Median Housing Price Model for D. M. Pan National Real Estate Company3 [Note: To complete this template, replace the bracketed text with your own content. Remove this note before you submit your...

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Use Assignment one and two to complete PROJECT 1
MAT 240 Project 1 - template to useScreenshot - directions for this project in detailAssignment II - is Assignment II that was turned in alreadyMat 240 Module Two Assignment Deborah Loberger - Assignment I that was turned in already


Median Housing Price Model for D. M. Pan National Real Estate Company3 [Note: To complete this template, replace the bracketed text with your own content. Remove this note before you submit your outline.] Report: Housing Price Prediction Model for D. M. Pan National Real Estate Company [Your Name] Median Housing Price Prediction Model for D. M. Pan National Real Estate Company1 Southern New Hampshire University Introduction [Describe the report: Include in this section a brief overview, including the purpose of the report and your approach.] Data Collection [Sampling the data: Outline how you obtained your sample data, including the response and predictor variables.] [Scatterplot: Insert a correctly labeled scatterplot of your chosen variables.] Data Analysis [Histogram: Insert the histogram of the two variables. Be sure to include appropriate labels.] [Summary statistics: Insert a table to show the summary statistics.] [Interpret the graphs and statistics: Describe the shape, center, spread, and any unusual characteristic (outliers, gaps, etc.) and what they mean based on your sample data and the graphs you created.] [Explain how these characteristics of the sample data compare to the same characteristics of the national population. Also, determine whether your sample is representative of the national housing market sales.] The Regression Model [Scatterplot: Include the scatterplot graph of the sample with a line of best fit and the regression equation.] [Based on your graph, explain whether a regression model can be developed for the data and how.] [Discuss associations: Explain the associations in the scatterplot, including the direction, strength, form in the context of your model.] [Find r: Calculate the correlation coefficient and explain how it aligns with your interpretation of the data from the scatterplot.] The Line of Best Fit [Regression equation: Insert the regression equation.] [Interpret regression equation: Interpret the slope and intercept in context.] [Strength of the equation: Interpret the strength of the regression equation, R-squared.] [Use regression equation to make predictions: Use the regression equation to make a sample prediction.] Conclusions [Summarize findings: Summarize your findings in clear and concise plain language. Outline any questions arising from the study that might be interesting for follow-up research.] Median Housing Price Prediction Model for D.M. Pan National Real Estate Company5 Housing Price Prediction Model for D.M. Pan Real Estate Company Deborah Loberger Southern New Hampshire University Module Two Notes [Copy and paste any relevant information from your Module Two assignment here to assist you in completing this assignment. This section is not graded and is only provided to help you easily review Module Two assignment information while completing this assignment.] Regression Equation From the above scatter plot, we have regression equation is y = 89.345x + 74676 Determine r We have r2 = 0.8117 The correlation coefficient r = = = 0.9009 Therefore, the correlation coefficient r is 0.9009 Strength: The closer the correlation coefficient r value is to (1 or -1) indicates the stronger the linear relationship. The obtained correlation coefficient r value (0.9009) is near to +1. Thus, it indicates that there is a strong correlation between square feet and listing price. Direction of association: The sign of ‘r’ determines how the two variables correlated i.e., either positive (or) negative. The positive sign in correlation coefficient r value (0.9009) explains that there is a strong positive correlation between square feet and listing price. Thus, increasing in the square feet leads to increases the corresponding Listing price and vice versa. i.e., Positive correlation. In other words, all the data points in a scatter plot seems to be form a straight line with the upward slope. Thus, we have sufficient evidence to tell that association direction is positive. Examine the Slope and Intercepts The simple regression equation is in the form of y= bo + b1*x Where b0 is the intercept of the regression equation b1 is the slope coefficients of the regression equation The regression equation we have is y = 89.345x + 74676 Intercept bo = 74676 and slope b1 = 89.345 Interpretation of slope b1: The slope of 89.345 means that for every additional square foot of land the listing price would be increases by 89.435$. Interpretation of intercept bo: The y intercept of the 74676 means that, if square foot of the land is zero (0) then we can expect to get a listing price would be 74676 The land value is 104468 when x is 0 It does not make sense in this context. Because, without land there will be no value that means at absence of land there is no cost value for land. R-squared Coefficient Coefficient of determination is denoted with r2, and it is defined as, how much % of variation in y variable can be explained by the variation in the values of x independent variable. The obtained coefficient of determination is r2 is 0.8117. Which reveals that approximately 82% of the variation in the listing price can be explained by the variation in the square feet. In other words, it can be said that the R – square value (82%) is significantly more than 60%, Which explains that the considered regression model is good fit for regression analysis. Conclusions No, there is no significant difference between selected region and overall homes in the United States. Since, there is a strong positive correlation between land price and square feet of the land. Selected region and as well as Overall homes in the United States shows the positive correlation. For every 100 square feet increase in the land, leads to increases the price land upto 100*89.345 = 8935$. Yes, we should use slope value because, for every additional square foot of land the listing price would be increases by 8935$. The best square feet range from the scatter plot is 1000 sq ft. – 3000 sq ft. Since, most of the values of variable square feet contains between 1000 to 3000. Further, least square feet values are outside the range of 3000 sq ft. Hence, the range 1000 sq ft – 3000 sq ft is best used for. Listing Price vs Square feet 169216281157130514931815514620811587159914161996162812931693357419781880197820183408163212391738143412432031198619991562236700191500174500166300225300270700581800218300248300266100160800304300254500148700278700347500270600248900228300257200323300254500145100253900187900213800246700256700188300204200 Selling Price Analysis for D.M. Pan National Real Estate Company2 Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company Deborah Loberger Selling Price and Area Analysis for D.M. Pan National Real Estate Company1 Southern New Hampshire University Introduction I have been hired by a D.M. Pan Real Estate Company to create a report that examines the relationship between the selling price of properties and their size in square feet. the Region of East North Central housing prices and housing square footage is the region I chose to examine. I will be determining this relationship with series of equations. I will be presenting my findings to the team at D.M. Pan Real Estate Company. Representative Data Sample Data Analysis I chose a region after using the (RAND) formula on excel on the given data. I then chose 30 random selections from within that region. The regional sample created reflects the national market because of the relation to the graphs. The data will vary from region to region because of the different pricing markets in certain areas on the nation. However, if you look at the relation my sample data in relation to the National statistics graph, they show similar numbers between both sets of data. Therefore, in this case, the samples I selected at random from my region are reflective on the National market being part of the same population. Scatterplot The Pattern There is a positive linear relationship between the listing price (y) and the square feet (x). A positive relationship means that as the x value increases, the y values also increase and vice versa. The coefficient of determination (R2) is large (0.8117) which shows that the regression equation is a good fit. This shows that 81.17% of the variation in the response variable (listing price) is explained by the predictor variable (square feet). The correlation coefficient (R) is 0.90. This indicates a strong association between the listing price (y) and the square feet (x). The scatterplot shows a linear shape because the line of best fit is a straight line.   The equation of the regression model is y=89.345x+74676 Slope = 89.345 y-intercept = 74676 For each increase in square feet (x), the listing price of the house increases by $89.345. y=89.345x+74676y=89.345(1800) +74676y=160821+74676y=$235,497 The chosen listing price of $235,497 for an 1,800 square foot house would be a good listing price. There are a few outliers in the sample data. Most of the houses are between 1,000 to 2,000 square feet. Two of the outliers are between 3,000 to 4,000 square feet and one outlier that is above 5,000 square feet. These outliers appeared randomly in the sampled data since I randomly selected the sample data from the population. These outliers may represent some farmhouses or mansions in the city outskirts or rural areas that usually have more land as compared to the normal houses in the city.
Answered Same DayNov 24, 2021

Answer To: Median Housing Price Model for D. M. Pan National Real Estate Company3 [Note: To complete this...

Subhanbasha answered on Nov 25 2021
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Median Housing Price Model for D. M. Pan National Real Estate Company    2
Report: Housing Price Prediction Model for D. M. Pan Na
tional Real Estate Company
[Your Name]
Median Housing Price Prediction Model for D. M. Pan National Real Estate Company    1
Southern New Hampshire University
Introduction
Describe the report: The major intention of the report is to get the insight about the square footage means that is it helpful to fix the listing price of a house that is the two variables are related or dependent. For knowing this insight from the given data we are going to perform the regression analysis which will allow us to make the linear relation between the variables that is what our intention.
Data Collection
Sampling the data: To take the sample data from population data I have used random function in the excel. Not used the entire data only selected particular location data. So, that the sample data will reflects the national market data. Here I filtered only 30 rows to do analysis.
Scatterplot:
Data Analysis
Histogram:
Summary statistics:
Interpret the graphs and statistics:
By observing the above histograms the data following right skewed distribution for both listing price and square footage. That the price for some of the square feet very...
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