculated to quantify the strength and direction of this relationship.
The regression equation is then developed, which includes the slope and intercept. The slope indicates the expected change in the median listing price for each additional square foot, while the intercept represents the estimated median listing price when the square footage is zero. The R-squared value is calculated to assess the proportion of variability in the median listing price that can be explained by the square footage.
Using the regression equation, predictions can be made for median listing prices based on different square footages. Finally, the findings are summarized, discussing any unexpected results and suggesting areas for further research.
This approach provides a systematic method for predicting housing prices, which can assist real estate professionals in setting appropriate listing prices based on property size.